The Value Employees Place on Health Insurance Plans: A Discrete-Choice Experiment

Poteet, Stephen; Craig, Benjamin M.

doi:10.1007/s40258-019-00507-1

Cited by 2 publications

(7 citation statements)

References 15 publications

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“…Allowing for different preference classes improved the model fit over a simple conditional logit model. 29 This is in line with recent research methodology, which has stated that accounting for preference heterogeneity can improve model fit. 3 Furthermore, the study also showed the importance of accounting for scale in addition to taste heterogeneity in respondents’ choices.…”

Section: Discussionsupporting

confidence: 82%

“…All models have been calculated with a balanced panel of subjects who completed 24 paired comparison ( N = 2086). Confirming the results of Poteet and Craig, 29 the reference model’s taste estimates are presented in the supplement (Supplemental Table S2 and Figure S2). The results confirm the tastes of attribute levels on choice.…”

Section: Resultssupporting

confidence: 77%

“…In 2018, a DCE was conducted to elicit preferences for health insurance plans. 29 The target population was US-based single adults who were between the ages of 26 and 64 y and enrolled in an employer-provided private health insurance plan ( N = 2098). This group was particularly chosen to identify the motivating factors that could prompt a healthier group of people to purchase a health insurance plan via a public marketplace and to ensure the viability of the public marketplace (i.e., the Affordable Care Act).…”

Section: Methodsmentioning

confidence: 99%

“…Both models were estimated using binary panel data from a DCE of health insurance demand among US single employees. 29…”

Section: Aims Of the Studymentioning

confidence: 99%

“…In the study, Poteet and Craig 29 identified the effects of different health insurance plan attributes that motivate single employees to switch from employer-based health insurance to the federally facilitated health insurance marketplace. Interested readers can find further discussion about the motivation of the study and the design of the experiment in the original article.…”

Section: Aims Of the Studymentioning

confidence: 99%

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Does Controlling for Scale Heterogeneity Better Explain Respondents’ Preference Segmentation in Discrete Choice Experiments? A Case Study of US Health Insurance Demand

2021

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Analyses of preference evidence frequently confuse heterogeneity in the effects of attribute parameters (i.e., taste coefficients) and the scale parameter (i.e., variance). Standard latent class models often produce unreasonable classes with high variance and disordered coefficients because of confounding estimates of effect and scale heterogeneity. In this study, we estimated a scale-adjusted latent class model in which scale classes (heteroskedasticity) were identified using respondents’ randomness in choice behavior on the internet panel (e.g., time to completion and time of day). Hence, the model distinctly explained the taste/preference variation among classes associated with individual socioeconomic characters, in which scales are adjusted. Using data from a discrete-choice experiment on US health insurance demand among single employees, the results demonstrated how incorporating behavioral data enhances the interpretation of heterogeneous effects. Once scale heterogeneity was controlled, we found substantial heterogeneity with 4 taste classes. Two of the taste classes were highly premium sensitive (economy class), coming mostly from the low-income group, and the class associated with better educational backgrounds preferred to have a better quality of coverage of health insurance plans. The third class was a highly quality-sensitive class, with a higher SES background and lower self-stated health condition. The last class was identified as stayers, who were not premium or quality sensitive. This case study demonstrates that one size does not fit all in the analysis of preference heterogeneity. The novel use of behavioral data in the latent class analysis is generalizable to a wide range of health preference studies.

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Section: Discussionsupporting

confidence: 82%

Section: Resultssupporting

confidence: 77%

Section: Methodsmentioning

confidence: 99%

“…Both models were estimated using binary panel data from a DCE of health insurance demand among US single employees. 29…”

Section: Aims Of the Studymentioning

confidence: 99%

Section: Aims Of the Studymentioning

confidence: 99%

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Does Controlling for Scale Heterogeneity Better Explain Respondents’ Preference Segmentation in Discrete Choice Experiments? A Case Study of US Health Insurance Demand

2021

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Preferences for public long-term care insurance among middle-aged and elderly residents: A discrete choice experiment in Hubei Province, China

Jia

et al. 2023

Front. Public Health

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ObjectiveIt is critical to incorporate residents' preferences into the design of long-term care insurance (LTCI). However, little is known about middle-aged and elderly residents' preferences for personalized need-related attributes of LTCI in China. Through a discrete choice experiment (DCE), we aimed to focus on the direct beneficiaries of LTCI and then elicit their preferences for LTCI under a hypothetical scenario of dysfunction.MethodsAttributes and levels were defined through a literature review and two rounds of expert consultations (n = 8). A D-optimal fractional factorial design was used to generate the DCE questionnaire. Face-to-face interviews with middle-aged and elderly residents were conducted in two cities in Hubei Province, China, between November and December 2020. A mixed logit model was utilized for estimation.ResultsFive attributes were identified and incorporated into the DCE questionnaire. A total of 390 participants completed DCE questionnaires. Care facilities, care content, reimbursement rate, caregivers, and annual premium per person all had a significant impact on residents' preferences. Residents had significantly higher preferences for the LTCI scheme with home and community-based care centers (β = 1.40, p < 0.01), multi-level services (β = 0.44, p < 0.01), 90% reimbursement rate (β = 0.37, p < 0.01), and sufficiently trained caregivers (β = 0.26, p < 0.01). Individual characteristics, such as gender, employment, and education level were the factors that drove heterogeneity in preferences for LTCI.ConclusionThis study provides new evidence on the preferences of middle-aged and elderly residents for personalized need-related public LTCI features. The design of the LTCI scheme in China needs to take these findings into account to maximize the utility for direct beneficiaries of LTCI and enhance their enrollment.

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The Value Employees Place on Health Insurance Plans: A Discrete-Choice Experiment

Cited by 2 publications

References 15 publications

Does Controlling for Scale Heterogeneity Better Explain Respondents’ Preference Segmentation in Discrete Choice Experiments? A Case Study of US Health Insurance Demand

Does Controlling for Scale Heterogeneity Better Explain Respondents’ Preference Segmentation in Discrete Choice Experiments? A Case Study of US Health Insurance Demand

Preferences for public long-term care insurance among middle-aged and elderly residents: A discrete choice experiment in Hubei Province, China

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