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. Tax morale or experimental studies on tax compliance are also beyond the scope of this paper 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.
2.1. Defining the Shadow Economy
It is difficult to make a precise definition of the shadow economy. It is often defined as comprising all currently unregistered economic activities that contribute to the officially calculated Gross National Product. 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”. 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
Non Monetary Transactions
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.
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. 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.
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. 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. 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).
Discussions continue regarding the “appropriate” methodology to assess the scope of the shadow economy. There are three methods of assessment:
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. 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 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.
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.
Table 2.2: Main Causes of the Increase of the Shadow Economy
Factors influencing the shadow economy
Influence on the shadow economy (in %)
(1) Increase of the Tax and Social Security Contribution Burdens
(2) Quality of State Institutions
(4) Specific Labour Market Regulations
(5) Public Sector Services
(6) Tax Morale
Influence of all Factors
(a) Average values of 12 studies.
(b) Average values of empirical results of 22 studies.
Source: Schneider (2009)
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.
Share of direct taxation
λ1 = 0.392**
(in % of GDP)
Share of indirect taxation
λ2 = 0.184(*)
Share of social security contribution
λ3 = 0.523**
Burden of state regulation (index of labour market regulation, Heritage Foundation, score 1 least regular, score 5 most regular)
λ4 = 0.226(*)
Quality of state institutions (rule of law, World Bank, score -3 worst and +3 best case)
λ5 = -0.314*
Tax morale (WVS and EVS, Index, Scale tax cheating always justified =1, never justified =10)
λ6 = -0.593**
Unemployment rate (%)
λ7 = 0.316**
GDP per capita (in US-$)
λ8 = -0.106**
λ 9= -0.613**
(in % of population 18-64)
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)
Change of local currency
λ12 = 0.320**
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. 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. 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.
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.
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. Moreover, by definition every activity in the shadow economy involves a “shadow labour market” to some extent: 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.
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“ 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:
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
4.2.1 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. 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 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
Source: OECD, Is Informal Normal?, Paris, 2009.
126.96.36.199 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. 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. 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.
188.8.131.52 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
Building and construction
Agriculture (incl. gardening), fishing and mineral extraction
Motor vehicle sales and repairs
Energy and water supply
Transport and telecommunications
Hotel and restaurant
Financial and business services
Public and personal services
Retail, wholesale and repair (excluding motor vehicles)
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%.. 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
If she earns DKK 300 per week
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
If he earns DKK 50,000 per year
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.
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%) . 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.
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.
4.2.3 Developing Countries – Earlier Results
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%. 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. 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%. 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 4.6: 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. 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)
Zaire (now Democratic Republic of Congo)
South and Southeast Asia
Table 4.9: Share of Informal Employment in Total Non-Agricultural Employment by five-year period and by country and region (in percent) – cont.
West Bank and Gaza Strip
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
1990 - 1999
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:
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
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.
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”. 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. 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.
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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. 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).
. 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.
. 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.
. 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).
. 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).
. 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).
. 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).
. 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).
 However, compare chapter 6, where it is shown, that shadow economy activities are partly captured in the
official statistics in some countries.
. 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).
. 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).
. 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.
 This part is taken from Feld and Schneider (2010, pp.115-116).
. The earlier study by Isachsen and Strøm (1985) for Norway does also not properly analyze the impact of deterrence on undeclared work.
 About 8000 observations overall.
. An earlier study by Merz and Wolff (1993) does not analyze the impact of deterrence on undeclared work.
. 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.
. 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.
 (Johnson, Kaufmann and Zoido-Lobatón 1998a, p. I).
 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).
. 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).
. 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!
. 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).
. Compare also Schneider, Buehn and Montenegro (2010), and Feld and Schneider (2010).
. 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).
. 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.
. This procedure is described in great detail in the paper Dell’Anno and Schneider (2004, 2009).
. 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).
. Compare also one of the latest OECD report with the title “Is Informal Normal: Toward More and Better Jobs” by the OECD (2009).
. For developing countries some literature about the shadow labour market exists (Dallago (1990), Pozo (1996), Loayza (1996), Chickering and Salahdine (1991) and OECD (2009)).
 Lemieux, Fortin and Fréchette 1994, p. 235.
. 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.
 The following results and figures are taken from the OECD (2009), executive summary.
. Shadow economy labour force consists of estimated full-time “black” jobs, including unregistered workers, illegal immigrants and second “black” jobs.
. 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.
. 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.
 Mann-Whitney U-Test, N=2104, p=0.00
. This parts follows closely Schneider and Enste (2002, part 5, pp. 43-51).
. 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.
. 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.
 Of the official labour force.
 See also Feld and Schneider (2010) and Schneider, Buehn and Montenegro (2010).
. This part is taken from Feld and Schneider (2010).
 Tanzi (1999) p. 347.
 The following text closely follows OECD (2011) pages 11 and 12, Box 2. Also the Table is taken from there.