Analyzing the Determinants of the Shadow Economy With a “Separate Approach”. An Application of the Relationship Between Inequality and the Shadow Economy

World Development Vol. xx, pp. xxx–xxx, 2015 0305-750X/Ó 2015 Elsevier Ltd. All rights reserved. www.elsevier.com/locate/worlddev

http://dx.doi.org/10.1016/j.worlddev.2015.08.026

Analyzing the Determinants of the Shadow Economy With a ‘‘Separate Approach”. An Application of the Relationship Between Inequality and the Shadow Economy ROBERTO DELL’ANNO* University of Salerno, Fisciano (Sa), Italy Summary. — This paper suggests a ‘‘separate” approach to analyze the determinants of the shadow economy (SE). It is applied to investigate the relationship between inequality and the SE on a cross-section of 118 countries. We disentangle the effect of inequality on the SE ratio by estimating both direct and indirect effects on both the numerator and denominator of the ratio separately. We find that an increase in inequality increases the SE ratio. This positive correlation is primarily due to a reduction in the official GDP rather than an increase in the SE. Ó 2015 Elsevier Ltd. All rights reserved. Key words — shadow economy, inequality, separate approach, unobserved economy

denominator (i.e., official GDP) disjointedly; we provide a method to calculate the effect of a determinant on the SE ratio by controlling for the double counting of a part of the SE in the SE ratio; and concerning the relationship between inequality and the SE, we find that the overall impact of inequality on the SE ratio is positive and higher than the effect estimated by the ‘‘ratio approach”. The paper is organized as follows. Section 2 addresses the definition of the SE and introduces the ‘‘separate approach”. Section 3 provides theoretical background on the interactions among inequality, official GDP and the SE. Section 4 describes the database, econometric models, and hypotheses and reports the empirical outcomes. Section 5 concludes.

1. INTRODUCTION The shadow economy (SE) is a subject of considerable interest, and the literature on the analysis of its determinants is particularly extensive (see Friedman, Johnson, Kaufmann, & ZoidoLobaton, 2000; Schneider, 2011; Schneider & Enste, 2000 for an overview). This paper aims at contributing to this issue by proposing an alternative approach to estimate the influence of a potential determinant on the SE ratio. The basic intuition of this research is to demonstrate that estimating the influence of an explanatory variable on a dependent variable measured as a ratio (hereinafter, ‘‘ratio approach”) may be not conclusive because it blurs the impact of the explanatory variable on the denominator (e.g., official economy) with the impact that it has on the numerator (e.g., SE). Accordingly, we propose calculating the overall effect, estimating both direct and indirect impacts on both the official and on the unobserved Gross Domestic Product (GDP) separately (hereinafter ‘‘separate approach”). An empirical application of this approach is conducted to explore the relationship between income inequality and the SE. The paper consists of two parts. In the first ‘‘methodological” part, we address the issue of the different approaches to define (and measure) the SE and introduce the ‘‘separate approach”. The second part of the paper applies the proposed methodological hints to investigate the relationship between the income distribution and the SE. Over the past two decades, several research works empirically supported the hypothesis that income inequality and the SE are positively correlated (e.g., Ahmed, Rosser, & Rosser, 2007; Chong & Gradstein, 2007; Rosser, Rosser, & Ahmed, 2000, 2003). We verify that this result is empirically validated both by utilizing the ‘‘ratio approach” and by applying the ‘‘separate approach”. In sum, the paper contributes to the existing literature in several ways. Following the order in which they are presented in the article, we attempt to reconcile the definitions of the SE utilized in economic research with the Non-Observed Economy (NOE) concept adopted by national statistical institutes; because the ‘‘ratio approach” may cause misinterpretation of the actual influence of an explanatory variable on a ratio variable, we propose estimating both the direct and indirect effects of the explanatory variable on the numerator (i.e., SE) and

2. DEFINING AND ANALYZING THE SHADOW ECONOMY IN EMPIRICAL RESEARCH (a) Defining the shadow economy We discuss two general approaches to define and measure the SE. On the one hand, the national accounting system (SNA) employs the label NOE to refer to ‘‘all productive activities that may not be captured in the basic data sources used for national accounts compilation” (UNECE, 2008, p. 2). Following the Eurostat’s (2005) ‘‘Tabular approach to exhaustiveness”, the SNA classifies seven sources of non-exhaustiveness for GDP estimates: (N1) Producers deliberately not registered to avoid tax and social security obligations; (N2) Producers deliberately not registered as a legal entity or as an entrepreneur because they are involved in illegal activities; (N3) Producers not required to register because they have no market output; (N4) Legal persons or (N5) registered entrepreneurs not surveyed due to a variety of reasons; (N6) Producers deliberately misreporting to evade taxes or social security contributions; and (N7) Other statistical deficiencies. For

* I am indebted to anonymous reviewers and editor for providing insightful comments and directions for additional work which has resulted in this paper. The usual disclaimer applies. 1

Please cite this article in press as: Dell’Anno, R. Analyzing the Determinants of the Shadow Economy With a ‘‘Separate Approach”. An Application of the Relationship Between Inequality and the Shadow Economy, World Development (2015), http://dx.doi.org/10.1016/j.worlddev.2015.08.026

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analytical purposes, OECD (2014) proposes a simplification of this classification in four types of NOE adjustments. It defines N1 + N6 as Underground production, N2 as Illegal production, N3 + N4 + N5 as Informal sector production (including those undertaken by households for their own final use) and N7 as Statistical deficiency. The second approach to define the SE is prevalent in economic research. Here, the adjectives informal, shadow, hidden, second, black, unrecorded, unofficial, unobserved, etc., are often utilized synonymously with terms such as economy, sector, market, and GDP. However, these labels refer to distinct phenomena and should be used appropriately (Bagachwa & Naho, 1995; Feige, 1990; Feige & Urban, 2008) to avoid misunderstandings. In this literature, a plurality of macro-econometric methods to estimate the SE is proposed. Among these methods, the Multiple Causes Multiple Indicators (MIMIC) approach and the currency demand approach are becoming dominant. Attempting to systematize the common definitions in this area of research, we identify two recent studies as benchmarks for the two most common sources of macro-econometric estimates of the SE, i.e., Buehn and Schneider (2012) for the MIMIC method and Alm and Embaye (2013) for the currency demand approach. The two studies adopt two different mainstream definitions of the SE. They differ in dealing with illegal activities in the SE. Specifically, Buehn and Schneider (2012, p. 141) define the SE as including all market-based legal production of goods and services that are deliberately concealed from public authorities to avoid payment of taxes or social security contributions, to avoid having to meet certain legal labor market standards, and to avoid complying with certain administrative procedures or statistical questionnaires. Following Smith (1984), Alm and Embaye (2013, p. 512) employ a somewhat broader definition of the SE that includes ‘‘all market-based goods and services (legal or illegal) that escape inclusion in official accounts”. In other words, while Buehn and Schneider (2012) include ‘‘all market-based legal production”, Alm and Embaye also consider market-based illegal production. Aiming to find a trait d’union between the most used labels in economic research (i.e., SE) and in national accounting system (i.e., NOE), we distinguish four types of GDP aggregates: recorded observed economy (GdpRO); recorded non-observed economy (GdpRNOE) and unrecorded non-observed economy (GdpUNOE). Given the foregoing definitions, we can label the total economic activity as GdpT = GdpRO + GdpNOE and the official (published) GDP as Gdpoff = GdpRO + GdpRNOE, where the total NOE is given by GdpNOE = GdpRNOE + GdpUNOE. Combining this classification with the seven sources of nonexhaustiveness for GDP estimates proposed by the Eurostat’s (2005) Tabular approach to exhaustiveness, we obtain a precise definition of the estimates of the SE ratio calculated by Alm and Embaye (2013) utilizing a modified Currency demand approach (SEMacro Curr ) and Buehn and Schneider (2012) utilizing MIMIC modeling (SEMacro MIMIC ). A preliminary explanation is required here. Following Alm and Embaye’s definition literally, we should include in the numerator only the GDP that ‘‘escapes inclusion in the official accounts”, i.e., GdpUNOE(N1+N6). However, the currency demand approach estimates a linear transformation of this value. In the last stage of the currency demand approach, the amount of the unobserved GDP is obtained by multiplying the stock of currency used to escape taxes and administrative

burdens (C*) by the velocity of money (V). Considering that the velocity of money is the ratio between the nominal (official) GDP and money supply, what a researcher obtains by multiplying C* by V is inevitably an estimate of the unobserved GDP that includes an additional share of the NOE in the same proportion – that we denote by b – in which the recorded NOE is included in the official GDP (hereinafter ‘‘currency demand bias”). Accordingly, we include (1 + b) in the numerator of SEMacro Curr . SEMacro Curr


ð1 þ bÞGdpUNOEðN1þN2þN6Þ GdpRO þ GdpRNOEðTotalÞ

ð1Þ

where b is the proportion of GdpRNOE on Gdpoff (GdpRNOE ¼ bGdpoff ). With reference to the MIMIC estimates of the SE ratio, the numerator of SEMacro Mimic follows (1) because of the calibration of the MIMIC model to the currency demand method. 1 SEMacro Mimic


ð1 þ bÞGdpUNOEðN1þN6Þ GdpRO þ GdpRNOEðTotalÞ

ð2Þ

This issue might be easily solved if the estimates of the imputed NOE were officially published and homogeneously estimated at the national level. However, this is not the normal case because national statistical offices do not regularly divulge the size of NOE adjustments in the official statistics. Moreover, for the countries where these data are available, these adjustments should be cautiously employed for crosscountries comparisons because of the differences in methodologies and practices followed by offices in estimating the NOE (OECD, 2014; UNECE, 2008). In general, assuming no measurement errors, the differences between the macro-econometric and statistical national accounting methods may be explained both by divergences in the coverage of the NOE types and by the factor (1 + b). For instance, the discrepancy between Alm and Embaye’s (2013) estimates and the size of adjustments in national accounting (SESNA ) should be equal to the imputed unobserved GDP yield by unregistered producers because they have no market output (N4 + N5), statistical discrepancies (N7) and unrecorded NOE for underground and illegal production divided by the official economy multiplied by the factor (1 + b) (i.e., SNA ¼ ð1 þ bÞGdpUNOEðN1þN2þN6Þ =Gdpoff ). Again, SEMacro curr  SE the discrepancy between the SE ratio obtained by Buehn and Schneider’s (2012) MIMIC specification and those obtained by the currency demand should be equal to the proportion of unobserved economy due to illegal activities (N2). However, given that the estimates obtained by currency approach calibrate the Buehn and Schneider’s (2012) MIMIC model, we cannot extrapolate N2 by comparing these two sources of data. Hence, in the following, we will assume that Macro the difference between SEMacro MIMIC and SECurr only depends on measurement errors. Concerning the consequence of this assumption, OECD (2014) states that N1 + N6 adjustments for NOE activities almost always represent the most significant part of the adjustments for non-exhaustiveness, reaching as much as 80% of all adjustments in some countries; therefore, we could suppose that our simplification does not significantly affect the results. In sum, the MIMIC and currency demand estimates of the SE approximately measure the following ratio: SEMacro
ð1 þ bÞ

GdpUNOE Gdp þ GdpRNOE RO

ð3Þ

Please cite this article in press as: Dell’Anno, R. Analyzing the Determinants of the Shadow Economy With a ‘‘Separate Approach”. An Application of the Relationship Between Inequality and the Shadow Economy, World Development (2015), http://dx.doi.org/10.1016/j.worlddev.2015.08.026

ANALYZING THE DETERMINANTS OF THE SHADOW ECONOMY WITH A ‘‘SEPARATE APPROACH”

However, grounding economic implications from this ratio is challenging. On the one hand, given that GdpNOE ¼ ð1 þ bÞGdpUNOE () GdpRNOE ¼ bGdpUNOE and assuming, realistically, that Gdpoff > GdpUNOE , the numerator of the SEMacro includes a lower GdpNOE than the actual one. On the other hand, the denominator includes a part of the numerator, i.e., GdpRNOE . As a consequence an unambiguous definition of the SE ratio for economic analysis may be the ratio between unobserved and observed economy. It takes into account both macro-econometric estimate of the SE ratio and proportion of NOE-adjustments in official GDP. Specifically, given that GdpUNOE ¼ Gdpoff SEMacro ð1 þ bÞ1 , we obtain a ratio where the numerator is the SE (or unobserved economy) and the denominator is the observed economy: SE


GdpUNOE þ GdpRNOE SEMacro þ bð1 þ bÞ ¼ GdpRO 1  b2

ð4Þ

In conclusion, by combining macro-econometric and SNA’s definitions, the SE includes all market-based goods and services (legal or illegal) that are not observed in the basic data sources utilized for national accounts compilation. (b) A separate approach to analyze the determinants of the shadow economy Problems in employing ratio variables have been described in the statistical literature for over a century (e.g., Pearson, 1897; Yule, 1910), and scholars continue to warn against this problematic practice (e.g., Kuh & Meyer, 1995). However, little attention has been paid to the use of ratio variables in the literature on the SE, where they remain popular. In general, we argue that estimating one regression for the numerator and another one for the denominator is a more appropriate method than the standard practice to regress the set of the potential determinants on the SE ratio. The separate approach avoids the risk of misinterpretation of empirical results that may occur if in addition to the effect of the determinant on the numerator (Hp.1: @N =@X –0), this factor also affects the denominator of the ratio (Hp.2: @D=@X –0). Furthermore, if a statistically significant relationship between numerator and denominator exists (Hp.3: @N =@D–0), computing the indirect effects in both the terms of the ratio is recommended. The rationale is that by using a separate approach, we can calculate an overall effect by combining the direct and indirect effects of X on both terms of the ratio. Applying this approach to the SE ratio, and given that the marginal effect of X on GDPNOE is unrelated to the statistical office’s ability to impute it in official GDP (@GdpUNOE =@X ¼ @GdpRNOE =@X ), then, we obtain the overall or total effect of X on SEMacro .

3

3. WHAT DOES THE LITERATURE SAY ABOUT THE RELATIONSHIPS AMONG INEQUALITY, THE SHADOW ECONOMY, AND THE OFFICIAL ECONOMY? In this second part of the article, we apply the separate approach to investigate the relationship between inequality and the SE. First, we theoretically support the hypotheses that should suggest the use of this method rather than the standard ‘‘ratio approach”. In particular, Sections 3(a) and 3(b) provide economic arguments and empirical evidence for the existence of statistically significant relationships between inequality and the SE (Hp. 1: @N =@X –0) and inequality and the official GDP (Hp. 2: @D=@X –0). Section 3(c) surveys the theoretical background supporting the inclusion of indirect effects in the analysis (Hp 3: @N =@D–0). (a) The relationship between income inequality and the shadow economy (Hp.1) Extensive research has been devoted to the study of the determinants of the SE, but explicit analyses of the link between the SE and income inequality are relatively scarce. The first published papers dealing empirically with the relationship between these two phenomena are those by Rosser et al. (2000, 2003). They found a positive correlation between inequality and the size of the SE as a percentage of official GDP within economies with low institutional infrastructure (i.e., Transition countries). This is because the SE reduces the amount of tax revenues, thereby reducing the effectiveness of a government’s redistributive policies. Notwithstanding, Rosser et al. (2000) conclude that the direction of causation in this relationship remains untested and unknown. Chong and Gradstein (2007) also find a robust positive relationship between income inequality and the SE. They argue that when inequality increases, the rich invest in rent seeking more and the poor invest less. Chong and Gradstein (2007) show that the SE ratio is larger with relation to weaker institutions, and the larger the income inequality. Likewise, utilizing a general equilibrium model in which public policy is based on the median voter, Hatipoglu and Ozbek (2011) provide theoretical support to the empirical evidence that the existence of a large SE coincides with less redistribution. Again, Ahmed et al. (2007) and Dell’Anno (2008) showed a positive relationship between income inequality and the size of the SE ratio in a global dataset and in Latin American countries, respectively. However, part of the literature notes that the sign of the relationship between the SE and inequality is hard to predict by macro-econometric analyses. For instance, Valentini (2009) notes two crucial aspects that have been scarcely considered in the literature. First, because income inequality is measured using ‘‘declared” incomes, the bias of the indexes of inequality may make these measures unreliable for comparisons among

ð5Þ

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countries with different sizes of the SE. Second, he argues that there are no reasons to suppose that a growth in unobserved income is uniform along income distribution. In particular, the sign of this correlation depends on the predominant nature of the shadow income. Accordingly, if the unobserved income is higher (lower) for the poorer than for the richer, we could have a positive (negative) relationship between the size of SE and income inequality, or vice versa. Eilat and Zinnes (2002) argue that the SE can affect income distribution through several channels, some increasing inequality and some decreasing it. Concerning the negative correlation between the SE and income inequality, Eilat and Zinnes (2002) state that if shadow activities are associated with anti-competitive conduct, it may transfer economic surplus from consumers to equity owners, increasing inequality. In contrast, if shadow activities provide employment to those with lower income, a ‘‘tax-free” SE may have a positive effect on income distribution. Consistent with this, the authors find evidence of a non-statistically significant relationship between the size of the SE and the Gini coefficient in Transition countries. In conclusion, the prevailing view is that an economically significant relationship between the SE and income inequality exists, although it may be concealed in the empirical analysis. However, when a statistically significant correlation is estimated, it is positive. (b) The relationship between income inequality and official GDP (Hp. 2) The relationship between inequality and the level of economic development has interested social scientists for many years, and it has been explored in many theoretical and empirical studies. Various theoretical explanations have been suggested that explain how inequality could affect economic development. Following Amendola and Dell’Anno (2015), this literature can be classified into two main strands: (1) political economy explanations and (2) purely economic explanations. A first group of political economy models argues that (1.a) a more unequal income distribution motivates more social demand for redistribution throughout the political process (e.g., Persson & Tabellini, 1994). Typically, transfer payments and associated taxation will distort economic decisions, and through this channel, inequality reduces growth. A second group of political economy models (1.b) assumes that a greater degree of inequality causes ‘‘political instability” (e.g., Alesina & Perotti, 1996) and motivates the poor to engage in crime and disruptive activities (Bourguignon, 1999). Through these dimensions of socio-political unrest, more inequality tends to reduce economic growth. With reference to ‘‘purely economic” explanations, a first group of models (2.a) assumes that due to the presence of imperfect capital markets, a more unequal distribution of assets means that an increased number of individuals do not have access to credit and, thus, cannot make productive investments. Through this channel, inequality would reduce economic growth (e.g., Galor & Zeira, 1993). A second group of models (2.b) assumes that income inequality noticeably reduces the future growth rate because of the positive effect of inequality on the overall rate of fertility (e.g., Becker, Murphy, & Tamura, 1990). Thus, a worsening in inequality jointly generates a rise in the fertility rate and a drop in the rate of investment in human capital, and this reduces the future growth rate of GDP. A third approach of ‘‘purely economic” models (2.c) claims that a more unequal distribution of incomes results in smaller domestic markets (Murphy,

Shleifer, & Vishny, 1989). The size of home demand is, thus, too small to generate markets large enough to fully develop local industries or to attract foreign direct investment. Following this approach, inequality reduces the growth rate as a consequence of a lower exploitation of economies of scale and of incentives to foreign direct investment. Accordingly, although several surveys show the findings of this strand of empirical research are mixed, the predominant view is that a statistically significant relationship between the official economy and income inequality exists. In particular, a higher inequality level is associated with lower economic development. (c) The relationship between official GDP and the shadow economy (Hp. 3) The analysis of the relationship between official production and the SE is one of the most relevant and challenging issue in this literature. Schneider and Enste (2000) state that the effect of the SE on economic growth remains considerably ambiguous, theoretically and empirically. The correlation between shadow and official may be both negative (dual hypothesis) and positive (structural hypothesis). According to the dual view shadow activities, creating unfair competition, interfere negatively with market allocation (Tokman, 1978). Then, the misallocation slows down economic growth. Loayza (1996) found empirical evidence of negative correlation between the SE and the growth rate of the official GDP per capita for 14 Latin American countries. The inverse relationship between the SE and economic growth is theoretically supported by the author’s hypothesis on the shadow economy’s congestion effect. Similarly, Eilat and Zinnes (2002) estimate an inverse relationship between the SE and official economy in Transition countries. The ‘‘structuralists” consider the shadow and official economy as intrinsically linked. According to this approach, shadow activities are inclined to meet the interests of increasing the competitiveness of regular productive units, providing cheap goods and services (Moser, 1978). Consequently, a growing official economy boosts the SE. The economic explanation is that the value-added created in the SE is spent (also) in the official economy. At the same time, more official production increases the demand for goods and services produced by unobserved activities. Various studies have supported the hypothesis of the beneficial effect of the SE on economic development. For instance, Adam and Ginsburgh (1985) estimate a positive relationship between the growth of the SE and the official economy under the assumption of the low probability of enforcement. Bhattacharyya (1999) presents clear evidence in the case of the United Kingdom (from 1960 to 1984) that the SE has a positive effect on several components of GDP (e.g., consumer expenditures, services, etc.). For Eilat and Zinnes (2002), the most obvious benefit of a SE is that it helps maintain economic activity when rent seeking and corruption raise the cost of official production. Because some of the income earned in the unobserved economy eventually is spent in the official economy, shadow activity may even have a positive effect on official growth and on tax revenues. Further empirical evidence of a positive correlation between the SE and official economy is also found by Tedds (2005) and Bovi and Dell’Anno (2010). An interesting result to rationalize these contradictory findings is reported by Schneider (2005). He estimates that while unofficial activities boost economic growth for developed economies, they reduce the growth rate of the official GDP for developing countries. As a result, the sample composition of the empirical analysis may indirectly

Please cite this article in press as: Dell’Anno, R. Analyzing the Determinants of the Shadow Economy With a ‘‘Separate Approach”. An Application of the Relationship Between Inequality and the Shadow Economy, World Development (2015), http://dx.doi.org/10.1016/j.worlddev.2015.08.026

ANALYZING THE DETERMINANTS OF THE SHADOW ECONOMY WITH A ‘‘SEPARATE APPROACH”

determine the sign of correlation between the official and unobserved economies. Conclusively, this survey has shown that although predicting the sign remains challenging, abundant evidence corroborates the hypothesis of a statistically significant relationship between the official economy and SE. For that reason, indirect effects should be included to compute the total effect of inequality on the SE ratio in the separate approach. Last but not least, we note that a positive correlation between the official GDP and SE should be expected because of SNA rules that prescribe to include the unobserved economy in official statistics, i.e., Gdpoff = GdpRO + GdpRNOE. As this section has shown, the main findings of the economic research concerning Hp.1, Hp.2, and Hp.3 support, in theory, the application of the proposed ‘‘separate approach” with indirect effects to analyze the relationship between the SE and inequality. The next section aims to verify whether these results are also empirically validated in our dataset. 4. EMPIRICAL ANALYSIS (a) Data and econometric approach We conduct cross-sectional regressions of a set of potential determinants of the official and SE for 118 countries, calculating the average values over the period 1999–2007. 2 The baseline regression to analyze the effect of inequality on the SE ratio follows Chong and Gradstein (2007): SEi ¼ a0 þ a1 Ineqi þ X b þ ei

with i ¼ 1; . . . ; 118

ð6Þ

where SE is the ratio between NOE and the official GDP in =Gdpoff the ratio approach (GdpNOE i i ) or the level of unobserved GDP per capita in the separate approach (GdpNOE ). i As a robustness check, we employ different model specifications and alternative indicators of the SE, inequality, and institutional quality. Measurement errors, sample bias, and endogeneity are the most relevant concerns for the empirical literature on this topic. In the following, we explain how we address these issues.

5

As concerns the measurement issues – probably the most puzzling topic regarding the study of the SE – we utilize different sources of estimates of both SE and inequality indexes. In particular, the regressions include as dependent variable the estimates of the SE obtained by the MIMIC approach off (GdpNOE Mimic =GdpPPP – Buehn & Schneider, 2012) and Alm and Embaye’s (2013) estimates based on the currency demand off approach (GdpNOE Curr =Gdpconst ). With reference to the proxies of income distribution (Ineq), we utilize the Gini index (Gini), the income share ratios of the top to the bottom, both quintiles (T20/B20) and deciles (T10/B10) of the population. 3 Table 1 summarizes the correlations among the key variables of this study. To address the sample bias issue, 4 we collected the largest cross-sectional dataset utilized in this area of research, i.e., 118 and 88 countries by employing estimates based on the MIMIC and currency demand approaches, respectively. The third relevant issue for this strand of empirical literature is endogeneity. Considering that a set of instrumental variables with a suitable coverage of the countries of our sample is not available for potential endogenous variables, we apply two alternative strategies. First, we use the observation of potential endogenous control variables in the year before the period used to estimate the country averages (i.e., 1999– 2007). That means utilizing the values observed in 1998, and if this observation was missed, we employ the first available observation in the sample period. Second, we replace potential endogenous control variables with a set of dummies on legal origins of the countries. These variables identify the origin of the Company Law or Commercial Code in each country and are extracted from Global Development Network Growth Database. They are British legal origin (LegBrit), French legal origin (LegFren), Socialist legal origin (LegSoc), German legal origin (LegGer), and Scandinavian legal origin (LegScan). The last one is excluded as a base category. Following a consolidated literature, a vector of control variables (X) is included in the regressions to reduce potential omitted-variables bias. These are 5 (logarithm of) official GDP per capita (Gdpoff), an index of rule of law (Rol), urban population as a percentage of total population (Urb), proportion of a country’s population that is employed (EmplR), share

Table 1. Correlation matrix GdpNOE Mimic Gdpoff PPP GdpNOE Mimic Gdpoff PPP

GdpNOE Mimic Gdpoff PPP GdpNOE Curr Gdpoff const

GdpNOE Curr Gdpoff const Gini Index Quintile ratio Decile ratio

1 (118) 0.318 (0.00;118) 0.603 (0.00;118) 0.547 (0.00;85) 0.556 (0.00;85) 0.606 (0.00;118) 0.336 (0.00;115) 0.355 (0.00;114) 0.350 (0.00;114)

GdpNOE Mimic

Gdpoff PPP

1 (118) 0.843 (0.00;118) 0.538 (0.00;85) 0.671 (0.00;85) 0.735 (0.00;118) 0.219 (0.02;115) 0.064 (0.50;114) 0.001 (0.99;114)

1 (118) 0.653 (0.00;85) 0.888 (0.00;85) 0.970 (0.00;118) 0.332 (0.00;115) 0.199 (0.03;114) 0.139 (0.14;114)

GdpNOE Curr Gdpoff const

1 (85) 0.466 (0.00;85) 0.623 (0.00;85) 0.290 (0.01;83) 0.205 (0.06;82) 0.154 (0.17;82)

GdpNOE Curr

Gdpoff const

1 (85) 0.914 (0.00;85) –0.212 (0.05;83) 0.115 (0.31;82) 0.076 (0.50;82)

1 (118) –0.302 (0.00;115) 0.188 (0.05;114) 0.137 (0.15;114)

Gini Index

Quintile ratio

Decile ratio

1 (115) 0.892 (0.00;114) 0.776 (0.00;114)

1 (114) 0.957 (0.00;114)

1 (114)

Note: in parenthesis p-value of H0: rxy = 0 and number of observations.

Please cite this article in press as: Dell’Anno, R. Analyzing the Determinants of the Shadow Economy With a ‘‘Separate Approach”. An Application of the Relationship Between Inequality and the Shadow Economy, World Development (2015), http://dx.doi.org/10.1016/j.worlddev.2015.08.026

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of taxes on income, profits and capital gains as a percentage of official GDP (TaxI), and a proxy of tax complexity – hours to prepare and pay taxes – (TaxC) 6; to account for the labor market determinants of the SE, we also include the ‘‘vulnerable” employment as a percentage of total employment (VunE). The definitions and sources of variables are provided in the Appendix (Table 4). Conclusively, we carry out a set of tests for residual normality and heteroskedasticity. With reference to the heteroskedasticity tests, we apply Breusch–Pagan (1979), Godfrey (1978) and Harvey (1976) tests. For the regressions where at least one of the two heteroskedasticity tests suggests rejecting the null hypothesis of no heteroskedasticity at the 5% significant level, White’s (1980) estimator is applied to provide consistent estimates of the coefficient covariances.

In this second step, we check whether the three hypotheses discussed in Section 3 are empirically validated. The validation of these hypotheses should guide the researcher on applying the separate approach with indirect effects. Hypothesis 1b. ((Separate approach) – Marginal effect on unobserved unrecorded GDP) Ceteris paribus, an increase in income inequality (marginally) increases the unobserved GDP per capita @GdpUNOE =@Ineq
d1 > 0.

Hypothesis 2. ((Separate approach) – Marginal effect on official GDP) Ceteris paribus, an increase in income inequality (marginally) decreases official GDP per capita @Gdpoff =@Ineq
c1 < 0.

(b) Model specifications and hypotheses The econometric analysis consists of four steps. The first step of our analysis replicates the results of the earlier literature. It aims to also validate the outcome of a positive correlation between the SE ratio and inequality in our sample. The benchmark specification regression is
þ X b þ ei SEMacro ¼ a0 þ a1 Ineqi þ a2 Log Gdpoff i with i ¼ 1; . . . ; 118 ð7Þ and the associated hypothesis is as follows: Hypothesis 1a. ((Ratio approach) – direct effect on the SE ratio) Ceteris paribus, an increase in income inequality directly increases the SE ratio: @SEMacro =@Ineq
a1 > 0: Specifically, a one-unit increase in the inequality index increases the SE ratio by a1 percentage points. In the second step, we estimate two regressions where the dependent variables are the numerator (GdpUNOE) and the denominator (Gdpoff) of the SE ratio:

¼ ½d0  Logð1 þ bÞ þ d1 Ineqi þ d2 Log Gdpoff Log GdpUNOE i i þ X b þ ei with i ¼ 1; . . . ; 118 ð8Þ
¼ ½c0 þ c2 Logð1 þ bÞ þ c1 Ineqi Log Gdpoff i
þ c2 Log GdpUNOE þ X b þ ei i with i ¼ 1; . . . ; 118 UNOE

ð9Þ

denotes both the level of unobserved unrecorded where Gdp GDP in purchasing power parity per capita ðGdpUNOE Mimic Þ when the estimates of the SE are extracted by Buehn and Schneider (2012) and the level of unobserved unrecorded GDP in constant 2000 US dollars per capita ðGdpUNOE Curr Þ if the source of the SE is Alm and Embaye (2013). This difference in the way to convert the nominal GDP in real values follows the original unit of measure of GDP employed by the two cited studies. As a result of log-transformation and given the small values of the estimated coefficients, d1 and c1 give us an approximation of the change in the SE for a one-unit increase in the inequality index. The interpretation of the coefficients d2 and c2 is given as an approximation of the expected percentage change in dependent variable when the official or unobserved (unrecorded) GDP increases by 1% (i.e., elasticity). In Appendix 2, we report OLS estimates for regression 9 (Tables 5 and 6), 10 (Tables 7 and 8) and 11 (Tables 9 and 10).

Hypothesis 3a. ((Test to include Indirect effects) – Marginal effect of official on GdpUNOE) Ceteris paribus, an increase in official GDP (marginally) increases unobserved GDP @GdpUNOE =@Gdpoff
d2 > 0.

Hypothesis 3b. (Marginal effect of unobserved on official GDP) Ceteris paribus, an increase in GdpUNOE (marginally) increases official GDP @Gdpoff =@GdpUNOE
c2 > 0. If the hypotheses 3(a) and 3(b) are verified, we calculate, in the third step, the direct and indirect effects of income inequality on the numerator and denominator of the SEMacro ratio adjusted for currency demand bias, i.e., multiplying by ð1 þ bÞ1 . In particular, we substitute (9) in (8) to obtain the overall effect of inequality on SE, i.e., ¼ ½ðd þ d c Þ=ð1  d c ÞIneq ; and (8) in (9) to get GdpUNOE 1 2 2 1 2 i i the total effect of inequality on official GDP, i.e., Gdpoff ¼ ½ðc1  c2 d1 Þ=ð1  d2 c2 ÞIneqi . As a result, from the i ratio of the previous total effects, we obtain a coefficient that can be compared in a straightforward manner with the (Log-linear specification) of regression (7) (a1 – i.e., ratio =Gdpoff ¼ ½ðd1 þ d2 c1 Þ=ðc1  c2 d1 ÞIneqi . approach): GdpUNOE i i Finally, because d2 c2  :0001, then 1  d2 c2 is negligible; therefore, the estimated marginal effects give us an approximation of the direct effects. Hypothesis 4. (Direct effect on unobserved unrecorded GDP) Ceteris paribus, an increase in income inequality (directly) increases the unobserved unrecorded GDP per capita: @GdpUNOE =@Ineq
d1 =ð1  d2 c2 Þ  d1 > 0.

Hypothesis 5. (Direct effect on official GDP) Ceteris paribus, an increase in income inequality (directly) decreases official GDP per capita: @Gdpoff =@Ineq
c1 =ð1  d2 c2 Þ  c1 < 0.

Hypothesis 6. (Indirect effect on unobserved unrecorded GDP) Ceteris paribus, an increase in the inequality index indirectly decreases unobserved unrecorded GDP: ð@GdpUNOE =@Gdpoff Þð@Gdpoff =@IneqÞ
d2 c2 =ð1  d2 c2 Þ  d2 c2 < 0.

Please cite this article in press as: Dell’Anno, R. Analyzing the Determinants of the Shadow Economy With a ‘‘Separate Approach”. An Application of the Relationship Between Inequality and the Shadow Economy, World Development (2015), http://dx.doi.org/10.1016/j.worlddev.2015.08.026

ANALYZING THE DETERMINANTS OF THE SHADOW ECONOMY WITH A ‘‘SEPARATE APPROACH”

Hypothesis 7. (Indirect effect on official GDP) Ceteris paribus, an increase in the inequality index indirectly increases official GDP: ð@Gdpoff =@GdpUNOE Þð@GdpUNOE =@IneqÞ
ðd1 c2 Þ= ð1  d2 c2 Þ  d1 c2 > 0. Concerning the signs of the total effects of the inequality on the numerator and denominator of the SE ratio, they are predicted by the knowledge on the relative size of the coefficients estimated by the separate regressions. In particular, given that jd1 j > jd2 c1 j and jc1 j > jc2 d1 j, the direct effects determine the signs of the total effects. Hypothesis 8. (Total effect on unobserved unrecorded GDP) Ceteris paribus, an increase in the inequality index increases =dIneqi
ðd1 þ c1 d2 Þ= unobserved unrecorded GDP: dGdpUNOE i ð1  d2 c2 Þ  d1 þ c1 d2 > 0.

Hypothesis 9. (Total effect on official GDP) Ceteris paribus, an increase in the inequality index decreases official GDP: dGdpoff i =dIneqi
ðc1 þ d1 c2 Þ=ð1  d2 c2 Þ  c1 þ d1 c2 < 0. Conclusively, in the fourth step, we derive direct, indirect and total effects of inequality on the SE ratio (i.e., Hp. 10, Hp. 11 and Hp. 12) by considering the variation of both the numerator (i.e., Hp. 1b; Hp. 3 and Hp. 8) and the denominator (i.e., Hp. 2; Hp. 6 and Hp. 9). Concerning the issue of the currency demand bias in the SEMacro ratio, we consider two scenarios i.e., with and without adjustments for currency demand bias. Accordingly, we modify the sample composition to estimate the averages of official and unobserved GDP by computing, under the hypothesis of adjustments, the averages only for the countries with available data on NOE adjustments in the official GDP. In particular, we denote by the subscript ‘‘A” the averages of the GDP calculated over all the 118 countries of the sample, while we indicate by the superscript ‘‘B” the average values of the GDP calculated over the 29 countries with available data on adjustments for NOE activities (b) (UNECE, 2008). Precisely, these NOE B A benchmark values are: GdpNOE Mimic ¼ $2;262; GdpMimic ¼ $3; 659; A A B GdpNOE ¼ $1; 174; GdpNOE ¼ $1; 467; Gdpoff ¼ $8; 742; PPP Curr Curr B A B Gdpoff ¼ $12; 920; Gdpoff ¼ $5; 116; Gdpoff PPP cons cons ¼ $6; 608; MacroðAÞ

SEMimic

22:95%;

MacroðBÞ

¼ 25:88%; SEMimic

MacroðBÞ SECurr

¼ 0:2832ð1 þ bÞ1 ; SECurr

¼ 0:2219ð1 þ bÞ

MacroðAÞ

1

¼

and b ¼ 16:16%.

Hypothesis 10. (Direct effect on the SE ratio) Ceteris paribus, an increase in the inequality index directly increases the SE ratio. The rationale for this expectation is that the inequality, on the one hand, directly increases the SE (Hp. 1b) and on the other hand, it decreases official GDP (Hp. 2). In quantitative terms, a one-unit increase in the inequality index directly d1 c1 MacroðAÞ percentage increases the SE ratio by aDir 1
1þc1 SE points or if the value of b is available, the change in the SE ratio for the currency demand bias is given by adjusted d1 c1 SEMacroðBÞ Dir a1 adj
1þc1 1þb percentage points. Hypothesis 11. (Indirect effect on the SE ratio) Ceteris paribus, an increase in the inequality index indirectly decreases the SE ratio.

7

Specifically, a one-unit increase in the inequality index indid2 c1 d1 c2 MacroðAÞ rectly increases the SE ratio by aInd per1
1þd1 c2 SE centage points, or if the value of b is available, the change in the SE ratio adjusted for the currency demand bias is given d2 c1 d1 c2 SEMacroðBÞ percentage points. by aInd 1 adj
1þd1 c2 1þb Given that hypotheses 10 and 11, we can hypothesize the following: Hypothesis 12. (Total effect on the SE ratio) Ceteris paribus, an increase in the inequality index increases the SE ratio. Explicitly, a one-unit increase in the inequality index d1 ð1c2 Þc1 ð1d2 Þ SEMacroðAÞ percentchanges the SE ratio by aTot 1
1þc1 þc2 d1 age points, or if data on b is available, the change in the SE ratio adjusted for the currency demand bias is given by 
1þðd1 þd2 c1 Þ  1 SEMacroðBÞ percentage points. aTot 1 adj 1þc1 þd1 1þb Conclusively, combining Hp.1a with Hp. 12, we estimate the bias in the marginal effect of inequality on the SE ratio estimated by the ratio approach. In Table 2, we label this outcome as follows: Result: The difference between marginal effect estimated by the ratio approach and total effect obtained by the separate approach. (c) Empirical outcomes Table 2 sums up the outcomes of the previous hypotheses. We report findings based on two indexes of inequality, i.e., Gini index and quintile ratio; two sources of the SE estimates obtained by macro-econometric methods, i.e., MIMIC and currency demand approach. Estimated outcomes give robust evidence that hypotheses 1–12 are empirically validated regardless of whether the coefficients are estimated by the MIMIC model or the currency demand approach as well as whether indexes of inequality are used. First, by applying the ratio approach, we validate the standard result that an increase in inequality increases the SE ratio. Looking at the results based on the MIMIC estimates of the SE, a one-unit increase in the Gini index increases the SE ratio by 0.87% (0.37 percentage points). Second, by replicating the analysis through the separate approach, we find that a one-unit increase in the Gini index directly increases the unobserved GDP per capita by 0.67% ($15 in PPP – Hp. 1b) and (directly) decreases the official GDP by 5.20% ($455 in PPP – Hp. 2). The inclusion of indirect effects is justified by a significant positive elasticity between the official and unobserved GDP; specifically, we estimate that a 1% increase in the official (unobserved) GDP increases unobserved (official) GDP by 0.86% (0.90). Indirect effects partially offset direct effects (Hp. 6 and 7) and the total effect of a one-unit increase in the Gini index increases the numerator by 0.62% (DGdpUNOE ¼ $14 – Hp. 8) and decreases the denominator by 5.2% (DGdpoff ¼ $453). Third, the direct and total effect of a change in the inequality index on the SE ratio is higher than the estimated effect obtained by the ratio approach. In quantitative terms, we find that the marginal effect estimated by the ratio approach is significantly biased downward with respect to the total effect estimated by the separate approach. In detail, a one-unit increase in the Gini index increases the SE ratio by 1.50 percentage points (6.1%), applying the separate approach, instead of 0.37 percentage points (0.87%) following the ratio approach.

Please cite this article in press as: Dell’Anno, R. Analyzing the Determinants of the Shadow Economy With a ‘‘Separate Approach”. An Application of the Relationship Between Inequality and the Shadow Economy, World Development (2015), http://dx.doi.org/10.1016/j.worlddev.2015.08.026

8

WORLD DEVELOPMENT Table 2. Summary of empirical outcomes Hypotheses

Estimated coefficients (model) Gini

Quartile ratio

MIMIC

Currency

Average

MIMIC

Currency

Average

Average

0.37 {0.87%}a (1–6)a

0.21 {0.57%}a (I–VI) a

0.29 {0.72%}a

0.60 {1.62%}a (1–6)b

0.24 {0.80%}a (I–IV)b

0.42 {1.21%}a

0.35 {0.96%}a

0.67% (1–6)c 5.20% (1–6)e 0.86% (1–6)c 0.90% (1–6)e

1.25% (I–VI)c 1.10% (I–VI)e 0.84% (I–VI)c 1.11% (I–VI)e

0.96%

1.50% (1–6)d 2.00% (1–6)f 0.88% (1–6)d 0.92% (1–6)f

0.50% (I–VI)d 1.20% (I–VI)f 0.88% (I–VI)d 1.14% (I–VI)f

1.00%

0.98%

1.60%

2.38%

0.88%

0.88%

1.03%

1.60%

Indirect Effects H6: Ind. Eff. on the GdpUNOE < 0 H7: Ind. Eff. on the Gdpoff > 0

0.04% 0.01%

0.01% 0.01%

0.03% 0.01%

0.02% 0.01%

0.01% 0.01%

0.01% 0.01%

0.02% 0.01%

Total Effects H8: Tot. Eff. on the GdpUNOE > 0 H9: Tot. Eff. on the Gdpoff < 0 b

0.62% 5.19%

1.24% 1.09%

0.93% 3.14%

1.48% 1.99%

0.49% 1.19%

0.99% 1.59%

0.96% 2.37%

Direct, Indirect and Total Effects on the SE ratio (percentage points) b Dir 1.60 0.55 H10: aDir 1 ; ½a1  > 0 [1.51] [0.45] H11: aIndir ; ½aIndir <0b 0.01 0.01 1 1 [0.01] [0.00] b Tot H12: aTot 1.59 0.54 1 ; ½a1  > 0 [1.50] [0.45] {6.13%} {2.35%}

1.07 [0.98] 0.01 [0.01] 1.06 [0.97] {4.24%}

0.92 [0.87] 0.01 [0.01] 0.92 [0.86] {3.54%}

0.39 [0.33] 0.00 [0.00] 0.39 [0.33] {1.70%}

0.66 [0.60] 0.01 [0.01] 0.65 [0.59] {2.62%}

0.87 [0.79] 0.01 [0.01] 0.86 [0.78] {3.43%}

Bias of the estimated effect by ratio approach (percentage points) Result: a1  aTot 1.22 0.33 1 < 0 [1.13] [0.24]

0.77 [0.68]

0.32 [0.26]

0.15 [0.09]

0.23 [0.17]

0.50 [0.43]

H1a:

@SEMacro @Ineq


a1 > 0

Marginal (Direct) Effects UNOE H1b(H4): @Gdp @Ineq
d1 > 0 H2(H5):

@Gdpoff @Ineq


c1 < 0

H3a:

@GdpUNOE @Gdpoff


d2 > 0

H3b:

@Gdpoff @GdpUNOE


c2 > 0

3.15% 0.85% 1.01%

Notes: In curly brackets, we report the expected percentage change in SE ratio for a unit increase in Inequality. a It is obtained by Log-linear specifications of the Eqn. (8). Details on these regressions are available upon request. b In square bracket, we report the adjusted effects for the currency demand bias.

Table 3. Simulated effects of a one-unit change in the Gini index (Sweden, Italy and Mexico) MIMIC Approach

Sweden b = 0.013;Gini = 26.8

Italy b = 0.167; Gini = 36.6

Mexico b = 0.121; Gini = 49.4

Currency Demand Approach

Var.

Baseline (PPP $)

Direct Effect

Indirect Effect

Total Effect

Baseline (Const. $)

Direct Effect

Indirect Effect

Total Effect

GdpUNOE Gdpoff GdpRNOE SEMacro adj SEMacro aDir;Ind;Tot 1

$ 5,705 $ 30,813 $ 401 18.5% 18.8%

$ 38.0 $ 1,602

$ 2.5 $ 1.9

$ 35.5 $ 1,600

$ 46.5 $ 319.6

$ 0.3 $ 4.0

$ 46.2 $ 315.5

19.7%

18.5%

19.7%

$ 3,646 $ 29,050 378 12.5% 12.7%

12.85%

12.55%

12.84%

1.15%

0.01%

1.14%

0.30%

0.00%

0.30%

GdpUNOE Gdpoff GdpRNOE SEMacro adj SEMacro aDir;Ind;Tot 1

$ 6,539 $ 28,241 $ 4,716 23.2% 27.0%

$ 43.6 $ 1,469

$ 2.9 $ 1.7

$ 40.7 $ 1,467

$ 51.5 $ 213.9

$ 0.4 $ 2.7

$ 51.1 $ 211.2

24.6%

23.1%

24.6%

$ 3,525 $ 19,449 $ 3,248 18.1% 21.2%

18.55%

18.12%

18.55%

1.43%

0.01%

1.42%

0.43%

0.00%

0.43%

UNOE

$ 4,555 $ 11,486 $ 1,390 39.7% 44.5%

$ 30.4 $ 597

$ 2.0 $ 0.7

$ 28.3 $ 597

$ 23.8 $ 65.7

$ 0.2 $ 0.8

$ 23.6 $ 64.9

42.1%

39.6%

42.1%

29.09%

28.41%

29.08%

2.45%

0.02%

2.43%

0.68%

0.01%

0.67%

Gdp Gdpoff GdpRNOE SEMacro adj SEMacro Dir;Ind;Tot a1

$ 1,698 $ 5,975 723 28.4% 31.9%

Please cite this article in press as: Dell’Anno, R. Analyzing the Determinants of the Shadow Economy With a ‘‘Separate Approach”. An Application of the Relationship Between Inequality and the Shadow Economy, World Development (2015), http://dx.doi.org/10.1016/j.worlddev.2015.08.026

ANALYZING THE DETERMINANTS OF THE SHADOW ECONOMY WITH A ‘‘SEPARATE APPROACH”

From a methodological perspective, we realize that the separate approach, in addition to an unbiased estimate of the effect of a determinant on SE ratio, may be helpful in terms of the analysis of policy implications. It is due to the fact that its property to provide a disentangled view of the effects and mechanisms of transmission between the potential determinant and the SE. A normative analysis of the implications of this proposition is beyond the scope of this paper but as an illustrative example, we simulate the effects of a public policy that generates, ceteris paribus, an increase of one-unit in the Gini index. We consider three economies, among the countries for which the shares of NOE adjustments in official statistics are available from (UNECE, 2008, Table 1), as indicative of low (Sweden, SEMacro ¼ 18:8%), medium (Italy, SEMacro ¼ 27%) and high (Mexico, SEMacro ¼ 44:5%), levels of the SE. Table 3 reports the estimated monetary effects. The simulated output shows that a rise of income inequality increases the SE ratio mainly because it reduces the denominator. As result, we could evaluate this policy as worsening in terms of welfare principally for losses in the official economy rather than for a boost in the SE. Conclusively, this simulation emphasizes also that in terms of policy analysis, the separate approach provides greater and useful information about the effects of a determinant on the SE than those obtainable estimating the marginal effect by the ratio approach. 5. CONCLUSIONS The paper has two main aims: to propose some methodological insights on empirical research on the SE, and by using a separate approach, estimate the relationship between income distribution and the SE. From a methodological perspective, we demonstrate that analyzing the effect of a determinant utilizing the SE ratio as a dependent variable may be misleading. That is, we suggest disentangling the effects of inequality on the SE ratio by estimating both the direct and indirect effects on the numerator and denominator separately. Furthermore, we propose (i) a definition of the SE consistent with both macro-econometric and SNA approaches and (ii) a method to correct the bias

9

of the estimated effect of any potential determinant of the unobserved economy due to the currency demand bias in the estimation of the numerator of the SE ratio. In the second part of the paper, we apply the proposed separate approach. The econometric analysis is conducted through a worldwide cross-section. We address the common weaknesses in this strand of the literature (i.e., sample bias, measurement errors in both the estimates of the SE and inequality indexes, and endogeneity issue by (i) collecting the widest cross-countries analysis in this field of the literature; (ii) utilizing annual averages based on nine-year averages to minimize measurement errors; (iii) testing robustness of outcomes with alternative indexes of both the SE and inequality; and (iv) controlling the estimates by ancillary regressions based only on exogenous explanatory variables. Despite this, our findings should be treated with some caution because of the intrinsic measurement issues in the SE estimates and the potential reverse causation between income inequality and official and/or unobserved GDP. Concerning the latter, the major problem is simply that of obtaining a suitable set of instrumental variables or obtaining panel data with an adequate sample size for a worldwide analysis is currently unavailable. Accordingly, further investigations on the consequences of endogeneity are required before empirical results can be conclusively validated. From a positive viewpoint, the actual overall effect of inequality on the SE is underestimated by the ratio approach. Specifically, we find that the higher is the equality of income, the lower is the SE, both in the absolute level and in terms of the ratio. In particular, depending on the SE proxy, a one-unit increase in the inequality index increases the SE ratio by 2.4% (currency demand approach) and 6.1% (MIMIC approach). For instance, an increase in income inequality measured by the Gini coefficient from German levels (30.9) to US levels (40.8) is expected to increase the relative size of the German SE by between 23.5% (Currency demand – from 12.7% to 15.6%) and 60.7% (MIMIC – from 16% to 25.7%). In conclusion, we believe that the use of the proposed separate approach is helpful in empirical research on the determinant of the SE.

NOTES 1. Buehn and Schneider (2012) used as the base value Schneider’s (2007) estimate of the SE obtained by currency demand approach in 2000 to calibrate MIMIC estimates. 2. We also conduct a panel estimation analysis by utilizing both Least Squares Dummy Variable (LSDV) and pooled-OLS estimators. This analysis corroborates the results obtained by cross-sectional models. However, we consider cross-sectional analysis more reliable than the results based on panel analysis because of a lack of data. In particular, the massive presence of missing values in the indexes of inequalities (62% of observations are missed) precludes the possibility of specifying LSDV regressions appropriately or to control for endogeneity by a generalized method of moments estimator because these estimators are not suitable for such a small sample size and without a dynamic model specification. As a result, from a theoretical point of view, the use of a cross-sectional analysis based on nine-year averages is a better strategy than a static panel specification to consider both contemporaneous (i.e., direct) and lagged (i. e., indirect) effects of inequality on the SE. Moreover, by utilizing annual averages, we also expect to minimize measurement errors.

3. Overall, by using the income earned by the top 10% of households and dividing that by the income earned by the poorest 10% of households (decile ratio), we obtain similar results to those shown with quartile ratio (T20/B20). For the sake of brevity, we do not show these results, but they are available upon request. 4. It is reasonable to assume that missing data are more numerous among developing countries. These countries have also a larger SE and more unequal income distribution than developed economies. In this sense, this is a potential source of sample bias in this literature. 5. We do not include other potential causes of the SE, such as the growth rate of GDP, unemployment rate, total tax burden, inflation rate, and regulation burden, because these variables have been included by both Buehn and Schneider (2012) and Alm and Embaye (2013) in the equation to estimate the SE. The only exception to this choice is the urbanization rate that is used by Alm and Embaye (2013) among the controls to estimate the SE ratio. We consider this variable in the vector of control variables: (1) to avoid the omission of a relevant variable –

Please cite this article in press as: Dell’Anno, R. Analyzing the Determinants of the Shadow Economy With a ‘‘Separate Approach”. An Application of the Relationship Between Inequality and the Shadow Economy, World Development (2015), http://dx.doi.org/10.1016/j.worlddev.2015.08.026

10

WORLD DEVELOPMENT

according to Kuznets (1955), urbanization followed by industrialization is an important factor in the shift of inequality; therefore, we include this variable in both regressions of official and unobserved GDP. However, qualitative results do not change by removing the urbanization rate by regressions with Alm and Embaye’s estimates; (2) to keep the same model

specification among regressions using both MIMIC and currency estimates of the SE ratio. 6. For the relevance of this variable in the SE, see Schneider and Neck (1993) and Thiessen (2010).

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Loayza, N. (1996). The economics of the informal sector: A simple model and some empirical evidence from Latin America. Carnegie-Rochester Conference Series onPublic Policy, 45, 129–162. Moser, C. N. (1978). Informal sector or petty commodity production: Dualism or independence in urban development. World Development, 6, 1041–1064. Murphy, K. M., Shleifer, A., & Vishny, R. (1989). Income distribution, market size and industrialization. Quarterly Journal of Economics, 104, 537–564. OECD – Organisation for Economic Co-operation and Development. (2014). Statistics brief, June No. 18, OECD Statistics Directorate, . Pearson, K. (1897). On a form of spurious correlation which may arise when indices are used in the measurement of organs. Proceedings of the Royal Society of London, 60, 489–498. Persson, T., & Tabellini, G. (1994). Is inequality harmful for growth?. American Economic Review, 84, 600–621. Rosser, J. B., Jr., Rosser, M. V., & Ahmed, E. (2000). Income inequality and the informal economy in transition economies. Journal of Comparative Economics, 28, 156–171. Rosser, J. B., Jr., Rosser, M. V., & Ahmed, E. (2003). Multiple unofficial economy equilibria and income distribution dynamics in systemic transition. Journal of Post Keynesian Economics, 25, 425–447. Schneider, F. (2005). Shadow economies around the world: What do we really know?. European Journal of Political Economy, 21, 598–642. Schneider, F. (2007). Shadow economies and corruption all over the world: new estimates for 145 countries. Economics E-Journal, 2007–9, July. Schneider, F. (Ed.) (2011). Handbook on the shadow economy. Cheltenham (UK): Edward Elgar Publishing Company. Schneider, F., & Enste, D. H. (2000). Shadow economies: Size, causes and consequences. Journal of Economic Literature, 38, 73–110. Schneider, F., & Neck, R. (1993). The development of the shadow economy under changing tax systems and structures. Finanzarchiv, 50 (3), 344–369. Smith, J. D. (1984). Market motives in the informal economy. In N. Gaertner, & W. Wenig (Eds.), The economics of the shadow economy (pp. 166–177). Heidelberg, Germany: Springer. Tedds, L. M. (2005). The underground economy in Canada. In C. Bajada, & F. Schneider (Eds.), Size, causes and consequences of the underground economy. UK: Ashgate Publishing. Thiessen, U. (2010). The shadow economy in international comparison: Options for economic policy derived from an OECD panel analysis. International Economic Journal, 24(4), 481–509. Tokman, V. E. (1978). An exploration into the nature of the informal– formal sector relationship. World Development, 6(9/10), 1065–1075. UNECE – United Nations Economic Commission for Europe (2008). Non-observed economy in national accounts - Survey of country practices (2008). United Nations Publication, ISSN: 0069-8458. . Valentini, E. (2009). Underground economy, evasion and inequality. International Economic Journal, 23(2), 281–290. White, H. (1980). A heteroskedasticity-consistent covariance matrix and a direct test for heteroskedasticity. Econometrica, 48, 817–838. Yule, G. Y. (1910). On the interpretation of correlation between indices or ratios. Journal of the Royal Statistical Society, 73, 644–647.

APPENDIX A 1 For regressions that include the estimates of the SE calculated by the MIMIC (Currency ratio) approach, the countries

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ANALYZING THE DETERMINANTS OF THE SHADOW ECONOMY WITH A ‘‘SEPARATE APPROACH”

in the sample are 118 (88 – the excluded countries are underlined): Albania, Angola, Argentina, Armenia, Austria, Azerbaijan, Bangladesh, Belarus, Belgium, Benin, Bhutan, Bolivia, Bosnia and Herzegovina, Brazil, Bulgaria, Burkina Faso, Burundi, Cambodia, Cameroon, Canada, Cape Verde, Central African Republic, Chad, Chile, China, Colombia, Comoros, Congo Dem. Rep., Congo Rep., Costa Rica, Cote d’Ivoire, Croatia, Dominican Republic, Ecuador, Egypt Arab Rep., El Salvador, Estonia, Ethiopia, Finland, Gabon, Gambia, Georgia, Germany, Ghana, Greece, Guatemala, Guinea, GuineaBissau, Haiti, Honduras, Hungary, India, Indonesia, Iran, Ireland, Israel, Italy, Jamaica, Kazakhstan, Kenya, Kyrgyz Rep., Lao PDR, Latvia, Lesotho, Liberia, Lithuania, Luxembourg, Macedonia, Madagascar, Malawi, Malaysia, Maldives, Mali,

11

Mauritania, Mexico, Moldova, Mongolia, Morocco, Mozambique, Nepal, Nicaragua, Niger, Nigeria, Norway, Pakistan, Panama, Paraguay, Peru, Philippines, Poland, Qatar, Romania, Russian Federation, Rwanda, Senegal, Sierra Leone, Slovenia, South Africa, Spain, Sri Lanka, Swaziland, Sweden, Switzerland, Syrian Arab Republic, Tajikistan, Tanzania, Thailand, Togo, Tunisia, Turkey, Uganda, Ukraine, United States, Uruguay, Venezuela, Vietnam, Yemen, Zambia. Values of variables are calculated as the average of available observations over the period 1999–2007 with exclusion of the averages based on the estimates of the SE calculated by currency demand approach. Given that Alm and Embaye (2013) report the estimates of the SE up to the 2006, for official and unobserved GDP based on the currency approach the averages are based on eight annual observations.

Table 4. Data sources Var. GdpNOE Mimic

Gdpoff PPP GdpNOE Curr

Gdpoff const Gini

T20/B20 T10/B10 Rol

Urb EmpR

TaxI

TaxC

VulE LegBrit LegFren LegSoc LegGer LegScan

Definition (Total) NOE per capita, PPP (constant 2005 international $). It is calculated by dividing Buehn and Schneider (2012) estimates by 100 and multiplying for official GDP per capita, PPP constant 2005 international $ Official GDP per capita, PPP (constant 2005 international $) (Total) NOE per capita (in constant 2000 US dollars). It is calculated by dividing Alm and Embaye’s (2013) estimates by 100 and multiplying for official GDP per capita, in constant 2000 US dollars Official GDP per capita (in constant 2000 US dollars) Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality Income share held by highest 20%/ Income share held by lowest 20% Income share held by highest 10%/ Income share held by lowest 10% Rule of Law: Estimate. It captures perceptions of the extent to which agents have confidence in and abide by the rules of society, and in particular the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence. Higher index means better institutional quality. Urban population (% of total). Urban population refers to people living in urban areas as defined by national statistical offices Employment to population ratio, 15+, total (%). It is the proportion of a country’s population that is employed. Ages 15 and older are generally considered the working-age population Taxes on income, profits, and capital gains are levied on the actual or presumptive net income of individuals, on the profits of corporations and enterprises, and on capital gains, whether realized or not, on land, securities, and other assets. (current LCU) divided to GDP (current LCU) Time to prepare and pay taxes (hours). Time to prepare and pay taxes is the time, in hours per year, it takes to prepare, file, and pay (or withhold) three major types of taxes: the corporate income tax, the value added or sales tax, and labor taxes, including payroll taxes and social security contributions Vulnerable employment is unpaid family workers and own-account workers as a percentage of total employment Legal origin: British Legal origin: French Legal origin: Socialist Legal origin: German Legal origin: Scandinavian

Data Source [code]

Mean

Max

Min

Obs

Buehn and Schneider (2012) – Table 3 * 0.01 * [NY.GDP. PCAP.PP.KD]

2,262

12,675

131.7

118

WDI – World Bank [NY.GDP. PCAP.PP.KD] Alm and Embaye (2013) * 0.01 * [NY.GDP.PCAP.KD]

8,742

65,224

283.5

118

1,174

10,997

40.9

85

WDI 2008– World Bank [NY. GDP.PCAP.KD] WDI – World Bank [SI.POV. GINI]

5,116

48,818

86.0

118

40.7

64.3

21.1

115

WDI – World Bank [SI. DST.05TH.20/SI.DST.FRST.20] WDI – World Bank [SI. DST.10TH.10/SI.DST.FRST.10] WGI – World Bank http:// info.worldbank.org/governance/ wgi/index.aspx#home

9.51

39.1

3.07

114

19.47

143

4.25

114

0.21

1.94

1.69

118

WDI – World Bank [SP.URB. TOTL.IN.ZS] WDI – World Bank [SL.EMP. TOTL.SP.ZS]

51.26

97.3

8.93

118

58.91

85.7

33.6

117

WDI – World Bank [100 * GC. TAX.YPKG.CN]/NY.GDP. MKTP.CN

4.40

20.5

0.00

118

WDI – World Bank [IC.TAX. DURS]

384.1

2600

0.00

118

WDI – World Bank [SL.EMP. VULN.ZS] GDN Growth Database. Available from: www.sscnet.ucla. edu/polisci /faculty/treisman/ Papers/what_have_we_ learned_data.xls

45.61

94.6

0.40

96

0.194 0.524 0.223 0.049 0.010

1 1 1 1 1

0 0 0 0 0

103 103 103 103 104

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12

WORLD DEVELOPMENT

APPENDIX 2 – ESTIMATED COEFFICIENTS The following tables report estimated models with two different measures of the SE (i.e., MIMIC and Currency demand) and income inequality (i.e., Gini, T20/B20).

Table 5. Dependent variable: unobserved unrecorded GDP as percentage of official GDP. MIMIC Approach

Gini Gini98 Log(Gdpoff)a Log(Gdpoff)98 Rol Rol98 Urb Urb98 EmplR EmplR98 TaxI TaxI98 VunE VunE98 TaxC LegBrit LegFren LegSoc LegGer Constant Obs. R2-Adjust. Het. Test1 b Het. Test2 c Norm.Testd

Currency Ratio Approach

1a

2a

3a

4a

5a

6a

Ia

IIa

IIIa

IVa

Va

VIa

0.31** – 3.68** – 1.05 – – – – – – – – – – – – – – 43.23*** 115

0.34* – 3.39 – – – 0.14 – 0.04 – 0.81** – 0.06 – 0.001 – – – – 41.35* 93

0.34**

– 0.42*** – 3.73 – 1.19 – 0.12 – 0.11 – 0.83*** – 0.01 – – – – – 40.59* 84

– 0.42*** – 3.60*** – – – – – – – – – – – 2.59 0.91 1.88 3.12 48.70*** 100

0.19** – 3.09*** – 1.42 – – – – – – – – – – – – – – 47.05*** 83

0.30*** – 4.60*** – – – 0.08 – 0.09 – 0.08 – 0.01 – 0.00 – – – – 55.48*** 70

0.20**

3.69*** – – – – – – – – – – – – 1.03 2.05 3.02 4.98* 54.25*** 100

– 0.36*** – 3.38** – 1.61 – – – – – – – – – – – – – 48.10*** 115

3.92*** – – – – – – – – – – – – 12.95*** 10.33*** 10.52*** 12.57*** 41.66*** 73

– 0.14 – 2.75*** – 2.06** – – – – – – – – – – – – – 46.01*** 83

– 0.23** – 3.57*** – 2.16* – 0.07* – 0.04 – 0.35 – 0.01 – – – – – 46.35*** 64

– 0.17* – 3.83*** – – – – – – – – – – – 13.09*** 10.32*** 10.01*** 12.36*** 41.72*** 73

0.264 0.169 0.424 0.040

0.294 0.067 0.012 0.439

0.247 0.815 0.000 0.037

0.302 0.168 0.811 0.158

0.435 0.038 0.132 0.055

0.277 0.761 0.000 0.279

0.428 0.919 0.717 0.483

0.488 0.422 0.275 0.734

0.441 0.728 0.000 0.375

0.410 0. 880 0.995 0.672

0.462 0.149 0.119 0.487

0.410 0.832 0.000 0.616

Notes: ***Denotes significant at 1% level; **Denotes significant at 5% level; *Denotes significant at 10% level. The numbers in parenthesis are the t-ratios. The p-value of F-test is equal to 0.000 for all the regressions. a For the SE estimates based on the MIMIC approach Gdpoff is the GDP per capita at PPP; for the SE estimates based on the currency demand approach, Gdpoff is GDP per capita at constant US dollars. b Breusch–Pagan (1979) and Godfrey (1978) Lagrange multiplier test where the null hypothesis is of no heteroskedasticity. We report the p-value of Fstatistic. c Harvey (1976) Test where the null hypothesis is of no heteroskedasticity; the p-values of F-statistic are reported. d Jarque–Bera Test, (p-value) the reported p-value is the probability that a Jarque–Bera statistic exceeds (in absolute value) the observed value under the null hypothesis. Therefore, a small probability value leads to rejection of the null hypothesis of a normal distribution.

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ANALYZING THE DETERMINANTS OF THE SHADOW ECONOMY WITH A ‘‘SEPARATE APPROACH”

13

Table 6. Dependent variables: unobserved unrecorded GDP as percentage of official GDP MIMIC Approach 1b T20/B20 T20/B20_98 Log(Gdpoff)a Log(Gdpoff)98 Rol Rol98 Urb Urb98 EmplR EmplR98 TaxI TaxI98 VunE VunE98 TaxC LegBrit LegFren LegSoc LegGer Constant Obs. R2-Adjust. Het. Test1b Het. Test2c Norm.Testd

2b

**

***

3b

4b

***

Currency Ratio Approach 5b

6b

Ib

IIb **

IIIb

**

**

IVb

Vb

VIb

0.59 – 3.92*** – 1.15 – – – – – – – – – – – – – – 62.48*** 114

0.64 – 3.44 – – – 0.13 – 0.03 – 0.84** – 0.07 – 0.001 – – – – 51.04** 92

0.68 – 4.04*** – – – – – – – – – – – – 0.80 3.27** 3.60 5.24** 65.40*** 99

– 0.52*** – 3.76*** – 1.80** – – – – – – – – – – – – – 60.67*** 115

– 0.50*** – 4.34** – 2.16** – 0.13 – 0.15 – 0.62* – 0.02 – – – – – 53.99*** 84

– 0.65*** – 4.16*** – – – – – – – – – – – 2.65 2.13 2.48 3.56 64.47*** 100

0.27 – 3.23*** – 1.59 – – – – – – – – – – – – – – 52.91*** 82

0.30 – 5.05*** – – – 0.12* – 0.08 – 0.002 – 0.001 – 0.001 – – – – 66.81*** 69

0.27 – 4.16*** – – – – – – – – – – – – 12.60*** 9.76*** 9.03*** 11.96*** 49.54*** 72

– 0.18* – 2.81*** – 2.22** – – – – – – – – – – – – – 50.31*** 83

– 0.18* – 3.57*** – 2.81** – 0.08 – 0.02 – 0.38 – 0.004 – – – – – 51.17*** 64

– 0.22* – 3.92*** – – – – – – – – – – – 12.80*** 9.69*** 9.46*** 11.70*** 48.19*** 73

0.281 0.447 0.618 0.187

0.339 0.178 0.149 0.597

0.323 0.723 0.000 0.118

0.356 0.433 0.685 0.256

0.453 0.090 0.532 0.723

0.323 0.740 0.000 0.367

0.449 0.949 0.977 0.676

0.472 0.404 0.274 0.965

0.477 0.773 0.000 0.430

0.438 0.127 0.068 0.887

0.584 0.127 0.068 0.392

0.417 0.925 0.000 0.886

See notes of Table 5.

Table 7. Dependent variable: logarithm of unobserved unrecorded GDP per capita MIMIC Approach 1c Gini Gini98 Log(Gdpoff)a Log(Gdpoff)98 Rol Rol98 Urb Urb98 EmplR EmplR98 TaxI TaxI98 VunE VunE98 TaxC LegBrit LegFren LegSoc LegGer Constant Obs.

2c **

***

3c

4c **

Currency Ratio Approach 5c

6c

Ic

IIc **

***

IIIc

IVc

Vc

VIc

*

0.01 – 0.88*** – 0.07 – – – – – – – – – – – – – – 0.50 115

0.01 – 0.85*** – – – 0.00 – 0.001 – 0.02** – 0.00 – 0.00 – – – – 0.44 93

0.01 – 0.87*** – – – – – – – – – – – – 0.08 0.06 0.10 0.15 0.42 100

– 0.006 – 0.87*** – 0.05 – – – – – – – – – – – – – 0.45 115

– 0.009** – 0.82*** – 0.07 – 0.00 – 0.00 – 0.03** – 0.004 – – – – – 0.36 84

– 0.007 – 0.87*** – – – – – – – – – – – 0.10 0.04 0.02 0.12 0.15 100

0.007 – 0.88*** – 0.03 – – – – – – – – – – – – – – 0.58*** 83

0.011 – 0.81*** – – – 0.002 – 0.003 – 0.002 – 0.002 – 0.00 – – – – 0.12 70

0.007 – 0.85*** – – – – – – – – – – – – 0.40*** 0.33*** 0.34*** 0.37*** 0.76*** 73

– 0.001 – 0.86*** – 0.00 – – – – – – – – – – – – – 0.08 83

– 0.005* – 0.78*** – 0.03 – 0.003 – 0.002 – 0.004 – 0.005** – – – – – 0.53 64

– 0.00 – 0.85*** – – – – – – – – – – – 0.38*** 0.33*** 0.37*** 0.34** 0.32 73

0.904 0.038 0.622 0.055

0.941 0.121 0.404 0.000

0.929 0.211 0.000 0.006

0.885 0.056 0.407 0.333

0.909 0.039 0.348 0.000

0.893 0.086 0.000 0.305

0.965 0.339 0.607 0.000

0.975 0.003 0.710 0.815

0.964 0.839 0.000 0.000

0.959 0. 300 0.041 0.142

0.972 0.000 0.082 0.979

0.958 0.301 0.000 0.085

R2-Adjust. Het. Test1b Het. Test2c Norm.Testd See notes of Table 5.

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14

WORLD DEVELOPMENT Table 8. Dependent variable: logarithm of unobserved unrecorded GDP per capita MIMIC Approach 1d T20/B20 T20/B20_98 Log(Gdpoff)a Log(Gdpoff)98 Rol Rol98 Urb Urb98 EmplR EmplR98 TaxI TaxI98 VunE VunE98 TaxC LegBrit LegFren LegSoc LegGer Constant Obs.

2d

***

3d

***

4d

***

Currency Ratio Approach 5d

6d

Id

IId **

IIId

**

IVd

Vd

VId

**

0.02 – 0.87*** – 0.08* – – – – – – – – – – – – – – 0.21 114

0.02 – 0.84*** – – – 0.00 – 0.00 – 0.02** – 0.00 – 0.00 – – – – 0.01 92

0.02 – 0.86*** – – – – – – – – – – – – 0.07 0.10 0.13 0.16 0.04 99

– 0.01** – 0.86*** – 0.05 – – – – – – – – – – – – – 0.04 115

– 0.01** – 0.81*** – 0.09 – 0.00 – 0.00 – 0.02* – 0.00 – – – – – 0.64 84

– 0.01*** – 0.86*** – – – – – – – – – – – 0.12 0.05 0.01 0.11 0.06 100

0.01 – 0.87*** – 0.04 – – – – – – – – – – – – – – 0.35** 82

0.01 – 0.80*** – – – 0.004* – 0.01 – 0.00 – 0.00 – 0.00 – – – – 0.25 69

0.01 – 0.85*** – – – – – – – – – – – – 0.39 0.31 0.29 0.35 0.48** 72

– 0.00 – 0.86*** – 0.00 – – – – – – – – – – – – – 0.07 83

– 0.00 – 0.77*** – 0.04 – 0.00 – 0.00 – 0.00 – 0.004** – – – – – 0.63 64

– 0.00 – 0.85*** – – – – – – – – – – – 0.39*** 0.33*** 0.39*** 0.36** 0.37*** 73

0.909 0.068 0.268 0.083

0.911 0.147 0.331 0.000

0.916 0.158 0.000 0.024

0.890 0.137 0.801 0.001

0.910 0.211 0.509 0.004

0.890 0.102 0.029 0.447

0.967 0.230 0.937 0.002

0.967 0.013 0.704 0.466

0.966 0.898 0.198 0.000

0.959 0. 132 0.305 0.000

0.972 0.000 0.053 0.962

0.958 0.238 0.006 0.099

R2-Adjust. Het. Test1b Het. Test2c Norm.Testd See notes of Table 5.

Table 9. Dependent variable: logarithm of official GDP per capita Official GDP per capita, PPP 1e Gini Gini98 Log(GdpUNOE)e Log(GdpUNOE)98 Rol Rol98 Urb Urb98 EmplR EmplR28 TaxI VunE TaxC LegBrit LegFren LegSoc LegGer Constant Obs. R2-Adjust. Het. Test1b Het. Test2c Norm.Testd

2e

3e

4e

Official GDP per capita, constant US $ 5e

6e

Ie

IIe

IIIe

IVe

Ve

VIe

0.01 – 0.99*** – 0.17** – – – – – – – – – – – – 1.58*** 115

0.01 – 0.63*** – 0.11** – 0.01** – 0.00 – 0.02** 0.01*** 0.00 – – – – 4.25*** 93

0.01 – 0.88*** – 0.12** – 0.01*** – 0.00 – – – – 0.22 0.31 0.31 0.43 1.85*** 100

– 0.02*** – 0.98*** – 0.22*** – – – – – – – – – – – 2.11*** 114

– 0.02*** – 0.78*** – 0.20** – 0.01** – 0.00 – – – 0.29 0.33** 0.28** 0.52* 2.91*** 99

– 0.02*** – – – – – 0.04*** – – – – – 0.84*** 0.84*** 0.82*** 0.80 6.26*** 100

0.01 – 1.08*** – 0.09** – – – – – – – – – – – – 0.97*** 83

0.01 – 1.02*** – 0.08 – 0.00 – 0.01 – 0.01 0.00 0.00 – – – – 1.18*** 70

0.01 – 1.19*** – 0.05 – 0.01* – 0.00 – – – – 0.41 0.35 0.37 0.35 0.78*** 73

– 0.01*** – 1.05*** – 0.14** – – – – – – – – – – – 1.54*** 78

– 0.01* – 1.12*** – 0.09 – 0.00 – 0.00 – – – 0.43*** 0.43*** 0.33*** 0.33* 1.42*** 67

– 0.02** – – – – – 0.06*** – – – – – 1.09 1.09 1.02 1.15 4.32*** 100

0.916 0.015 0.278 0.009

0.947 0.121 0.160 0.123

0.921 0.474 0.587 0.000

0.895 0.036 0.031 0.000

0.900 0.039 0.447 0.000

0.675 0.313 0.725 0.226

0.983 0.155 0.115 0.000

0.976 0.031 0.488 0.431

0.965 0.962 0.590 0.000

0.968 0.038 0.052 0.236

0.966 0.033 0.647 0.000

0.680 0.194 0.673 0.746

**

**

**

**

***

*

See notes of Table 5. e For the regressions 1e–6e GdpUNOE denotes unobserved unrecorded GDP based on the MIMIC approach; For the regressions Ie–VIe, GdpUNOE denotes unobserved unrecorded GDP based on the currency demand approach.

Please cite this article in press as: Dell’Anno, R. Analyzing the Determinants of the Shadow Economy With a ‘‘Separate Approach”. An Application of the Relationship Between Inequality and the Shadow Economy, World Development (2015), http://dx.doi.org/10.1016/j.worlddev.2015.08.026

ANALYZING THE DETERMINANTS OF THE SHADOW ECONOMY WITH A ‘‘SEPARATE APPROACH”

15

Table 10. Dependent variable: logarithm of official GDP per capita Official GDP per capita, PPP 1f T20/B20 T20/B20_98 Log(GdpUNOE)f Log(GdpUNOE)98 Rol Rol98 Urb Urb98 EmplR EmplR28 TaxI VunE TaxC LegBrit LegFren LegSoc LegGer Constant Obs. R2-Adjust. Het. Test1b Het. Test2c Norm.Testd

2f

3f

4f

Official GDP per capita, constant US $ 5f

6f

If

IIf

IIIf

IVf

Vf

VIf

0.02 – 1.01*** – 0.18** – – – – – – – – – – – – 1.24*** 114

0.02 – 0.64*** – 0.13** – 0.01** – 0.00 – 0.02** 0.01*** 0.00 – – – – 3.88*** 92

0.02 – 0.90*** – 0.12** – 0.01*** – 0.00 – – – – 0.24** 0.35*** 0.34*** 0.47* 1.35** 99

– 0.02*** – 0.99*** – 0.24*** – – – – – – – – – – – 1.49*** 114

– 0.01** – 0.74*** – 0.15* – 0.003 – 0.00 – – – 0.12 0.19 0.11 0.32 2.85*** 94

– 0.02** – – – – – 0.05*** – – – – – 0.99*** 0.98*** 1.01*** 1.03** 5.17*** 100

0.01 – 1.09*** – 0.09** – – – – – – – – – – – – 0.74*** 82

0.01 – 1.02*** – 0.10* – 0.00 – 0.01 – 0.01 0.00 0.00 – – – – 0.92** 69

– 0.01*** – 1.06*** – 0.16*** – – – – – – – – – – – 1.16*** 78

– 0.01*** – 1.06*** – 0.16*** – – – – – – – – – – – 1.16*** 78

– 0.01** – 1.21*** – 0.10** – 0.00 – 0.00 – – – 0.39 0.38 0.28 0.025 1.07** 67

– 0.017* – – – – – 0.06*** – – – – – 1.22*** 1.22*** 1.18*** 1.33** 3.41*** 100

0.918 0.039 0.323 0.015

0.947 0.215 0.060 0.106

0.925 0.189 0.000 0.005

0.893 0.025 0.158 0.000

0.913 0.004 0.000 0.000

0.665 0.356 0.000 0.469

0.968 0.251 0.417 0.000

0.976 0.129 0.001 0.176

0.977 0.011 0.098 0.000

0.977 0.011 0.098 0.000

0.966 0.117 0.905 0.000

0.670 0.238 0.000 0.597

***

***

***

**

**

See notes of Tables 5 and 9. f For the regressions 1f–6f GdpUNOE denotes unobserved unrecorded GDP based on the MIMIC approach; For the regressions If–VIf, GdpUNOE denotes unobserved unrecorded GDP based on the currency demand approach.

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Please cite this article in press as: Dell’Anno, R. Analyzing the Determinants of the Shadow Economy With a ‘‘Separate Approach”. An Application of the Relationship Between Inequality and the Shadow Economy, World Development (2015), http://dx.doi.org/10.1016/j.worlddev.2015.08.026