WEF_Future_of_Jobs_2023

(SERGIO PINHEIROFdUjs8) #1
Trend outlook

GTrelnodbs amol (^) stt rmeonsdt likse (^) lay nto ddr (^) ivteh iendiurs itmry tpranascfotr (^) moatnio (^) njo anbd cthreier eaxptieocnted impact job creation, ordered by net
effect (share of organizations surveyed)
Job creator Job displacer Net effect Global net effect
B- 1 r 0 o 0 %ader application of Environmental, Social and Governance (ESG) (^) +s 1 t 0 an 0 %dards
67 %
I-n 10 v 0 e%stments to facilitate the green transition of your business + 100 %
59 %
I-n 10 c 0 %reased adoption of new and frontier technologies + 100 %
53 %
B- 1 r 0 o 0 %adening digital access + 100 %
35 %
C- 10 o 0 n%sumers becoming more vocal on environmental issues + 100 %
34 %
R- 1 i 0 s 0 %ing cost of living for consumers + 100 %



  • 18 %
    S- 1 u 00 p%ply shortages and/or rising cost of inputs for your business + 100 %

  • 20 %
    S- 1 l 0 o 0 %wer global economic growth + 100 %

  • 63 %


TTeecchnholnogoielso mgoisets m (^) oastn lidke (^) lty htoe dirriv (^) eim indpusatcryt tr (^) anosnfo (^) rjomatbio cnr anead tthieoirn expected impact job creation, ordered
by net effect (share of organizations surveyed)
Job creator Job displacer Net effect Global net effect
E- 1 n 00 c%ryption and cybersecurity + 100 %
46 %
D- 10 ig 0 %ital platforms and apps + 100 %
45 %
B- 10 ig 0 %-data analytics + 100 %
44 %
I-n 10 te 0 %rnet of things and connected devices + 100 %
38 %
E- 1 d 00 u%cation and workforce development technologies + 100 %
30 %
C- 10 lo 0 %ud computing + 100 %
29 %
E- 1 - 00 c%ommerce and digital trade + 100 %
21 %
A- 1 r 0 t 0 i%ficial intelligence (e.g. machine learning, neural networks) + 100 %
18 %
Role outlook
CFivhe-uyerarn s itnru (^) ctfiurvale lab yeouar-rfosrce churn (percent) 21 %
Global 23 %
KRoelesy mrooslte ssele fcoterd bbyu osrgianniezsatsio (^) nstr saurnvseyfeodr (asm eaithtieor ngrowing, stable or declining), ordered by net role
growth, and their net growth and structural churn (percent)
ROLES NET^ GROWTH ECCOHNURONMY GCLHOUBRANL
AI and Machine Learning Specialists^90 %^40 %
Business Intelligence Analysts^48 %^35 %
Devops Engineers^21 %^26 %
Human Resources Specialists^11 %^12 %
Accountants and Auditors^14 %^19 %
Business Development Professionals^13 %^24 %
SMalanufes Ractepurreinsge,n Ttatecivhensic, (^) alW andhole sSalceie andntific... 6 % 14 %
General and Operations Managers^7 %^14 %
ACclecroksunting, Bookkeeping and Payroll 8 % 29 %
Assembly and Factory Workers^17 %^17 %
Data Entry Clerks^41 %^42 %
Administrative and Executive Secretaries^49 %^35 %
Contextual indicators
INDICATORS
Labour force participation 61 %
Vulnerable employment 16 %
Share of youth not in employment, education, or
training (NEET)^11 %
Unemployment rate 3 %
Unemployment rate among workers with basic
eUdnue.mployment rate among workers with^7 %
advanced edu.^2 %
Secondary Education Attainment 88 %
Tertiary Education Attainment 28 %
E 1 =asCoemp oanfi (^) efis ncdaninnotg e (^) asskilyi fillendd s (^) kelilemd pemloployyeeeess, 7 i=nC loomcpanalie lsab cano euasr (^) ilym fiarnd kskeilletd employees 3. 97
F 1 =ilCl (^) ovmacanciespanies canno (^) tb filly v (^) achanirinciegs (^) bfoy hreirinigg nfo (^) relabgin labouorur, 7 =Companies can fil lvacancies by hiring foreign
labour^3.^82
C 1 =oGuovnerntrmye (^) nint dvoeess tnmot ienvnestt iinn mmdi-icdar-ecerar traieneinrg (^) ,t 7 rai=Gnovinegrnment invests ni mid-career training 3. 04
W 5 +=oNrok geurarsan' (^) tReeig ofh rtigsh (^) tsI ndduee txo the breakdown of the rule of law, 1 =Sporadic violations of rights 3
L 10 e=vTheel woofr (^) stNat possioibnle alsc (^) oCreo, lomwepr llieance vels of cwomipthlian Labce, 0 =oTuher bResigt photsssible score, higher levels of
compliance^1
Economy Profile 1 / 2
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  • 50 % 0 50 %


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Working Age Population (Millions)
2 .
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