Foreign Direct Investment, Production Factors Productivity and Income Inequalities in Selected CEE Countries
Bibliographic record
Abstract
Abstract The issue of global economic inequality has inspired researchers to explore the potential connection between income inequalities and foreign direct investment (FDI), as it is one of the driving forces of globalization. Although there is a large body of theoretical as well as empirical studies linking these variables, the empirical literature on the relationship between FDI, production factors productivity and income inequalities is not conclusive because most scientists treat FDI as uniform. Therefore there is a lack of reliable empirical evidence on the distributional effects of FDI, especially in emerging countries, such as in Central and Eastern Europe (CEE). The research presented in the article fills this gap. The aim of the study is to analyze the impact of the inflow of foreign direct investment on the productivity of production factors (labor, capital and total factor productivity) and income inequality of households in four Central and Eastern European countries (Poland, the Czech Republic, Slovakia and Hungary) in the period 1990–2016. The four countries were selected for analysis as a classic example of European countries transforming their economic structures and similar in terms of the level of economic development. In turn, the choice of the analysis period was related to the availability of necessary statistical data. According to the theory of economics, the inflow of foreign direct investment should have a positive impact on production factors productivity as well as on income inequalities of households in investment receiving countries. In the study, a research method based on the study of economic literature in macroeconomics and international finance and econometric methods (vector autoregression models—VAR) was used. Results of the research suggest a significant and positive impact of greenfield investment inflow on labor productivity and total factor productivity, as well as a positive impact of brownfield investment inflow (mergers and acquisitions) on capital productivity in countries receiving investments. Moreover, the results also revealed the lack of a statistically significant impact of greenfield and brownfield investment on income inequalities in all of the examined countries. The statistical data used in the study came from the statistical databases of the Organization for Economic Cooperation and Development (OECD), the World Bank (World Development Indicators), World Income Inequality Database (United Nations University World Institute for Development Economics Research) and Total Economy Database (The Conference Board of Canada).
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".