Assortative mating, marital stability and the role of business cycles in the United States from 1968 to 2011

ABSTRACT

The strong negative correlation between divorce and a wide range of outcomes in terms of well-being, health, education and labour market performance has been well documented in the literature. Economic conditions have been found to affect marital stability. Shared gains from marriage also depend on spouses’ characteristics such as age, education, ethnicity and religious beliefs. This paper examines the relationship between these spousal characteristics and the probability of dissolution while taking into account business cycle fluctuations. Using data from the Panel Study of Income Dynamics 1968–2011 for the United States and employing a duration modelling strategy, findings reveal that differences in educational attainment and ethnicity between spouses increase the hazard of marital dissolution. However similarity in religious beliefs and ethnicity reduce the risk of divorce. A period of economic growth improves marital stability. However, ethnic differences are a significant predictor of marital division, even in times of economic prosperity.

KEYWORDS:

• Assortative mating
• marital stability
• business cycles

Disclosure statement

No potential conflict of interest was reported by the author.

Notes

¹ Aasve et al. (2007), Blanchflower and Oswald (2004), Richards et al. (1997)

² See Becker et al. (1977); Kalmijn (1998); Weiss and Willis (1997); Frimmel et al. (2013)

³ R. Conger et al. (1990); Liem and Liem (1990)

⁴ See R. D. Conger et al. (1994); Hardie and Lucas (2010); White and Rogers (2000); Bumpass et al. (1991); Jensen and Smith (1990) Jalovaara (2003) and Hansen (2005)

⁵ These authors have predominantly focused on assortative mating in people’s first marriages or cohabiting unions.

⁶ PSID (2013) url: https://psidonline.isr.umich.edu/Guide/documents.aspx

⁷ The number of individuals reporting more than two marriages was 3,844, while 2,663 reported all their marriages and 1,181 do not report all marriages.

⁸ Note that within the PSID, the information on ethnicity became available for both spouses in a household since survey year 1997 onwards. Please refer to the notes in the Appendix for details.

⁹ Note that the PSID was an annual survey from 1968–1997, thereafter it became biennial.

¹⁰ US Business Cycle Expansions and Contractions available at http://www.nber.org/cycles.html

¹¹ U.S regions are categorised as follows: (1)=North East: Division 1: New England- Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, Vermont; Division 2: Mid-Atlantic- New Jersey, New York, Pennsylvania (2)=MidWest: Division 3: East North Central- Illinois, Indiana, Michigan, Ohio, Wisconsin; Division 4: West North Central- Iowa, Kansas, Minnesota, Missouri, Nebraska, North Dakota, South Dakota (3)=South: Division 5: South Atlantic- Delaware, Florida, Georgia, Maryland, North Carolina, South Carolina, Virginia, Washington D.C, West Virginia; Division 6: East South Central- Alabama, Kentucky, Mississippi, Tennessee; Division 7: West South Central- Arkansas, Louisiana, Oklahoma, Texas (4)=West: Division 8: Mountain- Arizona, Colorado, Idaho, Montana, Nevada, New Mexico, Utah, Wyoming; Division 9: Pacific- Alaska, California, Hawaii, Oregon, Washington.

¹² Note that the probability of leaving the marriage declines over time, as seen in Figure 1.

¹³ This is because the elapsed duration since the start of the marriage spell is approximately 10 years, and ${\lambda }_{{\mathit{\tau }}_{\mathit{it}}}={\lambda }_{11}^{\ast }$ for ${\tau }_{\mathit{it}}\ge 11$ if the survival time is greater than 10

¹⁴ From column (1) of Table 3, the hazard ratio for H>W is 1.364, so the risk of dissolution is 1.364–1) 100 = 36.4%.

¹⁵ xtcloglog command is used if the frailty model has a Normal distribution and pgmhaz8 command is used if a Gamma distribution is assumed for unobserved heterogeneity.

¹⁶ We also note that in the case of estimates presented using the gamma frailty model, we do not obtain consistent estimates for the interaction terms with ethnicity and religion, therefore Table 5 does not show regressions estimates which included interaction terms on ethnicity and religion. This is due to technical difficulties in the computation of these analyses since the gamma variance is constrained to be positive, hence runs into convergence issues as it uses a slightly different computational method, compared to the gaussian. These technical problems were partly due to the reduced number of individual observations for the different categories in ethnicity and religion. Although further research is required to assess the suitability of either distribution, in our case it was simpler to assume a symmetric distribution to model the random effects.

¹⁷ Note that within the PSID, the information on ethnicity became available for both spouses in a household since 1997 onwards. Differences in sample sizes are due to non-response in ethnicity variables. Since this is a constant characteristic over time, we were able to recover this information for 3,644 couples (44,934 couple-years) who had responded to the ethnicity questions. Table A1 on sample selection criteria is shown in the Appendix.

¹⁸ We only interact the terms with economic boom since this was the significant variable. We observed that the number of observations decreases further since just over 24,000 observations (see Table A1) are non-missing in all controls included.

Additional information

Notes on contributors

Nikita Jacob

Nikita Jacob is based at the Centre for Health Economics, University of York. She received a Doctorate in Economics from the University of Essex. Her research interests are primarily in applied micro-econometrics, health, family, education and development economics.