Health Insurance Take‐up by the Near‐Elderly

Buchmueller, Thomas C.; Ohri, Sabina

doi:10.1111/j.1475-6773.2006.00593.x

Cited by 12 publications

(3 citation statements)

References 12 publications

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“…Yearly renewable term life insurance, USA −0.4 to −0.5 Pauly et al (2003) Term life insurance { XE "term life insurance" }, USA −0.66 Viswanathan et al (2007) Whole life insurance, USA −0.71 to −0.92 Babbel (1985) Health insurance { XE "health insurance" }, USA 0 to −0.2 Chernew et al (1997); Blumberg et al (2001); Buchmueller and Ohri (2006) Health insurance, Australia −0.35 to −0.50 Howeverler (1999) Farm { XE "crop insurance" } crop insurance, USA −0.32 to −0.73 Goodwin (1993) Note: Demand elasticities are all given with a minus sign because a rise in price implies a fall in demand.…”

Section: Market and Country Estimated Demand Elasticities Authorsmentioning

confidence: 99%

Why Insurers Are Wrong about Adverse Selection

Thomas

2018

Laws

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Insurers typically argue that regulatory limits on their ability to use genetic tests will induce 'adverse selection'; they say that this has disadvantages not just for insurers, but also for society as a whole. I argue that, even on its own terms, this argument is often flawed. From the viewpoint of society as a whole, not all adverse selection is adverse. Limits on genetic discrimination that induce the right amount of adverse selection (but not too much adverse selection) can increase 'loss coverage', and so make insurance work better for society as a whole.

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Section: Market and Country Estimated Demand Elasticities Authorsmentioning

confidence: 99%

Why Insurers Are Wrong about Adverse Selection

Thomas

2018

Laws

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“…A questionnaire survey about life insurance purchasing decisions produced an estimate of -0.66 (Viswanathan et al, 2007). For private health insurance in the US, several studies estimate demand elasticities in the range of 0 to -0.2 (Chernew et al, 1997;Blumberg et al, 2001;Buchmueller and Ohri, 2006). For private health insurance in Australia, Butler (1999) estimates demand elasticities in the range -0.36 to -0.50 (higher than in the US, perhaps because Australia's universal Medicare is a better substitute for private insurance than is available to most people in the US).…”

Section: Comparison With Empirical Demand Elasticitiesmentioning

confidence: 99%

Demand Elasticity, Risk Classification and Loss Coverage: When Can Community Rating Work?

Thomas

2009

ASTIN Bull.

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This paper investigates the effects of high or low fair-premium demand elasticity in an insurance market where risk classification is restricted. The effects are represented by the equilibrium premium, and the risk-weighted insurance demand or “loss coverage”. High fair-premium demand elasticity leads to a collapse in loss coverage, with an equilibrium premium close to the risk of the higher-risk population. Low fair-premium demand elasticity leads to an equilibrium premium close to the risk of the lower-risk population, and high loss coverage – possibly higher than under more complete risk classification. The demand elasticity parameters which are required to generate a collapse in coverage in the model in this paper appear higher than the values for demand elasticity which have been estimated in several empirical studies of various insurance markets. This offers a possible explanation of why some insurance markets appear to operate reasonably well under community rating, without the collapse in coverage which insurance folklore suggests.

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“…Three other assessments showed that many children leaving state CHIP coverage lost any form of insurance [ 3 - 5 ]. Other studies documented a drop in health insurance coverage in response to rising premiums using different data sets [ 6 - 8 ].…”

Section: Introductionmentioning

confidence: 99%

The impact of CHIP premium increases on insurance outcomes among CHIP eligible children

Nikolova

Stearns

2014

BMC Health Serv Res

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BackgroundWithin the United States, public insurance premiums are used both to discourage private health policy holders from dropping coverage and to reduce state budget costs. Prior research suggests that the odds of having private coverage and being uninsured increase with increases in public insurance premiums. The aim of this paper is to test effects of Children’s Health Insurance Program (CHIP) premium increases on public insurance, private insurance, and uninsurance rates.MethodsThe fact that families just below and above a state-specific income cut-off are likely very similar in terms of observable and unobservable characteristics except the premium contribution provides a natural experiment for estimating the effect of premium increases. Using 2003 Medical Expenditure Panel Survey (MEPS) merged with CHIP premiums, we compare health insurance outcomes for CHIP eligible children as of January 2003 in states with a two-tier premium structure using a cross-sectional regression discontinuity methodology. We use difference-in-differences analysis to compare longitudinal insurance outcomes by December 2003.ResultsHigher CHIP premiums are associated with higher likelihood of private insurance. Disenrollment from CHIP in response to premium increases over time does not increase the uninsurance rate.ConclusionsWhen faced with higher CHIP premiums, private health insurance may be a preferable alternative for CHIP eligible families with higher incomes. Therefore, competition in the insurance exchanges being formed under the Affordable Care Act could enhance choice.

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Health Insurance Take‐up by the Near‐Elderly

Abstract: The implied elasticities are comparable with results found in previous studies using very different data. Our estimates indicate that policy proposals for a Medicare buy-in or a nongroup tax credit will have a modest impact on take-up rates of near-elderly retirees.

Cited by 12 publications

References 12 publications

Why Insurers Are Wrong about Adverse Selection

Why Insurers Are Wrong about Adverse Selection

Demand Elasticity, Risk Classification and Loss Coverage: When Can Community Rating Work?

The impact of CHIP premium increases on insurance outcomes among CHIP eligible children

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