Low-skill labor-intensive tradables. equipment

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4 Average blue collar share (%) Commodity-based regional processing Manufacture of wood and wood products Manufacture of textiles, wearing apparel and leather products Manufacture of fabricated metals products Manufacture of other non-metallic Manufacture of basic metals Manufacture of furniture; mineral products manufacturing, n.e.c Manufacture of food, beverage and Manufacture of rubber and plastic Manufacture of transportation tobacco products products equipments Manufacture of electrical Manufacture of paper and paper equipment Manufacture of machinery and products; publishing and printing equipment n.e.c Manufacture of chemicals and chemical products Medium skill global innovators Manufacture of coke and refined petroleum products Capital-intensive regional processing Low-skill labor-intensive tradables Bubble size: Sector employment share in total manufacturing jobs (%) Yellow: Labor productivity Blue: High R&D Computing, electronics and optical equipment Manufacture of pharmaceutical products High skill global innovators Export value-to-output ratio (%) Sources: Calculations based on United Nations Industrial Development Organization (UNIDO) Industrial Statistics (INDSTAT) database; UN Comtrade database; University of Minnesota s Integrated Public Use Microdata Series (IPUMS) International database.

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8 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% China EAP ECA LAC MNA SAS SSA HIC Source: World Development Indicators database. Countries categorized by income level in China EAP ECA LAC MNA SAS SSA HIC Sources: ILOSTAT database, International Labour Organization (ILO); Key Indicators of the Labour Market (KILM) database, ILO; Groningen Growth and Development Centre (GGDC) 10-sector database, University of Groningen, Netherlands. HIC categorized by income level in 1994.

9 Ratio of change in absolute term of MVA (constant 2010 USD) UKR ARM TJK BRA LAO CHN 7 OMN LBR 6 MOZ QAT VNM BIH AFG GEO 5 YEM ETH UGA ALB RWA BGD SDN BLR 4 NGA IND SAU TZA AGO KOR IRN COG ARE TTO BWA JOR CZE EST LTU TKM SGP 3 MNG MYS PAK LSO BHR EGY TUR ZMB KAZ IRL LKA DOM BOLLBN PHL IDN NIC NER MKD GHASWE TGOPER SOMTHA CMR TUN PNG GAB 2 SWZ HND DZA ROU FIN ISR SVN ZAF MUS MWI CHL ECU PSE NPL SLV SEN MAR AUT CRI NLD URY FRA NOR JPN LVA ARG BEL AUS DNKCAN CHE NAM RUS MEX CUB GTM BFA KGZ GIN PRYCOL GMB KEN MLI MDG BDI PAN ERI BGR SRB VEN ESP DEU PRT PRI BEN NZL GBR ITAUSA MDA CAFHTI CIV GNB MRT SLE TCD HRV 1 AZE GRC IRQ JAM PRK SYR CYP UZB ZWE HKG LBY POL HUN SVK Change in the Share of Manufacturing Value Added (% points) HIC UMC LMIC LIC Source: World Development Indicators database. 0

10 middle-income UK DNK FIN CAN USA -6 SVK HUN CZE KOR MYS Food and beverages Computer and electronics Textiles and wearing apparel Transport equipment Food and beverages Computer and electronics Textiles and wearing apparel Transport equipment Source: United Nations Industrial Development Organization (UNIDO) Industrial Statistics (INDSTAT) database.

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12 Use of manufacturing robots over the years Funiture Motor vehicles, trailers and semi-trailers Office, Accounting and Computing machinery Electrical Machinery Machinery and equipment Fabricated Metal Prodcuts Basic Metals Non Metallic Products Rubber and Plastics Chemical and Chemical Products Pharmaceuticals, cosmetics Paper Wood and Furniture

13 Foreign value added, % of exports (WIOD 2013) Foreign value added, % of exports (WIOD 2016) Manufacturing import content, % of manufacturing exports (WITS, WIOD) Sources: Calculations based on World Input-Output Database (WIOD) (2013 and 2016 releases) and World Integrated Trade Solution (WITS) database Compound annual growth rate: electrical computer machinery fabricated metal transport equip rubber pharma non-metallic paper textiles chemicals furniture basic metal machinery wood food coke Compound annual growth rate: Source: WITS UN Comtrade. Note: Bubbles above the 45-degree line denote faster growth of trade in period relative to

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15 Number of robots per 1000 employees Servicification Food, beverages and tobacco products 3. Automation; rising exports Rubber and plastics products Fabricated metals Transport equipment Computer, electronics and optical equipment Electrical machinery and equipment 1. All 3 trends Furniture; manufacturing n.e.c. Bubble size: intensity of use of professional services: Large=high; Small=low Color: export intensity: Gold=high; Green=low Basic metals Machinery and equipment n.e.c. Wood and wood products Pharmaceutical products Coke and refined petroleum Paper and paper products; printing Other non-metallic mineral 5. Limited impact of products Textiles, wearing apparel and trends leather products Chemicals and chemical products 2. Export concentration Herfindahl Index Sources: Calculations based on United Nations Industrial Development Organization (UNIDO) Industrial Statistics INDSTAT database; International Federation of Robotics (IFR) World Robotics database; and UN Comtrade database.

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17 Source: Ahmed and Chen Laos Vietnam Russia Kenya Armenia Bolivia Chile Poland Turkey Colombia Korea Ukraine Cyprus Israel Lithuania Singapore Czech Republic France Japan Italy Ireland Greece Spain New Zealand United States China (Yunnan Province) Belgium Slovak Republic Sweden Slovenia Netherlands United Kingdom Denmark Germany Norway LMC UMC HIC Share of workers at high risk of automatibility (>70%) %

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19 Competitiveness Capabilities Connectedness Business environment Infrastructure Institutions Facilitate adjustment Workers skills Management capabilities Trade in goods Logistical performance New business models, new contracting, competition law Mobile finance Digital skills, creativity Enabling framework for data ecosystem Trade in services International data flow

20 Capabilities 2.0 USA CHE High Competitiveness KOR 1.5 NZL FIN Medium Competitiveness DNK JPN SWE GRC FRA Low Competitiveness SGP CAN GBR ESP BEL NOR 1.0 BLR AUS AUT DEU SVN HUN ISR TUR ARG RUS ITA PRT CZECHL EST HKG HRV LTU VEN BGR LVA POL 0.5 UKR TTO URY SRB SAU CYP BHR SVK BRA CRI LBN THA CHN MKD MDA 0.0 COL ARMALB MNG KAZ MUS ARE AZE BIH GEO DOM MYS PAN 0.3 JOR TUN SLV MEX PER 0.8 ECU EGY PHL JAM KGZ DZA ZAF VNM IND IDN PRY -0.5GTMMAR HND BOL OMN QAT MLI KWT BWA IRQ AGO YEM CMRBEN KHM LKA LSOKENAM PAK BGD TGO RWA TZA ETH NPL GIN GHA ZMB UGA NER BFA NIC AFGSENZWE CIV PNG MDG BDI MOZ NGA -1.0 COG MRT GMB MWI -1.5 Connectedness Sources: Calculations based on Kee, Nicita, and Olarreaga 2009; International Telecommunications Union s ICT Indicators Database; and the following World Bank databases: World Development Indicators, Worldwide Governance Indicators, Global Findex, Logistics Performance Index, and Services Trade Restrictiveness Index.

21 2.5 Capability Legend 1. Color = Competitiveness High Medium Low 2. Shape = risk of disruption given current 3Cs and expected changes O = low risk X = higher risk TUN LBN PHL MYS JOR INDMMR TUR BGR 0.5 SRB CRI THA MDA 0ARM CHN MUS PAN MEX -0.5 LSO -1 MAR LKA NAM COG ARG KOR HND PAK ITA CYP BIHDOM DNK JPN USA AUT DEU BWA NIC FIN GRC FRA SGP CAN GBR ESP BEL SVN HUN PRT CZE HRVEST LTU LVA SVK VNM ISR POL CHE SWE HKG -1.5 Connectedness Sources: Calculations based on Kee, Nicita, and Olarreaga 2009; International Telecommunications Union s ICT Indicators Database; and the following World Bank databases: World Development Indicators, Worldwide Governance Indicators, Global Findex, Logistics Performance Index, and Services Trade Restrictiveness Index.

22 2 Legend 1. Color = Competitiveness High Medium Low 2. Shape = risk of disruption given current 3Cs and expected changes O = low risk X = higher risk DNK ESP Capabilities SYR AFG EGY TUN ETH NPL MLI JOR IND MMR TUR MDA BGR IDN -0.5 GTM SRB CHN ALB PAN MAR MDG KHM LSOKEN LKA -1 SLV PAK MKD ITA BIH DOM KGZ BGD NIC UGA EST PRT HRVLTU MUS Connectedness VNM URY Sources: Calculations based on Kee, Nicita, and Olarreaga 2009; International Telecommunications Union s ICT Indicators Database; and the following World Bank databases: World Development Indicators, Worldwide Governance Indicators, Global Findex, Logistics Performance Index, and Services Trade Restrictiveness Index.

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