We assess the quality of the HRS’s measures of out-of-pocket medical spending and health insurance premia, both in the “core interviews” and in the “exit interview” data. We provide detailed evidence on the quality of the HRS insurance premia data, and we compare the HRS exit data to exit data in the MCBS. We document how changes in survey questions, including the introduction of “unfolding brackets,” affect the HRS measures. We document what we believe are errors in the HRS imputations and provide some suggestions for improving the accuracy of some imputed variables. Overall, we find the HRS data to be of high quality. However, we believe that many interesting variables in the HRS are under-utilized because users must perform imputations themselves.
We thank Jin Tan for excellent research assistance and Didem Bernard for helping us understand the MEPS data. The views expressed herein are those of the authors and do not necessarily reflect the views of the Federal Reserve Bank of Richmond or the Federal Reserve System.
Conflict of interest: None declared.
Research funding: Michigan Retirement Research Consortium (MRRC) and the Economic and Social Research Council (ESRC).
The MCBS and MEPS Datasets
This appendix describes the MEPS and MCBS datasets. Table 9 provides a summary comparison of these two datasets to the HRS.
|Data source||Survey, employer and administrative data available for merging||Survey data matched to provider data||Survey data reconciled with Medicare and Medicaid administrative data|
|Longitudinal design||Full panel, new cohorts added as they (roughly) reach age 50||Rotating panels, each panel lasting 2 years||Rotating panels, each panel lasting 4 years|
|Interview frequency||Every 2 years||Every 6 months||Every 4 months|
|Measurement unit||Household, spouses included||Household (subsample of NHIS)||Individual|
|Interview methodology||Mix of in-person and other||In-person||In-person|
|Institutional population||Not included in initial samples, but households followed into institutions||Not included||Included (by proxy)|
We use the 1996 to 2010 waves of the Medicare Current Beneficiary Survey (MCBS). The MCBS is a nationally representative survey of disabled and elderly (age-65+) Medicare beneficiaries. Although the sample misses elderly individuals who are not Medicare beneficiaries, virtually everyone aged 65+ is a beneficiary. The survey contains an over-sample of beneficiaries older than 80 and disabled individuals younger than 65. We exclude disabled individuals younger than 65, and use population weights throughout.
MCBS respondents are interviewed up to 12 times over a 4-year period, and are asked about (and matched to administrative Medicare claims data on) health care utilization over 3 of the 4 years, forming panels on medical spending for up to 3 years. We aggregate the data to an annual level. These sample selection procedures leave us 66,790 different individuals who contribute 152,193 person-year observations.
The MCBS’s unit of analysis is an individual. Respondents are asked about health status, income, health insurance, and health care expenditures paid out-of-pocket, by Medicaid, by Medicare, by private insurers, and by other sources. The MCBS survey data are then matched to Medicare records.
A key variable of interest is medical spending. This includes the cost of hospital stays, doctor visits, pharmaceutical, nursing home care, and other long-term care. The MCBS’s medical expenditure measures are created through a reconciliation process that combines survey information with administrative Medicare claims data and Medicaid recipiency data. As a result, the MCBS contains accurate data on Medicare payments and fairly accurate data on out-of-pocket, Medicaid, and other insurance payments. Out-of-pocket expenses include hospital, doctor and other bills paid out of pocket, but does not include insurance premia paid out of pocket. Because the MCBS includes information on people who enter a nursing home or die, its medical spending data are very comprehensive.
As shown in De Nardi et al. (2016c), the MCBS accurately measures the share of the population receiving Medicaid payments: the MCBS Medicaid recipiency rates of age 65+ “dual eligibles” (i.e. those who receive both Medicare and Medicaid) line up well with the aggregate statistics. The MCBS data matches aggregate Medicaid recipiency rates for the age 65+ population extremely well, which should not be surprising since MCBS uses administrative Medicaid data to create recipiency rates. However, because the MCBS does not capture those drawing Medicaid but not Medicare, the MCBS will likely understate the aggregate Medicaid recipiency rate by several percent. See De Nardi et al. (2016c), who analyze the MCBS data in detail.
We use data from the 1996 to 2012 waves of the Medical Expenditure Panel Survey (MEPS). The MEPS is a nationally representative survey. Respondents are asked about health status, health insurance, and health care expenditures paid out-of-pocket, by Medicaid, by Medicare, private insurers and by other sources. The MEPS data are matched to information provided by providers. Although it does not capture certain types of medical expenditures, such as nursing home expenditures, it captures most of the sources of medical spending that are faced by individuals in their 1950s and 1960s. See Sing et al. (2006) and Pashchenko and Porapakkarm (2016) for more on comparisons between the MEPS data and the aggregate statistics.
MEPS respondents are interviewed up to 5 times over a 2 year period, forming short panels. We aggregate the data to an annual level. We use the same sample selection rules for the MEPS that we use for the HRS. See French, von Gaudecker, and Jones (2016), who analyze MEPS data in detail.
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