Social Skills Improve Business Performance: Evidence from a Randomized Control Trial with Entrepreneurs in Togo
Bibliographic record
Abstract
Recent field experiments demonstrate that advice, mentorship, and feedback from randomly assigned peers improve entrepreneurial performance. These results raise a natural question: what is preventing entrepreneurs and managers from forming these peer connections themselves? We argue that entrepreneurs may be under-networked because they lack the necessary social skills—the ability to communicate effectively and interact collaboratively with new acquaintances—that allow them to match efficiently with knowledgeable peers. We use a field experiment in the context of a business training program in Togo to test if a short social skills training module increases the number and complementarity of peers that participants choose to learn from. We find that social skills training led entrepreneurs to match with 50% more peers and that more of those matches were based on complementary managerial skill. Finally, the training also increased entrepreneurs’ monthly profits by approximately 20%. Further analyses point to improvements in networking and advice as the drivers of performance improvements. Our findings suggest that social skills help entrepreneurs build relationships that create value for both themselves and their peers. This paper was accepted by Alfonso Gambardella, business strategy. Funding: This work was supported by the Ewing Marion Kauffman Foundation [Dissertation Fellowship] and the Strategic Management Society [SRF Dissertation Fellowship]. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2022.4334 .
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".