Disruption, not displacement: Environmental variability and temporary migration in Bangladesh.

Mass migration is one of the most concerning potential outcomes of global climate change. Recent research into environmentally induced migration suggests that relationship is much more complicated than originally posited by the 'environmental refugee' hypothesis. Climate change is likely to increase migration in some cases and reduce it in others, and these movements will more often be temporary and short term than permanent and long term. However, few large-sample studies have examined the evolution of temporary migration under changing environmental conditions. To address this gap, we measure the extent to which temperature, precipitation, and flooding can predict temporary migration in Matlab, Bangladesh. Our analysis incorporates high-frequency demographic surveillance data, a discrete time event history approach, and a range of sociodemographic and contextual controls. This approach reveals that migration declines immediately after flooding but quickly returns to normal. In contrast, optimal precipitation and high temperatures have sustained positive effects on temporary migration that persist over one to two year periods. Building on previous studies of long-term migration, these results challenge the common assumption that flooding, precipitation extremes and high temperatures will consistently increase temporary migration. Instead, our results are consistent with a livelihoods interpretation of environmental migration in which households draw on a range of strategies to cope with environmental variability.

Spatio-Temporal Dimensions of Child Poverty in America, 1990-2010.

The persistence of childhood poverty in the United States, a wealthy and developed country, continues to pose both an analytical dilemma and public policy challenge, despite many decades of research and remedial policy implementation. In this paper, our goals are twofold, though our primary focus is methodological. We attempt both to examine the relationship between space, time, and previously established factors correlated with childhood poverty at the county level in the continental United States as well as to provide an empirical case study to demonstrate an underutilized methodological approach. We analyze a spatially consistent dataset built from the 1990 and 2000 U.S. Censuses, and the 2006-2010 American Community Survey. Our analytic approach includes cross-sectional spatial models to estimate the reproduction of poverty for each of the reference years as well as a fixed effects panel data model, to analyze change in child poverty over time. In addition, we estimate a full space-time interaction model, which adjusts for spatial and temporal variation in these data. These models reinforce our understanding of the strong regional persistence of childhood poverty in the U.S. over time and suggest that the factors impacting childhood poverty remain much the same today as they have in past decades.