**Table S9:** Long Difference, IV Results using Least Cost Regional MST - RSDP I - III

||NT|L|Ur|ban|Crop|land|NT|L|Ur|ban|Crop|land|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Imp Rd.|0_._04<sup>_∗∗∗_</sup>|0_._02|0_._002<sup>_∗_</sup>|_−_0_._0000|0_._01<sup>_∗∗∗_</sup>|0_._02<sup>_∗∗∗_</sup>|0_._08<sup>_∗∗_</sup>|0_._04|0_._01|_−_0_._01|0_._04|0_._05|
||(0_._01)|(0_._01)|(0_._001)|(0_._0004)|(0_._004)|(0_._01)|(0_._04)|(0_._03)|(0_._02)|(0_._02)|(0_._04)|(0_._04)|
|Imp Rd._×NTL_96 Low||0_._0002||0_._001||_−_0_._02<sup>_∗∗∗_</sup>||0_._02||0_._02||_−_0_._06|
|||(0_._01)||(0_._001)||(0_._01)||(0_._03)||(0_._02)||(0_._06)|
|Imp Rd._×NTL_96 Med||0_._15<sup>_∗∗_</sup>||0_._003<sup>_∗∗_</sup>||_−_0_._01||0_._18<sup>_∗∗_</sup>||0_._04||_−_0_._004|
|||(0_._07)||(0_._001)||(0_._01)||(0_._09)||(0_._03)||(0_._02)|
|Imp Rd._×NTL_96 High||0_._25<sup>_∗∗∗_</sup>||0_._03<sup>_∗_</sup>||_−_0_._005||0_._17<sup>_∗_</sup>||0_._27<sup>_∗∗_</sup>||_−_0_._05<sup>_∗_</sup>|
|||(0_._09)||(0_._01)||(0_._01)||(0_._09)||(0_._13)||(0_._03)|
|Constant|0_._02|0_._02|0_._0005|0_._0003|_−_0_._01<sup>_∗_</sup>|_−_0_._01|_−_0_._18|_−_0_._16|_−_0_._09|_−_0_._07|_−_0_._15|_−_0_._16|
||(0_._02)|(0_._02)|(0_._001)|(0_._001)|(0_._01)|(0_._01)|(0_._13)|(0_._13)|(0_._08)|(0_._07)|(0_._15)|(0_._15)|
|Unit|1_km_|1_km_|1_km_|1_km_|1_km_|1_km_|_Keb._|_Keb._|_Keb._|_Keb._|_Keb._|_Keb._|
|1st Stage F-Stat|208_._63|62_._22|208_._63|62_._22|208_._63|62_._22|84_._51|25_._44|84_._51|25_._44|84_._51|25_._44|
|Observations<br>|1,271,082|1,271,082|1,271,094|1,271,094|1,271,094|1,271,094|10,220|10,220|10,220|10,220|10,220|10,220|
|Adjusted R<sup>2</sup>|0.003|0.01|0.0001|-0.002|-0.001|-0.001|-0.01|0.02|0.001|0.02|-0.003|-0.004|

Standard errors are clustered on Woredas. The models include a control for the log distance to the nearest targeted area. “Imp Rd.” indicates that a unit was near (within 5km of) an improved road. _NTL_ 96 Low, Medium, and High are dummy variables for different groups of baseline nighttime lights; groups are formed using 3-quantiles of the maximum value of nighttime lights within Woredas that had some positive nighttime lights. The excluded group is Woredas where the maximum value of nighttime lights at baseline was 0. When using 1x1km pixels as the unit of analysis, NTL is the inverse hyperbolic sine of nighttime lights of the 1x1km pixel, which has a similar interpretation as logs, and Urban and Cropland are binary variables indicating whether the pixel contains urban or cropland. When using Kebeles as the unit of analysis, NTL is the average nighttime lights within Kebeles, Urban is the total urban area within Kebeles, and Cropland is the total cropland area within Kebeles; we use the inverse hyperbolic sine transformation on all outcome variables.<sup>_∗_</sup> p _<_ 0.1;<sup>_∗∗_</sup> p _<_ 0.05;<sup>_∗∗∗_</sup> p _<_ 0 _._ 01

# **S8.5 Second Stage Results - Different Baseline Nighttime Light Groups**

Our primary models examine heterogeneity of impacts of road upgrades across four groups of baseline nighttime lights. To test the sensitivity of the results to different groups of baseline nighttime lights, we also estimate models using just two groups: Woredas with no nighttime lights and Woredas with some positive nighttime lights at baseline. Here, results are generally similar to when using four groups: road upgrades are associated with growth in nighttime lights and urban land, particularly in areas with higher baseline nighttime lights.

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