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[1] In this paper, we analyze the determinants of the number of large floods reported since 1990. Using the same sample of countries as Bradshaw et al. (2007), and, like them, omitting socioeconomic characteristics from the analysis, we found that a reduction in natural forest cover is associated with an increase in the reported count of large floods. This result does not hold in any of three new analyses we perform. First, we expand the sample to include all the developing countries and all countries for which data were available but were omitted in their study. Second, and more importantly, since forest management is just one possible channel through which humans can influence reported flood frequency, we account for other important human-flood interactions. People are typically responsible for deforestation, but they are also responsible for other land use changes (e.g., urbanization), for floodplain and flood emergency management, and for reporting the floods. Thus, in our analysis we account for population, urban population growth, income, and corruption. Third, we exploit the panel nature of the data to control for unobserved country and time heterogeneity. We conclude that not only is the link between forest cover and reported flood frequency at the country level not robust, it also seems to be driven by sample selection and omitted variable bias. The human impact on the reported frequency of large floods at the country level is not through deforestation.

We estimate the impact of large, catastrophic floods on internal armed conflict using global data on large floods between 1985 and 2009. The results suggest that while large floods did not ignite new conflict, they fueled existing armed conflicts. Floods and armed conflict are endogenously determined, and we show that empirically addressing this endogeneity is important. The estimated effects of floods on conflict prevalence are substantially larger in specifications that control for the endogeneity of floods, suggesting that treating natural disasters as exogenous phenomena may underestimate their impacts on sociopolitical outcomes.

While ethnic minorities, less-educated or less-skilled workers, and low-income workers are, in general, deemed more vulnerable to automation, the literature has not adequately investigated whether or not these sociodemographic groups perceive automation as a threat to their jobs. Using the 2019 Metro Atlanta Speaks survey, we find that high-income residents and residents with a graduate or a professional degree did not perceive automation as a threat to their jobs, but relatively older residents, blacks or African Americans, and low-income residents perceived automation as a threat to their jobs. Although Hispanics or Latinos and less-educated residents are identified to be more vulnerable to automation, they did not perceive automation as a threat to their jobs. Hence, automation is most likely to make Hispanics or Latinos and less-educated residents unemployed in metro Atlanta as they do not perceive automation as a threat to their jobs despite being deemed more vulnerable to automation.