industry classification) at the 4-digit level, sales revenue, total fixed assets, raw material input expenditure and the number of employees. All monetary variables are in units of national currency (i.e. thousand rupiah) and deflated at the industry-year level with the wholesale price index. We also observe the nominal average wage within a plant, computed as the ratio of total wage bill costs over the number of employees. This data set provides reliable information on the plant’s location at kapubaten or district-level (administrative III level). Although our analysis uses data from 2000-2015, district boundaries are made consistent with the first available year of this data set, that is, 1996. This helps control for changes in the administrative boundaries over time (e.g. district split-ups or mergers).

For countries considered in our study, plant-level product information is available with the varying Harmonized System (HS). In order to ensure consistency across the sample, this information on product-level details is concorded to relevant year, such that product codes are comparable over time. For each detailed product we observe the quantity produced, its value, and the unit in which its quantity is measured, e.g. kg, liter, etc. Product specific unit values are calculated by dividing total values over total quantities for each product. To ensure unit prices are comparable, we define products as unique product-unit combinations.

After merging manufacturing census data with the geospatial variables, the establishment-level data has complete information on 302,871 observations with 34,366 establishments engaged in 136 4-digit industries located across 291 districts and metros over our sample period 2000-2015. Similarly, the establishment-product level data contains the same number of establishments, and 7,826 products.

# A.2: Data on local demographic and geospatial variables

_Demographic variables:_ For population density and instruments used in IV strategy we construct geospatial variables and population characteristics for spatial units chosen in each country. Population and population density (population/land (km sq.) variables were sourced from Population: WorldPop - Unconstrained Topdown estimation modelling (100m).<sup>20</sup> Sociodemographic characteristics (the share of literate population over 15 years old) for Colombia and Indonesia are obtained at the relevant spatial level from the National Population Census (2005 for Colombia and 2000 and 2010 census for Indonesia).

_Geospatial variables:_ For Colombia, precipitation (millimeters, total recorded in the year, average across 19712000) and temperature (Celsius, annual, average across 1971-2000) was obtained from the Institute of Hydrology, Meteorology and Environmental Studies from Colombia (IDEAM).<sup>21</sup> For other countries, spatial variation in temperature was obtained from Willmott and K. Matsuura (2001) datasets and averaged over appropriate spatial units, while the data on precipitation is obtained from Harris et al. (2020).

Information about slope and terrain ruggedness in Colombia is obtained from Nunn and Puga (2012). The raw data calculates the average ruggedness and slope for grid cells around the globe. This paper uses the average slope and ruggedness for the grid cells within each municipality in Colombia.

Elevation and slope for all countries is obtained from the ASTER Global Digital Elevation Model (GDEM) Version 3 (ASTGTM),<sup>22</sup> while for Colombia it is obtained from the Universidad de los Andes Center for Studies on Economic Development (CEDE) Panel.<sup>23</sup>

> 20 See https://www.worldpop.org/project/categories?id=3

> 21 See http://www.ideam.gov.co/documents/21021/553571/Promedios+Climatol%C3%B3gicos+1971+-+2000.xlsx/857942de- <u>f9d7-4d5e-bb75-df984aabe55f</u>

> 22 See https://search.earthdata.nasa.gov/

> 23 See https://datoscede.uniandes.edu.co/es/catalogo-de-microdata

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