patterns of business creation, survival, and growth...

Patterns of Business Creation, Survival, and Growth:

Evidence from a Developing Country

Leora Klappery Christine Richmondz

November 2009

Abstract

We study rm dynamics using a novel database of all formally registered rms in

Cote dIvoire from 1977-1997, which account for about 60% of GDP. During this pe-

riod, Cote dIvoire experienced (separate) episodes of trade reform, scal reform, and

monetary reform, and we examine the impact of these shocks on private sector dy-

namics. First, we examine entry and exit patterns over this period and the role of new

and exiting rms versus incumbents in job creation and destruction. In comparison

with the U.S., new rms play a relatively important role in job creation and growth,

particularly during periods of political uncertainty. Yet, in absolute terms, incumbent

rms have a much larger impact on net employment. Next, we examine survival rates

We thank Francois Bourguignon for providing us the data and Daniel Dias, Sebastian Edwards, PhilEnglish, Vesela Grozeva, Ed Leamer, Phil Kim, Javier Miranda, Nico Voigtlander, Mark Wright, RomainWacziarg, and seminar participants at the World Bank and 2009 COST Workshop on Firm-level DataAnalysis in Transition and Developing Economies, George Mason University-Kauman Foundation BusinessCreation Symposium, and 2009 Northeast Universities Development Consortium for helpful comments.Financial assistance from UCLA CIBER is gratefully acknowledged by Christine Richmond. The opinionsexpressed do not necessarily represent the views of the World Bank, its Executive Directors, or the countriesthey represent.

yWorld Bank, Development Research Group. The World Bank. 1818 H St. N.W., Washington, DC20433. Tel: +1.202.473.8738; Email: [email protected].

zAnderson Graduate School of Management, UCLA. Anderson Graduate School of Management, UCLA.110 Westwood Plaza, Entrepreneurs Hall, Suite C525, Los Angeles, CA 90095-1481. Tel.: +1 310.825.8207;Email: [email protected].

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and nd that survival increases monotonically with rm size. Furthermore, higher

GDP growth increases survival rates, and periods of economic growth have the largest

impact on the reduction in the exit rates of small rms, relative to large rms. Finally,

we nd that all reforms decreased rm survival, although the impact varied by sector:

for instance, political reforms increased the exit rate of foreign-owned rms (relative

to domestic rms), while both trade and monetary reforms decreased survival rates of

rms in the trade sector.

JEL Classication: G18, G38, L51, M13

Keywords: Business Incorporation; Regulatory Barriers; Economic Growth

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1 Introduction

Firm entry and exit Schumpeterian creative destructionis critical for the continued

dynamism of the modern economy. Though evidence has linked entrepreneurship and eco-

nomic growth in developed countries, we have scarce evidence that a relationship exists

in developing countries. For instance, to what extent does entrepreneurship contribute to

job creation in countries with regulatory, legal, and tax barriers to entry and exit? In this

paper, we examine job creation and destruction, at both new and incumbent rms, in Cote

dIvoire, ranked 161 out of 181 countries in the Doing Business "Ease of Doing Business"

ranking (where 1 indicates the most favorable business environment) (World Bank, 2009).

We use a new and unique database that includes the complete census of registered

rms in Cote dIvoire from 1976 through 1997. This includes data collected at the time

of registration (ownership, sector, etc.) as well as annual reporting of total employment.

Unlike comparable studies that use survey data or manufacturing censuses, we can compute

over time exact formal sector entry and exit rates; growth and contraction at incumbent

rms; and the impact of macroeconomic and policy reforms on sectoral distribution and

growth.

First, we examine business dynamics in Cote dIvoire, using the complete census of

registered rms over the period 1976 to 1997. We decompose trends in job creation and

growth into entry of new rms and expansion of incumbent rms, and job destruction

into exit and contraction. We also illustrate trends in Cote dIvoire, relative to the U.S.

(Haltiwanger et al., 2008), and compare dierences and similarities in formal business

creation and destruction across very dierent investment climates.

Next, we examine the impact of three policy reforms on entrepreneurship: The rst

reform removed import quotas and other trade restrictions. The second reform reduced

public sector wages (in an attempt to reduce the budget decit). The third reform devalued

the currency by half. We study the eect of these scal, monetary, and trade reforms on

the creation of new rms, the average size of rms that incorporate, and the growth of

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incumbent rms, controlling for rm size, ownership, and age. Finally, we use a survival

analysis to study the impact of the business cycle and government reforms on rm exit.

New research on the survival of rms in low-income countries has important implications

for development strategies. In order for the private sector to act as an engine of growth

and advance the development process, it is necessary for rms to survive and grow. It is also

important for policymakers to understand and consider the impact of government policies

on private sector survival and growth.

Our paper proceeds as follows. Section 2 reviews previous literature related to rm

dynamics. Section 3 reviews the relevant economic and political history in Cote dIvoire.

Section 4 compares business dynamics in Cote dIvoire and the U.S. Section 5 discusses our

survival test methodology and results. Section 6 concludes.

2 Related Literature

Empirical studies of rm dynamics overwhelming focus on rm behavior in the US, Canada,

and other industrialized countries.1 In general, these studies nd that gross entry is sub-

stantial in most industries and is signicantly higher than net entry due to the high death

rate of infant rms.2 Successful entrants grow rapidly, so that an entrant cohorts initial

market share falls slowly. For instance, an early study of patterns of rm entry, growth, and

exit in US manufacturing industries over the period 1963-1982 shows that there is substan-

tial and persistent dierences in entry and exit rates across industries and that industries

with higher than average entry rates tend to also have higher than average exit rates (Dunne

et al., 1988). The authors also nd that most entrants and exiters are substantially smaller

than existing or continuing producers.

Recent cross-country studies have examined the impact of country characteristics, in

particular, the legal and regulatory environment, on rm entry and growth. For instance, a

1See Caves(1998) for a thorough survey of the literature on job turnover and rm mobility.2There is also a substantial literature on entry into an industry (possibly by a rm from another industry)

as distinguished from rm creation or entrepreneurship; see Gilbert (1989) for a comprehensive survey.

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comprehensive picture of entry rates across Europe (using a rm-level database of registered

rms) use a dierence-in-dierence approach to show that onerous entry regulations hamper

the creation of new rms, especially in industries that naturally should have high entry.

Furthermore, the consequences of regulatory barriers against entrepreneurship are seen, not

in young rms, but in older rms, who grow more slowly and to a smaller size (Klapper

et al, 2006). Another paper uses this database and a cross-country approach and nds

complementary evidence that entry regulations have a negative impact on rm entry (Desai

et al., 2003). A study using rm-level data from OECD countries to analyze rm entry and

exit nds that higher product market and labor regulations are negatively correlated with

the entry of SMEs in OECD countries (Scarpetta et al., 2002). Finally, a study of entry

rates in over 100 countries nds signicant relationships between entrepreneurial activity

and indicators of economic and nancial development and growth, the quality of the legal

and regulatory environment, and governance (Klapper et al., 2009).

Other studies have studied rm size distribution, i.e. the importance of small and

medium sized rms (SMEs) in the economy. For instance, a study of the creative destruction

process in 24 developed and developing countries over the 1990s nds that countries are

dominated by micro and small rms (with less than 20 employees), which accounts for more

than 80% of the rm population, yet only accounts for up to 30% of employment (Bartelsman

et al., 2004). A study of 76 countries nds a strong association between the importance of

SMEs and GDP per capita growth, although not necessarily a causal relationship (Beck et

al., 2005).

The shortcoming of most studies in low- and middle-income countries is their dependence

on survey data. For instance, surveys for ve Eastern and Southern African countries

(Botswana, Kenya, Malawi, Swaziland, and Zimbabwe) as well as the Dominican Republic

are used to examine the magnitude and determinants of rm births, deaths, and expansions

of micro and small enterprises (MSEs) (Mead and Liedholm, 1998). The authors nd that

the annual rate of new MSE start-ups averages over 20%, and that the vast majority of

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new rms were one-person establishments. For MSE closures, location plays a signicant

role with urban MSEs having an almost 25% greater chance of surviving the year, ceteris

peribus, compared to rural MSEs.

A number of studies have also used rm surveys to study the determinants of rm

growth. Using a survey of manufacturing rms in Cote dIvoire, Sleuwaegen and Goedhuys

(2002) nd that younger rms grow faster than older rms, yet larger entrants experience

greater growth opportunities that improve over time. A broad review of manufacturing

rm performance across developing countries nds that most economies are small, have

limited domestically produced intermediate inputs and capital equipment, face low rates

of secondary education and a lack of technicians, have limited infrastructure, experience

greater macroeconomic and relative price volatility than developed countries, have thin

credit markets with debt heavily skewed towards short-term instruments, and face weak

legal systems and higher instances of corruption (Tybout, 2000). Overall, these challenges

hamper the growth of small, entering rms.

Another important question in this literature is the determinants of rm survival. For

example, evidence from Portuguese manufacturing rms nds that drivers of a rms survival

include start-up size, with larger rms having a higher probability of survival, and fast

growing industries having a positive eect on rm duration. Furthermore, the probability

of rm failure decreases with the age of the rm, consistent with the view that rm entry

is a process of selection and learning (Mata and Portugal, 1994).

Finally, the literature examines the impact of macroeconomic and other shocks on private

sector behavior. For instance, large macroeconomic disturbances are found to aect the

rm turnover process. A study of the cyclical behavior of small versus large manufacturing

rms in the U.S. and the response to monetary policy, nds that small rms account for

a signicantly disproportionate share of the decline in manufacturing that follows a shift

to tight money (Gertler and Gilchrist, 1994). A related paper focusing specically on new

rms in the U.S. nds that startup rates are substantially shaped by both macroeconomic

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uctuations as well as industry-specic characteristics; macroeconomic expansion serves

as a catalyst for startup activity, but new-rm startups are promoted by a low cost of

capital as well as high unemployment rates (Audretsch and Acs, 1994). A study of rms

in West Germany rms nds that the exit of new plants is not responsive to aggregate

cyclical uctuations, rather, exit is large among new entrants at any stage of the business

cycle; however, among incumbent plants, the survival of older rms is more sensitive to the

aggregate cycle (Boeri and Bellmann, 1995).

Previous literature has also studied the impact of trade reform. An investigation of

employment patterns in Chilean manufacturing rms following a major trade liberalization

between 1975-1979 which made the economy more outward-oriented, removed quantitative

trade restrictions and slashed tari rates, privatized rms, relaxed government controlled

prices, and reformed nancial markets nds that in the years following the trade liber-

alization, rm size matters for job creation and destruction; job churning is very high in

both expanding and contracting industries; and job creation and destruction varies with

whether the rm is operating in the tradable or non-tradable sectors (Levinsohn, 1999).

The importance of rms size in determining survival is supported by another study that

examines rms in Ghana, Kenya, and Tanzania during the 1990s, when market reforms

were implemented to eliminate protection of the domestic rms (Harding, et al., 2004). To

summarize, the literature in developing countries generally nds that the main determinant

of rm exit is size, with small rms having much higher exit rates than large rms, and nd

no evidence that productivity impacts rm survival.

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3 Macroeconomic Background and Government Re-

forms

In 2006, Cote dIvoire was ranked 164 out of 177 in the World Bank Human Development

Index (World Bank, 2009).3 This followed a period of political instability and civil war

(2002 to 2003). Yet, Cote dIvoire remains one of the largest economies in the region and

accounts for close to 40% of the economic activity in the West Africa Economic Monetary

Union (WAEMU). Furthermore, the countrys relatively high GDP per capita (US$ 1,027

in 2007) suggests opportunities for private sector development and growth.

Cote dIvoire grew rapidly after achieving independence in 1960, and by 1974, nominal

GNI per capita rose from 190 to 450 CFA (over 300% in real terms). This expansion

was primarily due to growth in the agricultural sector as the country benetted from high

international prices of coee and cocoa, for which the country is a major exporter. In

addition, the countrys investment code and stable government and policies encouraged

foreign capital investment in agribusiness activities. By 1978, the World Bank declared,

The so-called Ivorian Miraclehas not been a matter of luck. . . and the countrys political

stability and favorable economic environment led international organizations to believe that

the country could be the rst African country to achieve "developed" status (Tuinder,

1978).4 However, with the collapse of coee and cocoa prices and the oil shock in 1979, the

Ivorian economy experienced a severe annualized contraction of 12% in 1980 and entered a

prolonged recessionary period that persisted through most of the 1980s. For most of this

period, GDP growth was less than 3% and private credit (as a percentage of GDP) sharply

declined from about 30% to less than 20%.

After defaulting on commercial bank loans in 1983, the country undertook a series of

foreign debt reschedulings with both o cial and private creditors. For the next decade,

3This section draws on Trebesch (2008), Reuda-Sabater and Stone (1992), and Library of CongressCountry Studies (1988).

4From 1960 to 1993, the country was led by a one-party rule: Houphouet-Boigny ruled from 1960 untilhis death in 1993.

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Cote dIvoire instituted a series of trade, scal, and monetary reforms, which we will show

impacted private sector entry, exit, and growth.

First, from 1985 to 1987, the government removed a series of import quotas, in order to

increase competition and reduce prices. The government removed quantitative restrictions

and unied eective rates of protection across intermediate and nal goods, which reduced

the competitive advantage of domestic manufacturers vis--vis foreign exporters (Harrison,

1994).

Following the implementation of this trade reform, domestic conditions deteriorated

drastically in 1987 with international coee prices dropping more than 30% (YoY), which

led to a scal decit greater than 8% of GNP and external debt ratios higher than 145% of

GNP, and caused the government to declare an o cial debt moratorium in May 1987. In

December 1987, the government reached a multi-year rescheduling of US$ 931 million with

Paris Club creditors and an agreement with London Club creditors worth US$ 1,575 million

in March 1988. In support of the agreements with creditors, the IMF extended US$ 240

million in loans in March 1988, and the government undertook a series of revenue-raising

measures.

While the countrys default episode was not fully resolved until 1998 (additional reschedul-

ings occurred in 1989 and 1997), the workout arrangements with creditors and IMF support

ensured access (albeit limited) to external capital markets. Yet by the late 1980s the gov-

ernment faced signicant di culties implementing IMF and World Bank mandated reforms,

particularly attempts to contain the public sector wage bill which amounted to than 60% of

non-interest expenditures. These moves led to protests by trade unions, strikes, demonstra-

tions, and led to a rise in student militancy. At the end of 1992, the government announced

its plans to abandon salary cuts (and introduce a multi-party political system), which de-

fused public protests.

Due to further deteriorations in the terms of trade, the Central Bank of West African

States (BCEAO) implemented an IMF-backed nominal devaluation of the CFA by 50% in

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mid-January 1994 in an eort to restore the regions international competitiveness. After

being pegged to the French franc at the same rate for 46 years, the currency value was

reduced from CFA/FRF 50 to CFA/FRF 100 (equivalent to CFA/EUR 656). Cote dIvoire

responded well to the devaluation as the government simultaneously undertook a series of

reforms to improve competitiveness and promote investment, including a revised investment

code, removal of price controls and export duties on key exports, and privatization program,

in order to maximize the benets associated with the devaluation.

To summarize, our empirical analysis will focus on the impact of private sector activity

following three government interventions:

Trade reform (1985-1987): Removal of import quotas;

Fiscal reform (1989-1990): Reduction in public sector wages;

Monetary reform (1994): 50% currency devaluation.

4 Data and Summary Statistics

The dataset is comprised of 5,941 unique rms in Cote dIvoire from 1976 through 1997,

which covers the complete census of formal, private sector corporations in the country.5

The data was hand-collected from the Registrar of Companies for the Modern Enterprise

Sector, and does not include a sample bias of exited rms. These rms are expected to pay

the full range of taxes, and may benet from bank nance and state technical assistance.

This dataset appears to be consistent with the overall private sector in Cote dIvoire, which

includes about 800 large, formally registered enterprises (with at least 50 employees), several

thousand formally and informally operating medium-sized enterprises (10-50 employees),

and several hundred thousand informal microentrepreneurs, which mostly generate self-

employment (Reuda-Sabater and Stone, 1992).

5The database excludes the primary sectors and state-owned rms.

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The structure of the data is an unbalanced panel of about 37,000 rm/year observations.

That is, there is detailed information tracking rms over time and the dataset includes rms

that enter over the course of the sample period, as well as rms that exit during the sample.

The variables of interest are information on sector classication at the two-digit sector

level and rm ownership, collected at the time of registration, and annual data on rm

employment, collected from required annual lings.6 Our dataset also includes balance

sheet and income statement nancial information, which is available for the sub-period

1976-1987.

We summarize this dataset in two dimensions: First, we provide the rst illustration of

rm dynamics in a low-income country, across sectors and time. This includes entry and

exit rates, as well as job creation and destruction rates and new versus incumbent rms.

Second, we present a comparison of formal sector dynamics in Cote dIvoire, relative to

private sector dynamics in the U.S. Following recent work by Haltiwanger et al. (2008), we

use the U.S. Bureau of the Census, Business Demography Statistics (BDS), which provides

detailed data on rm births and deaths and job creation and destruction, by rm size,

age, and sector.7 An important caveat is that we are not able to compare the two overall

economies: Data for the U.S. covers virtually the complete private sector, while data for

Cote dIvoire includes a very small number of rms that chose to formally register in a

challenging business environment. Yet, this comparison might shed light on barriers to

private sector job creation and growth, which encumbers many developing nations.

4.1 Business and employment formation

On average, there are about 2,000 registered rms per year, with on average 29 employees

(and median employment of 7), which employ about 100,000 individuals. Although this is a

6All registered companies are required to submit annual lings to the Registrar of Companies withnancial and employment information. Our panel includes only two missing rm/year observations.

7An important caveat is that US data is on the establishment (plant) level, while the Ivorian data is onthe rm (consolidated) level. However, nearly all rms in Cote dIvoire operate in only one location. Tomake the databases comparable, we exclude from the Cote dIvoire sample rms in the nancial, insurance,and real estate (FIRE) sectors.

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small fraction of overall employment, including individuals self-employed or employed in the

informal sector, the formal sector represents over 60% of GDP (OECD African Economic

Outlook, 2004). Figure 1, Panel A, shows that while the total number of registered rms in

a given year has remained at, on average, over the period, total employment in the formal

private sector declines over time. These are important trends for policymakers trying to

promote high-growth, employment generating, private sector rms. While average rm size

in Cote dIvoire has decreased over time, Panel B shows that average rm size in the U.S.

grew by over 7% over the same period.

Figure 2 presents summary statistics, by size buckets over the entire sample period. For

the purpose of our analysis, we dene smallas less than 10 employees; middleas 10-49

employees; largeas 50-149 employees; and very largeas more than 150 employees.

Economy-wide, 40% of rms have less than 10 employees and an additional 40% of rms

have 10-49 employees. Interestingly, we also nd in the U.S. that 80% of rms have less

than 50 employees, though a larger number, 62%, have less than 10 employees. The smaller

percentage of micro rms (with less than 10 employees) in Cote dIvoire might reect

the fact that because of burdensome regulations, high marginal tax rates, the absence of

monitoring and compliance (of both registration and tax regulations), and other weaknesses

in the business environment, many rms might nd it optimal to evade regulations and

operate in the informal sector. For instance, the cost of starting a business (as a percentage

of income per capita) is 133% in Cote dIvoire versus 0.7% in the U.S, and the minimum

capital required to start a business (as a percentage of income per capita) is 205% in Cote

dIvoire, versus 0% in the U.S (Doing Business, 2009). Previous literature has highlighted the

potential advantages to formal sector participation, including police and judicial protection

(and less vulnerability to corruption and the demand for bribe), access to formal credit

institutions, the ability to use formal labor contracts, and greater access to foreign markets

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(Schneider and Enste, 2000).8 The tendency of rms in Cote dIvoire to choose to stay

small and informal might be unable to realize their full growth potential.

Figure 3 shows the distribution of rms and employees, by sector. Although almost 50%

of rms are identied as service providers, 46% of employment is generated by the manufac-

turing sector (as compared to 37% of employment in the service sector). Similarly, we nd

about 50% of rms in the U.S. are service rms and generate about 40% of employment.

Yet, we nd a notable dierence in the contribution of the manufacturing sector in Cote

dIvoire, which comprises about 17% of rms and 46% of employment (as compared to 10%

of rms and 31% of employment in the U.S). On the one hand, it is surprising that manu-

facturing rms which require relatively higher levels of investment, human resources, and

capital remain a large percentage of rms in Cote dIvoire; for example, nancial devel-

opment (measured as private credit as a percentage of GDP) is only 33% over our sample

period. However, on average, approximately 70% of Ivorian rms are owned by foreigners

from industrialized countries with the majority of these owners being French (not shown).9

The lack of access to domestic nance might also explain the notable dierences in rm

size distribution, by sector. Though the manufacturing sector has the fewest number of

rms, average rm size is signicantly larger than it is for rms operating in the trade or

service sectors. However, the median rm size is small for all sectors, with only a median

of 8 employees in trade sector rms. We nd notable dierences in comparison to the

U.S. distribution. Most strikingly, across all sectors we nd less than half the number of

rms in the category of 10-49 employees, which is novel evidence that the missing middle

phenomena applies to sectors other than manufacturing.

For instance, within the manufacturing sector, we nd 44% of rms in Cote dIvoire are

small (less than 10 employees) and only 27% are large (more than 50 employees), relative to

14% and 42% of rms in the U.S., respectively; possibly, the growth of manufacturing rms

8The benets of formal sector registration might vary by industry. This is discussed in Section 5.3.9We also nd that the sectoral distribution of foreign- and domestically-owned rms is nearly identical

(not shown).

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in Cote dIvoire is limited by capital constraints. Service sector rms display an even more

disparate distribution: 72% of rms operate with less than 10 employees in Cote dIvoire,

relative to 45% of rms in the U.S. In contrast, close to 50% of rms operating in the

tradable sector in both Cote dIvoire and the U.S. have less than 10 employees, yet 28% of

rms in Cote dIvoire are large, while only 9% of tradable rms have at least 150 employees.

It might be the case that in developing countries with costly and timely barriers to starting

a business, only rms in wholesale and retail trade might have the greatest incentive to

formally register, for instance, in order to receive a Value Added Tax (VAT) number, which

might be required for domestic and international sales.

4.2 Firm and employment dynamics

In an eort to describe the entry and exit patterns of Ivorian and U.S. rms, we begin by

constructing measures of entry and exit as well as their size relative to other rms in the

industry. We count a rm as a new entrant if it appears for a second year, that is, we

exclude rms that survive less than a year.

Similar to Dunne et al. (1988), we dene the following variables, where bucket i is

equal to either 2-digit industry classications (manufacturing, services, or trade) or size

(micro, small, medium or large):

NEit = number of rms that enter bucket i between years t 1 and t

NTit = total number of rms in bucket i in year t, including rms that

enter bucket i between years t 1 and t

NXi(t1) = number of rms that exit bucket i between years t 1 and t

Next, we construct entry (ERit), exit (XRit), and turnover (TRit) rates as:

ERit = NEit=NTi(t1)

XRi(t1) = NXi(t1)=NTi(t1)

TRit = (NEit +NXit)=NTi(t1)

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The denominator for both entry and exit rates is the total number of rms in bucket i in

year t-1. This is because we want to capture the pool of potential exiting rms. These

measures are comparable to gross measures of entry and exit and allow us to compare our

ndings to those of US rm studies.

We also construct survival rates as the probability of exiting at t using the Kaplan-Meier

estimator such that

S(t) =tYj=1

[(n t)=(n j + 1)](t):

S(t) is the estimated survival function, n is the total number of rm lifespans, ()t is a

dummy equal to one for completed spells and 0 for censored spells. In our sample we nd

that the average lifespan of rms is 6.7 years, with a median of 5.0 years.

We follow Haltiwanger, et al.s (2008) analysis of business dynamics in the U.S. Firm-

level employment growth is :10

git = (Eit Eit1)=Xit; where Xit = :5 (Eit + Eit1)

and aggregate employment growth is given by the weighted average of rm-level growth:

gt = i(Xit=Xt)git; where Xt = iXit:

Employment growth can be decomposed into creation and destruction:

JCit = i(Xit=Xt)maxfgit; 0g

JDit = i(Xit=Xt)maxfgit; 0g:

Next we decompose job creation into entry and expansion and job destruction into exit

and contraction:

JC_Entryt = i(Xit=Xt)Ifgit = 2g;10This follows the methodology of Davis et al. (1994).

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where I is an indicator variable equal to one if the expression in the brackets hold and zero

otherwise, and git = 2 denotes an entrant.

JC_Exitt = i(Xit=Xt)Ifgit = 2g;

where I is an indicator variable equal to one if the expression in the brackets hold and zero

otherwise, and git = 2 denotes an exit.

We commence with a comparison of business dynamics across the U.S. and Cote dIvoire

over the sample (Figure 5). The rst observation is the high volatility of entry and turnover

rates in Cote dIvoire, which reect larger business cycle swings and macroeconomic instabil-

ity (that is, a strong correlation between real GDP and entry rates), political uncertainty,

and a more challenging business environment (Panels A and B). This is consistent with

Klapper et al. (2006) and Bartelsman, et al. (2009), which nd a (contemporaneous)

relationship between entry rates and the business environment.

The second striking feature of these graphs is that on average, the entry rates in Cote

dIvoire and the US are so similar (12.4% and 13.2%, respectively), across such dierent

economic, legal, and institutional environments. This is consistent with similar results

shown for a larger sample of over 100 countries in the World Bank Entrepreneurship Survey

database, which nds very low variation in entry rates of formally registered corporations

across a wide range of countries, e.g. the data nds, on average, entry rates of 8% in

middle- and low-income countries, versus 11% in industrialized countries (Klapper et al.,

2008). This is also consistent with Bartelsman et al., (2009), which nds very similar entry

rates across OECD countries. It is important to bear in mind that we are comparing only

formal sector dynamics; i.e. it is interesting to consider that rms that overcome the hurdles

of registering in a challenging business environment look similar to other registered rms

around the world.

We also compare one-year survival rates of rms in Cote dIvoire and the U.S. over our

sample period (Panel C). Similar to our ndings on entry rates, we nd high volatility of sur-

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vival rates in Cote dIvoire, which reect both larger business cycle swings (macroeconomic

instability), weaker bankruptcy laws, and greater political instability. However, again, an

interesting observation is that on average, the 2-year survival rates in Cote dIvoire and

the US are very similar, 83% and 84%, respectively. Taken together, this might, intrigu-

ingly, suggest naturalentry and exit rates of registered rms over time, regardless of the

investment climate.

Next, in Figure 6, we look at employment dynamics at new and exited rms (Panel A)

and jobs created and lost at incumbent rms, normalized to 100% (Panel B). Over time,

net employment is always positive, that is, more jobs are created at new rms then lost

at exiting rms. However, the absolute number of new jobs added to the private sector is

remarkably small on average, fewer than 3,000 formal, private sector jobs are added on

an annual basis. In comparison, the net number of new jobs generated at incumbent rms

is negative for all years from 1979 through 1995. The largest net job losses occurred during

the 1980 macroeconomic downturn over 10,000 jobs were shed in 1979 although net jobs

at incumbent rms remained negative even during periods of GDP growth.

In comparison to the U.S., an important implication is that new rm creation appears

to be relatively more important for job creation in Cote dIvoire, relative to incumbent

growth. This might suggest that expanding opportunities for entrepreneurial potential is

important for macroeconomic growth. An important caveat, however, is that jobs created

by new rms are highly volatile and not necessarily as good as those provided by large rms

(Haltiwanger et al., 2009). This suggests that economic and employment growth in Cote

dIvoire might be hampered by the inability of incumbent rms to grow, relative to growth

and job creation rates in developed countries, such as the U.S. This data can be interpreted

as quantitative evidence of the impact of barriers in the business environment such as

costly and timely procedures to obtain licenses, register property and movable collateral,

resolve disputes, and close a business (Doing Business, 2009) on private sector growth.

17

Finally, we compare average job ows by sector (Figure 7). The most prominent ob-

servation is that across all US all sectors, job growth is driven from incumbent expansion.

While we continue to see similar patterns of job creation arising from rm entry and job

destruction from rm exit, the dierence in dynamics is arising from job ows in expanding

and contracting rms.

5 Survival Model and Results

The previous section highlights barriers in employment creation at existing incumbent rms.

In order to better understand the barriers to growth, we begin by addressing the primary

question of what determines rm survival. Understanding the factors that appear to most

inuence rm survival is necessary to develop and implement policies that will improve and

help rms better adapt and prepare for challenges and promote opportunities for growth.

Our research aims to identify rm characteristics, such as sector and size, that predict

greater vulnerability to business cycles and policy interventions. First, we examine the

relationship between economic growth (measured by annual GDP growth rates) and rm

survival. Next, we explore the impact of specic monetary, scal, and trade reforms on rm

survival.

5.1 Firm dynamics and reforms

We begin by summarizing rm and job dynamics, relative to annual GDP growth and

economic reforms. Figure 8, Panels A and B, show the absolute number and one-year

growth rates of entering and exiting rms, by year, relative to one-year GDP growth. For

most of the period, we nd that the formal private sector both gains and looses about 200

rms per year, which is a growth and contraction rate equal to about 10%. This trend is

surprisingly stable over time and macroeconomic conditions. Panel C shows the impact of

policy reforms: Although GDP growth was relatively at during the reform periods, the

18

trade and scal reforms led to a sharp decrease in the number of rms. In particular, the

scal reform led to a relatively sharper decline in foreign-owned rms as political instability

increased. In contrast, the monetary devaluation (and growth in GDP) was associated with

an increase in new rm creation.

Next, in Figure 9, Panels A and B, we look at changes in new employment at new and

exited rms (Panel A) and jobs created and lost at incumbent rms (Panel B), relative

to annual GDP growth. Over time, net employment is always positive, i.e. more jobs

are created at new rms then lost at exiting rms. However, the absolute number of new

jobs added to the private sector is remarkably small on average, fewer than 3,000 formal,

private sector jobs are added on an annual basis. In comparison, the net number of new

jobs generated at incumbent rms is negative for all years from 1979 through 1995. The

largest net job losses occurred during the 1980 macroeconomic downturn over 10,000 jobs

were shed in 1979 although net jobs at incumbent rms remained negative even during

periods of GDP growth. Panel C shows that net employment at new/exited rms has

little variation over our sample period, while employment at incumbent rms appears more

sensitive to government reforms, in particular, the period following the introduction of scal

reforms.

Finally, Figure 10 shows net employment creation, by sector, relative to GDP and eco-

nomic reforms. Interestingly, we nd dierent patterns across sectors, suggesting that eco-

nomic and political shocks aect job creation and destruction dierently across sectors. For

instance, employment in trade is relatively at, perhaps because these jobs are driven by

external factors, while manufacturing jobs are most sensitive to macroeconomic uctuations

and government interventions, which might reect their greater size and capital dependency.

5.2 Survival test methodology

Our comprehensive panel data allows us to study the rm-level determinants of rm survival.

We estimate a discrete time duration model with time-varying covariates. The parametric

19

specication we consider is a proportional hazard model with a piecewise constant baseline

hazard. Specically, we use a baseline hazard with six parameters, j(j = 1; 2; 3; 4; 5; 6):

one for each of the rst four three-year periods and one for the remaining years. We also

accommodate unobserved individual heterogeneity with the inclusion of a multiplicative

error term in the hazard function, for which a gamma distribution with mean 1 and variance

2 is assumed.

The hazard function for rm i in period t is specied as being proportional to:

exp(f(Zi;Wt; Xit));

where Wt is a vector of macroeconomic variables and reform dummies, Zi is a vector that

includes dummies for rm sector, initial size, location, and owner, and Xit is a vector of

individual time varying characteristics of the rm, including current number of employees.

For rm i we dene it as the elapsed duration since the rms birth in period t and let

Ti be the duration of the spell. Under the assumption of a proportional hazard model with

a piecewise constant baseline hazard and unobserved heterogeneity, the hazard function can

be expressed by:

(itjZi;Wt; Xit; vi) = re exp(f(Zi;Wt; Xit))vi;

where vi is the unobserved component for rm i and re = j for it 5 and it = 6 for

it 6: Standard results imply that the survival function for Ti can be written as:

S(TijZi;Wt; Xit; vi) = exp(vi

T rXt=1

re exp(f(Zi;Wt; Xit))

):

Thus the contribution to the log-likelihood function from item i can be written as:

ln(Li) = ln[S(Ti 1jZi;Wt; Xit) &iS(TijZi;Wt; Xit)];

20

where &i is a dummy variable which equals 1 for completed spells and 0 for (right) censored

events.

5.3 Results

Table 1 shows summary statistics of our dependent and explanatory variables. Our depen-

dent variable is a dummy (0/1), equal to one in the rst year that the rm exits from the

sample, and equal to zero otherwise. On average, rms in our sample survive 6.6 years.

We include a series of rm characteristics as our dependent variables: First, we include a

dummy for rms located in the Abidjan (capital) region, which includes 90% of the sample.

Second, a dummy for foreign ownership is included, with 72% of rms being foreign owned.

Third, we include sector dummies to indicate rms in the manufacturing (18%) or trade

sectors (34%); we omit the service sector (49%). Finally we include size dummies to indicate

a rms entrysize at the time of registration: small (46%), medium (35%), or large (9%);

we omit the very largecategory (10%). Next, we include dummies indicating the three

reform periods: trade (1985-1987), scal (1989-1990), and monetary (1994). Finally, we in-

clude real annual GDP growth. In all specications we use leading GDP growth to address

the concern of endogeneity arising from the largest rms contributing the most towards

aggregate GDP (since rm exits occurs in the rst year after we no longer observe the rm).

To summarize our baseline hazard function in our regressions, we consider domestically-

owned rms located outside of the Abidjan commercial region, entering with at least 150

employees, and operating in the service sector. A correlation matrix of the explanatory

variables is shown in Table 2.

Table 3 shows the determinants of rm exit, where a positive coe cient indicates a

lower likelihood of survival. We do not nd any eect of operating in the capital and do

not nd that survival rates vary by sector. However, we nd that foreign rms exit more

frequently than domestically-owned rms, which might reect a higher voluntary exit rate

among foreigner owners who are more sensitive to economic and political volatility. We also

21

nd stark dierences in the exit rate of rms according to their initial size: the larger a

rm is at entry, the less likely it is to exit. Next, we nd a signicant eect of declining

GDP growth and lower survival rates. Finally, we nd that all reform periods are associated

with higher exit rates; the scal reform had the largest impact on survival, followed by the

monetary reform, and lastly the trade reform. In terms of economic signicance, the scal

reform raised the probability of exit by 66%, the monetary reform raised the probability of

exit by 40%, and the trade reform raised the probability of exit by 19%, all relative to the

baseline hazard.

Table 4 presents the impact of GDP growth on rm exit. We use interaction terms

to test if macroeconomic growth had a dierential impact on the exit rate of rms across

dierent sectors, ownership, and size. We nd that the business cycle has a marginal eect

on only the manufacturing sector, relative to service sector rms (at the 10% level) and

nd no marginal eect by rm ownership. However, we nd a strong eect of rm size:

smaller rms entering with 10-49 or 50-149 employees are most exposed to economic

downturns, relative to large rms entering with 150+ employees. This likely reects the

added vulnerability of small rms to downturns in the business cycle.

Finally, Table 5 examines the impact of government reforms on exit rates. From our

initial set of results presented in Table 3 we know that all reforms increased the exit rate for

rms, with the scal reform having the greatest impact. First, we nd that the manufactur-

ing sector experienced relatively higher exit rates during the scal crisis; we hypothesize that

this reects the larger, more capital intensive nature of rms in the industry. These results

are in line with previous literature showing that rms with larger market capitalization are

more politically connected, and uncertainty surrounding these political relationships can

negatively impact rmsperformances (Faccio 2006 and 2007 and Fisman, 2001). Next, we

nd that trade reform signicantly increased the probability of exit for rms operating in

the tradable sector, relative to the service sector, which was expected given the removal of

22

import quotas and increased competition facing the domestic economy. We nd no sector

or size specica impact of the monetary reform.

As a robustness check of our results, we estimate regression models using contempora-

neous log employment levels instead of entry size dummies. While not reported here, the

results are qualitatively similar.

Finally, an interesting nding is the shape of the baseline hazard (Figure 11). Our esti-

mates reveal an increasing baseline hazard function, which can be interpreted as suggesting

that that the probability of exit increases as the rm ages over time. This result is somewhat

surprising and counterintuitive, given that rms observed in developed countries exhibit an

increasing probability of survival as they age (see, for example, Audretsch and Mahmood,

1995 and Holmes et al., 2008). Furthermore, we do not observe an inection point where age

begins to increase the probability of survival, suggesting that rm vulnerability to exiting

does not decrease over time. It might be the case that rms become more visibleas they

age, which makes it more di cult to avoid costly operating and labor regulations.

6 Conclusion

We study rm dynamics using a database of all formally registered rms in Cote dIvoire

from 1977-1997, which account for about 60% of GDP. During this period, Cote dIvoire

experienced (separate) episodes of trade reform, scal reform, and monetary reform, and

we examine the impact of these shocks on private sector dynamics. First, we examine entry

and exit patterns over this period and the role of new and exiting rms versus incumbents

in job creation and destruction.

Next, we examine survival rates and nd that survival increases monotonically with size.

Furthermore, higher GDP growth increases survival rates, and periods of economic growth

have the largest impact on the reduction in the exit rates of small rms, relative to large

rms. Finally, we nd that all reforms decreased rm survival.

23

A comparison of business patterns in the U.S. and Cote dIvoire highlights the need to

encourage and inspire greater experimentation and innovation among African entrepreneurs.

Start-up rms in the U.S. generally enter small and face low survival rates, but those that do

survive grow and create new jobs, displacing those rms that do not innovate. Improvements

in the institutional environment such as easing rm entry and closure might promote

greater risk taking and high growth entrepreneurship.

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27

Figure 1: Comparison of Total Employment and umber of Firms, 1977-1997

Panel A: Cote dIvoire

1,000

1,200

1,400

1,600

1,800

2,000

2,200

-

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96

Number of Firms

Number of Employees

Total Employees Total Firms

Source: Cote dIvoire Centrale de Bilans des Entreprises, 1977-1997.

Panel B: U.S.

4,000

4,500

5,000

5,500

6,000

6,500

-

20,000

40,000

60,000

80,000

100,000

120,000

77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97

Number of Firms

(thousands)

Number of Employees

(thousands)

Total Employees Total Firms

Source: US Census Bureau's Longitudinal Business Database, 1977-1997.

Figure 2: Comparison of Firm Size Buckets, Average 1977-1997


40%

40%

12%

8%

Figure 3: Comparison of umber of Firms and Employment, by Sector,

Average 1977-1997


49%

34%

17%

Distribution of Firms

service

trade

manufacturing

37%

17%

46%

Distribution of Employment

service

trade

manufacturing

Panel B: U.S.

50%

40%

10%

Distribution of Firms

service

trade

manufacturing

40%

29%

31%

Distribution of Employment

service

trade

manufacturing

Source: Cote dIvoire Centrale de Bilans des Entreprises, 1977-1997 and the U.S. Census Bureaus Longitudinal

Business Database.

Figure 4: Comparison of Firm Size Buckets, by Sector, Average 1977-1997

Panel A: Manufacturing Sector

44%

29%

6%

21%

Figure 5: Comparison of Firm Dynamics, 1978-1997

Panel A: Average Entry Rates

0%

5%

10%

15%

20%

78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97

Cote d'Ivoire

United States

Panel B: Average Turnover Rates

0%

10%

20%

30%

40%

78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96

Cote d'Ivoire

United States

Panel C: Average Survival Rates, 1978-1995

70%

75%

80%

85%

90%

95%

78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95

Cote d'Ivoire

United States

Source: Cote dIvoire Centrale de Bilans des Entreprises, 1977-1997 and the U.S. Census Bureaus

Longitudinal Business Database.

Figure 6: Comparison of Employment Dynamics, Average 1978 1997

Panel A: Average Job Creation and Destruction Rates

Panel B: Average !umber of Employees, !ormalized to 100%

Source: Cote dIvoire Centrale de Bilans des Entreprises, 1977-1997 and the U.S. Census Bureaus

Longitudinal Business Database.

0%

4%

8%

12%

16%

20%

Cote d'Ivoire US Cote d'Ivoire US

Job Destruction Job Creation

Exit Exit Entry Entry

Contraction Contraction Expansion Expansion

0%

20%

40%

60%

80%

100%



Exit

Exit Entry Entry


Figure 7: Comparison of Employment Dynamics, by Sector, Average 1978 1997

Panel A: Manufacturing

Panel B: Services

Panel C: Trade

0%

4%

8%

12%

16%

20%



Exit Exit

EntryEntry

Contraction

Contraction

Expansion

Expansion

0%

4%

8%

12%

16%

20%



Exit Exit Entry Entry


0%

4%

8%

12%

16%

20%



Exit ExitEntry

Entry

Contraction

Contraction

Expansion

Expansion

Figure 8: Cote DIvoire Firm Dynamics, GDP Growth, and Economic Reforms

Panel A: !umber of Firms and GDP Growth

-15

-10

-5

0

5

10

15

0

50

100

150

200

250

300

350

78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96

GDP growth (annual %)

Number of Firms

Entry Exit GDP growth (annual %)

Panel B: Growth in !umber of Firms and GDP Growth

-15

-10

-5

0

5

10

15

0%

5%

10%

15%

20%

78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96


Annual Growth (%)

Entry Rates Exit Rates GDP growth (annual %)

Panel C: !et !ew Firms and Economic Reforms

-150

-100

-50

0

50

100

150

78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96

# of firm

s


Trade Fiscal Monetary

Figure 9:Cote DIvoire Job Dynamics, GDP Growth and Economic Reforms

Panel A: !umber of Jobs Created at !ew Firms and Lost at Exiting Firms

and GDP Growth

-15

-10

-5

0

5

10

15

-

5,000

10,000

15,000

20,000

25,000

78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96


Job Creation/Destruction

Jobs created at new firms Jobs lost at exiting firms GDP growth (annual %)

Panel B: !umber of Jobs Created and Lost at Incumbent Firms and GDP Growth

-15

-10

-5

0

5

10

15

-

5,000

10,000

15,000

20,000

25,000

78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96


Job Creation/Destruction

Jobs created at incumbent firms Jobs lost at incumbent firms GDP growth (annual %)

Panel C: !et Employment at Incumbent and !ew/Exit Firms and Economic Reforms

0

2000

4000

6000

8000

10000

-18000

-12000

-6000

0

6000

12000

78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96

Net Employment

Incumbants Entry/Exit



Figure 10: et Employment Growth, GDP Growth, and Economic Reforms,

by Sector, 1978-1997

Panel A: Manufacturing

-0.04

-0.035

-0.03

-0.025

-0.02

-0.015

-0.01

-0.005

0

-15%

-10%

-5%

0%

5%

10%

15%

78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97

Manufacturing

GDP

Panel B: Services

-0.04

-0.035

-0.03

-0.025

-0.02

-0.015

-0.01

-0.005

0

-15%

-10%

-5%

0%

5%

10%

15%

78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97

Services GDP

Panel C: Trade

-0.04

-0.035

-0.03

-0.025

-0.02

-0.015

-0.01

-0.005

0

-15%

-10%

-5%

0%

5%

10%

15%

78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97

Trade GDP



Figure 11: Baseline Hazard Function

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

baseline hazard rate

# years since entry

Table 1: Variables and Summary Statistics

Variable Definition Mean

Survival Age of firm when it dies (in years)

6.66

Abidjan Dummy (0/1), =1 if the firm is located in the Abidjan (capital) region, =0

otherwise

0.90

Foreign_owned Dummy (0/1), =1 if the firm is foreign owned, =0 if the firm is

domestically owned

0.72

Service Dummy (0/1), =1 if the firm operates in the service sector, =0 otherwise

0.49

Trade Dummy (0/1), =1 if the firm operates in the trade sector, =0 otherwise

0.34

Manufacturing Dummy (0/1), =1 if the firm operates in the trade sector, =0 otherwise

0.18

Entry_small Dummy (0/1), =1 if the firm enters operation with less than 10

employees, =0 otherwise

0.46

Entry_ medium Dummy (0/1), =1 if the firm enters operation with 10-49 employees, =0

otherwise

0.35

Entry_ large Dummy (0/1), =1 if the firm enters operation with 50-149 employees, =0

otherwise

0.09

Entry_very large Dummy (0/1), =1 if the firm enters operation with 150+ employees, =0

otherwise

0.10

Reform_trade Dummy 0/1, =1 in 1985-1987, the years during which trade reform was

undertaken

0.16

Reform_fiscal Dummy 0/1, =1 in 1989 and 1990, the years during which fiscal/political

reform was undertaken

0.10

Reform_monetary Dummy 0/1, =1 in 1994, the year during which monetary reform was

undertaken

0.05

GDP_growth Real one-year GDP growth

1.50

Table 2: Correlation Matrix

Note: Asterisk indicates significance at the 5% level.

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

Abidjan (1) 1.0000

Foreign_owned (2) -0.0391* 1.0000

Service (3) -0.1083* 0.0490* 1.0000

Manufacturing (4) 0.0687* 0.0334* -0.4445* 1.0000

Trade (5) 0.0590* -0.0783* -0.6970* -0.3325* 1.0000

Entry_small (6) -0.0028 -0.0458* 0.0846* -0.2547* 0.1148* 1.0000

Entry_medium (7) -0.0224* 0.0273* -0.0554* 0.0703* 0.002 -0.6776* 1.0000

Entry_large (8) -0.0149* 0.0265* -0.0767* 0.1816* -0.0646* -0.2848* -0.2300* 1.0000

Entry_very large (9) 0.0396* -0.0037 -0.0719* 0.2041* -0.0877* -0.2379* -0.1921* -0.0807* 1.0000

Reform_trade (10) 0.0068 0.0179* 0.0095 -0.0064 -0.0049 0.0081 -0.0001 -0.0087 -0.0073 1.0000

Reform_monetary (11) -0.0050 -0.0414* -0.0151* 0.0062 0.0109* 0.0291* -0.0126* -0.0081 -0.0118* -0.0968* 1.0000

Reform_fiscal (12) -0.0082 -0.0092 -0.0014 -0.0035 0.0043 0.0135* -0.0010 -0.0068 -0.0076 -0.1429* -0.0742* 1.0000

GDP_growth (13) -0.0108* -0.0405* -0.0238* 0.0119* 0.0155* 0.0148* -0.0078 0.0022 -0.0035 -0.0170* 0.2846* -0.1530*

Table 3: Survival Model: Determinants of Firm Exit

This table reports estimates using a discrete time-duration model with time-varying covariates The

dependent variables in all models is a dummy (0/1) equal to one in the first year that the firm exits from the

sample, and equal to zero otherwise. Explanatory variables are defined in Table 1. Standard errors are

shown below, ***, **, * indicate significance at the 1%, 5%, and 10% level, respectively.

(1) (2) (3)

Coeff S.E. Coeff S.E. Coeff S.E.

Abidjan -0.014 0.082 -0.020 0.080 -0.012 0.077

Foreign_owned 0.266 0.058 *** 0.255 0.057 *** 0.261 0.055 ***

Manufacture 0.006 0.077 0.006 0.075 -0.008 0.073

Trade -0.039 0.057 -0.041 0.055 -0.049 0.053

Entry_small 1.683 0.132 *** 1.650 0.130 *** 1.541 0.127 ***

Entry_medium 0.956 0.124 *** 0.938 0.121 *** 0.872 0.118 ***

Entry_large 0.502 0.157 *** 0.483 0.153 *** 0.441 0.148 ***

GDP_growth -0.016 0.004 *** -0.015 0.004 ***

Reform_trade 0.176 0.051 ***

Reform_fiscal 0.509 0.054 ***

Reform_monetary 0.337 0.080 ***

Constant -2.160 0.212 *** -2.176 0.207 *** -2.359 0.202 ***

1 -1.053 0.207 *** -0.986 0.204 *** -0.826 0.200 ***

2 -0.876 0.171 *** -0.859 0.169 *** -0.728 0.165 ***

3 -0.628 0.144 *** -0.623 0.142 *** -0.548 0.140 ***

4 -0.434 0.127 *** -0.432 0.125 *** -0.476 0.126 ***

5 -0.138 0.112 -0.157 0.111 -0.277 0.113 **

Gamma variance 1.168 1.077 0.960

# Observations 36529 36529 36529

Table 4: Survival Model: The Impact of GDP Growth





(1) (2) (3)

Coeff S.E. Coeff S.E. Coeff S.E.

Abidjan -0.020 0.080 -0.019 0.080 -0.019 0.080

Foreign_owned 0.255 0.057 *** 0.256 0.058 *** 0.258 0.057 ***

Manufacture 0.006 0.075 0.026 0.076 0.004 0.076

Trade -0.041 0.055 -0.032 0.056 -0.041 0.056

Entry_small 1.650 0.130 *** 1.650 0.130 *** 1.679 0.13 ***

Entry_medium 0.938 0.121 *** 0.937 0.121 *** 0.984 0.12 ***

Entry_large 0.483 0.153 *** 0.481 0.153 *** 0.539 0.16 ***

GDP_growth -0.016 0.004 *** -0.010 0.009 0.013 0.016

GDP_growth*foreign 0.000 0.009

GDP_growth*manufacture -0.019 0.011 *

GDP_growth*trade -0.009 0.008

GDP_growth*small -0.022 0.017

GDP_growth* medium

-0.042 0.017 **

GDP_growth*large -0.053 0.022 **

Constant -2.176 0.207 *** -2.177 0.207 *** -2.192 0.209 ***

1 -0.986 0.204 *** -0.991 0.204 *** -1.007 0.203 ***

2 -0.859 0.169 *** -0.865 0.169 *** -0.879 0.169 ***

3 -0.623 0.142 *** -0.628 0.142 *** -0.638 0.142 ***

4 -0.432 0.125 *** -0.436 0.125 *** -0.443 0.125 ***

5 -0.157 0.111 -0.162 0.111 -0.164 0.111

Gamma variance 1.077 1.077 1.088

# Observations 36529 36529 36529

Table 5: Survival Model: The Impact of Government Reforms





(1) (2) (3) (4) (5) (6) Coeff S.E. Coeff S.E. Coeff S.E. Coeff S.E. Coeff S.E. Coeff S.E.

Abidjan -0.012 0.077 -0.012 0.077 -0.012 0.077 -0.013 0.077 -0.013 0.077 -0.012 0.077 Foreign_owned 0.270 0.062 *** 0.262 0.055 *** 0.260 0.055 *** 0.260 0.055 *** 0.261 0.055 *** 0.261 0.055 *** Manufacture -0.007 0.073 -0.054 0.081 -0.057 0.059 -0.050 0.053 -0.050 0.053 -0.049 0.053 Trade -0.048 0.053 -0.049 0.054 -0.008 0.073 -0.009 0.072 -0.009 0.073 -0.008 0.073 Entry_small 1.541 0.127 *** 1.544 0.128 *** 1.539 0.127 *** 1.553 0.128 *** 1.537 0.127 *** 1.540 0.127 *** Entry_medium 0.872 0.118 *** 0.872 0.118 *** 0.870 0.118 *** 0.862 0.118 *** 0.844 0.121 *** 0.871 0.118 *** Entry_large 0.441 0.148 *** 0.444 0.149 *** 0.440 0.148 *** 0.434 0.148 *** 0.439 0.148 *** 0.478 0.153 *** GDP_growth -0.015 0.004 *** -0.015 0.004 *** -0.015 0.004 *** -0.015 0.004 *** -0.015 0.004 *** -0.015 0.004 *** Reform_trade 0.249 0.097 *** 0.154 0.054 *** 0.122 0.062 ** 0.189 0.079 ** 0.167 0.059 *** 0.185 0.051 *** Reform_fiscal 0.465 0.103 *** 0.470 0.058 *** 0.551 0.065 *** 0.611 0.082 *** 0.461 0.064 *** 0.512 0.055 *** Reform_monetary 0.359 0.130 *** 0.373 0.085 *** 0.345 0.098 *** 0.362 0.130 *** 0.331 0.092 *** 0.338 0.092 *** Reform_trade*foreign -0.095 0.110 Reform_fiscal*foreign 0.061 0.118 Reform_monetary*foreign -0.034 0.160 Reform_trade*manufacture 0.151 0.132 Reform_fiscal*manufacture 0.279 0.141 ** Reform_monetary*manufacture -0.247 0.226 Reform_trade*trade 0.156 0.098 * Reform_fiscal*trade -0.124 0.109 Reform_monetary*trade -0.020 0.159 Reform_trade*small -0.023 0.097 Reform_fiscal*small -0.171 0.105 * Reform_monetary*small -0.040 0.159 Reform_trade*medium 0.029 0.101 Reform_fiscal* medium 0.153 0.109 Reform_monetary* medium 0.023 0.172 Reform_trade*large -0.236 0.227 Reform_fiscal*large -0.071 0.223 Reform_monetary*large -0.001 0.170 Constant -2.367 0.202 *** -2.340 0.202 *** -2.356 0.202 *** -2.386 0.203 *** -2.356 0.201 *** -2.365 0.201 ***

1 -0.826 0.200 *** -0.841 0.200 *** -0.823 0.199 *** -0.801 0.201 *** -0.817 0.200 *** -0.821 0.199 *** 2 -0.727 0.166 *** -0.741 0.166 *** -0.726 0.165 *** -0.709 0.167 *** -0.721 0.166 *** -0.723 0.17 *** 3 -0.549 0.140 *** -0.558 0.140 *** -0.544 0.140 *** -0.536 0.141 *** -0.544 0.140 *** -0.544 0.140 *** 4 -0.476 0.126 *** -0.487 0.126 *** -0.478 0.126 *** -0.471 0.127 *** -0.475 0.126 *** -0.464 0.126 *** 5 -0.277 0.113 ** -0.291 0.113 *** -0.278 0.113 ** -0.294 0.114 *** -0.282 0.113 ** -0.272 0.113 ** Gamma variance 0.959 0.967 0.958 0.940 0.953 0.959 # Observations 36529 36529 36529 36529 36529 36529

patterns of business creation, survival, and growth...

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