Slack labor market conditions for women relative to men increase the marriage rate in the USA. This paper examines the long-term consequences of such marriages. Despite the significant effect on marriage timing, labor market conditions experienced in youth do not affect the probability that a woman will marry by the age of 30. Further, labor market conditions at the time of marriage are uncorrelated with the probability of divorce, spouses’ characteristics, or the number of children. These findings suggest that labor market fluctuations induce only intertemporal adjustments for marriage timing without affecting reservation match quality or total fertility.
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An important presumption of these studies is that women can shift labor supply intertemporary so that they can marry and bear children when the opportunity costs are low. In countries with more rigid labor market structures than the USA, it may be more difficult for women to find a job after child-bearing, and thus, there may be less room for intertemporal substitution between market work and family activities. Indeed, Gutiérrez-Domènech (2008) finds that a high unemployment rate delays marriage and fertility of Spanish women because potential mothers care not only of their current employment status but also in their expected possibilities to work afterward.
Among the studies conducted on fertility, the study of Dehejia and Lleras-Muney (2004), who investigate the relationship between the unemployment rate at the time of conception and the baby’s health outcome in the long run, has similarities with this paper in terms of empirical specification.
Another response that a woman could do when she faced bad labor market condition in her state is to move to another state where labor market conditions are better, and indeed, young people move away from states with high unemployment (Wozniak 2010). Although such moves may have weaken the actual effects of contemporaneous labor market conditions on marriage formation, empirically, there remains statistically significant effect. Also, endogenous mobility does not bias the estimates of the effect of labor market conditions experienced at the age of 18–20 in the state of birth, which is the main explanation variable of this paper because the state of birth is predetermined.
As a robustness check, I confirmed that replacing unemployment rates experienced between the ages of 18 and 20 with those between ages of 19 and 21 does not qualitatively change the results of any analyses in this paper. The ESM—Appendix also shows the results obtained using unemployment rates experienced between the ages of 22 and 24 for a subsample of 4-year college graduates. The basic results hold for this group, except that the average timing of marriage is about 3 years later than that of the rest of the sample.
I used linear OLS with the cohort-level data because it makes the comparison between the coefficients for the different ages easier than probit or logit. As a robustness check, ESM—Appendix shows that probit regressions which use the actual state of residence at age 18–20 for high school graduates and at age 22–24 for college graduates yield the marginal effects of unemployment rates of the similar size and statistical significance.
In the ESM—Appendix, I also report the results with controls for college education, which are qualitatively the same, even though the decision to acquire college education could be affected by the decision to marry. Such reverse causality from marriage to education might exist for the case of high school completion if high school students are forward looking and take in to account the prospect in the marriage market, but at least such decision is not affected by the unemployment rate at age 18–20.
I restrict my sample to women because fertility history is available only for women. Note that, since the SIPP is a household survey, most of the married men in the data are the spouses of women in the same data. Therefore, the results on marriage formation and dissolution are expected to be identical, except that the men are slightly older than the women, and I confirm that the results are actually very similar.
I do not retrieve data prior to 1978 because unemployment rates by gender are not available at the state level prior to 1978.
Another important drawback of the SIPP is the incomplete migration history. This issue is described in detail in the ESM—Appendix.
Another solution is to use a real panel dataset. In an earlier version circulated as Columbia PER Discussion Paper 0809-08, I also used the PSID for supplemental analyses. I confirmed that the results are qualitatively identical.
Another important difference is that panels prior to 1996 are overlapping. Until 1993, a new panel had been introduced every year. Later, these were replaced by the 1996 redesign that used larger, non-overlapping panels. Nevertheless, results from 1990 to 1993 panels are similar to those estimated using the 1996–2004 panels. Therefore, I believe that the change in the sampling scheme does not render any bias to this paper.
I have tried to do the same exercise with African-American women in the SIPP. However, the estimated coefficients of the unemployment rates have much larger standard errors, probably because of the smaller sample size and prevailed unmarried cohabitation. Also African-American population is concentrated to several large states such as New York, which might have made it difficult to identify the effect of unemployment rates net of state fixed effects. For Hispanics, in addition to the uneven distribution of the current residence, non-negligible fraction of the sample were born outside of the USA and thus cannot be assigned the unemployment rate at age 18–20.
Maine and Vermont are grouped together, and North Dakota, South Dakota, and Wyoming are grouped together because the original variable for the state of current residence in the SIPP is defined in such a way. Alaska, Washington DC, and Hawaii are dropped.
The state–year level gender-specific unemployment rates are not issued by the BLS but calculated from the micro-data by myself, so there is no official documentation about their sampling errors. However, according to BLS publication “Employment and Earnings (http://www.bls.gov/opub/ee/home.htm),” the sampling errors of the unemployment rate of white labor force in the entire USA are about 0.1%. Roughly speaking, the sampling errors of each state’s gender-specific unemployment rates should be approximately 0.1% times (the state’s population share in the US)^0.5. The population-weighted average of this value is about 8; thus, the sampling error is probably around 0.8%. Admittedly, 0.8% is very close to the size of remaining variations after controlling for the other gender’s unemployment rate, year dummies, and state dummies. Therefore, the estimated coefficients might be subject to attenuation bias from the measurement errors.
I tried switching between linear OLS and probit for binary dependent variables and the results were robust. See ESM—Appendix Section B1 for some of the robustness checks.
Robustness checks using the probit model are presented in the ESM—Appendix.
Dropping the previous year’s unemployment rates do not change the coefficient of unemployment rates experienced between the ages of 18 and 20 much.
One of the most recent follow-up studies is the one carried out by Lehrer (2008), which shows that the relationship between the age one marries and marital instability is strongly negative up to one’s late twenties.
Card and Lemieux (2000) show that the unemployment rate at age 18 has a slightly negative effect on the years of schooling. In contrast, Kondo (2007) shows that the effects of a recession at the time of entry into the labor market on wages and employment are negligible for non-Hispanic white women, despite persistent, significantly negative effects for men. Hershbein (2010) also show that women who graduate during a period of high unemployment reduce their labor supply in the short run but long-term impacts on labor supply is minimal.
It is true that a recession at graduation lowers earnings of male college graduates by about 1% for ten or more years after graduation (Kahn 2010; Genda et al. 2010). However, such effect is limited to male college graduates. The effects of a recession at entry on subsequent employment and earnings are temporary for male high school graduates (Genda et al. 2010) and even weaker and less persistent for women (Kondo 2007; Hershbein 2010).
The author’s tabulation from SIPP 2004
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This paper is a substantially revised version of “Female Labor Market Conditions and Family Formation,” Columbia PER Discussion Paper 0809-08. I am particularly indebted to my advisors, Janet Currie, Till von Wachter, and Bentley MacLeod, for their guidance and support. I also wish to thank Shubha Chakravarty, Pierre-André Chiappori, Lena Edlund, Joshua Goodman, Mariesa Herrmann, Fung-Mey Huang, Tümer Kapan, Dongshu Ou, Johannes Schmieder, Herdis Steingrimsdottir, Matthew Wai-Poi, Jane Waldfogel, and the editor and two anonymous referees of this journal for their helpful suggestions. I benefited from comments by the participants at student colloquia in Columbia, ZEW summer workshop, Asian conference 2009 on Applied Micro-Economics/Econometrics, seminars at the Institute for Statistical Research, Hitotsubashi University, Tohoku University, KISER Labor Workshop, Nihon University, and Kyoto University.
Responsible editor: Erdal Tekin
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Kondo, A. Gender-specific labor market conditions and family formation. J Popul Econ 25, 151–174 (2012). https://doi.org/10.1007/s00148-011-0367-7
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