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1、Job Creation andLocal Economic Development 2024THE GEOGRAPHY OFGENERATIVE AIJob Creation and Local Economic Development2024THE GEOGRAPHY OF GENERATIVE AIThis document,as well as any data and map included herein,are without prejudice to the status of or sovereignty overany territory,to the delimitati

2、on of international frontiers and boundaries and to the name of any territory,city or area.The statistical data for Israel are supplied by and under the responsibility of the relevant Israeli authorities.The use ofsuch data by the OECD is without prejudice to the status of the Golan Heights,East Jer

3、usalem and Israeli settlements inthe West Bank under the terms of international law.Note by the Republic of TrkiyeThe information in this document with reference to“Cyprus”relates to the southern part of the Island.There is no singleauthority representing both Turkish and Greek Cypriot people on the

4、 Island.Trkiye recognises the Turkish Republic ofNorthern Cyprus(TRNC).Until a lasting and equitable solution is found within the context of the United Nations,Trkiyeshall preserve its position concerning the“Cyprus issue”.Note by all the European Union Member States of the OECD and the European Uni

5、onThe Republic of Cyprus is recognised by all members of the United Nations with the exception of Trkiye.Theinformation in this document relates to the area under the effective control of the Government of the Republic of Cyprus.Please cite this publication as:OECD(2024),Job Creation and Local Econo

6、mic Development 2024:The Geography of Generative AI,OECD Publishing,Paris,https:/doi.org/10.1787/83325127-en.ISBN 978-92-64-56441-1(print)ISBN 978-92-64-62866-3(PDF)ISBN 978-92-64-34365-8(HTML)Job Creation and Local Economic DevelopmentISSN 2617-4960(print)ISSN 2617-4979(online)Photo credits:Cover I

7、rina_Strelnikova/Getty Images Plus.Corrigenda to OECD publications may be found at:https:/www.oecd.org/en/publications/support/corrigenda.html.OECD 2024 Attribution 4.0 International(CC BY 4.0)This work is made available under the Creative Commons Attribution 4.0 International licence.By using this

8、work,you accept to be bound by the terms of this licence(https:/creativecommons.org/licenses/by/4.0/).Attribution you must cite the work.Translations you must cite the original work,identify changes to the original and add the following text:In the event of any discrepancy between the original work

9、and the translation,only the text of original work should be considered valid.Adaptations you must cite the original work and add the following text:This is an adaptation of an original work by the OECD.The opinions expressed and arguments employed in this adaptation should not be reported as repres

10、enting the official views of the OECD or of its Member countries.Third-party material the licence does not apply to third-party material in the work.If using such material,you are responsible for obtaining permission from the third party and for any claims of infringement.You must not use the OECD l

11、ogo,visual identity or cover image without express permission or suggest the OECD endorses your use of the work.Any dispute arising under this licence shall be settled by arbitration in accordance with the Permanent Court of Arbitration(PCA)Arbitration Rules 2012.The seat of arbitration shall be Par

12、is(France).The number of arbitrators shall be one.3 JOB CREATION AND LOCAL ECONOMIC DEVELOPMENT 2024 OECD 2024 Preface Over the last five years,OECD countries have shown remarkable labour market dynamism,with employment rates at or near record-highs.In parallel,gender gaps in labour market participa

13、tion have narrowed in regions across the OECD.Strong labour demand has resulted in labour shortages in the most vibrant regions,while other regions continue to have untapped workforce potential.Similar disparities exist regarding labour productivity,with many regions with low employment rates also e

14、xhibiting lower levels of productivity.This edition of Job Creation and Local Economic Development provides new evidence on how the advent of Generative AI can contribute to closing these regional gaps,while continuing to boost economic growth in the most dynamic regions.Generative AI,in addition to

15、 automation and other digital breakthroughs,offers significant potential to boost productivity,particularly in urban areas where a third of workers expect to be able to complete many of their tasks twice as quickly.Generative AI can also help to address growing labour shortages,especially in regions

16、 with an ageing population or that are experiencing population decline.Sound policies are needed for all regions to unlock the full potential of generative AI,particularly rural regions that have further potential to boost jobs,productivity and incomes.Targeted programmes should focus on addressing

17、place-specific obstacles,whether they relate to a regions attractiveness to workers and capital,the quality of regional education and training systems,or regulatory frameworks.Adequate investment in digital infrastructure,not least to address existing,and often significant,urban-rural divides in acc

18、ess to high-speed internet,will also be needed.Through novel estimates for 35 OECD countries that show the degree of exposure of regional labour markets to Generative AI,this edition of Job Creation and Local Economic Development highlights the full potential and impact of Generative AI,as well as o

19、pportunities to ensure that all regions are able to benefit.In doing so,the report aims to provide policy makers at all levels of government,business and civil society with insights into the transformative potential of Generative AI for jobs,and recommendations to leverage AI to drive economic growt

20、h,enhance productivity,and create more resilient and inclusive labour markets.4 JOB CREATION AND LOCAL ECONOMIC DEVELOPMENT 2024 OECD 2024 Foreword Despite the recovery from COVID-19,regional and local economies in OECD countries continue to undergo significant transformations.An ageing workforce,sl

21、uggish productivity growth,persistent regional disparities,pervasive labour shortages across even more regions,and the rapid advancement of new technologies will require comprehensive transitions for both individuals and communities.These shifts underscore the need for adaptive strategies that suppo

22、rt workforce resilience and help regions remain competitive and resilient.Employment,education and training systems are not keeping pace with the changing demands for new skills.Technological advancements like artificial intelligence as well as structural shifts such as the decarbonisation of econom

23、ies imply greater need for new tools to facilitate job transitions and investments in upskilling and reskilling initiatives.Just as people will need to adapt to these changing requirements to find jobs where they live,places must also seize emerging opportunities for local economic development and j

24、ob creation.The rapid rise of new AI technologies could offer a strategic tool to OECD regions to address critical economic and labour market challenges,including labour shortages or labour productivity growth.Providing access to AI tools and training can help regions to access untapped talent and r

25、aise productivity.However,this requires the right enabling conditions,such as investments and deployment of AI in firms and preparing larger segments of the workforce with the skills to use AI tools effectively to complement their work.This 6th edition of Job Creation and Local Economic Development

26、closely examines the current health and recent evolution of regional labour markets in the OECD.It documents the uneven rise of labour shortages that hold back local economies,especially in jobs that are critical for the green and digital transition.Against this context,the report offers novel insig

27、hts into the geography of the labour market impact of Generative AI.It explores which jobs and which types of places are already exposed to Generative AI,meaning that AI could be a complement to boost productivity in a job or potentially render some jobs no longer necessary.The report discusses the

28、potential implications of increased adoption of Generative AI for urban and rural communities and workers and zooms in on place-based actions as well as policies to seize the opportunities that these technologies could yield to boost productivity growth and address labour shortages in ageing societi

29、es.This publication contributes to the work of the Co-operative Action Programme on Local Economic and Employment Development(LEED),created in 1982 to provide practical solutions about how to build vibrant communities with more and better jobs for all.It was approved by the Local Economic and Employ

30、ment Development Directing Committee via written procedure on 13 November 2024 CFE/LEED(2024)14/REV1.5 JOB CREATION AND LOCAL ECONOMIC DEVELOPMENT 2024 OECD 2024 Acknowledgements This publication was produced by the OECD Centre for Entrepreneurship,SMEs,Regions and Cities(CFE),led by Lamia Kamal-Cha

31、oui,Director,as part of the programme of work of the Local Employment and Economic Development(LEED)Programme.This publication was co-ordinated and managed by Lukas Kleine-Rueschkamp,under the supervision of Karen Maguire,Head of Division and Nadim Ahmad(Deputy Director,CFE).Lead authors for individ

32、ual chapters were Antonela Miho(Chapter 1),Laurenz Baertsch and Antonela Miho(Chapter 2),and Agustin Basauri(Chapter 3).Patricia Pealosa and Ana Krstanovic made effective contributions to the different chapters.Tahsin Mehdi(Statistics Canada)kindly provided data on Canada.This report benefited from

33、valuable comments and inputs from Wessel Vermeulen,Carlo Menon,Cem Ozguzel,Amal Chevreau,Lea Samek,Stijn Broecke,Marguerita Lane,Glenda Quintini,and Luis Aranda.The OECD would like to thank the delegates to the OECD LEED Directing Committee,including contributions from the report steering group,for

34、their valuable input.Eloisa Cozar Navarrete and Katrina Baker prepared the manuscript for publication.6 JOB CREATION AND LOCAL ECONOMIC DEVELOPMENT 2024 OECD 2024 Table of contents Preface 3 Foreword 4 Acknowledgements 5 Executive summary 11 1 The state of regional labour markets 14 In Brief 15 Intr

35、oduction 16 Regional disparities persist,highlighting the absence of regional convergence despite a strong recovery from the COVID-19 shock 17 Most regions face persistently low productivity growth,with little progress in closing regional gaps in labour productivity 29 Building resilient regional la

36、bour markets:the role of workers and firms 36 Conclusion 51 References 53 Notes 58 Annex 1.A.Additional background on policy 60 Annex 1.B.Additional results 62 2 Labour shortages across regional labour markets 70 In Brief 71 Introduction 72 Disparities in labour market tightness across regions remai

37、n large despite widespread increases in recent years 74 The extent of labour shortages depends on the characteristics of the regional economy 78 High-skilled occupations mainly drive regional labour shortages 81 High-productivity and contact-intensive industries experience the strongest labour short

38、ages 87 Demographic change will put additional pressure on labour market tightness 90 Potential policy levers to alleviate labour shortages 96 Policy recommendations to alleviate labour shortages 108 References 110 Notes 117 7 JOB CREATION AND LOCAL ECONOMIC DEVELOPMENT 2024 OECD 2024 3 Beyond autom

39、ation:Decoding the impact of Generative AI on regional labour markets 118 In Brief 119 Technological progress and AI:The future of local labour markets 120 Understanding the foundations:AI,automation,and labour market dynamics 122 Narrow-purpose technologies and automation:The consequences for local

40、 labour markets 126 The implications of Generative AI for regional labour markets 136 The role of AI in driving regional productivity and addressing labour market challenges 160 Navigating the future:public policy for jobs in the AI era 167 Policy recommendations to unlock Generative AIs potential i

41、n regional labour markets 177 References 179 Notes 186 Annex 3.A.Data coverage and measurements 188 FIGURES Figure 1.1.Employment rates are high,and regional differences exist across OECD countries 18 Figure 1.2.As employment rates reach a record high,there has been minimal regional convergence over

42、 the last decade 19 Figure 1.3.Employment rates are higher in capital-city regions,non-ageing regions,regions with a high share of green jobs and jobs in tradeable service sectors 20 Figure 1.4.Participation rates remain 10%higher in the top versus the bottom quintile of regions within a country,a d

43、ifference of almost 9%of the national median 21 Figure 1.5.Regional employment recovery is uneven in half of OECD countries 22 Figure 1.6.Metro and non-metro regions recovered congruently,despite greater COVID-19 incidence in the former 23 Figure 1.7.Regional employment recovered more widely than pa

44、rticipation rates 24 Figure 1.8.Age inequalities,but not gender,exacerbated over the last decade 27 Figure 1.9.Regions with large increases in the age inclusion gap saw NEET rates fall,and there is little link with changes to youth enrolment rates 28 Figure 1.10.Capital-city regions contribute most

45、to the age gap in participation rates while the gender gap is highest in non-capital-city regions 29 Figure 1.11.Within OECD countries,the leading regions productivity is,on average,double that of the least productive region 31 Figure 1.12.Most regions experienced only modest productivity growth ove

46、r the past decade 32 Figure 1.13.Within OECD countries,annual productivity growth kept in step for both the most and least productive regions 33 Figure 1.14.Productivity in the top quintile of regions remains over 50%higher than in the bottom quintile of regions 34 Figure 1.15.Capital-city regions a

47、nd regions with a higher share of green jobs or specialised in tradeable services lead productivity levels 35 Figure 1.16.Over the past ten years,labour productivity growth accompanied gains in participation but not employment 36 Figure 1.17.High-skilled jobs represent the highest share across OECD

48、regions 38 Figure 1.18.High-skilled jobs are replacing middle-skilled job 39 Figure 1.19.On average,more than 9 percentage points(a third of the OECD regional median)separate the region with the highest and lowest share of mismatched jobs within OECD countries 40 Figure 1.20.Over the past ten years,

49、the share of mismatch fell in capital-city regions,ageing regions and regions with a high relative share of green jobs 41 Figure 1.21.Regions specialise in a type of skill mismatch:those with more over-skilled workers tend to have fewer under-skilled workers 42 Figure 1.22.Within-country complementa

50、rity in the type of mismatch exists for half of OECD regions 43 Figure 1.23.There is considerable within-country range of over 5 percentage points between the region with the highest rate of self-employed vs.the lowest for almost half of OECD countries 44 Figure 1.24.Increase in post-pandemic within

51、-country disparities between the regions with the highest and least share of the self-employed 45 8 JOB CREATION AND LOCAL ECONOMIC DEVELOPMENT 2024 OECD 2024 Figure 1.25.Take-up of self-employment is greatest in regions facing higher unemployment rates,although the difference is narrowing 46 Figure

52、 1.26.Employment is moderately diversified in a few sectors across OECD regions 48 Figure 1.27.A majority of workers are employed by up to three sectors across OECD regions 49 Figure 1.28.Large disparities in the instance of mass layoffs across regions 51 Figure 2.1.Labour markets are tight in many

53、countries,despite some signs of easing post-2020 73 Figure 2.2.Labour markets are tight across OECD regions,with large dispersion in over half of countries 75 Figure 2.3.Employment and unemployment-based measures lead to similar tightness results 76 Figure 2.4.Labour markets have become tighter acro

54、ss all regions 77 Figure 2.5.Job switches do not explain increasing labour market tightness 78 Figure 2.6.Labour shortages are higher in regions that are more urban,have high employment rates,and rely more on tradable services 79 Figure 2.7.Labour market tightness is up to four times higher-than-ave

55、rage for the most affected occupations 83 Figure 2.8.ICT jobs experience particularly severe shortages across OECD regions 85 Figure 2.9.Green jobs are tighter than the average job in the vast majority of OECD regions 86 Figure 2.10.Shortages in green and ICT jobs tend to co-occur in OECD regions 86

56、 Figure 2.11.Industries differ substantially in terms of labour market tightness 88 Figure 2.12.Regions that are more reliant on high-growth industries experience stronger shortages 89 Figure 2.13.ICT and utilities are the tightest industries in more than half of all European regions 90 Figure 2.14.

57、Over 40%of OECD regions experienced a decline in the working age population over the past decade 91 Figure 2.15.Demographic pressure will tighten labour markets,especially in older regions 93 Figure 2.16.Ageing populations will affect almost all OECD countries,albeit to varying degrees 95 Figure 2.1

58、7.Most OECD regions will experience increases in tightness due to demographic change 96 Figure 3.1.Automation technologies involve a large set of technologies with varied degrees of generality 123 Figure 3.2.Key high-level dimensions of the OECD Framework for the Classification of AI Systems 124 Fig

59、ure 3.3.The share of jobs at high risk of automation can range from under 1%to 29%across OECD regions 128 Figure 3.4.The manufacturing sector leads in jobs at risk of automation by a significant margin 129 Figure 3.5.Although it is too early to assess the full impacts of automation in the labour mar

60、ket,there is currently little evidence of job destruction in regions more exposed to automation 131 Figure 3.6.Most regions have seen employment growth and a large share of those that did not experienced a drop in employment at high risk of automation 132 Figure 3.7.Overall job destruction across re

61、gions can be attributed to automation in only a handful of cases 133 Figure 3.8.Regions with a higher share of employment at risk of automation saw a small but significant boost in productivity 134 Figure 3.9.A quarter of workers are now exposed to Generative AI 138 Figure 3.10.Labour market exposur

62、e to Generative AI could range from 16%to 77%across regions 139 Figure 3.11.Labour demand has not yet reacted to Generative AI exposure 141 Figure 3.12.Exposure to Generative AI varies greatly across industries 142 Figure 3.13.Occupations with higher complementarity tend to require more education an

63、d/or training 143 Figure 3.14.Most job families contain occupations that can use Generative AI as a complement to their work 144 Figure 3.15.Labour markets in urban areas are significantly more exposed to Generative AI than non-urban areas.145 Figure 3.16.Cities are significantly more exposed than r

64、ural areas 146 Figure 3.17.Cities are,and will be,significantly more exposed to Generative AI 147 Figure 3.18.Regions with a low risk of automation are now highly exposed to Generative AI,and vice-versa 148 Figure 3.19.Highly educated workers are significantly more exposed to Generative AI,and this

65、gap will only increase 149 Figure 3.20.The overall trend in exposure across levels of education holds for all regions individually 150 Figure 3.21.Women are slightly more exposed to Gen-AI,a different trend from prior forms of automation 151 Figure 3.22.Men are consistently more exposed to narrow-pu

66、rpose technologies across regions,while women are most exposed to Generative AI in most regions 152 Figure 3.23.The gender gap in exposure to Generative AI reflects higher shares of women in exposed sectors 153 Figure 3.24.Software jobs are significantly more exposed while the cultural,creative and

67、health occupations are closer to the labour market average 155 Figure 3.25.Most cultural and creative occupations are more exposed than the average occupation,up to more than double 159 Figure 3.26.Almost half of health occupations are more exposed than the average occupation 160 Figure 3.27.Opportu

68、nities and challenges of adopting AI in public employment services 174 9 JOB CREATION AND LOCAL ECONOMIC DEVELOPMENT 2024 OECD 2024 Annex Figure 1.B.1.Employment did not increase faster over the past ten years depending on demographics or employment structure 62 Annex Figure 1.B.2.COVID-19 exacerbat

69、ed regional inequalities along age,but not gender 63 Annex Figure 1.B.3.Half of countries show significant regional dispersion in youth inactivity rates 63 Annex Figure 1.B.4.Within-country differences between regions with the highest and lowest NEET rates are growing 64 Annex Figure 1.B.5.Neither d

70、emographic nor economic structure is correlated with higher productivity growth 64 Annex Figure 1.B.6.Mismatch is lower in capital-city regions 65 Annex Figure 1.B.7.Record-low unemployment rates,with convergence continuing past Covid-19 recovery 65 Annex Figure 1.B.8.Across OECD regions,about 16%of

71、 workers are engaged in part-time work,with little regional dispersion in most countries 66 Annex Figure 1.B.9.Stark gender divides in the take-up of part-time work 67 Annex Figure 1.B.10.Little regional dispersion in the incidence of temporary employment,apart from in some Latin American countries

72、68 Annex Figure 1.B.11.There is little within-country variation in temporary and part-time employment,reflecting that take-up is driven by structural national policies 69 TABLES Table 3.1.Example of occupations by their exposure to Generative AI 137 Annex Table 3.A.1.Summary of exposure rubric 188 A

73、nnex Table 3.A.2.Employment by occupation data sources 188 BOXES Box 1.1.From crisis to recovery:Employment support policies during COVID-19 25 Box 1.2.Navigating the productivity puzzle:Factors,trends,and challenges facing OECD regions 30 Box 1.3.Defining sectoral diversification 47 Box 2.1.How wel

74、l do different measures of labour market tightness align?76 Box 2.2.Shortages vs.dynamism:disentangling drivers of labour market tightness 78 Box 2.3.Labour market tightness as a proxy for labour shortages 80 Box 2.4.How representative are online job postings data?81 Box 2.5.How does labour market t

75、ightness affect wages?84 Box 2.6.Lower labour force participation and employment rates among older workers intensify labour shortages 92 Box 2.7.Calculating demographic pressure on labour market tightness 93 Box 2.8.AI to the rescue:how automation alleviates local labour shortages in manufacturing a

76、nd agriculture 97 Box 2.9.Unlocking womens labour force participation:The case of the CAD 10 a day child care programme 99 Box 2.10.Answering the needs of business and the local community:The case of vocational schools in Nrpes,Finland 100 Box 2.11.Japans Act on Stabilization of Employment of Elderl

77、y Persons 101 Box 2.12.PES increasingly use labour market intelligence tools to facilitate job matching 102 Box 2.13.France adapts its large-scale national skills agenda to regional needs 104 Box 2.14 Skills for Success:Modernising VET and adult learning towards the twin transition 105 Box 2.15.Buil

78、ding teleworking potential:Ireland,Trento,and The Netherlands Case Studies 107 Box 3.1.Automation involves an ever-growing set of technologies 122 Box 3.2.Measuring jobs impacted by narrow-purpose automation technologies 127 Box 3.3.Digital upskilling in Australia,Romania,Korea,and Japan 134 Box 3.4

79、.Measuring exposure to Generative AI at the occupation level using O*NET data and expert surveys 136 Box 3.5.Alternative measures of AI exposure in labour markets 139 Box 3.6.The impact of Generative AI on job creation and destruction remains uncertain 140 Box 3.7.How complementary is Generative AI

80、to different occupations?142 Box 3.8.Use of AI in cultural and creative sectors 154 10 JOB CREATION AND LOCAL ECONOMIC DEVELOPMENT 2024 OECD 2024 Box 3.9.Using AI to deal with labour shortages in the health sector 156 Box 3.10.Use of AI in the programming industry 158 Box 3.11.Future Skills Centre:I

81、mproving AI skills and attitudes across Canada 162 Box 3.12.Regional AI and automation strategies for economic revitalisation 163 Box 3.13.Using AI to alleviate labour shortages in industries with tight labour markets 165 Box 3.14.Experimental evidence on the impact of Generative AI in the workplace

82、 168 Box 3.15.AI for task management in the workplace 169 Box 3.16.Initiatives to support AI adoption in SMEs 171 Box 3.17.Measuring SME digitalisation:2024 OECD D4SME Survey 172 Box 3.18.Guidelines for AI adoption in public employment services(PES)173 Box 3.19.Examples of AI applications in public

83、employment services(PES)175 Annex Box 1.A.1.Examples:policies for inclusive labour market participation 60 Annex Box 3.A.1.Measuring the degree of urbanisation 189 11 JOB CREATION AND LOCAL ECONOMIC DEVELOPMENT 2024 OECD 2024 Executive summary While most regions have benefitted from an employment bo

84、om,this has not translated into narrowing regional gaps Employment rates across OECD regions are at record highs but large disparities remain.In 2023,roughly 3 out of 5(59%)of OECD regions had employment rates over 70%.Within countries,employment is particularly high in metropolitan regions with a h

85、igh share of employment in tradeable sectors as well as“younger”labour markets.While the majority of regions have recovered from the COVID-19 pandemic,the recovery was faster and larger in metro regions.Overall,significant disparities persist,with little convergence between top-and bottom-performing

86、 regions in critical measures such as employment,labour force participation,and labour productivity.On average,regional employment rates differ by up to 10.5 percentage points in OECD countries.During this employment boom,gender inequalities in regional labour markets have narrowed but age dispariti

87、es have widened in most regions,to the detriment of younger workers.The gender gap between men and women in labour force participation has fallen in over four out of five(83%)of OECD regions,with two-thirds of OECD regions recording a fall of more than 1.5 percentage points.In contrast,disparities b

88、y age group increased in the majority of regions.The gap in labour force participation between youth(15-to 24-year-olds)and prime-age working population(25-to 64-year-olds)increased in three out of five(almost 60%)of regions,growing significantly by over 1.5 percentage points in half of OECD regions

89、.Young people are struggling more to integrate into the labour market,particularly in metropolitan regions.A troubling slowdown in labour productivity growth and stark differences among regions persist.Labour productivity growth has remained sluggish over the past decade,with half of OECD regions re

90、cording growth of less than 0.8%per year.While the least productive regions grew faster than the most productive ones over the last 10 years,it was not sufficient to result in significant regional convergence.As of 2022,the 20%most productive regions still recorded 50%higher labour productivity leve

91、ls than the 20%least productive regions in the same country.Overall,labour productivity remains considerably higher in capital regions(one-third higher)and regions specialised in tradable sectors(one-fifth higher)than in the rest of a country.A diversified skills base aligned with labour market need

92、s boosts resilience;however,OECD regions have recorded a noticeable increase in skills polarisation and struggle with large skills mismatches.On average,the share of middle-skilled jobs fell in four out of five OECD regions over the last decade,in many cases being replaced by high-skilled jobs,but a

93、lso in some cases low-skilled jobs.Skills mismatches(workers that are either under or overqualified for their jobs)persist and vary widely across regions,with one-third of countries exhibiting regional differences in skills mismatch of more than 10 percentage points between the regions with the high

94、est and lowest skills mismatches.12 JOB CREATION AND LOCAL ECONOMIC DEVELOPMENT 2024 OECD 2024 Most OECD regions are struggling with labour shortages Labour and skills shortages have become one of the most pressing policy concerns in most OECD regions,not only dynamic urban labour markets.Driven by

95、a combination of both cyclical and structural factors,labour markets have become extremely“tight”,as firms struggle to find suitable workers to fill vacancies at different skill levels.The consequences for both firms and local economies can be significant,holding back firm operations and investments

96、,inhibiting local economic growth,and creating obstacles for seizing new economic opportunities offered by technology or meeting environmental objectives.Regional labour shortages have risen substantially since 2019 and increasingly affect regions with previously low levels of labour shortages.Labou

97、r market tightness(defined as vacancies per employed person)increased significantly(e.g.50%in Germany,80%in the U.S.)compared to pre-COVID times(2019-2022).While the severity of labour shortages differs between countries,regional disparities are also significant.Within countries,the tightest regiona

98、l labour markets report on average five times more vacancies per employed person than the least tight regions.Labour shortages are particularly acute in regions focused on tradable services or high-growth industries.Many regions face significant labour shortages in jobs crucial for the green and dig

99、ital transitions.In almost all OECD regions(95%),labour shortages in Information and communication technologies(ICT)are higher than for other jobs,with on average twice as high labour market tightness.Labour shortages are also more pronounced for green jobs in nine out of ten(90%)regions.In European

100、 regions,labour shortages are on average more than 40%higher for green-task jobs than for other jobs.The scarcity of green and digital“talent”reflects the adjustment of local economies to the twin transition but could also indicate significant skills mismatches,resulting from structural labour marke

101、t transformation that has not yet been accompanied by the necessary change in education and training systems adapted to regional workforce needs.Widespread population ageing risks exacerbating labour shortages,especially in the regions with the oldest age structure.More than four in ten OECD regions

102、 experienced a shrinking working-age population over the past decade.If current population trends continued,average regional labour market shortages could increase by almost 9%within the next 20 years,and by nearly 16%in the oldest 20%of OECD regions(rising from one vacancy for every 21 working-age

103、persons to one vacancy for every 18 working-age persons).Policies designed to mitigate labour shortages need to reflect place-specific challenges,such as ageing,retaining and attracting(young)talent to remote regions and facilitating job transitions,taking account of the geographic distribution of j

104、obs.Generative AI will transform many jobs,but its impact will be greatest in regions that have been least exposed to past waves of automation Generative AI could have a much wider labour market impact than previous technologies that drove automation of tasks,affecting a broader group of people and

105、places.Across the OECD,around a quarter of workers are exposed to Generative AI,meaning 20%(or more)of their job tasks could be done at least 50%faster with the help of Generative AI.Exposure to AI will continue to grow,as new software is developed or integrated with Generative AI technologies,with

106、the share of workers who could be highly exposed(50%of their tasks could be done at least 50%faster with Generative AI)possibly ranging from 16%to more than 70%across OECD regions.In contrast to previous automation technologies,Generative AI excels in doing cognitive,non-routine tasks,shifting regio

107、nal labour market exposure,with regions concentrating industries such as education,ICT,or finance becoming most exposed to Generative AI.Regions previously considered to be at comparatively low risk of automation are the most exposed to Generative AI.Technology-led automation,including through other

108、 forms of AI,particularly affected non-metropolitan and manufacturing regions.In contrast,Generative AI has the potential to alter a 13 JOB CREATION AND LOCAL ECONOMIC DEVELOPMENT 2024 OECD 2024 significantly higher share of jobs in metropolitan regions.Exposure to Generative AI is greater for high-

109、skilled workers and women,while previous technology-led automation mainly affected low-skilled workers and men.While the exact effects of Generative AI on the geography of job creation and displacement remain to be seen,evidence from automation trends show overall net job creation.The share of jobs

110、at high risk of automation,including through forms of AI that predated Generative AI,ranges from around 1%to 29%in OECD regions.However,on average,higher regional risks of automation did not lead to overall reductions in employment over the past decade.Instead,an increase of 10%in the share of jobs

111、at high risk of automation is related to an increase of 5.6%in labour productivity over five years.Yet,in some regions,automation appears to have contributed directly to a loss of overall employment.Moreover,even though new job creation outweighed job losses in most regions,newly created jobs might

112、not have benefitted those workers who lost their jobs due to automation.New AI technologies could offer a strategic tool for OECD regions to address critical economic and labour market challenges,including labour shortages,and help boost sluggish labour productivity growth.Fostering the adoption of

113、AI technologies could yield a much-needed catalyst for productivity in regional economies.Providing access to AI tools and training can help regions to access untapped talent in low-skilled workers or workers with disabilities for whom many jobs were previously out of reach.In addition,AI technologi

114、es can be leveraged to directly supplement workers where feasible,helping to ease labour shortages and the effects of an ageing workforce.National place-based policies and local actions could foster resilience of regional economies and help seize the benefits of Generative AI National labour market

115、policies could draw lessons from the uneven recovery from COVID-19 and recent trends in regional labour market performance.By reflecting on the diverse impact and recovery from the pandemic,policy makers could take into consideration the different degrees of resilience to labour market shocks across

116、 regions and identify challenges and appropriate policy responses in light of ongoing transformations such as the green-digital twin transition.In trying to alleviate labour shortages,policy makers need to address their exact,underlying causes,which are often place specific.In some regions,labour sh

117、ortages might primarily be driven by a lack of available workers,a problem that could be exacerbated by ageing and a shrinking workforce.However,in other contexts,skills mismatches and gaps could be the main driver of labour shortages.Furthermore,some regions struggle with a lack of attractiveness t

118、o both attract and retain a skilled workforce.Finally,some regions might rely on employment in jobs that have become less attractive to workers due to lower job quality or work conditions,subsequently creating labour shortages.As such,the right mix of policy responses will need to consider the place

119、-specific factors behind labour shortages.To seize the opportunities of new technologies and respond to its labour market risks,policy makers could assess regional labour market exposure to different forms of AI.Working with the private sector could foster a better understanding of the job and skill

120、s changes that result from the spread of new forms of AI in different regions.This would provide the foundation for more effective up-and re-skilling programmes that are aligned with local labour market needs as well as tailored support for displaced workers.Public-private sector collaboration could

121、 help boost the adoption of AI tools,which could raise regional labour productivity,mitigate labour shortages,or offer a new tool to alleviate ageing in regions with significant population decline.Regional policy makers could also consider new opportunities that AI tools could bring such as promotin

122、g efficiency gains and enhancing the quality of regional public services or facilitating the labour market inclusion of people with disabilities.Collaboration with social partners to monitor job quality and worker rights should accompany these efforts.14 JOB CREATION AND LOCAL ECONOMIC DEVELOPMENT 2

123、024 OECD 2024 This chapter examines the current state of regional labour markets in the OECD,as well as recent and long-term trends in standard indicators such as employment,inclusion and productivity.It also assesses their resilience to the green,digital and demographic transitions.While most regio

124、ns have recovered since the COVID-19 crisis,regional convergence in employment and participation rates is limited.Despite record-high employment rates in many regions,the inclusion of certain groups in the labour market remains an issue.Labour productivity growth remains modest,and disparities betwe

125、en the most and least productive regions persist.Skill mismatches and non-traditional work are prevalent,and a lack of sectoral diversification may hamper the ability of regions to adapt.Mass layoffs pose additional challenges,especially given their prevalence and severity in some regions.1 The stat

126、e of regional labour markets 15 JOB CREATION AND LOCAL ECONOMIC DEVELOPMENT 2024 OECD 2024 In Brief The landscape of regional labour markets in the OECD over the past decade reveals a mixed picture of recovery and persistent disparities and challenges in productivity and skills.Healthy regional labo

127、ur markets promote economic growth,social inclusion,and overall well-being.This chapter examines their evolution in OECD regions over the past decade and in the aftermath of the COVID-19 pandemic,reporting on recent employment trends,productivity growth,skill polarisation and mismatch,non-traditiona

128、l work,and sectoral diversification.The analysis sheds light on the diversity of regional experiences and their differing abilities to withstand future shocks and transitions.This diversity highlights the need for differentiated actions given the specific labour market challenges of different types

129、of regions.Employment rates across OECD regions are at record highs,but significant regional disparities are widespread in some places.In 26 OECD countries,employment rates are above 70%in at least three-fourths of regions.In particular,employment is higher in capital-city regions,regions with a hig

130、h share of employment in green jobs and tradable sectors,and regions with younger working-age profiles.Almost seven in ten regions recovered both employment and participation rates relative to pre-COVID levels,although the recovery was more widespread for employment rates and in metro regions.There

131、is little convergence between top-and bottom-performing regions within countries in employment and participation rates or productivity.On average in OECD countries,ten percentage points separate the region with the highest and lowest employment.Participation and employment rates remain about 10 to 1

132、3%higher,respectively,in the top versus the bottom quintile of regions in a country.Many regions still struggle with the labour market inclusion of different groups.Age disparities in regional labour markets widened over the past decade,and gender inequalities narrowed,despite fears that COVID-19 wo

133、uld bring a lasting“she-cession”.The inclusion gap between youth(15-24 year-olds)and the prime-age working population(25-64 year-olds)grew in almost three in five(58%)regions,and the gender inclusion gap fell in five in six(83%)regions over the past ten years,with 20 countries seeing a rise in the a

134、ge gap and 31 countries seeing a fall in the gender gap in at least 70%of their regions.Capital-city regions exhibit the greatest age disparities in labour force participation rates,while gender disparities are most prominent in non-capital-city regions,by about seven and four percentage points,resp

135、ectively.Labour productivity growth remained sluggish over the past decade,with half of OECD regions recording growth rates of less than 0.8%per year.Within-country gaps between the most-and least-productive regions remain large,despite marginally higher productivity growth in lagging regions.In 202

136、2,in two-thirds of OECD countries,productivity in the most productive region is at least 50%higher than in the least productive region.The quintile of regions with the highest initial productivity,within a country,still has 50%higher productivity than the quintile of regions with the lowest initial

137、productivity.Capital-city regions and regions with a higher share of green jobs or jobs specialised in tradable services record significantly higher labour productivity than the national average.16 JOB CREATION AND LOCAL ECONOMIC DEVELOPMENT 2024 OECD 2024 A diversified skills base enhances the qual

138、ity and resilience of regional labour markets,yet many regions face rising skills polarisation and high skills mismatches.The share of middle-skilled jobs fell in four-fifths of OECD regions,and the share of low-skilled jobs grew in three-fourths of regions where the share of middle-skilled jobs fel

139、l.The falling middle may reflect changing labour market demands,but it has not resulted in a better alignment between workers skills and those needed by their jobs.Skill mismatches remain an issue for most OECD regions,with significant within-country differences of over ten percentage points in over

140、 one-third of countries.Mismatches fell over the past ten years in capital-city regions and in regions with a higher share of green jobs.Introduction The past decade has been marked by profound shifts for global economies.These shifts are driven by rapid technological change,the necessity of environ

141、mental sustainability,demographic pressures,and heightened geopolitical instability,all against the backdrop of recovery from the shock of the COVID-19 epidemic.Issues such as labour shortages,sluggish productivity and skill mismatches are increasingly relevant in the context of ongoing transformati

142、ons such as the green and digital transition,specifically the rise of artificial intelligence(AI).In many places too,the pressures of demographic change,with shrinking working-age populations,are further complicating the situation.Regional labour markets,given their size,specific characteristics,and

143、 degree of specialisation,face distinct challenges in adapting to these megatrends.To keep pace,transitions that build upon a regional labour markets strength may be necessary to not only navigate these challenges but to actively transform these challenges into opportunities.This chapter provides an

144、 overview of these recent trends in regional labour markets.Employment rates stand at a record high across OECD countries,even as the regional picture is more uneven.The average employment rate(share of the working-age population in employment)in the OECD reached over 70%in Q2 2024,surpassing this f

145、igure in almost two-thirds of OECD countries.These historic employment gains create benefits across demographic groups(OECD,20241).Most national labour market indicators,such as employment,unemployment,and participation rates,have recovered in most countries following the shock of the COVID-19 pande

146、mic.For example,by Q2 2023,unemployment and inactivity rates were,on average,half a percentage point and one percentage point,respectively,below pre-pandemic levels in OECD countries(OECD,20232).However,this pattern has not been true for all regions,contributing to persistent or growing regional ine

147、qualities in some countries.Employment rates fully recovered or exceeded their pre-pandemic levels by Q2 2022 in less than half of OECD regions across 33 countries(OECD,20233).The chapter will address the current situation and report on recovery into 2023.Labour shortages remain a prevalent and pers

148、istent issue in this high-employment context.As industries evolve and new technologies emerge,the demand for specific skills can sometimes outpace their supply,making it difficult for firms to fill needed positions.It may also be that available jobs are not attractive enough in terms of pay,working

149、conditions,location,or a combination of these.Even if 2023 saw real wage growth,wage gains remain below pre-pandemic levels(OECD,20244).This issue is further exacerbated by demographic change,as ageing populations combined with declining birth rates contribute to shrinking working-age populations.Re

150、gional labour market tightness increased by around 50%since 2019,affecting regions with high and low prior levels of shortages similarly.At the same time,the average difference between the relatively tightest and least tight regions is almost twice the national average,indicating substantial regiona

151、l variation in the extent of labour shortages(see Chapter 2).17 JOB CREATION AND LOCAL ECONOMIC DEVELOPMENT 2024 OECD 2024 Labour productivity is an important driver for reducing income inequality,yet regional differences remain large:levels in the most productive region are almost twice as high as

152、the least productive region,on average within OECD countries(OECD,20233).The trend of stagnating labour productivity adds to the challenge of widening regional inequalities in GDP per capita in more than half of OECD countries with available data.This is especially a challenge where large difference

153、s in productivity levels exist even where there were declines in GDP per capita inequality(OECD,20233).Active innovation and the diffusion of new technologies,for example,artificial intelligence,across regions,coupled with targeted investments in infrastructure such as digital technologies may be av

154、enues for boosting productivity(OECD,20233).Yet,whether artificial intelligence will support or replace workers depends on the task-based nature of jobs and the ability of AI to perform those tasks more efficiently(Nedelkoska and Quintini,20185).The issue is addressed in Chapter 3 through new estima

155、tes on occupational exposure to AI,such as large-language models.The regional-level analysis presents within-country disparities in AI exposure,affecting a broader group of people and places.It discusses the potential double-edged sword of the integration of AI in the workforce:whether it is product

156、ivity-boosting or leads to job displacement.Overall,the rapid pace of technological change and the shift towards greener economies creates a growing need for new skills and competencies and regions have different capacities to adapt.For example,while 18%of workers in the OECD have jobs with a signif

157、icant share of green tasks that promote environmental sustainability,the share of these“green-task”jobs shows a considerable range across regions,from 7%to more than 35%,and are especially concentrated in capital-city regions(OECD,20236).In light of these trends,this chapter examines the current sta

158、te of regional labour markets and their resilience to major transitions.The first section explores regional labour market dynamics over the past decade,touching upon the recovery from the COVID-19 crisis,and regional convergence.It considers recent developments and implications for employment,labour

159、 productivity and inclusion.The second section delves into different indicators linked to the ability of regional labour markets to adapt and benefit from the full potential of both workers and firms.For example,it investigates recent trends in the take-up of non-traditional work,skills polarisation

160、 and skills mismatch,as well as the sectoral concentration of employment and the incidence of mass layoffs.Regional disparities persist,highlighting the absence of regional convergence despite a strong recovery from the COVID-19 shock Across OECD regions,employment rates have reached record highs(Fi

161、gure 1.1).In 26 OECD countries,employment rates are above 70%in at least three in four regions,and in 20 countries,in all regions.In almost three in five regions(59%),employment stands at over 70%of the labour force and over 80%in almost one in ten regions(8%).Employment is particularly high in Icel

162、and,Switzerland,and the Netherlands,which all boast at least two regions in the top ten of employment rates across OECD regions.Four out of the top ten regions are in the Netherlands,which can likely be attributed to the high take-up of part-time work.On the other hand,the employment rate is lagging

163、 and below 60%in at least one-third of their regions for seven OECD countries.This is also the case in about one in seven(14%)OECD regions.This is a particular challenge for Trkiye and Italy,which have six(out of 26)and three(out of 21)regions,respectively,in the bottom ten of employment rates acros

164、s OECD regions.The lowest employment rate is in Choc(Colombia),likely due to its geographical isolation and lack of infrastructure.This is similar to the challenges faced by the Italian regions,which are all located in the south.While in Trkiye,low female labour market participation rates,which rang

165、e from 24%to 49%,limit employment rates.18 JOB CREATION AND LOCAL ECONOMIC DEVELOPMENT 2024 OECD 2024 Within-country regional differences are widespread.In 16 out of the 35 countries with more than one region,the difference between the top and bottom regions in employment rates is over ten percentag

166、e points.In Colombia,the difference exceeds 34 percentage points;in Italy,it is almost 30 percentage points and in Trkiye and Israel,almost 22 percentage points,highlighting particularly large disparities in these countries.Portugal has the smallest dispersion,among countries with at least five regi

167、ons,at about two percentage points.This is followed by Denmark and Norway,where the difference is about 3 and 3.6 percentage points,respectively.As many regions experience all-time high employment,the focus is shifting towards widespread labour shortages(see Chapter 2).Figure 1.1.Employment rates ar

168、e high,and regional differences exist across OECD countries Note:The figure shows the regional dispersion(highest,lowest and median value)in the employment rate for 15-64 year-olds in 2023.For Colombia,the data refers to 2022 due to data availability.The sample is all TL-2 regions in countries(inclu

169、ding the OECD accession countries of Bulgaria,Croatia and Romania)with available data.The employment rate is defined as the number of working-age employed persons out of the working-age population,where the working-age is defined as 15-64 year-olds.Source:OECD calculations based on the OECD Regional

170、 databases.Over the past decade,there has been minimal progress towards regional convergence in employment rates.In 2023,employment rates in the top quintile of regions in a country were more than 4%above the national median versus almost 6%in 2013(Figure 1.2).In contrast,employment in the bottom qu

171、intile of regions is almost 7%below the national median,versus almost 8%in 2013.Thus,there have been small decreases in within-country disparities over the past ten years.In 2013,the top quintile of regions had employment rates almost 16%higher than the bottom quintile,with the gap falling to 13%by

172、2023.Overall,the within-country difference in employment rates between top and bottom regions fell by about 2.5%of the national average over the past ten years.ViennaSalzburgTasmaniaCanberra RegionWalloniaFlemish RegionNorth WestSouth WestNewfoundland LabradorQuebecTicinoCentralLos LagosAysnChocNari

173、oBruncaCentralMoravia-SilesiaPragueBremenBavariaSouthCopenhagen RegionEstoniaEstoniaW.MacedoniaPeloponneseAndalusiaMadridEast and NorthlandHauts-de-FrancePays de la LoirePannonian CroatiaCity of ZagrebNorthBudapestSouthernEastern and MidlandJerusalemTel AvivReykjavik RegionOther RegionsCampaniaBolza

174、no-BozenKansaiHokurikuGyeongnamJejuCentre and WestVilniusLuxembourgLuxembourgLatviaLatviaChiapasBaja California S.LimburgN.BrabantAgder and Sr-stlandetWestern NorwayNorthlandWellingtonPodkarpaciaWarsaw RegionNorthAlgarveSouth EastBucharest-IlfovEast MiddleStockholmEastWestEastBratislavaS.E.Anatolia

175、E.ThraceWalesS.E.EnglandMississippiSouth DakotaRomaniaCroatiaBulgariaIcelandNetherlandsSwitzerlandJapanNew ZealandSwedenAustraliaNorwayGermanyEstoniaDenmarkCanadaAustriaCzechiaUnited KingdomLithuaniaHungaryFinlandIrelandPortugalUnited StatesSlovak RepublicSloveniaPolandLatviaLuxembourgFranceKoreaIta

176、lyIsraelSpainMexicoChileBelgiumGreeceColombiaCosta RicaTrkiye405060708090Employment rate(%)aaaMinimumRegional medianMaximum 19 JOB CREATION AND LOCAL ECONOMIC DEVELOPMENT 2024 OECD 2024 Figure 1.2.As employment rates reach a record high,there has been minimal regional convergence over the last decad

177、e Evolution of the employment rate relative to the national median,2013 to 2023 Note:The figure shows the evolution of the employment rate for the working-age population(15-64 year-olds),relative to the national median(which corresponds to 100 on the top graph),for the top and bottom 20%of regions w

178、hich account for at least 20%of the population in a country.The sample is all TL-2 regions in OECD countries with at least five regions and with data available over the entire period.The employment rate is defined as the number of working-age employed persons out of the working-age population,where

179、the working-age is defined as 15-64 year-olds.Source:OECD calculations based on the OECD Regional databases.Regions with younger age profiles(non-ageing regions),capital-city regions and regions where employment is specialised in green jobs or tradeable sectors lead employment rates(Figure 1.3).The

180、strongest predictor of high employment rates is the absence of an increase in the old-age dependency ratio:within-country employment levels are close to 4.5 percentage points higher,on average,in those regions than regions where the old-age dependency ratio rose.This is followed by the regional empl

181、oyment structure.In regions with an above-country-median employment share in green jobs or in tradeable goods or services,the employment rate is about 2.5 percentage points higher,on average,than in regions with a below-median share of green jobs or specialised in non-tradeable sectors.Finally,capit

182、al-city regions have employment rates that are two percentage points higher than non-capital-city regions.These regional differences in employment rates exist regardless of national characteristics or population size and account for country-level shocks.In contrast,none of these regional characteris

183、tics is correlated with within-country employment growth over the past ten years(Annex Figure 1.B.1).Regional characteristics in the same country,such as demographics,location or industry structure,are thus important for understanding current employment rates.Difference(Top 20%-Bottom 20%)2013201420

184、1520162017201820192020202120222023201320142015201620172018201920202021202220239510010511011511.011.512.012.513.013.5Relative employment rateTop 20%Bottom 20%Ratio(Top 20%to Bottom 20%)20 JOB CREATION AND LOCAL ECONOMIC DEVELOPMENT 2024 OECD 2024 Figure 1.3.Employment rates are higher in capital-city

185、 regions,non-ageing regions,regions with a high share of green jobs and jobs in tradeable service sectors Within-country correlation of the employment rate to selected characteristics,2023 or latest available year Note:*p-value0.01,*p-value0.05,*p-value=1.5 ppIncrease 1.5 ppDecrease=1.5 pp28 JOB CRE

186、ATION AND LOCAL ECONOMIC DEVELOPMENT 2024 OECD 2024 Figure 1.9.Regions with large increases in the age inclusion gap saw NEET rates fall,and there is little link with changes to youth enrolment rates Correlation between the ten-year change in the NEET rate(left)or the youth enrolment rate(right)and

187、the age inclusion gap,2013 to 2023 or closest available years Note:The figure shows the ten-year change in the difference in the participation rate for the prime-age working population(25-64 year-olds)and youth(15-24 year-olds)on the y-axis,and the ten-year change in the youth not in employment,educ

188、ation or training rate(18-24 year-olds)on the x-axis on the left graph and the ten-year change in the educational enrolment rate for youth(20-29 year-olds)on the x-axis on the right graph,over the years 2013 to 2023,or the closest available years for OECD TL-2 regions with available data.For the age

189、 inclusion gap,the last available year is 2022 for regions in Colombia(except for Choc where the data refers to 2010 to 2020),Korea,and the United Kingdom(except for North where the data refers to 2011 to 2021);for Mexico(except for Nayarit where the data refers to 2009 to 2019)the last year is 2020

190、.For youth enrolment rates,the ten-year period is 2012 to 2022 for Colombia,Latvia,New Zealand,and the United States;the initial year is 2014 for Estonia and Poland,and 2016 for Chile and Korea;for Austria,Australia,Belgium,Czechia,Denmark,Germany,Greece,Spain,Finland,France,Hungary,Italy,the Nether

191、lands,Norway,Poland,Portugal,Sweden,Slovak Republic,Switzerland,Trkiye,and the United Kingdom,the last available year is 2022.For NEET rates,the ten-year period refers to 2011 to 2021 for Sweden,to 2012 to 2022 for Australia,Belgium,Israel,Japan,Mexico,Slovak Republic,Spain,Switzerland,the United Ki

192、ngdom and the United States.The dotted line represents the correlation line,and the grey-shaded area represents the 95%confidence intervals between the two measures.The estimate of the correlation is listed on the top right of each graph with standard error in paratheses.Each dot represents a TL-2 r

193、egion.Outliers,defined as regions with values in the top or bottom 8%of the distribution,are not included.The participation rate is defined as the number of working-age employed persons or persons looking for work out of the working-age population in the same subgroup,where the working-age is define

194、d as 15-64 year-olds.The NEET rate is defined as the share of youth not in employment,education or training)out of the youth working-age population(15-24 year-olds).The educational enrolment rate is the share of individuals aged 15-29 year-olds enrolled in all types of schools and education institut

195、ions,including public,private and all other institutions that provide organised educational programmes according to the ISCED 2011 classification,regardless of education level,out of all individuals aged 15-29 year-olds.Source:OECD calculations based on the OECD Regional databases.Age disparities in

196、 labour force participation rates are highest for capital-city regions,while gender disparities are most prominent in non-capital-city regions(Figure 1.10).In 2023,capital-city regions had a gender inclusion gap that was almost four percentage points below that of non-capital-city regions.In contras

197、t,the age gap in participation rates was almost seven percentage points higher in capital-city 29 JOB CREATION AND LOCAL ECONOMIC DEVELOPMENT 2024 OECD 2024 regions.Both types of regions exhibit the same general trend over the past years in the evolution of the age and gender gap in participation ra

198、tes:the age gap is increasing over the decade while the gender gap is decreasing.Yet,in both cases,disparities between capital and non-capital-city regions are widening.From 2013 to 2023,they increased marginally by 0.3 percentage points for the gender inclusion gap and by 2.8 percentage points for

199、the age inclusion gap.Universities,and thus,a higher share of students,tend to be located in capital-city regions,which likely explains a part of the increase,as previously discussed.Figure 1.10.Capital-city regions contribute most to the age gap in participation rates while the gender gap is highes

200、t in non-capital-city regions Note:The figure shows the evolution of the difference in the participation rate for youth(aged 15 to 24)and the prime-age population(top panel)and the gender difference for males and females(bottom panel)for the working-age population(15-64 year-olds)for capital-city re

201、gions vs non-capital-city regions.The sample is all TL-2 regions in OECD countries with data available over the entire time period.The participation rate is defined as the number of working-age employed persons or persons looking for work out of the working-age population in the same subgroup,where

202、the working-age is defined as 15-64 year-olds.Source:OECD calculations based on the OECD Regional databases.Most regions face persistently low productivity growth,with little progress in closing regional gaps in labour productivity Labour productivity,defined as output per worker,is often cited as a

203、 primary driver of growth,well-being,and competitiveness in the global economy(see Box 1.2).It plays a role in enabling higher wages,improved living standards,and increased investments in public services and infrastructure.As economies globally face the challenges of technological adaptation and dem

204、ographic shifts,understanding the dynamics of their productivity allows regions to adapt to these challenges.Factors such as technological innovation,human capital,regulatory environments,and infrastructure development play significant roles in shaping productivity trends.However,recent years have w

205、itnessed a troubling slowdown in productivity growth across various advanced economies,accompanied by a disconnect between productivity gains and real wage increases.This scenario underscores the need for insightful policy interventions that can revive productivity while aiming for benefits that ext

206、end to workers and enhance the economic well-being of regions.Gender gap(Male-Female)Age gap(25 to 64 year-olds-15 to 24 year-olds)201320142015201620172018201920202021202220232013201420152016201720182019202020212022202332343638409111315Difference in participation rate(%)Capitol-city regionsNon-capit

207、al-city regions30 JOB CREATION AND LOCAL ECONOMIC DEVELOPMENT 2024 OECD 2024 Box 1.2.Navigating the productivity puzzle:Factors,trends,and challenges facing OECD regions Labour productivity,measured here as the output per worker,is a fundamental indicator of economic efficiency and a key driver of e

208、conomic growth and well-being.3 Higher labour productivity allows for increased wages and living standards since it implies more value is generated per worker or hour worked.This,in turn,can support higher income levels and greater investments in public services and infrastructure(Abiad,Furceri and

209、Topalova,201518).On a global scale,productivity improvements allow regions to maintain their competitiveness,as they reflect an economys ability to innovate and adapt to technological advancements(OECD,201819;Schwab and Zahidi,202020).Consequently,a focus on labour productivity growth is vital for d

210、riving local economic development and improving the quality of life in OECD regions.Productivity growth is driven by a confluence of factors,including technological advancements,human capital development,regulatory environments and infrastructure improvements(Syverson,201121).Technological innovatio

211、n,particularly in the digital and manufacturing sectors,has been identified as a key enhancer of productivity by enabling more efficient production processes and fostering new business models(OECD,202422;OECD,202323).For example,there is early micro-evidence that artificial intelligence,such as larg

212、e language models,may be productivity-enhancing,although long-run aggregate effects are not yet evident(Filippucci et al.,202424).Its impact on workers and occupations is also not yet clear(see Chapter 3).Human capital also plays a role:better-educated workforces adapt more swiftly to new technologi

213、es and processes,thereby increasing output(OECD,201925).In addition,regulatory frameworks that encourage competition and facilitate fair market conditions can significantly boost productivity by promoting efficiency among businesses(Nicoletti and Scarpetta,200326;Rubens,202327).Lastly,investments in

214、 infrastructure not only improve efficiency but also connect markets more effectively,enhancing productivity at both national and regional levels(OECD,20233).Despite its central role in driving economic growth and well-being,productivity growth slowed down significantly in recent years,turning negat

215、ive in the European Union,the United States and the OECD in 2022(OECD,202428).Several potential,and likely inter-linked,explanations lie behind this productivity slowdown rooted in macroeconomic,societal and technological shifts.For example,one theory relates to the slowdown in technological progres

216、s given the increasing difficulty of generating new ideas and fewer groundbreaking innovations(such as electricity and the internal combustion engine)versus advances in software and information technology(Bloom et al.,202029;Gordon,201730).Structural factors,such as ageing populations,the slowdown i

217、n trade and lower growth of allocative efficiency,also play a role(Goldin et al.,202431;Maestas,Mullen and Powell,202332;Daniele,Honiden and Lembcke,201933).Skills mismatches and inadequate investment in education and training,depressing the contribution of human capital,may have also hindered produ

218、ctivity improvements across various industries(World Bank Group,202134).Finally,difficulties in measuring labour productivity may also be a factor,albeit a small one that cannot fully explain the widespread phenomenon(Ahmad,Ribarsky and Reinsdorf,201735).An important caveat is the documented trend o

219、f the decoupling of productivity and real wage growth,which casts doubt on the ability of productivity gains to translate into improvements in well-being.A study across 24 OECD countries found that productivity increases decoupled from gains in real wages over the period 1995 to 2015,given declines

220、in total-economy labour shares and a partial measure of wage inequality(the ratio of median wages to average wages)(Schwellnus,Kappeler and Pionnier,201736).Increase in knowledge-based capital,technological change,and the rise of global 31 JOB CREATION AND LOCAL ECONOMIC DEVELOPMENT 2024 OECD 2024 W

221、ithin-country productivity differences are widespread,driven by a few top-performing regions.The most productive region is twice as productive as the least productive,on average(Figure 1.11).Labour productivity in the most productive region is more than three times higher than in the least productiv

222、e regions in 3 out of 33(9%)OECD countries and twice as high in 20 out of 33(60%)OECD countries with available data.This is mainly driven by a few regions that lead productivity within a country.In 30 out of 33 OECD countries,the median relative productivity level is below the national average,which

223、 is normalised to one.Overall,almost two-thirds(62%)of OECD regions have productivity levels below the national average.Figure 1.11.Within OECD countries,the leading regions productivity is,on average,double that of the least productive region Note:The figure shows the regional dispersion(highest,an

224、d lowest value)for labour productivity,relative to the regional median in the country for 2022 or the latest available year.The vertical line represents the national average,normalised to one.The data refers to 2021 for New Zealand,Norway,Switzerland and the United Kingdom;to 2020 for Australia and

225、Alaska(United States);and to 2018 for Nunavut,N.W.Territories,and Yukon regions in Canada.The sample is all TL-2 regions in countries(including the OECD accession countries of Bulgaria,Croatia and Romania)with available data,excluding Ireland.Source:OECD calculations based on the OECD Regional datab

226、ases.BurgenlandVorarlbergTasmaniaWestern AustraliaWalloniaBrussels RegionSouth CentralSouth WestPrince Edward I.N.W.TerritoriesEspace MittellandNorthwesternubleAntofagastaNarioMetaNorthwestPragueMecklenburg-VorpommernHamburgN.JutlandCopenhagen RegionEstoniaEstoniaE.Macedonia,ThraceAtticaExtremaduraB

227、asque CountrylandHelsinki-UusimaaCorsicale-de-FrancePannonian CroatiaCity of ZagrebS.TransdanubiaPestCalabriaLombardyJejuChungcheongCentre and WestVilniusLuxembourgLuxembourgLatviaLatviaChiapasCampecheDrentheN.HollandInnlandetJan Mayen and SvalbardNorthlandAucklandLublinWarsaw RegionNorthLisbon Metr

228、opolitanNorth EastBucharest-IlfovSmland with I.StockholmEastWestCentralBratislavaN.E.Anatolia E.IstanbulWalesGreater LondonMississippiD.of ColumbiaRomaniaCroatiaBulgariaItalyEstoniaLithuaniaLuxembourgLatviaSloveniaFinlandGermanySpainNew ZealandSwitzerlandTrkiyeAustriaColombiaBelgiumNetherlandsKoreaF

229、ranceSwedenUnited StatesHungaryPortugalDenmarkUnited KingdomGreecePolandAustraliaMexicoChileCzechiaNorwayCanadaSlovak Republic12Relative labour productivityaaaMinimumRegional medianMaximumvalue chains and income inequality are all cited as potential drivers of this phenomenon(Autor and Salomons,2018

230、37;Berlingieri,Blanchenay and Criscuolo,201738).Policies that call for higher minimum wages,unionisation,employer protection laws and reduced wage inequality may contribute to a positive link between productivity and wages over time,as well as to encourage upskilling of workers to reduce capital-lab

231、our substitution(Berlingieri,Blanchenay and Criscuolo,201738;OECD,201839).Many of these factors are likely to depend on regional characteristics;for example,gains from knowledge-based capital are likely to accrue in metropolitan regions.32 JOB CREATION AND LOCAL ECONOMIC DEVELOPMENT 2024 OECD 2024 I

232、n half of OECD regions,labour productivity growth over the past decade was below 0.8%per year(Figure 1.12).Labour productivity growth,nonetheless,varies widely across OECD regions,ranging from a decrease of about 5%in Greeces Western Macedonia region to increases of almost 6%in Chiles Los Lagos regi

233、on and almost 5%in several regions in Turkey(Southern Aegean;Western Black Sea West;and Northeastern Anatolia-East).Within-country differences are also significant among OECD countries.The median regional dispersion,i.e.the difference between the top and bottom region,in annual productivity growth o

234、ver the past ten years is about 1.4 percentage points.The highest dispersions were observed in Chile(8 percentage points),and Greece(6 percentage points).The lowest dispersions were observed in Belgium(0.5 percentage points)and Sweden(0.8 percentage points).In more than half of the countries with av

235、ailable data and with more than three regions,regional dispersion is above two percentage points.Figure 1.12.Most regions experienced only modest productivity growth over the past decade Note:The map shows the initial labour productivity in 2012 and the annual rate of labour productivity growth over

236、 the past ten years,from 2012 to 2022 or the closest available years.The initial year refers to 2013 for Chile.The last year refers to 2021 for New Zealand,Norway,Switzerland and the United Kingdom;to 2020 for Australia and Alaska(United States);and to 2018 for Nunavut,N.W.Territories,and Yukon regi

237、ons in Canada.Initial productivity levels are shown through the size of the circles and the change in labour productivity is shown through the colour scale.Labour productivity is measured as GDP(in USD 2015 PPP)per worker,using regional deflators.The sample is all TL-2 regions(including the OECD acc

238、ession countries of Bulgaria,Croatia and Romania)with available data,excluding Ireland.Source:OECD calculations based on the OECD Regional databases.Annual labour productivity growth,within countries,evolved similarly over the past decade(2012 to 2022)for regions with higher and lower levels of prod

239、uctivity,even if the least productive regions grew marginally faster(Figure 1.13).Between 2012 and 2022,productivity growth in the least productive regions grew faster than the least productive in all but two years.For most of the decade,the least productive regions grew,on average,at an annual rate

240、 of about 0.9%.During this time,the most productive regions experienced slightly more sluggish growth of 0.6%,on average,or even negative growth,as in the years 2012 and 2020.Indeed,in 2020 in the aftermath of the onset of the COVID-19 pandemic,both groups of regions experienced negative annual prod

241、uctivity growth,with the most productive regions 33 JOB CREATION AND LOCAL ECONOMIC DEVELOPMENT 2024 OECD 2024 seeing a downtick of almost 1.3 percentage points more than the least productive regions.Productivity growth in the most and least productive regions grew sharply and converged in 2021,with

242、 a less than 0.5 percentage point difference in annual growth rates.This may be due to the shifting composition of employment during the crisis,where smaller firms and lower-skilled workers dropped out of the labour market,mechanically raising the average(Kapsos,202140).In the most recent year,2022,

243、the most productive regions overtook the least productive regions in terms of growth.The gap in labour productivity growth reached 0.7 percentage points,mostly due to a decline in labour productivity growth in the least productive regions of almost 2.8 percentage points.This shift may represent a te

244、mporary fluctuation as the economy stabilised from the COVID-19 shock and distortions caused by furloughed workers are no longer as prevalent,or it may represent a longer-term trend that should be monitored.Figure 1.13.Within OECD countries,annual productivity growth kept in step for both the most a

245、nd least productive regions Annual labour productivity growth given initial productivity level,2012 to 2022 Note:The figure shows the evolution of the annual growth rate of labour productivity for the top and bottom 20%of regions within a country from 2012 to 2022 based on initial productivity level

246、s in 2012 which account for at least 20%of the population.The sample is all regions in countries with at least five regions and with data available over the entire time period,excluding Ireland.Source:OECD calculations based on the OECD Regional databases.Productivity gains among the least productiv

247、e regions were not enough to significantly narrow the gap between the top and bottom-performing regions.By 2022,productivity levels are almost 53%higher in regions in the top quintile of productivity than regions in the bottom quintile,compared to almost 56%in 2012,a decrease of less than two percen

248、tage points(Figure 1.14).Thus,although the least productive regions have marginally higher annual productivity growth,these gains do not show up in terms of relative productivity gains.The most productive regions have productivity levels almost 30%above the national median,on average,while the least

249、 productive regions have productivity levels that are almost 13%below the national median in 2022.This represents a difference of 41%of the national median,on average.The gap in productivity levels between the best-and worst-performing regions within a country highlights persistent regional inequali

250、ties within the OECD.While some regions are thriving with high productivity levels,others are struggling significantly,which could exacerbate socio-economic disparities.Difference(Top 20%-Bottom 20%)2012201320142015201620172018201920202021202220122013201420152016201720182019202020212022-2-10123-1.0-

251、0.50.00.5Labour productivity annual growth(%)Initial level in 2012:20%most productive regionsRegional median20%least productive regions34 JOB CREATION AND LOCAL ECONOMIC DEVELOPMENT 2024 OECD 2024 Figure 1.14.Productivity in the top quintile of regions remains over 50%higher than in the bottom quint

252、ile of regions Evolution of the labour productivity relative to the national median,2012 to 2022 Note:The figure shows the evolution of labour productivity,relative to the national median(which corresponds to 100 on the top graph),for the top and bottom 20%of regions which account for at least 20%of

253、 the population in a country.The sample is all TL-2 regions in OECD countries with at least five regions and with data available over the entire period,excluding Ireland.Source:OECD calculations based on the OECD Regional databases.Productivity levels are higher in capital-city regions and regions w

254、ith a higher share of green jobs or specialising in tradeable services(Figure 1.15).Regions that contain the capital city have higher productivity,far above non-capital-city regions,by over 1.7 standard deviations from the national mean.The industrial composition of employment also plays a role.In r

255、egions with an above-median share of green jobs or where employment is specialised in tradeable sectors,labour productivity is higher by almost 0.8 standard deviations from the national mean.In contrast,none of these characteristics are correlated with higher labour productivity growth(Annex Figure

256、1.B.5).Difference(Top 20%-Bottom 20%)20122013201420152016201720182019202020212022201220132014201520162017201820192020202120221001201404243Relative labour productivityTop 20%Bottom 20%Ratio(Top 20%to Bottom 20%)35 JOB CREATION AND LOCAL ECONOMIC DEVELOPMENT 2024 OECD 2024 Figure 1.15.Capital-city reg

257、ions and regions with a higher share of green jobs or specialised in tradeable services lead productivity levels Within-country standardised correlation of labour productivity to selected characteristics,2022 or latest available year Note:*p-value0.01,*p-value0.05,*p-value0.1.The graph shows the coe

258、fficient and 90%confidence intervals of separate multivariate regressions of labour productivity,standardised within each country,in the latest available year on a dummy for capital-city regions(large regions that include the capital city),regions with an above national median employment share in gr

259、een jobs in 2021,in tradeable services(ISIC broad sectors G to N),tradeable goods(ISIC sectors B,D,E)or neither tradeable goods nor services.The coefficient represents the change in productivity,measured in standard deviations from the national mean,for regions with the specified characteristic on t

260、he x-axis.Each regression also controls for the log of population in the latest available year,a dummy for the latest available year,and country fixed effects.The level of observation is the TL-2 region.The sample of countries includes all OECD countries with available data,excluding Ireland.The dat

261、a refers to 2021 for New Zealand,Norway,Switzerland and the United Kingdom;to 2020 for Australia and Alaska(United States);and to 2018 for Nunavut,N.W.Territories,and Yukon regions in Canada.Robust standard errors are clustered at the country level.Source:OECD elaboration based on the OECD Region an

262、d Cities databases.Productivity growth over the past decade complemented gains in labour market participation but not employment across OECD regions(Figure 1.16).There is a positive correlation between labour productivity growth and the change in labour force participation rates.For employment,this

263、correlation is null.Indeed,productivity gains can lead to job losses,for example,in the case of labour-saving technological change.Furthermore,job losses can mechanically raise labour productivity if output does not fall at the same rate as the number of workers.Nonetheless,productivity gains did go

264、 hand-in-hand with gains in participation rates:over four-fifths(83%)of regions where participation rates increased also exhibited overall productivity growth at the same time.Furthermore,about two-thirds(64%)of OECD regions are situated in the upper right quadrant of both graphs,indicating positive

265、 labour productivity gains with increases in both employment and participation rates over the past ten years.Additionally,almost three-fourths(74%)of regions experienced positive labour productivity growth,with an increase in either employment or participation,but not both.Yet,in almost one in twelv

266、e(8%)regions,productivity gains did not accompany either employment or participation gains.36 JOB CREATION AND LOCAL ECONOMIC DEVELOPMENT 2024 OECD 2024 Figure 1.16.Over the past ten years,labour productivity growth accompanied gains in participation but not employment Correlation between labour pro

267、ductivity growth and employment(left)or participation growth(right),2012 to 2022 or the closest available years Note:The figure shows the ten-year change in the employment rate(left)or the participation rate(right)on the x-axis,and the ten-year compound growth rate in labour productivity on the y-ax

268、is using the years 2012 to 2022,or the closest available years,for OECD regions with available data.For labour productivity,the first year refers to 2013 for Chile and the last year refers to 2021 for New Zealand,Norway,Switzerland and the United Kingdom;to 2020 for Australia and Alaska(United State

269、s);and to 2018 for Nunavut,N.W.Territories,and Yukon regions in Canada.The dotted line represents the correlation,and the grey shaded area represents the 95%confidence intervals between the two measures.Each dot represents a TL-2 region.Outliers are not shown.The employment rate is defined as the nu

270、mber of working-age employed persons out of the working-age population,where the working-age is defined as 15-64 year-olds.The participation rate is defined as the number of working-age employed persons or persons looking for work out of the working-age population,where the working-age is defined as

271、 15-64 year-olds.Source:OECD calculations based on the OECD Regional databases.Building resilient regional labour markets:the role of workers and firms Regional resilience involves the ability of a local economy to weather and recover from shocks through adaptative changes in economic structures and

272、 social arrangements(Martin and Sunley,201441).These shocks include economic recession,natural disasters,and structural changes accelerated by megatrends such as the green,digital and demographic transitions.Policies that support a dynamic business environment,diversified sectoral base and local ski

273、lls development are key to regional labour market quality,while at the same time,to building resilience in the face of external shocks.For example,targeted economic support measures,like maintaining employer-employee relationships and short-term business support schemes,can aid recovery without comp

274、romising efficiency but must be balanced to promote flexible labour markets.Active labour market policies,such as job-seeker support and skills development programmes,enhance regional resilience by improving employability and cushioning against unemployment during economic crises(Vermeulen,202242).L

275、astly,supporting vulnerable groups through 37 JOB CREATION AND LOCAL ECONOMIC DEVELOPMENT 2024 OECD 2024 in-work benefits,partial unemployment aid,and skills subsidies helps to build more equitable resilience.This section considers several indicators covering skill levels and mismatch,the take-up of

276、 non-traditional work on the worker side,and sectoral diversification and the case of mass layoffs on the firm side,to comment on the resilience of regional labour markets.High-skilled jobs dominate regional employment,as the share of middle-skilled jobs shrinks A diversified skills base in the regi

277、onal labour force can help to enhance the quality and resilience of regional labour markets.Skills diversity not only supports adaptability to changing economic conditions but also fosters innovation and productivity growth(Aghion and Howitt,200843;Acemoglu and Autor,201144).Effective skills develop

278、ment programmes,including vocational training and lifelong learning initiatives,play a pivotal role in equipping workers with the necessary competencies to thrive in evolving industries(European Commission,202045;Winthrop,Mcgivney and Fellow,201646).This is in addition to investments in high-quality

279、 education and training,which can enhance workforce flexibility and reduce vulnerability to economic shocks,thereby bolstering regional resilience(Heckman and Kautz,201247;World Bank Group,201948).Lastly,policies that promote skills matching,such as job placement services and apprenticeship programm

280、es,facilitate smoother transitions for workers and contribute to overall labour market efficiency(European Commission,202049;Autor,201450;OECD,201851).By prioritising a diverse skills agenda,regions can better withstand disruptions and position themselves for sustained economic growth in an increasi

281、ngly competitive global landscape.Occupations that require a high level of skills account for the largest proportion of jobs in OECD regions.In more than half(55%)of OECD regions,most workers are employed in high-skilled jobs,followed by three in eleven(27%)regions where most workers are in medium-s

282、killed jobs(Figure 1.17).This highlights a trend towards more advanced,professional,and technical occupations in the overall distribution of jobs within OECD regions.Yet,this distribution varies significantly across countries,and in some cases within countries,likely reflecting differences in educat

283、ion systems,labour market policies,and economic structures.Within-country differences tend to be driven by the capital-city region,where high-skilled jobs dominate.This is the case in Colombia,Greece,Korea,Mexico,and Portugal,where only the capital-city region has the high-skilled as the most common

284、 occupational skill level.In 5 out of 28 OECD countries with available data,there is at least one region where each of the skill levels dominates,reflecting significant within-country differences in the skill levels demanded by the labour market.38 JOB CREATION AND LOCAL ECONOMIC DEVELOPMENT 2024 OE

285、CD 2024 Figure 1.17.High-skilled jobs represent the highest share across OECD regions Note:The figure shows the most common job skill level and its share for OECD regions in 2023 or latest available year.For European Union countries,the data refers to 2022 and for Korea,to 2021.Job skill is defined

286、using ISCO occupational categories.Low-skilled corresponds to jobs in sales and services and un-skilled occupations(ISCO 5 and 9),medium-skilled workers hold jobs as clerks,craft workers,plant and machine operators and assemblers(ISCO 4,7 and 8),and high-skilled workers are those who have jobs in ma

287、nagerial,professional,technical and associated professional occupations(ISCO 1,2 and 3).The definition of skill is based on the educational level thought to be required of an occupation and does not consider skills not related to educational level.The sample is all TL-2 regions with available data.S

288、ource:OECD calculations based on national labour force surveys for the European Union(including the OECD accession countries of Bulgaria,Croatia and Romania),Canada,Chile,Colombia,Costa Rica,Korea,Mexico,the United States and the United Kingdom.Over the past decade,the share of middle-skilled jobs c

289、ontracted in OECD regions,as the share of high-skilled jobs increased.In four in five(80%)OECD regions with available data,the share of middle-skilled jobs fell over 2013 to 2023,falling significantly by over five percentage points in almost two in eleven(22%)regions(Figure 1.18).To a large extent,i

290、ncreasing demand for high-skilled jobs compensated for the falling share of middle-skilled jobs.The share of high-skilled jobs grew in three-fourths(75%)of regions where the share of middle-skilled jobs fell.In contrast,in a majority(63%)of OECD regions,the share of low-skilled jobs changed by less

291、than three percentage points.In one in eleven(11%)regions,the share of low-skilled jobs grew by over three percentage points and in tandem with the share of high-skilled jobs.While in more than half(53%)of regions,low-skilled and middle-skilled jobs fell together,as high-skilled jobs grew.Similar to

292、 the skill distribution across countries,this trend indicates a shift towards more managerial,professional,technical,and associated professional occupations at the cost of a decline in clerks,craft workers,plant and machine operators and assemblers.This may reflect a shifting skills demand,driven by

293、 changes in technology,automation,and the global economic landscape.In contrast,there is a consistent demand for low-skilled jobs in sales,services and unskilled occupations,despite these advancements in technology and economic shifts.39 JOB CREATION AND LOCAL ECONOMIC DEVELOPMENT 2024 OECD 2024 Fig

294、ure 1.18.High-skilled jobs are replacing middle-skilled job Correlation between the ten-year change in the regional skill distribution,2013 to 2023 or closest available years Note:The figure shows the ten-year change in the share of high-skilled jobs on the x-axis and the ten-year change in the shar

295、e of medium-skilled jobs on the y-axis.The colour of the point refers to the ten-year change in the share of low-skilled jobs.The ten-year period is 2013 to 2023,or closest available years.The time period refers to 2013 to 2022 for European Union countries,to 2015 to 2023 for the United Kingdom;and

296、2013 to 2021 for Korea.The dotted line represents the correlation line,and the grey shaded area represents the 95%confidence intervals between the two measures.The estimate of the correlation is listed on the top right of each graph with standard error in paratheses.Each dot represents a TL-2 region

297、.Outliers,defined as regions with values in the top or bottom 5%of the distribution,are not included.Job skill is defined using ISCO occupational categories.Low-skilled corresponds to jobs in sales and services and un-skilled occupations(ISCO 5 and 9),medium-skilled workers hold jobs as clerks,craft

298、 workers,plant and machine operators and assemblers(ISCO 4,7 and 8),and high-skilled workers are those who have jobs in managerial,professional,technical and associated professional occupations(ISCO 1,2 and 3).The definition of skill is based on the educational level thought to be required of an occ

299、upation and does not consider skills not related to educational level.The sample is all TL-2 regions in OECD countries with available data.Source:OECD calculations based on national labour force surveys for the European Union,Canada,Chile,Colombia,Costa Rica,Korea,Mexico,the United States and the Un

300、ited Kingdom.Regional disparities in over-and under-skilling highlight challenges in labour market alignment Skills mismatches,defined as discrepancies between the skills of workers and those demanded by employers,pose challenges for aligning labour market needs with available talent.4 These mismatc

301、hes can result in underemploymentwhere individuals work in jobs that do not fully utilise their skills,in in workers underqualified for the jobs they are employed in,or in job vacancies that remain unfilled due to a lack of qualified candidates.This friction can influence the economic performance of

302、 regions,weighing on growth and the capacity to respond to market changes effectively.As regions strive to enhance their economic resilience,understanding and addressing skill mismatches becomes increasingly important.40 JOB CREATION AND LOCAL ECONOMIC DEVELOPMENT 2024 OECD 2024 Skill mismatches are

303、 prevalent across OECD regions with almost one in three employed individuals working in jobs that do not match their skill level,regardless of whether they are over-or under-qualified(Figure 1.19).Countries such as Czechia,Lithuania,and Slovakia exhibit relatively low regional median skill mismatche

304、s,around 16.5%to 23%.Conversely,the top four OECD countries experiencing the greatest degree of mismatch,are Korea(41.8%),Costa Rica(41.1%),Colombia(40.5%),and the United Kingdom(40%).Low mismatch suggests effective alignment in most regions,given equally low regional dispersion of around 7.5 to 9 p

305、ercentage points difference between the region with the highest and lowest mismatch.Conversely,10 out of the 33 OECD countries with available data indicate large regional dispersion with a difference of over ten percentage points.The largest dispersions are present in high mismatch countries,such as

306、 Korea,Mexico and Colombia,where the difference is over 30 percentage points,20 percentage points and over 15 percentage points,respectively.The difference between the region with the highest and lowest mismatch is also large in the United States(21 percentage points),despite having a lower-than-ave

307、rage regional mismatch.Figure 1.19.On average,more than 9 percentage points(a third of the OECD regional median)separate the region with the highest and lowest share of mismatched jobs within OECD countries Note:The figure shows the regional dispersion(highest,lowest and median value)in the share of

308、 workers in mismatched jobs in 2023 or the latest available year.For European Union countries,the data refers to 2022,and for Korea,to 2021.Skill mismatch is calculated following the methodology of the Skills for Jobs Indicators of the OECDs Directorate for Employment,Labour and Social Affairs,where

309、by a worker is in a mismatched job when their educational skill level does not match the most common skill level of workers in that occupational group in that country.The sample is all TL-2 regions with available data.Source:OECD calculations based on national labour force surveys for the European U

310、nion(including the OECD accession countries of Bulgaria,Croatia and Romania),Canada,Chile,Colombia,Costa Rica,Korea,Mexico,the United States and the United Kingdom.Capital-city regions,ageing regions and regions with a higher share of green jobs saw the share of mismatched workers fall over the past

311、 ten years(Figure 1.20).Ageing regions,or regions where the old-age dependency ratio increased over the past five years,are correlated with a 3.2 percentage point Carinthia-StyriaBurgenland-Lower Austria-ViennaBrussels RegionWalloniaSouth WestNorth EastQuebecSaskatchewanZurichTicinoAntofagastaMauleB

312、ogot DistrictLa GuajiraCentralBruncaMoravia-SilesiaPragueSaxonyBremenCopenhagen RegionSouthEstoniaEstoniaAtticaS.AegeanMadridCantabriaWestlandAuvergne-Rhne-AlpesCorsicaCity of ZagrebAdriatic CroatiaW.TransdanubiaNorthEastern and MidlandNorthern and WesternIcelandIcelandApuliaFriuli-Venezia GiuliaSeo

313、ul RegionJejuVilniusCentre and WestLuxembourgLuxembourgLatviaLatviaChiapasSinaloaNetherlandsNetherlandsOslo and VikenNorthLesser PolandWarmian-MasuriaNorthAlgarveWestSouth EastUpper NorrlandSmland with I.EastWestCentralBratislavaGreater LondonE.MidlandsD.of ColumbiaNevadaRomaniaCroatiaBulgariaKoreaC

314、osta RicaColombiaUnited KingdomIcelandItalySpainNetherlandsAustriaMexicoNorwaySwitzerlandGreecePortugalGermanyDenmarkIrelandCanadaFranceUnited StatesSwedenChileEstoniaBelgiumFinlandLatviaSloveniaLuxembourgHungaryPolandSlovak RepublicLithuaniaCzechia204060Share of skill mismatch(%)aaaMinimumRegional

315、medianMaximum 41 JOB CREATION AND LOCAL ECONOMIC DEVELOPMENT 2024 OECD 2024 decrease in the share of mismatch.This is likely driven by both the exit of older workers with less education and an overall demand shift toward a more educated labour force.Job mismatch also fell by 1.4 percentage points in

316、 capital-city regions,and marginally by 0.2 percentage points in regions with a higher share of green jobs,compared to regions in the same country.The share of mismatched workers is lower in capital-city regions,compared to other regions in the same country(Annex Figure 1.B.6).In contrast,other demo

317、graphic(such as an ageing population),or economic(such as the sectoral employment composition)characteristic is not correlated with within-country differences in the share of mismatch.Figure 1.20.Over the past ten years,the share of mismatch fell in capital-city regions,ageing regions and regions wi

318、th a high relative share of green jobs Within-country correlation of the ten-year change in the share of mismatch(pp)to selected characteristics,2023 or latest available year Note:*p-value0.01,*p-value0.05,*p-value0.1.The graph shows the coefficient and 90%confidence intervals of separate multivaria

319、te regressions of the ten-year change in the share of job mismatch from 2013 to 2023(or closest available years)on a dummy for capital-city regions,ageing regions(defined as those that experienced an increase in the elder-dependency rate over the past five years),for an above national median employm

320、ent share in green jobs in 2021,in tradeable services(ISIC broad sectors G to N),tradeable goods(ISIC sectors B,D,E)or neither tradeable goods nor services.The coefficient(within-country correlation)presents the within-county percentage point difference in the share of mismatch based on the characte

321、ristic on the x-axis.Skill mismatch is calculated following the methodology of the Skills for Jobs Indicators of the OECDs Directorate for Employment,Labour and Social Affairs,whereby a worker is in a mismatched job when their educational skill level does not match the most common educational skill

322、level of workers in that occupational group in that country.Each regression also controls for the log of population in 2023 or latest available year and country fixed effects.For European Union countries,the data refers to 2013 to 2022,for Korea,to 2013 to 2021,and for the United Kingdom,to 2015 to

323、2023.The level of observation is the TL-2 region.The sample of countries includes all OECD countries.Robust standard errors are clustered at the country level.Source:OECD elaboration based on the OECD Regional databases.Regions with a higher prevalence of over-skilled workers generally exhibit lower

324、 levels of under-skilling(Figure 1.21).The mean difference between the share of underqualified and overqualified workers is around nine percentage points across OECD regions.The top five regions with the greatest difference are all in Canada,with over 30%of over-skilled workers and 1.5%to 2.3%of und

325、er-skilled workers.In contrast,the regions with the smallest differences between the share of over-skilled and under-skilled workers are in three different countries(Italy,Poland,Spain and the United States),with a difference of under 0.3 percentage points.Regions with a high share of either over-or

326、 under-skilled workers may be experiencing a high-or low-skill equilibrium,whereby the skills demand of jobs adapts to match the skills*-4-20Capital-city regionsAgeing regionsSh(green jobs)Tradeable services Tradeable goodsNon tradeablesWithin-country correlation42 JOB CREATION AND LOCAL ECONOMIC DE

327、VELOPMENT 2024 OECD 2024 supply of the population.This can be a particular issue in a low-skill equilibrium when employers adopt a price-based competition strategy that relies on low-skilled and standardised production(OECD,201452).Jeju region(Korea)stands out with a high share of both under-and ove

328、r-skilled workers at over 30%each,likely due to its economy specialised in agriculture,fishing and tourism,which differs from the rest of Korea,which is more industrialised.As such,workers in the same occupation in Jeju likely require different skills than their counterparts in the rest of Korea.The

329、 regional economic structure,industrial composition and educational system are likely to all contribute to this distinct distribution of skill mismatches.Figure 1.21.Regions specialise in a type of skill mismatch:those with more over-skilled workers tend to have fewer under-skilled workers Note:The

330、figure shows the share of over-and under-skilled workers for each OECD TL-2 region in 2023 or the latest available year.For European Union countries,the data refers to 2022,and for Korea,to 2021.Skill mismatch is calculated following the methodology of the Skills for Jobs Indicators of the OECDs Dir

331、ectorate for Employment,Labour and Social Affairs,whereby a worker is in a mismatched job when their educational skill level does not match the most common educational skill level of workers in that occupational group in that country.Over-skilled means that the worker has an educational skill level

332、above the most common educational skill level of their occupation.Under-skilled means that the worker has an educational skill level below the most common educational skill level of their occupation.Source:OECD calculations based on national labour force surveys for the OECD countries in the Europea

333、n Union,Canada,Chile,Colombia,Costa Rica,Korea,Mexico,the United States and the United Kingdom.Spatial variation in the incidence of over-and under-skill across regions highlights distinct patterns in how educational qualifications align with labour market demands.There exist opportunities in some countries to leverage complementarities between types of skill mismatch.For example,Illinois(United S