The Effect of the Affordable Care Act on Coverage and Labor Market Outcomes PRELIMINARY AND INCOMPLETE

MARK DUGGAN GOPI SHAH GODA EMILIE JACKSON

OCTOBER 2016

Motivation The Affordable Care Act (ACA) is the most significant reform to the U.S. health care system since the introduction of Medicare and Medicaid in 1965.

Primary goal: increase health insurance coverage

How? ◦ Medicaid expansions to cover all people below 138% FPL (occurred in some

states but not in others) ◦ Subsidies to provide incentives for coverage for people between 100-400%

FPL on new health insurance exchanges ◦ Mandate that all individuals are covered (or face a penalty) ◦ Incentives for employers to offer coverage

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Percentage of Persons 18-64 Uninsured, January 2010 - March 2016

Source: NCHS. National Health Interview Survey Early Release of Quarterly Estimates, retrieved September 12, 2016.

ACA Takes Effect

Research questions

1. How much of the reduction in uninsurance seen since 2014 was due to the ACA?

Motivation “CBO estimates that the ACA will reduce the total number of hours worked, on net, by about 1.5 percent to 2.0 percent during the period from 2017 to 2024, almost entirely because workers will choose to supply less labor—given the new taxes and other incentives they will face and the financial benefits some will receive.

The reduction in CBO’s projections of hours worked represents a decline in the number of full-time-equivalent workers of about 2.0 million in 2017, rising to about 2.5 million in 2024.”

Source: Congressional Budget Office (2015). “Appendix B: Updated Estimates of the Insurance Coverage Provisions of the Affordable Care Act,” from The Budget and Economic Outlook: 2015 to 2025.

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Labor Force Participation Rate

Seasonally Adjusted Ages 16 and over

ACA Takes Effect

Research questions

1. How much of the reduction in uninsurance seen since 2014 was due to the ACA?

2. What is the impact of the ACA on labor market outcomes?

Empirical approach (overview) Challenge: National reform no control group; difficult to understand how insurance coverage and labor market outcomes would have evolved absent the ACA.

Our approach: exploit geographic variation in expected treatment “intensity”

Identifying assumption: after controlling for substantial fixed differences across geographic areas that do not change over time, places with different expected treatment intensity would have evolved similarly absent the ACA.

Preview of findings ACA had a significant impact on health insurance coverage

◦ Subsidies and Medicaid expansion increase of 2.6 p.p. for 45-64 year-olds

Increase primarily due to increases in Medicaid and privately-purchased insurance coverage

◦ Medicaid coverage increases larger in states that chose to expand Medicaid; increase in states that did not expand suggest a “woodwork” effect

◦ Privately-purchased insurance coverage increases larger in states that did not choose to expand Medicaid

Increases happened differentially in places where expected treatment intensity was higher

No evidence that labor market outcomes changed in 2014 as a result of the ACA

ACA Overview

Two of the main features designed to increase health insurance coverage are:

1. Expansions of the Medicaid program to cover all individuals

under 138% FPL

2. Subsidies towards health insurance coverage purchased on health insurance exchanges for individuals between 100% and 400% FPL

Supreme Court decision on ACA’s Medicaid expansions

Constitutional challenge to ACA’s Medicaid expansion filed by Florida and joined by 25 additional states

On June 28, 2012, the Supreme Court ruled that the ACA’s Medicaid expansion was “unconstitutionally coercive”

The Supreme Court’s remedy was to “constrain the Federal government’s power in enforcing state compliance”

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Presentation Notes

As a result, only 25 states (including DC) chose to expand Medicaid to include all individuals less than 133% FPL by January 2014, and 32 states as of July 2016

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Presentation Notes

Figure shows how annual subsidy varies with income for a single 60-year-old facing the US average premium. No subsidy for individuals under 100% FPL Steeper slope between 133% and 300% as percentage of income towards health insurance increases Since subsidies are generated to cap the percentage of income paid towards health insurance, they absorb fluctuations in premiums due to age, regional health care costs, etc. As a result, subsidies are higher for people who face higher premiums (i.e. older individuals vs. younger individuals, individuals in high cost places vs. low cost places).

Coverage Gains Vary by State % Uninsured Expanded

Medicaid State 2013 2015

California 21.6 11.8 Yes

Colorado 17.0 10.3 Yes

Florida 22.1 15.7 No

Illinois 15.5 8.7 Yes

Kentucky 20.4 7.5 Yes

Massachusetts 4.9 3.5 Yes

New York 12.6 8.6 Yes

Oregon 19.4 7.3 Yes

Texas 27.0 22.3 No

Virginia 13.3 12.6 No

How could the ACA affect the labor market?

The ACA weakens the tie between employment and health insurance and could affect labor supply decisions through the following:

1. Subsidies for health insurance purchased through the exchanges

2. The expansion of Medicaid eligibility

3. Penalties on employers that decline to offer insurance

4. New taxes imposed on labor income

How could the ACA affect the labor market? (cont.)

The ACA could also affect labor demand through the following:

1. Provisions that waive penalties for firms who do not offer insurance and have less than 50 employees.

2. Provisions that waive penalties for firms who do not offer insurance to employees working less than 30 hours.

3. Employers with a large share of minimum wage employees may reduce employment if mandated to either offer coverage or face a penalty

Reasons why ACA could affect older workers in particular

1. Effective subsidies are highest for near-elderly workers

2. Rating regulations limit the ability of insurers to vary premiums by age

3. Evidence that labor supply elasticities are higher for individuals nearing retirement

Empirical approach Exploit geographic variation in expected “intensity” of treatment by ACA based on:

1. Pre-ACA share of region uninsured and under 138% FPL 2. Pre-ACA share of region uninsured and between 139-399% FPL 3. Medicaid expansion status

Hypotheses 1. Expansion states: Medicaid ↑, private coverage ↑ 2. Non-expansion states: Medicaid --, private coverage ↑ ↑ 3. Places with a larger share of population eligible for subsidies will have

larger increases in private coverage 4. Places with a larger share of population eligible for Medicaid will have

larger increases in Medicaid coverage (in expansion states) 5. Places with larger increases in coverage also have larger changes in labor

supply

Empirical approach We estimate the following regression:

𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖𝑖𝑖 = 𝛿𝛿 𝑃𝑃𝑃𝑃𝐼𝐼𝑃𝑃𝑖𝑖 + 𝛾𝛾 𝑃𝑃𝑃𝑃𝐼𝐼𝑃𝑃𝑖𝑖 × 𝐹𝐹𝐹𝐹𝐹𝐹𝑃𝑃𝑃𝑃𝐹𝐹𝑖𝑖 + 𝛽𝛽𝛽𝛽𝑖𝑖𝑖𝑖 + 𝜇𝜇𝑖𝑖 + 𝑦𝑦𝑖𝑖 + 𝜀𝜀𝑖𝑖𝑖𝑖

𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖𝑖𝑖 can represent any HI, private coverage, Medicaid for person i in year t

𝑃𝑃𝑃𝑃𝐼𝐼𝑃𝑃𝑖𝑖 is an indicator of 2014 or later

𝐹𝐹𝐹𝐹𝐹𝐹𝑃𝑃𝑃𝑃𝐹𝐹𝑖𝑖 represents pre-ACA characteristic(s) for person i’s region (standardized to have mean 0)

𝛽𝛽𝑖𝑖𝑖𝑖 includes demographic controls and age fixed effects; 𝜇𝜇𝑖𝑖 and 𝑦𝑦𝑖𝑖 represent region and year fixed effects

Standard errors clustered by region

Empirical approach

Interact 𝑃𝑃𝑃𝑃𝐼𝐼𝑃𝑃𝑖𝑖 and 𝑃𝑃𝑃𝑃𝐼𝐼𝑃𝑃𝑖𝑖 × 𝐹𝐹𝐹𝐹𝐹𝐹𝑃𝑃𝑃𝑃𝐹𝐹𝑖𝑖 with 𝐸𝐸𝛽𝛽𝑃𝑃𝐹𝐹𝐼𝐼𝐼𝐼𝐼𝐼𝑃𝑃𝐼𝐼𝑖𝑖 to determine whether estimates differ between Medicaid expansion and non-expansion states

Run similar specifications with labor market outcomes to examine effect of ACA on the labor market.

Identifying assumptions

Absent the ACA, geographic areas with larger shares of uninsured individuals under 400% FPL would have evolved similarly as those with smaller shares, after controlling for region-level characteristics that do not change over time and person-level demographics.

Places with a given share of uninsured individuals under 400% FPL in expansion states would have evolved similarly as those with a similar share in non-expansion states, absent the ACA.

Data American Community Survey (ACS) Public Use Microdata Sample (PUMS)

Household survey with ~3,540,000 annual subjects, very high response rate, and fine geographic identifiers

Sample restrictions ◦ 2010 - 2014 ◦ Civilians, ages 45-64 ◦ 4,435,742 person-year observations

Geographic identifiers ACS contains state of residence and Public Use Microdata Areas (PUMAs)

◦ PUMAs do not cross state borders ◦ Building blocks: census blocks or counties ◦ Minimum population of 100,000 individuals

PUMAs are redefined every decennial census ◦ 2000 census 2,071 PUMAs; used through 2011 ACS ◦ 2010 census 2,378 PUMAs; used for 2012 ACS and later ◦ No crosswalk available; we use a simulation methodology based on

population shares to create a consistent set of geographic regions over our sample period

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Presentation Notes

This map shows variation in the share of the population that is both uninsured and under 138% FPL across 2,351 PUMAs that we use in our analysis. Darker regions represent regions where this share is high (and the expected treatment intensity is high), while lighter regions represent places where this share is low. Regions could have a low value either because the uninsurance rates are low OR because poverty rates are low. Share < 138 % FPL & Uninsured in 2013 – summary statistics 2,3517.815.020.0042.56 Two with 0 (both in MA): Billerica, Andover, Tewksbury & Wilmington Towns Plymouth & Bristol Counties (Outside Brockton City) 7 lowest values are in MA Two highest values both in TX (McAllen): Hidalgo County (North & West) – 39% Hidalgo County (East)--Alamo & Donna Cities – 42.5%

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The full U.S. map makes it difficult to see the fine level of geographic variation in densely populated areas. These maps show the variation in the state of California and Los Angeles County up close. 69 PUMAs in single county.

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Presentation Notes

This map shows variation in the share of the population that is both uninsured and between139-399% FPL across 2,351 PUMAs that we use in our analysis. Darker regions represent regions where this share is high (and the expected treatment intensity is high), while lighter regions represent places where this share is low. Regions could have a low value either because the uninsurance rates are low OR because share 139-399% FPL is low. Share 139-399 % FPL & Uninsured in 2013 – summary statistics 2,3519.124.450.2729.60 11 lowest values are in MA: Norfolk (Northeast) & Middlesex (Southeast) Counties (West of Boston City) Essex County (Central)--Amesbury Town City Middlesex (West Central) & Worcester (East) Counties Billerica, Andover, Tewksbury & Wilmington Towns Middlesex County--Watertown Town City, Arlington, Belmont & Winchester Towns Middlesex County (Outside Lowell City) Boston City--Allston, Brighton & Fenway Middlesex (Southeast) & Norfolk (Northeast) Counties--Newton City & Brookline Town Berkshire County--Pittsfield City Worcester County (South) Worcester County (West Central) Five highest values: Miami-Dade County (East Central)--Miami City (West) Houston City (West)--Westpark Tollway, Between Loop I-610 & Beltway TX-8 Prince Georges County (Northwest)--College Park City & Langley Park Miami-Dade County (Northeast)--Hialeah City (South Central) Los Angeles County (Central)--LA City (Central/Koreatown)

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LA County 69 pumas.

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This figure shows baseline rates of insurance coverage in the ACS by age groups. The groups we focus on have the highest rates of uninsurance. Non-exclusive categories add up to more than one. Individuals age 25 and younger have higher rates of Medicaid coverage Individuals age 65+ have almost universal Medicare coverage and very low rates of uninsurance. None of the ACA provisions were focused on this group.

Labor market outcomes Not in the labor force (NILF) – not employed last week nor looking over last four weeks

Employed – last week

Self-employed – employed last week and reports working for self

Hours – usual hours worked per week in the past 12 months, conditional on employment last week

Part-time – employed last week and hours < 30

Research questions

1. How much of the reduction in uninsurance seen since 2014 was due to the ACA?

2. What is the impact of the ACA on labor market outcomes?

Results: overall health insurance coverage

(1) (2) (3)VARIABLES hicov hicov hicov

Post 3.244*** 3.243*** 2.457***(0.0944) (0.0921) (0.115)

Post*C_(Share <=138% FPL & Uninsured in 2013) 0.0937*** 0.0476(0.0264) (0.0358)

Post*C_(Share 139-399% FPL & Uninsured in 2013) 0.266*** 0.197***(0.0268) (0.0381)

Exp*Post 1.799***(0.140)

Exp*Post*C_(Share <=138% FPL & Uninsured in 2013) 0.221***(0.0500)

Exp*Post*C_(Share 139-399% FPL & Uninsured in 2013) 0.101**(0.0511)

Observations 4,435,742 4,435,742 4,435,742R-squared 0.066 0.066 0.066Pre-ACA Mean of Dependent Variable 84.26 84.26 84.26Pre-ACA Mean of Dependent Variable (Non-Exp States) 82.82 82.82 82.82Pre-ACA Mean of Dependent Variable (Exp States) 85.68 85.68 85.68

Slope = 0.27***

Slope = 0.05 (n.s.)

Slope = 0.30***

Slope = 0.20***

Slope = 0.28***

Slope = 0.09***

Slope = 0.13***

Slope = 0.01 (n.s.)

Slope = 0.00 (n.s.)

Slope = 0.02 (n.s.)

Slope = 0.10***

Slope = 0.08**

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Percentage of Persons 18-64 Uninsured, January 2010 - March 2016

Source: NCHS. National Health Interview Survey Early Release of Quarterly Estimates, retrieved September 12, 2016.

ACA Takes Effect

Overview of findings Overall health insurance coverage

◦ Increased by 1.85 p.p. in states that did not expand Medicaid, 3.34 p.p. in states that did as a result of Medicaid expansions and subsidies, accounting for approximately 80 percent of the increase in health insurance coverage

◦ Larger increases in places with higher share uninsured and < 138% FPL for expansion states only

◦ Larger increases in places with higher share uninsured and 139-399% FPL for both groups of states

Medicaid coverage ◦ Increased by 0.57 p.p. in non-expansion states, 2.3 p.p. in expansion states ◦ Larger increases in places with higher share < 138% FPL and uninsured in

both groups of states, but effect sizes are larger in magnitude for expansion states

Overview of findings (cont.) Privately purchased health insurance coverage

◦ Increased by 0.83 p.p. in non-expansion states, 0.64 p.p. in expansion states ◦ Larger increases in places with higher share uninsured and 139-399% FPL for

both groups of states

Employment outcomes (preliminary) ◦ Little evidence that labor supply changed differentially where increases in HI

coverage were larger in 2014 ◦ Additional years of data will shed more light on longer-run effect

Conclusions and next steps ACA had a substantial effect on health insurance coverage:

◦ Exchange subsidies account for a larger share in non-expansion states ◦ Medicaid accounts for a larger share in expansion states ◦ Little evidence of crowd-out of employer coverage

Little evidence of impact of ACA on labor market outcomes ◦ May change with the addition of more data

Next steps ◦ Incorporate 2015 data when available ◦ Examine heterogeneity across demographic groups ◦ Perform additional robustness and falsification exercises

Extra slides

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Presentation Notes

Point out Kentucky, West Virginia, New Mexico – states with largest increases in Medicaid enrollment; also states with a large percentage in poverty and larger baseline rates of uninsurance. https://www.medicaid.gov/medicaid-chip-program-information/program-information/downloads/june-2016-enrollment-report.pdf

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States that did not expand tend to have smaller Medicaid programs to begin with. Very little change in Medicaid enrollment. Some states saw reductions in Medicaid enrollment after the ACA.

Calculation of subsidies under the ACA

1. Determine income as a percentage of the FPL (varies based on family size).

2. Determine maximum percentage of income one is responsible for paying towards the cost of health insurance (varies from 2 percent at 100% FPL to 9.5% at 400% FPL).

3. Multiply this percentage by the cost of the 2nd lowest cost “silver tier” plan available on the exchange to determine the maximum premium payment the person is responsible for.

4. Subsidy = cost of 2nd lowest cost “silver tier” plan – maximum premium payment

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Exchange enrollment varies considerably across states as well. Mean = 2.62% of states population enrolled on exchange http://kff.org/health-reform/state-indicator/total-marketplace-enrollment-and-financial-assistance/ March 2016

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Enrollment hits 11.7 million (end of 2015) This is smaller than the increase in Medicaid enrollment One of the reasons states that did not expand Medicaid may have seen more people on the exchanges is the 100-133% FPL Mean = 4.34% of state’s population enrolled on the exchange

Literature Geographic variation as a proxy for expected treatment intensity:

◦ Finkelstein (2007), Finkelstein and McKnight (2008), Miller (2012)

Prior work on policy expansions and health insurance coverage: ◦ Cutler and Gruber (1996), Aizer and Grogger (2003), Long et al. (2009),

Kolstad and Kowalski (2012), Finkelstein et al. (2012), Hamersma and Kim (2013), Sonier et al. (2013)

Prior work on health insurance and labor market outcomes: ◦ Madrian (1994), Gruber and Madrian (2002) and references therein ◦ Massachusetts: Heim and Lin (2014), Heim and Lurie (2015) ◦ Medicaid expansions/contractions: Hamersma and Kim (2009), Strumpf

(2011), Garthwaite, Gross, Notodowigdo (2014), Dague, DeLeire and Leininger (2014), Baicker et al. (2014), Dave et al. (2015)

Literature Recent work on ACA and health insurance coverage

◦ Dependent care mandate: Cantor et al. (2012), Sommers and Kronick (2012), Sommers et al. (2013), Antwi, Moriya, Simon (2015)

◦ Medicaid expansions: Kaestner et al. (2015), Leung and Mas (2016), Sommers et al. (2014, 2015)

◦ Descriptive/trends: Long et al. (2014), Smith and Medalia (2014), Carman et al. (2015), Black and Cohen (2015), Courtemanche et al. (2016a)

◦ Other id strategies: Courtemanche et al. (2016), Frean, Gruber, Sommers (2016),

Recent work on ACA and labor market outcomes ◦ Dependent care mandate: Antwi, Moriya, Simon (2015), Bailey and Chorniy

(2015), Heim, Lurie and Simon (2015) ◦ Medicaid expansions: Kaestner et al. (2015), Leung and Mas (2016), Gooptu et al.

(2016), Moriya et al. (2016) ◦ Models/Simulations: Fang and Shephard (2015), Mulligan (2014), Heim, Hunter,

Lurie, and Ramnath (2014)

Our contributions

1. Use a difference-in-difference-in-difference (DDD) strategy that combines preexisting income distribution and uninsurance rates across geography

2. Using DDD strategy to examine labor supply outcomes

3. Employing a simulation methodology that allows us to use finer geographic areas across a broader time horizon

Outline 1. Describe empirical approach

2. Introduce data used in the analysis

3. Show results of the impact of the ACA on:

a. Health insurance coverage and source of coverage

b. Labor market outcomes

4. Conclusion

Baseline Demographics (2010-2013)

Examples of PUMA simulations PUMA00 PUMA10 PUMA10 Name Pop Factor

2701 8501 Santa Clara County (Northwest)--Mountain View, Palo Alto & Los Altos Cities 195,320 12702 8502 Santa Clara County (Northwest)--Sunnyvale & San Jose (North) Cities 144,475 12703 8502 Santa Clara County (Northwest)--Sunnyvale & San Jose (North) Cities 2,144 0.017

8503 Santa Clara County (Northwest)--San Jose (Northwest) & Santa Clara Cities 126,534 0.9833002 5301 Monterey County (North Central)--Seaside, Monterey, Marina & Pacific Grove Cities 52,046 0.261

5302 Monterey County (Northeast)--Salinas City 15,090 0.0765303 Monterey (South & East) & San Benito Counties 132,156 0.663

5412 3729 Los Angeles County (West Central)--LA City (West Central/Westwood & West Los Angeles 112 0.0013730 Los Angeles County (West Central)--LA City (Central/Hancock Park & Mid-Wilshire) 164,226 0.999

5413 3720 Los Angeles County (Central)--Burbank City 3,451 0.0163731 Los Angeles County (Central)--West Hollywood & Beverly Hills Cities 39,401 0.1793732 Los Angeles County (Central)--LA City (East Central/Hollywood) 176,714 0.805

5701 3763 Los Angeles County (South Central)--Long Beach City (North) 141,698 1

Heterogeneity across PUMAs

Average = 7.81

Heterogeneity across PUMAs

Average = 9.12

Health insurance coverage in the ACS Insurance from a current or former employer

Insurance purchased directly from an insurance company

Medicare

Medicaid, Medical Assistance, or any kind of government-assistance plan for those with low incomes or a disability

Tricare or other military health care

VA

Indian Health Service

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Presentation Notes

Series of bin-scatter plots that summarize the percent of the population with different types of health insurance coverage by single years of age. Health insurance coverage rises with age Shift in insurance coverage in 2014 for every single year of age Difference between 25 and 26 year olds due to dependent care mandate (our analysis sample omits 25 year olds)

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Can also examine how patterns differ in expansion states vs. non-expansion states. Expansion states show bigger increases in health insurance coverage than non-expansion states.

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Medicaid coverage slightly declines with age. In expansion states, Medicaid coverage increased dramatically. In non-expansion states, Medicaid coverage did increase but much more modestly.

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Privately-purchased insurance is relatively low. Increases with age. Increases slightly in expansion states, and to a greater extent in non-expansion states.

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No dramatic shifts in private employer coverage before and after ACA. Large share of overall health insurance coverage for these groups. Similar in non-expansion states. Some evidence that it declined for older ages.

Baseline labor market outcomes (2010-2013)

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Bin scatter plot shows very little change pre and post ACA.

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If anything, percent employed looks like it increased.

Robustness checks PUMAs – use only years with consistent boundaries

Using average of “share” variables from 2010-2013

Results: overall health insurance coverage

(1) (2) (3) (4) (5) (6)VARIABLES hicov hicov hicov hicov hicov hicov

Post 4.919*** 4.916*** 4.001*** 3.242*** 3.241*** 2.466***(0.125) (0.124) (0.154) (0.095) (0.0922) (0.115)

Post*C_(Share <=138% FPL & Uninsured in 2013) 0.0579*** -0.010 0.0963*** 0.049(0.0212) (0.027) (0.0266) (0.036)

Post*C_(Share 139-399% FPL & Uninsured in 2013) 0.158*** 0.140*** 0.265*** 0.200***(0.0243) (0.036) (0.0267) (0.038)

Exp*Post 2.130*** 1.774***(0.183) (0.140)

Exp*Post*C_(Share <=138% FPL & Uninsured in 2013) 0.241*** 0.223***(0.041) (0.050)

Exp*Post*C_(Share 139-399% FPL & Uninsured in 2013) 0.013 0.0917*(0.047) (0.051)

Observations 3,472,598 3,472,598 3,472,598 4,435,742 4,435,742 4,435,742 R-squared 0.104 0.104 0.104 0.065 0.065 0.065ymean 76.17 76.17 76.17 84.94 84.94 84.94

Ages 26-44 Ages 45-64

Results: Medicaid coverage (1) (2) (3) (4) (5) (6)

VARIABLES Any Mdcd Any Mdcd Any Mdcd Any Mdcd Any Mdcd Any MdcdAll StatesPost 2.855*** 2.854*** 1.273*** 2.751*** 2.752*** 1.366***

(0.102) (0.101) (0.108) (0.0865) (0.0850) (0.0871)Post*C_(Share <=138% FPL & Uninsured in 2013) 0.0805*** 0.0522*** 0.145*** 0.0881***

(0.0136) (0.0142) (0.0202) (0.0200)Post*C_(Share 139-399% FPL & Uninsured in 2013)

Exp*Post 3.362*** 2.996***(0.158) (0.131)

Exp*Post*C_(Share <=138% FPL & Uninsured in 2013) 0.191*** 0.288***(0.0258) (0.0356)

Exp*Post*C_(Share 139-399% FPL & Uninsured in 2013)

Observations 3,472,598 3,472,598 3,472,598 4,435,742 4,435,742 4,435,742R-squared 0.061 0.061 0.061 0.050 0.050 0.051ymean 11.82 11.82 11.82 10.22 10.22 10.22

Ages 45-64Ages 26-44

Results: privately-purchased health insurance coverage

(1) (2) (3) (4) (5) (6)VARIABLES Pvt Purch Pvt Purch Pvt Purch Pvt Purch Pvt Purch Pvt PurchAll StatesPost 1.192*** 1.192*** 1.613*** 1.127*** 1.126*** 1.622***

(0.0776) (0.0776) (0.0972) (0.0780) (0.0779) (0.101)Post*C_(Share <=138% FPL & Uninsured in 2013)

Post*C_(Share 139-399% FPL & Uninsured in 2013) 0.0522*** 0.0474*** 0.113*** 0.106***(0.0120) (0.0171) (0.0163) (0.0249)

Exp*Post -0.838*** -0.993***(0.124) (0.125)

Exp*Post*C_(Share <=138% FPL & Uninsured in 2013)

Exp*Post*C_(Share 139-399% FPL & Uninsured in 2013) -0.0146 -0.0149(0.0238) (0.0322)

Observations 3,472,598 3,472,598 3,472,598 4,435,742 4,435,742 4,435,742R-squared 0.017 0.017 0.017 0.017 0.017 0.017ymean 7.769 7.769 7.769 11.29 11.29 11.29

Ages 45-64Ages 26-44

Results: private employer health insurance coverage

(1) (2) (3) (4) (5) (6)VARIABLES Pvt Emp Pvt Emp Pvt Emp Pvt Emp Pvt Emp Pvt EmpAll StatesPost 0.125 0.124 0.349** -1.612*** -1.612*** -1.540***

(0.136) (0.136) (0.175) (0.116) (0.116) (0.148)Post*C_(Share <=138% FPL & Uninsured in 2013)

Post*C_(Share 139-399% FPL & Uninsured in 2013) 0.0636*** 0.0190 0.0729*** 0.0516(0.0195) (0.0294) (0.0225) (0.0327)

Exp*Post -0.383* -0.124(0.207) (0.181)

Exp*Post*C_(Share <=138% FPL & Uninsured in 2013)

Exp*Post*C_(Share 139-399% FPL & Uninsured in 2013) 0.0756* 0.0374(0.0391) (0.0453)

Observations 3,472,598 3,472,598 3,472,598 4,435,742 4,435,742 4,435,742R-squared 0.090 0.090 0.090 0.064 0.064 0.064ymean 58.01 58.01 58.01 63.25 63.25 63.25

Ages 26-44 Ages 45-64

Results: labor market outcomes (preliminary!)

(1) (2) (3) (4) (5) (6)VARIABLES NILF employed self_emp PT wage hours

Post 0.678*** 1.935*** -0.264*** 0.194*** 454.7*** 0.980***(0.120) (0.134) (0.0663) (0.0710) (143.4) (0.0626)

Post*C_(Pct <=138% FPL & Uninsured in 2013) 0.0171 -0.0227 0.0207* 0.00160 -61.67*** -0.0104(0.0209) (0.0251) (0.0118) (0.0126) (20.05) (0.0111)

Post*C_(Pct 139-399% FPL & Uninsured in 2013) 0.0236 0.0423* -0.00725 0.0229 -57.60** 0.0117(0.0232) (0.0250) (0.0151) (0.0157) (25.66) (0.0117)

Exp*Post -0.321** 0.445*** 0.0955 -0.0543 -127.9 0.159**(0.143) (0.157) (0.0848) (0.0919) (166.3) (0.0728)

Exp*Post*C_(Pct <=138% FPL & Uninsured in 2013) 0.0209 -0.00340 -0.00798 -0.00573 4.332 1.93e-05(0.0312) (0.0357) (0.0182) (0.0188) (31.01) (0.0158)

Exp*Post*C_(Pct 139-399% FPL & Uninsured in 2013) -0.0380 0.000526 0.0108 -0.0118 15.97 -0.000338(0.0341) (0.0368) (0.0217) (0.0220) (38.04) (0.0167)

Observations 3,472,598 3,472,598 3,472,598 3,472,598 3,472,598 3,472,598R-squared 0.043 0.043 0.018 0.017 0.143 0.075ymean 17.80 75.36 5.855 7.303 37369 30.63

Ages 26-44

Results: labor market outcomes (preliminary!)

(1) (2) (3) (4) (5) (6)VARIABLES NILF employed self_emp PT wage hours

Post 0.506*** 1.802*** -0.0266 0.0593 1,478*** 0.964***(0.114) (0.122) (0.0762) (0.0612) (162.7) (0.0550)

Post*C_(Pct <=138% FPL & Uninsured in 2013) 0.0631* -0.0375 -0.0287 0.00326 -125.5*** -0.0185(0.0348) (0.0316) (0.0212) (0.0162) (34.63) (0.0137)

Post*C_(Pct 139-399% FPL & Uninsured in 2013) -0.0107 0.0314 0.0386* 0.0200 -70.73* 0.00572(0.0352) (0.0340) (0.0217) (0.0162) (36.41) (0.0149)

Exp*Post 0.200 0.0659 0.0310 -0.101 144.1 0.0195(0.140) (0.150) (0.0906) (0.0792) (193.1) (0.0680)

Exp*Post*C_(Pct <=138% FPL & Uninsured in 2013) -0.00122 0.0108 0.0438 -0.0391 21.08 0.0131(0.0483) (0.0484) (0.0295) (0.0257) (57.68) (0.0211)

Exp*Post*C_(Pct 139-399% FPL & Uninsured in 2013) -0.0128 0.0413 -0.0573* -0.00435 -53.58 0.0162(0.0470) (0.0472) (0.0301) (0.0251) (59.60) (0.0210)

Observations 4,435,742 4,435,742 4,435,742 4,435,742 4,435,742 4,435,742R-squared 0.092 0.078 0.024 0.015 0.127 0.100ymean 27.24 67.78 8.584 7.104 41255 27.60

Ages 45-64

Heterogeneity across PUMAs

ACA Takes Effect

Summary Statistics - baseline

Potential factors associated with larger coverage expansions

PUMA-Level Summary Statistics

Variable N Mean SD Min MaxShare uninsured in 2013 2,351 20.32 9.51 0.63 70.40Share < 138 % FPL in 2013 2,351 19.22 8.98 2.38 59.08Share 139-399 % FPLin 2013 2,351 39.43 7.99 10.52 62.43Share < 138 % FPL & Uninsured in 2013 2,351 7.81 5.02 0.00 42.56Share 139-399 % FPL & Uninsured in 2013 2,351 9.12 4.45 0.27 29.60

Ages 25-64