Econometric methodology

In order to estimate the proposed hypotheses to explain the variations of corruption, the panel estimation methodologies used are based on equations (4.1) - (4.4) for the period 1995 to 2004. However, Treisman (2007) questions about the use of CPI data in longitudinal analysis due to the methodological change and alteration of the set of sources used for constructing CPI over the years by Transparency International. While the methodological adjustments and alterations of sources are considerable, their impact on the CPI is rather small. For example, as the 2002 CPI was determined with the earlier methodology, the result correlates 0.996 with the current one. In spite of the methodological changes, there exists a high numerical continuity of the CPI (Lambsdroff, 2002). As the effect of economic development on corruption is likely to be long term, it is better to consider using a longer time period than a single year. Also, the analysis of variance (ANOVA) of CPI values shows that the variation between countries explained 68 percent of the total variation, whereas 33 percent of the variation is within the countries over time (Appendix Table A4.4). The evidence thus supports the use of panel estimations in the analysis.

The use of panel data for the cross-country analysis can generate clusters or groups where the presence of clustering can lead to serious errors in statistical inference. Moulton (1986, 1990) in examining this issue claims that when the explanatory variables in a regression model are drawn from a population with grouped structure it can result in spurious regression when estimating the effects of variables. Because the regression errors are often correlated within the grouped structure, they fail to account for the correlation of errors within groups. To deal with this possibility, following

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The re-scaled value is calculated based on the following formula:

New value = [(Maxnew – Minnew) * (Oldvalue – Minold) / (Maxold – Minold)] + Minnew where Minold is minimum value of the old scale; Maxold is maximum value of the old scale; Oldvalue is actual value in the old scale; Maxnew is maximum value in the new scale and Newvalue is new converted value in the new scale.

Moulton, the cross-section standard errors-corrected regressions are estimated for the entire analysis. This allows for general correlation of observations within a cross- section or cross section heteroskedasticity. In addition, the period standard errors- corrected and generalised least square estimates are computed for the robustness checks. The results of these two types of estimations are shown in the Appendix Table A4.5 – A4.7. In addition, two-way fixed effects and two-way random effects are also estimated. Finally, the Generalised Method of Moments (GMM) estimation is utilised to estimate the effect on corruption using the lagged values of CPI.

As an alternative econometric methodology the study also estimates ordinary least square (OLS) with heteroskedasticity consistent standard errors for the average period 1995-2004 for the robustness check. The use of average data minimises the estimation inefficiency resulting from measurement error due to the difficulty of measuring the actual level of corruption across countries. To examine whether measurement error has an impact on average data, following You and Khagram (2005) the study also estimates single-year OLS regressions for 1995 and 2004.

To address the potential issue of simultaneous causation two-stage least square (2SLS) procedure is used. As the data for the infant mortality rate and clean sanitation are not available annually, hence two-stage least square regressions are estimated for cross- country only. As corruption is likely to reduce economic growth, OLS estimation may overestimate the coefficient for log (RGDP).25 For estimating 2SLS, some instrumental variable(s) is/are required which may affect economic development, but cannot be affected by corruption. The infant mortality rate and availability of clean sanitation variables, for example, represent some community health indicators which may have an impact on per capita income via productivity and they are highly correlated with economic development. The simple correlation between infant mortality rate and log (RGDP) is -0.87 and that between sanitation index and log (RGDP) is 0.79. As a country’s infant mortality rate and access to clean sanitation cannot be directly affected by corruption, however these variables could affect corruption via economic development. Therefore, infant mortality rate (INFM) and sanitation index (SAN) can be used as potential instruments for economic development.

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Some statistics for developed and developing nations also suggest that infant mortality and the level of economic development are highly correlated. For example, infant mortality rates average about 96 per 1000 live births in the least developed countries, compared with 8 in developed countries in 2002 (Todaro and Smith, 2006, p. 55). Like infant mortality, access to sanitation is also correlated positively with economic development. The World Health Organisation (2004) Report suggests that roughly 40 percent of the population do not have access to improved sanitation in Africa, while in Asia, 52 percent are without access to improved sanitation in 2000. However, North America and Europe have higher rates of access at over 90 percent. These two instrumental variables satisfy the required statistical properties.26

4.4 Estimation Results

This section analyses the panel least square estimation results of the relationship between corruption and real GDP per capita, as well as other socio-economic and institutional factors. Subsection 4.4.1 below focuses on per capita income and corruption association for all countries covered in the study. Subsection 4.4.2 analyses the relationship for three different groups of countries based on income classification, and subsection 4.4.3 estimates the relationship between corruption and real GDP per capita in a non-linear framework.