Washington University in St Louis
Institutional Affiliation: Washington University in St. Louis
NBER Working Papers and Publications
|August 2018||Does Machine Translation Affect International Trade? Evidence from a Large Digital Platform|
with , : w24917
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.
Published: Erik Brynjolfsson & Xiang Hui & Meng Liu, 2019. "Does Machine Translation Affect International Trade? Evidence from a Large Digital Platform," Management Science, vol 65(12), pages 5449-5460.
|Certification, Reputation and Entry: An Empirical Analysis|
with , , : w24916
Markets with asymmetric information will often employ third-party certification labels to distinguish between higher and lower quality transactions, yet little is known about the effects of certification policies on the evolution of markets. How does the stringency in quality certification affect the intensity and composition of entry, incumbents' reactions, and market outcomes? We use detailed administrative data and exploit a policy change on eBay to explore how a more selective certification policy affects entry and behavior across a rich set of online market segments. We find that after the policy change, entry increases and does so more intensely in markets where it is harder to become certified. The average quality of entrants also increases more in the more affected markets, while t...
|June 2018||Online Syndicates and Startup Investment|
with : w24777
Early crowdfunding platforms were based on a premise of disintermediation from professional investors, and relied on the ‘wisdom of the crowd’ to screen high quality projects. This becomes problematic when equity is involved, as the degree of asymmetric information between entrepreneurs looking for funding and the crowd is higher than in reward-based crowdfunding. As a result, platforms later experimented with incentives for professional investors to curate deals for crowd. We study how the introduction of such incentives influenced the allocation of capital on the leading US platform, finding that the changes led to a sizable 33% increase in capital flows to new regions. Professional investors use their reputation to vouch for high potential startups that would otherwise be misclassified ...