version of the VIIRS data (Elvidge et al. (2021)). Both data are constructed at an annual scale and are adjusted for clouds, flares, and other sources of noise. However, the two measures are not fully consistent with each other (Elvidge et al. (2013)). Therefore, we perform and report separate analysis for each sensor.<sup>16</sup>

# **3.1 Nighttime Light Luminosity and Sectoral Economic Activity**

Data on nighttime light luminosity is publicly available and reported to be a useful measure of urban economic activity. When one flies over a country by plane or views the many images available from satellites (e.g., Figure 4), it seems likely that these data capture the location of cities. Indeed, a large literature has confirmed this to be true across countries and time (e.g., Donaldson and Storeygard (2016)). What we are less confident about is whether changes in nighttime light luminosity are related to sub-national changes in economic activity.<sup>17</sup> More specifically, in our context, how do changes in nighttime lights correlate with changes in sectoral economic activity in Türkiye? This subsection shows that changes in nighttime lights are, in fact, correlated with changes in sectoral GVA. However, part of the association may come from capturing business cycle effects and other changes that are common to all provinces in a given year. When we remove the effect of these annual changes (by using year fixed effects), we find that (idiosyncratic) changes in construction and manufacturing are still associated with changes in nighttime lights, but the service and agriculture sectors are not.

Nighttime light emissions are more useful indicators of economic growth in urban areas. Therefore, we use the Global Human Settlement Layer (GHSL) constructed by Melchiorri et al. (2019) data to identify raster grid cells that are likely to be part of urban areas.<sup>18</sup> These data provide a lens through which we can better interpret changes in nighttime light emissions. In panels A-D in Table 4 we incorporate this data in different ways. Across all panels, we exclude rural provinces — provinces which contain no urban grid cells based on the 2000 GHLS data. Panel A in Table 4 sets a useful benchmark and is estimated by equation 3. We regress logdifferenced sectoral GVA or GDP ( ) on log differenced nighttime light luminosity (Δ ), pp pp which as in equation 2 also involves controlling for unobserved province level effects, province tt tt ΔΔOO ΔΔLL trends and has the standard errors clustered by province. = Δ pp pp pp tt tt pp tt ΔΔOO ΔΔLL + μμ + εε 16 Table 4 uses both measures in the same regression but includes year effects (which eliminates mean differences since the sensor measures are annual) and does not include changes in 2013 (which would have involved two different sensor measures).

17 Gibson and Boe-Gibson (2021) show that NTL changes are weakly correlated with GDP changes, controlling for year fixed effects, for counties in the United States. Their specification is in levels, while our specification uses log-differences (i.e., growth rates). Further, the US has a very large service sector; therefore, a greater fraction of economic activity is intangible, but it is does have better quality data. We are unsure how this result would transfer to countries that are less developed than the US.

18 https://ghsl.jrc.ec.europa.eu/download.php.

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