•

•

•

•

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Manufacture of textiles, wearing apparel and leather products

Manufacture of furniture; manufacturing, n.e.c

Manufacture of wood and wood products

Computing, electronics and optical equipment

Manufacture of other non-metallic mineral products

Manufacture of paper and paper products; publishing and printing

Manufacture of machinery and equipment n.e.c

Manufacture of fabricated metals products

Manufacture of pharmaceutical products

Manufacture of food, beverage and tobacco products

Manufacture of rubber and plastic products

Manufacture of transportation equipments

Manufacture of basic metals

Manufacture of electrical equipment

Manufacture of chemicals and chemical products

Manufacture of coke and refined petroleum products

20

30

40

50

60

70

80

90

100

0 10 20 30 40 50 60 70 80 90

Export value-to-output ratio (%)

Average blue collar share

(%)

Manufacture of textiles, wearing apparel and leather products

Manufacture of furniture; manufacturing, n.e.c

Manufacture of wood and wood products

Computing, electronics and optical equipment

Manufacture of other non-metallic mineral products

Manufacture of paper and paper products; publishing and printing

Manufacture of machinery and equipment n.e.c

Manufacture of fabricated metals products

Manufacture of pharmaceutical products

Manufacture of food, beverage and tobacco products

Manufacture of rubber and plastic products

Manufacture of transportation equipments

Manufacture of basic metals

Manufacture of electrical equipment

Manufacture of chemicals and chemical products

Manufacture of coke and refined petroleum products

20

30

40

50

60

70

80

90

100

0 10 20 30 40 50 60 70 80 90

Export value-to-output ratio (%)

Average blue collar share

(%) Bubble size: Sector employment share in total manufacturing jobs (%)

Yellow: Labor productivity

Blue: High R&D

Low-skill labor-intensivetradables

High skill global innovators

Medium skill global innovators

Capital-intensiveregional processing

Commodity-based regional processing

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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•

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

China EAP ECA LAC MNA SAS SSA HIC1994 2000 2005 2010 2015

0

5

10

15

20

25

30

35

40

45

China EAP ECA LAC MNA SAS SSA HIC

1990 2000 2010Source: World Development Indicators database. Countries categorized by income level in 1994 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.

ARE

AUS

AUTBEL

CANCHE

CYP

DEUDNK ESP

FINFRA

GBR

HKG

IRL

ISR

ITAJPNKWTNLDNOR

NZL PRT

QAT

SGP

SWE

USASRBARG

BHR

BRA

CHL

CZE

GAB

GRC

HUN

KOR

LBY

MEXMUS

MYS

OMN

PRI

SAU

SVN

TTO

URYZAF

AGO

BGR

BLR

BOL

BWA

COLCRICUB

DOM

DZA ECU

EST

GTM

HRV

IDN

IRN

IRQJAM

JOR

KAZLBN

LTU

LVAMAR

MDA

MKDNAM

PAN

PERPHLPNG

POL

PRK

PRYPSEROU RUSSLV

SVK

SWZ

SYR

THA

TKM

TUN

TUR

UKR

UZB

VEN

AFG

ALB

ARM

AZE

BDI BENBFA

BGD

BIH

CAF

CHN

CIV

CMR

COG

EGY

ERI

ETH

GEO

GHA

GINGMBGNB

HND

HTI

IND

KENKGZ

LAO

LBR

LKALSO

MDGMLI

MNG

MOZ

MRT

MWI

NER

NGA

NIC

NPL

PAK

RWA SDN

SEN

SLE

SOM

TCD

TGO

TJK

TZA

UGA

VNM

YEM

ZMB

ZWE

0

1

2

3

4

5

6

7

-30 -25 -20 -15 -10 -5 0 5 10 15Change in the Share of Manufacturing Value Added (% points)

HIC UMC LMIC LIC

Rat

io o

f cha

nge

in a

bsol

ute

term

of M

VA

(con

stan

t 201

0 U

SD)

Source: World Development Indicators database.

middle-income

-4

-3

-2

-1

0

UK DNK FIN CAN USA

Food and beverages Textiles and wearing apparel

Computer and electronics Transport equipment

-6

-3

0

3

6

SVK HUN CZE KOR MYS

Food and beverages Textiles and wearing apparel

Computer and electronics Transport equipment

Source: United Nations Industrial Development Organization (UNIDO) IndustrialStatistics (INDSTAT) database.

0

200000

400000

600000

800000

1000000

1200000

19

93

19

95

19

97

19

99

20

01

20

03

20

05

20

07

20

09

20

11

20

13

20

15

Use of manufacturing robots over the years Funiture

Motor vehicles, trailers andsemi-trailersOffice, Accounting andComputing machineryElectrical Machinery

Machinery and equipment

Fabricated Metal Prodcuts

Basic Metals

Non Metallic Products

Rubber and Plastics

Chemical and ChemicalProductsPharmaceuticals, cosmetics

Paper

Wood and Furniture

60

80

100

120

19

95

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

Foreign value added, % of exports(WIOD 2013)

Foreign value added, % of exports(WIOD 2016)

Manufacturing import content, % ofmanufacturing exports (WITS, WIOD)

food

textiles

wood

paper

coke

chemicals

pharmarubber

non-metallic

basic metal

fabricated metal

machinery

computerelectrical

machinery

transport equip

furniture

1

6

11

16

21

26

31

36

41

1 3 5 7 9 11 13 15C

om

po

un

d a

nn

ual

gro

wth

rat

e: 1

98

8-2

00

0Compound annual growth rate: 2001-2014

Source: WITS UN Comtrade. Note: Bubbles above the 45-degree line denote faster growth of trade in period 1988-2000 relative to Sources: Calculations based on World Input-Output Database (WIOD) (2013 and 2016 releases) and World Integrated Trade Solution (WITS) database.

Computer, electronics and optical equipment

Pharmaceutical products

Furniture; manufacturing n.e.c.

Textiles, wearing apparel and leather products

Machinery and equipment n.e.c.

Transport equipment

Electrical machinery and equipment

Chemicals and chemical products

Rubber and plastics products

Coke and refined petroleum

Basic metals

Paper and paper products; printing

Fabricated metals

Wood and wood products

Food, beverages and tobacco products

Other non-metallic mineral products

0.01

0.10

1.00

10.00

100.00

0.00 0.05 0.10 0.15 0.20

Nu

mb

er o

f ro

bo

ts p

er 1

00

0 e

mp

loye

es

Herfindahl Index

Bubble size: intensity of use of professional services: Large=high; Small=low

Color: export intensity: Gold=high; Green=low

1. All 3 trends

5. Limited impact of trends

3. Automation; rising exports

4. Servicification

2. Export concentration

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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0 2 4 6 8 10

NorwayGermanyDenmark

United KingdomNetherlands

SloveniaSweden

Slovak RepublicBelgium

China (Yunnan Province)United StatesNew Zealand

SpainGreeceIreland

ItalyJapan

FranceCzech Republic

SingaporeLithuania

IsraelCyprus

UkraineKorea

ColombiaTurkeyPoland

ChileBolivia

ArmeniaKenyaRussia

VietnamLaos

Share of workers at high risk of automatibility (>70%) %

LMC UMC HIC

Source: Ahmed and Chen 2017.

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•

Competitiveness

Business environment

Infrastructure

Institutions

Facilitate adjustment

New business models, new contracting, competition law

Mobile finance

Capabilities

Workers skills

Management capabilities

Digital skills, creativity

Enabling framework for data ecosystem

Connectedness

Trade in goods

Logistical performance

Trade in services

International data flow

DEU

SWE

BELGBR

AUT

FIN

FRADNK

CZE

CHE

LTUITA

NORESP

POL

HUNEST

LVASVK

PRT

SVN

CAN

ISR

USA

ARE

HKG

QAT

CYP

JPN

BHR

OMN

KOR

MNG

ZAF

BWA

SGP

RWA

CHL

MUS

AUS

MYS

NZL

HRV

GRC

TUR

SAU

BGR

SRB

BIH

KWT

MKDCHNMDAGEO

KAZALB

AZE

BLR

TUNIND

KEN

MAR

PAN

THA

RUS

UGA

CRI

VNM

UKR

GHALKANAM

SLVJOR MEX

ARM DOM

IDN

PER

ZMB

JAMPHL

TTO URY

PRY

COLBRA

LSOTZA

LBN

PAK

KGZ

BFANER

ECU

BGDMLI

TGOYEM NICBDI MOZ

BEN

HND

KHMIRQ

MWI

GTM

NPL

EGY

ETH GIN

VEN

DZA

AFGCOG

ARG

GMB

CMRCIV

BOL

NGA

AGO

MRTPNG

MDGZWESEN

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

-2.3 -1.8 -1.3 -0.8 -0.3 0.3 0.8 1.3 1.8

Cap

abili

tie

s

Connectedness

High Competitiveness

Medium Competitiveness

Low Competitiveness

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, WorldwideGovernance Indicators, Global Findex, Logistics Performance Index, and Services Trade Restrictiveness Index.

DEU

SWE

BELGBR

AUT

FIN

FRADNK

CZE

CHE

LTUITA

ESP

POL

HUNEST

LVASVK

PRT

SVN

CAN

ISR

USA

HKG

CYP

JPNKOR

SGP

BWA

MUSMYS

HRV

GRC

TUR

BGR

SRB

BIHCHNMDA

TUNIND MAR

PAN

THACRI

VNM

LKANAM

JOR MEX

ARM DOM

PHL

LSO

LBN

PAK NIC

HNDMMR

COG

ARG

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

-1.5 -1 -0.5 0 0.5 1 1.5

Cap

abili

ty

Connectedness

Legend1. Color = Competitiveness

2. Shape = risk of disruption given current 3Cs and expected changes

O = low risk X = higher risk

High Medium Low

Sources: Calculations based on Kee, Nicita, and Olarreaga 2009; International Telecommunications Union’sICT Indicators Database; and the following World Bank databases: World Development Indicators, WorldwideGovernance Indicators, Global Findex, Logistics Performance Index, and Services Trade Restrictiveness Index.

DNK

LTUITA

ESP

ESTPRT

MUS

HRV

SRB

BIH

MKDCHNALB

MAR

PAN

UGA

VNMSLV

DOM

URY

TUR

BGR

MDA

TUNIND

KENLKA

JOR

IDN

LSO PAK

KGZ

BGDMLI

NICKHM

GTM

NPL

EGY

ETHAFG

MMR

SYR

MDG

-1

-0.5

0

0.5

1

1.5

2

-2 -1.5 -1 -0.5 0 0.5 1 1.5

Cap

abili

tie

s

Connectedness

High Medium Low

Legend1. Color = Competitiveness

2. Shape = risk of disruption given current 3Cs and expected changes

O = low risk X = higher risk

Sources: Calculations based on Kee, Nicita, and Olarreaga 2009; International Telecommunications Union’sICT Indicators Database; and the following World Bank databases: World Development Indicators, WorldwideGovernance Indicators, Global Findex, Logistics Performance Index, and Services Trade Restrictiveness Index.

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