subnational level, particularly in the developing world. I thus use NTL data to expand the sample of Salvadoran municipalities in my analysis.

NTL density data come from the Defense Meteorological Satellite Program (DMS) - Operational Linescan System (OLS). Every night from 8 PM to 9:30 PM the DMSP captures night light intensity from space. The high-frequency measure of NTL is a six bits digital number calculated for each location (i.e., pixel) of approximately 0.86 square kilometers in the Equator (its annual measure is an average across all nights for a given year). A digital number can go from 0 (a non-lit area) to a maximum of 63. Panel (a) in Figure 5 shows the distribution of digital numbers across pixels in El Salvador and its neighboring countries for the year 2004. The darker the pixel, the higher the density of NTL. As can be seen in the map, the intensity of NTL is higher in the most developed parts of El Salvador, such as San Salvador, Santa Ana, and San Miguel.

It is important to note that the satellites’ technologies changed in 2013, which makes the direct comparison between the 2000-2012 and 2013-2018 periods difficult. To overcome this problem Li et al. (2020) created a harmonized global nighttime light dataset for the entire period for which NTL data is available (i.e., 1992–2018). I exploit this granular data to compute density of NTL at the municipality level by averaging across all pixels within the boundaries of each Salvadoran municipality. Panel (b) in Figure 5 depicts the average NTL density by municipality in 2004. A closer look at the data reveals that even within regions of El Salvador substantial heterogeneity in development exists across municipalities.<sup>5</sup>

# **2.2.1 Validation of the Proxy for Income at the Municipality Level**

To validate the use of NTL data as a proxy for income in El Salvador I first show NTL density is a strong predictor of income at the municipality level. For the unbalanced panel of municipalities for which income data is available, Table 2 shows that, indeed, NTL density does a great job predicting per capita household income, average household income, and per capita household labor income.<sup>6</sup> Even after controlling for municipality and year fixed effects, NTL density and the different measures of income are strongly and positively associated. All these associations are statistically significant under the standard levels of confidence. The implied elasticity from the different specifications in Table 2 ranges between 0.2 and 0.32. Moreover, the results in Table 2 show that using the average of NTL for two consecutive years does a better job than just using

> 5Li et al. (2020) recommends removing non-lit areas with low values of digital numbers. I removed from the analysis three municipalities with an average NTL density below 1.5 for the period of analysis. These excluded municipalities are Cacaopera and Joateca in the department of Moraz ́an, and Nuevo Ed ́en de San Juan in the department of San Miguel.

> 6All income measures come from SEDLAC’s harmonized versions of Encuesta de Hogares de Prop ́ositos M ́ultiples (EHPM). All waves from 2000 to 2018 were available through the World Bank’s DATALIBWEB.

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