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Learning from Neighbors and Differentiating Export Quality
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NETL
NSTL
万方数据
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'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.
NETL
NSTL
万方数据
