Econometrics report determinants affecting underground economy of european union from 2008 to 2021

Abstract 5 Introduction 6 1. Overview of the model 7 1.1. Literature review 7 1.1.1. Related published paper 7 1.1.2. Research gap 8 1.2. Theoretical framework 8 1.3. Empirical model 10 2. Model specification and data 13 2.1. Methodology 13 2.1.1. Method used to collect data 13 2.1.2. Method used to analyze data 13 2.2. Data description 13 2.2.1. Data description and interpretation 13 2.2.2. Correlation matrix between variables 15 3. Estimated model and statistical inferences 16 3.1. Testing the econometric model 16 3.2. Estimated model 17 3.3. Statistical inference 18 3.3.1. Testing for the overall significance of the model 18 3.3.2. Testing for the statistical significance of the individual coefficients 19 3.4. Testing for violations of the model 20 3.4.1. Detection of multicollinearity 20 3.4.2. Detection of heteroskedasticity 20 3.4.3. Detection of serial correlation – autocorrelation 21 3.4.4. Detection of crosssectional correlation – autocorrelation 21 3.4.5. Detection of omitted variables 22 3.5. Fixing the model 22 4. Recommendations 25 4.1. Policy implications 25 4.2. Limitations and future research 26 Conclusion 27 References 28 Dofile 29

FOREIGN TRADE UNIVERSITY FACULTY OF INTERNATIONAL ECONOMICS

-RESEARCH PAPER

Determinants affecting underground economy of european

Table of Contents

1.1 Literature review 7

1.1.1 Related published paper 7

1.1.2 Research gap 8

1.2 Theoretical framework 8

1.3 Empirical model 10

2 Model specification and data 13 2.1 Methodology 13

2.1.1 Method used to collect data 13

2.1.2 Method used to analyze data 13

2.2 Data description 13

2.2.1 Data description and interpretation 13

2.2.2 Correlation matrix between variables 15

3 Estimated model and statistical inferences 16 3.1 Testing the econometric model 16

3.2 Estimated model 17

3.3 Statistical inference 18

3.3.1 Testing for the overall significance of the model 18

3.3.2 Testing for the statistical significance of the individual coefficients 19 3.4 Testing for violations of the model 20

3.4.1 Detection of multicollinearity 20

3.4.2 Detection of heteroskedasticity 20

3.4.3 Detection of serial correlation – autocorrelation 21

3.4.4 Detection of cross-sectional correlation – autocorrelation 21 3.4.5 Detection of omitted variables 22 3.5 Fixing the model 22

4.1 Policy implications 25 4.2 Limitations and future research 26

The underground economy, also known as the informal, unofficial, or shadoweconomy, includes illegal activities and unreported income, from monetary or bartertransactions The relationship between the increase in the unofficial sector andeconomic growth has yet to be clearly demonstrated by theoretical research The mainproblem is that it would reduce the economy’s growth rate

The seven explanatory variables including GDP Growth rate, Tax Rate, Index ofEconomic Freedom, Unemployment Rate, Consumer Price Index, and Internet Userswill be examined to test their relationship with the underground economy In order toexamine the correlation between the mentioned variables, panel data was gatheredfrom 27 European countries between 2008 and 2021

Based on the results, quantitative analysis and explanation will be done to show theinfluence of each, the unaffected factors, and other removed ones from the model.Furthermore, other recommendations and perspectives of writers are offered for futurepolicy-making of governments

Keywords: underground economy, European Union

The mainstream economy and the underground economy have coexisted side by sidefor as long as human civilization has existed The underground economy has a biginfluence on a nation's overall economy even if it is not widely acknowledged

The underground economy goes by many names: shadow, informal, unobserved,unrecorded or unofficial economy The underground economy, in contrast to itsmainstream counterpart, involves not only illicit activity but also unreported earningsfrom the creation of legal products and services, whether through monetary or barterexchanges

It is challenging to determine the size of underground economies because, by theirvery nature, they are not subject to governmental control; as a result, the economicactivity neither generates tax returns nor appears in official statistical reports.However, even though the transactions are undetected, keeping track of outgoingexpenses can provide a sense of statistics In other words, spending that is not reflected

in documented transactions presumably reflects the scope of black market activities.indocumented transactions presumably reflects the scope of black market activities

In order to transfer the bulk of the illegal enterprises into the legal sector and enablethe government and policy framework, it is crucial to examine this issue in detail,identify the core factors that determine its size and growth, and develop policies thattarget those causes The issues with the shadow economy are too numerous anddetrimental to overlook, and their continued presence will ultimately lower overall taxreceipts and harm the macroeconomic policy framework

For this reason, we made the decision to investigate the elements influencing theeconomy of 27 nations over a 13-year period This paper will contribute to a deeperunderstanding of those important elements, including their significance for theoperation of the shadow economy as well as their involvement in the economies of thevarious nations

In this report, the content is organized as follows: The first part will be the state ofknowledge in this field, the second part will present the model specification and dataanalysis and validations in the determinants of the shadow economy The third partwill be about the estimated model and statistical inferences And the final part willsummarize all the results as well as present the limitations and directions for futureresearch

1 Overview of the model

1.1 Literature review

1.1.1 Related published paper

The very first beginning of studying the underground economy defined it as the output

of products and services (legal or illegal) not included in the official estimates of GDP(Smith, 1994) Schneider (1994) and Lubell (1991) believed that it is the activity that

is not included in the computation of the gross national product

As the confirmation of choosing the explained variable, Underground Economy Sizewas used by Friedrich Schneider’s studies (2004, 2005, 2010) The widespread of thisestimation has been recalculated by Mai Hassan and Friedrich in 2016 to give furtherinformation about the extent of the underground economy

First and foremost, the tax rate is mentioned in most of the articles about this topic.The higher the overall tax rate or lower monitoring, the stronger motivation for taxevasion and underreporting wages (Schneider and Williams 2013, Hassan andSchneider 2016) The tax burden can affect labor and increase the labor supply for theinformal economy in which they want to lower the tax wedge and work in the informalfield to minimize the gap between official wages and after-tax earnings (Thomas1992; Johnson, Kaufmann, and Zoido-Lobatón 1998a, b; Dell’Anno, Gomez-Antonioand Alanon Pardo 2007)

The connection between unemployment and the size of the underground economyshould be taken into consideration There may be a negative impact as the increase inthe unemployment rate would lead to a decline in both the formal and informal sectors(Gulzar et al., 2010; M Hassan & Schneider, 2016)

The inflation rate has been indicated as explanatory for the underground economy(Erdinç, 2016) The tax rate and tax distortions can be reduced since the government

no longer needs to impose high taxes in order to generate more revenue throughseigniorage Lower taxes encourage formal sector activity as opposed to production forthe illegal market (Gulzar, Junaid, & Haider, 2010)

Another regulation from the government can be known as limiting people’s freedom, itraises the labor costs and incentive people to work in the shadow sector The burdenplaced on businesses and people leads them to participate in the undergroundeconomy (Schneider 2011; Hassan and Schneider 2016)

Internet use is proven to have a negative effect on the growth of the undergroundeconomy Better insight into the drastic consequences of corruption or tax evasion can

be acknowledged through using the internet Awareness of risks would mitigate thepossibility of joining illegal activities (Elbahnasawy, 2014; Elgin, 2012) Corruptioncontributes a lot to the underground sector, consequently, reducing it results in thedecrease of the shadow economy size.

Another key factor is GDP growth as the higher the GDP growth, the lower themotivation to work in the shadow economy, ceteris paribus (Elbahnasawy, 2014;Elgin, 2012) However, due to the strict regulations and standards of government, it ischallenging for businesses and individuals to comply Therefore, GDP performancemay not only enhance development but also drive people into the unofficial sector

A very important aspect when measuring the underground economy is populationgrowth, while there is no direct link As population growth develops, it fuelscorruption which leads to an increase in the informal sector

1.2 Theoretical framework

The causes and factors that drive the shadow economy are various and numerous Themajority of the research has established a wide variety of elements that impact and aid

in the operation of this type of economy

Most studies on the underground economy (2004, 2005; 2010) employ FriedrichSchneider's estimates of it as a dependent variable These estimations are often used,which shows their accuracy The most current estimates of the size of the undergroundeconomy from Mai Hassan and Friedrich Schneider's study (2016) were used to putthe results of this study into context as they are among the most recent estimates andhave not been used in any prior studies

Tax income and tax rates are among the most important aspects of the shadoweconomy that are included in most literature publications The bigger the incentive fortax evasion and underreporting of salaries, the higher the total tax burden and/or lowerthe monitoring and enforcement (Schneider and Williams 2013, Hassan and Schneider2016) The distortion caused by the total tax burden affects leisure time spending

decisions and might boost the availability of workers in the shadow economy This taxwedge affects both the overall tax burden and the social security burden/payments,making them crucial components of the sustainability of the shadow economy.(Thomas 1992; Johnson, Kaufmann, and Zoido- Lobatón 1998a, b; Giles 1999a; Tanzi1999; Schneider 2003, 2005; Dell’Anno 2007; Dell’Anno, Gomez-Antonio andAlanon Pardo 2007).

There is evidence from several papers that the underground economy and inflation rateare related Earnings from seigniorage rise in tandem with inflation Because thegovernment no longer has to levy high taxes to increase income through seigniorage,both the tax rate and tax distortions may be decreased Lower taxes promote activity inthe legal economy as opposed to manufacturing for the black market The bulk of pastresearch (Erdinç, 2016; Gulzar, Junaid, & Haider, 2010; Schneider & Bajada, 2003)employed the consumer price index to calculate inflation

The relationship between the extent of the shadow economy and unemployment is upfor debate Given that an economic downturn would increase unemployment in boththe official and informal sectors, there may be a negative correlation The bulk ofearlier studies have calculated unemployment using the unemployment rate as apercentage of the labor force According to other studies (Gulzar et al., 2010; M.Hassan & Schneider, 2016; Kanniainen, Päkkönen, & Schneider, 2004; Saafi, Farhat,

& Haj Mohamed, 2015; Sarac, 2012; Savasan, 2003), other factors, notably the level

of education, have an effect on the extent of the underground economy

The literature claims that restrictions on people's freedom (of choice) in the formaleconomy include rules controlling the labor market and trade barriers As a result,people are more motivated to work in the shadow economy, which is why countrieswith greater levels of regulation often have a higher share of the shadow economy intheir overall GDP (Johnson, Kaufmann, and Shleifer 1997; Johnson, Kaufmann, andZoido-Lobatón 1998b; Friedman, Johnson, Kaufmann, and Zoido- Lobatón 2000;Kucera and Roncolato 2008; Schneider 2011; Hassan and Schneider 2016)

Internet usage has been shown to be negatively correlated with the expansion of theshadow economy As more individuals utilize the internet, they will become aware ofthe severe repercussions of actions like corruption or tax evasion Due to increasedknowledge and individual action to mitigate and scale back the hazards associated withsuch activities, the underground economy will decline (Elbahnasawy, 2014; Elgin,2012; Goel, Nelson, & Naretta, 2012; Shrivastava & Bhattacherjee, 2014)

Although there is no direct correlation between population growth and the shadoweconomy per se, corruption is a fundamental element of this dynamic, making it ahighly important consideration when attempting to quantify the shadow economy As aresult, there is a tenuous link between population increase and the shadow economy.The shadow economy grows as a result of population expansion, which alsoencourages corruption.

In this research, we will evaluate seven major factors that operate as driving forces inthe underground economy Following extensive study on the issue of the undergroundeconomy, we would like to propose the following independent factors that mayoperate as driving forces:

1.3 Empirical model

Definition of variables

- Underground economy (SUE)

Underground economy, also known as a shadow, informal, or parallel economy, refers

to economic activities not reported to the authorities in order to avoid taxes, socialsecurity contributions, labor laws, and regulations, and costs related to regulations.The underground economy includes not only illegal activities but also unreportedincome from the production of legal goods and services, either from monetary or

barter transactions There is no precise definition of the underground economy because

of its development and changes in regulations and taxation over time (IMF, 2002)

of domestic currency over time CPI in any month equals

100x Cost of thebasket ∈base period Cost of thebasket ∈that month

- Unemployment rate (UER)

The unemployment rate measures the shares of workers in the labor force who are notcurrently employed but are actively seeking a job The formula to calculate theunemployed rate is

Unemployment rate =Unemployed people Totallabor force x 100

- Internet user (IUR)

In the context of the survey on Internet use within households, an Internet user isdefined as someone who has used the Internet within the last three months, while aregular Internet user is defined as someone who has used the Internet at least once aweek within the reference period of the survey (the first three months of the calendaryear), regardless of locations to use

- Tax revenue rate (TRR)

Tax revenue is defined as the funds collected from taxes on income and profits; SocialSecurity taxes or “contributions”; taxes levied on goods and services, generally

categorized as “consumption taxes”; payroll taxes; taxes on the ownership and transfer

of property; and other taxes Total tax revenue as a percentage of GDP indicates theshare of a country’s output that the government collects through taxes It can beregarded as one measure of the degree to which the government controls theeconomy’s resources

Tax Revenue Rate = Taxrevenue GDP x 100

- Population growth (POPG)

Population growth can be defined as the increase in the number of people in a givenarea The three main factors that affect population growth are fertility rate, lifeexpectancy, and net immigration Population growth and economic growth are closelyrelated to one another, with both positive and negative effects on each: greaterpopulation growth can result in economic growth, but also can exacerbate the problem

of resource scarcity; economic growth affects population growth by either adapting tothe population growth or impeding future growth

- Index of Economic Freedom (IEF)

The Index of Economic Freedom is an annual index and ranking created in 1995 bythe Heritage Foundation and The Wall Street Journal to measure the degree ofeconomic freedom in the world’s nations

Variable Full variable name Measure unit Expected sign Source

SUE Size of Underground

TRR Tax revenue % of GDP + Worldbank

IEF Index of Economic

2.1.1 Method used to collect data

Our research uses annual macroeconomic data, including GDP growth, CPI,unemployment rate, Internet user, tax revenue, population growth and index ofeconomic freedom of 27 European countries for a 14-year period from 2008 to 2021.All data used in the paper is secondary and in the form of a panel It is taken fromreports and papers published by World Bank, and Heritage Foundation, which shows ahigh level of accuracy

2.1.2 Method used to analyze data

After collecting the necessary data, our group used Stata to analyze the dataset andinterpret the correlation matrix between variables In addition, we used Stata to choosethe most appropriate model among pooled OLS (POLS), FE, and RE to demonstratethe factors influencing the size of the underground economy of European countries inthe period 2008-2021

2.2 Data description

2.2.1 Data description and interpretation

We used panel data covering the period from 2008 to 2021 to assess the effects of theabove factors on the pace of the underground economy in 27 European nations

There are 369 observations in total, including those from every nation in the EuropeanUnion Therefore, we firmly believe that the sample is representative of the wholepopulation The data set will vary significantly due to these nations' varying levels of

development, clearly demonstrating how the independent variables have an influence

on the sub-variable

Summary statistics: Before analyzing the collected data, we will bring ingeneraldescription about the model and the parameters by using the command sum inSTATA This command reveals the Observations (Obs), Mean, Standard Deviation(Std Dev.) as well as Minimum (Min) and Maximum (Max) values of the variables.The results is shown in the following table:

Variable Obs Mean Std Dev Min Max

- SUE: The mean value of Shadow Economy Size in 27 countries in the period of

2008 to 2021 is 18.72656, the standard deviation is 7.034049, min value is 6.1 andmax value is 32.93 (% of GDP)

- GDPGR: The mean value of GDP Growth in 27 countries in the period of 2008 to

2021 is 1.406051, the standard deviation is 4.121146, min value is -14.83861 andmax value is 25.1765 (%)

- CPI: The mean value of Inflation rate (measured in CPI) in 27 countries in theperiod of 2008 to 2021 is 1.779284, the standard deviation is 1.973599, min value is

- 4.478103 and max value is 15.40232

- UER: The mean value of unemployment rate in 27 countries in the period of 2008

to 2021 is 8.641944, the standard deviation is 4.496867, min value is 2.01 and maxvalue is 27.47 (% of labor force)

- IUR: The mean value of the number of internet users in 27 countries in the period of

2008 to 2021 is 76.29486, the standard deviation is 13.86454, min value is 32.42and max value is 98.82242 (% of population)

- TRR: The mean value of the tax revenue rate in 27 countries in the period of 2008

to 2021 is 21.34963, the standard deviation is 4.806206, min 10.66701 value is andmax value is 46.04608 (% of GDP)

- POPG: The mean value of population growth rate in 27 countries in the period of

2008 to 2021 is 0.2103647, the standard deviation is 0.8368557, min value is 3.742377 and max value is 3.931356 (%)

IEF: The mean value of Economic freedom index in 27 countries in the period of

2008 to 2021 is 69.80212, the standard deviation is 5.647661, min value is 53.2 andmax value is 82 (%)

2.2.2 Correlation matrix between variables

We used the command corr SUE GDPGR CPI UER IUR TRR POPG IEF to identifythe correlation between independent variables and the dependent variable The resultsare shown as follows:

IEF -0.2032 0.0255 0.1174 -0.3967 0.3437 0.1430 0.1576 1.000

Correlation between the dependent variable and independent variables:

- Cor(SUE, GDPGR) = 0.0117 The correlation between the size of the underground

economy and GDP growth is positive and very weak

- Cor(SUE, CPI) = -0.0302 The correlation between the size of the underground

economy and the inflation rate is negative and very weak

- Cor(SUE, UER) = 0.0410 The correlation between the size of the underground

economy and the unemployment rate is positive and very weak

- Cor(SUE, IUR) = -0.3681 The correlation between the size of the underground

economy and the number of Internet users is negative and weak

- Cor(SUE, TRR) = 0.2221 The correlation between the size of the underground

economy and the tax revenue rate is positive and weak

- Cor(SUE, POPG) = 0.2094 The correlation between the size of the underground

economy and population growth is positive and weak

- Cor(SUE, IEF) = -0.2032 The correlation between the size of the underground

economy and the economic freedom index is negative and weak

Correlation between the dependent variable and independent variables: All

correlations between independent variables are quite low Furthermore, there is noperfect multicollinearity since the correlation between two variables is different from

±1

3 Estimated model and statistical inferences

3.1 Testing the econometric model