Adverse selection, efficiency and the structure of information
Bibliographic record
Abstract
Abstract This paper explores how the structure of asymmetric information impacts on economic outcomes in Akerlof’s (Q J Econ 84(3):488–500, 1970) Lemons model applied to the labour market and extended to admit a matching component between worker and firm. We characterize the nature of equilibrium and define measures of adverse selection and efficiency. We then characterize the joint distribution of outcomes—adverse selection, probability of trade, efficiency, profits, and wage—for the class of Gaussian basic games and information, and perform comparative statics with respect to a parsimonious parameterization of the information structure. We use this framework to revisit the classic issue, first addressed by Roy (Oxford Econ Pap 3(2):135-146, 1951), of selection into different sectors. We identify conditions under which an effect reversal—adverse selection at any realisation of public information but, overall, positive selection into the outside sector—can and cannot arise, and note the implications for empirical work. We also explore the divisions of expected total surplus between worker and firm that can be achieved as information varies. We show that, if the distribution of worker types is non-singular, any point in the set of possible surplus divisions can be achieved as a limit of a PBE for some information structure with asymmetric information. Finally, re-interpreting the model in an insurance context, where the matching component becomes consumer risk aversion, we use our framework to highlight sources of advantageous selection.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".