Pathways to retirement: patterns of labor force participation and labor market exit among the pre-retirement population by race, Hispanic origin, and sex.
OBJECTIVE
This study examines the pre-retirement labor force participation behavior of Black, White, and Hispanic men and women to determine how patterns of labor market exit differ among groups.


METHODS
We combine data from the first and second waves of the Health and Retirement Study and apply multinomial logit regression techniques to model labor force status in the first wave of the HRS and change over time.


RESULTS
Black, Hispanic, and female elderly persons experience more involuntary job separation in the years immediately prior to retirement, and the resulting periods of joblessness often eventuate in "retirement" or labor force withdrawal. Minority disadvantage in human capital, health, and employment characteristics accounts for a large part of racial and ethnic differences in labor force withdrawal. Nevertheless, Black men and Hispanic women experience more involuntary labor market exits than Whites with similar socioeconomic and demographic characteristics.


CONCLUSIONS
Workers most vulnerable to labor market difficulties during their youth confront formidable obstacles maintaining their desired level of labor force attachment as they approach their golden years. This has significant policy implications for the contours of gender and race/ethnic inequality among elderly persons, particularly as life expectancy and the size of the minority elderly population continue to increase.
Flippen
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University of Chicago, Population Research Center, USA.
Tienda
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D001741:Black or African American / Q000706:statistics & numerical data*; D000368:Aged; D003431:Cross-Cultural Comparison; D004651:Employment / Q000706:statistics & numerical data*; D005260:Female; D006630:Hispanic or Latino / Q000706:statistics & numerical data*; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D020415:Personnel Downsizing / Q000706:statistics & numerical data; D012044:Regression Analysis; D012179:Retirement / Q000706:statistics & numerical data*; D012737:Sex Factors; D012959:Socioeconomic Factors; D014481:United States; D044465:White People / Q000706:statistics & numerical data*