Daniela Nepote (p), Filomena Berardi (p), Fulvia Zunino, Salvatore Cominu, Santino Piazza

Jan Wedemeier

The purpose of this article is to provide a contribution aiming to highlight the opportunities and the threats posed within the transport components industry suppliers by the technological development over a significant period of time and how it effects the Piedmont Region.

The Piemont Region has traditionally been one of the leader of cars production, a specialization that has been reducing in economic importance over a long period but which remains the one of the main industrial assets. However this industry will face the increasingly challenging effects of the technological transition, from current and developing transport to future transport sustainability movements.
Investigation by the mean of a step-by-step research design based on multiple qualitative (surveys, case-studies, interviews) and quantitative methods (the analysis of official and non-official statistical sources) is taking place. The present contribution will focus on the possible effects of technological transition within the sector and on the innovation directions undertaken by the automotive industry components suppliers and by emerging players within the new mobility paradigm.
The new mobility paradigm seems to accelerate the process of “peripheralization” of the regional automotive industry. Therefore, the main trends related to the emerging paradigm of mobility (electrification, digitization, autonomous driving, mobility as a service) might be considered as possible avenues within which a re-position becomes possible.
Various means and policy responses might play a role in counteracting, or at least prevent the worst case scenarios, in the process of peripheralization, especially if informed by the transformative change approach for innovation policies.

Mihyun Seong (p)

Daniela Nepote

This research aims to discover spatial patterns of innovation through the location choices of firms over time. By investigating spatial patterns of new growing industries over a period of time, a model can be built to further investigate firm location choices. This model explores innovation patterns in space taking into account influential factors such as relatedness, physical network (distance from each other, distance to transportation hub), and agglomeration. Ultimately, the model aims to predict spatial patterns of innovation.

Bringing the location factors to the center of the study will contribute new insights of innovation processes. First, a new mechanism of innovation is explored using continuous space, unlike previous studies using primarily discrete spatial units. For example, relatedness studies in Evolutionary Economic Geography discovered a critical mechanism of innovation in regional development, but the location behavior of innovation has not been discovered using discrete units, nor the spatial and industrial variations of innovation. Although regression models, which incorporate spatial neighboring effects or spatial heterogeneity with dummy variables, are able to indicate spatial relationships, the locational distribution of innovation and its process on space over time cannot be identified. Second, a holistic approach of investigating innovation complements the conceptually discrete approach. Often innovation studies, especially relatedness studies, investigate innovation processes with separate focal points such as either production side (product space) or knowledge creation side (technology space). This type of approach provides generalized trends in certain parts of economic activities, but ignores a holistic view of development. Therefore, in this study, innovation patterns are investigated taking holistic economic activities into account such as knowledge creation part, producing part, supporting part, and consumption part of industries together, along with the influential factors.

To operationalize the research, aiming to answer how innovation diffuses in space, spatial statistics using R and GIS are used as the main methods, incorporating econometrics for the relatedness measurements. Utilizing longitudinal LISA data (firm data in the Netherlands) facilitates not only observing location patterns with firm location information, but also measuring industrial relatedness with multi-digit industrial codes (standard business classification). The location patterns of innovation are analyzed with the firm location information from LISA on GIS, which facilitates spatial analyses such as distance, emerging hotspots, and also visualization of the innovation patterns in space.

Racquel Claveria (p)

Mihyun Seong

Amid emerging evidence of weak association between urban agglomeration and firm innovation, I revisit the widely held notion that innovation is an urban phenomenon. To do so, I assemble a high-resolution dataset that spatially links geocoded firm-level data on internationally comparable, multidimensional innovation measures with gridded population density data and point-specific firm and geological variables for 23 developed and 35 developing countries. These innovation metrics correspond to product and process innovation, radical and incremental innovation, and R&D spending as innovation input. I find that contrary to prevailing theoretical expectations and empirical evidence, all innovation measures (product and process, radical and incremental, and R&D as innovation input) decline across density quartiles for my full sample and subsamples of developed and developing countries. Moreover, my probit estimation results indicate that similar to the alternative perspective that challenges the mainstream geography of innovation, internal factors such as research and development (R&D) and firm’s idiosyncrasies such as secrecy matter more to innovation. However, for developing countries, R&D is not internalized but rather depends on agglomeration economies.

Mariia Shkolnykova, Lasse Steffens, Jan Wedemeier (p)

Racquel Claveria

This study explores the regional convergence and divergence of economic development for Central and Eastern European (CEE) countries, including an in-depth analysis of different EU-border countries from the inside and outside of the EU (e.g., Ukraine and Poland). The authors analyze the impact of innovation and institutional factors on the development of regions inside these countries. Hence, the intention is to contribute to the literature on economic transitions driven by the regions. Furthermore, it identifies the reasons behind the differences or similarities in economic development between European and non-European CEE regions. The study aims to fill the existing gap by providing a comprehensive analysis of the regional convergence and/or divergence factors of innovation and institutions for the regions on both sides of the EU border in the period 2000-2020. By applying a standard panel regression three research questions are addressed: (1) Are institutions and innovations relevant for economic growth and are these framework conditions important for catching up for economic convergence? (2) Are there differences between European and non-European CEE country regions? (3) Do EU border regions stand out in this development? This paper concludes that for successful economic development and convergence innovations and institutions matter and the regional innovation systems need to be addressed by the current EU-policy smart specialization strategy.