Understanding Pastoralists’ Dynamic Insurance Uptake Decisions: Evidence from Four-year Panel Data in Ethiopia

Using a unique data set covering four years and six semi-annual sales periods of an index-based livestock insurance (IBLI) product in southern Ethiopia, we examine the dynamics of pastoralists’ demand for IBLI. We find that: (1) there is intertemporal dependence of an uptake decision, represented by correlations of unobserved household factors over time; (2) conditional on previous purchase decisions, factors related to continuing the purchase of IBLI to augment existing coverage and replace lapsing contracts differ significantly; (3) controlling for time-invariant household-fixed effects, neither a one-shot subsidy nor the uptake of others in one’s social network influence subsequent demand, whereas less vegetation and reduced insurance premiums induce households to purchase IBLI. Overall, our study provides rigorous micro-evidence to better understand the dynamic uptake of IBLI and signifies the importance of an empirical analysis that takes into account the dynamic demand structure.JEL Classification Codes: D12, D14, D81, G22, O12This work was made possible by the financial support provided by Cornell University, National Graduate Institute for Policy Studies (GRIPS), the International Livestock Research Institute (ILRI), the JSPS Grant-in-Aid for Scientific Research (B)-26301021, the US Agency for International Development (USAID) Agreement No. LAG-A-00-96-90016-00 through the Broadening Access and Strengthening Input Market Systems Collaborative Research Support Program (BASIS AM) Innovation Lab, the Department of Foreign Affairs and Trade through the Australia Development Research Awards Scheme, and the CGIAR Research Programs on Climate Change, Agriculture and Food Security, and on Dryland Systems.https://www.grips.ac.jp/list/jp/facultyinfo/takahashi-kazushi

Additional file 4 of Mapping age- and sex-specific HIV prevalence in adults in sub-Saharan Africa, 2000–2018

Additional file 4: Supplemental results.1. README. 2. Prevalence range across districts. 3. Prevalence range between sexes. 4. Prevalence range between ages. 5. Age-specific district ranges