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Labor Market and Insurance Coverage
Impacts Due to “Aging Out” of the Young Adult Provision of the Affordable Care Act
Heather Dahlen University of Minnesota, Applied Economics
The Young Adult Provision
Sept 23, 2010
Objective Data Methods Results Robustness Discussion
The Young Adult Provision
Sept 23, 2010
Allowed individuals to remain on a parent’s employer-sponsored insurance (ESI) plan until age 26
Objective Data Methods Results Robustness Discussion
The Young Adult Provision
Sept 23, 2010
Allowed individuals to remain on a parent’s employer-sponsored insurance (ESI) plan until age 26
Goal: Increase health insurance coverage for this group of relatively healthy, previously uninsured individuals (which it has)1
Objective Data Methods Results Robustness Discussion
1Sommers et. al (2012); Sommers and Kronick (2012); Cantor et. al (2012); O’Hara and Brault (2013); Antwi, Mariya, and Simon (2012)
The Young Adult Provision
Gave young adults an alternative path to health insurance coverage
Objective Data Methods Results Robustness Discussion
The Young Adult Provision
Gave young adults an alternative path to health insurance coverage
By relaxing the tie between employment and health insurance coverage, the employment/insurance choice set was altered
Objective Data Methods Results Robustness Discussion
The Young Adult Provision
Gave young adults an alternative path to health insurance coverage
By relaxing the tie between employment and health insurance coverage, the employment/insurance choice set was altered − Potential reduction of job lock, or reliance on
employment for health insurance coverage
Objective Data Methods Results Robustness Discussion
How does aging out of the young adult provision impact labor market and health insurance coverage outcomes?
Objective Data Methods Results Robustness Discussion
National Health Interview Survey (NHIS)
Detailed information for a nationally representative sample of non-institutionalized U.S. civilians − Health − Health insurance − Employment
Objective Data Methods Results Robustness Discussion
National Health Interview Survey (NHIS)
Detailed information for a nationally representative sample of non-institutionalized U.S. civilians − Health − Health insurance − Employment
Accessed through the Integrated Health Interview Survey (IHIS) − Minnesota Population Center and State Health Access
Data Assistance Center
Objective Data Methods Results Robustness Discussion
Key Measures
Includes respondent birth month and year as well as interview month and year
Able to more precisely account for time from 26th birthday (eligibility threshold)
Objective Data Methods Results Robustness Discussion
Outcomes
Employment: Labor force participation, employed, and full-time employment
Objective Data Methods Results Robustness Discussion
Outcomes
Employment: Labor force participation, employed, and full-time employment
Employment-related health insurance measures: Employer-sponsored insurance (ESI), offer of ESI
Objective Data Methods Results Robustness Discussion
Outcomes
Employment: Labor force participation, employed, and full-time employment
Employment-related health insurance measures: Employer-sponsored insurance (ESI), offer of ESI
Health Insurance: Plan quality compared to one year prior, type of insurance (public, private, and uninsured) − Non-group directly purchased private coverage
Objective Data Methods Results Robustness Discussion
Sample
Years: 2011-2013
Objective Data Methods Results Robustness Discussion
Sample
Years: 2011-2013
Ages: 24-28
Objective Data Methods Results Robustness Discussion
Sample
Years: 2011-2013
Ages: 24-28
N: 13,235
Objective Data Methods Results Robustness Discussion
Sample
Years: 2011-2013
Ages: 24-28
N: 13,235
Subpopulations: Separate models based on gender and marital status
Objective Data Methods Results Robustness Discussion
Objective Data Methods Results Robustness Discussion
Model
Regression Discontinuity (RD) design
Exploits the exogenous change in health coverage options that occurs at the age cutoff for the young adult provision program eligibility (age 26)
RD estimates the magnitude of the discontinuity in the outcome at the cutoff
Objective Data Methods Results Robustness Discussion
AGE
Outcome (%) Data Points
Objective Data Methods Results Robustness Discussion
Objective Data Methods Results Robustness Discussion
Objective Data Methods Results Robustness Discussion
Objective Data Methods Results Robustness Discussion
RD estimates the percentage point change in an
outcome at age 26
Model
Logistic regressions − Control for highest educational attainment, marital
status, region, health status, presence of a chronic health condition, US citizenship, race/ethnicity, poverty, gender (for full models), and year fixed effects
Objective Data Methods Results Robustness Discussion
Model
Treatment = 1 if age 26 or older Age = distance from 26 (in months)
Objective Data Methods Results Robustness Discussion
Model
Treatment = 1 if age 26 or older Age = distance from 26 (in months)
Objective Data Methods Results Robustness Discussion
Directly Purchased Private Insurance 4.4 pp increase (p<.05)
Objective Data Methods Results Robustness Discussion
Directly Purchased Private Insurance 4.4 pp increase (p<.05)
No other changes in
health insurance coverage
Objective Data Methods Results Robustness Discussion
Directly Purchased Private Insurance 4.4 pp increase (p<.05)
No other changes in
health insurance coverage
Prior to the individual mandate
Objective Data Methods Results Robustness Discussion
Directly Purchased Private Insurance 4.4 pp increase (p<.05)
No other changes in
health insurance coverage
Prior to the individual mandate
Waiting periods for employer-sponsored insurance eligibility
Objective Data Methods Results Robustness Discussion
Insurance Coverage is Worse (than 1 yr prior)
15.1 pp increase
Objective Data Methods Results Robustness Discussion
Insurance Coverage is Worse (than 1 yr prior)
15.1 pp increase
First interaction with the health insurance on own?
Objective Data Methods Results Robustness Discussion
Findings by Gender
Men – At age 26: Increases in labor force participation (+7.5 pp) and
directly purchased nongroup insurance (+6.2 pp)
Objective Data Methods Results Robustness Discussion
Findings by Gender
Men – At age 26: Increases in labor force participation (+7.5 pp) and
directly purchased nongroup insurance (+6.2 pp) Interest in remaining insured Were young men using the provision as a means of temporarily
exiting /delaying entry to the labor force?
Objective Data Methods Results Robustness Discussion
Findings by Gender
Men – At age 26: Increases in labor force participation (+7.5 pp) and
directly purchased nongroup insurance (+6.2 pp) Interest in remaining insured Were young men using the provision as a means of temporarily
exiting /delaying entry to the labor force? – Increases in health insurance coverage being worse (+12.2 pp)
Objective Data Methods Results Robustness Discussion
Findings by Gender
Men – At age 26: Increases in labor force participation (+7.5 pp) and
directly purchased nongroup insurance (+6.2 pp) Interest in remaining insured Were young men using the provision as a means of temporarily
exiting /delaying entry to the labor force? – Increases in health insurance coverage being worse (+12.2 pp)
Women – Large increase (+17.6 pp) in reporting of insurance coverage being
worse one year prior Higher healthcare utilization rates
Objective Data Methods Results Robustness Discussion
Findings for Unmarried Individuals
Men – Increase in employment (+7.9 pp) – Increase in labor force participation (+9.7 pp)
Objective Data Methods Results Robustness Discussion
Findings for Unmarried Individuals
Men – Increase in employment (+7.9 pp) – Increase in labor force participation (+9.7 pp)
Women – Increase in employer-sponsored insurance offers (+11.7pp) – Increase in health coverage being worse (+17.7 pp)
Objective Data Methods Results Robustness Discussion
Model Specification and Robustness Checks
1. Smoothness of the model covariates No significant jumps at age 26
Objective Data Methods Results Robustness Discussion
Model Specification and Robustness Checks
1. Smoothness of the model covariates No significant jumps at age 26
2. Respondent should not have control over the forcing
variable (the cut-point) Age is the forcing variable and this is naturally satisfied
Objective Data Methods Results Robustness Discussion
Model Specification and Robustness Checks
1. Smoothness of the model covariates No significant jumps at age 26
2. Respondent should not have control over the forcing
variable (the cut-point) Age is the forcing variable and this is naturally satisfied
3. No non-random sorting to one side of the threshold
Plotted the distribution of young adults around the eligibility threshold and this did not occur
Objective Data Methods Results Robustness Discussion
Model Specification and Robustness Checks
4. Model Fit. Estimated models for the following:
- A) Same age primary sample but earlier years (2004-2006): No significant results
Objective Data Methods Results Robustness Discussion
Model Specification and Robustness Checks
4. Model Fit. Estimated models for the following:
- A) Same age primary sample but earlier years (2004-2006): No significant results
- B) Only individuals younger than 26, same years as primary study (2011-2013), and artificial eligibility threshold: No significant results
Objective Data Methods Results Robustness Discussion
Model Specification and Robustness Checks
4. Model Fit. Estimated models for the following:
- A) Same age primary sample but earlier years (2004-2006): No significant results
- B) Only individuals younger than 26, same years as primary study (2011-2013), and artificial eligibility threshold: No significant results
- C) Only individuals older than 26, same years as primary study (2011-2013), and artificial eligibility threshold: No significant results
Objective Data Methods Results Robustness Discussion
Model Specification and Robustness Checks
4. Model Fit. Estimated models for the following:
- A) Same age primary sample but earlier years (2004-2006): No significant results
- B) Only individuals younger than 26, same years as primary study (2011-2013), and artificial eligibility threshold: No significant results
- C) Only individuals older than 26, same years as primary study (2011-2013), and artificial eligibility threshold: No significant results
Objective Data Methods Results Robustness Discussion
Model Specification and Robustness Checks
5. Sample Appropriateness. Estimated the following models: - A) Narrower age band: results are less precise
- B) Wider age band: includes individuals further removed from
the threshold and have had more time to adjust (however, many of the significant results from primary models remain)
- C) Restriction to unmarried
Objective Data Methods Results Robustness Discussion
First analysis of how loss of eligibility for the young adult provision alters labor market and health coverage choices
Objective Data Methods Results Robustness Discussion
First analysis of how loss of eligibility for the young adult provision alters labor market and health coverage choices
No change in uninsurance rate + increase in directly purchased coverage = young adults are interested in remaining insured
Objective Data Methods Results Robustness Discussion
First analysis of how loss of eligibility for the young adult provision alters labor market and health coverage choices
No change in uninsurance rate + increase in directly purchased coverage = young adults are interested in remaining insured
Larger labor market effects for unmarried men and women
Objective Data Methods Results Robustness Discussion
First analysis of how loss of eligibility for the young adult provision alters labor market and health coverage choices
No change in uninsurance rate + increase in directly purchased coverage = young adults are interested in remaining insured
Larger labor market effects for unmarried men and women
Increase in labor force participation for young men – Graduate school enrollment rates did not increase
during this time
Objective Data Methods Results Robustness Discussion
Large jumps in health insurance plan dissatisfaction at age 26
Objective Data Methods Results Robustness Discussion
Large jumps in health insurance plan dissatisfaction at age 26
– Health insurance marketplace education and outreach
coordinators can use the results for targeted marketing of young adults nearing a 26th birthday
− Smooth the coverage transition and reduce plan quality dissatisfaction
Objective Data Methods Results Robustness Discussion
