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What Factors Explain the Decline in Widowed Women’s Poverty?


Historically, women in widowhood in the United States have been vulnerable, with high rates of poverty. However, over the past several decades, their poverty rate has fallen considerably. In this article, we look at why this decline occurred and whether it will continue. Using data from the Health and Retirement Study linked to Social Security administrative earnings and benefit records, we address these questions by exploring three factors that could have contributed to this decline: (1) women’s rising levels of education; (2) their increased attachment to the labor force; and (3) increasing marital selection, reflecting that whereas marriage used to be equally distributed, it is becoming less common among those with lower socioeconomic status. The project decomposes the share of the decline in poverty into contributions by each of these factors and also projects the role of these factors in the future. The results indicate that increases in education and work experience have driven most of the decline in widows’ poverty to date, but that marital selection will likely play a large role in a continuing decline in the future. Still, even after these effects play out, poverty among widows will remain well above that of married women.

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Data Availability

Data for this analysis include HRS restricted data, including administrative data from the Social Security Administration and therefore are not publicly available. Code is available upon request.


  1. Authors’ calculations from the March Supplement to the Current Population Survey, 2018 (see Flood et al. 2018 for the data). If instead the Supplemental Poverty Measures are used, poverty rates are even more similar: 15.3% for those 65 and older and 15.1% for those ages 18–64. See Wimer et al. (2016).

  2. Authors’ calculation from the General Social Survey, 1972–2010 (Smith et al. 2011).

  3. For example, only one-half of married women aged 25–54 were in the labor force in 1975 compared with 75% in 2017 (based on our calculations from the CPS).

  4. Labor force participation among working-age men has been falling since the middle of the twentieth century, particularly among those with a high school degree or less (Krueger 2017). Among men aged 55–64, the general decline—attributed to the growth of the Social Security program—started to reverse in the mid-1980s because of factors such as changes to the earnings test, the delayed retirement credit, the shift from defined benefit to defined contribution pensions, improved health and longevity, and the decline of retiree health insurance (e.g., Coile 2019; Diamond and Gruber 1999; Munnell 2015). Among women aged 55–64, the long-running rise in participation has slowed since the Great Recession (e.g., Black et al. 2017; Krueger 2017).

  5. The theoretical underpinnings of this research go back to Becker (1974).

  6. This hypothesis is consistent with the literature on marriage premiums, which has consistently found that documented marriage wage premiums are mostly driven by selection or by the co-occurrence with transition to adulthood (Dougherty 2006; Killewald and Lundberg 2017; Ludwig and Brüderl 2018). The hypothesis would, however, also be consistent with the causal marriage wage premium hypothesis (e.g., Cheng 2016; Killewald and Gough 2013; Korenman and Neumark 1991).

  7. The literature has documented that widows experience significant losses of wealth upon husbands’ death due to medical and funeral expenses (Hurd and Wise 1989; McGarry and Schoeni 2005), and that a substantial share of poor widows were already poor prior to the death of their spouse (McGarry and Schoeni 2005; Sevak et al. 2003/2004).

  8. Ex-spouses are eligible for Social Security survivor benefits if the marriage lasted for 10 or more years.

  9. Goldin and Mitchell (2017) identified the increase in women’s labor force participation early in the life cycle and the prolonged phase-out at the end of the life cycle, leading to more work experience for the cohorts of women reaching retirement in the next few decades.

  10. The first wave of the HRS included only respondents born between 1931 and 1941, the oldest of whom were 61 in 1992. Widows ages 65–85 were first observed with the addition of the AHEAD cohort, born before 1924, in Wave 2.

  11. Specifically, the data are linked to the Cross-Wave Geographic Information (State) file, the Respondent Cross-Year Summary Earnings files, the Respondent Cross-Year Benefit file, and the Deceased Spouse Cross-Year Benefits file.

  12. Poverty is based on the prior year’s income and Census Bureau poverty thresholds. HRS records only total household income, which for a widow living by herself, equals her own income. The RAND codebook indicates that this measure minus food stamps is close to the census definition of income, with the exception of income from resident family members besides the respondent and spouse. Therefore, the HRS income measure would be higher than the census measure if a woman receives food stamps and would be lower if she lives with other adults who contribute to the household income (HRS includes only income from the respondent and spouse). However, because the HRS poverty rates closely track the widows’ poverty rate calculated from the CPS, this discrepancy likely has minimal effects on the estimates.

  13. A major shift that has taken place, and affects many retirees, is the change from defined benefit (DB) to defined contribution (DC) pensions. However, it is likely less of a factor for poverty. Although DCs are often less generous than DBs, workers in jobs that provide a DC benefit are still relatively well-off. Indeed, calculations in the HRS show that poverty rates among those that hold a DB or a DC are very low, while those that have neither have higher poverty rates. We test the hypothesis that the shift to DC retirement plans does not greatly affect the results, by also estimating models that include controls for whether the woman receives pension income from a DB or a DC. The coefficients on these variables are not statistically significant and do not affect the point estimates of the coefficients on the key variables of interest.

  14. As a robustness check, we also estimate alternative models that include controls for the late husbands’ years of education and years in the labor force. Including these controls does not change the results substantively. These results are available upon request. Controlling for calendar year implicitly controls for any secular increase or decrease in retirement ages occurring over this time period.

  15. The fact that some variables in this widowhood regression also appear in the initial poverty regression may seem problematic but is in fact a desired feature of the approach. For example, if married individuals who become widowed spent less time in the labor force or had lower education, on average—for instance, because their husbands had lower socioeconomic status and therefore higher mortality—then this should be reflected in the sample of women likely to be widows. Failing to control for education and labor force participation in Eq. (4) would lead to omitted variable bias and likely lead to a predicted sample of future widows who have higher education and labor force participation.

  16. To do this, we calculate the predicted values of the probability of widowhood using the coefficients from the widowhood regression and the younger married women’s characteristics. We then draw a random number between 0 and 1, the minimum and maximum probability of being a widow; if this number is smaller than the predicted value, she is included in the sample of future widows. A simpler approach would be to just assign anyone with a predicted probability of being a widow over .5 to the group of future widows. However, this approach would ignore the fact that some people who are unlikely to become widows will become widowed anyway, biasing the final sample toward women with lower socioeconomic status who are more likely to become widows.

  17. To test how well this model predicts out of sample poverty rates that are 15 years in the future, we predicted poverty rates for the years 2010–2014 using data from 1994 through t – 16 years (t – 8 waves) for predictions in year t. For instance, the poverty rate in year 2010 was predicted using a model estimated on a sample of widows in 1994. The predicted poverty rate in year 2012 used a sample of widows from 1994–1996, and so forth. The predicted poverty rates were close to the actual poverty rates. The results are available upon request.

  18. To determine the years in the labor force of the future widows, we assume that women retire at age 65. The SSA full retirement age (FRA) will be 67 for women reaching age 65 in 2029, but the average retirement age of women generally lags a few years behind the FRA. For example, see Munnell (2015).

  19. Some of these changes might be driven by an improvement in data quality over time. In the first waves of the HRS, less information on deceased spouses was available because they had passed away before the start of the HRS.

  20. As described in the Data and Methodology section, this sample is adjusted for the probability of being widowed. Table 3 shows the regression coefficients from the widowhood model used for this adjustment.

  21. Women are assumed to work until age 65. See footnote 18.

  22. Education is expected to go up only by 0.25 year, and LPF is expected to go up by 1.25 years (not shown).

  23. Munnell and Eschtruth (2018) noted that recent proposals along this line include Entmacher (2009), Estes et al. (2012), and Weller (2010).

  24. The 66% number reflects a situation in which the household received the husband’s benefit plus a 50% spousal benefit prior to his death and just his benefit after his death (100 / 150 = 66%). The 50% reflects a situation in which the household received two equally sized benefits prior to the husband’s death (as is the case for couples with similar earnings) and only the wife’s after his death.

  25. These benefits would be slightly less likely to apply to widows because many widows rely on husbands’ benefits. Still, because women increasingly rely on their own benefits, such a change could benefit even widows.

  26. The SSA actuaries evaluated a 2014 proposal with this feature from Senators Begich and Murray (Social Security Administration 2014).


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We are grateful for helpful comments from three anonymous reviewers, participants at the Retirement Research Consortium’s annual conference, and guidance from Christopher Tamborini and Sanders Korenman. The research reported herein was performed pursuant to a grant from the U.S. Social Security Administration (SSA) funded as part of the Retirement Research Consortium. The opinions and conclusions expressed are solely those of the authors and do not represent the opinions or policy of SSA, any agency of the federal government, or Boston College. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of the contents of this report. Reference herein to any specific commercial product, process or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply endorsement, recommendation or favoring by the United States Government or any agency thereof.


The research reported herein was performed pursuant to a grant from the U.S. Social Security Administration (SSA) funded as part of the Retirement Research Consortium.

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Alice Zulkarnain performed the econometric analysis. Geoffrey Sanzenbacher and Alicia Munnell assisted Alice Zulkarnain in writing the draft, designing the analysis, and performing background research.

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Correspondence to Alice Zulkarnain.

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Munnell, A.H., Sanzenbacher, G. & Zulkarnain, A. What Factors Explain the Decline in Widowed Women’s Poverty?. Demography 57, 1881–1902 (2020).

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  • Widows’ poverty
  • Education
  • Labor force participation
  • Marital selection