When it Rains, it Pours: Estimating the Spatial Spillover Effect of Rainfall

Abstract

A large fraction of the world’s poor rely on rain-fed agriculture, which makes them vulnerable to changes in rainfall patterns. In this paper, we examine whether spatial correlation in rainfall results in these households also being vulnerable to an adverse spatial-spillover effect. In particular, we use household-level panel data from India along with high-resolution meteorological data to show how rural household consumption varies with own-region rainfall as well as rainfall in neighboring areas. We find that while greater own rainfall has a positive effect on rural household consumption, greater rainfall in neighboring regions has an adverse spatial-spillover effect. Our results suggest that when this spillover effect is taken into account, the positive impact of own-region rainfall on household consumption falls by 38 percent.

The impacts of climate change on agriculture in sub-Saharan Africa: A spatial panel data approach

“…Overall, our result suggests that controlling for spatial effects provides larger estimates of the impacts of temperature and WDF on millet yield than those of non-spatial effects. This result is in line with earlier findings by Hossain and Ahsan (2018), Kumar (2011) that rainfall patches span longer periods and travel as underground water and through river channels to positively affect agricultural production in neighboring units.…”

“…Also, certain natural/climatic occurrences could impact bordering countries. Hossain and Ahsan (2018) find that greater amount of rainfall in neighboring units has adverse effect on own-unit economic outcomes because patches of rainfall span several geographic units.…”

“…Elhorst (2014), on the other hand, argues that the parameters of the other specific spatial models, such as the spatial Durbin model (SDM), are identifiable and free from the problem of overfitting. Consequently, we follow Harari and Ferrara (2018) and Hossain and Ahsan (2018) by controlling for spatial correlation in the regressors and dependent variable using the spatial Durbin model (SDM) and accounting for spatial dependence in the residuals via clustering standard errors by MPAs.…”