Do more rules lead to more corruption? Evidence using firm-level survey data for developing countries
This page in:
We hear all too often that regulation creates opportunities for corruption. If true, a practical way to fight corruption and its harmful effects would simply be to deregulate. However, is it in fact the case that more regulation increases corruption?
Economic theory is somewhat ambiguous and offers two contrasting views or theories on this. First, the public choice theory posits that regulation is pursued for the benefit of politicians and bureaucrats. Politicians use regulation both to create rents and to extract them through campaign contributions, votes, and bribes. In some cases, the main beneficiary of regulation is the industry while in others, it is the politicians and public officials. According to this theory, more regulation equates to more corruption. Second, the public interest theory argues that markets are prone to frequent failures and regulation is intended to correct for these deficiencies. Of course, corruption may still arise as implementing rules requires bureaucrats who may be corrupt, but this is a different issue altogether.
Economists resolve such theoretical ambiguities by letting the data talk. While there have been some empirical attempts at estimating the impact of regulation on corruption, they have been limited in several ways. For instance, they mostly employ macro-level indices of corruption based on the subjective opinions of experts, which are prone to perception bias. The Manual on Corruption Surveys prepared by the UN (UNDOC, UNDP and UNODC-INEGI 2018) argues that corruption measures based on the actual experience of individuals with corruption (obtained from survey data) are far superior than the corruption measures based on the opinions and perceptions of experts. Other regulation indices are measured by laws on the books rather than actual regulatory burden. However, there is reason to believe that it is regulatory implementation rather than heavy-handed regulatory policy that is responsible for bribery (Duvanova 2014).
In a recent study, Amin and Soh (2020), we attempt to address some of these problems in estimating the corruption-regulation nexus. The study uses firm-level survey data for 132 mostly developing countries collected by the World Bank’s Enterprise Surveys (ES) on the actual regulatory burden and corruption experienced by the firms. On the corruption front, the study uses overall and petty corruption. Overall corruption is defined as bribes typically paid by firms (as % of their annual sales) to public officials to “get things done.” Petty corruption arises when firms pay bribes to solicit the following: obtaining connections to electricity or water, a construction permit, an import or operating license, or inspections or meetings with tax officials. To assess regulatory burden, the study looks at the percentage of a firm’s senior management’s time that is spent dealing with business regulations (Time Tax).
The study finds that greater regulatory burden does indeed lead to higher overall and petty corruption. For the baseline specification, the overall bribery rate rises by about 0.03 of a percentage point for each percentage point increase in the regulatory burden. So, moving from least to highest regulatory burden in the sample under study leads to an increase in overall corruption by 2.7 percentage points. This is a large increase, given that the mean level of overall corruption in the sample is 1.1 percent. The finding is robust to various controls, specifications, and estimation methods including OLS (cross-section and repeated cross-section estimation), logit, and Poisson. Figure 1 illustrates the relationship graphically. Due caution is taken to ensure that it is indeed regulation that affects corruption and not the other way around; and that it is regulation and not some other determinant of corruption that regulation is proxying for.
Figure 1: Relationship between regulation and corruption
Source: Enterprise Surveys, World Bank.
Note: Figure 1B represents a partial scatter plot. It is based on residuals obtained from regressing overall corruption on country and industry fixed effects (Y-axis) and from regressing Time Tax (as defined above) on country and industry fixed effects (X-axis). Only the latest round of ES data (baseline sample) is used.
The findings have important implications for policy makers. Deregulation is indeed a powerful tool for controlling corruption. Policy reform has helped reduce opportunities for corruption in many countries. Where governments must impose regulations to correct for market failures or achieve redistributive goals, policymakers should carefully consider the demands that a new policy might place on institutional capacity. Without institutional capacity, well-intended policies can lead to poor outcomes and even greater corruption.
The study is a starting point and there is much left to explore. First, the issue of how much corruption depends on laws on the books vs. effective implementation is important. If it is the case that it is the implementation rather than the heavy-handed laws on the books that affect corruption, we can focus exclusively on effective implementation and not worry about the quantum of regulation as far as corruption is concerned. Second, petty corruption that arises in specific transactions is likely to be better correlated with regulations that are specific to those transactions rather than the overall regulatory burden on the firms. In which case, we may find even bigger effects of corruption than found in the study referenced above. Third, it is conceivable that the strength of the corruption-regulation relationship may vary across different types of firms and countries. A rigorous analysis of the issue will help shed light on exactly how regulations affect corruption. We look forward to more research along these and related lines in the future.