Firms in International Trade 1
Firms in International Trade 1
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发表时间：
2007
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通讯作者：
A. Bernard;Tuck
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作者：
A. Bernard;Tuck
尽管进出口是极为罕见的企业活动，但经济学家在讨论国际贸易时通常很少关注企业的作用。本文总结了贸易和非贸易公司之间的主要差异，展示了这些差异是如何对标准贸易模型提出挑战的，并展示了最近的“异质公司”国际贸易模型如何应对这些挑战。然后，我们利用交易层面的美国贸易数据来介绍一些关于公司和贸易的新的程式化事实。这些事实表明，广泛的贸易差额--即公司贸易的产品数量以及与它们进行贸易的国家数量--对于理解距离在抑制总贸易流量方面众所周知的作用至关重要。[1]伯纳德、詹森和肖特感谢国家科学基金会（SES-0241474、SES-0552029和SES 0550190），雷丁感谢普林斯顿大学和经济绩效中心（CEP）的研究支持。我们感谢人口普查局的吉姆·戴维斯及时披露了我们的数据分析，感谢吉姆·泰布特、吉姆·海因斯和JEP编辑委员会提供了非常有用的意见和建议。本文的研究是在伯纳德、詹森和肖特担任美国人口普查局波士顿研究数据中心、纽约研究数据中心和经济研究中心的特别宣誓身份研究员时进行的。我们感谢NSF为支持人口普查研究数据中心（SES 0550190和ITR-0427889）的基础设施赠款。所表达的研究结果和结论是作者的，并不一定反映人口普查局，NBER或作者所属的任何其他机构的观点。
Despite the fact that importing and exporting are extremely rare firm activities, economists generally devote little attention to the role of firms when discussing international trade. This paper summarizes key differences between trading and non-trading firms, demonstrates how these differences present a challenge to standard trade models and shows how recent “heterogeneous-firm” models of international trade address these challenges. We then make use of transaction-level U.S. trade data to introduce a number of new stylized facts about firms and trade. These facts reveal that the extensive margins of trade – that is, the number of products firms trade as well as the number of countries with which they trade – are central to understanding the well-known role of distance in dampening aggregate trade flows. 1 Bernard, Jensen, and Schott thank the National Science Foundation (SES-0241474, SES-0552029 and SES0550190) and Redding thanks Princeton University and the Centre for Economic Performance (CEP) for research support. We are grateful to Jim Davis of the Census Bureau for timely disclosure of our data analysis and to Jim Tybout, Jim Hines and the JEP editorial board for extremely helpful comments and suggestions. The research in this paper was conducted while Bernard, Jensen and Schott were Special Sworn Status researchers of the U.S. Census Bureau at the Boston Research Data Center, New York Research Data Center, and Center for Economic Studies. We thank the NSF for infrastructure grants that support Census Research Data Centers (SES0550190 and ITR-0427889). Research results and conclusions expressed are those of the authors and do not necessarily reflect the views of the Census Bureau, the NBER, or any other institution to which the authors are affiliated.