1. Empirical Studies of Marriage and Divorce

2. Korenman and Neumark, 1991 Does Marriage Really Make Men More Productive? How do we explain the male marriage premium? Three stories: 1. Marriage makes men more productive. 2. Employers favor married men. 3. Men who marry are positively selected.

3. Previous Research “First Generation”: Use cross-section data and control for things “Second Generation”: Essentially use fixed effects and IV strategies. Finding of a large male marriage wage premium very robust We know the premium exists, K & N try to figure out why. Use National Longitudinal Survey of Young Men, which follows men 14-24 in 1966, for 15 years.

4. Empirical Approach The “true” model is: (1) ln(W it ) = αX it + γMST it + A i + ε it A it is an unobserved characteristic of individual I which is assumed to be time-invariant. If we don’t control for A i, it is in the error term and can cause biased estimates of γ. How to control? First differences/differences-in-differences Fixed effects

5. Empirical Approach A slight modification of equation 2 gives us differences-in- differences estimate: ln(W it ) – ln(W it-1 ) = α(X it – X it-1 ) + γ(MST it – MSTi t-1 ) + v it Gets rid of Ai because it never changes over time. For fixed effects, subtract off mean (as in equation 2) or simply control for A i with dummies. Intuitively, asking: how does your wage change when your marital status changes (controlling for other things that might have changed)?

6. Table 2

7. Empirical Approach Interpretation of coefficient in 2 nd column: Being married is associated with a 6% wage increase. This appears to be statistically significant (since 0.06/0.03)>=2. Being divorced is associated with a 4% wage increase, but this is not statistically significant (since 0.04/0.03)<2. The (coefficient ÷ standard error) is the t-statistic for the null hypothesis that β=0. The 5% critical value for a two-sided test is 1.96 (~2). If t<1.96, we fail to reject the null hypothesis that β=0 at the 5% level. The p-value is the lowest level of statistical significance at which we can reject the null hypothesis.

8. EC 30031 - Prof. Buckles 8 y i =  0 +  1 X i1 + … +  k X ik + u i H 0 :  j = 0 H 1 :  j  0 c 0   -c  reject fail to reject

10. Table 2 (cont’d)

11. Table 2 Wage = β0 + β1 married + β2 married*yearsmarried + e If unmarried, wage = β0 + e. If just married (married=1 & yearsmarried=0), wage = β0 + β1 + e. If married 3 years, wage = β1 + 3 β2. Generally, effect of being married = β1 + β2* yearsmarried (take derivative) In Table 3, calculate marriage premium under different models, for someone with a marriage of average duration.

12. Table 2 What do you make of the fact that the premium is smaller in 1’ than in 1? Evidence of selection—once we account for unobserved, time-invariant characteristics, estimated premium is smaller. What do you make of the fact that the premium is smaller in 2’ than in 1’? Years married coefficient is like an interaction term. Tells us that the marriage premium emerges over time.

13. Conclusion Authors claim that selection story receives little support. Agree or disagree? Findings consistent with a story where men with higher “potential” are more likely to marry. Authors seem to prefer a productivity-enhancing story, but more work needs to be done. Question: data from 30+ years ago! Under each of the stories, how would we expect the premium to change as marriage has changed? 2015 meta-analysis by Leonard and Stanley found a 9-13% male marriage premium in U.S. that is not explained by selection. Conclude marriage makes men more productive.

14. Finlay and Neumark, 2010 Is Marriage Always Good for Children? Evidence from Families Affected by Incarceration Never-married motherhood associated with worse educational outcomes for children Children raised by never-married mothers more likely to repeat a grade in school, be expelled or suspended from school, receive treatment for an emotional problem than children living with both biological parents Explanations? * Selection (never-married mothers have worse characteristics, and this produces worse outcomes for their children) * Causal (something about your mother’s marital status causes you to have worse outcomes)

15. Finlay and Neumark, 2010 Is Marriage Always Good for Children? Evidence from Families Affected by Incarceration Marriage promotion policies used as strategy to improve outcomes of children of poor, single mothers  Ex. Healthy Marriage Initiative 2006  Focus of marriage in the TANF legislation

16. Finlay and Neumark, 2010 Is Marriage Always Good for Children? Evidence from Families Affected by Incarceration Identification strategy: uses incarceration rate as instrument for mom’s marital status 2 conditions:  Incarceration rate must be uncorrelated with child- outcome error term, other than through mom’s marital status  Incarceration rate must be correlated with marital status Satisfied?

17. First stage Increasing the incarceration rate from 0 to 1 would increase the likelihood that a mom was never-married by 52.8 percentage points. *or* increasing by 10 percentage points would increase the likelihood by 5.28 percentage points.

18. Ols and iv results

19. Conclusions For Hispanics, having a never-married mother decreases the likelihood of high-school-dropout by 8.2 percentage points. Partial correlations between never-married motherhood and child outcomes overstate the adverse effects of never-married motherhood Children of never-married mothers may have better outcomes, for this one outcome and for this local average treatment effect Simply encouraging marriage for poor, unmarried mothers may not improve outcomes for their children, and could even worsen them depending on which marriages form as a result of such policies

20. McKinnish, 2007 Sexually Integrated Workplaces and Divorce: Another Form of On-the-Job Search Are men and women who work in more sexually- integrated workplaces more likely to divorce? Theoretically: Integrated workplaces might lower search costs for married individuals, increasing their likelihood of divorce. But also lowers search costs for singles, which could lead to higher-quality matches and reduce the likelihood of divorce.

21. McKinnish, 2007 Sexually Integrated Workplaces and Divorce: Another Form of On-the-Job Search How might increased contact with opposite sex affect divorce? 1. Individual finds a more appealing spouse and divorces to marry that person. 2. Contact leads to an affair that leads to divorce, even if no subsequent marriage. 3. Changes individual’s perception of the outside alternatives. Also: “the random search process creates a meeting externality whereby one divorce (marginally) increases the remarriage probability of other divorcees.” –Chiappori and Weiss (2001)

22. Empirical Analysis: 1990 Census Data First, how does McKinnish measure sex-segregation? Each working person is identified as working in one of 235 industries and 501 occupations. For each industry-occupation cell, calculate the fraction of workers 18-55 who are female.

23. OLS Regression Model: Dependent variable = 1 if currently divorced. Note subscripts, vector notation. Empirical Analysis: 1990 Census Data

25. Interpret OLS results: Going from a job that is 0% female to one that is 100% female decreases the probability of divorce for women by 9.5 percentage points, ceteris paribus. -OR- Going from a job with a percent-female in the 75 th percentile to one that is in the 25 th percentile (so a 0.39 change) increases the probability of divorce by 3.7 percentage points, or by about 19%, ceteris paribus.

26. Empirical Analysis: 1990 Census Data

27. Interpret Fixed Effects results: Going from a job with a percent-female in the 75 th percentile to one that is in the 25 th percentile (so a 0.39 change) increases the probability of divorce by 1.4 percentage points, or by 7.2%, ceteris paribus. For men, the effect is 1.1 percentage points or 7.9%, ceteris paribus. Both are statistically significant at 0.1%.

28. Conclusion People who work with more members of the opposite sex are more likely to be divorced. Effects seem to be larger for women. Discussion: * Do you believe the results? * What are the weaknesses of this study? * What are the implications? * How might you improve or extend the paper?