AbstractExploiting regional heterogeneity in population dynamics across more than 10,000 municipalities in Germany, we provide robust empirical evidence that population aging depresses real estate prices and rents. Using millions of individual real estate listings and detailed demographic data at the municipality level, we estimate that average sales prices in 2020 would have been up to 12% higher if the population age distribution had been the same as in 2008. We show that population aging not only reduces prices but also increases the availability of real estate. In addition, we document substantial heterogeneity in price responses across dwelling type, property characteristics, and urban-rural status, suggesting that lower housing demand and life-cycle dissaving are driving our results. We predict that population aging will continue to exert downward pressure on house prices and exacerbate regional disparities in Germany.

AbstractThis research addresses global housing crisis, a secondary city of Dilla in Ethiopia as a case study. This study seeks to fill the empirical knowledge gap in popular housing typologies and tenure types in urban development in the Global South outside metropolitan areas. Drawing from an in-depth study of 18 households, the focus is on housing choices of middle- and low-income households in a secondary city of Dilla, Ethiopia. Such cities so far have been less in the focus of researchers, compared to capital regions. The studied housing types are tenures of homeownership, private rental and a rent-free model as housing types, with informal arrangements playing a role in the last two types. Our study underlines the necessity of not only acknowledging, repairing and formalizing the existing semi- and informal housing developments but also of proactively planning for peripheral housing developments and future expansion, a strategy with broader applicability to the region and the Global South. Such anticipating measures include the development of water and energy infrastructure, land earmarking for much-needed public rental housing, and the establishment of robust tenant rights through legislative foresight.

AbstractThe UK exhibits stark regional economic divides which have been a long running concern for policymakers. With the levelling up agenda taking shape, city-regions in the UK are developing innovation and business support policies in seeking to deliver on a range of goals from traditional productivity concerns to wider social and environmental imperatives. Drawing on interviews with key actors in the city-regions of Cardiff, Manchester and Glasgow, this paper points to an emerging directional change in innovation policy, yet we show that capacities to articulate and implement an inclusive innovation approach vary widely. The uneven landscape for innovation policy within each of the city-regions, in terms of the location of innovation assets but also the varied institutional and social legacies shaping innovation policy, is also brought into view. Central to the reshaping of innovation policy in all cases, however, are agents working in networks, fashioning narratives and marshalling data in efforts to mobilise new ways of practicing innovation policy within what remains a centralised approach to sub-national economic development.

In the context of regional sciences and migration studies, gravity and radiation models are typically used to estimate human spatial mobility of all kinds. These formal models are incorporated as part of regression models along with co-variates, to better represent regional specific aspects. Often, the correlations between dependent and independent variables are of non-linear type and follow complex spatial interactions and multicollinearity. To address some of the model-related obstacles and to arrive at better predictions, we introduce machine learning algorithm class XGBoost to the estimation of spatial interactions and provide useful statistics and visual representations for the model evaluation and the evaluation and interpretation of the independent variables. The methods suggested are used to study the case of the spatial mobility of high-school graduates to the enrolment in higher education institutions in Germany at the county-level. We show that machine learning techniques can deliver explainable results that compare to traditional regression modeling. In addition to typically high model fits, variable-based indicators such as the Shapley Additive Explanations value (SHAP) provide significant additional information on the differentiated and non-linear effect of the variable values. For instance, we provide evidence that the initial study location choice is not related to the quality of local labor-markets in general, as there are both, strong positive and strong negative effects of the local academic employment rates on the migration decision. When controlling for about 28 co-variates, the attractiveness of the study location itself is the most important single factor of influence, followed by the classical distance-related variables travel time (gravitation) and regional opportunities (radiation). We show that machine learning methods can be transparent, interpretable, and explainable, when employed with adequate domain-knowledge and flanked by additional calculations and visualizations related to the model evaluation.

Research collaboration is an important instrument for innovation and knowledge transfer. In this respect, barriers that prevent or impede research collaboration between research institutions and companies are of particular importance. In this paper, we elaborate on the importance of research collaboration barriers by conducting and analysing 77 expert interviews from ten German regions. We go beyond the existing literature by examining differences in the perception of different kinds of experts and by comparing different types of regions classified according to the presence of important actors, namely research institutions and large companies. Our results show that research collaboration barriers are perceived as very significant barriers to innovation across all region types. We find differences in the perception of research collaboration barriers between the experts. On the regional level, the presence of research institutions shifts the relevance from missing partners to other research collaboration barriers, while all research collaboration barriers are not perceived as significantly less important.

AbstractAn emerging field of research suggests that the policy and societal pressures for a green transition represent a “green window of opportunity” for peripheral regions. These regions often lag behind in overall innovation performance and may suffer from being places that don’t matter. At the same time, these are exactly the regions that the European Union is trying to support through several programmes, including Horizon Europe. This paper investigates the participation of organisations from peripheral regions in environmental projects funded by the Horizon Europe programme. To account for the multidimensional nature of regional peripherality, we define peripheral regions from a geographical, innovation and socio-economic perspective. We then analyse the relationship between these dimensions of regional peripherality and the extent to which regions benefit from Horizon environmental innovation projects in terms of participation, amount of funding and position in the overall network of project consortia.Our findings show a greater participation in Horizon environmental innovation projects for regions in Southern and Northern Europe, while within-country peripherality is negatively related to participation. At the same time, regions that are lagging in terms of innovation and socio-economic performance also receive less of this specific funding. Overall, geographical peripherality only tells a part of the story as several “places that don’t matter” for innovation and economic dynamism are also unable to benefit from these specific green windows of opportunity.

AbstractFamily firms are widely acknowledged to be the most predominant form of organization and hold a great relevance in most economies. Nevertheless, despite their popularity, research has thus far yielded inconsistent findings with regard to their innovative performance. This paper aims to address this research gap by focussing on a specific form of innovation: radical innovation. It seeks to determine the propensity of family firms to generate such innovations. Furthermore, by considering the heterogeneity between regions and firms, this paper also investigates the potential moderating effects of being located in a regional cluster and firm size. Based on various data sources, it is empirically shown that family firms are on average less capable of producing radical innovation than non-family firms. However, the corresponding regional context matters in this regard. By being located within regional clusters, family firms can reap the benefits of localization externalities, leading to produce more radical innovations than being located outside regional clusters.

AbstractIn the coming decades, most of Asia’s population will reside in megacities, vast urban regions accommodating 10–30 million people. However, Asian megacities will be at the same time situated in the countries whose national population is projected to decline rapidly in the coming decades. Hence, for scholars and policymakers of Asian countries, understanding how the socio-demography of mature, post-growth, megacities will evolve within space and time is crucial to envision long-term and effective spatial governance. Prior studies have shown that varied migration patterns among socio-demographic groups lead to synchronized re-urbanization, post-suburbanization, and urban shrinkage in mature city regions. However, existing studies have limitations: they often exclude large Asian megacities, lack micro-scale analyses, and use predefined spatial typologies/divisions that obscure detailed patterns. To address these research gaps, this study investigated sub-municipal spatiotemporal patterns in Tokyo, the largest Asian megacity, using micro-scale job-household data and unsupervised machine learning clustering. The study revealed that Tokyo, like Euro-American cities, has experienced regional synchronization of (re)urbanization and (post)suburbanization within a complex landscape of shrinkage. However, the synchronized sub/urban growth is not uniform across localities within Tokyo. Complex migration flows seem to create disparities in demographic growth and decline, emphasizing the need for collaborative governance among localities within a megacity. The study contributes to a wider audience who are interested not only in the evolution of cities but also in an emerging application of machine learning to quantitative urban analyses.