False Friends? On the Effect of Bureaucracy, Informality, Corruption and Conflict in Ukraine on Foreign and Domestic Acquisitions
Bibliographic record
Abstract
Ukraine had had its ups and downs in recent years. It has, for example, dramatically improved its ease of doing business (EOBB), and it has made some progress reducing the relative size and influence of its shadow economy (Shadow). But, the Russian invasion of 2014 (Conflict) forced it to take a few developmental steps backwards. In this paper, we consider the effect of these factors, positive and negative, on the number of mergers and acquisitions, involving Ukrainian firms. We construct a sample of 4030 acquisitions in the period 1 January 2000–31 December 2020. Our results suggest that while the number of acquisitions by domestic firms increases in efficiency (+EOBB), transparency (−Shadow) and peace (−Conflict), the number of foreign acquisitions increases in bureaucracy (−EOBB), in informality (+Shadow), and unrest (+Conflict). From an academic perspective, our findings fit with some recent work, while providing new insights too. From a policy perspective, our findings that the number of foreign acquisitions is negatively affected by Ukraine’s attempts to modernize and improve its economy and is positively affected by the ongoing conflict with Russia, makes us wonders what type of ‘false friends’ make such investments.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".