Table 1.
Main Findings From the Experimental and Quasi-Experimental Literature on the Impact of Health Insurance on Economic Outcomes, by Outcome Category.
| 1.1 Labor Force Participation. |
| Population of interest | Study | Study design | Finding |
|---|---|---|---|
| Young adults | |||
| 16- to 29-year-olds, except 26 | Antwi, Moriya, and Simon (2013) | Difference-in-difference comparing individuals most likely to be affected based on age with those above and below them in age | • No effect on probability of being employed, but reduced prevalence of FT work by 2 ppt (5.8%) and reduced hours of work by about 3% • No change in rates of job change |
| 22- to 35-year-olds, possibly affected by mandates earlier | Dillender (2014) | Triple-difference comparing changes for affected ages after the reforms relative to slightly older ages in states that implement the reform relative to those that do not | • Women saw 1.2-ppt (percentage point) decline in LFP. No change for men. Men saw 1.7-ppt decline in FT employment, but no change for women |
| 19- to 29-year-olds | Depew (2015) | Triple-difference comparing age criteria in states and adoption across states | • No effect on extensive margin for males or females • 3.7-ppt (5.67%) decrease in FT employment for females and 1.9 ppt (2.44%) decrease for males. Females decreased hours by 3.7% and males by 1.9% |
| 24- to 28-year-olds who are not married | Dahlen (2015) | Regression discontinuity design comparing 24- to 26-year-olds with 27- to 28-year-olds | • Aging out of provision increased employment for men by 8 ppt and increased the probability of being in the labor force by 10 ppt. No change for women |
| 19- to 29-year-olds | Heim, Lurie, and Simon (2014) | Triple-difference comparing young adults had access to benefits, age, and pre-post law | • Effects on employment (as measured by filing a tax return and receiving a W-2) were not statistically different from 0 |
| Adults | |||
| Oregon adults, ages 19–64 | Baicker, Finkelstein, Song, and Taubman (2014) | Oregon Health Insurance Experiment: IV for being enrolled in Medicaid using lottery as instrument; also show reduced form results | • No evidence of a change in the likelihood of being employed |
| Working individuals, ages 18–65 | Thurston (1997) | Difference-in-difference comparing Hawaii to rest of country | • No evidence of a change in hours worked |
| Ages 18–65 who were not self-employed | Buchmueller, DiNardo, and Valletta (2011) | Difference-in-difference comparing Hawaii with other states | • Interested in LR effects of the law • Increase in low hour (<20 hours/week) jobs in Hawaii of 1.4 ppt compared with other states, concentrated in quintiles with lowest concentration of ESHI • No effect on probability of employment |
| Ages 16+ | Dubay, Long, and Lawton (2012) | Difference-in-difference comparing Massachusetts with similar states as well as rest of country | • Little or no effect on private-sector employment or hours worked even when looking at results by firm size |
| Ages 18–64 who were not self-employed | Dillender, Heinrich, and Houseman (2016) | Difference-in-difference comparing outcomes in MA before and after reform with rest of country while controlling for unemployment rates | • No change in PT work for full population, but when constrained to individuals without bachelor’s degrees, find a 1.7 ppt (8%) increase in the probability of working PT hours |
| Employed individuals younger than 65 years | Moriya, Selden, and Simon (2016) | First difference with state and year FE | • No increases in 25–29 hours/week or decreases in 30–34 hours/week in 2014 or 2015. Does not seem to vary significantly across firm size, but employees of large firms did decrease working 30–34 hours/week by 0.25 ppt in 2015 • No significant change in involuntary PT work • 25–29 hours/week may have increased for individuals with no more than a HS diploma, but trend predated ACA, for older workers (60–64) slight increase in 25–29 hours/week |
| Employed in the private sector, nonagricultural | Mathur, Slavov, and Strain (2016) | Difference-in-difference comparing workers most likely to be close to minimum wage and industries likely to be affected with others | • No significant effect on odds of working 25–29 vs. 31 –35 hours |
| Ages 18–64 | Kolstad and Kowalski (2016) | Difference-in-difference using MA reform as exogenous variation in ESHI | • The change in hours is equal to a decrease of 0.96 hours/week |
| At-risk adult populations | |||
| HS degree or less | Kaestner et al. (2015) | Difference-in-difference comparing states that expanded Medicaid with those that did not and synthetic control approach | • No effects on employment at time of interview, usual hours/week worked or working 30 or more hours/week |
| Adults with less than 138% FPL | Gooptu et al.(2016) | Difference-in-difference comparing states that expanded Medicaid with those that did not | • No significant effect on transitioning from employed to unemployed out-of-labor force, job switching, or switching from FT to PT |
| Married adults | |||
| Wives 25 to 54 years old, and husbands were not nonworking, excluding couples on public insurance during the past year | Buchmueller and Valletta (1999) | Observational-multinomial logit hour hours worked and insurance; difference-in-difference comparing across wives’ insurance status, but within same hours category—no control for exogeneity of husband’s offer of health insurance | • Much less likely to work both PT and FT relative to not working in DD specifications • 11-ppt reduction (26%) in FT and receiving insurance if husband does (relative to husbands not offered insurance) This is concentrated mainly on women with children • 1.2-Ppt reduction in PT and receiving insurance if husband does |
| Married households where both partners are 19 to 64 years old and at least one spouse is employed outside household | Royalty and Abraham (2006) | Difference-in-difference with IV for spouse’s health insurance with spouse’s age and education and difference out effect paid sick leave to isolate effect of spouse’s insurance | • 10-point increase in probability of husband having insurance offer is associated with 1-point decrease in wife working FT (35 hours+) and having offer and 1.5-point decrease in wife working 20+ hours/week with offer • 10-point increase in probability of wife having insurance offer is associated with 2.1-point decrease in husband working FT (35 hours+) with offer and 1.9-point decrease in working 20+ hours/week with offer |
| Excludes couples where husband is not working, wife is younger than 25 or older than 54, Medicare/Medicaid recipients, self-employed wives | Kapinos (2009) | Difference-in-difference with IV for husband’s insurance offer with husband’s unions status and firm size and difference out effect having a pension to isolate effect of husband’s insurance | • Husband’s insurance offer has no effect on hours worked, but wives whose husband has Health insurance offer are 16% less likely to work and suggests effect has been increasing in magnitude overtime; wives whose husband has Health insurance offer are 23% less likely to work FT and effect has been again suggestive evidence that this has been increasing in magnitude overtime |
| 25–64 years of age | Buchmueller and Carpenter (2012) | Difference-in-difference comparing partnered gay men (women) with nonpartnered gay and straight men (women) | • No change in partnership or employment for gay men, but lesbian women were 7.6 ppt (14%) more likely to be in a partnership and 7.1 ppt less likely to be working FT |
| Couples where both members are 30–65 | Dillender (2015b) | Triple-difference comparing before and after states extended legal recognition, between same-sex and married opposite-sex couples | • LFP fell by 7.9 ppt (9%) for women, likelihood of both members being in the LF fell by 12.2 ppt, likelihood of one member in labor force increased by 10.2 ppt, no change in neither member in LF. These are concentrated in women with young children. No changes for men |
| Single mothers | |||
| Females aged 18–64 with 1+child younger than 18 | Winkler (1991) | Use form of state Medicaid generosity as exogenous variation in valuation with two-step estimate to correct for selection in hours decision | • Medicaid generosity decreases employment until control for region and urban/rural • 10% increase in Medicaid generosity causes average female employment probability to fall 0.9–1.3 ppt • 10% increase in Medicaid generosity causes employment probability to fall 0.61 (Miss.) to 2.1 (DC) ppt • Medicaid generosity has no effect on hours worked for female heads |
| Females aged 18–64 with 1+child younger than 18 years | Moffitt and Wolfe (1992) | Use individual valuation of Medicaid and private insurance to account for heterogeneity in health | • Increase in valuation of Medicaid/private insurance increases/decreases AFDC participation, but effect of private insurance is larger • Women with highest values of Medicaid are driving results • Increase in value of Medicaid coverage of $50 (~ 1/3) increases AFDC participation by 2 ppt (5.9%) and reduces employment rates by 5.5 ppt. Results for increasing private insurance valuation are opposite size and almost double in size • If every woman who worked was insured, 3.5- ppt reduction in AFDC and 7.6-ppt increase in employment rate |
| Females ages 18–55 with children younger than 15 years, not receiving Medicare or military health insurance, not reporting a handicap or ill health, and not a veteran | Yelowitz (1995) | Medicaid expansions of late 1980s and early 1990s as exogenous variation in eligibility | • Decoupling Medicaid and AFDC increased LFP by 0.9 ppt, or 1.4% and reduced AFDC caseload by 3.5%. These results are concentrated on ever married women |
| See Yelowitz, above | Ham and Shore-Sheppard (2005) | Medicaid expansions of late 1980s and early 1990s as exogenous variation in eligibility | • Increasing Medicaid eligibility had no effect on LFP (working 1+ week in last year) or on the number of hours worked using a Heckman selection model |
| Females ages 18–65 with at least one child younger than 15 years | Montgomery and Navin (2000) | Medicaid expansions of late 1980s and early 1990s as exogenous variation in eligibility, excluding spending on disabled and elderly | Medicaid expenditures have no effect on employment or hours worked or LFP when controlling for state FE Expansion of Medicaid is important—increase eligibility by 25% increases LFP by 0.034 ppt but no effect on hours |
| Women aged 19–44 and not in school, not ill, or disabled in previous year | Meyer and Rosenbaum (2001) | Difference-in-difference comparing single mothers with single childless women with variation in changes across time and states in how families are treated | • Medicaid had no significant effect on work as measured by probability of working last week or probability of working at all last year and small negative effects on hours worked |
| Females aged 18–55 with children | Pohl (2014) | Estimate partial equilibrium static discrete choice model with labor supply and insurance choice for mother and kid using exogenous variation in Medicaid eligibility across states and times; simulate changes when Medicaid is expanded and subsidies are introduced | • Labor supply increases by 4.5% at extensive margin and 2.2% at the intensive margin. These changes are largest for single mothers with medical conditions |
| Childless adults | |||
| Individuals aged 19–64 with family income ≤ 300% FPL who worked at least 1 week last year | Guy, Atherly, and Adams (2012) | Difference-in-difference comparing individuals in states that expanded access to childless adults with those in states that did not | • Public Health insurance eligibility is associated with a 2.2-percentage point decrease in FT employment, a 0.8-percentage point increase in the likelihood of PT employment, and a 1.4-percentage point increase in the likelihood of not working. Effects are stronger for those who are older (50–64) and in worse health |
| 21- to 64-year-olds with a bachelor’s degree or less and not in armed forces | Garthwaite, Gross, and Notowidigdo (2014) | Difference-in-difference comparing TN with other southern states and triple-difference focusing on childless adults | • Increase in employment of 2.5 ppt after disenrollment, which is concentrated among childless adults who saw a decrease of 4.6 ppt (6%). Evidence that change in labor supply is happening along the extensive margin. Results are larger for older individuals (40–64) |
| Nonelderly, nondisabled, childless adults | Dague, DeLeire, and Leininger (2014) | Regression discontinuity in Wl comparing applications just before freeze with applications just after freeze as well as propensity-score matching difference-in-difference | • Public insurance reduced the likelihood of employment by 2.4–5.9 ppt or 6.1 to 10.6 depending on specification |
| Pregnant women | |||
| Women who gave birth from 1985– 1996 when they were between 18 and 39 | Dave et al. (2015) | Reduced form using variation in public health insurance across states and time as exogenous variation | • 10-ppt increase in Medicaid eligibility is associated with a 2-ppt decrease in probability of being employed in the past year, a 1.8-ppt decrease in LFP in the past year, and no significant change in weeks worked in the past year. It also reduced weekly hours by 3.9% and conditional weekly hours by 0.9%. These results are concentrated on women with less than a HS degree |
| Other groups | |||
| Women diagnosed with breast cancer | Bradley et al. (2007) | First difference comparing women who have insurance from their own jobs with women who are insured through their husband | • Having insurance, even if husband was offered insurance, increased probability of working and work more hours 12 and 18 months after diagnosis |
| Individuals who received a kidney transplant | Page (2011) | Difference-in-difference comparing individuals in low-income treatment group with those in high (more likely to have private insurance) | • 8-ppt decrease in LFP for low-income treatment group 1 year after transplant. Effect jumps to 22.85 after correcting for proxy |
| SSI and D1 recipients | Coe and Rupp (2013) | Difference-in-difference using state-level variation in the access and affordability of health care for disabled individuals in both the nongroup and the Medicaid markets | • Medicaid buy-in programs have a positive, but small, effect on earning, increasing the likelihood of positive earnings by about 0.2–0.5 ppt. Medicaid generosity seems to have different effects on different program participants: The likelihood of earning among D1-only beneficiaries is lower, by 0.3 ppt, in states with high Medicaid coverage, while SSI-only beneficiaries are more likely to have positive earnings by 0.5 ppt. These effects are larger for sicker individuals |
| Married couples with male veteran where husband is between 55 and 64, wife was not a veteran, no active military personal | Boyle and Lahey (2016) | Difference-in-difference comparing wives of male veterans and nonveterans before and after VA benefits expansion | • For married men, 2.3% increase in not working, 14.7% increase in PT employment, no change in self-employment • For wives, 3%-4% increase in probability of working, average hours/week increases by about 0.5 hour, no change in hours conditional on working, log earnings increase by 3%. These results are driven by wives with HS degree or less and those with wealth below the median |
| 1.2 Earnings, Wages, and Other Labor Market Outcomes. |
| Population of interest | Study | Study design | Finding |
|---|---|---|---|
| Children | |||
| Children ages 0–5 not from AZ | Boudreaux, Golberstein, and McAlpine (2016) | Use timing of introduction of Medicaid as exogenous variation in exposure | • No statistically significant effect on income to poverty ratio, decile of family wealth, or economic index for the low income (< 150% FPL target population) |
| Children whose parents filed taxes every year from 1996- when child turned 18 | Brown, Kowalski and Lurie (2015) | Simulated IV using variation in eligibility across states and time as instrument for total years of eligibility | • Each additional year of eligibility from birth to age 18 increases cumulative tax payment by $247 for women, but results are not significant for men. Pooled together, 1 SD increase in Medicaid eligibility increases tax payments by 3.6%. • Each additional year of eligibility from birth to age 18, women earn $656 more from age 19–28, but no significant effect on men’s earnings |
| Individuals born between 1979 and 1993 who are 18+ and not born in AZ | Miller and Wherry (2015) | Simulated IV using variation in eligibility across states and time as instrument for pre-natal eligibility, and at different ranges 1–4, 5–9, 10–14, 15–18 | • 10-ppt increase in prenatal eligibility associated with increase in personal income of about $285 (2013 dollars). Using log of income, 10-ppt increase in prenatal eligibility increase average income by 1.3–1.5 ppt. Also ages 5–9 increases log income, but by smaller amount, 0.3–0.4 ppt |
| Young adults | |||
| Ages 25–35 who were possibly affected by dependent mandates | Dillender (2014) | Triple-difference comparing how outcomes changed for affected ages after the reforms relative to slightly older ages in states that implement the reform relative with those that do not | • No significant change in wages for men when controlling for education. Women saw an increase of 2.2%-2.4% for being previously treated and 2.5%-2.8% for currently treated |
| Adults | |||
| Adults aged 19–64 | Baicker et al. (2014) | Oregon Health Insurance Experiment: IV for being enrolled in Medicaid using lottery as instrument; also show reduced form results | • No statistically significant impact of Medicaid on amount of individual earnings or whether individual earnings are above the FPL for 2009 labor market activity |
| Working individuals, ages 18–65 | Thurston (1997) | Difference-in-difference comparing Hawaii to rest of country | • Wages in industries most affected by mandate shrunk relative to other industries in Health insurance, but grew relative to same industries in the United States |
| Working individuals, ages 18–65, not self-employed | Buchmueller, DiNardo and Valletta (2011) | Difference-in-difference comparing Hawaii with other states | • Interested in LR effects of the law • No detectible difference in wages |
| Ages 18–64 | Kolstad and Kowalski (2016) | Difference-in-difference using MA reform as exogenous variation in ESHI | • Compensating differential is −$1.35/hour which amounts to −$2,812/year |
| Married adults | |||
| Wives working FT with hourly wage of at least $2.00 | Olson (2002) | IV for Health insurance coverage of wife using husband’s firm size and union status | • Wives with Health insurance earn about 0.20 log points lower wage than they would if they took a job without Health insurance |
| Single adults | |||
| Employed FT (30+ hours/week), not married, not receiving Health insurance through other sources | Lluis and Abraham (2013) | Individual-level FE; also instrument for lagged wages using lagged skills and health and for current choice of benefits with past benefits | • Being offered Health insurance is associated with a decrease in wages of 1.8%. Results are sensitive to specification |
| Single mothers | |||
| Ages 18–55 with no more than a HS degree, up to 5 children younger than 19 years, not receiving disability benefits for themselves | Hamersma (2013) | Individual FE/IV of distance between twice lagged earnings and Medicaid threshold for lagged distance | • Medicaid and SCHIP threshold had no effect on earnings for workers • When studying heterogeneity in response, finds no change in monthly hours, but there is some evidence that workers with earnings below Medicaid threshold experience higher earnings growth when Medicaid threshold is increased. For example, worker who was $300 below threshold is predicted to have improved earnings growth rate of about 15% for $100 increase in Medicaid threshold |
| Pregnant women | |||
| Women who gave birth from 1985–1996 when they were between 18 and 39 | Dave et al. (2015) | Reduced form using variation in public Health insurance across states and time as exogenous variation | • There was no change in log wages conditional on working |
| Childless adults | |||
| Nonelderly, nondisabled, childless adults | Dague, Decker, Kaestner, & Simon (2014) | RD comparing applications just before freeze with applications just after freeze as well as propensity-score matching difference-in-difference | • Decrease in earnings of $200-$210/quarter (2010$) in RD and increase of $70-$ 120 in propensity score. Larger for older individuals |
| 21- to 64-year-olds with a bachelor’s degree or less, not in armed forces | Garthwaite, Gross and Notowidigdo (2014) | Difference-in-difference comparing TN with other southern states and triple-difference focusing on childless adults | • No significant change on wages |
| 1.3 Program Participation. |
| Population of interest | Study | Study design | Finding |
|---|---|---|---|
| AFDC/TANF | |||
| Female headed houses with children | Blank (1989) | Use form of state Medicaid generosity as exogenous variation in valuation | • Medicaid value had no effect on AFDC participation |
| Female aged 18–64 headed houses with 1+child less than 18 | Winkler (1991) | Use form of state Medicaid generosity as exogenous variation in valuation with two-step estimate to correct for selection in hours decision | • Replicate Blank’s findings that Medicaid has insignificant effect on AFDC participation when market value of Medicaid was used. However, when expenditures per dollar of state personal income as measure of Medicaid were used, increases participation in AFDC |
| Female aged 18–64 headed houses with 1+child younger than 18 years | Moffitt and Wolfe (1992) | Use individual valuation of Medicaid and private insurance to account for heterogeneity in health | • Increase in valuation of Medicaid/private insurance increases/decreases AFDC participation, but effect of private insurance is larger • Women with highest values of Medicaid are driving results • Increase in value of Medicaid coverage of $50 (~1/3) increases AFDC participation by 2 ppt (5.9%). Results for increasing private insurance valuation are opposite size and almost double in size • If every woman who worked was insured, 3.5-ppt reduction in AFDC and 7.6-ppt increase in employment rate |
| Female headed houses (ages 18–55) with children younger than 15 years not receiving Medicare or military health ins., no handicap or ill health, not a veteran | Yelowitz (1995) | Medicaid expansions of late 1980s and early 1990s as exogenous variation in eligibility | • Decoupling Medicaid and AFDC reduced AFDC caseload by 3.5%. These results are concentrated on ever married women |
| See Yelowitz, above | Ham and Shore-Sheppard (2005) | Medicaid expansions of late 1980s and early 1990s as exogenous variation in eligibility | • Increasing Medicaid eligibility had no effect on AFDC participation |
| Female headed houses with children | Decker and Selck (2012) | OLS using the timing of Medicaid introduction across states as exogenous variation | • Medicaid introduction increased AFDC caseloads by 3% in first year, 9% in second year, and 13% in third year. Permanent caseloads increased by almost 16% • Medicaid introduction increased chance of female heads participating in AFDC by 6.9 ppt or 16%. Ultimately, increases by about 12 ppt, or 28% |
| Food stamps | |||
| Adults aged 19–64 | Baicker et al. (2014) | Oregon Health Insurance Experiment: IV for being enrolled in Medicaid using lottery as instrument; also show reduced form results | • Winning the lottery increased probability of receiving food stamps by 2.5 ppt (4%) and increases unconditional annual household FS benefits by $73, or $3000 in annual benefits for new beneficiaries. The effects of being on Medicaid are about 4X larger. Probability of being newly covered by SNAP increases in first 3 months and continues to increase in subsequent 3-month increments out to 12–15 months |
| Nonelderly households | Yelowitz (1996) | Medicaid expansions | • Marginal effect of expanding eligibility was to increase FS participation by 0.58 ppt or 7.5% increase in FS caseload • True effect of expansions was to increase FS participation by 0.22 ppt (Medicaid explains 10% of FS growth) |
| Individuals born between 1979 and 1993 who are 18+ and not born in AZ | Miller and Wherry (2015) | Simulated IV using variation in eligibility across states and time as instrument for prenatal eligibility, and at different ranges 1–4, 5–9, 10–14, 15–18 | • 10-ppt increase in Medicaid prenatal eligibility decreased probability of having FS benefits by 0.6% |
| SSI and D1 | |||
| Adults aged 19–64 | Baicker et al. (2014) | Oregon Health Insurance Experiment: IV for being enrolled in Medicaid using lottery as instrument; also show reduced form results | • No statistically significant effect on SSDI or SSI benefit receipt. Possible evidence of increase in probability of receipt of TANF but results are small and not robust |
| 18- to 64-year-olds nonsingle parent households, White or African American not in AZ, not including women younger than 45 years | Yelowitz (1998) | Medicaid expansions; IV is average Medicaid expenditure for blind SSI recipients | • Increasing Medicaid expenditure by $1,000, increases SSI participation by 0.0537 ppt, or 13% of increase in SSI participation • For low permanent-income group, Medicaid explains about 20% of growth in SSI for this group |
| Ages 18–64 between October 2004 and September 2009 in MA and other states in NE census division | Maestas, Mullen, and Strand (2014) | Difference-in-difference comparing MA with other states in NE census division | • Disability applications increased by 3% (0.08/1000 working age residents) compared with neighboring states in 2008, but disappears in 2009. This is primarily driven by SSDI-only applications. • Low-insurance counties saw decrease in applications of 0.06 working-age residents in 2007 and 2008, even though SSDI-only applications increased by 0.04 in 2008 • SSDI-only applicants filed on 0.5–1 month later (on average) in low-insurance counties and 1–2 months earlier in high-insurance counties |
| MEPS sample | Li (2015) | Structural | • Calibrated general equilibrium model suggests that ACA will decrease percentage of working-age people receiving D1 by 0.3 ppt and increase LFP by 0.2 ppt |
| Workers compensation | |||
| NLSY sample born between 1957 and 1964 | Lakdawalla, Reville, and Seabury (2007) | Observational but individual FE, controlling for industry, state, establishment size | • Employer offer of Health insurance is associated with 0.4- to 1-ppt increase in probability of workplace injury • Injured workers in firms that offer Health insurance 14–17 ppt more likely to file a WC claim but actually having Health insurance is associated with 4- to 6-ppt increase in probability of filing a WC claim |
| TX individuals within 2 years of 26th birthday | Dillender (2015a) | RD exploiting jump in coverage through parents Health insurance after age 26 | • No significant change in claims after age 26 but number of bills paid for by WC increases 8.1 %, driven by strain and sprain bills as well as number of occupational disease bills |
| EITC | |||
| Children whose parents filed in every tax year from 1996-when child turns 18 | Brown, Kowalski and Lurie (2015) | Simulated IV using variation in eligibility across states and time as instrument for total years of eligibility | • Each additional year of eligibility from birth to age 18, women receive $109 less in EITC by age 27 and men receive $41 less. Women are 1.7% less likely to collect EITC, but there is no effect on extensive margin for men |
| 1.4 Education. |
| Population of interest | Study | Study design | Finding |
|---|---|---|---|
| Children | |||
| Fourth and 8th graders | Levine and Schanzenbach (2009) | Simulated IV for eligibility using Medicaid expansions of the 1980s and 1990s as variation with triple differences | • No effect on test scores separately in 4th or 8th grade, but 50-ppt increase in PHI eligibility at birth increases reading test scores by 0.091 SD (3 points) |
| Individuals born between 1980 and 1990 | Cohodes et al. (2016) | Simulated IV for eligibility using Medicaid expansions of the 1980s and 1990s as variation | • 10-Ppt increase in average Medicaid eligibility between 0 and 17 decreases HS dropout rate by 0.4 ppt (4%), increases likelihood of college enrollment by 0.3 ppt (0.5%), and increases 4-year college attainment rate by 0.7 ppt (2.5%). These effects are not driven by eligibility between birth and age 3 |
| Children whose parents filed in every tax year from 1996- when child turns 18 | Brown, Kowalski and Lurie (2015) | Simulated IV using variation in eligibility across states and time as instrument for total years of eligibility | • Female eligibles were more likely to have attended college at ages 20–22. At age 20, one additional year of eligibility increased likelihood of having ever attended college by 0.40 ppt. Results for men are not significant |
| Individuals born between 1979 and 1993 who are 18+ and not born in AZ | Miller and Wherry (2015) | Simulated IV using variation in eligibility across states and time as instrument for prenatal eligibility, and at different ranges 1–4, 5–9, 10–14, 15–18 | • 10-ppt increase in prenatal eligibility increased probability of graduating HS by 0.2 ppt (0.2%). Corresponds to coverage raising graduation rate by 7.3%. No significant effects on probability of attending some college or receiving a college degree |
| Children ages 0–5 not from AZ | Boudreaux, Golberstein and McAlpine (2016) | Use timing of introduction of Medicaid as exogenous variation in exposure | • No statistically significant effect on years of education, income to poverty ratio, decile of family wealth, or economic index for the low income (<150% FPL target population) |
| Young adults | |||
| Ages 17–23 | Jung, Hall, and Rhoads (2013) | Observational | • Availability of parental Health insurance increases the probability of being a FT student by 22%, decreases the probability of being a PT student by 2.6%, and decreases the probability of not enrolling in college by 19.4% • When sample consists only of students, the representative student is 6.5% more likely to enroll FT when parental Health insurance is available |
| Ages 25–35 who were possibly affected by dependent mandates | Dillender (2014) | Triple-difference comparing how completed education changes for affected ages after the reforms relative to slightly older ages in states that implement the reform relative with those that do not | • Men who were 18 years or younger at the time of the reform gain 0.173 years of education on average by the time they are older than 25, were 2.5 ppt more likely to have completed college by the time they are 26, and were 2.8 ppt more likely have attended some college, and increased completing HS by 1.5 ppt. Women saw very little effect on education—increased HS graduation by 1.6 ppt |
| Ages 19–29 | Depew (2015) | Triple-difference comparing age criteria in states and adoption across states | • No change in being a student, married, or having children |
| Ages 19–22 | Yaskewich (2015) | Difference-in-difference comparing Pennsylvania with New Jersey | • College enrollment in NJ was not statistically different than PA for full sample • Upper-income households (300%+ FPL) saw decrease of 8.6–9.3 ppt (14.4% and 27.0%) in college enrollment. Households where the young adult lived at home and the parent worked in a small firm saw larger effects |
| 1.5 Savings and Asset Accumulations. |
| Population of interest | Study | Study design | Finding |
|---|---|---|---|
| Households where head is not retired and younger than 65 | Starr-McCluer (1996) | Jointly estimate wealth and insurance coverage to try to control for selectivity of Health insurance using share of households heads in area who work for organizations with 100+ employees as instrument | • Households with insurance have significantly higher savings than households without coverage |
| Households with only one family, head ages 18–64, no members older than 64, state uniquely identified in SIPP | Gruber and Yelowitz (1999) | Simulated IV using total Medicaid dollars with eligibility and value of Medicaid as exogenous variations | • $1,000 Increase in Medicaid eligible dollars decreases odds of having positive assets by 0.81%, and wealth holdings fall by 2.51% conditional on having positive net wealth. For the Medicaid eligible population, these values are 4.2% and 12.8%, respectively • Medicaid program lowers asset holdings by between 25 and 32 cents for each dollar of eligibility, which amounts to lowered wealth holdings between $1,293 and $1,654. Expansions between 1984 and 1993 lowered wealth holdings by $567 to $722 • Having an asset test more than doubles the reduction in assets • For each $ 1,000 in eligibility, nondurable expenditures rise by 0.82%. For the eligible population, this is 4.2%, or $538 (1987$) |
| See, Gruber and Yelowitz, above | Maynard and Qiu (2009) | Simulated quantile IV with eligibility and value of Medicaid as exogenous variations | • $1,000 increase in Medicaid eligible dollars drops median net worth by 5.47% (greater than mean reported in Gruber and Yelowitz) • The effect of Medicaid dollars on assets is U-shaped in net-worth quantiles. No significant effect on lower quantiles (0–0.2) but increase monotonically in magnitude and significance until 0.6 quantile, and then increase in magnitude • Households in very bottom quantiles of net-worth do not respond to asset tests, while those in the middle do |
| No restrictions other than valid wealth data | Gittleman (2011) | Simulated IV using current Medicaid dollars with eligibility and value of Medicaid as exogenous variations | • Using same instrument as Gruber, Yelowitz, find reduction of wealth holdings of 28.0% for Medicaid eligibles. In aggregate, this is 0.8% reduction in wealth • Using current Medicaid dollars (as opposed to total) find no effect. Also show evidence that G-Y is driven by second-order interactions and depends on time period selected |
| Heads of households aged 19–50 years | Lee (2016) | Triple-difference comparing households with ESHI living with dependent child aged 19–25 to control group with child outside mandated ages | • Households with dependents ages 19–25 and ESHI increased shares of stocks in financial portfolio by 4.2 percentage points after ACA mandate with no significant reduction in shares of bonds or assets in interest-bearing accounts |
| 1.6 Household Well-Being. |
| Population of interest | Study | Study design | Finding |
|---|---|---|---|
| General households | |||
| Adults aged 19–64 | Finkelstein et al. (2012) | Oregon Health Insurance Experiment: IV for being enrolled in Medicaid using lottery as instrument |
• No significant effect on probability of declaring bankruptcy, judgments, or liens |
| Adults ages 21 –64 without an advanced degree | Gross and Notowidigdo (2011) | Simulated IV with Medicaid expansions of 1990s providing exogenous variation | • 10-ppt increase in eligibility for Medicaid reduces personal bankruptcies by 8% |
| Noninstitutionalized Americans | Sommers and Oellerich (2013) | Propensity-score matching individuals with Medicaid coverage to those without Medicaid coverage (either private insurance or uninsured) | • In 2010, Medicaid kept 2.1 million Americans out of poverty and 1.4 million out of extreme poverty. Without Medicaid, total OOP spending would increase from $376 to $871 per Medicaid enrollee and family income would drop from 149% to 143% of FPL |
| Citizens in 41 states | Flavin (2018) | Difference-in-difference comparing low-income citizens in states that expanded Medicaid with states that did not | • Moving from nonexpansion state to an expansion state is associated with more than 1/2 SD increase in SWB |
| Households with children | |||
| Households with children younger than 18 | Leininger, Levy, and Schanzenbach (2010) | Simulated IV with eligibility under CHIP providing exogenous state-level variation in access to Health insurance | • Eligibility associated with increase in nonhealth consumption of $5,477, which is concentrated in transportation and retirement/pension savings |
| Children younger than 9 years | Banthin and Selden (2003) | Difference in difference comparing children of different eligibility groups between 1987 and 1996 (eligibility is simulated) | • 7.4- to 8.7-ppt decrease in likelihood of family spending 10% or more of disposable income on medical care, depending on control group |
| Noninstitutionalized with incomes below 300% FPL | Saloner (2013) | Simulated IV with eligibility under CHIP providing exogenous state-level variation in access to Health insurance | • CHIP did not decrease food security or housing problems, even for low-income subsample |
| Reference person 18–64, unmarried with at least one never married child younger than 18 years | Schmidt, Shore-Sheppard, and Watson (2016) | Simulated IV with Medicaid eligibility across states as exogenous variation | • Medicaid did not have a statistically significant impact on food insecurity, but should be researched further |
| 1.7 Delayed Care due to Cost. |
| Population of interest | Study | Study design | Finding |
|---|---|---|---|
| Households with children | |||
| Nondisabled parents, ages 18–64 | Busch and Duchovny (2005) | Simulated IV using variation in eligibility across states and years | • 29-ppt increase in probability that one did not forgo needed care due to cost |
| Children younger than 18 years | Miller (2012) | Difference-in-difference comparing MA with other states in Northeast region | • 9-ppt decrease in forgone medical care because of cost |
| Nonelderly adults with at least one child and incomes ≤138% FPL | McMorrow et al. (2016) | Use Medicaid threshold that exploit exogeneity across states and time in eligibility | • Reduced delays in care due to cost in past 12 months by 3.1 ppt, unmet need for prescription meds due to cost by 3.1 ppt, and decreased unmet need for mental health care due to cost by 2.0 ppt |
| Young adults | |||
| Ages 19–34 | Wallace and Sommers (2015) | Difference-in-difference comparing those who were affected by dependent mandate with those who were not | • Proportion of young adults unable to see physician because of cost declined by 1.9 ppt |
| Adults | |||
| Adults aged 19–64 | Finkelstein et al. (2012) | Oregon Health Insurance Experiment: IV for being enrolled in Medicaid using lottery as instrument | • Decrease of 3.6 ppt (55%) in probability that refused treatment because of medical debt in past 6 months |
| Women with incomes ≥125% FPL | Pauly (2005) | Instrument for HI coverage using size of firm and marital status | • Large decrease in going without care needed for health, but hard to interpret because of categorical nature of explanatory and dependent variables |
| Ages 18–64 | Long (2008) | Difference between outcomes before and after MA reform | • Decrease in not getting needed care in the past year, especially for adults with income <300% FPL, decrease in not getting needed care because of cost, which was almost doubled for adults with income <300% FPL |
| Ages 18–64 | Zhu et al. (2010) | Difference-in-difference comparing MA with rest of New England | • Cost-related barriers improved for MA compared with New England for Whites and Blacks but not Hispanics, for individuals above 300% FPL and below 100% FPL |
| Ages 18–64 | Pande et al. (2011) | Difference-in-difference comparing MA with other New England states | • MA residents were 6.6 ppt more likely to forgo care because of cost, which was concentrated on the disadvantage subpopulation |
| Ages 18–64 | Sommers et al. (2015) | First difference comparing pre- and post-ACA as well as difference-in-difference comparing pre- and post-ACA adults above and below 138% of the poverty level | • Inability to afford care decreased 5.5 ppt when comparing pre- and post-ACA but was not statistically significant in difference-in-difference specification |
| Childless adults | |||
| Ages 19–64 | Guy (2010) | Difference-in-difference comparing childless adults eligible for expansions with childless adults not eligible but above 300% FPL | • 10-ppt increase in eligibility for programs with increased cost sharing lead to 0.22-ppt increase in likelihood of not forgoing needed care because of costs. For the traditional cost sharing, the result was an increase of 0.28 ppt |
Note. CHIP = Children’s Health Insurance Program; ACA = Affordable Care Act; SSDI = Social Security Disability Insurance; AFDC = Aid to Families with Dependent Children; TANF = Temporary Assistance for Needy Families; SSI = Supplemental Security Income; FPL = federal poverty level; HI = health insurance; LFP = labor force participation; PT = part-time; FT = full-time; HS = high school; OLS = ordinary least squares; IV = instrumental variable; FE = fixed effect; SNAP = Supplemental Nutrition Assistance Program; FS = food stamp; MEPS = Medical Expenditure Panel Survey; NLSY = National Longitudinal Survey of Youth; WC = workers’ compensation; EITC = earned income tax credit; SD = standard deviation; SWB = subjective well-being; LR, long run; RD, regression discontinuity.