Fertility progression in Germany: an analysis using flexible nonparametric cure survival models

Bremhorst, Vincent; Kreyenfeld, Michaela; Lambert, Philippe

doi:10.4054/DemRes.2016.35.18

Download

Article (Scientific journals)

Fertility progression in Germany: an analysis using flexible nonparametric cure survival models

Bremhorst, Vincent; Kreyenfeld, Michaela; Lambert, Philippe

2016 • In Demographic Research

Peer Reviewed verified by ORBi

Permalink
https://hdl.handle.net/2268/190356

DOI
10.4054/DemRes.2016.35.18

Files (1)Send to Details Statistics Bibliography Similar publications

Files

Full Text

BremhorstKreyenfeldLambert_DemographicResearch35-18.pdf

Publisher postprint (664.4 kB)

Download

All documents in ORBi are protected by a user license.

Send to

RIS BibTex APA Chicago Permalink X Linkedin

Details

Keywords :

Cure survival models; Fertility; Timing; Quantum; Parity progression; Germany

Abstract :

[en] OBJECTIVE This paper uses data from the German Socio-Economic Panel (GSOEP) to study the transition to second and third births. In particular, we seek to distinguish the factors that determine the timing of fertility from the factors that influence ultimate parity progression. METHODS We employ cure survival models, a technique commonly used in epidemiological studies and in the statistical literature but only rarely applied to fertility research. RESULTS We find that education has a different impact on the timing and the ultimate probability of having a second and a third birth. Furthermore, we show that the shape of the fertility schedule for the total population differs from that of ‘susceptible women’ (i.e., those who have a second or a third child). CONCLUSION Standard event history models conflate timing and quantum effects. Our approach overcomes this shortcoming. It estimates separate parameters for the hazard rate of having a next child for the ‘susceptible population’ and the ultimate probability of having another child for the entire population at risk.

Disciplines :

Social & behavioral sciences, psychology: Multidisciplinary, general & others

Author, co-author :

Bremhorst, Vincent; Université Catholique de Louvain - UCL

Kreyenfeld, Michaela; Max Planck Institute for Demographic Research, Rostock, Germany

Lambert, Philippe ; Université de Liège > Faculté des sciences sociales > Méthodes quantitatives en sciences sociales

Language :

English

Title :

Fertility progression in Germany: an analysis using flexible nonparametric cure survival models

Publication date :

26 August 2016

Journal title :

Demographic Research

eISSN :

1435-9871

Publisher :

Max-Planck Institute for Demographic Research/Max-Planck-institut fur Demografische Forschung, Germany

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

http://www.demographic-research.org/volumes/vol35/18/default.htm

Funders :

BELSPO - SPP Politique scientifique - Service Public Fédéral de Programmation Politique scientifique
FWB - Fédération Wallonie-Bruxelles [BE]

Available on ORBi :

since 05 January 2016

Statistics

Number of views

96 (10 by ULiège)

Number of downloads

194 (3 by ULiège)

More statistics

Scopus citations^®

Scopus citations^®
without self-citations

OpenCitations

Bibliography

Alter, G., Oris, M., and Tyurin, L. (2007). The shape of a fertility transition and analysis of birth intervals in Eastern Belgium. Paper presented to the Population Association of America meeting in New York.
Atchadé, Y.F. and Rosenthal, J.S. (2005). On adaptive Markov chain Monte Carlo algorithms. Bernoulli 11(5): 815-828. doi:10.3150/bj/1130077595.
Bastin, S. (2016). Partnerschaftsverläufe alleinerziehender Mütter: Eine quantitative Untersuchung auf Basis des Beziehungs-und Familienpanels. Wiesbaden: Springer VS. doi:10.1007/978-3-658-10685-0.
Bartus, T., Murinkó, L., Szalma, I., and Szél, B. (2013). The effect of education on second births in Hungary: A test of the time-squeeze, self-selection, and partner-effect hypotheses. Demographic Research 28(1): 1-32. doi:10.4054/DemRes.2013.28.1.
Bauer, G. and Jacob, M. (2010). Fertilitätsentscheidungen im Partnerschaftskontext: Eine Analyse der Bedeutung der Bildungskonstellation von Paaren für die Familiengründung anhand des Mikrozensus 1996-2004 [Fertility decisons in the partnership context: An analysis of the educational constellations of couples for family formation based on the microcensus 1996-2004]. Kölner Zeitschrift für Soziologie und Sozialpsychologie 62: 31-60. doi:10.1007/s11577-010-0089-y.
Beaujouan E. and Solaz A. (2013). Racing against the biological clock? Childbearing and sterility among men and women in second unions in France. European Journal of Population 29(1): 39-67. doi:10.1007/s10680-012-9271-4.
Begall, K. and Mills, M.C. (2012). The influence of educational field, occupation, and occupational sex segregation on fertility in the Netherlands. European Sociological Review 28(4): 1-23. doi:10.1093/esr/jcs051.
Berinde, D. (1999). Pathways to a third child in Sweden. European Journal of Population 15: 349-378. doi:10.1023/A:1006287630064.
Berkson, J. and Gage, R. (1952). Survival curve for cancer patients following treatment. Journal of the Americal Statistical Association 47: 501-515. doi:10.1080/01621459.1952.10501187.
Blossfeld, H.-P. and Huinink, J. (1991). Human capital investments or norms of role transition? How women's schooling and career affect the process of family formation. American Journal of Sociology 97(1): 143-168. doi:10.1086/229743.
Bremhorst, V. and Lambert, P. (2016). Flexible estimation in cure survival models using Bayesian P-splines. Computational Statistics and Data Analysis 93: 270-284. doi:10.1016/j.csda.2014.05.009.
Brooks, S. and Gelman, A. (1998). General methods for monitoring convergence of iterative simulations. Journal of Computational and Graphical Statistics 7: 434-455.
Chen, M.-H., Ibrahim, J.G., and Sinha, D. (1999). A new bayesian model for survival data with a surviving fraction. Journal of the American Statistical Association 94: 909-919. doi:10.1080/01621459.1999.10474196.
Chi, Y. and Ibrahim, J. G. (2006). Joint models for multivariate longitudinal and multivariate survival data. Biometrics 62(2): 432-445. doi:10.1111/j.1541-0420.2005.00448.x.
Eilers, P. and Marx, B. (1996). Flexible smoothing with B-splines and penalties (with discussion). Statistical Modeling 11(2): 89-121.
Eilers, P. and Marx, B. (2010). Splines, knots, and penalties. Wiley Interdisciplinary Reviews: Computational Statistics 2(6): 637-653. doi:10.1002/wics.125.
Gayawan, E. and Samson B.A. (2013). A Bayesian semiparametric multilevel survival modelling of age at first birth in Nigeria. Demographic Research 28(45): 1339-1372. doi:10.4054/DemRes.2013.28.45.
Gelman, A. and Rubin, D. (1992). Inference from iterative simulation using multiple sequences. Statistical Science 7(4): 457-511. doi:10.1214/ss/1177011136.
Gelman, A., Carlin, J.B., Stern, H.S., Dunson, D.B., Vehtari, A., and Rubin, D. (2013). Bayesian data analysis. 3rd ed. Boca Raton: Chapman and Hall/CRC.
Gerster, M., Keiding, N., Knudsen, L.B., and Strandberg-Larsen, K. (2007). Education and second birth rates in Denmark 1981-1994. Demographic Research 17(8): 181-210. doi:10.4054/DemRes.2007.17.8.
Geweke, J. (1992). Evaluating the accuracy of sampling-based approaches to calculating posterior moments. In: Bernardo, J., Berger, J., David, A., and Smith, A. (eds.). Bayesian statistics 4. Oxford: Clarendon: 169-193.
Goldstein, J.R. and Kreyenfeld, M. (2011). Has East Germany overtaken West Germany? Recent trends in order-specific fertility. Population and Development Review 37(3): 453-472. doi:10.1111/j.1728-4457.2011.00430.x.
Gottard, A., Mattei, A., and Vignoli, D. (2015). The relationship between education and fertility in the presence of a time varying frailty component. Journal of the Royal Statistical Society, Series A 178(4): 863-881. doi:10.1111/rssa.12097.
Gray, E., Evans, A., Anderson, J., and Kippen, R. (2010). Using split-population models to examine predictors of the probability and timing of parity progression. European Journal of Population 26(3): 275-295. doi:10.1007/s10680-009-9201-2.
Haario, H., Saksman, E. and Tamminen, J. (2001). An adaptive Metropolis algorithm. Bernoulli 7(2): 223-242. doi:10.2307/3318737.
Hoem, J.M. (1993). Classical demographic methods of analysis and modern event-history techniques. Stockholm Research Reports in Demography 75. Stockholm: Stockholm University.
Huinink, J. (1989). Das zweite Kind: Sind wir auf dem Weg zur Ein-Kind-Familie? [The second child: Are we on the path towards the one-child-family?]. Zeitschrift für Soziologie 18(3): 192-207.
Human Fertility Database. (2015). Cohort fertility: West Germany. Max Planck Institute for Demographic Research (Germany) and Vienna Institute of Demography (Austria). Available at www.humanfertility.org (data downloaded on July, 16 2015).
Jullion, A. and Lambert, P. (2007). Robust specification of the roughness penalty prior distribution in spacially adaptive Bayesian P-splines models. Computational Statistics and Data Analysis 51(5): 2542-2558. doi:10.1016/j.csda.2006.09.027.
Kravdal, Ø. (2001). The high fertility of college educated women in Norway: An artefact of the separate modelling of each parity transition. Demographic Research 5(6): 187-215. doi:10.4054/DemRes.2001.5.6.
Kreyenfeld, M. (2002). Time squeeze, partner effect or self-selection? An investigation into the positive effect of women's education on second birth risks in West Germany. Demographic Research 7(2): 15-48. doi:10.4054/DemRes.2002.7.2.
Kreyenfeld, M. and Andersson, G. (2014). Socio-economic differences in the unemployment and fertility nexus in cross-national comparison. Advances in Life Course Research 21: 59-73. doi:10.1016/j.alcr.2014.01.007.
Kreyenfeld, M. (2015). Economic uncertainty and fertility. Zeitschrift für Soziologie und Sozialpsychologie 67: 59-80. doi:10.1007/s11577-015-0325-6.
Kreyenfeld, M. and Konietzka, D. (2016). Childlessness in Germany. In: Kreyenfeld, M. and Konietzka, D. (eds.). Childlessness in Europe: Context, causes and consequences. Wiesbaden: Springer.
Lang, S. and Brezger, A. (2004). Bayesian P-splines. Journal of Computational and Graphical Statistics 13(1): 183-212. doi:10.1198/1061860043010.
Li, L. and Chloe, M. (1997). A mixture model for duration data: Analysis of second births in China. Demography 34(2): 189-197. doi:10.2307/2061698.
Lu, W. (2010). Efficient estimation for an accelerate failure time model with a cure fraction. Statistica Sinica 20: 661-674.
McDonald, J.W. and Rosina, A. (2001). Mixture modelling of recurrent event times with long-term survivors: Analysis of Hutterite birth intervals. Statistical Methods and Applilcations 10(1): 257-272.
Milewski, N. (2010). Fertility of immigrants: A two-generational approach in Germany. Berlin: Springer. doi:10.1007/978-3-642-03705-4.
Ní Bhrolcháin, M. (1986). Women's paid work and the timing of births: Longitudinal evidence. European Journal of Population 2(1): 43-70. doi:10.1007/BF01796880.
Ní Bhrolcháin, M. (2011). Tempo and the TFR. Demography 48(3): 841-861. doi:10.1007/s13524-011-0033-4.
Nitsche, N., Matysiak, A., Van Bavel, J., and Vignoli, D. (2015). Partners' educational pairings and fertility across Europe. Families and Societies working paper 2015/38. Stockholm: Families and Societies/University of Stockholm.
Oláh, L.S. (2003). Gendering fertility: Second births in Sweden and Hungary. Population Research and Policy Review 22(2): 171-200. doi:10.1023/A:1025089031871.
Oppermann, A. (2014). Exploring the relationship between educational field and transition to parenthood: An analysis of women and men in Western Germany. European Sociological Review 30(6): 728-749. doi:10.1093/esr/jcu070.
Peng, Y. (2003). Estimating baseline distribution in proportional hazards cure models. Computional Statistics and Data Analysis 42(1-2): 187-201. doi:10.1016/S0167-9473(02)00158-5.
Peng, Y. and Dear, K. (2000). A nonparametric mixture model for cure rate estimation. Biometrics 56(1): 237-243. doi:10.1111/j.0006-341X.2000.00237.x.
Pötzsch, O. (2010). Cohort fertility: A comparison of the results of the official birth statistics and of the microcensus survey 2008. Comparative Population Studies 35(1): 185-204.
Pötzsch, O. (2012). Geburtenfolge und Geburtenabstand: Neue Daten und Befunde [Birth order and birth spacing: New data and new evidence]. Wirtschaft und Statistik 2: 89-101.
Prskawetz, A. and Zagaglia, B. (2005). Second births in Austria. Vienna Yearbook of Population Research 3: 143-170.
Rizopoulos, D. (2012). Joint models for longitudinal and time-to-event data with applications in R. Boca Raton: Chapman and Hall/CRC. doi:10.1201/b12208.
Schmertmann, C.P. (2012). Calibrated spline estimation of detailed fertility schedules from abridged data. MPIDR Working Paper 2012-022. Rostock: Max Planck Institute for Demographic Research.
Tesching, K. (2012). Education and fertility: Dynamic interrelations between women's educational level, educational field and fertility in Sweden. [PhD thesis]. Stockholm: Stockholm University. http://www.demogr.mpg.de/publications/files/4565_1334049671_1_Full%20Text.pdf.
Tsodikov, A.D. (1998). A proportional hazard model taking account of long-term survivors. Biometrics 54(4): 1508-1516. doi:10.2307/2533675.
Upchurch, D.M., Lillard, L.A., and Panis, C.W.A. (2002). Nonmarital childbearing: Influences of education, marriage, and fertility. Demography 39(2): 311-329. doi:10.1353/dem.2002.0020.
Wagner, G.G., Frick, J.R., and Schupp, J. (2007). The German Socio-Economic Panel Study (SOEP): Scope, evolution and enhancements. Schmollers Jahrbuch 127: 139-169. doi:10.2139/ssrn.1028709.
Yamaguchi, K. and Ferguson, L. (1995). The stopping and spacing of childbirths and their birth-history predictors: Rational choice theory and event history analysis. American Sociological Review 60(2): 272-298. doi:10.2307/2096387.
Yakovlev, A. and Tsodikov, A. (1996). Stochastic models for tumor of latency and their biostatistical applications. Hackensack: World Scientific.