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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".