1.  Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA
ShadEcTaxBenefit.ppt Phone: Fax:
Prof. Dr. DDr.h.c. Friedrich Schneider Department of Economics Johannes Kepler University of Linz A-4040 Linz-Auhof
Recent Experience in the Use of Surveys and other Methods Measuring and Understanding the Shadow Economy, Tax and Benefit Fraud
February 2008
 Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA

2.  Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA
Introduction: Defining the Shadow Economy
The Size of the Shadow Economies of OECD and EU countries
2.1 DIMIMIC – Results of 21 OECD countries
2.2 Survey Results of 27 EU countries
2.3 Survey Results – Germany
2.4 Individual Analysis of Tax and Benefit Morale for Austria
Methods to Estimate the Size of the Shadow Economy:
3.1 Direct Approaches
3.2 Indirect Approaches
3.3 The Latent Estimation Approach
Concluding Remarks: Problems and Open Questions
4.1 Surveys
4.2 Estimations of national account statisticans
4.3 Monetary and/or electricity methods
4.4 DYMIMIC method
4.5 What did we learn?
February 2008
 Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA

3. 1. The Definition of the Shadow Economy
(1.1) The shadow economy includes all legal production and provision of goods and services that are deliberately concealed from public authorities for the following four reasons:
(i) To avoid payment of income, value added or other taxes,
(ii) To avoid payment of social security contributions,
(iii) To avoid having to meet certain legal standards such as minimum wages, maximum hours, safety standards, etc., and
(iv) To avoid complying with certain administrative procedures, such as completing statistical questionnaires or other administrative forms.
February 2008
 Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA

4. 1. The Definition of the Underground and Informal Household Economy
(1.2) Underground (classical crime) activities are all illegal actions that fit the characteristics of classical crime activities like burgarly, robbery, drug dealing, etc.
(1.3) Informal household economy consists of household enterprises that are not registered officially under various specific forms of national legislation.
(1.4) To a large extent these two sectors ((1.2) classical crime and (1.3) household production) are not included in the shadow economy activities.
February 2008
 Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA

5. Figure 1.1 Legal, Shadow, Illegal and Informal Economy
Illegal (criminal) underground economy
Shadow economy
Informal household economy
Legal/official economy
February 2008
 Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA

6.  Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA
2.1 The Size of the Shadow Economies: Econometric (DYMIMIC) Estimates for 21 OECD countries
Table DYMIMIC Estimation of the Shadow Economy of 21 highly developed OECD Countries, years 1990/91 to 2004/05– PART 1
February 2008
 Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA

7.  Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA
Notes:
t-statistics are given in parentheses (*); *; ** means the t-statistics is 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 is given for a all three equations in tables 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.
Table DYMIMIC Estimation of the Shadow Economy of 21 highly developed OECD Countries, years 1990/91 to 2004/05 – PART 2
February 2008
 Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA

8.  Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA
Figure Size of the Shadow Economy in 21 OECD COUNTRIES, in % of GDP, 2007
February 2008
 Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA
Source: Own calculations.

9. 2.2 Survey Results – 27 EU Countries
Eurobarometer pilot survey on undeclared work (UDW) in May/June 2007 done by tns Infratest Munich, 2007.
It was designed to empirically test the feasibility of a direct survey on undeclared work.
Source: tns Infratest, Munich 2007
February 2008
 Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA

10. Figure 2.2.1 Demand side – Total EU 27
2.2 Survey Results – 27 EU Countries
Figure Demand side – Total EU 27
"Have you in the last twelve months acquired any goods/services of
which you had a good reason to assume that they embodied undeclared work?"
Base: total population aged 15+
Source: tns Infratest, Munich, 2007
February 2008
 Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA

11. Figure 2.2.2 Demand side– Demand by country
2.2 Survey Results – 27 EU Countries
Figure Demand side– Demand by country
"Have you in the last twelve months acquired any goods/services
of which you had a good reason to assume that they embodied
undeclared work?"
EU-27
Source: tns Infratest, Munich, 2007
Base: total population aged 15+
February 2008
 Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA

12. 2.2 Survey Results – 27 EU Countries Base: total population aged 15+
Figure Supply side
"Did you yourself carry out any undeclared activities in the last 12 months for which you were paid in money or in kind? Herewith we mean again activities which were not or not fully reported to the tax or social security authorities and where the person who acquired the good or services was aware of this?"
Base: total population aged 15+
Source: tns Infratest, Munich, 2007
February 2008
 Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA

13. Figure 2.2.4 Supply side – Envelope wages
2.2 Survey Results – 27 EU Countries
Figure Supply side – Envelope wages
"Sometimes employers prefer to pay all our part of the regular salary or the remuneration for extra work or overtime hours cash-in-hand and without declaring it to tax or social security authorities. Did your employer pay all or part of your income in the last 12 months in this way?"
Base: DEPENDENT EMPLOYEES ONLY!
Source: tns Infratest, Munich, 2007
February 2008
 Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA

14. Figure 2.2.5 Supply side – Total supply by country
2.2 Survey Results – 27 EU Countries
Figure Supply side – Total supply by country
EU-27
Source: tns Infratest, Munich, 2007
Base: total population aged 15+
February 2008
 Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA

15. Figure 2.2.6 Comparison of supply and demand I
2.2 Survey Results – 27 EU Countries
Figure Comparison of supply and demand I
Certain discrepancy is plausible (UDW supposedly often done for more than one client)
Extraordinarily large differences in a country can e.g. indicate that…
large parts of UDW are done by suppliers with many clients (e.g. firms/self-employed)
large parts of UDW done by groups of people hardly/not captured by the survey (e.g. immigrants)
to buy UDW is much more accepted than to perform UDW
Source: tns Infratest, Munich, 2007
Base: total population aged 15+
February 2008
 Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA

16. 2.2 Survey Results – 27 EU Countries
Overall Evaluation
First attempt to measure undeclared work (UDW) European wide by the method of a survey; hence, character of a pilot study.
Face to face interviews and the number of cases per country is quite small. Consequence: Results are tentative, especially with respect to a deep analyses of the structures of undeclared work.
This questionnaire was never tested before in Eastern and Southern Europe. It was tested in a similar form in Denmark, Sweden, Germany, and the U.K.
February 2008
 Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA

17. 2.3 Survey Results: Germany
Table Do you regularly work in the shadow economy? (yes or no)? Germany, 2007
(1) Do you work regularly in the shadow economy?
Values in percent No
Yes
No answer
77,3
20,7
(25% male, 16% female) 2
(2) Do you regularly demand shadow economy activities?
69,2
30,8
(35.4% male, 26.5% female)
Representative questionnaire, Germany, January 2007
Source: IDW Koeln, Germany
February 2008
 Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA

18. 2.3 Survey Results: Germany
Table Reasons, why shadow economy activities are demanded, Germany, 2007
Reasons why shadow economy activities are demanded
Values in percent
(1) One saves money – or they are much cheaper than the official ones
(2) The tax and social security burden is much too high
(3) Due to the high labour costs in the official economy one would not demand these activities (extreme assumption: no shadow economy – 22% demand in the official economy; 30% do-it-themselves; and 48% no demand at all!)
(4) The firms offer them themselves
(5) It‘s so easy to get quick and reliable workers
90%
73%
68%
52%
31%
Representative questionnaire, Germany, January 2007, Source: IDW Koeln
February 2008
 Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA

19. Table A comparison of the Size of the German Shadow Economy using the survey and the DYMIMIC-method, year 2006
Various kinds of shadow economy activities/values
Shadow Economy in % of official GDP
Shadow Economy in bill. Euro
% share of the overall shadow economy
Shadow economy activities from labour (hours worked, survey results)
+ Material (used)
+ Illegal activities (goods and services)
+ already in the official GDP included illegal activities
5.0 – 6.0
3.0 – 4.0
4.0 – 5.0
1.0 – 2.0
117 – 140
70 – 90
90 – 117
23 – 45
33 – 40
20 – 25
25 – 33
7 - 13
Sum (1) to (4)
13.0 – 17.0
300 – 392
85 – 111
Overall (total) shadow economy (estimated by the DYMIMIC and calibrated by the currency demand procedure)
15.0
340
100
Source: Enste/Schneider (2006) and own calculations.
February 2008
 Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA

20. 2.3 Survey Results – Germany
Some remarks when comparing the values from the survey method with the total value added in the shadow economy sector achieved by the DYMIMIC method. The rather large difference can be “explained” with the following facts:
Table contains earnings and not the value added of the shadow economy. This means material is not considered.
Demanders are overwhelmingly households, the whole sector of the shadow economy activities between firms (which are especially a problem in the construction and craftsmen sectors) is not considered.
All foreign shadow economy activities are not considered.
The amount of income earned in the shadow economy, the hourly wage rate and hours worked per year vary considerably.
February 2008
 Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA

21. Shadow economy (in percentage of official GDP) in:
Table The Size of the Shadow Economy in Germany According to Different Methods (in percentage of official GDP)
Method/Source
Shadow economy (in percentage of official GDP) in:
1970
1975
1980
1985
1990
1995
2000
2005
Survey/IfD Allensbach, 1975 -
3.6 1)
4.1 2)
3.1 2)
1.3 3)
1.0 3)
Discrepancy between expenditure and income/(Lippert and Walker, 1997)
11.0
10.2
13.4
Discrepancy between official and actual employment/(Langfeldt, 1983)
23.0
38.5
34.0
Physical input method
(Feld and Larsen, 2005)
14.5
14.6
Transactions approach Feige (1989)
17.2
22.3
29.3
31.4
Currency demand approach/ (Kirchgässner, 1983)
(Langfeldt, 1983, 1984)
Schneider and Enste (2000)
3.1
6.0
10.3
12.1
11.8
12.6
4.5
7.8
9.2
11.3
12.5
14.7
Latent ((DY)MIMIC) approach/(Frey and Weck (1983))
Pickardt and Sarda (2006)
Schneider (2005, 2007)
5.8
6.1
8.2
9.4
10.1
11.4
15.1
16.3
4.2
10.8
11.2
12.2
13.9
16.0
15.4
Soft modelling/(Weck-Hannemann (1983))
8.3 4)
Feld and Larsen, 2005
Feld and Larsen, 2005
1) 1974; 2) 2001 and 2004; calculated using wages in the official economy; 3) 2001 and 2004; calculated using actual “black” hourly wages paid.
February 2008
 Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA

22. 2.4 Individual Analyis of Tax and Benefit Moral for Austria
2.4.1 Three Research Questions
Determinants of benefit morale?
 Taxpayer’s attitude toward claiming unjustified government benefits/subsidies.
Determinants of tax morale?
 Taxpayer’s attitude toward avoiding taxes.
Impact of Benefit - & tax morale on actual behavior (income).
Source: Martin Halla & Friedrich G. Schneider (2005).
February 2008
 Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA

23. 2.4 Individual Analyis of Tax and Benefit Moral for Austria
2.4.2 Tax Morale Literature
Intrinsic motivation (tax morale) is therefore a key determinant in understanding compliance behavior!
Using Canadian data Torgler (2003) shows that tax morale rises with financial satisfaction, age, patriotism, trust in government, religiosity and deteriorates with rising income.
Torgler and Schneider (2004) stress the influence of cultural differences in explaining tax morale and find strong evidence for interactions between culture and institutions.
Torgler and Schneider (2005) investigate tax morale in Austria. Societal variables (e.g. perceived tax evasion, patriotism, trust in legal system) are key determinants!
Source: Martin Halla & Friedrich G. Schneider (2005).
February 2008
 Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA

24. 2.4 Individual Analyis of Tax and Benefit Moral for Austria
2.4.2 Benefit Morale
To best knowledge of Halla and Schneider, no investigation of the individual taxpayer’s decision on claiming unjustified subsidies (e.g. by underreporting income) exists so far in the economic literature. Only Orviska and Hudson (2003) touch upon this issue.
From a theoretical point of view a subsidy is a negative tax, but…
…in empirical applications a distinction may be reasonable, since
the size of theoretical determinants (e.g. probability of detection) can differ,
Individuals may have different ‘opportunities’. Some may not have the opportunity to avoid taxes, but it may be feasible for them to claim unjustified subsidies,
Individuals may have distinct basic attitudes towards these two issues.
Source: Martin Halla & Friedrich G. Schneider (2005).
February 2008
 Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA

25. 2.4 Individual Analyis of Tax and Benefit Moral for Austria
2.4.3 Data
Austrian data from the European Values Survey (1990 & 1999).
Surveys contain information on basic attitudes, beliefs and human values covering religion, morality, politics, work and leisure.
In particular respondents were asked to evaluate on a ten-point scale:
‘cheating on tax if you have the chance’ (…) ‘can always be justified, never be justified, or something in between.’  tax morale
‘claiming state benefits which you are not entitled to’ (…) ‘can always be justified, never be justified, or something in between’.  benefit morale
N = 2,982  After cleaning data: N = 1,887 (N1990 = 835, N1999 = 1,052).
Source: Martin Halla & Friedrich G. Schneider (2005).
February 2008
 Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA

26. 2.4 Individual Analyis of Tax and Benefit Moral for Austria
2.4.3 Data and Descriptive Statistics
Table 2.4.1: Descriptive Statistics
Variable
Mean
Std. Dev.
Min
Max
Tax morale*
8.92
1.86 1 10
Benefit morale*
9.09
1.66
Income (100 € p.m.)**
13.03
6.25
24.99
Spearman‘s Rho (tax morale, benefit morale) = 0.43
*Ten is the highest tax- & benefit morale.
** Income is given on a household basis (ten ranges). To account for inflation we assign the lower bound of each range.
Source: Martin Halla & Friedrich G. Schneider (2005).
February 2008
 Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA

27. 2.4 Individual Analyis of Tax and Benefit Moral for Austria
2.4.4 Estimation of the Determinants of Tax & Benefit Morale
Closely related issues; we apply a system of equations:
Tax morale = α1 + β11 * benefit morale + β11 * income + Γ1 * Χ1 + ε1
Benefit morale = α2 + β21 * tax morale + β22 * income + Γ2 * Χ2 + ε2
If stated attitudes are translated in actual behavior, income is endogenous too,…
… because simultaneity prevails!
Halla and Schneider instrument income with chief wage earners sex & job rank and the socio-economic status of the household.
Source: Martin Halla & Friedrich G. Schneider (2005).
February 2008
 Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA

28. 2.4 Individual Analyis of Tax and Benefit Moral for Austria
Table SLS Estimation of Tax and Benefit Morale
Tax morale
Benefit morale -
0.463
(0.220)**
0.607
(0.282)**
Income
-0.024
(0.013)*
0.002
(0.013)
Age
-0.004
(0.005)
0.011
(0.003)***
Female
0.003
(0.094)
0.122
(0.077)
Married
0.182
(0.105)*
-0.043
(0.109)
School leaving age
-0.022
0.001
Tax morale
Benefit morale
Employed
-0.083
(0.130)
0.226
(0.096)**
1999
0.141
(0.180)
-0.480
(0.090)***
Religious
0.136
(0.053)***
-0.006
(0.061)
Patriotic
0.157
(0.101)
0.132
(0.093)
Distrust legal system
-0.137
(0.054)** -
Children
0.073
(0.032)**
Volunteer
(0.083)*
Observations
1,887
Basmann Statisticb
0.237
0.584
Sargan Statisticc
0.232
0.580
α Standard errors are in parenthesis.
*, ** and *** indicate a statistical significance at the 10-percent-level, 5-percent level and 1-percent level.
Dummies for the nine Austrian provinces are included in each regression. Base group is Vorarlberg.
b P-value of Basmann‘s test of overidentifying restrictions of all instruments (Basmann, 1960).
c P-value of Sargan‘s tests of overidentifying restrictions of all instruments (Sargan, 1958).
 A different estimation strategy applying ordered probit estimations in both stages of the estimation of the tax- & benefit morale eqs. Deliver qualitatively very similar results.
February 2008
 Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA

29. 2.4 Individual Analyis of Tax and Benefit Moral for Austria
2.4.5 Estimation of Impact on Actual Behavior
Income = α3 + β31 * tax morale + β32 * benefit morale + Γ3 * Χ3 + ε3
A neg. significant coefficient of tax morale indicates actual noncompliance behavior,…
A neg. significant coefficient of benefit morale shows actual claims of unjustified subsidies,…
… because in both cases a higher net wage is ceteris paribus expected!
Durbin-Wu-Hausman-Test for endogeneity of tax- & benefit morale: P-value = 0.01
 Endogeneity prevails; we instrument for tax- and benefit morale.
Instruments are: Confidence in the legal system, voluntary labor & children.
Test(s) for overidentifying restrictions confirm the instruments (e.g. P-value of Basmann‘s test = 0.62)
Source: Martin Halla & Friedrich G. Schneider (2005).
February 2008
 Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA

30. 2.4 Individual Analyis of Tax and Benefit Moral for Austria
Table SLS Estimation on Income
Independent Variable
Dependent Variable: Income
Tax morale
1.610
(1.293)
Benefit morale
-2.916**
(1.309)
Chief wage earner‘s job rank
0.113
(0.096)
Chief wage earner is female
-3.410***
(0.475)
Socioeconomic status
4.382***
(0.294)
Year
-0.066
(0.597)
Vienna
-0.112
(0.713)
Tirol
-1.827***
(0.728)
Constant
12.477
(6.280)
α Standard errors are in parenthesis.
*, ** and *** indicate a statistical significance at the 10-percent-level, 5-percent level and 1-percent level.
Dummies for the nine Austrian provinces are included in each regression. Base group is Vorarlberg.
b P-value of Basmann‘s test of overidentifying restrictions of all instruments (Basmann, 1960).
c P-value of Sargan‘s tests of overidentifying restrictions of all instruments (Sargan, 1958).
 A different estimation strategy applying ordered probit estimations in both stages of the estimation of the tax- & benefit morale eqs. Deliver qualitatively very similar results.
Observations
1,887
Basmann Statisticb
0.622
Sargan Statisticc
0.620
Source: Martin Halla & Friedrich G. Schneider (2005).
February 2008
 Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA

31. 2.4 Individual Analyis of Tax and Benefit Moral for Austria
2.4.5 Results of the 2SLS Estimation on Income
Not statistically significant impact of tax morale on household income!
Statistically significant impact of benefit morale on household income!
- One point higher benefit morale reduces household income by 292 € p.m.
Households with female chief wage earners have about 341 € lower income p.m.
A higher socio-economic status (self reported, measured on four-point scale) is associated with 438 € more income p.m.
Source: Martin Halla & Friedrich G. Schneider (2005).
February 2008
 Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA

32. 2.4 Individual Analyis of Tax and Benefit Moral for Austria
2.4.6 Conclusions
Tax morale (basic attitude towards avoiding taxes) and benefit morale (basic attitude towards claiming unjustified subsidies) have different determinants.
Halla and Schneider found empirical evidence that tax morale and benefit morale have different impact on realized behavior. Only statistically significant effect of benefit morale on income.
Low (high) benefit morale leads indeed (not) to unjustified claims on benefits and therefore to higher (lower) income!
These differences can be explained by:
Different probability of detection,
By a different penalty rate or by
More opportunities to cheat on the state by unjustified subsidies in contrast to tax evasion.
Source: Martin Halla & Friedrich G. Schneider (2005).
February 2008
 Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA

33.  Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA
3) Methods to Estimate the Size of the Shadow Economy
3.1 Direct Approaches
3.2 Indirect Approaches
3.3 The Model/Latent Estimation Approaches
February 2008
 Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA

34. 3) Methods to Estimate the Size of the Shadow Economy
3.1 Direct Approaches
These micro approaches employ either well designed surveys and samples based on voluntary replies or tax auditing and other compliance methods.
3.1.1 Survey-method
3.1.2 Tax-auditing-method
February 2008
 Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA

35. 3) Methods to Estimate the Size of the Shadow Economy
3.2 Indirect Approaches
These approaches, which are also called “indicator” approaches, are mostly macroeconomic ones and use various (mostly economic) indicators that contain information about the development of the shadow economy (over time).
3.2.1 The Discrepancy between National Expenditure and Income Statistics
3.2.2 The Discrepancy between the Official and Actual Labor Force
3.2.3 The Transactions Approach
3.2.4 The Currency Demand Approach
3.2.5 The Physical Input (Electricity Consumption) Method
3.3 The Model/Latent Estimation Approach
February 2008
 Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA

36. 3.2.4 The Currency Demand Approach
3) Methods to Estimate the Size of the Shadow Economy
3.2.4 The Currency Demand Approach
The basic regression equation for the currency demand, proposed by Tanzi (1983), is the following:
ln (C / M2)t = bO + b1 ln (1 + TW)t + b2 ln (WS / Y)t + b3 ln Rt + b4 ln (Y / N)t + ut
with b1 > 0, b2 > 0, b3 < 0, b4 > 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 (as a 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.
February 2008
 Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA

37.  Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA
3) Methods to Estimate the Size of the Shadow Economy – The Currency Demand Approach Cont.
The most commonly raised objections (criticism) against the current demand approach are:
Not all transactions in the shadow economy are paid in cash. The size of the total shadow economy (including barter) may thus be larger.
Most studies consider only one particular factor, the tax burden, as a cause of the shadow economy. If other factors also have an impact on the extent of the hidden economy, the shadow economy may be higher.
Blades and Feige, criticize Tanzi’s studies on the grounds that the US dollar is used as an international currency, which has to be controlled.
Another weak point is the assumption of the same velocity of money in both types of economies.
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 for most countries not the case, the calculation has to be corrected.
Finally, the assumption of no shadow economy in a base year is open to criticism.
February 2008
 Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA

38. 3.3 Multiple Indicators, Multiple Causes (MIMIC) approach
3. Methods to Estimate the Size of the Shadow Economy – The MIMIC (Latent) Approach
3.3 Multiple Indicators, Multiple Causes (MIMIC) approach
The MIMIC approach explicitly considers several causes, as well as the multiple effects of the informal economy.
The methodology makes use of the associations between the observeable causes and the observable effects of an unobserved variable, in this case the informal economy, to estimate the unobserved factor itself.
Formally, the MIMIC model consists two parts:
The structural equation model describes the „relationship“ among the latent variable (informal economy = IE) and its causes.
The measurement model represents the link between the latent variable IE and its indicators; i.e. the latent variable (IE) is expressed in terms of observable variables.
February 2008
 Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA

39. Multiple Indicators, Multiple Causes (MIMIC) approach
3. Methods to Estimate the Size of the Shadow Economy – The MIMIC (Latent) Approach – cont.
Multiple Indicators, Multiple Causes (MIMIC) approach
The model for one latent variable (IE) can be described as follows:
IE = γ‘ x + ν (1) Structural equation model
γ =λ IE + ε (2) Measurement model
where IE is the unobservable scalar latent variable (the size of the informal economy), γ‘ = (γ1…, γp) is a vector of indicators for IE, x‘ = (x1,…xq) is a vector of causes of IE, λ and γ are the (px1) and (qx1) vectors of the parameters and ε and ν are the (px1) and scalar errors.
February 2008
 Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA

40. Figure 3.1 General structure of a MIMIC Model
3. Methods to Estimate the Size of the Shadow Economy – The MIMIC (Latent) Approach – cont.
Figure 3.1 General structure of a MIMIC Model
Indicators
Causes λ1
x1t γ1
y1t – ε1t γ2 λ2
x2t
y2t – ε2t
IEt … γq λp …
xqt
ypt – εpt
February 2008
 Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA

41. 3.3 Multiple Indicators, Multiple Causes (MIMIC) approach
3. Methods to Estimate the Size of the Shadow Economy – The MIMIC (Latent) Approach – cont.
3.3 Multiple Indicators, Multiple Causes (MIMIC) approach
Equation (1) links the informal economy with ist indicators or symptoms,
while equation (2) associates the informal economy with ist causes.
Assuming that these errors are normally distributed and mutually uncorrelated with var(ν) = σ2 ν and cov(ε) = Θε, the model can be solved for the reduced form as a function of observable variables by combining equations (1) and (2):
γ = π x + μ (3)
where π = λ γ‘ , μ = λ ν + ε and cov(μ) = λ λ‘ σ2ν + Θε.
February 2008
 Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA

42.  Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA
4. Concluding Remarks
4. Concluding Remarks: Problems and Open Questions
4.1 Surveys
Quite often only households or only partly firms are considered
Non-responses and/or incorrect responses
Results of the financial volume of „black“ hours worked and not of value added
4.2 Estimations of national account statisticians (quite often the discrepancy method):
Combination of meso estimates/assumptions
Calculation method often not clear
Documentation and procedures often not public
February 2008
 Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA

43.  Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA
4. Concluding Remarks
4. Concluding Remarks: Problems and Open Questions
4.3 Monetary and/or electricity methods:
Some estimates are very high
Are the assumptions plausible?
Breakdown by sector or industry not possible!
4.4 DYMIMIC method
Only relative coefficients, no absolute values.
Estimations quite often highly sensitive with respect to changes in the data and specifications.
Difficulty to differentiate between the selection of causes and indicators
February 2008
 Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA

44. 4. Concluding Remarks: Problems and Open Questions
4.5 What did we learn?
No ideal or dominating method – all have serious problems and weaknesses.
If possible use several methods.
Much more research is needed with respect to the estimation methodology and empirical results for different countries and periods.
Experimental methods should be used to provide a micro-foundation.
February 2008
 Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA