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1、Regional Disparities in EuropeRavi Balakrishnan, Christian Ebeke, Melih Firat, Davide Malacrino, and Louise RabierWP/22/198IMF Working Papers describe research inprogress by the author(s) and are published toelicit comments and to encourage debate.The views expressed in IMF Working Papers arethose o
2、f the author(s) and do not necessarilyrepresent the views of the IMF, its Executive Board,or IMF management.2022SEP每日免费获取报告1、每日微信群内分享7+最新重磅报告；2、每日分享当日华尔街日报、金融时报；3、每周分享经济学人4、行研报告均为公开版，权利归原作者所有，起点财经仅分发做内部学习。扫一扫二维码关注公号回复：研究报告加入“起点财经”微信群。 2022 International Monetary FundWP/22/198IMF Working PaperEuropea
3、n DepartmentRegional Disparities in EuropePrepared by Ravi Balakrishnan, Christian Ebeke, Melih Firat, Davide Malacrino, and Louise RabierAuthorized for distribution by Ravi BalakrishnanSeptember 2022IMF Working Papers describe research in progress by the author(s) and are published to elicit commen
4、ts and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.ABSTRACT: While the level of disparities across regions in 10 advanced European economies studied in this pap
5、er mostly reflects productivity gaps, the increase since the Great Recession has resulted from diverging unemployment rates. Following the pandemic, this could be further exacerbated given teleworkability rates are lower in poorer regions than in high-income regions, making them ex-ante more vulnera
6、ble to the pandemics likely material impact on the prevalence of remote work. Preliminary evidence from 2020 confirms that regional disparities between countries increased during 2020. A further concern is that the pandemic might accelerate the automation of jobs across Europe, something which often
7、 happens following recessions. While lagging regions have lower ex-ante vulnerabilities against the routinization, the transformation of jobs through sectors with higher routinization rates in these regions could increase their vulnerability to technological change over time. The green transition co
8、uld also lead to challenges for regions that have benefitted from carbon-intensive growth strategies. Finally, the paper discusses the role for policiesincluding placed-based onesin reducing disparities in the face of the aforementioned short, medium, and long-term risks.RECOMMENDED CITATION: Balakr
9、ishnan, Ravi, Christian Ebeke, Melih Firat, Davide Malacrino, and Louise Rabier, 2022, “Regional Disparities in Europe.” IMF Working Paper WP/22/198.JEL Classification Numbers:R12, J2, J24, O44Keywords:Regional inequality; teleworkability; automation; climate changeAuthors E-Mail Address:RBalakrishn
10、animf.org; CEbekeimf.org; MFiratimf.org;DMalacrinoimf.org; LRabierimf.orgIMF WORKING PAPERSRegional Disparities in EuropeWORKING PAPERSRegional Disparities in EuropePrepared Ravi Balakrishnan, Christian Ebeke, Melih Firat, Davide Malacrino, and Louise Rabier11 The authors would like to thank Andy Jo
11、bst, Myrto Oikonomou, and Laura Papi for their helpful comments as well as participants in seminars at the European Commission, the ESM, and the IMF. They also thank Estefania Cohn Bech, Shiqing Hua, and Christine Rubio for excellent research and document production assistance.INTERNATIONAL MONETARY
12、 FUND1IMF WORKING PAPERSRegional Disparities in EuropeContentsIntroduction3Data and Measurement of Disparities4Anatomy of Regional Disparities5The Evolution of Disparities Since the Early 2000s5Components of the GDP per Capita6Productivity7Labor Markets7Beta-convergence8Potential Impact of the COVID
13、-19 Pandemic on Existing Disparities10A Snapshot from High-frequency Data: Mobility Index10Customer Interaction Intensity11Short-term Risks: Teleworkability11Longer-term Risks: Automation13Disparities and the Green Transition15Policy Discussions17Is There a Role for Place-based Policies?17Smoothing
14、the Effects of the Green Transition Across Different Regions18Conclusions19References20Appendix A. Figures and Tables22Appendix B. Disparities in Net Disposable Income26Appendix C. The Green/Growth Tradeoff: A Case Study of Two German Regions.28INTERNATIONAL MONETARY FUND2IMF WORKING PAPERSRegional
15、Disparities in EuropeIntroductionRegional disparities among European advanced economies (aggregating both within and across) declined until the Great Recession and, depending on the coverage, have flatlined or increased thereafter.1 Figure 1 shows that the trend for nine large EU advanced economies2
16、 and the United Kingdom is quite different than for all European economies, with the former displaying a marked increase in disparities.The COVID-19 pandemic has renewed interest in the connection between large economic downturns and disparities (IMF 2020). Considering the increase inregional dispar
17、ities that followed the last large recession in Europe, this paper offers novel insights on the risks of further economic divergence across European regions resulting from the pandemic. It specifically zooms in on the structural characteristics of regions that were already stagnating prior to the cr
18、isis, and who are now likely to face relatively stronger pandemic-related headwinds.The first part of the analysis documents the evolution of disparities in GDP per capita across European regions, decomposes disparities into within- and between-countries components, and separates the drivers of disp
19、arities into various economic components, such as employment, labor force participation, and productivity.The second part of the paper focuses on the COVID-19 pandemic and considers its likely impact on disparities across European regions, in the short, medium, and long run. For example, it assesses
20、 the extent to which the decline in mobility, low levels of teleworkability,3 and the exposure to routinization,4 are likely to trigger asymmetric effects of the pandemic across regions and shape divergent regional economic recoveries.Taking a long-run perspective, the paper also examines the impact
21、 of goals associated with the green transition planned by the European Union (EU). A close look at the data points suggests the need to carefully consider how to address the impact of the green transition on certain regions. In particular, those regions with a low GDP per capita level but high growt
22、h have traditionally been dependent on carbon-inefficient sectors both in terms of production and shares of employment. So, there will need to a strong policy focus on making sure such regions can transition smoothly to greener and more sustainable growth.Finally, we discuss how “place-based” polici
23、es might help reduce disparities, including the potential adverse effects of the pandemic. Besides noting the benefits of spatially-targeted policies on lagging regions, we argue that post-pandemic policies should focus on increasing the teleworkability of regions and improving1 The analysis examine
24、s the sources of disparities in GDP per capita across regions at the NUTS-2 level. Specifically, we use the coefficient of variation (CV) to measure dispersion across regions each year. Each component is further broken down into the contribution of each country. Another decomposition allows separati
25、ng the drivers of dispersion in GDP per capita into the dispersion of its components: regional labor productivity, the employment to labor force ratio (ELR), the labor force participation rate, and the working-age share.2 Austria, Finland, France, Germany, Greece, Italy, Netherlands, Portugal, Spain
26、 (and the United Kingdom). Figure A1 and A2 confirm that inequality went up during 2020, although data for the UK are not available in that year.3 Regional teleworkability rates are calculated following Dingel and Neiman (2020).4 Regional exposure to routinization/automation (indexes) are calculated
27、 following Autor and Dorn (2013).INTERNATIONAL MONETARY FUND3IMF WORKING PAPERSRegional Disparities in Europevulnerabilities to automation. Furthermore, we take two German regions to provide a micro-level analysis on potential green/growth tradeoffs that we stress in the paper. Overall, a holistic a
28、pproach will be needed to make sure that nobody (or no one region) gets left behind from climate change mitigation policies given the sectoral transformation they will induce.The paper is organized as follows. Section 2 presents the dataset and the concepts used in the paper. Section 3 shows the evo
29、lution of regional disparities across regions for the period before the COVID-19 pandemic. Section 4 discusses the channels by which the pandemic can potentially impact regional disparities. Section 5 discusses if there is a green/growth tradeoff related to climate change mitigation policies, while
30、Section 6 considers what mix of policies can help reduce disparities, including place-based policies. Section 7 concludes.Data and Measurement of DisparitiesThe analysis uses data on GDP, employment (aggregate, at the sector level, and by firm size), and population at the NUTS-2 regional level provi
31、ded by Eurostat, with a focus on nine EU countries (Austria, Finland, France, Germany, Greece, Italy, Netherlands, Portugal, Spain) and the U.K.These data are combined with occupation-customer interaction indexes from Koren and Peto (2020), occupation-teleworkability rates from Dingel and Neiman (20
32、20), and occupation-routinization indexes from Autor and Dorn (2013), to investigate the channels through which the COVID-19 pandemic is likely to impact European regional disparities. Moreover, the Google Community Mobility Report provides daily regional mobility data, and the European Pollutant Re
33、lease and Transfer Register (E-PRTR) has information on regional GHG emission levels.To assess the level and dynamics of disparities in GDP per capita across regions , the chosen measure is the unweighted square of Coefficient of Variation ( 2) as it can be decomposed into within- and between-countr
34、y components. Moreover, 2 displays a variables dispersion independent of its unit.5 Hence, while the standard deviation cannot be used to compare the level of dispersion across two different variables, 2 can be used to this end as it is scale-free, a useful feature leveraged in the analysis to compa
35、re disparities across the components of GDP per capita. The square of the CV can be written as:11 ( )2 =2 ( )2 =1 =1where and denote regional (the rc index denotes region r in country c) and average GDP per capita, respectively, and and denote the number of regions in country and the total number of
36、 regions across all countries considered. The measure lends itself to a useful decomposition into within- and between-country components as:2111 ( )2 = ()( )2 +( )222 =1 =1 =1 ( )25 As the CV is the ratio between the standard deviation and the mean of a variable and both statistics are measured in t
37、he same unit, their ratio is unit free. Squaring the CV does not change this feature of the measure.INTERNATIONAL MONETARY FUND4IMF WORKING PAPERSRegional Disparities in Europewhereanddenote the within- and between-country components of the2 in GDP per capita.Anatomy of Regional DisparitiesIn this s
38、ection, we analyze the trends in regional disparities in GDP per capita and its components. We provide a decomposition into within- and between-country components and assess the evolution of these components over time.6The Evolution of Disparities Since the Early 2000sFigure 2 shows that total dispa
39、rities across regions declined slightly through 2006. However, post-2006 total disparities started moving up slowly, to erase the earlier gains on the eve of the Great Recession. The divergence accelerated after the Great Recession and was mostly driven by increased between-country gaps, although, w
40、ithin-country disparities appear to be consistently higher than between-country disparities looking across the whole period. During 2020, disparities increased by about0.01 points in terms of the coefficient of variation, mostly driven by between-country gaps.7A decomposition of within- and between-
41、country disparities sheds light on the contribution of each country to both components. The left panel in Figure 3 shows that Germany and Italy have the largest disparities across their regions, consistent with substantial East/West and North/South differences in each country. The right panel shows
42、that the increase in between-country disparity after the Great Recession has been driven by relatively weaker GDP per capita growth in Greece and stronger growth in Germany.6 While our main analysis focuses on disparities in GDP per capita, appendix B reports other results on disparities in disposab
43、le income, emphasizing the role of tax and transfers.7 See appendix A. 2020 data excludes the UK following its exit from the EU.INTERNATIONAL MONETARY FUND5IMF WORKING PAPERSRegional Disparities in EuropeIn the next subsection, we propose a canonical decomposition of regional GDP per capita into its
44、 components to better isolate the main sources of regional disparities in GDP per capita.Components of the GDP per CapitaLeveraging the EUROSTAT data across NUTS-2 regions, GDP per capita can be decomposed into labor productivity, the employment to labor force ratio (ELR) (or one minus the unemploym
45、ent rate), the labor force participation rate, and working-age population share as follows:=Figure 4 shows that productivity is responsible for the largest fraction of disparities and drove up disparities in GDP per capita after the Great Recession together with the ELR. While productivity has prese
46、rved its prominent share, the dispersion in ELR has declined since 2013, pointing to a convergence in unemployment rates across countries in this period.We also find that within-country disparities appear to be the most important component of the dispersion of each variable except for the ELR (Figure 5). The increasing ELR dispersion explains growing GDP per c