1、 IMF Staff Discussion Notes(SDNs)showcase policy-related analysis and research being developed by IMF staff members and are published to elicit comments and to encourage debate.The views expressed in Staff Discussion Notes are those of the author(s)and do not necessarily represent the views of the I

2、MF,its Executive Board,or IMF management.2024 JAN Gen-AI:Artificial Intelligence and the Future of Work Prepared by Mauro Cazzaniga,Florence Jaumotte,Longji Li,Giovanni Melina,Augustus J.Panton,Carlo Pizzinelli,Emma Rockall,and Marina M.Tavares SDN/2024/001 -2024 International Monetary Fund SDN/2024

3、/001 IMF Staff Discussion Notes Research Department Gen-AI:Artificial Intelligence and the Future of Work Prepared by Mauro Cazzaniga,Florence Jaumotte,Longji Li,Giovanni Melina,Augustus J.Panton,Carlo Pizzinelli,Emma Rockall,and Marina M.Tavares*Authorized for distribution by Pierre-Olivier Gourinc

4、has January 2024 IMF Staff Discussion Notes(SDNs)showcase policy-related analysis and research being developed by IMF staff members and are published to elicit comments and to encourage debate.The views expressed in Staff Discussion Notes are those of the author(s)and do not necessarily represent th

5、e views of the IMF,its Executive Board,or IMF management.ABSTRACT:Artificial intelligence(AI)has the potential to reshape the global economy,especially in the realm of labor markets.Advanced economies will experience the benefits and pitfalls of AI sooner than emerging market and developing economie

6、s,largely because their employment structure is focused on cognitive-intensive roles.There are some consistent patterns concerning AI exposure:women and college-educated individuals are more exposed but also better poised to reap AI benefits,and older workers are potentially less able to adapt to th

7、e new technology.Labor income inequality may increase if the complementarity between AI and high-income workers is strong,and capital returns will increase wealth inequality.However,if productivity gains are sufficiently large,income levels could surge for most workers.In this evolving landscape,adv

8、anced economies and more developed emerging market economies need to focus on upgrading regulatory frameworks and supporting labor reallocation while safeguarding those adversely affected.Emerging market and developing economies should prioritize the development of digital infrastructure and digital

9、 skills.RECOMMENDED CITATION:Cazzaniga and others.2024.“Gen-AI:Artificial Intelligence and the Future of Work.”IMF Staff Discussion Note SDN2024/001,International Monetary Fund,Washington,DC.ISBN:979-8-40026-254-8 JEL Classification Numbers:E24,J24,J31,O33,O38 Keywords:Artificial Intelligence,Labor

10、Market,Job Displacement,Income Inequality,Advanced Economies,Emerging Market Economies,Low-Income Developing Countries Authors E-Mail Address:,FJaumotteimf.org,LLi4imf.org,GMelinaimf.org,APantonimf.org,CPizzinelliimf.org,ERockallstanford.edu,MMendestavaresimf.org *The authors thank Pierre-Olivier Go

11、urinchas and Antonio Spilimbergo for feedback and guidance and many IMF colleagues for useful comments.The views expressed herein are those of the authors and should not be attributed to the IMF,its Executive Board,or its management.Any remaining errors are the responsibility of the authors.-INTERNA

12、TIONAL MONETARY FUND 1 Contents Executive Summary _ 2I.Introduction _ 3II.AI Exposure and Complementarity_ 5III.Worker Reallocation in the AI-Induced Transformation _ 11IV.AI,Productivity,and Inequality _ 15V.AI Preparedness _ 19VI.Conclusions and Policy Considerations _ 22Annex I.Data _ 26Annex 2.A

13、dditional Information on AI Occupational Exposure and Potential Complementarity _ 28Annex 3.Methodology for the Worker Transition Analysis _ 29Annex 4.Model Details _ 32Annex 5.AI Preparedness Index _ 34References _ 36 Boxes 1.AI Occupational Exposure and Potential Complementarity1 _ 24 2.Artificial

14、-Intelligence-led Innovation and the Potential for Greater Inclusion1 _ 25 Figures 1.Employment Shares by AI Exposure and Complementarity:Country Groups and Select _ 8 2.Employment Share by Exposure and Complementarity(Selected Countries)_ 9 3.Share of Employment in High-Exposure Occupations by Demo

15、graphic Groups _ 10 4.Share of Employment in High-Exposure Occupations by Income Deciles _ 11 5.Occupational Transitions for College-Educated High-Exposure Workers for BRA and GBR _ 12 6.Life Cycle Profiles of Employment Shares by Education Level for Brazil and the United _ 13 7.1-Year Re-Employment

16、 Probability of Separated Workers _ 14 8.Estimated Wage Premia from Occupation Changes _ 15 9.Exposure to AI and to Automation and Income in the UK _ 17 10.Change in Total Income by Income Percentile _ 18 11.Impact on Aggregates(Percentage _ 18 12.AI Preparedness Index and _ 20 13.ICT Employment Sha

17、re and Individual Components of the AI Preparedness Index _ 21 STAFF DISCUSSION NOTES Gen-AI:Artificial Intelligence and the Future of Work INTERNATIONAL MONETARY FUND 2 Executive Summary Artificial intelligence(AI)is set to profoundly change the global economy,with some commentators seeing it as ak

18、in to a new industrial revolution.Its consequences for economies and societies remain hard to foresee.This is especially evident in the context of labor markets,where AI promises to increase productivity while threatening to replace humans in some jobs and to complement them in others.Almost 40 perc

19、ent of global employment is exposed to AI,with advanced economies at greater risk but also better poised to exploit AI benefits than emerging market and developing economies.In advanced economies,about 60 percent of jobs are exposed to AI,due to prevalence of cognitive-task-oriented jobs.A new measu

20、re of potential AI complementarity suggests that,of these,about half may be negatively affected by AI,while the rest could benefit from enhanced productivity through AI integration.Overall exposure is 40 percent in emerging market economies and 26 percent in low-income countries.Although many emergi

21、ng market and developing economies may experience less immediate AI-related disruptions,they are also less ready to seize AIs advantages.This could exacerbate the digital divide and cross-country income disparity.AI will affect income and wealth inequality.Unlike previous waves of automation,which h

22、ad the strongest effect on middle-skilled workers,AI displacement risks extend to higher-wage earners.However,potential AI complementarity is positively correlated with income.Hence,the effect on labor income inequality depends largely on the extent to which AI displaces or complements high-income w

23、orkers.Model simulations suggest that,with high complementarity,higher-wage earners can expect a more-than-proportional increase in their labor income,leading to an increase in labor income inequality.This would amplify the increase in income and wealth inequality that results from enhanced capital

24、returns that accrue to high earners.Countries choices regarding the definition of AI property rights,as well as redistributive and other fiscal policies,will ultimately shape its impact on income and wealth distribution.The gains in productivity,if strong,could result in higher growth and higher inc

25、omes for most workers.Owing to capital deepening and a productivity surge,AI adoption is expected to boost total income.If AI strongly complements human labor in certain occupations and the productivity gains are sufficiently large,higher growth and labor demand could more than compensate for the pa

26、rtial replacement of labor tasks by AI,and incomes could increase along most of the income distribution.College-educated workers are better prepared to move from jobs at risk of displacement to high-complementarity jobs;older workers may be more vulnerable to the AI-driven transformation.In the UK a

27、nd Brazil,for instance,college-educated individuals historically moved more easily from jobs now assessed to have high displacement potential to those with high complementarity.In contrast,workers without postsecondary education show reduced mobility.Younger workers who are adaptable and familiar wi

28、th new technologies may also be better able to leverage the new opportunities.In contrast,older workers may struggle with reemployment,adapting to technology,mobility,and training for new job skills.To harness AIs potential fully,priorities depend on countries development levels.A novel AI preparedn

29、ess index shows that advanced and more developed emerging market economies should invest in AI innovation and integration,while advancing adequate regulatory frameworks to optimize benefits from increased AI use.For less prepared emerging market and developing economies,foundational infrastructural

30、development and building a digitally skilled labor force are paramount.For all economies,social safety nets and retraining for AI-susceptible workers are crucial to ensure inclusivity.STAFF DISCUSSION NOTES Gen-AI:Artificial Intelligence and the Future of Work INTERNATIONAL MONETARY FUND 3 I.Introdu

31、ction Artificial intelligence(AI)promises to boost productivity and growth,but its impact on economies and societies is uncertain,varying by job roles and sectors,with the potential to amplify disparities.As a positive productivity shock,AI will expand economies production frontiers and will lead to

32、 reallocations between labor and capital while triggering potentially profound changes in many jobs and sectors.AI offers unprecedented opportunities for solving complex problems and improving the accuracy of predictions,enhancing decision-making,boosting economic growth,and improving lives.However,

33、precisely because of its vast and flexible applicability in numerous domains,the implications for economies and societies are uncertain(Ilzetzki and Jain 2023).AI represents a wide spectrum of technologies designed to enable machines to perceive,interpret,act,and learn with the intent to emulate hum

34、an cognitive abilities.Across this spectrum,generative AI(GenAI)includes systems such as sophisticated large language models that can create new content,ranging from text to images,by learning from extensive training data.Other AI models,in contrast,are more specialized,focusing on discrete tasks su

35、ch as pattern identification.Meanwhile,automation is characterized by its focus on optimizing repetitive tasks to boost productivity,rather than producing new content.The field of AI is experiencing a swift evolution,especially with the advent of GenAI,which has broadened AIs potential applications.

36、This suggests that its impact will expand to reshape job functions and the division of labor.One critical dimension to consider is the societal acceptability of AI.Acceptability may vary depending on job roles.Some professions may seamlessly integrate AI tools,while others could face resistance beca

37、use of cultural,ethical,or operational concerns.This uncertainty becomes especially pronounced in labor markets.Although AI holds the potential for production-oriented applications,its effect will likely be mixed.In some sectors where human oversight of AI is necessary,it could amplify worker produc

38、tivity and labor demand.Conversely,in other sectors,AI might pave the way for significant job displacements.A rise in aggregate productivity of the economy could however strengthen overall economic demand,potentially creating more job opportunities for most workers in a ripple effect.Moreover,this e

39、volution could also lead to the emergence of new sectors and job rolesand the disappearance of otherstranscending mere intersectoral reallocation.Beyond immediate job effects,another critical economic dimension is the capital income channel.As AI drives efficiency and innovations,those who own AI te

40、chnologies or have stakes in AI-driven industries may experience increased capital income.This shift could potentially exacerbate inequalities.AI challenges the belief that technology affects mainly middle and,in some cases,low-skill jobs:its advanced algorithms can now augment or replace high-skill

41、 roles previously thought immune to automation.While historical waves of automation and the integration of information technology affected predominantly routine tasks,AIs capabilities extend to cognitive functions,enabling it to process vast amounts of data,recognize patterns,and make decisions.As a

42、 result,even high-skill occupations,which were previously considered immune to automation because of their complexity and reliance on deep expertise now face potential disruption.1 Jobs that require nuanced judgment,creative problem-solving,or intricate data 1 Another historical example of technolog

43、y that hit the relatively educated is the introduction of the calculator.Before the widespread use of calculators,the role of accountants was considered a medium-to high-skill job,given that a significant portion of the population was uneducated.The introduction of calculators led to a reduction in

44、the number of accountants(Wootton and Kemmerer 2007).STAFF DISCUSSION NOTES Gen-AI:Artificial Intelligence and the Future of Work INTERNATIONAL MONETARY FUND 4 interpretationtraditionally the domain of highly educated professionalsmay now be augmented or even replaced by advanced AI algorithms,poten

45、tially exacerbating inequality across and within occupations.This shift challenges the conventional wisdom that technological advances threaten primarily lower-skill jobs and points to a broader and deeper transformation of the labor market than by previous technological revolutions.The impact of AI

46、 is also likely to differ significantly across countries at different levels of development or with different economic structures.Advanced economies,with their mature industries and service-driven economies,typically have a higher concentration of jobs in sectors that require complex cognitive tasks

47、.These economies are therefore both more susceptible to,yet better positioned to benefit from,AI innovations.Conversely,emerging market and developing economies,often still reliant on manual labor and traditional industries,may initially face fewer AI-induced disruptions.However,these economies may

48、also miss out on early AI-driven productivity gains,given their lack of infrastructure and a skilled workforce.Over time,the AI divide could exacerbate existing economic disparities,with advanced economies harnessing AI for competitive advantage while emerging market and developing economies grapple

49、 with integrating AI into their growth models.To inform the discussion on the potential impact of AI on the future of work and which policies countries should enact in response,this note aims to answer six questions.(1)Which countries are more exposed to AI adoption?Which countries are likely to ben

50、efit most?(2)How differently will AI affect workers within countries?Which segments of workers are likely to thrive and which face more risks?(3)Historically,how frequently did workers shift between roles now facing varying AI exposure?What insights do these shifts reveal about labor adaptability?(4

51、)In what ways could AI reshape income and wealth inequality?(5)What is the potential impact for growth and productivity?(6)Which countries appear better prepared for the AI transition?How can policies maximize gains and mitigate likely AI-related challenges?This note builds on a growing body of work

52、 that explores the impact of AI on labor markets and the macroeconomy.Many empirical studies so far have focused largely on the US,finding that many of the tasks of a significant portion of the workforce,including those of high-skilled workers,could be substantially replaced by AI(for example,Felten

53、,Raj,and Seamans 2021,2023;Eloundou and others 2023;Webb 2020).A few studies(OECD 2023;Albanesi and others 2023;Briggs and Kodnani 2023)adopt a cross-country approach;Gmyrek,Berg,and Bescond(2023)undertake a comprehensive review of emerging market economies and find less exposure to AI than in advan

54、ced economies;Colombo,Mercorio,and Mezzanzanica(2019)focus on the Italian labor market.These studies apply empirical approaches similar to those used in the automation literature(for example,Autor and Dorn 2013,Acemoglu and Restrepo 2022,Das and Hilgenstock 2022).This note contributes to the existin

55、g literature in four significant ways.First,while previous AI exposure measures often implicitly equate exposure with substitutability of human tasks,this note attempts to assess the potential for complementarity and substitution with labor,using the approach developed by Pizzinelli and others(2023)

56、.This method considers the wider social,ethical,and physical context of occupations,along with required skill levels,to discern whether AI may complement or replace roles.This adds to recent studies that have attempted to make this distinction using a purely task-based framework(Acemoglu and Restrep

57、o 2018,2022;Gmyrek,Bert,and Bescond 2023).Second,the note offers some initial insight into the potential for STAFF DISCUSSION NOTES Gen-AI:Artificial Intelligence and the Future of Work INTERNATIONAL MONETARY FUND 5 workers to make the transition from occupations at risk of displacement to those wit

58、h high AI-complementarity potential,drawing on microdata for one advanced and one emerging market economy.Third,it takes a deep look at how AI may affect income and wealth inequality within countries.It dissects AI exposure patterns across demographics and earnings levels and uses a model-based anal

59、ysis to evaluate AIs impact on labor and capital income inequality,as well as on income levels.Last,the note examines how AI preparedness for this technological shift may differ across countries at different income levels,using a very large sample of advanced and emerging market and developing econo

60、mies.With this analysis there are some important caveats.First,although in the model analysis activity grows in occupations with high AI complementarity and falls in low-complementarity occupationsmimicking sectoral reallocationsthe analysis on AI exposure assumes that sector sizes are fixed and tha

61、t the tasks required in each occupation are unchanged.Consequently,the results are more pertinent for the short to medium term.Over longer horizons,workers will likely migrate across different sectors and roles,or acquire new skills,and jobs will evolve.In addition,the analysis assumes that workers

62、within the same occupation will be affected in the same way,but there can be variation in the effects of AI.AI may also affect firm dynamics and market concentration(Babina and others,forthcoming),driving inequality between workers at different firms.Second,the study relies on the premise that tasks

63、 performed within similar occupations are homogenous around the world,while there can be significant cross-country variations.Third,the approach abstracts from linkages across occupations and countries(trade linkages),as well as from cross-border spillovers of AI exposure.Last,while the analyses on

64、workers AI exposure and societies preparedness use empirical approaches,the potential impacts on inequality and productivity are analyzed with a model.The latter therefore depend on potentially strong calibration assumptions.The pace of AI adoption,influenced by the time needed by firms to invest in

65、 any necessary physical capital and the reorganization required to capitalize on AI,is difficult to foresee.Likewise,the time required to exert aggregate macroeconomic effects,the impact on intersectoral reallocation of factors for production,the birth of new industries,and AIs exact implications fo

66、r economies and societies are challenging to predict.Any estimate embodies a level of uncertainty reminiscent of past introductions of general-purpose technologies,such as electricity.This uncertainty applies also to the results of this note.The remainder of the note is structured as follows.Section

67、 II illustrates the conceptual framework of AI exposure and complementarity and attempts to quantify empirically the degree of exposure to and complementarity with AI across countries and groups of workers within countries.Section III examines how easily workers have historically shifted across role

68、s now facing varying degrees of AI exposure and complementarity.Section IV uses a model to project potential implications of AI adoption for productivity,incomes,and inequality.Section V assesses countries AI preparedness in key policy areas.Section VI concludes and presents policy considerations.II

69、.AI Exposure and Complementarity II.1 Conceptual Framework Assessing the impact of AI on employment is complex because of its swift evolution,uncertainty in integration across production processes,and shifting societal perceptions.Given the rapid advance and evolving capabilities of AI-based technol

70、ogies,which production processes will integrate AI and which human STAFF DISCUSSION NOTES Gen-AI:Artificial Intelligence and the Future of Work INTERNATIONAL MONETARY FUND 6 tasks will be replaced or enhanced remain uncertain.Over time,the changing social acceptability of AI could also affect its in

71、tegration into production processes.This note refines a commonly used conceptual framework to better measure human works exposure to,and complementarity with,AI.To study the effect of technological innovation on jobs,it is standard to conceptualize individual occupations as a bundle of tasks and to

72、consider which tasks can be replaced or complemented by technology(see for instance Acemoglu and Restrepo 2022;and Moll,Rachel,and Restrepo 2022 for recent applications).Felten,Raj,and Seamans(2021,2023)define“exposure”to AI as the degree of overlap between AI applications and required human abiliti

73、es in each occupation.The analysis refines this approach by augmenting it with Pizzinelli and others(2023)index of potential AI complementarity.This index leverages information on the social,ethical,and physical context of occupations,along with required skill levels(see Box 1 for details).The index

74、 reflects an occupations likely degree of shielding from AI-driven job displacement and,when paired with high AI exposure,gives an indication of AI complementarity potential.For example,because of advances in textual analysis,judges are highly exposed to AI,but they are also highly shielded from dis

75、placement because society is currently unlikely to delegate judicial rulings to unsupervised AI.Consequently,AI will likely complement judges,increasing their productivity rather than replacing them.2 Conversely,clerical workers,who are also very exposed to AI but have a lower level of shielding,are

76、 more at risk of being displaced.The level of shielding and complementarity will likely evolve over time and at a different pace across countries,reflecting higher AI accuracy,which will decrease the chances for“hallucinations”AI system output that is not based on reality or a given context.Social p

77、references and available alternatives will also play a role(see Pizzinelli and others 2023 for quantitative illustrations of this phenomenon).For example,in low-income countries,where trained doctors are scarce,scalable AI-backed medical consultations may be viewed as an attractive option.The remain

78、der of this note refers to the complementarity potential driven by high AI exposure and high shielding more succinctly as“complementarity.”Joint consideration of exposure and complementarity indicates the types of labor market developments each occupation is more likely to experience with AI adoptio

79、n.Occupations with high exposure for which AI can autonomously complete tasks may see reduced human labor demand,leading to lower wages.Jobs that require human supervision over AI may experience a boost in productivity,which would raise labor demand and wages for incumbent workers.However,even in oc

80、cupations in which AI is likely to complement human labor,workers without AI-related skills risk reduced employment.Hence,the ease of acquiring AI-related skills will determine the ultimate impact of this technology.Based on these two criteria,occupations can be categorized into three groups:“high e

81、xposure,high complementarity”;“high exposure,low complementarity”;and“low exposure”(see Box 1).3 Although the indicators(and the thresholds adopted to define what is high and low,represented by their median values)are relative measures,this categorization highlights the overarching differences acros

82、s occupations in terms of their AI exposure and complementarity potential.High-exposure,high-complementarity occupations have significant potential for AI support,as AI can complement workers in their tasks and decision-making.However,there is limited scope for unsupervised use of AI in these roles.

83、These are primarily cognitive jobs with a high degree of responsibility and interpersonal interactions,such as those performed by surgeons,lawyers,and 2 One caveat is the possibility that increased productivity for certain high-exposure,high-complementarity jobs may lead to a decline in their demand

84、.3 As discussed in Box 1,complementarity is of limited relevance when AI exposure is limited.Hence,for the sake of simplicity,this note groups occupations with low exposure together regardless of their potential complementarity.STAFF DISCUSSION NOTES Gen-AI:Artificial Intelligence and the Future of

85、Work INTERNATIONAL MONETARY FUND 7 judges.In such roles,workers can potentially reap the productivity benefits from AI,provided they have the skills needed to interact with the technology.On the other hand,high-exposure,low-complementarity occupations are well positioned for AI integration,but there

86、 is a greater likelihood that AI will replace human tasks.This could lead to a decline in labor demand and slower wage growth for these jobs.Telemarketers are a prime example.Last,low-exposure occupations”have minimal or no potential for AI application.This group encompasses a diverse range of profe

87、ssions,from dishwashers and performers to others.This conceptual framework is subject to several caveats.First,the index of Felten,Raj,and Seamans(2021)and the complementarity measure discussed in Box 1 offer only a relative interpretation.In other words,these measures tell us whether a given occupa

88、tion is more or less exposed,or complementary,than others.Second,high complementarity can still result in displacement from occupations of workers who do not have the required skills or whose employers do not invest in the technology.Companies investing in these technologies earlier would solidify c

89、ommercial advantages over competitors.In other words,while the analysis assumes that workers within the same occupation will be affected in the same way,there can be variation in the effects of AI.Firms that are more successful at integrating AI may increase their productivity more than competitors

90、and pay higher wages,exacerbating intra-occupational inequality.Third,the conceptual framework provides only a static view of exposure and complementarity.In this regard,it does not speak to the existing or prospective availability of necessary IT infrastructure or to workers ability to acquire the

91、needed skills or to relocate across different occupations.Neither does it take into account the effects of ongoing integration of AI and robotics.In addition,it does not factor in potential changes in societal preferences,which will also shape regulations and could make unsupervised AI acceptable in

92、 a growing number of contexts or ban its use in others.On the macroeconomic side,it does not account for adoption speed and the factors influencing adoption,including costs borne by firms compared with productivity benefits.The conceptual framework also does not factor in feedback effects,which,for

93、examplethrough higher overall productivity as a result of AI adoptioncould boost labor demand for most types of jobs,partially offsetting potential negative impacts of AI.The note applies this categorization to appraise the exposure of the current employment structure to AI for a large number of cou

94、ntries.The definitions are applied to 142 countries using the online International Labour Organization(ILO)employment database and an internationally consistent classification of occupations.To examine within-country variation,a more granular level of the categorizationbased on more than 400 occupat

95、ion titlesis also applied to countries with good microdata coverage:two advanced economies(UK and US)and four emerging market economies(Brazil,Colombia,India,South Africa).4 II.2 Cross-Country Differences About 40 percent of workers worldwide are in high-exposure occupations;the share is 60 percent

96、in advanced economies,which indicates potentially large macroeconomic implications.Advanced economies have a greater share of high-exposure occupations,with either low or high complementarity,than emerging market economies and low-income countries(Figure 1,panel 1).In the average advanced economy,27

97、 percent of employment is in high-exposure,high-complementarity occupations,33 percent in high-exposure,low-complementarity jobs.In comparison,emerging market economies have corresponding shares of 16 and 4 Specifically,the analysis of the 142 countries from the ILO database uses 72 sub-major occupa

98、tion groups(2-digit level)of the International Standard Classification of Occupations(ISCO)-08 classification.The microdata analysis uses the 130 minor groups(3-digit)of the same classification for India and the 436 unit groups(4-digit)for the other five countries.See Annex 1 for details.STAFF DISCU

99、SSION NOTES Gen-AI:Artificial Intelligence and the Future of Work INTERNATIONAL MONETARY FUND 8 24 percent,respectively,and low-income countries have shares of 8 and 18 percent,respectively.5 A similar result emerges when looking at selected individual countries using more refined classifications(Fi

100、gure 1,panel 2).Almost 70 and 60 percent of UK and US employment,respectively,is in high-exposure occupations,approximately equally distributed between those that are high-and low-complementarity positions.High-exposure employment in emerging market economies ranges from 41 percent in Brazil to 26 p

101、ercent in India.Figure 1.Employment Shares by AI Exposure and Complementarity:Country Groups and Selected Individual Countries 1.Country Groups(Percent)2.Selected Countries(Percent)Sources:American Community Survey;Gran Encuesta Integrada de Hogares;India Periodic Labour Force Survey;International L

102、abour Organization;Labour Market Dynamics in South Africa;Pesquisa Nacional por Amostra de Domiclios Contnua;UK Labour Force Survey;and IMF staff calculations.Note:Country labels use International Organization for Standardization(ISO)country codes.AEs=advanced economies;EMs=emerging market economies

103、;LICs=low-income countries;World=all countries in the sample.Share of employment within each country group is calculated as the working-age-population-weighted average.The composition of the labor force in terms of broad occupational groups reflecting countries economic structure explains most of th

104、e differences in exposure and complementarity across countries.Figure 2 reports the employment shares by occupational groups for three countries with markedly different shares of employment in exposed occupations.The UK has a significant portion of employment in professional and managerial occupatio

105、ns,which exhibit high exposure and high complementarity,and in clerical support workers and technician occupations,generally high exposure and low complementarity.In India most workers are craftspeople,skilled agricultural workers,and low-skilled,or“elementary”workers;most of these are in the low-ex

106、posure category.Brazil represents a broadly intermediate case.5 There is heterogeneity behind average figures.In advanced economies the share of employment in high-exposure,high-complementarity occupations(HEHCs)ranges between 20.2 and 37.3 percent;the share in high-exposure,low-complementarity occu

107、pations(HELCs)ranges between 25.9 and 46.1 percent;and the share in low-exposure occupations(LEs)ranges between 22.5 and 53.6 percent.In emerging market economies,the ranges are 5.728.2 percent for HEHCs,10.434.7 percent for HELCs,and 46.175.9 percent for LEs.In low-income countries,the ranges are 2

108、35.3 percent for HEHCs,1.433 percent for HELCs,and 5496.1 percent for LEs.STAFF DISCUSSION NOTES Gen-AI:Artificial Intelligence and the Future of Work INTERNATIONAL MONETARY FUND 9 Figure 2.Employment Share by Exposure and Complementarity(Selected Countries)1.Brazil(Percent)2.United Kingdom(Percent)

109、3.India(Percent)Sources:India Periodic Labour Force Survey;Pesquisa Nacional por Amostra de Domiclios Contnua;UK Labour Force Survey;and IMF staff calculations.Note:The charts plot the total employment share by each of the nine 1-digit International Standard Classification of Occupations(ISCO)-08 oc

110、cupation codes.These findings suggest that advanced economies may be more susceptible to labor market shifts from AI adoption,materializing over a shorter time horizon than in emerging market economies and low-income countries.Given their high shares of employment in both low-and high-complementarit

111、y occupations,advanced economies may experience a more polarized effect from the structural transformation brought about by AI.On one hand,they face a greater risk of labor displacement and harmful income developments for workers in the high-exposure and low-complementarity occupations.On the other

112、hand,they are better positioned to take advantage early of the emerging AI growth opportunities as a result of their larger amount of employment in high-exposure and high-complementarity jobs.The net employment impact will depend on countries ability to innovate,adopt,and adapt to AI.Both advanced a

113、nd emerging market and developing economies are subject to considerable uncertainty surrounding these predictions.For example,in low-income countries AI adoption could mirror the swift adoption of mobile technology and lead to large marginal benefits from AI.In addition,with the appropriate digital

114、infrastructure in place,AI may also represent an opportunity for emerging market and developing economies to address skill shortages,especially in the health and education sectors,potentially increasing inclusion and productivity(Box 2).II.3 Within-Country Differences Beyond the overall exposure of

115、each country to AI,different groups within countries are likely to be affected differently.The advent of AI could exacerbate inequality within countries along various dimensions,such as the income level of individuals,their education level,or their gender.Understanding which groups are most vulnerab

116、le is essential to design policies that can mitigate those effects.Interestingly,while the overall exposure of countries to AI differs significantly between advanced and emerging market and developing economies,the patterns of exposure across individuals within countries are very similar for the two

117、 advanced economies and the four emerging market economies included in the granular microdata analysis.An important caveat is that findings may be different in other countries.Exposure is higher for women and for more educated workers but is mitigated by a higher potential for complementarity with A

118、I(Figure 3).In most countries,women tend to be employed in high-exposure occupations more than men(Figure 3,panel 1).Because this share is distributed approximately equally STAFF DISCUSSION NOTES Gen-AI:Artificial Intelligence and the Future of Work INTERNATIONAL MONETARY FUND 10 between low-and hig

119、h-complementarity jobs,the result can be interpreted to mean that women face both greater risks and greater opportunities.Exceptions to this pattern may be attributed to high shares of women in agricultural jobs,especially in countries where the farming sector is large(for example,India).Turning to

120、education,in all countries examined,higher education levels are associated with a greater share of employment in high-exposure occupations,but this is especially pronounced in occupations with high complementarity(Figure 3,panel 2).The higher level of exposure supports the popular view that,unlike a

121、utomation,AI could more strongly affect high-skilled workers.However,higher exposure is alleviated by greater potential for complementarity.Last,age differences do not exhibit a common pattern(Figure 3,panel 3).This is because the composition of different age cohorts in terms of gender and education

122、 is very distinct across countries,thus overshadowing age-based differences.In the UK and the US,younger groups have more college-educated individuals thanks to increased university attendance over the past 30 years;gender composition of age groups is similar.In emerging market economies and low-inc

123、ome countries,there are fewer people with higher education,but younger groups have more women thanks to recent rises in female labor participation.Figure 3.Share of Employment in High-Exposure Occupations by Demographic Groups 1.By Gender(Percent)2.By Education(Percent)3.By Age (Percent)Sources:Amer

124、ican Community Survey;Gran Encuesta Integrada de Hogares;India Periodic Labour Force Survey;Labour Market Dynamics in South Africa;Pesquisa Nacional por Amostra de Domiclios Contnua;UK Labour Force Survey;and IMF staff calculations.Note:The bars represent employment shares in high-exposure occupatio

125、ns.In panel 1,employment shares are conditional on each gender category.In panel 2,employment shares are conditional on each of the four education categories(middle school and below,high school,some college,college or higher).In panel 3,employment shares are conditional on each of the four age inter

126、vals.Country labels use International Organization for Standardization(ISO)country codes.Exposure is spread along the labor income distribution,but potential gains from AI are positively correlated with income.The share of employment in occupations at risk of displacement(high-exposure,low-complemen

127、tarity jobs;Figure 4,panel 1)is broadly similar across income quantiles(with a mildly positive slope in emerging market economies).This differs from previous waves of automation and information technology during which risks of displacement were highest for middle-income earners.Consistent with popul

128、ar discourse,AI differs from traditional automation by potentially affecting jobs of workers throughout the income distribution.However,employment in occupations that have a high potential for complementarity with AI(high-exposure,high-complementarity jobs;Figure 4,panel 2)is more concentrated in th

129、e upper-income quantiles.The correlation between earnings and potential complementarity is consistent with the findings on education level and is even more pronounced for emerging market economies(Figure 4,panel 3).This suggests that AIs gains will likely disproportionately accrue to higher-income e

130、arners,especially in countries such as India and,to a lesser extent,the US,where complementarity steadily rises at the top of the distribution.The phenomenon will likely be more muted in countries such as the UK,where the increase in complementarity plateaus at the top.STAFF DISCUSSION NOTES Gen-AI:

131、Artificial Intelligence and the Future of Work INTERNATIONAL MONETARY FUND 11 Figure 4.Share of Employment in High-Exposure Occupations and Potential Complementarity by Income Deciles 1.High-Exposure,Low-Complementarity (Percent)2.High-Exposure,High-Complementarity(Percent)3.Potential Complementarit

132、y Sources:American Community Survey;Gran Encuesta Integrada de Hogares;India Periodic Labour Force Survey;Labour Market Dynamics in South Africa;Pesquisa Nacional por Amostra de Domiclios Contnua;Pizzinelli and others(2023);UK Labour Force Survey;and IMF staff calculations.Note:Panel 1 shows the emp

133、loyment share in jobs with high exposure but low complementarity,and panel 2 presents the employment share in jobs with high exposure and high complementarity,each categorized by income deciles.Panel 3 shows the potential AI occupational complementarity from Pizzinelli and others(2023),averaged and

134、grouped by income deciles.Country labels use International Organization for Standardization(ISO)country codes.III.Worker Reallocation in the AI-Induced Transformation In the long term,workers will adjust to changing skill demands and sector shifts,with some potentially transitioning to high-AI-compl

135、ementarity roles and some struggling to adapt.The previous section provided a static picture of AI exposure based on the current employment composition of countries.Over time,however,workers are likely to adapt to the evolving labor market.Although the analysis on AI exposure and complementarity is

136、conducted at the occupational level,it is important to make a distinction between jobs and workers.AI adoption may destroy some jobs(and displace the associated workers)and create or enhance othersbut whether the incumbents are the ones who can reap the associated benefits is unclear.The employment

137、effects will likely depend on worker characteristics,which in turn will affect their adaptability.Historical data suggest that some workers may struggle to adapt to technology-induced shifts in the job market.6 Historical job transition patterns suggest how workers could adapt.This section analyzes

138、microdata from Brazil and the UK to examine worker transition across occupations with different current AI exposure and 6 In the US,Cortes,Jaimovich and Siu(2017)found that less-educated young men contributed to the decline in routine manual jobs since the 1980s,while women with intermediate educati

139、on led the fall in routine cognitive jobs.These workers often moved to low-wage occupations or nonemployment.Most of the reallocation took place through fewer moves into these occupations from unemployment and inactivity(Cortes and others 2020),suggesting that automation affected job seekers more th

140、an current workers.In the UK,Dabla-Norris,Pizzinelli,and Rappaport(2023)found that routine job decline affected women without college degrees differently across ages:older women shifted to higher-paying jobs,while younger ones went to lower-paying manual jobs.STAFF DISCUSSION NOTES Gen-AI:Artificial

141、 Intelligence and the Future of Work INTERNATIONAL MONETARY FUND 12 complementarity.7 It explores whether age and education affect transitions8 and how these characteristics affect incomes.In general,workers switch between similar types of occupations,indicating potentially limited flexibility in ad

142、justing to evolving labor markets.However,there is a significant fraction of switches across occupations with different levels of exposure to AI.Analyzing these dynamics can provide suggestive evidence on possible worker movements following AI adoption and help identify potentially vulnerable groups

143、.Figure 5.Occupational Transitions for College-Educated Workers in Brazil and the United Kingdom 1.Brazil(Percent)2.United Kingdom(Percent)Sources:Pesquisa Nacional por Amostra de Domiclios Contnua;UK Labour Force Survey;and IMF staff calculations.Note:“From”indicates the exposure category of the oc

144、cupation the individual had in the preceding quarter;“to”indicates the exposure category of the occupation the worker transitioned to.The share of transitions represents the average share of transitions in the“from”category for college-educated workers who go to the“to”category.Workers with a colleg

145、e education have historically shown a greater ability to transition into what are now jobs with high AI-complementarity potential.Both college-and non-college-educated workers frequently change occupations.The average yearly occupation-switching probability is 43.7 percent in Brazil and 29.8 percent

146、 in the UK for college-educated workers and 38 percent and 27 percent for non-college-educated workers.9 College-educated individuals working in what are or may become AI-intensive jobs tend to stay within such environments when they switch jobs,irrespective of AIs complementarity to their roles(Fig

147、ure 5).In addition,more than a third of those moving away from low-complementarity jobs shift toward roles with higher AI complementarity,which demonstrates a potential avenue for job growth.Non-college-educated workers are predominantly found in low-AI-exposure jobs and are less inclined to move to

148、 high-complementarity positions when they switch from high-exposure,low-complementarity occupations.10 7 Annex 3 provides details on the data used for the analysis,and Cazzaniga and others(forthcoming)describe the methodology and perform further analysis.The analysis in this section is conducted onl

149、y for the UK and Brazil because the labor force surveys for these two countries are structured as rotating panels,which allows for tracking individual workers over time.The analysis,however,comes with a caveat:cohort effects are not included because of the limited time series dimension of the data.8

150、 Gender is not directly discussed in this section because the main results presented below for each education group hold for both males and females separately.9 These values are broadly in line with other evidence on occupational mobility in advanced economies and emerging markets.For instance,for t

151、he US,Kambourov and Manovskii(2009)estimate a yearly occupation switching rate of 21 percent,while Moscarini and Vella(2008)estimate a monthly rate of 3.5 percent,equivalent to 34.7 percent annually.Meanwhile,for Brazil,Monsueto,Moreira Cunha,and da Silva Bichara(2014)estimate a 30 percent occupatio

152、n switching rate over a period of four months.10 Industry switches also happen,but the classification of AI exposure and complementarity has not been conducted at the industry level.While some occupations are industry-specific(for example,doctors typically work in health care),others are more versat

153、ile and can cross into other industries.STAFF DISCUSSION NOTES Gen-AI:Artificial Intelligence and the Future of Work INTERNATIONAL MONETARY FUND 13 Figure 6.Life-Cycle Profiles of Employment Shares by Education Level,Brazil and the United Kingdom 1.Brazil(Percent)2.United Kingdom(Percent)Sources:Pes

154、quisa Nacional por Amostra de Domiclios Contnua;UK Labour Force Survey;and IMF staff calculations.Note:The panels plot the estimated share of employment by age for each exposure category for college-and non-college-educated workers,according to the calculations described in Annex 3.AI adoption poses

155、 challenges but represents an opportunity for young college-educated workers careers.Figure 6 shows that college-educated workers often transition from low-to high-complementarity jobs in their 20s and 30s.Their career progression stabilizes by their late 30s to early 50s,when they usually have reac

156、hed senior roles and are less inclined to make significant job switches.Although non-college-educated workers show similar patterns,their progression is less pronounced,and they occupy fewer high-exposure positions.This suggests that young,educated workers are exposed to both potential labor market

157、disruptions and opportunities in occupations likely to be affected by AI.On one hand,if low-complementarity positions,such as clerical jobs,serve as stepping stones toward high-complementarity jobs,a reduction in the demand for low-complementarity occupations could make young high-skilled workers en

158、try into the labor market more difficult.On the other hand,AI may enable young college-educated workers to become experienced more quickly as they leverage their familiarity with new technologies to enhance their productivity.With the introduction of generative AI,the use of AI has itself become muc

159、h easier.A recent study shows that the productivity impact of an AI-based conversational assistant was greatest for less experienced and low-skilled customer support workers;the effect on experienced and highly skilled workers was minimal(Brynjolfsson,Danielle,and Raymond 2023).Older workers may be

160、less adaptable and face additional barriers to mobility,as reflected in their lower likelihood of reemployment after termination.Following job termination,older workers are less likely to secure new employment within a year than young and prime-age workers(Figure 7).Several factors can explain this

161、discrepancy.First,older workers skills,though once in high demand,may now be obsolete as a result of rapid technological advances.Moreover,after significant time in a particular location,they may have geographic and emotional ties,such as to a spouse and children,that discourage them from relocation

162、 for new job opportunities.Financial obligations accumulated over the years might also make them less likely to accept positions with a pay cut.Last,having invested many years,if not decades,in a particular sector or occupation,there may be a natural reluctance or even a perceptual barrier to a tran

163、sition to entirely new roles or industries.This may reflect a combination of comfort with familiar settings,concern about the learning curve in a new domain,or perceived age bias.These constraints are likely to be relevant also in the context of AI-induced disruptions.STAFF DISCUSSION NOTES Gen-AI:A

164、rtificial Intelligence and the Future of Work INTERNATIONAL MONETARY FUND 14 Figure 7.One-Year Reemployment Probability of Separated Workers 1.Brazil 2.United Kingdom Sources:Pesquisa Nacional por Amostra de Domiclios Contnua;UK Labour Force Survey;and IMF staff calculations.Note:The bars show the r

165、eemployment probability of workers who have recently(within the previous quarter)moved from employment to unemployment,which is defined as the share of these workers who are again employed one year later.“From”indicates the exposure category of the occupation the individual had before being unemploy

166、ed,while“to”indicates the exposure category of the occupation the worker transitioned to.“Prime age”refers to workers over 35 and under 55;“old”refers to workers 55 and older.Historically,older workers have demonstrated less adaptability to technological advances;artificial intelligence may present

167、a similar challenge for this demographic group.After unemployment,older workers previously employed in high-exposure and high-complementarity occupations are less likely to find jobs in the same category of occupation than prime-age workers(Figure 7).This difference in the reemployment dynamics can

168、reflect technological change,changes in workers preferences,and age-related biases or stereotypes in the hiring processes in high-complementarity and high-exposure occupations.Technological change may affect older workers through the need to learn new skills.Firms may not find it beneficial to inves

169、t in teaching new skills to workers with a shorter career horizon;older workers may also be less likely to engage in such training,since the perceived benefit may be limited given the limited remaining years of employment.This effect can be magnified by the generosity of pension and unemployment ins

170、urance programs.11 These channels align with Braxton and Taska(2023),which finds that technology contributes 45 percent of earnings losses following unemployment.This happens primarily because workers lacking new skills move to jobs where their existing skills are valued but that garner lower wages.

171、Occupational switches also affect workers incomes.In both the Brazil and the UK,progressing to high-exposure,high-complementarity occupations is associated with higher wages(Figure 8).12 Greater access to these types of jobs could thus be an significant driver of income growth for workers in advance

172、d and emerging market and developing economies.In Brazil(Figure 8,panel 1),workers switching to low-exposure from high-exposure occupations tend to experience a contraction in hourly wages.Hence,such transitions may be associated with income losses.11 See for example Yashiro and others(2022),who fin

173、d that in Finland,older workers in occupations more exposed to digital technologies are more likely to exit employment each year,and this effect is amplified when the workers can access an extension of benefits,known as the“unemployment tunnel,”which extends unemployment benefits until retirement.12

174、 A large amount of literature,starting with Kambourov and Manovskii(2009)finds that occupational mobility is an important driver of wage growth at the individual level and of wage inequality across workers.STAFF DISCUSSION NOTES Gen-AI:Artificial Intelligence and the Future of Work INTERNATIONAL MON

175、ETARY FUND 15 Figure 8.Estimated Wage Premiums from Changing Occupation(Percent)1.Brazil 2.United Kingdom Sources:Pesquisa Nacional por Amostra de Domiclios Contnua;UK Labour Force Survey;and IMF staff calculations.Note:“From”indicates the exposure category of the occupation the individual had in th

176、e preceding year,while“to”indicates the exposure category of the occupation the worker transitioned to.The premiums are relative to stayers;that is,they represent the increase or decrease in wages in relation to workers in the“from”category who did not switch occupations over a year.Wage premiums ar

177、e calculated according to the regression specification in Annex 2.95 percent confidence intervals for the point estimates are shown by whiskers.In summary,as AI reshapes the labor market,workers will likely adapt to shifting demands,with outcomes varying by education and age.Young college-educated w

178、orkers are the most vulnerable yet the most adaptable,often seesawing between job types.Historical patterns from Brazil and the UK reveal that high-exposure,high-complementarity roles offer wage premiums,while switching to low-exposure roles might decrease wages.The tendency for workers of all ages

179、to return to similar roles after unemployment suggests some labor market inflexibility.The ability to adjust is crucial for navigating AI-induced changes.Last,while the historical patterns examined in this section are informative,the structural transformation AI adoption will generate is still uncer

180、tain,and no one knows for sure how the labor market as a whole and individual workers will be able to adjust.IV.AI,Productivity,and Inequality In this section,a model-based analysis is used to evaluate the potential impact of AI adoption on the economy and inequality.This analytical approach serves

181、as a complement to the preceding empirical findings by examining broader effects on the economy,highlighting three critical channels through which AI may affect it:(1)labor displacement,(2)complementarity,and(3)productivity gains.These three channels are essential to gauging the potential impact of

182、AI adoption.First,AI adoption may shift tasks previously performed by labor to AI capital,leading to a reduction in labor income.Second,AI adoption may increase the importance of tasks that are not displaced by AI,particularly in occupations with high complementarity between human labor and AI.This

183、leads to a shift in value added and labor demand toward occupations with high AI complementarity and away from other occupations.Third,AI adoption may lead to broad-based productivity gains,boosting investment and increasing overall labor demand,which may offset some of the decline in labor income c

184、aused by AI-induced labor displacement.As a result,the overall impact of AI on income levels and inequality will depend on the extent to which gains in economic activity generated by AI-induced productivity compensate for any labor income losses.STAFF DISCUSSION NOTES Gen-AI:Artificial Intelligence

185、and the Future of Work INTERNATIONAL MONETARY FUND 16 To understand AIs impact on income levels and income inequality,both labor and capital income channels must be examined.A task-based model,detailed in Rockall,Pizzinelli,and Tavares(forthcoming),is developed.The model builds on the work of Drozd,

186、Taschereau-Dumouchel,and Tavares(2022)and Moll,Rachel,and Restrepo(2022).Agents differ by their labor productivity and asset holdings,offering a rich picture of the income and wealth distribution.AI is assumed to be adopted at its maximum potential and affects agents according to their AI exposure a

187、nd complementarity potential.Within this analytical framework,AIs effect on income operates primarily through the three channels mentioned above.AI adoption also leads to increases in the return on capital,raising capital income,which in turn increases wealth and wealth inequality consistently with

188、the initial distribution of asset holdings.The model is calibrated to the United Kingdom,a country that is highly exposed to AI adoption.Workers income is divided into three categories:(1)labor income,which can be positively or negatively exposed to AI depending on its degree of complementarity with

189、 workers skills;(2)capital income,which increases with AI adoption;and(3)benefits and other income(government benefits,pensions,and so forth).13 Figure 9,panel 1,shows that high-income workers have a much larger share of capital income than middle-and low-income workers,suggesting that this source o

190、f income may play a crucial role in determining the income inequality impact of AI adoption.Middle-and low-income workers total income depends more on labor income.The impact of AI on labor income will vary with workers AI exposure and complementarity.In line with the evidence presented in Section I

191、I,Figure 9,panel 2,shows that workers exposure to AI increases with their income.However,workers potential complementarity with AI also increases with income,albeit in the case of the UK,it peaks around the 75th percentile,declining slightly thereafter.The impact of AI is simulated by building three

192、 scenarios,which assume a labor share decline in line with comparable historical episodes associated with automation.The decrease in the labor share has historically been associated with routine-biased automation and,to a lesser extent,with increased trade,growing markups,and declining worker bargai

193、ning power resulting from the weakening of labor unions.14 Drawing on the change observed in the UK between 1980 and 2014 as a possible scenario,we assume that the labor share declines by 5.5 percentage points following the introduction of AI.This impact is spread across the income distribution,depe

194、nding on workers AI exposure and complementarity,as shown in Figure 9,panel 2.The three scenarios embed the same displacement of labor tasks via the capital deepening effect but are differentiated by(1)low-complementarity,if AI only mildly increases the demand for high-complementarity occupations;(2

195、)high-complementarity,if AI strongly supports the demand for high-complementarity occupations;and(3)high-complementarity and high productivity,if AI strongly complements high-complementary occupations,as in scenario(2),and further augments the productivity of the economy,predominantly through worker

196、s in high-complementarity occupations.The productivity increase is calibrated to generate close to a 1.5 percentage point increase in the workers average annual productivity growth rate in the first 10 years after AI adoption.This value is at the lower end of firm-level studies estimating the potent

197、ial impact of AI adoption on workers productivity(as discussed in Briggs and Kodnani 2023).15 13 While pension benefits are usually classified as ordinary income,pension fund income is classified as capital income.For simplicity,in Figure 9,panel 1,pension income is lumped together with government b

198、enefits and other income.14 See IMF(2017);Dao,Mitali,and Koczan(2019);and Bergholt,Furlanetto,and Maffei-Faccioli(2022)for factors that may explain the decline in the labor share.15 While the analysis presented in this section compares steady-state scenarios,the model would also allow for the study

199、of short-term dynamics toward the long-term steady state.STAFF DISCUSSION NOTES Gen-AI:Artificial Intelligence and the Future of Work INTERNATIONAL MONETARY FUND 17 Figure 9.Exposure to AI and to Automation and Income in the UK 1.Exposure of Income to AI(British pounds)2.Exposure and Complementarity

200、 by Income Percentiles(AI and complementarity index)Sources:UK Office for National Statistics,Wealth and Assets Survey;and IMF staff calculations.Note:Panel 1 shows three categories of workers income by total income percentiles:(1)wage income;(2)benefits,pensions,and other income;and(3)capital incom

201、e(rents and estimated investment income).In panel 2,AI exposure is measured as the share of total hours worked in a job in the top 30 percent of AI Occupational Exposure scores,from Felten,Raj,and Seamans(2021),weighted by hours worked.This threshold is chosen to make the analysis comparable with hi

202、storical episodes of automation.AI complementarity is measured by considering work contexts and skills,as discussed in Box 1 and in detail in Pizzinelli and others(2023).In the panel,we plot AI exposure and complementarity by total income percentiles.RHS=right scale.The impact of AI on labor income

203、inequality depends on the race between the degree of exposure to,and complementarity with,AI,and its boost to productivity.16 When AI has low complementarity with labor,AI adoption leads to a decline in labor income inequality(Figure 10)because of the displacement effect.At the top of the income dis

204、tribution the displacement effect is larger than the complementarity gains,leading to a labor income decline at the top.When AI is highly complementary to labor,the complementarity effect becomes stronger than the displacement effect,particularly in the upper half of the income distribution,leading

205、to a smaller share of high-income workers negatively affected by AI compared with the low-complementarity case.The share of workers negatively affected at the top drops from almost 15 percent to less than 5 percent.This high complementarity also leads to a decline in the labor income of those with l

206、ess complementary tasks,who are typically among low-income workers.As a consequence,labor income inequality increases.Last,when the AI productivity impact is also considered,labor income rises for all workers in the economy,even for the workers who have low exposure and those with high exposure and

207、low complementarity.The main reason is that higher productivity leads to higher demand for all factors of production in the economy,leading to increased labor income.However,labor income inequality rises because the increase is larger for workers with high AI complementarity.Unlike labor income ineq

208、uality,capital income and wealth inequality always increase with AI adoption(Figure 10).The main reason for the increase in capital income and wealth inequality is that AI leads to labor displacement and an increase in the demand for AI capital,increasing capital returns and asset holdings value.In

209、all scenarios,interest rates increase by almost 0.4 percentage point,with the potential to partially offset the decline in the natural rate of interest in the UK and advanced economies in general.17 Since in the model,as in 16 Annex 4 discusses two additional hypothetical scenarios that disentangle

210、the importance of exposure and complementarity.17 The increase in the interest rate is approximately of the same magnitude as the decline in the UK natural rate attributable to demographics(IMF 2023).STAFF DISCUSSION NOTES Gen-AI:Artificial Intelligence and the Future of Work INTERNATIONAL MONETARY

211、FUND 18 the data,high-income workers hold a large share of assets,they benefit more from the rise in capital returns.As a result,in all scenarios,independent of the impact on labor income,the total income of top earners increases because of capital income gains.These model simulations abstract from

212、possible changes in the definition of property rights,as well as changes in fiscal and redistributive policies,which can help reshape distributional outcomes(see,for example,Berg and others 2021,in the context of automation;and Klinova and Korinek 2021,in the context of AI).Figure 10.Change in Total

213、 Income by Income Percentile 1.Low Complementarity(Percent)2.High Complementarity(Percent)3.High Complementarity and High Productivity(Percent)Source:IMF staff calculations.Note:The panels show three scenarios from the model:(1)low complementarity,(2)high complementarity,and(3)high complementarity a

214、nd high productivity.For all scenarios,the calibrated change in the capital share is the same:5.5 percentage points,based on the change in the capital share during 19802014.The panels show the change in total income by income percentile,decomposed into the change in labor income in blue and the chan

215、ge in capital income in orange.For more details on the model see Annex 4.P=percentile.Under the high-complementarity,high-productivity scenario,the increase in total national income is largest and benefits all workers,although gains for those at the top are larger.In the first scenario,in which AI h

216、as low complementarity,the use of AI leads to an increase in output of almost 10 percent thanks to a combination of capital deepening and a small increase in total factor productivity(Figure 11).When higher complementarity is considered(second scenario),the AI impact on output and total factor produ

217、ctivity is similar to the impact in the low-complementarity scenario because these scenarios assume the same capital deepening and capital productivity gains.However,higher complementarity leads to sectoral reallocation,with labor demand and economic activity moving from low-to high-complementarity

218、occupations.Total income levels of low-income workers decline by 2 percent,while the gains at the top are almost 8 percent,leading to approximately the same increase in the level of national income as in the first scenario and an increase in labor income inequality.Last,when the productivity impact

219、is also considered,output increases by 16 percent between steady states,and total factor productivity Figure 11.Impact on Aggregates (Percentage points,left scale;percent,right scale)Source:IMF staff calculations.Note:The figure shows the change in the aggregate wage and wealth Gini between the init

220、ial and final distribution in each scenario,as well as the change in TFP and output.For more details on the model see Annex 4.RHS=right scale;TFP=total factor productivity.STAFF DISCUSSION NOTES Gen-AI:Artificial Intelligence and the Future of Work INTERNATIONAL MONETARY FUND 19 increases by almost

221、4 percent.These gains happen primarily in the first 10 years of the transition.Under this third scenario,despite the increase in labor income inequality,the total income level increases for all workers in the economy,ranging from 2 percent for low-income workers to almost 14 percent for high-income

222、workers.In emerging market and developing economies with higher initial inequality,AI could amplify wealth gaps and reduce wage disparity to a larger extent,but if the exposure to AI is lower and widespread,it could dampen these effects.An important issue is how model results may change when conside

223、ring two aspects pertinent to emerging market and developing economies:(1)higher initial levels of income and wealth inequality and(2)lower exposure to AI.Simulations suggest that higher initial income and wealth inequality could exacerbate wealth disparity,because AI-associated gains accrue predomi

224、nantly to top earners.At the same time,labor income inequality could decrease to a larger extent because of a higher concentration of AI-exposed workers at the top of the income distribution.The final effect,however,depends on the degree of complementarity,as in the case of advanced economies.In an

225、economy with fewer AI-exposed workers,the direct impact of AI on both income and wealth distribution may be less pronounced,given that fewer people stand to benefit from AI.18 Last,AIs potential to enhance public services,modernize finance,and bolster such sectors as agriculture and health care coul

226、d boost inclusion and productivity in emerging market and developing economies.Although these aspects are outside the scope of the model analysis,they are discussed in Box 2.Although the model simulations focus on within-country inequality,AI adoption may also have significant effects on global econ

227、omic disparity,driven by potential reshoring of activities to advanced economies.Such a shift could trigger reallocation of capital and labor from less developed regions,which are not as prepared to harness AI,toward more technologically advanced and AI-ready countries(Alonso and others 2022).Call c

228、enters located in emerging market economies are a potential example.These could be at risk of replacement by AI-driven solutions,subsequently leading to their relocation to advanced economies.In addition to labor reallocation,the increased profitability of firms that adopt AI may generate an influx

229、of capital from emerging market and developing economies to advanced economies,which could reduce equilibrium interest rates in advanced economies and exert downward pressure on capital income.19 Clearly,these dynamics are highly uncertain at this stage.It is also possible that,with sufficient inves

230、tment,AI may help emerging market and developing economies leapfrog in certain sectors,facilitating the offshoring of a broader selection of tasks and thus reducing cross-country inequality.V.AI Preparedness Preparedness for AI adoption is essential to harness its potential and mitigate its inherent

231、 risks.AI adoption can result in diverse labor market outcomes across countries,particularly regarding workforce reallocation and inequality.These likely varied outcomes are intertwined with countries structural and institutional frameworks.A countrys level of preparedness plays a pivotal role when

232、it comes to maximizing AIs benefits while managing downside risks,as historical episodes of technology adoption demonstrate(Cirera,Comin,and Cruz 2022).18 An important caveat regards the extent to which wealthy people in emerging market and developing economies have invested in foreign stocks likely

233、 to benefit from AI adoption.If such investment is significant,wealthy individuals may get higher returns on their foreign capital holdings even if domestic adoption is low,.19 A multicountry version of the model could investigate this and other relevant issues.STAFF DISCUSSION NOTES Gen-AI:Artifici

234、al Intelligence and the Future of Work INTERNATIONAL MONETARY FUND 20 This section proposes an AI Preparedness Index(AIPI),which covers multiple strategic areas for AI readiness.Drawing from the literature on the cross-country determinants of technology diffusion(for example,Keller 2004)and adoption

235、(for example,Nicoletti,Rueden,and Andrews 2020),the index is made up of a selected set of macro-structural indicators that are relevant for AI adoption.These are organized under four categories:(1)digital infrastructure,(2)innovation and economic integration,(3)human capital and labor market policie

236、s,and(4)regulation and ethics.Annex 5 contains the full list of subindicators and details on the index construction methodology.Although each component of the AIPI is important individually,preparedness for AI-induced structural transformation will likely rely on the collective performance in all ar

237、eas.For example,the digital infrastructure component,a crucial determinant of information and communications technology adoption(for example,Nicoletti,Rueden,and Andrews 2020)can lay the foundation for the diffusion and localized applications of AI technology.Nonetheless,such infrastructure would be

238、 of limited use absent a skilled workforce capable of leveraging digital platforms for innovative workplace applications(Bartel,Ichniowski,and Shaw 2007).Therefore,the human capital and labor market policies element,which incorporates the presence of social safety nets,assesses the prevalence and in

239、clusive distribution of digital skills within the labor force and the presence of policies that facilitate labor reallocation while safeguarding those harmed by AI-induced transitions(Nicoletti,Rueden,and Andrews 2020).Coupled with strong infrastructure,a digitally skilled labor force is vital for i

240、nnovation and economic integration(Autor,Levy,and Murnane 2003),which not only fosters domestic technological development through a vibrant R&D ecosystem but also promotes international trade and attracts foreign investment and new(AI)technologies(Bloom,Draca,and Van Reenen 2015).Last,the regulation

241、 and ethics dimension evaluates the extent to which the existing legal frameworks are adaptable to evolving new(digital)business models and the presence of strong governance for effective enforcement.Wealthier economies,including advanced and some emerging market economies,are generally better prepa

242、red than low-income countries to adopt AI,although there is considerable variation across countries(Figure 12).Broadly,advanced and some emerging market economies are highly exposed to potential disruptions from AIamid a substantial share of employment in highly exposed occupations.Yet these highly

243、exposed economies,notably the UK and US,as analyzed in Section II,are also well positioned to harness the benefits and mitigate the risks of AI thanks to their strong preparedness,particularly in digital infrastructure,human capital,and adaptable regulatory frameworks.On the other hand,low-income co

244、untries,although relatively less exposed,are underprepared across all dimensions to harness the benefits of AI.Notably,weak digital infrastructure and a less digitally skilled labor force are a concern.These Figure 12.AI Preparedness Index and Employment Share in High-Exposure Occupations Sources:Fr

245、aser Institute;International Labour Organization;International Telecommunication Union;United Nations;Universal Postal Union;World Bank;World Economic Forum;and IMF staff calculations.Note:The plot comprises 125 countries:32 AEs,56 EMs,and 37 LICs.The red reference lines are derived from the median

246、values of the AI Preparedness Index and high-exposure employment.Exes denote the average values for each corresponding country group.Circles represent the average values for each respective country group.AEs=advanced economies;EMs=emerging market economies;LICs=low-income countries.Country labels us

247、e International Organization for Standardization(ISO)country codes.STAFF DISCUSSION NOTES Gen-AI:Artificial Intelligence and the Future of Work INTERNATIONAL MONETARY FUND 21 cross-country differences risk amplifying the existing income gap between rich and poor economies,because advanced economies

248、expect productivity increases,as shown by the model-based simulations in the previous section.Figure 13.Information and Communications Technology Employment Share and Individual Components of the AI Preparedness Index 1.Digital Infrastructure 2.Human Capital and Labor Market Policies 3.Innovation an

249、d Integration 4.Regulation and Ethics Sources:Fraser Institute;International Labour Organization;International Telecommunication Union;United Nations;Universal Postal Union;World Bank;World Economic Forum;and IMF staff calculations.Note:ICT employment refers to people working in the information and

250、communications sector based on ISIC-Rev.4 classification.142 countries are included:35 AEs,67 EMs,and 40 LICs.Exes denote the average values for each corresponding country group.Circles represent the average values for each respective country group.Simple correlation(corr.)is also added for each cou

251、ntry group.AEs=advanced economies;EMs=emerging market economies;ICT=information and communications technology;LICs=low-income countries;ISIC=International Standard Industrial Classification.Reform prioritization should align with AI preparedness gaps.In this context,it is useful to distinguish betwe

252、en foundational AI preparednessdigital infrastructure and human capital that enable workers and firms to adopt AIand second-generation preparedness(innovation and legal frameworks).For economies with high AI exposure and strong foundational AI adoption preparedness(advanced economies and some emergi

253、ng market economies),more emphasis should be placed on strengthening their digital innovation capacity and adapting their legal and ethical frameworks to govern and foster AI advances.Accordingly,improvement in regulatory frameworkswhich are critical for broadening societal trust in AI toolsfollowed

254、 by innovation and integration,are the AI preparedness dimensions more strongly correlated with the size of the digital sector in advanced economies(Figure 13,panels 3 and 4).Regulatory frameworks need to mitigate cybersecurity risks as well(Carriere-Swallow and Haksar 2019;Haksar and others 2021),w

255、hich increase with widespread use of AI(Bank of America 2023)and may adversely affect firms performance(Jamilov,Rey,and Tahoun 2023).Where foundational preparedness is weak(low-income countries and some emerging market economies),STAFF DISCUSSION NOTES Gen-AI:Artificial Intelligence and the Future o

256、f Work INTERNATIONAL MONETARY FUND 22 investment in digital infrastructure and human capital should be prioritized to reap early gains from AI while paving the way for second-generation preparedness.In other words,while the capacity to innovate and strengthen regulatory frameworks for digital busine

257、sses is crucial in attracting(digital)investments in low-income countries,these frameworks will be less effective without strong AI infrastructure and a digitally skilled labor force.In some emerging market economies and low-income countries where foundational preparedness is not a strong binding co

258、nstraint,improvement in innovation and regulatory frameworks could catalyze private investment in digital innovations.The correlations reported in Figure 13(panels 1 and 2)corroborate these arguments,with digital Infrastructure and human capital strongly associated with the digital sector size in lo

259、w-income countries.With such investments,AI has the potential to improve the delivery of fundamental services such as education and health care and could perform complex tasks in areas where skilled labor is scarce.However,considering the costs associated with such investments and the limited fiscal

260、 space in many low-income countries,it would be prudent to focus spending on high-return projects.VI.Conclusions and Policy Considerations AI adoption may generate labor market shifts with significant cross-country differences.The exact implications of AI for economies and societies are challenging

261、to predict,embodying a level of uncertainty reminiscent of past introductions of general-purpose technologies,such as electricity.This uncertainty is particularly pronounced in labor markets,where AI offers productivity gains but also poses risks of job displacements.This notes findings highlight th

262、e significant portion of global employment that is exposed to AI,with advanced economies generally both more exposed but also better positioned to leverage this technology than most emerging market and developing economies.This dynamic suggests a potential widening of the digital divide and global i

263、ncome disparity.Women and highly educated workers are consistently more exposed to,but also more likely to benefit from,AI;older workers may be more likely to struggle during this technological transition.Both women,with their strong presence in the services sector,and highly educated workers,typica

264、lly employed in cognitive-intensive occupations,face greater AI exposure.Yet both groups also stand to gain the most from its integration.College-educated and younger people move more easily into high-complementarity jobs;older workers,however,face challenges in reemployment and adapting to new tech

265、nologies,mobility,and acquiring new job skills.Beyond its impact on income levels,which could increase for most workers,AI will also reshape wealth and income distribution.Capital deepening and the surge in productivity driven by AI hold the potential to elevate wage incomes for a broad range of wor

266、kers and to increase total income.This is more likely if AI exhibits significant complementarity with human labor in several roles and if the productivity boost is sufficiently strong.The enhanced economic activity and labor demand spurred by AI could offset the negative consequences of labor displa

267、cement.Unlike previous automation waves,which affected mostly middle-skilled workers,AIs displacement risks span the entire income spectrum,including high-income earners and skilled professionals.However,the potential for AI to complement jobs is positively correlated with income levels.As such,the

268、trajectory of labor income inequality hinges on how well AI complements tasks undertaken by high-income professionals.Model simulations suggest that with strong complementarity,high-wage earners might experience a disproportionate increase in their earnings,thereby intensifying labor income inequali

269、ty.This channel would amplify the increase in income and wealth inequality resulting from enhanced capital returns,which typically accrue to higher-earning people.These channels abstract from countries choices regarding the definition of AIs property rights and redistributive policies,which will ult

270、imately shape impacts on income and wealth distribution.STAFF DISCUSSION NOTES Gen-AI:Artificial Intelligence and the Future of Work INTERNATIONAL MONETARY FUND 23 Harnessing the advantages of AI will depend on countries preparedness and the ability of workers to adapt to this new technology.Advance

271、d and some emerging market economies are well positioned to harness AI thanks to their high exposure and preparedness.Other emerging market economies and low-income countries may find it difficult to harness potential AI benefits given their inadequate infrastructure,their workers lack of skills,and

272、 the absence of institutional frameworksputting them at risk of competitive disadvantage.Economic development stages influence preparedness priorities.Advanced and more developed emerging market economies should launch adequate regulatory frameworks to optimize the benefits of increased AI use and i

273、nvest in complementary innovations.Low-income countries and other emerging market economies should prioritize digital infrastructure and human capital.With such investments,AI could help alleviate skill shortages,expand the provision of health care and education,and improve productivity and competit

274、iveness in new sectors.The potential implications of AI demand a proactive approach from policymakers geared toward maintaining social cohesion.While long-term productivity gains from AI are likely,during the transition,job displacement and changes in income distribution could have substantial polit

275、ical economy implications.History shows that economic pressures can lead to social unrest and demands for political change.Ensuring social cohesion is paramount.Policies must promote the equitable and ethical integration of AI and train the next generation of workers in these new technologies;they m

276、ust also protect and help retrain workers currently at risk from disruptions.The cross-border nature of AI amplifies its ethical and data security challenges and calls for international cooperation to ensure responsible use,as recently laid out in the Bletchley Declaration,signed by 28 countries and

277、 the EU.Countries have varying capacity to address these issues,which highlights the need for harmonized global principles and local legislation.STAFF DISCUSSION NOTES Gen-AI:Artificial Intelligence and the Future of Work INTERNATIONAL MONETARY FUND 24 Box 1.Artificial Intelligence Occupational Expo

278、sure and Potential Complementarity Several studies have proposed definitions of AI exposure at the occupational level.The most common is the AI Occupational Exposure(AIOE)index of Felten,Raj,and Seamans(2021),measuring the correspondence between 10 AI applications and 52 human skills.This overlap be

279、tween AI and human abilities is then weighted by the degree of importance and complexity of such skills in each job.This index is interpreted in relative terms and reported as normalized or rescaled between 0 and 1.It is also agnostic about the implication of exposure for human labor.In other words,

280、it focuses on the relative likelihood of AIs integration into the functions of a given job,but it does not consider the likelihood of AI serving as a complementary technology or subsituting for human labor.Some studies build on the AIOE measure to attempt to answer this question.Pizzinelli and other

281、s(2023)propose a potential complementarity index to adjust the original AIOE measure.In this approach,greater potential complementarity reduces exposure.Hence,a higher complementarity-adjusted AIOE(C-AIOE)more explicitly reflects a higher chance of labor substitution.To develop this index,the author

282、s use O*NET,the same repository of occupational characterisitcs employed by Felten,Raj,and Seamans(2021),but draw from two different areas:work contexts and skills.Work contexts include social and physical aspects of how work in a given occupation is carried out.Using case-by-case judgment,the autho

283、rs argue that in some contexts societies may be less likely to allow unsupervised use of AI.For instance,the criticality of decisions and the gravity of the consequences of errors are two job aspects that may motivate societies to require humans to make final decisions or take actions.Judges and doc

284、tors,for example,despite high AI exposure,would still likely be human beings.Conceptually,exposure and complementarity can be thought of as two dimensions of relevance,as in Box Figure 1.1.At the first stage,exposure(x-axis)defines the scope for applying AI to carry out the main functions of a job.A

285、t the second stage,given the degree of potential application,a set of societal and technical concerns determines complementarity.For occupations with high exposure,low complementarity entails a relatively higher likelihood of AI replacing key tasks.In more acute cases,AI may lead to a decrease in th

286、e demand for the occupation altogether.This would in turn translate into reduced employment prospects,lower wages,and higher risk of displacment.High exposure combined with high complementarity entails a greater likelihood of workers in those jobs experiencing productivity growth and wage gains from

287、 adopting AI-driven technologies.However,these benefits will likely be contingent on possessing the skills needed to use AI.Without such skills,workers may be at a disadvantage and may experience lower compensation and reduced employment prospects.Last,at lower levels of exposure,complementarity bec

288、omes less relevant,because the tasks in an occupation that are likely to be either supported or replaced by AI are less integral components of the job itself(see Annex 2 for additional details).This box was prepared by Carlo Pizzinelli.Box Figure 1.1.Conceptual Diagram of AI Occupational Exposure(AI

289、OE)and Complementarity()Sources:Felten,Raj,and Seamans(2021);Pizzinelli and others(2023);and IMF staff calculations.Note:Red reference lines denote the median of AIOE and compementaity.STAFF DISCUSSION NOTES Gen-AI:Artificial Intelligence and the Future of Work INTERNATIONAL MONETARY FUND 25 Box 2.A

290、rtificial-Intelligence-Led Innovation and the Potential for Greater Inclusion Growing AI adoption has the potential to exacerbate cross-country and within-country inequality.This box argues,however,that there are also several avenues through which AI could be leveraged to foster inclusion in develop

291、ing economies.Enhancing inclusion in the delivery of public services that focus on boosting human capital,such as health care and education,as well as in agriculture and credit access,presents a promising avenue through which AI can augment productivity.One example is the transformative role of digi

292、talization in government technology(govtech).Historically,digitalization has helped modernize public finance by enhancing revenue collection and spending efficiency.It has also improved the delivery of social services,thereby fostering inclusion and reducing inequality(Amaglobeli and others 2023).No

293、tably,during COVID-19related lockdowns,nations such as Namibia,Peru,Zambia,and Uganda successfully used their digital infrastructure to expedite the distribution of financial aid.AI could amplify this wave of transformation by assisting in informed decision-making,identifying service gaps,detecting

294、fraud and corruption,and customizing local interventions.By streamlining bureaucratic tasks,AI tools could also free up time and resources,which could be better allocated to key sectors for inclusionfor example,agriculture,health care,and education.Interventions in these sectors benefit primarily th

295、e socially and economically vulnerable.In agriculture,AI could be leveraged to predict yields,optimize irrigation,and identify potential pests,thereby enhancing food security and productivity(IFC 2020).In health care,AI could assist in predictive analytics to foresee outbreaks,optimize resource allo

296、cation in hospitals,facilitate diagnoses,and make quality health care accessible and affordable even in areas with shortages of qualified medical staff(Wahl and others 2018;USAID 2019).In education,personalized learning experiences could be delivered through AI algorithms,reducing the human capital

297、divide in regions lacking qualified educators(UNESCO 2021).AI also holds the promise of advancing financial inclusion,specifically by using unconventional data to evaluate creditworthiness(IFC 2020).This would allow underserved communities to gain access to financial services that would otherwise be

298、 out of reach.Given the risks associated with AI technologiessuch as potential embedded bias and opaque outcomes(Shabsigh and Boukherouaa 2023)their deployment should be accompanied by stronger frameworks for monitoring and oversight(Boukherouaa and others 2021;FCA 2022).The expansion of digital fin

299、ancial services has historically been linked with increased inclusion.An IMF study(Sahay and ihk 2020)analyzed 52 emerging market and developing economies and underscored a marked rise in digital financial inclusion,with notable progress in Africa and Asia.COVID-19 further accelerated the growth of

300、digital financial services,which tend to benefit low-income households and small businesses while promoting economic growth and reducing inequality(Sahay and others 2017;Sahay and ihk 2020).While AI adoption promises transformative change,its successful implementation requires substantial investment

301、,political commitment,and safeguards for data security and privacy.This box was prepared by Giovanni Melina.STAFF DISCUSSION NOTES Gen-AI:Artificial Intelligence and the Future of Work INTERNATIONAL MONETARY FUND 26 Annex I.Data I.1 Descriptive Charts Annex Table 1.1.Data Sources for Stylized Facts

302、Figures Sources Economies Figure 1.Employment Shares by AI Exposure and Complementarity:1.Country Groups ILO 32 AEs,56 EMs,37 LICs Figure 1.Employment Shares by AI Exposure and Complementarity:2.Selected Countries ACS,GEIH,India PLFS,LMDSA,PNADC,UK LFS BRA,COL,GBR,IND,USA,ZAF Figure 2:Employment Sha

303、re by Exposure and Complementarity India PLFS,PNADC,and UK LFS BRA,GBR,IND Figure 3.Share of Employment in High-Exposure Occupations by Demographic Groups ACS,GEIH,India PLFS,LMDSA,PNADC,UK LFS BRA,COL,GBR,IND,USA,ZAF Figure 4.Share of Employment in High-Exposure Occupations and Potential Complement

304、arity by Income Deciles ACS,GEIH,India PLFS,LMDSA,Pizzinelli and others(2023),PNADC,and UK LFS BRA,COL,GBR,IND,USA,ZAF Figure 5.Occupational Transitions for College-Educated Workers for Brazil and the United Kingdom PNADC and UK LFS BRA,and GBR Figure 7.One-Year Reemployment Probability of Separated

305、 Workers PNADC and UK LFS BRA,GBR Figure 8:AI and Informality PNADC BRA Figure 12.AI Preparedness Index and Employment Share in High-Exposure Occupations FI,ILO,ITU,UN,UPU,WB,WEF 32 AEs,56 EMs,37 LICs Figure 13.Information and Communications Technology Employment Share and Individual Components of t

306、he AI Preparedness Index FI,ILO,ITU,UN,UPU,WB,WEF 35 AEs,67 EMs,40 LICs Box Figure 1.1:Conceptual Diagram of AI Occupational Exposure(AIOE)and Complementarity()Felten,Raj,and Seamans(2021),Pizzinelli and others(2023)Source:IMF staff.Note:Survey year considered:2019 for USA,ZAF,IND;2022 for COL,GBR,B

307、RA.Regarding survey sample size,2,239,553 for USA,238,251 for GBR,1,923,188 for BRA,919,459 for COL,69,420 for ZAF,420,720 for IND.American Community Survey(ACS);Gran Encuesta Integrada de Hogares(GEIH);India Periodic Labour Force Survey(PLFS);International Labour Organization(ILO);Labour Market Dyn

308、amics in South Africa(LMDSA);Pesquisa Nacional por Amostra de Domiclios Contnua(PNADC);UK Labour Force Survey(LFS).AEs=advanced economics;EMs=emerging markets;LICs=low-income countries.Country names use International Organization for Standardization(ISO)country codes.STAFF DISCUSSION NOTES Gen-AI:Ar

309、tificial Intelligence and the Future of Work INTERNATIONAL MONETARY FUND 27 I.2 Country Coverage Annex Table 1.2.Country Sample Coverage ISO3 Country Income Group ISO3 Country Income Group ISO3 Country Income Group SSD South Sudan LIC BOL Bolivia EM GEO Georgia EM AFG Afghanistan LIC IRN Iran EM SYC

310、 Seychelles EM CAF Central African Republic LIC PRI Puerto Rico AE MEX Mexico EM SOM Somalia LIC BGD Bangladesh LIC OMN Oman EM MRT Mauritania LIC SLV El Salvador EM QAT Qatar EM SDN Sudan LIC GTM Guatemala EM THA Thailand EM TCD Chad LIC EGY Egypt EM SRB Serbia EM LBY Libya EM SEN Senegal LIC CRI C

311、osta Rica EM COD Congo,Democratic Republic of the LIC MAC Macao SAR AE TUR Trkiye EM STP So Tom and Prncipe LIC PRY Paraguay EM URY Uruguay EM YEM Yemen LIC BWA Botswana EM KAZ Kazakhstan EM ETH Ethiopia LIC LBN Lebanon EM RUS Russia EM COM Comoros LIC SUR Suriname EM HUN Hungary EM MOZ Mozambique L

312、IC NAM Namibia EM SAU Saudi Arabia EM AGO Angola EM BLZ Belize EM BGR Bulgaria EM GNB Guinea-Bissau LIC GUY Guyana EM HRV Croatia AE HTI Haiti LIC GHA Ghana LIC GRC Greece AE IRQ Iraq EM KGZ Kyrgyz Republic LIC ROU Romania EM VEN Venezuela EM TLS Timor-Leste LIC CHL Chile EM COG Congo,Republic of LI

313、C BIH Bosnia and Herzegovina EM SVK Slovak Republic AE PNG Papua New Guinea LIC MAR Morocco EM POL Poland EM BDI Burundi LIC CPV Cabo Verde EM ITA Italy AE MLI Mali LIC JAM Jamaica EM ARE United Arab Emirates EM SLE Sierra Leone LIC TTO Trinidad and Tobago EM MYS Malaysia EM SYR Syria EM LKA Sri Lan

314、ka EM CYP Cyprus AE ZWE Zimbabwe LIC RWA Rwanda LIC LVA Latvia AE MDG Madagascar LIC BTN Bhutan LIC SVN Slovenia AE SWZ Eswatini EM ECU Ecuador EM CHN China EM BFA Burkina Faso LIC KEN Kenya LIC PRT Portugal AE TGO Togo LIC FJI Fiji EM CZE Czech Republic AE DJI Djibouti LIC BHS Bahamas,The EM ESP Sp

315、ain AE GAB Gabon EM KWT Kuwait EM MLT Malta AE GIN Guinea LIC TUN Tunisia EM LTU Lithuania AE MDV Maldives EM DOM Dominican Republic EM TWN Taiwan Province of China AE NER Niger LIC BLR Belarus EM BEL Belgium AE MMR Myanmar LIC AZE Azerbaijan EM IRL Ireland AE LAO Lao P.D.R.LIC ARG Argentina EM FRA

316、France AE NIC Nicaragua LIC MDA Moldova LIC ISL Iceland AE NGA Nigeria LIC VNM Vietnam LIC HKG Hong Kong SAR AE MWI Malawi LIC MKD North Macedonia EM NOR Norway AE CMR Cameroon LIC JOR Jordan EM CAN Canada AE HND Honduras LIC MNG Mongolia EM AUT Austria AE VCT St.Vincent and the Grenadines EM COL Co

317、lombia EM ISR Israel AE UZB Uzbekistan LIC PER Peru EM KOR Korea AE NPL Nepal LIC IND India EM AUS Australia AE TZA Tanzania LIC ARM Armenia EM GBR United Kingdom AE UGA Uganda LIC BRN Brunei Darussalam EM JPN Japan AE LSO Lesotho LIC ZAF South Africa EM LUX Luxembourg AE GMB Gambia,The LIC PHL Phil

318、ippines EM SWE Sweden AE BEN Benin LIC PAN Panama EM DEU Germany AE CIV Cte dIvoire LIC BRA Brazil EM NZL New Zealand AE TJK Tajikistan LIC MNE Montenegro EM CHE Switzerland AE PAK Pakistan EM BRB Barbados EM FIN Finland AE KHM Cambodia LIC UKR Ukraine EM EST Estonia AE LBR Liberia LIC BHR Bahrain E

319、M NLD Netherlands,The AE DZA Algeria EM IDN Indonesia EM USA United States AE ZMB Zambia LIC MUS Mauritius EM DNK Denmark AE LCA St.Lucia EM ALB Albania EM SGP Singapore AE STAFF DISCUSSION NOTES Gen-AI:Artificial Intelligence and the Future of Work INTERNATIONAL MONETARY FUND 28 Annex 2.Additional

320、Information on AI Occupational Exposure and Potential Complementarity Annex Figure 2.1,panel 1,plots the distribution of AI occupational exposure(AIOE)and complementarity for individual occupations within each major occupational group(that is,4-digit occupation within each major group of the Interna

321、tional Standard Classification of Occupations ISCO-08 classification).As is evident,some occupational groups are,on average,characterized both by high exposure and high complementarity,such as professionals,managers,and technicians.Others have both high exposure and low complementarity,such as cleri

322、cal workers.Another important observation is that,in general,compared with exposure,the dispersion of potential complementarity is larger within than across occupational groups,suggesting that the factors that may determine complementarity are cut across the spectrum of jobs.Given potential compleme

323、ntarity,a complementarity-adjusted AI occupational exposure(C-AIOE)measure can be constructed as follows:C-AIOE=AIOE*(1 MIN).The adjustment lowers exposure for occupations with higher values of relative to the occupation with the lowest complementarity(MIN).Annex Figure 2.1,panel 2 compares AIOE and

324、 C-AIOE.For professionals and managers,the average exposure is much lower after the complementarity adjustment.Meanwhile,clerical occupations,on average,have the highest complementarity-adjusted exposure,suggesting that they are the most vulnerable to disruption.Last,for occupational groups with ave

325、rage exposure that was already low,the adjustment does not substantially change their relative position in the ranking compared with the unadjusted measure.Annex Figure 2.1.AI Complementarity and Exposure across Major Occupational Groups 1.AIOE and Complementarity ()2.AIOE and C-AIOE Sources:Felten,

326、Raj,and Seamans(2021);Pizzinelli and others(2023);and IMF staff calculations.Note:The figure plots the distribution of the values of complementarity,unadjusted exposure AIOE(AI occupational exposure),and adjusted exposure C-AIOE(C for complementarity)across occupations specified by ISCO-08 codes.The

327、 boundaries of the whiskers is based on the 1.5 IQR value.The grouping is at the 1-digit ISCO-08 code level.ISCO=International Standard Classification of Occupations.*Technicians and associate professionals;*skilled agricultural,forestry,and fishery workers;*plant and machine operators and assembler

328、s.STAFF DISCUSSION NOTES Gen-AI:Artificial Intelligence and the Future of Work INTERNATIONAL MONETARY FUND 29 Annex 3.Methodology for the Worker Transition Analysis III.1 Data To analyze worker reallocation between occupations in Section III,this note uses the panel structure of the UK Labor Force S

329、urvey(LFS)and Brazils Pesquisa Nacional por Amostra de Domiclios Contnua(PNADC,National Continuous Household Sampling Survey).Both surveys have a similar design:households are interviewed quarterly,and they remain in the sample for five quarters(rolling replacement survey).Although the PNADC survey

330、identifies households across quarters,it does not identify the number of people within households.Thus,a matching algorithm must be used to identify individuals across quarters based on individual characteristics.The note uses the algorithm proposed by Ribas and Soares(2008)and implemented by Datazo

331、om.III.2 Constructing Worker Flows Using the panel data,it is possible to estimate the employment flows and construct the transition matrices shown in Annex Table 3.1.A transition from unemployment to inactivity(U2N),for example,is defined as happening when a worker is inactive in the current quarte

332、r but was unemployed in the previous quarter.Similarly,a transition from high-exposure employment to low-exposure employment(HE2LE)is defined as happening when a worker is employed in an occupation code with exposure above the median in the current quarter but was employed in an occupation code with

333、 exposure below the median in the previous quarter.An occupational switch,or transition,is defined as happening when a worker reports an occupation code in the quarter that differs from the occupation code reported in the previous quarter.This includes both job-to-job transitions(when the worker changes employer)and on-the-job transitions(when the worker switches occupations but remains with the s