My Research

The world is getting more complex …

Is it? How do we know? While we widely believe that “the world” has become more complex, showing this empirically is challenging. Beyond confirming our feelings, measuring complexity is also of great interest to scientists because complexity is widely accepted as a key (qualitative) dimension of knowledge that shapes the efforts required for its generation (invention), its (spatial) diffusion, and its economic value.

Don’t believe everything that’s written in the newspapers …

Sure, not everything we read or hear in the news reflects reality (fake news!). However, even when incorrect, news substantially impact our lives. Recent events like the BREXIT and the 2016 US election underline the impact of news on people’s behaviour. By providing and disseminating information as well as promoting specific views and opinions, news feeds into individuals’ decision-making processes. Consequently, existing research shows that news exerts a substantial influence on economic processes such as trade, tourism, and investments. My research aims at identifying and explaining differences in the content, volume, and tone of news between regions (subnational level).

Networks are everywhere …

But how do they look like? What drivers their development over time and space? My research deals with the structural characteristics of knowledge networks and their changes over time. A particular focus thereby lies on the identification of factors driving the evolution of such networks. The theoretical basis is primarily provided by the sociological literature on social networks (cf. Granovetter, 1985; Burt, 1992) and the so-called proximity approach by Boschma (2005) from the evolutionary economic geography literature. The latter highlights the relevance of social, cognitive, geographical, organizational, and institutional proximity for the establishment and effect of knowledge exchange relations.

Regions vary  in their ability to innovate …

However, how do we know that? What constitutes spatial units’ innovation performance? How can this variance be measured? What factors cause this variation? In my research, I promote the approach of measuring regional innovation performance as regions’ “innovation efficiency”. According to the well-known regional knowledge production approach, the concept of regional innovation efficiency describes the relationship between existing regional knowledge inputs and the results (output) of regional innovation activities. Amongst others, the open issues in this context are defining regional knowledge inputs that are transformed into innovation output and empirically considering region-external factors. Moreover, I explore the use of robust non-parametric efficiency analysis (e.g., see Daraio and Simar, 2007) as method for estimating innovation efficiency. In particular, the implementation of spatial relationships into this method seems to be a promising direction for future research.