Export quality differentiation in learning from neighbors

This paper explores how learning from neighboring firms affects new exporters’ product quality. We build a Bayesian learning model where new exporters can update their prior beliefs about the quality preference of foreign markets from their neighboring pioneering exporters. Our model shows that a new exporter raises its product quality when it receives a positive 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. Using highly disaggregated firm-product-country-level transaction data, we find robust evidences for these predictions. Our results also suggest the impact of neighboring signals on a new exporter’s quality is channeled through the imports of high-quality intermediate inputs and more fixed investment. In addition, we find that the learning effect are heterogeneous across firms and learning can influence other aspects of export performance.