Rainfall shocks push people away from the poverty line, making them poorer: Evidence from urban Ecuador

This study seeks to deepen the understanding of the interactions between weather extremes and vulnerable groups in urban areas. We use annual panels of household surveys from 2007 to 2019, weather information, and geographical characteristics of the territories in Ecuador to examine how rainfall shocks affect households’ poverty levels. By applying fixed effects models, we find that rainfall shocks, including excess and lack of rain, significantly worsen socioeconomic conditions, pushing poor urban households further down into poverty. These events disproportionately affect women, who are overrepresented in the informal labor market, and households living in highly susceptible areas, where exposure to environmental hazards intersects with economic vulnerability. Families in the lowest percentiles are most affected, underscoring their limited resilience and adaptive capacity. This study provides insights into the effects of rainfall shocks on disadvantaged urban populations in low and middle-income countries by integrating weather data, geographical characteristics, and socioeconomic vulnerabilities into the analysis. It offers a more comprehensive understanding of how weather shocks intersect with multiple dimensions of vulnerability, particularly for women and households in highly susceptible areas who are also experiencing poverty. Furthermore, it emphasizes the need for targeted interventions and resilience-building strategies to mitigate these adverse effects, especially for vulnerable populations. • Effects of rainfall shocks on disadvantaged groups in urban areas: women and households in highly susceptible areas. • Use of weather data, household panel surveys, and information on susceptibility to landslides, floods, and droughts. • Rainfall shocks push households further into poverty, with low-income families experiencing the most severe consequences. • Women and households in highly susceptible areas are disproportionately affected. • Use of multiple data sources to identify affected populations and develop targeted public policies.