The IZA Evaluation Dataset: Towards Evidence-Based Labor Policy-Making
Bibliographic record
Abstract
The evaluation of labor market policies has become an important issue in many European countries. In recent years, a number of them have opened their administrative databases for evaluation studies. The advantages of administrative data are straightforward: they are accurate, contain a large number of observations (in some cases the whole population) and usually cover a long period of time. However, the information contained in administrative data is normally limited to administrative purposes. Therefore, information that might be relevant for economic modeling is often absent. The IZA Evaluation Dataset aims to overcome such limitations for Germany by complementing administrative data from the Federal Employment Agency with innovative survey data. The administrative part of the dataset consists of a large random sample of inflows into unemployment in Germany from 2001 to 2008 and contains around 920,000 individuals. The complementary survey covers a panel of more than 17,000 individuals who entered unemployment between June 2007 and May 2008. They were initially interviewed shortly after becoming unemployed and then again one year later. In addition, a quarter of individuals were interviewed already after six months. The survey data also contain information on search behavior, ethnic and social networks, psychological factors, (non-)cognitive abilities, and attitudes. This paper describes the sampling and contents of the IZA Evaluation Dataset and outlines the future development.
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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 | 0.013 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".