WHEN DOES RELATIONAL CONTEXT HELP PREDICT TECHNOLOGY ENTRY? EVIDENCE FROM U.S. METROPOLITAN AREA

How does the inclusion of relational information improve predictions made us- ing history? We use U.S. metropolitan technology entry during 2000–2019 to construct a panel of 281,236 observations of 100 Core-Based Statistical Areas in- tegrating 541 CPC technology subclasses over the 10-year period. We investigate if relations with other cities forecast technology entry. We test if the benefit de- pends on how new the technology is relative to the city’s technology level. We use citation datasets to improve our forecast and measure the benefit against a baseline using the history and location of the focal city. The benefit is strongly dependent on technology novelty. For the most novel technology, the benefit is 0.016, and for the most familiar it is−0.002, for a difference of 0.018 (95% CI [0.002, 0.036]). The same effect is observed when using collaborations between inventors and when the same inventors work for the same company. As the col- laborations and company affiliations are similar—pairwise r = 0.81–0.97—we give greater weight to the results of the citation study and consider the effects of the collaborations and company affiliations as correlated views of a common diffusion signal rather than independent replications. Citation exposure, like ge- ography, predicts the outcome of interest. We consider the effect of relations, the intensity of relations, and other factors. Relational networks can be randomized, but the observed randomization achieves only Jaccard 0.645, so strong rela- tions and a “small world” effect constrain what can be learned. A graph neural network achieves forecasting accuracy of 0.6659. Conditional on technology nov- elty, we find the network captures relational information but the gradient is not significant; forecasts do not significantly vary from the baseline. The novelty of the technology may also condition the forecasts. The results jointly demonstrate that relational context enhances predictions pri- marily for technologically recent cases of competitive ent