Beyond the Great Recession: Labor Market Polarization and Ongoing Fertility Decline in the United States

Abstract

In the years since the Great Recession, social scientists have anticipated that economic recovery in the United States, characterized by gains in employment and median household income, would augur a reversal of declining fertility trends. However, the expected post-recession rebound in fertility rates has yet to materialize. In this study, I propose an economic explanation for why fertility rates have continued to decline regardless of improvements in conventional economic indicators. I argue that ongoing structural changes in U.S. labor markets have prolonged the financial uncertainty that leads women and couples to delay or forgo childbearing. Combining statistical and survey data with restricted-use vital registration records, I examine how cyclical and structural changes in metropolitan-area labor markets were associated with changes in total fertility rates (TFRs) across racial/ethnic groups from the early 1990s to the present day, with a particular focus on the 2006–2014 period. The findings suggest that changes in industry composition—specifically, the loss of manufacturing and other goods-producing businesses—have a larger effect on TFRs than changes in the unemployment rate for all racial/ethnic groups. Because structural changes in labor markets are more likely to be sustained over time—in contrast to unemployment rates, which fluctuate with economic cycles—further reductions in unemployment are unlikely to reverse declining fertility trends.

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Notes

  1. 1.

    Alternatively, displaced workers often exit the labor force entirely, enroll in workforce retraining programs, or pursue additional educational degrees and certifications (Janoski et al. 2014; McConnell et al. 2016).

  2. 2.

    I additionally estimate models using a data set that extends from 1991 to 2014, which relies on 82.3 million birth certificate records.

  3. 3.

    To account for comparability issues between SIC and NAICS industry classification codes in the extended period analysis (1991–2014), I construct an analogous domain-level grouping of SIC codes for CBP data between 1991 and 1997.

  4. 4.

    Because PUMAs are nested within states rather than within counties, MSA-level estimates have errors of commission and omission in which areas outside the MSA are included or areas within the MSA are not included, respectively. Most MSAs have a combined error of less than 0.1 % or less than 4.9 %, but 44 MSAs have an error of 5.0 % to 9.9 %, and 40 have an error of 10 % to 14.9 %. In other words, 296 MSAs have less than a 5 % geographic boundary error. In this analysis, I make the plausible assumptions that (1) the outlying geographic areas of MSAs are not excessively biasing the overall MSA population averages, and (2) subpopulation averages in outlying areas of MSAs are similar whether they happen to be immediately inside or outside the MSA boundary.

  5. 5.

    I conceptualize these fixed characteristics as aspects of geography, climate, city-specific cultural norms and mores, shared history, and place-specific socioeconomic and class distributions. To be sure, these metropolitan-area characteristics do change over time; but given the relatively brief period of analysis, I make the plausible assumption that metropolitan areas maintain a fixed set of social, cultural, and built-environment characteristics.

  6. 6.

    Kothari et al. (2013) noted that geographic mobility declined throughout the years of the Great Recession. For the geographic mobility that did occur, labor migration during the Great Recession varied for low- and high-skilled workers as well as across foreign-born and non-foreign-born workers (Cadena and Kovak 2016). In-migration from Mexico, for instance, decreased as a result of economic disruption to the construction and manufacturing sectors in the United States (Calnan and Painter 2017; Villarreal 2014).

  7. 7.

    Because the labor market measures and covariates are lagged by one year to approximate economic conditions at the time of conception, the prerecession/recession period (2006–2010) aligns with economic conditions in 2005–2009, and the post-recession period (2011–2014) aligns with economic conditions in 2010–2013.

  8. 8.

    County-level CBP data on industry composition are available from 1986 onward; however, county-level LAUS data are available from 1990 onward.

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Acknowledgments

The author is grateful for feedback from Jenna Nobles, Myra Marx Ferree, Marcy Carlson, Christine Schwartz, and Jennifer Laird. This research was supported by a core grant to the Center for Demography and Ecology at University of Wisconsin–Madison (P2C HD047873) as well as support from a training grant awarded to the Center for Demography and Ecology (T32 HD007014). All errors are the author’s own.

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Seltzer, N. Beyond the Great Recession: Labor Market Polarization and Ongoing Fertility Decline in the United States. Demography 56, 1463–1493 (2019). https://doi.org/10.1007/s13524-019-00790-6

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Keywords

  • Fertility
  • Great recession
  • Labor market polarization
  • Unemployment