Patterns of Industrialization and effects of country-specific conditions
IPD/JICA Task Force on Industrial Policy and Transformation
Jordan, June 5-6, 2014
Nobuya Haraguchi
Motivations
• To understand the process of industrial development
• To identify development patterns and structural change
• To find out the way in which country-specific conditions affect country’s industrial development
Studying development characteristics of manufacturing industries
Not only the patterns but also the development characteristics of manufacturing industries
– Use of real value added per capita– Analysis on output (value added), employment
and labor productivity together– Their changes as countries develop– Changes over time (time effects)
Patterns of Manufacturing Developmentand
Shift of Comparative Advantage
Manufacturing development patterns
Model
RVA – real value added per capitaEMP – employment-population ratioLP – labour productivityRGDP – real GDP per capita (in constant PPP 2005)RGDP2 – real GDP per capita square, RGDP3 – real GDP per capita cubicαc – country fixed effect e – unexplained residuali – manufacturing industry (ISIC 2 digit level - 18 industries)
• Unbalanced panel dataTime series from 1963 – 2010; 75-110 countries depending on the industry
• Model applied to large (with population more than 12.5 million) and small country groups separately
• In addition, we assessed the effects of population density, natural resource endowment and time periods on industrial development.
ictRVAlnictEMPln
ictLPln
ict
icctctct eRGDPRGDPRGDP 3
42
321 ln*ln*ln*
Non-parametric approach for estimation
Algeria
Argentina
Australia
Bangladesh
Canada
Sri Lanka
ChinaColombia
FranceGermany
India
Indonesia
Italy
Japan
Republic of KoreaMalaysia
Mexico
Morocco
Nepal
Netherlands
Pakistan
Peru
Philippines
PolandRomaniaRussian Federation
Viet Nam
South Africa
Spain
Sudan
Thailand
Turkey
Uganda
Egypt
United KingdomUnited States of AmericaUnited States of America
Venezuela (Bolivarian Republic of)
VA per cap
ita (U
S$)
403 1,097 2,981 8,103 22,026 59,874Real GDP per capita (US$)
Large countries: Basic metals
Source: UNIDO estimate based on UNIDO INDSTAT2
Estimated pattern with actual country observations
Algeria
Argentina Australia
Bangladesh
Brazil
Canada
Sri Lanka
China
Colombia
Ethiopia
FranceGermany
India
Indonesia
Iran (Islamic Republic of)
Italy
Japan
Kenya
Republic of Korea
Malaysia
MexicoMorocco
Nepal
Netherlands
NigeriaPakistan
Peru
Philippines
PolandRomania
Russian Federation
Viet Nam
South Africa
Spain
SudanThailand
Turkey
Egypt
United KingdomUnited States of AmericaUnited States of America
Venezuela (Bolivarian Republic of)
VA per
cap
ita (U
S$)
403 1,097 2,981 8,103 22,026 59,874Real GDP per capita (US$)
Large countries: Textiles
95% confidence intervals (after anti-log)
Source: UNIDO estimate based on UNIDO INDSTAT2
VA
per
cap
ita (U
S$)
1,097 2,981 8,103 22,026Real GDP per capita (US$)
Food and beverages TextilesWearing apparel ChemicalsBasic metals Fabricated metalsElectrical machinery and apparatus Motor vehicles
Large countries
Food and beverage
Textiles Wearing apparel
Chemical
Electrical machinery and apparatus
Motor vehicles
Fabricated metals
Basic metals
Development patterns
VA p
er c
apita
(US
$)
1,097 2,981 8,103 22,026Real GDP per capita (US$)
Food and beverages TextilesWearing apparel ChemicalsBasic metals Fabricated metalsElectrical machinery and apparatus Motor vehicles
Small countries
Textiles
Wearing apparel
Food and beverage
Chemical
Electrical machinery and apparatus
Fabricated metals
Basic metals
Motor vehicles
Development patterns
Motor vehiclesPrecision instruments
Electrical machinery and apparatusMachinery and equipment
Fabricated metalsBasic metals
Non-metallic mineralsRubber and plastic
ChemicalsCoke and refined petroleum
Printing and publishingPaper
Wood productsWearing apparel
TextilesTobacco
Food and beverages
0 10000 20000 30000 40000 50000
Large countries (population more than 12.5 million)
ela > 21<ela<20<ela<1ela = 0
Source: UNIDO estimate based on UNIDO INDSTAT2
Changes in growth rates
EP ∆∆ ∆∆ ∆∆ ∆∆ ∆ ∆ ∆ ∆ ∆ ∆ ∆ ∆ − − − − − − − − −VA ++ ++ ++ ++ ++ + + + + + + + + ∆∆ ∆∆ ∆∆ ∆∆ ∆∆ ∆∆ ∆∆ ∆∆LP ++ + + + + + + ∆∆ ∆∆ ∆∆ ∆∆ ∆∆ ∆∆ ∆∆ ∆∆ ∆∆ ∆∆ ∆∆ ∆∆ ∆∆ ∆∆
EP ∆ ∆ ∆ ∆ ∆ − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − −
VA + ++ ++ ++ ++ + + + + ∆∆ ∆∆ ∆∆ ∆∆ ∆ ∆ ∆ ∆ − − − −LP ++ + + + + + + + + + + + + + + + + + + + +
EP + ++ ++ ++ ++ + + ∆∆ ∆∆ ∆ ∆ ∆ − − − − − − − − − − − − − − − − − − − −
VA +++ +++ +++ ++ ++ + + ∆∆ ∆∆ ∆∆ ∆ ∆ ∆ ∆ − − − − − − −LP − ∆ ∆ ∆ ∆ ∆ ∆∆ ∆∆ ∆∆ ∆∆ ∆∆ ∆∆ ∆∆ ∆∆ ∆∆ ∆∆ ∆∆ ∆∆ ∆∆ ∆∆ ∆∆
Food
&
beve
rag
e
Texti
les
Wea
ring Ap
pare
l
+++ ++ + ∆∆∆ − −− e ≥ 2 2 > e ≥ 1.5 1.5 > e ≥ 1 1 > e ≥ 0.5 0.5 > e ≥ 0 0 > e ≥ - 0.5 - 0.5 > e ≥ -1 -1> e
1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 11000 12000 13000 14000 15000 16000 17000 18000 19000 20000 210000
0.1
0.2
0.3
0.4
0.5
0.6Employment, labour intensive industries
EP ra
tio
Wearing apparel
Food and beverage
Textiles
0
50
100
150
200
250
300
Value added, labour-intensive industriesVa
lue
adde
d pe
r cap
ita
Food & beverages
TextilesWearing apparel
6000 7000 8000 9000 10000 11000 12000 13000 14000 15000 16000 17000 18000 19000 20000 21000 22000 23000 24000 25000 26000
EP ∆∆ ∆∆ ∆∆ ∆∆ ∆∆ ∆ ∆ ∆ ∆ ∆ ∆ ∆ ∆ ∆ ∆ ∆ ∆ ∆ − − −
VA ++ ++ ++ ++ ++ + + + + + + + + + + + + + + + +
LP ∆∆ ∆∆ + + + + + + + + + + + + + + + + + + +
EP + + + ∆∆ ∆∆ ∆∆ ∆∆ ∆ ∆ ∆ − − − − − − − − − − − − − − − − − − − − −
VA +++ +++ +++ +++ +++ +++ +++ +++ +++ ++ ++ ++ ++ ++ ++ ++ ++ ++ ++ ++ ++
LP + + + + + + + ++ ++ ++ ++ ++ +++ +++ +++ +++ +++ +++ +++ +++ +++
+++ ++ + ∆∆∆ − −− e ≥ 2 2 > e ≥ 1.5 1.5 > e ≥ 1 1 > e ≥ 0.5 0.5 > e ≥ 0 0 > e ≥ - 0.5 - 0.5 > e ≥ -1 -1> e
Rubb
er
plas
ticEl
ectr
ical
m
achi
nery
0
0.05
0.1
0.15
0.2
0.25
0.3
Employment - late industriesEP
ratio
Rubber & plastic
Electrical machinery
0
50
100
150
200
250
300
350
400Value added - late industry
Valu
e ad
ded
per c
apita
Electrical machinery
Rubber & plastic
Effects of Country-Given Conditions
High Population Density High Resource Endowments
strongly
positive
Strongly PositiveMachinery and equipmentElectrical machinery and apparatus Motor vehicles ChemicalsRubber and plasticNon-metallic minerals Fabricated metals Food and beverages
Strongly Positive
Machinery and equipment
strongly negative
Textiles Paper Wood products Wearing apparel Tobacco Furniture, n.e.c.
Strongly Negative
Paper Rubber and plastic Non-metallic minerals Printing and publishing Wood products Food and beverages Motor vehicles Basic metals Chemicals Coke and refined petroleum Electrical machinery and apparatus Tobacco
Strongly Negative
Effects of Population Density and Resource Endowments on manufacturing value added
Large countries
Source: UNIDO estimate based on UNIDO INDSTAT2
Time Specific Effects
Emerging trends
.
.
.
.
Source: UNIDO estimate based on UNIDO INDSTAT2
Textiles
70s 80-85 85-90 90-95 95-00 00-05 05-10Tobacco - - - - - - -Textiles - - - - - - -Wood products - - - - - - -Coke, refined petro + - - - - -Chemical products - - - - -Non-metallic mineral - - - - -Basic metals - - - - -Fabricated metals - - - - - - -Machinery and equipment
+ + - - - -
Electrical machinery + + - - - -Motor vehicles - - - - -Furniture, nec + + + +
Emerging trends (Employment pattern)
Source: UNIDO estimate based on UNIDO INDSTAT2
70s 80-85 85-90 90-95 95-00 00-05 05-10Tobacco + - - - - -Textiles + - - -Wearing apparel + - - -Wood products + - - - - - -Paper + + + + + + -Chemical products + + + + + Rubber and plastic + + + + + + Basic metals + + + + + Electrical machinery + + + + + - +Motor vehicles + + + + + -
Emerging trends (Value added pattern)
Source: UNIDO estimate based on UNIDO INDSTAT2
Emerging characteristics of manufacturing industries since 1980
Emerging characteristics since
1980 Industry
Rising Rubber and plastic Declining Tobacco Textiles Paper Chemicals Non-metallic minerals Intensifying capital use Basic metals Fabricated metals
Electrical machinery and apparatus
Motor vehicles Intensifying labour use Furniture Stable Food and beverages
Source: UNIDO’s elaboration based on CIC 2009; UNIDO Database (UNIDO 2012a).
Country-Specific Effects
Country experiences
Source: UNIDO estimate based on UNIDO INDSTAT2
Country experiences
Source: UNIDO estimate based on UNIDO INDSTAT2
Country experiences
Source: UNIDO estimate based on UNIDO INDSTAT2
Country specific effects
Source: UNIDO estimate based on UNIDO INDSTAT2
Speed of manufacturing developmentIndustry Republic of Korea Malaysia Sri Lanka
Food and beverages 4.74 1.46 0.64Textiles 11.49 0.6 0.61Wearing apparel 13.37 0.66 1.43Chemicals 3.55 1.32 0.19Basic metals 3.62 0.38 0.03Fabricated metals 2.71 0.24 0.09Electrical machinery and apparatus 7.53 0.78 0.1Motor vehicles 5.28 0.4 0.13
Note: The speed is expressed as an increase in value added per capita divided by the number of years taken over the range of GDP per capita from US$ 3,000 to US$ 4,500.
Source: UNIDO calculations based on UNIDO INDSTAT2
Speed of structural change
Source: UNIDO estimate based UNIDO INDSTAT2
1965 1970 1975 1980 1985 1990 1995 2000 2005 20080
0.5
1
1.5
2
2.5
3
3.5
Korea, RepChinaPakistantColombia
Cons
umer
goo
ds/c
apita
l goo
ds v
alue
add
ed
ratio
$3,000 GDP per capita (PPP)
Value added per capita
$10,000
Comparative advantage
Country-specific and time effects
Industry A
Industry BSpeed
SpeedLevel deviation
Source: UNIDO’s elaboration
Graphic representation of the role of comparative advantage and country-specific and time effects in manufacturing development