The effect of Medicaid expansions for low-income children on Medicaid participation and private insurance coverage: evidence from the SIPP

Ham, John C.; Shore‐Sheppard, Lara

doi:10.1016/j.jpubeco.2003.07.011

Cited by 89 publications

(90 citation statements)

References 12 publications

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“…If we account for this effect by interpreting it as a transition from private coverage to Medicaid, then the CPS estimates are closer to the level of the SIPP estimates but are still slightly lower in magnitude. This is consistent with Ham and Shore-Sheppard (2005) which found that take-up rates were more similar across the CPS and SIPP when SIPP responses were annualized to be more like CPS data. When we account for the overlap group, the CPS estimates suggest a crowd-out rate of 90% which is reasonably close to the SIPP estimates of crowd-out.…”

supporting

confidence: 88%

“…Using this national population, the simulated instrument is constructed as the percent of people that would be eligible for Medicaid coverage under each state's individual eligibility rules for each year. When constructing these shares, I exclude respondents from the state whose laws are being used to simulate eligibility (Ham and Shore-Sheppard, 2005). These percents are then matched by state and year (based on the eligibility rules that constructed them) to respondents in the sample.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Medicaid expansions for the working age disabled: Revisiting the crowd-out of private health insurance

Wagner

2015

Journal of Health Economics

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supporting

confidence: 88%

mentioning

confidence: 99%

Medicaid expansions for the working age disabled: Revisiting the crowd-out of private health insurance

Wagner

2015

Journal of Health Economics

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“…Since our group of workers represents less than 0.0001% of the working population, this will hardly have an impact on the estimates. 19 Other factors that we could take into account are other macro variables such as changes in the unemployment insurance system; individual time invariant factors such as parental background and individual time varying factors such as health or the presence of children. Note that in this empirical illustration we have taken account of the time invariant cohort factor by taking only one birth cohort.…”

Section: Model With Covariatesmentioning

confidence: 99%

“…Some examples include: labour force participation (Heckman (1981), Hyslop (1999)); smoking (Becker et al (1994)); …rms exporting (Bernard and Jensen (2004)); stock market participation (Alessie et al (2004)) and taking up a welfare program (Gottschalk and Mo¢tt (1994) and Ham and Shore-Sheppard (2005)). The usual time-homogeneous …rst order Markov model for unit i (= 1; ::N ) in period t (t = 0; ::T ) is:…”

Section: Introductionmentioning

confidence: 99%

Dynamic binary outcome models with maximal heterogeneity

Browning

Carro

2014

Journal of Econometrics

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Most econometric schemes to allow for heterogeneity in micro behaviour have two drawbacks: they do not …t the data and they rule out interesting economic models. In this paper we consider the time homogeneous …rst order For comments and useful suggestions, we thank three referees, Enrique Sentana, Whitney Newey, Ivan Fernandez-Val, Sara Ayo, and participants at seminars at Boston University; MIT/Harvard; Yale University; Nu¢eld (Oxford); IFS (London); CEMFI; Manchester; Columbia, CAM (Copenhagen) and a conference at the Tinbergen Institute. The second author gratefully acknowledges that this research was supported by a Marie Curie International Outgoing Fellowship within the 7th European Community Framework Programme, by grants ECO2012-31358, ECO2009-11165 and SEJ2006-05710 from the Spanish Minister of Education, MCINN (Consolider-Ingenio2010), Consejería de Educación de la Comunidad de Madrid (Excelecon project); he also thanks the Department of Economics at the MIT at which he conducted part of this research as visiting scholar.y The …rst draft was titled "Identi…cation of the dynamic discrete choice model" and was presented at the CAM Summer Workshop (University of Copenhagen) in July 2007.Markov (HFOM) model that allows for maximal heterogeneity. That is, the modelling of the heterogeneity does not impose anything on the data (except the HFOM assumption for each agent) and it allows for any theory model (that gives a HFOM process for an individual observable variable). 'Maximal' means that the joint distribution of initial values and the transition probabilities is unrestricted.We establish necessary and su¢cient conditions for generic local point identi…cation of our heterogeneity structure that are very easy to check, and we show how it depends on the length of the panel.We apply our techniques to a long panel of Danish workers who are very homogeneous in terms of observables. We show that individual unemployment dynamics are very heterogeneous, even for such a homogeneous group. We also show that the impact of cyclical variables on individual unemployment probabilities di¤ers widely across workers. Some workers have unemployment dynamics that are independent of the cycle whereas others are highly sensitive to macro shocks.

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“…Many studies have shown that the uninsurance rate in the CPS is systematically higher than that measured by other surveys (Swartz, 1986;Bennefield, 1996;Bhandari, 2004;Ham and Shore-Sheppard, 2005). One possible reason for this is the retrospective perspective of the survey, which asks respondents between February and April about coverage during the entire previous calendar year.…”

mentioning

confidence: 96%

A Reevaluation of the Effects of State and Federal Dependent Coverage Mandates on Health Insurance Coverage

2018

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State governments have been passing laws mandating insurers to allow young adults to stay on their parents' health insurance plans past the age of 19 since the 1970s. These laws were intended to increase coverage, but research has been inconclusive on whether they were successful. We reconsider the issue with an improved approach featuring three key elements: a new, accurate dataset on state mandates; recognition that effects could differ greatly by age due to take up rate differences; and avoidance of endogenous characteristics when identifying mandate eligible young adults. We find the impact of the state mandates was concentrated among the 19 to 22 age group, for which dependent coverage increased sharply by about 6 percentage points. Overall coverage increased by almost 3 percentage points, with the difference explained by crowd out of public insurance. Crowd out of coverage through young adults own jobs was negligible. For those above age 22, we find little evidence of changes in coverage. We incorporate these insights into analysis of the Affordable Care Act (ACA) dependent coverage mandate, showing its effects were focused among those whom were previously ineligible for state mandates, or were eligible but older than 22. We argue the ACA's impact was broader because it had fewer eligibility conditions that implied parental dependence; young adults could be on their parents' insurance but still be relatively independent.

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The effect of Medicaid expansions for low-income children on Medicaid participation and private insurance coverage: evidence from the SIPP

Cited by 89 publications

References 12 publications

Medicaid expansions for the working age disabled: Revisiting the crowd-out of private health insurance

Medicaid expansions for the working age disabled: Revisiting the crowd-out of private health insurance

Dynamic binary outcome models with maximal heterogeneity

A Reevaluation of the Effects of State and Federal Dependent Coverage Mandates on Health Insurance Coverage

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