Effects of subnational regional corruption on growth strategies in emerging economies: Evidence from Russian domestic and international M&A activity
Bibliographic record
Abstract
Research Summary This article contributes to an improved understanding of the effects of subnational regional corruption on the external growth strategies of emerging economy firms. We examine the acquisition activity of firms in their home regions, in other parts of the country, and internationally. We consider four mechanisms through which a corrupt regional home context can affect firms’ acquisition behaviors: (a) corrosive deal deterrence, (b) deal facilitation, (c) corruption escape into less corrupt contexts, and (d) enhanced corruption ability to acquire in similarly corrupt environments. Based on an analysis of the acquisition activity of 2,981 Russian firms established in 40 regions in Russia from 2001 to 2008, we find evidence for the existence of both deal facilitating and escaping effects of home region corruption. Managerial Summary Corruption indicators regularly reveal that emerging countries are more corrupt than developed countries. While prior academic research has associated corruption with predominantly negative effects on business activity, an increasing number of globally successful companies have emerged from such corrupt environments. We advocate a more nuanced view of the effects of corruption on the growth strategies of emerging economy firms. We show that home region corruption can have multiple effects on a firm's geographic expansion. Specifically, we find that corruption in the home region helps regional firms expand their business through acquisitions when it is pervasive and nonarbitrary. At the same time, however, when expanding geographically, firms, on average, seem to prefer to diversify their assets in other regions or countries that are less corrupt.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".