Does corruption grease or sand the wheels of growth? ∗
PIERRE-GUILLAUME MÉON1 & KHALID SEKKAT2
1Large, Université Robert Schuman, Institut d’Etudes Politiques, 47 Ave de la Forêt Noire,67082 Strasbourg Cedex, France; E-mail: [email protected];2Dulbea, Université Libre de Bruxelles, 1050 Bruxelles, Belgium; E-mail: [email protected]
Accepted 1 October 2003
Abstract. This paper assesses the relationship between the impact of corruption on growthand investment and the quality of governance in a sample of 63 to 71 countries between 1970and 1998. Like previous studies, we find a negative effect of corruption on both growth andinvestment. Unlike previous studies, we find that corruption has a negative impact on growthindependently from its impact on investment. These impacts are, however, different dependingon the quality of governance. They tend to worsen when indicators of the quality of governancedeteriorate. This supports the “sand the wheels” view on corruption and contradicts the “greasethe wheels” view, which postulates that corruption may help compensate bad governance.
1. Introduction
Is corruption detrimental or beneficial to the economic activity? At firstsight the question seems ironic and even provocative. It is, however, stillcontroversial among economists.
Common wisdom views corruption as an impediment to developmentand growth. This view was recently supported by the results of the literat-ure aimed at quantifying the consequences of corruption and growth. Thatliterature was pioneered by Mauro (1995), who observed a significant negat-ive relationship between corruption and investment that extended to growth.Mauro (1995)’s findings were confirmed by Brunetti and Weder (1998) andMo (2001). As a result, international organizations (e.g. the IMF, the WorldBank, the UN or the OECD) gave the fight against corruption high priority.They took international initiatives (e.g. the UN resolution in 1998 or the 1999OECD’s “Convention on combating bribery”) and urged States to criminalizeand deter the bribery of foreign office holders.
∗ We thank an anonymous referee for very helpful comments, which substantially im-proved the paper. We also benefited from very useful discussions with participants to the“Institutions, growth and development” conference in Perpignan, 2003, and the 2003 AnnualMeeting of the European Public Choice Society, in Aarhus, and with seminar participants atthe University Robert Schuman of Strasbourg and the University Louis Pasteur of Strasbourg.We acknowledge financial support from the Research Fund at the ULB.
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In contrast, other researchers have suggested that graft may be beneficial.Leys (1965) questioned “the problem about corruption”. Bardhan (1997) re-called episodes of the history of Europe and the US which illustrate situationswhere corruption may have favored development by allowing entrepreneursto grow out of bribers. Furthermore, Beck and Maher (1986) and Lien (1986)argued that corruption may raise efficiency. The most popular justificationof the beneficial effects of corruption rests on the so-called “grease thewheels” hypothesis. Put forward by Leff (1964), Huntington (1968) and Leys(1965), that hypothesis suggests that corruption may be beneficial in a secondbest world because of the distortions caused by ill-functioning institutions.That argument is that an inefficient bureaucracy constitutes an impedimentto investment that some “speed” or “grease” money may help circumvent.In a nutshell, the “grease the wheels” hypothesis states that graft may actas a trouble-saving device, thereby raising efficiency hence investment and,eventually, growth.
The empirical evidence on the negative impact of corruption on growthand investment is not inconsistent with the “grease the wheels” hypothesis.The hypothesis implies that corruption may be beneficial in countries whereother aspects of governance are ineffective, but remain detrimental elsewhere.Existing evidence shows that corruption is on average associated with lowergrowth and investment but do not investigate to what extent such an associ-ation depends on the quality of governance. Actually there is little evidenceallowing a rigorous rejection of the “grease the wheels” hypothesis. Mauro(1995) has attempted to shed light of this issue by splitting his sample inhigh red tape and low red tape sub-samples of countries. He did not findany significant difference between the two sub-samples with respect to thenegative impact of corruption. However, the threshold for splitting the samplewas rather arbitrary (score of 5 or 7 of the red tape index) and the size ofthe sub-samples became too small to allow the inclusion of control vari-ables.1 Kaufman and Wei (2000) tackled the issue from a different angle.Using firm-level data, they tested whether corruption reduces the time thatfirms spend negotiating with foreign countries’ officials. They found thatmultinationals that pay more bribes also tend to spend more time negotiatingwith foreign countries’ officials, which contradicts the “grease the wheels”hypothesis. To our knowledge, there were no other attempts to rigorously testthe “grease the wheels” hypothesis.
This paper tests systematically the “grease the wheels” hypothesis at amacroeconomic level. It estimates the relationship between the impact ofcorruption, on investment and growth, and a wide range of indicators of thequality of governance. The results not only reject the “grease the wheels” hy-pothesis but are consistent with the reverse hypothesis: the “sand the wheels”
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hypothesis. It seems that corruption becomes even more harmful when gov-ernance is poor. In economics, there are well known situations where in thepresence of existing distortions an additional distortion may improve welfare.Our finding illustrates the opposite case where adding a distortion deteriorateswelfare.
The rest of the paper is organized as follows. The next section presentsthe theoretical underpinnings of the “grease the wheels” and the “sand thewheels” hypotheses. Section 3 describes the econometric approach. Section4 presents the results. Section 5 concludes.
2. The “grease the wheels” versus the “sand the wheels” hypothesis
The debate on the impact of corruption on economic performance goes bey-ond a “moralistic view” that unequivocally condemns corruption.2 The moraljudgement on corruption may bias the understanding of its economic con-sequences. One strand of the literature argues that corruption may take placein parallel with a low quality of governance and can, therefore, reduce theinconvenience of such low quality. This is the “grease the wheels” hypothesis.Another strand stresses that although bribery may have benefits if the qualityof governance is low, it may as well impose additional costs in the samecircumstances. The existence of such costs provides a rationale for the “sandthe wheels” hypothesis.
The core of the debate on the “grease” vs. the “sand the wheels” hypo-theses lies in the combination of corruption with a low quality of governance.While there are many aspects of governance that corruption may grease orsand, the literature has mainly focused on two. One concerns the ill func-tioning of bureaucracy (i.e. its failure to accomplish assigned goals; see Leff,1964) while the other refers to policy options by public authority. The extentto which corruption can grease or sand the wheels in the presence of a lowquality of governance is discussed below.
2.1. The “grease the wheels” hypothesis
The ill functioning of the bureaucracy is considered as the most prominentinefficiency that corruption could grease. Huntington (1968) stated: “In termsof economic growth, the only thing worse than a society with a rigid, over-centralized, dishonest bureaucracy is one with a rigid, overcentralized, honestbureaucracy”. There are various aspects of ill functioning of the bureau-cracy that can be compensated by corruption. A first one concerns slowness.Using a formal economic model, Lui (1985) showed that corruption couldefficiently lessen the time spent in queues. The reason is that bribes could
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give bureaucrats an incentive to speed up the process, in an otherwise slug-gish administration (see also Leys, 1964). Furthermore, Huntington (1968)argued that corruption could help surmount tedious bureaucratic regulationsand foster growth. According to him, such a phenomenon had been observedin the 1870’s and 1880’s in the United States, where corruption by railroad,utility and industrial corporations resulted in faster growth.
Another consequence of an ill-functioning bureaucracy concerns the qual-ity of civil servants. Leys (1964) and Bailey (1966) argued that corruption canamend a bureaucracy by improving the quality of its civil servants. If wagesin government service are insufficient, the existence of perks may constitutea complement that may attract able civil servants who would have otherwiseopted for another line of business.
Finally, Beck and Maher (1986) and Lien (1986) suggested that corruptionmay enhance the choice of the right decisions by officials. If bureaucratsdo not have enough information or are not competent for some decisions,corruption can replicate the outcome of a competitive auction. They formallyshowed that when attributing a government procurement contract the rankingof bribes can replicate the ranking of firms by efficiency. Moreover, if someinvestment projects are dependent on the attribution of a license, corruptionmay be an efficient way of selecting such projects. Here again, corruptionin the attribution of a government license is very similar to a competitiveauction. The intuition (Leff, 1964) is that licenses tend to be allocated to themore generous bribers, who can be the more efficient. Hence, the capacity tooffer a bribe is correlated with talent.
Turning to the other aspect of governance, some authors praise corruptionfor its role in allowing economic agents to escape the consequences of somepolicies. Bailey (1966) for instance argues that if bribes could help privateagents to evade a public policy designed to solve a particular problem, theymay thereby allow them to find an overlooked and better-suited solution. Thismay in turn allow an improvement of the policy’s outcome even in terms ofthe government’s objectives. Leff (1964) and Bailey (1966), also argue thatgraft may simply be a hedge against bad public policies. This is particularlytrue if institutions are biased against entrepreneurship, due for instance to anideological bias. By simply impeding inefficient regulations, corruption maythen limit their adverse effects. It may also result in an alteration of the policyin a way that is friendlier to growth.
It has also been argued that graft may in some circumstances improvethe quality of investments. This is the case (Leff, 1964) when governmentspendings are inefficient. If corruption is a means of tax evasion, it can reducethe revenue of public taxes. Provided the bribers can invest efficiently, theoverall efficiency of investment will be improved. In addition to the quality
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of investments, some authors argue that corruption may also raise the level ofinvestment. For instance, Leff (1964) asserts that corruption may constitutea hedge against other risks originating from the political system, such as ex-propriation or violence. If corruption helps mitigating those risks, investmentwill turn out less risky and may accordingly increase.
All the above-mentioned arguments share the presumption that corruptionmay positively contribute to growth and development, because it compensatesthe consequences of a defective bureaucracy and bad policies. One may nev-ertheless wonder whether corruption creates or reinforces other inefficienciesand whether bribers are always taking more efficient decisions than publicauthority. Although bribery may have benefits in a weak institutional envir-onment, it may as well impose additional costs in the same circumstances.The existence of such costs provides a rationale for the “sand the wheels”hypothesis.
2.2. The “sand the wheels” hypothesis
Starting with the ill functioning of bureaucracy, the positive impact of cor-ruption on slowness rests on the assumption that a civil servant can speed upan “exogenously” slow process. However, corrupt civil servants may causedelays that would not appear otherwise, just to get the opportunity to extracta bribe (Myrdal, 1968). Moreover, the ability of civil servants to speed upthe process can be very limited when the administration is made of a suc-cession of decision centers. In this case, civil servants at each stage can havesome form of veto power or some capacity to slow down a project. Usingindustrial organization models, Shleifer and Vishny (1993) show that the costof corruption can be higher when, say to get an authorization for a project,many independent agents are involved than when only one is. Bardhan (1997)reports that an Indian high official once declared that he could not be sure tobe able to move a file faster but could immediately stop it. The increased num-ber of transactions due to graft may well offset the increased efficiency withwhich transactions are carried out (Jain, 2001). Under these circumstancesone distortion adds up to the others instead of compensating them, which isprecisely the meaning of the “sand the wheels hypothesis”
At an aggregate level, the impact of corruption on the quality of civilservants is questionable. Kurer (1993) argued that corrupt officials have anincentive to create other distortions in the economy to preserve their illegalsource of income. For instance, a civil servant may have an incentive to ra-tion the provision of a public service just to be able to decide to whom toallocate that service in exchange for a bribe. Similarly a civil servant alsohas the incentive to limit new servants’ (especially competent ones) access tokey positions in order to preserve the rent form corruption. While individual
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bribers can indeed improve their own situation thanks to a perk, nothing isgained from corruption at the aggregate level.
The argument that corruption may enhance the choice of the right de-cisions is also subject to doubt. There are reasons to believe that agents payingthe highest bribe are not always able to improve efficiency. Rose-Ackerman(1997) argues that a firm may be able to pay the highest bribe simply becauseit compromises on the quality of the goods it will produce if it gets a license.Mankiw and Whinston (1986) show that entry on a market may be beneficialfor the firm but detrimental for welfare. In these cases, entry is, in general,subject to an authorization. Although entry is detrimental for welfare, the firmcan find it profitable to pay the bribe to get the authorization and enter themarket. Finally, if the profitability of a license is uncertain, the winner of theauction may be the more optimistic rather than the most efficient, a situationthat is known as the “winner’s curse”. In these cases, corruption is not thebest way to award a license. Thus, even if the analogy between corruptionand a competitive auction holds, there are situations where the winner is notenhancing efficiency.
Turning to the second category of institutional deficiencies (i.e. policyoptions by public authority), the argument in favor of corruption can becounter-balanced in various respects. The argument according to whichcorruption may raise both the quantity and the quality of investment is ques-tionable. There is evidence that this may not be true for public investment.Empirical evidence shows that higher corruption is associated with higherpublic investment (Tanzi and Davoodi, 1997) and that this results in a diver-sion of public spending towards less efficient allocations (Mauro, 1998). Inother words, corruption results in a greater amount of public investments inunproductive sectors, which is unlikely to improve efficiency and result infaster growth.
One may also doubt that corruption may be a hedge against risk in a polit-ically uncertain environment. This may only be true if corruption does notimply additional risk-taking. However, corruption is not a simple transaction.As it is illegal, the commitment to comply with the terms of the agreementmay indeed be very weak, which may lead to opportunism, especially onthe bribee’s part. As Bardhan (1997) points out, the inherent uncertainty ofcorrupt agreements may simply make the efficiency-enhancing mechanismsineffective. This presumption is supported by the results obtained by Camposet al. (1999) and Lambsdorff (2003) who observe that the unpredictability ofcorruption has an impact on investment and capital inflows that is independ-ent from the impact of the level of corruption. As a result, it is likely thatcorruption may increase the risks associated with a weak rule of law insteadof compensating it.
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3. The econometric approach
The above analysis has shown that the core of the “grease” vs. the “sandthe wheels” debate is not whether corruption reduces investment and growthin general. Instead, the concern is whether corruption increases or decreasesinvestment and growth when the quality of governance is low. When the qual-ity of governance is low, if corruption mitigates the negative effect of such asituation, investment and growth will be higher with corruption than withoutit, i.e. it greases the wheels. Alternatively, if corruption magnifies the negativeeffect of such a situation, investment and growth will be lower, i.e. corruptionsands the wheels. Although the situation of a country with a high quality ofgovernance is not directly addressed in this literature, it seems reasonableto assume that investment and growth should be lower with corruption thanwithout it. In this case corruption entails costs and has no imperfection togrease. The rest of the section describes the econometric model and data setsused for the analysis.
3.1. The model
Since the seminal works by Kormendi and Meguire (1985), Barro (1991) andMankiw et al. (1992), the modern growth literature, although quite dense, hasfocused on a common specification: cross-countries regression. Studies ofthe institutional and political determinants of growth have also widely usedthe same technique.3 It has then become standard to express the average rateof per capita growth (or the average rate of investment) of a given periodas a combination of a few explanatory variables. The economic variablesthat are typically included to explain long run macro-economic relationshipsare: GDP per capita in the initial year of the period under study, averagepopulation growth, initial school enrolment, investment ratio and a measureof openness to trade. Depending on the purpose of the empirical analysisadditional explanatory variables (e.g. war, ethnicity, corruption) are incor-porated. The objective of the present study is to examine how the qualityof governance affects the impact of corruption on investment and growth.Hence, two additional sets of explanatory variables are considered. One refersto corruption indices while the other concerns measures of the quality ofgovernance.
In econometric terms, examining whether growth and investment increaseor decrease with corruption when the quality of governance is low impliestesting how the latter affects the coefficient of corruption. Hence, the usual setof explanatory variables in the growth (or investment) regression is comple-mented by a corruption index and an interaction term defined as the product
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of that corruption index by a proxy for the other deficiencies. This results inthe following specification for the growth rate of per capita income:
log(yT) − log(y0) = α0 + α1∗ log(y0) + α2
∗ log(Sc0) + α3∗ [log(popT)
− log(pop0)] + α4∗ log(inv) + α5
∗ log(open)
+ [α6 + α7∗ log(gov)] ∗ log(cor) + µ
(1)where
log(yT) − log(y0) is the average growth rate of per capita in-come over the sample period
log(y0) is the initial per capita income
log(Sc0) is the initial level of schooling
log(popT) − log(pop0) is the average growth rate of population overthe sample period
log(inv) is the average ratio of investment to GDP overthe period
log(open) is the degree of openness of the economy
log(cor) is the corruption index
log(gov) is the governance indicator
µ is the error termThe investment specification is similar to Equation (1) except that the
dependent variable is the ratio of investment to GDP and that [log(popT) −log(pop0)] and log(inv) are not included.
The purpose of including per capita GDP in the first year of the sampleperiod is to take into account the absolute convergence effect highlightedin the neo-classical growth model. If convergence has taken place, α1 < 0.Similarly, population growth allows to take into account the negative (α3 < 0)effect of demographic growth on the growth rate of per capita income.
We also use the enrolment ratio in primary or secondary school in theinitial year, defined as the ratio of total enrolment, regardless of age, overthe population of the age group that officially corresponds to the level ofeducation shown. It is a common proxy for human capital (Mankiw et al.,1992). An improvement in human capital should boost growth and investment(α2 > 0).
Theory suggests that the impact of openness on growth should be positive(α5 > 0) although the empirical literature has not strongly confirmed suchan effect. Openness is defined as the ratio of exports plus imports to GDP.It is used as a proxy for the exposure of the economy to foreign markets.
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Levine and Renelt (1992) have found that it was robustly correlated with theinvestment share of GDP.
In this paper the parameters of interest are α6 and α7. Under the “grease thewheels” hypothesis corruption should have a positive impact on the economicactivity if the quality of governance is very low. In the sample very low qualityof governance implies log(gov) close to 0. With log(gov) close to zero, α6
should be positive for corruption to have a positive impact on the economicactivity. With high quality of governance the impact of corruption shouldbecome negative. In order to get such an impact α7 should be negative. Hencethe “grease the wheels” hypothesis will not be rejected if α6 > 0 and α7 < 0.
Under the “sand the wheels” hypothesis, corruption is harmful for growthand investment and becomes increasingly detrimental as governance deteri-orates. In this case, when the quality of governance is very low (log(gov)close to 0) α6 should be negative for corruption to still have a negative im-pact. For this impact to be more negative under low quality of governance(log(gov) close to 0) than under high quality (log(gov) far above 0) α7 shouldbe positive.
3.2. Data
In order to conduct tests we use three data sets: macroeconomic data,corruption indices and governance indicators.
3.2.1. Corruption dataWhile corruption is commonly defined as “the misuse of public power forprivate benefits” (see e.g. Jain, 2001), its proper measurement is more diffi-cult. Basically, one may classify available quantitative measures of corruptionthat allow cross-country comparisons into three broad categories.
A first set of indicators uses pools of experts that assess the level ofcorruption that prevails in a country. Very often, these ratings are producedby private risk-rating agencies, such as Business International Corporation,whose index was used by Mauro (1995).
A second type of indicators is based on surveys of residents and are usuallycarried out by international or non-governmental organizations. The indexprovided in the World Economic Forum’s Global Competitiveness Reportfalls in this category and was used by Wei (2000).
A third category combines the indices belonging to the previous two cat-egories. This has two main advantages.4 On the one hand, as basic indicatorsare by construction subjective, they may be biased. Composite indices mayinduce those biases to cancel out, therefore determining an average opinionregarding corruption. On the other hand, as composite indices aggregate sev-
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eral other indices, they can provide data for wider samples of countries, sincethey allow one index to fill the gaps left by another.
Given the above advantages, composite indices have been widely usedin the literature and will also be adopted in the present study. We use twocomposite indices to assess the consequences of corruption. This allows us totest the robustness of our results. The two indices are the Corruption Percep-tion Index (henceforth CPI) published by Transparency International and thecorruption index provided by the World Bank (henceforth WB).5
The CPI index is available directly on the Transparency International web-site. This index is computed yearly as an average of other indices. It rangesfrom zero to eight, the latter corresponding to an absence of corruption. Forclarity, we computed and used the opposite of that index in our analysis sothat an increase in the index can be directly interpreted as an increase in thelevel of corruption. To keep our sample as large as possible, we used the 1999vintage of the CPI index that is provided for 99 countries.
Unlike the CPI index, the World Bank’s corruption indicator is not anaverage of other indices. Instead, it is estimated thanks to an unobservedcomponent model, that is described in Kaufman et al. (1999a). As regardstheir composition, the CPI and the WB indices also differ insofar as theyaggregate slightly different sets of basic indicators of corruption.6 The twoindices therefore stand as two useful complements, since they aggregate twodifferent sets of indicators thanks to two different methods. The WB indicatorcan be found in the Governance database posted on the World Bank’s web-site. It combines information relative to the 1997–1998 period and rangesfrom –2.5 to +2.5. Like the CPI index, it is constructed so that an increasein the index reflects a better control of corruption. Kaufman et al. (1999a, b)accordingly sometimes refer to it as an indicator of probity. It was thereforere-scaled so as to increase with the level of corruption.
3.2.2. Governance dataTo test the “grease the wheels” vs. the “sand the wheels” hypotheses, oneneeds quantitative estimates of other dimensions of governance. Like corrup-tion, the other aspects of governance hardly lend themselves to an objectiveevaluation. They are proxied through surveys of experts or residents that areinherently subjective.
Kaufmann et al. (1999b) have applied the unobserved component modelused for corruption to construct indicators of governance. They classifiedavailable indicators of governance into five clusters and aggregated theminto five composite indices. Each composite indicator refers to a differentdimension of governance. It ranges from –2.5 to +2.5, higher values signalingbetter governance. The composition of indicators being described in detail in
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Kaufmann et al. (1999b), we will simply recall the definitions of each aspectof governance that those indicators aim at quantifying.
The first indicator, called “voice and accountability”, measures “the ex-tent to which citizens of a country are able to participate in the selection ofgovernments”. It accordingly assesses the openness of the political system.
The “lack of political violence” indicator “measures perceptions of thelikelihood that the government in power will be destabilized or overthrownby possibly unconstitutional and/or violent means”. This indicator thereforeprovides an assessment of the political risk associated with a country.
The third indicator, named “government effectiveness”, concerns the“perceptions of the quality of public service provision, the quality of thebureaucracy, the competence of the civil servants, the independence of thecivil service from political pressures, and the credibility of the government’scommitment to policies”.
The “regulatory burden” indicator captures “the incidence of market un-friendly policies such as price controls or inadequate bank supervision, aswell as perceptions of the burden imposed by excessive regulation”. The lastindicator is devoted to the “rule of law” and refers to “the extent to whichagents have confidence in and abide by the rules of society”.
3.2.3. Economic dataEconomic data are from the Growth Development Network database of theWorld Bank. More precisely, data concerning GDP per capita growth, invest-ment, population growth, and openness to trade, were found in the “macrotime series 2001” data set of the World Bank. School enrolment ratios weretaken from the “social indicators and fixed factor 2001” database. The samplespans the 1970–1998 period and covers developed and developing coun-tries from different regions of the World. Due to missing data, the totalnumber of observations used in any regression ranges from 63 to 71. Ap-pendix B presents the list of countries and the corresponding corruption andgovernance indicators.
Economic variables are averaged over the sample period. As pointed outby Easterly et al. (1993), it is not sure that the variation over time of countrycharacteristics adds much explanation to the regressions. This is especiallytrue when corruption indices are included in the set of explanatory variables.It can indeed be argued that corruption is a long-term institutional issue thatevolves slowly. Moreover, as Paldam (2002) suggests, indices of corruptionare likely to evolve even more slowly than the phenomenon that they aresupposed to gauge.7 Those indices are typically based on surveys and itis likely that respondents, who may be experts, firm managers or generalcitizens, found their answers on their past experience that is typically built
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over several years. The resulting inertia of corruption indices consequentlyprecludes any meaningful analysis of their variations over time, at least untillonger time series are available.
Furthermore, Transparency International insists upon the fact that thecomposition of the sample of corruption indicators that are aggregated tocompute the CPI index evolves over time, which makes year-to-year compar-isons of a country’s score risky. Thus the evolution of the score may simplyresult from changes in sample and methodology.8 Finally, some indices, likethe World Bank’s are only available for a single year.
4. The empirical analysis
The equations are estimated using Generalized Least Squares to correct forheteroscedasticity. Following Mankiw et al. (1992), all variables are taken inlogarithm.9 As the WB index takes values between –2.5 and +2.5, we replacedlog(WB) by log(3.5 – WB) in the regressions. Similarly, we replaced log(CPI)by log(11 – CPI). The interaction terms were defined as the product of a trans-formed corruption index by log(3.5-governance), where “governance” standsfor governance indicators drawn from the World Bank database. Given thesetransformations, higher values of the corresponding explanatory variablesmean higher level of corruption and higher quality of governance.
4.1. Per capita GDP growth rate
A preliminary investigation of the “grease the wheels” and the “sand thewheels” claims consists of examining how the impact of corruption variesdepending on the quality of governance. This is done by estimating Equation1 without the interaction term (i.e. setting α7 = 0) over different sub-samples.The sub-samples are constructed as follows. The observations in the initialsample are sorted according to the quality of governance (from the lowest tothe highest level). The first sub-sample includes the first 40 observations. Thesecond includes observations number 2 to 41 and so forth. The regressionover each sub-sample gives an estimated coefficient of corruption. Plottingthe successive coefficients of corruption, sheds light on the validity of the“grease the wheels” and the “sand the wheels” hypotheses. If the former isvalid the resulting curve should be decreasing, i.e. the coefficient becomesmore and more negative as one moves from low to high quality of governance.If the latter is valid the curve should be increasing.
For illustration, we conducted the investigation using the WB index ofcorruption and the rule of law index of governance. The results are presentedin Figure 1. The curve is clearly increasing, suggesting that the “sand the
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wheels” claim is right. However, in order to get firmer support for sucha claim one should rigorously test whether the change in coefficients issignificant or not. This is the purpose of Table 1.
There are four specifications. The basic specification includes all the ex-planatory variables in Equation (1) except the interaction term. Each of theother three specifications includes the interaction term. The results are repor-ted for three governance indicators: Rule of law, government effectivenessand lack of violence. To save on space, the results with the regulatory burdenand the voice and accountability indicators are not reported. The coefficientsof the interaction terms with these indicators are never significant neither inthe growth equation nor in the investment equation. This suggests that theaccountability of political leaders and the quality of the regulatory frameworkdo not modify the impact of corruption on growth.
The basic specification explains about 50% of the variation in growthrates. Across specifications, all coefficients have the expected sign, althoughnot always significantly. Initial GDP per capita enters the regressions witha negative sign and is in general significant, which means that we observethe usual convergence effect. The coefficient of primary school enrolment iscorrectly signed but always insignificant. Population growth enters the regres-sions negatively and is generally insignificant. Openness has a positive signbut is always insignificant. Finally, the average investment ratio is alwayssignificant and exhibits a positive coefficient, much in line with Levine andRenelt (1992)’s result.
As regards corruption, both indices appear in the regressions with a neg-ative coefficient. While the coefficient of the CPI is non-significant at the10% level, the WB’s is significant at 1%. This means that corruption tends tohamper growth. This result confirms previous studies that observed the samerelationship (Mauro, 1995 or Mo, 2001). It should be stressed, however, thatcorruption has a significant negative coefficient even when the investmentratio is included among the regressors. This suggests that beyond its potentialnegative impact on the accumulation of capital, corruption directly impactsgrowth. This result contrasts with Mauro (1995)’s who found no significantrelationship between corruption and growth once investment was includedamong the explanatory variables. Mo (2001) found a significant relationshipbetween corruption and growth even after controlling for the investment ratiobut the coefficient of corruption becomes insignificant when human capital istaken into account.
There are two possible explanations to the finding that corruption neg-atively affects growth independently of its potential impact on investment.The first one concerns public investment that can be used for bribees’ privateuse, or be concentrated in sectors that allow the greatest extraction of bribes
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Fig
ure
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83
Tabl
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Reg
ress
ions
resu
lts:
Gro
wth
rate
ofpe
rca
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me
Spe
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cati
onB
asic
spec
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Bas
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asic
spec
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icsp
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cati
on
wit
hin
tera
ctio
n:w
ith
inte
ract
ion:
wit
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tera
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n:
Rul
eof
law
Gov
ernm
ent
Lac
kof
viol
ence
effe
ctiv
enes
s
Exp
lana
tory
Cor
rupt
ion
inde
xC
orru
ptio
nin
dex
Cor
rupt
ion
inde
xC
orru
ptio
nin
dex
vari
able
sC
PI
WB
CP
IW
BC
PI
WB
CP
IW
B
Con
stan
t–7
.78
–3.5
4–4
.48
–3.2
5–2
.81
–2.8
1–6
.85
–2.5
1
(1.8
2)∗
(0.9
9)(1
.30)
(1.2
0)(0
.87)
(1.1
0)(1
.41)
(0.7
1)
GD
Ppe
rca
pita
–0.6
4–0
.95
–0.8
1–0
.93
–0.9
4–1
.07
–0.6
8–0
.99
(197
0)(1
.54)
(2.8
5)∗∗
∗(2
.38)
∗∗(3
.03)
∗∗∗
(3.5
4)∗∗
∗(4
.24)
∗∗∗
(1.6
3)(2
.98)
∗∗∗
Pri
mar
ysc
hool
ing
0.07
0.37
0.25
0.48
–2.3
90.
400.
080
.45
(197
0)(0
.08)
(0.7
8)(0
.26)
(1.0
5)(0
.29)
(0.7
4)(0
.09)
(0.9
4)
Gro
wth
rate
of–0
.45
–0.3
0–0
.23
–0.1
6–0
.28
–0.1
8–0
.40
–0.2
1
popu
lati
on(1
.68)
∗(1
.12)
(1.0
2)(0
.66)
(1.3
9)(0
.91)
(1.3
6)(0
.75)
Inve
stm
entt
o4.
424.
373.
663.
704.
103.
844.
233
.89
GD
Pra
tio
(5.7
1)∗∗
∗(6
.00)
∗∗∗
(5.2
7)∗∗
∗(6
.37)
∗∗∗
(6.5
7)∗∗
∗(6
.77)
∗∗∗
(4.7
1)∗∗
∗(5
.09)
∗∗∗
Ope
nnes
s0.
480.
070.
390.
110.
370.
200.
460
.11
(1.5
4)(0
.21)
(1.4
4)(0
.38)
(1.4
3)(0
.75)
(1.5
1)(0
.34)
CP
I–0
.63
–2.4
6–3
.29
–1.0
7
(1.4
7)(3
.14)
∗∗∗
(4.0
7)∗∗
∗(1
.03)
WB
–2.3
3–4
.25
–5.4
0–3
.61
(2.8
9)∗∗
∗(3
.71)
∗∗∗
(4.4
8)∗∗
∗(2
.30)
∗∗
84
Tabl
e1.
Con
tinu
ed
Spe
cifi
cati
onB
asic
spec
ifica
tion
Bas
icsp
ecifi
cati
onB
asic
spec
ifica
tion
Bas
icsp
ecifi
cati
on
wit
hin
tera
ctio
n:w
ith
inte
ract
ion:
wit
hin
tera
ctio
n:
Rul
eof
law
Gov
ernm
ent
Lac
kof
viol
ence
effe
ctiv
enes
s
Exp
lana
tory
Cor
rupt
ion
inde
xC
orru
ptio
nin
dex
Cor
rupt
ion
inde
xC
orru
ptio
nin
dex
vari
able
sC
PI
WB
CP
IW
BC
PI
WB
CP
IW
B
CP
Ixru
leof
law
1.52
(2.7
5)∗∗
∗W
Bx
rule
ofla
w2.
24
(2.4
8)∗∗
∗C
PIx
gove
rnm
ent
2.17
effe
ctiv
enes
s(3
.65)
∗∗∗
WB
xgo
vern
men
t3.
32
effe
ctiv
enes
s(3
35)∗∗
∗ .C
PIx
lack
of0.
34
viol
ence
(0.4
9)
WB
xla
ckof
1.2
4
viol
ence
(1.0
2)
N63
6763
6763
6763
67
R2
adj
0.49
0.51
0.57
0.57
0.60
0.61
0.49
0.5
2
All
inde
pend
ent
vari
able
sar
eex
pres
sed
inlo
gari
thm
.A
bsol
ute
t-st
atis
tics
are
disp
laye
din
pare
nthe
ses
unde
rth
eco
effi
cien
tes
tim
ates
.∗:
test
-sta
tist
icis
sign
ifica
ntat
the
10%
leve
l,∗∗
:si
gnifi
cant
atth
e5%
leve
l,∗∗
∗ :si
gnifi
cant
atth
e1%
leve
l.
85
(see e.g. Mauro, 1998, or Tanzi and Davoodi, 1997). The second one concernsincentives. Corruption may also distort the allocation of entrepreneurial talentand give an incentive to allocate agents’ energy to rent seeking instead ofother productive activities, like innovative activities in particular, as Bardhan(1997) suggests. It may also raise the share of the informal sector, as observedby Johnson et al. (1998).
When interaction terms are taken into account the results for control vari-ables remain broadly unchanged. In contrast, the goodness of fit improves.The adjusted R2, which takes account of the number of regressors, increasesmarkedly. With the rule of law and the government effectiveness indicatorsthe regressions explain up to 60% of variation in the growth rate, whichrepresents an increase of ten percentage points with respect to the basicspecification. The impact of corruption becomes significant with both gov-ernance indicators and the magnitude of the coefficients is systematicallylarger than with the basic specification. This is interpreted against the poolingof countries regardless of the quality of their institutions
With the rule of law and the government effectiveness indicators all in-teraction terms have a positive sign and are significant. This means thatbad governance increases the cost of corruption or that, inversely, goodgovernance alleviates the cost of corruption. Therefore, corruption does notappear as a way to circumvent bad governance (e.g. ineffective administrationor cumbersome bureaucracy) but as a way to make it more painful. In otherwords, the results of our regressions tend to reject the “grease the wheels”hypothesis. Consequently, curtailing corruption is most beneficial to countriessuffering from a weak rule of law or low government effectiveness. One canconclude that neglecting the role of other dimensions of governance leads tounderestimate the consequences of corruption on growth.
Regressions that include an interaction term between corruption and lackof violence do not perform better than the basic specification. The interactionterm never enters the relationship significantly and the level of corruptionmeasured by the CPI index remains insignificant. Moreover the explanatorypower of both regressions does not increase.
To summarize, the regressions that include the interaction of corruptionwith the rule of law and government effectiveness exhibit consistent results.Both improve the goodness of fit with respect to the basic specification. Bothindicators of corruption are significant with the expected negative sign. Thisis noteworthy, as the CPI index could not pass the 10% significance test inthe basic specification. The lack of significance of this variable in the basicspecification can thus be attributed to the omission of the interaction term thatblurred the relationship between corruption and growth.
86
The coefficient on the interaction term is always positive and significantfor both measures of governance. It therefore appears that a weak rule of lawor a low government effectiveness tend to make corruption more detrimentalto growth. It follows that the results of this section reject the “grease thewheels” hypothesis. Thus, when one looks at the impact of corruption ongrowth, one finds that it does not act as a substitute for government effective-ness or the rule of law. Instead, bad governance tends to increase the adverseeffect of corruption on growth.
Additional tests were conducted to check that our main result (i.e. thesignificance and sign of the corruption/governance interaction term) is robustto change in specification. For instance, the introduction of other interac-tion terms of the explanatory variables may equally improve the quality ofestimation and even remove the significance of the corruption/governance in-teraction term. The choice of an additional interaction term draws on the newgrowth literature, a central issue of which is the catch-up process by whichlagging countries converge to the performance of leading ones. Following aninfluential strand of the literature, such a process depends on the ability of lag-ging countries to successfully imitate new technologies. This in turn dependson human capital (see Benhabib and Spiegel, 1994, 2002, for discussions). Inour framework, the implication is that an interaction term between initial in-come and human capital variables is a potential relevant explanatory variable.Appendix A presents the estimation results of two variants of Equation 1. Onereplaces the interaction term between corruption and governance (rule of law)by the interaction term between initial income and human capital. The otherincorporates both interaction terms. The results show that the coefficient ofthe new interaction term is never significant and that, while the introduction ofthe interaction term between corruption and governance improves the qualityof fit, the introduction of the new one does not. Moreover, the introductionof the new interaction term does not impact the significance and sign of thecorruption/governance interaction term. To sum up, the latter result togetherwith those in Figure 1 and Table 1 lend strong support to the validity of the“sand the wheels” hypothesis.
4.2. The investment ratio
Before turning to the econometric analysis of the investment ratio, we con-ducted a similar preliminary investigation as in Section 4.1. The results arepresented in Figure 2 and also favor the validity of the “sand the wheels”claim. The curve is clearly increasing implying that the coefficient of corrup-tion becomes less and less negative as one moves from low to high quality ofgovernance. The results in Table 2 allow to rigorously test those hypotheses.
87
Fig
ure
2.E
stim
ated
impa
ctof
corr
upti
onon
grow
thac
cord
ing
toth
eru
leof
law
.
88
Tabl
e2.
Reg
ress
ions
resu
lts:
Log
arit
hmof
the
inve
stm
entr
atio
Spe
cifi
cati
onB
asic
spec
ifica
tion
Bas
icsp
ecifi
cati
onB
asic
spec
ifica
tion
Bas
icsp
ecifi
cati
on
wit
hin
tera
ctio
n:w
ith
inte
ract
ion:
wit
hin
tera
ctio
n:
Rul
eof
law
Gov
ernm
ent
Lac
kof
viol
ence
effe
ctiv
enes
s
Exp
lana
tory
Cor
rupt
ion
inde
xC
orru
ptio
nin
dex
Cor
rupt
ion
inde
xC
orru
ptio
nin
dex
vari
able
sC
PI
WB
CP
IW
BC
PI
WB
CP
IW
B
Con
stan
t1.
842.
802.
102.
582.
052.
752.
312
.74
(3.1
2)∗∗
∗(4
.37)
∗∗∗
(3.0
9)∗∗
∗(3
.98)
∗∗∗
(2.7
3)∗∗
∗(4
.21)
∗∗∗
(4.1
)∗∗∗
(4.5
3)∗∗
∗G
DP
per
capi
ta–0
.14
–0.1
6–0
.16
–0.1
4–0
.15
–0.1
6–0
.16
–0.1
5
(197
0)(2
.51)
∗∗(2
.24)
∗∗(2
.62)
∗∗∗
(2.0
3)∗∗
(2.2
7)∗∗
(2.1
1)∗∗
(2.9
8)∗∗
∗(2
.39)
∗∗P
rim
ary
scho
olin
g0.
510.
370.
470.
340.
490.
350.
450
.34
(197
0)(4
.63)
∗∗∗
(3.1
3)∗∗
∗(4
.14)
∗∗∗
(2.9
4)∗∗
∗(4
.20)
∗∗∗
(3.0
6)∗∗
∗(4
.7)∗∗
∗(3
.03)
∗∗∗
Ope
nnes
s0.
080.
050.
070.
060.
070.
062
0.05
0.0
6
(1.4
8)(1
.06)
(1.3
0)(1
.29)
(1.3
8)(1
.29)
(1.0
8)(1
.28)
CP
I–0
.12
–0.3
1–0
.23
–0.3
8
(1.8
9)∗
(2.6
1)∗∗
∗(1
.37)
(3.4
1)∗∗
∗W
B–0
.3–0
.50
–0.4
8–0
.57
(2.1
8)∗∗
(3.1
4)∗∗
∗(2
.19)
∗∗(4
.09)
∗∗∗
CP
1xru
leof
law
0.17
(2.5
1)∗∗
∗
89
Tabl
e2.
Con
tinu
ed
Spe
cifi
cati
onB
asic
spec
ifica
tion
Bas
icsp
ecifi
cati
onB
asic
spec
ifica
tion
Bas
icsp
ecifi
cati
on
wit
hin
tera
ctio
n:w
ith
inte
ract
ion:
wit
hin
tera
ctio
n:
Rul
eof
law
Gov
ernm
ent
Lac
kof
viol
ence
effe
ctiv
enes
s
Exp
lana
tory
Cor
rupt
ion
inde
xC
orru
ptio
nin
dex
Cor
rupt
ion
inde
xC
orru
ptio
nin
dex
vari
able
sC
PI
WB
CP
IW
BC
PI
WB
CP
IW
B
WB
xru
leof
law
0.29
(3.1
1)∗∗
∗C
PIx
gove
rnm
ent
0.09
effe
ctiv
enes
s(0
.9)
WB
xgo
vern
men
t0.
22
effe
ctiv
enes
s(1
.68)
∗∗C
PIx
lack
of0.
22
viol
ence
(3.0
9)∗∗
∗W
Bx
lack
of0
.33
viol
ence
(3.7
5)∗∗
∗
N67
7167
7167
7167
71
R2
adj
0.33
0.30
0.39
0.36
0.33
0.32
0.41
0.3
9
All
inde
pend
ent
vari
able
sar
eex
pres
sed
inlo
gari
thm
.Abs
olut
et-
stat
isti
csar
edi
spla
yed
inpa
rent
hese
sun
der
the
coef
fici
ente
stim
ates
.∗:t
est-
stat
isti
cis
sign
ifica
ntat
the
10%
leve
l,∗∗
:sig
nifi
cant
atth
e5%
leve
l,∗∗
∗ :si
gnifi
cant
atth
e1%
leve
l.
90
The specifications are similar to those in Table 1. The basic specificationexplains about one third of the variance in the investment ratio. Apart fromopenness, all control variables exhibit the expected coefficient and are signi-ficant. The initial GDP per capita in the base year exhibits a negative sign,which is in line with the standard convergence hypothesis. School enrolmententers all the regressions with a positive and significant sign, thereby re-emphasizing the role of human capital in fostering investment. Similarly, bothcorruption indices exhibit the expected negative sign and have a significantimpact on investment. This observation confirms previous studies like Mauro(1995), Brunetti and Weder (1998), Campos et al. (1999) or Mo (2001).
The inclusion of interaction terms does not affect the impact of the con-trol variables. All their coefficients remain consistent with the signs and thesignificance found with the basic specification. Moreover, the model fits thedata better with approximately 40% of the variance explained, which raisesthe percentage of the variance explained by up to ten percentage points.
As in the previous section, we examine the change in the coefficients ofthe corruption indices and the signs of the interaction terms. The differencebetween the basic specification and the others is quite similar to the onein Table 1. It appears that the magnitude of the coefficients of corruptionis systematically larger than with the basic specification. As before, this isinterpreted against the pooling of countries regardless of the quality of theirinstitutions.
The coefficients of corruption in the last three specifications reflect theimpact of corruption on investment in countries with the worst governance,whereas the coefficients in the basic specification measure the average impactof corruption on investment in the whole sample. Consequently, the highercoefficients of corruption in the former cases imply that corruption hindersinvestment more in countries whose governance is unsatisfactory than in therest of the sample. This statement is confirmed by the fact that the coeffi-cients on the interaction terms are all positive and almost always significant.This result means that corruption tends to further reduce investment as gov-ernance deteriorates. Therefore, like for growth, the result for investmentrejects the “grease the wheels” hypothesis. Instead of alleviating the costof bad governance, corruption impedes investment even more in countrieswhose governance is defective. Note that a robustness check, similar to theone with the growth equation, was conducted and also gives strong supportto our conclusion (see Appendix A).
It must be stressed that, unlike in Table 1, the results hold for both indicesof corruption and for the three indicators of governance.10 The only exceptionis the interaction between the CPI index and government efficiency. Accord-ingly, the results of Table 2 consistently suggest that corruption is a stronger
91
impediment to growth in countries that suffer from a low rule of law, badgovernment effectiveness and political violence. Our data therefore seem tofit the “sand the wheels” rather than the “grease the wheels” hypothesis.
5. Conclusion
In this paper, we tried to disentangle the interplay between corruption, invest-ment and growth, and the other dimensions of governance to test the “greasethe wheels” hypothesis against the “sand the wheels” one. We do so by addinginteraction variables to the set of variables that are usually used to explaininvestment and growth in cross-section analyses. Our results strongly rejectthe “grease the wheels” hypothesis in favor of the “sand the wheels” one.
We find that a weak rule of law, an inefficient government and politicalviolence tend to worsen the negative impact of corruption on investment.Moreover, we observe that corruption slows growth down even more in coun-tries suffering from a weak rule of law and an inefficient government, evenwhen one controls for investment. We therefore conclude that corruptionnot only impacts growth through reduced accumulation of capital but alsothrough other channels that have yet to be determined. The results imply thatreducing corruption would be more profitable to countries where other as-pects of governance are poor, which stands in sharp contrast with the opinionof those who view corruption as a lubricant.
Formal and in depth analysis of those channels by which corruption im-pacts growth paves the way for future research. Moreover, the assessmentof the level of corruption and the measurement of the quality of institutionsare still at the beginning and should be improved. Our analysis will con-sequently have to be carried out again in the future to take advantage of theimprovements in those measures and of the availability of longer time series.
Notes
1. See also Ades and di Tella (1997).2. The expression “moralistic approach” was used by Nye (1967) or Leys (1965).3. Brunetti (1997) provides a useful survey of that literature.4. They however share a common drawback that is that the definition of what they refer to
as corruption must remain fairly evasive, because each basic index uses a slightly differ-ent, though converging, definition. For instance, they do not allow making a distinctionbetween “petty” and “grand” corruption.
5. We also used Wei (2000)’s index but do not report the results. This index did not performas well as the others in the regressions.
6. The interested reader may find an exhaustive description of the composition of eachindicator in Lambsdorff (1999) and Kaufman et al. (1999b).
92
7. Paldam (2002) points out that annual movements in the CPI index are less than 0.1 point.8. Transparency International’s caution goes as far as refusing to present scores from various
years in a single table to prevent misleading comparisons.9. We also tested another specification where the levels of institutional variables instead of
their logarithms were taken into account. This did not affect our results.10. Hence the impact of political violence on growth works through its effect on investment.
References
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94
App
endi
xA
.R
obus
tnes
sch
eck
Spec
ifica
tion
Gro
wth
regr
essi
onIn
vest
men
treg
ress
ion
Spe
cifi
cati
onw
ith
Spe
cifi
cati
onw
ith
Spe
cifi
cati
onw
ith
Spe
cifi
cati
onw
ith
one
inte
ract
ion
two
inte
ract
ion
one
inte
ract
ion
two
inte
ract
ion
term
term
ste
rmte
rms
Exp
lana
tory
Cor
rupt
ion
inde
xC
orru
ptio
nin
dex
Cor
rupt
ion
inde
xC
orru
ptio
nin
dex
vari
able
sC
PI
WB
CP
IW
BC
PI
WB
CP
IW
B
Con
stan
t–7
.05
–4.3
5–8
.14
–6.4
61.
433.
741.
023
.21
(0.6
1)(1
.00)
(0.7
4)(1
.78)
(0.7
0)(3
.60)
∗∗(0
.50)
(2.7
1)∗∗
GD
Ppe
rca
pita
(197
0)–0
.67
–0.9
0–0
.65
–0.7
3–0
.12
–0.2
2–0
.11
–0.1
8
(1.0
1)(2
.20)
∗∗(1
.09)
(1.8
3)∗
(1.1
8)(2
.72)
∗∗(1
.05)
(2.1
8)∗∗
Pri
mar
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ing
(197
0)–0
.09
0.52
0.99
1.05
0.59
0.16
0.70
0.2
1
(0.0
4)(0
.72)
(0.3
9)(1
.83)
∗(1
.41)
(0.7
0)(1
.65)
∗(0
.83)
Gro
wth
rate
ofpo
pula
tion
–0.4
4–0
.31
–0.2
6–0
.19
(1.7
0)∗
(1.1
4)(1
.07)
(0.7
8)
Inve
stm
entt
oG
DP
rati
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424.
413.
643.
80
(5.6
2)∗∗
(6.0
7)∗∗
(5.0
2)∗∗
(6.4
7)∗∗
Ope
nnes
s0.
480.
070.
390.
110.
080.
060.
070
.07
(1.5
4)(0
.20)
(1.4
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(1.4
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.16)
(1.2
9)(1
.32)
95
App
endi
xA
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nued
Spec
ifica
tion
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ith
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ith
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ith
one
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ract
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two
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term
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Exp
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Cor
rupt
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rupt
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ptio
nin
dex
vari
able
sC
PI
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CP
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BC
PI
WB
CP
IW
B
CP
I–0
.64
–2.4
8–0
.12
–0.3
2
(1.4
7)(3
.13)
∗∗(1
.83)
∗(2
.66)
∗∗W
B–2
.33
–4.3
5–0
.27
–0.4
6
(2.8
7)∗∗
(3.8
5)∗∗
(1.9
5)∗
(2.7
0)∗∗
(GD
Ppe
rca
pita
x0.
03–0
.04
–0.1
6–0
.18
–0.0
20.
06–0
.05
0.0
4
scho
olin
g)(1
970)
(0.0
7)(0
.22)
(0.3
6)(0
.94)
(0.2
4)(1
.37)
(0.6
4)(0
.76)
CP
Ix
Rul
eof
law
1.55
0.19
(2.7
6)∗∗
(2.5
6)∗∗
WB
xR
ule
ofla
w2.
360
.26
(2.6
0)∗∗
(2.3
8)∗∗
N63
6763
6767
7167
71
R2
adj
0.48
0.50
0.55
0.56
0.32
0.32
0.38
0.3
6
96
Appendix B. List of countries in the sample and their corruption and governance indicators
Corruption Governance
Country CPI WB Lack of Government Rule of law
violence effectiveness
Angola –0.86 –1.78 –1.39 –1.23
Argentina 3.–0 27.0 51.0 26.0 32.
Australia 8.7 1.6 1.18 1.46 1.6
Austria 7.6 1.46 1.38 1.22 1.81
Belgium 5.3 0.67 0.82 0.88 0.8
Burkina Faso –0.37 –0.52 –0.06 –0.35
Bangladesh –0.29 –0.4 –0.56 –0.93
Bolivia 2.5 –0.44 –0.14 –0.22 –0.35
Botswana 6.1 0.54 0.74 0.22 0.5
Canada 9.2 2.06 1.03 1.72 1.55
Chile 6.9 1.03 0.45 1.17 1.09
China 3.4 –0.29 0.48 0.02 –0.04
Cote d’lvoire 2.6 –0.08 –0.14 –0.18 –0.33
Cameroon 1.5 –1.1 –0.72 –0.64 –1.02
Colombia 2.9 –0.49 –1.29 –0.06 –0.78
Costa Rica 5.1 0.58 0.91 0.55 0.55
Denmark 10.2 13.1 29.1 72.1 69.
Ecuador 2.4 –0.82 –0.47 –0.56 –0.72
Egypt 3.3 –0.27 –0.07 –0.14 0.13
Spain 6.6 1.21 0.58 1.6 1.03
Ethiopia –0.44 0.14 –0.15 0.27
Finland 9.8 2.08 1.51 1.63 1.74
France 6.6 1.28 0.65 1.28 1.08
United Kingdom 8.6 1.71 0.92 1.97 1.69
Ghana 3.3 –0.3 –0.1 –0.29 –0.01
Greece 4.9 0.82 0.21 0.56 0.5
Guatemala 3.2 –0.82 –0.75 –0.23 –1.11
Hong Kong 7.7 1.31 0.92 1.25 1.33
Honduras 1.8 –0.94 –0.33 –0.41 –0.9
Indonesia 1.7 –0.8 –1.29 –0.53 –0.92
India 2.9 –0.31 –0.04 –0.26 0.16
Ireland 7.7 1.57 1.43 1.36 1.39
Iceland 9.2 1.83 1.25 1.5 1.47
Israel 6.8 1.28 –0.46 0.69 0.97
Italy 4.7 0.8 1.16 0.77 0.86
Jamaica 3.8 –0.12 –0.34 –0.48 –0.73
Japan 6.0 72.1 15.0 84.1 42.
97
Appendix B. Continued
Corruption Governance
Country CPI WB Lack of Government Rule of law
violence effectiveness
Kenya 2.–0 65.–1 1.–0 9.–1 22.
Korea/Rep 3.8 0.16 0.16 0.41 0.94
Luxembourg 8.8 1.67 1.4 1.67 1.62
Morocco 4.1 0.13 0.09 0.27 0.68
Mexico 3.4 –0.28 –0.35 0.18 –0.47
Mozambique 3.5 –0.53 –0.53 –0.33 –1.05
Mauritius 4.9 0.34 1.14 0.17 1.28
Malaysia 5.1 0.63 0.55 0.71 0.83
Nigeria 1.6 –0.95 –1.05 –1.32 –1.1
Nicaragua 3.1 –0.84 –0.32 –0.55 –0.73
Netherlands 9.2 03.1 48.2 03.1 58.
Norway 8.9 1.69 1.41 1.67 1.83
New Zealand 9.4 2.07 1.42 1.57 1.82
Pakistan 2.2 –0.77 –0.65 –0.74 –0.76
Peru 4.5 –0.2 –0.53 0.17 –0.52
Philippines 3.6 –0.23 0.27 0.13 –0.08
Portugal 6.7 1.22 1.39 1.15 1.08
Paraguay 2.–0 96.–0 57.–1 1.–0 7.
Senegal 3.4 –0.24 –0.87 0.05 –0.1
Singapore 9.1 1.95 1.39 2.08 1.94
E] Salvador 3.9 –0.35 –0.02 –0.26 –0.66
Sweden 9.4 2.09 1.41 1.57 1.62
Thailand 3.2 –0.16 0.25 0.01 0.41
Tunisia 5.0 02.0 66.0 63.0 65.
Turkey 3.6 –0.35 –0.94 –0.41 –0.01
Taiwan/China 5.6 0.63 0.94 1.29 0.93
Tanzania 1.9 –0.92 0.57 –0.49 0.16
Uganda 2.2 –0.47 –0.98 –0.25 –0.01
Uruguay 4.4 0.43 0.35 0.62 0.27
Venezuela/RB 2.6 –0.72 –0.25 –0.85 –0.66
South Africa 5.0 3.–0 53.–0 01.–0 35.
Zambia 3.5 –0.61 0.–0 4.–0 4.
Zimbabwe 4.1 –0.32 –0.54 –1.13 –0.15
Note. Countries in bold enter the investment equation only.
