Studying health disparities by socioeconomic status (SES) is a long established research tradition. In recent years, interest in doing this in less developed and newly developing regions has been growing. This new interest in countries with emerging economies such as China, India and Brazil is well justified, not just because of their large populations but also because their rapid economic growth has been accompanied by increases in social inequality. However, unlike the well-documented positive association between SES and health in Western settings, findings from developing countries have revealed mixed and sometimes even contradictory patterns. In China, Philippines, and Thailand, for example, several studies found that certain lower status groups reported significantly better self-rated global health status, a widely used health indicator, than their better-off peers. One possible explanation pertains to the measurement bias in survey responses in that people of varying cultural, demographic, and SES backgrounds may adopt systematically different frames of reference in rating their overall health, an issue known as reporting heterogeneity. In this project, we propose to obtain bias-corrected SES gap estimates of self-rated health in Chinese adults. We will achieve this using anchoring vignettes, brief descriptions of hypothetical people or situations that survey respondents are asked to evaluate on the same scale as they use to assess their own situations. This project will draw data from the 2010-2014 waves of the China Family Panel Studies (CFPS), a newly launched nationally-representative longitudinal data collection project. Both vignettes and self-assessments were administered to all the adult respondents in the CFPS, yielding an unprecedentedly large sample size and hence greater statistical power compared to the previous research. Unlike previous studies using standard yet context-blind vignettes, the CFPS vignettes are designed specifically to anchor self-rated overall health in a general-purpose household survey where financial and time constraints prohibit collecting multiple domain-specific health ratings.
The specific aims of this proposal are: (1) to assess validity and effectiveness of vignettes methodology in adjusting reporting heterogeneity in self-rated health among Chinese adults; and (2) to estimate disparities in vignette- adjusted self-rated health by different dimensions of objective and subjective SES in contemporary China. The hierarchical ordered probit model and its variants will be employed to analyze the data. We will exploit longitudinal measures in the CFPS to address the endogeneity in the relationship between SES and health. The empirical findings from this project will contribute to the growing efforts of improving and applying the vignettes methodology in measuring health inequalities across different population subgroups from different cultures and societies. The findings will also shed new light on the debate about the varying importance of different dimensions of SES in determining health.

Public Health Relevance

This project aims to evaluate the validity and effectiveness of the vignettes methodology in correcting reporting heterogeneity in self-rated health among Chinese adults and to obtain bias-corrected estimates of the relative magnitudes of socioeconomic differentials in health as measured by a wide range of objective indicators and subjective status. This has important human development and health relevance because reporting heterogeneity can seriously bias health measures and engender erroneous inference about health inequalities by socioeconomic status.

Agency
National Institute of Health (NIH)
Institute
Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD)
Type
Small Research Grants (R03)
Project #
5R03HD082434-02
Application #
9303205
Study Section
Population Sciences Subcommittee (CHHD-W)
Program Officer
Bures, Regina M
Project Start
2016-06-24
Project End
2018-05-31
Budget Start
2017-06-01
Budget End
2018-05-31
Support Year
2
Fiscal Year
2017
Total Cost
$77,500
Indirect Cost
$27,500
Name
University of Michigan Ann Arbor
Department
Biostatistics & Other Math Sci
Type
Organized Research Units
DUNS #
073133571
City
Ann Arbor
State
MI
Country
United States
Zip Code
48109