The Performance Effects of Business Groups in Russia
Bibliographic record
Abstract
abstract This study analyses the impact of business group affiliation on firm performance during a time when business groups are newly formed, when the economic and institutional environment is changing, and when group survival is uncertain. Based primarily on a transaction cost approach, we develop two hypotheses, concerning profitability and risk sharing (redistribution) respectively. The positive profitability hypothesis proposes that company affiliation with a business group directly and positively affects the profitability of each affiliate. A positive direct effect emerges when each affiliate benefits from access to group resources. The redistribution hypothesis considers the simultaneous possibility that inter‐affiliate transfers of resources through internal markets are designed to redistribute profits among group members. We argue that variance‐reducing redistribution from strong to weak group members is linked to group survival in times of institutional change. Our empirical approach focuses on testing these two linked hypotheses (and their alternatives) using a relatively large, contemporary and time varying database of Russian firms. We also develop a framework that distinguishes among the four possible empirical outcomes associated with the hypotheses. Our results provide unambiguous support for the case where the impact of group membership on profitability is positive and redistribution is variance‐reducing. We term this outcome Business Group Robustness, and contrast it with other possible empirical outcomes.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".