The scale of wildfire destruction has grown exponentially in recent years, destroying nearly 25,000 buildings in the United States during 2018 alone. However, there is still limited research exploring how wildfires affect migration patterns and household finances. In this study, we evaluate the effects of wildfire destruction on in-migration and out-migration probability at the Census tract level in the United States from 1999 to 2018. We then shift to the individual level and examine changes in homeownership, consumer credit usage, and financial distress among people whose neighborhood suffered damaging fires. We pair quarterly observations from the Federal Reserve Bank of New York/Equifax Consumer Credit Panel with building destruction counts from the US National Incident Management System/Incident Command System database of wildfire events. Our findings show significantly heightened out-migration probability among tracts that experienced the most destructive wildfires, but no effect on in-migration probability. Among the consumer credit measures, we find a significant drop in homeownership among those treated by major fires. This is concentrated in people over the age of 60. Measures of credit distress, including delinquencies, bankruptcies, and foreclosures, improve rather than deteriorate after the fire, but the changes are not statistically significant. While wildfire effects on migration and borrowing are measurable, they are not yet as large as those observed following other natural disasters such as hurricanes.
In this paper, we infuse consideration of migration into research on economic losses from extreme weather disasters. Taking a comparative case study approach and using data from the Federal Reserve Bank of New York/Equifax Consumer Credit Panel, we document the size of economic losses via migration from 23 disaster-affected areas in the United States after the most damaging hurricanes, tornadoes, and wildfires on record. We then employ demographic standardization and decomposition to determine if these losses primarily reflect changes in out-migration or changes in the economic resources that migrants take with them (greater economic losses per migrant). Finally, we consider the implications of these losses for changing spatial inequality in the United States. While disaster-affected areas and those living in them differ in their experiences of and responses to extreme weather disasters, we generally find that, relative to the year before an extreme weather disaster, economic losses via migration from disaster-affected areas increase the year of and after the disaster, that these changes primarily reflect changes in out-migration (vs. the economic resources that migrants take with them), and that these losses briefly disrupt the status quo by temporarily reducing spatial inequality.
We introduce and provide the first comprehensive comparative assessment of the Federal Reserve Bank of New York/Equifax Consumer Credit Panel (CCP) as a valuable and underutilized data set for studying internal migration within the United States. Relative to other data sources on US internal migration, the CCP permits highly detailed cross-sectional and longitudinal analyses of migration, both temporally and geographically. We compare cross-sectional and longitudinal estimates of migration from the CCP to similar estimates derived from the American Community Survey, the Current Population Survey, Internal Revenue Service data, the National Longitudinal Survey of Youth, the Panel Study of Income Dynamics, and the Survey of Income and Program Participation. Our results establish the comparative utility and illustrate some of the unique advantages of the CCP relative to other data sources on US internal migration. We conclude by identifying some profitable directions for future research on US internal migration using the CCP, as well as reminding readers of the strengths and limitations of these data. More broadly, this paper contributes to discussions and debates on improving the availability, quality, and comparability of migration data.
This paper demonstrates that credit bureau data, such as the Federal Reserve Bank of New York Consumer Credit Panel/Equifax (CCP), can be used to study internal migration in the United States. It is comparable to, and in some ways superior to, the standard data used to study migration, including the American Community Survey (ACS), the Current Population Survey (CPS), and the Internal Revenue Service (IRS) county-to-county migration data. CCP-based estimates of migration intensity, connectivity, and spatial focusing are similar to estimates derived from the ACS, CPS, and IRS data. The CCP can measure block-to-block migration and it is available at quarterly rather than annual frequencies. Migrants’ precise origins are not available in public versions of the ACS, CPS, or IRS data. We report measures of migration from the CCP data at finer geographies and time intervals. Finally, we disaggregate migration flows into first-, second-, and higher-order moves. Individual-level panels in the CCP make this possible, giving the CCP an additional advantage over the ACS, CPS, or publicly available IRS data.