Corruption happens and it happens across the world. Although it is more common in poorer economies, corruption exists everywhere. A lingering debate that still provides a powerful research motivation is whether corruptions greases or sands the wheels of economic growth (Bardhan 1997, Pande 2008, Aidt 2009).
Those in favour of the greasing hypothesis argue that corruption facilitates trade that may not have happened otherwise and that it promotes efficiency by allowing private sector agents to circumvent cumbersome regulations (Leff 1964, Huntington 1968, Méon and Weill 2010).
Opponents of this view, meanwhile, have built a solid theoretical rebuttal by arguing that the greasing effect of corruption is only possible as a second best option in a bad institutional setting. Thus, in order to properly evaluate the effects of corruption we have to recognise its endogeneity with respect to institutions (Aidt 2009). Theoretical analyses and empirical evidence supporting this view abound, showing that corruption sands the wheels of growth. Mauro (1995) argue that corruption reduces investment across developing countries, thereby negatively affecting growth. Reinikka and Svensson (2004, 2005) find that corruption has detrimental effects on human capital accumulation.
Yet the empirical evidence on the economic consequences of corruption is still inconclusive (Svensson 2005). For example, the literature still provides support to phenomena such as the so-called Asian paradox (a positive correlation between corruption and growth in a number of fairly successful Asian economies, including China) even after accounting for the crucial intermediate effect of institutions that shape the more recent versions of the greasing the wheels hypothesis (Rock and Bonnett 2004, Li and Wu 2007).
A systematic review of the econometric evidence
In recent research together with Ahmad Saleh (Campos et al. 2010), we use meta-regression techniques to summarise this evidence and try to settle this question by shedding light on whether there is a genuine relationship between corruption and growth. We attempt to evaluate the direction of this relationship, and to identify the main determinants that may help explain the variance in the observed effects of corruption on growth. For these purposes, we have put together a unique data set comprising a total of 460 empirical estimates of the effect of corruption on growth from 41 different studies.
Using the quantitative information in this data base, what would a typical piece of empirical research on the effect of corruption on economic growth look like? Firstly, the typical study is likely to be written by authors in academia. The time window it covers is somewhat short, about nine years on average. The “median paper” does not control for endogeneity nor include country fixed effects. About half the papers in this literature use panel and the other half use cross-sectional data. The typical paper tends to favour the Transparency International index as its measure of corruption. It is also likely to use a large multi-region sample and have human capital among its explanatory variables. Yet variables reflecting institutional quality are among those least likely to be found in a typical study, which is a serious omission in light of the attention this receives in recent attempts to assess the “grease versus sand” debate in the corruption and growth literature.
Figure 1 shows that about 32% of these estimates indicate a significant and negative impact of corruption on growth, 62% suggest a statistically insignificant relationship, while approximately 6% provide support for a positive and significant relation. On this basis alone, we may be tempted to argue that the support for the sanding hypothesis is much greater than that for the greasing hypothesis, yet more than half of the results suggest that there is little statistical basis for this relationship. Why?
Figure 1. Histogram of the t-values of 460 coefficients of corruption on economic growth from 41 published and unpublished econometric studies (note: original t-values were multiplied by -1)
What are the reasons that explain the variation in the effects of corruption on economic growth (depicted in Figure 1)? We find that the main explanatory factors are:
- authors’ affiliation (authors whose main affiliation is academia systematically report less negative coefficients),
- the use of fixed-effects (which interestingly tend to increase the negative effect of corruption on growth),
- and the inclusion in the model of trade openness and institutions, which both tend to deflate the negative effect of corruption on economic growth.
Meta-regression analysis also tries to identify biases arising from the tendency of published academic papers to lean towards statistically significant results. A number of methods have been developed with the aim of identifying such bias and its direction, as well as to answer the question of whether the bias is so strong as to obscure any genuine relationship between the variables of interest.
We use a range of methodologies on the sample as a whole as well as separately for published and unpublished papers. For the sample as a whole, we find evidence for a bias in favour of negative and statistically significant results but different methodologies generate conflicting answers as to whether the bias is so severe as to obscure a genuine relationship between corruption and growth. The ambiguity recedes somewhat when the data is split into samples of published and unpublished papers. The results from the various techniques support the view that despite the bias, published research unveils a genuine negative relationship between corruption and growth and, interestingly, that is not the case among results in unpublished research. Two possible explanations are a stronger tendency of unpublished (e.g. policy oriented) papers to lean in favour of negative and significant results, and that the screening process of academic journals strips published papers of empirical deficiencies that may be the main reason for this ambiguity.
The main lessons from this stock-taking exercise on what we know about the impact of corruption on economic growth are as follows. The cross-country macro-econometric evidence provides rather limited support to the view that corruption greases the wheels of growth, with trade openness and institutional quality appearing to be crucial factors in mediating the effects of corruption on growth. In terms of future research, both data quality and the standards of the econometric modelling remain huge concerns in this area. Macro-data should be coupled with micro-evidence in trying to pinpoint the exact mechanisms and channels through which corruption affects growth (for example, is corruption principally a barrier to entry or just a tax on incumbent firms?). Future econometric modelling should try to establish clearer links to the theoretical debate (the common exclusion of institutional quality being a case in point) and be more attentive to endogeneity, reporting and errors-in-variables concerns.
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