The Transaction Cost Economics Theory of Trading Favors: The Case of Entrepreneurial Firms
Bibliographic record
Abstract
We analyze how the frequent use of dark favors, more specifically informal payments meant to function as bribes, affects institution- based transaction costs (degree of business obstacles) for privately held entrepreneurial firms in transition economies. Prior research on this subject matter has focused almost exclusively on how widespread the practice of dark favors is in various contexts, and on the determinants of dark favors’ levels. In contrast, we focus on a critical complementary issue, namely the frequency of dark favors, i.e., the regularity with which firms engage in bribing, and the performance outcomes thereof. We develop a transaction-cost-economics (TCE) based logic, augmented with insights from signaling theory, to predict the effects of frequency on dark favors’ outcomes, and assess the moderating impact of two governance-related parameters, namely the status of the company as a family firm, and the presence of business network governance. We test our hypotheses on a sample of 206 companies in 17 transition economies with data covering the 2002 and 2005 periods. We find support for our prediction that a higher dark favors’ frequency will negatively affect family firms because of comparatively stronger reputation impacts, as well as firms operating in business networks because of comparatively stronger information leakage.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".