Detecting Learning by Exporting and from Exporters
Jingfang Zhang and
Emir Malikov
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Jingfang Zhang: University of Kentucky
Journal of Productivity Analysis, 2023, vol. 60, issue 1, No 1, 19 pages
Abstract:
Abstract Existing literature at the nexus of firm productivity and export behavior mostly focuses on “learning by exporting,” whereby firms can improve their performance by engaging in exports. Whereas, the secondary channel of learning via cross-firm spillovers from exporting peers, or “learning from exporters,” has largely been neglected. Omitting this important mechanism, which can benefit both exporters and non-exporters, may provide an incomplete assessment of the total productivity benefits of exporting. In this paper, we develop a unified empirical framework for productivity measurement that explicitly accommodates both channels. To do this, we formalize the evolution of firm productivity as an export-controlled process, allowing future productivity to be affected by both the firm’s own export behavior as well as export behavior of spatially proximate, same-industry peers. This facilitates a simultaneous, “internally consistent” identification of firm productivity and the corresponding effects of exporting. We apply our methodology to a panel of manufacturing plants in Chile in 1995–2007 and find significant evidence in support of both direct and spillover effects of exporting that substantially boost the productivity of domestic firms.
Keywords: Exporting; Learning by exporting; Learning from exporters; Production function estimation; Productivity; Spillovers (search for similar items in EconPapers)
JEL-codes: D24 F10 L10 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:kap:jproda:v:60:y:2023:i:1:d:10.1007_s11123-023-00667-2
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DOI: 10.1007/s11123-023-00667-2
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