Natural-resources-seeking FDI and employment opportunities in developing countries: a temporal perspective
Why this work is in the frame
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Bibliographic record
Abstract
Purpose This study aims to analyze the short-, medium- and long-term impacts of natural-resources-seeking foreign direct investment (FDI) in the form of foreign multinational enterprise (MNE) land acquisitions on agricultural labor productivity in developing countries. The authors analyze if these land acquisitions disrupt fair and decent rural labor productivity or if the investments provide opportunities for improvement and growth. The influence of different country characteristics, such as economic development levels and governmental protection for the rural population, are acknowledged. Design/methodology/approach The study analyzes 570 land acquisitions across 90 countries between 2000 and 2015 via a generalized least squares regression. It distinguishes short- and long-term implications and the moderating role of a country’s economic development level and government effectiveness in implementing government protection. Findings The results suggest that natural resource-seeking FDI harms agricultural labor productivity in the short term. However, this impact turns positive in the long term as labor markets adjust to the initial disruptions that result from land acquisitions. A country’s economic development level mitigates the negative short-term impacts, indicating the possibility of finding alternative job opportunities in economically stronger countries. Government effectiveness does have no influence, presumably as the rural population in which the investment is partaking is in many developing countries, not the focus of governmental protectionism. Research limitations/implications The findings provide interesting insights into the impact of MNEs on developing countries and particularly their rural areas that are heavily dependent on natural resources. The authors identify implications on employment opportunities in the agricultural sector in these countries, which are negative in the short term but turn positive in the long term. Practical implications Moreover, the findings also have utility for policymakers. The sale of land to foreign MNEs is not a passive process – indeed, developing country governments have an active hand in constructing purchase contracts. Local governments could organize multistakeholder partnerships between MNEs, domestic businesses and communities to promote cooperation for access to technology and innovation and capacity-building to support employment opportunities. Social implications The authors urge MNE managers to establish new partnerships to ease transitions and mitigate the negative impacts of land acquisitions on agricultural employment opportunities in the short term. These partnerships could emphasize worker retraining and skills upgrading for MNE-owned land, developing new financing schemes and sharing of technology and market opportunities for surrounding small-holder farmers (World Bank, 2018). MNE managers could also adopt wildlife-friendly farming and agroecological intensification practices to mitigate the negative impacts on local ecosystems and biodiversity (Tscharntke et al. , 2012). Originality/value The authors contribute to the debate on the positive and negative impact of FDI on developing countries, particularly considering temporality and the rural environment in which the FDI is partaking.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it