AbstractThis study investigates whether local government expenditure correlate with the Quality of Life (QoL) of the local population and whether high public expenditure are indicative of high or low QoL. Data, including information on public expenditure and objective QoL indicators, were gathered for Finnish municipalities from several existing databases and cover a period of 2015–2019. A composite indicator was constructed to measure municipal QoL performance. The results indicate that there is a clear correlation between public expenditure and QoL: the higher the public expenditure the lower the QoL of the local population. This is due to a greater demand for public goods in municipalities that have low QoL levels. Further, QoL and public expenditure levels are a fairly constant phenomenon. There was no evidence that changes in public expenditure and QoL would affect each other in the short term. The added value of this paper is that it fills parts of the research gap concerning our knowledge on the empirical links between public expenditure and QoL at the local level.

This paper looks at the co-evolution of toxic industrial pollution and economic deprivation by means of spillovers from the plant’s production activities. Geolocalised facility-level data from the European Pollutant Release and Transfer Register (E-PRTR) are used to calculate annual chemical-specific pollution, weighted by its toxicity. We combine the latter with regional data on employment, wages, and demographics sourced from Cambridge Econometrics, covering more than 1200 NUTS‑3 regions in 15 countries, over the period 2007–2018. We employ quantile regressions to detect the heterogeneity across regions and understand the specificities of the 10th and 25th percentiles. Our first contribution consists in giving a novel and comprehensive account of the geography of toxic pollution in Europe, both at facility and regional level, disaggregated by sectors. Second, we regress toxic pollution (intensity effect) and pollutant concentration (composition effect) on labour market dimensions of left-behind places. Our results point to the existence of economic dependence on noxious industrialisation in left-behind places. In addition, whenever environmental efficiency-enhancing production technologies are adopted we observe associated labour-saving effects in industrial employment, but positive regional spillovers. Through the lens of economic geography, our results call for a new political economy of left-behind places within the realm of sustainable development.

Scholarly work in economic geography and regional science has recently seen a renewed interest in spatial inequalities, driven significantly by the debate on left-behind places and the resulting geographies of discontent. The plight of left-behind places calls for new place-based policy responses that address the specific challenges of these regions but that at the same time address grand societal challenges such as climate change, biodiversity loss, or pollution with synthetic chemicals. Despite growing attention among economic geographers and regional scientists to either green or inclusive regional development approaches, how to reconcile environmental sustainability and social inclusiveness in highly challenged left-behind places remains poorly understood. This editorial reflects on and critically discusses the literature on left-behind places and distils unifying conceptual characteristics of left-behindness. We argue that left-behindness is a temporal, relational, multidimensional, discursive, but not deterministic concept. The non-determinism of left-behindness opens up different choices for actors to shape regional futures. Imagining and negotiating these futures involves dealing with difficult potential trade-offs between environmental sustainability and social inclusiveness, some of which are explored by the articles in this special issue.

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.

The 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.