Assessment of multidimensional poverty alleviation effort in Tunisia

Chaouech, Olfa (2023): Assessment of multidimensional poverty alleviation effort in Tunisia.

Abstract

Long-term poverty data can support accurate decision-making by allowing decision-makers to track progress over time and identify areas in which additional resources or interventions may be required. In this study, we use the IPM to measure and assess poverty across regions and governorates in Tunisia, this index is a much more actionable and policy-relevant because it’s based on household survey information. However, such data, through its time-consuming, expensive, under-covered and labour-intensive, are available only once every ten years. Moreover, missing data is a common issue in survey datasets. Hence, we propose the deep learning method based on satellite images as a new and complementary approach to measure, evaluate and predict poverty in Tunisia.

• Item Type: MPRA Paper
• Language: English
• Keywords: Poverty alleviation, dual-cutoffs method, A-F model, Poverty prediction, Household survey, Deep learning, satellite imagery
• Subjects: I - Health, Education, and Welfare > I3 - Welfare, Well-Being, and Poverty > I32 - Measurement and Analysis of Poverty

References

Alkire, S., and Foster, J. E. (2011a). Counting and multidimensional poverty measurement. Journal of Public Economics, 95(7–8), 476–487.

Alkire, S., and Foster, J. E. (2011b). Understandings and misunderstandings of multidimensional poverty measurement. Journal of Economic Inequality, 9(2), 289–314.

Alkire, S., and Santos, M. E. (2014). Acute multidimensional poverty: A new index for developing countries. World Development, 59, 251–274.

Ayush, K., Uzkent, B., Burke, M., Lobell, D., and Ermon, S. (2020). Generating interpretable poverty maps using object detection in satellite images. arXiv preprint arXiv:2002.01612.

Blumenstock, J. (2020). Machine learning can help get COVID-19 aid to those who need it most. Nature.

Blumenstock, J., Cadamuro, G., and On, R. (2015). Predicting poverty and wealth from mobile phone metadata. Science, 350(6264), 1073–1076.

Brune, L., Karlan, D., Kurdi, S., and Udry, C. (2022). Social protection amidst social upheaval: Examining the impact of a multi-faceted program for ultra-poor households in Yemen. Journal of Development Economics, 155, 102780.

Cadamuro, G., Muhebwa, A., and Taneja, J. (2018). Assigning a grade: Accurate measurement of road quality using satellite imagery. arXiv preprint arXiv:1812.01699.

Deutsch, J., Silber, J., Wan, G., and Zhao, M. (2020). Asset indexes and the measurement of poverty, inequality and welfare in Southeast Asia. Journal of Asian Economics, 70, 101220.

Fisher, J. R., Acosta, E. A., Dennedy-Frank, P. J., Kroeger, T., and Boucher, T. M. (2018). Impact of satellite imagery spatial resolution on land use classification accuracy and modeled water quality. Remote Sensing in Ecology and Conservation, 4(2), 137–149.

Henderson, J. V., Storeygard, A., and Weil, D. N. (2012). Measuring economic growth from outer space. American Economic Review, 102(2), 994–1028.

Hu, S., Ge, Y., Liu, M., Ren, Z., and Zhang, X. (2022). Village-level poverty identification using machine learning, high-resolution images, and geospatial data. International Journal of Applied Earth Observation and Geoinformation, 107, 102694.

Huang, L. Y., Hsiang, S. M., and Gonzalez-Navarro, M. (2021). Using satellite imagery and deep learning to evaluate the impact of anti-poverty programs. National Bureau of Economic Research Working Paper No. 29105.

Jean, N., Burke, M., Xie, M., Davis, W. M., Lobell, D. B., and Ermon, S. (2016). Combining satellite imagery and machine learning to predict poverty. Science, 353(6301), 790–794.

Jerven, M. (2013). Poor numbers: How we are misled by African development statistics and what to do about it. Review of Constitutional Studies, 46(3), 336–340.

Mahabir, R., Croitoru, A., Crooks, A. T., Agouris, P., and Stefanidis, A. (2018). A critical review of high and very high-resolution remote sensing approaches for detecting and mapping slums. Urban Science, 2(1), 8.

Nasri, K., and Belhadj, B. (2017). Multidimensional poverty measurement in Tunisia: Distribution of deprivations across regions. Journal of North African Studies, 22(5), 841–859.

Pacifico, D., and Poege, F. (2017). Estimating measures of multidimensional poverty with Stata. The Stata Journal, 17(3), 687–703.

Pamies-Sumner, S. (2015). Development impact evaluations: State of play and new challenges. Agence Française de Développement.

Santos, M. E., and Alkire, S. (2011). Training material for producing national human development reports: MPI construction and analysis. Oxford: Oxford Poverty and Human Development Initiative.

Sen, A. K. (1982). Equality of what? Choice, welfare and measurement. In Choice, Welfare and Measurement, Oxford: Blackwell, 353–369.

Sen, A. K. (1997). On economic inequality. Oxford: Clarendon Press.

Sen, A. K. (1999). Development as freedom. Oxford: Oxford University Press.

Xie, M., Jean, N., Burke, M., Lobell, D., and Ermon, S. (2016). Transfer learning from deep features for remote sensing and poverty mapping. Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence.