Does regulatory discretion increase unofficial economy? Evidence from panel data

• Author: Ummad Mazhar
• Position: PhD (Economics), Assistant Director, Monetary Policy Department

Introduction

Existence of shadow economy or unrecorded business activity is a complex phenomena. It has been investigated from various perspectives in different disciplines. For economists the main cause for the existence of shadow economy is the excessive regulation of the private business activity. Previous literature allows us to identify three predictions about the link between shadow economy and regulation (Johnson et al. 1997 and 1998). First, that greater regulation of economic activity leads to greater unofficial economy. Second, a higher tax burden, as perceived by economic agents, turned them away from the official sector. Third, that corruption complements the unofficial activity (Dreher and Schneider, 2010).

The findings of the previous studies, although supportive, were either based on small sample (e.g. sample use by Johnson et al. (1998) comprises of 49 countries which reduces to as low as 34 observations in some specifications). Moreover, they lack coverage of East Asia and Africa, two biggest regions in terms of population and number of countries. This makes it difficult to tease out general conclusions from these studies.

This paper attempts to fill these gaps. It employs a larger data set of 160 countries (country coverage varies from 119 to 160 countries in different estimations depending on the availability of right hand side variables). In addition, unlike the previous studies, this study uses panel data set and also investigates the causal link between regulation and unofficial economy. It allows us to provide new insights. We find broadly similar results to previous studies i.e. various measures of regulation are increasing the size of the shadow economy. But the results from causality analysis are inconclusive. It may indicate the complexity of the shadow economy.

The rest of the paper is divided as follows. The second section describes the data and methodology. Third and fourth sections detail the results of simple and causality analysis respectively, while the fifth section concludes.

Data and Methodology

Following the original study, I have estimated the following empirical relation:

Unofficial_it = α + β[Regulatory Discretion]_it + γ[Control]_it + ε_it ,

Where, Unofficial_it denotes size of the unofficial sector as a percent of GDP for country i in the year t, α denotes the constant, and β is the coefficient, ε is the composite error term with usual assumptions. The Regulatory Discretion is captured in three different ways: (a) through different measures of the business regulation; (b) by using different measures of tax burden; and (c) by the indices of the rule of law and corruption. Each of these variables is used in turn to estimate the above equation controlling for the per capita GDP.

The data for the unofficial economy is from Schneider et al. (2010). They provide the largest available panel data set on unofficial economic activities, covering 162 countries from 1999 to 2007. They estimate the size of the shadow economy relative to the official GDP using the DYMIMIC (dynamic multiple causes, multiple indicators) method¹. For other explanatory variable, I have relied on various sources, attempting most of the time on theory consistent measures on the basis of previous studies².

Results³

Tables 1 (a and b) reproduce the results of Johnson et al. (1998) using their data set⁴. The explanatory variables include Regulation1 (which is Heritage Foundation’s business freedom index), Regulation2 (which is World Economic Forum’s measure of regulatory discretion); Regulation3 (which is Political Risk Services Group’s (PRSG) measure of bureaucratic quality); Regulation4 (which is Heritage Foundation’s measure of overall economic freedom); Taxation1(which is World Economic Forum’s (WEF) measure of tax burden); Taxation2 (which is Fraser institute’s measure of marginal income tax rate); Leg Env1 (which is PRSG’s measure of law and order); Leg Env2 (which is Heritage Foundation’s measure of property rights); Leg Env3 (which is Fraser Institute’s measure of equality of citizens before the law); Leg Env4 (which is World Governance Indicator’s measure of the rule of law); Corruption1 (which is transparency international’s index of corruption); Corruption2 (which is WEF’s measure of bribes in the public sector); Corruption3 (which is Impulse’s exporter bribery index); Corruption4 (which is WGI’s index of control of corruption); Corruption5 (which is PRSG’s measure of public sector corruption).

The results tell us that more restrictive regulations from business point of view, increase the size of the shadow economy (Columns 1a.1 to 1a.4 in Table 1a); greater tax burden is unsustainable with larger size of shadow economy (Columns 1a.5 and 1a.6 in Table 1a); more effective law and order implementation helps attract economic activity in official sector (Columns 1b.1 to 1b.3 in Table 1b); and public sector corruption has a negative effect on business decisions (Columns 1b.4 and 1b.5 in Table 1b) and positive on the size of the unofficial sector (Column 1b.6 in Table 1b). These tables mimic the results of Tables 1 and 2 in Johnson et al. (1998) study.

The results using panel data, are shown in tables 2 (a and b) and 3 (a and b). Following the recommendation of Beck and Katz (1995) we reported panel corrected standard errors which are robust against heteroskedasticity and autocorrelation⁵. In table 2a, I have used two measures of regulation (Regulation3 and Regulation5). The Regulation3 is similar to Johnsn et al. (1998) whereas Regulation5 is a new measure. Our results, like those of Johnsn et al. (1998), indicate a negative relation between the quality of governance and the size of the unofficial economy (columns 2a.1 and 2a.2). In the next two columns (2a.3 and 2a.4) we have used two measures of taxation (Taxation2 and Taxation3). The Taxation2 is similar to Johnsn et al. (1998) whereas Taxation3 is a new measure. The coefficients on these measures of taxation are positive and significant indicating that larger size of the shadow economy is not sustainable with lower tax rates.

In table 2b, we have presented the results of the affect of legal environment (Leg Env1 and Leg Env2) on the unofficial economy using two measures of legal environment. The first measure (Leg Env1) is similar to Johson et al. (1998) measure of law and order. Results indicate negative and significant impact of good legal environment on the unofficial economy (columns 2b.1 and 2b.2).

In table 2b columns (2b.3 and 2b.4), we have employed two measures of corruption (Corruption1 and Corruption2). Higher values of these indices are associated with lower corruption. Our results indicate that lower the corruption, lower the size of the unofficial economy.This results supports the Dreher and Schneider (2010) evidence that shadow economy and corruption are complements.

Causality Analysis

It is important to search empirically for the causal effects assumed in the theoretical studies. Therefore, we try to identify the causal impact of the regulatory discretion on the shadow economy. Given the difficulties in finding the instruments for all the three sets of our variables, I use Arellano and Bond (1991) estimator which uses the own past values of the endogenous regressors as instruments.

Tables 3(a and b) show the results. The two crucial assumptions of Arellano and Bond estimator are the absence of serial correlation in the error term beyond order 1 and the validity of the overidentifying restrictions. The bottom panel of the table provides test hypothesis on these two assumptions. As is clear from the table, in most of the cases there exists serial correlation beyond order one. While Sargan test clearly indicates that overidentyfying restrictions are not valid. Although the coefficients of our regressions are in line with the earlier findings but failure to satisfy the assumptions of the Arellano and Bond estimator do not permit a valid inference⁶.

Conclusions

We have replicated and reinvestigated the relationship between regulatory discretion and the size of the unofficial economy. In this respect, the paper endorses the findings of Johnson et al. (1998) and Dreher and Schnieder (2010) and adds two important dimensions to their results. First, it produces the same results using a much larger data set than the previous authors thus filling the important gap in terms of country coverage. Secondly, the paper attempts to discover the causal connection between the shadow economy size and business regulation. The results of the causal analysis using Arellano and Bond estimator suffer from weak instrument and serial correlation problems. A more rigorous causal analysis could be an important motivation for future research in this area.

References

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· Beck NL, Katz JN. 1995. What to do (and not to do) with time-series cross-section data. American Political Science Review 89: 634-647.

· Dreher A, Schneider F. 2010. “Corruption and the shadow economy: An empirical analysis”,. Public Choice, 144: 215-238.

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· Kaufmann D, Kraay A, Mastruzzi M. 2010. The Worldwide Governance Indicators: Methodology and Analytical Issues. World Bank Policy Research Working Paper No. 5430

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· Schneider F...