Learning to Import From Neighbors
Cui Hu and
Yong Tan ()
MPRA Paper from University Library of Munich, Germany
This paper studies how learning from neighboring firms affects the behaviors of new importers. We first develop a learning model in which firms update their beliefs about the import price in foreign markets based on several factors, including import prices and number of neighboring firms that import from the same country. The updating proceeds according to the Bayesian rule. The model predicts that a positive signal about import prices revealed by neighboring importers encourages entry and increases initial imports from the same country. The signal plays a stronger role when it is revealed by more neighbors. Using a transaction-level dataset of Chinese importers over the 2000-2006 period, we find supporting evidence for the model's predictions. Our results are robust to controlling for various fixed effects and different subsamples.
Keywords: Learning to Import; Bayesian Updating; Agglomeration; Uncertainty (search for similar items in EconPapers)
JEL-codes: D8 F1 F2 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-int
References: View references in EconPapers View complete reference list from CitEc
Citations Track citations by RSS feed
Downloads: (external link)
https://mpra.ub.uni-muenchen.de/78108/1/MPRA_paper_78108.pdf original version (application/pdf)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:78108
Access Statistics for this paper
More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC.
Series data maintained by Joachim Winter ().