World Economics - Insight , Analysis and Data

World Economics - Insight , Analysis and Data

The Shadow Economy and Shadow Economy Labour Force: What do we (not) know?

Friedrich Schneider - November 2011

In this paper the main focus lies on the development and the size of the shadow economy and of undeclared work (or shadow economy labour force) in OECD, developing and transition countries. Besides informal employment in the rural and non-rural sector also other measures of informal employment like the share of employees not covered by social security, own account workers or unpaid family workers are shown. The most influential factors on the shadow economy and/or shadow labour force are tax policies and state regulation, which, if they rise, increase both. Furthermore the discussion of the recent literature underlines that economic opportunities, the overall situation on the labour market, and unemployment are crucial for an understanding of the dynamics of the shadow economy and especially the shadow labour force.

1.  INTRODUCTION

This paper is mainly concerned with measuring the size and development of the shadow economy, black activities and undeclared work. Knowledge about the shadow economy and the shadow labour force is necessary to fighting tax evasion, an important policy goal in OECD countries, but this subject is not considered in this paper because too many additional aspects would be involved[1]. Tax morale or experimental studies on tax compliance are also beyond the scope of this paper[2] which is organized as follows: Section 2 presents theoretical considerations about the definition and measurement of the shadow economy and discusses also the main factors determining its size. In Section 3 the empirical results of the size and development of the shadow economy are discussed. In section 4 a detailed discussion of the size and development of the shadow economy labour force and its various aspects are presented. In section 5 the interaction between the shadow economy and unemployment is analyzed. In section 6 the adjustments of shadow economy measures in national accounts are presented. Finally section 7 concludes.

2.  SOME THEORETICAL CONSIDERATIONS ABOUT THE SHADOW ECONOMY

2.1.    Defining the Shadow Economy

It is difficult to make a precise definition of the shadow economy.[3] It is often defined as comprising all currently unregistered economic activities that contribute to the officially calculated Gross National Product.[4] Smith (1994, p. 18) defines it as “market-based production of goods and services, legal or illegal, that escapes detection in official estimates of GDP”. One of the broadest definitions is: “…those economic activities and the income derived from them that circumvent or otherwise avoid government regulation, taxation or observation”.[5] To reduce the scope for misinterpretation, Table 2.1 provides a taxonomy that could form a reasonable consensus definition of the underground (or shadow) economy.

 

Table 2.1: A Taxonomy of Types of Underground Economic Activities1)

Type of Activity

Monetary Transactions

Non Monetary Transactions

 

ILLEGAL ACTIVITIES

 

Trade with stolen goods; drug dealing and manufacturing; prostitution; gambling; smuggling; fraud, human-, drug-, and weapon-trafficking  

 

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

 

 

 

Tax Evasion

 

Tax Avoidance

 

Tax Evasion

 

Tax Avoidance

LEGAL ACTIVITIES

Unreported income from self-employment; wages, salaries and assets from unreported work related to legal services and goods

Employee discounts, fringe benefits

Barter of legal services and goods

All do-it-yourself work and neighbor help

1) Structure of the table is taken from Lippert and Walker (1997, p. 5) with additional remarks.

Table 2.1 shows that a broad definition of the shadow economy includes unreported income from the production of legal goods and services, either from monetary or barter transactions – and so includes all productive economic activities that would generally be taxable were they reported to the state (tax) authorities.

This paper uses a narrower definition of the shadow economy.[6] It 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 obligations, such as completing statistical questionnaires or other administrative forms.

This excludes typically illegal underground economic activities that fit the characteristics of classical crimes like burglary, robbery, drug dealing, etc. and the informal household economy consisting of all services and production.

 

2.2.    Measuring the Shadow Economy[7]

A clear definition of the shadow economy avoids ambiguities and controversies in assessing its size. In general, there are two types of underground economic activities: illicit employment and the production of goods and services mostly consumed within the household.[8] This paper focuses on the former and excludes illegal activities such as drug production, crime and human trafficking. The production of goods and services, consumed within the household such as childcare is also not analysed below. The paper focuses on productive economic activities that would normally be included in national accounts but which remain underground due to tax or regulatory burdens.[9] Although such legal activities contribute to the country’s value added, they are not captured in the national accounts because they are produced in illicit ways (e.g. by people without proper qualification or without a master craftsman’s certificate)[10].

Discussions continue regarding the “appropriate” methodology to assess the scope of the shadow economy.[11] There are three methods of assessment:

  1. Direct micro level procedures aimed at estimating the size of the shadow economy at one particular point in time. An example is the survey method;
  2. Indirect procedures using macroeconomic indicators to proxy the development of the shadow economy over time;
  3. Models using statistical tools to estimate the shadow economy as an “unobserved” variable.


This paper estimates the shadow economy of OECD countries based on a combination of the multiple indicators multiple causes (MIMIC) procedure and the currency demand method.[12] The MIMIC pro­ce­dure assumes that the shadow economy remains an unobserved phenomenon (latent variable) which can be estimated using quantitatively measurable causes of illicit employment, e.g. tax burden and regulation intensity, and indicators reflecting illicit activities, e.g. currency demand, official GDP and official working time. The MIMIC procedure, unfortunately, produces only relative estimates of the size and the development of the shadow economy. Thus, the currency demand method[13] is used to calibrate relative into absolute estimates by using two or three absolute values of the size of the shadow economy.

In addition, the shadow economy’s size is also estimated by survey methods (Feld and Larsen (2005, 2008, 2009)). To minimize the number of respondents dishonestly replying or declining to answer sensitive questions, structured interviews are undertaken (usually face-to-face) with respondents becoming slowly accustomed to the main purpose of the survey. As with the contingent valuation method (CVM) in environmental economics (Kopp et al. 1997), the first part of the questionnaire aims to shape respondents’ perception of the issue at hand. In a second part, questions about respondents’ activities in the shadow economy are asked, while the third part contains the usual socio-demographic questions.

In addition to the studies by Merz and Wolff (1993), Feld and Larsen (2005, 2008, 2009), Haigner et al. (2011) and Enste and Schneider (2006) for Germany, the survey method has been applied in the Nordic countries and in Great Britain (Isachsen and Strøm 1985, Pedersen 2003) as well as in the Netherlands (van Eck and Kazemier 1988, Kazemier 2006). The questionnaires underlying these studies are broadly comparable in design, but recent attempts by the European Union to provide survey results for all EU member states have run into difficulties in this area (Renooy et al. 2004, European Commission 2007): the wording of the questionnaires becomes more and more cumbersome depending on the culture of different countries with respect to the underground economy.

Each of these approaches to estimating the size of the shadow economy has its drawbacks. However, despite any prevailing biases no better economic data are currently available.

In tax compliance research, the most interesting data stem from actual tax audits by the US Internal Revenue Service (IRS). In the Taxpayer Compliance Measurement Program (TCMP), actual compliance behavior of taxpayers is observed and is used for empirical analysis (Andreoni, Erard and Feinstein 1998). The approach of the IRS is broader as tax evasion from all sources of income is considered, while the two methods discussed above aim at capturing the shadow economy or undeclared work and thus mainly measure tax evasion from labour income. Even the data obtained from the TCMP is biased however because the actually detected tax non-compliance could only be the tip of the iceberg. Although the perfect data on tax non-compliance does therefore not exist, the imperfect data in this area can still provide interesting insights also regarding the size, the development and the determinants of the shadow economy and of the shadow economy labour force.

 

2.3.    The Determinants of the Shadow Economy

Activities in the shadow economy in most cases imply the evasion of direct or indirect taxes, such that the factors affecting tax evasion will most certainly also affect the shadow economy. According to Allingham and Sandmo (1972) the benefits of tax non-com­pliance result from an individual’s marginal tax rate and true individual income. The individual’s marginal tax rate is obtained by calculating the overall marginal tax burden from indirect and direct taxes including social security contributions. Individual income generated in the shadow economy is usually labour income rather than capital income. The expected costs of non-com­pli­ance derive from deterrence efforts of the state. Tax non-compliance thus depends on the state’s auditing activities which raises the probability of detection and the fines individuals face when they are caught. Additional costs beyond pure punishment by the tax administration can take the form of psychic costs like shame or regret, but also additional pecuniary costs from loss of  reputation.

Kanniainen, Pääkönen and Schneider (2004) looking at labour supply decisions hypothesize that tax hikes unambiguously increase the shadow economy, while the costs for individual non-compliers resulting from moral norms appear to be mainly captured by state punishment although self-esteem also plays a role. A shortcoming is the neglected endogeneity of tax morale and good governance. In contrast, Feld and Frey (2007) argue that tax compliance is the result of a complicated interaction between tax morale and deterrence measures. Taxpayers need to know the rules of the game since deterrence measures signal the tax morale a society wants to elicit (Posner 2000a, b), but deterrence can also impact on the intrinsic motivation to pay taxes. Tax morale is increased if taxpayers perceive that the public goods received in exchange for their tax payments are valuable, but it also increases if political decisions follow fair procedures or if the treatment of taxpayers by the tax authorities is perceived to be friendly and fair. Tax morale is thus not exogenously given, but is influenced by deterrence, the quality of state institutions and by constitutional differences among states.

 This analysis suggests a rich set of variables that might influence the size of the shadow economy, but as labour supply decisions are also involved, labour and product market regulations are additionally important. Recent theoretical approaches thus suggest following a differentiated policy to contain the shadow economy’s expansion.

2.3.1 
Deterrence[14]
The theory of tax non-compliance derives unambiguous predictions as to the impact of deterrence measures. However, the strong focus of policies aimed at fighting the shadow economy, surprisingly little is known empirically about the effects of deterrence.. In their survey on tax compliance, Andreoni, Erard and Feinstein (1998) report that deterrence matters for tax evasion, but that the reported effects are rather small. Blackwell (2009) finds strong deterrence effects of fines and audits in experimental tax evasion. However, there is little evidence on the shadow economy since data on the legal background and the frequency of audits are not available internationally basis. They would also be difficult to collect even for the OECD member countries. Of the available emprirical work Pedersen (2003) reported significant negative effects of the subjectively perceived risk of detection on the probability of conducting undeclared work in the shadow economy for men in Denmark in 2001, for men in Norway in 1998/2002,[15] for men and women in Sweden in 1998, but found no significant effect for Great Britain in 2000. Moreover, van Eck and Kazemier (1988) report a significant negative impact of a high perceived probability of detection on participation in the hidden labour market for the Netherlands in 1982/1983. None of these studies included perceived fines and punishments as explanatory variables. In Germany the legal background is quite complicated, differentiating fines and punishment according to the severity of the offense and the true income of the non-complier. There are also regional disparities in sentencing arising from court directives in different Länder (States) whose tax authorities do not reveal the intensity of auditing.. Despite these difficulties. Feld, Schmidt and Schneider (2007) using data on fines and audits, conducted a time series analysis using the estimates of the shadow economy obtained by the MIMIC approach. According to their results, deterrence does not have a consistent effect on the German shadow economy with the possibility of ambiguity. The direction of causation could run from an impact of the shadow economy on deterrence instead of deterrence on the shadow economy. Feld and Larsen (2005, 2008, 2009) used individual survey data for Germany. Replicating Pedersen (2003), who reports a negative impact of the subjectively perceived risk of detection by state audits on the probability of working in the shadows for the year 2001, they extended it by adding subjectively perceived measures of fines and punishment, but found that these variables  do not exert a negative influence on the shadow economy in any of the annual waves of surveys.. The subjectively perceived risk of detection has a robust and significant negative impact in individual years only for women. In pooled regressions for the years 2004-2007[16], which minimizes sampling problems, the probability of detection had a significantly negative effect on the probability of men working in the shadow economy as well as women. The relationship was robust across different specifications.[17]  On balance, this is far from convincing evidence about the impact of of deterrence since according to theoretical analysis and plausibility it is always the combination of audits and fines that matters. The reasons for the unconvincing evidence of deterrence effects are discussed in the tax compliance literature by Andreoni, Erard and Feinstein (1998), Kirchler (2007) or Feld and Frey (2007). They range from interactions between tax morale and deterrence, thus the possibility that deterrence crowds out tax morale, to more mundane arguments like misperceptions of taxpayers. Likewise, these reasons could be important for the evidence on the deterrence effects on work in the shadow economy. As the latter mainly stems from survey studies, the insignificant findings for fines and punishment may also result from shortcomings in the survey design.

2.3.2 Tax and Social Security Contribution Burdens
In contrast to deterrence, almost all studies find that the tax and social security contribution burdens are among the main causes for the existence of the shadow economy.[18] Since taxes affect labour-leisure choices and stimulate labour supply in the shadow economy, the distortion of the overall tax burden is a major concern. The bigger the difference between the total labour cost in the official economy and after-tax earnings (from work), the greater is the incentive to reduce the tax wedge and work in the shadow economy. Since the tax wedge depends on the level and increase of the social security burden/payments and the overall tax burden, they are key features of the existence and the increase of the shadow economy.

2.3.3 Intensity of Regulations
An increased intensity of regulations reduces the freedom (of choice) for individuals engaged in the official economy. For example, labour market regulations, trade barriers, and labour restrictions for immigrants, all generate employment in the shadow rather than the official economy. Johnson, Kaufmann, and Zoido-Lobatón (1998b) find significant 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 (Deregulierungskommission/ Deregulation Commission 1991).[19]

Regulations lead to a substantial increase in labour costs in the official economy, but since most of these costs can be shifted to employees, regulations provide incentives to work in the shadow economy where they can be avoided. Johnson, Kaufmann, and Shleifer (1997) find evidence supporting the prediction that countries with higher general regulation of their economies tend to have a higher share of the unofficial economy in total GDP. They conclude that it is the enforcement of regulation and not the overall extent of regulation, which is the key factor driving firms into the shadow economy. Friedman, Johnson, Kaufmann and Zoido-Lobaton (2000) arrive at 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.

2.3.4 Public Sector Services
The relationship between the provision of public services and the shadow economy is dynamic and can work as a vicious or a virtous circle. An increase in the size of the shadow economy can lead to reduced state revenues which in turn reduce the quality and quantity of publicly provided goods and services. 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, providing even stronger incentives to participate in the shadow economy. Johnson, Kaufmann, and Zoido-Lobatón (1998a, b) present a simple model of this relationship. According to their findings smaller shadow economies occur in countries with higher tax revenues achieved by lower tax rates, fewer laws and regulations and less bribery facing enterprises. Countries with a better rule of law, 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 framework of regulations 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 countries 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.” [20]

Recently, various authors[21] have also considered the quality of public institutions as another key factor in the development of the informal sector. They argue that the efficient and discretionary application of tax systems and regulations by government may play a crucial role in the decision of conducting undeclared work, which is even more important than the actual burden of taxes and regulations. In particular, the corruption of bureaucracy and government officials seems to be associated with larger unofficial activity, while a good rule of law by securing property rights and contract enforceability, increases the benefits of formal economic activity.

Therefore, it is important to analyze theoretically and empirically the effect of political institutions on the shadow economy. If the development of the informal sector is considered a consequence of the failure of political institutions in promoting an efficient market economy, since entrepreneurs go underground when there is an inefficient public goods provision, then the effect of institutions on the individual’s incentive to operate unofficially can be assessed. In a federal system, competition among jurisdictions and the mobility of individuals act as constraints on politicians because “choices” will be induced that provide incentives to adopt policies which are closer to a majority of voters’ preferences. Frequently, efficient policies are characterized by taxation, mostly spent in productive public services since formal sector production benefits from a higher output of productive public services while it is negatively affected by taxation. The  shadow economy reacts in the opposite way. As actual fiscal policy moves closer to the majority of voters’ preferences in federal systems, the size of the informal sector goes down. This leads to the hypothesis that the size of the shadow economy should be lower in a federal system than in a unitary state, ceteris paribus.

2.3.5  Tax Morale
The efficiency of the public sector also has an indirect effect on the size of the shadow economy because it affects tax morale. As Feld and Frey (2007) argue, tax compliance is driven by a psychological tax contract that entails rights and obligations from taxpayers and citizens on the one hand, but from the state and its tax authorities on the other. Taxpayers are more inclined to pay their taxes honestly if they get valuable public services in exchange. However, taxpayers are honest even in cases when the benefit principle of taxation does not hold, i.e. for redistributive policies, if the political decisions underlying such policies follow fair procedures. Finally, the treatment of taxpayers by the tax authority plays a role. If taxpayers are treated like partners in a (tax) contract instead of subordinates in a hierarchical relationship, taxpayers are more likely to stick to their obligations of the psychological tax contract more easily. In addition to the empirical evidence reported by Feld and Frey (2007), and by Kirchler (2007) these authors present a comprehensive discussion of the influence of such factors on tax compliance.

Regarding the impact of tax morale on the shadow economy, there is scarce and only recent evidence. Using data on the shadow economy obtained by the MIMIC approach, Torgler and Schneider (2009) report the most convincing evidence for a negative effect of tax morale. They particularly address causality issues and establish a causal negative relation from tax morale to the size of the shadow economy. This effect is also robust to the inclusion of additional explanatory factors and specifications. These findings are also in line with earlier preliminary evidence by Körner et al. (2006). Using survey data, Feld and Larsen (2005, 2009) likewise report a robust negative effect of tax morale in particular and social norms in general on the probability of respondents to conduct undeclared work. Interestingly, the estimated effects of social norms are quantitatively more important than the estimated deterrence effects. Van Eck and Kazemier (1988) also report a marginally significant effect of tax morale on the participation in the hidden labour market.

2.3.6  Summary of the Main Causes of the Shadow Economy
In Table 2.2 an overview of a number of empirical studies summarizes the various factors influencing the shadow economy. The overview is based on the studies in which the size of the shadow economy is measured by the MIMIC or currency demand approach. As there is no evidence on deterrence using these approaches – at least with respect to the broad panel data base on which this table draws – the most central policy variable does not show up. This is an obvious shortcoming of the studies, but one that cannot be coped with easily due to the lack of internationally comparable deterrence data. In Table 2.2 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 morale accounts for some 22–25% of the variance of the shadow economy,[22] there is a third factor, “quality of state institutions”, accounting for 10-12% and a forth factor, “intensity of state regulation“ (mostly for the labour market) for 7-9%. In general Table 2.2 shows that the independent variables tax and social security burden, followed by variables tax morale and intensity of state regulations are the three major driving forces of the shadow economy.


 

Table 2.2: Main Causes of the Increase of the Shadow Economy

Factors influencing the shadow economy

Influence on the shadow economy (in %)

(a)

(b)

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

35-38

45-52

(2)         Quality of State Institutions

10-12

12-17

(3)         Transfers

5-7

7-9

(4)         Specific Labour Market Regulations

7-9

7-9

(5)         Public Sector Services

5-7

7-9

(6)         Tax Morale

22-25

-

Influence of all Factors

84-98

78-96

(a) Average values of 12 studies.

(b) Average values of empirical results of 22 studies.

Source: Schneider (2009)

 

3.   ESTIMATION AND SIZE OF THE SHADOW ECONOMIES IN HIGHLY DEVELOPED OECD COUNTRIES

 3.1.    Econometric Estimation

Following the theoretical considerations in section 2, I develop seven hypotheses below (all ceteris paribus), which will be empirically tested subsequently using the MIMIC approach:

1.      An increase in direct and indirect taxation increases the shadow economy.

2.      An increase in social security contributions increases the shadow economy.

3.      The more the country is regulated, the greater the incentives are to work in the shadow economy.

4.      The lower the quality of state institutions, the higher the incentives to work in the shadow economy.

5.      The lower tax morale, the higher the incentives to work in the shadow economy.

6.      The higher unemployment, the more people engage in shadow economy activities.

7.      The lower GDP per capita in a country, the higher is the incentive to work in the shadow economy.

As the sample consists of 21 highly developed OECD countries between 1990 and 2007 (pooled cross section time series data), the effect of deterrence cannot be empirically tested. As the size of fines and punishment and the probability of detection are only available for one or two countries across time. They are not considered here. The following estimation results thus rather correspond to the factors reported in Table 2.2 which are gained from an overview of existing studies.

 

Table 3.1: MIMIC 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, 2004/05 and 2006/07.


 

Cause Variables

Estimated Coefficients

 

Share of direct taxation

λ1 = 0.392**

 

(in % of GDP)

(3.34)

 

 

 

 

Share of indirect taxation

λ2 = 0.184(*)

 

(in % of GDP)

(1.74)

 

 

 

 

Share of social security contribution

λ3 = 0.523**

 

(in % of GDP)

(3.90)

 

 

 

 

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

λ4 = 0.226(*)

(2.03)

 

 

 

 

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

λ5 = -0.314*

(-2.70)

 

 

 

 

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

λ6 = -0.593**

(-3.76)

 

 

 

 

Unemployment rate (%)

λ7 = 0.316**

 

 

(2.40)

 

 

 

 

GDP per capita (in US-$)

λ8 = -0.106**

(-3.04)

 

Indicator Variables

Estimated Coefficients

 

Employment rate

λ 9= -0.613**

 

(in % of population 18-64)

(-2.52)

 

 

 

 

Average working time (per week)

λ10 = -1.00 (Residuum)

 

 

 

 

Annual growth rate of GDP (adjusted for the mean

λ11 = -0.281**

 

of all 22 OECD countries)

(-3.16)

 

 

 

 

Change of local currency

λ12 = 0.320**

 

per capita

(3.80)

 

 Test-statistics

RMSE1) = 0.0016* (p-value = 0.912)

 

 

Chi-square2) = 26.43 (p-value = 0.916)

 

 

TMCV3) = 0.051

 

 

AGFI4) = 0.772

 

 

N = 189

 

 

D.F.5) = 71

 

Notes: t-statistics are in parentheses (*); *; ** indicates significance at the 90%, 95%, or 99% confidence levels.

1)  Steiger’s 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 multinomial 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.

 

In Table 3.1 the econometric results using the MIMIC approach (latent estimation approach) are presented for these 21 OECD-countries for which I have nine data points of the years 1990/91, 1994/95, 1997/98, 1999/2000, 2001/02, 2002/03, 2003/04, 2004/05 and 2006/07. Besides the usual cause variables like direct and indirect taxation, social security contributions and state regulation I have added two further causal factors, i.e. tax morale and the quality of state institutions. In addition to the employment rate, the annual growth rate of GDP and the change of currency per capita, I use the average working time (per week) as an additional indicator variable.[23] 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. Also the independent variable quality of state institutions is statistically significant and quite important to determine whether one is engaged in shadow economy activities or not. The development of the official economy measured by unemployment and GDP per capita has a quantitatively important influence on the shadow economy. Turning to the indicator variables they all have a statistically significant influence and the estimated coefficients have the theoretically expected signs. The quantitatively most important independent variables are the employment rate and the change of currency per capita.[24] Summarizing, the econometric results demonstrate that in these OECD countries the social security contributions and the share of direct taxation have the biggest influence, followed by tax morale and the quality of state institutions[25].

 

3.2.  The Development and Size of the Shadow Economy in German-Speaking Countries
Existing estimates of the German shadow economy (measured in percentage of official GDP) are shown in table 3.2 (see also Feld et.al. 2007). The oldest estimate uses the survey method of the Institute for Demoscopy (IfD) in Allensbach, Germany, and shows that the shadow economy was 3.6% of official GDP in 1974. In a much later study, Feld and Larsen (2005, 2008) undertook an extensive research project using the survey method to estimate shadow economic activities in the years 2001 to 2006.[26] Using the officially paid wage rate, they concluded that these activities reached 4.1% in 2001, 3.1% in 2004, 3.6% in 2005 and 2.5% in 2006.[27] Using the (much lower) shadow economy wage rate these estimates shrink however to 1.3% in 2001 and 1.0% in 2004, respectively. If I consider the discrepancy method, for which I have estimates from 1970 to 1980, the German shadow economy is much larger: using the discrepancy between expenditure and income, I get approximately 11% for the 1970s, and using the discrepancy between official and actual employment, roughly 30%. The physical input methods from which estimates for the 1980s are available, “deliver” values of around 15% for the second half of that decade. The (monetary) transaction approach developed by Feige (1989) places the shadow economy at 30% between 1980 and 1985. Yet another monetary approach, the currency demand approach – the first person to undertake an estimation for Germany was Kirchgässner (1983, 1984) – provides values of 3.1% (1970) and 10.1% (1980). Kirchgässner’s values are quite similar to the ones obtained by Schneider and Enste (2000, 2002), who also used a currency demand approach to value the size of the shadow economy at 4.5% in 1970 and 14.7% in 2000. Finally, considering latent MIMIC estimation procedures, the first being conducted by Frey and Weck-Hannemann (1984), and later, Schneider and others followed for Germany, again, the estimations for the 1970s are quite similar. Furthermore, Schneider’s estimates using a MIMIC approach (Schneider 2005, 2009) are close to those of the currency demand approach.

Thus, one can see that different estimation procedures produce different results. It is safe to say that the figures produced by the transaction and the discrepancy approaches are rather unrealistically large: the size of the shadow economy at almost one third of official GDP in the mid-1980s is most likely an overestimate. The figures obtained using the currency demand and hidden variable (latent) approaches, on the other hand, are relatively close together and much lower than those produced by other methods (i.e. the discrepancy or transaction approaches). This similarity is not surprising given the fact that the estimates of the shadow economy using the latent (MIMIC) approach were measured by taking point estimates from the currency demand approach. The estimates from the MIMIC approach can be regarded as the upper bound of the size of the shadow economy. For the reasons outlined in Section 2, the estimates obtained from the survey approach provide for its lower bound. It should be noted that the “true” size of the shadow economy does not necessarily lie between both bounds, nor is it precluded that it is closer to the upper than the lower bound. But both benchmarks help us to understand the phenomenon pretty well.

 

3.3.  Size and Development of the Shadow Economy in 21 OECD Countries
In order to calculate the size and development of the shadow economies of the 21 OECD countries, it is necessary to overcome the disadvantage of the MIMIC approach, which is, that only relative sizes of the shadow economy are obtained. This means that another approach must be used to calculate absolute figures. In order to so using these MIMIC estimation results, the already available estimates from the currency demand approach for Austria, Germany, Italy and the United States are taken (from studies of Dell’Anno and Schneider 2003, Bajada and Schneider 2005, and Schneider and Enste 2002). The author has values of the shadow economy (in % of GDP) for various years for the above mentioned countries, so they can be used in a benchmark procedure to transform the index of the shadow economy from the MIMIC estimations into cardinal values.[28]

Table 3.3 presents the findings for 21 OECD countries until 2007. They clearly reveal that since the end of 1990’s the size of the shadow economy in most OECD countries continued to decrease. The unweighted average for all countries in 1999/2000 was 16.8% and dropped to 13.9% in 2007. This means, that since 1997/98 – the year in which the shadow economy was the biggest in most OECD countries, it has continuously shrunk. Only in Germany, Austria and Switzerland did this growing trend last longer to be reversed two or three years ago. The reduction of the share of the shadow economy from GDP between 1997/98 and 2007 is most pronounced in Italy (-5.0%) and in Sweden (-4.0). The German shadow economy ranges in the middle of the ranking, whereas Austria and Switzerland are located at the lower end. With 20% to 26%, South European countries exhibit the biggest shadow economies measured as a share from official GDP. They are followed by Scandinavian countries whose shadow economies’ shares in GDP range between 15 and 16%. 

One reason for the differences in the size of the shadow economy between these OECD countries includes, among others, that for example there are fewer regulations in an OECD country like the USA compared to the OECD country Germany where everything is forbidden, that is not explicitly allowed. The individual’s freedom is limited in many areas by far-reaching state interventions. Another reason is the large differences in the direct and indirect tax burden with the lowest being in the U.S. and Switzerland in this sample.


 


Table 3.2: The Size of the Shadow Economy in Germany According to Different Methods (in Percentage of Official GDP)



Table 3.3: The Size of the Shadow Economy (in % of Official GDP) in 21 OECD Countries between 1989/90 and 2007 
Estimated Using and MIMIC Method and the Currency Demand Approach to Calibrate the MIMIC values 



 
4.   SHADOW ECONOMY LABOUR FORCE AND LABOUR MARKET

 

4.1. Shadow Economy Labour Market

Having examined the size, rise and fall of the shadow economy in terms of value added over time, the analysis now focuses on the “shadow labour market”, as within the official labour market there is a particularly tight relationship and “social network” between people who are active in the shadow economy.[29] Moreover, by definition every activity in the shadow economy involves a “shadow labour market” to some extent:[30] Hence, the “shadow labour market” includes all cases, where the employees or the employers, or both, occupy a “shadow economy position“.

Illicit work can take many forms. The underground use of labour may consist of a second job after (or even during) regular working hours. A second form is shadow economy work by individuals who do not participate in the official labour market. A third component is the employment of people (e.g. clandestine or illegal immigrants), who are not allowed to work in the official economy. Empirical research on the shadow economy labour market is even more difficult than of the shadow economy on the value added, since one has very little knowledge about how many hours an average “shadow economy worker” is actually working (from full time to a few hours, only); hence, it is not easy to provide empirical facts.[31]

Why do people work in the shadow economy? In the official labour market, the costs firms (and individuals) have to pay when “officially” hiring someone are increased enormously by the burden of tax and social contributions on wages, as well as by the legal administrative regulation to control economic activity. In various OECD countries, these costs are greater than the wage effectively earned by the worker – providing a strong incentive to work in the shadow economy. More detailed theoretical information on the labour supply decision in the underground economy is given by Lemieux, Fortin and Fréchette (1994) who use micro data from a survey conducted in Quebec City (Canada). In particular, their study provides some economic insights regarding the size of the distortion caused by income taxation and the welfare system. The results of this study suggest that hours worked in the shadow economy are quite responsive to changes in the net wage in the regular (official) sector. Their empirical results attribute this to a (mis-) allocation of work from the official to the informal sector, where it is not taxed. In this case, the substitution between labour market activities in the two sectors is quite high. Their findings indicate, that “participation rates and hours worked in the underground sector also tend to be inversely related to the number of hours worked in the regular sector“[32] These results demonstrate a large negative elasticity of hours worked in the shadow economy with respect both to the wage rate in the regular sector as well as to a high mobility between sectors.

A study by Kucera and Roncolato (2008, p. 321) also deals with informal employment. They address two issues of crucial importance to labour market policy:

(i)              Intensive labour market regulations as one (major) cause of informal employment, and
(ii)            So-called “voluntary” informal employment.

 Kucera and Roncolato give a theoretical overview on both issues and also a survey of a number of empirical studies which analyze mainly the effect of official labour market regulations on informal employment where they find a significant and quantitatively important influence

 

 

4.2.       Shadow Economy Labour Force

4.2.1 World Wide Aspects – Latest Results

The following results on the extent of the shadow economy labour force are based on the OECD and World Bank database on informal employment in major cities and in rural areas, as well as on other sources mentioned in the footnotes of this chapter. The values of the shadow economy labour force are calculated in absolute terms, and as a percentage of the official labour force, under the assumption that the shadow economy in rural areas is at least as high as in the cities. This is a conservative assumption, since in reality it is likely to be even larger.[33] Survey techniques and, for some countries, the MIMIC-method and the method of the discrepancy between the official and actual labour force are used for estimation.

One of the latest studies has been undertaken by the OECD (2009) which provides world wide figures. This study[34] concludes that in many parts of the world over the period 1990 to 2007 informal employment is the norm, not the exception,. More than half of all jobs in the non-agricultural sectors of developing countries – over 900 million workers – can be considered informal. If agricultural workers in developing countries are included, the estimate rises to roughly 2,000 million people. The share of informal employment is also shown in figures 4.1 and 4.2 for Latin America and South East Asia. In some regions, including Sub-Saharan Africa and South Asia, over 80% of non-agricultural jobs are informal. Most informal workers in the developing world are self-employed and work independently, or own and manage very small enterprises. According to the OECD study (2009), informal employment is a result of both, people being excluded from official jobs and people voluntarily opting out of formal structures, e.g. in many middle income countries incentives drive individuals and businesses out of the formal sector.

To summarize, the OECD concludes  that 1.8 billion people work in informal jobs, compared to 1.2 billion who benefit from formal contracts and social security protection. Informal activity, excluding the agricultural sector, accounted for three quarters of the jobs in Sub-Saharan Africa, more than two thirds in South and South East Asia, a half in Latin America, the Middle East and North Africa, and nearly one quarter in transition countries. If agriculture is included, the informal share of the economy in the above mentioned regions is even higher (e.g. more than 90 % in South Asia). The OECD study  also finds that that more than 700 million informal workers “survive” on less than US$1.25 a day and some 1.2 billion on less than US$2 a day.

The study also concludes that the share of informal employment tends to increase during periods of economic turmoil. For example, during the Argentine economic crisis (1999-2002), the countries’ “official” economy shrank as by almost one fifth while the share of informal employment expanded from 48 to 52 percent. One can clearly see that even under strong economic growth, the share of non-agricultural employment and, the share of informal employment is rising strongly.



Figure 4.1: Informal Employment and GDP in Latin America and Southeast Asia

Latin America

 

 

Southeast Asia

 

Source: OECD, Is Informal Normal?, Paris, 2009.

 

Table 4.1.:  Estimates of the Size of the “Shadow Economy Labour Force” in Some OECD Countries 1974-1998



Table 4.2.: Development of „full time shadow economy workers“ and of illegal foreign workers of 1000 people in Germany, Austria and Switzerland over the period 1995 to 20091).


 

4.2.2 OECD-Countries

4.2.2.1 General Results

In Table 4.1 the estimates for the shadow economy labour force in highly developed OECD countries (Austria, Denmark, France, Germany, Italy, Spain and Sweden) are shown.[35] In Austria the shadow economy labour force is estimated at between 500,000 to 750,000 or 16% of the official labour force (mean value) in the years 1997-1998. In Denmark during the 1980s and 1990s the portion t of the Danish population engaged in the shadow economy ranged from 8.3% of the total labour force (in 1980) to 15.4% in 1994 – quite a remarkable increase of the shadow economy labour force; almost doubling over 15 years.

In France (in the years 1997/98) the shadow economy labour force reached between 6% and 12% of the official labour force or between 1.6 and 3.2 million workers.  In Germany this figure rose from 8% to 12% in 1974 to 22% (8 million) in the year 1997/98. For France and Germany this is again a very strong increase in the shadow economy labour force. In other countries the amount of the shadow economy labour force is also quite large too: in Italy 30%-48% (1997-1998), Spain 11.5%-32% (1997-1998) and Sweden 19.8 % (1997-1998). In the European Union about 30 million people were engaged in shadow economic activities in the years 1997-1998 and in all European OECD countries 48 million were working illicitly. These figures demonstrate that the shadow economy labour market is lively and may provide an explanation, why for example in Germany, one could observe such a high and persistent level of unemployment up to the year 2007.

Additionally, Table 4.1 contains a preliminary calculation of the total GDP per capita (including the official and the shadow economy GDP per capita) in US$ as a result of shadow labour market activities. In all of the countries investigated, total GDP per capita was much higher – on average in all countries around 40% greater than official data implied. This clearly shows that the productivity in the shadow economy is roughly as high as in the official economy – a clear indication, that the work effort (i.e. the incentive to work effectively) is as strong in the shadow economy as in the official one. In general these results demonstrate that the shadow economy labour force has reached a remarkable size in the developing countries as well as in highly developed OECD countries, even though the calculation still might have many errors.

Data about the share of the shadow economy labour force in highly developed countries is scarce. For three countries (compare Table 4.2), we have some data, these are Austria, Germany and Switzerland, where we have a shadow economy labour force calculated in terms of the number of full time shadow economy workers[36]. If we consider Germany, the full time shadow economy workers were about 7 million in 1995 increasing to 9.4 million in 2004 and decreasing again to 8.2 million in 2009. If we consider the illegal foreign shadow economy of full time workers in Germany, they are roughly one twelfth of the full time German or legal resident shadow workers. In 1995 they were 878,000, increasing to 1.2 million in 2002 and decreasing again to 968,000 in 2009. In Austria, the full time shadow economy workers numbered 575,000 in 1995, increased to 798,000 in 2004 and have decreased since to 713,000 in 2009. Table 4.2 clearly shows that the figures of the shadow economy work force in these highly developed countries Austria, Germany and Switzerland, are much smaller than the ones in developing countries.

 

4.2.2.2 Case Studies of Denmark and Germany

In this section  two case studies about the size and development of shadow economy labour markets in Denmark and in Germany will be presented and discussed.

The first study carried out by Hvidtfeldt, Jensen and Larsen (2011), investigated the size and development of undeclared work in Denmark over the years 2008-2010, but also going back to the year 1994. Hvidtfeld, Jensen and Larsen (2011, p. 1) claim that more than half of all Danes purchase undeclared work in the course of a year. The authors got this finding with the help of an interview survey of 2,200 randomly-selected Danes who were conducted by the Rockwool Foundation Research Unit in 2010. According to their survey, 52% of those questioned had had undeclared work done for them in the previous year and had paid in cash, in kind or through return services. Their survey (2011, p. 2) also showed that an additional 28% of Danes would be willing to buy more undeclared services, even though they had not actually done so within the previous year. In total, 80% of the Danish population are potential customers for undeclared work and only 20% said, they would refuse to pay for such services.

In table 4.3 the proportions of Danish men undertaking undeclared work in the previous 12 month (year 2010) are shown. Table 4.3 clearly says that 48% of such undeclared work is done in the construction sector, followed by agriculture with 47% and motor vehicle sales and repairs with 43%. The smallest amount is done in the public and personal services with 26%.

 

Table 4.3: Proportions of men who had carried out undeclared work in the previous 12 months

 

SECTOR

in percent

Building and construction

48%

Agriculture (incl. gardening), fishing and mineral extraction

47%

Motor vehicle sales and repairs

43%

Energy and water supply

(38%)

Manufacturing

36%

Transport and telecommunications

31%

Hotel and restaurant

(30%)

Financial and business services

28%

Public and personal services

26%

Retail, wholesale and repair (excluding motor vehicles)

26%

OVERALL

32%

Note: Figures in parentheses are based on fewer than 50 observations.

Source: Rockwool Foundation Research Unit, 2011, p. 5.

The authors also investigated the amount of undeclared work since the year 1994 and they come to the conclusion that Danes do roughly as much undeclared work today as they did 15 years ago. The latest figures, from 2008-2010, show that every fourth adult Dane carried out some kind of undeclared work in the course of a year. Those involved spent around three hours per week working on the undeclared labour market. This figure has not changed since mid 1994. Calculations of the amount of undeclared work in relation to GDP also show that the situation remains largely unchanged with such work standing at a level of 2.8%..[37] An interesting result of this study is the acceptance of black labour among the Danish population.

 

Table 4.4 A: Proportion of the Danish population  who find it acceptable that a schoolgirl should earn undeclared income for babysitting, 2007-2008.

 

If she earns DKK 200 per week

84%

If she earns DKK 300 per week

70%

Source: Rockwool Foundation Research Unit, March 2011, p.14

 

Table 4.4 B: Proportion of the Danish population who find it acceptable that a skilled tradesman should earn undeclared income, 2007-2008.

 

If he earns DKK 10,000 per year

47%

If he earns DKK 50,000 per year

27%

Source: Rockwool Foundation Research Unit, March 2011, p. 14.

 

The Danish population were asked to evaluate whether it wsa acceptable for a school girl to earnsome money in the shadow economy and the same question was asked about a skilled tradesman. The results are reported in table 4.4. They clearly show that there is a high acceptance of shadow economy labour work for a school girl compared to a well-established skilled tradesman with a reasonable high income. Not astonishing for the school girl the acceptance is 70% earning 300 DKK per week and 84% earning 200 DKK per week. For the tradesman to earn additional 10.000 DKK per year the acceptance drops down to 47% (below 50%) and if he earns more than 50.000 DKK per year the acceptance is only 27%. This demonstrates that Danes tolerate shadow economy earnings from low income earners but not from high income earners.

 

Additionally a new study by Haigner, Jenewein, Schneider and Wakolbinger (2011) investigated the informal labour supply and demand in Germany for the year 2010. In this study the authors use data from a representative survey among 2,104 German residents, conducted in May 2010. Questions on illegal behavior such as providing informal labour supply and demand are highly confidential and it is possible that survey respondents who have engaged in such activities do not want to declare that they have done so. In order to encourage honest answers, the interviewees have been read the following text (translated from German):

 

“The next set of questions deals with what is called black work. We survey these questions on behalf of a group of independent scientists, who will process the results within a study. By black work they mean the following: One works for somebody and agrees not to pay taxes for the payment. Both partners are better off because no value added tax, income tax or social security contributions are paid. Such procedures are frequently occurring, for example, in cleaning, gardening, baby-sitting, waiting at table, writing or programming. Also, work which is not taxed is prevalent in construction, renovation, car repair and taking care of elderly people.”

 

Moreover, if interviewers recognized that the interviewees hesitated to answer the questions on informal labour supply and demand, they would again note that the interview is confidential and that answers are confidential, anonymous and only for scientific use. The question on informal labour supply was as follows (translated from German):

 

“Have you, during the last year, worked for somebody in the way described above (black work)?”

 

The question on informal labour demand was (again translated from German):

 

“Have you, during the last year, demanded black work?”

 

Moreover, informal labour suppliers have been asked about the reasons for doing so, on the time when they have done such works (working time, weekends, vacations,…), on the sector in which they have worked, on the number of hours they have worked per month and on the estimated hourly wage they have received.

 

In order to grasp the general attitudes towards informal labour supply and demand, survey respondents were asked to declare their degree of agreement with a set of 13 statements on the topic. Possible answers were indicated on a scale ranging from -4 (total disagreement) to +4 (total agreement). Figure 4.2 shows the results. While there seems to be considerable awareness of the fact that informal labour reduces the tax revenues of the state, many people claim, on the other hand, that high tax rates make attractive the informal labour market. Interestingly, many people like informal labour because it is more rapidly available and more flexible than official labour, which is widely perceived to be subject to too strict regulations. Moreover, people, on average, do not agree with the statement that informal labour suppliers should be reported to the police, nor would many people report them to the police themselves. This shows that informal labour is, in Germany, perceived as a rather trivial offense.


Figure 4.2: Attitudes towards informal labour supply and demand


Source: Haigner et al. (2011)



(1)   Informal Labour Supply

Out of 2,104 respondents, 285 (13.55%) declared that they have been supplying informal labour during the year before the survey. Among men, the fraction of informal labour suppliers was significantly higher (18.82%) than among women (8.58%) [38]. Moreover, the authors find above average fractions of informal labour suppliers among the unemployed (29.29%) and people out of labour force “due to other reasons” (23.53%). Among pensioners (5.10%) and housewives and housemen (9.52%), the fraction is below the average, while it is close to the average among students (14.44%), apprentices (11.75%), self-employed persons (15.17%) and dependent employees (15.60%). Among persons not having completed compulsory education and those who have completed an apprenticeship, informal labour suppliers are overrepresented (24.24% and 20.41%), while they are underrepresented among persons with a university degree (7.19%).

(2)   Sectors of Informal Labour Supply

Figure 4.3 shows in which sectors informal labour supply takes place. Not surprisingly, crafts and technical occupations and private household services have the highest relative importance. In both branches, more than a quarter of informal labour suppliers are engaged. About 15% of informal labour suppliers declare to be working in other services, gardening/agriculture and construction. Fractions do not add up to 100% since multiple answers have been allowed.


Figure 4.3: sector of informal labour supply

 Source: Haigner et al.(2011)

 
(3)   Directly reported reasons

The authors have directly asked the survey respondents (declaring that they engaged in informal labour supply) for the reasons for doing so. Again, the results are as expected. Figure 4.4 shows that four in five declare to supply informal labour in order to earn more money. All other noted reasons are far less important. However, it is interesting to see, for example, that one in about eight informal labour suppliers do so because they do not want to lose transfer payments. In the German social system, pensioners as well as unemployment benefit and social assistance recipients face a full transfer cut and thus implicit marginal tax rates of 100% and more if they would officially supply labour.

 

More than one in five informal labour suppliers claim that a reason for doing so is that others do it as well. This result is in line with our (earlier reported) finding that German residents perceive, in general, informal labour supply and demand as a rather trivial offence. By the same token, slightly more than ten percent of informal labour suppliers claim that they do so because their customers want the demanded work to be done unofficially. Another ten percent say that they like the flexibility of informal labour supply.

Figure 4.4: Directly reported reasons for supplying informal labour


Source: Haigner et al. (2011)


 

4.2.3 Developing Countries – Earlier Results[39]

Table 4.5 shows the results of estimates of the shadow labour force for countries in Africa. Gambia has the largest shadow economy labour force with 80% of the official one, followed by Guinea with 79%, Benin with 76.9%, Rwanda with 75%, and the Republic of Congo with 50%.[40] Zimbabwe has the lowest rate of illicit work with 33.9% of the official labour force. For African countries, the figures show considerable variation and should really be seen as first and preliminary results. Under the assumption that this informal or shadow economy labour force is as productive as the official economy and contributes per capita a similar added value, the shadow economy GNP can be calculated, which is also shown in Table 4.4. Gambia has the largest shadow economy as a percentage of official GNP with 41.2%, followed by Guinea with 36.9%, and Rwanda with 38.7%. On average, the supply of illicit work in these 33 African countries is 54.2% (of the official labour force) and 24.6% of the population.

Table 4.6 illustrates the results for some Asian countries. Here, China, India, and Indonesia have to be examined more closely, as they are the three largest countries in Asia (regarding population). In China, it is estimated that 160 million people work in the shadow economy – 21.9% of the official labour force.[41] In India, 217 million people work illicitly – 50% of the official labour force. In Indonesia, 36.7 million people engage in shadow economic activities, this corresponds to 37.4% of the official labour force. In Pakistan, 29.4 million people or 60% work in the shadow economy. One realizes that in Asia the shadow economy labour force is quite high, a result also found in the OECD (2009) study. On the whole, the shadow economy labour force in these Asian countries makes up 46.5% of the official labour force and 19.6% of the population.

In Table 4.7 some Latin and South American states are shown. In absolute terms, Brazil has the highest shadow economy labour force with 37.4 million (49.2% of the official labour force), followed by Colombia with 9.7 million or 53.8%. Both Ecuador with 58.8%, and Peru with 54.6%, have a quite high rate of illicit work. Chile has the lowest rate, with 40%, as well as Paraguay with 41%, and El Salvador with 47.3% of the official labour force. Overall, the shadow economy labour force in these nine countries is 49.6% of the official labour force and 20.3% of the population.

 

4.2.4 Transition Countries – Earlier Results

Nine transition countries were analyzed (see Table 4.8.). Armenia has the highest rate with an illicit labour force of 75.5% of the official labour force, followed by Croatia with 70%, and Bulgaria with 63%. In absolute figures, Russia has by far the largest shadow economy labour force among the transition countries with 32.9 million illegal workers, followed by Rumania with 4.7 million, and Kazakhstan with 2.8 million. Slovenia has the lowest black labour force with 31%.[42] Generally, the shadow economy labour force in these nine countries is 49% of the official labour force and 23.9% of the population. Here the findings should be interpreted with great care, as these “transition” countries switched from a planned economy to a market economy and due to this official statistics had a lot of preliminary figures and calculation methods were difficult to use.


Table 4.5: Shadow economy labour force in Africa




Table 4.5: Shadow economy labour force in Africa – cont.



Table 4.6:  Shadow economy labour force in Asia



 

Table 4.7:  Shadow economy labour force in Latin and South America



 

Table 4.8:  Shadow economy labour force in transition countries




 

4.2.5 Developing and Transition Countries – Latest Results

Compared to the first estimates presented in the subchapters 4.2.3 and 4.2.4 there have been some newer studies with respect to estimate the size and development of the shadow economy labour force[43]. Kucera and Roncolato (2008, p.321) deal with informal employment. They address issues of crucial importance to labour market policy; first, the intensive labour market regulation is one major cause of informal employment, and second, the so called voluntary informal employment. Kucera and Roncolato give a theoretical overview on both issues and also a survey of a number of empirical studies, in which the effect of the official labour market regulations on informal employment is analyzed, where they find a significant and quantitatively important influence.

 

In Table 4.9 the share of informal employment in total non-agricultural employment by five-year period and by country and region is presented. From the table one clearly sees that in all countries the share of informal employment has increased over time. The share of informal employment in Algeria in the period of 1975-1979 was 21.8% and increased in the period of 2000-2007 to 41.3%. In India informal employment rose in the period of 1985-1989 from 76.2% to 83.4% from 1995-1999. In the Republic of Mali the share of informal employment (in percent of total non agricultural employment) was 63.1% from 1975-1979, and increased to 81.8% in 2000-2007. Table 5.5 clearly demonstrates that there is a very strong positive trend in the share of informal employment (in percent of total non agricultural employment).

 

Table 4.10 provides the share of informal employment in total non-agricultural employment by country, region and gender. If one splits up the share of informal employment (in percent of total non agricultural employment) by gender, we generally observe, that the share of women is significantly higher than the share of men. In North Africa (countries Algeria, Morocco, Tunisia, Egypt) the share of informal employment of women is 43.3% and the one of men 49.3% over the period 1990-1999. In Sub-Saharan Africa the share of women is 84.1%, the one of men 63.0%. In Latin America the share of women is 56.2% and the share of men 47.1%. Only in the region of West Asia and in the transition countries the share of men of informal employment is higher than the one of women. In West Asia (countries Lebanon, West Bank and Gaza Strip, Syria, Turkey, Yemen) the share of women is 31.1%, the share of men 43.4%.

 

In the Transition countries (Kyrgyzstan, Moldova, Russia) the share of women is 22.3% and the share of men 27.2%. We also see here some remarkable differences. In general the share of informal employment is rather large worldwide and certainly has important policy implications.

 

4.3 Further Indicators of the Shadow Labour Force

In this part some further indicators of the shadow economy labour force are discussed, as there are no exact measures of the shadow economy labour force, all measures which serve as proxies are shown.

 


Table 4.9: Share of Informal Employment in Total Non-Agricultural Employment by five-year period and by country and region (in percent)

 

 

 

 

Period

 

 

 

Region

1975-79

1980-84

1985-89

1990-94

1995-99

2000-07

North Africa

 

 

 

 

47.5

47.3

Algeria

21.8

 

25.6

 

42.7

41.3

Morocco

 

56.9

 

 

44.8

67.1

Tunisia

38.4

35.0

39.3

 

47.1

35.0

Egypt

58.7

 

37.3

 

55.2

45.9

Sub-Saharan Africa

 

 

 

76.0

 

 

Benin

 

 

 

92.9

 

 

Burkina Faso

 

 

70.0

77.0

 

 

Chad

 

 

 

74.2

95.2

 

Guinea

 

64.4

 

71.9

86.7

 

Kenya

 

 

61.4

70.1

71.6

 

Mali

63.1

 

78.6

90.4

94.1

81.8

Mauritania

 

69.4

80.0

 

 

 

Mozambique

 

 

 

73.5

 

 

Niger

62.9

 

 

 

 

 

Senegal

 

76.0

 

 

 

 

South Africa

 

 

 

 

 

50.6

Zaire (now Democratic Republic of Congo)

 

59.6

 

 

 

 

Zambia

 

 

 

58.3

 

 

Latin America

 

 

 

 

54.2

 

Argentina

 

 

 

47.5

53.3

 

Bolivia

 

 

 

56.9

63.5

 

Brazil

 

 

 

60.0

60.0

51.1

Chile

 

 

 

 

35.8

 

Colombia

 

 

 

 

38.4

 

Costa Rica

 

 

 

 

44.3

 

Dominican Republic

 

 

 

 

47.6

 

Ecuador

 

 

 

 

53.5

74.9

El Salvador

 

 

 

 

56.6

 

Guatemala

 

 

 

56.1

 

 

Haiti

 

 

 

 

92.6

 

Honduras

 

 

 

 

58.2

 

Mexico

 

 

 

55.5

59.4

50.1

Panama

 

 

 

 

37.6

49.4

Paraguay

 

 

 

 

65.5

 

Peru

 

 

 

 

 

67.9

Venezuela

 

 

 

38.8

46.9

49.4

South and Southeast Asia

 

 

 

 

69.9

 

India

 

 

76.2

73.7

83.4

 

Indonesia

 

 

39.2

 

77.9

 

Pakistan

 

 

39.0

 

64.6

 

Philippines

 

 

 

70.5

72.0

 

Thailand

 

 

57.4

51.4

51.5

 

 

 

Table 4.9: Share of Informal Employment in Total Non-Agricultural Employment by five-year period and by country and region (in percent) – cont.

Region

 

 

Periode

 

 

 

 

 

1975-79

1980-84

1985-89

1990-94

1995-99

2000-07

West Asia

 

 

 

 

 

43.2

Iran

 

 

43.5

 

 

48.8

Lebanon

 

 

 

 

 

51.8

West Bank and Gaza Strip

 

 

 

 

 

43.4

Syria

 

 

 

41.7

42.9

30.7

Turkey

 

 

 

 

30.9

33.2

Yemen

 

 

 

57.1

 

51.1

Transition countries

 

 

 

 

 

24.1

Kyrgyzstan

 

 

 

 

 

44.4

Moldova

 

 

 

 

 

21.5

Romania

 

 

 

 

5.4

22.0

Russia

 

 

 

 

 

8.6

Sources: OECD 2009, pages 34-35; and Charmes (2002, 2007, 2008) for the ILO Women and Men in the Informal Economy, 2002. For the most recent period: Heintz and Chang (2007) for the ILO, and for West Asia: Charmes (2007 and 2008). For detailed sources, see annex 2 A4. Stat.Link http://dx.doi.org/10.1787/533451351643

 


Table 4.10:  Share of Informal Employment in Total Non-Agricultural Employment, by

                   country, region and gender (in percent), 1190s and 2000s

Region

1990 - 1999

 

2000-2007

 

 

Women

Men

Women

Men

North Africa

43.3

49.3

 

 

Algeria

40.6

43.1

 

 

Morocco

46.8

44.0

 

 

Tunisia

39.2

53.2

 

 

Egypt

46.5

56.9

38.6

47.2

Sub-Saharan Africa

84.1

63.0

77.1

62.6

Benin

97.3

87.0

 

 

Chad

95.2

59.5

 

 

Guinea

86.7

65.6

 

 

Kenya

83.1

59.1

 

 

Mali

 

 

89.2

74.2

South Africa

58.4

43.6

64.9

51.0

Latin America

56.2

47.1

59.5

55.4

Bolivia

74.4

55.0

 

 

Brazil

67.3

54.7

52.3

50.2

Chile

43.9

30.9

 

 

Colombia

44.0

34.1

 

 

Costa Rica

48.0

42.1

 

 

Dominican Republic

49.7

46.5

 

 

Ecuador

 

 

76.9

73.2

El Salvador

68.6

45.7

 

 

Guatemala

69.4

46.5

 

 

Honduras

65.5

73.6

 

 

Mexico

55.0

54.3

53.5

47.8

Panama

40.8

35.5

50.4

48.7

Peru

 

 

72.0

65.1

Venezuela

47.3

46.7

52.1

47.5

South and Southeast Asia

72.7

70.2

 

 

India

85.7

82.9

 

 

Indonesia

77.2

78.0

 

 

Philippines

73.4

70.8

 

 

Thailand

54.3

49.1

 

 

West Asia

31.1

43.4

35.4

44.4

Lebanon

 

 

60.0

44.4

West Bank and Gaza Strip

 

 

20.2

46.8

Syria

34.6

42.8

 

 

Turkey

19.1

29.1

32.2

33.4

Yemen

39.7

58.2

29.3

52.8

Transition countries

 

 

22.3

27.2

Kyrgyzstan

 

 

40.9

47.1

Moldova

 

 

18.4

28.0

Russia

 

 

7.6

9.6

Source: OECD 2009, page 47; and Charmes (2002), for the ILO Women and Men in the Informal Economy, 2002. For the most recent period: Heintz and Chang (2007) for the ILO, and for West Asia:

 

4.3.1 The Share of Self-Employed in total Employment

 Figure 4.5: Share of self employed in total employment (average: from 1995 to 2008 or the

                    latest year available)



Source: OECD, STAN database, 2010, Paris; quoted from OECD (1022), p. 17, Figure 7.


The share of self-employment in total employment can be seen as one indicator of the significance of the shadow economy labour force. If we consider Figure 4.5 we clearly see, that Greece, Korea, Poland, Italy, Portugal have the highest share of self-employed (in percent of total employed) with a value of 48 % for Greece, a value of 26 % and 25 % for Poland and Italy respectively. As these values are highly correlated with the size of the shadow economy it is quite obvious that at least a great part of this self-employed work in the shadow economy, (too).

 

4.3.2 The Share of Employees not covered by Social Security Contributions

In Table 4.11 the share of employees without social security contributions are shown for some European countries. If we compare the single countries in Table 4.11 we clearly see that there are vast differences between the listed countries where in some the share of employees without any social security advantage is pretty high. The leader is Poland with a value between 65% and 57 % in the years 2007 and 2008, followed by France with 51,9 % and then followed by Spain with 41.5 %. Again the values in Table 4.3 give some indication about the size of the shadow economy labour force, as it is quite plausible that at least some of these work in the shadow economy.

 

Table 4.11: Share of employees not covered by social security contributions

 

Share of non-insured employees in

 

 Country

2007

2008

 

 

 

Austria

35,4

34,5

Belgium

38,8

36,2

Czech Republic

40,8

40,4

Estonia

34,6

33,9

Finland

23,0

23,5

France

51,9

--

Greece

37,1

37,3

Hungary

40,6

42,4

Iceland

13,4

13,3

Ireland

39,8

40,3

Italy

40,0

39,3

Luxembourg

34,6

32,6

Netherlands

17,7

21,6

Norway

12,2

13,2

Poland

65,3

57,0

Portugal

35,1

38,5

Slovak Republic

39,1

38,5

Slovenia

24,7

25,2

Spain

41,5

41,4

Sweden

22,7

22,0

Source: OECD calculation based on EU-SILC 2007 and 2008, quoted from OECD (2011), p. 18, Table 1.

 

4.3.3 The Share of Workers without an Employment Contract

In Figure 4.6 the share of workers without an employment contract is shown for various European countries. The leading country is Turkey with 44 %, followed by Ireland 39 % and Greece 39 %, then Israel 38 %. The lowest countries are Sweden and Finland with only 2 or 1 % share of workers without an employment contract.

 

Figure 4.6: Share of workers without an employment contract, 2006



Source. European Social Survey (ESS), 2008, quoted from OECD (2011), p. 18, Figure 8.

 

4.3.4 Summary of the Measures of Informal Employment

In an OECD study (OECD 2008) the organization focused on informal employment in seven member countries, the Czech Republic, Hungary, Korea, Mexico, Poland, the Slovak Republic and Turkey. Table 4.12 which is taken from this OECD study, nicely shows the alternative measures of informal employment and undeclared work. It is grouped in employees in informal job and own account workers, unpaid family workers, multiple job holders with undeclared income. The highest values for almost all of these seven categories has Mexico, followed by Turkey and then by Korea. Table 4.12 clearly shows, how difficult measurement of the informal or shadow economy labour force is.. In all categories there might be some shadow economy labour work, but it is very difficult to evaluate how large this figure is.

 

Table 4.12: Alternative measures of informal employment and undeclared work, Year 2006.

Source: OECD (2008), Paris, quoted from OECD (2011), p. 20, Table 3.1.

 

4.3.5 Shadow Economy Workers with Illegal Immigrant Background

In a number of European countries there are data  about shadow economy workers coming from illegal immigrants. A first estimate, undertaken again by OECD (2011), is shown in Figure 4.7. Considering these figures one realizes that the size again is increased with 4.4 % of total employment the highest in Greece, followed by the United States with 3.2 %, by Italy 2.0 % and at the lowest end Norway and Sweden with 0.5 % and 0.4 % of total employment. This table “confirms” the values of a similar size in table 4.2 for Germany, Switzerland and Austria. Both tables clearly show that illegal immigrant employment takes place, but from the size perspective it is rather small for most countries.


Figure 4.7: Illegal employed immigrants as a share of total employment1



1  The estimates of the number of employed illegal immigrants are calculated using the number of irregular migrants and assuming the same employment rate for illegal immigrants as for legal migrants.

Source: OECD Calculations based on OECD International Migration Outlook (2009) and OECD Economic Outlook Database (2010), quoted from OECD (2011), p. 21, Figure 10.

 

 

5.   SHADOW ECONOMY AND UNEMPLOYMENT[44]

There has been some discussion on the size of the shadow economy labour force and on the reasons for it, but comparatively little attention has been given to the relationship between unemployment and working in the shadow economy. As Tanzi (1999) points out, “the current literature does not cast much light on these relationships even though the existence of large underground activities would imply that one should look more deeply at what is happening in the labour market”[45]. Therefore, the objective of the paper by Bajada and Schneider (2009) is to examine the extent of participation in the shadow economy by the unemployed. They investigate the relationship between the unemployment rate and the shadow economy. Previous literature on this topic has suggested that the relationship between these two variables is ambiguous, predominantly because a heterogeneous group of people working in the shadow economy exists and there are also various cyclical forces at work, producing a net effect that is weakly correlated with unemployment. They provide a suggestion for disentangling these cyclical effects, so as to study the component of the shadow economy that is influenced directly by those who are unemployed. They refer to this effect as the ‘substitution effect’ which typically increases during declining periods of legitimate economic activity (and increasing unemployment). Equipped with this approach for measuring the ‘substitution effect’, they discover that a relationship exists between changes in the unemployment rate and shadow economy activity. Then by examining the growth cycle characteristics of the ‘substitution effect’ component of the shadow economy Bajada and Schneider (2009) determine that the growth cycles are symmetric (in terms of steepness and deepness) and that changes in the unemployment rate, whether positive or negative, had similar impacts on changes in the substitution effect component. They suggest that the shadow economy is a source of financial support during periods of unemployment for those genuinely wanting to participate in the legitimate economy. Although this does not exclude the possibility that long-term unemployed may also be participating in the shadow economy, it would appear that short-term fluctuations in unemployment directly contribute to short-term fluctuations in the shadow economy.

When Bajada and Schneider consider the various unemployment support programs across 12 OECD countries, there appears to be no real systematic relationship between the generosity of the social security systems and the nature of short-term shadow economic activity by the unemployed. Even the various replacement rates across the OECD countries appear to have little consequence on the rate at which the unemployed take on and cut back shadow economy activity. There is however some evidence to suggest that extended duration spell in unemployment lasts anywhere between less than 3 months to approximately 9 months.

On the whole Bajada and Schneider argue that dealing with unemployment participation in the shadow economy as a way of correcting the inequity it generates, is best handled by more stringent monitoring of those receiving unemployment benefits rather than reducing replacement rates as a way of encouraging re-integration into the work force. A strategy of reducing replacement rates would not only fail to maintain adequate support for those experiencing financial hardship during periods of unemployment, it is likely to have little impact on reducing participation by the unemployed who are willing and able to engage in shadow economy activity.



6. ADJUSTMENTS OF SHADOW ECONOMY MEASURES OF VALUE ADDED IN NATIONAL ACCOUNTS

Due to the strong increase of the size and development of the shadow economy (in value added terms) a number of countries have undertaken adjustments of this non observed economy measures in their national accounts[46]. OECD (2011, p.14) has detected seven adjustments necessitated by activities, which are included in some countries in their national accounts.

A1: A producer deliberately does not register to avoid tax and social security obligations.

A2: A producer deliberately does not register as a legal identity or as entrepreneur because he is involved in illegal activities.

A3: A producer is not required to register because he has no market output.

A4: A legal person not surveyed due to reasons such as business register is out of date or updating procedures are inadequate.

A5: Registered entrepreneurs may not be surveyed since  the statistical office does not conduct a survey of registered entrepreneurs.

A6: Cross output is under reported and/or intermediate consumption is overstated.

A7: Data is either not complete or not collected or not directly collectable and/or data are incorrectly handled.

If one considers those countries, which do some adjustment, one amazing thing is, that big adjustment takes place in Italy between 14.8 and 16,7 % and in Poland between 7.8 and 15.7 %. The largest adjustment takes place in Russia with 24.3 % and the smallest one in the United States with 0.8 %. Table 6.1 clearly shows that of those countries that do some adjustment, their adjustment is vastly different compared to other countries. Hence, this leads to the problem,  that for these countries starting from Australia and ending with the United States the measures of the size and development of the shadow economy in percent of official GDP is biased, because a part of the shadow economy has been already considered. This is certainly a further difficulty when comparing the size and development of shadow economies between countries.

 

 Table 6.1: Adjustment of non-observed economy in National Accounts, around 2000

O=according to output approach; E-according to expenditure approach; I=according to income approach; L=Lower bound; U=Upper bound; Source: United Nations, UN, 2008, quoted from OECD (2011), p. 12, Table 2.1.

 

7. CONCLUSIONS

In this paper some of the most recent developments in research on the shadow economy and undeclared work in highly developed OECD, developing and transition countries are shown. Besides the figures of the illicit work force in the rural and non-rural sector some other measures of the shadow economy labour force, like unpaid family workers, own account workers, multiple job holders, etc. are presented. The studies based on the MIMIC approach also report strong effects of tax morale, but underline the higher importance of tax policies and state regulation to increase the shadow economy.

The discussion of the recent literature shows that economic opportunities for employees, the overall situation on the labour market, not least unemployment are crucial for an understanding of the dynamics of the shadow economy. Individuals look for ways to improve their economic situation and thus contribute productively to aggregate income of a country. This holds regardless of their being active in the official or the unofficial economy.

Returning to the headline of my paper “The shadow economy and shadow economy labour force: What do we (not) know?”, there is certainly some knowledge about the size and development of the shadow economy and the size and development of the shadow economy labour force. For developing countries, the shadow economy labour force has reached a remarkable size according to OECD (2009) estimates, which is that in most developing countries the shadow economy labour force is higher than the official labour force. What we do not know are the exact motives that people work in the shadow economy and what is their relation and feeling if a government undertakes reforms in order to bring them back into the official economy?. Hence, more detailed micro studies are needed to obtain a more in-depth knowledge about people’s motivation to work either the shadow economy and/or in the official one.



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[1].         See Andreoni, Erard and Feinstein (1998) for the authoritative survey, Feld and Frey (2007) or Kirchler (2007) for broader interdisciplinary approaches, or the papers by Kirchler, Maciejov­sky and Schneider (2003), Kastlunger, Kirchler, Mittore and Pitters (2009), Kirchler, Hoelzl and Wahl (2007).

[2].         The authoritative scientific work on tax morale is by Torgler (2007). See also Torgler (2002) for a survey on experimental studies and Blackwell (2009) for a meta-analysis. 

[3].         This paper focuses on the size and development of the shadow economy for uniform countries and not for specific regions. Recently studies have been undertaken to measure the size of the shadow economy as well as the “grey” or “shadow” labour force for urban regions or states (e.g. California). See e.g. Marcelli, Pastor and Joassart (1999), Marcelli (2004), Chen (2004), Williams and Windebank (1998, 2001a, b), Flaming, Hayolamak, and Jossart (2005), Alderslade, Talmage and Freeman (2006), Brück, Haisten-DeNew and Zimmermann (2006). Herwartz, Schneider and Tafenau (2009) and Tafenau, Herwartz and Schneider (2010) estimate the size of the shadow economy of 234 EU-NUTS regions for the year 2004 for the first time demonstrating a considerable regional variation in the size of the shadow economy.

[4].         This definition is used, e.g., by Feige (1989, 1994), Schneider (1994a, 2003, 2005) and Frey and Pomme­rehne (1984). Do-it-yourself activities are not included. For estimates of the shadow economy and the do-it-yourself activities for Germany see Bühn, Karmann und Schneider (2009) or Karmann (1986, 1990).

[5].         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) or Feld and Larsen (2005, p. 25).

[6].         See 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.

7.         Compare also Feld and Schneider (2010).       

[8].         For a broader discussion of the definition issue see Thomas (1992), Schneider, Volkert and Caspar (2002), Schneider and Enste (2002, 2006) and Kazemier (2005a, b).

[9].         With this definition the problem of having classical crime activities included could be avoided, be­cause neither the MIMIC procedure nor the currency demand approach captures these activities: e.g. drug dealing is independent of increasing taxes, especially as the included causal variables are not linked (or causal) to classical crime activities. See e.g. Thomas (1992), Kazemir (2005a, b) and Schneider (2005).

[10]        However, compare chapter 6, where it is shown, that shadow economy activities are partly captured in the

          official statistics in some countries.

[11].      For the strengths and weaknesses of the various methods see Bhattacharyya (1999), Breusch (2005a, b), Dell’Anno and Schneider (2009), Dixon (1999), Feige (1989), Feld and Larsen (2005), Feld and Schneider (2010), Giles (1999a, b, c), Schneider (1986, 2001, 2003, 2005, 2006), Schneider and Enste (2000a, b, 2002, 2006), Tanzi (1999), Thomas (1992, 1999).

[12].      These methods are presented in detail in Schneider (1994a, b, c, 2005), Feld and Schneider (2010) and Schneider and Enste (2000b, 2002, 2006). Furthermore, these studies discuss advantages and disadvantages of the MIMIC- and the money demand methods as well as other estimation methods for assessing the size of illicit employment; for a detailed discussion see also Feld and Larsen (2005).

[13].      This indirect approach is based on the assumption that cash is used to make transactions within the shadow economy. By using this method one econometrically estimates a currency demand function including independent variables like tax burden, regulation etc. which “drive” the shadow economy. This equation is used to make simulations of the amount of money that would be necessary to generate the official GDP. This amount is then compared with the actual money demand and the difference is treated as an indicator for the development of the shadow economy. On this basis the calculated difference is multiplied by the velocity of money of the official economy and one gets a value added figure for the shadow economy. See footnote 10 for references discussing critically this method.

[14] This part is taken from Feld and Schneider (2010, pp.115-116).

[15].      The earlier study by Isachsen and Strøm (1985) for Norway does also not properly analyze the impact of deterrence on undeclared work.

[16] About 8000 observations overall.

[17].      An earlier study by Merz and Wolff (1993) does not analyze the impact of deterrence on undeclared work.

[18].      See Thomas (1992), Lippert and Walker (1997), Schneider (1994a, b, c, 1997, 1998a, b, 1999, 2000, 2003, 2005, 2009), Johnson, Kaufmann, and Zoido-Lobatón (1998a, b), Tanzi (1999), Giles (1999a), Mummert and Schneider (2001), Giles and Tedds (2002) and Dell’Anno (2003) as more recent ones.

[19].      The importance of regulation on the official and unofficial (shadow) economy is more recently investigated by Loayza, Oviedo and Servén (2005a, b). Kucera and Roncolato (2008) extensively analyze the impact of labour market regulation on the shadow economy.

[20] (Johnson, Kaufmann and Zoido-Lobatón 1998a, p. I).

 

[21] See e.g. Johnson et al. (1998a, b), Friedman et al. (2000), Dreher and Schneider (2009), Dreher, Kotsogiannis and Macorriston (2007, 2009), as well as Teobaldelli (2011), Schneider (2010) and Buehn and Schneider (2010).

[22].      The importance of this variable with respect to theory and empirical relevance is also shown in Frey (1997), Feld and Frey (2002a, 2002b, 2007) and Torgler and Schneider (2009).

[23].      Using this indicator variable the problem might arise that this variable is influenced by state regulation, so that it is not exogenous; hence the estimation may be biased, this problem applies for almost all causal variables!

[24].      The variable currency per capita or annual change of currency per capita is heavily influenced by banking innovations or payment; 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).

[25].      Compare also Schneider, Buehn and Montenegro (2010), and Feld and Schneider (2010).

[26].      In my paper there is no extensive discussion about the various methods to estimate the size and development of the shadow economy; I do also not discuss the strength and weaknesses of each method. See Schneider and Enste (2000), Schneider (2005), Feld and Larsen (2005, 2008, 2009), Pedersen (2003), and Giles (1999a, b, c).

[27].      Due to the extraordinarily low rate of participation based on a relatively small sample, the results for 2006 must be in­terpreted with extra great care. The results for 2006 should be re­garded as tentative and, at the most, as an indication that black activities do not appear to have increased from 2005 to 2006.

[28].      This procedure is described in great detail in the paper Dell’Anno and Schneider (2004, 2009).

[29].      Pioneering work in this area has been done by L. Frey (1972, 1975, 1978, 1980), Cappiello (1986), Lubell (1991), Pozo (1996), Bartlett (1998) and Tanzi (1999).

[30].      Compare also one of the latest OECD report with the title “Is Informal Normal: Toward More and Better Jobs” by the OECD (2009).

[31].      For developing countries some literature about the shadow labour market exists (Dallago (1990), Pozo (1996), Loayza (1996), Chickering and Salahdine (1991) and OECD (2009)).

[32] Lemieux, Fortin and Fréchette 1994, p. 235.

[33].      The assumption that the shadow economy labour force is at least as high in rural areas as in major cities, is a very modest one and is supported by Lubell (1991). Some authors (e.g., Lubell (1991), Pozo (1996), and Chickering and Salahdine (1991)) argue that the illicit labour force is nearly twice as high in the countryside as in urban areas. But since no (precise) data exists on this ratio, the assumption of an equal size may be justified arguing that such a calculation provides at least minimal figures.

[34]         The following results and figures are taken from the OECD (2009), executive summary.

[35].      Shadow economy labour force consists of estimated full-time “black” jobs, including unregistered workers, illegal immigrants and second “black” jobs.

[36].      These numbers of full time shadow economy workers are a “fiction”, because most people in these three countries are “part time” shadow economy workers. They are only calculated here to make the figure comparable to the work force in the official economy. Let me repeat, these full time shadow economy workers do not exist for Germany, Austria and Switzerland.

[37].  In this study a lot of interesting facts are reported, like who is working, like distribution of men and

       women in the shadow economy, like, how much is paid per hour in the different sectors, etc. Also it is

       investigated whether high income households demand more or less shadow economy work and it

       seems they demand more.

[38] Mann-Whitney U-Test, N=2104, p=0.00

[39]. This parts follows closely Schneider and Enste (2002, part 5, pp. 43-51).

[40]. These high values strongly indicate that a considerable number of these illicit workers also have (at

    least part-time) jobs in the official economy. Yet, the number of these ‘double-job-holders’ (official and

     unofficial at the same time) is unknown and may differ from country to country. The ratio of the shadow

     economy labour force as a percentage of the official one should be interpreted very cautiously, since

     it is unclear what this ratio actually stands for. Hence, an interpretation is very difficult. In addition,

     making comparisons between different countries is very complicated and such comparisons provide

     only  a very crude picture. Maybe the rate of the shadow economy labour force as a percentage of the

     population   is a somewhat better gauge.

[41]. The figure for China should be interpreted with great care as this country still has a communist regime

      with some regions under a capitalist system.

[42] Of the official labour force.

[43] See also Feld and Schneider (2010) and Schneider, Buehn and Montenegro (2010).

[44]. This part is taken from Feld and Schneider (2010).

[45] Tanzi (1999) p. 347.

[46] The following text closely follows OECD (2011) pages 11 and 12, Box 2. Also the Table is taken from there.



 
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Badri Bastakoti
576 days ago
good article




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