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Quarterly multidimensional poverty estimates in Mexico using machine learning algorithms/Estimaciones trimestrales de pobreza multidimensional en México mediante algoritmos de aprendizaje de máquina

Author

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  • Ratzanyel Rincón

    (The University of British Columbia)

Abstract

This article addresses the lack of timely information about multidimensional poverty in Mexico. Three machine learning algorithms —the LASSO logistic regression, random forest, and support vector machines— are trained with the ENIGH to find generalizable patterns of multidimensional poverty in the raw data. The fitted models are used to classify each individual in the ENOE as poor or non-poor to obtain aggregated poverty rates on a quarterly basis. These estimates are closer to the official levels of multidimensional poverty than the labor poverty measurement and provide an accurate poverty outlook more than a year ahead of the official measure.

Suggested Citation

  • Ratzanyel Rincón, 2023. "Quarterly multidimensional poverty estimates in Mexico using machine learning algorithms/Estimaciones trimestrales de pobreza multidimensional en México mediante algoritmos de aprendizaje de máquina," Estudios Económicos, El Colegio de México, Centro de Estudios Económicos, vol. 38(1), pages 3-68.
    • Handle: RePEc:emx:esteco:v:38:y:2023:i:1:p:3-68
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    File URL: https://estudioseconomicos.colmex.mx/index.php/economicos/article/view/435
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    References listed on IDEAS

    as
    1. World Bank, 2020. "Poverty and Shared Prosperity 2020," World Bank Publications - Books, The World Bank Group, number 34496.
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    3. Linden McBride & Austin Nichols, 2018. "Retooling Poverty Targeting Using Out-of-Sample Validation and Machine Learning," The World Bank Economic Review, World Bank, vol. 32(3), pages 531-550.
    4. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    5. Jon Kleinberg & Himabindu Lakkaraju & Jure Leskovec & Jens Ludwig & Sendhil Mullainathan, 2018. "Human Decisions and Machine Predictions," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(1), pages 237-293.
    6. Pave Sohnesen,Thomas & Stender,Niels, 2016. "Is random forest a superior methodology for predicting poverty ? an empirical assessment," Policy Research Working Paper Series 7612, The World Bank.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    multidimensional poverty; machine learning; LASSO logistic regression; random forest; support vector machines;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • D6 - Microeconomics - - Welfare Economics
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty

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