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on Economic Geography |
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Issue of 2024‒07‒22
three papers chosen by Andreas Koch, Institut für Angewandte Wirtschaftsforschung |
| By: | Lina Bjerke (Jönköping International Business School); Steven Bond-Smith (University of Hawai‘i at MÄ noa, University of Hawai‘i Economic Research Organization); Philip McCann (The University of Manchester and The Productivity Institute); Charlotta Mellander (Jönköping International Business School) |
| Abstract: | In this paper, we explore some little-known, but significant, economic geography features of the work-from-home (WFH) revolution. The increased practice of work from home following the pandemic has prompted a redistribution of working populations between urban and rural locations. Using a uniquely detailed and comprehensive individual-level nationwide Swedish micro-dataset, we analyze shifts in commuting distances pre- and post-pandemic and explore the association between teleworkability and changes in these distances. Teleworkability alone does not significantly influence the distance between home and work municipalities, yet we observe heterogeneity in the responses. As well as the widely-documented centrifugal ‘donut’-type spread effects localized within cities, our empirical work demonstrates that the work-from-home revolution also engenders a significant centripetal spatial ‘pull’ effect of large cities, as their hinterland shadow effects are magnified by the work-from-home revolution. This latter effect, which encourages workers to locate closer to the metropolitan areas, has not previously been seen or understood. |
| Keywords: | Working from home, agglomeration economies, regional distribution. |
| JEL: | R12 R23 |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:hae:wpaper:2024-3&r= |
| By: | Luca, Davide; Özgüzel, Cem; Wei, Zhiwu |
| Abstract: | The paper maps the diffusion of working from home across 30 European countries during the COVID-19 pandemic. We summarise the determinants of remote working and show that its uptake was lower than in the United States, and substantially uneven across/within countries, with most remote jobs concentrated in cities and capital regions. We then apply a variance decomposition procedure to investigate whether the uneven distribution of remote jobs can be attributed to individual or territorial factors. Results underscore the importance of composition effects as, compared with intermediate-density and rural areas, cities hosted more workers in occupations/sectors more amenable to working remotely. |
| Keywords: | COVID-19; Europe; remote work; telework; work from home |
| JEL: | R14 J01 |
| Date: | 2024–06–06 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:123880&r= |
| By: | Anabela Marques Santos; Francesco Molica; Carlos Torrecilla Salinas (European Commission, Joint Research Centre, Sevilla, Spain; European Commission, Joint Research Centre, Brussels, Belgium; European Commission, Joint Research Centre, Sevilla, Spain) |
| Abstract: | Artificial Intelligence (AI) is seen as a disruptive and transformative technology with the potential to impact on all societal aspects, but particularly on competitiveness and growth. While its development and use has grown exponentially over the last decade, its uptake between and within countries is very heterogeneous. The paper assesses the geographical distribution at NUTS2-level of EU-funded investments related to AI during the programming period 2014-2020. It also examines the relationship between this specialization pattern and regional characteristics using a spatial autoregressive model. Such an analysis provides a first look at the geography of public investment in AI in Europe, which has never been done before. Results show that in the period 2014-2020, around 8 billion EUR of EU funds were targeted for AI investments in the European regions. More developed regions have a higher specialization in AI EU-funded investments. This specialization also generates spillover effects that enhance similar specialization patterns in neighboring regions. AI-related investments are more concentrated in regions with a higher concentration of ICT activities and that are more innovative, highlighting the importance of agglomeration effects. Regions that have selected AI as an innovation priority for their Smart Specialization Strategies are also more likely to have a higher funding specialization in AI. Such findings are very relevant for policymakers as they show that AI-related investments are already highly spatially concentrated. This highlights the importance for less-developed regions to keep accessing to sufficient amounts of pre-allocated cohesion funds and to devote them for AI-related opportunities in the future. |
| Keywords: | Artificial intelligence; Public subsidy; Territorial specialization; Europe |
| JEL: | O31 R58 R12 O52 |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:mde:wpaper:181&r= |