Accurate estimates of the magnitude and spatial distribution of both formal and informal economic activity is necessary to achieve various social and economic goals of societies and countries at different levels of analysis. However, collection of data on economic variables, especially of national and sub-national income levels is problematic due to various shortcomings in the data collection process. Additionally, the informal economy estimates are often excluded from official statistics. Thus, developing alternative methods for estimating these economic activities may prove to be useful and necessary. This research demonstrates the potential of developing spatially explicit estimates of economic activity from nighttime satellite imagery as provided by the Defense Meteorological Satellite Program's Opertaional Linescan System (DMSP-OLS). The methods presented here are used to estimate formal and informal economic activity of Mexico and India at the sub-national level and to create a disaggregated global map of total economic activity. Regression models were developed between spatial patterns of nighttime imagery and Adjusted Official Gross State Product (AGSP) for the U.S. states. The regression parameters derived from the regression models of the U.S. were blindly applied to Mexico to estimate the Estimated Gross State Income (EGSI) at the sub-national level and the Estimated Gross Domestic Income (EGDI) at the national level. Comparison of the EGDI estimate of Mexico and official Gross National Income (GNI) statistic demonstrated that the informal economy and inflow of remittances for Mexico was about 50 percent larger than what was recorded in the official GNI statistic. However, when the regression parameters were applied to India, Gross State Income (GSI) was underestimated for most of the states and Union Territories (UTs) of India in comparison to their official GSP, although it provided a high correlation (r = 0.93) between them. This was probably because of the lower level of urbanization in India in comparison to the U.S. To adjust for the different levels of urbanization in the U.S. and India, the EGDI was multiplied by the ratio of the percentage of the population in urban areas for the two countries. Comparing the Adjusted Estimated Gross Domestic Income (AEGDI) with the official GNI statistic of India suggested that the magnitude of India's informal economy and the inflow of remittances may also be 50 percent larger than what was recorded in the official GNI value. Lastly, a global disaggregated map of total (formal plus informal) economic activity was created. This was done by multiplying the sum of light intensity values of the administrative units (states of China, India, Mexico, and the U.S., and other countries of the world) with computed unique coefficients. This provided estimated total economic activity (GSPIi and GDPIi ) for each administrative unit. The total economic activity values were spatially distributed (disaggregated) within each administrative unit using the percentage contribution of agriculture towards GDP for each country, combined with raster representations of the nighttime lights image and the LandScan population grid. This generated a spatially disaggregated 30 arc-second or one km2 map of estimated total economic activity.
","abstract_html":"<p>Accurate estimates of the magnitude and spatial distribution of both formal and informal economic activity is necessary to achieve various social and economic goals of societies and countries at different levels of analysis. However, collection of data on economic variables, especially of national and sub-national income levels is problematic due to various shortcomings in the data collection process. Additionally, the informal economy estimates are often excluded from official statistics. Thus, developing alternative methods for estimating these economic activities may prove to be useful and necessary. This research demonstrates the potential of developing spatially explicit estimates of economic activity from nighttime satellite imagery as provided by the Defense Meteorological Satellite Program's Opertaional Linescan System (DMSP-OLS). The methods presented here are used to estimate formal and informal economic activity of Mexico and India at the sub-national level and to create a disaggregated global map of total economic activity. Regression models were developed between spatial patterns of nighttime imagery and Adjusted Official Gross State Product (<em>AGSP</em>) for the U.S. states. The regression parameters derived from the regression models of the U.S. were blindly applied to Mexico to estimate the Estimated Gross State Income (<em>EGSI</em>) at the sub-national level and the Estimated Gross Domestic Income (<em>EGDI</em>) at the national level. Comparison of the <em>EGDI</em> estimate of Mexico and official Gross National Income (<em>GNI</em>) statistic demonstrated that the informal economy and inflow of remittances for Mexico was about 50 percent larger than what was recorded in the official <em>GNI</em> statistic. However, when the regression parameters were applied to India, Gross State Income (<em>GSI</em>) was underestimated for most of the states and Union Territories (UTs) of India in comparison to their official <em>GSP</em>, although it provided a high correlation (r = 0.93) between them. This was probably because of the lower level of urbanization in India in comparison to the U.S. To adjust for the different levels of urbanization in the U.S. and India, the <em>EGDI</em> was multiplied by the ratio of the percentage of the population in urban areas for the two countries. Comparing the Adjusted Estimated Gross Domestic Income (<em>AEGDI</em>) with the official <em>GNI</em> statistic of India suggested that the magnitude of India's informal economy and the inflow of remittances may also be 50 percent larger than what was recorded in the official <em>GNI</em> value. Lastly, a global disaggregated map of total (formal plus informal) economic activity was created. This was done by multiplying the sum of light intensity values of the administrative units (states of China, India, Mexico, and the U.S., and other countries of the world) with computed unique coefficients. This provided estimated total economic activity (<em>GSPI<sub>i</sub></em> and <em>GDPI<sub>i</sub></em> ) for each administrative unit. The total economic activity values were spatially distributed (disaggregated) within each administrative unit using the percentage contribution of agriculture towards <em>GDP</em> for each country, combined with raster representations of the nighttime lights image and the LandScan population grid. This generated a spatially disaggregated 30 arc-second or one km<sup>2</sup> map of estimated total economic activity.</p>","abstract_has_math":false,"creators":["Ghosh, Tilottama"],"institution":null,"degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":null,"degree_department":null,"school":null,"contributors":["Paul C. Sutton, Ph.D.","Rebecca C. Powell","Sharolyn Anderson","Christopher D. Elvidge"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2010,"date_issued":"2010-03-01T08:00:00Z","date_published":"2010-03-01T08:00:00Z","updated_at":"2026-07-24T02:03:12Z","subjects":["Disaggregated map of total economic activity","Informal economy","Nighttime lights satellite imagery","Economics","Social and Behavioral Sciences"],"languages":["en"],"rights":["Copyright is held by the author. User is responsible for all copyright compliance.
"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.du.edu/etd/239","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Paul C. Sutton, Ph.D.","Rebecca C. Powell","Sharolyn Anderson","Christopher D. Elvidge"]},{"key":"dc:creator","label":"Author","values":["Ghosh, Tilottama"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2001-01-01T08:00:00Z"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Disaggregated map of total economic activity","Informal economy","Nighttime lights satellite imagery","Economics","Social and Behavioral Sciences"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright is held by the author. User is responsible for all copyright compliance.
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"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Estimating Economic Activity from Space"]}]}],"canonical_facts":{"dc:contributor":["Paul C. Sutton, Ph.D.","Rebecca C. Powell","Sharolyn Anderson","Christopher D. Elvidge"],"dc:creator":["Ghosh, Tilottama"],"dc:date.available":["2001-01-01T08:00:00Z"],"dc:description.abstract":["Accurate estimates of the magnitude and spatial distribution of both formal and informal economic activity is necessary to achieve various social and economic goals of societies and countries at different levels of analysis. However, collection of data on economic variables, especially of national and sub-national income levels is problematic due to various shortcomings in the data collection process. Additionally, the informal economy estimates are often excluded from official statistics. Thus, developing alternative methods for estimating these economic activities may prove to be useful and necessary. This research demonstrates the potential of developing spatially explicit estimates of economic activity from nighttime satellite imagery as provided by the Defense Meteorological Satellite Program's Opertaional Linescan System (DMSP-OLS). The methods presented here are used to estimate formal and informal economic activity of Mexico and India at the sub-national level and to create a disaggregated global map of total economic activity. Regression models were developed between spatial patterns of nighttime imagery and Adjusted Official Gross State Product (AGSP) for the U.S. states. The regression parameters derived from the regression models of the U.S. were blindly applied to Mexico to estimate the Estimated Gross State Income (EGSI) at the sub-national level and the Estimated Gross Domestic Income (EGDI) at the national level. Comparison of the EGDI estimate of Mexico and official Gross National Income (GNI) statistic demonstrated that the informal economy and inflow of remittances for Mexico was about 50 percent larger than what was recorded in the official GNI statistic. However, when the regression parameters were applied to India, Gross State Income (GSI) was underestimated for most of the states and Union Territories (UTs) of India in comparison to their official GSP, although it provided a high correlation (r = 0.93) between them. This was probably because of the lower level of urbanization in India in comparison to the U.S. To adjust for the different levels of urbanization in the U.S. and India, the EGDI was multiplied by the ratio of the percentage of the population in urban areas for the two countries. Comparing the Adjusted Estimated Gross Domestic Income (AEGDI) with the official GNI statistic of India suggested that the magnitude of India's informal economy and the inflow of remittances may also be 50 percent larger than what was recorded in the official GNI value. Lastly, a global disaggregated map of total (formal plus informal) economic activity was created. This was done by multiplying the sum of light intensity values of the administrative units (states of China, India, Mexico, and the U.S., and other countries of the world) with computed unique coefficients. This provided estimated total economic activity (GSPIi and GDPIi ) for each administrative unit. The total economic activity values were spatially distributed (disaggregated) within each administrative unit using the percentage contribution of agriculture towards GDP for each country, combined with raster representations of the nighttime lights image and the LandScan population grid. This generated a spatially disaggregated 30 arc-second or one km2 map of estimated total economic activity.
"],"dc:format":["application/pdf"],"dc:identifier":["https://digitalcommons.du.edu/etd/239"],"dc:language":["en"],"dc:rights":["Copyright is held by the author. User is responsible for all copyright compliance.
"],"dc:subject":["Disaggregated map of total economic activity","Informal economy","Nighttime lights satellite imagery","Economics","Social and Behavioral Sciences"],"dc:title":["Estimating Economic Activity from Space"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."]},"updated_at":"2026-07-24T02:03:12Z"}