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on Economic Geography |
| By: | Pastore, Francesco; Semerikova, Elena |
| Abstract: | This paper aims to assess the role of migration as an adjustment mechanism device to favor convergence across states and regions of Russia. In contrast to previous studies, we use variations in the population of a region as a proxy of its net migration rate and apply spatial econometric methodology in order to distinguish the effect from the neighbouring regions. We provide descriptive statistical evidence showing that Russia has more/less/the same intense migration flows than the USA and EU. The econometric analysis shows that migration flows are sensitive to both regional income and regional unemployment differentials. Nonetheless, we find that internal migration is sensitive to regional unemployment and income differentials of neighbouring regions. Dependent on the welfare, pre- or after-crisis period, income in neighbouring regions can create out- or in-migration flows. The relatively high degree of internal mobility coupled with the low sensitivity of migration flows to the local unemployment rate of distant regions might explain why migration flows tends not to generate convergence, but rather divergence across Russian regions. |
| Keywords: | Internal and International migration,Adjustment mechanism,spatial econometrics,Russia |
| JEL: | F15 F22 J61 R23 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:glodps:445&r=all |
| By: | Krüger, Miriam; Kinne, Jan; Lenz, David; Resch, Bernd |
| Abstract: | In this paper, we introduce the concept of a Digital Layer to empirically investigate inter-firm relations at any geographical scale of analysis. The Digital Layer is created from large-scale, structured web scraping of firm websites, their textual content and the hyperlinks among them. Using text-based machine learning models, we show that this Digital Layer can be used to derive meaningful characteristics for the over seven million firm-to-firm relations, which we analyze in this case study of 500,000 firms based in Germany. Among others, we explore three dimensions of relational proximity: (1) Cognitive proximity is measured by the similarity between firms' website texts. (2) Organizational proximity is measured by classifying the nature of the firms' relationships (business vs. non-business) using a text-based machine learning classification model. (3) Geographical proximity is calculated using the exact geographic location of the firms. Finally, we use these variables to explore the differences between innovative and non-innovative firms with regard to their location and relations within the Digital Layer. The firm-level innovation indicators in this study come from traditional sources (survey and patent data) and from a novel deep learning-based approach that harnesses firm website texts. We find that, after controlling for a range of firm-level characteristics, innovative firms compared to non-innovative firms maintain more numerous relationships and that their partners are more innovative than partners of non-innovative firms. Innovative firms are located in dense areas and still maintain relationships that are geographically farther away. Their partners share a common knowledge base and their relationships are business-focused. We conclude that the Digital Layer is a suitable and highly cost-efficient method to conduct large-scale analyses of firm networks that are not constrained to specific sectors, regions, or a particular geographical level of analysis. As such, our approach complements other relational datasets like patents or survey data nicely. |
| Keywords: | Web Mining,Innovation,Proximity,Network,Natural Language Processing |
| JEL: | O30 R10 C80 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:zewdip:20003&r=all |
| By: | Luisa Dörr; Florian Dorn; Stefanie Gäbler; Niklas Potrafke |
| Abstract: | We examine how new airport infrastructure influences regional tourism. Identification is based on the conversion of a military air base into a regional commercial airport in the German state of Bavaria. The new airport opened in 2007 and promotes travelling to the touristic region Allgäu in the Bavarian Alps. We use a synthetic control approach and show that the new commercial airport increased tourism in the Allgäu region over the period 2008-2016. The positive effect is especially pronounced in the county where the airport is located. Our results suggest that new transportation infrastructure promotes regional economic development. |
| Keywords: | airports, tourism, regional development, transportation infrastructure, synthetic control method |
| JEL: | O18 Z38 L93 |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_8010&r=all |
| By: | Jaqueson K. Galimberti (School of Economics, Auckland University of Technology) |
| Abstract: | We evaluate the usefulness of satellite-based data on night-time lights for forecasting GDP growth across a global sample of countries, proposing innovative location-based indicators to extract new predictive information from the lights data. Our findings are generally favorable to the use of night lights data to improve the accuracy of model-based forecasts. We also find a substantial degree of heterogeneity across countries in the relationship between lights and economic activity: individually-estimated models tend to outperform panel specifications. Key factors underlying the night lights performance include the country’s size and income level, logistics infrastructure, and the quality of national statistics. |
| Keywords: | night lights, remote sensing, big data, business cycles, leading indicators |
| JEL: | C55 C82 E01 E37 R12 |
| Date: | 2019–12 |
| URL: | http://d.repec.org/n?u=RePEc:aut:wpaper:202002&r=all |
| By: | Damiaan Persyn (European Commission - JRC); Jorge Diaz-Lanchas (European Commission - JRC); Javier Barbero (European Commission - JRC); Andrea Conte (European Commission - JRC); Simone Salotti (European Commission - JRC) |
| Abstract: | Transport costs data are a key input for trade analysis and for industry-level studies focusing on spatial distribution and logistics. Surprisingly, good transport estimates at a detailed spatial level for the EU are not readily available. This Policy Insight presents a new freely available dataset containing estimates of distance and time related transport costs between all NUTS-2 EU regions. This Insight briefly illustrates both the main assumptions behind the construction of the dataset and its core characteristics. The estimates take into account both the time needed and the distance covered by a representative truck travelling along optimal routes between samples of points within the EU regions. The resulting dataset contains an origin-destination cost matrix in euros at the region pair level. Moreover, the sampling approach allows calculating average transport costs within each region. Both arithmetic and harmonic averages are considered - the latter may be more relevant for interaction modelling such as the estimation of trade gravity equations. |
| Keywords: | rhomolo, region, growth, transport costs, regional distance, EU |
| JEL: | C82 R12 R40 R41 |
| Date: | 2020–01 |
| URL: | http://d.repec.org/n?u=RePEc:ipt:iptwpa:jrc119412&r=all |
| By: | Boeing, Geoff (Northeastern University) |
| Abstract: | Computational notebooks offer researchers, practitioners, students, and educators the ability to interactively conduct analytics and disseminate reproducible workflows that weave together code, visuals, and narratives. This article explores the potential of computational notebooks in urban analytics and planning, demonstrating their utility through a case study of OSMnx and its tutorials repository. OSMnx is a Python package for working with OpenStreetMap data and modeling, analyzing, and visualizing street networks anywhere in the world. Its official demos and tutorials are distributed as open-source Jupyter notebooks on GitHub. This article showcases this resource by documenting the repository and demonstrating OSMnx interactively through a synoptic tutorial adapted from the repository. It illustrates how to download urban data and model street networks for various study sites, compute network indicators, visualize street centrality, calculate routes, and work with other spatial data such as building footprints and points of interest. Computational notebooks help introduce methods to new users and help researchers reach broader audiences interested in learning from, adapting, and remixing their work. Due to their utility and versatility, the ongoing adoption of computational notebooks in urban planning, analytics, and related geocomputation disciplines should continue into the future. |
| Date: | 2020–01–13 |
| URL: | http://d.repec.org/n?u=RePEc:osf:socarx:dxtq3&r=all |