cropland experienced less change over time. For example, 15,081 Kebeles (95.9%) contained some cropland in 1996 and 15,045 (95.7%) contained some cropland in 2016—a 0.2 percentage point change.

When considering only areas near (within 5km of) an improved road, urban land and luminosity are also heavily concentrated. Table 1 shows that out of the 14,933 Kebeles near an improved road, 626 (4%) registered positive nighttime light values at baseline, and this value grew to 1,267 by 2013 (a 102% increase) and further grew to 6, 214 (a 390% increase from 2013); however, much of that increase may be due to differences in underlying nighttime lights data (2013 uses DMSP-OLS data and 2016 uses DMSP-OLS-like data, which better captures low values of nighttime lights). Urban area saw similar growth over time, from 520 Kebeles with some urban area to 1,035 (a 99% increase). While a small proportion of Kebeles near an improved road contain lit or urban cells, a high proportion of lit or urban cells are near an improved road.

**Table 1:** Summary Statistics of Outcome Variables Across Kebeles

|Variable||All||(Within|Treated A<br> 5km of Im|reas<br>proved Road)|C<br>(_>_5km|ontrol Ar<br>of Improv|eas<br>ed Road)|
|---|---|---|---|---|---|---|---|---|---|
||1996|2013|2016|1996|2013|2016|1996|2013|2016|
|**_Average across Kebeles_**||||||||||
|Avg. NTL|0.35|0.57|1.88|0.37|0.6|1.9|0.01|0.02|1.45|
|Avg. Urban Area (km<sup>2</sup>)|0.072|0.156|0.189|0.066|0.156|0.189|0.159|0.207|0.225|
|Avg. Cropland Area (km<sup>2</sup>)|40.365|40.854|40.557|40.86|41.334|41.031|30.918|31.647|31.512|
|**_N Kebeles with Positive _**|**_Value_**|||||||||
|NTL|628|1,278|6,472|626|1,267|6,214|2|11|258|
|Urban|542|910|1,062|520|884|1,035|22|26|27|
|Cropland|15,081|15,044|15,045|14,368|14,333|14,332|713|711|713|
|N|15,714|||14,933|||781|||

Nighttime lights data come from Li et al. (2020), who provides an intercalibrated nighttime lights dataset derived from DMSP-OLS and VIIRS from 1992 to 2018; DMSP-OLS data from 1992–2013 is used, and VIIRS is used to simulate DMSP-OLS-like data from 2014 to 2018. Urban and cropland categoriescome from the ESA Globcover dataset. The table shows values in 1996 (first year of road data available), 2013 (last year of DMSP-OLS data available), and 2016 (last year of road data available, and where VIIRS is used to simulate DMSP-OLS-like data).

# **4 Empirical Strategy**

As shown in the previous two sections, Ethiopia experienced substantial changes in its road network (Figure 1) and in economic activity (Table 1). In this section, we exploit the temporal and spatial variation in roads and economic activity to estimate the effect of road upgrades.

We rely on three complementary empirical approaches to estimate the impact of road upgrades on economic activity and changes in land cover (urban land and cropland). First, we rely on a difference-indifference design that focuses on the impact of road upgrades on surrounding areas. Second, we rely on a long-difference approach that focuses on the benefits of the program for incidentally treated areas compared to the areas that did not benefit from the program. Third, we examine how improvements in market access 12