Essays on Gender and Health

The relationship between gender and health is complex. Although women live longer than men in almost every country throughout the world, women also tend to be sicker than men. While biological sex differences likely contribute to sex gaps in health, cross-national, historical, and life course variation suggest that social factors also play a role. This dissertation is composed of three chapters which examine social explanations for gender gaps in mortality and morbidity. The first chapter looks at the relationship between gender equality in the public sphere, and sex gaps in life expectancy throughout the world. I find that influence of gender equality on the sex gap in life expectancy depends on the level of economic development. The second chapter takes an historical perspective to examine the trend in the sex gap in depression in the United States between 1971 and 2008. In examining this trend, I find that the sex gap in depression has decreased over the past forty years, due to a decrease in depression among women that is primarily attributable to an increase in women\u27s labor force participation and attachment. In the third chapter, I examine the relationship between gender, aging, and depression using longitudinal data for the population over age fifty in the United States. In doing so, I find that age does not increase depression until age 75, after which point depression increases for both sexes, but particularly for men, leading to a reversal in the sex gap in depression at the end of the lifespan. Furthermore, while the majority of the age effect on depression is explained by social and health changes, I conclude that there is a net effect of age per se on depression after age 75

Linking Survey and Administrative Data to Measure Income, Inequality, and Mobility

Income is one of the most important measures of well-being, but it is notoriously difficult to measure accurately. Income data are available from surveys, tax records, and government programs, but each of these sources has important strengths and major limitations when used alone. We are linking multiple data sources to develop the Comprehensive Income Dataset (CID), a restricted micro-level dataset that combines the demographic detail of survey data with the accuracy of administrative measures. By incorporating information on nearly all taxable income, tax credits, and cash and in-kind government transfers, the CID surpasses previous efforts to provide an accurate and comprehensive measure of income for the population of U.S. individuals, families, and households. We use models to evaluate differences across the data sources and explore imputation methods and trends over time. The CID can enhance Census Bureau surveys and statistics through investigating measurement error, improving imputation methods, and augmenting surveys with the best possible estimates of income. It can also be used to improve the administration of taxes by the Internal Revenue Service and forecast and simulate changes in programs and taxes. Finally, the CID has substantial advantages over other sources to analyze numerous research topics, including poverty, inequality, mobility, and the distributional consequences of government transfers and taxes

Reconciling Parent-Child Relationships across US Administrative Datasets

Introduction Population data capture children, parents, relatives, and others moving in and out of households. The U.S. has seen falling marriage rates, and increases in multigenerational households and complex families, young children living with grandparents, and adult children living with parents. Robust parent-child linkages are critical to understand these demographic shifts. Objectives and Approach We construct and validate parent-child linkages over a century to observe how U.S. households are changing over time. The three largest person-based datafiles in the U.S. are the decennial censuses, the Social Security Administration transaction file, and individual tax returns from the Internal Revenue Service. These sources operationalize relationships differently, capture data at various frequencies, and gather the data for unique purposes. We use probabilistic matching to observe and reconcile parent-child relationships across these sources. The data include a variety of personal identifiers including name, date of birth, parents’ names, address, and place of birth that support matching and validation. Results We find that understanding the content, consistency, and coverage of the files before matching is critical for high quality linkages. The representativeness of the parent-child relationship file improves over time, with the weakest coverage for the Greatest Generation and the strongest coverage for Millennials. Coverage varies by source: tax data underrepresent non-white children and have duplicate records for SSNs, while names and dates of birth are missing from Census data. Multiple match rates differ among demographic groups and over time. In the matching process, the blocking variables rely on common variables across the population datasets. Our approach provides robust entity resolution for women, despite married-maiden name changes. We describe challenges due to data problems in old census records and validation changes in social security data. Conclusion/Implications We conduct a successful reconciliation of parent-child relationships in U.S. population level files. The project supports operational and research uses, such as the 2020 Census. We will extend this work using graph matching and will expand the method to validate other relationship links including spouses and siblings

Do family support environments influence fertility? Evidence from 20 European countries

Using data from two recent waves of the European Social Survey, we examine the relationship between macro-level supports for child rearing and individual-level fertility outcomes. We characterize country-level support environments across a broader set of domains than is typical, including supports from institutions, labor markets, extended families, and male partners. With rare exceptions, we find significant relationships between family support environment indicators and second or higher order births. In contrast, the relationship between family support environment indicators and first births is weaker and less often significant. This pattern accords with theory that practical considerations are more important for the second and subsequent births than for the transition to parenthood. Although most forms of support are positively related to fertility, we document a negative relationship between intergenerational exchange of support and higher order fertility. Our analyses also reveal that macro-level support environments are related to childbearing plans in much the same way as they are related to having a child, buttressing the argument that understanding the determinants of childbearing plans can help us to understand childbearing behavior

The Use and Misuse of Income Data and Extreme Poverty in the United States

Recent research suggests that the share of US households living on less than $2/person/day is high and rising. We reexamine such extreme poverty by linking SIPP and CPS data to administrative tax and program data. We find that more than 90% of those reported to be in extreme poverty are not, once we include in-kind transfers, replace survey reports of earnings and transfer receipt with administrative records, and account for ownership of substantial assets. More than half of all misclassified households have incomes from the administrative data above the poverty line, and many have middle-class measures of material well-being

B2: Measuring the Economy, Housing, and People

Moderator: Kitty Smith Evans Presenters: Marvin Ward Jr.: Our Economy is Evolving: Shouldn\u27t the Way We Measure It Evolve Too? Carla Medalia: Linking Survey and Administrative Data to Measure Income, Inequality, and Mobility John Haltiwanger: Minding Your Ps and Qs: Going from Micro to Macro in Measuring Prices and Quantities Misty L. Heggeness: Harnessing Administrative Records for Official Statistics on People and Households Laurie Goodman: Housing Affordability: Local and National Perspective