Extract 20 from the Economics of Artificial Intelligence: an Agenda

The Economics of Artiﬁ cial Intelligence

National Bureau of Economic Research Conference Report

The Economics of Artiﬁ cial Intelligence: An Agenda

Edited by Ajay Agrawal, Joshua Gans, and Avi Goldfarb

Chicago and London

The University of Chicago Press, Chicago 60637 The University of Chicago Press, Ltd., London © 2019 by the National Bureau of Economic Research, Inc. All rights reserved. No part of this book may be used or reproduced in any manner whatsoever without written permission, except in the case of brief quotations in critical articles and reviews. For more information, contact the University of Chicago Press, 1427 E. 60th St., Chicago, IL 60637. Published 2019 Printed in the United States of America

28 27 26 25 24 23 22 21 20 19 1 2 3 4 5

ISBN-13: 978-0-226-61333-8 (cloth) ISBN-13: 978-0-226-61347-5 (e-book) DOI: https:// doi .org / 10 .7208 / chicago / 9780226613475 .001 .0001

Library of Congress Cataloging-in-Publication Data Names: Agrawal, Ajay, editor. | Gans, Joshua, 1968– editor. | Goldfarb, Avi, editor. Title: The economics of artifi cial intelligence : an agenda / Ajay Agrawal, Joshua Gans, and Avi Goldfarb, editors. Other titles: National Bureau of Economic Research conference report. Description: Chicago ; London : The University of Chicago Press, 2019. | Series: National Bureau of Economic Research conference report | Includes bibliographical references and index. Identifi ers: LCCN 2018037552 | ISBN 9780226613338 (cloth : alk. paper) | ISBN 9780226613475 (ebook) Subjects: LCSH: Artifi cial intelligence—Economic aspects. Classifi cation: LCC TA347.A78 E365 2019 | DDC 338.4/70063—dc23 LC record available at https:// lccn .loc .gov / 2018037552

♾ This paper meets the requirements of ANSI/NISO Z39.48-1992 (Permanence of Paper).

DOI: 10.7208/chicago/9780226613475.003.0019

National Bureau of Economic Research

Offi cers Karen N. Horn, chair Kelly Horak, controller and assistant John Lipsky, vice chair corporate secretary James M. Poterba, president and chief Alterra Milone, corporate secretary executive offi cer Denis Healy, assistant corporate secretary Robert Mednick, treasurer

Directors at Large Peter C. Aldrich Diana Farrell Michael H. Moskow Elizabeth E. Bailey Jacob A. Frenkel Alicia H. Munnell John H. Biggs Robert S. Hamada Robert T. Parry John S. Clarkeson Peter Blair Henry James M. Poterba Kathleen B. Cooper Karen N. Horn John S. Reed Charles H. Dallara Lisa Jordan Marina v. N. Whitman George C. Eads John Lipsky Martin B. Zimmerman Jessica P. Einhorn Laurence H. Meyer Mohamed El-Erian Karen Mills

Directors by University Appointment Timothy Bresnahan, Stanford George Mailath, Pennsylvania Pierre-André Chiappori, Columbia Marjorie B. McElroy, Duke Alan V. Deardorff , Michigan Joel Mokyr, Northwestern Edward Foster, Minnesota Cecilia Rouse, Princeton John P. Gould, Chicago Richard L. Schmalensee, Massachusetts Mark Grinblatt, California, Los Angeles Institute of Technology Bruce Hansen, Wisconsin–Madison Ingo Walter, New York Benjamin Hermalin, California, Berkeley David B. Yoffi e, Harvard Samuel Kortum, Yale

Directors by Appointment of Other Organizations Jean-Paul Chavas, Agricultural and Applied Robert Mednick, American Institute of Economics Association Certiﬁ ed Public Accountants Martin J. Gruber, American Finance Peter L. Rousseau, American Economic Association Association Philip Hoff man, Economic History Gregor W. Smith, Canadian Economics Association Association Arthur Kennickell, American Statistical William Spriggs, American Federation Association of Labor and Congress of Industrial Jack Kleinhenz, National Association for Organizations Business Economics Bart van Ark, The Conference Board

Directors Emeriti George Akerlof Franklin Fisher John J. Siegfried Jagdish Bhagwati Saul H. Hymans Craig Swan Don R. Conlan Rudolph A. Oswald Ray C. Fair Andrew Postlewaite

Relation of the Directors to the Work and Publications of the National Bureau of Economic Research

1. The object of the NBER is to ascertain and present to the economics profession, and to the public more generally, important economic facts and their interpretation in a scientifi c manner without policy recommendations. The Board of Directors is charged with the responsibility of ensuring that the work of the NBER is carried on in strict conformity with this object. 2. The President shall establish an internal review process to ensure that book manuscripts pro- posed for publication DO NOT contain policy recommendations. This shall apply both to the proceedings of conferences and to manuscripts by a single author or by one or more co-authors but shall not apply to authors of comments at NBER conferences who are not NBER affi liates. 3. No book manuscript reporting research shall be published by the NBER until the President has sent to each member of the Board a notice that a manuscript is recommended for publication and that in the President’s opinion it is suitable for publication in accordance with the above principles of the NBER. Such notifi cation will include a table of contents and an abstract or summary of the manuscript’s content, a list of contributors if applicable, and a response form for use by Directors who desire a copy of the manuscript for review. Each manuscript shall contain a summary drawing attention to the nature and treatment of the problem studied and the main conclusions reached. 4. No volume shall be published until forty-fi ve days have elapsed from the above notifi cation of intention to publish it. During this period a copy shall be sent to any Director requesting it, and if any Director objects to publication on the grounds that the manuscript contains policy recommendations, the objection will be presented to the author(s) or editor(s). In case of dispute, all members of the Board shall be notifi ed, and the President shall appoint an ad hoc committee of the Board to decide the matter; thirty days additional shall be granted for this purpose. 5. The President shall present annually to the Board a report describing the internal manuscript review process, any objections made by Directors before publication or by anyone after publication, any disputes about such matters, and how they were handled. 6. Publications of the NBER issued for informational purposes concerning the work of the Bureau, or issued to inform the public of the activities at the Bureau, including but not limited to the NBER Digest and Reporter, shall be consistent with the object stated in paragraph 1. They shall contain a specifi c disclaimer noting that they have not passed through the review procedures required in this resolution. The Executive Committee of the Board is charged with the review of all such publications from time to time. 7. NBER working papers and manuscripts distributed on the Bureau’s web site are not deemed to be publications for the purpose of this resolution, but they shall be consistent with the object stated in paragraph 1. Working papers shall contain a specifi c disclaimer noting that they have not passed through the review procedures required in this resolution. The NBER’s web site shall contain a similar disclaimer. The President shall establish an internal review process to ensure that the working papers and the web site do not contain policy recommendations, and shall report annually to the Board on this process and any concerns raised in connection with it. 8. Unless otherwise determined by the Board or exempted by the terms of paragraphs 6 and 7, a copy of this resolution shall be printed in each NBER publication as described in paragraph 2 above. Contents

Acknowledgments xi Introduction 1 Ajay Agrawal, Joshua Gans, and Avi Goldfarb

I. AI as a GPT 1. Artifi cial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics 23 Erik Brynjolfsson, Daniel Rock, and Chad Syverson Comment: Rebecca Henderson 2. The Technological Elements of Artifi cial Intelligence 61 Matt Taddy 3. Prediction, Judgment, and Complexity: A Theory of Decision-Making and Artifi cial Intelligence 89 Ajay Agrawal, Joshua Gans, and Avi Goldfarb Comment: Andrea Prat 4. The Impact of Artifi cial Intelligence on Innovation: An Exploratory Analysis 115 Iain M. Cockburn, Rebecca Henderson, and Scott Stern Comment: Matthew Mitchell

vii viii Contents

5. Finding Needles in Haystacks: Artiﬁ cial Intelligence and Recombinant Growth 149 Ajay Agrawal, John McHale, and Alexander Oettl 6. Artiﬁ cial Intelligence as the Next GPT: A Political-Economy Perspective 175 Manuel Trajtenberg

II. Growth, Jobs, and Inequality 7. Artifi cial Intelligence, Income, Employment, and Meaning 189 Betsey Stevenson 8. Artifi cial Intelligence, Automation, and Work 197 Daron Acemoglu and Pascual Restrepo 9. Artifi cial Intelligence and Economic Growth 237 Philippe Aghion, Benjamin F. Jones, and Charles I. Jones Comment: Patrick Francois 10. Artifi cial Intelligence and Jobs: The Role of Demand 291 James Bessen 11. Public Policy in an AI Economy 309 Austan Goolsbee 12. Should We Be Reassured If Automation in the Future Looks Like Automation in the Past? 317 Jason Furman 13. R&D, Structural Transformation, and the Distribution of Income 329 Jeff rey D. Sachs 14. Artifi cial Intelligence and Its Implications for Income Distribution and Unemployment 349 Anton Korinek and Joseph E. Stiglitz 15. Neglected Open Questions in the Economics of Artifi cial Intelligence 391 Tyler Cowen Contents ix

III. Machine Learning and Regulation 16. Artifi cial Intelligence, Economics, and Industrial Organization 399 Hal Varian Comment: Judith Chevalier 17. Privacy, Algorithms, and Artifi cial Intelligence 423 Catherine Tucker 18. Artifi cial Intelligence and Consumer Privacy 439 Ginger Zhe Jin 19. Artifi cial Intelligence and International Trade 463 Avi Goldfarb and Daniel Trefl er 20. Punishing Robots: Issues in the Economics of Tort Liability and Innovation in Artifi cial Intelligence 493 Alberto Galasso and Hong Luo

IV. Machine Learning and Economics 21. The Impact of Machine Learning on Economics 507 Susan Athey Comment: Mara Lederman 22. Artifi cial Intelligence, Labor, Productivity, and the Need for Firm-Level Data 553 Manav Raj and Robert Seamans 23. How Artifi cial Intelligence and Machine Learning Can Impact Market Design 567 Paul R. Milgrom and Steven Tadelis 24. Artifi cial Intelligence and Behavioral Economics 587 Colin F. Camerer Comment: Daniel Kahneman Contributors 611 Author Index 615 Subject Index 625

Acknowledgments

This volume contains chapters and ideas discussed at the fi rst NBER Con- ference on the Economics of Artifi cial Intelligence, held in September 2017 in Toronto. We thank all the authors and discussants for their contributions. Funds for the conference and book project were provided by the Sloan Foun- dation, the Canadian Institute for Advanced Research, and the Creative Destruction Lab at the University of Toronto. At the Sloan Foundation, Danny Goroff provided guidance that improved the overall agenda. The NBER digitization initiative, under the leadership of Shane Greenstein, was a key early supporter. We thank our dean, Tiff Macklem. In addition, Jim Poterba at the NBER has been generous, giving us the fl exibility needed to bring this project together. Special thanks are due to Rob Shannon, Denis Healy, Carl Beck, and Dawn Bloomfi eld for managing the conference and logistics and to Helena Fitz-Patrick for guiding the book through the edito- rial process. Finally we thank our families, Gina, Natalie, Rachel, Amelia, Andreas, Belanna, Ariel, Annika, Anna, Sam, and Ben.

19 Artiﬁ cial Intelligence and International Trade

Avi Goldfarb and Daniel Treﬂ er

The last 200 years have produced a remarkable list of major innovations, not the least of which is artiﬁ cial intelligence (AI). Like other major innovations, AI will likely raise average incomes and improve well-being, but it may also disrupt labor markets, raise inequality, and drive noninclusive growth. Yet, even to the extent that progress has been made in understanding the impact of AI, we remain largely uninformed about its international dimensions. This is to our great loss. A number of countries are currently negotiating international agreements that will constrain the ability of sovereign governments to regulate AI, such as the North American Trade Agreement (NAFTA) and the Trans-Paciﬁ c Partnership (TPP)-11. Likewise, governments around the world are freely spending public funds on new AI clusters designed to shift international comparative advantage toward their favored regions, including the Vector Institute in Toronto and the Tsinghua- Baidu deep- learning lab around Beijing. The international dimensions of AI innovations and policies have not always been well thought out. This work begins the conversation. China has become the focal point for much of the international discussion. The US narrative has it that Chinese protection has reduced the ability

Avi Goldfarb holds the Rotman Chair in Artifi cial Intelligence and Healthcare and is professor of marketing at the Rotman School of Management, University of Toronto, and a research associate of the National Bureau of Economic Research. Daniel Trefl er holds the J. Douglas and Ruth Grant Canada Research Chair in Competitiveness and Prosperity at the University of Toronto and is a research associate of the National Bureau of Economic Research. The authors thank Dave Donaldson and Hal Varian for their thoughtful feedback. Trefl er acknowledges the support of the “Institutions, Organizations and Growth” Program of the Canadian Institute for Advanced Research (CIFAR). For acknowledgments, sources of research support, and disclosure of the authors’ material fi nancial relationships, if any, please see http:// www .nber .org/ chapters/ c14012.ack.

463 464 Avi Goldfarb and Daniel Trefl er of dynamic US fi rms such as Google and Amazon to penetrate Chinese markets. This protection has allowed China to develop signifi cant commercial AI capabilities, as evidenced by companies such as Baidu (a search engine like Google), Alibaba (an e-commerce web portal like Amazon), and Ten- cent (the developer of WeChat, which can be seen as combining the func- tions of Skype, Facebook, and Apple Pay). While no Chinese AI-intensive company has household recognition outside of China, everyone agrees that this will not last. Further, a host of behind- the- border regulatory asymmetries will help Chinese fi rms to penetrate Canadian and US markets. Even the Pentagon is worried. Chinese guided-missile systems are suffi - ciently sophisticated that they may disrupt how we think of modern warfare; large and expensive military assets such as aircraft carriers are becoming overly vulnerable to smart weapons.1 This may do more than transform the massive defense industry; these AI developments may radically shift the global balance of power. As international economists, we are used to hype and are typically dis- missive of it. Despite AI’s short life—Agrawal, Gans, and Goldfarb (2018) date its commercial birth to 2012—AI’s rapid insinuation into our daily economic and social activities forces us to evaluate the international implications of AI and propose best-policy responses. Current policy responses often rest on a US narrative of a zero- sum game in which either the United States or China will win.2 Is this the right premise for examining AI impacts and for developing AI policies? Further, calls for immediate action by prominent experts such as Bill Gates, Stephen Hawking, and Elon Musk will likely encourage governments to loosen their pocketbooks, but will government subsidies be eff ective in promoting broad- based prosperity or will subsidies become yet another form of ineff ective corporate welfare? What specifi c policies are likely to tip the balance away from ineff ective corporate handouts? Using comparative advantage theory, trade economists have thought long and hard about the right mix of policies for successfully promoting industry. Many of our theories imply a laissez-faire free-trade approach. However, since the early 1980s our theories have shown that certain types of government interventions may be successful, for example, Krugman (1980), Grossman and Helpman (1991), and the more informal theories of Porter (1990). These theories emphasize the role of scale and the role of knowledge creation and diff usion. Unfortunately, the precise policy prescriptions produced by these theories are very sensitive to the form of scale and the form

1. New York Times, Feb. 3, 2017. See also Preparing for the Future of Artifi cial Intelligence, Offi ce of the President, Oct., 2016. 2. For example, https:// www .economist .com/ news/ business/ 21725018-its- deep- pool- data -may- let- it- lead- artifi cial-intelligence- china- may- match- or- beat- america and http:// www .reuters .com/ article/ us- usa- china- artificialintelligence/ u- s- weighs- restricting- chinese - investment- in-artifi cial- intelligence- idUSKBN1942OX?il=0. Artifi cial Intelligence and International Trade 465 of knowledge creation/diff usion. And competition can play an important role too, for example, in Aghion et al. (2001, 2005) and Lim, Trefl er and Yu (2017). We therefore start in section 19.2 by identifying the key features of AI technology in regard to scale and knowledge. To date there are no models that feature the particular scale and knowledge characteristics that are empirically relevant for AI. In section 19.3 we use these features (a) to off er some suggestions for what an appropriate model might look like, and (b) to draw implications for policy. This leads to high- level thinking about policy. For example, it provides a foundation for assessing recent proposals put forward by AI researcher Geoff Hinton and others on the potential benefi t of public investments in AI.3 However, these models are not suffi ciently fi ne-grained to directly capture existing regulatory issues that “go behind the border” such as privacy policy, data localization, technology standards, and industrial regulation. In section 19.4 we therefore review the many behind- the-border policies that already impact AI and discuss their implications for comparative advantage and the design of trade agreements. We begin with a factual overview of the international dimensions of AI.

19.1 From Hype to Policy Statistics about where AI is being done internationally and how it is dif- fusing can be tracked in a number of ways, for example, the number of basic research articles, patents and patent citations produced in a region; the number of start-ups established in a region; or the market capitalization of publicly traded AI- based companies in a region. We look at two of these indicators: basic research and market capitalization. For the former, we collected time-series data on the institutional affi liation of all authors of papers presented at a major AI research conference, namely, the Association for the Advancement of Artiﬁ cial Intelligence (AAAI) Conference on Arti- ﬁ cial Intelligence. In table 19.1, we compare the 2012 and 2017 conferences. In 2012, 41 percent of authors were at US institutions, but by 2017 this was down to 34 percent. The two other largest declines were recorded by Canada and Israel. While these countries all increased their absolute number of participants, in relative terms they all lost ground to China, which leapt from 10 percent in 2012 to 23 percent in 2017. We have not examined patent numbers, but suggestive work by Fujii and Managi (2017) points to weaker international diff usion of AI: US technology giants such as IBM and Microsoft remain far and away the world’s dominant patent applicants. Another indication of the economic future of AI comes from the largest

3. “Artiﬁ cial Intelligence is the Future, and Canada Can Seize It” by Jordan Jacobs, Tomi Poutanen, Richard Zemel, Geoff rey Hinton, and Ed Clark. Globe and Mail, Jan. 7, 2017. 466 Avi Goldfarb and Daniel Treﬂ er

Table 19.1 Participants at a major AI conference

Country 2012 (%) 2017 (%) Change (%)

United States 41 34 –6 China 10 23 13 United Kingdom 5 5 0 Singapore 2 4 2 Japan 3 4 1 Australia 6 3 – 2 Canada 5 3 – 3 India 1 2 1 Hong Kong 3 2 –1 Germany 4 2 – 1 France 4 2 – 2 Israel 4 2 – 3 Italy 2 2 – 1 Other 10 10 0

Notes: Participation rates at the Association for the Advancement of Artifi cial Intelligence (AAAI) Conference on Artifi cial Intelligence. For example, of the papers presented at the 2017 conference, 34 percent of authors had a US affi liation. public companies in the world by market capitalization. Table 19.2 lists the twelve largest companies worldwide. What is striking about the table is the number of companies that might subjectively be described as “AI intensive.” Seven of the twelve companies are heavily engaged in AI (such as Alphabet/ Google), three are in fi nance (where the use of AI is growing rapidly), and one has a substantial pharmaceutical presence (where AI is likely to soon be reducing development costs). What makes table 19.2 relevant for international trade is the fact that two of the largest companies worldwide are now Chinese AI-intensive fi rms (Tencent and Alibaba). It is truly remarkable that two high-tech companies based out of China—private companies, not state-owned enterprises—are among the largest companies in the world. While we had to move beyond the round number of ten to make this point, it is striking nonetheless. It points to the major global shake-up that is coming. Some would conclude from tables 19.1 and 19.2 that almost all of the world’s largest companies will soon be competing directly against Chinese companies when—not if—these Chinese companies go global. In 2000, Robin Li signaled his agreement by moving to China to establish Baidu. The fl ood of US-trained talent returning to China has continued. This year, former Microsoft executive Qi Lu joined Baidu as chief operating offi cer (COO). In describing China, Lu writes, “We have an opportunity to lead in the future of AI.”4 Not everyone agrees. Some have argued that China’s AI-intensive companies will not be globally competitive until they compete head-on in China with global leaders such as Google. This fl ies in the face of

4. The Economist, July 15, 2017. Artiﬁ cial Intelligence and International Trade 467

Table 19.2 World’s largest public companies and AI exposure

Company Market value ($) AI exposure

1. Apple 754 High 2. Alphabet 579 High 3. Microsoft 509 High 4. Amazon 423 High 5. Berkshire Hathaway 411 Rising 6. Facebook 411 High 7. ExxonMobil 340 Low 8. Johnson & Johnson 338 Rising 9. JPMorgan Chase 314 Rising 10. Wells Fargo 279 Rising 11. Tencent Holdings 272 High 12. Alibaba 269 High

Notes: Market capitalization of the largest public companies as of March 31, 2017, from PWC (2017). “AI exposure” is our subjective assessment of the role of AI in company performance. a long history of Chinese export successes in other fi elds. Indeed, Sutton and Trefl er (2016) describe both theoretically and empirically how developing countries such as China initially enter new markets at a low level of quality, but over time develop the capabilities to deliver high- quality, internationally competitive goods and services. Many experts are weighing in on how to counter the “Chinese threat” and, more generally, how to enrich local economies through cluster policies that support sustained competitive advantage in AI- based market segments. Geoff Hinton and collaborators have convinced Canadian governments to develop a major AI institute that would “graduate the most machine- learning PhDs and master’s students globally” and “become the engine for an AI supercluster that drives the economy of Toronto, Ontario, and Canada.”5 Hinton also emphasizes the importance of access to data. “Why? Because for a machine to ‘think’ intelligently, it must be trained with lots of data.” While there are potential benefi ts from Hinton’s initiative, it raises two important points that loom large in our thinking. First, economists who specialize in clusters are deeply skeptical about the effi cacy of cluster policies (e.g., Duranton 2011). Such policies have failed more often than not, and the theoretical justifi cation for cluster policies is highly sensitive to assumptions about knowledge diff usion. For example, will Hinton’s PhDs stay in Canada and will the knowledge they generate be commercialized in Canada? Second, a host of behind-the- border regulations on privacy, data localization, technology standards, and industrial policy will aff ect the ability of Canadian fi rms to access data relative to their competitors in larger markets such as the

5. Globe and Mail, Jan. 7, 2017. 468 Avi Goldfarb and Daniel Treﬂ er

United States, Europe, and China. What is the current state of these domestic data regulations, how do they eff ect trade patterns, do they serve a public interest, are they being used as disguised protection to generate comparative advantage, and should they be covered by international trade agreements (as some would have been in the TPP e-commerce chapter)? The following sections help answer these questions and move us toward better policies for promoting AI and preventing both corporate welfare and welfare-reducing disguised protection.

19.2 The Technological Backdrop: Scale, Scope, Firm Size, and Knowledge Diff usion The Oxford English Dictionary defi nes AI as “the theory and development of computer systems able to perform tasks normally requiring human intelligence.” This has meant diff erent things at diff erent times. In the 1960s and 1970s, computer scientists approached this using rules, if-then state- ments, and symbolic logic. It worked well for factory robots and for playing chess. By the 1980s, it became clear that symbolic logic could not deal with the complexities of nonartifi cial settings, and AI research slowed substan- tially. Various approaches continued to be supported in a small number of locations, including by the Canadian Institute for Advanced Studies (CIFAR). The recent resurgence in AI research is driven by one such approach: the insight that computers can “learn” from example. This approach is often called “machine learning” and is a fi eld of computational statistics. The algorithm that has received the most attention is back propagation in neural networks, most notably through “deep learning,” but there is a large suite of relevant technologies including deep learning, reinforcement learning, and so forth. Because the current excitement about AI is driven by machine learning, we focus on this particular set of algorithms here. For our purposes, we need to zero in on those aspects of AI technology that are central to thinking about the economics of AI. We identify four aspects: economies of scale associated with data, economies of scale associated with an AI research team, economies of scope in the use of the team for multiple applications, and knowledge externalities. 19.2.1 Economies of Scale from Data Statistical predictions improve with the quantity and quality of data. Recall from statistics 101 that the quality of prediction increases with N (or, more precisely with root N). All else being equal, this means that companies that have more observations will generate more accurate predictions. It is in this sense that economies of scale matter. Still, because predictions increase in root N, then, while scale matters, there are decreasing returns to scale in terms of the accuracy of prediction. Artifi cial Intelligence and International Trade 469

It is subtler than this, however. Google and Microsoft both operate search engines. Google has claimed their search engine has higher market share because it has better quality.6 Microsoft has claimed the higher quality is a direct consequence of scale. By having more data, Google can predict what people want in their search results more accurately. Google responds that Microsoft has billions of search results. While Google has more data, surely the law of large numbers applies before one billion results. And so, more data does not give a meaningful advantage. Microsoft’s response is the essence of where economies of scale bind. While they have billions of searches, many search queries are extremely rare. Microsoft may only see two or three, and so Google can predict those rare queries much better. If people choose search engines based on quality diff erences in rare searches, then Google’s better data will lead to a substantial increase in market share. Having a larger share gives Google more data, which in turn improves quality and supports an even larger share. The source of economies of scale here is therefore in the form of direct network externalities. More customers generate more data, which in turn generates more customers. This is diff erent from the literature on two- sided markets and indirect network externalities. The network externalities re- semble the phone network, rather than externalities between buyers and sellers on a marketplace like Ebay. This is signifi cant in a trade context because the trade literature has emphasized two-sided matching, for example, in Rauch (1999) and McLaren (2000). This is also diff erent from all of the trade and market structure literature, which emphasize economies of scale that are driven by fi xed costs, so trade theory does not currently have models that are applicable to the AI technology environment. The direct network externalities environment leads to a core aspect of competition in AI: competition for data. The companies that have the best data make better predictions. This creates a positive feedback loop so that they can collect even more data. In other words, the importance of data leads to strong economies of scale. 19.2.2 Economies of Scale from the Overhead of Developing AI Capabilities Another source of economies of scale in AI involves the fi xed cost of building an AI capability within a fi rm. The main cost is in personnel. Much of the software is open source, and in many cases hardware can be purchased as a utility through cloud services. The uses of AI need to be big enough to justify the substantial cost of building a team of AI specialists. World leaders in AI command very high pay, often in the millions or tens of millions.

6. There is a chicken and egg problem, whether good algorithms drive market share or whether market share drives hiring that leads to better algorithms. For one point of view, see https:// www .cnet .com/ news/ googles- varian- search- scale- is- bogus/. 470 Avi Goldfarb and Daniel Treﬂ er

Top academic researchers have been hired to join Google (Hinton), Apple (Salakhutdinov), Facebook (LeCunn), and Uber (Urtasun). So far, there has been a meaningful diff erence between employing the elite researchers and others in terms of the capabilities of the AI being developed. 19.3.3 Economies of Scope Perhaps more than economies of scale, the fi xed cost of building an AI capacity generates economies of scope. It is only worth having an AI team within a company if there are a variety of applications for them to work on. Many of the currently leading AI fi rms are multiproduct fi rms. For example, Google parent Alphabet runs a search engine (Google), an online video service (YouTube), a mobile device operating system (Android), an autonomous vehicle division (Waymo), and a variety of other businesses. In most cases, the economies of scope happen on the supply side through AI talent, better hardware, and better software. Another important source of economies of scope is the sharing of data across applications. For example, the data from Google’s search engine might be valuable in helping determine the eff ectiveness of YouTube advertising, or its mapping services might be needed for developing autonomous vehicles. The sharing of data is a key source of international friction on disguised protection behind the border. Diff erences in privacy policies mean that it is easier to share data across applications in some countries compared to others. For example, when Ebay owned PayPal, it faced diff erent restrictions for using the PayPal data in Canada compared to the United States. We will return to this subject later. This contrasts with the main emphasis in the trade literature on economies of scope, which emphasizes the demand side. Economies of scope in AI do not seem to be about demand externalities in brand perception or in sales channels. Instead, they appear to be driven by economies of scope in innovation. A wider variety of potential applications generates greater incentives to invest in an AI research team, and it generates more benefi ts to each particular AI project due to the potential to share data across applications. 19.3.4 Knowledge Externalities There is a tension in discussing knowledge diff usion in the AI sphere. On the one hand, the spectacular scientifi c advances are often taught at universities and published in peer-reviewed journals, providing businesses and government personnel with quick and easy access to frontier research. Further, there is the migration of personnel across regions and countries as the above examples of Robin Li and Qi Lu show. This suggests that knowledge externalities are global in scope. On the other hand, AI expertise has also tended to agglomerate in several narrowly defi ned regions globally. As with other information technologies, much of the expertise is in Silicon Valley. Berlin, Seattle, London, Boston, Artifi cial Intelligence and International Trade 471

Shanghai, and to some extent Toronto and Montreal can all claim to be hubs of AI innovation. This suggests that AI involves a lot of tacit knowledge that is not easily codiﬁ ed and transferred to others. In fact, the traditional discussion of knowledge externalities takes on a more nuanced hue in the context of AI. Can these researchers communicate long distance? Do they have to be together? How important are agglomeration forces in AI? As of 2017, AI expertise remains surprisingly rooted in the locations of the universities that invented the technologies. Google’s Deep- Mind is in London because that is where the lead researcher lived. Then the ﬁ rst expansion of DeepMind outside the United Kingdom was to Edmon- ton, Alberta, because Richard Sutton, a key inventor of reinforcement learning, lives in Edmonton. Uber opened an AI offi ce in Toronto because it wanted to hire Raquel Urtasun, a University of Toronto professor. Generally, there are a small number of main AI research departments: Stanford, Carnegie Mellon University, the University of Toronto, and several others. Their location is often surprisingly disconnected from headquarters, and so companies open offi ces where the talent is rather than forcing the talent to move to where the company is. As we shall see, the exact nature of knowledge externalities is terribly important for understanding whether cluster and other policies are likely to succeed. The nature of these externalities also has some unexpected implications such as the implications of noncompete clauses (Saxenian 1994) and the asymmetries in access to knowledge created by asymmetries in who can speak English versus who can speak Chinese versus who can speak both.

19.3 Trade Theory and the Case for Industrial and Strategic Trade Policies There are many voices in the industrialized world arguing for industrial policies and strategic trade policies to promote rising living standards. Many of these voices point to the achievements of China as an example of what is possible. Much of what is claimed for China, and what was once claimed for Japan, is of dubious merit. China has redirected vast resources from the rural poor and urban savers toward state-owned enterprises that have massively underperformed. Those fi rms continue to be major players in the economy and a major drag on economic growth (Brandt and Zhu 2000). It is thus signifi cant that China’s greatest commercial successes in AI have come from private companies. So if we are to make the case for industrial and strategic trade policies, we cannot blithely appeal to Chinese state-directed successes. Rather, we must understand the characteristics of industries that increase the likelihood that government policy interventions will be successful. To this end, we start with a vanilla- specifi c factors model of international trade (Mussa 1974; Mayer 1974) in which the case for departures from free 472 Avi Goldfarb and Daniel Trefl er trade is weak. We then add on additional elements and examine which of these is important for policy success. The fi rst conclusion is that scale and knowledge externalities are critical. The second is that these two elements alone are not enough: their precise form also matters. 19.3.1 Scientists, Heterogeneous Scientists, and Superstar Scientists Many factors enter into the location decisions of AI fi rms including access to local talent, local fi nancing/ management, and local markets. In this section, we focus on the role of university- related talent. Among the participants of this conference are three head researchers at top AI companies: Geoff rey Hinton (University of Toronto and Google), Russ Salakhutdi- nov (Carnegie Mellon University and Apple), and Yann LeCun (New York University and Facebook). Each joined his company while retaining his academic position, and each continues to live near his university rather than near corporate headquarters. These three examples are not exceptional, as indicated by the above examples of DeepMind and Richard Sutton, and Raquel Urtasun and Uber. Scientists. We begin with the simplest model of trade that allows for two types of employees, scientists, and production workers. There are two industries, search engines and clothing. Production workers are employed in both industries and move between them so that their wages are equalized across industries. Scientists are “specifi c” to the search engine industry in that they are very good at AI algorithms and useless at sewing. We also assume that scientists and workers cannot migrate internationally. Then it is immediately obvious that the more scientists a country has, the larger will be both the size and service exports of the search engine industry. We start with this benchmark model because, in this setting, without scale or externalities there is no scope for market failure and hence there is no simple case for any trade policy other than free trade. For example, consider a policy of restricting imports of search engine services, as China has done with Google. This restriction helps Chinese scientists but can hurt Chinese production workers and consumers (Ruffi n and Jones 1977). There are several departures from this benchmark model that lead to welfare- enhancing export subsidies and other departures from free trade. As we shall see, the two most important are economies of scale and knowledge creation. However, we start instead with profi ts because profi ts are at the core of arguments supporting strategic trade policies (Krugman 1986). Since there are no profi ts in the specifi c factors model, we introduce profi ts by introducing scientists of heterogeneous quality. Heterogeneous Scientists. Consider an industry in which fi rms provide a search engine and generate advertising revenue. There is a continuum of scientists distinguished by their “quality” q. A fi rm is distinguished by the quality of its chief scientist and hence fi rms are also indexed by q. A higher- Artifi cial Intelligence and International Trade 473 quality scientist produces a better search engine. A fi rm engages in activity a that increases advertising revenues r(a) where ra > 0. Let p(q) be the propor- tion of consumers who choose fi rm q’s search engine. It is natural to assume that pq > 0 that is, a better scientist produces a more desirable search engine. The fi rm’s profi t before payments to the scientist is (a,q) = p(q)r(a) – c(a) where c(a) is the cost of the fi rm’s ad- generating activity. In this model the fi rm is essentially the scientist, but we can delink the two by assuming that the scientist is paid with stock options and so receives a fraction (1 – ) of the profi ts. It is straightforward to show that profi t (a,q) is supermodular in (a,q). This implies positive assortative matching; fi rms with better scientists engage in more ad-generating activity. This means that fi rms with better scientists will also have more users ( pq > 0), more revenues [∂r(a(q),q)/∂q > 0], and higher profi ts [∂(a(q),q)/∂q > 0]. Putting these together, better scientists anchor bigger and more profi table fi rms.7 To place this model into an international- trade setting, we assume that there are multiple countries, a second constant-returns- to-scale industry (clothing), and no international migration of scientists or workers. Because there are profi ts in the search engine industry, policies that expand that industry generate higher profi ts. This is the foundation of strategic trade policy. In its simplest form, if there are supernormal profi ts then tariff s and other trade policies can be used to shift profi ts away from the foreign country and to the domestic country. Strategic trade policy was fi rst developed by Brander and Spencer (1981) and variants of it have appeared in many of the models discussed below. Unfortunately, the case for strategic trade policy is not as clear as it might seem. Its biggest logical problem is the assumption of positive profi ts: if there is free entry, then entry will continue until profi ts are driven to zero.8 This means that any government policy that encourages entry of fi rms or training of scientists will be off set by ineffi cient entry of fi rms or scientists. Put simply, strategic trade policies only work if there are profi ts, but with free entry there are no profi ts (see Eaton and Grossman 1986). The conclusion we draw from this is that the model needs enriching before it can be used to justify trade policy. Before enriching the model, we note that there are two other compelling

7. The fi rst- order condition for advertising activities is a = ( pra – ca) = 0. We assume that the second-order condition is satisfi ed: aa < 0. Supermodularity is given 2 by ∂ (a,q)/ ∂a∂q = pqra > 0. The result that advertising activity levels a(q) are increasing in q comes from diff erentiating the first-order condition: pq ra + aaaq = 0 or aq = – pqra / aa > 0. The result that average revenues p(q)r(a) are increasing in q follows from ∂p(q)r(a(q))/∂q = pqr + praaq > 0. The result that profi ts (a(q),q) are increasing in q follows from ∂ (a,q)/∂q = aaq + pqr(a) = pqr(a) > 0 where we have used the fi rst-order condition ( a = 0). 8. Free entry implies that ex ante profi ts are zero. Of course, ex post profi ts (operating profi ts of survivors) are always positive; otherwise, survivors would exit. 474 Avi Goldfarb and Daniel Trefl er reasons for being skeptical about the effi cacy of strategic trade policy. First, such policies set up political economy incentives for fi rms to capture the regulatory process used to determine the amount and form of government handouts. Second, the logic of strategic trade policy fails if there is retaliation on the part of the foreign government. Retaliation generates a trade war in which both countries lose. Artifi cial intelligence meets all the conditions that Busch (2001) identifi es as likely to lead to a trade war. We now turn to enriching our model. Superstar Scientists.9 Strategic trade policies are more compelling in settings where scale and/or knowledge creation and diff usion are prevalent. To this end we follow section 19.2 in assuming that there are economies of scale in data. This will cause the market to be dominated by a small number of search engine fi rms; that is, it will turn our model into something that looks like a superstar model. To be more precise, it is a little diff erent from standard superstar models that make assumptions on the demand side (Rosen 1981). The superstar assumptions here are on the supply side. Modifying our model slightly, we introduce scale in data by assuming that the share of consumers choosing a search engine ( p(q)) is increasing at an 10 increasing rate ( pqq > 0); pqq > 0 implies that profi ts and scientist earnings increase at an increasing rate, that is, they are convex in q.11 This, in turn, implies that the distribution of fi rm size becomes highly skewed toward large fi rms. It also implies that the shareholders of large fi rms will make spectacular earnings, that is, the 1 percent will pull away from the rest of society. In this setting we expect that a small number of large fi rms will capture most of the world market for search engines. Further, these fi rms will be hugely profi table. We have in mind a situation like that found empirically in the search engine market. The top fi ve leaders are (billions of monthly visitors in parentheses): Google (1.8), Bing (0.5), Yahoo (0.5), Baidu (0.5), and Ask (0.3).12 If the Chinese government subsidizes Baidu or excludes Google from China, then Baidu captures a larger share of the market. This generates higher profi ts and higher earnings for shareholders within China, making China better off both absolutely and relatively to the United States. Depending on the details of the model, the United States may or may not be absolutely worse off . This example is very similar to the mid- 1980s discussions about commercial jet production. At a time when it was understood that there was room for only two players in the industry (Boeing and McDonnell Douglas were the leaders), the European Union (EU) heavily subsidized Airbus and ultimately

9. To our knowledge there are no superstar- and- trade models beyond Manasse and Turrini (2001), which deals with trade and wage inequality. 10. This is an ad hoc assumption, but to the extent that it has the fl avor of scale economies, we will see less ad hoc variants in the models reviewed below. 2 2 11. From a previous footnote, ∂ (a(q),q)/∂q = pq r(a). Hence ∂ (a(q),q)/∂q = pqq r + pq ra aq > 0. 12. Source: http:// www .ebizmba .com/ articles/ search- engines, July, 2017. Artifi cial Intelligence and International Trade 475 forced McDonnell Douglas to exit. These EU subsidies were enormous, but may nevertheless have been valuable for EU taxpayers.13 Our superstars model provides a more compelling case for government intervention because scale in data acts as a natural barrier to entry that pre- vents the free-entry condition from off setting the impacts of government policies. Thus, the government can benefi cially subsidize the education of AI scientists and/or subsidize the entry of fi rms, for example, by off ering tax breaks, subsidies, expertise, incubators, and so forth. This establishes that scale economies and the supernormal profi ts they sometimes imply strengthen the case for strategic trade policy. There is, however, one more assumption we have made that is essential to the argument for strategic trade policy, namely, that there are no international knowledge spillovers. In the extreme, if all the knowledge created, for example, by Canadian scientists, moved freely to the United States or China, then a Canadian subsidy would help the world, but would not diff erentially help Canada. This establishes the critical role of knowledge diff usion (in addition to scale) for thinking about government policies that promote AI. Empirics. What do we know about superstar eff ects empirically? Nothing from the trade literature. We know that superstars matter for the rate and direction of innovation in academic research. We know that universities have played a key role in developing AI expertise and that a small number of university-affi liated chief scientists have played a key role in developing new technologies. We also have some evidence of a knowledge externality. Azoulay, Graff Zivin, and Wang (2010) show that the death of a superstar scientist in a fi eld slows progress in the research area of the superstar. The fi eld suff ers as scientists associated with the deceased superstar produce less research. While Azoulay, Graff Zivin, and Wang do not consider AI, their work points to the existence of knowledge spillovers that are local rather than global. Inequality. This discussion has not had much to say about inequality. In our superstars model, industrial policy and strategic trade policies are successful precisely because they promote large and highly profi table fi rms. We know that these fi rms account for an increasing share of total economic activity and that they are likely major contributors both to falling labor shares (Autor et al. 2017) and to rising top-end inequality. Thus, the policies being supported by our model do not lead to broad-based prosperity. This cannot be ignored. Extensions. While the above model of AI science superstars is useful, it

13. The subsidies have continued unabated for over four decades. In 2016, the World Trade Organization (WTO) found that WTO- noncompliant EU subsidies were $10 billion. This does not include the WTO- compliant subsidies. Likewise, the WTO found comparable numbers for WTO- noncompliant US subsidies of Boeing. See Busch (2001) for a history. This raises the possibility that subsidies that are intended to get a ﬁ rm “on its feet” become permanent, which is yet another reason to be skeptical about strategic trade policies. 476 Avi Goldfarb and Daniel Treﬂ er has a number of other problems. It is beyond the scope of this chapter to resolve these problems through additional modeling. Instead, we highlight each problem and review the related international trade and growth litera- tures in order to provide insights into how the model might be improved and what the implications of these improvements are for thinking about trade and trade policy. The problems we cover are the following.

1. The scale assumption pqq > 0 is ad hoc. In subsection B below, we consider scale returns that are external to the fi rm and show that the form of the scale returns matters for policy. 2. In our model, there is no knowledge creation within fi rms and no knowledge diff usion across fi rms and borders. In subsection C below, we review endogenous growth models and show that the form of knowledge diff usion, whether it is local or global, matters for policy. 3. Our model ignores the geography of the industry and so does not speak to economic geography and “supercluster” policies. We review the economic geography literature in subsection D below. 4. In section E below we discuss the implications for supercluster policies.

19.3.2 Increasing Returns to Scale External to the Firm—A Basic Trade Model We start with a simple trade model featuring economies of scale whose geographic scope is variable, that is, regional, national, or international. The model captures the core insights of richer models developed by Ethier (1982), Markusen (1981), and Helpman (1984), along with more recent developments by Grossman and Rossi-Hansberg (2010, 2012). Firm i produces a homogeneous good using a production function

qi = Q F(Li,Ki), where Li is employment of labor, Ki is employment of capital, F displays Σ constant returns to scale, Q is industry output (Q = iqi), and 0 < < 1; Q is like a Solow residual in that it controls productivity. The idea is that a fi rm’s productivity depends on the output of all fi rms.14 If Q is world output of the industry, then productivity Q is common to all fi rms internationally and scale has no implications for comparative advantage. On the other hand if Q is national output of the industry, then the country with the larger output Q will have higher productivity Q and hence will capture the entire world market. Artifi cial intelligence as an industry has a technology that lies somewhere between national returns to scale (Q is national output) and international returns to scale (Q is international output). With national returns

14. Each fi rm ignores the impact of its output decision on Q so that returns to scale can be treated as external to the fi rm. Artifi cial Intelligence and International Trade 477 to scale, a government policy such as tariff s or production subsidies that increases domestic output will increase national welfare because the policy raises average productivity at home and also drive exports. Whether it helps or hurts the foreign country depends on a number of factors such as the strength of the scale returns (the size of a) and the size of the countries (Helpman 1984). Most important, the domestic benefi ts of industrial and trade policies depend on the geographic extent of scale, that is, how much of it is national versus international. Whether scale operates at the national or international level is not easy to assess and has not been attempted for AI. For the DRAM market in the 1980s, Irwin and Klenow (1994) show that external economies of scale were entirely international rather than national. Other evidence that AI economies are international is the fact that AI algorithms have been dis- seminated internationally via scientifi c journals and teaching, and research and development (R&D)-based AI knowledge has diff used internationally via imitation and reverse engineering. On the other hand, the colocation of AI researchers in Silicon Valley and a handful of other technology hubs is suggestive of national and even subnational returns to scale. Azoulay, Graff Zivin, and Wang (2010) also suggests the existence of subnational returns to scale. Clearly, more research is needed on the extent of national versus international returns to scale in AI. 19.3.3 Knowledge Creation and Diff usion: Endogenous Growth In the previous section, scale was external to the fi rm and, relatedly, fi rms did no research. We now introduce fi rm-level research. Conveniently, some of the key implications of fi rm-level innovation are similar to those from the previous section, namely, that trade policy depends in large part on the extent to which knowledge spillovers are national or international. To see this, we review the main endogenous growth models that feature international trade. These are Grossman and Helpman (1989, 1990, 1991), Rivera- Batiz and Romer (1991), and Aghion and Howitt (2009, ch. 15). In these models, fi rms conduct costly R&D and there is an externality that aff ects these costs. The dominant model in the trade literature features quality ladders (Gross- man and Helpman 1991) featuring vertical (quality) diff erentiation. The highest- quality fi rm takes the entire market and earns profi ts.15 Innovation improves the quality of the frontier fi rm by a constant pro- portion . At date t > 0, let n(t) be the number of quality improvements during the time interval (0,t) so that the frontier quality is n(t). Firms invest an amount r in R&D and this generates an endogenous probability p(r) of becoming the quality leader (with quality n(t)+1). A key feature of the R&D process is an externality: innovators stand

15. Ex post profi ts are needed in order to justify R&D expenses. However, these models have a free-entry condition that drives ex ante profi ts to zero. 478 Avi Goldfarb and Daniel Trefl er on the shoulders of giants in the sense that they improve on the frontier level of quality. Had they improved on their own quality, there would be no externality. A two- sector, two- country quality ladder model appears in Grossman and Helpman (1991). Grossman and Helpman assume that there is a standard constant-returns- to-scale sector and a quality sector.16 Another popular approach is Romer’s (1990) expanding- varieties model. Final goods producers combine varieties of intermediates using a constant elasticity of substitution (CES) production function so that there is love of variety. At any date t there is a measure N(t) of varieties. The marginal returns to new varieties are positive, but diminishing. The key “building on the shoulders of giants” externality is that the cost of developing a new variety is inversely proportional to the measure of varieties. As a result, innovation costs fall over time, generating endogenous growth. A one-sector, two-country extension appears in Rivera-Batiz and Romer (1991). A two- sector, two-country extension appears in Grossman and Helpman (1991). This brief review leads to a number of observations. As in the previous section, the benefi t of trade policy depends on whether the externality operates at the national or international levels; Q of the previous section is replaced here by either n(t) or N(t). Hence, if each fi rm builds on the international frontier n(t) or the international number of varieties N(t), then there are no implications for comparative advantage; however, if each fi rm builds on its national n(t) or national N(t) then the frontier country will develop an increasingly strong comparative advantage in the quality or expanding- varieties sector. With national- level externalities one country will capture the lion’s share of the quality/varieties sector. Further, a country can capture this sector by using R&D and trade policies. Endogenous growth models provide important insights into the details of R&D and trade policies. Research and development policies directly target the knowledge externality and so are preferred to (second- best) trade policies. One R&D policy avenue is to promote knowledge diff usion. This can be done through subsidies to nonprofi t organizations targeting local within-industry interactions and industry-university collaborations. A second R&D policy avenue is to promote knowledge creation through R&D subsidies that are available to all fi rms, universities, and students. There is a tension between these two avenues; knowledge diff usion can discour- age knowledge creation since knowledge diff usion to competitors reduces the returns to innovation. However, the tension is sometimes constructive: Silicon Valley emerged from the shadows of Massachusetts’ Route 128 partly because of an “open-source attitude” (Saxenian 1994) and Califor-

16. Placing endogenous growth into a two- sector model so as to facilitate a discussion of comparative advantage is not easy because the sector with improving quality slowly takes over the entire economy unless other price or nonprice “congestion” forces prevent this. Artifi cial Intelligence and International Trade 479 nian restrictions on noncompete clauses (Marx and Fleming 2012). It is less likely that diff usion of knowledge to foreign countries will be as benefi cial domestically. This class of models discourages policies that target individual fi rms or that “pick winners.” To understand why industry leaders should not be advantaged by policy, note that counterintuitively, industry leaders will be the least innovative fi rms due to the “market-stealing” eff ect. If an entrant innovates, it steals the market from the leader. If a leader innovates, it canni- balizes itself. Leaders therefore have less of an incentive to innovate. Aghion et al. (2001, 2005) address this counterintuitive result by developing a model in which leaders innovate in order to escape the competition. Aghion et al. (2017) and Lim, Trefl er, and Yu (2017) are currently developing international trade models featuring escape the competition. In the context of AI, none of the above endogenous growth models is ideal, leading us to conjecture about what an appropriate model might look like. The advantage of endogenous growth models is that they emphasize knowledge creation and diff usion. Thinking more deeply about AI development and commercialization, it is useful to distinguish two aspects of what is done in the AI research departments of large fi rms. First, they improve AI algorithms, which have the fl avor of quality ladders. (Recall that quality can be something that is perceived by consumers or, as is relevant here, something that reduces marginal costs.) Second, AI research departments develop new applications of existing AI; for example, Google uses AI for its search engine, autonomous vehicles, YouTube recommendations, advertising network, energy use in data centers, and so forth. This suggests an expanding-varieties model, but one that operates within the fi rm. We are unaware of any endogenous growth models that have both these features. Grossman and Helpman (1991) have the fi rst and Klette and Kortum (2004) have the second. Combining them in one model is not trivial and analytic results would likely have to be replaced with calibration.

19.3.4 New Economic Geography and Agglomeration The discussion in the previous section points to the possibility that knowledge spillovers are subnational, and this leads naturally to a theory of regional clusters such as Silicon Valley. New economic geography or NEG (Krugman 1980) does not typically consider knowledge spillovers, but it does consider other local externalities that drive regional clusters. Three mechanisms have been particularly prominent: (a) demand- side “home- market eff ects,” (b) upstream- downstream linkages, and (c) labor- market pooling. All of these theories feature two key elements: costs of trading across regions (e.g., tariff s) and increasing returns to scale at the firm level (which can be thought of as the fi xed costs of developing a new product). We explain the role of these two elements in the context of home- market eff ects. 480 Avi Goldfarb and Daniel Trefl er

Consider a model with CES monopolistic competition and two regions ( j = 1, 2). There are varieties of machines and the larger the set of machines to choose from, the more productive are the producers. Let Nj be the measure of machine varieties available in region j. Then with CES production func- 17 tions, productivity is proportional to Nj. The fundamental factor pushing for agglomeration is the strength of this love-of-variety/ productivity externality. (This is related to the externality in Romer’s expanding varieties model, which is also proportional to Nj.) As in previous models, the externality operates at the local level rather than at the international level. This externality encourages fi rms to colocate or agglomerate since the agglomeration of fi rms drives up Nj and productivity. The fundamental factor pushing against this agglomeration is trade costs: a fi rm can avoid trade costs by locating close to consumers rather than close to other producers. The main insight of this model is that in equilibrium a disproportionate share of the world’s fi rms will locate in a single region, and this region will thus have higher productivity. As a result, this region will be richer. Notice that fi rms are choosing to set up where the competition is greatest and where wages and property values are the highest. The above model of agglomeration has been extended in countless ways (e.g., Krugman and Venables 1995; Fajgelbaum, Grossman, and Helpman 2011; Duranton and Puga 2001) and it is easy to think of applications where the force for agglomeration is not the variety of machines, but the variety of knowledge held by fi rms. If this knowledge is tacit (meaning it cannot be codifi ed and transmitted in a document), then knowledge spillovers are only transmitted locally via face-to-face interactions. In this case, knowledge externalities lead fi rms to agglomerate. The result is regions like Silicon Valley. 19.3.5 Cluster Policies Cluster policies have long been the politician’s best friend, yet economists remain highly critical of them. In surveying the evidence for the success of these policies, Uyarra and Ramlogan (2012) write “There is no clear and unambiguous evidence that over the long term clusters are able to generate strong and sustainable impacts in terms of innovation, productivity or employment.” One of the world leaders in the economics of clusters, Gilles Duranton, titled his 2011 survey “‘California Dreamin’: The Feeble Case for Cluster Policies.” Yet clusters remain fashionable. In light of what we have described, the fi rst question is: When are cluster policies likely to succeed? The answer is that they are most likely to succeed when there is clear evidence of scale economies and of knowledge creation together with local knowledge diff usion. Artifi cial intelligence displays these

17. More precisely, productivity is proportional to N1/ (– 1) where > 1 is the elasticity of substitution between varieties. Artifi cial Intelligence and International Trade 481 characteristics, though the extent of international knowledge diff usion cannot be ignored. The second question is: What policies are likely to work? To answer this question we turn to the insights of Ajay Agrawal, Director of Rotman’s Creative Destruction Lab (CDL), and Michael Porter, the business guru of cluster policies. We start with Agrawal. Agrawal identifi es two problems with developing AI in the Canadian context. First, there is a shortage of people with the skills to scale up companies. Agrawal calls these people 1000Xers. Second, the cost of information about a start-up’s quality is so high that capital markets cannot identify the best and the brightest start-ups. Agrawal’s CDL addresses both of these problems by linking start-ups with serial entrepreneurs who can identify a good start-up, tap into 1000Xers for growth, and pass on valuable information about start-up quality to inves- tors globally. Another approach to the question of what policies are likely to work uti- lizes Porter’s (1990) diamond, which emphasizes four features of clusters: (a) factor conditions such as universities and an abundant supply of AI scientists, (b) home- market- demand externalities for AI, (c) externalities fl ow- ing from suppliers of specialized intermediate inputs into AI such as fi nancial services, and (d) a competitive environment. Items b–d involve eff ects that have already been described in our discussion of knowledge spillovers and lie at the heart of local agglomeration. Item a is a more conventional economic factor, that is, drive down the price of the key input by subsidizing its supply. Yet Porter’s research shows that many clusters are driven primarily by a. That is to say, the single most important policy in practice is simple: follow Hinton’s advice in training a large number of AI scientists locally. Our models also suggest two diffi culties with Hinton’s advice that must be shored up. First, there is international rather than national knowledge diff usion due to the fact that, for example, Canadian- trained scientists are likely to leave Canada for Silicon Valley, China, and other AI hotspots. This suggests value in programs like those used successfully in Singapore that require student loans to be repaid if the student does not work in Singapore for a minimum number of years. Second, scale in data is a huge problem for a small country like Canada. To understand appropriate solutions for this, we now turn to the details of national regulatory environments that aff ect data and the use of AI.

19.4 Behind- the- Border Trade Barriers: The Domestic Regulatory Environment Given these models, we next turn to the specifi c regulatory issues that are likely to impact trade policy. Many of the core trade issues around AI involve access to data. Data is a key input into AI, and there are a number of government policies that aff ect data access and data fl ows. To the extent 482 Avi Goldfarb and Daniel Trefl er these regulations vary across countries, they can advantage some countries’ AI industries. The models above suggest that this advantage can have con- sequences if there are economies of scale, local externalities, and/or rents. We highlight fi ve policies in particular. The fi rst three involve data: domestic privacy policy, data localization rules, and access to government data. The others are development of the regulation of AI application industries (such as autonomous vehicles) and protection of source code. Privacy policy, data localization, and source code access have already become signifi cant trade issues. For example, the TPP addresses all three of these, as do the US Trade Representative’s NAFTA renegotiation objectives. The US position is that strong Canadian and Mexican privacy rules, localization requirements, and access to foreign source code are all impediments to US exports of AI-related goods. In other words, the emphasis on trade policy in these areas is that regulation could be disguised protection that helps domestic fi rms and hurts foreign fi rms. In the discussion below, we explore the extent to which this starting assumption is appropriate. Privacy Regulation. Privacy regulation involves policies that restrict the collection and use of data. Such regulation diff ers across locations. Privacy policy has the power to limit or expand the ability of fi rms to use AI eff ectively. Restrictions on the use of data mean restrictions on the ability to use AI given the data available; however, restrictions on the use of data may also increase the supply of data available if it leads consumers to trust fi rms that collect the data. Although the theory is ambiguous, thus far, the empirical evidence favors the former eff ect on balance. Stricter privacy regulations reduce the ability of fi rms and nonprofi ts to collect and use data and therefore leads to less innovative use of data (Goldfarb and Tucker 2012). Thus, fi rms in some countries may benefi t from favorable privacy policy. We believe the most useful analogies for privacy policy in trade relate to labor and environmental regulations. Such regulations also diff er across countries for a variety of reasons. They could refl ect diff erences in preferences across countries, or could be perceived as normal goods that wealthier countries are willing to pay for but poorer countries are not (Grossman and Krueger 1995). There is room for reasonable disagreement on how data might be collected or used. Some countries will restrict the information used in prediction while others will not. For example, for insurance, the data that can be used varies by state, with diff erent states providing a variety of restrictions on the use of race, religion, gender, and sexual orientation in insurance.18 Even with such restrictions, if other variables provide surrogates for such categories, it is possible that fi rms may be forced to abandon AI methods entirely for more transparent prediction technologies. In terms of

18. http:// repository.law.umich .edu/ cgi/ viewcontent.cgi?article=1163&context=law_econ _current. Artifi cial Intelligence and International Trade 483 privacy policy, we think it is useful to take as given that there are diff erences across countries in their preferences for policies that restrict the collection and use of data. Given these diff erences in preferences, what are the implications for trade? Suppose that the optimal privacy policy for growing an AI industry involves relatively few restrictions on data. Artifi cial intelligence requires data, and so the fewer government restrictions on data collection, the more rapidly the industry grows.19 To the extent that young fi rms tend to grow by focusing on the domestic market, this will advantage the growth of AI fi rms in some countries relative to others. Thus, lax privacy policies may help domestic industry relative to countries with strict policies just as lax labor and environmental regulation may help the domestic industry. This suggests the potential of a “race to the bottom” in privacy policy. Evidence for such races has been found in enforcement of labor policies (e.g., Davies and Vadlamannati 2013) and in environmental policies (e.g., Beron, Murdoch, and Vijverberg 2003; Fredriksson and Milliment 2002). There is evidence that privacy regulation does disadvantage jurisdictions with respect to their advertising-supported software industries. In particular, Goldfarb and Tucker (2011) examined a change in European privacy regulation (implemented in 2004) that made it more diffi cult for European internet fi rms to collect data about their online customers. This regulatory change was particularly likely to reduce the eff ectiveness of advertising on websites that relied on customer- tracking data. Using a consistent measure of the eff ectiveness of thousands of online advertising campaigns, the results showed that European online advertising became about 65 percent less eff ective after the regulation took eff ect, compared to before the regulation and compared to advertising in other jurisdictions, mainly the United States. In other words, privacy regulation seemed to reduce the ability of companies to use data eff ectively. In a diff erent context, Miller and Tucker (2011) show that state- level privacy restrictions can reduce the quality of health care. While this evidence does not pertain to AI, just like AI, online advertising and health care use data as a key input. In other words, the same forces will likely be at play for privacy regulation that restricts the ability of AI to operate. Under strategic trade models, such races to the bottom are likely to matter if there are rents to be gained from AI. Under endogenous growth models with local spillovers and various agglomeration models, this could create an equilibrium in which the AI industry moves to the country with the most lax policies. Currently, privacy policies are much stricter in Europe than in the

19. Importantly, this is not a statement about the optimal privacy policy from the point of view of a ﬁ rm. If consumers have a preference for privacy, the private sector can provide it even in the absence of regulation. For a richer debate on this point, see Goldfarb and Tucker (2012) and Acquisti, Taylor, and Wagman (2016). 484 Avi Goldfarb and Daniel Treﬂ er

United States or China.20 Furthermore, there are a number of diff erences in such policies between the United States and China. This may give the United States and China an advantage over Europe in this industry. If stricter privacy policy is likely to hamstring domestic ﬁ rms in favor of foreign ones, we would expect policy to emphasize avoiding such a race to the bottom; however, recent trade negotiations have instead focused on privacy regulation as disguised protection. For example, this argument is at odds with the current US trade negotiation objectives, which want to weaken Canadian privacy laws. Based on the existing evidence from other data- driven industries, we believe this will help the Canadian industry relative to the US industry in the long run, even if it beneﬁ ts American companies that already do business in Canada in the short run. In addition, TPP’s chapter 14 on Electronic Commerce contains provisions that attempt to limit disguised protection, but contains almost no language that encourages harmonization in privacy policies beyond a request in Article 14.8.5 to “endeavor to exchange information on any such [personal information protection] mechanisms . . . and explore ways to extend these or other suitable arrangements to promote compatibility between them.” The words “endeavor” and “explore” are what are known in the trade policy literature as “aspirational” language and generally have no force. The CETA agreement is even more vague with respect to electronic commerce generally. The electronic commerce section, chapter 16, says little but “recognize the importance of” electronic commerce regulation and interoperability and that “the Parties agree to maintain a dialogue on issues raised by electronic commerce.”21 It is important to note that this is not a statement about company strategy. The market may discipline and provide consumer protection with respect to privacy. Apple, in particular, has emphasized the protection of the personal information of its customers as it has rolled out AI initiatives, and it is an open question whether this strategy will pay off in terms of consumer loyalty and access to better quality, if limited, data. We also want to emphasize that we do not have a position on the optimal amount of privacy as enforced by regulation. In fact, we think this is a diffi - cult question for economists to answer. Given that the empirical evidence suggests that privacy regulation, on balance and as implemented thus far, seems to reduce innovation, the determination of the optimal amount of privacy should not focus on maximizing innovation (through, as the TPP

20. Canada sits somewhere in the middle. Europe is strict on both data collection and its uses. Canada’s core restrictions involve use for a purpose diff erent from the collection context. The United States emphasizes contracts, and so as long as the privacy policy is clear, companies can collect and use data as they wish (at least outside of certain regulated industries like health and fi nance). 21. https:// ustr .gov/ sites/ default/ fi les/ TPP-Final- Text- Electronic- Commerce .pdf, http:// www .international.gc .ca/ trade-commerce/ trade-agreements- accords- commerciaux/ agr-acc / ceta- aecg/ text- texte/ 16 .aspx?lang=eng. Artifi cial Intelligence and International Trade 485 emphasizes in article 14.8.1, “the contribution that this [privacy protection] makes to enhancing consumer confi dence in electronic commerce”). Instead, it is a balance of the ethical value of (or even right to) privacy and the innovativeness and growth of the domestic AI industry. To reiterate, privacy regulation is diff erent from many other regulations because privacy (perhaps disproportionately) hamstrings domestic fi rms. Therefore, trade negotiations should not start with the assumption that privacy regulation is disguised protection. Instead, discussions should start with the public policy goal of the “social benefi ts of protecting the personal information of users of electronic commerce” that is also mentioned in article 14.8.1 of the TPP. Then, if needed, discussions can move to any particular situation in which a privacy regulation might really be disguised protection. As we hope is clear from the above discussion, domestic privacy regulations that restrict how fi rms can collect and use data are unlikely to be disguised protection. We next turn to two other regulations that might use privacy as an excuse to favor, rather than hamstring, domestic fi rms. Data Localization. Data localization rules involve restrictions on the ability of fi rms to transmit data on domestic users to a foreign country. Such restrictions are often justifi ed by privacy motivations. Countries may want data to stay domestic for privacy and (related) national security reasons. In particular, the argument for data localization emphasizes that governments want the data of their citizens to be protected by the laws of the domestic country. Foreign national security agencies should not have access to data that occurs within a country, and foreign companies should be bound by the laws of the country where the data were collected. The argument against such localization (at least in public) is technical: such localization imposes a signifi cant cost on foreign companies wanting to do business. They need to establish a presence in every country, and they need to determine a system that ensures that the data is not routed internationally (something that is technically costly, particularly for integrated communications networks such as within Europe or within North America). US- based companies have lobbied against such requirements.22 On the technical side, consider two parties, A and B, who reside in the same country. Internet traffi c between A and B cannot be confi ned within national borders without specifi c technical guidance (and some cost to quality) because the internet may route data indirectly. In addition, data on a transaction between A and B may be stored on a server located in a diff erent country. Furthermore, if A and B reside in diff erent countries, then the data on that transaction will likely be stored in both countries.23 Data localization is an issue for AI because AI requires data. And it often involves merging diff erent data sources together. The quality of aggregate

22. https://publicpolicy.googleblog .com/ 2015/ 02/ the- impacts- of-data- localization- on .html. 23. Dobson, Tory, and Trefl er (2017). 486 Avi Goldfarb and Daniel Trefl er predictions from AI will be lower if the scale of data is limited to within a country. In other words, localization is a way to restrict the possible scale of any country in AI, but at the cost of lower quality overall. Put diff erently, data localization is a privacy policy that could favor domestic fi rms. Unlike the consumer protection privacy policies highlighted above, it can favor domestic over foreign fi rms because the foreign-fi rm AI experts may not have access to the data. The TPP recognizes this and explicitly restricts it in Article 14.11.3a, which states that the cross- border transfer of information should not be restricted in a manner that would constitute “a disguised restriction on trade.”24 Privileged Access to Government Data. Another potential restriction on trade that might be justifi ed by privacy concerns involves access to government data. Governments collect a great deal of data. Such data might be valuable to training AIs and improving their predictions. Such data include tax and banking data, education data, and health data. For example, as the only legal provider of most health care services in Ontario, the Ontario government has unusually rich data on the health needs, decisions, and out- comes of 14 million people. If domestic fi rms are given privileged access to that data, it would create an indirect subsidy to the domestic AI industry. We think the most useful analogy in the current trade literature is the peren- nial softwood lumber trade dispute between Canada and the United States. In the softwood lumber case, most timber in Canada is on government- owned land, while in the United States, most timber is on privately owned land. The US complaints allege that Canadian timber is priced too low, and is therefore a government subsidy to the Canadian lumber industry. While there have been various agreements over the years, the disagreement has not been fully resolved. The superfi cial issue is what a fair price should be for access to government resources. The real issue is whether legitimate regulatory diff erences can be argued to convey unfair advantage and therefore constitute a trade-illegal subsidy. Government data can be seen similarly. Links between the state and the corporation vary by country, and this might help some corporations more than others. What is a fair price for access to the data? Importantly, governments may not want to give foreign fi rms access to such data for the same privacy and national security issues that underlie motivations for data local-

24. Related to the issue of data localization is the question of who owns data collected on domestic individuals by foreign individuals or fi rms. For example, consider an American company that uses Peruvians’ cell phones to gather data on agriculture and climate. Who owns the rights to that data? Are the Americans allowed to profi t from that data? Are contracts between the individual actors enough, or is there a need for international laws or norms? The data might not be collected if not for the private companies, but the companies use the data in their own interest rather than in the public interest or in the interest of the Peruvians who provided the data. The recent attempts at a joint venture between Monsanto and John Deere, along with the US Department of Justice antitrust concerns that scuttled the deal, highlight how tangible this issue is. Artifi cial Intelligence and International Trade 487 ization. Thus, seemingly reasonable diff erences across countries in their data access policies can end up favoring the domestic industry. Industrial Regulation. Most international agreements have a section on competition policy and industrial regulation. This is because regulation can be a source of unfair comparative advantage or disadvantage. In AI applications, this list is long. In addition to the points around data and privacy highlighted above, many applications of AI involve complementary technologies in which standards might not yet exist and the legal framework might still be evolving. For example, in autonomous vehicles, a variety of standards will need to be developed around vehicle- to-vehicle communication, traffi c signals, and many other aspects of automotive design. Most of these standards will be negotiated by industry players (Simcoe 2012), perhaps with some government input. As in other contexts, national champions can try to get their governments to adopt standards that raise costs for foreign competition. This leads to the possibility of international standards wars. This is particularly true of standards that are likely to involve a great deal of government input. For example, suppose governments require that the AI behind autonomous vehicles be suffi ciently transparent that investigators are able to determine what caused a crash. Without international standards, diff erent countries could require information from diff erent sensors, or they could require access to diff erent aspects of the models and data that underlie the technology. For companies, ensuring that their AI is compatible with multiple regulatory regimes in this manner would be expensive. Such domestic regulations could be a way to favor domestic fi rms. In other words, domestic technology standards around how AI interacts with the legal regime is a potential tool for disguised restriction on trade. The autonomous vehicle legal framework is evolving, with diff erent countries (and even states within the United States) allowing diff erent degrees of autonomy on their public roads. Drones are another example where, in the United States, the Federal Aviation Administration (FAA) strictly regu- lates American airspace, while China and some other countries have fewer restrictions. This may have allowed China’s commercial drone industry to be more advanced than the industry in the United States.25 Thus, regulation can also impact the rate of innovation and therefore comparative advantage. Source Code. To the extent that AI may discriminate, governments may demand information about the algorithms that underlie the AI’s predictions under antidiscrimination laws. More generally with respect to software, including AI, governments may demand access to source code for security reasons, for example, to reduce fraud or to protect national security. Thus, using consumer protection or national security as an excuse, governments

25. https:// www .forbes .com/ sites/ sarahsu/ 2017/ 04/ 13/ in- china- drone- delivery- promises - to-boost- consumption- especially- in-rural- areas/ #47774daf68fe. 488 Avi Goldfarb and Daniel Trefl er could reduce the ability of foreign fi rms to maintain trade secrets. Further- more, cyber espionage of such trade secrets may be widespread, but that is beyond the scope of this chapter.26 Broadly, this issue has been recognized in the TPP negotiations, with Article 14.17 emphasizing that access to source code cannot be required unless that source code underlies critical infrastruc- ture or unless the source code is needed to obey other domestic regulations that are not disguised restrictions on trade. Other policies that might aff ect the size of domestic AI industries include intellectual property, antitrust, R&D subsidies, and national security. If AI is the next important strategic industry, then all of the standard questions arise with respect to trade policies in these industries. We do not discuss these in detail because we think the trade- specifi c issues with respect to these policies are not distinct to AI, but are captured more generally by the discussion of innovation and trade. The main point for these other aspects of domestic policy with respect to AI and trade is that there are economies of scale in AI at the fi rm level. Furthermore, we expect some of the externalities from the AI industry to remain local.

19.5 AI and International Macroeconomics Before concluding, it is important to recognize that AI will have implications for international macroeconomics. For example, suppose that China does succeed in building a large AI industry. This will likely increase its trade surplus with the rest of the world, particularly in services. Furthermore, suppose that China manages to control wage infl ation through promoting migration from rural to urban areas, and by relaxing the one-child policy. Then, this is likely to put upward pressure on the renminbi (RMB) and downward pressure on the dollar. This will have implications for US labor markets. At the low end of the market, a weakening dollar might repatriate manufacturing jobs. At the high end of the market, skilled US workers will for the fi rst time be exposed to competition from a low- wage country. In isolation, this would reduce one dimension of domestic US inequality. If the Chinese market becomes open to US technology giants (and vice versa), both the Melitz (2003) model and the Oberfi eld (2018) model of trade predict that the giants will grow even larger. In the context in which these companies have already absorbed one- fi fth of US value added, and may have contributed to US top- end inequality, the impact of international trade in further growing these impacts may increase top-end inequality.

26. https:// obamawhitehouse.archives .gov/ sites/ default/ ﬁ les/ omb/ IPEC/ admin_strategy _on_mitigating_the_theft_of_u.s._trade_secrets .pdf. Artiﬁ cial Intelligence and International Trade 489

19.6 Conclusion How will artifi cial intelligence aff ect the pattern of trade? How does it make us think diff erently about trade policy? In this article we have tried to highlight some key points. First, the nature of the technology suggests that economies of scale and scope will be important. Furthermore, as a knowledge- intensive industry, knowledge externalities are likely to be important. Prior literature on other industries suggests that such externalities are often local, but more evidence is needed. Second, the trade models that are likely to be most useful in understanding the impact of AI are those that account for these points, specifi cally, scale, knowledge creation, and the geography of knowledge dif- fusion. These models suggest that whether AI- focused trade policies (or AI-focused investments in clusters) are optimal will depend very much on the presence of scale and the absence of rapid international knowledge diff u- sion. Third, we discussed whether and how regulation might be used to favor domestic industry. We highlighted that privacy policy that targets consumer protection is unlike many other regulations in that it is likely to hamstring domestic fi rms, even relative to foreign ones. So, rather than focusing trade discussions on how privacy policy might be used as a disguised restriction on trade, such discussions should emphasize regulatory harmonization so as to avoid a race to the bottom. In contrast, several other policies may be used to favor domestic fi rms including data localization rules, limited access to government data, industry regulations such as those around the use of drones, and forced access to source code. Generally, this is an exciting new area for trade research and policy. There is still much to learn before we have a comprehensive understanding of these questions.

References

Acquisti, Alessandro, Curtis Taylor, and Liad Wagman. 2016. “The Economics of Privacy.” Journal of Economic Literature 54 (2): 442–92. Aghion, Philippe, Antonin Bergeaud, Matthieu Lequien, and Marc Melitz. 2017. “The Impact of Exports on Innovation: Theory and Evidence.” Working paper, Harvard University. Aghion, Philippe, Nick Bloom, Richard Blundell, Rachel Griffi th, and Peter Howitt. 2005. “Competition and Innovation: An Inverted-U Relationship.” Quarterly Journal of Economics 120 (2): 701–28. Aghion, Philippe, Christopher Harris, Peter Howitt, and John Vickers. 2001. “Com- petition, Imitation and Growth with Step-by- Step Innovation.” Review of Eco- nomic Studies 68 (3): 467–92. 490 Avi Goldfarb and Daniel Treﬂ er

Aghion, Philippe, and Peter Howitt. 2009. The Economics of Growth. Cambridge, MA: MIT Press. Agrawal, Ajay, Joshua Gans, and Avi Goldfarb. 2018. Prediction Machines: The Simple Economics of Artifi cial Intelligence. Boston, MA: Harvard Business Re- view Press. Autor, David, David Dorn, Lawrence F. Katz, Christina Patterson, and John Van Reenen. 2017. “Concentrating on the Fall of the Labor Share.” NBER Working Paper no. 23108, Cambridge, MA. Azoulay, Pierre, Joshua S. Graff Zivin, and Jialan Wang. 2010. “Superstar Extinc- tion.” Quarterly Journal of Economics 125 (2): 549–89. Beron, Kurt J., James C. Murdoch, and Wim P. M. Vijverberg. 2003. “Why Co- operate? Public Goods, Economic Power, and the Montreal Protocol.” Review of Economics and Statistics 85 (2): 286–97. Brander, James A., and Barbara J. Spencer. 1981. “Tariff s and the Extraction of Foreign Monopoly Rents under Potential Entry.” Canadian Journal of Economics 14 (3): 371–89. Brandt, Loren, and Xiaodong Zhu. 2000. “Redistribution in a Decentralized Economy: Growth and Infl ation in Reform China.” Journal of Political Economy 108 (2): 422–39. Busch, Marc L. 2001. Trade Warriors: States, Firms, and Strategic-Trade Policy in High- Technology Competition. Cambridge: Cambridge University Press. Davies, Ronald B., and Krishna Chaitanya Vadlamannati. 2013. “A Race to the Bottom in Labor Standards? An Empirical Investigation.” Journal of Development Economics 103:1–14. Dobson, Wendy, Julia Tory, and Daniel Trefl er. 2017. “Modernizing NAFTA: A Canadian Perspective.” In A Positive NAFTA Renegotiation, edited by Fred Berg- sten, 36–49. Washington, DC: Petersen Institute for International Economics. Duranton, Gilles. 2011. “California Dreamin’: The Feeble Case for Cluster Policies.” Review of Economic Analysis 3 (1): 3–45. Duranton, Gilles, and Diego Puga. 2001. “Nursery Cities: Urban Diversity, Process Innovation, and the Life Cycle of Products.” American Economic Review 91 (5): 1454– 77. Eaton, Jonathan, and Gene M. Grossman. 1986. “Optimal Trade and Industrial Policy under Oligopoly.” Quarterly Journal of Economics 101 (2): 383–406. Ethier, Wilfred J. 1982. “National and International Returns to Scale in the Modern Theory of International Trade.” American Economic Review 72 (3): 389–405. Fajgelbaum, Pablo, Gene M. Grossman, and Elhanan Helpman. 2011. “Income Dis- tribution, Product Quality, and International Trade.” Journal of Political Economy 119 (4): 721–65. Fredriksson, Per G., and Daniel L. Millimet. 2002. “Strategic Interaction and the Determination of Environmental Policy across U.S. States.” Journal of Urban Economics 51 (1): 101–22. Fujii, Hidemichi, and Shunsuke Managi. 2017. “Trends and Priority Shifts in Arti- fi cial Intelligence Technology Invention: A Global Patent Analysis.” RIETI Dis- cussion Paper Series no. 17-E066, Research Institute of Economy, Trade, and Industry, May. Goldfarb, Avi, and Catherine Tucker. 2011. “Privacy Regulation and Online Adver- tising.” Management Science 57 (1): 57–71. ———. 2012. “Privacy and Innovation.” In Innovation Policy and the Economy, vol. 12, edited by Josh Lerner and Scott Stern, 65–89. Chicago: University of Chicago Press. Artifi cial Intelligence and International Trade 491

Grossman, Gene M., and Elhanan Helpman. 1989. “Product Development and International Trade.” Journal of Political Economy 97 (6): 1261–83. ———. 1990. “Trade, Innovation, and Growth.” American Economic Review Papers and Proceedings 80 (2): 86–91. ———. 1991. Innovation and Growth in the Global Economy. Cambridge, MA: MIT Press. Grossman, Gene M., and Alan B. Krueger. 1995. “Economic Growth and the En- vironment.” Quarterly Journal of Economics 110 (2): 353–77. Grossman, Gene M., and Esteban Rossi-Hansberg. 2010. “External Economies and International Trade Redux.” Quarterly Journal of Economics 125 (2): 829–58. ———. 2012. “Task Trade between Similar Countries.” Econometrica 80 (2): 593– 629. Helpman, Elhanan. 1984. “Increasing Returns, Imperfect Markets, and Trade Theory.” In Handbook of International Economics, edited by Peter B. Kenen and Ronald W. Jones, 325–65. Amsterdam: North-Holland. Irwin, Douglas A., and Peter J. Klenow. 1994. “Learning- by- Doing Spillovers in the Semiconductor Industry.” Journal of Political Economy 102 (6): 1200–27. Klette, Tor Jakob, and Samuel Kortum. 2004. “Innovating Firms and Aggregate Innovation.” Journal of Political Economy 112 (5): 986–1018. Krugman, Paul R. 1980. “Scale Economies, Product Diff erentiation, and the Pattern of Trade.” American Economic Review 70 (5): 950–59. ———. 1986. Strategic Trade Policy and the New International Economics. Cam- bridge, MA: MIT Press. Krugman, Paul R., and Anthony J. Venables. 1995. “Globalization and the Inequal- ity of Nations.” Quarterly Journal of Economics 110 (4): 857–80. Lim, Kevin, Daniel Trefl er, and Miaojie Yu. 2017. “Trade and Innovation: The Role of Scale and Competition Eff ects.” Working paper, University of Toronto. Manasse, Paolo, and Alessandro Turrini. 2001. “Trade, Wages, and Superstars.” Journal of International Economics 54 (1): 97–117. Markusen, James R. 1981. “Trade and the Gains from Trade with Imperfect Com- petition.” Journal of International Economics 11 (4): 531–51. Marx, M., and L. Fleming. 2012. “Non- compete Agreements: Barriers to Entry . . . and Exit?” In Innovation Policy and the Economy, vol. 12, edited by J. Lerner and S. Stern. Chicago: University of Chicago Press. Mayer, Wolfgang. 1974. “Short-Run and Long-Run Equilibrium for a Small Open Economy.” Journal of Political Economy 82 (5): 955–67. McLaren, John. 2000. “ ‘Globalization’ and Vertical Structure.” American Economic Review 90 (5): 1239–54. Melitz, Marc J. 2003. “The Impact of Trade on Intra- Industry Reallocations and Aggregate Industry Productivity.” Econometrica 71 (6): 1695–725. Miller, A. R., and C. Tucker. 2011. “Can Healthcare IT Save Babies?” Journal of Political Economy 119 (2): 289–332. Mussa, Michael L. 1974. “Tariff s and the Distribution of Income: The Importance of Factor Specifi city, Substitutability, and Intensity in the Short and Long Run.” Journal of Political Economy 82 (6): 1191–203. Oberfi eld, Ezra. 2018. “A Theory of Input- Output Architecture.” Econometrica 86:559–89. Porter, Michael E. 1990. “The Competitive Advantage of Nations.” Harvard Business Review 68 (2): 73–93. PWC. 2017. “Global Top 100 Companies by Market Capitalisation.” March 31, 2017, update. https://www .pwc .com/ top100. Accessed August 17, 2017. 492 Avi Goldfarb and Daniel Trefl er

Rauch, James E. 1999. “Networks versus Markets in International Trade.” Journal of International Economics 48 (1): 7–35. Rivera-Batiz, Luis A., and Paul M. Romer. 1991. “Economic Integration and Endog- enous Growth.” Quarterly Journal of Economics 106 (2): 531–55. Romer, Paul M. 1990. “Endogenous Technological Change.” Journal of Political Economy 98 (5): S71–102. Rosen, Sherwin. 1981. “The Economics of Superstars.” American Economic Review 71 (5): 845–58. Ruffi n, Roy J., and Ronald W. Jones. 1977. “Protection and Real Wages: The Neoclas- sical Ambiguity.” Journal of Economic Theory 14 (2): 337–48. Saxenian, AnnaLee. 1994. Regional Advantage Culture and Competition in Silicon Valley and Route 128. Cambridge, MA: Harvard University Press. Simcoe, Timothy. 2012. “Standard Setting Committees: Consensus Governance for Shared Technology Platforms.” American Economic Review 102 (1): 305–36. Sutton, John, and Daniel Treﬂ er. 2016. “Capabilities, Wealth and Trade.” Journal of Political Economy 124 (3): 826–78. Uyarra, Elvira, and Ronnie Ramlogan. 2012. “The Eff ects of Cluster Policy on Innovation Compendium of Evidence on the Eff ectiveness of Innovation Policy Intervention.” Technical Report, Manchester Institute of Innovation Research Manchester Business School, March. Contributors

Daron Acemoglu Erik Brynjolfsson Department of Economics MIT Sloan School of Management Massachusetts Institute of 100 Main Street, E62-414 Technology Cambridge, MA 02142 50 Memorial Drive Cambridge, MA 02142-1347 Colin F. Camerer Department of Economics Philippe Aghion California Institute of Technology Collège de France 1200 East California Boulevard 3 Rue D’Ulm Pasadena, CA 91125 75005 Paris, France Judith Chevalier Ajay Agrawal Yale School of Management Rotman School of Management 135 Prospect Street University of Toronto New Haven, CT 06520 105 St. George Street Toronto, ON M5S 3E6, Canada Iain M. Cockburn School of Management Susan Athey Boston University Graduate School of Business 595 Commonwealth Avenue Stanford University Boston, MA 02215 655 Knight Way Stanford, CA 94305 Tyler Cowen Department of Economics James Bessen George Mason University Technology & Policy Research 4400 University Drive Initiative Fairfax, VA 22030 Boston University School of Law 765 Commonwealth Avenue Patrick Francois Boston, MA 02215 Vancouver School of Economics University of British Columbia IONA Building, 6000 Iona Drive Vancouver, BC V6T 2E8, Canada

611 612 Contributors

Jason Furman Daniel Kahneman Harvard Kennedy School Woodrow Wilson School 79 John F. Kennedy Street Princeton University Cambridge, MA 02138 Princeton, NJ 08544-1013

Alberto Galasso Anton Korinek Rotman School of Management University of Virginia University of Toronto Monroe Hall 246 105 St. George Street 248 McCormick Road Toronto, ON M5S 3E6, Canada Charlottesville, VA 22904

Joshua Gans Mara Lederman Rotman School of Management Rotman School of Management University of Toronto University of Toronto 105 St. George Street 105 St. George Street Toronto, ON M5S 3E6, Canada Toronto, Ontario M5S 3E6, Canada

Avi Goldfarb Hong Luo Rotman School of Management Harvard Business School University of Toronto Morgan Hall 241 105 St. George Street Soldiers Field Road Toronto, ON M5S 3E6, Canada Boston, MA 02163

Austan Goolsbee John McHale University of Chicago Booth School 108 Cairnes Building of Business School of Business and Economics 5807 S. Woodlawn Avenue National University of Ireland Chicago, IL 60637 Galway H91 TK33, Ireland

Rebecca Henderson Paul R. Milgrom Harvard Business School Department of Economics Morgan Hall 445 Stanford University Soldiers Field Road 579 Serra Mall Boston, MA 02163 Stanford, CA 94305-6072

Ginger Zhe Jin Matthew Mitchell Department of Economics Rotman School of Management University of Maryland University of Toronto 3115F Tydings Hall 105 St. George Street College Park, MD 20742-7211 Toronto, ON M5S 3E6, Canada

Benjamin F. Jones Alexander Oettl Department of Management and Scheller College of Business Strategy Georgia Institute of Technology Kellogg School of Management 800 West Peachtree Street NW Northwestern University Atlanta, GA 30308 2211 Campus Drive Evanston, IL 60208 Andrea Prat Columbia Business School Charles I. Jones Uris Hall 624 Graduate School of Business 3022 Broadway Stanford University New York, NY 10027-6902 655 Knight Way Stanford, CA 94305-4800 Contributors 613

Manav Raj Chad Syverson Stern School of Business University of Chicago Booth School of New York University Business 44 West Fourth Street 5807 S. Woodlawn Avenue New York, NY 10012 Chicago, IL 60637

Pascual Restrepo Matt Taddy Department of Economics University of Chicago Booth School of Boston University Business 270 Bay State Road 5807 S. Woodlawn Avenue Boston, MA 02215 Chicago, IL 60637

Daniel Rock Steven Tadelis MIT Sloan School of Management Haas School of Business 100 Main Street, E62-365 University of California, Berkeley Cambridge, MA 02142 545 Student Services Building Berkeley, CA 94720 Jeff rey D. Sachs Center for Sustainable Development, Manuel Trajtenberg Earth Institute Eitan Berglas School of Economics Columbia University Tel Aviv University 535 West 116th Street, MC 4327 Tel Aviv 69978, Israel New York, NY 10027 Daniel Treﬂ er Robert Seamans Rotman School of Management Stern School of Business University of Toronto New York University 105 St. George Street 44 West 4th Street, KMC 7-58 Toronto, ON M5S 3E6, Canada New York, NY 10012 Catherine Tucker Scott Stern MIT Sloan School of Management MIT Sloan School of Management 100 Main Street, E62-536 100 Main Street, E62-476 Cambridge, MA 02142 Cambridge, MA 02142 Hal Varian Betsey Stevenson School of Information Gerald R. Ford School of Public Policy University of California, Berkeley University of Michigan 102 South Hall 5224 Weill Hall Berkeley, CA 94720-4600 735 South State Street Ann Arbor, MI 48109-3091

Joseph E. Stiglitz Columbia University Uris Hall 212 3022 Broadway New York, NY 10027

Author Index

Abadie, A., 531 Angermueller, C., 168 Ablon, L., 448 Aral, S., 43 Abrahamson, Z., 420 Arntz, M., 321 Abramovitz, M., 32 Aronoff , M., 497 Acemoglu, D., 23n1, 43, 89, 90, 105, 127, Arrow, K., 9, 43, 110, 118, 148, 364n11, 366, 141, 152, 197, 198, 201, 202, 203, 412 203n4, 204, 204n5, 205, 206, 208, 210, Arthur, B. W., 150 211, 212n7, 219, 220, 220n11, 223, 224, Asher, S., 525 225n14, 238, 240, 243, 271, 283, 293n3, Athey, S., 68, 425, 448, 449, 510, 514, 515, 376n27, 376n28, 554 516, 517, 519, 523, 524, 525, 527, 528, Acquisti, A., 410, 416, 424, 440n2, 444, 448, 529, 530, 531, 532, 533, 534, 536, 538 451, 457, 457n42, 459, 483n19 Atkeson, A., 42n19 Adee, S., 426 Autor, D. H., 7, 23n1, 30, 89, 198, 202n3, Agarwal, A., 82n12, 83 203, 208, 220n11, 238, 239n3, 240, 271, Aghion, P., 122, 172, 262, 262n19, 263, 265, 309, 322, 475, 555 267, 268, 373n23, 465, 477, 479, 495 Axelrod, R., 413 Agrawal, A., 5, 39, 90, 97n8, 150, 161, 167, Ayres, R., 201 241, 425, 464, 501 Azoulay, P., 475, 477 Aguiar, M., 386n36 Airoldi, E., 80 Babcock, L., 592n5 Akerlof, G., 378 Bai, J., 532 Akerman, A., 7, 303 Baker, D., 366n14 Alexopoulos, M., 555 Baker, G., 107 Allen, R. C., 204, 209 Banbura, M., 537 Alloway, T., 29 Barkai, S., 271 Alon, T., 43 Barrat, J., 350 Alpaydin, E., 92 Baslandze, S., 263, 264 Altman, S., 325 Bastani, H., 529 Alvarez-Cuadrado, F., 241n4 Baumol, W., 38n14, 238 Anderson, R., 406, 416 Bayati, M., 529, 531, 532, 536 Andrade, E. B., 592n3 Belloni, A., 93, 522 Andrews, D., 30 Bengio, S., 401

615 616 Author Index

Bengio, Y., 71, 74, 75, 75n6, 149, 168, 168n6, Cavusoglu, H., 450 172 Cette, G., 27 Benzell, S. G., 90, 336 Chandler, A. D., 206 Berg, A., 373n24 Chapelle, O., 529 Beron, K. J., 483 Chapman, J. P., 599 Bertocchi, G., 430 Chapman, L. J., 599 Bessen, J. E., 23n1, 203, 300, 413, 555 Chernozkhukov, V., 93, 522, 523, 527 Bhatt, M. A., 592n4 Chetty, R., 270 Bickel, P. J., 522 Chevalier, J., 577n5 Binmore, K., 589, 589n1, 593 Chiou, L., 428, 448 Bjorkegren, D., 516, 519 Chong, J.-K., 593, 595 Blake, T., 582 Christie, W. G., 414 Blattberg, R. C., 598 Chui, M., 331 Blei, D. M., 507, 510, 515, 532, 533, 534 Clark, C., 295 Bloom, N., 27, 150, 223, 259n15, 268, 560 Cockburn, I., 150 Bolton, P., 92, 95, 95n5, 96n7, 100, 112 Coey, D., 514 Boppart, T., 241n4, 295 Cohen, J., 555 Borenstein, S., 413 Cohen, L., 501 Bornmann, L., 153 Comin, D., 241n4, 295 Bostrom, N., 3, 286, 381, 382, 382n31 Corrigan, B., 597 Bottou, L., 79 Costa-Gomes, M. A., 593, 593n9, 594n9 Bousquet, O., 79 Courville, A., 71, 75n6 Bowen, W., 38n14 Cowen, T., 27, 150 Brandeis, L. D., 431, 459 Cowgill, B., 562 Brander, J. A., 473 Cranor, L. F., 424, 458 Brandimarte, L., 448 Crawford, V. P., 593, 593n9, 594n9 Brandt, L., 471 Criscuolo, C., 30 Bresnahan, T. F., 4, 39, 42, 116, 119, 120, 169, 176n2, 310 Dana, J., 599 Bridgman, B., 38 Danaylov, N., 253 Brooks, R., 124 Dasgupta, P., 366n15 Broseta, B., 593n9, 594n9 Datta, A., 433 Brown, J., 314 Dauth, W., 556 Brunskill, E., 528 David, P. A., 4, 41, 42n19, 119 Brynjolfsson, E., 23, 23n1, 28n7, 30, 39, 40, Davies, R. B., 483 42, 43, 47, 50, 89, 119, 120, 150, 197, Dávila, E., 354 201, 204, 309, 555, 557, 560, 563 Dawes, R. M., 597, 598, 599, 599n15 Brzeski, C., 556 Dawsey, K., 29 Buera, F. J., 293, 293n2 Deaton, A., 26, 85 Buffi e, E. F., 373n24 Della Vigna, S., 411 Bughin, J., 408 Delli Gatti, D., 361, 362n9, 380 Burk, I., 556 De Loecker, J., 30 Busch, M. L., 474, 475n13 Dennis, B. N., 293n2 Byers, J. W., 577n5 Dewatripoint, M., 112 Byrne, D. M., 29, 319 Diamond, A., 531 Dietvorst, B. J., 426 Camerer, C. F., 589, 589n1, 590, 593, Dimakopoulou, M., 529 594n10, 595, 598 Dimico, A., 430 Campbell, K., 450 Dobson, W., 485n23 Cardarelli, R., 29 Dogan, M., 107n9 Carley, M., 130, 132 Doi, E., 599 Case, A., 26 Domingos, P., 93 Catalini, C., 150, 425, 448, 449, 454 Doms, M., 562 Author Index 617

Dorantes, C., 450 Fortunato, M., 25 Dorn, D., 208 Fredriksson, P. G., 483 Dosi, G., 366n14 Frey, C. B., 291, 321, 331, 350n1, 553, 555, Doudchenko, N., 531 556 Dover, Y., 577n5 Friedman, J., 93 Drandakis, E., 376n27 Frosst, N., 76n7 Dubé, J.-P., 410 Fudenberg, D., 413, 594 Dudik, M., 528 Fujii, H., 465 Duﬂ o, E., 539 Fung, A., 459 Dunne, T., 562 Furman, J. L., 30, 122, 140, 150, 553 Dupuit, J., 296, 298 Duranton, G., 480, 567 Gaarder, I., 7, 303 Dwork, C., 453 Gabaix, X., 604 Dzamba, M., 116 Gaggl, P., 303 Gal, M. S., 583 Eckles, D., 538 Gal, P., 30 Edwards, D. D., 598 Galasso, A., 495, 496, 497, 498, 501 Edwards, J. S., 598 Gans, J., 5, 39, 90, 97n8, 171, 425, 454, 464, Edwards, L., 294 550 Edwards, W., 595 Garicano, L., 267 Eeckhout, J., 30 Geanakoplos, J., 354, 368n19 Egami, N., 510 Geank, 369 Einhorn, H. J., 598, 599 Gehring, J., 25 Elkin-Koren, N., 583 Gentzskow, M., 411 Elsby, M. W. L., 270, 329 Gibbons, R., 107 Engel, E., 295 Glaeser, E. L., 516, 537, 548 Engerman, S. L., 430 Goel, S., 516 Engstrom, R., 537 Goeree, J., 593 Erlingsson, U., 453 Goh, G., 151n1, 168 Ethier, W. J., 476 Goldberg, L. R., 597, 601n19 Etzioni, O., 410 Golden, J. M., 577n5 Evenson, R. E., 140 Goldenshluger, A., 529 Ezrachi, A., 414 Goldfarb, A., 5, 39, 90, 97n8, 148, 150, 161, 167, 425, 427, 448, 464, 482, 483, Fajgelbaum, P., 480 483n19, 550, 593n8 Farrell, J., 424 Goldin, C., 209, 322 Faure-Grimaud, A., 92, 95, 95n5, 96n7, Goldman, M., 536 100, 112 Gomez-Uribe, C., 603 Faust, D., 597 Good, I. J., 238, 253 Fehr, E., 358n8 Goodfellow, I., 66, 71, 75n6, 401 Feldman, M. P., 560 Goolsbee, A. D., 310, 312, 314 Feldstein, M., 29 Gopalan, P., 510 Feraud, R., 529 Gordon, R. D., 305, 310, 349 Fernald, J. G., 27, 29, 32, 319 Gordon, R. J., 27, 150, 175, 210, 223, Feurer, M., 70 259n15, 264 Filippas, A., 577n5 Graepel, T., 429 Finklestein, A., 503 Graetz, G., 201, 274, 319, 554 Fleming, L., 161, 479 Graff Zivin, J. S., 475, 477 Florêncio, D., 452 Graham, M., 459 Foellmi, R., 295 Green, J., 502 Forbes, S., 108 Greenstein, S., 119 Ford, M., 201, 204, 301 Greenwald, B., 354, 364n11, 366n15, Forsythe, R., 590n2 368n19, 369, 370, 412 618 Author Index

Gregory, T., 321 Hobijn, B., 270, 329 Griliches, Z., 116, 120, 170 Hoch, S. J., 598 Grissen, D., 516, 519 Hochreiter, S., 73, 76 Groover, M., 201, 205 Hodas, N., 151n1, 168 Groshen, E. L., 350 Hoff man, D., 444, 457 Gross, R., 451 Hoff man, M., 447n17 Grossman, G. M., 464, 476, 477, 478, 479, Hofman, J. M., 510 480, 482 Hogarth, R. M., 598 Grove, W. M., 597 Holmes, T. J., 43 Guerrieri, V., 293n3 Holmstrom, B., 112, 269 Gurun, U., 501 Holt, C., 593 Gutiérrez, G., 30 Hoos, H., 575 Guvenen, F., 29 Hornik, K., 74, 525 Hortaçsu, A., 43, 593n8 Ha, Y., 599 Horton, J. J., 577n5 Hahn, J., 522 Hotborn, T., 525 Haile, P. A., 515 Howitt, P., 122, 262n19, 263, 477 Hainmueller, J., 531 Hubbard, F., 494n2, 500 Hall, B. H., 132 Huber, P., 494 Hall, R. E., 47 Hunt, N., 603 Hansen, C., 93, 522 Hutter, F., 575 Hanson, G. H., 208 Hutter, M., 237n1 Hanson, R., 386n36 Harari, Y., 383 Imbens, G. W., 68, 517, 522, 523, 524, 525, Hardt, M., 80 529, 530, 536, 538 Harrell, E., 443, 444 Iriberri, N., 593, 594n10 Hart, O., 269 Irwin, D. A., 477 Hartford, J., 68, 75, 79n9, 526, 594 Isçan, T. B., 293n2 Haskel, J., 18 Hastie, T., 93, 526 Jaff e, A. B., 132, 150 Hatzius, J., 29 Jaravel, X., 312 Haugeland, J., 69 Jarrell, G., 502 Hawkins, J., 93 Jayadev, A., 366n14 Hay, B., 494n2, 499 Jean, N., 537 Hazan, E., 408 Jewitt, I., 112 He, K., 75 Jha, S., 94, 95 Heckman, J. J., 85, 182 Jiang, N., 528 Heifels, A., 116 Jin, G. Z., 442n4 Helpman, E., 169, 464, 476, 477, 478, 479, Joachims, T., 528 480 Johnson, E. J., 589, 593, 603 Hemous, D., 241 Jones, B. F., 127, 150, 155, 161, 259, 259n15, Hendel, I., 412 373n23 Henderson, R., 42, 43, 150 Jones, C. I., 46, 150, 152, 159, 160, 170n9, Herley, C., 452 171n9, 240, 252, 259n15, 373n23 Herrendorf, B., 204, 241n4 Jones, R. W., 472 Hersh, J., 537 Jordan, M. I., 509, 532 Hicks, J., 350, 368 Jovanovic, B., 41 Hillis, A., 516 Hinton, G. E., 68, 74, 76n7, 124, 125, 149, Kaboski, J. P., 293, 293n2 168, 168n6, 172 Kahn, L. B., 447n17 Hirschman, A., 178 Kahneman, D., 596, 600, 604 Hitt, L., 42, 43, 120 Kaldor, N., 240 Ho, T.-H., 592n3, 593, 595 Kallus, N., 528 Author Index 619

Kaplan, J., 3 Laibson, D., 606 Kaplow, L., 500 Lambrecht, A., 425, 426 Kapur, D., 150 Lancaster, K., 362 Karabarbounis, L., 270, 329 Landes, D., 206 Karagözog˘lu, E., 589 Lane, J., 554, 562 Katz, L. F., 7, 209, 322 Langford, J., 528 Kehoe, P. J., 42 Lanier, J., 84 Kehrig, M., 270 Lashkari, D., 241n4, 295 Kendrick, J. W., 43 Lawrence, R. Z., 294 Kennan, J., 590n2 LeCun, Y., 75, 149, 168, 168n6, 172 Kennedy, C., 376 Lederman, M., 108, 550 Keynes, J. M., 379 Legg, S., 237n1 Kislev, Y., 140 Leung, M. K. K., 121 Kitagawa, T., 527 Levin, J., 514, 515 Klayman, J., 599 Levine, D. K., 43 Kleinberg, J., 15, 507, 516, 518, 548, 588, Levine, S., 38 597 Levinthal, D., 161 Klenow, P. J., 310, 312, 477 Levitt, S. D., 43 Klette, T. J., 479 Levy, F., 37, 202n3, 238, 239n3 Kleven, H. J., 270 Lewis, E., 562 Knetsch, L., 604 Lewis, G., 526, 527 Ko, M., 450 Lewis, M., 596 Kogler, D. F., 560 Lewis, R. A., 541n2 Koh, D., 329 Lewis, W. A., 294 Kohno, T., 446 Leyton-Brown, K., 569, 575, 594 Kolb, D. A., 207 Li, D., 447n17, 529 Kollmeyer, C., 293n2 Li, L., 528, 529, 580, 581 Komarova, T., 538 Liang, A., 588, 594 Kominers, D., 501, 516 Lim, K., 465, 479 Kongsamut, P., 241n4, 295 Lindor, R., 494 Korinek, A., 354, 365, 366n14, 371, 373, Lipsey, R., 362 383, 384 List, J. A., 43 Korolova, A., 453 Litan, R. E., 441n2, 494 Kortum, S. S., 259, 259n15, 479 Liu, Y., 38 Kosinski, M., 429 Long, N., 241n4 Kotlikoff , L. J., 336, 344 Lovallo, D., 600 Krajbich, I., 592n6 Lowenstein, G., 448, 592n5 Kravitz, L., 325 Lu, Y., 529 Krisiloff , M., 325 Luo, H., 495, 496, 497, 498, 501 Krizhevsky, A., 68, 74, 125 Lusinyan, L., 29 Krueger, A. B., 7, 482 Lusted, L. B., 94 Krugman, P. R., 464, 472, 479, 480 Krussell, P., 265 Malthus, T. R., 384 Künzel, S., 527 Mamoshina, P., 168n7 Kurakin, A., 401 Managi, S., 465 Kurakin, P. R., 464 Mandel, M., 555 Kurzweil, R., 238, 253, 253n12, 350, 373n23, Manning, A., 379 381, 382 Mantoux, P., 200, 203 Kuznets, S., 205, 206 Manuelli, R. E., 205, 243n6 Manyika, J., 331 Lada, A., 540 Marchant, G., 494n2 Laff ont, J.-J., 514 Marco, A., 130, 132 LaGarda, G., 336 Markov, J., 93 620 Author Index

Markusen, J. R., 476 Murray, C., 325 Marthews, A., 425 Murray, F., 141 Marx, M., 479 Mussa, M. L., 471 Massey, C., 426 Mutz, R., 153 Masterov, D. V., 578, 579 Myers, A., 130 Matsuyama, K., 294, 295 Myerson, R. B., 568 Mayer, U. F., 578, 579 Mayer, W., 471 Naecker, J., 592 Mayzlin, D., 577n5 Naik, N., 516, 537 McAfee, A., 23, 23n1, 30, 40, 50, 89, 150, Nakamura, L., 29 201, 204, 309, 555 Nave, G., 590 McCall, J. J., 582 Neal, R. M., 74 McClelland, J. L., 602 Neelin, J., 589n1 McDonald, A. M., 424, 458 Neiman, B., 270, 329 McElheran, K., 560 Nekipelov, D., 538 McFadden, D., 85, 514 Nelson, P., 581 Mcguckin, R. H., 562 Nelson, R., 118, 161 McHale, J., 150, 241 Newbery, D., 362 McLaren, J., 469 Newell, A., 123 McSweeny, T., 583 Newhouse, D., 537 Meade, J. E., 362 Ng, A., 93, 509, 532 Meadows, M., 420 Ng, S., 532 Meehl, P. E., 596, 597 Ngai, L. R., 241n4, 294 Meltz, M. J., 488 Nickell, S., 293n2 Mestieri, M., 241n4, 295 Nielsen, M., 152, 161 Mian, A., 208 Nilsson, N., 122, 207, 208 Michaels, G., 201, 274, 319, 554 Nordhaus, W. D., 28, 152, 172, 238 Mikolov, T., 76 North, D. C., 577 Milgrom, P. R., 43, 111, 567, 569, 574, 577 Nosko, C., 577n5, 582 Miller, A., 428, 429, 448, 483 Miller, S. M., 201 O’Dea, B., 583 Milliment, D. L., 483 Odlyzko, A., 441n2 Minsky, M., 28, 208 Oettle, A., 241 Miremadi, M., 331 Olmstead, A. L., 204, 205, 206 Mishel, L., 322 Olsen, M., 241 Misra, S., 410 O’Mahony, S., 141 Mitchell, T., 39, 557, 563 O’Neil, C., 433 Mnih, V., 84 O’Reilly, B., 499 Mobius, M. M., 510 Orlikowski, W. J., 43 Mogstad, M., 7, 303 Orszag, P., 30 Mojon, B., 27 Osborne, M. A., 291, 321, 331, 350n1, 553, Mokyr, J., 29, 121, 150, 175, 201, 206, 209 555, 556 Monro, S., 78 Osindero, S., 74 Moore, G. E., 384n35, 498 Oskamp, S., 601 Moore, M., 496 Ossard, H., 514 Morris, D. Z., 37 Östling, R., 593n8 Mortensen, D. T., 379, 582 Ostrovsky, M., 568 Muellbauer, J., 85 Mullainathan, S., 169n8, 511, 512, 518, 588 Pajarinen, M., 556 Murdoch, J. C., 483 Pál, J., 510 Murnane, R. J., 202n3, 238, 239n3 Palfrey, T., 593 Murphy, K., 107 Parchomovsky, G., 494 Author Index 621

Pate, R. H., 420 Rosenblatt, F., 73, 124 Pelzman, S., 502 Rosenfeld, J., 322 Peretto, P. F., 241 Rossi-Hansberg, E., 476 Peterson, N., 599 Roth, A. E., 539, 567, 584, 589 Peysakhovich, A., 540, 592 Rousseau, P. L., 41 Phelps, E., 376n27 Rouvinen, P., 556 Philippon, T., 30 Rowthorn, R., 291, 293n2 Pierce, D. G., 413 Rubin, D. B., 517, 522, 527, 529 Pihur, V., 453 Rubinstein, A., 427 Piketty, T., 360 Ruffi n, R. J., 472 Pissarides, C. A., 241n4, 294, 379 Ruiz, F. J., 515, 533, 534 Polemarchakis, H., 354, 368n19, 369 Rumelhart, D. E., 73, 77, 124, 602 Polinsky, M., 494n2 Porter, M. E., 481, 494 Sabour, S., 76n7 Poschke, M., 241n4 Sachs, J. D., 336, 344 Posner, R. A., 439 Saez, E., 270, 360 Prantl, S., 263 S¸ahin, E., 270, 329 Pratt, G. A., 40 Salakhutdinov, R. R., 125 Proserpio, D., 577n5 Salomons, A., 23n1, 309, 555 Puga, D., 480 Samuelson, P., 376 Santaeulalia-Llopis, R., 329 Raghaven, M., 518 Saon, G., 25 Ramaswamy, R., 291, 293n2 Sawyer, J., 597 Ramlogan, R., 480 Saxenian, A.L., 4, 478 Rao, J. M., 516, 536, 541n2 Schierholz, H., 322 Rasmussen, W. D., 200, 203, 205n6, 206 Schmidhuber, J., 73, 76 Rauch, J. E., 469 Schmidt, K. M., 358n8 Rebelo, S., 241n4, 295 Schmitt, J., 322, 379 Recht, B., 80 Schmitz, J. A., 43 Redding, S., 293n2 Schmucki, R., 497 Reinsdorf, M. B., 29 Schultz, P. H., 414 Rennie, J., 80 Schumpeter, J., 148 Restrepo, P., 23n1, 43, 90, 105, 127, 152, 197, Schwartz, M., 568 201, 202, 203, 203n4, 204, 204n5, 206, Scotchmer, S., 121, 496, 502 208, 210, 211, 212n7, 219, 220, 220n11, Scott, S. L., 528 223, 224, 225n14, 238, 240, 241, 243, Seater, J. J., 241 271, 283, 376n27, 379, 554 Segal, I., 414, 569, 574 Rhode, P. W., 204, 205, 206 Seira, E., 514 Rhodes, E., 325 Seshadri, A., 205, 243n6 Rivera-Batiz, L. A., 477, 478 Shah, A. K., 517 Rivkin, J., 161 Shaked, A., 589n1 Robbins, H., 78 Shavell, S., 494n2 Roberts, J., 43, 111 Shaw, J. C., 123 Robinson, P. M., 524, 527 Shiller, B. R., 410 Rock, D., 557 Shroff , R., 516 Rodrik, D., 293 Silver, D., 63, 66, 453 Roesner, F., 446 Simcoe, T., 487 Rogerson, R., 204, 241n4 Simmons, J. P., 426 Romanosky, S., 444, 450, 457, 457n42 Simon, H. A., 107, 123, 207–8 Romer, P. M., 46, 122, 149, 153, 155n2, 159, Sims, C., 113 169, 171, 171n9, 172, 255, 477, 478 Simsek, A., 362n10 Rosenberg, N., 150, 169 Singer, Y., 80 622 Author Index

Slovic, P., 596 Taddy, M., 83, 526, 527 Smith, A., 590 Tadelis, S., 107, 577n5, 578, 579, 580, 581, Smith, M. D., 43 582 Smith, N., 29 Tang, J., 453 Sokoloff , K. L., 430 Taylor, C. R., 416, 424, 441n2, 448, 459, Solomonoff , R. J., 253n12 483n19 Solove, D., 447, 455 Tegmark, M., 382, 384n34 Soloveichik, R., 29 Teh, Y.-W., 74 Solow, R. M., 24, 46, 350 Telang, R., 444, 450, 457, 457n42 Somanchi, S., 444 Teodoridis, F., 150, 161 Song, J., 30 Tetenov, A., 527 Sonnenschein, H., 589n1 Thaler, R., 604 Sopher, B., 590n2 Thomas, K., 443 Sorensen, O., 161 Thomas, P., 528 Spencer, B. J., 473 Thompson, W. R., 82 Spezio, M., 594n10 Thrnton, B., 598 Spiegel, M., 589n1 Tibshirani, R., 93, 525, 527 Spiegel, Y., 412 Tirole, J., 106, 112, 264, 268, 269 Spier, K., 494, 499, 502 Topol, E. J., 94, 95 Spiess, J., 169n8, 511, 512 Tory, J., 485n23 Spindler, M., 522 Toulis, P., 80 Srivastava, N., 80 Trajtenberg, M., 4, 39, 116, 119, 132, 150, Stahl, D. O., 593 169, 176n2 Stantcheva, S., 360 Treﬂ er, D., 465, 467, 479, 485n23 Stein, A., 494 Troske, K. R., 562 Stern, A., 325 Tschantz, M. C., 433 Stern, S., 122, 141 Tucker, C. E., 148, 425, 426, 427, 428, 429, Stevenson, B., 194n5 448, 449, 482, 483, 483n19 Stigler, G. J., 439, 582 Turing, A. M., 123, 385 Stiglitz, J. E., 30n9, 354, 360, 362, 364, Tuzel, S., 210 364n11, 366n14, 366n15, 368n19, 369, Tversky, A., 596 370, 370n21, 371, 373, 376n27, 376n28, 378, 412 Ugander, J., 538 Stillwell, D., 429 Uyarra, E., 480 Stinchcombe, M., 74 Stivers, A., 442n4 Vadlamannati, K. C., 483 Stole, L., 268 Valentinyi, Á., 204, 241n4 Strehl, A., 527 Van der Laan, M. J., 522, 527 Streitwieser, M. L., 562 Van Seijen, H., 62, 84 Strotz, R. H., 427 Varian, H. R., 93, 310, 410, 413, 424, 425, Stucke, M. E., 414 440, 511 Stutzman, F., 451 Venables, A. J., 480 Suﬁ , A., 208 Vesteger, M., 420 Summers, L. H., 175 Vickrey, W., 567 Sutskever, I., 68, 74, 125 Vijverberg, W. P. M., 483 Sutton, J., 467, 589n1 Vincent, N., 270 Swaffi eld, J., 293n2 Vines, P., 446 Swaminathan, A., 528 Vinge, V., 238, 253, 253n12, 373n23 Sweeney, L., 433 Viscusi, K., 496, 498 Swire, P. P., 441n2 Vishnu, A., 151n1, 168 Syverson, C., 23, 29, 43, 210, 319, 320 Von Hippel, E., 499 Author Index 623

Von Weizacker, C. C., 376 Wright, J. R., 594 Von Winterfeldt, D., 595 Wu, L., 43 Vuong, Q., 514 Xiao, M., 593n8 Wager, S., 523, 525, 527, 528 Xie, D., 241n4, 295 Wagman, L., 416, 424, 441n2, 448, 459, Xu, H., 577 483n19 Wallach, I., 116 Yakovlev, E., 538 Wan, M., 534 Yang, S., 42, 47 Wang, J., 475, 477, 594n10 Yellen, J., 378 Warren, S. D., 431, 459 Yeomans, M., 517 Waseem, M., 270 Yildirim, P., 107n9 Wattal, S., 450 Yu, M., 465, 479 Weil, D., 459 Yudkowsky, E., 253n12 Weingast, B. R., 577 Weiss, A., 354 Zanna, L.-F., 373n24 Weitzman, M., 149, 151, 157, 171, 171n9, Zeevi, A., 529 172, 241 Zeileis, A., 525 Western, B., 322 Zeira, J., 152, 198, 212n7, 238, 239, 241 Westlake, S., 18 Zervas, G., 577n5 Whinston, M. D., 148 Zhang, M. B., 210 White, H., 74, 511 Zheng, Y., 329 Williams, H., 122, 364n11 Zhou, D., 529, 581 Williams, R., 124 Zhou, X., 580 Wilson, P. W., 593 Zhu, X., 471 Winter, S., 161 Zierahn, U., 321 Wolfers, J., 195n5 Zubizarreta, J. R., 523 Wooldridge, J. M., 522 Zweimüller, J., 295 Wright, G., 42n19 Zwiebel, J., 268 Wright, G. C., 303

Subject Index

Note: Page numbers followed by “f” or “t” refer to fi gures or tables, respectively. adoption, technological: implications of 351; inequality and, 320–23; internal speed of, for job market and inequality, agreements and, 463; international 310– 12 macroeconomics and, 488; knowledge adversarial artifi cial intelligence, 401 externalities and, 470–71; likely produc- aggregate productivity statistics, technolo- tivity eff ects of, and acceleration of, 45– gies and, 26–28 46; longer-term prospects of, 381–86; AI. See artifi cial intelligence (AI) market structure and, 262–63; as “next AlphaGo (Google), 63 big thing,” 175; political economy of, Amazon Go concept, 67 11, 394–95; prediction costs and, 92–93; applied artifi cial intelligence, 208 privacy and, 425–26; privacy concerns artifi cial intelligence (AI), 1; and automa- and, 423–24; for promoting trust in tion of production, 239–50; as basis online marketplaces, 576–81; recent for learning, 120–21; benefi t of more, approach to, 93; for reducing search 318–20; bibliometric data on evolution frictions, 581–83; return of Malthus of, 128–32; capital shares and, 270–74, and, 381–86; revolution, international 272–73f; in context, 84–85; defi ned, eff ects of, 393–94; in Schumpeterian 3–4, 62–67, 93, 122, 237, 468; econo- model with creative destruction, 276– mies of scale from data and, 468–69; 79; sectoral reallocation and, 263–64; economies of scale from overhead statistics on, 465–66, 466t; studies on of developing AI capabilities, 469– economic eff ect of, 556– 58; theory of 70; evolution of, 122–25; expected privacy in economics and, 424–26; as productivity eff ects of, 45– 46; firm tool, 16–17; world’s largest companies organization and, 264–70; future of and exposure to, 465–67, 467t. See also research on economics of, 17; as general machine learning (ML) purpose technology, 4–7, 39–41; in idea artifi cial intelligence capital, measuring, production function, 250–52; impact 46– 50 of, on innovation, 125–28; impact of artifi cial intelligence–general purpose tech- long-term decline in labor force partici- nology (GPT) era: education strategies pation rate and, 323–25; implications for, 179–82; human-enhancing innova- of, 349–53; income distribution and, tions vs. human-replacing innovations

625 626 Subject Index artifi cial intelligence–general purpose tech- consumer attitude, 448–49 nology (GPT) era (continued) consumer privacy: challenging issues in, for, 184–85; professionalization of 457–59; consumer attitude and, 448–49; personal services strategies for, 182–84; consumer risk and, 443–48; data risk top skills required for employment in, and, 442–43; nature of problem of, 442; 180– 81, 181t policy landscape in United States, 454– artifi cial intelligence revolution, inter- 57; supply side actions and, 450–54. national eff ects of, 393– 94 See also privacy Atomwise, case of, 115–16, 120, 154 consumer surplus, 11; distribution of, automatic teller machines (ATMs), security 391– 93 policy and, 416 contracting, 106–7 automation, 3–4, 105–6; basic model, 336– convolutional neural networks (CNNs), 41; Baumol’s cost disease and, 241–50; 75– 76, 75n6 to date, and capital shares, 270–74; cooperation, evolution of, 414 decline in labor share and, 329–31; cost disease, Baumol’s, 8–9, 238–39; auto- deepening of, 198, 204–5, 216–17; eco- mation and, 241–50 nomic adjustment and, 208–9; employ- creative destruction, 260–61 ment and, 190–91; excessive, 224–26; model of, 211–14; of production, and data, 61; acquisition methods, 403–4; de- artifi cial intelligence, 239–50; produc- creasing marginal returns of, 406, 407f; tivity and, 210–11; sector of economy economics of, 14; importance of, 13– aff ected by, 330–33; studies on employ- 14; important characteristics of, 404–6; ment on, 555–58; wages and, 200–211; localization rules, trade policies and, winners, 190; work and, 200–211; Zeira 485–86; persistence of predictive power model of growth and, 239–41 of, 427–28; privileged access to govern- average treatment eff ects, 522– 24 ment, trade policies and, 486–87; types of, and generation of spillovers, bandits (algorithms), problem of, 528–29 431– 34 Baumol’s cost disease. See cost disease, data access, 405–6 Baumol’s data generation, as pillar of artifi cial intel- BenchSci search technology, 153 ligence, 62f, 65–66 buy/make decisions (fi rm boundaries), data ownership, 405–6 107– 8 data persistence, 426–27; predictive power and, 427–28 capital accumulation, 198, 204–5, 216 data pipeline, 402 capital shares, and automation to date, data pyramid, 404, 405f 270– 74 data repurposing, 428–31 causal inference, new literature on, 519–34 data security: challenging issues in, 457– Children’s Online Privacy Protection Act of 59; policy landscape in United States, 1998 (COPPA), 454 454– 57 cloud- computing facilities, 402– 3 data spillovers, 431–34 cluster policies, 480– 81 data warehouses, 402–3 clusters, regional, theory of, 479 decision- making, baseline model for, 95– CNNs (convolutional neural networks), 75– 103; complexity and, 103– 8 76, 75n6 deepening of automation, 198, 204– 5, collusion, strategies for facilitating, 413– 14 216– 17 combinatorial- based knowledge production Deep Genomics, 154 function, 154–61; potential uses of new, deep learning, 3, 71–77, 400; as general pur- 170–71; with team production, 161–67 pose invention in method of invention, Communications Act (1986), 456 139–43; as general purpose technology, competition policy, innovations and, 141–43 133–39; as new discovery tool, 167–69; complexity, 103–4 patent systems and, 142 Subject Index 627 deep learning networks, 94 GPT. See general purpose technology (GPT) deep learning techniques, 25 Gramm-Leach- Bliley Act (GLBA), 454 deep neural networks (DNNs), 25–26, 61, growth: impact of artifi cial intelligence on, 63; structure in, 76–77 7– 9. See also economic growth demand, importance of, 301–2 destruction, creative, 260–61 Health Insurance Portability and Account- diff erence- in-diff erence models, 530–31 ability Act of 1996 (HIPAA), 454 digital information, 334–35 HEI (human-enhancing innovations), direct network eff ects, 412 184– 85 displacement eff ect, 8, 198, 208, 214 heterogeneous treatment eff ects, 524– 28 DNNs. See deep neural networks (DNNs) hierarchical Poisson factorization, 510 domain structure, as pillar of artifi cial intel- HIPAA. See Health Insurance Portabil- ligence, 62f, 63–65 ity and Accountability Act of 1996 double machine learning, 523–24 (HIPAA) HRI (human-replacing innovations), 184–85 economic growth: artifi cial intelligence and, human-enhancing innovations (HEI), 184–85 262–70; prospects for technology- human-replacing innovations (HRI), 184–85 driven, 149–53; Zeira model of automation and, 239–41. See also growth idea production function, artifi cial intelli- economics, impact of machine learning on gence in, 250–52 practice of, 15–16 implementation/ restructuring lags, as expla- education, factory model of, 180 nation for Solow paradox, 31–36 Electronic Communications Privacy Act of incentive auctions, machine learning and, 1986: (ECPA), 456 569– 76 employment: automation and, 190–91; income, artifi cial intelligence and, 189–90 levels of, and new technologies, 220–21; income distribution: artifi cial intelligence long-run vs. short run, 192–94; studies and, 351; impact of AI on, 11 on automation and, 555–57; work out- income inequality: artifi cial intelligence and, side of, 194–95 320–23; impact of AI on, 7–8, 11–12; evolution of cooperation, 414 speed of technological adoption and, 310– 12 factory model of education, 180 income redistribution, political economy of, Federal Trade Commission (FTC), 454–55 394– 95 fi rm boundaries (make/buy decisions), indirect network eff ects, 412 107– 8 industrial regulation, trade policies and, 487 fi rm-level data: need for, 558–59; strategies inequality. See income inequality for collecting, 561–62 information technology (IT), 24 fi rm-level research questions, 560–61 innovation, 115–18; competition policy and, fi rms: artifi cial intelligence and, 262–70; 141–43; early stage, 121–22; impact impact of machine learning on, 12 of artifi cial intelligence on, 125–28; institutions and, 141–43; management general purpose machine learning (GPML), and organization of, 140–41; product 67–71 liability and, 494 general purpose technology (GPT), 2, 65, institutions, innovations and, 141–43 119– 20, 169– 70; artifi cial intelligence intelligence- assisting innovation (IA), as, 4–7, 39–41; deep learning as, 133– 350– 51 39; viewing today’s Solow paradox International Federation of Robotics through previous history of, 44–45 (IFR), 16 generative adversarial networks (GANs), international macroeconomics, artifi cial 66– 67 intelligence and, 488 GPML (general purpose machine learning), international trade, economics of data 67– 71 and, 14 628 Subject Index invention of a method of inventing (IMI), machine learning– provision industries, 120– 21, 124 414– 15 inverted-U pattern, 293; simple model of, machine learning– using industries, 408f; 297– 301 boundaries and, 409– 10; fi rm size and, 409– 10; price diff erentiation and, 410– JDM (judgment and decision-making) re- 11; pricing and, 410; returns to scale search, 596–98 and, 411– 14; structure of, 406– 8 job displacement, 310–12 macroeconomics, international, artifi cial job losses, 291 intelligence and, 488 job markets, speed of technological adop- make/buy decisions (fi rm boundaries), tion and, 310–12 107– 8 jobs, impact of artifi cial intelligence on, 7–8, Maluuba, 62– 63 9– 11 market design, introduction to, 567– 69 judgment: in absence of prediction, 96–101; massive open online courses (MOOCS), as complements/substitutes to predic- 181 tion, 102–3; creating role for, 91; predic- matrix completion problem, 531–32 tion and, 91–92 matrix factorization, 51, 511 judgment and decision-making (JDM) meta technologies, 153 research, 596–98 ML. See machine learning (ML) model averaging, 511 knowledge creation, 477–79 MOOCS (massive open online courses), knowledge externalities, and artifi cial intel- 181 ligence, 470–71 knowledge spillovers, 479 neural networks, 72–74, 123, 124–25, 510, 511 labor: comparative advantage of, and new new economic geography (NEG), 479–80 tasks, 217–18; model of demand for, 211–14 online marketplaces, using artifi cial intelli- labor demand, technology and, 214–21 gence to promote trust in, 576–81 labor productivity growth, technologies and, optical lenses, invention of, 121 26– 28 optimism, sources of technological, 24–26 learning by doing, 412– 13 liability, innovation and, 494; empirical evi- Pareto improvement, 363 dence on, 496– 98; theoretical model of, patent systems, deep learning and, 142 494– 96 Pen Register Act (1986), 456 liability, tort, development of artifi cial intel- policy analysis, using methods of prediction ligence technologies and, 498–502 for, 516–19 political economy: of artifi cial intelligence, machine learning (ML), 3, 24–25; applica- 11; of income redistribution, 394–95; tions of, 401–2; defi ned, 509– 10; of technological disruptions, 176–79 double, 523–24; early use cases of, 510– prediction: in absence of judgment, 101–2; 15; for fi nding behavioral variables, artifi cial intelligence as tool for, 16–17; 587–96; general purpose, 67–71; human as complements/substitutes to judg- prediction as imperfect, 496–603; im- ment, 102–3; costs of, and AI, 92–93; pact of, 12–13, 15–17, 507–9; incentive judgment and, 91–92; using methods auctions and, 569–76; new literature of, in policy analysis, 516–19 on, 519–34; overview, 399–406; predic- premature deindustrialization, 393–94 tions about impact of on economics, price discrimination, artifi cial intelligence 534–42; regulation and, 12–15; super- and, 604 vised, 511–12; unsupervised, 510–11; principal components analysis, 74, 92, vertical integration and, 408–9. See also 510 artifi cial intelligence (AI) privacy, 13–14; artifi cial intelligence and, Subject Index 629

425–26; current models of economics search frictions, artifi cial intelligence for and, 424–25; data spillovers and, 431. reducing, 581–83 See also consumer privacy security policy, 416 privacy policy, 416–17 singularities, 253–61; examples of techno- privacy regulation, trade policies and, logical, 254–58; objections to, 258–61 482– 85 skills: mismatch of technologies and, 221– privileged access to government data, trade 23; technologies and, 209 policies and, 486–87 Solow paradox, 24; potential explanations production, automation of, and artifi cial for, 28–31 intelligence, 239–50 source code, trade policies and, 487–88 productivity: automation and, 210–11; miss- spectrum reallocation, 569–76, 572f ing growth of, and new technologies, spillovers, data, 431 223– 26 spreadsheet software, invention and impact productivity eff ects, 198, 203– 4, 214– 16 of, 90 productivity growth: low current, reasons stochastic gradient descent optimization, why it is consistent with future techno- 77– 81 logical growth, 41–44; rates of, tech- strategic trade policy, 473–74 nologies and, 26–28; slow, and future structural change, 293–96 productivity growth, 31–36 structural models, 532–34 productivity optimism, technology-driven superstar scientists, role of, 474–76 case for, 36–39 supervised machine learning, 511; methods for, 511–12 radiology, case of, 94–95 supplementary analysis, 530 random forest, 511 support vector machines, 511 R&D. See research and development (R&D) symbolic processing hypothesis, 123 recommender systems, 603 symbolic systems, 123 regional clusters, theory of, 479 regression trees, 511 tasks, new, 205–7; comparative advantage regularization on norm of matrix, 510 of labor and, 217–18; creation of, 198; regularized regression, 511 model of, 211–14 regulation, 12–15; machine learning and, technological changes: factor-biased, 376– 12– 15 77; and levels of employment, 220–21; reinforcement learning, 25, 66, 81–84, 400– types of, 212–13 401 technological disruptions, political economy reinstatement eff ect, 8, 198, 206 of, 176–79 research and development (R&D), 336; pro- technological growth, reasons why it is con- ductivity, eff ects of rise in, 341– 43 sistent with low current productivity research tools, economics of new, 118–22 growth, 41–44 robotics, 123–24; tort law and, 493–94. technological optimism, sources of, 24–26 See also robots technological progress: channels of inequal- robots, studies on, 554–55. See also robotics ity and, 365–70; determining scenarios Romer/Jones knowledge production func- that best describe economy, 363–64; tion, 151–52 endogenous, 364–65; fi rst- best scenario, 353–56; imperfect markets scenario, scale, economies of, and artifi cial intelli- 361–62; perfect markets but costly gence, 470 redistribution scenario, 358–61; perfect Schumpeterian model with artifi cial intel- markets ex post and no costs of redis- ligence, 276–79 tribution scenario, 356–58; welfare and, scientifi c discovery, rate of, 6 353– 65; worker- replacing, redistribu- scientists, role of, 472–73; superstar, 474–76 tion and, 370–77 scope, economies of, and artifi cial intel- technological singularities, examples of, ligence, 470 254– 58 630 Subject Index technological unemployment, 377–81 universal basic income (UBI), 312–14; cost technologies: future progress of, and low of replacing current safety net with, current productivity growth, 41–44; 325– 26 labor demand and, 214–21; mismatch unsupervised machine learning, 510–11 of skills and, 221–23; skills and, 209 USA Patriot Act (2001), 456 technology-driven economic growth, prospects for, 149–53 vertical integration, machine learning and, tort law, robotics and, 493 408–9 tort liability, 14 vertical research spillovers, 122 total factor productivity growth, 32 trade models, basic, 476–77 wages, automation and, 200–211 trade policies: data localization rules and, welfare, technological progress and, 353–65 485–86; industrial and strategic, case work, automation and, 200–211 for, 471–81; industrial regulation and, worker-replacing technological progress: 487; privacy regulation and, 482–85; dynamic implications of, 373–74; redis- privileged access to government data tributing innovators’ surplus and, 374– and, 486–87; role of university-related 76; redistribution and, 370–77; static talent, 472–76; source code and, 487– pecuniary externalities of, 371–72 88; strategic, 473–74 Zeira model of automation and growth, UBI. See universal basic income (UBI) 239–41 unemployment, technological, 377–81 “zero-shot” learning systems, 66