This article confronts monopsony theory’s predictions regarding workers’ wages with observed wage patterns over the business cycle. Using German administrative data for the years 1985 to 2010 and an estimation framework based on duration models, the authors construct a time series of the labor supply elasticity to the firm and estimate its relationship to the unemployment rate. They find that firms possess more monopsony power during economic downturns. Half of this cyclicality stems from workers’ job separations being less wage driven when unemployment rises, and the other half mirrors that firms find it relatively easier to poach workers. Results show that the cyclicality is more pronounced in tight labor markets with low unemployment, and that the findings are robust to controlling for time-invariant unobserved worker or plant heterogeneity. The authors further document that cyclical changes in workers’ entry wages are of similar magnitude as those predicted under pure monopsonistic wage setting.
Handbook of Labor Economics
What new tools and models are enriching labor economics?Developments in Research Methods and their Application, Volume 4A summarizes recent advances in the ways economists study wages, employment, and labor markets. Mixing conceptual models and empirical work, contributors cover subjects as diverse as field and laboratory experiments, program evaluation, and behavioral models. The combinations of these improved empirical findings with new models reveal how labor economists are developing new and innovative ways to measure key parameters and test important hypotheses. - Investigates recent advances in methods and models used in labor economics - Demonstrates what these new tools and techniques can accomplish - Documents how conceptual models and empirical work explain important practical issues
Adjustment Costs, Firm Responses, and Micro vs. Macro Labor Supply Elasticities: Evidence from Danish Tax Records
Abstract. We show that the effects of taxes on labor supply are shaped by interactions between adjustment costs for workers and hours constraints set by fi
AI Adoption and Inequality
There are competing narratives about artificial intelligence’s impact on inequality. Some argue AI will exacerbate economic disparities, while others suggest it could reduce inequality by primarily disrupting high-income jobs. Using household microdata and a calibrated task-based model, we show these narratives reflect different channels through which AI affects the economy. Unlike previous waves of automation that increased both wage and wealth inequality, AI could reduce wage inequality through the displacement of high-income workers. However, two factors may counter this effect: these workers’ tasks appear highly complementary with AI, potentially increasing their productivity, and they are better positioned to benefit from higher capital returns. When firms can choose how much AI to adopt, the wealth inequality effect is particularly pronounced, as the potential cost savings from automating high-wage tasks drive significantly higher adoption rates. Models that ignore this adoption decision risk understating the trade-off policymakers face between inequality and efficiency.
Consumer Spending during Unemployment: Positive and Normative Implications
Using de-identified bank account data, we show that spending drops sharply at the large and predictable decrease in income arising from the exhaustion of unemployment insurance (UI) benefits. We use the high-frequency response to a predictable income decline as a new test to distinguish between alternative consumption models. The sensitivity of spending to income we document is inconsistent with rational models of liquidity-constrained households, but is consistent with behavioral models with present-biased or myopic households. Depressed spending after exhaustion also implies that the consumption-smoothing gains from extending UI benefits are four times larger than from raising UI benefit levels. (JEL D14, D91, E21, E24, E70, J65)
Mind the Gap: AI Adoption in Europe and the U.S.
This paper combines international evidence from worker and firm surveys conducted in 2025 and 2026 to document large gaps in AI adoption, both between the US and Europe and across European countries. Cross-country differences in worker demographics and firm composition account for an important share of these gaps. AI adoption, within and across countries, is also closely linked to firm personnel management practices and whether firms actively encourage AI use by workers. Micro-level evidence suggests that AI generates meaningful time savings for many workers. At the macro level, in recent years industries with higher AI adoption rates have experienced faster productivity growth. While we do not establish causality, this relationship is statistically significant and similar in magnitude in Europe and the US. We do not find clear evidence that industry-level AI adoption is associated with employment changes. We discuss limitations of existing data and outline priorities for future data collection to better assess the productivity and labor market effects of AI.
Technological Disruption in the US Labor Market • The Aspen Institute Economic Strategy Group
DAVID DEMING, CHRISTOPHER ONG, LAWRENCE H. SUMMERS This paper explores past episodes of technological disruption in the US labor market, with the goal of learning lessons about the likely future impact of artificial intelligence (AI). The authors measure changes in the structure of the US labor market going back over a century in two ways. ...
Days not worked due to strikes and lockouts, 2023
In the publication Quality of Employment in Canada, the days not worked due to strikes and lockouts indicator measures annual changes in the number of person-days and hours not worked due to labour disputes. The article examines data compiled by Employment and Social Development Canada and the Labour Force Survey to better understand trends in hours lost due to labour disputes.
Political Aspects of Full Employment
Why do capitalists hate full employment? Because it weakens their power over workers.
The Hidden Tax on Blue-Collar Workers and Building in America
The unemployment insurance system has a design flaw that penalizes the construction industry, leading to fewer workers and lower productivity. Over time, that means less – and more expensive – housing.
When Does Worker Ownership Work? ESOPs, Law Firms, Codetermination, and Economic Democracy
The AI Layoff Trap
If AI displaces human workers faster than the economy can reabsorb them, it risks eroding the very consumer demand firms depend on. We show that knowing this is not enough for firms to stop it. In...
Thousands of CEOs just admitted AI had no impact on employment or productivity—and it has economists resurrecting a paradox from 40 years ago | Fortune
In the 1980s, economist Robert Solow made an observation that reminded economists of today’s AI boom: “You can see the computer age everywhere but in the productivity statistics.”
Will AI Destroy the Economy? (According to Economists: No.) | AI Reality Check | Cal Newport
報道の未来 新聞週間インタビュー(上)取材力で「不信はね返す」
インターネット上に情報が氾濫し、読者の新聞離れが止まらない。世界では地方紙の廃刊が相次ぎ、台頭する人工知能（AI）が既存の報道のあり方を問う。6日から始まった新聞週間に合わせたインタビュー連載の初回は日本最多の部数を持つ読売新聞グループ本社の山口寿一社長に聞いた。誤報、非常に痛恨――メディアへの不信が強まっています。「多くの人々が『自分たちが政治や行政の蚊帳の外にいる』と感じ、蚊帳の内側にい
Thousands of CEOs admit AI had no impact on employment or productivity—and it has economists resurrecting a paradox from 40 years ago | Fortune
In the 1980s, economist Robert Solow made an observation that reminded economists of today’s AI boom: “You can see the computer age everywhere but in the productivity statistics.”
Is A.I. creating jobs or killing them? Adding to inflation or fixing it? Boosting productivity or doing little? Nobody knows. And our data (public & private) isn't providing clear answers. My latest on the A.I. data gap and the challenge it poses for policymakers: nytimes.com/2026/07/02/business/economy/a…
A.I. Is Reshaping the Economy. Good Luck Measuring How.
www.nytimes.com