Best Selling Books by Erik Brynjolfsson

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 information work including management, professional and related occupations were more likely to shift toward working from home and had fewer people laid off or furloughed. We find no substantial change in results between the two waves, suggesting that most changes to remote work manifested by early April.

Remote work surged during the Covid pandemic but there is disagreement about the extent of the change. To address this question, we field a new, nationally-representative survey: the Remote Life Survey (RLS). We find that in October 2020, 31.6 percent of the continuously employed workforce always worked from home (WFH) and 21.9 percent sometimes or rarely WFH, totaling 53.5 percent. We compare our results with alternative measurement approaches, with a focus on government surveys and provide estimates on the impact of four factors: (a) differences among mail versus web-based survey respondents, (b) differences in the inclusion of self-employed workers, (c) the industry mix of the sample, and (d) the exclusion of people who were already remote pre-pandemic. We find that the last explanation (d) explains the bulk of the difference in estimates between the Current Population Survey (CPS) and other measures of remote work. Policymakers and researchers who turn to the BLS-CPS data series for an estimate of remote work prevalence in the American economy should note that it might be underestimating WFH levels by up to 25 percentage points. Under our preferred estimates, we find that about half of the U.S. workforce worked remotely at least one day each week as of December 2020.

General purpose technologies like information technology typically require complementary firm-specific investments to create value. These complementary investments produce a form of capital, which is typically intangible and which we call digital capital. We create an extended firm-level panel on IT labor investments (1990-2016) using data from LinkedIn. We then apply Hall''s Quantity Revelation Theorem to compute both prices and quantities of digital capital over recent decades. We find that 1) digital capital prices vary significantly over time, peaking around the dot-com boom in 2000, 2) significant digital capital quantities have accumulated since the 1990s, with digital capital accounting for at least 25% of firms'' assets by the end of our panel, 3) that digital capital has disproportionately accumulated in a small subset of “superstar” firms and its concentration is much greater than the concentration of other assets, and 4) that digital capital accumulation predicts firm-level productivity about three years in the future.

Information flows, and thus information technology (IT) are central to the structure of firms and markets. Using data from the U.S. Census Bureau, we provide firm-level evidence that increases in IT intensity are associated with increases in firm size and concentration in both employment and sales. Results from instrumental variables and long-difference models suggest that the effect is likely causal. The effect of IT on size is more pronounced for sales than employment, which leads to a decline in the labor share, consistent with the "scale without mass" theory of digitization. Furthermore, we find that IT provides greater benefits to larger firms by increasing their capability to replicate their operations across establishments, markets, and industries. Our findings provide empirical evidence suggesting that the substantial rise in IT investment is one of the main driving forces for the increase in firm size, decline of labor share, the growth of superstar firms, and increased market concentration in recent years.

In an effort to reveal the fine-grained relationships between IT use, patterns of information flows, and individual information-worker productivity, we study task level practices at a midsize executive recruiting firm. We analyze both project-level and individual-level performance using: (1) detailed accounting data on revenues, compensation, project completion rates, and team membership for over 1300 projects spanning 5 years, (2) direct observation of over 125,000 email messages over a period of 10 months by individual workers, and (3) data on a matched set of the same workers'' self-reported IT skills, IT use and information sharing. These detailed data permit us to econometrically evaluate a multistage model of production and interaction activities at the firm, and to analyze the relationships among key technologies, work practices, and output. We find that (a) IT use is positively correlated with non-linear drivers of productivity; (b) the structure and size of workers'' communication networks are highly correlated with performance; (c) an inverted-U shaped relationship exists between multitasking and productivity such that, beyond an optimum, more multitasking is associated with declining project completion rates and revenue generation; and (d) asynchronous information seeking such as email and database use promotes multitasking while synchronous information seeking over the phone shows a negative correlation. Overall, these data show statistically significant relationships among technology use, social networks, completed projects, and revenues for project-based information workers. Results are consistent with simple models of queuing and multitasking and these methods can be replicated in other settings, suggesting new frontiers for IT value and social network research.

Researchers have recently been able to understand organizations at an unprecedented level of detail using new digital records and electronic communication data. However, while digital communication is important in the modern workplace, face-to-face interaction still represents a large and important share of organizational communication, information exchange, socialization and informal coordination. Unfortunately, few techniques exist to collect face-to-face communication data at the same level of granularity as electronic communication. In this essay, we introduce and discuss a new set of research tools and methodologies collectively known as Sociometric Badges - wearable sensing devices designed to collect data on face-to-face communication and interaction in real time. To highlight opportunities and challenges for IS research, we discuss a) the design and function of the technology, b) potential opportunities for IS and management research, c) key trade-offs, challenges and research design choices, and d) important limitations of the tools and techniques. We believe this set of technologies, which will soon be publicly available for research purposes at low cost, could portend a dramatic improvement in our understanding of human behavior at unprecedented levels of granularity and therefore enable IS researchers to explore new research questions and more accurately address existing lines of research.

Dozens of markets of all types are in the early stages of a revolution as the Internet and related technologies vastly expand the variety of products that can be produced, promoted and purchased. Although this revolution is based on a simple set of economic and technological drivers, the authors argue that its implications are far-reaching for managers, consumers and the economy as a whole. This article looks at what has been dubbed the "Long Tail" phenomenon, examining how customers derive value from an important characteristic of Internet markets: the ability of online merchants to help consumers locate, evaluate and purchase a far wider range of products than they can typically buy via the traditional brick-and-mortar channels. The article examines the Long Tail from both the supply side and the demand side and identifies several key drivers. On the supply side, the authors point out how e-tailers'' expanded, centralized warehousing allows for more offerings, thus making it possible for them to cater to more varied tastes. On the demand side, tools such as search engines, recommender software and sampling tools are allowing customers to find products outside of their geographic area. The authors also look toward the future to discuss second order amplified effects of Long Tail, including the growth of markets serving smaller niches.

We study the staggered introduction of a generative AI-based conversational assistant using data from 5,179 customer support agents. Access to the tool increases productivity, as measured by issues resolved per hour, by 14 percent on average, with the greatest impact on novice and low-skilled workers, and minimal impact on experienced and highly skilled workers. We provide suggestive evidence that the AI model disseminates the potentially tacit knowledge of more able workers and helps newer workers move down the experience curve. In addition, we show that AI assistance improves customer sentiment, reduces requests for managerial intervention, and improves employee retention.

Digital technologies may make some tasks, jobs and firms more resilient to unanticipated shocks. We extract data from over 200 million U.S. job postings to construct an index for firms'' resilience to the Covid-19 pandemic by assessing the work-from-home (WFH) feasibility of their labor demand. Using a difference-in-differences framework, we find that public firms with high pre-pandemic WFH index values had significantly higher sales, net incomes, and stock returns than their peers during the pandemic. Our results indicate that firms with higher digital resilience, as measured through our pre-pandemic WFH index, performed significantly better in general, and in non-essential industries in particular, where WFH feasibility was necessary to continue operation. The ability to use digital technologies to work remotely also mattered more in non-high-tech industries than in high-tech ones. Lastly, we find evidence that firms with lower pre-pandemic WFH feasibility attempted to catch up to their more resilient competitors via greater software investment. This is consistent with a complementarity between digital technologies and WFH practices. Our study''s results are robust to a variety of empirical specifications and provide a first look at how WFH practices improved resilience to a major, unanticipated social and economic shock.

GDP is a measure of production and yet it is widely used as a proxy for well-being. It is particularly ill-suited for assessing the contributions of digital goods which are free to consumers and thus excluded from GDP measures. This underscores the need to develop new measures of well-being which can assess not only the contributions of digital goods but also welfare more generally. In Brynjolfsson, Eggers, and Gannamaneni (2017), we propose a new way of measuring consumer welfare using massive online choice experiments. This brief paper motivates the need for such an approach and introduces the method.

Recent years have seen dramatic changes in data storage and processing technologies. New opportunities to collect and leverage data have led many managers to change how they make decisions -- relying less on intuition and more on data. In this paper we provide the first systematic empirical study of the diffusion of data-driven decision-making and the factors influencing its adoption.

The success of online games such as “Second Life” and “World of Warcraft” shows the popularity of virtual worlds and reveals the economic systems embedded in them. A large number of players interact with each other in cyberspace, giving rise to an interesting phenomenon where players voluntarily create their own economies that involve trading virtual items or game money for real money. This real-for-virtual-money trading itself has become a several billion-dollar business. In this study, located in Korea, we analyze the economic impacts of the trading of virtual properties and the management strategies of the virtual economy (game) operators by using a two-period game theoretic model between the game players and game operators. In this model, players endogenously switch between seller and buyer roles. We find that real-money trading benefits game operators, and there exists an optimal amount in the supply of game items that maximizes their profits. We also find that the income disparity in the real world can be reduced when real-money trading is allowed. An empirical analysis with data from popular virtual worlds also confirms our findings that real-money trading benefits the game operators. Moreover, we find that playtime and the trading price of game items have a positive relationship. Our findings, from both the analytical and empirical analysis, strongly imply the importance of the embedded economic systems in virtual worlds.

We study the effects of occupational licensing on consumer choices and market outcomes in a large online platform for residential home services. We exploit exogenous variation in the time at which licenses are displayed on the platform to identify the causal effects of licensing information on consumer choices. We find that the platform-verified licensing status of a professional is unimportant for consumer decisions relative to review ratings and prices. We confirm this result in an independent consumer survey. We also use variation in regulation stringency across states and occupations to measure the effects of licensing on aggregate market outcomes on the platform. Our results show that more stringent licensing regulations are associated with less competition and higher prices but not with any improvement in customer satisfaction as measured by review ratings or the propensity to use the platform again.

We demonstrate how data from search engines such as Google provide an accurate but simple way to predict future business activities. Applying our methodology to predict housing market trends, we find that a housing search index is strongly predictive of future housing market sales and prices. For state-level predictions in the US, the use of search data produces out-of-sample predictions with a smaller mean absolute error than the baseline model that uses conventional data but lacks search data. Furthermore, we find that our simple model of using search frequencies beat the predictions made by experts from the National Association of Realtors by 23.6% for future US home sales. We also demonstrate how these data can be used in other markets, such as home appliance sales. In the near future, this type of “nanoeconomic” data can transform prediction in numerous markets, and thus business and consumer decision-making.

Digital versions of labor and capital can be reproduced much more cheaply than their traditional forms. This increases the supply and reduces the marginal cost of both labor and capital. What then, if anything, is becoming scarcer? We posit a third factor, ''genius'', that cannot be duplicated by digital technologies. Our approach resolves several macroeconomic puzzles. Over the last several decades, both real median wages and the real interest rate have been stagnant or falling in the United States and the World. Furthermore, shares of income paid to labor and capital (properly measured) have also decreased. And despite dramatic advances in digital technologies, the growth rate of measured output has not increased. No competitive neoclassical two-factor model can reconcile these trends. We show that when increasingly digitized capital and labor are sufficiently complementary to inelastically supplied genius, innovation augmenting either of the first two factors can decrease wages and interest rates in the short and long run. Growth is increasingly constrained by the scarce input, not labor or capital. We discuss microfoundations for genius, with a focus on the increasing importance of superstar labor. We also consider consequences for government policy and scale sustainability.