How do institutions affect corruption and the shadow economy?

Int Tax Public Finance (2009) 16: 773–796DOI 10.1007/s10797-008-9089-5

How do institutions affect corruption and the shadoweconomy?

Axel Dreher · Christos Kotsogiannis ·Steve McCorriston

Published online: 12 September 2008© Springer Science+Business Media, LLC 2008

Abstract This paper analyzes a simple model that captures the relationship betweeninstitutional quality, the shadow economy, and corruption. It shows that an improve-ment in institutional quality reduces the shadow economy and affects the corruptionmarket. The exact relationship between corruption and institutional quality is, how-ever, ambiguous and depends on the relative effectiveness of institutional quality inthe shadow and corruption markets. The analytics also show that the shadow econ-omy and corruption are substitutes. The predictions of the model are empiricallytested and confirmed.

Keywords Corruption · Shadow economies · Institutional quality

JEL Classification H39 · C31 · K49

A. DreherGeorg-August University Göttingen, Platz der Göttinger Sieben 3, 37073 Göttingen, Germanye-mail: [email protected]

A. DreherKOF Swiss Economic Institute, Zurich, Switzerland

A. DreherCESifo, Munich, Germany

C. Kotsogiannis (�) · S. McCorristonDepartment of Economics, University of Exeter Business School, Streatham Court, Rennes Drive,Exeter EX4 4PU, England, UKe-mail: [email protected]

S. McCorristone-mail: [email protected]

mailto:[email protected]



774 A. Dreher et al.

1 Introduction

Just as it is impossible not to taste the honey (or the poison) that finds itselfat the tip of the tongue, so it is impossible for a government servant not to eatup, at least, a bit of the king’s revenue.1

Corruption, by which we mean the abuse of public power for private gains, hasalways been present, in one form or another.2 It is, in general, thought to be endemic,pervasive, and a significant contributor to low economic growth, to distort investmentand provision of public services, and to increase inequality to such an extent thatinternational organizations like the World Bank have identified corruption as “thesingle greatest obstacle to economic and social development (World Bank 2001).”More recently, the World Bank has estimated that more than 1 trillion USD is paidin bribes each year and that countries that tackle corruption, improve governance andthe rule of law, could increase per capita incomes by a staggering 400% (World Bank2004). One has to recognize, though, that this argument is not supported unanimously.Routine corruption may be efficiency enhancing. As Leff (1964) puts it: “If the gov-ernment has erred in its decision, the course made possible by corruption may wellbe the better one” (p. 11). Corruption may also “grease the wheels” in the rigid publicadministration. As Huntington (1968) notes: “In terms of economic growth, the onlything worse than a society with a rigid, over-centralized, dishonest bureaucracy is onewith a rigid, over-centralized, honest bureaucracy” (p. 386).3

An important and, surprisingly, relatively unexplored element of corruption is itsrelationship with the shadow economy, defined as “all economic activities that con-tribute to the officially calculated (or observed) gross national product but are cur-rently unregistered” (Schneider and Enste 2000, p. 78).4 The shadow economy is,too, widely believed, and existing estimates also confirm, to be pervasive and a sig-nificant contributor to the erosion of tax and social security bases that may causesignificantly large budget deficits that render government policies ineffective. In un-derstanding the relationship of corruption with the shadow economy, it is importantto understand what causes the shadow economy. With the risk of oversimplification,two schools of thought can be identified.

1This quotation, attributed to Kautiliya (by R. P. Kangle), appears in Bardhan (1997, p. 1320).2Corruption, in the common usage of the word, can mean different things in different contexts. For a dis-cussion of some of the alternative denotations of the problem of corruption and its damaging consequences,see the insightful survey by Bardhan (1997). See also Klitgaard (1988), and Rose-Ackerman (1999).3Dreher and Gassebner (2007) and Méon and Weill (2008) provide recent evidence in favor of the “greasethe wheels” hypothesis. Another efficiency argument in favor of corruption is to look upon it as the“speed of money” which considerably reduces the slow-moving queues in public offices. For a criticismof this efficiency-enhancing view of corruption, see Tanzi (1998), Kaufmann and Wei (1999), and Rose-Ackerman (1999). Bardhan (1997), Jain (2001), and Aidt (2003) provide comprehensive accounts of thelatest developments on corruption. See also Rose-Ackerman (1978, 1999), and Lambsdorff (1999). Kauf-mann et al. (1999) provide a comprehensive account of empirical work on corruption.4Arguably, a precise definition is difficult to be given. The working definition adopted here seems to bethe most widely used. See also Friedman et al. (2000) and Schneider and Enste (2002).

How do institutions affect corruption and the shadow economy? 775

One school of thought identifies high tax and social security burdens as the prin-cipal causes.5 Economic agents, the story goes, are not willing to pay high taxes andso are driven out of the official economy. In doing so, they attempt to keep all eco-nomic profits for themselves. This view is closely associated with the literature ontax evasion, a subset of underground economic activities.6

The second school of thought identifies institutional quality—bureaucracy, regula-tory discretion, rule of law, corruption, and a weak legal system—as the main causesof driving economic agents underground. This view is based on the presumptionthat the Leviathan government is not (sufficiently) constitutionally constrained and,hence, uses its coercive powers to exploit the citizenry. Although economic agentsmay be willing to incur a reasonable fiscal burden, they are not willing to acceptextortionate demands. A natural response of economic agents to this government be-havior is to go underground losing all publicly provided benefits.7 Such behaviorreveals that there is a relationship between corruption (and the misbehavior of gov-ernment in general) and the shadow economy. But what is the precise relationship? Isit that the existence of the shadow economy imposes a constraint on the magnitude ofcorruption, and so they are substitutes, or that it is conducive to corruption and so theyare complements? Theoretically, both types of relationship may stand. Indeed, Choiand Thum (2005) show that the shadow economy mitigates government-induced dis-tortions leading to enhanced economic activities in the official sector: corruption andthe shadow economy are then substitutes.8 This is a view that seems to be in line withRose-Ackerman (1997) who notes that “going underground is a substitute for bribery,although sometimes firms bribe officials in order to avoid official taxes” (p. 21). Al-ternatively, Friedman et al. (2000) show that bureaucracy and the shadow economyare positively related. When faced with weak economic institutions and red tape en-trepreneurs go underground hiding their activities. As a consequence, tax revenues,as well as the quality of public administration, fall further reducing a firm’s incentiveto remain official. Using an inspector-tax payer model, Hindriks et al. (1999) alsoshow that the shadow economy is a complement to corruption. This is because, inthis case, the tax payer colludes with the inspector so the inspector under reports thetax liability of the tax payer in exchange for a bribe.9

The empirical evidence, though limited, is in favor of corruption and the shadoweconomy being complements. Johnson et al.’s (1998) investigation of 49 countries

5See, for instance, among others, Tanzi (1982, 1999), Frey and Pommerehne (1984), Schneider (1994a,1994b, 1997), and Giles (1999). For a recent contribution that explores the extent to which moral senti-ments may control shadow activities, see Kanniainen et al. (2005).6The seminal work on tax evasion is Allingham and Sandmo (1972). A thorough account of theoreticalcontributions can be found in Cowell (1990).7As a manager in deciding against investing in Russia notably puts it: “It doesn’t matter who it is: fireinspector, zoning committee member, mayor for that region, anybody can come and shut you down infive min. The fire guy could come, find fire hazards, and demand $50,000 into his overseas account. Theyknow that if you shut down production for a few days you will lose a lot more.” (The quotation appears inFriedman et al. 2000 and is attributed to Wilson 1996).8The official market and the shadow economy are, however, complements. We address this in Sect. 2.9The approaches taken by the contributions of Friedman et al. (2000), and Hindriks et al. (1999), thoughinsightful, differ from the one taken in the present analysis in two respects: the way the incidence ofcorruption is modeled and the endogenous determination of the size of the markets.


in Latin America, the OECD, and the former Soviet Union block find a statisticallysignificant positive relationship between the various measures of corruption and theshadow economy. As they note “. . . the relationship between the share of the unoffi-cial economy and the rule of law (including corruption) is strong and consistent acrossseven measures. Countries with more corruption have higher shares of the unofficialeconomy” (p. 391). Using data for 69 countries, Friedman et al. (2000) find evidencethat “more bureaucracy, greater corruption, and a weaker legal environment are allassociated with a larger unofficial economy . . .”, (p. 460).10 They conclude that “. . .poor institutions and a large unofficial economy go hand in hand” (p. 460). Address-ing the issue of causality they show—by using instruments such as long-standinglinguistic fractionalization, the origin of the legal system, religious composition ofthe population and latitude—that the causal link runs from weak economic institu-tions to the size of the shadow economy. As they put it: “[the] results show thereis an exogenous component of ‘institutions’ that is significantly correlated with thesize of the unofficial economy” (p. 460).11 Though the fundamental importance ofthe quality of institutions for combating corruption and the shadow economy is wellrecognized, to the best of our knowledge, only limited attempts have been made toarticulate, and empirically estimate, the relationship between institutional quality, theshadow economy, and corruption.12 This is the objective of this paper.

In particular, this paper considers a theoretical model which captures in a starkway the relationship between institutional quality, the shadow economy and corrup-tion. The model relates to Shleifer and Vishny (1993) who analyze a bureaucracyissuing permits to perform some economic activity in exchange for bribes. The paperalso relates to Bliss and Di Tella (1997) who show that corruption affects the numberof firms in a free entry equilibrium and argue that the number of firms in the mar-ket place cannot be treated as exogenous. The model draws upon Choi and Thum(2005) with the key departure being in the explicit (but exogenous) specification ofinstitutional quality. The theoretical model shows that corruption and shadow econ-omy are substitutes in the sense that the existence of the shadow economy reducesthe propensity of officials to demand grafts from firms. It also shows that institutionalquality has a differential effect on the shadow economy and the corruption marketand as a consequence its effect on corruption is ambiguous. The predictions of themodel are then empirically tested. The empirical estimations confirm that the shadoweconomy and corruption are substitutes and that an improvement in institutional qual-ity reduces both the shadow economy and the magnitude of corruption. The resultsalso lend support to the hypothesis that better institutional quality reduces corrup-tion. Such an empirical prediction has an interesting policy implication. While it istrue that better institutions reduce the extent of the shadow economy, for the inci-dence of corruption to be reduced attention should be focused not only on the role ofthe government and regulations and the rule of law, but also on the differential impactof variables pertaining to institutional quality on corruption and the shadow market.

10They also find no evidence, after controlling for GDP that higher taxes are associated with a smallerunderground sector.11Focusing on regulation, Johnson et al. (1997) show that countries with more regulation of theireconomies tend to have a higher share of the unofficial economy in GDP.12This probably reflects the fact that only recently data that proxy these variables have become available.


The organization of the paper is as follows. In Sect. 2, we introduce a simplemodel that investigates the relationship between institutional quality, corruption, andthe shadow economy. In Sect. 3, we present data and the empirical methodology. InSect. 4, we present and discuss the results. Finally, Sect. 5 concludes.

2 The structure of the model

The model is familiar from Choi and Thum (2005); the key departure is in the ex-plicit specification of institutional quality. Corruption is defined, following Shleiferand Vishny (1993), “as the sale by government officials of government property forpersonal gain” (p. 599). There is a continuum of firms, each of which is characterizedby an “earning ability” parameter θ . The individual characteristic θ—distributed withcumulative probability F(θ) with F ′(θ) ≥ 0—is known to the firms but, importantly,not to the officials. There are two markets in this economy; an official within whichcorrupt officials operate and the shadow market. What distinguishes the former fromthe latter market is the cost of operation. In particular, in order to operate in the officialmarket, firms must purchase a permit which is government property from an officialwho is corrupt at price denoted by m.13 To avoid expropriation by government offi-cials firms can enter the shadow market.14 Entering the shadow market is, however,not cost free. The (expected) cost is the fine associated with the firm operating in theshadow economy. To minimize the possibility of getting caught firms typically scaledown their degree of operation, an issue that we turn to shortly. Independent of theirearning ability and market of operation, firms also incur fixed operating costs, suchas the purchase of capital, denoted by ρ > 0.

Central to this paper is how institutional quality, in the presence of a free mar-ket equilibrium, affects both markets. It is not difficult for one to be convinced thatthe quality of the institution impacts differently in the two markets. Consider, forinstance, the rule of law. One would expect that an improvement in the rule of lawreduces the incidence of illegal activities taking place in the two markets but it doesso to different degrees since the nature of these activities are different (see footnote14). To capture this, it is assumed that institutional quality, denoted by e, reducesthe incidence of illegal activities in the official market—denoted by q(e),15 with16

q ′(e) > 0—and also the incidence of illegal activities in the shadow market, denotedby σ(e), with σ ′(e) > 0, but q(e) �= σ(e) and q ′(e) �= σ ′(e).

There are two stages in the sequence of events. In the first stage, and for giveninstitutional quality, the (corrupt) officials decide (anticipating the number of firms

13This implies that the officials expropriate not only the firms but also the government property.14It is implicitly assumed that there is no rent extraction in the shadow economy. If firms are caught operat-ing in the shadow economy they are fined but the revenues are not observed and so cannot be expropriatedby the corrupt officials.15Another way to interpret this is as technology. The effectiveness of the institution in the two marketsshould be interpreted broadly to include, for instance, elements such as, among others, the complexity ofthe tax system and regulatory discretion. For a study on the effect of the complexity of the tax system onthe size of the shadow economy, see Schneider and Neck (1993).16A derivative of a function of one variable is indicated by a prime and of many variables with a subscript.


operating in the official market) what level of graft m to set in order to maximizegraft revenues and, in the second, for a given level of graft, firms decide to eitherenter the official market, the shadow economy market, or not to enter the marketsat all.17 Officials thus have Leviathan preferences and any resources diverted fromthe public sector are for personal gain only. To investigate the precise relationshipbetween the corrupt and shadow markets and their relationship with the quality ofthe institutions, the strategy is to consider the behavior of public officials when theshadow market is not present and when it is.

2.1 Magnitude of corruption in the absence of the shadow market

A firm of earning ability θ and operating cost ρ that operates in the official economyhas profits

π = θ − ρ − mq(e). (1)

The term mq(e) in (1) captures the monetary cost of institutional quality to a firm.This cost is increasing in e, reflecting the fact that an improvement in institutionalquality results in the typical firm being more likely to be caught engaging in corruptactivities. This specification might allow for the fact that (an improvement of) insti-tutional quality does not only affect corrupt officials, as will be shown shortly, butalso affects the policing of firms caught engaging in corrupt activities. A marginalfirm will enter the official market if and only if it realizes nonnegative profits, that is,following from (1), if and only if

θ ≥ ρ + mq(e) ≡ θ. (2)

Condition (2) defines a cutoff level of θ , denoted by θ , such that all firms with earningability above this will enter the official market by purchasing the permit at the costof m. The proportion of firms then that will enter the market is 1 − F(θ). Denote fornotational convenience

G(θ) ≡ 1 − F(θ), (3)

with G′(θ) ≤ 0 (to avoid uninteresting cases, we take it throughout that G′ < 0).In the absence of a shadow economy, a corrupt official chooses the level of graft

m to maximize graft revenues given by

R = mG(θ), (4)

with the necessary condition being

Rm = G + mG′q(e) = 0 ≡ T (m, e,ρ). (5)

Equation (5) implicitly defines the equilibrium level of graft m∗(e, ρ). It is assumedthat the second order condition, evaluated at m∗(e, ρ), given by

Tm = q(e)(2G′ − GG′′/G′) < 0, (6)

17There is so commitment on the part of corrupt public officials: once announced, public officials cannotrenege on the level of graft.


is satisfied and so (5) has a unique solution. A property of (5) that will prove usefulbelow is that18

m∗′(e) = −Te/Tm (7)

= −m∗q ′(e)/q(e) < 0, (8)

and so an increase in institutional quality e reduces the equilibrium level of graftdemanded by the officials.

It can be also shown that total equilibrium graft revenues, denoted by R∗, decreasewith institutional quality, too. To see this, evaluate (4) using (5) and perturb to find

R∗′(e) = (m∗(e)

)2G′q ′(e) < 0, (9)

where the inequality sign follows from G′ < 0 and q ′(e) > 0. That total equilibriumgraft revenues decrease with institutional quality is intuitive. For institutional qual-ity, e, does not as an envelope property affect graft revenues through the equilibriumlevel of graft m∗(e), but only through q(e); for given m∗(e) an increase in e increasesthe cutoff level of θ of the marginal firm (since some firms are caught misbehaving)reducing the official market and, therefore, total graft revenues. This, of course, doesnot imply that the equilibrium size of the official market changes. In fact, it does not.To see this, notice that the size of the official market is given by H(e) = G(θ(e)).Differentiating H and evaluating using (5) and (8) gives, as an envelope property,H ′(e) = 0. The intuition behind this follows from the fact that an increase in institu-tional quality, on the one hand, reduces the official market through an increase in thecutoff level of θ , for given level of graft m�(e), as noted previously, but, on the otherhand, it increases the official market through an increase in the level of θ , for givenlevel q(e).19 On the balance, these two effects cancel out.

The analysis now turns to the case in which firms face the possibility of, insteadof exiting the market completely, going underground in order to avoid extortionatedemands.

2.2 Magnitude of corruption in the presence of the shadow market

Consider now an economy in which there is an underground sector. As noted previ-ously, if a firm enters the shadow market it pays no graft but it requires to scale downthe level of the economic activity so as not to be detected. To capture this in a simpleway, it is assumed that for a given σ(e), the cost of entering the shadow economyis θσ (e). Denoting variables pertaining to the shadow economy with a superscript s,profits are given by

π s = θ − ρ − θσ (e). (10)

It is also natural to assume that the cost of entering the shadow market is not toohigh, that is, σ(e) < 1. Consider now the choice that a typical firm with characteristic

18Throughout this subsection, the dependence of the functions on ρ (being fixed) is suppressed.19The latter effect can be seen clearly by combining (2), (3), and (8).


θ faces. Comparison of (1) and (10) reveals that

π ≥ πs iff θ ≥ mq(e)/σ (e),

π < πs iff ρ/(1 − σ(e)

) ≤ θ < mq(e)/σ (e),

πs < 0 iff θ < ρ/(1 − σ(e)

).

(11)

It is thus the case that if θ ≥ mq(e)/σ (e) then the marginal firm participates in corrup-tion, whereas if ρ/(1 − σ(e)) ≤ θ < mq(e)/σ (e) then it enters the shadow economy.With a sufficiently low θ < ρ/(1 −σ(e)), the firm makes negative profits and, hence,ceases activity altogether.

Denoting total graft by Rs officials, choosing m, and anticipating the market equi-librium in (11), maximize

Rs = mG

(mq(e)

σ (e)

), (12)

with necessary condition

Rsm = G + mG′q(e)/σ (e) = 0 ≡ Z(m,e), (13)

which implicitly defines the optimal graft ms∗(e) with, for later use,

ms∗′ = −Ze/Zm, (14)

= −ms∗e−1(εq − εσ ), (15)

where εh ≡ h′(e)e/h > 0 denotes the elasticity of h = q,σ with respect to institu-tional quality e. Equation (15) reveals that the change in equilibrium graft, due toa change in institutional quality e, takes the opposite sign of εq − εσ . This is intu-itive. Consider, for instance, εq > εσ . In this case, corruption is easier to be detected,relative to the illegal activities taking place in the shadow market, and so the publicofficials respond to an increase in institutional quality by reducing the level of graftdemanded, that is, ms∗′ < 0. Analogous reasoning applies to εq < εσ .

It is now straightforward to see that the equilibrium level of graft, denoted by Rs∗,when the shadow economy exists is less than the level of graft when the shadoweconomy does not exist, that is, ms∗(e) < m∗(e). To see this evaluate (12) at theoptimal level of graft m∗(e) and then, starting from m∗(e), consider a small changedm to find

dRs∗ = G

(σ(e) − 1

σ(e)

)dm < 0, (16)

with the inequality following upon σ(e) < 1. Clearly, then, since Rs∗ is strictly con-cave in m, ms∗(e) < m∗(e) and so optimal graft under the existence of the shadoweconomy is smaller than without. This is intuitive. The shadow economy, for giveninstitutional quality, imposes a constraint on the officials. When firms face a highgraft, they have the option of going underground. The lower ms∗(e), the more firmsenter the official economy. Hence, when the shadow economy exists, corruption islower and so the official economy is larger implying that the shadow market and the


official economy are complements. Putting this differently, the shadow economy andthe corruption market are substitutes.20

What about total graft revenues? One would expect that these depend on the rela-tive magnitudes of εq and εσ . Indeed, perturbing optimal graft Rs∗, and evaluating atms∗, using (13), one obtains

Rs∗′ = (ms∗)2qG′

σe(εq − εσ ), (17)

and so with G′ < 0, total graft revenues decrease (increase) in institutional qualitywhen εq > εσ (εq < εσ ). The intuition of this is similar to the one given when theshadow economy was not present. An increase in institutional quality does not affectequilibrium revenues through the graft ms∗(e), but only via the cutoff level of θ ,thereby reducing the proportion of firms entering the official market.21 Improvedinstitutional quality, however, does not affect the size (number of firms) of the officialmarket but it does affect the magnitude of graft revenues. This is for the same reasonas that given above but modified here to incorporate the shadow market. Specifically,with εq > εσ (εq < εσ ), the cutoff level of θ increases (decreases) thereby reducing(increasing) the number of firms entering the official market. But this is not the endof the story. Following (15), to maintain maximum graft revenues, officials reduce(increase) the equilibrium level of graft undoing the change in the cutoff level of θ .Thus, overall, the number of firms in the official market remains the same.

An increase in institutional quality e, however, unambiguously reduces the sizeof the shadow economy. To see this notice first that, following the discussion inthe preceding paragraph, with the equilibrium size of the official market being un-affected by institutional quality there are no firms contemplating entering the offi-cial market after an improvement in institutional quality has taken place. What in-stitutional quality affects, however, is the marginal firm that decides to stay in theshadow market or of exiting the market completely. It is straightforward to showthat the size of the shadow economy decreases with changes in institutional qual-ity, e. This is because an increase in institutional quality increases the number offirms that exit the shadow market. To see this, notice that for θ = ρ/(1 − σ(e)) theproportion of firms exiting the shadow economy is given by, abusing notation some-what, S(e) = F(ρ/(1 − σ(e))). An increase in institutional quality then, followingS′(e) = F ′ρσ ′(e)/(1 − σ(e))2 > 0, implying that for the marginal firm to stay in theshadow market requires a higher earning ability.

The preceding discussion offers two testable hypotheses. Firstly, the theoreticalanalysis has shown that the size of the shadow market unambiguously decreases withinstitutional quality (hypothesis 1). Secondly, the analysis has also emphasized thatthe existence of the shadow market reduces the magnitude of corruption, and so theshadow market and corruption are substitutes (hypothesis 2). The effect of institu-tional quality on the magnitude of corruption has been found to be ambiguous and

20This reconfirms the result of Choi and Thum (2005).21It can be easily seen that with, as an envelope property, ms∗(e) being unaffected by institutional quality,the threshold θ is increasing (decreasing) in e if and only if εq > εσ (εq < εσ ).


to depend on the relative effectiveness of institutional quality in the two markets.If it is the case, for instance, that institutions are more effective in combating cor-ruption (and so more costly for the firms to engage in corrupt activities) relative tothe shadow economy, in the sense that εq > εσ , then a further improvement in insti-tutional quality reduces the magnitude of corruption. Officials, anticipating that theofficial market is less profitable for firms, reduce the level of graft demanded. Butthe opposite may also be true: if the effectiveness of the institutions is biased towardcombating the shadow economy, in the sense that εq < εσ , then (and for the reasonexplained above) institutional quality may exacerbate corruption. While such a resultdoes not offer a testable hypothesis, its empirical estimation is of central concern heresince it will reveal whether institutional quality affects (if it does) more strongly theshadow economy than the corruption market.

Having described the theoretical model, and stated the hypotheses to be tested, wenow turn to the description of the data and some methodological issues.

3 Data description and methodological issues

The theoretical model of Sect. 2.2 suggests that the empirical estimation should con-sider a simultaneous system of two equations, with one equation determining (con-trolling for other variables that influence the endogenous variables) the shadow econ-omy as a function of institutional quality, and the second equation determining theextent of corruption as a function of the shadow economy and the level of institu-tional quality.22 The basic regressions for a cross-section of countries thus take theform

shadowi = β1 + β2ei + β3xi + κi, (18)

corruptioni = β4 + β5shadowi + β6ei + β7zi + ui, (19)

where the dependent variables are the extent of the shadow economy and corruptionin country i, ei denotes institutional quality in country i, x, and z are vectors ofcontrol variables that affect the shadow economy and corruption, respectively, and κi

and ui represent error terms.23

22It is worth emphasizing that the theoretical model does not allow the shadow economy to be influencedby corruption. The constraint of the corrupt officials is coming from the existence of the shadow economy.To put it differently, the shadow economy is a by-product of corruption. We have, however, also estimatedthe model (not reported) allowing for corruption to be among our set of explanatory variables. The coef-ficient of this variable has been insignificant at conventional levels. This is true when estimation is withOLS, 3SLS, or 2SLS and using the determinants of corruption as shown in column 2 of Table 1 below asinstruments. According to the Durban–Wu–Hausman test the exogeneity of corruption cannot be rejectedat conventional levels of significance. The results are available upon request.23We have checked for the influence of outliers using an algorithm that is robust to them. This robust re-gression technique weights observations in an iterative process. Starting with OLS, estimates are obtainedthrough weighted least squares, where observations with relatively large residuals receive smaller weight.This procedure results in estimates not being overly influenced by any specific observation. The resultsobtained under such procedure are qualitatively unchanged.


The data for the shadow economy are taken from Schneider (2005a, 2005b) whohas calculated the size and development of the shadow economy for 145 countries,including developing, transition, and highly developed OECD countries, over the pe-riod 1999 to 2003. This data set is, to the best of our knowledge, the only one thatis consistently estimated with one method and for a large number of countries.24 De-scriptive statistics reveal that according to these data, the average size of the shadoweconomy in percentage of official gross domestic product (GDP) is fairly substantialand amounts to 32%.25 To increase the number of observations, all data are averagesover the period 2000–2002.

To measure corruption, the estimations employ a well known and widely used in-dex provided by the International Country Risk Guide (ICRG)26 that ranges from0—representing highest corruption—to 6 (no corruption). The maximum number ofcountries in the sample for which both data for the shadow economy and corruptionare available is 108. The choice of the variables to be included in the vectors of controlvariables follows the previous literature. In particular, the determinants of the shadoweconomy follows Friedman et al. (2000) who explain the size of the shadow economyby employing per capita GDP, indices measuring the tax burden, over-regulation, thequality of the legal system, and the ICRG index of corruption. While Friedman et al.(2000) include only one variable at the time, in addition to GDP, we choose to includeall variables simultaneously. We thus, contrary to Friedman et al. (2000), control forrelevant determinants of the shadow economy rather than presenting simple correla-tions. From the estimations, we exclude the index of over-regulation due to its highcorrelation (0.66) with one of the variables of interest, government effectiveness.27

We also include regional dummies for the East Asia and Pacific region, East Eu-rope and Central Asia, Middle East and North Africa, South Asia, West Europe, NorthAmerica, and Latin America and Caribbean, which are jointly significant at the 1%level. The (log of) GDP per capita is taken from the World Bank’s (2005) World De-velopment Indicators and is measured in constant 2,000 USD. The index of tax bur-den is provided by the Heritage Foundation and includes the top marginal tax rateson individual and corporate income and total tax revenue as a share of GDP. A higherscore (on a scale of 1 to 5) implies a lower burden of taxation. Institutional qualityis measured with an index constructed by the World Bank (Kaufmann et al. 2003).The World Bank’s government effectiveness indicator ranges from −2.58 to 2.59,

24Schneider (2005a, 2005b) estimates the size of the shadow economy with the help of structural equationmodeling (dynamic multiple causes, multiple indicators, DYMIMIC). The DYMIMIC method is basedon the statistical theory of unobserved variables which considers multiple causes and multiple indicatorsof the phenomenon to be measured. For the estimation, a factor-analytic approach is used to measure thehidden economy as an unobserved variable over time. The unknown coefficients are estimated in a set ofstructural equations within which the “unobserved” variable cannot be measured directly.25Descriptive statistics of all variables together with their sources and definitions are reported in Appen-dices A and B. The countries included in the present study are listed in Appendix C.26Note that the focus of this index is on capturing political risk involved in corruption. Since this index isavailable for a greater number of countries than those provided by Transparency International, it is widelyused in empirical studies. We do, however, test for the robustness of the results employing the index ofcorruption compiled by Transparency International. The results do not depend on this choice.27If included, the index of over-regulation is completely insignificant throughout, while the results arequalitatively unchanged.


with higher scores showing “better” environments. We test for the robustness of ourresults by employing alternative measures of institutional quality and corruption be-low.

Regarding the determinants of corruption, we follow Dreher et al. (2007) and in-clude the age of democracy and gross school enrollment, in addition to (log) GDPper capita and the index of government effectiveness. These variables are standard instudies that estimate the causes of corruption. We measure a country’s age of democ-racy as the number of years since 1900 a country has a democracy score continuouslygreater than zero (Marshall and Jaggers 2000), while school enrollment is taken fromthe World Bank (2005). Again, we have included the regional dummies introducedabove. However, as they are jointly (and individually) completely insignificant, weomit them from the final specification. Depending on which covariates are includedin the regressions, our sample comprises between 78 and 135 countries.

The model in (18) and (19) is estimated as a system of simultaneous equations, em-ploying Three-Stage Least Squares (3SLS). In the first stage, 3SLS uses instrumentsfor all endogenous variables. These instruments are the predicted values resultingfrom a regression of each endogenous variable on all exogenous variables includedin the system. The second stage consistently estimates the covariance matrix of theequation errors using the residuals from a two-stage least squares (2SLS) estimationof each equation. In the third stage GLS estimation, employing the covariance matrixestimated in the second stage and the instruments in place of the endogenous vari-ables is performed. For comparison, we also report 2SLS. While this estimator is alimited information procedure neglecting information contained in the second equa-tion, it is nevertheless consistent. We also provide OLS regressions for comparison.The next section presents the results.

4 Econometric results

Table 1 presents the main results. According to the OLS regressions presented incolumns 1 and 2, the shadow economy decreases with higher GDP per capita, betterinstitutional quality (as proxied by greater government effectiveness), and a lower fis-cal burden, while corruption decreases with better government effectiveness, and theage of democracy, at least at the 10% level of significance. The results also show thatschool enrollment does not affect corruption at conventional levels of significance,and neither does the shadow economy.

Column 3 of Table 1 allows for the endogeneity of the shadow economy and es-timates the equations simultaneously. The previous results, with the exception of thefiscal burden which is no longer significant, are confirmed by the 3SLS regressions.28

28The insignificant coefficient of fiscal burden is in line with the recent contribution of Friedman et al.(2000). According to their results, the fiscal burden is not significantly correlated with the size of theshadow economy when GDP per capita is controlled for. In a follow-up to our study, Dreher and Schneider(2007) also find an insignificant impact of fiscal burden on the size of the shadow economy. Note thatthe exclusion of the fiscal burden would not change the results. When including the three componentsof the index of fiscal burden—the top marginal tax rates on individual and corporate income and totaltax revenue as a share of GDP—individually, the coefficients are also completely insignificant, while theshadow economy remains significant at the 5% level at least.


Table 1 Corruption and the shadow economy

(1) (2) (3) (4)

Shadow (No) corruption Shadow (No) corruption (No) corruption

index index index

(log) GDP per capita −3.006 −0.015 −3.038 0.063 0.053

(2.81)∗∗∗ (0.14) (2.42)∗∗ (0.55) (0.59)

Government effectiveness −4.477 0.744 −4.185 1.025 1.070

(2.69)∗∗∗ (4.06)∗∗∗ (2.08)∗∗ (4.44)∗∗∗ (4.96)∗∗∗Fiscal burden −2.332 −0.590

(1.90)∗ (0.43)

Shadow economy 0.001 0.039 0.041

(0.11) (2.02)∗∗ (2.52)∗∗Age of democracy 0.006 0.005 0.004

(1.94)∗ (1.72)∗ (1.22)

School enrollment 0.004 0.002 0.005

(0.77) (0.37) (1.01)

Constant 67.049 2.372 60.348 0.690 0.433

(7.09)∗∗∗ (2.63)∗∗∗ (5.72)∗∗∗ (0.56) (0.42)

Method OLS OLS 3SLS 3SLS 2SLS

Number of countries 135 108 108 108 108

Sargan–Hansen test (p-value) 0.28

Excluded instrument F -statistic 11.83

Anderson LR (p-value) 0.00

Cragg–Donald statistic (p-value) 0.00

R-squared 0.57 0.49 0.56 0.39 0.38

Notes:

1. Absolute value of t statistics in parentheses: ∗ denotes significance at 10%, ∗∗ at 5% and ∗∗∗ at 1%2. Estimates of the Shadow economy include regional dummies3. In 2SLS, the shadow economy is instrumented with the variables included in column 14. Reported statistics: Sargan–Hansen test of overidentification of all instruments (p-value reported),

F -statistic of excluded instruments test, Anderson canonical correlation LR test of underidentifica-tion (p-value reported), and the Cragg–Donald statistic (p-value reported)

Most importantly, however, the results show that an increase in the shadow economyreduces corruption at the 5% level of significance thereby confirming hypothesis 1.This result is also confirmed by the 2SLS regression reported in column 4 of Table 1.

According to the estimated coefficients of column 3, an increase in governmenteffectiveness by 0.1 points (on a scale from −2.58 to 2.59) reduces the size of theshadow economy in percent of GDP by approximately 0.42 percentage points andreduces corruption by 0.1 points. Hypothesis 2 is thus also confirmed. Calculated aselasticities, an increase in government effectiveness by 1% reduces the size of theshadow economy by approximately 0.25% and, respectively, corruption by 0.07%.Turning to the effect of the shadow economy on corruption, column 3 shows that an


increase in the shadow economy in percent of GDP by one point reduces corruptionby approximately 0.04 points. The elasticity of this is approximately 0.43%.

The effect of institutional quality on corruption has both a direct and an indirecteffect (through the shadow economy) summarized, albeit in an implicit form, in theelasticities εq, εσ identified in Sect. 2.29 The empirical estimation, however, allowsus to obtain an unambiguous sign of the effect of institutional quality on corruption.To obtain this, notice that, following (18) and (19), a change in institutional qualitygenerates an impact on the equilibrium degree of corruption equal to

∂[corruptioni]∂ei

= β2β5 + β6, (20)

where β2, β5, β6 are the estimated coefficients of column 3. In particular, β2β5 rep-resents the indirect effect of institutional quality on the corruption market, equal to−4.185 × 0.039, whereas β6 gives the direct effect of institutional quality on cor-ruption and is equal to 1.025. It is thus the case that the direct effect β6 = 1.025is substantially larger than the indirect effect given by β2β5 = 0.163, and so insti-tutional quality reduces corruption. To put it differently, an increase in governmenteffectiveness by 0.1 points reduces corruption by 0.087 points.

For the 2SLS specification, we report a range of specification tests on the valid-ity of these results. As can be seen from Table 1, the Sargan–Hansen test does notreject the over identifying restrictions at conventional levels of significance. The An-derson canonical correlations LR statistic and the Cragg–Donald χ2 statistic, whichare both tests of whether the equation is identified, do also not reject the specificationat conventional levels of significance. An F -test on the joint significance of the in-struments in the first stage regression (conditional on all exogenous variables in thesystem) shows that they are good predictors of the size of the shadow economy, andare significant at the one percent level. The F -statistic also exceeds the critical rule-of thumb value of 10 (Staiger and Stock 1997). Finally, we tested for the exogeneityof the shadow economy using the Durbin–Wu–Hausman test. According to this test,the exogeneity of the shadow economy is rejected at the 5% level. It is worth men-tioning that in this case the estimated coefficients are very similar to those reportedpreviously.

While our theoretical model assumes that institutional quality exogenously affectscorruption and the shadow economy, Table 2 allows for the potential endogeneity ofgovernment effectiveness. It does so by using instruments suggested by Friedman etal. (2000): a country’s latitude, and French, socialist, German, and Scandinavian le-gal origin. These variables have also been shown to be correlated with institutionaldevelopment across a wide range of countries in La Porta et al. (1999). Again test-ing for the exogeneity of the shadow economy, the Durbin–Wu–Hausman test showsthat the assumption of exogeneity of the shadow economy is rejected at the 5% level,while exogeneity cannot be rejected at conventional levels of significance for govern-ment effectiveness. Consequently, the more efficient results reported in Table 1 arepreferable to those of Table 2.

29These effects are implicit, by construction, in (17). They do, however, appear explicitly in (20).


Table 2 Corruption, shadow economy, and government effectiveness

(1) (2)

Shadow (No) corruption Government (No) corruption

index effectiveness index

(log) GDP per capita 1.291 0.190 0.374 0.086

(0.48) (1.32) (8.50)∗∗∗ (0.58)

Government effectiveness −13.136 0.722 0.828

(2.27)∗∗ (2.04)∗∗ (1.81)∗Fiscal burden −1.332

(0.81)

Shadow 0.036 0.031

(2.20)∗∗ (1.86)∗Age of democracy 0.008 0.008

(2.40)∗∗ (1.94)∗School enrollment 0.005 0.007

(0.93) (1.90)∗Legal origin French −0.309

(2.87)∗∗∗Legal origin Socialist −0.615

(3.27)∗∗∗Legal origin German 0.021

(0.09)

Legal origin Scandinavian −0.196

(0.73)

Latitude 0.015

(3.24)∗∗∗Constant 32.781 −0.598 −2.879 0.134

(1.78)∗ (0.53) (9.63)∗∗∗ (0.15)

Method 3SLS 3SLS 3SLS 2SLS

Number of countries 101 101 101 101


Excluded instr. F -statistic 9.25

(government effectiveness)


(shadow)



R-squared 0.48 0.46 0.76 0.50

Notes:

1. Absolute value of t statistics in parentheses: ∗ denotes significance at 10%, ∗∗ at 5%, and ∗∗∗ at 1%2. Estimates of the Shadow economy include regional dummies3. In 2SLS, the shadow economy and government effectiveness are instrumented with the variables that

are used to explain them in column 14. Reported statistics: Sargan–Hansen test of overidentification of all instruments (p-value reported),



Turning to the results, Table 2 shows a similar pattern as those reported above.At the 5% level of significance, the shadow economy and corruption decrease withgreater government effectiveness, while the shadow economy reduces corruption(column 1). These results also hold when estimated with 2SLS (column 2) instead of3SLS. The coefficients show a similar magnitude as compared to Table 1. The impactof the shadow economy on corruption is again very similar to the one presented in Ta-ble 1 above. According to column 1, an increase in the size of the shadow economy, inpercent of GDP, by one point reduces corruption by approximately 0.04 points. Theeffect of government effectiveness on the contrary differs to some extent from thosereported above. An increase by 0.1 points reduces the size of the shadow economyby approximately 1.3 percentage points and, respectively, corruption by 0.07 points.Again, calculating the equilibrium change in corruption (and so the direct and indirecteffects) resulting from a 0.1 point increase in government effectiveness, these coeffi-cients show a reduction by 0.25 points. In this case, too, an increase in institutionalquality (as proxied by government effectiveness) reduces corruption.

We also test for the robustness of the results regarding the choice of indicator ofcorruption and institutional quality. As an alternative indicator for (the absence of)corruption, we use Transparency International’s Corruption Perception Index. Thisindex ranges from 0 to 10, with higher values showing less corruption. As can beseen, replacing the index of corruption (Table 3) and, respectively, institutional qual-ity (Table 4) does not change the results reported previously.

According to Table 3, government effectiveness reduces the size of the shadoweconomy at the 10% level of significance, while—at the 1% level—government ef-fectiveness reduces corruption. Again, corruption decreases with a greater shadoweconomy at the 1% level of significance. Note, however, that in this case the num-ber of countries is reduced to 78. These results are confirmed according to Table 4where, however, the size of the shadow economy is not significantly affected by insti-tutional quality when estimated with 3SLS (but significant at the 5% level accordingto 2SLS). According to Table 3 (Table 4), an improvement in the different indicesof institutional quality by 0.1 reduces the size of the shadow economy by approxi-mately 1.2 (0.23) and, respectively, corruption by 0.45 (0.06), while an increase inthe shadow economy in percent of GDP, by one percentage point reduces corrup-tion by 0.18 (0.04) points. Through its effect on the size of the shadow economy,the improvement of institutional quality increases the size of the shadow economyby 2.14 (0.09) percentage points. Again, the overall equilibrium effect of improvedinstitutional quality on corruption remains positive according to these estimations.

Arguably, one may wonder to what extent the result regarding the substitutabilityof corruption and the shadow economy is driven by the fact that a substantial pro-portion of countries in the sample are high income countries. Indeed, this is an issueinvestigated by Dreher and Schneider (2007) who argue, in a follow-up to this study,that corruption and the shadow economy might be substitutes in high income coun-tries only, but might be more likely to be complements in low income countries. If thiswere true, the results of the present analysis would be driven by the substitutive rela-tionship between corruption and the shadow economy in high income countries.30 As

30It has to be noted though that Dreher and Schneider (2007) find no support for this hypothesis usingperceptions-based indices of corruption.


Table 3 Corruption (TI), shadow economy, and government effectiveness

(1) (2)


TI index effectiveness TI index

(log) GDP per capita −0.127 −0.181 0.410 0.086

(0.03) (0.57) (7.49)∗∗∗ (0.31)


(1.72)∗ (5.91)∗∗∗ (5.30)∗∗∗Fiscal burden −0.072

(0.04)

Shadow economy 0.175 0.110

(4.68)∗∗∗ (3.63)∗∗∗Age of democracy 0.010 0.015

(2.23)∗∗ (3.42)∗∗∗School enrollment −0.004 0.005

(0.43) (0.35)

Legal origin French −0.432

(3.91)∗∗∗Legal origin Socialist −0.750

(3.54)∗∗∗Legal origin German −0.139

(0.57)

Legal origin Scandinavian −0.213

(0.83)

Latitude 0.016

(3.13)∗∗∗Constant 39.076 −1.238 −3.068 −1.996

(1.72)∗ (0.48) (8.60)∗∗∗ (0.86)







(shadow)



R-squared 0.55 0.17 0.79 0.57

Notes:





Table 4 Corruption, shadow economy, and rule of law

(1) (2)

Shadow (No) corruption Rule of (No) corruption

index law index

(log) GDP per capita −3.851 0.281 0.169 0.199

(3.10)∗∗∗ (2.52)∗∗ (1.76)∗ (1.88)∗Rule of Law −2.283 0.592 0.421

(0.79) (3.00)∗∗∗ (2.25)∗∗Fiscal burden −0.883

(0.61)


(2.46)∗∗ (2.04)∗∗Age of democracy 0.009 0.013

(2.99)∗∗∗ (3.51)∗∗∗School enrollment 0.006 0.008

(1.01) (1.98)∗∗Legal origin French −0.420

(1.78)∗Legal origin Socialist −0.958

(2.36)∗∗Legal origin German −0.095

(0.19)

Legal origin Scandinavian 0.159

(0.27)

Latitude 0.040

(3.96)∗∗∗Constant 76.460 −3.779 1.861 −2.324

(7.25)∗∗∗ (2.58)∗∗∗ (2.86)∗∗∗ (2.27)∗∗





(rule of law)

Excluded instrument F -statistic 6.45

(shadow)



R-squared 0.56 0.37 0.45 0.46

Notes:

1. Absolute value of t statistics in parentheses: ∗ denotes significance at 10%, ∗∗ at 5%, and ∗∗∗ at 1%2. Estimates of the Shadow economy include regional dummies3. In 2SLS, the shadow economy and rule of law are instrumented with the variables that are used to

explain them in column 14. Reported statistics: Sargan–Hansen test of overidentification of all instruments (p-value reported),



Table 5 Corruption, shadow economy, and government effectiveness (high income countries excluded)

(1) (2)


index effectiveness index

(log) GDP per capita −0.019 0.197 0.323 0.074

(0.01) (1.31) (5.93)∗∗∗ (0.54)


(2.12)∗∗ (1.96)∗ (2.04)∗∗Fiscal burden −2.643

(1.02)


(3.13)∗∗∗ (2.12)∗∗Age of democracy −0.000 −0.001

(0.02) (0.10)

School enrollment 0.004 0.008

(0.83) (2.07)∗∗Legal origin French −0.233

(1.77)∗Legal origin Socialist −0.280

(1.26)

Legal origin German −0.224

(0.47)

Legal origin Scandinavian

Latitude 0.007

(1.22)

Constant 45.557 −1.090 −2.467 0.004

(2.68)∗∗∗ (0.96) (6.80)∗∗∗ (0.00)







(shadow)



R-squared 0.17 0.13 0.41 0.08

Notes:





a robustness test, we thus reestimate the model by excluding all high income coun-tries from the sample.31 Table 5 presents the results. Inspection of this table revealsthat all previous results remain both quantitatively and qualitatively similar at sim-ilar levels of significance. It is thus the case that the substitutability of corruptionand the shadow economy emphasized by the theoretical approach, and confirmed bythe empirical estimations does not seem to driven by the inclusion of high incomecountries.

5 Summary and concluding remarks

This paper has taken a step toward understanding the relationship between institu-tional quality, corruption, and the shadow market. It developed a simple model andconfirmed existing results, associated with Choi and Thum (2005), that corruptionand shadow markets are substitutes in the sense that the existence of the shadow mar-ket is associated with smaller levels of graft. It has also been shown that the effect ofinstitutional quality on the shadow market is unambiguously negative, whereas the ef-fect of institutional quality on the magnitude of corruption is ambiguous and dependson the relative effectiveness of institutional quality on the two markets. These predic-tions were tested using data from between 78–135 countries. The empirical estima-tion confirmed the prediction that institutional quality reduces the shadow economyand corruption. The total effect of institutional quality on corruption was estimated tobe negative and significant. These results were shown to be robust to different econo-metric specifications and indicators of corruption and government effectiveness.

The results thus do lend support to the hypothesis that better institutional qualityreduces corruption, but they do so under an important qualification. While it is truethat better institutions reduce the extent of the shadow economy, for the incidenceof corruption to be reduced attention should be focused not only on the role of thegovernment and regulations and the rule of law but also on the differential impact ofvariables pertaining to institutional quality on corruption and the shadow market.

The analysis presented here has, of course, a number of limitations. Firstly, theincidence of corruption, and its effect on the official market, has taken a very partic-ular form departing, in particular, from the possibility that corruption may affect thepublic finances of a country in a number of ways (including the propensity of offi-cials to shift expenditure towards less-productive investments or military spending).Secondly, the shadow economy has taken a particular form ignoring, too, the pos-sibility that unreported economic activity may affect the public finances with directconsequences for corruption. It remains an open question at this point whether—byallowing, for instance, economic agents that are subject to different expropriationmechanisms32 to exit the market—the shadow economy and corruption will remainto be substitutes.

31This classification follows the World Bank’s (2006) definition for high income countries (2004 grossnational income exceeds 10,066 USD). The sample is restricted to 76 countries.32Such as the one taken by Hindriks et al. (1999).


Acknowledgements We thank Christian Conrad, two anonymous referees, and the editor of the journal,Michael Devereux, for many constructive comments that have considerably improved the paper. This pa-per has also benefited from comments from seminar participants at the Centre for Economic and BusinessResearch (Copenhagen) and the Autonomous University of Barcelona, and conference participants at theEuropean Public Choice Society Annual Meeting 2005, and the Public Choice Society Meeting, New Or-leans, 2005. Kotsogiannis acknowledges support from the Catalan Government Science Network (ProjectNo. SGR2005-177) and the Spanish Ministry of Education and Science Research Project (SEJ2006-4444).The usual caveat applies.

Appendix A

Table 6 Descriptive statistics

Mean Std. dev. Min Max

Shadow 31.70 12.67 6.90 68.10

(No) corruption 3.28 1.33 0.00 6.00

(No) corruption, TI 5.20 2.61 0.00 9.95

Government effectiveness −0.02 0.95 −2.58 2.59

Rule of Law 3.66 1.52 0.27 6.00

(log) GDP per capita 7.50 1.55 3.98 10.71

Age of democracy 19.03 30.45 0.00 101

School enrollment 96.69 20.96 19.09 162.76

Fiscal burden 3.83 0.75 1.30 5.00

Latitude 24.86 16.34 0.23 64.22

Appendix B

Table 7 Set of countries in the data set

Albania Burkina Faso Djibouti Guinea

Algeria Burundi Dominican Republic Haiti

Angola Cambodia Ecuador Honduras

Argentina Cameroon Egypt, Arab Rep. Hong Kong, China

Armenia Canada El Salvador Hungary

Australia Chad Equatorial Guinea India

Austria Chile Estonia Indonesia

Azerbaijan China Ethiopia Iran, Islamic Rep.

Bangladesh Colombia Fiji Ireland

Belarus Congo, Dem. Rep. Finland Israel

Belgium Congo, Rep. France Italy

Benin Costa Rica Gabon Jamaica

Bolivia Cote d’Ivoire Georgia Japan

Bosnia and Herzegovina Croatia Germany Jordan

Botswana Cyprus Ghana Kazakhstan

Brazil Czech Rep. Greece Kenya

Bulgaria Denmark Guatemala Korea, Rep.


Table 7 (Continued)

Kuwait Namibia Romania Thailand

Kyrgyz Rep. Nepal Russian Federation Togo

Lao PDR Netherlands Rwanda Tunisia

Latvia New Zealand Samoa Turkey

Lebanon Nicaragua Saudi Arabia Uganda

Lesotho Niger Senegal Ukraine

Lithuania Nigeria Sierra Leone United Arab Emirates

Madagascar Norway Singapore United Kingdom

Malawi Oman Slovak Rep. United States

Malaysia Pakistan Slovenia Uruguay

Mali Panama South Africa Uzbekistan

Mauritania Papua New Guinea Spain Venezuela, RB

Mexico Paraguay Sri Lanka Vietnam

Moldova Peru Sweden Yemen, Rep.

Mongolia Philippines Switzerland Zambia

Morocco Poland Syrian Arab Rep. Zimbabwe

Mozambique Portugal Tanzania

Appendix C: Definition and sources of variables

Shadow: Size of the shadow economy in percent of GDP calculated with struc-tural equation modeling (dynamic multiple causes, multiple indicators, DYMIMIC).Source: Schneider (2005a, 2005b).

(No) corruption, index: Based on the analysis of a world-wide network of experts,on a range from 0—representing highest corruption—to 6 (no corruption). Source:International Country Risk Guide (2006).

(No) corruption, TI: Corruption Perception Index, ranging from 0 to 10, with highervalues showing less corruption. Source: Transparency International (2006).

Government effectiveness: Ranges −2.31 to 2.22, with higher scores showing ‘bet-ter’ environments. Source: Kaufmann et al. (2003).

Rule of law: Based on the analysis of a world-wide network of experts, on a rangefrom 0 to 6, with higher values reporting “better” environments. Source: Interna-tional Country Risk Guide (2006).

(log) GDP per capita: Measured in constant 2,000 USD. Source: World Bank (2005).Age of democracy: Number of years since 1900 a country has a democracy score

continuously greater than zero. Source: Marshall and Jaggers (2000).School enrollment: Ratio of total enrollment, regardless of age, to the population of

the age group that officially corresponds to the level of education shown. Primary ed-ucation provides children with basic reading, writing, and mathematics skills alongwith an elementary understanding of such subjects as history, geography, naturalscience, social science, art, and music. Source: World Bank (2005).

Fiscal burden: The index of the fiscal burden includes the top marginal tax rates onindividual and corporate income, as well as a measure of total tax revenue as a


portion of GDP. A score of 1 signifies a low fiscal burden, while a score of 5 signifieshigh fiscal burden. Source: Heritage (2006).

Latitude: Absolute value of latitude. Source: Easterly and Sewadeh (2001).Regional dummies: Dummies for the East Asia and Pacific region, East Europe and

Central Asia, Middle East and North Africa, South Asia, West Europe, North Amer-ica, and Latin America and Caribbean.

Legal origin: Dummies for French, socialist, German, and Scandinavian legal origin.British legal origin is the excluded category. Source: La Porta et al. (1999).

References

Aidt, T. S. (2003). Economic analysis of corruption: a survey. Economic Journal, 113, 632–652.Allingham, M., & Sandmo, A. (1972). Income tax evasion: a theoretical analysis. Journal of Public Eco-

nomics, 1, 323–38.Bardhan, P. (1997). Corruption and development: a review of issues. Journal of Economic Literature, 3,

1320–1346.Bliss, C., & Di Tella, R. (1997). Does competition kill corruption? Journal of Political Economy, 105,

1001–1023.Choi, J., & Thum, M. (2005). Corruption and the shadow economy. International Economic Review, 46(3),

817–836.Cowell, F. (1990). Cheating the government. Cambridge: MIT Press.Dreher, A., & Gassebner, A. (2007). Greasing the wheels of entrepreneurship? The impact of regulations

and corruption on firm entry (KOF Working Paper 166). ETH Zurich.Dreher, A., & Schneider, F. (2007). Corruption and the shadow economy: an empirical analysis. Unpub-

lished manuscript.Dreher, A., Kotsogiannis, C., & McCorriston, S. (2007). Corruption around the world: evidence from a

structural model. Journal of Comparative Economics, 35(3), 443–466.Easterly, W., & Sewadeh, M. (2001). Global development network growth database. http://www.

worldbank.org/research/growth/GDNdata.htm.Frey, B. S., & Pommerehne, W. (1984). The hidden economy: state and prospect for measurement. Review

of Income and Wealth, 30, 1–23.Friedman, E., Johnson, S., Kaufmann, D., & Zoido-Lobaton, P. (2000). Dodging the grabbing hand: the

determinants of unofficial activity in 69 countries. Journal of Public Economics, 76, 459–493.Giles, D. E. (1999). Measuring the hidden economy: implications for econometric modelling. Economic

Journal, 109, F370–F380.Heritage (2006). http://www.heritage.org/.Hindriks, J., Muthoo, A., & Keen, M. (1999). Corruption, extortion and evasion. Journal of Public Eco-

nomics, 74, 395–430.Huntington, S. (1968). Political order in changing societies. New Haven: Yale Univ. Press.International Country Risk Guide (2006). http://www.prsgroup.com/ICRG.aspx.Jain, A. K. (2001). Corruption: a review. Journal of Economic Surveys, 15, 71–121.Johnson, S., Kaufmann, D., & Shleifer, A. (1997). The unofficial economy in transition (Brookings Papers

on Economic Activity). Washington, DC.Johnson, S., Kaufmann, D., & Zoido-Lobaton, P. (1998). Regulatory discretion and corruption. American

Economic Review, 88, 387–392.Kanniainen, V., Pääkkönen, J., & Schneider, F. (2005). Determinants of shadow economy: theory and

evidence. Mimeo, Johannes Kepler University Linz.Kaufmann, D., & Wei, S. J. (1999). Does “grease money” speed up the wheels of commerce (NBER

Working Paper 7093). Cambridge, MA.Kaufmann, D., Kraay, A., & Zoido-Lobatón, P. (1999). Governance matters (Policy Research Working

Paper 2196). World Bank.Kaufmann, D., Kraay, A., & Mastruzzi, M. (2003). Governance matters III (Draft Paper). World Bank.Klitgaard, R. (1988). Controlling corruption. Berkeley: University of California Press.Lambsdorff, Graf J. (1999). Corruption in empirical research—a review (Transparency International

Working Paper). November 1999.

http://www.worldbank.org/research/growth/GDNdata.htm

http://www.worldbank.org/research/growth/GDNdata.htm

http://www.heritage.org/

http://www.prsgroup.com/ICRG.aspx


La Porta, R., Lopez-De-Silanes, F., Schleifer, A., & Vishny, R. (1999). The quality of government. Journalof Law, Economics and Organization, 15, 222–279.

Leff, N. H. (1964). Economic development through bureaucratic corruption. American Behavioural Scien-tist, 8, 8–14.

Marshall, M., & Jaggers, K. (2000). POLITY IV PROJECT—Political regime characteristics and transi-tions 1800–1999. http://www.cidcm.umd.edu/polity/.

Méon, P.-G., & Weill, L. (2008). Is corruption an efficient grease? (Working Papers of LaRGE 2008-06).University of Strasbourg.

Rose-Ackerman, S. (1978). Corruption: a study in political economy. New York: Academic.Rose-Ackerman, S. (1997). Corruption and development. In Annual bank conference on development eco-

nomics. Washington: The World Bank.Rose-Ackerman, S. (1999). Corruption and government: causes, consequences and reform. Cambridge:

Cambridge Univ. Press.Schneider, F. (1994a). Measuring the size and development of the shadow economy: can the causes be

found and the obstacles be overcome? In H. Brandstaetter & W. Güth (Eds.), Essays on economicpsychology (pp. 193–212). Berlin: Springer.

Schneider, F. (1994b). Can the shadow economy be reduced through major tax reforms? An empiricalinvestigation for Austria. Public Finance, 49, 137–52.

Schneider, F. (1997). The shadow economies of Western Europe. Journal of Institute of Economic Affairs,17, 42–48.

Schneider, F. (2005a). Shadow economies of 145 countries all over the world: estimation results of theperiod 1999–2003 (Discussion paper). University of Linz, Department of Economics, Linz, Austria.

Schneider, F. (2005b). Shadow economies around the world: what do we really know? European Journalof Political Economy, 21(3), 598–642.

Schneider, F., & Enste, D. H. (2000). Shadow economies: size, causes and consequences. Journal of Eco-nomic Literature, XXXVIII, 77–114.

Schneider, F., & Enste, D. H. (2002). The shadow economy: an international survey. Cambridge: Cam-bridge Univ. Press.

Schneider, F., & Neck, R. (1993). The development of the shadow economy under changing tax systemsand structures. Finanzarchiv N. F, 50, 344–69.

Shleifer, A., & Vishny, R. (1993). Corruption. Quarterly Journal of Economics, 108, 599–617.Staiger, D., & Stock, J. H. (1997). Instrumental variables regression with weak instruments. Econometrica,

65(3), 557–586.Tanzi, V. (Ed.) (1982). The underground economy in the United States and abroad. Lexington: D.C. Heath.Tanzi, V. (1998). Corruption around the world-causes, consequences, scope and cures. International Mon-

etary Fund Staff Papers, 45, 559–594.Tanzi, V. (1999). Uses and abuses of estimates of the underground economy. Economic Journal, 109,

338–40.Transparency International (2006). http://www.transparency.org/.Wilson, D. (1996). Acer computer in Russia. Fuqua School of Business, Duke University.World Bank (2001). http://www.worldbank.org/publicsector/anticorrupt/index.ctm.World Bank (2004). http://www.worldbank.org/WBSITE/EXTERNAL/NEWS. Accessed 21 April 2004.World Bank (2005). World development indicators. CD-ROM, Washington, DC.World Bank (2006). World development indicators. CD-ROM, Washington, DC.

http://www.cidcm.umd.edu/polity/

http://www.transparency.org/

http://www.worldbank.org/publicsector/anticorrupt/index.ctm

http://www.worldbank.org/WBSITE/EXTERNAL/NEWS

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How do institutions affect corruption and the shadow economy?