Political Risk Investing in Emerging Markets versus Economic Reality
Bibliographic record
Abstract
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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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".