Differences across countries and time in household expenditure patterns: implications for the estimation of equivalence scales
Bibliographic record
Abstract
When comparing economic well-being using income or expenditures, an equivalence scale is often used to adjust for differences in characteristics that affect needs. For example, a family of two is assumed to need more income than a single person, but not twice as much due to the economies of scale in consumption. In this study, we ask whether it is appropriate to use a common equivalence scale when comparing economic well-being across countries and/or time if consumption expenditure patterns differ? Based on an Engel methodology, we estimate equivalence scales for a diverse set of countries (Canada, France, Israel, Poland, South Africa, Switzerland, Taiwan, United States) in different time periods (1999–2012). We find considerable differences in economies of scale across countries, as well as increases over time. Notably, we find that economies of scale are larger than those implied by the widely accepted ‘square root of household size’ equivalence scale. Our results indicate that using a common equivalence scale to compare economic well-being across countries and/or time is misleading. Specifically, if economies of scale are understated (as is the case when using the ‘square root of household size’), the relative poverty experienced by larger versus smaller families is being overstated.
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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 | 0.041 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".