Networking Frictions and Entrepreneurial Learning in Developing Economies
Bibliographic record
Abstract
Relationships with peers help entrepreneurs learn and improve firm performance. Recent scholarship confirms that events—social gatherings such as mixers, conferences, or training programs—can help entrepreneurs build valuable social connections. Yet, for entrepreneurs in developing economies, networking frictions may make connecting with peers challenging and undermine the benefits of events. We argue that when networking frictions are high, the value of events will lie more in connecting neighbors rather than bringing together distant peers. In the presence of networking frictions, neighbors are both less likely to be someone the entrepreneur has already learned from and easier to sustain a relationship with. To test this argument, we use data from a series of networking events in Togo during which entrepreneurs were randomly assigned to meet with peers from across the city of Lomé. We find that entrepreneurs who were assigned to neighboring peers were much more likely to sustain a relationship, learn from their peer’s management knowledge, and in turn benefit more: Profits increase by 10% when entrepreneurs get to know peers who are located on average 1 km closer to them. Our results highlight the central role that networking frictions play in shaping who entrepreneurs in developing economies can successfully learn from. This paper was accepted by Lamar Pierce, organizations. Funding: Ewing Marion Kauffman Foundation [Dissertation Fellowship] and the Strategic Management Society [SRF Dissertation Fellowship]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.00281 .
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".