Analysis of impacts of turning 26 (i.e., becoming ineligible for parental insurance coverage under the ACA young adult dependent coverage provision) on labor market and health insurance coverage.

1. 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

2. The Young Adult Provision
 Sept 23, 2010
Objective Data Methods Results Robustness Discussion

3. 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

4. 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)

5. The Young Adult Provision
 Gave young adults an alternative path to health
insurance coverage
Objective Data Methods Results Robustness Discussion

6. 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

7. 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

8. How does aging out of the young adult
provision impact labor market and health
insurance coverage outcomes?
Objective Data Methods Results Robustness Discussion

9. 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

10. 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

11. 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

12. Outcomes
 Employment: Labor force participation, employed, and
full-time employment
Objective Data Methods Results Robustness Discussion

13. 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

14. 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

15. Sample
 Years: 2011-2013
Objective Data Methods Results Robustness Discussion

16. Sample
 Years: 2011-2013
 Ages: 24-28
Objective Data Methods Results Robustness Discussion

17. Sample
 Years: 2011-2013
 Ages: 24-28
 N: 13,235
Objective Data Methods Results Robustness Discussion

18. 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

19. 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

20. Objective Data Methods Results Robustness Discussion
AGE
Outcome
(%)
Data Points

21. Objective Data Methods Results Robustness Discussion

22. Objective Data Methods Results Robustness Discussion

23. Objective Data Methods Results Robustness Discussion

24. Objective Data Methods Results Robustness Discussion
RD estimates the
percentage point
change in an
outcome at age 26

25. 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

26. Model
 Treatment = 1 if age 26 or older
 Age = distance from 26 (in months)
Objective Data Methods Results Robustness Discussion

27. Model
 Treatment = 1 if age 26 or older
 Age = distance from 26 (in months)
Objective Data Methods Results Robustness Discussion

28. Directly Purchased Private Insurance
 4.4 pp increase (p<.05)
Objective Data Methods Results Robustness Discussion

29. Directly Purchased Private Insurance
 4.4 pp increase (p<.05)
 No other changes in
health insurance
coverage
Objective Data Methods Results Robustness Discussion

30. 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

31. 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

32. Insurance Coverage is Worse
(than 1 yr prior)
 15.1 pp increase
Objective Data Methods Results Robustness Discussion

33. 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

34. 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

35. 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

36. 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

37. 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

38. 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

39. 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

40. Model Specification and Robustness Checks
1. Smoothness of the model covariates
 No significant jumps at age 26
Objective Data Methods Results Robustness Discussion

41. 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

42. 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

43. 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

44. 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

45. 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

46. 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

47. 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

48.  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

49.  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

50.  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

51.  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

52.  Large jumps in health insurance plan dissatisfaction at age
26
Objective Data Methods Results Robustness Discussion

53.  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

54. Thank-you!
Heather Dahlen
heather.dahlen@gmail.com