Immigration and Provision of Public Goods: Evidence at the Local Level in the U.S.
Bibliographic record
Abstract
Using U.S. county-level data from 1990 to 2010, we study the causal impact of immigration on the provision of local public goods. We uncover substantial heterogeneity across immigrants with different skills, mainly due to the asymmetric impact immigrants have on the per capita tax base and local revenues. In the absence of full insurance through intergovernmental transfers, the changes in per capita revenues are reflected in changes in the provision of local public services: per capita public expenditures decrease with the arrival of low-skilled immigrants and increase with the arrival of high-skilled immigrants. While the two types of immigrants offset each other on average, spatial differences in the population shares of low- and high-skilled immigrants lead to unequal fiscal effects across U.S. counties. We find the estimated impact to differ across various public services and for second-generation immigrants.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".