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Same-occupation spouses: preferences or search costs?

Abstract

Married individuals match with spouses who share their occupation more frequently than should happen by chance if marriage markets are large frictionless search markets covering a particular geographic area. This suggests that either there is a preference for same-occupation matches or that search costs are lower within occupation. This paper uses 2008–2015 data from the American Community Survey to analyze same-occupation matching among a sample of recently married couples. Our empirical strategy compares the difference in wages between same-occupation husbands and different-occupation husbands across occupations with different percent male workers. Under a preference explanation, this difference should become less negative as the share of males in the occupation increases. Under a search cost explanation, this difference should become more negative as the share of males increases. Our results are consistent with the search cost explanation. Furthermore, using an occupation-specific index of workplace communication, we demonstrate that the results are most consistent with the search cost mechanism for occupations with a greater degree of workplace communication. Finally, we show that matching on field of degree for couples in which both spouses have a college degree is also consistent with the search cost explanation.

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Notes

  1. 1.

    Bruze (2011) shows that movie actors, whose job does not require a specific level of education, are more likely to marry spouses with their same level of education. He concludes that preferences play a significant role in explaining educational assortative matching.

  2. 2.

    Details on the procedure of calculating random matching by occupation, following the procedure from Fryer (2007), are in the Appendix.

  3. 3.

    An additional explanation for within-occupation matching is that married individuals might recruit their partners into their own occupations after matching. For example, in Table 1, it might be that the very few women who report being fishers, hunters, or trappers are in that category because their husbands brought them into the occupation, generating a very large within-occupation matched rate for women in that occupation (0.60). This explanation is less likely to be relevant for married individuals with college degrees, because entrance into an occupation often requires investment in specialized education and training.

  4. 4.

    This raises the question of how, in this two-sided matching model, the non-smoking women are able to attract higher-quality smoking husbands. It is not because the smoking men value non-smoking wives (Chiappori et al. assume that smokers, unlike non-smokers, are indifferent to smoking status). Rather, the smoking men face a shortage of smoking wives, and it is this gender imbalance that drives some smoking men to match with non-smoking wives.

  5. 5.

    Banerjee et al. (2013) analyze an Indian marriage market in which individuals care about caste and other attributes. While matching by caste may seem very applicable to matching by occupation, there is a crucial difference; there is sex balance across castes, but sex imbalance across occupations. A key finding of Banerjee et al. (2013) is that, under the assumption of sex balance, sorting on other attributes does not change much in the presence of preferences for same-caste matching. Chiappori et al. (forthcoming) have a similar prediction for the case in which men and women smoke at the same rate. The prediction we are using from Chiappori et al. is driven by the sex imbalance across groups.

  6. 6.

    There is a substantial literature on models of marriage markets with search costs. Unlike the perfect assortative matching predicted in frictionless markets, in the presence of search costs, markets will develop a class structure in which individuals match with one of a range of acceptable partners rather than their ideal partner (Burdett and Coles 1997; Bloch and Ryder 2000; Smith 2006; Jacquet and Tan 2007). These models, however, do not allow search costs to vary across different types of potential partners or across different marriage markets.

  7. 7.

    It may seem more realistic to have a model in which individuals allocate some effort to within-occupation search and some effort to outside-occupation search. But in this simple search framework, one search pool will always dominate the other in the marginal return to additional search, so it would never be optimal for an individual to allocate effort to both pools. A potential model extension would be to allow search costs to increase as an individual searches in a given pool, perhaps if the search pool has a limited number of options, so that at some point it becomes optimal for an individual to switch to the other search pool.

  8. 8.

    A similar empirical prediction is generated by the two-sided search market with frictions in Moen (1997). In his model, submarkets vary by wage and search costs (workers per vacancy). Submarkets with higher wages attract more workers per vacancy and therefore have greater search costs. A comparison of identical workers who matched in different submarkets will find that the worker who matched in the market with greater search costs is receiving a higher wage.

  9. 9.

    This may at first seem counter-intuitive because we expect lower search costs to generate matches with higher-quality husbands. But in the analysis, we are not comparing women across occupations facing different search costs. If we were, then we would predict that women in occupations with lower search costs should on average match with higher-quality husbands. Instead, we are comparing women in the same occupation, facing the same within-occupation search costs, who match either with same-occupation husbands or with different-occupation husbands.

  10. 10.

    Our results are very similar if we instead just use the midpoint of the reported hour interval when calculating the hourly wage, specifically, if we use hour values of 7, 20, 33, 43.5, 48.5, and 51, respectively, for the reported intervals 1–13, 14–26, 27–39, 40–47, 48–49, and 50–52.

  11. 11.

    Stevenson and Wolfers (2011), in calculations with SIPP data, find that 91% of women married in the late 1990s were married at their 5th anniversary and 77% were married at their 10th anniversary. Kreider and Ellis (2011) report similar marriage survival statistics. Our findings are similar if we restrict the sample to couples who have been married for 2 years or less. Van Kammen and Adams (2014) show that spouses with similar occupational characteristics are more likely to divorce.

  12. 12.

    Because the eliminated occupations are small, results using the full sample in which the small occupations are retained are quite similar to those in which they are eliminated. Because there may be a concern that the military operates as a somewhat distinct marriage market, we also confirmed that results are robust to excluding couples in which either or both of the husband and wife work in the military.

  13. 13.

    Other papers have used both husband’s education and wife’s education as measures of quality. We do not use this approach here, because it is important to control for education on the right-hand side of the equation in order to avoid bias due to unobserved heterogeneity in wife’s quality.

  14. 14.

    The results for husband’s wage are robust to including non-working wives in the sample. We cannot estimate results for the wage ratio if the sample includes non-working wives.

  15. 15.

    It is likely that, even in the absence of preferences for same-occupation spouses, individuals prefer spouses from high-wage occupations. A certain amount of same-occupation matching will occur just from individuals in high-wage occupations assortatively matching with spouses in high-wage occupations. But this form of sorting will be picked up by the occupation fixed effects. If both female doctors and female lawyers agree that male doctors are the most desirable spouses, there is no reason for female doctors to disproportionately sacrifice to marry a male doctor. The focus of our analysis is whether the male doctor is disproportionately attractive to female doctors compared to women from other occupations.

  16. 16.

    Because the interaction term is zero unless husband and wife share the same occupation, it is equivalent to the interaction of Same_Occ with the percent male in husband’s occupation.

  17. 17.

    It could be the case the individuals who select into low-communication workplaces are particularly adverse to social interaction and therefore consider search outside the workplace disproportionately difficult. If that were so, then individuals in low-communication workplaces should be even more willing to accept a low-quality match within occupation rather than engage in wider search. This would work against our finding a negative estimate for the coefficient on Same_Occ*%Male*Comm.

  18. 18.

    As was the case with the occupation-level characteristics in Eqs. (1)–(3), field of degree characteristics are calculated on the larger sample of men and women ages 25–45 who report a field of degree in the 2008–2015 ACS, unconditional on marital status.

  19. 19.

    We do not report the coefficient estimate for the main effect of the same-occupation variable, because this coefficient is only interpretable in combination with the coefficients on the interaction of the same-occupation variable with the four occupation-level wage characteristics.

  20. 20.

    Specifically, the coefficient (standard error) on Same_Occ*%Male is 0.185 (1.50) using wife’s wage and 0.038 (0.043) using logged wife’s wage.

  21. 21.

    Percent male in wife’s occupations included in the sample ranges from a maximum of 93.5% to a minimum of 2.0%.

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Acknowledgements

We gratefully acknowledge helpful comments from Chris Bollinger, Murat Iyigun, and Rick Mansfield and two anonymous referees as well as seminar participants at the University of Kentucky, Osaka University, University of Melbourne, University of Sydney, University of New South Wales, UC-Santa Barbara, Deakin University, SEA annual meetings, SOLE/EALE World Congress, and the IZA/SOLE transatlantic meetings.

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Correspondence to Terra McKinnish.

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Appendix: Random matching

Appendix: Random matching

In Table 2, we report the probability a married man (woman) in occupation k is married to a woman (man) who also works in occupation k under random matching by occupation (conditional on non-random matching by education).

We calculate these random matching probabilities adapting Fryer (2007)’s approach for random matching by race.

There are seven steps to the calculation:

  1. Step 1:

    Restrict the sample to married mixed-sex couples. Divide married men and women into two education categories based on college degree completion. Calculate for men (women) in each education category the proportion whose spouse is in each education category (e.g., the proportion of male college graduates married to female college graduates)

  2. Step 2:

    For men (women) in each education category, calculate the number in each occupation category.

  3. Step 3:

    Multiply the numbers from step 2 (e.g., the number of male college graduates in occupation 1) by the proportions from step 1 (e.g. ,the proportion of male college graduates married to female college graduates) to calculate the expected number of pairings between men (women) in each education/occupation group and women (men) in each education group (e.g., the expected number of pairings between male college graduates in occupation 1 and female college graduates).

  4. Step 4:

    Using the numbers from step 2, calculate for men (women) in each education category the share in each occupation category.

  5. Step 5:

    Multiply the expected number of pairings from step 3 by the shares from step 4 to calculate expected pairings by education/occupation group (e.g., multiply the expected number of pairings between male college graduates in occupation 1 and female college graduates times the share of female college graduates in occupation 1. This produces the expected number of pairings between male college graduates in occupation 1 and female college graduates in occupation 1).

  6. Step 6:

    Find the total expected number of same-occupation pairings in each occupation group by summing over the relevant pairings in step 5 (e.g., for men in occupation 1, calculate the total number of expected pairings with women in occupation 1).

  7. Step 7:

    Convert the expected number of same-occupation pairings to probabilities by dividing by the number of men (women) in the occupation from step 2.

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Mansour, H., McKinnish, T. Same-occupation spouses: preferences or search costs?. J Popul Econ 31, 1005–1033 (2018). https://doi.org/10.1007/s00148-017-0670-z

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Keywords

  • Marital matching
  • Occupation
  • Sex composition
  • Search costs

JEL classification

  • J12
  • J24