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The marriage unemployment gap

Sekyu Choi and Arnau Valladares-Esteban

Abstract

In this paper we document that married individuals face a lower unemployment rate than their single counterparts. We refer to this phenomenon as the marriage unemployment gap. Despite dramatic demographic changes in the labor market over the last decades, this gap has been remarkably stable both for men and women. Using a flow-decomposition exercise, we assess which transition probabilities (across labor force states) are behind this phenomenon: For men, the main driver is the higher job losing probabilities faced by single workers. For females, the participation margin also plays a crucial role.

JEL Classification: E24; J12; J64

Acknowledgement

We thank the comments of the editor, Karel Mertens and one anonymous referee. We are indebted to Nezih Guner, Stefania Albanesi, Yuliya Kulikova, Joan Llull, Brendon McConnell, and seminar participants at UAB, CEA-Universidad de Chile, ENTER Jamboree 2013, 2014 SAEe, University of Southampton, and the 2013 SED meetings in Seoul, South Korea for thoughtful comments and discussion. Sekyu Choi gratefully acknowledges financial support from the Spanish Ministry of Economy and Competitiveness through grant ECO2012-32392 and through the Severo Ochoa Programme for Centres of Excellence in R&D (SEV-2011-0075). All errors are ours.

A Appendix

A.1 Figures of non-adjusted data

Figure 7: Employment rate by marital status (in percentage). CPS 1976:1–2013:12. Series smoothed using a 12-month moving average. All individuals aged 16 or more. Grey bars denote NBER recession dates.

Figure 7:

Employment rate by marital status (in percentage). CPS 1976:1–2013:12. Series smoothed using a 12-month moving average. All individuals aged 16 or more. Grey bars denote NBER recession dates.

Figure 8: Unemployment rate by marital status (in percentage). CPS 1976:1–2013:12. Series smoothed using a 12-month moving average. All individuals aged 16 or more. Grey bars denote NBER recession dates.

Figure 8:

Unemployment rate by marital status (in percentage). CPS 1976:1–2013:12. Series smoothed using a 12-month moving average. All individuals aged 16 or more. Grey bars denote NBER recession dates.

Figure 9: Labor market transitions for males (probability in percentage). CPS 1976:2–2013:12. Corrected for time aggregation bias. Series smoothed using a 12-month moving average. All individuals aged 16 or more. Grey bars denote NBER recession dates.

Figure 9:

Labor market transitions for males (probability in percentage). CPS 1976:2–2013:12. Corrected for time aggregation bias. Series smoothed using a 12-month moving average. All individuals aged 16 or more. Grey bars denote NBER recession dates.

Figure 10: Labor market transitions for females (probability in percentage). CPS 1976:2–2013:12. Corrected for time aggregation bias. Series smoothed using a 12-month moving average. All individuals aged 16 or more. Grey bars denote NBER recession dates.

Figure 10:

Labor market transitions for females (probability in percentage). CPS 1976:2–2013:12. Corrected for time aggregation bias. Series smoothed using a 12-month moving average. All individuals aged 16 or more. Grey bars denote NBER recession dates.

A.2 Our method of controlling for observables vs. marginal effects probit

In this section we compare our method to control for observables and the results from a Probit regression. Figure 11 compares the difference between the unemployment rate of single and married individuals in our adjusted sample with the marginal effect of being single in the following Probit model:[10]

(3)Pr(U=1X)=Φ(β0×single+β1×X)

where U is a dummy variable that takes value 1 if the individual is unemployed and 0 otherwise, single is a dummy variable taking value 1 if the individual is not married and 0 otherwise, the vector X is the set of observable characteristics we use in the construction of our adjusted sample, and Φ is the Cumulative Distribution Function of the standard normal distribution.[11] We estimate the probit model by maximum likelihood.

In the adjusted sample, both married and single individuals present the same observable characteristics. Hence, the difference between the unemployment rate of single and married individuals reflects the different probabilities of being unemployed conditional on observables. This is equivalent to estimating the Probit model in Equation 3 and computing the marginal effect of being single (or married) controlling for observables. These results indicate that, both the exact matching method we use to control for the effects of observables and using a Probit model to clean out the effects of observables, deliver similar results. We choose to use exact matching because it does not require to assume a particular parametric relationship between observables and labor market outcomes.

Figure 11: Unemployment rate. CPS 1976:1–2013:12. The solid line (Artificial Sample) represents the difference between the unemployment rate of single and married individuals in our adjusted sample (in percentage points). The dashed line (Marginal Effects) is the marginal effect (probability in percentage) of being single computed from the estimation of the Probit model in Equation 3. Series smoothed using a 12-month moving average. All individuals aged 16 or more. Grey bars denote NBER recession dates.

Figure 11:

Unemployment rate. CPS 1976:1–2013:12. The solid line (Artificial Sample) represents the difference between the unemployment rate of single and married individuals in our adjusted sample (in percentage points). The dashed line (Marginal Effects) is the marginal effect (probability in percentage) of being single computed from the estimation of the Probit model in Equation 3. Series smoothed using a 12-month moving average. All individuals aged 16 or more. Grey bars denote NBER recession dates.

A.3 Gaps in transitions

Figure 12: Labor market transitions for males (probability in percentage). CPS 1976:1–2013:12. Corrected for time aggregation bias and classification error. Adjusted sample to control for observables (see main text). Series smoothed using a 12-month moving average. All individuals aged 16 or more. Grey bars denote NBER recession dates.

Figure 12:

Labor market transitions for males (probability in percentage). CPS 1976:1–2013:12. Corrected for time aggregation bias and classification error. Adjusted sample to control for observables (see main text). Series smoothed using a 12-month moving average. All individuals aged 16 or more. Grey bars denote NBER recession dates.

Figure 13: Labor market transitions gaps for females (probability in percentage). CPS 1976:1–2013:12. Corrected for time aggregation bias and classification error. Adjusted sample to control for observables (see main text). Series smoothed using a 12-month moving average. All individuals aged 16 or more. Grey bars denote NBER recession dates.

Figure 13:

Labor market transitions gaps for females (probability in percentage). CPS 1976:1–2013:12. Corrected for time aggregation bias and classification error. Adjusted sample to control for observables (see main text). Series smoothed using a 12-month moving average. All individuals aged 16 or more. Grey bars denote NBER recession dates.

A.4 Subgroups

Figure 14: Unemployment rate (in percentage) by subgroup, males. CPS 1976:1–2013:12. Adjusted sample to control for observables (see main text). Series smoothed using a 12-month moving average. All individuals aged 16 or more. Grey bars denote NBER recession dates.

Figure 14:

Unemployment rate (in percentage) by subgroup, males. CPS 1976:1–2013:12. Adjusted sample to control for observables (see main text). Series smoothed using a 12-month moving average. All individuals aged 16 or more. Grey bars denote NBER recession dates.

Figure 15: Unemployment rate (in percentage) by subgroup, females. CPS 1976:1–2013:12. Adjusted sample to control for observables (see main text). Series smoothed using a 12-month moving average. All individuals aged 16 or more. Grey bars denote NBER recession dates.

Figure 15:

Unemployment rate (in percentage) by subgroup, females. CPS 1976:1–2013:12. Adjusted sample to control for observables (see main text). Series smoothed using a 12-month moving average. All individuals aged 16 or more. Grey bars denote NBER recession dates.

A.5 Decomposition exercise for females from 1985

Figure 16: Counterfactual unemployment rates (in percentage) for single females, aged 16+, from 1980:1 to 2013:12.

Figure 16:

Counterfactual unemployment rates (in percentage) for single females, aged 16+, from 1980:1 to 2013:12.

Figure 17: Counterfactual unemployment rates (in percentage) for single females, aged 16+, from 1985:1 to 2013:12.

Figure 17:

Counterfactual unemployment rates (in percentage) for single females, aged 16+, from 1985:1 to 2013:12.

Table 2:

Contribution of each separate transition probability to the marriage unemployment gap, females. Second, third, and forth columns are the value of the statistic Sgap(XZ)=1t=t0t=T[u~ts(XZ)utm]2/t=t0t=T[utsutm]2, where XZ is the related transition probability (see main text for details). Higher numbers imply a higher contribution to the gap.

Transition1980 onwards1985 onwardsAll sample
EU0.740.790.60
EO−2.87−2.22−2.75
UE0.400.400.38
UO0.410.470.26
OE0.360.400.28
OU0.560.710.30

A.6 Composition of the EU transition

We use the CPS information on the reason of job separation to compute the share of individuals in the EU transition that report layoff, quit, or other as the reason for their job separation.

Figure 18: Share of individuals in EU transition by reported job separation reason (in percentage). CPS 1976:1–2013:12. Series smoothed using a 12-month moving average. All individuals aged 16 or more. Grey bars denote NBER recession dates.

Figure 18:

Share of individuals in EU transition by reported job separation reason (in percentage). CPS 1976:1–2013:12. Series smoothed using a 12-month moving average. All individuals aged 16 or more. Grey bars denote NBER recession dates.

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Published Online: 2017-6-6

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