# **3.1 Data Sources**

The road data covers the stock of existing roads and their improvement between 1996 and 2016 annually, capturing RSDP phases I-IV. Three sources of data went into constructing the dataset (created and provided by the ERA): (1) federal road data from ERA, which includes dates of budget disbursements towards RSDP; (2) regional road network studies implemented at various times within selected regions that captured the inventory and conditions of roads; and (3) a nationwide road network inventory and condition survey implemented in 2014 and 2015. Speed limits were assigned to each road segment based on the pavement type and condition of the road, and road improvements are indicated by an increase in the speed limit (see Appendix S2 for a table showing ERA’s classification for road types, conditions, and speeds).

Our first outcome variable is nighttime lights, which has been shown to be a strong predictor of local economic activity (Donaldson and Storeygard, 2016; Sutton and Costanza, 2002; Doll et al., 2006; Ghosh et al., 2013), local GDP (Henderson et al., 2012), and measures of welfare such as local levels of asset wealth (Weidmann and Schutte, 2017). Nighttime lights data come from two data sources: the Defense Meteorological Program, Operational Line Scan System (DMSP-OLS) and the Visible Infrared Imaging Radiometer Suite (VIIRS). Both sources are made available by the National Oceanic and Atmospheric Administration (NOAA). We rely on both sources to capture the full study period from 1996 to 2016; DMSP-OLS is available from 1992 to 2013 at a roughly 1km resolution, and VIIRS is available from 2012 to the present at a roughly 750m resolution. Two challenges must be addressed to construct a consistent time series based on the two nighttime lights data sources. First, DMSP-OLS data is captured over time using six different satellites; differences in the satellites, including the sensors and time of orbit, have resulted in significant differences in light values captured from different satellites across years (Wu et al., 2013; Zhang et al., 2016). Second, there are notable differences between DMSP-OLS and VIIRS data: VIIRS has a finer resolution, can capture nighttime lights at both lower and higher magnitudes, and suffers less from “blooming,” whereby lights in one pixel impact surrounding pixels (Elvidge et al., 2013). We therefore rely on a dataset from Li et al. (2020), who address both issues to develop a harmonized global nighttime lights dataset from 1992 to 2018; Li et al. (2020) use a stepwise calibration approach to create a consistent DMSP-OLS dataset.<sup>5</sup> One key difference between the calibrated DMSP-OLS data and the simulated DMSP-OLS-like data (i.e., VIIRS data simulated to be like DMSP-OLS) is that the DMSP-OLS-like data captures small values of

> 5The stepwise calibration approach refers to a four-step process, where DMSP-OLS satellites are individually calibrated to match earlier years; the process starts with earlier satellites and moves sequentially to later satellites. The method produces nighttime lights trends that more accurately track trends in electricity consumption compared to DMSP-OLS calibration methods developed by Zhang et al. (2016) and Elvidge et al. (2014). See Li and Zhou (2017) for details on how the dataset is created.

9