Research Paper

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The Shadow Economy
Unemployment, Self Employment, and Recession
By Bobby Canell
Abstract: This paper explores the topics of the shadow economy, employment data and
recession. I analyze the impact of unemployment and self employment on the shadow economy
in a unique way by looking at the impact during different business cycle periods. I estimate
panel data from seven countries over the 1999-2007 time frame. The conclusions based on the
data results are twofold. First, it shows that the unemployment rate has a significant effect on the
shadow economy during recessions but not during periods of growth. Second, I conclude that
self employment has a statistically significant effect on the shadow economy regardless of
business cycle period.
1. Introduction
The definition of the shadow economy that I use for this research is a legal good or
business that is evading taxes or regulations. An example of this is a person who owns a
restaurant but does not claim all of the income he receives from the restaurant, thus evading
taxes. The business is legal, however, the tax evasion is illegal. This would not include
someone who is selling illegal drugs, prostitution, gun running or other illegal businesses or
illegal goods. This version of the shadow economy is sometimes known as the informal sector.
This paper explores how unemployment and self employment effect the shadow economy
in recession years and in growth years in order to compare the results. This is an important and

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understudied area of research. This is particularly relevant for practical uses for governments
during recessions and may influence government policies in regards to unemployment, taxes,
retraining and reeducation. This research may be used to examine unemployment benefits,
minimum wage laws, Federal Pell Grants, and tax structure, among other related issues.
Other scholarly papers have observed the effects of unemployment on the shadow
economy. The argument for positive correlation is that people officially unemployed will seek to
supplement their income causing the shadow economy to increase as unemployment increases.
Empirically, however, studies have shown an ambiguous relationship between the
unemployment rate and the shadow economy, this does not account directly for the business
cycle (Schneider and Enste (2000)). This paper will take into account business cycles and takes
the stance that unemployment and the shadow economy are positively correlated as shown by the
data. That is, as the unemployment rate rises during a recession, the shadow economy rises as
well. Bajada and Schneider state that "when the economy experiences significant contractions
and the unemployment rate increases, this unemployed labour may resurface as clandestine
employment in the shadow economy. Although compensated by welfare benefit payments, the
decrease in disposable income from being unemployed may foster some dependency on shadow
economy activity." (Bajada and Schneider (2009)).
2. Literature Review
This research adds to the literature by examining links between labor markets and the
shadow economy in different business cycle periods i.e., recession periods and growth periods.
There are two main fields of study in the literature related to this research. The first field is the
measurement of the shadow economy and papers in that field are finding ways to measure the

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size of the shadow economy. The second field is freedom and includes those papers that
examine how freedom, in particular economic freedom effect the shadow economy.
Relevant to this paper is how the shadow economy is calculated. There are several
attempts in other scholarly papers to accurately measure the shadow economy. This field of
study's main goal is to acquire an effective and accurate measurement tool for the shadow
economy. While economic freedom may play a role in this it is not the main focus of the paper
[(Dobre and Alexandru (2009)), (Ruge (2010)), (Schneider, Buehn (2010) and Schneider et al.
(2012)), and (Thießen (2010))]. Papers in this field may include tax variables and tax morale in
their analysis although the main focus is not examining the effects of economic freedom. This is
one way of measuring the shadow economy. Other papers will use money supply or electricity
use to determine the size of the shadow economy (Takala and Viren (2010)).
The second field relevant to this paper that is widely researched is regarding economic
freedom. One viewpoint in this field is to examine freedom as it pertains to legal structure and
property rights [(Enste (2010)), (Freytag, et al. (2013)), (Pieroni and d'Agostino (2012)), and
(Britton et al. (2004))]. The negative correlation between a stronger legal structure and property
rights and the shadow economy has been documented in these studies. Other papers examine the
link between economic freedom and economic growth, they find a positive correlation between
the two [(de Haan and Sturm (1999)) and (Panahi et al. (2014))].
I use data from Elgin and Oztunah (2012) for my measurement of the shadow economy.
This is measured as a percentage of GDP and is calculated using a two sector dynamic general
equilibrium model using capital stocks, formal employment, and aggregate consumption. This
model may include indirectly the other measurements such as electricity through the aggregate

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consumption variable. Missing from the literature however, is an inclusion of business cycles.
This is where this paper will make its significant contribution.
Studies that examine the effect of unemployment on the shadow economy [(Dobre and
Alexandru (2009)) and (Bajada and Schneider (2009))] find that a rise in the unemployment rate
in the formal sector leads to a larger shadow economy. These papers do not distinguish between
recession and expansion, whereas this paper will.
3. Data
This research uses panel country data from the following 7 countries: Canada, France,
Germany, Italy, Japan, United Kingdom, and United States. These countries were chosen
because of the availability and reliability of the data. The data are annual and stretch from 1999
to 2007. This timeframe was also chosen due to availability of the data. As stated earlier the
dependent variable in this analysis is the shadow economy, which is measured as a percentage of
GDP and this data comes from Elgin and Oztunah (2012). Elgin and Oztunah's model uses the
definition of the shadow economy that this paper has adopted, as seen above.
There are two independent variables of interest in this paper. The first variable of interest
is the unemployment rate. This is calculated by the World Bank: World Development Indicators.
As stated above the arguments can be made for a positive coefficient and a negative coefficient.
I argue that the unemployment rate and the shadow economy have a positive correlation during a
recession and as the unemployment rate rises during a recession the shadow economy will rise as
well. This paper shows that the unemployment rate has a larger effect on the shadow economy
during a recession as opposed to a period of expansion. The theoretical justification is that

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during a recession people may claim unemployment and still attempt to work in the informal
sector to supplement their income, even though they are technically considered unemployed.
During a recession the decrease in disposable income for the unemployed, even with government
welfare, will force the unemployed to seek supplemental income in the shadow economy.
The second variable of interest is self employment. This is measured as a percentage of
total employment, and is also taken from the World Bank: World Development Indicators. The
positive correlation between the percentage of self employed and the shadow economy has been
thoroughly examined [(Dobre and Alexandru (2009)) and (Schneider, Buehn (2010))]. The
theoretical validation is that the self employed have an easier time evading taxes due to filing tax
returns, and being able to take more deductions (Bordignon, and Zanardi (1997)). An example
of this is someone that mows lawns, and is paid in cash. It would be very easy for that person to
not claim some or even all of that income to the government. Even those who own their own
business can find it easier to cheat on their taxes. This is true because these groups are allowed a
greater number of deductions (Bordignon, and Zanardi (1997)). This can also be due to
operating a cash business that lacks proper accounting and cash handling procedures.
There are three control variables that are used in the regressions. The first of the control
variables is credit market regulations. This figure comes from the Fraser Institute (2009) and it
is used to measure the rules under which banks and financial institutions operate. The higher the
number, the less restrictions placed on the bank or financial institution. Theory would suggest
that the greater the freedom, the lower the shadow economy. This would give us a negative
correlation between credit market regulations and the shadow economy. That is, as the freedom
in the credit market increases the shadow economy decreases.

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The second control variable in this paper is the natural log of the gross domestic product
per capita. This information was taken from the World Bank: World Development Indicators.
Schneider et al. note that "The situation of the official economy also plays a crucial role in
people’s decision to work or not to work in the shadow economy" and "in a booming official
economy, people have a lot of opportunities to earn a good salary and 'extra money' in the
official economy. This is not the case in an economy facing a recession; more people try to
compensate their income losses from the official economy through additional shadow economy
activities." (Schneider et al. (2010)).
The final control variable is government size and is calculated by the World Bank: World
Development Indicators as government expenditures divided by gross domestic product. This is
used to capture the effects of government fiscal freedom and taxation. This will theoretically
have a positive correlation with the shadow economy. As this is a proxy for taxes and
government fiscal freedom theory would suggest that greater freedom and lower taxes, thus a
lower size of government would reduce the size of the shadow economy, thus a positive
correlation.
In order to account for the recession I acquired a recession dummy variable calculated by
the St. Louis Federal Reserve. This dummy variable includes all seven of the countries used in
this analysis. With the aim of discovering the impact of the business cycle, I create an
interaction term between my independent variables of interest and the recession dummy.
In this paper I use a two stage least squares regression. For my independent variables of
interest I include instruments to correct for endogeneity caused by reverse causality. By using a

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lag variable as an instrument, I am able to correct for the endogeneity of the variables. I also run
the unemployment and self employment regressions separately to prevent multicollinearity.
The model I use is a linear regression model. It is as follows:
Shadowit = b0+b1Laborit+b2(Labor*Recession)+b3Recession+b4Xit+Eit
Where the dependent variable shadow is the shadow economy as a percentage of GDP, b0 is the
constant, b1Laborit is the labor variable unemployment or self employment,
b2(Labor*Recession) is the interaction term between the labor variable and the recession dummy,
b3Recession is the recession dummy, b4Xit are the control variables and Eit is the error term.
There are two questions that are central to this paper and must be answered. These
hypotheses are as follows:
Hypothesis 1: Does the labor market effect the size of the shadow economy?
Hypothesis 2: Does the effect differ over the course of the business cycle?
In order to answer these questions I use an interaction term to receive results for a period of
expansion and a period of recession. The cases and their results are listed below. The dummy
variable, recession, will be at 1 when there is a recession and 0 when there is an expansion. This
means in case 1, labor is multiplied by 0 and b2 then equals zero. In case 2, the recession dummy
takes on a value of 1 and so the total effect of the labor market variable during a recession will be
b1+b2.
Case 1: The effect of the labor market on the shadow economy during an expansion.
Result 1: b1

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Case 2: The effect of the labor market on the shadow economy during a recession.
Result 2: b1+b2
Case 1 will be able to answer part of hypothesis 1, as the result will show the labor market effect
on the size of the shadow economy during an expansion. Case 2 will be able to answer the
remaining part of hypothesis 1, as the result will show the labor market effect on the size of the
shadow economy during a recession. With case 1 and case 2 I can answer hypothesis 2 as I will
be able to compare the results during an expansion against a recession.
4. Results
In order to answer the hypotheses this paper first splits the variables in two, first testing
the effect of unemployment on the shadow economy and then the effect of self employment on
the shadow economy. As stated earlier, if these terms were in the same equation there would be
issues of multicollinearity which would give us large standard errors and may corrupt the
integrity of the regressions. An ordinary least squares regression for each of the independent
variables of interest are below in Table 1 and Table 2. Table 1 has the variable unemployment
rate and Table 2 has the variable self employment.

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Table 1 OLS for Unemployment Rate
Number of
Observations 55
R-sq: within 0.9
between 0.2355
overall 0.0859
Shadow Economy Coefficient Std. Err.
credit regulation -0.1493541*** (.0459118)
ln(GDP/capita) -0.9605744*** (.1246489)
government size -0.0371855*** (.0114875)
unemployment rate 0.0952535*** (.0188006)
recession -0.0203878*** (.0515425)
_constant 27.36512*** (1.466404)
***, **, * denote significance at the 1, 5,and10% significance levels respectively.
Table 2 OLS for Self Employment
Number of
Observations 55
R-sq: within 0.908
between 0.6536
overall 0.6257
Shadow Economy Coefficient Std. Err.
credit regulation -0.337825 (.0547985)
ln(GDP/capita) -1.402253*** (.125591)
government size -0.049865*** (.0111295)
self employment 0.1858914*** (.0330549)
recession -0.0940393* (.0477768)
_constant 29.51248*** (1.276637)
Table 1 includes my variable of interest unemployment rate, and Table 2 includes my
variable of interest self employment. I ran separate regressions for these variables as running

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them together would give me multicollinearity. These results show that the unemployment rate
and self employment are both statistically significant at the 1% level. Table 1 shows that the
unemployment rate has a positive coefficient of .095 and is highly significant. Table 2 shows
that self employment has a coefficient of positive .186 and is highly significant. These tables are
ordinary least squares regressions and there is a possibility of endogeneity as stated above. In
order to fix the problem of endogeneity I use a two stage least squares regression using
instrumental variables for unemployment and self employment. Table 1 shows all variables at a
significance level of 1%. The unemployment variable shows a positive correlations with the
shadow economy. In Table 2 we can see that the natural log of GDP per capita, government
size, and self employment are significant at the 1% level and the recession dummy is significant
at the 10% level.
In Table 3 and Table 4 I will use a two stage least squares regression that will include
lags for instrumental variables. This will correct for endogeneity in the regressions.
Table 3 2SLS Unemployment Rate without Recession
Number of
observations 55
F( 4, 44) 96.33
Prob > F 0
Total (centered) SS 4.269024337
Total (uncentered) SS 4.269024337
Residual SS 0.431329442
Centered R2 0.899
Uncentered R2 0.899
Root MSE 0.09479

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shadow economy Coefficients Std. Err.
unemployment rate 0.1072768*** (0.0216619)
credit regulation -0.1345091*** (0.0416766)
ln(gdp/cap) -0.9195335*** (0.0815261)
government size -0.0366752*** (0.010924)
Table 3 shows the labor variable, unemployment rate, without the recession dummy or
interaction term included. In this table all variables are statistically significant. This table is
created by using an two stage least squares regression. The unemployment rate uses the lag
instrument. This corrects for problems of endogeneity in the variable. There is a positive
coefficient for unemployment of .107 and this is significant at the 1% level. The positive
coefficient for unemployment means as unemployment increases the shadow economy increases
as well. This gives us general base readings without the recession. Table 4 will show the same
test with the self employment variable instead of the unemployment variable.
Table 4 2SLS Self Employment without Recession
Number of
observations 55
F( 4, 44) 91.53
Prob > F 0
Centered R2 0.8944
Uncentered R2 0.8944
Root MSE 0.09692

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self employment .2366604*** (.051929)
credit regulation .0484832 (.0703607)
ln(gdp/cap) -1.314028*** (.1113135)
government size -.0516299*** (.0114551)
Table 4 shows the labor variable self employment. As in Table 3, this regression uses a
lag instrument for self employment. As indicated in the table, self employment, the natural log
of gdp per capita and government size are all significant at the 1% level. Self employment has a
positive correlation with the size of the shadow economy at .237, so as self employment as a
percentage of total employment increases the shadow economy increases as well.
Table 5 displays a two stage least squares regression with the unemployment rate and the
recession variable. Table 5 uses instrumental variables for both unemployment and the labor -
recession interaction term.
Table 5 2SLS Unemployment with Recession
Number of
observations 55
F( 6, 42) 12.82
Prob > F 0
Centered R2 0.5151
Uncentered R2 0.5151
Root MSE 0.2077

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unemployment rate -0.01661 (0.1748659)
unemployment rate
recession 0.1826418 (0.2570392)
credit regulation -0.2811101 (0.2195638)
ln(gdp/cap) -0.5262378 (0.6503786)
government size -0.0368064 (0.0239711)
recession -1.220624 (1.699862)
By combining the unemployment rate and unemployment rate in a recession from Table
5, using a linear combination of estimators, I receive the output in Table 6. This will show the
coefficient for case 2, which is the total effect of the unemployment rate during a recession on
the shadow economy.
Table 6 Linear Combination of Employment Rate and Employment Rate Recession
(1) .1660318 (0.1024958)
This coefficient is a combination of the unemployment rate, which is my b1, and my
interaction term which is b2. This coefficient is significant at the 10.5% level. As stated above

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for case 1, the effect of the labor market on the shadow economy during an expansion is b1. That
is -0.01661 and it is slightly negative and insignificant. This is the unemployment rate's effect
on the shadow economy during an expansion. For case 2, the effect of the labor market on the
shadow economy during a recession, I use this linear combination coefficient, which is .1660318
and this number is significant at the 10.5% level. This means that during a growth period the
size of the shadow economy is not affected by the unemployment rate. However during a
recession the unemployment has a positive, significant effect on the size of the shadow economy.
During an expansion the unemployment rate has a coefficient of -.017 and is insignificant. This
would mean that a one percentage point increase in unemployment would decrease the shadow
economy .017 percentage points. However, when we examine Table 6, this shows a positive
coefficient of .166 and is significant at the 10.5% level. This means that a one percentage point
increase in the unemployment rate during a recession would increase the size of the shadow
economy by .166 percentage points.
In Table 7 I use the same method I used in Table 5, a two stage least squares regression,
to get estimates for self employment and self employment in a recession. Both self employment
and self employment in a recession have a lag instrument.

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Table 7 2SLS Self Employment with Recession
Numberof observations 55
F( 6, 42) 55.74
Prob> F 0
Total (centered)SS 4.269024337
CenteredR2 0.8866
UncenteredR2 0.8866
Root MSE 0.1004
shadoweconomy Coefficients Std.Err.
Selfemployment .2907857* (.1610926)
selfemploymentrecession -.0057603 (.0368748)
Creditregulation .0673129 (.0975146)
Ln(gdp/cap) -1.572231*** (.344274)
Governmentsize -.0556641*** (.016031)
Recession -.0254533 (.4590824)
As I did with Table 5, I will use a linear combination of estimators to show the
coefficient for case 2, which is the total effect of self employment as a percentage to total
employed during a recession on the shadow economy. This linear combination of estimators is
Table 8.
Table 8 Linear Combination of Self Employment and Self Employment Recession
shadoweconomy Coefficients Std.Err.
(1) .2850254** (.1267343)

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This coefficient is a combination of the self employment rate, which is my b1, and my
interaction term which is b2. This coefficient is significant at the 5% level. For case 1, the effect
of the labor market on the shadow economy during an expansion is b1. That is .2907857 and it is
positive and significant at the 10% level. This is self employment's effect on the shadow
economy during an expansion. For case 2, the effect of the labor market on the shadow economy
during a recession, I use this linear combination coefficient, which is .2850254 and this number
is significant at the 5% level. The difference between b1 and b2 is very small and these numbers
are both positive which means that self employment as a percentage of total employed has a
large and statistically significant effect on the size of the shadow economy regardless of business
cycles. During an expansion period self employment has a positive coefficient of .291 and is
significant at the 10% level. This means that during an expansion a one percentage point
increase in the self employment as a percentage of total employment would result in a .291
percentage point increase in the size of the shadow economy. In a recession period the
coefficient is a positive .285 and is significant at the 5% level. This means that during a
recession if the self employment as a percentage of total employment would result in a .285
percentage point increase in the size of the shadow economy.
5. Conclusion
The shadow economy is an ever increasing topic for economic research, with greater
information and longer timelines the research in the field continues to grow. One of the
underrepresented topics in the shadow economy research is examining business cycles and their
effect on the shadow economy. In this paper I attempted to do just that by using two stage least

17
squares regressions on my two main variables of interest, the unemployment rate and the self
employed as a percentage of total employed. There are two main questions that this paper
attempts to answer. The first question is does the labor market effect the size of the shadow
economy? The second question is does the effect differ over the course of the business cycle?
By using the data from Table 5 and 6 we can see the effect that the unemployment rate
has on the size of the shadow economy, both in expansion periods and recession periods. I found
that the unemployment rate during a period of growth is insignificant, however during a period
of recession the effect of the unemployment rate on the size of the shadow economy is
significant.
When considering the hypotheses for the unemployment rate this study finds that the
unemployment rate does have a significant effect on the size of the shadow economy. The paper
also finds that the effect does indeed differ over the course of the business cycle as seen in Table
5 and Table 6, as there is a significant difference between the unemployment rate during a period
of expansion and a period of recession.
Self employment as a percentage of total employed is the second labor variable that this
papers asks its two questions. Again, the paper asks does the labor market effect the size of the
shadow economy, and secondly does the effect differ over the course of the business cycle?
By using data from Table 7 and Table 8 this paper shows that self employment as a
percentage of total employment has a positive coefficient and is significant in both recession and
growth. This positive coefficient means that as more people become self employed as a
percentage of the total employed then the size of the shadow economy will increase.

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Examining the hypotheses for self employment this paper shows that self employment
has a significant effect on the size of the shadow economy. Self employment, however, does not
have a significant difference between a growth period and a recession period. The paper
concludes that business cycles do not affect the coefficients of self employment's effect on the
size of the shadow economy in a significant way.
This paper addresses a topic that may have policy implications. The shadow economy is
a topic that governments would like to have more information about as it will affect the countries
tax revenues and social spending. Thus countries will attempt to reduce the size of the shadow
economy. Specific policies that can be employed due to this paper would be a focus on reducing
unemployment during recessions. As unemployment has a significant positive effect on the size
of the shadow economy a reduction of unemployment during a recession would reduce the size
of the shadow economy. This could mean more emphasis on retraining and reeducation during
recessions to lower the unemployment rate during recessions.
This paper found that self employment has a positive effect on the size of the shadow
economy regardless of business cycles. As governments attempt to reduce the size of the
shadow economy they could look to reducing the effect that self employment has on the size of
the shadow economy. In order to do this there are several possible policies that could be
implemented. Changes to the tax code are a possible solution. This could include moving
towards a consumption based tax as opposed to an income based tax. Reducing regulations and
bureaucracy would also reduce the incentive for people to circumvent these regulations.

19
Bibliography
Bajada, Christopher and Friedrich Schneider (2009): "Unemployment and the Shadow Economy
in the oecd", Revue économique 5/2009 (Vol. 60).
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Economisti e Annali di Economia 56, 169-210.
Britton, Charles R and Richard K Ford and David E R Gay (2004): "Economic Freedom,
Property Rights, and the Informal Underground Economy" Journal of Private Enterprise; Fall
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de Haan, Jakob and Jan-Egbert Sturm (1999): "On the relationship between economic freedom
and economic growth" European Journal of Political Economy Vol. 16 2000. pp. 215–241.
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Freytag, Andreas and Friedrich Schneider and Sebastian Spiegel (2013): " The Influence of
Economic Freedom on the Shadow Economy in OECD and European Countries: Some
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Schneider, Friedrich and Andreas Buehn and Claudio E. Montenegro (2010): " Shadow
Economies All over the World New Estimates for 162 Countries from 1999 to 2007" The World
Bank Development Research Group Poverty and Inequality Team & Europe and Central Asia
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Data Sources
Elgin and Oztunah (2012)
Fraser Institute (2009)- www.freetheworld.com.
St. Louis Federal Reserve
World Bank World Development Indicators

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