survey data with GPS coordinates for most surveys and are available for several rounds per country. The DHS is a repeated cross-section of enumeration areas (EA), with approximately 20–30 households enumerated per EA. Five rounds of survey data are covered in Nigeria (1990, 2003, 2008, 2013, 2018), four in Cameroon (1991, 2004, 2011, 2018), and one in Chad (2014). Niger did not conduct DHSs for the period of interest.

We use DHS data for which we have access to the occupation of the individuals as well as a proxy for their location. In order to construct the shares of employment per sector, we use respondents’ answers to questions about their current occupation. We first compute the shares of nonworking individuals and then group all working individuals into three sectors: agriculture, manufacturing, and services. We aggregate individual responses within each EA and then generate an unbalanced panel of districts that had at least one EA during a survey round. The DHS–provided GPS coordinates for EA locations are not perfectly reliable because of the common random displacement applied to GPS coordinates for anonymity.<sup>9</sup> We aggregate EAs per geographic location.

**Use of infrastructure** In addition of access to infrastructure, we consider how uses of roads, electricity and ICT infrastructure impact the structure of employment. We include variables from the DHS surveys that cover use of electricity as reported by households, ownership of cars and motorcycles, ownership of land phone and mobile phone, and use of internet. We aggregate the answers at the subnational level of interest as percentage of households that have access to electricity, own a car, a motorcycle, a land phone, a mobile phone, or use internet. The analysis of use of infrastructure complements the analysis of infrastructure investments. Not all variables are available for the whole period so we restrict our analysis to the period 2008-2018.

**District characteristics** We use additional data to control for district heterogeneity. We use population data from the Global Human Settlement Layer (GHSL),<sup>10</sup> land categories from the European Space Agency land cover (see Defourny (2017)), distance from the coast from the Global Self-consistent, Hierarchical,<sup>11</sup> High-resolution Geography Database (GSHHG), distance to the border,<sup>12</sup> access to a city larger than 50,000 inhabitants from the Malaria Atlas Project,<sup>13</sup> temperature from Land Processes Distributed Active Archive Center,<sup>14</sup> and elevation

> 9DHS coordinates of rural (urban) EAs are randomly displaced within a 0-10 kilometer (0-5 kilometer) radius

> 10GHSL: Population count from the Global Human Settlement Layer. Based on population data from Gridded Population of the World v4.10 polygons, distributed across cells using the Global Human Settlement Layer global layer. Source data provided in 9 arc-second (250m) grid cells.

> 11Distance to the coast (on land only) is measured in meters. It is derived using World Vector Shorelines (Wessel and Smith, 1996).

> 12Distance to country borders is measured in meters. It is derived using the database of Global Administrative Areas (GADM) 2.8 ADM0 (Country) boundaries.

> 13Data incorporate data from Open Street Map (OSM) and the Google roads database. See Weiss et al. (2018) 14Yearly daytime land surface temperature are from Wan and Hook (2015).

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