Economic Freedom of North America 2024 Full Dataset
Bibliographic record
Abstract
Economic Freedom of North America 2024 measures the degree to which governments in North America permit their citizens to make their own economic choices. The full dataset of the report encompasses data from the 10 Canadian provinces, the 50 U.S. states, the 31 Mexican states and Mexico City, and the U.S. territory of Puerto Rico. It contains an all-government index for comparison of all 93 jurisdictions across all three countries and three subnational indices—one for each country—for comparison of individual jurisdictions (provincial, state, and local governments) within the same country. In the all-government index—which takes account of federal as well as provincial or state policies—the most economically free jurisdiction in North America is New Hampshire; followed by Idaho, Oklahoma, and South Carolina tied for 2nd; and Florida and Indiana tied for 5th. Alberta is the highest-ranking Canadian province, tied for 12th place with Tennessee, South Dakota, Colorado, and Texas. The next-highest Canadian province is British Columbia, which is tied with Massachusetts, Minnesota, and New Mexico for 43rd. Puerto Rico ranks 61st below all U.S. states and Canadian provinces. The highest-ranked Mexican state, Baja California, ranks 62nd. The lowest-ranking jurisdictions in the index are Campeche, Colima, and Ciudad de México. Average economic freedom across all 93 jurisdictions has fallen every year since 2017 and is now slightly above its all-time low. Incomes in the freest top 25% of North American jurisdictions were 21 times higher than in the least-free. From 2013 to 2022, the population of the freest U.S. states grew 10 times faster and total employment grew 3 times faster than in the least-free states.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.024 |
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".