Upload
lamlien
View
222
Download
5
Embed Size (px)
Citation preview
•
•
•
•
••
•••
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.
•
••
•
•
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.
•
•
•
•
•
•
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.
•
•
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.
•
•
•
•
•
••
••
•••
••
•••