Economic development is uneven among as well as within countries. In addition to differences in economic development between countries, we also observe wide disparities in economic (mis)fortunes between subnational regions. This variation is often explained by productivity differences which allow some countries (or regions) to prosper while others fall behind. Even though these differences in productivity are driven by a large number of characteristics of the economy, technological progress is considered as the most essential factor for productivity gains and economic growth. However, it is clear that not all knowledge has the same quality or value. In an industrial/innovation policy sense, knowledge or technologies that are harder to be imitated and diffused in geographical space offer more sustained competitive advantage for the innovating firms and regions. In this context, the concept of knowledge complexity has been developed to empirically approach the elusive notion of knowledge quality. In this paper we explore the link between regional knowledge complexity and total factor productivity (TFP) by adopting a spatial econometric modelling approach. The modelling approach is inspired by the regional knowledge capital model (KCM) that relates knowledge to regional TFP. As the qualitative dimension of knowledge has been neglected so far, we augment the regional KCM with a knowledge complexity measure. We employ an empirical model in the form of a (fixed effects) Spatial Durbin Model which allows to identify spillover effects of knowledge complexity on regional productivity. Preliminary results indicate that regions may benefit from neighbouring regions that can produce complex knowledge, in contrast to regions that have a high conventional knowledge capital stock.