Don’t Blame the Weather: Federal Natural Disaster Aid and Public Corruption

Do episodes of extreme weather conditions contribute to the geographic patterns in public corruption across the United States? Leeson and Sobel (2008) argue that they do. Using data culled from the annual report to Congress by the Public Integrity Section (PIN) of the Department of Justice (DOJ), they show that convictions for public corruption are substantially higher in states that are frequently hit by natural disasters, such as those along the US Gulf Coast. Leeson and Sobel argue that this finding has a simple explanation: the influx of federal aid that follows in the wake of a natural disaster creates many new opportunities for fraudulent appropriation by public officials, thereby increasing corruption.

In this study, we use a new and more detailed dataset to revisit the relationship between federal disaster aid and public corruption. Although the PIN data employed by Leeson and Sobel (2008) have been used extensively in the empirical literature, they are not particularly well suited to testing the hypothesis that disaster aid promotes public corruption. If bad weather indirectly leads to an increase in corruption via the disaster aid channel, then we would expect the relationship between the amount of federal disaster aid received by a state and the number of public officials convicted of corruption-related offenses to manifest itself primarily among state and local officials. This expectation arises from the manner in which the Federal Emergency Management Agency (FEMA) oversees and distributes disaster aid.

Once an official disaster declaration has been issued, FEMA takes the lead role in coordinating federal disaster relief. Disaster aid funds are provided directly to individuals, to state governments, and to local governments. Although some FEMA officials and some officials at other federal agencies that also distribute disaster aid might themselves be involved in corruption, we would not expect most federal officials who work within a state to have significant opportunities to fraudulently appropriate disaster relief funds. To the extent that federal aid creates new opportunities for public corruption, we expect these opportunities to be highly concentrated in state and local government agencies.

Most of the anecdotal evidence cited by Leeson and Sobel (2008) is consistent with this view. For example, they note that after flooding in Buchanan County, Virginia, in 2002, “county officials embarked on a frenzy of bribe solicitation for relief-related reconstruction contracts that ended in 16 indictments for public corruption.” Because the PIN data used by Leeson and Sobel (2008) do not distinguish between federal, state, and local officials, their findings could be influenced by the presence of a large number of federal officials in the dataset, which account for about half of all PIN corruption convictions. In addition, Cordis and Milyo (forthcoming) identify several other issues that raise concerns about the reliability of the PIN data.

First, the numbers presented in the annual PIN report to Congress are not internally consistent. The aggregate annual number of convictions listed in the report does not match the number of convictions obtained by aggregating the district-level data in the report, and there are large unexplained swings in the former number over time. Second, the aggregate annual number of convictions from the PIN report is much larger than the aggregate annual number of convictions shown in the statistical report of the Executive Office for United States Attorneys (EOUSA). Cordis and Milyo (forthcoming) argue that the most likely source of this discrepancy is that the PIN data include a large number of convictions of postal service employees for stealing and destroying mail. Although destroying, stealing, or tampering with mail is a federal crime, it is not corruption as it is usually described in the literature.

To overcome these shortcomings of the PIN data, we conduct our analysis using a new dataset obtained from the Transactional Records Access Clearinghouse (TRAC), a research organization affiliated with Syracuse University. The TRAC database contains records on all publicly available criminal cases in federal courts, including offenses by federal, state, and local public employees for official misconduct or misuse of office. These data can be disaggregated down to the individual case level, making it possible to analyze corruption by referrals, convictions, and penalties imposed, and to segment convictions by type of charge, type of public official, and geographic district. The ability to do so allows us to test the Leeson and Sobel (2008) hypothesis using convictions for FEMA, state, and local officials (i.e., using a convictions series that excludes all non-FEMA federal officials). Not only is this aspect of the TRAC data important for our analysis, it will likely be a boon for future empirical research on the causes and consequences of public corruption.

We begin the empirical analysis by replicating the Leeson and Sobel (2008) panel data regressions for their sample period, which covers the years 1990–1999. The results of these regressions using the PIN data are very similar to those that they report. The coefficient estimates point to a statistically significant relationship between the amount of federal disaster aid received by a state in a given year and the number of corruption convictions in the next few years. In addition, this relationship appears to be relatively robust to the use of different regression specifications and sets of controls.

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