# **4.1 Baseline strategy: OLS with fixed effects**

For our baseline strategy, we estimate the following equation using OLS: Where _Empijct_ is the outcome variable, measuring whether individual _i_ in country _c_ and at time _t_ is employed. In regressions using skilled/unskilled worker as the dependent variable, _Empict_ measures whether individual _i_ in country _c_ and time _t_ is a skilled or unskilled worker. _Roadict_ measures proximity of individual _i_ in country _c_ at time _t_ to the road networks, and _Electict_ measures proximity of individual _i_ in country _c_ at time _t_ to the electricity grids. _Roadict × Electict_ measures the interaction between proximity to road and proximity to electricity grid.<sup>4</sup> In our main specification, the parameters _α_ 1 measure the marginal effect on employment of moving 1 km closer to the road networks in locations within zero km to an electricity grid, and _α_ 2 measure the marginal effect on employment of moving 1 km closer to the electricity grids in locations within zero km to a main road.<sup>5</sup> The parameter _β_ measures the incremental effect of moving closer to both the road networks and the electricity grids. If all these parameters are positive, then roads and electricity are complements in the process of job creation. If on the contrary, _β_ is negative while both _α_ 1 and _α_ 2 are positive, then the two types of infrastructure are substitutes. Being complements (substitutes) means that one type of infrastructure is more (less) useful when the other type is supplied.

Our identification strategy exploits both spatial and temporal variation in the expansion of the road networks, and mostly spatial variation in the expansion of the electricity grids.<sup>6</sup> The term _μc_ captures country fixed effects; and _θt_ captures survey year fixed effects. Controlling for year fixed effects accounts for all the time-variant factors common to all grid cells, and that may affect infrastructure and job creation. _X_<sup>_′_</sup> _ict_ is a vector of individual-level controls including age, gender, residence in a urban or rural location, education, and average temperature computed at the grid cell level. The identifying assumption is that the error term, _εict_ , is uncorrelated with each of the variables measuring proximity to infrastructure given all the aforementioned controls.

# **4.2 Instrumental variables approach**

For our second identification strategy, we use an instrumental variables approach. In fact, inferring causation from the OLS regression requires that proximity to the road networks and the electricity grid be uncorrelated with the error term of equation (1) given individuals’ characteristics, and the country

> 4Electricity grids are often located along main roads, which can make the identification of having access to the grid alone difficult (Moneke, 2020). In this paper, we keep all three variables, proximity to road, proximity to electricity grid and their interaction term of the following reasons. First, we consider the distance to the closest road, which might be different from the distance to the road along which the closest electricity grid point can be found. This analysis differs from Moneke (2020) which uses binary access variables and finds that all districts with access to the electric grid have a main road, which makes the identification of access to the grid alone impossible. Second, the correlations between our three variables are positive but not very high. For example, the correlation between proximity to road and proximity to electricity grid is 0.38 in the LSMS sample and 0.34 in the DHS sample.

> 5This interpretation stems from the fact that each variable that measures proximity to a given infrastructure takes only “negative” values, with greater values implying greater access; the highest access is therefore achieved when the value of the variable is zero.

> 6We follow a research design similar to that of Hjort and Poulsen (2019), in which temporal variation in connectivity to the electricity grid comes from the fact that survey samples vary across years in the DHS and the LSMS for each country.

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