FILSAFAT KORUPSI - Direktori File UPI

Underdevelopment
(The dotted lines denote causal relationships established by previous studies, while the real lines represent
hypothesized causal relationships of this study.)
In order to test my hypotheses and to identify causal mechanisms, I conduct rigorous
empirical research including both quantitative and qualitative methods. Although large-N
quantitative studies have an advantage in identifying correlations between an explanatory
variable(s) and the dependent variable controlling for plausible covariates, and thus may
be less vulnerable to omitted variables bias than small-N case studies, they are often
vulnerable to endogeneity bias and weak at identifying causal mechanisms. Comparative
historical case studies can be useful for establishing causal direction and illuminating
causal mechanisms by examining the historical sequence and intervening causal process
between an independent variable(s) and the outcome of the dependent variable
(Rueschemeyer and Stephens 1997). On the other hand, a small-N case study alone
cannot generalize a theory.
In my quantitative study of the causal effect of inequality on corruption, I attempt to
overcome the deficiencies of previous studies by employing improved methodologies and
using better data. Previous studies were vulnerable to measurement error and did not
adequately address the problem of endogeneity. Like previous studies, I use measures of
perceived levels of freedom from corruption such as Transparency International‘s
Corruption Perceptions Index (CPI) and Kaufmann, Kraay, and Mastruzzi‘s (2003)
Control of Corruption Indicator (CCI). Unlike previous studies, I use averaged data for
both perceived freedom from corruption (for the period of 1996-2002) and the
independent variables including income inequality (Gini index and mean/median ratio as
a measure of skewness), democracy (political rights score from Freedom House),
economic development (per capita income), and trade openness (percentage of imports
plus exports over GDP) for a long period of 1971-1996. By using averaged data instead
of single-year data, I expect to reduce the measurement error.
I also employ an instrumental variables method to cope with the problem of reverse
causation. Economic development and trade openness may also be influenced by
corruption, so I use instrumental variables for these endogenous variables as well. In
addition, I examine whether inequality affects norms and perceptions about corruption
significantly. If confirmed, this will be an additional piece of evidence for the effect of
inequality on corruption.
In my quantitative study of correlates of social trust, I use data on social trust and other
individual characteristics from the World Values Surveys and European Values Study
that were conducted in 1995-97 and in 1999-2001. Like many previous studies, I use the
binary responses to the question, ―Do you agree that most people can be trusted or you
can‘t be too careful in dealing with other people?‖ as a measure of social trust. There
must be substantial measurement error in social trust, but this survey question seems to
capture both trust and trustworthiness. Unlike previous studies, I employ a multilevel
hierarchical logit model of analysis to estimate how much individual-level factors and
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