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on Economic Geography |
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Issue of 2026–05–25
eight papers chosen by Andreas Koch, Institut für Angewandte Wirtschaftsforschung |
| By: | Denis F. Alves (Federal University of Pernambuco, Brazil); André L. S. Chagas (University of Sao Paulo, Brazil); Roberta M. Rocha (Federal University of Pernambuco, Brazil); Raul M. Silveira Neto (Federal University of Pernambuco, Brazil) |
| Abstract: | We examine the local economic impacts of the opening of the RioMar Shopping Mall in Recife, Brazil, in 2012 using a georeferenced panel of firms covering 2005–2019. We exploit variation in proximity to the mall through concentric distance rings (up to 500 meters, 500 meters–1 km, and 1–2 km) and implement a Difference-in-Differences (DiD) framework combined with Propensity Score Matching (PSM). We find strong and spatially concentrated effects: firms within 500 meters increase employment and wages substantially, with effects that decay monotonically with distance but remain statistically significant up to 2 kilometers. Event study estimates support the parallel trends assumption and show no evidence of pre-treatment dynamics. We show that these effects are primarily driven by the expansion of incumbent firms rather than new firm entry, indicating that agglomeration economies operate through the intensification of existing activity. We also document important heterogeneities: gains are larger for firms with more skilled workers, in managerial occupations, and in consumption- and service-related sectors. However, benefits are unevenly distributed across demographic groups, with stronger effects for male and white workers. These results are robust to alternative control groups and to placebo tests based on both timing and spatial location. Overall, we show that large urban commercial developments act as localized economic hubs, generating positive but spatially bounded externalities. Our findings contribute to the literature on place-based policies and urban agglomeration by providing causal evidence from developing countries such as Brazil, where such interventions play a central role in shaping urban economic dynamics. |
| Keywords: | Shopping Malls; Local Economic Effects; Spatial Externalities; Agglomeration Economies; Difference-in-Differences |
| JEL: | R10 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ris:nereus:022483 |
| By: | Luigi Capoani; Margarita Shnaider; Piergiorgio Martini |
| Abstract: | This paper investigates how geopolitical conflict reshapes trade patterns, focusing on the economic consequences of the Russo-Ukrainian war on European and global trade flows. War is conceptualized as a shock that increases bilateral trade costs within a structural gravity model, rather than as a force acting against trade flows, amplifying frictions in territories closer to the epicenter and reducing the economic attractiveness of major trade routes. The empirical analysis combines an Extended Gravity Model based on bilateral trade data from 2019 and 2023 with geographic, institutional, and political factors, including sanctions regimes and energy specialization. The findings show that war not only reduces trade volumes but also operates multiplicatively on trade frictions, influencing both the intensity and direction of trade disruptions, with more pronounced effects in the central corridors of the European market. As a result, some trade relationships collapse while others are redirected towards less exposed regions; furthermore, policy choices are decisive in shaping trade flows and contribute to isolating the Russian economy by creating a policy-induced trade void around the target country, while mechanisms such as the EU Single Market facilitate the internal reallocation of trade flows, preserving economic cohesion. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2605.16334 |
| By: | Massimo Giannini |
| Abstract: | This paper assesses whether NASA Black Marble nightlight intensity can serve as an early indicator of annual taxable income at the Italian municipal level, where official data are released with a 12--18 month lag. Using a panel of 7{, }631 municipalities over 2012--2021, we compare four recurrent neural network architectures (LSTM, BiLSTM, GRU, Transformer) against six benchmarks: simple persistence, panel fixed effects, autoregressive distributed lag, and two spatial econometric specifications (SAR, Spatial Durbin) on a queen-contiguity matrix. Models are trained on 2012--2019 and evaluated out-of-sample on 2020--2021 with a cross-sectional Diebold--Mariano test. A single-layer GRU achieves a median forecast error of 1.07 million euros across the cross-section of municipalities -- approximately $4\%$ of the median municipal IRPEF income of 29 million euros -- statistically dominating every benchmark (DM $>4$ against persistence, $>40$ against spatial linear models, all $p |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2605.08782 |
| By: | Santiago Picasso |
| Abstract: | A stylized factin modern economies is that the more developed a country is, the greater the weight of the service sector.The economics of complexity has provided a new perspective that explains this growth in modern economies.However, thestudy of economic complexity through the standard measure of thecomplexity index presents an increasingly relevant omission in understanding the economic process and its growth.Ingeneral, the data used to measure the EconomicComplexity Index(ECI) are based on information about goods;however, there is a lack of informationon services.This paper proposes an ew methodology to retrieve information on the economic complexity in services.Forthis purpose, the US input-output matrix is used.This work is novel because, thanks to the structure of the data as a network, it is possible to infer them is sing information on the complexity of services. Using a machinelearning method, it ispossible to impute the complexity index for 146services, a level of disaggregation, that is strikingly higher than in other works.The index recovered by this method is consistent with previous results that found service sectors to be more complex than goods.The second result shows that the more restricted the core is in the center of the network, the greater the centrality of services and their complexity.Finally, the results confirm the relevance of the economic complexity index. However, the ECI forservices is better than the ECI for goods for predicting growth;aone-unit increase in the ECI of services increases GDP growth by more than 1 percentage point. |
| Keywords: | Economic Complexity; Services Sector; Input–Output Networks; Machine Learning; k-Nearest Neighbors; Structural Transformation; Economic Growth; Spatial Econometrics |
| JEL: | C45 C55 O11 O14 O47 L80 |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:ude:wpaper:0126 |
| By: | Salvo, Carla (Sapienza University of Rome); Weisdorf, Jacob (Sapienza University of Rome, CAGE, & CEPR) |
| Abstract: | Northern Italy is markedly richer than the rest of the country. The origins of this regional divide have long been the subject of debate. We trace relative regional development back to the end of antiquity using newly assembled data on ecclesiastical building activity as a proxy for economic performance. We identify two pre-modern golden ages in the 10th to 13th and 15th to 16th centuries, both plausibly interrupted by major plague outbreaks. Our evidence suggests that the North South gap emerged more than a millennium ago, around 900 CE, when the North pulled ahead and re tained its lead thereafter. We also find that Italian unification further amplified this northern advantage. |
| Keywords: | Church building, regional development, economic growth JEL Classification: O11, N30, N60, Z12 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:cge:wacage:803 |
| By: | Lee, Kamwoo; Blankespoor, Brian; Newhouse, David |
| Abstract: | Fine-grained spatial data are critical for informed decision-making in domains ranging from economic planning to environmental management. However, many statistics are only available for coarse administrative units, necessitating techniques for fine-scale spatial disaggregation. This paper introduces a graph neural network (GNN) based framework for disaggregating aggregated indicators to a finer spatial resolution. The GNN approach leverages graph representations of spatial units to incorporate both feature information and spatial relationships, addressing challenges of heterogeneity and data sparsity. The approach also adopts the H3 hierarchical hexagonal indexing system to define fine-resolution cells, providing a globally consistent, multi-resolution spatial grid well suited to graph-based modeling. The paper demonstrates the framework using gross domestic product (GDP) as a representative example, disaggregating national or regional GDP to fine-resolution cells. The proposed methodology is applicable to a broad class of aggregate indicators, offering a flexible and scalable tool for spatial analysis of economic, social, and environmental statistics. The results show that the framework produces high-resolution estimates that are consistent with known aggregates and aligned with ancillary covariate patterns. This general-purpose approach to spatial disaggregation enables more detailed mapping of indicators like GDP and beyond, unlocking finer insights from coarse data. |
| Date: | 2026–04–23 |
| URL: | https://d.repec.org/n?u=RePEc:wbk:wbrwps:11360 |
| By: | Jorge Arbache; Otaviano Canuto |
| Abstract: | Decarbonization is reconfiguring global relative prices. As clean energy, natural capital, and location-specific assets become dominant industrial inputs, the relative cost of producing low-carbon goods is increasingly determined by geography. Two systematic distortions explain why the expected reallocation of investment toward renewable-rich economies remains incomplete. First, industrial policy interventions, including subsidies, trade barriers, and certification systems, disconnect effective prices from underlying structural costs. Second, institutional failures create demand uncertainty that leaves structurally competitive projects unbankable. Together, these distortions generate static misallocation, leading to slower technological learning, higher fiscal burdens, delayed emissions reductions, and suppressed industrial opportunities in developing economies. This paper is part of broader research on powershoring and green comparative advantage, which focuses on the idea that decarbonization is a spatial and price reorganization of global production, in addition to a technological transition. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:ocp:rpaeco:pp13_26 |
| By: | Alm, Bastian (BMWE); Fuchs, Michaela (Institute for Employment Research (IAB), Nuremberg, Germany); Sujata, Uwe (Institute for Employment Research (IAB), Nuremberg, Germany); Weyh, Antje (Institute for Employment Research (IAB), Nuremberg, Germany) |
| Abstract: | "We analyze structural change in Germany and its counties between 1993 and 2024. Thereby, special focus is put on regional differences in the degree of specialization on specific sectors and on its relation with regional structural change. We discuss the relevance of our findings for upcoming changes in German regional policy measures." (Author's abstract, IAB-Doku) ((en)) |
| Date: | 2026–05–21 |
| URL: | https://d.repec.org/n?u=RePEc:iab:iabkbe:202609 |