What Attracts Foreign Direct Investment Inflows in the United States
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The present study evaluates the relative impact of industry and state specific economic factors on inward FDI in several US states that compete for the same inward FDI. It appears that relative labor productivity, relative spending on education, and relative crime rate are important in inter-state competition for the same inward FDI. Also, when the contest in attracting inward FDI comes down to two states, relative tax incentives also become important in attracting FDI inflows. Acknowledgments Jane Cloran and Nikolaos Kalios provided invaluable research assistance. Financial support from the Board of Research, Babson College is gratefully acknowledged. Notes 1See for example, CitationCulem (1988), CitationCoughlin et al. (1990), Citation(1991), CitationFriedman et al. (1992), CitationHead et al. (1995), Citation(1999), CitationBillington (1999), CitationHatzius (2000), and CitationAxarloglou (2004). 2See CitationCoughlin et al. (1990), Citation(1991), CitationFriedman et al. (1996), and CitationHead et al. (1995), Citation(1999). 3See CitationHead et al. (1995), Citation(1999). 4See CitationBartik (1989) and CitationWheat (1986). 5For example, CitationFriedman et al. (1992), CitationWoodward (1992), and CitationCoughlin et al. (1991). 6As CitationFriedman et al. (1996) have shown. 7See CitationWoodward (1992), Barrell and Pain (1998), and CitationHead et al. (1995), among others. 8"Mercedes Plant Said to be Set for Alabama," Wall Street Journal, 09/29/1993. 9These data were maintained by the International Trade Administration (ITA), the US Department of Commerce, and were discontinued after 1994. 10The ten states in the sample were the top states in total FDI received for the period between 1974 and 1994. These states together received approximately 63% of all FDI inflows in US manufacturing that recorded by the ITA from 1974 to 1994. 11See, for example, the Wall Street Journal article "Mercedes Plant Said to Be Set for Alabama," 9/29/1993. 12Notice that the two proxies for labor productivity are strongly correlated (correlation of 0.730). 13In estimations we also used the share of industry and state specific real gross product out of the same industry's real gross product in all ten states in the sample, as an alternative proxy for agglomeration effects. The empirical results do not change much, and therefore we do not report them in the article. 14The correlation between the state per capita real income and labor productivity in our sample is 0.549. A (*) or (**) next to a reported coefficient indicates its significance at 0.01 and 0.05 levels. 15See, for example, "Sates: Over There Over There," Financial World (4/17/1990), or "U.S. Governor's Travel Abroad 'Opens the Door' to Increased Exports and Jobs for their States," Business America (5/7/1990), or "Ante up: States' Bidding War Over Mercedes Plant Made for Costly Chase—Alabama Won the Business, But Some Wonder if It Also Gave Away the Farm—Will Image Now Improve?" Wall Street Journal (11/24/1993). 16"Alabama's Winning of Mercedes Plant Will Be Costly, with Major Tax Breaks," Wall Street Journal, 9/30/1993. 17The weights are the relative share of real state gross product in each industry out of the real gross product in the same industry and year in the remaining nine states together. 18For instance, (SAPL ijt ) is the share of (APL ijt ) in industry (i) and state (j) for year (t) out of the average labor productivity in the same industry and year in the remaining nine states in the sample. A (*) or (**) next to a reported coefficient indicates its significance at 0.01 and 0.05 levels. aThis finding is consistent with CitationWoodward (1992). 20"The Americas: How Canada Scares Away Investors and Talent," Wall Street Journal, 1/2/1998. A (*) or (**) next to a reported coefficient indicates its significance at 0.01 and 0.05 levels. Additional informationNotes on contributorsKostas Axarloglou Assistant Professor of Business Economics
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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 it