Learning to Export from Neighbors

50 Pages Posted: 16 Apr 2014

See all articles by Ana Fernandes

Ana Fernandes

University of Sussex

Heiwai Tang

Johns Hopkins University - Paul H. Nitze School of Advanced International Studies (SAIS); The University of Hong Kong - Faculty of Business and Economics; CESIfo; Kiel Institute for the World Economy

Date Written: March 15, 2014

Abstract

This paper studies how learning from neighboring firms affects new exporters’ performance and dynamics. We develop a statistical decision model in which a firm updates its prior belief about demand of a foreign market based on the number of neighbors currently selling there, the level and heterogeneity of their export sales, and the firm’s own prior knowledge about the market. A positive signal about demand inferred from neighbors’ export performance raises the firm’s probability of entry and initial sales in the market, but lowers post-entry growth, conditional on survival. These learning effects are stronger when there are more neighbors revealing the signal or when the firm is less familiar with the market. Decisions to exit are independent of the prevalence of neighboring export activities. We find supporting evidence from the transaction-level export data for all Chinese exporters over 2000-2006. Our findings are robust to controlling for firms’ supply shocks, countries’ demand shocks, and city-country fixed effects.

Keywords: learning to export, knowledge spillover, uncertainty, export dynamics

JEL Classification: F10, F20

Suggested Citation

Fernandes, Ana and Tang, Heiwai, Learning to Export from Neighbors (March 15, 2014). CESifo Working Paper Series No. 4699, Available at SSRN: https://ssrn.com/abstract=2425106

Ana Fernandes

University of Sussex ( email )

Sussex House
Falmer
Brighton, Sussex BNI 9RH
United Kingdom

Heiwai Tang (Contact Author)

Johns Hopkins University - Paul H. Nitze School of Advanced International Studies (SAIS) ( email )

1740 Massachusetts Avenue, NW
Washington, DC 20036-1984
United States

The University of Hong Kong - Faculty of Business and Economics ( email )

Pokfulam Road
Hong Kong
China

CESIfo ( email )

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Munich, DE-81679
Germany

Kiel Institute for the World Economy ( email )

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Germany

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