Demographic Trends: Forecast of the Child Population from 0 to 3 years old for the Year 2030
Article Sidebar
Main Article Content
Abstract
This paper estimates the number of children aged 0-3 living in poverty in Ecuador, using combined data from the 2014 Living Conditions Survey (LCS) and the 2010 Population and Housing Census (CPV), as the current data do not provide sufficient detail by region. To do so, we employ the Small Area Estimation (SAE) method, based on the Fay-Herriot model, which uses multiple linear regression to integrate survey and census data. This approach allows the analysis of specific sub-populations, such as cantons or districts, where surveys alone do not provide reliable results due to small samples. In addition, consumption-based poverty is simulated and the child population is projected up to 2030, comparing these projections with estimates from the Economic Commission for Latin America and the Caribbean (ECLAC) to verify their accuracy. The main purpose of this research is to provide accurate and detailed information on children in poverty, thus supporting the creation of effective public policies in Ecuador.
Downloads
Metrics
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
References
A. Gutiérrez, X. Mancero, and S. Guerrero, "Poverty mapping in Latin America: ECLAC experiences on small area estimation," Statistical Journal of the IAOS, vol. 38, no. 3, pp. 1021–1033, 2022.
Comisión Económica para América Latina y el Caribe (CEPAL), Panorama social de América Latina y el Caribe 2024: Desafíos de la protección social, [Online]. Available: https://www.cepal.org/es/publicaciones/80858-panorama-social-america-latina-caribe-2024-desafios-la-proteccion-social .
Banco Mundial, Panorama de la pobreza y la desigualdad en América Latina y el Caribe , Washington, D.C.: Banco Mundial, 2024.
J. N. Rao and I. Molina, Small Area Estimation , John Wiley & Sons, 2015.
DANE, Encuesta Nacional de Calidad de Vida 2023 , Departamento Administrativo Nacional de Estadística de Colombia, 2023. [Online]. Available: https://microdatos.dane.gov.co/index.php/catalog/827 .
INEGI, Censo de Población y Vivienda 2024 , Instituto Nacional de Estadística y Geografía de México, 2024. [Online]. Available: https://www.inegi.org.mx/app/buscador/default.html?q=SAE .
A. Sen, Development as Freedom , Oxford University Press, 1999.
Banco Mundial, Pobreza: Panorama general , 2024. [Online]. Available: https://www.bancomundial.org/es/topic/poverty/overview .
W. H. Greene, Econometric Analysis (8th ed.), Pearson Education, 2018.
A. Arias-Salazar, A. Gutiérrez, S. Guerrero-Gómez, X. Mancero, N. Rojas-Perilla, and H. Zhang, "Small area estimation for composite indicators: the case of multidimensional poverty incidence," Journal of Official Statistics , 0282423X241300751, 2023.
Eurostat, City Statistics - Small Area Estimation , European Commission, 2024. [Online]. Available: https://ec.europa.eu/eurostat/statistics-explained/index.php?title=City_statistics_-_small_area_estimation
Statistics Canada, Canadian Community Health Survey - Annual Component (CCHS), [Online]. Available: https://www23.statcan.gc.ca/imdb/p2SV.pl?Function=getSurvey&SDDS=3226 .
A. Molina, J. Rosero, M. León, R. Castillo, F. Jácome, D. Rojas, et al., Reporte de pobreza por consumo Ecuador 2006-2014 , 2016.
INEC-BM, Reporte de Pobreza por Consumo Ecuador 2006-2014, Quito: Instituto Nacional de Estadística y Censos, 2014.
S. Dercon, Assessing Vulnerability , World Bank Publications, 2001.
Z. Villa Juan-Albacea, Small Area Estimation of Poverty Statistics , No. 2009-16, PIDS Discussion Paper Series, 2009.
D. M. Stukel and J. N. K. Rao, "Estimation of regression models with nested error structure and unequal error variances under two and three stage cluster sampling," Statistics & Probability Letters , vol. 35, no. 4, pp. 401–407, 1997.
Y. You and J. Rao, "A pseudo-empirical best linear unbiased prediction approach to small area estimation using survey weights," Canadian Journal of Statistics, vol. 30, no. 3, pp. 431–439, 2002.
J. N. K. Rao and I. Molina, Small Area Estimation: A Review of Methods Based on the Application of Mixed Models , 2015. [Online]. Available: https://www.researchgate.net/publication/252669243_Small_Area_Estimation_A_Review_of_Methods_Based_on_the_Application_of_Mixed_Models .
C. Elbers, J. O. Lanjouw, and P. Lanjouw, Small Area Estimation of Poverty Indicators , ResearchGate, 2003. [Online]. Available: https://www.researchgate.net/publication/230806613_Small_Area_Estimation_of_Poverty_Indicators .
UNICEF, The State of the World’s Children 2023 , [Online]. Available: https://www.unicef.org/reports/state-worlds-children-2023 .
Studocu, Variables dummy de estadística , [Online]. Available: https://www.studocu.com/ec/document/instituto-tecnologico-bolivariano-de-tecnologia/administracion/variables-dummy-de-estadistica/15406065 .
J. Jiang and P. Lahiri, "Mixed model prediction and small area estimation," Test , vol. 15, pp. 1–96, 2006.
I. Molina, J. N. K. Rao, and G. S. Datta, "Small area estimation under a Fay-Herriot model with preliminary testing for the presence of random area effects," Survey Methodology , vol. 41, no. 1, pp. 1–20, 2015.
M. Ghosh and J. N. Rao, "Small area estimation: an appraisal," Statistical Science , vol. 9, no. 1, pp. 55–76, 1994.
W. Lutz, W. P. Butz, and K. E. Samir (Eds.), World Population and Human Capital in the Twenty-First Century , OUP Oxford, 2014.
A. Van Delden, B. J. Du Chatinier, and S. Scholtus, "Accuracy in the application of statistical matching methods for continuous variables using auxiliary data," Journal of Survey Statistics and Methodology , vol. 8, no. 5, pp. 990–1017, 2020.
M. Ghosh and J. N. Rao, "Small area estimation: an appraisal," Statistical Science , vol. 9, no. 1, pp. 55–76, 1994.
I. Molina and Y. Marhuenda, Small Area Estimation for Mapping Local Indicators , 2019. [Online]. Available: https://www.makswell.eu/attached_documents/3_small-area-estimation-for-mapping-local-indicators.pdf .
P. Corral, I. Molina, A. Cojocaru, and S. Segovia, Guidelines to Small Area Estimation for Poverty Mapping , Washington: World Bank, 2022.
United Nations Population Fund, Small Area Estimation for Mapping Local Indicators , 2019. [Online]. Available: https://www.unfpa.org/sites/default/files/pub-pdf/19-310_SAE_Brochure_A4-SINGLE-PROOF6.pdf .
[14] Asian Development Bank, Introduction to Small Area Estimation Techniques: A Practical Guide for National Statistics Offices , 2020. [Online]. Available: https://www.adb.org/publications/small-area-estimation-guide-national-statistics-offices .
World Bank, Guidelines to Small Area Estimation for Poverty Mapping , 2023. [Online]. Available: https://www.worldbank.org/en/events/2023/02/07/guidelines-to-small-area-estimation-for-poverty-mapping .
RTI International, Small Area Estimation , [Online]. Available: https://www.rti.org/brochures/small-area-estimation .
A. de Mesa, Los sistemas de pensiones en América Latina: institucionalidad, gasto público y sostenibilidad financiera en tiempos del COVID-19 , 2020.
J. M. Wooldridge, Introductory Econometrics: A Modern Approach (6th ed.), Cengage Learning, 2016.
M. C. Nguyen, P. A. C. Rodas, J. P. W. De Azevedo, and Q. Zhao, "Small area estimation and Poverty map in Stata," in 2017 Stata Conference , No. 20, 2017.
M. H. Boyle et al., "The influence of economic development level, household wealth and maternal education on child health in the developing world," Social science & medicine , vol. 63, no. 8, pp. 2242–2254, 2006.
F. F. J. Bogliaccini, Metas del milenio y desarrollo: las configuraciones del desarrollo social en América Latina , Documento de Trabajo del IPES, Montevideo, 2008.
J. N. K. Rao and I. Molina, Small Area Estimation: A Review of Methods Based on the Application of Mixed Models , 2015. [Online]. Available: https://www.researchgate.net/publication/252669243_Small_Area_Estimation_A_Review_of_Methods_Based_on_the_Application_of_Mixed_Models .
E. A. Herrera Herrera, Comparación de estimadores directos e indirectos en estimación de áreas pequeñas , Universidad Santo Tomás, 2018. [Online]. Available: https://repository.usta.edu.co/bitstream/handle/11634/12530/2018edgarherrera.pdf?sequence=1
R. E. Fay and R. A. Herriot, "Estimates of income for small places: An application of James-Stein procedures to census data," Journal of the American Statistical Association , vol. 74, no. 366, pp. 269–277, 1979.
G. N. López Fuertes, Estimación de la población de niñas y niños de 0 a 3 años del ecuador, según Pobreza por Consumo, mediante la metodología Small Area Estimation (SAE) hasta el año 2021 , Master's thesis, Quito: UCE, 2019.
Instituto Nacional de Estadística y Censos (INEC), Guía de Uso de Base de Datos , 2021. [Online]. Available: https://www.ecuadorencifras.gob.ec/documentos/web-inec/EMPLEO/2021/Febrero-2021/202102_%20Guia%20de%20usuario%20BDD_ENEMDU.pdf
J. Parra, Introducción a las variables ficticias o variables dummy , [Online]. Available: https://www.javierparra.net/ecoknowmic/introduccion-a-las-variables-ficticias-o-variables-dummy/ .
Studocu, Variables dummy de estadística , [Online]. Available: https://www.studocu.com/ec/document/instituto-tecnologico-bolivariano-de-tecnologia/administracion/variables-dummy-de-estadistica/15406065 .
M. C. Howard, Regresión con Variables Dummy en Jamovi , [Online]. Available: https://mattchoward.com/regresion-con-variables-dummy-en-jamovi/ .
M. Carvajal, Análisis de datos y detección de valores atípicos en econometría , Quito: Ediciones Económicas, 2004.
T. Hastie, R. Tibshirani, J. H. Friedman, and J. H. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction , vol. 2, New York: Springer, 2009.
A. C. King, M. P. Rao, and C. D. Tregillis, "Econometric Analysis," in Litigation Services Handbook: The Role Of The Financial Expert , 2017, pp. 1–62.
R. McElreath, Statistical Rethinking: A Bayesian Course with Examples in R and Stan , Chapman and Hall/CRC, 2018.
D. Pfeffermann, "New important developments in small area estimation," Quality control and applied statistics , vol. 59, no. 1, pp. 103–106, 2014.
B. W. Silverman, Density Estimation for Statistics and Data Analysis , Routledge, 2018.
J. Fox, Applied Regression Analysis and Generalized Linear Models , Sage Publications, 2015.
N. Tzavidis et al., "A framework for producing small area estimates based on the Fay-Herriot model," Statistical Modelling , vol. 19, no. 4, pp. 309–328, 2019.
World Health Organization (WHO), Global Strategy Data: Maternal, Newborn, Child, Adolescent and Ageing , [Online]. Available: https://platform.who.int/data/maternal-newborn-child-adolescent-ageing/global-strategy-data .
Save the Children, World more dangerous than ever for children , [Online]. Available: https://www.savethechildren.org.uk/news/media-centre/press-releases/world-more-dangerous-than-ever-for-children#:~:text=Save%20the%20Children%20report%20reveals,day%20were%20killed%20or%20maimed .
A. F. Correa, On the measurement of multidimensional poverty as a policy tool: empirical applications to Chile, Colombia, Equador and Peru , 2017.
S. K. Smith, J. Tayman, and D. A. Swanson, State and Local Population Projections: Methodology and Analysis , Springer, 2019.
United Nations, Principles and Recommendations for Population and Housing Censuses , United Nations Statistics Division, 2020.
S. H. Preston, P. Heuveline, and M. Guillot, Demography: Measuring and Modeling Population Processes , Blackwell Publishing, 2022.
National Research Council, Reengineering the 2020 Census: Risks and Challenges , National Academies Press, 2020.
D. Pfeffermann, "Small Area Estimation: A Review and Some Directions," Statistica Sinica , vol. 29, no. 1, pp. 289–326, 2019.
OECD, Challenges of the 2022 Census and the Implications for Data Comparability , Organisation for Economic Co-operation and Development, 2022.
J. Foster, J. Greer, and E. Thorbecke, "A class of decomposable poverty measures," Econometrica: journal of the econometric society , pp. 761–766, 1984.
Instituto Nacional de Estadística y Censos (INEC), Encuesta Nacional de Empleo, Desempleo y Subempleo (ENEMDU) , Quito, Ecuador: INEC, 2023.
P. Samaniego, "Evolución de la pobreza y la desigualdad en Quito," Questiones Urbano Regionales. Revista del Instituto de la Ciudad , vol. 1, no. 2, pp. 77–94, 2013.
N. G. Mankiw and M. P. Taylor, Economía , Cengage Learning, 2021.
J. De Gregorio, Macroeconomía , McGraw-Hill, 2007.
R. Prebisch, El desarrollo económico de América Latina y sus principales problemas , Naciones Unidas, 2012.
Instituto Nacional de Estadística y Censos (INEC), Boletín de pobreza: Encuesta Nacional de Empleo, Desempleo y Subempleo (ENEMDU), Junio 2024 , Quito, Ecuador: INEC, 2024. [Online]. Available: https://www.ecuadorencifras.gob.ec/documentos/web-inec/POBREZA/2024/Junio/202406_Boletin_pobreza_ENEMDU.pdf .
J. Andrade, E. Marinho, and G. Lima, "Crecimiento económico y concentración del ingreso: sus efectos en la pobreza del Brasil," CEPAL , 2017. [Online]. Available: https://repositorio.cepal.org/bitstream/handle/11362/42693/1/RVE123_Araujo.pdf .
del Ecuador, O. S., Situación de la niñez y adolescencia en Ecuador. Una mirada a través de los ODS , 2019.
[48] INEC, Proyecciones Demográficas 2022-2030 , 2024. [Online]. Available: https://www.ecuadorencifras.gob.ec/proyecciones-poblacionales/ .
R. L. Chambers and R. Clark, An Introduction to Model-Based Survey Sampling with Applications , Oxford University Press, 2012.
CELADE-División de Población de la CEPAL y División de Población de las Naciones Unidas, Estimaciones y proyecciones de población a nivel nacional, de los 20 países de América Latina , CEPAL, 2023. [Online]. Available: https://www.cepal.org/es/temas/estimaciones-proyecciones-poblacion/estimaciones-proyecciones-poblacion-nivel-nacional-los-20-paises-america-latina .
UNICEF, Estado Mundial de la Infancia 2024 , Fondo de las Naciones Unidas para la Infancia, 2024.
Institute for Health Metrics and Evaluation, TL Capstones global fertility , 2024. [Online]. Available: https://www.healthdata.org/sites/default/files/2024-03/TL%20Capstones%20global%20fertility_ES.pdf .
Dialoguemos, ¿Cuáles son los países de América Latina con la natalidad más baja y más alta? , 2024. [Online]. Available: https://dialoguemos.ec/2024/10/cuales-son-los-paises-de-america-latina-con-la-natalidad-mas-baja-y-mas-alta/ .
P. Corral, I. Molina, A. Cojocaru, and S. Segovia, Guidelines to Small Area Estimation for Poverty Mapping , Washington: World Bank, 2022.
T. Lynch et al., "Definitions, theories, and measurement of stress in children," Journal of Pediatric Nursing , vol. 66, pp. 202–212, 2022.
G. D. E. Loaiza, "Impacto de la inversión social en la reducción de la pobreza y la desigualdad en Ecuador: Análisis de políticas públicas y resultados 2010-2023," Ciencia y Educación , vol. 5, no. 12, pp. 122–138, 2024.
N. Tzavidis, L. C. Zhang, A. Luna, T. Schmid, and N. Rojas-Perilla, "From start to finish: a framework for the production of small area official statistics," Journal of the Royal Statistical Society: Series A (Statistics in Society) , vol. 181, no. 4, pp. 927–979, 2018.
Food and Agriculture Organization of the United Nations (FAO), [Título del recurso] , [Online]. Available: https://openknowledge.fao.org/items/65139780-d06c-4b7c-a2cd-3ed4256eaa1c .
Economic Commission for Latin America and the Caribbean (ECLAC), Social Panorama of Latin America and the Caribbean 2024: Accessible Version , [Online]. Available: https://www.cepal.org/en/publications/81294-social-panorama-latin-america-and-caribbean-2024-accessible-version .
S. Parandekar, R. Vos, and D. Winkler, Ecuador: Crisis, poverty, and social protection , 2002.
A. V. Banerjee and E. Duflo, Poor Economics: A Radical Rethinking of the Way to Fight Global Poverty , Public Affairs, 2011.
A. Deaton, The Analysis of Household Surveys: A Microeconometric Approach to Development Policy , World Bank Publications, 1997.
C. Elbers, J. O. Lanjouw, and P. Lanjouw, "Micro–Level Estimation of Poverty and Inequality," Econometrica , vol. 71, no. 1, pp. 355–364, 2003.
M. F. M. Macías and M. M. B. Salinas, "Programas de servicios sociales para erradicar la pobreza en el Ecuador," ECA Sinergia , vol. 12, no. 1, pp. 59–69, 2021.
G. Wan, "Regional income inequality in rural China, 1985–2002: Trends, causes and policy implications," in Understanding Inequality and Poverty in China: Methods and Applications , London: Palgrave Macmillan UK, 2008, pp. 89–116.
I. A. Eyzaguirre, A. Y. Iwama, and M. E. Fernandes, "Integrating a conceptual framework for the sustainable development goals in the mangrove ecosystem: a systematic review," Environmental Development , p. 100895, 2023.
W. Naudé, M. McGillivray, and S. Rossouw, Measuring the vulnerability of subnational regions , No. 2008/54, WIDER Research Paper, 2008.
S. Alkire and M. E. Santos, "Measuring acute poverty in the developing world: Robustness and scope of the multidimensional poverty index," World Development , vol. 59, pp. 251–274, 2014.
A. Ahrens, C. Aitken, and M. E. Schaffer, "Using machine learning methods to support causal inference in econometrics," in Behavioral Predictive Modeling in Economics, 2021, pp. 23–52.
P. R. Voss, D. D. Long, R. B. Hammer, and S. Friedman, "County child poverty rates in the US: a spatial regression approach," Population Research and Policy Review , vol. 25, pp. 369–391, 2006.
N. Shrestha, "Factor analysis as a tool for survey analysis," American journal of Applied Mathematics and statistics , vol. 9, no. 1, pp. 4–11, 2021.
J. Cohen, Statistical Power Analysis for the Behavioral Sciences , Routledge, 2013.
A. Field, Discovering Statistics Using IBM SPSS Statistics , Sage Publications Limited, 2024.
J. Hentschel, Combining census and survey data to study spatial dimensions of poverty: A case study of Ecuador , vol. 1928, World Bank Publications, 1998.
C. Elbers, J. O. Lanjouw, and P. Lanjouw, Micro-level estimation of welfare , vol. 2911, World Bank Publications, 2002.
UNICEF, The state of the world's children 2006: excluded and invisible , UNICEF, 2005.
D. Pfeffermann, "New important developments in small area estimation," Quality control and applied statistics , vol. 59, no. 1, pp. 103–106, 2013.
Economic Commission for Latin America and the Caribbean (ECLAC), Small area estimation (SAE): Resources , [Online]. Available: https://statistics.cepal.org/portal/sae/resources.html?lang=en .
F. O. Rico, C. F. T. Piñerez, and N. Ramirez-Vargas, "A Review of the Use of Small Area Estimation in Colombia," Revista Colombiana de Estadística , vol. 47, no. 2, p. 407, 2024.
A. Zgodic et al., "Estimates of childhood overweight and obesity at the region, state, and county levels: a multilevel small-area estimation approach," American journal of epidemiology , vol. 190, no. 12, pp. 2618–2629, 2021.
T. F. Crossley and L. J. Curtis, "Child poverty in Canada," Review of Income and Wealth , vol. 52, no. 2, pp. 237–260, 2006.