The Effect of Enterprise Zone-Related Tax Savings on Economic Development: A Generalized Propensity Score Approach
Bibliographic record
Abstract
Since their onset in the early 1980s, enterprise zones (EZ) have been utilized for the revitalization of traditional downtown areas or old industrial and manufacturing areas that have undergone a protracted period of decline. Businesses within EZs often receive some combination of labor and/or capital tax incentives. This article examines various measures of economic development during the period 2006 to 2015 for Indiana firms receiving at least one tax incentive. This relationship is examined using a generalized propensity score model in an effort to remove the potential endogeneity between tax savings to each EZ business and economic development. The results suggest that, on average, employment tends to rise more significantly for firms receiving a modest amount in tax incentives than for those receiving above-average amounts. However, this is not the case for capital investment, where firms receiving modest amounts do not invest any differently than firms receiving above-average amounts. Additionally, the results suggest that fewer savings are being translated into higher wages for employees as firms continue to receive tax incentives.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".