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February 2008  Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA 1 Recent Experience in the Use of Surveys and other Methods Measuring and Understanding.

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Presentation on theme: "February 2008  Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA 1 Recent Experience in the Use of Surveys and other Methods Measuring and Understanding."— Presentation transcript:

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

2 February 2008  Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA 2 1)Introduction: Defining the Shadow Economy 2)The Size of the Shadow Economies of OECD and EU countries 2.1DIMIMIC – Results of 21 OECD countries 2.2Survey Results of 27 EU countries 2.3 Survey Results – Germany 2.4 Individual Analysis of Tax and Benefit Morale for Austria 3)Methods to Estimate the Size of the Shadow Economy: 3.1Direct Approaches 3.2Indirect Approaches 3.3The Latent Estimation Approach 4)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?

3 February 2008  Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA 3 (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. 1. The Definition of the Shadow Economy

4 February 2008  Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA 4 (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. 1. The Definition of the Underground and Informal Household Economy

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

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

7 February 2008  Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA 7 Table DYMIMIC Estimation of the Shadow Economy of 21 highly developed OECD Countries, years 1990/91 to 2004/05 – PART 2 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 ) 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.

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

9 February 2008  Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA 9 (1)Eurobarometer pilot survey on undeclared work (UDW) in May/June 2007 done by tns Infratest Munich, (2)It was designed to empirically test the feasibility of a direct survey on undeclared work. 2.2 Survey Results – 27 EU Countries Source: tns Infratest, Munich 2007

10 February 2008  Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA 10 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, Survey Results – 27 EU Countries

11 February 2008  Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA 11 EU-27 Base: total population aged 15+ Figure Demand side– Demand by country Source: tns Infratest, Munich, 2007 "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?" 2.2 Survey Results – 27 EU Countries

12 February 2008  Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA 12 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, Survey Results – 27 EU Countries

13 February 2008  Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA 13 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, Survey Results – 27 EU Countries

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

15 February 2008  Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA 15 Figure Comparison of supply and demand I (1)Certain discrepancy is plausible (UDW supposedly often done for more than one client) (2)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 Survey Results – 27 EU Countries

16 February 2008  Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA 16 Overall Evaluation (1)First attempt to measure undeclared work (UDW) European wide by the method of a survey; hence, character of a pilot study. (2)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. (3)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. 2.2 Survey Results – 27 EU Countries

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

18 February 2008  Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA 18 Table Reasons, why shadow economy activities are demanded, Germany, Survey Results: Germany Reasons why shadow economy activities are demandedValues 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

19 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 – – – – – – – – – – – Sum (1) to (4)13.0 – – – 111 Overall (total) shadow economy (estimated by the DYMIMIC and calibrated by the currency demand procedure) Source: Enste/Schneider (2006) and own calculations.

20 February 2008  Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA 20 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: (1)Table contains earnings and not the value added of the shadow economy. This means material is not considered. (2)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. (3)All foreign shadow economy activities are not considered. (4)The amount of income earned in the shadow economy, the hourly wage rate and hours worked per year vary considerably. 2.3 Survey Results – Germany

21 February 2008  Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA 21 Table The Size of the Shadow Economy in Germany According to Different Methods (in percentage of official GDP) Method/SourceShadow economy (in percentage of official GDP) in: Survey/IfD Allensbach, ) ) 3.1 2) ) 1.0 3) Discrepancy between expenditure and income/(Lippert and Walker, 1997) Discrepancy between official and actual employment/(Langfeldt, 1983) Physical input method (Feld and Larsen, 2005) Transactions approach Feige (1989) Currency demand approach/ (Kirchgässner, 1983) (Langfeldt, 1983, 1984) Schneider and Enste (2000) Latent ((DY)MIMIC) approach/(Frey and Weck (1983)) Pickardt and Sarda (2006) Schneider (2005, 2007) Soft modelling/(Weck-Hannemann (1983)) ) ) 1974; 2) 2001 and 2004; calculated using wages in the official economy; 3) 2001 and 2004; calculated using actual “black” hourly wages paid. Feld and Larsen, 2005

22 February 2008  Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA Three Research Questions (1)Determinants of benefit morale?  Taxpayer’s attitude toward claiming unjustified government benefits/subsidies. (2)Determinants of tax morale?  Taxpayer’s attitude toward avoiding taxes. (3)Impact of Benefit - & tax morale on actual behavior (income). 2.4 Individual Analyis of Tax and Benefit Moral for Austria Source: Martin Halla & Friedrich G. Schneider (2005).

23 February 2008  Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA Tax Morale Literature  Intrinsic motivation (tax morale) is therefore a key determinant in understanding compliance behavior! (1)Using Canadian data Torgler (2003) shows that tax morale rises with financial satisfaction, age, patriotism, trust in government, religiosity and deteriorates with rising income. (2)Torgler and Schneider (2004) stress the influence of cultural differences in explaining tax morale and find strong evidence for interactions between culture and institutions. (3)Torgler and Schneider (2005) investigate tax morale in Austria. Societal variables (e.g. perceived tax evasion, patriotism, trust in legal system) are key determinants! 2.4 Individual Analyis of Tax and Benefit Moral for Austria Source: Martin Halla & Friedrich G. Schneider (2005).

24 February 2008  Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA Benefit Morale (1)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. (2)From a theoretical point of view a subsidy is a negative tax, but… (3)…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. 2.4 Individual Analyis of Tax and Benefit Moral for Austria Source: Martin Halla & Friedrich G. Schneider (2005).

25 February 2008  Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA 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 (N 1990 = 835, N 1999 = 1,052). 2.4 Individual Analyis of Tax and Benefit Moral for Austria Source: Martin Halla & Friedrich G. Schneider (2005).

26 February 2008  Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA Data and Descriptive Statistics Table 2.4.1: Descriptive Statistics 2.4 Individual Analyis of Tax and Benefit Moral for Austria Source: Martin Halla & Friedrich G. Schneider (2005). VariableMeanStd. Dev.MinMax Tax morale* Benefit morale* Income (100 € p.m.)** 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.

27 February 2008  Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA Estimation of the Determinants of Tax & Benefit Morale (1)Closely related issues; we apply a system of equations: -Tax morale = α 1 + β 11 * benefit morale + β 11 * income + Γ 1 * Χ 1 + ε 1 -Benefit morale = α 2 + β 21 * tax morale + β 22 * income + Γ 2 * Χ 2 + ε 2 (2)If stated attitudes are translated in actual behavior, income is endogenous too,… (3)… because simultaneity prevails! (4)Halla and Schneider instrument income with chief wage earners sex & job rank and the socio-economic status of the household. 2.4 Individual Analyis of Tax and Benefit Moral for Austria Source: Martin Halla & Friedrich G. Schneider (2005).

28 February 2008  Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA 28 Table SLS Estimation of Tax and Benefit Morale 2.4 Individual Analyis of Tax and Benefit Moral for Austria Tax moraleBenefit morale Tax morale (0.220)** Benefit morale0.607 (0.282)** - Income (0.013)* (0.013) Age (0.005) (0.003)*** Female0.003 (0.094) (0.077) Married0.182 (0.105)* (0.109) School leaving age (0.013)* (0.013) Tax moraleBenefit morale Employed (0.130) (0.096)** (0.180) (0.090)*** Religious0.136 (0.053)*** (0.061) Patriotic0.157 (0.101) (0.093) Distrust legal system (0.054)** - Children (0.032)** Volunteer (0.083)* Observations1,887 Basmann Statistic b Sargan Statistic c 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.

29 February 2008  Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA Estimation of Impact on Actual Behavior Income = α 3 + β 31 * tax morale + β 32 * benefit morale + Γ 3 * Χ 3 + ε 3 (1)A neg. significant coefficient of tax morale indicates actual noncompliance behavior,… (2)A neg. significant coefficient of benefit morale shows actual claims of unjustified subsidies,… (3)… because in both cases a higher net wage is ceteris paribus expected! (4)Durbin-Wu-Hausman-Test for endogeneity of tax- & benefit morale: P-value = 0.01 (5)  Endogeneity prevails; we instrument for tax- and benefit morale. (6)Instruments are: Confidence in the legal system, voluntary labor & children. (7)Test(s) for overidentifying restrictions confirm the instruments (e.g. P-value of Basmann‘s test = 0.62) 2.4 Individual Analyis of Tax and Benefit Moral for Austria Source: Martin Halla & Friedrich G. Schneider (2005).

30 February 2008  Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA 30 Table SLS Estimation on Income 2.4 Individual Analyis of Tax and Benefit Moral for Austria Independent VariableDependent Variable: Income Tax morale1.610 (1.293) Benefit morale-2.916** (1.309) Chief wage earner‘s job rank (0.096) Chief wage earner is female *** (0.475) Socioeconomic status4.382*** (0.294) Year (0.597) Vienna (0.713) Tirol-1.827*** (0.728) Constant (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. Observations1,887 Basmann Statistic b Sargan Statistic c Source: Martin Halla & Friedrich G. Schneider (2005).

31 February 2008  Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA Results of the 2SLS Estimation on Income (1)Not statistically significant impact of tax morale on household income! (2)Statistically significant impact of benefit morale on household income! - One point higher benefit morale reduces household income by 292 € p.m. (3)Households with female chief wage earners have about 341 € lower income p.m. (4)A higher socio-economic status (self reported, measured on four-point scale) is associated with 438 € more income p.m. 2.4 Individual Analyis of Tax and Benefit Moral for Austria Source: Martin Halla & Friedrich G. Schneider (2005).

32 February 2008  Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA 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. (1)Low (high) benefit morale leads indeed (not) to unjustified claims on benefits and therefore to higher (lower) income! (2)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. 2.4 Individual Analyis of Tax and Benefit Moral for Austria Source: Martin Halla & Friedrich G. Schneider (2005).

33 February 2008  Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA 33 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

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

35 February 2008  Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA 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) The Discrepancy between National Expenditure and Income Statistics The Discrepancy between the Official and Actual Labor Force The Transactions Approach The Currency Demand Approach The Physical Input (Electricity Consumption) Method 3.3The Model/Latent Estimation Approach 3) Methods to Estimate the Size of the Shadow Economy

36 February 2008  Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA 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 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. 3) Methods to Estimate the Size of the Shadow Economy

37 February 2008  Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA 37 The most commonly raised objections (criticism) against the current demand approach are: (1)Not all transactions in the shadow economy are paid in cash. The size of the total shadow economy (including barter) may thus be larger. (2)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. (3)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. (4)Another weak point is the assumption of the same velocity of money in both types of economies. (5)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. (6)Finally, the assumption of no shadow economy in a base year is open to criticism. 3) Methods to Estimate the Size of the Shadow Economy – The Currency Demand Approach Cont.

38 February 2008  Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA 38 (1)The MIMIC approach explicitly considers several causes, as well as the multiple effects of the informal economy. (2)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. (3)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. 3.3 Multiple Indicators, Multiple Causes (MIMIC) approach 3. Methods to Estimate the Size of the Shadow Economy – The MIMIC (Latent) Approach

39 February 2008  Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA 39 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‘ = (γ 1 …, γ p ) is a vector of indicators for IE, x‘ = (x 1,…x q ) 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. Multiple Indicators, Multiple Causes (MIMIC) approach 3. Methods to Estimate the Size of the Shadow Economy – The MIMIC (Latent) Approach – cont.

40 February 2008  Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA 40 Figure 3.1 General structure of a MIMIC Model Causes Indicators x 1t x 2t … x qt y 1t – ε 1t y 2t – ε 2t y pt – ε pt … IE t γ1γ1 γ2γ2 γqγq λ1λ1 λ2λ2 λpλp 3. Methods to Estimate the Size of the Shadow Economy – The MIMIC (Latent) Approach – cont.

41 February 2008  Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA 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) = σ 2 ν and cov(ε) = Θ ε, 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‘ σ 2 ν + Θ ε. 3. Methods to Estimate the Size of the Shadow Economy – The MIMIC (Latent) Approach – cont.

42 February 2008  Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA Concluding Remarks: Problems and Open Questions 4.1 Surveys (1)Quite often only households or only partly firms are considered (2)Non-responses and/or incorrect responses (3)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): (1)Combination of meso estimates/assumptions (2)Calculation method often not clear (3)Documentation and procedures often not public 4. Concluding Remarks

43 February 2008  Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA Concluding Remarks: Problems and Open Questions 4.3 Monetary and/or electricity methods: (1)Some estimates are very high (2)Are the assumptions plausible? (3)Breakdown by sector or industry not possible! 4.4 DYMIMIC method (1)Only relative coefficients, no absolute values. (2)Estimations quite often highly sensitive with respect to changes in the data and specifications. (3)Difficulty to differentiate between the selection of causes and indicators 4. Concluding Remarks

44 February 2008  Prof. Dr. Friedrich Schneider, University of Linz / AUSTRIA Concluding Remarks: Problems and Open Questions 4.5 What did we learn? (1)No ideal or dominating method – all have serious problems and weaknesses. (2)If possible use several methods. (3)Much more research is needed with respect to the estimation methodology and empirical results for different countries and periods. (4)Experimental methods should be used to provide a micro-foundation.


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