Rugged Labor
Minimum Wages and the Rise of the Robots
Erik Brynjolfsson et al.
Stanford Working Paper, February 2026
Abstract:
This paper studies how minimum wage policy affects firms' adoption of automation technologies. Using both state-level measures of robot exposure and novel plant-level data on industrial robot imports linked to U.S. Census microdata from 1992-2021, we show that increases in minimum wages raise the likelihood of robot adoption in manufacturing. Our preferred identification exploits discontinuities at state borders, comparing otherwise similar firms exposed to different wage floors. Across specifications, a 10 percent increase in the minimum wage increases robot adoption by roughly 8 percent relative to the mean.
The Twenty-four Hour Economy or Rolled-up Sidewalks: Trends in Work Timing and Their Causes
Jeff Biddle & Daniel Hamermesh
NBER Working Paper, January 2026
Abstract:
We demonstrate nearly steady trends from 1973-2023 in the U.S. in the timing of when people work for pay, away from evening and night hours toward "usual" daytime hours. The trend is related to changes in rising educational attainment, to increased real incomes, and the increased wage premium for undesirable work times -- evenings and nights -- that we document. The trend exists in all major industries except retail, in which changes in technology biased work away from daytime hours. The trend was accelerated by the sharp increase in telework that occurred after the Covid pandemic, an increase that was especially concentrated during daytime hours. While we observe the same phenomenon in France from 1966 to 2010, we do not in the U.K. from 1974-2015, arguably because of the very sharp decline in unionization in the U.K. and the changes in retailing.
Past Automation and Future A.I.: How Weak Links Tame the Growth Explosion
Charles Jones & Christopher Tonetti
Stanford Working Paper, January 2026
Abstract:
How much of past economic growth is due to automation, and what does this imply about the effects of A.I. and automation in the coming decades? We perform growth accounting using a task-based model for key sectors in the U.S. economy. Historically, TFP growth is largely due to improvements in capital productivity. The annual growth rate of capital productivity is at least 5pp larger than the sum of labor and factor-neutral productivity growth. The main benefit of automation is that we use rapidly-improving machines instead of slowly-improving humans on an increasing set of tasks. Looking to the future, we develop an endogenous growth model in which the production of both goods and ideas is endogenously automated. We calibrate this model based on our historical evidence. Two key findings emerge. First, automation leads economic growth to accelerate over the next 75 years. Second, the acceleration is remarkably slow. By 2040, output is only 4% higher than it would have been without the growth acceleration, and by 2060 the gain is still only 19%. A key reason for the slow acceleration is the prominence of "weak links" (an elasticity of substitution among tasks less than one). Even when most tasks are automated by rapidly improving capital, output is constrained by the tasks performed by slowly-improving labor.
Building Pro-Worker Artificial Intelligence
Daron Acemoglu, David Autor & Simon Johnson
NBER Working Paper, February 2026
Abstract:
This paper defines pro-worker technologies, including Artificial Intelligence, as technologies that make human skills and expertise more valuable by expanding worker capabilities. Our conceptual framework distinguishes among five categories of technological change: labor-augmenting, capital-augmenting, automating, expertise-leveling, and new task-creating. Only the last category is unambiguously pro-worker, generating demand for novel human expertise rather than commodifying it. We illustrate these distinctions through hypothetical and real-world examples spanning aviation maintenance, electrical services, custodial work, education, patent examination, and gig delivery. While AI's capacity to automate work is substantial, we argue that its potential to serve as a collaborator, by extending human judgment, enabling new tasks, and accelerating skill acquisition, is equally transformative and currently underexploited. We identify market failures, including misaligned firm and developer incentives, path dependence, and a pervasive pro-automation ideology, that may lead to underinvestment in pro-worker AI. We consider nine policy directions that would change incentives, including targeted investments in health care and education, tax code reform, antitrust enforcement, and intellectual property protections for worker expertise.
Who is using AI to code? Global diffusion and impact of generative AI
Simone Daniotti et al.
Science, 19 February 2026, Pages 831-835
Abstract:
Generative coding tools promise big productivity gains, but uneven uptake could widen skill and income gaps. We train a neural classifier to spot AI-generated Python functions in over 30 million GitHub commits by 160,097 software developers, tracking how fast, and where, these tools take hold. Currently AI writes an estimated 29% of Python functions in the US, a shrinking lead over other countries. We estimate quarterly output, measured in online code contributions, consequently increased by 3.6%. AI seems to benefit experienced, senior-level developers: they increased productivity and more readily expanded into new domains of software development. In contrast, early-career developers showed no significant benefits from AI adoption. This may widen skill gaps and reshape future career ladders in software development.
Enhancing Worker Productivity Without Automating Tasks: A Different Approach to AI and the Task-Based Model
Ajay Agrawal, John McHale & Alexander Oettl
NBER Working Paper, January 2026
Abstract:
The task-based approach has become the dominant framework for studying the labor-market effects of artificial intelligence (AI), typically emphasizing the replacement of human workers by machines. Motivated by growing empirical evidence that contemporary AI is more often used as a tool that augments workers, this paper develops two related task-based models in which AI enhances worker productivity without automating tasks. Abstracting from capital, we develop a pair of related task-based models that examine how technological progress in AI that provides new tools to augment workers affects aggregate productivity and wage inequality. Both models emphasize the role of human capital in intermediating the effects of AI-related technological shocks. In the first model, AI use requires specialized expertise, and technological progress expands the set of tasks for which such expertise is effective. We show that a larger supply of AI expertise amplifies the productivity gains from improvements in AI technology while attenuating its adverse effects on wage inequality. The second model focuses on non-AI skills, allowing AI tools to alter the set of tasks that workers can perform given their skills. In equilibrium, workers allocate across tasks in response to wages, generating an endogenous distribution of skills across the task space. A central result is that aggregate productivity and wage inequality depend on different global properties of this equilibrium distribution: productivity is particularly sensitive to thinly staffed tasks that create bottlenecks, while wage inequality is driven by the concentration of workers in a narrow set of tasks. As a result, improvements in AI tools can induce non-monotonic co-movement between productivity and inequality. By linking these mechanisms to multidimensional human capital — including AI expertise and higher-order non-AI skills — the paper highlights the role of education and training policies in shaping the economic consequences of AI-driven technological change.
Replacing labour with capital: Evidence from aggregate mobility shocks
Bharadwaj Kannan, Roberto Pinheiro & Harry Turtle
Labour Economics, February 2026
Abstract:
Do firms respond to labour mobility shocks? We construct an overlapping generations model where policies restricting labour mobility present firms with an important trade-off. Firms leverage their monopsony power to reduce late-career wages while early-career workers demand a wage premium to join the restricted sector. In response to higher labour turnover costs, firms alter their optimal capital-labour ratio. We confirm these predictions in the data by exploiting the statewide adoption by state supreme courts of the inevitable disclosure doctrine (IDD) as a valid legal doctrine intended to protect trade secrets by restricting labour mobility. Post-IDD, early-career workers receive higher starting wages, late-career workers experience slower wage growth, firms raise investment by 3.5%, and their capital-labour ratio by 5.5%. Our results suggest that firms respond meaningfully to labour mobility shocks by replacing labour with capital.
How Do Workers Think About The Costs and Benefits of Freelance Work? New Evidence From a Survey Experiment
Edward Freeland, Andrew Garin & Dmitri Koustas
NBER Working Paper, February 2026
Abstract:
We examine how workers perceive the trade-offs of freelancing using a novel survey design that explores the nature of workers' perceptions of their own jobs and the implications of work arrangements for their take-home pay. We find that, across several alternative classifications of freelance work, workers in such arrangements make less per hour than traditional employees, but report having greater control of when, where, and how they work. We find that on average, self-employed workers spend an additional 5 to 8 percentage points of gross pay covering unreimbursed expenses relative to traditional employees. However, when asked about expectations of net pay in freelance and traditional employment jobs with the same gross pay, respondents who received no quantitative information expected net pay to be higher in freelance arrangements than in employment arrangements, on average. This pattern reversed among respondents who were randomly assigned to receive customized estimates of their expected total expense and tax burdens in each arrangement, who estimated that freelance arrangements would generate lower net lower earnings than employment arrangements (consistent with the estimates we provided to them). This suggests that workers may not be fully aware of the tax and expense burdens freelance workers are responsible for. Interestingly, we find similar results both for workers who are currently employees in their main job and those who are currently self-employed, suggesting that the low salience of the tax and expense burdens associated with freelance work are not merely driven by those with no self-employment experience.
Productivity dynamics among union locals in the United States
Thomas Breda, Alex Bryson & John Forth
Economic Inquiry, forthcoming
Abstract:
Using panel data on union locals in the United States we examine the dynamics of the union sector, investigating the impact of inter-union competition on locals' productivity and survival. We find low entry rates, high exit rates and high levels of productivity dispersion in the sector. The entry of new locals is not associated with productivity improvements among incumbents but has a small negative association with locals' survival rates. These findings indicate that inter-union competition is not effective in raising productivity and the effects of creative destruction are weak, with these processes likely insufficient to stem the sector's overall decline.
Well-being Increases in Age Among Workers: Evidence From 103 Countries
David Blanchflower & Alex Bryson
NBER Working Paper, January 2026
Abstract:
We examine how workers' and non-workers' wellbeing varies by age across 171 countries in eight international surveys. In 103 countries (60%) we find evidence that workers' wellbeing rises with age and workers' illbeing falls with age. This relationship appears to have strengthened over time in some countries. Patterns are different among non-workers and are sensitive to survey mode. Where surveys are conducted using Computer-Assisted Web-based Interviews (CAWI) non-workers' wellbeing is U-shaped, but this is less clear-cut when the data are collected with Computer-Assisted Telephone Interviews (CATI). The change in the age profile of workers' wellbeing may reflect changes in selection into (out of) employment by age, changes in job quality, or changes in young workers' orientation to similar jobs over time. But changes in smartphone usage -- often the focus of debate regarding declining young peoples' wellbeing -- are unlikely to be the main culprit unless there are sizeable differences in smartphone usage across young workers and non-workers, which appears unlikely.