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Abstract Both the number of disasters in the U.S. and federal outlays following disasters are rising. FEMA’s Public Assistance (PA) is a key program for rebuilding damaged public infrastructure and aiding local and state governments in recovery. It is the primary post-disaster source of recovery funds. Between 2000 and 2019, more than $125B (adjusted, 2020 dollars) was awarded through PA. While all who qualify for PA should have equal opportunity to receive aid, not all do, and the factors influencing how the program has been administered are complex and multifaceted. Lacking an understanding of the factors positively associated with historical receipt of aid, there is little way to objectively evaluate the efficacy of the PA program. In this work, we evaluate the salient features that contribute to the number of county-level PA applicants and projects following disasters. We use statistical learning theory applied to repetitive flooding events in the upper Midwest between 2003 and 2018 as a case study. The results suggest that many non-disaster related indicators are key predictors of PA outlays, including the state in which the disaster occurred, the county’s prior experience with disasters, the county’s median income, and the length of time between the end of the disaster and the date when a disaster is declared. Our work suggests that indicators of PA aid are tied to exposure, bureaucratic attributes, and human behavior. For equitable distribution of aid, policymakers should explore more disaster-relevant indicators for PA distribution. 

Abstract As researchers collect large amounts of data in the social sciences through household surveys, challenges may arise in how best to analyze such datasets, especially where motivating theories are unclear or conflicting. New analytical methods may be necessary to extract information from these datasets. Machine learning techniques are promising methods for identifying patterns in large datasets, but have not yet been widely used to identify important variables in social surveys with many questions. To demonstrate the potential of machine learning to analyze large social datasets, we apply machine learning techniques to the study of migration in Bangladesh. The complexity of migration decisions makes them suitable for analysis with machine learning techniques, which enable pattern identification in large datasets with many covariates. In this paper, we apply random forest methods to analyzing a large survey which captures approximately 2000 variables from approximately 1700 households in southwestern Bangladesh. Our analysis ranked the covariates in the dataset in terms of their predictive power for migration decisions. The results identified the most important covariates, but there exists a tradeoff between predictive ability and interpretability. To address this tradeoff, random forests and other machine learning algorithms may be especially useful in combination with more traditional regression methods. To develop insights into how the important variables identified by the random forest algorithm impact migration, we performed a survival analysis of household time to first migration. With this combined analysis, we found that variables related to wealth and household composition are important predictors of migration. Such multi-methods approaches may help to shed light on factors contributing to migration and non-migration. 

Environmental change interacts with population migration in complex ways that depend on interactions between impacts on individual households and on communities. These coupled individual-collective dynamics make agent-based simulations useful for studying environmental migration. We present an original agent-based model that simulates environment-migration dynamics in terms of the impacts of natural hazards on labor markets in rural communities, with households deciding whether to migrate based on maximizing their expected income. We use a pattern-oriented approach that seeks to reproduce observed patterns of environmentally-driven migration in Bangladesh. The model is parameterized with empirical data and unknown parameters are calibrated to reproduce the observed patterns. This model can reproduce these patterns, but only for a narrow range of parameters. Future work will compare income-maximizing decisions to psychologically complex decision heuristics that include non-economic considerations. 

Abstract Climate change is expected to increase the frequency and intensity of natural hazards such as hurricanes. With a severe shortage of affordable housing in the United States, renters may be uniquely vulnerable to disaster‐related housing disruptions due to increased hazard exposure, physical vulnerability of structures, and socioeconomic disadvantage. In this work, we construct a panel dataset consisting of housing, socioeconomic, and hurricane disaster data from counties in 19 states across the East and Gulf Coasts of the United States from 2009 to 2018 to investigate how the frequency and intensity of a hurricane correspond to changes in median rent and housing affordability (the interaction between rent prices and income) over time. Using a two‐stage least square random‐effects regression model, we find that more intense prior‐year hurricanes correspond to increases in median rents via declines in housing availability. The relationship between hurricanes and rent affordability is more complex, though the occurrence of a hurricane in a given year or the previous year reduces affordable rental housing, especially for counties with higher percentages of renters and people of color. Our results highlight the multiple challenges that renters are likely to face following a hurricane, and we emphasize that disaster recovery in short‐ and medium‐term should focus on providing safe, stable, and affordable rental housing assistance.