The Impact of Minimum Wages on Wages, Work and Poverty in Nicaragua
Bibliographic record
Abstract
In this paper we use an individual- and household-level panel data set to study the impact of changes in legal minimum wages on a host of labor market outcomes including: a) wages and employment, b) transitions of workers across jobs (in the covered and uncovered sectors) and employment status (unemployment and out of the labor force), and c) transitions into and out of poverty. We find that changes in the legal minimum wage affect only those workers whose initial wage (before the change in minimum wages) is close to the minimum. For example, increases in the legal minimum wage lead to significant increases in the wages and decreases in employment of private covered sector workers who have wages within 20% of the minimum wage before the change, but have no significant impact on wages in other parts of the distribution. The estimates from the employment transition equations suggest that the decrease in covered private sector employment is due to a combination of layoffs and reductions in hiring. Most workers who lose their jobs in the covered private sector as a result of higher legal minimum wages leave the labor force or go into unpaid family work; a smaller proportion find work in the public sector. We find no evidence that these workers become unemployed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".