Reporting Heterogeneity and Health Disparities Across Gender and Education Levels: Evidence From Four Countries

Abstract

I use anchoring vignettes from Indonesia, the United States, England, and China to study the extent to which differences in self-reported health across gender and education levels can be explained by the use of different response thresholds. To determine whether statistically significant differences between groups remain after adjusting thresholds, I calculate standard errors for the simulated probabilities, largely ignored in previous literature. Accounting for reporting heterogeneity reduces the gender gap in many health domains across the four countries, but to varying degrees. Health disparities across education levels persist and even widen after equalizing thresholds across the two groups.

Appendix: Description of Data Sets

Indonesian Family Life Survey (IFLS)

I use the 2007 wave of the IFLS, an ongoing longitudinal household survey of individuals in 13 of the 27 Indonesian provinces, representative of 80 % of the Indonesian population. This article uses information from the individual-level demographic and health status modules. IFLS 4 also randomly chose 2,500 households to participate in the health vignette module. In selected households, all adults over 40 were asked the six domain-specific health questions. Crucially, the IFLS included three anchoring vignettes per health domain in addition to the above self-reports. Although all vignette households were asked all of the self-reported health questions, time constraints meant that each vignette household was assigned to respond to anchoring vignettes for only two randomly chosen domains of the six, leaving between 1,100–1,300 individuals per domain. During the interview, the interviewers read aloud a vignette like the one described in the Anchoring Vignettes section (see Online Resource 1 for a list all of the vignettes). The interviewers then repeated the domain-relevant question from the list of self-report questions in the Data section (of course, replacing the word “you” with the name of the hypothetical vignette person). The gender of the hypothetical individuals, depicted through their names, was randomized at the household level. Answers to the health status questions and anchoring vignettes form the outcome variables of interest for this analysis.

Purposely focusing on a set of simple explanatory variables in order to facilitate comparisons with the three other data sets, I use gender, age, and education levels. Specifically, I create one dummy variable for males, one for high school graduates, and one for those who completed primary but not high school.

Health and Retirement Study (HRS)

Since 1992, the HRS has interviewed a representative sample of Americans older than 50, reinterviewing the original sample and adding new cohorts every two years. In 2007, an “off-year” between two main interview years, the Disability Vignette Study (DVS) was sent out as a mail survey to a subsample, of which 81.7 % (more than 4,000) responded. This survey included the exact same anchoring vignettes for the same six domains found in the IFLS vignette modules, except with American instead of Indonesian names. Unlike the IFLS, two versions of the questionnaires, which ordered the questions differently and used different genders for the hypothetical individuals, were used.

I combine data from this off-year study with data from the most recent main survey prior to it, which took place in 2006. From the 2006 interviews, I obtain the basic explanatory variables: age, gender, and educational attainment. Because the vast majority of HRS respondents are high school graduates, I use college graduation as my “high-education” group and high school graduates (who have not completed college) as my “medium-education” group.

English Longitudinal Study of Aging (ELSA)

Similar to the HRS, the ELSA is a longitudinal panel of individuals aged over 50 living in England (Marmot et al. 2014). Since 2002, the representative sample, which was initially drawn from the Health Survey for England, has been reinterviewed every two years. The ELSA sample was also refreshed at Waves 3, 4, and 6. I use data from the third wave, collected during 2006 and 2007, which included self-completion vignette questionnaires that were handed out to a randomly selected one-third of the sample (and completed by almost 2,500 individuals). Individuals were asked to rate their own health in the six domains and then to respond to the same vignettes found in the IFLS and HRS. Unlike the other data sets, which randomized the genders of vignette individuals in varying ways, the ELSA had only one version of the questionnaire, which had the same names (and thus genders) assigned to the same questions for all respondents. The vignette genders alternated throughout the questionnaire, with one-half of the vignette individuals assigned female names and the other one-half male names.

Along with respondent age and gender, I use degree qualifications as my education variable because precise years of schooling are not included in this survey. The “high-education” category includes those who have received their A-levels or higher, while the “medium-education” category includes all qualifications lower than A-levels. This leaves those with no qualifications as the low-education group.

China Health and Retirement Longitudinal Study (CHARLS)¹⁹

Finally, I also use data from the first wave of the CHARLS, conducted in 2011 (Zhao et al. 2013). Very similar to the other two longitudinal aging studies described earlier (the HRS and ELSA), the CHARLS has interviewed a representative sample of over 17,000 Chinese residents aged 45 and older and plans to follow up with the respondents every two years. The CHARLS is one of very few Chinese surveys that include domain-specific self-reports and vignette questions, which are asked as part of the full in-person interview for a random subsample of households. Like in the IFLS, each vignette household is randomly assigned to two of the six domains, resulting in around 1,100 to 1,300 respondents per domain. The genders of the hypothetical individuals are also randomized at the household level.

Because high school graduation rates for this sample are so low (less than 10 %), I use junior high school completion as my “high-education” cutoff and primary school completion as the boundary between the medium- and low-education groups.

Self-report Distributions

Figures 2 and 3 explore within-country differences across gender and education. Figure 2 depicts the distribution of self-report responses by gender for each data set separately. On each domain graph, I report the p value corresponding to the Pearson chi-squared statistic for the test of the null hypothesis that the distribution of the responses is the same for males and females. In the IFLS and CHARLS, for pain, cognition, affect, and sleep, males and females have significantly different self-report distributions, with males disproportionately falling in the healthiest category.

Fig. 2

Distribution of self-reports by gender

In the HRS, male and female distributions in the cognition, affect, and sleep domains are significantly different. In the ELSA, the domains that exhibit significant gender differences are pain, sleep, and affect. Like in the IFLS and CHARLS, males fall into the healthiest category more often than females.

Figure 3 shows even more drastically different distributions of self-reports, this time between high-education and lower-education groups (for which I pool the medium- and low-education categories). In virtually all domains in all four samples (with the exception of cognition and affect in the IFLS), the distributions are significantly different, with the higher education group disproportionally represented in the healthiest categories.

Fig. 3

Distribution of self-reports by education