Distributed Ledger Technology (DLT): A Game Changer for MNEs in Emerging Markets
Bibliographic record
Abstract
Global trade determines how we live. Technology determines the extent of the market and the ease of trade. The transportation revolution reduced costs and cut travel times. The communication revolution (ICT) improved the quality and quantity of information in the global market and cut the cost of global trade in goods and services, including labor. Global trade has become a B2B market wherein multinational enterprises (MNEs) are major players. While MNEs began as major companies in developed countries, their success in importing labor from the emerging market through production of consumer goods in the developed countries led to emerging MNE markets. In an earlier paper on MNEs in emerging markets, Agmon suggested that blockchains reduce the cost of using the global price mechanism, and both production and consumption decisions can be made by individuals in a global market. In this paper, we discuss the case of the multinational industry of venture capital-supported small start-ups, wherein individuals with ideas for better goods, production processes, and services approach capital markets in major countries for financing their ideas. The accompanying distributed ledger technology (DLT) takes global trade a step further by opening up the possibility of global trade among individuals and loosely organized, task-oriented groups of individuals located in both developed and emerging economies. In a DLT world with decentralized markets, no transaction costs, and perfect information, the key to global trade will lie in the capabilities of the individual, or a specific task-oriented, loosely organized group of individuals. Small countries are finding it increasingly difficult to compete in international markets. We seek to examine whether the conceptual framework of DLT, when implemented in a small country that chooses to export ideas rather than products, thereby eliminating the need for a complex supply chain, can be the first empirical example of the DLT concept as a “game changer”. The experience of the Israeli VC industry points to exciting potential through the application of the mindset and the unique legal and financial structures of the “start-up nation”, wherein an economy was created that relies on small and frequently changing high-tech firms. In a country where VC investment capital is entirely imported, there is more room for investment in DLT technologies. Such an economy is compatible with the DLT concept and provides a unique empirical example of the DLT technological change’s effect on the economy of a small country.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".