Direct and Indirect Effects of Investment Incentives in Slovakia
Bibliographic record
Abstract
Countries trying to attract foreign direct investment often use various tools to influence the foreign investor’s allocation decision including public subsidies in the form of investment incentives. However, the effects associated with providing these incentives are often questioned, especially in light of the need to achieve at least a minimum level of attractiveness of the business environment. The primary aim of the present study was to examine the effects of investment incentives on foreign direct investment inflows (direct effect) and on selected macroeconomic variables (indirect effects) under the conditions in Slovakia. Findings showed that the preference of specific forms of investment incentives by the government of the Slovak Republic changed slightly in the observed period of 2002–2019. The results of the regression analysis further suggest that while financial incentives have a positive statistically significant direct effect on foreign direct investment inflows, in the case of fiscal incentives, this effect is the opposite. In terms of indirect effects of investment incentives, only a reduction in the unemployment rate through foreign direct investment was found. The study contributes to the literature by providing evidence on the effects of various forms of investment incentives and by offering some implications for investment promotion policy.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".