We survey 15,000 Americans over several waves to investigate whether, how, and why working from home will stick after COVID-19. The pandemic drove a mass social experiment in which half of all paid hours were provided from home between May and October 2020. Our survey evidence says that about 25% of all full work days will be supplied from home after the pandemic ends, compared with just 5% before. We provide evidence on five mechanisms behind this persistent shift to working from home: diminished stigma, better-than-expected experiences working from home, investments in physical and human capital enabling working from home, reluctance to return to pre-pandemic activities, and innovation supporting working from home. We also examine some implications of a persistent shift in working arrangements: First, high-income workers, especially, will enjoy the perks of working from home. Second, we forecast that the post-pandemic shift to working from home will lower worker spending in major city centers by 5 to 10%. Third, many workers report being more productive at home than on business premises, so post-pandemic work from home plans offer the potential to raise productivity as much as 2.4%.

Recently, many have predicted an imminent automation revolution, and large resulting job losses. Others have created metrics to predict new patterns in job automation vulnerability. As context to such claims, we test basic theory, two vulnerability metrics, and 251 O*NET job features as predictors of 1505 expert reports regarding automation levels in 832 U.S. job types from 1999 to 2019.

We find that pay, employment, and vulnerability metrics are predictive (R²~0.15), but add little to the top 25 O*NET job features, which together predict far better (R²~0.55). These best predictors seem understandable in terms of traditional kinds of automation, and have not changed over our time period. Instead, it seems that jobs have changed their features to become more suitable for automation.

We thus find no evidence yet of a revolution in the patterns or quantity of automation. And since, over this period, automation increases have predicted neither changes in pay nor employment, this suggests that workers have little to fear if such a revolution does come.

[Keywords: automation, wages, employment, occupations, artificial intelligence, technology]

Our automation data analysis found a few surprising results…But the most interesting surprise, I think, is that while, over the last twenty years, we’ve seen no noticeable change in the factors that predict which jobs get more automated, we have seen job features change to become more suitable to automation. On average jobs have moved by about a third of a standard deviation, relative to the distribution of job automation across jobs. This is actually quite a lot. Why do jobs change this way?

Consider the example of a wave of human colonization moving over a big land area. Instead of all the land becoming colonized more densely at same rate everywhere, what you instead see is new colonization happening much more near old colonization. In the U.S., dense concentrations started in the east and slowly spread to the west. There was little point in clearing land to grow stuff if there weren’t enough other folks nearby to which to sell your crops, and from which to buy supplies.

…Now think about the space of job tasks as a similar sort of landscape. Two tasks are adjacent to other tasks when the same person tends to do both, when info or objects are passed from one to the other, when they take place close in place and time, and when their details gain from being coordinated. The ease of automating each task depends on how regular and standardized are its inputs, how easy it is to formalize the info on which key choices depend, how easy it is to evaluate and judge outputs, and how simple, stable, and mild are the physical environments in which this task is done. When the tasks near a particular task get more automated, those tasks tend more to happen in a more controlled stable environment, the relevant info tends to be more formalized, and related info and objects get simpler, more standardized, and more reliably available. And this all tends to make it easier to automate such tasks. Much like how land is easier to colonize when nearby land is more colonized.

…We have long been experiencing a wave of automation passing across the space of job tasks. Some of this increase in automation has been due to falling computer tech costs, improving algorithms and tools, etc. But much of it may simply be the general potential of this tech being realized via a slow steady process with a long delay: the automation of tasks near other recently automated tasks, slowly spreading across the landscape of tasks.

As a new general-purpose technology, robots have the potential to radically transform employment and organizations.

In contrast to prior studies that predict dramatic employment declines, we find that investments in robotics are associated with increases in total firm employment, but decreases in the total number of managers. Similarly, we find that robots are associated with an increase in the span of control for supervisors remaining within the organization. We also provide evidence that robot adoption is not motivated by the desire to reduce labor costs, but is instead related to improving product and service quality.

Our findings are consistent with the notion that robots reduce variance in production processes, diminishing the need for managers to monitor worker activities to ensure production quality. As additional evidence, we also find robot investments predict improved performance measurement and increased adoption of incentive pay based on individual employee performance. With respect to changes in skill composition within the organization, robots predict decreases in employment for middle-skilled workers, but increases in employment for low-skill and high-skilled workers. We also find robots not only predict changes in employment, but also corresponding adaptations in organizational structure. Robot investments are associated with both centralization and decentralization of decision-making authority depending upon the task, but decision rights in either case are reassigned away from the managerial level of the hierarchy.

Overall, our results suggest that robots have distinct and profound effects on employment and organizations that require fundamental changes in firm practices and organizational design.

[Keywords: robot, AI, employment, productivity, organizational change]

Recent advances in artificial intelligence and robotics have generated a robust debate about the future of work. An analogous debate occurred in the late nineteenth century when mechanization first transformed manufacturing. We analyze an extraordinary dataset from the late nineteenth century, the Hand and Machine Labor study carried out by the US Department of Labor in the mid-1890s. We focus on transitions at the task level from hand to machine production, and on the impact of inanimate power, especially of steam power, on labor productivity. Our analysis sheds light on the ability of modern task-based models to account for the effects of historical mechanization.

[Summary by Jack Clark & Matthew van der Merwe:

Quantifying automation in the Industrial Revolution: We all know that the Industrial Revolution involved the substantial substitution of human labour for machine labour. This 2019 paper from a trio of economists paints a clear quantitative picture of automation in this period, using the 1899 US Hand and Machine Labor Study⁠.

The dataset: The HML study is a remarkable data-set that has only recently been analyzed by economic historians. Commissioned by Congress and collected by the Bureau of Labor Statistics, the study collected observations on the production of 626 manufactured units (eg. ‘men’s laced shoes’) and recorded in detail the tasks involved in their production and relevant inputs to each task. For each unit, this data was collected for machine-production, and hand-production.

Key findings: The paper looks at transitions between hand-labour and machine-labour across tasks. It finds clear evidence for both the displacement and productivity effects of automation on labour:

• 67% of hand tasks transitioned 1-to-1 to being performed by machines and a further 28% of hand tasks were subdivided or consolidated into machine tasks. Only 4% of hand tasks were abandoned.
• New tasks (not previously done by hand) represented one-third of machine tasks.
• Machine labour reduced total production time by a factor of 7.
• The net effect of new tasks on labour demand was positive—time taken up by new machine-tasks was 5× the time lost on abandoned hand-tasks

Matthew’s view: The Industrial Revolution is perhaps the most transformative period in human history so far, with massive effects on labour, living standards, and other important variables. It seems likely that advances in AI could have a similarly transformative effect on society, and that we are in a position to influence this transformation and ensure that it goes well. This makes understanding past transitions particularly important. Aside from the paper’s object-level conclusions, I’m struck by how valuable this diligent empirical work from the 1890s, and the foresight of people who saw the importance in gathering high-quality data in the midst of this transition. This should serve as inspiration for those involved in efforts to track metrics of AI progress.]

This paper raises basic questions about the process of economic growth. It questions the assumption, nearly universal since Solow’s seminal contributions of the 1950s, that economic growth is a continuous process that will persist forever. There was virtually no growth before 1750, and thus there is no guarantee that growth will continue indefinitely. Rather, the paper suggests that the rapid progress made over the past 250 years could well turn out to be a unique episode in human history. The paper is only about the United States and views the future from 2007 while pretending that the financial crisis did not happen. Its point of departure is growth in per-capita real GDP in the frontier country since 1300, the U.K. until 1906 and the U.S. afterwards. Growth in this frontier gradually accelerated after 1750, reached a peak in the middle of the 20^th century, and has been slowing down since. The paper is about “how much further could the frontier growth rate decline?”

The analysis links periods of slow and rapid growth to the timing of the three industrial revolutions (IR’s), that is, IR #1 (steam, railroads) from 1750 to 1830; IR #2 (electricity, internal combustion engine, running water, indoor toilets, communications, entertainment, chemicals, petroleum) from 1870 to 1900; and IR #3 (computers, the web, mobile phones) from 1960 to present. It provides evidence that IR #2 was more important than the others and was largely responsible for 80 years of relatively rapid productivity growth between 1890 and 1972. Once the spin-off inventions from IR #2 (airplanes, air conditioning, interstate highways) had run their course, productivity growth during 1972–96 was much slower than before. In contrast, IR #3 created only a short-lived growth revival between 1996 and 2004. Many of the original and spin-off inventions of IR #2 could happen only once—urbanization, transportation speed, the freedom of females from the drudgery of carrying tons of water per year, and the role of central heating and air conditioning in achieving a year-round constant temperature.

Even if innovation were to continue into the future at the rate of the two decades before 2007, the U.S. faces six headwinds that are in the process of dragging long-term growth to half or less of the 1.9% annual rate experienced between 1860 and 2007. These include demography, education, inequality, globalization, energy/​​​environment, and the overhang of consumer and government debt. A provocative “exercise in subtraction” suggests that future growth in consumption per capita for the bottom 99% of the income distribution could fall below 0.5% per year for an extended period of decades.

So we have perhaps five eras during which the thing whose growth is at issue—the universe, brains, the hunting economy, the farming economy, and the industrial economy—doubled in size at fixed intervals. Each era of growth before now, however, has eventually switched suddenly to a new era having a growth rate that was between 60 and 250× as fast. Each switch was completed in much less time than it had taken the previous regime to double—from a few millennia for the agricultural revolution to a few centuries for the industrial one. These switches constituted singularities…A few exceedingly rare innovations, however, do suddenly change everything. One such innovation led to agriculture; another led to industry.

…If current trends continue, we should have computer hardware and brain scans fast and cheap enough to support this scenario in a few decades…Though it might cost many billions of dollars to build one such machine, the first copy might cost only millions and the millionth copy perhaps thousands or less. Mass production could then supply what has so far been the one factor of production that has remained critically scarce throughout human history: intelligent, highly trained labor.

…The relative advantages of humans and machines vary from one task to the next. Imagine a chart resembling a topographic cross section, with the tasks that are “most human” forming a human advantage curve on the higher ground. Here you find chores best done by humans, like gourmet cooking or elite hairdressing. Then there is a “shore” consisting of tasks that humans and machines are equally able to perform and, beyond them an “ocean” of tasks best done by machines. When machines get cheaper or smarter or both, the water level rises, as it were, and the shore moves inland.

This sea change has two effects. First, machines will substitute for humans by taking over newly “flooded” tasks. Second, doing machine tasks better complements human tasks, raising the value of doing them well. Human wages may rise or fall, depending on which effect is stronger. Wages could fall so far that most humans could not live on them. For example, in the 1920s, when the mass-produced automobile came along, it was produced largely by machines, with human help. So machines dominated that function—the assembly of cars. The resulting proliferation of machine-assembled cars raised the value of related human tasks, such as designing those cars, because the financial stakes were now much higher. Sure enough, automobiles raised the wages of machinists and designers—in these cases, the complementary effect dominated. At the same time, the automobile industry lowered the pay of saddle makers and stable hands, an example of the substitution effect.

So far, machines have displaced relatively few human workers, and when they have done so, they have in most cases greatly raised the incomes of other workers. That is, the complementary effect has outweighed the substitution effect—but this trend need not continue. In our graph of machines and humans, imagine that the ocean of machine tasks reached a wide plateau. This would happen if, for instance, machines were almost capable enough to take on a vast array of human jobs. For example, it might occur if machines were on the very cusp of human-level cognition. In this situation, a small additional rise in sea level would flood that plateau and push the shoreline so far inland that a huge number of important tasks formerly in the human realm were now achievable with machines. We’d expect such a wide plateau if the cheapest smart machines were whole-brain emulations whose relative abilities on most tasks should be close to those of human beings.

…Together these effects seem quite capable of producing economic doubling times much shorter than anything the world has ever seen. And note that this forecast does not depend on the rate at which we achieve machine intelligence capabilities or the rate at which the intelligence of machines increases. Merely having computer-like machines able to do most important mental tasks as well as humans do seems sufficient to produce very rapid growth.

We explore the effect of computerization on productivity and output growth using data from 527 large U.S. firms over 1987–1994. We find that computerization makes a contribution to measured productivity and output growth in the short term (using 1-year differences) that is consistent with normal returns to computer investments. However, the productivity and output contributions associated with computerization are up to 5× greater over long periods (using 5-year to 7-year differences). The results suggest that the observed contribution of computerization is accompanied by relatively large and time-consuming investments in complementary inputs, such as organizational capital, that may be omitted in conventional calculations of productivity. The large long-run contribution of computers and their associated complements that we uncover may partially explain the subsequent investment surge in computers in the late 1990s.

To understand the economic value of computers, one must broaden the traditional definition of both the technology and its effects. Case studies and firm-level econometric evidence suggest that: 1) organizational “investments” have a large influence on the value of IT investments; and 2) the benefits of IT investment are often intangible and disproportionately difficult to measure. Our analysis suggests that the link between IT and increased productivity emerged well before the recent surge in the aggregate productivity statistics and that the current macroeconomic productivity revival may in part reflect the contributions of intangible capital accumulated in the past.

…What We Now Know About Computers and Productivity: Research on computers and productivity is entering a new phase. While the first wave of studies sought to document the relationship between investments in computers and increases in productivity, new research is focusing on how to make more computerization effective. Computerization does not automatically increase productivity, but it is an essential component of a broader system of organizational changes which does increase productivity. As the impact of computers becomes greater and more pervasive, it is increasingly important to consider these organizational changes as an integral part of the computerization process.

This is not the first time that a major general purpose technology like computers required an expensive and time-consuming period of restructuring. Substantial productivity improvement from electric motors did not emerge until almost 40 years after their introduction into factories [7]⁠. The first use involved swapping gargantuan motors for large steam engines with no redesign of work processes. The big productivity gains came when engineers realized that the factory layout no longer had to be dictated by the placement of power transmitting shafts and rods. They re-engineered the factory so that machines were distributed throughout the factory, each driven by a separate, small electric motor. This made it possible to arrange the machines in accordance with the logic of work flow instead of in proximity to the central power unit.

It has also taken some time for businesses to realize the transformative potential of information technology to revolutionize work. However, the statistical evidence suggests that revolution is occurring much more quickly this time.

Many observers of recent trends in the industrialized economies of the West have been perplexed by the conjecture of rapid technological innovation with disappointingly slow gains in measured productivity. A generation of economists who were brought up to identify increases in total factor productivity indexes with “technical progress” has found it quite paradoxical for the growth accountants’ residual measure of “the advance of knowledge” to have vanished at the very same time that a wave of major innovations was appearing-in microelectronics, in communications technologies based on lasers and fiber optics, in composite materials, and in biotechnology…This latter aspect of the so-called “productivity paradox” attained popular currency in the succinct formulation attributed to Robert Solow: “We see the computers everywhere but in the productivity statistics.”

…If, however, we are prepared to approach the matter from the perspective afforded by the economic history of the large technical systems characteristic of network industries, and to keep in mind a time-scale appropriate for thinking about transitions from established technological regimes to their respective successor regimes, many features of the so-called productivity paradox will be found to be neither so unprecedented nor so puzzling as they might otherwise appear.

…Computer and dynamo each form the nodal elements of physically distributed (transmission) networks. Both occupy key positions in a web of strongly complementary technical relationships that give rise to “network externality effects” of various kinds, and so make issues of compatibility standardization important for business strategy and public policy (see my 1987 paper and my paper with Julie Bunn, 1988). In both instances, we can recognize the emergence of an extended trajectory of incremental technical improvements, the gradual and protracted process of diffusion into widespread use, and the confluence with other streams of technological innovation, all of which are interdependent features of the dynamic process through which a general purpose engine acquires a broad domain of specific applications (see Timothy Bresnahan and Manuel Trajtenberg, 1989). Moreover, each of the principal empirical phenomena that make up modem perceptions of a productivity paradox had its striking historical precedent in the conditions that obtained a little less than a century ago in the industrialized West, including the pronounced slowdown in industrial and aggregate productivity growth experienced during the 1890–1913 era by the two leading industrial countries, Britain and the United States (see my 1989 paper, pp. 12–15, for details). In 1900, contemporary observers well might have remarked that the electric dynamos were to be seen “everywhere but in the productivity statistics!”

Many observers of contemporary economic trends have been perplexed by the contemporary conjuncture of rapid technological innovation with disappointingly slow gains in measured productivity. The purpose of this essay is to show modern economists, and others who share their puzzlement in this matter, the direct relevance to their concerns of historical studies that trace the evolution of techno-economic regimes formed around “general purpose engines”. For this purpose an explicit parallel is drawn between two such engines—the computer and the dynamo. Although the analogy between information technology and electrical technology would have many limitations were it to be interpreted very literally, it nevertheless proves illuminating. Each of the principal empirical phenomena that go to make up modern perceptions of a “productivity paradox”, had a striking historical precedent in the conditions that obtained a little less than a century ago in the industrialized West. In 1900 contemporaries might well have said that the electric dynamos were to be seen “everywhere but in the economic statistics”. Exploring the reasons for that state of affairs, and the features of commonality between computer and dynamo—particularly in the dynamics of their diffusion and their incremental improvement, and the problems of capturing their initial effects with conventional productivity measures—provides some clues to help understand our current situation. The paper stresses the importance of keeping an appropriately long time-frame in mind when discussing the connections between the information revolution and productivity growth, as well as appreciating the contingent, path-dependent nature of the process of transition between one techno-economic regime and the next.

[Keywords: productivity slowdown; diffusion of innovations; economics of technology; information technology; electric power industry]