TECHNOLOGICAL UN/EMPLOYMENT by Camilla A. Hrdy * Assistant Professor of Law University of Akron School of Law Draft dated November 20, 2017 Updated Draft Available on SSRN: https://ssrn.com/abstract=3011735
ABSTRACT
Advances in artificial intelligence and robotics threaten the fate of the human worker. Intellectual property (IP) plays a role in this transition—and it’s not what we might expect. According to the Patent & Trademark Office, “IP-intensive industries” create more and higher paying jobs than other industries. State and local governments also operate under this assumption, providing major subsidies to tech companies that agree to locate in the region in the hopes they will hire local workers. The insight of this article, however, is that a significant subset of the innovations covered by IP, from manufacturing and warehousing robots to self-driving cars, significantly reduce the need for human labor. They are labor-displacing as well as labor-saving. Therefore, to the extent IP works as an incentive to innovate in these areas, IP actually contributes to what this article calls “technological un/employment”—the simultaneous creation and elimination of jobs resulting from technological advance. Historically, innovation has created more jobs than it has destroyed. But job loss is an inevitable externality of market-driven innovation. The fear of impending catastrophic labor displacement has generated proposals to tax companies that replace humans with machines. This article shows that IP law itself can be used to effectuate similar goals by altering incentives to innovate.
* With thanks for invaluable insights from Bruce Boyden, Molly Brody, Stewart Graham, Julia Knight, Alex Lemann, Mark Lemley, Michael Madison, Alan Marco, Matiangai Sirleaf, Robert Merges, Ion Meyn, Kali Murray, Tejas Narechania, Peter Norman, and Ted Sichelman. Thanks also to attendees and organizers of IP Scholars 2017 at Cardozo Law and the Marquette Junior Faculty Works-In-Progress Conference.
TABLE OF CONTENTS I. Introduction II. Technological Un/employment – A Framework A. Labor-Saving Innovation – A Double Edged Sword 1. Automation – Labor Without Humans 2. A Tendency Towards Partial (Not Complete) Automation 3. Technologies That Permit Self-Service B. Technological Employment – Why Humans Still Have Jobs 1. Job Generation 2. Demand-Boosting 3. Spillovers C. Technological Unemployment – Why This Time May Be Different 1. More And Better Automation 2. Decreasing Quality of Work Draft dated November 20, 2017 2 TECHNOLOGICAL UN/EMPLOYMENT
3. Increasing Inequality III. Intellectual Property’s Impact on Technological Un/employment A. Privilege Regimes B. Modern Intellectual Property Rights C. Intellectual Property’s Impact on Technological Un/employment 1. The Incentive Effect 2. The Distribution Effect IV. The Case For A “Pro-Employment” Intellectual Property Regime A. The Hayekian View – Intellectual Property As Handmaiden of the Market B. Job Loss As An Externality of Innovation C. Prior Proposals For Correcting The Externality 1. Universal Basic Income 2. Taxes On Companies That Automate 3. Tax Credits For Companies That Add Jobs D. Employing Intellectual Property Law 1. Subject Matter Bars For Labor-Displacing Inventions 2. Fast Track Procedures For Labor-Generating Inventions E. Coordinating Intellectual Property and the Tax System 1. Labor-Displacing Patent Tax 2. Labor-Generating Patent Credits F. Altering Incentives To Automate Without Killing Incentives To Innovate V. Conclusion Appendix: Labor-Saving Patents
IP-intensive industries directly accounted for 27.9 million jobs either on their pay-rolls or under contract in 2014[.] [T]hey also indirectly supported 17.6 million more supply chain jobs throughout the economy. In total, IP-intensive industries directly and indirectly supported 45.5 million jobs, about 30 percent of all employment.
U.S. Patent & Trademark Office, Intellectual Property and the U.S. Economy, 2016
We are being afflicted with a new disease of which some readers may not yet have heard the name, but of which they will hear a great deal in the years to come—namely, technological unemployment. This means unemployment due to our discovery of means of economising the use of labour outrunning the pace at which we can find new uses for labour.
John Maynard Keynes, Economic Possibilities for our Grandchildren, 1930
I. Introduction In 1589, William Lee visited Queen Elizabeth I, seeking an interview to request a patent for his new stocking frame knitting machine. The machine’s major benefit to humanity was that it would reduce the hours spent on hand knitting clothing and other cloth items. The Queen refused to grant the patent, observing, “Thou aimest high, Master Lee. Consider thou what the
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invention could do to my poor subjects. It would assuredly bring to them ruin by depriving them of employment, thus making them beggars.” Lee thereafter failed to obtain a patent in France and again in England, when Elizabeth’s successor James I also denied his patent for the same reason: mechanization of knitting would put people out of work, and a patent to operate the technology would therefore be contrary to the people’s interests.1 Fast forward over four hundred years. The U.S. Patent & Trademark Office (USPTO) has just issued its latest report on the impact of intellectual property rights on the economy and the workforce. According to the USPTO report, “IP-intensive industries”—defined as industries that rely heavily on intellectual property rights, including patents, trademarks, or copyrights2—create more jobs than other industries, and wages are 47% higher.3 The report’s conclusions appear to vindicate the predictions of legislators, during the latest round of patent reform, that intellectual property rights “spur innovation and create jobs.”4 The Obama White House made similar assertions, issuing a formal statement that reforming the patent system would not “just increase transparency and certainty for inventors, entrepreneurs, and businesses, but help grow our economy and create good jobs.”5 Which story is right? Do intellectual property rights “create good jobs” for “inventors, entrepreneurs, and businesses”? Or do intellectual property rights in fact “depriv[e] [people] of employment, thus making them beggars?” This article seriously considers this question, and seeks to bring the broader discussion of innovation’s impact on human work into the intellectual property field.6 The article’s main insight is as follows. Even if intellectual property rights are somehow causally responsible for job creation for people who work in “IP-intensive” industries such as pharmaceuticals, motion pictures, and robotics7, the subject matter of a subset of those intellectual property rights are labor-saving, and more specifically, labor-displacing,
1 This story is documented in DARON ACEMOGLU & JAMES ROBINSON, WHY NATIONS FAIL 182-83 (2012). See discussion in Part III.B. 2 Trade secrets were not measured in the USPTO Report. Empirical studies on trade secrets, while not impossible to conduct, are relatively rare for various reasons, not least of which that trade secrets are kept secret. Michael Risch, Empirical Methods in Trade Secret Research in PETER S. MENELL & DAVID L. SCHWARTZ (EDS.), RESEARCH HANDBOOK ON THE ECONOMICS OF INTELLECTUAL PROPERTY LAW, VOL. II (2016). 3 INTELLECTUAL PROPERTY AND THE U.S. ECONOMY: 2016 UPDATE, UPDATING INTELLECTUAL PROPERTY AND THE U.S. ECONOMY: INDUSTRIES IN FOCUS, 2012 (“USPTO REPORT”). See Part III.C. 4 To give just one example, Senator Udall stated to his colleagues in discussions surrounding the America Invents Act of 2011 (AIA) that “as we work to rebuild our economy, get Americans back to work, and win the global economic race, we should all appreciate this effort to spur innovation and create jobs.” Statement by Senator Udall, March 8, 2011, 3 Patent Reform A Legislative History of the America Invents Act William H. Manz ed. S1348 2012 (“Patent reform is an important issue for Colorado's economy and, of course, our national economy. High-tech innovators represent over 12,000 jobs in Colorado, and they are an important part of our economic recovery.”). 5 Administration of Barack Obama, 2011, Statement on Senate Passage of Patent Reform Legislation, March 8, 2011, 2 Patent Reform: A Legislative History of the Leahy-Smith America Invents Act (William H. Manz, ed.) 1 (2012). 6 Mark Lemley recently tackled a related but distinct question: what is the role of IP law in producing artificial scarcity when technological developments like automation and 3D printing reduce the costs of production so low that it becomes difficult to make a profit? Lemley discusses the impact of some of these new technologies on employment but only tangentially. See Mark Lemley, IP in a World Without Scarcity, 90 N.Y.U. L. REV. 460 (2015) 7 This is a major “if”, which I explore in Part III.C. See also USPTO REPORT, supra note 3, at 25-29 (listing the major industries identified as being “IP-intensive”).
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innovations.8 Labor-displacing innovations, in turn, are responsible for what economists call technological unemployment—job loss that results from technological change.9 Autonomous vehicles provide one striking example of technological unemployment occurring today, in real time.10 Companies like Uber, Alphabet, and Tesla are striving feverishly to perfect “self-driving” vehicles, and rely heavily on intellectual property rights (primarily patents. trade secrets, and trademarks) in order to achieve the excess rents of a right to exclude others from copying and competing in this space.11 The result is higher returns for the owners of intellectual property rights in self-driving vehicles, and higher wages for the roboticists and engineers whose skills are necessary to generate this intellectual property.12 But self-driving vehicles, if successfully commercialized, could spell the end of paid employment for taxi drivers, Uber drivers, truck drivers, and millions of other people whose jobs involve driving for a living. The threat to these workers’ livelihoods is arguably one of the major social crises of the day.13 Are the intellectual property rights that helped give rise to self-driving vehicles in some sense responsible for these lost jobs? Are they in some sense responsible for the unequal division of rewards between “IP-generating workers”14 and everyone else? Without overstating the causal relationship between intellectual property rights and innovation, I suggest that, at least at the margins, intellectual property facilitates the process by which innovation puts some people out of work, and exacerbates the divide between the “have-jobs” and the “have-nots.”15 The implications of this connection for IP scholars should be striking, perhaps disturbing. If intellectual property is successful in its primary aim to promote innovation, the subject matter of some of those intellectual property rights, from self-driving cars to “self-driving databases”16—may be responsible for eliminating the jobs of others. While legislators highlight intellectual property’s “job creation” potential, intellectual property, like innovation itself, has a double-sided impact on employment. Both sides of this process—what the article terms “technological un/employment”—must be considered in order to understand what is happening in the innovation economy and intellectual property’s role in this process. The normative question, of course, is what do about this? If the pace of labor-saving innovation, counterintuitively, risks toppling the current structure of society, how should the law respond? Several prominent businesspeople have argued that some form of government action may be necessary. Tesla’s CEO Elon Musk has stated his view that “[artificial intelligence] is the biggest risk that we face as a civilization”, and speculated that policymakers should try to slow
8 I use the term “labor-saving innovation” to refer to innovations that reduce the amount of labor required to complete a task that would otherwise be performed by a human. “Labor-displacing innovations” are a subset of labor-saving innovations: they drastically reduce the amount of labor required to complete a task that is or would otherwise be performed by a paid human worker, and thus lead to significant worker displacement. See Part II.A. 9 I provide a brief history of the concept of technological unemployment at the beginning of Part II and discuss the concept in depth in Part II.C. 10 I discus this example at length in Part III.C.2. 11 I explain IP’s exclusion-incentive mechanism in Part III.B. 12 I assess evidence for the hypothesis that IP contributes to higher wages for IP-generators in Part III.C.2. 13 See, e.g., Brishen Rogers, The Social Costs of Uber, 82 U. CHI. L. REV. DIALOGUE 85, 101 (2015). See also discussion in Part IV.B. 14 I define IP-generating workers in Part III.C.2. 15 Part III explores the complex relationship between intellectual property and technological un/employment in depth. 16 Oracle is currently running an advertising campaign for a fully automated databases, referring to them as “World’s First ‘Self-Driving’ Database.” I discuss this example of a labor-displacing innovation in Part II.A.1.
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down development and potentially even give people a living stipend (called a “universal basic income”) to help them get along without paid work.17 Microsoft founder Bill Gates has made similar statements. Especially if companies “are going to cross the threshold of job replacement [sic] all at once,” we ought to be willing to use policies like taxation to at least “slow down the speed” of automation.18 This article’s thesis, that intellectual property itself impacts the amount and pace of automation, yields another surprising insight. Rather than a “robot tax” like Gates proposes19, we could, at least theoretically, use intellectual property law itself to affect incentives to innovate. If a company seeks a patent for an invention that the company unabashedly proclaims will allow adopters to eliminate 1,000,000 jobs in two years, maybe that patent should not be granted. If my thesis is correct, denying patents for labor-displacing technologies would not actually stop innovators from inventing them. But, similar to a tax, it might marginally deter and slow down the pace of invention, commercialization, and adoption.20 The article proceeds in three parts. Part II lays out the current debate over innovation’s impact on employment, and explain the consensus that innovation tends to both create and destroy jobs.21 Drawing on research by economists and economic historians, I explain the various mechanisms by which innovation is theorized to create jobs—a process I call technological employment.22 I then explain why some people are currently contend that we are entering a new phase where technological employment will be outpaced by technological unemployment.23 Part III explores how intellectual property rights should be expected to affect the process of technological un/employment. Applying standard intellectual property theory, I hypothesize that intellectual property has two related types of effects on the process of technological un/employment: the Incentive Effect, under which intellectual property magnifies existing market incentives to invent and commercialize labor-displacing technologies24, and the Distribution Effect, under which intellectual property exacerbates the division between IP- generators and non-IP generators and the related division between “high-skill” and “low-skill” workers observed by economists.25 Both of these effects are purely theoretical, but they generate
17 Tim Higgins, Tesla Boss Warns on Artificial Intelligence, THE WALL STREET JOURNAL, Monday July 17, 2017, B1. Musk speculates the solution might have to be a universal basic income, saying that “[t]here is a pretty good chance we end up with a universal basic income, or something like that, due to automation… I am not sure what else one would do. I think that is what would happen.” Catherine Clifford, Elon Musk: Robots Will Take Your Jobs, Government Will Have To Pay Your Wage, CNBC.COM, Nov. 4, 2016 https://www.cnbc.com/2016/11/04/elon- musk-robots-will-take-your-jobs-government-will-have-to-pay-your-wage.html On National Public Radio (NPR) Musk stated that "AI is a fundamental existential risk for human civilization, and I don't think people fully appreciate that.” He also stated his concern that " by the time we are reactive in AI regulation, it's too late.” See http://www.npr.org/2017/07/17/537686649/elon-musk-warns-governors-artificial-intelligence-poses-existential-risk 18 See “The robot that takes your job should pay taxes, says Bill Gates”, QUARTZ, February 17, 2017, https://qz.com/911968/bill-gates-the-robot-that-takes-your-job-should-pay-taxes/ 19 See Part IV.C.2. 20 See Part IV.D. 21 See Part II.A. 22 See Part II.B. 23 See Part II.C. 24 See discussion in Part II.C.1. 25 See discussion in Part II.C.2. See also ERIK BRYNJOLFSSON & ANDREW MCAFEE, AGAINST THE MACHINE: HOW THE DIGITAL REVOLUTION IS ACCELERATING INNOVATION, DRIVING PRODUCTIVITY, AND IRREVERSIBLY TRANSFORMING EMPLOYMENT AND THE ECONOMY 39-47 (2011).39 (2011) (“Even when technological progress
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testable predictions. I review existing evidence and find some preliminary support for both hypotheses.26 Part IV switches to the normative, addressing what policymakers should do differently in light of the connection between intellectual property and technological un/employment. If intellectual property’s role is merely to prop up innovation already occurring in the market then no action should be taken at all.27 But I show that job loss can be conceptualized as a negative externality of labor-saving innovation—suggesting that in fact corrective measures may be appropriate, even for those who see intellectual property through the lens of the free market.28 I show that, counterintuitively, the intellectual property regime can provide an avenue for altering incentives to generate labor-displacing innovations in the first place.29 II. Technological Un/employment Just over twenty years ago, the popular writer Jeremy Rifkin wrote in his book, The End of Work, that “high-tech automated production” is “[l]ike a deadly epidemic inexorably working its way through the marketplace…destroying lives and destabilizing whole communities in its wake.” Rifkin predicted the end or near-end of manual labor in factories within the “next twenty to thirty years.”30 These sorts of concerns have a long pedigree.31 The Luddite riots of 1811- 1816 occurred because workers feared the introduction of textile machinery would threaten their jobs.32 In the late nineteenth century Karl Marx predicted that an “automatic system of machinery” would eventually replace human laborers altogether.33 During the Great Depression John Maynard Keynes wrote about the “disease” of “technological unemployment” afflicting the
increases productivity and overall wealth, it can also affect the division of rewards, potentially making some people worse off than they were before the innovations.”). 26 See Part III.C.1-2. 27 See Part IV.A. 28 See Part IV.B. 29 See Part IV.D. 30 See JEREMY RIFKIN, THE END OF WORK: THE DECLINE OF THE GLOBAL LABOR FORCE AND THE DAWN OF THE POST-MARKET ERA 3, 8-9 (1995). Rifkin has since followed up these predictions with his book, RIFKIN, THE MARGINAL COST SOCIETY: THE INTERNET OF THINGS, THE COLLABORATIVE COMMONS, AND THE ECLIPSE OF CAPITALISM (2011), in which he argues that technologies that enable an ever-lower costs of production “might bring marginal costs to near zero, making goods and services priceless, nearly free, and abundant, and no longer subject to market forces.” The book is blogged on by Professor Brett Frischmann at http://www.huffingtonpost.com/brett- frischmann/fixed-costs-zero-marginal-cost-society_b_5124945.html 31 See Joel Mokyr, Chris Vickers, & Nicolas L. Ziebarth, The History of Technological Anxiety and the Future of Economic Growth: Is This Time Different? 29 J. ECON. PERSP. 31, 31-50 (2015). See also FORD, supra, at 29-34 (discussing concerns over job loss as a result of technology in the 1960s and 70s); RIFKIN, THE END OF WORK, supra, at 81-89 (discussing concerns over automaton in the same period). 32 See recounting of Luddite riots in ACEMOGLU & ROBINSOn, supra note 1, at 310. 33 See RIFKIN, THE END OF WORK, supra, at 16-17 (discussing Marx’s argument that increasing productivity would necessarily lead to the gradual replacement of humans with machines) (discussing Karl Marx, Das Kapital: Kritik der politischen Ökonomie, 1867).
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contemporary workforce.34 And so on.35 The issue has been widely studied in the field of economics36 and in public policy.37 And yet clearly humans still have jobs.38 The oft-stated reason is that technology creates new jobs even as it takes away old ones. For example, “[t]he invention of, say, the internal combustion engine put buggy-whip makers out of business, but it created many more jobs in the manufacture, advertising, sales and maintenance of automobiles.” 39 In order to reflect this reality, I use what I hope is a more accurate if verbally unwieldy term—“technological un/employment—in order to mitigate negative connotations that come with the term technological unemployment, and avoid the erroneous conception that technological developments like automation necessarily destroy jobs on an aggregate level. To give a simple example of how the two-sided process works, do a Google search for the term “self-driving car.” Searching for the phrase “self-driving car jobs” provides results for job postings40 and artilces with titles like “Who's hiring for self-driving car jobs”?41 A Google search for the similar phrase “self-driving car kill jobs” reveals results about the negative impact of autonomous vehicles on jobs and ideas for how to save the jobs of human drivers.42 How is it that a single technology can have such a disparate perceived impact on social welfare? This part of the paper explains precisely how both sides of this process work. In doing so it provides a framework for predicting how technological un/employment may play out for current and unforeseen technological shifts in the years to come. A. Labor-Saving Innovation – A Double Edged Sword The driving force behind technological un/employment is innovation. The typical economics definition of innovation is applying new ideas in order to generate “value” in a commercial enterprise—with value typically being measured in the form of higher profits.43 Value can be realized in two ways: either by generating some benefit for which consumers are
34 Mokyr et al, supra note 27, at 31-32. See also John Maynard Keynes, Economic Possibilities for our Grandchildren (1930). 35 For further discussion of other predicions see Moykr et al supra note 27, at 31-56. 36 See, e.g., Carl Benedikt Frey & Michael A. Osborne, The Future of Employment: How Susceptible Are Jobs To Computerisation?, Oxford Martin School, University of Oxford, September 17, 2013, at 5-13 (discussing a long line of economics research on technology’s impact on jobs). See also CLAUDIA GOLDIN & LAWRENCE KATZ, THE RACE BETWEEN EDUCATION AND TECHNOLOGY (2008). 37 See, e.g., DARRELL M. WEST, WHAT HAPPENS IF ROBOTS TAKE JOBS? THE EMERGING IMPACT OF ROBOTS ON EMPLOYMENT AND PUBLIC POLICY, CENTER FOR TECHNOLOGY INNOVATION AT BROOKINGS, October 2015. 38 See David Autor, Why Are There Still So Many Jobs, 29 J. ECON. PERSP. 3, 3-4 (2015) (discussing history of concerns about technological unemployment and reasons there are still jobs). 39 Lewis M. Andrews, Robots Don’t Mean the End of Human Labor, THE WALL STREET JOURNAL, August 24, 2015, at A13. 40 Indeed: Google Self Driving Car Project Jobs, https://www.indeed.com/q-Google-Self-Driving-Car-Project- jobs.html 41 Marco della Cava, Who's hiring for self-driving car jobs, October 17, 2016, USA TODAY, https://www.usatoday.com/story/tech/news/2016/10/17/google-ford-not-only-names-self-driving-car- jobs/92315206/ 42 See, e.g., Mark Fahey, Driverless cars will kill the most jobs in select US states, CNBC.COM, Friday, 2 September 2016; Jack Stewart, Robot & Us: Self-Driving Trucks Are Coming To Save Lives and Kill Jobs, WIRED, May 5, 2017. 43 Innovation’s “value” is multifaceted and depends on one’s reference point. Innovation’s perceived benefits for different sectors of society are discussed in more detail in the citations in note 40 infra.
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willing to pay or by increasing productivity by lowering cost per output.44 These two types of innovation are often divided into two distinct types. The first type is a “product innovation,” a new good or service consumers want.45 The second type is a process innovation, the introduction of a new technique for production that allows a reduction in costs.46 The pill (birth control) and optical lenses, for example, are product innovations.47 The printing press is a process innovation—it drastically lowered publishers’ cost per output of reproducing written materials.48 Not all innovations necessarily reduce the need for human labor. They may possess advantages that have nothing to do with labor reduction. Anesthesia, for example, invented in 1846, was, in the words of James Fallows, a first step towards “distinguish[ing] surgery from torture[.]”49 Its purpose is to make people healthier and more comfortable, not to reduce labor. However, an important subset of innovations are explicitly labor-saving— meaning that their very goal is to reduce or eliminate the human labor required to complete a task.50 Process innovations, by definition, reduce the costs of achieving a given output, which often means reducing the human labor required, which in turn means hiring fewer workers.51 Think, for example, of a farm that begins using tractors, and many fewer people, to harvest tomatoes, or a factory that begins using robots instead of human workers to weld steel.52 Product innovations like the pill or anesthesia are generally viewed as more “labor-friendly.”53 They tend to create a new kind of benefit that consumers want, which in turn will likely require hiring workers to meet that demand. Nonetheless, product innovations are not always labor-friendly.54 For example, if
44 See Camilla A. Hrdy, Patent Nationally, Innovate Locally, 31 BERKELEY TECH. L. J. 1301, 1310-11 (2017) (citing CHRISTINE GREENHALGH & MARK ROGERS, INNOVATION, INTELLECTUAL PROPERTY, AND ECONOMIC GROWTH 4-5 (2010)). See also Stuart Graham, Cheryl Grim, Alan Marco & Javier Miranda, Business Dynamics of Innovating Firms: Linking U.S. Patents with Administrative Data on Workers and Firms, U.S. CENSUS BUREAU, CENTER FOR ECONOMIC STUDIES (CES), July 2015, at 2 (stating that innovations are either “new products or services that satisfy a previously unmet need or processes that provide existing goods and services in new and more efficient ways”). 45 A product innovation means the introduction of a new product or service, or a significant improvement on an existing product or service, for which consumers are willing to pay. GREENHALGH & ROGERS, supra note 40, at 4. 46 A process innovation means the introduction of a process or method of operation that reduces the cost of outputs—for instance, by reducing the amount of time or labor required to make some output. Id. See also id. at 9 (explaining how product and process innovations are related). 47 James Fallows, The Fifty Greatest Breakthroughs Since The Wheel, The Atlantic, November 2013, at 7. Available at https://www.theatlantic.com/magazine/archive/2013/11/innovations-list/309536/ 48 Id. at 5, 16. 49 Fallows, supra, at 4. 50 C.f. MERRIAM-WEBSTER DICTIONARY, https://www.merriam-webster.com/dictionary/laborsaving (defining labor- saving as “adapted to replace or decrease human labor”). 51 GREENHALGH & ROGERS, supra note 40, at 4. See also James Bessen, How Computer Automation Affects Occupations: Technology, Jobs, and Skills (October 3, 2016), Boston Univ. School of Law, Law and Economics Research Paper No. 15-49, https://ssrn.com/abstract=2690435, at 10 (“Technological innovations sequentially improve discrete steps in production processes over a long period of time. Labor is affected when technology automates discrete tasks.”). 52 Claire Cain Miller, The Long-Term Jobs Killer Is Not China. It’s Automation, THE NEW YORK TIMES, Dec. 21, 2016, https://www.nytimes.com/2016/12/21/upshot/the-long-term-jobs-killer-is-not-china-its-automation.html (“Over time, automation has generally had a happy ending: As it has displaced jobs, it has created new ones.”). 53 See, e.g., Vincent Van Roy, Daniel Vertesy & Marco Vivarelli, Innovation and Employment in Patenting Firms: Empirical Evidence from Europe, IZA DP No. 9147, June 2015, at 3 (“While these controversies [over technological unemployment] center on the overall employment effect of process innovations, there is less debate about the positive employment effect of product innovations.”). 54 See id. (“[E]ven the labor-friendly impact of product innovation may be more or less powerful.”).
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everyone goes out and buys a Roomba (an automated vacuum cleaner), then fewer humans will be needed to vacuum floors, and fewer people will pay for maid services.55 Labor-saving innovations are good for the companies that adopt them to reduce costs. They are also good for the companies that make, sell, or license labor-saving innovations. For example, iRobot Corp., the company that invented Roombas sold a million Roombas within a year of releasing the product. The product sells for as much as 700 dollars.56 iRobot Corp. can also make revenues by licensing other companies the right to make and sell Roombas, since it owns the intellectual property required to do so.57 Labor-saving innovations also tend to be seen as good for human happiness and wellbeing. Few people would want to spend their time vacuuming floors if they could choose to do something else, the thinking goes, so the Roomba is making life better.58 But labor-saving innovations are not always good for workers. If the labor saved by the innovation is, or would otherwise be, performed by a (paid) human worker, then the innovation may not just labor-saving. It may be labor-displacing. I define labor-displacing innovations, a subset of labor-saving innovations, as innovations that drastically reduce the amount of labor required to complete a task that is or would otherwise be performed by a paid human worker, and thus lead to drastic worker displacement. Importantly, the decision whether to call an innovation labor-saving or labor-displacing is subjective and at least in part political. For example, Oracle, one of the most profitable software companies in the world,59 is currently running an advertising campaign for the “Oracle Automonous Database,” which “offers total automation based on machine learning and eliminates human labor, human error, and manual tuning.” The advertisement shown below has run in the print edition of The Wall Street Journal and on the internet.60
55 See Hiawatha Bray, Robots taking over in warehouses, The Boston Globe, May 1, 2017, at 3 (discussing iRobot Corp., the maker of the popular Roomba automated vacuum cleaner). Available at https://www.bostonglobe.com/business/2017/05/01/robots-taking-over- warehouses/PV9i4ZDvBc9mtHSSAvRGjJ/story.html 56 Jolie Kerr, The History Of The Roomba, FORTUNE, November 29, 2013, http://fortune.com/2013/11/29/the- history-of-the-roomba/ 57 iRobot Corp. has several patents, including U.S. Patent No. US7761954B2, for an “Autonomous surface cleaning robot for wet and dry cleaning.” It also has trademark registrations. See U.S. Trademark Registration No. 2742056. 58 iRobot Corp.’s slogan for Roomba is “Cleaner floors. Every day. All at the push of a button.” https://content.abt.com/documents/66782/ComparisonChart_Roomba.pdf 59 http://www.oracle.com/us/corporate/oracle-fact-sheet-079219.pdf 60 This ran in the print edition of The Wall Street Journal, November 16, 2017, A1. The ad is also on the company’s website, https://www.oracle.com/database/autonomous-database/feature.htmland
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This invention’s primary purpose is to eliminate human labor, and human error, and to reduce the costs of operating a database. Whether this invention is labor-saving or labor- eliminating depends on whom you ask. Workers and management will have different ideas, as likely will liberals and conservatives. Therefore, in this article, I often use the terms “labor- saving” and “labor-displacing” interchangeably, while recognizing that others may disagree with my characterizations, and that not all labor-saving innovations are necessarily labor-displacing. 1. Automation – Labor Without Humans Almost any technological shift can ultimately displace some jobs. For example, if we move away from coal mining to using other sources of energy, this shift inherently means there will be fewer workers in the coal mining industry. Workers in that industry will have been displaced. But this type of ordinary creative destruction is not particularly concerning for aggregate labor policy because the jobs have not gone away; they have just gone elsewhere. The main type of labor-displacing innovation, even in the time of the Luddites, has come from a certain kind of technological development: “automation.” While not all automation would constitute a labor-displacing innovation, the discussion below will show that some types of automation seem guaranteed to fall into this category if they don’t already. Automation is a term of art that refers to using non-human technology, such as a robot, a computer, an algorithm, to accomplish a commercial task that would otherwise be carried out by a human.61 The agent of automation need not be a robot that looks like a human. It simply must, in an economic sense, “substitute” for a human in performing work.62
61 See Bessen, supra note 43, at 3 (“Automation of an occupation happens when machines take over one or more tasks, either completely performing those tasks or reducing the human labor time needed to perform them.”); see also, e.g., Raja Parasuraman et al., A Model for Types and Levels of Human Interaction with Automation, 30 IEEE TRANSACTIONS ON SYSTEMS, MAN AND CYBERNETICS (2000). See also Frey & Osborne, supra, at 2, n. 1 (“We refer to computerisation as job automation by means of computer-controlled equipment.”). 62 Jack Balkin, The Three Laws of Robotics in the Age of Big Data, 78 OHIO STATE L.J. at 8-9 (2017) (“[R]obots, AI agents, and algorithms substitute for human beings. They operate as special purpose people.”).
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Automation can be tempting for businesses because it can lower costs by reducing the amount of paid human labor required to achieve an output and improve performance significantly, by improving accuracy, quality, and speed, and in some cases by achieving “superhuman” capabilities.63 Innovation, in turn, can be expected to respond to these market forces. To give a simple example, it is not hard to see why cost-conscious restaurants would want to automate the task of cooking and preparing food by using a robot instead of a human cook— this would drastically lower costs and allow restaurants to satisfy orders at an unprecedented pace. Start-ups such a Momentum Machines, whose robot can make 400 hamburgers in an hour and save restaurants up to $90,000 a year, have begun selling and using these machines in the marketplace.64 Although cost-cutting is typically associated with private companies like Tesla and Google, innovations in automation also come from universities and governmental entities in the public sector, which are under pressure to save taxpayer money.65 For instance, in her engaging story in The New Yorker, Sheelah Kolhatkar describes a regional government in Denmark that has begun integrating robotics into its hospital system in order to reduce costs.66 Rather than human orderlies, the government’s robotics team took “small, mobile robots with movable arms, designed for use in warehouses, and refashioned them, so that they could carry supplies to doctors and nurses.”67 Since humans were costing the hospital “thirty-three to thirty four dollars an hour,” and the robots cost “under a dollar per hour,” this led to significant cost-savings for public taxpayers.68 With cost-cutting and efficiency advantages like these, it is no surprise innovation is moving towards more, and better, automation. Several important technologies are currently in
63 MCKINSEY GLOBAL INSTITUTE, A FUTURE THAT WORKS: AUTOMATION, EMPLOYMENT, AND PRODUCTIVITY, January 2017, at 3 (“MCKINSEY REPORT”) (“With recent developments in robotics, artificial intelligence, and machine learning, technologies not only do things that we thought only humans could do, but also can increasingly do them at superhuman levels of performance.”). Id. at 11 (“The deployment of automation technologies could bring a range of performance benefits for companies. … They include, but are not limited to, greater throughput, higher quality, improved safety, reduced variability, a reduction of waste, and higher customer satisfaction.”) 64 In 2012, a robotics startup called Momentum Machines invented a machine that can make “400 made-to-order hamburgers in an hour. … [T]he robot can slice toppings, grill a patty, and assemble and bag the burger without any help from humans.” Using the robot allows a restaurant to “replace two to three full-time line cooks” and save “up to $90,000 a year in training, salaries, and overhead costs. The company is now opening a restaurant using the machine. See Melia Robinson, This robot-powered restaurant could put fast food workers out of a job, BUSINESS INSIDER, June 30, 2016, http://www.businessinsider.com/momentum-machines-is-hiring-2016-6 65 Kolhatkar, Dark Factory, supra, at 70-81 (observing that automation for purposes of cost-cutting is not limited to the private sector). See also Cade Metz, Teaching AI Systems To Behave Themselves, THE NEW YORK TIMES, August 13, 2017 (discussing AI research being done in Google and Tesla labs as well as at Stanford and Berkeley), https://www.nytimes.com/2017/08/13/technology/artificial-intelligence-safety-training.html
Significant literature within the IP field discusses the impact of “market pull” on the direction of innovation at universities, whose funding can come from private entities as well as taxpayers, especially when the results can be patented. See, e.g., Brett Frischman, The Pull of Patents, 77 FORDHAM L. REV. 2143, 2159 (2009) (symposium essay discussing how patent incentives can aggravate the market’s pull on the direction of university research by biasing companies in favor of research with market potential). 66 Id. at 74. 67 Id. 68 Id.
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use, or being developed, that can perform tasks otherwise done by humans.69 The first, most obvious example is the human-like robot that can perform human functions.70 Along with food preparation71, robots are now capable of performing a range of functions, from driving a vehicle, to milling steel72, to testing phones and electronics73, to boat racing74, to providing mental therapy.75 According to the Brookings Institute, there will be an estimated 1.9 million robots in commercial as of 2017, and robotics is expected to rise from a $15 billion sector now to $67 billion by 2025.76 And then there are all the automation technologies that do not resemble humans at all. Take drones, so-called “unmanned aerial vehicles.” 77 Drones can now perform a huge range of jobs formerly or still performed by humans. To name only a few: package delivery78, going to war79, crop-dusting80, disaster aide81, insurance claims inspection.82 According to World Economic Forum, commercial drone sales are expected to rise from $2.5 million in 2016 to $7 million in 2020.83 Probably the most powerful new form of automation comes from the computer revolution. Algorithms implemented on general purpose computers are increasing their capacity to take on human tasks, especially when combined with the massive amount of data obtained through the internet.84 Quantifying algorithms’ use in the economy is virtually impossible because they are used everywhere, and often in secret, with little transparency as to their function and capability.85 Computer algorithms have permitted the near-total automation of a variety of
69 See generally WEST, supra note 36, at 2, 2-6. 70 See generally WEST, supra note 36, at 2-4. 71 Robinson, supra note 53, at 1. 72 Biesheuvel, supra note 58, at 16. 73 T-Mobile’s custom-built phone-testing robot “Tappy” got a lot of press and eventually was part of a trade secret dispute when workers at Huawei stole Tappy’s mechanical finger. See, e.g., T-Mobile v. Huawei, 2017 WL 951065 (W.D. Wash. Mar. 10, 2017). 74 Metz, supra, at 1. 75 See Megan Molteni, The Chatbot Therapist Will See You Now, WIRED, June 7, 2017, https://www.wired.com/2017/06/facebook-messenger-woebot-chatbot-therapist/ (“Created by a team of Stanford psychologists and AI experts, Woebot uses brief daily chat conversations, mood tracking, curated videos, and word games to help people manage mental health.”). 76 WEST, supra note, at 2. 77 See generally WEST, supra, at 6. On drones and privacy issues, see generally, Margot Kaminski, Drone Federalism, 4 CAL. L. REV. CIRCUIT 57 (2013). 78 Elizabeth Weizse, Amazon delivered its first customer package by drone, USA TODAY, Dec. 14,2016, https://www.usatoday.com/story/tech/news/2016/12/14/amazon-delivered-its-first-customer-package- drone/95401366/ 79 John Yoo, Embracing the Machines: Rationalist War and New Weapons Technologies, 105, CALIF. L. REV. 101 (2017); but see Rebecca Crootof, The Killer Robots Are Here: Legal and Policy Implications, 36 CARDOZO L. REV. 1837 (2015) (exploring legal implications of autonomous weapons systems including unclear liability). 80 See marketing of “Spraying Drone” at http://sprayingdrone.com 81 Ambulance drones are used to rapidly deliver defibrillators to people in cardiac arrest. Drones To The Rescue, THE NEW YORK TIMES, June 19, 2017, https://www.nytimes.com/2017/06/19/health/drones-by-air.html 82 Nicole Freidman, Drones Speed Up Insurance Claims, THE WALL STREET JOURNAL, Saturday/Sunday, August 5- 6, 2017, at B1. 83 World Economic Forum, Future of Drones and Tomorrow's Airspace, https://www.weforum.org/projects/civil- drones-for-tomorrow-s-commerce 84 See generally WEST, supra note 36, at 4. 85 On increasing use of algorithms and secrecy, see FRANK PASQUALE, BLACK BOX SOCIETY (2013).
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tasks that would otherwise be done by humans, including internet searching,86 data collection, processing and analysis,87 stock picking,88 and designing investment strategies.89 Use this info on medical industry? Medical monitoring Entire market for healthcare Remote patient monitoring, measure and healthcare automation projected to biological, chemical, or physical analytics systems90 surpass $50 billion by processes92, drug delivery93, analysis 201591 of medical data94, read radiology reports95
2. A Tendency Towards Partial (Not Complete) Automation At a conceptual level, automation can be either “complete” or only “partial”.96 Complete automation occurs when a machine fully replaces the entire occupation of a human by performing all tasks the human previously performed.97 Potential examples on the horizon include self-driving cars and delivery drones. Partial automation is where a machine is used only to perform some of the tasks of an occupation performed by a human, such as where a CEO uses
86 Barry Schwartz, How Google uses machine learning in its search algorithms, SEARCH ENGINE LAND, October 18, 2016 (discussing Google’s use of search algorithms to improve internet searching, both with and without human assistance), available at https://searchengineland.com/google-uses-machine-learning-search-algorithms-261158 87 The Oracle autonomous database, mentioned above, is a compelling example. https://www.oracle.com/database/autonomous-database/feature.htmland 88 Bailey McCann, The Artificial-Intelligent Investor, THE WALL STREET JOURNAL, Monday, November 6, 2017, at R13(discussing increasing use of AI in stock picking and investment management). 89 Using algorithms to give financial advice for individual clients is also becoming a reality. See Hugh Son, Robot Advisers Can Be Conflicted, Too, BLOOMBERG BUSINESSWEEK, July 31, 2017, at 28-29 (“Wealth management units at big U.S. banks including Morgan Stanley and Bank of America Corp. have rushed to build so-called robo-adviser services.”). 90 See generally WEST, supra note 36, at 5-6. 91 This estimate is by Transparency Market Research (TMR). See http://www.prnewswire.com/news- releases/healthcare-automation-market-to-reach-us5898-billion-by-2025-smarter-technologies-up-the-game-for- automated-solutions-in-healthcare-industry-622761234.html 92 WEST, supra, at 5-6. See also Mike Montgomery, The Future Of Health Care Is In Data Analytics, FORBES, Oct. 26, 2016. 93 Claire Chapuis, Pierrick Bedouch, Maxime Detavernier, Michel Durand, Gilles Francony, Pierre Lavagne, Luc Foroni, Pierre Albaladejo, Benoit Allenet, & Jean-Francois Payen, Automated drug dispensing systems in the intensive care unit: a financial analysis, 19 CRIT. CARE 318 (2015) (measuring costs saved by implementing automated drug dispensing systems in intensive care units). Notably, Google Patents brings up 208,836 results for patents on a “drug delivery system”. 94 “Health care analytics” means using big data and advanced processing tools to predict healthcare outcomes and streamline other aspects of healthcare. See http://www.mckesson.com/healthcare-analytics/healthcare-analytics/ See also McKinsey’s website for its Health Care Analytics & Delivery group, http://www.mckinsey.com/solutions/healthcare-analytics 95 Tom Simonite, IBM’s Automated Radiologist Can Read Images and Medical Records, MIT TECHNOLOGY REVIEW, Feb. 4, 2016, https://www.technologyreview.com/s/600706/ibms-automated-radiologist-can-read-images- and-medical-records/ (discussing new software such as “Avicenna” that can “read medical images and written health records” and “could help radiologists work faster and more accurately.”) 96 See Bessen, How Computer Automation Affects Occupations: Technology, Jobs, and Skills, supra note 43, at 4-5. 97 Bessen, supra, at 5-7 (discussing the difference between complete and partial automation).
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email or a “virtual assistant” to set up appointments rather than her secretary, but still uses her secretary for other tasks like dealing with publicity, getting coffee, or greeting clients.98 According to Boston University economist and economic historian James Bessen, partial automation has been the norm, while complete automation has been rare. 99 Indeed, most of the technologies depicted above are not capable of performing, or are not used to perform, a job without any human involvement. For example, even in steel factories, where robots perform all mining and milling operations, humans are still used to install, operate, and supervise the robots that do the milling.100 According to Bessen, this difference is “critical” because complete automation “implies a net loss of jobs; partial automation does not.”101 In other words, automation can affect an industry, profession, or one person’s job, but not necessarily lead to that industry, profession, or job’s disappearance. One common type of partial automation is technology that enables customers to serve themselves rather than relying on the skills of a paid employee. As Bessen observes, self-service is not technically the same as “automation” because self-service technology does not actually permit more output per human input. Someone still has to do the work—it’s just that it’s the customer rather than a paid employee.102 Examples of shifts to self-service abound. They include the check-out kiosks at grocery stores and pharmacies, self-service kiosks at airports, online ticket sales that do not require going through a human agent, and using online marketplaces to select and purchase consumer items like clothing, rather than going to a brick and mortar store.103 Bessen suggests that the negative impact of self-service on labor should be less severe than automation, since someone still has to do the job. 104 But I am not sure this logic follows. Even if a technology does not fully replace a profession or even a certain task with a machine, this can still negatively impact remaining human work. First, partial automation can make a task so efficient that it reduces the overall quantity of people who must be hired in a certain profession.105 For instance, even if it doesn’t automate the entre job of a farm laborer, a harvesting machine can make farm laborers’ role smaller, and thus reduce hiring in the profession. Second, as I discuss more fully in Part III.C., partial automation can significantly reduce the quality of the remaining tasks that humans perform in terms of wages and job satisfaction.
98 Lynn Peril, Do Secretaries Have A Future? THE NEW YORK TIMES, April 26, 2011 (“[W]e’re living through the end of a recession in which around two million administrative and clerical workers lost their jobs after bosses discovered they could handle their calendars and travel arrangements online and rendered their assistants expendable.”) 99 Bessen, How Computer Automation Affects Occupations: Technology, Jobs, and Skills, supra note 43, at 4-5. 100 See Thomas Biesheuvel, 500,000 Tons of Steel. 14 Jobs. A mill in Austria shows how automation in steelmaking augues less employment—but better conditions, BLOOMBERG BUSINESSWEEK, June 26, 2017, at 16 (describing a nearly deserted steel mill except for “three technicians who sit high above the line, monitoring output on a bank of flatscreens.”). 101 Id. at 5. 102 Bessen, supra note 43, at 7 (“[T]his is not an example of automation. Shifts such as these represent a change in who is performing the work without necessarily changing the amount of labor required per unit output.”). 103 For a case study analysis of the shift to self-service in grocery stores, see MCKINSEY REPORT, supra, at 53-65 (noting that “self-checkout kiosks are becoming commonplace and are a short step from automatic payment and shipping.”). 104 Bessen, supra note 43, at 7. 105 See id. at 6.
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For example, farm laborers who once performed high skill functions that required extensive experience may be reduced to the role of picking up the remaining tomatoes left behind by a harvesting machine. Lastly, partial automation, can bleed into total replacement once the capacity of the machines improves or some other factor makes using machines more attractive than it used to be. For example, self-service technologies that let people “do it themselves” can very quickly turn into automation technologies that let the “machines do it instead”. Take the example of self- service check out. Some pharmacies and grocery stores already use self-service kiosks that let customers check out on their own. But now Amazon is developing cashierless checkout that allows customers to enter a store and leave without ever waiting in line or entering payment information.106 This is not self-service or partial automation. The entire task of making a payment has been automated. This trend may well become more common.107 B. Technological Employment A question that is often asked lately is: if technology is advancing rapidly and permitting ever-increasing amounts of automation, why are there still jobs at all? 108 The reason, as numerous economists and economic historians have documented, is that most labor-displacing innovations create new jobs even as they eliminate others. I call this process “technological employment.” Below I identify the three main mechanisms discussed in the literature for how technological employment occurs. These mechanisms are very important for explaining both why we still have jobs despite the mechanization of the Industrial Revolution, and why, at a theoretical level, economists, policymakers, and the USPTO can safety claim that innovation “creates good jobs.” 1. Job Generation The first mechanism of technological employment is job generation. Job generation refers to where an innovation adds jobs into the economy by generating new demand for skills or facilitating peoples’ ability to meet current demand for skills. The most basic example of job generation is the invention of a new product or process that generates demand for a certain type of skill set. Invention of anesthesia, for example, created demand for anesthesiologists trained in administering anesthesia. Invention of the tractor created demand for people with the skills needed make, use, maintain, install, and operate farm machinery.109 Today, developments in artificial intelligence has contributed to skyrocketing demand for software engineers, roboticists, and other AI talent.110 Job generation can also happen when innovation opens up new ways to serve existing demand for skills. A contemporary example is the ride-sharing industry created by Uber
106 “AmazonGo” is currently being tested to Amazon employees in Seattle. Laura Stevens, Amazon Delays Opening of Cashierless Store to Work Out Kink: Retailer must fix glitches in technology that automatically charges customers, THE WALL STREET JOURNAL, March 27, 2017 107 See ALEC ROSS, THE INDUSTRIES OF THE FUTURE 78-89 (2017) (discussing new ways to make payments without going through cashiers or in some cases without using banks). 108 Autor, Why Are There Still So Many Jobs?, supra, at 4 (“Clearly, the past two centuries of automation and technological progress have not made human labor obsolete[.]”). 109 CLAUDIA GOLDIN & LAWRENCE KATZ, THE RACE BETWEEN EDUCATION AND TECHNOLOGY 176-179 (2008) (discussing hiring and jobs in companies like National Cash Register Company, General Electric, and Deere Tractor Company). 110 Cade Metz, Artificial intelligence has contributed to skyrocketing demand for AI talent, THE NEW YORK TIMES, Oct. 22, 2017, at B1.
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Technologies, which allows car owners to enlist with Uber and drive people around for pay.111 Uber’s ride-sharing technology has facilitated the paid work of millions of drivers, some of whom would not otherwise have jobs.112 Another example is “Wag!”, an app which allows people to apply for jobs as dog walkers, and matches them with dog owners seeking walkers.113 This innovation has not necessarily created new demand for dog walkers, but it has enhanced dog walkers’ ability to meet existing demand. (As I’ll explain in a bit, it may also do the former). Pure job generation is like alchemy, creating jobs where there were none. But in reality job generation does not happen in a vacuum. Even as a new technology creates jobs for some, it may take away the jobs of others. The tractor creates jobs for people who can make and operate tractors, but threatens manual farm jobs.114 Uber opens up millions of jobs for car owners who can now drive for pay, as well as for a few thousand highly educated workers at Uber’s San Francisco headquarters, like software engineers, product designers, and lawyers.115 But Uber’s business also threatens the jobs of traditional taxi drivers, who now have to compete with a larger pool of suppliers of driver services. This does not mean innovations like the tractor or Uber are necessarily net job destroyers. They may well be net job generators, so long as they create substitute occupations116 that require sufficiently similar skills as the old ones that have gone away.117 If pure job generation is like alchemy, job substitution is like an exodos. Taxi drivers can now be Uber drivers. Manual laborers who once worked on farms can move into other industries that require similar skills and education.118
111 Uber is now operating in around 377 major cities around the world. Paul Gil, See How Uber Works and the Pros and Cons, July 1, 2017 https://www.lifewire.com/how-does-uber-work-3862752 112 Though precisely how many drivers Uber hires is unclear, former Uber CEO Travis Kalanick bragged in 2014 that “in 2015 alone Uber will generate over 1 million jobs in cities around the world.” Alison Girswold, How to Speak Uber: Brag about all the jobs you’re creating. Ignore the fact that they’re not actual jobs, Slate, April 16, 2015 (“http://www.slate.com/articles/business/moneybox/2015/04/uber_says_it_ll_generate_1_million_jobs_this_year_de pends_how_you_define.html See also Rogers, supra, at 89 (“Uber may be creating what once appeared impossible: a functioning market for car-hire services that is governed largely by supply and demand.”). Id. at 98-99 (discussing whether Uber drivers qualify as employees or independent contractors). 113 See Laine Higgins, Want A Job As A Dog Walker? It’s Just Like Getting Into Harvard, The Wall Street Journal, Friday, Oct. 6, 2017, at A1. 114 For discussion of technological un/employment surrounding substitution of the tractor for farm horses and the mechanization of agriculture generally, see Derek Thompson, How the Tractor (Yes, the Tractor) Explains the Middle Class Crisis, March 13, 2012, The Atlantic, https://www.theatlantic.com/business/archive/2012/03/how-the- tractor-yes-the-tractor-explains-the-middle-class-crisis/254270/ (“It would be churlish to suggest that the most important consequence of the tractor was the elimination of farm horse jobs. 115 https://www.bizjournals.com/sanfrancisco/slideshow/2017/01/03/meet-san-francisco-10-biggest-employers.html 116 Bessen, How Computer Automation Affects Occupations: Technology, Jobs, and Skills, supra note 43, at 9-10 (defining an occupation as a bundle of tasks that can be performed by people with similar skills and observing that tasks can be transferred from one occupation to another). 117 .As Bessen puts it, even if an innovation reduces jobs in one industry, it can offset these losses by generating “job growth in different occupations or industry segments.” Bessen, Don't Blame Technology for Persistent Unemployment, SLATE, Sep. 30, 2013 (arguing technology does not have only negative impacts on employment in part because of the creation of substitute jobs); Bessen, Toil and Technology, FINANCE & DEVELOPMENT, March 2015, Vol. 52, No. 1 (arguing that “innovative technology is displacing workers to new jobs rather than replacing them entirely”). 118 Thompson, supra, at 1 (“Workers have been flowing away from our most productive (and most mechanized) industries like manufacturing and toward our least productive service jobs like nursing and teaching for decades
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2. Demand-Boosting The second mechanism of technological employment is what I refer to as “demand- boosting.”119 Demand-boosting predicts that hiring will increase as a result of labor-saving process innovations that permit more output at lower cost, and consequently lower prices. This in turn increases consumption and creates new demand for output and hiring of workers.120 Demand-boosting arguments are usually made in conjunction with job generation arguments. The idea is that both mechanisms occur at once: demand for a company’s output rises in response to increasing productivity and falling prices, and new or substitute jobs then emerge that need to be filled in order to meet that new demand.121 Technology writer Greg IP gives the example of the development of Lotus 1-2-3, Microsoft Excel, and other ‘smart’ spreadsheets. These programs made it far easier to manipulate large amounts of data regarding businesses’ costs and revenues, and totally altered the painstaking job of bookkeepers. “Suddenly, you could change one number—say, this year’s rent—and instantly recalculate costs, revenues and profits years into the future.”122 By simplifying the task of bookkeeping, spreadsheets “pummeled demand for bookkeepers”, Ip writes, but nonetheless “spreadsheets drove costs down and demand up”, and made “many tasks possible, such as modelling alternate scenarios. This meant new kinds of jobs for workers with different skills. “[P]eople who could run numbers on the new software became hot commodities.”123
now. … The pessimistic take on our employment crisis is that something has changed, and there is no modern manufacturing sector to absorb millions of modern replaced workers.”). 119 GREENHALGH & ROGERS, supra note 40, at 268-69. Another term sometimes used is the “compensation theory.” Van Roy et al, supra, at 2 (“[T]he so-called ‘compensation theory’… puts forward the view that process innovations lead to more efficient production and thus, assuming competitive markets, increasing demand and hence employment.”). 120 Jerry Kaplan of Stanford University, for instance, makes this argument, stating his view that concerns about smart machines replacing human work are overblown because even if today’s robots reduce the need for human labor, they also
[make] the remaining workers more productive and their companies more profitable. These profits then find their way into the pockets of employees, stockholders and consumers (through lower prices). This newfound wealth, in turn, increases demand for products and services, compensating for lost jobs by employing even more people.
Jerry Kaplan, Don’t Fear The Robots, THE WALL STREET JOURNAL, Saturday/Sunday, July 22-23, 2017, at C3. 121 As Bessen puts it,
[l]abor augmenting change does not necessarily lead to job losses in automated occupations for two reasons. First, greater productivity might reduce prices and thus increase product demand, offsetting the labor-saving effect. Second, increasing the productivity of one occupation might induce a substitution with other occupations; work may be transferred to the newly more productive occupation.
Bessen, How Computer Automation Affects Occupations: Technology, Jobs, and Skills, supra note 43, at 3. For another example (one of many), see, e.g., Michael Jones, Yes, The Robots Will Steal Our Jobs. And That’s Fine. Those Jobs Will Be Replaced With New Ones, THE WASHINGTON POST, February 17, 2016, https://www.washingtonpost.com/posteverything/wp/2016/02/17/yes-the-robots-will-steal-our-jobs-and-thats- fine/?utm_term=.580af02df13c (“[A] focus on technology’s substitutionary (or replacement) role fails to appreciate how it also can be complementary. Job loss in some occupations will certainly continue, but it will be accompanied by gains in different fields, just as in the past.”). 122 Greg IP, We Survived Spreadsheets. We’ll Survive AI, THE WALL STREET JOURNAL, Thursday, August, 3, at A2. 123 Id.
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Another example of the “demand-boosting argument” can be seen through the example of the automated burger joint, noted above. Momentum Machines, responding to complaints that its technology will eliminate jobs, says that even if its robotic burger-makers eliminate the jobs of food-line cooks, this may “actually promote job growth” since, if it succeeds in lowering costs and offering cheaper burgers, Momentum Machines will eventually have to expand and “have to hire new employees to grow their technology and to staff additional restaurant locations.”124 In his 1995 book The End of Work, Rifkin was quite skeptical of these claims, derisively calling this the “trickle down technology argument.”125 But at least so far demand-boosting, in conjunction with job generation and/or substitution, appears to be doing a lot of work to offset jobs that labor-saving technology eliminates. We might think, for instance, that the introduction of factories and automation of the production of goods during the Industrial Revolution would have eliminated jobs, as the Luddites feared. But according to economic historians like Joel Mokyr, the shift of production into factories in fact vastly multiplied the number of jobs available.126 For instance, the mechanized plow factories introduced in the nineteenth century127, and the assembly lines introduced by Henry Ford in the early twentieth century128, required far more human workers to operate them than the small artisan shops they replaced.129 As Mokyr puts it, “[i]n the end, the fears of the Luddites that machinery would impoverish workers were not realized, and the main reason is well understood. The mechanization of the early 19th century could only replace a limited number of human activities. At the same time, technological change increased the demand for other types of labor … [including] such obvious jobs as mechanics to fix the new machines,” as well as “jobs for supervisors to oversee the new factory system and accountants to manage enterprises operating on an unprecedented scale.”130 (In the next section, Part II.C., I will talk about what happens when technology can replace more than just a “limited number of human activities.”)
124 Robinson, supra, at 1. 125 “The conventional economic wisdom,” Rifkin states, “is that new technologies boost productivity, lower the costs of production, and increase the supply of cheap goods, which, in turn, stimulates purchasing power, expands markets, and generates more jobs.” RIFKIN, THE END OF WORK, supra, at 15. 126 Frey & Osborne, supra, at 7 (“[D]espite the employment concerns over mechanisation, unskilled workers have been the greatest beneficiaries of the Industrial Revolution.”) (citing Joel Mokyr, THE LEVER OF RICHES: TECHNOLOGICAL CREATIVITY AND ECONOMIC PROGRESS (1990) and GREGORY CLARK, A FAREWELL TO ALMS: A BRIEF ECONOMIC HISTORY OF THE WORLD 194-199 (2007) (discussing the increasing wealth of people in the Industrial Revolution and linking this to increases in productivity). 127 See Frey & Osborne, supra, at 7, n. 9 (“The production of plows nicely illustrates the differences between the artisan shop and the factory. In one artisan shop, two men spent 118 man-hours using hammers, anvils, chisels, hatchets, axes, mallets, shaves and augers in 11 distinct operations to produce a plow. By contrast, a mechanized plow factory employed 52 workers performing 97 distinct tasks, of which 72 were assisted by steam power, to produce a plow in just 3.75 man-hours.”). 128 Id. at 9 (“Crucially, the new assembly line introduced by Ford in 1913 was specifically designed for machinery to be operated by unskilled workers (Hounshell, 1985, p. 239). Furthermore, what had previously been a one-man job was turned into a 29-man worker operation, reducing the overall work time by 34 percent (Bright, 1958). The example of the Ford Motor Company thus underlines the general pattern observed in the nineteenth century, with physical capital providing a relative complement to unskilled labour, while substituting for relatively skilled artisans[.]”). 129 Id. at 7-9. 130 See Mokyr, Vickers, & Ziebarth, supra note 31, at 35.
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To test the demand-boosting prediction, Bessen uses the historic case study of adoption of the automated teller machine (ATM).131 Adopted in the 1970s and 80s, we might think ATM would eliminate the jobs of human bank tellers. But Bessen finds that, even though the ATM “took over cash handling tasks” and reduced work for human tellers, “the number of fulltime equivalent bank tellers has grown since ATMs were widely deployed during the late 1990s and early 2000s. Indeed, since 2000, the number of fulltime equivalent bank tellers has increased 2.0% per annum, substantially faster than the entire labor force.”132 Bessen’s explanation for why employment in the bank teller profession did not fall, and in fact rose, is that “the ATM allowed banks to operate branch offices at lower cost”, which lowered the prices of, and increased demand for, banking services, which “prompted [banks] to open many more branches” to meet the new demand. This led to hiring of both bank tellers and other related jobs in the banking sector, thereby “offsetting the erstwhile loss in teller jobs.”133 3. Spillovers A final, immensely important mechanism of technological employment is spillovers: employment growth in other areas of the economy, particularly in the services sector, as a result of productivity gains in the high-tech sector.134 The general idea is that wherever there are productivity-enhancing innovations and high-paying jobs, there are likely to be higher levels of consumption by workers with disposable incomes. Thus, the economic gains from highly productive industries are spread throughout the overall regional economy.135 In his important book The New Geography of Jobs (2012), labor economist at UC Berkeley, Enrico Moretti, explores the relationship between the tech sector and the non-tech sector, and finds that there are more and better-paying jobs for workers in local services located in so-called “brain hubs”—metropolitan areas with higher shares of college-educated workers, higher shares of patents, and higher salaries.136 Moretti hypothesizes that this “multiplier effect”—spillovers for non-tech workers located in brain hubs—is due in part to the fact that workers in the high-tech sector have more disposable income to spend on local services and more money to spend on services like yoga classes, construction, and real estate.137 “How can the high-tech multipiler effect be so much larger than that of other industries?” Moretti asks. “What is so special about high tech?” The answer, Moretti says, is two-fold. First, high tech workers are “very well paid” in terms of salaries and benefits, and so have more disposable income to spend on local services like hairdressers, therapists, and yoga instructors.138 Second,
131 Bessen, How Computer Automation Affects Occupations, supra note 43, at 1-4. 132 Id. at 6. 133 Id. 134 See, e.g., David Autor and Anna Salomons, Does Productivity Growth Threaten Employment?, June 19, 2017, at 5 (concluding that “the reason that industry-level productivity growth typically raises net employment is because productivity growth in each sector — particularly in services — generates employment growth spillovers elsewhere in the economy. These spillovers are sufficiently large that they more than offset employment losses in industries making rapid productivity gains.”). 135. Id. See also ENRICO MORETTI, THE NEW GEOGRAPHY OF JOBS 13, 55–63, 97-101 (2012). 136. MORETTI, supra, at 55–63 (discussing research suggesting that attracting scientists, software engineers, and other high-skill workers to a region increase demand for local services such as restaurants, hairdressers, therapists, and yoga instructors). 137. MORETTI, supra, at 61–62 (explaining why the “high-tech multiplier effect” is so much larger than that of other industries). 138 Id. at 61. I explore the question of wages in the high-tech sector in Part III.C.2.
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high-tech companies’ operations themselves require many local business services, such as graphic designers, marketers, business consultants, and security guards.”139 The upshot of Moretti’s work is that wherever there is innovation, there are likely to be more and better jobs for others outside the innovation sector. 140 C. Technological Unemployment – Why This Time May Be Different Now for the dark side. Technological unemployment is defined as job loss brought about by technological change.141 As by now is clear, the formula is not as simple as “automation automatically destroys jobs.” And economists who study the issue agree that no matter how good it gets at automating tasks, technology has not yet put everyone out of work. To the contrary, innovation has, on aggregate, created more jobs than it has destroyed.142 But in the last decade or so, prominent commentators have cast doubt on whether what happened in the past, with technological employment outpacing technological unemployment, will hold true in the future. There are several reasons for doubt. 1. More And Better Automation The technological employment mechanisms discussed above—job generation, demand boosting, and spillovers—all require that at least some tasks be left that only humans can perform effectively. But if this is no longer true, if all humans can be replaced with machines, then there is no way for these mechanisms to operate. If, for example, all production is performed by robots, then it does not matter how much new demand is generated by more cost- efficient production and lower prices. There will not be jobs in the industry of production for humans to do. If the switch to machines moves across all industries, then there will be no jobs. Period. This hypothesis—that humans will eventually all be replaced by technology—once seemed outlandish, relegated to the concerns of socialists whose true concern was the low quality and unequal distribution of jobs, not their disappearance.143 But in the past two decades, the increasing pace of developments in robotics and AI, and the vast array of labor-saving innovations already in commercial use, has left many of us (myself included) with the sense that we are indeed at the beginning of a major shift in how human labor is conducted and compensated.144
139 Id. at 61-62. 140. MORETTI, supra, at 61–62. See also id. at 97–99 (finding a positive correlation between the number of skilled workers in the city and the salary of their unskilled neighbors). 141 See definition in Mokyr et al in note 27 supra. 142 See, e.g., Autor and Salomons, supra, at 1 (concluding, in part, that “over the 35+ years of data that we study, we find that productivity growth has been employment-augmenting rather than employment-reducing; that is, it has not threatened employment.”). 143 See discussion of Marx and Rifkin above. 144 See, e.g., ALEC ROSS, INDUSTRIES OF THE FUTURE 27-28 (2017) (“[T]he current moment in the field of robotics is very much like where the world stood with the Internet 20 years ago. We are at the beginning of something: chapter one, page one…”). For just a sampling of recent media articles expressing anxiety about technological unemployment, see, e.g., Nida Najar, Tech Jobs Cut in India. A Reason? Technology, THE NEW YORK TIMES, Monday, June 26, 2017, at B2; Rachel Abrams & Robert Gebeloff, Another Blow For a Battered Work Force: E- Commerce Causes Retail Jobs to Dry Up in Old Steel Towns, THE NEW YORK TIMES, Monday, June 26, 2017, A1; Smarter Machines Cause Mass Unemployment, THE ECONOMIST, http://www.economist.com/news/special- report/21700758-will-smarter-machines-cause-mass-unemployment-automation-and-anxiety; Sheelah Kolhatkar, Dark Factory, THE NEW YORKER, Oct. 23, 2017, at 70-81; Robert C. Allen, Lessons From History For the Future of
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The worry has now gained traction among “establishment” economists. As The New York Time’s Eduardo Porter puts it, “[m]ost economists still reject” the hypothesis that humans, like the work horses of history, may soon become obsolete, “but the conventional economic consensus is starting to fray. The productivity figures may not reflect it yet but new technology does seem more fundamentally disruptive than technologies of the past.”145 In two books, Race Against The Machine (2011) and The Second Machine Age (2014), economists at MIT Erik Brynjolfsson and Andrew McAfee contend that machines, rather than other factors like globalization, are the major source of unemployment today.146 They predict that in the near future, automation—use of machines to perform tasks previously done by humans—will have more pervasive effects on employment that it has in the past.147 “[A]s computers get more powerful,” they write in The Second Machine Age, “companies have less need for some kinds of workers. Technological progress is going to leave behind some people, perhaps even a lot of people, as it races ahead. … there’s never been a worse time to be a worker with only ‘ordinary’ skills and abilities to offer, because computers, robots, and other digital technologies are acquiring these skills and abilities at an extraordinary rate.”148 Economists are not the only ones expressing concern. As mentioned in the introuduction, several famous company executives such as Bill Gates and Elon Must, have chimed in. In his entertaining and informative book, The Rise of the Robots (2015), software executive Martin Ford expresses alarm at the increasing pace and pervasiveness of automation, and speculates that the machines may soon replace everyone, not just people with “ordinary skills”.149 Ford recounts what he calls the “seven deadly trends”, including the increasing prevalence, quality, and pace of automation, that “taken together, suggest a “transformative role for advancing information technology.”150 In his dramatic telling, no one is safe from these development. “[R]obotics and advanced self-service technologies are increasingly deployed across nearly every sector of the economy,” Ford writes. Now, “[t]he machines are coming for the high-wage, high-skill jobs as well.”151 Others have made similar predicitons.152 Several empirical studies give meat to these doomsday predictions. One of the most publicized studies is by scholars at the Oxford Martin School at University of Oxford, who
Work, NATURE, Oct. 19, 2017, at 321-324. See also, e.g., BRYNJOLFSSON & MCAFEE, supra note 19, at 1-11; MARTIN FORD, THE RISE OF THE ROBOTS: TECHNOLOGY AND THE THREAT OF A JOBLESS FUTURE xii (2015). 145 See Eduardo Porter, Contemplating The End of the Human Workhorse, THE NEW YORK TIMES, Wednesday, June 8, 2016, at B1, B6 (discussing debates among economists regarding whether smart-learning machines will destroy large swaths of current occupations, from manual laborers to lawyers). This article was renamed in the online version. See Jobs Threatened by Machines: A Once ‘Stupid’ Concern Gains Respect, THE NEW YORK TIMES, June 7, 2016, https://www.nytimes.com/2016/06/08/business/economy/threatened-by-machines-a-once-stupid-concern- gains-respect.html 146 See BRYNJOLFSSON & MCAFEE, supra note 19, at 1-11. See also BRYNJOLFSSON & MCAFEE, THE SECOND MACHINE AGE (2014). 147 See BRYNJOLFSSON & MCAFEE, supra note 19, at 9 (“[C]omputers are not doing many things that used to be the domain of people only The pace and scale of this encroachment into human skills is relatively recent and has profound economic implications.”). 148 BRYNJOLFSSON & MCAFEE, THE SECOND MACHINE AGE, supra note 161, at 11. 149 See MARTIN FORD, THE RISE OF THE ROBOTS: TECHNOLOGY AND THE THREAT OF A JOBLESS FUTURE (2015). 150 FORD, supra, at 34-51. See also id at 29-61 (discussing various reasons this time may be different). 151 Id. at 27. 152 See, e.g., KLAUS SCHWAB, THE FOURTH INDUSTRIAL REVOLUTION 1-15 (2016)
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estimate that 47% of all U.S. jobs are at “high risk” of being automated.153 In their own work and in a study co-authored with Citi, the authors report that major industries at high risk of automation include accommodation and food services, transportation and warehousing, real estate and rental and leasing, retail trade, wholesale trade, administrative and support services, manufacturing, construction, and finance and insurance. 154 The Citi/Oxford Martin study highlights the implications of impending improvements in “additive manufacturing” (3D printing).155 3D printing, the authors write, will “digitise production and enable deeper levels of automation across the manufacturing workflow”, and “could drive manufacturing to new levels of efficiency by automating the human elements of production (both design and operations).”156 A more recent McKinsey Global Institute report less bombastic, predicting that few entire occupations will be replaced in the near future. 157 However, many types of jobs will be significantly affected by partial automation, as technology improves and businesses face increasing pressure to adopt machines in order to achieve cost and performance benefits.158 Specifically, the McKinsey report finds that “the proportion of occupations [jobs] that can be fully automated” by adopting “currently demonstrated” technology is “very small, less than 5 percent in the United States.”159 However, “[a]utomation will nonetheless affect almost all occupations, not just factory workers and clerks, but also landscape gardeners and dental lab technicians, fashion designers, insurance sales representatives, and CEOs, to a greater or lesser degree”, depending on “the types of work activity that they entail”.160 The report concludes, somewhat strikingly, that “as a rule of thumb, about 60 percent of all occupations have at least 30 percent of activities that are technically automatable.”161
153 Carl Benedikt Frey & Michael A. Osborne, The Future of Employment: How Susceptible Are Jobs To Computerisation?, Oxford Martin School, University of Oxford, September 17, 2013, at 38 (“According to our estimate, 47 percent of total US employment is in the high risk category, meaning that associated occupations are potentially automatable over some unspecified number of years, perhaps a decade or two.”), http://www.oxfordmartin.ox.ac.uk/downloads/academic/The_Future_of_Employment.pdf 154 Id. See also TECHNOLOGY AT WORK V2.0: THE FUTURE IS NOT WHAT IT USED TO BE, CITI GPS: GLOBAL PERSPECTIVES & SOLUTIONS, January 2016 (Hereafter, CITI/OXFORD MARTIN STUDY). The full Citi/Oxford Martin Study is available at http://www.oxfordmartin.ox.ac.uk/downloads/reports/Citi_GPS_Technology_Work_2.pdf See also Sarah Nassauer, Retailers Are Checking Out Automation, THE WALL STREET JOURNAL, Thursday, July 20, 2017, at B3 (discussing the results of the report by Citi Research co-authored with scholars at the Oxford Martin School). 155 Three-dimensional (“3D”) printing technology essentially “enables physical objects to be ‘printed’ as easily as words can be printed on a page[.]” Daniel Harris Brean, Asserting Patents to Combat Infringement via 3D Printing: It's No 'Use', 23 FORDHAM INTELLECTUAL PROPERTY, MEDIA & ENTERTAINMENT L. J. 771, 771 (2013). 3D printing works using computer aided design programs (CAD files), which “function as digital ‘blueprints’ that can be used by manufacturers to make products to exact specifications in a factory setting.” Id. at 773-774. 156 See CITI/OXFORD MARTIN STUDY, supra, at 87. 157 MCKINSEY GLOBAL INSTITUTE, A FUTURE THAT WORKS: AUTOMATION, EMPLOYMENT, AND PRODUCTIVITY, January 2017, at 1 (“Given currently demonstrated technologies, very few occupations—less than 5 percent—are candidates for full automation today, meaning that every activity constituting these occupations is automated”). 158 Id. at 10-16. 159 Id. at 1. See also id. at 32. 160 Id. at 32. 161 Id. (emphasis added) (“In the United States, the country for which we have the most complete data, about 46 percent of time spent on work activities across occupations and industries is technically automatable based on currently demonstrated technologies.”).
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Of course, the fact that many activities are “technically automatable” does not mean they will be automated. Rather, as mentioned above, the McKinsey report posits that the decision to automate in any given case ultimately depends not just on technological factors, but on economic and social factors.162 But, read together, the upshot of both the Oxford Martin studies and the McKinsey report is that the technology is getting better; the market forces that favor automation are strong; and a lot of people are going to be affected, many of them negatively, by automation in the near future. A closely related driver of anxiety is the fear that, if automation continues, soon all workers will be robots, not humans. If this happens, then the future will not have as much worker-driven consumption as in the past. As explained above, technological employment via demand-boosting relies on the idea that more productivity leads to higher wages and lower prices, which leads to more disposable income, which permits more consumption, which necessitates hiring. As one academic at Stanford, Jerry Kaplan, puts the classic demand-boosting argument, “technology like self-driving cars will ultimately save people a lot of money, which they can then spend on vacations, clothes, restaurant dinners, concert tickets, spa days and more.” 163 “That means increased demand for flight attendants, hospitality workers, tour guides, bartenders, dog walkers, tailors, chefs, ushers, yoga instructors and masseuses, even as artificial intelligence reduces the need for drivers, warehouse workers and factory operators.164 But demand-boosting arguments don’t work very well if people aren’t making wages to spend. Martin Ford draws out this argument (as Rifkin did twenty years earlier) in The Rise of the Robots, observing that if machines are the workers then there will be no humans with disposable income to spend on services and consumer products. 165 As Ford puts it, “machines do not consume.” 166 “So if automation eliminates a substantial fraction of the jobs that consumers rely on, or if wages are driven so low that few people have discretionary income, then it is difficult to see how a modern mass market economy could continue to thrive.”167 Combined with the fear that technology “is just getting too good at what we do”, the upshot of this point is that, if automation of human work becomes even more pervasive, there will no longer be much job generation because there are no tasks to be performed by humans; and there will no boost in demand for output because unemployed humans won’t have any disposable income to spend. 2. Decreasing Quality of Work Many commentators are skeptical about these fears. They have two basic critiques. First, they observe that the ability of machines to perform the work of most humans is still highly in doubt. For example, Ken Goldberg, a robotics expert at University of California, Berkeley, has
162 Id. at 10-12. See also CITI/OXFORD MARTIN STUDY, supra, at 89 (discussing barriers to automation including “the economic cost of investment” in new automation technologies and “[o]ther barriers besides costs … such as finding the right skills for the jobs available and systems lock in with proprietary systems, even though robots using open platforms are increasing.”). 163 Kaplan, supra, at C3 (“Many consumers are likely to conclude in the next decade or so that they no longer need to have a car of their own. … [This] will save the typical American family more than $5000 a year according to think tank RethinkX.”). 164 Id. 165 FORD, supra, at 193-198. See RIFKIN, THE END OF WORK, supra, at 15-20 (discussing the limits to demand as the driver of employment). 166 FORD, supra, at 196. See also Autor, supra, at 7 (discussing the limits to demand as the driver of employment). 167 FORD, supra, at 197.
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stated that even though robots are “going to become increasingly human”, “the gap between humans and robots will remain—it’s so large that it will be with us for the foreseeable future.”168 As Kaplan, also a skeptic, puts it, “[e]ven if can machines can “recognize cats, drive cars, beat world-champion Go players, identify cancerous lesions and translate from one language to another”, this does not mean they can perform most tasks in today’s economy.169 Second, they stress that none of this came true when people made similar arguments in the past. “The weavers rioted”, the argument goes, “but the weaving looms didn’t put everyone out of work.”170 “In the face of such evidence,” they ask, “why do so many experts and futurists continue to warn of an impending crisis?”171 I do not have reason to disagree that machines are not yet and may never be able to replace human work entirely. I also agree that machines did not replace all human work in the past and that, at present, partial automation is more common than complete automation. 172 But technological un/employment is about far more than just the ability of technology to perform human jobs and its impact on the overall quantity of jobs left over. It’s about technology’s impact on the quality of remaining jobs. Even when adoption of machines in a business or industry does not lead to full elimination of an industry, profession, or individual job, this can still significantly reduce their quality in terms of both wages and satisfaction. In my terminology, technology can either “augment” or “diminish” human work. a. Augmentation Augmentation is where labor-saving technology substantially improves the quality of human work by making workers more productive and higher-performing, and thereby increasing demand for their particular skills, which usually means an increase in wages.173 Several major
168 ALEC ROSS, THE INDUSTRIES OF THE FUTURE 27 (2017) (quoting Ken Goldberg). 169 “The crux of [the technological unemployment argument,]” Kaplan states, “is that the coming wave of artificially intelligent computers and robots can do virtually any job that a human can do, so everyone’s job is on the chopping block.” But, Kaplan says, this is not the case. “What all of these [now-automated] tasks have in common, is that they involve finding subtle patterns in very large collections of data, a process that goes by the name of machine learning. The kinds of data vary, of course. It might be pixels in cat photos, bytes streaming from a dashboard camera, millions of computer-generated games of Go, digital X-rays or volumes of human-translated documents. These programs present no more of a threat to human primacy than did automatic looms, phonographs and calculators, all of which were greeted with astonishment and trepidation by the workers they replaced when first introduced.
Kaplan, supra, at C3 . 170 Kaplan, for instance, reiterates that jobs didn’t disappear when tasks were automated in the past. “[D]espite centuries of progress in automation and recurrent warnings of a jobless future,” he writes, “total employment has continued to increase relentlessly, even with bumps along the way.” Kaplan, supra, at C3. 171 Id. 172 As noted above, both Professor Bessen and the McKinsey report also share the conviction that “complete automation”, versus “partial automation”, is unlikely the near future. See Part II.B.1. 173 The notion that technology will augment rather than replace much of human work is the thesis of several recent books addressing technological un/employment. A prominent example is THOMAS DAVENPORT & JULIA KIRBY, ONLY HUMANS NEED APPLY: WINNERS AND LOSERS IN THE AGE OF SMART MACHINES (2016) (arguing that will not necessarily eliminate human jobs but will replace certain tasks and therefore “augment” many types of jobs). See also Jeanne Meister, Future Of Work: Three Ways To Prepare For The Impact Of Intelligent Technologies In Your Workplace, FORBES, July 6, 2016, https://www.forbes.com/sites/jeannemeister/2016/07/06/future-of-work-three- ways-to-prepare-for-the-impact-of-intelligent-technologies-in-your-workplace/#2fa2bc4360b6
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professions are likely to benefit from automation in the form of greater efficiency, improved performance, and higher wages. These include, to name just a few, mechanical engineers, pharmacists, chief executive officers, and microbiologists, all of whom cannot be replaced by machines and instead benefit from technologies that compliment their skill sets.174 One extreme example of augmentation is the rise of the quant, so-called “new kings of wall street”, who are able to use electronic trading algorithms to achieve much higher returns than ordinary human analysis based on fundamentals.175 Another example is the physician who is able to use artificial intelligence systems like “Watson Health”, derived from IBM’s Watson, to search through patient records and research papers, in order to help doctors diagnose and treat ailments.176 Law is, I think, another example of augmentation. Most lawyers would probably say they are very grateful for search engines like West Law that help them to do case law research. The next generation of this technology is a so-called “legal assistant” called Ross (also derived from IBM’s Watson), which helps lawyers sift through cases and answers legal questions in “plain English”.177 b. Diminution Diminution is the flip side of augmentation. Diminution occurs when labor-saving technology substantially reduces demand for the skills of certain human workers. Even if technology doesn’t replace humans entirely, it can lower their wages and decrease their satisfaction, leaving humans merely filling in gaps left by machines.178
The notion that machines will “augment” human capacities frequently accompanies demand-boosting arguments. See, e.g., Kaplan, supra, at C3 (stating that “[in reducing the need for human labor, robots] make the remaining workers more productive and their companies more profitable. These profits then find their way into the pockets of employees, stockholders and consumers (through lower prices).”). 174 Frey & Osborne, supra, at 57-58. 175 Gregory Zuckerman & Bradley Hope, The Quants Run Wall Street Now, THE WALL STREET JOURNAL, May 21, 2017, https://www.wsj.com/articles/the-quants-run-wall-street-now-1495389108 (“Up and down Wall Street, algorithmic-driven trading and the quants who use sophisticated statistical models to find attractive trades are taking over the investment world.”). 176 As a representative from IBM’s Watson research unit put it, ‘It’s an augmentation to the human to enable what the human does best and what the computer does best.” Tom Sullivan, Cognitive computing will democratize medicine, IBM Watson officials say, HelthCareITNews, April 27, 2017 (“Artificial intelligence tools will augment physicians’ jobs and one day be able to provide Memorial Sloan Kettering levels of care in places as far as rural China, Big Blue representatives said at Health Datapalooza.”). See also MCKINSEY REPORT, supra, at 56 (“Automated diagnostic advice is thus likely to augment doctors’ decision making before fully automated diagnosis, except perhaps in special instances, such as radiology, or cell pathology (checking for abnormalities through a microscope).’). See also Tim O’Reilly, Don’t replace people. Augment them, MEDIUM.COM, July 17, 2016 (“I used to be legally blind without huge coke-bottle glasses. My eyes were fixed by an augmented surgeon able to do something that had been previously impossible.”), https://medium.com/the-wtf-economy/dont-replace-people- augment-them-8bea60cb80ac 177 http://www.rossintelligence.com See also Karen Turner, Meet Ross, The Newly Hired Legal Robot, THE WASHINGTON POST, Meet Ross, May 16, 2016, https://www.washingtonpost.com/news/innovations/wp/2016/05/16/meet-ross-the-newly-hired-legal- robot/?utm_term=.4ecd334e908b See also Eric Talley, Is the Future of Law a Driverless Car? Assessing How the Data Analytics Revolution Will Transform Legal Practice (working paper, 2017) (arguing that machine learning and AI approaches are “overwhelmingly likely to continue to penetrate law” but expressing skepticism that AI will “categorically displace” human lawyers from the practice of law), https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3064926 178 See FORD, supra, at 3 (describing various modern jobs that involve “filling the gaps between the machines”, such as factory workers performing minor task at the end of a production process that is otherwise fully automated.)
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An example comes from the manufacturing industry. At Steelcase, a large manufacturer of office furniture, the company has steadily introduced automation, such as computerized work stations and computer-assisted arms, which now perform tasks once done entirely by humans.179 The New Yorker features the company in an article about the machines’ impact on remaining workers at the factory. 180 As the author puts it, employees (sometimes called “meat robots” by their peers) “follow a strict automated protocol” and they “need little training. Even the drill [used to affix parts of furniture being assembled] was attached to a computer-assisted arm; the worker just had to move it to the right position and let the machine do its magic. A decade ago, industrial robots assisted workers in their tasks. Now workers—those who remain—assist the robots in theirs.”181 Diminution is related to the more narrowly defined phenomenon of “deskilling”— where machines replace highly skill humans, and relegate remaining humans to performing low-skill tasks to supplement the machines—as occurred during the transition from artisanal production to mass production in factories in the nineteenth and early twentieth centuries.182 Historically, as mentioned above, deskilling in fact increased the number of unskilled workers needed to operate machines and meet growing demand; but it did take away much of the skill required to perform these jobs and drastically altered their nature. 183 Unlike deskilling, diminution is a broader term that refers to where technology reduces the need for a profession or for its core skills, and reduces wages according. Diminution can happen to very low skill jobs, like driving. According to Commerce Department economists, the approximately 3.8 million people who drive taxis, trucks, and other vehicles for a living—none of whom would be considered high-skill workers—may either be displaced by self-driving vehicles or simply see their wages fall drastically as fewer people demand rides from human drivers.184 Diminution can also happen to very high skill jobs. For example, translating languages was once the sole domain of skilled human translators. 185 But thanks to improving translation technologies like Google Translate, now “[i]t is much easier for machines (and humans) to translate between closely related languages.”186 Humans are not fully replaced because at least some translations are too complex or context-specific for machines to do alone.187 But for many
179 Kolhatkar, supra, at 70. 180 Id. at 71 (“… As automation depresses wages, jobs in factories become both less abundant and less appealing.”) 181 Id. at 71 182 Frey & Osborne, supra, at 7-8 ("An important feature of nineteenth century manufacturing technologies is that they were largely ‘deskilling’ – i.e. they substituted for skills through the simplification of tasks[.]”) 183 Id. at 7-9. 184 Ben Leubsdorf, Self-Driving Cars Could Transform Jobs Held by 1 in 9 U.S. Workers, THE WALL STREET JOURNAL, August 14, 2017, https://blogs.wsj.com/economics/2017/08/14/self-driving-cars-could-transform-jobs- held-by-1-in-9-u-s-workers/ 185 Translation platforms cannot replace humans: But they are still astonishingly useful, THE ECONOMIST, April 29, 2017, https://www.economist.com/news/books-and-arts/21721357-they-are-still-astonishingly-useful-translation- platforms-cannot-replace-humans. 186 Id. 187 On the front end, high-end translations systems must be first “trained” by huge volumes of text translated by humans. On the back end, for some kinds of translations, humans “will at the very least have to be at the wheel to vet and edit the output of automatic systems.” Id. (“[N]eural-translation systems aren’t ready to replace humans any time soon. Literature requires far too supple an understanding of the author’s intentions and culture for machines to
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purposes, “Google Translate is faster, cheaper, and often as good as a human interpreter.”188 Human translators’ job in many cases has been reduced to mere “clean-up” of work done by machine translation systems.189 Note that the same technology can diminish the jobs of some people, and augment the jobs of others. For instance, the Commerce Department report notes that an additional 11.7 million workers, who drive as only part of their work, such as personal care aides, police officers, and plumbers, may actually “benefit from greater productivity and better working conditions” in performing their core tasks—personal care, policing, and plumbing, respectively.190 In other words, there is a significant skew in the distribution of benefits from this technology—a point on which I elaborate further below. 3. Increasing Inequality Even if innovation creates more jobs than it destroys, it tends to generate unequal returns for different members of society.191 There are two different trends. a. Skill-Biased Technical Change The first trend is that technological advance has been found to be “skill-biased,” meaning that, as technology advances, high-skill tend to become more valuable, while low skill workers tend to become less valuable.192 “The central idea,” Lawrence Katz and Claudia Goldin wrote in their 2008 book The Race Between Technology And Education, “is that certain technologies are difficult for workers and consumers to master, at least initially. …[E]mployees who are slow to grasp new skills will not be promoted and might see their earnings reduced. Those who are quicker will be rewarded.”193 The upshot is that even as some workers are left behind or made obsolete by machines, others are rewarded.194 A growing divergence in the wages of educated versus non-educated workers supports the skill-biased nature of technological change As Brynjolfsson and McAfee describe the current state of the data in in Race Against the Machine, discussing work by economists Daron Acemoglu and David Autor, “[o]ver the past 40 years, weekly wages for those with a high school degree have fallen and wages for those with a high school degree and some college have
do the job. And for critical work—technical, financial or legal, say—small mistakes (of which even the best systems still produce plenty) are unacceptable[.]”) 188 Id. 189 Why translators have the blues: A profession under pressure, THE ECONOMIST, May 27, 2017, https://www.economist.com/news/books-and-arts/21722609-profession-under-pressure-why-translators-have-blues (discussing how translators’ jobs have been reduced in many cases to mere “clean-up” of machines’ work). 190 Id. 191 For a concise discussion of the “winners and losers” ushered in by advances in technology, including the disparate gains for high-skill and low-skill workers, see BRYNJOLFSSON & MCAFEE, supra note 19, at 39-47. 192 CHRISTINE GREENHALGH & MARK ROGERS, INNOVATION, INTELLECTUAL PROPERTY, AND ECONOMIC GROWTH 268 (2010) (“[T]he predominant view is … that high-skilled workers will benefit from new technology, while low- skilled workers will suffer a loss of demand leading to lower wages and higher unemployment.”). See also, e.g., Autor & Solomons, supra, at 4 (discussing “skill-biased demand shifts”, such as declines in the real wages of less- educated workers in both the United States and Germany over the last two to three decades and stating that “technological change can directly reduce demand for broad skill groups even if they do not diminish labor demand in aggregate.”). 193 CLAUDIA GOLDIN & LAWRENCE KATZ, THE RACE BETWEEN EDUCATION AND TECHNOLOGY 90 (2008). 194 See id. at 94-99 (arguing that there is evidence for the thesis that skill-biased technological change is partly responsible for growing division in wages between high and low skilled workers, even though education and supply of skilled workers is also a factor).
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stagnated. On the other hand, college-educated workers have seen significant gains, with the biggest going to those who have completed graduate training.” 195 This is despite the fact that the supply of educated workers increased during that same workers—suggesting that skilled labor really is in serious demand. 196 b. Job Polarization The second trend has been called “job polarization.” This refers to the “simultaneous growth of high-education, high-wage jobs at one end” of the occupational skill spectrum, and the rise of “low-education, low-wage jobs at the other end” of the occupational skill spectrum.197 As Autor explains, jobs that cannot easily be replaced by machines are typically either abstract tasks, like academic thinking or corporate strategizing, or they are manual tasks, like massage therapy or yoga instruction. 198 These tasks are “generally found at opposite ends of the occupational skill spectrum—in professional, managerial, and technical occupations on the one hand, and in service and laborer occupations on the other[.]”199 The problem area, the “hollowing out”, is in between: middle skill jobs with some education that can be easily replaced by machines.200 This prediction seems to be borne out in the data provided in the Oxford Martin study mentioned above. 201 In their data set, the jobs least likely to be replaced by automation are in the categories of advanced degrees and bachelor degrees—such as CEOs, surgeons, specialized coders, and lawyers—and people with non-degree professional training on the other—like hair dressers, fitness trainers, and nurses.202 Meanwhile, they find that middle-skill jobs for people with little formal education, whose tasks are highly routine and predictable, such as clerks, waiters, food prep workers, human resources assistants, and real estate agents, and paralegals, are especially vulnerable to automation.203 The upshot of job polarization is, again, that the impact of innovation on labor is highly uneven across society. Workers with irreplaceable skills are in higher demand; but workers who can easily be replaced find themselves with no jobs or jobs with very low wages.204
195 BRYNJOLFSSON & MCAFEE, supra note 19, at 39-40. See also id. at 39-42 (discussing recent work on skill biased technical change). 196 Id. at 40. 197 Autor, Why Are There Still So Many Jobs, supra, at 12 (2015). 198 Id. 199 Id. 200 Id. (“[T]his reasoning implies that computerization of “routine” job tasks may lead to the simultaneous growth of high-education, high-wage jobs at one end and low-education, low-wage jobs at the other end, both at the expense of middle-wage, middle education jobs—a phenomenon that Goos and Manning (2003) called “job polarization.”). See also CITI/OXFORD MARTIN STUDY, supra, at 38-39 (discussing the “risk of job polarisation as the new jobs (replacement and expansion) will be created on the top or the bottom of the job sector. At all skill levels, most jobs in demand will be characterised by non-routine tasks which are not easily replaced by technology or organisational change.”). 201 Frey & Osborne, supra 108, at 57-72 (containing chart of professions and risk of automation). 202 Id. See also Mark Whitehouse & Dorothy Gambrell, How Screwed Is Your Job?, BLOOMBERG BUSINESSWEEK, June 26, 2017, at 52-53 (containing pictographic of professions and risk of automation based on Frey and Osborne data). 203 Id. at 53. 204 GREENHALGH & ROGERS, supra note 40, at 268. See also BRYNJOLFSSON & MCAFEE, RACE AGAINST THE MACHINE, supra note 19, at 39 (“Even when technological progress increases productivity and overall wealth, it can also affect the division of rewards, potentially making some people worse off than they were before the innovations.”).
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c. The Skills Gap There is an important caveat to these distributional concerns. The reason for the division in wages between high and low skill workers is not just technological change. Rather, as Katz and Goldin emphasize in their book, the increasing supply of skilled workers relative to the demand for skilled workers is also very important.205 In a sense, they argue, education and skills training are in a “race” against technology. Their data shows that in the first part of the twentieth century workers were sufficiently skilled to take on the available jobs, but that in the latter part of the century, “technology raced ahead of educational gains.” There were too few people with the requisite education, and too many people without it.206 This is called a “skills gap”: where there is higher demand for people with a certain skill set than there is supply.207 So, for example, roboticists with the knowledge to work on self-driving car design are highly paid not just because their education and skills are complimentary to this technology when others’ (like truck drivers’) are not. It is also the fact that there are so few roboticists around to meet the hiring demand, and so many truck drivers. This suggests that education can alleviate some of the inequality in gains from technology. If we train everyone in the skills needed to take on new jobs ushered in by innovations in robotics and algorithms, then more people can experience the resulting employment benefits. However, education may not be a perfect solution to technological un/employment problem. As Ford puts it, the numbers simply don’t seem to add up.208 Ford depicts the job market like a pyramid with many low skill jobs at the bottom and only a few high skill jobs at the top.209 “It’s becoming increasingly clear,” Ford writes, that “robots, machine learning algorithms, and other forms of automation are gradually going to consume much of the base of the jobs skills pyramid.”210 Even by investing in “still more education and training”, Ford is skeptical that we can “cram everyone into that shrinking region at the very top.”211 III. Intellectual Property’s Impact on Technological Un/employment The consensus of the work discussed above has been that technologies that permit significant labor-saving both eliminate and create employment for humans, and significantly affect the quality and distribution of remaining human work.212 The most alarming quadfecta of potentiality is that machines will soon take over jobs performed near the top of the skills pyramid
205 Id at 99-102. Frey and Osborne discuss Goldin’s and Katz’s large body work in Frey & Osborne, supra, at 9-12. For a more complete list of their articles, see id. at 50. 206 GOLDIN & KATZ, supra, at 8. 207 See Phil Britt, Skills Gap: Training Exists, But Automate Experts Say More Is Needed, ROBOTICS BUSINESS REVIEW, April 10, 2017, (discussing the views of a recent whitepaper, ‘Work in the Automation Age: Sustainable Careers Today and Into the Future’ that there is a need for better education to meet job openings in fields including advanced manufacturing and robotics), https://www.roboticsbusinessreview.com/manufacturing/skills-gap-training- exists-automate-experts-say-needed/ On the skills gap and the need for more programs to help workers gain the skills they need to keep pace with technology, see also, e.g., CITI/OXFORD MARTIN STUDY, supra, at 48-49 (discussing report finding that in the UK “over 12 million people and a million small businesses do not have the skills to prosper in the digital era” and a McKinsey estimate that “by 2020, there will likely be a shortage globally of 40 million high skilled workers unless adequate training and education is given.”). 208 FORD, supra, at 252 (“The numbers simply don’t work.”). 209 FORD, supra, at 252-253. See also SCHWAB, supra, at 45 (discussing fears of a “hollowing out of the job skills pyramid”, leading to “growing inequality and a rise in social tensions unless we prepare for these changes today.”). 210 Id. at 252. 211 Id. at 253. 212 See Part II.
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as well as the bottom; threaten worker-driven consumption as a driver of demand; reduce the quality of remaining jobs in terms of wages and satisfaction; and exacerbate inequality between a large number people with replaceable skills, on the one hand, and a small number of people with skills that cannot be replaced, on the other.213 This part shows that intellectual property—mainly patents, trade secrets, and trademarks—plays a role in generating technological un/employment and may contribute to these trends. In short, I hypothesize that intellectual property rights facilitate and increase the pace of technological un/employment and enhance the unequal distribution of returns between “IP-generating” workers (who have the skills to generate IP intellectual property and “non-IP- generating” workers (those who lack the skills needed to generate intellectual property). Even if my hypothesis proves true—that intellectual property enhances technological un/employment trends—this does not mean intellectual property is bad any more than it means innovation is bad. However, it does raise the question of whether intellectual property law should be modified in order to take into account its subjects’ perverse effects on human work. Whether intellectual property law should in fact be modified, and to what degree, is of course a normative question, which I address in Part IV. A. Privilege Regimes Our first intuition may be that the presence or absence of intellectual property makes no difference for employment at all. This is because modern intellectual property rights only provide a right to exclude others from using the covered innovation. 214 Intellectual property covering a particular technology does not give a company the right or permission to use it, let alone guarantee that they will do so and be successful.215 On the flip side, absent intellectual property rights, companies are still free to adopt innovations like drones and self-driving cars, so long as they do not run afoul of health and safety or other regulations.216 In the next section I will argue that this intuition is wrong, that in fact intellectual property should be predicted to have an impact on employment. But first it is worth understanding that, unlike today, historically there was absolutely no question that intellectual property rights could influence employment. The first instances of institutionalized patent protection in the Western World occurred in fifteenth century Venice and sixteenth century Great Britain, and thereafter in colonial America.217 Patents in this period were a “privilege” to practice an innovation that had to be sought from the monarch, rather than a property right granted through an administrative scheme.
213 See Part II.C. 214 35 U.S.C. § 154(a)(1) (“Every patent shall contain a short title of the invention and a grant to the patentee, his heirs or assigns, of the right to exclude others from making, using, offering for sale, or selling the invention throughout the United States or importing the invention into the United States…”). See also 17 U.S.C. § 106 (“[T]he owner of copyright under this title has the exclusive rights to do and to authorize any of the following…”) 215 See Ted Sichelman, Commercializing Patents, 62 STAN. L. REV. 341, 341 (2010) (noting that few patents are ever commercialized). 216 Various regulations external to intellectual property regulate the use of emerging technologies. See, e.g., Gregory N. Mandel, Regulating Emerging Technologies, 1 L., INNOVATION & TECH. 75, 75 (2009) (discussing the challenges of regulating emerging technologies); see also Carla Reyes, Moving Beyond Bitcoin to an Endogenous Theory of Decentralized Ledger Technology Regulation: An Initial Proposal, 61 Vill. L. Rev. 191 (2016) (discussing ways to regulate Bitcoin and other payments systems that operate using “distributed ledger technology”).
217 See ROBERT MERGES & JOHN DUFFY, PATENT LAW AND POLICY: CASES AND MATERIALS 3-5 (5th ed. 2011). See also Camilla A. Hrdy, State Patent Laws In The Age of Laissez Faire, 28 BERKELEY TECH. L. J. 45, 60-65 (2013) (describing state and colonial patents in early America).
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They conferred both a right to exclude others from using a qualifying invention and permission from the sovereign to use the invention in the jurisdiction.218 Several scholars working in the fields of intellectual property and history of science have elucidated the economic function of patents in privilege regimes. Herbert Hovenkamp analogizes patents to early corporate charters: both were viewed as a means to induce socially productive activity. “Just as the early American states viewed corporate charters as granting private entrepreneurs a special right or privilege to induce the creation of infrastructure,” Hovenkamp writes, “the patent was an inducement to introduce useful technology. Under this model both corporate charters and initially patents were granted selectively to private developers who promised to furnish the state with something that would contribute economic growth or infrastructure.”219 With such broader economic goals in mind, Margio Biagioli has explained, privilege-granting regimes made the decision to confer a patent based on a variety of factors other than novelty and disclosure—including the invention’s likely “impact on local labor, commerce, and prices.”220 Oren Bracha similarly emphasizes privilege regime’s special emphasis on the public good. “Patent petitions and the patent grants,” Bracha observes, opened with recitals of the specific benefits that the patentee offered to the realm and the crown. … The descriptions in the recitals were not general assertions of utility, but rather accounts of the specific and tangible benefits offered. These benefits might have included decreasing unemployment, providing relief to a ‘decayed town,’ or supplying a needed commodity superior in price or quality to imported ones.221 In privilege regimes, if an inventor came to the sovereign seeking a patent to use the technology in the region, and that technology was likely to have a negative impact on the work force, it was (so far as I can tell) less likely the sovereign would grant that patent. What is more, unlike today, denying a privilege was effectively a total bar on practicing the technology – it meant the applicant would not be able practice the invention in the realm at all. For example, Mario Biagioli describes Galileo’s efforts to obtain a privilege to operate his new water pump in Venice in 1594. Biagioli notes that, “the Provveditori [the officials charged with evaluating the invention] seemed to support [Galileo’s] request based on their assessment of the local utility the invention would have after being put to work. Terminally swampy Venice had a soft spot for
218 See Hrdy, State Patent Laws in the Age of Laissez Faire, supra, at 58 (“In England, the word “patent” had referred to special privileges granted by the monarch and recorded in “open letters” to become matters of public record.”) (citing 2 WILLIAM BLACKSTONE COMMENTARIES *346–47 (1768); ROBERT MERGES & JOHN DUFFY, PATENT LAW & POLICY, 3 n.6 (4th ed. 2007)). C.f. Adam Mossoff, Who Cares What Thomas Jefferson Thought About Patents? Reevaluating the Patent “Privilege” in Historical Context, 92 CORNELL L. REV. 953 (2007) (casting doubt on the notion that early American patent rights were seen as “privileges” in the modern sense of the term). 219 Herbert Hovenkamp, The Emergence of Classical American Patent Law, 58 ARIZ. L. REV.. 263, 267 (2016) (emphasis added) (citing HOVENKAMP, ENTERPRISE AND AMERICAN LAW, 1836–1937, at 17–41 (1991)); see also Hrdy, State Patent Laws in the Age of Laissez Faire, supra, at 100-104. 220 See Mario Biagioli, Patent Republic: Representing Inventions, Constructing Rights and Authors, 73 SOCIAL RESEARCH 1129, 1134 (2006) (“[Privilege granting authorities] assessed the local utility and novelty of the invention, its impact on local labor, commerce, and prices, and carried out preliminary checks to see whether someone else had already received a privilege for it[.]”). See also Hrdy, State Patent Laws In The Age of Laissez Faire, supra, at 60-65, 95-96 (discussing consideration of social utility in state patent laws and earlier privilege regimes). 221 Oren Bracha, The Commodification of Patents, 1600-1836, 38 LOYOLA OF LOS ANGELES L. REV, 177, 186-187 (2004)
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water pumps, and Galileo promised a very efficient one.”222 However, one wonders whether officials would have granted Galileo the privilege to operate his water pump in the realm if Galileo had instead insisted that his water pump’s main advantage would be to reduce employment for Venetian workers in the irrigation industry. There are indeed documented instances of privilege-granting regimes denying patents for labor-displacing inventions. The introduction mentioned William Lee’s unsuccessful attempt to achieve a patent for his knitting machine in England and France, which the Queen of England predicted would bring her subject to “ruin by depriving them of employment[.]”223 Another example comes from Venice, courtesy of Professor Stefania Fusco. The petitioner, Maria Bessea Brancaleoni, sought a patent for “a machine that could be used to either to spin and [sic] wind several kinds of materials.” The authority responsible for reviewing her petition stated that the invention was “ingenious and beautiful and could easily accomplish” what Brancaleoni had promised. However, the officials also expressed reservations and warned the Signoria (the issuing authority) to be careful, because “if the device proved to be effective (as was likely to be the case) it would be to the detriment of the poor, because this machine would cause unemployment among poor [women].”224 This demonstrates that, both in England and in Venice, at least some patents were reviewed for their impact on labor, and were potentially denied if found to be to the detriment of workers. This changed in early American patent law. As Bracha, Biagioli, and others have observed, the first U.S. Patent Act of 1790 shifted the focus of patents from generating local utility in the socioeconomic sense to disclosing new information.225 Nonetheless, in the first few decades, employment remained a factor that could at least come up in discussions surrounding patent’s “utility” under what is today Section 101 of the Patent Act.226 Bracha gives the example of Eli Whitney’s patent for his cotton gin, which was challenged in Whitney v. Carter (1810).227 According to Bracha, when the cotton gin’s utility was questioned, Whitney’s counsel responded by cataloguing the public benefits conferred by the cotton gin, including that (so he argued) the cotton gin provided “a lucrative employment” for “[i]ndividuals who were depressed with poverty” and “sunk in ideleness[.]”228 As I discuss further in Part III, this aspect of the utility
222 Id. at 1132-34. 223 See citation in note 1. 224 This example is courtesy of Professor Stefania Fusco. Professor Fusco’s original translation is on file with the author. 225 See Biagioli, supra, at 1138 (discussing American patent law’s shift in focus from local economic benefits to disclosure of new information in a patent specification). See also Hrdy, State Patents As A Solution To Underinvestment In Innovation, 62 U. Kan. L. Rev. 487, 493-95, 511-512 (2014). 226 The Patent Acts of 1790 and 1793 required that inventions sought to be patented be “useful.” See, e.g., Patent Act of 1790, ch. 7, § 1, 1 Stat. 109, 109–10 (repealed 1793). As discussed further below, utility was initially interpreted as referring, among other things, to social utility. See Bedford v. Hunt, 3 F. Cas. 37, 37 (C.C.D. Mass. 1817) (“By useful invention, in the statute, is meant such a one as may be applied to some beneficial use in society, in contradistinction to an invention, which is injurious to the morals, the health, or the good order of society.”). 227 See 29 F.Cas.1070(C.C.D.Ga.1810) (No.17,583), discussed in Bracha, supra, at 230-231. 228 Whitney’s attorney stated,
With regard to the utility of this discovery, the court would deem it a waste of time to dwell long on this topic. Is there a man who hears us who has not experienced its utility? The whole interior of the Southern states was languishing, and its inhabitants emigrating, for want of some objects to engage their attention,
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requirement—its focus on economic development rather than simply novelty and disclosure— thereafter faded from patent law.229 Under the current interpretation of Section 101, a patent would never be denied due to its negative impact on the workforce. But the point remains that, back when patents were evaluated for economic utility and denying a patent meant denying permission to practice B. Modern IP Rights Patents in the United States today are not what they were in the early privilege regimes of England and Venice. Today, the Patent Act gives only a right to exclude others from using the covered invention for the term of the patent. This distinction between right to operate and right to exclude is very important in our discussion. Before, denying a patent meant denying the privilege to use the technology at all in the jurisdiction. Today, denying a patent does not deny the right to use the invention—it just denies the right to exclude others (or in the case of trade secrets, some others) from doing so. Thus, even if a government wished to effectuate a ban on employment- killing inventions, denying intellectual property rights would not necessarily effectuate this goal. However, the intuition that modern intellectual property rights would have no impact on technological un/employment is wrong. Standard intellectual property theory suggests intellectual property rights should still have an impact on both the magnitude and pace of technology’s replacement of human labor. A primer in how modern intellectual property rights work is needed. There are four major IP regimes: patents, copyrights, trade secrets, and trademarks. As I discuss in more detail below, each form of IP right gives its owner the right exclude others from using the protected subject matter. This right to exclude is thought to provide an incentive to invent and commercialize new or fairly new innovations, and to undertake high-cost, high-risk research, by allowing companies to obtain profits that cannot easily be competed away.230 In addition, the
and employ their industry, when the invention of this machine at once opened views to them which set the whole country in active motion. From childhood to age, it has presented us a lucrative employment. Individuals who were depressed with poverty, and sunk in idleness, have suddenly risen to wealth and respectability. Our debts have been paid off, our capitals increased, and our lands have trebled in value. We cannot express the weight of obligation which the country owes to this invention; the extent of it cannot now be seen. Some faint presentiment may be formed from the reflection that cotton is rapidly supplanting wool, flax, silk, and even furs, in manufactures, and may one day profitably supply the want of specie in our East-India trade. Our sister states also participate in the benefits of this invention; for, besides affording the raw materials for their manufactories, the bulkiness and quality of the article afford a valuable employment for their shipping.
Whitney, 29 F.Cas. at 1072. See also Bracha, supra, at 231 (quoting and discussing the arguments in Whitney, 29 F.Cas. at 1072). BRACHA: The reported cases of the time indicate that this was not an exception. When the utility question was discussed, courts were often provided with substantive evidence and arguments regarding the social benefits and effects of the relevant inventions.307 CASES IN BRACHA TO CHECK: Langdon v. De Groot, 14 F. Cas. 1099 (C.C.S.D.N.Y. 1822) (No. 8059); Stanley v. Whipple, 22 F. Cas. 1046, 1048 (C.C.D.Ohio 1839) (13,286). Cf WILLARD PHILIPS, THE LAW OF PATENTS FOR INVENTIONS 137 (1837) (referring disdainfully to "some of the earlier cases in Pennsylvania and Massachusetts" in which substantive inquiries into the merits of potentially infringing inventions were undertaken). 229 On the demise of “moral” or “beneficial” utility see discussions and citations in Part III.D. 230 See Robert P. Merges, Uncertainty and the Standard of Patentability, 7 HIGH TECH. L. J. 1, 2–3 (1992) (theorizing that patents’ main incentive value should be judged on whether the existence of a patent system causes the “marginal inventor” to undertake R&D whose technical and commercial success is highly uncertain at the
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race for priority over intellectual property rights—particularly in patent law where one inventor achieves universal priority—should accelerate the pace at which creation and deployment of these technologies occurs.231 The upshot is that, when presented with the decision to innovate or not innovate, a company is theoretically more likely to choose to innovate due to the option for intellectual property protection and the chance for outsize returns. Below I quickly explain how each intellectual property regime utilizes a right to exclude as an incentive to invent and commercialize. (This general explanation will be obvious to most readers who should feel free to skip this.) 1. Patents Patents provide a 20-year-long right to exclude others from a new, nonobvious, and useful (i.e. operable) invention.232 Patents thereby give the inventor an incentive to invent and commercialize the technology knowing they’ll have a 20-year period during which they can raise prices without worrying about being undercut by another entity selling an identical or equivalent invention.233 Patents are strict liability—they permit exclusion of even innocent defendants who did not copy the invention or did not intend to infringe.234 2. Trade Secrets Trade secret law is emerging as one of the most important ways to protect new technology.235 Trade secret law protects both totally novel inventions and “non-innovative” subject matter such as data collections and customer lists so long as they have independent economic value to others from being kept secret and have been kept secret using “reasonable” efforts.236 Trade secrets’ incentive mechanism is slightly different than patent because trade secret law only provides a right to exclude others who obtain the innovation by improper means or in breach of a duty of confidentiality (i.e. those who “misappropriate”).237 But the principle by which trade secrets operate is the same: the right to exclude gives an incentive to innovate, as well as the freedom to exchange information internally.238 Companies consider the right to protection under trade secret law a very important way to protect innovations that can be kept
outset). See also GREENHALGH & ROGERS, supra, at 272 (IP rights allow companies to achieve “excess profits that cannot easily be competed away by other firms in the short run.”). For in depth discussion of intellectual property’s effects on incentives to commercialize, see, e.g., Camilla A. Hrdy, Commercialization Awards, 2015 WIS. L. REV. 13, 27-39 (2015). 231 This can be thought of the as the “acceleration theory” of intellectual property. See, e.g., Michael Abramowicz & John F. Duffy, The Inducement Standard of Patentability, 120 YALE L.J. 1590, 1599 (2010) (noting that a “growing body of literature . . . views the patent system as attempting not so much to increase but to accelerate innovation.”). 232 Patent Act, 35 U.S.C. §§ 101, 102, 103, 112. 233 See Kewanee v. Bicron, 416 U.S 470, 480 (1974) (“The patent laws promote [the Progress of Science and useful arts] by offering a right of exclusion for a limited period as an incentive to inventors to risk the often enormous costs in terms of time, research, and development.”). 234 See Robert Merges, A Few Kind Words for Absolute Infringement Liability in Patent Law, (July 10, 2014), available at https://ssrn.com/abstract=2464756 235 Sharon K. Sandeen & Christopher B. Seaman, Toward a Federal Jurisprudence of Trade Secret Law, 31 BERKELEY TECH. L. J. __ (forthcoming), https://ssrn.com/abstract=2958042 236 Defend Trade Secrets Act, 18 U.S.C. § 1839(3), (6). See also Kewanee, 416 U.S. at 482 (“[T]rade secret law protects items which would not be proper subjects for consideration for patent protection under 35 U. S. C. § 101”). 237 Kewanee, 416 U.S. at 490 (“[T]rade secret law does not forbid the discovery of the trade secret by fair and honest means, e. g., independent creation or reverse engineering[.]”) 238 Kewanee, 416 U.S. at 493 (stating that trade secret law “encourages the development and exploitation of those items of lesser or different invention than might be accorded protection under the patent laws” and “promotes the sharing of knowledge”.).
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secret, such as processes, even if they do not provide a right to exclude against the whole world like patents.239 3. Trademarks Trademarks also affect incentives to innovate, though in a different way than patents and trade secrets. Trademarks provide sellers the right to exclude others from using their source- identifying names, logos, and trade dress (e.g. distinctive packaging or designs) in ways that are likely to confuse consumers as to source or affiliation.240 According to modern trademark theory, trademark law has two basic purposes. The first is to avoid consumer confusion as to the source of products and services and to reduce consumers’ costs of searching for what they want (“search costs”). The second is to maintain sellers’ incentives to invest in “quality” through the assurance that consumers who like what they get will come back to the seller, rather than to others who use the seller’s marks in a confusing way.241 Although the primary function of trademarks is often said to be to prevent consumer confusion, trademarks still theoretically provides a significant incentive to innovate because they allow the sellers of innovative products and services to prevent others from passing off their own offerings as those of the trademark owner. In other words, one type of “quality” incented by trademarks is innovation. 242 Take the following example. If Oracle invents a new kind of automated database that collects and organizes data without significant ongoing human intervention243, this may well be a technology that businesses would like to adopt, and it may well be higher quality than other similar market offerings. However, if other sellers of competing database technologies can pretend to be Oracle—for instance by using the Oracle® name or other source-distinguishing
239 See, e.g., Richard C. Levin, Alvin K. Klevorick, Richard R. Nelson, Sidney G. Winter, Richard Gilbert and Zvi Griliches, Appropriating the Returns from Industrial Research and Development, 3 BROOKINGS PAPERS ON ECONOMIC ACTIVITY 783, 783-831 (1987) (using surveys to test the importance of patens as compared to trade secrecy and first-mover advantage). But see, e.g., W. Nicholson Price II, Making Do in Making Drugs: Innovation Policy and Pharmaceutical Manufacturing, 55 B.C. L. REV. 491 (2014) (arguing that IP incentives to innovate in pharmaceutical manufacturing methods are too low in part because trade secrecy is not a sufficient incentive). 240 Lanham Act, 15 U.S.C. §§ 1127, 1114, 1125. 241 See, e.g., Robert Bone, Hunting Goodwill: A History of the Concept of Goodwill in Trademark Law, 86 B.U. L. REV. 547, 547-567 (2006). 242 Economists of innovation have stated the innovation incentive function of trademarks as follows:
Trademarks are used to signal to consumers that the product is of a certain, consistent quality. … The signaling argument for trademarks is linked to the basic justification for IPRs: firms would be reluctant to invest in new product innovation if the new product could not be distinguished from innovators.
See GREENHALGH & ROGERS, supra, at 40 (emphasis added). See also William M. Landes and Richard A. Posner, Trademark Law: An Economic Perspective, 30 J. LAW & ECON. 265. 265-309 (1987) (discussing trademarks as an incentive to invest in product quality).
The innovation-incentive function of trademarks, as opposed to their function in incenting “quality” more generally, is I think widely recognized in the IP field, but has been underexplored in the IP scholarship. For discussion of the variety of trademark theories within IP scholarship, see Mark McKenna, The Normative Foundations of Trademark Law, 82 NOTRE DAME L. REV. 1839, 1844-49 (2007) (summarizing conventional justifications for trademark law, and arguing that trademark law originated not as a form of consumer protection but as a “producer-centered” form of unfair competition law intended to protect producers from “illegitimate attempts to divert their trade.”). 243 https://www.oracle.com/database/autonomous-database/index.html
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characteristics—than it will be at least more difficult for Oracle to profit from its investment in a superior innovation. Possession of other vehicles of market exclusivity such as patents would be for naught if consumers cannot distinguish the true Oracle product from contenders. Oracle should be theoretically more likely to invest in inventing this type of above-the-rest innovation with the security of trademark protection. 4. Copyrights Copyrights provide a 70-plus-year-long right to exclude others from using a fixed and original work of authorship.244 Unlike patents, copyrights require a showing of actual copying, along with “substantial similarity” to protected expression, so are not totally strict liability like patents are.245 Protectable subject matter may include aspects of technological innovations that can be copyrighted, such as software code (protected as a “literary work”.246 But copyright does not require novelty, and does not generally provide protection for “useful articles.”247 Copyright is thus, I think, far less likely to have a negative impact on labor than patents and trade secrets, which are more likely to cover technological innovations that implicate automation or other labor-saving improvements. In sum, intellectual property rights, particularly patents, trade secrets, and trademarks, provide not-insignificant incentives to innovate by providing a right to exclude, which increases the returns from innovation by permitting innovators to obtain excess profits that cannot be competed away. The chance to obtain an intellectual property right is not necessarily a “but for” cause of why an innovation is invented and adopted, but is viewed as one of several more-or-less important factors. C. Intellectual Property’s Impact on Technological Un/employment Assuming standard intellectual propery theory holds true, then intellectual property rights should be predicted to have two kinds of effects on the process of technological un/employment. 1. The Incentive Effect The first effect is the Incentive Effect. The Incentive Effect predicts that the incentives generated by intellectual property’s right-to-exclude magnify, and accelerate the pace of, technological un/employment. In other words, intellectual property rights facilitate the simultaneous creation and elimination of jobs that result from advances in technology. a. How The Incentive Effect Works In Theory The Incentive Effect works as follows. As explained above, the chance to obtain an intellectual property right enables its owner to constrain competition and increase returns, and thereby increases the incentive to invent and commercialize a particular innovation. Within the entire universe of innovation, at least some subset of these innovations will involve automation or other labor-saving improvement.248 The availability of intellectual property should, in theory,
244 17 U.S.C. § 102. 245 Patrick R. Goold, Unbundling the “Tort” of Copyright Infringement, 102 VA. L. REV. 1833, 1844 (2016) (“To prove infringement, the owner must show (1) that the defendant factually copied protected expression (meaning the defendant must perform one of the actions listed in Section 106),71 and (2) that the copying was qualitatively or quantitatively so extensive as to amount to an “improper appropriation.”). 246 Software code is protected as a “literary work” under 17 U.S.C. § 102(a) 247 Feist Publications, Inc., v. Rural Telephone Service Co., 499 U.S. 340 (1991). See also § 101 (defining a “useful article” for purposes of pictorial, sculptural, and graphical works) 248 I discussed the types of technological improvements likely to involve labor-saving in Part II.B.1.
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make it more likely that this improvement will invented, commercialized, and adopted in industry. Obviously, intellectual property is not the only factor influencing invention and adoption of a labor-saving development. Several factors besides technical and commercial feasibility affect the decision to automate including the cost of human labor alternatives and performance benefits of using machines versus humans. 249 Thus, just because an automation solution is technically possible and may yield better results does not mean businesses will choose to adopt it.250 The case of agricultural machinery provides a compelling example. For some crops, the availability of cheap farm workers, not technological feasibility, is the major determinant of whether growers use machines at harvest or people.251 Nonetheless, the technical feasibility of automating a job is an absolutely essential factor affecting the decision to automate. If the technological capability doesn’t exist, then a task simply cannot be automated. Therefore, again in theory, intellectual property should increase the chances that technologies that eliminate or reduce human labor are adopted and used to replace human workers. In addition, per the acceleration theory, IP should also increase the pace at which this process occurs.252 The Incentive Effect generates a testable hypothesis. Call the entire universe of innovation I, and call the labor-saving subset of all innovation, IL. The Incentive Effect predicts that intellectual property rights should on aggregate increase the overall size of IL by providing the opportunity to exclude others from using the protected innovation. Thus, the size of IL. in the IP presence of intellectual property rights, call it IL , should be greater than the size of IL in the 0 absence of IP, call it IL . IP 0 The Incentive Effect: IL > IL It is important to emphasize that I am not arguing that intellectual property rights enlarge only the size of IL. Rather, intellectual property rights enlarge the size of the entire universe of I, including IL. In other words, I do not have any reason to suspect that there is something about the inherent nature of intellectual property’s incentive mechanism—the right to exclude—that leads businesses to prefer labor-saving innovations when they would otherwise adopt different innovations that rely on human labor.253 Intellectual property rights do not, so far as I know, “distort” innovation towards labor-saving innovations versus non-labor-saving innovations. But, IP 0 if the Incentive Effect (IL > IL ) holds true, then intellectual property rights should increase the overall universe of innovations that are labor saving. To explain the point by way of example, the
249 A 2017 McKinsey Global Institute Report identifies five factors that influence a business’s decision to automate a particular task: technical feasibility, commercial feasibility, the supply and cost of human labor alternatives, performance and cost benefits associated with using machines versus humans, and any regulatory or social acceptance issues that may affect the decision to automate. MCKINSEY GLOBAL INSTITUTE, A FUTURE THAT WORKS: AUTOMATION, EMPLOYMENT, AND PRODUCTIVITY, January 2017, at 10-12. 250 Id. 251 See, e.g., Binyamin Applebaum, With Fewer Immigrants, More Jobs? Not So, Economists Say, THE NEW YORK TIMES, Friday, August 4, 2017, at A1 (recounting how a ban on use of farmworkers from Mexico in 1964 led California tomato growers to use tomato picking machines rather than hiring more expensive American workers). 252 See note 168 supra. 253 Some have argued, for instance, that patents, which provide a right to exclude in exchange for disclosure, may lead innovators to prefer certain types of inventions that are easier to exclude and more difficult to keep secret, such as process innovations. See, e.g., Amy Kapczynski & Talha Syed, The Continuum of Excludability and the Limits of Patents, 122 YALE L.J. 1900, 1905 (2013) (arguing that “patents will systematically underreward research because they yield less than full appropriability”).
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presence of IP does not mean Google is more likely to invent a fully automated car as opposed to an improved human-operated car. But the presence of IP does mean Google is more likely to invent new and improved cars, including fully automated cars. b. Evidence For The Incentive Effect Now to the empirical question: do the innovation incentives created by intellectual IP 0 property result in more labor-saving innovation than we would otherwise have? Is IL > IL ? I am willing to make two assumptions. First, that the existence of intellectual property rights generally increases the incentive to innovate on aggregate and at least makes innovation happen faster than it otherwise would.254 Second, I am also willing to assume that, if a labor-saving technology has been invented and commercialized, it is far more likely to be adopted in a business than it otherwise would be.255 But what I still do not satisfactorily know is whether the IP size of the universe of labor-saving innovations incented by intellectual property (IL ) is very big. For example, if it turns out that most automation technology was invented and commercialized without resort to intellectual property, then this would completely disprove my hypothesis. If, on the other hand, it turns out that most labor-innovations are/were protected by intellectual property then this would at least not disprove the hypothesis. So this leaves the IP following query: how big is IL ? Below I discuss various ways this has been tested and explore new ways to test it. First, for the sake of completeness, I will have to review some of the major studies in the area of IP and employment that have not answered my question. i. Studies Measuring the Impact of Innovation on Employment As explained in Part II, the role of technology in creation or eliminating employment is the subject of an entire field of study among economists and economic historians, dating back to at least the eighteenth century.256 So we might assume these studies had already answered the question of IP’s role. But in fact, they have not. These studies frequently use patents merely as proxies for innovation in order to observe innovation’s impact on technological un/employment.257 Obviously, more work should continue to be done in this area in the coming years as the technological movements afoot unfold. But these studies do not measure the impact of intellectual property rights themselves on technological un/employment. They do not help me answer the question I am after: do intellectual property rights increase the size of labor-saving innovation?
254 It is nearly impossible to causally link technological advances, on aggregate or in individual cases, to the incentive effect of intellectual property rights. The empirical evidence remains inconclusive despite many years of research. But I am willing to make this assumption. See ROBERT MERGES, JUSTIFYING INTELLECTUAL PROPERTY (2011). For a recent attempt to use surveys to answer this question, see e.g., Christopher Buccafusco, Zachary Burns, Jeanne Fromer, & Christopher Sprigman, Experimental Tests of Intellectual Property Laws’ Creativity Thresholds, 93 TEX. L. REV. 1921 (2014). 255 See caveat above in note 177 supra that other factors also play a role such as the cost of human labor alternatives. 256 See, e.g., Mokyr et al, supra, at 33 (“From the late 18th century onward, the debate centered on how technological progress affected workers and how these effects might differ between the short run and the long run.”). 257 For example, in a 2015 paper exploring “the possible job creation effect of innovation activity” several empiricists, analyzing data covering almost 20,000 patenting firms from Europe over the period 2003-2012, finds that higher levels of innovation, as measured by forward-weighted patent citations, has a positive impact on firms in high-tech manufacturing sectors but not for firms in low-tech manufacturing and services. Vincent Van Roy, Daniel Vertesy & Marco Vivarelli, Innovation and Employment in Patenting Firms: Empirical Evidence from Europe, IZA DP No. 9147, June 2015.
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ii. Studies Measuring Employment in “IP-Intensive Industries” In the introduction I mentioned that the U.S. Patent & Trademark Office (USPTO) has been exploring the impact of intellectual property rights on employment. These studies seek to assess the correlation between intellectual property rights and job creation, and have concluded that industries that rely upon intellectual property rights—called “IP-intensive industries”—add more jobs than other industries. I will quickly highlight three of these studies before explaining IP why, in fact, they are also largely useless for answering my question about the size of IL . In their 2015 paper, Business Dynamics of Innovating Firms: Linking U.S. Patent Data with Administrative Data on Workers and Firms, Stuart Graham and Alan Marco, the former and current Chief Economist at the USPTO, respectively, along with two scholars from the U.S. Census Bureau, find that “patenting firms, particularly young patenting firms, disproportionally contribute jobs to the U.S. economy.” 258 The authors compile a database tracking “patenting firms” (firms with higher numbers of patents per R&D dollar) as well as the network of inventors employed at those firms. They then determine the job creation rates and the job destruction rates at patenting firms as compared to non-patenting firms over the 2005 to 2008 period. They find that “patenting firms create more jobs than non-patenting firms for all age classes except among the youngest firms [.]. … By contrast, non-patenting firms on average shed more jobs than do patenting firms across almost all age classes, with the youngest non-patenting firms shedding the most jobs.” 259 The USPTO then expanded this methodological approach to look at other forms of intellectual property besides patents. In 2012, the Department of Commerce issued a report titled Intellectual Property and the U.S. Economy: Industries in Focus. The report, and its 2016 update, Intellectual Property and the U.S. Economy: 2016 Update, identified a number of “IP- intensive” industries—those that have more patents, trademarks, or copyrights per size—and estimated their impact on the U.S. economy.260 The 2016 update identifies 81 industries (from among 313 total) as IP-intensive and concludes that that “directly accounted for 27.9 million jobs either on their pay- rolls or under contract in 2014” and “indirectly supported 17.6 million more supply chain jobs throughout the economy.”261 In total, IP-intensive industries “directly and indirectly supported 45.5 million jobs, about 30 percent of all employment.”262
258 Stuart Graham, Cheryl Grim, Tariqul Islam, Alan Marco, and Javier Miranda, Business Dynamics of Innovating Firms: Linking U.S. Patent Data with Administrative Data on Workers and Firms, April 1, 2015, http://www.law.northwestern.edu/research- faculty/searlecenter/events/roundtable/documents/Miranda_Business_Dynamics_Innovating_Firms_04062015.pdf, at 1 259 Id. at 32. 260 “IP-intensive” means IP count per size, which is generally determined by dividing the IP count by number of employees. USPTO REPORT, supra note 3, at 7-9 (explaining definition of IP-intensive for patent, copyright, and trademark respectively). 261 USPTO REPORT, supra note 3, at ii. “While IP-intensive industries directly accounted for 27.9 million jobs either on their pay- rolls or under contract in 2014, they also indirectly supported 17.6 million more supply chain jobs throughout the economy. In total, IP-intensive industries directly and indirectly supported 45.5 million jobs, about 30 percent of all employment.” Id. 262 USPTO REPORT, supra note 3, at ii. The report also concludes that “Trademark-intensive industries are the largest in number and contribute the most employment with 23.7 million jobs in 2014 (up from 22.6 million in 2010). Copyright-intensive industries supplied 5.6 million jobs (compared to 5.1 million in 2010) followed by
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The USPTO’s findings are similar to those of the European Patent Office and the Office for Harmonization in the Internal Market (OHIM), which together published comparable reports in 2013 and 2015 using European Union (EU) data. Their 2013 study found that “IP-intensive industries” in Europe, in the 2008–2010 period, “directly employed 56.5 million Europeans, which accounted for almost 26% of all jobs for the period”.263 If accurate, hiring and wages in in “IP-intensive industries” is great information to have. But these studies are flawed for measuring intellectual property’s impact on technological un/employment. At best, all that these studies prove is that intellectual property rights are correlated with more jobs and higher-paying jobs in the industries that possess them than in the industries that do not possess them. This finding has two major limitations for my purposes. First, it does not indicate that there is any direct causal linkage between the incentive effect created by intellectual property rights and the job gains documented. As the USPTO Report concedes, “[o]ur methodology aims to measure the intensity of IP use, but does not directly measure the extent to which IP incentivizes the creation of new goods and services. We find differences in employment, wages, value added, and other outcomes that are correlated with IP use…[But] our methodology does not permit us to attribute those differences to IP alone.” 264 Indeed, some people responsible for these studies, have expressed doubts to me that these reports do a good job of proving intellectual property, versus simply innovation, is causally responsible for any of these effects.265 Second, these studies measure the impacts of intellectual property on the jobs of people working within IP-intensive industries; they do not measure IP’s impact on people working outside of those industries. These studies tell us nothing about how the subject matter protected by intellectual property rights affects the occupations of others who do not work in IP-intensive industries. For example, what percentage of the innovations generated in the “IP-intensive” industries are labor-saving? Returning to my hypothetical model above, we do not know the size IP of IL—the subset of innovations that are labor saving—or the size of IL —the subset of innovations that are labor-saving when modified by the incentive of intellectual property rights. iii. Patents On Labor-Saving Innovations I hypothesize that the universe of labor saving innovation is larger with IP than without IP IP. But how can we know for sure? One blunt way to measure the size of IL is to simply count patent filings to see how many of the inventions protected by patents implicate labor-saving or automation. There are many famous patented historical inventions, such as cotton harvesting machines266 and automated teller machines (ATM’s)267, that implicated technological
patent-intensive industries with 3.9 million jobs (3.8 million in 2010).” Trade secrets are not measured. See note 2 supra. 263 Id. at 3 (discussing EU study). 264 Id. at 2 (emphasis added) (“As in any area of research, no single study will yield the complete picture.”). See also id. at 4 (“noting that While this study [the 2015 OHIM study] does not identify the causal impact of IPRs, it provides detailed evidence of a high correlation between IPR-ownership and economic performance.”). 265 Author is checking on permission to use interviewees’ names. 266 Cotton-Harvester, U.S. Patent No. 526209A, Ely Whitney, 1894, Object: To produce a “simple and durable apparatus” for harvesting cotton, “capable of operation by unskilledlabor”, and “having a large capacity for work” . 267 Credit card automatic currency dispenser, U.S. Patent No. 3,761,682, Barnes, Chastain, Wetzel, 1972, Object: “To provide the consumer with a source of ready cash without the expense of branch banking”, “to make cash available to bank customers on a 24 hour basis.”
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un/employment and can be seen in the patent record. The fact that these inventions were patented does not tell us whether those patents actually facilitated or accelerated the pace at which cotton- harvesting machines or ATMs, respectively, altered the labor force. But it does indicate that the inventors perceived a patent as worth the bother of obtaining one. This, in turn, supports the connection between patents and technological un/employment. Famous inventions like the cotton harvester and the ATM are hardly the only examples of patented inventions that may trigger technological un/employment. A search for “labor-saving” in Google Patents reveals over 90,000 results, such as labor-saving long-arm gardening shears268, a labor-saving materials dispenser269, and a labor-saving consolidated checkout system (one of many patents on self-service checkout terminals)270. The term “automation” yields 424,928 results, including several patents involving “sales force automation”271 and “home automation system”272. The term “autonomous vehicle” alone yields 42,488 results, several of which are owned by Uber Technologies. 273 (I discuss Uber and self-driving vehicles further in the next section). To illustrate how these labor-saving patents are presented, take NCR Corp’s274 U.S. Patent No. 6522772B1 for a labor-saving consolidated checkout system. The patent discusses at length it goal to “reduce labor costs” associated with the retail grocery or supermarket industry275, and states that it is accordingly an “object of the present invention to provide a self- service checkout terminal which reduces the number of occasions in which an employee of the retailer must intervene in the customer's transaction relative to self-service checkout terminals which have heretofore been designed.” 276 In other words, the object of the invention, and the reason for to invent and patent the technology in the first place, is that it can eliminate the need for human labor. This suggests, though by not means proves, that the chance for a patent provided an incentive to invent and commercialize self-service checkout kiosks, and that patents thus facilitated or at least sped up the pace of resulting technological un/employment. Again, a major caveat is necessary: even if it is true that patents incentivized invention of all of these labor-saving technologies and that they were eventually adopted in the marketplace, this does not mean that they eliminated more labor than they created. For example, NCR Corp.’s self-service kiosks may take away jobs from some retail clerks. But they also generate profits and good-paying jobs for employees of NCR Corp, as well as the people who manufacture, distribute, and install the NCR Corp. checkout kiosks in pharmacies and grocery stores, and so
268 U.S. Patent No. 7530172B1. 269 U.S. Patent No. 5592760A. 270 U.S. Patent No. 5497853A. 271 See, e.g., U.S. Patent No. 7340410B1. 272 See, e.g., U.S. Patent No. 6473661B1. 273 See, e.g., Patent No’s 9557183, 9603158, 9616896, 9672446, 9432929. For a complete list of Uber’s patents, see Justia Patents, http://patents.justia.com/assignee/uber-technologies-inc?page=2 274 NCR Corp., initially named National Cash Registry Company, was founded in 1894 by John H. Patterson, “maker of the first mechanical cash registers.” NCR Corp. has been developing machines to facilitate consumer transactions, including cash registers, ATMs, and self-service kiosks for over a century. See https://www.ncr.com/company/company-overview/history-timeline NCR Corp. is also a major patent owner. Patterson’s first patent for a cash register has a grant date of 1889. U.S. Patent No. CA32621A. 275 The patent states in the “Background of the Invention,” for instance, that “[i]n the retail industry, the largest expenditures are typically the cost of the goods sold followed closely by the cost of labor expended. …” U.S. Patent No. 6522772B, 11, L14-19. 276 U.S. Patent No. 6522772B1, 3, 30-34.
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on. 277 In other words, an expanding universe of labor-saving innovation does not necessarily mean increasing unemployment. iv. Case Study: Electronic Trading Algorithms Case studies allow for more granular analysis of the link between intellectual property and technological un/employment and an opportunity to explore the phenomenon “within its real-life context.”278 I use the case of the increasing adoption of electronic trading algorithms in the financial sector. This is one of the most alarming shifts in the labor force today because it shows that even highly skilled individuals can be displaced by current technology.279 At the same time, financial innovation is an area where intellectual property rights, especially patents and trade secrets, have played an important role.280 Examining this case in more depth can give us at least some insight on whether intellectual property is causally related to this potentially life- altering shift.281 Up until the last two decades or so, trading on Wall Street trading still relied mainly on humans using good judgment, gut instincts, and ruthless information-gathering to make winning trades and obtain outsize returns for investors.282 This is steadily is changing because financial firms, from Goldman Sachs to boutique hedge funds, are increasingly turning to computer algorithms to effectuate trades, as well as perform other tasks such as data analysis and designing “robo-based” investment strategies for clients.283
277 Indeed, National Cash Register was “at the forefront” of a larger group of employers in the early twentieth century, such as John Deere and General Electric, seeking to hire high school educated talent. See GOLDIN & KATZ, supra, at 76-77 (discussing how NCR Corp. created new jobs for people with the skills to use, maintain, and install cash register machinery in the early twentieth century). 278 See Ryan Holte, Trolls Or Great Inventors: Case Studies of Patent Assertion Entities, 59 ST. LOUIS U. L. J. 1, 19- 20 (2014) (“Case studies are an important part of empirical research used to illustrate or disprove theories proposed in other analyses. They are an ‘empirical inquiry that investigates a contemporary phenomenon in depth and within its real-life context.’) (quoting ROBERT K. YIN, CASE STUDY RESEARCH: DESIGN AND METHODS 18 (4th ed. 2009). 279 As Forbes reports “[a]lthough it's unlikely that machines will completely displace humans—at least in our lifetime—the conversation is gaining momentum in the world of investing.” John Reese, The Stock Picking Robots Are Coming, FORBES, April 18, 2017, https://www.forbes.com/sites/investor/2017/04/18/the-stock-picking-robots- are-coming/#4e9b9a6d5d93 280 On IP in the financial sector, see generally Megan M. La Belle & Heidi Mandanis Schooner, Big Banks and Business Method Patents, 16 U. PENN. J. BUS. LAW 431, 469-477 (2014). 281 I rely on a recent series of articles in the popular media. See, e.g., Gregory Zuckerman & Bradley Hope, The Quants Run Wall Street Now, THE WALL STREET JOURNAL, May 22, 2017, at A1, available online at https://www.wsj.com/articles/the-quants-run-wall-street-now-1495389108; see also, e.g., Jack Ewing, Robocalypse Now? Bankers Ask, Will Automation Kill Jobs?, THE NEW YORK TIMES, Thursday, June 29, 2017, at B4. To obtain background on trading and quants, I also spoke with several individuals, including a manager of a small boutique hedge fund in Silicon Valley, and several analysists at Wall Street firms. However, they requested their identities and firms remain anonymous. 282 These ideas have been around for a long time but have been gaining traction. According to a 2012 piece by Bloomberg’s Colin Read, academic discussions about applying mathematical concepts to the stock market began around the 1950s, and large investment houses began experimenting thereafter. They began “using these techniques in the 1970s and ’80s to guide trades.” See Colin Read, History of Algorithmic Trading Shows Promise and Perils, BLOOMBERG VIEW, August 8, 2012, https://www.bloomberg.com/view/articles/2012-08-08/history-of-algorithmic- trading-shows-promise-and-perils 283 Justin Baer, Wall Street’s Endangered Species: The College Jock, THE WALL STREET JOURNAL, May 26, 2017 A1 (discussing reports that “one time athletes” are being replaced for “math stars” as trading goes electronic). See also Reese, supra, at 1 (“In the realm of investment and fund management, the use of robo-based investment strategies is also on the rise, using ‘rules’ based strategies and quantitative metrics to select stocks.”).
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The adoption of electronic algorithms has two disparate impacts on the labor force. On the one hand is technological employment.284 The demand for “quants”— highly skilled individuals who typically have Ph.D’s in engineering or mathematics and “who use sophisticated statistical models to find attractive trades”—has skyrocketed.285 The reason firms are fighting tooth and nail for quant talent is that only the quants can achieve the high returns firms and investors desire.286 Quants have, in other words, been augmented by electronic trading technology: their skills are in higher demand relative to available supply, and their wages accordingly lower. 287 On the other side of this shift is technological unemployment. Ordinary human analysts who lack the capacity to employ algorithms are experiencing a simultaneous decline in demand even as the quants’ salaries rise.288 We would hardly consider these analysts low-skill workers (I myself attended college with many of them), but they are nonetheless “lower skill” workers in this specific context. Their professions have been diminished by electronic trading technology: their skills are in lower demand relative to available supply, and their wages are accordingly lower. 289 Wall Street analysts are unlikely to evoke much pity and may have an easier time finding substitute work.290 But they are in this sense not unlike the truck drivers I discuss in the next section. So did intellectual property rights play a role in the adoption of electronic trading algorithms, and therefore in the augmentation of quants and the diminution of ordinary analysts?291 We can tackle this question by assessing the forces that likely drove the change from using humans to primarily electronic trading.292 There was certainly significant “market pull” to adopt machine-based trading, including cost reduction (given the comparatively high cost of human analysts in the finance sector) and, more so, the chance for significantly higher returns from using machines versus humans to effectuate trades.293 Another factor may have been the recent crack-downs on insider trading. Adopting automated trading algorithms becomes
284 See Part II.B.2. 285 Zuckerman & Hope, supra, at A1, A10 (reporting that quants are “gradually taking over the investment world.”). See also id at A1 (writing that “[l]ast year, a bidding war for Mr. Poyarkov [a quant] broke out among hedge-fund heavyweights Renaissance Technologies LLC, Citadel LLC and TGS Management Co. When it was over, he went to work at TGS in Irvine, Calif., and could earn as much as $700,000 in his first year, say people familiar with the contract.”). 286 Studies show that quants using algorithms outperform ordinary analysts in market returns by between 1 and 5%, which can add up to billions of dollars in market returns. See id. at A10 (“The computers are outperforming humans at picking investments.”). 287 See Part II.C.3., defining augmentation. 288 Baer, supra, at A1 (discussing reports that “one time athletes” are being replaced for “math stars” as trading goes electronic). 289 See Part II.C.3., defining diminution. 290 See Nathaniel Popper, The Robots Are Coming for Wall Street, THE NEW YORK TIMES MAGAZINE, Feb. 25, 2016 (“Goldman employees who lose their jobs to machines are not likely to evoke much pity.”), https://www.nytimes.com/2016/02/28/magazine/the-robots-are-coming-for-wall-street.html?_r=0 . 291 See Part II.C.3 (explaining augmentation and diminution). 292 I discuss the McKinsey factors that affect businesses’ decision to automate in Part II.1.a. See McKinsey Report, supra, at 10-12. 293 As Forbes reports, “The use of robots in the workplace has allowed companies to lower production costs, increase efficiency, and fatten margins. … Capital markets firms are now turning to 'automation' to reduce costs, provide better service, and even make complex regulatory implementations work more efficiently.’” Reese, supra, at 1 (quoting PricewaterhouseCoopers).
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comparatively more attractive if it’s harder for humans to (legally) get insider information on trades.294 But market forces favoring adoption mean little if the underlying technology isn’t there. Inventing, testing, and developing trading algorithms is not easy. It requires plentiful simulation and trial and error. Nor is it cheap. Developing software, after all, cannot be done without hiring the quants to develop the algorithms.295 Moreover, the risk of competitors copying a trading strategy is debilitating. Once a trading strategy is adopted by other firms, its value falls drastically, depleting the profits of using the tool in the first place. There are historical examples of this type of “scooping” happening to financial innovators.296 Heavy reliance on intellectual property to prevent imitation would thus likely have been perceived as critical. Two forms of intellectual property would make undertaking these up-front costs less risky: patents and, more so, trade secrets. Patents on financial innovations were common in the financial industry in the 1990s, when Wall Street firms scrambled to get patents on business methods related to trading. While the legality of those patents was to some degree in limbo297, the patent record reveals plentiful patents in the banking and financial services industries.298 But trade secrecy is likely preferred to protect source code and algorithms that can easily be kept secret. Not surprisingly, several trade secrets cases from the last decade involve attempts to protect employees from departing with financial trading strategies and source code.299 In sum, although the point can be debated, the presence of patents and especially trade secrets in the financial industry during a crucial period in the development of trading technology might well have been a driving factor in providing the incentive to invent and commercialize financial innovations that enable electronic trading, and made it technologically feasible to partially automate the task.
294 See Zuckerman & Hope, supra, at A10. 295 Read, supra, at 1. 296 For example, Bloomberg’s Colin Read speculates that the “personal-computer revolution in the ’80s and ’90s” permitted “broad adoption” of trading houses’ “proprietary techniques” for trading, and thus usurped their major technological advantage. Read, supra, at 1. Another example Read gives is the downfall of Long-Term Capital Management (LTCM), a hedge fund founded by John Meriwether in 1994. Id. LTCM “used computers to detect very small and fleeting differentials in securities prices to make huge profits in global bond and derivatives markets in the late ’90s.” Id. But the hedge fund’s “profits were fleeting” because “[t]heir computer trading algorithms were soon imitated by others, which required LTCM to seek out new methodologies and markets. Soon, such risk taking led to the fund’s downfall.” Id. The New York Federal Reserve bailed out LTCM in 1998. Id. 297 Whether business methods qualify as patents was a big controversy that played out in various cases, beginning with the Federal Circuit’s decision in State Street Bank v. Signature Financial Group, 149 F.3d 1368 (Fed. Cir. 1998), which expanded the boundaries of subject matter eligible for patent protection to include any process, including business methods, that produces a “useful, concrete and tangible result” and culminating with Alice v. CLS Bank, 134 S. Ct. 2347 (2014), where the Supreme Court invalidated a patent on a computerized trading platform and announced a more restrictive test for software and business method patents. 298 See La Belle & Schooner, supra, at 469-477. See also Bilski: Crossroads Of Wall Street And State Street, Law 360, October 6, 2008 (“[A] quick search of the [USPTO] database reveals that these and other companies have amassed significant patent portfolios on inventions in the banking and financial services industries.”). 299 See, e.g., Quantlab Technologies, Limited (BVI) v. Kuharsky, et ano., No. 16-20242 (5th Cir. June 22, 2017) (finding plaintiff’s former employees and a business associate liable for misappropriating and using trade secrets pertaining to high frequency trading strategies); see also United States v. Aleynikov, 676 F. 3d 71 (2d Cir. 2012) (reversing on jurisdictional grounds jury conviction of former computer engineer for Goldman Sachs who was found guilty under the Economic Espionage Act for stealing trade secret source code relating to high frequency trading).
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Again, the net result for net employment in the financial industry is not necessarily negative. Even though adoption of electronic trading algorithms means fewer jobs for “finance bros”, it means new work for some highly skilled quants. 300 As I’ll discuss further in the next section, these quants are likely to be very well paid, in part due to their ability to generate valuable intellectual property: namely, the techniques, algorithms, and source code that can be protected from competitors by patents and trade secrets. These well paid quants may in turn may spend more on services like barber shops and nail salons, generating spillovers for the lower skilled workforce.301 And then there is demand-boosting.302 In theory, assuming using electronic trading reduces fees and decreases the costs and risks of trading on the stock market, then more people should seek out the advantages of investing in the stock market through electronically- traded funds.303 This should in turn lead to more hiring of quants. The cycle of technological employment in trading has a theoretical end point. Already, algorithms have made it possible to automate the skill sets of some highly educated Wall Street analysts. As The New York Times Magazine puts it, “it is exactly Goldman’s privileged status that makes the threat to its workers so interesting. If jobs can be displaced at Goldman, they can probably be displaced even more quickly at other, less sophisticated companies, within the financial industry as well as without.” 304 Conceivably, the algorithms could become so intelligent that the quants themselves are no longer necessary to operate them. As Jeffrey Sachs and his colleagues explore in a recent paper, it is at least theoretically possible for the creators of “code”—the software, rules, instructions, and associated data that permits automating outputs— to develop such sophisticated code that they end up replacing themselves.305 It would indeed be greatly ironic if some of the same patent rights that banks fought to achieve in the 1990s are partly responsible for their demise. The issue of IP’s impact on the process of technological un/employment can be explored other case studies using the same framework, as briefly illustrated in Table 2 in the Appendix. 1. The Distribution Effect The Incentive Effect is the most basic way in which IP rights affect technological un/employment. It can be boiled down to the claim that intellectual property probably facilitates and accelerates the pace at which innovation creates and eliminates jobs. The second effect of IP on employment is what I call the Distribution Effect. Under the Distribution Effect, intellectual property magnifies the division between the wages of high skill employees who are capable of
300 This is either pure job generation or job substitution—depending on whether the quants would otherwise have paying jobs if not for their role in the finance sector. See Part II.B.2.a. 301 See Part II.B.2.c. 302 See Part II.B.2.b. 303 According to Reese at Forbes, “[t]he robo trend” in the world of investing “is resulting in efficiencies and lower fees, which are huge draws for clients, especially since many of these strategies are earning better returns than many actively managed funds.” Reese, supra, at 1. 304 Popper, supra, at 1. 305 Jeffrey Sachs and colleagues explore whether this could happen—which, echoing Marx, they call immiseration— using a model and recommend policies. See Seth G. Benzell Laurence J. Kotlikoff Guillermo LaGarda Jeffrey D. Sachs, Robots R Us, NBER Working Paper 20941, http://www.nber.org/papers/w20941, at 1, 22-23 See also Part II.C.1.
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generating intellectual property, on the one hand, and low-skill employees, whose core skills are more easily replaced by machines. a. How The Distribution Effect Works The Distribution Effect has two parts. First, ownership of valuable intellectual property increases returns for innovator firms by giving them a right to exclude that permits them to obtain excess profits that cannot be competed away, and thereby increases wages for those employees who are necessary to generate that intellectual property. 306 Second, at least some subset of that intellectual property permits automation and contributes to lower wages for people whose core skills are easily replaced by labor-saving technology, which, under the Incentive Effect described above, is in turn incented by intellectual property. The upshot is that intellectual property increases the division between “IP-generators”, on the one hand (the winners), and “non-IP generators”, on the other (the losers).307 The main hypothesis generated by the Distribution Effect is that wages for employees who are likely to generate intellectual property, WIP, should be exponentially higher than the wages of employees who lack the capacity to generate intellectual property, W 0. Distribution Effect: WIP >> W0 The upshot of this hypothesis is that intellectual property rights magnify the gulf between the exceptionally high-skill, IP-generating workforce at the top, and the lower-skill, non-IP generating workforce at the bottom. Autonomous vehicles provide an illustration of the Distribution Effect’s predictions. Start-ups are raising millions of dollars in funding in order to develop self-driving vehicle technology.308 Companies and the government poor billions into this research309, and salaries for people working in the area, such as roboticists and engineers who design robotics systems, are famously high.310 For instance, an entire troop of roboticists previously in academia at Carnegie
306 This assume some rents from IP-enhanced innovation go to workers. As I show below economists have attempted to test whether rents from innovation go towards wages for workers. 307 For purposes of simplicity, I divide these into two groups: IP-generators and non-IP-generators. I assume that IP- generators are both high-skill workers and less likely to be replaced by machines, and that non-IP-generators are both low-skill workers and more likely to be replaced by machines. Other commentators on the effects of technological un/employment on wages frequently assume there is only one group of people—high-skill workers versus low-skill workers—while recognizing this is not precise. See, e.g., GOLDIN & KATZ, supra, at 95-96 (“In this simplified depiction, the work-force consists of two groups, the skilled or highly educated (Ls) and the unskilled or less educated (Lu)”). 308 Max Chafkin & Josh Eidelson, These Truckers Work Alongside the Coders Who Are Trying to Eliminate Their Jobs, BLOOMBERG BUSINESSWEEK, June 26, 2017, at 62 (discussing Starsky, Inc., one of a “handful of companies trying to seize a piece of the trucking industry’s $700 billion in annual revenue”, which raised “$5 million in seed capital from, among others, Y Combinator, the Silicon Valley venture fund and incubator.”). See also Liza Lin, China Self-Drive Firm Gets Daimler Funding, THE WALL STREET JOURNAL, Tuesday July 25, 2017, at B2 (discussing Daimler’s investment in a Chinese startup Momenta of $46 million). 309 Bill Vlasicjan, U.S. Proposes Spending $4 Billion on Self-Driving Cars, THE NEW YORK TIMES, January 14, 2016, https://www.nytimes.com/2016/01/15/business/us-proposes-spending-4-billion-on-self-driving-cars.html; see also Toyota spending $1B on self-driving car research, REUTERS, Friday, November 6, 2015, https://www.cnbc.com/2015/11/06/toyota-spending-1b-on-self-driving-car-research.html 310 See, e.g., Johana Buiyan, Ex-Googler Sebastian Thrun says the going rate for self-driving talent is $10 million per person: Now he wants to train more engineers for the fast-growing industry, since there are simply not enough, ROCODE, September 2016, https://www.recode.net/2016/9/17/12943214/sebastian-thrun-self-driving-talent-pool10.
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Mellon were apparently lured away by the prospect of huge salaries at Uber’s new start-up “Otto”, to work on self-driving vehicles.311 Part one of the Distribution Effect hypothesizes that one reason for these expenditures on self-driving car research, and the reason these people are so well paid is the expectation that at the end of the road, they will generate valuable intellectual property like patents and trade secrets, which can be licensed to others and used to prevent competitors from competing away profits. The recent choice of Waymo to enforce its trade secrets against Uber, its main competitor in the self-driving tech field312, certainly suggests that IP relating to self-driving vehicles technology has significant value.313 Their ability to generate intellectual property is obviously not the only reason autonomous vehicle experts are so well paid. Other crucial factors are the scarcity of talent in the self-driving car field (i.e. the “skills gap”314) and the underlying value that self-driving vehicles can bring to businesses who can use them to reduce costs.315 But, the Distribution Effect hypothesizes, without the potential to generate legally protectable intellectual property, these people might not be quite so well paid. Meanwhile, Part two of the Distribution Effect hypothesizes that some of the very same intellectual property that allows IP-generators to achieve higher profits simultaneously threatens the jobs, and lowers the wages, of non-IP-generators like truck drivers. If Otto, Waymo, and the other self-driving vehicle start-ups successfully develop and commercialize self-driving vehicles, this could spell the end of paid employment for the millions people whose jobs involve driving for a living.316 Even now, the wage differential is striking. While base pay for engineers in the self- driving vehicle field is well over $200,000 per year, trucker drivers’ median pay is around
311 Otto has around 50 employees and was co-founder Anthony Levandowski, who sold Otto to Uber for $700 million. See Johana Buiyan, Inside Uber’s self-driving car mess, RECODE, March 24, 2017, https://www.recode.net/2017/3/24/14737438/uber-self-driving-turmoil-otto-travis-kalanick-civil-war 312 Waymo claims that Uber stole trade secrets through Waymo’s former employee Levandowski, when he left Waymo to work at Uber. See Waymo LLC v. Uber Technologies, Inc., No. C 17-00393 WHA, 2017 WL 2123560 (N.D. Cal. May 15, 2017) (bringing claims against Uber for theft of trade secrets under the DTSA as well as under the California Uniform Trade Secrets Act). 313 The connection between litigated IP and valuable IP is seen as strong. See, e.g., John Allison, Mark Lemley, Kimberly Moore, & Derek Trunkey, Valuable Patents, 92 GEO. L. J. 435 (2004) (noting that litigated patents tend to be much more valuable than others on average, and that valuable patents are much more likely than others to be litigated). 314 I discussed the skills gap, where there are not enough people with the requisite skills to fill the jobs ushered in by new technology, in Part II.C.4. 315 I discussed the temptation to automate in order to reduce costs in Part II.B.1. The current prediction is that self- driving vehicles could significantly reduce Uber’s costs in running its ride-sharing business, see, e.g., Rogers, supra, at 100-101 (discussing the temptation for Uber to switch to autonomous vehicles), and reduce costs for many other companies as well, such as Amazon. See, e.g., Laura Stevens & Tim Higgins, Amazon Forms Team to Focus on Driverless Technology, THE WALL STREET JOURNAL, April 24, 2017, https://www.wsj.com/articles/amazon-team- focuses-on-exploiting-driverless-technology-1493035203 (citing experts’ view that Amazon’s self-driving car initiative could help “overcome one of [Amazon’s] biggest logistical complications and costs: delivering packages quickly. Amazon could use autonomous vehicles including trucks, forklifts and drones to move goods. In addition, driverless cars could play a broader role in the future of last-mile delivery, enabling easier package drop-offs[.]”). 316 See Chafkin & Eidelson, supra, at 62 (observing the irony of the fact that truck drivers currently work at start- ups, alongside the people who are profiting from a technology that will one day replace truck drivers).
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$40,000 per year. 317 This difference would not be problematic if the numbers were different— for instance, if there were more jobs available for people to be roboticists working on autonomous vehicles than truck drivers. But at least currently, companies developing autonomous vehicles hire comparatively few human workers in relation to their net worth.318 The American Trucking Association reports that there are “approximately 3.5 million” professional truck drivers in the United States.319 b. Evidence For The Distribution Effect Is intellectual property actually responsible for the division in wages between IP- generators like Otto’s co-founder Anthony Levandowski and non-IP-generators like truck drivers? The above account is purely theoretical, and autonomous vehicles is obviously just one anecdote. However, there is some evidence of intellectual property’s Distribution Effect at work. There are two different evidentiary questions to explore. First: does IP contribute to higher wages for IP-generators (part one of the Distribution Effect)? Second, does IP contribute to lower wages for non-IP-generators (part two of the Distribution Effect)? With respect to the second question, I have already assessed in depth whether intellectual property causally affects technological un/employment as per the Incentive Effect.320 But I have not yet explored the further question of whether the existence of a larger universe of labor-saving innovation due to IP intellectual property (I L ) contributes to lower wages for people who may be replaced by that labor-saving technology. Again, isolating IP’s effect on returns and wages, as opposed to other factors, is extremely difficult.321 But, taken as a whole, the evidence does I think provide some (albeit slim) support for the notion that IP contributes to higher wages for employees necessary to generate IP, and on the flip side, lower wages for those who cannot do so. i. Wages For High Skill Versus Low Skills Workers The most basic piece of support for the Distribution Effect is the fact that since World War II, wages for high-skilled works have risen while wages for low-skilled workers have fallen.322 The primary explanatory thesis for this inequity has been that technology increases
317 Id. (noting that “specialists in AI and machine learning—are even better paid and even harder to hire. Google has been known to pay its self-driving car engineers millions or even tens of millions.”). Paysa estimates the base salary for self driving car engineers is $233,000. https://www.paysa.com/salaries/self-driving-car-engineer--t See also Alan Ohnsman, Autonomous Car Race Creates $400k Engineering Jobs For Top Silicon Valley Talent, FORBES, March 27, 2017, https://www.forbes.com/sites/alanohnsman/2017/03/27/autonomous-car-race-creates-400k-engineering- jobs-for-top-silicon-valley-talent/ 318 Chafkin & Eidelson, supra, at 62 (noting that Otto “had fewer than 100 employees when Uber Technologies Inc. acquired it for $700 million.”). See also WEST, supra note 36, at 6 (“Many of the large tech firms have achieved broad economic scale without a large number of employees. For example, Derek Thompson writes that ‘Google is worth $370 billion but has only about 55,000 employees – less than a tenth the size of AT&T’s workforce in its heyday [in the 1960s].’”). 319 The total number of people employed in the industry, including those in positions that do not entail driving, “exceeds 8.7 million.” http://www.alltrucking.com/faq/truck-drivers-in-the-usa/ 320 As said above, the theoretical case that IP, if it promotes innovation, must also thereby promote labor-saving innovation, is quite strong. And there is some slim evidence, such as the sheer number of patents filed on labor- saving technology. 321 As mentioned above, the USPTO report stresses that this connection is tenuous. USPTO REPORT, supra note 3, at i (“While our methodology does not permit us to attribute [differences in economic indicators such as employment, wages, and value added] to IP alone, the results provide a useful benchmark.”). 322 Using data from the last several decades, several economists have found a correlation between technological advancement, on one hand, and increased wages for high-skill workers and decreased wages for low-skill workers,
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demand for high-skill workers and decreases demand for low-skill workers (skill-biased technological change).323 In theory, it could be that intellectual property rights exacerbate this division by increasing demand for high-skill workers capable of generating IP-protected rents, and decreasing demand for low-skill workers who cannot do so and who, per the Incentive IP Effect, are also more easily replaced by labor-saving innovations stimulated by IP (IL ). The mere fact of this skew by no means proves intellectual property is causally responsible for the skew between high and low skill wages. Indeed, as noted in Part II.C.4, another key factor besides just technology that favors high skill is the supply of workers available. Nonetheless, the skew between wages for high versus low skill workers to some degree supports my hypothesis that people with the skills needed to generate IP may have higher wages because of the returns from that IP, and vice versa. ii. Wages In Innovative Firms A key piece of the Distribution Effect is the contention that employees who are necessary to generate intellectual property rights (IP-generators) will be paid more due to the fact that a firm cannot obtain a right to exclude without those employees. A key link in this claim is the prediction that employers will share some of the profits obtained as a result of intellectual property protection with workers. These profits are sometimes referred to as “rents” generated by innovation.324 Fortunately, the question of whether companies share excess profits obtained from monopoly power due to patents (called “rent sharing”) has been explored in several papers in the field of economics.325 In a 1996 study of over 600 publicly traded British firms from 1945 to 1983, wages for innovating firms were found to be 12% higher than in non-innovating firms.326 The authors found this to be consistent with the theory that rents generated by innovation are shared with workers and that this rent sharing is a significant determinant of higher wages.327 This study strongly supports my hypothesis that IP-generators are paid more than non-IP- generators, and that these higher wages are specifically due to the excess profits companies can obtain from relying on patents and other IP to limit copying and competition.328 Another study of 1000 UK production firms from 1986 to 1995 is also supportive of the IP-wages connection. 329 However, interestingly, the study found that trademark applications and R&D expenditures were associated with higher wages than the industry average, but that patents were not.330 One possible explanation the authors give for this difference is that patents “are
on the other. See, e.g. BRYNJOLFSSON & MCAFEE, RACE AGAINST THE MACHINE, supra note 19, at 39 (discussing “technical change that increases the relative demand for high-skill labor while reducing or eliminating the demand for low-skill labor”). 323 This is the skill-biased technological change thesis, discussed in Part II.C.4. See also GOLDIN & KATZ, supra, at 94-99. 324 See John Van Reenan, The Creation and Capture of Rents: Wages and Innovation in a Panel of U. K. Companies, 111 THE QUARTERLY JOURNAL OF ECONOMICS, 195, 195–226 (1996). 325 See id. See also GREENHALGH & ROGERS, supra note 40, at 272-73, 277-278. 326 Van Reenan, supra, at 195. 327 Id. See also GREENHALGH & ROGERS, supra note 40, at 277 (discussing the Van Reenen study and noting that the study shows that the innovation rents variable is “a significant determinant of higher wages”, with as much as 20- 30% of rents generated through innovation going to workers). 328 GREENHALGH & ROGERS, supra, at 277 (noting that at least two studies support the practice of sharing innovative rents from workers). 329 Id. 330 GREENHALGH & ROGERS, supra note 40, at 277.
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taken out well before a product launch”, whereas trademarks are obtained closer to the “commercial introduction” of the innovation. 331 If we take anything from this explanation, I think it should be the principle that innovations un-commercialized patents are, economically speaking, valueless, and start-ups that go under before commercialzing presumably won’t be paying high wages. 332In any case, this finding certainly conflicts with those of the USPTO report, discussed below, where high levels of patenting was associated with higher-than-average wages, and the effect was more pronounced than for trademarks. iii. Wages In “IP-Intensive” Industries The third piece of evidence for the hypothesis that IP-generators are paid higher wages is the strongest, at least on its face. The 2016 USPTO report on IP and the economy, discussed above, found, using U.S. Census Bureau and USPTO data, that wages in “IP-intensive” industries—industries in which companies own more intellectual property per size—tend to be 46% higher than in other industries that are not classified as IP-intensive.333 The report found the effect was more profound in patent and copyright-intensive industries than in trademark- intensive industries (which is the opposite of the finding of the study above, that trademarks were correlated with higher wages while patents were not).334 The OHIM 2015 study of European data, mentioned above, likewise found that IP-owning EU firms “earn, on average, 29 percent more in revenue per employee and pay, on average, 20 percent more in wages.” 335 Again, even assuming this data is perfect (and many doubt it is), causality is very difficult to prove. Even the USPTO concedes that the causal connection between IP rights themselves and the 47% wage premium is not clear. It may not be IP, specifically, that makes wages higher in IP-intensive industries or IP that makes wages lower in non-IP-intensive industries—it could just as likely be another common factor such as skill level. iv. Wages In High-Patent “Brain Hubs” Fourth, wages in geographic regions with high levels of innovation, the “brain hubs” like Silicon Valley, tend to be higher than wages in other regions.336 Again, this does not link higher pay to intellectual property rights; it just tells us that in places like California—where property prices are higher and people have more education, etcetera—tend to be paid more. That said, research by economic geographer Enrico Moretti suggests that geographic regions with higher
331 Id. The authors speculate that another reason for the difference may be that trademarks are more associated with product innovations (innovations sold to consumers in a market), whereas patents related to “a mixture of product and process innovations” (improvements in methods of production), and so might have more “ambiguous” effects for workers. Id. I am uncertain about the value of this explanation. 332 See Ted Sichelman, Commercializing Patents, 62 STAN. L. REV. 341 (2010). 333 USPTO REPORT, supra note 3, at ii. Specifically, the USPTO report found that
[p]rivate wage and salary workers in IP-intensive industries continue to earn significantly more than those in non-IP- intensive industries. In 2014, the average weekly wage of $1,312 was 46 percent higher (up from 42 percent in 2010) than for workers in non-IP-intensive industries[.]
Id. at 19. 334 Id. 335 Id. at 4 (discussing 2015 study by OHIM). 336 ENRICO MORETTI, THE NEW GEOGRAPHY OF JOBS 71-77, 93-94 (2012).
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levels of patenting per entity tend to have higher wages than other regions.337 Specifically, the metropolitan areas that generate the most patents per capita, such as the San Francisco-San Jose region of California, Boston, Massachusetts-New Hampshire, Austin, Texas, and Seattle-Everett, Washington are the same states that have higher salaries and higher shares of workers with a college degree.338 The caveat here, again, is that we do not know that patents are causing high salaries—it is just as likely to be education level or regional factors like the weather. What do we make of all of this data? Together, it suggests the following correlation: where there are intellectual property rights there are higher wages, and where there are not intellectual property rights there are lower wages. Is this correlation causal? Again, I cannot prove that it is. But I suspect that the ability of firms to raise prices and restrict output, as well as the ability to license resulting IP, probably contributes in some degree to higher wages for people with the skills necessary to generate IP. In turn, I suspect that at least some of those same profitable innovations also contribute to lower wages for people who can easily be replaced by them. IV. The Case For A “Pro-Employment” IP Law If the hypotheses generated in Part III are correct, then intellectual property facilitates and accelerates the pace of technological un/employment, and exacerbates the wedge between well- paid IP-generators and lower-paid non-IP-generators. The upshot is more and better jobs for IP- generators like “quants”, computer programmers, and robotocists, but fewer and worse jobs for everyone else. This contradicts the hopes of the legislators and officials quoted in the introduction, who appear to think that intellectual property rights like patents “spur innovation and create jobs.”339 Therefore, this part explores whether intellectual property law should be altered in some way to be more “pro-employment” than it currently is. An overarching question posed by the assertion that intellectual property should be good for jobs is whether the purpose of the intellectual property system is simply to facilitate innovation in the private sector, or instead has higher social aims, such as helping people achieve satisfying lives and reigning in inequality.340 My own normative take is that intellectual property can and should be designed to do both. It can enhance market incentives to innovate, while also mitigating some of the negative impacts those innovations entail for society. Below I draw out the tension between these views. I posit that unemployment is a negative externality of innovation and that, to the extent the externality risks becoming
337 MORETTI, supra, at 82-86, 72-97. I discuss the correlation between education levels, salaries, and patenting in brain hubs in Hrdy, Patent Nationally, Innovate Locally, 31 BERKELEY TECH. J. 1301, 1317-22 (2017). 338 MORETTI, supra, at 93 (stating that college graduates in brain hubs make about 50% more than college graduates in the bottom group). See also id. at 83-85 (listing top patents per capita); 94 (listing salaries of college grads and non college grads in these metro regions). See also Jonathan Rothwell et al., Patenting Prosperity: Invention and Economic Performance in the United States and its Metropolitan Areas, BROOKINGS INST. (Feb. 2013), at 12–13, https://www.brookings.edu/research/patenting-prosperity-invention-and- economic-performance-in-the-united- states-and-its-metropolitan-areas/ (finding that “most U.S. patents—63 percent—are developed by people living in just 20 metro areas, which are home to 34 percent of the U.S. population. … For patents applied for from 2007 to 2011, the metro areas with the highest number per capita are San Jose; Burlington, VT; Rochester, MN; Corvallis, OR; and Boulder, CO.”). 339 See quote in notes 4 and 5 supra. 340 See generally MADHAVI SUNDER, FROM GOODS TO A GOOD LIFE: INTELLECTUAL PROPERTY AND GLOBAL JUSTICE (2012).
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catastrophic for society, law should play a role in mitigating it. I discuss a variety of solutions, ranging from a universal human income to tax breaks for companies that hire workers. I argue that IP law itself can be designed to alter incentives to innovate in a way that reduces the overall quantity of labor-displacing innovations available for adoption in the marketplace. A. The Hayekian View – Intellectual Property As Handmaiden of the Market The “Hayekian view” of intellectual property law views intellectual property rights mainly as handmaiden of the free market.341 On this view, IP is an especially effective mechanism for stimulating innovation because IP rights do not interfere with the natural progression of private markets and the forces of supply and demand.342 Innovation finance, direct government spending on innovation through mechanisms such as research grants and tax credits, requires government to tell people at some level what to invest in.343 In contrast, intellectual property rights simply permit creators to internalize the benefits of their innovations by excluding others and charging higher prices for goods and services that they would have made anyway. 344 Thus, while innovation finance is “government-set”, IP rights are what Daniel Hemel and Lisa Ouellette call “market-set” incentives.345 The upshot of the Hayekian view is that the government does not, and should not, do very much in terms of telling markets what to do.346 When seen in this light, the fact that intellectual property may have a negative impact on employment by increasing to the size of the universe of labor-saving innovations is not necessarily a problem at all. If, like Queen Elizabeth, legislators today choose to reward or penalize innovations that eliminate too much human employment, this might lead to all sorts of long-term problems. Firms might eschew automation and invest in inefficient “make work” technologies like multi-human-driven motor vehicles or manufacturing methods that require using humans instead of robotic arms. Worse still, workers would not be incentivized to educate themselves for the industries of the future. Unless government somehow succeeds in completing halting automation in its tracks, the “skills-gap”—the division between
341 Friedrich Hayek was an Austrian economist famous for objecting to John Maynard Keyes’ view that government should subsidize demand in order to stimulate spending and employment. See NICHOLAS WAPSHOTT, KEYNES HAYEK: THE CLASH THAT DEFINED MODERN ECONOMICS (2012). 342Amy Kapczynski has observed the linkage between IP theory and the views of Hayek. Amy Kapczynski, The Cost of Price: Why and How to Get Beyond Intellectual Property Internalism, 59 U.C.L.A. L. REV. 970, 982-983 (2012) (“IP scholarship has been deeply influenced by [Harold] Demsetz’s argument (and behind it, the lurking shadow of Friedrich Hayek)”). 343 “Innovation finance” refers to direct public financing for innovation, such as research grants, tax incentives, or public venture capital, in lieu of intellectual property rights. See Camilla A. Hrdy, Patent Nationally, Innovate Locally, supra note 40, at 1304. See also SUZANNE SCOTCHMER, INNOVATION AND INCENTIVES 242–43 (2004) (“[A] single innovation may be funded in two ways: by the public sector out of general revenue, and through proprietary prices under an intellectual property regime.”). 344. As Suzanne Scotchmer put it, a major “lure of intellectual property” as opposed to “public sponsorship” of innovation is that IP automatically “tap[s] ideas for invention” that already pervade in the market[.]” SCOTCHMER, supra, at 38. 345 See Daniel J. Hemel & Lisa Larrimore Ouellette, Beyond the Patents–Prizes Debate, 92 TEX. L. REV. 303, 327 (2013) (defining patents as a form of “market-set” incentive as compared to “government-set” incentives like research grants). See also, e.g., Peter Lee, The Push And Pull Of Patents, 77 FORDHAM L. REV. 2225 (2009) (discussing variety of scholarship in the IP field addressing the question of whether patents exert a significant “pull” on university research, encouraging scientists to favor research with commercial potential). 346 For fuller discussion see Hrdy, Patent Nationally, Innovate Locally, supra note 40, at 1324-25 (“IP relies on innovators and private markets to determine the technical and commercial value of an innovation.”).
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what types of skills the market needs and what is available—would only grow larger, and workers would be worse off.347 One past example, discussed by economics writer Mark Levinson, is France’s response to unemployment in the 1970s. France’s policies to create jobs, Levinson writes, included subsidizing labor-intensive industries that would be most likely to hire large numbers of workers. “These were not the sorts of ideas that would make investors want to put their money in France[,]” Levinson writes, and eventually leaders changed course as they worried about France’s ability to “adapt to a world of rapid technological change and intense global competition.”348 Today, governments will likely explore similar options, if they aren’t already. For example, developing countries with a lot of low skill human labor, like India, may find it in their interests to ban labor-displacing technology like driverless cars. India’s transportation minister has already made public comments about potentially banning self-driving cars. 349 The spirit behind such measures is presumably to halt or at least slow down the pace of automation for the sake of short-term prosperity. But this has the same risks as France’s decision in the 70s—unless all countries sign a compact not to introduce automated vehicles, India will find it difficult to adapt and will ultimately lose business to foreign competitors. For these reasons, intellectual property law is typically seen—I think quite rightly—as a primarily market-directed solution to the problem of incenting innovation in comparison to other legislative options.350 The major doctrinal signal of this feature of IP is that in order to qualify for protection, technologies are almost never judged by their impact on social welfare.351 Intellectual property rights are not denied, or granted, based on whether they “create jobs.” To the contrary, intellectual property rights are granted irrespective of external government goals. They simply magnify existing market inclinations to invent and commercialize. But intellectual property’s Hayekian foundation is of course why we have this problem in the first place. It is intellectual property’s market-magnifying nature (I posited in Part III) that contributes to technological un/employment. It is like a catch 22. If the commentators are right that technological development is gearing towards far fewer jobs, and more inequality in who gets those jobs, then policy solutions are needed. But if the United States uses policy to halt or slow down the pace of change, then this may lead to lower productivity and reduce the country’s standing in the world.352 B. Job Loss As An Externality of Innovation This is not where I would like to end. The free market principles behind the Hayekian view do not prohibit, and indeed encourage, law or regulation in the face of externalities.353 An
347 Part II.C.4.c. 348 MARC LEVINSON, AN EXTRAORDINARY TIME: THE END OF THE POSTWAR BOOM AND THE RETURN OF THE ORDINARY ECONOMY (2016). 349 “‘We will not allow driverless cars in India. We don't need it,’ Transport Minister Nitin Gadkari was quoted as telling reporters on Monday. ‘Each car gives a job to a driver. Driverless cars will take away those jobs,’ Gadkari added.” See http://money.cnn.com/2017/07/25/technology/india-driverless-cars-jobs/index.html 350 See, e.g., Kenneth Dam, The Economic Underpinnings of Patent Law, 23 J. LEGAL STUDIES, 247, 247-248 (1994). But see Mark Lemley, The Regulatory Turn in IP Law, 36 HARV. J. L. & PUB. POLICY (2012). 351 For a discussion of the demise of assessment of social utility in nineteenth century patent law, see, e.g., Oren Bracha, Owning Ideas: A History of Anglo-American Intellectual Property, 99-100 (June 2005) (Ph.D. dissertation, Harvard Law School). 352 Again, an international compact to ban automation is an interesting idea. 353 GREGORY MANKIW, PRINCIPLES OF MICROECONOMICS 11-13 (2010).
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externality is a cost or a benefit conferred on another that is not taken into account by private decisions absent legal or regulatory intervention.354 For example if an energy company produces pollution without raising its costs, this may impose external harms on others in the community. On the flip side, it is the existence of a positive externality—new ideas that are free for others to copy—that leads us to create intellectual property rights to allow innovators to protect their creations and obtain some monetary reward.355 Employment loss can theoretically be conceptualized as a negative externality of productivity-enhancing innovation.356 Labor-saving technologies lower costs and increase profits for companies that adopt them, and generate returns for creators, who (if they are not the same as the adopter) can sell or license the technology to others.357 But they simultaneously produce negative effects for workers who no longer have jobs. These negative effects on workers are not generally internalized by either adopters or creators of labor saving innovation.358 The mass technological unemployment that can result from these decisions is, in other words, a negative externality of labor-saving innovations, not unlike pollution. I am hardly the only one to make this observation. As one economist puts it, Technological developments have contributed enormously to the improvement of human living conditions, replacing physical labor with machines and expanding mental capacity with computer automation. Nevertheless, as we have also seen throughout history, technology can be as destructive as it is constructive. … [T]echnology is creating new jobs but it is also destroying some old ones even faster. In economics, negative consequences not directly accounted for in a transaction, but borne by other third parties (society, future generations, the ecology, and so forth), are known as negative externalities.359 In the words of another economist, “[improvements in technology] make work less taxing, living conditions more comfortable, and health care more extensive. Even though everyone in society benefits from improvements in technology, it does create negative externalities for some segments in the short run.”360 In their paper on technology’s skewed impact on employment, economists Daron Acemoglu & Pascual Restrepo similarly assume that job loss is an externality
354 Brett M. Frischmann & Mark A. Lemley, Spillovers, 107 COLUM. L. REV. 257, 262 (2007) (“[P]ositive (or negative) externalities are benefits (costs) realized by one person as a result of another person's activity without payment (compensation). Externalities generally are not fully factored into a person's decision to engage in the activity.”). 355 Id. 356 Daron Acemoglu & Pascual Restrepo, The Race Between Machine and Man: Implications of Technology for Growth, FACTOR SHARES AND EMPLOYMENT 30 (2016), https://economics.mit.edu/files/11512. 357 See Robert Merges, A Transactional View of Property Rights, 20 BERKELEY TECH. L.J. 1477 (2005) (viewing the key function of patents as facilitating disclosure and transfer of information related to innovations from creators to the most effective developers). 358 Harm to a company’s brand is one way in which companies can suffer from the choice to lay off large numbers of workers. See Margaret Chon, Tracermarks, 53 HOUSTON L. REV. 421, 428-29 (2017) (discussing consumer boycotts and the transaction costs involved in getting one going). 359 Ernest Chi-Hin Ng, “Taxing the Robots and Other Externalities”, Buddhistdoor Global, 2017-03-17, https://www.buddhistdoor.net/features/taxing-the-robots-and-other-externalities. 360 Loren Nerhus, Automation and the Labor Force, MAJOR THEMES IN ECONOMICS 65, 66 (2014) (“For example, when the assembly line was introduced, it caused unemployment for the craftsmen and artisans who made the product from start to finish. The technological improvement, the assembly line, created more value for society, but destroyed jobs for some.”).
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of labor-saving innovation, noting that automation “reduces employment…and this has first- order effect on workers”. “[I]innovators do not internalize this externality.”361 To be clear, externalities are everywhere. Minor job loss brought about by innovation is part and parcel of societal advance. But there is a growing sense that the externality is getting out of control, especially for certain segments of the populations. Return to the example of the drivers. Labor law scholar Brishen Rogers has voiced concern about the coming fall-out from the development self-driving vehicles.362 “After the ride-sharing drivers’ jobs are eliminated,” Rogers wonders, “what happens to mail-and package-delivery drivers whom Uber also hopes to displace? What happens to supermarket clerks and other retail clerks as more and more shopping goes online, with goods delivered by—yes—Uber? What happens to the fast-food workers displaced as Uber or some other ‘Internet of things’ company delivers fast food on demand in driverless cars?”363 In Rogers’ view, “ideally the stunning productivity gains promised by new technologies” like self-driving cars could “reduce society’s need for work that is deadening to the human spirit”, but “without far-reaching changes to our social safety net,” this shift “would render tens of millions destitute.”364 In other words, at least some people think the externality is severe enough to warrant notice. Especially when we take into account my points in Part I.C.3—that technological unemployment is about the quality of remaining jobs, not just the quantity—the fact that millions of workers have been or will soon be replaced or diminished in the near future is, I’d say, a catastrophic externality. C. Prior Proposals For Correcting the Job Loss Externality So what is the solution? The classic way to “correct” externalities is to use law and regulation to shape incentives. For instance, regulations ban pollutants or tax polluters in order to force them to internalize the negative effects of pollution and reduce their inclination to pollute.365 IP laws like patents, copyrights, and trade secrets, improve innovators’ ability to internalize the profits of their creations and thereby provides incentives to innovate at more efficient levels for the benefit of all.366 In a similar vein we could use law to force the relevant actors—either creators or adopters of automation technologies—to internalize the social costs of this innovation on workers. Below I identify the main policy solutions commentators have so far proposed for mitigating the impact of technology on jobs. These proposals seek to fulfill two intertwined but distinct functions: provide some form of subsidy for displaced workers, and alter the incentives of the actors whose decisions to replace human workers with machines create job loss externalities. 1. Universal Basic Income
361 See Acemoglu & Restrepo, supra mote 292, at 30 362 Rogers, supra note 13, at 100 (observing that Uber may eventually “wield the possibility of shifting to driverless cars to prevent drivers from organizing,” and that “there is little reason to think it will not do so when feasible.”) 363 Id. at 101. 364 Id. (“I worry that such a crisis in the low-wage labor market is close on the horizon, and that society is unprepared to deal with it.”) 365 See Richard Epstein, Waste & the Dormant Commerce Clause, 3 GREEN BAG 29, 35– 36 (1999) (discussing ways to limit shipments of waste into other states’ borders in order to prevent externalities). 366 R. Polk Wagner, Information Wants to Be Free: Intellectual Property and the Mythologies of Control, 103 COLUM. L. REV. 995 (2003) (explaining theory that IP serves to preserve incentives to generate information in the face of spillovers).
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If workers are displaced by technology and find themselves out of work, they are presumably going to need some form of income to survive. One option is a universal basic income (UBI): a guaranteed base-level income funded through the tax system that would give people a way to survive even without having paying jobs. The Finnish government is currently experimenting with this option. In the Finnish trial, a partial UBI is adopted that amounts to around 560 Euros (around 650 U.S. dollars) each month. Currently, the target group consists solely of social welfare recipients such as people already on unemployment.367 But the idea is that in future a UBI could be for everyone, not just people on welfare already. Alternatively, the UBI could be entirely need-based and be available only for the unemployed (like the current Finnish program) or for people who fall below a minimum poverty threshold (i.e. welfare.)368 Mainstream thinkers such as Robert Reich of University of California, Berkeley have publically supported a universal human income.369 Professor Rogers, discussed above, has also expressed support.370 Even some people in the private sector have stated that we may need to turn to “Keynesian policies” (i.e. government spending supported by taxation) to help those whose jobs have been displaced.371 In my view, the UBI has several problems associated with it. First, it is not clear general taxpayers are willing to pay for a UBI. Presumably, the tax would need to come from general tax collections, a progressive tax upon the uber-wealthy, or (as I discuss below) specifically from
367 See Luisa Scarcella and Michaela Georgina Lexer, The effects of artificial intelligence on labor markets – A critical analysis of solution models from a tax law and social security law perspective, Working Paper, presented at We Robot 2017, Yale Information Society Project (discussing various iterations of a “unconditional basic income” (UBI) and the Finnish government’s experiment with a partial UBI), available at http://www.werobot2017.com/wp- content/uploads/2017/03/Conference-Paper-LEXER-SCARCELLA-WeRobot2017.pdf 368 A pure Rawlsian solution would redistribute specifically to the most disadvantaged. JOHN RAWLS, A THEORY OF JUSTICE (1971). 369 Reich has stated that a universal basic income “will almost certainly be part of the answer” to a jobless future. “The economy we’re heading toward”, Reich states, could offer millions of people more free time to do what they want to do instead of what they have to do to earn a living. But to make this work, we’ll have to figure out some way to recirculate the money from the handful of people who design and own i-Everythings, to the rest of us who will want to buy i-Everythings.
One answer: A universal basic income – possibly financed out of the profits going to such labor replacing innovations, or perhaps even a revenue stream off of the underlying intellectual property.
Robert Reich, Why We’ll Need a Universal Basic Income, September 29, 2016, http://robertreich.org/post/151111696805 370 At Yale Law School’s Spring 2017 conference on the legal implications of robots and artificial intelligence, Rogers, along with European scholars Luisa Scarcella and Michaela Georgina Lexer, argued in favor of adopting a UBI in order to alleviate some of the short and long-term suffering of workers displaced by the shift to self-driving cars. See The Return of the Blog: WeRobot 2017, April 2, 2017, http://www.lawandai.com/2017/04/02/the-return- of-the-blog/ 371As one A.I. inventor puts it, it is “unavoidable that large chunks of the money created by A.I. will have to be transferred to those whose jobs have been displaced. This seems feasible only through Keynesian policies of increased government spending, presumably raised through taxation on wealthy companies.” Jonathan Taplin, Can the Tech Giants Be Stopped?, THE WALL STREET JOURNAL, Saturday/Sunday, July 15-16, at C2 (quoting (“Very few politicians have been willing to grapple with the possibility of mass unemployment caused by AI and robotics, but others see clear policy implications.”).
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companies that profit from automating work. This may not gain political traction in the current political climate.372 Second, the prospect of guaranteed subsistence payments could have perverse effects on peoples’ incentives to work and gain the satisfaction that comes with (paid) employment. In at least some iterations, the UBI comes with an obvious “incentive trap:” people may lose the incentive to work since they know they can rely on a UBI. Especially if the UBI is based to some degree on need, then people who are unemployed may not wish to re-enter the labor market and lose entitlement to parts of their benefits, since “even though they started working, they do not end up with more money than through social benefits.”373 Some people see this as a positive. It could be good, Reich suggests, if people could “have more free time to do what they want instead of what they have to do to earn a living”.374 I am less optimistic about the draws of a no- work future. Perhaps a superior option to a universal income is a “skills training fund” that helps re- train workers to take on new jobs. Whereas a UBI gives everyone enough income to afford basic necessaries without working, the goal of the skills training fund is to give everyone the best opportunity to obtain work in the new “automated economy.” 375 The skills training idea has some support among economists.376 A Citi survey on policy responses to automation ranked investing in education as the “most likely” response.377 As I have discussed in other work, skills training programs are already administered by several state and local governments.378 I personally prefer the skills training fund to the UBI and see improved education, particularly at the local level, as essential for resolving current skills gaps.379 Yet training people to work in the “automated economy” does not ensure a complete solution to technological unemployment of the future. According to some of the commentators discussed in Part II.C., it could be that, due to the pervasiveness and quality of automation, no matter what skills they have people will not have jobs.
372 Sonia Sodha of The Guardian, for instance, observes that while some polls show support for the idea of a basic income “[the poll] invariably fails to ask voters whether they would be prepared to countenance the sort of tax levels needed to fund it. A basic income would therefore require a fundamental shift in our politics: leaders who are comfortable advocating unpopular tax rises.” Sonia Sodha, Is Finland’s basic universal income a solution to automation, fewer jobs and lower wages? THE GUARDIAN, Sunda, February 19, 2017 (emphasis added), https://www.theguardian.com/society/2017/feb/19/basic-income-finland-low-wages-fewer-jobs 373 See Scarcella & Lexer, supra, at 9. See also id. at 10 (noting that “the Finnish Government’s experiment focuses on the question of whether a basic income can be seen as a (dis)incentive to take up a job”). 374 Reich, supra note 307, at 1. 375 See, e.g., Claire Cain Miller & Jess Bidgood, Preparing Young Children For The Automated Economy, THE NEW YORK TIMES, Tuesday, August 1, 2017, at A15 (“Jobs are likely to be very different [in the automated economy], but we don’t know which will still exist, which will be done by machines and which new ones will be created. To prepare, children need to start as early as preschool, educators say.”). 376 Bessen, for instance, conceptualizes education as a positive externality, like basic research, and sees public funding for skills training as essential. JAMES BESSSEN, LEARNING BY DOING: THE REAL CONNECTION BETWEEN INNOVATION, WAGES AND WEALTH (2015). 377 Citi/Oxford report, supra, at 98 (showing results of Citi survey asking people to rank policy responses to the risks of automation impacting labor). 378 See Hrdy, Patent Nationally, Innovate Locally, supra note 40, at 1372-73 (discussing local programs directed at enhancing science, technology, engineering, and math (STEM) training). 379 Camilla A. Hrdy, Some Reflections on Localism, Innovation, and Jim Bessen's New Book "Learning by Doing", WRITTEN DESCRIPTION, August 1, 2015, http://writtendescription.blogspot.com/2015/08/some-reflections-on- innovation-localism.html
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This leads to my third and final critique: neither a guaranteed income nor a high quality, skills-focused education do anything, on their own, to alter the state of labor-saving technology or to mitigate the strong incentives of companies to automate work as soon as it is cost effective to do so. A more complete solution would also seek to alter the incentives of adopters of labor- saving technology (and potentially of creators as well) in order to intentionally nudge innovation towards a future in which people can work in satisfying jobs if they want to. The options I discuss below do this, seeking to intentionally alter incentives to adopt technology that eliminates human labor. 2. Taxes On Companies That Automate The UBI would potentially alleviate the hardship of workers who are or will be replaced by machines. Another related set of proposals focuses on altering companies’ incentives to automate work by using the tax system. One proposal that has received a lot of attention to tax companies that choose to replace human employees with machines in order to force them to internalize the negative externalities and at least marginally alter their incentives to automate.380 The tax proceeds could in turn go towards a UBI, a skills-training fund, or some other form of subsidy for displaced workers or the poor. In an interview, Bill Gates recently discussed two iterations of this: either taxing profits derived from adopting labor-saving innovations381 and taxing the owners of robots used in businesses to replace workers (a so-called “robot tax”).382 The latter would be paid from the company that adopts the robot at a rate that replicates the level that would be paid by human workers performing the same tasks.383 3. Tax Credits For Companies That Add Jobs The opposite of a robot tax is a job creation credit: a tax credit for companies that start businesses or implement innovations that ultimately add a certain number of jobs.384 Like the robot tax on automating firms, a job credit should theoretically dampen firms’ incentives to adopt labor-saving innovations and make work for people who might otherwise be displaced. But it does so using a carrot versus a stick.385 Rather than taxing companies that use machines and
380 Acemoglu and Restrepo, for example, theorize that, “[i]n addition to the usual taxes and subsidies to internalize monopoly markups and technological externalities, the social planner will need to impose a tax on automation … in order to combat the tendency of the decentralized equilibrium to automate excessively.” Acemoglu & Restrepo, supra, at 30 (emphasis added). See also Ng, supra, at 1 (“Some of these negative externalities can be addressed through taxation and/or surcharges.”) 381 “Certainly there will be taxes that relate to automation[,]” Gates predicted. “There are many ways to take that extra productivity and generate more taxes. … Some of it can come on the profits that are generated by the labor- saving efficiency there.” Full interview in “The robot that takes your job should pay taxes, says Bill Gates”, QUARTZ, February 17, 2017, https://qz.com/911968/bill-gates-the-robot-that-takes-your-job-should-pay-taxes/ 382 Gates explained this “robot tax” idea as follows:
Right now, the human worker who does, say, $50,000 worth of work in a factory, that income is taxed and you get income tax, social security tax, all those things. If a robot comes in to do the same thing, you’d think that we’d tax the robot at a similar level. Id. 383 See id. 384 Acemoglu and Restrepo theorize something akin to a job creation credit, stating that, along with or in lieu of a tax on automation, government could offer “a subsidy on the creation of new tasks” in order to dampen incentives to “automate excessively.” Acemoglu & Restrepo, supra, at 30. 385 On carrots versus sticks in innovation policy, see Ian Ayers & Amy Kapczynski, Innovation Sticks: The Limited Case for Penalizing Failures to Innovate, 81 U. CHI. L. REV. 1781 (2015).
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giving the proceeds to displaced workers, job creation credits increase companies’ incentives to hire human workers. The notion of a job creation tax credit is not just theoretical. As I have previously discussed, state and local governments currently employ a wide range of job creation tax credits—many of them specifically directed at start-ups and companies that invest in innovative technologies.386 An example of a recent high-profile job creation incentive was the city of Reno’s gift of a billion dollars in tax incentives to electric car maker Tesla, in exchange for Tesla’s promise to add 6500 permanent jobs. 387 Tesla cannot automate these jobs without breaching its arrangement with the city. The goal of these job creation incentives—I argue—is not just to stimulate local hiring and technological employment by attracting profitable companies into the region, though this is certainly a major goal.388 It is also to intentionally dampen firms’ natural inclination to adopt labor-saving technologies. If these job creation credits were more widely adopted or federalized we might see wide-scale alternations in the types of innovations that are adopted. 389 D. Employing Intellectual Property Law The taxation ideas discussed above all share certain features that make them potentially attractive policy solutions for mitigating the job loss externality. First, they force companies to internalize at least some of the cost their choices impose on workers and thus alter their incentives to automate work. Second, they redistribute some of the proceeds from automation- enabling innovations to displaced workers. Third, they are to some degree market-set.”390 Unlike an outright ban or a patent subject matter bar, taxes don’t tell firms what to do or prohibit them from doing what they want. They just take a small slice of companies’ profits or give a subsidy for taking a certain action. Here I argue that intellectual property law itself provides another potential avenue for effectuating the same goals. This seems counterintuitive. IP law is first and foremost a way to encourage innovation, not a way to affect employment. But if we accept my premise in Part II that intellectual property rights aggravate technological unemployment and aggravate the division of rewards between certain types of workers, and if we accept that job losses caused by innovation are potentially sufficiently catastrophic to warrant intervention, then by the same token intellectual property law itself can be used to mitigate these problems. The key is that, unlike the proposals discussed above—the robot tax and the job creation credit—intellectual property levers would allow policymakers to target decisions of innovators themselves: that is
386 See Hrdy, Patent Nationally, Innovate Locally, supra note 40, at 1371-72. 387 See Jeff Muskus & Jillian Goodman, eds., Reno-vating, BLOOMBERG BUSINESSWEEK, June 26, 2017, at 19 (“[In]n 2014 Tesla Inc. chose the Tahoe Reno Industrial Center to build what will be the largest factory in the world and promised to create 6,500 permanent jobs in exchange for $1.3 billion in tax incentives.”). 388 Hrdy, Patent Nationally, Innovate Locally, supra note 40, at 1354 65-67 (discussing the localized benefits of state innovation incentives, including “gains in employment”, “retention of a highly educated labor force”, “expansion of the tax base” and “growth in related service industries.”) (quoting Terrance McGuire, Note, A Blueprint for Disaster? State Sponsored Venture Capital Funds for High Technology Ventures, HARV. J.L. & TECH. 419, 419 (1994)). 389 See Camilla Hrdy, with Jennifer Kangas, The (Anti)Innovation State, Working Paper, On File With The Author. Data currently being compiled and is on file with the author. 390 Hemel & Ouellette, supra, at 303 (arguing that tax credits, like patents, are a “market set” way to promote innovation because innovators still get to choose what to invest in and how much).
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the people who create the innovations that are eventually used in commerce to displace or create jobs, and who own the rights to those innovations. 1. Subject Matter Bar For Labor-Displacing Inventions The bluntest and most obvious way to use intellectual property law to mitigate the job loss externality is to deny patents on technologies that are predicted to eliminate large numbers of human jobs. The most natural way to accomplish this goal would be to reinvigorate the utility requirement of Section 101 of the Patent Act.391 As presently interpreted by the Patent Office and courts, the utility requirement does not scrutinize the moral or economic implications of inventions.392 There are only two main requirements for utility. The patent must show the invention is “operable” (i.e. work for its stated purpose)393