Natural-resources-seeking FDI and employment opportunities in developing countries: a temporal perspective
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,000 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».