Abstract
Multi‐attribute choices are commonly analyzed in economics to value goods and services. Analysis assumes individuals consider all attributes, making trade‐offs between them. Such decision‐making is cognitively demanding, often triggering alternative decision rules. We develop a new model where individuals aggregate multi‐attribute information into meta‐attributes. Applying our model to a choice experiment (CE) dataset, accounting for attribute aggregation (AA) improves model fit. The probability of adopting AA is greater for: homogenous attribute information; participants who had shorter response time and failed the dominance test; and for later located choices. Accounting for AA has implications for welfare estimates. Our results underline the importance of accounting for information processing rules when modelling multi‐attribute choices.
| Original language | English |
|---|---|
| Pages (from-to) | 1291-1305 |
| Number of pages | 15 |
| Journal | Health Economics |
| Volume | 30 |
| Issue number | 6 |
| Early online date | 19 Mar 2021 |
| DOIs | |
| Publication status | Published - 1 Jun 2021 |
Keywords
- Multi-attribute choices
- Attributes aggregation
- Information processing
- Choice modelling
- Choice experiment
- information processing
- choice modelling
- choice experiment
- attributes aggregation
- multi-attribute choices
Access to Document
- Genie_etal_weighting_or_aggregating_VOR
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