This work examines the effects of innovation on wage dynamics among German manufacturing workers. We combine linked employer-employee data with patent data from PatStat to study the causal impact of innovation at the local-industrial level and across workers’ occupations. To address endogeneity, we construct an instrument for local-industrial trends in innovation that exploits the network of patent citations. Our estimates show that in more innovative sectors, both average and lower-quartile wage growth accelerated, while top-quartile wage growth did not. This pattern reflects a consolidation of routine and manual workers’ wages in innovative sectors. We then investigate how labor market institutions, namely Collective Bargaining Agreements (CBAs), mediate the returns of innovation on wages at the micro level. We find that routine and manual workers affected by bargained wage increases benefited from innovation, whereas abstract workers in the same industries were not responsive to innovation through the collective bargaining channel. Labor market institutions thus represent a fundamental but often overlooked dimension in shaping the relationship between innovation and wage dynamics.

Since its conception, relatedness has emerged as a ductile framework to study countries’ and regions’ productive and technological capabilities. More recently, this framework has faced a growing number of critiques, both for its policy implications, and its conceptual outlines. Scholars have been largely scrutinizing relatedness for its theoretical and methodological limitations (Whittle and Kogler, 2020; Bathelt and Storper, 2023). This work explores the feasibility of a Shift-Share Instrumental Variable (SSIV) design in the relatedness density framework. On the one hand, it establishes a solid empirical strategy for relatedness analysis. On the other hand, it redefines relatedness as a measure of exposure, reframing relatedness theory in the light of such intuition. The relatedness density of technological sector in a region, indeed, represents how exposed it is to aggregate shocks in technological coevolution. This work assesses whether and under which assumptions we can identify the effects of relatedness density using an exogenous-shifts SSIV design. We use EPO patent data on 635 technological classes (IPC 4-digit codes). First, we underline the necessary identification assumptions for such a SSIV and a set of balancing tests on the technologies’ characteristics. We, hence, identify quasi-experimental variations from exogenous shifts, by leveraging cooccurrences of technologies across patents in a different technological space. We conclude that our proposed SSIV is relevant and exogenous and can be used to rigorously analyze a relatedness density framework.