Bibliographic record
Abstract
We ask why, in many circumstances and many environments, decision-makers choose to act on a time-regular basis (e.g. adjust every six weeks) or on a stateregular basis (e.g. set prices ending in a 9), even though such an approach appears suboptimal. The paper attributes regular behaviour to adjustment cost heterogeneity. We show that, given the cost heterogeneity, the likelihood of adopting regular policies depends on the shape of the benefit function: the flatter it is, the more likely, ceteris paribus, is regular adjustment. We provide sufficient conditions under which, when policymakers differ with respect to the shape of the benefit function (as in Konieczny and Skrzypacz, 2006), the frequency of adjustments across markets is negatively correlated with the incidence of regular adjustments. On the other hand, if policymakers differences are due to the level of adjustment costs (as in Dotsey, King and Wolman, 1999), then the correlation is positive. To test the model we apply it to optimal pricing policies. We use a large Austrian data set, which consists of the direct price information collected by the statistical office and covers 80% of the CPI over eight years. We run cross-sectional tests, regressing the proportion of attractive prices and, separately, the excess proportion of price changes at the beginning of a year and at the beginning of a quarter, on various conditional frequencies of adjustment, inflation and its variability, dummies for good types, and other relevant variables. We find that the lower is, in a given market, the conditional frequency of price changes, the higher is the incidence of time- and state-regular adjustment. JEL Classification: E31, L11, E52, D01
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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; both teacher heads agree on what is shown here.
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".