| Abstract: | The Modeling Income in the Near Term (MINT) data system projects retirement income for persons retiring in the 1990s through 2020. Using those data, we examine the economic well-being of divorced women at retirement. The MINT data system improves upon previous estimates of Social Security benefits by: Measuring and projecting years of marriage to determine if the 10-year requirement has been met, Projecting lifetime earnings until retirement and eligibility for Social Security retirement benefits, and Estimating lifetime earnings of former spouses. MINT also makes independent projections of each retiree's income from pensions, assets, and earnings (for working beneficiaries). As a result of changes in marital patterns, MINT projects that the proportion of women who are divorced will increase. At the same time, the proportion of those women who are eligible for auxiliary benefits is projected to decrease, for two main reasons. First, changes in women's earnings and work patterns result in more women receiving retired-worker benefits based on their own earnings. Second, an increased number of divorced women will not meet the 10-year marriage requirement for auxiliary benefits. Despite the projected decrease over time in eligibility rates for auxiliary benefits, the level of Social Security benefits is projected to change little between the older and younger birth cohorts of divorced women entering retirement. According to the MINT data, the most vulnerable of divorced women will be those who have not met the 10-year marriage requirement. Poverty rates will be higher for them than for all other divorced women. This group of divorced women is projected to grow as more and more women divorce from shorter marriages. With more women divorcing and with fewer divorced women meeting the 10-year marriage requirement, the proportion of economically vulnerable aged women will increase when the baby boom retires. Further research is warranted on this long neglected subject. Analyses of divorced women's economic well-being by major socioeconomic characteristics such as race and ethnicity and education are of particular interest. Such analyses can be supported by the MINT data system. |
