Senior Economist The World Bank
Bibliographic record
Abstract
In Russia, ROSSTAT, the Statistical Agency of the Federation, monitors poverty based on the annual Household Budget Survey. Although the HBS collects very detailed information on household consumption, the information on other dimensions of well-being is very limited. This has restricted both the analyses that can be undertaken with HBS, and the methodological choices for the construction of the welfare aggregate. Aware of these limitations, ROSSTAT revised the HBS questionnaire in the last quarter of 2005. In this paper, we use a multi-topic household survey, NOBUS, which collects a richer set of data on consumption and household characteristics, to illustrate two points. First, we show how household welfare and poverty can be measured with a survey specially designed for this purpose. Second, we investigate the impact of alternative methodological choices on poverty and inequality, such as accounting for the consumption of durables, housing, subsidies for privileged citizens and rural-urban differences in the cost of living. We compare different welfare aggregates – constructed to take into account the data limitations of HBS or the methodological choices currently endorsed by ROSSTAT – with a benchmark aggregate constructed according to the recommendations of renowned international
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.147 | 0.109 |
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".