Political Risk Investing in Emerging Markets versus Economic Reality
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Notice bibliographique
Résumé
Investment risk? is always accompanied with ?return?, it is one of the most important aspects to evaluate when doing business by private firms or making new decision on overseas investments by governments. According to the report ?World Investment and political Risk? provided by the Multilateral Investment Guarantee Agency, investors keep ranking political risk as a prime obstacle for investments into developing markets (Multilateral Investment Guarantee Agency, 2014). The term ?emerging markets? originally brought into fashion in the 1980?s by the World Bank economist Antoine van Agtmael. Emerging markets are the world?s fastest growing economies, contributing to a great deal of the world?s explosive growth of trade. By 2020, the five biggest emerging markets? share of world output will double to 16.1 percent from 7.8 percent in 1992 (Marr & Reynard, 2010). Since the year 2000 share of emerging economies in global GDP (in Purchasing Power Parity) has increased from 37 percent to 50 percent in 2013 (Boumphrey & Bevis, 2013). They are critical participants in the world?s major political, economic, and social affairs and are seeking a larger voice in international politics and a bigger slice of the global economic pie. Recently some events such as Arab Spring, a conflict between Russia and Ukraine, and protests in Brazil against corn upt spending when organizing the World Football championship have increased political risk in those markets. As a result, the issues of political risk analyzed in this article are currently relevant. The aim of the article is to research political risk and its influence on business investments in emerging markets as well as the methods to evaluate such risk precisely as much as possible. This article begins with the introduction to theories relevant for the analysis of the topic. It also presents the political risk and its influences on operations in a emerging market. Then the case study is presented with food industry is chosen for analysis and with application to Russian-Lithuanian situation after Russia has put the sanctions on import of food products (vegetables, meat, fish, milk and dairy products) from the EU member states, Australia, the US, Canada and Norway for a year.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
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 découle