In our paper “The Impact of a New Workplace Technology on Employees,” (published in the Oxford Bulletin of Economics and Statistics) we investigate how introducing new technologies affects non-monetary working conditions—specifically overtime, training participation, and perceived labor intensity—using detailed German worker-level data from the Socio-Economic Panel (2014–2020). Our analysis reveals that these effects are most pronounced immediately after implementation and vary significantly across occupational and industrial contexts.
This paper investigates the impact of the implementation of a new workplace technology on worker outcomes such as overtime, training and perceived productivity. While the effects of new technologies on firm outcomes are widely discussed, the impact on workers still remains debated. (joint work with Marek Giebel)
Abstract:
Detailed worker-level data for Germany allows us to compare the outcomes for workers which are exposed to a change in workplace technologies to the group of workers which are not exposed in the periods before and after the introduction. Moreover, we exploit the perception of the new technology by employees to determine the dependency of the effects on this dimension of heterogeneity. Our estimates imply strongest impacts of new technologies in the first year of the implementation for overtime, training and perceived productivity. In addition, we show that the positive effects of a new technology vanish after the introduction period. Finally, changes in worker outcomes are dependent on the nature of the introduced technology. Positive effects on real worker outcomes therefore primarily occur when workers also tend to perceive it.
Research Approach
We employed difference-in-differences regressions combined with inverse probability weighting to isolate causal effects. Our dataset tracked 5,892 workers across diverse industries, comparing those exposed to new workplace technologies against a control group. We focused on three core outcomes:
- Overtime: Measured through binary indicators and inverse hyperbolic sine-transformed hours.
- Training: Captured via participation rates and training intensity.
- Labor intensity: Assessed using self-reported changes in work demands. We further analyzed heterogeneity using occupational substitution potential (routine-task share) and industry-level dynamics (R\&D, organizational capital, ICT investments).
Key Findings
Our results highlight three critical patterns:
- Temporal dynamics: Technology adoption sharply increases overtime (4.4%), training participation (6.7%), and perceived labor intensity (31.4%) in the first year. These effects fade in subsequent years, indicating adaptation.
- Occupational heterogeneity: Workers in high-substitution-potential occupations (e.g., clerical roles) showed stronger responses:
- 8.9% higher overtime hours.
- 5.4% more training measures post-adoption.
- 40.5% greater labor intensity.
- Industry dynamics: Effects amplified in industries with:
- High R&D organizational capital: 8.0% overtime surge and 36.8% labor intensity rise.
- High ICT equipment investment: Training jumped 7.7%.
- Low software investment: Overtime hours increased 4.7% and labor intensity 40.1%.
Broader Implications
These findings offer actionable insights:
- Firms: Prioritize training and workload management during technology transitions to mitigate short-term productivity dips and burnout risks.
- Policymakers: Support reskilling in high-substitution occupations (e.g., through tax incentives for training programs).
- Workers: Proactive skill development is crucial in dynamic industries to leverage new technologies complementarily.
Conclusion
Our research demonstrates that new workplace technologies act as short-term shocks, driving measurable changes in work patterns. The effects are strongest in technologically dynamic industries and routine-task occupations, underscoring the need for targeted organizational and policy responses to balance productivity gains with employee well-being.
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