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on Economic Geography |
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Issue of 2025–04–07
eight papers chosen by Andreas Koch, Institut für Angewandte Wirtschaftsforschung |
| By: | Fabian Bald; Marcel Henkel |
| Abstract: | This paper proposes that spatial policies improve economic outcomes by reducing barriers to supplying labour, with heterogeneous effects across demographic groups. Using quasi-experimental variation in Germany’s fiscal transfer system, we estimate higher employment elasticities for female workers, with the strongest impact in places where public childcare supply is smaller. We propose a quantitative spatial model incorporating location decisions and group-specific frictions to labour force participation. We establish that optimal spatial policy would not unambiguously direct resources to low-wage areas but additionally target regions with high labour supply elasticities, yielding substantial welfare and labour force gains in the aggregate. This paper argues that accounting for differential employment responses significantly alters optimal place-based policy design, highlighting a novel channel for addressing efficiency and equity concerns in ageing economies. |
| Keywords: | Place-Based Policies, Local Public Goods, Labour Force Participation, Fiscal Transfers, Spatial Sorting |
| JEL: | H41 H73 J16 J22 J61 R23 R58 |
| Date: | 2025–03–17 |
| URL: | https://d.repec.org/n?u=RePEc:bdp:dpaper:0063 |
| By: | Yijia Chen; Kangmin Wu; |
| Abstract: | Artificial intelligence (AI) is a key driver of the Fourth Industrial Revolution. Despite growing interest in the geography of AI, our understanding of how AI integrates into regional contexts remains limited. In response, we examine the integration of AI into regional technological domains in China and the United States using patent data. Theoretically, we develop a framework by introducing the concepts of intra- and extra-regional AI relatedness. Our findings reveal that the integration of AI into regional technological domains is positively associated with both intra-regional and extra-regional AI relatedness. Additionally, extra-regional AI relatedness can moderate the lack of intra-regional AI relatedness. |
| Keywords: | integration of artificial intelligence, intra-regional AI relatedness, extra-regional AI relatedness, regional technological domains, China, the United States |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:egu:wpaper:2507 |
| By: | Paola Conconi; Fabrizio Leone; Glenn Magerman; Catherine Thomas |
| Abstract: | This paper provides a new explanation for the dominance of multinational corporations (MNCs) in international trade: after being acquired by an MNC, firms face lower entry frictions in countries in which their global parent already has a presence. We provide a model of firms’ export and import choices that delivers firm-level gravity regressions to isolate these “MNC network effects” from other channels through which multinationalownership can affect firms’ trade participation. We estimate the model combining rich administrative data for Belgium with data on MNCs’ global affiliate networks. Event study results reveal that acquired firms are more likely to start exporting to and importing from countries that belong—or that are exogenously added—to their parental network. The effects are stronger when new affiliates are geographically and culturally close to their direct parent, which can facilitate transfer of information about the global parent’s network. Combining the structure of our model with the empirical estimates, we find that MNC network effects have a large impact on firm growth. The effects of MNC ownership extend beyond the boundaries of the multinational: new affiliates are also more likely to start trading with countries that are geographically and culturally close to the MNC network, even if their parent has no affiliates there. |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:eca:wpaper:2013/389310 |
| By: | R. Zelli; M.G. Pittau; A. Vaiano |
| Abstract: | According to the World Bank report (Gill & Raiser, 2012), the EU has become the modern world's greatest "convergence machine". While the process of convergence has been acknowledged at country level, results at regional level are still unclear. Using the most advanced techniques, we assess convergence across European NUTS2 regions over forty years. The distributional dynamic approach unveils different perspectives that traditional methods have overlooked. We conclude that a process of catching-up between low- and middle-income regions has been in progress, while wealthy regions have been drifting away. |
| Keywords: | mixture models;EU regions;Club convergence |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:cns:cnscwp:202507 |
| By: | A.C. Pinate; M. Dal Molin; M.G. Brandano |
| Abstract: | This paper analyses the relationship between institutional quality and green innovation in Italian regions (NUTS2). We examine how varying levels of institutional quality influence the regional capacity to generate green innovation, disentangling the effects related to economic institutions (corruption, government effectiveness, and regulatory quality) from the impacts associated with political institutions (rule of law and voice and accountability). Using a panel of data for 2004–2018 on green patents, we use an instrumental variable IV approach to control for endogeneity and several robustness checks. Our results show that the most important drivers of green innovation are related to the quality of political institutions. These findings remain robust, even when checking for economic and environmental controls, demonstrating that green innovation is more related to political decisions and social capital than innovation in general is. |
| Keywords: | regional green innovation;green patents;Institutional Quality;italy |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:cns:cnscwp:202508 |
| By: | Laurent Bailly (Public Health Department at CHU Nice); Rania Belgaied (Université Côte d'Azur, France; GREDEG CNRS); Thomas Jobert (Université Côte d'Azur, France; GREDEG CNRS); Benjamin Montmartin (Skema Business School) |
| Abstract: | During the period from January 4 to February 14, 2021 the spread of the COVID epidemic peaked in the city of Nice, with a worrying number of infected cases. The spatial dynamics of the pandemic revealed explicit geographical patterns. This article focuses on analyzing the spatial pattern of virus spread and assessing the geographical factors influencing this distribution. Thus, in this article, spatial modeling was carried out to examine geographical disparities in terms of distribution, incidence and prevalence of the virus, while taking socio-economic factors into account. A multiple linear regression model was used to identify the key socio-economic variables affecting the spread of COVID-19 in Nice. Global and local spatial autocorrelation was measured using Moran and LISA indices, followed by spatial autocorrelation analysis of the residuals. Similarly, we used a global regression model and local models (the Geographically Weighted Regression (GWR) model and the Multiscale Geographically Weighted Regression (MGWR) model), to assess the influence of socio-economic factors that vary on a global and local scale, in order to adopt the most appropriate model explaining the spread of the disease. The results confirm that covid-19 is strongly spatially correlated, and that spatial analysis is an essential step in implementing effective preventive measures. The various global and local models identified four significant variables with regard to vulnerability to COVID disease in Nice. Our results reveal a marked geographical polarization, with affluent areas in the southeast contrasting sharply with disadvantaged neighborhoods in the northwest. Neighborhoods with low LHDI, low levels of education, social housing and immigrant populations. These latter factors all point to worrying values. On the other hand, people who use public transport are significantly negatively correlated with contamination by the virus. These results underline the importance of geographically predicting COVID-19 distribution patterns to guide targeted interventions and health policies in Nice. Understanding these spatial patterns using models such as MGWR can help guide public health interventions and inform future health policies, particularly in the context of pandemics. |
| Keywords: | COVID-19, Spatial analysis, Spatial autocorrelation, Public health, Geographic Information System (GIS) |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:gre:wpaper:2025-04 |
| By: | Glyn Wittwer |
| Abstract: | This paper describes a method of combining national GTAP regions with sub-national detail. The approach extends the sub-national TERM methodology to create a family of models named GlobeTERM. In each model, the master database includes 74 sectors, based on GTAP with electricity split into 9 generation sectors plus a distribution sector. The other 64 sectors are those in on GTAP version 11c. In most examples, one country within GTAP is split into sub-national regions, while retaining the other 159 GTAP regions in the master database. Examples include China, Germany, UK and USA. Another version represents Europe's regions at the NUTS-2 level. Using the US version of GlobeTERM, an illustrative simulation examines the impacts of the imposition of large bilateral tariffs between USA and China. The aggregation for this scenario depicts swing states separately. While almost all US regions lose in the short run from the imposition of high bilateral tariffs, there are winning and losing states in the long run. |
| Keywords: | Computable general equilibrium, regional economics, tariffs |
| JEL: | R15 C68 D58 B17 |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:cop:wpaper:g-350 |
| By: | Keisuke Kondo (Research Institute of Economy, Trade and Industry and Research Institute for Economics and Business Administration, Kobe University, JAPAN) |
| Abstract: | This paper introduces the new Stata command moransi, which allows users to easily compute global and local Moran's I statistics in Stata. The fundamental feature of the moransi command is that the spatial weight matrix is constructed internally within a sequence of the program code. The additional information required in the dataset to implement this command are the latitude and longitude of regions. This paper presents two applied examples of the moransi command to deepen the understanding of global and local spatial autocorrelation. |
| Keywords: | Moransi; Moran's I; Global indicators of spatial association; Local indicators of spatial association; Spatial lag |
| JEL: | C87 |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:kob:dpaper:dp2025-03 |