Path dependent network advantage.

We analyze network volatility as something akin to the hum of a running engine. People active in a network produce vibration and wiggle where the connections and the network structure around these people changes frequently. We want to know how that vibration and wiggle affects their network advantage. We distinguish four dimensions to network volatility (churn, variation, trend, and reversals), measure them with panel data on a population of bankers, then add them to analysis predicting compensation from status and structural-hole measures of network advantage. We find that volatility does not affect performance directly. It creates a slope adjustment that enhances the returns to network advantage. We identify two stability traps that destroy advantage, but the key is not to avoid the traps so much as to avoid them in a particular way. The volatility that enhances is reversal: a pattern in which network advantage is lost then regained, producing oscillations in network metrics, as would be produced by a behavioral pattern of serial closure over time. Bankers who go through reversals enjoy significantly higher returns to their network advantage. In the absence of reversals, in fact, there are no returns to network advantage. We infer a "serial closure" hypothesis in which reversals are the result of bankers engaging and disengaging closed networks. Networks built through serial closure provide advantage well beyond the level expected from structure alone. We present evidence consistent with the hypothesis, but testing remains for future research. What we can say here is that the way a network develops has substantial implications for the advantage it provides.