The Economic Impact of Artificial Intelligence

 

Ramin Toloui

Professor of the Practice

Stanford University

rtoloui@stanford.edu

 

Economic Policy Seminar (Econ 101)

Department of Economics

Fall Quarter 2020

Mon & Wed 1:00-2:50pm

Course Description

 

The objectives of this class are to provide students with:

 

(1) frameworks for analyzing the prospective impact of artificial intelligence/machine learning on employment, wages, inequality, industrial organization, international competition, and governance;
(2) historical and empirical perspective on the impact of technological revolutions and automation on living standards, economic activity, income distribution, and social institutions; and
(3) a structured opportunity to learn and practice a range of oral and written communication skills that are critical to future success in diverse fields.

 

Requirements

 

Course objectives will be accomplished by examining the history of mechanization and automation; trends in labor market and industrial concentration in recent decades alongside IT adoption; and methods economists are using to analyze the impact of AI.  We will explore these topics through lectures, discussion sessions, and wide-ranging readings. Key requirements include:

 

Active participation in all class discussions (20 points). Contributing meaningfully to these discussions requires thoughtful reflection on advance readings for each class. Participation is a key determinant of the overall course grade.

 

Leadership of a class discussion session (12 points). Successful moderation entails effectively framing key themes to be unpacked and eliciting participation from peers.

 

Brief written summary of discussion sessions for three weekly readings (7 points). Reporting on meetings is a vital task in business, academia, and government – doing so effectively requires an ability to extract key points, summarize disagreements, and be succinct. (450-500 words)

 

Final Paper (51 points). Students will identify a question of interest, specify an approach to answering that question, and produce a comprehensive final product. Various milestones during the quarter provide opportunities to incorporate guidance from the instructor and TA: initial proposal (750‐1000 words, 7 points*), partial draft (1250‐1750 words, 7 points*), full draft (2500‐3500 words, 7 points*), and final submission + response to comments (2500‐3500 words excluding response to comments, 30 points). For the first three submissions (marked with *), full points will be awarded as long as the assignments are submitted on time and meet specified guidelines.

 

Final Presentation (10 points). Students will present the key findings of their final paper to their peers in a presentation of approximately 10 minutes during the last week of the course.

 

Course Gameplan

 

Week 1:  What Do Economists Need to Know About Artificial Intelligence?

Define artificial intelligence and machine learning; explain how machine learning helps solve Polayni’s paradox and enables automation of new categories of tasks previously beyond the capabilities of machines; highlight dangers of algorithmic bias; demystify how neural networks work.

 

Discussion Readings

• Brynjolfsson, Erik and Andrew McAfee (2017). “The Business of Artificial Intelligence: What it Can—and Cannot—Do for Your Organization.” Harvard Business Review, July. (https://starlab-alliance.com/wp-content/uploads/2017/09/The-Business-of-Artificial-Intelligence.pdf) – September 14
• Levy, Frank (2018). “Computers and Populism: Artificial Intelligence, Politics, and Jobs in the Near Term.” Oxford Review of Economic Policy, Volume 34, Number 3, pp. 393–417. (https://www.russellsage.org/sites/default/files/gry004.pdf) – September 16
• Buolamwini, Joy and Timnit Gebru (2018). “Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification.” Proceedings of Machine Learning Research, Vol. 81, pp. 1–15. (http://proceedings.mlr.press/v81/buolamwini18a/buolamwini18a.pdf) – September 16

 

Supplementary and Technical Readings

• Lewis-Kraus, Gideon (2016). “The Great AI Awakening.” New York Times, December 14. (https://www.nytimes.com/2016/12/14/magazine/the-great-ai-awakening.html)
• Susskind, Daniel (2018). “Re-Thinking the Capabilities of Machines in Economics.” Oxford Department of Economics Discussion Paper 825, April. (https://www.economics.ox.ac.uk/materials/working_papers/45/825-susskind-april18.pdf)
• Lee, Nicol Turner, Paul Resnick, and Genie Barton (2019). “Algorithmic Bias Detection and Mitigation.” Brookings Institution, October 30. (https://www.brookings.edu/research/algorithmic-bias-detection-and-mitigation-best-practices-and-policies-to-reduce-consumer-harms/)
• Goodfellow, Ian, Yoshua Bengio, and Aaron Courville (2016). “Introduction” in Deep Learning. Cambridge: The MIT Press, pp. 1-26. (https://www.deeplearningbook.org/contents/intro.html)

 

Week 2:  A Brief History of Technological Revolutions and Living Standards
Provide an overview of the history of economic development from the agricultural revolution to the Industrial Revolution; explain how mechanization facilitated an escape from the Malthusian trap; describe worker displacement and unequal distribution of benefits (Engel’s pause) as new technologies were adopted; describe how diffusion of innovation led to new products and services that generated employment and rising living standards, epitomized by post-WWII United States.

 

Discussion Readings

• Craig, Lee (2016). “Antebellum Puzzle: The Decline in Heights at the Onset of Modern Economic Growth.” In John Komlos and Inas Kelly (Eds.), The Oxford Handbook of Economics and Human Biology, Oxford: Oxford University Press, pp. 751-764. (https://searchworks.stanford.edu/articles/edsoho__edsoho.9780199389292.013.12) – September 21
• Bessen, James (2015). “When Does Technology Raise Wages?” (Chapter 5) and “How the Weavers Got Good Wages” (Chapter 6) in Learning By Doing: The Real Connection between Innovation, Wages, and Wealth, New Haven: Yale University Press, pp. 71-100). (https://ebookcentral.proquest.com/lib/stanford-ebooks/detail.action?docID=3421590) – September 23
• Gordon, Robert (2016). “The Great Leap Forward from the 1920s to the 1950s: What Set of Miracles Created It?” (Chapter 16) in The Rise and Fall of American Growth, Princeton: Princeton University Press, pp. 535-565. (https://ebookcentral.proquest.com/lib/stanford-ebooks/detail.action?docID=4987197) – September 23

 

Supplementary and Technical Readings

• Frey, Carl Benedict (2019). “The Industrial Revolution and Its Discontents” (Chapter 5) in The Technology Trap: Capital, Labor, and Power in the Age of Automation. Princeton: Princeton University Press, pp. 112-139.
• Mokyr, Joel, Chris Vickers, and Nicolas Ziebarth (2015). “The History of Technological Anxiety and the Future of Economic Growth: Is This Time Different?” Journal of Economic Perspectives. 29(3): 31-50. (https://pubs.aeaweb.org/doi/pdfplus/10.1257/jep.29.3.31)
• Katz, Lawrence and Robert Margo (2014). “Technical Change and the Relative Demand for Skilled Labor: The United States in Historical Perspective.” In L. Boustan, C. Frydman, and R.A. Margo (Eds.), Human Capital in History. Chicago: University of Chicago Press and NBER, 15-57. (https://www.nber.org/chapters/c12888.pdf)
• Keynes, John Maynard (1930). “Economic Possibilities for Our Grandchildren.” (http://www.econ.yale.edu/smith/econ116a/keynes1.pdf)

 

Week 3:  Does Technology Explain Rising Inequality Since the 1970s?
Describe the rising inequality and wage polarization that has characterized labor market patterns in the United States since the 1970s; review the key explanations for these outcomes, including (i) skill-biased technological change (SBTC) & the race between technology and education (ii) job polarization from information technology adoption; (iii) demographics; (iv) international trade and globalization; and (v) decline of labor unions.

 

Discussion Readings

• Beaudry, Paul, David Green, and Benjamin Sand (2016). “The Great Reversal in the Demand for Skill and Cognitive Tasks.” Journal of Labor Economics, 34(1), pp. S199-S247 (Section II from pp. S207-S220 may be skimmed). (https://searchworks.stanford.edu/articles/edsbl__CN601040138) – September 28
• Mishel, Lawrence and Josh Bivens (2017). “The Zombie Robot Argument Lurches On: There is No Evidence that Automation Leads to Joblessness or Inequality.” Economic Policy Institute Report, May. (https://www.epi.org/files/pdf/126750.pdf) – September 30
• Russo, John and Sherry Lee Linkon (2009). “The Social Costs of Deindustrialization” in Manufacturing a Better Future for America, pp. 149-174. (https://drive.google.com/file/d/1blzkkSX_c1INQZVFlAOcTQyXguOH0lA0/view) – September 30

 

***Paper Proposal Due - Friday, October 2 @ 11:59pm***

 

Supplementary and Technical Readings

• Autor, David (2015). “Why Are There Still So Many Jobs? The History and Future of Workplace Automation.” Journal of Economic Perspectives, 29(3), Summer, pp. 3–30
  (https://pubs.aeaweb.org/doi/pdfplus/10.1257/jep.29.3.3)
• Goldin, Claudia and Lawrence Katz (2007). “The Race Between Education and Technology: The Evolution of U.S. Educational Wage Differentials, 1890-2005.” National Bureau of Economic Research Working Paper 12984, March. (https://www.nber.org/papers/w12984.pdf)
• Autor, David, David Dorn, and Gordon Hanson (2015). “Untangling Trade and Technology: Evidence from Local Labour Markets.” The Economic Journal, 125, May, pp. 621-646. (https://economics.mit.edu/files/11564)
• Farber, Henry, Daniel Herbst, Ilyana Kuziemko, and Suresh Naidu (2018). “Unions and Inequality Over the Twentieth Century: New Evidence from Survey Data.” National Bureau of Economic Research Working Paper 24587, May. (https://www.nber.org/papers/w24587.pdf)

 

Week 4:  How Has Technology Affected Productivity, Market Structure and Industrial Concentration?

Provide an overview of the changes in industrial concentration since the 1980s; review empirical work on the rise of superstar firms and implications for compensation of labor and capital; describe network effects and the economics of “winner-take-all” markets; evaluate the hypotheses to explain the puzzle of low productivity growth; analyze the potential impact of algorithms on competition.

 

Discussion Readings

• Appelbaum, Eileen (2017). “Domestic Outsourcing, Rent Seeking, and Increasing Inequality.” Review of Radical Political Economics, 49(4), pp. 513–528. (https://journals.sagepub.com/doi/pdf/10.1177/0486613417697121) – October 5
• Harrington, Joseph (2019). “Developing Competition Law for Collusion by Autonomous Agents.” Journal of Competition Law & Economics, pp. 1-33. (https://joeharrington5201922.github.io/pdf/jcle18.pdf) – October 7
• Stucke, Maurice and Ariel Ezrachi (2016). “Is Your Digital Assistant Devious?” Oxford Legal Studies Research Paper No. 52. (https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2828117) – October 7

 

Supplementary and Technical Readings

• Brynjolfson, Erik, Daniel Rock, and Chad Syversson (2017). “Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics.” National Bureau of Economic Research Working Paper 24001, November. (https://www.nber.org/papers/w24001.pdf)
• David Autor, David Dorn, Lawrence Katz, Christina Patterson, and John Van Reenen (2020). “The Fall of the Labor Share and the Rise of Superstar Firms.” Quarterly Journal of Economics, May. (https://economics.mit.edu/files/12979)
• Barwise, Patrick and Leo Watkins (2019). “The Evolution of Digital Dominance” in Martin Moore and Damian Tambini, eds., Digital Dominance: The Power of Google, Amazon, Facebook, and Apple, pp 21-46. (http://www.lse.ac.uk/law/Assets/Documents/orla-lynskey/orla-3.pdf)
• Barth, Erling, Alex Bryson, James Davis, and Richard Freeman (2016). “It’s Where You Work: Increases in the Dispersion of Earnings Across Establishments and Individuals in the United States.” Journal of Labor Economics. 34(S2), pp. S67 S97. (https://scholar.harvard.edu/files/freeman/files/wp_20447_-_its_where_you_work_2015.pdf)

 

Week 5:  How Can We Analyze the Prospective Impact of Artificial Intelligence?
Examine the channels through which artificial intelligence may affect the demand for various types of jobs, relations between capital and labor, consumption demand for different types of goods and services, distribution of income and wealth, and the overall structure of economic activity.

 

Discussion Readings

• Acemoglu, Daron and Pascual Restrepo (2019). “The Wrong Kind of AI? Artificial Intelligence and the Future of Labour Demand.” Cambridge Journal of Regions, Economy and Society, November. (https://economics.mit.edu/files/18782) – October 12
• AI Now Institute (2019). “AI, Power, and Control” in AI Now 2019 Report, pp. 14-24 (https://ainowinstitute.org/AI_Now_2019_Report.pdf) – October 14
• Ajay Agrawal, Joshua Gans, and Avi Goldfarb (2019). “Artificial Intelligence: The Ambiguous Labor Market Impact of Automating Prediction.” Journal of Economic Perspectives, Spring, 33(2), 31–50. (https://pubs.aeaweb.org/doi/pdf/10.1257/jep.33.2.31) – October 14

 

Supplementary and Technical Readings

• Webb, Michael (2020). “The Impact of Artificial Intelligence on the Labor Market.” Stanford University unpublished working paper, January. (https://www.michaelwebb.co/webb_ai.pdf)
• Nordhaus, William. (2015). “Are We Approaching an Economic Singularity? Information Technology and the Future of Economic Growth.” National Bureau of Economic Research Working Paper 21547. (https://www.nber.org/papers/w21547.pdf)
• Sachs, Jeffrey and Lawrence Kotlikoff (2012). “Smart Machines and Long-Term Misery.” National Bureau of Economic Research Working Paper 18629, December. (https://www.nber.org/papers/w18629.pdf)
• Thompson, Derek (2015). “A World Without Work.” The Atlantic, July/August. (https://www.theatlantic.com/magazine/archive/2015/07/world-without-work/395294/)

 

Week 6:  What are the Global Economic and Geopolitical Consequences of AI?
Analyze the potential impact of artificial intelligence on global trade patterns, paths to economic development, relationships between citizens and governments, and competition among nations.

 

Discussion Readings

• Lee, Kai-Fu (2018). “The Four Waves of AI.” White paper adapted from AI Superpowers: China, Silicon Valley, and the New World Order. Boston: Houghton Mifflin. (http://storage.googleapis.com/aisp-assets/pdf/the-four-waves-of-ai.pdf?mtime=20180926123520) – October 19
• Baldwin, Richard (2019). “Telemigration and the Globotics Transformation” in The Globotics Upheaval: Globalization, Robotics, and the Future of Work, Oxford: Oxford University Press, pp. 115-145. (https://ebookcentral.proquest.com/lib/stanford-ebooks/detail.action?docID=5626386) – October 21
• Ünver, H. Akın (2018). “Artificial Intelligence, Authoritarianism and the Future of Political Systems.” Cyber Governance and Digital Democracy 2018/9 Paper, Centre for Economics and Foreign Policy Studies, July. (https://edam.org.tr/wp-content/uploads/2018/07/AKIN-Artificial-Intelligence_Bosch-3.pdf) – October 21

 

***Partial Draft Due - Friday, October 23 @ 11:59pm***

 

Supplementary and Technical Readings

• Rodrik, Dani (2018). “New Technologies, Global Value Chains, and the Developing Economies.” Pathways for Prosperity Commission Background Paper Series No. 1. Oxford. (https://drodrik.scholar.harvard.edu/files/dani-rodrik/files/new_technologies_global_value_chains_developing_economies.pdf)

·       Pratt, Gill (2015). “Is a Cambrian Explosion Coming for Robotics?” Journal of Economic Perspectives, Summer, 29(3), pp. 51-60. (https://pubs.aeaweb.org/doi/pdfplus/10.1257/jep.29.3.51)

• Wang, Binbin and Xiaoyan Li (2017). “Big Data, Platform Economy and Market Competition: A Preliminary Construction of Plan-Oriented Market Economy System in the Information Era.” World Review of Political Economy, 8(2) Summer, pp. 138-161. (https://www.researchgate.net/publication/318611551_Big_Data_Platform_Economy_and_Market_Competition_A_Preliminary_Construction_of_Plan-Oriented_Market_Economy_System_in_the_Information_Era)
• Roberts, Anthea, and Henrique Choer Moraes and Victor Ferguson (2019). “Toward a Geoeconomic Order.” Journal of International Economic Law 22(4), pp. 655-676 (https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3540818)

 

Week 7:  Can Artificial Intelligence Help Solve Problems of Economics and Governance?
Examine the potential for artificial intelligence to address Baumol’s cost disease of the service sector, improve health and education, expand opportunity, and create a more inclusive society.

 

Discussion Readings

• Poo, Ai-Jen (2019). “The Caring Professionals” in The Age of Dignity: Preparing for the Elder Boom in a Changing America, pp. 71-105. (https://searchworks.stanford.edu/articles/nlebk__907262) – October 26
• Topol, Eric (2019). “Deep Empathy” (Chapter 13) in Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again, New York: Basic Books. (https://searchworks.stanford.edu/articles/nlebk__1850752) – October 26
• Lanier, Jaron and E. Glen Weyl (2018). “A Blueprint for a Better Digital Society.” Harvard Business Review, September. (https://canvas.stanford.edu/files/6015865/download?download_frd=1) – October 28

 

***October 28 – Guest Speaker: JARON LANIER***

 

Jaron Lanier is one of the most creative thinkers on the impact of technology on society and decisions that will shape its future. His books include Who Owns the Future?, You Are Not A Gadget, Dawn of the New Everything, and Ten Arguments for Deleting Your Social Media Accounts Right Now. We look forward to welcoming him to class.

 

***Full Draft Due - Friday, October 30 @ 11:59pm***

 

Supplementary and Technical Readings

• Trajtenberg, Manuel (2018). “AI as the Next GPT: A Political Economy Perspective.” NBER Working Paper 24245, January. (https://www.nber.org/papers/w24245.pdf)
• Obermeyer, Ziad, Brian Powers, Christine Vogeli, and Sendhil Mullainathan (2019). “Dissecting Racial Bias in an Algorithm used to Manage the Health of Populations.” Science, 355(6464), October 25, pp. 447-452. (https://science.sciencemag.org/content/366/6464/447)
• Heller, Nathan (2018). “Who Really Stands to Win from Universal Basic Income?” The New Yorker, July 9 & 16 Issue. (https://www.newyorker.com/magazine/2018/07/09/who-really-stands-to-win-from-universal-basic-income)
• Helland, Eric and Alexander Tabarrok (2019). Why Are the Prices So Damn High? Health, Education, and the Baumol Effect. Arlington: Mercatus Center. (https://www.mercatus.org/system/files/helland-tabarrok_why-are-the-prices-so-damn-high_v2.pdf)

 

Weeks 8 and 9:  Individual Meetings to Discuss Full Drafts & Final Presentations

 

Week 10:  Final Presentations by Students (November 16 and 18)

 

***Final Paper & Response to Comments Due - Friday, November 20 @ 11:59pm***

 

Course Readings

 

All course readings are designated in the Course Gameplan section above.

 

Readings must be completed before the accompanying lecture/discussion. It is impossible to absorb the material and contribute meaningfully (see “Active participation in all class discussions” above) without completing the readings in advance.

 

All readings are available online, either through publicly available links or via online books that can be accessed through Stanford Libraries SearchWorks.

 

No Late Work & Other Course Policies

 

All courses taught in the Stanford Department of Economics are governed by a common set of course management rules. A document explaining these rules can be found on the Economics Department website at https://economics.stanford.edu/undergraduate/major/economics-common-syllabus. Please be sure to read this document in its entirety, and contact me if you have any questions. Note that it is your responsibility to familiarize yourself with these policies, and failure to do so does not constitute grounds for exceptions from these policies.

 

Late work submitted after designated deadlines will not be accepted (it will be marked as a zero).

 

Common Deadlines

 

Paper Proposal (end of Week 3) - Friday, October 2 @ 11:59pm
Partial Draft Paper (end of Week 6) - Friday, October 23 @ 11:59pm
Full Draft Paper (end of Week 7) - Friday, October 30 @ 11:59pm
Final Paper + Response to Comments (end of Week 10) - Friday, November 20 @ 11:59pm
Summary of Discussion Session - due by Friday @ 11:59pm at the end your assigned week.

 

Please see “Reference on Course Requirements” in the Files tab of Canvas for additional details on course requirements.

 

Office Hours

 

Ramin Toloui’s office hours will be Wednesdays 3:00-4:00pm via Zoom. Office hours for TA Brian Higgins will be Tuesdays 9:30-10:30am via Zoom.

 

Final Paper Idea Generation / Paper Proposal

 

A word of warning and preparation: the process of finding a good topic for your final paper is challenging! Getting an early start is very important, so that by Week 4 you can get down to writing rather than still refining a topic.

 

Start with a broad idea that you are interested in, and voraciously read/skim all you can find on it. Based on what you find, you may modify your topic or be led in a new direction.

 

You will likely encounter some dead ends and switchbacks. That is not wasted effort, but rather a natural part of the process.

 

In terms of generating some initial ideas, have a look through the syllabus for a broad selection of readings on various topics. Skim those papers, and check out their bibliographies.

 

Another useful resource to explore is the hub for the National Bureau of Economic Research (NBER)’s work on artificial intelligence (https://www.economicsofai.com). There you will find content presented at several conferences on economics/AI, as well as recommended papers under various topic headings.