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A Closer Look at the Second Demographic Transition in the US: Evidence of Bidirectionality from a Cohort Perspective (1982–2006)

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Abstract

Second demographic transition (SDT) theory posits that increased individualism and secularization have contributed to low fertility in Europe, but very little work has directly tested the salience of SDT theory to fertility trends in the US. Using longitudinal data from a nationally representative cohort of women who were followed throughout their reproductive years (National Longitudinal Survey of Youth 1979 cohort, NLSY79), this study examines the role of several key indicators of the SDT (secularization, egalitarianism, religious affiliation, and female participation in the labor market) on fertility behavior over time (1982–2006). Analyses employ Poisson estimation, logistic regression, and cross-lagged structural equation models to observe unidirectional and bidirectional relationships over the reproductive life course. Findings lend support to the relevance of SDT theory in the US but also provide evidence of “American bipolarity” which distinguishes the US from the European case. Furthermore, analyses document the reciprocal nature of these relationships over time which has implications for how we understand these associations at the individual-level.

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Notes

  1. In one case, completed fertility behavior is measured using a different specification: the total number of children ever born by 2006 (range = 0–11, mean = 1.83, standard deviation = 1.41). (See Poisson models presented in Table 3.).

  2. Women who were unemployed or out of the labor market received a value on this variable corresponding with the mean number of hours worked among employed women in the sample in a given year. This allows for a straightforward interpretation of the effect of the number of hours worked net of employment status, since point estimates in regression models indicate the effect of an independent variable on the dependent variable, net of all other covariates in the model. This logic is similar to calculating a predicted probability as a value of x changes, when all other values on the right side of the equation are held constant at their mean value. Thus, the point estimates from subsequent regression models can be interpreted as the effect of being employed, and the effect of the number of hours worked net of employment status.

  3. Similar to hours worked, unemployed women were assigned the mean level of job satisfaction for employed women in that year. This point estimate from subsequent regression equations can be interpreted as the effect of job satisfaction net of employment status.

  4. Consistent with the coding strategy for women’s participation in the labor market, women who remained childless by the 2006 interview were assigned the mean value of both indicators among mothers in the sample.

  5. Although Moors (2003, 2008) advocates for the use of latent class analysis to determine attitude profiles for the prediction of fertility behavior, these data do not include a sufficient number of variables within each domain to adequately produce such profiles. Instead, this analysis utilizes sparser but broader data to examine the impact of SDT indicators on multiple parity progressions over time to determine a similar profile.

  6. Mplus provides superior estimation of models including ordered or dichotomous dependent variables. Other programs geared towards structural equation modeling such as AMOS use algorithms which assume all dependent variables are continuous measures. Additionally, two goodness-of-fit statistics are provided for each model. Since the sample size is somewhat large, the χ2 statistic (which compares the observed and predicted covariances, testing the null hypothesis that the model fits the data perfectly) fails to provide the best measure of model fit. Instead, I draw upon the Tucker–Lewis Index (TLI) which performs well for large sample sizes and adjusts for model complexity. This statistic compares model fit between the given model and the independence model, and tends to range between 0 and 1 with higher scores conferring better model fit. Scores of 0.90 are required to accept the model as a good fit for the data. I also reference the root means square error of approximation (RMSEA) which adjusts for error in the population, thus making it ideal for use with large population-level samples. Scores less than 0.05 indicate adequate approximation (Curran et al. 2003).

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Acknowledgments

The author wishes to thank Paul Amato, Alan Booth, and Nancy Landale for helpful comments on earlier drafts. Support for this work was provided by the Eunice Kennedy Shriver National Institute of Child Health and Human Development, Interdisciplinary Training in Demography (Grant No. T32 HD007514, PI: Gordon DeJong) to the Pennsylvania State University Population Research Institute. Opinions reflect those of the author and not necessarily those of the granting agencies.

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  1. Carolina Population Center, University of North Carolina, 123 West Franklin Street, Chapel Hill, NC, 27516, USA 

    Jennifer B. Kane

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Correspondence to Jennifer B. Kane.

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Kane, J.B. A Closer Look at the Second Demographic Transition in the US: Evidence of Bidirectionality from a Cohort Perspective (1982–2006). Popul Res Policy Rev 32, 47–80 (2013). https://doi.org/10.1007/s11113-012-9257-2

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