IDEAS home Printed from https://ideas.repec.org/p/nbr/nberwo/24917.html

Does Machine Translation Affect International Trade? Evidence from a Large Digital Platform

Author

Listed:
  • Erik Brynjolfsson
  • Xiang Hui
  • Meng Liu

Abstract

Artificial intelligence (AI) is surpassing human performance in a growing number of domains. However, there is limited evidence of its economic effects. Using data from a digital platform, we study a key application of AI: machine translation. We find that the introduction of a machine translation system has significantly increased international trade on this platform, increasing exports by 17.5%. Furthermore, heterogeneous treatment effects are all consistent with a substantial reduction in translation-related search costs. Our results provide causal evidence that language barriers significantly hinder trade and that AI has already begun to improve economic efficiency in at least one domain.

Suggested Citation

  • Erik Brynjolfsson & Xiang Hui & Meng Liu, 2018. "Does Machine Translation Affect International Trade? Evidence from a Large Digital Platform," NBER Working Papers 24917, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:24917
    Note: IO ITI PR
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/w24917.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    More about this item

    JEL classification:

    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty
    • F1 - International Economics - - Trade
    • F14 - International Economics - - Trade - - - Empirical Studies of Trade
    • O3 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nbr:nberwo:24917. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.