Assessing a Causal Relationship Between Foreign Direct Investment and Human Capital: The Case of the Netherlands
Bibliographic record
Abstract
Foreign direct investment is seen by many countries as an important source of capital. The flow of foreign direct investment in the world has been increasing over the past couple of years and many countries have adopted various institutional policies with the hope of attracting more foreign direct investment. Besides capital, these countries also get to benefit from the other advantages that these investments bring with them, such as the newest technology and managerial skills. FDI, besides boosting capital formation, also increases the quality of capital stock. Countries can only reap all the benefits of foreign direct investment if they have a sufficient amount of human capital. Human capital is seen by many researchers as one of the most important determinants of FDI. With the world becoming more knowledge-based and globalized the importance of human capital became very significant not only to individuals, but also to countries in their competitive advantages. The purpose of this research is to find out what the relationship is between foreign direct investment and human capital in the Netherlands. The Netherlands is a small country in Europe which has been a global leader in the inflow and outflow of FDI the past couple of years. This study uses the Augmented Dickey-Fuller test and the Johansen cointegration test to determine whether or not there is a causal relationship between the variable’s foreign direct investment and secondary school enrollment, which is used as a proxy for human capital. The study observed that there is no causal relationship between foreign direct investment and human capital in the Netherlands. The inflow of human capital into the Netherlands is determined by other factors.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".