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Published in: Social Indicators Research 2-3/2021

17-01-2020 | Original Research

Mapping Poverty of Latin American and Caribbean Countries from Heaven Through Night-Light Satellite Images

Authors: Maria Simona Andreano, Roberto Benedetti, Federica Piersimoni, Giovanni Savio

Published in: Social Indicators Research | Issue 2-3/2021

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Abstract

The adoption of the Sustainable Development Goals in September 2015 by the United Nations General Assembly is calling National Statistics Offices worldwide to underpin a data revolution. Indeed, these organizations should extend both the scope and disaggregation of the data traditionally produced, and measure new economic, social and environmental phenomena, leaving none behind. There is a growing consensus that, in the digital era, earth observation might strengthen traditional data sources and statistics in monitoring sustainable well-being, facilitating the transformative agenda that official statisticians should implement in the forthcoming years. This research analyses how earth observation, under the form of observable nightlights, might help in mapping poverty data, thus filling in existing gaps of official statistics for a core sustainable development indicator. The empirical analyses show that there are indeed considerable advantages from the use of satellite remote sensing information, the so-called ‘views from the above’, in facing the increasing demand from policy makers and the public at large. The analysis shows that publicly and freely available information from the space might be a key source of information to derive—through an unbalanced fractional panel-data model—spatially disaggregated and continuous-time estimations of poverty gap, headcount, and Gini indices for 20 Latin American and Caribbean countries.

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Appendix
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Footnotes
1
The set of information generally used in poverty estimations includes consumer prices, purchasing power parities, population censuses and national accounts.
 
2
For example, the way household surveys are designed, implemented and processed across countries and within the same country over time. This might include the choice of the income versus consumption main target of the survey, the number of consumption items considered, the treatment and coverage of non-food and seasonal items, the use of recall versus a diary approach, and the recall period.
 
3
These are, with ISO codes in parentheses: Argentina (ARG), Belize (BLZ), Bolivia (BOL), Brazil (BRA), Chile (CHL), Colombia (COL), Costa Rica (CRI), Dominican Republic (DOM), Ecuador (ECU), Guatemala (GTM), Honduras (HND), Jamaica (JAM), Mexico (MEX), Nicaragua (NIC), Panama (PAN), Paraguay (PRY), Peru (PER), El Salvador (SLV), Uruguay (URY) and Venezuela (VEN).
 
4
Non-integer values may occur in years where two satellites are available (the final image value \(DN\) is equal to the average of the two values captured by the two satellites orbiting during the same calendar year).
 
5
It should be noted that LandScan data estimates uses satellite information from different sources, including DMSP nighttime lights in previous versions, to perform a spatial allocation of census reported population numbers based on models developed with spatially disaggregated data.
 
6
This level corresponds to the Level-1 Nomenclature of Territorial Units for Statistics (called NUTS 1) used by the European Commission and the Territorial Level 2 (called TL2) used by the Organization for Economic Co-operation and Development.
 
7
The test statistics reported for the estimated models are those provided by these packages and might not include standard tests, i.e. for residual autocorrelation and heteroskedasticity, used in standard contexts. Lack of these statistics reflects in part the specific nature of the estimated models, characterized by their non-linear panel unbalanced structures. Indeed, the test statistics included in the R packages aim essentially at verifying the misspecification of the models used.
 
8
Results obtained for the estimation of the indices at US$1.90 and US$3.20, qualitatively similar to those for the \(PH550\) and \(PG550\) versions, are only reported for completeness in concise way in Table 7, after discussion of the main results.
 
9
Benchmarking has not been possible for PG and the Gini index as those measures do not depend on population counts.
 
10
In 2013, Brazil recorded (jointly with Colombia) the higher value of the national Gini index among the 20 countries considered in this work.
 
Literature
go back to reference Ahrens, H., & Pincus, R. (1981). On two measures of unbalancednees in a one-way model and their relation to efficiency. Biometrics Journal,3, 227–235.CrossRef Ahrens, H., & Pincus, R. (1981). On two measures of unbalancednees in a one-way model and their relation to efficiency. Biometrics Journal,3, 227–235.CrossRef
go back to reference Bennett, M. M., & Smith, L. C. (2017). Advances in using multitemporal night-time lights satellite imagery to detect, estimate, and monitor socioeconomic dynamics. Remote Sensing of Environment,192, 176–197.CrossRef Bennett, M. M., & Smith, L. C. (2017). Advances in using multitemporal night-time lights satellite imagery to detect, estimate, and monitor socioeconomic dynamics. Remote Sensing of Environment,192, 176–197.CrossRef
go back to reference Bonferroni, C. (1930). Elementi di statistica generale. Firenze: Libreria Seber. Bonferroni, C. (1930). Elementi di statistica generale. Firenze: Libreria Seber.
go back to reference Chen, S., & Ravallion, M. (2008). The developing world is poorer than we thought, but no less successful in the fight against poverty. Policy Research Working Paper World Bank, 4703 Chen, S., & Ravallion, M. (2008). The developing world is poorer than we thought, but no less successful in the fight against poverty. Policy Research Working Paper World Bank, 4703
go back to reference Croft, T. A. (1979). The brightness of lights on earth at night, digitally recorded by DMSP satellite. Palo Alto, CA: Stanford Research Institute Final Report prepared for the U.S. Geological Survey.CrossRef Croft, T. A. (1979). The brightness of lights on earth at night, digitally recorded by DMSP satellite. Palo Alto, CA: Stanford Research Institute Final Report prepared for the U.S. Geological Survey.CrossRef
go back to reference Davidson, R., & Mackinnon, J. G. (1981). Several tests for model specification in the presence of multiple alternatives. Econometrica,49, 781–793.CrossRef Davidson, R., & Mackinnon, J. G. (1981). Several tests for model specification in the presence of multiple alternatives. Econometrica,49, 781–793.CrossRef
go back to reference Doll, C. N. H. (2008). CIESIN thematic guide to night-time light remote sensing and its applications. Palisades: Center for International Earth Science Information Network (CIESIN), Columbia University. Doll, C. N. H. (2008). CIESIN thematic guide to night-time light remote sensing and its applications. Palisades: Center for International Earth Science Information Network (CIESIN), Columbia University.
go back to reference Eidenshing, J. C., & Faundeen, J. L. (1994). The 1 km AVHRR global land data set: First stages in implementation. International Journal of Remote Sensing,15, 3443–3462.CrossRef Eidenshing, J. C., & Faundeen, J. L. (1994). The 1 km AVHRR global land data set: First stages in implementation. International Journal of Remote Sensing,15, 3443–3462.CrossRef
go back to reference Elvidge, C. D., Baugh, K. E., Anderson, S. J., Sutton, P. C., & Ghosh, T. (2012). The night light development index (NLDI): a spatially explicit measure of human development from satellite data. Social Geography,7, 23–35.CrossRef Elvidge, C. D., Baugh, K. E., Anderson, S. J., Sutton, P. C., & Ghosh, T. (2012). The night light development index (NLDI): a spatially explicit measure of human development from satellite data. Social Geography,7, 23–35.CrossRef
go back to reference Elvidge, C. D., Sutton, P. C., Ghosh, T., Tuttle, B. T., Baugh, K. E., Badhuri, B., et al. (2009). A global poverty map derived from satellite data. Computers and Geosciences,35, 1652–1660.CrossRef Elvidge, C. D., Sutton, P. C., Ghosh, T., Tuttle, B. T., Baugh, K. E., Badhuri, B., et al. (2009). A global poverty map derived from satellite data. Computers and Geosciences,35, 1652–1660.CrossRef
go back to reference Fabrizi, E., Ferrante, M. R., & Trevisano, C. (2016). Bayesian beta regression models for the estimation of poverty and inequality parameters in small areas. In M. Pratesi (Ed.), Analysis of poverty data by small area estimation (pp. 299–314). Chichester: Wiley.CrossRef Fabrizi, E., Ferrante, M. R., & Trevisano, C. (2016). Bayesian beta regression models for the estimation of poverty and inequality parameters in small areas. In M. Pratesi (Ed.), Analysis of poverty data by small area estimation (pp. 299–314). Chichester: Wiley.CrossRef
go back to reference Ferrari, S. L. P., & Cribari-Neto, F. (2004). Beta regression for modelling rates and proportions. Journal of Applied Statistics,31, 799–815.CrossRef Ferrari, S. L. P., & Cribari-Neto, F. (2004). Beta regression for modelling rates and proportions. Journal of Applied Statistics,31, 799–815.CrossRef
go back to reference Gini, C. (1914). Sulla misura della concentrazione e della variabilità dei caratteri. Atti del Reale Istituto Veneto di Scienze, Lettere ed Arti,LXXIII, 1203–1248 (English translation in Metron, LXIII, 3–38). Gini, C. (1914). Sulla misura della concentrazione e della variabilità dei caratteri. Atti del Reale Istituto Veneto di Scienze, Lettere ed Arti,LXXIII, 1203–1248 (English translation in Metron, LXIII, 3–38).
go back to reference Hausman, J. A., & Leonard, G. K. (1997). Superstars in the national basketball association: economic value and policy. Journal of Labor Economics,15, 586–625.CrossRef Hausman, J. A., & Leonard, G. K. (1997). Superstars in the national basketball association: economic value and policy. Journal of Labor Economics,15, 586–625.CrossRef
go back to reference Henderson, J. V., Storeygard, A., & Weil, D. N. (2012). Measuring economic growth from outer space. The American Economic Review,102, 994–1028.CrossRef Henderson, J. V., Storeygard, A., & Weil, D. N. (2012). Measuring economic growth from outer space. The American Economic Review,102, 994–1028.CrossRef
go back to reference Huang, Q., Yang, X., Gao, B., Yang, Y., & Zhao, Y. (2014). Application of DMSP/OLS nighttime light images: A meta-analysis and a systematic literature review. Remote Sensing,6, 6644–6866. Huang, Q., Yang, X., Gao, B., Yang, Y., & Zhao, Y. (2014). Application of DMSP/OLS nighttime light images: A meta-analysis and a systematic literature review. Remote Sensing,6, 6644–6866.
go back to reference Imhoff, M. L., Lawrence, W. T., Stutzer, D. C., & Elvidge, C. D. (1997). A technique for using composite DMSP/OLS ‘city lights’ satellite data to map urban area. Remote Sensing of Environment,61, 361–370.CrossRef Imhoff, M. L., Lawrence, W. T., Stutzer, D. C., & Elvidge, C. D. (1997). A technique for using composite DMSP/OLS ‘city lights’ satellite data to map urban area. Remote Sensing of Environment,61, 361–370.CrossRef
go back to reference Li, S., Zhang, T., Yang, Z., Li, X., & Xu, H. (2017). Night time light satellite data for evaluating the socioeconomics in Central Asia. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences,42, 1237–1243.CrossRef Li, S., Zhang, T., Yang, Z., Li, X., & Xu, H. (2017). Night time light satellite data for evaluating the socioeconomics in Central Asia. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences,42, 1237–1243.CrossRef
go back to reference Molina, I., & Rao, J. N. K. (2010). Small area estimation of poverty indicators. Canadian Journal of Statistics,38, 369–385.CrossRef Molina, I., & Rao, J. N. K. (2010). Small area estimation of poverty indicators. Canadian Journal of Statistics,38, 369–385.CrossRef
go back to reference Neal, J., Burke, M., Xie, M., Davis, W. M., Lobell, D. B., & Ermon, S. (2016). Combining satellite imagery and machine learning to predict poverty. Science,353, 790–794.CrossRef Neal, J., Burke, M., Xie, M., Davis, W. M., Lobell, D. B., & Ermon, S. (2016). Combining satellite imagery and machine learning to predict poverty. Science,353, 790–794.CrossRef
go back to reference Paolino, P. (2001). Maximum likelihood estimation of models with beta-distributed dependent variables. Political Analysis,9, 325–346.CrossRef Paolino, P. (2001). Maximum likelihood estimation of models with beta-distributed dependent variables. Political Analysis,9, 325–346.CrossRef
go back to reference Papke, L. E., & Wooldridge, J. M. (1996). Econometric methods for fractional response variables with an application to 401(k) plan participation rates. Journal of Econometrics, 11, 619–632. Papke, L. E., & Wooldridge, J. M. (1996). Econometric methods for fractional response variables with an application to 401(k) plan participation rates. Journal of Econometrics, 11, 619–632.
go back to reference Papke, L., & Wooldridge, J. (2008). Panel data methods for fractional response variables with an application to test pass rates. Journal of Econometrics,145, 121–133.CrossRef Papke, L., & Wooldridge, J. (2008). Panel data methods for fractional response variables with an application to test pass rates. Journal of Econometrics,145, 121–133.CrossRef
go back to reference Pratesi, M. (2016). Analysis of poverty data by small area estimation. Chichester: Wiley.CrossRef Pratesi, M. (2016). Analysis of poverty data by small area estimation. Chichester: Wiley.CrossRef
go back to reference Ramalho, E. A., Ramalho, J. J. S., & Murteira, J. M. R. (2011). Alternative estimating and testing empirical strategies for fractional regression models. Journal of Economic Surveys,25, 19–68.CrossRef Ramalho, E. A., Ramalho, J. J. S., & Murteira, J. M. R. (2011). Alternative estimating and testing empirical strategies for fractional regression models. Journal of Economic Surveys,25, 19–68.CrossRef
go back to reference Ramalho, E. A., Ramalho, J. J. S., & Murteira, J. M. R. (2014). A generalized goodness-of-functional form test for binary and fractional regression models. The Manchester School,82, 488–507.CrossRef Ramalho, E. A., Ramalho, J. J. S., & Murteira, J. M. R. (2014). A generalized goodness-of-functional form test for binary and fractional regression models. The Manchester School,82, 488–507.CrossRef
go back to reference Ramalho, E. A., Ramalho, J. J. S., & Coelho, L. M. S. (2016). Exponential regression of fractional-response fixed-effects models with an application to firm capital structure. Journal of Econometric Methods,7, 1–18.CrossRef Ramalho, E. A., Ramalho, J. J. S., & Coelho, L. M. S. (2016). Exponential regression of fractional-response fixed-effects models with an application to firm capital structure. Journal of Econometric Methods,7, 1–18.CrossRef
go back to reference Rao, J. N. K., & Molina, I. (2015). Small area estimation. Hoboken, NJ: Wiley.CrossRef Rao, J. N. K., & Molina, I. (2015). Small area estimation. Hoboken, NJ: Wiley.CrossRef
go back to reference Small, C., & Elvidge, C. D. (2013). Night on earth: Mapping decadal changes of anthropogenic night light in Asia. International Journal of Applied Earth Observation and Geoinformation,22, 40–52.CrossRef Small, C., & Elvidge, C. D. (2013). Night on earth: Mapping decadal changes of anthropogenic night light in Asia. International Journal of Applied Earth Observation and Geoinformation,22, 40–52.CrossRef
go back to reference Tarsitano, A. (1989). The Bonferroni index of income inequality. In C. Dagum & M. Zenga (Eds.), Income and wealth distribution, inequality and poverty (pp. 228–242). Berlin: Springer. Tarsitano, A. (1989). The Bonferroni index of income inequality. In C. Dagum & M. Zenga (Eds.), Income and wealth distribution, inequality and poverty (pp. 228–242). Berlin: Springer.
go back to reference The World Bank. (2018). Piecing together the poverty puzzle. Washington, DC: The World Bank.CrossRef The World Bank. (2018). Piecing together the poverty puzzle. Washington, DC: The World Bank.CrossRef
go back to reference UN-ECLAC. (2019). Statistical yearbook for Latin America and the Caribbean 2018. Santiago de Chile: UN Economic Commission for Latin America and the Caribbean. UN-ECLAC. (2019). Statistical yearbook for Latin America and the Caribbean 2018. Santiago de Chile: UN Economic Commission for Latin America and the Caribbean.
go back to reference United Nations. (2014). A world that counts—Mobilising the data revolution for sustainable development. New York: Independent Expert Advisory Group on a Data Revolution for Sustainable Development. United Nations. (2014). A world that counts—Mobilising the data revolution for sustainable development. New York: Independent Expert Advisory Group on a Data Revolution for Sustainable Development.
go back to reference United Nations. (2015). Transforming our world. The 2030 agenda for sustainable development. A/RES/70/1, Seventieth session, 21 October 2015, Agenda items 15 and 116. New York: General Assembly. United Nations. (2015). Transforming our world. The 2030 agenda for sustainable development. A/RES/70/1, Seventieth session, 21 October 2015, Agenda items 15 and 116. New York: General Assembly.
go back to reference Xu, H., Yang, H., Li, X., Jin, H., & Li, D. (2015). Multi-scale measurement of regional inequality in mainland China during 2005–2010 using DMSP/OLS night light imagery and population density grid data. Sustainability,7, 13469–13499.CrossRef Xu, H., Yang, H., Li, X., Jin, H., & Li, D. (2015). Multi-scale measurement of regional inequality in mainland China during 2005–2010 using DMSP/OLS night light imagery and population density grid data. Sustainability,7, 13469–13499.CrossRef
go back to reference Yi, K., Tani, H., Li, Q., Zhang, J., Guo, M., Bao, Y., et al. (2014). Mapping and evaluating the urbanization process in northeast China using DMSP/OLS nighttime light data. Sensors,14, 3207–3226.CrossRef Yi, K., Tani, H., Li, Q., Zhang, J., Guo, M., Bao, Y., et al. (2014). Mapping and evaluating the urbanization process in northeast China using DMSP/OLS nighttime light data. Sensors,14, 3207–3226.CrossRef
go back to reference Wang, W., Cheng, H., & Zhang, L. (2012). Poverty assessment using DMSP/OLS night-time light satellite imagery at a provincial scale in China. Advances in Space Research,49, 1253–1264.CrossRef Wang, W., Cheng, H., & Zhang, L. (2012). Poverty assessment using DMSP/OLS night-time light satellite imagery at a provincial scale in China. Advances in Space Research,49, 1253–1264.CrossRef
go back to reference Wooldridge, J. M. (2019). Correlated random effects models with unbalanced panels. Journal of Econometrics, 211, 137–150.CrossRef Wooldridge, J. M. (2019). Correlated random effects models with unbalanced panels. Journal of Econometrics, 211, 137–150.CrossRef
Metadata
Title
Mapping Poverty of Latin American and Caribbean Countries from Heaven Through Night-Light Satellite Images
Authors
Maria Simona Andreano
Roberto Benedetti
Federica Piersimoni
Giovanni Savio
Publication date
17-01-2020
Publisher
Springer Netherlands
Published in
Social Indicators Research / Issue 2-3/2021
Print ISSN: 0303-8300
Electronic ISSN: 1573-0921
DOI
https://doi.org/10.1007/s11205-020-02267-1

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