Number of young adults are covered under their parents’ health insurance


I am looking into CPS microdata in R to analyze how many young adults (ages 18-26) are covered under their parents’ health insurance in 2022.

The list of variables I am looking into are AGE and HIMCAIDLY. I understand I can attach characteristics of the father and mother to the variable HIMCAIDLY to create variables representing the father’s health insurance status and the mother’s health insurance status.

However, I am unsure:

  • How I would make the connection of “young adults covered under their parents’ health insurance.”

  • Should I be using ACS data instead?

Any advice is appreciated.

Thank you for your time.

My understanding of your question is that you would like to identify persons age 18-26 who are covered by their parents’ health insurance, and you are unsure how to go about this and whether the CPS or the ACS is better for your purposes. Please correct me if I misunderstand your post. This identification is possible using CPS data, and potentially with ACS data. I will explain how you might go about making this identification using both data sources.

In IPUMS CPS, there are several variables that identify the type of health insurance coverage a respondent has, as well as variables that identify who in the household is the policy holder. I will provide an example of how I would identify persons age 18-26 who have employer-based health insurance coverage under a parent’s plan. You can generalize this example to determine how to make the specific identification you are interested in.

I would use the variable AGE to determine who in my sample is between 18 and 26. You can find all variables on health insurance available from IPUMS CPS here. I would use the variable GRPCOVLY to identify those who were covered by employment-based group health last year. Finally, I would use the variables GRPWHO1 and GRPWHO2 (report the line number of the persons in the household who were the policyholders of the employment-based insurance during the previous year) in conjunction with the line numbers of the respondents’ parents to determine whether a parent was the policyholder for the respondent’s employment-based insurance coverage. You can determine parents’ line numbers by attaching the LINENO of the mother and father when you create your extract, or with the variables MOMLOC and POPLOC. You may notice that this identification method relies on covered individuals living in the same household as the parent(s) who hold the insurance policy. You may also, therefore, be interested in the variable GRPDEPLY, which identifies respondents who were covered as dependents on an employment-based health plan last year, regardless of whether they live with their parent(s). This variable does not distinguish between people who are dependents of their parent(s) or dependents of some other person (such as a spouse), so you will need to make your own determination about how to use the variable, if at all. Using these types of variables in conjunction with MARST may help you better identify individuals who are dependents on a parent’s health plan. You may also be interested in the health insurance unit (HIU) variables in IPUMS CPS. Health insurance units, which define the individuals covered under a health plan, are not reported directly by respondents, but are defined by researchers at the State Health Access Data Assistance Center (SHADAC) based on family interrelationships. You can read more about the creation of the HIUID variable in the description metadata and in the HIURULE variable metadata.

In IPUMS USA, health insurance variables report the type of insurance coverage a respondent has, if any. Like in IPUMS CPS, there are also HIU variables created by SHADAC. Respondents age 18-26 who are part of the same HIU as their parent(s) may reasonably be assumed to be covered under their parents’ health insurance plan. As in IPUMS CPS, you can use the attach characteristics feature to attach parents’ values of PERNUM or you can use MOMLOC and POPLOC to determine parents’ location in the household. Note that, similar to the identification method I recommend using CPS data, with this method, you cannot explicitly identify respondents who do not live with their parents and who are covered by their parents’ health plan. You may again consider using other relevant variables, such as insurance coverage type, MARST, and EMPSTAT to make some additional assumptions about who is likely to be covered by their parents’ health plan.

A third source of data you may consider using is IPUMS NHIS, which provides data from the National Health Interview Survey. While the sample sizes in the NHIS are smaller than in the ACS and CPS, much more detailed information about health insurance is available. See variable HIP1RELPOLICY, for example, which reports adult respondents’ relationship to the policyholder.