Effects of infertility insurance mandates on fertility

Schmidt, Lucie

doi:10.1016/j.jhealeco.2006.10.012

Cited by 118 publications

(129 citation statements)

References 25 publications

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“…First, our results provide strong evidence that the states were able to effect changes in health insurance coverage despite ERISA limits on its regulatory authority, a finding consistent with evidence found by authors studying other types of mandates (Gruber, 1994;Schmidt, 2007;Bitler and Schmidt, 2012;Bitler andCarpenter, 2016, 2017). To the extent that researchers have found effects of the state mandates difficult to pin down, this has more to do with having correct data on the mandates and identifying the population of people likely to take-up coverage than it does with ERISA.…”

Section: Resultssupporting

confidence: 89%

“…Why would they be so eager to adopt policies with limited effectiveness? Moreover, states have been shown able to affect other types of health plan benefits despite ERISA limitations (Gruber, 1994;Schmidt, 2007;Bitler and Schmidt, 2012;Bitler andCarpenter, 2016, 2017). A second possible explanation is information, as Cantor et al (2012a) speculated that the state mandates made people more aware of the ACA, while Akosa Antwi et al (2013) suggested they may not have understood the state mandates well.…”

Section: Introductionmentioning

confidence: 99%

“…The age of the statute was, once again, likely an influence in these mis-codings. Also similar to Louisiana, the relatively minor change to the law in 2009 is probably the cause of the 2009 record for the law held by Depew (2015) and Trudeau and Conway (2018 (Gruber, 1994), infertility treatments (Schmidt, 2007;Bitler and Schmidt, 2012), and cancer screening (Bitler andCarpenter, 2016, 2017). Additionally, there is some reason to think that state mandates may influence health plans that are not explicitly subject to state authority.…”

mentioning

confidence: 99%

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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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Section: Resultssupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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

2018

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“…The mandates have also been shown to increase fertility, especially among those likely to have been affected (Bundorf, Henne, & Baker, 2007;Bitler, 2008;Jain, Harlow, & Hornstein, 2002;Schmidt, 2007 (Bitler & Schmidt, 2010;Collins, 2001;Neumann, 1997;Neumann, Gharib, & Weinstein, 1994). Neumann et al (1994) $38,000 to $800,000 depending on assumptions about which couples select into using IVF and whether or not the increased incidence and cost of multiple births are taken into account in the calculation (Bitler & Schmidt, 2010;Collins, 2001;Neumann, 1997;Neumann, Gharib, & Weinstein, 1994;Reynolds et al, 2003).…”

Section: A Factsmentioning

confidence: 99%

The Efficiency of a Group-Specific Mandated Benefit Revisited: The Effect of Infertility Mandates

Lahey

2011

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in ABSTRACTThis paper examines the labor market effects of state health insurance mandates that increase the cost of employing a demographically identifiable group. State mandates requiring that health insurance plans cover infertility treatment raise the relative cost of insuring older women of child-bearing age. Empirically, wages in this group are unaffected, but their total labor input decreases. Workers do not value infertility mandates at cost, and so will not take wage cuts in exchange, leading employers to decrease their demand for this affected and identifiable group. Differences in the empirical effects of mandates found in the literature are explained by a model including variations in the elasticity of demand, moral hazard, ability to identify a group, and adverse selection. JEL Classification Codes: I18, J23, J13

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“…Such data resources, if updated regularly, would allow for more tests of mandates' impact on the use of specific services, on the move away from other services that are close substitutes but not mandated, and on firms' decisions to selfinsure health benefits to avoid mandates; they also would test changes in the composition of employment and wages in response to particularly expensive mandates. One such mandate, infertility services, has been estimated to increase first-birth rates among older women (Schmidt 2007). This recent evidence, together with Gruber's (1994) findings that mandated maternity coverage is associated with lower wages for married women, points to the importance of maintaining a reliable database of mandate policies.…”

Section: Data Needs For Researchmentioning

confidence: 99%

Data Needs for Policy Research on State-Level Health Insurance Markets

Simon¹

2008

INQUIRY

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Private and public health insurance provision in the United States operates against a backdrop of 50 different regulatory environments in addition to federal rules. Through creative use of available data, a large body of research has contributed to our understanding of public policy in state health insurance markets. This research plays an important role as recent trends suggest states are taking the lead in health care reform. However, several important questions have not been answered due to lack of data. This paper identifies some of these areas, and discusses how the Agency for Healthcare Research and Quality could push the research agenda in state health insurance policy further by augmenting the market-level data available to researchers. As states consider new forms of regulation and assistance for their insurance markets, there is increased need for better warehousing and maintenance of policy databases.Americans face a patchwork of different markets providing health insurance. These markets, be they private or public, are governed by several sets of regulations enacted at the state as well as federal level. The variation that exists in policies, particularly at the state level, has spawned a large literature assessing the policies' impacts on health insurance and related outcomes. Yet several new questions could be answered if additional market-level data were collected. The Agency for Healthcare Research and Quality (AHRQ) has an opportunity to further the research by facilitating the creation of such resources. In this paper, I briefly review the available market-level data, 1 research questions that have been explored with these measures, and identify additional questions that could be answered with new data. This paper begins with a review of the different segments of markets for health insurance. Regulating Private vs. Public Health InsuranceThe private insurance market consists of two separate domains-group and nongroup markets-which are further split in the manner shown in Figure 1. To convey a sense of the relative sizes of some of these components, in 2004 about 161.5 million individuals (or 63.3% of all nonelderly Americans) held employer health insurance, while 11.2% of the nonelderly held coverage through Medicaid/State Children's Health Insurance Program (SCHIP), 2.3% held Medicare/Tricare coverage, 5.4% had directly

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Effects of infertility insurance mandates on fertility

Cited by 118 publications

References 25 publications

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

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

The Efficiency of a Group-Specific Mandated Benefit Revisited: The Effect of Infertility Mandates

Data Needs for Policy Research on State-Level Health Insurance Markets

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