Social capital and entrepreneur resilience: Entrepreneur performance during violent protests in Togo
Bibliographic record
Abstract
Abstract Research Summary This study explores how entrepreneurs' social capital affects their resilience to localized shocks. Using a unique longitudinal survey of entrepreneurs during a surge of violent protests in Togo during 2017 and 2018, I explore how different kinds of relationships affect entrepreneurs' performance. Results show that proximity to violent protests caused entrepreneurs' profits to drop by 20%. This decrease, however, was mitigated by entrepreneurs' ties to their local communities and by their non‐colocated advice relationships, which were ties to geographically distant advisers. In contrast, colocated advisers, those who were spatially proximate, were harmful to their performance. These findings show that social capital can have conflicting effects on entrepreneurs' resilience, depending on the kinds of relationships they consist of and how those relationships are exposed to the shock. Managerial Summary Relationships are critical to entrepreneurs' performance. Yet, during local crises, such as violent protests, it can be difficult to know which relationships to rely on. Studying entrepreneurs in Togo during a sudden surge of violent protests, I found that two kinds of relationships reduced the negative impact of the localized shock: ties to local communities and advisers located outside the crisis area. In contrast, advisers located nearby, who were also affected by the crisis, amplified the protests' negative effects. These findings suggest that entrepreneurs who can afford to build stronger ties to their local communities and have more distant advisers may be better positioned to minimize losses during localized shocks.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".