Comparative research on corruption has always faced challenges on how to reliably measure this phenomenon. Indicators based on perceptions of or experience with corruption are the most common approaches, but these methods have also faced criticism regarding limitations to their conceptual and measurement validity. A number of scholars have thus sought to develop alternative, more objective, measures of corruption. Following this line of research, this paper relies on audit reports from Brazilian municipalities to construct a concrete indicator of political corruption. Data collection exploits the setup of randomized multiple audit rounds to construct a unique panel of 140 municipalities covering five administrative terms between 1997 and 2013. A first empirical application of data is presented, testing the potential deterrent effect of electoral accountability on future corruption levels.
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This is naturally associated with the fact that corruption acts are most often illegal, but recent discussions have highlighted the existence of legal forms of corruption, understood as “abuses of public office or entrusted power for private gain”, in explaining different patterns of corruption across countries (Kaufmann and Vicente 2011). The political influence of economic actors exercised through legal campaign financing is a good example of this (Johnston 2005).
These indices are largely based on the perceived frequency of corruption reported in different surveys of country experts, business people, and citizens (Treisman 2007).
Another study that puts into question the validity of perception-based indicators is the analysis by Olken (2009), who finds discrepancies between corruption perceptions and a more concrete measure of corruption, based on an estimate of missing expenditures in the implementation of road projects in Indonesian villages.
Ferraz and Finan (2008, 2011) and Brollo (2010) have compiled corruption indicators from audit reports originating from the same audit program in Brazil but did not employ the same panel structure.
Based on data published by the National Treasury Department (STN) for 2012, a mean of 86 % of municipal revenue comes from intergovernmental transfers (from both federal and state levels), and the share of transfers from the federal government reaches 48 % on average.
As a rule, the jurisdiction for external oversight regarding municipal administrations lies with state-based Courts of Accounts, which are responsible, among other things, for assessing and passing opinion on yearly financial and managerial reports submitted by each municipality. However, their effectiveness in performing this control function is often weakened by factors such as delays in the analysis of such reports, the limitation of audits to formal aspects and a considerable degree of politicization in the appointment of the Courts’ members, which reduces their independence as watchdogs (Arantes et al. 2005; Loureiro et al. 2009; Weitz-Shapiro et al. 2015).
Earlier lottery rounds had slightly different rules, and the number of municipalities selected gradually increased from only 5 in the first edition to 26 in the second, 50 from the third to the ninth rounds, and finally 60, which has been the standard since then. Also, the population threshold was 250,000 or 300,000 in earlier rounds, and was eventually increased as well.
An exception is made with regards to one group of five less populated northern states, from which a total of two municipalities from two different states is selected in each round.
The coding of the material is a relatively cumbersome process and, due to time and resource constraints, it was not possible to collect the data for the municipalities that did not fulfill these criteria.
In Brazil, mayors are elected for a 4-year term and are allowed to run for only one consecutive term after that. They may run again only after a hiatus of 4 years in which another mayor has been in power.
This criterion could only be applied in the case of elections until 2004, as after that no data was available on electoral coalitions.
It is not uncommon in Brazilian municipalities for local politics to be marked by the dominance of certain political dynasties that remain in power for longer periods of time. In such cases, incumbent mayors that cannot be re-elected often present a relative or even their spouse as a candidate to continue their “legacy” in office.
A few other cases were excluded in which audit 1 took place before an election, but its results were released only shortly before or after election day, since the relevant information about corruption could not have reached voters. Cases in which the relevant election had only one registered candidate were also disregarded.
Dozens of specific concrete situations exemplifying each category were identified in the coding. As they are not always explicitly related to the occurrence of corruption in the audit reports, the coding process included an inductive component as well, through which the different occurrences described by the auditors were considered as associated with corruption violations when at least part of the reports characterized them as linked to suspicions of fraud, favoritism, diversion, or marked-up prices. A detailed list with all types of occurrences considered in the classification of corruption violations can be obtained from the author.
Due to the conflict of interest associated with such cases, procurement legislation in Brazil (Law 8666/1993) explicitly prohibits the hiring of companies owned by public officials employed in the respective hiring agency, with the understanding that this implies potential access to privileged information and favoritism. Analogously, the jurisprudence of the Federal Court of Accounts (TCU) has applied the same understanding to consider illegal the hiring of companies owned by relatives of public officials as well.
Previous studies (Ferraz and Finan 2008, 2011) included the share of funds linked to corruption violations in the total amount of audited funds as an alternative measure of corruption. This was also calculated for the sample considered in this study but was not used due to reliability and measurement error concerns, given that many of the reports do not include this information while describing some of the irregularities.
Ferraz and Finan (2011), for instance, find the mean number of irregularities per municipality to be around 2.5, and Brollo (2010) estimates 1.8 as the mean number of violations in her sample. The data collected for this study, on the other hand, finds the mean number of corruption violations for all audited periods at 8.4.
The coding for this variable was based on the time periods mentioned in association with each of the service orders covered in the reports, complemented by data from Portal da Transparência (http://transparencia.gov.br/convenios/) on cofinancing grants implemented in the municipalities.
Interestingly, this is not necessarily limited to single municipalities, and in a few cases, the replicated “techniques” indicate the functioning of more organized corruption schemes extending to several municipalities or even regions. In the sample at hand, for instance, a similar practice of procurement fraud with restricted competition in the award of school transportation services, combined with blatant overinvoicing in the contract amounts, is visible in almost all the municipalities in the state of Ceará. In another state, Rio Grande do Norte, one of the audit reports makes explicit mention to a parallel investigation that uncovered an accounting firm responsible for fabricating documents of simulated tenders with fake firms in multiple municipalities. An even larger case of fraud in the purchase of ambulances, which became know nationwide in 2006 as the Sanguessugas (“Leeches”) scandal, was uncovered after the CGU audits found evidence of fraud in tenders favoring the same group of companies in municipalities of several states.
Actually, this learning effect is not restricted to cases of procurement fraud. A similar replication pattern can be observed in many cases with the other kinds of violations as well, where the same corruption practices are often used in the application of funds from different programs.
The sources for these variables are described in more detail in Section 3.
Variable definitions and the respective sources are summarized in “Appendix B.”
More importantly, regional fixed-effects may also capture unobserved cultural differences that cannot be accounted for by the other indicators available.
The latter is based on a broader interpretation of electoral accountability where politicians may face a longer time horizon, and even if incumbent mayors themselves are not directly eligible for reelection, they still have a stake in their party’s or political group’s remaining in power. Therefore, they may still face some incentives to refrain from corruption in order to improve the chances of that group’s securing another term in office.
An alternative approach to estimating the effect of electoral accountability on corruption over time would be to observe the change in corruption levels across periods. However, this would be associated with endogeneity problems, because corruption in the period before the election is expected to be affected ex ante by the probability of electoral accountability in the future. The use of future corruption levels, i.e., corruption after the occurrence of accountability as a dependent variable, has the objective of minimizing endogeneity concerns, since future corruption is posterior to the occurrence of electoral accountability and arguably does not affect it ex ante.
The same models presented in Table 4 were also tested with the original (non-log-transformed) values for corruption violations. However, even with robust estimation, the observed residuals in all models are not normally distributed, as generally assumed in linear regression. Moreover, all models do not pass a Ramsey test for omitted variables, which indicates that there are problems with model fit regarding the functional form. In addition to linear regression, Poisson and negative binomial regression with the original variable were tested as well, given the count nature of the corruption indicator. Nevertheless, those models were also found to offer an inadequate fit, with the exception of the negative binomial model with the broad sample, the only one to indicate a correct link function for the data structure. In any case, none of these models show a statistically significant effect of electoral accountability on corruption. The results for the fully specified models are reported in Table 9 in “Appendix A.”
The recent literature on corruption in Brazil has seen the emergence of a number of studies exploiting the availability of the audit-based corruption data in the examination of the relationship between corruption and several other factors. A few examples include studies on the impact of e-government (Vieira 2012), participatory mechanisms of oversight (Vieira 2014), increased government transfers (Brollo et al. 2013), and gender (Brollo and Troiano 2015) on corruption and mismanagement at the municipal level.
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Mondo, B.V. Measuring Political Corruption from Audit Results: A New Panel of Brazilian Municipalities. Eur J Crim Policy Res 22, 477–498 (2016). https://doi.org/10.1007/s10610-016-9306-1
- Political corruption
- Measurement of corruption
- Electoral accountability