Herrera Gómez, Marcos and Cid, Juan Carlos (2015): Fecundidad, determinantes socioeconómicos e interacciones sociales.Un análisis de heterogeneidad espacial para la Argentina.
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Abstract
The relationship between fertility and socioeconomic determinants can be influenced by social behavior at a local level. Using the geographical distance to approximate the social effects, this paper analyzes the spatial heterogeneity of the impact of economic conditions on departmental fertility in Argentina. Using geographically weighted regressions, the role of social interactions is confirmed in the local variability of the explanatory factors. The main determinant for the level of fertility is education, followed by poverty and marital status. In the change of the fertility level, marital status appears as the main factor, followed by education.
Item Type: | MPRA Paper |
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Original Title: | Fecundidad, determinantes socioeconómicos e interacciones sociales.Un análisis de heterogeneidad espacial para la Argentina. |
English Title: | Fertility, socioeconomic determinants and social interactions. Spatial heterogeneity analysis to Argentina. |
Language: | Spanish |
Keywords: | Fecundidad, Interacciones sociales, GWR, Heterogeneidad espacial |
Subjects: | C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C21 - Cross-Sectional Models ; Spatial Models ; Treatment Effect Models ; Quantile Regressions J - Labor and Demographic Economics > J1 - Demographic Economics > J13 - Fertility ; Family Planning ; Child Care ; Children ; Youth |
Item ID: | 66318 |
Depositing User: | marcos herrera |
Date Deposited: | 31 Aug 2015 15:47 |
Last Modified: | 26 Sep 2019 15:30 |
References: | Anselin, L. (1988). Spatial econometrics: Methods and models. Dordrecht: Kluwer Academic Publishers. Becker, G. (1960). “An economic analysis of fertility”. En Demographic and economic change in developed countries, editado por National Bureau of Economic Research, 209-240. Princeton: Princeton University Press. Brock, W. y Durlauf, S. (2001). “Ineraction-based models”, en Handbook of Econometrics, editado por James Heckman y E Leamer. Amsterdam: North-Holland. Brunsdon, C., Fotheringham, A. y Martin, C. (1996). “Geographically weighted regression: a method for exploring spatial nonstationarity”, Geographical analysis, 28(4): 281-298. Brunsdon, C., Fotheringham, A. y Martin, C. (1998). “Geographically weighted regression”, Journal of the Royal Statistical Society: Series D (The Statistician), 47(3): 431-443. Calvo, E. y Escolar, M. (2003). “The local voter: a geographically weighted approach to ecological inference”, American Journal of Political Science, 47(1): 189-204. CELADE/CEPAL (1996). Impacto de las tendencias demográficas sobre los sectores sociales en América Latina: contribución al diseño de políticas y programas. Santiago de Chile: CEPAL/BID. Chackiel, J. (2004). “La transición de la fecundidad en América Latina 1950-2000”, Papeles de Población, 10(41): 9-58. Chackiel, J. y Martínez Pizarro, J. (1992). “La Transición demográfica en América Latina y el Caribe desde 1950”, en IV Conferencia Latinoamericana de Población, Vol. 1., Santiago de Chile: CELADE. Charlton, M. y Brunsdon, C. (1997). “Two techniques for exploring non-stationarity in geographical data”, Geographical Systems, 4: 59-82. Conley, T. y Topa, G. (2002). “Socio-economic distance and spatial patterns in unemployment”, Journal of Applied Econometrics, 17(4): 303-327. Durlauf, S. y Walker, J. (2001). “Social interactions and fertility transition”, en Diffusion processes and fertility transition: Selected Perspectives, editado por John Carterline. Washington: National Academy Press. Fotheringham, A. (1997). “Trends in quantitative methods 1: stressing the local”, Progress in Human Geography, 21: 88-96. Fotheringham, A., Charlton, M. y Brunsdon, C. (2001). “Spatial variations in school performance: a local analysis using geographically weighted regression”, Geographical and Environmental Modelling, 5(1): 43-66. Fotheringham, A., Charlton, M. y Brunsdon, C. (2002). Geographically weighted regression: the analysis of spatially varying relationships. New York: Wiley & Sons. Huang, Y. y Lueng, Y. (2002). “Analysing regional industrialisation in Jiangsu province using geographically weighted regression”, Journal of Geographical Systems, 4(2): 233-249. Lanza, N. y Valeggia, C. (2014). “Cambios demográficos en una población rural de la etnia Toba del norte de Argentina”, Latin American Research Review, 49(2): 107-128. López, E. y Mario, S. (2009). “La fecundidad en la Argentina 1996-2006: convergencias y divergencias”, Población, 2(4): 41-57. Lu, B., Charlton, M., Harris, P. y Fotheringham, A. (2014). “Geographically weighted regression with a non-Euclidean distance metric: a case study using hedonic house price data”, International Journal of Geographical Information Science, 28(4): 660-681. Manski, C. (1993). “Identification of endogenous social effects: The reflection problem”, Review of Economic Studies, 60(3): 531-542. Mario, S. y Pantelides, E. (2011). “Análisis regional de los determinantes próximos de la fecundidad en la Argentina”, en XI Jornadas Argentinas de Estudios de Población. Asociación de Estudios de Población de la Argentina, Neuquén. McMillen, D. (2004). “Employment densities, spatial autocorrelation, and subcenters in large metropolitan areas”, Journal of Regional Science, 44(2): 225-244. Moran, P. (1950). “Notes on continuous stochastic phenomena”, Biometrika, 37(1-2): 17-23. Nakaya, T., Charlton, M., Fotheringham, A. y Brunsdon, C. (2014). “GWR4 version 4.0.80. Application for geographically weighted regression modeling”, National Centre for Geocomputation, National University of Ireland Maynooth and Department of Geography, Ritsumeikan University, Japan. Nakaya, T., Fotheringham, A., Charlton, M. y Brunsdon, C. (2009). “Semiparametric geographically weighted generalised linear modelling in GWR 4.0”, en 10th International Conference on GeoComputation, editado por Lees, Brian y Laffan, Shawn, UNSW, Sydney. Pantelides, E. y Rofman, A. (1983). “La transición demográfica argentina: un modelo no ortodoxo”, Desarrollo Económico, 22(8): 511-534. Schultz, P. (1981). Economics of population. Reading: Addison-Wisley. Schultz, P. (2002). “Fertility transition: economic explanations”, en International encyclopedia of the Social and Behavioral Sciences, editado por Smelser, N. y Baltes, P. Oxford: Pergamon. Topa, G. (2001). “Social interactions, local spillovers and unemployment,” The Review of Economic Studies, 68(2): 261-295. Wang, Q., Ni, J. y Tenhunen, J. (2005). “Application of a geographically- weighted regression analysis to estimate net primary production of Chinese forest ecosystems”, Global Ecology and Biogeography, 14(4): 379-393. Weeks, J. (2003). “The role of spatial analysis in demographic research”, en Spatially integrated social science: examples in best practice, editado por Goodchild, Michael y Janelle, Donald. New York: Oxford University Press. Weeks, J., Getis, A., Hill, A., Gadalla, S. y Rashed, T. (2004). “The fertility transition in Egypt: intraurban patterns in Cairo”, Annals of the Association of American Geographers, 94(1): 74-93. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/66318 |