Real unemployment in Kazakhstan: an analysis based on international methodologies
Bibliographic record
Abstract
The article examines the issue of the incomplete reflection of the real situation in the labor market of the Republic of Kazakhstan by official statistics. The authors present a comparative analysis of alternative unemployment indicators used in the United States and Canada, and adapt the methodologies of the U.S. Bureau of Labor Statistics and Statistics Canada to the socio-economic conditions of Kazakhstan. The study provides a detailed description of extended indicators that include the long-term unemployed, discouraged workers, and individuals employed part-time for economic reasons. Based on data from the Bureau of National Statistics of Kazakhstan, the United States Bureau of Labor Statistics, and the National Statistical Office of Canada, the authors calculated alternative unemployment rates, which made it possible to identify the extent of hidden underutilization of labor resources. The article demonstrates that, despite the comparability of the official unemployment rate with that of developed countries, Kazakhstan is characterized by higher values of extended indicators, reflecting structural and institutional problems in the labor market. The authors analyzed the dynamics of hidden unemployment for the period 2017–2023 and found its decrease from 3,4 % to 2,7 %, indicating a gradual recovery of employment after economic shocks. At the same time, regional disparities and the persistent share of temporarily underemployed workers highlight the uneven development of the labor market. The obtained results emphasize the need to improve the employment monitoring system, expand statistical accounting, and develop measures aimed at increasing the involvement of the economically inactive population in labor activity.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".