Estimación del PIB a nivel subnacional utilizando datos satelitales de luminosidad: Perú, 1993-2018

International Borders, Integration and Economic Development: Evidence from Argentina

“…Intuitively, the higher the standard deviation, the higher the inequality observed in the data (Ciaschi, 2021;González, Cantero, & Szyszko, 2022;Mendez & Santos-Marquez, 2021;Montalvo, Reynal-Querol, & Muñoz, 2021;Seminario & Palomino, 2022;Valenzuela Vega, 2022). In the context of measuring economic inequality through satellite night luminosity, the standard deviation refers to the variation in night luminosity among different pixels within a district.…”

“…Night luminosity maps have been widely used in similar studies (Chen and Nordhaus, 2011;Doll, Muller, and Elvidge, 2000;Elvidge et al, 1997;Henderson, Storeygard, and Weil, 2012;Seminario and Palomino, 2022;Sutton, Elvidge, Ghosh et al, 2007;Stymne and Jackson, 2000) and to estimate urban population (Amaral et al, 2006), population density (Sutton et al, 1997), construct poverty maps at the global (Elvidge et al, 2009a), regional (Noor et al, 2008), or national level (Wang et al, 2012), as well as to evaluate rural poverty and inequality (Smith and Wills, 2018) and locate forest fires (Fuller and Fulk, 2000).…”

“…The use of night luminosity satellite images has gained popularity in various research studies to approximate economic activity, its evolution, and its concentration at the national and district levels. Existing literature shows a strong correlation between luminosity, GDP, and local levels of economic development (Chen & Nordhaus, 2011;Doll, Muller, & Elvidge, 2000;Elvidge et al, 1997;Henderson, Storeygard, & Weil, 2012;Seminario & Palomino, 2022;Sutton, Elvidge, Ghosh et al, 2007;Stymne & Jackson, 2000). Additionally, luminosity maps have been used to estimate urban population (Amaral et al, 2006) and population density (Sutton et al, 1997), as well as to construct poverty maps at the global (Elvidge et al, 2009a), regional (Noor et al, 2008, or national level (Wang et al, 2012), assess rural poverty and inequality (Smith & Wills, 2018), and locate forest fires (Fuller & Fulk, 2000).…”

“…The records of the satellite images provide a luminosity scale ranging from 0 to 63, with each value having a precision of 30 arc seconds (approximately 1 km² at the equator) 2) , and each pixel storing data in 6 bits (i.e., up to 64 1) Other studies have employed satellite imagery for inequality analysis. For example, González, Cantero, and Szyszko (2022); Mendez and Santos-Marquez (2021); Montalvo, Reynal-Querol, and Muñoz (2021); Seminario and Palomino (2022); Valenzuela Vega (2022). 2) According to Argentina's geographical latitude, the area represents 0.8 km² in the north, 0.32 km² in the south, positions).…”

Measuring Regional Inequality in the Andean Countries: A Multiple-Stage Nested Theil Decomposition Using Night Light Emissions

“…In Ecuador, Cabrera-Barona et al ( 2020) mapped urban representations of the Ecuadorian Amazon using a geospatial approach, and Mejía Juárez (2020) analyzed the evolution of urban uses using the magnitude and intensity of nighttime light data. In Peru, Seminario and Palomino (2022) estimated subnational GDP from 1993 to 2018. carried out a spatiotemporal analysis relating light emissions with multiple variables to evaluate the socioeconomic crisis that suffers Venezuela, while Maldonado (2023) actually combined the absence of high intensity of lights and gridded population datasets to estimate 2000-2020 rural poverty rates at different geographic levels of Venezuela. Furthermore, satellite data as an alternative source is particularly important for analyzing developing countries, which are often limited or lack robust subnational indicators to diagnose regional disparities.…”