Transparency and Health as Complementary Public Goods

 

James Stodder and Peter W. Schroth

Rensselaer Polytechnic Institute*

 

Presented at the XIII World Congress of the International Economics Association;

Lisbon, Portugal, September 2002. 

 

ABSTRACT: The social basis of corruption helps resolve a paradox:  that a rationally selfish state has the incentive to limit corruption, but may not have the means. "Transparency" (lack of corruption plus openness to public scrutiny) cannot be provided by government alone. Transparency depends upon other basic public goods that, like transparency itself, require voluntary inputs by private households.  Health is a "socially produced" public good, at least in part.  While transparency has already been shown to promote public health outcomes, we present evidence that the reverse causality also holds, so that health and transparency are mutually reinforcing.  It is argued that anti-corruption incentives in poor countries must be linked with (a) investment in basic public infrastructure and (b) retooling business and government institutions toward traditional forms of social-reciprocal enforcement of honesty, such as rotating savings and credit associations (ROSCAs).

Journal of Economic Literature codes: K420, F230, O170

 

1.       Introduction

 

We do not want to know if life improves when Togo becomes Denmark; we want to know if life improves when a poor Togo becomes a richer Togo.

William Easterly (1999)

 

There is widespread agreement that corruption is bad for growth; why, then, are many countries that desire growth nevertheless so corrupt?  Identifying the self-interests of kleptocratic elites (Charap & Harm 1999, Olson 2000) provides only a partial answer, because there are many more parties to corruption than the governmental and business elites.  For large scale corruption to be effective in its own terms, there must also be widespread cooperation by middle class professionals, small business owners, and probably even most urban families. 

Corruption is not merely the strong taking from the weak; it is a public bad, a culture of corruption maintained by daily actions, and by millions of willing participants.  This much is well known to most people, but what is not so obvious is the logical connection between the publicness of the corruption bad and the difficulty in devising effective incentives against it, especially in developing countries.

One aim of this paper is to explain why anti-corruption incentives are so difficult to implement.  We begin with the paradox that rationally self-interested state actors will wish to end widespread corruption, which is demonstrably a terrible barrier to growth.  Corruption, therefore, should be strictly limited even by a self-interested state, one that is, in the memorable formulation of Mancur Olson (2000), a "stationary bandit," maximizing the economic surplus it can extract.  It is elementary to show that decentralized and uncontrolled graft makes a state "weakly corrupt," i.e., too corrupt for its own selfish good:  charging multiple corruption "taxes" that are too high to maximize its total tax revenue.[1]  The obvious fact that such states are too weak to reform themselves – too corrupt to rationalize their own corruption – is a hint that the sources of corruption may be beyond direct state control.

Transparency (the relative absence of corruption) is a public good analogous to clean air or water (the relative absence of pollution).  As such, its provision can be expected to be closely tied to the success of a society in providing other basic public goods, such as education, health, and economic security.  The new political economy literature models the causality between corruption and these other goods as simple and unidirectional: corruption slows down growth, while both corruption and slower growth damage these pubic goods.  We demonstrate that some of these public goods and bads are bound together in a simultaneously determining relationship, part of a socio-cultural whole.  The way in which they are connected has important implications for development and for anti-corruption policy.

To be clear, we agree with the dominant anti-corruption literature (Gupta et al. 1998, 2000):  transparency directly promotes health, education, and economic security, even after accounting for its indirect benefit through faster growth.  But that is only half the story.  We further show that the reverse is also true, that improvements in at least one of these public goods, health, also encourage transparency.  Thus, causality is not unidirectional; it is simultaneous.  The econometric methodology for doing this is a straightforward instrumental variable approach, the same as that used by Gupta and many others in the literature.


We argue that human capital investment into traditional community networks is an effective way of preventing "corruption" within the networks themselves; i.e., to prevent cheating on fellow community members.  A key aspect of the problem of corruption in modern bureaucracies, in business and government, is the impersonalization and separation of these structures from traditional community networks.

Corruption can be prevented only by large scale investment in the public goods of enforcement and monitoring, by traditional social network monitoring, or, in most cases, by institutions that have evolved out of compromises between the two.  The Securities and Exchange Commission (SEC) in the United States, for example, grew out of the self-policing organizations of stockbrokers themselves.

It is possible to find traditional social and modern public forms of providing these public goods.  The Grameen Bank "micro-financing" institution (see www.grameen-info.org) is perhaps the best-known traditional analogy to the modern SEC example, but a host of other examples have emerged in developing countries, and now constitute a veritable micro-finance revolution (Beasley et al. 1993, De Soto 2000).  The World Bank now supports a network of micro-financing programs around the world.[2]  These programs seek to legalize and securitize the vast informal sector in all the cities of the developing world, where De Soto estimates as much as half their population earns its living.  To the extent that modernization destroys traditional social networks, however, the process of monitoring corruption is made that much more difficult.  We show that corruption can be made worse if other basic public goods are in short supply.  The old sources of such goods, networks of traditional reciprocity, have been undermined by modernization.  At the same time, their modern replacements, the state and market, have not had much success providing such public goods.

 

2.       The Paradox of Persistent, Immiserizing Corruption

That corruption is negatively correlated with growth is easy to show.  The "new economics of growth" literature shows a strong positive correlation between economic growth and most indicators of human happiness:  health, education, democracy, and transparency (Barro 1997).  Another intellectual current, "the new political economy of corruption" literature, has shown that corruption is negatively correlated with growth, and hence also inimical to the progress of human happiness. (See Mauro 1995; Lopez & Mitra 2000; Gupta et al. 2000.)

There is now a substantial body of published research into the effects of corruption.  For instance, Maoro (1997, p. 91) found a strong negative correlation between perceived corruption, on one hand, and GDP and investment, on the other:

This means that if a given country were to improve its corruption “grade” from 6 out of 10 to 8 out of 10, its investment-GDP ratio would rise by almost 4 percentage points and its annual growth of GDP per capita would rise by almost half a percentage point.

 

Generally similar conclusions were reached by Burki & Perry (1998), Hines (1995), Wei (2000), and Rose-Ackerman (1978, 1997, 1999).[3]

These findings are particularly important when linked with recent estimates by Reisen & Soto (2001) on the relation between foreign investment and growth.  Using a panel data set of 44 developing countries over the period 1986 to 1997, these authors find that both foreign direct investment (FDI) and foreign portfolio investment correlate positively and significantly with long term GDP growth.  As we shall see, panel data sets allow us to estimate directly the effects of change within countries over time, not just differences between countries, at more or less the same time.

Despite the conclusion these of studies – that a government curtailing corruption should reap large payoffs in growth and welfare – effective anti-corruption incentives are weak to non-existent in much of the developing world.  After persistent prodding by the Clinton administration, the Organization of American States (OAS),[4] the Organization for Economic Cooperation and Development (OECD),[5] and the European Union[6] have moved toward institutionalizing anti-corruption standards similar to those in the United States Foreign Corrupt Practices Act (FCPA) of 1977.[7] 

With the notable exceptions of the OAS Convention and the Council of Europe’s Criminal Law Convention, there has been little anti-corruption action by developing countries, but very recent events include hopeful signs.  For example, no African country has or ever had a law criminalizing foreign bribery, as far as we have been able to determine (Schroth & Sharma 2002), but the Prevention of Corruption Bill, introduced by the Government in South Africa in April 2002, not only will reach that but, if adopted in its current form, will be, in many respects, the harshest and most sweeping anti-bribery law of any country in the world.[8] No African country is a party to any anti-bribery convention, but South Africa is discussing possible accession to the OECD Convention.  Also, one of the last acts of the Organization of African Unity, before it was replaced by the African Union, was a proposed OAU Convention on Combating Corruption; although it does not appear to be a priority at present (and has not yet appeared on the new African Union web site), with a bit of help from the OECD, it may in time be signed and ratified.

The OECD Convention is designed for investor countries – the payers of bribes – not for the host countries in which they are paid, so most of Africa doesn’t qualify.  Why then do most countries find it so hard to take measures that are in the economic interests of the great majority?  And why is this especially true for the poorest of the poor?

 

3.      Transparency as a Public Good

a.      A Socially Produced Public Good

That private spillovers, externalities, can have public consequences for good or ill is one of the staples of economic theory (Heal & DasGupta 1980).  Insofar as property or liability rights have not been established to adequately cover and encourage market-based voluntary transfers among these spillover goods and bads, the efficiency implication is very clear.  Receiving no market sanction, negative spillovers will be overproduced; receiving no market reward, positive spillovers will be underproduced – in each case, relative to an efficient level of provision.  Although policy prescriptions differ widely, this basic argument is entirely uncontroversial among economists, even staunch libertarians (Friedman 1982).

Transparency is self-evidently a public good.  It is a good because most of the public benefits from transparent institutions in the form of lower transaction costs and reduced risk.  It is a classically public good because my use of this benefit does not “use up” the amount of it available to anyone else, and thus will not affect its price. In fact, my use of transparent procedures may, in some cases, even increase the amount of transparency available to everyone else.  In particular, this public good of transparency is created primarily as the positive spillover of millions of “private” acts.  That is, transparency is usually enforced by accounting standards and by laws.  But it can be maintained only by the largely voluntary or habitual compliance of individuals, including accountants, lawyers, and official legal guardians. 

It is virtually always the case that the transparent act confers benefits extending beyond the individuals who practice it.  It not only sets standards and expectations among others who will benefit from the good provided, but in some cases may directly and significantly reduce corruption costs to many other people.  The conferring of external benefits becomes problematic when the private cost to the individual who produces the transparent practice outweighs the private benefit accruing to him or her.  This may be the case because, when transparency is not well established, insistence on such procedures may entail substantial economic and personal risks. 

Transparency is far from unique in being a public good that is primarily produced by voluntary provision within long-term social networks, instead of by direct governmental production, in the way that a government builds, say, a hydro-electric dam. [9]  Most public goods can be and are produced as by voluntary acts within social networks and communities, both for self-interested reasons of long-term reciprocity and for emotional commitments to values and to other human beings. (See Frank & Scitovsky 1992; Axelrod & Cohen 2000.)

The implications for anti-corruption policy are straightforward in theory, if difficult in their policy implications.  If transparency is a public good produced by both governmental provision and voluntary social networks, it should be correlated with the society's success in providing other public goods meeting basic human needs, especially those also produced partially by diffuse social networks.  This paper examines the correlation between corruption and another such (partially) public good: health care.  Health care is indeed positively correlated with transparency, at least in this cross-section of countries.   (We also tested the relationship between measures of Education and Transparency, and found an empirical correlation quite similar to that between Health and Transparency.  Our results were short of statistical significance, however, and will not be presented here.)

If health care is both a product of, and a contributor to, the “social capital” accumulated within voluntary social networks, then its provision should not only be correlated with the provision of other public goods, such as transparency, but also should help determine the levels of their provision.  This is the argument about causality that we address in the econometric methods of within-country panel estimates, discussed in the next section.

First, let us look at a simple model of cost-complementarity in the provision of health care (H) and transparency (T), and two individuals (1, and 2).  The demand for H, a straightforward public good, is found by the summing of the demands by both 1 and 2:

Ph1 = a - bH,  Ph2 = c - dH     =>        Ph = Ph1 + Ph2 = (a+c) - (b+d)H.

 

The demand for T is formed similarly:

                        Pt1 = e - fT,  Pt2 = g - hT        =>        Pt = Pt1 + Pt2 = (e+g) - (f+h)T.

 

 

H and T are cost complements in the sense that the more of one is produced, the cheaper it is to produce the other.  Simple cost functions would be as follows:

 

C(H)  = j + kH + mH2 - nHT  => MCh = (k -nT) + 2mH,  and

C(T)  = q + rH + sH2 - tHT      => MCt = (r -tH) + 2sT.

Below is a simple graph of the supply and demand for transparency:

Note that for parameters such as shown above, the relation between the provision of health care (H) and transparency (T) is:           

a)      If there is free riding, but with full provision of H, then T will be undervalued by society and move from E1 to E3, even though its cost of production is correct.

 

b)      If H falls, but with no free riding (so we have both layers of demand), this pushes T provision from E1 to E2 – i.e., from an intercept as low as r-tH, to one as high as r.  Transparency is now being correctly valued by demand, but has become too costly to provide adequately.

 

c)      If there is both free riding and non-provision of the complementary good H, then transparency may disappear altogether, moving from E1 to intercept e.  Transparency is both not adequately valued by societal demand, and has become too costly to supply.  

 

The policy recommendations of this argument are fairly straightforward.  For a set of public goods which are cost complements, “free riding” is not just one good at a time.  Under-provision of any one of the interlinked public goods can have drastic consequences on many others – even if there is no free riding on these other public goods by themselves  – as in case b) above.  An anti-corruption strategy, therefore, cannot concentrate on corruption alone, and certainly not just on punishing it.  It must rely on building up the traditional social networks that can and do maintain, at least to some degree, both transparency and other public goods, such as health.  And it must recognize that, to the extent modernization and economic growth dismantle and dissolve these traditional values and networks, modernization will promote rather than diminish corruption.

This debilitating backwash of modernization is now taken seriously by development economists and anthropologists studying the social capital invested in traditional organizations such as Rotating Savings and Credit Associations (“ROSCAs”).  ROSCAs are found throughout the developing world (Beasley et al. 1993, Stodder 1995, De Soto 2000), and there is an impressive record of projects like Grameen Bank that have built upon them, rather than trying to surpass them.  Grameen Bank has a rate of loan repayment better than that of many commercial banks in the developed world.  Therefore, a policy of anti-corruption should concentrate on:

a)      providing basic needs public goods, such as health, education, economic security, and public safety, both directly and through traditional social networks.

b)      shoring-up those traditional social networks themselves, by promoting business, governmental and communal forms that are based on these forms of accumulated social capital, rather than predicated upon their dismantling and dissolution.

 

These points are re-examined in the conclusion.

 

b.      Variation Within (as Opposed to Between) Countries: Income versus Transparency and the Rule of Law

 

With the exception of the Reisen & Soto study, the papers cited above are based on cross-sectional data tests.  A widely-cited paper by Maoro (1997), for example, is sub-titled, "A Cross-Country Analysis."  To test the emerging consensus on growth, Easterly (1999) constructed a large international panel data set on income growth and standard indicia of progress under development – transparency, health, and education. 

Easterly argues that it is highly misleading to assume that this variation between rich and poor countries provides a map for income growth within any single country:

The bad news is that other key modernization or development indicators like democracy, good institutions, human rights, years of schooling, school enrollment ratios, and life expectancy do not robustly improve with income controlling for country effects and sometimes even have the wrong sign. (Easterly 1999, p. 16.)

 

The present paper builds upon Easterly's findings. We use his panel data to show how corruption interacts with other quality-of-life variables, such as health.  Testing for within country variation, we show that improvements in health can improve transparency, independently of the negative effect of income upon transparency.  That is, even after accounting for the positive relation of income upon the health, the effect of higher income upon freedom from corruption (transparency) remains negative.[10]

Why does it not work, this seemingly reasonable use of cross-sectional variation to map the historical trajectory of development?  One depressing possibility is that variations between countries that are largely fixed and immune to policy even in the reasonably long run may explain most cross-country variation in development.  Easterly & Levine (1997), Sachs & Warner (1997), and other empirical investigators have suggested that fixed factors (such as access to the sea, location in the tropics, ethno-linguistic fragmentation, and resource abundance) may explain more between-country income variation than any policy-relevant variables. 

It is also true that transparency and health are higher in the richer countries, and their correlation can be shown to be highly significant.  This leads to the “virtuous circle” argument based on cross sectional evidence.  But, as will be seen from the panel data estimates, the within-country variation between income and transparency is also significant, and strongly negative.[11]

 

4. Empirical Estimates:  The Problem of the Direction of Causality; Health Care and Corruption

Here we wish to assess accurately the effect of both health and income on corruption.  Easterly shows in Tables 1 and 2 (and we do not repeat his regressions showing) that:

1)      Higher income improves life expectancy between countries, but harms it within countries.  Higher income tends to improve infant mortality, both between and within countries.

2)      Income growth improves transparency and rule of law between, but harms them within, countries. 

In contrast to 2), we show that higher life expectancy and lower infant mortality improve both transparency and rule of law, both within and between countries. Because income pushes healthcare up but transparency down, the interesting question is their joint effect on transparency.  Here we follow the standard practice of the literature (Pritchett & Summers 1996), and model income as more determinative of health care than vice versa. A truly complete model would test for co-determination here, and our estimation procedure in no way rules this out.  Lacking appropriate data "instruments" to determine income growth, however, our instrumental variable approach will look merely at "one side," the effect of income on health care.

We begin applying instrumental variables to our health care variables – life expectancy and infant mortality.  To begin with, we must model the determination of life expectancy as dependent on both income and other instrumental variables: length of paved highways, and cars per capita.  That ordinary least squares (OLS) regression is shown in Table 1 below:

TABLE 1.       Dependent variable: LIFE EXPECTANCY, Observations = 107

                        Ordinary Least Squares (OLS) Estimates

 

Mean dependent variable

62.857

R-Squared

0.742

Standard Error Regression

10.421

Adjusted R-Squared

0.729

 

 

 

 

Variable 

Coefficient

T-statistic

P-value

Constant

46.654

31.617

[.000]

Sanitation

  0.165

  6.878

[.000]

Highway Distance

  2.08E-02

  0.950

[.344]

Cars per Capita

 -8.738

-0.904

[.368]

Per Capita Income

  1.40E-03

 3.407

[.001]

CO2 per Capita

 -0.257

-0.950

[.344]

 

Note that there is likely multicollinearity or functional dependency between variables here: for example, between Cars per Capita and CO2 per Capita.  If we were concerned about estimates of the coefficients, this would be a problem.  Because we are interested only in generating a "fitted" form of life expectancy, however, multicollinearity is of no concern.

Next, we show the estimates of the between-country and within-country effects of life expectancy upon transparency.  The between- and within-country equations are shown here as (1a) and (1b), relating to the Tables 2a and 2b, respectively:

Transparency i = Constant    + b*(Life Expectancy i )  + c*(Per Capita Income i ) +  ε i            (1a)

Transparency it = Constant i + b*(Life Expectancy it ) + c*(Per Capita Income it ) +  ε it          (1b)

 

where i = (1, 2, …, C) the number of countries, and t = (1, 2, ..T), the number of time periods observed.  Note that in the following data, the numbers of countries is at most 106, and the maximum number of times periods is 2 (1980 and 1990). 

 

TABLE 2: Dependent variable: TRANSPARENCY,  O = 107,  C = 70, T = 2, P = 37,

where O = # Observations, C = # Countries, T = Maximum # of Time Periods, P = # of Paired observations (number of countries with both time periods observed.

 

TABLE 2a. BETWEEN COUNTRY Estimates (OLS on Country means, 70 Observations):

 

Mean dependent variable

3.400

R-Squared

0.543

Standard Error Regression

1.648

Adjusted R-Squared

0.530

 

 

 

Variable 

Coefficient

T-statistic

P-value

Constant

-1.987

-0.947

[.347]

Life Expectancy (fitted)

 7.63E-02

 1.976

[.052]

Per capita INCOME

 1.23E-04

 1.647

[.104]

 

TABLE 2b. WITHIN COUNTRY Estimates (Fixed Effects, 107 Observations):

 

Mean dependent variable

3.327

R-Squared

0.943

Standard Error Regression

1.753

Adjusted R-Squared

0.827

Durbin-Watson Statistic

2.00

[1.00]

 

Variable 

Coefficient

T-statistic

P-value

Constant i

Life Expectancy (fitted)

  0.110

 2.298

[.028]

Per Capita INCOME

-3.63E-04

-2.654

[.012]

 

Hausman test of H0: RE vs. FE: CHISQ(2) = 13.337,  P-value = [.0012]

 

In Table 2b's within-country estimates, the coefficient on Life Expectancy is positive and significant, while that on the Income term has turned significantly negative. Comparing this with Table 2a, note that the positive effect of Life Expectancy upon Transparency is seriously understated in these between-country estimates.  The coefficient on Life Expectancy in Table 2b is almost twice as high as in 2a, as well as having a higher t-statistic.  Similarly, the coefficient on per-capita Income in Table 2b is not only of the opposite sign from that in Table 2a, but is also far more significant.

In the within-country estimates of the effect of income alone on transparency (not shown here), income showed a negative correlation with transparency.  The present estimates strengthen that basic conclusion, by adding the "omitted variable" of health care.   Because life expectancy is positively correlated with transparency, while income itself is negatively correlated with both transparency and life expectancy, a biasing of the results can be expected to hold when life expectancy is omitted from the equation.  Income's negative influence on transparency looks smaller when “flying solo,” without the positive offset of life expectancy, than when life expectancy is included (2b).  The coefficient values are |-2.24 E-04| < |-3.63 E-04|, respectively. 

Along with the data on transparency, Easterly's data set has observations on the "Rule of Law," both assembled by the IRIS Country Risk Database (University of Maryland).  Now it might be thought that transparency and respect for the rule of law are virtually the same thing, and indeed the two series are highly correlated.  But there are interesting exceptions that make the difference worth studying.

An authoritarian state may be relatively uncorrupted without having much rule of law.   The highly militarized states of Algeria and Israel, for example, show respectable Transparency scores of 4 and 5, but fall to 2 and 1 for  their Rule of Law scores, respectively (both scores can take values between 0 and 6).  At the other extreme, states may have strong traditions for the rule of law, and still be relatively open to corruption.  Portugal and Bulgaria seem to fit this pattern, with the high scores for Rule of Law (6 and 5), but unimpressive levels of Transparency (both scoring 3). 

 

The equations on rule of law have functional forms similar to (1a), (1b):

 

RuleLaw i = Constant    + b* Transparency I  + c*(Income i )   + ε i                                        (2a)

RuleLaw it = Constant i + b* Transparency it + c*(Income it )  + ε it                            (2b)

 

 

 

 

TABLE 3: Dependent variable: RULE OF LAW,  O = 107,  C = 70, T = 2, P = 37,

where O = # Observations, C = # Countries, T = Maximum # of Time Periods,

P = # of Paired observations (number of countries with both time periods observed).

 

TABLE 3a.   BETWEEN COUNTRY Estimates (OLS on Country means, 70 Observations):

Mean dependent variable

2.961

R-Squared

0.604

Standard Error Regression

1.800

Adjusted R-Squared

0.592

 

 

 

 

Variable 

Coefficient

T-statistic

P-value

Constant

-0.497

-0.233

[.817]

Life Expectancy (fitted)

 3.65E-02

 0.931

[.355]

 Per capita INCOME

 2.39E-04

 3.145

[.002]

 

TABLE 3b. WITHIN COUNTRY Estimates (Fixed Effects, 107 Observations):

 

Mean dependent variable

2.935

R-Squared

0.977

Standard Error Regression

1.881

Adjusted R-Squared

0.932

Durbin-Watson Statistic

2.00

[1.00]

 

Variable 

Coefficient

T-statistic

P-value

Constant i

Life Expectancy (fitted)

  7.40E-02

  2.301

[.027]

Per Capita INCOME

-2.59E-04

-2.811

[.008]

 

Hausman test of H0: RE vs. FE:  CHISQ(2) = 27.407,  P-value = [.0000]

 

Interpreting these results in terms of elasticities, we can take the regressions in Tables 2b and 3b, and evaluate them at the sample means for Transparency, Rule of Law, Life Expectancy and Per Capita Income (3.327, 2.935, 62.86 years, and $4,917, respectively):        

 

Table 4: Life Expectancy versus Income – Effects on Transparency and Rule of Law

 

Effects of

Coefficient

Elasticity

Life Expectancy on Transparency

  0.110

  2.078

                Income on Transparency

-3.63E-04

-0.537

 

Life Expectancy on Rule of Law

  7.40E-02

 1.585

                 Income on Rule of Law

-2.59E-04

-0.434

 

These results are rather striking.  A one percent increase in Life Expectancy yields better than a 2.0 percent and 1.5 percent improvement in Transparency and Rule of Law, respectively.  Those elasticities are almost 4 times as great as the elasticities on income, and in the opposite direction.  We conclude that improving life expectancy may be a cost-effective way to improve other public goods linked to corruption, much more so than an increase in income itself.

Turning to another measure of general health, infant mortality, the results are very similar.   Infant mortality is a bad, so the negative association of income mortality with income, both between and (not shown) within countries, is clearly a good thing. The OLS result is here:

TABLE 5.       Dependent variable: INFANT MORTALITY, Observations = 105

                        Ordinary Least Squares (OLS) Estimates

 

Mean dependent variable

61.676

R-Squared

0.755

Standard Error Regression

46.718

Adjusted R-Squared

0.743

Variable 

Coefficient

T-statistic

P-value

Constant

133.811

21.102

[.000]

Sanitation

   -0.838

-7.821

[.000]

Highways

   -0.065

-0.680

[.498]

Cars Per Capita

   10.784

 0.255

[.799]

Per Capita INCOME

    -3.51E-03

-1.925

[.057]

CO2 Per Capita

    -0.147

-0.118

[.906]

 

TABLE 6:       Dependent variable: TRANSPARENCY,  O = 105,  C = 69, T = 2, P = 36,   

where O = # Observations, C = # Countries, T = Maximum # of Time Periods,  

P = # of Paired observations (number of countries with both time periods observed)

 

TABLE 6a.   BETWEEN COUNTRY (OLS on Country means, 69 Observations) Estimates:

 

Mean dependent variable

3.442

R-Squared

0.586

Standard Error Regression

1.697

Adjusted R-Squared

0.574

 

 

 

 

Variable 

Coefficient

T-statistic

P-value

Constant

  3.905

 5.047

[.000]

Infant Mortality (fitted)

-0.018

-2.403

[.019]

 Per capita INCOME

 1.29E-04

 2.021

[.047]

 

TABLE 6b. WITHIN COUNTRY (Fixed Effects, 105 Observations) Estimates:

 

Mean dependent variable

3.371

R-Squared

0.944

Standard Error Regression

1.783

Adjusted R-Squared

0.830

Durbin-Watson Statistic

2.000

[1.000]

 

Variable 

Coefficient

T-statistic

P-value

Constant I

Infant Mortality (fitted)

-2.00E-02

-2.190

[.035]

Per Capita INCOME

-3.10E-04

-2.381

[.023]

 

Hausman test of H0: RE vs. FE: CHISQ(2) = 12.251,  P-value = [.0022]

Now consider the relationship between Infant Mortality and the Rule of Law variable.

 

TABLE 7:  Dependent variable: RULE OF LAW,  O = 105,  C = 69, T = 2, P = 36,

where O = # Observations, C = # Countries, T = Maximum # of Time Periods,

P = # of Paired observations (number of countries with both time periods observed)

 

 

TABLE 7a.   BETWEEN COUNTRY Estimates (OLS on Country means, 69 Observations):

 

Mean dependent variable

3.047

R-Squared

0.615

Standard Error Regression

1.851

Adjusted R-Squared

0.604

 

 

 

 

Variable 

Coefficient

T-statistic

P-value

Constant

2.440

2.998

[.004]

Infant Mortality (fitted)

-9.22E-03

-1.173

[.245]

 Per capita INCOME

2.36E-04

3.495

[.001]

 

 

TABLE 7b. WITHIN COUNTRY Estimates (Fixed Effects, 105 Observations):

 

Mean dependent variable

3.005

R-Squared

0.979

Standard Error Regression

1.917

Adjusted R-Squared

0.935

Durbin-Watson Statistic

2.000

[1.000]

Variable 

Coefficient

T-statistic

P-value

Constant i

Infant Mortality (fitted)

-1.39E-02

-2.241

[.032]

Per Capita INCOME

-2.21E-04

-2.562

[.015]

 

Hausman test of H0: RE vs. FE: CHISQ(2) = 25.660,  P-value = [.0000]

 

Similar to the elasticity comparisons of Life Expectancy and Income (Table 5), we can do a computation with Infant Mortality, Income, and their effects on Transparency and Rule of Law. The sample means for these variables are 61.67 (per thousand births), $5,016, 3.371, and 3.005, respectively.  Using the coefficients in Tables 6b and 7b, this leads to the following elasticities:

 

Table 8: infant mortality versus Income – Effects on Transparency and Rule of Law

 

Effects of

Coefficient

Elasticity

 Infant Mortality on Transparency

-2.00E-02

-0.366

                Income on Transparency

-3.10E-04

-0.461

 

  Infant Mortality on Rule of Law

-1.39E-02

-0.285

                 Income on Rule of Law

-2.21E-04

-0.369

 

The signs on Infant Mortality and Income are in the same direction: both are bads from the point of view of Transparency and the Rule of Law.  We see that their elasticities are also comparable, with Income somewhat more “corrupting” than Infant Mortality.

As a final point on our empirical work, we should note that we performed similar estimates on the public goods of Education (years of schooling) and income Inequality (the Gini coefficient, as a public "bad" serving as proxy for lack of income security).  The results on Inequality were very far from statistical significance.  Tests on our Education variables, however, were just short of statistical significance, and all of the same sign as the tests on our Health variables.  These results will not be presented here; the variables themselves are given in the Appendix. 

 

4.  Summary and Conclusions

The argument of this paper is that transparency is a public good created largely by capital in social networks, rather than by direct governmental production alone.  This hypothesis is supported by within-country estimates, based on extensive panel data, demonstrating a determining influence of health care upon transparency, so that its greater provision encourages greater transparency and rule of law.  This does not in any way deny the reverse causality established by earlier literature, i.e., that greater transparency and rule of law also encourage better health care.

Thus, there is a positive determining influence for health care upon corruption, at least in the structural sense of exogeneity.  But there still may be no very direct causal link: both improvements might be caused by some third term.  So how might better public health improve transparency and the rule of law?  There is evidence that good health is highly associated with strong community and family networks (Castles 1994, Cox et al. 1997).  Health is a good most people prize highly, so that much leisure time and disposable income are invested in promoting it.  The same social networks that promote good health may also promote greater long-term relationships of trust, which is the social basis of transparency and the rule of law.

Mutual aid delivered through community health care may also allow developing countries to leverage traditional forms of social capital used to promote honesty and transparency.  The example of Grameen Bank has been widely noted, and is built upon the informal ROSCA networks found throughout the developing world (Beaseley et al. 1993). Literacy and numeracy can only aid such leverage.  Another example may be the use of modern communication technologies to connect village school programs with university programs, such as that of the University of South Africa (UNISA), which has been involved in distance education since 1946, and is also used to promote public health. It is now using the Internet to reach out to the world and in the year 2000 had over 8000 students outside South Africa, 6000 of them from other African countries (Schroth & Sharma 2002).[12]

The findings of this paper support of a policy that not only strives to improve the level of basic public goods like education and health, but also attempts to complement and strengthen the social networks responsible for their provision in traditional societies.  These traditional values and networks in developing countries now appear, rather than standing as the great roadblocks to transparency and capital investment, as their most secure foundation (Beasley et al. 1993, De Soto 2000).

A program of strengthening these social networks and allying them with the market becomes all the more important in light of the within-country evidence in Easterly (1999) that corruption is encouraged by income growth.  Capitalist modernization has been based largely upon dismantling and degrading traditional values and networks.  To the extent it has been so based, it has delayed the establishment of transparent institutions, and thus hindered its own development.

Whenever within-country effects of income growth are qualitatively at odds with the cross-sectional evidence, as in Easterly (1999) and the present paper, there are arguments that the cross-sectional evidence remains more fundamental.  Recall that our fixed effects estimates explain most of the between country differences by individual country differences, and that the time period of change is typically very short: in the case of this paper, just one decade. It is uncertain, therefore, whether these estimates really capture the most of the socioeconomic changes wrought by sustained income growth.  It may be that the “corruption enhancing” effects of growth and poor health care are strictly short-term.  Easterly's results would become a mild qualification to the prevailing pro-growth optimism: the negative backwash of growth would then be only a minor friction on the upwardly spiraling “virtuous circle” of income growth.  

There are many more questions than can be answered, or even seriously posed, by a paper like this.  A closing thought, however, on why social networks should not be ignored:   modern capitalism in Western Europe and in Japan was not built upon the simple destruction of traditional social networks.   Rather, what made these societies so successful was precisely their ability to mobilize these traditional forms and transform them toward capitalism (Polanyi 1962; Rosser & Rosser 1999).  Polanyi, a student of Max Weber, stressed that capitalist social relations in Europe rest upon an underpinning of “bourgeois community values” (in the non-Marxist and entirely approving sense of that phrase), built up over centuries of medieval trade and town life.   Without such a broad social basis, the institutions of a well-functioning capitalism are impossible.  Development programs must fight and punish corruption, of course.  But they must also nurture the social basis of transparency.  That social basis will be found in traditional social networks and the norms of reciprocity that support the vast informal sector.

 

REFERENCES

 

Axelrod, Robert & Michael Cohen (2000), Harnessing Complexity: Organizational Implications of a Scientific Frontier, New York: Free Press.

Barro, Robert (1997), Determinants of Economic Growth: A Cross-Country Empirical Study, Cambridge (MA): MIT Press.

Besley, Timothy, Stephen Coate & Glenn Loury (1993), “The Economics of Rotating Savings and Credit Associations,” American Economic Review, September 83(4), pp.792– 810.

Burki, Shahi & Guillermo Perry (1998), Beyond the Washington Consensus: Institutions Matter

"Viewpoints," Washington, DC: World Bank.

Castles, Ian (1994), How Australians Use Their Time, Australian Bureau of Statistics, Catalog No. 4153.0, Canberra: Australian Government Printing Service.

Charap, Joshua & Christian Harm (1999), “Institutionalized Corruption and the Kleptocratic State,” IMF Working Paper WP/99/91, www.imf.org/external/­pubs/­ft/­wp/­1999/­wp9991.pdf

Council of Europe (1999), Criminal Law Convention on Corruption, 27 Jan. 1999, Europ. T.S. No.173.

Cox, Donald, Emmanuel Jimenez & Wlodek Okrasa (1997), "Family Safety Nets and Economic Transition: Worker Households in Poland," Review of Income and Wealth, June: 191-209.

De Soto, Hernando (2000), The Mystery of Capital: Why Capitalism Triumphs in the West and Fails Everywhere Else, New York: Basic Books.

Easterly, William (1999), "Life During Growth: A Compendium of Political, Social and Environmental Indicators of What Gets Better and What Gets Worse from Low to High Income," Journal of Economic Growth 4(3), September, pp. 239-276.

Easterly, William and Ross Levine (1997), "Africa's Growth Tragedy: Policies and Ethnic Divisions," Quarterly Journal of Economics, 112(4), November, pp. 1203-50.

Eliot, Kimberly Ann, ed. (1997), Corruption and the Global Economy, Washington, D.C.: Institute for International Economics.

Engle, Robert F. and David F. Hendry (1983), "Exogeneity," Econometrica, 51(2), March: 277-304.

Frank, Robert H. & Tibor Scitovsky (1992), The Joyless Economy: The Psychology of Human         Satisfaction, Oxford: Oxford University Press.

Friedman, Milton (1982), Capitalism and Freedom, Chicago: University of Chicago Press.

Gupta, Sanjeev, Hamid Davoodi & Erwin Tiongson (2000), "Corruption and the Provision of Health Care and Education Services," IMF Working Paper, Fiscal Affairs Department, www.imf.org/external/pubs/cat/longres.cfm?sk=3652.0

Gupta, Sanjeev, Hamid Davoodi & Rosa Alonso-Terme (1998), "Does Corruption Affect Income Inequality and Poverty?," IMF Working Paper, Fiscal Affairs Department, www.imf.org/­external/pubs/cat/longres.cfm?sk=2629.0

Heal, Geoffrey & P.S. DasGupta (1980), Economic Theory and Exhaustible Resources,  Cambridge:            Cambridge University Press.

Hines, Jr., James R. (1995) “Forbidden Payment: Foreign Bribery and American Business After

1977,” NBER Working Paper 5266.

Inter-American Convention Against Corruption (1997) OEA/Ser. K/XXXIV.1, CICOR/doc. 14/96 rev. 2 (29 Mar. 1996), 35 ILM 724 (1996).

Lopez, Ramon & Siddhartha Mitra (2000) "Corruption, Pollution, and the Kuznets Environment            Curve," Journal of Environmental Economics and Management 40, 137-150.

Maoro, Paolo (1995), “Corruption and Growth,” Quarterly Journal of Economics, 110(3): 681-712.

___________ (1997), “The Effects of Corruption on Growth, Investment, and Government Expenditure:  A Cross-Country Analysis,” in Kimberly, Ann Elliott, ed., Corruption and the Global Economy, Washington, D.C.: Institute for International Economics.

OECD (1998) Convention on Combating Bribery of Foreign Public Officials in International Business Transactions, OECD/DAFFE/IME/BR(97)16/FINAL (18 Dec. 1997), 37 ILM 1 (1998), S. Treaty Doc. No. 105-43.

Olson, Mancur (2000), Power and Prosperity: Outgrowing Communist and Capitalist Dictatorships, New York: Basic Books.

Organization of African Unity (2001), “Draft OAU Convention on Combating Corruption,” Expt/­Draft/­OAU/­Conv/­Comb/­Corruption (I).

Pritchett, Lant & Larry Summers (1996), “Wealthier Is Healthier,” Journal of Human Resources,        31(4): pp. 841-868.

Polanyi, Karl (1962), The Great Transformation, Boston: Beacon Press.

Reisen, Helmut, and Marcelo Soto (2001) "Which Types of Capital Inflows Foster Country Growth?" International Finance, 4(1).

Republic of South Africa (2002), Prevention of Corruption Bill, Government Gazette No. 23336, 18 April 2002.

Rose-Ackerman, Susan (1999), Corruption and Government: Causes, Consequences and Reform, Cambridge (UK): Cambridge University Press.

___________________ (1997) “The Political Economy of Corruption,” in Kimberly, Ann Elliott, ed., Corruption and the Global Economy, Washington, D.C.:  Institute for International Economics.

___________________ (1978) Corruption: A Study in Political Economy, New York: Academic Press.

Rosser, J. Barkley, Jr. & Marina Rosser (1999), "The New Traditional Economy: A New Perspective for Comparative Economics?" International Journal of Social Economics, 26(6) pp. 763-778.

Sachs, Jeffrey D. & Andrew Warner (1997) “Fundamental Sources of Long-run Growth,” American Economic Review, Papers and Proceedings, 87, No. 2: 184-88, May.

Schroth, Peter W. (2002a), "43 Years of Transnational Law Against Corruption (of Which 40 in the United States Alone)", in David Birch & Jonathan Batten, eds., Governance and Corporate Social Responsibility in the New Millennium:  Proceedings of the 2001 Conference, Burwood, VIC, Australia:  Deakin University.

_______________ (2002b), "The United States and the International Bribery Conventions," American Journal of Comparative Law, 50(supp.) pp.593-622.

_______________ (2002c), “Why Have the Corruption Treaties’ Provisions on Accounting and Control Failed to Overcome the Business Judgment Rule?,” Academy of International Business, Proceedings of the 2002 Northeast Conference, Salisbury, MD:  Salisbury University.

_______________ (2003), "Fostering Informed and Responsible Management:  The Failure of the Corruption Treaties: Provisions on Accounting and Control," Research in International Business and Finance,17, pages ___-___ (forthcoming).

Schroth, Peter W. & Preeti Sharma (2002) "Fighting Corruption in Africa: Transnational Law and New Communication Technologies," in 2002 Proceedings of the International Academy of African Business and Development:  Expanding the Horizons of African Business and Development, Port Elizabeth, South Africa:  University of Port Elizabeth pp. 549-556.

Stodder, James (1995) "The Evolution of Complexity in primitive Exchange," Journal of Comparative Economics, Vol. 20 (in two parts): "Theory," Issue 1, February, pp. 1-31 and "Empirical Tests," Issue 2, April, pp. 190-210.

____________ (1993) "Ex-Communist Economics: the Logic of Capital Reform," New York Economic Review, 24(4).

Wei, Shang-Jin (1997) "Why is Corruption So Much More Taxing Than Tax? Arbitrariness Kills," NBER Working Paper No. w6255.

____________ (2000) “How Taxing Is Corruption on International Investors?” Review of Economics & Statistics, 82(1).

 

 

 

Appendix (A):  Country Data in the Easterly Data Set, Basic Indicators

 

The Data in this table are the basic ones used in this study; the instrumental variables used to derive the fitted estimates can be downloaded by going to www.worldbank.org/research/peg/, then clicking on the link for Working Paper Number 17. Below there are C = 106 Countries, P = 76 with T = 2 time period observations, and S = 30 with only one observation.  O = 182 observations in all. The variable definitions are as follows:

 

Table A1: Data Sources

VARIABLE

Data Sources

 

NAME

Name of Country

 

YEAR

Year of Observation

 

TRANSP

Transparency

IRIS Int'l Country Risk data (Note: This variable is labeled 'Corruption' in the Easterly Data, even though it is increasing as corruption decreases.  We have chosen 'Transparency' as less confusing.

RULELAW

Rule of law

IRIS Int'l Country Risk data

INCOME-PC

Income per capita

Summers, R. & A. Heston. 1991. "The Penn World Table (Mark 5): an expanded set of international comparisons, 1950-88." Quarterly Journal of Economics 106 (2): 327-68 (with 1993 on line update from NBER).

NSMORT

Mortality - Infant                     

WRI (World Resources, Inc.)

NSSCHL

Average schooling years in the total population over age 25

Barro-Lee

NSLIFEX

Life expectancy at age zero

Barro-Lee

GINIDC

Gini coefficient (decade avg)

Gini coefficient (decade avg)

 

In the table below, names are in bold if they occur without a second period observation:

 

Table B1: Basic Data

NAME

YEAR

TRANSP

RULELAW

INCOME-PC

NSMORT

NSSCHL

NSLIFEX

GINIDC

ALGERIA

1980

2

2

       $2,758

112

1.868

58.4

38.73

ALGERIA

1990

4

2

       2,777

74

2.394

61.5

#N/A

ARGENTINA

1980

3

2

       6,506

41

6.63

69.3

#N/A

ARGENTINA

1990

4

3

       4,706

32

6.676

70.2

#N/A

AUSTRALIA

1980

6

6

      12,520

13

10.076

74.5

34.267

AUSTRALIA

1990

5

6

      14,445

9

10.241

75.8

41.72

AUSTRIA

1980

6

6

      10,509

17

6.224

72.7

28.669

AUSTRIA

1990

5

6

      12,695

9

6.642

73.7

#N/A

BANGLADESH

1980

0

1

       1,085

137

1.681

47.9

35.723

BANGLADESH

1990

0

1

       1,390

119

1.974

49.9

28.27

BELGIUM

1980

6

6

      11,109

14

8.794

73.2

24.461

BELGIUM

1990

5

6

      13,232

9

9.152

74.3

26.968

BOLIVIA

1980

1

1

       1,989

131

3.971

49.9

#N/A

BOLIVIA

1990

2

1

       1,658

98

4.285

52.2

42.04

BRAZIL

1980

4

4

       4,303

79

2.976

62.8

53.858

BRAZIL

1990

4

4

       4,042

63

3.486

64.3

#N/A

BULGARIA

1990

3

5

       6,203

14

#N/A

#N/A

27.797

BURKINA FASO

1990

4

3

          511

126

#N/A

46.4

#N/A

CAMEROON

1980

3

3

       1,194

102

1.758

53

49

CAMEROON

1990

3

3

       1,226

74

2.23

55.1

#N/A

CANADA

1980

6

6

      14,133

12

10.158

74.7

31.569

CANADA

1990

6

6

      17,173

7

10.369

76.2

30.52

CHILE

1980

4

5

       3,892

47

5.986

69.5

54.607

CHILE

1990

3

4

       4,338

18

6.45

71.3

52.35

CHINA

1990

4

3

       1,324

32

#N/A

68.8

36.033

COLOMBIA

1980

3

2

       2,946

59

4.233

63.1

47.9

COLOMBIA

1990

3

1

       3,300

40

4.533

65

50.023

CONGO

1990

3

2

       2,211

84

3.143

57.7

#N/A

COSTA RICA

1980

5

4

       3,717

30

4.814

71.7

44.42

COSTA RICA

1990

5

4

       3,499

16

5.333

73.4

#N/A

COTE D'IVOIRE

1990

3

2.5

       1,213

#N/A

#N/A

51.7

#N/A

 

 

 

 

 

 

 

 

 

NAME

YEAR

TRANSP

RULELAW

INCOME-PC

NSMORT

NSSCHL

NSLIFEX

GINIDC

CYPRUS

1990

3

4

       8,368

11

7.128

75.4

#N/A

CZECHOSLOVAKIA, FORMER

1990

5

5

       4,095

#N/A

#N/A

#N/A

24.557

DENMARK

1980

6

6

      11,342

9

10.139

74.1

26.554

DENMARK

1990

6

6

      13,909

8

10.331

74.9

29.063

DOMINICAN REPUBLIC

1980

3

3

       2,343

#N/A

3.709

63.3

47.608

DOMINICAN REPUBLIC

1990

3

3

       2,166

#N/A

4.177

65.2

49

ECUADOR

1980

4

4

       3,238

82

5.401

63.1

44.53

ECUADOR

1990

3

4

       2,755

63

5.584

65

43

EGYPT

1980

1

1

       1,645

131

#N/A

57.1

#N/A

EGYPT

1990

2

2

       1,912

65

#N/A

59.6

32

EL SALVADOR

1980

2

1

       2,014

87

3.283

57.3

#N/A

EL SALVADOR

1990

2

1

       1,824

59

3.574

60.2

#N/A

FINLAND

1980

6

6

     10,851

9

9.609

73.2

25.545

FINLAND

1990

6

6

      14,059

6

9.491

75

23.549

FRANCE

1980

6

6

      11,756

11

5.968

74.9

32.05

FRANCE

1990

5

5

      13,904

8

6.523

76.6

#N/A

GABON

1980

2

2

       4,797

122

#N/A

48.4

#N/A

GABON

1990

2

2

       3,958

103

#N/A

51.1

#N/A

GAMBIA, THE

1990

3

3

          799

143

0.837

42.4

#N/A

GERMANY, FORMER FDR

1980

6

6

     11,920

#N/A

8.457

73.2

#N/A

GERMANY, FORMER FDR

1990

5

6

      14,341

#N/A

8.544

74.3

#N/A

GHANA

1980

1

1

          976

103

2.382

51.6

36.32

GHANA

1990

4

3

          902

90

3.216

53.2

33.94

GREECE

1980

3

3

       5,901

25

6.556

74.3

35.738

GREECE

1990

5

3

       6,768

11

6.727

75.9

#N/A

GUATEMALA

1980

2

1

       2,574

82

2.342

58

58.66

GUATEMALA

1990

2

1

       2,127

59

2.591

60.8

#N/A

GUINEA

1990

4

3

          767

145

#N/A

#N/A

#N/A

GUINEA-BISSAU

1990

2

1

          689

151

0.56

38.7

56.12

GUYANA

1980

1

0

       1,927

67

4.603

65.3

#N/A

GUYANA

1990

1

1

       1,094

56

5.109

65.9

40.22

HAITI

1980

0

0

       1,033

121

1.455

51.9

#N/A

HONDURAS

1980

2

1

       1,519

90

2.698

60.2

54.98

HONDURAS

1990

2

2

       1,377

68

3.561

63.2

53.726

HONG KONG

1980

6

6

       8,719

#N/A

6.734

74.1

41.493

HONG KONG

1990

5

4

     14,849

#N/A

7.511

75.8

45

HUNGARY

1990

5

5

       5,357

17

10.745

69.9

27.874

ICELAND

1980

6

6

      11,566

9

7.398

#N/A

#N/A

ICELAND

1990

6

6

      13,362

6

7.889

#N/A

#N/A

INDIA

1980

3

3

          882

129

2.715

53.7

31.428

INDIA

1990

2

1

       1,264

96

3.046

56.7

31.628

INDONESIA

1980

1

1

       1,281

105

3.086

54.7

33.957

INDONESIA

1990

0

2

       1,974

75

3.75

58.5

34.592

IRAN, ISLAMIC REPUBLIC OF

1980

1

1

       3,434

#N/A

2.323

60.6

42.9

IRAN, ISLAMIC REPUBLIC OF

1990

3

1

       3,392

#N/A

3.281

#N/A

#N/A

IRAQ

1980

2

1

       7,242

84

2.456

62.4

#N/A

IRELAND

1980

6

5

       6,823

15

7.605

72.6

35.804

IRELAND

1990

5

5

       9,274

8

8.008

73.5

#N/A

ISRAEL

1980

5

1

       7,895

18

9.135

72.8

#N/A

ISRAEL

1990

5

1

       9,298

11

9.41

75

#N/A

ITALY

1980

3

5

      10,323

18

5.83

74.7

31.853

ITALY

1990

4

5

      12,488

10

6.276

76.3

28.302

JAMAICA

1980

2

1

       2,362

26

3.602

71.3

50.73

JAMAICA

1990

2

2

       2,545

17

4.162

73.5

39.825

JAPAN

1980

6

6

      10,072

9

8.166

76

35.2

JAPAN

1990

5

5

      14,331

5

8.458

77.3

35

JORDAN

1980

3

1

       3,384

65

2.933

62.7

36.968

JORDAN

1990

3

2

       2,919

44

4.308

65

40.66

KENYA

1980

3

2

          911

88

2.444

54.9

57.3

KENYA

1990

3

3

          911

72

3.093

57.1

54.39

KOREA, REPUBLIC OF

1980

3

3

       3,093

#N/A

#N/A

#N/A

35.335

KOREA, REPUBLIC OF

1990

2

2

       6,673

#N/A

#N/A

#N/A

#N/A

KUWAIT

1980

3

1

      20,018

34

4.294

70.8

#N/A

LIBERIA

1980

1

1

          927

167

1.345

51.5

#N/A

LUXEMBOURG

1990

6

6

      16,280

9

#N/A

#N/A

#N/A

MADAGASCAR

1990

4

3.5

          675

120

#N/A

52.7

43.44

MALAWI

1980

5

1

          554

177

2.271

43.7

58.3

MALAWI

1990

4

2

          519

149

2.577

45.4

#N/A

MALAYSIA

1980

6

4

       3,799

34

4.489

66.9

48.168

NAME

YEAR

TRANSP

RULELAW

INCOME-PC

NSMORT

NSSCHL

NSLIFEX

GINIDC

MALAYSIA

1990

4

3

       5,124

17

5.361

68.9

#N/A

MALI

1990

2

2

          531

169

0.819

46.3

#N/A

MEXICO

1980

2

4

       6,054

58

3.512

66.6

52.453

MEXICO

1990

3

3

       5,827

41

4.418

68.1

50.31

MONGOLIA

1990

4

3

       1,842

68

#N/A

#N/A

#N/A

MOROCCO

1980

1

1

       1,941

110

#N/A

57.3

43.763

MOROCCO

1990

3

2

       2,151

82

#N/A

59.8

39.2

MOZAMBIQUE

1990

4

2

          760

155

1.077

47

#N/A

MYANMAR

1980

1

3

          505

114

1.464

#N/A

#N/A

NAMIBIA

1990

3

2

       2,854

80

#N/A

#N/A

#N/A

NETHERLANDS

1980

6

6

      11,284

10

8.199

75.7

29.17

NETHERLANDS

1990

6

6

      13,029

9

8.572

76.5

29.163

NEW ZEALAND

1980

6

6

      10,362

14

12.141

73.2

35.314

NEW ZEALAND

1990

6

6

      11,513

11

12.039

74.4

40.21

NICARAGUA

1980

3

1

       1,853

97

2.735

58.4

#N/A

NICARAGUA

1990

5

2

       1,294

71

3.776

61.9

50.32

NIGERIA

1980

1

1

       1,438

124

#N/A

47.7

36.26

NIGERIA

1990

2

1

          995

105

#N/A

50.1

39.31

NORWAY

1980

6

6

      12,141

9

10.316

75.7

27.036

NORWAY

1990

6

6

      14,902

8

10.382

76.5

28.906

PAKISTAN

1980

1

1

       1,110

130

1.737

53

33.586

PAKISTAN

1990

2

1

       1,394

109

1.92

55.5

31.15

PANAMA

1980

3

1

       3,392

32

5.982

70.3

51.97

PANAMA

1990

2

2

       2,888

23

6.3

71.6

#N/A

PAPUA NEW GUINEA

1990

3

3

       1,425

59

1.654

53.2

#N/A

PARAGUAY

1980

3

1

       2,534

53

4.627

66.3

#N/A

PARAGUAY

1990

0

2

       2,128

49

4.704

66.7

#N/A

PERU

1980

2

2

       2,875

105

5.442

58

45.023

PERU

1990

3

1

       2,188

88

5.786

60.3

41.935

PHILIPPINES

1980

0

1

       1,879

62

6

61

44.027

PHILIPPINES

1990

2

1

       1,763

45

6.481

62.8

45

POLAND

1990

5

4

       3,820

17

8.412

71.2

26.685

PORTUGAL

1980

3

6

       4,982

30

3.23

71.4

35.353

PORTUGAL

1990

5

5

       7,478

14

3.827

72.9

34.442

SAUDI ARABIA

1980

3

1

      13,750

75

#N/A

59.7

#N/A

SENEGAL

1980

3

1

       1,134

112

1.989

45.2

#N/A

SENEGAL

1990

3

2

       1,145

87

2.393

46.7

54.12

SIERRA LEONE

1990

2

3

          901

154

1.721

40.3

#N/A

SINGAPORE

1980

6

6

       7,053

13

3.691

71.5

42.41

SINGAPORE

1990

4

5

      11,710

8

4.553

72.5

#N/A

SOUTH AFRICA

1990

5

1

       3,248

58

4.955

59.4

62.3

SPAIN

1980

6

5

       7,390

16

5.152

74.9

26.378

SPAIN

1990

4

4

       9,583

8

5.585

76.3

#N/A

SRI LANKA

1980

3

2

       1,635

44

5.183

68

43.868

SRI LANKA

1990

3

0

       2,096

28

5.371

69.8

30.1

SUDAN

1980

1

1

          866

131

0.64

46.7

#N/A

SUDAN

1990

2

1

          757

108

0.909

49

#N/A

SWEDEN

1980

6

6

      12,456

8

9.469

75.9

30.287

SWEDEN

1990

6

6

      14,762

6

9.447

76.4

28.877

SWITZERLAND

1980

6

6

      14,301

10

9.666

75.8

34.313

SWITZERLAND

1990

6

6

      16,505

7

9.088

76.7

#N/A

SYRIAN ARAB REPUBLIC

1980

1

1

       4,467

#N/A

3.106

61.6

#N/A

SYRIAN ARAB REPUBLIC

1990

2

2

       3,897

#N/A

3.987

64

#N/A

TAIWAN, CHINA

1980

5

6

       4,459

#N/A

6.365

72.4

29.442

TAIWAN, CHINA

1990

4

5

       8,063

#N/A

6.999

72.8

30.139

TANZANIA

1980

1

3

          480

125

2.425

49.7

#N/A

THAILAND

1980

4

3

       2,178

56

3.765

62.2

47.037

THAILAND

1990

3

4

       3,580

32

5.081

63.8

50.264

TOGO

1980

2

2

          731

117

1.623

49.5

#N/A

TOGO

1990

2

2

          641

94

2.132

52

#N/A

TRINIDAD AND TOBAGO

1980

2

4

      11,262

#N/A

6.599

68.2

41.72

TRINIDAD AND TOBAGO

1990

3

4

       7,764

#N/A

6.503

69.4

#N/A

TUNISIA

1980

3

3

       2,527

88

1.918

61.9

43.143

TUNISIA

1990

3

2

       2,910

49

2.478

64.4

40.493

TURKEY

1980

3

3

       2,874

120

2.616

61

43.945

TURKEY

1990

2

2

       3,741

68

3.294

63.1

#N/A

UGANDA

1980

1

1

          534

114

1.641

46

33

UGANDA

1990

3

1

          554

108

1.917

47.3

40.78

UNITED ARAB EMIRATES

1980

3

1

      31,969

38

#N/A

68.2

#N/A

NAME

YEAR

TRANSP

RULELAW

INCOME-PC

NSMORT

NSSCHL

NSLIFEX

GINIDC

UNITED KINGDOM

1980

6

6

      10,167

14

8.345

73.8

29.621

UNITED KINGDOM

1990

5

5

      13,217

9

8.648

74.7

32.35

UNITED STATES

1980

6

6

      15,295

14

11.888

73.7

36.878

UNITED STATES

1990

5

6

      18,054

10

11.787

74.9

37.158

URUGUAY

1980

3

3

       5,091

42

5.8

70.1

#N/A

URUGUAY

1990

3

3

       4,602

24

6.454

70.8

#N/A

VENEZUELA

1980

2

3

       7,401

43

4.93

68.5

42.965

VENEZUELA

1990

3

4

       6,055

36

5.368

69.6

49.12

ZAIRE

1980

1

1

          476

115

1.738

49.2

#N/A

ZAMBIA

1980

1

3

          971

94

3.459

50.1

#N/A

ZAMBIA

1990

2

1.5

          689

86

4.349

52.1

43.51

ZIMBABWE

1980

3

1

       1,206

86

2.395

55

#N/A

 

 

It is noteworthy that all examples of countries with scores of 5 or 6 for both Transparency and Rule of Law were from the countries of North-West or Central Europe or peopled largely by their immigrants (e.g., Australia), with the following exceptions: Taiwan, Hong Kong, Singapore, and Japan.

 

Appendix (B): Examining the Regression Outliers

 

It is often instructive to check which observations the regressions do not able handle particularly well.  This was done here, and the inconsistencies exhibit a rather striking consistency of their own.  All of the countries with absolute error terms greater than 0.5 (on a 7 point scale) are either "Latin" in their culture or colonial legacy, or they are African.  We have no theories on the reasons behind this pattern, but it is certainly interesting.

 

Table B1:  Transparency Regressed on Life Expectancy

 

 

 

 

 

 

Estimated

Standard

 

 

Variable

Coefficient

Error

t-statistic

P-value

Constant i

-

-

-

 

Income-PC

-3.63E-04

1.37E-04

-2.65416

[.012]

LifeEx (fitted)

0.109551

0.047682

2.29755

[.028]

 

 

The following table shows those regressions where the estimated Transparency differed from the Observed by more than 0.5 points.

 

Table B2: Outliers for Regression of Transparency on Life Expectancy

 

 

 

Coefficient

Variable

Variable

Estim.

Observ.

Estim. minus

Bias

Year

Country

Constant

Income-PC

LifeEx (fitted)

Transp.

Transp.

Observ. Transp.

Estim.

1980

ECUADOR

-1.62245

3238

55.3

3.26

4.0

-0.7

under

1990

ECUADOR

-1.62245

2755

58.0

3.74

3.0

0.7

over

1980

GHANA

-3.23589

976

52.7

2.19

1.0

1.2

over

1990

GHANA

-3.23589

902

58.2

2.81

4.0

-1.2

under

1980

ITALY

-0.63614

10323

75.0

3.84

3.0

0.8

over

1990

ITALY

-0.63614

12488

76.0

3.16

4.0

-0.8

under

1980

PANAMA

-3.09236

3392

58.5

2.09

3.0

-0.9

under

1990

PANAMA

-3.09236

2888

64.3

2.91

2.0

0.9

over

1980

PARAGUAY

-4.37733

2534

65.5

1.88

3.0

-1.1

under

1990

PARAGUAY

-4.37733

2128

57.2

1.12

0.0

1.1

over

1980

PHILIPPINES

-5.01573

1879

61.1

1.00

0.0

1.0

over

1990

PHILIPPINES

-5.01573

1763

60.7

1.00

2.0

-1.0

under

1980

SPAIN

0.16239

7390

70.7

5.23

6.0

-0.8

under

1990

SPAIN

0.16239

9583

73.8

4.77

4.0

0.8

over

1980

SUDAN

-3.73478

866

51.1

1.55

1.0

0.6

over

1990

SUDAN

-3.73478

757

49.8

1.45

2.0

-0.6

under

1980

UGANDA

-3.68546

534

50.0

1.59

1.0

0.6

over

1990

UGANDA

-3.68546

554

57.4

2.41

3.0

-0.6

under

1980

ZAMBIA

-4.57559

971

59.6

1.60

1.0

0.6

over

1990

ZAMBIA

-4.57559

689

56.8

1.40

2.0

-0.6

under

 

We can also consider the direction of change in Transparency, according to our regressions.  For each pair of observations on a country here, we have bolded the larger of the two estimates and observations on the dependent variable. When the bolded values are in the same year, the regression correctly predicts the direction of change.     

 

We will see very much the same pattern of outliers when we use Child Mortality variable, rather than Life Expectancy, as the dependent variable.

 

 

Table B3: Transparency Regressed on Child Mortality

 

 

 

 

 

 

Estimated

Standard

 

 

Variable

Coefficient

Error

t-statistic

P-value

Constant i

-

 

-

-

Income-PC

-3.10E-04

1.30E-04

-2.38085

[.023]

Child.Mort. (fitted)

-0.020462

9.34E-03

-2.18983

[.035]

 

 

 

 

Table B4: Outliers for Regression of Transparency on Child Mortality

 

 

 

Coefficient

Variable

Variable

Estimate

Observ

Estim. minus

Bias of

Year

Country

Constant

Income-PC

Mortality (fitted)

Transp.

Transp.

Observ. Transp.

Estimate

1980

ECUADOR

6.29551

3238

99.4

3.26

4.0

-0.7

under

1990

ECUADOR

6.29551

2755

83.2

3.74

3.0

0.7

over

1980

GHANA

4.68724

976

107.0

2.20

1.0

1.2

over

1990

GHANA

4.68724

902

78.4

2.80

4.0

-1.2

under

1980

ITALY

7.19094

10323

10.5

3.78

3.0

0.8

over

1990

ITALY

7.19094

12488

5.1

3.22

4.0

-0.8

under

1980

PANAMA

4.83749

3392

82.4

2.10

3.0

-0.9

under

1990

PANAMA

4.83749

2888

51.1

2.90

2.0

0.9

over

1980

PARAGUAY

3.58555

2534

46.7

1.84

3.0

-1.2

under

1990

PARAGUAY

3.58555

2128

86.6

1.16

0.0

1.2

over

1980

PHILIPPINES

2.93285

1879

66.0

1.00

0.0

1.0

over

1990

PHILIPPINES

2.93285

1763

67.8

1.00

2.0

-1.0

under

1980

SPAIN

8.04508

7390

27.5

5.19

6.0

-0.8

under

1990

SPAIN

8.04508

9583

13.3

4.81

4.0

0.8

over

1980

SUDAN

4.17514

866

116.4

1.53

1.0

0.5

over

1990

SUDAN

4.17514

757

120.5

1.47

2.0

-0.5

under

1980

UGANDA

4.22259

534

119.6

1.61

1.0

0.6

over

1990

UGANDA

4.22259

554

81.1

2.39

3.0

-0.6

under

1980

ZAMBIA

3.34266

971

70.7

1.60

1.0

0.6

over

1990

ZAMBIA

3.34266

689

84.3

1.40

2.0

-0.6

under

 

Note that these results for Rule of Law are almost identical to those for Transparency.  The Error terms (in the second to last column) are identical to the first decimal place in every case.   There-fore the same pattern holds, of over-estimating the Transparency of the Latin countries, while under-estimating that of the African countries shown.

 

Next we turn to the regressions for Rule of Law.

 

Table B5: Rule Law Regressed on Life Expectancy

 

 

 

 

 

 

Estimated

Standard

 

 

Variable

Coefficient

Error

t-statistic

P-value

Constant i

-

 

-

-

Income-PC

7.40E-02

3.22E-02

2.30E+00

[.027]

LifeEx (fitted)

-2.59E-04

9.21E-05

-2.811

[.008]

 

 

Table B6: Outliers for Regression of Rule of Law on Life Expectancy

 

 

 

Coefficient

Variable

Variable

Estimated

Observed

Estim. minus

Bias of

Year

Country

Constant

Income-PC

LifeEx (fitted)

Rule Law

Rule Law

Obs. Rule Law

Estimate

1980

GHANA

-1.86147

976

52.7

1.79

1.0

0.8

over

1990

GHANA

-1.86147

902

58.2

2.21

3.0

-0.8

under

1980

PARAGUAY

-2.43742

2534

65.5

1.75

1.0

0.8

over

1990

PARAGUAY

-2.43742

2128

57.2

1.25

2.0

-0.8

under

1990

PERU

-2.11651

2875

56.5

1.32

2.0

-0.7

under

1980

PERU

-2.11651

2188

59.0

1.68

1.0

0.7

over

1990

ZAMBIA

-1.84248

971

59.6

2.32

3.0

-0.7

under

1990

ZAMBIA

-1.84248

689

56.8

2.18

1.5

0.7

over

 

Once again, all the outliers are Latin or African.  A similar pattern will be seen in the regressions using Child Mortality.

 

Table B7: Transparency Regressed on Child Mortality

 

 

 

 

 

 

Estimated

Standard

 

 

Variable

Coefficient

Error

t-statistic

P-value

Constant i

-

 

-

-

Income-PC

-3.10E-04

1.30E-04

-2.38085

[.023]

Child.Mort. (fitted)

-0.020462

9.34E-03

-2.18983

[.035]

 

 

Table B8: Outliers for Regression of Rule of Law on Child Mortality

 

 

 

Coefficient

Variable

Variable

Estimated

Observed

Estim. minus

Bias of

Year

Country

Constant

Income-PC

Child Mortality (fitted)

Rule Law

Rule Law

Observ. Rule Law

Estimate

1980

GHANA

3.49653

976

107.0

1.79

1.0

0.8

over

1990

GHANA

3.49653

902

78.4

2.21

3.0

-0.8

under

1980

PARAGUAY

2.94254

2534

46.7

1.73

1.0

0.7

over

1990

PARAGUAY

2.94254

2128

86.6

1.27

2.0

-0.7

under

1990

PERU

3.23503

2875

92.0

1.32

2.0

-0.7

under

1980

PERU

3.23503

2188

77.0

1.68

1.0

0.7

over

1990

ZAMBIA

3.51119

971

70.7

2.31

3.0

-0.7

under

1990

ZAMBIA

3.51119

689

84.3

2.19

1.5

0.7

over

 

 

Once again, the pattern of error terms is almost identical in Tables B6 and B8, with nearly identical terms in the second to last column -- except in the case of Paraguay.



*               Lally School of Management and Technology, Rensselaer Polytechnic Institute, 275 Windsor Street, Hartford, Connecticut 06120-2991, USA.  Professor Stodder can be reached at +1 860 548-7860 or Stodder@rh.edu.  Professor Schroth can be reached at +1 860 548-7845 or Schroth@rh.edu.

 

[1]               The unpredictability of chaotic corruption may actually be worse for private businesses than its rate of extraction, which is already too high to be revenue maximizing.  This is the conclusion of an empirical study by Wei (1997).

[2]               The current World Bank website for Micro and SME (Small to Medium Enterprise) Financing is http://wbln0018.worldbank.org/html/FinancialSectorWeb.nsf/SearchGeneral?openform&Rural+and+Microfinance/SMEs&Links. Although the URL may change, the World Bank site, www.worldbank.org, is itself easily searchable.

[3]               However, Rose-Ackerman (1997, p. 31) points out that “Some countries alleged to be corrupt have experienced high levels of economic growth.  In Indonesia, Thailand, and Korea, corruption and growth have gone together.”

[4]               The Inter-American Convention Against Corruption entered into force in 1997.

[5]               The OECD's Convention on Combating Bribery of Foreign Public Officials in International Business Transactions entered into force in 1999.

[6]               The Council of Europe’s Criminal Law Convention on Corruption entered into force on 1 July 2002.  It may be significant that the signatories include several Eastern European countries that both have a reputation for corruption and have not participated in other anti-corruption efforts.

[7]               While they do represent significant progress, the OAS and OECD Conventions and the various national implementing laws fall short of the laws already in force decades ago in the United States.  Neither of these conventions addresses the tax deductibility of bribes, which was eliminated – if it ever really existed – in the United States in 1958 (§ 162(c)(1) of the Internal Revenue Code, 26 U.S.C), although a separate OECD initiative has made some progress in this respect (Schroth 2002a, 2003).  The FCPA’s anti-bribery provisions include political parties and candidates, which are not covered in the conventions.  The U.S. laws in general remain far more detailed, and are backed by far harsher punishment, than those of any other country.  Also, unlike any other country, the United States has an established record of vigorous enforcement (Schroth 2002b).  The accounting provisions of the OAS and OECD Conventions appear similar in form to the long established U.S. law, but have turned out to be a toothless tiger (Schroth 2002c, 2003).  It is likely to be some time before other countries match the frequently severe and vigorous enforcement, both by an expert and experienced bureaucracy and by the private lawsuits of competitors and investors, of the U.S. laws in this area.

[8]               For example, subsection 3(1) provides, “A person is guilty of an offence if he or she, directly or indirectly, corruptly accepts or agrees to accept for himself or herself or for any other person any gratification as an inducement to do or not to do anything or as a reward for having done or not having done anything.”  Subsection 4(1) is similar, with the substitution of “gives or agrees to give” and words such as “corruptly” and “gratification” are defined in extremely broad terms.  Several provisions of the bill seem to invite constitutional challenge as infringements of the rights of the accused, setting the stage for a confrontation between the world’s strongest anti-bribery law and the world’s most liberal bill of rights.

[9]               The well known problem of corruption of the “transitional” economies within the former Soviet Union may stem from the very absence of this social cohesion, which is something that a central government cannot produce directly on its own (Stodder 1993).  Anti-corruption campaigns within the former Soviet Union, and within the People's Republic of China today, seem to be almost entirely founded on something which central governments can produce, namely fear of the severe punishments that await those who get caught.

[10]             Here transparency (where the high-scoring countries are the least corrupt) is coded by the IRIS International Country Risk data, and income is in purchasing power parity (PPP) terms from the Penn World Tables.  (Easterly's data set and details on all variables used in this paper are available at the World Bank working paper website, www.worldbank.org/research/peg/, Working Paper Number 17.)

[11]             For a counter-argument that cross-sectional tests may be more important – see conclusions in the final section.

[12]  Enrollment figures available at www.unisa.ac.za/about/gen_stats.html .