Determining Consumers’ Discount Rates with Field Studies
Bibliographic record
Abstract
Because utility/profits, state transitions, and discount rates are confounded in dynamic models, discount rates are typically fixed for the purpose of identification. The authors propose a strategy of identifying discount rates. The identification rests on imputing the utility/profits using decisions made in a context in which the future is inconsequential, the objective function is concave, and the decision space is continuous. They then use these utilities/profits to identify discount rates in contexts in which dynamics become material. The authors exemplify this strategy using a field study in which cell phone users transitioned from a linear to a three-part-tariff pricing plan. They find that the estimated discount rate corresponds to a weekly discount factor (.90), lower than the value typically assumed in empirical research (.995). When using a standard .995 discount factor, they find that the price coefficient is underestimated by 16%. Moreover, the predicted intertemporal substitution pattern and demand elasticities are biased, leading to a 29% deterioration in model fit and suboptimal pricing recommendations that would lower potential revenue gains by 76%.
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How this classification was reachedexpand
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 | 0.021 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".