In Chapter 1, I combine longitudinal administrative firm-level data from Germany with 8,000 local tax changes for identification to show that local tax hikes (cuts) increase (decrease) the local manufacturing share. Firm-level results reveal that this is due to wage, employment, firm entry, and labor productivity in the service sector being more responsive to a tax shock than in manufacturing. With this evidence in mind, I calibrate a two-sector model with heterogeneous firms and profit tax to show that, owing to different structural parameters, a corporate tax cut disproportionately benefits service firms, contributing to the sectoral reallocation from manufacturing to service. In Chapter 2, we derive a European Herfindahl-Hirschman concentration index from 15 micro-aggregated country datasets. We show that European concentration rose due to a reallocation of economic activity towards large and concentrated industries. Over the same period, productivity gains from an increasing allocative efficiency of the European market accounted for 50% of European productivity growth while markups stayed constant. Using country-industry variation, we show that changes in concentration are positively associated with changes in productivity and allocative efficiency. This holds across most sectors and countries and supports the notion that rising concentration in Europe reflects a more efficient market environment rather than weak competition and rising market power. In chapter 3, We study the consequences of the Covid-19 pandemic and related policy support on productivity. We employ an extensive micro-distributed exercise to access otherwise unavailable individual data on firm performance and government subsidies. Our cross-country evidence for five EU countries shows that the pandemic led to a significant short-term decline in aggregate productivity and the direct support to firms had only a limited positive effect on productivity developments.

Firm training is widely regarded as crucial for protecting workers from automation, yet there is a lack of empirical evidence to support this belief. Using internationally harmonized data from over 90,000 workers across 37 industrialized countries, we construct an individual-level measure of automation risk based on tasks performed at work. Our analysis reveals substantial within-occupation variation in automation risk, overlooked by existing occupation-level measures. To assess whether firm training mitigates automation risk, we exploit within-occupation and within-industry variation. Additionally, we employ entropy balancing to re-weight workers without firm training based on a rich set of background characteristics, including tested numeracy skills as a proxy for unobserved ability. We find that training reduces workers’ automation risk by 3.8 percentage points, equivalent to 8% of the average automation risk. The training-induced reduction in automation risk accounts for 15% of the wage returns to firm training. Firm training is effective in reducing automation risk and increasing wages across nearly all countries, underscoring the external validity of our findings. Training is similarly effective across gender, age, and education groups, suggesting widely shared benefits rather than gains concentrated in specific demographic segments.

We analyse the impact of robot adoption on employment composition using novel micro data on robot use in German manufacturing plants linked with social security records and data on job tasks. Our task-based model predicts more favourable employment effects for the least routine-task intensive occupations and for young workers, with the latter being better at adapting to change. An event-study analysis of robot adoption confirms both predictions. We do not find adverse employment effects for any occupational or age group, but churning among low-skilled workers rises sharply. We conclude that the displacement effect of robots is occupation biased but age neutral, whereas the reinstatement effect is age biased and benefits young workers most.