To assist developing countries, it has been the official policy of many aid agencies and development banks to alleviate poverty through promoting economic development and reducing corruption (International Monetary Fund, 2010; United Nations, 2009a; World Bank, 2009). The agencies and banks continue to use Gross Domestic Product per capita (GDP per capita) as a main indicator of economic development and have identified corruption as a key impediment to improving a country's economic growth (International Monetary Fund, 2000; United Nations, 2009b; World Bank, 2009). To encourage countries to reduce corruption they undertook two key strategies: First, they prioritized aid to countries demonstrating 'good' governance, and second, they made economic development loan provisions which require recipients to limit corruption (Kaufmann and Kraay, 2002; Kurtz and Schrank, 2007). Clearly, many international agencies see a cause-effect link between corruption and economic development.
Researchers have been long searching for a better understanding of these linkages. Their study results can be placed in two broad categories: theory and empiricism. The general conclusions, from a theoretical perspective, are that corruption could retard economic growth through, for example, generating sociopolitical instability (Mo, 2001), encouraging bureaucratic processes specifically to support corruption (Jain, 2001) and reducing openness to trade (Pellegrini and Gerlagh, 2004). The argument that corruption could provide a minor stimulus for economic growth has been mostly rejected (Jain, 2001; Mo, 2001; Pellegrini and Gerlagh, 2004).
There are also theoretical reasons to conclude that high economic growth could lead to reduced corruption. For example, economic growth helps to pay for the monitoring necessary to identify corrupt practices, it strengthens the political institutions which can control corrupt behavior and it reduces the discrepancy between corrupt and legitimate income earners (Jain, 2001).
The empirical studies' results are less clear, especially on the cause-effect relationship between corruption and economic growth. Mauro (1996), Gupta et al. (1998), Mo (2001) and Pellegrini and Gerlagh (2004) concluded that high levels of corruption lead to lower economic growth. Mauro (1996) and Gupta et al. (1998) utilized cross-section data and Two-Stage Least Squares (2SLS) with instrumental variables (to deal with possible biases due to endogenous variables) to demonstrate a significant negative relationship between corruption and economic growth. Mo (2001) and Pellegrini and Gerlagh (2004) utilized cross-sectional data and 2SLS with transmission channels to demonstrate a significant negative relationship between corruption and economic growth.
In contrast, Kurtz and Schrank (2007) established the opposite cause-effect pathway, that is: high economic growth leads to reduced corruption. Their divergent analysis was based on time-series crosssectional data and a General Linear Model (GLM), which allowed them to test for a relationship between corruption and future development growth (rather than co-temporal growth). They found no significant relationship between corruption and future economic growth, though they found evidence that development can lead to a reduction in corruption. They also suggest that many of the conclusions from previous studies, which found a corruption/economic growth cause-effect pathway, are an artifact of using a combination of cross-section data with OLS or 2SLS with instrumental variables.
To add to the confusion, Kaufmann and Kraay (2002) found what may be a feedback loop between corruption and development, suggesting that increased economic development could lead to short-term increases in levels of corruption, as more money is available to foster it.
Despite the confusion, most analysts agree that there is a very strong negative correlation between measures of existing corruption and economic development (Kurtz and Schrank, 2007). However, the negative correlation does not explain how changes in corruption will affect development, or how changes in development will affect corruption. Given that the theoretical arguments and empirical evidence showing complex linkages in the cause-effect pathways, the links between corruption and economic growth requires further investigation.
The objective of the paper, then, is to look for further empirical evidence on both the existence and direction of cause-effect linkages between corruption and economic development. The study represents a unique contribution in its application of Structural Equation Modeling (SEM) to the problem.
The measure of corruption is based on a 'perceived' corruption index. The data set used to indicate corruption was taken from Transparency International's Corruption Perception Index (CPI) (TI, 2008). The CPI is a 'pole of poles' based on surveys of experts and businesses and their perception of corruption in the public officials and politicians of a particular country. Annual figures are available from 1995, starting with 41 countries and increasing to over 180 countries by 2007. The index ranges from 0 to 10, with 0 indicating a high level of perceived corruption.
The economic development indicators are from the GDP per capita statistics provided by the USDA Economic Research Service (USDA, 2008). They are based on GDP per capita expressed in nominal, year 2000, $US. Complete data was available for 80 countries. The combined data was summarized at 4 points in time: 1998, 2001, 2004, and 2007; this naturally resulted in the observations of three periods of change. Logarithmic transformations were completed on the CPIs and GDP per capita; Changes were calculated as the differences in the transformed...
Are a country's corruption and development related?: a longitudinal cross-lagged structural equation model analysis.
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