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The Impact of Affordable Care Act Medicaid Expansions on Applications to Federal Disability Programs

Priyanka Anand, Jody Schimmel Hyde, Maggie Colby and Paul O’Leary

Abstract

In this paper, we estimate the impact of Medicaid expansions via the Patient Protection and Affordable Care Act (ACA) on applications to federal disability programs in 14 states that expanded Medicaid in January 2014. We use a difference-in-differences regression model to compare disability application rates in geographic areas within states that expanded Medicaid to rates in areas of non-expansion states that were carefully selected using a matching approach that accounts for state Medicaid policies pre-ACA as well as demographic and socioeconomic characteristics that might influence disability application rates. We find a slower decrease in Supplemental Security Income (SSI) application rates after Medicaid expansions in expansion states relative to non-expansion states, with application rates declining in both state groups from 2014 through 2016. Our analysis of the impact of the Medicaid expansions on Social Security Disability Insurance (SSDI) application rates was inconclusive for reasons we discuss in the paper.

Funding source: U.S. Social Security Administration

Award Identifier / Grant number: Disability Research Consortium, 1-DRC12000001-01-0

Funding statement: U.S. Social Security Administration, Funder Id: 10.13039/100005225, Grant Number: Disability Research Consortium, 1-DRC12000001-01-0.

Acknowledgements

The authors would like to thank Lauren Hula, Andrew McGuirk, Swaati Bangalore, and Charles Hanley at Mathematica Policy Research for their efforts in the data analysis and Neil McCray at George Mason University for research assistance. We also acknowledge valuable input received from Randall Brown and David Stapleton at Mathematica on the research design and manuscript, as well as comments about the study design received from Jack Gettens, Kosali Simon, and participants at the 2016 Disability Research Consortium Annual Research Meeting. The research reported herein was performed pursuant to a grant from the U.S. Social Security Administration (SSA) funded as part of the Disability Research Consortium. The opinions and conclusions expressed are solely those of the author(s) and do not represent the opinions or policy of SSA or any agency of the Federal Government.

Methods Appendix: Matching Model Details

We estimated a separate propensity score model at the PUMA level for each group of states shown in Table 1. Each propensity score model contained a mutually exclusive set of states; neither treatment nor comparison PUMAs were included in more than one model. We used the same set of covariates in each of the state group models because we did not have a strong reason to believe that the set of predictors should vary by group. The reason that we developed the propensity score model separately by state group is that we believed that the relationship between Medicaid expansions and SSI/SSDI may have varied across those groups, and individual models allowed us the best matched comparison sample for each group of expansion states. The variables included in the propensity score model and estimated coefficients for each of the four models are provided in Appendix Table 3.

After running these models separately, a propensity score was calculated for each PUMA. With those scores in hand, we used nearest-neighbor matching with replacement, allowing for up to four matches per expansion PUMA, with a caliper of 0.1 standard deviations of the overall estimated propensity score. Traditionally, the standard in the matching literature has been to use a caliper of 0.2 standard deviations (Rosenbaum and Rubin 1985; Stuart 2010); we used an even smaller caliper of 0.1 standard deviations to ensure high quality matches. We limited the number of matches to four, given findings that increasing the number of matches beyond five tends to increase the bias in the estimated treatment effect (Austin 2010).

To yield better match quality, we excluded treatment and comparison PUMAs that had an estimated propensity score smaller than 0.1 or larger than 0.9, as has been suggested in the literature (Crump et al. 2009). While excluding cases with propensity scores outside this interval improves match quality of the remaining cases, in our case, it significantly reduced the number of matched PUMAs. Before trimming, 487 of 547 expansion PUMAs had at least one match, and 237 of 861 potential comparison PUMAs were matched to one or more expansion PUMAs; after trimming the number of matched expansion PUMAs fell from 487 to 257. Appendix Table 4 highlights the number of PUMAs that were matched in each state. Because we exclude about half of the PUMAs and populations in each state from our analysis, it is important to recognize that the impact estimates based on the trimmed samples may not be generalizable to all PUMAs. Nonetheless, our inspection of the untrimmed results (not shown) highlighted that our overall estimates were not significantly different from the trimmed version and in the state-level estimates, so our general conclusions would have remained unchanged. These results are available to interested readers upon request.

Table 3:

Coefficients from Propensity Score Logit Model, by State Group.

State group 1State group 2State group 3State group 4
VariableCoefficient [standard error]
Percentage change in the unemployment rate, 2010–20130.147***−0.0210.271***0.001
[0.02][0.04][0.06][0.04]
Size of working-age population, 2013−0.000−0.0000.0000.000
[0.00][0.00][0.00][0.00]
Percentage change in the working-age population, 2010–2013−0.241***−0.091*−0.0020.028
[0.05][0.05][0.08][0.07]
Population density0.001***0.000**0.001***0.001***
[0.00][0.00][0.00][0.00]
Percentage uninsured−0.164***−0.584***−0.443***−0.641***
[0.06][0.09][0.14][0.15]
Percentage privately insured−0.067**−0.405***0.017−0.227***
[0.03][0.07][0.07][0.08]
Percentage white−0.193***−0.033−0.0090.063
[0.04][0.03][0.07][0.08]
Percentage black−0.307***−0.446***0.0010.046
[0.04][0.08][0.07][0.08]
Percentage Hispanic−0.081***−0.039**0.234***0.267***
[0.03][0.02][0.07][0.07]
Percentage with income < 137% FPL−0.0560.257***0.0930.119
[0.05][0.07][0.10][0.09]
Median gross rent, 2013−0.0010.012***−0.006***−0.001
[0.00][0.00][0.00][0.00]
Percentage of the population ages 25–34−0.0410.258**−0.179−0.243*
[0.07][0.10][0.12][0.14]
Percentage of the population ages 35–64−0.0280.162**0.0870.230**
[0.05][0.07][0.08][0.09]
Percentage of the population over age 64−0.089*0.0260.009−0.194
[0.05][0.10][0.12][0.12]
Percentage change in SSDI-only applications, 2010–2011−0.049**−0.025−0.056*0.011
[0.02][0.02][0.03][0.03]
Percentage change in SSDI-only applications, 2011–2012−0.094***−0.031−0.0150.029
[0.02][0.03][0.03][0.03]
Percentage change in SSDI-only applications, 2012–2013−0.034−0.035−0.0350.079***
[0.02][0.03][0.03][0.03]
Percentage change in SSI applications, 2010–2011−0.0310.094***0.0060.044
[0.02][0.03][0.03][0.03]
Percentage change in SSI applications, 2011–2012−0.084***0.0280.016−0.034
[0.02][0.03][0.03][0.03]
Percentage change in SSI applications, 2012–2013−0.064***0.006−0.017−0.046
[0.02][0.03][0.03][0.03]
R20.5680.7030.6140.426
Number of observations553422233200

  1. Source: Authors’ calculations, using data derived from SSA’s SDR, the ACS, and BLS unemployment statistics.

  2. *Denotes p-value < 0.10, **denotes p-value < 0.05, and ***denotes p-value < 0.01. Values are derived from the three-year ACS estimates from 2010–2012 unless otherwise noted. Demographic and socioeconomic characteristics were calculated among working-age adults.

Table 4:

Number of PUMAs in State-level Regressions and 2013 application counts.

Unmatched PUMAsMatched PUMAs
ExpansionNon-ExpansionExpansionNon-ExpansionSSI unmatched regression sample sizeSSI matched regression sample sizeSSDI unmatched regression sample sizeSSDI matched regression sample size
Overall54786125718739,41612,43039,42412,432
Arkansas20350164824,6641,79224,6681,792
Kentucky34350274125,0561,90425,0601,904
New Jersey73350273226,1461,65226,1521,652
Rhode Island735031024,30036424,304364
Washington56350265125,6722,15625,6762,156
West Virginia13350113024,4681,14824,4721,148
Arizona54308162125,6161,03625,6201,036
Colorado42308223225,2781,51125,2841,512
New Mexico1830891724,60872824,612728
Connecticut2611481324,83258824,836588
Illinois88114242126,5681,26026,5721,260
North Dakota51141424,24413924,248140
Nevada1889101824,60878424,612784
Ohio9389574126,7082,74426,7122,744

  1. Source: Data derived from authors’ calculations using SSA’s SDR, the ACS, and BLS unemployment statistics.

  2. Notes: In the unmatched models, the 547 PUMAs in each expansion state are compared to all 861 PUMAs in non-expansion states, not limited to the PUMAs within the state group. In the matched models, PUMAs from expansion states could only match to PUMAs from states in the same state group. The number of observations in each regression include the number of matched PUMAs, multiplied by the number of quarters of data available (as many as 28 quarters).

Table 5:

Coefficients from Difference-in-Differences Model.

SSISSDI-only
PostQuarterlyQuarterly with leadsPostQuarterly Quarterly with leads
VariableMatchUnmatchMatchUnmatchMatchUnmatchMatchUnmatchMatchUnmatchMatchUnmatch
Expanded0.1080.113*0.044**0.035*
[0.12] [0.06][0.02][0.02]
Expanded* (q)−0.0090.022−0.075−0.0410.0040.020−0.050−0.013
[0.09][0.06][0.05][0.03][0.03][0.02][0.05][0.02]
Expanded* (q + 1)0.1300.0610.064*−0.0030.064**0.029*0.009−0.004
[0.10][0.05][0.03][0.03][0.03][0.02][0.04][0.02]
Expanded* (q + 2)0.1270.0550.061−0.0090.0470.012−0.007−0.021
[0.09][0.06][0.06][0.06][0.04][0.02][0.04][0.03]
Expanded* (q + 3)0.1400.158**0.075**0.094***0.0450.040*−0.0100.007
[0.12][0.07][0.03][0.03][0.03][0.02][0.04][0.02]
Expanded* (q + 4)0.1500.116*0.084*0.0520.051**0.053**−0.0040.020
[0.13][0.06][0.04][0.03][0.02][0.02][0.04][0.02]
Expanded* (q + 5)0.1620.140**0.096*0.076*−0.0040.023−0.058*−0.009
[0.11][0.06][0.05][0.04][0.03][0.02][0.03][0.03]
Expanded* (q + 6)0.1080.1030.0420.0390.065**0.0220.010−0.011
[0.11][0.06][0.05][0.05][0.02][0.03][0.05][0.04]
Expanded* (q + 7)0.1020.180**0.0360.116**0.0480.060*−0.0060.028
[0.15][0.09][0.05][0.05][0.04][0.03][0.03][0.03]
Expanded* (q + 8)0.0610.134−0.0050.0710.057**0.062**0.0030.030
[0.13][0.08][0.05][0.05][0.02][0.03][0.04][0.04]
Expanded* (q + 9)0.0700.1120.0040.0480.082**0.0350.0280.003
[0.11][0.07][0.05][0.05][0.03][0.02][0.06][0.03]
Expanded* (q + 10)0.1300.1090.0640.0450.0350.015−0.019−0.018
[0.13][0.07][0.06][0.06][0.03][0.03][0.06][0.04]
Expanded* (q + 11)0.1370.174*0.0710.109*0.0280.042−0.0260.010
[0.17][0.10][0.07][0.06][0.04][0.03][0.04][0.03]
Expanded* (q − 16)−0.110−0.064−0.066−0.034
[0.15][0.10][0.06][0.03]
Expanded* (q − 15)−0.093−0.133−0.083−0.043
[0.16][0.11][0.06][0.03]
Expanded* (q − 14)−0.140−0.171−0.022−0.030
[0.20][0.12][0.08][0.04]
Expanded* (q − 13)−0.083−0.015−0.0430.009
[0.11][0.08][0.04][0.02]
Expanded* (q − 12)−0.081−0.024−0.036−0.017
[0.14][0.08][0.06][0.03]
Expanded* (q − 11)−0.009−0.083−0.039−0.040*
[0.14][0.09][0.04][0.02]
Expanded* (q − 10)−0.068−0.118−0.047−0.056**
[0.14][0.08][0.05][0.02]
Expanded* (q − 9)−0.049−0.000−0.058−0.019
[0.09][0.06][0.04][0.02]
Expanded* (q − 8)−0.050−0.013−0.065−0.028
[0.13][0.07][0.06][0.02]
Expanded* (q − 7)−0.037−0.087−0.035−0.057**
[0.13][0.08][0.04][0.02]
Expanded* (q − 6)−0.090−0.096−0.060−0.049*
[0.16][0.09][0.05][0.02]
Expanded* (q − 5)−0.104−0.022−0.094***−0.034
[0.11][0.06][0.03][0.02]
Expanded* (q − 4)−0.039−0.023−0.103***−0.037*
[0.11][0.06][0.04][0.02]
Expanded* (q − 3)−0.040−0.059−0.081*−0.038
[0.09][0.06][0.04][0.02]
Expanded* (q − 2)−0.066−0.112*−0.046−0.051*
[0.09][0.06][0.04][0.03]
R20.9480.9490.9480.9490.9480.9500.9010.8990.9010.8990.9020.899
F-statistic (p-value) of test that pre-period quarters jointly equal to zero 1.99 [0.06]2.15

[0.04]
4.71 [0.00]5.33 [0.00]
Number of observations12,43039,41612,43039,41612,43039,41612,43239,42412,43239,42412,43239,424
Number of applications per 1000 working-age adults in Q4 2013 in comparison PUMAs1.71.91.71.91.71.91.11.11.11.11.11.1

  1. Source: Authors’ calculations, using data derived from SSA’s SDR, the ACS, and BLS unemployment statistics.

  2. Notes: *Denotes p-value < 0.10, **denotes p-value < 0.05, and ***denotes p-value < 0.01. The model includes quarterly unemployment, quarterly calendar indicators, and PUMA fixed effects. Heteroskedasticity-robust standard errors are clustered at the PUMA level, with weights accounting for the PUMA's population and the matching algorithm as described in the text. Time period q refers to quarter 1, 2014.

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Published Online: 2019-02-23

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