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This paper is forthcoming as a chapter in The Political Economy of Corruption, ed. by Arvind K. Jain (London: Routledge). The authors wish to thank Emanuele Baldacci, Luiz De Mello, Gabriela lnchauste, Arvind Jain, and Luc Leruth for their comments. The usual disclaimer applies.
Governments provide a wide range of services in social sectors, as measured by intermediate health care and education indicators (e.g., immunization and school enrollment) and outcomes (e.g., literacy and mortality)
Government revenues are lower according to these two models because different government agencies act as independent rent-seeking, monopolist providers of complementary goods and services or because of corrupt and extortionist tax inspectors.
Alesina (1999) discusses this “vicious cycle” in developing countries and contrasts it with a “virtuous cycle” in developed countries.
Kauftnann, Kraay, and Zoido-Lobaton (1999a) test a simple association between two social indicators and various measures of governance.
This is similar to Alam’s (1989,1990) model, where managers increase their illicit revenues by reducing output. The result is also consistent with studies of benefit incidence of public social spending, which point to significant leakages. Benefits from public spending disproportionately accrue to the rich; the poor simply do not utilize public services as intensively as the rich, despite the fact that the poor tend to have lower levels of health care and education achievements (Castro-Leal and others, 1999; Davoodi and Sachjapinan, forthcoming). Corruption, of course, is not the only reason for this leakage.
The CIET social audits are available via the Internet: http://www.ciet.org.
Available via the Internet: http://www.unibas.ch/wwz/wifor/staff/bf/survey/index.html.
In the survey, corruption was defined as irregular payments made to officials, and corruption uncertainty as firms asked to pay more, in addition to irregular payments. Respondents were required to rate their responses from I (worst) to 6 (best). Respondents were also asked to rate the quality of health care services and efficiency of government services provided in their country, following the same scale. For ease of interpretation, this paper re-scales the corruption and uncertainty indices from 1 (best) to 6 (worst), with higher values of each index representing higher corruption and higher uncertainty.
See Pradhan and Campos (1999) and Wei (1997) for the concepts of corruption unpredictability and corruption uncertainty, both of which are found to have adverse impact on development.
The regression produces an adjusted R-squared of 0.13. with variations explained mostly by corruption and not by the quality of health care provision:
where t–statistics are in parentheses. The regression does not control for other determinants of child mortality, hence the low R-squared, and does not address the endogeneity of corruption or reverse causality. These issues are discussed in Section IV of this paper.
Each cell is calculated as one standard deviation around the mean; the results are the same when two standard deviations are used. Child mortality rates refer to under-age-5 mortality rates.
The survey does not provide data on the quality of education service provision; the indicator of the efficiency of government services is used instead. The regression produces an adjusted R-squared of 0.29:
Not all informal charges are necessarily bribe payments.
The Economist magazine (1994) reports on the theft of medical supplies.
Cross-sectional data are averages of each variable by country over the 1985–97 period. The actual number of observations varies depending on specifications. Descriptive statistics are provided in the appendix.
See, for example, Pritchett and Summers (1996) on the nonlinear relationship between income and health.
As noted later in the robustness tests, however, using the graft index in place of the PRS/ICRG index yields the same overall results.
The indicators have been endorsed by the OECD, the World Bank, and the UN; the Common Country Assessment (CCA) of the UN Development Assistance Framework; the UN/CCA Task Force on Basic Social Services; and by the UN Statistical Commission under the Minimum National Social Data Set (MNSDS).
Other data are taken from Davoodi and Sachjapinan (forthcoming), and International Monetary Fund, World Economic Outlook (1999).
The link between income and social indicators is well documented. See, for example, Jack (1999) for a brief survey of the relevant health literature.
In general, the estimated coefficient of per capita income in the mortality regressions is consistent with previous cross-country estimates. Wang and others (1999), for example, suggest that income elasticity for child mortality in 1990 was −0.71 in low-income and middle-income countries, which is close to the estimate of −0.73 obtained in this paper.
Dependency ratios have been used in regressions of education outcomes. See, for example, Tan and Mingat (1992). Behrman, Duryea, and Szekely (1999) suggest that dependency ratios change the relative share of public resources available for school-age children. This effect may hold for health as well.
According to Schultz’s (1993) survey of the literature, studies suggest that mortality is higher for rural, low-income households. Plank (1987) finds that access to education is typically better in urban areas.
For lack of annual data from 1985–97, 1990 data on average years of education of adult females are used. The regressions also hold when 1990 data on average years of education of all adults are used.
Shi (2000) finds that access to potable water and sewerage connection have a significant impact on child mortality.
This follows Davoodi and Sachjapinan (forthcoming). To keep the sample size as large as possible, this paper restricts the controls to the strongest and consistent determinants of health outcomes, limiting the sample to 31 countries.
These results also hold when pupil-teacher ratio is added as well.
Corruption, for example, is correlated with public spending on education and health. The health regression results generally hold even when per capita health expenditures, including private spending, is used in place of public health spending.
La Porta, Lopez-de-Silanes, Shleifer, and Vishny (1999) also find that countries that are less developed have higher catholic and Muslim populations, and countries with French or socialist laws tend to have inferior measures of government performance, including higher corruption.
Further difficulties arise if per capita income is regarded as endogenous to health care and education indicators. Good health or better education could raise living standards, thus implying reverse causality. To address this concern, the initial value of per capita income (1985) is used as an instrument for per capita income averaged over the 1985–97 period.
The other control variables are assumed to act as their own instruments.
Removal of apparent outliers in Figure 4, for example, those associated with child mortality rates well above 200, in fact strengthens the relationship.
This is not surprising, considering the high degree of correlation between the PRS/ICRG index and the graft indicator.
These indices are both scaled from 0 (most corrupt) to 10 (least corrupt).
The TI corruption index is also significantly correlated with immunization and persistence rates, controlling for all these variables. These results are available from authors upon request.
In the two-way classification case, the high corruption dummy takes a value of one when corruption scores are above the mean (or median) and zero otherwise. In the three-way classification case, a high corruption dummy takes a value of one when corruption scores are greater than one standard deviation above the mean and zero otherwise. The medium corruption is defined as one when corruption scores lie within one standard deviation around the mean and zero otherwise. The low corruption dummy takes a value of one when corruption scores are less than one standard deviation below the mean.
When high corruption is used as benchmark, the dummy for low corruption is significant but medium corruption is not.
A country is classified under high corruption if its corruption score is higher than the median graft score; otherwise, it is classified under low corruption. The results hold when the mean is used in place of the median.
Average years of education in the female population was dropped as a control variable, due to lack of annual data. These results are in Appendix Table 2. These results hold when the regressions also control for physicians per 1,000 and, for a much smaller sample, safe water and sanitation.
It is, however, possible that the supply of basic public services is constrained in some countries, and would need to be expanded.
This participatory principle underlies the preparation of recently introduced poverty reduction strategy papers for the Highly Indebted Poor Countries (HIPCs). It is envisioned that all stakeholders in the economy will participate in the process leading to the preparation of antipoverty programs, including for health and education, that are consistent with the overall macroeconomic framework.