Predicting Economic Activity Using Atmospheric NO2 Satellite Data: Evidence from Local Economic Indicators in Japan

This study evaluates the effectiveness of satellite-derived tropospheric nitrogen dioxide (NO2) concentrations as a proxy for economic activity in Japan. While nighttime light (NTL) data has been widely used to approximate economic output, recent research has highlighted its' key limitations. In particular, the relationship between NTL and economic outcomes weakens in sub-sample analyses with shorter time spans or restricted geographic coverage. NTL data also faces several key limitations: saturation in dense urban areas reduces measurement accuracy, capturing nighttime emissions fails to account for essential daytime economic activity, inconsistent sensors across different satellites introduce measurement variability, and the technology's sensitivity diminishes when differentiating economic development beyond certain brightness thresholds. Our results show that NO2's effectiveness as an economic proxy is highly dependent on spatial resolution. Using 0.25 degree esolution NO2 data, we find statistically significant relationships with prefecture-level GDP across multiple sectors. Mining shows the strongest elasticity (3.02%), followed by electricity, gas, and water (1.51%), and manufacturing (0.48%). Agriculture, forestry, and fisheries exhibit negative associations (-0.11%), consistent with vegetation serving as NO2 sinks. However, when using higher resolution 0.1 degree NO2 data, these relationships largely disappear, with most coefficients becoming statistically insignificant and sometimes counterintuitive. These findings highlight the importance of matching satellite data resolution to the geographic scale of economic analysis, with coarser resolution being optimal for prefecture-level analysis in Japanese context. This research demonstrates NO2's potential as a more reliable alternative to NTL for economic monitoring when appropriately calibrated. This study examines the effect of exports on subnational income and regional inequality between urban (trade hub) and rural (non–trade hub) areas, using nighttime luminosity as a proxy for economic activity. We construct a country-period panel dataset covering 104 countries, based on five-year average data from 1997 to 2020. Trade hub areas are defined as the union of areas within a 30 km or 50 km radius of each of the three largest ports and three international airports in a country, while all remaining areas are classified as non–trade hub areas. To address endogeneity, we employ a two-stage least squares (2SLS) approach, using predicted trade as an instrumental variable. Predicted trade is derived from a dynamic gravity equation in which time dummies are interacted with sea and air transport distances. This instrument captures variation in transportation costs driven by technological advances that have shifted trade from sea to air, thereby influencing trade volumes. Our results show that a 1\% increase in exports raises nighttime luminosity by 0.3% in trade hub areas and by 0.06\% in non–trade hub areas. Export growth also leads to population increases in trade hub areas, but not in non–trade hub areas. Furthermore, we find that a 1% increase in exports raises nighttime luminosity per capita by 0.18% in trade hub areas and by 0.06% in non–trade hub areas. These findings suggest that while exports stimulate economic activity in trade hubs, population inflows partially offset per capita gains. Nonetheless, exports significantly exacerbate regional inequality.