Methodology - how will we achieve the FINNOV project aims?

First, by linking innovation dynamics to financial dynamics, the study contributes to developing a Schumpeterian and evolutionary analysis of the finance of innovation tied to structural changes in real production conditions at the firm and industry level across the European economy.

Second, well beyond the original Schumpeterian framework, the project addresses the political economies of different institutional arrangements linking the mechanisms of finance allocation with real market dynamics and industry evolution. In that, some of the related research questions resonate with quite similar ones addressed in the recent neo-Schumpeterian literature (cf. Aghion and Howitt, 2005). Some of the answers are likely to point towards the same causal mechanisms – for example, concerning the importance of innovative exploration especially when countries are not too far from the international technological frontier. Other issues are much more controversial, and, indeed, this project is meant to significantly advance our understanding of phenomena such as the relative importance of incumbents vs. new entrants in the exploration and exploitation of new innovative market opportunities and the underlying role of different mechanisms of financial allocation in shaping the mix of incumbent vs. new entrant based innovative activity.

Third, the political economy of finance allocation and corporate governance is far from being neutral in terms of income distribution, obviously between innovative vs. laggard firms but also within firms (between owners and managers and between managers and workers). Moreover, different finance/industry arrangements affect differently the balance between 'creative accumulation' vs. 'creative destruction' of both knowledge and corporate competitive abilities. And with that come also different balances between employment creation and destruction. This is yet another domain where our project is meant to yield novel comparative knowledge based on detailed longitudinal micro data.