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Inequality Aversion in Health: Eliciting Aversion to Univariate Health Inequalities Across Different Health
Sindre A Horn1, Ole F Norheim2, Mathias Barra3
1Bergen Centre for Ethics and Priority Setting (BCEPS), Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway.
Most people prefer to improve health for the disadvantaged, even with some loss in overall health gains. However, individual preferences for health inequality aversion varied across different health distributions, challenging assumptions in health policy modeling.
Area of Science:
- Health Economics
- Public Health Policy
- Social Welfare Theory
Background:
- Health inequality aversion parameters are crucial for health-related social welfare functions (HRSWFs) when balancing health maximization and equalization.
- Empirical data on public preferences for inequality aversion can ensure health policies align with societal values.
Purpose of the Study:
- To elicit inequality aversion parameters from a large population sample using different health distributions.
- To inform Atkinson and Kolm-Pollak type HRSWFs.
- To compare elicited parameters across varying inequality distributions.
Main Methods:
- Recruited a representative Norwegian population sample.
- Utilized two sets of benefit trade-off tasks with varying baseline health inequality.
- Employed univariate health distributions for analysis.
- Tested for intra-individual concurrence of parameters across distributions.
Main Results:
- 946 responses were analyzed.
- Median Atkinson inequality aversion parameter was 3.52; Kolm-Pollak parameter was 0.05.
- No convincing concurrence was found between elicited parameters from the two task sets.
Conclusions:
- Respondents generally favor trading some health gains to benefit the worse-off.
- Lack of concurrence suggests individual distributional preferences may not meet assumptions of constant relative or absolute inequality aversion.
- Findings highlight complexities in applying inequality aversion parameters in health policy.
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