Learning to Export from Neighbors

46 Pages Posted: 16 Jul 2014

See all articles by Ana Fernandes

Ana Fernandes

University of Exeter

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: June 2014

Abstract

This paper studies how learning from neighboring firms affects new exporters' performance. We develop a statistical decision model in which a firm updates its prior belief about demand in a foreign market based on several factors, including 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, conditional on survival, lowers its post-entry growth. These learning effects are stronger when there are more neighbors to learn from or when the firm is less familiar with the market. We find supporting evidence for the main predictions of the model from transaction-level data for all Chinese exporters from 2000 to 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: F1, F2

Suggested Citation

Fernandes, Ana and Tang, Heiwai, Learning to Export from Neighbors (June 2014). Centro Studi Luca d'Agliano Development Studies Working Paper No. 370, Available at SSRN: https://ssrn.com/abstract=2466337 or http://dx.doi.org/10.2139/ssrn.2466337

Ana Fernandes (Contact Author)

University of Exeter ( email )

Northcote House
The Queen's Drive
Exeter, Devon EX4 4QJ
United Kingdom

Heiwai Tang

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 )

Poschinger Str. 5
Munich, DE-81679
Germany

Kiel Institute for the World Economy ( email )

P.O. Box 4309
Kiel, Schleswig-Hosltein D-24100
Germany

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