Abstract
Poverty has emerged as one of the chronic dilemmas facing the development of human society during the twenty first century. Accurately identifying regions of poverty could lead to more effective poverty-alleviation programs. This study used a new type of remote-sensing data, NPP-VIIRS, to locate poverty-stricken areas based on nighttime light, taking Chongqing Municipality as a sample, and constructed a multidimensional poverty index (MPI) system, guided by a well-known and widely used conceptual framework of sustainable livelihood. A regression model was constructed and results were correlated with those using the average nighttime light index. The model was then tested on Shaanxi Province, and average relative error of the estimated MPI was only 11.12%. These results showed that multidimensional poverty had a high spatial concentration effect at the regional scale. We then applied the index nationwide, at the county scale, analyzing 2852 counties, which we divided into seven classifications, based on the MPI: extremely low, low, relatively low, medium, relatively high, high, and extremely high. Eight hundred forty-eight counties in 26 provinces were identified as multidimensionally poor. Among these, 254 were absolutely poor counties and 543 were relatively poor counties; 195 of these are not on the list of poverty-stricken counties as identified by income levels alone. By improving the accuracy of targeting, this method of identifying multidimensional poverty areas could help the Chinese government improve the effectiveness of poverty reduction strategies, and it could also be used as a reference for other countries or regions that seek to target poor areas that suffer multidimensional deprivation.
Similar content being viewed by others
References
Alkire, S., & Foster, J. (2011). Counting and multidimensional poverty measurement. Journal of Public Economics, 95(7–8), 476–487.
Anderson, G., Pittau, M. G., & Zelli, R. (2011). Partially identified poverty status: A new approach to measuring poverty and the progress of the poor. Working Papers, 12(4), 469–488.
Baugh, K., Hsu, F. C., Elvidge, C. D., & Zhizhin, M. (2013). Nighttime lights compositing using the VIIRS day-night band: Preliminary results. Proceedings of the Asia-Pacific Advanced Network, 35, 70–86.
Chen, X. (2015). Explaining subnational infant mortality and poverty rates: What can we learn from night-time lights? Spatial Demography, 3(1), 27–53.
Chen, Y., & Ge, Y. (2015). Spatial point pattern analysis on the villages in China’s poverty-stricken areas. Procedia Environmental Sciences, 27, 98–105.
DFID. (1999–2005). Sustainable livelihoods guidance sheets. London: Department for International Development (UK). http://www.eldis.org/go/home&id¼41731&type¼Document#.U9DchbeKDIU.
Elvidge, C. D., Baugh, K. E., Kihn, E. A., Kroehl, H. W., Davis, E. R., & Davis, C. W. (1997). Relation between satellite observed visible-near infrared emissions, population, economic activity and electric power consumption. International Journal of Remote Sensing, 18(6), 1373–1379.
Elvidge, C. D., Sutton, P. C., Ghosh, T., Tuttle, B. T., Baugh, K. E., Bhaduri, B., et al. (2009). A global poverty map derived from satellite data. Computers & Geosciences, 35(8), 1652–1660.
Ghosh, T., Anderson, S. J., Elvidge, C. D., & Sutton, P. C. (2013). Using nighttime satellite imagery as a proxy measure of human well-being. Sustainability, 5, 4988–5019.
Glauben, T., Herzfeld, T., Rozelle, S., & Wang, X. (2012). Persistent poverty in rural China: Where, why, and how to escape? World Development, 40(4), 784–795.
Henderson, J. V., Storeygard, A., & Weil, D. N. (2012). Measuring economic growth from outer space. American Economic Review, 102(2), 994–1028.
Jean, N., Burke, M., Xie, M., Davis, W. M., Lobell, D. B., & Ermon, S. (2016). Combining satellite imagery and machine learning to predict poverty. Science, 353(6301), 790–794.
Kingwhyte, M., & Sun, Z. X. (2011). The impact of China’s market reforms on the health of chinese citizens: Examining two puzzles. China An International Journal, 8(1), 1–32.
Li, X., Xu, H., Chen, X., & Li, C. (2013). Potential of NPP/VIIRS nighttime light imagery for modeling the regional economy of China. Remote Sensing, 5, 3057–3081.
Liu, M., & Murphy, R. (2014). The political economy of earmarked transfers in a state-designated poor county in western China: Central policies and local responses. China Quarterly, 200(200), 973–994.
Liu, Y. H., & Xu, Y. (2015). Geographical identification and classification of multi-dimensional poverty in rural China. Acta Geographica Sinica, 70(6), 993–1007. (in Chinese).
Liu, Y. H., & Xu, Y. A. (2016). Geographic identification of multidimensional poverty in rural China under the framework of sustainable livelihoods analysis. Applied Geography, 73, 62–76.
Lü, X. (2015). Intergovernmental transfers and local education provision—Evaluating China’s 8-7 national plan for poverty reduction. China Economic Review, 33(4), 1199–1210.
Noor, A. M., Alegana, V. A., Gething, P. W., Tatem, A. J., & Snow, R. W. (2008). Using remotely sensed night-time light as a proxy for poverty in Africa. Population Health Metrics, 6(1), 1–13.
Ravallion, M., & Chen, S. (2007). China’s (uneven) progress against poverty. Journal of Development Economics, 82(1), 1–42.
Riskin, C. (1994). Chinese rural poverty: Marginalized or dispersed? American Economic Review, 84, 281–284.
Rogers, S. (2014). Betting on the strong: Local government resource allocation in China’s poverty counties. Journal of Rural Studies, 36, 197–206.
Sen, A. K. (1976). Poverty: An ordinal approach to measurement. Econometrica, 44(2), 219–231.
Sen, A. K. (1985). Commodities and capabilities. Amsterdam: Elsevier Science.
Shi, K., Yu, B., Huang, Y., Hu, Y., Yin, B., Chen, Z., et al. (2014). Evaluating the ability of NPP/VIIRS nighttime light data to estimate the gross domestic product and the electric power consumption of China at multiple scales: A comparison with DMSP-OLS data. Remote Sensing, 6(2), 1705–1724.
Stanley, C. R., Lawie, D., & Stanley, C. R. (2007). Average relative error in geochemical determinations: Clarification, calculation, and a plea for consistency. Exploration and Mining Geology, 16(3), 267–275.
Stirling, C. M., Harris, D., & Witcombe, J. R. (2006). Managing an agricultural research programme for poverty alleviation in developing countries: An institute without walls. Experimental Agriculture, 42(2), 127–146. https://doi.org/10.1017/S0014479705003340.
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(8), 1253–1264.
Ward, P. S. (2016). Transient poverty, poverty dynamics, and vulnerability to poverty: An empirical analysis using a balanced panel from rural China. World Development, 78, 541–553.
World Bank. (2009). From poor areas to poor people: China’s evolving poverty reduction agenda. An assessment of poverty and inequality in China.
Wu, Y., & Qi, D. (2016). The breadth and depth of multidimensional child poverty in China. International Journal of Social Welfare, 25(4), 373–387.
Xue, L., Wang, M., & Xue, T. (2013). Voluntary’ poverty alleviation resettlement in China. Development and Change, 44, 1–22.
Yang, J., & Mukhopadhaya, P. (2016). Disparities in the Level of Poverty in China: Evidence from China Family Panel Studies 2010. Social Indicators Research, 128, 1–40.
Yao, S., Zhang, Z., & Hanmer, L. (2004). Growing inequality and poverty in China. China Economic Review, 15(2), 145–163.
You, J., Wang, S., & Roope, L. (2017). Intertemporal deprivation in rural China: Income and nutrition. Journal of Economic Inequality, 5, 1–41. https://doi.org/10.1007/s10888-017-9352-z.
Yu, B. L., Shi, K. F., Hu, Y. J., Huang, C., Chen, Z. Q., & Wu, J. P. (2015). Poverty evaluation using NPP/VIIRS Nighttime Light Composite Data at the county level in China. IEEE Journal of Selected Topics In Applied Earth Observations and Remote Sensing, 8(3), 1217–1229.
Yu, J. (2013). Multidimensional poverty in China: Findings based on the CHNS. Social Indicators Research, 112(2), 315–336.
Funding
This study was funded by the National Nature Science Foundation of China (No. 41661025), the Scientific Research Fund for the Provincial Universities of Gansu (No. 2016A-001), and Research ability promotion project for young teachers of Northwest Normal University (No. NWNU-LKQN-16-7). Also, I thank International Science Editing for English editing.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
About this article
Cite this article
Pan, J., Hu, Y. Spatial Identification of Multi-dimensional Poverty in Rural China: A Perspective of Nighttime-Light Remote Sensing Data. J Indian Soc Remote Sens 46, 1093–1111 (2018). https://doi.org/10.1007/s12524-018-0772-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12524-018-0772-4