NBER

Daniel Rock

MIT Sloan School of Management
100 Main Street, E62-412
Cambridge, MA 02142

E-Mail: EmailAddress: hidden: you can email any NBER-related person as first underscore last at nber dot org
Institutional Affiliation: University of Pennsylvania

NBER Working Papers and Publications

June 2020COVID-19 and Remote Work: An Early Look at US Data
with Erik Brynjolfsson, John J. Horton, Adam Ozimek, Garima Sharma, Hong-Yi TuYe: w27344
We report the results of a nationally-representative sample of the US population during the COVID-19 pandemic. The survey ran in two waves from April 1-5, 2020 and May 2-8, 2020. Of those employed pre-COVID-19, we find that about half are now working from home, including 35.2% who report they were commuting and recently switched to working from home. In addition, 10.1% report being laid-off or furloughed since the start of COVID-19. There is a strong negative relationship between the fraction in a state still commuting to work and the fraction working from home. We find that the share of people switching to remote work can be predicted by the incidence of COVID-19 and that younger people were more likely to switch to remote work. Furthermore, states with a higher share of employment in inf...
October 2018The Productivity J-Curve: How Intangibles Complement General Purpose Technologies
with Erik Brynjolfsson, Chad Syverson: w25148
General purpose technologies (GPTs) such as AI enable and require significant complementary investments, including co-invention of new processes, products, business models and human capital. These complementary investments are often intangible and poorly measured in the national accounts, even when they create valuable assets for the firm. We develop a model that shows how this leads to an underestimation of productivity growth in the early years of a new GPT, and how later, when the benefits of intangible investments are harvested, productivity growth will be overestimated. Our model generates a Productivity J-Curve that can explain the productivity slowdowns often accompanying the advent of GPTs, as well as the increase in productivity later. We use our model to analyze empirically the h...
January 2018Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics
with Erik Brynjolfsson, Chad Syverson
in The Economics of Artificial Intelligence: An Agenda, Ajay Agrawal, Joshua Gans, and Avi Goldfarb, editors
We live in an age of paradox. Systems using artificial intelligence match or surpass human-level performance in more and more domains, leveraging rapid advances in other technologies and driving soaring stock prices. Yet measured productivity growth has declined by half over the past decade, and real income has stagnated since the late 1990s for a majority of Americans. We describe four potential explanations for this clash of expectations and statistics: false hopes, mismeasurement, redistribution and implementation lags. While a case can be made for each explanation, we argue that lags have likely been the biggest contributor to the paradox. The most impressive capabilities of AI, particularly those based on machine learning, have not yet diffused widely. More importantly, like other gen...
November 2017Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics
with Erik Brynjolfsson, Chad Syverson: w24001
We live in an age of paradox. Systems using artificial intelligence match or surpass human level performance in more and more domains, leveraging rapid advances in other technologies and driving soaring stock prices. Yet measured productivity growth has declined by half over the past decade, and real income has stagnated since the late 1990s for a majority of Americans. We describe four potential explanations for this clash of expectations and statistics: false hopes, mismeasurement, redistribution, and implementation lags. While a case can be made for each, we argue that lags have likely been the biggest contributor to the paradox. The most impressive capabilities of AI, particularly those based on machine learning, have not yet diffused widely. More importantly, like other general purpos...

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