Data and Code for: Hurricane Maria and La Crisis Boricua on Healthcare Supply in Puerto Rico
Bibliographic record
Abstract
Due to the financial crisis facing Puerto Rico, many medical professionals on the island have left. Former Governor Rossello passed Act 14 in April of 2017 hoping to stave off the exodus of physicians. Act 14 reduces the income tax charge on medical services from 30 percent to 4 percent for 15 years. During the same year Puerto Rico experience a devastating category 4 hurricane, which left the island without power or water for several months. We will use a difference in difference estimation to estimate the effects of this change in the marginal tax rate to both keep physicians on the island as well as attract new physicians to the island from the mainland US. We use data from the Quarterly Census of Employment and Wages, the May Occupational Employment Statistics counts, and the AAMC Report on Residents. We find the number of healthcare providers decreased by 6.5 percent. The number of family physicians and pediatricians fell by 17.5 percent and 62 percent respectively. However, the number of registered nurses increased by 2.7 percent. Although the levels of healthcare providers decreases, the rate of healthcare provides per capita actually increased during this time period since the population decreases more rapidly than the fall in the level of healthcare providers.<br><br>The zip file includes a STATA do file, readme file, and the following data files.<br>AAMC Report of Resident xls file contains counts of residents by state and year<br>May Occupational Employment Statistics Counts 2000-2019<br>Quarterly Census of Employment and Wages establishment counts of physician offices by county by quarter from 2000 to 2019<br><br><br>
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| 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".