This paper proposes to use dynamic treatment models to analyze the effects of fertility on labor market interactions. It argues that when large data sets are available the dynamic potential outcome model is an interesting modeling framework because it allows the careful consideration of the selection issues coming from the interaction of fertility and labor market decisions at different ages. It allows explicitly considering their dependence on the labor market and fertility history realized up to that period. There is no need to collapse the ‘endogeneity’ problem into a static setting since the dynamic nature and timing of the interaction can be explicitly addressed. Furthermore, the paper argues that this approach allows defining relevant parameters of interest in a more precise way. Based on artificial data, the approach is implemented and issues that may come up in practical applications of this approach are discussed.
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References
Abbring, J.H. (2003). Dynamic Econometric Program Evaluation. IZA Discussion Paper 804.
Adsera, A. (2005a). Labor Market Performance and the Timing of Births. University of Chicago, Department of Economics.
Adsera, A. (2005b). Where Are the Babies Gone? Labor Market Conditions and Fertility in Europe. IZA Discussion paper 1576.
Angrist, J.D. and W.N. Evans (1998). Children and Their Parents’ Labor Supply: Evidence from Exogenous Variation in Family Size. The American Economic Review 88: 450–477.
Arroyo, C. and J. Zhang (1997). Dynamic microeconometric models of fertility choice: A survey. Journal of Population Economics 10: 23–65.
Connelly, R. (1996). Comments on the Fertility Employment Interaction. Population and Development Review 22, Supplement: 290–294.
Dawid, A.P. (1979). Conditional Independence in Statistical Theory. Journal of the Royal Statistical Society B 41: 1–31.
Del Boca, D. and R. Sauer (2005). Life Cycle Employment and Fertility Across Institutional Environments. mimeo.
Di Tommaso, M.L. (1999). A trivariate model of participation, fertility and wages: the Italian case. Cambridge Journal of Economics 23: 623–640.
Ding, W. and S.F. Lehrer (2003). Estimating Dynamic Treatment Effects from Project STAR. mimeo.
Francesconi, M. (2002). A Joint Dynamic Model of Fertility and Work of Married Women. Journal of Labor Economics 20: 336–380.
Geweke, J. (1984). Inference and Causality in Economic Time Series. In: Handbook of Econometrics, Vol. 2, eds. Z. Griliches and M. D. Intriligator. Amsterdam: North-Holland.
Gill, R.D. and J.M. Robins (2001). Causal Inference for Complex Longitudinal Data: The Continuous Case. The Annals of Statistics 2001: 1–27.
Granger, C.W.J. (1969). Investigating Causal Relations by Econometric Models and Cross-Spectral Methods. Econometrica 37: 424–438.
Granger, C.W.J. (1986). Comment. Journal of the American Statistical Association 81: 967–968.
Heckman, J.J. (2000). Causal Parameters and Policy Analysis in Economics: A Twentieth Century Retrospective. Quarterly Journal of Economics 115: 45–97.
Heckman, J.J. and J.J. Walker (1990). The Relationship between Wages and Income and the Timing and Spacing of Births: Evidence from Swedish Longitudinal Data. Econometrica 58: 1411–1441.
Heckman, J.J., R.J. LaLonde and J.A. Smith (1999). The Economics and Econometrics of Active Labor Market Programs. In: Handbook of Labor Economics, Vol. 3A, eds. O. Ashenfelter and D. Card. Amsterdam: North-Holland.
Holland, P.W. (1986). Statistics and Causal Inference. Journal of the American Statistical Association 81: 945–970.
Hotz, J.V. and R.A. Miller (1988). An Empirical Analysis of Life Cycle Fertility and Female Labor Supply. Econometrica 56: 91–118.
Hotz, J.V., J.A. Klermann and R.J. Willis (1997). The Econometrics of Fertility in Developed Countries. In: Handbook of Population and Family Economics, eds. M. R. Rosenzweig and O. Stark. Amsterdam: Elsevier.
Imbens, G.W. (2000). The Role of the Propensity Score in Estimating Dose-Response Functions. Biometrika 87: 706–710.
Imbens, G.W. (2004). Nonparametric Estimation of Average Treatment Effects under Exogeneity: A Review. Review of Economics and Statistics 86(1): 4–29.
Klepinger, D., S. Lundberg and R. Plotnick (1999). How Does Adolescent Fertility Affect the Human Capital and Wages of Young Women? The Journal of Human Resources 34: 421–448.
Lechner, M. (1998). Eine empirische Analyse der Geburtenentwicklung in den neuen Bundesländern aus der Sicht der neoklassischen Bevölkerungsökonomie. Zeitschrift für Wirtschaft- und Sozialwissenschaften (ZWS) 118: 463–488.
Lechner, M. (2001a). The Empirical Analysis of East German Fertility after Unification: An Update. European Journal of Population 17: 61–74.
Lechner, M. (2001b). Identification and Estimation of Causal Effects of Multiple Treatments under the Conditional Independence Assumption. In: Econometric Evaluation of Active Labour Market Policies, eds. M. Lechner and F. Pfeiffer. Heidelberg: Physica.
Lechner, M. (2009). Sequential Causal Models for the Evaluation of Labor Market Programs. Journal of Business & Economic Statistics 27: 71–83.
Lechner, M. (2008a). A Note on Endogenous Control Variables in Causal Studies. Statistics and Probability Letters 78: 190–195.
Lechner, M. (2008b). Matching estimation of dynamic treatment models: Some practical issues, in: D. Millimet, J. Smith, and E. Vytlacil (eds.), Advances in Econometrics: Volume 21, Modelling and Evaluating Treatment Effects in Econometrics 289–333.
Lechner, M. and Miquel, R. (2001). A Potential Outcome Approach to Dynamic Programme Evaluation – Part I: Identification. Discussion paper 2001–07, Department of Economics, University of St. Gallen; revised 2005.
Lechner, M. and S. Wiehler (2007). Does the Order and Timing of Active Labour Market Programmes Matter? Discussion paper 2007–38, Department of Economics, University of St. Gallen.
Neyman, J. (1923). On the Application of Probability Theory to Agricultural Experiments. Essay on Principles. Section 9, translated in Statistical Science 5 (1990): 465–480.
Miquel, R. (2002). Identification of Dynamic Treatments Effects by Instrumental Variables. University of St. Gallen, Discussion paper, 2002–11.
Miquel, R. (2003). Identification of Effects of Dynamic Treatments with a Difference-in-Differences Approach. University of St. Gallen, Discussion paper, 2003–06.
Moffitt, R. (1984). Profiles of Fertility, Labour Supply and Wages of Married Women: A Complete Life-Cycle Model. Review of Economic Studies 51: 263–278.
Pötter, U. and H.-P. Blossfeld (2001). Causal Inference from Series of Events. European Sociological Review 17 (1): 21–32.
Robins, J.M. (1986). A new approach to causal inference in mortality studies with sustained exposure periods – Application to control of the healthy worker survivor effect. Mathematical Modeling 7:1393–1512; with 1987 Errata to: A new approach to causal inference in mortality studies with sustained exposure periods – Application to control of the healthy worker survivor effect. Computers and Mathematics with Applications 14:917–921; 1987 Addendum to: A new approach to causal inference in mortality studies with sustained exposure periods – Application to control of the healthy worker survivor effect. Computers and Mathematics with Applications 14:923–945; and 1987 Errata to: Addendum to ‘A new approach to causal inference in mortality studies with sustained exposure periods – Application to control of the healthy worker survivor effect’. Computers and Mathematics with Applications 18: 477.
Robins, J.M. (1989). The Analysis of Randomized and Nonrandomized AIDS Treatment Trials Using a New Approach to Causal Inference in Longitudinal Studies. In: Health Service Research Methodology: A Focus on Aids, eds. L. Sechrest, H. Freeman and A. Mulley. Washington, D.C.: Public Health Service, National Centre for Health Services Research.
Robins, J.M. (1997). Causal Inference from Complex Longitudinal Data. Latent Variable Modelling and Applications to Causality. In: Lecture Notes in Statistics, ed. M. Berkane. New York: Springer-Verlag.
Robins, J.M. (1999). Association, Causation, and Marginal Structural Models. Synthese 121: 151–179.
Robins, J.M., S. Greenland and F. Hu, (1999). Estimation of the Causal Effect of a Time-varying Exposure on the Marginal Mean of a Repeated Binary Outcome. Journal of the American Statistical Association 94: 687–700.
Rosenbaum, P.R. and D.B. Rubin (1983). The Central Role of the Propensity Score in Observational Studies for Causal Effects. Biometrika 70: 41–50.
Rosenbaum, P.R. and D.B. Rubin, (1984). Reducing bias in observational studies using subclassification on the propensity score. Journal of the American Statistical Association 79: 516–524.
Rosenfeld, R.A. (1996). Women Work Histories. Population and Development Review 22, Supplement: 199–222.
Rubin, D.B. (1974). Estimating Causal Effects of Treatments in Randomized and Nonrandomized Studies. Journal of Educational Psychology 66: 688–701.
Rubin, D.B. (2004). On principles for modeling propensity scores in medical research. Pharmacoepidemiology and Drug Safety 13: 855–857.
Sims, C.A. (1972). Money, Income, and Causality. American Economic Review 62: 540–552.
Troske, K.R. and A. Voicu (2004). Joint estimation of sequential labour force participation and fertility decisions using Markov chain Monte Carlo techniques. Columbia: University of Missouri, Department of Economics.
Wiener, N. (1956). The Theory of Prediction. In: Modern Mathematics for Engineers, Series 1, ed. E.F. Beckenham, Chapter 8. New York: McGraw-Hill.
Zhao, Z. (2004). Using Matching to Estimate Treatment Effects: Data Requirements, Matching Metric and a Monte Carlo Study. The Review of Economics and Statistics 86: 91–107.
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Lechner, M. (2009). Sequential Potential Outcome Models to Analyze the Effects of Fertility on Labor Market Outcomes. In: Engelhardt, H., Kohler, HP., Fürnkranz-Prskawetz, A. (eds) Causal Analysis in Population Studies. The Springer Series on Demographic Methods and Population Analysis, vol 23. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-9967-0_3
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