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Diffusion of Childbearing Within Cohabitation

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Demography

Abstract

The article analyzes the diffusion of childbearing within cohabitation in Norway, using municipality data over a 24-year period (1988–2011). Research has found substantial spatial heterogeneity in this phenomenon but also substantial spatial correlation, and the prevalence of childbearing within cohabitation has increased significantly over time. We consider several theoretical perspectives and implement a spatial panel model that allows accounting for autocorrelation not only on the dependent variable but also on key explanatory variables, and hence identifies the key determinants of diffusion of childbearing within cohabitation across space and over time. We find only partial support for the second demographic transition as a theory able to explain the diffusion of childbearing within cohabitation. Our results show that at least in the first phase of the diffusion (1988–1997), economic difficulties as measured by increased unemployment among men contributed to the diffusion of childbearing within cohabitation. However, the most important driver for childbearing within cohabitation is expansion in education for women.

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Notes

  1. We calculated these percentages from our original sample of municipalities and weighted them using the number of inhabitants. Thus, our numbers differ slightly from the official figures from Statistics Norway. However, because the official statistics include numbers only for the period 2001–2011, we use the data from our original sample when presenting the time trend for the whole country.

  2. During the period 1988–2011, administrative changes were aimed at reducing the overall number of municipalities, which has changed slightly from year to year. To have a balanced panel (which is necessary for our statistical analysis), we referred to the administrative subdivision that was in place at the beginning of the period we study (i.e., a total of 435 municipalities in 1988).

  3. The Moran’s I index (Moran 1950) is formally described as follows:

    $$ I=\frac{n}{{\displaystyle {\sum}_{i=1}^n{\displaystyle {\sum}_{j\ne i}^n{w}_{ij}}}}\frac{{\displaystyle {\sum}_{i=1}^n{\displaystyle {\sum}_{j\ne i}^n{w}_{ij}\left({y}_i-\overline{y}\right)\left({y}_j-\overline{y}\right)}}}{{\displaystyle {\sum}_{i=1}^n{\left({y}_i-\overline{y}\right)}^2}}, $$

    where y i is the value assumed by the variable in the ith location, \( \overline{y} \) is the sample mean, w ij is the spatial weight assigned to the jth location, and n the number of spatial units (see the Methods section for a definition of spatial weight). Like the conventional correlation coefficient, the Moran’s I index ranges between –1 (perfect negative spatial autocorrelation: e.g., a location with a high value of the variable is surrounded by locations with low values of the variable) and 1 (perfect positive spatial autocorrelation: i.e., similar values are clustered together in space). An index value close to 0 indicates random spatial distribution: that is, no spatial autocorrelation.

  4. Local indicators of spatial association allow the decomposition of global indicators, such as the Moran’s I, into the contribution of each individual municipality. In this way, it is possible to identify local spatial clusters. To produce Fig. 3, we used the spatlsa command in Stata (Pisati 2001).

  5. The model is estimated using the xsmle procedure in Stata (Belotti et al. 2013). Details about the estimation procedure can be found in Elhorst (2010a) and Lee and Yu (2010).

  6. Following the procedure described in LeSage and Pace (2009), we evaluate the statistical significance of the spatial direct and indirect effects using simulations to compute the standard errors.

References

  • Aassve, A., Sironi, M., & Bassi, V. (2013). Explaining attitudes towards demographic behaviour. European Sociological Review, 29, 316–333.

    Article  Google Scholar 

  • Anselin, L. (1988). Spatial econometrics: Methods and models. Dordrecht, The Netherlands: Kluwer Academic Publishers.

    Book  Google Scholar 

  • Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical Analysis, 27, 93–115.

    Article  Google Scholar 

  • Anselin, L., Le Gallo, J., & Jayet, H. (2008). Spatial panel econometrics. In L. Matyas & P. Sevestre (Eds.), The econometrics of panel data, fundamentals and recent developments in theory and practice (3rd ed., pp. 625–660). Dordrecht, The Netherlands: Kluwer Academic Publishers.

    Google Scholar 

  • Arpino, B., Esping-Andersen, G., & Pessin, L. (2015). How do changes in gender role attitudes towards female employment influence fertility? A macro-level analysis. European Sociological Review. Advance online publication. doi:10.1093/esr/jcv002

  • Baller, R. D., Anselin, L., Messner, S. F., Deane, G., & Hawkins, D. F. (2001). Structural covariates of U.S. county homicide rates: Incorporating spatial effects. Criminology, 39, 561–588.

  • Becker, G. S. (1981). A treatise on the family. Cambridge, MA: Harvard University Press.

    Google Scholar 

  • Belotti, F., Hughes, G., & Mortari, A. P. (2013). xsmle: Stata module for spatial panel data models estimation [Statistical Software Components S457610]. Chestnut Hill, MA: Boston College, Economics Department.

  • Berk, R. A., Western, B., & Weiss, R. E. (1995). Statistical inference for apparent populations. Sociological Methodology, 25, 421–458.

    Article  Google Scholar 

  • Bernhardt, E. (2004). Is the second demographic transition a useful concept for demography? Vienna Yearbook of Population Research, 2, 25–28.

    Article  Google Scholar 

  • Billari, F., & Liefbroer, A. (2004). Is the second demographic transition a useful concept for demography? Introduction to a debate. Vienna Yearbook of Population Research, 2, 1–3.

    Article  Google Scholar 

  • Billy, J. O. G., & Moore, D. E. (1992). A multilevel analysis of marital and non-marital fertility in the U.S. Social Forces, 70, 977–1011.

    Article  Google Scholar 

  • Bongaarts, J., & Watkins, S. C. (1996). Social interactions and contemporary fertility transitions. Population and Development Review, 22, 639–682.

    Article  Google Scholar 

  • Boyle, P. (2003). Population geography: Does geography matter in fertility research? Progress in Human Geography, 27, 615–626.

    Article  Google Scholar 

  • Casterline, J. B. (2001). Diffusion processes and fertility transition: Introduction. In J. B. Casterline (Ed.), Diffusion processes and fertility transition: Selected perspectives (pp. 1–38). Washington, DC: National Academies Press.

    Google Scholar 

  • Chi, G., & Zhu, J. (2008). Spatial regression models for demographic analysis. Population Research and Policy Review, 27, 17–42.

    Article  Google Scholar 

  • Cleland, J., & Wilson, C. (1987). Demand theories of the fertility transition: An iconoclastic view. Population Studies, 41, 5–30.

    Article  Google Scholar 

  • Cliquet, R. L. (1991). The second demographic transition: Fact or fiction? Strasbourg, France: Council of Europe.

    Google Scholar 

  • Coale, A. J., & Watkins, S. C. (1986). The decline of fertility in Europe. Princeton, NJ: Princeton University Press.

    Google Scholar 

  • Coleman, D. (2004). Why we don’t have to believe without doubting in the “second demographic transition”—Some agnostic comments. Vienna Yearbook of Population Research, 2, 11–24.

    Article  Google Scholar 

  • Debarsy, N., Ertur, C., & LeSage, J. P. (2012). Interpreting dynamic space–time panel data models. Statistical Methodology, 9, 158–171.

    Article  Google Scholar 

  • de Castro, M. C. (2007). Spatial demography: An opportunity to improve policy making at diverse decision levels. Population Research and Policy Review, 26, 477–509.

    Article  Google Scholar 

  • Elhorst, J. P. (2010a). Spatial panel data models. In M. M. Fischer & A. Getis (Eds.), Handbook of applied spatial analysis (pp. 377–407). Heidelberg, Germany: Springer.

    Chapter  Google Scholar 

  • Elhorst, J. P. (2010b). Applied spatial econometrics: Raising the bar. Spatial Economic Analysis, 5, 9–28.

    Article  Google Scholar 

  • Elhorst, J. P. (2012). Dynamic spatial panels: Models, methods, and inferences. Journal of Geographical Systems, 14, 5–28.

    Article  Google Scholar 

  • Entwisle, B. (2007). Putting people into place. Demography, 44, 687–703.

    Article  Google Scholar 

  • Esping-Andersen, G. (2009). The incomplete revolution: Adapting to women’s new roles. Cambridge, MA: Polity Press.

    Google Scholar 

  • Hornik, R., & McAnany, E. (2001). Mass media and fertility change. In J. B. Casterline (Ed.), Diffusion processes and fertility transition: Selected perspectives (pp. 208–239). Washington, DC: National Academies Press.

    Google Scholar 

  • Klüsener, S., Perelli-Harris, B., & Gassen, N. S. (2012). Spatial aspects of the rise of nonmarital fertility across Europe since 1960: The role of states and regions in shaping patterns of change. European Journal of Population, 29, 137–165.

    Article  Google Scholar 

  • Lee, L.-F., & Yu, J. (2010). Estimation of spatial autoregressive panel data models with fixed effects. Journal of Econometrics, 154, 165–185.

    Article  Google Scholar 

  • LeSage, J. P., & Pace, R. K. (2009). Introduction to spatial econometrics. Boca Raton, FL: Chapman and Hall/CRC.

    Book  Google Scholar 

  • Lesthaeghe, R. J., & Lopez-Gay, A. (2013). Spatial continuities and discontinuities in two successive demographic transitions: Spain and Belgium, 1880–2010. Demographic Research, 28(article 4), 77–136. doi:10.4054/Dem.Res.2013.28.4

  • Lesthaeghe, R. J., & Neels, K. (2002). From the first to the second demographic transition: An interpretation of the spatial continuity of demographic innovation in France, Belgium and Switzerland. European Journal of Population, 18, 325–360.

    Article  Google Scholar 

  • Lesthaeghe, R. J., & Neidert, L. (2006). The second demographic transition in the United States: Exception or textbook example? Population and Development Review, 32, 669–698.

    Article  Google Scholar 

  • Lesthaeghe, R., & Surkyn, J. (2008). When history moves on: The foundations and diffusion of a second demographic transition. In R. Jayakody, W. G. Axinn, & A. Thornton (Eds.), International family change: Ideational perspectives (pp. 81–118). Mahwah, NJ: Lawrence Erlbaum Association.

    Google Scholar 

  • Lesthaeghe, R. J., & Vanderhoeft, C. (2001). Ready, willing, and able: A conceptualization of transitions to new behavioral forms. In J. Casterline (Ed.), Diffusion processes and fertility transition: Selected perspectives (pp. 240–264). Washington, DC: National Academies Press.

    Google Scholar 

  • McDonald, P. (2006). Low fertility and the state: The efficacy of policy. Population and Development Review, 32, 485–510.

    Article  Google Scholar 

  • McDonald, P. (2013). Societal foundations for explaining low fertility: Gender equity. Demographic Research, 28(article 34), 981–994. doi:10.4054/DemRes.2013.28.34

  • Messner, S. F., & Anselin, L. (2004). Spatial analyses of homicide with areal data. In M. F. Goodchild & D. G. Jannelle (Eds.), Spatially integrated social science (pp. 127–144). New York, NY: Oxford University Press.

    Google Scholar 

  • Micheli, G. A. (2004). Claiming for a demologic approach to demographic change. Vienna Yearbook of Population Research, 2, 29–34.

    Article  Google Scholar 

  • Montgomery, M. R., & Casterline, J. B. (1996). Social learning, social influence, and new models of fertility. Population and Development Review, 22, 151–175.

    Article  Google Scholar 

  • Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37, 17–23.

    Article  Google Scholar 

  • Oppenheimer, V. K. (1994). Women’s rising employment and the future of the family in industrial societies. Population and Development Review, 20, 293–342.

    Article  Google Scholar 

  • Palloni, A. (2001). Diffusion in sociological analysis. In J. B. Casterline (Ed.), Diffusion processes and fertility transition: Selected perspectives (pp. 67–114). Washington, DC: National Academies Press.

    Google Scholar 

  • Parent, O., & LeSage, J. P. (2010). A spatial dynamic panel model with random effects applied to commuting times. Transportation Research Part B: Methodological, 44, 633–645.

    Article  Google Scholar 

  • Perelli-Harris, B., & Gerber, T. P. (2011). Nonmarital childbearing in Russia: Second demographic transition or pattern of disadvantage? Demography, 48, 317–342.

    Article  Google Scholar 

  • Perelli-Harris, B., Kreyenfeld, M., Sigle-Rushton, W., Keizer, R., Lappegård, T., Jasilioniene, A., & Di Giulio, P. (2012). Changes in union status during the transition to parenthood in eleven European countries, 1970s to early 2000s. Population Studies: A Journal of Demography, 66, 167–182.

    Article  Google Scholar 

  • Perelli-Harris, B., Sigle-Rushton, W., Kreyenfeld, M. R., Lappegård, T., Keizer, R., & Berghammer, C. (2010). The educational gradient of childbearing within cohabitation in Europe. Population and Development Review, 36, 775–801.

    Article  Google Scholar 

  • Pisati, M. (2001). Tools for spatial data analysis. Stata Technical Bulletin, 60, 21–37.

    Google Scholar 

  • Potter, J. E., Schmertmann, C. P., Assunção, R. M., & Cavenaghi, S. M. (2010). Mapping the timing, pace, and scale of the fertility transition in Brazil. Population and Development Review, 36, 283–307.

    Article  Google Scholar 

  • Potter, J. E., Schmertmann, C. P., & Cavenaghi, S. M. (2002). Fertility and development: Evidence from Brazil. Demography, 39, 739–761.

    Article  Google Scholar 

  • Rogers, E. M. (1995). Diffusion of innovations (4th ed.). New York, NY: Free Press.

    Google Scholar 

  • Schmertmann, C. P., Assunção, R. M., & Potter, J. E. (2010). Knox meets Cox: Adapting epidemiological space-time statistics to demographic studies. Demography, 47, 629–650.

    Article  Google Scholar 

  • Sobotka, T. (2008). Overview chapter 6: The diverse faces of the second demographic transition in Europe. Demographic Research, 19(article 8), 171–224. doi:10.4054/DemRes.2008.19.8

  • Surkyn, J., & Lesthaeghe, R. (2004). Value orientations and the second demographic transition (SDT) in northern, western and southern Europe: An update. Demographic Research, Special Collection 3(article 3), 45–86. doi:10.4054/DemRes.2004.S3.3

  • Thomson, E. (2013, September). Forerunners of the forerunners: Swedish cohabiting parents 1968–1980. Keynote address, Nordic Demographic Symposium, Tönsberg, Norway.

  • Thornton, A., Axinn, W. G., & Xie, Y. (2008). Marriage and cohabitation. Chicago, IL: University of Chicago Press.

    Google Scholar 

  • Tolnay, S. E. (1995). The spatial diffusion of fertility: A cross-sectional analysis of counties in the American South, 1940. American Sociological Review, 60, 299–308.

    Article  Google Scholar 

  • Trost, J. (1978). A renewed social institution: Non-marital cohabitation. Acta Sociologica, 21, 303–315.

    Google Scholar 

  • Upchurch, D. M., Lillard, L. A., & Panis, C. W. A. (2002). Nonmarital childbearing: Influences of education, marriage, and fertility. Demography, 39, 311–329.

    Article  Google Scholar 

  • Valente, T. W. (1995). Network models of the diffusion of innovations. New York, NY: Hampton Press.

    Google Scholar 

  • Valkonen, T., Blomgren, J., Kauppinen, T. M., Martikainen, P., & Mäenpää, E. (2008). The effects of regional socioeconomic and cultural characteristics on the spatial patterns of the second demographic transition in Finland. Demographic Research, 19(article 61), 2043–2056. doi:10.4054/DemRes.2008.19.61

  • Van Bavel, J. (2004). Diffusion effects in the European fertility transition: Historical evidence from within a Belgian town (1846–1910). European Journal of Population, 20, 63–85.

    Article  Google Scholar 

  • Van de Kaa, D. J. (1987). Europe’s second demographic transition. Population Bulletin, 42, 1–57.

    Google Scholar 

  • Van de Kaa, D. J. (2004). Is the second demographic transition a useful research concept? Questions and answers. Vienna Yearbook of Population Research, 2, 4–10.

    Google Scholar 

  • Voss, P. R. (2007). Demography as a spatial social science. Population Research and Policy Review, 26, 457–476.

    Article  Google Scholar 

  • Watkins, S. C. (1987). The fertility transition: Europe and the third world compared. Sociological Forum, 2, 645–675.

    Article  Google Scholar 

  • Weeks, J. R. (2004). The role of spatial analysis in demographic research. In M. F. Goodchild & D. G. Janelle (Eds.), Spatially integrated social science (pp. 3–21). New York, NY: Oxford University Press.

    Google Scholar 

  • Weeks, J. R., Gadalla, M. S., Rashed, T., Stanforth, J., & Hill, A. G. (2000). Spatial variability in fertility in Menoufia, Egypt, assessed through the application of remote-sensing and GIS technologies. Environment and Planning A, 32, 695–714.

    Article  Google Scholar 

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Acknowledgments

This research has received support from the project “Consequences of Demographic Change” (CODEC), funded by the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013) ERC Grant agreement No. 201194 and from the project “Family Dynamics, Fertility and Family Policy” funded by the Research Council of Norway (202442/S20). We are also grateful for support from the NordForsk Research-based Network for Register-Based Life Course Studies. The authors would like to thank two anonymous reviewers for their valuable comments and suggestions; Elisabeth Thompson, Federico Belotti, Andrea Piano Mortari, and Manudeep Bhuller; participants to the 2013 Annual Meeting of the Population Association of America, New Orleans; and participants to the 2013 workshop “Changing Families and Fertility Choices,” Oslo.

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Authors and Affiliations

  1. Department of Social Statistics and Demography and ESRC Centre for Population Change, University of Southampton, Social Sciences, Building 58 Highfield Campus, Southampton, SO17 1BJ, UK

    Agnese Vitali

  2. Department of Policy Analysis and Public Management and Carlo F. Dondena Centre for Research on Social Dynamics and Public Policies, Bocconi University, Milan, Italy

    Arnstein Aassve

  3. Research Department, Statistics Norway, Oslo, Norway

    Trude Lappegård

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  1. Agnese Vitali
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  2. Arnstein Aassve
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  3. Trude Lappegård
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Correspondence to Agnese Vitali.

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Vitali, A., Aassve, A. & Lappegård, T. Diffusion of Childbearing Within Cohabitation. Demography 52, 355–377 (2015). https://doi.org/10.1007/s13524-015-0380-7

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