The Impact of Perceptions in Averting‐decision Models: An Application of the Special Regressor Method to Drinking Water Choices
Bibliographic record
Abstract
Households' monetary valuation of water quality is a prerequisite for efficient water resource management and the valuation of water quality protection policies. Individuals are commonly questioned about their perception of risk in valuation surveys based on stated‐preference methods and revealed‐preference methods such as averting‐behavior models. These subjective and often discrete measures are commonly used to explain individuals' actions to protect themselves against these risks. Perceptions appear as endogenous variables in traditional theoretical averting‐decision models but, quite surprisingly, endogeneity of perceived risk is not always controlled for in empirical studies. In this article, we argue that perceptions have to be treated as endogenous to averting decisions in order to produce accurate and reliable measures of households' valuation of water quality improvements. We present various binary averting decision models featuring an endogenous discrete variable (such as risk perception). In particular, we compare the traditional bivariate probit model with the special regressor model, which is less well‐known and relies on a different set of assumptions. In the empirical illustration using household data from Australia, Canada, and France, we study how the perceived health impacts of tap water affect a household's decision to drink water from the tap. Individuals' perceptions are found to be endogenous and significant for all models, but the estimated marginal effect is sensitive to the chosen model. Our empirical application also includes some tests of the special regressor estimator's sensitivity to underlying assumptions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".