Learning from Neighbors and Differentiating Export Quality

This paper explores how learning from neighboring firms affects new exporters' product quality. It builds a Bayesian learning model to study how new exporters revise their prior beliefs about foreign customers' preferences for product quality from neighboring pioneering exporters. The model shows that a new exporter improves its product quality when it receives a positive quality-preference signal from its neighbors. The learning process of a firm depends on the number of neighbors, the level and heterogeneity of their export quality, and its own prior knowledge of the market. Highly disaggregated firm–product–country level transaction data provide robust evidence for this. The results also suggest that the impact of neighboring signals on a new exporter's quality can be channeled through the importation of high-quality intermediate inputs and more fixed investment. Learning effects are heterogeneous across firms and learning can influence other aspects of export performance.