Social Security and Divorce

2.1 Social Security’s 10-Year Rule

People contribute to Social Security through payroll taxes, and employers match the employee contribution. Upon retiring, workers can receive Social Security benefits if they accumulated at least 40 quarters of earnings over their work lives. The size of the benefit, or the Primary Insurance Amount (PIA), is computed based on the average of the worker’s highest 35 years of indexed monthly earnings. ^[3]

If their ex-spouses are still alive, former spouses are eligible for spousal benefits of 50 % of the primary earners’ PIA if their marriages lasted at least 10 years before ending in divorce. If their ex-spouses are deceased, former spouses are eligible for spousal benefits equal to the primary earners’ full PIA if their marriages lasted at least 10 years before ending in divorce. ^[4] Even former spouses who qualify for Social Security on their own earnings’ histories can still receive spousal benefits if the spousal benefits are greater than what they would receive based on their own earnings. Divorced people whose marriages lasted fewer than 10 years are not eligible for any spousal benefits.

The Social Security Administration (SSA) defines eligibility based on formal marriage length. A separation delayed until 10 years of marriage would still be considered intact. Remarrying results in the individual no longer being eligible for spousal benefits from a previous marriage; however, if a subsequent marriage ends in divorce, the person can be eligible for spousal benefits from any previous marriages that lasted at least 10 years. An ex-spouse remarrying does not affect an individual’s eligibility (Social Security Administration 2013). ^[5]

The 10-year rule was part of a 1977 Social Security law and went into effect in 1979. While the main purpose of the 1977 law was to ensure the financial stability of Social Security, it also changed the length of marriage requirement for spousal benefits from 20 to 10 years because marriages were ending more quickly than before (Dickert-Conlin and Meghea 2004).

The vast majority of spousal benefits go to women since they tend to have lower PIAs. In 2006, approximately 8 % of people receiving Social Security received it through the spousal benefit, and approximately 98 % of people receiving spousal benefits were women. The ex-husband’s full PIA is much more likely to be larger than the woman’s PIA than half of the ex-husband’s PIA is. For this reason, a majority of divorced wives will receive benefits based on their deceased ex-husbands’ PIA if their ex-husbands die (Butrica and Smith 2012). As more women enter the labor force and earn higher wages, more women are receiving Social Security without the spousal benefit (Social Security Administration 2013 and Goda, Shoven, and Slavov 2007).

2.2 Conceptual Framework – Heterogeneity by Age at the Time of Marriage

The impact of the 10-year rule likely varies with age. As retirement is nearer for older people, they are likely more focused on Social Security benefits. Young people, on the other hand, tend to be myopic in thinking about retirement. Young people also likely do not know the value of the spousal benefit as earnings typically peak later in life, whereas older people generally have a better idea about whether or not spousal benefits would increase their Social Security payments. ^[6]

Even if young people are perfectly rational and forward thinking, they still may not be influenced by Social Security’s 10-year rule because they have time to marry again and to achieve spousal benefits through another spouse. Since married people are no longer eligible for spousal benefits from previous marriages, young people would have to go through most of their adult lives unmarried or have subsequent marriages end in divorce to claim spousal benefits from divorces that occurred in their twenties or early thirties. Approximately 69 % of women and 78 % of men remarry after divorce (Schoen and Standish 2001), and young divorced people are much more likely to remarry than older divorced people (Brown, Lee, and Bulanda 2006), suggesting young divorced people likely expect to remarry. Not remarrying is a smaller price to pay for older adults. Older couples are also more likely to respond to the 10-year rule because a higher percentage of couples from older cohorts have disparate earnings’ records. Younger generations of women have more parity with their husbands and would be less likely to benefit from the 10-year rule.

Throughout the remainder of the paper, I consider how the 10-year rule influences divorce for the full sample as well as for three broad age groups. For the couple-level Vital Statistics data, the groups are couples with women younger than 25 at marriage, couples with women 25–44 at marriage, and couples with women 45 or older at marriage. With the individual-level ACS data, the groups are women younger than 25 at the start of marriage, women ages 25–44 at the start of marriage, and women 45 or older at the start of marriage. I focus on the age of the woman since women are more likely to receive spousal benefits. To ensure that these broad age groups are appropriate, I also consider smaller age bins, which produce noisier results but provide a fuller picture of how the response to the 10-year rule varies by age.

3.1 Data

The first set of results uses Vital Statistics data from 1985 to 1995. The Vital Statistics data were compiled by the National Center for Health Statistics and contain information from divorce certificates collected at the state level. About half of all states participated in the program. While some states provided a random sample to the National Center for Health Statistics, other states provided data on all divorce certificates. These data are not nationally representative as the non-reporting states come disproportionately from the South and Mountain West. ^[7] I restrict the sample to couples who divorced within 4 years of their 10-year anniversaries.

The Vital Statistics data have several advantages. First, they contain information on the month and year of marriage and divorce, meaning I can calculate the duration of marriage in months. Second, the data set is large. For the years 1985–1995, the data contain information on 2,008,923 divorces. Of these, 1,818,591 contain the ages of the spouses and the information necessary to compute the duration of marriage in months. Finally, these data come straight from divorce certificates and are likely very accurate.

3.2 Empirical Strategy

3.3 Results

Figure 1 shows the number of divorces by marriage length for all ages and for different age groups. For older couples in particular, divorces gap up immediately after 10-year anniversaries. For couples in the middle age group, divorces also appear to increase slightly at 10-year anniversaries. For couples where the woman was 25 or younger at the time of marriage, divorces appear to trend more smoothly.

Figure 1:

Divorces by marriage length, from the 1985 to 1995 Vital Statistics data.

The β coefficients from eq. [1] are shown in Table 1 and reveal a statistically significant increase in the divorce rate for people 25 and older at the time of marriage as well as for the full sample. For couples where the woman was at least 45 or older at marriage, divorces increase by 23 % at 10-year anniversaries. For middle-aged couples, divorces increase by over 4 % immediately after 10-year anniversaries. For younger couples, there is only weak evidence of an increase in divorces after 10-year anniversaries. For the full sample, divorces increase by 3.3 %. ^[10]

These results provide evidence that age is a major factor in how divorce decisions respond to the 10-year rule. To further explore how the response to the 10-year rule varies with age, I replicate the analysis using 5-year age bins in Table 2. With one exception, the point estimates rise with each age bin, though they are generally not statistically significantly different from each other. These results reveal a nonlinear response to the 10-year rule. For women in the three older age bins, the estimated discontinuities are statistically significant and large. For women who married at 56–60, the estimated increase in divorces is over 60 %. ^[11]

I next implement the bunching procedure described in Section 3.2 to produce estimates of the missing mass of divorces during the 6 months before 10-year anniversaries and the bunching that takes place during the 6 months after 10-year anniversaries. The results are shown in the top panel of Table 3 and suggest that approximately 2 % of divorces occurring in 6 months after 10-year anniversaries are delayed from the 6 months before. For couples with women who were 45 or older at marriage, approximately 11.8 % of divorces occurring during the 6 months after 10-year anniversaries appear to be retimed. For couples with women who married younger, 1.8 % of divorces occurring in the 6 months after divorce are retimed.

In panels B and C, I set the bunching regions to be 1 and 1.5 years, respectively. The results from both bunching regions suggest that there are extra divorces after 10-year anniversaries for couples with women who were 45 or older at marriage. For couples with women who married younger, the results from panels B and C are contradictory. In panel B, the results imply that there is a missing mass of divorces immediately prior to 10-year anniversaries for couples with women who were younger than 45 at marriage. In panel C, the results imply that there are extra divorces after 10-year anniversaries for couples with women who were younger than 45 at marriage. The results from the bunching analysis for younger women being sensitive to the bunching window likely suggests that g does a poor job predicting what would happen within the bunching region using data far from the cutoff for younger women. Thus, the results for the larger bunching windows should be interpreted with caution for younger couples.

Panel D displays results with the bunching region set to be 2 years before and after 10-year anniversaries. With this wide bunching region, there is only evidence of bunching for couples where the woman was 45 or older at the time of marriage. There are approximately 9.1 % more divorces occurring during years 10 and 11 than the estimated distribution predicts. The estimate of divorces missing during years 8 and 9 is statistically indistinguishable from 0, suggesting that the extra divorces may not be merely retimed from years 8 and 9. These results for older couples are consistent with the results from the 1- and 1.5-year bunching windows.

The results from the wider bunching regions suggest that older people may not have divorced if they would never have received spousal benefits or that they delay divorce from very early on in marriages for spousal benefits. These results make interpreting the results with the bunching region set to be 6 months before and after difficult to interpret for older individuals. It appears that the 10-year rule affects divorce rates for older couples into the 11th year after marriage, meaning estimates of ŷ_before and ŷ_after might be biased downward when the bunching region is set to 6 months.

The results presented in this section are most comparable to Dickert-Conlin and Meghea (2004), who find no immediate impact of the 10-year rule’s implementation using Vital Statistics data from 1975 to 1980. ^[12] These differences suggest that while people may not have changed their behavior because of Social Security’s 10-year rule immediately, older people in particular soon began adjusting their divorce timing so that they could receive spousal benefits after divorce.

4.1 Data

While the Vital Statistics data are ideal for examining how the 10-year rule influences the distribution and timing of divorces, the Vital Statistics data lack many demographic and labor force variables, meaning they do not allow for knowing characteristics of couples the 10-year rule influences. Examining characteristics of couples is important because economic theory suggests that couples with large disparities between the primary and secondary earners should be the most responsive to the 10-year rule. The Vital Statistics records are also strictly a flow measure, whereas we are also interested in if and how the stock of marriages changes at the 10-year mark. Because of these issues and to focus on more recent data, I examine Social Security’s 10-year rule using Integrated Public Use Microsample Series (IPUMS) ACS data from 2008 to 2011.

Beginning in 2008, the ACS began asking people the year their most recent marriage began. I subtract people’s answers to this question from the survey year to calculate the years since their marriages began. I focus on people over the age of 17 who married 5–14 years prior. Because I am interested in how the likelihood of being divorced changes, only non-widowed ever-married people are included in the sample. With the ACS, the unit of observation is the individual because I can only identify both members of the couple when the couple is still married. Because women make up the vast majority of people receiving spousal benefits and to keep the results concise, I focus on women with the ACS analysis. ^[13] Since the SSA considers couples who have separated as having intact marriages, I code separated people as still being married. As with the Vital Statistics, the ACS also has the advantage of being large. The final sample contains 809,912 observations. This large sample size contrasts with the PSID, which also has information on marriage histories. The PSID only has data on 16,361 marriages, the vast majority of which are comprised of young people.

Despite its advantages, the ACS has three issues. The first is that we only know the length of marriage in years, which means that brief delays in divorce are difficult to observe. Unless divorces are delayed for considerable lengths of time or there is a spike in divorce rates after 10 years that is not from retimed marriages, we may not find any evidence the 10-year rule affects the likelihood of divorcing even if it does. For this reason, the ACS analysis focuses primarily on the divorces of older individuals. The second limitation is that the ACS only asks respondents the years of their most recent marriages. This means that if someone gets a divorce after 10 years of marriage and then remarries, she will not show up in the data as having been divorced after 10 years of marriage. Since divorce rates rise after 10-year anniversaries, the estimates would be biased toward 0 if this happens. Asking about the year of the most recent marriage but not the year of divorce also means that I do not know the length of marriages for divorced people, which leads to me studying the years since marriages began and current marital statuses. Third, Social Security PIA is calculated based on lifetime labor earnings, while the ACS only asks about current earnings. Using current earnings is especially problematic with older individuals since many are retired. To mitigate this concern, I use people’s education levels and the average earnings of their prior occupations to study what kind of marriages end immediately after 10-year anniversaries.

To explore the characteristics of couples who divorce, I create a series of indicator variables that capture within-couple specialization that are equal to 1 if the woman is in a certain type of marriage and 0 otherwise. Since people who are no longer married have a value of 0 for these indicator variables, these variables will allow for understanding what types of marriages end at 10-year anniversaries. The first indicator variable equals 1 if both members of the couple are in the labor force at the time of the interview. I create a separate variable equal to 1 if an individual is married to someone with a different labor force participation status than herself. Since current labor force participation is likely a poor proxy for lifetime earnings, I also take advantage of the ACS question that asks people if they have ever worked over the last 5 years. I create a variable equal to 1 if the woman is married to someone with the same answer to this question as herself and a separate variable equal to 1 if the woman is married to someone with a different answer to this question.

If people report that they have worked in the last 5 years, the ACS asks them about their occupation. I compute the mean earnings of each occupation and then create a variable equal to 1 if women are in marriages where one member is in an occupation that earns at least 50 % more on average than the other’s occupation. I create another variable equal to 1 if the woman is in a marriage with an occupational earnings difference of less than 50 %. When people have not worked during the last 5 years, they are assigned an occupational earnings of 0 and are thus identified as being in marriages with wide earning disparities if their spouses worked at all. Finally, I create an indicator variable equal to 1 if women are married to men with the same education levels as well as an indicator equal to 1 if women are married to men with different education levels. Education is an attractive measure because it predicts lifetime earnings and has the advantage of generally being fixed from an early age.

Means of key variables are shown in Table 4. As with the estimates, the means are weighted using IPUMS weights. An important difference between older and younger women in the sample is how many times they have been married. Only 6 % of younger women have been married more than once, while 83 % of the older sample has had multiple marriages.

4.2 Empirical Strategy

With the ACS data, I consider what happens to the likelihood of being married at 10-year anniversaries by estimating the following equation:

where i indexes the individual, t indexes the year, s indexes the state, y is either an indicator equal to 1 if the individual is married, X is a set of individual covariates that includes years of education, a vector of indicator variables for race, and a vector of indicator variables for age, γ is a full set of year and state interactions, f is a smooth function representing the profile of the dependent variable with respect to the length of marriage l in years, and D is an indicator equal to 1 if the marriage happened at least 10 years prior. I model f as a quadratic polynomial on either side of 10-year anniversaries. Including state-by-year fixed effects means eq. [3] accounts for any state differences and any state-specific shocks over time. For example, state-wide law changes regarding the division of assets and changes to a state’s economy are both captured by these fixed effects.

I omit several indicator variables from eq. [3] to avoid multicollinearity. Specifically, I omit the indicator for Alabama in 2008, the indicator for being white, and the indicator for the earliest possible age in each regression. The β coefficients can be interpreted as the percentage-point change in the likelihood of being married at 10 years since the beginning of marriages for people who have ever married.

4.3 Results

Figure 2 shows how the likelihood of remaining married trends with years since marriages began. The likelihood of remaining married trends smoothly at 10-year anniversaries for all women except for those who were 45 or older at the time of marriage. For women 45 and older at the time of marriage, the likelihood of being married gaps down at 10-year anniversaries.

Figure 2:

Probability of remaining married by years since the marriage began, from the 2008 to 2011 ACS.

The results from estimating eq. [3] with indicator variables for remaining married as the dependent variable are shown in Table 5. The estimates suggest that the likelihood of being married falls by 3.2 percentage points or 3.8 % as marriages of older women cross the 10-year threshold. This estimate implies that the likelihood of being divorced increases by 19.4 % as marriages of older women cross the 10-year mark. Since I classify separated women as being married, the likelihood of being married would trend smoothly at 10-year anniversaries if people were moving out before 10-year anniversaries but waiting to file the paperwork. The large coefficient indicates that older people are delaying changes in living arrangements as well.

These results are consistent both with couples retiming their divorces and with there being extra divorces after 10-year anniversaries for older women. Couples opting to forgo divorcing at 9 years of marriage would mean the 10-year rule causes the likelihood of remaining married at 9 years to be artificially high. When these divorce-delaying couples divorce at 10 years of marriage, the likelihood of being married would gap down even if the 10-year rule only affected marriage timing. On the other hand, if there were extra divorces at 10-year anniversaries that did not come from couples delaying divorce, the likelihood of remaining married would also gap down at 10-year anniversaries.

Table 6 shows results from using narrower age bins. As in Table 2 with the Vital Statistics data, Table 6 indicates nonlinearities in response to the 10-year rule based on age. The estimated change in the likelihood of remaining married is negative and statistically significant for women who married from age 41 to age 55. For women who married at younger ages, there is no evidence of changes in the likelihood of remaining married at 10-year anniversaries.

Next I estimate variations of eq. [3] to examine the characteristics of marriages that end at 10 years. ^[14] I focus the discussion on older people since they are the ones for whom a discontinuity was documented in Table 5. In column 1 of the top panel of Table 7, the dependent variable equals 1 if the individual is in a marriage where both members have the same labor force participation. A negative and significant β coefficient would imply that the marriages that end abruptly at 10-year anniversaries are those where members have the same labor force participation. The coefficient on crossing the 10-year mark is statistically indistinguishable from 0, suggesting that it is not couples with identical labor force participation statuses that drive the fall in marriage probabilities documented in Table 5. In column 1 of the bottom panel of Table 7, the dependent variable equals 1 if the individual is married to someone with a different labor force status than herself. The coefficient of –0.035 suggests that the marriages that end are those where spouses had different labor force statuses.

Column 2 of Table 7 displays β estimates from eq. [3] with the indicator variables based on whether or not both members of the couple have ever worked over the last 5 years as the dependent variables. These results indicate that couples whose marriages end are those where both members answered the same to having ever worked in the last 5 years. In column 3, the dependent variables are based on average earnings of the spouses’ occupations. The coefficient of –0.027 when the dependent variable is an indicator for couples having been in occupations with different average earnings suggests that it is couples with different earning potentials who divorce at their 10-year anniversaries.

The last column of Table 7 evaluates how similar the education levels are for couples who divorce at 10-year anniversaries. In the last column of the top panel of Table 7, the dependent variable is 1 if women are married to husbands with the same education levels. In all cases, the coefficients are statistically insignificant. In the bottom panel, the dependent variable is an indicator equal to 1 if the woman is married to someone with a different education level. The estimate of –0.032 for older couples indicates that the couples who divorce are those where one member has more education than the other member.

These results are most comparable to Goda, Shoven, and Slavov (2007), who also study how marriage stocks change at 10-year anniversaries and find small, statistically insignificant differences in divorce probabilities at 10 years of marriage between couples with and without large earning disparities between the primary and secondary earners. My estimates of the change in the likelihood of being divorced at 10-year anniversaries are small for the full sample as well. Unlike the PSID, though, the ACS allows for studying heterogeneity based on age and reveals important differences in age at marriage.

We would not expect divorces from the 10-year rule to comprise a large share of total divorces since the vast majority of marriages are not near their 10-year anniversaries and because many factors unrelated to Social Security influence divorce. Nevertheless, the ACS allows for performing a back-of-the-envelope calculation to better understand the percent of divorces for people who married over the age of 44 that are influenced by the 10-year rule. According to the ACS, 98,611 marriages where the woman was 45 or older at marriage ended in divorce in 2011. Of these divorces, 9,049 ended at marriage durations of 10 and 11. The Vital Statistics bunching estimate implies that 9.1 %, or 823, of those divorces in years 10 and 11 are additional divorces that would not have occurred in years 10 and 11 without the sudden change in spousal benefits after divorce at 10-year anniversaries. These numbers imply that the 10-year rule influenced about 0.84 % of divorces in 2011 for older couples, which is a small but nontrivial share of divorces.

4.4 Heterogeneity, Robustness, and Placebo Tests

I now test for heterogeneity based on marital status, consider the robustness of these results to various estimation choices, and conduct placebo analyses. Table 8 displays results with the dependent variable being an indicator for being married, but the results are similarly robust for other outcome variables.

I first test for differences between people in their first marriages and people who have been married more than once. Heterogeneity by marital history may exist because people who have been married multiple times may be more aware of divorce rules than people who are on their first marriages. Alternatively, people who are on subsequent marriages may be eligible for spousal benefits from previous marriages and may therefore not care about reaching the 10-year mark in their current marriages. Column 1 displays results with the sample restricted to people who have only married one time, while column 2 displays results with the sample restricted to only people who have had multiple marriages. For people married more than once, the point estimate of the fall in the likelihood of remaining married at 10 years is larger in absolute value for the full sample and for people who married at 45 or older. For people married only once, the fall in the likelihood of remaining married is statistically insignificant. These results suggest that older people in subsequent marriages may be more responsive to the 10-year rule.

For the main analysis, I coded an individual as being married if she was separated because the SSA uses the official marriage length in determining eligibility for the spousal benefit. In column 3, I test the sensitivity of the results to defining being married as being 0 if people are separated. The results are similar to the original estimates.

All of the ACS estimates are weighted using the IPUMS sample weights. In their review of econometric issues associated with survey weights, Solon, Haider, and Wooldridge (2013) suggest considering both weighted and unweighted estimates. Column 4 tests the sensitivity of the estimates to not using weights. The point estimate for the change in the likelihood of being married at 10 years of marriage for older people is statistically indistinguishable from the prior estimate and from 0. ^[15]

Another possible concern is that the controls in eq. [3] may be inadequate, which would be the case if the effects of demographic variables change at the cutoff. To consider this possibility, I supplement eq. [3] with interactions of years of education, age, and the nonomitted race indicators with quadratic polynomials with respect to years since the start of marriages. The results are shown in column 5 and are almost identical to the original results, suggesting returns to demographic variables changing at the threshold do not drive the fall in the likelihood of remaining married at 10-year anniversaries. A related concern is that many factors affect divorce that I do not control for, such as health, income, and the presence of children in the household. These factors changing discontinuously at 10-year anniversaries could potentially bias the results. ^[16] In column 6, I supplement eq. [3] with controls for the presence of children, the individual’s annual earnings, and indicator variables for having a cognitive difficulty, an ambulatory difficulty, self-care difficulty, vision difficulty, or difficulty living alone. The estimates are very similar to the original estimates.

An alternative to estimating eq. [3] using ordinary least squares (OLS) would be to estimate the model using a probit regression. Column 7 of Table 8 displays average marginal effects from probit regressions of eq. [3]. The estimates of the average change in the likelihood of being married at 10-year anniversaries from the probit regressions are nearly identical to the OLS estimates.

One might also be concerned that f is not flexible enough to capture the likelihood of remaining married trending smoothly with years since marriages began for older people. In columns 8 and 9, I replicate the analysis using marriage durations of 9 and 11 years as placebo cutoffs. One of the estimates of abrupt changes in the likelihood of being married at these marriage lengths is statistically significant at the 10 % level. An issue with using the years immediately before and after the cutoff is that f can be influenced by the cutoff at 10 years. As a further check, I replicate the analysis setting marriage durations of 6 through 8 and 12 through 14 as the cutoffs. The point estimates are significant at least the 10 % level 12.5 % of the time, which is close to the 10 % we would expect from chance. These results provide evidence that f can sufficiently account for the marriage profile trending smoothly.

Finally, I include widows in the sample and consider how the likelihood that women are widows changes at 10-year anniversaries. The likelihood that a woman classifies herself as a widow could change at 10-year anniversaries if divorced women are more likely to consider themselves widows rather than divorced if their ex-husbands die while they are receiving spousal benefits. However, we would be concerned if the likelihood of being a widow changes dramatically at 10-year anniversaries.

Figure 3 shows how the likelihood of being a widow changes with years since marriages began. Estimates of eq. [3] with the dependent variable being an indicator equal to one if the woman is a widow are shown in Table 9. The profiles with respect to years since marriage do not provide evidence of large spikes at 10-year anniversaries. The change in the likelihood of being a widow is estimated to increase by 0.3 percentage points and is significant at the 5 % level. None of the estimated discontinuities are statistically significant for any of the age groups. Although statistically significant for the full sample, the estimate is 91 % smaller than the estimate for the decrease in the likelihood of remaining married after 10 years of marriage and does not indicate that widowhood increases dramatically at 10-year anniversaries.

Figure 3:

Probability of being widowed by years since the marriage began, from the 2008 to 2011 ACS.