The Short-Term Effects of Generative Artificial Intelligence on Employment: Evidence from an Online Labor Market

References

• Abadie A, Athey S, Imbens GW, Wooldridge JM (2023) When should you adjust standard errors for clustering? Quart. J. Econom. 138:1–35.Crossref, Google Scholar
• Acemoglu D, Autor D (2011) Skills, tasks and technologies: Implications for employment and earnings. Handbook of Labor Economics, vol. 4 (Elsevier, New York), 1043–1171.Google Scholar
• Acemoglu D, Restrepo P (2018) Artificial intelligence, automation, and work. The Economics of Artificial Intelligence: An Agenda (University of Chicago Press, Chicago), 197–236.Google Scholar
• Acemoglu D, Restrepo P (2020) Robots and jobs: Evidence from US labor markets. J. Political Econom. 128:2188–2244.Crossref, Google Scholar
• Acemoglu D, Autor D, Hazell J, Restrepo P (2022) Artificial intelligence and jobs: Evidence from online vacancies. J. Labor Econom. 40:S293–S340.Crossref, Google Scholar
• Agrawal A, Gans JS, Goldfarb A (2019a) Artificial intelligence: The ambiguous labor market impact of automating prediction. J. Econom. Perspective 33:31–50.Crossref, Google Scholar
• Agrawal A, Gans JS, Goldfarb A (2019b) Exploring the impact of artificial intelligence: Prediction versus judgment. Inform. Econom. Policy 47:1–6.Crossref, Google Scholar
• Allen R, Choudhury P (2022) Algorithm-augmented work and domain experience: The countervailing forces of ability and aversion. Organ. Sci. 33:149–169.Link, Google Scholar
• Argyres NS, Zenger TR (2012) Capabilities, transaction costs, and firm boundaries. Organ. Sci. 23:1643–1657.Link, Google Scholar
• Autor DH, Levy F, Murnane RJ (2003) The skill content of recent technological change: An empirical exploration. Quart. J. Econom. 118:1279–1333.Crossref, Google Scholar
• Bailey DE (2022) Emerging technologies at work: Policy ideas to address negative consequences for work, workers, and society. Industry Labor Related Rev. 75:527–551.Crossref, Google Scholar
• Bailey DE, Faraj S, Hinds PJ, Leonardi PM, von Krogh G (2022) We are all theorists of technology now: A relational perspective on emerging technology and organizing. Organ. Sci. 33:1–18.Link, Google Scholar
• Balasubramanian N, Ye Y, Xu M (2022) Substituting human decision-making with machine learning: Implications for organizational learning. Acad. Management Rev. 47:448–465.Crossref, Google Scholar
• Barach MA, Horton JJ (2021) How do employers use compensation history? Evidence from a field experiment. J. Labor Econom. 39:193–218.Crossref, Google Scholar
• Berg J, Raj M, Seamans R (2023) Capturing value from artificial intelligence. Acad. Management Discovery 9(4):424–428.Crossref, Google Scholar
• Bertrand M, Duflo E, Mullainathan S (2004) How much should we trust differences-in-differences estimates? Quart. J. Econom. 119:249–275.Crossref, Google Scholar
• Brynjolfsson E, Hui X, Liu M (2019) Does machine translation affect international trade? Evidence from a large digital platform. Management Sci. 65:5449–5460.Link, Google Scholar
• Brynjolfsson E, Li D, Raymond LR (2023) Generative AI at work. NBER Working Paper No. 31161, National Bureau of Economic Research, Cambridge, MA.Crossref, Google Scholar
• Brynjolfsson E, Mitchell T, Rock D (2018a) What can machines learn and what does it mean for occupations and the economy? AEA Papers Proc., vol. 108 (American Economic Association, Nashville, TN), 43–47.Crossref, Google Scholar
• Brynjolfsson E, Rock D, Syverson C (2018b) Artificial intelligence and the modern productivity paradox: A clash of expectations and statistics. The Economics of Artificial Intelligence: An Agenda (University of Chicago Press, Chicago), 23–57.Google Scholar
• Burtch G, Lee D, Chen Z (2023) The consequences of generative AI for UGC and online community engagement. Preprint, submitted May 1, https://dx.doi.org/10.2139/ssrn.4521754.Google Scholar
• Cameron AC, Gelbach JB, Miller DL (2008) Bootstrap-based improvements for inference with clustered errors. Rev. Econom. Statist. 90:414–427.Crossref, Google Scholar
• Cao X, Zhang D (2020) The impact of forced intervention on AI adoption. Preprint, submitted September 16, https://dx.doi.org/10.2139/ssrn.3640862.Google Scholar
• Cengiz D, Dube A, Lindner A, Zipperer B (2019) The effect of minimum wages on low-wage jobs. Quart. J. Econom. 134:1405–1454.Crossref, Google Scholar
• Cheng Z, Lee D, Tambe P (2022) Innovae: Generative AI for understanding patents and innovation. Preprint, submitted April 13, https://dx.doi.org/10.2139/ssrn.3868599.Google Scholar
• Choudhury P, Starr E, Agarwal R (2020) Machine learning and human capital complementarities: Experimental evidence on bias mitigation. Strategic Management J. 41:1381–1411.Crossref, Google Scholar
• Cockburn IM, Henderson R, Stern S (2018) The impact of artificial intelligence on innovation: An exploratory analysis. The Economics of Artificial Intelligence: An Agenda (University of Chicago Press, Chicago), 115–146.Google Scholar
• Cowgill B, Dell’Acqua F, Deng S, Hsu D, Verma N, Chaintreau A (2020) Biased programmers? Or biased data? A field experiment in operationalizing AI ethics. Proc. 21st ACM Conf. Econom. Comput. (Association for Computing Machinery, New York), 679–681.Google Scholar
• Dell’Acqua F, McFowland E, Mollick ER, Lifshitz-Assaf H, Kellogg K, Rajendran S, Krayer L, et al.. (2023) Navigating the jagged technological frontier: Field experimental evidence of the effects of AI on knowledge worker productivity and quality. Working paper, Harvard Business School Technology & Operations Management Unit, Cambridge, MA.Google Scholar
• Eloundou T, Manning S, Mishkin P, Rock D (2023) Gpts are gpts: An early look at the labor market impact potential of large language models. Preprint, submitted March 17, https://arxiv.org/abs/2303.10130.Google Scholar
• Faraj S, Pachidi S, Sayegh K (2018) Working and organizing in the age of the learning algorithm. Inform. Organ. 28:62–70.Crossref, Google Scholar
• Farber HS, Herbst D, Kuziemko I, Naidu S (2021) Unions and inequality over the twentieth century: New evidence from survey data. Quart. J. Econom. 136:1325–1385.Crossref, Google Scholar
• Feigenbaum J, Gross DP (2024) Answering the call of automation: How the labor market adjusted to mechanizing telephone operation. Quart. J. Econom. 139(3):1879–1939.Crossref, Google Scholar
• Felten EW, Raj M, Seamans R (2018) A method to link advances in artificial intelligence to occupational abilities. AEA Papers Proc., vol. 108 (American Economic Association, Nashville, TN), 54–57.Crossref, Google Scholar
• Felten EW, Raj M, Seamans R (2023) Occupational heterogeneity in exposure to generative AI. Preprint, submitted April 19, https://dx.doi.org/10.2139/ssrn.4414065.Google Scholar
• Furman J, Seamans R (2019) AI and the economy. Innovative Policy Econom. 19:161–191.Crossref, Google Scholar
• Furman JL, Teodoridis F (2020) Automation, research technology, and researchers’ trajectories: Evidence from computer science and electrical engineering. Organ. Sci. 31:330–354.Link, Google Scholar
• Goldberg S, Johnson G, Shriver S (2023) Regulating privacy online: An economic evaluation of the GDPR. Amer. Econom. J. Microeconom. 16(1):325–358.Google Scholar
• Goldfarb A, Trefler D (2018) Artificial intelligence and international trade. The Economics of Artificial Intelligence: An Agenda (University of Chicago Press, Chicago), 463–492.Google Scholar
• Grant RM (1996) Toward a knowledge-based theory of the firm. Strategic Management J. 17:109–122.Crossref, Google Scholar
• Horton JJ (2010) Online labor markets. Saberi A. ed. Proc. 6th Internat. Workshop Internet Network Econom. (Springer-Verlag, Berlin, Heidelberg), 515–522.Google Scholar
• Horton JJ (2017) Price floors and employer preferences: Evidence from a minimum wage experiment. Preprint, submitted January 16, https://dx.doi.org/10.2139/ssrn.2898827.Google Scholar
• Jin GZ (2018) Artificial intelligence and consumer privacy. The Economics of Artificial Intelligence: An Agenda (University of Chicago Press, Chicago), 439–462.Google Scholar
• Kellogg KC, Valentine MA, Christin A (2020) Algorithms at work: The new contested terrain of control. Acad. Management Ann. 14:366–410.Crossref, Google Scholar
• Krakowski S, Luger J, Raisch S (2023) Artificial intelligence and the changing sources of competitive advantage. Strategic Management J. 44:1425–1452.Crossref, Google Scholar
• Lebovitz S, Lifshitz-Assaf H, Levina N (2022) To engage or not to engage with AI for critical judgments: How professionals deal with opacity when using AI for medical diagnosis. Organ. Sci. 33:126–148.Link, Google Scholar
• Lifshitz-Assaf H, Ng F, Kittur A, Kraut R (2019) Using technology to augment professionals, instead of replacing them, for innovative problem solving. Working paper, Harvard Business School, Cambridge, MA.Google Scholar
• Lipsitz M, Starr E (2022) Low-wage workers and the enforceability of noncompete agreements. Management Sci. 68:143–170.Link, Google Scholar
• Liu J, Xu X, Li Y, Tan Y (2023) Generate the future of work through AI: Empirical evidence from online labor markets. Preprint, submitted August 9, https://arxiv.org/abs/2308.05201.Google Scholar
• Luo X, Qin MS, Fang Z, Qu Z (2021) Artificial intelligence coaches for sales agents: Caveats and solutions. J. Marketing 85:14–32.Crossref, Google Scholar
• Luo X, Tong S, Fang Z, Qu Z (2019) Frontiers: Machines vs. humans: The impact of artificial intelligence chatbot disclosure on customer purchases. Marketing Sci. 38:937–947.Abstract, Google Scholar
• Masclans R, Hasan S, Cohen WM (2023) The commercial potential of science and its realization: Evidence from a measure using a large language model. Preprint, submitted June 25, https://dx.doi.org/10.2139/ssrn.4481928.Google Scholar
• Mintzberg H (1980) Structure in 5’s: A synthesis of the research on organization design. Management Sci. 26:322–341.Link, Google Scholar
• Mokyr J, Vickers C, Ziebarth NL (2015) The history of technological anxiety and the future of economic growth: Is this time different? J. Econom. Perspective 29:31–50.Crossref, Google Scholar
• Mollick E, Mollick L (2023) Assigning AI: Seven approaches for students, with prompts. Preprint, submitted June 13, https://arxiv.org/abs/2306.10052.Google Scholar
• Murray A, Rhymer J, Sirmon DG (2021) Humans and technology: Forms of conjoined agency in organizations. Acad. Management Rev. 46:552–571.Crossref, Google Scholar
• Noy S, Zhang W (2023) Experimental evidence on the productivity effects of generative artificial intelligence. Preprint, submitted March 6, https://dx.doi.org/10.2139/ssrn.4375283.Google Scholar
• OpenAI (2023) Gpt-4 technical report. Preprint, submitted March 15, https://arxiv.org/abs/2303.08774.Google Scholar
• Overby E, Slaughter SA, Konsynski B (2010) Research commentary—The design, use, and consequences of virtual processes. Inform. Systems Res. 21:700–710.Link, Google Scholar
• Peng S, Kalliamvakou E, Cihon P, Demirer M (2023) The impact of AI on developer productivity: Evidence from github copilot. Preprint, submitted February 13, https://arxiv.org/abs/2302.06590.Google Scholar
• Pfeffer J, Salancik G (2015) External control of organizations—Resource dependence perspective. Organizational Behavior, vol. 2 (Routledge, Oxfordshire, UK), 373–388.Google Scholar
• Raisch S, Krakowski S (2021) Artificial intelligence and management: The automation–augmentation paradox. Acad. Management Rev. 46:192–210.Crossref, Google Scholar
• Reshef O (2023) Smaller slices of a growing pie: The effects of entry in platform markets. Amer. Econom. J. Microeconom. 15:183–207.Crossref, Google Scholar
• Schanke S, Burtch G, Ray G (2021) Estimating the impact of “humanizing” customer service chatbots. Inform. Systems Res. 32:736–751.Link, Google Scholar
• Sun C, Shi ZJ, Liu X, Ghose A (2019) The effect of voice AI on digital commerce. NYU Stern School of Business, HKUST Business School Research paper. Preprint, submitted November 5, http://dx.doi.org/10.2139/ssrn.3480877.Google Scholar
• Syverson C (2004) Market structure and productivity: A concrete example. J. Political Econom. 112:1181–1222.Crossref, Google Scholar
• Tambe P, Cappelli P, Yakubovich V (2019) Artificial intelligence in human resources management: Challenges and a path forward. California Management Rev. 61:15–42.Crossref, Google Scholar
• Troncoso I, Fu R, Malik N, Proserpio D (2023) Algorithm failures and consumers’ response: Evidence from Zillow. Preprint, submitted July 24, https://dx.doi.org/10.2139/ssrn.4520172.Google Scholar
• Tschang FT, Almirall E (2021) Artificial intelligence as augmenting automation: Implications for employment. Acad. Management Perspective 35:642–659.Crossref, Google Scholar
• Tucker C (2018) Privacy, algorithms, and artificial intelligence. The Economics of Artificial Intelligence: An Agenda (University of Chicago Press, Chicago), 423–437.Google Scholar
• van Inwegen E, Munyikwa ZT, Horton JJ (2023) Algorithmic writing assistance on jobseekers’ resumes increases hires. NBER Working Paper No. 30886, National Bureau of Economic Research, Cambridge, MA.Google Scholar
• Webb M (2019) The impact of artificial intelligence on the labor market. Preprint, submitted November 15, https://dx.doi.org/10.2139/ssrn.3482150.Google Scholar
• Yilmaz ED, Naumovska I, Aggarwal VA (2023) AI-driven labor substitution: Evidence from Google Translate and ChatGPT. Preprint, submitted April 17, http://dx.doi.org/10.2139/ssrn.4400516.Google Scholar
• Zammuto RF, Griffith TL, Majchrzak A, Dougherty DJ, Faraj S (2007) Information technology and the changing fabric of organization. Organ. Sci. 18:749–762.Link, Google Scholar
• Zhang M, Sun T, Luo L, Golden J (2021) Consumer and AI co-creation: When and why human participation improves AI creation. USC Marshall School of Business Research Paper, Los Angeles.Google Scholar