A population‐level approach to distributional weighting
Bibliographic Data
| ID | 6415142 |
|---|---|
| Authors | Daniel Acland (0000-0001-7698-6986, Goldman School of Public Policy University of California Berkeley California USA), Steven Raphael (0000-0001-9243-2182, Goldman School of Public Policy University of California Berkeley California USA) |
| Year | 2025 |
| Volume | 96 |
| Issue | 2 |
| Pages | 363-399 |
| Publication date | 2025-05-14 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Annals of Public and Cooperative Economics (JOURNAL) |
| Journal identifiers | ISSN: 1370-4788 • E-ISSN: 1467-8292 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1111/apce.12505 |
| OpenAlex | W4410358352 |
| Language | EN |
| Citations received | 1 |
| References cited | 29 |
Distributional weighting to address concerns about diminishing marginal utility of income in benefit–cost analysis has been the topic of increased interest in recent years. Concern has been expressed about the practicability of distributional weighting, given limitations on data and on the analytical capacities of agencies. This paper contributes to a small but growing literature that attempts to provide guidance and real‐world examples of distributional weighting. We develop a methodology for calculating what we call ‘population weights’, which, once computed for a given population by an analyst, can be used by other analysts to implement distributional weighting on similar populations, without those analysts needing information on income distribution or the cost or benefit experienced by households at different income levels within those populations. These population weights can be calculated without knowing the costs or benefits received by households at different income levels, using proxies for cost or benefit that may be observable or about which, in the absence of data, assumptions can be made in some cases. We implement the methodology on an example regulation and present results that we believe provide useful information to decision‐makers, even in the absence of estimates of unweighted costs and benefits
Econometrics · Geography · Population · Sociology · Statistics · Weighting · Computer Science · Demography · Economic and Environmental Valuation · Global Health Care Issues · Global Maternal and Child Health · Mathematics · Medicine
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| Unique citing works | 1 |
|---|---|
| Citations per year | 1 |
| Citation span | 2025 - 2025 (1) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 1 |