Women in Transition: The Dynamic Effects of Inward FDI on Female Employment in the Economy and Across Sectors
Bibliographic record
Abstract
This paper examines the effects of inward Foreign Direct Investment (FDI) on the female employment rate in the economy and the share of female employment across sectors. The empirical analysis is implemented through the Generalized Method of Moments (GMM) System estimator for dynamic panel models using different empirical specifications and FDI openness indicators. The main results show that the overall effects of inward FDI on the national female employment rate are not statistically significant. However, they reveal that inward FDI has promoted the share of female employment in the service sector and has led to decreases in the share of female employment in agriculture. The FDI effects on the share of female employment in the industrial sector are found to be statistically insignificant. These results are generally supported when running the empirical analysis through alternative FDI openness indicators. Also, supplementary analysis reveals some variations in the magnitude of these effects over different national income categories. The findings in this paper emphasize FDI’s gendered influences in the labour market. They are consistent with the prevalence of macroeconomic channels through which inward FDI impacts female employment across sectors, and they encompass the underlying implications of various counteracting microeconomic 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".