**Table S4:** Long Difference, OLS Results - RSDP I - III

||NT|L|Urba|n|Crop|land|NT|L|Urb|an|Crop|land|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Imp Rd.|0_._02<sup>_∗∗∗_</sup>|0_._005|0_._001<sup>_∗∗∗_</sup>|_−_0_._0001|0_._001|0_._002|0_._03<sup>_∗∗_</sup>|0_._003|0_._01<sup>_∗_</sup>|_−_0_._001|0_._005|0_._01|
||(0_._01)|(0_._004)|(0_._0002)|(0_._0001)|(0_._001)|(0_._002)|(0_._01)|(0_._01)|(0_._01)|(0_._005)|(0_._01)|(0_._01)|
|Imp Rd._×NTL_96 Low||0_._001||0_._001||_−_0_._004||0_._02||0_._02||_−_0_._01|
|||(0_._01)||(0_._001)||(0_._003)||(0_._02)||(0_._01)||(0_._03)|
|Imp Rd._×NTL_96 Med||0_._08<sup>_∗∗_</sup>||0_._001<sup>_∗_</sup>||0_._002||0_._13<sup>_∗∗_</sup>||0_._02||0_._01|
|||(0_._04)||(0_._001)||(0_._004)||(0_._06)||(0_._02)||(0_._02)|
|Imp Rd._×NTL_96 High||0_._19<sup>_∗∗∗_</sup>||0_._01<sup>_∗∗_</sup>||_−_0_._0001||0_._22<sup>_∗∗_</sup>||0_._20<sup>_∗∗_</sup>||_−_0_._04<sup>_∗_</sup>|
|||(0_._06)||(0_._005)||(0_._01)||(0_._10)||(0_._08)||(0_._02)|
|Constant|0_._04<sup>_∗∗∗_</sup>|0_._04<sup>_∗∗∗_</sup>|0_._002<sup>_∗∗∗_</sup>|0_._002<sup>_∗∗∗_</sup>|0_._004|0_._004|_−_0_._02|_−_0_._01|_−_0_._10<sup>_∗∗_</sup>|_−_0_._09<sup>_∗∗_</sup>|_−_0_._02|_−_0_._02|
||(0_._01)|(0_._01)|(0_._001)|(0_._001)|(0_._01)|(0_._01)|(0_._06)|(0_._06)|(0_._05)|(0_._04)|(0_._07)|(0_._07)|
|Unit|1_km_|1_km_|1_km_|1_km_|1_km_|1_km_|_Keb._|_Keb._|_Keb._|_Keb._<br>|_Keb._|_Keb._|
|Observations<br>|1,271,082|1,271,082|1,271,094|1,271,094<br>|1,271,094|1,271,094|10,220|10,220|10,220|10,220|10,220|10,220|
|Adjusted R<sup>2</sup>|0.004|0.01|0.0002|0.002|0.0000|0.0001|0.003|0.02|0.001|0.02|-0.0001|-0.0001|

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

# **S8.3 First Stage Results**

Table S5 shows first stage results of IV models for different variations of the minimum spanning trees and using both 1x1km pixels (models 1–4) and Kebeles (models 5–8) as the unit of analysis. The MSTs are all strongly associated with roads improved from RSDP I-III; the F-Stat on models using 1x1km pixels range from 194-250, and the F-Stat on models using Kebeles range from 31-88.

**Table S5:** First Stage, RSDP I-III

|||||Near Impro|ved Road||||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Near Least Cost MST|0_._29<sup>_∗∗∗_</sup><br>||||0_._24<sup>_∗∗∗_</sup><br>||||
||(0_._02)||||(0_._03)||||
|Near Min. Distance MST||0_._27<sup>_∗∗∗_</sup><br>||||0_._15<sup>_∗∗∗_</sup><br>|||
|||(0_._02)||||(0_._03)|||
|Near Least Cost MST: Regional|||0_._26<sup>_∗∗∗_</sup><br>||||0_._22<sup>_∗∗∗_</sup><br>||
||||(0_._02)||||(0_._02)||
|Near Min. Distance MST: Regional||||0_._25<sup>_∗∗∗_</sup>||||0_._15<sup>_∗∗∗_</sup>|
|||||(0_._02)||||(0_._03)|
|Constant|0_._89<sup>_∗∗∗_</sup><br>|0_._98<sup>_∗∗∗_</sup><br>|0_._92<sup>_∗∗∗_</sup><br>|0_._99<sup>_∗∗∗_</sup><br>|2_._67<sup>_∗∗∗_</sup><br>|3_._06<sup>_∗∗∗_</sup><br>|2_._69<sup>_∗∗∗_</sup><br>|3_._02<sup>_∗∗∗_</sup><br>|
||(0_._08)|(0_._08)|(0_._08)|(0_._08)|(0_._15)|(0_._14)|(0_._15)|(0_._15)|
|Unit|1_km_|1_km_|1_km_|1_km_|_Keb._|_Keb._|_Keb._|_Keb._|
|1st Stage F-Stat|250_._2|213_._3|208_._6|194_._8|88_._1|31_._3|84_._5|35_._9|
|Observations<br>|1,271,082|1,271,082|1,271,082|1,271,082|10,220|10,220|10,220|10,220|
|Adjusted R<sup>2</sup>|0.21|0.19|0.21|0.19|0.22|0.20|0.22|0.20|

Standard errors are clustered on Woredas. “Near” indicates that the unit was within 5km of the MST or improved road. Least Cost MSTs minimize the estimated construction cost of connecting targeted areas (construction cost is a function of slope and land cover), while Min. Distance MSTs minimize the Euclidean distance between targeted areas. Regional MSTs first compute targeted areas within Ethiopia’s regions, and are then appended together.<sup>_∗_</sup> p _<_ 0.1;<sup>_∗∗_</sup> p _<_ 0.05;<sup>_∗∗∗_</sup> p _<_ 0 _._ 01 S13