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TRANSCRIPT
An Assessment of the Investment Climate
in Tanzania
May 2009
Revised Draft
DO NOT CITE OR QUOTE WITHOUT PERMISSION
Volume 2: Detailed Results and Econometric Methodology
ii
ABBREVIATIONS AND ACRONYMS
2SLS Two Stage Least Squares
AfDB African Development Bank
ASCA Accumulating Savings and Credit Associations
BMK Bahati Milk Kiosk
CET Common External Tariff
CPI Corruption Perception Index
CRDB Cooperative and Rural Development Bank
EAC East African Community
FDI Foreign Direct Investment
GDP Gross Domestic Product
GFCF Gross fixed capital formation
HIPC Heavily Indebted Poor Countries Initiative
IC Investment Climate
ICA Investment Climate Assessment
IDA International Development Association
ILO International Labour Organization,
IMF International Monetary Fund
IPP Independent Power Producer
IPTL Independent Power Tanzania Limited
ISO International Standards Organization
JAST Joint Assistance Strategy for Tanzania
LLC Limited Liability Company
LPI Logistics Performance Index
LTD Large Taxpayers Department
MDRI Multilateral Debt Relief Initiative
MFI Microfinance Institution
MKUKUTA National Strategy for Growth and the Reduction of Poverty (Swahili translation)
MW Megawatt (1,000,000 watts)
NBC National Bank of Commerce
NMB National Microfinance Bank
NPV Net Present Value
NSGRP National Strategy for Growth and the Reduction of Poverty
OLS Ordinary Least Squares
PAYE Pay As You Earn
PPP Price Purchasing Parity
PSI Policy Support Instrument
REER Real Effective Exchange Rate
ROSCA Rotating Savings and Credit Association
SACCO Savings and Credit Cooperative
SME Small and Medium-Sized Enterprise
SMLE Small, Medium-Sized and Large Enterprise
SSA Sub-Saharan Africa
TANESCO Tanzania Electric Supply Company Limited
TAP Tax Administration Project
iii
TE Technical Efficiency
TFP Total Factor Productivity
TIN Taxpayer Identification Number
TMP Tax Modernization Project
TRA Tanzania Revenue Authority
TSH Shilling (Currency)
UN United Nations
UNDP United Nations Development Program
UNIDO United Nations Industrial Development Organization
US United States
USAID United States Agency for International Development
VAT Value Added Tax
iv
TABLE OF CONTENTS
Abbreviations and Acronyms ......................................................................................................... ii
Table of Contents ........................................................................................................................... iv
Chapter 1: Introduction ................................................................................................................... 7
I. Comparator Countries ................................................................................................ 7
II. Macroeconomic Background ................................................................................... 11
III. The World Bank Enterprise Survey ......................................................................... 19
Chapter 2: An Analysis of Firm Performance .............................................................................. 22
I. Firm Performance .................................................................................................... 22
II. Competition.............................................................................................................. 35
III. Profitability .............................................................................................................. 37
Chapter 3: Perceptions about the Investment Climate .................................................................. 39
I. Perceptions about constraints ................................................................................... 39
II. Main perceived constraints in the 2006 Enterprise Survey ..................................... 41
III. Differences in perceptions across different firms .................................................... 43
IV. Comparisons with earlier surveys ............................................................................ 44
V. Summary .................................................................................................................. 48
Chapter 4: Employment Creation and Human Capital Accumulation .......................................... 50
I. Characteristics of workers in the worker survey...................................................... 50
II. Employment creation ............................................................................................... 52
III. Worker education and skills ..................................................................................... 54
IV. Firm Training ........................................................................................................... 56
V. Wages ....................................................................................................................... 59
v
VI. Summary .................................................................................................................. 62
Chapter 5: Access to Finance ........................................................................................................ 64
VII. Background .............................................................................................................. 64
VIII. Perceptions about access to finance ......................................................................... 71
IX. Objective measures of access to finance .................................................................. 73
X. Summary .................................................................................................................. 84
Chapter 6: Infrastructure, Taxation, and Regulation and Governance ......................................... 86
I. Infrastructure in Tanzania ........................................................................................ 86
II. Taxes ...................................................................................................................... 100
III. Regulation and Corruption ..................................................................................... 106
Chapter 7: Informality................................................................................................................. 114
I. Informality ............................................................................................................. 114
II. Microenterprises and SMLEs ................................................................................ 116
III. Registered and Unregistered Microenterprises ...................................................... 121
IV. Sole Proprietorships and Limited Liability Companies ......................................... 128
V. Competition with the Informal Sector ................................................................... 129
References ................................................................................................................................... 132
Appendices .................................................................................................................................. 142
Appendix 1.1: Enterprise Survey in Tanzania—Survey Design ........................................ 142
Appendix 1.2: Comparison of Samples from 2003 and 2006 Surveys............................... 148
Appendix 2.1: Technical Efficiency in Tanzania ................................................................ 150
Appendix 3.1: Differences in Perceptions by Firm Type. ................................................... 158
Appendix 3.2: Differences in Perceptions by Year. ........................................................... 163
Appendix 4.1: Econometric Analysis of Training. .............................................................. 167
Appendix 4.2: Econometric Analysis of Wages .................................................................. 172
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Appendix 5.1: Econometric Analysis of Perceptions about Access to Credit ..................... 176
Appendix 6.1: Effect of Generator Ownership on Firm Performance ................................. 179
Appendix 6.2: Comparison of Doing Business Indicators ................................................. 180
Appendix 6.3: Differences in the Investment Climate across Firms .......................................... 182
I. Differences by region ............................................................................................. 182
II. Differences by sector ............................................................................................. 183
III. Differences for exporters ....................................................................................... 184
Appendix 7.1: Other Factors that Affect Perceptions about Informality ............................. 185
Endnotes ...................................................................................................................................... 189
7
CHAPTER 1: INTRODUCTION
Sustained improvements in living standards depend on broad-based economic growth.
This will only take place when firms invest in workers, capital and technology. But firms will
only do this when the rewards are high enough to earn a fair return on their investment and the
risks associated with investment are not too high.
The investment climate is made of things that affects the risks and returns associated with
investment (Stern, 2002a; Stern, 2002b; Stern, 2002c). In its broadest definition, the investment
climate includes things such as a country‘s climate, its endowment of natural resources, and its
location. For operational purposes, however, the Investment Climate Assessment (ICA) focuses
on things that are directly affected by government policy. These include macroeconomic
stability, labor market regulations, worker education and skills, financial markets, infrastructure,
regulation, and the institutional arrangements that affect the security of property rights, the rule
of law and governance.
The goal of the ICA for Tanzania is to evaluate the investment climate in Tanzania in all
its operational dimensions and to promote policies to strengthen the private sector. The ICA will
largely be based on results from a large firm-level survey (the Enterprise Survey or the World
Bank Enterprise Survey) that collects information on firm performance, the cost of doing
business, the regulatory environment, the labor market, and access to finance. The survey, which
is described in detail in Appendix 1.1, covered manufacturing, retail trade and other services in
five urban areas. A separate survey was done of microenterprises, including informal
microenterprises. Information from the surveys will be supplemented with information from
other sources including the Doing Business Report; analytical reports by the World Bank, the
International Monetary Fund, other international organizations and the Government of Tanzania;
and academic papers and reports. The report will also compare results from the 2006 survey
with results from an earlier Enterprise Survey from 2003 (see Box).
I. Comparator Countries
One advantage of the Enterprise Survey over other firm-level surveys is that Enterprise
Surveys have been completed in over 100 countries throughout the world. The surveys use a
uniform questionnaire and sampling methodology, allowing cross-country comparisons of both
firm performance and investment climate constraints.1 This makes it possible to assess how
Tanzania compares with other countries.
Throughout the report, Tanzania will be benchmarked against two groups of countries
with respect to both firm performance and the investment climate. First, Tanzania will be
compared with other low income countries in Sub-Saharan Africa (SSA), especially within the
region. Enterprise Surveys have been conducted, or about to be conducted, in about 25 to 30
low-income countries in SSA—including Kenya and Uganda. About 15 of these surveys have
been conducted in either late 2006 or early 2007. Comparing Tanzania either with the entire
sample of countries, and particularly with other countries in East Africa, will give some idea
about how Tanzania compares to other low-income countries in SSA where Enterprise Surveys
have been completed. Since an earlier survey was conducted in Tanzania in 2003 (see Box)—
8
although this survey only covered the manufacturing sector—comparisons will also be made
with Tanzania in 2003 where possible (see Appendix 1.2 for a discussion of cross-time
comparisons).
Although regional comparisons are interesting, it is important to note that relatively few
countries in the SSA have successfully managed to sustain rapid growth over long periods of
time and thus been able to enter the ranks of middle-income economies. Moreover, many that
have done so have done so due primarily to natural resources. For example, Botswana, which
was once among the poorest countries in the world, has successfully broken into the ranks of
middle-income economies due to its very successful management of its abundant natural
resources—Botswana is the largest producer of diamonds in the world by value of production.2
Although Botswana‘s success in avoiding the natural resource curse, which has been attributed to
its good policies and strong institutions, is encouraging, resource-based growth is more difficult
for larger countries with fewer resources on a per capita basis to follow.3
Box: The 2003 Investment Climate Assessment
The 2008 Investment Climate Assessment is the second investment climate assessment for
Tanzania. An earlier assessment, based upon a survey completed in 2003, was completed in 2004.
The results of the earlier report were:
Firms in Tanzania were not highly competitive. Although labor productivity was higher than in
Uganda, it was lower than in the more successful manufacturing countries such as Kenya, India
and China. Productivity was a particular problem for small enterprises. Finally, human capital
and technology use was relatively low. Workers were less well educated than in Kenya or
Uganda and firms were less likely to use computers and e-mail than competitors from other low
income countries.
Problems with competitiveness were also reflected in firm‘s poor export performance. Firms in
Tanzania were about 18 percentage points less likely to export than similar firms in Kenya. The
report argued that this also partly reflected problems with trade and customs regulations.
Burdensome regulation contributed to high levels of informality. The report notes that
estimates suggest that up to 58 percent of gross national income is generated by the informal
sector—higher than in Kenya, Uganda, India or China. In part, this seemed to reflect the heavy
burden of regulation and corruption—both of which can discourage firms from becoming
formal. It took 35 days and cost an amount equal to 304 percent of per capital GNI to register a
new business. Moreover, managers of formal firms reported that they spend about 15 percent
of their time dealing with government regulations. This also contributed to corruption—firms
were more likely to report that they needed to pay bribes to get things done than in the
comparator countries.
There were problems in several additional areas of the investment climate that were obstacles to
firm operations and growth. Firms were most likely to say that tax rates, electricity, tax
administration, corruption, and the cost of and access to financing were obstacles to their firm‘s
operations and growth. The objective data was generally consistent with the idea that these
were problems. Firms were less likely to have loans, reported spending more time with tax
officials and had greater losses due to power outages than in the best performing comparator
countries.
One of the goals to the 2008 investment climate assessment will be to see how much progress has
been made in these areas since the previous survey
Source: Regional Program on Enterprise Development (2004b).
9
Moreover, Tanzania should be a more attractive location for investment than many of its
neighbors, which other than Kenya are mostly small and land-locked. Tanzania will therefore
also be compared with additional countries in SSA that are more attractive for investment either
due to their coastal location or because they have successfully diversified out of primary
production into other sectors (Mauritius, South Africa and Swaziland). These countries should
be better comparators in the respect that they will be more attractive for investment than
Tanzania‘s mostly small, land-locked neighbors.
Another successful strategy that countries—especially in East Asia—have followed to
enter the ranks of middle-income economies is through export-oriented manufacturing.
However, with a few exceptions such as Lesotho, few low income countries in Sub-Saharan
Africa have been successful in manufacturing and even fewer have managed to enter
international markets for manufactured goods (see Figure 1). Although there has been
considerable debate over why this is the case, many authors have suggested that the investment
climate plays a role. Using firm-level data, Zeufack (2002) argues that neither endowments nor
observable and unobservable skills explain the poor export performance of textile and garment
firms in Ghana and Kenya relative to similar firms in India. Rather, he argues that weak
institutions explain much of the difference. Similarly, Biggs and others (1996) argue that
although task-level efficiency was lower for garment producers in Zimbabwe, Kenya, and Ghana
than for similar firms in India or China, lower wages offset much of the difference. They argue
that other factors such as poor infrastructure, difficulties associated with access to credit, and
high transactions costs constrain export opportunities in Africa. Finally, Eifert and others (2008)
argue that indirect costs due to problems in the investment climate such as inadequate
infrastructure, corruption and regulation explain why firms in Sub-Saharan Africa find it hard to
compete on international markets with producers in Asia.4
Figure 1: Few low-income countries in Sub-Saharan Africa have diversified into manufacturing.
Source: World Bank (2008c).
0
5
10
15
20
VA
in
ma
nu
factu
rin
g (
% o
f G
DP
) Value added in manufacturing (% of GDP)
10
Because of the problems that many countries in Sub-Saharan Africa have had
diversifying into manufacturing—and because of concern that this is because of investment
climate problems in these countries—Tanzania‘s investment climate will also be compared with
the investment climate in low and middle-income countries in East Asia and Sub-Saharan Africa
that are more attractive investment locations—many of which have successfully diversified out
of primary production into producing and exporting manufactured goods. Therefore, in addition
to the broad comparisons with low-income countries elsewhere in Sub-Saharan Africa, Tanzania
will also be compared with the following countries:
Regional Comparators (small and land-locked): Uganda, Rwanda, Burundi.
Economies in Sub-Saharan Africa that are more attractive investment locations: Kenya,
Mauritius, South Africa and Swaziland.
Successful Manufacturing Economies in East Asia: China, Malaysia, and Thailand.
Manufacturing is more important in these economies with respect to its contribution to
Gross Domestic Product (GDP) than it is in Tanzania (see Figure 2). Value-added in
manufacturing is equal to about 7 percent of GDP in Tanzania. This is slightly lower than in
Kenya (about 12 percent of GDP) and considerably lower than in the best performing countries
in Sub-Saharan Africa (between about 20 and 40 percent of GDP) and the comparator countries
in East Asia (between about 30 and 40 percent).
Because earlier surveys before 2006 covered only the manufacturing sector—and most of
the surveys outside of Africa were conducted in 2004 or 2005, comparisons with countries
outside of Africa include only manufacturing firms. When Tanzania is being compared only with
Figure 2: Manufacturing is less important in Tanzania than in the ‘manufacturing’ comparator countries.
Source: World Bank (2008c).
0
5
10
15
20
25
30
35
40
VA
in m
anufa
ctu
ring (
% o
f G
DP
) Value added in manufacturing (% of GDP)
11
the other countries in Africa where surveys were completed in 2006 or 2007, firms from all
sectors will be included.5
II. Macroeconomic Background
Tanzania is a low income country with GDP per capita equal to $995 (United States [US]
$ in Price Purchasing Parity [PPP] adjusted prices) and a population of 39 million in 2006
(World Bank, 2008c). The country‘s macroeconomic performance has been strong. The country
has grown strongly over the past decade, while maintaining low inflation and adequate
international reserves. The once state-controlled socialist economy has been transformed into a
free market in recent decades.
Although Tanzania has been growing rapidly it remains a poor country, especially in
rural areas. Recent estimates suggest that 36 percent of the population lives below the national
poverty line (United Nations Development Program, 2007), with poverty higher in rural areas
(39 percent) than in urban areas (29 percent). The World Bank estimated that 58 percent of the
population lived on one dollar a day (PPP) in 2000 and 90 percent lived on two dollars a day (see
Table 1).
Table 1: There is still significant poverty and income inequality in Tanzania.
Country Year % below $1 (PPP) per day Year Income share of poorest quintile
Tanzania 2000 57.8 2000 7.3
Burundi 1998 54.6 1998 5.1
Rwanda 2000 51.7 2000 5.3
Kenya 1997 22.8 1997 6.0
South Africa 2000 10.7 2000 3.5
Swaziland 1995 8.0 2000 4.3
Uganda 2002 57.4 2002 5.7
Source: United Nations ( 2008).
Broader measures of poverty (i.e., measures that take into account things other than
income) also suggest that poverty remains high. Tanzania was ranked 159th
out of 177 countries
in the United Nations Development Program (UNDP) Human Development Index for 2007/2008
(United Nations Development Program, 2007), which measures health, education and income.
This is an improvement from 2006, when Tanzania ranked 162nd
, but Tanzania ranks lower than
Uganda (158th
) or Kenya (154th
). Measures that improve the business environment for small and
medium enterprises (SMEs) should reduce poverty (World Bank, 2007d). In this respect,
improving the investment climate should reduce poverty, especially in urban areas.6
Economic Growth
Tanzania‘s economy has grown rapidly in recent years (see Figure 3). GDP growth
averaged 6.5 percent per year between 2000 and 2007, a significant increase from the 1990s,
when growth averaged 3.1 percent per year. GDP growth was very strong in 2005, reaching 7.4
percent before slowing to 6.7 percent in 2006 (World Bank, 2008c). The slowdown in 2006 was
largely due to drought-induced hydropower shortages and high prices for imported fuel
(International Monetary Fund, 2007b).7 Growth recovered, however, in 2007 and recent
12
International Monetary Fund (IMF) estimates suggest that growth should remain high (about 7.5
percent) in 2008 despite upheavals in world markets (International Monetary Fund, 2008b).
Because population growth has been slower in the 2000s than it was in the 1990s (see
Figure 3), per capita GDP growth has outpaced GDP growth. After averaging -0.8 percent
between 1990 and 1994, per capita growth accelerated reaching 1.1 percent between 1995 and
1999 and to 3.9 percent per year between 2000 and 2007.
Although both per capita growth and growth have been slower than in China and per
capita growth has been slower than in Thailand, growth has been faster than in other countries in
the region and in most of the other comparator countries (see Figure 4).
Figure 3: GDP and per capita GDP have both grown quickly over the past decade.
Source: World Bank (2008c).
-4
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8
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% g
row
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GDP growth
GDP per capita growth
Population growth
13
The fastest growing sector is mining (see Table 2). Between 2001 and 2006, mining
grew at an average rate of 15.7 percent per year. Construction has also been growing quickly,
average 10.6 percent over the same period. Trade and tourism and manufacturing have also been
growing relatively quickly. In contrast, agriculture has been growing more modestly with
average growth averaging only 5 percent per year since 2001.
Table 2: Mining and quarrying, along with construction, are the fastest growing sectors.
2001 2002 2003 2004 2005 2006 Average
Agriculture 5.5 5.0 4.0 5.8 5.2 4.1 4.9
Manufacturing 5.0 8.0 8.6 8.6 9.0 8.6 8.0
Mining and quarrying 13.5 15.0 18.0 15.4 15.7 16.4 15.7
Trade & tourism 6.7 7.0 6.5 7.8 8.2 8.4 7.4
Construction 8.7 11.0 11.0 10.8 11.9 10.0 10.6
Transport & Communication 6.3 6.4 5.0 6.0 6.4 7.5 6.3
Finance & Business Services 3.3 4.8 4.4 4.4 5.3 5.5 4.6
Electricity & Water 3.0 3.1 4.9 4.5 5.1 -1.8 3.1
Public Administration 3.5 4.1 4.1 4.3 5.1 5.1 4.4
Source: The President‘s Office, Planning and Privatization (2007).
Despite rapid growth in some non-traditional sectors, agriculture remains the largest
sector in Tanzania, accounting for 45 percent of GDP in 2006 (see Figure 5). About 80 percent
of the population lives in rural areas (International Monetary Fund, 2007b). Manufacturing is
considerably less important, accounting for only about 6 percent of GDP in 2006, construction
accounts for an additional 6 percent, while mining and quarrying—despite rapid growth and its
importance with respect to exports—accounts for only about 3 percent of GDP.
Figure 4: Although Tanzania compares more favorably with the comparator countries with respect
to GDP growth, per capita growth has also been strong.
Source: World Bank (2008c).
0 5 10 15
Burundi
Swaziland
Kenya
Mauritius
South …
Thailand
Malaysia
Rwanda
Uganda
Tanzania
China
Ave. GDP Growth (%)
Average GDP Growth (00-06)
-5 0 5 10
Burundi
Swaziland
Kenya
Uganda
Rwanda
South Africa
Mauritius
Malaysia
Tanzania
Thailand
China
Ave per capita GDP growth (%)
Ave. Per Capita GDP Growth (00-06)
14
Investment
Despite Tanzania‘s fast growth, gross fixed capital formation has remained relatively low
(see Figure 6). Gross fixed capital formation (GFCF) was equal to about 18 percent of GDP
between 2000 and 2006. Although this is far lower than in China (38 percent of GDP) and
Thailand (25 percent of GDP), it is comparable to many countries in the region (e.g., Kenya,
Tanzania and Rwanda). GFCF has remained between about 17 and 19 percent since 2000.
Figure 5: Agriculture remains an important sector in Tanzania.
Source: National Bureau of Statistics (2007).
Figure 6: Gross fixed capital formation has been relatively high.
Source: World Bank (2008c).
Agriculture46%
Mining and
Quarrying3%
Manufacturing6%
Construction6%
Services39%
0
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15
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25
30
35
40
45
Buru
ndi
South
A
fric
a
Kenya
Tanzania
Sw
azila
nd
Rw
anda
Uganda
Mauritius
Mala
ysia
Thaila
nd
Chin
a
GF
CF
(%
of
GD
P)
GFCF as % of GDP (average 00-06)
15
Foreign Direct Investment
In the late 1990s, inward foreign direct investment (FDI) increased greatly reaching 6
percent of GDP in 1999 and remaining above 5 percent of GDP in 2000 (see Figure 7). This has
largely been due to large inflows of FDI into the gold mining sector. Since this time, FDI has
fallen slightly as FDI has shifted to expanding and improving existing mines rather than in new
mines (Economist Intelligence Unit, 2007a).
Despite the drop since 1999-2000, foreign direct investment (FDI) remains high. On
average, FDI was equal to about 3.8 percent of GDP between 2000 and 2006, slightly higher
than in Thailand (3.7 percent), Uganda (3.5 percent) and China (3.2 percent) and significantly
higher than other countries in the region such as Rwanda, Kenya and Burundi (see Figure 7).
Economic Policy
The Government‘s Development Plan, the National Strategy for Growth and the
Reduction of Poverty (NSGRP or MKUKUTA in Swahili) is in effect from 2005 until 2010. The
plan‘s goals include annual growth of between 6 and 8 percent. The plan centers around three
clusters: 1) growth of the economy and reduction of income poverty, 2) improvement of quality
of life and well-being and 3) governance and accountability. Emphasis is placed on direct
budget support to the government from donors to increase spending on priority areas such as
education, health, agriculture linked to the MKUKUTA goals. MKUKUTA is supported by a
World Bank Joint Assistance Strategy for Tanzania (JAST). Discussed by the board at the end
of April 2007 for the period 2007/08 through 2009/10, the strategy aims to coordinate donors,
harmonize aid modalities and make aid more effective. This approach also relies more on
government systems and processes.
Figure 7: Although FDI has slowed since 1999 and 2000, it remains very high in Tanzania.
Source: World Bank (2008c).
0 2 4 6
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
Inward FDI (% of GDP)
FDI in Tanzania (% of GDP)
0 2 4 6
Burundi
Kenya
Rwanda
Mauritius
South Africa
Swaziland
Malaysia
China
Uganda
Thailand
Tanzania
Inward FDI (% of GDP)
FDI (% of GDP, ave. 00-06)
16
Tanzania is also carrying out a three-year Policy Support Instrument (PSI) with the IMF.
Under this arrangement the IMF monitors the Tanzanian economy, but as financing is no longer
needed does not provide any financing. This program replaces the previous Poverty Reduction
and Growth Facility which ended in December 2006. Under the PSI, emphasis is being placed
on the efforts of the Government to increase domestic revenue with improved tax and customs
policies and administrative systems. Also, efforts are being made to increase credit available to
the private sector through financial sector reforms. Additional growth is to be stimulated by
undertaking measures to improve the business climate, by immediately addressing the energy
crisis, increasing transparency and improving the regulatory environment.
Macroeconomic Stability
Prudent economic management has allowed the Government to significantly reduce
inflation since the early 1990s, when it exceeded 20 percent per year. It had fallen into the single
digits by 1999 and fell further through 2004 (see Figure 8). There was a sharp increase in
inflation in 2005, to over 8 percent, due to drought-related energy shortages and the high cost of
imported oil. It has remained relatively high since 2005, averaging over 6 percent in 2006 and
2007 (see Figure 8). Inflation accelerated further in 2008, reaching over 11.6 percent on an
annual basis by September 2008, largely due to rising food prices.8
There has been a steady depreciation of the official exchange rate over the last decade.
Although the exchange rate has largely been determined by market forces, the Bank of Tanzania
limits short-term fluctuations with occasional interventions on the inter-bank foreign exchange
markets. International reserves have been steady and were at 4.4 months worth of imports in
2007 (International Monetary Fund, 2007b).
Figure 8: Although inflation is lower than in the 1990s, it has increased in recent years.
Source: World Bank (2008c).
0
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10
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25
30
35
40
1…
1…
1…
1…
1…
1…
1…
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1…
2…
2…
2…
2…
2…
2…
2…
2…
Inflation (
CP
I)
Average annual CPI inflation (percent)
17
The real effective exchange rate (REER) has been more volatile. Between 1995 and
2001 the REER appreciated by almost 50 percent. It then depreciated rapidly after the Bank of
Tanzania reduced aid absorption in 2001 (Hobdari, 2008), returning to its 1995 level by the
middle of the 2000s (World Bank, 2007d). Estimates suggested that the exchange was slightly
undervalued relative to its equilibrium level at the time of the survey.9 In this respect, despite the
increase in gold exports, it is unlikely that this has had a significant impact on the exporting
potential of manufacturers at the time of the survey.
Fiscal Performance
Budget expenditures are planned at 27.9 per cent of GDP during 2007/08 (up from 23.5
percent in 2006/07 and 16 percent in 1999/2000) (International Monetary Fund, 2007b;
International Monetary Fund, 2008a). Although tax revenues have been consistently lower than
expenditures, they have been increasing. Tax revenues were projected to be 15.2 percent of GDP
in 2007/08, up from 13.3 percent in 2006/2007 and 11.5 percent in 2005/06. The increase is due
to tax and customs administration reforms have resulted in increased revenues.
The resulting government deficit has been quite large (see Figure 9). It was projected to
be 11.2 percent of GDP in 2007/08, up from 10.4 percent in 2005.06 and 9.1 percent in 2005/06
and 2006/07. Grants have covered an increasingly large share of the deficit. Grants increased
from 5.4 percent of GDP in 2005/06 to 7.8 percent in 2007/08.
External Debt
Tanzania‘s external debt is at a sustainable level at net present value (NPV) of public and
publicly guaranteed external debt is estimated at 16 percent of GDP and the ratio of the NPV of
debt to exports is below 150 percent. This development follows significant debt relief (US$3.8
Figure 9: External grants have been moderating fiscal deficits.
Source: International Monetary Fund (2007b; 2008a).
-3000
-2500
-2000
-1500
-1000
-500
0
2004/05 2005/06 2006/07 2007/08
Deficits (
bill
ions o
f T
anzania
shill
ings)
before grants after grants
18
billion) under the Enhanced Heavily Indebted Poor Countries Initiative (HIPC) and the
Multilateral Debt Relief Initiative (MDRI) (International Monetary Fund, 2007b). HIPC involves
the cancellation of bilateral, multilateral and commercial debt. The reduction of debt frees funds
planned for high-priority social expenditures and critical projects to increase growth. MDRI is
for countries such as Tanzania, who have already reached the ―completion point‖ of the HIPC
initiative. The MDRI initiative involves the cancellation of debt to the World Bank‘s
International Development Association (IDA), the International Monetary Fund (IMF) and the
African Development Bank (AfDB). This program provides additional debt relief in order to
support the attainment of the United Nations (UN) Millennium Development Goals. Debt
sustainability also helped by growth in exports.
Trade Policy
The East African Community (EAC) was established on July 7, 2000 by its three
members: Tanzania, Kenya and Uganda. The emergence of the EAC has succeeded in
expanding the local markets of each member. The EAC countries accord each other at least Most
Favored Nation (MFN) treatment. In January 2005, the EAC Common External Tariff (CET)
came in to force and will be reviewed in 2009. The average applied tariff rate is 12.9 percent.
While the average rate of tariff protection in Tanzania and Kenya has come down, in Uganda it
has increased (Secretariat, 2007) and external tariffs of the customs union remain high. Both
Uganda and Tanzania are concerned about the threat of competition from Kenya when the
internal tariffs among members are eliminated in 2010. This may mean the continuance of non-
tariff barriers to trade as the tariffs among EAC members are further reduced (Economist
Intelligence Unit, 2007b).
Trade
Since the beginning of the decade, exports have increased significantly (see Table 3). In
1999, exports were equal to about 15 percent of GDP. By 2006, they had increased to 22 percent
of GDP. Despite the increase, the trade balance has not improved significantly over this period
since imports have also increased significantly—from 26 percent of GDP in 1999 to 28 percent
in 2006. As a result, the trade balance has not improved since 2000, remaining between about 6
and 8 percent of GDP over this period. The current account deficit has, however, increased
significantly over this period—mostly because net current transfers have fallen as a share of
GDP over this period.
Table 3: Trade balance, 1999-2006.
1999 2000 2001 2002 2003 2004 2005 2006
Current account balance -10 -5 -2 1 -1 -3 -6 -10
Trade Balance -11 -7 -8 -7 -7 -7 -7 -6
Imports 26 24 24 24 26 30 27 28
Exports 15 17 16 17 20 22 21 22
Source: World Bank (2008c).
A significant reason for the substantial increase in exports is the large increase in exports
of gold (see Figure 10). Between 2000 and 2006, gold exports, including ore, increased from
about $114 million in 2000 (17 percent of exports) to about $770 million (46 percent of exports)
19
in 2006. Most remaining exports are of agricultural goods and food products. Manufactured
goods makes up as smaller share of exports.
III. The World Bank Enterprise Survey
The main source of information for Investment Climate Assessment is the World Bank
Enterprise Survey that was conducted in late 2006. Information from the survey will be
supplemented with information from other sources including the Doing Business Report;
analytical reports by the World Bank, the International Monetary Fund, other international
organizations and the Government of Tanzania; and academic papers and reports.
Two surveys were conducted as part of the World Bank Enterprise Survey. The surveys
and sampling are described in detail in Appendix 1.1. The first survey covered establishments
with five or more employees in the manufacturing, retail trade, and other service sectors.10
The
survey did not cover agriculture, mining, financial services or government services (e.g., health
and education). Firms were sampled from a comprehensive list of establishment kindly provided
by National Bureau of Statistics in for Mainland Tanzania and by the Office of the Chief
Statistician for Zanzibar.11
The second survey covered establishment with fewer than five
employees in the same sectors. Informal firms were included in the microenterprise survey.12
Characteristics of Surveyed Firms
Table 4 presents unweighted sample sizes by sector. The Small, Medium-sized and
Large Enterprise (SMLE) sample is evenly divided between manufacturing, retail trade, and
other services. Because the manufacturing sample covers most establishments in the sector,
while the retail trade and other services samples are only sub-samples, weights are used
throughout the report to appropriately combine information across sectors for all summary
statistics. The microenterprise sample is dominated by retail establishments, which make up
Figure 10: The increase in exports is mostly due to large increase in gold exports.
Source: UN Comtrade/World Bank WITS.
$0
$250,000
$500,000
$750,000
$1,000,000
200
0
200
1
200
2
200
3
200
4
200
5
200
6
US
$ (
000s)
Exports by sector (US$, 000s)
Fish
Coffee
Tobacco
Gold
20
close to three-quarters of the sample—there are only a small number of light manufacturing
firms and other service firms.
Table 4: Unweighted Sample Size, by Sector.
SMLEs Microenterprises
Total 419 65
Manufacturing 273 27
Food 70 9
Garments 51 9
Other 152 9
Retail 65 27
Other Services 81 11
Source: World Bank Enterprise Survey.
Because manufacturing firms are over-sampled relative to other sectors, they make up a
smaller share of the weighted sample (27 percent) than of the unweighted sample. Retail trade
enterprises make up about 23 percent of the weighted sample, while other services make up the
remaining 51 percent. About 70 percent of the sample is from Dar es Salaam, about 12 percent
from Arusha, about 14 percent from Zanzibar with the remaining 4 percent from Mbeya. About
14 of the manufacturers exported at least some of their output.
The sample is heavily weighted towards small and medium-sized enterprises—only about
5 percent of the weighted sample had over 100 employees. Most firms were at least partly
indigenously owned (82 percent), although a substantial minority was partly Asian-owned. Less
than half of the firms had any female owners. Very few firms were majority foreign-owned,
only about 8 percent of the sample.
Table 5: Sample characteristics of SMLEs, weighted.
Percent of Sample
(Weighted)
Percent of Sample
(Weighted)
Dar es Salaam 70 Manufacturing 27
Arusha 12 Retail 23
Mbeya 4 Other Services 51
Zanzibar 14
Any female owner 31
Exporters (manufacturing) 14 Any black owner 82
Non-Exporters (manufacturing) 86 Any white owner 3
Any Asian owner 18
Micro (less than 5 employees) 0 Any Lebanese owner 3
Small (5-19 employees) 72
Medium (20-99 employees) 23 Foreign-owned 8
Large (100 and up) 6 Domestically owned 92
Source: World Bank Enterprise Survey.
Table 6 presents similar data for microenterprises. In comparison to the SMLE sample,
the microenterprises were more likely to be partly indigenously owned (97 percent of
microenterprises compared to 82 percent of SMLEs) and were very unlikely to be even partly
white or Asian-owned. The microenterprise was heavily dominated by retail establishments (42
percent) and light manufacturing (42 percent), with a few firms involved in other services (17
percent). As in most counties where microenterprise surveys have been completed,
21
microenterprises are primarily involved in domestic markets—only a couple of the
microenterprises in the light manufacturing sector exported anything.
Table 6: Sample characteristics of microenterprises.
Percent of Sample
(Unweighted)
Percent of Sample
(Unweighted)
Dar es Salaam 66 Manufacturing 42
Arusha 8 Retail 42
Mbeya 14 Other Services 17
Zanzibar 12
Any female owner 35
Exporters (manufacturing) 4 Any black owner 97
Non-Exporters (manufacturing) 96 Any white owner 0
Any Asian owner 0
Micro (less than 5 employees) 100 Any Lebanese owner 2
Small (5-19 employees) 0
Medium (20-99 employees) 0 Foreign-owned 0
Large (100 and up) 0 Domestically owned 100
Source: World Bank Enterprise Survey.
22
CHAPTER 2: AN ANALYSIS OF FIRM PERFORMANCE
Before looking at the investment climate, it is useful to look at firm performance. This
gives context for the results in later chapters and can provide useful information on some aspects
of the investment climate that might be particularly binding for growth. This chapter therefore
looks at how SMLEs in Tanzania perform when compared with SMLEs in Uganda and Kenya,
successful exporters in Sub-Saharan Africa, successful exporters in other regions and other
countries in Sub-Saharan Africa. The different measures of firm performance indicate how
competitive SMLEs are in both international and domestic markets. While this chapter provides
an overview of how well firms in Tanzania perform, later chapters assess how the investment
climates affect their competitiveness.
I. Firm Performance
As a preliminary analysis of SMLEs‘ competitiveness, this section examines several
traditional measures of firm productivity. The chapter first examines labor productivity—the
amount of output per worker that firms produce. It then looks at capital stock and capital
productivity in the manufacturing sector and compares labor productivity with labor costs, to
obtain the unit labor costs. These measures are compared across different SMLEs within
Tanzania and also to SMLEs in other countries. This is followed by an analysis of total factor
productivity, a measure of firm performance that takes into account use of both capital and labor.
To ensure that the results are comparable across countries, and because the standard
methodology is only appropriate for the manufacturing sector, results in this chapter only cover
SMLEs in that sector unless otherwise noted.
Labor Productivity
Labor productivity, the per worker output that the firm produces less the cost of raw
materials (such as iron or wood) and intermediate inputs (such as engine parts or textiles) used to
produce the output, is a basic measure of firm productivity. Labor productivity is higher in firms
that produce more with fewer workers and raw materials. Differences in labor productivity can
be the result of differences in technology, differences in organizational structure, differences in
worker skills, differences in management ability, or differences in the amount of machinery and
equipment that the firm uses. Because labor productivity does not take the use of capital (i.e.,
machinery and equipment) into account, it will generally be higher in firms that use machines in
place of labor (i.e., firms that are capital intensive).
Labor productivity is comparable in Tanzania to other low income countries in Sub-
Saharan Africa. Value-added per worker is about $3,000 in Tanzania (see Figure 11). Since
value-added worker is between about $1,000 and $4,000 for most low-income countries in the
region, this puts Tanzania towards the upper end of productivity among countries in SSA.
Tanzania, however, lags behind the leading countries in the region in this respect. For example,
labor productivity is considerably higher in Kenya—about $7,000 per worker. It is also lower
than in any of the middle-income countries in Africa for which comparable data are available
(e.g., Botswana or Namibia).
23
Although labor productivity is similar to other countries in Sub-Saharan Africa and is
higher than many neighboring countries (e.g., Rwanda, Burundi, and Uganda), labor productivity
compares less favorably with successful manufacturers in Africa and East Asia. Labor
productivity is slightly higher in Tanzania than in India. But it is less than half as high as in
Kenya, Swaziland or Mauritius and is less than one-third as high as in China, Malaysia, or South
Africa (see Figure 12).
One problem with labor productivity is that it is generally lower in firms that are labor
intensive (i.e., firms that use little capital per worker). Since firms in some sectors (e.g.,
garments) tend to more labor intensive than others, if industry in a country is a concentrated in
these sectors, then productivity might appear to be artificially low. One way of dealing with this
is to calculate total factor productivity—a measure of productivity that takes the firms‘ use of
capital and the firm‘s sector and size into account. Another approach is to focus on a single sub-
sector of manufacturing. In general, since labor intensity will vary less within a single sub-sector
than in manufacturing overall, this will partially reduce these concerns.
Figure 12, therefore, shows comparisons for the garments and food, beverage and
agroprocessing sectors. The garments sector is chosen because garments are internationally
traded and there is a relatively well established production technology. Tanzania compares
Figure 11: Labor productivity in Tanzania is comparable to labor productivity in other low income countries
in Sub-Saharan Africa.
Source: World Bank Enterprise Surveys.
Note: Data were collected between 2002 and 2006 depending on country. Data collected prior to or after 2005 is
converted to 2005 figures using GDP deflators and to US dollars using 2005 exchange rates. See notes to Table 7
for more information on how value added is calculated. Several African countries with investment climate date on
labor productivity are excluded from the graph for presentational purposes. South Africa is omitted since labor
productivity is far higher than in other countries in Sub-Saharan Africa. This does not affect the relative position of
Tanzania on the graph. Cross-country comparisons are for manufacturing firms only.
$0
$5,000
$10,000
$15,000
$20,000
Ghana
Eth
iopia
Gam
bia
Guin
ea-C
onakry
Ma
da
ga
sca
r
Guin
ea-B
issau
Bu
run
di
Uganda
Rw
anda
Mala
wi
Benin
Ta
nzania
Maurita
nia
Eritr
ea
Za
mbia
Lesoth
o
Burk
inaF
aso
Angola
Nig
eria
Ma
li
Kenya
Sw
azila
nd
Mauritiu
s
Bots
wana
Senegal
Nam
ibia
Valu
e-a
dded p
er
work
er
(2005 U
S$) Value Added per Worker (in 2005 US$)
24
somewhat less favorably when focusing on this sector alone. Labor productivity in the garment
sector remains lower than in most of the successfully manufacturing economies (e.g., Swaziland,
Kenya, China and Thailand). But it is also lower than in India and Uganda, even though overall
labor productivity was higher in Tanzania. Consistent with the idea that firms are not very
competitive, relatively few garment firms from Tanzania sell on international markets—only 15
percent of the SMLEs exported any part of their output compared to 70 percent in South Africa
and 85 percent in Mauritius.
Garments, however, is not a particularly important sector in Tanzania. It is therefore
interesting to look at the agroprocessing sector (see Figure 12). Tanzania compares more
favorably with respect to this sector than it does for the garment sector. Once again, productivity
at the median firm is slightly higher than in the regional comparators other than Kenya and is
also higher than in India. The median firm, however, is slightly less productive than in the other
comparator countries. Overall, although Tanzania compares less favorable with respect to the
garments sectors, these results suggest that the numbers from the overall manufacturing sectors
are probably reasonable. Firms in Tanzania lag behind firms in the best performing countries,
although they appear more productive than firms in some low-income countries in SSA.
Figure 12: Although labor productivity is higher than in many nearby countries, labor productivity is
lower than in economies that have been more successful in manufacturing.
Source: World Bank Enterprise Surveys.
Note: See footnotes to Table 7 and Figure 11 for methodology. Cross-country comparisons are for
manufacturing firms only. Countries are omitted if there are less than 10 firms with productivity data in that
sector.
0 20000 40000
Tanzania
Burundi
Uganda
Rwanda
Kenya
Swaziland
Mauritius
South …
India
Thailand
China
Malaysia
Value-Added per Worker(US$ 2005)
All SMLEs
0 10000 20000
Tanzania
Burundi
Uganda
Swaziland
Kenya
Mauritius
South …
India
China
Thailand
Malaysia
Value-added per Worker (US$ 2005)
Garment SMLEs
0 20000 40000
Tanzania
Uganda
Rwanda
Burundi
Kenya
Mauritius
Swaziland
South …
India
China
Thailand
Malaysia
Value-added per Worker (US$ 2005)
Agroprocessing SMLEs
25
Table 7: Median productivity, by enterprise characteristics.
Value-Added
per Worker
Labor Cost
per Worker
Unit Labor
Costs
Capital Per
Worker
(sales value)
Capital
Productivity
(Sales Value)
All $3,006 $797 29% $2,625 133%
Sector
Garments $2,189 $507 28% $443 393%
Food and Beverage $3,654 $1,013 30% $2,657 178%
Chemicals $17,273 $1,325 8% $14,394 100%
Construction Materials $3,030 $744 21% $3,543 179%
Furniture $1,631 $689 43% $1,181 129%
Paper & Publishing $5,083 $988 28% $10,066 88%
Plastics $5,707 $1,059 11% $8,642 65%
Other $4,259 $1,107 27% $4,543 81%
Size
Small (5-19 employees) $2,145 $677 35% $1,107 180%
Medium (20-99 employees) $5,006 $1,092 20% $4,482 112%
Large (more than 100 employees) $14,187 $1,297 12% $14,763 60%
Manager Education
University or higher $5,811 $1,119 20% $6,327 88%
Vocational $1,987 $667 41% $1,119 163%
Secondary or less $1,724 $709 41% $580 257%
Exports
Exporters $6,083 $689 12% $7,268 133%
Non-exporter $2,676 $824 30% $2,188 134%
Foreign Ownership
Foreign Owned $14,187 $1,703 12% $21,415 57%
Domestic $2,613 $744 31% $2,000 141%
Internet Use
Internet User $9,729 $1,263 14% $9,491 98%
Non-user $1,958 $667 42% $1,045 180%
Training
Formal Training Program $4,874 $988 24% $3,951 145%
No Formal Training Program $2,613 $749 31% $2,126 129%
Bank Credit
Bank Credit $9,212 $1,114 13% $7,268 94%
No Bank Credit $2,336 $720 35% $1,303 163%
Source: World Bank Enterprise Survey.
Notes: See Figure 11 for description of exchange rates used to convert data to 2005 prices. All values are medians
for enterprises with available data. Value added is calculated by subtracting intermediate inputs and energy costs
from sales from manufacturing. Workers include both permanent and temporary workers. Capital is the sales value
of machinery and equipment (i.e., the amount the manager thinks his machinery and equipment would cost if sold in
its current condition) . Labor cost is the total cost of wages, salaries, allowances, bonuses and other benefits for both
production and non-production workers. Unit labor costs are labor costs divided by value-added and capital
productivity is value added divided by the sales value of machinery and equipment.
Within Tanzania there are significant differences in labor productivity between firms in
different sector, of different sizes and by other firm characteristics. As noted above, firms in the
garment sector tend to be less productive than firms in other sectors. The median garment firm
produces about $2,200 of output per worker—less than any other sector except for furniture.
There is a strong negative correlation between capital intensity—the amount of capital the firm
has per worker and labor productivity. Firms in sectors that use a lot of machinery and
equipment produce more per worker than firms in sectors that use less capital. For example,
26
firms in the garment and furniture sectors—the two sectors with the lowest labor productivity—
use less capital than firms in other sectors, while firms in the chemicals sector—the sector with
the highest labor productivity—use more capital per worker than any other sector.
Labor productivity increases with firm size. The median small enterprise produces about
$2,100 of output per worker compared to about $5,000 for the median medium-sized enterprise
and about $14,200 per worker for the median large enterprise. A similar pattern can be observed
for capital intensity. The median small enterprises uses about $1,100 of capital per worker,
compared to $4,500 and $14,800 per worker for medium-sized and large enterprises. This
suggests that one reason why labor productivity is higher for large firms is that these firms are
more capital intensive.
Although large firms are more productive than small firms in most countries in Sub-
Saharan Africa including Kenya and Uganda (see Figure 13), the difference is particularly large
in Tanzania. In fact, although small enterprises in Tanzania are less productive than small firms
in Kenya, large firms are more productive on average than similar firms in Kenya.
There are other differences in productivity. As in most countries, exporters are more
productive than non-exporters and foreign-owned firms are more productive than domestically
owned firms. Firms with training programs are more productive than firms without training
programs, firms that use technology more intensively are more productive, and firms that have
bank credit are more productive than those without. Finally, firms with university educated
managers are more productive than firms with less well educated managers. All these
differences are statistically significant at a 5 percent level or higher.
Figure 13: Although small firms in Tanzania are relatively unproductive, large firms appear to be relatively
productive.
Source: World Bank Enterprise Surveys.
Note: See footnotes to Table 7 and Figure 11 for methodology. Cross-country comparisons are for manufacturing
firms only.
$0
$5,000
$10,000
$15,000
$20,000
Kenya Tanzania Uganda
Valu
e-a
dded p
er
work
er
(2005 U
S$)
Small
Medium
Large
27
Although these differences are large and statistically significant, these differences do not
control for other differences between different types of firms. For example, although exporters
are on average more productive than non-exporters, they are also more likely to be foreign-
owned, use technology more intensively, are larger than non-exporters and use capital far more
intensively than other firms do. Similarly, since size and ownership are also correlated with
export behavior, they might be more productive for these reasons rather than because of their
export status. In the section on total factor productivity, these differences are controlled for
making it possible to see whether exporters are more productive than non-exporters after
controlling for differences in size, technology use, capital intensity and foreign ownership.
It is also important to remember that correlation does not imply causation. For example,
although firms with loans are more productive than firms without loans, this could be because
banks are more likely to give loans to firms that are already more productive or it could be
because firms that receive loans can invest in making themselves more productive. Similarly,
exporters might be more productive than non-exporters because only productive firms can enter
international markets or might be more productive because exposure to foreign markets improves
access to foreign technologies.
Labor Costs
The cost of labor, which includes wages, salaries, bonuses, other benefits, and social
payments, is comparable to other low income countries in Sub-Saharan Africa. For the median
firm, labor costs are close to $800 per worker. This is fractionally higher than in Uganda,
Burundi or Rwanda. Since labor productivity is also higher than in these other countries, this
suggests that Tanzanian firms should be relatively competitive in regional markets. Wages are
about one-third to one-quarter lower than in most middle-income countries in Sub-Saharan
Africa and are also only about one-half the level of labor costs in Kenya.
Although the median firm in Tanzania spends more per worker on wages, salaries and
other benefits than in several other countries in the region, wages are lower than in most of the
more successful manufacturing economies in Sub-Saharan Africa and Asia (see Figure 15). For
example, the median firm in China spends about $1,250 per worker on wages, salaries, and
benefits and the median firm in Thailand spends about $1,750 per worker—50 percent higher
than and twice as high as in Tanzania respectively. In this respect, the cost of labor does not
appear to be a major drag on firm competitiveness.
28
Although labor cost per worker gives some indication of labor costs, differences in labor
costs can reflect differences in things such as worker education and worker skills. That is, labor
costs might be low because the cost of labor is low or might be low because workers are poorly
educated or unskilled and, hence, are less productive. Because wages and productivity are both
relatively low in Tanzania, firms could potentially remain competitive despite low labor
productivity.
Unit labor costs (labor costs as a percent of value-added) are a measure of labor costs that
make it easier to assess the net impact of labor costs on competitiveness by taking differences in
productivity into account when assessing labor costs. Unit labor costs are higher when higher
labor costs are not fully reflected in higher productivity. When unit labor costs are higher (i.e.,
when labor costs are higher compared to productivity), all else equal, firms will find it more
difficult to compete on international markets than when they are lower. Although unit labor
costs are not the only factor that affect competitiveness—for example, they do not take the cost
of capital or capital intensity into account—they are a better measure of competitiveness than
labor costs alone.
Tanzania compares relatively favorably with other low-income countries in Sub-Saharan
Africa with respect to unit labor costs. Although wages are slightly higher in dollar terms than in
Rwanda, Burundi, and Uganda, labor productivity is also higher. Because the difference is
greater for labor productivity, unit labor costs are actually lower than in any of these other
countries.
Figure 14: Labor costs are comparable to—or lower than—other low income countries in Sub-Saharan
Africa.
Source: World Bank Enterprise Surveys.
Note: See footnotes to Table 7 and Figure 11 for methodology. Cross-country comparisons are for manufacturing
firms only.
$0
$1,000
$2,000
$3,000
$4,000
Guin
ea-C
onakry
Ghana
Eth
iopia
Madagascar
Gam
bia
Buru
ndi
Mala
wi
Uganda
Rw
anda
Mozam
biq
ue
Ta
nzania
Congo, D
R
Guin
ea-B
issau
Benin
Nig
er
Eritr
ea
Mali
Burk
ina F
aso
Maurita
nia
Nig
eria
Za
mbia
Kenya
Lesoth
o
Sw
azila
nd
Angola
Bots
wana
Cam
ero
on
Se
ne
ga
l
Cape V
erd
e
Nam
ibia
Mauritiu
sPer
work
er
labor
costs
(2005 U
S D
olla
rs)
Labor costs per worker (US$)
29
Unit labor costs also compare favorably with some of the successful manufacturing
countries in Sub-Saharan Africa and Asia. Although unit labor costs are considerably higher
than in China or India and slightly higher than in Kenya and Thailand, they are about the same as
in Swaziland, are slightly lower than in Malaysia and are far lower than in South Africa and
Mauritius. Compared with these economies, the low level of productivity in Tanzania is set off
with relatively low wages. This suggests that productivity would have to improve if Tanzanian
firms were to remain competitive while paying higher wages.
Within Tanzania, there are some interesting patterns with respect to wages (see Table 7).
Not surprisingly, wages tend to be lower in sectors where labor productivity is lower. Wages
costs are lowest in the garment and furniture sectors—the two sectors where labor productivity is
lowest—and highest in chemicals sectors—the sector with the highest median productivity.
Although most measures of firm performance cannot be calculated for firms in the retail
trade and services sectors, it is possible to calculate labor costs for these firms. Although
manufacturing is often seen as a high wages sector, wages are lower in the manufacturing sector
than in the retail trade or service sectors. Labor costs are about $800 per worker in the
manufacturing sector, $900 per worker in the retail trade sector and $1300 per worker in the
service sector. The differences between the medians for retail trade and manufacturing and other
services and manufacturing are statistically significant.
Wage costs also tend to be greater for larger firms. Median labor costs are about $1,300
per worker for large firms, $1,100 per worker for medium-sized firms and $700 per worker for
Figure 15: Although labor productivity is higher than in many nearby countries, costs are lower than
in economies that have been more successful in manufacturing and so remain competitive.
Source: World Bank Enterprise Surveys.
Note: See footnotes to Table 7 and Figure 11 for methodology. Cross-country comparisons are for
manufacturing firms only.
$0 $5,000 $10,000 $15,000
Tanzania
Burundi
Uganda
Rwanda
Kenya
Swaziland
Mauritius
South Africa
India
China
Thailand
Malaysia
Labor Costs per Worker (US$ 2005)
Per worker labor cost (2005 US$)
-20% 0% 20% 40% 60%
Tanzania
Burundi
Uganda
Rwanda
Kenya
Swaziland
Mauritius
South Africa
China
India
Thailand
Malaysia
Unit Labor Cost (as % of value-added)
Unit labor costs
30
small firms. The differences between large and medium-sized and between medium-sized and
small firms are statistically different from zero at a 5 percent level or higher. Because labor
productivity increases more rapidly with size than per worker labor costs, unit labor costs tend to
fall with firm size.13
That is, although large firms pay more per worker than small firms their
productivity is relatively higher than their labor costs.
There are some other differences. Wages costs tend to be greater for foreign-owned
firms than domestically owned firms, for firms that used the Internet, and for firms with bank
credit. However, as for large firms and medium-sized firms, productivity tends to be relatively
higher for these firms than wages costs are, meaning that unit labor costs are actually lower for
these firms. The difference between exporters and non-exporters with respect to labor costs is
small and statistically insignificant. But because exporters are more productive, unit labor costs
are lower for exporters than non-exporters.
Capital Productivity
Differences in labor productivity often reflect differences in capital use. Firms that have
more capital usually produce more output per worker than firms with less capital. For this
reason, this chapter also looks at capital intensity, how much capital the firm has per worker, and
capital productivity, how much the firm produces relative to the capital it has.
Although these measures provide some context for the previous results, it is important to
note that it is more difficult to measure capital than it is to measure labor (e.g., it is relatively
easy to measure wages and number of workers). Because most machinery is long-lived,
providing services over a long period of time, it is difficult to measure its contribution to output
in a single year. As capital ages, it becomes less productive (i.e., it depreciates in value) and will
eventually stop producing anything, either by breaking or becoming obsolete. Although
accounting rules for depreciating machinery and equipment exist, these often bear little
resemblance to true rates of economic depreciation—and can vary across countries. The book
value of capital (i.e., the value of capital included in company accounts) is therefore not an
especially accurate measure of the value of capital—especially for small firms that do not keep
detailed audited accounts.
31
As an alternate measure of the value of capital, recent World Bank Enterprise Surveys
have also asked firm managers how much it would cost to replace their equipment in its current
condition. Although this is a useful measure of capital—and provides an additional check on
results—in practice, markets for used capital are thin. Because of this, firm managers might not
know the true value of their capital—especially if the equipment is old or if they have not
purchased similar equipment for several years. These questions were not asked in older surveys
for countries outside of Africa, meaning that this section use the book value of capital when
making comparisons with countries outside of Africa. However, because the sales value
provides a more intuitive measure of the value of machinery and equipment, sales value is used
for comparisons within Africa and comparisons within Tanzania. In practice, the results within
Africa are not highly sensitive to the measure of capital that is used.
The median firm in Tanzania uses about $2,600 of capital per worker (valued at sales
value). This is slightly higher than regional comparators such as Uganda, Burundi and Rwanda
(between about $1,700 and $2,400) and is higher than many other low-income countries in the
region (see Figure 16).14
It is less, however, than in many of the countries in sub-Saharan Africa
where productivity is highest (e.g., Senegal, Kenya, Botswana, or Namibia). This suggests that
the differences in labor productivity between these countries and Tanzania are at least partly due
to differences in capital use—rather than to other differences such as technology, worker skills or
Figure 16: Although firms in Tanzania are less capital intensive than in the most capital intensive countries,
they are more capital intensive than in many low-income countries in the region.
Source: World Bank Enterprise Surveys.
Note: See footnotes to Table 7 and Figure 11 for methodology. Cross-country comparisons are for manufacturing
firms only. South Africa and Nigeria are omitted for presentational purposes since the median firms are more
capital intensive than in the other countries (between $13,000 and $15,000 per worker). This figure uses sales value
of capital.
$0
$2,500
$5,000
$7,500
$10,000
Guin
ea-C
onakry
Ghana
Congo, D
R
Buru
ndi
Gam
bia
Guin
ea-B
issau
Madagascar
Eth
iop
ia
Uganda
Mauritiu
s
Angola
Sw
azila
nd
Burk
ina F
aso
Mala
wi
Rw
anda
Nig
er
Ta
nzania
Mozam
biq
ue
Mali
Lesoth
o
Maurita
nia
Cape V
erd
e
Senegal
Benin
Bots
wana
Eritr
ea
Za
mbia
Cam
ero
on
Kenya
Nam
ibiaC
apital per
work
er
(Sale
s V
alu
e, 2005 U
S$)
Capital per worker (sales value, in 2005 US$)
32
worker education. This will be investigated further in the next section on total factor
productivity.
Although firms in Tanzania are slightly more capital intensive on average than firms in
most other countries in Sub-Saharan Africa, they are less capital intensive than firms in the
successful manufacturing economies (see Figure 17). For example, the median firm in India has
about twice as much capital per worker as the median firm in Tanzania, the median firm in
Thailand has about four times as much, and the median firms in South Africa, Malaysia and
China have nearly ten times as much capital.
Although capital per worker gives an idea about how much capital firms use, it does not
provide much information on how productively that capital is being used. Capital productivity,
the ratio of value added to the net book value of machinery and equipment, measures how
productively firms use capital. It is analogous for capital to (the inverse of) unit labor costs for
labor. Capital productivity is higher in firms that produce a lot of output with only a small
amount of machinery and equipment. Hence, capital productivity is generally higher for labor
intensive firms (i.e., firms that rely relatively heavily on labor to produce their output) since they
produce a lot of output, due to their heavy use of labor, with relatively little capital.
Given that firms are labor intensive is relatively higher (or capital intensity is relatively
lower) in Tanzania than in most of the comparator countries, it is not surprising that capital
productivity is relatively high. Although lower than in Swaziland or South Africa, capital
productivity is higher than in most of the successful manufacturing economies.
Figure 17: Firms in Tanzania are less capital intensive than in countries with successful
manufacturing industries in Sub-Saharan Africa and Asia.
Source: World Bank Enterprise Surveys.
Note: See footnotes to Table 7 and Figure 11 for methodology. Cross-country comparisons are for
manufacturing firms only. This figure uses book value of capital.
$0 $2,000 $4,000 $6,000 $8,000
Tanzania
Burundi
Rwanda
Uganda
Swaziland
Kenya
South Africa
India
Thailand
Malaysia
China
Capital per worker (US$, book value)
Capital Intensity
0% 200% 400% 600% 800%
Tanzania
Uganda
Rwanda
Burundi
Kenya
South Africa
Swaziland
China
India
Thailand
Malaysia
Capital over value-added (book value)
Capital Productivity
33
Total Factor Productivity
The results presented in the previous subsection have some drawbacks. The main
problem is that when considered in isolation, labor productivity can present incomplete evidence
on firm performance. Technical efficiency (TE)—which is analogous for firm-level analysis to
total factor productivity (TFP) in macroeconomic and sectoral studies—avoids some of the
problems associated with labor productivity by taking both capital and labor use into account
simultaneously. Differences in TE between firms (e.g., between firms in different countries or
between exporters and non-exporters) are due to differences in things other than capital or labor.
For example, differences in TE might be due to differences in firm organization, management
efficiency, worker skills or education, or the investment climate. To the extent that differences
in technology are not embedded in the machinery and equipment that the firm uses, differences
in technical efficiency can also reflect technological differences.
The econometric methodology used to calculate TE is described in detail in Appendix
2.1. The appendix also explains how TE numbers are calculated, provides more detail on the
results in the chapter, and discusses various limitations of this analysis.
Although productivity is lower in Tanzania than in the best performing countries in Sub-
Saharan Africa, it is higher than in many other countries in SSA and than most of the regional
comparators (see Figure 18). For example, TE is about 40 percent lower in for the median firms
in Rwanda and Burundi and about 35 percent lower in Uganda. The median firm in Kenya,
however, is about 41 percent higher. All of these differences are statistically significant.
Tanzania compares slightly more favorably with respect to TE than it does with respect to
labor productivity (compare Figure 11 and Figure 18). This could be partly reflect that firms in
Tanzania are not particuarly capital intensive and might also reflect differences due to
differences with respect to size and sector.
There is some evidence that productivity has improved since 2003. When firms from
both the 2003 and 2006 surveys are included in the large cross-country model (see Table 39), the
firms in the 2006 survey were about 9 percent more productive on average than similar firms in
the 2003 survey. This suggests an annual increase of about 3 percent using the cross-sectional
approach. Although this suggests productivity improvements, the difference is not statistically
significant (i.e., the null hypothesis that TFP is the same in the two periods cannot be rejected at
conventional significance levels). This suggests that the apparent difference might be due to
sampling variation. The results from the balanced panel approach are similar. The results from
the balanced panel analysis suggest that total factor productivity increased by 20 percent between
2003 and 2006, suggesting an average increase of 6 percent per year. However, as for the cross-
sectional analysis, this difference is not statistically significant, due to high dispersion around the
estimated mean.
Although this suggests that productivity might have increased since 2003, there are
several reasons for caution. First, the differences are not statistically significant, suggesting that
the growth might be more apparent rather than actual. Second, one problem with TFP
calculations is that calculations can be affected by price differences. That is, ideally we would
have a physical measure of output for TFP regressions. In practice, however, it is difficult to
34
obtain physical measures of output and, instead, most analyses using Enterprise Survey data use
sales (i.e., output multiplied by unit price) as the dependent variable (i.e., a sales generating
function).15
With firms producing heterogeneous products, this can be problematic if some firms
have market power.16
That is, firms with market power that charge high prices for their output
(e.g., monopolists) would appear more productive than a similar firm in competitive markets that
have to charge lower prices even if their physical output were the same.17
If market power has
increased in Tanzania in recent years, this could appear as productivity increases.
So what are the factors that affect firm productivity in Tanzania? Although many factors
could drive productivity differentials, an important factor is the role of an adverse business
environment. The Enterprise Survey asks firms about various costs and losses incurred due to a
poor business climate. Details of these costs, and their implication on competitiveness, are
discussed in the Chapter 4 on Finance, Chapter 5 on labor markets, and Chapter 6 on other
aspects of the Investment Climate.
It is also interesting to look at differences in TE between different firms in Tanzania. The
empirical results, discussed in detail in Appendix 2.1, suggest that the most robust associations
between enterprise characteristics and TE are: (i) firms that use technology more intensively are
more productive than other firms. In particular, firms that are International Standards
Organization (ISO) certified and those that have their own website are much more efficient than
firms that do (51 percent and 43 percent respectively) and (ii) enterprises that provide their own
transportation are about 32 percent more efficient than less vertically integrated firms As
Figure 18: TE is similar or slightly higher in Tanzania than in most low income in SSA—although it is lower
than in the best performing countries.
Source: World Bank Enterprise Surveys.
Note: See Appendix 2.1 for description of methodology. Cross-country comparisons are for manufacturing firms
only
-100%
-50%
0%
50%
100%
150%
200%
250%
Gam
bia
Mozam
biq
ue
Eth
iopia
Madig
ascar
Rw
anda
Lesoth
o
Buru
ndi
Eritr
ea
Ghana
Uganda
Mala
wi
Guin
ea-B
issau
Benin
Mali
Guin
ea
Congo, D
R
Maurita
nia
Nig
eria
Nig
er
Ta
nzania
Za
mbia
Burk
ina F
aso
Se
ne
ga
l
Kenya
Angola
Cam
ero
on
Cape V
erd
e
Bots
wana
Sw
azila
nd
Mauritiu
s
Nam
ibia
South
Afr
ica
TE
rela
tive to T
anzania
TE relative to Tanzania (0 means as productive as Tanzania)
35
discussed in the appendix, it is difficult to draw strong conclusions from this analysis due to the
possiblity of reverse causation.
Although foreign-owned firms and exporters are slightly more productive in terms of TE
than other firms, the difference is small and might be due to sampling variation. This suggests
that the higher labor productivity (see previous subsection) of these firms might reflect that they
are far more capital intensive, especially for foreign-owned firms, that they are in more
productive sectors, or that they use technology more intensively rather than being foreign-owned
or being exporters.
II. Competition
It is more difficult to measure how much competition firms face, than to measure many
other aspects of firm performance and the business environment. Because of this, the World
Bank Enterprise Survey asks several questions that approach the issue in different ways. First,
the survey asks about market share in local markets. Market share is defined as the
establishment‘s sales for its main product line divided by total sales of all firms in these product
lines in local markets. In general, competition is lower when average market share is higher. A
second question, which was added in the more recent round of investment climate assessments,
asks about the number of competitors in the firms‘ main market for its main product line.
Neither question is perfect, both having issues with conception and implementation. On
a conceptual level, it is generally difficult to define the firm‘s market. Does it include just a very
local area around the firms, the entire metropolitan area where the firms is located, one or two
large metropolitan areas or the entire country? Similarly, it is also difficult to define the product
line. For example, does a firm whose main product is a pilsner compete only against other beer
makers that sell pilsners, against firms that sell all types of beer, against firms that sell all types
of alcoholic beverages, against all firms that make beverages of any type, or even against all
leisure goods? In practice, the questions on the Enterprise Survey do not define these issues
precisely—and it would be very difficult to do so—and so it is left up to the manager to define
the extent of the market themselves.
Second, neither measure is a perfect measure of competition. If three or four firms divide
a market between them (e.g., based upon geography), they might face relatively low levels of
competition while only having modest market share. Similarly, if a large domestic firm
competes with several import brands that all have tiny market shares and face high barriers to
entry, it may face a large number of competitors but only a modest level of competition.
Finally, on a practical level, during interviews managers often appear to have problems
with the concept of market share. This appears to be a particularly significant problem for
managers of small firms, especially those without formal business training.
With these provisos in mind, competition does not appear to be particularly low in
Tanzania. The average firm reported that its market share was about 10 percent. This is lower
than in regional competitors (e.g., Uganda, Rwanda or Burundi). Although it might not be
surprising that firms report lower market share than in small economies (e.g., Mauritius,
36
Rwanda, Burundi or Swaziland), they also report lower market share than in larger economies
such as Thailand, China and South Africa.
The second question on number of competitors has only been asked on the more recent
surveys in Africa, meaning that it is not possible to compare results with most of the comparator
countries. What evidence there is, however, also suggests that competition is fairly high in
Tanzania. About 10 percent of firms said that they did not have any competitors in local
markets, higher than in most of the larger countries for which similar data are available (e.g., DR
Congo, Ghana Kenya, and Uganda) but more than in many of the smaller economies in Sub-
Saharan Africa (e.g., Swaziland, Botswana, Guinea-Bissau and Rwanda). Moreover, firms were
more likely to say that they had five or more competitors than in any of the comparator countries
except for Kenya and Ghana. In this respect, competition does not appear to be particularly low
in Tanzania.
As in most countries, large firms report less competition than small firms. About 10
percent of small manufacturing firms said that they faced no competitors in local markets and the
average firm reported a market share of about 6 percent. In comparison, about 17 percent of
large manufacturing firms said they faced no competition and the average large firm said that
they had about 23 percent of local markets.
Figure 19: Although it is difficult to measure competition accurately, Tanzanian firms appear to face
as much or more competition than firms elsewhere in Africa.
Source: World Bank Enterprise Surveys.
Note: Data varies between 2002 and 2007, depending on survey period for each country. Market share is
the average reported market share in local markets. Cross-country comparisons are for manufacturing
firms only.
0 20 40 60
Tanzania
Uganda
Rwanda
Burundi
SouthAfrica
Swaziland
Mauritius
Thailand
China
Malaysia
Share of Local market
Share of local market (percent)
0% 50% 100%
Namibia
Kenya
Burundi
Uganda
Congo, DR
Ghana
Gambia
Mauritania
Tanzania
Angola
Botswana
Guinea-Conakry
Rwanda
Swaziland
Guinea-Bissau
% of firms
% of firms with no and many competitors
Monopoly Competition
37
Keeping in mind that large firms typically face less competition than smaller firms, it is
important to note that large firms in Tanzania do appear to face particularly low levels of
competition. About 17 percent of large firms in Tanzania said that they face no competition in
domestic markets. In comparison, only about 5 percent of large firms in Uganda and Kenya said
the same. Moreover, most large firms operate mostly in domestic markets. About 5 percent of
large firms said that their most important market was international, compared to about 20 percent
of firms in Uganda, Kenya and Burundi.
III. Profitability
At the enterprise-level, profitability is associated with better firm performance. Firms
that are more productive and that have lower overhead costs will be more profitable than other
firms because they manage to produce more output at lower cost. But at an industry or country
level, high profitability could also reflect a lack of competition—especially in countries like
Botswana where firms sell mostly in domestic markets. When markets are less competitive,
firms will be able to earn higher profits than in more competitive markets where profits will
typically be competed away. Given the problems associated with measuring competition directly
(see discussion in previous section), this is a useful check on the previous results.
Although the World Bank Enterprise Survey does not collect any data on taxation,
meaning that profits can only be calculated before taxation, it is possible to calculate several
before-tax measures of profitability in a consistent way for the most recent set of World Bank
Enterprise Surveys in Africa. Because comparable data are not available for earlier years, the
comparisons focus on the fifteen surveys completed in 2006-07. Given that profit taxes do not
appear especially high in Tanzania (see Chapter 7) compared to other countries in the region, it
Figure 20: large firms in Tanzania face little competition.
Source: World Bank Enterprise Surveys.
Note: Includes all firms, not just manufacturing firms.
0%
5%
10%
15%
20%
25%
Tanzania
Uganda
Kenya
Buru
ndi
Rw
anda
% o
f la
rge f
irm
s
No competitors in main market
Main market is international
38
does not seem likely that results would be markedly different, however, even if after-tax
measures were available. Because, as discussed earlier, capital is difficult to measure accurately,
this section focuses on return on sales (profits over sales) rather than return on assets (profits
over capital).
Firms in Tanzania appear, on average, to be relatively profitable. Although the median
firm‘s pre-tax return on sales (21 percent) is lower than in Kenya, Swaziland or Namibia—two
of which are among the more successful manufacturing economies—it is higher than in most
low-income countries in Sub-Saharan Africa (see Table 8).
Return on sales is higher for the median large firm than it is for the median small firm.18
Although this is not particularly uncommon—it is true in about half of countries with enough
large firms—large firms appear to be particularly profitable. Return on sales is higher for the
median large firm in Tanzania than it is in 10 of 12 countries in Sub-Saharan Africa. In contrast,
the median for small firms is higher than in only 9 of 14 countries. Overall, this suggests that
large firms appear to be particularly profitable in Tanzania.
Table 8: Profitability (return on sales), by firm size.
All Small Large
Mauritania 3% 3% 0%
Gambia 9% 3% ---
Ghana 9% 9% 8%
Guinea-Bissau 10% 9% ---
Rwanda 12% 8% 16%
Uganda 13% 13% 21%
Congo, DR 15% 17% 15%
Botswana 17% 13% 36%
Guinea-Conakry 18% 18% 20%
Angola 19% 19% 9%
Burundi 20% 20% -1%
Tanzania 21% 18% 29%
Kenya 24% 17% 23%
Namibia 28% 28% 20%
Swaziland 31% 38% 35%
Source: World Bank Enterprise Survey.
Note: Data is from between 2006-2007. Return on sales is sales less costs (materials, wages,
other miscellaneous costs) divided by sales. This is used rather than return on assets due to
difficulty of measuring assets accurately. Comparisons are for manufacturing firms only.
Observations presenting medians for groups with less than five observations are dropped.
As with productivity, return on sales is also higher for exporters and foreign-owned
firms. Return on sales was about 29 percent for the median foreign-owned firm compared to
about 19 percent for the median domestic firm and was 28 percent for the median exporter
compared to about 20 percent for the median non-exporter. Although these firms tend to be
more capital intensive—and therefore have higher costs associated with depreciation—and to
have higher labor costs than domestic firms and non-exporters, these additional costs are not as
large as the differences in productivity.
39
CHAPTER 3: PERCEPTIONS ABOUT THE INVESTMENT CLIMATE
In addition to collecting information on firm productivity, the Enterprise Survey also
collects information on the investment climate—including on topics such as infrastructure,
access to finance, taxes, competition from the informal sector and corruption. Firms are asked
two kinds of questions in the surveys: (i) subjective questions about what managers see as the
major obstacles that their firm faces; and (ii) objective questions such as production lost due to
power outages, whether the firm has a loan or overdraft facility, and amount of time managers
spend dealing with government regulations. The report uses both types of information—and
supplementary information from other sources—to assess constraints to enterprise operations and
growth in Tanzania and to compare constraints in Tanzania with constraints in the comparator
countries.
I. Perceptions about constraints
As a starting point for the analysis of the investment climate, this chapter looks at what
enterprise managers say are the biggest obstacles that they face. Since enterprise managers know
more about the immediate problems facing their businesses than government officials, academic
researchers, or other outside experts, it makes sense to take their concerns about the investment
climate seriously.
Although it is important to take this information seriously, it is also important to realize
that perceptions are not a perfect measure of the investment climate. First, enterprise managers‘
interests might not always be consistent with society‘s interests. Most managers would like
subsidized credit or to be charged less for electricity or water if they believed that the cost of
providing these services would be borne by someone else. Similarly, most managers would be
happy to face less competition even if the cost to society outweighed the benefits to their firm. It
is important, therefore, to keep the costs of interventions in mind and to think about how policy
changes will affect other stakeholders (e.g., workers and taxpayers) before adopting programs to
reduce constraints.
Second, cultural differences or persistent differences in expectations about how the
investment climate should look might affect perceptions. For example, expectations about
political freedom and freedom of speech might affect whether managers are willing to complain
to interviewers about the investment climate more than it affects their willingness to answer
objective questions.19
This can make cross-country comparisons of perception-based data
difficult and means that these comparisons should be treated carefully.
Third, although managers may be aware of a problem, they might not be aware of the
underlying causes. For example, they might know that it is difficult to get bank loans to finance
new investment, but might not know the underlying reasons for this (e.g., lack of competition in
the banking sector, government debt issues crowding out private investment, or problems with
land registration that prevent firms from using land as collateral). As a result, additional
information is needed to assess how to release any given constraint.
40
Fourth, the views of managers of existing enterprises might not reflect the obstacles that
potential entrepreneurs and new entrants might face. For example, managers of existing
enterprises that have already completed registration procedures might not be concerned about
entry costs even if they remain high. Further, they might rate some issues as lesser problems
because they have structured their businesses in ways to minimize those costs. For example, if
transportation costs are especially high in some areas, existing firms might only be located close
to transportation facilities or might provide their own transport. This does not mean that
improving transportation would not be useful. Finally, if investment climate constraints are
particularly binding, then there might be very few firms that rely heavily upon that area of the
investment climate.20
For example, if the ports and custom facilities are particularly poor, very
few firms might operate in export-oriented industries. It is important, therefore, to think about
how constraints might affect new and potential entrants as well as how they affect the managers
of the existing firms interviewed during the survey.
Finally, it is difficult to aggregate perceptions across firms. Constraints affect different
firms to different degrees and perception-based data cannot be aggregated as easily as objective
data (for example, costs measured in local currency). This makes it difficult to rank obstacles.
For example, it is not clear whether an issue that one firm considers a very serious problem and
another firm considers a minor problem, is more or less of a problem on aggregate than one that
both consider a moderately serious problem. Because of these concerns, in addition to using
objective data in later chapters of the Investment Climate Assessment, this chapter looks at two
measures of perceptions; the share of firms that say whether an issue is a serious problem and the
share that say it is the biggest obstacle that they face. This makes it possible to check that the
results based upon the perception-based indices are robust to small changes in the way the
question is asked.
Although the concerns about perception-based data are serious, it is important not to
overemphasize these problems. Recent work suggests that perception-based measures line up
reasonably well with objective macro- and micro-economic indicators even on a cross-country
basis.21
That is, despite concerns about subjective measures, they seem to provide useful
information. Moreover, some things are very difficult or costly to measure objectively—for
example, how ‗fair‘ or ‗reliable‘ the court system is. In these cases, perceptions give valuable
information that would be difficult to obtain in other ways.
It is also important to remember that there are concerns about objective data as well—
particularly for sensitive and difficult questions.22
In comparison to many of the objective
questions, the perception questions are both relatively easy for the managers to answer—no
implicit or explicit calculations are needed—and many would appear to be less sensitive than
their objective counterpart questions. For example, it would seem to be less controversial for a
manager to say that corruption is a problem than to answer objective questions such as whether
‗firms like their firm‘ typically pay bribes or whether inspectors requested bribe payments during
their last inspection.23
Because of these concerns, although this assessment uses the perception-based data as a
starting point for the analysis, this information will be supplemented with objective measures of
the investment climate taken from the Enterprise Survey and other sources when possible and
appropriate. In addition, although cross-country comparisons of perception-based data (e.g.,
41
comparing the number of firms that complain about an issue between countries) can provide
some context to results using objective data, concerns about cross-country comparisons of
perceptions will mean that the later chapters will mostly use objective data for cross-country
comparisons when this information is available.
II. Main perceived constraints in the 2006 Enterprise Survey
The Enterprise Survey asks firm managers to say how great an obstacle each of 17 areas
of the investment climate is to the current operations of their business. They respond by rating
each on a five-point scale between ‗no obstacle‘ and a ‗very severe obstacle‘. Figure 21 shows
the percent of each type of firm that rated each area as a ‗major‘ or ‗very severe obstacle‘.
For both SMLE and microenterprise managers, the performance of the power sector
stands out as the biggest constraint that they face. Close to nine out of ten firm managers said
that power was a serious problem. This was significantly higher than the number that rated any
of the other constraints as a serious problem. There was also broad agreement on the second
most serious constraint—about four out of ten SMLE manager and about five out of ten
microenterprise managers said that access to finance was a serious problem.
For most other areas of the investment climate, far fewer firms had complaints. Between
two and three out of ten SMLE and microenterprise managers report that macroeconomic
instability and competition with the informal sector were serious problems and over one third of
SMLE managers and about one fifth of microenterprise managers said that tax rates were a
serious problem.
Figure 21: SMLEs and microenterprises have similar views on the investment climate in Tanzania—with
electricity and access to finance rating far above other constraints.
Source: World Bank Enterprise Survey.
0%
20%
40%
60%
80%
100%
% o
f firm
s s
ayi
ng issue is s
erious p
roble
m
SMLEs
Microenterprises
42
SMLE and microenterprise managers also seemed to broadly agree on the areas of the
investment climate that were less serious problems. Relatively few (one-fifth or less) of
enterprise managers of either type of enterprise rated telecommunications, political instability,
worker skills and education, crime, transportation, the courts, and most areas of regulation (e.g.,
labor, trade, or business registration) as serious problems.
There were, however, some differences in perceptions of SMLE and microenterprise
managers. One notable difference is that SMLE managers were more likely to complain about
tax rates—over one in three SMLE manager compared to one-fifth of microenterprise managers.
Another smaller difference is that SMLE managers were more likely to say that access to land
and worker education were serious problems. None of these differences, however, were
statistically significant after controlling for other differences between SMLEs and
microenterprises (e.g., ownership and size).
Although as noted, in theory, results can look very different when firms are asked about
the biggest constraint rather than being asked how great an obstacle a given area is, this is not the
case in Tanzania. Electricity dominated enterprise concerns by this measure as well—about
three quarters of enterprise managers rated power as the biggest problem (see Figure 4). A
considerably smaller number rated access to finance as the biggest problem (about one in ten).
Few firms—less than one in twenty—said that any other area of the investment climate was the
biggest problem that they faced. Even for those areas that a significant number said was a
serious problem such as tax rates and macroeconomic instability, only a very small number of
firm managers said that it was the biggest problem. In this respect, the results looking at the
biggest obstacle emphasize the over-riding concern associated with access to power.
Figure 22: Responses were also similar for SMLEs and microenterprises when managers were asked
about the biggest constraint that they faced.
Source: World Bank Enterprise Survey.
Power72%
Finance9%
Tax Rates3%
Macro Instability
4% Other12%
SMLEs
Power75%
Finance8%
Tax Rates5%
Macro Instability
1%
Other11%
Microenterprises
43
III. Differences in perceptions across different firms
As discussed above, although managers of SMLEs and microenterprises have similar
views about the investment climate, their views are not identical. Not surprisingly, even within
these two broad groups of managers, there are often differences in views about the investment
climate and the major constraints that they face. These differences can be due to differences in
expectations (e.g., foreign-owned firms might have expectations based upon their experience in
their home countries) or differences in experiences (e.g., large firms might find it easier to get
loans due to having better connections or better access to collateral). This section looks at
differences in perceptions across different types of firm in more detail. Later chapters address
whether the differences in objective indicators are consistent with the differences in perceptions.
A more detailed econometric analysis is presented in Appendix 3.1. This section focuses on
those differences that are both statistically significant (i.e., not due to sampling variation) and
economically important.
There were relatively few large, statistically significant differences in perceptions by firm
type. Most notably, power was consistently the area of the investment climate that firms said
was a serious problem. Firms of all types and all sizes said that power was a serious constraint
on their operations. For example, although large firms were more likely to say that power was a
serious problem than small firms were (100 percent compared to 85 percent), it ranked as the top
constraint for both types of firms based upon the percent of firms that said it was a serious
problem (see Figure 24). The small difference could reflect that large firms are more capital
intensive than smaller firms (see Chapter 2) and that, therefore, it is harder for them to deal with
outages.
As in many countries, large firms were less likely to say that access to finance was a
serious problem than small firms were—40 percent of large firms said it was a serious problem
compared to 28 percent of small firms (see Figure 24). The lower level of concern among
managers of large firms could be because managers of large firms find it easier to develop a
working relationship with banks, that they are more likely to have collateral, or that they are
more established than small firms. Although the difference appears large, it is important to note
that access to finance consistently ranks among the top constraint for firms of all sizes. Access to
finance ranked as the second greatest constraint for small firms, but also accounted as the third
greatest constraint for medium and large firms.
A more significant difference with respect to access to finance was that foreign-owned
firms were less likely to say that it was a problem than domestic firms. About 42 percent of
domestic firms said it was a serious constraint compared to only about 22 percent of foreign-
owned firms. Moreover, access to finance did not rank among the top concerns of foreign-
owned firms, ranking 11th
out of 17 constraint for foreign-owned firms compared to 2nd
for
domestic firms. Although this could be because banks and other financial intermediaries in
Tanzania are more willing to lend to foreign-owned firms, there are other possible reasons for the
difference. For example, foreign-owned firms might be more profitable—and so can more easily
finance investment from retained earnings—or might be able to rely upon parent companies or
banks in their home countries.
44
IV. Comparisons with earlier surveys
Firms have been asked in similar ways about the major constraints that they face in 24
several earlier studies, including a 2003 Enterprise Survey and the 1999 World Business
Environment Survey (WBES).25
Comparisons with results from these earlier surveys can give
some information on how the investment climate has changed over time. Comparisons with
earlier surveys are complicated by several factors, however. First, as discussed in Appendix 2,
differences in sample frames can make it difficult to compare results from different surveys.
Second, the different surveys often ask similar questions about perceptions about the investment
climate in different ways. Lists of areas often differ between surveys.26
Moreover, questions
about the investment climate are sometimes asked as questions about the biggest constraint and
sometimes about the severity of constraints.27
Even when firms are asked to rate obstacles on a
scale (e.g., from ‗no‘ obstacle to ‗very severe‘ obstacle), the scales are often different and often
have different descriptions.28
The 2003 Enterprise Survey
The 2003 Enterprise Survey was very similar to the 2006 survey. Although there were
some differences in the list of constraints—access to finance and the cost of finance were asked
about separately in 2003, firms were asked about ‗anti-competitive and informal practices‘ not
‗practices of competitors in the informal sector‘ and firms were not asked about political
instability in the 2006 survey the list of constraints was very similar in the two surveys. In the
2003 survey, firms‘ greatest concerns were tax rates, electricity, cost of finance, tax
administration, corruption, access to finance and macroeconomic instability.
Figure 23: Other than electricity, fewer enterprises said that most other areas of the investment climate
were serious problems in 2006 than in 2003.
Source: World Bank Enterprise Survey.
0%
20%
40%
60%
80%
100%
120%
Electricity Access to finance
% o
f firm
s s
ayi
ng issue is s
erious
pro
ble
m
% of firms saying areas were serious problems, by firm size
Small
Medium
Large
45
These were similar to the concerns firms expressed in 2006. In particular, electricity, tax
rates, macroeconomic instability and corruption remained among the top concerns in 2006.
There were, however, some noticeable differences in perceptions.
Firms were far more likely to say that electricity was a serious problem in 2006 than they
were in 2003. About 70 percent of firms said electricity was a serious problem in 2003
(the second biggest constraint) compared to about 90 percent in 2006. Although
problems in the power sector are not a new problem, the magnitude of the problem
appears to have increased. Given the crisis in the power sector in 2006, this is probably
not surprising.
Except for transportation, firms were less likely to say that all other areas of the
investment climate were serious problems in 2006 than they were in 2003. It is not
immediately clear why this is the case. One possibility is that most areas of the
investment climate have improved since 2003. But another possibility is that the crisis in
the power sector overshadowed problems in other parts of the investment climate. The
scale used in the Enterprise Survey, with firms ranking problems from ‗no problem‘ to a
‗very severe problem‘ is not an absolute scale. Without an absolute anchor as to what
constitutes a major problem, it is likely that managers used the power crisis as a reference
point for their rankings. For example, they might have thought ‗corruption is a less
serious problem than power and since we said power was a very severe problem, we
should corruption as less of a constraint.‘ As a result, firms might have been less likely
to say that other areas of the investment climate were problems in 2006 than they were in
Figure 24: Other than electricity, fewer enterprises said that most other areas of the investment climate
were serious problems in 2006 than in 2003.
Source: World Bank Enterprise Surveys.
0%
20%
40%
60%
80%
100%
% s
ayi
ng issue is s
erious p
roble
m
% of firms saying areas were serious problems in 2003 and 2006
2003
2006
46
2003 because they were perceived as far less serious than the power crisis in 2006. For
these reasons, it will be interesting to compare objective measures of the investment
climate between 2003 and 2006.
Although fewer managers said that most areas of the investment climate were serious
problems in 2006 than in 2003, differences were larger for some obstacles than for others.
In particular, far fewer firms said tax administration was a serious problem in 2006 than
in 2003 (about 35 percent fewer) and it fell from the 3rd
to the 6th
greatest constraint
among the 15 constraints common to the two surveys. Tax rates and corruption were also
rated as serious constraints by far fewer firms in 2006, although they ranked among the
top concerns in both surveys.
Crime and transportation declined less than other constraints, although neither ranked
among the top constraints in either survey. Both moved from among the least concerns in
the 2003 survey to somewhere near the middle in the 2006 survey.
In 2006, more firms said ―access to finance‖ was a serious constraint than any area of the
investment climate except power. In 2003, it also ranked below tax rates, tax
administration, and corruption. Moreover, the decline in the percent of firms that said it
was a serious problem was smaller for ‗access to finance‘ than for most other constraints.
It is important to note, however, that the wording of this constraint changed between the
two surveys. In 2003, it was described as ―access to finance (collateral)‖ and there was a
separate question for ―cost of finance (interest rates).‖ In 2006, it was described as
access to finance (availability and cost). Given that cost of finance was the second
largest constraint (after tax rates) in 2003, the discrepancy might be due to change in
wording rather than a change in availability of financing.
These differences remain significant after controlling for changes in sample composition
and when only looking at the firms that were in both surveys (see Appendix 3.2). This strongly
suggests that the differences are real rather than being due to changes in sample.
The Global Competitiveness Report.
The Global Competitiveness Report (GCR) also reports how firms see the investment
climate assessments.29
The surveys are mostly delivered through face-to-face interviews in
developing countries—although not uniformly so (2006). However, as noted in Lall (2001) the
‗data are not collected rigorously. The sampling methodology appears to vary somewhat from
country to country and it is not clear that sampling frames are representative of the economy or
how firms are sampled from the frame. World Economic Forum (2006) notes that the samples
are not entirely random (e.g., large firms with international experience are preferred). In
Tanzania, large firms with over 100 employees and foreign-owned firms appear to be
overrepresented in the sample in the 2005/06 sample.
Firms are asked to select the five areas of the investment climate that are the most
problematic (out of 14 possible areas) and rank those five from 1 (most problematic) and 4 (least
problematic). Although the numbers are not directly comparable to the numbers from the
Enterprise Survey, the results from the GCR have many similarities to the results from the
47
Enterprise Survey. Tanzania was first included in the 2003/04 Global Competitiveness report,
along with about 15 other countries in Sub-Saharan Africa. The results from the 2003/04 report
were based upon the responses of only a small number of firms (about 45). Since the 2004/05,
responses have been based on large samples (over 100 firms). For this reason, and because the
results from the small sample used in the 2003/04 report do not appear similar to the other
results, this report focuses on the later surveys that relied upon larger samples.
With these provisos in mind, it is interesting to look at the results from this report and to
compare them with the results from the Enterprise Survey. There are many similarities. In
particular, the two most common concerns in 2006 were infrastructure and access to finance—
similar to the results from the Enterprise Survey. Tax rates and corruption also rank among the
top concerns. Moreover, labor regulation, and political instability do not appear to be serious
constraints in either survey.
There are other similarities with the results from the Enterprise Surveys. First, concern
about infrastructure appears to be income increasing over time. Concern about crime is also
increasing—it was not among the top concerns in 2004, but is more important by 2006. Concern
appears to have declined a little, however, in 2007. Finally, concern about tax regulation appears
to be falling. In this respect, many of the results appear consistent with results from the
Enterprise Surveys.
World Business Environment Survey
The World Bank conducted the World Business Environment Survey in 1999/2000.30
Although in most regions, the survey was delivered in face-to-face interviews, as in the earlier
survey, the surveys were mostly delivered by mail in the Africa region. Samples were drawn
from the company registers in most countries and the same set of minimum sampling guidelines
Figure 25: Despite methodological differences, results from the Global Competitive Report and the
World Bank Enterprise Surveys are similar.
Source: World Economic Forum (2005; 2006; 2007; 2008).
0
5
10
15
20
25
Rankin
g (
hig
her
valu
es m
ean g
reate
r pro
ble
m)
Ranking of constraints from Global Competitive Report2004
2005
2006
2007
48
was applied in each case. As a result, the samples were more likely to be representative across
countries in the WBES than they were in the earlier survey. The WBES also covered both
manufacturing and services.
The perceptions data from this survey is different from the perceptions data in are not
identical in the two surveys—the data in the World Business Environment Survey are based
upon responses of both service and manufacturing enterprises, the scale in that survey was a
four-point scale (no problem, minor problem, moderate problem and major problem) rather than
a five-point scale and some categories were different—it provides a useful comparison on some
dimensions
First, the major constraints were similar in the two surveys. High tax rates were most
likely to be seen as a major problem in both years and high interest rates, tax administration, and
corruption were rated as major problems in both periods. Although the 1999 survey did not ask
about the power sector specifically, infrastructure in general was seen as a major problem by
many enterprises. One notable difference between the two surveys is that enterprises appear to
be less likely to rate crime as a serious problem in the 1999 survey. It ranked as only a minor
problem in the 1999 survey, but appeared to be a greater problem by 2003.
V. Summary
As well as collecting information on firm performance, the Enterprise Survey also
collects both objective and subjective data on the investment climate. As a starting point for the
Figure 26: High tax rates, high interest rates and infrastructure were the biggest concerns in the 1999
World Business Environment Survey.
Source: World Business Environment Survey.
0%
20%
40%
60%
80%
% o
f firm
s s
ayi
ng a
rea w
as s
erious p
roble
m % of firms saying areas were serious problem in 1999 WBES survey
49
analysis, this chapter looks at the subjective information on the investment climate—those things
that managers said were the greatest constraints on their enterprises‘ operations.
The area of the investment climate that managers were most likely to be concerned about
was electricity. Close to 90 percent of firms said it was a major or very severe problem and close
to three quarters said it was the biggest problem that they faced. There was very broad
agreement on this—enterprises in all sectors and of all sizes, including microenterprises,
expressed concern about power.
Other than power, which was the greatest concern for firms of all types, the next most
common concerns were access to finance, tax rates, competition with the informal sector,
macroeconomic instability, and corruption. In general, few firms rate most areas of regulation,
political instability, and other areas of infrastructures as serious problems.
In general, managers of firms in different sectors, of different sizes and with different
types of owner broadly agreed about the biggest constraints that their firm faced. In particular,
power was consistently the greatest concern across all types of firm. There were, however, some
differences. Foreign-owned firms and large firms were less concerned about access to finance
than other firms and exporters were more concerned about crime. But for the most part,
differences were relatively modest.
Comparisons with earlier surveys suggest a few interesting trends over time. First, power
was a significantly greater concern in the 2006 survey than in earlier surveys. Second, concern
about most other areas of the investment climate was less in 2006 than in 2003. This could
reflect across-the-board improvements in the investment climate or could reflect that concern
about power in 2006 overwhelmed other concerns. Later chapters, focusing on the objective
data, will assess the extent to which the investment climate has improved since 2003. Some
areas, most notably tax administration, show the most significant improvements, while others
such as crime and transportation appear to show very little improvement or even a deterioration.
50
CHAPTER 4: EMPLOYMENT CREATION AND HUMAN CAPITAL
ACCUMULATION
The Government of Tanzania recognizes that the economy will need to create high
paying jobs if poverty is to be reduced. Tanzania‘s National Strategy for Growth and the
Reduction of Poverty (MKUKUTA) emphasizes the role that job creation and the reduction of
unemployment play in reducing poverty (Vice President's Office, 2005). Similarly, the National
Employment Policy notes that ―the need to create more and better jobs, enhance gender equality,
improve the access to employment opportunities by all, and generate more decent employment,
is the major challenge to poverty eradication, economic growth, social development and social
integration‖ (United Republic of Tanzania, 2008, p. 10).
The rapid growth of the labor force amplifies worries about job creation. Between 2000
and 2006, the labor force grew at a rate of 4.1 percent per year—equivalent to 800,000 new
workers entering the labor force every year (United Republic of Tanzania, 2008). This rapid
growth means that there is concern about whether the market can absorb the young workers
entering the labor force and about whether these workers will have adequate education and skills.
Given the growing labor force and the continuing skills shortages, it is important to
understand the determinants of employment growth and investment in human capital. The
Enterprise Survey offers an opportunity for looking at both issues. Because microenterprises are
relatively unconcerned about worker skills and invest little in them and, more importantly,
because the worker surveys and questions on training were only delivered to SMLEs in the
manufacturing sector, the focus of the analysis will be on these firms.
The results in this chapter suggest that foreign-owned firms, large firms and exporters are
more likely to invest in their workers and reward them for their skills. These firms make up a
dynamic side of the labor market where better jobs are created. Unfortunately most workers, and
particularly young workers, work for smaller firms. Because of this, a two-pronged approach is
needed. Supporting the growth of smaller firms by addressing the constraints they face and
encouraging entry of new large firms, many of which are likely to be foreign-owned, are both
important parts of a strategy to strengthen employment growth and deepen skills.
I. Characteristics of workers in the worker survey
In addition to interviewing firm managers about firm performance and the investment
climate, up to 10 workers were interviewed in about half of the manufacturing firms in the
Enterprise Survey about their education, skills, and wages and about other aspects of their jobs
(see Appendix 2.1 for a full description). This section discussed some of the characteristics of
these workers.
Gender. Although over the 1990s more jobs were created for women than for men,
recent labour force data show that this trend was reversed in the first half of the current decade
(United Republic of Tanzania, 2008, p. 10). Despite this, women make up slightly more than
half of the workforce, when including the agricultural workforce. This is consistent with a study
of sixteen newly privatized firms in Tanzania, which found that despite the context of a male-
51
dominated society and the withdrawal of the socialist emphasis on equal treatment of men and
women with respect to employment and wages, the ratio of female to male employees rose in the
post-privatization period. This is different from other countries in Sub-Saharan African, where
overall employment levels fell from their pre-privatization levels (Due and Temu, 2002).
In the Enterprise Survey, roughly one in four of the interviewed workers were women. A
large number of women are employed in non-production jobs (28 percent compared with 10
percent of men). Female production workers were more likely to be skilled (42 percent of
female workers) than unskilled (22 percent). In contrast, male production workers were more
likely to be unskilled (about 37 and 44 percent respectively). About 6 percent of women were
professionals.
Although the sample is not representative of women overall (i.e., it only covers
manufacturing), it is encouraging that educated women are more likely to be in professional jobs
than men are. Indeed in the sample, female professionals made up a greater share of female
workers than male professionals made up of male workers (7 percent compared with 5 percent).
Overall, however, because there were more male workers, professionals were over twice as
likely to be male as female and skilled production workers were almost three times as likely to
be male.
Women in the worker sample tended to have more human capital than men. They were
better educated on average in terms of formal education (10.6 years of education versus 10.4 for
men) and were more likely to have received training (31 percent have received some form of
training against 19 percent for men).
Female workers made up a greater share of the workforce in female-owned firms (43
percent of workers in female-owned firms compared with 21 percent in other firms).31
These
workers, however, were slightly less well educated than other female workers, possibly because
many female-owned firms are in low-skill sectors. The concentration of women is very high in
some manufacturing sub-sectors such as garments (46 percent), chemicals (35 percent) and metal
and metal products (33 percent). Women also appear to be more concentrated in exporting and
foreign owned firms.
Given that an uneven distribution of household duties makes it difficult for women to
work full-time in many countries, at least in the OECD, it is surprising that women were slightly
more likely to work full-time than men in the Enterprise Survey sample. Despite this, women
work shorter hours on average than male workers (59 hours compared with 61).32
Further,
women workers are more likely than men to be single, despite being roughly the same age. This
suggests that household duties might play a greater role in determining activity on the labor
market rather than the choice of full-time versus part-time work.33
Young workers. There has been much concern about growing unemployment among
youth, with the Government recently issuing a Youth Employment Action Plan. A recent study
on youth employment and the transition from school to work (Kondylis and Manacorda, 2006)
found that young workers were more likely to be unemployed than adults at any point in time
due to friction in the employment market. Reasons included employment legislation, labor
regulation, and hiring and firing rules that disproportionately penalize new workers. Lower
52
skills and weaker attachment to the labor market also lead to higher rates of unemployment
among young workers. Young workers are more sensitive to the economic cycle and are more
likely to be among the long-term unemployed. Unemployment rates are particularly high among
urban youth. Young women are also especially affected due in part to inactivity based on poor
labor market prospects.
The Enterprise Survey data can be disaggregated by age, making it possible to focus on
the characteristics of younger workers (aged between 16 and 25). This group accounts for about
23 percent of the workers in the Enterprise Survey. On average, young workers are less well
educated than older workers (9.4 years compared with 10.7 years respectively), are less likely to
have had any form of training (16 percent compared with 24 percent), and are even less likely to
receive that training from the firm (6 percent compared with 18 percent).
Young people are more likely to work in small firms than older workers are, emphasizing
the importance of these firms in the transition from school to work. Whereas 65 percent of
workers between 16 and 25 are employed by small firms with less than 20 workers, only 43
percent of workers over 25 are. Similarly, only 6 percent of young workers work in large firms
with over 100 employees compared with 17 percent of older workers.
Human capital. The workers in the Enterprise Survey are relatively well-educated. The
average full-time worker has 10.4 years of education. Skilled production workers have 10.5 years
on average, whereas unskilled production workers have only 9.3 years of education. Managers
have 13.9 years of education on average and professionals have 14.8 years. The most educated
managers are in export-oriented firms (16.7 years).
Only 10 percent of workers in the sample have not completed primary education (i.e.
have less than 7 years of education). Within manufacturing, the sectors whose workers have the
most years of education are chemicals and machinery and equipment (13 years on average).
Workers in the non-metallic minerals and garment sectors have the fewest years of education (9
and 10 years respectively).
About 22 percent of workers have received any type of training, and 15 percent have
received firm-provided training. As already mentioned, younger people are less likely to have
received training, especially training provided by the firm. Male workers are also less likely to
have received any training than female workers (20 percent and 31 percent respectively).
II. Employment creation
The Enterprise Survey includes recall data on the total number of employees that the firm
had in 2002. This means that it is possible to compare firms where employment has been
growing with those where it has not. These comparisons must, however, be treated carefully.
First, there are well-known problems with the accuracy of recall data.34
Second, these
comparisons are mostly for successful firms (i.e., firms that existed in 2002 and managed to
survive). The fact that about 70 percent of firms in the sample reported that employment had
increased over this period compared with only 12 percent that reported that they reduced the size
of the workforce and 16 percent who reported that they had reduced the size of the workforce
emphasizes this fact.
53
The fastest growing firms were firms that were initially microenterprises in 2002 (i.e.,
that had less than 5 employees in 2002). The average microenterprise that grew enough by 2005
to enter the Enterprise Survey grew at an annual rate of over 20 percent per year between 2002
and 2005 (see Figure 27). It is very important to note that this does not imply that all
microenterprises grow very quickly. The reason for this is that only microenterprises that grew
very quickly would be in the Enterprise Sample. If you had a microenterprise that grew at
slowly, shrank, or went out of business, it would be excluded from the sample. That is because
microenterprises that had less than five enterprises in 2002 would still have less than five
enterprises in 2005 unless they grew quickly and would therefore be excluded.
Otherwise there was little difference in average growth rates by initial firm size. Keeping
in mind that the average growth rates might be biased upwards for small enterprises (i.e., small
enterprises that shrunk between 2002 and 2005 might be excluded from the sample if they
shrunk too much—below five employees in 2005), average growth rates were relatively similar
for large and small firms (5 percent and 4 percent per year respectively). Medium-sized firms
grew slightly more quickly (7 percent per year).
It is important to note that this is based upon initial size. If comparisons are based upon
final size, then large firms grew most quickly (i.e., firms that ended up as large had grown more
quickly than other enterprises). This is because many firms that grew quickly between 2002 and
2005 had become large by 2005 even if they were initially small or medium-sized. Non-
exporters grew more quickly than exporters and foreign-owned firms grew slightly more quickly
than domestic firms.
Figure 27: Different types of firms grew at different rates.
Source: World Bank Enterprise Survey.
Note: Size categories are based upon initial number of employees in 2002. To reduce the influence of outliers,
growth rates are average annual log growth rates [1/3 * (ln (workers in 2005)-ln (workers in 2002))]. In
addition, outliers with growth rates more than 3 standard deviations greater or less than the mean are excluded.
0%
5%
10%
15%
20%
25%
All
Mic
ro
Sm
all
Me
diu
m
La
rge
Exp
ort
ers
Non
-E
xp
ort
ers
Fo
reig
n
Do
me
stic
Ave
rag
e A
nn
ua
l G
row
th R
ate
Ave. Annual Growth Rate for Employment
54
III. Worker education and skills
About 20 percent of SMLE managers said that inadequately workers were a serious
problem for their firm. Although worker education was not among the very top concerns of
SMLE managers, this still suggests a moderate level of concern. Microenterprise managers were
less likely to be concerned—only 8 percent said it was a serious problem.35
Demand for skills by different types of firms
Although most of the differences in firms‘ perceptions about worker education and skills
were not statistically significant after controlling for other factors, there is some evidence that
managers of foreign-owned firms were more likely to be concerned about worker skills than
managers of domestically owned firms. This is a concern because foreign firms‘ ability to access
expertise from their home countries is limited by rules that set a ceiling on the number of foreign
workers that can receive work permits.
Although managers of foreign-owned firms were more concerned about education, they
do not appear to hire better educated workers than domestic firms (see Figure 28). Although
fewer workers in domestic firms had technical or vocational training, more had a general
secondary education. Indeed, there was little difference in terms of the percentages having only
a primary education (28 percent for domestic and 27 percent for foreign) or university education
(8 percent for both).
The structure of employment, however, was slightly different. While the share of skilled
workers is just slightly higher in foreign-owned firms (45 percent versus 41 of locally owned
firms), the share of unskilled production workers is considerably lower (18 versus 35 percent).
Figure 28: Workers are better educated on average in larger enterprises.
Source: World Bank Enterprise Survey.
0%
25%
50%
75%
100%
All Small Medium Large Foreign Domestic
% o
f w
ork
ers
, b
y e
du
ca
tio
n le
ve
l
None/Other Primary Secondary Technical/Vocational University
55
The reason for this is that the share of non-production workers is almost twice as high in foreign-
owned firms (30 versus 17 percent).
As discussed in Chapter 2, large firms tend to be more capital intensive and more
productive than small firms. They also appear to be more skills-intensive (see Figure 28). A
larger share of the workers interviewed as part of the worker survey were university educated in
large manufacturing firms than in small manufacturing firms (17 percent of workers in large
firms compared with 9 percent in medium-sized firms and only 4 percent in small firms).
Similarly, fewer workers have only a primary education in large firms (18 percent, 28 percent
and 31 percent respectively).
This is consistent with previous studies using data from the 2003 Enterprise Survey.
Goedhuys (2007) surveys learning and product innovation in Tanzanian manufacturing and
commercial farming firms. All of the aspects of learning that are analyzed are strongly and
significantly correlated with size. Larger firms have more highly skilled worker labor force,
invest more in training and R&D activities and are more capital intensive.
Comparisons with the 2003 Survey
As discussed in Chapter 3, firm managers appeared to be less concerned about worker
education in the 2006 Enterprise Survey than they were in the 2003 Enterprise Survey (see
Figure 29). About 28 percent of the managers of the manufacturing SMLEs in the 2003
Enterprise Survey said worker education was a serious problem, compared with 19 percent of
managers of similar firms in the 2006 survey.36
Although the drop in concern might be
encouraging, it is important to note that this might reflect the effect of the power crisis, which
appears to have muted complaints about other aspects of the investment climate between the two
surveys (see discussion in Chapter 3).
Figure 29: Workers interviewed in the 2006 Enterprise Survey appear better educated on average than
workers in the 2003 Enterprise Survey.
Source: World Bank Enterprise Survey.
0%
25%
50%
75%
100%
2003 2006
% o
f w
ork
ers
, b
y e
du
ca
tio
n le
ve
l
None/Other Primary Secondary Technical/Vocational University
56
But also consistent with the lower levels of concern in the 2006 survey, workers appear
better education on average in the 2006 Enterprise Survey. Although cross-time comparisons
between the 2003 and 2006 surveys are difficult (see Appendix 1.2), data from the Enterprise
Surveys suggests that the average education of workers in manufacturing firms in Tanzania
increased between 2003 and 2006.37
Among the workers interviewed as part of the Enterprise
Survey, there were more workers that had completed secondary and vocational training and
fewer workers who had completed less than secondary education in 2006 (see Figure 29). There
were, however, also fewer workers that had completed university education.
IV. Firm Training
Given current debates on skill shortages but also the rise in workers educational
qualifications, the way workers acquire human capital is of crucial importance. It is therefore
interesting to look at the characteristics of firms that provide training and workers that receive
training. Because of concern about the robustness of results, the empirical analysis focuses on
those firm and worker characteristics that are significantly and robustly correlated with training
in the econometric analysis presented in Appendix 4.1.
Worker Characteristics
Better educated and more highly skilled workers were more likely to have received
training than other workers were (see Figure 30). Whereas about 44 percent of workers with a
university education had received training only about 10 percent of workers with a primary
education had. Similarly, about 55 percent of managers had received training compared with 33
percent of professionals, 25 percent of skilled workers and only 12 percent of unskilled workers.
This suggests complementarity between education and firm- and worker-financed training.
Not surprisingly, there is a lot of overlap between education levels and profession within
the firm. For example, whereas 49 percent of unskilled workers have a primary education or less
and only 1 percent have a university education, only 23 percent of managers have a primary
education or less and 68 percent have a university education. It is therefore possible that the
reason that better educated workers are more likely to receive training is that they are more likely
to be managers or professionals.
57
This does not seem to be the case, however. Better educated workers are more likely to
receive training than less well educated workers even after controlling for profession (see
Appendix). That is, better educated skilled workers are more likely to receive training than less
well educated skilled workers.
As noted earlier, female workers are both better educated and more likely to receive
training than male workers are. Whereas 31 percent of female workers have ever received
training and 20 percent have received training in their current firms, only 20 percent of male
workers had ever received training and only 14 percent had received training in their current
firm. This remains true after controlling for education and profession. That is, female workers
do not appear to only receive more training because they are better educated or are less likely to
be unskilled workers (see Appendix 6.1).
Firm Characteristics
The probability that a worker receives training does not depend only on the
characteristics of the worker—it also depends upon the characteristics of the firm that employs
the worker. Firm owners and managers decide whether they should invest in their workers and
also decide who they will train and the content of the training. Looking at the types of firms that
provide training provide information on these decisions.
About 36 percent of manufacturing firms reported having formal training programs in
2006 Enterprise Survey. This was slightly lower than in 2003, when 48 percent reported having
training programs (see Table 9). It is important to note, however, that large firms appear to have
been oversampled in the 2003 survey making comparisons more difficult. Comparing only panel
firms, the percent of firms with formal training programs appears to be about the same or
possibly to have even increased (about 54 percent in 2003 to 59 percent in 2006). This suggests
Figure 30: Better educated and more highly skilled workers are more likely to receive training.
Source: World Bank Enterprise Survey.
-20%
0%
20%
40%
60%
Te
rtia
ry
Se
co
nd
ary
Pri
ma
ry
Ma
na
ge
rs
Pro
fessio
na
ls
Skill
ed
Non
-Pro
du
ctio
n
Un
skill
ed
Fe
ma
le
Ma
le
% o
f w
ork
ers
tra
ine
d% of workers ever trained
% of workers trained at firm
58
that the drop observed in the raw data might be primarily due to the change of samples rather
than an actual drop in training.
Table 9: Although fewer firms in the 2006 survey provided training, this appears to be due to sample
differences between the two surveys.
2003 2006
% of firms that have formal training programs
All manufacturing SMLEs 48% 36%
Panel Firms 54% 59%
Source: World Bank Enterprise Surveys.
Note: Averages for all manufacturing firms only include manufacturing firms in cities covered in both
surveys. Averages for panel firms are averages for firms that were in both the 2003 and 2007 surveys.
Because the 2003 survey only covered manufacturing comparisons are only for manufacturing. Averages
for panel firms are unweighted.
In general, larger, more formal firms are more likely to have training programs than other
firms (see Figure 31). Whereas only about 28 percent of small manufacturing SMLEs had
training programs, 40 percent of medium-sized SMLEs and 68 percent of large SMLEs in this
sector did. Similarly, firms with audited accounts were more likely to have training programs,
again emphasizing the link between formality and whether the firm provides training.38
Previous studies of foreign owned firms in Tanzania have found that foreign owned firms
investment more in both their workers and in new technology. Using data from the 2003
Enterprise Survey for Tanzania, Goedhuys (2007) finds that foreign firms train their workforce
more intensely and invest more in equipment than domestic firms do.39
This is also true in the
2006 Enterprise Survey. Whereas about 71 percent of foreign-owned firms have formal training
programs only about 33 percent of domestic firms do (see Figure 31). This is not just because
they are larger and more formal. In the econometric analysis in Appendix 2.1, the difference
between foreign and domestic firms with respect to providing training remains statistically
significant after controlling for these other factors.
59
One slightly anomalous result is that firms with part-time and temporary workers are
more likely to provide training than other firms. It is not clear why this is the case given that
long-term investment in full-time workers would seem to be more profitable than similar
investment in part-time or temporary workers. This could be because firms that have a relatively
small full-time workforce are more likely to have to provide some easy-to-implement training for
their part-time workers. It is, however, important to note that there was no evidence that part-
time workers were more likely to receive training in the individual-level regressions.
After controlling for ownership and firm-size, there is little evidence that other firm
characteristics affect training decisions. In particular, exporters do not appear to be more likely
to provide training than non-exporters after controlling for size and ownership.
V. Wages
The private returns to education and experience can provide a powerful incentive for
individual workers to invest in basic education and additional training. Understanding how
characteristics of workers and firms affect wage remuneration makes it possible to assess the
incentives that firms and workers have in improving education and skills. As in the previous
section, the results presented in this section are supported by a detailed econometric analysis, the
results of which are presented in Appendix 6.2.
Education, training and wages
Better educated workers receive significant higher wages in Tanzania than other workers
do. An analysis of the Enterprise Survey data suggests that wages increase by about 7 to 8
percent for each additional year of education. Other studies in Africa have found similar returns.
Using data from the mid-1990s, Bigsten and others (2000) found that wages increased on
Figure 31: Large firms and foreign-owned firms are more likely to have formal training programs.
Source: World Bank Enterprise Survey.
-25%
0%
25%
50%
75%
All
Sm
all
Mid
-Siz
ed
Larg
e
Dom
estic
Fo
reig
n
No A
udited A
ccounts
Audited A
ccounts
Fu
ll-tim
e O
nly
Part
-tim
e
% o
f firm
s w
ith t
rain
ing p
rogra
ms
% of firms with training program
60
average by about 8 percent for 5 African countries (Cameroon, Ghana, Kenya, Zambia and
Zimbabwe) that they looked at.40
Experience appears to have a modest positive effect in some
specifications—with an extra year of experience associated with an increase in wages of about 3
to 4 percent. This result, however, is not consistently statistically significant after controlling for
other things that might affect wages.
This is higher than returns to education in Tanzania in the early 1990s. Between 1993
and 2001, average marginal returns rose from 6 percent to 9 percent for the young workers aged
less than 30 and from 8 to 13 percent for older workers (Soderbom and others, 2006). Although
the estimate from the 2006 Enterprise Survey data suggests that returns might be slightly lower
than in 2001, it appears that they remain higher than in 1993.
There is some evidence that training can also increase wages. After controlling for both
firm and worker characteristics, workers that have received training earn about 19 percent more
than other workers. Although this suggests that training improves wages and productivity, it is
also possible that this is because managers select the best workers for training when budgets are
limited. Moreover, as noted in the Appendix, this result is also not highly robust. There is some
evidence that rather than reflecting high returns to training, this result is due to unobserved firm
characteristics affecting both training decisions and wages. That is, the econometric results
suggest that the correlation between wages and training might be due to more productive firms
paying higher wages and training their workers more.
Firm Characteristics
As noted in Chapter 2, large firms and foreign-owned firms report that they pay their
workers more than other firms do. The evidence from the worker survey is consistent with this.
Moreover, even after controlling for worker skills and education and other firm characteristics,
there is evidence that foreign-owned firms pay their workers more than similar workers in
domestic firms. Workers in foreign owned manufacturing firms earn about 24 percent more than
similar workers in domestic manufacturing firms.
Similarly, there is some evidence that large firms pay their workers about 30 to 40
percent more than similar workers in small and medium-sized firms. Previous studies found
similar results in 1990s. In particular, Kingdon and other (2006) showed that there were
significant wage gaps between large and small firms in Tanzania, Ghana, Kenya and Nigeria.
In the Enterprise Survey, 10 percent of the workers that were interviewed reported wages
below the mandated minimum wage when the survey was run (the ―old‖ minimum wage). Given
that wages are generally higher in larger firms, it is not surprising that most of these workers
worked in firms with less than 50 workers (see Figure 32). About 13 percent of workers in small
firms reported wages below the old minimum wage compared with 7 percent of workers in mid-
sized firms and no workers in large firms. If the new minimum wage set in January 2008 were
enforced, more workers (about 20 percent of those interviewed in the Enterprise Survey) would
have been below the threshold. Moreover, this is true for workers across the spectrum of firm
sizes. Indeed, many of the workers in larger firms reported wages below the new minimum
wage.
61
Another issue is that it is likely that enforcement of those minimum wages would be
highly difficult and most likely somewhat ad hoc. Further, the fact that exemptions have been
granted to larger firms and firms that export appears to be counter to what the data show, as well
as introducing a significant source of non-transparency in the system.
It is important to note that the results in this chapter are broadly consistent with the
analysis in Chapter 2, which also suggested that wages were higher in large and foreign-owned
firms. The results in this chapter, however, suggest that the higher wages are not simply due to
large and foreign-owned firms hiring better educated or more experienced workers. These
results also suggest that some form of rent sharing might be occurring. Under the rent-sharing
hypothesis that firms and workers share rents in such a way that an identical worker will earn
more in more profitable enterprises such as large and foreign-owned enterprises (see Chapter 2).
Other Worker Characteristics
Before controlling for firm characteristics, union members appear to earn more than
similar non-members. The difference is large—about 25 to 30 percent. However, after
controlling for firm characteristics, the difference becomes small and statistically insignificant
(i.e., it might be due to sampling variation). This suggests that the wage premium might due to
unionized firms paying more to all workers irrespective of the workers‘ union membership.
Gender, marital status and full-time status are not significantly correlated with wage
levels after controlling for other factors. The fact that there is no evidence that women are paid
less than men with similar characteristics and in similar firms is surprising. Previous studies
using earlier data from between 1991 and 1995 found large differences between wages for men
and women in Tanzania.41
Figure 32: Many workers are paid less than the new minimum wage.
Source: World Bank Enterprise Survey.
Note: Firms with more than 300 workers are excluded because few firms are in this size category.
0
5
10
15
20
25
Total 5-49 50-99 99-299
% o
f w
ork
ers
be
low
min
imu
m w
ag
e
workers below old minimum wage
workers below new minimum wage
62
VI. Summary
About 20 percent of SMLE managers said that inadequately workers were a serious
problem for their firm. Although worker education was not among the very top concerns of
SMLE managers, this still suggests a moderate level of concern. Microenterprise managers were
less likely to be concerned—only 8 percent said it was a serious problem. Managers of foreign-
owned firms were more likely to be concerned about worker skills than other managers. This is
a concern because foreign firms‘ ability to access expertise from their home countries is limited
by rules that set a ceiling on the number of foreign workers that can receive work permits.
Given that managers of foreign-owned firms are more concerned about worker skills than
other managers are, it is not surprising that foreign-owned firms invest more in their workers
than other firms do. The difference is relatively large—whereas about 71 percent of foreign-
owned firms have programs to provide formal training to their workers, only about 33 percent of
domestically owned firms do. Large firms are also more likely to have training programs than
other firms. Whereas only about 28 percent of small manufacturing SMLEs had training
programs, 40 percent of medium-sized SMLEs and 68 percent of large SMLEs in this sector did.
This investment in their workers might partly explain why large firms are more productive than
small firms in Tanzania (see Chapter 3).
These firms appear to reward their workers better than other firms do. Even after
controlling for worker skills and education, large firms and foreign-owned firms pay their
workers more than other firms do. Workers in foreign owned manufacturing firms earn about 24
percent more than similar workers in domestic manufacturing firms. Similarly, there is some
evidence that large firms pay their workers about 30 to 40 percent more than similar workers in
small and medium-sized firms. This suggests that some form of rent sharing might be occurring.
Under the rent-sharing hypothesis, firms and workers share rents in such a way that an identical
worker will earn more in more profitable enterprises such as large and foreign-owned enterprises
(see Chapter 3).
Returns to education are relatively high in Tanzania. Better educated workers receive
significant higher wages in Tanzania than other workers do—wages increase by about 7 to 8
percent for each additional year of education. This suggests that both that there is demand for
educated workers and that workers are rewarded for becoming better educated. In this respect,
high returns to education should encourage people to complete their education.
About 10 percent of the workers that were interviewed for the Enterprise Surveys
reported earning less than the mandated minimum wage that was in place when the survey was
run (the ―old‖ minimum wage). Given that wages are generally higher in larger firms, it is not
surprising that most of these workers worked in firms with less than 50 workers (see Figure 32).
About 13 percent of workers in small firms reported wages below the old minimum wage
compared with 7 percent of workers in mid-sized firms and no workers in large firms.
A new, higher, minimum wages was set in January 2008. If the new minimum wage is
enforced, more workers (about 20 percent of those interviewed in the Enterprise Survey) would
have been below the threshold. Moreover, this is true for workers across the spectrum of firm
63
sizes. Indeed, many of the workers in larger firms reported wages below the new minimum
wage.
64
CHAPTER 5: ACCESS TO FINANCE
Firms in Tanzania are very concerned about access to finance. About 40 percent of
SMLEs and about 50 percent of microenterprises said that access to finance was a serious
constraint for their enterprises. Managers were more likely to say that access to finance was a
problem than any other area of the investment climate except power.
This chapter looks at additional evidence, including objective indicators, on access to
finance in Tanzania. The first section discusses the institutional framework in Tanzania. The
second section provides additional information on perceptions about access to finance. The third
section compares objective data on access to finance in Tanzania with the comparator countries
and compares access across different types of firms within Tanzania. The final section
concludes.
VII. Background
Since the early 1990s, the Government of Tanzania has implemented a series of reforms
that have liberalized the banking sector and reduced the dominant role of the state. In 1991, the
Government passed the Banking and Financial Institutions Act, which gave the Bank of
Tanzania supervisory and regulatory power and allowed private banks to operate in Tanzania.
The first private banks started operating in 1993, although their market share was initially very
low. The largest bank in the sector, the state-owned National Bank of Commerce (NBC), had a
market share of 80 percent of deposits at this time.
In 1996, the Government of Tanzania started to privatize state-owned banks.42
The first
bank to be privatized was the Cooperative and Rural Development Bank (CRDB), a smaller
state-owned bank with a market share of about 5 percent of deposits. It was recapitalized and
was then sold on the stock market. In 1997, the Government started the privatization process for
the much larger NBC. After recapitalizing the bank, closing 20 branches and reducing the
number of employees from 10,000 to 8,000, the Government split the bank into two banks, the
new NBC and the National Microfinance Bank (NMB). The new NBC was planned as a
conventional bank operating mostly in urban areas while the NMB was intended to ensure that
the rural population would continue to have access to financial services. As a result, most
branches went to NMB (95 out of 130). Although NBC was sold to the Absa group in 1999, the
Government initially failed to find a buyer for NMB. NMB was taken over by management
consultants, before having 49 percent of its shares sold to Rabobank, a large Dutch bank with
extensive experience in microfinance, in 2005.
Banking Sector. In late 2008, 35 tier one financial institutions—25 banks and 10 other
financial institutions—operated in Tanzania. NMB is the largest bank in terms of branch
network, with 108 branches throughout the country. NMC had a branch network of 40 branches
and CRDB had a network of 22 branches. Most other banks have only 1 or 2 branches and no
other bank had more than 7 branches (Standard Chartered Bank).
NMB, NBC and CRDB accounted for about 49 percent of sector assets and about 51
percent of deposits at the end of 2007. Although NMB is larger in terms of branch network, it
65
was about the same size as CDRB in terms of assets and deposits (about 17 percent and 19
percent respectively for each) and is only slightly larger than NBC (14 percent and 15 percent
respectively).43
Credit to the Private Sector. The banking sector has been growing quickly. Credit to the
private sector increased from about 10 percent of GDP in June 2006 to about 15 percent of GDP
by June 2008 (see Table 10).
Despite this growth, the sector remains small by international standards. At the end of
2007, credit to the private sector was about the same size as in Uganda or Rwanda (see Figure
12). It is far lower, however, than in the other comparator countries. For example, credit to the
private sector was equal to about 25 percent of GDP in Burundi, Kenya, and Swaziland and
between 80 and 120 percent of GDP in Mauritius, Thailand, South Africa, Malaysia and China.
Other measures of financial sector development such as bank credit and money and quasi-money
tell a similar story.
Under these circumstances, it is not surprising that access to finance is relatively low.
According to data from a recent survey by FinScope (see Box), only about 9 percent of
individuals had access to financial services at a formal provider such as a bank or an insurance
company and only about 2 percent had access at a semi-formal provider such as a microfinance
institution (MFI). Access is particularly limited for low-income individuals, individuals in rural
areas and individuals with little education.
Figure 33: The financial sector is not highly developed in Tanzania.
Source: World Bank (2008c).
Note: Data for Uganda are for 2006 and data for Rwanda are for 2005. All other data are for 2007.
0 100 200
Tanzania
Uganda
Rwanda
Burundi
Kenya
Swaziland
Mauritius
Thailand
South …
Malaysia
China
% of GDP
Credit to private sector
0 100 200
Tanzania
Swaziland
Rwanda
Uganda
Kenya
Burundi
South …
Thailand
Mauritius
Malaysia
China
% of GDP
Bank Credit
0 100 200
Tanzania
Rwanda
Uganda
Swaziland
Burundi
Kenya
South …
Thailand
Mauritius
Malaysia
China
% of GDP
Money and Quasi-money
66
Many people without bank accounts said that they did not have one because they did not
have regular income (about two-thirds), did not have a job (about one-quarter), did not have
Box: Access to financial services for households
Few people in Tanzania have access to financial services. According to a recent FinScope survey conducted by the
Financial Services Deepening Trust Tanzania, only about 9 percent of individuals had access to financial services at
a formal provider such as a bank or an insurance company and only about 2 percent had access at a semi-formal
provider such as an MFI. A larger share—about 35 percent—had access at an informal provider such as an
Accumulating Savings and Credit Associations (ASCA) or a Rotating Savings and Credit Associations (ROSCA).
About 54 percent of individuals had did not use either formal or informal financial service providers. Not
surprisingly, access to formal financial service providers was more limited in rural areas (7 percent of individuals
compared with 18 percent in urban areas).
Education and income were important factors associated with access. Better educated individuals were far more
likely to have access to financial services than less well-educated individuals were. Close to 90 percent of people
with a university education had access to financial services compared with less than 10 percent of people with a
primary education or less. Similarly about 30 percent of individuals with salaried employment used formal financial
institutions compared with less than 10 percent of those without.
The 89 percent of the population without a bank account at a formal or semi-formal financial institution were asked
why they did not have one. Multiple responses were allowed. By far the most common response was a lack of
regular income—about two thirds (63 percent) said that this was a reason. Significant numbers also said that they
did not have money to save (33 percent), did not have a job (28 percent) or that they had too little money to make it
worthwhile (17 percent).
Financial illiteracy was a lesser, although still significant concern—21 percent of individuals said that they did not
know how to open an account. About one-fifth of individuals said that the nearest bank was too far away. Bank
charges also played a role—about 20 percent said that charges were too high and 17 percent said that it is too
expensive to have an account.
Very few households had loans from formal financial institutions. About half of the population reported that they
had ever had any type of loan from either a formal or informal source and about one-quarter percent reported that
they currently had a loan. Most loans, however, were from informal or semi-formal providers, with family or
friends being the most common source. About 38 percent of individuals with loans reported having a loan from a
family or friend and 33 percent from a kiosk. In contrast only about 4 percent had a loan from a bank and only 6
percent reported having a loan from an MFI. Loans from Savings and Credit Cooperatives (SACCOs) and ASCAs
were slightly more common (9 percent and 6 percent). Very few reported loans from informal money lenders (4
percent).
Individuals who had never borrowed from a financial institution were asked why they had not done so. Individuals
could give multiple answers. The most common reason was that they did not need a loan (35 percent). A significant
number said that they did not have enough money (35 percent) or were concerned that they would not have enough
money to repay the loan (33 percent). A significant number (25 percent) said that they did not know where to get a
loan and 16 percent said that there was nowhere nearby to do so.
There was also concern about interest rates and other bank charges. About 18 percent of individuals said that
charges were too high and about 10 percent said that they did not believe in paying interest. Collateral was also a
concern (14 percent). Few respondents (less than 5 percent) gave other reasons such as not having identification,
being too young or not being allowed to by their spouse.
In summary, access to financial services is very limited in Tanzania—only 9 percent of people have a bank account
and only 4 percent have a loan. Most of the problems with access relate to broader problems in providing these
services to people with limited and unstable income and resources. Other factors also play a role, however. For
example, many respondents were concerned about bank charges and physical access to banks. Financial illiteracy
also appears to play a role.
Source: Steadman Group (2007); FinScope (2007).
67
money to save (about one third) or had too little money to make it worthwhile. In this respect,
limited access to finance appears to reflect other types of economic marginalization.
Long-Term Financing. Access to long-term funds is particularly underdeveloped and the
ability of banks to extend term credit beyond about five years is extremely limited due to their
dependence upon short-term deposits. Long-term loans of over 5 years made up only about 7
percent of total bank loans in June 2008—slightly higher than two years earlier (see Table 10).
In more developed economies, banks are more easily able to provide longer-term project
financing because they can obtain long-term debt in the capital markets.
Table 10: Selected Indicators for the Tanzania Financial Sector.
Indicator June 2006 June 2007 June 2008
Banking Sector
Credit to the private sector (% of GDP) 9.8% 12.5% 14.9%
Non-performing loan (% of total) 5.9% 7.9% 6.3%
Medium-term loans (2-5 years, % of total) 16.3% 21.8% 27.5%
Long-term loans (5 years or longer) 5.2% 4.5% 7.1%
Stock Market
Turnover (as % of market capitalization) 2.5% 3.1% 2.2%
Market capitalization (% of annual GDP) 5.2% 4.7% 6.0%
Interest Rate
Lending Rate 15.4% 15.7% 14.8%
Deposit Rates 2.5% 2.6% 2.8%
Interest Rate Spread 12.9% 13.1% 12.0%
Source: Bank of Tanzania.
Stock Market Capitalization. Market capitalization in Tanzania is also low. In June 2008,
it was equal to about 6 percent of GDP (see Table 10). Although this is slightly higher than in
Uganda and close to market capitalization in Swaziland, it is far lower than in the other
comparator countries. For example, market capitalization was equal to about 45 percent of GDP
in Kenya, about 80 percent in Thailand and was equal to more than 100 percent of GDP in the
other comparator countries (see Figure 34).
Turnover is also very low—averaging between about 2 and 3 percent of market
capitalization between 2006 and 2008. This is far lower than in Kenya (about 11 percent of
market capitalization) and the other comparator countries (between 50 and 180 percent of market
capitalization). The low market capitalization and low market turnover suggests that market
development is relatively limited.
Housing Financing. Since the early 1990s, the Government has taken a number of
initiatives to encourage the development of mortgage financing. One thing that it has done is to
license a number of banks and non-bank financial institutions to spur competition and to
encourage the introduction of new products including medium and long-term facilities to support
mortgage financing. In 1999, the Government put in place land legislation that was supposed to
fully support mortgage financing in the country.
68
The Government has also established commercial courts to speed up the resolution of
contractual disputes, including bank accommodations on mortgages. This appears to have been
successful—Tanzania compares relatively favorably with other countries with respect to generic
contractual disputes. The Doing Business report estimates the time it takes to resolve a simple
commercial dispute over non-payment for delivered goods that the buyer claims are sub-
standard.44
It takes 462 days and costs 14.3 percent of the claim to resolve the case in Tanzania.
In the best performing economy on this measure (Hong Kong, China), it takes 211 days and costs
14.5 percent of the claim. Based upon the number of procedures, the length of time, and the
cost, Tanzania ranks 33rd
in the World (World Bank, 2008a). In comparison, it takes 535 days
and costs 44.9 percent of the claim in Uganda (117th
in the World) and takes 465 days and costs
26.7 percent of the claim in Kenya (107th
in the World).
Despite the demand for housing and the reforms that the Government has instituted,
housing financing remains extremely limited. This has contributed to a serious shortage of urban
housing in Tanzania, with about 70 percent of residents of Dar es Salaam living in unplanned
settlements with limited access to services. The wealthy mostly finance housing construction
with cash, while middle and low income households that cannot get credit generally build
incrementally.45
One major roadblock for would-be lenders is legal impediments that exist in
land law and problems with foreclosure procedures. These issues will need to be resolved to
allow the mortgage market to develop.
Collateral. Land registration is related to the issue of property financing and collateral.
The Doing Business report records the full sequence of procedures to transfer property title from
one business to another so that it can be used for collateral or to be sold. The report looks at a
standardized transaction of transferring a land and a 2 story warehouse from one limited liability
Figure 34: Stock market capitalization and turnover is low in Tanzania.
Source: World Bank (2008c).
Note: Data for Uganda are for 2006. All other data are for 2007.
0 100 200 300 400
Tanzania
Uganda
Swaziland
Kenya
Thailand
Malaysia
China
South Africa
% of GDP
Stock market capitalization (% of GDP)
0 50 100 150 200
Tanzania
Swaziland
Uganda
Kenya
Malaysia
South …
Thailand
China
% of market capitalization
Turnover (% of market capitalization)
69
firm to another in Dar es Salaam.46
The standardized transaction includes 9 procedures that take
about 73 days to complete and the cost is equal to about 4.4 percent of the property value (see
Table 11). In the best performing economy (Saudi Arabia), it takes two days and the cost is 0.0
percent of the property value. Based upon this, Tanzania ranks 142nd
out of 181 countries on this
measure in the Doing Business Report. In comparison, it takes 227 days and costs 4.1 percent of
the property value in Uganda (167th
in the world) and takes 64 days and costs 4.1 percent of the
property value in Kenya (119th
in the world).
Table 11: Procedures to register land in Dar es Salaam.
No Procedure Time: Cost:
1* Obtain an official search at the Land Registry 14 days TZS 2,000-4,000
2* Obtain clearance by the Land Ministry of payment of land tax for
ten years 1 day no cost
3* Obtain a property tax clearance from the Municipality for the last
10 years 1 day no cost
4* Obtain a valuation report 2 days See Note.
5 A government valuer inspects the property to determine its value 7 days Paid in Procedure 5
6* Notarization and execution of the sale agreement and preparation
of the transfer deed 1 day About 3% of value
7 Obtain approval for the transfer 14 - 21 days TZS 5,000 approval fee
8 Obtain a capital gains tax certificate from the Tanzania Revenue
Authority 14 - 21 days no cost
9 The transfer deed is delivered to the Land Officer for its recording
under the name of the buyer at the Lands Registry 14 days See Note.
Source: World Bank (2008a).
Note: Procedures 1 through 3 are done simultaneously; procedure 6 is done simultaneously with procedure 5 and
procedure 4 is done simultaneously with procedure 7. The valuation fee is calculated by using the following
formula: (Property Value - 200,000) * (1.25/1000) + 550 + valuation approval fee of 0.01% of property value (in
Shillings). The fee for the transfer deed is 1% of property value (Stamp duty) + Registration Fee as follows:
(Property value - 100,000) * (2.5/1000) + 1000 (in Shillings).
The full list of procedures is shown in Table 11. The most time consuming procedures
are getting approval for the transfer from the Commissioner of Land, getting a capital gains tax
certificate from the Tanzania revenue Authority and getting the transfer deed recorded at the
Land Registry. The procedures in Table 11 are for Dar es Salaam. In most secondary cities, the
procedures are more time consuming (World Bank, 2007c). Similar procedures took between
114 (Mwanza) and 268 days (Kigoma) in six of the eight secondary cities covered in a World
Bank report that looked at land registration in secondary cities in Tanzania in 2007. It took less
time in Zanzibar (only 53 days).47
The delays in the secondary cities were primarily due to delays in processing title deeds
in Dar es Salaam. Local procedures were generally completed fairly rapidly (about 40 days in
most cases). Based upon interviews with local businessmen, the authors of the World Bank
report that documented these procedures note that there is a lot of individual variation in
completing procedures—one local businessman reported that he had been waiting for close to
five years for his title deed to come from Dar es Salaam.
One side effect of these delays is that most entrepreneurs in Tanzania choose to rent their
premises rather than purchase them—purchasing existing premises and obtaining the title deed is
70
not normal practice (World Bank, 2007c). Given the importance of land and buildings as
collateral, this can obviously impact access to financing.
Interest Rates. Lending rates remain high in Tanzania. Between June 2006 and June
2008, they were between 14.8 percent and 15.7 percent (see Table 10). This was slightly higher
than in Kenya (13.6 percent), about the same as in Rwanda (16.1 percent) and significantly lower
than in Uganda (18.7 percent) and Mauritius (21.1 percent). It was, however, considerably
higher than in the other comparator countries (between about 6 and 11 percent). This suggests
that although interest rates are not likely to be as constraining as they are in Uganda, they are
more constraining than in the best performing countries (see Figure 35).48
Legal protections for creditors and investors. The Doing Business report also collects
information on legal protections for creditors and investors. The measure of investors looks at
the strength of legal protections that minority shareholders have against directors‘ misuse of
corporate assets for personal gain. The measure of creditor rights measures the legal rights of
lenders and borrowers and the sharing of credit information.
Tanzania ranks 84th
out of 181 economies on the getting credit measure. In comparisons,
Uganda ranks 109th
and Kenya ranks 5th
. On the index of legal rights, Tanzania scores 8 of 10.
It loses two points on the index because creditors do not have absolute priority to their collateral
either inside or outside of bankruptcy procedures. In comparison, Uganda gets a 7 on this index
and Kenya gets a 10. Tanzania compares less favorably with respect to credit bureau coverage,
scoring a 0 on this index.
Tanzania ranks about the same on the protecting investors measures—88th
out of 181
economies. In comparison, Uganda ranks 126th
and Kenya ranks 88th
. In general, Tanzania
compares better on the sub-indices related to shareholder lawsuits (8 out of 10) than on the sub-
Figure 35: Lending Rates are higher in Tanzania than in the best performing countries.
Source: World Bank (2008c) for 2007 for other countries; Bank of Tanzania for Tanzania.
0
5
10
15
20
25
Tanzania
Chin
a
Mala
ysia
Thaila
nd
South
A
fric
a
Sw
azila
nd
Kenya
Rw
anda
Uganda
Mauritius
Inte
rest R
ate
Lending Rate (%)
71
indices related to the directors‘ liability (4 out of 10) and disclosure (3 out of 10). More details
are available on the Doing Business website (www.doingbusiness.org).
VIII. Perceptions about access to finance
Firms included in the enterprise survey
The Enterprise Survey collected some detailed data on firms‘ use of bank credit in
Tanzania. Among the SMLEs that were surveyed, relatively few reported having either a loan or
overdraft. Only 16 percent of SMLEs in Tanzania reported having a bank loan and only 12
percent reported having an overdraft. Because some firms had both a loan and an overdraft
facility, this means that about 22 percent of SMLEs in Tanzania had some type of bank credit.
Not surprisingly, microenterprises were less likely to have bank credit—only about 17 percent
reported that they did.
In addition to asking whether the firm has a loan, firms are also asked whether they
applied for a loan in the fiscal year before the survey. If they did, they are asked whether their
application was rejected and, if so, why. If they did not apply, they are asked why not.
Given that relatively few firms had loans or overdrafts at the time of the survey, it is not
surprising that few firms had applied for a loan in the previous year—about 19 percent of
SMLEs and 21 percent of microenterprises (see Figure 36). Few firms had had applications
rejected—less than 7 percent of SMLEs and 12 percent of microenterprises had had an
application rejected.49
In part this is because few firms applied for a loan. The small number of
firms that applied for loans implies that many of the firms that did apply were rejected—about
Figure 36: Most firms did not apply for a new loan in 2005 and a substantial proportion did not apply
because they did not need to.
Source: World Bank Enterprise Survey.
Applied -Accepted
12%
Applied-Rejected
7%
Did not Apply -
No Need19%
Did not Apply -Other
Reason62%
SMLEs by whether applied for loan in past year
Applied -Accepted
9%
Applied-Rejected
13%
Did not Apply -
No Need9%
Did not Apply -Other
Reason69%
Microenterprises by whether applied for a loan in past year
72
one-third of SMLEs and about three fifths of microenterprises that applied for a loan had the
application rejected. That is, although only a small number of firms had loan applications
rejected, this is mostly because few firms applied. Given the high level of concern about access,
the modest number of applications suggests that self-selection plays an important role in firms
not having loans.
A large number of SMLE managers (about 19 percent) said that they had not applied
because they did not want a loan. In contrast, few microenterprise managers said the same
(about 9 percent). The remainder of the firms had not applied for other reasons such as interest
rates being too high or that they did not have sufficient collateral. Reasons for not applying are
discussed further below.
Perceptions about access to finance for firms with and without bank financing
About 40 percent of SMLE managers and about 50 percent of microenterprise managers
said that access to finance was a serious problem for their firms‘ operations. As discussed in
chapter 3, larger firms, firms in the retail trade sector, and foreign-owned firms were less likely
to say that access to finance was a serious problem than other firms were. Also consistent with
previous work, firms in Zanzibar were more likely to say that access to finance was a serious
constraint than firms on the mainland were.50
This section looks a little more at data on perceptions about access to finance to see
which types of firms are concerned about access to finance.51
It is important to note that the
question on access explicitly refers to both availability and the cost of the loan (i.e., interest rate).
Moreover, availability can be interpreted more broadly to mean the terms of the loans that are
available not just whether the firm can get any loan. For example, firms might be concerned
about availability of long-term lending even if small short-term loans are available for working
capital. To partially address this, this section looks at whether the most serious complaints are
from firms without any credit products, which might indicate that the high level of concern
reflects the availability of credit, or from firms that have credit, which indicates that the high
level of concern reflects the terms of the loans (e.g., high interest rates or insufficient loan size or
maturity).
The results are a little ambiguous. Firms with credit were generally less likely to say that
access to credit was a serious concern than firms without credit (see Figure 37). About 54
percent of SMLEs and 45 percent of microenterprises without credit said that access to credit
was a serious problem compared with 36 percent and 26 percent for SMLEs and
microenterprises with credit. The difference, however, is not statistically significant after
controlling for other factors such as firm size or sector that might affect views about access to
credit (see Appendix 5.1).
As noted above, firms were also asked whether they applied for a loan in the year prior to
the survey. If they did, they were asked whether the application was successful. If they did not,
they were asked why not. These questions are discussed in greater detail in the next section. In
this section, however, we look at whether firms that applied and were accepted, firms that
applied and were rejected and firms that did not apply because they said they did not need a loan,
73
were less concerned than firms that did not apply for other reasons (e.g., interest rates are too
high, they did not have sufficient collateral, or loan terms were not attractive).
In general, firms that said that they did not apply for a loan because they did not need one
were less likely to say that access to credit was a serious problem than firms that applied and
were rejected or did not apply for other reasons.52
Firms that applied and were rejected were
more likely to say that access to finance was a problem than firms that had applications accepted,
although the difference was not statistically significant after controlling for other things such as
sector and size.
In summary, much of the evidence suggests—although not conclusively so—that concern
is higher among firms that do not have loans than among firms that do have loans. This
suggests, in contrast to Uganda for example, that much of the concern is related to access for
firms without access rather than the terms of that access for firms with access.53
IX. Objective measures of access to finance
In addition to asking firms about whether they see access to finance as a problem, firm
managers are also asked about their use of several credit products (e.g., loans or overdrafts),
characteristics of their most recent loan (e.g., interest rate, maturity, and year that the loan was
approved), whether they applied for a loan recently, why they did not apply for a loan if they did
apply for one, and whether the application was rejected and if so why. This section looks at
Figure 37: Firms both with and without loans were concerned about access to finance in Tanzania.
Source: World Bank Enterprise Survey.
Note: Credit means firm has either a loan or overdraft; ―Applied and accepted‖ means firm applied for a loan and
application was accepted in year prior to survey. ―Applied and rejected‖ means application was rejected in year
prior to survey. ―Did not apply—no need‖ means firm did not apply and said that this was because it did not need
a loan. ―Did not apply – other reason‖ means firm did not apply because interest rates were too high, collateral
requirements were too tight, manager thought the firm would be rejected, could not get sufficient maturity or
amount or application procedures were too complex or the manager said ‗other‘ reason. See Table 12.
0% 25% 50% 75% 100%
All
Credit
No Credit
Applied and accepted
Applied and rejected
Did not apply - no need
Did not apply - other reason
% saying access was serious problem
% of SMLEs that said that access to finance was serious problem
0% 25% 50% 75% 100%
All
Credit
No Credit
Applied and accepted
Applied and rejected
Did not apply - no need
Did not apply - other reason
% saying access was serious problem
% of microenterprises that said that access to finance was serious problem
74
these objective indicators and compares some with similar indicators for the comparator
countries and among different types of firms within Tanzania.
Use of Credit
SMLEs in Tanzania report using bank credit to a similar degree or slightly less than
SMLEs in other countries in region, although Tanzania compares less favorably with respect to
longer-term financing for new investment than short-term financing for working capital. On
average, SMLEs report that they finance about 6 percent of their working capital needs with
bank financing and about 8 percent of their new investment in the same way (see Figure 38). In
comparison, SMLEs in Uganda report financing 4 percent of their working capital and 13
percent of their new investment with bank financing and SMLEs in Kenya report financing 7
percent of their working capital and 14 percent of their new investment with bank financing.
SMLEs in Rwanda, Burundi and Swaziland generally use bank financing slightly more than
SMLEs in Tanzania, but the difference is not large—between 6 and 16 percent for working
capital and between 12 and 17 percent for new investment.
Similarly, SMLEs in Tanzania tend to be about as dependent upon retained earnings for
both working capital and new investment (71 percent and 85 percent) as firms in Uganda (75
percent and 78 percent) and Kenya (73 percent and 78 percent). The very low level of bank
financing for new investment and the large gap between how much working capital and how
much new investment SMLEs finance with retained earnings further suggest that there are issues
related to getting long-term loans that are suitable for long-term investment.
Figure 38: SMLEs in Tanzania use bank financing less than firms in best performing countries
although to a comparable degree to other countries in the region.
Source: World Bank Enterprise Surveys.
0% 25% 50% 75% 100%
Tanzania
Rwanda
Swaziland
Burundi
Kenya
Uganda
Thailand
Malaysia
Mauritius
South Africa
% of working capital financed in different ways
Working Capital
Retained Earnings Banks Trade Finance Other
0% 25% 50% 75% 100%
Tanzania
Burundi
Rwanda
Swaziland
Uganda
Kenya
Thailand
Malaysia
Mauritius
South Africa
% of new investment financed in different ways
New Investment
Retained Earnings Banks Trade Finance Other
75
The difference between Tanzania and the other comparator countries is considerably
larger than the difference between Tanzania and the comparator countries in the region. Firms in
South Africa finance 17 percent of their working capital and 16 percent of their new investment
with bank financing and only 66 percent and 59 percent with retained earnings. Firms in
Mauritius and the three Asian comparators finance over 30 percent of their working capital and
over 34 percent of their new investment with bank financing. Moreover, in all of the other
countries except Mauritius they finance less that one half of their investment and working capital
with retained earnings.
Responses to other questions paint a similar picture. Only 16 percent of SMLEs in
Tanzania reported having a bank loan compared with about 17 percent in Uganda, 21 percent in
Kenya and over 50 percent in Mauritius, Thailand, China and Malaysia. Similarly, only 12
percent reported having an overdraft compared with 16 percent in Uganda, 21 percent in Kenya,
29 percent in China and over 70 percent in Malaysia, Thailand and Mauritius. Overall, these
results suggest that SMLEs in Tanzania generally use bank financing slightly less than or about
the same as SMLEs in other countries in the region, but far less than SMLEs in the best
performing economies.
Microenterprises
Because microenterprise surveys have only been conducted in other countries in Sub-
Saharan Africa, comparisons of access to credit are only possible for these countries. As in all
countries, microenterprises are less likely to have bank loans and overdrafts than SMLEs.
Whereas about 22 percent of SMLEs in Tanzania reported having a loan or overdraft, only 17
percent of microenterprises reported the same (see Figure 5).
Figure 39: Few microenterprises have bank credit in Tanzania—although the gap between microenterprises
and SMLEs is smaller than in many other countries.
Source: World Bank Enterprise Surveys.
Note: Credit means firm has either a loan or overdraft.
-20%
0%
20%
40%
60%
Tanzania
Uganda
Sw
azila
nd
Kenya
Buru
ndi
Rw
anda
% o
f firm
s that
have b
ank c
redit
% of microenterprises and SMLEs with bank credit
SMLEs
Microenterprises
76
Although SMLEs were less likely to have bank credit than in the other comparator
countries, microenterprises were more likely to have bank credit than in some of the
comparators. Only about 10 percent of microenterprises in Uganda and 12 percent of
microenterprises in Burundi had either loans or overdrafts compared with 17 percent of
microenterprises in Tanzania. In this respect the gap between microenterprises and SMLEs is
more modest in Tanzania than in most other countries.
This is broadly consistent with the perceptions data. Although microenterprises were
more likely to say that access to finance was a problem than SMLEs in Tanzania (51 percent
compared with 41 percent), the gap was also smaller than in most other low-income countries in
the region. For example, about 71 percent of microenterprises and 48 percent of SMLEs said
that access to finance was a serious concern in Uganda and 76 percent of microenterprises but
only 41 percent of SMLEs said the same in Kenya.
Access by firm type
Although fewer firms use bank financing on average in Tanzania than in most of the
comparator countries outside of East Africa, there are large differences in access between firms
within Tanzania. Larger firms were generally less likely to say that access to finance was a
problem than small firms (see Figure 40). Whereas about 40 percent of managers of small
enterprises said that access to finance was a problem, only about 28 percent of managers of large
enterprises said the same.54
Consistent with perceptions, the objective indicators also show that large firms have
better access to finance than small firms do. Almost all of the large enterprises reported than
they had a bank account, where only about 84 percent of small enterprises reported that they did.
Figure 40: Larger firms have better access to credit than small firms.
Source: World Bank Enterprise Surveys.
Note: Credit means firm has either a loan or overdraft. Includes firms in all sectors.
0%
25%
50%
75%
100%
Access to finance serious obstacle
Have bank account
Have overdraft Have loan Have bank credit
% of working capital
financed with bank credit
% of investment
financing with bank credit
% o
f firm
s
% of SMLEs with access to finance, by sizeSmall
Medium
Large
77
Similarly, whereas 37 percent of large enterprises had an overdraft and 83 percent had a bank
loan, only 6 and 10 percent of small enterprises did. Large enterprises also financed
considerably more of their working capital and new investment with bank financing than small
enterprises did.
Although it is common for large enterprises to have better access than small enterprises
do, the difference appears relatively large in Tanzania. Indeed, large manufacturing enterprises
appear to have better access to finance than large manufacturing enterprises in many of the
comparator countries (see Figure 41). Although it is important to treat the numbers somewhat
cautiously due to the small number of large manufacturing enterprises in some country samples,
especially in Sub-Saharan Africa, a greater share of large firms in Tanzania reported that they
had a loan than in many of the regional comparator countries. Further, access is as high or
higher for large manufacturing firms in Tanzania than for similar firms in many of the other
comparator countries (e.g., Swaziland, China and South Africa). Moreover, what differences
there are probably reflect demand for financing as well as the availability of financing. In this
sense, the banks appear to provide the good access for large firms in Tanzania.
Given the large differences in access overall between these countries and Tanzania, this is
all the more remarkable. It is important to note, however, that the large difference in overall
access rates are not just because small firms in Tanzania have worse access than in most of the
comparator countries outside of the region. It also reflects that small firms are generally more
common in SSA than they are in many of the comparator countries. That is, they make up a
larger share of the sample in Tanzania and elsewhere in SSA.
Figure 41: Large enterprises in Tanzania have relatively good access compared with other countries.
Source: World Bank Enterprise Surveys.
Note: Credit means firm has either a loan or overdraft. Cross-country comparisons are only for manufacturing
SMLEs.
0%
25%
50%
75%
100%
Tanzania
Uganda
Rw
anda
Kenya
Buru
ndi
Sw
azila
nd Chin
a
South
A
fric
a
Mauritius
Thaila
nd%
of
larg
e f
irm
s w
ith b
ank c
redit
% of large manufacturing firms with bank credit
78
Reasons for not applying for a loan
In addition to being asked about whether they currently had loans or overdrafts, managers
were also asked whether their firm had applied for a new loan in 2005. Managers that said no
were asked why they had not done so. About one-quarter of firms that had not applied for a loan
said that they had not done so because they did not need one (see Table 12). The percent of
firms was similar for firms with existing loans in 2005 (21 percent) and without loans (24
percent). This suggests that a large number of firms with loans would like to borrow more if
terms were more attractive (i.e., 79 percent of firms with loans in 2005 that did not apply for
additional financing did not do so because of things other than that they did not need a loan).
Microenterprise managers were less likely to say that they did not need a loan (only 12 percent
of microenterprise managers) and were more likely to say that collateral requirements were too
high (25 percent) and that application procedures were too complicated (37 percent).
Table 12: Most firms that did not apply for a loan in 2005 said either that they did not need one or
application procedures were too complex.
Tanzania Kenya
SMLEs
Uganda
SMLEs SMLEs
Micro All Already have loan No Loan
No need for loan 24% 21% 24% 12% 38% 37%
Application procedures too complex 26% 20% 26% 37% 9% 6%
Interest rates are not favorable 20% 20% 16% 27% 36%
Collateral requirements too high 14% 14% 25% 14% 12%
Size or Maturity are insufficient 7% 28% 7% 4% 3% 3%
Did not think it would be approved 3% 17% 2% 4% 5% 2%
Other 7% 14% 7% 2% 5% 4%
Source: World Bank Enterprise Survey.
Note: Table shows percent of firms in each group that gave each reason. Only firms that did not apply for a loan
were asked this question.
Fewer firms reported that they did not apply because they did not need one in Tanzania
than in the successful manufacturing countries with comparable data—38 percent of firms in
Kenya and 70 percent of firms in Swaziland that did not apply for a loan said they did not need
one. It was also lower than in Uganda (37 percent), Rwanda (44 percent) or Burundi (36
percent).
The most common reason that firms gave for not applying for loans was that application
procedures were too complex. Over one-quarter of firms said that this was the main reason why
they did not apply. This was far higher than in most of the comparator countries—for example,
only 9 percent of firms in Kenya and 6 percent of firms in Uganda said the same. SMLEs that
already had a loan were only slightly less likely to say that this was the case than SMLEs without
loans (20 percent compared with 26 percent).55
About one-fifth of firms said that the main reason was that interest rates were too high.
Although this is quite high, it was lower than in either Uganda (36 percent) or Kenya (27
percent), where high interest rates were the most common response other than that the firm did
not need a loan. In this respect, interest rates appear less binding in Tanzania than in these two
countries.
79
Fewer firms gave other reasons. About 14 percent said that collateral requirements were
too high—although this was mostly a concern for firms without an existing loan, about 7 percent
of firms said that size or maturity was insufficient and about 3 percent said that they did not think
they would be approved. Firms with loans were more concerned about getting a loan that was
large enough and long-term enough and were more likely to think that their application would
not be approved. Consistent with the idea that even firms with loans were not happy with the
size and maturity of the loans they can get, over one-quarter of firms with loans that did not
apply for an additional loan in 2005 said that the size and maturity of loans they could get were
insufficient.
Loan rejections
Firms that applied for loans were asked whether they had had any loan applications
rejected and whether all of their applications were rejected. About 35 percent of SMLEs in
Tanzania that applied for a loan in 2005 said that at least one application has been rejected and
about 26 percent said that all applications were rejected. This was higher than in either Uganda
or Kenya. About 25 percent of firms in Uganda and 19 percent of firms in Kenya that had
applied for a loan said that at least one application had been rejected and about 18 percent and 13
percent had had all applications rejected in the two other countries. Microenterprises were more
likely to have had applications rejected. About 57 percent of firms had had at least one
application rejected and half had all applications rejected.
Because few firms applied for a loan and only about one-quarter had had an application
rejected, relatively few firms had actually been rejected (about 29 SMLEs).56
These firms were
then asked why their application was rejected. Although it is difficult to draw very strong
conclusions based upon the small number of rejections, the most common responses—about 28
percent of the firms who had been rejected—said that the main reason was that collateral was
inadequate. Significant numbers also said that the main reason was that their application was
incomplete (about 23 percent) or that they were not profitable enough (about 23 percent as well).
The large number of firm reporting incomplete applications would appear consistent with the
previous results where significant numbers of firms did not apply because of complicated
application procedures.
Credit constraints
The previous analysis suggests that although few firms have loans, a significant share of
these firms do not want loans—many firms that had not applied for loans did not want loans. A
useful way to explore credit constraints is to divide firms into three groups: firms that had a loan,
firms that did not have a loan because they were unable to get one, and firms that did not have a
loan but did not want one.57
Only the second group—firm whose loan applications had been
rejected and firms that did not apply because they did not think they would get one, did not have
collateral or who found the process too difficult—are usually considered to be credit constrained.
In contrast, the final group, which is made up of firms that did not apply because they did not
want to incur debt, did not need a loan, or found the terms of available loans (e.g., interest rates,
size and maturity) unattractive, is not generally considered to be credit constrained.
80
In 2005, about 18 percent of SMLEs in Tanzania had a loan—about the same as in
Uganda (about 18 percent) and slightly lower than in Kenya (about 29 percent). Although many
of the remaining SMLEs did not want loans (about 40 percent of all SMLEs or about half of
SMLEs that did not have a loan), the percent of firms that wanted loans but could not get a loan
was considerably higher in Tanzania than in Uganda or Kenya. The difference with Uganda
appears to primarily be due to the large number of firms in Uganda that reported that they did not
want a loan because interest rates are too high.
Table 13: SMLEs that are credit constrained in Tanzania
Tanzania Uganda Kenya
Had loan (in 2005) 18 21 29
Want loan and could not get loan (in 2005) 42 22 26
Did not want loan (in 2005) 39 57 45
Source: World Bank Enterprise Surveys.
Note: Includes firms in all sectors (not just manufacturing). Only includes SMLEs.
Microenterprises were far more likely to be credit constrained (i.e., firms that want a loan
but cannot get one) than larger enterprises. About 61 percent of microenterprise managers said
this was the case, compared to 50 percent of small enterprise managers, 27 percent of medium-
sized enterprise managers, and only 2 percent of large enterprise managers. Only about one-fifth
of microenterprise managers said that they did not want a loan. This further emphasizes that
large enterprises do not appear to be credit constrained in Tanzania, while microenterprises and
small enterprises are.
Table 14: Firms that are credit constrained in Tanzania, by firm size
Micro Small Medium-Sized Large
Had loan (in 2005) 18 12 22 87
Want loan and could not get loan (in 2005) 61 50 27 2
Did not want loan (in 2005) 20 38 50 11
Source: World Bank Enterprise Surveys.
Note: Includes firms in all sectors (not just manufacturing).
It is important to note that although a relatively large number of firms reported that they
did not want loans, this does not mean that they would not want loans if terms (interest rates,
maturity and size) were more attractive. A second caveat is that for firms that did not apply for
loans because they did not need or want one, it is not clear why this is the case. Some of the firm
owners might be able to finance their operations completely from retained earnings or their own
funds and therefore not need external financing for this reason. Some might not want to invest
(e.g., if they feel that it would require more of their time to expand than they wanted to spend
managing the company). Some might be too risk averse to borrow. And some might have
become so discouraged given past attempts to obtain external financing that they have essentially
given up trying to do so. In the final case, they might have become so used to the idea that they
cannot get external financing that they tell an interviewer that they do not want it rather than
admit that they are unable to get external financing. Unfortunately, there is no way to assess this
given the current information in the survey.58
81
Loan Characteristics
In addition to asking broad questions about whether they have a loan or overdraft and if
not, why not, firms with loans are asked about the most recent loan they received. Because many
firms with loans said that access to finance was a serious problem in Tanzania, information about
the loans can provide some perspective on the why this is.
Most of the loans were relatively recent (see Table 15). About 80 percent of firms with
loans reported that they had got their most recent loan in the three years prior to the survey. Not
surprisingly, most loans were for relatively short period. About 40 percent of loans were for a
year or less and only 9 percent of loans were for more than 60 months. The median length was
slightly longer than in Uganda (only 12 months). It was, however, shorter than in either Kenya
or Swaziland (36 months). Long-term loans (more than 60 months) were also more common in
both Kenya and Swaziland (13 and 39 percent of loans respectively).
Table 15: Characteristics of most recent loan.
Obs. 10th
percentile
25th
percentile Median
75th
percentile
90th
percentile Mean
Year loan was approved 83 2002 2004 2005 2005 2006 2004
Duration of loan 83 12 12 24 36 50 28
Collateral (% of loan value) 72 20 60 130 150 200 124
Interest Rate 82 9 10 14 18 21 15
Loan relative to assets (total, book) 54 4% 12% 24% 54% 149% 57%
Loan relative to assets (M&E, repl.) 53 3% 6% 15% 41% 69% 38%
Loan relative to sales 72 2% 8% 12% 47% 115% 33%
Source: World Bank Enterprise Survey.
The median firm in Tanzania reported that the interest rate on its most recent loan was 14
percent (see Figure 42), with most firms (over 80 percent) reporting interest rates between 9 and
21 percent. This was lower than in many other low-income countries in the region. For
example, the median firm in Uganda reported that the interest rate on its most recent loan was 22
percent—higher than 90 percent of loans in Tanzania. Median firms in Rwanda and Burundi
also reported higher interest rates than in Tanzania. Interest rates are, however, higher than in
the middle-income comparator countries, where the median firms reported interest rates between
11 and 12.5 percent.
82
Loan amounts were fairly modest. The median loan amount was equal to about 12
percent of sales. This was roughly the same as in Uganda (13 percent), Kenya (9 percent) and
Swaziland (13 percent). Nearly all firms with loans (92 percent) reported that the collateral was
required. The median level of collateral was 130 percent of the value of the loan, with most
firms reporting between collateral of between 60 and 150 percent of the value of the loan.
Firms were also asked about the type of collateral they used—land and buildings,
machinery and equipment, and accounts receivable. Firms can report using more than one type
of collateral for any given loan. In developed banking sectors, firms can use machinery and
equipment and accounts receivable as collateral, rather than land or buildings. As in most low-
income countries the most common form of collateral was land and buildings (66 percent of
firms). This is slightly lower than in Uganda (70 percent of firms), Burundi (75 percent) or
Rwanda (81 percent). But it is higher than in Kenya (50 percent) or Swaziland (37 percent).
Significant numbers of firms also reported using other types of collateral. Close to 40 percent of
firms reported using accounts receivable as collateral and a similar number reported using
machinery and equipment. This is higher than in Uganda (18 and 28 percent respectively),
similar to Swaziland (30 percent and 50 percent) and lower than in Kenya (48 and 60 percent) or
Swaziland.
In general, loans in Tanzania compare favorably with loans in other low-income
countries in the region. For example, interest rates are lower, loan durations are slightly longer,
and collateral other than land is more common than in Uganda. Tanzania compares less
favorably with the more successful manufacturing countries such as Kenya or Swaziland.
Figure 42: Interest rates reported by firms are higher in Tanzania than in many of the countries that have
successfully diversified into manufacturing.
Source: World Bank Enterprise Surveys.
0
5
10
15
20
25
Tanzania
Kenya
Rw
anda
Buru
ndi
Uganda
South
A
fric
a
Mauritius
Sw
azila
nd
rate
(perc
ent)
Interest Rate
Inflation (00-06)
83
Comparisons with 2003
An important question is what has been happening with respect to access to finance
between 2003 and 2006. As discussed in Chapter 3, SMLEs in the 2006 survey were less likely
to say access to finance was a problem than SMLEs in the 2003 survey, suggesting that access
might have improved since 2003.
It is, however, difficult to compare perceptions about access to finance between the two
surveys. The main concern is that the wording of this constraint changed between the two
surveys. In 2003, it was described as ―access to finance (collateral)‖ and there was a separate
question for ―cost of finance (interest rates).‖ In 2006, it was described as access to finance
(availability and cost). Given that cost of finance was the second largest constraint (after tax
rates) in 2003, it is not clear that comparing rankings between these two surveys is appropriate.
Another thing that makes it difficult to compare responses to the perception questions
between the two surveys is the impact that power crisis had on the perception-based indicators.
As discussed in Chapter 3, the scale used in the Enterprise Survey, where firms ranking problems
from ‗no problem‘ to a ‗very severe problem‘, is not an absolute scale. As a result, firms might
have been less likely to say that other areas of the investment climate were problems in 2006
because they were perceived as far less serious than the power crisis. That is, in 2006, the
managers might have been looked at access to finance and rated is as only a moderate problem
because it was so much less of a problem than the power crisis. In contrast, in 2003, without the
power crisis to anchor their responses, they might have said it was a major problem even if
access was similar in the two surveys.
Taking these concerns into account, there are some reasons to think that perceptions
about access to finance might have deteriorated even though fewer managers ranked it as a
serious problem in 2006. First, the relative ranking appears to have got worse. In 2006, more
firms said ―access to finance‖ was a serious constraint than any area of the investment climate
except power. In 2003, it also ranked below tax rates, tax administration, and corruption in terms
of the percent of firms that said it was a problem. Second, the decline in the percent of firms that
said it was a serious problem was smaller for ‗access to finance‘ than for most other constraints.
In summary, because of the concerns noted above, the perception data does not provide strong
evidence for either an improvement or a decline in perceptions about access to finance between
the two surveys.
It is therefore, natural to look at objective indicators. SMLEs in the 2006 survey were far
less likely to have overdrafts than firms in the 2003 survey and were about as likely to have loans
(see Table 16). As discussed in Appendix 2.2, however, comparisons between the 2003 and
2006 surveys are difficult. One particular problem is that the 2006 sample contained far more
small firms. This is a concern because the previous analysis suggests that small firms find access
to finance particularly difficult. The difference in perceptions between the two surveys might,
therefore, be due to changes in sample rather than changes in access to finance.
84
Table 16: Comparisons of access to finance between 2003 and 2006.
All firms Small firms Large firms Panel firms only
2003 2006 2003 2006 2003 2006 2003 2006
Have overdraft 36% 19% 7% 6% 50% 42% 34% 40%
Have loan 22% 22% 12% 9% 72% 72% 21% 30%
Have bank credit 42% 31% 16% 13% 86% 83% 40% 53%
Source: World Bank Enterprise Survey.
Note: Comparisons are only for manufacturing firms and only for firms in cities covered in both surveys.
To try to lessen this concern, two additional comparisons are made between 2003 and
2006. First, access is compared for small and large firms separately. Second, access is
compared for the panel firms. Among small firms, there was little change between 2003 and
2006 in terms of access. About 7 percent of small firms had an overdraft and 12 percent had a
loan in 2003 compared with 6 percent and 9 percent in 2006. For the small number of large
firms, large firms were as likely to have loans in 2006 but less likely to have overdrafts as large
firms in 2003.
Stronger evidence comes from looking only at the panel firms—firms interviewed in both
2003 and 2006. The panel firms were more likely to have loans and overdrafts in 2006 than in
2003. Discounting the results for the whole sample because of concerns about comparability
between the two samples, these results suggest that access to finance is either about the same in
the two years or might have improved slightly—assuming that the panel results are more reliable
than the simple cross-sectional comparisons even after controlling for size. More specifically,
access to loans appears to have improved or stayed the same, although it is harder to reach strong
conclusions with respect to overdrafts.
X. Summary
The results from this chapter suggest that the financial sector reflects the general structure
of Tanzania‘s private sector. The higher end segment of the market, large firms in urban areas
have reasonable access to finance. In fact, their access is no worse than access for similar firms
in other developing countries. In contrast, the lower market segment that includes
microenterprises and the rural poor does not have good access to financial services. SMEs in
particular appear to have more limited access than in the best performing developing countries.
The relatively low level of access observed among the lower market segment might be
difficult to deal with directly without addressing other problems in the investment climate. For
example, the FinScope study suggests that many individuals that do not use financial services do
not earn enough to match the needs of providers in the market. Similarly, as discussed in Chapter
7, productivity and profitability is low among the microenterprises in the Enterprise Survey.
Under these circumstances, it is possible that the marginal cost of providing financial service to
many of these individuals would be greater than the benefits of providers exceeds customer
benefits in these market segments.
The Enterprise Survey also shows that entrepreneurs are risk-averse—a large proportion
of the people that did not apply for a loan did not want a loan. This reflects a cautious approach
to leveraging existing balance sheets, the informal and cash nature of enterprises and the short-
term nature of the market.
85
So what can be done? One important thing is to reduce the high transaction costs
associated with providing service to small and micro firms and low income individuals.
Although it would be difficult to change many of the fixed costs of service, this suggests that
improving the business environment by improving contract enforcement would be very useful.
In many countries, high interest rates are a serious deterrent to expanding access.
Although there was some concern about high interest rates in Tanzania, it does not seem that this
is the most important limiting factor for firms without loans that would like to have one.
Moreover, it appears to be less important than in many other countries in the region. Two issues
appear more problematic than interest rates—cumbersome loan application processes and
collateral. Improving the loan application process would therefore seem to be an important goal.
86
CHAPTER 6: INFRASTRUCTURE, TAXATION, AND REGULATION AND
GOVERNANCE
This chapter discusses several remaining areas of the investment climate that are not
covered in previous chapters: infrastructure, taxation, and regulation and governance. Several of
these areas were rated as serious obstacles by enterprise managers (see Chapter 3). Other areas
that were serious concerns are discussed in other chapters. Access to finance, which was rated as
a serious problem by about four out of ten SMLE managers and about half of microenterprise
managers, is discussed in Chapter 5. Competition with informal firms, which was rated as a
serious problem by one quarter of SMLE managers and three out of ten microenterprise
managers, is discussed in Chapter 7. Overall, these two areas ranked as the second and fourth
greatest concerns for both SMLE and microenterprise. Finally, macroeconomic instability,
which ranked as the third greatest concern for microenterprise managers and the fifth greatest
concern for SMLE managers, is discussed in Chapter 1.
Firms were very concerned about several aspects of the areas of the investment climate
discussed in this chapter (see Chapter 3). As discussed previously, by far the greatest concern
was power, with close to nine out of ten SMLE and microenterprises managers saying that it was
a serious problem and close to three quarters saying that it was the biggest problem that they
faced. Similarly, tax rates ranked as the third greatest concern for SMLE managers.
Microenterprise managers were less concerned about tax rates—only about one in five
microenterprise managers said that it was a serious problem compared with close to two out of
five SMLE managers. This probably reflects the high levels of evasion among informal
enterprises. Corruption and tax administration also ranked as serious concerns for both
microenterprise and SMLE managers—although as noted in Chapter 3 concern about both seems
to have declined since the 2003 Enterprise Survey.
I. Infrastructure in Tanzania
As in many countries in Sub-Saharan Africa, access to and the quality of infrastructure is
a serious problem in Tanzania. This section looks at evidence from the recent Enterprise Survey
and from other sources on how infrastructure affects firm performance in Tanzania.
Electricity
Tanzania has substantial and diverse potential sources for energy including biomass,
natural gas, hydropower, coal, geothermal, solar and wind power, much of which is currently
untapped. Wood accounts for about 90 percent of total energy production (i.e., not electricity
generation only), while hydroelectric sources account for about 2 percent of power and oil-
derived products account for about 8 percent (World Bank, 2007b).
In 2002, the time of the most recent population and housing census, about 10 percent of
households in mainland Tanzania and about 24 percent of households in Zanzibar had electricity
(National Bureau of Statistics, 2006a). Although the number of households with connections
had increased by between 150,000 and 170,000 household by mid 2007, access remained low
87
even at this time (World Bank, 2007b). Regionally, access is highest in Dar es Salaam, with
about 42 percent of households having access in the city.
Tanzania‘s power sector is dominated by a single vertically integrated national utility,
Tanzania Electric Supply Company Limited (TANESCO) (see Box). TANESCO is fully
government owned. In addition to TANESCO, there are several independent power projects
(IPPs). TANESCO operates the main hydroelectric generating plants and some thermal
generating plants. The two largest private IPPs are Independent Power Tanzania Limited (IPTL)
with 103 Megawatts (MW) of installed capacity and Songas with an installed capacity of
189MW.
Box: Tanzania Electric Supply Company Limited
Tanzania‘s power sector is dominated by a single vertically integrated national utility—
TANESCO. As a government company under the Ministry of Energy and Minerals, it has
traditionally not been profitable. Quality of service has deteriorated in recent years, with system
losses increasing from 23 percent in 2003 to over 35 percent in 2005 due to lack of investment in
the transmission and distribution network. TANESCO has not been able to invest its own
resources in transmission and distribution in recent years, because tariffs have been below cost
recovery levels. Estimates in 2007 suggested that current blended cost of generation is about 11
cents, compared with average retail tariff of about 8 cents at that time.
Privatization, discussed since the late 1990s, never materialized due to local opposition.
However, a management contract with South Africa‘s NetGroup result in gains in productivity,
particularly in terms of commercial loss reductions and increased collections. These gains,
however, have been eclipsed by the increasing technical losses.
In 2006, the Government decided to not to privatize TANESCO. A new managing director with a
commercial background was appointed. The Government has developed a financial recovery plan
for TANESCO, approved in February 2007, with the objective of restoring complete financial
sustainability. To achieve this, tariffs were increased by 6 percent in 2007 and an additional 40
percent increase was requested for 2008. In the end, a 23 percent increase was allowed in early
2008.
Source: World Bank (2007b).
Poor quality infrastructure can negatively impact firm growth and competitiveness. When
power is unreliable or if it takes a long time to get a connection, firm owners will choose to
locate their firms in regions with reliable power supply. This reduces firm entry in areas with
unreliable supply (see Chapter 8). Existing firms, especially those that are technologically
advanced, are also vulnerable to poor electricity supply. To cope with outages, existing firms
often adopt labor intensive production methods that are less vulnerable to supply disruptions. In
countries, such as Tanzania, where power problems are particularly acute, firms often invest in
generators. But this is costly both in terms of both higher operating costs and the high capital
cost of purchasing a generator. For those firms that cannot purchase generators, the impact can
be even more significant as they are forced to reduce output. In these ways, unreliable power
can increase costs and reduce competitiveness.
88
The problem that power outages impose of firms in many countries in Sub-Saharan
Africa has been well documented. In late July 2007, the New York Times reported that 25 out of
the 44 countries in SSA were experiencing crippling power shortages at the time. Evidence from
the Enterprise Surveys confirms this—concern about outages was high in many of the low-
income countries where Enterprise Surveys were completed in 2006 and 2007 and firms in many
of these countries were reporting high losses due to unreliable power (see Figure 43). Although
concern was very high in Tanzania, more than seven in ten firms said that power was a serious in
about half of the low-income countries where surveys were conducted in 2006 and 2007.
Similarly, the average firm reported more than 9 outages in an average month in 2005 in 10 out
of 16 countries in SSA including Tanzania. Tanzania compares more unfavorably on the
perception-based comparisons than based upon the objective measures. The most likely reason
for this, which is discussed below, is that although the survey was conducted during a serious
power crisis, the objective measure of outages is for a period before the crisis.
Firms reported more outages in 2005 and 2006 than in 2003. In 2003, firm managers
reported an average of about 4 outages per month compared to close to 10 in 2005. This is true
whether we look at all manufacturing firms or only panel firms (i.e., firms interviewed in both
2003 and 2006).
Figure 43: Power is a serious problem in many countries in Sub-Saharan Africa
Source: World Bank Enterprise Surveys.
Note: Comparisons based upon data from the surveys in Africa in 2006/07 are for all firms (not just
manufacturing firms). Outliers (firms more than 3 standard deviations from the mean) are dropped for days
of outages when calculating means. Data are for year prior to survey (2005 for Tanzania).
0% 25% 50% 75% 100%
Tanzania
Ghana
Uganda
Guinea-Conakry
Gambia
Guinea-Bissau
Burundi
Congo
Senegal
Mali
Rwanda
Nigeria
Mauritania
Kenya
Mozambique
Zambia
% of firms
% reporting power is serious problem
0 10 20 30
Guinea-Conakry
Nigeria
Tanzania 2006
Gambia
Congo
Senegal
Uganda
Burundi
Rwanda
Ghana
Tanzania 2005
Guinea-Bissau
Kenya
Mauritania
Mali
Zambia
Mozambique
No of days of outages
Ave. days with outages in an ave. month
89
Table 17: Outages were more common in 2005 than they were in 2003.
2003 2005
Number of outages per month
All manufacturing SMLEs 4.4 9.2
Panel Firms 4.2 10.8
Source: World Bank Enterprise Surveys.
Note: Averages for all manufacturing firms only include manufacturing firms in cities covered in both
surveys. Averages for panel firms are averages for firms that were in both the 2003 and 2007 surveys.
Because the 2003 survey only covered manufacturing comparisons are only for manufacturing. Averages
for panel firms are unweighted.
Although, as discussed below, the poor performance of the power sector has been a
problem for many years, it is important to note that the Enterprise Survey took place during a
serious power crisis. Growing demand and a steep drop in hydroelectric generation capacity was
leading to load shedding and almost daily outages for many firms (see Box). At the time of the
survey, it was not clear how long this crisis would continue for adding to uncertainty. Although
some of the objective indicators refer to the period before the crisis, perceptions and some
indicators are likely to reflect the temporary impact of the crisis.
Box: The 2006 Power Crisis.
At the time of the survey, the Tanzania power system had about 1,192 MW of installed
generating capacity (permanent plants). A significant part of this capacity is hydroelectric.
Hydroelectric capacity accounted for about 562 MW of total installed capacity (47 percent of
the total), with the remainder (630MW) was thermal capacity.
A serious drought that lasted for several years resulted in a significant and continuous drop in
reservoir water levels. This resulted in available hydropower capacity dropping from 562 to
300MW. This shortage meant that TANESCO started to ration power in February 2006. This
resulted in serious load shedding and almost daily outages in some parts of the country.
Whereas the average firm in the Enterprise Survey reported about 9 outages per month in
2005, the average firm reported about 22 outages per month in 2006. It was estimated that the
2006 energy crisis might have reduced GDP growth by as much as 2 percentage points.
Significant rainfalls during the ‗short rains‘ seasons in late 2006 and early 2007 restored full
hydro capacity, with the company lifting power rationing on December 28th
, 2006. This
allowed TANESCO to avoid further generation shortfalls. TANESCO also contracted about
140MW of leased capacity for two years and 40 MW of leased capacity for one-year. About
150MW was scheduled to come on line by the end of 2008.
Although generation capacity is now sufficient for the near term, the crisis in 2006 greatly
affected the private sector and highlighted the impacts of power shortages and uncertainty on
business growth. Moreover, it was not a new problem—Tanzania has faced three energy
crises over the last decade. This emphasizes the importance of improving sector performance
in the medium-term.
Source: World Bank (2007b).
Given that the survey was conducted during the crisis, it is not surprising that enterprises
were far more likely to say that power was a serious obstacle than any other area of the
investment climate (see Chapter 3). Close to 90 percent SMLEs and microenterprises reported
90
that electricity was a serious obstacle. Similar numbers of SMLEs in the manufacturing sector
said the same (see Figure 5)
Although, as discussed above, power is a serious problem in many other countries in SSA
and firms in many of these other countries also said that power was a serious obstacle (see Figure
43), Tanzania compares less favorably with countries outside of SSA (see Figure 5). More firms
reported that power was a serious obstacle than in any of the other comparator countries. About
30 percent of firms in China and India said that power was a serious problem and less than 15
percent of the firms said this was the case in the other comparator countries.
Given the extent of the crisis, it is not surprising that firms were far more likely to say
that power was a problem during the 2006 survey than in the 2003 survey. About 75 percent of
SMLEs in the 2003 Enterprise Survey said electricity was a serious problem, compared with
almost 90 percent of SMLEs in the 2006 Enterprise Survey. Although this suggests that the high
level of concern might be temporary due to the crisis, it is important to note that firms in
Tanzania were more likely to say that crisis was a problem in 2003 than firms in any of the
comparator countries outside of East Africa.
Although it is interesting to compare perceptions across countries, it is difficult make
policy inferences based on subjective data alone. As discussed in detail in Chapter 3, although
differences in subjective impressions could be the result of actual differences between countries,
they could also be the result of different expectations or even different ideas about whether it is
acceptable to criticize the government. For example, entrepreneurs in a country with a power
sector that has been historically reliable might have higher expectations about how the system
should operate. As a result, they might be less tolerant of the same level of outages than similar
Figure 44: SMLEs in Tanzania were more likely to say that power was a problem than in most of the
comparator countries.
Source: World Bank Enterprise Surveys.
Note: Cross-country comparisons are for manufacturing firms only. Comparisons within Tanzania are only for those
provinces and firm size classes covered in both surveys.
0
25
50
75
100
Tanzania
06
Tanzania
03
South
A
fric
a
Mauritius
Mala
ysia
Thaila
nd
Sw
azila
nd
Chin
a
India
Kenya
% o
f firm
s
% of manufacturing SMLEs saying power is a serious problem
91
firms in the country with a historically unreliable power sector that are used to dealing with
inadequate or non-functioning infrastructure. Because of the difficulties of interpreting
subjective data, it is useful to look at objective data on outages as well as subjective data.
The objective data also suggest that the poor performance of the power sector is a
significant burden on firms in Tanzania. Frequent and long power outages result in high indirect
costs and lost sales. For SMLEs reporting outages in an average month in 2005—and most firms
did report outages even before the crisis—the average firm reported losses that were equal to
almost 10 percent of sales (see Figure 45).
It is important to note that these losses were for the fiscal year before the crisis hit
Tanzania—that is, the question was asked about fiscal year 2005 (not fiscal year 2006). Since
the crisis did not hit Tanzania until early 2006, it is likely that this is lower than losses during the
crisis. Consistent with this, firms reported far more frequent outages during the crisis than on
average in 2005. On average, managers report about 9.1 outages per month in an average month
in 2005. For the month prior to the survey (i.e., during the crisis), the average manager reported
an average of 22.2 outages. This probably explains the discrepancy between the perception-
based and objective measures in Figure 43. The perception-based measure probably reflects the
firm managers concerns about the ongoing crisis, while the objective measure of outages (about
9 per month) is for before the crisis. Tanzania would compare far less favorably based upon the
data for 2006. This also emphasizes that problems in the power sector were serious even before
the crisis—although they became much worse after the crisis.
Although losses in Tanzania were similar to losses in other countries in the region, they
were far higher than in the successful manufacturing economies in East Africa and Asia even
before the crisis hit Tanzania. Average losses were less than 2 percent of sales in China, Thailand
and Malaysia—far lower than in Tanzania in either 2003 or 2005. This gives firms in these
countries a large cost advantage over firms from Tanzania. The high losses due to outages will
make if difficult for Tanzanian firms to compete against firms from Asia and even from regional
competitors such as Kenya, which has a slightly better situation in this respect.
92
Although losses were high in Tanzania in the 2005, they were also quite high in the 2003
Enterprise Survey (Regional Program on Enterprise Development, 2004b). In both cases about
the average firm reported losses equal to about 7 and 9 percent of sales (see Table 18). Although
as discussed in detail in Appendix 1.2, differences in sampling might make it difficult to
compare results from the two surveys, in this case the difference does not appear to be due to
this. The similar levels of losses are visible looking only at firms that were interviewed in both
2003 and 2006 (i.e., the panel firms).
Table 18: Losses due to outages were similar in 2003 and 2005.
2003 2005
% of sales lost due to outage
All manufacturing SMLEs 7.1 8.7
Panel Firms 8.6 7.4
Source: World Bank Enterprise Surveys.
Note: Averages for all manufacturing firms only include manufacturing firms in cities covered in both
surveys. Averages for panel firms are averages for firms that were in both the 2003 and 2007 surveys.
Because the 2003 survey only covered manufacturing comparisons are only for manufacturing. Averages
for panel firms are unweighted.
One way that firms can cope with outages is to adopt low technology or labor intensive
production processes. Another is to self-generate power. Although self generation can reduce
losses due to outages by allowing firms to continue production, generating power is usually far
more costly than using power from the grid. In practice, less than half of firms reported owning
generators in 2006 (see Figure 46). This is somewhere in the middle of the East African
comparator countries—higher than in Burundi or Uganda and lower than in Rwanda or Kenya.
Figure 45: SMLEs in Tanzania reported very high losses due to power outages.
Source: World Bank Enterprise Surveys.
Note: Outliers more than three standard deviations from the mean are excluded. Cross-country comparisons are for
manufacturing firms only.
0
2
4
6
8
10
12
Tanzania
Tanzania
2003
Kenya
Rw
anda
Buru
ndi
Uganda
South
Afr
ica
Chin
a
Thaila
nd
Mala
ysia
Mauritius
Losses (
% o
f sale
s)
Losses dues to unreliable power as percent of sales
Mean Median
93
Compared with Tanzania, and the other countries in East Africa, relatively few firms
have generators in the successful manufacturing countries. This probably most reflects that
generators are not needed. For example, that only about 10 percent of firms in South Africa
reported having generators probably reflects the historically reliable power sector in that
country.59
Similarly, relatively few firms in Thailand, China or Malaysia reported owning
generators. In all of these countries, losses due to outages were low despite the low level of
generator ownerships (see Figure 45).
Power outages are likely to impose different burdens for firms with and without
generators. An interesting question, therefore, is whether having a generator ownership has a
significant impact on losses due to outages. If firms were accurately assessing sales lost due to
power outages, it seems plausible that firms with generators should report lower losses.
Although production would be interrupted during the switchover to generator usage and might
only be able to run their generators for limited periods or might not be able to operate all
equipment when using the generator if the generator is not powerful enough, it seems likely that
their losses should still be less severe than firms without any source of backup power.
In practice, however, the difference in losses between the two groups is small and is not
statistically significant. The average enterprise with a generator report losing 9 percent of their
sales due to power outages. In comparison, the average enterprise without a generator reports
losses equal to 8.5 percent of sales. The medians are also about the same—the median firms
with and without generators reported losses of 5 and 4 percent respectively.
Why is there little difference in losses due to outages? One possibility is that firms with
generators might suffer from more frequent outages than firms without generator. If
infrastructure quality varies significantly between regions between or within cities, then a firm in
one of those areas (i.e., a firm that faces outages most frequently) might be more willing to invest
Figure 46: More firms own generators in Tanzania than in most comparator countries outside of the region.
Source: World Bank Enterprise Surveys.
Note: Cross-country comparisons are for manufacturing firms only.
0
20
40
60
80
Tanzania
Uganda
Buru
ndi
Rw
anda
Kenya
South
A
fric
a
Thaila
nd
Chin
a
Mala
ysia
Sw
azila
nd
Mauritius
India
% o
f firm
s w
ith g
enera
tors
% of firms with owned or shared generators
94
in a generator than a firm in an area with more reliable power. If this were the case then firms
with generators might report more frequent outages and have higher losses than firms without
generators. In practice, however, this does not appear to be the case. The number of power
outages does not appear to differ between the two groups.
Another possibility is that firms that have generators are more capital intensive than other
firms and so might be more vulnerable to outages for this reason. This appears to be the case—
the median firm with a generator reports that the sales value of its capital was US $7,276 per
worker compared with only $849 per worker for firms without generators. It is, however,
important to keep in mind that generators are very expensive pieces of equipment and, therefore,
this might affect the comparison. Other possibilities are that the measure of sales lost obtained
through our surveys is an ex-ante measure, capturing the severity of the power problem for a
particular enterprise regardless of its availability of substitute power or that managers implicitly
include the extra cost of operating a generator when estimating losses due to outages.
Few studies have examined the impact of power constraints on enterprise growth,
productivity and investment. To look at this question, results from some simple regressions are
presented in Appendix 6.1. The results show that even after controlling for differences due to
firm size, ownership, sectors and exporting status, firms that owned generators in 2006 were
more productive, and grew faster (measured by employment) than firms without. Firms were
also more likely to make investments, but this difference was insignificant. After controlling for
these other factors, enterprises with generators were 96 percent more efficient those without.
Despite incurring more than double the costs to operate a generator, these firms are far better off
than others within Tanzania. Given that the manufacturing sector as a whole has very low
productivity (see Chapter 2), this implies that the lack of adequate power supply, the low access
to generators for SMEs puts these firms, which comprise a majority of the private sector, at a
severe competitive disadvantage both locally and internationally, compared with firms in other
countries with a more stable power supply.
Transport
The ability of a country to connect firms, suppliers and consumers to global supply
chains efficiently is essential for its competitiveness. Transportation did not rank among the
very top concerns of firms in Tanzania. Of the 16 areas of the investment climate that were
asked about in the Enterprise Survey, it ranked as the 11th
greatest constraint (see Chapter 3).
This was higher, however, than in 2003 when it ranked towards the bottom of the list.
About one in five SMLE managers in Tanzania—and slightly fewer microenterprise
managers—said that transportation was a serious constraint. Although this was fewer than in
any of the nearby countries such as Uganda and Kenya, it was more than in most of the
successful manufacturing countries in East Asia and Sub-Saharan Africa (see Figure 47).
This was also about the same as in the 2003 Enterprise Survey. As discussed in Chapter
3, however, the power crisis appears to have resulted in most other problems falling down the list
of concerns. That is, managers concerned about the crisis were less likely to complain about any
other area of the investment climate. As a result, although about the same percent of managers
95
said transportation was a problem in 2003 and in 2006, it ranked among the very lowest concerns
in the 2003 Enterprise Survey but towards the middle in the 2006 Enterprise Survey.
In addition to asking a general question about whether transportation was a severe
obstacle to enterprise operations and growth, the 2003 Enterprise Survey also included several
additional questions that provide more detailed information on what aspects of transportation are
most problematic. Enterprise managers in that survey were most likely to say that sealed roads
and railways were severe problems—18 and 9 percent of enterprise managers with access to
these services rated these services as severe problems. Although similar questions were not asked
in the 2006 Enterprise Survey, objective data on losses due to theft and breakage indicate that
road conditions remain problematic.
As discussed previously, it is difficult to compare perceptions across countries (see
Chapter 3 and the sub-section in this chapter on electricity). Because of this, it is useful to look
at objective measures of time and money costs. In addition to asking firms about whether
transportation is a serious obstacle, the Enterprise Survey data also asks firms about the losses
due to theft or breakage in transit for goods being shipped domestically.
The objective data also suggests that the transportation system is a problem in Tanzania.
Losses due to theft and breakage during transportation are higher in Tanzania than in the
comparator countries elsewhere in Sub-Saharan Africa (see Figure 48).60
On average, SMLEs in
Tanzania reported losses equal to close to 1.5 percent of shipment value. This is higher than in
most of the comparator countries, where losses mostly average between 0.5 and 1.5 percent of
shipment values.
Figure 47: More firms are concerned about transportation in Tanzania than in most comparator countries
outside of the region.
Source: World Bank Enterprise Surveys.
Note: Cross-country comparisons are for manufacturing firms only. Comparisons within Tanzania are only for
those provinces and firm size classes covered in both surveys.
0
20
40
60
Tanzania
Tanzania
2003
Buru
ndi
Uganda
Rw
anda
Kenya
South
Afr
ica
Mala
ysia
Thaila
nd
Mauritius
Sw
azila
nd
Chin
a
% o
f firm
s
Percent of firms that say transportation is a serious obstacle
96
Consistent with the idea that transportation has become a greater constraint in recent
years, firm managers tended to report higher losses due to theft and breakage in 2006 than they
did in 2003. In 2006, managers reported average losses of about 1.4 percent, compared to 0.7
percent in 2003. A similar although more exaggerated pattern can be seen looking at panel firms
alone.
Table 19: Losses during transportation appear to have become worse since 2003.
2003 2006
Losses during domestic transportation (% of shipment value)
All manufacturing SMLEs 0.7 1.4
Panel Firms 0.5 2.2
Source: World Bank Enterprise Surveys.
Note: Averages for all manufacturing firms only include manufacturing firms in cities covered in both
surveys. Averages for panel firms are averages for firms that were in both the 2003 and 2007 surveys.
Because the 2003 survey only covered manufacturing comparisons are only for manufacturing. Averages
for panel firms are unweighted.
International Trade
The 2004 Investment Climate Assessment noted that Tanzanian firms did not perform
well with respect to exporting (Regional Program on Enterprise Development, 2004b). This
remained true in 2006. Only about 14 percent of Tanzanian SMLEs in the manufacturing sector
exported any part of their output (see Figure 49). This is very low compared with most of the
comparator countries that have been successful with respect to entering manufacturing. For
example, close to half of SMLEs in Kenya and more than half of SMLEs in South Africa,
Mauritius, Thailand and Malaysia export. Even in China about one-quarter of the manufacturing
firms exported—this is high considering that firms in countries with large domestic market are
Figure 48: Losses due to theft and breakage are high in Tanzania.
Source: World Bank Enterprise Surveys.
Note: Cross-country comparisons are for manufacturing firms only.
0.0
0.5
1.0
1.5
2.0
Ta
nza
nia
Bu
run
di
Sw
azila
nd
Rw
an
da
Uga
nd
a
Ke
nya
Lo
sse
s a
s %
of sh
ipm
en
t va
lue
Losses due to theft and breakage during transportation (% of value)
97
far less likely to export than firms based in smaller markets. Tanzania compares poorly even
with landlocked neighbors such as Uganda (20 percent of firms) and Rwanda (about 28 percent
of firms).
As noted above, the 2004 ICA also noted that few Tanzanian firms exported in that
survey either. Although a greater share of the firms in 2003 survey exported (27 percent
compared to only 14 percent in 2006), it is important to control for firm size when making these
comparisons. As discussed in Appendix 1.2, the 2003 survey appears to have oversampled large
firms—and large firms are far more likely to export than small firms are.
Therefore a better comparison might be to look at only panel firms. Among the panel
firms, the difference between is smaller (17 percent compared to 21 percent) and is actually
reversed with the panel firms exporting more in 2006 than in 2003. This suggests that the
change in survey composition probably explains most of the drop between 2003 and 2006 in
terms of exporting in the raw data.
Table 20: Although exporting remains low, it might be slightly higher than in 2003.
2003 2006
% of firms that export
All manufacturing SMLEs 27% 14%
Panel Firms 17% 21%
Source: World Bank Enterprise Surveys.
Note: Averages for all manufacturing firms only include manufacturing firms in cities covered in both
surveys. Averages for panel firms are averages for firms that were in both the 2003 and 2007 surveys.
Because the 2003 survey only covered manufacturing comparisons are only for manufacturing. Averages
for panel firms are unweighted.
Figure 49: Fewer firms from Tanzania export than in the most successful of the comparator countries.
Source: World Bank Enterprise Surveys.
Note: Data are for manufacturing firms only.
0
25
50
75
100T
an
za
nia
Bu
run
di
Uga
nd
a
Rw
an
da
Ke
nya
Chin
a
So
uth
A
fric
a
Th
aila
nd
Ma
uri
tiu
s
Ma
laysia
% o
f firm
s
Percent of firms that export
98
As well as the relatively modest number of exporting firms, it is also important to note
that most SMLEs that do export do so to neighboring countries. The most common export
destinations for the mostly small and medium-sized manufacturing firms in the Enterprise
Survey are nearby countries such as Kenya, Mozambique, Uganda, Malawi and Zambia. Only
about one-quarter of SMLEs that export, export overseas to developed economies in Europe,
Asia or North America. In comparison, over 90 percent of exporters, exported some part of their
output to neighboring countries.
There are, of course, many factors that can affect exporting. The relatively low
productivity among Tanzanian SMLEs (see Chapter 2) is likely to make it difficult for Tanzanian
firms to enter foreign markets. It is, however, important to note that wages are also relatively
low in Tanzania, even compared to productivity, suggesting that wage levels are not the primary
factor discouraging exporting.
Other things that might affect exporting include the regulatory burden associated with
exporting and inefficiencies at the ports. The Doing Business report collects information on the
cost of exporting a standardized cargo of goods to an overseas destination using the port that
firms in the country most frequently use for exports. The Doing Business report documents all
forms that need to be completed, the time it takes to complete all steps associated with exporting
and importing included customs clearance, completing all forms, port procedures and inland
transportation.
The financial costs of exporting and importing containers of standardized goods are
higher for firms in Tanzania than in any of the comparator countries in East Asia, but lower than
all countries in East Africa (see Table 21). The cost is more that double that of China, Thailand
and Malaysia. This suggests that one of the reasons why few firms in Tanzania export, especially
compared to firms in Asia, is that the high cost of transportation makes Tanzanian exports
uncompetitive.
Table 21: Doing Business indicators for Tanzania and comparator countries for trading across borders.
Rank
Documents
for export
(number)
Time for
export
(days)
Cost to
export
(US$ per
container)
Documents
for import
(number)
Time for
import
(days)
Cost to
import
(US$ per
container)
Tanzania 103 5 24 $1,262 7 31 $1,475
Uganda 145 6 39 $3,090 7 37 $3,290
Kenya 148 9 29 $2,055 8 26 $2,190
Rwanda 168 9 42 $3,275 10 42 $5,070
Burundi 170 9 47 $2,147 10 71 $3,705
Thailand 10 4 14 $625 3 13 $795
Mauritius 20 5 17 $725 6 16 $677
Malaysia 29 7 18 $450 7 14 $450
China 48 7 21 $460 6 24 $545
South Africa 147 8 30 $1,445 9 35 $1,721
Swaziland 154 9 21 $2,184 11 33 $2,249
Source: World Bank (2008a).
99
Other cross-country studies lead to similar conclusions. Based upon a large survey of
over 5,000 freight forwarders throughout the world, the Logistics Performance Index (LPI) ranks
logistics and transportation in 150 countries throughout the world based upon the performance in
seven areas (see Table 22).61
There are several similarities with the results from the Doing
Business report. Tanzania ranked 137th
out of 150 countries. This puts it far behind most of the
strong performing manufacturing economies such as South Africa, Malaysia and China, and
lowest amongst all regional comparators also except Rwanda. The most serious concerns related
to infrastructure and logistics, although Tanzania compares unfavorably with the comparator
countries on most of the sub-indices.
Table 22. Logistics Performance Index.
Country Rank LPI Customs Infrast-
ructure
Int’l
shipment
Logistics
competence
Tracking
&
tracing
Domestic
logistics
costs
Timeliness
Tanzania 137 2.08 2.07 2.00 2.08 1.92 2.17 3.33 2.27
South Africa 24 3.53 3.22 3.42 3.56 3.54 3.71 2.61 3.78
Malaysia 27 3.48 3.36 3.33 3.36 3.40 3.61 3.13 3.95
China 30 3.32 2.99 3.20 3.31 3.40 3.37 2.97 3.68
Thailand 31 3.31 3.03 3.16 3.24 3.31 3.25 3.21 3.91
India 39 3.07 2.69 2.90 3.08 3.27 3.03 3.08 3.47
Kenya 76 2.52 2.33 2.15 2.79 2.31 2.62 2.75 2.92
Uganda 83 2.49 2.21 2.17 2.42 2.55 2.33 3.63 3.29
Burundi 113 2.29 2.20 2.50 2.50 2.50 2.00 2.33 2.00
Mauritius 132 2.13 2.00 2.29 2.20 1.75 2.25 2.67 2.33
Rwanda 148 1.77 1.80 1.53 1.67 1.67 1.60 3.07 2.38
Source: Arvis and others (2007).
Note: Scores are on a five-point scale based upon subjective assessments by freight forwarders and other logistics
professionals with high values denoting strong performance. The table presents average scores.
In addition to asking questions related to the manager‘s perceptions about transportation
services, the Enterprise Survey also asked about delays in ports and customs for enterprises that
import capital goods and intermediate inputs and export their final product. In all relevant
questions, the questions ask how long it takes from the time that goods arrive at the point of
entry or exit to the time that they clear customs. The reason for this approach—rather than trying
to break down the delays into separate components (e.g., those due to customs and those due to
the port)—is that enterprise managers often only know about the total delay, not who is
responsible for the delay.
Related to the measures above, firms in the Enterprise Survey that directly import and
export goods are asked how long it takes goods to go from arriving at the point of entry until
they have cleared customs. In addition to clearing customs, this also includes the time that it
takes to complete other port procedures.
Tanzania compares less favorably on this measure with respect to the regional
comparators than some of the other measures discussed above (see Figure 50). SMLEs from
Tanzania reported longer delays than in any of the other countries in the region, except Rwanda,
for both imports and exports. The average delay was also longer than in any of the successful
manufacturing comparator countries except China. For example, the average time for exports to
complete procedures is 4 days for Swaziland, 4.4 days for Mauritius and 4.6 days for South
Africa compared with 5.7 days for Tanzania.
100
II. Taxes
SMLE managers were more likely to say that tax rates were a problem than any other
areas of the investment climate other than power and access to finance, with close to 40 percent
saying it was a serious problem (see Chapter 3). Even managers of microenterprises, many of
whom appear to evade at least some of their tax liability (see Chapter 7), were concerned about
tax rates—microenterprise managers were more likely to say that tax rates were a problem than
any area except power, access to finance, macroeconomic instability and competitors in the
informal sector.
In contrast, managers were less concerned about tax administration. It ranked as the
eighth most serious concern for SMLE managers and the sixth most serious concern for
microenterprise managers, with only about 20 percent of both types of firms saying it was a
serious problem. Moreover, concern among SMLE managers had declined considerably since
earlier surveys. Of the areas asked about in both the 2003 and 2006 surveys, tax administration
ranked as the third greatest constraint in the 2003 Enterprise Survey. This is consistent with
other evidence that suggests that concern about tax administration has declined. Concern about
tax regulation declined significantly between 2004 and 2007 in the Global Competitiveness
Report (World Economic Forum, 2005; 2008).
Figure 50: It takes less time for imports and exports to complete customs and port/border procedures in
Tanzania than in many of the comparators.
Source: World Bank Enterprise Surveys.
Note: Data on exports not available for Burundi. Cross-country comparisons are for manufacturing firms only.
0 2 4 6 8
Tanzania
Uganda
Kenya
Rwanda
Thailand
Malaysia
Swaziland
Mauritius
South Africa
China
No. of days for exports
Ave. time for exports to complete procedures
0 5 10 15 20
Tanzania
Uganda
Kenya
Burundi
Rwanda
Swaziland
Malaysia
Thailand
Mauritius
South Africa
China
No of days for imports
Ave. time for imports to complete procedures
101
Tax Rates
Although the high level of concern about tax rates suggests that they are seen as a serious
obstacle in Tanzania, it is important to note that tax rates are typically among the greatest
concerns in Enterprises Surveys. Indeed, tax rates rank among the top three obstacles in over
half of Enterprise Surveys in low-income countries and in over two-thirds of countries in Sub-
Saharan Africa (World Bank, 2004).62
In this respect, it is not surprising that they also rank
among the top concerns in Tanzania.
Although this emphasizes that concern about tax rates is very common throughout the
world, other evidence suggests that concern is somewhat high in Tanzania. Over 40 percent of
manufactuing SMLEs said that tax rates were a serious problem for their firms (see Figure 12).
Compared with other countries in the region, this is not particularly high. Although fewer than
40 percent of manufacturing SMLEs in Burundi said the same, more firms in Rwanda, Uganda
and Tanzania said that taxes were a serious problem. In general, firms in the comparator
countries from other regions were less likely to say that tax rates were a problem. For example,
fewer than 25 percent of manufacturing SMLEs in South Africa, Malaysia or Thailand said that
tax rates were a serious constraint.
Given the high level of concern about tax rates, a natural quesiton is whether the
objective data also suggest that tax rates might be a particular problem in Tanzania. The
evidence here is somewhat mixed (see Table 23). The corporate tax rate is 30 percent. Although
it is far lower in Mauritius (only 15 percent), corporate tax rates are between 27 and 33 percent
in most of the comparator countries. In contrast, the value-added tax (VAT) rate is relatively
Figure 51: Firms were more likely to say that tax rates were a problem in Tanzania than they were in
most of the other comparator countries.
Source: World Bank Enterprise Surveys.
Note: Cross-country comparisons are for manufacturing firms only.
0 20 40 60
Tanzania
Burundi
Rwanda
Uganda
Kenya
South Africa
Malaysia
Thailand
Swaziland
Mauritius
India
China
% of firms
% saying that tax rates are a serious problem
0 20 40 60
Tanzania
Burundi
Rwanda
Uganda
Kenya
South Africa
Malaysia
Thailand
Mauritius
Swaziland
India
China
% of firms
% saying tax administration is a serious problem
102
high. At 20 percent, it is higher than in most of the comparator countries about the same as in
most of the comparator countries (mostly between about 14 and 18 percent).63
Table 23: Tax rates and revenue in Tanzania and comparator countries.
Country
Name
Tax Revenue
(% of GDP, 2006)
Top Corporate
Tax Rate
(% of profit)
Sales Tax or VAT
(% of sales)
Social Security
Contributions
(% of salaries)
Total tax rate
(% of profit)
Tanzania --- 30 20 10 45.1
Mauritius 18 15 15 6 22.2
Rwanda --- 30 18 3 33.7
South Africa 29 29 14 --- 34.2
Malaysia 18 27 10 --- 34.5
Swaziland 27 30 14 --- 36.6
Thailand 17 30 7 5 37.8
Kenya 19 30 16 5 50.9
Uganda 13 30 18 10 34.5
India 11 30 --- 12 71.5
China 9 33 17 44 79.9
Burundi --- --- 17 4 278.7
Source: World Bank (2008a; 2008c).
Although headline tax rates provide some information on the burden of taxation, they can
be misleading when considered in isolation. Differences in definitions of tax rates, depreciation
rates, investment incentives and loss carry-forwards can have a large impact on the effective tax
rate that firms actually pay for any given marginal tax rate. The Doing Business report (World
Bank, 2008a) calculates the total tax rate for a representative firm in each country.64
This is the
amount of corporate taxes and other taxes that this representative firm would pay as a percent of
profits after accounting for various deductions and exemptions. Tanzania compares less
favorably on this measure. The total tax rate is 45.1 percent of profits in Tanzania. This is higher
than in most of the comparator countries except India, China, Kenya and Burundi. Moreover, this
calculation does not take value added taxes into account.65
In summary, although taxes are not
generally within or close to the levels observed in the comparator countries, they are on the
upper end of the range.
Although tax rates do appear to be higher than in the countries with the lowest tax
burdens, other factors might also affect perceptions about tax rates. In particular, firms might be
dissatisfied with tax rates because they are concerned that they do not get value for money from
their taxes. That is, firms are more likely to be concerned about tax rates when they feel their tax
payments are being used efficiently by the Government. As discussed in the next section,
Tanzania does not compare very favorably with respect to either regulatory quality or
government effectiveness (see Figure 55).
Another possibility is that some managers‘ concerns about tax rates might reflect concern
about the impact that tax rates have on their firms‘ competitiveness rather than their concern
about the actual level of taxes. If managers feel that tax rates make it difficult for them to
compete with informal firms or formal competitors that evade taxes, then this could affect
perceptions about tax rates. That is, firms might be less satisfied with taxes when they feel they
are borne by all firms—not just the manager‘s own firm.
103
Of course, as with measuring informality (see Chapter 6), it is very difficult to accurately
measure tax evasion. Few firm managers will willingly admit that they illegally evade taxes in
an interview format, especially if they believe it might result in legal problems or affect their tax
liabilities. To partially avoid this problem, the question is asked indirectly—firm managers are
asked ―what percentage of total sales would you estimate the typical firm in your area of activity
would report for tax purposes?‖ A similar question is asked about workers: ―what percentage of
the total workforce would you estimate the typical establishment in your line of business declares
for tax purposes?‖
It is important to note two things however. First, although it is possible that this might
overestimate tax evasion, it is far more likely that this will underestimate tax evasion. That is, it
seems more reasonable that firm managers that are evading taxes will lie and say they are not
than firm managers that are complying with taxes will lie and say that they don‘t. Second, it is
not clear that this bias will affect either the relative rankings across countries or the relative
rankings across time. That is, managers in all countries have similar incentives to lie. Moreover,
it is not clear that managers in Tanzania have different incentives to lie in 2007 than they did in
2003.
The average firm manager said that ‗firms like theirs‘ reported about 50 percent of
revenues to the authorities for tax purposes (see Figure 52). The average manager of a
manufacturing firm reported a slightly higher share—about 52 percent of revenues. Reporting
was even lower for workers, with the average firm manager saying that ‗firms like theirs‘
reported only 51 percent of workers for tax purposes. Both of these are lower than in most of the
comparator countries.
Figure 52: Firms in Tanzania report less revenues and workers to tax authorities than in most of the
comparator countries.
Source: World Bank Enterprise Surveys.
Note: Cross-country comparisons within Africa include all firms not just manufacturing firms.
0 25 50 75 100
Tanzania
Swaziland
Uganda
Namibia
Kenya
Burundi
Rwanda
% of revenues
% of revenues reported for tax purposes
0 25 50 75 100
Tanzania
Uganda
Kenya
Swaziland
Namibia
Rwanda
Burundi
% of workers
% of workers reported for tax purposes
104
Moreover, tax evasion appears to have increased since 2003 (see Table 24). Although, as
noted earlier, it is difficult to make comparisons across time between the two surveys, managers
of manufacturing firms said that ‗firms like theirs‘ reported about 72 percent of revenues to the
authorities in the 2003 survey—far higher than the 53 percent of revenues that the average
manufacturing firm reported in 2007. Moreover, a decline, although smaller, is also evidence
when only looking at the panel firms (i.e., firms that were interviewed and answered the question
in both 2003 and 2006).
Table 24: Tax evasion appears to be higher in 2006 than in 2003.
2003 2007
% of income reported for tax purposes
All manufacturing SMLEs 72 53
Panel Firms 71 60
Source: World Bank Enterprise Surveys.
Note: Averages for all manufacturing firms only include manufacturing firms in cities covered in both
surveys. Averages for panel firms are averages for firms that were in both the 2003 and 2007 surveys.
Because the 2003 survey only covered manufacturing comparisons are only for manufacturing. Averages
for panel firms are unweighted.
Although it is difficult to draw strong conclusions about tax evasion from Enterprise
Surveys, tax evasion does appear to be a significant problem in Tanzania and it seems possible
that it is increasing. Moreover, if anything, this is likely to underestimate the extent of tax
evasion. That is, firm managers are probably more likely to lie and say they do not evade taxes
when they do, than they are to lie and say they evade taxes when they do not.
Tax Administration
SMLE managers were far less likely to say that tax administration was a problem than
they were to say that tax rates were a problem. Only about two out of ten managers said that it
was a serious problem (see Figure 5). This makes tax administration the eighth greatest
constraint based upon the percent of managers that said it was a serious problem. Moreover,
concern is not significantly higher than in most of the comparator countries.
Objective data on tax administration, however, suggests that the burden is roughly
comparable with the burden in the comparator countries. Managers in the Enterprise Survey
were asked how many visits or required meetings the firm‘s management had with tax officials.
The average firm report about 3 meetings—higher than in some countries but far lower than in
many others. For example, the average firm in China reported 14 meetings.
The Doing Business report also collects information on the burden of tax administration.
For a representative enterprise, the report estimates the firm has to make 48 tax payments and
that it would take about 178 hours to complete these requirements. Again, this is somewhere
near the average for the comparator countries (see Table 25). Overall, the evidence from the
Enterprise Surveys and the Doing Business report suggest that although the burden of tax
administration is not particularly high, it is also not particularly low.
105
Table 25: Tax administration in Tanzania and the comparator countries.
Visits or required meetings
with tax officials
Annual tax payments
(number)
Time completing forms
(hours)
Mean Median
Tanzania 2.8 2.0 48 172
Thailand 1.7 1.0 23 264
Swaziland 1.9 1.0 33 104
Mauritius 2.1 1.0 7 161
Burundi 2.1 1.0 32 140
Uganda 2.9 2.0 32 222
South Africa 3.3 1.0 9 200
Rwanda 4.0 1.0 34 160
Malaysia 5.2 1.0 12 145
Kenya 5.5 1.0 41 417
China 14.4 6.0 9 504
Source: World Bank Enterprise Surveys; World Bank (2008a).
Notes: The first columns are average and medians for manufacturing firms from the Enterprise Survey. The second
and third columns are from the Doing Business report.
As discussed above and in Chapter 3, perceptions about tax administration have
improved in recent years. More firms said that tax administration was a problem in 2003 than in
2007 and its relative position among the constraints has improved since 2003—it ranked as the
third greatest constraint in the 2003 survey (of the constraints asked about in both surveys). This
observed improvement suggests that recent reforms of the Tanzania Revenue Authority (TRA),
which have been supported since 1999 by the World Bank through the Tax Administration
Project (TAP) and the Tax Modernization Project (TMP), have been successful in improving tax
administration (see Box).66
Table 26: Tax inspections appear to be less common in 2006 than in 2003.
2003 2006
Tax inspections per year
All manufacturing SMLEs 10.6 2.8
Panel Firms 10.6 2.7
Source: World Bank Enterprise Surveys.
Note: Averages for all manufacturing firms only include manufacturing firms in cities covered in both
surveys. Averages for panel firms are averages for firms that were in both the 2003 and 2007 surveys.
Because the 2003 survey only covered manufacturing comparisons are only for manufacturing. Averages
for panel firms are unweighted.
Consistent with the idea that tax administration has become less burdensome, firms
reported fewer tax inspections in 2006 than they did in 2003. The median number fell from close
to 11 per year in 2003 to only about 3 per year by 2006. This is true whether looking at all firms
or only the panel firms.
The ―Paying Taxes‖ indicator did not appear in the Doing Business report until the 2006
report, reflecting conditions at the beginning of 2005. At this time, it was estimated that the
representative company would have to make 47 tax payments per year and that it would take 172
hours to fill out all forms. This is roughly the same as in 2007. The reason for the improvement
in perceptions despite the stability of the Doing Business indicator could either be due to timing
106
(e.g., the improvement took place before 2005) or that the improvements were in areas not
captured in the Doing Business report.
Box: Tax Administration Reform in Tanzania
Before 1995 Tanzania had serious problems with revenue collection. To address these
problems the Government of Tanzania radically reformed tax administration. The Government
creating a semi-autonomous revenue agency (Tanzania Revenue Authority - TRA), which started
operating in July 1996. The World Bank supported these reforms through the Tax Administration
Project (TAP) [1999-2006] and the Tax Modernization Project (TMP) [2006-2009].
The reforms substantially improved revenue collection. The tax yield more than doubled
from SHS 1.4 trillion in FY 2003/04 to SHS 3.4 trillion in FY 2007/08, raising the tax/GDP ratio
to 14.9 percent. Broadening the tax base substantially increased the fairness of the tax system, as
the number of registered taxpayers increased from 190,000 in July 2003 to 381,000 in March
2008.
The reforms have also resulted in the modernization of the TRA. Reforms included
strengthening of the Large Taxpayer Department (LTD), merging the VAT and income tax
departments into a single Domestic Revenue Department and developing an integrated IT system.
Interaction with stakeholders improved through a modern taxpayer service program and regular
taxpayer feedback surveys. Steps were also been taken to improve governance in revenue
administration, and a TRA anti-corruption strategy was designed as part of the National Anti-
Corruption Strategy and Action Plan. Moreover, carrying out of Internal Quality Audits for
readiness of attaining ISO 9001:2000 certification for the entire organization, implementing
Compliant Traders Scheme for 54 traders, and the introduction of New Euro trace Database
Management System have contributed to improve the service quality provided by TRA. The
TMP also developed tax payer‘s education capacity by developing and implementing the
Taxpayer‘s Charter and conducting the Stakeholder‘s Forum. As a result, the percentage of tax
payer‘s awareness on tax education programs increased from 46% in June 06 to 76% in June 08.
III. Regulation and Corruption
Few firm managers said that the specific areas of regulation asked about on the Enterprise
Survey were serious obstacles to their firms‘ operations. Only about on in five SMLE managers
said that business licensing and registration was a serious problem, only about one in eight said
that customs and trade regulation was a serious problem, and only about one in twenty said labor
regulation was a serious problem. Concern was even more modest for microenterprise managers
(see Chapter 3). None of these specific areas of regulation ranked among the managers‘ top
concerns.
Although this might suggest regulation is not a serious problem in Tanzania, it is
important to note that the narrow measures that the survey asks about might not be representative
of the overall burden of regulation. Moreover, the question on business registration and
licensing might underestimate the burden that this imposes upon start-ups. Existing enterprises
that have already completed registration procedures might be far less concerned about
registration and licensing than potential start-ups are.
Other evidence suggests that regulation might be a broader concern. Most importantly,
there was serious concern about informality and corruption. About three out of ten SMLE
managers and two out of ten microenterprise managers said that informality was a serious
problem. Although slightly fewer managers said that corruption was a serous problem, it still
ranked among the top concerns. Both corruption and informality should be seen as symptoms of
107
other problems in the investment climate. Although tackling these issues demands that the
Government takes a broad approach doing things such as improving the skills and integrity of the
bureaucracy, strengthening government procurement, and actively pursuing individuals accused
of breaking laws, reducing the burden of regulation is often an important component.67
Objective measures of the burden of regulation
Objective, and broader, measures of regulation suggest that although the burden of
regulation is lower than in many of the comparator countries especially within East Africa, the
burden is high compared with the best performing economies in the world. The Doing Business
report (World Bank, 2008a) collects detailed information on laws and regulations in a variety of
areas in 179 countries throughout the world and ranks the countries based upon the burden of
regulation (e.g., the time and cost of completing certain regulation tasks for a representative
business). Rather than interviewing firms, the Doing Business report collects detailed
information on regulations from lawyers, accountants, shippers, and other agents.
Many of the measures in the Doing Business report are based upon legal requirements
related to regulatory procedures rather than on how regulations are applied in practice. For
example, the measures of labor regulation, getting credit, and protecting investors are based
solely upon a detailed analysis of laws in these areas.68
Some measures are based upon a
combination of legal requirements and expert assessments of the application of these legal
requirements. For example, the ‗starting a business‘ indicator includes measures related to the
number of procedures that a firm has to complete to start a limited liability company and the time
to complete these procedures. The number of procedures is based upon the legal requirements
that need to be met and assumes that the firm completes each step. The measure of time is based
both upon the legal requirements and upon expert assessments of how long it takes to complete
each step (i.e., the time does not depend only upon the legal requirements and can be affected by
how the procedures are applied). For many of these procedures, the time it takes to complete a
procedure will depend upon how quickly the agency charged with implementing the procedure
can complete it (i.e., the efficiency of the agency involved) as well as the complexities of the
legal requirements. That is, the time measure depends partly upon the application of the
procedures.
All measures in the Doing Business also assume that laws are complied with (i.e., that
firms do not avoid or evade the legal requirements). For many, although not all, of the indicators
and sub-indicators in the Doing Business report, regulations might be legally avoided or illegally
evaded.69
For example, the starting a business measure does not consider the extent that small,
informal enterprises (illegally) ignore all or some these of requirements. Similarly, the firm
owner could avoid some of the costs of starting a limited liability company by not registering the
firm as a limited liability company. In this case, this could be done by registering as a sole
proprietorship. Or in some countries the firm manager might be able to speed up the process and
avoid the fees associated with registering by bribing the officials that are responsible for
registration.
Tanzania ranks 127th
out of 179 countries in the Doing Business report (World Bank,
2008a). This is slightly worse than two neighboring countries—Uganda and Kenya (see Figure
53). It is more significantly worse than most of the successful manufacturers. For example,
108
Thailand ranks 13, Malaysia ranks 20, Mauritius ranks 24 (the highest ranked country in Sub-
Saharan Africa), and South Africa ranks 32 (the second highest ranked county in Sub-Saharan
Africa).
The Enterprise Survey provides additional information on the application of regulation.
In particular, the Enterprise Survey asks firms how much time their senior management spends
dealing with regulatory requirements. This complements the Doing Business data by providing
additional information on the application of laws and regulations. For the most part, Tanzania
compares more favorably on this measure than it does on the Doing Business indicators. The
average manager reported spending a little less than 5 percent of their time dealing with
regulatory requirements in Tanzania. This was considerably higher than managers in Thailand
(only 2 percent of their time) and similar to that of firms in Swaziland, Uganda and Rwanda
(about 4 to 5 percent of their time). However, it is lower than in the other comparator countries
and is considerably lower than in China (over 20 percent of their time). The difference in results
could reflect that the second measure is based mostly on firm experience while the Doing
Business measure is primarily, although not exclusively, based upon legal requirements.
Although this could be because regulations are enforced efficiently, a more likely explanation is
that there is a gap between legal requirements and enforcement.
In addition to the overall ranking, the Doing Business report ranks countries in each area
that the report covers. Tanzania compares particularly badly on a few of the measures (see
Figure 54). These include employing workers (140th
), registering property (142nd
) and dealing
with construction permits (172nd
).
Figure 53: Although Tanzania compares favorably with many of the comparator countries with respect
to the burden of regulation—especially in the region—there is room for improvement.
Source: World Bank Enterprise Surveys; World Bank (2008a).
Note: Higher numbers on Doing Business ranking means more restrictive regulation. Cross-country
comparisons using Enterprise Survey data are for manufacturing firms only.
0 50 100 150 200
Tanzania
Thailand
Malaysia
Mauritius
South Africa
Kenya
China
Swaziland
Uganda
India
Rwanda
Burundi
Ranking on Doing Business
Ranking in 2009 Doing Business report
0 5 10 15 20 25
Uganda
Thailand
Swaziland
Tanzania
Rwanda
Burundi
Kenya
Malaysia
South Africa
Mauritius
China
% of time
% of management time dealing with regulations
109
The closest subjective measures related to registering property and in the Investment
Climate Assessment is the question on access to land. About one in five SMLEs and about one
in ten microenterprises said that access to land was a problem. At least for SMLEs, this suggests
that access to land is a moderate concern, ranking 10th
of the 17 constraints (see Chapter 3).
On one final measure, employing workers, Tanzania compares very unfavorably with the
other countries, ranking 140th
in the world. It is probably surprising therefore that very few firms
said that labor regulation was a serious constraint (only about 5 percent of SMLEs and none of
the microenterprises). Also consistent with the idea that labor regulation is not a particularly
serious constraint, only about 10 percent of microenterprises said that labor regulation
discouraged firms from becoming formal.
So what explains the large divide between the Doing Business indicators and the
perceptions of firm managers with respect to labor regulations? One plausible explanation is that
the gap is due to a gap between legal requirements and implementation. Although some of the
Doing Business indicators, such as starting a business which is discussed above, depend partly
on how the regulations are applied, the employing workers indicator is based purely on the
content of the law. If laws are enforced and interpreted in ways that are favorable to firms, firm
managers might not be particularly concerned about them even if the regulations and laws appear
unfavorable on paper.
A second factor that could lead to differences between firm perceptions and the Doing
Business indicators is avoidance and evasion. As discussed earlier, it might be possible to avoid
or evade some regulations either legally or illegally. Perceptions are likely to be affected both
by the difficulty of completing the procedure (something that is captured in the Doing Business
indicators) and the ease of avoiding it either legally or illegally (something that is not captured in
Figure 54: Although Tanzania compares favorably on some components of the Doing Business indicators, it
compares less favorable on others.
Source: World Bank (2008a).
Note: Higher numbers on Doing Business ranking means more restrictive regulation.
0
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110
the Doing Business indicators). This could lead to a divergence between some of the Doing
Business indicators and firm perceptions.
Consistent with the idea that labor regulations are not enforced very rigorously for many
firms, medium-sized and large firms were generally more likely to say that labor regulations
were a serious obstacle than small firms were. None of the microenterprise managers (0 to 5
employees) and only 2 percent of managers of small enterprises said that labor regulations were
a problem, 14 percent of managers of medium-sized enterprises and 6 percent of managers of
large enterprises said the same. Since it is reasonable to assume that labor regulations are more
tightly enforced for medium-sized and large enterprises than for microenterprises and small
enterprises, this makes sense.
Corruption
As noted above, corruption is a common symptom of over-regulation. About one-fifth of
SMLE managers and one quarter of manufacturing SMLE managers said that corruption was a
serious problem in Tanzania. Although this is relatively high, it does not place corruption among
the very top concerns of Tanzanian firms. Moreover, it is also not particularly high compared
with the comparator countries. For example, about one-fifth of manufacturing SMLEs in
Burundi and one-quarter of manufacturing SMLEs in Uganda, China, India and Swaziland said
that corruption was a serious problem (see Table 27).
The objective data on corruption is generally consistent with the perception-based data,
suggesting that corruption is a serious problem but no more so than in many of the comparator
countries. Although over half of firms report that bribe payments are needed to get things done
in Tanzania, this is not exceptionally high compared with the comparator countries, especially
those in the region. Close to half of SMLE managers said bribe were needed in Burundi, Uganda
and India and close to three-quarters said the same in Kenya and China.
Table 27: Corruption in Tanzania and comparator countries.
% saying corruption is
a serious constraint
% reporting bribes needed
‗to get things done‘
Rank on CPI
(Transparency International)
Tanzania 25% 51% 94
Rwanda 9% 17% 111
Burundi 17% 47% 131
Uganda 23% 52% 111
Kenya 51% 70% 150
Malaysia 14% --- 43
Thailand 18% --- 84
South Africa 16% 13% 43
Swaziland 25% 41% 84
India 26% 47% 72
China 27% 73% 72
Source: World Bank Enterprise Surveys (first two columns); Transparency International (2007) (final column).
Note: CPI is Corruption Perception Index. Ranking is for 2007 and is out of 179 countries. Cross country
comparisons are for manufacturing firms only
111
Tanzania compares slightly less favorably on Transparency International‘s ―Corruption
Perceptions Index‖ (Transparency International, 2007) with the comparator countries from
outside the region, ranking worse than any of these countries including China and India. It
compares more favorably with other countries from the region. Rwanda and Uganda rank 111th
,
Burundi ranks 131st and Kenya ranks 150
th—all worse than Tanzania (94
th). It is important to
note that this measure is broader than the measure in the Enterprise Survey, which focuses on
petty corruption (i.e., bribe payments to bureaucrats to get things done such as getting licenses or
avoiding taxes). In contrast, the broader measure also covers ‗grand corruption‘, which includes
things such as payments to senior officials, ministers, and heads of state.
Both the Transparency International Index and a broader measure produced by Kaufmann
and Kraay suggest that corruption has improved in recent years. Between 2003 and 2007,
Tanzania improved from about the 20th
percentile in 2003 to the 40th
percentile in 2007 with
respect to control of corruption. Similarly, it improved from a score of 2.5 out of 10 in 2003
(92nd
out of 133 countries) on the Corruption Perceptions Index to 3.4 out of 10 (94th
out of 180
countries) in 2007.
The results from the Enterprise Survey do not suggest much of an improvement over this
period. About 37 percent of managers of manufacturing SMLEs said that bribes were needed in
the 2003 survey, compared with 51 percent in the 2006 survey. Although this might suggest that
corruption has increased, comparisons between the two surveys are difficult due to different
sampling methodologies. Looking only at the panel firms (i.e., firms interviewed in both years)
further suggests that corruption has not fallen and might have actually increased. About 38
percent of firms surveyed in both periods reported that bribes are required to get things done in
2003; this increased to 52 percent in 2006. Differences in this measure and the broader measures
could be due to improvements in grand or systematic corruption that are not yet reflected in
improvements in petty corruption—small payments to bureaucrats.
Table 28: Petty bribes have not become any less common—and might have become more common—since
2003
2003 2006
% of firms reporting bribes
All manufacturing SMLEs 37% 51%
Panel Firms 38% 55%
Source: World Bank Enterprise Surveys.
Note: Averages for all manufacturing firms only include manufacturing firms in cities covered in both
surveys. Averages for panel firms are averages for firms that were in both the 2003 and 2007 surveys.
Because the 2003 survey only covered manufacturing comparisons are only for manufacturing. Averages
for panel firms are unweighted.
Other areas of Governance
Much of what has been discussed is based on the traditional measures of corruption as the
―abuse of public office for private gain‖ (Hellman and Kaufmann, 2002). Behind this definition
is the image of a predatory state, often seen as a large ―grabbing hand‖, demanding payments
from firms for the benefit of politicians, high officials and bureaucrats.70
The link between
corruption and over-regulation implicitly reflects this view of corruption.
112
It is, however, important to note that regulation does not always lead to corruption. That
is, many countries manage to combine regulation with clean government. So why is this? One
reason for this is that as well as reflecting problems with in the regulatory environment,
corruption also reflects broader problems related to governance and the quality of institutions.71
It is therefore useful to look at broader measures of governance.
The World Bank has developed broad measures of governance that are meant to capture
six different aspects of governance and institutions. These measures, which have been calculated
by combining information from many different sources, include measures of political freedom,
control of corruption, political stability, government effectiveness, the rule of law and regulatory
quality (see Box).72
Tanzania ranked below the median on all six indicators in 2006 (see Figure 55).Although
it has made significant improvements in the rankings of control of corruption, other measures
remain the same. Also, the results from the Enterprise Survey indicate that petty bribery remains
common, informality continues to be a problem, and majority of firms report some degree of tax
evasion.
Box: Different Aspects of Governance
In recent years, many researchers and practitioners have tried to produce aggregate statistics
that can be used to compare the quality of governance across countries and for single countries over
time. Few of these studies cover the entire world or all topics. Further, although the studies often
cover similar topics, responses and questions are usually not comparable across surveys. In order to
increase country coverage, Kaufmann, Kraay, and Mastruzzi (2007) combined information from as
many as 60 mostly subjective indices from other sources to produce six measures that capture
different aspects on regulation, corruption and governance. The six measures are:
Voice and Accountability: The extent to which citizens of the country are able to participate in the
selection of government.
Political Stability: The likelihood that the government will be destabilized or overthrown by possibly
unconstitutional and/or violent means including terrorism.
Government Effectiveness: The quality of public service provision and the government bureaucracy,
the competence and independence of the civil service and the credibility of the government‘s
commitment to adhering to announced policies. This measure primarily focuses on ‗inputs‘ that
governments‘ need to implement good policies and deliver public goods.
Regulatory Quality: The quality of government policies. This measure is ‗output‘ rather than ‗input‘
based, in that it focuses on the prevalence of market-unfriendly policies such as price controls or
inadequate bank supervision, as well as perceptions about the burden imposed on businesses by
regulation.
Rule of Law: the extent to which individuals have confidence in and abide by the rules of society.
This includes perceptions about the incidence of crime (both violent and non-violent), the
effectiveness and predictability of the judiciary, and the enforceability of contracts.
Control of Corruption: the extent of corruption (i.e., the illegal use of public power for private gain).
113
Figure 55: Although governance remains a concern in Uganda, it has improved in most areas since 2002.
Source: Kaufmann and others (2007).
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114
CHAPTER 7: INFORMALITY
Because of differing definitions of ‗informality‘ and the difficulty of measuring informal
activity, it is very difficult to definitively determine the size of the informal sector. Nevertheless,
several studies have tried to measure informal activity in Tanzania and to look at the
characteristics of firms, workers, and activities that might be considered informal.
During the early 1980s, informality was discouraged. For example, the Nguvu Kazi
campaign against ―unproductive elements‖ was followed in the early 1980s. The political
environment changed in the 1990s and 2000s as the economy was liberalized and in 2004
President Mkapa announced that the informal sector was a key element of society and the
economy, with great potential for ingenuity and productivity, and was vital to the prosperity of
the country.73
Recent studies argue that the informal sector has grown rapidly in recent years as a
result of economic liberalization and increased tolerance for many informal sector activities that
were previously illegal. Privatization and public service reform have also contributed because
laid-off government and parastatal workers have had to find new sources of income (Zacchia,
2007).
Given the increased tolerance for informality, it is not surprising that most estimates
suggest that informality grew rapidly as the economy in the 1990s and 2000s.74
By 1991,
estimates suggested that the urban informal sector contributed between about 15 and 20 percent
of total GDP—this was higher than estimates of about 10 percent in the mid-1980s and was
roughly equal to the Sub-Saharan Africa average.75
Estimates suggest that the size increased
further in the 1990s and 2000s, with the largest estimates suggesting that the size of the informal
economy might have reached close to 60 percent of GDP by 2000.76
I. Informality
One problem with studies on informality is that it is difficult to identify ‗informal
enterprises‘. There are two reasons for this: (i) it is difficult to define informality in a clear and
concise way, especially in a way that allows for cross-country comparisons; and (ii) it is difficult
to identify informal firms both in terms of defining a sample frame and in terms of identifying
firms that are informal within a sample.
Commonly used definitions of the informal economy usually focus on economic
activities that are not measured or visible to the Government. For example, Schneider and
Klinglmair (2004) lists two related definitions:
Smith (1994, p. 18) defines [the informal economy] as ‗market-based production
of goods and services, whether legal or illegal that escapes detection in the official
estimates of GDP.‘ Or to put it another way, one of the broadest definitions of it,
includes those economic activities and the income derived from them that
circumvent or otherwise avoid government regulation, taxation, or observation.‘
These definitions focus on activities, rather than firms, being formal or informal. For example,
work performed by unregistered temporary workers that were paid under the table would be
115
classified as ‗informal‘ under the second definition even if the firm was registered with the
business registrar, the firm paid corporate income taxes on most of its income and the firm
complied with labor regulations for its permanent workforce.77
Although definitions based upon activities are a useful way of thinking about informality
at the macroeconomic level, a more natural way to think about informality at the firm-level is
define informality based upon the firm-level behavior. For example, informality could be based
upon whether the firm is registered with relevant government agencies or whether the firm
generally complies with government regulations and taxes.
In practice, however, either approach can be problematic. Firms are often required to
register with a large number of government agencies. For example, a limited liability company
in Tanzania has to obtain a trading license from the regional trade officer; register with the
registrar of companies; register with the Tanzania Revenue Authority for a Taxpayer
Identification Number (TIN) and several additional taxes, register for workmen‘s compensation
insurance at the National Insurance Corporation and obtain a registration number from the
National Social Security Fund (World Bank, 2008a). Registration requirements often vary based
upon sector, legal status (e.g., sole proprietorship, limited liability) and firm size. In some
countries, for example, firms operating as sole proprietorships are not required—and in some
cases not even allowed—to register with the company registrar.78
This can make it difficult to
define exactly what constitutes registration even within a single country, let alone in a way that
allows comparisons across countries. For example, if a small trading company has a trading
license issued by the local authorities but is not registered to pay taxes, would this be enough to
make the firm ‗formal‘? Similarly, if a small company that falls fractionally above the sales
limit for the value-added tax is registered with the company registrar, has a tax identification
number and a trading license, but is not registered for the VAT, would this make it informal?
How about if the same firm fell fractionally below the sales limit for the VAT?
Similarly, a definition based upon compliance with rules associated with taxation and
regulation would also be problematic in some ways. That is, it is not immediately clear what
level of non-compliance would make a firm ‗formal‘ or ‗informal‘. Many firms fail to comply
with all written regulations, especially when some laws and regulations are enforced selectively,
and tax evasion is also common. On the other hand, many firms will comply with some
regulations (i.e., local zoning regulations or payment of some local fees) while still being largely
informal. Knowing exactly where to draw the line is difficult.
This appears to be the case in Tanzania, with many ‗informal‘ firms having some form of
license from the local authority, being registered as a sole proprietorship or being registered
under a business name. In 2005, the Instituto Libertad y Democracia conducted an informal
business survey in Dar es Salaam (400) over 90 percent of all companies interviewed reported
that this was the case. However, as in many low-income countries, most operators are not fully
compliant with the laws. Traders often operate close to market opportunities rather than from
their registered location and businesses registered under a business name might not have all
required licenses.79
Even when a definition could be agreed upon (e.g., registration with the tax authorities or
payment of VAT), it is difficult to measure many of these things. One problem is that it is
116
difficult to locate ‗informal firms‘. In particular, sampling frames based upon official lists from
government agencies are likely to undersample ‗informal firms‘ almost by definition. Moreover,
government lists are likely to be out-of-date due to rapid turnover in the informal sector. The
difficulty of locating firms without fixed premises will make sampling informal firms even more
difficult. As a result, most attempts at sampling informal firms rely on some type of area
sampling—either of households or firms. But even this can be difficult. Many individuals are
involved in small-scale part-time side activities that involve trading goods and services for cash
or other goods or services that would not be considered full-scale businesses even if they provide
some cash or bartered goods or services.
Even after getting an appropriate sampling frame and defining a firm or business
appropriately, it is difficult to gather information on informal behavior. Not unsurprisingly, firm
managers are often unwilling to discuss sensitive issues such as registration and compliance with
tax laws and other regulations. Questions on non-compliance with tax laws, regulation and
registration requirements are likely to overestimate compliance—managers of firms that do not
comply are probably more likely to either refuse to answer or to lie than managers of firms that
do comply. Although, in theory, it would be possible to demand to see evidence of tax
registration, for example, it is difficult to do this practically in an interview format—respondents
are often unwilling to be challenged on issues such as these. Such demands are likely to have
reputational risks for the organization performing the interviews, to undermine participation and
to make the interviewee uncooperative on other issues.
Because of these issues, rather than relying on a single definition to define informality,
this chapter uses several approaches. In addition to ensuring that results are not highly
dependent on a single definition of informality, this approach also recognizes that informal
behavior lies along a continuum rather than being a single dimension. Many ‗informal‘ firms
will be formal in some ways (e.g., registered with the municipality even if unregistered with the
company registrar) and many ‗formal‘ firms will evade some portion of taxes that they should be
paying or regulatory requirements. The comparisons that are made between microenterprises
and SMLEs, are between registered and unregistered microenterprises, and between SMLEs that
are sole proprietorships and SMLEs that are limited liability companies.
II. Microenterprises and SMLEs
For many reasons, including the size of establishments and their expected high rate of
turnover, microenterprises often exhibit a higher level of ―informality‖ than SMLEs do. Hiding
from government officials, such as inspectors and tax officials is far easier for small, young
firms, especially those without a fixed location, than it will be for SMLEs with fixed premises.
As a result, many microenterprises are informal to some degree.
Because of this, comparisons of microenterprises with SMLEs can provide some useful
information on the differential effect that investment climate constraints have on informal and
formal firms. As discussed in the next sections, however, these comparisons can be slightly
misleading since some SMLEs exhibit informal behaviour and many microenterprises are likely
to be formal to some degree. That said, however, these comparisons provide a useful starting
point for the analysis.
117
For the most part, managers of microenterprises and SMLEs had similar concerns about
the investment climate (see Chapter 3). Managers of both types of firms were more likely to say
that electricity was a serious problem than any other area of the investment climate and were
most likely to say it was the most serious problem. Further, many managers of both types of
enterprise said that access to finance, macroeconomic instability and competition with informal
firms were serious problems. Similarly, few managers of either type of enterprise said that most
areas of regulation, political instability, the judiciary, or worker education were serious
problems.
There were some differences, however. Most notably, fewer microenterprise managers
said that tax rates were a serious obstacle—about one-fifth of microenterprise managers
compared to about one-third of SMLE managers. The lower level of concern about tax rates is
consistent with the idea that informality and tax evasion are more common among
microenterprises.80
Differences in perceptions can be due to differences in outcomes, but can also be due to
differences in expectations. For example, if microenterprise managers do not realistically expect
to be able to get loans, they might be less likely to say that access to finance is a serious problem
than managers of larger enterprises even if access is more difficult for them. It is therefore
useful to look at objective indicators of the investment climate as well as perceptions.
Although the small sample size among microenterprises makes it difficult to find
statistically significant differences between microenterprises and SMLEs, most of the differences
are consistent with the idea that microenterprises are less productive and sophisticated than
SMLEs. Microenterprises were far less likely to keep audited accounts, were younger, were less
likely to use e-mail or their own website, were less likely to have generators or their own
transportation and their managers were less likely to be university educated (see Table 29).
The average age of the microenterprises was only 6 years old—only about half of the
average age of firms in the SMLE sample. In comparison, the average microenterprise was close
to 8 years old in Uganda. Although this emphasizes the instability of many small enterprises, it
also emphasizes that many of the microenterprises had been operating for a substantial time.
More that three-quarters of the sample were over two years old and close to 10 percent were over
ten years old.
118
Table 29: Differences between SMLEs and microenterprises with respect to objective variables.
SMLEs Micro
Firm Characteristics
Has audited accounts (% of firms) 51% 6% ***
Age (years, average) 11 6 ***
Firm exports (% of firms) 5% 5%
Owns land (% of firms) 44% 25%
Percent of land owned by firm (average) 41 25
Annual value added per worker (median, US$)23
$3,006 $1,133 ---3
Annual wage cost per worker (median, US$)23
$797 $260 ---3
Technology Use
Uses e-mail (% of firms) 42% 9% ***
Uses own website (% of firms) 16% 3% *
Workers
Manager has university education (% of firms) 42% 17% ***
Part-time workers (% of workers) 9% 7%
Has training program2 36% 19%
Ave. Worker has primary education (% of firms)2 36% 25%
Infrastructure
Days of power outages (per month, average)1 9 11
Days of water outages (average, per month)12
6.1 2.0 ***
Has generator (% of firms)2 46% 8% **
Uses own transportation (% of firms)2 35% 8% **
Losses during transportation12
1.4 0.7
Crime
Cost of crime (% of sales, average)1 38% 43% **
Cost of security (% of sales, average) 132% 83%
Finance
Has bank accounts (% of firms) 86% 66% ***
Has loan or overdraft (% of firms) 22% 17%
Investment
Has invested in previous fiscal year (% of firms) 52% 37%
Investment (as % of sales, average)1 6% 3%
Tax and Regulation
% of revenue reported to tax authorities (average) 50 44 **
All revenues to tax authorities (% of firms) 29% 26%
Says 'firms like theirs' pay bribes (% of firms) 49% 38% *
Bribes (as % of sales, average) 2.3 1.6 **
Time spent dealing with regulations (average) 5.2 4.7
Number of tax inspections (average)1 3 2 *
Source: World Bank Enterprise Survey.
Note: Data includes firms in all sectors except where noted. 1
Outliers more than 3 s.d. from dictionary dropped. 2
Data only for manufacturing firms. 3 Test for statistical significance omitted.
* Means different at a 10% significance level; ** 5% level; *** 1% level.
As discussed in Chapter 5, access to finance appears to be a greater problem for
microenterprises than for SMLEs. They were less likely to have bank credit (17 percent of
microenterprises compared to 22 percent of SMLEs) and less likely to have bank accounts (66
percent compared to 86 percent). As noted in Chapter 5, however, the gap between
microenterprises and SMLEs is smaller than in most other countries with similar data available.
In some ways, microenterprises face different problems than SMLEs. The burden of
regulation, in particular, appears lower for microenterprises than for SMLEs. Managers report
119
spending less time dealing with government regulations and also report fewer tax inspections. It
is important to note, however, that the burden of regulation is not zero—about half of
microenterprise managers report that they spend some of their time dealing with government
regulations and close to two thirds reported that they were inspected by tax officials at least once
in the previous year. In comparison, about 36 percent of SMLE managers say senior
management spends no time dealing with regulatory requirements and only 15 percent said that
they did not have any required meetings on inspections with the tax authority.
Corruption and informality are often thought to go hand in hand. On the one hand, firms
that don‘t register or don‘t comply with government regulations and tax laws are more
vulnerable to demands for bribes than larger enterprises are. In this respect, microenterprises
might be more vulnerable to demands for bribes than SMLEs are. On the other hand,
microenterprises are less visible to regulators and other bureaucrats and so might be less
vulnerable to bribe demands for this reason. It seems that the second mechanism dominates in
Tanzania—microenterprise managers are less likely to report bribe payments and report lower
bribe payments on average than SMLE managers.
As noted earlier, microenterprise managers were less likely to say that tax rates were a
serious problem than SMLE manager were. Consistent with the idea that this is partly due to tax
evasion, microenterprise managers were also less likely to say that ‗enterprises like theirs‘
reported all their revenues to the tax authorities and estimated that on average ‗enterprises like
theirs‘ reported only 44 percent of revenues to the authorities compared to 50 percent for SMLE
managers.
Access to Infrastructure
Less than half of micro firms in Tanzania reported reliable access to basic infrastructure
services (see Figure 56). Although 94 percent of microenterprises with a permanent location
reported having an electricity connection, 75 percent complained of power outages, and even
though 49 percent had a water connection, 18 percent said they had insufficient water supply for
production. Only 46 percent had a public sewage connection, and 20 percent reported a mainline
telephone connection. Nevertheless, establishments seemed to cope with substitute services, as 8
percent owned or shared power from a generator, and 83 percent used cell phones. In addition,
88 percent operated in a permanent, non-movable structure (though 23 percent said the space
was the owner‘s house).
120
Productivity
The Enterprise Survey collected some basic information on the performance of
microenterprises in Tanzania. Because of concerns about comparability, the analysis is restricted
to microenterprises in the manufacturing sector. The productivity data used in this section needs
to be treated with some care. Data were collected for only a relatively modest number of
microenterprises and the quality of productivity data is generally lower for the microenterprises
in the microenterprise survey, most of whom do not collect as detailed accounting data, than it is
for SMLEs in the SMLE survey. Although it is important to keep the data limitations in mind,
the data do provide some interesting comparisons.
Microenterprises in Tanzania are far less productive than SMLEs. The median
microenterprise in the manufacturing sector produced about $1,100 of output per worker—only
about one-third of the output of the median SMLE. As a result, wages are far lower on
average—about $260 per worker per year compared to about $800 per worker per year for
SMLEs
Labor productivity is lower in almost all of the countries in Sub-Saharan Africa where
compared data are available. The gap is large in middle income economies, although it is also
large in Kenya—a low-income country with a relatively productive modern sector. The gap in
productivity between SMLEs and microenterprises, however, is particularly large in Tanzania.
As discussed in Chapter 3, although manufacturing SMLEs are less productive in Tanzania than
in the middle-income economies and the best performing low-income economies in Sub-Saharan
Africa, they are more productive than in many low income economies. In contrast, there is little
difference in productivity for microenterprises between the low-income economies—labor
Figure 56: Most microenterprises are based in permanent structures, but less than half have access to
reliable infrastructure.
Source: World Bank Enterprise Survey.
0
25
50
75
100
Ele
ctr
icity
Wate
r
Pu
blic
se
wa
ge
Ma
inlin
e
tele
ph
on
e
co
nn
ectio
n
In o
wn
er's
ho
use
In
pe
rma
ne
nt
str
uctu
re
% o
f firm
s
% of microenterprises with utility connections and in permanent locations
121
productivity is between about $1000 and $1300 of value added per worker per year for median
microenterprises in most countries.
III. Registered and Unregistered Microenterprises
Although many microenterprises will behave informally to some degree, even among
microenterprises there are likely to be significant differences in the level of informality that they
exhibit. The microenterprise survey, which covers establishments with less than 5 employees,
provides some useful additional information on informality.
Registration Status
One set of questions in the microenterprise survey ask microenterprise managers whether
they are registered with various government institutions. Although, as discussed above,
responses should be treated cautiously given that firm managers have an incentive to be less than
fully truthful when responding to these questions, many managers report not being registered
with all agencies. The survey asks firms whether they are registered with any one of the
following institutions:
i) The Office of the Registrar or other government institution responsible for approving
company names
ii) The Office of the Registrar, the local courts, or other government institutions
responsible for formally registering enterprises
iii) Any municipal agency for an operating, trade or general business license
iv) The tax administration or other agency responsible for tax registration (e.g., if they
have obtained a tax identification number).
Figure 57: The difference between microenterprises and SMLEs in terms of productivity is greater in
Tanzania than in most other low income countries.
Source: World Bank Enterprise Surveys.
$0
$4,000
$8,000
$12,000
$16,000
Valu
e a
dded p
er
work
er
(2005 U
S$)
SMLEs Microenterprises
122
Most of the microenterprises reported that they were registered with at least one agency,
although 15 firms (23 percent) had no type of registration. Microenterprises were most likely to
report that they were registered with the municipal authorities—about three quarters of
microenterprises reported having a municipal license. In addition, about half of firms claimed
that they were registered with the tax authorities. Fewer firms reported being registered with the
Registrar of Companies or to having a trade name registered.
It is important to note that this probably overestimates the level of formality in the
economy. If there are concerned about the confidentiality of their responses, are concerned that
if they admit to being unregistered that the authorities might make them become formal or are
merely reticent about admitting that they are operating in the grey economy, it is possible that
managers will not admit that they are not registered during the interview process. Because of
concerns about tax penalties, managers of firms that are not registered to pay taxes might not be
willing to admit that they are not registered to do so. In summary, although these self-reported
numbers should be treated with caution, this suggests that many microenterprises might be at
least partly formal.
Reasons for Not Registering
Registering can be both costly and time consuming for microenterprises (see Box).
Microenterprise managers were asked what they saw as the biggest about the barriers to
becoming formal. The question was asked to managers who reported that their firm was
registered and to managers that reported their firm was not. In general, microenterprise
managers were most likely to say that taxes and the financial cost of registration were the most
serious obstacles related to registration (see Figure 59). About 36 percent of microenterprise
Figure 58: Most micro-enterprises report being registered with at least one public agency.
Source: World Bank Enterprise Survey.
0
25
50
75
100
Company name registered
Commercially registered
Tax registration Municipal license
% o
f firm
s
% of firms registered with various government agencies
123
managers said that tax rates were a serious obstacle and about 30 percent said that the
administrative burden of complying with taxes was a problem.
Box: A milk kiosk in Dar es Salaam: A case study
Bahati milk kiosk (BMK) is a small milk shop located at a busy street corner in down town Dar es Salaam. The
owner is a single mother with two young children, who started BMK as a backyard enterprise to supplement her
cash income. Each day she would buy about 20 liters of raw milk from a hawker who ferried them in on a bicycle
from suburban farmers about 10 kilometers outside of Dar. BMK‘s initial investment was a charcoal stove, a
casserole, several thermos flasks, plastic mugs, a small table and two sittings forms for customers (about 48,000
Tanzanian shillings or US$30). The entrepreneur raised the money by participating in a ROSCA. BMK‘s main
products were hot milk and milk with coffee. Within a year BMK became a popular destination for young people in
the evening and sales soared. Soon the owner had purchased about 70 liters of milk a day from several vendors.
One day she was served with a notice from the local government to get a business license or close BMK. The
process was lengthy and costly:
1. Inspection of the premises by health inspectors (3 days of follow-up plus taxi fare for the inspectors i.e. about Tsh
20,000).
2. Putting up wall tiles, a wash basin, water heater and plumbing (28 days of supervision plus TSH 400,000 for
materials and labor).
3. Purchase of a batch pasteurizer (biomass fuel, TSH 300,000), a utensil cabinet with glass front (TSH 35,000), a
deep freezer (TSH 600,000), 2 plastic tables and 8 plastic chairs (TSH 360,000).
7. Submitting a profit and loss statement to TRA to determine provisional tax (TSH 50,000 Accountant fee).
8. Obtaining a Taxpayer payer Identification number from TRA with a provisional tax of TSH 300,000 a year.
9. Obtain a license from the local government (Tsh 30,000).
It took nearly half a year to get all the necessary permits. Since BMK was operating illegally during this period,
rent-seeking from enforcers cost BMK about TSH 180,000. When the process was complete, the enterprise was
formally launched, attracted more customers and increasing its sales to 200 liters a day within year. At this point the
owner started to process and pack both fresh and cultured milk, employing 6 persons.
The batch pasteurizer and deep freezer were loaned from a hire purchase organization for women entrepreneurs with
30 percent interest rate per year. The load required collateral of a cash deposit equivalent to 15 percent of the loan
and the equipment and references from two referees. Repayment was in monthly installments. Despite these costs,
BMK remained profitable after it had completed the registration process.
Problems associated with taxes are discussed in more detail in Chapter 6. It is worth
noting, however, that although Tanzania compares favorable with other countries in the region
with respect to both the tax rate (45 percent of profits including all taxes) and the time it takes to
complete tax forms (172 hours per year), this partly reflects the poor performance of other
countries in Sub-Saharan Africa in this respect. Overall, Tanzania ranks 109th
out of 181
countries with respect to taxes in the Doing Business report.
Managers were also concerned about the registration process. Over a quarter of firm
managers said that the financial cost of registration was a serious concern. In contrast to the
questions about the financial cost of registration, there were far less concern about the non-
financial aspects of registration. In particular, few firms said that either the time to register or
the availability of information about registration were serious problems.
124
The Doing Business report presents detailed information on the financial and time costs
of business registration throughout the world (World Bank, 2008a). As of the beginning of 2008,
the 12 procedures associated with registration took 29 days to complete and the total cost was
41.5 percent of per capita gross national income. The most costly procedure was getting the
certificates of incorporation and commencement from the registrar of companies (see Table 30).
Based upon this, the Doing Business report ranked Tanzania as 109th
out of 181 countries with
respect to starting a business.
Figure 59: Microenterprise managers were most likely to say that the financial and administrative burden
associated with taxation was a serious constraints to registering,
Source: World Bank Enterprise Survey.
0%
5%
10%
15%
20%
25%
30%
35%
40%P
oo
r in
form
atio
n o
n
reg
iste
rin
g
La
bo
r re
gu
latio
n
Tim
e to
re
gis
ter
Oth
er
ad
min
istr
ative
b
urd
en
Min
imu
m
ca
pita
l re
qu
ire
me
nts
Cost o
f re
gis
tra
tio
n
Ta
x
ad
min
istr
atio
n
Ta
x r
ate
s
% o
f firm
s
% of firms that said that each area discouraged registration
125
Table 30: Time and Cost to Start a Business in Tanzania.
Procedure
Time to
complete
:
Cost to complete:
1. Apply for clearance of the proposed company name at the Registrar of
Companies 1 day no charge
2. Apply for a certificate of incorporation and of commencement to Registrar
of Companies 7 days TZS 206,200
3. Apply for TIN with the Tanzania Revenue Authority 2 days no charge
4. Income tax officials inspect the office site of the new company* 1 day, no charge
5. Apply for Pay As You Earn (PAYE) with the Tanzania Revenue Authority* 1 day, no charge
6. Apply for business license from the regional trade officer (depending on the
nature of business) 7 days no charge
7. Have the land and town-planning officer inspect the premises and obtain his
signature* 1 day transport cost, trivial
8. Have the health officer inspect the premises and obtain his signature* 1 day transport cost, trivial
9. Apply for VAT certificate with the Tanzania Revenue Authority 4 days no charge
10. Receive VAT/stamp duty inspection 1 day no charge
11. Register for the workmen‘s compensation insurance at the National
Insurance Corporation or other alternative insurance policy 1 day no charge
12. Obtain registration number at the National Social Security Fund (NSSF) 7 days no charge
Source: World Bank (2008a).
* Simultaneous with previous action.
The Doing Business report focuses on procedures with Dar es Salaam. The burden of
registration, however, can be even greater for firms outside of Dar es Salaam. A 2002 study by
UNDP/International Labor Organization (ILO)/United Nations Industrial Development
Organization (UNIDO) noted that at that time there was only one business registration office in
the entire country, meaning all entrepreneurs had to travel to Dar es Salaam to obtain licenses.
Similar problems were observed in a 2007 study by the World Bank (World Bank, 2007c), which
noted that procedures associated with incorporation and getting clearance for the company name
took far longer outside of Dar es Salaam (i.e., steps 1 and 2 in Table 30). Whereas these two
steps take about 8 days in Dar es Salaam (see Table 30), they took between 10 and 23 days in the
eight locations outside of Dar covered in the World Bank report. In five of the eight locations
(all but Zanzibar, Mbeya, and Mwanza), the required forms were not available locally.
Moreover, even when available locally, the forms still had to be submitted in the capital, calling
for a costly journey to Dar es Salaam.
Despite this, the microenterprises outside of Dar es Salaam did not generally say that the
time needed to complete registration procedures was burdensome. Only 13 percent of
microenterprises outside of Dar es Salaam said that the time to complete registration procedures
was a serious barrier to registering and only 15 percent said that getting information on
registration procedures was a serious barrier. This was, however, higher than in Dar es Salaam.
Only eight percent of the firms in Dar es Salaam said that the time to complete procedures was a
serious obstacle and none said that gathering information on procedures was a serious obstacle.
Firms that were not registered were generally more likely to say that each area was a
significant obstacle than firms that were not registered. For example, about 46 percent of firms
that were not registered said that taxes were a serious obstacle for registering compared to only
23 percent of firms that were registered. However, the relative rankings were similar. In
126
particular, tax rates, tax administration and the financial cost of registration were the greatest
concerns for both registered and unregistered firms.
Previous studies have also noted the burden of regulation can be high for informal
enterprises. A 2002 study by UNDP/ILO/UNIDO, which followed up on a previous similar
study by United States Agency for International Development (USAID), attempted to identify
the main regulatory constraints on micro and small entrepreneurs in Tanzania. The studies found
difficult regulatory hurdles in the areas of reporting, regulatory environment, business locating,
and hiring, discouraged many informal businesses from registration.
Although Tanzania also ranks relatively poorly with respect to employing workers in the
Doing Business report—140th
out of 181 countries in the 2009 report—microenterprises were
less likely to say that restrictive labor regulation discouraged firms from registering. Labor
restrictions are outlined in the Tanzanian constitution. Any work by children is prohibited as is
nighttime work by youth.
Differences between registered and unregistered microenterprises
Although, as discussed above, it is likely that some of the firms that claim to be registered
might not be, it is interesting to compare registered and unregistered enterprises with respect to
the objective variables that were compared for microenterprises and SMLEs. Keeping this in
mind and noting that the small sample size makes it difficult to find statistically significant
differences, it is interesting to note that in many ways the self-reported unregistered
microenterprises appear different from self-reported registered microenterprises (see Table 31).
In particular, self-reported unregistered firms are less likely to have audited accounts, are
less likely to export, are less likely to own their own land, use e-mail and the world wide web
less intensively, have less well educated managers, are less likely to have bank accounts and
loans, and invest less than registered firms do. They were also less likely to have generators. As
with the comparisons between microenterprises and SMLEs, this suggests that the unregistered
are less sophisticated and probably less productive than registered microenterprises.
127
Table 31: Differences between registered and unregistered microenterprises with respect to objective
variables.
Registered Unregistered
Firm Characteristics
Has audited accounts (% of firms) 15% 0% **
Age (years, average) 5 7
Firm exports (% of firms) 8% 3%
Owns land (% of firms) 27% 24%
Percent of land owned by firm (average) 27 24
Technology Use
Uses e-mail (% of firms) 19% 3% **
Uses own website (% of firms) 4% 3%
Workers
Manager has university education (% of firms) 23% 13%
Part-time workers (% of workers) 7% 8%
Infrastructure
Days of power outages (per month, average)1 13 10
Crime
Cost of crime (% of sales, average)1 0.35 0.48
Cost of security (% of sales, average) 1.52 0.36 ***
Finance
Has bank accounts (% of firms) 69% 64%
Has loan or overdraft (% of firms) 19% 15%
Investment
Has invested in previous fiscal year (% of firms) 46% 31%
Investment (as % of sales, average)1 5% 1% **
Tax and Regulation
% of revenue reported to tax authorities (average) 51 39
All revenues to tax authorities (% of firms) 31% 23%
Says 'firms like theirs' pay bribes (% of firms) 42% 36%
Bribes (as % of sales, average) 2.1 1.3
Time spent dealing with regulations (average) 8 2 **
Number of tax inspections (average)1 2 1 **
Source: World Bank Enterprise Survey.
Note: Data includes firms in all sectors except where noted. Because of the small number of observation, variables
for which data were only available for manufacturing firms are omitted. 1
Outliers more than 3 s.d. from dictionary
dropped. * Means different at a 10% significance level; ** 5% level; *** 1% level.
There is also some evidence that unregistered firms manage to avoid the burden of
regulation. The average manager of an unregistered microenterprise reported spending about 2
percent of their time dealing with regulations and reported one tax inspection per year. In
comparison, the average manager of a registered firm report spending 8 percent of time dealing
with regulation and report 2 tax inspections per year. More than half of the managers of
unregistered microenterprises reported spending no time dealing with regulatory requirements—
compared to about 40 percent of registered microenterprises.
Tax evasion also appears to be higher among unregistered firms. On average, managers
of microenterprises reported that they believe that ‗firms like theirs‘ report about 44 percent of
revenues to the tax authorities. But managers of unregistered firms reported that ‗firms like
theirs reported less than 40 percent of revenues to the authorities compared to over 50 percent for
managers of registered microenterprises. This is consistent with the idea that the unregistered
firms are less formal than their registered counterparts.
128
IV. Sole Proprietorships and Limited Liability Companies
The SMLE survey did not include any information on whether the firm is registered or
not. The reason for the omission is that it is assumed that most SMLEs will be visible enough to
have to register with at least some government agencies. Moreover, given that the sampling
frame and the list of firms used for sampling were obtained from government agencies—the
National Bureau of Statistics in for Mainland Tanzania and from the Office of the Chief
Statistician for Zanzibar—this is probably not an unreasonable assumption.
Although it is not possible, therefore, to divide the SMLE sample into ‗registered‘ and
‗unregistered‘ firms like it was for the microenterprise sample, it is possible to separate limited
liability companies (LLCs), most of which are privately held, from unlimited liability firms,
which include sole proprietorships and partnerships. Limited liability can be seen as another step
toward more formality as in involves further separation of individual ownership and the firm
identity. Moreover, registering an LLC is more time consuming and costly than registering a
sole proprietorship. In Tanzania, registering as a sole proprietorship only requires a business
license and company registration, whereas LLCs require more detailed information including
Memorandum and Articles of Association and additional information including the list of
directors, details of nominal shares, particulars of the director or managers.81
The sole proprietorships do appear to be different from the limited liability companies
(see Table 32). They are less likely to have audited accounts, are younger, are less likely to
export, are less likely to own land, are less likely to use e-mail or have their own website, less
likely to have generators or their own transportation, and are less likely to have university
educated managers. They were also less likely to have bank accounts or loans.
They are also, on average, less productive than limited liability companies. The median
limited liability company produces about $6,000 of output per worker compared to about $2,000
of output per worker for the median sole proprietorship. This was higher than the median
microenterprise, however (see Table 29). They were also less likely to invest and invested less
on average than limited liability companies—but more than the average microenterprise.
Although they were less productive than limited liability companies, there is less
evidence that they are less formal. In particular, the average sole proprietorship said that ‗firms
like theirs‘ reported about 48 percent of their output to the tax authorities compared to 47 percent
for the average limited liability companies. The average sole proprietorship did report that
senior management spent only 4 percent of their time dealing with government regulations
compared to 6 percent for the average limited liability company. The difference, however, is not
statistically significant.
129
Table 32: Differences between limited liability companies and sole proprietorships with respect to objective
variables.
LLCs Sole Proprietorship
Firm Characteristics
Has audited accounts (% of firms) 81% 28% ***
Age (years, average) 13 9 ***
Firm exports (% of firms) 7% 1% ***
Owns land (% of firms) 65% 33% ***
Percent of land owned by firm (average) 61 30 ***
Annual value added per worker (median, US$)23
$6,083 $1,908
Annual wage cost per worker (median, US$)23
$1,114 $689
Technology Use
Uses e-mail (% of firms) 56% 27% ***
Uses own website (% of firms) 27% 7% ***
Workers
Manager has university education (% of firms) 59% 26% ***
Part-time workers (% of workers) 14% 6% **
Has training program2 45% 30% **
Ave. Worker has primary education (% of firms)2 38% 32%
Infrastructure
Days of power outages (per month, average)1 9 10
Days of water outages (average, per month)12
6.6 4.5
Has generator (% of firms)2 64% 27% ***
Uses own transportation (% of firms)2 54% 18% ***
Losses during transportation12
1.8 1.0 **
Crime
Cost of crime (% of sales, average)1 0.42 0.38 ***
Cost of security (% of sales, average) 1.49 1.10 ***
Finance
Has bank accounts (% of firms) 94% 81% ***
Has loan or overdraft (% of firms) 30% 14% ***
Investment
Has invested in previous fiscal year (% of firms) 64% 35% ***
Investment (as % of sales, average)1 7% 4% **
Tax and Regulation
% of revenue reported to tax authorities (average) 47 48
All revenues to tax authorities (% of firms) 23% 27%
Says 'firms like theirs' pay bribes (% of firms) 44% 46%
Bribes (as % of sales, average) 2.4 1.8
Time spent dealing with regulations (average) 6 4
Number of tax inspections (average)1 3 3
Source: World Bank Enterprise Survey.
Note: Data includes firms in all sectors except where noted. 1
Outliers more than 3 s.d. from dictionary dropped. 2
Data only for manufacturing firms. 3 Test for statistical significance omitted.
* Means different at a 10% significance level; ** 5% level; *** 1% level.
V. Competition with the Informal Sector
One reason why informality is a concern is the effect that it has on government‘s ability
to achieve social goals through both direct spending and regulation. Since informal firms evade
taxes, informality erodes the fiscal base resulting in lower government revenues and a higher tax
burden on formal firms that do comply with tax laws. Moreover, to the extent that informal
130
firms do not comply with government regulations, it can undermine other government policies
and ultimately reduce trust in the rule of law and government effectiveness.
But informality can also be a problem for firms that do pay their taxes and comply with
government regulations. Since informal firms avoid the cost of complying with laws and
regulations, they have an unfair advantage over formal firms. That is, inefficient informal firms
can survive and even drive more competitive formal firms out of business by avoiding the costs
associated with taxation and regulation.
Although competition with informal firms was a lesser concern than electricity or access
to finance, it remains a relatively serious concern. About 29 percent of SMLEs and about 25
percent of microenterprises, some of whom might also be informal, said that competition from
the informal sector was a serious constraint on doing business. It is notable that managers of
microenterprises were less likely to say competition with the informal sector than managers of
SMLEs were. This is not the case in most countries where similar Enterprise Surveys have been
conducted. For example, about 48 percent of microenterprise managers in Uganda said that
competition from the informal sector was a serious constraint on doing business, compared to
only 39 percent of SMLE managers (Regional Program on Enterprise Development, 2008d).
This does not appear to be because of differences in observable characteristics between firms in
the microenterprise and SMLE samples—the difference remains statistically insignificant even
after controlling for other things that might affect perceptions about competition with the
informal sector (see Chapter 3 and Appendix 7.1).
The section provides a summary of the firms that appear to be most affected by
competition with the informal sector. Appendix 7.1 provides a more detailed econometric
analysis of these same issues.
Size
In many countries, concern about competition with informal firms decreases consistently
with size. This does not appear to be the case in Tanzania. As noted above, microenterprise
managers were no more likely that SMLE managers to say that competition with informal firms
was a serious problem were. In fact, overall, size does not appear to be a major factor in firm
complaints about competition with informal firms (see Figure 60). After controlling for other
things that might affect perceptions, managers of smaller firms were no more likely to say that
competition with the informal sector was a problem than managers of large firms were. In fact,
managers of microenterprises and very small enterprises were less likely to say that competition
with informal firms was a problem (about 25 percent of firms in these size categories) than
managers of small and medium-sized enterprises (about 35 percent).
Managers of large enterprises were the least likely to say that competition with informal
firms was a serious problem—only about 17 percent of managers of large enterprises said this.
This might be because large firms are far more productive than their smaller competitors. It is
important to note that what concern there is about informality among large enterprise managers
might at least partly reflect concern about counterfeit goods. In many countries in the region,
this has become a serious problem.82
131
Limited Liability Companies
As discussed above, limited liability can be seen as a step towards increased formality—
registering as a limited liability company is more costly in terms of both time and money than
becoming a sole proprietorship. Consistent with the idea that these firms are more ‗formal‘ than
other firms, managers of limited liability firms were far less likely to say that competition with
the informal sector was a serious concern than managers of other firms were—about 21 percent
of limited liability SMLEs compared to 29 percent of other SMLEs. This was also true for
microenterprises. Only about 17 percent of managers of limited liability microenterprises said
that competition with the informal sector was a serious concern compared to 26 percent of
microenterprises that were sole proprietorships.
Figure 60: Small and medium-sized enterprises were slightly more concerned about informality than other
firms, but size is not a significant driver of complaints.
Source: World Bank Enterprise Survey.
0%
10%
20%
30%
40%
% o
f firm
s that
said
info
rmal secto
r is
serious p
roble
m
% of firms that said the competition with informal sector is serious problem
132
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APPENDICES
Appendix 1.1: Enterprise Survey in Tanzania—Survey Design
Provided by EEC Canada
Survey coverage
The World Bank Enterprise Survey in Tanzania targeted establishments located in Dar-
es-Salaam, Arusha, Mbeya, and Zanzibar in the following industries (according to ISIC, revision
3.1): all manufacturing sectors (group D), construction (group F), retail and wholesale services
(sub-groups 52 and 51 of group G), hotels and restaurants (group H), transport, storage, and
communications (group I), and computer and related activities (sub-group 72 of group K). For
establishments with five or more full-time permanent paid employees, this universe was
stratified according to the following categories of industry:
1. Manufacturing: Food and Beverages (Group D, sub-group 15);
2. Manufacturing: Garment (Group D, sub group 18);
3. Manufacturing: Other Manufacturing (Group D excluding sub-groups 15 and 18);
4. Retail Trade: (Group G, sub-group 52);
5. Rest of the universe, including:
Construction (Group F);
Wholesale trade (Group G, sub-group 51);
Hotels, bars and restaurants (Group H);
Transportation, storage and communications (Group I);
Computer related activities (Group K, sub-group 72).
The survey also sampled a selection of micro establishments (establishments with less
than five full-time permanent paid employees) from the targeted universe, without stratification
by industry.
Sampling methodology
Different sampling methodologies were used for the two samples, microenterprises with
less than 5 employees and small, medium and large enterprises (SMLEs) with five or more full-
time permanent employees.
SMLEs
Establishments with five or more full-time paid permanent employees A satisfactory list
of establishments was sourced from the National Bureau of Statistics in for Mainland Tanzania
and from the Office of the Chief Statistician for Zanzibar.83
These lists were merged together
into a master list which was used to establish the initial population size, and then to set the target
143
sample size for each stratum. During the survey period, the master list was updated as new
information regarding establishments that had closed or were out-of-scope was gathered. The
final population size in all strata and locations was 7,300, with the vast majority of
establishments operating in the rest of the universe, and manufacturing strata.
In Tanzania, the survey includes panel data collected from establishments surveyed in the
2003 PICS in Tanzania. That survey included establishments in all three manufacturing strata
distributed across the entire country. In order to collect the largest possible set of panel data, an
attempt was made to contact and survey every establishment in the panel, provided it was located
in one of the four cities covered by this survey and operated in the universe under study. The
remainder of the sample (including the entire rest of universe and retail sample in each city) was
selected at random from the master list by a computer program.
In addition to the firm-level survey, individual-level data was collected from workers
matched in half of the sampled firms in the manufacturing sector. The firms included in the
worker sample were randomly selected from the original list of firms. Up to 10 workers were
selected in each of the firms in the worker sample. To the extent possible, workers in each firm
are selected randomly. Ideally, this is done from a list when it is available. If not, workers are
selected by walking through the work area and selecting workers randomly from throughout the
work area.
Microenterprises
The microenterprise stratum covers all establishments in the targeted categories of
economic activity with less than 5 employees. Because of the small size of establishments, their
expected high rate of turnovers, the high level of informality of establishments in these size
categories, it is difficult to obtain trustworthy information on firms from official sources. For
these reasons, and to ensure that informal (i.e., unregistered) enterprises were included in the
sample, EEC Canada used an area sampling approach to estimate the population of
establishments and select the sample in this stratum.
The main steps in this approach are to:
i) select districts and specific zones of each district where a large number of
microenterprises operate;
ii) count all micro establishments in these specific zones;
iii) based on this count, create a virtual list and select establishments at random from that
virtual list;
iv) based on the ratio between the number selected in each specific zone and the total
population in that zone, create and apply a skip rule for selecting establishments in
that zone.
The districts and the specific zones were selected after discussions with national sources
including business associations and the National Bureau of Statistics. The EEC team then went
in the field to verify these sources and to count microenterprises. Once the count for each zone
was completed, the numbers were sent back to EEC head office in Montreal, where the count
was converted into one list of sequential numbers for the whole survey region, and a computer
144
program performed a random selection of the determined number of establishments from the list.
Then, based on the number that the computer selected in each specific zone, a skip rule was
defined to select micro establishments to survey in that zone. The skip rule for each zone was
sent back to the EEC field team.
In Tanzania, enumerators were sent to each zone with instructions as to how to apply the
skip rule defined for that zone as well as how to select replacements in the event of a refusal or
other cause of non-participation.
145
Population and sample size
Table 33: Population size by stratum and sampling region
Dar es Salaam Arusha Mbeya Zanzibar Total
Manufacturing 1,287 179 66 407 1,939 Food and beverages 146 30 4 95 275 Garments 293 35 39 27 394 Other manufacturing 848 114 23 285 1,270 Retail 1,227 154 53 220 1,654 Rest of the universe 2,612 529 151 415 3,707 Micro 73,830 9,624 4,188 12,535 100,177
Total 78,956 10,486 4,458 13,577 107,477
Table 34: Final sample size by stratum and sampling region
Dar es Salaam Arusha Mbeya Zanzibar Total
Manufacturing 188 30 10 45 273 Food and beverages 37 13 2 18 70 Garments 43 1 1 6 51 Other manufacturing 108 16 7 21 152
Retail 43 7 6 9 65 Rest of the universe 55 8 8 10 81 Micro 43 5 9 8 65
Total 329 50 33 72 484
146
Table 35: Approached, refused, unavailable, and surveyed by stratum and sampling region
Dar es Salaam Arusha Mbeya Zanzibar Total
App. Ref.
Unav
ail. Surv. App. Ref.
Unav
ail. Surv. App. Ref.
Unav
ail. Surv. App. Ref.
Unav
ail. Surv. App. Ref.
Unav
ail. Surv.
Manufacturing 252 57 7 188 42 12 0 30 12 2 0 10 47 2 0 45 353 73 7 273 Food and beverages 48 11 0 37 16 3 0 13 4 2 0 2 19 1 0 18 87 17 0 70
Garments 43 0 0 43 1 0 0 1 1 0 0 1 6 0 0 6 51 0 0 51
Other manufacturing 161 46 7 108 25 9 0 16 7 0 0 7 22 1 0 21 215 56 7 152 Retail 43 0 0 43 7 0 0 7 6 0 0 6 9 0 0 9 65 0 0 65
Rest of the universe 65 10 0 55 8 0 0 8 8 0 0 8 10 0 0 10 91 10 0 81
Total 360 67 7 286 57 12 0 45 26 2 0 24 66 2 0 64 509 83 7 419
Table 36: Refused, unavailable, and surveyed as percentage of approached by stratum and sampling region
Dar es Salaam Arusha Mbeya Zanzibar Total
App. %
Ref. %
Unav. %
Surv. App.
% Ref.
% Unav
% Surv.
App. %
Ref. %
Unav %
Surv. App.
% Ref.
% Unav
% Surv.
App. %
Ref. %
Unav % Surv.
Manufacturing 252 22.6% 2.8% 74.6% 42 28.6% 0.0% 71.4% 12 16.7% 0.0% 83.3% 47 4.3% 0.0% 95.7% 353 20.7% 2.0% 77.3% Food and beverages 48 22.9% 0.0% 77.1% 16 18.8% 0.0% 81.3% 4 50.0% 0.0% 50.0% 19 5.3% 0.0% 94.7% 87 19.5% 0.0% 80.5%
Garments 43 0.0% 0.0% 100. 0% 1 0.0% 0.0%
100.0% 1 0.0% 0.0%
100.0% 6 0.0% 0.0%
100.0% 51 0.0% 0.0% 100.0%
Other manufacturing 161 28.6% 4.4% 67.08
% 25 36.0% 0.0% 64.0% 7 0.0% 0.0% 100.0
% 22 4.6% 0.0% 95.5% 215 26.1% 3.3% 70.7%
Retail 43 0.0% 0.0% 100.0
% 7 0.0% 0.0% 100.0
% 6 0.0% 0.0% 100.0
% 9 0.0% 0.0% 100.0
% 65 0.0% 0.0% 100.0%
Rest of the universe 65 15.4% 0.0% 84.62
% 8 0.0% 0.0% 100.0
% 8 0.0% 0.0% 100.0
% 10 0.0% 0.0% 100.0
% 91 11.0% 0.0% 89.0%
Total 360 18.6% 1.94% 79.4% 57 21.1% 0.00% 79.0% 26 7.69% 0.00% 92.3% 66 3.03% 0.00% 97.0% 509 16.3% 1.38% 82.3%
147
Table 37: Sample weights by stratum and sampling region
Dar es Salaam Arusha Mbeya Zanzibar Total
Manufacturing 6.85 5.97 6.60 9.04 7.10 Food and beverages 3.95 2.31 2.00 5.28 3.93 Garments 6.81 35.00 39.00 4.50 7.73 Other manufacturing 7.85 7.13 3.29 13.57 8.36 Retail 28.53 22.00 8.83 24.44 25.45 Rest of the universe 47.49 66.13 18.88 41.50 45.77 Micro 1,716.98 1,924.80 465.33 1,566.88 1,541.18
Total 239.99 209.72 135.09 188.57 222.06
148
Appendix 1.2: Comparison of Samples from 2003 and 2006 Surveys
In addition to tracking the current state of the investment climate, one of the goals of this
investment climate assessment is to assess how the investment climate has changed over time.
As discussed in the main report, an earlier Enterprise Survey was conducted in 2003.84
A natural
question is what needs to be done to compare the results from the two surveys. The 2006 survey
was a random stratified sample (see Appendix 1) with weights provided. The 2003 survey was a
random sample, but without any stratification within the manufacturing sector.
A first issue is that the survey coverage is different in the two surveys. In particular,
whereas the 2003 survey only covered manufacturing and covered ten cities, the 2006 survey
covers additional sectors—services and retail trade—as well as manufacturing and only covers 4
urban areas. In addition, a small number of firms in the 2003 sample had less than 5 employees
(18 firms). Problems associated with survey coverage are relatively easy to resolve. To make the
results more comparable, comparisons between the two surveys will only be made for the
manufacturing sector in the areas covered in the 2006 survey and for firms with more than five
employees. Although this improves comparability between the two surveys, this does reduce the
size of the 2003 sample (from about 276 to 158 firms) and also means that the numbers
presented in this report for 2003 will differ slightly from the numbers presented in the 2003
Investment Climate Assessment (Regional Program on Enterprise Development, 2004a).
A second concern is the comparability of the original sample frames for the two surveys.
For the 2006 survey, as described in Appendix, the sample frame was based upon lists provided
by the National Bureau of Statistics. The lists were updated lists based upon the 2003/05
Business Survey—a census of business establishments in fixed locations in urban areas of
Tanzania.85
A stratified sample was drawn based upon this sampling frame and weights were
calculated.
The 2003 survey was also primarily based upon lists provided by the National Bureau of
Statistics. The list provided by the National Bureau of Statistics, however, was less complete
than the list used for the 2006 survey. A business census had not been conducted since 1988 and
so the list provided for that survey was less complete. Although the list provided by NBS was
updated with additional lists provided by local governments and business associations in each
region, the final list is likely to be less complete than the list used for the more recent 2006
survey. This could potentially make it difficult to compare results from the 2003 and 2006
surveys, especially if there are systematic differences in the firms in the two sample frames (e.g.,
if large firms were overrepresented in the 2003 survey).
One way to assess whether the second problem appears to affect the sample is to look at
the distribution of firms in the two samples. Although differences could be due to differences in
the population of firms in 2003 and 2006, it would seem that changes in the population
characteristics would be likely to be relatively modest between 2003 and 2006—especially
because the 2006 sample frame is based upon lists provided by the National Bureau of Statistics
that in part depend on earlier surveys including the 2003/05 Business Survey.
149
There are some similarities between the two samples. About two-thirds of both samples
are from Dar es Salaam, with only a modest number of firms from Mbeya. There are more firms
from Zanzibar in the 2006 survey and fewer firms from Arusha, although the differences are not
large.
But there are some differences. Garment firms are more important in the 2006 samples,
accounting for about one-fifth of firms in the sample compared to about one-twentieth in 2003.
Although, in part, this could reflect an increase in garment exports—garment exports from
Tanzania to the United States increased from $300,000 to $3,000,000 between 2002 and 2006,
most of the garment firms in the sample are small firms with less than 20 employees (80 percent)
concentrated entirely on the domestic market (95 percent).86
Moreover, about 70 percent of the
garment firms reported that they were operational before 2002. In this respect, it seems plausible
that garment firms were underrepresented in the 2003 survey. Similarly, firms in agro
processing appear to be overrepresented.
Another notable difference is that medium and large firms appear to be overrepresented.
About one-third of firms in the 2003 sample had fewer than 20 workers, compared to close to
two-thirds in the 2006 survey. Since small firms are less likely to export, are less likely to be
foreign-owned and are more likely to be indigenously owned, this might explain discrepancies
between the 2003 and 2006 surveys in this respect as well.
Table 38: Sample characteristics of manufacturing firms, 2003 and 2006
Percent of Sample
(Weighted)
Percent of Sample
(Weighted)
2003 2006 2003 2006
Dar es Salaam 68 66 Food 32 14
Arusha 17 9 Garments 4 20
Mbeya 4 3 Other Manufacturing 64 65
Zanzibar 11 21
Any female owner 7 22
Exporters 27 14 Any black owner 48 77
Non-Exporters 73 86 Any white owner 8 5
Any Asian owner 36 21
Micro (less than 5 employees) 0 0 Any Lebanese owner 6 4
Small (5-19 employees) 30 58
Medium (20-99 employees) 44 29 Foreign-owned 20 10
Large (100 and up) 27 12 Domestically owned 80 90 Source: Enterprise Survey.
Overall, these results suggest that the 2003 sample frame might have been less
comprehensive than the 2006 sample frame, possibly omitting some small enterprises. This
makes it more difficult to compare results from the two surveys. To try to reduce problems of
comparability, comparisons between the two surveys will be made by comparing only
manufacturing firms with five or more employees in the regions covered by the 2006 survey. In
addition, differences between the two surveys will often be checked either by comparing results
for subsets of firms (e.g., small firms only) and through regression analysis that controls for
differences between types of firm.
150
Appendix 2.1: Technical Efficiency in Tanzania
Although measures of firm productivity such as labor productivity provide useful
information on firm performance, they can be misleading when considered in isolation. To get
an overall assessment of productivity, it is necessary to take both capital and labor use into
account simultaneously by calculating technical efficiency. This measure is analogous to the
macroeconomic concept of total factor productivity and the terms are often used interchangeably.
Differences in technical efficiency (TE) or total factor productivity (TFP) are those differences in
output that cannot be explained by differences in the use of labor, capital and other inputs.
Differences in TE across firms can be due to things such as differences in the quality of workers,
the quality of management, the technology used (as long as it isn‘t embodied in capital), or firm
organization. Firms for which TE is higher are more efficient—they produce more with fewer
inputs.
In addition to taking into account both capital and labor use, TE has several additional
advantages over labor productivity:
1. Because TE is calculated in a regression framework, it is possible to control for multiple
things when calculating it. For example, when comparing average TE across countries it
is possible to control for differences in sector composition and firm size (by not imposing
constant returns to scale).
2. The regression framework also makes it possible to estimate an augmented production
function to look at differences between different types of firms while controlling for
capital and labor use and other firm characteristics. Controlling for other firm
characteristics is important. For example, exporters tend to be more productive than
other firms. However, if they are more likely to work in some sectors than others—and
there are sectoral differences in productivity—then it is difficult to know whether this is
due to sectoral differences or other differences such as differences in technology. Within
a regression framework it is possible to control for multiple factors (e.g., sector,
ownership, or export status) including investment climate-related factors simultaneously.
Methodology
Mechanically, TE is calculated as a residual from a regression of the log of output (either
value-added or revenue) on labor, capital, and other intermediate inputs. 87
Using a formulation
based upon value-added, the estimation in this chapter assumes a Cobb-Douglas Production
function with a coefficient α on capital and a coefficient β on labor. Constant returns to scale can
be imposed by forcing β=1-α, but this is not done here since it seems that large firms are often
more productive than similar small firms. The formula is therefore:88
iLiii KAY (1)
Where Y is value-added for firm i, K is a measure of capital (e.g., the book value or
replacement value of capital), L is the number of workers and A is total factor productivity.
Constant returns to scale are not imposed allowing the model to essentially control for
151
differences in productivity by firm size. The higher that A is, the more output the firm produces
with the same amount of capital and labor. Taking natural logs of both sides implies that:
iiii lky lnlnln (2)
That is, the firm‘s productivity is equal to a constant, μ, and an additional firm-specific
measure of productivity, εi.89
It is easy to generalize this into a more general ‗augmented‘
production function where the error term is:
iii CDIv iC (3)
This implies that:
iiiii CDlky IC lnlnln i (4)
Where ICi is characteristics of the firm or the investment climate for that firm and CD
are a set of country dummies that are set to ―1‖ if firm i is in that country and ―0‖ if not. The
inclusion of Country Dummies (CDi) controls for country levels differences that might affect the
productivity of all firms in that country. Including country dummies is also useful because doing
so means that the coefficients on other variables will not be affected by exchange rate variation
(i.e., the dummies will control for exchange rates in cross-country regressions where monetary
variables are in logs), exchange rates can make the coefficients on the country dummies difficult
to interpret.
It is also possible to look at these dummies to assess the average level of productivity in
that country relative to other countries. Comparisons must be made carefully because the
coefficients on the country dummies will depend upon the exchange rate as well as on
productivity differences. If a country‘s exchange rate is overvalued relative to its long-run
equilibrium value, the coefficient on that country‘s dummy will appear artificially large (as will
value added per worker and other variables after being converted into a common international
currency).
Under some assumptions, equation (4) can be estimated by Ordinary Least Squares
(OLS). In particular, when firm characteristics are omitted (i.e., when equation (2) is estimated),
the coefficients can be estimated with OLS if capital and labor are uncorrelated with the error
term. That is any shock or firm specific factors that affect productivity must be uncorrelated
with the firms‘ decisions regarding capital and labor choices. This would be violated if, for
example, managers were aware of something that affected productivity and allowed this to affect
their hiring, firing or investment decisions. For example, if a firm received some technical
advice from one of their suppliers or buyers that improved the firm‘s productivity and then the
manager decided to hire more workers to take advantage of this improved know-how, this would
violate this assumption.
Characteristics of the firm or the investment climate for the firm can also be directly
included in the OLS regression as long as these characteristics are exogenous. For example, if
becoming an exporter makes a firm more productive (e.g., through exposure to foreign markets)
then a dummy variable indicating that the firm was an exporter could be included in the
152
regression so long as the causation does not run in the opposite direction. In the case mentioned
above reverse causation could be a problem if a firm became more productive and decided that
this productivity boost meant that it could start exporting.
Rather than including firm or investment climate characteristics directly in the model, it
is possible to first estimate equation (2) through OLS or another more robust estimation method,
obtain estimates of TE by calculating ε for each firm from equation (2) and then regress the
residuals on the firm and investment climate characteristics (e.g., estimating equation (3)). An
advantage of this approach is that it might be possible to estimate equation (2) using a robust
technique such as the method suggested by Levinsohn and Petrin (2003) and then use something
such as two-stage least squares (2SLS) in the second stage if some of the firm or investment
climate characteristics were thought to be endogenous.90
The drawback of this second approach is that if the firm level or investment climate
characteristics are correlated with the labor and capital variables then the estimates of the
coefficients in equation (2) will be biased.91
As a result, the ε‘s will be estimated incorrectly and
the coefficients from the second stage will be biased. It seems likely that this will often be the
case. Escribano and Guasch (2005), argue that ―this is almost always the case since the inputs
are correlated with the Investment Climate (IC) variables and least squares estimators of
[equation 2] are inconsistent and biased.‖ For this reason, estimation is done in a single step in
this report.
A final concern is that for analyses with firms from multiple sub-sectors of
manufacturing, this approach essentially assumes that firms use the same production
technologies. Since the analysis includes firms from more than one sub-sector of manufacturing,
a more flexible estimation technique would allow firms in different sector to use different
production technologies. This can be done mechanically by including a full set of sector
dummies and interacting these dummies with the measures of labor and capital to allow different
technologies in different sectors—that is, this allows labor and capital intensities to different in
different sectors.
The augmented production function then becomes:
ijiji
j
ijjijjjij Dlk CIC loglogVAlog ij (5)
The coefficients on labor and capital, β and γ, are assumed to vary between sectors.
Sector dummies, α,, are also included to allow for systematic differences in productivity across
sectors. In cases where the samples are large enough (i.e., in the regressions to calculate average
TFP levels between countries), the augmented production function is estimated.
Cross-Country Results
A first question is how TFP in Uganda compares to TFP in other countries in Sub-
Saharan Africa. This can be doen by estimating equation (5) above including firms from all
countries in the region and looking at the country dummies. Because TFP does not have any
natural units, TFP is calculated as the difference between that country and Uganda.
153
Tanzania compares quite favorably with other countries in Sub-Saharan Africa with
respect to total factor productivity. TFP is higher than in many other countries in SSA and than
most of the regional comparators (see Figure 18). For example, it is about 40 percent lower for
the median firms in Rwanda and Burundi and about 35 percent lower in Uganda. TFP is about
41 percent higher for the median firm in Kenya. All of these differences are statistically
significant at conventional significance levels. TFP is lower in Tanzania than in the most
successful manufacturing countries in Africa such as South Africa, Mauritius and Swaziland.
Changes in Productivity over Time
Has manufacturing productivity in Tanzania improved over time? Ideally, this question
would be answered with census data. This would make it possible to control for entry and exit
and to control for changes as production shifts from more to less productive sectors. This
information, however, is not available for Tanzania. In the absence of such information, it is
possible to use data from the Enterprise Surveys to try to assess productivity changes. The
enterprise survey that was conducted in 2003 used similar sampling procedure and questionnaire
Figure 61: TFP is similar or slightly higher in Tanzania than in most low income in SSA—although it is lower
than in the best performing countries.
Source: World Bank Enterprise Surveys.
Note: See Appendix 2.1 for description of methodology. Cross-country comparisons are for manufacturing firms
only
-100%
-50%
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150%
200%
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Gam
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Eth
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Nig
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anzania
TFP relative to Tanzania (0 means as productive as Tanzania)
154
and so, in theory, it should be possible to assess average changes in productivity between the two
surveys by looking at average levels of TFP in the two surveys.
When making these comparisons, it is important to note that these comparisons
effectively look at average changes in productivity after controlling for changes in firm size and
sector. That is because the regressions control for sector by allowing the production function to
vary across sector and size by not imposing constant returns to scale. As a result, changes in
productivity due to changes in the composition of firms with respect to size or sector are omitted
in the estimates of productivity changes.
This highlights that one problem when making the comparisons across time is that the
estimates might be sensitive to differences in the composition of the samples. Although the
estimation controls for firm size and sector, other differences in sample composition could affect
the comparisons (e.g., if exporters or innovative or technologically advanced firms are
oversampled). Moreover, to the extent that the model is misspecified, the controls for size and
sector might not be perfect. As a result, it is possible that TFP might be over or underestimated
if the sample is not broadly representative. Given that there are some differences between the
2003 and 2006 samples (see Appendix I), this might be a concern.
One way around this is to focus only on the panel firms (i.e., the firms that were
interviewed in both 2003 and 2006). Because this compares the productivity of the same firms,
concern that the two samples might not be comparable is less pronounced. However, this
introduces new problems. An important question is whether the firms included in the initial
survey are broadly representative of firms in the economy in the first place. If they were and all
panel firms were reached in the second round of the survey, and births of new firms and deaths
of old firms have been modest, then they should be broadly representative of firms for the entire
period. In practice, however, a significant number of firms did not participate in the second
survey. One problem is that this means that there is some danger of survivorship bias – that is,
the firms that survive between 2003 and 2006 are likely to be the ‗better‘ firms and so looking at
productivity changes for these firms might not be representative of overall productivity changes
for all firms. Another danger is that a large number of firms from the 2003 survey could either
not be located or refused to be interviewed in the 2006. If these omissions are not random, this
will also make comparisons difficult. As a result, even if the original sample was broadly
representative of the economy as a whole, it is less clear that the final panel sample is
representative.
For this reason, it is interesting to compare results from the two surveys including all
firms (cross-sectional approach) and then only including panel firms (balanced panel approach)
to check the robustness of results. When firms from both the 2003 and 2006 surveys are
included in the large cross-country model (see Table 39), the firms in the 2006 survey were
about 9 percent more productive on average than similar firms in the 2003 survey. This suggests
an annual increase of about 3 percent using the cross-sectional approach. Although this suggests
productivity improvements, the difference is not statistically significant (i.e., the null hypothesis
that TFP is the same in the two periods cannot be rejected at conventional significance levels).
This suggests that the apparent difference might be due to sampling variation.
155
The results from the balanced panel approach are similar. The results from the balanced
panel analysis suggest that total factor productivity increased by 20 percent between 2003 and
2006, suggesting an average increase of 6 percent per year. However, as for the cross-sectional
analysis, this difference is not statistically significant, due to high dispersion around the
estimated mean. The large point estimate from the balanced panel approach might be to
problems associated with survivorship bias.
Table 39: Productivity Changes over Time –Balanced Panel.
Tanzania
Dependent Variable Log of Value Added
Number of Observations 85
Sector Dummies Yes
Production Function
Capital 0.41***
(natural log) (0.077)
Labor 0.66***
(natural log) (0.139)
Year Dummies
Dummy indicating observation is for 2006 0.18
(dummy) (0.275)
Intercept 4.06***
(0.764)
Adjusted R-squared 0.77
Source: Authors‘ calculations based upon data from World Bank Enterprise Survey.
Note: Balanced panel is a panel of firms that were in both the 2003 and 2006 surveys. Regressions are for
manufacturing firms only.
***, **, * Significant at 1, 5 and 10 percent significance levels
Productivity Differences Within Tanzania
The previous sub-sections compared productivity levels across different countries in Sub-
Saharan Africa and in Tanzania over time. This sub-section compares difference in productivity
across firms within Tanzania. Because different aspects of the investment climate are likely to
affect productivity differently in different countries, the sample only includes firms from
Uganda. The results are shown in Table 40. The table presents several specifications, including
various firm level variables such as whether the firm: (i) has ISO certification; (ii) has a company
website; (iii) has a formal training program; and (iv) exports. Several additional variables such as
whether the firm has its own generator and whether the firm provides its own transportation are
included to capture the internalization of negative externalities.
The table presents four specifications. The first and second model examine differences in
total factor productivity due to differences in ownership structure and whether the firm exports.
The second specification replaces a dummy variable indicating that the firm is partly foreign
owned with a second variable indicating that the firm is majority foreign owned. The third
specification adds some additional variables indicating enterprise learning channels. These
include dummy variables indicating that the firm has ISO certification, that it has its own
website, that it has a formal training program for its workers, and a dummy variable indicating
that the manager has a university degree. The fourth model adds a final set of variables
indicating whether the firm owns a generator and whether it provide its own transportation to
capture internalization of negative externalities due to adverse business environment.
156
Results from the cross-sectional models presented below need to be interpreted with
caution. One particular concern is that although some of the variables may affect firm
performance, reverse causation is also a concern. For example, it is possible that the process of
obtaining ISO quality certification might improve firm performance. That is, the process of
quality improvement requires that firms carefully assess their processes and organizational
performance and that this in turn might affect their efficiency. Alternatively, the process of ISO
certification might improve either the quality of their products or act as a signal of quality,
allowing the firm to command higher prices on international markets. In these cases, ISO
certification would actually improve measured TFP. But it is also possible that causation might
partly run in the opposite direction. That is, firms that are already producing quality goods and
are already well organized might find it easier to become internationally certified than other
firms. This interpretation is consistent with other evidence that suggests that although
international certification provides a useful market signal that a firm is adhering to a recognized
management system there is mixed evidence that it actually improves quality and organizational
performance in the firm (Guasch and others, 2007). Problems with reverse causation cannot
easily be controlled for in a cross-sectional framework. As a result, it it important to note that the
empirical results show correlation not causation.
A second issue is multicollinearity, This problem exists when the explanatory variables
are highly correlated with each other, making it difficult to assess which are the main drivers of
productivity. For example, foreign-owned manufacturing firms are more likely to have ISO
certification than other firms (correlation of 0.28 between the two variables). This makes it
difficult to isolate the impact of ISO certification from the impact of exporting. To address this
issue, we have examined the degree of correlation between the explanatory variables, and also
estimated productivity adding each explanatory variable at a time. These results are similar to the
regressions reported in Table 40. The only difference is that foreign ownership is significant
when included alone. Other variables that are insignificant in the full model remain insignificant
in the partial models.
The most robust results are for the variables representing different aspects of technology
use. In particular, enterprises that are ISO certified and those that have their own website are
much more efficient than firms that do not (51 percent and 43 percent respectively). Enterprises
that provide their own transportation are about 32 percent more efficient than less vertically
integrated firms.
The coefficients on the other variables are not statistically significant, indicating that
there is not a strong association between these enterprise characteristics and firm productivity.
For example, the point estimates of the coefficients suggest that firms with enterprise training
programs and university educated managers are more productive than other firms (3 and 12
percent respectively), but the differences are not statistically significant. Similarly enterprises
that have generators and provide their own transportation appear to be more productive, although
once again the differences are not statistically significant. Finally, after controlling for other
characteristics, exporting firms and foreign firms are not statistically significantly more efficient
than non-exporters and domestic enterprises.
157
Table 40: Determinants of Enterprise Productivity in Tanzania.
(1) (2) (3) (4)
Dependent Variable Value Added
Number of Observations 213 213 213 213
Sector Dummies Yes Yes Yes Yes
Production Function
Capital 0.38*** 0.38*** 0.36*** 0.33***
(natural log) (0.034) (0.033) (0.034) (0.035)
Labor 0.59*** 0.57*** 0.51*** 0.49***
(natural log) (0.075) (0.074) (0.078) (0.078)
Region
Dar-es-Salaam 0.42*** 0.43*** 0.45*** 0.37***
(dummy) (0.143) (0.140) (0.139) (0.144)
Additional Controls
Firm has any foreign ownership 0.22
(dummy) (0.184)
Firm is majority foreign owned 0.52*** 0.31 0.29
(dummy) (0.198) (0.203) (0.202)
Firm exports 0.10 0.11 0.13 0.09
(dummy) (0.191) (0.188) (0.186) (0.185)
Firm has ISO Certification 0.48*** 0.41***
(dummy) (0.161) (0.163)
Firm has own website 0.40** 0.36**
(dummy) (0.180) (0.179)
Firm has training program 0.01 0.03
(dummy) (0.124) (0.123)
Manager has a university degree 0.12 0.11
(dummy) (0.149) (0.148)
Firm own a generator 0.20
(dummy) (0.151)
Firm provides own transportation 0.28*
(dummy) (0.154)
Constant 5.08*** 5.20*** 5.45*** 5.66***
(dummy) (0.334) (0.332) (0.336) (0.347)
Adj. R Squared 0.84 0.84 0.85 0.84
Source: Authors‘ calculations based upon data from World Bank Enterprise Survey.
Note: Regressions are for manufacturing firms only.
***, **, * Significant at 1, 5 and 10 percent significance levels
158
Appendix 3.1: Differences in Perceptions by Firm Type.
One way of assessing whether there were differences in perceptions across different types
of firms would be simply to compare average responses across firms of different types. For
example, it would be possible to look at how many firms of different types rated a particular
investment climate issue as their biggest constraint or how many firms rated it as a major or very
severe constraint.
Although this approach is intuitive, it has at least two problems associated with it. First,
the sub-samples of different types of firms are often relatively small. For example, there are
fewer than 50 white-owned firms, only 37 exporters, and only 28 foreign-owned firms in the
sample. This makes it difficult to assess whether differences are due to random variation in
responses or due to actual systematic differences in perceptions.
Second, there are also systematic differences in other firm characteristics across types of
firms. For example, foreign-owned firms tend to be both slightly larger than domestic firms (an
average of 221 workers compared to an average of 42 workers) and more likely to export (28
percent compared to 13 percent). Differences in perceptions between foreign and domestic firms
might therefore reflect differences in size or export behavior rather than differences in
ownership.
To deal with this, this section presents econometric results that deal with both these
issues. First, by using a multivariate regression approach, it is possible to look at differences in
perceptions after controlling for other systematic differences between firms. Second, it is
possible to look at the statistical significance of the results (i.e., to see whether the probability
that differences are likely to be due to random variation in responses is high or not).
Methodology.
The methodology is similar to the methodology used in a recent paper by Gelb,
Ramachandran, Shah and Turner (2006). Because of concerns about pooling the data from the
SMLE and microenterprise surveys, the results focus on differences among the SMLEs in the
sample. The microenterprise sample, with only about 120 firms, is too small to do a similar
analysis.
The question of how different factors, including ownership, affect access to credit for
microenterprises is examined by estimating different versions of the equation below:
iSectorExporterSizeOwnershipICabout Perception 5i43i21i (3.1)
The dependent variables are dummy variables indicating whether the manager of firm i rates that
area of the investment climate as a major or very severe obstacle. The independent variables are
a set of five dummy variables indicating firm ownership (whether the firm has a white, Asian or
black owner, whether the firm has a female owner, and whether the firm is foreign owned), firm
size (number of workers), a dummy variable indicating whether the firm exports, and a series of
dummies indicating sector of operations. The error term is assumed to be normally distributed.
159
Because the dependent variable is a dummy variable, the model is estimated using standard
maximum likelihood estimation. Results from the regression for each of the obstacles are shown
in Table 41.
Empirical Results
a) Firm Size
Previous work looking mostly at low-income countries in Africa suggests that large firms
are more likely to complain about most aspects of the investment climate, with the exception of
access to finance and access to land, than smaller firms.92
A similar pattern appears to hold in
Tanzania. Large firms were more likely to say that 12 (of 17) areas of the investment climate
were serious problems than small firms were (see Table 41). The differences, however, were
mostly statistically insignificant.
The coefficient on firm size (number of workers) was statistically significant at a 10
percent level or higher in five of the regressions. Large firms were significantly more likely to
say that electricity, labor regulation, and courts were serious problems than small firms and were
significantly less likely to say that political instability and access to finance were serious
obstacles.
Although the differences are statistically significant at conventional significance levels, it
is important to note that the differences do not appear to have a significant effect on many of the
rankings. In particular, electricity remains the top constraint for small, medium and large
firms—although large firms are more likely to say that electricity is a significant problem than
smaller firms (100 percent of large firms compared to 85 percent of small firms). Moreover,
about 70 percent of small firms, 80 percent of medium-sized firms, and 85 percent of large firms
said that electricity was the biggest obstacle they faced. Although large firms report greater
concern than small firms, electricity remains the biggest concern for firms of all sizes.
Similarly, few firms of any type saw courts, labor regulation or political instability as
serious problems—they ranked among the bottom five constraints for firms of all sizes. Finally,
access to finance ranked as the second greatest constraint for small firms (40 percent of small
firms) and the third greatest for medium and large firms (44 percent and 28 percent respectively).
b) Exporters
In many countries, exporters were more likely to be concerned about trade and customs
regulations, telecommunications or macroeconomic instability (exchange rate instability) due to
the impact that these have on trade.93
This does not appear to be the case in Tanzania—for most
areas of the investment climate differences in perceptions between exporters and non-exporters
were both small and statistically insignificant.
Exporters were 14 percentage points more likely to say that crime was a serious problem
than non-exporters and were 10 percentage points less likely to say that access to land was a
serious problem after controlling for other factors that might affect. These differences are
statistically significant at a ten percent level or higher.
160
Of the two differences, the difference with respect to crime is more notable. Crime
ranked among the top concerns of exporters—34 percent said it was a major problem making it
the fifth greatest constraint. In contrast, only 16 percent of non-exporters said the same—the 11th
greatest constraint. In contrast, although the exporters were less likely to say that access to land
was a serious constraint, it did not rank among the top concerns of either exporters or non-
exporters. Less than one in five exporters and non-exporters said that it was a serious constraint.
c) Foreign-Owned Firms
After controlling for other things that affect perceptions (e.g., size and export status),
differences between foreign-owned firms and domestic firms were both small and statistically
insignificant in most cases. The only exception was that foreign-owned firms were 17
percentage points less likely to say that access to finance was a serious problem. Although this
could be because banks and other financial intermediaries in Tanzania are more willing to lend to
foreign-owned firms, there are other possible reasons for the difference. For example, foreign-
owned firms might be more profitable—and so can more easily finance investment from retained
earnings—or might be able to rely upon parent companies or banks in their home countries.
These issues will explored in more detail in Chapter 5 of Volume 2.
d) Other Ownership Variables
After controlling for other factors, managers of female-owned firms were more likely to
say that transportation was a serious problem and less likely to say that tax rates, macroeconomic
instability, and political instability were serious problems. Managers of African-owned firms
were also less likely to say that tax rates and macroeconomic instability were serious problems.
e) Sectors
For most obstacles, there were only minor differences in perceptions between firms in
different sectors after accounting for other differences (e.g., size, export status, ownership).
Firms in the service sector were 6 percentage points more likely to say electricity was a serious
problem than firms in the retail trade sector and firms in services and manufacturing were 13 and
11 percentage points more likely to say that access to finance was a serious problem than firms
in the retail trade sector. Finally firms in the manufacturing sector were 10 percentage points
more likely to say that trade and customs regulations were a serious problem than other firms
were.
Although these differences were relatively large and statistically significant, they do not
appear to have a significant impact on relative orderings. Electricity still ranked as the greatest
constraint for firms of all types, access to finance ranked among the top three constraints, and
trade and customs regulation did not rank among the top constraints for firms in any sector. In
this respect, the results do not appear to be very different.
161
Table 41: Effect of enterprise characteristics on perceptions about different aspects of the investment climate.
Electricity Access to
Finance
Access to
Land Tax Rates Transport
Macro-
Economic
Instability
Tax
Admin.
Comp. with
Informal
Sector
Crime
Number of Observations 476 476 476 476 476 476 476 476 476
Microenterprise 0.059* -0.024 -0.042 -0.098 -0.040 0.080 -0.016 0.053 0.005
(dummy) (1.67) (-0.28) (-0.66) (-1.17) (-0.68) (1.11) (-0.23) (0.66) (0.068)
Number of Workers 0.037** -0.046* 0.029 0.001 0.007 -0.011 -0.026 0.016 0.032
(log) (1.99) (-1.66) (1.41) (0.021) (0.34) (-0.47) (-1.18) (0.65) (1.60)
Exporter 0.063 0.081 -0.102* 0.023 0.046 0.057 0.013 0.064 0.145**
(dummy) (1.15) (0.93) (-1.71) (0.28) (0.76) (0.79) (0.19) (0.82) (2.10)
Foreign-owned 0.017 -0.169* 0.027 -0.141 0.089 0.028 0.094 -0.117 -0.053
(dummy) (0.21) (-1.73) (0.35) (-1.62) (1.19) (0.34) (1.17) (-1.44) (-0.79)
Female Owner -0.003 -0.011 -0.028 -0.126** 0.082** -0.085** -0.048 0.006 0.065
(dummy) (-0.088) (-0.20) (-0.71) (-2.43) (2.10) (-1.99) (-1.12) (0.12) (1.55)
African Owners -0.046 -0.032 0.018 -0.151** 0.002 -0.075 -0.066 -0.006 -0.088*
(dummy) (-1.21) (-0.48) (0.37) (-2.34) (0.047) (-1.37) (-1.25) (-0.11) (-1.70)
Manager has University Education 0.048 -0.042 -0.074* 0.105* -0.030 -0.085* 0.050 0.049 -0.114***
(dummy) (1.59) (-0.77) (-1.78) (1.94) (-0.76) (-1.86) (1.10) (0.98) (-2.68)
Services 0.062* 0.133* -0.010 -0.039 -0.053 0.055 -0.011 0.085 0.019
(dummy) (1.88) (1.74) (-0.18) (-0.53) (-1.03) (0.85) (-0.18) (1.17) (0.32)
Manufacturing 0.044 0.113* 0.002 0.023 0.008 0.012 0.037 0.045 0.030
(dummy) (1.32) (1.74) (0.048) (0.36) (0.18) (0.22) (0.70) (0.74) (0.58)
Pseudo R-Squared 0.09 0.02 0.02 0.05 0.02 0.03 0.02 0.01 0.04
Source: Authors calculations based on Enterprise Survey data.
***, **, * Significant at 1, 5 and 10 percent significance levels.
162
Table (continued).
Business
Reg. and
Licensing
Worker
skills and
education Telecom Corruption
Trade and
Customs
Regulation
Labor
Regulation
Political
Instability Courts
Number of Observations 476 476 476 475 476 419 476 476
Microenterprise -0.021 -0.074 0.007 0.052 -0.017 -0.013 0.059
(dummy) (-0.32) (-1.18) (0.19) (0.70) (-0.28) (-0.30) (1.26)
Number of Workers 0.015 0.010 0.004 0.034 -0.010 0.019** -0.034** 0.027**
(log) (0.72) (0.52) (0.33) (1.61) (-0.55) (2.03) (-2.07) (2.51)
Exporter -0.068 0.042 -0.002 0.032 0.024 -0.014 0.036 0.012
(dummy) (-1.09) (0.67) (-0.057) (0.49) (0.44) (-0.52) (0.67) (0.35)
Foreign-owned 0.066 0.024 0.109 -0.001 0.090 -0.026 0.041 0.021
(dummy) (0.85) (0.34) (1.64) (-0.019) (1.38) (-1.05) (0.64) (0.56)
Female Owner -0.017 0.044 0.032 -0.036 -0.030 0.009 -0.054* 0.026
(dummy) (-0.43) (1.11) (1.43) (-0.86) (-0.83) (0.41) (-1.93) (1.16)
African Owners 0.019 0.007 0.056** -0.034 -0.052 0.008 -0.075* 0.005
(dummy) (0.39) (0.16) (1.97) (-0.66) (-1.21) (0.36) (-1.84) (0.21)
Manager has University Education -0.049 0.075* -0.004 -0.072 0.039 0.037 -0.071** -0.007
(dummy) (-1.19) (1.83) (-0.17) (-1.62) (1.05) (1.62) (-2.24) (-0.30)
Services 0.035 0.084 0.016 0.031 0.012 0.029 0.026 -0.025
(dummy) (0.59) (1.33) (0.53) (0.47) (0.19) (0.62) (0.62) (-0.94)
Manufacturing 0.010 0.028 -0.021 0.034 0.098** 0.027 0.009 -0.046
(dummy) (0.21) (0.55) (-0.75) (0.63) (2.13) (0.82) (0.27) (-1.56)
Pseudo R-Squared 0.01 0.04 0.06 0.02 0.05 0.09 0.08 0.06
163
Appendix 3.2: Differences in Perceptions by Year.
An interesting question is whether perceptions have changed significantly since 2003.
Although it is possible to simply compare the percent of firms in the 2003 and 2006 surveys that
said that each area was a significant problem, differences in the percentages could be the result
of changes in the sample between the two surveys rather than the result of changes in perception.
In particular, the firms in the 2003 survey are mostly in the manufacturing sector and tend to be
slightly larger than the firms in the 2006 survey. To see whether this affects the comparisons
between the two surveys, this Appendix uses regression analysis to see whether differences in
perceptions remain significant after controlling differences between the two samples.
The first analysis is repeated cross-sectional regression that pools all data for the two
years and adds a time dummy that is set to one for the 2006 observations and to zero for the 2003
observations to the regressions from the previous Appendix. If the time dummy is statistically
significant, this suggests that firms had different perceptions about that area of the investment
climate in 2006 than in 2003 after controlling for sector, size, ownership, export behavior and
firm age.
The results from this estimation are shown in Table 42. After controlling for other firm
differences, firms were about 30 percentage points more likely to say that electricity was a
problem in 2006 than in 2003. Firms were also no more likely to say that transportation, crime
and access to finance were serious problems in 2006 than they were in 2003—although the
coefficients are negative they are not statistically significant at conventional significance levels.
For all other areas of the investment climate, firms were less likely to say that they were serious
problems in 2006 than they were in 2003. They were less likely to say that telecommunications
(7 percentage points), access to land (18 percentage points), tax rates (28 percentage points), tax
administration (36 percentage points), trade regulation (14 percentage points), labor regulation (6
percentage points), business licensing (14 percentage points), worker education and skills (11
percentage points), macroeconomic instability (25 percentage points), and corruption (32
percentage points) in 2006 than they were in 2003.
As a robustness check, Table 43 shows differences in perceptions for panel firms between
2003 and 2006. That is, for the subset of firms that were interviewed in 2003 and 2006, the
regressions compare their answers in the two surveys and sees if their responses were different in
the two surveys. To do this, a fixed effects model is estimated that includes enterprise-level
fixed-effects (i.e., enterprise level dummy variables) for those enterprises for which multiple
observations are available (i.e., enterprises included in both the 2003 and 2006 surveys). This
analysis is interesting because the firm-level fixed effects control for a far greater degree of firm
diversity than firm controls. Because most firm characteristics do not change much over the
period and because including additional controls reduces sample size, no additional control
variables are included.
Because of the difficulty in obtaining consistent estimates in maximum likelihood models
with a large number of individuals but few time observations (Neyman and Scott, 1948), the
model is estimated using the Logit model proposed by Chamberlain (1980). This reduces sample
size, since firms can only be included if they report the obstacles is a serious problem in only one
164
of the two periods. For firms that say it is a serious problem in neither or both periods the firm-
level fixed effects perfectly predict their decision—and the firm has to be dropped.
In general, the results are similar. In particular, panel firms were far more likely to say
that electricity was a problem in 2006 than the same firms were in 2003, were neither more nor
less likely to say that transportation was a problem and were less likely to say that the other areas
of the investment climate were serious problems. The only difference with the cross-sectional
results were that firms were less likely to say that crime and access to finance were problems in
the panel analysis, but there was no difference in the cross-sectional comparisons. This suggests
that differences in these areas might reflect differences in sample rather than differences in
perceptions.
165
Table 42: Differences in Perceptions between 2003 and 2006 (pooled cross-section).
Telecom Electricity Transport Access to
Land Tax Rates
Tax
Admin.
Trade and
Customs
Regulation
Courts
Number of Observations 344 337 361 358 356 362 362 345
Year (2006) -0.070** 0.292*** -0.012 -0.176*** -0.283*** -0.360*** -0.137** -0.157***
(Dummy) (-2.12) (5.42) (-0.23) (-3.09) (-4.20) (-5.77) (-2.51) (-4.02)
Number of Workers 0.022* 0.031 0.016 0.048* -0.037 -0.063** 0.003 0.033**
(log) (1.65) (1.44) (0.66) (1.77) (-1.12) (-2.11) (0.10) (2.34)
Exporter -0.015 -0.002 0.103* -0.120* 0.047 0.053 0.035 0.041
(dummy) (-0.49) (-0.029) (1.66) (-1.83) (0.56) (0.70) (0.54) (1.07)
Foreign-owned -0.027 -0.047 -0.015 0.062 0.013 0.240** 0.000 -0.009
(dummy) (-0.70) (-0.64) (-0.21) (0.69) (0.13) (2.51) (0.0054) (-0.24)
Female Owner 0.097** 0.011 0.102 -0.027 -0.191** -0.133* -0.052 0.102**
(dummy) (2.33) (0.19) (1.63) (-0.39) (-2.26) (-1.76) (-0.78) (1.98)
African Owners 0.042 -0.001 0.063 0.043 -0.188*** -0.018 -0.080 -0.003
(dummy) (1.61) (-0.027) (1.35) (0.78) (-2.74) (-0.30) (-1.50) (-0.100)
Manager has University Education -0.019 0.105** -0.018 -0.069 0.122* 0.132** 0.101* 0.003
(dummy) (-0.65) (2.19) (-0.34) (-1.15) (1.66) (2.02) (1.76) (0.10)
Pseudo R-Squared 0.17 0.18 0.05 0.11 0.14 0.15 0.09 0.21
Source: Authors calculations based on Enterprise Survey data.
Note: Includes all firms in either of the 2003 and 2006 surveys. ***, **, * Significant at 1, 5 and 10 percent significance levels.
Labor
Regulation
Worker
skills and
education
Business
Reg. and
Licensing
Access to
Finance
Macro-
Economic
Instability
Corruption Crime
Number of Observations 353 358 361 366 362 366 357
Year (2006) -0.056* -0.108** -0.135** -0.064 -0.252*** -0.325*** -0.089
(Dummy) (-1.69) (-2.01) (-2.48) (-0.99) (-4.22) (-5.29) (-1.58)
Number of Workers 0.028* 0.051** 0.054** -0.068** -0.047 0.010 0.019
(log) (1.91) (2.01) (2.10) (-2.10) (-1.63) (0.32) (0.72)
Exporter -0.020 0.040 -0.012 0.008 0.127* 0.095 0.150**
(dummy) (-0.60) (0.64) (-0.18) (0.10) (1.68) (1.26) (2.13)
Foreign-owned -0.025 -0.087 -0.088 -0.105 -0.043 -0.040 -0.103
(dummy) (-0.69) (-1.28) (-1.28) (-1.09) (-0.49) (-0.47) (-1.35)
Female Owner 0.011 0.125* -0.096 -0.020 -0.068 -0.063 0.091
(dummy) (0.27) (1.84) (-1.54) (-0.26) (-0.95) (-0.83) (1.32)
African Owners -0.017 -0.082 0.020 -0.061 -0.098* -0.012 -0.073
(dummy) (-0.56) (-1.53) (0.38) (-0.94) (-1.66) (-0.20) (-1.29)
Manager has University Education 0.009 0.057 -0.009 -0.101 -0.037 -0.082 -0.075
(dummy) (0.25) (1.01) (-0.16) (-1.46) (-0.58) (-1.22) (-1.24)
Pseudo R-Squared 0.11 0.11 0.08 0.07 0.12 0.10 0.06
166
Table 43: Differences in Perceptions over Time (Panel Regressions).
Telecom Electricity Transport Tax Rates
Tax
Admin.
Trade and
Customs
Regulation
Courts
Observations 16 28 24 52 56 46 22
Number of Firms 8 14 12 26 28 23 11
year -0.649* 1.792** 0.000 -1.204*** -1.099** -1.041** -2.303**
(dummy) (-1.82) (2.35) (0.00) (-2.59) (-2.52) (-2.19) (-2.20)
Pseudo R-squared 0.456 0.408 0 0.221 0.189 0.172 0.561
Source: Authors calculations based on Enterprise Survey data.
Note: Only firms that were in both the 2003 and 2006 surveys are included in the regressions. Only firms that rated
an obstacle as a major obstacle in one year only can be included in the regressions.
***, **, * Significant at 1, 5 and 10 percent significance levels.
Labor
Regulation
Worker
skills and
education
Business
Reg. and
Licensing
Access to
Finance
Macro-
Economic
Instability
Corruption Crime
Observations 22 46 46 50 60 62 32
Number of Firms 11 23 23 25 30 31 16
year -2.303** -1.281** -1.897*** -0.754* -1.609*** -2.674*** -1.099*
(dummy) (-2.20) (-2.53) (-3.06) (-1.76) (-3.29) (-3.66) (-1.90)
Pseudo R-squared 0.561 0.245 0.441 0.0956 0.350 0.655 0.189
167
Appendix 4.1: Econometric Analysis of Training.
Given current debates on skill shortages but also the rise in workers educational
qualifications, the way workers acquire human capital is of crucial importance. Through
individual and firm-level regressions we analyze the profile of workers who have received
training and of the firms which provide it.
Worker-level regressions
The Enterprise Survey includes worker-level data on both contemporaneous training and
training received in the past. It also asks whether the training was external or in-firm and whether
it was financed by the firm or by the worker. Because the data also includes information on the
worker, it is possible to look at what kinds of workers are more likely to receive training. The
empirical analysis focuses on a number of worker characteristics include formal education,
gender, union membership, and experience, looking at how these affect participation in formal
training programs.
The econometric model for the worker level regressions is a probit model with the
following specification:
Yijk = + Wijk + Fjk + ijk (2)
Yijk is an indicator for whether a worker i in firm j and sector k received any training or firm-
based training. Wijk is a set of worker attributes including schooling, experience, tenure, gender
and working hours. Fjk captures a set of firm-characteristics. ijk captures unobserved
individual/firm characteristics that affect training. The model is estimated without weights.
Empirical Results
Although separate regressions are done for any training and firm-financed training, the
findings are very similar. The most likely reason for this is that firms finance the majority of
training: about 22 percent of workers have received any training and about 15 percent have
received training provided by the firm.
The empirical results show (see Table 44):
1. Better educated workers are more likely to receive training, though their likelihood of
receiving firm provided training partly depends on the characteristics of the firm they
work for.
2. Other things being equal, and particularly when controlling for firm characteristics,
women have higher chances of getting training.
3. Workers‘ years of experience, and working full-time versus part-time are not
significantly associated with the likelihood of having received training.
4. Marital status, while not significantly associated to the likelihood of having received
any type of training, is negatively related to the likelihood of having receiving firm
provided training. This might capture workers characteristics beyond age and
168
experience, suggesting that single workers might either be more mobile and so that
returns on the firm-provided training is lower.
5. Managers are more likely to have received training than other groups.
6. At the firm level, workers in large firms with 100 employees or more are more likely
to have received training, and particularly firm provided training. Working in foreign
owned firms and in firms which export does not appear to have a significant
independent effect in general.94
These results are interesting as they both confirm and challenge some prior expectations.
The positive relation between schooling and training is in line with human capital theory‘s
prediction that workers with more schooling are more likely to receive training as the cost of
their training is lower or returns are higher.95
The higher likelihood of women to receive training
is more puzzling, though it might be a reflection of the higher selectivity that women face in the
market.96
Firm-level regressions
Firm owners and managers decide whether they should invest in their workers and also
decide who they will train and the content of the training. Looking at the types of firms that
provide training provide information on these decisions.
The probability that the firm provides training is estimated as a function of firm
characteristics. The model is specified as:
Tij = + Xij + j + ij (1)
Tij is a dummy variable that is set to 1 if firm i in sector j has a formal training program and 0 if
it does not. Since the dependent variable is a dummy variable, the model is estimated as a
standard probit model without weights. The independent variables, Xij, are observable firm
characteristics thought to affect the provision of training. A series of dummy variable, j,
representing fixed sector characteristics that determine the desirability to provide training such as
average levels of capital intensity and skill complementarities in production are included in some
model specifications. The error term, ij, represents unobserved firm characteristics that
potentially affect training.
The set of firm characteristics Xij include a size dummy indicating that the firm is large.
Firm size can affect the likelihood of training provision in a number of ways. Firstly, large firms
might be so as a result of training or a common factor such as ‗high quality management‘ or
access to liquidity which affects both employment growth and the propensity to train. Second, to
the extent that training is associated with fixed costs (including the space to provide training),
larger firms face lower per-worker costs of training provision.
The regression also controls for export status, foreign ownership and firm vintage. Both
of these measures are proxies for firm quality (Roberts and Tybout, 1996). Firms facing
international competition are more likely to invest in the quality of their workers. Similarly,
firms with foreign ownership are more likely to provide training as a result of greater liquidity or
169
peer effects. Firm age is a measure of quality and or competitive pressure (Hopenhayn, 1992).
Given that the effect of age is ambiguous, the regression includes a quadratic to capture non-
linear effects of age on the propensity to provide training.
The returns to training are likely related to the level of formal education of the worker. In
this direction the regressions control for the average level of education in the firm. The
regression therefore includes an indicator for whether the average worker in the firm has more
than 6 years of schooling.
Bargaining power of workers is likely to affect firm-based training. On the one hand,
workers with more bargaining power will induce firms to invest in worker skills. On the other
hand, if workers value other non-skills related investments, then training is less likely. The
proportion of workers that are seasonal captures the extent to which a firm relies on a stable
workforce. A higher proportion of seasonal workers will likely be associated with a lower
propensity to provide training. The regression also include a measure of the extent to which
firm‘s are engaged in HIV prevention/treatment activities. Firms that invest in prevention and
treatment are firms that are sensitive to the skill-composition of their workforce and are more
likely to provide training.97
Finally the regressions include a series of controls that capture other measures of firm
competitiveness and liquidity such as capacity utilization and whether the firm‘s accounts are
externally audited.
Empirical Results.
The firm-level training regressions (see Table 45) show:
1. Firms with foreign ownership are significantly more likely to provide training. This
result is quite robust across the different model specifications.
2. Firms that employ more than 100 people are also more likely to provide training. The
size of the coefficient and its statistical significance decline, however, when more
controls are included.
3. Firms that are externally audited are also more likely to provide training. This could
be because they are more formal or more competitive.
4. The coefficient on ratio of part-time workers to full-time workers is positive and
statistically significant. This could be because firms that have a relatively small full-
time workforce are more likely to have to provide some easy-to-implement training
for their part-time workers. It is, however, important to note that there was no
evidence that part-time workers were more likely to receive training in the individual-
level regressions.
5. In contrast to previous results using the 2003 Enterprise Survey data (Ramachandran
and others, 2007), there was no evidence that firms that have HIV prevention
programs were more likely to provide training.
6. Coefficients on other firm-level variables such as firm age, percent of workers in a
union, capacity utilization and a dummy indicating that the firm exports are not
consistently statistically significant.
170
Tables
Table 44: Probability that worker receives training (worker level probit regressions)
Any training in the past?
(Dummy Variable)
Firm financed/provided training in the past?
(Dummy Variable)
Observations 608 556 556 554 608 556 556 554
Worker Characteristics
Schooling 0.024*** 0.010* 0.011** 0.009* 0.020*** 0.006 0.007* 0.003 (Years) [0.005] [0.005] [0.005] [0.005] [0.004] [0.004] [0.004] [0.004]
Experience 0.005 0.000 0.002 0.004 0.003 -0.001 0.002 -0.000
(Years) [0.008] [0.009] [0.008] [0.009] [0.007] [0.007] [0.007] [0.007] Experience Squared -0.000 0.000 -0.000 -0.000 -0.000 0.000 -0.000 -0.000
(Years) [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000]
Female 0.120*** 0.094** 0.064 0.103** 0.084** 0.082** 0.045 0.061 (dummy) [0.044] [0.045] [0.045] [0.048] [0.040] [0.039] [0.038] [0.040]
Single -0.034 -0.024 -0.025 -0.081** -0.070** -0.069** (dummy) [0.039] [0.040] [0.042] [0.032] [0.032] [0.033]
Union Member 0.138*** 0.073* 0.134*** 0.133*** 0.054 0.107***
(dummy) [0.041] [0.043] [0.049] [0.037] [0.037] [0.039] Full-time 0.073 0.072 0.026 0.070 0.065 0.052
(dummy) [0.095] [0.099] [0.115] [0.087] [0.086] [0.093]
Type of Worker Professional -0.112 -0.143** -0.150** -0.021 -0.067 -0.079
(dummy) [0.080] [0.062] [0.062] [0.088] [0.059] [0.052]
Skilled Production Worker -0.120 -0.151* -0.144 -0.040 -0.073 -0.079 (dummy) [0.093] [0.086] [0.089] [0.081] [0.068] [0.067]
Unskilled Production Worker -0.215** -0.231*** -0.199** -0.090 -0.102 -0.086
(dummy) [0.088] [0.084] [0.088] [0.079] [0.069] [0.068] Non-Production Worker -0.148** -0.166*** -0.185*** -0.083 -0.104** -0.106**
(dummy) [0.073] [0.064] [0.060] [0.066] [0.050] [0.048]
Firm Characteristics Large Firm 0.265*** 0.371*** 0.332*** 0.434***
(dummy) [0.059] [0.082] [0.058] [0.084]
Exporter -0.029 -0.027 (dummy) [0.057] [0.045]
Large Exporter -0.194*** -0.138***
(interaction) [0.038] [0.024] Foreign-Owned 0.079 0.051
(dummy) [0.060] [0.050]
Age of Firm -0.014** -0.002 (years) [0.007] [0.004]
Age of Firm Squared 0.000** 0.000
(years) [0.000] [0.000]
Pseudo R2 0.05 0.08 0.12 0.18 0.05 0.09 0.18 0.25
Source: Authors‘ calculations based upon World Bank Enterprise Survey data
Note: Probit regressions, unweighted observations, robust standard errors in brackets. *** p<0.01, ** p<0.05,
*p<0.1. For dummy variables, results are marginal effects for discrete change of dummy variable from 0 to 1.
171
Table 45: Probability that firm has a formal training program (firm level probit regressions)
Dependent Variable Firm has formal training program
(dummy)
Observations 271 265 264 271 265 264
Industry Fixed Effects Included? No No No Yes Yes Yes
Large Firm 0.255** 0.253* 0.186 0.241* 0.236* 0.182
(dummy) [0.130] [0.131] [0.142] [0.132] [0.134] [0.144]
Exporter 0.133 0.111 0.070 0.127 0.105 0.069
(dummy) [0.106] [0.110] [0.111] [0.108] [0.112] [0.112]
Large Exporter -0.204 -0.197 -0.168 -0.200 -0.181 -0.158
(interaction) [0.151] [0.153] [0.165] [0.152] [0.159] [0.169]
Foreign-Owned 0.252*** 0.224** 0.197* 0.247*** 0.214** 0.189*
(dummy) [0.095] [0.102] [0.102] [0.096] [0.104] [0.104]
Age of Firm -0.001 0.002 0.000 -0.001 0.003 0.001
(years) [0.008] [0.008] [0.009] [0.008] [0.008] [0.009]
Age of Firm Squared 0.000 0.000 0.000 0.000 0.000 0.000
(years) [0.000] [0.000] [0.000] [0.000] [0.000] [0.000]
Average worker has > 6 years of school. 0.023 0.032 0.021 0.030
(dummy) [0.065] [0.066] [0.066] [0.066]
Percent of workers in union 0.000 0.000 0.000 0.000
(percent) [0.001] [0.001] [0.001] [0.001]
Percent of workers part-time or temporary 0.002** 0.002** 0.002** 0.002**
(percent) [0.001] [0.001] [0.001] [0.001]
Firm has HIV prevention program 0.027 0.016
(dummy) [0.078] [0.082]
Capacity Utilization 0.001 0.001
(percent) [0.002] [0.002]
Firms accounts are audited externally 0.151** 0.150**
(dummy) [0.072] [0.073]
Pseudo R-squared 0.12 0.12 0.12 0.12 0.12 0.12
Source: Authors‘ calculations based upon World Bank Enterprise Survey data
Note: Probit regressions, unweighted observations, robust standard errors in brackets. *** p<0.01, ** p<0.05, *
p<0.1. For dummy variables, results are marginal effects for discrete change of dummy variable from 0 to 1.
172
Appendix 4.2: Econometric Analysis of Wages
The private returns to education can provide a powerful incentive for individual workers
to invest in their human capital. Through wage regressions we look at the determinants of wages
to understand which characteristics of workers and firms carry a premium.
As in the previous appendix on training, we run two sets of regressions. The first uses
worker-level data to see what worker and firm characteristics are associated with higher wages.
The second uses firm-level data and focuses on firm characteristics.
Worker-level regressions
The worker-level model is specified as follows:
Log (wages)ijk = + Hijk + Fjk + k + j + ijk (3)
The dependent variable is the log of monthly earnings (wages plus allowances) for worker i in
firm j in sector k. Following Mincer (1974), Hijk is a set of worker characteristics. The Mincerian
framework is augmented with firm-level controls Fjk that capture some firm-level characteristics
that might affect how firms set wages. Some regressions include fixed effects for sectors ( k)
and firms ( j). The models are unweighted ordinary least squares (OLS) regressions.
Empirical Results
The regressions (see Table 46) show that:
1. Wages are about 7 to 8 percent higher for each additional year of education that the
worker has. This is similar to levels estimated in earlier studies. Bigsten and others (2000)
found in a sample of 5 African countries (Cameroon, Ghana, Kenya, Zambia and
Zimbabwe) an average 8 percent return is similar regressions that included firm fixed
effects—although the return was less when they included job tenure. Other evidence based
on manufacturing employees points to rising returns in Tanzania since the 1990s.
Between 1993 and 2001, average marginal returns rose from 6 percent to 9 percent for the
young workers aged less than 30 and from 8 to 13 percent for older workers (Soderbom
and others, 2006). This could be due to the adoption of more market oriented policies.
2. Experience appears to have a modest positive effect in some specifications but is only
mildly significant.
3. Workers in firms with more than 100 employees are paid between 29 and 44 more than
similar workers in smaller firms. Similarly, workers in foreign-owned firms are paid
between 24 percent more than similar workers in domestic firms. This is consistent with
other studies that have found similar results.
4. Gender, marital status and full-time status are not significantly correlated with wage levels
after controlling for other factors. The fact that there is no evidence that women are paid
less than men with similar characteristics and in similar firms is surprising. Previous
studies using earlier data from between 1991 and 1995 found large differences between
wages for men and women in Tanzania.98
173
5. Receiving training was positively and significantly correlated with wages in Tanzania.
The point estimates of the coefficients in the models excluding firm fixed effects suggest
that wages were between 19 and 29 percent higher for workers that received training.
Once firm fixed effects are included, the coefficient become much smaller, suggesting
only a 6 percent wage premium, and becomes statistically insignificant. This suggests that
at least in part the wage premium for training might reflect the fact that firms that are
likely to train their workers also pay higher wages irrespective of whether that worker
receives training or not. A recent study of Tanzania‘s manufacturing sector comparing the
benefits of education versus vocational training found that increases in earnings to
vocational school after primary are over 20 percent for workers in large firms but
significantly less (10 percent) if they work in a small one. For both vocational and
academic education the type of job matters for wages and wages increase more in large
than small firms. The returns to students who are successful in the academic educational
stream are far greater than the returns to any form of vocational or technical training
(Kahyarara and Teal, 2007).
6. Wages were between 11 and 31 percent high for workers in unions. The coefficient
become statistically insignificant once firm controls and sector dummies are included or
when firm level dummies are included. This suggests that the wage premium might due
to unionized firms paying more to all workers irrespective of the workers‘ union
membership.
Firm-level regressions
Similar firm level wage regressions have been run to explore further how firm
characteristics are correlated to worker remuneration. The following specification is estimated in
which competing wage-setting mechanisms are represented by one or more control variables.
Ln (wages)ij = + Xij + j + ij
The dependent variable is the average level of wages for firm i in sector j paid to production or
non-production workers, Xij is a set of controls that include our proxies for each of the
mechanisms outlined above. j represents sector specific effects and ij captures unobserved firm
characteristics affecting wages.
Empirical Results
The coefficients on fewer variables are statistically in the firm-level regressions (see
Table 47). The most likely reasons for this are that there are fewer observations and that the
aggregate data are less precise.
In the regression for production workers, the only significant coefficients are on the
variables representing that the firm is foreign-owned and the percent of workers that have more
than six years of education. For non-production workers the only statistically significant
coefficient is on the variable for percent of workers belonging to a union. These results confirm
well-known findings such as the role that education and firm size play in driving wages and are
consistent with results from the individual-level regressions.
174
Tables
Table 46: Wage regressions (OLS, worker-level)
Dependent Variable Log of Wages
Observations 559 551 544 544 544 542
Firm fixed effect No No No Yes No No
Industry fixed effect No No No No Yes Yes
Worker Characteristics
Schooling 0.098*** 0.093*** 0.086*** 0.057*** 0.083*** 0.079***
(Years) [0.010] [0.010] [0.010] [0.007] [0.010] [0.010]
Experience 0.023 0.031* 0.035* 0.016 0.038** 0.031*
(Years) [0.018] [0.018] [0.018] [0.010] [0.018] [0.018]
Experience Squared 0.000 0.000 0.000 0.000 0.000 0.000
(Years) [0.001] [0.001] [0.001] [0.000] [0.001] [0.001]
Female 0.031 0.013 0.004 -0.029 -0.039 -0.077
(dummy) [0.087] [0.088] [0.089] [0.046] [0.090] [0.090]
Single 0.015 0.037 0.004 0.056 0.08
(dummy) [0.087] [0.087] [0.042] [0.086] [0.085]
Union Member 0.307*** 0.256*** 0.083 0.129 0.108
(dummy) [0.081] [0.082] [0.094] [0.089] [0.097]
Full-time -0.475** -0.489** -0.034 -0.360* -0.316
(dummy) [0.200] [0.199] [0.109] [0.204] [0.204]
Any training 0.292*** 0.057 0.192** 0.186**
(dummy) [0.090] [0.078] [0.090] [0.093]
Firm Characteristics
Large Firm 0.434*** 0.333**
(dummy) [0.111] [0.148]
Exporter 0.302**
(dummy) [0.125]
Large Exporter -0.111
(interaction) [0.232]
Foreign-Owned 0.240**
(dummy) [0.108]
Age of Firm 0.004
(years) [0.007]
Age of Firm Squared 0.000
(years) [0.000]
F test 30.67 20.41 18.93 12.9 16.38 11.79
R-squared 0.18 0.21 0.22 0.19 0.22 0.24
Source: Authors‘ calculations based upon World Bank Enterprise Survey data
Note: OLS regressions, unweighted observations, robust standard errors in brackets. *** p<0.01, ** p<0.05, *
p<0.1.
175
Table 47: Wage regressions (OLS, firm-level)
Production Non-production
Average Wage Average Wage
Observations 270 267 266 201 199 199
Industry FE Yes Yes Yes Yes Yes Yes
Large Firm 0.069 0.008 -0.102 0.188 0.087 -0.007
(dummy) [0.194] [0.194] [0.205] [0.257] [0.251] [0.272]
Exporter -0.091 -0.100 -0.130 -0.071 -0.045 -0.081
(dummy) [0.161] [0.161] [0.160] [0.225] [0.223] [0.229]
Large Exporter -0.090 -0.093 -0.039 0.079 0.026 0.070
(interaction) [0.315] [0.313] [0.308] [0.416] [0.407] [0.412]
Foreign-Owned 0.487*** 0.485*** 0.454*** 0.297 0.279 0.228
(dummy) [0.144] [0.145] [0.144] [0.188] [0.185] [0.190]
Age of Firm -0.006 -0.007 -0.008 0.007 0.000 -0.001
(years) [0.009] [0.009] [0.009] [0.012] [0.012] [0.012]
Age of Firm Squared 0.000 0.000 0.000 0.000 0.000 0.000
(years) [0.000] [0.000] [0.000] [0.000] [0.000] [0.000]
Average worker has > 6 years of school. 0.250** 0.299*** 0.163 0.212
(dummy) [0.098] [0.098] [0.144] [0.148]
Percent of workers in union 0.001 0.001 0.006*** 0.005***
(percent) [0.001] [0.001] [0.002] [0.002]
Percent of workers part-time or temporary -0.002 -0.002 -0.005 -0.004
(percent) [0.002] [0.002] [0.003] [0.003]
Firm has formal training program 0.139 0.094
(dummy) [0.100] [0.145]
Capacity Utilization 0.003 0.005
(percent) [0.003] [0.004]
Firm has bank loan or overdraft 0.053 0.010
(dummy) [0.126] [0.167]
Firms accounts are audited externally 0.142 0.208
(dummy) [0.111] [0.188]
F test 2.729 2.753 2.655 1.507 2.684 2.199
R-squared 0.06 0.09 0.12 0.05 0.12 0.14
Source: Authors‘ calculations based upon World Bank Enterprise Survey data
Note: OLS regressions, unweighted observations, robust standard errors in brackets. *** p<0.01, ** p<0.05, *
p<0.1.
176
Appendix 5.1: Econometric Analysis of Perceptions about Access to Credit
Chapter 3 looks at whether there are systematic differences in firm perceptions across
different types of firms in Tanzania. The results suggested that, as in most countries, large firms
were less likely to say that access to finance was a problem than smaller firms. Given that large
firms are probably more likely to have access to collateral, are more likely to have established
relationships with banks, and are less likely to fail, this is probably not surprising. Similarly,
foreign-owned firms, who can often rely on financing from parent companies and who might
have access to finance in their home country, are less likely to say that access to finance was a
problem. Finally, firms in the retail trade sector are also less likely to say access to finance is a
serious problem.
Using the methodology similar to the methodology used in Gelb, Ramachandran, Shah
and Turner (2006) and Beck and others (2006), and described in Chapter 3, this section extend
the analysis to look at additional factors that might affect perceptions about finance. First, the
analysis looks at whether firms have different perceptions about access to finance in different
regions of the country. Second, several factors that might affect access to finance—whether the
firm owns land, whether the firm has audited accounts, and whether the firm is a limited liability
company. Since firms with land have better access to collateral and the presence of audited
accounts or limited liability might increase information on the firm for bank offices, these could
affect access to finance. Finally, the analysis looks at whether the firm has a loan or overdraft,
whether the firm has been rejected for a loan, and whether the firm does not think it needs a loan
affects affect perceptions. This could give an idea about what aspect of financing is the greatest
concern. For example, if most complaints are from firms with credit, this could indicate concern
about the terms of the credit (e.g., about interest rates or loan duration) rather than simply
whether the firm can get a loan or overdraft or not.
Empirical results
For the most part including additional variables does not have a large effect on the
previous results from Chapter 3. Foreign owned firms remain less likely to say that access to
finance is a serious problem and firms in the retail trade sector remain less likely to say that
access to finance is a serious problem. The coefficient on firm size remains negative—although
it becomes statistically insignificant in most of the additional specifications—indicating that
larger firms are less likely to see access to finance as a serious constraint that smaller firms are.
Region. Several dummies indicating region are included in the base regression in column
2 of Table 48. The coefficients on the dummies are often statistically significant. One notable
regional difference is that firms in Zanzibar are more likely to say access to finance is a serious
constraint than firms in other regions are. This is consistent from earlier results using the 2003
Tanzania survey that also found access to finance was a greater problem in Zanzibar than on the
mainland (Regional Program on Enterprise Development, 2007c) and indicates that the situation
on Zanzibar has not improved significantly since the previous survey. Other than in Zanzibar,
firms in the other regions of Tanzania were generally less, nor more, likely to say that access to
finance was a serious problem than in Dar es Salaam.
177
Additional Variables. The coefficients on the dummy variables representing whether the
firm owns its land and buildings and whether it has audited accounts are statistically insignificant
at conventional significance levels. Since land and buildings are the most common form of
collateral, whether the firm has land or buildings to use as collateral might affect access to credit.
As discussed in the Chapter, banks seem to accept other forms of property as collateral (e.g.,
accounts receivable and machinery and equipment) and so it might not be surprising that land
ownership does not appear to affect access to credit significantly.
Firms that are limited liability were considerably less likely to say that access to credit
was a serious problem than other firms. After controlling for other factors, limited liability firms
were about 20 percentage points less likely to say that access to credit was a serious problem
than other firms were. This might reflect that being a limited liability company makes the firm
more formal and, therefore, makes access to credit easier. As noted in the next section, however,
limited liability companies do not appear to be more likely to have credit (i.e., they were no more
likely to apply for loans and were more likely to be rejected). The results may therefore reflect
that managers of limited liability firms have less need for external financing rather than that they
have better access.
Access to Finance. An interesting question is to see whether firms with or without loans
are more likely to say access to finance is a serious problem. Since the question on perceptions
about access to finance explicitly refers to both whether the firm has access and the terms of that
access (e.g., interest rates), it is not only firms without loans that see access to finance as a
problem. For example, firms without loans might be less concerned about access to financing if,
for example, they are less likely to want financing (e.g., if they can finance their operations
through internal funds). Or firms with loans might be more concerned about access to financing
if they do not have loans that suit their demands well (e.g., if loan durations are too short, interest
rates too high, or they received less than they want).
When a dummy variable indicating that firm has either a loan or an overdraft is included
in the regression, the coefficient is negative but is not statistically significant (see column 4).
This suggests that managers of firms with loans are also concerned about access to finance.
Dividing the firms into firms with loans and firms with overdrafts leads to a similar conclusion
(see column 5).
The survey also contains some additional questions for firms that applied for loans in the
year before the survey. Firms that applied for loans were asked whether their applications were
rejected and firms that did not apply for loans were asked why not. Based upon these questions,
several additional variables are included noting whether the firm applied for a loan in the year
prior to the survey, whether the loan application was rejected, and whether the firm manager said
that his firm did not apply for a loan because it did not need a loan. The coefficients on the first
two variables are statistically insignificant—managers of firms that applied for a loan and had
that application accepted and managers of firms applied but were rejected were no more likely to
say access to finance was a greater or lesser problem than managers of firms that did not apply.
Managers who said that they did not apply because they did not need one, however, were about
20 percentage points less likely to say access was a problem than other managers.
178
Table 48: Differences in firm perceptions about access to finance in Tanzania
Firm says access to credit is a serious problem
(dummy)
Observations 476 476 470 469 469 469 469
Firm has bank credit -0.059
(dummy) (-0.96)
Firm has loan -0.006 -0.058 -0.053
(dummy) (-0.091) (-0.73) (-0.61)
Firm has overdraft -0.046 -0.044 -0.041
(dummy) (-0.60) (-0.57) (-0.53)
Firm applied for a new loan 0.090 0.000
(dummy) (1.22) (0.0043)
Firm did not need a loan -0.190***
(dummy) (-2.88)
Firm was rejected for a loan 0.088
(dummy) (0.74)
Microenterprise -0.024 -0.008 -0.005 0.008 -0.003 -0.005 0.008
(dummy) (-0.28) (-0.089) (-0.054) (0.087) (-0.033) (-0.054) (0.087)
Workers -0.046* -0.040 -0.029 -0.019 -0.024 -0.025 -0.020
(natural log) (-1.66) (-1.44) (-0.97) (-0.61) (-0.76) (-0.79) (-0.64)
Female Owners -0.011 -0.016 -0.027 -0.023 -0.024 -0.030 -0.047
(dummy) (-0.20) (-0.30) (-0.51) (-0.42) (-0.45) (-0.55) (-0.86)
Exporter 0.081 0.089 0.086 0.090 0.089 0.073 0.056
(dummy) (0.93) (1.00) (0.95) (1.00) (0.98) (0.80) (0.61)
Foreign-Owned -0.169* -0.171* -0.137 -0.141 -0.134 -0.138 -0.156
(dummy) (-1.73) (-1.72) (-1.32) (-1.35) (-1.29) (-1.33) (-1.51)
African-Owned -0.032 -0.040 -0.029 -0.032 -0.029 -0.029 -0.055
(dummy) (-0.48) (-0.60) (-0.43) (-0.47) (-0.42) (-0.42) (-0.79)
Manager is university education -0.042 -0.021 0.019 0.020 0.020 0.019 0.026
(dummy) (-0.77) (-0.38) (0.33) (0.34) (0.35) (0.33) (0.44)
Sector - Other Services 0.133* 0.163** 0.156* 0.151* 0.153* 0.164** 0.168**
(dummy) (1.74) (2.08) (1.96) (1.89) (1.92) (2.04) (2.06)
Manufacturing 0.113* 0.130** 0.130* 0.131* 0.132** 0.140** 0.133*
(dummy) (1.74) (1.97) (1.95) (1.95) (1.97) (2.07) (1.94)
Region – Mbeya -0.068 -0.102 -0.094 -0.098 -0.105 -0.113
(dummy) (-0.77) (-1.14) (-1.04) (-1.07) (-1.15) (-1.24)
Region – Zanzibar 0.459** 0.441** 0.436** 0.438** 0.443** 0.444**
(dummy) (2.27) (2.13) (2.09) (2.11) (2.14) (2.15)
Region – Arusha -0.289*** -0.287*** -0.285*** -0.286*** -0.287*** -0.292***
(dummy) (-3.64) (-3.52) (-3.48) (-3.49) (-3.52) (-3.60)
Firm owns land 0.026 0.025 0.023 0.021 0.026
(dummy) (0.48) (0.46) (0.42) (0.39) (0.48)
Firm has audited accounts 0.004 0.006 0.006 -0.001 0.031
(dummy) (0.063) (0.095) (0.089) (-0.019) (0.48)
Firm is Limited Liability Company -0.164*** -0.165*** -0.163*** -0.160*** -0.158***
(dummy) (-2.80) (-2.81) (-2.77) (-2.71) (-2.66)
Pseudo R-squared 0.02 0.06 0.07 0.07 0.07 0.07 0.09
Source: Authors calculations based on Enterprise Survey data.
Note: All regressions are probit regressions with marginal effects reported rather than coefficients ***,**, * Significant at 1 and 5 percent significance levels
179
Appendix 6.1: Effect of Generator Ownership on Firm Performance
Table 49: Regression Analysis: Impact of Generator Ownership on Enterprise Performance.
Growth (2003-2006) Productivity Probability of Investing
Observations 237 235 269
Generator Use
Firm owns generator 0.04*** 0.64*** 0.22
(dummy) (0.016) (0.166) (0.192)
Inputs
Capital ─ 0.19*** ─
(natural log) ─ (0.032) ─
Workers in 2003 -0.03*** ─ ─
(natural log) (0.007) ─ ─
Workers in 2005 ─ 0.73*** 0.30***
(natural log) ─ (0.085) (0.099)
Firm Characteristics
Age of firm -0.02* -0.08 -0.01
(years, natural log) (0.011) (0.098) (0.120)
Firm is foreign owned 0.03 0.33 0.46*
(dummy) (0.020) (0.209) (0.282)
Firm exports 0.04** 0.25 0.13
(dummy) (0.020) (0.217) (0.279)
Firm is in special economic zone -0.02 -0.13 0.52***
(dummy) (0.015) (0.149) (0.187)
Firm is in food processing sector 0.03 0.05 -0.74***
(sector dummy) (0.021) (0.205) (0.261)
Sector Dummies
Textile and Garments 0.04** -0.60*** -0.15
(sector dummy) (0.024) (0.229) (0.288)
Wood and Furniture 0.02 -0.43** -0.40
(sector dummy) (0.022) (0.215) (0.268)
Metal Working 0.01 -0.27 -0.23
(sector dummy) (0.027) (0.271) (0.347)
Chemical 0.03 0.85*** -0.02
(sector dummy) (0.031) (0.308) (0.418)
Intercept 0.18*** 7.50*** -1.45***
(0.046) (0.479) (0.557)
Adjusted R-Squared 0.0995 0.766 ---
Source: Author‘s calculations based upon World Bank Enterprise Survey data.
180
Appendix 6.2: Comparison of Doing Business Indicators
Table 50: Comparison of Doing Business Indicators in 2003 and 2008
Year 2003 or earliest available 2008
Starting a Business (Rank) 109
Procedures (number) 13 12
Time (days) 31 29
Cost (% of income per capita) 200.3 41.5
Min. capital (% of income per capita) 0 0
Dealing with Construction Permits (Rank) 172
Procedures (number) 21 21
Time (days) 308 308
Cost (% of income per capita) 3,105 2,087
Employing Workers (Rank) 140
Difficulty of Hiring Index 89 100
Rigidity of Hours Index 40 40
Difficulty of Firing Index 50 50
Rigidity of Employment Index 60 63
Firing costs (weeks of wages) 18 18
Registering Property (Rank) 142
Procedures (number) 9 9
Time (days) 77 73
Cost (% of property value) 7.4 4.4
Getting Credit (Rank) 84
Legal Rights Index 8 8
Credit Information Index 0 0
Public registry coverage (% adults) 0 0
Private bureau coverage (% adults) 0 0
Protecting Investors (Rank) 88
Disclosure Index 3 3
Director Liability Index 3 4
Shareholder Suits Index 6 8
Investor Protection Index 4 5
Paying Taxes (Rank) 109
Payments (number) 47 48
Time (hours) 172 172
Total tax rate (% profit) 43.8 45.1
Trading Across Borders (Rank) 103
Documents for export (number) 7 5
Time for export (days) 30 24
Cost to export (US$ per container) $822 $1,262
Documents for import (number) 13 7
Time for import (days) 51 31
Cost to import (US$ per container) $917 $1,475
Enforcing Contracts (Rank) 33
Procedures (number) 38 38
Time (days) 462 462
Cost (% of debt) 14.3 14.3
Closing a Business (Rank) 111
Time (years) 3 3
Cost (% of estate) 22 22
Recovery rate (cents on the dollar) 21.9 21.3
Source: World Bank (2003; 2008a).
Note: Data are for 2008 (from Doing Business 2009) and earliest period for which data are available. This is 2003
except registering property and getting credit (2004) and dealing with construction permits, protecting investors,
paying taxes and trading across borders (2005). Ranks are not available for early years.
181
182
APPENDIX 6.3: DIFFERENCES IN THE INVESTMENT CLIMATE ACROSS FIRMS
Although much of the focus of the main report has been on the overall investment
climate, it is also possible to look at differences across regions, across sectors, and across
exporters and non-exporters. Before making these breakdowns, it is important to note that the
samples can be relatively small in some cases (e.g., there are only between 20 and 30 firms in the
cities outside of Dar es Salaam). For this reason, we present some results from simple
hypothesis tests on whether the differences in means are statistically significant. It is, however,
important to keep the small sample size in mind even for statistically significant differences.
I. Differences by region
In general, the firms in Dar es Salaam and Arusha tend to be larger in terms of the
number of workers that they have (44 and 66 workers on average) compared with about 20 in
Mbeya and Zanzibar. They also tend to be more sophisticated in terms of being more likely to
have audited accounts, using e-mail, owning their own land and having better educated
managers. In general, firms in Arusha appear to be more sophisticated than firms in Dar es
Salaam on average
Table 51: Average of investment climate variables by region.
Dar es Salaam
Zanzibar
Mbeya
Arusha
Observations (unweighted) 286
64
24
45 Has audited accounts (% of firms) 51% *** 36% *** 16% *** 82% ***
Age (years, average) 11
12
8
12
Firm exports (% of firms) 5%
5%
0%
5%
Uses e-mail (% of firms) 40%
37% *** 11% *** 68% *** Uses own website (% of firms) 14%
22%
8%
28% ***
Manager has university education (% of firms) 44% *** 23% *** 23%
57% ***
Part-time workers (% of workers) 9%
4% *** 6%
16% Days of power outages (per month, average)1 7.9 * 9.4
6.8
15.8 ***
Cost of crime (% of sales, average)1 0.3 ** 0.4
0.9
0.7 ***
Cost of security (% of sales, average) 1.4
0.7 *** 1.9
1.7 ** Has bank accounts (% of firms) 88% *** 63% *** 99%
99% ***
Has loan or overdraft (% of firms) 20%
9% *** 20%
51% ***
Has invested in previous fiscal year (% of firms) 56% ** 46%
25% *** 43% Investment (as % of sales, average)1 7.4% ** 6.0%
1.2% *** 2.5%
% of revenue reported to tax authorities (average) 50
44 *** 85 *** 48
All revenues to tax authorities (% of firms) 25% ** 37%
69% *** 29% Owns land (% of firms) 41%
32% *** 16% * 86% ***
Percent of land owned by firm (average) 39
26 *** 16 * 82 ***
Says 'firms like theirs' pay bribes (% of firms) 43% ** 74% *** 67%
48% Bribes (as % of sales, average) 1.8 ** 3.8 * 2.4
4.0 **
Time spent dealing with regulations (average) 4.4
2.9 *** 5.0
12.7 ***
Number of tax inspections (average)1 2.8 * 2.2
2.7
2.1 * Have generator 54% *** 12% *** 5% * 78% ***
Provide own transportation 38%
15% *** 5% * 74% ***
Losses due to breakage and theft during transportation 1.3
0.7 ** 0.7
3.8 ***
Days of water outages 6.2
9.4 * 1.7
2.0 **
Firm provides training (% of firms) 36%
24% *** 18%
75% ***
Percent of workforce with only primary education 35%
43%
10%
35% Firm competes with informal firms (% of firms) 70%
44%
83%
57%
Source: World Bank Enterprise Surveys.
***, **, * Average is different than average for other firms at 1%, 5% and 10% significance levels
183
The previous Zanzibar Investment Climate Assessment noted that Zanzibar appears to
face some challenges that are more severe that those faced by firms on the mainland (Regional
Program on Enterprise Development, 2007c). Many of these observations also appear to hold in
2006. In particular, firms in Zanzibar are less likely to have access to finance, have less well
educated managers and workers, and are less likely to provide training to their workers. As in
the previous Zanzibar ICA, firms also reported that the burden of regulation was lower than
firms on the mainland. In contrast to the 2003 ICA, there is no evidence that corruption is less
serious in Zanzibar.
Other problems appear to affect firms in all cities covered in the survey. In particular,
although there are some differences across cities, power outages are common compared to the
best performing countries and losses during transportation are high. Dar es Salaam compares
more favorably with cities on the mainland with respect to the extent of corruption and the
burden of regulation.
II. Differences by sector
Manufacturing firms tend to be more sophisticated than firms in the retail trade sector. In
particular, they are more likely to have audited accounts, are more likely to export, have better
educated managers, and are more likely to have bank credit. Differences between manufacturing
and other service firms are more modest.
Table 52: Differences in investment climate variables by sector
Manufacturing
Retail
Services
Has audited accounts (% of firms) 56% *** 40% *** 54%
Age (years, average) 14 *** 8 *** 11 *
Firm exports (% of firms) 14% *** 2% ** 1% ***
Uses e-mail (% of firms) 39%
34%
47%
Uses own website (% of firms) 15%
10%
20%
Manager has university education (% of firms) 48% *** 37% ** 41%
Part-time workers (% of workers) 9%
6%
11%
Days of power outages (per month, average)1 9.2
10.4
8.3 *
Cost of crime (% of sales, average)1 0.5
0.4
0.3
Cost of security (% of sales, average) 1.5
1.4
1.2
Has bank accounts (% of firms) 83%
81%
90%
Has loan or overdraft (% of firms) 31% *** 16% * 20% **
Has invested in previous fiscal year (% of firms) 63% *** 37% *** 53%
Investment (as % of sales, average)1 7.1%
2.7% ** 7.7%
% of revenue reported to tax authorities (average) 53
50
48
All revenues to tax authorities (% of firms) 30%
29%
28%
Owns land (% of firms) 49% *** 28% *** 48% ***
Percent of land owned by firm (average) 46 *** 27 *** 45
Says 'firms like theirs' pay bribes (% of firms) 51%
56%
45%
Bribes (as % of sales, average) 2.2
2.9 * 2.1
Time spent dealing with regulations (average) 5.0
3.4 * 6.0
Number of tax inspections (average)1 2.8
2.5
2.6
Source: World Bank Enterprise Surveys.
***, **, * Average is different than average for other firms at 1%, 5% and 10% significance levels
In general, there are only modest sectoral differences with respect to the investment
climate. The number of power outages, the cost of crime and security, the number of tax
inspections and tax evasion appear similar across sectors. The burden of regulation appears to be
slightly lower and corruption appears to be more costly for retail trade firms.
184
III. Differences for exporters
Exporters tend to be very different from non-exporters. They are larger (122 workers
compared to 38), more productive (see Chapter 2), and have many other characteristics that
suggest that they are more formal. For example, their managers are better educated—66 percent
of managers have a university education compared to 53 percent and 41 percent for non-
exporters. They are also more likely to use e-mail, have their own website, have audited
accounts, own land, and provide training to their workers.
Table 53: Differences in investment climate variables by exporter status
Exporters Non-exporters
Has audited accounts (% of firms) 80% 50% ***
Age (years, average) 19 11 ***
Firm exports (% of firms) 100% 0% ***
Uses e-mail (% of firms) 75% 40% ***
Uses own website (% of firms) 27% 16% ***
Manager has university education (% of firms) 66% 41% ***
Part-time workers (% of workers) 9% 9%
Days of power outages (per month, average)1 9.1 9.1
Cost of crime (% of sales, average)1 0.6 0.4
Cost of security (% of sales, average) 1.0 1.3
Has bank accounts (% of firms) 93% 86%
Has loan or overdraft (% of firms) 52% 21% ***
Has invested in previous fiscal year (% of firms) 78% 50% ***
Investment (as % of sales, average)1 6.4% 6.4%
% of revenue reported to tax authorities (average) 66 49 **
All revenues to tax authorities (% of firms) 46% 28% ***
Owns land (% of firms) 61% 43% ***
Percent of land owned by firm (average) 58 41 **
Says 'firms like theirs' pay bribes (% of firms) 57% 49% **
Bribes (as % of sales, average) 2.0 2.3
Time spent dealing with regulations (average) 6.9 5.1 ***
Number of tax inspections (average)1 3.2 2.6 **
Have generator 70% 42% ***
Provide own transportation 56% 32% ***
Losses due to breakage and theft during transportation 2.6 1.2 ***
Days of water outages 6.4 6.0
Firm provides training (% of firms) 52% 34% **
Percent of workforce with only primary education 35% 36%
Although they are different in some ways, they are also affected by problems in the
investment climate. For example, they are as likely to face water and power outages, the cost of
crime and security is about the same for exporters and non-exporters, the cost of corruption is
similar, and the burden of regulation is higher for exporters than non-exporters. This final result
could be because they are larger and are therefore more visible to inspectors and regulators or
because of the time it takes them to deal with trade and customs regulations. Not surprisingly,
they also report higher losses due to breakage and theft during transportation.
The are, however, better equipped to deal with problems in the investment climate. For
example, they are more likely to have generators, are more likely to provide their own
transportation and are more likely to have loans and overdrafts. These should all place them
better to cope with problems in the investment climate.
185
Appendix 7.1: Other Factors that Affect Perceptions about Informality
Chapter 3 includes an econometric analysis of differences in firm characteristics that
appear to affect perceptions about all areas of the investment climate. The analysis in that
chapter shows that there were few significant differences with respect to views about
competition from the informal sector among firms of different types. In particular, small
enterprises were no more likely to say that competition was a problem than larger firms. The
analysis in this section extends the analysis in Chapter 3, looking at additional factors that appear
to affect perceptions about competition from informal firms.
Methodology.
The methodology used in this section is an extension of the analysis in Chapter 3 of this
volume, which is based upon the methodology in Gelb and others (2007). The question of how
different factors, including ownership, affect access to credit for microenterprises is examined by
estimating different versions of the equation below:
iVariablesAdditionalsticsCharacteriFirmiIC
The dependent variables are dummy variables indicating whether the manager of firm i rates that
area of the investment climate as a major or very severe obstacle. The independent variables are
the variables included in Chapter 3 (size, ownership, sector, education of manager, and export
status) and a set of additional variables that might affect perceptions about competition with
informal firms. The error term is assumed to be normally distributed. Because the dependent
variable is a dummy variable, the model is estimated using standard maximum likelihood
estimation. The coefficients in Table 41 are marginal effects calculated at the means of all
variables. For dummy variables, they can be interpreted as the difference in the likelihood that a
firm of that type will say that that area of the investment climate is a serious problem.
The additional variable include variables representing the share of revenues that the firm
reports to the tax authorities, various measures of competition, and a dummy variable indicating
that the firm competes with informal firms. Questions about the level of competition that the
firm faces were only asked to manufacturing firms, while the question on whether the firm
reports unregistered competitors is asked only to retail firms.
Empirical Results
Table 41 shows the empirical results from the regressions. For the most part, including
additional variables does not have a large effect on the results from Chapter 3. In particular,
none of the control variables related to size, ownership, export behavior or manager education
are consistently correlated with the firms‘ level of concern about competition from informal firm.
Interestingly, model specifications that exclude the microenterprise sample (i.e.., columns 4-6),
managers of retail firms appear less likely to say that competition with the informal sector is a
problem than managers of firms in other sectors. This is also true in the base specification
(column 1) when the microenterprises are excluded. The coefficients on other variables,
however, remain statistically insignificant when microenterprises are excluded.
186
Tax Evasion. One interesting question is how tax evasion affects perceptions about
competition from informal firms. It is possible that firms that pay their taxes consistently and in
full might be more concerned about competition from informal firms since informal firms will
have a significant tax advantage over formal firms that pay their taxes.
In practice, however, it is difficult to measure tax compliance—firm managers are often
unwilling to answer questions about illegal behavior such as tax evasion or corruption openly
and so it is hard to accurately estimate the magnitude of the problem. Although the Enterprise
Survey does not use the most sophisticated techniques to try to get honest answers, the question
is asked indirectly.99
The actual question is ―what percentage of total annual sales would you
estimate a typical establishment in your sector of activity reports for tax purposes?‖ The indirect
phrasing allows the manager to answer the question without implicating him or herself of tax
evasion.
The indirect phrasing allows the manager to answer indirectly and so should encourage
honesty. This, in turn, should allow an estimate of the average level of sales that firms report to
the tax authority or at least the average level that managers believe that other firms in their sector
report to the tax authority. It is less clear, however, how to treat individual firm responses. In
particular, it is not clear whether managers are referring to how much of sales their own
enterprises report or whether they are referring to what they estimate other enterprises in the
same sector report. For example, managers might know that they are personally very dishonest
and report nothing to the tax authorities but might suspect that other managers in the same sector
are far more honest and report everything to the tax authorities. If managers in this situation
answer the question as it is asked, they would say 100 percent (since other firms report 100
percent of sales). If they understand the indirect phrasing is intended to elicit information on
their own behavior without requiring them to admit to tax evasion, they would answer 0 percent.
The indirect question, therefore, makes if difficult to draw strong conclusions from the
regressions.
The coefficient on the percent of sales reported is negative, but is small and is statistically
insignificant. This suggests that there isn‘t a strong link between this measure of tax evasion and
concern about competition from the informal sector. One possible explanation for this (assuming
that firms answer the question thinking about their own level of tax compliance) is that formal
firms do not generally perceive themselves as competing directly with informal firms and, as a
result, are not any more concerned about competition with informal firms than firms that are
evading taxes.
Firm competes with informal firms. Although the question was not asked to firms in all
sectors, SMLEs in the retail sector were asked directly whether they compete with formal firms.
About two-thirds of retail SMLEs said that they did. Not surprisingly, retail firms that said they
competed with informal firms were more likely (about 18 percentage points more likely) to say
that competition with informal firms was a serious problem than firms that did. Given that only
one-fifth of retail SMLEs said that competition with informal firms was a serious problem, this
difference is large. The results from the two questions, therefore, appear to be broadly
consistent—firms that see themselves as competing with informal firms are more likely to say
that competition with informal firms is a serious problem.
187
Limited Liability Companies. As discussed in the chapter, limited-liability companies are
often seen as more ‗formal‘ than sole proprietorships. Consistent with this, limited liability
companies were far less likely to say that competition with the informal sector were 14
percentage points less likely to say that they saw competition with informal firms as a serious
problem than other firms were even after controlling for size and sector. This emphasizes the
divide between formal and informal firms in Tanzania.
Competition. It is possible that concern about competition with informal firms partly
reflects unease about competition in general. Firms might not know how formal or informal
their competitors are and firms in competitive industries might believe that their competitors
have an unfair advantage because they evade taxes or do not comply with regulations.
This does not seem to be the case. Firms with at least three competitors were no more
likely to say that competition with informal firms was a problem than firms with fewer
competitors. Similarly, market share was also not correlated with concern about informality.
This suggests that concern about competition from informal firms is not simply reflecting
concern about competition in general (i.e., from both formal and informal firms).
Table 54: Impact of tax evasion and competition on perceptions about competition with informal competitors
Column 1 2 3 4 5 6
Informal Competitors
Observations 472 471 472 68 259 404
Sample All All All Retail
Only
Manufacturing
Only
All
(no micro)
Revenues report to tax authorities -0.000
(% of revenues) (-0.14)
Firm is limited liability company -0.141***
(-2.89)
Firm competes with informal firms 0.180*
(dummy) (1.71)
Firm has more than 3 competitors 0.061
(dummy) (1.00)
Local market share -0.001
(% of market) (-0.40)
Workers 0.016 0.016 0.033 0.117 0.029 0.014
(natural log) (0.65) (0.66) (1.31) (1.60) (0.88) (0.53)
Exporter 0.064 0.067 0.065 -0.041 -0.015
(dummy) (0.82) (0.86) (0.83) (-0.45) (-0.18)
Foreign-Owned -0.117 -0.117 -0.102 -0.166* -0.121
(dummy) (-1.44) (-1.43) (-1.23) (-1.72) (-1.49)
Female-Owned 0.006 0.008 -0.003 0.092 0.009 0.021
(dummy) (0.12) (0.17) (-0.058) (0.91) (0.12) (0.40)
African-Owned -0.006 -0.011 -0.002 0.141 -0.029 -0.019
(dummy) (-0.11) (-0.18) (-0.031) (1.21) (-0.40) (-0.31)
Manager has university education 0.049 0.046 0.079 -0.016 0.035 0.061
(dummy) (0.98) (0.91) (1.55) (-0.18) (0.50) (1.13)
Services 0.085 0.084 0.088 0.395* 0.187**
(dummy) (1.17) (1.17) (1.21) (1.68) (2.13)
Manufacturing 0.045 0.043 0.048 0.144**
(dummy) (0.74) (0.71) (0.79) (2.08)
Microenterprise 0.053 0.053 0.054
(dummy) (0.66) (0.66) (0.68)
Pseudo R-squared 0.01 0.01 0.03 0.17 0.02 0.02
Source: Authors‘ calculations based on World Bank Enterprise Survey.
* Coefficient significant at a 10% significance level; ** 5% level; *** 1% level.
188
189
ENDNOTES
1 The earliest Enterprise Surveys were conducted in 2000. In the first few years, there was some minor variations in
both questionnaire design and sampling design. One particular difference is that most early surveys covered only
manufacturing. For this reason cross-country comparisons mostly focus on manufacturing firms. Since 2005, the
surveys have become even in terms of both sampling and questionnaire design. In addition, weights, which were
generally not calculated in the early surveys, have been consistently calculated since 2005.
2 Diamond mining accounted for about one-third of GDP in 2004/05. This includes other mining, although mining
is dominated by diamonds (International Monetary Fund, 2006).
3 See, for example, Leith (2005) and Acemoglu and others (2003) for a discussion of the institutional characteristics
that have allowed Botswana to do this. Regional Program on Enterprise Development (2007a) discusses the
Investment Climate in Botswana in more detail.
4 Using firm-level data for seven countries in SSA, including Tanzania, Clarke (forthcoming) shows that restrictive
trade and customs regulation have affected manufacturing exports from Africa, including from Uganda.
5 See endnote 1. Weights will be used to ensure comparability.
6 Reducing poverty in rural areas is also vital in this respect. Recent studies have suggested that agricultural growth,
which could be achieved through improvements in market access and increases in agricultural productivity, could
have a significant impact on poverty in Tanzania (Treichel, 2005; Vice President's Office, 2005; World Bank,
2006b).
7 The International Monetary Fund (IMF) estimated that problems in the energy sector problems slowed growth by 2
percent (International Monetary Fund, 2007a).
8 Data from the National Bureau of Statistics.
9 A study by the IMF in 2004 found the REER to be in line with fundamentals, (International Monetary Fund, 2004).
Since 2004, the REER has depreciated by about 10 percent which the IMF considers to be consistent with the terms
of trade deterioration (International Monetary Fund, 2007b). Recent estimates suggest that the currency might have
been slightly undervalued at the end of 2007 and that a mild appreciation might occur over the next few years
(Hobdari, 2008).
10 Weights will be used to ensure comparability.
11 See National Bureau of Statistics (2006b; 2006c) and Office of Chief Government Statistician (2005)
12 See Appendix 1.1 for a full description of the area sampling methodology used to include informal enterprises.
13 The difference between large and medium-sized firms is only statistically different from zero at a 12 percent
significance level. The difference between small and medium-sized firm is statistically different from zero at a 1
percent significance level.
14 When comparing based upon the book value of capital, the median firm is less capital intensive than the median
firm in Uganda.
15 See, for example, Pakes (2008).
16 This concern can be lessened—although not eliminated—by using good sector specific price deflators.
Unfortunately, even these were not available for Tanzania.
17 See, for example, the discussion by Levinsohn (2008) on the Escribano-Guasch methodology for TFP calculations
(2005; 2008; 2005).
18 The difference is statistically significant. Using a non-parametric test, the medians are statistically different in the
two samples at a 5 percent significance level (Chi-Squared [1]=3.9, p-level=0.05).
190
19
This appears to be true for both sensitive and less sensitive questions. Jensen et al (2008) show that non-response
patterns and lying reduce measured corruption in politically repressive environments. But similar patterns also
appear for less sensitive questions. In particular, Clarke et al (2006) show that firms appear to complain more about
access to finance in countries that are more free politically than in other countries after controlling for other country
and firm characteristics.
20 Hausmann and Velasco (2005) illustrate this point with an analogy to camel and hippos. They note that the few
animals that you find in the Sahara will be camels, which have adapted to life in the desert, rather than hippos,
which depend heavily upon water. Asking the camels about problems associated with life in the desert might not
adequately represent the views of the missing hippos.
21 See, for example, Gelb et al (2006) for work using data from Africa or Hellman and others (1999) for work using
data from Eastern Europe and Central Asia.
22 For example, some work has shown that managers in Africa appear to find it difficult to answer questions that
involve calculating percentages. Clarke (2008) shows that managers that report bribes as a percentage of sales
report bribe payments that are between four and fifteen times higher when they report them in monetary terms. This
does not appear to be due to outliers, differences between firms that report bribes in monetary terms and firms that
report them as a percent of sales, and the sensitivity of the corruption question. Lying is also a problem. Azfar and
Murrell (forthcoming) show that even broad questions about corruption, including questions about ‗firm like yours‘,
suffer from serious problems with lying and non-response that can lead to substantial underestimates of the extent of
corruption..
23 In the Enterprise Survey, many objective questions on sensitive questions are asked indirectly to reduce these
concerns. For example, on the issue of corruption, firms are asked the question ―we‘ve heard that establishments are
sometimes required to make gifts or informal payments to public officials to get things done with regard to customs,
taxes, licenses, regulations, services etc. On average, what percentage of total annual sales, or estimated annual
value, do establishments like this one pay in informal payments/gifts to public officials for this purpose?‖ There are
also a series of direct questions about bribe requests for licenses and utility connections and during inspections. For
example, in the question on utility connections, firm managers that reported applying for utility connections were
asked whether ‗a gift or informal payment was expected or requested‘ not whether a bribe was paid. Thus, they can
admit that a bribe was requested without actually admitting whether it was paid. Azfar and Murrell (forthcoming)
argue that even broad questions about corruption, including questions about ‗firm like yours‘, suffer from serious
problems with lying and non-response.
24 That is, focusing on results that do not appear to be due to chance but also looking at whether the differences
appear to affect rankings significantly. For example, although manufacturing firms were more likely to say that
trade regulation was a serious problem than non-manufacturing firms, it did not rank among the top constraints for
either type. Although only 4 percent of non-manufacturing firm managers said it was a serious problem (meaning it
ranked as the 17th
constraint for non-manufacturing firms) and 20 percent of manufacturing firm managers said the
same, it still only ranked as the 12th
greatest constraint for manufacturing firms. That is, it was not particularly
constraining for either type of firm.
25 The WBES is discussed in detail in Batra and Stone (2002). Regional Program on Enterprise Development
(2004c) discusses results from the 2003 survey.
26 For example, the 2003 survey asked about ‗cost of finance‘ and ‗access to finance‘ separately, while the 2006
survey asked only about ‗access to finance (availability and cost)‘. The WBES survey asked about ‗infrastructure‘
rather than ‗electricity, telecommunications, and transportation‘ separately.
27 Different lists of constraints are probably particularly troublesome when the questions are phrased as ―what are
the biggest constraints you face‖. Even when ‗other‘ is offered as a potential answer, firm managers appear to
usually choose a constraint from the list.
28 Iarossi (2006, p. 61-62) discusses the design of these questions in the context of business environment surveys.
29 See World Economic Forum (2005; 2006; 2007).
191
30
The World Business Environment Survey is described in greater detail in Batra and others (2002) and Hellman
and others (1999).
31 Note that this does not appear to be related to marital status as the distribution of single and married workers is
roughly the same in male and female owned firms.
32 Note however that part time work appears marginal, with 96 percent of the sample of workers working full time.
Interestingly, working part-time seems an option for either low end (unskilled production jobs) or high level
workers.
33 Note that it is not possible to explore this hypothesis with this dataset as there is no data on non-working women
34 See, for example, Iarossi (2006) for a discussion of the accuracy of recall data in the Enterprise Surveys.
35 The difference between microenterprise and SMLE managers was statistically significant even after controlling
for other things that might affect perceptions (see Chapter 3).
36 As noted in Appendix 1.2, the 2003 survey covered only manufacturing and so comparisons between the two
surveys are made for manufacturing firms only.
37 Comparisons between the worker surveys are particularly difficult because of the absence of weights that can be
applied to individual workers in the two surveys. That is, although workers are selected randomly within firms and
firms are selected randomly within the country, the probability of a worker being selected will depend both upon the
probability of the firm being selected and the probability of the worker being selected in the firm. In practice, the
probability of the worker being selected within the firm will depend at least partly on firm size.
38 These results remain statistically significant after controlling for the other characteristics. See the econometric
analysis in Appendix 4.1.
39 They are also better connected to the internet and have better financial endowments (Goedhuys, 2007). Smaller
and medium sized firms partly offset the scale disadvantages they face by collaborating more intensely with other
local firms on product development, marketing and technology
40 Similar returns have been found in other developing countries (Psacharopolous, 1993; 1994). The returns
observed in Tanzania are, however, slightly lower than in middle-income countries in Southern Africa. Similar
estimates for Botswana, Namibia and South Africa suggest returns between about 7 and 10 percent, 7 and 11
percent, and 7 and 12 percent respectively (Clarke and others, 2007; Clarke and others, 2008; Regional Program on
Enterprise Development, 2008a; Regional Program on Enterprise Development, 2008b). Returns were lower in
Swaziland—between about 2 and 2.5 percent per year (Regional Program on Enterprise Development, 2008c).
41 A study of wage and productivity premiums in three countries in SSA, Tanzania, Kenya and Zimbabwe, found the
wage premium for males to be highest in Tanzania (Van Biesebroeck and others, 2007).
42 Cull and Spreng (2008) provide a full description of the bank privatization process in Tanzania, focusing on the
privatization of NBC/NMB.
43 Data from Directorate of Banking Supervision (2008).
44 Aspects of the contractual arrangement, the firms involved, and the location of the firms are standardized for
comparison. It is assumed that the case in Tanzania goes through the commercial courts.
45 See World Bank (2006a).
46 All aspects of the transaction are standardized in terms of both buyers and sellers and the property. In particular,
the property is fully owned by the sellers, with no mortgages attached at the time of sale, no title disputes, and no
building code or other violations. The property is not used for any special purpose that would require additional
permits, has no occupants (legal or illegal). The full description is include in World Bank (2008a).
47 The report also notes that it took 41 days in Mbeya. However, the report notes that this appears to be based upon
a very optimistic assessment of how long it would take to complete procedures in Dar es Salaam.
192
48
For Uganda, see Regional Program on Enterprise Development (2008d).
49 Some firms with successful loan applications in 2005 did not have active loans at the time of the survey. It is
possible if they had short-term loans approved in early 2005 (e.g., for a year or less) that had expired by the time of
the survey (late 2006).
50 See Regional Program on Enterprise Development (2007c)
51 This analysis is based in part on the econometric analysis in Appendix 5.1.
52 The difference is statistically significant at conventional significance levels after controlling for other things (see
Appendix 5.1).
53 See Regional Program on Enterprise Development (2008d)
54 The difference remains even after controlling for other factors that might affect access to finance (see Appendix
5.1 and Chapter 3).
55 Because most firms that did not apply did not have a loan, the percentages for all SMLEs and those that did not
have one are almost identical.
56 Only 8 microenterprises had a loan application rejected, meaning that there are too few for analysis.
57 The methodology used in the Uganda CEM (World Bank, 2007f) is based upon a methodology proposed by
Bigsten and others (2003). Unfortunately, given the current survey structure, it is not possible to neatly divide firms
into these groups. Most notably, this is because firms were not asked whether they had a loan in 2005. Firms were
assumed to have a loan in 2005 if they either applied for a loan and that application accepted in 2005 or if they had a
loan in 2006. This will exclude some firms that had a loan before 2005 that ended before 2006 and will include
some firms that did not have a loan in 2005 but who applied for a loan and got that loan in 2006.
58 Unfortunately, because the questions are asked differently between the 2003 and 2006 surveys, it is not possible to
make direct comparisons between the 2003 and 2006 surveys. For the same reason, it is not possible to compare
Uganda with the Asian comparators or with South Africa and Mauritius.
59 Data for South Africa are for 2005, before the recent problems in the power sector.
60 Comparable data are not available for the other comparator countries
61 The Logistics Performance Index (LPI) is based on information collected from logistics professionals and freight
forwarders. The respondents are asked to rate the performance of countries that they do business in along seven
areas of logistics. Performance is evaluated on a five-point scale (1 for lowest, 5 for highest).
62 For countries with Enterprise Surveys conducted before 2005. Although similar information is not available for
more recent surveys, it seems that concern has also remained high in these surveys.
63 The relatively high VAT rate was also noted in the 2004 Investment Climate Assessment (Regional Program on
Enterprise Development, 2004b).
64 This measure is similar to an average effective tax rate (i.e., rather than a marginal effective tax rate).
65 The Doing Business report assumes that sales taxes and value-added taxes are passed on to consumers and so does
not consider them in the total tax rate (World Bank, 2008a).
66 See World Bank (2007a; 2008b) for more detail.
67 Many studies have found that both are linked to burdensome regulations, red-tape and taxation. See Friedman and
others (2000), Djankov and others (2002a), Djankov (2002b), Johnson and others (1998), Schneider (2000),
Schneider and Klinglmair (2004), Shleifer and Vishny (1993), Svensson (2005) and World Bank (2003).
68 The indicators rely upon there being established practices in each area. For example, indicators of the
requirements to close a business are only calculated when actual cases have been resolved using these procedures.
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69
Of course, some things measured by the Doing Business indicators might be difficult or impossible to avoid. For
example, if there is no credit registry, an individual firm will have no way of avoiding or evading this problem either
legally or illegally.
70 Studies of the investment climate have focused much less on the role that firms play in shaping the investment
climate: where the choice is made to devise rules of the game that systematically benefit particular, privileged
companies at the expense of society (Desai and Pradhan, 2005).
71 Over the past decade, studies have emphasized the importance of good governance, institutions, the rule of law
and the protection of property rights with the absence of corruption and with economic growth. Mauro (1995) is an
early study that looks at the relationship between corruption and growth. However, other studies such Keefer and
Knack (1997) and Knack and Keefer (1995) have linked broader measures of institutions and governance with
economic growth. More recent studies have tried to control for the potential endogeneity of institutions. See, for
example, Acemoglu and others (2001).
72 Some recent studies have looked at these measures and have questioned whether the six different concepts really
measures distinct aspects of governance (Guasch and Knack, 2008; Thomas, 2007). Kaufman and Kraay (2007;
2008) responds to these criticisms.
73 See Muller (2008)
74 Between 1985 and 1991, which was during and immediately following liberalization, it was estimated that the
urban informal sector‘s share of GDP increased from 10.3 to 14.5 percent (World Bank, 1996).
75 Charmes (2000) estimated based upon a 1991 survey that the informal sector contributed 22 percent of total GDP,
43 percent of non-agricultural GDP and 20 percent of total employment. World Bank (1996) estimates that the
urban informal sector‘s share of GDP was 14.5 percent in 1991.
76 Schneider (2002) estimated that the informal sector was equal to about 58 percent of GDP, although other
estimates suggested lower shares. Masare (2000) estimates the informal sector contributed 22 percent of total GDP
(43 percent of non-agricultural GDP and 20 percent of total employment)—shares roughly the same as the SSA
average. According to the Bureau of Statistics non-monetary GDP is estimated at 25% of total GDP. An ILO
informal sector survey published in 2004 suggests that the informal sector employs twice as many people compared
to the formal sector.
77 Some definitions focus on workers rather than activities. For example, Perry and others (2006) focuses on
employment using a social protection definition that defines informal work based upon a definition of paid workers
that are unregistered with social security.
78 For example, only limited liability companies, close corporations and foreign and external companies can register
with the Registrar of Companies in Botswana (Ministry of Trade and Industry, 2005). Natural persons can register
business names, however.
79 A recent survey focusing on micro trading in mainland Tanzania noted that even traders with access to legal
trading space would move to areas where a higher return was expected, thereby violating and contravening town and
council laws.
80 As discussed in Chapter 3, the difference in the likelihood that SMLE and microenterprise managers see tax rates
as a serious problem is not statistically significant. The small size of the microenterprise survey (only 65 firms),
however, makes it difficult to find statistically significant results.
81 The process is described in Tanzania Revenue Authority (2007) and the Tanzania National Website (Government
of Tanzania, 2008)
82 See, for example, Rocks and Halperin (2008)
83 See National Bureau of Statistics (2006b; 2006c) and Office of Chief Government Statistician (2005)
84 Results from this survey are discussed in Regional Program on Enterprise Development (2004a)
85 See National Bureau of Statistics (2006b; 2006c) and Office of Chief Government Statistician (2005)
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86
Data from AGOA website (http://www.agoa.info/index.php?view=trade_stats&story=apparel_trade), downloaded
on September 20, 2007.
87 Following Caves (1990), the analysis in this chapter uses value-added rather than sales is used as the dependent
variable. Caves (1990) found that measures of TE (technical efficiency) based upon revenues (gross output) were
far more sensitive to small changes in functional assumptions with respect to calculating efficiency.
88 It is possible to make other assumptions about the functional form of the production function (e.g., to assume a
trans-log production function), although this does not appear to have a significant impact on results in most cases.
See, for example, the analysis from the Investment Climate Assessment for Turkey (World Bank, 2007e).
89 Breaking the firm specific measure of productivity is mostly for convenience—that is it means that it is possible to
assume that vi has a mean of zero. In practice the two terms could be merged into a single term where vi has a non-
zero mean.
90 Gatti and Love (forthcoming) do this allowing access to credit to be endogenous in the second step.
91 This is due to omitted variable bias. It is discussed in more detail in Chapter 7 in Kumbhakar and Lovell (2000)
and Escribano and Guasch (2005).
92 See Gelb and other (2006). A similar pattern was also observed in Swaziland, where larger firms were also more
likely to say that most aspects of the investment climate were serious constraints (Regional Program on Enterprise
Development, 2007b).
93 For example, in South Africa, exporters were far more concerned about the instability of the Rand against other
currencies than other firms (Clarke and others, 2007; Clarke and others, forthcoming; Regional Program on
Enterprise Development, 2006)
94 There is a significant negative effect for large firms which export – this is likely to be related to sampling issues as
only 37 workers fall into this category, and this subsample appears to have fewer professional workers and more
unskilled workers than the overall sample.
95 To the extent that training is voluntary, this positive correlation could also reflect self-selection into the training
linked to same non-observable characteristics responsible for greater success in schooling.
96 Given that fewer women than men work, those who are on the market have specific characteristics (such as
motivation, or ability) which make them more likely to be successful, and therefore occupy places where training
might be most productive from a firm‘s point of view.
97 Ramachandran and others (2005) find that high-skill intensity firms in East Africa are more likely to invest in a
number of health-enhancing activities.
98 A study of wage and productivity premiums in three countries in SSA, Tanzania, Kenya and Zimbabwe, found the
wage premium for males to be highest in Tanzania (Van Biesebroeck and others, 2007).
99 Techniques of getting honest answers to sensitive question are discussed in Iarossi (2006) and Recanatini and
others (2000)