July 2007
C:/Pfusch/ShadEconomyCorruption_July2007.doc
Revised version

Shadow Economies and Corruption all over the
World: New Estimates for 145 Countries
by
Friedrich Schneider*)

Abstract:

Estimations of the shadow economies for 145 countries, including developing, transition
and highly developed OECD economies over 1999 to 2005 are presented. The average
size of the shadow economy (as a percent of "official" GDP) in 2004/05 in 96 developing
countries is 36.7%, in 25 transition countries 38.8% and in 21 OECD countries 14.8%. An
increased burden of taxation and social security contributions, combined with a labour
market regulation are the driving forces of the shadow economy. Furthermore, the results
show that the shadow economy reduces corruption in high income countries, but increases
corruption in low income countries. Finally, the various estimation methods are discussed
and critically evaluated.
JEL-class.: O17, O5, D78, H2, H11, H26.
Keywords: shadow economy of 145 countries, tax burden, tax moral, quality of state
institutions, regulation, DYMIMIC and other estimation methods

*) Professor of Economics, Dr. DDr.h.c. Friedrich Schneider, Department of Economics,
Johannes Kepler University of Linz, A-4040 Linz-Auhof, Austria. Phone: 0043-732-24688210, Fax: -8209. E-mail: friedrich.schneider@jku.at, http://www.econ.jku.at/Schneider.

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Contents
1

Introduction .......................................................................................................................3

2

Some Theoretical Considerations about the Shadow Economy....................................4
2.1

Defining the Shadow Economy ................................................................................................. 4

2.2

The Main Causes of the Shadow Economy ............................................................................... 6

2.2.1
2.2.2
2.2.3
2.2.4
2.2.5

3

Tax and Social Security Contribution Burdens ............................................................................... 6
Intensity of Regulations .................................................................................................................. 7
Public Sector Services..................................................................................................................... 8
Public Opinion about the Shadow Economy................................................................................... 8
Summary of the Main Causes of the Shadow Economy ............................................................... 13

The Size of the Shadow Economy for 145 Countries ...................................................14
3.1

Econometric Results ............................................................................................................... 14

3.2

The Size of the Shadow Economies for 145 Countries for 1999/2000 to 2004/2005.............. 21

3.2.1
3.2.2
3.2.3
3.2.4
3.2.5

Developing Countries.................................................................................................................... 21
Transition Countries...................................................................................................................... 25
Highly developed OECD-Countries.............................................................................................. 26
South West Pacific Islands............................................................................................................ 27
Communist Countries.................................................................................................................... 28

4

Corruption and the Shadow Economy: Substitutes or Compliments?)......................29

5

Summary and Conclusions .............................................................................................33

6 Appendix 1: Methods to Estimate the Size of the Shadow Economy: The DYMIMIC
and Currency Demand Approach ........................................................................................35
6.1
6.1.1
6.1.2

6.2
6.2.1
6.2.2
6.2.3
6.2.4
6.2.5

Direct Approaches .................................................................................................................. 35
Survey Method .............................................................................................................................. 35
Tax Auditing Method .................................................................................................................... 38

Indirect Approaches................................................................................................................ 39
The Discrepancy between National Expenditure and Income Statistics ....................................... 39
The Discrepancy between the Official and Actual Labour Force ................................................. 40
The Transactions Approach .......................................................................................................... 40
The Currency Demand Approach.................................................................................................. 41
The Physical Input (Electricity Consumption) Method................................................................. 44

6.3

The Model Approach .............................................................................................................. 46

6.4

Summarizing the Critical Remarks ......................................................................................... 49

6.5 The Size and Development of the Shadow Economies of 145 Countries over 1999/2000 to
2004/2005 .......................................................................................................................................... 50

7

Appendix 2: Definition of the variables and data sources ...........................................54

8

References.........................................................................................................................57

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1 Introduction
As corruption and shadow economic activities are a fact of life around the world, most
societies attempt to control these activities through various measures like punishment,
prosecution, economic growth or education. To gather information about the extent of
corruption and the shadow economy and its relationship or who is engaged in corrupt and/or
underground activities, the frequency with which these activities are occurring and their
magnitude of them, 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 the relationship between corruption and shadow economy
activities on the goods and labour market, because all individuals engaged in these activities
wish not to be identified. Hence, doing research in these two areas can be considered as a
scientific passion for knowing the unknown.
Although substantial literature1) exists on single aspects of the hidden or shadow economy
and a comprehensive survey has been written by Schneider (the author of this paper) and
Enste (2000), the subject is still quite controversial2) as there are disagreements about the
definition of shadow economy activities, the estimation procedures and the use of their
estimates in economic analysis and policy aspects3). Nevertheless around the world, there are
some indications for an increase of the shadow economy but little is known about the
development and the size of the shadow economies in transition, developing and developed
countries over the latest period 1999 to 2005.
Hence, the goal of this paper is threefold: (i) to undertake the challenging task of estimating
the shadow economy for 145 countries all over the world4) (ii) to provide some insights into
1)

The literature about the "shadow", "underground", "informal", "second", "cash-" or "parallel", economy is
increasing. Various topics, on how to measure it, its causes, its effect on the official economy are analyzed. See
for example, survey type publications by Frey and Pommerehne (1984); Thomas (1992); Loayza (1996); Pozo
(1996); Lippert and Walker (1997); Schneider (1994a, 1994b, 1997, 1998a, 2003, 2005, 2007); Johnson,
Kaufmann, and Shleifer (1997), Johnson, Kaufmann and Zoido-Lobatón (1998a, 1998b); Belev (2003);
Gerxhani (2003) and Pedersen (2003). For an overall survey of the global evidence of the size of the shadow
economy see Bajada and Schneider (2005), Schneider and Enste (2000, 2002, 2006) and Alm, Martinez and
Schneider (2004), and Kazemier (2005a)
2)
Compare e.g. in the Economic Journal, vol. 109, no. 456, June 1999 the feature "controversy: on the hidden
economy”.
3)
Compare the different opinions of Tanzi (1999), Thomas (1999), Giles (1999a,b) and Pedersen (2003), and
Janisch and Brümmerhoff (2005).
4)
This paper focuses on the size and development of the shadow economy for countries and does not show any
disaggregated values for specific regions. Lately some first studies were undertaken to measure the size of the
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the main causes of the shadow economy, and (iii) to explore the relationship between shadow
and corruption. In section 2 an attempt is made to define the shadow economy and some
theoretical considerations about the reasons why it is increasing. Section 3 presents the
econometric estimation results and the calculation of the size of the shadow economy in 145
countries in the period 1999/2000 to 2004/05. In section 4 two hypotheses about the
relationship between the shadow economy and corruption are derived and some empirical
results are shown. In section 5 a summary is given and some policy conclusions are drawn.
Finally in the three appendices (chapters 6, 7 and 8) the various methods to estimate the
shadow economy are presented and critically evaluated, a definition of the variables and data
sources are given, and the descriptive statistics of the variables are shown.

2 Some Theoretical Considerations about the Shadow Economy
2.1

Defining the Shadow Economy

Most authors trying to measure the shadow economy face the difficulty of how to define it.
One commonly used working definition is all currently unregistered economic activities that
contribute to the officially calculated (or observed) Gross National Product5). 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." Or to put it in another way, one of the
broadest definitions of it includes…"those economic activities and the income derived from
them that circumvent or otherwise avoid government regulation, taxation or observation"6).
As these definitions still leave open a lot of questions, table 2.1 is helpful for developing a
better feeling for what could be a reasonable consensus definition of the underground (or
shadow) economy.
From table 2.1, it becomes clear that a broad definition of the shadow economy includes
unreported income from the production of legal goods and services, either from monetary or
shadow economy as well as the "grey” or "shadow” labour force for urban regions or states (e.g. California).
Compare e.g. Marcelli, Pastor and Joassart (1999), Marcelli (2004), Chen (2004), Williams (2004a, b, 2005a, b,
2006), Williams and Windebank (1999, 2001a, b), Flaming, Haydamack, and Jossart (2005) and Alderslade,
Talmage and Freeman (2006), and Brueck, Haisten-DeNew and Zimmermann (2006).
5)
This definition is used for example, by Feige (1989, 1994), Schneider (1994a, 2003, 2005, 2007) and Frey and
Pommerehne (1984). Do-it-yourself activities are not included. For estimates of the shadow economy and the doit-yourself activities for Germany see Karmann (1986, 1990), and Buehn, Karmann and Schneider (2007).
6)
This definition is taken from Del’Anno (2003), Del’Anno and Schneider (2004) and Feige (1989); see also
Thomas (1999), Fleming, Roman and Farrell (2000).
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barter transactions – and so includes all economic activities that would generally be taxable
were they reported to the state (tax) authorities. In this paper the following more narrow
definition of the shadow economy is used7). The shadow economy includes all market-based
legal production of goods and services that are deliberately concealed from public authorities
for the following reasons:
(1) to avoid payment of income, value added or other taxes,
(2) to avoid payment of social security contributions,
(3) to avoid having to meet certain legal labour market standards, such as minimum
wages, maximum working hours, safety standards, etc., and
(4) to avoid complying with certain administrative procedures, such as completing
statistical questionnaires or other administrative forms.
Hence, in this paper, I will not deal with typical underground, economic (classical crime)
activities, which are all illegal actions that fit the characteristics of classical crimes like
burglary, robbery, drug dealing, etc. I also exclude the informal household economy
which consists of all household services and production. This paper also does not focus on
tax evasion or tax compliance, because it would get too long, and moreover tax evasion is
a different subject, where already a lot of research has been undertaken8).
Table 2.1: A Taxonomy of Types of Underground Economic Activities1)
Type of Activity

Illegal
Activities

Monetary Transactions

Non Monetary Transactions

Trade with stolen goods; drug dealing
and manufacturing; prostitution;
gambling; smuggling; fraud; etc.

Barter of drugs, stolen goods,
smuggling etc. Produce or
growing drugs for own use. Theft
for own use.

Tax Evasion

Tax
Avoidance
Employee
discounts,
fringe benefits

Tax Evasion

Tax Avoidance

Unreported income
Barter of legal All do-itfrom selfservices and
yourself work
employment; wages,
and neighbour
goods
salaries and assets
help
from unreported
work related to legal
services and goods
1)
Structure of the table is taken from Lippert and Walker (1997, p. 5) with additional remarks.

Legal
Activities

7)

Compare also the excellent discussion of the definition of the shadow economy in Pedersen (2003, pp.13-19)
and Kazemier (2005a) who use a similar one.
8)
Compare, e.g. the survey of Andreoni, Erard and Feinstein (1998) and the paper by Kirchler, Maciejovsky and
Schneider (2002).
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2.2
2.2.1

The Main Causes of the Shadow Economy
Tax and Social Security Contribution Burdens

In almost all studies9) 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.
Since taxes affect labour-leisure choices, and also stimulate labour supply in the shadow
economy, the distortion of the overall tax burden is a major concern for economists. The
bigger the difference between the total cost of labour 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, they latter are key features of the existence and
the increase of the shadow economy.
But even major tax reforms with major tax rate deductions will not lead to a substantial
decrease of the shadow economy10). Such reforms will only be able to stabilize the size of the
shadow economy and avoid a further increase. Social networks and personal relationships, the
high profit from irregular activities and associated investments in real and human capital are
strong ties which prevent people from transferring to the official economy. For Canada, Spiro
(1993) found similar reactions of people facing an increase in indirect taxes (VAT, GST).
This fact makes it even more difficult for politicians to carry out major reforms because they
may not gain a lot from them.
Empirical results of the influence of the tax burden on the shadow economy is provided in the
studies of Schneider (1994b, 2000, 2004, 2005) and Johnson, Kaufmann and Zoido-Lobatón
(1998a, 1998b); they all found statistically significant evidence for the influence of taxation
on the shadow economy. This strong influence of indirect and direct taxation on the shadow
economy is further demonstrated by discussing empirical results in the case of Austria and the
Scandinavian countries. For Austria the driving force for the shadow economy activities is the
direct tax burden (including social security payments); it has the biggest influence, followed
by the intensity of regulation and complexity of the tax system. A similar result has been
found by Schneider (1986) for the Scandinavian countries (Denmark, Norway and Sweden).
In all three countries various tax variables: average direct tax rate, average total tax rate
9)

See Thomas (1992); Lippert and Walker (1997); Schneider (1994a,b, 1997, 1998a,b, 2000, 2003b, 2005,
2007); Johnson, Kaufmann, and Zoido-Lobatón (1998a,1998b); Tanzi (1999); Giles (1999a); Mummert and
Schneider (2001); Giles and Tedds (2002) and Dell’Anno (2003), just to quote a few recent ones.
10)
See Schneider (1994b, 1998b) a similar result of the effects of a major tax reform in Austria on the shadow
economy. Schneider shows that a major reduction in the direct tax burden did not lead to a major reduction in the
shadow economy. Because legal tax avoidance was abolished and other factors, like regulations, were not
changed; hence for a considerable part of the tax payers the actual tax and regulation burden remained
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(indirect and direct tax rate) and marginal tax rates have the expected positive effect (on
currency demand) and are highly statistically significant. These findings are supported by
studies of Kirchgaessner (1983, 1984) for Germany, and by Klovland (1984) for Norway, and
Sweden, too.
In this study an attempt will be made to investigate the influence of the direct and indirect tax
burden as well as the social security payments on the shadow economy for developing,
transition and highly developed countries over the period 1999 to 2005.

2.2.2

Intensity of Regulations

Increased intensity of regulations is another important factor which reduces the freedom (of
choice) for individuals engaged in the official economy11). One can think of labour market
regulations, trade barriers, and labour restrictions for foreigners. Johnson, Kaufmann, and
Zoido-Lobatón (1998b) find significant overall empirical evidence of the influence of (labour)
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 labour 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. Empirical evidence supporting the model of
Johnson, Kaufmann, and Shleifer (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, is found in their empirical analysis. They conclude that it is the enforcement of
regulation which is the key factor for the burden levied on firms and individuals, and not the
overall extent of regulation - mostly not enforced - which drives firms into the shadow
economy. Friedman, Johnson, Kaufmann and Zoido-Lobaton (1999) reach a similar
conclusion. In their study every available measure of regulation is significantly correlated
with the share of the unofficial economy and the estimated sign of the relationship is
unambiguous: more regulation is correlated with a larger shadow economy.
These findings demonstrate that governments should put more emphasis on improving
enforcement of laws and regulations, rather than increasing their number. Some governments,
however, prefer this policy option (more regulations and laws), when trying to reduce the
shadow economy, mostly because it leads to an increase in power for the bureaucrats and to a
higher rate of employment in the public sector.
unchanged.
11)
See for a (social) psychological, theoretical foundation of this feature, Brehm (1966, 1972), and for a (first)
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2.2.3

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. Johnson, Kaufmann, and Zoido-Lobatón (1998a/b)
present a simple model of this relationship. Their findings show that smaller shadow
economies appear in countries with higher tax revenues if achieved by lower tax rates, fewer
laws and regulations and less bribery facing enterprises. Countries with a better rule of law,
which is financed by tax revenues, also have smaller shadow economies. Transition countries
have higher levels of regulation leading to a significantly higher incidence of bribery, higher
effective taxes on official activities and a large discretionary regulatory framework and
consequently a higher shadow economy. Their overall conclusion is that "wealthier countries
of the OECD, as well as some in Eastern Europe, find themselves in the ‘good equilibrium’ of
relatively low tax and regulatory burden, sizeable revenue mobilization, good rule of law and
corruption control, and a [relatively] small unofficial economy. By contrast, a number of
countries in Latin American and the former Soviet Union exhibit characteristics consistent
with a ‘bad equilibrium’: tax and regulatory discretion and burden on the firm is high, the rule
of law is weak, and there is a high incidence of bribery and a relatively high share of activities
in the unofficial economy." (Johnson, Kaufmann and Zoido-Lobatón 1998a p. I). First results
of the influence of corruption on the shadow economy and vice versa are reported in chapter 4
of this section.

2.2.4

Public Opinion about the Shadow Economy

The perception of citizens/voters about the shadow economy and their (moral) reaction to this
phenomenon is also an important factor, i.e. under which circumstances people decide to
work in the shadow economy. There are a number of empirical studies which investigate the
tax morale of people and their attitudes towards the shadow economy12). In this section some
results for Germany are shown which clearly demonstrate that people have no bad (moral)
feeling when working in the shadow economy. In table 2.2 for the year 2007 it is investigated
application to the shadow economy, Pelzmann (1988).
12
Compare Halla and Schneider (2005), Torgler (2002), Torgler and Schneider (2005, 2007), Feld and Frey
(2005), and Feld and Larsen (2005).
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whether people regularly work in the shadow economy or not. 20.7% of the German
respondents say "yes", and 30.8% of the respondents regularly demand shadow economy
activities. In table 2.3 some reasons are asked for why shadow economy activities are
demanded. The most important result is, one saves money – or shadow economy activities are
much cheaper than the official ones. The second most important reason is that tax and social
security burden is too high (73% of the respondents) and reason number 3 is that due to the
much higher labour costs in the official economy one would not demand these activities.
Especially the third answer is interesting, because this result clearly demonstrates that only
22% of the demanded shadow economy activities have substitutive character (i.e. they would
be demanded in the official economy if there would be no shadow economy) and 30% of the
respondents answer that they would do it themselves. From this survey result one can
conclude that roughly 48% of these activities would not take place if there were no shadow
economy. In table 2.4 examples of some hourly wage rates of shadow economy activities in
Germany are shown and what is surprising here is the huge range of wage rates in the shadow
economy, for example the varying "price" for an hour of shadow market work by a painter
ranges from € 9 to € 17. Table 2.4 clearly demonstrates also the large difference (a
multiplicative factor between 4 and 5) between the wage rates in the shadow economy and in
the official one.
In table 2.5 important attitudes held by Germans regarding what may be classified as a
"Kavaliersdelikt" are shown13). These results convincingly demonstrate for the years 1996 to
2003 that roughly two thirds of the German population treat shadow economy activities as a
"Kavalierdelikt", whereas only a third treats a small theft such as "stealing a newspaper from
a box", as a "Kavaliersdelikt". In table 2.6 value statements of the German population with
respect to the shadow economy are shown, and again, two thirds say that without shadow
economy earnings one can not keep the achieved standard of living and only a third of the
population asked finds that shadow economy activities lead to great losses of tax revenues and
social security payments to the state. What are most amazing in table 2.6 are the attitudes of
the German population with respect to punishment of shadow economy activities: only
between 9% and 3% of the asked German population questioned are convinced that shadow
economy workers should be reported to the authorities and prosecuted! One gets a similarly
low figure when asking whether a shadow economy worker is detected, he should be severely
punished. Only between 7% and 3% of those asked say, "yes". This clearly shows that there is
no bad (moral) feeling about working in the shadow economy among the German population.
13

"Kavaliersdelikte”: in english peccadillos

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The results are quite similar for Austria.
Table 2.2: Work in the Shadow Economy – Survey Results for 2007
(1) Do you work regularly in the shadow economy?
No
Yes
No answer
(2) Do you regularly demand shadow economy activities?

Values in percent
77,3
20,7
(25% male, 16% female)
2
Values in percent

No
Yes

69,2
30,8
(35.4% male, 26.5%
female)

Representative questionnaire, Germany, January 2007
Source: IDW Koeln, Germany

Table 2.3: Reasons for Shadow Economy Activities – Survey Results for Germany,
January 2007
Reasons why shadow economy activities are demanded

Values in
percent

(1) One saves money – or they are much cheaper than the official ones

90%

(2) The tax and social security burden is much too high

73%

(3) Due to the high labour costs in the official economy one would not

68%

demand these activities (extreme assumption: no shadow economy – 22%
demand in the official economy; 30% do-it-themselves; and 48% no demand
at all!)

52%

(4) The firms offer them themselves

31%

(5) It‘s so easy to get quick and reliable workers
Representative questionnaire, Germany, January 2007, Source: IDW Koeln

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Table 2.4: Hourly wage rates of shadow economy activities – Survey Results for
Germany, 2004
Activity/Type of

Town/Area

Worker

Painter

Mechanics

Wage rate in the

Wage rate in the

shadow economy

official economy (in

(in €)

€)

Berlin

10 – 17

München

9 – 15

Rhein/Rhur

10 – 12

Hamburg

13 – 23

Berlin

15 – 19

München

15 – 23

Cost of moving

Berlin

household furniture

München

and other goods

Rhein/Rhur

42

58

300 – 380
400 – 450

1.800

350 – 420

(distance 300km)

Representative questionnaire, May 2003, Source: Schneider (2004)

Table 2.5: Values/Attitudes of the German population regarding the shadow economy
Question: What are "Kavaliersdelikte" (negligible delicts)?
Statement

To demand activities in the shadow
economy
To drive a car too fast
To undertake shadow economic
activities oneself
To steal a newspaper from a box
Not to send children to school
To be dishonest when completing
tax declarations
Not to go to work (e.g. to skive on a
Monday)
To drive when drunk

German Population (in % Yes)
May
1996

May
1998

May
2001

Nov./Dec.
2002

Nov./Dec.
2003

55

64

60

68

67

42

43

44

45

46

36

41

33

36

38

28

29

31

30

28

25

27

24

18

16

22

22

18

-

18

18

17

16

13

12

9

4

7

3

4

Source: Schneider (2004)

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Table 2.6: Value Judgements/Attitudes from the German population regarding the
Shadow Economy
Statement
German Population (in % Yes)
May May May Nov./Dec. Nov./Dec.
1996 1998 2001
2002
2003
Without shadow economy earnings one cannot
keep up the standard of living
It‘s the state’s/government’s own fault that the
shadow economy is so popular and large,
because the tax and social security burden is
too high
In the last 2-3 years I have taken advantage of
shadow economic activities
Due to shadow economic activities the state
loses a great amount of tax revenues and social
security payments
In the neighbourhood one can observe a
significant number of shadow economic
activities
I think shadow economy workers should be
reported to the authorities and prosecuted
If a shadow economy worker is detected he
should be punished severely (high financial
fines)

62

69

69

70

71

63

67

57

66

67

26

38

34

36

39

29

25

30

28

26

-

-

24

28

32

9

4

6

3

3

7

4

5

7

3

Source: Schneider (2004)

Table 2.7: A comparison of the size of the German Shadow Economy using the survey
and the DYMIMIC-method, year 2006.
Shadow
Shadow
Fictive jobs
% share of
Economy in
Various kinds of shadow economy
Economy
(full time
the overall
% of
activities/values
in bill.
equivalent)
shadow
official
Euro
millions
economy
GDP
Shadow economy activities from
5.0 – 6.0
117 – 140
2.1 – 2.4
33 – 40
labour (hours worked)
+ Material (used)
3.0 – 4.0
70 – 90
1.2 – 1.5
20 – 25
+ Illegal activities (goods and
4.0 – 5.0
90 – 117
1.5 – 2.1
25 – 33
services)
+ already in the official GDP
1.0 – 2.0
23 – 45
0.4 – 0.8
7 - 13
included illegal activities
Sum (1) to (4)
13.0 – 17.0 300 – 392
5.2 – 6.8
85 – 111
Overall (total) shadow economy
15.0
340
6.0
100
(estimated by the DYMIMIC and
calibrated by the currency
demand procedure)
Source: Enste/Schneider (2006) and own calculations.

Finally, in table 2.7, a comparison between the size of the German shadow economy, using
the survey and the DYMIMIC method, is undertaken. Also an attempt is made to explain the
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quite often observed, large differences using a macro (DYMIMIC) and/or currency demand
approach to estimate the size of the German shadow economy; e.g. for 2006, we obtain a
value of 15% of “official” GDP. Using the survey method, in which the value added of
shadow economy from labour activities is captured, one obtains a value between 5 and
6%14). Hence, there is quite a huge difference. The first difference originates from the survey
method, where usually not the total overall value added is asked, but only the value added of
shadow economy work. If one adds material, one might come up with another 3-4% and one
has to add other illegal activities (prostitution, gambling and totally illegal working firms in
the construction sector). Hence, one has to add another 4-5% of the size of these activities
measured in per cent of official GDP. Finally, official national account authorities (also in
Germany) add (or include) already some shadow economy activities in the “official” GDP, so
one has to include another 1-2% black activities to official GDP, which sums up roughly to
15%. One also realizes that if one measures these different kinds of shadow activities in per
cent of overall shadow economy activities that shadow economy activities from labour (hours
worked) has the bigged size with 33-40%, followed by illegal activities in the shadow
economy with a size of 25-35%. Table 2.7 quite nicely demonstrates how the differences
between the size of the shadow economy using the survey method and compared with the
macro approach DYMIMIC and/or curreny demand can be explained.
2.2.5

Summary of the Main Causes of the Shadow Economy

In table 2.7 an overview of a number of empirical studies summarizes the various factors
influencing the shadow economy. In table 2.7 two columns are presented, showing the various
factors influencing the shadow economy with and without the independent variable, "tax
morale". This table clearly demonstrates that the increase of tax and social security
contribution burdens is by far most important single contributor to the increase of the shadow
economy. This factor does explain some 35–38% or 45–52% of the variance of the shadow
economy with and without including the variable "tax morale". The variable tax moral
accounts for some 22–25% of the variance of the shadow economy15, and finally there is a
third factor, "intensity of state regulation "(mostly for the labour market). In general table 2.7
shows that the independent variables tax and social security burden, followed by variables tax

14)

Compare also Figure 5.1, where the values using the survey method by Feld and Larsen (2005) vary between
3–4% of “official” GDP.
15
The importance of this variable with respect to theory and empirical importance is also shown in Feld and
Frey (2002, 2002a and 2005), Frey (1997), Torgler (2002) and Torgler and Schneider (2005, 2007)
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13

morale and intensity of state regulations are the three major driving forces of the shadow
economy.

Table 2.7: Main Causes of the Increase of the shadow economy
Factors influencing the shadow economy

Influence on the shadow economy (in%) 1)
(1)

(2)

35-38%

45-52%

(2) Intensity of State Regulations

8-10%

10-15%

(3) Social Transfers

5-7%

5-8%

(4) Specific Labour Market Regulations

5-7%

5-8%

(5) Public Sector Services

5-7%

5-8%

(6) Tax Morale

22-25%

-

Overall influence

76-94%

70-90%

(1) Increase of the Tax and Social
Security Contribution Burdens

1) Average values of 15 studies
2) Average values of empirical results of 28 studies.
Source: Schneider (2004)

3 The Size of the Shadow Economy for 145 Countries
3.1

Econometric Results

In tables 3.1 to 3.3 the econometric estimations using the DYMIMIC approach (latent
estimation approach) are presented for the 96 developing countries, the 28 (25) transition and
3 communist countries and the 21 industrialized (highly developed) OECD-countries of our
sample16). This grouping was necessary because the available data is different for these
countries. For the 96 developing countries and the 28 transition and communist countries the
estimation was done for five different points of time 1999/2000, 2001/02 2002/03, 2003/04
and 2004/05 and for the 21 OECD countries I have eight data points of time 1990/91,
1994/95, 1997/98, 1999/2000, 2001/02, 2002/03, 2003/04 and 2004/05. For the developing

16)

The classification which country is a developing country follows the one done by the World Bank (2002)
using a benchmark per capita income of USD 9.265 or less. The others with a higher income are either transition
or industrialized countries (here 21 OECD countries). The grouping of the transition countries is done following
the grouping in the OECD country studies (Paris, various years).
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14

and transition countries I use as cause variables the following: share of direct and indirect
taxation (including custom duties in % of GDP) as the two tax burden variables; burden of
state regulation (Index of regulation, Heritage Foundation, 2006), unemployment quota and
GDP per capita as three cause variables for the status of the "official" economy. As indicator
values I use the employment quota (in % of the population between 18 and 64), annual rate of
GDP, and annual rate of local currency per capita17).For the OECD countries I use as
additional cause variables the burden of social security payments, the tax morale, quality of
state institutions and an index of the regulation of the labour market, and an additional
indicator variable average working time per week.
The estimation results for the 96 developing countries in Middle and South America, Africa,
Asia and the South West Pacific Islands are shown in table 3.1. All estimated coefficients of
the independent cause variables are statistically significant and have the theoretically expected
signs. If one first considers the two tax burden variables, one realizes that the share of direct
taxation is just statistically significant (90% confidence level) and the size of the estimated
coefficient has roughly half the size of the value of the share of indirect taxation and custom
duties, which is statistically highly significant. One can interpret this to mean that direct
taxation is a less important for the development of the shadow economy in developing
countries, compared to indirect taxation and custom duties. If one turns to the burden of state
regulation, this variable is highly significant statistically, like the two variables, measuring the
official economy, unemployment quota and GDP per capita. As a single independent variable,
the burden of state regulation has the quantitatively largest impact on the size of the shadow
economy, showing that state regulation is the most important factor for the size of the shadow
economy in developing countries. But also the official labour market is quite important: the
unemployment quota has the second largest estimated coefficient and influence on the shadow
economy in the 96 developing countries in Middle and South America, Africa, Asia and the
South West Pacific Islands. If we turn to the indicator variables, we see that the employment
quota, as well as the change of local currency per capita, have the expected negative and
positive influence and are highly statistically significant, respectively18).

17)

Here I have the problem, that in some developing and transition countries the US-$ (or Euro) is also a widely
used currency, which is not considered here, because I got no reliable figures of the amount of US-$ (Euro) in
these developing and transition countries.
18)
The estimation results are in general robust, if other indicator variables are used as residuum; e.g. if the
variable currency per capita is used as residuum the share of direct taxation becomes insignificant as well as the
variable GPD per capita.
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In table 3.2 the DYMIMIC estimation results are presented for the 25 transition countries in
Central and East Europe, former Soviet Union countries and 3 communist countries19. Again
all estimated coefficients of the cause variables are statistically significant and similar: as in
the case of the developing countries. The two tax burden variables have together the
quantitatively largest impact on the size of the shadow economy. Contrary to the results found
in the 96 developing countries, the cause variable, "share of direct taxation" (including social
security payments) has a highly significant statistical influence with the expected positive
effect on the shadow economy. Also the independent variable "share of indirect taxation" has
now a highly significant statistical influence, but the estimated coefficient is somewhat
smaller than compared to the one the share of direct taxation (including social security
payments). The variable, "unemployment quota" has also the expected positive influence, is
highly statistically significant, and has the second largest estimated coefficient. The indicator
variables, "employment quota", and, "the annual rate of currency per capita" have the
theoretically expected signs and are statistically highly significant.
Finally, in table 3.3 the results for 21 highly developed OECD countries are shown. For these
countries the availability of data is somewhat better: Not only have I more data points over
time, but also I have three additional cause variables, tax morale (an index), quality of state
institutions and now, as a separate variable, the burden of social security payments (in % of
official GDP). The additional indicator variable is the average working time (per week)20).
The estimated coefficients of all eight cause variables are statistically significant and have the
theoretically expected signs. The tax and social security burden variables are quantitatively
the most important ones, followed by the tax morale variable which has the single biggest
influence; hence the tax payers' attitude towards the state institutions/government is quite
important to determine whether one is engaged in shadow economy activities or not. Also the
development of the official economy measured in unemployment and GDP per capita has a
quantitatively important influence on the shadow economy. Turning to the four indicator
variables they all have a statistically significant influence and the estimated coefficients have

19

How useful it is to conclude the three communist countries in this estimation, is an open and debatable
question, as these countries have only a somewhat limited market system. Hence they may not fit in this sample,
which may be a point of criticism. Hence the calculated shadow economy figures may have a different meansing
and should be interpreted with great care.
20)
Using this indicator variable one has the problem that, of course, this variable is influenced by state
regulation, so that this variable is not really exogenous; hence the estimation may be biased.
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16

the theoretically expected signs. The quantitatively most important independent variables are
the employment quota and change of currency per capita21).
Summarizing, the econometric results demonstrate that for all three groups of countries the
theoretical considerations about the causes of the shadow economy in section 2 can be
confirmed: The direct tax (and social security) payment and indirect tax (+ customs tariff)
burden variables are the driving forces of the growth of the shadow economy for all three
types of countries (developing, transition and highly developed OECD countries), followed
by the measure of state (labour market) regulation and, as measures of the official economy,
the unemployment quota and GDP per capita. In the developing countries has the largest
influence has the burden of state regulation, followed by the unemployment quota and the
share of indirect taxation. In the transition countries direct taxation (including social security
payments) has the largest influence, followed by the unemployment quota and share of
indirect taxation. In the highly developed OECD countries, the social security contributions
and the share of direct taxation wield the biggest influence, followed by tax morale and the
quality of state institutions. From these results we see that there are some differences, which
influence the shadow economy according to these three different country groups.
In order to calculate the size and development of the shadow economies of 145 countries, I
have to overcome the disadvantage of the DYMIMIC approach, which is that one gets only
relatively estimated sizes of the shadow economy and one has to use another approach to get
absolute figures. In order to calculate absolute figures of the size of the shadow economies
from these DYMIMIC estimation results, I use the already available estimations from the
currency demand approach for Australia, Austria, Germany, Hungary, Italy, India, Peru,
Russia and the United States (from studies of Chatterjee, Chaudhury and Schneider (2006),
Del’Anno and Schneider (2004), Bajada and Schneider (2003, 2005), Alexeev and Pyle
(2003), Schneider and Enste (2002) and Lacko (2000)). As I have absolute values of the
shadow economy (in % of GDP) for various years for the above mentioned countries, I can
use a benchmark procedure to transform the index of the shadow economy from the
DYMIMIC estimations into absolute values.22)

21)

The variable currency per capita or annual change of currency per capita is heavily influenced by banking
innovations; hence this variable is pretty unstable with respect to the length of the estimation period. Similar
problems are already mentioned by Giles (1999a) and Giles and Tedds (2002).
22)
This procedure is described in great detail in the paper Del’Anno and Schneider (2005).
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Table 3.1: DYMIMIC Estimations of the size of the shadow economy of 96 developing
countries in Middle and South America, Africa, Asia and the South West Pacific Islands
1999/00, 2001/02, 2002/03, 2003/04 and 2004/05
Cause Variables
Share of direct taxation
(in % of GDP)

Estimated Coefficients
λ1 = 0.14(*)
(1.70)

Share of indirect taxation
and customs duties (in % of GDP)

λ2 = 0.234**
(3.01)

Burden of state regulation (Index, Heritage
Foundation: score 1 most economic freedom,
5 least economic freedom)

λ3 = 0.274**
(2.61)

Unemployment quota (%)

λ4 = 0.317**
(4.12)

GDP per capita (in US-$)

λ5 = -0.143*
(-2.21)
λ6 = 0.241(*)
(1.31)

Lagged endogenous variable
Indicator Variables
Employment quota
(in % of population 18-64)
Annual rate of GDP
Change of local currency
per capita
Test-statistics

λ7 = -0.603*
(-2.86)
λ8 = -1 (Residuum)
λ9 = 0.371**
(4.07)
RMSE1) = 0.0010(*) (p-value = 0.903)
Chi-square2) = 8.50 (p-value = 0.913)
TMNCV3) = 0.056
AGFI4) = 0.721
N = 480
D.F.5) = 41

Notes:
t-statistics are given in parentheses (*); *; ** means the t-statistics are statistically significant at the
90%, 95%, or 99% confidence level.
1) Steigers Root Mean Square Error of Approximation (RMSEA) for test of close fit; RMSEA < 0.05;
the RMSEA-value varies between 0.0 and 1.0.
2) If the structural equation model is asymptotically correct, then the matrix S (sample covariance
matrix) will be equal to Σ (θ) (model implied covariance matrix). This test has a statistical validity
with a large sample (N ≥ 100) and multinomial distributions; both are given for all three equations
in tables 3.1-3.3 using a test of multi normal distributions.
3) Test of Multivariate Normality for Continuous Variables (TMNCV); p-values of skewness and
kurtosis.
4) Test of Adjusted Goodness of Fit Index (AGFI), varying between 0 and 1; 1 = perfect fit.
5) 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.
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Table 3.2: DYMIMIC Estimation of the Shadow Economy of 25 Central and East
European and former Soviet Union Countries and 3 Communist Countries, 1999/00,
2001/02, 2002/03, 2003/04 and 2004/05
Cause Variables
Share of direct taxation
+ share of social security payments
(in % of GDP)

Estimated Coefficients
λ1 = 0.387**
(3.03)

Share of indirect taxation
+ customs duties (in % of GDP)

λ2 = 0.294*
(2.62)

Burden of state regulation (Index, Heritage
Foundation: score 1 most economic freedom,
5 least economic freedom)

λ3 = 0.202*
(2.56)

Unemployment quota (%)

λ4 = 0.345**
(3.21)

GDP per capita (in US-$)

λ5 = -0.194**
(-2.88)
λ6 = 0.214(*)
(1.80)

Lagged endogenous variable
Indicator Variables
Employment quota
(as % of total population 18-64)
Annual rate of GDP
Change of local currency
per capita

Test-statistics

λ7 = -0.612**
(-3.57)
λ8 = -1.00 (Residuum)
λ9 = 0.406**
(3.20)
RMSE1) = 0.0010(*) (p-value = 0.889)
Chi-square 2) = 342.66 (p-value = 0.701)
TMCV3) = 0.084
AGFI4) = 0.682
N = 140
D.F.5) = 34

Notes:
t-statistics are given in parentheses (*); *; ** means the t-statistics are statistically significant at the
90%, 95%, or 99% confidence level.
1) Steigers Root Mean Square Error of Approximation (RMSEA) for test of close fit; RMSEA < 0.05;
the RMSEA-value varies between 0.0 and 1.0.
2) If the structural equation model is asymptotically correct, then the matrix S (sample covariance
matrix) will be equal to Σ (θ) (model implied covariance matrix). This test has a statistical validity
with a large sample (N ≥ 100) and multinomial distributions; both are given for all three equations
in tables 3.1.1-3.1.3 using a test of multi normal distributions.
3) Test of Multivariate Normality for Continuous Variables (TMNCV); p-values of skewness and
kurtosis.
4) Test of Adjusted Goodness of Fit Index (AGFI), varying between 0 and 1; 1 = perfect fit.
5) 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.
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19

Table 3.3: DYMIMIC Estimation of the Shadow Economy of 21 highly developed
OECD Countries, 1990/91, 1994/95, 1997/98, 1999/2000, 2001/02, 2002/03, 2003/04 and
2004/05
Cause Variables
Share of direct taxation
(in % of GDP)

Estimated Coefficients
λ1 = 0.384**
(3.06)

Share of indirect taxation
(in % of GDP)

λ2 = 0.196(*)
(1.84)

Share of social security contribution
(in % of GDP)

λ3 = 0.506**
(3.86)

Burden of state regulation (index of labour
market regulation, Heritage Foundation,
score 1 least regular, score 5 most regular)

λ4 = 0.213(*)
(1.96)

Quality of state institutions (rule of law,
World Bank, score -3 worst and +3 best
case)

λ5 = -0.307**
(-2.61)

Tax morale (WUS and EUS, Index, Scale tax
cheating always justified =1, never justified
=10)

λ6 = -0.582**
(-3.66)

Unemployment quota (%)

λ7 = 0.324**
(2.61)

GDP per capita (in US-$)

Indicator Variables
Employment quota
(in % of population 18-64)

λ8 = -0.106**
(-3.04)
λ9= -0.165(*)
(-1.66)
Estimated Coefficients
λ10= -0.626**
(-2.72)

Average working time (per week)

λ11 = -1.00 (Residuum)

Lagged endogenous variable

Annual rate of GDP (adjusted for the mean
of all 22 OECD countries)
Change of local currency
per capita

Test-statistics

λ12 = -0.274**
(-3.03)
λ13 = 0.312**
(3.74)
1)
RMSE = 0.0016* (p-value = 0.903)
Chi-square2) = 26.43 (p-value = 0.906)
TMCV3) = 0.049
AGFI4) = 0.763
N = 168
D.F.5) = 67

Notes: t-statistics are given in parentheses (*); *; ** means the t-statistics are statistically significant at
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20

the 90%, 95%, or 99% confidence level.
1) Steigers Root Mean Square Error of Approximation (RMSEA) for test of close fit; RMSEA < 0.05;
the RMSEA-value varies between 0.0 and 1.0.
2) If the structural equation model is asymptotically correct, then the matrix S (sample covariance
matrix) will be equal to Σ (θ) (model implied covariance matrix). This test has a statistical validity
with a large sample (N ≥ 100) and multinomial distributions; both are given for all three equations
in tables 3.1.1-3.1.3 using a test of multi normal distributions.
3) Test of Multivariate Normality for Continuous Variables (TMNCV); p-values of skewness and
kurtosis.
4) Test of Adjusted Goodness of Fit Index (AGFI), varying between 0 and 1; 1 = perfect fit.
5) 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.

3.2

The Size of the Shadow Economies for 145 Countries for 1999/2000 to
2004/2005

When showing the size of the shadow economies over the five periods of time (1999/2000,
2001/2002, 2002/2003, 2003/2004 and 2004/2005) for the 145 countries which are quite
different in location and developing 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 DYMIMIC and the currency demand methods have shortcomings;
these are discussed in appendix (chapter 5)23). Due to these shortcomings a detailed discussion
of the (relative) ranking of the size of the shadow economies is not conducted.

3.2.1

Developing Countries24

The results of the shadow economies for developing countries are divided by continent into
Africa, Asia, and Central and South America, and are shown in Tables 3.2.1-3.2.3. The results
for thirty-seven African countries are shown in Table 3.2.1. If we first consider the
development of the shadow economies in these thirty-seven African countries from
1999/2000 to 2004/2005, we realize that shadow economies in these African nations have
increased. On average, the size of these thirty-seven African shadow economies was 41.3%
(of official GDP) in 1999-2000, and increased to 42.8% in 2004/2005 but highest average
value with 43.2 occurred in the years 2002/03 and 2003/04, since then we have a slight
decrease to 42.8% in 2004/05. Turning to the latest results for 2004/2005, Zimbabwe, Nigeria
and Tanzania (with 64.6, 59.5 and 58.2% respectively) have by far the largest shadow
23)

See also Thomas (1992, 1999), Tanzi (1999), Pedersen (2003) and Ahumada, Alveredo, Cavanese A and P.
Cavanese (2004), Janisch and Brümmerhoff (2005), Schneider (2005) and Breusch (2005a, 2005b).
24)
For an extensive and excellent literature survey of the research about the shadow economy in developing
countries see Gerxhani (2003),who stresses thoroughout her paper that the distinction between developed and
developing countries with respect to the shadow economy is of great importance. Due to space reasons this point
is not further elabourated here; nor are the former results and literature discussed. Compare Schneider and Enste
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21

economies, and the country in the median position is Mozambique with 43.5%. South Africa
has the lowest shadow economy, with 28.2%, followed by Lesotho with 32.3%, and Namibia
with 32.4%.
The large shadow economy in Africa (and in other developing countries) is only to
some extent an issue of tax burdens and regulation, given the simple fact that the limited local
economy means that citizens are often unable to earn a living wage in a legitimate manner.
Working in the shadow economy is often the only way of achieving a minimal standard of
living.
Table 3.2.1: The Size of the Shadow Economy in Thirty-Seven African Countries
Shadow Economy (in % of official GDP) using the
DYMIMIC and Currency Demand Method
No.

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34

Country
Algeria
Angola
Benin
Botswana
Burkina Faso
Burundi
Cameroon
Central African Republic
Chad
Congo, Dem. Rep.
Republic of Congo
Cote d'Ivoire
Egypt, Arabian Republic
Ethiopia
Ghana
Guinea
Kenya
Lesotho
Madagascar
Malawi
Mali
Mauritania
Morocco
Mozambique
Namibia
Niger
Nigeria
Rwanda
Senegal
Sierra Leone
South Africa
Tanzania
Togo
Tunisia

1999/00
34.1
43.2
47.3
33.4
41.4
36.9
32.8
44.3
46.2
48.0
48.2
43.2
35.1
40.3
41.9
39.6
34.3
31.3
39.6
40.3
42.3
36.1
36.4
40.3
31.4
41.9
57.9
40.3
45.1
41.7
28.4
58.3
35.1
38.4

2001/02
35.0
44.1
48.2
33.9
42.6
37.6
33.7
45.4
47.1
48.8
49.1
44.3
36.0
41.4
42.7
40.8
35.1
32.4
40.4
41.2
43.9
37.2
37.1
41.3
32.6
42.6
58.6
41.4
46.8
42.8
29.1
59.4
39.2
39.1

2002/03
35.6
45.2
49.1
34.6
43.3
38.7
34.9
46.1
48.0
49.7
50.1
45.2
36.9
42.1
43.6
41.3
36.0
33.3
41.6
42.1
44.7
38.0
37.9
42.4
33.4
43.8
59.4
42.2
47.5
43.9
29.5
60.2
40.4
39.9

2003/04
34.8
45.3
49.3
34.2
43.8
39.4
34.4
46.3
48.4
50.4
50.5
45.4
36.3
42.7
43.8
41.7
35.4
32.8
41.9
42.7
44.0
37.4
37.3
42.9
33.0
44.1
59.6
42.4
47.8
44.1
29.0
59.1
40.6
39.4

2004/05
33.9
45.0
49.8
33.8
43.1
39.7
33.6
46.9
47.8
50.8
51.1
44.7
35.4
42.0
43.2
41.0
34.8
32.3
41.2
41.9
43.2
36.8
36.7
43.5
32.4
44.2
59.5
41.6
48.2
44.3
28.2
58.2
39.4
38.3

(2000)
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22

35 Uganda
36 Zambia
37 Zimbabwe
Unweighted Average

43.1
48.9
59.4
41.3

44.6
49.7
61.0
42.3

45.4
50.8
63.2
43.2

45.8
50.2
63.9
43.2

44.9
49.3
64.6
42.8

Source: Own calculations.
In Table 3.2.2, the results for twenty-eight Asian countries are shown. It is somewhat difficult
to treat all Asian countries equally, because some, such as Israel, Singapore, and Hong Kong,
are highly developed, while others, such as Thailand and Nepal, are still developing. The
average shadow economy in the region increased from 28.5% in 1999/2000, to 29.8% of
official GDP in 2004/2005; however in 2002/03 the shadow economies of most Asian
countries reached a peak value with 30.4% (average value over the 28 countries) which
decreased to 29.8% (average) in 2004/05. Looking at individual countries25) for the year
2004/2005, with 53.6% Thailand has by far the largest shadow economy, followed by
Cambodia with 52.2%, and Sri Lanka with 48.8% of official GDP. The median country is the
Republic of South Korea with 27.6% of official GDP, surrounded by Yemen with 27.3% and
United Arab Emirates with 26.5%. Singapore, Hong Kong and Saudi Arabia have the lowest
shadow economies with 12.1%, 15.6%, and 18.4% of official GDP, respectively.
It is somewhat astonishing that the average size of the Asian shadow economies is
considerably smaller than the shadow economies of African and Latin American states––this
is partly due to the fact that there are a greater number of developed countries, which have
smaller shadow economies located in Asia. It should be noted, however, that the average
increase of shadow economies in the region is slightly more rapid than in Africa. This is not
surprising, given that the size of the average African shadow economy is already more than
eleven percentage points higher than its Asian counterpart. There is simply more room for
growth in Asia.
In Table 3.2.3, the sizes of shadow for twenty-one Central and South American countries are
shown. Averaging the figures over all twenty-one Central and South American countries, the
shadow economy increased from 41.1% in the year 1999/2000 to 42.2% of official GDP in
2004/2005; however, in 2002/03 the shadow economies of most Central and South American
countries reached a peak value with 43.4% (average of over the 21 countries), which
decreased then to 42.2% (average) in 2004/05. If I turn to the size of the shadow economy for
single countries for 2004/2005, Bolivia has the largest shadow economy with 67.2%,
followed by Panama with 62.2% and Peru with 58.2% of official GDP. The median country is

25)

The case of India has been extensively investigated by Chatterjee, Chaudhury and Schneider (2006).

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Brazil with 41.8% and at the lower end is Chile with 19.4%, Costa Rica with 26.3%, and
Argentina with 27.2% of official GDP.
The sizes of the shadow economies of African and Central and South American
countries are generally similar. This is partly due to the factors mentioned earlier; for the
majority of citizens in many of these countries, the only way to ensure a decent standard of
living is to turn to the black market. As income inequality is much more pronounced in most
Central and South American countries, compared to Africa, the rate of increase in shadow
economy activity in Central and South America is higher.
Table 3.2.2: The Size of the Shadow Economy in Twenty-Eight Asian Countries
Shadow Economy (in % of official GDP) using the
DYMIMIC and Currency Demand Method
Country
1 Bangladesh
2 Bhutan
3 Cambodia
4 Hong Kong, China
5 India
6 Indonesia
7 Iran, Islamic Republic
8 Israel
9 Jordan
10 Korea, Republic
11 Kuwait
12 Lebanon
13 Malaysia
14 Mongolia
15 Nepal
16 Oman
17 Pakistan
18 Papua New Guinea
19 Philippines
20 Saudi Arabia
21 Singapore
22 Sri Lanka
23 Syrian Arab Republic
24 Taiwan, China
25 Thailand
26 Turkey
27 United Arab Emirates
28 Yemen, Rep.
Unweighted Average
No.

1999/00
35.6
29.4
50.1
16.6
23.1
19.4
18.9
21.9
19.4
27.5
20.1
34.1
31.1
18.4
38.4
18.9
36.8
36.1
43.4
18.4
13.1
44.6
19.3
25.4
52.6
32.1
26.4
27.4
28.5

2001/02
36.5
30.5
51.3
17.1
24.2
21.8
19.4
22.8
20.5
28.1
20.7
35.6
31.6
19.6
39.7
19.4
37.9
37.3
44.5
19.1
13.4
45.9
20.4
26.6
53.4
33.2
27.1
28.4
29.5

2002/03
37.7
31.7
52.4
17.2
25.6
22.9
19.9
23.9
21.6
28.8
21.6
36.2
32.2
20.4
40.8
19.8
38.7
38.6
45.6
19.7
13.7
47.2
21.6
27.7
54.1
34.3
27.8
29.1
30.4

2003/04
38.3
32.7
52.9
16.4
25.9
23.6
20.2
23.2
21.2
28.2
21.2
36.5
32.0
20.6
40.2
19.2
39.2
38.0
45.1
19.3
13.0
48.3
21.7
27.0
54.3
33.9
27.2
28.2
30.3

2004/05
38.0
33.1
52.2
15.6
25.1
24.0
19.7
22.6
20.4
27.6
20.7
37.1
31.4
21.2
39.3
18.6
39.5
37.3
44.3
18.4
12.1
48.8
21.2
26.3
53.6
33.2
26.5
27.3
29.8

Source: Own calculations.

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Table 3.2.3: The Size of the Shadow Economy in Twenty-One Central and South American
Countries
Shadow Economy (in % of official GDP) using the DYMIMIC
and Currency Demand Method
Country
1 Argentina
2 Bolivia
3 Brazil
4 Chile
5 Colombia
6 Costa Rica
7 Dominican Republic
8 Ecuador
9 El Salvador
10 Guatemala
11 Haiti
12 Honduras
13 Jamaica
14 Mexico
15 Nicaragua
16 Panama
17 Paraguay
18 Peru
19 Puerto Rico
20 Uruguay
21 Venezuela, RB
Unweighted Average
No.

1999/00
25.4
67.1
39.8
19.8
39.1
26.2
32.1
34.4
46.3
51.5
55.4
49.6
36.4
30.1
45.2
64.1
27.4
59.9
28.4
51.1
33.6
41.1

2001/02
27.1
68.1
40.9
20.3
41.3
27.0
33.4
35.1
47.1
51.9
57.1
50.8
37.8
31.8
46.9
65.1
29.2
60.3
29.4
51.4
35.1
42.2

2002/03
28.9
68.3
42.3
20.9
43.4
27.8
34.1
36.7
48.3
52.4
58.6
51.6
38.9
33.2
48.2
65.3
31.4
60.9
30.7
51.9
36.7
43.4

2003/04
28.6
68.0
42.6
20.3
43.0
27.1
34.4
36.1
48.1
51.1
59.3
50.8
39.2
32.6
48.8
64.1
32.4
59.1
29.6
50.8
36.1
43.0

2004/05
27.2
67.2
41.8
19.4
42.7
26.3
34.8
35.2
47.2
50.3
59.6
49.3
38.4
31.7
48.1
62.2
33.1
58.2
28.2
49.2
35.4
42.2

Source: Own calculations.

3.2.2

Transition Countries

The measurement of the size and development of the shadow economies in the transition countries
has been undertaken since the late 1980s starting with the work of Kaufmann and Kaliberda
(1996), Johnson et al. (1997) and Lacko (2000). They all use the physical input (electricity)
method (see Appendix 7.1.2.5) and come up with quite large figures. In the work of Alexeev and
Pyle (2003) and Belev (2003) the above mentioned studies are critically evaluated arguing that the
estimated sizes of the unofficial economies are to a large extent a historical phenomenon and
partly determined by institutional factors.
In table 3.2.4 the size and development of the shadow economy of 25 East and Central European
and former Soviet Union countries are presented. Turning again first to the development of the
size of the shadow economy over time, the average size of the shadow economy of these 25 East
and Central European countries was 38.1% of official GDP in 1999/2000 and increased to 38.8%
in 2004/2005; however, the average size of the shadow economies of these 25 East and Central
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European and Former Soviet Union countries reached a peak value of 40.1% in 2002/03 and since
then declined to 38.8% in 2004/05. The highest shadow economies are in Georgia, Azerbaijan and
the Ukraine with 66.4%, 59.4% and 55.3%. The median country is Bulgaria, surrounded by Serbia
and Montenegro with 37.3% and Romania with 35.4%. At the lower end are the Czech Republic
with 18.3%, the Slovak Republic with 18.2% and Hungary with 24.3% of official GDP.

Table 3.2.4: The Size of the Shadow Economy in 25 East and Central European and
Former Soviet Union Countries
Shadow Economy (in % of official GDP) using the DYMIMIC
and Currency Demand Method
Country
1 Albania
2 Armenia
3 Azerbaijan
4 Belarus
5 Bosnia and Herzegovina
6 Bulgaria
7 Croatia
8 Czech Republic
9 Estonia
10 Georgia
11 Hungary
12 Kazakhstan
13 Kyrgyz Republic
14 Latvia
15 Lithuania
16 Macedonia, FYR
17 Moldova
18 Poland
19 Romania
20 Russian Federation
21 Serbia and Montenegro
22 Slovak Republic
23 Slovenia
24 Ukraine
25 Uzbekistan
Unweighted Average
No.

1999/00
33.4
46.3
60.6
48.1
34.1
36.9
33.4
19.1
38.4
67.3
25.1
43.2
39.8
39.9
30.3
34.1
45.1
27.6
34.4
46.1
36.4
18.9
27.1
52.2
34.1
38.1

2001/02
34.6
47.8
61.1
49.3
35.4
37.1
34.2
19.6
39.2
67.6
25.7
44.1
40.3
40.7
31.4
35.1
47.3
28.2
36.1
47.5
37.3
19.3
28.3
53.6
35.7
39.1

2002/03
35.3
49.1
61.3
50.4
36.7
38.3
35.4
20.1
40.1
68.0
26.2
45.2
41.2
41.3
32.6
36.3
49.4
28.9
37.4
48.7
39.1
20.2
29.4
54.7
37.2
40.1

2003/04
35.0
48.4
60.8
50.5
36.2
37.4
34.7
19.2
39.1
67.3
25.3
45.4
41.4
40.4
31.3
36.8
49.5
28.2
36.2
48.2
38.2
19.1
28.2
54.9
36.3
39.5

2004/05
34.3
47.6
59.4
50.8
35.3
36.5
34.1
18.3
38.2
66.4
24.3
44.6
40.6
39.4
30.2
36.9
49.1
27.3
35.4
47.3
37.3
18.2
27.3
55.3
35.4
38.8

Source: Own calculations.

3.2.3

Highly developed OECD-Countries

The size and development of the shadow economies of 21 highly developed OECD countries
are shown in table 3.2.5.

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Table 3.2.5: The Size of the Shadow Economy in 21 OECD Countries
Shadow Economy (in % of official GDP) using the DYMIMIC
and Currency Demand Method
Country
1 Australia
2 Austria
3 Belgium
4 Canada
5 Denmark
6 Finland
7 France
8 Germany
9 Greece
10 Ireland
11 Italy
12 Japan
13 Netherlands
14 New Zealand
15 Norway
16 Portugal
17 Spain
18 Sweden
19 Switzerland
20 United Kingdom
21 United States
Unweighted Average

1999/00
14.3
9.8
22.2
16.0
18.0
18.1
15.2
16.0
28.7
15.9
27.1
11.2
13.1
12.8
19.1
22.7
22.7
19.2
8.6
12.7
8.7
16.8

2001/02
14.1
10.6
22.0
15.8
17.9
18.0
15.0
16.3
28.5
15.7
27.0
11.1
13.0
12.6
19.0
22.5
22.5
19.1
9.4
12.5
8.7
16.7

2002/03
13.5
10.9
21.0
15.2
17.3
17.4
14.5
16.8
28.2
15.3
25.7
10.8
12.6
12.3
18.4
21.9
22.0
18.3
9.4
12.2
8.4
16.3

2003/04
13.1
10.1
20.4
14.8
16.7
16.4
13.8
16.1
27.4
14.8
24.8
9.4
12.0
11.6
17.6
21.1
21.2
17.2
9.0
11.7
8.2
15.6

2004/05
12.8
9.3
19.6
14.1
16.1
15.8
13.2
15.3
26.3
14.1
23.2
8.8
11.1
10.9
16.8
20.4
20.5
16.3
8.5
10.3
7.9
14.8

Source: Own calculations.
If I consider again the development of the size of the shadow economies of these 21 OECD
countries, I realize for the first time that the size of the shadow economy of these countries
has decreased over the period 1999/2000 to 2004/2005. The average size of the shadow
economy in 1999/2000 of these countries was 16.8% of official GDP; it decreased to 14.8%
in 2004/2005. If I consider single countries, Greece, Italy and Spain have by far the largest
shadow economy in 2004/2005 with 26.3%, 23.2% and 20.5% of official GDP. The median
country is Ireland with 14.1% of official GDP surrounded by Germany with 15.3%26) and
Canada with 14.1%. At the lower end are the United States, Switzerland and Japan with a
shadow economy of 7.9%, 8.5% and 8.8% of official GDP.

3.2.4

South West Pacific Islands

The size and development of the shadow economies of 10 South West Pacific islands are
presented in table 3.2.6.
26)

Pickhardt and Sarda-Pous (2006) reach very similar values of the shadow economy for Germany using a

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Table 3.2.6.: The Size of the Shadow Economy in 10 South West Pacific Islands

Shadow Economy (in % of official GDP) using the
DYMIMIC and Currency Demand Method
Country
1 Fiji
2 Kiribati
3 Maldives
4 Marshall Islands
5 Micronesia, Fed. Sts.
6 Palau
7 Samoa
8 Solomon Islands
9 Tonga
10 Vanuatu
Unweighted Average

1999/00
33.6
34.1
30.3
28.1
31.3
28.4
31.4
33.4
35.1
30.9
31.7

2001/02
34.3
35.0
31.4
29.0
32.1
29.2
32.6
34.5
36.3
31.7
32.6

2002/03
35.1
35.3
32.0
29.6
33.2
30.0
33.5
35.3
37.4
32.5
33.4

2003/04
34.6
34.8
31.6
28.7
32.6
29.2
33.1
34.6
36.8
32.0
32.8

2004/05
33.8
34.0
30.9
27.9
31.9
28.4
32.8
34.0
35.8
31.4
32.1

Source: Own calculations.
If I again consider the development over time, the average size of the shadow economy of
these 10 South West Pacific islands countries increased from 31.7% in the year 1999/2000 to
32.1% in the year 2004/2005; however, the average value of these 10 West Pacific Islands
reached a peak value of 33.4% in 2002/03 and since then declined to 32.1% in 2004/05.
Considering the largest shadow economy is in Tonga, with 35.8%, followed by the Solomon
Islands with 34.0% and Kiribati with 34.0%. In the middle field is Micronesia and Samoa
with a shadow economy of 31.9% and 32.8% of official GDP. The lowest shadow economy
have the Marshall Islands and Palau with a shadow economy of 27.9% and 28.4%.

3.2.5

Communist Countries

In this last section the size and development of the shadow economies of three communist
countries (China, Laos and Vietnam) are presented. The results are shown in table 3.2.7.
Table 3.2.7: The Size of the Shadow Economy in 3 Communist Countries
Shadow Economy (in % of official GDP) using the DYMIMIC
and Currency Demand Method
Country
1 China
2 Lao PDR
3 Vietnam
Unweighted Average
No.

1999/00
13.1
30.6
15.6
19.8

2001/02
14.4
31.9
16.9
21.1

2002/03
15.6
33.4
17.9
22.3

2003/04
16.1
33.9
16.9
22.3

2004/05
16.6
33.2
16.1
22.0

Source: Own calculations.
combination of a MIMIC and Currency Demand Method.
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The average size of the shadow economy in the above mentioned countries in 1999/2000 was
19.8%, and by 2004/2005 had increased to 22.0%. Laos has the largest shadow economy with
33.2% and Vietnam the lowest with 16.1%. It should be clear that the shadow economy in
these countries, and especially in China (16.6%), which is partly a market economy and yet
still a planned socialist economy, is difficult to interpret. It should be more seen as a parallel
economy, where especially farmers and small firms in rural regions produce additional
products to earn some (extra) money. It is an open question whether the findings of these
shadow economies can be compared to others. That is one reason why they are shown in this
paper in an extra section and should be interpreted with caution with respect to their size and
the label "shadow" or "grey" economy.

4 Corruption and the Shadow Economy: Substitutes or
Compliments?27)
Quite often shadow economy and corruption28) are seen as "twins", who need each other or
fight against each other. This means for a social scientist that, theoretically, corruption and the
shadow economy can be either complements or substitutes. Choi and Thum (2004) present a
model where the option of entrepreneurs to go underground constrains a corrupt official’s
ability to ask for bribes. Dreher, Kotsogiannis and McCorriston (2005a/b) extend the model to
the explicit specification of institutional quality. The model shows that corruption and shadow
economy are substitutes in the sense that the existence of the shadow economy reduces the
propensity of officials to demand graft.
Johnson et al. (1998), on the contrary, model corruption and the shadow economy as
complements. In their full-employment model, labour can be either employed in the official
sector or in the underground economy. Consequently, an increase in the shadow economy
always decreases the size of the official market. In their model, corruption increases the
shadow economy, as corruption can be viewed as one particular form of taxation and
regulation (driving entrepreneurs underground). Hindriks et al. (1999) also show that the
shadow economy is a complement to corruption. This is because, in this case, the tax payer

27)

This section is taken from Dreher and Schneider (2006), pages 4, 5 and 14 as well as table 4.1.
According to Dreher and Schneider (2006), corruption is commonly defined as the misuse of public power for
private benefit.
28)

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colludes with the inspector so the inspector under-reports the tax liability of the tax payer in
exchange for a bribe29).
Theoretically, the relationship between corruption and the shadow economy is thus unsettled.
There is, however, reason to believe that the relationship might differ among high and low
income countries. In high income countries, the official sector provides public goods like the
rule of law, enforcement of contracts, and protection by an efficient police. Usually, only
craftsmen or very small firms have (or take) the option of going underground. In this case, the
shadow economy is hidden from tax inspectors and other officials. In other words, there are
no bribes necessary or possible to buy the way out of the official sector. In high income
countries – typically showing comparably small levels of corruption – individuals confronted
with a corrupt official always have the choice to bring the official to court. Moreover, in high
income countries corruption quite often takes place, for example, to bribe officials to get a
(huge) contract from the public sector (e.g. in the construction sector). This contract is then
handled in the official economy and not in the shadow economy. Hence, corruption in high
income countries can be a means to achieve certain benefits which make work in the official
economy easier, e.g., winning a contract from a public authority, getting a licence (e.g. for
operating taxes or providing other services or getting the permission to convert land into
"construction ready" land, etc.). In high income countries people thus bribe in order to be able
to engage in more official economic activities. As Schneider and Enste (2000) point out, at
least two thirds of the income earned in the shadow economy is immediately spent in the
official sector. The shadow economy and the official sector might thus be complements. The
corresponding increase in government revenue and strengthened institutional quality is likely
to decrease corruption. The prediction of a negative (substitutive) relation between corruption
and the shadow economy is in line with the models of Choi and Thum (2004) and Dreher,
Kotsogiannis and McCorriston (2005a).30)
In low income countries, on the contrary, we expect different mechanisms to prevail. Instead
of working partly in the official sector and offering additional services underground as in
high-income countries, enterprises completely engage in underground activity. Examples for
enterprises operating completely underground are restaurants, bars, or haircutters – and even
big production companies. One reason for this is that public goods provided by the official
sector are, in many developing countries, less efficient compared to high income countries.
29)
30)

See Dreher and Siemers (2005) for a formalization of this argument.
Consequently, Dreher, Kotsogiannis and McCorriston (2005a) test their model employing data for OECD

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30

Big companies, however, are comparably easy to detect and – in order to escape taxation and
punishment – they have to bribe officials, thereby increasing corruption. Corruption often
takes place in order to pay for activities in the shadow economy, so that the shadow economy
entrepreneur can be sure not to be detected by public authorities. Here, shadow economy and
corruption are likely to reinforce each other, as corruption is needed to expand shadow
economy activities and – at the same time – underground activities require bribes and
corruption. To get some additional income from the shadow economy entrepreneur, it is
natural for public officials to ask for bribes and thus benefit from the shadow market. In low
income countries, we therefore expect a positive (complementary) relationship between
corruption and the shadow economy. This corresponds to the predictions of the models of
Hindriks et al. (1999) and Johnson et al. (1997).
In summary, following Dreher and Schneider (2006), I expect:
Hypothesis 1: In low income countries, shadow economy activities and corruption are
complements.
Hypothesis 2: In high income countries, shadow economy activities and corruption are
substitutes.
These two hypotheses are tested for a cross-section of 120 countries and a panel of 70
countries for the period 1994 to 2002.31) Table 4.1 summarizes the empirical results of Dreher
and Schneider (2006). Overall, they show that an increase in perceived corruption over time
also increases the shadow economy. This confirms the models of Johnson et al. (1998) and
Hindriks et al. (1999). Across countries, however, greater perceived corruption does not lead
to a greater shadow economy. To some extent this also supports the results of Méon and
Sekkat (2004) showing the within-country variation to be important in their analysis of
corruption on foreign direct investment and exports.
Regarding the impact of the shadow economy on perceived corruption, these results for the
overall sample are similar to those for the other way round. In the cross-country regressions,
all coefficients are completely insignificant. An increase in the shadow economy over time
increases corruption according to the fixed and random effects estimator, but not when the
endogeneity of the shadow is controlled. Turning to the sub-samples, the results show that
higher perceived corruption significantly reduces the shadow economy in high income
countries only.
31)
For the description of the data, the estimation techniques used, and the various specification see Dreher and
Schneider (2006, chapters 3 and 4).
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countries, confirming the models of Choi and Thum (2004) and Dreher, Kotsogiannis and
McCorriston (2005a). In low income countries, on the contrary, corruption tends to increase
with a higher shadow economy, again confirming the models of Johnson et al. (1998) and
Hindriks et al. (1999). This is true for the impact of perceived corruption in the within-groups
specification and actual corruption in all specifications.
Table 4.1: Empirical Results of the Relationship between the Shadow Economy and
Corruption
Dependent Variable:
Shadow Economy
Corruption
Independent Variable:
Corruption
Shadow Economy
Estimation technique
All
Low
High
All
Low
High
ICRG index of corruption
OLS
1.88
3.57
-0,84
0.00
0.01
-0.07
(1.20) (1.34) (0.97)
(0.41)
(1.14)
(3.57***)
Robust regression
1.32
0.00
(0.82)
(0.43)
IV, set 1
3.72
3.12
5.41
-0.03
-0.01
-0.09
(1.17) (0.86) (1.40)
(1.28)
(0.42)
(1.57)
IV, set 2
-4.04
5.14
-1.85
-0.02
-0.02
-0.11
(1.33) (0.78) (1.91*)
(0.66)
(0.46)
(1.45)
Panel, fixed effects
1.34
1.36
0.69
0.09
0.10
0.09
(2.63**) (1.42) (1.98**) (2.88***) (2.77***)
(0.76)
Panel, random effects
1.59
0.02
(4.81***)
(2.64***)
Panel IV
3.46
0.01 (0.12)
(3.48***)
TI index of corruption
OLS
-0.06
(2.35**)
World Bank Index of corruption
OLS
-0.01
(2.76**)
DKM index of corruption
OLS
0.04
0.06
-0.10
(1.77*) (2.49**)
(1.50)
Robust regression
0.04
(1.69*)
IV, set 1
0.14
0.10
-0.32
(2.59**) (2.65**)
(1.22)
IV, set 2
0.12
0.12
0.04
(2.45**) (2.50**)
(0.19)
Notes: Higher values represent more corruption; corruption indices used: ICRG International Country
Risk Guide; TI=Transparency International; World Bank Index of Corruption; and DKM-Index of
Dreher, Kotsogiannis and McCorriston.

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Instruments for the shadow economy are: (1) Credit Market Regulations (Fraser), Minimum Wage
Regulation (Fraser), Government Effectiveness (World Bank); (2) Starting a Business (Duration), Starting
a Business (Costs), Flexibility to Hire, Flexibility to Fire.
Instruments for corruption are: (1) Fiscal Burden (Heritage), Regulation of Prices (Fraser), Rule of Law
(World Bank), Democracy; (2) Ethnic Fractionalization, Religious Fractionalization, Latitude, French
Legacy, Socialist Legacy, German Legacy, Scandinavian Legacy.
* denotes significant at 10% level; ** significant at 5% level; *** significant at 1% level
Source: Dreher and Schneider (2006, table 12).

5 Summary and Conclusions
There have been many obstacles to overcome to measure the size of the shadow economy, to
analyze its consequences on the official economy and the interaction between corruption and
the shadow economy, but as this paper shows some progress has been made. I provided
estimates of the size of the shadow economies for 145 countries for five periods of time
(1999/2000, 2001/2002, 2002/2003, 2003/04 and 2004/05) using the DYMIMIC and for the
econometric estimation the currency demand approach for calibrating the values into absolute
ones. Coming back to the headline of this paper, some new knowledge/insights are gained
with respect to the size and development of the shadow economy of 145 countries,32) and to
the relationship between the shadow economy and corruption leading to four conclusions:
The first conclusion from these results is that for all countries investigated the shadow
economy has reached a remarkably large size; the summarized results are shown in table 5.1.
This table clearly shows that the average size of the shadow economies of all seven groups of
countries (mostly developing countries in Africa, Central and South America, Asia, Transition
countries, highly developed OECD countries, South Pacific Islands and Communist
Countries) reached a peak value of 35.2% of official GDP in 2002/03 and sine then declined
to 34.5% in 2004/05. Hence the first conclusion is that in later years the shadow economies of
most of these countries is modestly shrinking!

32)

In the appendix some critical discussion of these two methods is given; they have well known weaknesses
(compare also Pedersen, 2003).
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Table 5.1: Average Size of the Shadow Economy for Developing, Transition and OECDCountries in % of official GDP
Average Size of the Shadow Economy – Value added in % of
official GDP using DYMIMIC for estimation and Currency
Countries/Year
Demand method for calibration (Number of Countries)
Mostly developing countries:
Africa

16.8
(21)

2000/2001
42.3
(37)
42.1
(21)
29.5
(28)
39.1
(25)
16.7
(21)

2002/2003
43.2
(37)
43.4
(21)
30.4
(28)
40.1
(25)
16.3
(21)

2003/2004
43.2
(37)
43.0
(21)
30.3
(28)
39.5
(25)
15.6
(21)

2004/2005
42.8
(37)
42.2
(21)
29.8
(28)
38.8
(25)
14.8
(21)

South Pacific Islands

31.7
(10)

32.6
(10)

33.4
(10)

32.8
(10)

32.1
(10)

Communist Countries

19.8
(3)

21.1
(3)

22.3
(3)

22.3
(3)

22.0
(3)

Unweighted Average over
145 Countries
Source: Own calculations.

33.6

34.5

35.2

34.9

34.3

Central and South America
Asia
Transition countries
Highly developed
Countries

OECD

1999/2000
41.3
(37)
41.1
(21)
28.5
(28)
38.1
(25)

The second conclusion is that shadow economies are a complex phenomenon present to an
important extent in all type of economies (developing, transition and highly developed).
People engage in shadow economic activity for a variety of reasons, among the most
important of which we can count are government actions, most notably, taxation and
regulation.
Considering a public choice perspective a third conclusion for highly developed countries is
that a government may not have a great interest to reduce the shadow economy due to the fact
that:
(i) tax losses my be moderate, as at least 2/3 of the the income earned in the shadow economy
is immediately spent in the official economy,
(ii) income earned in the shadow economy increases the standard of living of at least 1/3 of
the working population,

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(iii) between 40 and 50% of the shadow economy activities have a complementary character,
which means that additional value added his created, which increases the official (overall)
GDP, and
(iv) people who work in the shadow economy have less time for other things like going to
demonstrations, etc.
Considering these three conclusions, it is obvious that one of the big challenges for every
government is to undertake efficient incentive orientated policy measures in order to make
work less attractive in the shadow economy and hence to make the work in the official
economy more attractive. In a number of OECD countries this policy direction has been
successfully implemented and this has led to a reduction of the shadow economy.
The fourth conclusion is that the shadow economy reduces corruption in high income
countries (substitution effect) and increases corruption in low income countries
(complementary effect).

6 Appendix 1: Methods to Estimate the Size of the Shadow
Economy: The DYMIMIC and Currency Demand Approach
As has already been mentioned in chapters 2 and 3, estimating the size and development of a
shadow economy is a difficult and challenging task. In this appendix, I give a short but
comprehensive overview of the currency demand and the DYMIMIC-approach; each is
briefly discussed as well as critically evaluated.33)
6.1
6.1.1

Direct Approaches
Survey Method

These are micro approaches that employ both well designed surveys and samples based on
voluntary replies, or tax auditing and other compliance methods. Sample surveys designed to
estimate the shadow economy are widely used in a number of countries34). The main
disadvantage of this method is that it presents the flaws of all surveys. For example, the
average precision and results depend greatly on the respondent’s willingness to cooperate, it is
33)

A discussion and critical evaluation of all used approaches is given in Schneider (2005, 2007).
The direct method of voluntary sample surveys has been extensively used for Norway by Isachsen, Klovland
and Strom (1982), and Isachsen and Strom (1985). For Denmark this method is used by Mogensen et. al. (1995)
in which they report "estimates" of the shadow economy of 2.7 percent of GDP for 1989, of 4.2 percent of GDP
for 1991, of 3.0 percent of GDP for 1993 and of 3.1 percent of GDP for 1994. In Pedersen (2003) estimates of
the Danish shadow economy contain the years 1995 with 3.1% up to 2001 with 3.8%. This method is also used
34)

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difficult to assess the amount of undeclared work from a direct questionnaire, most
interviewers hesitate to confess fraudulent behaviour, and responses are of uncertain
reliability, which makes it difficult to calculate a real estimate (in monetary terms) of the
extent of undeclared work. The main advantage of this method lies in the detailed information
about the structure of the shadow economy, but the results from these kinds of surveys are
very sensitive to the way the questionnaire is formulated35).
In order to demonstrate the difficulties of calculating a macro estimation for a whole country
from survey results of shadow economy activities (from single individuals) the following
example is used: in Austria the author undertook a representative questioning of the Austrian
population in order to estimate the size of the shadow economy in the construction and
craftsman sector (including repairing) in November/December 2002 considering three groups.
1. A representative sample of the Austrian population between 16 and 65 years old,
2. 55 self-declared shadow economy workers in the construction and craftsmen
sector, and
3. 320 managers (owners) of construction and craftsmen firms.
The following results were gained: (1) Among the Austrian population (potential labour
force) are 918,000 Austrians who supplied shadow economy activities in the construction and
craftsmen sector. Their average hourly earning in the shadow economy varies between €15.30
and €15.60, and the average yearly income from shadow economy activities varies between
€1,117.00 and €1.142.00. This means that 73 hours per year were worked in the shadow
economy.
(2) Among the 55 self-declared shadow economy workers I got a wage rate of €11.50 per
hour and annual earnings in the shadow economy of €2,480.00 using the fact that these
groups worked 245 hours per year in the shadow economy.
(3) Managers (owners) of construction and craftsmanship firms report a wage rate for shadow
economy workers of €17 per hour and average earnings per year of €4,590.00, assuming that
270 hours per year were used for shadow economy activities by their employees/workers. The
questioned managers also state: 21% of the managers questioned also stated that more than
50% of their employees work in the shadow economy, 41% indicated a figure of less than
50% and 34% reported that no-one in the firm works in the shadow economy. To summarize,
62% of the managers acknowledge that a large percentage of their employees work in the
by Williams (2004a).
35)
The advantages and disadvantages of this method are extensively dealt by Pedersen (2003), Mogensen et. al
(1995) and Feld and Larsen (2005) in their excellent and very carefully done investigations. Compare also the
careful and detailed studies by Kazemier (2005a,b), who extensitively discusses the pros .and cons of this
method.
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shadow economy. Further results are that 7% of the managers think that their employees work
between 0 and 2 hours per week in the shadow economy; 29% assume that they work between
6 and 10 hours, 28% between 3 and 5 hours and 14% think that their employees work more
then 10 hours per week in the shadow economy; 22% of all managers have no knowledge of
this fact. In principle 39% of managers are not in favour (do not support) moonlighting by
their workers and 61% are in favour (do support) - an amazingly high percentage!
Finally in table 6.1 the aggregate values of the size of the shadow economy in the construction
and craftsmen sector in the year 2002 are presented, based on questionnaire findings. Table
6.1 clearly demonstrates that the size of the shadow economy in the construction and
craftsmen sector varies considerably from a total value of €2.6 billion up to €4.2 billion.
These differences originate from different hourly wages rates, ranging from €11.50 to €17 and
from the different amount of hours worked per year in the shadow economy ranging from 245
to 270. Hence the survey method "covers" between 31.2% and 50.9 % of the value obtained
by a macro approach (mimic method). These results still leave a considerable leeway, but the
rather large differences may be explained by the following facts:
1. Table 5.1 contains earnings and not the value added of the shadow economy.
2. Shadow economy demanders are overwhelmingly households, the whole area of the
shadow economy activities between firms (which are especially a problem in the construction
and craftsmen sectors) are not considered.
3. All foreign shadow economy activities achieved by foreigners (illegal immigrants) are not
considered.
4. The amount earned in the shadow economy (hourly wage rate and hours worked per year),
vary considerably.

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Table 6.1: Size of the supplied shadow economy in the construction and craftsmen
sector, Austria 2002, based on the questionnaire findings

Variable/Indicator

Worked hours and earning in the shadow economy
results from
results from
managers of
results from
managers of
results from
construction
declared
construction
declared
and
moonlighters
and craftsmen
moonlighters
craftsmen
(1)
firms
(3)
firms
(2)
(4)

∅ hourly shadow
economy wage rate

€11.5

€17

€11.5

€17

∅ average yearly
€2,814
€4,165
€3,105
€4,590
earning
∅ amount of hours
worked in the shadow
245
245
270
270
economy per year per
worker
∅ aggregated yearly
amount
of
hours
225.1 million
225.1 million
248.1 million 248.1 million
worked in the shadow
economy 1)
Total earnings of the
€2,588.65
€3,826.7
€2,853.15
€4,217.7
shadow economy in
million
million
million
million
the year 2002
Total
shadow
economy earnings in
% of the value added
of
the
shadow
economy
in
the
31.2
46.1
34.4
50.9
construction
and
craftsmanship sector
(including repairing);
absolute value €8,284
billion in 2002
1) Basis of the calculation: 918,864 shadow economy workers in the construction and
craftsmen sector. Source: Own calculations.

6.1.2

Tax Auditing Method

Estimates of the shadow economy can also be based on the discrepancy between income
declared for tax purposes and that measured by selective checks. Fiscal auditing programmes
have been particularly effective in this regard. Since these programs are designed to measure
the amount of undeclared taxable income, they may also be used to calculate the shadow
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38

economy.36) However, a number of difficulties beset this approach. First, using tax
compliance data is equivalent to using a (possibly biased) sample of the population. In
general, the selection of tax payers for tax audit is not random but based on properties of
submitted (tax) returns that indicate a certain likelihood of (tax) fraud. Consequently, such a
sample is not a random one of the whole population, and estimates of the shadow based upon
a biased sample may not be accurate. Second estimates based on tax audits reflect only that
portion of shadow economy income that the authorities succeed in discovering, and this is
likely to be only a fraction of hidden income.
A further disadvantage of these two direct methods (surveys and tax auditing) is that they lead
only to point estimates. Moreover, it is unlikely that they capture all "shadow" activities, so
they can be seen as providing lower bound estimates. They are unable to provide estimates of
the development and growth of the shadow economy over a longer period of time. As already
argued, they have, however, at least one considerable advantage – they can provide detailed
information about shadow economy activities and the structure and composition of those who
work in the shadow economy.

6.2

Indirect Approaches

These approaches, which are also called "indicator" approaches, are mostly macroeconomic
ones and use various economic and other indicators that contain information about the
development of the shadow economy (over time). Currently there are five indicators that
leave some "traces" of the shadow economy.

6.2.1

The Discrepancy between National Expenditure and Income Statistics

This approach is based on discrepancies between income and expenditure statistics. In
national accounting the income measure of GNP should be equal to the expenditure measure
of GNP. Thus, if an independent estimate of the expenditure site of the national accounts is
available, the gap between the expenditure measure and the income measure can be used as an
indicator of the extent of the black economy.37) Since national accounts statisticians are
36)

In the United States, IRS (1979, 1983), Simon and Witte (1982), Witte (1987), Clotefelter (1983), and Feige
(1986). For a more detailed discussion, see Dallago (1990) and Thomas (1992).
37)
See, e.g., Franz (1983) for Austria; MacAfee (1980) O’Higgins (1989) and Smith (1985), for Great Britain;
Petersen (1982) and Del Boca (1981) for Germany; Park (1979) for the United States. For a critical survey, see
Thomas (1992).
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anxious to minimize this discrepancy, the initial discrepancy or first estimate, rather than the
published discrepancy, should be employed as an estimate of the shadow economy. If all the
components of the expenditure site are measured without error, then this approach would
indeed yield a good estimate of the scale of the shadow economy. Unfortunately, however,
this is not the case. Instead, the discrepancy reflects all omissions and errors everywhere in
the national accounts statistics as well as the shadow economy activity. These estimates may
therefore be very crude and of questionable reliability.38)

6.2.2

The Discrepancy between the Official and Actual Labour Force

A decline in participation of the labour force in the official economy can be seen as an
indication of increased activity in the shadow economy. If total labour force participation is
assumed to be constant, then a decreasing official rate of participation can be seen as an
indicator of an increase in the activities in the shadow economy, ceteris paribus.39) One
weakness of this method is that differences in the rate of participation may also have other
causes. Also, people can work in the shadow economy and have a job in the "official’
economy. Therefore such estimates may be viewed as weak indicators of the size and
development of the shadow economy.

6.2.3

The Transactions Approach

This approach has been most fully developed by Feige.40) It is based upon the assumption that
there is a constant relation over time between the volume of transaction and official GNP, as
summarized by the well-known Fisherian quantity equation, or M*V = p*T (with M = money,
V = velocity, p = prices, and T = total transactions). Assumptions also have to be made about
the velocity of money and about the relationships between the value of total transactions
(p*T) and total (=official + unofficial) nominal GNP. Relating total nominal GNP to total
transactions, the GNP of the shadow economy can be calculated by subtracting the official
GNP from total nominal GNP. However, to derive figures for the shadow economy, one must
also assume a base year in which there is no shadow economy and therefore the ratio of p*T

38)

A related approach is pursued by Pissarides and Weber (1988), who use micro data from household budget
surveys to estimate the extent of income understatement by self-employed.
39)
Such studies have been made for Italy, see e.g., Contini (1981) and Del Boca (1981); for the United States,
see O’Neill (1983), for a critical survey, see again Thomas (1992).
40)
For an extended description of this approach, see Feige (1996); for a further application for the Netherlands,
Boeschoten and Fase (1984), and for Germany, Langfeldt (1984).
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to total nominal (official = total) GNP was "normal" and would have been constant over time,
if there had been no shadow economy.
This method, too, has several weaknesses, such as the required assumptions of a base year
with no shadow economy, and of a "normal" ratio of transactions to nominal GNP. Moreover,
to obtain reliable shadow economy estimates, precise figures of the total volume of
transactions should be available, and this availability might be especially difficult to achieve
for cash transactions, because they depend, among other factors, on the durability of bank
notes in terms of the quality of the paper on which they are printed.41) Also, the assumption is
made that all variations in the ratio between the total value of transaction and the officially
measured GNP are due to the shadow economy. This means that a considerable amount of
data is required in order to eliminate financial transactions from "pure" cross payments, which
are legal and have nothing to do with the shadow economy. In general, although this approach
is theoretically attractive, the empirical requirements necessary to obtain reliable estimates are
so difficult to fulfil that its application may lead to doubtful results.

6.2.4

The Currency Demand Approach

The currency demand approach was first used by Cagan (1958), who calculated a correlation
of the currency demand and the tax pressure (as one cause of the shadow economy) for the
United States over the period 1919 to 1955. 20 years later, Gutmann (1977) used the same
approach but without any statistical procedures. Cagan’s approach was further developed by
Tanzi (1980, 1983), who econometrically estimated a currency demand function for the
United States for the period 1929 to 1980 in order to calculate the shadow economy. His
approach assumes that shadow (or hidden) transactions are undertaken in the form of cash
payments, so as to leave no observable traces for the authorities. An increase in the size of the
shadow economy will therefore increase the demand for currency. To isolate the resulting
"excess" demand for currency, an equation for currency demand is econometrically estimated
over time. All conventional possible factors, such as the development of income, payment
habits, interest rates, and so on, are controlled. Additionally, such variables as the direct and
indirect tax burden, government regulation and the complexity of the tax system, which are
assumed to be the major factors causing people to work in the shadow economy, are included

41)

For a detailed criticism of the transaction approach see Boeschoten and Fase (1984), Frey and Pommerehne
(1984), Kirchgaessner (1984), Tanzi (1982a,b, 1986), Dallago (1990), Thomas (1986, 1992, 1999), Giles
(1999a), Pederson (2003), and Janisch and Brümmerhoff (2005) and Breusch (2005a, 2005b).

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in the estimation equation. The basic regression equation for the currency demand, proposed
by Tanzi (1983), is the following:
ln (C / M2)t = βO + β1 ln (1 + TW)t + β2 ln (WS / Y)t + β3 ln Rt + β4 ln (Y / N)t + ut
with β1 > 0, β2 > 0, β3 < 0, β4 > 0
where
ln denotes natural logarithms,
C / M2 is the ratio of cash holdings to current and deposit accounts,
TW is a weighted average tax rate (to proxy changes in the size of the shadow economy),
WS / Y is a proportion of wages and salaries in national income (to capture changing payment
and money holding patterns),
R is the interest paid on savings deposits (to capture the opportunity cost of holding cash) and
Y / N is the per capita income.42)
Any "excess" increase in currency, or the amount unexplained by the conventional or normal
factors (mentioned above) is then attributed to the rising tax burden and the other reasons
leading people to work in the shadow economy. Figures for the size and development of the
shadow economy can be calculated in a first step by comparing the difference between the
development of currency when the direct and indirect tax burden (and government
regulations) are held at their lowest value, and the development of currency with the current
(much higher) burden of taxation and government regulations. Assuming in a second step the
same income velocity for currency used in the shadow economy as for legal M1 in the official
economy, the size of the shadow can be computed and compared to the official GDP.
The currency demand approach is one of the most commonly used approaches. It has been
applied to many OECD countries,43) but has nevertheless been criticized on various
grounds.44) The most commonly raised objections to this method are:

42)

The estimation of such a currency demand equation has been criticized by Thomas (1999) but part of this
criticism has been considered by the work of Giles (1999a,b) and Bhattacharyya (1999), who both use the latest
econometric technics.
43)
See Karmann (1986 and 1990), Schneider (1997, 1998a), Johnson, Kaufmann and Zoido-Lobatón (1998a),
and Williams and Windebank (1995).
44)
See Thomas (1992, 1999); Feige (1986); Pozo (1996); Pedersen (2003) and Ahumada, Alvareda, Canavese A.
and P. Canavese (2004); Janisch and Brümmerhof (2005); and Breusch (2005a,b).
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(i)

Not all transactions in the shadow economy are paid in cash. Isachsen and Strom
(1985) used the survey method to find out that in Norway, in 1980, roughly 80% of all
transactions in the hidden sector were paid in cash. The size of the total shadow
economy (including barter) may thus be even larger than previously estimated.

(ii)

Most studies consider only one particular factor, the tax burden, as a cause of the
shadow economy. But others (such as the impact of regulation, taxpayers’ attitudes
toward the state, "tax morality" and so on) are not considered, because reliable data for
most countries are not available. If, as seems likely, these other factors also have an
impact on the extent of the hidden economy, it might again be higher than reported in
most studies.45)

(iii)

As discussed by Garcia (1978), Park (1979), and Feige (1996), increases in currency
demand deposits are due largely to a slowdown in demand deposits rather than to an
increase in currency caused by activities in the shadow economy, at least in the case of
the United States.

(iv)

Blades (1982) and Feige (1986, 1996), criticize Tanzi’s studies on the grounds that the
US dollar is used as an international currency. Instead, Tanzi should have considered
(and controlled) the presence of US dollars, which are used as an international
currency and are held in cash abroad.46) Moreover, Frey and Pommerehne (1984) and
Thomas (1986, 1992, 1999) claim that Tanzi’s parameter estimates are not very
stable.47)

(v)

Most studies assume the same velocity of money in both types of economies. As
argued by Hill and Kabir (1996) for Canada and by Klovland (1984) for the
Scandinavian countries, there is already considerable uncertainty about the velocity of

45)

One (weak) justification for the use of only the tax variable is that this variable has by far the strongest impact
on the size of the shadow economy in the studies known to the authors. The only exception is the study by Frey
and Weck-Hannemann (1984) where the variable "tax immorality" has a quantitatively larger and statistically
stronger influence than the direct tax share in the model approach. In the study of Pommerehne and Schneider
(1985), for the U.S., besides various tax measures, data for regulation, tax immorality, minimum wage rates are
available, the tax variable has a dominating influence and contributes roughly 60-70% of the size of the shadow
economy. See also Zilberfarb (1986).
46)
In another study by Tanzi (1982, esp. pp. 110-113) he explicitly deals with this criticism. A very careful
investigation of the amount of US-$ used abroad and the US currency used in the shadow economy and to
"classical" crime activities has been undertaken by Rogoff (1998), who concludes that large denomination bills
are the major driving force for the growth of the shadow economy and classical crime activities are due largely
to reduced transactions costs.
47)
However in studies for European countries Kirchgaessner (1983, 1984) and Schneider (1986) reach the
conclusion that the estimation results for Germany, Denmark, Norway and Sweden are quite robust when using
the currency demand method. Hill and Kabir (1996) find for Canada that the rise of the shadow economy varies
with respect to the tax variable used; they conclude "when the theoretically best tax rates are selected and a range
of plausible velocity values is used, this method estimates underground economic growth between 1964 and
1995 at between 3 and 11 percent of GDP." (Hill and Kabir [1996, p. 1553]).
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money in the official economy, and the velocity of money in the hidden sector is even
more difficult to estimate. Without knowledge about the velocity of currency in the
shadow economy, one has to accept the assumption of "equal" money velocity in both
sectors.
(vi)

Ahumada, Alvaredo, Canavese A. and P. Canavese (2004) show that the currency
approach, together with the assumption of equal income velocity of money in both the
reported and the hidden transaction is only correct if the income elasticity is 1. As this
is not the case for most countries, the calculation has to be corrected.

(vii)

Finally, the assumption of no shadow economy in a base year is open to criticism.
Relaxing this assumption would again imply an upward adjustment of the size of the
shadow economy.

6.2.5

The Physical Input (Electricity Consumption) Method

(1) The Kaufmann – Kaliberda Method48)

To measure overall (official and unofficial) economic activity in an economy, Kaufmann and
Kaliberda (1996) assume that electric-power consumption is regarded as the single best
physical indicator of overall (or official plus unofficial) economic activity. Now, overall
economic activity and electricity consumption have been empirically observed throughout the
world to move in lockstep with an electricity to GDP elasticity usually close to one. This
means that the growth of total electricity consumption is an indicator for growth of overall
(official and unofficial) GDP. By having this proxy measurement for the overall economy and
then subtracting from this overall measure the estimates of official GDP, Kaufmann and
Kaliberda (1996) derive an estimate of unofficial GDP. This method is very simple and
appealing. However, it can also be criticized on various grounds:
(i)

Not all shadow economy activities require a considerable amount of electricity (e.g.
personal services), and other energy sources can be used (gas, oil, coal, etc.). Only a
part of the shadow economy will be captured.

(ii)

Over time, there has been considerable technical progress, so that both the production
and use of electricity are more efficient than in the past, and this will apply in both
official and unofficial uses.

48)

This method was used earlier by Lizzeri (1979), Del Boca and Forte (1982), and then was used much later by
Portes (1996), Kaufmann and Kaliberda (1996), Johnson, Kaufmann and Shleifer (1997). For a critique see
Lackó (1998).

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(iii)

There may be considerable differences or changes in the elasticity of electricity/GDP
across countries and over time.49)

(2) The Lackó Method

Lackó (1996, 1998, 1999, 2000) assumes that a certain part of the shadow economy is
associated with the household consumption of electricity. This part comprises the so-called
household production, do-it-yourself activities, and other non registered production and
services. Lackó further assumes that in countries where the portion of the shadow economy
associated with the household electricity consumption is high, the rest of the hidden economy
(or the part Lackó cannot measure) will also be high. Lackó (1996, pp.19 ff.) assumes that in
each country a part of the household consumption of electricity is used in the shadow
economy.
Lackó’s approach (1998, p.133) can be described by the following two equations:
ln Ei
with

= α1 ln Ci + α2 ln PRi + α3 Gi + α4 Qi + α5 Hi + ui (1)
α1 > 0, α2 < 0, α3 > 0, α4 < 0, α5 > 0
= β1 Ti + β2 (Si – Ti) + β3 Di

Hi

(2)

with β1 > 0, β2 < 0, β3 > 0
where
i: the number assigned to the country,
Ei: per capita household electricity consumption in country i in Mtoe,
Ci: per capita real consumption of households without the consumption of electricity in
country i in US dollars (at purchasing power parity),
PRi: the real price of consumption of 1 kWh of residential electricity in US dollars (at
purchasing power parity),
Gi: the relative frequency of months with the need of heating in houses in country i,
Qi: the ratio of energy sources other than electricity energy to all energy sources in household
energy consumption,
Hi: the per capita output of the hidden economy,
Ti: the ratio of the sum of paid personal income, corporate profit and taxes on goods and
services to GDP,
Si: the ratio of public social welfare expenditures to GDP, and
49)

Johnson, Kaufmann and Shleifer (1997) make an attempt to adjust for changes in the elasticity of
electricity/GDP.

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45

Di: the sum of dependants over 14 years and of inactive earners, both per 100 active earners.
In a cross country study, Lackó econometrically estimates equation (1) substituting Hi by
equation (2). The econometric estimation results can then be used to establish an ordering of
the countries with respect to electricity use in their respective shadow economies. For the
calculation of the actual size (value added) of the shadow economy, Lackó must, furthermore,
know how much GDP is produced by one unit of electricity in the shadow economy of each
country. Since these data are not known, she takes the result of one of the known shadow
economy estimations carried out for a market economy with another approach for the early
1990s, and she applies this proportion to the other countries. Lackó used the shadow economy
of the United States as such a base (the shadow economy value of 10.5% of GDP taken from
Morris (1993)), and then she calculates the size of the shadow economy for other countries.
Lackó’s method is also open to criticism:
(i)

Not all shadow economy activities require a considerable amount of electricity and
other energy sources can be used.

(ii)

Shadow economy activities do not take place only in the household sector.

(iii)

It is doubtful whether the ratio of social welfare expenditures can be used as the
explanatory factor for the shadow economy, especially in transition and developing
countries.

(iv)

It is questionable which is the most reliable base value of the shadow economy in
order to calculate the size of the shadow economy for all other countries, especially for
the transition and developing countries.

6.3

The Model Approach50

All methods described so far that are designed to estimate the size and development of the
shadow economy 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, labour, and money markets. An even more important critique is that the causes
50)

This part is derived from a longer study by Aigner, Schneider, and Ghosh (1988, p. 303), applying this
approach for the United States over time; for Germany this approach has been applied by Karmann (1986 and
1990). The pioneers of this approach are Weck (1983), Frey and Weck-Hannemann (1984), who applied this
approach to cross-section data from the 24 OECD countries for various years. Before turning to this approach
they developed the concept of "soft modeling" (Frey, Weck, and Pommerehne (1982), Frey and Weck (1983a
and 1983b)), an approach which has been used to provide a ranking of the relative size of the shadow economy
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46

that determine the size of the shadow economy are taken into account only in some of the
monetary approach studies that usually consider one cause, the burden of taxation. The model
approach explicitly considers multiple causes leading to the existence and growth of the
shadow economy, as well as the multiple effects of the shadow economy over time.
The empirical method used is quite different from those used so far. It is based on the
statistical theory of unobserved variables, which considers multiple causes and multiple
indicators of the phenomenon to be measured. For the estimation, a factor-analytic approach
is used to measure the hidden economy as an unobserved variable over time. The unknown
coefficients are estimated in a set of structural equations within which the "unobserved"
variable cannot be measured directly. The DYMIMIC (dynamic multiple-indicators multiplecauses) model consists in general of two parts, with the measurement model linking the
unobserved variables to observed indicators.51) The structural equations model specifies
causal relationships among the unobserved variables. In this case, there is one unobserved
variable, or the size of the shadow economy; this is assumed to be influenced by a set of
indicators for the shadow economy’s size, thus capturing the structural dependence of the
shadow economy on variables that may be useful in predicting its movement and size in the
future. The interaction over time between the causes Zit (i = 1, 2, …, k) the size of the shadow
economy Xt, in time t and the indicators Yjt (j = 1, 2, …, p) is shown in Figure 6.1.
Figure 6.1: Development of the shadow economy over time.

Causes

Xt-1

Indicators

↓
Z1t
Z2t
...

Y1t
Development of the shadow
economy over time

Y2t

Xt

...

Zkt

Ypt

in different countries.
51)
The latest papers dealing extensively with the DYMIMIC or MIMIC approach, its development and its
weaknesses are from Del’Anno (2003) and the excellent study by Giles and Tedds (2002), as well as Breusch
(2005a, 2005b), Schneider (2005), and Pickhardt and Sarda-Pous (2006).
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47

There is a large body of literature52) on the possible causes and indicators of the shadow
economy, in which the following three types of causes are distinguished:

Causes
(i)

The burden of direct and indirect taxation, both actual and perceived. A rising burden
of taxation provides a strong incentive to work in the shadow economy.

(ii)

The burden of regulation as proxy for all other state activities. It is assumed that
increases in the burden of regulation give a strong incentive to enter the shadow
economy.

(iii)

The "tax morality" (citizens’ attitudes toward the state), which describes the readiness
of individuals (at least partly) to leave their official occupations and enter the shadow
economy: it is assumed that a declining tax morality tends to increase the size of the
shadow economy.53)

Indicators
A change in the size of the shadow economy may be reflected in the following indicators:
(i)

Development of monetary indicators. If activities in the shadow economy rise,
additional monetary transactions are required.

(ii)

Development of the labour market. Increasing participation of workers in the hidden
sector results in a decrease in participation in the official economy. Similarly,
increased activities in the hidden sector may be expected to be reflected in shorter
working hours in the official economy.

(iii)

Development of the production market. An increase in the shadow economy means
that inputs (especially labour) move out of the official economy (at least partly), and
this displacement might have a depressing effect on the official growth rate of the
economy.

The latest use of the model approach has been undertaken by Giles (1999a, 1999b, 1999c) and
by Giles, Tedds and Werkneh (2002), Giles and Tedds (2002), Chatterjee, Chaudhury and
Schneider (2006), Bajada and Schneider (2005), and Pickhardt and Sarda-Pous (2006). They
52)

Thomas (1992); Schneider (1994a, 1997, 2003, 2005); Pozo (1996); Johnson, Kaufmann and Zoido-Lobatón
(1998a, 1998b); Giles (1997a, 1997b, 1999a, 1999b, 1999c); Giles and Tedds (2002), Giles, Tedds and Werkneh
(2002), Del’Anno (2003) and Del’Anno and Schneider (2004).
53)
When applying this approach for European countries, Frey and Weck-Hannemann (1984) had difficulty in
obtaining reliable data for the cause series, besides the ones for the direct and indirect tax burden. Hence, their
study was criticized by Helberger and Knepel (1988), who argue that the results were unstable with respect to
changing variables in the model and over the years.
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48

basically estimate a comprehensive (sometime dynamic) MIMIC model to get a time series
index of the hidden/measured output of New Zealand, Canada, Germany, India or Australia,
and then estimate a separate "cash-demand model" to obtain a benchmark for converting this
index into percentage units. Unlike earlier empirical studies of the hidden economy, they paid
proper attention to the non-stationary, and possible co-integration of time serious data in both
models. Again this DYMIMIC model treats hidden output as a latent variable, and uses
several (measurable) causal variables and indicator variables. The former include measures of
the average and marginal tax rates, inflation, real income and the degree of regulation in the
economy. The latter include changes in the (male) labour force participation rate and in the
cash/money supply ratio. In their cash-demand equation they allow for different velocities of
currency circulation in the hidden and recorded economies. Their cash-demand equation is not
used as an input to determine the variation in the hidden economy over time – it is used only
to obtain the long-run average value of hidden/measured output, so that the index for this ratio
predicted by the DYMIMIC model can be used to calculate a level and the percentage units of
the shadow economy. Overall, this latest combination of the currency demand and DYMIMIC
approach clearly shows that some progress in the estimation technique of the shadow
economy has been achieved and a number of critical points have been overcome.
However, objections can also be raised against the (DY)MIMIC method, i.e.:
(1) instability in the estimated coefficients with respect to sample size changes,
(2) instability in the estimated coefficients with respect to alternative specifications,
(3) difficulty of obtaining reliable data on cause variables other than tax variables, and
(4) the reliability of the variables grouping into "causes" and "indicators" in explaining the
variability of the shadow economy.
6.4

Summarizing the Critical Remarks

In table 6.2 some more general weaknesses/criticisms of the different methods of estimating
the shadow economy are summarized. Table 6.2 clearly shows that each method has its
strength and weaknesses and that we are far away from having an ideal or most preferred
estimation method. When undertaking the difficult and challenging task of estimating the
shadow economy, all methods have weaknesses and it is important to report and to consider
them, and to treat the size and development of shadow economy with great care.

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Table 6.2: Some critical Points of the Different Estimation Methods
1. Surveys
(1) Quite often only households or only partly firms are considered
(2) Non-responses and/or incorrect responses
2. Estimations of national account statisticians (quite often the discrepancy method):
(1) Combination of meso estimates/assumptions
(2) Often not published
(3) Documentation and procedures often not public
3. Monetary and/or electricity methods:
(1) Some estimates are very high
(2) Are the assumptions plausible?
(3) Breakdown by sector or industry possible?
4. DYMIMIC method
(1) only relative coefficients, no absolute values
(2) estimations quite often highly sensitive with respect to changes in the data.

6.5

The Size and Development of the Shadow Economies of 145 Countries
over 1999/2000 to 2004/2005

Finally, the results of the size and development of the shadow economies of 145 countries are
shown (and the countries are listed in alphabetical order) in table 6.3.
Table 6.3: The Size of the Shadow Economy of 145 Countries
Shadow Economy (in % of official GDP) using the DYMIMIC
and Currency Demand Method
No.

1
2
3
4
5
6
7
8
9
10
11

Country
Albania
Algeria
Angola
Argentina
Armenia
Australia
Austria
Azerbaijan
Bangladesh
Belarus
Belgium

1999/00
33.4
34.1
43.2
25.4
46.3
14.3
9.8
60.6
35.6
48.1
22.2

2001/02
34.6
35.0
44.1
27.1
47.8
14.1
10.6
61.1
36.5
49.3
22.0

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2002/03
35.3
35.6
45.2
28.9
49.1
13.5
10.9
61.3
37.7
50.4
21.0

2003/04
35.0
34.8
45.3
28.6
48.4
13.1
10.1
60.8
38.3
50.5
20.4

2004/05
34.3
33.9
45.0
27.2
47.6
12.8
9.3
59.4
38.0
50.8
19.6
50

Shadow Economy (in % of official GDP) using the DYMIMIC
and Currency Demand Method
Country
12 Benin
13 Bhutan
14 Bolivia
Bosnia and
15 Herzegovina
16 Botswana
17 Brazil
18 Bulgaria
19 Burkina Faso
20 Burundi
21 Cambodia
22 Cameroon
23 Canada
24 Central African Republic
25 Chad
26 Chile
27 China
28 Colombia
29 Congo, Dem. Rep.
30 Costa Rica
31 Cote d'Ivoire
32 Croatia
33 Czech Republic
34 Denmark
35 Dominican Republic
36 Ecuador
37 Egypt, Arabian Republic
38 El Salvador
39 Estonia
40 Ethiopia
41 Fiji
42 Finland
43 France
44 Georgia
45 Germany
46 Ghana
47 Greece
48 Guatemala
49 Guinea
50 Haiti
51 Honduras
52 Hong Kong, China
53 Hungary
54 India
55 Indonesia
56 Iran, Islamic Republic
57 Ireland
58 Israel
59 Italy
60 Jamaica

No.

1999/00
47.3
29.4
67.1

2001/02
48.2
30.5
68.1

2002/03
49.1
31.7
68.3

2003/04
49.3
32.7
68.0

2004/05
49.8
33.1
67.2

34.1
33.4
39.8
36.9
41.4
36.9
50.1
32.8
16.0
44.3
46.2
19.8
13.1
39.1
48.0
26.2
43.2
33.4
19.1
18.0
32.1
34.4
35.1
46.3
38.4
40.3
33.6
18.1
15.2
67.3
16.0
41.9
28.7
51.5
39.6
55.4
49.6
16.6
25.1
23.1
19.4
18.9
15.9
21.9
27.1
36.4

35.4
33.9
40.9
37.1
42.6
37.6
51.3
33.7
15.8
45.4
47.1
20.3
14.4
41.3
48.8
27.0
44.3
34.2
19.6
17.9
33.4
35.1
36.0
47.1
39.2
41.4
34.3
18.0
15.0
67.6
16.3
42.7
28.5
51.9
40.8
57.1
50.8
17.1
25.7
24.2
21.8
19.4
15.7
22.8
27.0
37.8

36.7
34.6
42.3
38.3
43.3
38.7
52.4
34.9
15.2
46.1
48.0
20.9
15.6
43.4
49.7
27.8
45.2
35.4
20.1
17.3
34.1
36.7
36.9
48.3
40.1
42.1
35.1
17.4
14.5
68.0
16.8
43.6
28.2
52.4
41.3
58.6
51.6
17.2
26.2
25.6
22.9
19.9
15.3
23.9
25.7
38.9

36.2
34.2
42.6
37.4
43.8
39.4
52.9
34.4
14.8
46.3
48.4
20.3
16.1
43.0
50.4
27.1
45.4
34.7
19.2
16.7
34.4
36.1
36.3
48.1
39.1
42.7
34.6
16.4
13.8
67.3
16.1
43.8
27.4
51.1
41.7
59.3
50.8
16.4
25.3
25.9
23.6
20.2
14.8
23.2
24.8
39.2

35.3
33.8
41.8
36.5
43.1
39.7
52.2
33.6
14.1
46.9
47.8
19.4
16.6
42.7
50.8
26.3
44.7
34.1
18.3
16.1
34.8
35.2
35.4
47.2
38.2
42.0
33.8
15.8
13.2
66.4
15.3
43.2
26.3
50.3
41.0
59.6
49.3
15.6
24.3
25.1
24.0
19.7
14.1
22.6
23.2
38.4

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51

Shadow Economy (in % of official GDP) using the DYMIMIC
and Currency Demand Method
No.

61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110

Country
Japan
Jordan
Kazakhstan
Kenya
Kiribati
Korea, Republic
Kuwait
Kyrgyz Republic
Lao PDR
Latvia
Lebanon
Lesotho
Lithuania
Macedonia, FYR
Madagascar
Malawi
Malaysia
Maldives
Mali
Marshall Islands
Mauritania
Mexico
Micronesia, Fed. Sts.
Moldova
Mongolia
Morocco
Mozambique
Namibia
Nepal
Netherlands
New Zealand
Nicaragua
Niger
Nigeria
Norway
Oman
Pakistan
Palau
Panama
Papua New Guinea
Paraguay
Peru
Philippines
Poland
Portugal
Puerto Rico
Republic of Congo
Romania
Russian Federation
Rwanda

1999/00
11.2
19.4
43.2
34.3
34.1
27.5
20.1
39.8
30.6
39.9
34.1
31.3
30.3
34.1
39.6
40.3
31.1
30.3
42.3
28.1
36.1
30.1
31.3
45.1
18.4
36.4
40.3
31.4
38.4
13.1
12.8
45.2
41.9
57.9
19.1
18.9
36.8
28.4
64.1
36.1
27.4
59.9
43.4
27.6
22.7
28.4
48.2
34.4
46.1
40.3

2001/02
11.1
20.5
44.1
35.1
35.0
28.1
20.7
40.3
31.9
40.7
35.6
32.4
31.4
35.1
40.4
41.2
31.6
31.4
43.9
29.0
37.2
31.8
32.1
47.3
19.6
37.1
41.3
32.6
39.7
13.0
12.6
46.9
42.6
58.6
19.0
19.4
37.9
29.2
65.1
37.3
29.2
60.3
44.5
28.2
22.5
29.4
49.1
36.1
47.5
41.4

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2002/03
10.8
21.6
45.2
36.0
35.3
28.8
21.6
41.2
33.4
41.3
36.2
33.3
32.6
36.3
41.6
42.1
32.2
32.0
44.7
29.6
38.0
33.2
33.2
49.4
20.4
37.9
42.4
33.4
40.8
12.6
12.3
48.2
43.8
59.4
18.4
19.8
38.7
30.0
65.3
38.6
31.4
60.9
45.6
28.9
21.9
30.7
50.1
37.4
48.7
42.2

2003/04
9.4
21.2
45.4
35.4
34.8
28.2
21.2
41.4
33.9
40.4
36.5
32.8
31.3
36.8
41.9
42.7
32.0
31.6
44.0
28.7
37.4
32.6
32.6
49.5
20.6
37.3
42.9
33.0
40.2
12.0
11.6
48.8
44.1
59.6
17.6
19.2
39.2
29.2
64.1
38.0
32.4
59.1
45.1
28.2
21.1
29.6
50.5
36.2
48.2
42.4

2004/05
8.8
20.4
44.6
34.8
34.0
27.6
20.7
40.6
33.2
39.4
37.1
32.3
30.2
36.9
41.2
41.9
31.4
30.9
43.2
27.9
36.8
31.7
31.9
49.1
21.2
36.7
43.5
32.4
39.3
11.1
10.9
48.1
44.2
59.5
16.8
18.6
39.5
28.4
62.2
37.3
33.1
58.2
44.3
27.3
20.4
28.2
51.1
35.4
47.3
41.6
52

Shadow Economy (in % of official GDP) using the DYMIMIC
and Currency Demand Method
Country
111 Samoa
112 Saudi Arabia
113 Senegal
114 Serbia and Montenegro
115 Sierra Leone
116 Singapore
117 Slovak Republic
118 Slovenia
119 Solomon Islands
120 South Africa
121 Spain
122 Sri Lanka
123 Sweden
124 Switzerland
125 Syrian Arab Republic
126 Taiwan, China
127 Tanzania
128 Thailand
129 Togo
130 Tonga
131 Tunisia
132 Turkey
133 Uganda
134 Ukraine
135 United Arab Emirates
136 United Kingdom
137 United States
138 Uruguay
139 Uzbekistan
140 Vanuatu
141 Venezuela, RB
142 Vietnam
143 Yemen, Rep.
144 Zambia
145 Zimbabwe
Unweighted Average
No.

1999/00
31.4
18.4
45.1
36.4
41.7
13.1
18.9
27.1
33.4
28.4
22.7
44.6
19.2
8.6
19.3
25.4
58.3
52.6
35.1
35.1
38.4
32.1
43.1
52.2
26.4
12.7
8.7
51.1
34.1
30.9
33.6
15.6
27.4
48.9
59.4
33.6

2001/02
32.6
19.1
46.8
37.3
42.8
13.4
19.3
28.3
34.5
29.1
22.5
45.9
19.1
9.4
20.4
26.6
59.4
53.4
39.2
36.3
39.1
33.2
44.6
53.6
27.1
12.5
8.7
51.4
35.7
31.7
35.1
16.9
28.4
49.7
61.0
34.5

2002/03
33.5
19.7
47.5
39.1
43.9
13.7
20.2
29.4
35.3
29.5
22.0
47.2
18.3
9.4
21.6
27.7
60.2
54.1
40.4
37.4
39.9
34.3
45.4
54.7
27.8
12.2
8.4
51.9
37.2
32.5
36.7
17.9
29.1
50.8
63.2
35.2

2003/04
33.1
19.3
47.8
38.2
44.1
13.0
19.1
28.2
34.6
29.0
21.2
48.3
17.2
9.0
21.7
27.0
59.1
54.3
40.6
36.8
39.4
33.9
45.8
54.9
27.2
11.7
8.2
50.8
36.3
32.0
36.1
16.9
28.2
50.2
63.9
34.9

2004/05
32.8
18.4
48.2
37.3
44.3
12.1
18.2
27.3
34.0
28.2
20.5
48.8
16.3
8.5
21.2
26.3
58.2
53.6
39.4
35.8
38.3
33.2
44.9
55.3
26.5
10.3
7.9
49.2
35.4
31.4
35.4
16.1
27.3
49.3
64.6
34.3

Source: Own calculations.

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53

7 Appendix 2: Definition of the variables and data sources
(1) GDP per capita on PPP basis
GDP per capita is based on purchasing power parity [PPP]. PPP GDP is gross domestic
product converted to international dollars using purchasing power parity rates. An
international dollar has the same purchasing power over GDP as the U.S. dollar has in the
United States. GDP is the sum of gross value added by all resident producers in the economy
plus any product taxes and minus any subsidies not included in the value of the products. It is
calculated without making deductions for depreciation of fabricated assets or for depletion
and degradation of natural resources. Data are in current dollars.
Source: World Bank, International Comparison Programme database.

(2) Annual GDP per capita Growth Rate
Out of this GDP per capita values for the observed 145 countries the independent variable
annual GDP per capita Growth Rate has been calculated using the formula

Per Capita Growth =

(GDPpct − GDPpct −1 )
GDPpct −1

Source: World Bank, International Comparison Programme database; own calculation by
authors.

(3) Shadow Economy

The variable Shadow Economy is defined as the informal sector [shadow economy] in percent
of official GDP. The estimations for the size of the shadow economy are undertaken using the
DYMIMIC and the currency demand approaches; using the values calculated in section 3.
This variable is available for five points in time namely the years 1999/00, 2001/02, 2002/03,
2003/04 and 2004/05.
Source: Own calculation by the author.

(4) Share of Direct Taxation (in % of GDP)

Source: OECD, Paris 2003, Taxing Wages and World Bank (Washington D.C.), 2003,
Governance Indicators.

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(5) Share of Indirect Taxation and Custom Duties in % of GDP

Source: See Share of Direct Taxation.

(6) Burden of State Regulation

Burden of State regulation, index of regulation, where a score of 1 signifies an economic
environment most conductive to economic freedom, whereas a score of 5 signifies least
economic freedom. Source: Heritage Foundation 2005, Index of Economic Freedom,
Washington, D.C.

(7) Employment Quota (in % of population between 18 and 64)

Source: OECD, Paris, various years, Employment Outlook.

(8) Unemployment Quota (% of unemployed in the working force)

Source: OECD various years, Employment Outlook.

(9) Change of Currency per Capita, Annual Rate of Currency per Capita

Source: World Bank National Accounts Data and OECD National Accounts Data Files,
Washington and Paris, various years.

(10) Tax Morale (Index)

Source: European Values Study, EUROPEAN VALUES STUDY, various years/Release 1,
The

Netherlands,

Germany:

Tilburg

University,

Zentralarchiv

für

Empirische

Sozialforschung, Cologne (ZA), Netherlands Institute for Scientific Information Services
(NIWI),

Amsterdam

[producer],

2006.

Germany:

Zentralarchiv

für

Empirische

Sozialforschung, Cologne [distributor], 2006. Inglehart, Ronald et.al. World Values Surveys
and European Values Surveys, 1981-1984, 1990-1993 and 1995-1997 [Computer file].

(11) Quality of Institutions

index =0 lowest quality, =100 highest quality, Source World Bank, various years.

(12) Social Security Burden

Definition: social security payments (employers and employees) in % of GDP, Source OECD,
Paris, various years.
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55

(13) Quality of State Institutions

Quality of state institutions, World Bank rule of law index, ranges from -3 to +3, with higher
scores showing better environments, i.e. the higher the score the better is the rule of law in
that respective country. Source: Index development; Kaufmann, D.; Kraay, A. and M.
Mastruzzi, 2003, Governments Matters III: Governments Indicators for 1996/2002, World
Bank Policy Research Working Papers 3106, World Bank, Washington D.C.; source index
World Bank, Washington, various years.

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