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unpredictable informal ones and are willing to pay more of the latter. In reality, there is a
substitution between this two; this reflects the trade-off between costly incorrupt bureaucracy
and cheaper incorrupt one. Hence, tested variables may take either negative or positive signs
in the estimation, depending on which effect will come out as the stronger one.
Methodology and Discussion of the Empirical Results
The corruption indexes come not only with a point estimate of corruption in a given country,
but also with a standard error precision of the estimate. This additional data was used to
produce Weighted Least Squares estimation, where weights are the inverse of the standard
error of precision. This approach, proposed first by Treisman (2000) now became a standard
in the literature on corruption (Treisman, 2003, Gering and Thacker, 2005, among others).
This enables to give more weight to corruption scores that are more reliable in the regression.
A novelty in this paper is that Jackknife robust standard errors were used in the estimation.
The reason for this is a possibility of heterogeneity and correlated errors in the regression.
This is important since corruption indices may be systematically biased. Both the experts and
people surveyed on corruption may have consistently biased perceptions. What is not covered
in the paper is that there exists a probability of causality bias. In order to tackle this, IV
regression could be used, however, there are no examples of good instruments in the literature
on corruption as argued by Treisman (2000). It was tested with a trimmed regression whether
the results are affected by outliers and the results were consistent with those obtained without
trimming. When reading the empirical testing section, it is nevertheless important to bear in
mind that empirical research on corruption is still a relatively new field of inquiry. As a result,
the econometric methods used in the estimations may seem to be less advanced then the
techniques used in applications more original to the core of economic thought. Definitely,
more discussion is needed on the subject of proper approach to the estimation of economic
linkages of corruption.
Initially, the observations are pooled over the three years in the dataset and the model is
estimated with Weighted Least Squares (WLS). However, it has been shown that WLS may
sometimes suffer from heterogeneity bias in cross-country framework: the results of
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