Spatial-sectoral Home and Host Country Assessment of United States Direct Investent in Canada
Notice bibliographique
Résumé
There is a large body of empirical research describing the geographic patterns of, and the underlying reasons for, foreign direct investment (FDI). Generally, this research has been conducted at an aggregate level both spatially and sectorally. However, it is certain that more disaggregated approaches can yield important insights. This is particularly true for developed economies where regional differences influence investment decisions. General macro level knowledge can be assumed but regional-based knowledge will be less certain (Qu and Green 1997; Mariotti and Piscitello 1995). In this paper, we provide a detailed spatial assessment of US foreign direct investment in Canada disaggregated by industrial sector, province and state. Moreover, we explore the importance of established economic conditions (as measured by trade flows, number of establishments and distance) on the spatial choices made by US foreign direct investors operating within the highly-integrated and compatible Canadian and US economies. Due to this explicit home and host location viewpoint in explaining spatial-sectoral direct investments patterns, we believe this itemised spatial appraisal of Canada FDI from the US is distinctive. The host country approach is typified by Dunning's (1977) Eclectic Paradigm of International Production. According to Dunning's model, a firm is unlikely to invest directly in a foreign country if firm (ownership)-specific, internalisation and country (location)-specific advantages are missing (see also Rugman et al 1985). Firm-specific advantages (which are chiefly knowledge-based or technology-oriented attributes) and internalisation properties (the ability to keep these advantages intact) provide the `competitive edge' that a given multinational enterprise (MNE) has over rival firms operating in a market. However, consideration of differences in location-specific advantages is also important. In other words, host locations that are most frequently chosen are those that allow for the most profitable use of an MNE's firm-specific advantages. Some of the more common host country considerations that Dunning and others have recognised as important to foreign direct investors include: * Market factors -- which consists of not only market size and growth potential but also the ability to maintain market shares and to promote trade between the subsidiary and parent company (and, thereby to realise transfer pricing advantages); * Natural and created resource endowment; * Cost factors -- which encompass factors influencing the cost of production (such as labour, energy and supporting industry); * Societal and a financial infrastructure -- such as credit, legal, and educational facilities; * Transportation and communication infrastructures; * Economies of centralisation for RD * Artificial barriers -- such as import controls on the trade of goods and services and exchange rate differentials; * The investment climate -- which consists mainly of political stability, general attitudes toward FDI (which includes corporate tax rates and regulations), and industrial incentives and disincentives; and * Cultural differences and similarities (Dunning 1977, 1993; Rugman 1980). Although Dunning (1993) also recognised that home countries may benefit to varying degrees from the location-specific decisions of their MNEs, his theory is predominantly based on demonstrating what criteria enable host countries to attract FDI. In 1990, Porter added to the FDI literature by providing a theory that helps to explain the global pattern of FDI (and trade) largely from the perspective of differing home country characteristics. With respect to FDI, Porter claims that firms that have attained success within the global market have done so because of their ability to extend their home-based advantages abroad. The home country, then, acts as an incubator and outward FDI is shaped by the country's: qualities of labour, appropriateness of production factors, market and demand conditions, support industries, competitive atmosphere, government policy and so on. …
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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 | 0,000 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».