Informal Employment and Business Cycles in Emerging Economies: The Case of Mexico
Bibliographic record
Abstract
This paper documents how informal employment in Mexico is countercyclical, lags the cycle and is negatively correlated to formal employment. This contributes to explaining why total employment in Mexico displays low cyclicality and variability over the business cycle when compared to Canada, a developed economy with a much smaller share of informal employment. To account for these empirical findings, a business cycle model is built of a small, open economy that incorporates formal and informal labor markets, and the model is calibrated to Mexico. The model performs well in terms of matching conditional and unconditional moments in the data. It also sheds light on the channels through which informal economic activity may affect business cycles. Introducing informal employment into a standard model amplifies the effects of productivity shocks. This is linked to productivity shocks being imperfectly propagated from the formal to the informal sector. It also shows how imperfect measurement of informal economic activity in national accounts can translate into stronger variability in aggregate economic 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".