of the European Commission provides this georeferenced data set including both topsoil and subsoil carbon measurements via the European Soil Data Centre (ESDAC) upon request.

These indicators of environmental quality are used together with a set of common control variables to model two dependent variables, namely poverty and GDP per capita.

# **Instrumental variable (measure that delivers exogenous variation)**

Mean average annual rainfall is employed as the instrument for annual changes in vegetation vigor (NPP & NDVI) and topsoil carbon. The data are sourced from the Climatic Research Unit in the National Center of Atmospheric Research (NCAR, 2017). The data set contains geographically gridded multiple weather time series from 1901 onwards. We use averages per year and province. A detailed discussion of the validity of using rainfall as an instrument is provided in the research design section.

# **Control variables**

We use a variety of highly relevant control variables to isolate the effect of soil fertility and vegetation vigor on our dependent variables. This is particularly important in the cross‐sectional specifications, as they do not have the luxury of province fixed effects. To capture the effect of different terrains, a topographic ruggedness index is employed. This index (see Nunn & Puga, 2012) is given by the square root of the sum of the squared differences in elevation between a given point on the earth surface and the eight points that lie 30 arc‐seconds away in the eight major directions of the compass (North, North‐East, East, etc.). Hence, the ruggedness index captures small‐scale terrain irregularities based on elevation differences.

We also include land use categories (i.e. cropland, forest land, grass land, urban land or other) in the regression. Each _LU_ indicator is measured as a share of the total geographic area. The original data are provided as global raster files with a spatial resolution of 300m by the Land Cover (LC) project of the Climate Change Initiative (CCI) led by the European Space Agency (ESA).

Soil type is a categorical variable that is used as a control variable in the cross‐sectional specifications. There are 12 different categories of soil types: Alfisol, Andisol, Ardisol, Emtisol, Gelisol, Histosol, Inceptisol, Mollisol, Oxisol, Spodosol, Ulisol, and Vertisol. Additionally, where there is no soil, the area may be classified as either rock or sand. These soil categories are the result of millennia old geological and ecological processes and are a crucial determinant of soil quality. While a province may have several soil types, we assign the most prevalent soil type to each province. This classification using the aforementioned 12 + 2 soil types follows the soil classification system of the USDA system of soil taxonomy.

We include road density (mean kilometers of roads per province) and population as standard control variables. In contrast to similar works, we do not rely on population data that are modeled using land use or night light data (e.g. Amaral et al., 2005). Such data sets might raise severe endogeneity issues when acting as an explanatory variable for environmental quality. On our level of aggregation, we can rely on census and survey estimates, which greatly minimizes this risk of endogeneity. A detailed description of all variables, including data sources, can be found in table 1. For summary statistics, please refer to table 5 in the appendix.

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