Labor Dynamics
Paper Session
Sunday, Jan. 3, 2027 8:00 AM - 10:00 AM (EST)
- Chair: Mary Ann Bronson, Georgetown University
Early Retirement, Capital Adjustment and Technology Adoption
Abstract
Older workers are often viewed as obstacles to innovation, suggesting that their exit allows firms to reallocate resources toward new capital and technology. I argue instead that older, experienced workers support both the continuity of current production and the capacity to integrate new technologies into existing operations. The key empirical challenge is that when retirements are anticipated, firms have time to transfer knowledge internally, making the productivity value of older workers difficult to observe. I address this by studying a 2014 German pension reform that unexpectedly lowered the early retirement age for experienced workers by up to 29 months, inducing a sudden and unanticipated loss of long-tenured employees. Firms exposed to the reform reduce capital accumulation, delay technology adoption, and experience subsequent declines in revenue and value added, consistent with the erosion of firm-specific human capital. To interpret these findings, I develop a stylized model in which older workers transfer uncodified, firm-specific knowledge that is essential for maintaining legacy capital and integrating new technologies into firms’ operations. The model predicts, and the data confirm, that unexpected retirements weaken firms’ ability to sustain production and slow the pace of technological upgrading.Short-Term Relief, Long-Term Pain? The Impact of Short-Time Work on Employment
Abstract
This paper studies whether short-time work (STW) stabilized jobs during the COVID-19 pandemic or instead delayed labor market adjustment. Using a novel administrative panel linking monthly STW claims to the universe of German establishments from 2009 to 2022, I document an unprecedented expansion of STW, with nearly one-fourth of establishments and workers enrolled by mid-2020. Establishments that used STW were systematically larger, lower-wage, and slower-growing than non-users before the pandemic. Across multiple empirical approaches, STW participation is associated with persistently weaker employment growth through 2022 and higher workforce turnover, but only modest differences in wages and firm survival. Instrumental variables estimates based on prior program experience reinforce this pattern, although robustness checks indicate that these estimates should not be given a strong causal interpretation. The results suggest that job-retention subsidies can provide short-run relief while also slowing medium-run reallocation, highlighting the need for more targeted and dynamically calibrated STW policies.Social Media Entrepreneurship
Abstract
Recent survey evidence suggests that working in Social Media (SoMe) ranks among the top career aspirations of young cohorts. This paper studies how young SoMe Entrepreneurship (e.g., being a content creator) shapes subsequent career trajectories. We build the first comprehensive dataset of SoMe entrepreneurs and combine our definition with extensive register data on firm and person characteristics in Norway. SoMe entrepreneurs are younger, predominantly female, and more likely to operate as sole proprietors than traditional entrepreneurs. At the same time, they are more likely to transition to limited liability companies (LLCs), achieve higher revenues upon doing so, and pursue managerial roles later in life -- consistent with SoMe being a low cost entrepreneurial experimentation channel. To quantify the long-term effects of young SoMe entrepreneurship, we develop an OLG model of traditional and SoMe entrepreneurship.Leading Firms and the Future of Work
Abstract
This paper develops an approach to forecasting occupational change by identifying leading firms whose workforce composition anticipates the future occupational structure of their industry as other firms catch up over time. We develop a theoretical model with technology diffusion and frontier-laggard dynamics showing that leading firms are early adopters of technology and foreshadow broader labor demand adjustments. Using administrative data for French firms, we document substantial changes in occupational structure over time, mostly arising within industries and firms. To forecast occupational change, we first identify leaders using a machine-learning model based on firm characteristics and compare this approach with a productivity-based leader definition. We then estimate a forecasting model that combines current and lagged occupational structures of industries and leaders. In out-of-sample assessments, the machine-learning, leader-based approach improves forecasting accuracy by more than 21% relative to a baseline model without leader information. The predicted occupational changes are largely orthogonal to those implied by automation-risk indices, suggesting that the forces reshaping labor demand are not reducible to a single technological channel. Finally, we apply the model to predict the occupational structure of the French economy in 2034.Who Benefits from Million Dollar Plants? The Missing Local Beneficiaries
Abstract
We study who receives jobs in the industries catalyzed by Million Dollar Plants (MDPs) during 2000-2015. Comparing winning vs. runner-up counties, people working the winning county are on average 1.2% more likely to work in the MDP's four-digit industry compared to the runner-up. This effect takes about nine years post MDP announcement to materialize. Surprisingly, however, there is no employment effect for the initial residents of the winning county compared to runner-up counties; nor do we observe any other difference in economic benefits. These results suggest MDPs generate the promised jobs but source their workforce from outside the winning county.JEL Classifications
- J2 - Demand and Supply of Labor