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George Alter, Income, Disability, and Old Age Mortality among Early Twentieth Century Railroad Retirees
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por : Demography Today on Mar 26, 2018
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Duración: 00:42:25

George Alter, Income, Disability, and Old Age Mortality among Early Twentieth Century Railroad Retirees - Descripción

This study with co-author Samuel Williamson looks at the effect of income on survival after retirement in the early 20th century. In 1900 the Pennsylvania Railroad, which was one of the largest employers in the U.S., imposed mandatory retirement on its employees at age 70. Workers could retire as early as age 65 if they were disabled or had the approval of supervisors. The railroad employed a very diverse workforce, and even managers and executives were covered by the retirement program. Since retirement records include information about wages, we can ask whether income and mortality were correlated before the advent of effective medical treatment. We find that early retirement was most common among workers with the lowest pre-retirement income as well as certain well-paid occupations like conductor and engineer. Mortality after age 70 was inversely related to income; higher-paid workers had longer lives after retirement.

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George Alter, Income, Disability, and Old Age Mortality among Early Twentieth Century Railroad Retirees - Biografía

George Alter is Research Professor in the Institute for Social Research and Professor of History at the University of Michigan. His research integrates theory and methods from demography, economics, and family history with historical sources to understand demographic behaviors in the past. From 2007 to 2016 Alter was Director of the Inter-university Consortium for Political and Social Research, the world’s largest archive of social science data, and he is a past president of the Social Science History Association. He has been active in international efforts to promote research transparency, data sharing, and secure access to confidential research data. He is currently engaged in projects to share longitudinal data in historical demography, to automate the capture of metadata from statistical analysis software, and to compare fertility transitions in contemporary and historical populations.

 

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