measuring the shadow economy in zimbabwe: using the...

Global Journal of Contemporary Research in Accounting, Auditing and Business Ethics (GJCRA)

An Online International Research Journal (ISSN: 2311-3162) 2015 Vol: 1 Issue: 1

232 www.globalbizresearch.org

Measuring the Shadow Economy in Zimbabwe:

Using the Monetary Method

JDG Nhavira

University of Zimbabwe, Zimbabwe.

Email: [email protected]

_____________________________________________________________________

Abstract

The main purpose of this study is to estimate the size of the shadow economy in Zimbabwe

using the monetary method. The monetary method or the currency approach has become a

popular way of measuring the size of the shadow economy. It assumes that cash is employed

to make transactions that agents want hidden from official records. It is based on the

difference between declared income and the income implied by the observed currency

demand. The major conclusions of this study can be expressed as follows: The size of the

shadow economy has increased as a proportion of GDP from 25.6 per cent to 33.0 per cent

in 2013 while the total value of tax evasion has increased from ZW $243,005,524.7 in 1979

to USD $880908339 in 2013. The evidence also demonstrates that the rate of growth of the

informal economy has been higher than the formal economy.

___________________________________________________________________________

Key words: Underground, unreported, unrecorded, undeclared, informal, shadow, GDP,

national statistics

JEL: O17, 011 H26, E59

http://www.globalbizresearch.org/

mailto:[email protected]




1. Introduction

The main purpose of this study is to estimate the size of the shadow economy in

Zimbabwe using the monetary method as in Tanzi (1982, 1983). More specifically, the

present work addresses the question of the size of the shadow (unreported) economy in

Zimbabwe and estimates the revenues that are lost due to tax evasion.

Economists have been interested in the issue of the shadow economy since the last decade

of the 20th century. The shadow economy refers to any economic activity that is unrecorded in

national statistics. As a vicious cycle, undeclared economic activities of economic agents

reduce the tax revenues, leading to tax authorities increasing taxes to compensate for the loss

of tax revenues, stimulating another increase in the share of unreported incomes.

Characterising the nature of the shadow economy and its mechanism is essential for

determining the optimal strategies for the government. Undeclared transactions results in

underestimating the official GDP which has implications for macroeconomic management

and stability as any fiscal and monetary policy decision would be based on biased official

statistics. Furthermore, projections for expenditure are lower as most countries base their

budget projections with reference to the official GDP figure.

Therefore, determining the size of the shadow economy is important because it distorts

the GDP figure since national accounts do not register a number of economic transactions.

The size (averages) of the shadow economy varies from 11 per cent of registered GDP for

OECD members to 20 per cent for emerging economies and 39 per cent for developing

countries (Schneider, 2000; Ahumada, Avarado, Canavesse and Grosman, 2009). Thus one of

the most important challenges that national tax systems confronts is the problem of taxing

unreported economic activities.

However, by its very nature, measuring the shadow economy, is not an easy task. The

matter is further complicated by the existence of different concepts and different estimation

methods tied to particular concepts. This has resulted in estimation methods developing into

an important theoretical and empirical issue.

The increase in the shadow economy creates problems for governments and policy

makers. It accelerates corruption and advocacy, lowers the official economy‟s income,

distorts economic information by overstating unemployment, understating growth rates, and

reduces government revenue. There are several reasons attributed to the increase in shadow

economy. These include increasing tax burdens and social security contributions, increased

regulations in the official economy, particularly in the labour markets, poor governance and

the presence of significant corruption in government operations.





However, everything is not all doom and gloom. The shadow economy encourages

entrepreneurship and innovation, generates income which is spent in the official economy,

induces prices in the official economy to decline in order to remain competitive, and provides

employment and a source of income (Greenidge Holder and Mayers, 2005).

The monetary method or the currency approach has become a popular way of measuring

the size of the shadow economy. It assumes that cash is employed to make transactions that

agents want hidden from official records. It is based on the difference between declared

income and the income implied by the observed currency demand. The popularity of the

monetary method arises from the fact that it covers all money transactions and requires no

expensive surveys that are restricted to a sample and therefore permits regular estimations to

trace the evolution of the size of the shadow economy with relatively easily available data.

In examining the data and the results one should take the following historical highlights

into account. In 1999-2001 violent farm invasions took place in Zimbabwe disrupting

agricultural production and in the process undermining the country‟s industrial bass which

was highly dependent on agriculture. Secondly, monetary policy in Zimbabwe changed

drastically from 2003-2008 with the central bank becoming development banking oriented

and engaging in quasi-fiscal activities (Gono, 2003-2009). This period culminated in

hyperinflation which was brought to a halt through dollarization in 2009 February. This also

marked the demonetisation of the Zimbabwe dollar.

The next section presents a brief outline of the evolution of the monetary method. Section

3 discusses the data source, data description and methodology based on the econometric

estimation of the demand for currency. Section 4 presents the empirical results and section 5

concludes.

2. Literature Review

Transactions made using cash are difficult to trace. This is why the monetary method of

measuring the shadow economy is based on the assumption that cash is the preferred medium

for making transactions that economic agents wish concealed from official records. In

contrast, assets recorded in financial institutions and their uses are recorded in such a manner

that transactions made with them can be easily inspected. Thus where the amount of currency

employed in hidden transactions can be estimated then this amount could be multiplied by the

income-velocity of money to get a measure of the size of the shadow economy.

There are various monetary approaches which researchers have used to measure the size

of the shadow economy thus far-

(i) Gutmann’s approach:

In 1977, Gutmann estimated the shadow economy of the United States (U.S.) estimating

it at $200 billion. His research was based on four key assumptions: Cash is the shadow





economy‟s medium of exchange; high taxes give impetus to the shadow economy; changes in

taxation and other restrictive regulations alters the ratio of currency to demand deposits; and

identified 1937-1941 as the period in which there was an absence of an underground economy

in the U.S.

Feige (1979) criticised Gutmann (1977) „s assumptions as follows; that the first assumption

was unrealistic as most shadow economy money is initially externalised and then

reintroduced into the economy through business loans. Furthermore, irregular purchases can

take place by, cheque, debit cards with little risk of detection. The third assumption of

Gutmann (1977) is criticised on the basis that the ratio of currency to demand deposits alters

over time due to various factors.

(ii) Feige’s approach:

Feige (1979) measured the underground economy of the United States of America based

on the quantity theory of money: MV+M‟V‟= PT

Where,

M = Currency notes

M‟= Demand deposits

V = Velocity of the currency notes

V‟= Velocity of money of the demand deposits

P = Composite price index of existing and newly created goods.

T = Physical volume of transaction.

Feige‟s approach was to first determine M, M‟V and V‟ and thereafter determine PT. The

result for PT is then divided by the observed income to GNP, which gives the estimate of the

shadow economy. In this regard the observed income py is the product of the price index of

newly created goods and services and y is the real income. In the absence of a shadow

economy, the nominal GNP derived should equal the official GNP. When there was no

shadow economy, i.e. 1939 Feige found that the estimated ratio of PT/GNP was 10.3 per cent

which he regarded as normal. However, his estimations for GNP for 1976 and 1978 and

divided by the 1939 normal ratio he derived the nominal GNP. The difference between his

derivation and the official GNP provided the estimate for the shadow economy.

Interestingly, neither Gutmann (1977) nor Feige (1979) employed econometric estimates

of the demand for currency. They determined the quantity of money reserved for shadow

transactions by reference to a prior point in time described as free of a shadow economy. The

size of the shadow economy is obtained by multiplying the income-velocity of circulation

(which is assumed to be the same for the official and the shadow economies) by that amount

of money.

(iii) Tanzi’s approach:





Tanzi (1980; 1983) built his approach based on the insights gained by Cagen (1958) who

was interested in estimating the long run behaviour of the currency ratio over the period 1875-

1955. He identified a number of factors that influenced this currency ratio (defined as

currency/ money supply) as follows: The opportunity cost of holding currency; expected real

income per capita; the volume of retail trade (cash being more preferred); the volume of travel

per capita (cash being more preferred); the degree of urbanisation; the rate of tax on

transactions. Cagen (1958) pointed out that there is a direct and positive relationship between

the income tax rates and the currency ratio.

Tanzi‟s approach has been criticised by Feige (1986) contemptuously dismissing Tanzi‟s

contribution to the literature as being merely his relaxation of his third assumption regarding

treatment of the ratio of currency to checkable deposits in the reported economy.

Furthermore, Feige (1986) also criticised Tanzi for using a multiplicative functional form to

estimate the observed currency ratio.

However, the criticism continued. Even Tanzi‟s choice of variables were singled out for

criticism. Earlier, Acharya (1984) had criticised Tanzi, inter alia, for employing Gross

National Product as a proxy for output. Meanwhile, Hassan and Suk-Yu (2010) argued that

the choice of dependent variable is really at the discretion of the researcher. Notwithstanding

the various criticism levelled against Tanzi and his approach, it is still the best method

available for obtaining an estimate of the size of the shadow economy. By definition, the

shadow economy is difficult to estimate precisely because it is hidden.

Schneider and Kinglmair (2004) estimated the size and official economy of 24 African

countries, in particular, Zimbabwe. Their estimate of the shadow economy for Zimbabwe is

around 59.4 per cent of GNP. In another study Saunders for 2001/2002 and 2002/2003

estimated the shadow economy using the DYMIMIC and monetary method as 61 per cent and

63.2 per cent respectively for the periods.

Using the monetary method, Makochekanwa (2012) estimated the second economy in

Zimbabwe. He found that the shadow economy had escalated from about 10 per cent of

registered GDP in 1980 to 70 per cent in 2008 before declining to 52 per cent in 2009.

Tanzi (1980) based his study on two assumptions; that the shadow economy is fuelled by

high taxes; second that currency is employed for transaction and storing wealth purposes. The

model subjected to empirical tests is as follows:

LnC/M2 = β0 + β1 lnT + β2 lnWS/NI + β3lnR + β4ln Y+e (1)

Where,

C/M2 = Ratio of currency holdings to money defined as M2

T1 = Ratio of personal income taxes to personal income net of transfers‟.





T2 = Top bracket statutory tax rate.

T3 = Weighted-average rate on interest income

Ws/NI = Ratio of wages and salaries in national income.

R = Interest rate

Y= Per capita income

e = Error term

(iv) The Monetary Method

Henry (1976) argued that the shadow economy tends to use higher denomination notes.

He, therefore, suggested that bills of $100 and above be withdrawn from circulation to

facilitate analysis of the change in the composition of total currency in circulation. This

approach has been criticised on the basis that it ignores the effect of high inflation in the

economy as the currency holding would alter over time thereby making large bills appear

relatively small.

The research by Tanzi (1982, 1983) employed econometric estimates of the demand for

currency. The estimated equation of the demand for currency is not dependent on determining

a historical point where no shadow economy existed as in the Gutmann (1977) study. It also

recognises that income velocity depends on income and the opportunity cost of holding cash

and not solely on those variables that motivate economic agents to conduct hidden

transactions.

In conclusion, the literature demonstrates the existence of a widespread interest and concern

from researchers, academics and policy makers about the shadow economy around the world.

In contrast interest in Sub-Saharan Africa, in the topic seems to be lacking. This is most likely

due to the difficulties and frustrations inherent in this particular field. It is hoped that this

paper will contribute to the literature and spark debate. This is so because economic decisions

that rely on incomplete official macroeconomic data are more likely to be ineffective.

3. Data Source, Data Description and Methodology

This study uses an indirect method known as the monetary approach which employs an

aggregated data set to estimate a money demand equation in order to determine the size of the

informal/ underground economy in Zimbabwe. The monetary method is used extensively in

the literature to estimate the underground economy. This approach has been utilised by (Filho,

2012; Ahumada, Alvaredo and Canavese, 2007; Iqbal, Qureshi and Mahmood, 1998;

Greenige, Holder and Mayers, 2005; Ahumada, Alvarado, Canavese and Grosman, 2009;

Ahmed and Ahmed, 1995;shabsigh, 1995; Haque, 2013) It is based on the concept that the

underground economy‟s activities are concealed from authorities and therefore depend on





currency to carry out transactions. It follows therefore, that the underground economy uses

relatively more cash than the visible economy.

A surge in informal activity increases money demand. Consequently, variables that

influence the underground economy such as direct and indirect tax burden should ideally be

included in the estimated money demand equation. In this paper, government expenditure

ratio to GDP is used to represent the tax burden, also included is real GDP per capita, real

interest rates (to incorporate opportunity cost of holding cash) and the ratio of currency to

money supply or M2.

3.1 Data Source

Annual data on currency in circulation, M1 and M2, Real Gdp per capita annual growth

rate, Government expenditure are drawn from the World Bank data base. Data are collected

from 1979 -2013. The theoretical justification for the selected variables in the empirical

model is as follows:

(i) This research hypothesizes that as the taxation level increases economic agents

are encouraged to evade taxes through the use of currency and thereby raising the

demand for currency and consequently, the ratio of currency holdings to money

currency/M2.

(ii) The real interest rate is included as it is expected that a higher real interest rate

may increase the opportunity cost of currency holdings thereby leading to a

decline in currency demand. Consequently, the effect of an increase in real

interest rate on the demand for currency is expected to be negative.

(iii) An increased level of economic development reflected and defined in terms of

annual growth rate in per capita gross domestic product is anticipated to decrease

the demand for currency as economic development is assumed to replace

currency by other financial instruments. Consequently, we anticipate a decline in

currency/M2 ratio when the economy experiences rapid economic growth.

3.2 Data Description

Data used in this model are currency money supply ratio, Government expenditure, and

real rate of interest. The most widely used measure of broad money is M2. This is the main

measure of the money supply and is the economic indicator generally used to measure the

amount of liquidity in the economy as it is relatively easy to track. For tracking the variable

that induces economic agents to conceal transactions, we use government expenditure as ratio

of GDP.

3.3 Methodology

At the commencement of the analysis a correlation matrix is specified to test for

relationships among the variables and the time series are tested for stationarity using





Augmented Dickey-Fuller (1979) and Kwiatkowski, Phillips, Schmidt, and Shin (KPSS,

1992). The model estimation is based upon the work of Tanzi (1980, 1983) model but with

some modification to suit the Zimbabwean economy. Moreover, currency demand methods

including those of Tanzi (1980, 1983) original model are built upon the regression model with

multiple time series variables. As previously alluded to the model has been utilised by (

Bagachawa and Naho, 1994; Filho, 2012; Ahumada, Alvaredo and Canavese, 2007; Iqbal,

Qureshi and Mahmood, 1998; Greenidge, Holder and Mayers, 2005; Ahumada, Alvarado,

Canavese and Grosman, 2009; Ahmed and Ahmed, 1995; Shabsigh, 1995; Haque, 2013). The

first step is to specify and estimate a demand function for currency with the following

assumptions -

3.3.1 Model assumptions

All activities in the underground economy rely on currency for transactions. This

includes RTGS and debit cards. Even where taxes were absent, the currency ratio would be

impacted by illegal and criminal activities such as gambling or smuggling to name a few.

The velocity of money for currency in the underground economy is the same as that

of narrow money M1 or that of the legal money or an average of these two.

The higher the government expenditure as a proportion of GDP the larger the

underground economy and the money demand.

The second step is to run the model again and set GE equal to zero to obtain an estimate of

the amount of cash demanded in the absence of incentives to conceal transactions.

The third step is to obtain the difference between observed currency and the currency under

no incentives which will reveal the hidden economy.

The fourth step is to multiply the above result by the velocity of circulation.

3.3.2 Empirical Model

The basic regression equation for the currency demand as suggested by Tanzi (1980, 1983), is

the following:

Cr= f ( RGP+GE+Ri + Dep int+ (m1/m2)t-1

Where,

Cr = Currency holdings ratio

RGP= real GDP per capita growth rate (annual)

GE = Government expenditure % of GDP

Ri = Real interest rate

Dep int = Deposit int rate (annual)

(M1/M2) t-1 = Lagged currency ratio

4. Empirical Results





The demand for currency has been estimated by regression analysis and the results are

reported as follows:

Dependent variable: Cash holdings

The dependent variable is cash holdings and the model summary in table 1 below highlights

that R squared = 1.000 which implies that cash holdings can be explained by the predictor

variables and that all the variation in demand for currency is explained by the estimated

ANOVA equation.

Table 1: Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .934a .873 .849 15.76786

a. Predictors: (Constant), Government Expenditure (%) GDP, GDP Per

Capita Growth (%), M1/m2, Deposit Interest, Real Interest Rate (%)

Table 2: The ANOVA equation highlights the F-statistic which indicates a perfect fit with the equation.

Model Sum of Squares df Mean Square F Sig.

1 Regression 46064.371 5 9212.874 37.055 .000b

Residual 6712.885 27 248.625

Total 52777.256 32

a. Dependent Variable: Cash Holdings

b. Predictors: (Constant), Government Expenditure (%) GDP, GDP Per Capita Growth (%), M1/m2, Deposit

Interest, Real Interest Rate (%)

In table 3, the coefficients of the predictor variables for deposit interest, lagged M1M2, are

positive and statistically significant at the 0.001 level. Government expenditure is statistically

significant at the .05 level. Whilst real interest coefficient is negatively correlated to cash

holdings and GDP per capita growth rate but both are statistically insignificant.

Table 3: Coefficientsa

Model

Unstandardized Coefficients

Standardized

Coefficients

t Sig. B Std. Error Beta

1 (Constant) -17.503 20.896 -.838 .410

Deposit Interest 1.378 .246 .956 5.604 .000

M1/m2 .768 .154 .431 4.975 .000

Real Interest Rate (%) -.085 .058 -.278 -1.462 .155

GDP Per Capita Growth (%) .688 .548 .106 1.255 .220

Government Expenditure (%)

GDP 2.231 .949 .261 2.351 .026

a. Dependent Variable: Cash Holdings





Table3a: Summary statistics and results from regression analysis

Variable Correlation cashholding

Multiple regression

weights b

β

Constant -17503

Dep. int .799** 1.378 .956

Govt Exp -.174 2.231 .261

GDP PCAP -.478** .688 .106

Real int .499** -.085 -.278

M1/m2 .562** .768 .431

Correlation matrix

Below in table 4 is a correlation matrix. It reports the results of 15 correlations. Statistical

hypotheses are:

H0: р = 0 [there is no actual correlation]

HA: р ≠ 0 [this is a correlation]

The null hypothesis was rejected in the following instances - Cash holdings ratio correlate

positively with deposit interest, real interest, and statistically significant at the 0.01 level

whilst m1/m2 is statistically significant at the 0.05 level. GDP per capita growth rate and

Government expenditure has a negative sign and is statistically not significant. Furthermore,

there is no evidence of multi-collinearity.

Table 4: Correlation Matrix

Deposit

Interest

Real

Interest

Rate (%)

GDP Per

Capita

Growth (%)

Government

Expenditure (%)

GDP M1/m2

Cash

Holdings

Deposit Interest Pearson

Correlation 1 .859** -.548** -.508** .317 .799**

Sig. (2-

tailed)

.000 .001 .002 .068 .000

N 35 34 35 35 34 35

Real Interest

Rate (%)

Pearson

Correlation .859** 1 -.319 -.659** .382* .499**

Sig. (2-

tailed) .000 .066 .000 .028 .003

N 34 34 34 34 33 34

GDP Per Capita

Growth (%)

Pearson

Correlation -.548** -.319 1 .157 -.125 -.478**

Sig. (2-

tailed) .001 .066 .368 .480 .004

N 35 34 35 35 34 35

Government

Expenditure (%)

Pearson

Correlation -.508** -.659** .157 1 -.019 -.174





GDP Sig. (2-

tailed) .002 .000 .368 .915 .318

N 35 34 35 35 34 35

M1/m2 Pearson

Correlation .317 .382* -.125 -.019 1 .562**

Sig. (2-

tailed) .068 .028 .480 .915 .001

N 34 33 34 34 34 34

Cash Holdings Pearson

Correlation .799** .499** -.478** -.174 .562** 1

Sig. (2-

tailed) .000 .003 .004 .318 .001

N 35 34 35 35 34 35

** Correlation is significant at the 0.01 level (2-tailed).

*Correlation is significant at the 0.05 level (2-tailed).

Furthermore, in order to arrive at the estimates of the shadow economy and tax evasions, it is

necessary to compute the components of illegal money, legal money, tax evasion and velocity

of money as indicated below:

Illegal money

Subsequent to the estimation of currency demand above, the size of the shadow economy and

tax evasion were computed as follows (Iqbal, Qureshi and Mahmood, 1999; Ahmed and

Hussain, 2008):

The equation for the level of illegal money can be expressed as follows:

(IM)= [ (M1/M2)t – (M1/M2)wt]…….. (2)

Legal money

The difference between the sum of currency (M1) and total money supply (M2) and the

estimated illegal money yields legal money (LM).

The equation for legal money can be expressed as follows:

LM = M2 –IM ……………. ……. ….. (3)

Velocity of money

The income velocity (IV) of money can be expressed as an equation as follows:

IV = GDP/LM …… ………. ……….. (4)

It is assumed that the velocity of illegal money is the same as that of legal money. Thereafter

an estimate of the shadow economy can be obtained by multiplying illegal money by the

income velocity of money. The equation for the shadow economy can be expressed as

follows:

SE = IM * IV ………. …….. ……… …… (5)

Tax Evasion





Finally the level of total tax evasion (TE) in Zimbabwe can be computed multiplying the

estimates of the shadow economy (SE) by government expenditure (GE/GDP) as follows:

TE = SE * (GE/GDP) ……….. …….. …….. …… (6)

4.1 Estimates of the Shadow Economy and Tax evasion

The size of the underground economy and tax evasion - The estimates of the level of the

shadow economy and tax evasion for the period 1979-2013 are reported in table 4 below. The

results therein highlight that the shadow economy grew from zero 1979 to US $

7,684,784,664 in 2013. Column (f) reports that the shadow economy as a proportion of GDP

was around 0 per cent in 1979 which escalated to 60.0 per cent in 2013. The level of tax

evasion is reported in column (g). It indicates that the level of tax evasion rose from zero to

US$ 1,600,797,513 in 2013. The estimates of tax evasion are based on a firm assumption that

that incomes in the shadow economy would be taxed at the same rate as those in the formal or

visible economy.

As regards the growth rates of the shadow economy as compared to the formal economy

as depicted in column (g) and (h) reports that the shadow economy grew at rates of 00.0, -

19,0 1210,4 -585,5 180,6 -81,5 -1149,5 and 15,8 in 1979, 1991, 1992, 2000, 2003, 2008 ,

2009, 2013correspondingly, while growth rates in the visible economy for the same periods

were 00,0 -1.6, -21.9, -2.5, -9.7, -16.6, 84.7 per cent and 2,6. Each of these dates is related to

an economic event. 1979 is one year before independence, 1991 was the year of economic

structural adjustment programme commencement, 1992 was the year financial liberalisation

began, 2000 was the year land invasions began, 2003 was the year when currency shortages

began, 2008 was the hyperinflationary year prior to dollarization, 2009 was dollarization year

and 2013 is the present day or four year of a dollarized economy.

The apparent faster growth rates of the shadow economy appears to be a major factor

contributing to government fiscal deficit since government expenditure expands with the

overall economy (including formal and informal) while tax revenues grow at the slower pace

of the formal economy.

Table 5: Estimates of the Shadow Economy in Zimbabwe

Velocity Shadow Growth

rate Growth

rate

Year Illegal legal of Legal Shadow Tax Economy of

shadow of GDP

Money Money Money Economy Evasion

(% of GDP)

Economy %

(a) (b) ( C ) ( d) ( e ) ( f) ( g ) ( h)

1979 -459793683 1126393683 4,6 -2113437919 -387476910 -40,8

1980 347288575,4 862511425 7,7 2689233516 509766733,7 40,3 -227,2 29,0

1981 306951393 1092448607 7,3 2251000490 364536923,8 28,1 -16,3 20,0

1982 724223522,6 1022176477 8,4 6050473907 1124983975 70,9 168,8 6,6





1983 665956794,5 1011600751 7,7 5111238953 883883485,4 65,8 -15,5 -9,1

1984 699151446,6 1146048553 5,5 3875139495 776585705 61,0 -24,2 -18,2

1985 811894101,5 1360805899 4,1 3363343448 679510066,4 59,7 -13,2 -11,3

1986 770420717,2 1494079283 4,2 3206060823 661988080 51,6 -4,7 10,3

1987 1362291000 1516209000 4,4 6056880449 1415228275 89,8 88,9 8,4

1988 1643211395 1833888605 4,3 7002247666 1924713418 89,6 15,6 15,9

1989 1354411510 2966088490 2,8 3783801759 707208062,3 45,7 -46,0 6,0

1990 531251655,7 1501073618 5,9 3108719713 604525676,7 35,4 -17,8 6,0

1991 332050683,3 1139507088 7,6 2518115012 405913089,2 29,1 -19,0 -1,6

1992 925792120,1 189421887 35,6 32997558287 7971573229 488,7 1210,4 -21,9

1993 757166095,6 746434000 8,8 6658186642 995203818,1 101,4 -79,8 -2,8

1994 983449261,7 629961857 10,9 10757205625 1795786393 156,1 61,6 5,0

1995 879332566,7 1025634412 6,9 6096881909 1098228900 85,7 -43,3 3,2

1996 1032190530 1168351253 7,3 7556355072 1280033703 88,3 23,9 20,3

1997 877255486,5 1688714874 5,1 4430951355 722913258,4 51,9 -41,4 -0,3

1998 770684021,6 689789558 9,3 7152753345 1128994164 111,7 61,4 -24,9

1999 942513166,1 284207823 24,1 22743102363 4046200324 331,6 218,0 7,1

2000 2154992143 -368068407 -18,2

-39168822474

-9504451864 -585,5 -272,2 -2,5

2001 967535011,1 2326253898 2,9 2818848372 498730104,1 41,6 -107,2 1,3

2002 4709481124 4901928456 1,3 6093127944 1092103006 96,1 116,2 -6,4

2003 3011427099 1008796791 5,7 17097819155 3063289734 298,5 180,6 -9,7

2004 3922173099 -2,1E+09 -2,8

-10840983396

-2276674811 -186,7 -163,4 1,4

2005 4598536675 -1,986E+09 -2,9

-13328562406

-2027443615 -231,6 22,9 -0,9

2006 10338798353 -4,818E+09 -1,1

-11681239831

-687168207,1 -214,6 -12,4 -5,4

2007 13916759290 -8,396E+09 -0,6 -8771413121

-281402370,6 -165,8 -24,9 -2,8

2008 -1,249E+23 3,393E+23 0,0 -1625465724

-33275250,18 -36,8 -81,5 -16,6

2009 5963200,657 2851349,7 2860,8 17059391004 1606111468 209,1 -1149,5 84,7

2010 4108706,861 17129880,4 552,1 2268273429 328826835,7 24,0 -86,7 15,9

2011 8278766,086 22211008 493,3 4083742503 734126222,3 37,3 80,0 15,9

2012 12584440,73 23648411,8 527,4 6637164038 1353317084 53,2 62,5 13,8

2013 13591305,33 22641547,2 565,4 7684784664 1600797513 60,0 15,8 2,6

5. Conclusions and Policy Implications

This study has estimated the existence of a shadow economy and tax evasion in

Zimbabwe over the period 1979- 2013 using the monetary method as employed by Tanzi

(1980, 1983) and by Iqbal Qureshi and Mahmood (1999) among others. The cost of the

shadow economy and its increasing size must be large (7.6bn US$). Particularly, when one

estimates the loss of tax revenues and the demand on government services by the nefarious

economic activities taking place in the shadow economy should be a major contributing factor

for any fiscal deficit. This leads to an implied higher uncertain cost of doing business





particularly when the element of discretion that is exercisable by public officials is pervasive,

acts as a deterrent to the implementation of a private sector –led development strategy.

The major conclusions of this study can be expressed as follows:

a) The size of the shadow economy has increased as a proportion of GDP from 40.3

per cent in 1980 to 60.0 per cent of GDP in 2013. [That is from 2.6bn Zimbabwe

dollars in 1980 to 7.6 bn USD in 2013. The population in 1980 stood at 7.6 million 50

per cent of whom were above 15 years of age dividing this by 3.8 million means 1000

Zimbabwe dollars of income per adult was unreported then. In 2013 the population

stood at 13 million 50 per cent of whom were above 15 years of age dividing this by

6 million means 1100 USD of income per adult was unreported. By 2008

unemployment in Zimbabwe was estimated at around 80 per cent. However, the most

recent census report argues that the majority of Zimbabweans engaged in selling

vegetables on street corners, barbers, flea market stall keepers are gainfully employed

therefore unemployment in Zimbabwe is estimated at 11 per cent by Zimstat (2013).

b) The total value of tax evasion has increased from Zimbabwe dollars 509766733.70 to

USD$1,600,797,513 in 2013. [In 1980 the exchange rate was Zimbabwe dollar one =

one USD.]

c) The evidence also demonstrates that the rate of growth of the informal economy has

been higher than the formal economy.

In contrast to the Makochekanwa (2012) study which reported that during the

hyperinflationary period the shadow economy was 70 per cent of GDP in 2008 and 52 per

cent in 2009 when the hyperinflation was halted through dollarization, this study found that

the shadow economy was -36.8 per cent and 209.10 per cent respectively. As can be seen the

results differ depending on the objectives of the study.

[It should be noted that as inflation accelerated official statistics were no longer being

reported. In any event the shadow economy became the “official economy” as goods and

services were available in the shadow economy in exchange for United States dollars.]

This study does not attempt to present a detailed plan that would reduce the size of the

informal economy. Actions required for reform agenda include economic liberalisation, fiscal

discipline (austerity), greater space for the private sector, tax reforms consisting of lower rates

and a broadening of the tax base, enhanced transparency in decision-making, and policy

consistency are some of the areas in which policy alterations need to be made. Isolated piece-

meal actions would be a hindrance. Corruption in particular requires a comprehensive

measure of reforms in order to successfully tackle the problem and should be a crucial plank

in the reforms of government.

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