The Health and Retirement Study: What Is It and What Is Its Potential Use to Researchers?
The Health and Retirement Study: What is it and what is its potential use to researchers?
Lori Gonzalez, PhD
What is the HRS?
Since its inception, the Health and Retirement Study (HRS) has provided researchers across disciplines with a wealth of data on the lives of America’s older adults. Researchers have used the HRS to study things like spending and saving before and after retirement, physical and mental health, family life and asset transfers, how older adults spend their time, expectations regarding retirement, genotypes that predict Alzheimer’s and cross-national comparisons of retirement (HRS, 2014). This paper describes the HRS, major modifications to the study, reviews articles that critique the study, provides examples of research that uses the study, and explores possible future research projects.
The HRS began a new generation of survey research conducted on the lives of older adults. The survey replaced the Retirement History Study, a study conducted between 1969-1979. The Retirement History Study was in need of replacement as it no longer captured the retirement experiences of older Americans. The Retirement History Study, for example, underrepresented blacks, Hispanics, and women. By contrast, the HRS is nationally representative and includes an oversampling of blacks and Hispanics.
The survey has had several objectives since its beginning in 1992. The primary objective was to design a survey that could be used across disciplines to understand decisions regarding retirement from every angle—financial, social, health and so on. The survey seeks to uncover the lives of people before retirement and after and how the following factors interact to affect decisions to retire or not: wealth and income, wealth accumulation and depletion, disability, health, family, program enrollment, dissaving, institutionalization, and health declines.
Other nations have patterned retirement studies after the HRS. The data being collected are similar enough to the HRS that cross national comparisons can be made. For example, in Britain, the English Longitudinal Study of Aging is comparable to the HRS. Sweden, Denmark, France, Belgium, the Netherlands, Germany, Switzerland, Austria, Spain, Italy and Greece all also have retirement studies that are comparable to the HRS. Israel, Ireland, the Czech Republic, Poland and parts of Asia are developing similar surveys. These surveys allow researchers to uncover differences and similarities in retirement experiences across cultures.
How Does the Study Work?
The HRS has been conducted every two years since 1992. The most recent release is from 2012. The following describes the study cohorts, sampling procedures, and how individuals enter and leave the survey.
The HRS consists of several cohorts. The first, the Health and Retirement Survey cohort, consists of persons aged 51 through 61 or those born between 1931 and 1941. The HRS cohort was administered the survey every two years between 1992 and 2010. The second cohort, Assets and Health Dynamics Among the Oldest Old (AHEAD) cohort, is made up of persons aged 70 or older (those born in 1923 or earlier) and was administered in 1993 and every two years after that. The third cohort, the Children of the Depression (CODA), are people born between 1924 and 1930. The fourth cohort, the War Baby cohort are persons born between 1942 and 1947. The fifth cohort, the Early Boomers consists of people born between 1948 and 1953. The final cohort, the Mid Boomers, were born between 1954 and 1959.Figure 1 below summarizes the study years and cohorts.
Figure 1: HRS Study Years and Cohorts
Source: “Growing Older in America: The Health & Retirement Study”
The response rate for the HRS is relatively high. Figure 2 below shows the numbers of respondents and response rates by cohort and year.
Figure 2. HRS Respondents and Response Rate by Cohort and Year
As previously mentioned, there is an oversampling of blacks (1.86:1), Hispanics (1.72:1), as well as Floridians (2:1). The study initially did not interview those who were institutionalized (ex: those in nursing facilities or jail), but if a respondent eventually became institutionalized, they were included in the study. The HRS sample design was a multi-stage area probability sample design with four distinct selection stages. The AHEAD sample design is the same as the HRS sample design with one exception—those who were born in 1914 or earlier were selected using a dual frame approach. The War Babies cohort also used the HRS sample design with the difference that some of the respondents were age ineligible spouses of the HRS, or AHEAD age eligible respondents who were already included in the study. The Children of the Depression cohort was drawn from Health Care Financing Administration files (HRS, 2014).
Questions in the survey are asked of the individual (if they are not married) or of the individual and/or their spouse. Each question, if the person is married, is answered only by one of the two. How this is done is not consistent across household or across surveys. The survey is conducted on the telephone unless the person does not have a telephone or can’t use the telephone for the duration of the survey due to health reasons (HRS, 2014).
HRS Major Modifications
Over time there have been several major modifications to the HRS, mostly involving the addition of questions, cohorts, and nursing home residents. Questions relating to cognitive and psychosocial measures have been added throughout the years and in 2006, the survey began collecting DNA samples and biomarker data (lung capacity, grip strength, walking ability and blood pressure). In 1998 the HRS saw a major change in the target population with those who are 50 years or older being surveyed every two years. Prior to 1998 the HRS was limited to those born between 1931 and 1941 and those who were born in 1923 or before. Also taking place in 1998 was the merging of the HRS and AHEAD samples, the addition of those born between 1942 and 1947, and those who were born between 1924 and 1930. Although the nursing home population was initially excluded from the 1992 survey, they have since been included and by 2000, the sample is representative of the U.S. nursing home population (HRS, 2014).
Future endeavors will include locating HRS respondents in other studies that began earlier in the HRS respondents’ lives to have a better lifecourse understanding of events that led up to retirement. The hope is to be able to have a complete picture of individual’s lives from birth until death (HRS, 2014).
Critiques of the HRS
One set of literature looking at the HRS focuses on evaluating the study itself. Several issues have been examined including population representativeness, sample attrition, sample questions, respondent reporting, and sample weights. This section reviews that body of literature.
The HRS has been critiqued for its representativeness of the larger population. These studies have largelyfocused on specific topics. One study (Lackman and Spiro, III 2002), for example,looked at the HRS for its representativeness of the population for studies of cognition. This study found that the AHEAD cohort is suitable enough to make generalizations to the larger population and that comparisons can be made across socioeconomic status, race/ethnicity, and across other surveys including the Americans’ Changing Lives and Established Populations for Epidemiologic Studies of the Elderly. The study also noted some weaknesses of the HRS including: 1) theinability to compare “completing tasks” data over time as questions asked about differenttasks, 2) it’s not clear if the battery can detect dementia, and 3) the telephone battery might have been biased against those who have hearing problems. Another study (Bricker and Engelhardt, 2007) examined HRS population representativeness in the HRS earnings data. The study compared W-2 data for HRS respondents in 1991 and in 2003 and found that measurement error for men and women are higher than in other studies, but that the error for women is smaller than for men. A study by Meijer and Karoly (2010) compared SSA records to HRS records for low-income representativeness, which is important to health research as some diseases can be more prevalent among the low-income population. The researchers found that the HRS for all of the years examined, had complete records. They did, however, caution that breaking the low-income sample into small groups (SSI and Medicaid beneficiaries) might produce population estimates that do not reflect the larger population. With regard to race/ethnicity, one study compared response rates in 2004 to response rates in 2008 and found that minorities were more likely than whites to respond to the survey, except to some supplemental questionnaires like the biomarker survey (Ofstedal and Weir, 2011).
Other critiques have focused on sample attrition in the HRS. The HRS is different than most other surveys in that if a participant drops out of one wave of the survey, they are asked to participate in the next one unless they have requested not to be contacted in the future. One study (Kapteyn et al., 2006) examined sample attrition for the 1931-1941 cohort for the 2002 study year and found that sample attrition was low. The study did, however, find that when the sample of those who came back to the study (the balanced sample) were excluded, there was significant selection bias for the financial variables. Therefore, the researchers recommended using the unbalanced sample for analyses, although it can be difficult to include those who skip waves in panel analyses. Michaud and colleagues (2011) came to a similar conclusion in their study of the 1931-1941 cohort. They found that when the balanced sample was used, there was significant bias especially when looking at the wealth variables. Another study (Banks et al., 2010) compared the HRS to the ELSA for the years, 2002, 2004, and 2006 and found that attrition is four times higher in the ELSA study compared to the HRS for the 55-65 and 70-80 age groups.
Researchers have examined how the HRS survey questions have changed over time and how those questions are being used in academic research. Jackson and colleagues (2011), for example, looked at how the number of questions have increased over time and how researchers have used the questions in their work. They found that since 2002 the number of questions have increased from 413 to 581. When they looked at which questions were being used for research articles between 2006 and 2009, they found that researchers were primarily using questions relating to health, wealth, income and employment despite the wealth of other topics covered in the HRS. In another study that compared the ELSA, SHARE, KLOSA, JSTAR, CHARLS and LASI on survey content for chronic diseases, researchers found that the HRS did not contain questions about benign tumors, lung disease, asthma, liver disease, stomach or other digestive disease, peptic ulcer disease, kidney disease, Parkinson’s disease, osteoporosis, or hip or femoral fracture—a limitation of the HRS (Hu and Lee 2011). Other studies have looked at the family and relationship variables in the HRS and found that with regards to family, there are a large number of questions. Relationship questions are included, but are not as plentiful as the family questions. The study also collects extensive siblings’ data, however it is seldom used(Bianchi, 2011). Another study looked at the questions regarding health insurance and Medicare. Ayanian and colleagues (2011) found that since 2002 there have been improvements in the types of coverage questions. For example, the HRS began collecting data about how individuals choose their insurance company (e.g., advertisements, etc.), long-term care coverage, prescription drug coverage, number of hospitalizations, doctor visits, satisfaction with medical care. They also noted that the time that it takes to link individuals in the HRS to Medicare claims data has shortened. The study also noted several issues with HRS data including: the insurance section is overly lengthy compared to other sections, Medicare claims data cannot identify contract identifiers or geographic location, and there is no Medicare Part D data. Levy and Gutierrez (2009) in their review of the insurance section also note that it’s comprehensive. They caution, however, that in 1992, respondents were asked about their insurance coverage and their spouse’s and then in 1996 and after, both the respondent and their spouses were asked separately. The implication is that when using the 1994 data, you must also use the 1992 data since they are linked. From 1996 and on, this is not the case. Another study focused on economic data collected and concluded that although the data are, for the most part, comprehensive, the assets section contains some outliers and the pensions section is missing a significant amount of data (Venti, 2011).
Yet, other critiques have looked at the HRS for respondent bias. One study (Gouskova 2013) looked at the issue of telescoping in reporting inheritance (reporting that one received an inheritance more recently than they’ve actually received one, causing the inheritance to be reported in more than one wave of the study), and found that between 1992 and 2008 five to ten percent of individuals reported that they received an inheritance in more than one wave. Gouskova developed an algorithm that can reduce duplicate inheritance reporting. Another study looked at respondent bias in medical spending reporting and found that 21 percent of respondents do not provide an exact dollar amount in at least one category of medical spending (Goldman et al., 2011). Similarly, Venti (2011) found that respondents had difficulty in reporting exact dollar amounts for pensions. In a study of arthritis prevalence compared rates in the HRS to the NHIS between 1992 and 2002 and found that rates raise between 1992 and 1995, drop to 1992 levels in 1996, rises between 1998 and 2002 not due to natural rates, but instead due to reporting and measurement error in the HRS (Wilson and Howell 2005).
Finally, the HRS makes available sample weights to account for uneven response across waves, non-participation, and differential probability of selection (Gouskova, 2013). The sample weights intend to ensure that the HRS is representative of the target population in terms of race/ethnicity, gender, age and marital status. When compared to the Current Population Survey (CPS), Gouskova (2013) found that with regard to race/ethnicity, the HRS matched the CPS before 2004. However, the 2004, 2006, and 2008 weights underestimate older cohort minorities and overestimate younger cohort minorities. Gouskova concludes that each cohort needs its own race adjustment.
Examples of Research Using the HRS
This section offers examples of the types of studies that have used HRS data. Thousands of studies have used the data. A full list of articles that have used HRS data can be found here: Below I summarize three recent studies.
The first (Chen and Feeley, 2013) examined the role of social support (the perceived emotional support from one’s social network) and social strain (the negative interactions with one’s social network) in well-being and loneliness of adults aged 50 and older. The researchers used the 2008 Leave-Behind Questionnaire of the HRS to test whether social support from one’s spouse, children, family and friends was associated with loneliness and well-being and whether or not higher social strain from these sources is related to loneliness and well-being. Their models controlled for age, gender, education, marital status, and self-reported health status. Social support was measured by asking participants the following questions about their spouse, children, family members, and friends separately: 1) “How much do they really understand the way you feel about things?”, 2) “How much can you rely on them if you have a serious problem?”, and 3) “How much can you open up to them if you need to talk about your worries?” Social strain was measured by asking participants the following questions: 1) “How often do they make too many demands on you?”, 2) “How much do they criticize you?”, 3) “How much do they let you down when you are counting on them?”, and 4) How much do they get on your nerves?” They found that with regard to social support, that support from friends and especially from one’s spouse reduced loneliness and that social support from one’s spouse was most important, followed by one’s children, and finally by one’s friends in improving well-being. With regards to social strain, Chen and Feeley found that strain coming from one’s spouse had the strongest, negative effect on well-being. In sum, Chen and Feeley (2013) were able to use a subset of HRS data to demonstrate the relationship between social support, social strain, loneliness and well-being among older adults.