Effects of Occupational Licensing on DACA Recipients: A Synthetic Control Approach
Bibliographic record
Abstract
Since 2014, 17 states have allowed DACA recipients to acquire occupational or professional licenses. This policy change benefits DACA recipients, eases the labor shortage, and boosts the economy. This paper evaluates the impacts of this policy change on labor market outcomes of DACA recipients, using the generalized synthetic control method to create counterfactuals for treated units using control group information. Our results suggested that granting licensing increases the wages of DACA recipients. Moreover, granting licensing seems to raise education attainment, such as more DACA recipients finishing associate degrees. However, these positive effects are only shown in the short term (the first two to three years after the policy change). Then, gradually, we find no differences in the labor market outcomes of DACA recipients in the treated group relative to its control. In conclusion, even though access to licenses does improve labor market outcomes for DACA recipients, we are still questioning how effective this policy change is.
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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 | 0.017 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".