Estimating Gravity Model in the Czech Republic: Empirical Study of Impact of IFRS on Czech International Trade
Bibliographic record
Abstract
Purpose: In this paper, we test the influence on foreign trade and FDI by using the gravity model within the EU. The influence of IFRS is also tested, although we might expect that its influence will be smaller than that of other factors. Design/Methodology/Approach: According to the gravity model, countries are trading according to their proximity and also according to the size of their GDP. Negative influence is played by trade barriers and positive by common traditions and a common political background. Big countries trade a lot between each other, e.g., the USA and Canada on the same continent or the USA and Germany in different continents. Smaller countries, like the Czech Republic, do not have such an impact on the scale of world trade. The size of exports /imports is influenced by the fact of whether or not they are part of some trading bloc, e.g., the EU in Europe or NAFTA in America. Accounting rules, namely IFRS, are expected to be perceived as a positive influence on the world trade of a particular country and a country´s FDI (Foreign Direct Investment). Findings: Contrary to our expectations, we have found that the influence of IFRS is not insignificant and is more pronounced after the year 2010 which coincides with the change of local regulations. Practical Implications: The findings establish an interesting signal relating to perceiving the increasing quality of the Czech economic environment including accounting regulations. Originality/Value: Based on our methodology accounting rules, namely IFRS, are expected to be perceived as a positive influence on the world trade of a particular country and a country´s FDI (Foreign Direct Investment).
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".