Local Geographic Distance and Business Practice Non-Contagion

In Simple Words

Imagine two neighboring food stalls in a market. A consultant teaches one owner a new way to organize inventory and keep accounts. The neighboring owner sees the consultant visiting and sees the new record books, but does not copy the method because they do not understand the underlying financial calculations or do not perceive a clear profitability gain from spending extra time on bookkeeping. Without direct, tailored coaching or clear visible profit gains, business practices do not naturally diffuse to neighboring firms simply through physical proximity.

Research question

Development programs often rely on peer-to-peer knowledge spillovers, assuming that non-treated microenterprises located near trained peers will observe, learn, and passively adopt improved business management practices.

Participants

160 microenterprise tailors across 8 geographic clusters in Accra, Ghana, spaced between 1 to 10 minutes walk apart or in adjacent market stalls.

Study Design

Geographic spillover analysis measuring adoption of 35 recommended business practices among control tailors as a function of minimum distance to consulting tailors and the density of consulting tailors within a 0.5 km radius over 8 survey rounds.

Results

Minimum distance to a consulting tailor had no statistically significant effect on business practice adoption by control tailors (coefficient = -0.0304, p > 0.10), and the number of consulting tailors within 0.5 km similarly showed no significant spillover effect (coefficient = 0.00779, p > 0.10).

Takeaway

Do not rely on geographic proximity or peer diffusion to spread complex managerial practices across microenterprise clusters; direct intervention is required for practice adoption.

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Plain-English glossary

Original explanations, informed by the references below. Examples are illustrations, not data from the studies above. The research text stays unchanged.

Statistically significant

A label used when a result passes a chosen statistical threshold, often p < 0.05. It does not show how large or useful an effect is. A result that is not statistically significant does not prove there is no effect.

ExampleA study reports p = 0.03 against a 0.05 threshold. It passes that test, but the effect could still be too small to matter.

American Statistical Association · Statement on p-values ↗Read more about statistically significant →

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