![]() This is because differential survival tends to bias these patterns. In demographic research, they matter when presenting age patterns of a variable (e.g., income or wealth) derived from either cross-sectional or synthetic panel data. For individuals, they inform decisions about retirement savings and investments in financial products. ![]() In financial markets, they are important for designing annuities and life insurance. In the arena of public policy, they help policymakers and analysts assess how much various groups benefit from public programs, such as Social Security and health care. Nations with a smaller gradient appear to be Belgium, France, and Italy, while the United States, England, and Sweden appear to have a larger gradient.Įstimates of differences in survival by socioeconomic status play an important role in multiple domains. Wealthier people have higher survival probabilities than those who are less wealthy, but the strength of the association differs across countries. We then use this approach to compare differential survival by SES for 10 European countries and the United States. To show that subjective survival probabilities are informative proxies for actual survival when estimating differential survival, we compare estimates of differential survival based on actual survival with estimates based on subjective probabilities of survival for the same sample. Rather than using observations of actual survival, we relate individuals’ subjective probabilities of survival to SES variables in cross section. We present an alternative method for acquiring information on differential survival by SES. ![]() Cross-country estimates have proven even more difficult because the measures of SES need to be comparable internationally. Reliably estimating differential survival in a single country has been challenging because it requires rich panel data with a large sample size. Yet, to date, such studies have been rare. ![]() Cross-country comparisons of differential survival by socioeconomic status (SES) are useful in many domains. ![]()
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