Financial Development and Shadow Economy in European Union Transition Economies

Abstract

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.

Financial Development and Shadow Economy in European Union Transition Economies

Yilmaz BayarUak University, Turkey [email protected]

Omer Faruk OzturkUsak University, Turkey [email protected]

The shadow economy has been a serious problem with varying dimensions in all the income group economies and has signicant adverse eects 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 nancial sector and institutional quality during 20032014 period in European Union transition economies employing panel data analysis. The empirical ndings suggested a cointegrating relationship among shadow economy, nancial sector development and institutional quality. Furthermore, nancial development and institutional quality aected the shadow economy negatively in the long term.

Key Words: shadow economy, nancial development, institutional quality, panel data analysis

jel Classication: c23, g20, h11, h26, o17

Introduction

Shadow economy is also called as informal economy, unofficial economy, irregular economy, black economy. Similarly, there have been no consensus on the denition 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 classied 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): 157173

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 ination (Singh, Jain-Chandra, and Mohommad 2012a; Schneider and Williams 2013).

Shadow economy is a very serious problem for the economy, because it has signicant 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 beneted 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 of the eu. Transition economies of eu generally underwent decreases in the volume of shadow economy and improvements in nancial sector and institutional quality proxied by economic freedom index as seen in table1. 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 dened 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 TransitionEconomies

Country Year () () ()

Bulgaria . .

. . .

Croatia . . .

. . .

Czech Republic . . .

. . .

Estonia . . .

. . .

Hungary . .

. .

Poland . . .

. .

Romania . . .

. . .

Slovakia . .

. . .

Slovenia . . .

. . .

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 Mrz (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 com(2012)173.

There have been no studies on the interaction among shadow economy, development of nancial 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, nancial sector development and institutional quality on in eu transition member countries during the 20032014 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, nancial 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 ndings of empirical analysis. Finally, the fourth sections concludes the study.

Literature Review

A great number of studies have researched the eect of improvements in nancial 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 nancial sector, but few studies have researched the interaction between shadow economy and improvements in nancial sector and revealed that improvements in nancial 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 nancial sector development in Italian debt markets during the 19972003 period and revealed that shadow economy prevented development of nancial sector, but nancial 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 19952007 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 nancial intermediation and tax evasion and the model suggested that the economies with lower development of nancial 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 nancial sector. Their model projected that nancial development may reduce the tax evasion and shadow economy by contributing to the rms providing cheaper nance. They also tested their theoretical model by using Italian microeconomic data and empirical ndings also veried 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 nancial sector and ination and their model suggested that higher nancial development reduces the shadow economy. They also tested their model by a dataset including 150 countries during 19802009 period and empirical ndings 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 nancial sector development. The studies have predominantly revealed that the improvements in the institutions reduce the shadow economy (Torgler and Schneider 2007; Dreher, Kotsogiannis, and McCorriston 2009; Singh, Jain-Chandra, and Mohommad 2012a; Razmi, Falahi, and Montazeri 2013; Iacobuta, Socoliuc, and Clipa 2014; Shahab, Pajooyan, and Ghaari 2015) as seen in table 2.

table 2 Literature Summary on the Relation between Institutional Quality andShadow Economy

Study Sample and study period

Method Major ndings

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, 19901993

Panel regression Institutional quality aected shadow economy negatively.

Dreher, Kotsogiannis, and McCorriston (2005)

Friedman, Kauf-mann, and Zoido-Lobaton 2000

18 oecd countries, 19982002

Structural equation modelling

Institutional quality aected shadow economy negatively.

Torgler and Schneider (2007)

86100 countries, 1990, 1995, and 2000

Panel regression Institutional quality aected shadow economy negatively.

Schneider (2007) 145 countries,

19992005

Panel regression Institutional quality aected shadow economy negatively/positively in high/low income countries

Dreher, Kotsogiannis, and McCorriston (2009)

145 countries, 19992003

Panel regression Institutional quality aected 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 ndings

Enste (2010) 25 oecd countries, 19952005

Panel regression Deregulation aected shadow economy negatively.

Torgler, Schneider, and Macintyre (2010)

59 countries, 19901999

Panel regression Institutional quality aected shadow economy negatively.

Singh, Jain-Chandra, and Mohommad (2012b)

100 countries Panel regression Institutional quality aected shadow economy negatively.

Ruge (2012) 35 countries (mostly from oecd)

Structural equation model

Institutional quality aected shadow economy negatively.

Quintano and Mazzocchi (2012)

33 European countries, 2005 2010

Structural equation model

Regulatory efficiency had negative impact on shadow economy.

Manolas et al. (2013)

19 oecd countries, 20032008

Panel regression Institutional quality aected shadow economy negatively.

Razmi, Falahi, and Montazeri (2013)

51 Organisation of Islamic Cooperation member countries, 1999 2008

Dynamic panel regression

Institutional quality aected shadow economy negatively.

Kuehn (2014) 21 oecd countries

Modelling Institutional quality aected shadow economy negatively.

Iacobuta, Socoliuc, and Clipa (2014)

eu countries Panel data analysis

Institutional quality aected shadow economy negatively.

Remeikiene and Gaspareniene (2015)

Lithuania, 2000 2011

Regression analysis

Financial development and institutional quality aected shadow economy negatively.

Shahab, Pajooyan, and Ghaari (2015)

25 developed and developing countries, 1999 2007

Static and dynamic panel regression

Institutional quality aected shadow economy negatively.

Data and Method

We researched the relationship among shadow economy, development of nancial sector and improvement in institutional quality in the eu

Managing Global Transitions

Financial Development and Shadow Economy 163

transitional economies during 20032014 period employing cointegration analysis of Basher and Westerlund (2009) and causality test of Dumitrescu and Hurlin (2012).

data

In this study, we used the data of shadow economy based on the mimic method by Schneider, Raczkowski, and Mrz (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 nancial 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 beneted from Stata 14.0, Winrats 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, andMrz (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 nancial sector and quality of governing institutions have potential to aect shadow economy negatively, because economic units are motivated to operate in formal economy in case nancial sector provides cheap nancing. On the other hand institutional quality is the main factor which designs and regulates the environment which rms operate. So we expected that countries with better institution have less shadow economy. Therefore, we establish our model as follows:

shaec = f (dcrd, efr) (1)

In this function, shaec denotes the shadow economy as a percent of gdp, while dcrd represents the development level of nancial 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 signicance of this correlation is tested with lm test in equation (3) developed by Breusch and Pagan (1980).

Yit = i + iyi,t +

pi

j=1

ciji,tj + dit + hiyt1

+

pi

j=0

yi,tj + i,t. (2)

lm = T

N1

i=j

N

j=i+1

2ij 2N(N1)/2. (3)

In equation (3) ij is the correlation among the residuals obtained estimation of each equation by ordinary least squares. lm exhibits chi square distribution, while T goes to innity and N is xed.

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: 1 = 2 = = n = , for all the is):

~

adj = N N1

E(it) Var(it)

. (4)

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 signicance of the break dummy coefficient dierently 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:

Z(M) = 1

N

N

i=1

M1+1

j=1

Tij

S2it

(Tij Tij1)2

2i

. (5)

t=Tij1+1

Sit =

ts=Tij1+1st andit is a residual vector obtained from an effi

cient estimator like fully modied least squares. 2i is variance estimator based onit. 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 modied 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

166 Yilmaz Bayar and Omer Faruk Ozturk

WHNCN,T =

1 N

N

i=1

Wi,T. (6)

ZHNCN,T =

N2K (WHNCN,T K)

dN, T

N(0, 1). (7)

ZHNCN,T =

N[WHNC

N,T N1 N

i=1 E(Wi,t)]

N1

N(0, 1). (8)

Ni=1 Var(Wi,t)

dN, 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 ndings indicated a cross-sectional dependency among the series.

table 4 Results of cdlm1 Test

Variable Test statistic Probability

shaec . .

dcrd . .

efr . .

We employed adjusted delta tilde test of Pesaran, Ullah, and Yamagata (2008) and the ndings were given in table 5. Since the null hypothesis (slope coefficients are homogenous) was rejected at 1 signicance level, we concluded that there was heterogeneity.

table 5 Results of Adjusted Delta Tilde Test

Test Test statistics Probability ~

adj. . .

panel unit root tests

Panel data analysis requires that the variables should be I(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 ndings denoted that all the variables were I(1).

table 6 Results of cips Test

Test shaec dcrd efr

cips .* .* .*

notes * Signicant 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 ndings were given in table 7.

table 7 Results of Narayan and Popp (2010) Panel Unit Root Test

Country Test statistic tb1, tb2

shaec dcrd efr

Bulgaria .* .* .* ,

Croatia .* .* .* ,

Czech Republic .* .* .* ,

Estonia .* .* .* ,

Hungary .* .* .* ,

Poland .* .* .* ,

Romania .* .* .* ,

Slovakia .* .* .* ,

Slovenia .* .* .* ,

notes * Signicant at 5 level. Critical values are 5.882, 5.263, and 4.941 at the 1, 5, and 10 signicance levels, respectively for model 2 with 50.000 replications for endogenous two breaks test.

The results indicated that the series were I(1) with structural breaks. The dates of structural breaks showed that recent nancial crises, global nancial crisis and Eurozone debt crisis, induced signicant structural shifts in the series of dcrd and efr.

We also used the dierent versions of the panel lm unit root tests considering and not considering structural and the ndings tests were given in table 8. The ndings 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 dierent test statistics were obtained depending on the cross-correlations. The rst test statistic ignores the cross-correlations, while the second test statistic regards the cross-correlations by considering the Pesarans 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 ca statistic with two breaks .

notes * 0.05 signicance level.

basher and westerlund (2009) cointegration test

We employed Basher and Westerlund (2009) model which allows structural breaks in constant and trend and the ndings were presented in table 9. The ndings 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 Eects) method of Pesaran (2006) and the cointegrating coefficients of the panel were estimated with ccmge (Common Correlated Mean Group Eects) method of Pesaran (2006) and the ndings were given in table 10 (p. 169). The ndings revealed that development of nancial sector and improvements in institutional quality decreased the shadow economy.

dumitrescu and hurlin (2012) causality test

We investigated causal relationship among shadow economy, nancial development and institutional quality with causality test of Dumitrescu

Managing Global Transitions

Financial Development and Shadow Economy 169

table 10 Long run Cointegrating Coefficients

Country dcrd efr

Coefficient t-statistic Coefficient t-statistic

Bulgaria .* . .* .

Croatia .* . .* .

Czech Republic .* . .* .

Estonia .* . .* .

Hungary .* . .* .

Poland .* . .* .

Romania .* . .* .

Slovakia .* . .* .

Slovenia .* . .* .

Panel .* . .* .

notes * Signicant 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 . .

Zhnc . . bar . .

dcrd does not homogeneously cause shaec Whnc . .

Zhnc . . bar . .

shaec does not homogeneously cause efr Whnc . .

Zhnc . . bar . .

efr does not homogeneously cause shaec Whnc . .

Zhnc . . bar . .

and Hurlin (2012) and the ndings were given in table 11. The ndings revealed bidirectional causality both between shaec and dcrd and between shaec and efr.

Conclusion

We researched the relationship among shadow economy, development of nancial sector and institutional over the period 20032014 in eu tran-

Volume 14 Number 2 Summer 2016

170 Yilmaz Bayar and Omer Faruk Ozturk

sition economies beneting from Basher and Westerlund (2009) cointegration test and Dumitrescu and Hurlin (2012) causality test. Our ndings revealed that there was a cointegrating relationship among shadow economy, development of nancial sector and institutional quality. Moreover, development of nancial 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 nancial development and shadow economy and institutional quality. So our ndings veried an interaction among shadow economy, development of nancial 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 veried that nancial development and institutional quality are important factors aecting shadow economy. In this regard, improvements in nancial sector and institutional quality will be useful in combat with shadow economy considering our ndings, 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): 44970.

Basher, S. A., and J. Westerlund. 2009. Panel Cointegration and the Monetary Exchange Rate Model. Economic Modelling 26 (2): 50613.

Bittencourt, M., R. Gupta, and L. Stander. 2014. Tax Evasion, Financial Development and Ination: Theory and Empirical Evidence. Journal of Banking & Finance 41:194208.

Blackburn, K., N. Bose, and S. Capasso. 2012. Tax Evasion, the Shadow Economy and Financial Development. Journal of Economic Behavior & Organization 83:24353.

Bose, N., S. Capasso, and M. A. Wurm. 2012. The Impact of Banking Development on the Size of Shadow Economies. Journal of Economic Studies 39 (6): 62038.

Bovi, M. 2003. The Nature of the Shadow Economy: Some Evidence from oecd Countries. Journal for Institutional Innovation, Development and Transition 7:6070.Breusch, T. S., and A. R. Pagan. 1980. The Lagrange Multiplier Test and Its

Applications to Model Specication in Econometrics. Review of Economic Studies 47 (1): 23953.Capasso, S., and T. Jappelli. 2013. Financial Development and the Shadow

Economy. Journal of Development Economics 101:16778.

Managing Global Transitions

Financial Development and Shadow Economy 171

Dreher, A., C. Kotsogiannis, and S. McCorriston. 2005. How do Institutions Aect Corruption and the Shadow Economy? University of Exeter Economics Department Discussion Papers Series 05/05, University of Exeter.

. 2009. How Do Institutions Aect Corruption and the Shadow

Economy? International Tax and Public Finance 16:77396. Dumitrescu, E. I., and C. Hurlin. 2012. Testing for Granger Non-Causality in Heterogeneous Panels. Economic Modelling 29 (4): 145060. Enste, D. H. 2010. Regulation and Shadow Economy: Empirical Evidence for 25 oecd Countries. Constitutional Political Economy 21 (3): 23148.

European Commission. 2015. Conditions for Membership. http://ec

.europa.eu/enlargement/policy/conditions-membership/index_en.htm Friedman, E. S., J. D. Kaufmann, and P. Zoido-Lobaton. 2000. Dodging the

Grabbing Hand: The Determinants of Unofficial Activity in 69 Countries. Journal of Public Economics 76:45993.

Gobbi, G., and R. Zizza. 2007. Does the Underground Economy Hold Back

Financial Deepening? Evidence from the Italian Credit Market. cep Discussion Paper 789, Centre for Economic Performance, London. Granger, C. W. J. 1969. Investigating Causal Relations by Econometric

Models and Cross-Spectral Methods. Econometrica 37 (3): 42438. Hassan, M. K., B. Sanchez, and J. S. Yu. 2011. Financial Development and

Economic Growth: New Evidence from Panel Data. Quarterly Review of Economics and Finance 51:88104.

Iacobuta, A. O., O. R. Socoliuc, and R. I. Clipa. 2014. Institutional Determinants of Shadow Economy in eu Countries: A Panel Data Analysis. Transformations in Business and Economics 13 (3c): 48395. Im, K. S., J. Lee, and M. Tieslau. 2010. Panel lm Unit Root Tests with

Trend Shifts. http://papers.ssrn.com/sol3/papers.cfm?abstract_id =1619918Kuehn, Z. 2014. Tax Rates, Governance, and the Informal Economy in

High-Income Countries. Economic Inquiry 52(1):40530.

Lee, J., M. Strazicich. 2003. Minimum Lagrange Multiplier Unit Root Test with Two Structural Breaks. Review of Economics and Statistics 85 (4): 10829.

Levine, R. 1997. Financial Development and Economic Growth: Views andAgenda. Journal of Economic Literature 35:688726.

Lumsdaine, R., and D. Papell. 1997. Multiple Trend Break and the Unit-

Root Hypothesis. Review of Economics and Statistics 79:21218. Manolas, G., K. Rontos, G. Sfakianakis, and I. Vavouras. 2013. The Determinants of the Shadow Economy: The Case of Greece. International Journal of Criminology and Sociological Theory 6 (1): 103647.

Volume 14 Number 2 Summer 2016

172 Yilmaz Bayar and Omer Faruk Ozturk

Narayan P. K., and S. Popp. 2010. A New Unit Root Test with Two Structural Breaks in Level and Slope at Unknown Time. Journal of Applied Statistics 37:142538.

Pesaran, M. H. 2004. General Diagnostic Tests for Cross Section Dependence in Panels. iza Discussion Paper 1240, Institute for the Study of Labor, Bonn.

. 2006. Estimation and Inference in Large Heterogenous Panels with

Multifactor Error Structure. Econometrica 74:9671012.. 2007. A Simple Panel Unit Root Test in the Presence of Cross Section Dependence. Journal of Applied Econometrics 22 (2): 265312. Pesaran, M. H., A. Ullah, T. Yamagata. 2008. A Bias-Adjusted lm Test of

Error Cross-Section Independence. Econometrics Journal 11 (1): 10527. Quintano, C., and P. Mazzochi. 2013. The Shadow Economy beyond European Public Governance. Economic Systems 37:65070.

Razmi, M. J., M. A. Falahi, and S. Montazeri. 2013. Institutional Quality and Shadow Economy of 51 oic Member Countries. Universal Journal of Management and Social Sciences 3 (2): 114.

Remeikiene, R., and L. Gaspareniene. 2015. Evaluation of the Shadow Economy Inuencing Factors: Lithuanian Case. Paper presented at the 9th International Conference on Business Administration, Dubai, United Arab Emirates.

Restrepo-Echavarria, P. 2015. Measuring Underground Economy Can BeDone, but It Is Difficult. https://www.stlouisfed.org//media/

Publications/RegionalEconomist/2015/January/PDFs/ underground.pdfRuge, M. 2012. Public Governance and the Shadow Economy. http://www

.culturaldiplomacy.org/academy/content/pdf/participant-papers/2012 -03-biec/Public_Governance_and_the_Shadow_Economy_-_Narcus _Ruge.pdfSahoo, P., and R. K. Dash. 2013. Financial Sector Development and Domestic Savings in South Asia. Economic Modelling 33:38897. Schneider, F. 2007. Shadow Economies and Corruption all over the World:

New Estimates for 145 Countries. Economics 9:147.. 2013. The Shadow Economy in Europe. https://www.visa-europe.fr

/media/images/shadow20economy20white20paper-58-8752.pdf Schneider, F., and D. H. Enste. 2000. Shadow Economies: Size, Causes, and

Consequences. Journal of Economic Literature 38:77114.

Schneider, F., K. Raczkowski, and B. Mrz. 2015. Shadow Economy and Tax

Evasion in the eu. Journal of Money Laundering Control 18 (1): 3451. Schneider, F., and C. C. Williams. 2013. The Shadow Economy. http:// papers.ssrn.com/sol3/papers.cfm?abstract_id=2286334Shahab, M. R., J. Pajooyan, and F. Ghaari. 2015. The Eect of Corruption

Managing Global Transitions

Financial Development and Shadow Economy 173

on Shadow Economy: An Empirical Analysis Based on Panel Data in Developed and Developing Countries. magnt Research Report 3 (3): 163240.

Singh, A., S. Jain-Chandra, and A. Mohommad. 2012a. Out of the Shadows. Finance & Development 49 (2): 425. . 2012b. Inclusive Growth, Institutions, and the Shadow Economy. imf Working Paper wp/12/47, International Monetary Fund, Washington, dc.

Torgler, B., and F. Schneider. 2007. Shadow Economy, Tax Morale, Governance and Institutional Quality: A Panel Analysis. iza Discussion Paper 2563, Institute for the Study of Labor, Bonn.

Torgler, B., F. Schneider, and A. Macintyre. 2010. Shadow Economy, Voice and Accountability and Corruption. http://www.econ.jku.at/members /Schneider/les/publications/LatestResearch2010/Torgler_Schneider _Macintyre.pdfZhang, J., L. Wang, and S. Wang 2012. Financial Development and Economic Growth: Recent Evidence from China. Journal of Comparative Economics 40 (3): 393412.

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/4.0/).

Volume 14 Number 2 Summer 2016