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AbstractReliable testing data for new infectious diseases like COVID‐19 is scarce in developing countries making it difficult to rapidly diagnose spatial disease transmission and identify at‐risk areas. We propose a method that uses readily available data on bi‐lateral migration channels combined with COVID‐19 cases at respective migrant destinations to construct a spatially oriented risk index. We find significant and consistent association between our measure and various types of outcomes including actual COVID‐19 cases and deaths, indices of government policy responses, and community mobility patterns. Results suggest that future pandemic models should incorporate migration‐linkages to predict regional socio‐economic and health risk exposure.

AbstractThe effects of marriage between biological relatives on the incidence of childhood genetic illness and mortality are of major policy significance, as rates of consanguinity exceed 50% in various countries. Empirical research on this question is complicated by the fact that consanguinity is often correlated with poverty and other unobserved characteristics of households, which may have independent effects on mortality. This study has developed an instrumental variables empirical strategy to re-examine this question, based on the concept that the availability of unmarried cousins of the opposite gender at the time of marriage creates quasi-random variation in the propensity to marry consanguineously. Using primary data collected in Bangladesh in 2006–07 and Pakistan in 2009–10, the study found that previous estimates of the impact of consanguinity on child health were biased and falsely precise. The study also empirically investigated the social and economic causes of consanguinity (including marital quality) and concludes that marrying a cousin can have positive economic effects for one's natal family, by allowing deferral of dowry payments until after marriage.