Financial Development and Shadow Economy in European Union Transition Economies Yilmaz Bayar U§ak University, Turkey yilmaz.bayar@usak.edu.tr Omer Faruk Ozturk Usak University, Turkey omerfaruk.ozturk@usak.edu.tr The shadow economy has been a serious problem with varying dimensions in all the income group economies and has significant adverse effects on the development of economies. Therefore, all the countries have tried to combat with the shadow economy by taking various measures. This study researches the interaction among shadow economy, development of financial sector and institutional quality during 2003-2014 period in European Union transition economies employing panel data analysis. The empirical findings suggested a cointegrating relationship among shadow economy, financial sector development and institutional quality. Furthermore, financial development and institutional quality affected the shadow economy negatively in the long term. Key Words: shadow economy, financial development, institutional quality, panel data analysis jel Classification: C23, G20, H11, H26, 017 Introduction Shadow economy is also called as informal economy, unofficial economy, irregular economy, black economy. Similarly, there have been no consensus on the definition of shadow economy, but it generally includes all the unrecorded transactions which should be in the gross domestic income (Schneider and Enste 2000). The shadow economy is classified as undeclared work and underreporting. The undeclared work generally consists of wages which businesses and workers do not declare to the governments for tax evasion, while underreporting means that economic units do report their income incompletely for tax evasion (Schneider 2013). Also measurement of shadow economy is very hard due to its invisible structure. However, size of shadow economy generally is measured by direct methods using surveys and samples which consist of vol- Managing Global Transitions 14 (2): 157-173 158 Yilmaz Bayar and Omer Faruk Ozturk untary replies and tax audits etc. or by indirect methods including multiple indicator multiple cause (mimic), dynamic mimic (dymimic), currency demand approach, transactions approach and electricity consumption (physical input) approach (Restrepo-Echavarria 2015). Finally, major causes underlying shadow economy have been weak public administration and legal regulations, growing tax burden and social insurance payments, weak tax morale, strict regulations concerning labour market, corruption, deterrence and inflation (Singh, Jain-Chandra, and Mohom-mad 2012a; Schneider and Williams 2013). Shadow economy is a very serious problem for the economy, because it has significant direct or indirect adverse implications for many components of economic and social life in a country. In this regard, the statistics related to the countries with high level of shadow economy are unreliable and incomplete. Therefore, it makes difficult the public policy planning and policymaking. On the other hand restricted contribution to official economy show that resources of an official economy are not benefited by most of the economic units and this in turn poses a challenge for the economic growth (Singh, Jain-Chandra, and Mohommad 2012a). European Union (eu) transition economies have experienced an economic transformation with transition from centrally planned economies to free market economies as of Berlin Wall fall. The integration process with the eu also accelerated the transition process, because these countries have made many structural reforms to meet the existing standards ofthe eu. Transition economies of eu generally underwent decreases in the volume of shadow economy and improvements in financial sector and institutional quality proxied by economic freedom index as seen in table 1. The countries participated to the eu earlier such as Czech Republic, Estonia and Hungary experienced more progress in reduction of shadow economy when compared to Romania, Bulgaria and Croatia. The main criteria of the eu membership are defined as follows (European Commission 2015): • stable institutions promoting democracy, the rule of law, human rights and respect for and protection of minorities, • a functioning market economy and the capacity to cope with competition and market forces in the eu, • ability to implement the obligations of membership such as taking actions in harmony with the aims of the eu. So the countries also decreased the size of underground economy in- Managing Global Transitions Financial Development and Shadow Economy 159 table 1 Shadow Economy, Financial Sector and Economic Freedom in eu Transition Economies Country Year (1) (2) (3) Bulgaria 2003 35.9 25.95 57 2014 31.0 60.66 65.7 Croatia 2003 32.3 45.08 53.3 2014 28.0 69.36 60.4 Czech Republic 2003 19.5 24.53 67.5 2014 15-3 50.38 72.2 Estonia 2003 30.7 50.77 77.7 2014 27.1 69.07 75-9 Hungary 2003 25.0 36.70 63 2014 21.6 43-90 67 Poland 2003 27.7 27.98 61.8 2014 23-5 51.91 67 Romania 2003 33.6 13.74 50.6 2014 28.1 37.87 65.5 Slovakia 2003 18.4 31.18 59 2014 14.6 50.39 66.4 Slovenia 2003 26.7 40.52 57.7 2014 23-5 55.02 62.7 notes Column headings are as follows: (1) shadow economy (% of gdp), (2) domestic credit to private sector (% of gdp), (3) Economic Freedom Index. The data of shadow economy, domestic credit to private sector and economic freedom index were respectively obtained from Schneider, Raczkowski, and Mroz (2015), World Bank (http://data.worldbank.org/indicator/FS.AST.PRVT.GD.ZS), and Heritage Foundation (http://www.heritage. org). directly, while trying to meet the criteria of eu membership. However, there have been no general programs in the eu to combat with shadow economy yet, while European Commission launched some initiatives such as com(2012)722 and C0m(2012)i73. There have been no studies on the interaction among shadow economy, development of financial sector and institutional quality in eu transition economies in the literature. Therefore, this study will be an early empirical study which investigates the interaction among shadow economy, financial sector development and institutional quality on in eu transition member countries during the 2003-2014 period employing Volume 14 • Number 2 • Summer 2016 160 Yilmaz Bayar and Omer Faruk Ozturk panel data. In this context, we will sum up the literature related to the nexus among shadow economy, financial sector development and institutional quality in the next section. Then data and method will be given in the second section, the third section provides the major findings of empirical analysis. Finally, the fourth sections concludes the study. Literature Review A great number of studies have researched the effect of improvements in financial sector on various economic variables such as economic growth, income distribution, savings, competitiveness, technological progress (Levine 1997; Hassan, Sanchez, and Yu 2011; Ang 2011; Zhang, Wang, and Wang 2012; Sahoo and Dash 2013). However, most of them have concentrated on the nexus between economic performance and development of financial sector, but few studies have researched the interaction between shadow economy and improvements in financial sector and revealed that improvements in financial sector has decreased the shadow economy (Blackburn, Bose, and Capasso 2012; Bose, Capasso, and Wurm 2012; Capasso and Jappelli 2013; Bittencourt, Gupta, and Stander 2014). In this context, Gobbi and Zizza (2007) investigated the nexus between shadow economy and financial sector development in Italian debt markets during the 1997-2003 period and revealed that shadow economy prevented development of financial sector, but financial sector development had no statistically impact on shadow economy. Bose, Capasso, and Wurm (2012) researched the interaction between shadow economy and improvements in banking sector in 137 countries during 1995-2007 period employing panel regression and revealed a negative relationship between shadow economy and banking sector development. Blackburn, Bose, and Capasso (2012) also developed a theoretical model including financial intermediation and tax evasion and the model suggested that the economies with lower development of financial sector experiences higher rates of shadow economy and tax evasion. In another study, Capasso and Jappelli (2013) developed a theoretical model on the nexus between shadow economy and development of financial sector. Their model projected that financial development may reduce the tax evasion and shadow economy by contributing to the firms providing cheaper finance. They also tested their theoretical model by using Italian microeconomic data and empirical findings also verified their theoretical model. Bittencourt, Gupta, and Stander (2014) also developed a model on the relationship among shadow economy, development Managing Global Transitions Financial Development and Shadow Economy 161 of financial sector and inflation and their model suggested that higher financial development reduces the shadow economy. They also tested their model by a dataset including 150 countries during 1980-2009 period and empirical findings supported the predictions of their theoretical model. The literature on the nexus between shadow economy and institutional quality is richer when compared to the literature about the interaction between shadow economy and financial sector development. The studies have predominantly revealed that the improvements in the institutions reduce the shadow economy (Torgler and Schneider 2007; Dreher, Kot-sogiannis, and McCorriston 2009; Singh, Jain-Chandra, and Mohommad 2012a; Razmi, Falahi, and Montazeri 2013; Iacobuta, Socoliuc, and Clipa 2014; Shahab, Pajooyan, and Ghaffari 2015) as seen in table 2. table 2 Literature Summary on the Relation between Institutional Quality and Shadow Economy Study Sample and study period Method Major findings Friedman, Kaufmann, and Zoido-Lobaton 2000 69 countries Panel regression Corruption had positive impact on shadow economy, while legal environment had negative impact on shadow economy. Bovi (2003) 21 oecd countries, 1990-1993 Panel regression Institutional quality affected shadow economy negatively. Dreher, Kot-sogiannis, and McCorriston (2005) 18 oecd countries, 1998-2002 Structural equation modelling Institutional quality affected shadow economy negatively. Torgler and Schneider (2007) 86-100 countries, 1990,1995, and 2000 Panel regression Institutional quality affected shadow economy negatively. Schneider (2007) 145 countries, 1999-2005 Panel regression Institutional quality affected shadow economy negatively/positively in high/low income countries Dreher, Kot-sogiannis, and McCorriston (2009) 145 countries, 1999-2003 Panel regression Institutional quality affected shadow economy negatively. Continued on the next page Volume 14 • Number 2 • Summer 2016 162 Yilmaz Bayar and Omer Faruk Ozturk table 2 Continued from the previous page Study Sample and study period Method Major findings Enste (2010) 25 oecd countries, 1995-2005 Panel regression Deregulation affected shadow economy negatively. Torgler, Schneider, and Macin-tyre (2010) 59 countries, 1990-1999 Panel regression Institutional quality affected shadow economy negatively. Singh, Jain-Chandra, and Mohommad (2012b) 100 countries Panel regression Institutional quality affected shadow economy negatively. Ruge (2012) 35 countries (mostly from oecd) Structural equation model Institutional quality affected shadow economy negatively. Quintano and 33 European Mazzocchi (2012) countries, 20052010 Structural equation model Regulatory efficiency had negative impact on shadow economy. Manolas et al. (2013) 19 oecd countries, 2003-2008 Panel regression Institutional quality affected shadow economy negatively. Razmi, Falahi, and Montazeri (2013) 51 Organisation of Islamic Cooperation member countries, 19992008 Dynamic panel regression Institutional quality affected shadow economy negatively. Kuehn (2014) 21 oecd countries Modelling Institutional quality affected shadow economy negatively. Iacobuta, Socol-iuc, and Clipa (2014) eu countries Panel data analysis Institutional quality affected shadow economy negatively. Remeikiene and Gaspareniene (2015) Lithuania, 20002011 Regression analysis Financial development and institutional quality affected shadow economy negatively. Shahab, Pa-jooyan, and Ghaffari (2015) 25 developed and developing countries, 19992007 Static and dynamic panel regression Institutional quality affected shadow economy negatively. Data and Method We researched the relationship among shadow economy, development of financial sector and improvement in institutional quality in the eu Managing Global Transitions Financial Development and Shadow Economy 163 transitional economies during 2003-2014 period employing cointegra-tion analysis of Basher and Westerlund (2009) and causality test of Du-mitrescu and Hurlin (2012). DATA In this study, we used the data of shadow economy based on the mimic method by Schneider, Raczkowski, and Mroz (2015) as a proxy for the shadow economy. Moreover, we used domestic credit to private sector as a percent of gdp as a proxy for financial development, because the capital markets in our sample still have been at the early stages of development. Finally, we took the economic freedom index of Heritage Foundation (http://www.heritage.org) as a proxy for institutional quality, because index of economic freedom is calculated based on rule of law, limited government, regulatory efficiency and open markets. The data description was given in table 3. We benefited from Stata 14.0, Win rats Pro. 8.0 and Gauss 11.0 programs for econometric analysis. table 3 Data Description Variable Symbol Source Shadow economy (% of gdp) SHAEC Schneider, Raczkowski, and Mroz (2015) Domestic credit to private sector (< % of GDP) DCRD World Bank (http://data .worldbank.org/indicator/ FS.AST.PRVT.GD.ZS) Economic freedom index EFR Heritage Foundation (http://www.heritage.org) econometric methodology In this study, we tested the heterogeneity of the variables with adjusted delta test of Pesaran, Ullah, and Yamagata (2008) and cross-sectional independency was tested with cd lm1 test of Breusch and Pagan (1980). Then, we tested stationarity of the series with cips test of Pesaran (2007) regarding considering cross-sectional dependency, Im, Lee, and Tieslau (2010), and Narayan and Popp (2010) unit root tests considering structural breaks. The cointegration test of Basher and Westerlund (2009) was employed to test cointegrating relationship among variables. Finally causal relationship among the series was tested with test by Dumitrescu and Hurlin (2012). Volume 14 • Number 2 • Summer 2016 164 Yilmaz Bayar and Omer Faruk Ozturk ECONOMETRIC MODEL The development of financial sector and quality of governing institutions have potential to affect shadow economy negatively, because economic units are motivated to operate in formal economy in case financial sector provides cheap financing. On the other hand institutional quality is the main factor which designs and regulates the environment which firms operate. So we expected that countries with better institution have less shadow economy. Therefore, we establish our model as follows: SHAEC = /(DCRD, EFr) (l) In this function, shaec denotes the shadow economy as a percent of gdp, while DCRD represents the development level of financial sector and efr represents the quality of institutions. We expect a negative relationship among shaec, DCRD and efr considering the theoretical and empirical literature. CROSS-SECTIONAL AND HOMOGENEITY TESTS Cross-sectional independency and homogeneity of the variables are determinative for us to select the econometric tests used in the future stages of the study. The cross-sectional independency among the variables will be analyzed by cdlm1 test of Breusch and Pagan (1980), because T (time dimension) = 12 is higher than N, cross-sectional dimension = 9. The cdlm test statistic values are obtained from the equation (2). It is expected that there is a simultaneous correlation among the residuals of this equation (Pesaran 2004) and the statistical significance of this correlation is tested with lm test in equation (3) developed by Breusch and Pagan (1980). pi AY it = ai + ßiyit + ^ CijAit-j + dit + hy-j=1 pi + ^ nAyij-j + Si,t. (2) j=0 N-1 N LM = TZZ ~ *N(N-i)/2. () i=j j=i+1 In equation (3) pij is the correlation among the residuals obtained estimation of each equation by ordinary least squares. lm exhibits chi square distribution, while T goes to infinity and N is fixed. Managing Global Transitions Financial Development and Shadow Economy 165 We tested the homogeneity of the variables with adjusted delta tilde test of Pesaran, Ullah, and Yamagata (2008) and the test statistic is calculated as follows (h0: — — ■■■ — fin — j6,for all the ^s): Aadj = iN-- . (4) ^Vbr{Zit) panel unit root tests cips, Im, Lee, and Tieslau (2010), and Narayan and Popp (2010) unit root tests will be employed to analyze integration levels of the variables. cips test based on cadf test of Pesaran (2007) considers cross-sectional dependency but ignores the structural breaks. However, unit root tests of Narayan and Popp (2010) and Im, Lee, and Tieslau (2010) regard structural breaks in the series. Narayan and Popp (2010) unit root test determines the dates of structural breaks by maximizing the significance of the break dummy coefficient differently from Lumsdaine and Papell (1997) and Lee and Strazicich (2003) unit root tests. Finally, Im, Lee, and Tieslau (2010) panel lm unit root test considers possible heterogeneous breaks in constant and trend and also makes the adjustments in case of cross-correlations. basher and westerlund (2009) cointegration test Basher and Westerlund (2009) cointegration test regards cross-sectional dependency and multiple structural breaks and allows for maximum three structural breaks, while testing cointegrating relationship among the series. The test statistics of the model (h0: There is cointegration among the variables for all the cross-sections) is as follows: N Mt+1 Tij . S2 \ Sit = ELt„ Wst and Wit is a residual vector obtained from an efficient estimator like fully modified least squares. <2 is variance estimator based on Wit. The test statistic exhibits a standard normal distribution and the hypotheses of the test are as follows: dumitrescu and hurlin (2012) causality test Dumitrescu and Hurlin (2012) causality test is a modified version of Granger (1969) causality test regarding heterogeneity. The following test statistics are calculated in the context of the test (Dumitrescu and Hurlin 2012): Volume 14 • Number 2 • Summer 2016 168 Yilmaz Bayar and Omer Faruk Ozturk N 1=1 z™c = V f <:w™c - k)kt^n(o> i}- (7) / MObl). (8) ylN-^VariW») N'T ^ °° Empirical Analysis CROSS-SECTIONAL TEST AND HOMOGENEITY TEST We tested the cross-sectional dependence with cdlm1 test of Breusch and Pagan (1980), because time dimension is higher than cross-sectional dimension (T = 12, N = 9). The results were given in table 4 and since probability values were lower than 5%, the null hypothesis (cross-sectional independency) was rejected. So the findings indicated a cross-sectional dependency among the series. table 4 Results of cdlm1 Test Variable Test statistic Probability shaec 9.523 0.001 DCRD 7.226 0.034 efr 9.821 0.010 We employed adjusted delta tilde test of Pesaran, Ullah, and Yamagata (2008) and the findings were given in table 5. Since the null hypothesis (slope coefficients are homogenous) was rejected at 1% significance level, we concluded that there was heterogeneity. table 5 Results of Adjusted Delta Tilde Test Test Test statistics Probability ^ adj. 28.97 PANEL UNIT ROOT TESTS Panel data analysis requires that the variables should be 1(0) to avoid the possible spurious relationship among the series. First we analyzed integration levels of the variables with cips test of Pesaran (2007) regarding the cross-sectional dependence among the series and the results of Managing Global Transitions Financial Development and Shadow Economy 167 the test were given in table 6. The findings denoted that all the variables were I(i). table 6 Results of cips Test Test SHAEC DCRD EFR cips 7-532* 8.002* 7.271* notes * Significant at the 0.05 level. Secondly, we employed unit root tests of Narayan and Popp (2010) and Im, Lee, and Tieslau (2010) regarding structural breaks. In this context, we applied the second model of Narayan and Popp (2010) test which allows two breaks in both level and trend and the findings were given in table 7. table 7 Results of Narayan and Popp (2010) Panel Unit Root Test Country Test statistic TB 1,TB2 sHAEC DCRD EFR Bulgaria 4.764* 4.732* 6.834* 2008, 2009 Croatia 9.328* 6.543* 5.925* 2009, 2012 Czech Republic 6.035* 4.007* 5.112* 2009, 2012 Estonia 9.692* 5.328* 6.733* 2008, 2009 Hungary 7.551* 3.982* 8.492* 2009, 2012 Poland 8.634* 7.831* 5.629* 2008, 2009 Romania 5.992* 9.447* 4.227* 2008, 2009 Slovakia 8.426* 6.263* 4.752* 2009, 2010 Slovenia 9.113* 4.771* 6.994* 2009, 2012 notes * Significant at 5% level. Critical values are -5.882, -5.263, and -4.941 at the 1%, 5%, and 10% significance levels, respectively for model 2 with 50.000 replications for endogenous two breaks test. The results indicated that the series were 1(1) with structural breaks. The dates of structural breaks showed that recent financial crises, global financial crisis and Eurozone debt crisis, induced significant structural shifts in the series of dcrd and efr. We also used the different versions of the panel lm unit root tests considering and not considering structural and the findings tests were given in table 8. The findings denoted that the variables had unit root when the structural breaks were disregarded. On the other hand when we ap- Volume 14 • Number 2 • Summer 2016 168 Yilmaz Bayar and Omer Faruk Ozturk plied the version considering two structural breaks, two different test statistics were obtained depending on the cross-correlations. The first test statistic ignores the cross-correlations, while the second test statistic regards the cross-correlations by considering the Pesaran s ca procedure. The results indicated that the variables were stationary when the cross-sectional dependence was ignored. However, the variables were not stationary, when the cross-sectional was considered. table 8 Results of Panel lm Unit Root test Panel lm test statistic without break Panel lm test statistic with two breaks Panel lm test c a statistic with two breaks notes * 0.05 significance level. basher and westerlund (2009) cointegration test We employed Basher and Westerlund (2009) model which allows structural breaks in constant and trend and the findings were presented in table 9. The findings revealed that there was cointegrating relationship between the variables of our study with structural breaks and cross-sectional dependency. table 9 Results of Basher and Westerlund (2009) Cointegration Test Test statistic Probability value 56.987 0.258 notes Probability values obtained by using bootstrap with 1.000 simulations. estimation of long run cointegrating coefficients The individual cointegrating coefficients were estimated with cce (Common Correlated Effects) method of Pesaran (2006) and the cointegrating coefficients of the panel were estimated with ccmge (Common Correlated Mean Group Effects) method of Pesaran (2006) and the findings were given in table 10 (p. 169). The findings revealed that development of financial sector and improvements in institutional quality decreased the shadow economy. dumitrescu and hurlin (2012) causality test We investigated causal relationship among shadow economy, financial development and institutional quality with causality test of Dumitrescu Managing Global Transitions -0.234 -7-335* -0.872 Financial Development and Shadow Economy 169 table 10 Long run Cointegrating Coefficients Country dcrd efr Coefficient i-statistic Coefficient i-statistic Bulgaria -0.089* -3-854 -0.053* -4.263 Croatia -0.112* -4.012 -0.114* -5.883 Czech Republic -0.108* -4-348 -0.156* -3.915 Estonia -0.142* -5.924 -0.083* -3.772 Hungary -0.096* 6-993 -0.145* -6.834 Poland -0.063* -5.326 0.102* -3.992 Romania -0.135* -3.261 0.081* -4.036 Slovakia -0.152* -4.772 0.126* -5.823 Slovenia -0.133* -3.725 0.105* -6.432 Panel -0.146* -4.045 0.170* -3.886 notes * Significant at 5% level. table 11 Results of Dumitrescu and Hurlin (2012) Causality Test Null hypothesis Test Statistics Prob. shaec does not homogeneously cause dcrd WHNC 3.632 0.000 ^HNC 5.943 0.001 Z - bar 6-523 0.013 dcrd does not homogeneously cause shaec WHNC 5.998 0.000 ZHNC 3.642 0.022 Z - bar 4.022 0.000 shaec does not homogeneously cause efr WHNC 6.531 0.000 ZHNc 5.773 0.011 Z - bar 4.254 0.004 efr does not homogeneously cause shaec whnc 3.992 0.000 ZHNc 2.880 0.000 Z - bar 3-638 0.032 and Hurlin (2012) and the findings were given in table 11. The findings revealed bidirectional causality both between shaec and dcrd andbe-tween shaec and efr. Conclusion We researched the relationship among shadow economy, development of financial sector and institutional over the period 2003-2014 in eu tranVolume 14 • Number 2 • Summer 2016 170 Yilmaz Bayar and Omer Faruk Ozturk sition economies benefiting from Basher and Westerlund (2009) cointe-gration test and Dumitrescu and Hurlin (2012) causality test. Our findings revealed that there was a cointegrating relationship among shadow economy, development of financial sector and institutional quality. Moreover, development of financial sector and improvements in institutional quality decreased the shadow economy in the long run. Finally, the results of causality test revealed a two-way causality between shadow economy and financial development and shadow economy and institutional quality. So our findings verified an interaction among shadow economy, development of financial sector and institutional quality and were consistent with the predictions of theoretical studies and the results of empirical studies in the literature. This study also verified that financial development and institutional quality are important factors affecting shadow economy. In this regard, improvements in financial sector and institutional quality will be useful in combat with shadow economy considering our findings, theoretical and empirical literature. References Ang, J. B. 2011. 'Savings Mobilization, Financial Development and Liberalization: The Case of Malaysia.' Review of Income and Wealth 57 (3): 449-70. Basher, S. A., and J. Westerlund. 2009. 'Panel Cointegration and the Monetary Exchange Rate Model.' Economic Modelling 26 (2): 506-13. Bittencourt, M., R. Gupta, and L. Stander. 2014. 'Tax Evasion, Financial Development and Inflation: Theory and Empirical Evidence.' 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'Shadow Economy, Voice and Accountability and Corruption.' http://www.econ.jku.at/members /Schneider/files/publications/LatestResearch2010/Torgler_Schneider _Macintyre.pdf Zhang, J., L. Wang, and S. Wang 2012. 'Financial Development and Economic Growth: Recent Evidence from China.' Journal of Comparative Economics 40 (3): 393-412. This paper is published under the terms of the Attribution-NonCommercial-NoDerivatives 4.0 International (cc by-nc-nd 4.0) License (http://creativecommons.org/licenses/by-nc-nd/4.o/). Volume 14 • Number 2 • Summer 2016