Identificación de la pobreza en Colombia: Comparación entre el índice de pobreza multidimensional y el sistema de identificación de beneficiarios de programas sociales

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

Alkire, S., Kanagaratnam, U., & Suppa, N. (2020). The global multidimensional poverty index (MPI) 2020. https://www.ophi.org.uk/wp-content/uploads/OPHIMPIMN492020.pdf

Alkire, S., Roche, J. M., Ballon, P., Foster, J., Santos, M. E., & Seth, S. (2015). Multidimensional poverty measurement and analysis. Oxford University Press.

Alkire, S., & Seth, S. (2008). Measuring multidimensional poverty in India: A new proposal (Working Paper 15). Oxford Poverty and Human Development Initiative.

Alkire, S., & Shen, Y. (2017). Exploring multidimensional poverty in China: 2010 to 2014. En Research on economic inequality. Emerald Publishing Limited.

Angulo, R., Díaz, Y., & Pardo, R. R. (2016). The Colombian multidimensional poverty index. Measuring poverty in a public policy context. Social Indicators Research, 127, 1-38.

Atkinson, A. B. (1987). On the measurement of poverty. Econometrica: Journal of the Econometric Society, 55(4), 749-764.

Atkinson, A. B. (2019). Measuring poverty around the world. Princeton University Press.

Banco Mundial —BM (2022). Poverty and shared prosperity 2022: Correcting course. The World Bank.

Barocas, S., Bradley, E., Honavar, V., & Provost, F. (2017). Big data, data science, and civil rights. arXiv preprint arXiv:1706.03102.

Boltvinik, J. (2013). Medición multidimensional de la pobreza. América Latina de precursora a rezagada. Revista Sociedad y Equidad, 5, 4-29.

Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32.

Celis, L. E., Huang, L., Keswani, V., & Vishnoi, N. K. (2019). Classification with fairness constraints. A meta-algorithm with provable guarantees. En Proceedings of the conference on fairness, accountability, and transparency (pp. 319-328). Association for Computing Machinery.

Chen, R. J., Chen, T. Y., Lipkova, J., Wang, J. J., Williamson, D. F. K., Lu, M. Y., & Mahmood, F. (2021). Algorithm fairness in ai for medicine and healthcare. ArXiv, abs/2110.00603.

Chouldechova, A., & Roth, A. (2020). A snapshot of the frontiers of fairness in machine learning. Communications of the ACM, 63(5), 82-89.

Conover, W. J. (1999). Practical nonparametric statistics (vol. 350). John Wiley & sons.

Corbett, S., & Goel, S. (2018). The measure and mismeasure of fairness. A critical review of fair machine learning. arXiv preprint arXiv:1808.00023.

Decancq, K., & Lugo, M. (2013, 01). Weights in multidimensional indices of wellbeing. An overview. Econometric Reviews, 32(1), 7-34. http://dx.doi.org/10.1080/07474938.2012.690641

Departamento Nacional de Planeación —DNP. (2012). Metodologías oficiales y arreglos institucionales para la medición de la pobreza en Colombia. Consejo Nacional de Política Económica y Social. https://colaboracion.dnp.gov.co/cdt/conpes/social/150.pdf

Departamento Nacional de Planeación —DNP. (2016, 5 diciembre). Declaración de importancia estratégica del sistema de identificación de potenciales beneficiarios (Sisben IV). Consejo Nacional de Política Económica y Social. https://colaboracion.dnp.gov.co/CDT/Conpes/Econmicos/3877.pdf

Eubanks, V. (2018). Automating inequality. How high-tech tools profile, police, and punish the poor. St. Martin’s Press.

Greco, G. (2018). Setting the weights: The women’s capabilities index for malawi. Social Indicators Research, 135(2), 457-478. https://doi.org/10.1007/s11205-016-1502-3

Keddell, E. (2019). Algorithmic justice in child protection. Statistical fairness, social justice and the implications for practice. Social Sciences, 8(10). https://doi.org/10.3390/socsci8100281

Kuhn, M., & Wickham, H. (2020). Tidymodels: A collection of packages for modeling and machine learning using tidyverse principles [Computer software manual]. https://www.tidymodels.org

Laderchi, C. R., Saith, R., & Stewart, F. (2003). Does it matter that we do not agree on the definition of poverty? A comparison of four approaches. Oxford Development Studies, 31(3), 243-274. https://doi.org/10.1080/1360081032000111698.

Lepenies, P. (2020). Metrics, politics and definitions. How poverty lost its social context and what this means for current debates. En V. Beck, H. Hahn, & R. Lepenies (eds.), Dimensions of poverty: Measurement, epistemic injustices, activism (pp. 57-73). Springer International Publishing. https://doi.org/10.1007/978-3-030-31711-9_4

Lepri, B., Staiano, J., Sangokoya, D., Letouze, E., & Oliver, N. (2017). The tyranny of data? the bright and dark sides of data-driven decision-making for social good. Transparent data mining for big and small data (pp. 3-24). Springer International Publishing. https://doi.org/10.1007/978-3-319-54024-5_1

Lucci, P., Bhatkal, T., & Khan, A. (2018). Are we underestimating urban poverty? World Development, 103, 297-310. https://doi.org/10.1016/j.worlddev.2017.10.022

Maccio, J., & Mitchell, A. (2023). Medición multidimensional de pobreza en ciudades segregadas. Evidencia de la ciudad de Buenos Aires. Revista Desarrollo y Sociedad, 93, 101-137. https://doi.org/10.13043/DYS.93.3

Mahamallik, M., & Sahu, G. B. (2011). Identification of the poor. Errors of exclusion and inclusion. Economic and Political Weekly, 46(9), 71-77.

Menardi, G., & Torelli, N. (2014). Training and assessing classification rules with imbalanced data. Data Mining and Knowledge Discovery, 28, 92-122.

Putnam, D. (2020). Poverty as a social relation. En V. Beck, H. Hahn, & R. Lepenies (eds.), Dimensions of poverty. Measurement, epistemic injustices, activism (pp. 41-55). Springer International Publishing. https://doi.org/10.1007/978-3-030-31711-9_3

Raji, I. D., Smart, A., White, R. N., Mitchell, M., Gebru, T., Hutchinson, B., & Barnes, P. (2020). Closing the AI accountability gap. Defining an end-to-end framework for internal algorithmic auditing. En Proceedings of the 2020 conference on fairness, accountability, and transparency (pp. 33-44). https://doi.org/10.48550/arXiv.2001.00973

Roelen, K. (2017). Monetary and multidimensional child poverty: A contradiction in terms? Development and Change, 48(3), 502-533.

Schonlau, M., & Zou, R. Y. (2020). The random forest algorithm for statistical learning. The Stata Journal, 20(1), 3-29.

Sen, A. (1985). Commodities and capabilities. Oxford University Press.

Sen, A. (1992). Inequality reexamined. Harvard University Press.

Sen, A. (1999). Development as freedom. Anchor Books.

Sen, A. (2018). Collective choice and social welfare. Harvard University Press.

Sohnesen, T. P., & Stender, N. (2017). Is random forest a superior methodology for predicting poverty? An empirical assessment. Poverty & Public Policy, 9(1), 118-133.

Tran, V. Q., Alkire, S., & Klasen, S. (2015). Static and dynamic disparities between monetary and multidimensional poverty measurement. Evidence from Vietnam. En T. I. Garner & K. S. Short (eds.), Measurement of poverty, deprivation, and economic mobility (vol. 23, p. 249-281). Emerald Publishing Ltd.

United Nations Development Programme (UNDP) & Oxford Poverty and Human Development Initiative —OPHI (2021). Global multidimensional poverty index 2021 – Unmasking disparities by ethnicity, caste and gender. United Nations Development Programme and Oxford Poverty and Human Development Initiative. https://ophi.org.uk/wp-content/uploads/UNDPOPHIGMPI2021ReportUnmasking.pdf

United Nations Development Programme (UNDP) & Oxford Poverty and Human Development Initiative —OPHI. (2022). 2022 global multidimensional poverty index (MPI). Autor.

United Nations —UN. (2015). Transforming our world: The 2030 Agenda for Sustainable Development Transforming our world: The 2030 Agenda for Sustainable Development. Autor. https://unctad.org/system/files/official-document/ares70d1en.pdf

Usmanova, A., Aziz, A., Rakhmonov, D., & Osamy, W. (2022a). Utilities of artificial intelligence in poverty prediction. A review. Sustainability, 14(21). https://doi.org/10.3390/su142114238

Usmanova, A., Aziz, A., Rakhmonov, D., & Osamy, W. (2022b). Utilities of artificial intelligence in poverty prediction. A review. Sustainability, 14(21), 14238.

Viera, A. J., & Garrett, J. M. (2005). Understanding interobserver agreement: The kappa statistic. Fam med, 37(5), 360-363.

Villatoro, P., & Santos, M. E. (2019). ¿quiénes son pobres? Análisis de su identificación en América Latina. Problemas del Desarrollo, 50(199), 3-29.

Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L. D., François, R., & Yutani, H. (2019). Welcome to the tidyverse. Journal of Open Source Software, 4(43), 1686. https://doi.org/10.21105/joss.01686

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