Introduction/Objectives: Poverty is a multidimensional phenomenon that affects various aspects of well-being across territories. This study aims to develop a Synthetic Municipal Poverty Index (SMPI) for the state of Querétaro, with the objective of assessing socioeconomic disparities among municipalities and prioritizing areas for public policy intervention.

Methodology: A quantitative approach with a deductive, non-experimental design was employed. Socioeconomic data from 16 municipalities in Querétaro were collected from official sources such as INEGI and CONEVAL. The SMPI was constructed using Principal Component Analysis (PCA), a technique applied to determine the weights of selected indicators. Index validation was carried out through hierarchical clustering and the XGBoost machine learning model, ensuring the robustness and accuracy of the index.

Results: The findings reveal significant disparities in poverty levels across the analyzed municipalities. Income and food-related sub-indices emerged as the main determinants of poverty. Validation using the XGBoost model demonstrated high predictive performance of the SMPI, reinforcing its value as an analytical tool.

Conclusions: The SMPI serves as a reliable instrument for measuring multidimensional poverty at the municipal level. Its application supports the identification of priority areas and contributes to the design of more targeted and effective public policies. This index provides a solid framework for evidence-based decision-making and the development of comprehensive strategies to reduce poverty at the territorial level.

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Keywords:
Poverty,
synthetic index,
Querétaro,
principal component analysis, clustering,
supervised learning

01_RSN_16(35)_76_Synthetic

Alkire, S., & Foster, J. (2007). Counting and multidimensional poverty measurement, OPHI Working Paper 7, University of Oxford.

Alkire, S., & Foster, J. (2011). Counting and multidimensional poverty measurement. Journal of Public Economics, 95(7-8), 476-487. https://doi.org/10.1016/j.jpubeco.2010.11.006

Alonso, J. C. (2004). Números índices. Apuntes de Economía. https://repository.icesi.edu.co/items/5f9b8cea-273a-7785-e053-2cc003c84dc5

Álvarez, P. S., Trujillo Ubaldo, E., & Pérez Robles, K. (2021). Determinantes de la pobreza rural en México: caso de 5 localidades. Comunicaciones en Estadística, 12(2), 153-171. https://doi.org/10.15332/23393076/5642

Boltvinik, J., & Damián, A. (2020). Medición de la pobreza de México: análisis crítico comparativo de los diferentes métodos aplicados. Recomendaciones de buenas prácticas para la Medición de la Pobreza en México y América Latina.

Burbano, C., Londoño, H. H., & Pérez, C. A. (2022). Regiones para vivir. Índice sintético de calidad de vida departamental. Revista de Economía del Caribe, 23, 7-32. https://doi.org/10.14482/ecoca.23.3010

Chen, T., & Guestrin, C. (2016). XGBoost. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785-794. https://doi.org/10.1145/2939672.2939785

Chinnala, B. R. (2024). Development strategies and governance in India: Predicaments and challenges. Taylor & Francis.

CONEVAL. (2022). Medición de la pobreza. Glosario. https://www.coneval.org.mx/Medicion/Paginas/Glosario.aspx#:~:text=Vulnerables

De la Fuente, F. (2014). Números índices. Apuntes de Economía, 4.

de Waal, A. (2025). Hunger in global war economies: Understanding the decline and return of famines. Disasters, 49(1). https://doi.org/10.1111/disa.12661

Dios Palomares, R., Alcaide, D., Diz, J., Jurado, M., Prieto, A., Morantes, M., & Zúñiga, C. A. (2015). Análisis de la eficiencia de sistemas agropecuarios en América Latina y el Caribe mediante la incorporación de aspectos ambientales. Revista Científica, XXV(1), 43-50. https://www.redalyc.org/articulo.oa?id=95934122007

Dorin, F., Perrotti, D., & Goldszier, P. (2018). Los números índices y su relación con la economía. In Comisión Económica para América Latina y el Caribe (CEPAL). https://www.cepal.org/es/publicaciones/43974-numeros-indices-su-relacion-la-economia#:~:text=Los%20n%C3%BAmeros%20%C3%ADndices%20constituyen%20el,tambi%C3%A9n%20para%20realizar%20comparaciones%20internacionales

Everitt, B. S., Landau, S., Leese, M., & Stahl, D. (2011). Cluster Analysis. Wiley. https://doi.org/10.1002/9780470977811

FAO. (2015). The State of Food Insecurity in the World. https://openknowledge.fao.org/server/api/core/bitstreams/63863832-4cb5-4e05-9040-4b22d9a92324/content

Fields, G. (2001). Poverty: Concepts and dimensions. International Symposium on Poverty: Concepts and Methodologies.

Gawusu, S., Jamatutu, S. A., Zhang, X., Moomin, S. T., Ahmed, A., Afriyie Mensah, R., Das, O., & Ackah, I. (2024). Spatial analysis and predictive modeling of energy poverty: Insights for policy implementation. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4817830

Gerszon, D., Yonzan, N., Lakner, C., Castaneda Aguilar, R. A., & Wu, H. (2021). Updated estimates of the impact of COVID-19 on global poverty: Turning the corner on the pandemic in 2021? https://blogs.worldbank.org/en/opendata/updated-estimates-impact-covid-19-global-poverty-turning-corner-pandemic-2021

Grajales-Marín, A. F., Sepúlveda-Murillo, F. H., Tapia, A., & Tabares, A. (2025). Spatial analysis of socioeconomic data and its relationship with illicit crops in Nariño-Colombia. PLoS ONE, 20(1), e0316709. https://doi.org/10.1371/journal.pone.0316709

Hotelling, H. (1933). Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology, 24(6), 417.

Ishmuradova, I. I., Chistyakov, A. A., Klebanov, L. R., Sliusar, V. V., Sliusar, M. V., Devyatkin, G. S., & Drobysheva, N. N. (2024). A decadal review of the role of communication-mobile technologies in promoting digital inclusion: Digital divide. Online Journal of Communication and Media Technologies, 14(3), e202438. https://doi.org/10.30935/ojcmt/14709

Jarupunphol, P., Buathong, W., Kuptabut, S., & Sudjarid, W. (2024). Assessing decision tree, random forest, and XGBoost models for human capital readiness predictions in low-income areas. Multidisciplinary Science Journal, 7(6), 2025296. https://doi.org/10.31893/multiscience.2025296

Ji, Q., Yang, J., Chu, Y., Chen, H., & Guo, X. (2024). Inequality of rural residents’ income in China since the targeted poverty alleviation strategy: New trends, causes, and policy implications. Research in Cold and Arid Regions. https://doi.org/10.1016/j.rcar.2024.09.002

Jolliffe, I. T., & Cadima, J. (2016). Principal component analysis: A review and recent developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2065), 20150202. https://doi.org/10.1098/rsta.2015.0202

Kaufman, L., & Rousseeuw, P. J. (1990). Finding groups in data: An introduction to cluster analysis. Wiley. https://doi.org/10.1002/9780470316801

Li, X., & Zhang, C. (2025). Spatiotemporal trends of poverty in the United States, 2006-2021. Applied Spatial Analysis and Policy, 18(1), 3. https://doi.org/10.1007/s12061-024-09604-8

Lipton, M., & Ravallion, M. (1995). Poverty and policy. Handbook of Development Economics, 3, 2551-2657.

Mitra, A., & Das, D. (2018). Inclusive growth: Economics as if people mattered. Global Business Review, 19(3), 756-770. https://doi.org/10.1177/0972150917713840

Molina, J. (2022). Áreas funcionales y mercados locales de trabajo en Andalucía [Universidad de Granada]. https://hdl.handle.net/10481/77138

Pham, T. M., Dinh, H. T., Pham, T. A., Nguyen, T. S., & Duong, N. T. (2024). Modeling of water scarcity for spatial analysis using Water Poverty Index and fuzzy-MCDM technique. Modeling Earth Systems and Environment, 10(2), 2079-2097. https://doi.org/10.1007/s40808-023-01884-2

Ribeiro, L. C. de S., Santos, G. F. dos, Cerqueira, R. B. de, Sousa Filho, J. F. de, Tresinari, E. M., & Rocha, A. R. F. da. (2024). The role of emergency aid in mitigating the local effects of the COVID-19 pandemic: Evidence from Espírito Santo, Brazil. Spatial Economic Analysis, 1-21. https://doi.org/10.1080/17421772.2024.2332339

Safi, L. (2024). Review of Afghanistan’s development pattern from 2001 to 2021: A relative lack of sustainability, inclusiveness, and prosperity. Journal of Social and Economic Development, 26(1), 333-352. https://doi.org/10.1007/s40847-023-00268-7

Sahin, A. N. (2024). Investigating the impact of climate variables on the organic honey yield in Turkey using XGBoost machine learning. Journal of the Science of Food and Agriculture, 105(1), 84-92. https://doi.org/10.1002/jsfa.13806

Sánchez-Ancochea, D. (2021). The surprising reduction of inequality during a commodity boom: What do we learn from Latin America? Journal of Economic Policy Reform, 24(2), 95-118. https://doi.org/10.1080/17487870.2019.1628757

Santos, M. E., & Villatoro, P. (2016). A multidimensional poverty index for Latin America. Review of Income and Wealth, 64(1), 52-82. https://doi.org/10.1111/roiw.12275

Saraví, G. A. (2004). Segregación urbana y espacio público: los jóvenes en enclaves de pobreza estructural. Revista de la CEPAL. https://repositorio.cepal.org/server/api/core/bitstreams/bcd6e487-7d0b-4990-a117-a72d1acc1d69/content

Schumann, L. A., & Moura, L. B. A. (2015). Índices sintéticos de vulnerabilidade: Uma revisão integrativa de literatura. In Ciencia e Saude Coletiva, 20(7), 2105-2120. https://doi.org/10.1590/1413-81232015207.10742014

Sen, A. (1981). Issues in the Measurement of Poverty. In Measurement in public choice (pp. 144-166). Springer.

Sen, A. (2000). Social exclusion: Concept, application, and scrutiny. Office of Environment and Social Development, Asian Development Bank.

Sen, A., Rajan, R., & Banerjee, A. (2020). Huge numbers may be pushed into dire poverty or starvation… we need to secure them. The Indian Express, 18.

Sevilla, J. A. (2019). El poder de los gobernadores. Conceptualización y medición en los ejecutivos locales mexicanos. Colombia Internacional, 100, 201-235. https://doi.org/10.7440/colombiaint100.2019.09

Sharma, A., Dean, B. P., & Bezjian, J. (2024). Reducing poverty by digitization: What role do information and communication technologies (ICTs) play in reducing poverty? Journal of Strategy and Management. https://doi.org/10.1108/JSMA-06-2023-0129

Sobczak, E., Bartniczak, B., & Raszkowski, A. (2021). Implementation of the no poverty sustainable development goal (SDG) in Visegrad Group (V4). Sustainability, 13(3), 1030. https://doi.org/10.3390/su13031030

Spicker, P. (2009). Definiciones de pobreza: doce grupos de significados. Pobreza: un Glosario Internacional, 291-306.

Strabidis, O., Salgado, C., Santiago, M., & Vázquez, S. (2019). El contexto social y su relación con el análisis de la pobreza rural. In Pobreza. La realidad detrás de sus perpetradores y productores, 59-84.

Thorbecke, E. (2013). Multidimensional poverty: Conceptual and measurement issues. In The many dimensions of poverty (pp. 3-19). Palgrave Macmillan UK. https://doi.org/10.1057/9780230592407_1

Tikadar, B., & Swami, D. (2024). Understanding the variability of residential energy poverty in India. Utilities Policy, 93, 101878. https://doi.org/10.1016/j.jup.2024.101878

Townsend, P. (1979). Poverty in the United Kingdom: A survey of household resources and standards of living. University of California Press. https://doi.org/10.1525/9780520325760

Wang, F., & Wang, Y. (2024). How geographic accessibility and rural governance mitigate the impact of multiple risks on rural households’ well-being: Evidence from the Dabie Mountains in China. Journal of Geographical Sciences, 34(6), 1195–1227. https://doi.org/10.1007/s11442-024-2245-8

Wani, N. R., Rather, R. A., Farooq, A., Padder, S. A., Baba, T. R., Sharma, S., Mubarak, N. M., Khan, A. H., Singh, P., & Ara, S. (2023). New insights in food security and environmental sustainability through waste food management. Environmental Science and Pollution Research, 31(12), 17835-17857. https://doi.org/10.1007/s11356-023-26462-y

Zafar, I., Rasool, R., Kausar, T., Ayaz, M. M., Fatima, H., Shoukat, W., & Ain, Q. ul. (2025). Intervening with Fish Genetics and Breeding Programs to End Hunger and Achieve Food Security and Nutrition. A global perspective. In Food Security, Nutrition and Sustainability Through Aquaculture Technologies (pp. 129-172). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-75830-0_8

Ziccardi, A. (2019). Nueva arquitectura espacial, pobreza urbana y desigualdad territorial. Polis, 15(1), 7-31.

Zuniga-Gonzalez, C. A. (2023). TFP Bioeconomy Impact post Covid-19 on the agricultural economy. PLOS ONE, 18(11), e0288885. https://doi.org/10.1371/journal.pone.0288885

Dimensions

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