Bacci, Silvia and Bartolucci, Francesco and Pieroni, Luca (2012): A causal analysis of mother’s education on birth inequalities.
Download (1MB) | Preview
We propose a causal analysis of the mother’s educational level on the health status of the newborn, in terms of gestational weeks and weight. The analysis is based on a finite mixture structural equation model, the parameters of which have a causal interpretation. The model is applied to a dataset of almost ten thausand deliveries collected in an Italian region. The analysis confirms that standard regression overestimates the impact of education on the child health. With respect to the current economic literature, our findings indicate that only high education has positive consequences on child health, implying that policy efforts in education should have benefits for welfare.
|Item Type:||MPRA Paper|
|Original Title:||A causal analysis of mother’s education on birth inequalities|
|Keywords:||birthweight, finite mixtures, intergenerational health trasmission, latent class model, structural equation models|
|Subjects:||J - Labor and Demographic Economics > J1 - Demographic Economics > J13 - Fertility; Family Planning; Child Care; Children; Youth
I - Health, Education, and Welfare > I1 - Health > I12 - Health Production
I - Health, Education, and Welfare > I2 - Education and Research Insititutions > I21 - Analysis of Education
C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models; Multiple Variables > C30 - General
|Depositing User:||Francesco Bartolucci|
|Date Deposited:||12. May 2012 23:51|
|Last Modified:||18. Feb 2013 21:23|
Abrevaya, J. and Dahl, C. M. (2008). The effects of birth inputs on birthweight. Journal of Business & Economic Statistics, 26:379–397.
Agresti, A. (2002). Categorical Data Analysis. John Wiley & Sons, Hoboken.
Almond, D., Chay, K. Y., and Lee, D. S. (2005). The costs of low birth weight. The Quarterly Journal of Economics, 120(3):1031–1083.
Almond, D., Currie, J., and Simeonova, E. (2011). Public vs. private provision of charity care? evidence from the expiration of hill-burton requirements in florida. Journal of Health Economics, 30(1):189–199.
Ansari, A., Jedidi, K., and Jagpal, S. (2000). A hierarchical bayesian methodology for treating heterogeneity in structural equation models. Marketing Science, 19(4):328–347.
Arminger, G., Stein, P., and Wittenberg, J. (1999). Mixtures of conditional mean- and covariance-structure models. Psychometrika, 64(4):475–494.
Bartolucci, F. and Forcina, A. (2006). A class of latent marginal models for capture-recapture data with continuous covariates. Journal of the American Statistical Association, 101:786–794.
Becker, G. S. (1981). Altruism in the family and selfishness in the market place. Eco- nomica, 48(189):1–15.
Becker, G. S. (1985). Human capital, effort, and the sexual division of labor. Journal of Labor Economics, 3(1):33–58.
Behrman, J. R. and Rosenzweig, M. R. (2002). Does increasing women’s schooling raise the schooling of the next generation? American Economic Review, 92(1):323–334.
Bollen, K., Rabe-Hesketh, S., and Skrondal, A. (2008). Structural equation models. In Box-Steffensmeier, J. M., Brady, H., and Collier, D., editors, Oxford Handbook of Political Methodology, chapter Structural equation models, pages 432–455. Oxford University Press.
Breierova, L. and Duflo, E. (2004). The impact of education on fertility and child mor- tality: Do fathers really matter less than mothers? NBER Working Papers 10513, National Bureau of Economic Research, Inc.
Case, A. and Paxson, C. (2011). The long reach of childhood health and circumstance: Evidence from the whitehall ii study. Economic Journal, 121(554):183–204.
Chou, S.-Y., Liu, J.-T., Grossman, M., and Joyce, T. (2010). Parental education and child health: Evidence from a natural experiment in taiwan. American Economic Journal: Applied Economics, 2(1):33–61.
Conti, G., Heckman, J., and Urzua, S. (2010). The education-health gradient. American Economic Review, 100(2):234–38.
Cox, D. and Wermuth, N. (2004). Causality: a statistical view. International Statistical Review, 72(3):285–305.
Currie, J. (2011). Inequality at birth: Some causes and consequences. American Economic Review, 101(3):1–22.
Currie, J. and Moretti, E. (2003). Mother’s education and the intergenerational trans- mission of human capital: Evidence from college openings. The Quarterly Journal of Economics, 118(4):1495–1532.
Dasgupta, A. and Raftery, A. E. (1998). Detecting features in spatial point processes with cluster via model-based clustering. Journal of the American Statistical Association, 93:294–302.
Dawid, A. (2002). Influence diagrams for causal modelling and inference. International Statistical Review, 70:161–189.
Dempster, A. P., Laird, N. M., and Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm (with discussion). Journal of the Royal Statistical Society, Series B, 39:1–38.
Dolan, C. and van der Maas, H. (1998). Fitting multivariate normal finite mixtures subject to structural equation modeling. Psychometrika, 63(3):227–253.
Duncan, O. (1975). Introduction to structural equation models. Academic Press, New York.
Freedman, D. (1999). From association to causation: some remarks on the history of statistics. Statistical Science, 14(3):243–258.
Goldberger, A. (1972). Structural equation models in the social sciences. Econometrica: Journal of Econometric Society, 40:979–1001.
Goodman, L. A. (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika, 61:215–231.
Greenland, S., Pearl, J., and Robins, J. (1999). Causal diagrams for epidemiologic research. Epidemiology, 10:37–48.
Hernan, M. and Robins, J. (2012). Causal Inference. Chapman & Hall/CRC.
Holland, P. (1986). Statistics and causal inference. Journal of American Statistical Association, 81:945–960.
Jedidi, K., Jagpal, H., and DeSarbo, W. (1997). Stemm: a general finite mixture structural equation model. Journal of Classification, 14:23–50.
Kramer, M. S. (1987). ntrauterine growth and gestational duration determinants. Pedi- atrics, LXXX:502–511.
Lam, D. (1988). Marriage markets and assortative mating with household public goods: Theoretical results and empirical implications. Journal of Human Resources, 23(4):462–87.
Lauritzen, S. (1996). Graphical models. Claredon Press, Oxford.
Lazarsfeld, P. F. (1950). The logical and mathematical foundation of latent structure analysis. In S. A. Stouffer, L. Guttman, E. A. S., editor, Measurement and Prediction, New York. Princeton University Press.
Lazarsfeld, P. F. and Henry, N. W. (1968). Latent Structure Analysis. Houghton Mifflin, Boston.
Lefgren, L. and McIntyre, F. (2006). The relationship between women’s education and marriage outcomes. Journal of Labor Economics, 24(4):787–830.
Lindeboom, M., Llena-Nozal, A., and van der Klaauw, B. (2009). Parental education and child health: Evidence from a schooling reform. Journal of Health Economics, 28(1):109–131.
McCrary, J. and Royer, H. (2011). The effect of female education on fertility and in- fant health: Evidence from school entry policies using exact date of birth. American Economic Review, 101(1):158–95.
McCullagh, P. (1980). Regression models for ordinal data (with discussion). Journal of the Royal Statistical Society, Series B, 42:109–142.
McLachlan, G. and Peel, D. (2000). Finite mixture models. Wiley Series in Probability and Statistics.
Muthen, B. (1989). Latent variable modeling in heterogeneous populations. Psychome- trika, 54:557 – 585.
Muthen, B. (2002). Beyond sem: general latent variable modeling. Behaviormetrika, 29(1):81–117.
Neyman, J. (1923). On the application of probability theory to agricultural experiments. essays on principles. section 9. Statistical Science, 5:465–480.
Oakes, D. (1999). Direct calculation of the information matrix via the EM algorithm. Journal of the Royal Statistical Society, Series B, 61:479–482.
Pearl, J. (1998). Graphs, causality, and structural equation models. Sociological methods and research, 27:226–284.
Pearl, J. (2000). Causality: models, reasoning, and inference. Cambridge University Press, New York.
Pearl, J. (2009). Causal inference in statistics: an overview. Statistics Surveys, 3:96–146.
Pearl, J. (2011). The causal foundations of structural equation modeling. In Hoyle, R., editor, Handbook of structural equation modeling. Guildford Press.
Pencavel, J. (1998). Assortative mating by schooling and the work behavior of wives and husbands. American Economic Review, 88(2):326–29.
Qian, Z. (1998). Changes in assortative mating: The impact of age and education, 1970-1990. Demography, 35(3):279–92.
Roeder, K. and Wasserman, L. (1997). Practical bayesian density estimation using mixtures of normals. Journal of the American Statistical Association, 92:894–902.
Rosenzweig, M. R. and Schultz, T. P. (1983). Estimating a household production function: Heterogeneity, the demand for health inputs, and their effects on birth weight. Journal of Political Economy, 91(5):723–46.
Rosenzweig, M. R. and Wolpin, K. I. (1991). Inequality at birth : The scope for policy intervention. Journal of Econometrics, 50(1-2):205–228.
Rubin, D. (1974). Estimating causal effects of treatments in randomized and non randomized studies. Journal of Educational Psychology, 66:688–701.
Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6(2):461–464.
Skrondal, A. and Rabe-Hesketh, S. (2004). Generalized Latent Variable Modeling: Multilevel, Longitudinal, and Structural Equation Models. Chapman & Hall/CRC.
Skrondal, A. and Rabe-Hesketh, S. (2005). Structural equation modeling: categorical variables. In Encyclopedia of Statistics in Behavioral Science. Wiley.
Vermunt, J. and Magidson, J. (2005). Structural equation models: mixture models. In Everitt, B. and Howell, D., editors, Encycopledia of Statistics in Behavioral Science, pages 1922–1927. Wiley.
Weiss, Y., Chiappori, P., and Iyigun, M. (2009). Investment in schooling and the marriage market. American Economic Review, 99(5).
Wright, S. (1921). Correlation and causation. Journal of Agricultural Research, 20:557–585.