Bayesian Spatial-Temporal Analysis of the Effect of Female Schooling on Fertility in the Municipalities of Mexico, 1970-2020
DOI:
https://doi.org/10.5944/etfvi.15.2022.33816Keywords:
Bayesian model; INLA; spatio-temporal evolution; fertility ratesAbstract
The purpose of the research is to analyze the spatio-temporal evolution of the total fertility rate (TFR) from the effect generated by the changes in female schooling during the period 1970-2020, in the municipalities of Mexico. Six competing Bayesian models were fitted using the integrated nested Laplace approximation method available in the R-INLA package, whose purpose is to capture the spatio-temporal behavior of both female schooling and the effect that spatial and/or temporal random alterations have exerted on the TFR. The results show that the fertility decline observed throughout the period analyzed can be attributed to the increase in female schooling levels, in accordance with different theories on fertility analysis. However, the decline has had a more pronounced impact in the municipalities where, at the beginning of the study period, fertility rates were below the national average, which caused the formation of clusters with low fertility levels in specific areas of the national territory. The application of the spatio-temporal models made it possible to identify the formation of spatial clusters with high and low fertility rates and revealed the differences between the North-central and South-Southeastern regions of Mexico in terms of fertility.
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References
Akaike, H. (1973). Information theory and an extention of the maximum likelihood principle. In 2nd International Symposium on Information Theory, 1973 (pp. 267-281). Akademiai Kiado.
Bivand, R., Gómez-Rubio, V. & Rue, H. (2015). Spatial Data Analysis with R-INLA with Some Extensions. Journal of Statistical Software, 63(20), 1 - 31. https://doi.org/10.18637/jss.v063.i20
Bongaarts, J. (1978). A framework for analyzing the proximate determinants of fertility. Population and Development Review, 4(1), 105-132. https://doi.org/10.2307/1972149
Bongaarts, J., & Watkins, S. C. (1996). Social interactions and contemporary fertility transitions. Population and development review, 22(4), 639-682. https://doi.org/10.2307/2137804
Blangiardo, M. & Cameletti, M. (2015). Spatial and Spatio-Temporal Bayesian Models with R-INLA. Chichester, UK: John Wiley & Sons.
Cacique, I. (2003). Uso de anticonceptivos en México: ¿Qué diferencia hacen el poder de decisión y la autonomía femenina?. Papeles de Población, México, 9(35) 209-232.
Caldwell, J., (1968). Population Growth and Family Change in Africa: The New Urban Elite in Ghana. Australian National University Press.
Caldwell, J. C. (1982). Theory of fertility decline. Academic Press.
Caldwell, J., Caldwell, P., McDonald, P. F., & Schindmayr, T. (2006). Demographic transition theory. Springer.
Carlsson, G. (1966). The decline of fertility: Innovation or adjustment process. Population Studies, (20), 149-179.
Casterline, J. B. (2001). Diffusion Processes and Fertility Transition: selected perspectives. Washington, D.C.: Division of Behavioral and Social Sciences and Education. National Research Council.
Chakiel, J. & Schkolnik, S. (2004). América Latina: los sectores rezagados en la transición de la fecundidad. En CEPAL, La fecundidad en América Latina: ¿transición o revolución?, Serie:Serie Seminarios y Conferencias – CEPAL, -LC/L. 2097-P-2004-36, pp. 51-73.
Chesnais, J.C. (1992). The demographic transition: Stages, patterns, and economic implications. Oxford University Press.
Cleland, J. & C. Wilson. (1987). Demand Theories of the Fertility Transition: An Iconoclastic View. Population Studies (41), 5-30.
Demeny, P. (1972). Early fertility decline in Austria-Hungria: A lesson in demographic Transition. En Glass, DV. & Revelle, R. (edits). Population and Social Change. E. Arnold, London. 157-172.
Easterlin, R. A., & Crimmins, E. N. (1985). The fertility revolution: A demand–supply analysis. Chicago, IL: University of Chicago Press.
Freedman, R. (1979). Theories of fertility decline: A reappraisal. Social Forces, 58(1), 1-17. https://doi.org/10.2307/2577781
González, G., Palma, Y., & Montes, M. D. L. (2007). Análisis regional de los determinantes próximos de la fecundidad en México. Papeles de población, 13(51), 213-245.
Gómez-Rubio, V. (2020). Bayesian Inference with INLA. Chapman & Hall/CRC Press.
Juárez, F, Quilodrán, J. (1990). Mujeres pioneras del cambio reproductivo en México, Revista Mexicana de Sociología, IISUNAM, 52(1), 33-49.
Juárez, F., Quilodrán, J. & Zavala de Cosío, M. (1989). De una fecundidad natural a una controlada: México 1950-1980. Estudios Demográficos y Urbanos, El Colegio de México, 4(1), 5-51.
Kitson, G. C. (1992). Portrait of divorce: Adjustment to marital breakdown. Guilford Press.
Lesthaeghe, R. (1983). A century of demographic and cultural change in Western Europe: An exploration of underlying dimensions. Population and Development Review, 9, 411-435.
Lillard, L. A. & Waite, L. J. (1993). A joint model of childbearing and marital disruption. Demography, 30, 653-681
Maitra, P. (2004). Effect of socioeconomic characteristics on age at marriage and total fertility in Nepal. Journal of health, Population and Nutrition, 22 (1), 84-96.
Martino, S. & Rue, H. (2008). Implementing Approximate Bayesian Inference using Integrated Nested Laplace Approximation: A manual for the inla program. Department of Mathematical Sciences, NTNU, Norway.
Massey, D. S. (2002). A brief history of human society: The origin and role of emotions in social life. American Sociological Review, 67(1), 1-29. https://doi.org/10.2307/3088931
Medina Hernández, E. J. (2012). Diferenciales regionales de la fecundidad según el nivel educativo de las mujeres colombianas en edad fértil. Sociedad y economía, (23), 205-234.
Menkes Bancet y Héctor Hernández Bringas (2005). Población, crisis y perspectivas demográficas en México. CRIM-UNAM.
Mesa, A. F. A., Rodríguez, D. L., & Garavito, S. F. (2012). Determinantes de la fecundidad en el Departamento de Antioquia. Criterio Libre, 10(17), 25-52.
Mier y Terán, M. (1992). Descenso de la fecundidad y participación laboral femenina en México. Notas de Población, 20 (56), 143-171.
Mier y Terán, M. y Partida, B. (2001).Niveles, tendencias y diferenciales de la fecundidad en México, 1930-1997, en José Gómez de León Cruces y Cecilia Rabell Romero (coords.), La población de México. Tendencias y perspectivas sociodemográficas hacia el siglo XXI, México, Consejo Nacional de Población / Fondo de Cultura Económica, pp. 168-203.
Moraga, P. (2019). Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny. Chapman and Hall/CRC Biostatistics Series.
Norton, R. (1983). Measuring marital quality: A critical look at the dependent variable. Journal of Marriage and the Family, 45, 141–151. https://doi.org/10.2307/351302
Notestein, F. (1953). Economic problem of population change. In Proceedings of the Eighth International Conference of Agricultural Economics. London: Oxford University Press, pp. 13-31.
Núñez Medina, G. (2021). Análisis espacio-temporal de la evolución de los niveles de fecundidad en los municipios de México, 1970-2020. Notas de Población, 113, 39-60.
Páez, O. & Zavala, M. (2016). Tendencias y determinantes de la fecundidad en México: las desigualdades sociales. En Coubès, M.; Solís, P. y Zavala, M. (coords.) Generaciones, curso de vida y desigualdad social en México. El Colegio de México y El Colegio de la Frontera Norte.
Palma, Y. (2005). Políticas de población y planificación familiar. DemoS. (16), 24-25. https://doi.org/10.22201/%256798
Puyol R. (1987). El uso de los modelos de difusión espacial de innovaciones en el estudio geográfico de la fecundidad. Anales de Geografía de la Universidad Complutense. (7), 185-191.
R Core Team. (2016). R: A Language and Environment for Statistical Computing. Vienna.: R Foundation for Statistical Computing.
Riebler, A., Sørbye, S.H., Simpson, D. & Rue, H. (2016). An intuitive Bayesian spatial model for disease mapping that accounts for scaling. Statistical Methods in Medical Research, 25(4), 1145-1165. https://doi.org/10.1177/0962280216660421
Romo, R. & Sánchez, M. (2009). El Descenso de la Fecundidad en México, 1974-2009: a 35 años de la puesta en marcha de la nueva política de población. CONAPO. La situación demográfica de México 2009: 35 años de la política de población. Distrito Federal, México: Consejo Nacional de Población.
Rue, H., Martino, S. & Chopin, N. (2009). Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 71(2), 319-392.
Schrödle, B. & Held L. (2011). Spatio-temporal disease mapping using INLA. Environmetrics. 22 (6), 725-734. https://doi.org/10.1002/env.1065
Sharafifi, Z., Asmarian, N., Hoorang, S. & Mousavi, A. (2018). Bayesian spatio-temporal analysis of stomach cancer incidence in Iran, 2003–2010. Stoch Environ Res Risk Assess. (32), 2943-2950. https://doi.org/10.1007/s00477-018-1531-3
Sollova-Manenova, V. (2022). Fecundidad, trabajo y educación de la mujer en el Estado de México, 1990. Papeles de Población, 4(15), 127-144.
Tobler, W.R. (1970). A computer movie simulating urban growth in the Detroit region. Economic Geography, 46(Supplement): 234-240.
Wang, X., Yue, Y. R., & Faraway, J. J. (2018). Bayesian Regression Modeling with INLA. UK: Chapman and Hall/CRC.
Welti, C. (1980). Estimación del cambio en el nivel de fecundidad de la población del área metropolitana de la ciudad de México entre 1964 y 1976. Investigación demográfica en México-1980. 297-311.
Welti, C. (1998) Determinantes próximos de la fecundidad. Demografía II. Programa Latinoamericano de Actividades en Población, IIS-UNAM, México.
Zavala de Cosio, M. (1992). Cambios de fecundidad en México y políticas de población. México: Fondo de Cultura Económica USA.
Zavala de Cosio, M. (2010). Las variables determinantes de la fecundidad. Métodos clásicos, Avances recientes, perspectivas. X Reunión Nacional de Investigación Demográfica en México. (1). 1-15.
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