By estimating (1), we estimate a reduced form and assume that all relevant income and consumption effects, resulting from variation in soil nutrients, have evolved into an equilibrium effect captured by parameter b: Our estimation approach closely follows the approach used by Michalopoulos and Papaioannou [27], also including many of the same control variables. We estimate (1) using OLS in Stata and since the soil **data** is a cross-section so is our estimation approach. Observations are arranged at the lowest possible grid cell level, in our case grid cells at a resolution of 5km x 5km. We cluster standard errors twice, using the multiway clustering approach developed by Cameron et al. [28] (available through the reghde command available for Stata developed by Correia [29]) in order to account for unobserved spatial correlations. In each specification, we cluster at the country level and either at the regional level (for which most health **data** is provided) or at the level of precolonial institutions (also see **data** description).