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on Economic Geography |
| By: | S. Usai; G. Filia; A. Tidu; U.M. Gragnolati |
| Abstract: | This paper assesses the spatial concentration of employment at plant level in Italy between 2007 and 2021. We rely on a comprehensive data set including both manufacturing and service sectors at 3-digit ATECO. Our key measure of spatial concentration is theM function, which we analyze both at the aggregate and local level. In this way, we trace how the spatial concentration of economic activities has evolved across various geographic scales, while also keeping track of which local economies have contributed to such change. |
| Keywords: | Spatial concentration;M function;Industrial clusters;structural change |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:cns:cnscwp:202512 |
| By: | Riukula, Krista (ETLA - The Research Institute of the Finnish Economy); Väänänen, Touko (Aalto University) |
| Abstract: | We study the impact of transport-induced agglomeration on workers' earnings, as well as the productivity and costs of establishments, in the capital region of Finland using comprehensive individual- and establishment-level registry data. To our knowledge, we are the first to jointly examine firm- and worker-level effects of agglomeration. We find that improved workplace-to-workplace accessibility increases employees’ annual earnings, particularly among workers in smaller firms. However, we find no statistically significant effects on value added or labour costs per worker at the establishment level. We propose two potential explanations for this discrepancy: (1) differences in the composition of workers between the worker- and establishment-level analyses due to, for example, new hires, and (2) rising costs associated with increased agglomeration. Further analysis reveals that enhanced accessibility leads to higher establishment employment and increased operating expenses, such as rents. Taken together, these findings suggest that the benefits of agglomeration are primarily shared between workers and property owners. |
| Keywords: | transport project, productivity, agglomeration, accessibility |
| JEL: | R41 R42 R12 |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18103 |
| By: | Jeannerat, Hugues; Butzin, Anna; Carvalho, Luís; Manniche, Jesper |
| Abstract: | While knowledge has long been central to theories of innovation-led regional development, its conceptualization within the emerging transformative innovation paradigm has remained largely implicit and undertheorized. This paper draws on insights from sustainability transitions, organizational learning, and higher education studies to develop a perspective on the action-oriented nature of knowledge, as it increasingly associates with the matters of directionality, materiality and structuration. Based on this, we articulate an idea of transformative knowledge through a triple lens, emphasising interdependencies between knowledge for action (goal- and mission-oriented), knowledge by action (generated through experimentation), and knowledge as action (situated in practice and everyday life). We apply this lens to discuss the outlines of transformative knowledge regions, proposing an expansion in the repertoire of regional innovation interventions. In doing so, the paper broadens the epistemic contours of knowledge in regional development and contribute to current debates on challenge- and mission-oriented regional innovation policy. |
| Keywords: | transformative learning, sustainability transitions, regional innovation policy, mission innovation, valuation |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:iatdps:324867 |
| By: | Giulia Ferrante; Luca Buzzanca; Arsene Perrot |
| Abstract: | This study argues that regional development theories and policymaking have overlooked the role of local cultural characteristics and social cohesion in influencing local development through cohesion networks, proxim- ities and territorial identity. Supporting this statement through the Italian case study, we provide causal evidence from Difference-in-Differences estimates of the effect of social cohesion fostered by local cultural characteristics’ recognition in mitigating depopulation trends in peripheries after a place-based policy’s implementation. Linguistic Minorities-hosting municipalities retained 2.9 more inhabitants per 1, 000 per year, a relevant but heterogeneously distributed effect with proximities-induced spillovers. This frames cultural characteristics as local public goods and policymaking tools. |
| Keywords: | Linguistic Minorities; Local Development; Proximities; Public goods; Social Cohesion; Territorial Capital |
| JEL: | R11 R58 Z13 |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:egu:wpaper:2530 |
| By: | Yating Ru (Asian Development Bank); Elizabeth Tennant (Cornell University); David Matteson (Cornell University); Christopher Barrett (Cornell University) |
| Abstract: | Recent studies harnessing geospatial big data and machine learning have significantly advanced poverty mapping, enabling granular and timely welfare estimates in traditionally data scarce regions. While much of the existing research has focused on overall out-of-sample predictive performance, there is a lack of understanding regarding where such models underperform and whether key spatial relationships might vary across places. This study investigates spatial heterogeneity in machine learning-based poverty mapping, testing whether spatial regression and machine learning techniques produce more unbiased predictions. We find that extrapolation into unsurveyed areas suffers from biases that spatial methods do not resolve; welfare is overestimated in impoverished regions, rural areas, and single sector-dominated economies, whereas it tends to be underestimated in wealthier, urbanized, and diversified economies. Even as spatial models improve overall predictive accuracy, enhancements in traditionally underperforming areas remain marginal. This underscores the need for more representative training datasets and better remotely sensed proxies, especially for poor and rural regions, in future research related to machine learning-based poverty mapping. |
| Keywords: | poverty mapping;machine learning;spatial models;East Africa |
| JEL: | C21 C55 I32 |
| Date: | 2025–09–05 |
| URL: | https://d.repec.org/n?u=RePEc:ris:adbewp:021518 |
| By: | E. Marrocu; R. Paci; L. Serafini |
| Abstract: | This paper investigates the impact of digital and green programmes within Smart Specialisation Strategies on regional productivity growth across European regions. It examines the combined influence of digital and green priorities (Twin Transition) and how their effects vary according to regions' initial economic conditions. The analysis reveals a U-shaped relationship - the Twin Transition is positively and significantly associated with productivity growth in low-productivity regions, whereas regions with intermediate productivity levels exhibit weaker or even negative associations. Conversely, high-productivity regions experience modest yet stabilising effects. These findings highlight the significance of the middle-income trap and the need for context-sensitive policy design. |
| Keywords: | Green policies;Digital policies;Twin Transition;Smart Specialisation Strategy;regional economic growth;european regions |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:cns:cnscwp:202510 |
| By: | Terstriep, Judith; Angstmann, Marius |
| Abstract: | In this discussion paper, we explore the concept of "societal innovativeness" as a key driver of transformative regional development, particularly within the context of regions facing structural weaknesses. Our explorative study delves into the multifaceted nature of societal innovativeness, which encompasses a broad range of social, cultural, and institutional factors that collectively enable regions to address complex societal challenges. We propose a comprehensive conceptual framework that identifies six core components-values and norms, capabilities, power relations, system-level agency, narratives and imaginaries, and exogenous factors-as integral to fostering societal innovativeness. By establishing a theoretical foundation, we aim to bridge theoretical concepts with practical applications, offering pathways for regions to enhance their innovative capacities. Our hypotheses, grounded in this framework, emphasise the interplay between these components, aiming to encourage inclusive and sustainable regional development. Future research, through empirical testing across diverse regional contexts, will further validate and refine this framework, thereby enhancing its applicability and providing valuable insights into practical strategies that empower regions to navigate and thrive amidst societal challenges. |
| Keywords: | societal innovativeness, society, regional development, transformation, structural change, transformative regional development, grand societal challenges |
| JEL: | Z1 O10 O3 O35 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:iatdps:324868 |
| By: | Manabu Nose (Keio University, Faculty of Economics); Yasuyuki Sawada (University of Tokyo, Faculty of Economics, Graduate School of Economics) |
| Abstract: | This paper examines the nonlinear effects of a large-scale highway construction project in the Greater Mekong Subregion, which connects the historically conflict-affected borderlands of northern Vietnam to the country’s industrial core. Employing a market access framework with geo-coded highway network and firm-level panel data, we estimate the causal impact of improved interregional connectivity, while accounting for spillovers via production input-output linkages. To address endogeneity issues arising from non-random route placements, we construct least-cost path spanning tree networks. Our instrumental variable estimates reveal that enhanced market access spurred manufacturing firm agglomeration and employment growth, particularly in peripheral rural areas. We further explore the underlying sources of polycentric development patterns, finding pronounced effects in second-tier cities characterized by less intense competition and better access to national road networks. Our findings are robust to controls for industrial zones, underscoring the pivotal role of the upgraded highway connectivity in transforming previously marginalized regions and supporting economy-wide industrialization over the past decade. |
| Keywords: | spatial structural transformation, market access, treatment spillover, agglomeration, core-periphery |
| JEL: | O14 O18 O22 O25 R12 R32 R58 |
| Date: | 2025–05–30 |
| URL: | https://d.repec.org/n?u=RePEc:keo:dpaper:dp2025-010 |
| By: | Gorjian, Mahshid |
| Abstract: | This study brings together current advances in the statistical and methodological foundations of spatial economics, focusing on the use of quantitative models and empirical approaches to investigate the distribution of economic activity over geographic space. We combine classical principles with modern approaches that emphasize causal identification, structural estimation, and the use of statistical and computational tools such as spatial econometrics, machine learning, and big data analytics. The study focuses on methodological challenges in spatial data analysis, such as spatial autocorrelation, high dimensionality, and the use of Geographic Information Systems (GIS), while also discussing advances in the design and estimation of quantitative spatial models. The focus is on contemporary empirical applications that use natural experiments, quasi-experimental approaches, and advanced econometric tools to examine the effects of agglomeration, market access, and infrastructure policy. Despite significant advances, significant challenges remain in resilient model identification, dynamic analysis, and the integration of statistical approaches with new types of geographic data. This page focuses on statistical methodologies and serves as a resource for economists and the broader statistics community interested in spatial modeling, causal inference, and policy evaluation. |
| Keywords: | statistical methodology, causal inference, spatial econometrics, machine learning, quantitative models, spatial statistics, GIS. |
| JEL: | C01 C1 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:125636 |
| By: | McKenzie Humann; Jordan Rappaport |
| Abstract: | Metropolitan areas are a fundamental unit of economic analysis. Broadly defined, they are unions of built-up locations near each other among which people travel between places of residence, employment, and consumption. Despite the importance of metropolitan areas, metropolitan Core-Based Statistical Areas and other official U.S. delineations considerably stray from this broad definition. We develop a simple algorithm to better match it, using commuting flows among U.S. census tracts in 2000. Three judgmental parameters govern the threshold strength of commuting ties between locations to include them in the same metropolitan area, the maximum separating distance between locations, and the threshold density of outlying settlement. A parameterization that balances encompassing commuting flows and excluding sparsely settled land delineates 361 Kernel-Based Metropolitan Areas (KBMAs), in aggregate capturing almost all the population and employment of metropolitan CBSAs in a small fraction of their land area. We benchmark KBMAs against two alternative parameterizations, one that prioritizes encompassing commuting flows and one that prioritizes excluding less built-up and less near locations. |
| Keywords: | metropolitan areas; commuting; City size; metropolitan statistical areas |
| JEL: | R12 R14 R23 |
| Date: | 2025–04–11 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedkrw:101733 |