Bibliographic record
Abstract
What is the welfare effect of a price change? This simple question is one of the most relevant and controversial questions in microeconomic theory and its different answers can lead to severe heterogeneity in empirical results. This paper returns to this question with the objective of providing a general framework for the use of theoretical contributions in empirical works, with a particular focus on poor people and poor countries. Welfare measures (such as Equivalent Variation or Consumer's Surplus) and computational methods (such as Taylor's approximations or the Vartia method) are compared to test how these choices result in different welfare measurement under different price shock scenarios. As a rule of thumb and irrespective of parameter choices, welfare measures converge to approximately the same result for price changes below 10 percent. Above this threshold, these measures start to diverge significantly. Budget shares play an important role in explaining such divergence, whereas the choice of demand system has a minor role. Under standard utility assumptions, the Laspeyers and Paasche variations are always the outer bounds of welfare estimates and consumer surplus is always the median estimate. The paper also introduces a new simple welfare approximation, clarifies the relation between Taylor's approximations and the income and substitution effects, and provides an example for treating nonlinear pricing. Stata codes for all computations are provided in annex.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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 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".