Abstract
The study that gives rise to this article arises from the results obtained in the framework of a collaboration agreement signed by the Statistical Office of Rosario City and the School of Statistics of the Faculty of Economic Sciences and Statistics (National University of Rosario). Among its objectives is to obtain probabilistic fertility forecasts for Rosario City. For this, based on vital statistics, estimates and population projections, probable scenarios, past and future, are constructed, both for the global fertility rate and the specific fertility rates. The results of this study, based on the application of probabilistic prognostic models, allow to know structures and trends, past and future, of fertility, so that diagnoses can be generated that are useful for the evaluation and management of the health system or good for the development of new public policies. The results indicate that Rosario had, has and will continue to have a change in fertility patterns faster and more marked than the national average. Although this fact is to be expected in a context marked by advances in public health (which allow access to more and better reproductive health care), the methodology used here is based solely on the extrapolation of trends, therefore, the backprojection must be carefully analyzed.
References
Andreozzi, L. y Blaconá, M.T. (2014). Análisis de la mortalidad por edad y sexo mediante modelos para datos funcionales. Santiago de Chile. Instituto Interamericano de Estadística: Revista Estadística, 66, 65-89.
Añaños, M. C. (1993). Composición social y comportamientos de unión en madres adolescentes. Rosario, 1980-1991. Buenos Aires, Argentina: Centro de Estudios de Estado y Sociedad/ Centro de Estudios de Población (CEDES/CENEP)
Bongaarts, J. (2015). Global Fertility and Population Trends. New York: Seminars in Reproductive Medicine, 33(1), 5-10.
Bongaarts, J. y Bulatao, R. A. (2000). Beyond Six Billion: Forecasting the World’s Population. Panel on Population Projections, Committee on Population, National Research Council. Washington, DC: The National Academies Press .
Booth, H., Hyndman, R., Tickle, L., y de Jong, P. (2006). Lee-Carter mortality forecasting: a multi-country comparison of variants and extensions. Demographic Research, 15(9), 289–310.
Brouhns, N., Denuit, M., y Vermunt, J.K. (2002). A Poisson log-bilinear regression approach to the construction of projected lifetables. Insurance: Mathematics and Economics, 31(3), 373–393.
Cavallini, C. (1996). Regulación de la Fecundidad: Estimación de Características Sensibles en la Población Femenina de la Ciudad de Rosario. Rosario, Argentina: Fondo de Población de las Naciones Unidas (UNFPA) y Escuela de Estadística, de la Facultad de Ciencias Económicas y Estadística de la Universidad Nacional de Rosario.
García Guerrero, V. M. y Ordorica Mellado, M. (2012). Proyección estocástica de la mortalidad mexicana por medio del método de Lee-Carter. Estudios Demográficos y Urbanos, 27(2), 409-448. Recuperado de
http://www.redalyc.org/articulo.oa?id=31226408004.
Guerrero Guzmán, V. M. y González Pérez, C. (2007). Pronósticos estadísticos de mortalidad y su impacto sobre el sistema de pensiones de México. Recuperado de http://estadistica.itam.mx/sites/default/files/u486/178-174gonzalez_guerrero.pdf
Hyndman, R. y Booth, H. (2007). Stochastic population forecasts using functional data models for mortality, fertility and migration. International Journal of Forecasting, 24(3), 323-342. Recuperado de https://doi.org/10.1016/j.ijforecast.2008.02.009.
Hyndman, R. y Ullah, M. (2007). Robust forecasting of mortality and fertility rates: A functional data approach. Computational Statistics and Data Analysis, 51(10), 4942– 4956. DOI: http://dx.doi.org/10.1016/j.csda.2006.07.028.
Jong, P. D. y Tickle, L. (2006). Extending Lee-Carter mortality forecasting.
Mathematical Population Studies, 13(1), 1–18.
Lee, R. y Carter, L. (1992). Modeling and Forecasting U. S. Mortality. Journal of the American Statistical Association, 87, 659–671.
Lee, R. y Miller, T. (2001). Evaluating the performance of the Lee-Carter Method for Forecasting Mortality. Demography, 38, 537–549.
Lee, R. D. y R. Rofman. (1994). Modelación y Proyección de la Mortalidad en Chile. Notas de Población, 22(59),183-213.
Lee, R. y Tuljapurkar, S. (1994). Stochastic population forecasts for the United States: Beyond high, medium, and low. Journal of the American Statistical Association, 89, 1175–1189.
Pantelides, E. A. (1982). Las mujeres de alta fecundidad en la Argentina: pasado y futuro. Buenos Aires, Argentina: CENEP
Pantelides, E. A. (1989). La fecundidad argentina desde mediados del siglo XX. Buenos Aires, CENEP. DOI: https://doi.org/10.13140/2.1.1761.2488.
Pantelides, E. A. (2006). La transición de la fecundidad en la Argentina 1869-1947. Buenos Aires, Argenitna: CENEP.
Pantelides, E. y Rofman, A. (1983). La transición demográfica argentina: Un modelo no ortodoxo. Desarrollo Económico, 22(88), 511-534. Recuperado de
https://doi.org/10.2307/3466332.
Renshaw, A., & Haberman, S. (2003). Lee-Carter Mortality Forecasting: A Parallel Generalized Linear Modelling Approach for England and Wales Mortality Projections. Journal of the Royal Statistical Society. Series C (Applied Statistics), 52(1), 119-137. Recuperado de http://www.jstor.org/stable/3592636.
Sacco, N. y Andreozzi, L. (2017). Proyecciones y retroproyecciones probabilísticas de las tasas de fecundidad por edad (1895-2040). Revista Latinoamericana de Población, 11(20), 79-104. DOI: https://doi.org/10.31406/relap2017.v11.i1.n20.4.