Substitution Effects in Spatial Discrete Choice Experiments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper explores spatial substitution patterns using a choice experiment to estimate the non-market benefits of environmental quality improvements at different sites presented as labelled alternatives. We develop a novel modelling approach to estimate possible disproportional substitution patterns among these alternatives by including cross-effects in site-specific utility functions, combining mixed and universal logit models. The latter model allows for more flexibility in substitution patterns than random parameters and error-components in mixed logit models. The model is relevant to any discrete choice study that compares multiple sites that vary in their comparability and that may be perceived as (imperfect) substitutes. Applying the model in an empirical case study shows that accounting for cross-effects results in a better model fit. We discuss the validity of welfare estimates based on the inclusion of cross-effects. The results demonstrate the importance of accounting for substitution effects in spatial choice models with the aim to inform policy and decision-making.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it