Won’t you be my neighbor? Geography, peer learning, and entrepreneur performance in Togo
Bibliographic record
Abstract
Entrepreneurs, especially in developing economies, rely on peers for advice and managerial knowledge. While a growing body of work shows that introducing entrepreneurs to new peers outside their immediate neighborhood and social circles improves performance, these results are seemingly at odds with work on geographic spillovers which suggests that entrepreneurs are especially likely to learn and benefit from their neighbors. We explore these diverging predictions using data from a training program in Togo, during which entrepreneurs were randomly assigned to meet and talk to peers from across the city of Lomé. We find that meeting neighbors increases entrepreneurs’ performance more than does meeting more distant peers. Profits increase by 10% when entrepreneurs get to know three neighbors who are on average 1 km closer. In additional analyses we find evidence that, in this context, entrepreneurs tend to be locally “under networked:” they are likelier to stay in touch with neighbors than with more distant peers and neighbors possess novel managerial knowledge. These results suggest that entrepreneurs in our context, and potentially in other developing economies, are often under networked, making neighbors particularly impactful for their performance.
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 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 | 0.006 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".