This paper explores the association between corruption and trade policy. A non-technical theoretical framework first outlines the effects of corruption on non-tariff barriers at the political and bureaucratic level. Both parts have incentives to accept bribes in exchange for increasing barriers. These considerations include politicians’ re-election motives, implying that free information may weaken the association between corruption and trade barriers. I employ panel data on corruption and non-tariff barriers in three periods 1995–2005. The results show that corruption tends to lead to higher non-tariff barriers. The effectiveness of corruption in buying barriers varies with the degree of press freedom and GDP per capita.
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A previous working paper version of this paper (available online) outlined a simple, mathematical formulation of the following intuitive arguments.
The Fraser Institute actually makes this point in their annual report, stating that administrative factors influencing trade are sometimes “the result of inefficiency while in other instances they reflect the actions of corrupt officials seeking to extract bribes” (Gartzke et al. 2005, 8).
Note that although the question on regulatory barriers mentions official tariff barriers and might therefore simply proxy for tariffs, this problem is solved in the empirics by the inclusion of the average tariff rate. This way, the measured effects come to pertain to the NTBs and not official barriers.
These data are from Bjørnskov (2008), calculated on the basis of the party categorizations in Beck et al. (2001); this source also provides the measure of political competition. Although political ideology is difficult to quantify, a problem that has been discussed in a voluminous literature (Keman 2007; McDonald et al. 2007; Potrafke 2009; Powell 2009), most different coding schemes correlate highly with each other despite their different procedures and starting points, and tend to yield similar results (Potrafke 2010).
A number of studies have recently started using the more conservative binary democracy indicator from Cheibub et al. (2010). However, within the present sample, the two indices produce virtually the same scores. I therefore opt for the better known measure from Freedom House.
The human rights database is outlined in CIRI (2007). An earlier version of this paper also included a small extreme bounds analysis, including government expenditures, legal quality, average years of school and the secondary schooling completion ratio, the number of veto players in politics, population size, a dummy for the Nordic countries, the Fraser Institute measure of regulatory freedom, and a dummy for presidential systems. These and similar variables were chosen based on existing literature on corruption and on concerns that specific variables might confound NTBs and corruption (Treisman 2000, 2007; Aidt 2003; Glaeser and Saks 2006; Potrafke 2012). Likewise, I performed a set of country jackknife exercises. However, all findings proved to be robust in an extreme bounds sense and robust in the jackknife. These analyses are all available in an online appendix.
The tests reported in this subsection are naturally not the only robustness tests one would ideally perform. An appendix available online provides further support for the main estimates by adding a set of additional control variables, performing a small extreme bounds analysis, region and country jackknife estimates, and testing the stability of estimates to allowing for an AR 1 disturbance within the error terms. All central estimates remain stable and significant.
The Kaufmann indices have been criticized due to the very high correlations between the six components of institutional quality and capacity, which may invalidate the use of any single index and violate their comparability over time (Knack and Langbein 2010; Shah 2008). Instead, the data are more likely to be reflections of the overall institutional environment at one point in time instead of precise measures of corruption. In the present data, the simple correlation between the two corruption indices is above 0.9, yet the correlation between the Kaufmann control of corruption index and the Fraser Institute index of legal quality is virtually the same as that with corruption. Running a fixed effects regression of the Kaufmann corruption index on the index from Transparency International reveals that it only explains about 20 % of the within-country variation. Hence, it is unclear whether the Kaufmann data measure corruption or the rule of law.
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See Table 6.
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Bjørnskov, C. Can bribes buy protection against international competition?. Rev World Econ 148, 751–775 (2012). https://doi.org/10.1007/s10290-012-0128-z
- Trade policy
- Press freedom