"This thesis assesses the impact of innovation on employment, using a selection of International Patent Classifications (IPC) as proxies for technological development. It uses a 15-country, 8-sector and 15-year dynamic panel dataset, specifying a system Generalized Methods of Moments (GMM) econometric model to test this relationship. It specifically evaluates whether advances in robotics – measured by the operationalization of a robotics patent index – impact labor markets differently than other patent classifications. This dissertation suggests that robotics patents are mildly positively associated with employment, and that the results from other patent classifications are insufficiently robust for reliable conclusions. Overall, this paper supports the notion that technological progress is a driver of wage and job-polarization, and that it contributes to the current OECD trend of rising inequality. While the results from this thesis do not support the notion that innovation will result in technological unemployment in the near future, it does raise a number of new questions that indicate further research on this topic is essential. Specifically, this dissertation suggests approaching this topic by drawing on more empirical data, thus bridging the gap between on the one hand theoretical models and projections, and on the other hand empirical evidence. "

Support: Digital