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on Economic Geography |
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Issue of 2024‒03‒25
four papers chosen by Andreas Koch, Institut für Angewandte Wirtschaftsforschung |
| By: | Giovanni Dosi; Anna Snaidero |
| Abstract: | This paper delves into geographical agglomeration patterns of economic activities focusing on the connection between these agglomeration tendencies and sectoral patterns of innovative activities. Within a broad evolutionary perspective, we refine upon incumbent statistical models, trying to distinguish between intra- and inter-sectoral agglomerative forces, conditional on different types of sectoral innovative activities. Utilizing data spanning three distinct years, a decade apart, we investigate the systematic nature of spatial distributions, the relationship between agglomeration drivers and technological paradigms, and shifts in agglomerative tendencies over time. Our findings suggest that economic space is far from uniform, but the spatial heterogeneity differs across sectors as it is driven by various factors, including increasing returns, urbanization advantages, and sector-specific forms of knowledge generation and diffusion. |
| Keywords: | spatial agglomeration, evolutionary economic geography, increasing returns, externalities, knowledge specificities, Pavitt taxonomy |
| Date: | 2024–03–11 |
| URL: | http://d.repec.org/n?u=RePEc:ssa:lemwps:2024/07&r=geo |
| By: | Tobias R\"uttenauer |
| Abstract: | This handbook chapter provides an essential introduction to the field of spatial econometrics, offering a comprehensive overview of techniques and methodologies for analysing spatial data in the social sciences. Spatial econometrics addresses the unique challenges posed by spatially dependent observations, where spatial relationships among data points can significantly impact statistical analyses. The chapter begins by exploring the fundamental concepts of spatial dependence and spatial autocorrelation, and highlighting their implications for traditional econometric models. It then introduces a range of spatial econometric models, particularly spatial lag, spatial error, and spatial lag of X models, illustrating how these models accommodate spatial relationships and yield accurate and insightful results about the underlying spatial processes. The chapter provides an intuitive understanding of these models compare to each other. A practical example on London house prices demonstrates the application of spatial econometrics, emphasising its relevance in uncovering hidden spatial patterns, addressing endogeneity, and providing robust estimates in the presence of spatial dependence. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.09895&r=geo |
| By: | Moritz Kuhn; Iourii Manovskii; Xincheng Qiu |
| Abstract: | Spatial differences in labor market performance are large and highly persistent. Using data from the United States, Germany, and the United Kingdom, we document striking similarities across these countries in the spatial differences in unemployment, vacancies, and job filling, finding, and separation rates. The novel facts on the geography of vacancies and job filling are instrumental in guiding and disciplining the development of a theory of local labor market performance. We find that a spatial version of a Diamond-Mortensen-Pissarides model with endogenous separations and on-the-job search quantitatively accounts for all the documented empirical regularities. The model also quantitatively rationalizes why differences in job-separation rates have primary importance in inducing differences in unemployment across space while changes in the job-finding rate are the main driver in unemployment fluctuations over the business cycle. |
| Keywords: | Unemployment; Search and matching; Vacancies; Local labor markets |
| JEL: | J64 E24 E32 R13 J63 |
| Date: | 2024–02–29 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedmoi:97900&r=geo |
| By: | Yoshifumi Konishi (Department of Economics, Keio University); Akari Ono (Graduate School of Economics, Keio University) |
| Abstract: | Economists are increasingly concerned with the heterogeneous impacts of transportation infrastructure investments on economic outcomes, particularly the phenomenon known as the gStraw Effect h: Core cities that were already in economic prosperity may gain more, and peripheral cities may lose, from large transportation projects. We empirically investigate whether such an effect manifests in the case of the Great Seto Bridges in Japan, a 70-billion-dollar project implemented as part of the gBuilding-a-new-Japan h initiative in the 1980s-1990s. We employ the recently developed recentered instrumental variable approach in the difference-in-differences design, exploiting the sharp decline in transport costs and its differential impacts on market access levels across cities of different economic prosperity as exogenous sources of variation. We find that, contrary to the straw effect, large peripheral cities gain more than core cities, rather than lose, from the megaproject. We also demonstrate that the distribution of winners and losers from the megaproject depends on where the associated cost reductions occur in the existing network structures. |
| Keywords: | Market Access, Transportation Investment, Core-Periphery Model, Economic Geography, Quantitative Spatial Model |
| JEL: | O18 R4 R11 R12 |
| Date: | 2024–02–20 |
| URL: | http://d.repec.org/n?u=RePEc:keo:dpaper:2024-003&r=geo |