As climate change drives more severe droughts, floods and wildfires, the government spends billions helping victims. We found that disaster aid follows and perpetuates inequities in the U.S. economy.
NPR analyzed 40,000 federal property buyouts we obtained via FOIA lawsuit. Most were in neighborhoods that are over 85 percent white. We focused on how inequities in disaster funding affect families and communities. In Houston, we found that richer, whiter neighborhoods are more resilient to floods. We profiled two adjacent towns in New Jersey, which had different economic fortunes driven by disaster aid. We released the entire database to the public.
On April 18, 2019, Sen. Elizabeth Warren (D-Mass.) and Rep. Bennie G. Thompson (D-Miss.), Chairman of the House Committee on Homeland Security, formally asked the Government Accountability Office to determine the extent to which the structure and administration of federal disaster relief programs “exacerbate racial and socioeconomic inequities in the United States and the extent to which they have a disparate impact on Native tribal nations.” Their letter explicitly cited the NPR series. The GAO agreed to study the issue. Relevant documents:
Sen. Warren news release on the request 4/18/19: https://www.warren.senate.gov/oversight/letters/warren-thompson-seek-gao-review-of-federal-disaster-aid-programs-impact-on-inequality
Sen. Warren news release on GAO agreeing to the study, 6/11/2019: https://www.warren.senate.gov/oversight/reports/gao-agrees-to-investigate-federal-disaster-aid-programs-impact-on-inequality
Also, during the reporting process and since our report was published, multiple academic sociologists have told NPR they are following up on questions raised by the trends NPR uncovered. Those academic studies are still forthcoming.
We analyzed the data via SPSS and Microsoft Access to track the demographic inequities in the disaster buyouts, in part by linking the database to U.S. Census datafiles from the American Community Survey. We linked the data in several waves. Each wave of census data was tied to the timeframe appropriate to the buyout, so that we could use the demographics that existed at the time of the buyouts. This was essential as some areas were dynamic demographically, as a potential effect of the buyouts. We found this in New Jersey, where two similar towns were profiled, highlighting the economic and social differences associated with disaster aid in both places. We used ESRI ArcMap to validate property coordinates, as a guide to finding disaster buyout clusters visually and to make decisions about where in the country to report.
What was the hardest part of this project?
The hardest part of this project was obtaining the data. We filed a FOIA request for from the Federal Emergency Management Administration (FEMA) in 2014. It was denied, as was a subsequent appeal. We then sued FEMA in U.S. District Court (Civil Action No. 17-91.) We won the suit and obtained the data in November, 2017. Once we had the data we were able to convince some officials within the agency to cooperate in explaining it on background, and in getting an interview with the administrator who is responsible for the buyout program. The difficulty of getting the data from the government factored into our decision to provide the entire database to the public, to increase the overall transparency of the program and allow local communities to understand their own buyout patterns.
What can others learn from this project?
Main lessons from this project:
An agency’s justification for withholding data may not be validated by a court.
Even if release of the data is without precedent (as this was), a court will still weigh the pros and cons of public release.
A reporter’s prospects for obtaining legal assistance is correlated with the merit of their claim. Our attorneys agreed to take this case in part because they believed it would succeed.
When faced with the fact that their data will be released, a government custodian of the data is often motivated to explain it. Once released, they may have no interest in seeing it misinterpreted. That could potentially reflect poorly on them as well as on the news organization.