The impacts of China’s FDI on employment in Thailand’s industrial sector
Bibliographic record
Abstract
Purpose – This study aims to analyze the effect of inward foreign direct investment (FDI) on new job creation. This study pays attention to factors interrelated to China’s FDI by using the case of Thailand. Design/methodology/approach – Using time series data from 2001 to 2014, this paper explores the driving forces and reduction potentials of employment in Thailand’s industrial sector with consideration for dynamic changes within the vector autoregression model. Findings – The results show that government expenditure plays a dominant role in increasing employment in Thailand’s industrial sector and exports plays a dominant role in decreasing employment in Thailand’s industrial sector. All variables are co-integrated and the analysis of the impulse–response function also turns out to be synchronous. Furthermore, in the short term, exports are more critical than China’s FDI in industrial sectors in reduction potentials of employment in Thailand’s industrial. Practical/implications – Policies should be devised to increase skilled labour and improve the equality of infrastructure in the country to attract more FDI into the economy and for quick adjustment purposes in case of shock to the system. Originality/value – The paper uncovers some important factors influencing employment in Thailand’s industrial sector under study and provides a guide-map for policymakers.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".