The Relationship Between Economic Freedom, State Growth and Foreign Direct Investment in US States
Bibliographic record
Abstract
Researchers have identified economic freedom, growth rate of the economy, per capita income, unemployment rate, etc as determinants of foreign direct investment (FDI) inflows into the United States as a country. Whether or not these economic variables also determine FDI at the states’ level is often excluded from the literature. This paper attempts to fill that gap by using a panel data from 1984 through 2007 for all 50 states. We employ the random effects regression model and find that both economic freedom and growth rate in each state are significant positive determinants of FDI inflows. This result is consistent with that of Ray (1989) who shows that high economic growth in the U. S. leads to more FDI inflows. Bengoa and Sanchez-Robles (2003), and Kapuria-Foreman (2007) document similar results for Latin American countries. In addition, we show that both per capita income and unemployment rate exhibit significant negative relations with FDI. These results are consistent with that of Edwards (1992) and Jaspersen, Aylward, and Knox (2000), but inconsistent with that of Tsai (1994) and Lipsey (1999). We attribute the negative relation between FDI and per capita income to the fact that states with higher per capita income tend to discourage FDI inflows since higher per capita income translates into higher wages. The observed inverse relation between FDI and unemployment rate is due to the fact that states with high unemployment rates are more prone to crime, and therefore deters risk-averse foreign investors from assuming a lasting interest in those 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".