Previous studies have shown that the risk of divorce is low during the first months of marriage; it then increases, reaches a maximum, and thereafter begins to decline. Some researchers consider this pattern consistent with the notion of a “seven-year itch,” while others argue that the rising-falling pattern of divorce risk is a consequence of misspecification of longitudinal models because of omitted covariates or unobserved heterogeneity. The aim of this study is to investigate the causes of the rising-falling pattern of divorce risk. Using register data from Finland and applying multilevel hazard models, the analysis supports the rising-falling pattern of divorce by marriage duration: the risk of marital dissolution increases, reaches its peak, and then gradually declines. This pattern persists when I control for the sociodemographic characteristics of women and their partners. The inclusion of unobserved heterogeneity in the model leads to some changes in the shape of the baseline risk; however, the rising-falling pattern of the divorce risk persists.
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I conducted experiments to investigate the sensitivity of residual variance to the share of population with repeated episodes. As expected, the estimate for the residual variance depended little on how large the share of population with repeated episodes was (100 %, 50 %, 25 %, 10 %, and 5 %) in the context where the intraclass correlation was moderate to strong (i.e., the durations of an individual were positively correlated).
The identification of the model with unobserved heterogeneity was thus based on the existence of multiple marriages (and divorces) for some women. It is likely that there were more disruption-prone women (or women with unmeasured characteristics that made them disruption-prone) among this group than among women who had been married only once. Therefore, some assumptions of my statistical model might not be fully met (e.g., normality of the residuals), and I might overestimate or underestimate the true amount of heterogeneity in the population (e.g., unmeasured individual values or personality traits that made some women more and others less likely of experiencing divorce). However, the proposed approach was the only way of estimating unobserved heterogeneity from the data without imposing strong assumptions on data. Alternatively, one could have used all women to identify unobserved heterogeneity; however, this approach would have required strong assumptions about distribution of both the residuals and the baseline risk (i.e., the risk of divorce by marriage duration), the shape of which is the main interest of this study. My approach was thus well justified; I also tested sensitivity of the results to underestimation and overestimation of the amount of unobserved heterogeneity in the population (see footnote 3).
I conducted additional analysis to explore how sensitive the shape of the baseline was to the estimates for the residual variance/standard deviation. The shape of the baseline became less pronounced with an increase in the variance/standard deviation, as expected (see Fig. S1 in Online Resource 1). However, the value of 3.0 for the standard deviation (9.0 for the variance) was needed to substantially modify the shape of the baseline. This suggests that a woman with unmeasured characteristics that place her at 1 standard deviation above the average had 20 times higher hazard of separation (exp(3.0) = 20.1) than a woman with average unobserved characteristics (e.g., in the middle in the liberal-conservative scale), while a woman at 1 standard deviation below the average had a 95 % lower risk of divorce (exp(–3.0) = 0.05). Empirical analysis gave no support to (or even no indication of) the existence of such enormous unobserved heterogeneity in the data.
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The author is grateful to three anonymous referees and former Editor Stewart Tolnay for valuable comments and suggestions on a previous version of this article. The author also thanks Statistics Finland for providing the register data used in this study, as well as Mrs. Marianne Johnson for valuable suggestions when preparing the data order. The analyses made in this study are based on the Statistics Finland Register Data at the Max Planck Institute for Demographic Research (TK-53-1662-05).
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Kulu, H. Marriage Duration and Divorce: The Seven-Year Itch or a Lifelong Itch?. Demography 51, 881–893 (2014). https://doi.org/10.1007/s13524-013-0278-1
- Multilevel hazard models