Artificial intelligence (AI) is one of the most important technological advances of our era. Recent progress of AI and, in particular, machine learning (ML), has dramatically increased predictive power in many areas such as speech recognition, image recognition, and credit scoring (Agrawal et al. , Brynjolfsson and Mitchell , Mullainathan and Spiess ). Unlike the last generation of information technology that required humans to codify tasks explicitly, ML is designed to learn the patterns automatically from examples (Brynjolfsson and Mitchell ). This has opened a broad new frontier of applications and economic implications that are, as yet, largely undeveloped. AI has been called a general-purpose technology, like the steam engine and electricity, whose capabilities span beyond specific applications. If this is true, then AI should ultimately lead to fundamental changes in work, trade and the economy.
Nonetheless, empirical evidence documenting concrete economic effects of using AI is largely lacking. In particular, contributions from AI have not been found in measures of aggregate productivity. Brynjolfsson et al.  argue that the most plausible reason for the gap between expectations and statistics is due to lags in complementary innovations and business procedure reorganization. If the gap is indeed due to lagged complementary innovation, the best domains to empirically assess AI impacts are settings where AI applications can be seamlessly embedded in an existing production function because complementary innovations are already in place. In particular, various digital platforms are at the forefront of AI adoption, providing ideal opportunities for early assessments of AI’s economic effects.
In this paper, we provide evidence of direct causal links between AI adoption and economic activities by analyzing the effect of the introduction of eBay Machine Translation (eMT) on eBay’s international trade. As a platform, eBay mediated more than 14 billion dollars of global trade among more than 200 countries in 2014. The focal AI technology, eMT (from here on also referred to as the policy), is an in-house machine learning system that statistically learns how to translate among different languages. We exploit the discrete introduction of the policy for several language pairs, most notably English-Spanish, as a natural experiment, and study its consequences on U.S. exports on eBay via a difference-in-difference (DiD) estimation strategy. The identification compares the post-policy change in U.S. exports for the treated countries with that of the control countries (i.e., all other countries that U.S. sellers export to on eBay). For instance, we find that eMT increases U.S. exports to Spanish-speaking Latin American countries by 17.5%-20.9% on eBay, depending 1 on the length of the pre- and post-policy time windows we evaluate. To mitigate potential spillover effects, we also use a second control group: offline U.S. exports to the same set of countries treated with eMT, for the DiD estimation. The results are similar, and the comparisons of the policy with either of the two control groups are statistically indistinguishable from each other. In the online appendix, we use U.S. exports to Brazil as a third control group, and also study the two rollouts of eMT in the EU. In each case, the results remain qualitatively unchanged.
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