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New Estimates for the Shadow Economies All over the World

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This paper presents estimations of the shadow economies for 162 countries, including developing, Eastern European, Central Asian, and high income OECD countries over 1999 to 2006/2007. According to our estimations, the weighted average size of the shadow economy (as a percentage of 'official' GDP) in Sub-Saharan Africa is 37.6%, in Europe and Central Asia (mostly transition countries) 36.4% and in high income OECD countries 13.4%. We find that an increased burden of taxation (direct and indirect ones), combined with (labour market) regulations and the quality of public goods and services as well as the state of the 'official' economy are the driving forces of the shadow economy.
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2010/ShadowEconomySchneiderBuehnMontenegro/SHADOW ECONOMIES_ITax.doc
New Estimates for the Shadow Economies all over the World
1
Friedrich Schneider
2
, Andreas Buehn
3
, and Claudio E. Montenegro
4
This Version: September 06, 2010
Abstract
This paper presents estimations of the shadow economies for 162 countries, including
developing, Eastern European, Central Asian, and high income OECD countries over 1999
to 2006/2007. According to our estimations, the weighted average size of the shadow
economy (as a percent of "official" GDP) in 2007 in developing countries is 28.3%, in
transition countries (Eastern European and Central Asian countries) 41.1% and in the high
income OECD countries 19.4%. We find that an increased burden of taxation (direct and
indirect ones), combined with (labor market) regulations and the quality of public goods
and services as well as the state of the “official” economy are the driving forces of the
shadow economy.
JEL-class: O17, O5, D78, H2, H11, H26.
Keywords: Shadow Economy of 162 Countries, Tax Burden, Quality of State Institutions,
Regulation, MIMIC Model
1
Responsibility for the content of this paper is ours and should not be attributed to our affiliated institutions. This is
a background paper for In from the Shadow: Integrating Europe’s Informal Labor, a World Bank regional report
on the informal sector in Central, Southern Europe and the Baltic countries (Task number P112988). We would
like to thank suggestions and comments received at the 2010 Annual Meeting of the Public Choice Society
(Monterrey, CA), the 2010 Annual Meeting of the European Public Choice Society (Izmir, Turkey), and the
workshop Shadow Economy, Tax Policy, and Labour Markets in International Comparison: Options for
Economic Policy.
2
Friedrich Schneider, Department of Economics, Johannes Kepler University of Linz, A-4040 Linz-Auhof,
Austria. Phone: +43-732-2468-8210, Fax: +43-732-2468-8209. E-mail: friedrich.schneider@jku.at.
3
Andreas Buehn, Technische Universität Dresden, Faculty of Business and Economics, Chair for Economics, esp.
Monetary Economics, 01062 Dresden, Email: andreas.buehn@tu-dresden.de.
4
Claudio E. Montenegro, Development Research Group, Poverty and Inequality Unit, The World Bank; and
Department of Economics, Universidad de Chile. Email: cmontenegro@worldbank.org.
2
1. Introduction
Information about the extent of the shadow economy, who is engaged, the frequency of these
activities, and their magnitude is crucial for making effective and efficient decisions regarding
the allocations of a country’s resources in this area. Unfortunately, it is very difficult to get
accurate information about shadow economy activities on the goods and labor market, because
all individuals engaged in these activities do not wish to be identified. Hence, doing research in
this area can be considered as a scientific passion for knowing the unknown.
Although substantial literature exists on single aspects of the hidden or shadow economy and
comprehensive surveys have been written by Schneider and Enste (2000), and Feld and
Schneider (2010), the subject is still quite controversial as there are disagreements about the
definition of shadow economic activities, the estimation procedures and the use of their
estimates in economic analysis and policy aspects.
5
Nevertheless, there are some indications for
an increase of the shadow economy around the world, but little is known about the
development and the size of the shadow economies in developing, Eastern European and
Central Asian (mostly the former transition countries), and high income OECD countries over
the period 1999 to 2006/2007. This paper is an attempt to fill this gap by using the same
estimation technique and almost the same data sample.
Hence, the goal of this paper is twofold: (i) to undertake the challenging task of estimating the
shadow economy for 162 countries all over the world and (ii) to provide some insights into the
main causes of the shadow economy using a unique database of the size and trend of the
shadow economy for 162 countries between 1999 and 2006/2007. This is an improvement
compared to previous work, because we successfully “created” a unique dataset and used the
MIMIC estimation method for all countries with the explicit goal to have a comparable shadow
economy data set.
5
Compare the different opinions of Tanzi (1999), Thomas (1999), Giles (1999a,b) and Pedersen (2003).
3
2. Some Theoretical Considerations about the Shadow Economy
One commonly used working definition of the shadow economy is all currently unregistered
economic activities that contribute to the officially calculated (or observed) Gross National
Product.
6
Smith (1994, p. 18) defines it as “market-based production of goods and services,
whether legal or illegal, that escapes detection in the official estimates of GDP.” In this paper
the following more narrow definition of the shadow economy is used: the shadow economy
includes all market-based legal production of goods and services that are deliberately concealed
from public authorities to avoid payment of income, value added or other taxes; payment of
social security contributions; compliance with certain legal labor market standards, such as
minimum wages, maximum working hours, safety standards, etc.; and compliance with certain
administrative procedures, such as completing statistical questionnaires or administrative
forms. Given this definition, important determinants of the shadow economy are:
a) Tax and Social Security Contribution Burdens
It has been ascertained that the overall tax and social security contribution burdens are among
the main causes for the existence of the shadow economy.
7
. The bigger the difference between
the total cost of labor in the official economy and the after-tax earnings (from work), the greater
is the incentive to avoid this difference and to work in the shadow economy. Since this
difference depends broadly on the social security burden/payments and the overall tax burden,
the latter are key features of the existence and the increase of the shadow economy.
The concrete measurement of the tax and social security contribution burdens is not easy to
define, because the tax and social security systems are vastly different among the countries. In
6
This definition is used for example, by Feige (1989, 1994), Schneider (2005, 2007), Feld and Schneider (2010)
and Frey and Pommerehne (1984). Do-it-yourself activities are not included. For estimates of the shadow economy
and the do-it-yourself activities for Germany see Buehn et al. (2009).
7
See Schneider (1986, 2005, 2007); Johnson et al. (1998a,1998b); Tanzi (1999); Giles (1999a); Giles and Tedds
4
order to have some general comparable proxies, we use the following causal variables: (1)
Indirect taxes as a proportion of total overall taxation (positive sign expected); (2) Share of
direct taxes: direct taxes as proportion of overall taxation (positive sign expected); (3) Size of
government: general government final consumption expenditures (in percent of GDP, which
includes all government current expenditures for purchases of goods and services; positive sign
expected); (4) Fiscal freedom as subconent of the Heritage Foundation’s economic freedom
index measures the fiscal burden in an economy; i.e. top tax rates on individual and corporate
income. The index ranges from 0 to 100, where 0 is least fiscal freedom and 100 maximum
degree of fiscal freedom (negative sign expected).
b) Intensity of Regulations
Increased intensity of regulations is another important factor which reduces the freedom (of
choice) for individuals engaged in the official economy. One can think of labor market
regulations such as minimum wages or dismissal protections, trade barriers such as import
quotas, and labor market restrictions for foreigners such as restrictions regarding the free
movement of foreign workers. Johnson et al. (1998b) find significant overall empirical
evidence of the influence of (labor) regulations on the shadow economy; and the impact is
clearly described and theoretically derived in other studies, e.g. for Germany (Deregulation
Commission 1990/91). Regulations lead to a substantial increase in labor costs in the official
economy. But since most of these costs can be shifted to the employees, these costs provide
another incentive to work in the shadow economy, where they can be avoided. Their empirical
evidence supports the model of Johnson et al. (1997), which predicts, inter alia, that countries
with more general regulation of their economies tend to have a higher share of the unofficial
economy in total GDP.
(2002); Feld and Schneider (2010).
5
To measure the intensity of regulation or the impact of regulation on the decision of whether to
work in the official or unofficial economy is a difficult task, and we try to model this by using
the following causal variables: (1) Business freedom: it is a subcomponent of the Heritage
Foundation’s economic freedom index; it measures the time and efforts of business activity. It
ranges from 0 to 100, where 0 is least business freedom and 100 maximum business freedom
(negative sign expected); (2) Economic freedom: Heritage Foundation economic freedom index
which ranges from 0 to 100, where 0 is least economic freedom and 100 maximum economic
freedom (negative sign expected); (3) Regulatory quality: World Bank´s regulatory quality
index including measures of the incidents of market-unfriendly policies, such as price controls
or inadequate bank supervision, as well as perceptions of the burdens imposed by excessive
regulation in areas, such as foreign trade and business development. It scores between -2.5 and
+2.5 with higher scores corresponding to better outcomes (negative sign expected).
c) Public Sector Services
An increase of the shadow economy can lead to reduced state revenues which in turn reduce the
quality and quantity of publicly provided goods and services. Ultimately, this can lead to an
increase in the tax rates for firms and individuals in the official sector, quite often combined
with a deterioration in the quality of the public goods (such as the public infrastructure) and of
the administration, with the consequence of even stronger incentives to participate in the
shadow economy. The provision and especially the quality of the public sector services is thus
also a crucial causal variable for people’s decision to work or not work in the shadow economy.
To capture this effect, we have the following variable: Government Effectiveness from the
World Bank´s Worldwide Governance Indicators. It captures perceptions of the quality of
public services, the quality of the civil service and the degree of its independence from political
pressures, the quality of policy formulation and implementation, and the credibility of
6
government’s commitment to such policies. The scores of this index lie between -2.5 and +2.5
with higher scores corresponding to better outcomes (negative sign expected).
d) Official Economy
As it has been shown in a number of studies (Enste and Schneider, 2006; Feld and Schneider,
2010) the situation of the official economy also plays a crucial role of people’s decision to
work or not to work in the shadow economy. In a booming official economy, people have a lot
of opportunities to earn a good salary and “extra money” in the official economy. This is not
the case in an economy facing a recession and more people try to compensate their losses of
income from the official economy through additional shadow economy activities. In order to
capture this, we will use the following variables: (1) GPD per capita based on Purchasing
Power Parity (PPP), measured in constant 2005 US$. PPP as gross domestic product converted
to international dollars using PPP rates (negative sign expected); (2) Unemployment rate
defined as total unemployment in percent of total labour force (positive sign expected); (3)
Inflation rate: GDP deflator (annual rate in percent); inflation is measured by the annual growth
rate of the GDP implicit deflator, it shows the rate of price changes in the economy as a whole
(positive sign expected); (4) Openness: openness corresponds to trade (in percent of GDP).
Trade is the sum of exports and imports of goods and services, measured as a share of gross
domestic product (negative sign expected).
Because the shadow economy cannot be directly measured, we have to use indicators in which
shadow economy activities are reflected. Here, we use the following indicators:
e) Monetary Indicators
Given that people who engage in shadow economy transactions do not want to leave traces,
7
they conduct these activies in cash. Hence, most shadow economy activities are reflected in an
additional use of cash (or currency). To take into account this, we use the following two
indicators: (1) M0 / M1: M0 corresponds to the currency outside the banks; the usual definition
for M! is M0 plus deposits; (2) Currency / M2: It corresponds to the currency outside the banks
as a proportion of M2.
f) Labour Market Indicators
Shadow economy activities are also reflected in labour market indicators. We use the following
two: (1) Labour force participation rate: Labour force participation rate is a proportion of the
population that is economically active, supply of labour for the production of goods and
services during a specified period; (2) Growth rate of the total labour force: Total labour force
compromises people aging 15 and older who meet the International Labor Organisation´s (ILO)
definition of the economically active population: all people who supply labour for the
production of goods and services during a specified period.
g) State of the Official Economy
Also, shadow economy activities are reflected in the state of the official economy. For this
reason, we include the following two indicators: (1) GDP per capita: GDP per capita is gross
domestic product converted to international dollars using Purchasing Power Parity rates,
divided by the population; (2) Growth rate of GDP per capita, as (1), but the annual growth rate
of the GDP per capita.
3. The Size of the Shadow Economy for 162 Countries
3.1 Econometric Methodology
Estimating the size and trend of the shadow economy is a difficult and challenging task.
8
Methods designed to estimate the size and trend of the shadow economy such as the
currency demand approach or the electricity approach consider just one indicator that ”must”
capture all effects of the shadow economy. However, it is obvious that shadow economy effects
show up simultaneously in the production, labor, and money markets. The empirical method
used in this paper is based on the statistical theory of unobserved variables, which considers
multiple causes and multiple indicators of the phenomenon to be measured, i.e. it explicitly
considers the multiple causes leading to the existence and growth of the shadow economy, as
well as the multiple effects of the shadow economy over time.
8
In particular, we use the
Multiple Indicators Multiple Causes (MIMIC) model for the empirical analysis.
The main idea behind this model is to examine the relationship between an unobserved variable
and a set of observable variables using covariance information. In particular, the MIMIC model
compares a sample covariance matrix, i.e. the covariance matrix of the observed variables, with
the parametric structure imposed on it by a hypothesized model.
9
The MIMIC model presented
in this paper considers the shadow economy as the unobserved variable and analyzes its
relationship to the observed variables using the covariance matrix of the latter. For this purpose,
the unobserved variable is in a first step linked to the observed indicator variables in a factor
analytical model also called measurement model. Second, the relationships between the
unobserved variable and the observed explanatory (causal) variables are specified through a
structural model. Thus, a MIMIC model is the simultaneous specification of a factor model and
a structural model. In this sense, the MIMIC model tests the consistency of a “structural” theory
through data and has two goals: (i) estimating the parameters (coefficients, variances, etc.) and
(ii) assessing the fit of the model. Applying this to the shadow economy research, these two
goals mean (i) measuring the relationships of a set of observed causes and indicators to the
shadow economy (latent variable), and (ii) testing if the researcher’s theory or the derived
8
The pioneers of this approach are Frey and Weck-Hannemann (1984) who applied this approach to cross-section
data from the 24 OECD countries for various years.
9
hypotheses, as a whole, fit the data.
3.2 Econometric Results
Table 1 presents seven different specifications because we think it is interesting to see which
variables turn out to be significant, especially if one uses subsamples of countries, where more
and different causal variables are available. We believe that it is interesting to see which
variables have an influence on the size and trend of the shadow economy, if we have more and
better data available. The ideal situation of course would be, a large data set for all countries
over the total period 1996 up to 2007, but this is unfortunately not the case.
For the total sample two estimations are shown, one for the 151 countries over 1996 to 2007
and, with more causal variables, one sample for 120 countries over 1996 to 2006. In addition to
the total sample estimations, econometric estimations using the MIMIC approach are presented
for 98 (88) developing countries, 21 Eastern European and Central Asian (mostly former
transition) countries; and 25 high income OECD-countries. For the developing countries, two
estimations with and without the direct tax burden rate as causal variable are presented; without
direct tax burden rate the number of development countries increase from 88 to 98. For the high
income OECD countries again two estimations are shown, one over the period 1996 to 2006
and one over the period 1996 to 2007. For the 98 (88) developing countries and the 21 Eastern
European and Central Asian countries, the estimations were done over the period 1994 to 2006
and for the 25 OECD countries over the period 1996 to 2007. For the total sample of 151(120)
countries we use data for the period from 1996 up to 2007(2006).
For the developing countries we use as cause variables the following six: share of direct
taxation (direct taxes in percent of overall taxation), size of government (general government
final consumption expenditure, in percent of GDP) as proxy for indirect taxation and a variable,
9
A general overview about the SEM approach is given in e.g. Bollen (1989).
10
fiscal freedom (an index consisting of top individual income tax rate, top individual corporal
tax rate, and total tax revenues as percent of GDP) as three tax burden variables in a wide
sense; regulatory intensity for state regulation, and the business freedom index (which is
composed of the following components: time to open a business, financial costs to start a
business, minimum capital stock to start a business, and costs for obtaining a licence), the state
of economy with the two variables: the unemployment rate and GDP per capita. As indicator
variables we use growth rate of GDP per capita, the labor force participation rate (people over
15 economically active in % of total population), and as currency we use M0 divided by M1.
For the Eastern European and Central Asian (mostly former transition) countries, we use as
cause variables the size of government, the fiscal freedom index, for state regulation the
business freedom index, and for the state of the economy the unemployment rate, inflation rate
and openness (sum of export and imports of goods and services, in percent of GDP). As
indicators, we use the growth rate of GDP per capita, the growth rate of total labor force, and
the ratio M0 over M1. For the 25 OECD countries, we use the total tax burden (total tax
revenues in percent of GDP), the fiscal and business freedom indices, a regulatory quality
index, and the unemployment rate. As indicator variables, we use GDP per capita, the labor
force participation rate and a measure for currency (M0 over M2). For the total sample of 151
countries we use as cause variables the size of the government, the unemployment rate,
government effectiveness, and the GDP per capita. As indicators we use currency (M0 over
M1), the growth rate of GDP per capita, and the labor force participation rate. For the 120
countries, we have additional causal variables. Here we include the size of the government, the
fiscal freedom index, the share of direct taxation, the business freedom index, the
unemployment rate, government effectiveness, and the GDP per capita. As indicator variables
we use currency (M0 over M1), the growth rate of GDP per capita, and the growth rate of total
labor force.
11
The estimations results for the 98 developing countries over the period 1994 to 2006 are shown
in specification 1, and the estimation results for the 88 developing countries (including direct
taxation) over the same period are shown in specification 2. In both estimations, all estimated
coefficients of the cause variables have the theoretically expected signs. Except for the
unemployment rate, all other cause variables are statistically significant, at least at the 90-
percent confidence level. The share of direct taxation and the size of government are highly
statistically significant, as well as the fiscal freedom and the business freedom variable. Also,
the GDP per capita is in both equations highly statistically significant with the expected
negative sign. If we turn to the indicator variables, the labor force participation rate and the
growth rate of GDP per capita are in both equations highly statistically significant. The test
statistics are also quite satisfactory.
In specification 3, the MIMIC estimation result for the 21 Eastern European and Central Asian
(mostly former transition) countries over the period 1994 to 2006 is shown. The size of
government and the fiscal freedom variable (both capturing the overall state burden), they are
highly statistically significant causes and have the expected signs. Turning to regulation, the
economic freedom variable has the expected negative significant sign. As these countries
experienced periods of high inflation, we include the inflation rate which has the expected
positive, highly significant sign. The variable openness, modelling in a certain way the
transition process, is also statistically significant. Considering the indicator variables, the
growth rate of the total labor force is statistically significant, as well as the growth rate of GDP
per capita. Also, here the test statistics are quite satisfactory.
In specifications 4 and 5, the estimation results for the 25 high income OECD countries are
shown over the period 1996 to 2006 and 1996 to 2007.
10
In specification 4, the two variables
capturing government burden (total tax burden and fiscal freedom) are highly statistically
10
A number of variables is not available for 2007, hence we have two different sets of cause variables.
12
significant and have the expected sign. The unemployment rate has the expected sign and is at
95 percent confidence level statistically significant. The two variables capturing the regulatory
burden, i.e., business freedom and regulatory quality, have the expected signs and are highly
statistically significant. Turning to the indicator variables, the labor force participation rate and
currency (ratio of M0 over M2) are both highly statistically significant. Also, the test statistics
for this equation are quite satisfactory. Specification 5 excludes fiscal and business freedom
which allows us to estimate the model up to the year 2007. All causal variables are highly
statistically significant and have the expected signs. as the same is true for the indicators.
Specifications 6 and 7 present two estimations of 151 and 120 countries. In specification 6 we
present the results of 151 countries estimated over the period 1996 to 2007. Turning first to the
causal variables, we see that the size of government has the expected positive sign and is highly
statistically significant. The same holds for the two variables which describe the state of the
economy, the unemployment variable, statistically significant with a positive sign, and GDP
per capita, which is highly statistically significant with the expected negative sign. Turning to
the indicator variables, the growth rate of GDP per capita and the labor force participation rate
have the expected signs and are highly statistically significant. If we reduce this sample to 120
countries, we can include more causal variables and the results are presented in specification 7.
Here, we see that as we have three variables capturing the burden of taxation (in a wide sense):
the size of government, fiscal freedom and share of direct taxation. All three have the expected
signs and are statistically significant. As regulatory variables we have business freedom and
government effectiveness which, again, have the expected negative signs and are statistically
significant. For the state of the economy, we have the unemployment rate, which is not
statistically significant, and GDP per capita, statistically significant with the expected negative
sign. For the indicators, we have currency (M0 over M1), the labour force participation rate and
GDP per capita, being statistically significant and showing the expected sign.
13
Summarizing the results, we can say that for all groups of countries, the theoretical
considerations of the causes of the shadow economy in section 2 behave according to our
expectations. However, the estimated coefficients in table 1 are quite different in magnitude
from one specification to the next. Because it is rather difficult to come up with an explanation
for the exact differences in the magnitude of the coefficients, we only present a general
interpretation for this observation. With respect to the indices measuring regulation in one way
or the other, i.e the fiscal freedom and business/economic freedom indices, our results suggest
that regulation is a much more important determinant in developed and transition countries than
in developing ones. It seems that – for the reason that the burden of regulation is on average
higher in developed and transition countries as more rules, regulations, and administrative
procedures are in place the importance of regulation being a determinant of the shadow
economy increases with the level of development. On the contrary, in developing countries in
which regulation is often less burdensome, the coefficients of the fiscal and business freedom
indices are much smaller and hence regulation is a less important determinant of the shadow
economy. Regarding the unemployment rate, the results are comparable. It does not influence
the shadow economies in developing countries but in transition and the OECD countries. It
seems that higher unemployment rates due to on average more regulated and hence less flexible
labor markets significantly contribute to the size and trend of the shadow economies in OECD
countries. In developing countries however, unemployment is not a significant determinant of
the shadw economy. In these countries, the income earned in the shadow economy guarantees
subsistence of families. Comparing specifications 3 and 5, the unemployment rate seems to be a
more important determinant in OECD than in transition countries.
The estimation results further show a slightly different impact of “policy causal variables
compared to non-policy “economic” causal variables across the different groups of countries. In
general economic variables, i.e. the level of development and the state of the economy
14
measured by the GDP per capital and the unemployment rate are very important determinants
of the shadow economy. The estimated coefficient indicate that an improvement of economic
conditions would reduce the size of the shadow economy at most. Of course, for the
unemployment rate this is only true for transition and highly developed OECD countries.
Comparing the impact of the policy variables such as the different measures of the tax burdern
and regulation on the shadow economy across the estimated specifications also reveals
interesting results. A reduction of the regulatory burden and improvement of
business/economic freedom in transition and OECD countries leads to a much higher reduction
of the shadow economy than it would in developing countries; which is clearly indicated by the
(much) larger coefficients of these variables. Fiscal freedom, however, is similarily important
across all groups of countries.
[Table 1 here]
3.2 The Size of the Shadow Economies for 162 Countries from 1999 to 2006/2007
The estimated MIMIC coefficients allow us to determine only relatively estimated sizes of the
shadow economies, describing their pattern over time. In order to calculate the sizes and trends
of the shadow economies, we must convert the MIMIC index into “real world” figures
measured in percentage of official GDP. This final step requires an additional procedure so
called benchmarking or calibration procedure. Unfortunately, no consensus exists in the
literature which benchmarking procedure to use. The methodology we use was promoted by
Dell’Anno (2007) and Dell’Anno and Solomon (2008).
In the first step, the MIMIC model
index of the shadow economies is calculated using the structural equation (1), i.e. by
multiplying the coefficients of the significant causal variables with the respective time series.
For the numerical example of specification 1 the structural equation is given as
15
t 1t 2 t 3t 4 t
0.14 x 0.06 x 0.05 x 0.27 x
η
= ⋅ − ⋅ − ⋅ −
%
.
11
(1)
Secondly, this index is converted into absolute values of the shadow economies taking a base
values in a particular base year. The base values necessary for this final step of the calibration
procedure are from the year 2000 and taken from Schneider (2007) who estimated the shadow
economies in 145 countries around the world using the MIMIC and the currency demand
approach. Thus, the size of the shadow economy
t
ˆ
at time
t
is given as:
t
t 2000
2000
ˆ
η
η η
η
=
%
%
,
(2)
where
t
%
denotes the value of the MIMIC index at
t
according to equation (1),
2000
η
%
is the
value of this index in the base year 2000, and
2000
η
is the exogenous estimate (base value) of the
shadow economies in 2000. Applying this benchmarking procedure, the final estimates of the
shadow economies can be calculated.
12
Of course, when showing the size of the shadow economies for countries which are quite
different in location and development stage, one should be aware that such country
comparisons give only a rough picture of the ranking of the size of the shadow economy in
these countries and over time, because the MIMIC and the currency demand methods have
shortcomings (see e.g. Breusch (2005) and Ahumada et al. (2007). Table 2 shows (in
alphabetical order) the development of the shadow economy in 162 countries between 1999
and 2007. The lowest level of informality for any country in the world is 8.7 % of GDP
(Switzerland), and the highest is 67.5 (Georgia).
[Table 2 here]
We turn now to analyze the estimates by the countries’ stage of development. Table 2 presents
at its bottom line the simple unweighted yearly average which is not the average informality for
11
x
1t
is size of government, x
2t
and x
3t
are the fiscal and business freedom index, and x
4t
represents GDP per capita.
12
The base values originate from the year 2000 except for some developing countries, for which we sometimes
used base values from the year 2005 because of data availability.
16
the World but the average World’s informality when one weights every country equally. In
order to measure how much of the GDP in the world is really informal, we weighted by total
country GDP. In particular, for every country/year we weighted the rate of informality by the
total GDP. This gives us the GDP in current Billion US dollars that is informal for each
country/year. Then we added up this amount and divided it by the total GDP of the sample. The
same had also been done for the sub-samples of developing, transition, and OECD countries.
According to these calculations, tabe 3 shows much lower rates of informal GDP for the world
as a whole, with an average of 17.3%. The results with respect to the countries’ development
stage are very impressive too: the averages of the weighted yearly informality estimates
demonstrate that transition countries have the largest shadow economies (with an average of
37%) followed by the developing countries (with an average of 26.9%). At the bottom of the
distribution we find the OECD countries with and average of 13.7%, which is consistent with
the fact that richer economies have lower informality rates.
[Table 3 here]
Finally, we present the informality measurement country by country in a world map view using
the country’s simple average over the years. Countries shown with darker colors in figure 2
indicate countries with higher level of informality. Among them are for example Azerbaijan,
Bolivia, Peru, Panama, Tanzania, and Zimbabwe. Countries shown with lighter color indicate
countries with lower levels of informality. Among them are for example Austria, Japan,
Luxembourg, Switzerland, the United States, and the United Kingdom.
[Figure 1 here]
5. Summary and Conclusions
There are many obstacles to overcome when measuring the size of the shadow economy and
when analyzing its consequences on the official economy. But, as this paper shows, some
17
progress can be made. We provide estimates of the size of the shadow economies for 162
countries over the period 1999 to 2006/2007 using the MIMIC procedure for the econometric
analysis; and a benchmarking procedure to calibrate the estimated MIMIC into absolute values
of the size of the shadow economy. The new insights gained from our analysis of the sizes and
trends of the shadow economy of 162 countries lead to three conclusions:
The first conclusion is that for all countries investigated the shadow economy has reached a
remarkably large size of an weighted average value of 17.3% of official GDP over 162
countries over 1999 to 2007. However, the average size of the shadow economies of all of these
162 countries (developing, Eastern European and Central Asian and high income OECD
countries) increased only modestly from 16.1% of official GDP in 1999 to 19.4% of official
GDP in 2007. The second conclusion is that shadow economies are a complex phenomenon
present to an important extent in developing, transition as well as highly developed economies.
People engage in shadow economic activities for a variety of reasons. Among the most
important are government actions, most notably, taxation and regulation. The third conclusion
is that there are regional disparities in the level of informality, but obviously also regional
clusters. At the top level of informality we find Sub-Saharan Africa, while OECD countries
show the lowest level.
Considering these three conclusions, it is apparent that one of the big challenges for every
government is to undertake efficient incentive orientated policy measures in order to make
working in the shadow economy less attractive and, hence, to make working in the official
economy more attractive. Successful implementation of such policies may lead to a
stabilization, or even reduction, of the size of the shadow economies. Of course, even after 20
years of intensive research the size, causes, and consequences of the shadow economy are still
controversily debated in the literature and further research is necessary to improve our
understanding about the shadow economy.
18
Table 1. MIMIC Model Estimation Results
Independent variables
Specification 1
98 Developing
Countries
(1994 - 2006)
Specification 2
88 Developing
Countries
(1994 - 2006)
Specification 3
21 Transition
Countries
(1994 - 2006)
Specification 4
25 High
Income
OECD
Countries
(1996 - 2006)
Specification 5
25 High
Income
OECD
Countries
(1996 - 2007)
Specification 6
151 Countries
(1996 - 2007)
Specification 7
120 Countries
(1996 - 2006)
Causal variables
Size of government 0.14 (5.97)*** 0.15 (5.57)*** 0.18 (3.49)*** 0.05 (2.64)*** 0.10 (3.77)***
Share of direct taxation 0.06 (2.57)** 0.05 (2.39)**
Total tax burden 0.05 (2.05)** 0.06 (1.78)*
Fiscal freedom -0.06 (2.90)***
-0.03 (1.69)* -0.08 (1.68)* -0.07 (2.84)***
-0.04 (2.08)**
Business freedom -0.05 (2.18)** -0.05 (2.33)** -0.23 (5.93)***
-0.04 (1.84)*
Economic freedom -0.09 (1.91)*
Unemployment rate 0.01 (0.67) -0.00 (0.06) 0.08 (1.84)* 0.05 (1.89)* 0.11 (3.16)*** 0.04 (2.08)** 0.02 (0.89)
GDP per capita -0.27 (8.79)***
-0.26 (6.87)***
-0.38
(15.89)*** -0.33 (9.15)***
Regulatory quality -0.21 (5.45)***
-0.31 (6.50)***
Government
effectiveness -0.05 (2.64)***
-0.04 (2.11)**
Openness -0.15 (2.47)**
Inflation rate 0.22 (2.83)***
Indicator variables
Growth rate of GDP per
capita -1.01 (7.88)***
-1.39 (6.70)***
-0.76 (4.41)***
-0.79
(10.93)*** -0.99 (8.42)***
GDP per capita -1.52 (6.71)***
-1.25 (8.36)***
Labor force participation
rate 0.05 (0.59) 0.02 (0.14) -1.11 (5.45)***
-1.03 (7.70)***
-0.19 (3.15)***
Growth rate of labor
force -0.83 (3.90)***
-0.16 (1.76)*
Currency 1 1 1 1 1 1 1
19
Statistical tests
RMSEA (p-value) 0.03 (0.99) 0.03 (0.99) 0.00 (1.00) 0.00 (0.88) 0.00 (0.99) 0.03 (1.00) 0.02 (1.00)
Chi-square (p-value) 38.70 (0.00) 44.43 (0.02) 17.75 (0.91) 17.74 (0.60) 3.55 (0.94) 29.95 (0.00) 51.82 (0.03)
AGFI 0.98 0.98 0.97 0.95 0.99 0.99 0.98
Degrees of freedom 20 27 27 20 9 13 35
Number of observations 1045 741 213 145 243 1563 942
Note: Absolute z-statistics in parentheses. ***, **, * denote significance at the 1, 5, and 10% significance level. All variables are used as their
standardized deviations from mean. According to the MIMIC models identification rule (see also section 3.1), one indicator has to be fixed to an a
prior value. We have consistently chosen the currency variable. The degrees of freedom are determined by 0.5(p+q)(p+q+1)–t; with p= number of
indicators; q = number of causes; t = the number for free parameters.
20
Table 2. Ranking of 162 Countries in Alphabetical Order
Years
No. Country 1999
2000
2001
2002
2003
2004
2005
2006
2007
Country
Av.
1 Albania 34.9
35.3
35.7
35.9
36.2
36.7
36.9
37.3
37.7 36.3
2 Algeria 34 34.1
34.4
34.9
35.8
36.6
37.3
37.3
37.1 35.7
3 Angola 41.6
41.6
41.9
42.8
43 43.1
45 45.9
47.6 43.6
4 Argentina 25.6
25.4
24.7
23.3
24.4
25.3
26.1
27 27.8 25.5
5 Armenia 46 46.3
47.2
48.1
48.8
49.1
50 50.7
51.7 48.7
6 Australia 14.2
14.3
14.3
14.4
14.7
14.8
14.8
14.9
15 14.6
7 Austria 9.6 9.8 9.9 9.8 9.8 9.8 9.8 10 10.1 9.8
8 Azerbaijan 60.2
60.6
60.9
61.2
62.2
62.7
64.7
67.6
69.6 63.3
9 Bahamas, The 26.1
26.2
26 26 25.5
25.1
25.8
26.2
26.2 25.9
10 Bahrain 18.2
18.4
18.6
18.8
19 19.3
19.7
- - 18.9
11 Bangladesh 35.2
35.6
35.7
35.5
35.6
35.7
36 36.7
37 35.9
12 Belarus 47.9
48.1
48.3
48.6
49.2
50.1
51.1
52.1
53 49.8
13 Belgium 21.7
22.2
22.3
22.4
22.4
22.6
22.6
22.9
23.1 22.5
14 Belize 42.4
43.8
44.3
44.2
45.2
45.5
45.4
45.9
45.6 44.7
15 Benin 48.5
49.4
49.8
50 50.2
50.1
49.8
50 50.4 49.8
16 Bhutan 29.2
29.4
29.6
29.7
30.1
30.1
30.5
30.6
31.1 30.0
17 Bolivia 67.2
67.1
66.6
66.5
66.5
67.3
69.9
71.3
70.7 68.1
18 Bosnia &
Herzegovina 33.9
34.1
34.2
34.3
34.7
34.6
35 35.3
35.4 34.6
19 Botswana 33 33.4
33.6
33.5
33.8
34 34.1
34.5
34.8 33.9
20 Brazil 38.8
39.8
39.7
39.7
40 40.9
41.1
41.8
43 40.5
21 Brunei Darussalam 30.8
31.1
31.2
32 32.3
31 30.4
31.4
31 31.2
22 Bulgaria 36.5
36.9
37.2
37.7
38.3
39 39.7
40.4
41.2 38.5
23 Burkina Faso 41.5
41.4
41.5
41.4
42.4
42.7
43 43 43.1 42.2
24 Burundi 40.4
40 39.8
40 39.8
39.8
39.7
39.8
39.8 39.9
25 Cambodia 49.8
50.1
50.6
50.2
51 51.4
52.4
53.4
54.2 51.5
26 Cameroon 32.3
32.8
33.2
33.4
33.9
34 33.9
34.2
34.2 33.5
27 Canada 15.7
16 16.1
16.2
16.3
16.4
16.5
16.6
16.6 16.3
28 Cape Verde 35.7
36.1
36.3
36.3
36.5
36.4
36.8
38 38.7 36.8
29 Central African Rep.
51.5
51.7
51.2
50.1
46.9
46.5
46.9
48.1
48.9 49.1
30 Chad 46.6
46.2
46.9
47.4
48.4
51.2
51.6
51 50.5 48.9
31 Chile 19.7
19.8
20 20 20.2
20.5
20.7
20.9
21.1 20.3
32 China 13 13.1
13.2
13.3
13.4
13.6
13.7
14 14.3 13.5
33 Colombia 38.8
39.1
39.3
39.4
40.4
41.2
42.3
43.4
45.1 41.0
34 Comoros 40 39.6
40.2
41.6
41.7
40.2
41.3
40.9
39.8 40.6
35 Congo, Dem. Rep. 48.8
48 47.8
47.9
49 49.2
49.3
49.3
49.4 48.7
36 Congo, Rep. 46.8
48.2
49.2
49.7
49.7
50.3
51.9
53.3
52 50.1
37 Costa Rica 26.3
26.2
26 26 26.3
26.5
26.8
27.4
28.3 26.6
38 Côte d'Ivoire 44.9
43.2
42.1
41 40.5
40.4
40.2
39.7
39.6 41.3
39 Croatia 33 33.4
33.6
34.2
34.7
35.2
35.5
36 36.5 34.7
40 Cyprus 28.3
28.7
29.2
29.6
29.2
29.3
29.7
30.1
30.8 29.4
41 Czech Republic 18.9
19.1
19.3
19.4
19.5
19.8
20.4
20.9
21.2 19.8
42 Denmark 17.7
18 18 18 18 18.2
18.4
18.9
19 18.2
43 Dominican Republic
31.8
32.1
31.8
32.1
32.1
31.8
32.5
33.2
33.6 32.3
44 Ecuador 34.7
34.4
35.2
35.6
36.1
37.4
38.3
38.7
38.8 36.6
21
45 Egypt, Arab Rep. 34.7
35.1
35 34.5
34.8
35.2
35.4
36.1
37 35.3
46 El Salvador 46.1
46.3
46.4
47 47.4
47.6
48 48.7
49.5 47.4
47 Equatorial Guinea 33 32.8
33.7
34.1
34.4
34.9
35.1
35 35.5 34.3
48 Eritrea 42.6
40.3
41.2
41.3
40.3
40 40 39.4
39.2 40.5
49 Estonia - 38.4
38.8
39.3
40 40.3
41.1
41.9
42.3 40.3
50 Ethiopia 39.9
40.3
41.2
41 40.5
42 43.1
44.5
45.7 42.0
51 Fiji 34.3
33.6
33.9
34.6
34.7
35.3
35.8
36.2
34.6 34.8
52 Finland 17.8
18.1
18.3
18.4
18.5
18.6
18.8
19.1
19.2 18.5
53 France 14.8
15.2
15.4
15.3
15.4
15.5
15.6
15.6
15.7 15.4
54 Gabon 49.9
48 48.7
48.4
48.5
48 48.3
48 48.8 48.5
55 Gambia, The 44.1
45.1
45.5
43.1
44.8
46.4
46.6
47.8
49.3 45.9
56 Georgia 66.2
67.3
67.4
67.4
68.7
69.2
69.5
71.1
72.5 68.8
57 Germany 15.6
16 16.1
16 15.8
15.9
16 16.4
16.7 16.1
58 Ghana 41.8
41.9
42 42.2
42.5
42.9
44.3
45.3
45.6 43.2
59 Greece 28.9
28.7
29.2
29.4
30 30.4
30.6
31 31 29.9
60 Guatemala 51.4
51.5
51.4
51.8
52.3
52.5
52.7
53.9
55 52.5
61 Guinea 39.5
39.6
39.9
40.4
40.4
40.6
40.8
40.3
40 40.2
62 Guinea-Bissau 38.8
39.6
39.6
38.5
37.7
37.3
37.5
37.7
37.6 38.3
63 Guyana 33.8
33.6
33.8
33.5
33.3
33.8
33 33.4
33.3 33.5
64 Haiti 56 55.4
54.7
54.3
54.4
53.4
53.7
53.8
53.7 54.4
65 Honduras 48.9
49.6
49.4
49.6
50.3
50.9
52 53.1
54.2 50.9
66 Hong Kong, China 16.2
16.6
16.6
16.6
16.8
17.3
17.7
18.2
18.6 17.2
67 Hungary 24.8
25.1
25.4
25.7
25.8
26.1
26.2
26.5
26.4 25.8
68 Iceland 15.8
15.9
16 15.8
15.9
16.3
16.7
16.7
16.8 16.2
69 India 23 23.1
23.4
23.6
24 24.2
24.5
25 25.6 24.0
70 Indonesia 19.1
19.4
19.4
19.5
19.7
20 20.2
20.5
20.9 19.9
71 Iran, Islamic Rep. 18.7
18.9
18.8
19.1
19.6
19.9
19.7
20.1
20.5 19.5
72 Ireland 15.7
15.9
15.9
15.9
15.8
16 16.2
16.3
16.4 16.0
73 Israel 21.2
21.9
21.6
21.1
21.2
21.7
22 22.6
23 21.8
74 Italy 26.5
27.1
27.5
27.4
27.2
27.2
27.1
27.3
27.4 27.2
75 Jamaica 36.4
36.4
36.6
36.6
38.6
39.1
38.9
40.2
40.5 38.1
76 Japan 11 11.2
11.2
11.1
11.2
11.5
11.7
12 12.1 11.4
77 Jordan 19.4
19.4
19.6
19.9
20.1
20.6
20.9
21.4
21.7 20.3
78 Kazakhstan 42.6
43.2
43.9
44.5
45.4
45.9
46.7
47.7
48.2 45.3
79 Kenya 35 34.3
34.7
33.8
33.9
34.9
36 37.7
39.4 35.5
80 Korea, Rep. 26.7
27.5
27.7
28.1
28.2
28.5
28.7
29 29.4 28.2
81 Kuwait 20.1
20.1
19.9
19.9
20.9
21.5
22.2
22.5
- 20.9
82 Kyrgyz Republic 41 41.2
41.6
41 41.9
42.6
42.4
42.6
43.6 42.0
83 Lao PDR 30.3
30.6
31 31.2
31.4
31.8
32.3
32.8
33.2 31.6
84 Latvia 39.6
39.9
40.4
40.9
41.4
42 42.7
43.7
44.3 41.7
85 Lebanon 34.1
34.1
34.5
34.7
35 35.9
35.9
35.4
36.2 35.1
86 Lesotho 30.9
31.3
31.5
31.6
31.9
32.5
32.4
33.3
33.8 32.1
87 Liberia 42.3
43.2
43.2
43.3
41.6
41.2
41.6
42 42.3 42.3
88 Libyan Arab
Jamahiria 35.5
35.1
35.8
36.5
35.3
36.4
37.3
38.5
39.6 36.7
89 Lithuania 30.2
30.3
30.7
31.2
31.9
32.2
32.8
33.4
34 31.9
90 Luxembourg 9.6 9.8 9.8 9.8 9.8 9.8 9.9 10 10.2 9.9
91 Macao, China 12.9
13.1
13.2
13.4
13.7
14.2
14.4
14.6
15.3 13.9
22
92 Macedonia, FYR 34.9
35.7
34.8
35.1
35.5
36.4
36.9
37.7
38.8 36.2
93 Madagascar 39.1
39.6
40.4
34.7
36 37.7
38.5
39.5
40.6 38.5
94 Malawi 40.7
40.3
38.3
36.5
37.5
38.3
38.2
39.4
41.1 38.9
95 Malaysia 30.1
31.1
30.6
30.7
31 31.4
31.7
32.2
32.6 31.3
96 Maldives 30.3
30.3
30.6
31.2
31.4
31.8
31 31.3
32.1 31.1
97 Mali 42.1
42.3
43.8
44.4
44.7
44 44.5
44.7
44.7 43.9
98 Malta 26.8
27.1
26.9
27 26.7
26.7
26.9
27.2
27.7 27.0
99 Mauritania 36.7
36.1
36.2
36.4
36.4
37.2
37.9
40.8
- 37.2
100 Mauritius 22.9
23.1
23.3
23.2
23.5
23.8
23.8
24 24.3 23.5
101 Mexico 29.5
30.1
30 29.9
29.7
30.1
30.3
31 31.3 30.2
102 Moldova 44.6
45.1
46.1
45.8
45.7
46.2
46.8
46 - 45.8
103 Mongolia 18.5
18.4
18.5
18.8
19.1
19.5
19.8
20.1
20.5 19.2
104 Morocco 36.3
36.4
37.1
37.3
37.8
38.7
37.9
39.8
39.8 37.9
105 Mozambique 39.5
40.3
40.2
40.8
40.8
40.9
41.6
42 - 40.8
106 Myanmar 53.6
52.6
53.7
54.5
56.3
56.2
57.4
- - 54.9
107 Namibia 31.4
31.4
31.6
31.5
32.2
33.1
33.3
34.1
34.4 32.6
108 Nepal 36.4
36.8
36.9
36.5
36.7
36.8
36.9
37.3
37.5 36.9
109 Netherlands 12.9
13.1
13.1
13 12.9
13 13 13 13.2 13.0
110 New Zealand 12.6
12.8
13 13.2
13.4
13.6
13.5
13.5
13.6 13.2
111 Nicaragua 44.7
45.2
45.1
44.9
45.4
46.2
46.6
46.8
47.2 45.8
112 Niger 42.1
41.9
43 43.7
44.4
43.2
44.4
45.6
- 43.5
113 Nigeria 57.8
57.9
58 58.2
59.5
60.8
62.1
62.9
- 59.7
114 Norway 19 19.1
19.2
19.2
19.2
19.7
19.7
20 20.2 19.5
115 Oman 18.7
18.9
19.3
19.3
19.4
19.5
19.8
20.2
- 19.4
116 Pakistan 36.6
36.8
36.6
36.8
37.4
38.3
38.8
39.8
40.1 37.9
117 Panama 63.4
64.1
63.5
63.1
63.9
64.7
66.4
68.1
- 64.7
118 Papua New Guinea 36.7
36.1
35.4
35.1
35.1
35.2
34.9
35.1
35.7 35.5
119 Paraguay 41.8
39.8
39.9
39.5
40.6
41.5
41.6
42.5
- 40.9
120 Peru 59.7
59.9
59.6
60.8
61.2
61.9
62.7
64.2
66.3 61.8
121 Philippines 42.7
43.3
43.6
44.1
44.7
45 46.6
47.2
48.4 45.1
122 Poland 27.5
27.6
27.6
27.5
27.7
27.9
28.3
28.7
29.1 28.0
123 Portugal 22.4
22.7
22.8
22.7
22.4
22.3
22.2
22.2
22.5 22.5
124 Quatar - 17.8
17.5
17.8
17.3
19.4
18.4
- - 18.0
125 Romania 34.6
34.4
35.1
35.4
36.1
37 37.3
38.3
38.9 36.3
126 Russian Federation 45.1
46.1
47 47.8
48.8
49.5
50.1
50.8
52 48.6
127 Rwanda 40.1
40.3
40 40.7
39.9
40.4
41.4
41.5
- 40.5
128 Saudi Arabia 18.1
18.4
18 17.5
18.5
19.1
19.4
19.5
20 18.7
129 Senegal 45.2
45.1
45.6
45.1
45.8
46.9
47.8
47.8
48.4 46.4
130 Sierra Leone 40.3
40.2
41.2
43.3
43.8
44.2
44.3
45 45.6 43.1
131 Singapore 12.9
13.1
12.9
12.9
13.1
13.4
13.5
13.8
14 13.3
132 Slovak Republic 18.9
18.9
19 19.2
19.5
19.7
20.2
20.6
21.1 19.7
133 Slovenia 26.9
27.1
27.5
27.6
27.8
28 28.4
28.9
29.5 28.0
134 Solomon Islands 35.1
33.4
32.3
31.9
32.1
33 33.4
33.6
34.2 33.2
135 South Africa 28.4
28.4
28.4
28.8
29 29.7
30.4
30.9
31.7 29.5
136 Spain 22.4
22.7
22.9
23 23 22.9
23 23 23.1 22.9
137 Sri Lanka 44 44.6
44.6
45.1
45.3
45.2
45.7
46.2
47 45.3
138 Sudan 34.1
- - - - - - - - 34.1
139 Suriname 39.9
39.8
40.3
40.8
41.5
42.9
43.3
43.9
44.7 41.9
23
140 Swaziland 39.4
41.4
41.5
41.8
42.5
42.7
43.4
43.8
- 42.1
141 Sweden 18.9
19.2
19.3
19.4
19.6
19.9
19.8
20.2
20.4 19.6
142 Switzerland 8.4 8.6 8.6 8.6 8.4 8.6 8.7 8.9 9.1 8.7
143 Syrian Arab
Republic 19.3
19.3
19.4
19.5
19.3
19.5
19.6
19.9
20.1 19.5
144 Taiwan 25.1
25.4
25.1
25.4
25.6
26 26.2
26.6
26.9 25.8
145 Tajikistan 42.9
43.2
43.5
43.8
44.3
44.8
45 45.3
45.5 44.3
146 Tanzania 58 58.3
58.9
59.7
60.1
60.6
61.3
61.9
63 60.2
147 Thailand 51.8
52.6
52.8
53.8
55.1
55.8
56.4
56.9
57.2 54.7
148 Togo 35.8
35.1
34.8
35.7
35.3
35.2
35.2
35.6
- 35.3
149 Trinidad and
Tobago 34.1
34.4
34.5
34.4
35.4
35.7
35.9
36.8
37.3 35.4
150 Tunisia 38.1
38.4
38.9
39 39.4
39.9
40 40.9
41.4 39.6
151 Turkey 31.5
32.1
31.4
31.8
32.4
33.2
34.2
34.7
35.2 32.9
152 Uganda 42.7
43.1
43.3
43.3
43.7
43.8
44 45.1
45.8 43.9
153 Ukraine 51.7
52.2
53 53.7
55 55.9
57 57.5
58.1 54.9
154 United Arab
Emirates 26.5
26.4
25.8
25.3
26.5
27.5
28 29.4
- 26.9
155 United Kingdom 12.6
12.7
12.8
12.8
12.9
13 13 13.1
13.2 12.9
156 United States 8.6 8.7 8.7 8.6 8.7 8.8 8.9 8.9 9 8.8
157 Uruguay 51.7
51.1
50.5
48.2
48.6
51.1
53 53.7
56 51.5
158 Venezuela, RB 33.4
33.6
33.7
31.7
30.2
32.3
33.7
35.3
36.3 33.4
159 Vietnam 15.4
15.6
15.7
15.9
16 16.1
16.5
16.6
16.8 16.1
160 Yemen, Rep. 27.1
27.4
27.5
27.6
27.7
27.8
28.2
28 28 27.7
161 Zambia 48.5
48.9
49.5
49.7
50.4
51.2
51.7
53.1
54.3 50.8
162 Zimbabwe 59.2
59.4
57.4
56.1
55.2
56.6
56.8
56.6
56.1 57.0
Time Average 33.7
33.8
33.9
33.9
34.2
34.6
35.0
35.6
35.5
24
Table 3. Weighted Average Informality by Country Type
Informal GDP in Current Billion US Dollars
Year World / 162
Countries
(% of World GDP)
Developing
Countries
(% of GDP of
Developing
Countries)
Transition
Countries
(% of GDP of
Transition
Countries)
OECD Countries
(% of GDP of
OECD Countries)
1999 4,959
(16.1)
1,385
(26.7)
310
(33.3)
3,225
(13.1)
2000 5,205
(16.3)
1,505
(26.9)
350
(34.6)
3,314
(13.2)
2001 5,176
(16.4)
1,468
(26.3)
368
(35.5)
3,302
(13.3)
2002 5,392
(16.4)
1,462
(26.1)
422
(35.8)
3,465
(13.4)
2003 6,210
(16.8)
1,643
(26.3)
532
(36.5)
3,976
(13.7)
2004 7,223
(17.3)
1,953
(26.7)
700
(37.6)
4,500
(13.9)
2005 8,081
(18.0)
2,342
(27.3)
885
(38.8)
4,779
(14.1)
2006 9,084
(18.7)
2,801
(27.9)
1,090
(40.1)
5,110
(14.3)
2007 10,636
(19.4)
3,437
(28.3)
1,422
(41.1)
5,680
(14.6)
25
Figure 1. World View of Informality
26
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... Selection of particular indicators depends on understanding of the SE and its definition. There are various definitions of the SE (Smith, 1994;Schneider & Enste, 2000;Feld & Schneider, 2010;Schneider et al., 2010), but the most commonly used definition was suggested by Schneider et al. (2010), stating that the SE represents "goods and/or services the income received for which is deliberately hidden from authorities to evade income, VAT or other taxes, social insurance contributions, avoiding compliance with particular legal labour market regulations such as minimum wages, maximal duration of working hours, safety standards, etc." Borlea et al. (2017) argue that the shadow economy comprises two major components: undeclared work (which refers to the salaries that employees and businesses do not declare to avoid taxation or labour market regulations) and underreported business revenue. In addition, the authors state that circumvention of regulations, tax evasion and lower tax revenues are common characteristics of corruption and the SE. ...
... Selection of particular indicators depends on understanding of the SE and its definition. There are various definitions of the SE (Smith, 1994;Schneider & Enste, 2000;Feld & Schneider, 2010;Schneider et al., 2010), but the most commonly used definition was suggested by Schneider et al. (2010), stating that the SE represents "goods and/or services the income received for which is deliberately hidden from authorities to evade income, VAT or other taxes, social insurance contributions, avoiding compliance with particular legal labour market regulations such as minimum wages, maximal duration of working hours, safety standards, etc." Borlea et al. (2017) argue that the shadow economy comprises two major components: undeclared work (which refers to the salaries that employees and businesses do not declare to avoid taxation or labour market regulations) and underreported business revenue. In addition, the authors state that circumvention of regulations, tax evasion and lower tax revenues are common characteristics of corruption and the SE. ...
... The higher path coefficient amounting to -0.609, which indicates the link between wealth and development and the SE, proposes that higher economic development and social well-being lead to a smaller size of the SE. Such results can be explained by the fact that citizens and economic entities in advanced post-Soviet economies are not motivated enough to engage in the SE activities because their income in the formal sector is sufficiently high to achieve a satisfactory quality of living and working conditions, as suggested by Schneider et al., (2010) relationship between the political environment and the size of the SE. This value indicates that improvement in a country's political environment (which implies the rule of law, higher government effectiveness, and a satisfactory level of democracy and political stability) results in a lower level of the SE. ...
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... In our analysis we refer to this general meaning and we will use the terms underground, informal, unofficial synonymously. 2 Past review studies, such as Schneider et al. (2010), Schneider (2011), and Ulyssea (2020), have shown that informality is associated with various factors and consequences, ranging from poor governance to underdevelopment. Studies like Goel and Nelson (2016) and , have used various econometric methods to identify robust correlates of informality and address modeling uncertainty. ...
... Indirect approaches use various macroeconomic indicators, from which it is possible to extract important information about the trend of the informal economy over time: the transaction approach; the currency demand approach; the electricity consumption method; and some other methods based on the discrepancy between national expenditure and income statistics, or between the official and actual labour force. Model-based approaches include the Multiple Indicators, Multiple Causes (MIMIC) model (as in Schneider, 2007or in Schneider et al., 2010 or general equilibrium models (as in Elgin et al., 2021). ...
... Employing panel data for 150 countries over the period 1980-2009, Bittencourt et al. (2014 establish an impact of financial development on informality. They compare two measures of the size of the informal economy: the MIMIC method developed by Schneider et al. (2010) and a general equilibrium model-based measure developed by Elgin and Otzunali (2012). 11 Financial development is captured in terms of banking sector efficiency, measured as the average overhead cost in percent of the banking sector's total assets. ...
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The paper estimates the Portuguese Shadow Economy (SE) from 1977 to 2004 and tests the statistical relationships between the SE and other economic variables. In order to carry out the econometric analysis, a multiple indicators multiple causes (MIMIC) model with means and intercepts is applied. The main causes of the Portuguese SE are analyzed and economic policies to reduce it are suggested. An appraisal on the reliability of estimates and an alternative benchmark strategy for the MIMIC approach are proposed.
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In their book Taxes and the Canadian Underground Economy, David E.A. Giles and Lindsay M. Tedds describe a hidden economy in Canada that is large and growing rapidly. They estimate an amount of additional income - unobserved by the authorities and untaxed - ranging from a low of 3.46 percent of official GDP in 1976 to a high of 15.64 percent of GDP in 1995. Others have questioned these findings, but the econometric method employed by Giles and Tedds is complex. In this paper, Trevor Breusch peels back the separate layers of model fitting, prediction, and benchmarking and reveals the origins of their results. He shows that the time path of their estimates has little or no connection with underground income as a percentage of GDP and that the overall level of their estimates is a result of numerical accidents.