©2015 Asian Productivity Organization©2015 Asian Productivity Organization

Contents
Abbreviations
Forewordviii

1 Introduction

1ix

1.1 Databook 20151

1.2 List of Contributors

2

1.3 Map of Countries Covered

4

2 Overview

5

3 Economic Growth13

3.1 Economic Scale and Growth14

3.2 Catching Up in Per Capita GDP

23

3.3 Sources of Per Capita GDP Gap

3139

4.1 Final Demand Compositions

39

4.2 Consumption and Investment

44

4.3 Expenditure-Side Growth Decomposition

51

5 Productivity

61

5.1 Per-Worker Labor Productivity

61

5.2 Per-Hour Labor Productivity

67

5.3 Total Factor Productivity

72

5.4 Sources of Labor Productivity Growth83

5.5 Energy Productivity

90

6 Industry Perspective95

6.1 Output and Employment95

6.2 Industry Growth103

6.3 Labor Productivity by Industry

111

7 Real Income

121

7.1 Real Income and Terms of Trade121

7.2 Trading Gain and Productivity Growth130

Appendix

133

A.1 GDP Harmonization

133

A.2 Capital Stock

138

A.3 Rate of Return and Capital Services

140

A.4 Hours Worked

142

A.5 Other Data

145

A.6 Industry Classification

146

References

149

iii

©2015 Asian Productivity Organization

4 Expenditure

Contents

Box
Box

1

PPP Revisions by the 2011 ICP Round

17

Box

2

Population and Demographic Dividend

35

Box

3

National Accounts in Asian Countries

52

Box

4

The Size of the Informal Sector

59

Box

5

Turning Point in China

64

Box

6

Sensitivity of TFP Estimates

94

Box

7

Improvement in Labor Quality

117

Box

8

Wages of Workers and Income Level

119

Box

9

Mining Value Added in Resource-Rich Countries

131©2015 Asian Productivity Organization

Table
Table

1

GDP using Exchange Rate, 1970, 1980, 1990, 2000, 2010, and 2013

14

Table

2

GDP using PPP, 1970, 1980, 1990, 2000, 2010, and 2013

16

Table

3

GDP Growth, 1990–1995, 1995–2000, 2000–2005, 2005-2010, and 2010–2013

20

Table

4

Population, 1970, 1980, 1990, 2000, 2010, and 2013

24

Table

5

Per Capita GDP using Exchange Rate, 1970, 1980, 1990, 2000, 2010, and 2013

25

Table

6

Per Capita GDP, 1970, 1980, 1990, 2000, 2010, and 2013

27

Table

7

Country Groups Based on the Initial Economic Level and the Pace of Catching Up

30

Table

8

Final Demand Shares in GDP, 1970, 1990, 2000, 2010, and 2013

39

Table

9

Per-Worker Labor Productivity Levels, 1970, 1980, 1990, 2000, 2010, and 2013

63

Table 10

Per-Worker Labor Productivity Growth, 1990–1995, 1995–2000, 2000–2005, and 2005–2013

65

Table 11

Per-Hour Labor Productivity Levels, 1970, 1980, 1990, 2000, 2010, and 2013

68

Table 12

Per-Hour Labor Productivity Growth, 1990–1995, 1995–2000, 2000–2005, and 2005–2013

70

Table 13

Output Growth and Contributions of Labor, Capital, and TFP, 1970–2013

78

Table 14

Role of TFP and Capital Deepening in Labor Productivity Growth, 1970–2013

90

Table 15

Energy Productivity Levels, 1980, 1990, 2000, 2010, and 201292

Table 16

Country Groups Based on the Current Economic Level and the Pace of Catching Up

95

Table 17

Output Growth by Industry, 2000–2013

107

Table 18

Labor Productivity Growth by Industry, 2000–2013

114

Table 19

Real Income and Terms of Trade, 1970–2013, 1995–2000, 2000–2005, and 2005–2013

122

Table 20

Asset Classification and Parameters in Hyperbolic Function

138

Table 21

Input-Output Tables and Supply and Use Tables

138

Table 22

Average Ex Post Real Rate of Return in Asia

141

Table 23

Sources of Labor Data

143

Table 24

Industry Classification – Concordance with ISIC Rev.3

146

Table 25

Industry Classification – Concordance with ISIC Rev.4

147

iv

Figure1

GDP Growth of Asia, the EU, Japan, and the US, 1970–2013

13

Figure2

Share of Asia in World GDP in 2013 and Projection for 2020

13

Figure3

Price Level Indices of GDP, 201115

Figure4

Regional GDP of Asia and the EU, Relative to the US, 1970–2013

18

Figure5

GDP of China, India, and Japan, Relative to the US, 1970–2013

18

Figure6

Regional GDP of South Asia, ASEAN, CLMV, and GCC, Relative to the US, 1970–2013

19

Figure7Country Contributions to Regional GDP Growth, 1970–1990 and 1990–2013

21

Figure8Correlation of GDP Growth, 1990–2000

22

Figure9Correlation of GDP Growth, 2000–2013

23

Figure 10

Share of Asian Population in the World, 2013

23

Figure 11

Per Capita GDP using Exchange Rate of Japan and Australia, Relative to the US, 1970–2013

26

Figure 12

Per Capita GDP using Exchange Rate of the Asian Tigers, Relative to the US, 1970–2013

26

Figure 13

Per Capita GDP of Japan, the EU, and Australia, Relative to the US, 1970–2013

28

Figure 14

Per Capita GDP of the Asian Tigers, Relative to the US, 1970–2013

28

Figure 15

Per Capita GDP of China, India, and ASEAN, Relative to the US, 1970–2013

28

Figure 16

Per Capita Non-Mining GDP in Oil-Rich Countries and Japan, 2013

29

Figure 17

Initial Level and Growth of Per Capita GDP, 1970–2013

30

Figure 18

Labor Productivity and Employment Rate Gap Relative to the US, 1990 and 201331

Figure 19

Sources of Per Capita GDP Growth, 1990–2000 and 2000–2013

32

Figure 20

Share of Female Employment

33

Figure 21

Employment Rates, 1970, 1990, and 2013

33

Figure 22

Final Demand Shares in GDP of China, 1952–2013

40

Figure 23

Final Demand Shares in GDP of the US, 1929–2013

40

Figure 24

Final Demand Shares in GDP, 1995 and 2013

41

Figure 25

Ratio of Dependent Population and Consumption Share in GDP, 2013

42

Figure 26

Export and Import Shares in GDP, 1995 and 2013

43

Figure 27

Long-Term Trend of Household Consumption Share in GDP, 1970–2013

45

Figure 28

Household Consumption by Purpose, 2013

46

Figure 29

Engel Curve of Japan during 1949–2013 and Levels of Asian Countries in 2013

47

Figure 30

Long-Term Trend of Investment Share in GDP, 1970–2013

48

Figure 31

FDI Inflows, 2000–2013

48

Figure 32

FDI Inflow Ratio and Business Environment, 2000–2013

49

Figure 33

Investment Share by Type of Asset, 1970 and 2013

49

Figure 34

Long-Term Trend of Net Export Share in GDP, 1970–2013

50

Figure 35

Final Demand Contributions to Economic Growth, 1990–2000 and 2000–2013

51

Figure 36

Final Demand Contribution Shares to Economic Growth of the Asian Tigers, 2000–2013

54

Figure 37

Impacts of Global Financial Crisis and Recoveries, 2007–2013

54

Figure 38

Impacts of Asian Financial Crisis, 1997–1998

55

Figure 39

Final Demand Decomposition of Real GDP Growth, 1970–2013

56

Figure 40

Labor Productivity Level by Per-Worker GDP, 2013

62

Figure 41

Labor Productivity Trends of China and India, 1970–2013

62

Figure 42

Labor Productivity Level Relative to the US, 1970–2013

66

Figure 43

Labor Productivity Gap by Per-Worker and Per-Hour GDP Relative to the US, 2013

67

Figure 44

Labor Productivity Trends in Japan and the Asian Tigers, 1970–2013

68v©2015 Asian Productivity Organization

Figure©2015 Asian Productivity Organization

Contents

Figure 45

Labor Productivity Growth, 1970–2013, 1970–1990, and 1990–2013

69

Figure 46

Labor Input Growth, 1970–2013, 1970–1990, and 1990–2013

69

Figure 47

Labor Productivity Trends of Japan in 1885–2013 and Levels of Asian Countries in 2013

71

Figure 48

Time Durations Taken to Improve Labor Productivity by Japan and the Asian Tigers

71

Figure 49

TFP Growth, 1970–2013, 1970–1990, and 1990–2013

73

Figure 50

Sources of Economic Growth, 1970–2013

74

Figure 51

Contribution Shares of Economic Growth, 1970–2013

74

Figure 52

Sources of Economic Growth, 1970–1985, 1985–2000, and 2000–2013

76

Figure 53

Contribution Shares of Economic Growth, 1970–1985, 1985–2000, and 2000–2013

76

Figure 54

Comparison of Sources of Economic Growth with OECD Countries, 2000–2013

77

Figure 55

Comparison of TFP Contribution Shares with OECD Countries, 2000–2013

77

Figure 56

Individual Countries’ Growth Accounting Decomposition, 1970–2013

80

Figure 57

IT Capital Contribution to Capital Input Growth of Japan and the US, 1970–2013

82

Figure 58

IT Capital Contribution to Capital Input Growth of the Asian Tigers, China, and India, 1970–2013

83

Figure 59

Individual Countries’ Growth Accounting Decomposition (year-on-year), 1970–2013

84

Figure 60

Capital Deepening, 1970–2013, 1970–1990, and 1990–2013

86

Figure 61

Capital Productivity Growth, 1970–2013, 1970–1990, and 1990–2013

86

Figure 62

Capital Productivity Trends in Japan and the Asian Tigers, 1970–2013

87

Figure 63

Capital Productivity Trends in China and India, 1970–2013

87

Figure 64

Sources of Labor Productivity Growth, 1970–2013

87

Figure 65

Contribution Shares of Labor Productivity Growth, 1970–2013

87

Figure 66

Sources of Labor Productivity Growth, 1970–1985, 1985–2000, and 2000–2013

88

Figure 67

Contribution Shares of Labor Productivity Growth, 1970–1985, 1985–2000, and 2000–2013

88

Figure 68

Decomposition of Labor Productivity Growth, 1970–2013

89

Figure 69

Shares of Asia in World Energy Consumption and CO2 Emission, 201292

Figure 70

Labor Productivity and Energy Productivity, 201293

Figure 71

Sources of CO2 Emission Growth, 2000–2012

93

Figure 72

Industry Shares of Value Added, 2013

96

Figure 73

Manufacturing Share and TFP Growth, 2000–2013

97

Figure 74

Industry Shares of Value Added in Manufacturing, 2013

97

Figure 75

Industry Shares of Value Added and Employment by Country Group, 1980, 1990, 2000, and 2013

98

Figure 76

Long-Term Trends of Value-added Share in the Agriculture Sector, 1970–2013

100

Figure 77

Industry Shares of Employment, 2013

101

Figure 78

Employment Share of Agriculture in Japan during 1885–2013 and Levels of Asian Countries in 2013

101

Figure 79

Long-Term Trends of Employment Share in the Agriculture Sector, 1970–2013

102

Figure 80

Job Creation in Manufacturing, 1970–2013

103

Figure 81

Industry Origins of Economic Growth, 1990–2000 and 2000–2013

104

Figure 82

Contribution of Manufacturing to Economic Growth, 1990–2000 and 2000–2013

105

Figure 83

Contribution of Service Sector to Economic Growth, 1990–2000 and 2000–2013

106

Figure 84

Industry Origins of Output Growth in Manufacturing, 1990–2000 and 2000–2013

108

Figure 85

Industry Origins of Regional Economic Growth, 1990–2000 and 2000–2013

109

Figure 86

Industry Origins of Asian Economic Growth, 2000–2013

110

Figure 87

Industry Origins of Economic Growth, 1970–2013

112

Figure 88

Industry Origins of Labor Productivity Growth, 1990–2000 and 2000–2013

115

Figure 89Contribution of Manufacturing to Labor Productivity Growth, 1990–2000 and 2000–2013

116

vi

Contribution of Service Sector to Labor Productivity Growth, 1990–2000 and 2000–2013

116

Figure 91

Effect of Net Income Transfer on GDP, 1970–2013

123

Figure 92

Real Income and Real GDP Growth, 1970–2013

124

Figure 93

Trading Gain Effect, 2005–2013

125

Figure 94

Price of Crude Oil, 1986 January–2015 May

125

Figure 95

Decomposition of Real Income Growth, 1970–2013 and 2000–2013

126

Figure 96

Decomposition of Real Income Growth, 1973–1979 and 1996–1998

127

Figure 97

Sources of Real Income Growth, 1970–2013

128

Figure 98

Trading Gain Effect and Labor Productivity Growth, 1970–2013

130

Figure 99

Adjustment of FISIM

134

Figure 100

FISIM Share in GDP, 2000–2013

134

Figure 101

Software Investment Ratio and GFCF Ratio to GDP, 2005

135

Figure 102

Adjustment of R&D

136

Figure 103

R&D Share in GDP, 2013

137

Figure 104

Capital-Output Ratio, 1980 and 2013

139

Figure 105

Ex Post Real Rate of Return in Asia, 1970–2013

140

Figure 106

Average Annual Hours Worked Per Worker Relative to the US, 2000–2013

144

Figure B1

Revisions of PPP for GDP by the 2011 ICP Round17

Figure B2.1

Distribution of the World’s Population in Different Regions, 1950–2100

35

Figure B2.2

Asian Countries’ Population Size and Projection, 1970, 2013, and 2050

36

Figure B2.3

Proportion of the Dependent Population, 2013

36

Figure B2.4

Demographic Dividend, 1950–2100

37

Figure B3

Implementation of the 1968, the 1993, and the 2008 SNA

52

Figure B4Employee Share and GDP Level, 2013

59

Figure B5

Price of Labor Relative to Capital in China, Japan, and the Asian Tigers, 1970–2013

64

Figure B6.1

Labor Income Share for Employees, 2013

94

Figure B6.2

Sensitivity of TFP Estimates by the Change of Income Share, 1970–2013

94

Figure B7.1

Contributions of Labor Quality to Growths in Japan and the US, 1955–2012

117

Figure B7.2

Average Schooling Years of Workers, 1970–2013

117

Figure B8

Average Wage and Per Capita GNI, 2013

119

Figure B9

Trading Gain Effect and Value-added Share in Mining Sector, 1970–2013

131

vii

©2015 Asian Productivity Organization

Figure 90

Abbreviations©2015 Asian Productivity Organization

ADB
AEC
AIIB
APO
APO20

Asian Development BankASEAN Economic Community
Asian Infrastructure Investment Bank
Asian Productivity Organization
20 member economies of the Asian Productivity Organization: Bangladesh,
Cambodia, the Republic of China, Fiji, Hong Kong, India, Indonesia, Islamic Republic of Iran, Japan, the Republic of Korea, the Lao PDR, Malaysia, Mongolia,
Nepal, Pakistan, the Philippines, Singapore, Sri Lanka, Thailand, and Vietnam
AQGM		 Asian quarterly growth map
ASEAN
Association of Southeast Asian Nations: Brunei, Cambodia, Indonesia, the Lao
PDR, Malaysia, Myanmar, the Philippines, Singapore, Thailand, and Vietnam
Brunei, Indonesia, Malaysia, the Philippines, Singapore, and Thailand
ASEAN6
APO20 plus the People’s Republic of China, the Kingdom of Bhutan, Brunei,
Asia24and Myanmar
Asia24 plus GCC countries
Asia30
Cambodia, the Lao PDR, Myanmar, and Vietnam
CLMV
consumer price index
CPI
European UnionEU
15 member economies of the European Union prior to enlargement: Austria,
EU15
Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain, Sweden, and the United Kingdom
EU28
European Union: EU15 plus Bulgaria, Republic of Croatia, Cyprus, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Romania, Slovak
Republic, and Slovenia
FDI
foreign direct investment
FISIM
financial intermediation services indirectly measured
GCC
Gulf Cooperation Council: Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and theUAEgross domestic productGDPgross fixed capital formation
GFCF
gross national income
GNI
International Comparisons Program
ICP
International Labour Organization
ILO
International Monetary Fund
IMF
International Standard Industry Classification
ISIC
information technology
IT
Keio Economic Observatory, Keio University
KEO		
Lao PDR		 Lao People’s Democratic Republic
less developed countries
LDCs
non-profit institutions serving households
NPISHsOrganisation for Economic Co-operation and Development
OECDpurchasing power parity
PPP
quarterly national accounts
QNA
Regional Comprehensive Economic Partnership
RCEP
Republic of China
ROC
research and development
R&D
System of National Accounts
SNA
total factor productivity
TFP
Trans-Pacific Partnership
TPP
United Arab Emirates
UAE
United Nations
UN
United Nations Statistics Division
UNSD
United States
US

viii

Foreword
Productivity plays a pivotal role in the acceleration of economic growth. Forecasts indicate that the APO region will remain the global growth leader overthe medium term. The APO supports member economies in acquiring practical, state-of-the-art tools and knowledge to foster productivity at industry
and enterprise levels. It is formulating a new program approach intended toalign efforts on pivotal productivity topics with the best available technologies
and practices. Capacity-building efforts are being streamlined and upgradedthrough the use of ICT. Expansion of e-learning courses will strengthen the industry, service, and public sectors in the region and in turn enhance national
competitiveness.
The APO is also emphasizing its policy advisory role. Productivity data are indispensable for designing and assessing policies for socioeconomic development.
Policymakers and business leaders alike depend on economic data to makeinformed decisions at macro and micro levels. The collection and comparisonof such detailed data enable the APO to offer evidence-based policy guidance
to its member economies, contributing to sustainable socioeconomic development through enhanced productivity and competitiveness.
I am happy to introduce this new edition of the APO Productivity Databook,reflecting the dynamic economic and productivity performance in the AsiaPacific region while offering comparisons with other major economies aroundthe world. This publication is the result of research conducted under the APO
Productivity Databook Project of the Secretariat Research and Planning Department in collaboration with Keio Economic Observatory, Keio University, Tokyo.
My gratitude goes to Professor Koji Nomura for his leadership and project management. I also thank all of the contributors for developing the productivity
database and this publication. The APO will continue to work with its members
and their national statistics offices to improve data quality for more precise productivity measurement and more informed policy formulation.
I hope that readers will find this publication to be a useful reference for multiple
purposes in their own work.

Mari Amanoix©2015 Asian Productivity Organization

Secretary-General
Asian Productivity Organization
Tokyo, September 2015

1.1 Databook 2012

1 Introduction
1.1 Databook 2015

1

This is the eighth edition in the APO Productivity Databook series. The publication aims to provide across-country comparison of economic growth and productivity levels of Asian economies in relation
to global and regional economies. The focus of the Databook is on long-term analysis. This is achievedby examining a country’s economic growth and productivity performance, as well as the sources andindustry origins of the growth. This complete analysis provides readers with a more comprehensive
description and comparison of a given country’s economic structure and characteristics.
Baseline indicators on economic growth and labor productivity are calculated for 30 Asian economies,
representing the 20 Asian Productivity Organization (APO20) member economies and the 10 nonmember economies in Asia. This edition covers the period from 1970 to 2013. The APO20 includes:
Bangladesh, Cambodia, the Republic of China (ROC), Fiji, Hong Kong, India, Indonesia, the Islamic Republic of Iran (Iran), Japan, the Republic of Korea (Korea), the Lao People’s Democratic Republic (Lao
PDR), Malaysia, Mongolia, Nepal, Pakistan, the Philippines, Singapore, Sri Lanka, Thailand, and Vietnam. The 10 non-member economies in Asia are: the People’s Republic of China (China), the Kingdomof Bhutan (Bhutan) – newly covered in this edition, Brunei, Myanmar, and the Gulf Cooperation Council (GCC) that consists of Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates(UAE). In addition, Australia, the European Union (EU), Turkey, and the United States (US) are included
as reference economies.

To analyze the overall productivity improvement as well as partial productivity improvement (laborproductivity and capital productivity), this Databook project constructs estimates of capital services
appropriate to the concept of capital input introduced in the 2008 SNA. In particular in this edition,
the research and development (R&D) is newly treated as a factor of production. Based on these estimates, the sources of economic growth in each economy are further decomposed to factor inputs oflabor and capital and total factor productivity (TFP) for 19 Asian economies – Bangladesh, Cambodia,
the ROC, Fiji, Hong Kong, India, Indonesia, Iran, Japan, Korea, Malaysia, Mongolia, Pakistan, the Philippines, Singapore, Sri Lanka, Thailand, Vietnam, and China – along with the US as a reference economy.
It is a notable achievement that TFP estimates for Cambodia and for the APO20 as the region (excluding Nepal and the Lao PDR) are newly developed in this edition. In addition, the energy productivity
estimates are newly presented in this edition as a reflection of the impending need to improve energy
productivity as a policy target for pursuing sustainable growths of the Asian countries.
The official national accounts and metadata information used for constructing the APO Productivity
Database 2015 have been provided by national experts in APO member economies through1©2015 Asian Productivity Organization

The productivity measures in this report are based on data and estimates collated for the APO Productivity Database project since September 2007, as a joint research effort between the APO and the Keio
Economic Observatory (KEO), at Keio University, Tokyo. Estimates are based primarily on the System of
National Accounts in 2008 (2008 SNA). In this edition, some significant revisions on the national accounts were incorporated. New developments for the upgrading of statistics systems in APO member
economies have resulted in Bangladesh, the ROC, Indonesia, Mongolia, and Singapore, publishing
their accounts based on the 2008 SNA during 2014–2015. While there are movements toward upgrading the SNA, some countries, such as Cambodia, the Lao PDR, and Nepal, have yet to fully introduce
the earlier version 1993 SNA. Because the varying SNA adaptions among the member economies canresult in discrepancies between data definitions and coverage, data harmonization is necessary forcomparative productivity analyses. This Databook attempts to reconcile these national accounts variations which are based on the different concepts and definitions. This is done by following the 2008
SNA and providing harmonized estimates for international comparison.

1 Introduction

questionnaires designed at KEO. The names of these experts are listed in Section 1.2. The submitted
data was then examined and processed at KEO where further information was collected on labor,
production, prices, trades, and taxes as required. This edition reflects the revisions to the official national accounts and other statistical data published as of May 2015. The project was managed by Koji
Nomura (Keio University), under the consultancy of Professors Dale W. Jorgenson (Harvard University)and W. Erwin Diewert (University of British Columbia) and with coordination by Yasuko Asano (APO).
The text, tables, and figures of this edition of the Databook were authored by Koji Nomura and Fukunari Kimura (Keio University), with support from the research assistants Shinyoung Oh, Hiroshi Shirane, Naoyuki Akashi, Rie Kinoshita, and Shiori Nakayama. The Databook project appreciates Eunice Ya
Ming Lau for her contribution to developing the foundation of Databook series and Trina Ott for her
review of the draft.

1.2 List of Contributors
Authors of This Report

National Experts

Dr. Koji Nomura

Bangladesh
Mr. Ziauddin Ahmed

APO Productivity Database Project Manager,
Associate Professor, KEO, Keio University,
2-15-45 Mita, Minato-ku, Tokyo, 108-8345,
Japan

Dr. Fukunari Kimura
Professor, Department of Economics,
Keio University

Research Assistants at KEO, Keio University
Ms. Shinyoung Oh
Mr. Hiroshi Shirane
Mr. Naoyuki Akashi
Ms. Rie Kinoshita
Ms. Shiori Nakayama
APO Officer
Ms. Yasuko Asano©2015 Asian Productivity Organization

Program Officer, Research and Planning
Department, Asian Productivity Organization,
1-24-1 Hongo, Bunkyo-ku, Tokyo, 113-0033,
Japan

Joint Director, Bangladesh Bureau of Statistics,
Ministry of Planning, Parishankhyan Bhaban,
E-27/A, Agargaon, Sher-e-Bangla Nagar,
Dhaka–1207

Cambodia
Mr. Chettra Keo
Director, National Accounts Department, National
Institute of Statistics, Ministry of Planning, #84, St.
130, Don Penh, Phnom Penh

Republic of China
Mr. Wei-Jie Huang
Chief, National Accounts Section, Bureau of
Statistics, Directorate-General of Budget,
Accounting, and Statistics (DGBAS), Executive
Yuan, No. 2, Guangzhou St., Zhongzheng District
Taipei, 10065

Fiji
Ms. Navilini Singh
Senior Statistician, Fiji Bureau of Statistics, Ratu
Sukuna House, PO Box 2221, Government
Buildings, Suva

India
Dr. Kolathupadavil Philipose SunnyGroup Head (Economic Services), National
Productivity Council, Lodhi Road,New Delhi, 110003

2

1.2 List of Contributors

Director of Industrial Statistics, Statistics
Indonesia – BADAN PUSAT STATISTIK, Jalan Dr.
Sutomo No.6-8, Jakarta 10710, Kotak Pos 1003,
JakartaIslamic Republic of Iran
Mr. Behzad Mahmoodi
Deputy of Monitoring, Auditing and Assessment
Department, National Iranian Productivity
Organization, Management and Planning
Organization of Iran (MPO), Daneshsara St.,
Baharestan Sq. Tehran

Japan
Mr. Yutaka Suga
Research Official, National Wealth Division,
National Accounts Department, Economic andSocial Research Institute, Cabinet Office,
Government of Japan, 3-1-1 Kasumigaseki,
Chiyoda-ku, Tokyo, 100-8970

Lao PDR
Mr. Phousavanh Chanthasombath
Deputy Director of National Accounts Division,
Department of Economic Statistics, Lao Statistics
Bureau, Ministry of Planning and Investment, Ban
Sithanneau, Souphanouvong Road, Vientiane

Malaysia
Ms. Hezlin Suzliana Binti Abdul Halim
Assistant Director, National Accounts Statistics
Division, Department of Statistics, Malaysia, Ting.
3, Unit 01-05, Wisma Minlon, Batu 12 Lebuhraya
Sg. Besi, 43300 Seri Kembangan, Selangor

Mongolia
Ms. Bayarmaa Baatarsuren
Statistician, Macro-economic Statistical
Department, National Statistical Office ofMongolia, Government Building III,
Ulaanbaatar-20a

Nepal
Mr. Rajesh Dhital

1

Director, Establishment Census and Survey
Section, Central Bureau of Statistics, Thapathali,
Kathmandu

Pakistan
National Productivity Organization
2nd Floor, Software Technology Park,
Constitution Avenue, F-5/1, Islamabad

Philippines
Ms. Estela Tormon De Guzman
Director (Interim Assistant National Statistician),
Philippine Statistics Authority, Ground Floor,
Solicarel Bldg II, R. Magsaysay Blvd. Sta. Mesa,
ManilaSingapore
Mr. Tan Jian Qi
Senior Officer, SPRING Singapore, 1 Fusionopolis
Walk, #01-02 South Tower, Solaris, Singapore,
138628

Sri Lanka
Mr. Weerasinghe Wasala Mudiyanselage Ananda
Sarath Premakumara
Director Statistics, Department of Census andStatistics, Ministry of Finance and Planning, No.
306/7, Polduwa Road, Baththaramulla

ThailandMr. Wirot Nararak
Director, National Accounts Office, National
Economic and Social Development Board, 962
Krung Kasem Road, Bangkok, 10100

VietnamMr. Duong Manh Hung
Deputy Director, National Accounts Department,
General Statistic Office of Vietnam, No. 6B Hoang
Dieu, Ba Dinh District, Hanoi

3©2015 Asian Productivity Organization

Indonesia
Mr. Sulthani Emil Azman©2015 Asian Productivity Organization

APO member economies
Non-member Asian economies included in Databook 2015

1 Introduction

1.3 Map of Countries Covered4

Overview

2 Overview
In 2014, most of the Asian developing economies achieved steady economic growth. The average annual growth of GDP at constant market prices in 2010–2013 in Asia30 and East Asia reached 5.4% and5.8%, respectively (Table 3 in Chapter 3). Although the growth performance widely varies, the countries in the region mostly kept conservative macroeconomic management and continuously outperformed the rest of the world. Since many Asian countries were net importers of energy, low petroleum
prices helped them keep inflation low and sustain the pace of economic growth.

2

Advanced economies performed better than the previous year, particularly due to a strengthened
recovery of the US economy. The average annual growth of GDP at constant market prices in 2010–2013 in the US was 2.0%. However, the European economy seemed to follow a low growth path in aslow recovery from the Global Financial Crisis (GFC). The average annual growth of GDP at constantmarket prices in 2010–2013 in EU15 and EU28 was as low as 0.3% and 0.4%, respectively. The situation
in some countries, particularly Greece, remains unstable. The Japanese economy looks stable goingback to the before-GFC normal performance though the concrete outcomes of Abenomics will taketime. The average annual growth of GDP at constant market prices in 2010–2013 in Japan was 0.9%.
The recent World Economic Outlook by the International Monetary Fund (IMF) presented a pessimistic
view with the permanent lowering of potential growth rates in advanced economies (IMF, 2015).
China is adjusting for a bit slower growth path – “new normal.” The Chinese economy maintains vigorous energy, achieving 7.9% for the average annual growth of GDP at constant market prices in 2010–2013. However, to sustain economic growth at the pace of above 7% appears to be increasingly
difficult. How far the structural adjustment and economic reform progresses is the key, which would
influence all of Asia. Other areas of developing Asia may need to rely more on their own markets in thenear future.

While the overall perspectives of economic growth in the Asian economies are bright, three issues
should be watched closely in the coming years. First, in the era of globalization, countries must be
prepared for possible turbulence in the asset and financial markets. Since the GFC, international capital flows have slowed substantially. However, relatively good economic performance as well as open
capital markets in some of the Asian economies likely has attracted excessive capital inflows. The current management of macroeconomic fundamentals is stronger than in the era of the Asian currency

5©2015 Asian Productivity Organization

Industrialization in developing East Asia has been characterized by the aggressive use of a new typeof international division of labor called international production networks (Ando and Kimura, 2005) or
the 2nd unbundling (Baldwin, 2011). Latecomers in the region, Cambodia, the Lao PDR, and Myanmar,
have rapidly grown in the past two decades, reaching $1,070, $1,650, and $1,300, respectively, in the
per capita GDP using a 2013 exchange rate (Table 5 in Chapter 3). However, the easy catch-up period
has almost passed, which will require these countries to develop production networks and remove
bottlenecks to achieve sustained economic growth. The “Thailand-Plus-One” investment by machinery parts producers and creation of fragmented satellite factories off Thailand is a step in the right
direction. Vietnam presented much deeper involvement in production networks after the GFC, reaching $1,930 in the per capita GDP using a 2013 exchange rate. Quantitatively, the Philippines, Indonesia, Thailand, Malaysia, and Singapore did experience much expansion in production networks,
however they developed their networking with a larger number of parts and components as well as
trading partners. The per capita GDP using a 2013 exchange rate reached $2,800, $3,690, $6,480,$10,460, and $55,980, respectively. The South Asian countries have not taken full advantage of international production networks, although some have advocated “Look East” policies. The per capita
GDP using a 2013 exchange rate in Nepal, Bangladesh, Pakistan, and India is $770, $1,000, $1,210, and$1,540, respectively. There is ample room for them to adopt a novel development strategy with production networks from ASEAN member states and China.

2 Overview

crisis; and the financial world is more globalized. A slight shift may trigger sudden massive outflows ofcapital, resulting in speculative attack. The financial authority must monitor asset and financial markets with nurturing credibility.
Several external events could trigger such turmoil in Asia. Tapering policy for monetary easing in the
US is a constant concern. Additionally, a possible negative shock may come from the EU, perhaps
starting from Greece. A recent bubble in China’s asset market is also a concern. These potential events
demonstrate the need for the Asian economies to be prepared for external shocks.
Second, although a series of policies favorable to trade and investment have been the basis of strong
and sustained economic growth in Asia, some inconsistent economic policies have been observed
recently. In working to establish a healthy democracy, politics have played a role in policies thatare popular, yet not positive for economic growth. Some such policies include the introduction of
non-tariff barriers in Indonesia, excessive income redistribution for rural areas in Thailand, high minimum wage policies in Thailand and Cambodia, and sticking to Bumiputera policies in Malaysia. While
the overall political mood is not consistently anti-trade or anti-investment, policymakers and poli­ticians should be cognizant of policy inconsistency that could cause serious damage in the economic
growth path.
Third, 2015 will be a landmark year for economic integration in East Asia and Asia-Pacific, with threemajor events on the horizon. While the political implication of the ongoing Trans-Pacific Partnership(TPP) negotiation is well documented, possible economic consequences should also be highlighted.
Once TPP concludes, it will set a new standard of deep liberalization for goods and services trade as
well as investment. It will also lead to the development of a new series of international rule including
government procurement, intellectual property rights protection, competition policy (particularly
with state-owned enterprises), and a dispute settlement mechanism (including investor-state disputesettlements). A number of political concessions will certainly mitigate radical aspects of the agreement, but TPP will still be a landmark agreement, particularly for East Asia. Among 12 negotiating
countries, only five East Asian countries, namely Brunei, Japan, Malaysia, Singapore, and Vietnam, arecurrently included. However, other East Asian countries such as Korea, the Philippines, and even China
will certainly consider their accession.

©2015 Asian Productivity Organization

ASEAN-centered advances will also impact economic integration. ASEAN will complete efforts to construct the ASEAN Economic Community (AEC) by the end of 2015. Arguably, AEC will become themost successful economic integration ever established in the developing world. Among six forerunners in ASEAN, tariffs are already zero for more than 99% of the traded goods, and four latecomers arealso cutting tariffs as scheduled. Business-friendly rules of origin as well as trade facilitation help thecreation of production networks in the region. Because the initial wording of the AEC Blueprint in2007 was too abstract and ambitious, some commitments such as services liberalization and skilledlabor movements likely will be carried over after 2016. The achievement should be appreciated as afoundation of integrated production base in the whole ASEAN.
A component of integration commitments is the negotiation over the Regional Comprehen­sive Economic Partnership (RCEP). This is intended to provide a mega-FTA covering the extended
East Asia including ASEAN 10 plus Japan, Korea, China, Australia, New Zealand, and India. The negotia­tion started in May 2013, but some delay in setting up the negotiating modality has been observed
since August 2014. Although the direct cause of the delay is a passive negotiating attitude from
India, the overall enthusiasm toward the high level of agreements seems to be lacking. The spectrumof RCEP negotiation over policy modes is quite broad, with a geographical extension that would cover an entire area of production networks including China and India. Regaining energy in the6Overviewnegotiation is a hope because the advancement of TPP negotiation may provide positive influence on
RCEP negotiation.

2

China’s initiation of a new approach toward development issues is the third major event to advanceeconomic integration. China proposed to establish a new international financial institution called
Asian Infrastructure Investment Bank (AIIB). China is deeply committed to regional infrastructure development in the form of bilateral aid or foreign direct investment as well as being an importantmember of the Asian Development Bank (ADB) and the World Bank. However, the establishment of
AIIB, officially planned for the end of 2015, will upgrade China’s commitment to Asia and other developing regions.
It is a big step for China to establish a new international financial institution. With this bold effort, wecertainly need to review the role and practices of existing bilateral and international development
institutions. This may be a good opportunity to reexamine the basic logic of rigid DAC (Development
Assistance Committee) rule for development assistance, and loose OECD (Organisation for Economic
Co-operation and Development) guidelines for multinational enterprises, and others. At the sametime, it may be appropriate to apply a new policy discipline on AIIB and China’s other international
commitments to encourage them to participate in forming international rules. These are three big
events in this region that will come in the year 2015 and beyond. How we take advantage of these
newly created opportunities will deeply influence our future.
Asia’s economic vitality warrants considerable attention to the rapid and vigorous changes in its economic performance in the short run. To fully understand this economic dynamism, it is essential tograsp its growth performance, structural changes, and the advancement of its economic development within a context of its middle-and long-term performance. Asia, in particular, consists of a variety of countries at different development stages, with diversified resource endowments, and undervarious political regimes. The APO Productivity Databook provides concise information and useful
insights into the basis of growth performance and economic structure of Asian countries by presenting such long-term data analysis.
International comparisons of economic performance are never a precise science; instead, they are
fraught with measurement and data comparability issues. Despite best efforts in harmonizing data,some data uncertainty remains. Operating within a reality of data issues, some of the adjustments inthe Databook are necessarily conjectural, while others are based on assumptions with scientific rigor.
In addressing this shortcoming, findings drawn from the research are cross-referenced against othersimilar studies. Such magnitude of variations in the economic indicators is often subject to a certain
degree of data uncertainty.
Bearing in mind these caveats, the main findings from our analysis are as follows:
Recent economic growth of Asiaand the second largest economy in the world, after the US. On this measure, Asia30 was 41% and
53% larger than the US and EU15 in 2013, respectively (Table 1).
u Based on GDP adjusted for purchasing power parity (PPP),1 the weight of the world economy iseven more tilted toward Asia, with Asia30 being 1.57 times and 1.77 times larger than the US and
EU15 in 2013, respectively. China has overtaken Japan as the largest Asian economy since 1999;
and its size was 98% relative to the US in 2013. India surpassed Japan, replacing it as the second

7©2015 Asian Productivity Organizationu In terms of exchange-rate-based GDP, China overtook Japan in 2010 as the largest economy in Asia

2 Overview

largest economy in Asia in 2008. In 2013, the total GDP of the three largest Asian economies alone
was 67% larger than the US economy (Table 2 and Figure 5).
u During the period 1990–2013, Asia30 grew at 5.5% on average per annum, compared with 2.4%

and 1.6% in the US and EU15, respectively. Japan was the slowest growing economy among the
Asia30 at 1.0%, compared with 26 of the 30 Asian economies with over 4.0% of annual economic
growth (Table 3 and Figure 1).
u For the past two decades (1990–2013), China and India have emerged as the driving force propel-ling Asia forward, accounting for 45% and 16% of regional growth, respectively (Figure 7).
u The global financial crisis slowed Asia30’s growth significantly from a recent peak of 8.2% during

2006–2007, to 4.8% during 2007–2008 and further to 4.0% during 2008–2009, before rebounding
strongly to 7.9% during 2009–2010. This is in comparison to the deep recession of –2.8% and –4.6%
experienced by the US and EU15, respectively, during 2008–2009 (Figure 1).
u The correlation coefficients between China and other Asian economies strengthened between

the two decades. This suggests that China has become more integrated within the Asian economy.
For most Asian countries, the correlation with the US and EU15 has also grown stronger (Figures 8
and 9).
Catching up in per capita GDP
u Our results show the outcome of the dramatic development effort of the four Asian Tigers.2 Singa-pore and Hong Kong have managed to close a per capita GDP gap with the US of around 60% injust under four decades. Singapore has even surpassed the US since 1993, and in 2013 its per capita GDP was 52% higher. In contrast, veteran Japan has fallen behind, widening its gap with the US
to 28%. In 2013, the ROC’s and Korea’s per capita GDP was 82% and 64% of the US level, respectively (Table 6 and Figure 14).
u Despite their rapid growth, due to their population size, per capita GDP of China and India was 23%

and 10% of the US in 2013, respectively. However, this represents a tenfold increase in China’s relative per capita GDP over the last four decades. The level achieved by Asia30 was 21% of the US,
indicating that there is ample room for catch-up (Table 6 and Figure 15).
u Asia’s huge per capita GDP gap with the US is predominantly explained by its labor productivity

gap. With the exception of the Asian Tigers, Japan, and Iran, all Asian countries have a labor productivity gap of 50% or higher (Figure 18).
u For most countries in Asia, the majority of per capita GDP growth can be explained by improve-©2015 Asian Productivity Organization

ment in labor productivity. However, the employment rate contribution relative to labor productivity was also highly significant in Bhutan, Bangladesh, Nepal, Singapore, Bangladesh, and Pakistan
in 2000–2013 (Figure 19).

1: This Databook based on the new PPP estimates of the 2011 International Comparisons Program (ICP) round published in April
2014. This has the significant effect of raising the relative sizes of Asian economies against the base economy, the US.
2: Refers to Hong Kong, Korea, Singapore, and the ROC.

8

Overviewu There is a significant variation in Asia’s employment rate from 25% to over 60% at present. The

employment rate has been rising in most Asian countries and is more than 10 percentage points
above the US in Singapore, Myanmar, Cambodia, Thailand, Vietnam, and China (Figure 21).

2

Changes in demand compositionu With a few exceptions, household consumption is the biggest component of final demand. In re-cent years, Asia30’s consumption ratio has dropped to 47.4% of GDP in 2013, largely reflecting thetrend in China. This compares to 68.5% in the US, 57.2% in EU15, and 55.6% in Australia (Table 8).
u The share of household consumption in GDP tends to be more volatile, dropping in countries thatare undergoing rapid development. As countries get richer, the household consumption sharetends to rise. At the other end of the spectrum, countries with low income and a high dependent
population (under-15, over-65) sustain a high consumption ratio to GDP (Figures 24 and 25).
u Overall, Asia invests more than the US/EU15 as a share of its GDP. Lately this gap has been widen-

ing. Historically, Australia’s investment share has been sandwiched between that of Asia and the
US/EU15. In 2013, Asia30 invested 36.2% of its GDP, compared with 19.3% for the US, 19.0% for
EU15, and 27.1% for Australia (Table 8 and Figure 30).
u China faces huge internal and external imbalances. The investment share of GDP (at 50.3%) as thebiggest component in final demand and the household consumption share plummeted to 33.5%
in 2013. In contrast, the weight of net exports has been rising in the past decade, although it isdeclining in recent years due to weak foreign demand (Figure 22).
u GCC economies are unusually skewed toward net exports because of their oil. Net exports ac-

counted for 25.5% of final demand in 2013, compared with Asia30’s 2.6% and China’s 2.4%. Only
the US and South Asia run trade deficits of a more significant nature, which accounted for –3.0%
and –4.0% of final demand, respectively, in 2013 (Table 8).
u Basic necessities account for a high proportion of household consumption in lower-income coun-tries, according to the cross-country version of Engel’s Law, which says that basic necessities willaccount for a high proportion of household consumption for a lower per capita income group andvice versa. They spend 30–60% of total consumption for food, which corresponds to Japan’s experience in the 1950s and the 1960s (Figures 28 and 29).
u In the 2000s, investment recovered in the Asian economies and drove growth. For Singapore andthe ROC, however, the strength of net exports was still the dominant force behind their economic
growth. The growth slowed in the US and EU15, and the contributions of government consumption to growth nearly tripled as contributions from investment took a plunge (Figures 35 and 39).

u For most Asian countries, the per capita GDP gap with the US is largely explained by their laborproductivity shortfalls of 80% or more against the US level. Only Singapore and Hong Kong haveeffectively closed that gap. The relative labor productivity of Asia24 was 20% of the US in 2013
(Table 9 and Figure 40).
u Growth of per-worker GDP in Asia has outstripped that in the US, allowing catch-up. In particular,

the low-income countries appeared to experience a labor productivity growth spurt in the 2000s.

9©2015 Asian Productivity Organization

Labor productivity

2 Overview

China achieved the fastest labor productivity growth of 9.3% on average per year in 2005–2013,
followed by Mongolia’s 7.2% and India’s 6.7%; this compares with the US’s 1.1%. Japan’s 0.7%
growth over the same period was the weakest performance among the Asian Tigers and Japan(Table 10 and Figure 42).
u The productivity gap based on GDP per hour is generally wider between Asian countries and theUS. While the adjustments are negligible for most Asian countries, the productivity gap significantly widened by 14–32 percentage points for the Asian Tigers, suggesting that people work
much longer hours than in the US (Figure 43).
u Most Asian countries experience faster growth in GDP per hour than the US. Among them, China’s

performance is the most outstanding, with average annual productivity growth doubling from
4.3% to 8.9% between 1970–1990 and 1990–2013, compared to the US at 1.5% and 1.8% over thesame periods (Figure 45).
u Mapped onto Japan’s historical trajectory of GDP per hour, most Asian countries cluster around thelevel that Japan achieved in the 1950s and early 1970s, with the Asian Tigers being the clear frontrunners, sprinting away from the pack (Figure 47).
Total factor productivity
u Eleven of the 19 Asian countries compared experienced faster TFP growth than the US over theperiod 1970–2013, with China in a league of its own. Its TFP growth was at 3.1% on average per
year, compared with those of Thailand at 2.1% in second place and the US at 0.8%. With TFP growing at 0.4% on average per year, Singapore’s productivity performance has been weak relative to its
economic counterparts (Figure 49).
u Over the past four decades, economic growth in Asia has been predominantly explained by thecontribution of capital input, but the role of TFP growth should not be underestimated. Its contribution accounted for over 20% of economic growth in 11 of the 19 Asian countries compared, with
it being most prominent in Sri Lanka (38%), China (36%), Cambodia (36%), and Pakistan (34%)(Figure 51).
u The composition of economic growth is shifting over time. In the past two decades, the contribu-

tion of capital input (especially of non-IT capital) has been getting progressively smaller in Asia,
falling to a share of below 55% on average, while the contribution of TFP is getting progressively
more significant, rising to a share of above 35% on average in 2000–2013 (Figures 53 and 59).
u The evident rise in the contribution of information technology (IT) capital is noteworthy. By the©2015 Asian Productivity Organization

2000s, it had risen to above 5% in most Asian countries compared, while accounting for around
one-third of economic growth in Japan and the US. The allocation shift towards IT capital started
two decades earlier in the US than in any Asian country (Figures 53 and 57).
u Over the past decades, it has been observable that economic growth has decelerated in the earlystarters (Japan and the Asian Tigers). Their experience lends support to the likelihood of an eventual slowdown in China; the question is more likely “when,” than “if.” TFP growth slowed from its
former peaks achieved in the late 1970s or late 1980s until recent years when countries experienced TFP resurgence (Figure 56).

10

Overview

Capital deepening and capital productivity

2u Capital deepening appears to be an accompanying process of rapid economic development. The

early starters (i.e., Japan and the Asian Tigers) underwent more rapid capital deepening in the initial period whereas the reverse is true for the currently emerging Asian economies. For example,
the rise in capital–labor ratio decelerated from 10.7% on average per year to 7.2% in Korea between 1970–1990 and 1990–2013, whereas it doubled in China from 5.2% to 10.8% (Figure 60).
u Capital deepening tends to go hand in hand with deterioration in capital productivity. China’s per-formance is particularly impressive as its acceleration in capital deepening over the past two decades did not compromise its capital productivity as much as the early starters in the early period(Figure 61).
u Over a long period – stretching four decades – a downward trend in labor productivity growth canbe seen among the early starters, but there is a step up in China and India. Singapore’s productivity performance, albeit robust compared with other mature economies like the US, has been very
modest against its Asian counterparts (Figure 68).
Industry structureu Evidence supports the view that a country’s industry structure transforms with its economic devel-

opment. There is a broad negative correlation between the share of agriculture in total GDP and
per capita GDP. Finance, real estate, and business activities increase in weight as countries move up
income levels, whereas mining is the sector that defines the oil-exporting countries (Figure 72).
u Manufacturing is a significant sector, accounting for over 20% of total value added in most Asian

economies. It is particularly prominent in Korea, China, the ROC, Thailand, Malaysia, and Indonesia,
in which higher TFP growths are measured in 2000–2013 (Figure 73). Asian manufacturing is dominated by machinery and equipment in the richer Asian economies while their poorer counterparts
concentrate on light manufacturing such as textiles and the food industry (Figure 74).
u While Asian countries are diversifying away from agriculture, the sector still dominates employ-ment, accounting for 37% of total employment in 2013 for Asia30, down from 61% in 1980. Its
share in total value added decreased more moderately, from 14% to 10% over the same period.
Shifting out of agriculture into more efficient sectors will boost economy-wide productivity (Figures 75 and 78).
u Manufacturing is a main absorption sector for workers who have been displaced from the agricul-

Industry origins of economic growthu Our results support the observation that China and India have taken different development paths,

with the former relying more on the traditional growth engine of manufacturing and the latter onservices. In the past two and a half decades China has been undergoing a slight transition, with its
growth shifting away from manufacturing-driven to more services-driven. In the period 2000–2013,11©2015 Asian Productivity Organizationture sector, especially in the initial stages of economic development. In Korea and the ROC, expansions to manufacturing output could account for the increase of employment in the 1970s and the1980s. Since the 1990s, however, the manufacturing sector has no longer been an absorption sector of employment, regardless of the sound expansion of production in this sector (Figure 80).

2 Overview

the contributions to economic growth by manufacturing and services were 34% and 45%, respectively, compared with 44% and 34% in the 1990s (Figures 82 and 83).
u In contrast, growth in India has always been more driven by services, the contributions of whichare 61% in the 1990s and 65% in 2000–2013, while manufacturing usually contributes one-sixth orless (Figures 82 and 83).
u A total of 27% of Asia30’s regional growth originated from the expansion of manufacturing in

2000–2013, 60% of which was accounted for by China. In other words, China’s manufacturing alone
contributed 17% to regional growth (Figure 86).
u The importance of manufacturing as a contributor to overall labor productivity growth has neverwaned in Korea and the ROC. However, manufacturing has never been a major contributor in India
in its recent development process or in Hong Kong and Sri Lanka in 2000–2013 (Table 18 and Figure 89).
Real income and terms of tradeu Real GDP could systematically underestimate (or overestimate) growth in real income if terms oftrade improve (or deteriorate). It is generally observed that the trading gain effect is more significant in the short term than in the long term. Our findings confirm this observation, with the exceptions in some oil-exporting countries such as Kuwait and Brunei, where trading gain has always
been positive and significant (Table 19 and Figure 97).
u Positive net primary income from abroad also bolsters a country’s real income. In Japan and thePhilippines, net primary income from abroad has been rising steadily, albeit at different magnitudes. In Japan, it rose from 0.6% of GDP in 1990 to 3.5% in 2013, compared with 1.5% in 1990 and
32.2% in 2013 in the Philippines. Singapore’s historical margin fluctuates within a large range whencompared with other rich economies – from +2.0% in 1997 to –7.0% in 2004, but on the whole, it
has been more negative than positive (Figure 91).
u Our results show that for most countries studied, the difference between growth of real GDP andreal income (reflecting the combined effect of trading gain and net primary income from abroad)was within the margin of ±20% over the long period from 1970–2013; Kuwait and Brunei appear tobe the outliers (Figure 92).
u The six countries that have been enjoying a trading gain over 1% per annum in the past four de-©2015 Asian Productivity Organization

cades are all oil-exporting countries. Among them, only Iran managed to achieve a positive growthin labor productivity. In contrast, export-oriented, high productivity Asian countries have been facing a deteriorating trading gain position as a price of their own success (Figure 98).
Asia is a diverse regional economy in which countries have embarked on their own journey of economic development at different times and different paces. As shown by our analysis, nearly all countries are making concerted efforts to move away from agriculture and accumulate capital in order toimprove their growth potential and catch up with the West. Their efforts are yielding results beyond
just impressive growth rates. The evidence gained from our research confirms that countries’ capital
accumulation is accompanied by strong productivity improvements. Through the statistics and data
presented in this report, one manages to catch a glimpse of the current unparalleled economic dynamics inherent in the region.

123.1 Economic Scale and Growth

3 Economic Growth
In the past two decades, the story of the world economy belonged to Asia, featuring its steady rise ineconomic prowess. Before the mid-1980s, the fortune of Asia closely followed that of Japan but 1988
marked the start of their paths decoupling. The Asian economy is no longer defined by Japan alone(Figure 1). Asian growth consistently has been outperforming the West over the past two decades.
With the exception of 1997–1999, when the economy was adversely affected by the Asian financial
crisis, Asia30 has been growing faster than the US and EU15 by more than 3 to 4 percentage points on
average per year. This gap has been widening in recent years. In 2009, at the height of the global financial storm, the growth differentials were 6.8 and 8.5 percentage points with the US and EU15, respectively. In 2010, simultaneous large-scale fiscal stimulus packages helped major economies reboundstrongly, before growth slowed again in 2011. The fortunes of economies were mixed in 2012. The
slowdown in growth was less pronounced in Asia than in the previ%
10
ous year. Plagued by the euro crisis,8
EU15 saw their economy shrink by
6
0.6%, whereas the US and Japanese
4
economies improved. Despite that,2
the differences in growth perfor0
mance have been sustained.
−2
Japan
Asia30

−4
−6
1970

1975

US

EU15

1980

1985

1990

1995

2000

2005

2010

Figure 1 GDP Growth of Asia, the EU, Japan, and the US,
1970–2013_Annual growth rate of GDP at constant market prices

Sources: Official national accounts in each country, including author adjustments.

2013

2020

APO20
23 %

Others
22 %

Asia
45 %

Asia30
41 %
EU28
17 %

EU15
15 %
US
16 %

Other Asia
3%

Others
20 %

Asia
50 %

Asia30
46 %

EU15
13 %
EU28
15 %

APO20
24 %

US
15 %

Other Asia
3%

To better understand the dynamics
of the long-term economic growthwithin the region, the remainder
Figure 2 Share of Asia in World GDP in 2013 and Projecof this chapter details countries’
tion for 2020
_Share of GDP using constant PPP
diverse development efforts andachievements since the 1970s,
Source: IMF, World Economic Outlook Database, April 2015.
through cross-country level comparisons of GDP and other related
performance indicators. To facilitate international level comparisons, harmonized GDP for each of theindividual countries 3 is expressed in its equivalent in a common currency unit (customarily in the US
dollar), using a set of conversion rates between the individual national currencies. The choices forconversion rates are exchange rate and PPP.
3: The database used in the Databook series includes author adjustments made to better harmonize GDP coverage across countries.
See Appendix 1 for the GDP harmonization in this Databook.

13©2015 Asian Productivity Organization

It is therefore no surprise that thecenter of gravity in the global economy is gradually shifting toward
Asia. In 2013, the Asian economy
contributed 45% (41% for Asia30) ofworld output, compared with the
US and EU28, each accounting for
16% and 17%, respectively (Figure2). The IMF (2015) projects the Asianshare in world output will continue
to rise, reaching 50% (46% for
Asia30) by 2020. In contrast, theoutput shares of each of the US and
EU28 will shrink by a similar extent
to 15%.

3

3 Economic Growth

Table 1 GDP using Exchange Rate, 1970, 1980, 1990, 2000, 2010, and 2013
_GDP at current market prices, using annual average exchange rate
1970
Japan
China
India
Iran
PakistanIndonesia
Bangladesh
Korea
ThailandPhilippines
ROC
Saudi Arabia
Malaysia
Hong Kong
KuwaitMyanmar
Sri Lanka
Singapore
Vietnam
UAE
Nepal
Cambodia
Qatar
BahrainOman
Fiji
BruneiMongolia
Bhutan(regrouped)
APO20
358 171.3
Asia24453 216.6
Asia30464 221.6
East Asia
320 152.8
87 41.7
South Asia
35 16.6
ASEAN
ASEAN630 14.3
5 2.2
CLMV
11 5.1
GCC(reference)
1,076 514.5
US1,249 597.5
EU15Australia
Turkey1980(%)

209 100.0
92 43.964 30.4
11 5.410 4.8
10 4.7
10 4.7
9 4.3
7 3.5
7 3.2
6 2.8
5 2.6
4 1.9
4 1.8
3 1.4
3 1.3
3 1.2
2 0.9
1 0.6
1 0.5
1 0.5
1 0.4
1 0.3
0 0.2
0 0.1
0 0.1
0 0.1
0 0.1
0 0.0

45
24

21.6
11.4

(regrouped)
APO20
Asia24
Asia30
East Asia
South Asia
ASEAN
ASEAN6
CLMV
GCC(reference)
US
EU15
Australia
Turkey

1,767
2,083
2,339
1,548
240
196
188
8
257

159.7
188.2
211.4
139.9
21.7
17.7
17.0
0.7
23.2

2,863 258.7
3,327 300.6
173
91

1990

(%)

Japan
1,107 100.0
305 27.5
China
190 17.2
India
Saudi Arabia 165 14.9
97 8.8Iran
80 7.2Indonesia
Korea
65 5.9UAE
44 4.0ROC
42 3.8Thailand
33 3.0Philippines
33 3.0
Kuwait30 2.7
Hong Kong
29 2.625 2.2
Malaysia
24 2.2
Pakistan
Bangladesh
19 1.7Singapore
12 1.1
8 0.7
Qatar
6 0.6
Oman
6 0.5
Myanmar
5 0.5
Brunei
Sri Lanka
4 0.4
3 0.3
Bahrain
3 0.2
Nepal
1 0.1
Fiji
1 0.1
Vietnam
1 0.1
Cambodia
0 0.0
Mongolia
0 0.0
Bhutan

15.6
8.2

(%)

Japan
China
India
Korea
ROC
Indonesia
Saudi Arabia
IranThailandHong Kong
UAE
Philippines
Malaysia
Pakistan
Singapore
Bangladesh
KuwaitOman
Sri Lanka
Qatar
Vietnam
MyanmarBahrain
Nepal
Brunei
Cambodia
MongoliaFiji
Lao PDR
Bhutan(regrouped)
APO20
Asia24
Asia30
East Asia
South Asia
ASEAN
ASEAN6
CLMV
GCC(reference)USEU15

3,182 100.0
392 12.3
335 10.5
279 8.8
167 5.2
127 4.0
118 3.7
95 3.089 2.877 2.451 1.647 1.545 1.444 1.439 1.231 1.019 0.6
12 0.4
8 0.3
7 0.2
7 0.2
5 0.2
5 0.1
4 0.1
3 0.1
2 0.1
2 0.0
1 0.0
1 0.0
0 0.0

Australia
Turkey

324
200

4,581
4,982
5,194
4,098
424
365
350
14
212

144.0
156.6
163.2
128.8
13.3
11.5
11.0
0.5
6.7

5,980 187.9
6,392 200.9
10.2
6.3

2000
Japan
4,886
China
1,209
Korea
562
India
482
ROC
331
Saudi Arabia 190
Hong Kong
172
Indonesia
168
Thailand
127
Iran
110
UAE
106
Singapore96
Malaysia
95
Philippines
81
Pakistan
72
Bangladesh
51
Kuwait38
Vietnam
33
Oman
20
Qatar
18
Sri Lanka
17
8
Bahrain7
Myanmar
6
Nepal
6
Brunei
Cambodia
4
2
Fiji2
Lao PDR
1
Mongolia
0
Bhutan(regrouped)
7,298
APO20
8,521
Asia24
8,901
Asia307,161
East Asia
629
South Asia
618
ASEAN
ASEAN6
572
46
CLMV
381
GCC
(reference)
10,285
US  
9,893
EU15
10,999EU28
409
Australia
268
Turkey

(%)

100.0
24.8
11.5
9.9
6.8
3.9
3.5
3.4
2.6
2.3
2.2
2.0
1.9
1.7
1.5
1.1
0.8
0.7
0.4
0.4
0.3
0.2
0.1
0.1
0.1
0.1
0.0
0.0
0.0
0.0
149.4
174.4
182.2
146.6
12.9
12.6
11.7
0.9
7.8
210.5
202.5
225.1
8.4
5.5

2010
China
6,035
Japan
5,693
India
1,673
Korea
1,094
Indonesia
756
Saudi Arabia 532
Iran
477
ROC
446
Thailand
342
UAE
294
Malaysia
248
Singapore
236
Hong Kong
229
Philippines
200
Pakistan
175
Qatar
127
Kuwait118
Vietnam
117
Bangladesh
115
Oman
60
Sri Lanka
50
42
Myanmar
26
Bahrain
19
Nepal
14
Brunei
Cambodia11
7
Mongolia
7
Lao PDR
3
Fiji
2
Bhutan(regrouped)
11,898APO20
17,990
Asia24
19,146
Asia30
East Asia 13,504
South Asia 2,034
1,972
ASEAN
ASEAN6
1,795
177
CLMV
1,156
GCC
(reference)
14,964US  
14,581EU15
16,756EU28
1,291
Australia
740
Turkey

(%)

100.0
94.3
27.7
18.1
12.5
8.8
7.9
7.4
5.7
4.9
4.1
3.9
3.8
3.3
2.9
2.1
2.0
1.9
1.9
1.0
0.8
0.7
0.4
0.3
0.2
0.2
0.1
0.1
0.1
0.0
197.2
298.1
317.3
223.8
33.7
32.7
29.7
2.9
19.2
248.0
241.6
277.7
21.4
12.3

2013
China
9,366
Japan
5,103
India
1,917
Korea
1,306
Indonesia
911
Saudi Arabia 752
ROC
511
Iran
511
Thailand
424
UAE
413
Malaysia
313
Singapore
302
Hong Kong
275
Philippines
272
Pakistan
222
Qatar
207
Kuwait179
Vietnam173
Bangladesh
154
Oman
79
Sri Lanka
68
64
Myanmar
33
Bahrain
21
Nepal
16
Brunei
Cambodia16
13
Mongolia
11
Lao PDR
4
Fiji
2
Bhutan(regrouped)
12,526
APO20
21,974
Asia24
23,638Asia30
East Asia 16,574
South Asia 2,383
2,503
ASEAN
ASEAN6
2,239
263
CLMV
1,664
GCC
(reference)
16,768US  
15,481EU15
17,917EU28
1,528
Australia
833
Turkey

(%)

100.0
54.5
20.5
13.9
9.7
8.0
5.5
5.5
4.5
4.4
3.3
3.2
2.9
2.9
2.4
2.2
1.9
1.8
1.6
0.8
0.7
0.7
0.4
0.2
0.2
0.2
0.1
0.1
0.0
0.0
133.7
234.6
252.4
176.9
25.4
26.7
23.9
2.8
17.8
179.0
165.3
191.3
16.3
8.9

©2015 Asian Productivity Organization

Unit: Billions of US dollars.
Sources: Official national accounts in each country, including author adjustments.
Note: See Appendix 1 for the adjustments made to harmonize GDP coverage across countries.

3.1 Economic Scale and Growth
Table 1 provides snapshot-level comparisons of Asian countries, based on GDP at current market prices using exchange rates,4 for the six separate years of 1970, 1980, 1990, 2000, 2010, and 2013. By thismeasure, Japan had been the largest economy in Asia until 2010 when China finally overtook Japan’s
position to become the second-largest economy in the world after the US. Japan clearly surged ahead
between the 1970 and 1990 comparisons; dwarfing the relative size of all other Asian economies and14

3.1 Economic Scale and Growth

reducing the US lead from five times to less than twotimes its economy. The turn of Japan’s fortune came in1990, when the country’s excessive growth years of thelate 1980s ended and its descent began. Thereafter, stagnation in Japan combined with vibrant growth in developing Asia has resulted in the rapid erosion of Japan’s
prominence in the regional economy. The countries thatmake up the four largest Asian economies (China, Japan,
India and Korea) have been consistent, with their positions rather secure in the past two decades, whereas
ASEAN as a group has been demonstrating vigor in catching up since 2000. On this measure, Asia30 was 41% and
53% larger than the US and EU15 in 2013, respectively.
Comparisons based on exchange rates however, could
appear arbitrary as movements in exchange rates can bevolatile and subject to short-term or substantial fluctuations of speculative capital flows and government intervention. Furthermore, comparisons based on exchange
rates typically underestimate the size of a developing
economy and, in turn, the perceived welfare of its residents. The scale of economy rankings change dramatically when international price differences are properly taken
into account.5

56 Australia
35
−23
−29
−30
−31
−34
−38
−41
−42
−43
−44
−46
−49
−50
−51
−52
−56
−58
−59
−59
−59
−64
−65
−67
−67
−67
−68
−69
−69
−71
−72

−80 −60 −40 −20 0

Japan
Korea
Singapore
Hong Kong
UAE
Qatar
KuwaitTurkeyFiji
BruneiBahrain
China
ROC
Oman
Saudi Arabia
Malaysia
Iran
Mongolia
Philippines
Indonesia
Thailand
BhutanSri Lanka
Nepal
Cambodia
Vietnam
India
Bangladesh
Lao PDR
Myanmar
Pakistan3

20 40 60 %Figure 3 Price Level Indices of GDP,
2011

_Ratio of PPP to exchange rate (reference
country=US)

4: The exchange rates used in this Databook are the adjusted rates, which are called the Analysis of Main Aggregate (UNSD database) rates in the UN Statistics Division’s National Accounts Main Aggregate Database. The AMA rates coincide with the IMF rates(which are mostly the annual average of market or official exchange rates) except for some periods in countries with official fixedexchange rates and high inflation, when there could be a serious disparity between real GDP growth and growth converted to US
dollars based on IMF rates. In such cases, the AMA adjusts the IMF-based rates by multiplying the growth rate of the GDP deflator
relative to the US.
5: This is because exchange rates embody the trade sector bias (i.e., is more influenced by the prices of traded than non-traded
goods and services) and thus do not necessarily succeed in correcting the price differentials among countries. As developing
economies tend to have relatively lower wages and, in turn, lower prices for non-traded goods and services, a unit of local currency has greater purchasing power in the local economy than reflected in its exchange rate.
6: It is therefore important to note that any international GDP comparisons are sensitive not only to revisions in national accounts
but also to revisions in multilateral PPPs, which are currently benchmarked every six years. PPPs for most Asian countries havebeen revised downward, compared with what they would have been by extrapolating the 2005 benchmark PPP (see Box 1). Thishas the effect of raising the relative sizes of these economies against the base economy.

15©2015 Asian Productivity Organization

Figure 3 shows the extent to which the exchange rates
Sources: Analysis of Main Aggregate rates by United
Nations Statistics Division (UNSD) and PPP by World
have failed to reflect countries’ price differentials properly
Bank (2014).
relative to the US, based on the PPP estimates of the 2011
International Comparisons Program (ICP) round published in April 2014. With the exception of Japan andAustralia, exchange rates systematically under-represent the relative purchasing power for all thecountries covered in this report. The underestimation is substantial for some, ranging from 23% for
Korea to 72% for Pakistan. Thus, the exchange-rate-based GDP considerably underestimates the
economic scales in real terms for those countries. By taking into account the international pricedifferentials, PPP rectifies the trade sector bias, and in turn the relative size of economies can be more
adequately measured.6

3 Economic Growth

Table 2 GDP using PPP, 1970, 1980, 1990, 2000, 2010, and 2013
_GDP at constant market prices, using 2011 PPP, reference year 2013
1970

1980

(%)

Japan
2,457 100.0
India
948 38.6
China
728 29.6
Saudi Arabia 655 26.7
Indonesia
458 18.6
Iran
390 15.9
UAE
203 8.3
Korea
200 8.2
Philippines
199 8.1
Thailand181 7.4
ROC
149 6.1
Pakistan
145 5.9
Kuwait117 4.8
Malaysia
100 4.1
Bangladesh
90 3.7
Hong Kong
83 3.454 2.2
VietnamSingapore
51 2.1
Sri Lanka
39 1.6
Qatar
31 1.328 1.2Oman
27 1.1
Myanmar27 1.1
Brunei17 0.7Nepal
16 0.6BahrainMongolia
7 0.3
4 0.1
Fiji
0 0.0
Bhutan(regrouped)
APO20
Asia24
Asia30
East Asia
South Asia
ASEAN
ASEAN6
CLMV
GCC(reference)
US  
EU15

(regrouped)
APO20
Asia24
Asia30
East Asia
South Asia
ASEAN
ASEAN6
CLMV
GCC(reference)
US  
EU15

Australia
Turkey

3,626
4,150
4,607
2,295
980
601
532
73
474

232.2
265.8
295.1
147.0
62.8
38.5
34.0
4.7
30.3

5,040 322.8
6,232 399.2
284
241

18.2
15.5

Australia
Turkey

5,777
6,707
7,784
3,832
1,312
1,172
1,076
98
1,095

235.1
273.0
316.9
156.0
53.4
47.7
43.8
4.0
44.6

6,885 280.2
8,519 346.8
380
360

1990

(%)

Japan
1,561 100.0
India
705 45.2
China
398 25.5
Iran
280 18.0
Saudi Arabia 248 15.9
Indonesia
205 13.1
Kuwait145 9.3
Philippines
112 7.2
Thailand95 6.1
Pakistan
91 5.8
Korea
83 5.3
Bangladesh
83 5.3ROC
55 3.5
Malaysia
45 2.9Vietnam
42 2.7
Hong Kong
35 2.224 1.5
Sri Lanka
Singapore
22 1.4Myanmar18 1.2
Qatar
18 1.2
11 0.7
Brunei11 0.7UAE
Oman
10 0.7Bahrain
8 0.5
Mongolia
4 0.2
Fiji
2 0.1
Bhutan
0 0.0

15.5
14.6

(%)

Japan
3,902
1,765
China
1,627
India
840
Indonesia
Saudi Arabia 617
515
Korea
Iran
506
Thailand
385
ROC
328
Pakistan
292
Philippines
243
UAE
208
Malaysia
182
159
Hong Kong
135
Bangladesh
Singapore108
Vietnam
92
89
Kuwait63
Oman
59
Sri Lanka
36
Qatar
31
Myanmar
26
Nepal
21
Brunei18
Bahrain
12
Mongolia
9
Cambodia
7
Lao PDR
4
Fiji
1
Bhutan(regrouped)
9,626
APO20
11,665
Asia24
12,723
Asia306,935
East Asia
South Asia 2,232
1,995
ASEAN
1,849
ASEAN6
148
CLMV
1,062
GCC
(reference)
9,558
US
10,879
EU15

100.0
45.2
41.7
21.5
15.8
13.2
13.0
9.9
8.4
7.5
6.2
5.3
4.7
4.1
3.4
2.8
2.4
2.3
1.6
1.5
0.9
0.8
0.7
0.5
0.5
0.3
0.2
0.2
0.1
0.0

512
599

13.1
15.4

Australia
Turkey

246.7
299.0
326.1
177.7
57.2
51.1
47.4
3.8
27.2
245.0
278.8

2000
China
4,774
4,376
Japan
2,768
India
1,270
Indonesia
1,008
Korea
Saudi Arabia 808
Iran
748
ROC
627
Thailand599
Pakistan
470
Malaysia
369
UAE
341
Philippines
340
234
Hong Kong
223
Bangladesh
Singapore215
Vietnam200
157
Kuwait101
Oman
98
Sri Lanka
69
Qatar
62
Myanmar
43
Nepal
30
Bahrain
26
Brunei17
Cambodia13
Lao PDR
Mongolia
11
Fiji
6
2
Bhutan(regrouped)
13,806
APO20
18,938Asia24
Asia3020,471
East Asia 11,306
South Asia 3,706
3,169
ASEAN
2,867
ASEAN6
303
CLMV
1,537
GCC
(reference)
13,405
US
13,597EU15
15,437EU28
725
Australia
860
Turkey

(%)

100.0
91.7
58.0
26.6
21.1
16.9
15.7
13.1
12.6
9.8
7.7
7.1
7.1
4.9
4.7
4.5
4.2
3.3
2.1
2.0
1.4
1.3
0.9
0.6
0.5
0.4
0.3
0.2
0.1
0.0
289.2
396.7
428.8
236.8
77.6
66.4
60.0
6.4
32.2
280.8
284.8
323.3
15.2
18.0

2010
China
12,987
India
5,679
Japan
4,722
Indonesia
2,115
Korea
1,554
Iran
1,351
Saudi Arabia 1,294
ROC
943
Thailand938
Pakistan
758
Malaysia
596
Philippines
542
UAE
506
Vietnam406
Bangladesh
384
Singapore
378
Hong Kong
349
Kuwait
238
Qatar
237
Myanmar
195
163
Sri Lanka
142
Oman
62
Nepal
52
Bahrain38
Cambodia
Brunei30
26
Lao PDR
Mongolia
22
Fiji
6
5
Bhutan(regrouped)
21,072
APO20
34,389Asia24
Asia3036,870
East Asia 20,675
South Asia 7,083
5,284
ASEAN
4,616
ASEAN6
668
CLMV
2,482
GCC
(reference)
15,779US  
15,417EU15
17,730EU28
981
Australia
1,264
Turkey

(%)

100.0
43.7
36.4
16.3
12.0
10.4
10.0
7.3
7.2
5.8
4.6
4.2
3.9
3.1
3.0
2.9
2.7
1.8
1.8
1.5
1.3
1.1
0.5
0.4
0.3
0.2
0.2
0.2
0.0
0.0
162.3
264.8
283.9
159.2
54.5
40.7
35.5
5.1
19.1
121.5
118.7
136.5
7.6
9.7

2013
China
16,478
India
6,718
Japan
4,858
Indonesia
2,513
Korea
1,696
Saudi Arabia 1,542
Iran
1,275
Thailand
1,051
ROC
1,021
Pakistan
841
Malaysia
694
Philippines
643
UAE
587
Vietnam
480
Bangladesh
462
Singapore
434
Hong Kong
383
Qatar
303
Kuwait282
Myanmar239
201
Sri Lanka
159
Oman
71
Nepal
58
Bahrain47
Cambodia
Lao PDR
32
32
Mongolia
30
Brunei7
Fiji
6
Bhutan(regrouped)
23,458
APO20
40,211
Asia24
Asia3043,143
East Asia 24,467
South Asia 8,299
6,163
ASEAN
5,365
ASEAN6
798
CLMV
2,932
GCC
(reference)
16,768US  
15,541EU15
17,965EU28
1,069
Australia
1,464
Turkey

(%)

100.0
40.8
29.5
15.3
10.3
9.4
7.7
6.4
6.2
5.1
4.2
3.9
3.6
2.9
2.8
2.6
2.3
1.8
1.7
1.4
1.2
1.0
0.4
0.4
0.3
0.2
0.2
0.2
0.0
0.0
142.4
244.0
261.8
148.5
50.4
37.4
32.6
4.8
17.8
101.8
94.3
109.0
6.5
8.9

©2015 Asian Productivity Organization

Unit: Billions of US dollars (as of 2013).
Sources: Official national accounts in each country, including author adjustments.
Note: See Appendix 1 for the adjustments made to harmonize GDP coverage across countries.

Table 2 repeats the same snapshot level comparisons of Asian countries as in Table 1, but based on
GDP at constant market prices using constant PPP for Asian countries. By correcting for international
price differentials, Asia30 has been expanding rapidly. It was 157%, instead of 41%, larger than the US
economy in 2013, having overtaken it in 1975 (Figure 4).7 East Asia (China, the ROC, Hong Kong, Japan,
7: This compares with the finding in Databook 2013, which were based on the 2005 benchmark PPP, that the economic size ofAsia30 overtook the US in 1988.

16

3.1 Economic Scale and Growth

Box 1

PPP Revisions by the 2011 ICP RoundPurchasing power parities (PPPs) are indispensable inputs into economic research and policy analysis
involving cross-country comparisons of macroeconomic aggregates. They affect a double conversionof macroeconomic measures, estimated in national currencies and price levels, into comparable crosscountry volume measures. These are expressed in a common currency and at a uniform price level. PPPs
are price relatives that show the ratio of the prices in national currencies of a single or composite goods
and services in different countries. They are compiled within the International Comparisons Program(ICP). Comparisons are made from the expenditure side of GDP. To this end, the ICP compiles PPPs byholding worldwide surveys at regular intervals (currently, every six years) to collect comparable price andexpenditure data for the whole range of final goods and services that make up the final expenditures
on GDP. In April 2014, the new benchmark PPP estimates were published by the ICP 2011 round. For anumber of methodological improvements, see Eurostat-OECD (2012) and World Bank (2014).

It is inevitable that they will be compared with the resultsof the previous round in 2005, which has provided thebenchmark estimate for the past Databook series in 2009–2013. Figure B1 shows the revisions of PPPs in Asian countries at the 2011 ICP round, in comparison with the 2005
ICP round. The 2011 benchmark PPP for most of the Asian
countries are lower than suggested by their extrapolated
equivalents from the 2005 benchmark, with a difference
ranging from +3% for Korea to –47 % for Myanmar. With
the exception of Singapore, it is observed that revisions
for the more mature economies are much smaller (ranging within ±4%) than those for the rapidly developing
economies (with downward revisions larger than 10%).
Therefore, the impact of the PPP revisions is to raise therelative size of Asian economies, moving them closer tothe level of the more mature economies. More specifically,
the PPP revisions for India and China are –24% and –16%,
respectively. As a result, the relative positions of India andChina have improved considerably in cross-country levelcomparisons after PPP revisions at the 2011 ICP round.

3
1
1
−1
−4
−4
−13
−14
−16
−16
−18
−21
−22
−23
−24
−27
−28
−28
−29
−31
−31
−34
−35
−36
−37
−39
−39
−40
−41
−45
−45
−47

−50

−40

−30

−20

−10

0

Korea
Australia
Hong KongJapan
ROC
TurkeyCambodia
BhutanSingapore
China
Iran
VietnamBangladesh
Malaysia
India
Brunei
UAE
Philippines
ThailandSri Lanka
Nepal
Pakistan
Lao PDR
Mongolia
Fiji
Saudi Arabia
Qatar
BahrainOman
KuwaitIndonesia
Myanmar

10 %Figure B1 Revisions of PPP for GDP
by the 2011 ICP Round_Ratio of the 2011 ICP PPP to the 2005 ICP
PPP (extrapolated for 2011)

Source: World Bank, World Development Indicators
2014.

These revisions by the 2005 ICP round have a property topartly offset the past upward revisions by the 2005 ICP round for many Asian countries. As shown in the
Databook 2009 (Box 1, p.14), the 2005 benchmark PPP for most of the Asian countries were upwardly
revised compared to their extrapolated equivalents from the 1993 benchmark estimates that had beenused in the Databook 2008. For example, the PPP estimates were upwardly revised by 55% and 65% (thusthe internationally comparable measures of GDP in 2005 were reduced by 36% and 40%) for India andChina, respectively.
Singapore is an exceptional country, in which the PPP has been downwardly revised (thus the relative sizeof the economy has been upwardly revised) by both of the two revisions of the ICP 2005 and 2011 rounds.
continued on next page >17©2015 Asian Productivity Organization

Chapter 3 mainly provides the cross-country comparisonof economic volumes. To obtain comparable volume measures, the Databook uses the constant PPP approach,which relies not on a time series of PPPs, but on one of thebenchmark estimates. The Databook has used the new
benchmark estimates by the ICP 2011 round since lastyear’s publication. The use of this approach creates national series for volumes at the prices of a common reference year (i.e., 2013), and deflates these by the PPP for a
fixed year (i.e., 2011).

3

3 Economic Growth> continued from previous page

The PPP for Singaporean GDP was revised by –29% and by –16% in the ICP 2005 and 2011 rounds, respectively. Based on the constant PPP approach, the revision by the ICP 2011 round advanced the years when
the Singapore economy has surpassed Japan and the U.S. to 1980 (from 1993) and 1992 (from 2004), respectively, as a measure of per capita GDP. It may require further examination if this revision provides anappropriate view. Generally speaking, the cross-country level comparison has to face a much larger opportunity to be revised, compared to the cross-country growth comparison. The readers should bear inmind these caveats.

Korea, and Mongolia) caught up with the US in 2006 from a low base of 46% in 1970. In contrast, EU15
has been experiencing a gradual relative decline in economic size, from 124% of the US economy in1970 to a low of 93% in 2013. Based on GDP using constant PPP, the weight of the world economy iseven more tilted toward Asia than portrayed by GDP using exchange rates. This reflects the fact thatnearly all Asian countries increase in relative size after international price differentials have been properly taken into account.
The relative size of China’s economy in 2013 was 339% or more than three times that of Japan, compared with 183% when exchange rates are used in Table 1. Considering that the Chinese economy was
only 26% that of Japan and 56% that of India in 1970, represents remarkable growth. On this measure,
China overtook Japan after 1999 to become the leading economy in Asia (Figure 5).8 Similarly, its sizein 2013 increased from 56% to 98% relative to the US economy after adjusting for their price differences. Assuming that China and the US also grow at the usual pace as they have displayed since 2000,
China might overtake the US economy in 2014.
Given that PPP for India have been revised by –24% in the 2011 ICP round (see Box 1), the effects havebeen to raise the relative size of India. Relative to Japan, the Indian economy has been increasing from
44% in 1970 to 138% in 2013, surpassing Japan and replacing it as the second largest economy in Asia
in 2008. In 2013, the total GDP of the three largest Asian economies alone was 67% larger than the
US economy.

US=100

US=100

250

US

100

220

China

80

190
60

160
EU15

130
100

Asia30
40

US

70

20

APO20

India

East Asia

40
1970 1975 1980 1985 1990 1995 2000 2005 2010©2015 Asian Productivity Organization

Japan

0
1970 1975 1980 1985 1990 1995 2000 2005 2010

Figure 4 Regional GDP of Asia and the EU,
Relative to the US, 1970–2013

Figure 5 GDP of China, India, and Japan,
Relative to the US, 1970–2013

2011 PPP

2011 PPP_Indices of GDP at constant market prices, using

Sources: Official national accounts in each country, including author adjustments.

_Indices of GDP at constant market prices, using

Sources: Official national accounts in each country, including author adjustments.

8: The shift of the benchmark year PPP estimates from 2005 to 2011 has the effect of bringing forward the year when China overtook
Japan in relative GDP to 1999, from 2002 in Databook 2013.

18

3.1 Economic Scale and Growth

Figure 6 shows the rapid expansion of the relative size of the South Asian economy (consisting of Bangladesh, Bhutan, India, Nepal,
Pakistan, and Sri Lanka), 81% of which was accounted for by India in 2013. ASEAN also
showed vigor in their catch-up effort. They wereon par with the South Asian economy in 1996–
1997 before the setback caused by the Asian financial crisis of 1997–1998 took hold, setting
them on a lower growth path, once again opening up a divergence. In contrast, the progress of
GCC9 countries lagged for two decades. Only inthe past decade has it picked up and brought
the relative size of the country group back to its
previous peak of the early 1980s.10

US=100

50
40

ASEAN

3

30
20

ASEAN6South Asia

GCC

10
CLMV

0
1970 1975 1980 1985 1990 1995 2000 2005 2010

Figure 6 Regional GDP of South Asia, ASEAN,
CLMV, and GCC, Relative to the US, 1970–2013_Indices of GDP at constant market prices, using 2011
PPP

Sources: Official national accounts in each country, including au-

thor adjustments.
Countries’ relative performance is also transformed when economic growth is used as theyardstick. Table 3 presents cross-country comparisons of real GDP growth in Asia, covering the 1990s and 2000s.11 The rankings vary from period toperiod and are no longer dominated by the economic giants. In fact, small developing Asian countries, like Qatar, Myanmar, Cambodia, Vietnam, the Lao PDR, and Mongolia, are equally capable of exhibiting exuberant growth. As labor costs are edging up in China (see Box 5, p. 64), the workshop of
the world has started shifting its location to the neighboring countries such as Cambodia, the Lao
PDR, Myanmar, and Vietnam. To capture the dynamism, a new country group, called CLMV, is formed
for the Databook to track from this edition onward. They are clearly the faster growing group among
the ASEAN countries, at 7.6% on average per year compared with 4.8% managed by ASEAN6 in the
period 1990–2013.

9: GCC consists of Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the UAE. These GCC countries display economic characteristics
very different from those of other Asian economies due to their preponderant reliance on the oil and energy sector. In 2012, thesecountries account for about 34% of the world’s proven crude oil reserves and possess at least 21% of the proven global natural
gas reserves (GCC Secretariat General, 2014).
10: In interpreting the results in this report, one must bear in mind that conventional GDP tends to overstate the income of these oilexporting countries since it does not account for the depletion of natural resource stock, and in turn a large part of their GDP may
not be sustainable. Besides, GDP growth can underestimate the growth of real income available to the country brought about by
a favorable change in terms of trade, and vice versa. For an oil-exporting country, the growth wedge of the two measures could
be significant in the face of volatile oil prices. See Chapter 7.
11: Annual data maximize the use of available information and data, and are normally published two to three years in arrears. For
more timely analysis, quarterly economic data are used as they are normally published within a month and a half of the reference
period and are subsequently revised as more data become available. A trade-off always exists between data timeliness and precision.

19©2015 Asian Productivity Organization

At the other end of the table, Japan consistently has been struggling at the bottom over the past
two decades (1990–2013), with an average growth of 1.0% per year, compared with Asia30’s 5.5% andthe fastest growth of 9.7% achieved by China. During this period, only three Asian countries – Brunei,Fiji, and Japan – grew slower than the US (2.4%), and only Japan grew slower than EU15 (1.6%). The
divergence of growth performance between the Asian countries on the one hand and the US and
EU15 on the other was even more pronounced if focusing on the most recent years, with Asia30
growing at 5.4% on average per annum, compared with 2.0% in the US and 0.3% in EU15 in the pe­riod 2010–2013.

3 Economic Growth

Table 3 GDP Growth, 1990–1995, 1995–2000, 2000–2005, 2005-2010, and 2010–2013_Average annual growth rate of GDP at constant market prices
1990–1995

1995–2000

China
Malaysia
KuwaitSingapore
VietnamThailandKorea
Indonesia
ROC
Cambodia
Lao PDR
Oman
Myanmar
PakistanBahrain
Sri Lanka
Hong Kong
Bangladesh
India
Nepal
BhutanIran
UAE
Brunei
Philippines
Saudi Arabia
Fiji
Qatar
Japan
Mongolia(regrouped)
APO20
Asia24
Asia30
East Asia
South Asia
ASEAN
ASEAN6
CLMV
GCC(reference)
US  
EU15

11.6
9.3
9.2
8.3
8.1
8.1
8.1
7.5
7.2
6.5
6.2
5.7
5.7
5.5
5.3
5.3
5.2
5.0
5.0
4.9
3.8
3.7
3.6
3.1
2.8
2.8
2.7
2.4
1.4
−2.8

Australia
Turkey

3.2
3.2

4.3
5.7
5.5
5.5
5.1
7.3
7.3
7.4
3.8
2.6
1.6

Qatar
China
MyanmarVietnam
Cambodia
BhutanUAE
Lao PDR
ROC
India
Singapore
Korea
Bangladesh
Malaysia
Sri Lanka
Nepal
Bahrain
Iran
Pakistan
Philippines
Oman
Mongolia
Hong Kong
Saudi Arabia
KuwaitFiji
BruneiJapan
ThailandIndonesia(regrouped)
APO20
Asia24
Asia30
East Asia
South Asia
ASEAN
ASEAN6
CLMV
GCC(reference)
US  
EU15
EU28
Australia
Turkey2000–2005
10.6
8.3
8.2
7.3
7.0
6.8
6.3
6.0
5.8
5.7
5.5
5.3
5.1
4.9
4.9
4.8
4.2
4.1
4.0
3.9
3.7
2.7
2.6
2.6
2.1
2.0
1.4
0.9
0.7
0.7
3.1
4.3
4.3
4.5
5.4
2.4
1.9
7.4
3.7
4.2
2.8
2.8
3.8
4.1

Myanmar
China
Cambodia
Qatar
VietnamBhutanKuwait
Iran
India
Mongolia
Lao PDR
BahrainPakistan
UAE
Thailand
Malaysia
Bangladesh
Singapore
Korea
Indonesia
Philippines
Hong Kong
Sri Lanka
ROC
Saudi Arabia
Nepal
Brunei
Fiji
Japan
Oman
(regrouped)
APO20
Asia24
Asia30
East Asia
South Asia
ASEAN
ASEAN6
CLMV
GCC(reference)
US  
EU15
EU28
Australia
Turkey2005–2010
12.1
9.4
9.0
8.0
8.0
7.7
7.2
6.8
6.5
6.3
6.2
5.9
5.9
5.4
5.3
5.2
5.0
4.8
4.6
4.6
4.5
4.1
4.0
4.0
3.7
3.1
2.1
2.0
1.2
1.0
4.3
5.7
5.6
5.6
6.3
5.2
4.8
9.0
4.6
2.5
1.8
1.9
3.4
4.5

Qatar
Myanmar
China
BhutanIndia
Lao PDR
Cambodia
Singapore
Mongolia
Sri Lanka
VietnamBangladesh
Oman
Saudi Arabia
Indonesia
Bahrain
Iran
Philippines
Nepal
Malaysia
ROC
Korea
Hong Kong
Thailand
Pakistan
UAE
KuwaitFiji
BruneiJapan(regrouped)
APO20
Asia24
Asia30
East Asia
South Asia
ASEAN
ASEAN6
CLMV
GCC(reference)
US  
EU15
EU28
Australia
Turkey2010–2013
16.6
10.7
10.6
9.0
7.8
7.8
6.5
6.5
6.4
6.2
6.2
5.9
5.7
5.7
5.6
5.4
5.0
4.8
4.4
4.4
4.2
4.0
3.8
3.7
3.7
2.5
1.2
0.7
0.7
0.3
4.4
6.6
6.5
6.8
7.2
5.3
5.0
7.5
5.3
0.8
0.7
0.9
2.7
3.2

Mongolia
Qatar
China
Lao PDR
Cambodia
Sri Lanka
MyanmarBangladesh
Saudi Arabia
Indonesia
Philippines
KuwaitIndia
Vietnam
Malaysia
BhutanUAE
Nepal
Singapore
Oman
ThailandBahrainPakistan
Hong Kong
Fiji
Korea
ROC
Japan
BruneiIran(regrouped)
APO20
Asia24
Asia30
East Asia
South Asia
ASEAN
ASEAN6
CLMV
GCC(reference)
US  
EU15
EU28
Australia
Turkey1990–2013

12.9
8.2
7.9
7.7
7.1
7.1
6.8
6.1
5.8
5.8
5.7
5.6
5.6
5.6
5.1
5.0
4.9
4.7
4.6
3.9
3.8
3.6
3.5
3.1
3.1
2.9
2.7
0.9
0.7
−1.9
3.6
5.4
5.4
5.8
5.4
5.3
5.1
6.1
5.7
2.0
0.3
0.4
2.9
4.9

China
Qatar
MyanmarCambodia
VietnamLao PDR
BhutanIndia
Singapore
Malaysia
Bangladesh
Sri Lanka
Korea
Bahrain
KuwaitROC
Indonesia
Pakistan
UAE
Mongolia
Thailand
Nepal
Philippines
Oman
Iran
Saudi Arabia
Hong Kong
Fiji
BruneiJapan(regrouped)
APO20
Asia24
Asia30
East Asia
South Asia
ASEAN
ASEAN6
CLMV
GCC(reference)
US  
EU15
EU28
Australia
Turkey

9.7
9.2
8.9
7.3
7.2
6.7
6.6
6.2
6.0
5.8
5.4
5.4
5.2
5.0
5.0
4.9
4.8
4.6
4.5
4.4
4.4
4.3
4.2
4.0
4.0
4.0
3.8
2.0
1.7
1.0
4.0
5.5
5.5
5.6
5.9
5.1
4.8
7.6
4.5
2.4
1.6
1.6
3.2
3.9

©2015 Asian Productivity Organization

Unit: Percentage.
Sources: Official national accounts in each country, including author adjustments.
Note: See Appendix 1 for the adjustments made to harmonize GDP coverage across countries.

The change of guards in Asia is clearly illustrated in Figure 7. While Japan was the standard-bearer inyesteryears, China and India have emerged as the driving force propelling Asia forward over the past
two decades (1990–2013) and accounting for 45% and 16% of regional growth, respectively. Despite
being the slowest growing economy in Asia, Japan has remained the fifth largest contributor to regional growth in 1990–2013, due to its size.

20

3.1 Economic Scale and Growth

Looking at the five sub-periods inTable 3, growth in the reference
countries, namely the US, EU15,
and Australia, revived in the latter
half of the 1990s, before it deteriorated in the subsequent two periods in the 2000s. Both the US
and EU15 went through deep recessions in 2009, following the
global financial storm. Consequently, the US managed a
growth of 0.8% on average per
year in the period from 2005 to2010. EU15 fared worse as they
dipped into recession again in2012 on the stress of the euro crisis. They managed an average annual growth of only 0.3% in the
period from 2010 to 2013, a sharp
contrast to a resurgence of the US
economic growth of 2.0% in thesame period. Growth in Australia
has been faster than that in the
US and EU15, and sustained by,among other things, China’s surging demand for commodities
even through the turbulent yearsof global financial crisis.

1970–1990
Japan
China
India
Indonesia
Saudi Arabia
Korea
Iran
Thailand
ROC
UAE
Pakistan
Philippines
Malaysia
Hong Kong
Singapore
Oman
VietnamBangladesh
Sri Lanka
Qatar
MyanmarBrunei
Nepal
BahrainMongolia
Cambodia
Lao PDR
Fiji
BhutanKuwait−10

1990–2013
29.4

16.0
10.8
7.9
6.2
5.1
3.8
3.4
3.3
3.1
2.4
1.8
1.7
1.5
1.1
0.7
0.6
0.5
0.4
0.3
0.2
0.2
0.2
0.2
0.1
0.1
0.1
0.0
0.0

−0.7
0

10

20

30 %

China
India
5.6
Indonesia
4.4
Korea
3.9
Japan
Iran 2.9
Saudi Arabia 2.9
ROC 2.6
Thailand 2.4
Pakistan 2.0
Malaysia 1.8
UAE 1.3
Vietnam 1.3
Philippines 1.3
Singapore 1.1
Bangladesh 1.1
Hong Kong 0.8
Qatar 0.7
Kuwait 0.7
Myanmar 0.6
Sri Lanka 0.5
Oman 0.3
Nepal 0.2
Bahrain 0.1
Cambodia 0.1
Lao PDR 0.1
Mongolia 0.0
Brunei 0.0
Bhutan 0.0
Fiji 0.0
0

10

45.3
15.9

3

20

30

40

50 %

Figure 7 Country Contributions to Regional GDP Growth,
1970–1990 and 1990–2013_Contribution share to the growth of gross regional products (growthrate of Asia30=100)

Sources: Official national accounts in each country, including author adjustments.
Note: The starting periods for Nepal, the Lao PDR, and Cambodia are 1974, 1984, and1987, respectively.

Based on Table 3, one might assume that Asia has not been even slightly affected by the global financial crisis, as Asia30’s growth rate accelerated from 5.6% to 6.5% between 2000–2005 and 2005–2010.
However, Asia30’s growth slowed significantly from a recent peak of 8.2% in 2007, to 4.8% in 2008 andfurther to 4.0% in 2009, before rebounding strongly to 7.9% in 2010. Growth moderated again to 5.4%
in the period from 2010–2013, partly reflecting the impact of the fiscal stimulation in response to thecrisis. Out of the 30 countries, 12 Asian economies experienced negative growth in 2009. Japan went
through the deepest contraction of 5.6%. Of the Asian Tigers, only Korea managed a narrow escapefrom recession with 0.7% growth in 2009, likely due to strengthening an economic relationship with
China which recorded 9.0% growth in 2009.

21©2015 Asian Productivity Organization

Growth in Asia has gone from strength to strength, with a blip in the second half of the 1990s due tothe Asian financial crisis. Fastest acceleration has been achieved by South Asia, from an annual average growth rate of 5.1% in 1990–1995 to 7.2% in 2005–2010, compared with 5.5% and 6.8% for East
Asia, respectively. Among all country groups, ASEAN6 was most impacted by the Asian financial crisisof 1997, which slowed its average annual growth drastically from 7.3% to 1.9% in 1990–1995 and1995–2000, respectively (see Figure 6). More than one decade later, it has not yet fully recovered its
pre-crisis growth vitality, with the 2010–2013 average annual growth rate 2.2 percentage points lower
than in the first half of the 1990s. CLMV on the other hand has been the fastest growing country groupin Asia.

3 Economic Growth

China (CHN)
1.0
Hong Kong (HKG) 0.5 1.0
Japan (JPN)
0.0 0.7 1.0
Korea (KOR)
0.2 0.8 0.7 1.0
Mongolia (MGL) −0.4 −0.4 −0.2 −0.1 1.0
ROC (ROC)
0.5 0.8 0.6 0.7 −0.7 1.0
Bangladesh (BAN) 0.4 −0.0 −0.0 −0.1 0.1 −0.2 1.0
Bhutan (BTN)−0.5 −0.2 −0.2 −0.1 0.8 −0.5 0.2 1.0
India (IND)
0.1 −0.4 −0.4 −0.1 0.6 −0.4 0.6 0.6 1.0
Iran (IRN)
0.0 0.1 0.2 −0.1 −0.4 0.0 0.5 −0.1 −0.1 1.0
0.1 0.1 −0.2 0.2 0.0 −0.1 0.3 −0.1 0.2 0.4 1.0
Nepal (NEP)
0.4 0.3 0.4 0.4 −0.5 0.7 0.4 −0.2 0.1 0.3 −0.1 1.0
Pakistan (PAK)
0.3 0.3 −0.0 0.1 0.1 −0.0 −0.3 −0.1 −0.3 −0.3 0.2 −0.6 1.0
Sri Lanka (SRI)
Brunei (BRN)
0.3 0.4 0.4 0.6 −0.3 0.6 0.3 0.0 0.2 0.3 0.0 0.9 −0.5 1.0
Cambodia (CAM) −0.3 0.2 0.1 0.5 0.1 0.3 −0.4 0.4 0.1 −0.2 −0.1 0.2 −0.3 0.5 1.0
Fiji (FIJ)
0.3 −0.1 −0.4 0.1 0.1 0.1 0.4 0.2 0.8 −0.0 0.2 0.4 −0.5 0.4 0.4 1.0
Indonesia (IDN)
0.5 0.9 0.8 0.9 −0.3 0.8 0.1 −0.3 −0.2 0.0 0.1 0.5 0.2 0.5 0.1 −0.0 1.0
Lao PDR (LAO)
0.4 0.4 0.1 0.5 0.3 0.3 0.3 0.4 0.6 −0.4 −0.1 0.3 0.0 0.4 0.4 0.6 0.5 1.0
Malaysia (MAL)
0.5 0.9 0.8 0.9 −0.3 0.7 0.1 −0.2 −0.1 0.1 0.1 0.4 0.2 0.5 0.2 0.0 1.0 0.5 1.0
Myanmar (MYA) −0.0 0.1 −0.2 0.1 0.3 −0.2 0.4 0.7 0.5 0.1 0.2 0.1 0.0 0.2 0.4 0.4 −0.1 0.5 0.0 1.0
Philippines (PHL) 0.1 0.4 0.3 0.4 0.6 −0.1 0.3 0.4 0.4 −0.1 0.2 −0.2 0.3 −0.1 −0.0 0.1 0.4 0.7 0.5 0.3 1.0
Singapore (SIN)
0.6 0.9 0.5 0.8 −0.2 0.6 0.0 −0.2 −0.1 −0.0 0.2 0.1 0.5 0.2 0.1 0.0 0.9 0.6 0.9 0.1 0.6 1.0
Thailand (THA)
0.6 0.7 0.5 0.8 −0.5 0.8 0.2 −0.4 −0.0 0.2 0.3 0.7 −0.0 0.8 0.2 0.3 0.8 0.4 0.8 0.0 0.1 0.7 1.0
Vietnam (VIE)
0.5 0.3 0.3 0.1 0.3 0.0 0.7 0.1 0.4 −0.0 −0.1 0.2 0.2 0.2 −0.4 0.1 0.5 0.6 0.4 0.1 0.7 0.4 0.3 1.0
Bahrain (BHR)
0.3 0.1 −0.2 −0.1 −0.4 0.0 0.3 −0.5 −0.2 0.4 0.8 −0.0 0.3 −0.2 −0.5 −0.0 0.0 −0.3 0.1 0.0 −0.2 0.1 0.2 −0.1 1.0
Kuwait (KWT)
0.6 0.1 −0.4 −0.2 −0.1 0.0 0.6 0.1 0.4 0.0 0.1 0.3 0.1 0.1 −0.2 0.5 −0.0 0.5 0.0 0.6 0.1 0.2 0.1 0.4 0.4 1.0
Oman (OMN)
0.5 0.5 0.4 0.1 −0.6 0.5 0.1 −0.5 −0.6 0.1 −0.2 0.3 0.4 0.0 −0.4 −0.5 0.4 −0.1 0.4 −0.1 −0.1 0.4 0.3 0.3 0.3 0.3 1.0
Qatar (QAT)−0.2 −0.1 −0.1 −0.4 0.3 −0.4 −0.1 0.3 -0.0 −0.4 −0.6 −0.3 0.1 −0.5 −0.1 −0.3 −0.3 0.1 −0.3 0.2 0.1 −0.2 −0.7 0.1 −0.2 0.2 0.2
Saudi Arabia (SAU) −0.2 0.2 0.5 −0.0 −0.6 0.3 −0.3 −0.4 −0.8 0.5 −0.4 0.3 −0.2 0.1 −0.1 −0.6 0.1 −0.6 0.0 −0.4 −0.5 −0.2 0.0 −0.3 −0.0 −0.4 0.5
UAE (UAE)−0.2 0.3 0.3 0.3 0.6 −0.2 0.1 0.8 0.3 −0.1 −0.3 −0.1 0.2 0.1 0.4 −0.1 0.2 0.5 0.2 0.6 0.6 0.3 −0.1 0.4 −0.6 0.0 −0.0
Australia (AUS)
0.2 −0.5 −0.7 −0.5 0.5 −0.6 0.4 0.3 0.7 −0.4 0.0 −0.3 0.0 −0.4 −0.3 0.5 −0.4 0.4 −0.4 0.3 0.3 −0.2 −0.4 0.4 0.0 0.6 −0.3
Turkey (TUR)
0.1 0.1 0.2 −0.2 −0.0 −0.2 0.5 −0.1 −0.1 0.3 0.3 −0.0 0.3 −0.2 −0.7 −0.4 0.1 −0.2 0.1 0.0 0.2 0.1 −0.1 0.4 0.6 0.3 0.6
EU15−0.7 −0.3 −0.0 −0.1 0.5 −0.4 −0.3 0.8 0.1 −0.1 −0.4 −0.2 −0.2 0.0 0.6 −0.2 −0.4 0.0 −0.3 0.4 0.0 −0.4 −0.4 −0.3 −0.7 −0.4 −0.4
US−0.2 −0.3 −0.6 −0.3 0.6 −0.6 0.3 0.8 0.6 −0.2 −0.1 −0.3 −0.0 −0.3 0.2 0.4 −0.4 0.5 −0.4 0.7 0.4 −0.2 −0.5 0.1 −0.2 0.5 −0.4
If greater than 0.55

1.0
0.0 1.0
0.4 −0.2
0.4 −0.7
0.3 0.1
0.3 0.1
0.5 −0.6

US

TUR

EU15

AUS

UAE

Group 5
SAU

QAT

OMN

KWT

BHR

VIE

THA

SIN

Group 4
PHL

MYA

MAL

LAO

IDN

FIJ

CAM

BRN

SRI

PAK

Group 3

NEP

IRN

IND

BTN

BAN

ROC

Group 2
MGL

KOR

JPN

HKG

CHN

Group 1

1.0
0.0 1.0
0.0 0.1 1.0
0.7 −0.1 −0.3 1.0
0.5 0.7 −0.1 0.4 1.0

If less than −0.55

Figure 8 Correlation of GDP Growth, 1990–2000_Correlation of GDP growth at constant market prices©2015 Asian Productivity Organization

Sources: Official national accounts in each country, including author adjustments.

It has been a subject of much debate whether the Asian economy has decoupled from the US and
EU15. If it has, the world economy will be substantially less volatile. Park and Shin (2009) show that
East Asia has seen a marked increase in intra-regional trade and, at the same time, diversified its export markets to other parts of the world resulting in an output movement that is more idiosyncratic
than before. In turn, East Asia is less dependent on the US. Such increased self-subsistence is a necessary adaptation. In recent years the US has become less and less reliable as an outlet of China’s final
goods export. In contrast, the impact of Asia’s extra-regional integration with the global financial markets on business cycle synchronicity is less clear-cut. While deep financial markets allow more risk diversification, and the smoothing out of consumption, closer integration also provides the conduit for
financial contagion. East Asia still suffers from the flight for quality when a crisis strikes. As the impactof the global financial crisis was filtering through, Asia initially seemed immune to the adverse effects.
However, once global investors began to retreat from the region and the financial menace began toinexorably spread through the real economy, Asia, too, started to slow.
Figures 8 and 9 compare the correlation coefficients of growth rates among countries in the 1990s
and the period from 2000 to 2013, respectively. Countries are grouped by region. Overall, the fortunesof the reference countries have become increasingly tied to Asia in a pro-cyclical manner. It is22

3.2 Catching Up in Per Capita GDP

1.0
China (CHN)
Hong Kong (HKG) 0.6 1.0
Japan (JPN)
0.2 0.7 1.0
Korea (KOR)
0.4 0.5 0.6 1.0
Mongolia (MGL) −0.1 0.4 0.4 0.0 1.0
0.5 0.8 0.8 0.7 0.2 1.0
ROC (ROC)
Bangladesh (BAN) 0.4 0.4 0.2 −0.2 0.5 0.1 1.0
Bhutan (BTN)
0.6 0.4 0.2 0.6 −0.1 0.7 −0.0 1.0
0.6 0.5 0.4 0.1 −0.3 0.5 0.3 0.2 1.0
India (IND)
0.6 0.4 0.1 0.6 −0.4 0.5 −0.3 0.5 0.3 1.0
Iran (IRN)
0.2 0.0 −0.1 −0.3 0.3 0.2 0.3 0.4 0.2 −0.2 1.0
Nepal (NEP)
0.4 0.6 0.3 0.2 0.0 0.3 0.1 0.1 0.4 0.4 −0.0 1.0
Pakistan (PAK)
0.3 0.6 0.4 0.0 0.6 0.6 0.5 0.4 0.3 −0.1 0.7 0.2 1.0
Sri Lanka (SRI)
0.2 0.3 0.4 0.6 −0.0 0.4 −0.2 0.2 −0.1 0.5 −0.4 0.2 −0.0 1.0
Brunei (BRN)
Cambodia (CAM) 0.5 0.8 0.7 0.4 0.4 0.5 0.4 0.1 0.3 0.3 −0.2 0.7 0.2 0.3 1.0
Fiji (FIJ)−0.4 0.2 0.4 0.3 0.4 0.3 −0.3 −0.1 −0.2 −0.1 −0.2 0.0 0.1 0.2 0.1 1.0
Indonesia (IDN)
0.4 0.5 0.3 0.0 0.6 0.5 0.8 0.3 0.2 −0.3 0.6 −0.1 0.9 −0.2 0.2 −0.1 1.0
Lao PDR (LAO)
0.3 0.3 0.1 −0.2 0.4 0.2 0.7 0.0 0.3 −0.4 0.5 −0.1 0.7 −0.2 −0.1 −0.0 0.8 1.0
Malaysia (MAL)
0.3 0.8 0.8 0.5 0.6 0.8 0.3 0.5 0.2 0.1 0.3 0.3 0.7 0.3 0.6 0.4 0.6 0.2 1.0
Myanmar (MYA)
0.6 0.3 0.2 0.3 −0.4 0.3 −0.3 0.3 0.4 0.7 −0.2 0.6 −0.2 0.2 0.4 −0.3 −0.4 −0.4 0.1 1.0
Philippines (PHL)
0.2 0.7 0.9 0.3 0.5 0.7 0.4 0.2 0.5 −0.2 0.2 0.2 0.6 −0.0 0.5 0.4 0.6 0.4 0.8 −0.0
Singapore (SIN)
0.6 0.9 0.8 0.6 0.2 1.0 0.3 0.6 0.6 0.4 0.2 0.3 0.7 0.3 0.4 0.3 0.6 0.4 0.8 0.2
0.2 0.5 0.8 0.5 0.1 0.7 −0.1 0.4 0.3 0.2 −0.2 0.3 0.2 0.5 0.5 0.3 0.1 −0.2 0.7 0.4
Thailand (THA)
0.4 0.5 0.3 0.4 −0.1 0.4 −0.1 0.3 0.3 0.7 −0.3 0.7 −0.1 0.4 0.7 −0.1 −0.2 −0.6 0.3 0.7
Vietnam (VIE)
0.6 0.5 0.4 0.3 0.1 0.4 0.2 0.2 0.5 0.4 0.1 0.6 0.1 −0.0 0.7 −0.2 0.1 −0.1 0.4 0.7
Bahrain (BHR)
Kuwait (KWT)
0.3 0.5 0.4 0.1 0.5 0.3 0.2 0.1 −0.0 0.2 0.1 0.6 0.3 0.4 0.7 0.0 0.2 −0.2 0.6 0.2
Oman (OMN)−0.0 −0.2 −0.2 −0.3 0.0 −0.3 0.5 −0.3 0.1 −0.5 −0.1 −0.4 −0.0 −0.5 −0.2 −0.2 0.3 0.6 −0.3 −0.3
Qatar (QAT)
0.7 0.5 0.1 0.3 0.2 0.4 0.4 0.4 0.3 0.3 0.2 0.2 0.5 0.1 0.1 0.1 0.5 0.7 0.3 0.2
Saudi Arabia (SAU) 0.3 0.5 0.3 −0.1 0.6 0.4 0.5 0.2 0.0 −0.1 0.4 −0.0 0.7 0.1 0.2 −0.1 0.7 0.5 0.6 −0.2
UAE (UAE)
0.2 0.7 0.6 0.3 0.6 0.4 0.3 0.0 0.1 0.1 0.0 0.7 0.4 0.4 0.8 0.4 0.2 0.1 0.7 0.3
Australia (AUS)
0.4 0.3 0.3 0.4 0.1 0.2 0.0 0.2 0.1 0.5 −0.2 0.4 −0.1 0.7 0.5 0.0 −0.3 −0.3 0.2 0.3
Turkey (TUR)
0.4 0.8 0.7 0.5 0.5 0.9 0.2 0.5 0.3 0.3 0.3 0.5 0.8 0.4 0.5 0.4 0.5 0.2 0.9 0.1
EU15
0.5 0.8 0.8 0.7 0.4 0.6 0.2 0.3 0.2 0.5 −0.3 0.4 0.2 0.6 0.8 0.3 0.2 −0.1 0.7 0.3
US
0.2 0.8 0.9 0.5 0.5 0.7 0.2 0.2 0.3 0.2 −0.1 0.6 0.4 0.5 0.8 0.5 0.2 −0.1 0.8 0.3
If greater than 0.55

US

TUR

EU15

AUS

UAE

Group 5
SAU

QAT

OMN

KWT

BHR

VIE

THA

SIN

Group 4
PHL

MYA

MAL

LAO

IDN

FIJ

CAM

BRN

SRI

PAK

Group 3

NEP

IRN

IND

BTN

BAN

ROC

Group 2
MGL

KOR

JPN

HKG

CHN

Group 13

1.0
0.7 1.0
0.7 0.5 1.0
0.0 0.3 0.3 1.0
0.4 0.3 0.4 0.6 1.0
0.3 0.2 0.4 0.5 0.5 1.0
0.0 −0.1 −0.3 −0.6 −0.2 −0.6 1.0
0.2 0.5 −0.0 −0.1 0.2 −0.1 0.3 1.0
0.3 0.5 0.1 −0.0 0.1 0.5 0.0 0.4
0.6 0.4 0.5 0.4 0.5 0.8 −0.4 0.2
0.0 0.1 0.3 0.5 0.3 0.7 −0.6 −0.1
0.6 0.8 0.5 0.4 0.3 0.6 −0.4 0.3
0.5 0.6 0.5 0.6 0.6 0.6 −0.3 0.3
0.7 0.7 0.7 0.6 0.5 0.7 −0.4 0.0

1.0
0.4
0.1
0.5
0.3
0.3

1.0
0.5 1.0
0.6 0.2 1.0
0.7 0.7 0.6 1.0
0.8 0.5 0.8 0.8 1.0

If less than −0.55

Figure 9 Correlation of GDP Growth, 2000–2013_Correlation of GDP growth at constant market prices

Sources: Official national accounts in each country, including author adjustments.

interesting to note that China’s correlation with the US and EU15 has moved from negative to positive.
Correlation among the East Asian countries (Group 1) has strengthened over time and their correlation with the US, EU15, and South Asian countries(Group 2) has strengthened as well. Although the in2013
ter-regional correlation in the Southeast Asian countries (Group 3) is stable, their correlation with the US
Asia
and EU15 has grown much stronger. Therefore, com60 %Othersparisons of the correlation coefficients of growth be28 %
APO20
35 %
tween the two periods lend support to an increase,
not a decrease, in business cycle synchronicity.
Asia30EU15

Performance comparisons based on whole-economy
GDP do not take into account the population size andcan in turn exaggerate the wellbeing of countries withlarge populations. Asia is the world’s most populous

6%US
5 % Other Asia
4%

56 %

Figure 10 Share of Asian Population in
the World, 2013
Source: IMF, World Economic Outlook Database, April 2015.

23©2015 Asian Productivity Organization

3.2 Catching Up in Per Capita GDP

EU28
7%

3 Economic Growth

Table 4 Population, 1970, 1980, 1990, 2000, 2010, and 2013
1970
China
India
Indonesia
Japan
Bangladesh
Pakistan
VietnamPhilippines
Thailand
Korea
Iran
MyanmarROC
Sri Lanka
Nepal
Malaysia
CambodiaHong Kong
Lao PDR
Singapore
Mongolia
Fiji
Bhutan

829.9
539.1
116.2
104.7
71.5
60.6
42.7
36.7
34.4
32.2
28.4
27.3
14.8
12.5
11.3
10.9
6.78
3.96
2.51
2.07
1.25
0.52
0.29

(%)

41.5
27.0
5.8
5.2
3.6
3.0
2.1
1.8
1.7
1.6
1.4
1.4
0.7
0.6
0.6
0.5
0.3
0.2
0.1
0.1
0.1
0.0
0.0

0.21 0.0
Bahrain
0.75 0.0
Kuwait0.68 0.0
Oman
0.11 0.0
Qatar
Saudi Arabia         5.80 0.3
UAE0.25 0.0
0.13 0.0
Brunei(regrouped)
1133.1 56.7
APO20
1990.7 99.6
Asia24
1998.8 100.0
Asia30  
986.8 49.4
East Asia
South Asia 695.3 34.8
ASEAN
279.7 14.0
ASEAN6200.4 10.0
CLVM
79.3 4.0
7.80 0.4
GCC(reference)
205.1 10.3
US  
341.9 17.1
EU15
439.9 22.0
EU28   12.6 0.6
Australia
35.6 1.8
Turkey

1980
China
India
Indonesia
Japan
Bangladesh
Pakistan
VietnamPhilippines
Thailand
Iran
Korea
MyanmarROC
Sri Lanka
Nepal
Malaysia
CambodiaHong Kong
Lao PDR
Singapore
Mongolia
Fiji
Bhutan

987.1
689.0
147.5
117.1
85.4
82.6
53.7
48.1
44.8
38.8
38.1
31.8
17.9
14.7
14.6
13.9
6.59
5.06
3.20
2.41
1.66
0.63
0.41

(%)

40.1
28.0
6.0
4.8
3.5
3.4
2.2
2.0
1.8
1.6
1.6
1.3
0.7
0.6
0.6
0.6
0.3
0.2
0.1
0.1
0.1
0.0
0.0

0.34 0.0
Bahrain1.37 0.1
Kuwait1.09 0.0
Oman
0.22 0.0
Qatar
Saudi Arabia         9.84 0.4
UAE1.04 0.0
0.19 0.0
Brunei(regrouped)
1425.7 58.0
APO20
2445.2 99.4
Asia242459.5 100.0
Asia30  
East Asia 1166.8 47.4
South Asia 886.7 36.1
ASEAN
352.2 14.3
ASEAN6256.9 10.4
CLVM
95.3 3.9
13.9 0.6
GCC(reference)
227.2 9.2
US  
357.1 14.5
EU15
461.6 18.8
EU28   14.7 0.6
Australia
44.7 1.8
Turkey1990
China
1143.3
833.9
India
179.4
Indonesia
123.6
Japan
112.1
Pakistan
Bangladesh 109.0
Vietnam
66.0
Philippines
60.7
Iran
55.1
Thailand
54.5
Korea
42.9
Myanmar
38.9
ROC
20.4
18.1
Malaysia
18.1
Nepal
Sri Lanka
17.0
8.88
Cambodia
5.70
Hong Kong
4.14
Lao PDR
Singapore
3.05
2.07
Mongolia
0.74
Fiji
0.54
Bhutan

(%)

38.9
28.3
6.1
4.2
3.8
3.7
2.2
2.1
1.9
1.9
1.5
1.3
0.7
0.6
0.6
0.6
0.3
0.2
0.1
0.1
0.1
0.0
0.0

0.49 0.0
Bahrain2.13 0.1
Kuwait1.63 0.1
Oman
0.42 0.0
Qatar
Saudi Arabia         16.2 0.6
UAE1.77 0.1
0.25 0.0
Brunei(regrouped)
1735.4 59.0
APO20
2918.4 99.2
Asia242941.6 100.0
Asia30  
East Asia 1338.0 45.5
South Asia 1090.6 37.1
ASEAN
434.0 14.8
ASEAN6316.0 10.7
CLVM
117.9 4.0
22.6 0.8
GCC(reference)
249.6 8.5
US  
366.1 12.4
EU15475.2 16.2
EU28   17.1 0.6
Australia
56.5 1.9
Turkey2000
China
1267.4
1014.0India
206.3
Indonesia
137.9
Pakistan
126.9
Japan
Bangladesh 124.1
Vietnam
77.6
Philippines
76.5
Iran
64.2
Thailand
60.6
Korea
47.0
Myanmar44.7
Malaysia
23.5
22.7
Nepal
22.3
ROC
Sri Lanka
19.1
11.9
Cambodia
6.67
Hong Kong
5.22
Lao PDR
Singapore
4.03
2.39
Mongolia
0.80
Fiji
0.56
Bhutan

2010

(%)

37.3
29.9
6.1
4.1
3.7
3.7
2.3
2.3
1.9
1.8
1.4
1.3
0.7
0.7
0.7
0.6
0.4
0.2
0.2
0.1
0.1
0.0
0.0

0.64 0.0
Bahrain2.14 0.1
Kuwait2.40 0.1
Oman
0.61 0.0
Qatar
Saudi Arabia         20.1 0.6
UAE3.00 0.1
0.32 0.0
Brunei(regrouped)
2053.8 60.5
APO20
3366.9 99.1
Asia243396.4 100.0
Asia30  
East Asia 1472.7 43.4
South Asia 1318.4 38.8
ASEAN
510.7 15.0
ASEAN6371.2 10.9
CLVM
139.5 4.1
28.9 0.9
GCC(reference)
282.2 8.3
US  
378.2 11.1
EU15
487.0 14.3
EU28   19.0 0.6
Australia
67.8 2.0
TurkeyChina
1340.91176.7
India
237.6
Indonesia
173.5
Pakistan
Bangladesh 147.2
128.1
Japan
Philippines
92.3
Vietnam
86.9
Iran
74.3
Thailand
65.9
Korea
49.4
Myanmar48.0
Malaysia
28.6
26.4
Nepal
23.2
ROC
Sri Lanka
20.7
14.0
Cambodia
7.02
Hong Kong
6.26
Lao PDR
Singapore
5.08
2.76
Mongolia
0.86
Fiji
0.72
Bhutan

(%)

35.3
31.0
6.3
4.6
3.9
3.4
2.4
2.3
2.0
1.7
1.3
1.3
0.8
0.7
0.6
0.5
0.4
0.2
0.2
0.1
0.1
0.0
0.0

1.23 0.0
Bahrain2.98 0.1
Kuwait2.77 0.1
Oman
1.70 0.0
Qatar
Saudi Arabia         27.3 0.7
UAE8.26 0.2
0.39 0.0
Brunei(regrouped)
2366.8 62.3
APO203756.8 98.8
Asia243801.7 100.0
Asia30  
East Asia 1551.3 40.8
South Asia 1545.2 40.6
ASEAN
585.1 15.4
ASEAN6430.0 11.3
CLVM
155.1 4.1
44.2 1.2
GCC(reference)
309.3 8.1
US  
398.8 10.5
EU15503.2 13.2
EU28   22.0 0.6
Australia
73.7 1.9
Turkey2013
China1360.71240.9
India
246.7
Indonesia
183.1
Pakistan
Bangladesh 153.8
127.3
Japan
Philippines
97.2
Vietnam
89.7
Iran
77.2
Thailand
66.5
Korea
50.2
Myanmar49.2
Malaysia
29.9
27.1
Nepal
23.4
ROC
Sri Lanka
20.5
14.7
Cambodia
7.19
Hong Kong
6.64
Lao PDR
Singapore
5.40
2.89
Mongolia
0.88
Fiji
0.75
Bhutan

(%)

34.6
31.6
6.3
4.7
3.9
3.2
2.5
2.3
2.0
1.7
1.3
1.3
0.8
0.7
0.6
0.5
0.4
0.2
0.2
0.1
0.1
0.0
0.0

1.23 0.0
Bahrain3.31 0.1
Kuwait3.86 0.1
Oman
2.11 0.1
Qatar
Saudi Arabia         28.8 0.7
UAE9.15 0.2
0.41 0.0
Brunei(regrouped)
2471.2 62.9
APO20
3882.3 98.7
Asia243931.5 100.0
Asia30  
East Asia 1571.7 40.0
South Asia 1626.1 41.4
ASEAN
606.4 15.4
ASEAN6446.2 11.3
CLVM
160.2 4.1
48.5 1.2
GCC(reference)
316.5 8.1
US  
403.0 10.3
EU15
505.2 12.8
EU28   23.1 0.6
Australia
76.7 2.0
Turkey©2015 Asian Productivity Organization

Unit: Millions of persons.
Sources: Population census and other official data in each country, including author interpolations.

region. In 2013, it accounted for 60% of the world’s population (56% for Asia30), with China and India
alone accounting for more than one-third (Figure 10). In addition, there is a significant difference inthe population sizes among Asian economies. Table 4 presents the population. Six countries’ populations were over 100 million in 2013 (in addition the Philippine population hit 100 million in 2014), but
the populations are less than 20 million in 13 economies of Asia30. Based on per capita GDP, which
adjusts for the differences in population size, Asia’s rising economic giants (China and India) are still24

3.2 Catching Up in Per Capita GDP

Table 5 Per Capita GDP using Exchange Rate, 1970, 1980, 1990, 2000, 2010, and 2013
_GDP at current market prices per person, using annual average exchange rate
1970

1980

(%)

2.00 100.0
0.96 48.2
0.93 46.3
0.43 21.4
0.40 19.8
0.39 19.6
0.36 17.8
0.28 14.0
0.21 10.6
0.21 10.5
0.21 10.4
0.18
9.2
0.17
8.4
0.14
6.9
0.12
5.9
0.12
5.9
0.11
5.5
0.10
4.9
0.09
4.7
0.09
4.4
0.09
4.3
0.03
1.4

Japan
Hong Kong
Singapore
Iran
ROC
Fiji
Malaysia
Korea
ThailandPhilippines
Indonesia
BhutanChina
Sri Lanka
Pakistan
Mongolia
India
Bangladesh
Myanmar
Nepal
Cambodia
VietnamBahrain
KuwaitOman
Qatar
Saudi Arabia        
UAEBrunei(regrouped)
APO20
Asia24
Asia30
East Asia
South Asia
ASEAN
ASEAN6
CLVM
GCC(reference)
US  
EU15

1.88 94.3
3.96 198.2
0.40 19.8
4.96 248.5
0.93 46.5
4.28 214.3
1.49 74.4

5.25 262.6
3.65 182.9

10.30
Bahrain
21.63
Kuwait
5.79
Oman
35.29
Qatar
Saudi Arabia         16.79
UAE
42.28
28.28
Brunei(regrouped)
1.24
APO20
0.85
Asia24
Asia300.95
1.33
East Asia
0.27
South Asia
ASEAN
0.56
ASEAN6
0.73
CLVM
0.08
GCC
18.45
(reference)
12.60
US
9.32
EU15

Australia
Turkey3.57 178.8
0.67 33.6

Australia
Turkey

0.32
0.23
0.23
0.32
0.13
0.12
0.15
0.06
1.36

15.8
11.4
11.6
16.2
6.3
6.2
7.5
3.0
68.1

1990

(%)

9.45 100.0
5.70 60.3
5.00 52.9
2.51 26.5
2.37 25.0
1.92 20.3
1.78 18.8
1.70 18.0
0.74 7.9
0.69 7.3
0.54 5.7
0.31 3.3
0.31 3.3
0.29 3.1
0.29 3.1
0.29 3.0
0.28 2.9
0.22 2.3
0.19 2.0
0.18 1.9
0.11 1.2
0.02 0.2

Japan
Hong Kong
Singapore
Fiji
Iran
ROC
Malaysia
Korea
Thailand
BhutanSri Lanka
Philippines
Pakistan
Bangladesh
Cambodia
India
China
MyanmarMongolia
Nepal
Indonesia
Vietnam

109.0
228.8
61.3
373.4
177.6
447.3
299.2
13.1
9.0
10.1
14.0
2.9
5.9
7.8
0.9
195.2
133.3
98.6

11.78 124.6
2.03 21.5

Japan
Hong Kong
Singapore
ROC
Korea
MalaysiaFiji
IranThailandPhilippines
Mongolia
Indonesia
BhutanSri Lanka
India
Pakistan
China
Bangladesh
Nepal
Lao PDR
Cambodia
MyanmarVietnam(%)

25.74 100.0
13.49 52.4
12.77 49.6
8.17 31.7
6.52 25.3
2.50 9.7
1.86 7.2
1.72 6.7
1.63 6.3
0.77 3.0
0.77 3.0
0.71 2.8
0.51 2.0
0.49 1.9
0.40 1.6
0.39 1.5
0.34 1.3
0.29 1.1
0.25 1.0
0.21 0.8
0.20 0.8
0.13 0.5
0.10 0.4

9.25 35.9
Bahrain8.98 34.9
Kuwait7.20 28.0
Oman
17.69 68.7
Qatar
Saudi Arabia         7.26 28.2
UAE
28.94 112.4
12.92 50.2
Brunei(regrouped)
2.64 10.3
APO20
1.71 6.6
Asia24
Asia301.77 6.9
3.06 11.9
East Asia
0.39 1.5
South Asia
ASEAN
0.84 3.3
ASEAN6
1.11 4.3
CLVM
0.12 0.5
GCC
9.35 36.3(reference)
23.95 93.1
US  
17.46 67.8
EU15Australia
Turkey

18.98
3.55

73.7
13.8

2000
Japan
Hong Kong
Singapore
ROC
Korea
Malaysia
Fiji
ThailandIranPhilippines
China
Sri Lanka
Indonesia
BhutanMongolia
Pakistan
India
VietnamBangladesh
Lao PDR
Cambodia
Nepal
Myanmar(%)

38.49 100.0
25.76 66.9
23.79 61.8
14.88 38.7
11.95 31.0
4.04 10.5
2.11 5.5
2.09 5.4
1.72 4.5
1.06 2.8
0.95 2.5
0.89 2.3
0.82 2.1
0.78 2.0
0.60 1.6
0.52 1.4
0.48 1.2
0.42 1.1
0.41 1.1
0.32 0.8
0.31 0.8
0.28 0.7
0.16 0.4

13.18
Bahrain
17.97
Kuwait
8.21
Oman
29.28
Qatar
Saudi Arabia         9.44
UAE
35.33
17.81
Brunei(regrouped)
3.55
APO20
2.53
Asia24
Asia302.62
4.86
East Asia
0.48
South Asia
ASEAN
1.21
ASEAN6
1.54
CLVM
0.33
GCC
13.16
(reference)
36.45
US
26.16
EU15
22.59
EU28
21.49
Australia
3.96
Turkey

34.2
46.7
21.3
76.1
24.5
91.8
46.3
9.2
6.6
6.8
12.6
1.2
3.1
4.0
0.8
34.2
94.7
68.0
58.7
55.8
10.3

2010
SingaporeJapan
Hong Kong
Korea
ROC
Malaysia
Iran
ThailandChina
Fiji
Indonesia
Mongolia
Sri Lanka
BhutanPhilippines
India
VietnamLao PDR
Pakistan
MyanmarCambodia
Bangladesh
Nepal

(%)

46.57 100.0
44.46 95.5
32.55 69.9
22.15 47.6
19.26 41.4
8.66 18.6
6.41 13.8
5.18 11.1
4.50 9.7
3.67 7.9
3.18 6.8
2.61 5.6
2.42 5.2
2.21 4.8
2.16 4.6
1.42 3.1
1.35 2.9
1.11 2.4
1.01 2.2
0.87 1.9
0.81 1.7
0.78 1.7
0.72 1.5

20.84 44.8
Bahrain39.72 85.3
Kuwait21.46 46.1
Oman
74.94 160.9
Qatar
Saudi Arabia         19.50 41.9
UAE
35.57 76.4
Brunei35.67 76.6
(regrouped)
5.03 10.8
APO20
4.79 10.3
Asia24
Asia305.04 10.8
8.71 18.7
East Asia
1.32 2.8
South Asia
ASEAN
3.37 7.2
ASEAN6
4.17 9.0
CLVM
1.14 2.5
GCC
26.16 56.2
(reference)
48.37 103.9
US36.56 78.5
EU15
33.30 71.5
EU28
58.62 125.9
Australia
10.04 21.6
Turkey2013
SingaporeJapan
Hong Kong
Korea
ROC
Malaysia
China
Iran
ThailandFiji
Mongolia
Indonesia
Sri Lanka
Philippines
BhutanVietnamLao PDR
India
Myanmar
Pakistan
Cambodia
Bangladesh
Nepal

(%)

55.98 100.0
40.09 71.6
38.24 68.3
26.00 46.4
21.87 39.1
10.46 18.7
6.88 12.3
6.62 11.8
6.38 11.4
4.62 8.3
4.36 7.8
3.69 6.6
3.30 5.9
2.80 5.0
2.38 4.2
1.93 3.4
1.65 3.0
1.54 2.8
1.30 2.3
1.21 2.2
1.07 1.9
1.00 1.8
0.77 1.4

3

26.78 47.8
Bahrain54.26 96.9
Kuwait20.61 36.8
Oman
98.18 175.4
Qatar
Saudi Arabia         26.08 46.6
UAE
45.18 80.7
40.06 71.6
Brunei(regrouped)
5.07 9.1
APO20
5.66 10.1
Asia24
Asia306.01 10.7
10.55 18.8
East Asia
1.47 2.6
South Asia
ASEAN
4.13 7.4
ASEAN65.02 9.0
CLVM
1.64 2.9
GCC
34.32 61.3
(reference)
52.98 94.6
US
38.41 68.6
EU15
35.47 63.4
EU28
66.03 118.0
Australia
10.87 19.4
Turkeysubstantially less well-off when compared with the US standard. Conversely, the Asian Tigers fare exceptionally well.
Table 5 presents cross-country comparisons of per capita current-price GDP, using exchange rates asconversion rates.12 However, given the volatile nature of exchange rates, snapshot comparisons likethose presented in Table 5 can appear arbitrary. Rather, long-term trends of nominal per capita GDP

25©2015 Asian Productivity Organization

Unit: Thousands of US dollars.
Sources: Official national accounts in each country, including author adjustments.
Note: See Appendix 1 for the adjustments made to harmonize GDP coverage across countries.

3 Economic Growth

US=100 in each year

US=100 in each year

160

120

140
Japan
120
100

100

US
Singapore

80

US

60

Hong Kong

80
60

40
Australia

ROC

Korea

40

20

20
1970 1975 1980 1985 1990 1995 2000 2005 201001970 1975 1980 1985 1990 1995 2000 2005 2010

Figure 11 Per Capita GDP using Exchange
Rate of Japan and Australia, Relative to theUS, 1970–2013

Figure 12 Per Capita GDP using Exchange
Rate of the Asian Tigers, Relative to the US,
1970–2013

annual average exchange rate, relative to the US

annual average exchange rate, relative to the US_GDP at current market prices per person, using
Sources: Official national accounts in each country, including author adjustments.

_GDP at current market prices per person, using
Sources: Official national accounts in each country, including author adjustments.

provide a better guide of relative movements. Based on this measure, Japan closed in on the US levelin the late 1980s and peaked in 1995, reflecting the strong yen (Figure 11). Figure 12 shows comparisonsamong the Asian Tigers. Singapore and Hong Kong have been moving closely with one another for
three and a half decades until the mid-2000s, when Singapore sprinted ahead of Hong Kong.13 Hong
Kong’s per capita GDP peaked in 1997, the year when Hong Kong was returned to China, and subsequently plummeted until 2004. Singapore followed a similar path – peaking in 1996, and falling to an
all-time low in 2002 before the surge of recent years. The ROC and Korea moved together but at alower level than Singapore and Hong Kong. In Asia, Japan and Singapore are the two countries thathave income levels almost equivalent to the US. However, this view is considerably revised if focusing
on production or real income per capita, using PPP as the conversion rates (Table 6).
In terms of per capita GDP at constant prices using PPP, Japan was the first country in Asia to start
catching up with the US. By 1970, its per capita GDP was 61% of the US, quite a distance ahead ofother Asian countries. Japan had been closing the gap with the US steadily until 1991 (86%), but the
gap widened again when the impact of the long recession of the 1990s started to manifest itself.14 In
recent years, Japan’s level has stabilized to around 70–73% of the US (Figure 13).

©2015 Asian Productivity Organization

Japan’s per capita GDP was the highest among Asian countries until it was overtaken by Singapore
in 1980.15 The result highlights the outcome of the dramatic development effort made by the AsianTigers, as shown in Figure 14. Not only were they inching to the top, they were constantly closing
the gap with the US. Starting from a level of 42% the US in 1970, Singapore surpassed the US in 1993.16
12: In Myanmar the first census in three decades was conducted between March 30 and April 10 in 2014 and showed that the total
population was 51 million, which was considerably below the official estimate of 61 million. Reflecting this revision, the per capita
GDP is upwardly revised, compared to the results in the Databook 2014.
13: Singapore’s population comprises not only Singaporean citizens but also non-citizens who have been granted permanent residence in Singapore as well as non-permanent residents such as employment pass holders, work permit holders, and student passholders. It is known that many workers and students commute to Singapore from outside the country every day. According to themost recent census, the share of Singaporean citizens with respect to total population was 74% in 2000, the share of permanent
residents who are not Singaporean citizens was 7%, and the share of non-permanent residents was 19%.
14: Jorgenson, Nomura, and Samuels (2015) indicated that the manufacturing sector was the main contributor to the catching-up
process of the Japanese economy in the 1960s, and that, by 1980, the US–Japan TFP gap for the manufacturing sector had almost
disappeared. Japanese manufacturing productivity relative to the U.S. peaked at 103.8 in 1991 and deteriorated afterward, leaving
a current gap that is almost negligible.

26

3.2 Catching Up in Per Capita GDP

Table 6 Per Capita GDP, 1970, 1980, 1990, 2000, 2010, and 2013

_GDP at constant market prices per person, using 2011 PPP, reference year 2013
1970
Japan
Singapore
IranHong Kong
Fiji
Malaysia
ROC
Philippines
Mongolia
ThailandKorea
Sri Lanka
Indonesia
Pakistan
India
Bangladesh
BhutanVietnam
MyanmarChina

36.9 247.6
Bahrain
194.1 1301.4
Kuwait14.9 99.6
Oman
164.8 1105.0
Qatar
Saudi Arabia         42.7 286.5
UAE
42.5 285.0
85.9 576.0
Brunei(regrouped)
3.2 21.5
APO20
2.1 14.0
Asia24
2.3 15.5
Asia302.3 15.6
East Asia
1.4
9.5
South Asia
ASEAN
2.1 14.4
ASEAN6
2.7 17.8
CLVM
0.9
6.2
GCC
60.7 407.1(reference)
24.6 164.8
US18.2 122.2
EU15
Australia
Turkey1980(%)

14.9 100.0
10.4 69.7
9.9 66.1
8.9 59.3
4.2 28.3
4.1 27.7
3.7 25.0
3.0 20.4
3.0 20.3
2.7 18.4
2.6 17.3
1.9 12.7
1.8 11.8
1.5 10.0
1.3
8.8
1.2
7.8
1.1
7.2
1.0
6.5
0.7
4.5
0.5
3.2

22.5 150.9
6.8 45.4

SingaporeJapan
Hong Kong
Iran
ROC
Malaysia
Fiji
Korea
Philippines
Mongolia
ThailandIndonesia
Sri Lanka
Pakistan
India
Nepal
BhutanBangladesh
Vietnam
Myanmar
China

46.9
Bahrain
85.6
Kuwait
26.1
Oman
Qatar
139.8
Saudi Arabia         66.6
UAE
194.8
144.3
Brunei(regrouped)
4.1
APO20
2.7
Asia24
3.2
Asia303.3
East Asia
1.5
South Asia
ASEAN
3.3
ASEAN6
4.2
CLVM
1.0
GCC
78.7
(reference)
30.3
US
23.9
EU15
Australia
Turkey1990(%)

21.3 100.0
21.0 98.6
16.4 77.0
10.1 47.2
8.3 39.2
7.2 33.8
5.5 26.0
5.3 24.7
4.1 19.5
4.1 19.4
4.0 19.0
3.1 14.6
2.6 12.3
1.8 8.3
1.4 6.5
1.1 5.4
1.1 5.3
1.1 5.0
1.0 4.7
0.9 4.1
0.7 3.5

220.6
402.1
122.5
656.9
313.0
915.4
678.2
19.0
12.9
14.9
15.4
7.0
15.6
19.7
4.9
370.0
142.4
112.1

25.9 121.6
8.0 37.8

SingaporeJapan
Hong Kong
ROC
Korea
Malaysia
Iran
ThailandFiji
Mongolia
Indonesia
Philippines
Sri Lanka
Pakistan
BhutanIndia
Lao PDR
China
Nepal
VietnamBangladesh
Cambodia
Myanmar(%)

35.5 100.0
31.6 89.0
27.9 78.6
16.1 45.3
12.0 33.9
10.0 28.3
9.2 25.9
7.1 19.9
5.9 16.8
5.6 15.7
4.7 13.2
4.0 11.3
3.4 9.7
2.6 7.4
2.3 6.5
2.0 5.5
1.7 4.7
1.5 4.4
1.5 4.1
1.4 3.9
1.2 3.5
1.0 2.8
0.8 2.3

37.5
Bahrain
42.0
Kuwait
38.9
Oman
86.0
Qatar
Saudi Arabia         38.1
UAE
117.1
81.7
Brunei(regrouped)
5.5
APO20
4.0
Asia24
4.3
Asia305.2
East Asia
2.0South Asia
ASEAN
4.6
ASEAN6
5.8
CLVM
1.3
GCC
46.9
(reference)
38.3
US
29.7
EU15

105.8
118.5
109.6
242.5
107.3
330.3
230.4

30.0
10.6

84.6
29.9

Australia
Turkey

15.6
11.3
12.2
14.6
5.8
13.0
16.5
3.5
132.3
108.0
83.8

2000
Singapore
Hong Kong
Japan
ROC
Korea
Malaysia
Iran
ThailandFiji
Indonesia
Sri Lanka
Mongolia
Philippines
China
BhutanPakistan
India
VietnamLao PDR
Nepal
Bangladesh
Cambodia
Myanmar(%)

53.4 100.0
35.1 65.8
34.5 64.6
28.1 52.7
21.4 40.2
15.7 29.4
11.6 21.8
9.9 18.5
6.9 13.0
6.2 11.5
5.1 9.6
4.8 9.0
4.4 8.3
3.8 7.1
3.7 7.0
3.4 6.4
2.7 5.1
2.6 4.8
2.4 4.5
1.9 3.5
1.8 3.4
1.5 2.7
1.4 2.6

46.6
Bahrain
73.5
Kuwait
42.2
Oman
112.6
Qatar
Saudi Arabia         40.1
UAE
113.8
79.5
Brunei(regrouped)
6.7
APO20
5.6
Asia24
6.0
Asia307.7
East Asia
2.8
South Asia
ASEAN
6.2
ASEAN6
7.7
CLVM
2.2
GCC
53.1
(reference)
47.5
US
36.0
EU15
31.7
EU28
38.1
Australia
12.7
Turkey

87.3
137.6
79.0
210.9
75.2
213.2
149.0
12.6
10.5
11.3
14.4
5.3
11.6
14.5
4.1
99.5
89.0
67.4
59.4
71.3
23.8

2010
Singapore
Hong Kong
ROC
Japan
Korea
Malaysia
Iran
ThailandChina
Indonesia
Sri Lanka
Mongolia
Fiji
BhutanPhilippines
India
Vietnam
Pakistan
Lao PDR
MyanmarCambodia
Bangladesh
Nepal

(%)

74.4 100.0
49.7 66.8
40.7 54.7
36.9 49.5
31.4 42.2
20.8 28.0
18.2 24.4
14.2 19.1
9.7 13.0
8.9 12.0
7.9 10.6
7.9 10.5
7.4 9.9
6.7 9.0
5.9 7.9
4.8 6.5
4.7 6.3
4.4 5.9
4.1 5.5
4.1 5.5
2.7 3.6
2.6 3.5
2.4 3.2

42.5 57.1
Bahrain
79.9 107.4
Kuwait51.0 68.5
Oman
139.5 187.3
Qatar
Saudi Arabia         47.5 63.8
UAE
61.2 82.2
77.0 103.5
Brunei(regrouped)
8.9 12.0
APO20
9.2 12.3
Asia24
9.7 13.0
Asia3013.3 17.9
East Asia
4.6 6.2
South Asia
ASEAN
9.0 12.1
ASEAN6
10.7 14.4
CLVM
4.3 5.8
GCC
56.2 75.4(reference)
51.0 68.5
US38.7 51.9
EU1535.2 47.3
EU28
44.5 59.8
Australia
17.1 23.0
Turkey2013
Singapore
Hong Kong
ROC
Japan
Korea
Malaysia
Iran
Thailand
China
Mongolia
Indonesia
Sri Lanka
Fiji
BhutanPhilippines
India
Vietnam
MyanmarLao PDR
Pakistan
Cambodia
Bangladesh
Nepal

(%)

80.3 100.0
53.3 66.4
43.7 54.4
38.2 47.5
33.8 42.1
23.2 28.8
16.5 20.6
15.8 19.7
12.1 15.1
11.0 13.7
10.2 12.7
9.8 12.2
7.9 9.8
7.4 9.3
6.6 8.2
5.4 6.7
5.4 6.7
4.9 6.0
4.8 6.0
4.6 5.7
3.2 4.0
3.0 3.7
2.6 3.3

3

47.6 59.2
Bahrain85.3 106.2
Kuwait41.3 51.5
Oman
143.8 179.1
Qatar
Saudi Arabia         53.5 66.6
UAE
64.1 79.9
75.0 93.4
Brunei(regrouped)
9.5 11.8
APO20
10.4 12.9
Asia2411.0 13.7
Asia3015.6 19.4
East Asia
5.1 6.4
South Asia
ASEAN
10.2 12.7
ASEAN612.0 15.0
CLVM
5.0 6.2
GCC
60.5 75.3(reference)
53.0 66.0
US
38.6 48.0
EU15
35.6 44.3
EU28
46.2 57.5
Australia
19.1 23.8
Turkey15: Among the mature economies in Asia, Singapore is a unique country, in which the PPP was downwardly revised from the 2005
ICP to the 2011 ICP (see Box 1). This shift has the significant effect of bringing forward the year when Singapore overtook Japan (orUS) in relative per capita GDP to 1980 (1993 for the US), from 1993 (2004 for the US) as estimated in the Databook 2013, based on
the 2005 ICP. Although this edition follows the 2011 ICP results, it may require a further examination if this time-series level comparison based on the constant PPP approach can provide an appropriate picture, especially for Singapore.
16: Generally, Singapore’s GNI is lower than its GDP, and over the past four decades, the divergence was the largest in 2004 with GNI
equivalent to 93.0% of GDP (see Figure 91 in Chapter 7). On the other hand, the US GNI never goes outside +1.6% of GDP. However, Singapore’s lead of 52% over the US in 2013 was large enough that their relative positions would be independent of whether
GNI or GDP was used.

27©2015 Asian Productivity Organization

Unit: Thousands of US dollars (as of 2013)
Sources: Official national accounts in each country, including author adjustments.
Note: See Appendix 1 for the adjustments made to harmonize GDP coverage across countries.

3 Economic Growth

US=100 in each year

100
90
80

US

Australia

US=100 in each year

160
140

Singapore

120
EU15

100

US
Hong Kong

80
70
60

60
40
Japan

ROC

20

50
1970 1975 1980 1985 1990 1995 2000 2005 2010

Korea
01970 1975 1980 1985 1990 1995 2000 2005 2010

Figure 13 Per Capita GDP of Japan, the EU,
and Australia, Relative to the US, 1970–2013

Figure 14 Per Capita GDP of the Asian Tigers,
Relative to the US, 1970–2013

2011 PPP, relative to the US

using 2011 PPP, relative to the US_GDP at current market prices per person, using
Sources: Official national accounts in each country, including author adjustments.

In 2013, Singapore had a per capita GDP which
was 52% above the US. It became the richest
economy in Asia, representing a remarkableachievement. Hong Kong holds second place,with a per capita GDP similar to the US. Japan’s
per capita GDP, at 72% of the US, or around 48%of the group leader (Singapore), is similar to
the EU15. The ROC and Korea trail behind theother two Asian Tigers at 82% and 64% of theUS, respectively.

_Ratio of per capita GDP at constant market prices,
Sources: Official national accounts in each country, including author adjustments.

US=100 in each year

24
20
16
12

ASEAN

8
India
4

China
0
1970 1975 1980 1985 1990 1995 2000 2005 2010©2015 Asian Productivity Organization

The relative performance of China and India, theFigure 15 Per Capita GDP of China, India, and
ASEAN, Relative to the US, 1970–2013
two most populous countries in the world, is di_Ratio of per capita GDP at constant market prices,
minished in this measure due to their populausing 2011 PPP, relative to the UStion. Their per capita GDP is 22.9% and 10.2% of
Sources: Official national accounts in each country, including authe US in 2013, respectively (Figure 15). Howevthor adjustments.
er, this should not taint the remarkable progress
made over the past decades, especially by China
where the per capita GDP was less than 2% of
the US in 1970. China’s relative per capita GDP has increased tenfold in four decades. The income gap
between the US and the majority of Asian countries is still sizable,17 indicating significant opportunity
for catch up.
Table 6 presents individual figures for seven oil-rich economies (Brunei and the six GCC countries). At
first glance, figures in 1970, and to a lesser extent in 1990, suggest these economies enjoyed anincome many times that of Japan and the US. For example, in 1970, Kuwait, Qatar, and Brunei had aper capita GDP 12.6 times, 10.7 times, and 6.0 times that of Japan, respectively. However, the

17: Per capita GDP may have underestimated the welfare of people in some countries. In the ROC, Hong Kong, and Japan, for example, GNI is consistently higher than GDP although the fluctuations are within +6%. The Philippines is the exception where thedivergence between GNI and GDP has been increasing and has become significant for the past two decades, and GNI was morethan 30% higher than GDP in the 2010s (see Figure 91 in Chapter 7).

28

3.2 Catching Up in Per Capita GDP

measurement of GDP as an indicator of production is misleading for these countries, as it
erroneously includes proceeds from the liquidation of a natural resource stock as part ofthe income flow. In other words, GDP overestimates income from the oil-exporting economies because it does not account for
depletion of their natural resource assets. To
give a rough indication of the extent of distortion, Figure 16 provides comparisons of percapita GDP excluding production of the mining sector (i.e., crude oil, natural gas, and soon). The non-mining GDP per person in Brunei
and GCC economies, such as the UAE, Bahrain,
and Kuwait, is almost similar to Japan’s level,although total GDP per capita is much larger.

Qatar

61.7

Brunei

81.2

36.5

41.3

UAE

35.2

26.6

Japan

34.4

3.1 37.5

Bahrain

33.4

11.9 45.3

Kuwait27.7

59.4

Saudi Arabia

26.8

26.3

Oman

22.4

19.9 42.3

Malaysia

61.8

3
87.1

53.1

Non-mining GDP
Mining GDP

19.2 3.2 22.4

Thousands of US dollars (as of 2013)

Iran 14.0 3.1 17.1
0

30

142.9

77.8

60

90

120

150

Figure 16 Per Capita Non-Mining GDP in OilRich Countries and Japan, 2013

_GDP at constant market prices per person, using 2011
PPP, reference year 2013

Sources: Official national accounts in each country, including author
adjustments.

Catching up with the per capita GDP level ofadvanced economies is a long-term processthat could take several decades to accomplish. Empirical evidence suggests there may be a negative
correlation between per capita GDP level and the speed of catching up, with some exceptions. With
the possibility of adopting successful practices and technologies from the more advanced economies,
less advanced economies are poised to experience faster growth in per capita GDP, enabling them tocatch up to average income level. However, as income levels approach those of the more advanced
countries, their economic growth rates are expected to gradually decline over time.18

Table 7 summarizes Figure 17 by country groups. Four levels of per-capita income groups are defined:
Group-L1, with per capita GDP at or above 60% of the US; Group-L2, from 20% to under 60%; Group-L3,
from 8% to under 20%; and Group-L4, below 8%. Likewise, countries are also grouped according
to the speed of their catch-up with the US: Group-C1, at 3% per annum or above; Group-C2, from 1%
to under 3%; Group-C3, from 0% to under 1%; and Group-C4, under 0%. The speed of catch-up with
the US is defined as the difference in the average annual growth rate of per capita real GDP between
each country and the US. Table 7 shows that many Asian countries (not belonging to Group-C4)
have managed to close the gap in per capita real GDP with the US over the last four decades,
18: The OECD (2015) observes that GDP per capita has broadly converged in the OECD countries since the 1970s. However, moreadvanced economies that started with high income levels in the 1970s have had lower rates of catch-up, stagnated or recently
diverged vis-à-vis the US.

29©2015 Asian Productivity Organization

Figure 17 plots countries’ initial per capita GDP levels against their respective average growth rates
per year between 1970 (or the initial year data first became available for the country in question) and2013. If the two variables have a correlation coefficient of –0.5 (i.e., a negative relationship of medium
strength), the higher the initial income level, the slower the average growth rate per year is expected.
However, this is not always true. Low-income countries like Nepal, Bangladesh, the Philippines, and Fiji
have failed to catch up, while Thailand and Malaysia could be expected to have grown even fastergiven their initial income levels. The Asian Tigers have enjoyed robust growth in the past four decades,
but Korea and the ROC, with their lower initial per capita GDP, have sustained higher growth rates
than Singapore and Hong Kong. Relative to the Asian Tigers, China appears to be at the start of thecatch-up process. Mature economies like the US, EU15, and Japan shared similar growth experiences(around 2% on average per year, in the past four decades).

3 Economic Growth

%

5

%

Growth rate of per capita GDP during 1970–2013

Cambodia

MyanmarGrowth rate of per capita GDP during 1970–2013=1970, =1990, =2013

China

Bhutan7

Indonesia

Lao PDR

4

8=1970, =1990, =2013

Vietnam

6

Sri Lanka
India

Korea

ROC

5

3

Singapore

Mongolia

Thailand

Pakistan4

Nepal

Hong Kong
Malaysia

Bangladesh

2

3Philippines

Turkey

Fiji

2

Japan
US

EU15

1

Australia

Iran

1
Thousands of US dollars (as of 2013)

Thousands of US dollars (as of 2013)

0

0
0

2

4

6

8

10

12

Per capita GDP at constant prices

0

10

20

30

40

50

60

70

80

90

Per capita GDP at constant prices

Figure 17 Initial Level and Growth of Per Capita GDP, 1970–2013_Level and average annual growth rate of GDP at constant market prices, using 2011 PPP, reference year 2013
Sources: Official national accounts in each country, including author adjustments.
Note: The starting periods for Nepal, the Lao PDR, and Cambodia are 1974, 1984, and 1987, respectively.

although some are more successful
than others.

Table 7 Country Groups Based on the Initial Economic
Level and the Pace of Catching Up_Level and average annual growth rate of GDP at constant market©2015 Asian Productivity Organization

prices, using 2011 PPPFrom Table 7 one can see the initial
Annual rate of catch-up to the US
Initial GDP
economic level does not fully exlevel
(C2)
(C3)
(C4)
(C1)
to the US
1% <–< 3%
0% <–< 1%< 0%> 3%
plain the catch-up process. If it did,
the table would have been popuBrunei, Bahrain,(L1)
Japan, EU15,
Kuwait, Qatar,
lated diagonally from the bottom
Oman
Saudi Arabia,
60% <UAE, Australia
left corner to top right corner. Of
the Asia30 countries, five achieved(L2)
Singapore
Hong Kong
TurkeyIran
20% <–< 60%
a very fast catch-up (over 3% per(L3)
Malaysia, Mongolia,
year on average) between the reFiji, Philippines
ROC, Korea
8% <–< 20%
Thailand
spective starting years of their data
Bhutan,India,
series and 2013. Their initial per
Bangladesh,Indonesia, Lao PDR,
(L4)
Cambodia, China
Nepal,
Myanmar, Sri Lanka,< 8%
capita GDP level classifies themPakistan
Vietnaminto the three groups: Singapore
from Group-L2, the ROC and Korea
Sources: Official national accounts in each country, including author adjustments.
Note: The annual catch-up rates are based on the difference in the growths of perfrom Group-L3, and Cambodia andcapita GDP at constant prices between each country and the US during 1970–2013.
China from Group-L4. Eleven counThe starting years for some countries are different due to data availability: Cambodia(1987), the Lao PDR (1984), and Nepal (1974).
tries in Group-C4 experienced deterioration in their relative income
level against the US with lowincome countries like Fiji and the Philippines failing to take off. The seven high-income countries inGroup-C4 are all GCC countries except Australia. It is worth noting that GCC countries had an exceptionally high GDP (a distortion, as aforementioned) at the beginning of the period. Japan was the only
Asian non-oil-exporting country with a high-income level in 1970. But, like EU15, it has since failed toachieve further parity with the US.

30

3.3 Sources of Per Capita GDP Gap

3.3 Sources of Per Capita GDP Gap
To further understand the diverse performance in the Asian group, per capita GDP can be broken into
two components: labor productivity (defined here as real GDP per worker); and the employment
rate.19 Figure 18 shows the percentage point differences in per capita GDP decomposed into the contributions by the labor productivity gap and the employment rate gap, relative to the US in 1990 and2013.20 Most of the Asian countries display a huge per capita GDP gap with the US. This is predominantly explained by their relative labor productivity performance. With the exception of the AsianTigers, Japan, and Iran, all the other Asian countries had labor productivity gaps of more than 50%
against the US in 2013. At the top end of performance, estimates show Singapore was 19% above
while Hong Kong was 7% below the US labor productivity level. The labor productivity gaps of theother two Asian Tigers are stillsizable against the US, at 16%
and 40% for the ROC and Korea,
1990
respectively. In most countries,
the effect of the employment
rate is to widen the per capita%
20
GDP gap. However, in recent
5
4
3
2
0
−7
−8
years more Asian countries−21 −13−20 0−14 −9−33−20−49 −43 −7
have employment rates higher−56 −57−72 −85 −84 −80 −73 −78 −87−80 −93−40−93 −83−15−77 −82 −90 −72 −64−97 −92 −89−99 −85 −72 −71
than the US, with the effect of−43 −23−60−11−18
narrowing the gap.
−13 −20
MyanmarBangladesh
CLMV
Vietnam
Nepal
China
Lao PDR
India
South Asia
BhutanPakistan
Sri Lanka
Asia24
Philippines
Asia30ASEAN
Indonesia
East Asia
APO20
Mongolia
ASEAN6Fiji
ThailandIran
Malaysia
TurkeyKorea
ROC
Hong Kong
EU15
Australia
Japan
Singapore

3

−80

−1

−17

−4 −3 −7

−22 −20 −13 −6 −8−5−4−12 −22−3 −10

−13

−7

2013

%

Nepal
Bangladesh
Cambodia
Pakistan
Lao PDR
Myanmar
CLMVSouth Asia
Vietnam
India
Philippines
BhutanFiji
APO20
Sri Lanka
ASEAN
Indonesia
Asia24
Asia30
Mongolia
ASEAN6
China
East Asia
ThailandIran
TurkeyMalaysia
Korea
Japan
EU15
ROC
Australia
Hong Kong
Singapore

40
33

20
0

1

3 10 88

9 8 12

2

8

−20
−40
−60
−80
−100

5 8 19−21−16−17 −7−37−41 −40−34 −6
−1
−51
4 6

−75 −101 −81 −81 −83 −75 −83 −79 −68−76 −79−88−86 −82−32 −5−96 −102 −94 −99 −98 −80 −73 −69 −79 −78
−22

−7

−17

−9

−9 −8 −3

Employment rate

−13−7 −12

−2 −1 −1 −11 −1

Labor productivity

Per capita GDP

Figure 18 Labor Productivity and Employment Rate Gap
Relative to the US, 1990 and 2013

_Decomposition of per capita GDP gap at constant market prices, using
2011 PPP

Sources: Official national accounts in each country, including author adjustments.

19: Employment rate is measured as the number of workers relative to the population, to ensure consistency with the definition of labor productivity (i.e., GDP per worker) that is measured in all APO member economies. In Section 5.2, labor productivity measures
are provided based on hours worked for some selected countries. Also, in the computation of TFP in Section 5.3, hours worked
data are used.
20: The gap of country x’s per capita GDP relative to the US is decomposed into the sum of the gap of labor productivity and employment rate with respect to the US, as in:
In (GDPxt / POPxt ) − In (GDPUSt / POPUSt ) = In (GDPxt / EMPxt ) − In (GDPUSt / EMPUSt ) + In (EMPxt / POPxt ) − In (EMPUSt / POPUSt )Gap of per capita GDP

Gap of labor productivity

Gap of employment rate

where POPxt is population of country x in period t and EMPxt is the number of employment of country x in period t.

31©2015 Asian Productivity Organization

Figure 19 focuses on explaining a country’s per capita GDP
growth by its components:
namely labor productivity growth;and the change in the em­
ployment rate for the periods
1990–2000 and 2000–2013,
respectively.21 For most countries in Asia, the per capita
GDP growth can be explainedby improvement in labor productivity. However, this should
not lead us to underestimate
the role of changes in the employment rate. On average,
Asia30’s per capita GDP grew by
3.3% per year between 1990 and
2000, and accelerated to 4.6%

−100

3 Economic Growth

1990–2000

%

10
8
6
4

0.7

0.6 0.5
1.1 1.6

8.9
5.4 5.1 5.1

2

0.9

0.7 0.2
1.1

4.4 3.9 3.8 3.8 3.9

2.9

0.1 0.4 0.2
1.1
3.9 3.5 3.6

0.1

2.7 3.30

0.2 0.1 0.3 0.4
4.3

0.3

0.5 0.3

3.1 3.2 2.9 2.6 2.8 2.4 2.8−0.9

2.0 2.1−0.1 −0.1−2

0.1 0.3 0.3
0.3
1.3
1.0
2.2 1.9 1.7 1.6 2.4 1.1
1.1
0.4 1.3 1.3 −0.3
−0.6
−0.1
−0.2
−1.1

Mongolia
Japan
Philippines
GCC
Fiji
Turkey
EU15
APO20
US
Hong Kong
Iran
Australia
Nepal
PakistanIndonesia
ASEAN6ASEAN
South Asia
Asia30India
ThailandAsia24
Bangladesh
Lao PDR
Cambodia
East Asia
Sri Lanka
Singapore
Malaysia
Bhutan
CLMV
Myanmar
ROC
Korea
VietnamChina

2000–2013

%

10
8

0.8

6
4

0.9 1.1
8.8 9.0

1.5 1.3

5.2

4.6
2.0

0
−2

0.9

3.3
5.5 5.3 4.5 4.4

2

0.7
5.4

0.1 0.1 0.3

4.1 4.6 4.5 4.3

1.6 0.4 0.7 1.0 0.8 0.5 0.7 0.6
2.3

3.5 3.1

0.3 1.4 0.7 0.9

2.6 2.7 2.9 2.6 2.6 2.8

0.5 0.3 1.3
0.4
0.9
1.8 2.4 2.1 2.2 2.3 1.3 1.4 1.0 1.1

1.5
0.6
1.7
0.9
−0.1 −0.7−0.7 −0.1

EU15
Japan
US
GCC
Fiji
Australia
Pakistan
Nepal
APO20
Iran
Malaysia
Philippines
Singapore
TurkeyHong Kong
ROC
ASEAN6
Korea
Thailand
ASEAN
Indonesia
Bangladesh
South Asia
Asia30
Asia24
Sri Lanka
India
BhutanLao PDR
East Asia
Vietnam
Cambodia
CLMV
Mongolia
China
Myanmarper year between 2000 and 2013.
The earlier period captured thedampening effect of the Asianfinancial crisis of the late 1990s.
Emerging from the crisis, bothlabor productivity growth andemployment growth strengthened. For most countries, laborproductivity explains a larger
share of per capita GDP growththan employment. Additionally,
the employment rate contribution relative to labor productivity was also highly significant
in countries such as, Bhutan(62%), Nepal (48%), Singapore(43%), Bangladesh (42%), andPakistan (41%).

China’s improvement was themost impressive, achieving per
Employment rate
Labor productivity
Per capita GDP
capita GDP growth of 8.9% and9.0% per year on average in the
Figure 19 Sources of Per Capita GDP Growth, 1990–2000
and 2000–2013
two periods, respectively. Im_Decomposition of average annual growth rate of per capita GDP atprovement in labor productivity
constant market prices, using 2011 PPP
explains almost all of that growth.
Sources: Official national accounts in each country, including author adjustments.
According to official statistics,22
Note: The starting period for Cambodia are 1991.
Myanmar achieved a similar performance to China in growthterms, with per capita GDP growth of 5.5% and 9.6% per year on average in the two periods. However,this growth was from a very low base; even in 2013, Myanmar’s per capita GDP was only 40% of China’s(see Table 6). Like China, Myanmar’s per capita GDP growth has been predominantly explained by labor productivity. In both periods Japan had a deteriorating employment rate. With an aging population (see Box 2), this pattern may well continue. To sustain per capita GDP growth, China’s labor productivity growth will have to accelerate to counteract the negative effect of its employment rate. The
US also experienced a deteriorating employment rate in the recent period, which was a drag on percapita GDP growth. In contrast, falling labor productivity was the drag in GCC countries.

©2015 Asian Productivity Organization

Most countries also have an employment rate short of the US level. In the case of Iran, Turkey, andPakistan, the employment rate is significantly less than the US, further reinforcing the poor productivity
performances of these countries (Figure 18). It is no coincidence they are among the countries with the
21: Country x’s per capita GDP is decomposed into the product of its labor productivity and employment rate, as in:
In (GDPxt / POPxt) = In (GDPxt / EMPxt) + In (EMPxt / POPxt)
where POPxt is population of country x in period t and EMPxt is the number of
Per capita GDP

Labor productivity

Employment rate

employment of country x­in period t.
22: The author would caution readers as to the reliability and quality of Myanmar’s official statistics (especially a decade from the late
1990s, based on our observations), which have been questioned. Researchers have suggested that this is not consistent withother variables closely correlated with GDP, such as energy use. Non-official estimates put GDP growth at less than half of the official estimates. See The Economist Intelligence Unit (2010). Nonetheless, official statistics from Myanmar are presented in this report, as there is no comprehensive and transparent alternative data source.

32

3.3 Sources of Per Capita GDP Gap

lowest shares of female workers in employment, at
17%, 30% and 22%, respectively (Figure 20). In contrast, a handful of countries such as Cambodia, China, and Thailand, had higher employment rates
than the US, counteracting the negative impact of
their productivity performances. In Singapore, thepositive gap in employment rate further reinforced
the already impressive relative labor productivity
performance, pulling ahead of the US in per capita
GDP. More specifically, Singapore’s labor productivity was 19 percentage points higher than the US
level, but its employment rate was 33 percentage
points higher, giving an overall per capita GDP
which was 52% higher than the US in 2013.
All other things being equal, increasing employment and improving labor productivity could present a policy trade-off in the short term, as theycannot be achieved simultaneously. If the policy
target is to increase employment, productivity may
suffer in the short term as marginal and less-productive workers are recruited,bringing down the average
productivity performance.
The huge labor productivity
gap between Asia and the
US – discussed in Chapter 5 –%65should be considered in thecontext of the generally high60
employment rate in Asia.

50
49
49
49
48
48
47
46
46
46
45
45
44
44
43
43
42
41
39
38
38

3

35
33
32
30
26
22
21
19
17
17
13
13
13

0

10

20

30

40

50

60 %

Figure 20 Share of Female Employment_Ratio of female to total employment

Sources: Population census or labor survey in each country.

55
50
45
40
35
30
25
2013

1990

1970

Figure 21 Employment Rates, 1970, 1990, and 2013
_Ratio of employment to total population

Sources: Employment and population data by national statistical office in each country.

33©2015 Asian Productivity Organization

20
Singapore
MyanmarCambodia
Thailand
VietnamChina
Lao PDR
Hong Kong
Japan
Australia
Korea
Nepal
US
ROC
Brunei
Inodonesia
EU15
Bhutan
Malaysia
Philippines
Bangladesh
Mongolia
India
Sri Lanka
Fiji
TurkeyPakistan
Iran

Figure 21 shows cross-country comparisons of employment rates in 2013, basedon the labor statistics ofeach country. Employment
consists of employees, ownaccount workers, and con­tributing family workers.
Singapore, Myanmar, andCambodia lead the Asiangroup with employment
rates of over 60%, around 14
and 18 percentage points
higher than the US and
EU15, respectively, in 2013. It
is clear that employment
rates have been rising in
Asia.23 The fastest catch-up
countries (i.e., those in GroupLao PDR (2005)
Cambodia (2013)
Vietnam (2013)
Hong Kong (2013)
Mongolia (2012)
Canada (2013)
US (2013)
EU28 (2013)
Australia (2013)
Bhutan (2013)
Thailand (2013)
China (2010)
ROC (2013)
Singapore (2012)
Japan (2013)
Nepal (2011)
Korea (2013)
Brunei (2011)
Philippines (2013)
Indonesia (2013)
Malaysia (2013)
Sri Lanka (2013)
Kuwait (2011)
Fiji (2007)
Turkey (2013)
India (2010)
Pakistan (2013)
Bahrain (2010)
UAE (2008)
Oman (2010)
Iran (2010)
Bangladesh (2011)
Saudi Arabia (2013)
Qatar (2013)

3 Economic Growth©2015 Asian Productivity Organization

C1) are also countries with the largest surge in employment rates over the past four decades: China,
Korea, Cambodia and the ROC. However, China seems to have exhausted its capacity for further
improvement as its employment rate changed little between 1990 and 2013 at 57%. Some of thecountries in Group C2 also experienced significant improvements in employment rates (for example,
Indonesia and Vietnam). While there are exceptions, generally countries that have failed to catch up
also tend to make less vigorous improvements over the period, and in turn continue to have lower
employment rates.

23: Japan is the only exception where the employment rate in 2013 was lower than that in 1970. This reflects, among other things, its
aging population. US employment rates also shows weakening in the recent period, with levels in 2013 lower than that in 1990 (i.e.,
48% compared with 51%).

34

3.3 Sources of Per Capita GDP Gap

Population and Demographic Dividend

Box 2

According to the United Nations (UN) (2013), the world’s population is estimated to reach 7.2 billion in2013, of which Asian countries account for 60.0%. The region is by far the most populous in the world.
China and India account for 19.3% and 17.5% of the world’s population, respectively. It has been observedthat falling fertility rates and rising living standards go hand in hand, although the direction of causality isless certain. The evolution of the demographic structure implies dynamics in a society that are not captured by the overall population size or growth. As people’s economic behavior, aspirations, and needs varyat different stages of life, changes in a country’s age structure can have a significant impact on its economic growth via supply-side and demand-side impacts.

3

The world’s fertility rate is converging to the replacement level (the level at which a country’s population
stabilizes). According to the UN, the number of children a woman is expected to have in her reproductive
years has dropped by more than half, from about 5.0 to 2.5 in the last 60 years, compared to the replacement level of 2.1 children, one of them a girl. There is regional divergence in this trend. In the last 60 years,
the total fertility rate dropped from about 6.7 children to 2.6 in Central America, and from about 5.6 children to 1.6 (below the replacement level), in East Asia. In comparison, some parts of Africa have seen onlya modest drop in total fertility, which today remains at more than five children per woman. What is evenmore staggering is the pace of change. For example, it took Britain over 130 years (1800–1930) to halve its
fertility rate, while it took Korea only 20 years to achieve it. This is echoed around the world. This widespread social revolution has been heralded by a complex mix of economic and social development. Economic growth, greater access for women to education, income-earning opportunities, and sexual andreproductive health services, all have been contributing factors to this trend. Coupled with changes in themortality rate, such a trend can dramatically alter the age profile of a country’s population, bringing with it
economic implications.
The growth rate of the world’s population has slowed from its peak of around 2.0% in the 1970s to today’s
1.2% per year. With falling fertility rates, the UN projects the world’s population growth rate will decelerate
to 0.49% per year by 2050 and further to 0.09% by 2100. Even so, the world population will still increase by
one-third in the next 40 years, from 6.9 billion to 9.5 billion and a further 13% to 10.8 billion by 2100. Theseestimates are based on the medium-fertility variant, but with only a small variation in fertility, particularlyin the more populous countries, the total could be higher (10.9 billion by 2050 and 16.6 billion in 2100) or
lower (8.3 billion in 2050 and 6.8 billion in 2100).
Much of this increase is expected to come from high-fertility countries, which comprise 39 out of the 55
countries in Africa, nine in Asia, six in Oceania, and four in Latin America. In contrast, low-fertility countries
include all countries in Europe except Iceland and Ireland, 19 out of the 51 in Asia, 14 out of the 39 in theAmericas, two in Africa (Mauritius and Tunisia), and one in Oceania (Australia). Figure B2.1 depicts this shift

Least developed countries
Less developed regions, excluding
least developed countries
More developed regions
1.2

10
8

0.3

4
0.2

0

2.9

Billion

Oceania
Northern America
Latin America and the Caribbean
Europe0.4
Africa0.7
Other Asia
0.7
ASEAN
0.3
China
0.5
1.4
India
0.7

10

1.8

8

0.7

6

2

2.4

6
5.6

6.4

6.7

6.6

4

4.3
2.7

2

1.5
0.8

1.0

1.2

1950

1975

2000

1.3

1.3

1.3

1.3

2025

2050

2075

2100

0

0.5
0.5

1950

0.3
0.7
0.4
0.5
0.3
0.9

0.5
0.8

0.4
0.8

0.7

0.5
0.7
0.6

0.7
3.4

2.3

4.2

0.8

1.2

1.4

1.4

0.9
0.5

0.7

0.8

0.8

1.4

1.4

1.2

1.1

1.6

1.6

1.5

2050

2075

2100

1.3

0.6

1.0

1.4

1975

2000

2025

1.3
0.7

Figure B2.1 Distribution of the World’s Population in Different Regions, 1950–2100
Source: UN (Department of Economic and Social Affairs), World Population Prospects: The 2012 Revision.
continued on next page >35©2015 Asian Productivity Organization

Billion3 Economic Growth> continued from previous page

2050

1400
1200
1000
800
600
400
350
300
250
200
150
100
50
30

scale by 5 millions©2015 Asian Productivity Organization1970

scale by 50 millions

Figure B2.3 shows the demographic make-up of countries in2013 (the population proportions of the under-15 and over65 age groups, which together
make up the dependent population). Ranking the countries by
the share of old-age population,filters the rich economies to the
top end. These economies also
have a relatively low share of theyoung age group compared toless developed countries. Thissuggests that demographic transition tends to run parallel witheconomic progress, although
the direction of causation is notcertain. As countries move fromhigh to low mortality and fertility
rates, the demographic transition

2013

1600

25
20
15
10
5
0

Brunei
BhutanFiji
Bahrain
Qatar
Mongolia
KuwaitOman
Singapore
Lao PDR
Hong Kong
UAE
Cambodia
Sri Lanka
Australia
ROC
Nepal
Saudi Arabia
Malaysia
Myanmar
Korea
ThailandTurkeyIranVietnamPhilippines
Japan
Bangladesh
PakistanIndonesia
US
EU28
India
China

According to the projection,
Asia’s share will decline from its
60.2% today to 54.1% in 2050
and 43.4% in 2100, while Africa’s
share will rise from today’s 14.9%
to 25.1% and 38.6%, respectively. Figure B2.2 shows the current
population size of individual
Asian countries compared withthe 1970 level and its 2050 projection. As can be seen from thechart, China’s population is expected to more or less stabilize
around the current level. China
has socially engineered thechange with its one-child policy,
which has made its current population 300–400 million lower
than it would have been otherwise. In less than two decades,
India is projected to overtake
China as the most populous
country in the world.

Millionscale by 200 millions

in the distribution of the world
population with the share fromthe more developed regions
gradually declining from 17.9%
to 13.6% in 2050 and 11.8% in
2100, compared with 32.2% in
1950. Conversely, the share of
the least developed countries is
depicted as rising from today’s
12.1% to a projected 19.0% in
2050 and 27.0% in 2100, up from
7.7% in 1950.

Figure B2.2 Asian Countries’ Population Size and Projection,
1970, 2013, and 2050
Source: World Bank, World Development Indicators 2014.

11.1

19.4

33.2

24.6
26.4
22.7
23.6
26.0
28.9

Japan
Australia
Hong Kong
US
Korea
ROC
Singapore
ThailandChina
TurkeySri Lanka
Vietnam
Iran
Myanmar
Fiji
Nepal
India
Indonesia
MyanmarBhutanBangladesh
Philippines
Cambodia
Pakistan
BruneiMongolia
Lao PDR
Saudi Arabia
Oman
Bahrain
Kuwait
Qatar
UAE

12.9

18.9

14.8
14.3
16.1
18.2
16.4

29.1
28.9

23.4
28.1
30.0
32.7
31.9
33.8
24.5
27.2
36.7
29.0
23.5
22.1
24.8

40 %
Age 0–14

30

13.6
14.9

20

10

0

7.7
6.7
6.6
5.8
5.5
5.4
5.4
5.3
5.2
5.1
4.8
4.8
4.6
4.5
4.4
3.8
3.8
3.7
2.9
2.8
2.4
2.3
0.8
0.4

0

5

25.1

14.4
14.2
14.1
12.1
11.5
10.2
9.7
9.7

10

15

20

25 %

Age over 65

Figure B2.3 Proportion of the Dependent Population, 2013
Sources: Population census and official national accounts in each country.

continued on next page >36

3.3 Sources of Per Capita GDP Gap> continued from previous page

produces a “boom” generation that is larger than those immediately before and after it. As this boom generation gradually works through a nation’s age structure, it produces a demographic dividend of economic growth as people reach their prime.
Using demographic data since 1950 and UN projections up to 2100, Figure B2.4 tracks changes in the ratioof the working population (aged 15–64) to dependent population (aged under 14 and over 65) over time.
The higher the ratio, the more favorable its demography for economic growth. Japan could have capitalized the demographic dividend in the 1960s, when its GDP growth was over 10% on average per year forten years. Similarly, China, Hong Kong, Korea, Singapore, and Thailand are poised for the prospect of suchdemographic dividend in the 2000s and 2010s, whereas, based on projections, Indonesia will have to wait
for such opportunity until the 2020s and 2030s, and India until the 2040s. The reaping of this dividend,
however, is far from automatic. A favorable demography can work wonders to produce a virtuous cycle of
Dependent population (age under 14 and over 65)=1.0
Hong Kong
wealth creation only if it is com­3.0
Japan
bined with appropriate health, labor,
Korea
2.5
Singaporefinancial, human capital, and growthUS
enhancing economic policies. The
2.0
presence of these complementary
1.5
factors cannot be taken for granted,
but needs to be cultivated in order to
1.01950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
earn the demographic dividend. As
the analysis of the Databook show,
Dependent population (age under 14 and over 65)=1.0
the contribution of labor to economChina
3.0
India
ic growth has been smaller thanIndonesia
2.5
those of capital and TFP for mostThailandAustralia
countries (Figure 53, p. 76). This
2.0
means that countries should not be
1.5
afraid of aging too much as long as
fairly high growth rates of capital and
1.0
TFP are maintained. Nevertheless,1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
understanding the demographic
shift and its implications is highly relFigure B2.4 Demographic Dividend, 1950–2100
evant for economic projections, providing valuable foresight for economic
Source: UN (Department of Economic and Social Affairs), World Population Prospects: The 2012 Revision.
policy making.

©2015 Asian Productivity Organization

37

3

4.1 Final Demand Compositions

4 Expenditure
In national accounts, GDP is measured by three approaches: production by industry; expenditure on
final demand; and income to factor inputs. In theory, these three approaches are accounting identities, and should yield the same result, but in reality, they differ by statistical discrepancies. Decompositions of GDP are valuable in understanding the structure, and in turn the behavior, of an economy. In
this chapter, the economic insights are drawn from analyzing the composition of countries’ expenditure (the demand side). The decomposition of output growth into input growth and TFP growth (thesupply side) is analyzed in Chapter 5, while the industry structure of countries is presented and analyzed in Chapter 6.

4

4.1 Final Demand Compositions
From Table 8, one can see that country groups display distinctive features in their final demand composition, reflecting their development stage and economic makeup. With the differences in emphasis
and vulnerabilities, their behavior and reaction to economic shocks are obviously quite diverse. Table
8 presents comparisons of final demand shares of nominal GDP, covering: (1) household consumption, including consumption of non-profit institutions serving households (NPISHs); (2) government
consumption; (3) investment or, in national accounts terminology, gross fixed capital formation (GFCF)
plus changes in inventories; and (4) net exports (exports minus imports).
For most countries, household consumption is by far the biggest component of GDP.24 The GCC countries, Brunei, and China are the exceptions. Over the past four decades, the share of household
Table 8 Final Demand Shares in GDP, 1970, 1990, 2000, 2010, and 2013
_Share of final demands with respect to GDP at current market prices

Asia24

59.5

55.3

55.6

48.8

48.5

11.1

12.2

13.6

13.1

13.5

29.9

32.5

28.2

36.2

37.0

−0.5

0.0

2.7

1.9

1.0

Asia30

57.0

54.8

54.5

48.0

47.4

11.5

13.3

14.1

13.3

13.7

28.8

31.1

27.4

35.7

36.2

2.8

0.7

3.9

3.1

2.6

East Asia

50.8

50.4

51.1

42.0

41.2

10.9

13.2

15.4

14.5

15.0

37.7

34.8

31.5

40.2

42.0

0.6

1.6

2.0

3.3

1.8

South Asia

75.6

65.7

67.2

62.8

64.1

8.8

11.6

11.8

11.2

11.0

16.0

25.2

22.5

30.9

28.9

−0.4

−2.5

−1.5

−4.9

−4.0

ASEAN

69.0

60.7

58.2

56.1

56.6

12.4

9.6

9.3

10.6

11.2

23.3

30.6

23.6

28.1

29.5

−4.8

−0.8

8.9

5.1

2.7

ASEAN6

68.6

59.3

57.3

55.1

55.7

10.5

9.6

9.5

11.1

11.7

23.4

31.4

23.3

27.4

29.7

−2.4

−0.3

9.9

6.4

3.0

CLMV

76.7

84.0

69.7

67.5

60.6

27.3

9.0

8.4

6.7

8.3

19.3

14.2

24.3

31.4

30.1 −23.3

−7.3

−2.4

−5.5

1.0

GCC

35.2

49.8

41.9

36.6

32.2

14.8

25.4

20.5

16.3

17.3

18.9

15.8

18.1

28.6

25.1

9.0

19.5

18.5

25.5

31.1

China

55.4

46.8

46.3

34.1

33.5

11.2

14.1

15.6

13.1

13.8

33.3

36.4

35.7

49.1

50.3

0.1

2.7

2.4

3.7

2.4

India

74.0

62.4

64.8

59.7

61.1

9.4

11.9

12.8

11.6

11.4

16.7

27.1

23.2

33.2

30.6

−0.1

−1.4

−0.9

−4.4

−3.0

Japan

47.8

51.5

54.7

57.2

58.9

10.7

13.0

16.4

19.0

19.8

40.3

34.6

27.5

22.6

24.0

1.2

0.9

1.4

1.2

−2.7

Australia

54.2

57.9

58.7

53.9

55.6

13.9

18.1

17.6

17.9

17.9

32.2

24.3

23.4

27.2

27.1

−0.3

−0.2

0.2

1.0

−0.4

US

60.2

64.0

66.0

68.2

68.5

18.1

15.9

14.0

16.9

15.2

21.4

21.5

23.6

18.4

19.3

0.4

−1.3

−3.7

−3.4

−3.0

EU15

56.8

57.0

57.7

57.3

57.2

15.9

19.4

19.1

21.6

21.1

27.8

24.3

22.8

20.2

19.0

−0.5

−0.7

0.4

0.9

2.7

Unit: Percentage.
Sources: Official national accounts in each country, including author adjustments.
Note: Final demand shares in country groups are computed by using the PPP for GDP. Household consumption includes consumption of
NPISHs. Investment includes GFCF plus changes in inventories.

24: Based on our Metadata Survey 2015 on national accounts for APO member economies, Japan is an exceptional country that estimates GDP from its expenditure side. In other countries, GDP is estimated from the production side (value added in industries),and some countries record statistical discrepancy as the difference in the estimates between production-based GDP and the sumof final expenditures. In this Databook, statistical discrepancy is mainly attributed to household consumption when data is recorded. Readers should keep in mind that it can have some impacts on the share of final demand: e.g., it accounts for 2.5% of GDP
in 1990 in the Thailand SNA published in January 2015.

39©2015 Asian Productivity Organization

APO20

Household consumption
Government consumption
Investment
Net exports
1970 1990 2000 2010 2013 1970 1990 2000 2010 2013 1970 1990 2000 2010 2013 1970 1990 2000 2010 201359.8 56.9 58.8 57.7 59.1 11.0 11.9 12.8 13.1 13.3 29.6 31.8 25.6 28.3 27.7 −0.5 −0.6
2.7
0.8 −0.1

4 Expenditureconsumption for mature economies tends to bestable and trending upward in recent years, It is
more volatile and largely trending downward ineconomies undergoing rapid transformation,such as the Asian Tigers in the 1970s and 1980s,
and India and China in the present day, as theinvestment share increases for their development effort.

%

70
60

Household consumption

50
40

Investment

30

Exports

20
Imports (−)

100China’s household consumption has been trend1952
1962
1972
1982
1992
2002
2012
ing downward as a share of GDP. It fell from
55.4% in 1970 to 46.3% in 2000. This compares
Figure 22 Final Demand Shares in GDP ofwith the early Communist era when household
China, 1952–2013_ Share of final demands with respect to GDP at
consumption was more volatile and at a higher
current market prices
level of over 60% of GDP (Figure 22). China wasless well-off then. Figure 22 shows how houseSources: National accounts by National Bureau Statistics of China,
including author interpolation.
hold consumption share and investment share
25
mirror each other. As the decline in household
consumption share accelerated in the 2000s,
plummeting to 33.5% in 2013, the investment share rose rapidly to 50.3% of GDP from 35.7% in 2000.
Investment has overtaken household consumption as the largest component in GDP expendituresince 2003, and the divide shows no sign of narrowing. The falling share of household consumption
may partially reflect the falling labor income share of GDP and/or an uneven distribution of economic
gain between the rich and the poor in these countries. There also is a notably rapid rise in exports asa share of GDP since the 1980s when China began to open its economy, from around 5.0% or below in
the 1950s and 1960s to its peak of 36.5% in 2006
before softening to 23.3% in 2013.
%80©2015 Asian Productivity Organization

With a low consumption ratio, coupled with anunsustainable rise in investment and an overdependence on exports, China faces huge internaland external imbalances. If not addressed, this
could jeopardize its medium-term growth prospects. A low consumption share of GDP is not
merely a reflection of consumer behavior or
preference, but a manifestation of an array of underlying distortions in the economy. An undervalued currency with a wide range of factor pricedistortions which favors the production of tradables over non-tradables, may result in an unusually low consumption ratio and a heavy
reliance on exports. Lax corporate governanceof state-owned enterprises is not conducive to70

Household consumption

60
50
40
30

Investment

20
10

Imports (−)

Exports0
1929 1939 1949 1959 1969 1979 1989 1999 2009

Figure 23 Final Demand Shares in GDP of theUS, 1929–2013_ Share of final demands with respect to GDP atcurrent market prices

Sources: National accounts by Bureau of Economic Analysis, US.

25: The Chinese official statistics on household consumption could be misleading. Zhang and Tain (2013), for example, point out
three potential sources of a significant downward bias in Chinese consumption data. Firstly, the method used to impute rents forowner-occupiers does not take into account land costs, and in turn greatly underestimates the market values of housing. Secondly, private consumption on company accounts is misclassified as business costs (i.e., intermediate consumption), or investment
expenditure. Thirdly, sample selection bias (under-representation of high income households) and reporting errors also contributeto the underestimation of household consumption. The authors suggest that taking into account these factors could add 10–15
percentage points to China’s consumption, which would bring it to a level more comparable with other East Asian countries.

40

4.1 Final Demand Compositions

distribution of dividends and therefore, in effect, may act to subsidize investment. Additionally, in theabsence of a social safety net, well-developed domestic financial markets may provide a strong incentive for precautionary saving on the part of households (Eichengreen, Park, and Shin 2011). All ofthese factors suggest that there are policy levers available to the government to impede or rebalance
the economy.

4

In recent years, even labor-abundant China faced a tightened supply of surplus labor at its coasts, putting an upward pressure on wages (see Box 5, p. 64). This could be good news for the world, as ahigher labor share of GDP will enable higher household consumption, helping the domestic market
fulfill its potential. This will make China less dependent on foreign demand as well as generate demand for foreign products. Early signs that the Chinese economy may have started moving in theright direction were evident when the decline in the consumption ratio halted (even turning up
slightly since its recent trough in 2009) and external imbalances narrowed to 2.4% in 2013, which is
the lowest since 2004. Since the peak of 8.7% in 2007, net exports have been shrinking. Only time willtell if this is the start of a more persistent trend that reflects fundamental adjustments to the underlying economy.

1995

%

125
100

15
27
6 20
5 8

75
50

95

−15

8

8 12 10 14−6424 24 26 21 29

26

9 11 11 15
7 8

73 73 71 67

34

0

8

8 13 11 16 9

−15 −1 −8 −9 −9 −2 −9 0 −1 −2 −1 0

−2 −2 0

−1

−4

15

37

25
0

1

1

0

11
14 30 28 43 39
11 10

58 57 55 55 54 54 54 53 52

0

−7

21
28

1

2

8

10

10

35 21 43 20 40 15
14

22

12

23

21

50 50 50 49 47 43

−1

−4

4

1

17
35 35

42
34

32
14

8 25 3143 42 41 36

33

−5

1
20 28 20 22 19 30

39

2

1
29 31

3
24 19

48

3

53

3

0

5

3
9

9

9

1

20

3

2

2

25 22 2130
30
37 37 36
22 28 30 26 29
44 43 43
42
16
50
9
23
6 11 11 8 13 20 21
17 11 13
27 27
17 29
25
9
12 18 15 17
10 14 15 7 13 13 14
14 16
15 16
10 14 17 21 2231
81 80 79 74
73 70 68 67 66
16 18
65 64 64 61 61 59 59
57 57 57 57 56 56 56 54 53 51 51 51 50
50 49 47
41 40 3811
33 32 31 30
25 23
15
−5 −7
−7
−6
0
0 −3
−1
−3
−3 −9
−4
−4
−20
−23
−22
−28
11 11

50

1

2013

%

75

0

9 12 18 20 12 13 21 15 1766 65 65 64 62 62 62 62 61 60 59 58

125
100

1

23 33 34 30 25 22
17 28
32 32

Qatar
BruneiSingapore
China
KuwaitBhutanSaudi Arabia
Malaysia
GCC
East Asia
Oman
Korea
Thailand
ROC
Japan
Bahrain
Asia30
Asia24EU15Australia
APO20
ASEAN6
ASEAN
Fiji
India
Iran
Hong Kong
UAE
Indonesia
US
South Asia
Mongolia
TurkeySri Lanka
Pakistan
Philippines
Vietnam
CLMV
Myanmar
Nepal
Bangladesh
Cambodia−25

881 80 78 76 74

25
0

14 23 27 25 20 24

5

5 11 15 15 13

24

27

30 28

34

27

27 23

−25

Government consumption

Investment

Net exports

Figure 24 Final Demand Shares in GDP, 1995 and 2013

_Share of final demands with respect to GDP at current market prices
Sources: Official national accounts in each country, including author adjustments.
Note: Household consumption includes consumption of NPISHs. Investment includes GFCF plus changes in inventories.

41©2015 Asian Productivity Organization

Qatar
Brunei
KuwaitSaudi Arabia
Oman
GCC
China
Singapore
BahrainEast Asia
Asia30
Asia24Myanmar
UAE
Korea
Malaysia
Iran
Thailand
ROC
Australia
ASEAN6
Mongolia
ASEAN
BhutanIndonesia
EU15
Japan
APO20
CLMV
India
South Asia
VietnamFiji
Hong Kong
Sri Lanka
US
TurkeyPhilippines
Bangladesh
Cambodia
Nepal
Pakistan
Household consumption

4 Expenditure

In contrast to China, the share of household consumption was relatively stable in the US at around
62–63% for the 1970s and 1980s before edging up to 68.5% of GDP in 2013. From a historical perspective, the current level is below that experienced during the Great Depression in the US, when the
consumption share was over 75%, even as high as 82% in 1932, and above its all-time low of below
50% in 1944 during World War II (Figure 23). The share of household consumption in EU15, which is ataround 57%, has stayed fairly stable over the past four decades. The Asian average, meanwhile, has
hovered in the lower 50% range until recently when the gap with EU15 widened, largely reflecting thetrend in China (Table 8). Australia’s consumption ratio has never exceeded 60% of GDP and has dippedin the past decade to 55.6% in 2013, reflecting a pickup in the investment share. Within Asia, all regions display a decline in household consumption ratios. South Asia maintains the highest share, despite its fall from 75.6% in 1970 down to 64.1% in 2013.

©2015 Asian Productivity Organization

Overall, Asian countries invest significantly more than the US and EU15 as a share of GDP. Historically,
the gap in the investment share between Asia30 and EU15 never exceeded 10 percentage points.
However, since the beginning of the 1990s, it has started to widen (except for the period of the Asian
Financial Crisis). In 2013, the difference was over 17 percentage points. In the 1970s, EU15 was investing on average 4% more of their GDP than the US. Thereafter, the EU15 investment share converged
to the US level. They were out of synch with each other temporarily in the late 1980s and early 1990s.
For the past five years, a divergence has opened up with the US investment share of GDP declining
faster than that of EU15 (Figure 30.3). In 2013,
investment accounted for 19.3% and 19.0% of% Household consumption share in GDP
90
final demand in the US and EU15, respectively,
compared with 37.0% for Asia24. Australia’s inPakistan
Cambodia80vestment level has been closer to the level of
Nepal
Bangladesh
the APO20 than the US/EU15. In 2013 it acPhilippines
Turkey70
UScounted for over a quarter of final demand.
Hong Kong
Sri Lanka
The share of investment in China is the biggest
Fiji
VietnamIndia
60
final demand component of GDP since 2004.
BhutanJapan
Mongolia
Indonesia
At 48.7% in 2013, it is likely unsustainable in
ThailandAustralia
ROC
Iran
UAE
50
the long term. East Asia has the highest investKorea
Malaysia
Myanmarment ratio among the Asian regions. While
BahrainSouth Asia caught up with them in 2007, since40
Singapore
then the paths of the two regions diverged in
China
Saudi Arabia
Oman
opposite directions. Now South Asia is con30
verging with ASEAN countries, the investment
KuwaitBruneiintensity of which has not recovered since the20
Qatar
Asian financial crisis of the late 1990s.
Compared to other components of final demand, the contribution of net exports to the
Asian economy has always been more volatile.
Having increased in the Asia24 between 1990
and 2000 from –0.0% to 2.7%, the contributionof net exports has decreased to 1.0% in 2013.
This compares with the oil-exporting GCC
countries at 9.0% in 1990, rising to 18.5% in2010 and further to 25.5% in 2013.26 Including

10

10

15

20

25

30

35

40

Figure 25 Ratio of Dependent Population andConsumption Share in GDP, 2013

_Shares of dependent population (age under 14 andover 65) to total population and consumption share in
GDP at current market prices

Sources: Population data by national statistical office in each country; World Bank, World Development Indicators 2014; official national
accounts in each country with author estimates.

26: The recent increase is mainly due to that the price of
crude oil has steadily risen to over USD 100 per barrel since 2010 and held until the middle of 2015. See Figure 94 in Chapter 7.

42

45 %

Dependent population ratio

4.1 Final Demand Compositions

1995

%

100

91143

181
78

80
60

59

57

52

40

22

20

46

44

37

20

44

42

36

28

25

18 19

28

11 11 1410

−12−17−14−18−18−25 −19−29 −27−12
−26
−28
−36
−40
−42
−45 −43
−53
−59
−67

−15

9

1
−2

0
−20
−40
−60
−80
−100

41

26

−7

−165

32 34 32

31

22

4

−37
−41
−47 −48 −43−41 −46

−147
−94

Cambodia
Nepal
Philippines
Sri Lanka
VietnamThailandBangladesh
BhutanHong Kong
Malaysia
Pakistan
US
India
Korea
MyanmarMongolia
Australia
TurkeyIndonesia
Oman
Fiji
Qatar
Japan
ROC
EU15
China
Brunei
UAE
Iran
Saudi Arabia
KuwaitBahrain
Singapore

the GCC countries, the contribution of net exports to the GDP of
Asia30 was 2.6% in 2013, compared to 2.8% in 1970 when net
exports accounted for nearly athird of final demand in GCC
countries. In the US, there is anobservable trend of persistent
deficit between exports and imports, which has considerably
expanded from the beginning of
the 1980s to 5.6% in 2006 before
narrowing to 3.0% in 2013. South
Asia is the only Asian region thatconsistently runs a fluctuating
trade deficit over the years. Lately, it has become historically sizable at 7.4% of GDP in 2012 andnarrowed to 4.0% in 2013. In
EU15, net exports have becomea positive component in the past
two decades, accounting for2.7% in 2013.

2013

%

100
80

96

192
70

75

68

74

60

75

83

82
70

229

67

61

54

52

40

28

21 24

23

20

−40
−60
−80

16

28

25

41 39
25

13

13

20 22

12

0

0
−20

56

41

−26 −26
−32
−46

−19
−31
−53

−60
−72
−76

−21

−38
−49
−65

−17
−20
−21−25−18−28 −32 −32 −27−32
−66

−81
−228

−40

−61
−63
−76

27: The Lao PDR is also in the bottom income bracket and the share of dependent population is the highest among Asian countries(41%); it is, however, omitted from Figure 24 and Figure 25 because of a lack of final demand data.

43©2015 Asian Productivity Organization

Nepal
Mongolia
BhutanFiji
Sri Lanka
Bangladesh
Pakistan
TurkeyCambodia
Philippines
US
India
Japan
Indonesia
Australia
MyanmarHong Kong
Vietnam
China
Thailand
EU15
Korea
Iran
ROC
Malaysia
UAE
Saudi Arabia
Oman
Singapore
Bahrain
Qatar
Brunei
Kuwait−168−100
The regional averages disguisethe great variation displayed byindividual countries. Figure 24
shows the cross-country comExports
Imports
Net exports
parisons of final demand sharesin current-price GDP in 1995 andFigure 26 Export and Import Shares in GDP, 1995 and 20132013. Countries are arranged in_Share of exports and imports with respect to GDP at current market
descending order of their houseprices
hold consumption shares. AlSources: Official national accounts in each country, including author adjustments.
though most countries fall to theright of the US, there are a handful of Asian countries that have ahigher consumption ratio than the US. Bangladesh, Cambodia, Nepal, Pakistan, and the Philippines
fell to the left of the US in both years of comparisons. The high consumption rate in these countries
could be explained by the difference in demographic conditions. Figure 25 shows that countries witha high proportion of dependent population (age under 14 and over 65) tend to have a high household
consumption share in their GDP. The aforementioned five countries have higher shares of dependent
population with over 36% in 2013. On the other hand, the variation of consumption rates is also related to the income level. Countries with a low income will struggle to defer consumption. It is no
coincidence that countries clustered on the left of Figure 24 tend to be those in the bottom income
groups among the countries studied in this report (see Table 16, p. 95).27

4 Expenditure

A deficit in net exports can be associated with high household consumption. At the other end of thespectrum, GCC and other oil-exporting countries tend to cluster at the low end of household consumption share of GDP in both years of comparison. The average household consumption share for
GCC countries has been squeezed by net exports (which in turn are dominated by erratic oil revenues), from 49.6% in 1995 to 32.2% in 2013, as shown in Figure 24.28 Given that a large part of GCC
countries’ GDP is not sustainable income, it may in fact be prudent for oil-exporting countries not to
consume beyond their sustainable levels and instead purposefully invest to generate a steady income
stream in the eventuality of oil depletion, regardless of how distant this may seem now. Among thenon-oil-exporting Asian countries, Singapore had the smallest household consumption share. However, since 2002 China has replaced Singapore in that position, with a share of 33.4% in 2013.
Figure 26 presents the export and import shares in GDP as a decomposition of net exports in 1995
and 2013. Net exports are particularly important in a handful of economies. In 2013 in Singapore
export shares were at 192%, and in Hong Kong 229%, reflecting their port function in Asia. This explains why the total values of exports and imports are exceptionally high, relative to the size of GDP
in these economies.29

4.2 Consumption and Investment
Figure 27 shows the long-term trends of household consumption share of GDP for Asian economies
and some country groups. Countries are grouped according to the levels of per-capita income in2013.30 The Asian Tigers have been the consistent high performers, coming at the top for most of thelevel indicators presented in Chapter 3. As seen in Figure 27.1, Singapore and Korea showed the most
rapid relative retrenchment in household consumption as a share of GDP in their initial stage of development of the 1970s and 1980s. While the downward trend continues in Singapore, it has halted andbeen mildly reversed in Korea since the late 1980s. Between 1970 and 2013, the household consumption share of GDP fell from 69.0% of GDP to 37.7% and from 73.5% to 50.9% in Singapore and Korea,
respectively. In contrast, household consumption as a share of GDP, at 66.3% in 2013, has been risingin Hong Kong since the mid-2000s. The household consumption share did fall from 66.2% in 1970 tonearly 55% in the late 1980s, but it was subsequently reversed. Similarly, relative household consumption fell in the ROC, from 55.9% in 1970 to under 50% in the mid-1980s. Since then, it has been on an
upward climb until the 2000s when it stabilized at around 55%.
The consumption share in Japan has been rising slowly since 1970, from just under 50% in 1970 to
58.8 in 2013. With a rapidly aging population, this rising trend can be expected to continue. Japan’sshare of dependent population stood at 37.9% in 2013 (Figure 25), nearly 60% of which was accounted
for by the over-65 age group.

©2015 Asian Productivity Organization

Figure 27.5 illustrates the observations of Table 8, plotting Asian group averages against those of thereference countries. The US household consumption share has been climbing since the mid-1980s to
28: It should also be noted that the shares are calculated in current market prices. Revenues from oil exports are notoriously erratic.
It is possible that a sudden surge in export revenues relative to imports can squeeze the shares of other components of final demand without any real change in the underlying behavior in the economies. For example, Qatar has the smallest share of household consumption, which shrank from 32.6% in 1995 to 14.6% in 2013, while over the same period, net exports swung from 1.0%
to 42.7%. Similarly, net exports for GCC countries as a whole swung from 7.6% to 25.5%, squeezing household consumption from
49.6% in 1995 to 32.2% in 2013.
29: The 2008 SNA requires that the trade values should be recorded to reflect a change in ownership of goods, rather than accounting for goods moved for processing without incurring actual transactions. Singapore and Hong Kong already introduced the 2008
SNA, the revisions from the 1993 SNA on the export and import data were very minor.
30: Table 16 in Chapter 6 defines four levels of per capita GDP groups in 2013: Group-L1, with per capita GDP above 60% of the US;
Group-L2, from 30% to under 60%; Group-L3, from 10% to under 30%; and Group-L4, below 10%. They are presented in Figure27.1, Figure 27.2, Figure 27.3, and Figure 27.4, respectively. The same country groups are applied in Figures 30 and 34.

44

4.2 Consumption and Investment

%

100
90

%

Hong Kong
Korea
Singapore

100

Japan
ROC

90

%

Iran
Thailand

Malaysia
Turkey100

Bhutan
India

90

China
Indonesia
PhilippinesFiji
Mongolia
Sri Lanka
Vietnam

80

80

80

70

70

70

60

60

60

50

50

50

40

40

40

30
1970 1975 1980 1985 1990 1995 2000 2005 201030
1970 1975 1980 1985 1990 1995 2000 2005 201030
1970 1975 1980 1985 1990 1995 2000 2005 2010

Figure 27.1

Figure 27.2

Figure 27.3

%

%

100
90

Bangladesh
Nepal

Cambodia
Pakistan

100
90

4

%

APO20
US
Australia

Asia30
EU15

100
90

East Asia
ASEAN

South Asia
ASEAN6
CLMV

80

80

80

70

70

70

60

60

60

50

50

50

40

40

40

30
1970 1975 1980 1985 1990 1995 2000 2005 201030
1970 1975 1980 1985 1990 1995 2000 2005 201030
1970 1975 1980 1985 1990 1995 2000 2005 2010

Figure 27.4

Figure 27.5

Figure 27.6

Figure 27 Long-Term Trend of Household Consumption Share in GDP, 1970–2013_Share of household consumption with respect to GDP at current market pricess
Sources: Official national accounts in each country, including author adjustments.

The decomposition of household consumption reveals a huge diversity of consumption patterns
among individual countries, partly reflecting their income levels and partly the idiosyncratic characteristics of the society. Figure 28 strongly illustrates the cross-country version of Engel’s Law, whichsays that basic necessities will account for a high proportion of household consumption for a lower
per capita income group and vice versa. More specifically, countries where food and non-alcoholic
beverages account for a large proportion of consumption tend to have low income (i.e., in groups L3
or L4 in Table 16, p. 95). The other end of the spectrum is occupied by the rich Asian countries, namely,
the Asian Tigers and Japan.

31: It is worth noting that the GDP share of government consumption in EU15 was 7.6 percentage points higher than the average of
Asia24 in 2013 (Table 8). In fact, when it comes to welfare measurement, actual individual consumption, as opposed to household
consumption, is preferred because the former takes into account expenditures by NPISHs and government expenditures on individual consumption goods and services (such as education and health) in addition to household consumption.

45©2015 Asian Productivity Organization

over 70% of GDP since 2008, from a level of around 62%. Today the US level is more than about 10%
higher than that of EU15 and the APO20.31 The share in EU15 has been stable, fluctuating within a narrow range between 57% and 60% since the mid-1990s. In 1970, household consumption accounted
for around 60% of GDP in APO countries. In contrast, the consumption share for Asia30 declined rapidly from 57.0% to below 50% over the past decade. This largely reflects China’s recent household
consumption behavior (Figure 22) as it gained gravity in the regional economy.

4 Expenditure

US
Singapore
Australia
Korea
Hong KongJapan
ROC
Malaysia
ThailandIndia
Mongolia
Iran
Indonesia
Cambodia
BhutanSri Lanka
Philippines
Bangladesh
Fiji%
Figure 29 traces the decreasing100
4 5 3 5
4
4
Miscellaneous goods and2
8 2
11 14 13 13
13 2 10 6
2 2serviceslong-term path of Japan’s Engel’s
14 15
2
9 3 2 1618 14
1990 2 43
3 5
Restaurants and hotels
Curve for the period 1949–2013.
4
8
4
4
3
3
6
4
6
3 15 10 9 6
3
8 7
Education3
80 2 3 42 25 5 3 4 7
The countries’ levels in 2013 are
2 2
14
5
9 2
3
5
2 4
3
6 6 4
Recreation and culture
9995
mapped against Japan’s experi4
6
4
70 12 17 11
2 15 59
10
Communication
3 8 8
4
2
3
14
6
ence (as circles). Among the se2
7
4
3 4
2 14
4
3
Transport
10
8 4
4
60
12
4
22
lected countries, it is staggering
2 6
3
17
11
19 4
13 13
Health
4 32
12
2
3
14
5 8
13
to note that in 2013, 52.9% of Ban18
50
6
Furnishings, household
13 4 3 4 4
9
11 21
5
7
gladesh’s household consumpequipment, and routine
6
6
4
4
3
5
11
15
3 4 4
40
household maintenance7
7 8tion was spent on food and non3 6 5
4
Housing, water, electricity,
15
25
3
6 4
19
4
alcoholic beverages at one end of18
30 57
gas and other fuels
24
53
3
17
3
the spectrum, compared with
Clothing and footwear
2
43
16 19
40 39
20
4 4
6
35 33
Alcoholic beverages,
only 6.7% in the US at the other32 32 30
2 3 2 2 3
25
tobacco and narcotics
4 3
21
10
3
end. This translates into the fact
2
1
14 14 13 13
Food and non-alcoholic
10 7
7that low-income countries spend
beverages
030–50% of their GDP on food andnon-alcoholic beverages, whichcorresponds to Japan’s experience in the 1950s and the 1960s.
Figure 28 Household Consumption by Purpose, 2013
Eating out, recreation and culture
Sources: Official national accounts in each country.
are luxuries that the least well-off
Note: For data of Hong Kong, transportation includes communication; recreation andcountries cannot afford in conculture includes hotels; miscellaneous goods and services include restaurants. For
data of Sri Lanka, transportation includes communication; food and non-alcoholic
trast to their richer counterparts.
beverages includes alcoholic beverages, tobacco and narcotics. For Fiji and Mongolia,
Besides food and non-alcoholic
the observation periods are 2009 and 2012, respectively.
beverages, housing/utilities andtransportation are the other twolarge spending categories. In rich economies, these two categories account for bigger shares inhousehold consumption than food and non-alcoholic beverages. Idiosyncratic spending, such as education in Korea and Cambodia accounting for 5.9% and 5.0% of household consumption, respectively,
and health in the US, accounting for one-fifth of consumption, are not reflected in other countries.

©2015 Asian Productivity Organization

Figure 30 looks at the long-term trend of investment share (including R&D investment) in GDP acrosscountries. Historically, an investment share in the region of 40% or above seems to be unsustainable
in the long run. We see that Japan’s investment share of GDP steadily declined over the past decades
from 40.2% in 1970 to 23.9% in 2013 (Figure 30.1).32 In the initial period, Singapore also sustained aninvestment share of 40% or above. Since the mid-1980s, however, it has seen a downward trend, inspite of its fluctuations. In 2013, the investment ratio was 29.0%.33 The investment share hit around
40% in the ROC and Korea at different times but these were nothing more than temporary spikes(Figure 30.1).
In contrast, the investment share in China and India has been rising. India in particular has been investing very aggressively since 2000, coming as close as 6.1 percentage points to China’s 42.5% sharein 2007. Since then, the gap has widened to 19.7 percentage points in 2013 as investment in India
softened (Figure 30.3). At 50.3% in 2013, China’s investment share has reached a level previously
32: The Japan’s current share of gross investment is almost equivalent to the share of consumption of fixed capital (CFC) in GDP. Thus
the net investment is close to zero.
33: Although Singapore’s investment ratio in 2013 is higher than that of Japan, it is of note that Singapore has succeeded to sustain ahigher ex-post rate of returns on capital (12.6% for the period 2010–2013, based on our estimates in Table 22 in Appendix 3) thanthat of Japan (4.5% for the same period). Korea is another country which confronts of the decreases in the ex post rate of returnon capital. In 2010–2013, Korea’s rate of return reached to 6.6%, which is similar to that of Japan in the early 1990s.

46

4.2 Consumption and Investment

unseen in Asia. If history is any
guide, the contribution of investment to final demand in China will
drop eventually. South Asia and East
Asia’s investment shares are dominated by the effort in India andChina, respectively. ASEAN’s investment share was previously around35%, but it fell sharply to the lowest
point of 17.8% in 1999 in the aftermath of the Asian financial crisis.
Since then, it has been slowly inching up, reaching 29.5% in 2013. In
the past two and a half decades, theinvestment share in GCC countries
has fluctuated between 15–30% of
GDP (Figure 30.6).

Food consumption share in 2013 relative to Japan

0.7
Japan (1949–2013)

Fiji

0.6

Bangladesh
Lao PDR

4

0.5
Philippines
Sri Lanka
BhutanCambodia Indonesia
Mongolia
India
Iran

0.4

0.3

Thailand
Malaysia

0.2

Japan
ROC
Hong Kong Korea

0.1

Singapore

US

0.0
1940

1950

1960

1970

1980

1990

2000

2010

2020

The role of foreign direct investFigure 29 Engel Curve of Japan during 1949–2013 andment differs considerably among
Levels of Asian Countries in 2013
_Share of food in household consumptionn
Asian countries. Figure 31 shows
the FDI (foreign direct investment)
Sources: Official national accounts in each country. The historical data of Japan isbased on JSNA by ESRI, Cabinet Office of Japan.
inflows as a percentage of GFCF
Note: Food is defined as sum of food and non-alcoholic beverages and alcoholicduring 2000–2013, for the Asianbeverages, tobacco and narcotics. For Fiji and Mongolia and the Lao PDR, the observation periods are 2009, 2012, and 2005, respectively.
economies with some EU countries
for comparison. In almost half ofAsia30 (14 countries), the FDI inflows are over 10% share of GFCF. In particular, they are outstanding in the two global cities of the
Asian Tigers, Hong Kong (109% of GFCF) and Singapore (75%), which have recorded a remarkableachievement in their economic growths in the 2000s. Two countries in which extremely small FDI inflows were realized in this period, Nepal (0.9%) and Japan (0.7%), might consider a domestic reform
for lowering barriers to entry, therefore encouraging international investment.

34: The EIU’s business rankings model examines ten separate criteria or categories, covering the political environment, the macroeconomic environment, market opportunities, policy towards free enterprise and competition, policy towards foreign investment,
foreign trade and exchange controls, taxes, financing, the labor market and infrastructure. Each category contains a number ofindicators that are assessed by the EIU for the last five years and the next five years. The number of indicators in each category varies from 5 (foreign trade and exchange regimes) to 16 (infrastructure), and there are 91 indicators in total. Each of the 91 indicators
is scored on a scale from 1 (very bad for business) to 5 (very good for business).

47©2015 Asian Productivity Organization

It is an important policy target for low-income countries to create a business-enabling environment;
and for middle-income countries to improve many kinds of business environments. Based on the EIU’s(Economist Intelligence Unit, The Economist) ranking 2014–2018 (covering 82 countries in theworld), 34 Singapore (1st) and Hong Kong (3rd) are in the top 10% of the covered countries. In contrast,
Bangladesh (69th), Pakistan (74th), and Iran (81th) are in the bottom 10%. Figure 32 plots this businessenvironment score and the FDI inflows ratio, for the countries presented in Figure 31 (excluding thosein which the FDI inflows ratio is over 25%). There is a positive correlation between these two, withsome outlier countries like Japan. Improving business environment is a necessary condition for attracting FDI.

4 Expenditure

%

%

50

Hong Kong
Korea

Japan
ROC
Singapore

40

50

%

Iran
Thailand70

Malaysia
Turkey60

40

BhutanIndia
Mongolia
Sri Lanka

China
Indonesia
Philippines
Vietnam

Fiji

50
30

30

20

20

10

10

0
1970 1975 1980 1985 1990 1995 2000 2005 201001970 1975 1980 1985 1990 1995 2000 2005 201001970 1975 1980 1985 1990 1995 2000 2005 2010

Figure 30.1

Figure 30.2

Figure 30.3

40
30
20

%

50

10

%

%

Bangladesh
Nepal

50

Cambodia
Pakistan

APO20
USAustralia50

Asia30EU1540East Asia
ASEAN
CLMV

South Asia
ASEAN6
GCC

40

40

30

30

30

20

20

20

10

10

10

0
1970 1975 1980 1985 1990 1995 2000 2005 201001970 1975 1980 1985 1990 1995 2000 2005 201001970 1975 1980 1985 1990 1995 2000 2005 2010

Figure 30.4

Figure 30.5

Figure 30.6

Figure 30 Long-Term Trend of Investment Share in GDP, 1970–2013_Share of investment with respect to GDP at current market prices
Sources: Official national accounts in each country, including author adjustments.

Figure 33 focuses on investment components, showing the nominal investment share of seven typesof assets for some selected countries.35 For most countries, investment is still very much constructionbased (i.e., in dwellings, non-residential buildings, and other structures). However, the expansion of IT
capital in the past four decades is significant in the US, Japan, the Asian Tigers, and Malaysia – even at%

120

FDI inflows as a percentage to GFCF, an average in the period 2000–2013
109.3

100
80
60
40
20

©2015 Asian Productivity Organization

61.0
44.6 41.8

33.5

27.8

22.7

19.0 18.3 17.7

15.2 15.1 13.6 13.1 12.8 12.5

11.8 11.7 10.2 10.1 9.5 9.3 9.2 8.5

6.5 6.4 6.4 5.5 5.3 5.1 4.2 4.1 4.0 3.9 3.5 3.4 2.8

Figure 31 FDI Inflows, 2000–2013_FDI Inflows as a Percentage of GFCF, an average of the ratios during the period 2000–2013
Source: United Nations Conference on Trade and Development (UNCTAD), World Investment Report 2014.

48

0.9 0.7

Japan
Nepal
Indonesia
Bangladesh
KuwaitIran
Bhutan
ROC
Korea
India
Italy
Sri Lanka
US
Philippines
China
TurkeyPakistan
Oman
Qatar
Germany
Lao PDR
Australia
FranceThailandSpain
Malaysia
Saudi Arabia
UAE
CanadaVietnam
MyanmarBahrainUK
Netherlands
Cambodia
Mongolia
Fiji
BruneiSingapore
Hong Kong0

75.0

4.2 Consumption and Investment

the current price comparisons. The realterm comparisons are conducted at theflow and stock levels in Chapter 5. The
ROC, Korea, Japan, and the US invested
in R&D activities by more than 13% of
total investment in 2013. Among the
Asian Tigers however, the two global
cities (Singapore and Hong Kong) havea smaller share of R&D in GFCF – 7.5%
and 3.0%, respectively, in 2013.

% FDI inflows as a percentage to GFCF, an average in the period 2000–2013

25

UK
20

Bahrain

%

100
80
60
40
20

Saudi Arabia
Thailand
Pakistan

5

India

Iran

Malaysia
Australia

0Germany

Qatar

China
Italy

BangladeshIndonesia

US
Korea

ROC

Kuwait
Japan

2

4

6

8

10

Business environment rankings score 2014–18

Figure 32 FDI Inflow Ratio and Business Environment,
2000–2013_ FDI inflows as a percentage of GFCF and business environ-ment score

Sources: United Nations Conference on Trade and Development (UNCTAD),World Investment Report 2014; The Economist Intelligence Unit (2014).

1970

2013

%

100
80
60
40
20
0

9
18 13 14 24
23 20 22 23
26 27 26 30 53
26 20 2930
36
35
3 11 8
40
6
5
3
10 7
7
4
5 8 1115 9
3 17 84 134
5 3
9 42 5 83 20
3
14
13 9
4
4 13
14
13
10
21
8
3
13 21
15
9 3 2724 30 9
21 22
8
25 21
8
8
19 30 6
16
29
24
10
19
18
15
13
15 10
15
29
26
32
25
21 13 2031
7 8 1011 18 13
32 30
15
19
7
24 23 18 1814
16 16 13 13 13 12 11 10
9 9 8 6 5 2

Mongolia
Philippines
India
Fiji
Indonesia
ROC
Thailand
Malaysia
CambodiaHong Kong
Korea
Japan
China
US
Iran
Pakistan
Sri Lanka
VietnamSingapore
Bangladesh

Transport equipment

TurkeyPhilippines
Sri Lanka

10
16
15 15 25 29 24 18
24
25 28 435 30 40 35 30
2 32 4
7
41
42
17
2 12
12 6
4
13
2
4 8
5
12
10
4
11
12 12
10
13
11 16
23 13
55 11 21 456
8 5
11 24 16 24 22
8
32
11
14
18
20
8
22
34 15 9 201825
8 15 20 2114
15 13 16 13
46 40
13 18 17
24
38
14
19 27
26 23 23 23 22
19 19 19 18 16 15
11 9 8 5 3Dwellings

Spain
FranceChina
Philippines
Sri Lanka
ROC
Thailand
Malaysia
PakistanIndonesia
Japan
Singapore
US
Fiji
Hong Kong
Korea
Vietnam
Iran
India
Mongolia
Bangladesh

0Canada

UAE

15

10

Figure 34 plots the long-term trend of
net export share in GDP from 1970 to2013. Among the selected countries, India can be identified as prone to running a trade deficit, which deteriorated
rapidly from the mid-2000s to 6.9% of
GDP in 2012, but sharply improved to
3.0% in 2013 (Figure 34.3).36 In contrast,
net exports, which were previously ahuge drag on the Asian Tigers, Singapore, and Korea in the 1970s, have improved their position rapidly. In recent
years, net exports are making a positive
contribution to GDP for all of the AsianTigers. The share of net exports in

4

Vietnam

Non-residential buildings
R&D

Other structures
IT capital

Other non-IT

Figure 33 Investment Share by Type of Asset, 1970 and 2013

35: The investment data by type of assets includes our own estimates for the countries where data is not available. Although our estimates are constructed based on 11 classifications of assets (see Table 20 in Appendix 2), newly including the R&D investment inthis edition of the Databook (see Appendix 1), they have been aggregated into seven assets for the purposes of this table. The IT
capital is defined as IT hardware, communications equipment, and computer software.
36: The Indian government established the National Manufacturing Competitiveness Council (NMCC) in September 2004 to enhance
manufacturing competitiveness. By developing this policy direction, the Prime Minister, Shri Narendra Modi, launched the “Makein India” initiative with an aim to give the Indian economy global recognition in September 2014.

49©2015 Asian Productivity Organization

Sources: Official national accounts in each country, including author adjustments based on input–output tables and trade data.

4 Expenditure

%

%

32
24

Hong Kong

Japan

Korea

ROC

32

%

Iran

Malaysia

ThailandTurkey20

BhutanIndia
Philippines

China
Indonesia
Sri Lanka

Fiji
Mongolia
Vietnam

24

10

16

0

8

−10

0

−20

−8

−30

−24
1970 1975 1980 1985 1990 1995 2000 2005 2010−161970 1975 1980 1985 1990 1995 2000 2005 2010−40
1970 1975 1980 1985 1990 1995 2000 2005 2010

Figure 34.1

Figure 34.2

Figure 34.3

Singapore

16
8
0
−8
−16

%

30
20

%

%

Bangladesh

Cambodia

Nepal

Pakistan

10

10
8

APO20
US
Australia12

Asia30
EU15

6

6

0

4

−6

0
2

−12

−10
0
−20

−2

−18
East Asia
ASEAN
CLMV

South Asia
ASEAN6

−30

−4

−24

−40
1970 1975 1980 1985 1990 1995 2000 2005 2010

−61970 1975 1980 1985 1990 1995 2000 2005 2010−30
1970 1975 1980 1985 1990 1995 2000 2005 2010

Figure 34.4

Figure 34.5

Figure 34.6

Figure 34 Long-Term Trend of Net Export Share in GDP, 1970–2013_Share of net exports with respect to GDP at current market prices
Sources: Official national accounts in each country, including author adjustments.

Singapore is particularly large, at 23.2% in 2013, compared with 5.0%, 9.1%, and 0.6% for Korea, theROC, and Hong Kong, respectively (Figure 34.1). China is another country that has changed its net
export position, transforming it into a significant positive contribution to final demand. The net export share of GDP peaked at 8.7% in 2007. Since then, it has lagged to 2.4% in 2013.

©2015 Asian Productivity Organization

Japan had enjoyed a trade surplus for most of the period compared, but recently its trade balance hasturned negative amounting to –0.9% in 2011 deepening to –2.7% in 2013 (Figure 34.1). In the aftermath of the triple disaster (earthquake, tsunami, and nuclear) in 2011, Japan had to increase the imports of natural gas and coal to meet the increase of thermal power generation as a result of the
shutdown of its nuclear power plants. This trend may change in response to its new energy policy,
which will in turn reduce imports.
Figure 34.3 illustrates the external imbalance of the world’s major economies. Both the US and EU15
faced a trade deficit at the beginning of this period. While EU15 managed to recover, being in surplus
since the early 1990s (within a range of 0–2% of GDP), the US position has significantly deteriorated
since the middle of the 1990s, despite a tremendous effort to restore its trade balance in the late1980s. In 2013, the size of the US trade deficit stood at 3.0% of its GDP, compared to its recent dip to
5.6% of GDP in 2006. Australia has been running a trade deficit for most of this period. Only in the past50

4.3 Expenditure-Side Growth Decomposition

few years has its trade balance been in surplus. In contrast, Asia30’s trade has been in surplus
continuously and a near mirror-image of the US. Asia30’s net exports share of GDP was 2.6%, compared to the recent peak of 5.3% in 2013. Addressing this external imbalance has been highlighted asa necessary step to healthy and sustained growth in the world economy.

4

Japan
EU15
Saudi Arabia
Brunei
Philippines
US
Australia
Indonesia
TurkeyHong Kong
Bangladesh
ThailandBahrain
Iran
Pakistan
Oman
India
Sri Lanka
KuwaitMyanmar
UAE
BhutanCambodia
Korea
Qatar
ROC
Singapore
Malaysia
Vietnam
China

The time series of ASEAN’s trade balance has a clear structural break which is marked by the Asian financial crisis of 1997 (Figure 34.4). The impact was a trade balance spike in 1998 at 10.0%, up from
–0.1% in the previous year. Trade balance moderated over time to the more normal level of 2.7% in
2013. In recent years, the tradeperformance of CLMV has been
1990–2000%
strong and is in surplus for the first
10 0.4
0.1
time since 1970.37 Its improve8
4.1
1.3 1.1
ment has been rapid, from a defi0.2
3.2
1.2 1.5
6
2.1 3.1
cit of 9.3% in 2007 to a surplus of
0.8 4.2 1.3
2.6 2.8
1.5 1.0 0.7 0.2 0.4
0.4
2.0
2.0 2.4
0.7 0.0
1.6
4
0.7
0.7 0.7 0.8 0.0
0.5 1.4 1.6
1.4
0.1
0.5
1.6 1.4 1.0 0.6
0.6 0.8
1.0% in 2013. This should not be
0.6
0.6
1.1 1.2
1.3
0.7
0.8 0.7
0.1
0.3 0.5 0.3
0.4
0.1 0.5 0.2 0.2
0.2
4.5
2.6
1.4 0.3 0.5
2 3.4 4.3
3.8 3.6 0.5
3.6
3.6 3.4
1.0 0.3 0.2
3.0
3.0
2.9 2.7
a surprise when CLMV is picking
2.8
2.4 2.3 2.7 2.7
0.6 0.7
2.2 2.2 2.5 2.4 2.7 2.0 2.4
0.4
1.8
0.8
0.8 0.6 1.3 0.7
0.1
0up the slack from China as the
0.0
−0.1
−0.1
−0.2
−0.3
−0.3−0.1−0.3−0.1−0.4−0.7−0.4−0.4−2.2−2
workshop of the world. If the timeseries of China’s net exports is−4
any guide, CLMV’s trade surplus
could continue to expand for years
to come.
2000–2013%12

4.3 Expenditure-Side
Growth Decomposition

10

Figure 35 shows the decomposition of the average annual economic growth by final demand forthe periods 1990–2000 and 2000–
2013, respectively. Here, Asia30
grew faster in the latter period
than the former (at 5.2% on average per annum compared with
4.7%, as presented in Table 2).38
The earlier period embodied theatypical economic event of the
Asian financial crisis, which caused
some erratic contributions by the
final demand components observed in some countries in the

4

8

0.3
3.4
0.8
5.9

6

2
0
−2

6.9

5.9 2.0
0.5

3.0 3.1

0.1
2.9
2.0
2.0
2.1
1.3 1.1 0.2
1.0
0.8
2.8 1.9
0.3
0.6
1.8 1.5
0.3
0.4 0.6 1.4 2.1 2.0
1.6
0.9
0.9
1.4 0.7
0.6
1.1
6.4
0.5 0.4
1.1
0.6
2.4 0.2
0.7
0.8 1.4
0.9
0.2
4.3
0.7
0.5 0.1
4.1
3.9 3.5 0.4
1.0
0.6
3.5
3.4
3.4
3.4
3.1
3.1
0.2 0.1 0.7
2.9
0.2 2.4
2.6
2.3
2.2 1.8 1.4 2.8 2.6 1.6 2.8 2.2
1.8 0.2 0.3 0.3
1.5
1.3
1.4 0.5 0.6 1.1−0.9
0.0 −0.2
−0.1
−0.1
−0.2
−0.2−0.2−0.3−1.5−0.2−0.4−0.1
−0.6
−0.7
−2.3
−0.8
−0.8
−2.3
−1.0
−1.2
−3.3
1.3

3.1

0.6 0.4 1.1

2.3

Brunei
Japan
EU15
US
Australia
Iran
ROC
Nepal
Hong Kong
Oman
Korea
UAE
TurkeyKuwaitSaudi Arabia
Thailand
Pakistan
Philippines
Malaysia
Indonesia
Singapore
Bangladesh
Sri Lanka
Bahrain
BhutanVietnam
India
Cambodia
China
Qatar
Myanmar−4

1.7

Household consumption
Investment

Government consumption
Net exports

Real GDP

Figure 35 Final Demand Contributions to Economic
Growth, 1990–2000 and 2000–2013_ Decomposition: Average annual growth rate of GDP at constantmarket prices

37: The huge deficit of CLMV in the 1970s due to a large impact by the Vietnam War.
38: The Törnqvist quantity index is adopted for calculating the growth of real GDP. Using this index, the growth of real GDP into the
products of contributions by final demands can be decomposed:
In (GDPt / GDPt−1) = ∑ i (1/2) (sit + sit−1) In (Qit / Qit−1)
where Qit is quantity of final demand i in period t and sit is expenditure share ofReal GDP growth

Contribution of final demand ifinal demand i ­in period t. Thus, the real GDP growth may diverge from the official estimates or those presented in Table 3 (p. 20).

51©2015 Asian Productivity Organization

Sources: Official national accounts in each country, including author adjustments.

4 Expenditure

Box 3

National Accounts in Asian Countries

Understanding data comparability is essential for the construction of an international database, and requires continuous effort and expert knowledge. Between December 2014 and March 2015, the APO Productivity Database project conducted the Metadata Survey 2015 on the national accounts and otherstatistical data required for international comparisons of productivity among the APO member economies.
Broadly speaking, cross-country data inconsistency can arise from variations in one or more of the three
aspects of a statistic: definitions, coverage, and methodology. The international definitions and guidelines
work to standardize countries’ measurement efforts. However, country data can deviate from the international best practice and vary in terms of omissions and coverage achieved. Countries can also vary in theirestimation methodology and assumptions in benchmark and/or annual revisions. This may account forpart of the differences observable in the data, as well as interfere with comparisons of countries’ underlying economic performance.
Most of the economic performance indicators in this report are GDP-related. The surveys therefore put
much emphasis on discerning countries’ GDP compilation practices. In the Databook 2015, the 2008 SNA
is used as the standard, noting how countries’ practices deviate from it. Since there are differences between the 2008 SNA and its predecessors (1993 SNA or 1968 SNA) in some concepts and coverage, it isimportant to know in which year in the data series definitions and classification started to switch over. This
allows identification in breaks in the time series. Figure B3 presents the current situation in compilations
and data availability of the backward estimates based on the 1968 SNA, the 1993 SNA, and the 2008 SNA(including the future plan for introducing the 2008 SNA), based on our Metadata Survey 2015. For example,

1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015
Bangladesh
Cambodia
ROC
Fiji
India
Indonesia
Iran
Japan
Korea
Lao PDR
Malaysia
Mongolia
Nepal
Pakistan
Philippines
Sri Lanka
Thailand©2015 Asian Productivity Organization

Vietnam

68
93
08
68
93
68
93
08
68
93
08
68
93
08
68
08
68
93
68
93
08
68
93
08
68
93
08
68
93
08
68
93
08
68
93
08
68
93
08
68
93
08
68
93
08
68
93
68
93
08

1973

1980

1988

1951
1951
1951

2000

1996
1993
1993

2014
2009
2005

1968

1974

1995

2003
2005

1978

1950
1960

1999
2004

1991

1978
1980

2000

2000

N.A.(Before 1993 SNA was introduced, Material Product System had been used.)
1980

2008

2005

1988

2000

1972

1998

1975
1986

1990
1989

2015

2006
2004
2006

1998
1998

1975

2012

2010
2000

1981

2015

2007

1995

1975

Introduction year

2014

2002

1960
1975
(mixture of 1953 SNA until 1968)

1972

2016
2004

1990

1946

2015

2006

1986

1970

2010

2010

1981

1955

1953

2008
2007

1970

1959

1953

2014

2015
2013
2011
2011

2001

2015
2012

1993

2015

Backward estimates and implementation

Figure B3 Implementation of the 1968, the 1993, and the 2008 SNA
Source: APO Metadata Survey 2015.
continued on next page >52

4.3 Expenditure-Side Growth Decomposition> continued from previous page

Japan started to publish national accounts based on the 1968 SNA in 1978 (backward estimates based on
the 1968 SNA are available from 1955 at present) and national accounts based on the 1993 SNA in 2000
(backward estimates based on the 1993 SNA are available from 1980 at present), and plans to introduce
the 2008 SNA as of the end of 2016.
As Figure B3 suggests, countries differ in their year of introduction, the extent of implementation, andbackward estimates available. According to the survey response, five economies (Bangladesh, the ROC,
Indonesia, Singapore, and Mongolia) conducted the switchover in the period 2014–2015 after the publication of the last year edition of the Databook and 10 countries are currently 2008 SNA compliant (partly or
fully). While there are movements toward upgrading the SNA, Cambodia, Lao PDR, and Nepal have yet to
fully introduce the 1993 SNA.

4

The starting year of the official 2008 or 1993 SNA-compliant time series varies a great deal across countries,
reflecting the differences in the availability of backward estimates. Countries may have adopted the
2008/1993 SNA as the framework for their national accounts, but the extent of compliance in terms ofcoverage may also vary. The APO Productivity Database tries to reconcile the national accounts variations
based on the metadata information and our investigation, and provide harmonized estimates for international comparison. See Appendix 1 for details of the adjustments.

late 1990s. In the 1990s overall, the engine of growth for most countries in Asia was household consumption, while investment growth was more subdued.39
On the back of the Asian financial crisis, investment growth surged strongly. Its impact on real GDP
growth became more significant in Asia in the 2000s, especially in the fast-growing economies. For
example, investment contributed 5.9 percentage points in China, 3.4 percentage points in Myanmar,
3.0 percentage points in India, and 3.1 percentage points in Vietnam. China grew by 10.6% on average
per year in the latter period. The role played by investment has strengthened, with its contribution toeconomic growth expanding between the two periods from 43.4% to 55.9%, squeezing the contribution of net exports from 3.8% to 2.7%, and that of household consumption from 35.9% to 29.1%.
However, for Singapore and the ROC, the strength of net exports was the real economic story, accounting for 49.8% and 64.1% of their economic growth on average per year between 2000 and 2013,
respectively.40 Even in the other two Asian Tigers, net exports accounted for 23.9% and 18.2% of Korea’s and Hong Kong’s economic growth, respectively (Figure 36). In contrast, net exports have been adrag on economic growth in India over both periods, making a negative contribution of –2.2% and–3.8%, respectively.

39: The exceptions are some of the oil-producing countries, which enjoyed a positive contribution from net exports bigger than mostcountries, and China, which experienced the fastest economic growth among the countries studied, averaging 9.3% per year,
43.4% of which was driven by investment, compared with 35.9% by household consumption. This compares with average annual
growths of 3.4% in the US and 2.3% in EU15. The contribution from household consumption was 70.3% and 56.8%, whereas investment growth accounted for 35.2% and 22.9% of overall growth in the US and EU15, respectively.
40: In the real income term, the trading gain effect ran counter to welfare for those countries. See Figure 93 in Chapter 7.

53©2015 Asian Productivity Organization

In some of these economies, the contribution of household consumption to economic growth wassqueezed – for example, from 35.9% in 1990–2000 to 29.1% in 2000–2013 in China, from 36.7% to26.3% in Singapore, and from 54.1% to 34.0% in the ROC. In contrast, the role played by household
consumption in economic growth increased in the US and Japan, from 70.3% to 81.9% and from
61.2% to 74.9%. Overall economic growth in Japan slowed from 1.1% to 0.7% between the two periods compared. This was a sluggish performance, especially relative to the acceleration that most Asian
economies experienced. Also, in the latter period net exports made negative contributions in morecountries than previously, with its impact in certain oil-exporting countries particularly large.

4 Expenditure

In the 2000s, economic growth slowed in both the
US and EU15 from 3.4% on average per year in
1990–2000 to 1.7% in 2000–2013, and from 2.3% to
1.0%, respectively. In terms of contributions, household consumption increased from 70.3% to 81.9%
and government spending tripled from 5.0% to10.5% in the US over the two periods. Investment in
the US took a plunge, however, from a contributionof 35.2% to 6.1% over the two periods. On the contrary, its net exports improved from –10.5% to 1.6%.
EU15 had a similar pattern where the contributionof government spending doubled over the two periods from 14.6% to 29.3%, making up the slack inthe contribution of investment which went from
22.9% to –2.5%, while household consumption remained more or less stable. Its net exports also improved from 5.6% to 22.2%.

%

120
100
80

Household consumption

5
16
34

8

64

Government consumption

42

60

24

18

Investment

50

20
0

6

16

26

60
40

16

18

Net exports−3

Hong Kong

Korea

Singapore

ROC

Figure 36 Final Demand Contribution
Shares to Economic Growth of the AsianTigers, 2000–2013_Shares of final demand contributions to growthrate of GDP at constant market prices
Sources: Official national accounts in each country, including
author adjustments.

Figure 37 shows the impacts of the global financial
crisis and countries’ path of recovery from the viewpoint of final demand between 2007 and 2013. The adverse impact of the crisis was felt throughinvestment in most countries, and to a lesser extent, through net exports. Drastic contraction in invest­-­ment became commonplace in countries from 2008–2009. China’s robust growth in investment was a
result of prompt active policy intervention in face of the potential detrimental effects of the crisis on
the economy, and shrinking net exports. Hong Kong and Japan also suffered from the negative impact of net exports on growth. Investment rebounded strongly in 2009–2010 with favorable policy
levers, but moderated in the subsequent years when the effects of policy faded out. Only China andSingapore sustained their robust investment growth. The global financial crisis hit the US and EU15
earlier and deeper with retrenchment in household consumption as well as investment. These economies subsequently recovered, but the EU15 was pulled down again in 2011–2012 when it faced its%

2007–2008

%

15

15

10

10

2008–2009%2009–2010

%

5

0

0

%

2011–2012

%

10

10

10

12

8

8

8

6

6

4

4

2

2

0

0

0

−2

−2

0

−3

−4

−4

−2

9
5

2010–2011

15

6
4

6
3
−5

−5

−10

−10

Sources: Official national accounts in each country, including author adjustments.

54

EU15

_Annual growth rate of GDP at constant market prices and contributions of final demands

US

Figure 37 Impacts of Global Financial Crisis and Recoveries, 2007–2013

GDP

Japan
India
China
Hong Kong
ROC
Korea
Singapore

Net exports

EU15

US

©2015 Asian Productivity Organization

2

Japan
India
China
Hong Kong
ROC
Korea
Singapore

Investment

EU15

US

Government consumtion

Japan
India
China
Hong Kong
ROC
Korea
Singapore

EU15

US

Japan
India
China
Hong Kong
ROC
Korea
Singapore

EU15

US

Japan
India
China
Hong Kong
ROC
Korea
Singapore

EU15

US

Japan
India
China
Hong Kong
ROC
Korea
Singapore

Household consumption

2012–2013

4.3 Expenditure-Side Growth Decomposition

15
10
5

4

0
−5
−10
−15
−20
−25
Australia
EU15
US
China
VietnamBahrainCambodiaOman
Sri Lanka
India
Bangladesh
Fiji
ROC
Qatar
Myanmar
Pakistan
Saudi Arabia
TurkeyKuwait
UAE
Iran
Japan
BhutanSingapore
Malaysia
Korea
Philippines
Hong Kong
BruneiThailandIndonesia

In comparison, the impact of the
Asian financial crisis was more contained. Figure 38 suggests that the
impact was contained within Asia,
except for the handful of countries
affected, it marked an exceptional
time. In 1998, investment took anosedive in Indonesia, Korea, Malaysia, Singapore, and Thailand.
Household consumption also fell,albeit to a lesser extent. The crisis
however greatly boosted thesecountries’ net exports, likely tohave benefitted from the rapid devaluation of the Asian currencies,
except the Japanese yen, at thetime of the crisis. This helped tobolster the impacted economies
against the retrenchment in othercomponents of final demand.

%

20

Household consumption
Investment

Government consumption
Net exports

GDP

Figure 38 Impacts of Asian Financial Crisis, 1997–1998_Annual growth rate of GDP at constant market prices and contributions of final demands

Sources: Official national accounts in each country, including author adjustments.

Figure 39 shows how the contribution of economic growth by final demand varies across countries
and over time for the period 1970–2013. The immediate impact of the global financial crisis of 2007–2008 is represented in the data. Most countries felt an adverse impact in 2008 and 2009, with the exception of India where in 2009 growth rebounded strongly from a slowdown in the previous year. The
impact on the Asian countries varied both in magnitude and nature. Japan’s recession was particularly deep with the economy falling by 1.1% and 5.6% in 2008 and 2009, respectively, compared with
2.1% growth in 2007. The economic retrenchment in Japan was deeper than the –2.9% in the US and–4.7% in the EU15 in 2009. Besides Japan, other Asian countries either experienced a mild recession ora growth slowdown. Even so, relative to their rapid growth the magnitude of the impact could still be
substantial. For example, growth in Singapore dropped from 9.5% in 2007 to 0.2% and 0.4% in 2008
and 2009, respectively. Similarly, growth in Hong Kong slowed from 6.3% in 2007 to 2.1% 2008 before
moving into the negative zone of –2.5% in 2009. The corresponding real GDP growth figures for the
ROC were 6.7% in 2007, 0.8% in 2008, and –2.2% in 2009. India’s growth slowed from 10.3% in 2007 to
3.5% in 2008 before bouncing back to 8.3% in 2009. In contrast, the slowdown in China was gradualalthough it lasted longer. From 13.1% in 2007, growth decelerated to 10.6% in 2010, and further
dipped to 8.6% in 2013, which was the slowest in more than a decade. Most countries experienced arebound (strongly in some cases) in 2010, but it was generally temporary, wearing off and resulting ina more subdued growth trajectory beyond 2010. For example, growth in Japan swung from 4.3% in2010 to –0.5% in 2011 (reflecting a country-specific economic shock, namely the triple disaster of2011) and 1.7% in 2013, and similarly from 10.9% to 4.3% and 8.0% in India, from 15.4% to 6.2% and4.3% in Singapore, and from 10.2% to 3.6% and 2.2% in the ROC. The US also bounced back strongly
in 2010 from –2.9% in 2009 to 2.6%, which subsequently slowed in 2011 to 1.6% before returning to
2.2% in 2013. A similar pattern can be seen in the EU15 when the economy revived in 2010 from a–4.7% growth in 2009 to 2.1%, which slowed to 1.6% in 2011. Growth went negative (at –0.6%) again
in 2012 under the impact of a region-specific economic shock (the euro crisis). For Asia30, the figures
were 8.7% in 2010 and 5.9% in 2013.

55©2015 Asian Productivity Organization

specific adverse economic shock
originated from the euro crisis.

4 Expenditure%
60

Bahrain%
15

Bangladesh

10

45

20

5

30

Bhutan

%
30

10

0

15

−10

−10

−15

−15

−30

−20

%
30

0

−5

0

1971 1976 1981 1986 1991 1996 2001 2006 2011

Brunei−20

1971 1976 1981 1986 1991 1996 2001 2006 2011%20

Cambodia

−30

16

12

12

10

8

8

0

4

4

−10
−20

%
18

1971 1976 1981 1986 1991 1996 2001 2006 2011

ROC

0

0

−4

−4

−8

%
15

12

10

6

5

0

0

−6

−5

−12

−10

China

%
20

16

20

1971 1976 1981 1986 1991 1996 2001 2006 2011

1971 1976 1981 1986 1991 1996 2001 2006 2011

Hong Kong−8

1971 1976 1981 1986 1991 1996 2001 2006 2011

India

%
12
9
6
3

%
18

1971 1976 1981 1986 1991 1996 2001 2006 2011

Indonesia

12

16

6

8

0

0

−6

−8

−12

−16

−18

−24

−24

−32

%
18

1971 1976 1981 1986 1991 1996 2001 2006 2011

Korea

%
200

−150

−24

−200

1971 1976 1981 1986 1991 1996 2001 2006 2011

Mongolia

30
20
10
0
−10

1971 1976 1981 1986 1991 1996 2001 2006 2011

Household consumption

%
20

4
2
0
−2
−4
1971 1976 1981 1986 1991 1996 2001 2006 2011

Kuwait−61971 1976 1981 1986 1991 1996 2001 2006 2011

Malaysia

%
18
12
6
0
−6
−12
−18

1971 1976 1981 1986 1991 1996 2001 2006 2011

Myanmar

−24

12

10

9

5

6

0

3

−5

0

−10

−3
1971 1976 1981 1986 1991 1996 2001 2006 2011

Government consumption

1971 1976 1981 1986 1991 1996 2001 2006 2011

Nepal

%
15

15

−15

Japan

6

−100

−18

1971 1976 1981 1986 1991 1996 2001 2006 2011

8

−50

−12

−6

%
10

0

−6

56

Iran

50

0

−20

1971 1976 1981 1986 1991 1996 2001 2006 2011

100

6

%
40

3

150

12

©2015 Asian Productivity Organization

%
24

0

Investment−61971 1976 1981 1986 1991 1996 2001 2006 2011

Net exports

Real GDP

4.3 Expenditure-Side Growth Decomposition

%
40

Oman

%
15

30

12

20

9

10

6

0

3

−10

0

−20

−3

−30

−6

%
30

1971 1976 1981 1986 1991 1996 2001 2006 2011

Qatar

20
10

%
60

Pakistan

Philippines

%
15
10
5
0
−5
−10

1971 1976 1981 1986 1991 1996 2001 2006 2011

Saudi Arabia−15

1971 1976 1981 1986 1991 1996 2001 2006 2011

Singapore

%
18

40

12

20

6

0

0

−20

−6

4

0
−10
−20
−30

%
18

1971 1976 1981 1986 1991 1996 2001 2006 2011

Sri Lanka

−40

%
20

12

10

6

0

0

−10

−6

−20

1971 1976 1981 1986 1991 1996 2001 2006 2011

Thailand−121971 1976 1981 1986 1991 1996 2001 2006 2011

UAE

%
90

60

30

−12

%
30

1971 1976 1981 1986 1991 1996 2001 2006 2011

Vietnam

−30

%
9

20

6

10

3

0

0

−10

−3

−20

−6

0

1971 1976 1981 1986 1991 1996 2001 2006 2011

US

−30

1971 1976 1981 1986 1991 1996 2001 2006 2011

EU15

%
6
4
2
0

%
8

1971 1976 1981 1986 1991 1996 2001 2006 2011

Australia

6

%
15

−2
−4

1971 1976 1981 1986 1991 1996 2001 2006 2011

Turkey

APO20

8
6

5

2

4

0

2

0

0

−5

−2

−2
−10

−4

%
12

1971 1976 1981 1986 1991 1996 2001 2006 2011

%
10

10

4

−6

−6

1971 1976 1981 1986 1991 1996 2001 2006 2011

−15

−4

1971 1976 1981 1986 1991 1996 2001 2006 2011−61971 1976 1981 1986 1991 1996 2001 2006 2011

Asia30

10
8
6
4
2
0

−4

1971 1976 1981 1986 1991 1996 2001 2006 2011

Household consumption

Government consumption

Investment

Net exports

Real GDP

Figure 39 Final Demand Decomposition of Real GDP Growth, 1970–2013
Sources: Official national accounts in each country, including author adjustments.

57©2015 Asian Productivity Organization−2

4 Expenditure

The channels through which economic growth was adversely impacted also varied across countries.
Japan’s recession in 2009 was largely accounted for by a sharp fall in investment (4.0 percentage
points) and, to a lesser extent, a fall in net exports (1.6 percentage points). Meanwhile the 0.4% growthof government spending canceled out the 0.4% fall in household consumption. Similarly, in the ROC,
investment fell by 5.2% in 2009, while household consumption and net exports grew, albeit more
slowly than previously. Growth in government consumption was stronger to negate some of the adverse impacts. In Singapore, net exports and investment in 2008 accounted for –8.8 percentage points
and 7.3 percentage points of the final demand growth, respectively. The reverse was true in 2009 withnet exports accounting for 4.3 percentage points and investment –3.9 percentage points of final demand growth. In China, the financial contagion was through trade. Net exports were the only
component to fall (by 2.3 percentage points) in 2009, while other final demand components expanded handsomely. In the subsequent years, investment growth softened from an all-time high of 9.5
percentage points in 2009 to the decade-norm of 5.1 percentage points in 2013. This contributed to a
slowdown in growth. It is worth noting that the level of investment growth in China in the past decadeis unprecedented historically. Hong Kong also took a hard hit in terms of net exports in 2009, whichfell by 3.0 percentage points. Household consumption growth slowed considerably in 2009 to 0.1
percentage point before bouncing back to its normal range of 3–5%.

©2015 Asian Productivity Organization

In the US and EU15, the vulnerability in 2009 was in investment and household consumption. Consumers were cautious with their spending as households repaired their balance sheets and job prospects became uncertain. Household consumption fell by 1.1 percentage points and 0.8 percentage
points, whereas investment fell by 3.6 percentage points and 3.9 percentage points in the US and
EU15, respectively. In the subsequent years, there was no further retrenchment in these activities,
which still struggled to grow. Final demand components returning to growth was marked by governments in the US and EU15 going through a period of fiscal austerity in 2011 and 2012, when government consumption contracted marginally. As the euro crisis deepened and was answered with further
austerity policy, both household consumption and investment contracted in 2012. That is, of the four
final demand components, only net exports were growing. Japan was the only Asian country in which
the global financial storm of 2007–2008 caused deeper retrenchment in its economy than the Asianfinancial crisis of 1997–1998 (Figure 38).
It is difficult to understand the oil-exporting economies fully without analyzing the oil market in parallel. Its volatility can be observed clearly from Figure 39, with huge peaks and valleys, particularly in the
1970s. The oil booms of the 1970s brought benefits, but the downturn was a detriment. Net exports
remain erratic, but overall volatility seems to have reduced in the past two decades. Qatar experienced the fastest GDP growth among the oil-exporting countries in recent years with very strong investment growth. However, its economy remains very dependent on oil and gas and related industries,
which accounted for 53% of its GDP in 2013 (Figure 72, p. 96) – roughly 80% of its export earnings, and
70% of government revenues in the 2000s.41 In contrast, Bahrain has diversified into a regional banking and financial center and benefited from the regional boom in recent years. Even so, petroleum
production and processing accounted for 26.9% of its GDP in 2013 (Figure 72) – about 60% of export
earnings, and 75% of government revenues in the 2000s.42 The economic fortunes of these countries
are therefore intimately tied with the rest of the world via their dependence on the oil and gas industry. For example, demand for oil has been driven by the rapid growth in emerging economies. If, forinstance, China’s growth slows, the demand for oil will also relent. Their future depends on how wellthey can diversify away from oil and gas while the stock of natural resources remains.

41: Data from the series of Annual Statistical Abstract, State of Qatar.
42: Data from the Ministry of Finance, Kingdom of Bahrain.

58

4.3 Expenditure-Side Growth Decomposition

Box 4

The Size of the Informal Sector

The definition of “the informal sector” varies depending on the purposes and the context of discussion.
One statistical definition of the informal sector is provided by the 15th ICLS resolution of the International
Labour Organization (ILO) in 1993 as follows:
The informal sector units are divided into two subsets:(a) Informal own-account enterprises. These are household enterprises owned and operated by ownaccount workers, either alone or in partnership with members of the same or other households, which
may employ contributing family workers and employees on occasional basis but do not employ employees
on a continuous basis.
(b) Enterprises of informal employers. These are household enterprises owned and operated by employers,either alone or in partnership with member of the same or other households, which employ one or more
employees on a continuous basis. Enterprises may be considered informal if they meet one of the following
criteria: (a) small size of the enterprise in terms of employment, (b) non-registration of the enterprise, and (c)non-registration of its employees (ILO, 2013, pp. 249–250).

4

Examples of the informal sector include unpaid work in a family enterprise, casual wage labor, home-based
work, and street vending.
The informal sector in less developed countries (LDCs) is huge. Compared with workers in the formal sector, those in the informal sector are typically paid poorly and supply labor in low-quality working conditions without legal protection or official social protection. Some part of the informal sector exists for the
purpose of tax evasion, but the dominant portion in LDCs provides “the only opportunity for many poor
people to secure their basic needs for survival” (ILO, 2013, p.3). Encouraging labor movements from theinformal sector to the formal sector is one of the most important developmental issues in many LDCs.
How far the informal sector is counted in the national accounts depends on the country. The size of theinformal sector is not directly comparable across countries. However, we can loosely grasp the significanceof the informal sector by looking at “the number of employment” and “the number of employees.”% Share of employee to employment in 2013100

Australia

90

China

80

Singapore

ROC

Malaysia
Mongolia

70

Hong Kong

US

Japan
EU15

Korea

TurkeyPhilippines

60

Sri Lanka
50

IranIndonesia

40

Fiji
Thailand
Pakistan
VietnamBhutanCambodia
Nepal
20
India
Bangaldesh
10
30

0

Thousands of US dollars (as of 2013)

0

10

20

30

40

50

60

70

80

Per capita GDP in 2013 (using 2011 PPP, reference year 2013)

Figure B4 Employee Share and GDP Level, 2013
Sources: Official national accounts in each country, including author adjustments;
APO Productivity Database 2015.
continued on next page >59©2015 Asian Productivity Organization

The number of employment is estimated so as to be consistent with
the national accounts, which tries tocapture economic activities of thewhole economy though some partof workers in the informal sector
would be missing. On the other
hand, the data for the number of
employees seem to be drawn fromofficial labor surveys and thus arelikely to exclude most of the employment in the informal sector. Therefore, a difference between thenumber of employment and thenumber of employees is loosely regarded as employers/self-employedworkers in the formal sector andworkers in the informal sector. Although statistical problems are evident, particularly for the treatmentof the employment data in the agricultural sector, we can still clearly
see that the number of employees issubstantially lower than the numberof employment in LDCs.

4 Expenditure> continued from previous page

Figure B4 plots the ratio of the number of employees to the number of employment (the vertical axis)
against PPP-adjusted per capita GDP (the horizontal axis) in 2013 for a number of countries. Employee
ratios tend to be higher as countries have higher income. However, even among LDCs, employee ratios
have substantial variation; low in most of the South Asian countries while relatively high in ASEAN
Member States.
The policy implication is profound. First, LDCs with low employee ratios are likely facing difficulties in encouraging labor movements from informal to formal sectors. The reasons could be on the demand side,
the supply side, or the combination of both. The growth of the formal sector, particularly the manufacturing sector and modern services sectors, may not create enough jobs. The gap of human capital between
informal and formal sectors may be too large. Urban living conditions may be too harsh and expensive toattract rural people to urban areas. Governments must find and resolve bottlenecks to make labor movements smoother.

©2015 Asian Productivity Organization

Second, raising minimum wages is recently a popular policy in many countries including Thailand, Indonesia, and Cambodia, but may deter labor movements from informal to formal sectors. Minimum wages are
typically enforced only in the formal sector, and wage levels in the informal sector remain low. Raisingminimum wages too high may reduce the labor demand in the formal sector, make labor movements
more difficult, and in the end negatively impact people in the informal sector. Although the betterment oflabor conditions is certainly important, raising minimum wages too high may cause adverse effects for
economic development.

60

5.1 Per-Worker Labor Productivity

5 Productivity
Productivity performance is crucial to a country’s economic prospect. As the cost to input more factors of production (labor and capital) may increase, productivity gains, which enable an economy toproduce more for the same amount of inputs, are the only route to sustainable economic growth in
the long run. It follows that monitoring and improving national productivity capability (the supply
side of the economy) are important aspects of public policy, especially when many countries are facing aging population.

5

Used as a ratio of an output volume measure to an input volume measure, productivity is simple as a
notion. When it comes to applying it, however, one quickly realizes the complexity in operationalizing
this notion to suit different purposes, especially in a world with data limitations. Consequently, there
are different measures of productivity for different purposes, and different estimation approaches anddefinitions subject to the data used. In the Databook, national accounts are the basis for productivity
estimates, and, in turn, growth accounting with the appropriate choice of index numbers is adopted
as an estimation approach.43 Two productivity measures are mainly presented in this chapter, namelylabor productivity and TFP.
Labor productivity can be measured in a number of ways, depending on the definitions of output andlabor input measures. The preferred measure is the basic-price GDP at constant prices per actual hour
worked, which adjusts to allow for different work patterns across countries and across time.44 However, total actual hours worked cannot be collated for all countries. In order to include all countries
and define the Asian country groups, the labor productivity measure in terms of GDP per worker isused in Section 5.1. As workers in high-performing Asian countries tend to work longer hours on average than those in the US, the worker-based labor productivity gaps, in this instance, cast the Asian
countries in a particularly favorable light. Section 5.2 sees the focus shift to alternative estimates oflabor productivity measure, namely GDP per hour worked for some selected Asian countries. In Section 5.3 and 5.4, capital input is included as another key factor of production and the TFP estimates arepresented for 19 Asian countries and the US,45 based on the estimates of capital services (see Appendix 3). The improvement in energy productivity, as one of the measures of partial factor productivities,
is becoming an important policy target for pursuing sustainable growth of the Asian countries. In Section 5.5, the estimates of energy productivity are newly measured in this edition of the Databook.

5.1 Per-Worker Labor Productivity

43: The growth accounting approach is based on the microeconomic production theory and the nominal accounting balance of input and output of production. See OECD (2001) for a presentation of definitions, theoretical foundations, and a number of practical issues in measuring productivity.
44: GDP is valued at basic prices in this chapter, as opposed to GDP at market prices used in the previous chapters. GDP at basic prices
is defined as GDP at market prices, minus net indirect taxes on products. As most Asian countries do not provide official estimatesfor GDP at basic prices in their national accounts, they are calculated based on available tax data. See Appendix 1 for the methods
employed for our calculations.
45: In this Databook, the TFP estimate was newly developed for Cambodia. The estimates for factor incomes for Vietnam are revised
through the examinations with Vietnam National Productivity Institute (VNPI) in May 2014.
46: Cross-country level productivity comparisons are notoriously difficult to make and hence subject to much data uncertainty. Estimates should therefore be taken as indicative for broad groupings rather than precise ranking. The level of labor productivity in
Singapore was slightly lower than the US level in 2011, in the Databook 2013 which was based on the 2005 benchmark PPP. However, in this Databook, it was upwardly revised by 16% due to the use of the new 2011 benchmark PPP (See Box 1, p. 17).

61©2015 Asian Productivity Organization

Figure 40 presents the cross-country comparisons of labor productivity levels in 2013, measured as
GDP per worker in US dollars. The countries naturally bundle into groups. On this measure, Singapore
is the leading economy, more than 10% larger than the US level.46 Hong Kong and the ROC follow atsome distance. While Iran is close to the top, it is worth noting that it has the lowest employment rate
in Asia (Figure 21, p. 33). Japan took the fourth place, with productivity levels at 34% below the US.

5 Productivity

Korea, Iran, and Malaysia followed.47 Thereafter, anumber of countries from among the Asia group followed with labor productivity levels at less than 25%of the US, pulling down the average performance ofthe group to 21% for the APO20, 20% for Asia30, and
18% for ASEAN. Bringing up the rear were China andIndia, with productivity levels that were 18% and 12%of the US level, respectively.

Singapore
US
Hong Kong
ROC
Australia
EU15
Japan
Korea
Iran
TurkeyMalaysia
Mongolia
East Asia
ASEAN6
ThailandSri Lanka
APO20
Indonesia
Asia30
Asia24
ASEAN
China
Fiji
BhutanPhilippines
Pakistan
India
South Asia
Lao PDR
Vietnam
CLMV
MyanmarBangladesh
Cambodia
Nepal

Table 9 presents the comparison of the per-worker labor productivity level. In 2000–2013, the APO20 as agroup achieved little change in its labor productivity
relative to the US, stagnating at around 20%, while
Asia24’s rose from 13% to 19%. In 2000, Hong Kong
sustained a productivity gap of 19% with the US, but
by 2013 the gap narrowed to around 7%. In contrast,
the relative productivity level of Japan against the US
has deteriorated over the last two decades.
China and India are the two giant and fast-emerging
economies in Asia. China began with one-third of India’s productivity levels in 1970. Four decades later itshows signs of pulling ahead of India, as shown in Figure 41. China’s relative performance against the US
moved up from 2% in 1970 to 7% in 2000 and 18% in
2013, compared with the corresponding figures of
6%, 8%, and 12% for India.48

121.9
107.6
99.8
90.6
86.6
77.6
71.4
61.5
59.3
50.9
50.2
25.2
25.1
24.8
24.5
24.5
22.0
21.9
21.8
20.4
19.4
18.8
18.3
15.9
15.7
14.7
13.3
12.8
8.4
8.4
7.8
7.7
7.0
4.9
4.8

0

20

Thousands of US dollars (as of 2013)

40

60

80

100

120

Figure 40 Labor Productivity Level by
Per-Worker GDP, 2013

_GDP at constant basic prices per worker,
using 2011 PPP, reference year 2013©2015 Asian Productivity Organization

Source: APO Productivity Database 2015.

The figures for GCC countries and Brunei are uncharacteristically high, especially in 1970. There
are noticeable variations within the countrygroup. The atypically high figures in the early period reflect the natural resource rents (the valueof the resource over and above the cost of extraction) which are erroneously included in the
GDP of these countries. The extent of exaggeration appears to be proportional to their oil production. Saudi Arabia has the largest proven oil
reserves in the world and is the largest world oil
exporter. Kuwait has the fourth-largest oil reserves in the world. In addition, Qatar has become the fourth-largest exporter of liquefied
natural gas. In contrast, Bahrain has the smallest
oil reserves compared to its peers. Its dependence on oil is therefore considerably lower.
Consequently, it has worked to diversify its economy over the past decade (see Figure 87, p. 112).49

Thousands of US dollars (as of 2013)

18
15
China

12
9
6

India

3
0
1970 1975 1980 1985 1990 1995 2000 2005 2010

Figure 41 Labor Productivity Trends of China
and India, 1970–2013_GDP at constant basic prices per worker, using
2011 PPP, reference year 2013

Source: APO Productivity Database 2015.

Table 10 presents the growth comparison of per-worker labor productivity. When labor productivity
growth is compared, the ranking of countries is substantially reshuffled. In the 2000s there was a surge

625.1 Per-Worker Labor Productivity

Table 9 Per-Worker Labor Productivity Levels, 1970, 1980, 1990, 2000, 2010, and 2013
_GDP at constant basic prices per worker, using 2011 PPP, reference year 2013
1970

1980

(%)

1990

(%)

(%)

2000

(%)

2010

(%)

2013

(%)

Iran
SingaporeJapan
Hong Kong
Fiji
Malaysia
ROC
Philippines
Korea
Mongolia
Sri Lanka
Indonesia
Thailand
Pakistan
India
Bangladesh
VietnamChina

37.1 100.0
31.2 84.0
27.7 74.6
21.8 58.6
15.2 41.0
12.9 34.6
11.7 31.6
9.3 24.9
8.1 21.9
7.7 20.7
6.1 16.4
5.5 14.8
5.2 14.0
5.0 13.5
3.7 10.0
3.5 9.4
2.4 6.4
1.0 2.7

Singapore
Iran
Japan
Hong Kong
ROC
Malaysia
Fiji
Korea
Mongolia
Philippines
Sri Lanka
Indonesia
Thailand
Pakistan
Bangladesh
India
Vietnam
Nepal
MyanmarChina

44.0 100.0
40.0 90.9
39.6 89.9
36.0 81.8
22.1 50.3
19.7 44.8
14.5 32.9
13.3 30.2
11.6 26.4
10.9 24.8
8.9 20.1
8.7 19.8
7.3 16.5
5.9 13.4
3.6 8.3
3.6 8.2
2.5 5.6
2.4 5.4
1.6 3.6
1.5 3.4

SingaporeJapan
Hong Kong
Iran
ROC
Malaysia
Korea
Fiji
Mongolia
ThailandIndonesia
Sri Lanka
Philippines
Pakistan
BhutanIndia
Bangladesh
Nepal
Lao PDR
Vietnam
China
Myanmar65.6 100.0
57.4 87.4
57.0 86.8
40.0 61.0
38.5 58.7
26.0 39.6
25.6 39.0
15.3 23.3
12.9 19.6
11.3 17.3
10.9 16.6
10.8 16.4
10.1 15.4
9.3 14.2
8.3 12.7
4.9 7.5
4.0 6.0
3.4 5.1
3.2 4.9
2.8 4.2
2.4 3.7
1.6 2.4

Singapore
Hong Kong
ROC
Japan
Iran
Korea
Malaysia
ThailandFiji
Sri Lanka
Indonesia
Mongolia
Pakistan
BhutanPhilippines
India
China
Bangladesh
VietnamLao PDR
Nepal
Cambodia
Myanmar96.7 100.0
71.0 73.4
64.3 66.5
63.5 65.6
44.6 46.1
42.8 44.2
38.1 39.4
17.4 18.0
15.9 16.4
14.4 14.9
13.9 14.4
12.4 12.8
12.3 12.7
12.2 12.6
11.5 11.9
6.7 7.0
5.8 6.0
5.2 5.4
4.7 4.9
4.6 4.8
4.1 4.3
2.7 2.8
2.5 2.5

Singapore
Hong Kong
ROC
Japan
Iran
Korea
Malaysia
ThailandSri Lanka
Indonesia
Mongolia
Fiji
China
BhutanPakistan
Philippines
India
VietnamLao PDR
MyanmarBangladesh
Nepal
Cambodia

116.9 100.0
97.7 83.5
87.5 74.8
69.7 59.6
64.9 55.5
59.3 50.7
47.9 41.0
22.4 19.1
20.9 17.8
19.2 16.4
18.3 15.7
17.3 14.8
15.0 12.8
14.4 12.3
14.3 12.2
14.0 11.9
11.1 9.5
7.5 6.4
7.2 6.2
6.6 5.6
6.4 5.5
4.5 3.9
4.1 3.5

Singapore
Hong Kong
ROC
Japan
Korea
Iran
Malaysia
Mongolia
ThailandSri Lanka
Indonesia
China
Fiji
BhutanPhilippines
PakistanIndia
Lao PDR
Vietnam
MyanmarBangladesh
Cambodia
Nepal

121.9 100.0
99.8 81.9
90.6 74.4
71.4 58.6
61.5 50.5
59.3 48.7
50.2 41.2
25.2 20.7
24.5 20.1
24.5 20.1
21.9 18.0
18.8 15.5
18.3 15.1
15.9 13.1
15.7 12.9
14.7 12.0
13.3 10.9
8.4 6.9
8.4 6.9
7.7 6.3
7.0 5.8
4.9 4.0
4.8 3.9

Bahrain
KuwaitOman
Qatar
Saudi Arabia
UAE

131.9 355.2
612.1 1649.0
120.8 325.4
305.4 822.7
213.9 576.3
98.7 265.8

Bahrain
KuwaitOman
Qatar
Saudi Arabia
UAE
Brunei(regrouped)
APO20
Asia24
Asia30
East Asia
South Asia
ASEAN
ASEAN6
CLMV
GCC(reference)
US
EU15

119.6
240.0
165.3
259.0
215.1
363.6
420.7

271.7
544.9
375.5
588.1
488.5
825.8
955.3

Bahrain
KuwaitOman
Qatar
Saudi Arabia
UAE
Brunei(regrouped)
APO20
Asia24
Asia30
East Asia
South Asia
ASEAN
ASEAN6
CLMV
GCC(reference)
US
EU15

89.1
97.4
176.7
166.5
118.7
226.0
203.1

135.7
148.3
269.2
253.7
180.9
344.2
309.4

105.6
189.3
152.8
222.8
132.6
194.9
182.8

109.1
195.7
158.0
230.3
137.1
201.5
189.0

62.6
121.2
93.1
158.6
124.5
123.6
143.0

Australia
Turkey61.0
27.9

Bahrain
KuwaitOman
Qatar
Saudi Arabia
UAE
Brunei(regrouped)
APO20
Asia24
Asia30
East Asia
South Asia
ASEAN
ASEAN6
CLMV
GCC(reference)
US
EU15
EU28
Australia
Turkey

76.4
146.4
82.7
212.6
144.1
149.4
160.4

56.7 128.7

Bahrain
KuwaitOman
Qatar
Saudi Arabia
UAE
Brunei(regrouped)
APO20
Asia24
Asia30
East Asia
South Asia
ASEAN
ASEAN6
CLMV
GCC(reference)
US
EU15
EU28
Australia
Turkey

73.2
141.7
108.8
185.4
145.5
144.5
167.2

Australia

Bahrain
KuwaitOman
Qatar
Saudi Arabia
UAE
Brunei(regrouped)
APO20
Asia24
Asia30
East Asia
South Asia
ASEAN
ASEAN6
CLMV
GCC(reference)
US
EU15
EU28
Australia
Turkey(regrouped)
8.8 23.8
APO20
5.2 14.0
Asia24
5.8 15.6
Asia305.0 13.4
East Asia
4.1 11.0
South Asia
6.7 18.0
ASEAN
7.4 20.0
ASEAN6
4.0 10.7
CLMV
278.0 748.8
GCC(reference)
56.2 151.3
US
39.7 107.0
EU15Australia

49.4 133.1

10.7 24.2
6.5 14.7
7.5 17.1
6.8 15.5
4.1 9.3
8.2 18.7
10.6 24.0
2.4 5.5
242.5 550.7
62.3 141.4
51.3 116.5

13.8 21.0
8.1 12.3
8.8 13.4
8.4 12.8
5.5 8.3
10.1 15.3
12.8 19.6
2.7 4.1
136.0 207.2
72.8 110.9
61.3 93.4
93.0
42.6

16.3 16.9
11.2 11.6
12.1 12.6
12.4 12.8
7.3 7.5
13.0 13.5
17.1 17.7
3.9 4.1
154.7 159.9
88.0
71.9
64.3
75.5
35.4

91.0
74.4
66.5
78.1
36.6

20.2 17.3
17.8 15.2
19.0 16.2
21.5 18.4
11.0 9.4
17.5 14.9
22.3 19.1
6.9 5.9
142.7 122.1
105.7
76.6
70.4
82.2
49.7

90.4
65.6
60.2
70.3
42.5

5

62.7
120.1
67.9
174.4
118.2
122.6
131.6

22.0 18.1
20.4 16.8
21.8 17.9
25.1 20.6
12.8 10.5
19.4 16.0
24.8 20.3
7.8 6.4
141.5 116.1
107.6
77.6
71.6
86.6
50.9

88.3
63.7
58.7
71.0
41.8

47: Note that the workers aged over 65 are excluded from labor input in Malaysia, due to the definition in labor survey in Malaysia.
This edition of the Databook does not adjust the difference in coverage of workers, which can be defined differently among countries. Thus readers should mind that our estimates of the labor productivity for Malaysia in Figure 40 would be underestimated atleast by 1%, if the omitted workers were included.
48: If the comparisons were with the region’s leader at different times, India’s relative labor productivity has actually fallen, while China
has managed to make a substantial leap to close in on the leader, albeit from a very low level.
49: The GCC countries have also been experiencing high population growth, especially in the late 1970s and the early 1980s. In
2000–2013, this has somewhat stabilized at around 3.0% per year, except in the Qatar and the UAE where the population grew at
9.5% and 8.6%, respectively. The working-age population has been expanding accordingly. Employment is erratic from one year
to another, and this will be reflected in the labor productivity figures.

63©2015 Asian Productivity Organization

Unit: Thousands of US dollars (as of 2013).
Source: APO Productivity Database 2015.

5 Productivity

Box 5

Turning Point in China

The Lewis model (Lewis, 1954) or the Fei-Ranis model (Fei and Ranis, 1964), which established development economics as a respectable academic discipline in the late 1950s and 1960s, proposed the conceptof a turning point, where a developing economy transforms itself from an unskilled-labor-abundant economy with seemingly unlimited supply of labor, to a labor-scarce industrial economy. The Chinese economy
seems to have reached its turning point in the latter half of the 2000s, based on the APO Productivity Database 2015.
Figure B5 presents the price of labor, relative to capital in China, Japan, and the Asian Tigers. The price oflabor is defined as the average wage (total labor compensation, including our estimates of wages for selfemployed and family workers) over total hours worked. The price of capital is estimated by the ex-post
approach for measuring user cost of capital (see Appendix 3). The relative price index of labor on capital isnormalized as 1.0 in 1970 in each country.

©2015 Asian Productivity Organization

In Japan the price of labor increased at the beginning of the 1970s. The price of labor increased
for Korea and the ROC in the late 1980s and the
beginning of the 1990s, respectively. In these periods, China’s low price of labor could be a main
source of superior price competitiveness in labor-intensive manufacturing. The turning point
was around 2008, when the price of labor started to increase very sharply, relative to capital.
Such a turning point emerges when a country
makes effective movements of labor from agricultural/rural/informal sectors to industrial/urban/formal sectors. This turn was a great
achievement for China, addressing the serious
concern of income disparity and working to­ward alleviation of poverty. The Chinese economy has overcome its first-round of economic
development issues and now faces new challenges to move beyond the upper middleincome plateau.

1970=1.0

50

45
40
35
30

China
ROC
Hong Kong
Korea
Singapore
Japan

25
20
15
10
5
0
1970 1975 1980 1985 1990 1995 2000 2005 2010

Figure B5 Price of Labor Relative to Capitalin China, Japan, and the Asian Tigers, 1970–
2013
Source: Official national accounts in each country, including author adjustments; APO Productivity Database 2015.

in labor productivity growth among low-income countries. While they were scattered in the earlier
periods, by 2000–2005 the six countries with the fastest labor productivity growth were all fromGroup-L4 (as defined in Table 7 in Section 3.2). In the latest period 2005–2013, five out of the top six
were from Group-L4 and one from Group-L3. Among them, China has sustained rapid productivity
growth in the past two decades. Its growth accelerated to an average of 9.3% per year in 2005–2013
from 7.2% per year in 1995–2000 and 8.7% per year in 2000–2005. This compares with India at 6.7%,
3.9%, and 3.0% over the same periods. Labor productivity growth amongst the Asian Tigers wassteady, ranging from 3.0% to 3.3% on average per year in 2000–2005. This performance was sustainedin 2005–2013, except in Singapore. While Singapore’s average annual productivity growth slowed
significantly to 0.9%, the others enjoyed growth of about 2.4% in 2005–2013. The 2000s were an era
when labor productivity deteriorated in GCC countries. The decline accelerated from –0.4% to –0.9%
between the two halves of the 2000s.
As a group, Asia24 achieved the highest labor productivity growth in recent years, reaching 5.1% on
average per year in 2005–2013, up from 3.8% in 2000–2005. Within Asia, labor productivity growthhas been accelerating in both South Asia and East Asia, to 5.4% and 5.9% in 2005–2013, respectively.
South Asia displayed a newfound vigor in recent years. In contrast, average annual productivity

645.1 Per-Worker Labor Productivity

Table 10 Per-Worker Labor Productivity Growth, 1990–1995, 1995–2000, 2000–2005, and 2005–2013_Average annual growth rate of GDP at constant basic prices per worker, using 2011 PPP
1990–1995

1995–2000

2000–2005

7.2
6.4
5.5
5.5
5.3
4.8
4.6
3.9
3.9
3.5
3.4
2.5
2.3
2.2
1.7
1.6
1.6
1.6
1.4
1.2
1.1
1.1
0.7
0.7
0.6
0.6
0.4
0.2
−1.6
−1.9

1.5
1.9

China
Oman
Qatar
MyanmarVietnamROC
Korea
Lao PDR
India
Singapore
Cambodia
BhutanPhilippines
Bangladesh
Mongolia
Nepal
Sri Lanka
Saudi Arabia
Japan
Fiji
Pakistan
Malaysia
Iran
UAE
BahrainHong Kong
Thailand
KuwaitIndonesia
Brunei(regrouped)
APO20
Asia24
Asia30
East Asia
South Asia
ASEAN
ASEAN6
CLMV
GCC(reference)
US
EU15

2.3
1.3

Australia
Turkey2.0
3.4

KuwaitChina
Thailand
Malaysia
Indonesia
Korea
ROC
VietnamBhutanPakistan
Cambodia
Singapore
Sri Lanka
Hong Kong
Lao PDR
MyanmarBangladesh
Bahrain
Nepal
India
Iran
Japan
Saudi Arabia
Qatar
Philippines
BruneiFiji
Mongolia
UAE
Oman(regrouped)
APO20
Asia24
Asia30
East Asia
South Asia
ASEAN
ASEAN6
CLMV
GCC(reference)
US
EU15

13.1
10.6
8.1
6.6
6.4
5.7
5.5
5.4
5.2
4.5
4.3
4.2
4.1
3.8
3.3
3.3
3.2
2.8
2.4
2.3
1.4
0.6
0.6
0.3
0.2
−0.2
−0.4
−2.3
−3.7
−9.3

Australia
Turkey

2.1
3.9
3.7
4.3
2.5
4.9
5.8
2.9
0.6

1.2
2.7
2.7
3.4
3.2
0.2
−0.1
4.8
1.9
2.3
1.3

Myanmar
China
VietnamLao PDR
Indonesia
Cambodia
Malaysia
IranHong Kong
Singapore
Korea
ROC
India
PakistanThailandMongolia
Sri Lanka
Bangladesh
Fiji
Japan
Oman
Philippines
KuwaitNepal
Saudi Arabia
Brunei
BhutanQatar
UAE
Bahrain(regrouped)
APO20
Asia24
Asia30
East Asia
South Asia
ASEAN
ASEAN6
CLMV
GCC(reference)
US
EU15
EU28
Australia
Turkey2005–2013

10.4
8.7
5.5
4.1
3.7
3.6
3.6
3.5
3.3
3.2
3.2
3.0
3.0
3.0
2.9
2.7
2.5
1.6
1.5
1.2
1.1
1.1
0.9
0.5
−0.2
−0.6
−0.7
−0.8
−1.8
−4.7
1.5
3.8
3.7
4.8
2.6
3.2
3.0
6.1
−0.4
2.2
0.9
1.3
1.3
5.9

China
MyanmarMongolia
India
Cambodia
Sri Lanka
Lao PDR
BhutanVietnamIndonesia
Philippines
Bangladesh
Korea
Thailand
ROC
Hong Kong
Nepal
Iran
Malaysia
Saudi Arabia
Singapore
Fiji
Japan
Pakistan
Qatar
BahrainBruneiUAE
KuwaitOman(regrouped)
APO20
Asia24
Asia30
East Asia
South Asia
ASEAN
ASEAN6
CLMV
GCC(reference)
US
EU15
EU28
Australia
Turkey1990–2000

9.3
7.8
7.2
6.7
5.1
5.1
4.9
3.8
3.7
3.4
3.2
2.8
2.6
2.5
2.4
2.2
1.5
1.4
1.2
1.2
0.9
0.8
0.7
0.4
−0.1
−1.1
−1.3
−2.2
−3.8
−8.4
2.8
5.1
5.0
5.9
5.4
3.0
2.8
4.8
−0.9
1.1
0.4
0.5
0.9
0.8

China
KuwaitVietnam
Korea
ROC
MyanmarThailandSingapore
Bhutan
Malaysia
Cambodia
Lao PDR
India
Qatar
Sri Lanka
Pakistan
BangladeshIndonesia
Hong Kong
Nepal
Bahrain
Philippines
Saudi Arabia
Iran
Japan
Fiji
Mongolia
BruneiOman
UAE(regrouped)
APO20
Asia24
Asia30
East Asia
South Asia
ASEAN
ASEAN6
CLMV
GCC(reference)
US
EU15
EU28
Australia
Turkey2000–2013

8.9
6.6
5.4
5.1
5.1
4.4
4.3
3.9
3.8
3.8
3.7
3.6
3.1
2.9
2.9
2.8
2.7
2.4
2.2
2.0
1.7
1.3
1.1
1.1
1.0
0.4
−0.3
−1.0
−1.5
−1.5
1.7
3.3
3.2
3.9
2.9
2.6
2.8
3.9
1.3
1.9
1.6
0.0
2.1
2.4

China
MyanmarMongolia
India
Lao PDR
Cambodia
Vietnam
Sri Lanka
Indonesia
Korea
Thailand
ROC
Hong Kong
Philippines
Bangladesh
Iran
Malaysia
BhutanSingapore
Pakistan
Nepal
Fiji
Japan
Saudi Arabia
Qatar
Brunei
KuwaitUAE
BahrainOman(regrouped)
APO20
Asia24
Asia30
East Asia
South Asia
ASEAN
ASEAN6
CLMV
GCC(reference)
US
EU15
EU28
Australia
Turkey

9.0
8.8
5.5
5.2
4.6
4.5
4.4
4.1
3.5
2.8
2.6
2.6
2.6
2.4
2.3
2.2
2.1
2.0
1.8
1.4
1.1
1.1
0.9
0.6
−0.4
−1.0
−2.0
−2.0
−2.5
−4.7

5

2.3
4.6
4.5
5.4
4.3
3.1
2.9
5.3
−0.7
1.5
0.6
0.8
1.0
2.8

growth in the US slowed abruptly to 1.1% between 2005 and 2013, after a decade of over 2.0% growth
per year. The EU15 shows signs of weakening as well, slowing in every successive period from 1.9% in
the first half of the 1990s to 0.4% in the most recent period of 2005–2013. Japan’s labor productivity
growth performed closer to that of other mature economies. Having managed to grow at 1.3% on
average per year for a decade in 1995–2005, labor productivity growth in Japan has slowed to 0.7%
per year on average since 2005.

65©2015 Asian Productivity Organization

Unit: Percentage.
Source: APO Productivity Database 2015.

5 Productivity

US=100

US=100120

120

Cambodia
ROC
Singapore

100

China
Korea

100

80

80

60

60

40

40

20

20

0
1970 1975 1980 1985 1990 1995 2000 2005 2010

120
100

Hong Kong
Lao PDR
MyanmarVietnam

India
Malaysia
Sri Lanka

01970 1975 1980 1985 1990 1995 2000 2005 2010

Figure 42.1: Group-C1 Countries
US=100BhutanIndonesia
Mongolia
ThailandFigure 42.2: Group-C2 Countries
US=100

Bangladesh

Japan

Nepal

Pakistan

EU15

Turkey

120
100

80

80

60

60

40

40

20

20

0
1970 1975 1980 1985 1990 1995 2000 2005 2010

Figure 42.3: Group-C3 Countries

Fiji

Iran

Philippines

Australia

01970 1975 1980 1985 1990 1995 2000 2005 2010

Figure 42.4: Group-C4 Countries

Figure 42 Labor Productivity Level Relative to the US, 1970–2013_Indices of GDP at constant basic prices per worker, using 2011 PPP
Source: APO Productivity Database 2015.

©2015 Asian Productivity Organization

Figure 42 shows labor productivity levels relative to the US (=100) for Asian countries. The samegrouping as in Section 3.2, based on the speed of catch-up with the US in per capita GDP, is used here.
Broadly speaking, countries that are catching up faster with the US in per capita GDP (Group-C1) arealso faster catching up in labor productivity (Figure 42.1). Similarly, countries with deteriorating relative per capita GDP (Group-C4) also present signs of deterioration of or little change against the US in
terms of labor productivity (Figure 42.4).
Among the countries that are catching up with the US in per capita GDP (Group-C1 and Group-C2),
the Asian Tigers have made a tremendous effort in improving their relative labor productivity over the
past four decades. Singapore passed the US in the middle of the 1990s and Hong Kong closed the gap
from 61% in 1970 to 7% in 2013 (Figures 42.1 and 42.2). Similarly, the ROC and Korea reduced a gap ofaround 80% initially to 16% and 43% by 2013, respectively (Figure 42.1). Malaysia is making steadyprogress, raising its relative productivity level from 23% of the US in 1970 to 47% in 2013 (Figure 42.2).
The rest of the countries in these two groups all display an initial relative labor productivity levelof below 15%, but have shown signs of a strong and promising start in their catch-up process in thepast decade.
Countries that have managed a modest catch-up with the US (Group-C3) or have a declining per
capita GDP against the US (Group-C4) are also those with stagnant or deteriorating relative labor665.2 Per-Hour Labor Productivity

productivity. Japan is the only high-income Asian country in this group, while the rest (except Iran) are
all low-income countries with per capita GDP less than 30% of the US. Japan showed strong catch-up
behaviors in the earlier period, with relative labor productivity peaking at 79% of the US in 1991. Since
then the gap has widened again to over 30% in 2013. Similarly EU15, a reference economy with highincome, has seen its productivity gap double against the US since 1995, from 14% to 28% in 2013;
whereas the low-income countries have managed little catch-up (Figure 42.3) or a declining relative
productivity level (Figure 42.4). Iran (a Group-L2 country) experienced a drastic decline in its relativelabor productivity from its former peak of 97% in 1976 to 48% in 1988, before recovering to 63% in
2011. As a result of the strengthened sanctions against Iran, however, labor productivity to date has
declined drastically.

5

5.2 Per-Hour Labor Productivity

50: Cross-country comparisons of hours worked are notoriously difficult, not least because harmonized data is rarely readily available.
In the countries studied, three published their total hours worked as part of their official statistics, but not for the whole period
studied in this report, and the publications may have been constructed based on different methodologies. Some countries only
published estimates for average weekly hours worked, which need estimates of number of weeks worked to derive the total average hours worked per worker. Others may have only estimated benchmark hours worked available, which are then extrapolated
to form a series. Consequently, growth of employment and growth of total hours worked become identical, as in the case of
China and Thailand. In reading the results, it is therefore important to bear in mind the data limitations. This edition of the Databook newly uses the improved time-series estimates of average hours worked with considering the changes in the compositionsof workforces. See Appendix 4 for an explanation of the estimation procedure of total hours worked.

67©2015 Asian Productivity Organization

Singapore

Australia

EU15

Hong Kong

ROC

Japan

Turkey

Korea

Iran

Malaysia

ThailandSri Lanka

Indonesia

China

Philippines

Pakistan

India

VietnamBangladesh

Nepal

Cambodia

The per-worker-based labor productivity gaps presented in Section 5.1 are most likely conservative
estimates, since workers in high-performing Asian countries tend to work longer hours than those in
the US on average. To adjust for this discrepancy, total hours worked are constructed in our databasefor 18 Asian countries, although the quality of the estimates may vary considerably across countries.50
Figure 43 shows how the productivity gap against the US in 2013 varies depending on which measureof labor productivity is used.51 The productivity gap with the US widens for all Asian countries when
the differences in working hours
are taken into account. However,for 11 of these countries, the adjustments are within 2–5 percent­age points, and hence are not%13
deemed as statistically significant.
100
In contrast, the choice of labor−7−10
productivity measure makes a
−16
−20
−18
−20
significant difference for the−24 −20−28−30−29
previously highperforming coun−33
−34
−36
−40
tries in their relative performance.
−43
−45
−50
On a per-hour GDP basis, the−53−53−57 −57−60−61
labor productivity gap with the−65−70
US widens by 14–32 percent−77 −77
−80
−80
−81
−82
Per-worker GDP−84 −83−85
age points for the Asian Tigers.
−87−88 −86
−90
−89
−90
−90
−92
Per-hour GDP−93−96−96 −95 −94−95
Europeans generally work fewer−100−97
hours. This is reflected in comparisons of hourly labor productivity
Figure 43 Labor Productivity Gap by Per-Worker and PerHour GDP Relative to the US, 2013
showing EU15 in a more favor_GDP at constant basic prices per worker and hour, using 2011 PPP
able light against the US, albeit
only marginally.
Source: APO Productivity Database 2015.

5 Productivity

Table 11 Per-Hour Labor Productivity Levels, 1970, 1980, 1990, 2000, 2010, and 2013
_GDP at constant basic prices per hour, using 2011 PPP, reference year 2013
1970

1980

(%)

Iran
SingaporeJapan
Hong Kong
Malaysia
ROC
Philippines
Sri Lanka
Indonesia
Korea
Thailand
Pakistan
India
Bangladesh
VietnamChina

15.3 100.0
14.1 92.4
13.1 85.6
8.9 58.1
5.8 38.0
4.8 31.7
4.0 26.0
3.9 25.3
2.9 19.3
2.9 19.2
2.1 14.0
2.1 13.8
1.7 11.2
1.6 10.7
1.0 6.3
0.5 3.1(reference)US31.6 207.0

1990

(%)

(%)

Singapore
Japan
Iran
Hong Kong
ROC
Malaysia
Philippines
Korea
Indonesia
Sri Lanka
Thailand
Pakistan
Bangladesh
India
Nepal
VietnamChina

20.7 100.0
19.1 92.2
16.5 79.5
15.2 73.2
9.2 44.5
8.9 42.9
4.9 23.5
4.8 23.3
4.6 22.3
4.5 21.5
3.1 14.8
2.6 12.3
1.7 8.3
1.7 8.1
1.3 6.3
1.0 4.9
0.7 3.4

SingaporeJapan
Hong Kong
ROC
Iran
Malaysia
Korea
Indonesia
Sri Lanka
ThailandPhilippines
Pakistan
India
Bangladesh
Nepal
VietnamChina

29.0 100.0
28.4 98.2
25.5 88.0
16.7 57.8
16.5 56.8
11.6 39.9
9.3 32.2
5.7 19.6
5.5 19.1
4.9 16.9
4.7 16.1
4.0 13.7
2.3 7.9
2.0 6.9
1.8 6.3
1.1 3.9
1.1 3.9(reference)US36.5 176.4(reference)US

43.0 148.6

Australia

30.9 149.2

Australia
Turkey34.2 118.0
15.0 51.7

2000

(%)

40.3 100.0
34.8 86.4
31.5 78.0
28.8 71.4
18.3 45.5
16.7 41.3
16.3 40.4
7.3 18.2
7.3 18.1
7.1 17.6
5.4 13.5
5.2 13.0
3.1 7.8
2.8 6.8
2.6 6.4
2.3 5.8
2.0 5.0
1.1 2.8

SingaporeJapan
Hong Kong
ROC
Iran
Korea
Malaysia
ThailandSri Lanka
Indonesia
Philippines
Pakistan
India
China
Bangladesh
Nepal
Vietnam
Cambodi(reference)
US
EU15
Australia
Turkey52.0 129.0
43.7 108.5
42.4 105.2
18.3 45.3

2010
Singapore
Hong Kong
ROC
Japan
Iran
Korea
Malaysia
Sri Lanka
ThailandIndonesia
China
Philippines
Pakistan
India
VietnamBangladesh
Nepal
Cambodia(reference)
US
EU15
Australia
Turkey(%)

50.3 100.0
43.0 85.5
41.5 82.5
40.2 79.9
27.5 54.6
26.2 52.0
20.8 41.4
10.4 20.6
9.7 19.2
9.4 18.6
6.8 13.5
6.6 13.2
6.2 12.3
5.2 10.3
3.1 6.3
2.7 5.4
2.5 4.9
1.6 3.2
63.8 126.8
48.3 96.0
48.5 96.4
26.5 52.6

2013
Singapore
Hong Kong
ROC
Japan
Korea
Iran
Malaysia
Sri Lanka
ThailandIndonesia
China
Philippines
Pakistan
India
VietnamBangladesh
Nepal
Cambodia(reference)
US
EU15
Australia
Turkey(%)

52.7 100.045.7 86.8
43.0 81.6
41.1 78.1
27.9 53.0
25.1 47.6
22.3 42.3
12.0 22.8
10.8 20.5
10.6 20.1
8.6 16.4
7.4 14.1
6.5 12.4
6.2 11.7
3.6 6.8
3.0 5.6
2.5 4.7
1.9 3.7
64.5 122.5
49.3 93.6
51.7 98.2
27.8 52.8

Unit: US dollar (as of 2013).
Source: APO Productivity Database 2015.

©2015 Asian Productivity Organization

Based on GDP at constant basic prices per hour worked, US labor productivity has been able to sustain
a big lead over even the Asian high performers (Table 11).52 In 1970, the US productivity level was
nearly 2.5 times that of Japan. This gap was reduced to around 34% in 1990. Since 1990, Japan’s pacein closing the gap has slowed. By 2013, a sizable gap of 36% remained. The gap between the US and
the Asian leader, Singapore, has been narrowing
with a very slow pace and the productivity gap
Thousands of US dollars (as of 2013)60
of 18% still remains in 2013. This is in contrast
ROC
Hong Kong
50
with the picture painted by the per-worker proJapan
Korea
ductivity measure, in which the Asian leaders
40
Singapore
have overcome or almost closed the gap with30
the US (Figure 42).
The levels of labor productivity for the top six
economies – Japan, Iran, and the four Asian Tigers – maintained their relative positions foralmost four decades. The progress of labor productivity in these countries during 1970–2013 isshown in Figure 44. Within four decades, GDP
per hour has roughly tripled for Japan and Singapore. Hong Kong and the ROC have improvedby five and eight times in this period and haveovercome Japan in 2006 and 2010, respectively.

20
1001970 1975 1980 1985 1990 1995 2000 2005 2010

Figure 44 Labor Productivity Trends in Japanand the Asian Tigers, 1970–2013_GDP at constant basic prices per hour, using 2011
PPP, reference year 2013

Unit: Thousands of US dollars (as of 2013).
Source: APO Productivity Database 2015.

51: The labor productivity gap for country x is country x’s labor productivity divided by the US’s labor productivity in Figure 43.
52: Note that the differentials in the labor quality per hour worked among countries have not been accounted for in this comparison:labor productivity will tend to be overestimated if labor quality has been rising, and vice versa.

685.2 Per-Hour Labor Productivity

They were ahead of Korea, despite the duo’s effort in catching up with Japan by 2.6% per year on average, respectively, over the past four decades (1970–2013). If they were to maintain this effort at thesame pace, it would take Korea 20 years to finally draw level with Japan.
Over the past four decades, hourly labor productivity growth ranged from 1.4% (Bangladesh) to 6.7%
(China) on average per year, compared with the US at 1.7% and Australia at 1.5% (Figure 45). Amongthe 18 Asian countries compared, only Bangladesh, Iran, Nepal, and the Philippines grew slower thanthe US. Between the two sub-periods (1970–1990 and 1990–2013), there is a notable deceleration in
the hourly productivity growth for 10 of 17 Asian countries (excluding Cambodia). For example, 2.7
percentage points and 2.3 percentage points were shaved off productivity growth in the earlier periodin Hong Kong and Japan, respectively. Five Asian countries managed to accelerate their productivity
improvement after 1990. Among these, China’s performance is the most outstanding, with productivity growth more than doubling from 4.3% to 8.9% between the two sub-periods.

5

The deceleration of labor productivity growth between the two sub-periods reflects weaknesses in
output growth in most countries. Figure 46 shows all countries except Bangladesh experienced a1970−2013%10

1970−1990

1990−2013
8.9

8
6.7

6

6.2

5.2 5.1

5.3

5.0
4.3 4.1

3.83.8 3.7

4

3.13.1 3.0 3.03.0

2.72.62.6 2.5

2

1.71.6 1.51.5

3.9

3.6 3.5

4.8

4.3 4.1

3.3 3.2
2.0

1.4 1.2

1.8

1.5 1.4

1.0 0.9 0.8 0.8

3.7

3.4 3.4

2.8 2.72.7 2.6

2.5

2.1 2.0

1.81.8 1.81.7 1.6

1.3

0.4 0.3

Nepal
Japan
Bangladesh
US
Australia
Iran
Philippines
Pakistan
Hong Kong
Singapore
TurkeyIndonesia
Malaysia
Sri Lanka
ThailandCambodia
ROC
India
Korea
Vietnam
China

TurkeyIranVietnamPhilippines
Australia
Bangladesh
India
US
Sri Lanka
Nepal
PakistanIndonesia
Malaysia
SingaporeJapan
Thailand
China
Hong Kong
Korea
ROC

Iran
Bangladesh
Philippines
Australia
Nepal
US
TurkeyPakistan
Sri Lanka
Japan
Indonesia
India
VietnamSingapore
Malaysia
Cambodia
ThailandHong Kong
ROC
Korea
China

0

5.8

Figure 45 Labor Productivity Growth, 1970–2013, 1970–1990, and 1990–2013_Average annual growth rate of GDP at constant basic prices per hour

Source: APO Productivity Database 2015.
Note: The starting periods for Australia, Cambodia, Nepal, and Turkey are 1978, 1993, 1974, and 1988, respectively.

1970−2013%5

1970−1990

1990−2013

4.5
3.9

3.8

3.7
3.2

3
2
1
0

2.8

2.7 2.6 2.6 2.5

2.4 2.3 2.3 2.2

2.2

1.9 1.8 1.7

3.5 3.4

3.7 3.6
3.2 3.2 3.1 3.1

1.7 1.7

3.0 2.9

2.7 2.7 2.7 2.6

2.5

2.3
1.7

1.1

3.4
3.0

2.6

2.2 2.2 2.2

2.1 2.0

1.8
1.5

1.5
0.6

1.3 1.2

0.9 0.8 0.8

0.7

0.5

−0.1
−0.6

−1

Japan
Korea
US
China
ROC
ThailandFiji
Hong Kong
Mongolia
India
Sri Lanka
Indonesia
Vietnam
Iran
Philippines
Pakistan
Malaysia
Singapore
Bangladesh
CambodiaJapan
Bangladesh
US
Hong Kong
Iran
Pakistan
ROC
India
Sri Lanka
ThailandMongolia
Philippines
Korea
China
VietnamFiji
Malaysia
Indonesia
Singapore

Japan
US
Korea
ROC
Hong Kong
Thailand
China
Mongolia
Fiji
India
Sri Lanka
IranPakistan
Philippines
Bangladesh
VietnamIndonesia
Malaysia
Cambodia
Singapore

Figure 46 Labor Input Growth, 1970–2013, 1970–1990, and 1990–2013_Average annual growth rate of total hours worked
Source: APO Productivity Database 2015.
Note: The starting period for Cambodia is 1993.

69©2015 Asian Productivity Organization

4

5 Productivity

Table 12 Per-Hour Labor Productivity Growth, 1990–1995, 1995–2000, 2000–2005, and 2005–2013_Average annual growth rate of GDP at constant basic prices per hour, using 2011 PPP
1990–1995

1995–2000

China
ThailandIndonesia
Korea
Malaysia
ROC
Vietnam
Cambodia
Sri Lanka
Hong Kong
Pakistan
Singapore
Bangladesh
Nepal
India
Japan
Iran
Philippines(reference)
US

10.5
7.7
6.3
6.3
6.0
5.7
4.7
4.4
4.3
4.2
4.1
3.6
2.8
2.6
2.4
2.1
1.4
0.7

Australia
Turkey

2.1
1.2

1.5

China
Vietnam
Korea
ROC
India
Singapore
Cambodia
Nepal
Philippines
BangladeshJapan
Sri Lanka
Pakistan
Malaysia
Iran
ThailandHong Kong
Indonesia(reference)
US
EU15
Australia
Turkey2000–2005

7.5
6.7
5.3
5.2
3.9
3.1
2.4
2.4
2.4
2.4
2.0
1.4
1.3
0.8
0.7
0.4
0.0
−1.8
2.3
1.7
2.2
2.8

China
Vietnam
Korea
IranSingapore
Malaysia
ROC
Indonesia
PakistanThailandHong Kong
India
CambodiaJapan
Sri Lanka
Philippines
Nepal
Bangladesh(reference)
US
EU15
Australia
Turkey2005–2013

7.4
6.7
4.2
3.7
3.7
3.5
3.4
3.4
3.3
3.3
3.1
3.0
2.9
1.8
1.7
1.3
−0.2
−0.8
2.5
1.3
1.9
6.0

China
India
Sri Lanka
Cambodia
Korea
Philippines
VietnamIndonesia
ROC
Hong Kong
ThailandBangladesh
Malaysia
Iran
Singapore
Japan
Nepal
Pakistan(reference)
US
EU15
Australia
Turkey1990–2000
9.6
6.6
5.1
4.9
3.8
3.1
3.1
2.9
2.9
2.8
2.7
2.2
1.7
1.6
1.1
1.0
0.9
0.7
1.1
0.7
1.3
1.5

China
Korea
Vietnam
ROC
Thailand
Malaysia
Singapore
India
Cambodia
Sri Lanka
Pakistan
Bangladesh
Nepal
Indonesia
Hong Kong
Japan
Philippines
Iran(reference)
US
EU15
Australia
Turkey2000–2013
9.0
5.8
5.7
5.4
4.1
3.4
3.3
3.1
3.0
2.8
2.7
2.6
2.5
2.2
2.1
2.0
1.5
1.1
1.9
1.7
2.2
2.0

China
India
Vietnam
Cambodia
Korea
Sri Lanka
Indonesia
ROC
ThailandHong Kong
Malaysia
Iran
Philippines
Singapore
PakistanJapan
Bangladesh
Nepal(reference)
US
EU15
Australia
Turkey

8.8
5.2
4.5
4.1
4.0
3.8
3.1
3.1
3.0
2.9
2.4
2.4
2.4
2.1
1.7
1.3
1.1
0.5
1.7
0.9
1.5
3.2

Unit: Percentage.
Source: APO Productivity Database 2015.
Note: The annual average growth rate for Cambodia during 1990–1995 replicates their annual average growth rates of
1993–1995 due to absent data.

©2015 Asian Productivity Organization

slowdown in hours-worked growth between the sub-periods, which should have worked to boost
labor productivity growth, all other things being equal.53 For labor productivity growth to slow imp­lies that output growth must have been decelerating more than labor input in percentage points. In
China, output growth was reinforced by the slower pace of labor input growth to result in an ex­traordinary surge in labor productivity growth. Labor input growth slowed to 0.8% per year on average in the latter period, from 3.2% in the previous period. Japan was the only economy to experiencean actual fall in labor input in the period from 1990 to 2013. This served to compensate for a slug­gish output growth during said period; and sustain a positive labor productivity growth of 1.6% peryear on average.
Table 12 more closely examines the sub-period from 1990–2013, providing the growth rates of
per-hour-based labor productivity since 1990. The growth patterns of individual countries generally
follow their counterparts closely in per-worker productivity growth, as illustrated in Table 10. In
some countries the two measures diverge greatly and are not at all consistent through the periods
compared.54 This contrast was particularly stark in the first half of the 1990s, when Japan’s hourly
productivity growth was 2.1% compared with 0.6% in per-worker productivity growth. How­ever, the divergence narrowed to 0.4 percentage points in the 2000s. Korea is another country in

53: By definition, positive labor productivity growth occurs when output grows faster than labor input. Figures 45 and 46 therefore
tend to have an inverse relationship, namely that the higher the labor input growth, the lower the labor productivity growth,
other things being equal.
54: For China and Thailand, both measures give the same productivity growth. This is a result of a statistical construct in our current
database rather than the underlying trend.

705.2 Per-Hour Labor Productivity

Labor productivity gap in 2013 relative to Japan(Japan's level in 2013=1.0)

1.3
1.2

Singapore
Japan (1885−2013)
Hong Kong

1.1

ROC

1.0
0.9

5

0.8
0.7
0.6
0.5

Korea
Iran
Malaysia

One can identify where countries
0.4
are today in terms of their hourly
Sri Lanka
0.3
Thailandproductivity performance against
China
Indonesia
Philippines
0.2
a backdrop of Japan’s historical
India
Pakistanexperience. Figure 47 traces the
0.1
Cambodia Nepal
VietnamBangladesh
long-term path of Japan’s per0.0
hour labor productivity for the1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010
period 1885–2013 along the red
Figure 47 Labor Productivity Trends of Japan in 1885–2013
line, expressed as relative to Jaand Levels of Asian Countries in 2013
pan’s 2013 level (set equal to_GDP at constant basic prices per hour, using 2011 PPP
1.0).55 A structural break is obSources: For historical data of Japan, the sources of GDP are Long-Term Economic
served during World War II when
Statistics by Ohkawa, Takamatsu, and Yamamoto (1974) during 1885–1954 and theoutput collapsed. Countries’ relaJSNA by ESRI, Cabinet Office of Japan, during 1955–2013 (including author adjustments). Hours worked data is based on KEO Database, Keio University, during 1955–
tive hourly productivity levels
2013. During 1885–1954, the average hours worked per person are assumed to beagainst Japan in 2013 are thenconstant. For the labor productivity level of Asian countries in 2013, it is based on the
APO Productivity Database 2015.
mapped against Japan’s growthexperience (as circles). By so doing, a corresponding year can be
1970
1980
1990
2000
2010
located when Japan’s hourly pro1991
1970
Japan (21)ductivity level was the closest to
1992
1977
Hong Kong (15)the country in question. The three
countries with the lowest hourly
1986
2000
ROC (14)productivity in 2013 (Cambodia,
1995
2013
Korea (18)
Nepal, and Bangladesh) see levels
corresponding to Japan’s in the
Figure 48 Time Durations Taken to Improve Labor Produc1920s. Even if they manage Japan’s
tivity by Japan and the Asian Tigers
long-term productivity growth ofSource: See Figure 47.
2.8% on average per year, thismeans it will take them over a cen­tury to catch up with the Asianleader’s current position (Singapore, Hong Kong, the ROC, and Japan). Most Asian countries are
clustered around Japan’s level in the 1950s and early 1970s. Among them, China has been leading thecatch-up effort, with productivity growing three times faster than Japan’s long-term average (Table12), followed by India and Vietnam.

55: While mindful that level comparisons of productivity among countries and over periods, are subject to a great degree of data uncertainty, they should provide a rough sketch of the productivity divergence in Asia.

71©2015 Asian Productivity Organization

which hourly productivity growth
was consistently higher than its
per-worker counterpart. Insteadof narrowing, the divergence
widened to 1.2 percentage points
in the second half of the 2000s.
Hours worked in the ROC havealso grown at a slower rate thannumber of workers. The por­tion ranged from 0.2 to 0.5 percentage points.

5 Productivity

In pole position are the Asian Tigers, of which Singapore, Hong Kong, and the ROC have already surpassed Japan. Figure 48 compares the time periods taken by each country to raise its labor productivity from 30–70% of Japan’s level today (unit of measurement on the y-axis of Figure 47). What Japan
had achieved in the 21 years from 1970 to 1991, Hong Kong, the ROC, and Korea managed to achievein 15, 14, and 18 years, respectively (Figure 48). Although the speed of catch-up for latecomers issomewhat increasing, most Asian countries will still take a long time to catch up with the leaders, currently clustered near Japan’s 1960 levels in Figure 47.

5.3 Total Factor Productivity
Labor productivity in the previous sections is only a one-factor or partial-factor productivity measureand does not provide a full perspective of production efficiency. An observation of low labor pro­ductivity could suggest production inefficiency, but it could also reflect different capital intensities
in the chosen production method under the relative labor-capital price faced by the economy concerned. By observing movements in labor productivity alone, it is not easy to distinguish which is thecase. In populous Asian economies, which are relatively abundant in low-skilled labor, productionlines may be deliberately organized in a way to utilize this abundant, and hence relatively cheap,
resource. It follows that the chosen production method is most likely to be (low-skilled) laborintensive and with little capital, manifested in low labor productivity. This is why economists analyze
TFP, which is GDP per unit of combined inputs, to arrive at a more complete picture of a country’s
production efficiency.56
Capital input is a key factor for measuring TFP. It is defined by capital services – the flow of services
from productive capital stock, as recommended in the new SNA.57 The required basis for estimating
capital services is the appropriate measure of capital stock. The SNA recommends constructing
the national balance sheet accounts for official national accounts, however, this is not common practice in the national accounts of many Asian countries.58 Even where estimates of net capital stocks areavailable for the whole economy, assumptions and methodologies can differ considerably among
countries. In response to this challenge, harmonized estimates for productive capital stocks andcapital services have been constructed and compiled within the APO Productivity Database, built
on the same methodology and assumptions.59 In this methodology, changes in the quality of capitalare incorporated into the measurement of capital services in two ways: changes in the composi­tion are captured by explicitly differentiating assets into ten types and; an appropriate and harmonized deflator is used for IT capital to reflect the rapid quality change embodied in IT-related assets(see Appendix 2).

©2015 Asian Productivity Organization

The current APO Productivity Database estimates capital services and TFP for 19 Asian economies forwhich long-time investment data by type of asset are available or estimated.60 Their economic growth
56: Different types of inputs and outputs are aggregated by using index numbers, and TFP is calculated as the output quantity index
divided by the input quantity index. In this chapter, the Törnqvist index is used for aggregating labor and 11 types of capital inputs.
57: See the chapter on capital services and the national accounts of the 2008 SNA (United Nations, 2009). The second edition of theOECD Capital Manual (2009) provides a comprehensive framework for constructing prices and quantities of capital services.
58: Based on our metadata survey, half of APO member economies do not estimate the balance sheet accounts within the official national accounts; these countries are Bangladesh, the ROC, Indonesia, Korea, the Lao PDR, Mongolia, Nepal, Sri Lanka, and Vietnam(but the National Wealth Survey is available in the ROC and Korea for some selected years).
59: The Department of Statistics Malaysia developed a new set of comprehensive capital stock statistics in April 2011 following theOECD Capital Manual (2009). The correlations between these official estimates (Department of Statistics Malaysia, 2013) and ourestimates for the period of 1970–2012 are high; they are 88.0% and 93.7% for the growth rates of net and productive capital stock,
respectively. In this report, capital input is defined as capital services computed from our estimates of productive capital stock, so
as to ensure that the same methodology and same asset classification are applied for the 19 Asian countries compared.

725.3 Total Factor Productivity

is decomposed into sources from factor inputs and TFP based on the methodology developed byJorgenson and Griliches (1967). This report defines output as GDP at constant basic prices, and factors
inputs as labor, IT capital, and non-IT capital.61 Labor input is measured by total hours worked (except
for Fiji and Mongolia), without adjustments for changes in labor quality.62
Cross-country comparisons of TFP growth for the 19 Asian countries, the APO20,63 and the US are
shown in Figure 49 for the period 1970–2013, and the two sub-periods 1970–1990 and 1990–2013.
The average annual growth rate of TFP during the entire observation period ranges from almost 0–2%,
with the exception of China which has achieved considerably high growth of TFP over 3%. Taking the
US as the reference economy, with TFP growth of 0.8% on average per year, 11 Asian economies
achieved higher TFP growth than the US.

5

Looking at the sub-periods (1970–1990 and 1990–2013), one can discern that the two were not identical and, in fact, had quite significant differences in terms of the magnitude of growth and countries’
relative performance. Eleven of the 19 Asian countries experienced acceleration in TFP growth. China
and Iran accelerated the most between the two sub-periods: from 1.7% to 4.3%, and from –1.4% to
1.3%, respectively. More modestly, the TFP growths in India and Sri Lanka improved from 0.6% on average per year in the earlier period to 2.4% since 1990 and from 1.1% to 2.6%, respectively. Mongolia’s
TFP also improved between the two periods.64 Three countries saw their productivity growth morethan halved: Thailand,65 Hong Kong, and Japan. TFP growths in Korea, Indonesia, Malaysia, and the US
were changed slightly.

1970−2013

1970−1990

1990−2013

%

4.5

3.0

4.3

3.1

2.9

2.7
2.1

1.5

1.91.9 1.81.8

1.7 1.6

1.5
0.90.9 0.80.8

0.0

2.6

2.3

2.7 2.6
2.0

1.7

1.5
1.11.1 1.0

0.6 0.5

0.4 0.4

0.2 0.2

0.0

0.9 0.8

0.7 0.6

0.5

0.3

2.4 2.3

2.0

1.8

1.61.6

1.4 1.3

1.2 1.1

1.0 0.9

0.8 0.7

0.6 0.5

0.4

0.1−0.2−0.3−0.3
−0.6

−1.5

−1.4

Bangladesh
Japan
Malaysia
Hong Kong
Fiji
Indonesia
US
Singapore
Philippines
APO20
Iran
Thailand
Pakistan
ROC
Korea
Mongolia
Vietnam
India
Sri Lanka
Cambodia
China

Iran
Mongolia
Fiji
Philippines
Singapore
Bangladesh
Malaysia
India
APO20
US
Japan
Indonesia
Sri Lanka
Vietnam
Korea
China
Pakistan
ROC
Hong Kong
ThailandIran
Bangladesh
FijiSingapore
Philippines
Malaysia
Japan
Mongolia
US
Indonesia
APO20
Hong Kong
India
Korea
Vietnam
Pakistan
Sri Lanka
ROC
ThailandCambodia
China

Figure 49 TFP Growth, 1970–2013, 1970–1990, and 1990–2013

60: In measuring TFP, income generated from domestic production should be separated into labor compensation and returns tocapital. The national accounts readily provide the estimates of labor compensation for employees as a component of value added;labor compensation for the self-employed is not separately estimated but is combined with returns to capital in mixed income,
except China, where labor remuneration in the national accounts includes labor income for the self-employed (Holz, 2006). In the
Databook, it is assumed that the per-worker wages for self-employed and family workers are 20% to 80% of the per-worker wage
for employees in the countries where the appropriate wage data is not available, in order to measure total labor compensation.
See Box 6 (p. 94) for sensitivity of our assumptions to the TFP results.
61: IT capital is defined as a composite asset of IT hardware (computers and copying machines), communications equipment, andcomputer software.
62: The failure to take into account improvements in labor quality leads to TFP overestimation. The current APO Productivity Databaseestimates the labor quality index for only a handful of countries, and covering more Asian countries is the next challenge. See Box
7 (p.117).
63: The APO20 defined in this chapter does not include the Lao PDR and Nepal.

73©2015 Asian Productivity Organization

Source: APO Productivity Database 2015.
Note: The labor inputs for Fiji and Mongolia are defined by numbers of employment. The starting period for Cambodia is 1993. The APO20
does not include the Lao PDR and Nepal.

5 Productivity

In terms of its contribution to economic growth, TFP has played a significant role in fast-growing Asian
economies over the past decades. During the period 1970–2013, China achieved the fastest output
growth of 8.7% on average per year. This is followed by Singapore and Korea, growing at 7.0% and6.9% on average per year, respectively (Figure 50). From these GDP growths, the TFP contribution accounted for over 30% of economic growth in seven of the 19 Asian economies compared (Figure 51).
Among them, TFP contribution was the largest in Sri Lanka (38%), Thailand (37%), Cambodia (36% for
1993–2013), China (36%), Pakistan (34%), Vietnam (31%)66, and India (30%). The TFP accounted forabout a quarter of economic growth in Hong Kong (28%), Japan (24%), the ROC (29%), and Korea(24%). In contrast, TFP performance was erratic in Singapore, resulting in its relatively small contribution of only 6% to economic growth over the same period.
China’s productivity performance was outstanding in this period. The average TFP growth was 3.1%
per year during 1970–2013 (Figure 49). This compares with the long-run estimates of 3.8% during
1978–2005 in Holz (2006) and also 3.8% during 1978–2004 in Bosworth and Collins (2008). The Chinese experience of long-term TFP growth of about 3.0% is not unprecedented in Asia. According toJorgenson and Nomura (2005), Japan achieved an annual TFP growth of 3.1% during 1960–1973, evenafter improvements in labor quality had been taken into account in the estimation of labor growth(and, as such, eliminating overestimation in TFP).67 Both the ROC and Korea68 achieved a TFP growth
%

%

9

TFP

8
7

3.1
2.7

1.7

4.3

1.9

1.8

0.2 2.4
1.0

3.9
0.6
1.8

80

0.9

4.1

2.2
0.1

2

120

TFP

100

0.5

4.1

4

0

Labor

6

0.4

5

1

IT Capital

Output6

3

Non-IT Capital

3.5

2.1

1.5

1.6 1.8

0.8
1.9

0.9 0.4 0.2

3.2
2.1
2.1

2.7 2.1

1.8

1.6

3.2

2.3

2.0 2.1

30
0.2
0.6

0.9
0.1
0.9 0.1
0.2
1.7
0.1
0.4
0.2
0.4
1.5
1.5 1.5 1.3
1.4 1.6 1.6 1.1
1.4 1.7
1.0 1.0
0.9 0.7
0.9
0.8 0.8
0.3
0.0

0.4 0.4

0.3 0.0 0.2

0.3 0.459

0.0
2.4 0.8

0.1 0.0 0.1

40
20

3
12

32

IT Capital

15

16
28 30
31
34 38
37

37

56 51

21

Labor
11

38

49 39 35

4

1

9
26

5
6

6

14 14

23

3

2
5

28 29

7

52

69
31

64 53
33

20 17

1

2
67

2

2

28 30 26

24
33

5
4

8
29

50

64 55

50
2

0

8
24 29

36 36

60

Non-IT Capital

3

8

16 18

3
34

15

57

41
27 25
11
−1

−1

©2015 Asian Productivity Organization

Japan
Fiji
US
Iran
Bangladesh
Philippines
APO20
Mongolia
Sri Lanka
Pakistan
India
Hong Kong
Thailand
VietnamIndonesia
Malaysia
ROC
Korea
Singapore
Cambodia
China

Japan
Fiji
US
Iran
Bangladesh
Philippines
APO20
Mongolia
Sri Lanka
Pakistan
India
Hong Kong
Thailand
VietnamIndonesia
Malaysia
ROC
Korea
Singapore
Cambodia
China

Figure 50 Sources of Economic Growth,
1970–2013

Figure 51 Contribution Shares of Economic
Growth, 1970–2013

Source: APO Productivity Database 2015.
Note: The labor inputs for Fiji and Mongolia are defined bynumbers of employment. The starting period for Cambodia is
1993. The APO20 does not include the Lao PDR and Nepal.

Source: APO Productivity Database 2015.
Note: The labor inputs for Fiji and Mongolia are defined by numbers of employment. The starting period for Cambodia is 1993.
The APO20 does not include the Lao PDR and Nepal.

64: In Mongolia, subsoil assets may have a significant role in economic growth, although they are omitted in our measures of capital
inputs.
65: Warr (2006) shows that the average annual TFP growths of Thailand were 2.0% in the period of economic boom (1986–1996),–9.0% during the Asian financial crisis (1996–1998), and 1.6% in the period of recovery (1998–2002). These compare with our estimates of 3.3%, –7.9%, and 2.4%, respectively. The contribution rates of TFP and labor quality (to economic growth) in Vu (2013) are
estimated as 0.7% and 0.3%, respectively, on average per year during 1990–2010. The sum of both (1.0%) is comparable with ourestimate of TFP growth of 1.2% in 1990–2010.
66: In the national program on “improving productivity and quality of products of Vietnamese enterprises to 2020” approved in May
2010, the Vietnamese government has a target to raise the TFP contribution on GDP growth to at least 35% by 2020.
67: In the same period 1960–1973, the average annual contribution rate of labor quality improvement to growth is measured as 0.54%
in Jorgenson and Nomura (2005). As a measure of the TFP contribution that is comparable with the estimates in this Databook,their estimate can be recognized as 3.6% per year during the same period.

74

5.3 Total Factor Productivityof 2.7% and 2.3%, respectively, during the period 1985–2000,69 as shown in the second chart of Figure
52. Since 2000, TFP growth was 2.9% in India.
In the long run, TFP growth has insignificant impact on economic growth for the Philippines and Fiji,
while labor input growth explained 52% and 33% of their economic growth, respectively (Figure 51).
Looking at the breakdown of the period in Figure 52, one can see the Philippines and Fiji were runningan overall negative TFP growth only in the period 1970–1985, at –1.6% and –1.2% on average per annum, respectively.70 Negative TFP growth can be caused by many things, including a rapid, temporary
decline in demand or the inefficient use of resources by political interventions to the economy. This isunlikely to be sustainable in the long run. As shown in the year-on-year changes of growth decomposition in each country (Figure 59), the Philippine’s TFP fell severely in the beginning of the 1980s, inwhich the economy declined by 15.4% for two years from 1983–1985 under the regime of Ferdinand
Marcos. In Mongolia, negative TFP growths are observed before the transition to market economy
in 1992.

5

It is clear from Figure 51 that economic growth was predominantly explained by the contribution ofcapital input in most of the Asian countries, which ranged from 32% (for 1993–2013) in Cambodia and
35% in Sri Lanka to 78% in Japan. Among the Asian Tigers, the contribution of capital services ranged
from 56% in Hong Kong to 68% in Singapore, whereas in China and India, it accounted for 52% and
41% of economic growth, respectively. This compares with 46% in the US, of which 15 percentage
points were contributed by IT capital, a share unmatched by Asian countries. Japan has been leading
Asian countries in terms of contribution from IT capital (11% of economic growth) whereas in other
Asian countries it has been 1–9%, with China and India trailing behind.
One prevalent characteristic of Asian countries is their investment intensity as a share of GDP (Figure
30, p. 48), and in turn its contribution to economic growth (Figures 51 and 53). There is policy significance in identifying the driver(s) behind the rapid economic growth in Asian countries. If growth has
been driven by capital accumulation more than capital assimilation, the Asian model may prove to be
too expensive for many less well-off countries to emulate. According to our findings (Figures 52 and53), it is true that, historically, capital accumulation has played a much more significant role in the
Asian countries than in the US. However, the relative contribution shares are not constant across countries and over time. There have been periods when (and in some countries where) capital assimilation
as reflected in TFP growth also contributed significantly toward driving growth.

68: Note that economic growth at the aggregate level for Korea has been revised upward considerably in the Korean System of National Accounts (KSNA) published in 2010. The main revisions stem from the introduction of a chain index in Korea’s system ofnational accounts. As a result, Korea’s GDP growth at constant market prices has been revised up from 7.0% to 8.6% on averagein the 1970s, from 8.4% to 9.3% in the 1980s, and from 5.9% to 6.3% in the 1990s. In addition, by the KSNA revision based on the2008 SNA, these are further revised to 8.8%, 9.4%, and 6.7%, respectively.
69: The National Statistics, Republic of China, published the TFP estimates for the period 1982–1999. The correlation of TFP growth
rates between their estimates and ours is 0.89 for the whole period. For 1985–2000, our estimate is around 1 percentage point
smaller than their estimate of 3.6% (1985–1999).
70: Negative TFP growth for both countries is also observed in other studies. Baier, Dwyer, and Tamura (2006) estimate the averageannual growth rate of TFP of Fiji at –0.75% during 1960–2000. Cororaton (2002) shows that the average annual TFP growth of thePhilippines was –1.09% during 1970–2000.

75©2015 Asian Productivity Organization

Looking at Figure 53, capital accumulation was the dominant factor in the early period 1970–1985,
typically explaining two-thirds to three-quarters of economic growth achieved. In Thailand, Pakistan,
China, and Hong Kong, however, the contribution of TFP growth was still significant, accounting for
27–40% of their respective economic growth. In the subsequent periods, the contribution of capital
input became progressively smaller, falling to a share of below 55% on average, while the contribution
of TFP became progressively more significant, rising to a share of above 35% on average in 2000–2013.

5 Productivity

1970−1985%

10

2000−2013

1985−2000

8 1.7 0.9

2.4
5.2 4.24
0.5

2.0 0.8

0.5

2.0 2.3

0.1

1.2

2.3 3.1

3.5

0.1
1.6

4.1

3.2 0.6
0.0 0.0

2.7 1.7

4.1 3.9

0.1 0.8
0.1

1.1 0.1

0.1
0.2 0.0 0.1
0.1
0.0 0.3
0.1 2.2 2.8 0.3 0.9
1.9 1.9
1.7 1.8 2.1 1.5 1.5 1.6 2.0 0.1 1.5 1.3 1.8
0.0
1.2
1.1 0.3
1.0 0.8
0.8
0.3
−0.1

−1.0

2.0 1.6

4.6
3.3

2.0

2.2 3.3

0.1

0.8

4.1

2.6 1.5

0.2

5.3

3.6 3.9

2 0.4 0.3

02.7 2.9

6.3
5.1 5.5

0.4

2.3 1.2

0.6

2.3 2.0

6

3.6

3.8

0.1

0.4

2.9
1.9 2.3

0.7

2.6

1.3
2.9
3.1 2.3

0.8
1.1
2.7

3.1 2.4

1.2

5.1
1.0

2.1

2.4

1.7 0.8

−0.2
−1.2

−1.2
−1.6
−1.9

1.6
1.8

2.3

0.2

2.5 0.2 2.8

1.2 1.0 0.6

0.3 0.2
0.2
0.1
0.1 0.8
0.7 0.6 1.6
0.5 0.1
0.4 0.1
0.1 0.5 0.4 2.2
0.1 1.6
1.8
1.7 1.6 1.5 1.3 1.7 0.5 1.5 0.1 0.4 0.2
1.2 0.1
1.2 1.0 0.8
1.0
1.0
1.0
1.0
0.7
0.7 0.9
0.4 0.7

3.7

1.1

2.3

2.0

1.7

1.1

2.2

1.7 2.4 1.6
0.6
0.1

0.9

1.7

1.7 1.3

2.5

1.7
0.5
0.4 2.9
1.0
2.0 1.2 1.8 1.1 0.6
0.2 0.2 0.4 0.6 2.0
0.3 2.5 0.2
0.3
0.6 1.0 1.0 1.3 1.1 1.5 1.2 1.1 1.0 0.7 0.3 0.2 0.4 0.3 0.2 0.7 0.7
0.6 0.6 0.3 0.5 0.3 0.0 0.4 0.1
0.3
0.5 0.0 0.2
0.1
−0.5
−0.3

−2

Non-IT Capital

IT Capital

Japan
US
Fiji
ROC
Hong Kong
Korea
Iran
APO20
Thailand
Pakistan
Malaysia
Philippines
Indonesia
Singapore
Sri Lanka
Bangladesh
Vietnam
India
Cambodia
Mongolia
China

Mongolia
Japan
Fiji
Iran
US
Philippines
APO20
Sri Lanka
Bangladesh
Hong Kong
Indonesia
Pakistan
India
Thailand
Vietnam
Malaysia
Cambodia
ROC
Singapore
Korea
China

Bangladesh
US
FijiPhilippines
Iran
India
VietnamJapan
APO20
Sri Lanka
PakistanThailandMongolia
Indonesia
Malaysia
China
Hong Kong
Singapore
Korea
ROC

TFP

Labor

OutputFigure 52 Sources of Economic Growth, 1970–1985, 1985–2000, and 2000–2013
Source: APO Productivity Database 2015.
Note: The APO20 does not include the Lao PDR and Nepal.

1970−1985%

120

1985−2000

100
19

1

11

9

16

30 27

80

4

2

13

60
57

75 61

64
47 53
7
3 1

19 21

0

20 20 23

−12

1

2

29 2 25
13

0
23

2

83
2
7

50 53

37

33

23 6
8

38 40

39

9 13
37

63

45
11

45
30

25

17

25

31

56

66
27

51
45

9

2

33

52

4 3

30 30

40

20

34

52

49 42

2 1

3

2 17

21
23

16

3

2

53

2 10 5

59

32 2

21

23
37

42
44 34

35

41

10
6

51

4 32 3
14 19

4
20

6 12
3

30

38

39

43 44

55

55 3

32

68

−8

−9

61

1

40

32 48

22

27 22

22 20

37
61

45 14

4 7 13 46

36

7 8

48
10

4

16 14 14 7 13

39
18

11
3

8 26 22 28
9

2

2

−96

−33

Mongolia
Japan
Fiji
Iran
US
Philippines
APO20
Sri Lanka
Bangladesh
Hong Kong
Indonesia
Pakistan
India
Thailand
Vietnam
Malaysia
Cambodia
ROC
Singapore
Korea
China

Japan
US
Fiji
ROC
Hong Kong
Korea
Iran
APO20
Thailand
Pakistan
Malaysia
Philippines
Indonesia
Singapore
Sri Lanka
Bangladesh
Vietnam
India
Cambodia
Mongolia
China−53 −46 −37

TFP

21
55 41

42

9

24

62 50

34 43

34

38

42
6
10
7
33 30
30 34
1 25 6 31 24 25 27 25
22 22
17
16
15
14
13 13 11−1

Bangladesh
US
FijiPhilippines
Iran
India
VietnamJapan
APO20
Sri Lanka
PakistanThailandMongolia
Indonesia
Malaysia
China
Hong Kong
Singapore
Korea
ROC

−20

26

1

25

48

35

73 1 1

87

59

4

16

1
5 4

41

30

49

33 42

40

6

17

25

45
46 66

87

4
2

32

40 35

80

20

2000−2013
12811879 52

Non-IT Capital

IT Capital

Labor

Figure 53 Contribution Shares of Economic Growth, 1970–1985, 1985–2000, and 2000–2013©2015 Asian Productivity Organization

Source: APO Productivity Database 2015.
Note: The APO20 does not include the Lao PDR and Nepal.

The evident rise in the contribution of IT capital is also noteworthy. In 1970–1985, IT capital accounted
for less than 5% of economic growth in all Asian countries, except Japan and Singapore. By the 2000s,
the IT capital share rose to above 5% in most countries, with the exceptions of Bangladesh, Cambodia,
Fiji, India, Iran, Mongolia, Pakistan, Sri Lanka, and Indonesia. Between 1985–2000 and 2000–2013, thecontribution of IT capital more than doubled in Malaysia and Vietnam, from 4% to 13%, and from 3%
to 6%, respectively. Hong Kong sustained an IT share of around 10% in the same period. This yearly
accumulation of IT investment may have paved the way for countries to capitalize on the productivity
gain from the IT revolution. Reflecting on these results, capital accumulation appears to be a necessary step to economic growth. Countries may go through cycles of capital accumulation and76

5.3 Total Factor Productivity

TFP

%

9

CapitalLabor

Output

3.6

6

4.1

2.6

1.3
2.9
3.2 2.3

1.1

1.6

2.6

3

5.6
3.0

2.8
2.5

0

0.3

1.0

2.8

4.1
2.2

1.8

1.1

2.0

2.8 0.7

2.9
1.1 1.5 1.2 1.1 1.0

1.0 1.3

0.9

1.7
2.4

2.5

2.0

−0.5

2.7

1.7 1.7 1.3

0.5 0.1

5

1.4 1.1 0.4 0.3
0.0
0.7 0.4
0.6 0.6 0.6
0.2
1.3 0.8
0.8 1.1
1.1 0.8 0.6 0.4 0.6
0.8
0.2 1.1 0.6 0.7
0.6 0.7
0.8
0.7 0.6
0.5 0.3
0.4
0.4 0.5 0.5 0.6 0.4
0.4 0.7 0.5
0.3
0.3 0.6 0.5
0.1
0.0
0.1 −0.1
0.2 0.1 0.0 0.2 −0.3 −0.1 0.0
0.0 −0.5
1.3

2.3 1.6 2.1

1.1 1.2

Italy

DenmarkJapan

Netherlands

Germany

FranceFinland

Belgium

Spain

Austria

UK

US

Fiji

Switzerland

SwedenCanadaIreland

New ZealandAustralia

ROC

Hong Kong

Korea

Thailand

IranPakistan

Malaysia

Philippines

Indonesia

Singapore

Sri Lanka

Bangladesh

Vietnam

India

Cambodia

Mongolia

China

Figure 54 Comparison of Sources of Economic Growth with OECD Countries, 2000–2013
Sources: APO Productivity Database 2015 for APO member economies and China and the US. OECD Stat (Dataset: Multi-Factor Productivity)
and OECD (2015) for OECD countries (except Japan and Korea).
Note: The ending year for Portugal is 2011.

%90

TFP contribution share, 2000–2013
Japan

80
70
Germany
Fiji

60

ThailandMongolia

50

India Sri Lanka

40
Pakistan

China
Philippines

Finland
Korea
UK

Hong Kong
Austria
SwedenUS
ROC

Figure 54 places our estimates
France30
Indonesia
Cambodiaamong those of OECD for 17 other
Singapore
Switzerland
Malaysia
Ireland
20
OECD countries to give readers a
Vietnam Iran
CanadaNetherlands
wider perspective.71 Countries are
Australia
10
arranged according to their average
New Zealand Belgium
economic growth per annum for
0
Portugal
Denmark
Spain
the past decade (2000–2013), in de(Thousands of US dollars)
Bangladesh−10scending order. In so doing, the
0
10
20
30
40
50
60
70
80
90
Per capita GDP, 2013
wedge in economic growth is clearly displayed, with all Asian countries
Figure 55 Comparison of TFP Contribution Shares with(barring Fiji and Japan) having been
OECD Countries, 2000–2013
filtered out to occupy the top end.
Sources: APO Productivity Database 2015 for APO member economies and China
Asian countries are also among
and the US. OECD Stat (Dataset: Multi-Factor Productivity) and OECD (2015) forOECD countries (except Japan and Korea).
those that experienced the fastest
Note: The ending year for Portugal is 2011.
TFP growth in the 2000s: 3.6% in
China, 4.1% in Mongolia, 2.9% in India, 2.6% in Cambodia, 2.3% in Sri Lanka, 1.7% in Hong Kong, and 1.7% in both Korea and Thailand.
Though growing at a more subdued pace, the contribution made by TFP in the slower-growing, mature economies should not be underestimated. Figure 55 plots per capita GDP levels in 2013 and the
TFP contribution shares in the period 2000–2013, for 19 Asian countries with comparison of OECD
countries (as circles). The roles of TFP contribution are also large in some mature economies, rather
than the low-income countries; for example, TFP accounted for more than 35% of economic growth inJapan, Germany, Finland, Austria, Sweden, the UK, and the US in this period.

77©2015 Asian Productivity Organization

assimilation. Although a prerequisite, capital accumulation does notguarantee TFP growth. Some countries may be more capable thanothers in reaping the benefits
through capital assimilation. The
reason as to why this is so, is beyond
the scope of this report.

5 Productivity

China

1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2013
1970–2013

5.8
6.3
10.2
7.6
11.6
8.3
9.4
10.7
7.9
8.7

1.0
1.0
2.4
2.2
0.6
0.7
0.4
0.2
0.2
1.0

(18)
(17)
(23)
(29)
(5)
(8)
(4)
(2)
(3)
(12)

0.0
0.0
0.0
0.1
0.1
0.2
0.7
0.6
0.4
0.2

(1)
(1)
(0)
(1)
(1)
(2)
(7)
(5)
(5)
(3)

4.2
4.0
3.4
4.4
3.7
4.2
4.4
5.7
5.1
4.3

(73)
(64)
(34)
(58)
(32)
(50)
(47)
(54)
(64)
(50)

Fiji

1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2013
1970–2013

5.6
3.7
0.7
3.7
2.7
2.0
2.0
0.7
3.1
2.7

4.1
2.8
1.4
1.0
1.9
0.7
0.3
0.4
0.8
1.5

(73) 0.1(76) 0.1(205) 0.0(27) 0.1(70) 0.2(32) 0.0(15) 0.1(56) 0.1(25) −0.1(57) 0.1

(2)
(2)
(6)
(3)
(7)
(−1)
(3)
(11)
(−2)
(2)

2.0
2.0
1.3
0.0
1.0
0.7
0.4
0.1
0.0
0.9

(35) −0.5 (−10)(53) −1.1 (−31)(182) −2.1 (−292)(1) 2.6 (69)(38) −0.4 (−15)(35) 0.7 (34)(21) 1.2 (61)(18) 0.1 (15)(−2) 2.4 (78)(33) 0.2
(8)

India

1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2013
1970–2013

2.8
3.1
5.0
5.8
5.0
5.7
6.5
7.8
5.6
5.2

2.0
2.3
1.5
1.7
1.6
1.1
2.2
0.5
−0.2
1.5

(69)
(75)
(30)
(30)
(33)
(20)
(33)
(6)
(−4)
(28)

0.0
0.0
0.0
0.1
0.1
0.1
0.1
0.2
0.2
0.1

(1)
(1)
(1)
(1)
(1)
(2)
(2)
(3)
(4)
(2)

1.6
1.8
1.6
1.7
1.9
2.1
1.9
3.4
3.1
2.1

(56) −0.7 (−26)(58) −1.0 (−33)(31) 1.9 (39)(29) 2.4 (41)(39) 1.3 (27)(36) 2.4 (42)(30) 2.3 (35)(44) 3.7 (47)(56) 2.5 (45)(39) 1.6 (30)1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2013
1970–2013

9.5
−2.8
3.8
1.3
3.7
4.1
6.8
5.0
−1.9
3.5

0.9
(9)
1.5 (−52)
1.1
(30)
1.2
(87)
0.8
(22)
1.2
(28)
1.1
(16)
0.2
(4)
0.4 (−20)
0.9
(27)

0.1
0.1
0.0
0.1
0.1
0.1
0.2
0.2
0.1
0.1

(1)
(−2)
(1)
(4)
(2)
(2)
(3)
(3)
(−6)
(3)

4.6 (49) 3.9
5.6 (−195) −9.9
2.2 (57) 0.5
0.2 (13) −0.1
0.9 (24) 1.9
1.1 (26) 1.8
2.5 (36) 3.1
3.0 (60) 1.6
1.7 (−89) −4.1
2.4 (69) 0.0

0.5
1.2
4.3
0.9
7.1
3.2
4.0
4.2
2.2
3.1

(9)
(19)
(43)
(12)
(62)
(39)
(42)
(39)
(28)
(36)

(41)
(349)
(13)
(−4)
(53)
(43)
(45)
(33)
(215)
(1)

Labor

IT

CapitalNon-IT

TFP

Cambodia

0.0
0.0
0.0
0.1
0.1
0.1
0.1
0.1
0.2
0.1

Output1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2013
1970–2013

7.6
7.0
9.0
6.5
7.1
7.5

1.6
2.5
4.2
1.5
1.1
2.4

(22)
(35)
(47)
(24)
(15)
(32)

0.1
0.1
0.1
0.2
0.2
0.1

(1)
(2)
(1)
(3)
(3)
(2)

1.0
2.3
2.4
2.3
2.5
2.2

(13)
(33)
(26)
(36)
(36)
(30)

4.9
2.1
2.3
2.5
3.3
2.7

(64)
(30)
(26)
(38)
(47)
(36)

ROC

(27)
(29)
(51)
(31)
(28)
(32)
(73)
(43)
(34)
(41)

IT

1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2013
1970–2013

9.3
10.6
6.9
8.9
7.2
5.8
4.0
4.2
2.7
6.8

2.0
2.0
1.1
1.2
1.0
0.3
0.2
0.2
0.8
1.0

(22)
(18)
(16)
(13)
(13)
(5)
(6)
(4)
(29)
(14)

0.5
0.4
0.4
0.3
0.3
0.7
0.6
0.1
0.1
0.4

(5)
(4)
(5)
(4)
(4)
(12)
(14)
(3)
(4)
(6)

6.5
5.1
3.8
3.1
3.5
3.3
2.3
1.7
1.1
3.5

(70)
(48)
(55)
(35)
(49)
(57)
(58)
(41)
(42)
(51)

0.2
3.2
1.6
4.3
2.4
1.5
0.9
2.2
0.7
1.9

(3)
(30)
(23)
(49)
(34)
(26)
(22)
(53)
(26)
(29)

Hong Kong

−0.5
1.1
1.9
1.4
1.4
1.7
3.6
2.5
2.1
1.7

Labor

1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2013
1970–2013

6.3
10.9
5.6
7.4
5.2
2.6
4.1
3.8
3.1
5.6

1.8
1.9
0.9
0.4
0.5
1.3
0.6
0.4
0.6
0.9

(29)
(17)
(16)
(6)
(9)
(51)
(14)
(9)
(19)
(17)

0.2
0.2
0.3
0.4
0.4
0.7
0.5
0.3
0.3
0.4

(3)
(2)
(5)
(6)
(9)
(27)
(13)
(8)
(9)
(7)

2.8
3.7
4.2
3.1
3.5
2.9
1.4
1.2
1.0
2.7

(44) 1.5 (24)(34) 5.1 (47)(76) 0.2(3)(42) 3.5 (47)(67) 0.8 (16)(111) −2.3 (−88)(33) 1.7 (40)(32) 1.9 (51)(32) 1.2 (40)(49) 1.5 (28)Indonesia

−2.0
3.7
3.7
4.4
5.0
5.1
5.0
5.9
6.1
4.0

Output1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2013
1970–2013

8.3
7.8
4.7
7.5
7.5
0.7
4.6
5.6
5.8
5.8

2.1
1.7
2.2
2.8
0.8
1.5
0.7
1.8
0.9
1.6

(26)
(22)
(47)
(38)
(11)
(209)
(15)
(33)
(15)
(28)

0.0
0.1
0.1
0.1
0.2
0.1
0.2
0.2
0.2
0.1

(1)
(2)
(3)
(2)
(3)
(20)
(3)
(4)
(4)
(3)

3.8
4.6
4.2
2.5
3.2
3.0
1.9
2.4
2.9
3.2

(46) 2.4 (28)(60) 1.3 (17)(89) −1.8 (−39)(34) 2.0 (27)(42) 3.3 (44)(416) −4.0 (−545)(40) 1.9 (41)(43) 1.2 (21)(50) 1.8 (30)(55) 0.9 (15)Japan

Bangladesh

1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2013
1970–2013

CapitalTFP
Non-IT(−1) 0.4 (−21) −1.9 (95)(1) 0.7 (20) 1.8 (50)(1) 1.5 (40) 0.3(8)(2) 2.1 (49) 0.8 (19)(2) 2.2 (44) 1.3 (25)(3) 2.9 (58) 0.4(7)(3) 3.0 (61) −1.8 (−36)(3) 3.1 (53) 0.1(1)(3) 3.0 (49) 0.8 (13)(2) 2.1 (52) 0.2
(4)

Iran

Table 13 Output Growth and Contributions of Labor, Capital, and TFP, 1970–2013

1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2013
1970–2013

4.3
4.3
4.3
4.9
1.4
0.9
1.2
0.3
0.9
2.6

−0.3 (−7)
0.9
(22)
0.4
(8)
0.4
(8)
−0.4 (−24)−0.6 (−72)−0.3 (−26)−0.4 (−138)0.1(10)0.0 (−1)

0.4
0.2
0.2
0.4
0.3
0.3
0.4
0.2
0.0
0.3

(8) 5.0 (114) −0.7 (−16)(5) 2.6 (60) 0.6 (13)(5) 2.0 (46) 1.8 (41)(9) 2.1 (43) 2.0 (40)(22) 1.8 (128) −0.4 (−26)(35) 1.0 (112) 0.2 (25)(32) 0.4 (31) 0.8 (63)(56) 0.1 (40) 0.4 (142)(4) −0.2 (−21) 1.0 (107)(11) 1.7 (67) 0.6 (24)©2015 Asian Productivity Organization

Table 13 and Figure 56 show the growth accounting decomposition for individual countries in fiveyear intervals covering the period 1970–2013. The relative importance of drivers behind economic

71: The multi-factor productivity in the OECD Productivity Database (OECD, 2015), referred as TFP in this report, defines total input
as the weighted average of the growth rates of total hours worked and capital services. Comparing OECD’s TFP estimates for thewhole economy with ours, there are mainly two differences in assumptions. Firstly, capital services of residential buildings areincluded in our estimates of capital input in order to be consistent with output that includes the imputed cost of owner-occupied
housing. Secondly, the compensation of capital is defined in our estimates as the residual of the value added and the compensation of labor (compensations for employees, self-employed persons, and contributing family workers), whereas the OECD defines
it as the imputed value of capital services based on the assumptions of an ex-ante rate of returns on capital. Thus, although both
apply the same Törnqvist index, the weights to aggregate labor and capital can differ. Other than these, our methodology and assumptions in measuring capital services are designed to be largely consistent with the OECD methodology, and the impact of thedifferences in assumptions on the volume estimates of capital services is judged to be limited.

78

Mongolia

1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2013
1970–2013

6.5
5.4
6.6
3.8
−2.8
2.7
6.3
6.4
12.9
5.0

0.6
0.9
0.9
2.0
−0.2
0.4
1.5
0.4
0.9
0.8

(9)
(17)
(13)
(53)
(6)
(14)
(24)
(7)
(7)
(16)

0.1
0.1
0.1
0.1
0.1
0.1
0.2
0.4
0.3
0.2

(1)
(2)
(2)
(3)
(−3)
(5)
(3)
(5)
(3)
(3)

Philippines

1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2013
1970–2013

5.7
5.9
−1.4
5.3
2.8
3.9
4.5
4.8
5.7
4.1

3.0
(53)
1.7
(28)
1.8 (−132)
0.9
(17)
1.1
(40)
0.6
(16)
1.2
(27)
0.9
(19)
1.0
(18)
1.4
(34)

0.1
(2)
0.1
(2)
0.1 (−10)
0.1
(2)
0.1
(2)
0.4 (11)0.6 (13)
0.2
(5)
0.1
(2)
0.2
(5)

2.0 (36) 0.5
3.3 (55) 0.9
2.7 (−200) −6.0
0.7 (13) 3.6
1.8 (64) −0.2
2.6 (66) 0.3
1.8 (40) 0.9
1.6 (32) 2.1
1.6 (28) 3.0
2.0 (50) 0.4

(9)
(15)
(442)
(67)
(−6)
(7)
(19)
(44)
(52)
(11)

Sri Lanka

1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2013
1970–2013

4.2
5.6
5.0
3.3
5.3
4.9
4.0
6.2
7.1
5.0

2.0
1.4
2.0
0.2
0.6
2.1
1.4
0.6
1.4
1.3

(48)
(25)
(41)
(6)
(12)
(43)
(36)
(10)
(20)
(26)

0.0
0.0
0.1
0.0
0.1
0.2
0.2
0.3
0.1
0.1

(1)
(1)
(2)
(1)
(1)
(4)
(6)
(4)
(1)
(2)

1.9
2.0
2.8
1.1
0.3
0.8
1.6
1.9
2.6
1.6

(45)
(36)
(56)
(34)
(6)
(17)
(41)
(31)
(37)
(33)

(6)
(37)
(1)
(59)
(81)
(36)
(18)
(55)
(41)
(38)

1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2013
1970–2013

1.8
3.5
6.2
4.4
8.1
7.3
8.0
6.2
5.6
5.7

1.2
1.2
3.3
2.0
2.4
0.5
0.8
2.1
0.7
1.6

(70)
(35)
(53)
(46)
(30)
(7)
(10)
(34)
(13)
(29)

0.0
0.1
0.1
0.2
0.2
0.3
0.3
0.5
0.4
0.2

(0)
(2)
(1)
(4)
(2)
(4)
(4)
(9)
(7)
(4)

0.5
1.1
0.4
1.5
2.0
3.0
3.3
4.3
3.3
2.1

(25) 0.1(4)(30) 1.1 (32)(6) 2.5 (40)(33) 0.7 (16)(24) 3.6 (44)(42) 3.5 (48)(41) 3.6 (45)(70) −0.8 (−12)(60) 1.1 (20)(37) 1.8 (31)

4.7 (73) 1.2 (18)
5.4 (100) −1.0 (−19)
5.9 (89) −0.3 (−5)
3.7 (98) −2.1 (−55)
0.9 (−31) −3.5 (128)0.2(8) 2.0 (74)0.2
(3) 4.3 (69)
3.0 (47) 2.6 (40)
5.6 (44) 6.1 (47)
3.2 (64) 0.8 (16)

0.3
2.1
0.1
1.9
4.3
1.8
0.7
3.4
2.9
1.9

Malaysia

0.2
0.4
0.3
0.6
0.4
0.6
0.6
0.2
0.1
0.4

1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2013
1970–2013

7.7
8.2
5.1
6.9
9.3
4.9
5.2
4.4
5.1
6.4

1.8
1.7
1.3
1.9
1.4
1.7
0.6
1.2
1.2
1.4

(23)
(21)
(27)
(27)
(16)
(34)
(12)
(27)
(23)
(23)

0.1
0.1
0.1
0.2
0.3
0.5
0.7
0.6
0.4
0.3

CapitalTFP
Non-IT(1) 4.8 (62) 1.1 (15)(1) 4.9 (60) 1.5 (18)(2) 6.0 (119) −2.4 (−47)(2) 3.0 (43) 1.9 (27)(3) 5.8 (63) 1.7 (19)(10) 5.1 (103) −2.3 (−47)(13) 2.3 (44) 1.6 (31)(14) 2.0 (45) 0.6 (14)(9) 2.3 (46) 1.2 (23)(5) 4.1 (64) 0.5(8)

Pakistan

(24)
(24)
(15)
(19)
(14)
(0)
(6)
(−8)
(14)
(14)

IT

1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2013
1970–2013

3.6
5.8
7.4
6.6
5.5
4.0
5.9
3.7
3.5
5.2

1.6
1.0
1.3
1.3
0.8
1.9
1.9
2.6
1.3
1.5

(44)
(18)
(17)
(21)
(14)
(48)
(32)
(72)
(38)
(30)

0.0
0.0
0.0
0.1
0.1
0.0
0.1
0.1
0.0
0.0

(1)
(0)
(0)
(1)
(1)
(1)
(1)
(2)
(1)
(1)

2.0
2.3
2.7
3.0
2.7
1.3
0.7
0.8
0.2
1.8

(57)
(40)
(36)
(45)
(49)
(32)
(13)
(21)
(7)
(35)

Singapore

2.3
1.8
1.4
1.9
1.1
0.0
0.3
−0.3
0.4
1.0

Labor

1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2013
1970–2013

9.1
8.3
6.6
8.3
8.3
5.5
4.8
6.5
4.6
7.0

2.5
2.4
1.4
2.2
2.1
1.1
0.5
2.5
1.3
1.8

(28)
(29)
(21)
(26)
(26)
(20)
(11)
(38)
(29)
(26)

0.6
0.4
0.6
0.8
0.9
0.7
0.6
0.6
0.5
0.6

(6)
(5)
(9)
(10)
(11)
(12)
(12)
(8)
(11)
(9)

8.0
5.3
5.8
3.0
3.4
4.1
2.2
2.2
2.4
4.1

(88) −2.0 (−22)(64) 0.2(3)(88) −1.2 (−18)(37) 2.3 (27)(41) 1.8 (22)(74) −0.4 (−7)(46) 1.5 (31)(34) 1.3 (19)(53) 0.3(7)(59) 0.4(6)

Thailand

9.4
7.5
8.9
9.8
8.1
5.3
4.7
4.2
2.9
6.9

Output1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2013
1970–2013

5.5
7.5
5.3
9.8
8.1
0.7
5.3
3.7
3.8
5.6

−0.2
3.8
1.0
2.8
0.2
0.2
1.0
0.7
0.0
1.1

(−3)
(50)
(19)
(28)
(3)
(21)
(19)
(20)
(1)
(20)

0.1
0.2
0.2
0.3
0.5
0.3
0.2
0.4
0.3
0.3

(1)
(2)
(3)
(3)
(6)
(41)
(4)
(11)
(9)
(5)

2.2
2.0
1.9
2.3
4.8
2.6
0.6
1.2
1.2
2.1

(39) 3.5 (63)(27) 1.5 (20)(35) 2.3 (43)(24) 4.4 (45)(60) 2.5 (31)(347) −2.3 (−310)(12) 3.4 (64)(32) 1.4 (37)(31) 2.3 (60)(38) 2.1 (37)

US

Korea

1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2013
1970–2013

CapitalTFP
Non-IT(2) 6.0 (64) 0.9 (10)(6) 6.7 (88) −1.3 (−17)(4) 3.9 (45) 3.2 (36)(6) 4.2 (43) 3.1 (32)(5) 4.4 (55) 2.1 (26)(11) 3.1 (59) 1.6 (30)(12) 2.2 (47) 1.7 (35)(4) 1.9 (45) 2.5 (59)(4) 1.8 (60) 0.7 (22)(6) 3.9 (56) 1.7 (24)Vietnam

5.3 Total Factor Productivity

1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2013
1970–2013

2.6
3.6
3.3
3.3
2.6
4.2
2.5
0.8
2.0
2.8

0.5
(18)
1.6
(44)
0.8
(25)
1.2
(37)
0.7
(26)
1.2
(27)
0.0 (−1)−0.5 (−63)
1.0
(47)
0.7
(25)

0.2
0.3
0.4
0.5
0.4
0.7
0.6
0.3
0.2
0.4

(8)
(7)
(13)
(15)
(16)
(17)
(22)
(43)
(9)
(15)

1.3
1.1
0.9
1.0
0.6
0.8
0.9
0.7
0.3
0.9

(49)
(31)
(29)
(29)
(24)
(19)
(34)
(98)
(14)
(31)

Output

Labor

IT

0.0
2.4
3.5
2.2
2.0
0.8
3.2
0.2
1.9
1.8

0.6
0.6
1.1
0.6
0.9
1.5
1.1
0.2
0.6
0.8

5

(−1)
(42)
(47)
(33)
(36)
(19)
(54)
(5)
(55)
(34)

(24)
(17)
(34)
(19)
(34)
(36)
(45)
(22)
(30)
(29)

growth changes over time. It is a common experience in most countries that a large part of the vibrant
growth in the initial period is driven by input growth. TFP growth becomes more prominent andmakes a steady contribution in the later periods. Hong Kong’s TFP growth peaked at 5.1% in 1975–
1980, and was robust at 3.5% in 1985–1990, when TFP growth also peaked in the ROC, Korea,
Singapore, and Japan, at 4.3%, 3.1%, 2.3%, and 2.0%, respectively. Thereafter, TFP growth slowed until
recent years when countries experienced a productivity growth resurgence. This resurgence is also
shared by Malaysia and the Philippines. TFP growth in Mongolia has been particularly strong since1995. It also has bounced back in Indonesia72 and Thailand73 from a negative standing, following the
Asian financial crisis of the late 1990s, but has lapsed again since 2005. In contrast, the US experienced
a surge in TFP growth in the second half of the 1990s, which was sustained into the early 2000s before
the adverse cyclical effect hit in 2005–2013.

79©2015 Asian Productivity Organization

Unit: Average annual growth rate (percentage), contribution share in parentheses.
Source: APO Productivity Database 2015.

5 Productivity

Bangladesh

%

10

6
4
2
0
–2

0.4 3.6
1.8 0.3 0.8
1.4
1.9
1.1
2.1
1.5
0.4 0.7
0.1
–0.5

1.3
1.4

0.3

1.7

2.9 3.0 3.1
2.2
0.1 0.1 0.1 0.2
–1.8

5.1

–2

1.6 0.8
5.8 5.1 0.6 1.2
2.3
3.0
2.1
0.1 0.1 0.1 0.2 0.3 0.5 0.7 0.6
4.8 4.9

6.0

1.9

–2.3

–2.4

2005–2013

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980–4

1.7
1.1 1.5 1.3
1.4 1.7
1.9
1.8 1.7

1970–1975

2005–2013

2000–2005

1995–2000

1990–1995

1985–1990

Labor

0

2005–2013

1.8
0.2 0.4 0.3 0.6 0.4 0.6 0.6 0.1
–1.3

2.2

2005–2013

4
1.7
0.3

2000–2005

1.6

1995–2000

1.9 1.1

1990–1995

6

1985–1990

1980–1985

1975–1980

Malaysia%

8

2000–2005

Indonesia
2.4 1.3
2.0
1.7 2.2
3.3
1.4
2.1
2.8 0.8 1.5
1.9 1.5
3.0
4.6 4.2
0.7
3.8
2.5 3.2
1.9 2.6
0.1 0.1 0.1 0.2 0.1 0.2 0.2
–1.8
–4.0

1970–1975–6

1995–2000–3

1990–1995

0

3.3
3.1 3.5
1985–1990

3

3.8

1980–1985

1975–1980

2005–2013

2000–2005

1995–2000

1990–1995©2015 Asian Productivity Organization

0.9
0.2 1.6
2.3 0.4
1.5
0.5 0.4 0.4 0.3 0.3 0.7 0.6 0.1
1970–1975

2005–2013
0.2

1.8

1980–1985

IT capital

2.2

6

1.5
1.2 1.0 0.3

1.1
6.5

10

3.9 4.2 4.4 3.1

1975–1980–20

2.0 1.6 4.3
2.4

9

2.1

6.7

2
0

2

3.1
3.2

4

2

6.0

1970–1975

2005–2013

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980

Non-IT capital

0.9 1.8
2.3

1.4

0.8
2.0 2.1 1.8 0.2
1.0 0.4 0.7
0.4 0.2 0.2 0.4 0.3 0.3 0.4 0.1
–0.4
– 0.3
–0.3 –0.2–0.4 –0.6–0.7

1985–1990

4

2000–2005

1995–2000

1990–1995

1985–1990

0.4

0.4
2.6

1970–1975

2005–2013

2000–2005

TFP

1995–2000

1990–1995

1985–1990–1

1980–1985–12

1975–1980

0
1970–1975

–8

0.9

6

5.5

Korea

%

1.8 2.0

4.4

3.3
2.1 1.9
1.6 1.8 1.6 1.7 1.9
0.1 0.1 0.1 0.1 0.1 0.2–0.7 –1.0

10
8

6

0.2 3.2
2.0%

1.6 1.1
2.0 2.3 1.5 1.7

1980–1985

1

–2

0.2

2.3 3.3

1975–1980

–9.9

2

–0

8

0.4

2.4 1.3 2.4
1.9

4
2

3.4

India

1970–1975

2005–2013

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980

1970–1975

Japan

5.0

1980–1985

1.8

0.6

0.6

0.7

4.0

0.1 0.1 0.2 0.7 0.5
1975–1980

0.2 3.5 0.8
1.9 0.9
1.3
0.4 0.5
1.7
1.7
3.7 4.2 3.1 3.5 2.9 0.6 0.4
2.8
1.4
0.2 0.2 0.3 0.4 0.4 0.7 0.5 1.1
0.3
–2.3
1.5

4
3

0

3.2

4.4 3.7 4.2
4.2 4.0
3.4
1970–1975

2005–2013

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980

2005–2013

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980

1970–1975

1.9

2

2.2
0.5 1.2
1.0 1.0 2.4

8

5

1.5

4

7.1
0.9

6

6

3.1
0.5
1.8 1.1 0.3
4.6 5.6 1.1 1.2 0.8 1.2
2.5
2.5
0.1 0.1 2.2 0.2 0.9 1.1
0.1–0.1 0.1 0.1 0.2 -0.5

–4

–3

6

4.3

%

5.1

%

3.9

0

3
0

Iran

%

12

4

1970–1975

6
1.0
0.5

–3

0.9

Hong Kong

9

1.2
0.3
0.4
0.1

2.4

2.3
1.0
0.1 0.1 0.1 0.2%

2.8
1.4 2.6 1.9
0.7
2.0 2.0
0.7
1.0 1.0 0.7
0.2
0.1 0.1 1.3–0.5 –1.1
0.1 –0.4
–2.1

1.4
2.4

1.6

12

4.1

8

0

2.8
4.2

2.5

2

2005–2013

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980

6

0

4.9

4

10

8

2.1

ROC

%

12

10

2.3

6

Fiji

%

3

8

China

%

12

2.4

–1.9
1970–1975–4

Cambodia%8

OutputLooking at the decomposition of economic growth in China and India, the two key drivers have been
non-IT capital input growth and TFP growth. While the contribution from non-IT capital has been relatively stable in terms of percentage points, it is TFP performance that has more bearing in determining
the overall economic growth over time. For example, the low economic growth that China experienced
in 1985–1990 was explained largely by the lack of TFP growth. Similarly, when output growth slowedfrom its peak in the latter half of the 1990s, it was due to the slowdown in TFP growth from 7.1% on
average per annum in the previous period to 3.2%. Thereafter, output growth has accelerated to reflect the pickup in TFP growth in the 2000s. In India, TFP growth was a drag in the 1970s. Since then, it
has accelerated and has increasingly accounted for a greater proportion of economic growth. In
2005–2013, India achieved TFP growth of 3.3% – its highest in the past four decades. Through trial and72: Van der Eng (2008) provides estimates of capital stock for Indonesia and Van der Eng (2009) shows that annual average TFP growthincreased from –4.4% during 1995–2000 to 1.7% during 2000–2007 in Indonesia. Warr (2006) also finds that TFP growth increased
from –8.4% during 1996–1998 to 1.5% during 1998–2002.
73: Bosworth (2005) shows that annual average TFP growth increased from –4.6% during 1996–1999 to 2.1% during 1999–2004 in
Thailand. Warr (2006) also finds that TFP growth increased from –9.0% during 1996–1998 to 1.6% during 1998–2002.

80

5.3 Total Factor Productivity

Mongolia

%

10
0.9

0
–2

2.0

5.9
4.7 5.4

4.3

0.6

4.0
2.0
0.9 0.4 1.5
0.1 0.1 0.1 0.1 0.1 0.2 0.2 0.3–0.2 0.1 0.2–1.0 –0.3
–2.1
3.7

2.2

5.3 5.8

5

2005–2013

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980

1970–1975

2005–2013

3.6
1.6

US

1
0

1.1

0.6

0.6 1.6
1.2 0.9 1.2 1.1
0.8
0.5
0.7
0.8 0.9 0.3
1.3 1.1 0.9 1.0 0.6
0.1
0.6
0.7 0.6
0.2 0.3 0.4 0.5 0.4
0.3

2005–2013

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980

1970–1975

2005–2013

–1

1.5

0.6

3
2

0.8

2000–2005

1995–2000

3.1

1.0

2.5

2.4

0.1
2.8

2.2

2.2
1.4

0.1

0.1

3.4
2.7 2.5 2.5

0.6 0.4 0.4 0.6 0.4 0.5 0.8 0.5–0.5

2005–2013

2000–2005

1995–2000

Non-IT capital

1990–1995

1985–1990

1980–1985

1975–1980–1

1970–1975

2005–2013

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980

1970–1975

TFP

3.5

2.4 0.5

1990–1995

0

1.7

0.9

4.7

2

0.4 0.3 0.3 0.5 0.4 0.4 0.4 0.3–0.5

0.6 2.2
1.1

3
1

–3

2.1 1.1

0.9
1.5 2.1
0.5
4.1
3.0 3.4
2.2 2.3
0.6 0.4 0.6 0.8 0.9 0.7 0.6 0.5
–0.4
–1.2
–2.0
8.0

5

3.9
3.0 3.3
1.2 3.3
1.2
1.5 2.0
0.5 1.1 0.4 0.2 0.2 0.3 0.3 0.5
0.1 0.1–0.1
1985–1990

4

1.4 1.8

1

2.0

1980–1985

0.8 0.5

0.1

1.1

1975–1980

2.4 2.1 2.4 2.0

0.7

1970–1975

3.1

1.6 1.6

2005–2013

3.8

0.2

5

1.0 0.2
1.0 1.2 0.5
0.5

3

2000–2005

1.5 1.0

1995–2000

6
0.8

2.4 1.4 2.3 1.8

East Asia7

2.0

1990–1995–2

1985–1990

0

–4

%

6

4

–2
1980–1985

7

2.5

2

0.2

4
3.6

6

0.2 3.4 1.7
4.8 2.6
2.3
1.0 0.5
2.2 2.0 1.9
1.2
0.1 0.2 0.2 0.3 0.5 0.3 0.6 0.4–0.2–2.3 0.2

2.5

%

8

2.5

1.0

1975–1980

APO20

%

Vietnam%10

0.2
3.5 3.8 2.3 2.8

1970–1975

2005–2013

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980

1970–1975

1.6
1.1 0.6 0.8
0.3 0.2 0.2 0.2
0.1

2000–2005

0.2

1995–2000

0
2.2

–8

6

0

–6
1990–1995

1.4

2

–6.0

–4

9

3

1985–1990

0.1

2.1

0.9

1.1

–2

Singapore%12

1.7 1.8
3.6

1980–1985

2.0

1.9
2.8

4

0.7

4.4

1.5

6

Philippines

0.3 0.9
2.4
0.6 1.2
1.0
3.3 2.7
2.6 1.8
0.9 1.8
2.0
1.6
0.1 0.1 0.1 0.7 0.1 0.4 0.6 0.2
0.1 –0.2
3.0

1975–1980

Thailand

0.9
0.5

1970–1975–1

2005–2013

4.3

0
0.8

2000–2005

1.8

2.0
1.4

2
1.9

3.2

0.8

1995–2000

2.1

0.8

3.2

1.9
1.9 2.1
2.7 3.0 2.7
2.0 2.3
1.3 0.7
0.1 0.1
0.1 0.6

%

0.1

1.3

1990–1995

2.0

–1

1

1.3

1985–1990

302

1.6 1.0

1980–1985

4

0.3

2

3

2
2.0

8

5

4

2.4

4

10

6

5

5

1975–1980

70

6

3.5 2.2

1970–1975

2005–2013

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980

Sri Lanka

%

1

%

4

6

0

–3.5
1970–1975–4

3.9

1.2 0.9
0.6

4
2

8
7

8
6

Pakistan%

IT capital

Labor

OutputFigure 56 Individual Countries’ Growth Accounting Decomposition, 1970–2013
Source: APO Productivity Database 2015.

Tracking the size and growth of IT capital has become a standard practice in productivity research,following attempts to establish the driving force behind productivity resurgence in developed economies. This started with the US in the 1990s. Unlike technological advancements in the past, whichwere largely confined to manufacturing, IT is a technology that can permeate the economy and bringabout significant production gains in, for example, wholesale and retail, banking and finance, andtransportation and telecommunications (service sectors that have traditionally struggled with slow
productivity growth). Given the share of the service sector in the economy (Figure 75, p. 98), the potential and implications for economic development and productivity gains could therefore be immense. A frequent question asked by policymakers and researchers is how best to capitalize on the
productivity potential invited by this IT revolution. As with non-IT capital, it involves a process of81©2015 Asian Productivity Organization

error, China and India invested first and then learned how to combine inputs efficiently. Both havereaped the benefits of their efforts in robust TFP growth, while the contribution from labor input
growth dwindles over time in the two countries.

5 Productivity

accumulation and assimilation. IT capability becomes a factor which determines an economy’s longterm growth prospects.74
Japan has been leading Asian countries in terms of IT capital contribution to economic growth (Figures
51 and 53). Japan’s shift in capital allocation took off in earnest in the mid-1990s, with the contributionof IT capital to capital input growth rising from a low of 12% in 1995 to a peak of 66% in 2009 (Figure57).75 It took place in a period when Japan’s overall investment growth slowed significantly after the
economic collapse of the early 1990s (Figure 39, p. 56). After years of excesses, Japan shifted away from
non-IT to IT capital as a profitable investment. In contrast, the US started its shift toward IT capitalmuch earlier than any Asian economy and over a longer period of time. For two decades (between
1983 and 2004), IT capital accounted for over 30% of US capital input growth, reaching a height ofover 50% in the late-1990s and the late-2000s. In recent years, the slowdown in total capital growth
has concentrated more on non-IT capital, resulting in spikes in the contribution of IT capital in both
Japan and the US. The findings here are in accordance with Jorgenson, Ho, and Stiroh (2005). Based on
their measurement, IT capital in the 1980s contributed 31.9% of the growth of total capital inputs in
the US, but only 13.5% in Japan.76 Since 1995, the Japanese economy had been rapidly shifting its
capital allocation from non-IT to IT capital. In 2002, the contribution of IT capital in Japan rose to53.3%, which is higher than the 49.8% for the US.
A similar allocation shift to IT capital is also found in the Asian Tigers (Figure 58).77 In Korea, the ROC,
and Hong Kong, the contribution of IT capital to total capital input peaked at about 30% at the turn ofthe millennium, from a share of 10% or below before 1995. In contrast, Singapore had two local
peaks – the first at the end of 1980s when the contribution of IT capital reached 28%; the second in
2005–2006 when it peaked again at 25%. China was a latecomer in terms of investing in IT capital witha surge in its contributions only taking off around 2000 and peaking at 15% in the early 2000s. There
has not been as big a drive in IT pickups in India as in other Asian countries. Rather, the process has
Japan

%

%

70

60

6.0

60

50

5.0

40

4.0

30

3.0

1.5

2.0

0

0.0

10

–1.0

0
1971

1986

1991

1996

2001

2006

Non-IT capital (right-axis)

2011

2.0

30

1.0

1981

2.5

40

10

1976

%

3.0

50

20

–10
1971

US

%

7.0

70

1.0

20

IT capital (right-axis)

0.5
0.0
1976

1981

1986

1991

1996

2001

2006

2011

IT capital contribution to capital input growth

Figure 57 IT Capital Contribution to Capital Input Growth of Japan and the US, 1970–2013©2015 Asian Productivity Organization

Source: APO Productivity Database 2015.

74: The 2008 SNA formally acknowledges the IT sector’s importance to the modern economy and has made it more easily identifiable
and separable in industry classification and asset type.
75: Japan’s capital services recorded negative growth in 2009–2013, for the first time after World War II, although IT capital services
increased. This period has been omitted from our calculations of the IT capital contribution share in total capital input in Figure 57.
76: Based on our estimates, IT capital contributes 35.0% in the US and 17.9% in Japan to the growth of total capital input. Although
the estimates in the 1980s in this report are somewhat higher than the industry-level estimates in Jorgenson, Ho, and Stiroh (2005)
and Jorgenson and Nomura (2005), the trends of both the US and Japan shown in Figure 57 are very similar to Figure 3 in Jorgenson and Nomura (ibid.).

82

5.4 Sources of Labor Productivity Growth

%

%

40

30

40

ROC
Hong Kong
Korea
Singapore

30

20

20

10

10

0
1971

1976

1981

China
India

1986

1991

1996

2001

2006

2011

0
1971

5
1976

1981

1986

1991

1996

2001

2006

2011

Figure 58 IT Capital Contribution to Capital Input Growth of the Asian Tigers, China, and India,
1970–2013
Source: APO Productivity Database 2015.

been gradual with a clear step-up in effort from a minimal level in the early 1990s. The share of IT
capital reached 8% in the early 2000s before lessening recently.

5.4 Sources of Labor Productivity Growth
Although TFP more accurately measures how efficiently an economy utilizes its factor inputs,
labor productivity and its drivers are of interest because of the close link to GDP per capita. Within thesame growth accounting framework, average labor productivity growth at the aggregate level can be
broken down into effects of capital deepening (as measured by capital input per hour worked),which reflects the capital–labor substitution, and TFP. In other words, these factors are key in fosteringlabor productivity.

77: The quality of the data on investment for IT capital (IT hardware, communications equipment, and computer software) varies considerably among countries. If the official estimates are not available in their national accounts, the investment data by type of asset in benchmark Input–Output Tables (IOT) and the time-series IOTs (if available) are used to separate IT capital investment fromGFCF in the national accounts. In the years when the IOTs are not available, domestic production and import data (UN Comtrade
Database) for IT hardware and communication equipment is used to interpolate the estimates of IT investments. Thus, data inconsistency could pose a problem. Where software is excluded from the GFCF definition compliant to the 1968 SNA, software investment is estimated as described in Appendix 1. In addition, the constant-quality prices for IT capital are hardly available for mostAsian countries. If they are not available, the prices for IT capital are estimated by harmonizing Japan’s price indices, as describedin Appendix 2. Thus, readers are cautioned about data uncertainty and should expect that the decompositions of contributionsof capital services into IT and non-IT capital may be considerably revised for some countries, when more reliable data sources for
estimation become available.

83©2015 Asian Productivity Organization

Capital deepening has been taking place in all of the countries compared, albeit to various degrees(Figure 60). Experience of countries suggests that capital deepening is an accompanying process of
rapid economic development. The relatively early starters (Japan and the Asian Tigers) underwent
more rapid capital deepening than the other countries compared; and in the earlier rather than thelatter period. The reverse is true for the emerging Asian economies, where concerted efforts weremade to increase capital intensity in the latter period. In 1990–2013, China, Vietnam, India, Indonesia,
and Thailand moved up to occupy the top spots among the Asian Tigers, while Singapore and Japan
stepped down in the rankings. In 1970–1990, the capital–labor ratio was rising by 10.7% and 9.3% on
average per year in Korea and the ROC, respectively. Over the subsequent two decades it slowed to5 Productivity%
12

Bangladesh

8

9
9

0

6
3

6

–4

0

3

–8

–3

–12

0

–16

–3
1971 1976 1981 1986 1991 1996 2001 2006 2011

ROC

–6
–9
1971 1976 1981 1986 1991 1996 2001 2006 2011

Fiji

%
15
12

12

3

3

0

4
0

–3

0

–4

–6

–3

–9

–8

–6

–12

–12

1971 1976 1981 1986 1991 1996 2001 2006 2011

India

1971 1976 1981 1986 1991 1996 2001 2006 2011

15
10
5
0

–4

–5

–8

–4
–8
–12

–12

–10

–16

–15

–20

Japan

–20
1971 1976 1981 1986 1991 1996 2001 2006 2011

1971 1976 1981 1986 1991 1996 2001 2006 2011

Korea

%
15

8
6
4

8
4
0
–4

0

–2
–4

–3

–6

–6

–8

–8
–12
–16

–9
1971 1976 1981 1986 1991 1996 2001 2006 2011

TFP

Malaysia

%
12

9

3

0

1971 1976 1981 1986 1991 1996 2001 2006 2011

12

6

2

Iran

%
20

8
0

0

1971 1976 1981 1986 1991 1996 2001 2006 2011

12
4

4

%
10

Indonesia%
16

8Hong Kong

%
16

8

6

6

1971 1976 1981 1986 1991 1996 2001 2006 2011

12

9

9

%
12

China

%
15
12

12

4

%
15

Cambodia%15

1971 1976 1981 1986 1991 1996 2001 2006 2011

Non–IT capital

IT capital

1971 1976 1981 1986 1991 1996 2001 2006 2011

Labor

Output©2015 Asian Productivity Organization

7.2% and 5.8% respectively. Meanwhile, China’s pace doubled between the two periods, from 5.2% to10.8% on average per year. In Vietnam, it has accelerated to 7.6% since 1990. In the US, the pace ofcapital deepening also increased from 2.1% to 2.2% between the two sub-periods.
While labor productivity steadily improved for all countries (Figure 45), the growth rate of capital productivity (as the other measure of partial productivity) remains negative for almost all countries during 1970–2013 (Figure 61). Although rates of capital deepening in Korea and the ROC were outstanding,at 8.9% and 7.4% per year, on average during this period, their capital productivity experienced the
sharpest decline of 3.6 and 2.3% per year, respectively (Figure 62). In contrast, the deterioration ofcapital productivity (by 1.4%) was relatively mild in China as shown in Figure 61, despite its fast capital
deepening of 8.2% (Figure 60).

84

5.4 Sources of Labor Productivity Growth%18

Mongolia

Pakistan

%
10
8

12

8

6
6

4

0

2

4
0
–4

0
–6
–12

–2

–8

–4

–12

1971 1976 1981 1986 1991 1996 2001 2006 2011%15

Singapore

1971 1976 1981 1986 1991 1996 2001 2006 2011

Sri Lanka

%
10

12

8

9

6

6

4

3

2

0

0

12
8
4
0
–4

–2
–4

–8

–9

–6

–12

Vietnam1971 1976 1981 1986 1991 1996 2001 2006 2011

12
9
6
3
0
–3
–6
–9

1971 1976 1981 1986 1991 1996 2001 2006 2011

APO20

%
8

6

6

4

4

2

2

0

0

–2

–2
–4

–4

1971 1976 1981 1986 1991 1996 2001 2006 2011

%
10

US

%
8

15

Thailand

%
16

–6

%
18

5

1971 1976 1981 1986 1991 1996 2001 2006 2011–3

1971 1976 1981 1986 1991 1996 2001 2006 2011

Philippines%
121971 1976 1981 1986 1991 1996 2001 2006 2011

1971 1976 1981 1986 1991 1996 2001 2006 2011

East Asia

8
6
4
2
0
–2
–4
–6

1971 1976 1981 1986 1991 1996 2001 2006 2011

TFP

Non–IT capital

IT capital

Labor

OutputFigure 59 Individual Countries’ Growth Accounting Decomposition (year-on-year), 1970–2013

Looking at the two sub-periods of 1970–1990 and 1990–2013, overall the rate of deterioration in capital productivity for all countries was slower in the latter period. China’s performance is particularly
impressive. Its acceleration in capital deepening in the latter period did not compromise its capital
productivity as much as the early starters (Figure 63). In 1990–2013, China’s capital–labor ratio rose by
10.8% whereas its capital productivity fell by 1.8%. This compares with Korea’s performance in 1970–
1990 when its capital–labor ratio rose by 10.7% while capital productivity fell by 5.0%.
Labor productivity growth can be decomposed into contributions from capital deepening and TFP
growth. Capital deepening should raise labor productivity, all other things being equal. It remains the85©2015 Asian Productivity Organization

Source: APO Productivity Database 2015.

5 Productivity

1970−2013%

1970−1990

1990−2013

12

10.8

10.7

10
8

8.9

9.3
8.2

7.5

7.4

6

5.2 5.0 4.8

44.74.77.6

7.1
5.7 5.5 5.4

4.3 3.9

3.83.75.8

5.2 5.0

4.8 4.7 4.54.4
3.4 3.3

3.3 3.2
2.22.2 2.02.0 1.8

2

7.2

2.62.6 2.4

2.12.1

1.7

0.7

4.3 4.0

3.9

3.1 2.9

2.3 2.2

1.0

1.7 1.60

1.3

0.8 0.5

–0.8

-2

Fiji
Iran
Pakistan
Mongolia
PhilippinesUS
Sri Lanka
Japan
Singapore
Hong Kong
Malaysia
Cambodia
Bangladesh
ThailandIndonesia
India
ROC
Korea
Vietnam
China

VietnamFiji
Sri Lanka
Bangladesh
USPakistan
India
Philippines
ThailandIranMongolia
China
Indonesia
Malaysia
Hong Kong
Japan
Singapore
ROC
Korea

Fiji
Pakistan
Sri Lanka
IranPhilippines
US
Mongolia
Bangladesh
Vietnam
India
ThailandCambodia
Malaysia
Hong Kong
Japan
Indonesia
Singapore
ROC
China
Korea

Figure 60 Capital Deepening, 1970–2013, 1970–1990, and 1990–2013
Source: APO Productivity Database 2015.
Note: The labor inputs for Fiji and Mongolia are defined by numbers of employment. The starting period for Cambodia is 1993.

1970−2013%

1970−1990

2
1
0

–1
–2

1.4
0.9 0.8

0.8 0.7–0.1–0.3 –0.4–0.5 –0.5 –0.7–0.7–0.8 –0.8–0.4 –0.6

–0.9
–1.4

1.1 1.0

0.8
0.3 0.3

0.1–0.5–0.5 –0.5 –0.5
–1.0–1.0 –1.1–1.1–1.6–1.9 –2.1 –2.1

–2.2

–1.0 –1.2
–1.8

–2.3

–3
–4

1990−2013

1.5–2.1 –2.1

–2.4

–5

–1.7 –1.8–2.0–2.5 –2.6–3.0 –3.1–3.6–1.3 –1.4

–2.6

–3.2
–3.9
–5.0

Bangladesh
Vietnam
Korea
Indonesia
China
ROC
Hong KongJapan
Malaysia
ThailandCambodia
Singapore
US
India
Philippines
Fiji
Pakistan
Iran
Sri Lanka
Mongolia

Korea
Singapore
Japan
ROC
Iran
Mongolia
Indonesia
Malaysia
Philippines
India
Bangladesh
Fiji
China
US
Hong Kong
Sri Lanka
PakistanThailand
Vietnam

Korea
ROC
Japan
Singapore
Indonesia
Bangladesh
Malaysia
China
Hong Kong
Iran
India
Philippines
Vietnam
CambodiaUS
Mongolia
Fiji
ThailandSri Lanka
Pakistan

Figure 61 Capital Productivity Growth, 1970–2013, 1970–1990, and 1990–2013
Source: APO Productivity Database 2015.
Note: The starting period for Cambodia is 1993.

©2015 Asian Productivity Organization

prime motor of labor productivity growth, generally explaining 50% of it. Taking the US as the reference economy, with contribution share of capital deepening to labor productivity growth of 50.4% on
average in 1970–2013, it has been a main engine to enhance labor productivity in 12 Asian countries(Figure 65). The exceptions to this observation are Cambodia, India, Sri Lanka, Thailand, Pakistan, andVietnam, in which the role of TFP has been more significant.
Within this long period, the composition of labor productivity growth has seen substantial shifts (Figures 66 and 67). In the earlier period 1970–1985, TFP growth was enjoyed by 11 out of the 18 Asian
countries compared (excluding Cambodia). It was a significant drag on labor productivity growth infour countries (Iran, the Philippines, Fiji, and Singapore). During the middle period 1985–2000, allcountries (except Mongolia) achieved positive TFP growth to bolster labor productivity growth. By
2000–2013, TFP growth had become the dominant driver of labor productivity growth in 12 of the 19
countries compared. At the same time, the contribution from IT capital deepening was also strengthening, from a range of 0–12% in 1970–1985, to 2–20% in 1985–2000, and 2–21% in 2000–2013. Thismay have accounted for a boost of countries’ TFP performance. In the mid period 1985–2000, thecontribution of IT capital deepening in the US was ahead of Asian countries accounting for 28% of86

5.4 Sources of Labor Productivity Growth

1970=1.0

1970=1.0

1.0

1.0

0.8

0.8

1.2

1.2

0.6

0.6
ROC
Hong Kong
Korea
Singapore
Japan

0.4
0.2

5

0.4
China
India

0.2

0.0
1970 1975 1980 1985 1990 1995 2000 2005 2010

0.0
1970 1975 1980 1985 1990 1995 2000 2005 2010

Figure 62 Capital Productivity Trends inJapan and the Asian Tigers, 1970–2013

Figure 63 Capital Productivity Trends in
China and India, 1970–2013

Source: APO Productivity Database 2015.

Source: APO Productivity Database 2015.

%

%

7

TFP
IT capital deepening

6
5

Non-IT capital deepening
Labor productivity

120
TFP

Non-IT capital deepening

4

80

32
46

29 28

38 40

0.5 0.4

2.1
2.7

0

3.5 3.2

2.8
1.9

0.9
1.8 1.6

2.3 2.275
0.8 0.640
1.9 1.8

2.0 1.8 1.8

62

0.8 0.4 0.2

1.4
1.1 1.3
0.5 0.9 1.1 1.0
0.9
0.6 0.8
0.2
0.4 0.2 0.1 0.1 0.2
0.4
0.3
0.3
0.2 0.1 0.3 0.5 0.2 0.1 0.1 0.1 0.3 0.1
0.2

67 66

28

3

7

9

6

90

59
39

37 44
25 31

25
7

52

83

66

38

20
0

50

71

55 50

51

4

30

72 68

72

60
1.5

23

58 54

56

1.9

3

1

12

17 14

3.1
1.7

2

IT capital deepening

100

3

9

15

6

3

4

5

11

4

1

23
12

5

7

9

Fiji
Iran
Bangladesh
Philippines
US
Pakistan
Sri Lanka
Japan
Mongolia
Indonesia
India
VietnamSingapore
Malaysia
Cambodia
ThailandHong Kong
ROC
Korea
China

Fiji
Iran
Bangladesh
Philippines
US
Pakistan
Sri Lanka
Japan
Mongolia
Indonesia
India
VietnamSingapore
Malaysia
CambodiThailandHong Kong
ROC
Korea
China

Figure 64 Sources of Labor Productivity
Growth, 1970–2013

Figure 65 Contribution Shares of Labor
Productivity Growth, 1970–2013

Source: APO Productivity Database 2015.
Note: The starting period for Cambodia is 1993.

Source: APO Productivity Database 2015.
Note: The starting period for Cambodia is 1993.

Figure 68 and Table 14 show the decomposition of labor productivity growth for individual countries
in five-year intervals covering the period 1970–2013. Productivity is procyclical in nature. In turn, it isdifficult to discern fundamental shifts from short-term fluctuations. However, over a period spanning
four decades, it can be observed that labor productivity growth in the two fast-growing emerging
Asian economies (China and India) is accelerating. China has clearly leapt from a growth rate of around
4% in the 1970s to a rate of 8–10% in the 2000s, with its transition period in the early 1990s. India’s
passage to accelerating labor productivity growth is more gradual than China’s, from almost zero in
the 1970s to 6.6% in 2005–2013. Both TFP growth and capital deepening took a leap in 2005–2013 toreinforce the positive trend. In contrast, the early starters (Japan and the Asian Tigers) have been
experiencing a slowdown in labor productivity growth since their heights of the late 1980s. In both87©2015 Asian Productivity Organizationlabor productivity growth. Coincidentally, this was also the period when the share of TFP growth was
the largest, at 60%.

5 Productivity

1970−1985%

1985−2000

2000−2013

10
8
3.6

6
1.7 0.94
2

3.9 4.3
2.3 2.6

2.7
1.5

0.1

0.5

3.4

3.0

2.6

0.6

2.4 4.2

4.0

2.3

2.0
2.6

3.2

2.8

0.8 0.8

3.3

2.8 2.6

0.7
1.2
2.5

4.1 2.9 1.3

0.4
0.6 0.4

2.0 1.6

1.5 2.6

2.9 2.7

2.6
0.8

1.8 2.0

1.2

TFP

2.2

IT capital deepening

Non-IT capital deepening

2.3

1.6 1.3

2.8

1.9

Bangladesh
Fiji
Japan
US
Pakistan
Singapore
Philippines
Iran
Malaysia
Hong Kong
Thailand
ROC
Indonesia
Sri Lanka
Korea
Cambodia
Vietnam
India
Mongolia
China

0.1 1.2 0.1 1.3
1.2
1.0 0.6 0.6
0.7
0.4
0.5
0.4 0.3 0.2 0.0 0.1 0.3 0.1 0.4 0.1 0.1 0.0 0.3 0.0 0.0 0.0 0.0 0.0 0.1 0.0
−0.1
−0.7
−1.2
−1.0
−1.9
−1.6

1.0
1.5
1.6
1.1 1.4 0.1
0.8 0.2 1.0 1.2
0.1 0.5 0.4 0.3 0.2 0.5 0.6 0.3 0.1 0.1 0.1 0.1 0.4 0.1 0.1 0.2 0.5 0.1 0.0 0.3
0.1
0.0
0.0
−1.0−1.2

1.7

1.7 0.9
2.4
1.1
1.8 1.1
0.6
1.7
1.5
1.1
1.4
0.8 0.8 1.4 0.6 0.6 0.7 1.1 1.4
1.4
1.3
0.3
0.3
0.3
0.5 0.3 0.2 0.4 0.1 0.3 0.2 0.2 0.3 0.3 0.4 0.5 0.1 0.3 0.4 0.0 0.4 0.2 0.0 0.1−0.1
0.0−0.5

4.8

Mongolia
Iran
Fiji
US
Philippines
BangladeshIndonesia
Japan
Sri Lanka
Cambodia
Pakistan
India
Malaysia
Singapore
Hong Kong
VietnamThailand
ROC
Korea
China

-2

2.3 2.0

Fiji
Philippines
Bangladesh
Vietnam
India
Iran
Sri Lanka
USPakistanIndonesia
Malaysia
Singapore
ThailandJapan
Mongolia
China
Hong Kong
Korea
ROC

0

3.8

Labor productivity

Figure 66 Sources of Labor Productivity Growth, 1970–1985, 1985–2000, and 2000–2013
Source: APO Productivity Database 2015

1970−1985%
120

1985−2000

115

100
28
80

3

14

17

287
7

19

47 44

13

13

18
38

49
65

53

46

34
61

67

22 17

37
63

99
66

78

95

79

57

79

68

81
54

35

49 56
20

32

0

6 5 4 1 3 7 3
−2

39

45

33
12

2 2 0

16

3 5 2 3 5

−3

58

97 94

79

34

13
2 8 7 8 4

18

44

55

59

63

51

43
58

46

36

38
76

94

46

103

12

62

54

37

13
8 2 2 3 42
5 4 8

49
42

28

21

20
6
−1

54

55

80

64 77
63

35

30

40
55 60
74

45

47

135

34

36

43

33 58

48
11

−10

27

27

30

12
6 5 3 8 3 7 5 5 8 9

97

39

13

21
6

11

23 19

18
2
−5

1 10
3

−51 −66

−405
−30

−45

Fiji
Philippines
Bangladesh
Vietnam
India
Iran
Sri Lanka
USPakistanIndonesia
Malaysia
Singapore
ThailandJapan
Mongolia
China
Hong Kong
Korea
ROC

Mongolia
Iran
Fiji
US
Philippines
BangladeshIndonesia
Japan
Sri Lanka
Cambodia
Pakistan
India
Malaysia
Singapore
Hong Kong
VietnamThailand
ROC
Korea
China

Bangladesh
Fiji
Japan
US
Pakistan
Singapore
Philippines
Iran
Malaysia
Hong Kong
Thailand
ROC
Indonesia
Sri Lanka
Korea
Cambodia
Vietnam
India
Mongolia
China

−20

90

33

52

60

40

2000−2013
485 1401043169212−27−192 −115

TFP

Non-IT capital deepening

IT capital deepening

Figure 67 Contribution Shares of Labor Productivity Growth, 1970–1985, 1985–2000, and 2000–2013©2015 Asian Productivity Organization

Source: APO Productivity Database 2015.

Hong Kong and Korea, labor productivity growth appeared to stabilize in the 2000s, but at a lower rate
than previously. Singapore’s productivity performance, albeit robust, compared with other matureeconomies like the US, has been very modest against its Asian counterparts. A recent peak of 3.1–3.6%
in the 1990s is compared with over 6% in Hong Kong, the ROC, and Korea in the late 1980s. The US
clearly enjoyed a labor productivity growth spurt in the late 1990s (2.3%) and early 2000s (2.5%), theorigin of which attracted much research attention at the time. In recent years, it has returned to its
long-term average of under 2%.

885.4 Sources of Labor Productivity Growth

3
2
1

6

1.3 0.4

0.3

1.9

1.7

0.8

0
–1

–1.9

2005–2013

2000–2005

1995–2000

1990–1995

1985–1990

2005–2013

2000–2005

1995–2000
–2.3

–2.4

2005–2013

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980

Philippines

1990–1995

1985–1990

1980–1985

1.6
4.5 1.9 4.0
2.7
1.3 0.8
1.0
0.5
0.1 0.1 0.1 0.2 0.4 0.6 0.4

2.8 2.9

Singapore

%

8

0.9

0.3
2.4
3.6
1.7
0.9
0.5 2.1 1.3
0.8 0.4 0.5 0.5
0.5
0.1
0.1
0.1
0.1
0.1
–0.2

6
4
2

0.2

1.5
4.0 2.3 1.8
2.9
1.7
1.1
0.5
0.9
0.5 0.3 0.5 0.6 0.6 0.5 0.5 0.3
–0.4
–0.1
–1.2
–2.0
2.7

2

1.1
0.6

1
0

1.5

1.1

0.6 0.9
0.9 0.3
0.1
1.0 0.6 0.4
0.3 0.2
0.5
0.6
0.2
0.6
0.3
0.2 0.2 0.4 0.4 0.4

2005–2013

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980

1970–1975

2005–2013

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980

1970–1975–1

2005–20133

2.9 2.9 2.7
2.5 0.7
1.1
0.5 1.1
0.1 0.2
0.1 0.1 0.3 0.3 0.4
–0.1
–0.6
–1.6

2000–2005

1995–2000

1990–1995

US%

IT capital deepening

1985–1990

1980–1985

1975–1980

Vietnam

6.0

1970–1975

2005–2013

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980

1970–1975

2005–2013

2000–2005

Non-IT capital deepening

1.7
1.1 1.5

1970–1975

–4

3.6

–2

Malaysia

0

2005–2013

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980

2005–2013

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980–4

1980–1985–2

3.5 3.60

1975–1980

2.0
1.9
0.2 0.4 0.3 0.5 0.4 0.6 0.5 0.1–1.3
1970–1975

2005–2013

–2

–4.0

1970–19752

42

1975–1980

2005–2013

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980

1970–1975

1.7
3.8 3.1
3.2 2.96

2.5 3.4 1.7
2.3
0.7
1.5 1.4 0.8
0.1 0.1 0.2 0.2 0.5 0.3 0.1 0.3
–0.2
–0.2
–2.3

0

1970–1975

2005–2013

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980

2005–2013

4

1.6

5.4

8

2

–1.8

8

1.8

4.4

%

4.4 4.7
2.3

1.9 1.4
2.0
3.1 2.6
2.8
2.0 1.4 1.3
1.2
0.1 0.1 0.1 0.2 0.1 0.1 0.2

%

–2

3.5

1970–1975

2005–2013

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980

1970–1975

TFP

–4

–8

2000–2005

1.7
1.8
0.8
0.1 0.2 0.2–0.1 –0.6

2000–2005

1.2 1.0
0.1

2000–2005

0.8

1970–1975

2005–2013

2000–2005

0.3
–0.3

0

0

4

5

3.3

1.3

–2

–6

1995–2000

1

4.3
0.7

0.1

1995–2000

1.9

2.1

2.1

2.3 3.1

–6.0

2.5

2.4

2

2.4

–2

Thailand1.6

Indonesia–4

1990–1995

–1

2.1

%

0.1 0.8
0.1 0.1 0.5 0.1–0.2

1985–19902

1995–2000

1.2 1.5
0.2

01980–19853

1990–1995

1

3.0

2.9

1.1
0.4 0.3 0.3 0.3 0.3 0.7 0.5 0.1

1.7 2.1 0.8

6
3.2

0
3.2

8

4

1990–1995

2.4

2.2

2.0

2.3

6
3.2 3.1 2.1

4

2

%

5

0

2
3.5

1975–1980

6

2

3.1

0

4

Korea

%

4

1970–1975

2005–2013

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980

1970–1975

Sri Lanka%

1985–1990

4.3
3.0
2.0
1.1
0.1 0.1 0.1 0.2 0.3
0.1 0.1 0.1 0.1–0.5–1.0 –0.3
–1.8
–2.1
–3.5

–2

0.9
3.7

8

1.0 1.4 0.7
0.7 0.6 0.9 0.9 0.1 0.1 0.1
0.2–0.7 –1.0

4

2.0

Pakistan

3
4.1

1

2.4 1.5

5.0

2–2
1980–1985

3.9

1.9 2.4 1.3

2
0

4.3
1.6

6

8

2.0 1.8 1.8 2.1 1.4 0.8
0.6 0.7
0.4 0.2 0.2 0.4 0.3 0.4 0.4 0.2
0.1
–0.4
–0.7

5

1.2

4.1

1.8

%

6

1985–1990

0.6

1975–1980

Mongolia

5.3

3.3 3.8
2.4
1.5
0.1 0.1 0.2 0.6 0.5%

0.2

1970–1975

2005–2013

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980–1

1970–1975–128

1980–1985

5.1

2

0

4.1

India

0.9

3

–9

3.2 3.0

3
0.9

6

1

%

1975–1980

1970–1975

2005–2013

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980

1970–1975

–9.9

3.8

Japan

6

4

–6

1.7 1.7
3.3 2.7 3.0
1.7
1.6 2.1
0.9 0.8
0.2 0.2 0.3 0.4 0.4 0.6 0.5 0.3

%

5

0

0.5 1.2 4.3

0.2 3.2

4

4

0.8

0.2

–3

3.1
3.6 3.9
2.0
0.5
1.9 1.8 0.5 0.1
0.1
0.9
0.1
–1.4 –0.6 –1.1
0.1 –0.5
–0.1

2

5

3.3

–2.3

Iran

4

4.0

3.4

3.2

8

1.5

0

3.9

–1

1995–2000

–2.1

4

0

1990–1995

–1.1

7.1

6

3

6

–4

1985–1990

–0.5

6

8

%

3.5

5.1

1.0
0.5 0.3
0.1
0.1 0.2–0.1–0.4 –0.2
–0.4

9

0

Hong Kong9

0.7 1.2
0.6 0.8

%

–2

2.0

6

–1

2

1980–1985

–1

2.6

1

4

2.3

0.2 0.5
0.1 0.1 0.1 0.2
–0.6

%

2

0

2.1

1975–1980

3

8
7

6

4.9

2

1970–1975

2005–2013

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980

1970–1975

Fiji

%

–3

2.8

3

ROC

%

10

0

–2

–2

5

1

–1.8

China

%

12

4

1.8

1.4
0.3 1.3
1.0
0.6
0.2 0.5 0.1 0.1 0.1 0.1 0.1

0Cambodia%

Labor productivity

Figure 68 Decomposition of Labor Productivity Growth, 1970–2013
Source: APO Productivity Database 2015.

89©2015 Asian Productivity Organization

Bangladesh%5 Productivity

Bangladesh

1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2013
1970–2013

−1.2
2.1
0.8
2.2
2.8
2.4
−0.8
1.8
2.8
1.4

Capital deepeningIT
Non-IT
0.0 (−2)
0.6 (−52)
0.0
(1)
0.2
(11)
0.0
(4)
0.5
(59)
0.1
(3)
1.3
(61)
0.1
(3)
1.4
(51)
0.1
(5)
1.9
(80)
0.1 (−10)
1.0 (−123)
0.1
(6)
1.6
(91)
0.2
(6)
1.8
(65)
0.1
(5)
1.1
(83)

China

1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2013
1970–2013

3.7
4.2
5.9
3.4
10.6
7.2
8.7
10.3
7.6
6.8

0.0
0.0
0.0
0.1
0.1
0.2
0.6
0.5
0.4
0.2

Fiji

1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2013
1970–2013

−0.5
−0.5
−1.2
2.3
−0.4
1.2
1.5
0.1
2.0
0.4

0.0 (−5)0.0 (−3)0.0 (−2)0.1(3)0.2 (−39)
0.0 (−3)
0.1
(4)
0.1
(48)
−0.1 (−4)
0.0
(9)

0.0
(−8)
0.6 (−115)
0.8 (−67)−0.4 (−17)
−0.2
(40)
0.5
(44)
0.3
(17)
0.0 (−24)−0.3 (−17)
0.2
(39)

−0.5
−1.1
−2.1
2.6
−0.4
0.7
1.2
0.1
2.4
0.2

(113)
(218)
(169)
(113)
(100)
(59)
(80)
(76)
(121)
(52)

India

1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2013
1970–2013

0.0
−0.4
2.8
3.3
2.4
3.9
3.0
7.0
6.0
3.0

0.0
0.0
0.0
0.0
0.1
0.1
0.1
0.2
0.2
0.1

(97)
(−5)
(1)
(1)
(2)
(3)
(3)
(3)
(4)
(3)

0.7
(-)
0.6 (−174)
0.9
(31)
0.9
(27)
1.0
(42)
1.4
(36)
0.7
(22)
3.1
(44)
3.2
(54)
1.3
(44)

−0.7
−1.0
1.9
2.4
1.3
2.4
2.3
3.7
2.5
1.6

(-)
(279)
(68)
(72)
(56)
(60)
(75)
(53)
(42)
(54)

Iran

Table 14 Role of TFP and Capital Deepening in Labor Productivity Growth, 1970–2013

1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2013
1970–2013

7.5
−6.0
1.4
−1.4
1.4
0.7
3.7
4.3
−3.0
1.2

0.1
0.0
0.0
0.0
0.0
0.1
0.1
0.1
0.1
0.1

(1)
(−1)
(2)
(−3)
(3)
(9)
(4)
(3)
(−3)
(6)

3.6
(48)
3.9 (−64)
0.9
(64)
−1.4
(98)
−0.6 (−41)−1.1 (−148)
0.5
(13)
2.5
(59)
1.0 (−34)
1.0
(90)

3.9
−9.9
0.5
−0.1
1.9
1.8
3.1
1.6
−4.1
0.0

(51)
(165)
(34)
(4)
(137)
(239)
(83)
(38)
(137)
(4)

0.5
1.2
4.3
0.9
7.1
3.2
4.0
4.2
2.2
3.1

(13)
(28)
(74)
(27)
(68)
(45)
(46)
(41)
(29)
(46)

Cambodia

(86)
(71)
(25)
(71)
(31)
(52)
(47)
(54)
(66)
(51)

1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2013
1970–2013

4.4
2.4
2.9
4.5
5.6
3.7

0.1
0.1
0.1
0.2
0.2
0.1

(2)
(4)
(3)
(4)
(3)
(3)

−0.6
0.2
0.5
1.9
2.1
0.9

(−13)
(9)
(18)
(42)
(38)
(25)

4.9
2.1
2.3
2.5
3.3
2.7

(111)
(87)
(79)
(55)
(59)
(72)

ROC

(153)
(88)
(36)
(37)
(46)
(15)
(233)
(3)
(29)
(12)

TFP

1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2013
1970–2013

5.6
7.2
5.0
6.9
5.7
5.2
3.4
3.9
1.1
5.1

0.4
0.3
0.3
0.3
0.3
0.7
0.5
0.1
0.1
0.3

(8)
(5)
(6)
(4)
(5)
(13)
(16)
(2)
(5)
(7)

5.0
3.7
3.1
2.3
2.9
3.0
2.1
1.6
0.4
2.8

(88)
(51)
(61)
(34)
(52)
(58)
(60)
(41)
(35)
(55)

0.2
3.2
1.6
4.3
2.4
1.5
0.9
2.2
0.7
1.9

(4)
(44)
(32)
(62)
(43)
(29)
(25)
(57)
(60)
(38)

Hong Kong

3.2
3.0
1.5
2.4
3.3
3.8
4.1
5.5
5.0
3.5

−1.9
1.8
0.3
0.8
1.3
0.4
−1.8
0.1
0.8
0.2

Capital deepeningIT
Non-IT

1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2013
1970–2013

3.3
7.4
3.8
6.6
4.2
0.0
3.1
3.2
2.0
3.8

0.2
0.2
0.3
0.4
0.4
0.6
0.5
0.3
0.2
0.3

(5)
(3)
(7)
(7)
(10)
(-)
(16)
(9)
(12)
(9)

1.6
2.1
3.3
2.7
3.0
1.7
0.9
0.9
0.6
1.9

(49)
(28)
(88)
(41)
(71)
(-)
(30)
(30)
(28)
(51)

1.5
5.1
0.2
3.5
0.8
−2.3
1.7
1.9
1.2
1.5

(46)
(69)
(5)
(53)
(19)
(-)
(54)
(61)
(61)
(40)

Indonesia

(1)
(1)
(1)
(2)
(1)
(3)
(7)
(5)
(5)
(3)

Labor
Productivity

TFP

1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2013
1970–2013

4.4
4.6
0.9
3.3
6.3
−1.8
3.4
2.2
4.0
3.0

0.0
0.1
0.1
0.1
0.2
0.1
0.1
0.2
0.2
0.1

(1)
(3)
(11)
(3)
(3)
(−6)
(4)
(7)
(5)
(4)

2.1
(46)
3.1
(69)
2.6 (291)
1.2
(36)
2.8
(44)
2.0 (−112)
1.4
(40)
0.9
(40)
2.1
(51)
2.0
(67)

2.4
1.3
−1.8
2.0
3.3
−4.0
1.9
1.2
1.8
0.9

(53)
(29)
(−202)
(61)
(53)
(218)
(56)
(53)
(44)
(29)

Japan

Labor
Productivity

1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2013
1970–2013

4.8
2.8
3.8
4.2
2.1
2.0
1.8
1.1
0.8
2.7

0.4
0.2
0.2
0.4
0.3
0.4
0.4
0.2
0.0
0.3

(8)
(7)
(6)
(10)
(17)
(18)
(23)
(19)
(4)
(11)

5.1
2.0
1.8
1.8
2.1
1.4
0.6
0.4
−0.3
1.8

−0.7
0.6
1.8
2.0
−0.4
0.2
0.8
0.4
1.0
0.6

(−14)
(20)
(47)
(47)
(−18)
(11)
(43)
(40)
(131)
(23)

(107)
(73)
(47)
(43)
(102)
(71)
(34)
(41)
(−35)
(66)

©2015 Asian Productivity Organization

5.5 Energy Productivity
In 2012, in order to produce 40.5% of the world output in Asia30 (18.1% and 16.5% in the EU28 andthe US, respectively), 41.2% (13% and 16%) of world energy was consumed and 48.6% (10.7% and15.6%) of world CO2 was emitted, as shown in Figure 69. This implies that Asia has lower energy productivity (defined as a ratio of output per energy consumption) and higher carbon intensity of energy
at the aggregate level. It is key to improve energy productivity and carbon intensity in the growing
economies of Asia in order to reduce CO2 emissions in the world in the long run.
The average level of energy productivity in Asia was inferior to the EU28 and the US by 31% and 5%,
respectively, in 2012. There is a considerable diversity in Asia however, reflecting the differences in

90

(1)
(4)
(3)
(−4)
(−3)
(7)
(6)
(6)
(3)
(5)

3.8
4.1
4.1
0.1
1.1
−0.5
−1.8
2.2
4.3
1.8

(76)
(129)
(105)
(−6)
(−47)
(−27)
(−68)
(43)
(41)
(66)

1.2
−1.0
−0.3
−2.1
−3.5
2.0
4.3
2.6
6.1
0.8

(23)
(−33)
(−8)
(110)
(150)
(120)
(161)
(50)
(57)
(28)

Philippines

1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2013
1970–2013

1.1
3.1
−4.6
3.7
0.7
2.4
1.3
2.7
3.7
1.5

0.1
0.1
0.1
0.1
0.0
0.4
0.5
0.2
0.1
0.2

(5)
(2)
(−2)
(3)
(6)
(17)
(36)
(6)
(2)
(12)

0.5
2.1
1.3
0.0
0.8
1.7
0.0
0.5
0.7
0.9

(47)
(69)
(−29)
(0)
(120)
(72)
(−2)
(17)
(18)
(59)

0.5
0.9
−6.0
3.6
−0.2
0.3
0.9
2.1
3.0
0.4

(48)
(28)
(132)
(97)
(−26)
(12)
(66)
(76)
(80)
(30)

1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2013
1970–2013

−0.1
2.9
1.4
3.0
4.3
1.4
1.7
5.3
4.9
2.6

0.0 (−34)
0.0
(2)
0.1
(5)
0.0
(1)
0.0
(1)
0.1
(9)
0.2
(12)
0.3
(5)
0.1
(1)
0.1
(4)

−0.3
0.8
1.2
1.0
−0.1
−0.6
0.8
1.6
1.9
0.6

(556)
(26)
(90)
(34)
(−2)
(−41)
(47)
(30)
(40)
(25)

0.3
2.1
0.1
1.9
4.3
1.8
0.7
3.4
2.9
1.9

(−422)
(72)
(5)
(66)
(101)
(131)
(41)
(65)
(59)
(72)

1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2013
1970–2013

−0.6
1.4
1.0
1.3
4.7
6.7
6.7
2.4
4.3
3.0

0.0
0.0
0.0
0.1
0.1
0.3
0.3
0.4
0.4
0.2

−0.6 (110)0.2(12)−1.6 (−162)
0.5
(35)
1.1
(22)
2.9
(43)
2.9
(43)
2.7 (113)
2.8
(65)
1.1
(37)

0.1
1.1
2.5
0.7
3.6
3.5
3.6
−0.8
1.1
1.8

(−14)
(84)
(258)
(54)
(75)
(53)
(53)
(−32)
(26)
(58)

(4)
(3)
(4)
(11)
(3)
(4)
(4)
(19)
(9)
(6)

Malaysia

0.1
0.1
0.1
0.1
0.1
0.1
0.2
0.3
0.3
0.1

Pakistan

5.1
3.1
3.9
−1.9
−2.3
1.7
2.7
5.1
10.7
2.8

4.0
4.5
2.2
3.0
6.0
0.8
3.5
1.4
2.3
3.1

1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2013
1970–2013

0.2
3.7
4.9
4.0
4.1
1.3
3.3
0.1
1.7
2.6

0.0
0.0
0.0
0.1
0.1
0.0
0.1
0.0
0.0
0.0

(8)
(0)
(0)
(2)
(2)
(1)
(2)
(39)
(1)
(1)

0.2 (120)
1.2
(34)
1.5
(30)
1.7
(44)
2.1
(50)
0.5
(41)
0.1
(2)
−0.1 (−127)−0.2 (−15)
0.8
(31)

0.0
2.4
3.5
2.2
2.0
0.8
3.2
0.2
1.9
1.8

(−27)
(66)
(70)
(55)
(48)
(58)
(97)
(188)
(114)
(68)

Singapore

Mongolia

1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2013
1970–2013

1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2013
1970–2013

Capital deepeningIT
Non-IT
0.0
(1)
2.8
(70)
0.1
(2)
2.9
(65)
0.1
(4)
4.5 (203)
0.1
(4)
1.0
(33)
0.2
(4)
4.0
(67)
0.4
(50)
2.7 (329)
0.6
(18)
1.3
(37)
0.5
(36)
0.3
(21)
0.3
(14)
0.8
(35)
0.3
(9)
2.3
(75)

1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2013
1970–2013

4.5
3.2
3.3
3.4
3.6
3.1
3.7
0.8
1.5
3.1

0.5
0.3
0.5
0.6
0.6
0.5
0.5
0.2
0.3
0.5

(11)
(9)
(15)
(18)
(18)
(18)
(14)
(31)
(22)
(15)

6.0
2.7
4.0
0.5
1.1
2.9
1.7
−0.7
0.9
2.2

(134)
(85)
(120)
(15)
(31)
(95)
(46)
(−94)
(56)
(71)

−2.0
0.2
−1.2
2.3
1.8
−0.4
1.5
1.3
0.3
0.4

(−45)
(7)
(−35)
(67)
(51)
(−12)
(40)
(163)
(22)
(14)

Thailand

5.5
4.4
6.7
6.6
6.3
5.3
4.2
4.8
2.2
5.2

Sri Lanka

Labor
Productivity

1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2013
1970–2013

5.9
1.5
3.8
5.4
7.7
0.4
3.3
2.2
3.7
3.8

0.1
0.1
0.2
0.2
0.5
0.3
0.1
0.3
0.3
0.2

(1)
(7)
(4)
(4)
(7)
(67)
(4)
(16)
(9)
(6)

2.3
−0.2
1.4
0.8
4.7
2.5
−0.2
0.4
1.1
1.4

(40)
(−11)
(37)
(14)
(60)
(559)
(−7)
(21)
(30)
(38)

3.5
1.5
2.3
4.4
2.5
−2.3
3.4
1.4
2.3
2.1

(59)
(104)
(59)
(82)
(33)
(−525)
(103)
(64)
(61)
(56)

US

Korea

1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2013
1970–2013

Capital deepeningIT
Non-IT
0.2
(3)
4.4
(80)
0.4
(9)
5.4 (121)
0.3
(5)
3.2
(47)
0.5
(8)
2.9
(44)
0.4
(6)
3.8
(60)
0.6
(11)
3.1
(59)
0.5
(13)
2.0
(48)
0.2
(4)
2.1
(44)
0.1
(4)
1.4
(66)
0.4
(7)
3.2
(62)

Vietnam

5.5 Energy Productivity

Labor
Productivity

1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2013
1970–2013

1.9
1.0
1.9
1.3
1.5
2.3
2.5
1.6
0.4
1.7

0.2
0.2
0.4
0.4
0.4
0.6
0.6
0.4
0.1
0.4

(11)
(20)
(20)
(32)
(25)
(28)
(22)
(23)
(28)
(22)

1.0
0.2
0.4
0.3
0.2
0.1
0.9
1.0
−0.3
0.5

(55)
(19)
(23)
(19)
(16)
(6)
(34)
(66)
(−93)
(28)

0.6
0.6
1.1
0.6
0.9
1.5
1.1
0.2
0.6
0.8

(34)
(61)
(57)
(49)
(59)
(66)
(44)
(11)
(164)
(50)

TFP
0.9
−1.3
3.2
3.1
2.1
1.6
1.7
2.5
0.7
1.7

(17)
(−30)
(48)
(48)
(34)
(30)
(39)
(52)
(30)
(32)

TFP
1.1
1.5
−2.4
1.9
1.7
−2.3
1.6
0.6
1.2
0.5

(28)
(33)
(−106)(63)(29)(−279)
(45)
(43)
(51)
(17)

5

industrial structure and energy efficiencies of industries and household among economies. Table 15
presents the snapshot level comparisons of energy productivity since 1980. Japan’s energy productivity level is almost equivalent to that in the EU15 since 2000, and 33% and 86% higher than the US andChina, respectively, in 2012.
Figure 70 placed countries on the two partial productivity indicators of labor and energy, measured in2012. Less-developed countries with lower labor productivity (such as the Philippines, Sri Lanka, andBangladesh) tend to have higher energy productivity. One of the effective strategies to improve laborproductivity in such countries is to expand the manufacturing sector (as shown in Figure 73 in Section

91©2015 Asian Productivity Organization

Unit: Average annual growth rate (percentage), contribution share in parentheses.
Source: APO Productivity Database 2015.

5 Productivity

6.1, there is a positive correlation
between the TFP growth and the
manufacturing share). This frequently follows the deteriorationin energy productivity.

Energy Consumption

CO2 Emission

Japan

Japan

Others
27 %

India

APO20
19 %

Asia
44 %

Asia 30
41%

APO20
18 %

Others
22 %
EU28
11 %

India
Asia
52 %

Asia 30
49 %

EU15
9%

As a next stage of economic
EU15
China
11 %
US
Other
China
growth, well-developed countries
US
EU28
16 % Other
Asia
16 %
13 %
%
3
Asia
will be able to pay more attention
3%
to improving energy productivity
by abolishing implicit or explicit
subsidies on energy prices andFigure 69 Shares of Asia in World Energy Consumption and
CO2 Emission, 2012levying heavier taxes on energy
consumptions. The C-shape relaSources: IEA, CO2 Emissions from Fuel Combustion 2014; IEA, Energy Balances of OECDCountries 2014; IEA, Energy Balances of non-OECD Countries 2014.
tionship found between laborand energy productivities corresponds to the so-called Environmental Kuznets curve, as an inversed U-shape relationship between environmental quality (at the yaxis) and economic development (at the x-axis).
Figure 71 decomposes the sources of CO2 emission growth (from fuel combustion) in Asian countries
during 2000–2012, based on the so-called Kaya identity. The growth in CO2 emissions is decomposedto three components: changes in real GDP; carbon intensity of energy; and energy intensity of GDP

Table 15 Energy Productivity Levels, 1980, 1990, 2000, 2010, and 2012

_GDP at constant basic prices per energy consumption (toe), using 2011 PPP, reference year 2013©2015 Asian Productivity Organization

1980

1990

(%)

(%)

29.4 100.0
20.3 69.3
12.4 42.4
12.4 42.3
12.1 41.2
11.6 39.6
11.1 37.9
10.8 36.9
10.3 35.3
10.3 34.9
9.2 31.3
7.9 26.9
7.1 24.3
6.1 20.8
5.2 17.8
4.2 14.5
3.6 12.3
2.3 8.0

18.5
31.8

Hong Kong
SingaporeJapan
Malaysia
ThailandPhilippines
Bangladesh
ROC
Indonesia
Sri Lanka
IranPakistan
Korea
India
Vietnam
Nepal
Mongolia
China(reference)
US
EU15

7.2
10.8

24.4
36.8

27.7
44.0

Australia
Turkey

8.5
13.3

28.8
45.2

Hong Kong
Singapore
Iran
Malaysia
Philippines
ThailandBangladeshJapan
Indonesia
Sri Lanka
ROC
Pakistan
Korea
India
Vietnam
Nepal
China

27.5 100.0
22.7 82.6
14.1 51.1
13.2 47.9
11.3 41.2
10.8 39.2
10.5 38.2
10.1 36.6
9.1 33.0
8.4 30.5
7.8 28.4
6.3 22.9
5.8 21.1
5.0 18.1
3.8 13.7
3.4 12.4
1.3 4.7(reference)
US
EU15

5.1
8.8

Australia
Turkey

7.6
12.1

2000
Singapore
Hong Kong
Philippines
Bangladesh
ROC
Sri Lanka
Japan
Malaysia
ThailandIndonesia
Pakistan
India
Iran
Vietnam
Korea
Mongolia
Cambodia
China(reference)
US
EU15
EU28
Australia
Turkey(%)

24.4 100.0
24.1 98.7
13.4 54.9
13.3 54.4
12.5 51.4
12.3 50.6
12.2 50.0
11.8 48.5
10.8 44.1
10.3 42.2
8.9 36.5
8.2 33.5
8.0 32.8
7.2 29.7
7.1 29.2
6.9 28.1
5.5 22.5
5.1 21.1
8.4
12.1
11.8
9.8
13.2

34.4
49.7
48.2
40.0
54.1

2010
Hong Kong
Singapore
Philippines
Sri Lanka
BangladeshJapan
Indonesia
ROC
Malaysia
India
PakistanThailand
Korea
Iran
Cambodia
Mongolia
Vietnam
China(reference)
US
EU15
EU28
Australia
Turkey(%)

38.9 100.0
23.1 59.3
21.5 55.2
17.2 44.3
15.2 39.1
14.0 35.9
13.9 35.7
13.6 34.8
13.0 33.3
11.3 29.0
10.6 27.1
10.1 25.9
9.0 23.0
8.6 22.0
8.3 21.3
7.8 20.0
7.6 19.5
7.5 19.2
10.2
13.6
13.2
12.0
14.4

26.2
35.0
33.9
30.9
37.1

2012
Hong Kong
Singapore
Philippines
Sri Lanka
Bangladesh
ROC
Japan
Indonesia
Malaysia
India
PakistanThailand
Korea
Cambodia
Mongolia
Iran
China
Vietnam(reference)
US
EU15
EU28
Australia
Turkey(%)

40.0 100.0
24.4 61.1
23.5 58.7
17.9 44.7
15.9 39.8
14.9 37.3
14.7 36.8
14.7 36.7
12.8 32.0
11.4 28.4
11.0 27.4
10.1 25.2
9.0 22.5
8.6 21.4
8.5 21.2
7.9 19.9
7.9 19.8
7.7 19.2
11.1
14.5
14.1
12.3
14.3

27.7
36.4
35.3
30.8
35.8

Unit: Thousands of US dollars per toe (as of 2013).
Sources: Official national accounts in each country, including author adjustments; IEA, Energy Balances of OECD Countries 2014; IEA, Energy Balances of non-OECD Countries 2014; APO Productivity Database 2015.

92

5.5 Energy Productivity(the inverse of energy productivity). In many
countries, the production expansion (real GDP
growth) is the most significant factor to explain
the growth of CO2 emissions. With an exception of Thailand, Iran, and Singapore, energy
productivity has been improved in many Asian
countries in this period, but these improvements are not enough to offset an expansionof energy consumption (except in the Philippines, Hong Kong, and Japan).

Labor productivity(US dollar/hours worked)

70

US
60

50

Australia

EU15

Singapore

5

ROC
Japan

40

30
Iran

Korea

TurkeyMalaysia

Italy
UK
EU15
CanadaGermany
EU28
US
FranceJapan
Singapore
ROC
Hong Kong
Australia
Philippines
Pakistan
Korea
Indonesia
TurkeySri Lanka
Nepal
Mongolia
Thailand
Malaysia
Iran
Cambodia
India
Bangladesh
China
Vietnam

20
On the other hand, in many Asian economies,
the carbon intensity of energy has increased,
Sri Lanka
Thailand10
Indonesia
mainly due to an expansion of coal consumpChina Pakistan
Philippines
India
Vietnamtion. Japan achieved some improvement in
Bangladesh
Cambodia0energy efficiency in this period, but the carbon
0
5
10
15
20
25
30
Energy productivity
intensity of energy had to be increased due to(Thousands of US dollars/toe)a very low operation rate of nuclear power
plants after the Fukushima Daiichi nuclear diFigure 70 Labor Productivity and Energy Prosaster in 2011.78 Singapore realized a large
ductivity, 2012
improvement in carbon intensity of energy by
Sources: Official national accounts in each country, including author
the shift from oil to LNG in electricity power
adjustments; IEA, Energy Balances of OECD Countries 2014; IEA, Energy
Balances of non-OECD Countries 2014; APO Productivity Databasegeneration.79 This helped to offset the increas2015.
es in CO2 emission accompaniedby strong economic growth, re%gardless of no considerable im14
GDP
Carbon intensity of energy
Energy intensity of GDP
CO2
provement in energy productivity.
12
2.3
Decoupling in the growths of
10
GDP and CO2 emission seems to
3.4
1.8
8
1.8
3.0
be realized in only a few devel1.4
6
1.2
1.6
0.1 0.1
oped countries, especially in the
9.7
0.6
2.0
1.5
0.4
0.0
4
7.6
7.6
0.1
6.9
6.7
EU. However, this may be due to
5.6
5.4
5.5
5.2
4.2 4.5 4.7
4.6 4.9 4.5
4.1
2
0.2
3.0 3.9 3.9
3.6
the production shift to Asian coun1.0
1.7 1.3 1.1 2.0 1.1 1.6
0.7 1.2
0.1
0 −0.5−0.1−0.1−0.5−0.4−0.2−0.4−0.7−0.8
−0.5
−0.7
−1.0
tries. For sustainable growth of the
−1.3
−1.5
−1.6−1.6−1.6−1.8−1.8−1.9−1.9−1.7−1.5−3.0−0.5−2.3−1.5−1.5−2.7−3.1 −2.9
−3.6
−3.7
−2
−4.2
−1.5
−4.4
−1.2
−4.7
world economy, improvements−1.8−4
in energy productivity and carbon−6
intensity of energy are recognized as one of the important
policy targets in Asian economies.

Figure 71 Sources of CO2 Emission Growth, 2000–2012

78: According to the FEPC (The Federation of Electric Power Companies of Japan), the rate of utilized capacity of nuclear power plants
was 67.3% in the fiscal year 2010 (the share of nuclear in power generation was 28.6%), but after the disaster, 23.7% in 2011, 3.9%
in 2012, 2.3% in 2013, and 0.0% in 2014.
79: In Singapore, the share of natural gas in electricity power generation reached to 84.3% in 2012 from 18.5% in 2000, compared tothe decrease in the share of oil in power generation from 80.0% in 2000 to 13.0% in 2012 (IEA, Energy Balances of non-OECD Countries 2014.).

93©2015 Asian Productivity Organization

Sources: Official national accounts in each country, including author adjustments; IEA,
Energy Balances of OECD Countries 2014; IEA, Energy Balances of non-OECD Countries
2014; IEA, CO2 Emissions from Fuel Combustion 2014.

5 Productivity

Box 6

Sensitivity of TFP Estimates

TFP computations based on the growth accounting framework depends on data that is sometimes difficult
to observe. One difficult data point is calculating the wages for the self-employed and unpaid family workers. As a crude approximation in this report, it is assumed that per-worker wages for the self-employed andcontributing family workers are 20–80% of the per-worker wage for employee in the countries where theappropriate wage data is not available. This approximation is made in order to estimate the labor compensation for total employment. The future review on this assumption affects TFP estimates directly throughthe revision of factor income shares and indirectly through the estimates of the ex post rate of return andthus the aggregate measure of capital services.
The right panel of Figure B6.1 presents the labor income share (the ratio of compensation for employees tothe basic-price GDP) based on the official national accounts (including author adjustments in basic-priceGDP for some countries) in 19 Asian countries and the US in 2013. The left panel of the figure illustrates theemployee share to total employment. There is a large divergence in labor income
100 % 80
60
40
20
0
0
10 20 30 40 50 60 %56
57
Sri Lanka
share for employees among
88
57
China
the Asian countries. This does
94
54
US
94
53
Hong Kong
not necessarily reflect differ87
52
Japan
ences in the number of em44
52
Fiji
73
48
Korea
ployees in total employment.
79
45
ROC
Although Malaysia and Mon94
44
Singapore
41
42
Pakistangolia have a high employee
37
42
Vietnamshare of 78% and 70%, the la58
37
Philippines
78
35
Malaysia
bor income share is only 35%
15
35
Bangladesh
and 29% in 2013, respectively.
44
34
ThailandIndia
Indonesia
Mongolia
Cambodia
Iran

70
24
54

33
32
29
22
21

Share of employees to total employment

Labor income share for employees

Figure B6.1 Labor Income Share for Employees, 2013
Sources: Official national accounts in each country, including author adjustments.

%

4

3.9

3

3.1

2

2.3

3.1
2.7
2.3

2.5
2.1
1.7

2.7
1.9

2.1
1.8
1.4

2.5
1.7
0.8

2.0
1.6
1.2

2.0
1.5
1.1

0

1.4
0.9
0.4

1.0
0.8
0.6

1.1
0.8
0.5

1.1
0.6
0.1

Japan

2.0
1.8
1.6

Mongolia

2.1
1.9
1.7

TFP(vL+10%)
TFP
TFP(vL−10%)

US

1.2

1

1.0
0.5
0.0

0.7 0.9
0.5
0.4 0.4 0.3
0.2 0.2 0.2
0.2 −0.1 0.2 −0.2 0.0
−0.2

−1
Iran

Bangladesh

FijiSingapore

Philippines

Malaysia

Indonesia

Hong Kong

India

Korea

Vietnam

Pakistan

Sri Lanka

Thailand

ROC

Cambodia

94

50

China

©2015 Asian Productivity Organization

19

Figure B6.2 illustrates the sensitivity of TFP estimates bychanging the factor income
share during the period from1970 to 2013. In general, thegrowth rate of capital input ishigher than that of labor input, so the higher income
share of labor results in higher
estimates of TFP growth. In
other words, labor productivity is improved much fasterover a given period than capital productivity, the growth ofwhich tends to be frequently
negative (see Figures 45 and61). The TFP estimate reflects
the improvement of laborproductivity more when thelabor income share increases.
In Malaysia, with TFP growthof 0.5% on average during theperiod 1970–2013, the true
estimate could be 1.0% if thecurrent labor income share isunderestimated by 10%.

Figure B6.2 Sensitivity of TFP Estimates by the Change ofIncome Share, 1970–2013
Source: APO Productivity Database 2015.
Note: The labor inputs for Fiji and Mongolia are defined by number of employment.

6 Industry Perspective
This chapter provides the industry origins of economic growth and labor productivity improvementin Asian economies. Industry decomposition allows an insight into the source of a country’s economic dynamics, which, in turn, determines its overall performance and characteristics, its strengths, andits vulnerabilities. On one hand, a broad industry base reflects diversification and sophistication in theeconomy, and in turn is more resourceful in weathering economic shocks. On the other hand, relianceon a narrow industry-base leaves economies more vulnerable to shocks and more susceptible to volatility. Industry structure is a key indicator of an economy’s stage of development. As a rough sketch, at
one end of the spectrum are predominantly agricultural- and rural-based economies, while at theother end the agriculture sector is negligible and the service sector is the dominant economic base.
The middle realm is occupied by manufacturing as the main driver of economic growth. As an economy matures, its depth and sophistication increases and its resilience to economic shocks should be
strengthened accordingly. Furthermore, the different composition of economic activities among
countries is also one of the main sources of the huge gap in average labor productivity at the aggregate level, as observed in Chapter 5. By analyzing the industry structure of Asian economies, one canclearly trace the path of economic development and identify countries’ respective stages based on
their characteristics.80

6

6.1 Output and Employment
Table 7 (p. 30) in Section 3.2 introduces a country grouping according to stages of development (asmeasured by per capita GDP relative to the US). Table 16 regroups
Table 16 Country Groups Based on the Current Economic
countries based on the same set
Level and the Pace of Catching Upof criteria as in Table 7, but applies

Comparing Table 16 with Table 7,
it is notable that 12 of the 30 Asian
economies have moved up in income group, whereas 15 havestagnated. Among them, themost upwardly mobile countries
are the ROC and Korea, both in
the fast catch-up group. They
have moved up two income levels
during the past four decades to_Level and average annual growth rate of GDP at constant market
prices, using 2011 PPP
Per capita
GDP level to
the US in 2013
(L1)
60% <

Annual rate to catch-up to the US
(C1)
>3%

(C2)
1% <–< 3%(C3)
0% <–< 1%

(C4)
< 0%

ROC, Korea,
Singapore

Hong KongJapan, EU15,
Oman

Bahrain, Brunei,
Kuwait, Qatar,
Saudi Arabia,
UAE, Australia

Malaysia,
ThailandTurkeyIran(L2)
30% <–< 60%(L3)
10% <–< 30%
(L4)
< 10%

China

Bhutan, India,
Indonesia, Mongolia,
Sri Lanka, Vietnam

Cambodia

Lao PDR, Myanmar

Fiji, Philippines
Bangladesh,
Nepal,
Pakistan

Sources: Official national accounts in each country, including author adjustments.
Note: The annual catch-up rates are based on the data during 1970–2013. The starting
years for some countries are different due to data availability: Cambodia (1987–), theLao PDR (1984–), and Nepal (1974–).

80: Constructing the industry origins of labor productivity growth requires confronting a large volume of data from different sources.
Issues of data inconsistency arising from fragmentation of national statistical frameworks can present enormous hurdles toresearchers in this field. The industry data in this chapter is mainly based on official national accounts. Where back data is notavailable, series are spliced together using different benchmarks and growth rates. Data inconsistencies in terms of concepts,
coverage, and data sources have not been fully treated although levels of breakdown are deliberately chosen to minimize thepotential impact of these data inconsistencies. In this sense, the industry data in APO Productivity Database should be treated asa work in progress and it is difficult to advise on data uncertainty. These data will be further developed and examined in the near
future. Readers should bear these caveats in mind in interpreting the results.

95©2015 Asian Productivity Organization

it to 2013 income levels. The difference in relative per capita GDP
between the two tables reflects
the impact of their catch-up efforts since 1970, or the year of firstrecorded data.

6 Industry Perspective

join Japan in the top income group.
Singapore and Hong Kong havealso moved up one income groupto the L1 level. Malaysia and Thailand have moved up one level toL2. Both China and India have
moved up to L3, although they are
in different catch-up groups. Indonesia and Vietnam (in Group-C2)
have also improved their income
level to L3. This means the numberof lowest-income countries has decreased from 12 at the start of theperiod to six (Cambodia, the Lao
PDR, Myanmar, Bangladesh, Nepal,
and Pakistan) as of 2013. As expected, there were few movements incountry groups with little or nocatch-up.

MyanmarCambodia
Nepal
Pakistan
Lao PDR
VietnamIndia
BhutanMongolia
Indonesia
Bangladesh
Fiji
ThailandPhilippines
Sri Lanka
China
Malaysia
TurkeyIranAustralia
Korea
Saudi Arabia
ROC
US
Japan
Oman
BruneiUAE
Bahrain
Kuwait
Qatar
Hong Kong
Singapore
100 %

1
20
1 5
20
14
4
1
1 8
15
8
8
16
10
1 6 1 7
17
9
12
14
3
22
20
11
7
25
14
15
15
19
4 7
24
8 4 7
12
11
3 5
17
4
12
18
17
11
2
2 8
17
7
19
18
13
15
3 9
15
8
10
8
17
18
12
17
15
7
11
15
10 2 6
17
11
1 10
14
7
9
14
23
11
2
1 8
15
11
11
13
18
21
23
19
16
17
13 1
16
14
4
33
18
7
13
11
28
14
3 6
19
6
20
11 1
20
12
2 10
20
13
14
11 2
18
10
5
2 7
12
7
12
10
30
15
10
2 4
17
7
13
9
24
13
3 5
16
16
15
8 2
17
18
18
15
8
17
7
12 2 8
11
12
8
20
2 9
8 3 8
30
2 5
12
7
21
2
31
19
44
9
5
10
2
10 1 5
15
13
21
4
19
2
29
21
12
8
32
13
12 2 4
27
2 6
14
10
16
1
20
30
47
7 5
8
1
10 1 61652
1
12 1 3 4 4 5
18
37
12
8
16
1
8 2 9
8
27
1 6 6
6
20
15
18
58
10
6 12 3 5
15
53
12
10
5 6 3
10
29
10
38
12 4
17
1 5
21
11
32
18
11
35
34
33

0

10

20

30

40

50

60

1. Agriculture
3. Manufacturing
5. Construction
7. Transport, storage, and communications
9. Community, social, and personal services

70

80

90

Group-L4

Group-L3(except for
Bangladesh,
Myanmar, and,Thailand)Group-L2(except for
China)

Group-L1

2. Mining
4. Electricity, gas, and water supply
6. Wholesale and retail trade, hotels, and restaurants
8. Finance, real estate, and business activities©2015 Asian Productivity Organization

Countries at the lower rungs of thedevelopment ladder tend to have abigger agriculture sector as a shareof value added.81 Figure 72 shows
the industry composition82 of the
Figure 72 Industry Shares of Value Added, 2013
Asian economies in 2013, and indiSources: Official national accounts in each country, including author adjustments.
cates a broad, negative correlation
between the share of the agriculture sector and the relative per capita GDP against the US. Half of the Asian countries compared havean agriculture sector accounting for over 10% of total value added. They all have a relative per capita
GDP that is 30% below the US. Among them, the six countries with the biggest agricultural share are
all in the lowest income group (with a per capita GDP less than 10% of the US). In contrast, the agriculture sector is 10% or less of the total value added for Group-L2 countries, compared with 3% or less forGroup-L1 economies. In particular, agriculture accounts for less than 1% in the US, and is negligible inHong Kong and Singapore. Note also how finance, real estate, and business activities grow in importance as one moves up income levels. The finance sector is especially prominent in Hong Kong (38%),
Singapore (32%), and the US (32%). Mining appears to be what defines oil-exporting countries, typically accounting for over 40% of total value added, except in Bahrain (27%), Iran (18%), and the UAE(37%), which are countries that have managed to diversify mining. Finance is the biggest sector in
Bahrain, accounting for 20% of total value added, whereas it is the second largest sector (16%) in the
UAE, following mining.

81: In Chapter 5, GDP is adjusted to be valued at basic prices (including our estimates, if the official estimates at basic prices are notavailable). However, the definition of GDP by industry differs among countries in this chapter due to data availability. GDP is valued at factor cost for Fiji and Pakistan; at basic prices for Cambodia, Hong Kong, India, Korea, the Lao PDR, Mongolia, Nepal, andSingapore; at producers’ prices for Iran, the ROC and the Philippines; and at market prices for Bangladesh, Indonesia, Japan, Malaysia, Sri Lanka, Thailand, and Vietnam.
82: The nine industries are 1–agriculture; 2–mining; 3–manufacturing; 4–electricity, gas, and water supply; 5–construction; 6–wholesale and retail trade, hotels, and restaurants; 7–transport, storage, and communications; 8–finance, real estate, and business activities; and 9–community, social, and personal services. See Appendix 6 for the concordance with the ISIC, Revision 3.

96

6.1 Output and Employment

Figure 74 shows the breakdown of
the manufacturing sector, comprising nine sub-industries, for 17 selected Asian countries and the US.84
The dominance of machinery andequipment in Asian manufacturing
can be clearly seen, particularly in
Singapore (63% of manufacturing’s
total value added) and the ROC(62%), Korea (53%), and Japan(48%). These compare with 42%
in the US. At the other end arecountries dominated by light manufacturing (e.g., the food products,
beverages, and tobacco products
sector in the Philippines, Sri Lanka,Fiji, and Mongolia; and the textiles,
wearing apparel, and leather products sector in Cambodia and Bangladesh). Coke, refined petroleum
products, chemicals, rubber, and% TFP growth during 2000–2013

4.5

Mongolia

4.0

China

3.5
3.0

India
Cambodia

2.5

ThailandSri Lanka
2.0
Hong Kong

1.5
1.0

Pakistan

Iran
US

0.5

Philippines
Indonesia
VietnamSingapore
Malaysia

Fiji

6

Korea
ROC

Japan

0.0
Bangladesh

−0.5
−1.0

0

5

10

15

20

25

30

35 %

Manufacturing share, 2013

Figure 73 Manufacturing Share and TFP Growth, 2000–
2013
Sources: Official national accounts in each country, including author adjustments; APO Productivity Database 2015.

3
8 1 Sri Lanka
15
2 3 Mongolia
4 Fiji
4 1 8
3 Philippines
17
3 4
3 4
11
1 Indonesia
38
29
8
3
29
7
28
8
15
11
Hong Kong
4 2
33
5 Thailand
22
23
7 12
12
18
20
26
16
4 2
Iran
8
22 3 3 2
14
66
Cambodia
3848
5 Japan
16
13
11 4
4 3
39
1 Malaysia
34
12
1 3 2
42
4 US
29
2 3
12
12 4
7
3
11
10
12
50
21 4
Bangladesh
6
10
27
6
14
2 3
22
10
India
5 1 8 1 Kuwait
75
5 2 3
62
5 ROC
4 3 2
15
3 5
15
53
1 Korea
16
3
4
5 2
63
4 Singapore
25
4 2
11
51
51
51
49

0

20

18

4
6
19

13
11
6 11

40

60

1
3 2
8

18

6

7
3 3

80

100%

3-1. Food products, beverages, and tobacco products
3-2. Textiles, wearing apparel, and leather products
3-3. Wood and wood products
3-4. Paper, paper products, printing, and publishing
3-5. Coke, refined petroluem products, chemicals, rubber, and plastic products
3-6. Other non-metallic mineral products
3-7. Basic metals
3-8. Machinery and equipment
3-9. Other manufacturing

Figure 74 Industry Shares of Value Added in Manufacturing, 2013
Sources: Official national accounts in each country, including author adjustments.

83: In Mongolia, the two world-class large mines of Tavan Tolgoi (coal mine) and Oyu Tolgoi (copper and gold mine) started producing concentrate from the mine as of the beginning of the 2010s.

97©2015 Asian Productivity Organization

For fostering productivity in theless-developed countries, it is important to adopt existing technologies from the advanced economies.
In this view, manufacturing is a key
sector in propelling countries tomake a leap in economic development. It accounts for around 20%
or more of total value added innine of the 30 Asian countries compared. Among these, manufacturing is the largest sector in Korea,
China, and ROC, equivalent toaround 30% of total value added,
while in Thailand, Malaysia, and Indonesia it accounts for a quarter ormore. Figure 73 shows there is apositive correlation between ourestimates of TFP growth in Chapter
5 during 2000–2013 and the sharesof manufacturing in 2013, except
Hong Kong, India and Mongolia,83
who have a higher share of services
and mining, respectively.

6 Industry Perspective

plastic products are also a prominent subsector, not least in Kuwait, where they account for two-thirdsof the country’s manufacturing value added.
Figure 75 shows the industry shares of value added and employment by the four country groups
based on 2013 income levels, compared with the Asia30 average and the US for the years 1980, 1990,
2000, and 2013.85 The first thing to note is that in 2013, the service sector accounted for the largest
share of total value added in all country groups, independent of their economic development.86 Thatsaid, Group-L1 has always had the biggest service sector among all Asian countries. This has becomemuch more distinctive as over time the bulk of the economy in this group continues to shift heavily
toward services. By 2013, the service sector accounted for 61% of total value added in Group-L1, compared to 79% in the US and 56% in Group-L2.87 The weight of the service sector is similar in Group-L3
and Group-L4 at 49% to 53%. This reflects the relative importance of manufacturing to the former, andagriculture for the latter, at their particular stages of development.
The second noteworthy point is that Asia30 remains a region dominated by agriculture as far as employment is concerned, despite its downward trend. In the past three decades, the agricultural employment share for Asia30 dropped from 61% in 1980 to 37% in 2013. In the past three decades, thevalue-added share of agriculture in Group-L3 has more than halved from 30% in 1980 to 13% in 2013,
1980

%

100
90
80
70
60
50

18

22

13 4

17

12
7

12

18

1
3
9
5

7
7

11

10

10 8
1
2
8 6
3
5
11
5
2

13

8
2

30

21

9
1

23

8
1
18

23

20

9

15

11

3
10

6

9
1
3

7 66

6
16

15 5

13

13

21

34

11

61

4

13

19
6
15

8 9

12

14

5
19

8
13

5
2 6
1
21

8 12

3 1

4

9
12
8

12
5
2

13
1

1
3

1
4

3

13
7

10
19

13 15
23

27

14
26

7
7 5

2

5

61

2

58

23

13
57

49

26

31

6

1
11 4
9

9 18
13
17 7
1

13 20

1
18

8

2013

15

13

14
4
1
18 10

14 14

8

48

4
2 6

5
2
18

6
15 13
1 2

8

20

10

11

5

13
7

20
14

17

5
2

14
12
4

9
1

9

7 16

1

2
1

16

19

11
1
6
15

17 17
27
14

6
1

7

13

14

4
2
16

9

9

5

24

20
8

22

12

5
14
37

13

32

1

2 48
27

43

7 15
2
1

39
8

2
5
14

25

19

1

Emp.
V.A.

Emp.
V.A.

Emp.
V.A.

Emp.
V.A.

Emp.
V.A.

Emp.
V.A.

Emp.
V.A.

Emp.
V.A.

Emp.
V.A.

Emp.
V.A.

Emp.
V.A.

Emp.
V.A.

Emp.
V.A.

Emp.
V.A.

Emp.
V.A.

Group Group Group Group Asia30 US
L-1
L-2
L-3
L-4

2. Mining
5. Construction
8. Finance, real estate, and business activities

13

10

13

13 15

23

11

10

8

7

30

8 6

5
29

5

2
4

15
17

1

11
53

15

11

8 6
1

6
3

15 16

36

38

19

17
7

13

14 16
23

26

10

2

15

2
4

10

52
34

17 15

1
5

4
2

22

20

2

6
1
3

26

3 9

7

14

4
17
3

6

1 1
2 2

5
1

7

15

Group Group Group Group Asia30 US
L-1
L-2
L-3
L-4

1. Agriculture
4. Electricity, gas, and water supply
7. Transport, storage, and communications

8

6

4
2 6
1
18

14

13

12 13

8
16

17

24

3
14 5

4

13 1

4
2

31

38

8

3

Emp.
V.A.

Emp.
V.A.

Emp.
V.A.

Emp.
V.A.

Emp.
V.A.

Emp.
V.A.

Emp.
V.A.

Group Group Group Group Asia30 US
L-1
L-2
L-3
L-4

25

5
6 10

7

16

11

12

23 23

20

3 1
2 3

6 1
2

6

1
3

2000

14 11

7

4
2

5

12

10

9
3 1

Emp.
V.A.

17

2
11 4

7

22

8

2

64

10 10

Emp.
V.A.

16

1

59

36

13

4
1

26

12 14

3

12 1

18

22

24

7

11

30

0

2
4

9 10

1

13

9

7

40

10

1990

13

Group Group Group Group Asia30 US
L-1
L-2
L-3
L-4

3. Manufacturing
6. Wholesale and retail trade, hotels, and restaurants
9. Community, social, and personal services

Figure 75 Industry Shares of Value Added and Employment by Country Group, 1980, 1990,
2000, and 2013©2015 Asian Productivity Organization

Sources: Official national accounts in each country, including author adjustments.

84: Manufacturing consists of nine sub-industries: 3.1–food products, beverages, and tobacco products; 3.2–textiles, wearing apparel,and leather products; 3.3–wood and wood products; 3.4–paper, paper products, printing, and publishing; 3.5–coke, refined petroleum products, chemicals, rubber, and plastic products; 3.6–other non-metallic mineral products; 3.7–basic metals; 3.8–machineryand equipment; and 3.9–other manufacturing. See Appendix 3 for the concordance with ISIC, Revision 3.
85: The group averages as industry share of value added are based on a country’s industry GDP, using exchange rates for the whole
economy without consideration of the differences in relative prices of industry GDP among countries.
86: The service sector is defined in this Databook as 6–wholesale and retail trade, hotels, and restaurants; 7–transport, storage, andcommunications; 8–finance, real estate, and business activities; and 9–community, social, and personal services.
87: If Figure 72 was to rank by the size of the service sector, Hong Kong would top the table at 92.9%, followed by the US (78.6%), andother Group-L1 countries, namely the ROC (64.8%), Japan (70.4%), and Singapore (75.1%). Fiji is an exception, with a large service
sector share (65.5%) relative to its per capita GDP level.

986.1 Output and Employment

with the most rapid shift taking place in the 1990s. Employment in the sector also was cut by one-third
over the same period. In contrast, the least well-off countries have not been as successful in diversifying away from agriculture, which accounted for 24% of total value added and 48% of employment in2013, compared with 34% and 64%, respectively, in 1980. In the meantime, the richest economies
continued to squeeze agriculture even though it had a share of only 4% in total value added and 16%
in total employment in 1980. By 2013, the figures had fallen to 1% and 4%, respectively.
Comparisons of the value added and employment shares also reveal some interesting facts. Agriculture is the only industry sector that consistently has a disproportionately higher employment sharethan justified by its share in value added across all country groups. This suggests that agriculture is still
highly labor-intensive and/or there may be a high level of underemployment in the sector in Asia,
both of which imply that the labor productivity level is low compared to other industry sectors.88 Thus,
countries with a big agriculture sector often have low per capita GDP, and shifting out of agriculture
will help boost economy-wide labor productivity. The US is an exception, where its agricultural valueadded share and employment share are similar; suggesting that labor productivity in this sector ishigher than that experienced in Asian countries. The reverse is true for the sector of finance, real estate, and business activities, which often generate a much bigger value-added share than suggested
by its employment share. In 2013, the sector accounted for 32% of total value added generated by
20% of employment in the US, and 14% and 2%, respectively, in Asia30. While the value-added shareof the sector has grown by 10 percentage points in the US over the past three decades, it has onlygrown by 3 percentage points in Asia30.

6Figure 76 shows how the share of the agriculture industry in total value added shrank over time in the
Asian economies. This could reflect the actual decline in agricultural output and/or the relatively rapid
expansion in other sectors. Despite the broad spread, the downward trend is unmistakable, even forGroup-L4 countries. The share of the agriculture sector displays a long-term declining trend in all
countries, albeit at different paces and at different starting times. Looking at the available data, theshare of agriculture in most Asian countries (excluding the oil-exporting countries) clustered aroundthe 30–50% band in the 1970s, trending down to the 10–20% band by 2013. Vietnam and Mongolia
are two countries where the agriculture sector experienced similar declines but within a much shorter
88: Gollin, Parente, and Rogerson (2004) and Caselli (2005) demonstrate the negative correlation between employment share of agriculture and GDP per worker. They show that the agriculture sector was relatively large in less well-off countries and agriculturallabor productivity was lower than that in other sectors.
89: If Figure 72 was to rank by the size of the manufacturing sector, China would lead with a share of 29.9%, followed by Thailand andKorea at 27.7% and 31.0%, respectively.

99©2015 Asian Productivity Organization

The third point to note is that the industry structure in Asian countries differs from that in the US regarding the relative importance of manufacturing, even in Group-L1 countries, where manufacturingaccounts for 18% of the economies’ value added, compared with 12% in the US in 2013. The US economy is highly skewed toward the service sector, accounting for 79% of the total value added, compared with an average of 61% in Group-L1 countries. Certainly, its share of finance, real estate, andbusiness activities at 32% was much larger than the share in Group-L1 countries, at 17%. This suggeststhat Asian economies could experience further deindustrialization and a shift in prominence toward
services as they continue to mature. The relative prominence of manufacturing in the Asian region­al economy as a whole is reflected in the fact that income groups are not filtered out by the sizeof a country’s manufacturing sector.89 In Asia, the manufacturing employment share is typicallysmaller than the value-added share it generates. Furthermore the value-added share of the sector
has been shrinking in the high-income groups (i.e., Group-L1 and Group-L2) whereas in Group-L3
countries it has been relatively stable, and slowly expanding in Group-L4, reflecting their different
developmental stage.

6 Industry Perspective

%

%

30

Bahrain
ROC
Japan
Kuwait
Qatar
Singapore

25
20

BruneiHong Kong
Korea
Oman
Saudi Arabia
UAE
Australia

30

20
15

10

10

5

5
0

0

1970 1975 1980 1985 1990 1995 2000 2005 2010

1970 1975 1980 1985 1990 1995 2000 2005 2010

Group-L2

Group-L1%

50

Malaysia
Turkey

25

15

60

Iran
Thailand%

BhutanIndia
Philippines

China
Indonesia
Sri Lanka

Fiji
Mongolia
Vietnam

70
60

40

50

30

40

20

30

10

20

0

10
1970 1975 1980 1985 1990 1995 2000 2005 2010

Group-L3

Bangladesh
Lao PDR
Nepal

Cambodia
Myanmar
Pakistan1970 1975 1980 1985 1990 1995 2000 2005 2010

Group-L4

Figure 76 Long-Term Trends of Value-added Share in the Agriculture Sector, 1970–2013
Sources: Official national accounts in each country, including author adjustments.

©2015 Asian Productivity Organization

period (from the late 1980s and mid-1990s, respectively). The relative decline of agriculture was most
rapid in Korea, from 28.9% of total value added in 1970 to 2.3% in 2013. In many countries, the shareof the agriculture sector more than halved between 1970 and 2013: for example, from 44% to 14% inIndonesia, from 42% to 18% in India, and from 33% in 1972 to 13% in Bangladesh. In China, the shareof this sector also significantly declined, from 36% in 1970 to 10% in 2013.
Despite the relative decline of agriculture’s share in total value added, employment in the sector for
Asia as a whole still accounted for 40% of total employment in 2013. Figure 77 shows countries’ industry shares in total employment, and ranks them by size of employment in the agriculture sector.90
Group-L4 and Group-L3 countries and Thailand cluster at the top in Figure 77, with the share of agricultural employment ranging from 30% (Sri Lanka) to 73% (Nepal). Figure 78 traces the historicaltrajectory of Japan’s employment share of agriculture for the period 1885–2013 and the countries’
levels in 2013, mapped against Japan’s experience (as circles). Large shares of agriculture employ­ment over 30% in 13 countries correspond to Japan’s level at the end of the 1950s and the onset ofhigh economic growth. This may indicate there is much room for improving labor productivity andper capita income.

90: Data for the Lao PDR and Myanmar are unavailable for Figure 77.

100

6.1 Output and Employment

It is the manufacturing sector
that largely absorbs workers
who have been displaced fromthe agriculture sector, especially in the initial stages of economic development. Figure 80
traces the trajectory of growth
rates of GDP and employment
in combination with manufacturing for several Asian countries and the US over the past
four decades. Each dot represents the average annual
growth rate in the 1970s, 1980s,
1990s, and 2000s (2000–2013).
The growth rate in the 2000s is
illustrated by a white dot. If
manufacturing GDP and employment grow at the samerate, a dot will be on a 45degree line through the origin
running from the lower left toupper right quadrants. Despite
positive gains in manufacturing
GDP for the US and Japan, theoverall growth in manufacturing employment was negative,
except during the 1970s for the
US and the 1980s for Japan.

Nepal
Bhutan
India
13
41
20
4
10
48
Cambodia
7 2 1015
51247
Bangladesh
3 1 1017614
147
Vietnam
12
5
16
1 7
14
44
Pakistan
13
2 3
21
7
14
40
Thailand
15
4 3
21
7
14
1
34
Indonesia
17
6
7
23
6
1 8
31
Philippines
21
41
12
1 10
19
1
31
China
23
8 2
17
5 7 2 730
Mongolia
18
7 4
16
7
18
1
30
Sri Lanka
18
7
6
20
1 7
20
21
Turkey
17
4
10
15
15
1
17
1
20
Iran
20
9
5
26
1 10
16
12 1
Malaysia
22
3
14
21
7
7
18
9 1
Fiji
25
14
8
22
7
17
6
Korea
47
4 2
17
16
5 1 6 1
Saudi Arabia
20
9
5
25
8
27
5
ROC
41
4
8
18
1 8
15
5
Japan
33
5 3
11
37
41 6 1
Oman
43
5
7
21
32 8 1 9
Australia
41
10
8
17
3 2 8 1 10
UAE
36
5 4
21
20
3 3 6 2
Brunei
43
20
5
14
5
11 9
US
27
6 2
13
38
7 1
1 6
Qatar
57
3 10
10
12 4 1 11
Kuwait
41
9 3
14
20
1 12
Bahrain
32
8
10
20
14
15
Singapore
26
19
12
30
8
3
Hong Kong
0
10
20
30
40
50
60
70
80
90 100 %
73

56

52

6 13

11

1. Agriculture
3. Manufacturing
5. Construction
7. Transport, storage, and communications
9. Community, social, and personal services

11

9

21 6
16

6 13
4 2

8

11

5 3

8

Group-L4(except for
Bhutan, India,
and Vietnam)

Group-L3(except for
Thailand)6

Group-L2(except for Fiji)Group-L1

2. Mining
4. Electricity, gas, and water supply
6. Wholesale and retail trade, hotels, and restaurants
8. Finance, real estate, and business activities

Figure 77 Industry Shares of Employment, 2013
Sources: Official national accounts in each country, including author adjustments.

Agriculture share in total employment

0.8

Nepal

Japan (1885–2013)

0.7
0.6
0.5
0.4
0.3

BhutanIndia
VietnamBangladesh
Cambodia

PakistanThailandIndonesia
China

Philippines
Mongolia

Sri Lanka
0.2

Iran
Malaysia

0.1
0.0

Fiji

ROC Brunei
Korea
Singapore

Hong Kong
1881 1891 1901 1911 1921 1931 1941 1951 1961 1971 1981 1991 2001 2011

Figure 78 Employment Share of Agriculture in Japan during
1885–2013 and Levels of Asian Countries in 2013
Sources: Official national accounts in each country, including author adjustments. The
sources of historical data of Japan are Long-Term Economic Statistics by Ohkawa,
Takamatsu, and Yamamoto (1974) during 1885–1954 and Population Censuses since 1920.

101©2015 Asian Productivity Organization

The trend of employment shareover time (Figure 79) suggeststhat the relative decline in
the share of agriculture in to­tal value added has been accompanied by a downward
trend in its share in total employment.91 This trend is unmistakable in most of thecountries plotted in Figure 79.92
Between 1970 and 2013, theemployment share in agriculture dropped from 50% to 6%
in Korea and from 20% to 5% inJapan. Employment in agriculture also fell rapidly in the ROC,
from 25% in 1978 to 5% in
2013. In China, the share has
declined from 71% in 1978 to
31% in 2013.

6 Industry Perspective

%

%

60

Bahrain
ROC
Japan
Kuwait
Qatar
Singapore

50
40

BruneiHong Kong
Korea
Oman
Saudi Arabia
UAE
Australia

80
70

Iran
Thailand

Malaysia
Turkey

60
50

30
40
20

30

10

20

0
1970 1975 1980 1985 1990 1995 2000 2005 2010101970 1975 1980 1985 1990 1995 2000 2005 2010

Group-L1

Group-L2

%

%

80

100

70

90

60

Bangladesh
MyanmarCambodia
Nepal
Pakistan

80

50
70
40
60

30
20
10

50
BhutanChina
India
Philippines

Fiji
Indonesia
Sri Lanka

Mongolia
Vietnam40

01970 1975 1980 1985 1990 1995 2000 2005 201030
1970 1975 1980 1985 1990 1995 2000 2005 2010

Group-L3

Group-L4

Figure 79 Long-Term Trends of Employment Share in the Agriculture Sector, 1970–2013
Sources: Official national accounts in each country, including author adjustments.

©2015 Asian Productivity Organization

In Korea and the ROC, expansions of manufacturing output could allow for increases of employment
in the 1970s and the 1980s, but since the 1990s manufacturing has not been an absorption sector of
employment, regardless of the sound expansion of production in this sector (Figure 80). The experiences of Singapore, Indonesia, and Thailand are closer to the 45-degree line through the origin, which
implies well-balanced growth of output and employment in the manufacturing sector. The job creation role of manufacturing remains effective or increasingly more important in Indonesia and Pakistan, but it is diminishing rapidly in India and Iran.

91: Nepal’s employment-by-industry figures are constructed by interpolating benchmark data taken from its labor force survey as wellas its population census. Figure 79 indicates that its share of agriculture has increased since 1999. This reflects the employment
share of agriculture at 66% in the population census of 2001 and its share of 74% in the labor force survey of 2008.
92: However, the decline in a share does not always reflect an actual fall in employment for the agriculture sector; rather, it could
reflect total employment rising faster than employment in agriculture. Countries that have been experiencing a consistent fall in
actual employment in the agriculture sector are, for example, the ROC, Hong Kong, Japan, and Korea, whereas in Cambodia, India,
Iran, Nepal, and Pakistan, actual employment has been rising. Other countries such as Thailand, Indonesia, Singapore, Malaysia,
and Vietnam have no established trend in employment growth. China, however, has seen actual employment in agriculture fallingsince the turn of the millennium.

102

6.2 Industry Growth

%

2

%

10

Growth of
Employment in Mnf
1980s

1

0

−2

0

2

4

6

Growth of
GDP in Mnf

4

6%

2

1970s

−1

−2

−8

−4

2000s

1990s

Growth of
Employment in Mnf

8

0
−2

1970s
Growth of
GDP in Mnf

0

4

8

12

16 %

1980s

6

−4
−6

−3

−8

Japan
US
Australia

ROC
Hong Kong
Korea

2000s

−10

1990s

−4

−12
%

%

10

6

Growth of
Employment in Mnf
1970s

Growth of
Employment in Mnf

5

8
China
Indonesia
Singapore
Thailand

6

2000s

India
Iran
Pakistan
Sri Lanka

4

3
4

1970s

2

1980s

2

0

Growth of
GDP in Mnf

1990s

0

1990s

1

2000s

5

10

15

20

25 %

1980s
Growth of
GDP in Mnf

0
0

2

4

6

8

10 %

Figure 80 Job Creation in Manufacturing, 1970–2013_Average annual growth rates of GDP at constant prices and number of employment
Sources: Official national accounts in each country, including author adjustments.
Note: Each dot represents the average annual growth rate in manufacturing (mnf ) in the 1970s, 1980s, 1990s, and 2000s (2000–
2013). The white dots indicate the rate in the latest decade.

In Section 3.1, it can be seen that, as a region, growth in Asia30 accelerated in the most recent period
2005–2013, averaging 6.1% per annum, up from 5.6% in 2000–2005. China and India have been thetwo main drivers among the Asian economies, accounting for 45% and 16% of the region’s growthduring 1990–2013, respectively (Figure 7, p. 21). However, looking at the industry composition, theorigins of economic growth in China and India are quite different. Bosworth and Collins (2008) indicate that China’s economic growth has been fueled by industry sector expansion,93 whereas India’s
economic growth has been led by service sector expansion, based on their observation during 1978–
2004. Although the findings broadly support their conclusion, it also discerns that the nature of
growth in China may have started shifting more toward services in recent years.
Our results show that manufacturing was the biggest contributor to economic growth in China until
the 2000s when the service sector overtook manufacturing in this respect (Figure 81).94 The gap
93: The industry sector in Bosworth and Collins (2008) is equivalent to the industry groups 2–5 in this report.

103©2015 Asian Productivity Organization

6.2 Industry Growth6 Industry Perspective%

12

1990−2000
10

2000−2013

0.7

0.5

0.5

4.6 3.3

8

6

0.2
4.4

1.1

2

0

0.2
1.8

0.7

2.4

1.3 1.7

0.6

0.9

0.5 0.3
0.6
1.5 0.2
0.7 1.1
0.7
1.0
0.2 0.6
0.6
0.9 0.4
0.3
0.2 0.5
1.7
0.8
1.0
0.5 0.6
0.7 0.2 1.8
1.7
1.3
0.2
0.9
1.2 0.3 0.2 0.5
0.3
0.6
0.6 0.2
0.2
0.5
0.8
1.0 0.9 0.3
0.9
0.2
0.4 0.6 1.2
1.3
0.8
0.2 1.3 1.0
1.2
0.5 1.2 2.2
0.3 0.5
0.3
0.2
0.5
0.6
0.7 0.2 1.3 1.7 1.6 1.1 0.6
0.5
0.7 2.1
0.5 1.1 0.3 0.7
0.4 0.2
0.2 0.8
1.0
0.8
1.2
0.6
0.7 1.9 1.1 0.8
0.2
1.8
0.8
0.3
0.2
0.4
0.2 1.0
1.2
0.9 0.5 1.0 0.5
1.6
0.4 0.3 0.7
0.4
0.6 0.4 0.7 0.8 0.7 0.3
0.6
1.7
0.8
1.0 0.4 0.6 0.8 1.2
0.4
0.5 0.7 0.6
0.6 0.7 0.5
1.0 0.7
1.5 0.3
0.5
0.4
0.6 0.3 0.4 0.2
0.7 0.8
1.5
0.5
0.6
0.6 0.7 0.7
0.3
1.7
1.6
0.9
0.5 0.5
0.8
1.1
0.7
0.2 0.6
0.6
1.1
0.8
0.6
0.4
0.8 0.5
0.7
1.4 0.7
0.3 0.2
1.4
1.1
0.6
0.9
0.9 0.7 0.6 0.6
0.9 0.9 0.7 0.4 0.8 0.7 1.0
0.9 0.3 0.7
0.7
0.5
0.5 0.6 0.3 0.8
0.4 0.4
0.3
0.4 0.5 0.5 0.4
0.3 0.3
0.2
0.2
−1.3
3.3

0.3

0.4 0.4
0.5
1.4
2.4
0.7
0.6
0.2
2.1
0.3
3.5
0.3
0.5
0.9
1.1 1.0 0.7
3.9
0.3
1.2
0.3
1.3
0.3 0.2
1.0
2.1 0.2
0.9
0.2
0.2
1.2
0.6 0.6
0.3
2.0
0.3
1.5 1.3 0.3
1.3
0.2 0.3
0.3 0.7 0.9
0.7
1.3
0.2
1.5
0.5
0.4
1.1
1.3 1.0
0.7
0.8
0.2 0.7
0.5
0.4 0.8
0.7
1.0
0.3 0.5
1.2 0.2
0.5
0.2 0.3 0.5 0.4
0.7 0.7 0.5
0.5
0.2 1.6
0.5 0.7
0.4
1.4
0.6 0.8 0.9 0.2
0.7 0.2
0.6
0.2
0.8
0.5
0.4 1.4 0.3
0.4 0.7 0.6 0.2
0.6 0.7 1.0 0.8 0.3 0.7 0.5 0.7
0.5 0.2
0.5
0.9
0.2
1.8
0.5
0.3
0.6
0.5
1.4
0.4
1.3
0.8 1.2
0.7 0.6
0.4
0.5 0.4 0.5 0.6
0.8 1.0 0.5
0.7
1.4 0.4
0.4
1.4 0.3
1.0
0.4 0.3
0.2 0.3
1.0
1.6 0.5 0.5 1.0 0.5 0.5 0.4 0.3 0.6
0.5 1.2 0.3
0.3 0.6 0.2
0.7 1.2
0.4
1.0
1.0 0.8 0.7 0.8
1.0 0.8
1.0
0.4 0.8 0.4
0.6
0.6 0.8 0.5 0.6 0.7 0.6 0.3 0.4
0.5
0.2 0.5
0.4 0.6 0.6 0.2 0.5 0.3
0.4
0.2 0.2
0.2−0.4−0.2 −0.3 −0.2 −0.5
−0.3
−0.5
1.4

4

0.5

0.9 2.3

1.4

−0.8

−2

Brunei
Japan
US
Fiji
Australia
ROC
Iran
Pakistan
Nepal
Hong Kong
Korea
ThailandTurkeySaudi Arabia
Malaysia
KuwaitUAE
Oman
Philippines
Singapore
Indonesia
Sri Lanka
Bahrain
Bangladesh
Vietnam
India
Mongolia
BhutanCambodia
Lao PDR
China
Myanmar
Qatar

MongoliaJapan
Fiji
Brunei
Saudi Arabia
Philippines
Hong Kong
US
Australia
TurkeyIndonesia
Pakistan
Oman
ThailandBahrain
Bangladesh
Nepal
KuwaitIran
BhutanSri LankaIndia
Lao PDR
ROC
Qatar
Myanmar
Korea
Cambodia
UAE
Malaysia
Singapore
Vietnam
China
1. Agriculture

2. Mining

3. Manufacturing

4. Electricity, gas, and water supply

5. Construction

6. Wholesale and retail trade, hotels, and restaurants

7. Transport, storage, and communications

8. Finance, real estate, and business activities

9. Community, social, and personal services

Real GDP

Figure 81 Industry Origins of Economic Growth, 1990–2000 and 2000–2013_Industry decomposition: Average annual growth rate of GDP at constant prices
Sources: Official national accounts in each country, including author adjustments.

between contributions of manufacturing and services was the widest in the early 1990s; narrowing in
the late 1990s until a redress in 2000–2013, with manufacturing and services accounting for 34%(Figure 82) and 45% (Figure 83) of economic growth, respectively. In contrast, economic growth inIndia always has been dominated by services. Its growth has only become more pronounced overtime. The contribution of manufacturing and services to economic growth were 15% (Figure 82) versus 65% (Figure 83) in 2000–2013, compared with 16% and 61% in 1990–2000. The increased prominence of the service sector has weakened, not so much manufacturing’s hold, but agriculture’s, where
the contribution fell from 13% in the late 1990s to 9% in the latest period of comparisons.

©2015 Asian Productivity Organization

Manufacturing has sustained its prominence in Thailand, Korea, and the ROC, contributing 30%, 41%,
and 50% to economic growth in 2000–2013, respectively. Its importance is modest in Singapore at
23% (Figure 82). In Hong Kong, it has been a drag on economic growth in the past decade or so. During the Asian crisis, the most impaired economies were Thailand and Indonesia, and the sectors whichbore the brunt were construction, wholesale and retail trade, hotels, and restaurants, and finance, realestate, and business activities. In contrast, manufacturing played a significant role in bolstering the
economy at the time (Figure 82).

94: The Törnqvist quantity index is adopted for calculating the growth of real GDP. Using this index, the growth of real GDP into the
products of contributions by industries can be decomposed:
In (GDPt / GDPt−1) = ∑ j (1/2) (sjt + sjt−1) In (Qjt / Qjt−1)
where Qjt is real GDP of an industry j in period t and sjt is the nominal GDP share
Real GDP growth

Contribution of an industry jof an industry j in period t.

104

6.2 Industry Growth

1990−2000
5.0 4.0 3.0 2.0 1.0 0.0 −1.0 %
4.4
2.4
2.4
2.1
1.8
1.6
1.5
1.4
1.4
1.3
1.3
1.3
1.2
1.0
0.9
0.9
0.7
0.7
0.7
0.6
0.5
0.5
0.5
0.5
0.5
0.4
0.4
0.3
0.2
0.2
0.1
−0.4
−0.8

Contribution

2000−2013
−30

China
Malaysia
Korea
Cambodia
Singapore
Indonesia
Thailand
VietnamROC
UAE
Sri Lanka
Lao PDR
TurkeyIran
India
Bangladesh
Bahrain
Nepal
US
Philippines
BhutanMyanmarOman
Fiji
Saudi Arabia
Pakistan
Brunei
KuwaitJapan
Australia
Qatar
Hong Kong
Mongolia

0

30

60 %
44

4.0 3.0 2.0 1.0 0.0 −1.0 %
3.3
2.3

33

1.8

36

1.8

30

1.7

25

1.7

40

1.3

34
19

1.3

23

1.2

18

1.2

25

1.0

22

1.0
1.0

33
19

0.9

16

0.9

20

0.9

17

0.9

15

0.8

21

0.8

21

0.8
0.7

11

0.7

9

0.7

12

0.6

28

0.4

17

0.3

10

0.3

18

0.2

5

0.1

19

0.1

5

0.1

1

0.1

−14

−0.1

45

Contribution share

Contribution

−30
China
MyanmarCambodia
ROC
Vietnam
Korea
Bangladesh
ThailandIndonesia
Singapore
India
Malaysia
Philippines
Bahrain
Qatar
Sri LankaOman
Lao PDR
TurkeyIranPakistan
Saudi Arabia
BhutanMongolia
Kuwait
UAE
Japan
US
Fiji
Nepal
Australia
BruneiHong Kong

0

30

60 %
34

22
25
50
27
41
22
30
24
23

6

15
22
20
16
8
16
19
11
19
22
21
16
9
9
9
7
48
11
8
2
3
28
−3

Contribution share

Figure 82 Contribution of Manufacturing to Economic Growth, 1990–2000 and 2000–2013
Sources: Official national accounts in each country, including author adjustments.

Economic growth in the Asian Tigers was also dominated by the service sector, albeit more so in Singapore and Hong Kong than in the ROC and Korea, where manufacturing remained a significant force.
The service sector accounted for 49% of growth in the ROC for the period 2000–2013, 54% in Korea,
73% in Singapore, and 101% in Hong Kong, counterbalancing the negative contribution of 2% by
manufacturing (Figures 82 and 83). These compare with 93% in the US, to counterbalance the negative contribution of 9% by construction. In the 2000s, growth in Hong Kong was highly skewed toward
wholesale and retail trade, hotels, and restaurants, accounting for 40% of growth. This compares with
95: The computer software industry in India depends considerably on export demands. According to India’s Input–Output Table 2006–
2007, 82% of the output in computer and related activities is exported. This export is equivalent to 14.8% of total exports in Indiaand is the second-largest export product (among 130 products in this table).
96: In 2014, India was the 6th largest producer (3.8 million) of motor vehicles (89.7), following Korea (4.5), Germany (5.9), Japan (9.8), theUS (11.7), and China (23.7), based on a survey by OICA (International Organization of Motor Vehicle Manufacturers). India moved
up in the rankings from 15th (0.8) in 2000 to 12th (1.6) in 2005.

105©2015 Asian Productivity Organization

The service sector plays an equal, if not more important, role in Asian economic growth. Services
made the biggest contribution to economic growth in all Asian countries except Qatar (Figure 83). The
story behind India’s recent growth has been one of services. Modern information and communication
technology have allowed India to take an unusual path in its economic development, bypassing a
stage when manufacturing steers growth.95 Within the service sector, contribution is quite evenly
spread among the sub-sectors, more recently the iron/steel and motor vehicle sectors have been
intensively developed.96 For further improvement in per capita GDP and to capitalize on the demographic dividend (see Box 2, p. 35), expansion of labor-intensive manufacturing may be required inIndia for greater job creation.

6 Industry Perspective

1990−2000
5.0 4.0 3.0 2.0 1.0 0.0 −1.0 %
4.8
4.6
4.4
4.0
3.7
3.4
3.4
3.3
3.2
3.1
2.9
2.9
2.7
2.6
2.5
2.5
2.5
2.4
2.3
2.3
2.2
2.1
2.0
1.9
1.8
1.8
1.7
1.4
1.2
1.1
1.0
0.9

Contribution

Singapore
UAE
ROC
Malaysia
Korea
India
China
Vietnam
KuwaitHong Kong
Sri Lanka
Iran
BhutanAustralia
Cambodia
Nepal
US
Bangladesh
BahrainThailand
Pakistan
Oman
MyanmarTurkeyQatar
Lao PDR
Philippines
Indonesia
Japan
Saudi Arabia
Fiji
Brunei−0.3 Mongolia

2000−2013
0

30

60

90 %

5.0

4.0

65

4.5

64

4.4

3.0

2.0

1.0

4.3

73
55

4.3

56

4.1

61

4.0
4.0

34

3.8

44

3.7

67
103

3.3

56

3.3

59

3.3
3.0

51

3.0

78

3.0

37

3.0

53

2.9

75

2.9

52
53

2.7

52

2.7

53

2.5

49

2.4
2.4

32

2.3

54
29

2.2

30

2.2
2.2

57

1.9

37

1.7

96
40

1.4
52

1.3

39

1.2

19

0.5

Contribution share

Contribution

0.0 %Mongolia
India
Qatar
China
MyanmarHong Kong
Bahrain
Lao PDR
Singapore
Sri Lanka
Cambodia
Bangladesh
TurkeyUAE
Philippines
Bhutan
Malaysia
VietnamIndonesia
Oman
KuwaitSaudi Arabia
Thailand
Nepal
Iran
Korea
Australia
Pakistan
ROC
US
Fiji
Brunei
Japan

0

30

60

90 %
63
65

39
45
39
101
72
49
73
60
45
56
70
66
62
41
66
46
53
59
55
55
55
59
64
54
72
54
49
93
78
646
80

Contribution share

Figure 83 Contribution of Service Sector to Economic Growth, 1990–2000 and 2000–2013
Sources: Official national accounts in each country, including author adjustments.

©2015 Asian Productivity Organization

22% in Singapore and 18% in the ROC. In contrast, the sector contributed only 8% to Korea’s growthover the same period. Finance, real estate, and business activities also played an important role, contributing 42% to growth in Hong Kong, 30% in Singapore, and 14% in the ROC.
The oil-exporting countries have different industry structures from other countries, with their relianceon mining for growth. The sector is volatile in nature and could in turn give rise to big swings in its
economies from one period to another. In 2000–2013, mining accounted for 43% of economic growthin Qatar, 30% in Kuwait, 18% in Saudi Arabia, and only 1% in Iran, reflecting a drop in the demand toward the end of 2008 and 2009 (Figure 81). Still, it has been a drain on growth, in some cases a quite
significant one. Its contribution was –1% in Bahrain and –679% in Brunei, reflecting a reduction in oilor gas production. These countries would do well to diversify. Bahrain has been successful in branching into finance, real estate, and business activities, which accounted for 30% of the 5.5% overall
growth over the same period. Oman also sustained growth of 4.6% on average per year, 59% of which
originated from the service sector. Brunei has not managed as well, with dismal growth of 0.2% on
average per year between 2000 and 2013. Oil and gas production activities are also reflected in Mongolia and the Lao PDR, where mining accounted for 15% and 18% of overall economic growth, respectively, in the 2000s.
For some Asian countries, agriculture is still the biggest sector. The four countries in which the agriculture sector has the largest share in total value added are Myanmar, Cambodia, Nepal, Pakistan, andthe Lao PDR (Figure 72). For the period 2000–2013, agriculture in Myanmar, Nepal, and Cambodia had
the highest contribution to economic growth among all Asian countries, accounting for 32%, 30%,106

6.2 Industry Growth

Table 17 Output Growth by Industry, 2000–2013

8. Finance, realestate, andbusiness activities

9. Community,social, andpersonal services

Total economy

5.0
6.5
12.2
4.9
7.4
11.7
3.1
2.1
5.8
7.7
6.2
3.9
0.2
2.8
3.2
9.8
6.3
11.0
9.8
2.2
6.1
3.6
6.0
14.6
9.4
5.9
5.2
3.7
4.4
7.4

(0.4)
(1.0)
(0.8)
(0.2)
(1.1)
(1.2)
(0.6)
(0.4)
(1.6)
(1.3)
(0.9)
(0.6)
(0.0)
(0.3)
(0.2)
(1.9)
(1.0)
(2.2)
(2.1)
(0.3)
(0.5)
(0.7)
(1.0)
(0.9)
(0.7)
(1.1)
(1.2)
(0.7)
(0.8)
(1.2)

8.6
7.5
7.7
4.1
8.0
8.4
3.3
2.3
4.0
10.2
11.5
6.9
1.3
4.7
10.4
8.3
6.1
12.3
15.1
6.4
12.5
3.9
6.5
19.7
11.1
4.1
8.3
5.7
8.0
7.8

(0.5)
(0.8)
(0.8)
(0.1)
(0.6)
(0.7)
(0.2)
(0.4)
(0.4)
(0.8)
(0.7)
(0.5)
(0.1)
(0.4)
(0.6)
(0.4)
(0.4)
(1.2)
(1.6)
(0.6)
(0.7)
(0.5)
(0.5)
(0.7)
(0.5)
(0.5)
(1.0)
(0.4)
(0.6)
(0.3)

6.4
4.4
9.2
3.8
8.3
10.5
2.6
1.9
4.8
9.6
6.6
5.5
0.3
4.0
4.5
8.7
6.7
8.1
21.2
4.2
6.4
6.5
6.6
15.1
6.9
5.6
5.6
6.2
6.7
5.4

(1.6)
(0.5)
(0.7)
(0.2)
(0.7)
(1.0)
(0.5)
(0.3)
(1.7)
(1.5)
(0.6)
(0.8)
(0.0)
(0.8)
(0.7)
(0.6)
(0.8)
(0.7)
(0.0)
(0.5)
(0.6)
(0.6)
(1.1)
(1.7)
(0.6)
(1.5)
(0.7)
(0.8)
(1.1)
(0.7)

8.6
4.8
5.3
3.8
8.8
9.9
2.1
1.3
1.7
6.0
5.3
2.6
1.1
3.5
6.1
10.2
5.8
3.2
10.8
7.0
6.3
1.1
2.7
10.8
3.9
4.7
4.0
3.5
7.8
6.9

(1.4)
(1.0)
(0.7)
(0.7)
(0.9)
(1.4)
(0.4)
(0.2)
(0.3)
(0.8)
(0.5)
(0.4)
(0.3)
(0.6)
(0.9)
(0.9)
(0.7)
(0.4)
(0.3)
(0.8)
(0.9)
(0.2)
(0.4)
(1.1)
(0.6)
(0.5)
(0.4)
(0.5)
(0.5)
(0.7)

5.5
5.9
7.2
0.2
7.3
9.5
3.5
1.7
4.0
6.8
5.2
3.5
0.6
4.1
4.5
7.6
4.5
7.2
10.3
3.8
4.6
3.6
4.8
10.9
4.4
5.1
5.5
4.3
4.6
6.3

(0.2)
(0.4)
(0.4)
(0.3)
(0.5)
(0.3)
(0.3)
(0.5)
(0.4)

4.4
6.3
6.4
6.5
6.9
6.1
5.5
8.3
7.2

(0.7)
(0.9)
(0.9)
(0.8)
(1.1)
(1.0)
(0.9)
(0.7)
(0.6)

5.6
6.6
6.8
5.5
8.9
8.6
7.9
10.9
10.4

(0.5)
(0.5)
(0.5)
(0.5)
(0.8)
(0.6)
(0.6)
(0.7)
(0.5)

4.9
6.1
6.2
5.1
9.0
6.3
6.4
5.7
7.0

(0.7)
(0.8)
(0.8)
(0.7)
(1.3)
(0.8)
(0.8)
(0.5)
(0.8)

2.9
4.9
4.9
4.9
5.3
4.8
4.5
7.5
5.2

(0.5)
(0.8)
(0.8)
(0.9)
(0.7)
(0.5)
(0.5)
(0.6)
(0.7)

3.9
5.8
5.7
6.0
6.2
5.2
4.7
6.6
4.9

(0.2) −0.1 (−0.0) −2.7 (−0.1)(0.1) 1.3 (0.0) 4.8 (0.4)(0.8) 4.7 (0.1) 4.3 (0.2)

1.1
2.9
3.7

(0.1)
(0.4)
(0.6)

3.4
3.5
5.5

(0.3)
(0.3)
(0.8)

2.3
3.1
5.9

(0.7)
(0.6)
(0.9)

1.0
3.1
4.0

(0.3)
(0.9)
(0.6)

1.5
3.0
4.3

5. Construction

4. Electricity, gas,
and water supply

7. Transport,
storage, andcommunications

3. Manufacturing

6. Wholesale andretail trade, hotels,
and restaurants

Bahrain
Bangladesh
BhutanBrunei
Cambodia
China
ROC
Fiji
Hong Kong
India
Indonesia
Iran
Japan
Korea
KuwaitLao PDR
Malaysia
Mongolia
Myanmar
Nepal
Oman
Pakistan
Philippines
Qatar
Saudi Arabia
Singapore
Sri Lanka
Thailand
UAE
Vietnam(regrouped)
APO20
Asia24
Asia30
East Asia
South Asia
ASEAN
ASEAN6
CLMV
GCC(reference)
US
Australia
Turkey2. Mining

1. Agriculture___Average annual growth rate of industry GDP at constant prices−0.9 (−0.0)
3.7 (0.6)
2.2 (0.5)
2.4 (0.0)
4.5 (1.5)
4.1 (0.5)
0.5 (0.0)
0.3 (0.0)−2.5 (−0.0)
3.3 (0.6)
3.5 (0.5)
2.2 (0.2)−1.2 (−0.0)
1.1 (0.0)
9.0 (0.0)
2.9 (0.9)
2.9 (0.3)
3.4 (0.3)
7.1 (3.3)
3.3 (1.1)
2.5 (0.0)
2.6 (0.6)
2.7 (0.3)
5.4 (0.0)
1.3 (0.0)
0.9 (0.0)
3.0 (0.4)
2.4 (0.2)−3.1 (−0.0)
3.5 (0.7)−0.5 (−0.1) 7.1 (0.9) 11.5 (0.1) 7.2 (0.6)
7.3 (0.1) 7.9 (1.3) 8.4 (0.1) 7.4 (0.5)
10.9 (0.2) 8.0 (0.7) 11.3 (1.7) 7.7 (1.2)−2.1 (−1.3) 0.3 (0.1) 3.1 (0.0) 4.5 (0.1)
18.0 (0.1) 10.2 (1.8) 10.6 (0.1) 8.3 (0.5)
10.1 (0.5) 10.1 (3.3) 10.1 (0.3) 11.2 (0.7)−3.3 (−0.0) 6.1 (1.8) 3.6 (0.1) −0.7 (−0.0)
0.3 (0.0) 0.9 (0.1) 3.0 (0.1) 3.2 (0.1)−2.5 (−0.0) −3.0 (−0.1) 1.2 (0.0) 0.5 (0.0)
3.3 (0.1) 6.6 (1.0) 5.7 (0.1) 7.8 (0.6)
1.2 (0.1) 4.7 (1.2) 7.1 (0.1) 6.7 (0.5)
0.2 (0.1) 5.8 (0.8) 5.5 (0.1) 1.9 (0.1)−7.4 (−0.0) 1.4 (0.3) −1.9 (−0.0) −1.9 (−0.1)−0.6 (−0.0) 5.8 (1.7) 4.2 (0.1) 0.9 (0.1)
2.8 (1.3) 5.8 (0.4) 11.4 (0.2) 4.4 (0.1)
34.6 (1.4) 8.7 (0.8) 6.4 (0.2) 9.2 (0.5)
0.0 (0.0) 3.7 (1.0) 4.6 (0.1) 5.1 (0.2)
6.6 (1.1) 8.0 (0.6) 4.0 (0.1) 10.9 (0.5)
11.6 (0.1) 17.0 (2.3) 10.3 (0.1) 16.0 (0.6)
3.8 (0.0) 1.4 (0.1) 5.3 (0.1) 3.2 (0.2)
0.2 (0.2) 9.7 (0.9) 8.8 (0.1) 18.5 (0.7)
4.7 (0.2) 5.9 (0.7) 2.1 (0.0) 2.9 (0.1)
9.0 (0.1) 4.3 (1.0) 4.2 (0.2) 4.7 (0.3)
8.8 (4.6) 9.8 (0.9) 7.7 (0.1) 19.6 (1.0)
1.9 (0.8) 7.1 (0.7) 9.6 (0.1) 6.2 (0.3)
0.0 (0.0) 5.0 (1.2) 3.5 (0.1) 3.6 (0.1)
12.8 (0.2) 4.6 (0.9) 6.6 (0.2) 8.3 (0.6)
5.0 (0.1) 4.4 (1.3) 5.0 (0.1) 3.1 (0.1)
1.3 (0.5) 3.2 (0.3) 9.4 (0.2) 6.6 (0.6)
2.3 (0.2) 9.4 (1.7) 10.1 (0.3) 7.8 (0.5)

2.9
3.5
3.4
3.7
3.2
3.9
3.1
5.0
0.9

(0.3)
(0.4)
(0.3)
(0.3)
(0.6)
(0.5)
(0.3)
(1.4)
(0.0)

1.6
5.1
3.9
9.8
3.9
1.4
1.2
3.3
2.4

(0.1)
(0.2)
(0.3)
(0.3)
(0.1)
(0.1)
(0.1)
(0.2)
(1.0)

4.4
7.0
7.0
7.6
6.5
5.0
4.4
11.0
6.4

(0.9)
(1.7)
(1.6)
(2.1)
(1.0)
(1.3)
(1.2)
(1.8)
(0.6)

2.3
1.7
1.9

(0.0)
(0.1)
(0.2)

1.8
4.0
1.3

(0.1)
(0.3)
(0.0)

1.0
3.1
4.0

2.9
5.8
6.0
6.0
5.4
5.8
5.1
9.9
9.8

(0.1)
(0.1)
(0.1)
(0.2)
(0.1)
(0.1)
(0.1)
(0.2)
(0.1)

3.5
6.1
6.3
6.0
7.5
6.2
5.6
9.2
7.7

6

and 21% of growth, respectively.97 In the latest period, agricultural output continued expanding in the
majority of Asian countries, suggesting that the shrinkage in its value-added share (Figure 76) over
97: In Myanmar, agriculture accounted for over 34.7% of GDP in 2013. Since 1988, the government has continued its modest steps toliberalize the sector and marketing controls have been made less onerous. As a result, farm production has increased. According
to official statistics, the quality of which has been questionable, this sector accounted for 31.6% of GDP growth in 2000–2013.

107©2015 Asian Productivity Organization

Unit: Average annual growth rate (percentage), contribution in parentheses.
Sources: Official national accounts in each country, including author adjustments.

6 Industry Perspective

the recent period is more a result of
rapid growth in other sectors than
any actual contraction of the sector.

2000–2013

%

10

0.5

1.3

16

8

12

8

8.3
0.6
0.4

0

4

2.0 1.5
0.4 0.9

4

0.8

6
12.6

0.6
0.4
0.7 0.3 0.8
0.6
4.1
0.3
0.4
1.8
1.5

1.0

1.7 0.8 0.7 1.1
0.5 0.9 0.3
0.4
0.4
3.6
1.7 1.6 1.5
1.7 5.6
2.3
5.1
1.0 0.7
0.6 0.3 0.3
0.6
3.6
0.3
0.5
0.3
0.4 0.9 1.8 0.6
0.4
1.3
1.3
0.5 1.0
0.7 1.2 1.4 0.7−0.4−1.0
−0.5
−0.3

2

0

0.5
0.6

0.3 0.4
0.5 0.5

0.9
4.8

4.7

0.7

4.3

2.0 1.2 0.6

0.8

2.1 2.5

0.8
0.4
0.4 0.6 0.5

2.5

2.0 2.3
0.6−0.3

0.5 1.5

1.0 1.8 1.3 1.9
0.3
−0.5

Japan
Malaysia
Indonesia
Philippines
ThailandSingapore
Kuwait
Korea
ROC
Iran
India
Bangladesh
CambodiaJapan
US
Philippines
Indonesia
ROC
India
ThailandIran
Bangladesh
Korea
Malaysia
Cambodia

Comparisons across the countrygroups in Table 17 reveal that Asia
enjoyed more vibrant growth thanthe US in all sectors. It is notablethat the US was more directly affected by the global financial crisisof 2008–2009 than Asia. Overall
construction retrenched in the US
in the 2000s, while growth has beenstrongest in CLMV and the GCC
countries at 9.2% and 7.7% per year
on average, respectively. Apartfrom construction, the other fastgrowing sectors in CLMV and the
GCC countries were transportation,
storage, and communications (atover 10% per year on average), presumably reflecting their effort inbuilding and upgrading infrastructure for their development needs.
Finance, real estate, and businessactivities also enjoyed robust expansion at 9.0% per year on average in South Asia. Manufacturing
has been growing at 11.0% per year
on average in CLMV, comparedwith 4.4% in ASEAN6.

1990–2000%

20

3-1. Food products, beverages, and tobacco products
3-2. Textiles, wearing apparel, and leather products
3-3. Wood and wood products
3-4. Paper, paper products, printing, and publishing
3-5. Coke, petroluem, chemicals, rubber, and plastic products
3-6. Other non-metallic mineral products
3-7. Basic metals
3-8. Machinery and equipment
3-9. Other manufacturing
3. Manufacturing

Figure 84 Industry Origins of Output Growth in Manufacturing, 1990–2000 and 2000–2013___Sub-industry decomposition: Average annual growth rate of GDP
at constant prices of manufacturing
Sources: Official national accounts in each country, including author adjustments.

©2015 Asian Productivity Organization

Figure 84 presents the sub-industry origins of average annual growth of manufacturing GDP for selected Asian countries for the periods 1990–2000 and 2000–2013.98 Manufacturing in Asia has been
dominated by 3-8 (machinery and equipment) accounting for 40% or more of overall manufacturing
growth in half of the Asian countries compared. In the ROC and Korea, it was about 80%. The subsector 3-1 (food products, beverages, and tobacco products) is the largest contributor in the Philippines for 2000–2013, accounting for 47% of manufacturing output growth. In Bangladesh andCambodia, manufacturing growth has been dominated by the sub-sector of 3-2 (textiles, wearing apparel, and leather products), whereas in Kuwait, and to a lesser extent Singapore and Malaysia, it is 3-5(coke, petroleum, chemicals, rubber, and plastic products).
Figure 85 contrasts industry contributions to economic growth for the periods 1990–2000 and 2000–
2013, as well as between the US and Asian averages.99 Even within such a short period, one can see
98: The Törnqvist quantity index is adopted for calculating the growth of real GDP of manufacturing. Using this index, the growth ofreal GDP of manufacturing into the products of contributions by sub-industries of manufacturing can be decomposed:
In (GDPt / GDPt−1)= ∑ j (1/2) (sjt + sjt−1) In (Qjt / Qjt−1)
where Qjt is real GDP of a sub-industry j in period t and sjt is the nomReal GDP growth of manufacturing

Contribution of a sub-industry jinal GDP share of a sub-industry j in period t.

108

6.2 Industry Growththat the industry structure of growth ischanging. The first striking feature is thedominance of manufacturing in Asian
countries. Between 1990 and 2000, its con­tribution to economic growth in Asia24
was 32% compared to 21% in the US.
Although its significance has fallen inrecent years, it still accounted for 29% of
economic growth in Asia24 between 2000
and 2013, compared with 11% in the US.
This, however, masks a divergence within
Asia. In the earlier period, manufacturingaccounted for 38% of growth in East Asia
but only 16% in South Asia. The corresponding figures were 35% and 16% in
2000–2013. The differential is somewhat
narrowing.

1990−2000%

120

2000−2013100

18
17 14 14 15 15

9

10 10

13 13 11
16

16
9

9

12
14 14
3
3

4
3

2
3

16 16

7
3

18

4
3

4
3

19

5
3

2
3
32 31 38
24

33
16

35

2

3

5

8

8

7

4
5

15

9

19

5
9

14 14 11 21
9

9

12
15
17

6
3
15

23

4
3

21
1
0

8

15 17

13

11

14

10 17

6
3

6
3

8
29 28

35

23

1
7

4

5
6

5
4

4

7
2

28

2
25
16

6

18

6

13

18
7
2

5
2

48

11

7
19

6
3

12 11

12

15 15

10

26 23
7

9

30
8

3
1

2

0

4
8

11

12

18

20

13 14 14 16 12 10 11812

10 10 1040

12

13 13 14

80

60

15

2

2

10

9

9
3

18

12
24

2

3

9

21 20 11

7

4
2
0
−9

0

US

GCC

CLMV

ASEAN

ASEAN6

East Asia

South Asia

Asia30

APO20

Asia24

US

GCC

CLMV

ASEAN

ASEAN6

East Asia

South Asia

Asia30

APO20

Asia24

In ASEAN, manufacturing’s contribution−20
was reduced to 25% in 2000–2013 from
33% in the 1990s, while wholesale and retail trade, hotels, and restaurants increased from 16% to 19%. In the US, the
1. Agriculture
2. Mining
3. Manufacturing
4. Electricity, gas, andfinance, real estate, and business activities
water supply
5. Construction
sub-sector made the biggest contribution
6. Wholesale and retail trade,
7. Transport, storage, andhotels, and restaurants
communications
in both periods, accounting for 30% of
9. Community, social, and
8. Finance, real estate, andpersonal services
business
activities
economic growth in 1990–2000 and rising
to 48% in 2000–2013. In contrast, its conFigure 85 Industry Origins of Regional Economic
tribution in Asia24 was 14% in the period
Growth, 1990–2000 and 2000–2013
2000–2013. Mining in GCC countries took
_Contribution sharea hit in 2008–2009 due to the downturn in
Sources: Official national accounts in each country, including author adthe world economy. Consequently, thejustments.
contribution of mining fell from 23% to
20% between the two periods while construction’s share increased from 6% to 9%. Finance, real estate, and business activities became thebiggest contributors of economic growth in GCC countries, with its share rising from 12% to 17% between the two periods.

99: Asian averages are calculated using the Törnqvist index to aggregate the growth rates of industry GDP of each country based on
the two-period average of each country’s shares of industry GDP to the gross regional products as weights.
100: The average growth rate of the Asian economy for 2000–2013 is set at 100%. Asian economic growth is calculated as the sum of
the contributions over countries and industries:∑ x (1/2) (Sxt + Sxt−1) ∑ j (1/2) (sx,t j + sx,t−1j ) In (Qx,t j / Qx,t−1j )where Qx,t j is real GDP of an industry j in a country x in period t, sx,t j is GDP share ofContribution of an industry j in a country x

an industry j in a country x with respect to GDP of a country x in period t and sxt is GDP share of a country x with respect to the regional GDP in period t. All the industries whose contribution is more than 0.25% are shown in Figure 86.

109©2015 Asian Productivity Organization

Figure 86 presents industry contributions to regional economic growth in Asia30 during 2000–2013,
decomposing Figure 7 in Section 3.1 into countries’ industry origins.100 In each industry contribution,
the top eight countries are presented. The top four industries in terms of contributions to regional
growth were manufacturing (27%), community, social, and personal services (18%), wholesale and6 Industry Perspective

3. Manufacturing (26.7%)
16.6

0

5

10

China

1.2 1.2 0.8

2.3

India

15

4.5

20

Indonesia

25

Korea

Japan

30 %
Rest of the world

9. Community, social, and personal services (18.0%)
7.1

0

4.7

4
China

1.9

8

Bhutan0.9 0.5

2.9

12

India

16

Japan

20 %

Indonesia

Rest of the world

6. Wholesale and retail trade, hotels, and restaurants (14.3%)
6.4

0

2

2.9

4

China

6

India

0.9

8

Indonesia

0.4 0.4

3.2

10

12

Saudi Arabia

14 %

Iran

Rest of the world

8. Finance, real estate, and business activities (13.5%)
5.0

0

3.5

2

4

China

0.6 0.6 0.5

6

India

8

Korea

3.3

10

12

Indonesia

14 %

Iran

Rest of the world

7. Transport, storage, and communications (8.9%)
3.4

0
China

1.9

2

4

India

Indonesia

0.4 0.3

0.7

2.2

6

8

Japan

10 %

Iran

Rest of the world

5. Construction (6.1%)
3.5

0

1

1.3

2

China

India

3

0.5

4

Indonesia

0.2 0.2

5

Saudi Arabia

0.4

6%

UAE

Rest of the world

1. Agriculture (5.6%)
2.5

0

1.4

1
China

India

0.5

2

3

Indonesia

Myanmar

0.2 0.2

4

0.7

5
Pakistan

6%
Rest of the world

2. Mining (4.4%)
2.7

0
China

0.5

1

2

Saudi Arabia

Qatar

0.4

0.2 0.2

3

0.5

4

India

Kuwait5%
Rest of the world

4. Electricity, gas, and water supply (2.5%)©2015 Asian Productivity Organization

1.6

0

0.5
China

0.3

1
India

Saudi Arabia

1.5

0.1 0.1 0.1

0.4

2
Iran

2.5
Korea

Figure 86 Industry Origins of Asian Economic Growth, 2000–2013_Contribution to regional growth of GDP at constant prices, using 2011 PPP
Sources: Official national accounts in each country, including author adjustments.

110

3%

Rest of the world

6.3 Labor Productivity by Industry

retail trade (14%), and finance, real estate, and business activities (14%). A total of 27% of Asian economic growth originated from the expansion of its manufacturing sector, two-thirds of which wasaccounted for by China. In other words, China’s manufacturing sector alone accounted for nearly 17%of the region’s economic growth. This was followed by China’s community, social, and personal services (7.1%) and wholesale and retail trade, hotels, and restaurants (6.4%).
Over a period of four decades there has been a noticeable shift in the industry origins of economic
growth (Figure 87). For the ROC and Korea, manufacturing has been a clear driving force behind economic growth on the whole. In the decade between the mid-1980s and the mid-1990s, however, theimportance of manufacturing retreated in the ROC temporarily while the economy developed its service sector. Since the mid-1990s, the role of manufacturing in the ROC has increased again, althoughcompared to its heydays of the 1970s and 1980s its impact in terms of percentage points is much reduced. In Singapore, finance, real estate, and business activities, as well as wholesale and retail trade,hotels, and restaurants are important drivers alongside the manufacturing sector. Working within thedata constraints, Hong Kong appears a clear service-driven economy in recent years. While the lack ofdiversification of the oil-exporting countries cannot be missed; historically, the dominance of the mining sector influenced the economic volatility of these countries. In recent years the GCC countries
have been making efforts in diversifying, especially into the service sector, with different degrees ofsuccess. Bahrain and Oman are leading the way and have yielded results. The largely agriculturalcountries are Myanmar, the Lao PDR, Cambodia, Nepal, and Pakistan, and, to a lesser extent, Vietnam
and Bangladesh. In the Philippines, construction was driving economic growth in the first half of theperiod, but it never recovered its dominance after its crash in the mid-1980s. In the second half, economic growth was better balanced, with the development of finance, real estate, and business activities in particular.

6

6.3 Labor Productivity by Industry
Section 5.1 discusses per-worker measures of labor productivity performance in level terms, and identifies a large gap between Asia as a whole and the US. In 2013, Singapore and Hong Kong were thecountries that had labor productivity levels comparable to the US. Besides these two, the best performers in Asia achieved productivity levels that were at least 40% of the US. However, Asia collectively was dragged down by a long tail of countries with labor productivity of less than 25% of the US
level. This pulled down the average performance to 20% of the US for the APO20 and 19% for Asia24(Table 9, p. 63). In growth terms, however, Asia’s performance far exceeded the US, allowing the countries to gradually close the gap with the US over time. Labor productivity growth in Asia24 was 5.1%
per year on average between 2005 and 2013, compared to 1.1% in the US (Table 10, p. 65).

101: Labor productivity growth in Table 18 is defined simply as per-worker GDP at constant prices by industry (vj). The industry decomposition of labor productivity growth for the whole economy (v) in Figure 88 (industry contribution in Table 18) is based on
the equation v = ∑ jwjvj* where the weight is the two-period average of value-added shares. In this decomposition, the number ofworkers as a denominator of labor productivity (vj*) is adjusted, weighting the reciprocal of the ratio of real per-worker GDP by industry to its industry average. Thus, the industry contribution (wjvj*) is emphasized more in industries in which the per-worker GDP
is higher than the industry average, in comparison with the impact (wjvj) of using the non-adjusted measure of labor productivity.
102: The data presented in this chapter is subject to bigger uncertainty than those in previous chapters and the quality across countries is also more varied. Employment data of the less developed countries often lacks frequency as well as industry details. Neither
does the industry classification of employment data necessarily correspond to those of industry output data. Consequently, thequality of labor productivity estimates at the industry level is compromised. Furthermore, estimates of the manufacturing sector
should be of better quality than those of the service sector as many countries have occasional manufacturing censuses, but do
not have a similar census covering the service sector.

111©2015 Asian Productivity Organization

Table 18 presents cross-country comparisons in labor productivity growth by industry101 for the period 2000–2013.102 Positive labor productivity growth was achieved across all sectors for Asia24. If one

6 Industry Perspective

Bahrain%50
40

Bangladesh

%
12

20

9

30

15

6

10

20

3

10

5
0

0

0

−10

−3

−5

−20

−6

−10

1971 1976 1981 1986 1991 1996 2001 2006 2011

Brunei

%
20
10
0
−10
−20
−30

Bhutan%25

1971 1976 1981 1986 1991 1996 2001 2006 2011

ROC

%
15
12
9

1971 1976 1981 1986 1991 1996 2001 2006 2011

Cambodia

%
15

12

9

9

6

6

3

3

0

0

−3

−3

Hong Kong

%
12

China

%
15

12

1971 1976 1981 1986 1991 1996 2001 2006 2011

1971 1976 1981 1986 1991 1996 2001 2006 2011

1971 1976 1981 1986 1991 1996 2001 2006 2011

India

%
9

8

6

4

3

0

0

−4

−3

6
3
0
−3

1971 1976 1981 1986 1991 1996 2001 2006 2011

Indonesia

%
10

−8

1971 1976 1981 1986 1991 1996 2001 2006 2011

Iran

%
20

−6

1971 1976 1981 1986 1991 1996 2001 2006 2011

Japan

%
10
8

5

10

0

0

−5

−10

−10

−20

−15

−30

6
4
2
0
−2
−4
−6

1971 1976 1981 1986 1991 1996 2001 2006 2011

Korea

%
15
12

1971 1976 1981 1986 1991 1996 2001 2006 2011

Kuwait

%
60
40

9

5
0

−20

0

−5

−40

−3
1971 1976 1981 1986 1991 1996 2001 2006 2011

Malaysia

%
12

−60

1971 1976 1981 1986 1991 1996 2001 2006 2011

Mongollia

%
12
8

8
4

0

0

−4

1971 1976 1981 1986 1991 1996 2001 2006 20110−101971 1976 1981 1986 1991 1996 2001 2006 2011

1. Agriculture
2. Mining
5. Construction
6. Wholesale and retail trade, hotels, and restaurants
9. Community, social, and personal services

112

Myanmar

%
15

−5

−12
−16

1971 1976 1981 1986 1991 1996 2001 2006 2011

5

−8
−4

−10

10

4

©2015 Asian Productivity Organization

Lao PDR

%
15

0

3

−8

1971 1976 1981 1986 1991 1996 2001 2006 2011

10

20

6

−6

−8

−15

1971 1976 1981 1986 1991 1996 2001 2006 2011

3. Manufacturing
7. Transport, storage, and communications
Real GDP

4. Electricity, gas, and water supply
8. Finance, real estate, and business activities

6.3 Labor Productivity by Industry

%
8

Nepal

6

Oman

%
30

8

20

6

4

10

4

0

2

2
0

0
−10

−2
−4

%
9

1971 1976 1981 1986 1991 1996 2001 2006 2011

Philippines

6
3
0
−3
−6
−9

%
15

1971 1976 1981 1986 1991 1996 2001 2006 2011

Singapore−20−21971 1976 1981 1986 1991 1996 2001 2006 2011

Qatar

%
25

−4

20

18

15

12

10

6

5

0

0

−6

−5

−12

−10

−18

−15

1971 1976 1981 1986 1991 1996 2001 2006 2011

Sri Lanka

%
10

−24

8

12

6

8

6

4

4

3

2

0

0

0

−4

−3

−2

−6

−4

UAE

%
8

1971 1976 1981 1986 1991 1996 2001 2006 2011

Australia−121971 1976 1981 1986 1991 1996 2001 2006 2011

US

%
8

2
0

0

0

Thailand42201971 1976 1981 1986 1991 1996 2001 2006 2011

6

4

40

−20

Vietnam

%
10

6

60

6

−8
1971 1976 1981 1986 1991 1996 2001 2006 2011

8

80

Saudi Arabia

%
16

9

1971 1976 1981 1986 1991 1996 2001 2006 2011

1971 1976 1981 1986 1991 1996 2001 2006 2011

%
24

12

%
100

Pakistan

%
10

−2

−2

−4
1971 1976 1981 1986 1991 1996 2001 2006 2011−41971 1976 1981 1986 1991 1996 2001 2006 2011

Turkey

%
10
8

6

6
4

4

2

2
0

0

−2
−2
−4

−4
1971 1976 1981 1986 1991 1996 2001 2006 2011−61971 1976 1981 1986 1991 1996 2001 2006 2011

1. Agriculture
2. Mining
5. Construction
6. Wholesale and retail trade, hotels, and restaurants
9. Community, social, and personal services

3. Manufacturing
7. Transport, storage, and communications
Real GDP

4. Electricity, gas, and water supply
8. Finance, real estate, and business activities_Industry decomposition: Average annual growth rate of GDP at constant prices
Sources: Official national accounts in each country, including author adjustments.

113©2015 Asian Productivity Organization

Figure 87 Industry Origins of Economic Growth, 1970–2013

6 Industry Perspective

focuses on the regional economy, the findings highlight the fact that service industries no longer
hamper an economy’s productivity performance, but are as capable as manufacturing in achieving
productivity growth. In fact, there are no significant differences between manufacturing and some
services in Asia24; i.e., manufacturing (at 4.9% on average per year), electricity (4.3%), agriculture(4.2%), and transport, storage, and communications (4.0%). Construction was the sector with theslowest productivity growth at 1.1%.
Table 18 Labor Productivity Growth by Industry, 2000–2013

2.8
4.3
4.4
3.6
6.4
2.6
3.7
0.7
4.4

2. Mining
(0.1)
(0.2)
(0.3)
(0.3)
(0.1)
(0.1)
(0.1)
(0.2)
(0.9)

3.1
4.9
4.9
5.2
5.2
2.3
2.5
4.3
2.8

(0.7)
(1.4)
(1.3)
(1.7)
(0.8)
(0.9)
(0.9)
(1.3)
(0.4)

(0.1) −2.5 (−0.2)(0.1) 1.1 (0.0)(0.1) 1.1 (0.0)(0.2) 2.5 (0.1)(0.1) −1.0 (−0.0)(0.1) 0.4 (0.0)(0.1) 0.5 (0.0)(0.2) 0.4 (0.2)(0.1) −1.6 (−1.0)−1.6 (0.0)−5.5 (0.2)−1.7 (−0.0)

4.0
1.6
1.9

(0.5) 0.6 (0.0) −1.7 (−0.1)(0.2) −2.8 (−0.0) 1.7 (0.1)(0.3) −0.8 (0.1) 2.0 (0.0)−1.8 (−0.8) −3.0 (−0.3) 6.2 (1.7) 0.6 (−1.6) −2.4
3.3 (0.5) 2.8 (0.4) −4.3 (0.4) 3.2 (0.8)
2.6−0.6 (−0.8) −0.4 (−0.1) 3.3 (0.2) 4.7 (1.1) −2.3
−1.3 (−0.2) 0.4 (0.4) −0.4 (0.6) −0.8 (−0.0)
4.0
7.5 (0.8) 6.6 (0.6) 8.0 (1.0) 7.3 (0.9)
9.0
1.3 (0.2) 2.9 (0.1) −0.1 (0.2) 0.6 (0.1)
2.4
0.3 (0.0) 2.0 (0.3) −1.9 (0.3) 1.7 (0.4)
1.1
4.8 (1.2) 2.3 (0.2) 1.2 (1.1) −0.3 (−0.2)
2.5
4.5 (0.9) 7.0 (0.7) 5.5 (1.4) 6.0 (0.8)
5.2
4.1 (0.5) 10.7 (0.7) −2.6 (0.4) 0.7 (−0.1)
3.3
1.5 (0.2) 2.7 (0.1) 1.1 (0.6) 2.8 (0.4)
1.6
0.0 (0.0) 0.9 (0.1) 0.5 (0.1) 0.1 (−0.1)
0.8
3.0 (0.4) 2.6 (0.2) 0.0 (0.4) −0.6 (−0.2)
2.8
0.9 (−0.2) 5.6 (0.4) −6.6 (−0.1) −2.3 (−3.3) −1.9
1.4 (−0.2) 2.1 (0.2) −0.2 (0.3) 3.6 (0.2)
1.6
6.0 (1.4) 9.0 (1.0) −2.2 (0.6) 0.2 (−0.2)
4.9
1.6 (0.3) 1.5 (0.5) 1.0 (0.5) 7.4 (0.9)
1.2
1.6 (0.0) 2.5 (0.2) 0.4 (0.4) 0.5 (−1.0) −3.9
−0.8 (0.0) 0.1 (0.3) 7.5 (0.5) −0.6 (−0.1)
0.4
2.7 (0.3) 3.8 (0.3) −1.3 (0.8) −0.1 (−0.1)
2.3
3.3 (−0.7) 3.3 (−0.1) 8.8 (1.4) 2.2 (−1.1) −1.5
5.6 (−0.1) 7.2 (0.3) 6.4 (0.5) −0.3 (−1.3) −0.1
2.2 (0.4) 0.7 (0.2) 0.4 (1.2) 0.0 (−0.9)
1.5
2.3 (0.7) 4.5 (0.7) 0.0 (0.5) 2.7 (0.1)
3.8
1.6 (0.2) 5.9 (0.4) 2.4 (0.7) 0.2 (0.1)
2.6−1.7 (−0.4) 0.8 (0.1) −7.2 (0.0) −0.8 (−2.4) −2.1
2.4 (0.5) 6.2 (0.3) −7.9 (0.6) 1.5 (0.2)
3.9
1.6 (0.3)
2.9 (0.5)
2.9 (0.5)
2.9 (0.4)
3.4 (0.8)
2.9 (0.4)
2.8 (0.3)
2.4 (0.1)
2.4 (−0.2)

2.8
4.0
4.2
3.7
5.0
6.6
6.1
8.1
3.5

(0.3) 0.4
(0.4) 2.0(0.4) 2.0(0.4) 2.7(0.6) 4.3
(0.5) −1.5(0.5) −1.0(0.6) 3.3(0.2) −0.3(0.6) 1.3 (0.3)
2.1(0.7) 2.6 (0.5)
4.6(0.7) 2.5 (0.4)
4.5(0.6) 2.4 (0.4)
5.5(1.2) 4.8 (0.7)
4.3
3.1(0.6) 0.6 (0.0)(0.6) 0.6 (0.0)2.8(0.1) 1.3 (0.2)
4.2(0.5) −0.5 (−1.7) −0.8

1.2
1.4
1.3

4.2
2.2
3.1

(0.3) 1.6
(0.2) 0.9(0.7) −1.6

(0.6) −0.1 (−0.2)(0.5) 0.3 (−0.2)(0.5) 1.0 (0.1)

(0.2)
(0.0)
(0.1)

Unit: Average annual growth rate (percentage), contribution in parentheses.
Source: APO Productivity Database 2015.

114

Total economy

0.5
3.4
2.2
7.6
4.7
−3.3
−3.9
0.6
−1.7

9. Community,social, andpersonal services−6.0 (−1.5)
0.2 (0.2)−5.1 (−1.3)−4.4 (0.2)
7.3 (0.4)−1.0 (−0.0)−6.2 (−0.3)
0.4 (0.0)−1.0 (−0.0)
0.6 (0.2)−2.6 (−0.5)−0.2 (−0.0)
0.1 (0.0)
1.2 (−0.4)
0.8 (−0.2)
3.1 (0.1)−0.8 (0.1)−0.8 (−4.2)−2.2 (−0.3)
1.3 (0.1)
2.8 (−4.9)−2.6 (−0.9)
0.2 (−0.3)
5.6 (0.5)−0.9 (−0.1)
5.8 (−0.1)−0.2 (0.1)

8. Finance, realestate, andbusiness activities

18.3 (0.2)
8.4 (0.1)
0.5 (0.0)−4.7 (0.0)
7.7 (0.3)
3.8 (0.1)−2.6 (−0.2)
2.1 (0.0)
8.0 (0.1)
5.1 (0.1)
3.7 (0.1)−2.1 (−0.0)
1.6 (0.1)
7.0 (0.1)
1.4 (0.1)−3.1 (−0.0)
4.9 (0.1)−1.8 (0.0)−1.6 (0.0)
3.4 (0.1)
2.8 (0.1)
5.8 (0.1)
0.9 (0.1)
10.6 (0.2)
6.0 (0.1)
1.3 (0.1)
1.9 (0.3)

7. Transport,
storage, andcommunications

5. Construction

(0.1)
(0.7)
(0.0)
(1.3)
(2.8)
(1.5)
(0.3)
(0.2)
(1.0)
(0.9)
(0.6)
(0.6)
(1.7)
(0.1)
(1.0)
(0.6)
(0.1)
(0.6)
(0.1)
(0.9)
(0.1)
(0.5)
(0.9)
(0.5)
(1.1)
(0.0)
(1.0)

6. Wholesale andretail trade, hotels,
and restaurants

4. Electricity,gas, and watersupply4.3 (0.0) 2.3
6.2 (0.1) 2.6−4.3 (−1.3) 0.7
11.7 (0.1) 4.9
8.4 (0.5) 7.6
4.5 (0.0) 5.1
0.0 (0.1) 1.8
0.0 (0.0) 4.6
4.7 (0.1) 6.4−3.5 (0.1) 2.2
0.2 (0.1) 4.6−1.5 (−0.0) 3.2−0.2 (−0.0) 5.7−1.5 (1.5) 0.4−9.6 (−0.1) 3.8−3.8 (0.8) 7.8−2.5 (0.0) 1.1−1.6 (0.2) 5.6−3.8 (0.2) 1.2
1.3 (0.1) 3.5−5.3 (4.0) 1.9
1.4 (0.9) 4.7
0.0 (0.0) 3.6
10.7 (0.2) 2.8
2.0 (0.1) 3.2−4.0 (0.5) −0.3
0.0 (0.2) 3.3

1. Agriculture©2015 Asian Productivity Organization

Bahrain−4.1 (−0.1)
Bangladesh 1.0 (−0.8)
Brunei−4.0 (−0.1)
Cambodia
4.7 (1.5)China
7.3 (1.7)ROC
2.8 (0.2)Fiji
2.2 (0.2)
Hong Kong −1.9 (−0.0)
India
2.4 (0.1)
Indonesia
3.9 (0.6)
Iran
1.0 (0.0)Japan
0.7 (0.1)
Korea
4.1 (0.3)
Kuwait6.9 (0.0)
Malaysia
2.7 (0.3)
Mongolia
3.8 (0.6)
Nepal−0.1 (−1.2)
Oman
0.1 (0.0)
Pakistan
0.3 (−0.4)
Philippines
1.3 (−0.2)
Qatar−1.0 (−0.2)
Saudi Arabia −2.1 (−0.1)
Singapore −6.3 (−0.0)
Sri Lanka
3.3 (0.4)
Thailand1.6 (−0.1)
UAE−0.7 (0.1)
Vietnam
3.6 (0.8)(regrouped)
APO20
2.0 (−0.1)
Asia244.2 (0.7)
Asia304.2 (0.7)
East Asia
6.7 (1.3)
South Asia
1.9 (−0.1)
ASEAN
3.8 (0.4)
ASEAN6
2.9 (0.3)
CLMV
5.0 (1.4)
GCC−1.4 (−0.0)(reference)US
3.1 (0.0)
Australia
4.2 (0.1)
Turkey4.7 (1.0)

3. Manufacturing_Average annual growth rate of industry labor productivity

1.3
1.0
3.0

6.3 Labor Productivity by Industry

%
10
1.7

1990−2000

1.2

8

2000−2013

0.3

0.6

0.5

6
2.8

0.6

4

4.3 0.2
0.8
1.1

2

0

0.3
0.3
2.7

1.3 1.7 0.2 2.2 0.3

0.6
1.3
0.4 1.4
0.6 0.5
0.3 0.4 0.6
0.5
0.7
0.3
0.4
0.7 0.4 0.6 1.9 0.7
0.5
0.3
0.2 0.2
0.2
0.6 0.6 1.0 0.4 0.5
0.2
0.3
0.3
0.3 0.2 0.2
0.7
0.7
0.8 0.2
0.4 0.3
0.3 0.3 0.2 1.1 0.1 0.7
1.2 0.6 1.0
0.4 0.8
0.6 0.3 0.6
0.9 0.3
0.3
0.8
0.4
0.2 0.4 0.5
0.8
1.4
0.3 0.8 0.5 0.4
0.5 0.2 0.4
0.9
1.0 0.4
0.2 0.3
0.4 0.5 0.3 0.7 0.3 0.5 0.5 0.3
0.7
0.6
0.5
0.5
0.5
0.5
0.2 0.5 0.3 0.3 0.3
0.3
0.3
0.2
−0.2
−0.2
−0.2
−0.3
−0.3
−0.4 −0.3 0.2 −0.2
−0.4
−0.5 −0.2−0.2 −0.2 −0.4
−0.2
−0.3
−0.2 −0.4
−0.3
−0.3
−0.2
−1.2

0.2
0.2
0.3 0.5
0.5 0.4

0.6
1.0

0.8
0.4
0.3
0.4 0.9 0.6 1.5 0.8 0.2
0.8

0.7

0.3
0.2 0.5 0.6
0.2
1.0 0.5 0.9 1.0

6

0.7 0.2 0.2

0.3
0.2
0.9
0.3
0.5 1.2
0.9
0.2
0.6 0.4
0.3 0.7 0.2 0.3 1.7
0.2
0.5 0.5
1.5
0.4
1.0 1.4
1.0
0.2
0.3 0.2
0.5
0.5
0.2
0.2 0.2
0.3
0.2
1.0 0.2 0.3 0.7
0.2 0.5
0.7
0.4 0.4
0.3
0.4
0.3
0.4
0.7
0.3
0.2 0.6 1.2 0.3
0.2
0.2
1.1
0.6
0.3
0.6
0.9 0.8 0.6 0.6
0.3
0.5 0.4 0.5 0.2 0.7 0.8
0.6 0.9 0.4 0.5
0.6
0.2 0.8 0.3
0.4
0.4
−0.2
0.2
−0.2 −0.5
−0.3
−0.3
−0.2
−0.2 −0.2−0.2−0.2−0.8 −0.2
−0.9
−0.4
−1.2
−0.2
−0.2
−0.3
0.6

1.4

1.3

1.1

−2

PakistanJapan
Australia
Fiji
Nepal
US
Singapore
Iran
Malaysia
Philippines
ROC
Hong Kong
Bangladesh
Thailand
Korea
TurkeyIndonesia
Sri Lanka
Vietnam
Cambodia
Mongolia
India
China

Mongolia
Fiji
Philippines
Hong Kong
Japan
Nepal
US
TurkeyAustralia
Indonesia
IranPakistan
Bangladesh
Myanmar
Sri Lanka
Cambodia
India
Thailand
Malaysia
Singapore
ROC
Korea
Vietnam
China
1. Agriculture

2. Mining

4. Electricity, gas, and water supply

5. Construction

6. Wholesale and retail trade, hotels, and restaurants

7. Transport, storage, and communications

8. Finance, real estate, and business activities

9. Community, social, and personal services

3. Manufacturing

Real GDP

Figure 88 Industry Origins of Labor Productivity Growth, 1990–2000 and 2000–2013_Industry decomposition: Average annual growth rate of GDP at constant prices
Source: APO Productivity Database 2015.

Figure 88 shows the industry origins of average labor productivity growth per year in two periods:
1990–2000 and 2000–2013.103 In the past two decades, the role played by agriculture (both positive
and negative) has been diminishing in Asian countries. While the importance of manufacturing has
never waned in some countries (e.g., Korea, the ROC, China, and Thailand), it has not been a major
contributor in India in its recent development process, or in Hong Kong and Sri Lanka in the 2000s.

Traditionally, it has been difficult for the service sector to realize productivity growth, but modern
advancements in information and communication technology have changed this. Many IT-intensive
users are located in this sector, which is capable of capturing the productivity benefits arising from IT
utilization. The growing importance of these services has been observed in explaining the productivity growth in Western economies of recent decades. In Asia, the contribution from services matches103: Not all Asian countries are included, as employment by industry sector is not available for some countries.

115©2015 Asian Productivity Organization

The manufacturing sector has been a major driving force behind productivity growth in most Asian
countries, as shown in Figure 89. In the 1990s, manufacturing accounted for a significant part of laborproductivity growth in Hong Kong (83%), Indonesia (53%), and China (49%). Nevertheless, its role haslessened in 2000–2013 to 8%, 27%, and 32%, respectively. In contrast, contributions from manufacturing strengthened from 54% to 60% in Korea, from 29% to 64% in the ROC, and from 56% to 82% inJapan between the two periods. In other economies, however, like Sri Lanka, Nepal, and Mongolia, inthe 2000s manufacturing played a negligible role.

6 Industry Perspective

1990−2000
5.0 4.0 3.0 2.0 1.0 0.0 −1.0 %
4.3
2.7
1.7
1.4
1.3
1.3
1.1
1.1
1.0
0.8
0.7
0.7
0.7
0.6
0.6
0.4
0.4
0.3
0.3
0.3

2000−2013
−30

China
Korea
Singapore
Cambodia
ROC
Malaysia
VietnamIndonesia
ThailandHong Kong
Bangladesh
US
TurkeyJapan
Sri Lanka
Philippines
India
Pakistan
IranAustralia
0.0
Myanmar
−0.2
Nepal
−0.2
Fiji
−0.4 Mongolia

Contribution

0

30

60 %

3.0

49

2.8

2.0

54

0.0 %

1.0

1.7

41

1.5

39

1.3

29

1.1

33

1.0

22

1.0

53

1.0

24

0.9

83

0.9

32

0.9

39

0.7

35

0.6

56

0.6

21

0.6

46

0.5

10

0.5

13
12

0.3

13

0.3
0.2

−1

0.2

−9

0.1

78
27

0.1

Contribution share toaggrergate labor productivity

0

China
Korea
ROC
Cambodia
Thailand
Vietnam
Malaysia
India
Singapore
Indonesia
Philippines
Bangladesh
Japan
Mongolia
Iran
Sri Lanka
US
Fiji
TurkeyHong Kong
Australia
Pakistan
Nepal

20

40

60

80 %

32
60
64
33
43
26
63
19
61
27
39
28
82
13
36
14
35
30
11
8
16
30
6

Contribution

Contribution share toaggrergate labor productivity

Figure 89 Contribution of Manufacturing to Labor Productivity Growth, 1990–2000 and
2000–2013
Source: APO Productivity Database 2015.

1990−2000
3.0

2.0

1.0

0.0 −1.0 %

2.8
2.5
2.2
2.2
2.0
1.9
1.8
1.4
1.4
1.3
1.3
1.3
1.2
1.2
1.0
0.8
0.7
0.5
0.4
0.3
0.1
0.1
0.1

©2015 Asian Productivity Organization

Contribution

2000−2013

−30

ROC
India
Vietnam
China
Malaysia
Singapore
Cambodia
Korea
Sri Lanka
Bangladesh
Pakistan
IranAustralia
Nepal
US
ThailandTurkeyMyanmarJapan
Hong Kong
Philippines
Indonesia
Mongolia
−0.8
Fiji

0

30

60 %

4.0

62

3.9
71

45

3.0

2.0

1.0

0.0 %

3.3
2.8

26

2.2
50

2.2

45

2.1

52

2.1

28

1.5

51

1.5

59

1.5

60

1.4

61

1.4

60

1.3

68

1.0

58

0.8

20

0.8

38

0.8

20

0.8

40

0.7

29

0.7

16

0.5

5

0.5

−4

0.1

303

Contribution share toaggrergate labor produtcivity

Contribution

India
China
Mongolia
Hong Kong
Nepal
Bangladesh
Sri Lanka
VietnamTurkeyIndonesia
ThailandIranPhilippines
Fiji
Cambodia
US
Singapore
Pakistan
Korea
ROC
Malaysia
Australia
Japan

0

20

40

60

80 100 %
74

37
57
90
180
84
54
39
51
44
54
84
55
90
21
65
54
179
27
29
34
46
9

Contribution share toaggrergate labor productivity

Figure 90 Contribution of Service Sector to Labor Productivity Growth, 1990–2000 and
2000–2013
Source: APO Productivity Database 2015.

116

6.3 Labor Productivity by Industry

Box 7

Improvement in Labor Quality

This edition of the Databook defines labor inputs as the simple sum of the economy-wide hours worked.
The estimates of number of workers and average hours per worker have improved in this edition (see Appendix 4 for the details). In productivity analysis, however, labor inputs are expected to be quality adjusted
in order to reflect workforce heterogeneity, as recommended in the SNA 2008 (United Nations, 2009). In

%

2.4
1.8

Labor quality
Hours worked

1.7

0.6

0.6
0.9 0.0
0.3
US
Japan

0.0

6

1.3

0.6

1.2

1.0 0.7
0.2

1.2
0.7 0.6
0.0

1.1

0.2
0.2

0.4

0.8

0.6

0.2

0.6
0.2

0.4 1.0
0.5

0.1

−0.4

0.4 0.2

−0.3

0.8

1.0

0.4

0.4 0.1−0.6

0.3 0.2−0.3 −0.1

0.3 0.3−0.4 −0.3

0.2 0.3
0.0

1955–60 1960–65 1965–70 1970– 75 1975–80 1980–85 1985–90 1990–95 1995–2000 2000–05 2005–10 2010–12

Figure B7.1 Contributions of Labor Quality to Growths in Japan and the US,
1955–2012
Source: Jorgenson, Nomura, and Samuels (2015).

Figure B7.1 shows the contributions oflabor quality and hours worked to economic growths in Japan and the US
since 1955, by Jorgenson, Nomura,and Samuels (2015). Although the US
sustained a steady pace of labor quality contribution of 0.1–0.3% on average
per year to the economic growths overa half century, the contributions of labor quality were substantially changedin the catching up process of the Japanese economy to the US. The labor
quality improvement had a significant
contribution to growth by 0.7–1.1% on
average per year during 1955–1980.
These impacts have decreased, but labor quality changes remain factorsthat enhanced the growths by 0.3–
0.4% for two decades after 1990 evenwhen the Japan’s hours worked began
to decrease.

Years

14

Japan
Korea
ROC

13
12

Fiji
Sinagpore
Hong Kong
Sri Lanka
China
Mongolia
Iran
Thailand
Malaysia
Vietnam
PakistanIndonesia
Philippines

11
10
9
8
7

India
Bangladesh

6

Nepal
5
4
Cambodia

3
2
1
1970

1975

1980

1985

1990

1995

2000

2005

2010

Figure B7.2 Average Schooling Years of Workers,
1970–2013
Sources: Population census and labor survey in each country, including author adjustments
continued on next page >117©2015 Asian Productivity Organization

the stage of high economic growth, labor quality growth can be a significant
factor as well as the increase in hours
worked, improvement in education attainment of workers, and a shift fromthe self-employed (e.g., in agriculture
or informal service sectors) to the employees (e.g. in manufacturing or formal service sectors).

6 Industry Perspective> continued from previous page

The TFP growths estimated in Chapter 5 include the contributions of labor quality improvements by definition. On the analogy of the experiences of Japanese economy, it may be reasonable that the current estimates of the TFP growths include the contributions of labor quality improvements about 0.5–1.0% per year
in Asian economies. Although it is a very data-demanding exercise, the APO Productivity Database project
has spent several years collecting the official data on number of workers, average hours worked per worker, and hourly wages by type of labor categories for the Asian economies. This data was necessary to develop a harmonized database of quality adjusted labor input (QALI) and to identify an impact of labor
quality improvement in TFP growths. A first set of the estimates will be available in 2016.
Figure B7.2 presents the time-series comparisons of the average schooling years observed in terms ofworkers since 1970, based on our work-in-progress estimates. Although there is a significant range in 2013
from 3 years (Cambodia) to 13 years (Japan) in average schooling years of workers, the average years havebeen increased since 1970 in almost all economies in Asia.

that of manufacturing. Among the four industries in the service sector, three are potentially IT-employing industries: wholesale and retail trade, hotels, and restaurants; transport, storage, and communications; and finance, real estate, and business activities.

©2015 Asian Productivity Organization

Figure 90 presents the contribution of services in labor productivity growth by country. In 2000–2013,services were contributing at least one-third or more to labor productivity growth in most Asian
countries. The contribution was predominant in Hong Kong and India, accounting for 90% and 74% oflabor productivity growth, respectively. It also accounted for around two-thirds or more of labor productivity growth in the US, Sri Lanka, and Singapore. Japan had the lowest share from the service
sector, accounting for less than one-tenth of labor productivity growth. There is an expansion of therole played by services in China between these two periods, from 26% to 37%. The contribution ofservices was also highly significant in South Asian countries like Bangladesh, Bhutan, India, Nepal, andPakistan over the same term. Finance, real estate, and business activities made the largest contribution of 1.4, 1.2 and 1.1 percentage points in India, Singapore, and Hong Kong, respectively.

118

6.3 Labor Productivity by Industry

Box 8

Wages of Workers and Income Level

Figure B8 plots per-worker average wages for employees against per capita GNI, using annual average exchange rates for selected countries in 2013 (taking the logarithms). The overall trend is a positive association; the higher average wages, the higher the per capita income. Of course, average wages are not equal
to GNI per capita. First, some adjustments are needed for the number of workers in one family. Second,
income from capital must be counted. If you inspect Figure B8, some countries are off the simple regression line. One outlier is Singapore, which is below the regression line. This likely reflects a large proportionof foreign workers out of total labor force who are paid lower than local workers.
Other off-lines are ASEAN member states including Cambodia, Vietnam, the Philippines, Indonesia, Thailand, and Malaysia. They have relatively low wages vis-à-vis income levels. Is it because they set unfairly low
wages? Probably not. Rather, in these countries, labor movements from the informal to formal sectors orfrom rural to urban are relatively smooth, which pushes down average wages of employees. These countries indeed gain competitiveness in the manufacturing sector and achieve rapid decreases in the population below the poverty line.

6

In contrast, the South Asian countries including Nepal, Bangladesh, Pakistan, India, and Sri Lanka are above
the regression line perhaps because they face a difficulty in labor movements from informal to formal orfrom rural to urban. The reasons may reside in both labor supply and demand. Presumably, education gaps
between rural and urban are too big, or stunted modernization is too serious in rural areas. Perhaps too,poor urban infrastructure may cause high living costs and poor security conditions in urban areas. In either
case, these countries suffer from an unfavorable position for the smooth growth of manufacturing sector.

Log of per-worker average wage in 2013 (using 2013 exchange rate)

2.0

US Australia
EU15 Japan
Hong Kong
Korea

1.6

Singapore

ROC
Turkey

1.2
Fiji

Iran

Sri Lanka
0.8

Bangaldesh
Pakistan
Nepal

Malaysia

ThailandChina
Indonesia

India

Philippines

Mongolia

Vietnam

0.4
Cambodia
0.0
–0.4

0.0

0.4

0.8

1.2

1.6

2.0

Log of per capita GNI in 2013 (using 2013 exchage rate)

Figure B8 Average Wage and Per Capita GNI, 2013

119©2015 Asian Productivity Organization

Sources: Official national accounts in each country, including author adjustments; APO Productivity Database 2015.

7 Real Income
The standard GDP concept does not adequately measure welfare. Among its shortcomings is theneglect of the terms-of-trade effect. An improvement in the terms of trade (the relative prices of acountry’s exports to imports), unambiguously raises real income and in turn welfare.104 In many ways,
a favorable change in the terms of trade is synonymous with technological progress, making it possible to get more for less. That is, for a given trade balance position, a country can either import more
for what it exports, or export less for what it imports.

7.1 Real Income and Terms of Trade7

By focusing on production per se, the real GDP concept does not capture this beneficial effect of the
improvement in the terms of trade.105 In contrast, real income focuses on an economy’s consumption
possibilities, and in turn captures the impact of a change in the relative price of exports to imports.
Real income growth attributed to changes in the terms of trade can be significant when there arelarge fluctuations in import and export prices and the economy under concern is highly exposed tointernational trade, as many Asian economies are (see Figure 26, p. 43). For example, real income
growth for oil-exporting countries was more than double that of real GDP growth in recent years (asin Saudi Arabia and Brunei). Meanwhile, there has been no significant difference between real income
growth and real GDP growth in Myanmar, which is a relatively closed economy (see Figure 34, p. 50
and Figure 97 for the expenditure-side and the income-side, respectively). In the 2000s, the trading
gain has also driven a significant wedge between real income and real GDP in Australia. That is partly
due to a fall in import prices, but owes more to the rising prices of its commodity exports.
The distinction between real income and real GDP lies in the differences between the corresponding
deflators. Real GDP is calculated from a GDP deflator aggregating prices of household consumption,
government consumption, investment, exports, and imports,106 while real income is calculated fromthe prices of domestic expenditure, consisting of household consumption, government consumption, and investment. Therefore, real income can be understood as the amount of domestic expenditurethat can be purchased with the current income flow.107 As such, real income captures the purchasing
power of the income flow. Furthermore, the Databook adopts the concept of gross national income(GNI) instead of GDP in its estimation of real income, to take into account net income transfer fromabroad. Applying the method proposed by Diewert and Morrison (1986), the annual growth rate ofreal income can be fully attributed to three components: annual growth rate of real GDP, real income
growth attributed to changes in prices of exports and imports (referred to as the trading gain),108 andthe effect of net income transfer.109( )Pt

In (GNIt−1) − In P t−1D = In ( GNIt−1/GDP t−1 ) + In (GDPt / GDPt−1) − (1/2) ∑ i (sit + sit−1) In (Pit / Pit−1) +GNIt

GNIt/GDP t

D

Real income growth(

Income transfer effect)

Real GDP growth()(1/2) (sXt + sXt−1) In ( PXt / PXt−1 ) − In ( PDt / PDt−1 ) − (1/2) (sMt + sMt−1) In ( PMt / PMt−1 ) − In ( PDt / PDt−1 )Real income growth attributed to changes in the terms of trade (=trading gain)

where Pit is price of final demand i in period t and sit is expenditure share of final demand i in period t. D is domestic expenditure,
X is export, and M is import. Note that the real GDP growth based on this formulation may differ from that used in other chapters,
since the implicit Törnqvist quantity index is adopted for calculating it.

121©2015 Asian Productivity Organization

104: See Diewert and Morrison (1986) and Kohli (2004).
105: Kohli (2004) elaborates: “if real GDP is measured by a Laspeyres quantity index, as it is still the case in most countries, an improvement in the terms of trade will actually lead to a fall in real GDP.”
106: The weight for import price changes is negative. Thus, if import prices decrease, this tends to raise the GDP deflator.
107: This definition of real income is the same as in Kohli (2004, 2006). An alternative definition is nominal GDP deflated by the priceof household consumption; this is adopted by Diewert, Mizobuchi, and Nomura (2005) and Diewert and Lawrence (2006).
108: The term “trading gain” is used by some authors (Kohli, 2006). This term is adopted in this report.
109: Real income growth can be decomposed into two components as follows:7 Real Income

A general observation is that over a long period of time the trading gain effect is, on average, small,but over a shorter period could be very significant.110 The findings presented in Table 19 confirm thisobservation. Excluding the oil-exporting countries, the trading gain effect in 17 out of 22 economies
compared, fell within the margin of ±10% of real GDP growth on average for the long period of 1970–2013. Movements in terms of trade have been consistently unfavorable to the ROC and Korea. In the
Table 19 Real Income and Terms of Trade, 1970–2013, 1995–2000, 2000–2005, and 2005–2013_Average annual growth rate of real income, real GDP, trading gain, and net primary income transfer from abroad©2015 Asian Productivity Organization

Bahrain
5.72
Kuwait
5.59
Oman
8.13
Qatar
6.83
Saudi Arabia 7.31
UAE
10.44

4.87
0.88
6.39
6.23
4.88
9.93

1.18 −0.32
4.31
0.41
1.61
0.14
0.64 −0.04
1.54
0.87
0.18
0.32

0.00
0.11

3.78 0.12
0.15
4.36 −0.31 −0.08(reference)
USEU15

2.74 2.78 −0.06
0.02
2.08 2.12 −0.03 −0.01

Australia
Turkey3.47 3.29 0.22 −0.03
4.17 4.31 −0.12 −0.03

Australia
Turkey

4.05
3.98

6.54
7.64
2.81
9.79
4.29
4.71
2.86

1.33
4.64
4.82
4.54
4.55
1.74
5.15

−0.02
−1.17
0.21
−2.30
−0.07
−0.08
0.00

2.49 −0.03
0.07
1.76 0.07
0.11
1.85 0.07
0.05
3.37 1.17 −0.22
4.61 0.27 −0.19

Trading gain

4.19 0.09
2.87 −0.07

Bahrain
7.85
Kuwait
11.10
Oman
7.83
Qatar
12.03
Saudi Arabia 8.76
UAE
6.37
Brunei8.01(reference)
US
2.53
EU15
1.94
EU28
1.96
Australia
4.31
Turkey4.68

Net primary income
from aboad

−0.35
0.34
−0.38
−0.97
−0.21
−0.42
0.00

Real GDP

2.87
4.40
4.15
5.81
2.02
1.87
3.59

11.96 11.96 0.00
0.00
11.54 6.22 5.54 −0.24
10.97 10.32 0.55
0.10
10.11 10.37 0.00 −0.26
8.89 7.17 2.02 −0.30
8.15 7.68 0.57 −0.09
7.31 5.35 1.20
0.77
7.01 7.25 −0.32
0.07
6.86 6.75 0.37 −0.26
5.71 5.85 −0.78
0.63
5.50 4.67 0.72
0.11
5.40 4.25 −0.28
1.44
5.35 5.18 −0.05
0.23
4.63 5.15 −0.01 −0.52
3.92 4.96 0.20 −1.25
3.82 4.53 −0.70 −0.01
3.67 4.29 −0.99
0.38
3.06 4.09 −0.98 −0.05
2.95 3.55 −0.68
0.07
2.57 3.81 −1.44
0.21
2.11 2.28 0.35 −0.52
1.07 1.20 −0.33
0.20Real income

3.51
1.62
4.10
8.62
2.65
6.56
1.81

Bahrain
6.04
Kuwait
6.36
Oman
7.86
Qatar
13.47
Saudi Arabia 4.47
UAE
8.01
Brunei5.39(reference)
US
4.28
EU15
2.91

Trading gain

MyanmarMongolia
China
Cambodia
Iran
Vietnam
Malaysia
India
BhutanPakistan
Sri Lanka
Philippines
Bangladesh
ThailandSingapore
Korea
Indonesia
Hong Kong
Nepal
ROC
Fiji
Japan

2005–2013
Net primary income
from aboad

2.62
−0.26
0.08
−0.27
1.66
−0.03
0.01
−0.75
0.15
−0.31
−0.15
−0.61
0.10
−0.38
−0.05
−0.01
1.00
0.09
−0.81
−0.01

Real GDP

Bhutan10.04 7.51 −0.09
Vietnam
7.37 7.41 0.21
China
7.07 7.78 −0.78
Singapore
6.17 6.30 0.14
Philippines
5.84 3.04 1.14
ROC
5.73 5.90 −0.14
India
5.32 5.48 −0.17
Malaysia
5.20 5.54 0.41
Iran
5.20 2.73 2.30
Cambodia5.17 5.44 0.04
Sri Lanka
4.81 5.03 −0.07
Myanmar4.34 4.91 0.04
Bangladesh 4.15 4.02 0.02
Pakistan
3.59 3.99 −0.02
Korea
3.22 5.23 −1.96
Hong Kong
2.75 2.39 0.37
Fiji
2.33 2.44 −1.12
Japan
0.80 0.88 −0.16
Indonesia −0.60 −0.39 0.62
Thailand−0.93 0.27 −1.20

Real income

Net primary income
from aboad

−0.01
−0.09
−0.05
−0.02
0.07
0.33
−0.03
−0.07
0.05
−0.02
−0.14
−0.10
0.10
0.12
0.69
0.17
0.08
0.09

2000–2005
Trading gain

Net primary income
from aboad

8.64 −0.01
7.33 −0.22
6.42 0.42
7.04 −0.42
7.10 −0.87
5.54 0.22
5.23 0.59
5.67 0.06
5.60 −0.06
5.38 −0.03
5.69 −0.39
5.37 −0.21
3.15 1.74
5.11 −0.30
3.72 −0.01
3.33 −0.21
2.55 0.20
2.65 −0.31

Real GDP

Trading gain

8.62
7.01
6.79
6.60
6.29
6.10
5.79
5.65
5.60
5.34
5.16
5.07
5.00
4.93
4.40
3.29
2.83
2.42

Real income

Real GDP

China
Singapore
Malaysia
Korea
ROC
BhutanIndonesia
MyanmarHong Kong
India
ThailandSri Lanka
IranPakistan
Philippines
Bangladesh
Fiji
Japan

1995–2000

Real income

1970–2013

China
10.72 10.73 −0.04
0.03
Myanmar10.57 10.57 0.00
0.00
Mongolia
8.79 9.79 −0.46 −0.53
India
7.34 7.42 −0.01 −0.07
Vietnam
7.04 6.62 0.75 −0.33
Cambodia6.70 6.76 0.94 −1.00
Sri Lanka
6.42 6.59 0.01 −0.19
Bangladesh 6.04 6.07 −0.45
0.42Indonesia
5.84 6.31 −0.70
0.23
Bhutan5.57 6.01 0.19 −0.63
Philippines
5.52 5.07 −0.17
0.61Singapore
5.27 5.91 −1.11
0.47
Malaysia
4.99 4.58 0.29
0.12
Nepal
4.65 3.82 0.68
0.15
Thailand3.38 3.77 −0.25 −0.14
Pakistan
3.29 3.68 −0.78
0.39
Hong Kong
3.13 3.53 −0.58
0.18
Korea
3.09 3.48 −0.58
0.19
Iran
1.87 0.90 0.87
0.10
ROC
1.82 3.60 −1.82
0.05
Japan
0.19 0.55 −0.51
0.16
Fiji
−1.09 0.97 −1.56 −0.51
Bahrain6.68 5.45 2.62 −1.40
Kuwait5.34 2.08 3.71 −0.46
Oman
8.77 4.45 4.44 −0.11
Qatar
14.29 11.65 1.74
0.88
Saudi Arabia 6.43 4.21 2.05
0.16
UAE
5.70 3.54 2.34 −0.18
Brunei3.90 −1.54 5.38
0.07
(reference)US
1.24 1.22 −0.06
0.08
EU15
0.44 0.57 −0.12 −0.02
EU28
0.62 0.74 −0.11
0.00
Australia
3.51 2.77 0.53
0.21
Turkey3.34 3.75 −0.42
0.01

Unit: Percentage.
Sources: Official national accounts in each country, including author adjustments.
Note: See footnote 109 for the definition of real GDP growth, real income growth, and trading gain growth. The starting years for somecountries are different due to data availability during 1970–2013: Brunei (1989–), Cambodia (1993–), Mongolia (2000–), Nepal (2000–), andVietnam (1989–).

110: Short-term trends in export and import prices cannot continue indefinitely. Negative and positive trading gain effects in shorter
periods cancel each other out. In the end, the accumulated effect over a long period of time often becomes negligible.

1227.1 Real Income and Terms of Tradeshort term, the spread of the trading gain effect is wider across countries. Australia has benefitted
from the continual surge in commodity prices since the early 2000s, as such, its terms of trade havebeen turning strongly in its favor. The trading gain effect in Australia has therefore been rising from
3% on average per year in 1995–2000, to 35% in 2000–2005, and 19% in 2005–2013 of its real GDP
growth. In terms of percentage points, the trading gain added 0.12, 1.17, and 0.53 percentage points
to real GDP growth in the three consecutive periods. For the oil-exporting countries, the trading gain
effect is almost always positive and significant, making it possible to sustain a rise in purchasing power with little real GDP growth in countries, such as Brunei, Kuwait, and UAE in 2005–2013.
Over the past four decades, net primary income from abroad has not moved outside the margin of±10% of real GDP growth on average for all 29 countries compared, except for the Philippines, Kuwait,
and Saudi Arabia. Net primary income from abroad has been a long-term significant contribution to
the purchasing power of the Philippines, with remittances from large number of overseas workers.
When its real GDP growth slowed (during the late 1990s), net primary income from abroad played aneven greater role in cushioning the real income of Filipinos. Over the past four decades, net primary
income from abroad augmented real GDP growth by 3.2% and 0.6% on average per year in Japan andthe US, respectively. This has grown to be more significant (29% and 6%, respectively), in both countries as real GDP growth slowed from 2005–2013.

7

Figure 91 plots the time series of net primary income from abroad as a percentage of GDP. The role ofnet primary income from abroad has been shifting from negative to positive in Hong Kong, with thetransition taking place in the mid-1990s leading up to the handover of Hong Kong from British rule to
China in 1997. Since then, net primary income from abroad has been positive. A shift in the role of net
primary income from abroad has also taken place in Korea from negative to a more or less neutral
position in the 2000s. It has held positive in the ROC, oscillating around +2% of GDP, since 1980. Singapore’s net primary income from abroad displayed the largest fluctuations, ranging from +2.0% in
1997 to –7.0% in 2004, but on the whole, it has been more negative than positive. In Japan and thePhilippines, net primary income from abroad has risen strongly, albeit at different magnitudes. In Japan, it rose from 0.6% of GDP in 1990 to 3.5% in 2013, compared with 1.5% in 1990 and 32.2% in 2013
in the Philippines. In the US, it has always been positive, fluctuating within +1.6% of GDP, whereas in
the EU15 it was marginally negative for the three decades between 1975 and 2005 before turning
mildly positive.

%

6
4

%

ROC
Korea

Hong Kong
Singapore

4

3

2

%

China
Japan
US

India
EU15

40

30

2

20

1

10

0

0

−1

−10

Bangladesh
Fiji
Malaysia
Pakistan

Cambodia
Indonesia
Mongolia
Philippines

0

−4
−6
−8

1970 1975 1980 1985 1990 1995 2000 2005 2010−21970 1975 1980 1985 1990 1995 2000 2005 2010−20

1970 1975 1980 1985 1990 1995 2000 2005 2010

Figure 91 Effect of Net Income Transfer on GDP, 1970–2013
Sources: Official national accounts in each country, including author adjustments.

123©2015 Asian Productivity Organization−2

7 Real Income

Combining both the trading gain
effect and net primary income fromabroad, real income growth formost of the countries compared fellwithin the margin of ±20% of real
GDP growth (Figure 92). Kuwait andBrunei appear to be the outliers,
with real income growth being 6.3
times and 3.7 times their respective
long-term dismal real GDP growthof 0.9% and 1.3%.111%

12Real income growth(+20%)
UAE

10
China
8
Saudi Arabia(−20%)

Oman
VietnamSingapore
Malaysia
Korea
BhutanROC
MyanmarIndonesia
KuwaitBahrainIndia Hong Kong
Thailand
IranPakistan
Sri Lanka
Brunei
Philippines
TurkeyAustralia
Bangladesh
Fiji
US
Japan
EU15
Qatar

6

4

©2015 Asian Productivity Organization

Unlike the oil-exporting countries,
at any one time roughly half of the2
Asian countries compared sustained a negative trading gain effect, albeit to variable extents,
0
whereas the impact from net pri0
2
4
6
8
10
12 %
Real GDP growthmary income from abroad was relatively less pronounced. The period
Figure 92 Real Income and Real GDP Growth, 1970–2013
of 1995–2000 reflects the impact of_Average annual growth rate of GDP at constant prices and real
the Asian financial crisis. For Thaiincome
land, the trading gain effect more
Sources: Official national accounts in each country, including author adjustments.
than outweighed the small positive
Note: The starting years for some countries are different due to data availability
during 1970–2013: Brunei (1989–), Cambodia (1993–), Mongolia (2000–), Nepal
average real GDP growth per year
(2000–), and Vietnam (1989–).
(0.3%), giving rise to a marginal fallin real income of –0.9%. In Korea,
the negative trading gain also shaved 37% off real GDP growth of 5.2%, producing real income growthof 3.2%. At the start of the 2000s, the Asian economy recovered from the financial crisis, but the trading gain effect ran counter to welfare for some countries, with a negative impact that only intensified
after 2005. For example, in the ROC, the trading gain effect caused real income growth to be 38%
lower than real GDP growth in the period 2000–2005. However, in the period 2005–2013 it wiped out
51% of the attractive 3.6% real GDP growth on average per year, leaving real income to grow at 1.8%.
Similarly, in Korea the trading gain effect caused real GDP growth to overestimate real income growthby 16% in the first half of the 2000s, which increased to 17% in the years 2005–2013 (Table 19 and
Figure 93). In Japan, the negative trading gain effect more than wiped out the 0.5 percentage pointsof real GDP growth, leaving real income to actually fall by 0.2% per year on average in the period
2005–2013.
In contrast, the trading gain worked to counterbalance falling real GDP in Brunei, leaving it with a robust, real income growth of 3.9%, despite its contracting real GDP of 1.5% in the period 2005–2013(Table 19). In Saudi Arabia, real income growth was more than 153% faster than its real GDP growth.
This takes place against the backdrop of strong oil prices, which spiked in mid-July 2008 to USD 145
per barrel. After dropping sharply to USD 30 per barrel by the end of 2008 (reflecting the fall in demand by the global financial crisis), oil has steadily risen to, and held at, over USD 100 per barrel since2010 through the middle of 2014 (Figure 94). In the US, the trading gain effect has been unfavorable
111: According to Kohli (2004) study on real income of 26 OECD countries during 1980–1996, the trading gain on average over theentire period varies across countries, from the smallest effect of –0.8% (–30.9% of real income growth) per year in Norway to thelargest of 0.63% (29.4% of real income growth) per year in Switzerland.

1247.1 Real Income and Terms of Trade

more often than not, but its positive net primary income
from abroad has worked to counterbalance it and the difference between real GDP and real income growth is reduced. For example, in the latest period 2005–2013, thetrading gain effect shaved 0.06 percentage points off real
GDP growth of 1.22%. It was counterbalanced by the positive effect from net primary income from abroad, which
added 0.08 percentage points to real GDP growth, leaving
real income growth slightly higher than real GDP.

5.4
4.4
3.7
2.6
2.3
2.1
1.7
0.9
0.9
0.7
0.7
0.5
0.3
0.2
0.0
0.0

BruneiOman
KuwaitBahrain
UAE
Saudi Arabia
Qatar
Cambodia
Iran
Vietnam
Nepal
Australia
Malaysia
BhutanSri Lanka
Myanmar
India
China
US
EU28
EU15
Philippines
ThailandTurkeyBangladesh
Mongolia
Japan
Hong Kong
Korea
Indonesia
Pakistan
Singapore
Fiji
ROC

7

0.0
Figure 95 provides the results of further decomposition of0.0
the trading gain into the terms-of-trade effect and the real
−0.1
−0.1
exchange rate effect in Asian countries for the period−0.1
1970–2013.112 The terms-of-trade effect is the part of real
−0.2
−0.2
income growth attributed to the change in the relative−0.4
price between exports and imports. The real exchange
−0.4
−0.5
rate effect refers to the part of real income growth attrib−0.5
uted to changes in the relative prices of traded goods and
−0.6
−0.6
domestically consumed goods. By applying this result, real
−0.7
−0.8
income growth can be decomposed into real GDP growth,−1.1
terms-of-trade effect, real exchange rate effect, and net
−1.6
−1.8
primary income from abroad. The first chart in Figure 95
−2
0
2
4
6%
applies this break-down to Asian countries for the period
1970–2013. It shows that the real exchange rate effect isFigure 93 Trading Gain Effect, 2005–
generally much smaller than the terms-of-trade effect, im2013
plying that the relative prices of traded versus domesti_Average percentage points
cally consumed goods have been largely stable in most
Sources: Official national accounts in each country,
countries. The exception is Kuwait where the real exincluding author adjustments.
change rate effect accounted for 31% of real income
growth. This might have reflected the weight of oil in thecomposition of their traded goods. The second chart shows the decomposition for the most recent
period 2000–2013. It shows that the trading gain, particularly the terms-of-trade effect, is highly significant and favorable for the oil-exporting countries, but is significant and negative in a handful of
Asian economies such as Hong Kong, the ROC, Indonesia, Korea, Fiji, and Pakistan.

Dollars per barrel

145.3

150

106.8

125

113.4

109.4 110.2

100
77.1

75
50

68.0

41.1

59.2

75.4

0
Jan 02,
1986

30.3
10.8

Jan 02,1988

Jan 02,1990

Jan 02,1992

Jan 02,1994

Jan 02,
1996

Jan 02,
1998

Jan 02,2000

Jan 02,
2002

Jan 02,
2004

Jan 02,2006

Jan 02,2008

Jan 02,2010

Jan 02,2012

Jan 02,2014

Figure 94 Price of Crude Oil, 1986 January–2015 May
Source: US Energy Information Administration, WTI spot prices FOB (Cushing, Oklahoma).

125©2015 Asian Productivity Organization

43.4

25

7 Real Income

EU15
Japan
US
Fiji
Bangladesh
Australia
TurkeyPhilippines
Pakistan
Iran
Sri Lanka
ThailandIndia
KuwaitHong Kong
MyanmarBahrainIndonesia
BhutanROC
Korea
Malaysia
Qatar
Singapore
Saudi Arabia
Oman
China
UAE

Figure 96 shows the decomposition of average annual real income growth covering two periods ofmajor economic shocks faced by the Asian economies: during 1973–1979, which includes the two oil
price hikes in 1974 and 1979; and 1996–1998 to capture the impact of the Asian financial crisis. Highoil prices improved the terms for oil-exporting countries, such as Iran and Indonesia, and worsened
the terms of trade for oil-importing countries. During the Asian financial crisis, the terms-of-trade effect was still the predominant factor in determining the difference between real income growth andreal GDP growth. In Brunei, theterms-of-trade effect further reinforced the negative real GDP
1970–2013%12
growth of –6.3%, reducing its
real income growth a further 8.1
9percentage points. In Iran, the1.4 0.9
0.6
6
negative terms-of-trade effect
0.7 1.1
1.0
9.9
1.7
8.6
1.8
0.7
counteracted the 1.0% real GDP
7.3
7.1
7.0
6.4
6.2 6.43
5.5 5.2 4.9 5.7 5.6
5.4 5.7 5.4
5.1
4.9
2.6
4.3
3.7
growth, giving real income
3.3 3.3 2.5 2.8 2.7
3.2
2.1
0.9
0
growth of –1.5%. In Indonesia,−0.8
the trading gain effect worked to−3
counterbalance the contraction
in real GDP, whereas in Thailand,
it reinforced the negative real
GDP growth. In the Philippines,
2000–2013%
although the strong favorable
15
0.6
terms-of-trade effect was moder12 2.4
ated by the negative real ex1.3 0.7
9
1.0 0.7
change rate effect, the resulting
2.0
0.9
6 10.94.0
3.1
real income growth more than
2.7
10.6
2.1 0.8
11.1
1.4
8.4
1.7
8.1
113
7.4
7.0
0.8
tripled the real GDP growth.
3
5.9 6.3 5.9
5.7
5.5 5.5
3.8

0−3−0.7−0.7

4.0 4.9

4.2

3.9 4.8

3.3

4.5 3.7 4.3 4.1

−0.7

−0.9

3.0 3.9 3.7 3.7−0.8−1.7

1.7 1.0

0.8

1.5
−1.0

Fiji
Japan
EU15
US
ROC
Hong Kong
Korea
Australia
TurkeyThailand
Nepal
Pakistan
IranSingapore
Indonesia
Philippines
Brunei
Bangladesh
Malaysia
UAE
Sri Lanka
BhutanBahrainIndia
Saudi Arabia
Vietnam
KuwaitCambodia
Oman
Mongolia
China
Myanmar
Qatar

Figure 97 shows this decomposition of real income in each Asian
country, along with the US, EU15,
Australia, and Turkey114 from 1970,or the year of first data collec­tion for the country in question.
The trading gain can be positiveor negative, depending on the direction of change in the terms oftrade. Its impact is modest for

4.2

Real exchange rate effect
Net primary income from abroad

Terms-of-trade effect
Real income

Real GDP

Figure 95 Decomposition of Real Income Growth, 1970–2013 and 2000–2013
Sources: Official national accounts in each country, including author adjustments.

112: Following Kohli (2006), trading gain can be decomposed into two components as follows:

(

)

(

)

(1/2) (sXt + sXt−1) In ( PXt / PXt−1 ) − In ( PDt / PDt−1 ) − (1/2) (sMt + sMt−1) In ( PMt / PMt−1 ) − In ( PDt / PDt−1 ) =©2015 Asian Productivity Organization

Real income growth attributed to changes in the terms of trade (=trading gain)()(1/4) (sXt + sXt−1 + sMt + sMt−1) In ( PXt / PXt−1 ) − In ( PMt / PMt−1 ) +Terms-of-trade effect()(1/2) (sXt + sXt−1 − sMt − sMt−1) (1/2) In ( PXt / PXt−1 ) + (1/2) In ( PMt / PMt−1 )− In ( PDt / PDt−1 )Real exchange rate effect113: Kohli (2006) calculated the trading gain, the terms-of-trade effect, and the real exchange rate effect of Canada during 1982–2005.
The average annual trading gain over the entire period is very low, at 0.1%. This is small by the standard of Asian economies. However, the trading gain later became significant, especially for the three years 2002–2005. Over these years, the average trading gainis 1.6% per year. This effect is decomposed into a terms-of-trade effect of 1.4% and a real exchange rate effect of –0.1%.

1267.1 Real Income and Terms of Trade

1973–1979%

0.5
3.7 1.2
1.2

5

7.5

5.7

1.4
3.3

5
1.1
0.7

2.2

9.8
6.6

7.9

8.7 8.5

7.3
4.2

0

1996–1998

%

10

10

5.8

4.1

5.1

0.5

−1.1

5.2

0
5.4 5.2

4.4 4.4 4.8

1.5−0.9 −1.3

1.1
7.3−0.7 −0.6−0.7
3.5 3.7 3.6 2.9 2.8
2.7

7.3

1.0
1.2
1.1
1.4
5.1 4.6 6.0 4.4 4.6 3.0 6.9 1.5
1.0
2.5 2.6 2.9 2.8 4.4
1.5
1.3 1.2−0.8−1.6 −0.9 −0.7
−1.7
−3.1

1.2
2.2
1.0
−2.6
−2.2
−1.1
−6.2 −5.1 −6.3

−5

−0.9
−1.4 −0.7−0.5 −0.8

−10

−8.1

−15

−5

BruneiThailandIndonesia
Korea
Fiji
Iran
Japan
Malaysia
Hong Kong
Myanmar
EU15
Bangladesh
Singapore
Pakistan
BhutanPhilippines
Australia
US
Cambodia
India
ROC
China
TurkeyVietnam
Sri Lanka

Bangladesh
EU15
US
Japan
Australia
India
TurkeyPhilippines
MyanmarBhutanSri Lanka
Pakistan
China
ThailandFiji
Singapore
ROC
Hong Kong
Iran
Malaysia
Indonesia
Korea
Real exchange rate effectTerms-of-trade effect

7

Real GDPNet primary income from abroad

Real income

Figure 96 Decomposition of Real Income Growth, 1973–1979 and 1996–1998_Decomposition: Average annual growth rate of real income

Sources: Official national accounts in each country, including author adjustments.

114: There are several studies on the decomposition of real income growth for other countries: Kohli (2004) for 26 OECD countries
during 1980–1996, Kohli (2006) for Canada during 1981–2005, and Diewert and Lawrence (2006) for Australia during 1960–2004.

127©2015 Asian Productivity Organization

most countries, adding less than ±1 percentage point to annual real GDP growth, except for some oilrich countries. In the short term, one sees extreme spikes in trading gain. For instance, as a consequence of the first oil price shock, the improvement in the terms of trade was responsible for around
80% of the 40.4% increase in real income in Iran in 1974. The opposite was true in EU15, where thenegative trading gain effect counterbalanced real GDP growth, leaving virtually no growth to real income in the period 1974–1975. The effect of the second oil spike can be seen in the early 1980s. Sri
Lanka, Malaysia, and Indonesia also experienced volatile variations in trading gains in the
1970s. The trading gain has been working against Singapore and the ROC’s welfare for most of theperiod covered.

7 Real Income

Bahrain%60

Bangladesh

%
10

%
24

40

5

18

20

0

12

0

−5

6

−20

−10

0

−40

−15

−6

−60

1971 1976 1981 1986 1991 1996 2001 2006 2011

Brunei

%
24

−20

18

12

15

6

12

0

9

−6

6

−12

3

−24

−12

Cambodia

%
21

18

−18

1971 1976 1981 1986 1991 1996 2001 2006 2011

%
15

ROC

%
18

−3

China

9
6
3
0
−3
−6

1971 1976 1981 1986 1991 1996 2001 2006 2011

Hong Kong

%
16

15

1971 1976 1981 1986 1991 1996 2001 2006 20111201971 1976 1981 1986 1991 1996 2001 2006 2011

Bhutan

%
12

12

9

8

6

4

3

1971 1976 1981 1986 1991 1996 2001 2006 2011

India

12
9
6
3

0

0

−4

−3

0
−3
−6

1971 1976 1981 1986 1991 1996 2001 2006 2011

Indonesia

%
20

−8

36

10

27

5

18

0

9

−5

0

−10

−9

−15

−18

−20

−27

1971 1976 1981 1986 1991 1996 2001 2006 2011

Korea

%
20
15

Iran

%
45

15

−6

1971 1976 1981 1986 1991 1996 2001 2006 2011

%
8

4
2
0
−2
−4
−6

1971 1976 1981 1986 1991 1996 2001 2006 2011

Kuwait

%
90

%
20

5
0

0

0

−30

1971 1976 1981 1986 1991 1996 2001 2006 2011

Mongolia%
25©2015 Asian Productivity Organization

Malaysia

10

30

−5

−60

12

15

9

10

6

5

3

0

0

−5

−3

−10

−6
1971 1976 1981 1986 1991 1996 2001 2006 2011

Real GDP128−51971 1976 1981 1986 1991 1996 2001 2006 2011

−9

−10

Myanmar

%
15

20

−15

1971 1976 1981 1986 1991 1996 2001 2006 2011

15

5

−10

Japan

6

60

10

1971 1976 1981 1986 1991 1996 2001 2006 2011%81971 1976 1981 1986 1991 1996 2001 2006 2011

Nepal

6
4
2
0

1971 1976 1981 1986 1991 1996 2001 2006 2011

Trading gain

Net primary income from abroad−21971 1976 1981 1986 1991 1996 2001 2006 2011

Real income

7.1 Real Income and Terms of Trade

%
50

Oman

40

Pakistan

%
12

%
15
10

9

30

5

6

20

0
3

10
0

−5

0

−10

−10
−3

−20
−30

%
40

1971 1976 1981 1986 1991 1996 2001 2006 2011

Qatar

30

−6

−15
1971 1976 1981 1986 1991 1996 2001 2006 2011−20

Saudi Arabia

%
60

%
24

12

15

8

0

4

0

−10

0
−15

−20
1971 1976 1981 1986 1991 1996 2001 2006 2011

Sri Lanka

15

−30

−8

1971 1976 1981 1986 1991 1996 2001 2006 2011

Thailand

%
15

%
100

40

0

20
−5

−5

0

−10

−10
1971 1976 1981 1986 1991 1996 2001 2006 2011

Vietnam−15−20
1971 1976 1981 1986 1991 1996 2001 2006 2011

−40

US

%
8

%
6

9

6

4

6

4

2

3

2

0

0

0

−2

−3

−2

−4

−6

%
8

UAE

60

0

%
12

1971 1976 1981 1986 1991 1996 2001 2006 2011

80

5

5

7

−4

10

10

−15

Singapore

16
30

10

%
20

1971 1976 1981 1986 1991 1996 2001 2006 2011

20

45

20

−30

Philippines1971 1976 1981 1986 1991 1996 2001 2006 2011

Australia

6−41971 1976 1981 1986 1991 1996 2001 2006 2011−61971 1976 1981 1986 1991 1996 2001 2006 2011

EU15

1971 1976 1981 1986 1991 1996 2001 2006 2011

Turkey

%
12
8

4

4

2
0

0

−4

−2
−4

1971 1976 1981 1986 1991 1996 2001 2006 2011

Real GDP−81971 1976 1981 1986 1991 1996 2001 2006 2011

Trading gain

Net primary income from abroad

Real income

Unit: Percentage.
Sources: Official national accounts in each country, including author adjustments.

129©2015 Asian Productivity Organization

Figure 97 Sources of Real Income Growth, 1970–2013

7 Real Income

7.2 Trading Gain and Productivity Growth% Trading gain effect3

KuwaitBrunei©2015 Asian Productivity Organization

When the trading gain is highly
favorable, it can breed a sense of2
Irancomplacency with productivity
performances suffering as a reOman
sult. Resource-rich economies are
Bahrain Saudi Arabia
susceptible to this pitfall because1
they are poised to reap some exIndonesia
tremely positive trading gains
Vietnam Malaysia
Qatar
when commodity prices turn inAustralia
CambodiaFiji
Bhutan Singapore
China
UAE
their favor over a sustained period
EU150Hong Kong
TurkeyMyanmarPhilippinesof time. While commodity prices
India
US
Sri Lanka
BangladeshJapan
can rise, they can also fall. This is
Korea
Pakistan Thailand
when countries’ real income
ROC
growth could suffer if fundamen−1
−4.5
−3.0
−1.5
0.0
1.5
3.0
4.5
6.0
7.5 %
tals for real GDP growth are weak.
Labor productivity growthOver the past four decades, only
five countries have enjoyed a faFigure 98 Trading Gain Effect and Labor Productivity
vorable trading gain effect of overGrowth, 1970–2013
1% per year. They are Kuwait, BruSources: Official national accounts in each country, including author adjustments;
nei, Iran, Oman, Bahrain, and
APO Productivity Database 2015.
Note: The starting years for some countries are different due to data availability durSaudi Arabia (all oil-exportinging 1970–2013: Bhutan (1990–), Brunei (1989–), Cambodia (1993–), Myanmar (1977–),
countries). Only Iran among themand Turkey (1988–).
could achieve a significant positive growth in labor productivity(Figure 98). Australia is a rising economy that has benefited from recent hikes in commodity prices.
These are likely to stay for a period of time, as a response to the vibrant growth in the emerging
economies, especially China. The surge in its TFP in the 1990s stopped around the end of the century
before turning negative about five years ago.115 This was just at the stage when they were enjoying an
all-time-high positive trading gain effect, with real income growth faster than real GDP growth by
26% during 2005–2013 (Table 19). A resource-rich country can suffer from “Dutch disease,” which is aphenomenon in which a country’s currency is pushed up by the commodity boom, making otherparts of its economy less competitive and potentially increasing the country’s dependence on natural
resources.116 This is how resource abundance can easily lead to resource dependence. A way to counteract Dutch disease is broad-based, robust productivity growth and industry diversification, in which
Bahrain and Oman have shown some success (see Section 6.2 and Figure 81, p. 104).
Figure 98 also shows that many Asian countries have succeeded in achieving high growth of laborproductivity while having to accept a deteriorating trading gain over the long run. These countries are
typically resource importers whose voracious demand for commodities pushes up their import prices.
Meanwhile, export prices tend to fall as a result of their achievement in productivity improvement,
resulting in unfavorable movements in terms of trade. This is particularly the case in countries where

115: The Economist, 28 May 2011, “Special Report on Australia: No worries?”
116: The term was originated by The Economist in 1977 (The Economist, 26 November 1977, “The Dutch Disease.”) to describe theoverall decline of the manufacturing and the subsequent economic crisis in the 1960s in the Netherlands after the discovery of
the large natural gas field in the North Sea in 1959.

130

7.2 Trading Gain and Productivity Growth

Box 9

Mining Value Added in Resource-Rich Countries

Resource price hikes are certainly boons for resource-exporting economies when prices are increasing.
Figure B9 illustrates trading gain effects (average annual growth rates due to trading gains in 1970–2013)and value-added shares of the mining sector in 1970 and 2013 in selected Asian economies. It indicatesthat large trade gainers typically have dominant mining sectors, petroleum and natural gas in particular.
Provided resource prices continuously rise, these countries continue to gain from the positive terms-oftrade effects.
However, what would happen if resource prices fell, or natural reserves were depleted? Then the story of
the Dutch disease may apply. Richness in natural resources may become a curse if they do not have competitive industries other than mining. Figure B9 shows some of the trading gainers (i.e. Brunei, Oman, Qatar, and UAE) actively reduced their share of the mining sector over time, which could reflect the intentionof developing industries other than mining. However,
Figure 98 shows that labor% Trading gain effect, 1970−2013
5.0
productivity growth rates in=1970 (starting year), =2013
these countries after 1990 reKuwait
mained low, or even negative.
4.0
Even if they wanted to startindustrialization, their high income and strong local curBrunei
rency would not easily allow
3.0
them to develop a manufacturing sector or an internationally competitive service
2.0
Iranindustry. Another concern is
Oman
their heavy dependence on
Bahrain Saudi Arabia
foreign workers, both skilled
1.0
and unskilled.
Qatar

Indonesia

0.0Australia
0

10

Malaysia
Vietnam
20

UAE
30

40

50

60

70

80

90 %

Value-added shares in Mining, 1970 and 2013

Figure B9 Trading Gain Effect and Value-added Share in Mining Sector, 1970–2013
Sources: Official national accounts in each country, including author adjustments.
Note: The starting years for some countries are different due to data availability during
1970–2013: Brunei (1989–) and Vietnam (1986–) for trading gain effect, Brunei (1974–),
UAE (1972–), Bahrain (1975–), Malaysia (1987–), and Vietnam (1986–) for value-added
share of mining sector.

economic growth is highly dependent on export promotion. In such instances, a negative trading
gain is partly a side-effect of productivity success. Although the trading gain effect partly negates
their real GDP growth, they are better positioned than before their development took off, and without
productivity improvements.

131©2015 Asian Productivity Organization

On the other side of coin are theresource/energy-importing
economies. Most of these suffered from negative trading
gain effects, losing a part oftheir economic growth due
to resource price hikes, particularly in the 2000s (Table
19, p. 122). However, it has
actually strengthened theircompetitiveness in manufacturing and other productive
activities for the future.

7

Appendix
A.1

GDP Harmonization

This edition of the Databook incorporates some significant revisions to the national accounts. Observing new developments for upgrading of statistics systems in Asian economies, Bangladesh, the ROC,
Indonesia, Singapore, and Mongolia published their national accounts based on the 2008 SNA in 2014or 2015. Based on our Metadata Survey 2015 for the APO member economies (see Box 3, p. 52), 10
economies are already the 2008 SNA-compliant in Asia and others (Cambodia, Iran, Japan, the Lao
PDR, Nepal, Sri Lanka, Thailand, and Vietnam) are the 1993 SNA-compliant, although it should be
noted that the extent of compliance in terms of coverage may vary. The different statuses of SNA
adaptions among economies are responsible for the huge variations of data definitions and coveragein national accounts, calling for data harmonization to better perform comparative productivity analyses.

App.

This edition largely follows the concepts and definitions of the 2008 SNA and tries to reconcile thenational accounts variations, in particular on the difference in the treatment of research and development (R&D), military weapon system, software investment, and financial intermediation services indirectly measured (FISIM).117 In order to make a long-time series data for the Databook series, it isinevitable to use the past estimates based on the 1968/1993 SNA, with exceptions the ROC and Korea
who already published the backward estimates based on the 2008 SNA until the 1950s. In addition,some extra adjustments are necessary to harmonize the long-term estimates of GDP. Procedures forthese adjustments are explained below.

1) FISIMFISIM is an indirect measure of the value of financial intermediation services provided, but for which
financial institutions do not charge explicitly (United Nations, 1993: para. 6.124). It represents a significant part of the income of the finance sector. The 1993 SNA recommends that FISIM should be allocated to users (to individual industries and final demands). This is in contrast to the 1968 SNA, where
the imputed banking services were allocated exclusively to the business sector. The common practice
was to create a notional industry that buys the entire service as an intermediate expense and generates an equivalent negative value added. As such, the imputed banking services have no impact on
GDP. Therefore, the 1993/2008 SNA recommendation, if fully implemented, will impact on industry
GDP and the overall GDP for the total economy (by the part of FISIM allocated to final demands).

117: The introductions of the 2008 SNA are usually conducted with the benchmark revisions. Thus in some countries there are large
revisions in data due to the uses of the newly available survey (e.g. a new survey on services) or of the new benchmark data (e.g.
a new development of the supply and use table), not largely due to the revision from the 1993 SNA. The information required
to reconcile the different benchmark-year series is collected for the APO member countries through our questionnaire to thenational experts in our project. In January 2015, Brunei published the new national accounts based on the 2008 SNA and somelarge differences are found in comparison with the past estimates based on the 1993 SNA. However, this edition used the past estimates with our additional capitalization of R&D, since the sources of the difference between the two estimates are not clear andthe latest data covers only the period since 2010.

133©2015 Asian Productivity Organization

Among the 20 APO member economies, four countries –Cambodia, the Lao PDR, Nepal, and Sri
Lanka – do not allocate FISIM to final demands in their official national accounts, as a result of themstill not following the 1993/2008 SNA recommendation. Thus, the GDP values in these countries are
smaller than others by definition. In addition, even in the countries whose national accounts follow
the 1993/2008 SNA’s recommendation on FISIM, the available data sometimes does not cover thewhole periods of our observations. To harmonize the GDP concept among countries and over periods,
final demands of FISIM are estimated for those countries in the APO Productivity Database, using
available estimates of value added in Imputed Bank Service Charge (IBSC) or financial intermediation(in instances where IBSC data is not available). The ratios of value added of IBSC or financial

Appendix

intermediation on FISIM allocatedto final demand are assumed tobe identical with the average ratios observed in the countries inwhich data is available. Figure 99
describes the countries, years,
and methods to adjust FISIM in
the official national accounts. As
described, in instances where
both value added data are notavailable, the trend of the FISIM
share on GDP is applied to extrapolate past estimates (although
the impacts on GDP are minor).
Figure 100 plots per capita GDP
levels in 2013 and the FISIM share
in GDP in the 2000–2013 (including both of the original estimatesin the official national accounts
and our estimates). In countries
where GDPs are adjusted, the proportions by which author adjustments for FISIM increases GDP
stand at 0.6–1.1% for Nepal, Brunei, and the Lao PDR and less than
0.4% GDP in others.118©2015 Asian Productivity Organization

2) Software
The 2008 SNA recommends thecapitalization of intellectual property products (IPP), which changes not only the size of GDP but
also the size of capital input.
One of the IPP capitalized in the
Databook is computer software,
which includes pre-packaged soft­ware, custom software, and ownaccount software. Among APO
member economies, 10 economies have capitalized all three
types of software. Another three
countries exclude own-accountsoftware in their capitalizationand in one country only custom
software is capitalized. For the1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
Bangladesh
Cambodia
Fiji
India
Indonesia
Iran
Japan
Lao PDR
Malaysia
Mongolia
Nepal
Pakistan
Philippines
Sri Lanka
Thailand
VietnamBhutanBrunei
MyanmarBahrain
KuwaitOman
Qatar
Saudi Arabia
UAE
Turkey

1970
1970
1970
1970

1950
1951

1980

1995
1993
1994

1977

2013
2003

1960
1959

2009
1990

1955

1979
1981 1985

2013

1970

1955

1999

1960

1988
1974

1964

2013

1960

2005

1946

1967
1959

1997

1972
1970

1951

2013
1989
1986

1955
1970
1970 1974

2004

1980

1999

1958

2013
2013

1990
1975

1970

2005
1982

1962

2013
2013
2013
2013
2013

1970
1970 19751970
1970 1975
1960

1997

Adjustment using value added of imputed bank service charge
Adjustment using value added of financial intermediation
Adjustment using the average trend of FISIM share in GDP

Figure 99 Adjustment of FISIM
Source: APO Productivity Database 2015.

%

3.5

FISIM share in GDP (average in the 2000−2013)

Hong Kong
3.0

2.5
Australia
2.0

India
Philippines
Fiji

1.5

BhutanPakistan

SingaporeJapan

ThailandChina
Malaysia
Indonesia
Mongolia Turkey
Iran
1.0 Nepal
VietnamBangladesh
Lao PDR
0.5
Cambodia
0.0

ROC
Brunei

Myanmar Sri Lanka
0

10

US

Korea

Thousands of US dollars (as of 2013)

20

30

40

50

60

70

80

Per capita GDP in 2013 (using 2011 PPP, reference year 2013)

Offical national accounts in each country, including author adjustment
Our estimates using value added in financial intermediation
Our estimates using value added in imputed bank service charge

Figure 100 FISIM Share in GDP, 2000–2013
Sources: Official national accounts in each country and author estimates.

118: In this edition of the Databook, our estimates of FISIM are replaced to the official estimates for Bangladesh (revised from 0.5% to
0.9%, as the average share of FISIM on GDP during the period 2000–2013), Indonesia (from 1.1% to 1.3%), and Pakistan (from 1.1%
to 1.2%).

134

A.1 GDP Harmonization

APO Productivity Database, tentative adjustments have been madeto harmonize data to include
all software.
Among the countries studied, thedata for software investment isavailable for the ROC, Japan, Korea,
Mongolia, the Philippines, Singapore, Thailand, and China. To harmonize data, a country’s GDP isadjusted to include software investment (through its software industry) by using the ratio between
software investment and GDP (software ratio) and the tangible GFCF to
GDP ratio (GFCF ratio). Data fromthe OECD Productivity Database
and the APO Productivity Databasesuggest an inverse relationship between these two ratios (Figure 101).
Countries with a low GFCF ratio
tend to be those with high per capita GDP, and the observed data suggest that IT tends to play a more
important role in these countries
than in less developed countries.

%

Software Investment/GDP

2.0

SwedenUS
Denmark

1.5

Finland
Korea
Hong Kong
New Zealand
Switzerland
UK
ROC
Netherlands
Canada2.5

JapanFrance1.0Germany

Singapore
Austria
Italy

Australia
Spain

App.

VietnamFiji Indonesia
Sri Lanka
Ireland
Philippines Nepal
India
Portugal
ThailandBangladesh
Pakistan Malaysia IranMongolia
Cambodia

0.5

0.0
13

18

23

28

33

China

38

43 %

Tangible GFCF/GDPCountries in which software is included
Countries in which software is excluded
Countries in which own account is excluded
Countries in which custom and own account software are excluded
Countries in which pre-package and own account software are excluded

Figure 101 Software Investment Ratio and GFCF Ratio toGDP, 2005
Sources: OECD Productivity Database and author estimates.

Another problem arises from partial software capitalization. There are three types of software: prepackaged software, custom software, and own-account software. Countries may have capitalized one
or two types of software, but software investment data is often not available separately. The Databook
attempt’s to adjust for the varied level of capitalization across countries by adding the type of software not capitalized to countries’ GDP.

3) Valuables
Valuables are defined as “goods of considerable value that are not used primarily for purposes of production or consumption but are held as stores of value over time” (United Nations, 1993: para. 10.7).
They are held under the expectation that their prices will not deteriorate and will rise in the long run.
Valuables consist of precious stones and metals such as diamonds; art-works such as paintings and135©2015 Asian Productivity Organization

The Databook applies the inverse relationship between these two ratios observed from the OECD
countries and national accounts in Asian non-OECD countries to estimate the software ratio in 2006
for those APO member economies that do not capitalize software investment. The estimated ratios forindividual countries in 2006 gradually taper off as one move back in time. However, there is an exception. Countries at the very early stage of economic growth are found to have a GFCF ratio as low as
countries with high per capita GDP, but for a different reason. The low GFCF ratio is explained by thefact that these countries have not experienced economic development yet, and in turn this does not
imply an important role for software investment. In this report, Cambodia, the Lao PDR, and Nepal are
regarded as countries at the very early stage of economic development, and are assigned Vietnam’s
software ratio accordingly, which is the lowest of all APO member economies.

Appendix

sculptures; and other valuables
such as jewelry made from stones
and metals. In a small number ofcountries, such as, India, Iran, Mongolia, Vietnam, and Bhutan, net
acquisitions of valuables are recorded as a part of gross capital formation. For example, the SNA in India
has included it since 1999, accounting for 1.4% of GDP for India on aver­age during 1999–2013. The current
decision is to harmonize the databy excluding net acquisition ofvaluables from GDP in this editionof the Databook.

4) Consumption of Fixed
Capital of Assets Owned byGovernment

1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
Bangladesh
Cambodia
Fiji
India
Indonesia
Iran
Japan
Lao PDR
Malaysia
Mongolia
Nepal
Pakistan
Philippines
Sri Lanka
Thailand
VietnamBhutanBrunei
China
MyanmarBahrain
KuwaitOman
Qatar
Saudi Arabia
UAE
Turkey

1970
1970
1970

1995
1993

2002
2010

1958

1950
1951

2003
2000
2001

1970
1959

2009

1955
2002
1996 2004
1997

1981
1955
1960

1970
1964

2009

1974
1990

1960
1946 1950

1997
1996

1959
1990

1951

2002

1955
1970
1970
1952
1952

1996
1997

1958

2011

2002 2005

1989

2013
2003

1970
1997

1962

1960

1970
1970
1970
1970
1970

2011
2003
2011
1996

Adjustment using R&D expenditure
Adjustment using the average trend of R&D share in GFCF
Adjustment using the average trend of R&D share in GDP

At the end of 2011, Thailand officialFigure 102 Adjustment of R&D
ly switched to the 1993 SNA, and its
Source: APO Productivity Database 2015.
national accounts became compatible with the 1993 framework forthe first time. In this series, government consumption includes the consumption of fixed capital (CFC)
owned by the government since 1990. In order to construct the long time-series data in the Databook
series, the past data based on the 1968 SNA has been adjusted to be consistent with the new series.
In the Databook, government capital stock and its CFC for the period 1970–1989 are estimated andthe past government consumption and GDP are adjusted accordingly. A similar adjustment on the
CFC of the assets owned by government was conducted for Bangladesh (for the period 1970–1995),
Malaysia (1970–1999), and Mongolia (1970–2004).

©2015 Asian Productivity Organization

5) R&D
This edition of the Databook newly capitalizes the R&D by following the 2008 SNA recommendations.
In the countries who still do not follow the 2008 SNA, the R&D expenditures are not allocated to GFCF
(but to intermediate uses). As a result the GDP values in these countries are smaller than others bydefinition. To harmonize the GDP concept among countries and over periods, the R&D investment isestimated for those countries in the APO Productivity Database. As a preferable approach, the data on
the R&D expenditure are collected based on the official surveys in each country, in order to estimate
the R&D investment.119 Figure 102 describes the countries, years, and methods to estimate R&D investment and adds it to GFCF in the official national accounts. If the data on R&D expenditures are notavailable, as a crude estimate, the trend of R&D investment shares on GFCF or GDP are applied to extrapolate past estimates. Figure 103 plots the per capita GDP and the R&D investment share in GDP in
2013. The impacts on GDP by our adjustment of the additional R&D investment are less than 1.0% of
GDP for all countries in 2013.

119: In the case of Japan, in which the official estimates of the R&D investment data are not published yet, the R&D investment series
are developed based on the related expenditures in Survey on the Research and Development by the Statistics Bureau of Japan,
covering the period of 1952–2013 by Nomura.

136

A.1 GDP Harmonization

6) GDP at basic prices%

4.5

R&D investment share in GDP, 2013

Korea
GDP can be valued using differ4.0
ent price concepts: factor cost,
basic prices, and market prices.
3.5
Japan
If the price concept is not stanROC
dardized across countries, it will
3.0
interfere with the international
Australia
USA
2.5
comparisons. All the countries
covered in this Databook offi2.0
China
Singaporecially report GDP at market prices (or at purchasers’ prices), but
1.5
this is not true for GDP at factor
cost and GDP at basic prices.
Thailand
1.0 Myanmar
TurkeyIndia
Malaysia
BhutanInternational comparisons inHong Kong
Iran
Nepal
0.5
Philippines
Chapter 3 (on economic scale
Pakistan
Mongolia
Thousands of US dollars (as of 2013)
Cambodia
Sri Lanka
and growth) and Chapter 4 (onBruneiIndondsia
0.0
final demand) are based on
0
10
20
30
40
50
60
70
80
Fiji
Bangladesh
Per capita GDP in 2013 (using 2011 PPP, reference year 2013)
GDP at market prices. However,VietnamLao PDRby valuing output and input at
Offical estimates of R&D investment
Our estimates based on R&D expenditure, including author adjustments
the prices that producers actuOur estimates using the average trend of R&D share in GFCF
Our estimates using the average trend of R&D share in GDP
ally pay and receive, GDP at basic prices is a more appropriate
Figure 103 R&D Share in GDP, 2013
measure of countries’ output
than GDP at market prices for
Sources: Official national accounts; Surveys on R&D in each country; World Bank, World
Development Indicators 2014, including author adjustments.
international comparisons of TFP
and industry performance as
it is a measure from the pro­ducers’ perspective. Hence, Chapter 5 on whole-economy productivity performance is based on GDP
at basic prices, including our estimates.

App.

Readers should bear these caveats in mind in interpreting the results in Chapter 6, since the definition
of GDP by industry differs among countries due to data availability. GDP is valued at factor cost for Fiji,and Pakistan, at basic prices for Cambodia, Hong Kong, India, Korea, the Lao PDR, Mongolia, Nepal,
and Singapore, at producers’ prices for Iran, the ROC, and the Philippines, and at market prices for
Bangladesh, Indonesia, Japan, Malaysia, Sri Lanka, Thailand, and Vietnam. In this sense, APO industry
data should be treated as a work in progress as it is difficult to advise on data uncertainty. These issues
will be developed and examined in the future.

137©2015 Asian Productivity Organization

These concepts of GDP differ in the treatment of indirect tax and subsidies (and import duties). The
difference between GDP at basic prices and GDP at market prices is “taxes on products” minus “subsidies on products.” “Taxes on products” are the indirect taxes payable on goods and services mainly
when they are produced, sold, and imported, and “subsidies on products” are subsidies payable on
goods and services mainly when they are produced, sold, and imported. Since GDP at basic prices isavailable for some economies, such as Hong Kong, India, Korea, Mongolia, Nepal, Singapore, and Sri
Lanka, a GDP at basic prices, needs to be constructed for all other countries. In order to obtain GDP atbasic prices, “taxes on products” and “duties on imports” are subtracted from GDP at market prices,which is available for all the countries studied, and “subsidies on products” is added. The main data
sources for estimating “taxes on products” and “subsidies on products” are tax data in national accounts, the IMF’s Government Finance Statistics, and the input-output tables in each country.

Appendix

A.2

Capital Stock
Table 20 Asset Classification andParameters in Hyperbolic Function

At present, half of APO member economies publish estimates of capital stocks in their systems of national accounts. Even where estimates are available, users must be
mindful of differences in methodologies and assumptions
used to estimate capital stock, and a large diversity in thetreatment of quality adjustment in price statistics among
countries. In the APO Productivity Database 2015, a harmonized methodology has been applied in estimating
capital stock and capital services, covering 19 Asian economies: Bangladesh, Cambodia, China, the ROC, Fiji, Hong
Kong, India, Indonesia, Iran, Japan, Korea, Malaysia, Mongolia, Pakistan, the Philippines, Singapore, Sri Lanka, Thailand, and Vietnam, and the US as a reference country.

©2015 Asian Productivity Organization

Quality changes in the aggregate measure ofcapital input can originate from two kinds ofsources, namely the composition change bytype of asset, and the quality change in eachtype of asset. To take the composition changeof assets into account, the current databaseclassifies 11 types of assets (shown in Table 20).
For countries in which detailed investment
data is not available from national accounts,
the 11 types of investment data are estimatedbased on the benchmark input–output tables
and our estimates of the commodity flow
data of domestic production and export/import of assets. The input-output tables andsupply and use tables are listed in Table 21.
The starting years for estimating capital stock
based on the perpetual inventory method is1901 for the US, 1951 for the ROC, 1952 for
China, 1953 for Korea, 1955 for Japan, 1960 forSingapore, 1961 for Hong Kong, and 1970 forother countries.

Tβ

1. IT hardware

7

0.50

2. Communications equipment

15

0.50

3. Transportation equipment

15

0.50

4. Other machinery and equipment

15

0.50

5. Residential buildings

30

0.75

6. Non-residential buildings

30

0.75

7. Other construction

40

0.75

8. Cultivated assets

10

0.50

9. R&D100.50

10. Computer software

3

0.50

11. Other intangible assets

7

0.50

Source: APO Productivity Database 2015.

Table 21 Input-Output Tables and Supply and
Use Tables
Input–Output Tables and Supply and Use Tables
ROC
Fiji

Benchmark (1981, 1984, 1986, 1989, 1991, 1994, 1996, 1999,
2001, 2004, 2006, 2011), Annual (2006–2012)
1972, 1982, 2005

India

1993/1994, 1998/1999, 2003/2004, 2006/2007, 2007/2008

Indonesia

1971, 1975, 1980, 1985, 1990, 1995, 2000, 2005

Iran

1999, 2001

Japan

1960, 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005, 2011
Benchmark (1960, 1963, 1966, 1970, 1975, 1980, 1985, 1990,

Korea

1995, 2000, 2005, 2010), Updated (1973, 1978, 1983, 1986-1988,

Malaysia

1993, 1998, 2003, 2006–2012)
1978, 1983, 1987, 1991, 2000, 2005, 2010

Mongolia

1970, 1977, 1983, 1987, 2000, 2005, 2010

Pakistan

1975/1976, 1984/1985, 1989/1990, 1990/1991

Philippines 1961, 1965, 1969, 1974, 1979, 1985, 1988, 1994, 2000, 2006
Singapore

1973, 1978, 1983, 1988, 2000, 2005, 2007, 2010

Sri Lanka

2006

Thailand

1975, 1980, 1985, 1990, 1995, 1998, 2000, 2005

Vietnam

1996, 2000, 2007

China

1987, 1992, 1997, 2002, 2007

Brunei2005

Turkey1973, 1979, 1985, 1990, 1998, 2002

It is well known that prices of constant-quality IT capital have been falling rapidly. For cross-country
comparisons, it has been noted that there is great diversity in the treatment of quality adjustment inprice statistics among countries. Cross-country comparisons will be significantly biased if some countries adjust their deflators for quality change while others do not. Price harmonization is sometimes
used in an attempt to control for methodological differences in the compilation of price indexes, under the assumption that individual countries’ price data fails to capture quality improvements. Assuming that the relative price of IT to non-IT capital in the countries compared is set equal to the IT to
X
non-IT prices relative in the reference country, the harmonized price is formulated as: ∆ ln P̃ITX = ∆ ln PnlT
refref+ (∆ ln PIT − ∆ ln PnlT ), where the superscript X denotes the country included in the comparisons, PIT isthe price of IT capital, and PnIT is the price of non-IT capital. The price of IT capital in country X, P̃ITX , isrefX
in the reference country and PnlTin X. Schreyer (2002)
computed by the observed prices PITref and PnlT

138

A.2 Capital Stock

GDP=1.0

4.5

3.8

3.2

3.1

2.9

2.5

2.9

2.9

2.7 2.8

2.7

2.7

2.2

2.0

2.4

2.4
2.0

1.9

2.3

2.3 2.2

2.1

1.8

1.8

1.6

2.1 2.0
1.6

1.5

Cambodia

1.5

1.8

1.6

2.4 2.4

1.9

Pakistan

1.7

2.6

2.6

2.3

2.0

2013

3.5

3.5
3.0

1980

4.0

4.0

1.1

1.0
0.5
0.0

Sri Lanka

Philippines

India

VietnamBangladesh

Malaysia

US

Singapore

Fiji

Mongolia

Hong Kong

Iran

Thailand

ROC

China

Indonesia

Korea

Japan

and Schreyer, Bignon, andDupont (2003) applied priceharmonization to OECD capital services, with the US as areference country, since thepossible error due to usinga harmonized price index
would be smaller than thebias arising from comparing
capital services based on national deflators.

App.

In this Databook, the same
Figure 104 Capital-Output Ratio, 1980 and 2013
price harmonization method___Ratio of the beginning-of-period net capital stock to basic-price GDP at
is applied to adjust the qualcurrent prices
ity improvement for IT hardSource: APO Productivity Database 2015.
ware and communications
Note: The estimate for Cambodia is not available for 1980.
equipment in countries where
the appropriate qualityadjusted price data is not available, with Japan’s prices as a reference country. A similar procedure
was applied in cases where the prices for some assets were not available, to estimate missing data
based on the relative price of these assets to total GFCF. In measuring capital services, this Databooklargely follows the framework of the OECD Productivity Database.120 The OECD assumes the truncatednormal distribution as profiles for asset discarding (retirement) and the hyperbolic distribution as profiles for asset decaying. The age-efficiency profile is defined as a combined distribution of discard anddecay of assets. The age-efficiency profile in each asset is based on the two parameters in the hyperbolic function: T (average service life) and β (–∞<β≤1). The hyperbolic function becomes one-hoss
shay (no decay until T) when β=1 and linear when β=0. These two parameters are set, as shown in Table 20. The estimates of productive capital stock by type of asset are used in measuring capital services (see Appendix 3).

120: See OECD (2015) and the website of the OECD productivity statistics (http://www.oecd.org/std/productivity-stats/). The projectappreciates Maria Belen Zinni (Statistics Directorate, OECD) for her supports.

139©2015 Asian Productivity Organization

Figure 104 presents the estimated capital-output ratio (stock coefficient) that is defined by the ratio of
the beginning-of-period net capital stock (all types of produced fixed assets owned by private andpublic institutions) to the basic-price GDP at current prices. Japan has the highest capital-output ratio
among Asian countries, at 3.8. However, the ratio may not work well for cross-country comparisonssince the price differential between that for GDP and fixed assets in each country is not accounted for.
Compared to the 1980 level in each country, all Asian countries except Mongolia, Pakistan, and thePhilippines have an increasing trend of capital-output ratio, unlike the ratio in the US, which is stable.

Appendix

Rate of Return and Capital Services

A.3

In the analysis of production and productivity, capital service provides an appropriate concept ofcapital as a factor of production. The fundamental assumption in measuring capital services is proportionality between the (productive) capital stock and capital services in each type of asset. Thus, thegrowth rates of capital services can differ from that of capital stock only at the aggregate level. For
aggregating different types of capital, the user costs of capital by type of asset should be estimated.

%
50

Bangladesh

%
50

China

40

40

40

30

30

30

30

20

20

20

20

10

10

10

10

1970 1980 1990 2000 2010

%
50

Fiji

Hong Kong%
50

1970 1980 1990 2000 2010
%
50

India

1970 1980 1990 2000 2010
%
50

40

40

40

40

30

30

30

30

20

20

20

20

10

10

10

10

0

0

%
50

Iran

0
1970 1980 1990 2000 2010

1970 1980 1990 2000 2010

Japan

%
50

Korea

1970 1980 1990 2000 2010

%
50

40

40

40

40

30

30

30

30

20

20

20

20

10

10

10

10

1970 1980 1990 2000 2010

1970 1980 1990 2000 2010

%
50

0

0

0

Mongolia

Pakistan%
50

Philippines1970 1980 1990 2000 2010

%
50

40

40

40

40

30

30

30

30

20

20

20

20

10

10

10

10

0

0

%
50

Sri Lanka

01970 1980 1990 2000 2010

1970 1980 1990 2000 2010

Thailand%50

Vietnam1970 1980 1990 2000 2010

%
50

40

40

40

40

30

30

30

30

20

20

20

20

10

10

10

10

0

0

0

1970 1980 1990 2000 2010

1970 1980 1990 2000 2010US01970 1980 1990 2000 2010

Figure 105 Ex Post Real Rate of Return in Asia, 1970–2013
Source: APO Productivity Database 2015.

Singapore01970 1980 1990 2000 2010%
50

Malaysia

01970 1980 1990 2000 2010%
50

Indonesia

01970 1980 1990 2000 2010

%
50

ROC

0

0

0
1970 1980 1990 2000 2010

140

%
50

40

0

©2015 Asian Productivity Organization

Cambodia%
50

1970 1980 1990 2000 2010

A.3 Rate of Return and Capital Services

This Appendix outlines the methodology of the user cost of capital estimation and presents the estimated results of endogenous rate of return for Asian countries in the APO Productivity Database 2015.
k
The user cost of capital of a new asset (with type of asset denoted as k of the period t), ut,0
, is defined
k
k
k
k
k
k
as qt−1,0 {rt + (1 + t ) P,t,0 − t }, where rt, P,t,0, and qt,0 are the expected nominal rate of return, cross-sectionkk/ qt−1,0−1).
depreciation rate, and asset price, respectively. The asset-specific inflation tk is defined as (qt,0The OECD assumes the country-specific ex ante real rate of return r * that is constant for the whole
period, and defines the nominal rate of return as rt = (1 + r *)(1 + ρt) − 1, where ρt represents the expected overall inflation rate, defined by a five-year centered moving average of the rate of change of
the CPI (see Schreyer, Bignon, and Dupont, 2003).

App.

One of the main difficulties in applying the ex ante approach for measuring user cost of capital is obtaining proper estimates for real rates of return, which can differ considerably among countries andover time. On the other hand, the ex post approach originated by Jorgenson and Griliches (1967) allows an estimation based on observed data. Assuming constant returns to scale and competitive markets, capital compensation can be derived from the summation of the capital service cost Vtk for each
asset, which is defined as the product of the user cost of capital and the productive capital stock (i.e.,k kSt ). Based on this identity and the n-equations of user cost of capital, the n+1 variables
Vt = Σk Vtk = Σk ut,0kof ut,0 and rt are simultaneously determined, using the observed capital compensation Vt as the totalkis not independent
sum of Vtk that is not observable in each asset. Note that the depreciation rate P,t,0of the estimated rt.
The estimated results of the ex post real rate of return based on rt* = (1 + rt) / (1 + ρt)−1 for 19 Asian
countries and the US are shown in Figure 105. Although there are large fluctuations in countries likeThailand, Mongolia, and Vietnam, many Asian countries may exhibit decreasing trends in the (endogenous) real rate of return, while the US holds a stable rate of around 10%. Table 22 presents the fiveyear-averages of the estimated rates for ex post real rate of return during 1970–2013. In 2010–2013,
the real rate of return ranged from 4.5% for Japan and 6.6% for Korea to 20.3% in Malaysia and 21.9%
for Cambodia. Using these ex post estimates, the aggregate capital services are measured in this report. The difference caused by the ex ante and ex post approaches may provide a modest difference inthe growth measure of capital services, regardless of the substantial differences in the rates of returnand capital compensations (Nomura, 2004).
Table 22 Average Ex Post Real Rate of Return in Asia
12.3

13.1

9.7

15.4

21.4
29.2
11.4
20.4
11.3
33.4
25.0
14.5
28.9
27.0
11.9
21.9
9.6
26.5
24.4
14.9
7.5
10.8

16.5
19.6
12.5
24.2
16.0
32.9
21.7
7.0
22.5
27.6
10.6
23.8
13.5
18.5
33.0
11.5
12.7
9.3

14.2
13.8
5.0
17.6
11.3
31.4
10.0
6.2
11.6
17.0
10.5
19.7
10.4
15.2
16.4
5.0
30.2
6.9

13.1
18.8
2.6
22.6
10.4
15.1
7.8
8.5
15.1
13.8
13.2
23.8
11.7
12.9
10.3
10.1
9.9
8.3

16.4
19.3
14.8
15.9
15.3
13.9
9.5
13.1
21.0
6.8
12.2
15.1
−5.4
20.8
9.5
14.9
9.8
11.2
26.0
7.8

13.3
30.1
10.2
14.3
7.0
11.7
9.0
17.6
13.2
4.6
8.9
15.5
23.7
10.9
14.4
12.4
7.8
9.6
14.5
10.1

12.3
17.7
12.6
16.5
7.2
9.9
11.3
17.7
24.2
4.9
8.6
16.8
2.1
13.7
19.9
11.1
9.1
9.3
15.5
10.9

10.6
4.0
14.4
14.8
5.1
13.6
12.0
22.0
28.5
5.4
7.9
20.6
14.8
9.4
17.3
16.8
9.0
10.2
12.2
10.0

11.4
21.9
9.6
10.6
−0.1
11.0
7.5
14.7
19.2
4.5
6.6
20.3
18.1
11.2
15.1
12.6
10.4
10.5
10.1
9.1

Unit: Percentage
Source: APO Productivity Database 2015.
Note: The starting year is 1993 for Cambodia.

141©2015 Asian Productivity Organization

1970–1974 1975–1979 1980–1984 1985–1989 1990–1994 1995–1999 2000–2004 2005–2009 2010–2013
Bangladesh
Cambodia
China
ROC
Fiji
Hong Kong
India
Indonesia
Iran
Japan
Korea
Malaysia
Mongolia
Pakistan
Philippines
Singapore
Sri Lanka
Thailand
VietnamUSAppendix

A.4

Hours Worked

Labor volume can be measured in three measurement units: number of persons in employment,
number of filled jobs, and hours actually worked. Given the variations in working patterns and employment legislation both over time and across countries, hours worked, if accurately measured, offers the most time-consistent and somewhat internationally comparable unit measuring the volume
in each of different types of labor. This is the primary underlying reason for the importance of choosing hours actually worked in productivity analysis, but in reality, due to the difficulty in accurately estimating average hours actually worked, it is not always available or comparable across countries. The
large variety of data sources, definitions, and methodologies available in estimating these labor market variables often leads to a fragmentation of labor market statistics of an individual country concerned, dubious data quality, and incomparability across countries. Here follows an attempt to outline
some of these intricate measuring issues.

©2015 Asian Productivity Organization

Data on labor volume comes from two main statistical surveys on establishment and household, withrespective strengths and weaknesses. Establishment surveys are surveys of firms with stratified sample frames by the size of establishments. The concentration of total employment in a relatively small
number of establishments means that this sampling strategy is cost effective in delivering high precision labor market estimates with fairly small sampling error. Questionnaires are designed to be close
to the concepts used in company administration. This has both strengths and weaknesses. On the one
hand, data collected is of high quality and accuracy. On the other hand, changes in legislation andregulation could be a source of instability to the definitions, and in turn of the data collected. Furthermore data that companies do not collect for administrative purpose, such as unpaid hours and worker characteristics, are unavailable. This greatly limits the varieties of labor market data that can becollected through establishments. Employment as measured is necessarily based on jobs rather thanon persons employed, as persons holding multiple jobs with different establishments cannot be identified and will be counted more than once. Information on hours is on paid hours rather than hours
actually worked. Certain categories of employment, most notably the self-employed, are not covered.
Sometimes small firms, informal employment (occupies more than 50% in some developing Asian
countries) or the public sector is also excluded. As a result of these limitations, labor market data fromestablishment surveys often requires a raft of adjustments for omissions and definition modifications
during the compilation process.
Household-based labor force surveys (LFS), in contrast, have full coverage of the economy, although
they sometimes incorporate age or geographic exclusions and may have imperfect coverage of thearmed forces and other institutional households. Nonetheless, they provide valuable data on certain
employment groups such as the self-employed and unpaid family workers, and on the rate of multiple
job holding. Employment status in LFS is independently determined and is not subject to the criteria
used in company records. Most countries follow the International Labour Organization (ILO) definitions. As LFS’ are surveys from the socio-economic perspective, they also provide rich data on worker
characteristics that are relevant to productivity analysis. The major weakness of the LFS, however, isdata precision. By relying on the recollection of the respondents, their response also depends on perception. Response errors could, therefore, arise from confusion of concepts and imprecise recollectionof the respondents concerning work patterns and pay during the reference week. Another source oferrors originates from proxy response, which relies on the proxy’s perception and knowledge of another household’s member. A high level of proxy responses could, therefore, reduce the reliability ofdata collected.

142

A.4 Hours Worked

The common practice of statistical offices has been to combine information from both establishment
and household surveys, with a view to making use of the most reliable aspects of each of the surveys.
This seems to be the most promising avenue forward in improving the quality and consistency of dataon labor input. However, statistical offices could still differ a great deal in their methodologies, especially in estimating the annual average hours worked per job/per person, depending on their starting
points, namely LFS data or enterprise data. All these have to be taken into account in international
comparisons of productivity.
In productivity analysis, ideally, labor volume should be quality adjusted in order to reflect workforce
heterogeneity, as recommended in the SNA 2008. To adjust total hours worked for quality would
require information on worker characteristics so as to distinguish the workforce into different
types, which are then weighed by their marginal productivity and approximated by their respective
shares of total compensation. Deriving a quality adjusted labor input (QALI) measure is a datademanding exercise. Even if LFS provides the required information, researchers often run into the con­sistency issues discussed above, as well as sample size problems as they break down the workforce
into fine categories.

121: The project is developing the QALI database covering the Asian countries. The first estimates will be available in 2016 and beplaced as more appropriate measures of labor inputs in TFP computation in Chapter 5.

143©2015 Asian Productivity Organization

The APO Productivity Database 2015 defines labor inputs as the simple sum of hours worked. Hours
worked are defined as the economy-wide hours worked by employees, the self-employed, and contributing family workers. Japanese and US’s national accounts publish estimates of the total hours
worked, as recommended by the SNA. Other
Asian countries do not publish hours worked
in their national accounts. For these countries
Table 23 Sources of Labor Datathe procedure of constructing economy-wide
Sources of Labor Dataannual hours worked consists of two steps; for
Bangladesh Labor Force Survey, Population Census
Cambodia Socio-Economic Survey, Labor Force Survey
many Asian countries first, an average weekly
Yearbook of Manpower Survey Statistics in Taiwan Area,
ROC
hours worked is obtained and the number of
Taiwan Statistical Data Book
Annual Employment Survey, Population Census, Estimates byworkers collated from official statistics, such as
Fiji
FIBOS (Fiji Islands Bureau of Statistics), Labor Force Survey
LFS. The data used in this edition is listed in
Annual Earnings and Household Survey, General Household
Hong Kong
Survey, Population Census
Table 23. Some countries do not fully provide
India
Census of India, Employment and Unemployment Survey
the time-series data of average weekly hours
Indonesia
Labor Force Situation in Indonesia
worked. This edition of the Databook newly
Iran
Population Census, Labor Force Survey
Japan
Labor Force Survey, National Accounts
considered the changes in the composition of
Census on Basic Characteristics of Establishment, Economically
workforces (determined by four kinds of attriKorea
Active Population Survey, Employment Structure Survey
Monthly Labor Survey, Population and Housing Census
butes; gender, education, age, and status of
Lao PDR
Population Census, ADB Key Indicators for Asia and the Pacificemployment) in the estimation process of the
Economic Report Various issues, Malaysia Economic
Malaysiatime-series average hours, although the previStatistics-Time Series, Labor Force Survey Report
Labour Force Survey, Mongolian Statistical Yearbook,ous editions has used a linear interpolation or a
Mongolia
Population and Housing Survey
fixed average hour for the periods in which the
Nepal
Labor Force Survey, Population Census
Labor Force Survey, Pakistan Statistical Yearbook, Pakistan
data is not available without considering the
Pakistan
Economic Survey, Population Census
121changes in the composition. Multiplying thePhilippines Labor Force Survey, Philippines Statistical Yearbook
Labor Force Survey, Singapore Yearbook of Manpower
average hours worked by the number of workSingapore
Statistics
ers gives economy-wide averages of weekly
Central Bank of Sri Lanka Annual Report, Labor Force Survey,
Sri Lanka
Population and Housing Census
hours worked. Second, the number of weeks
ThailandLabor Force Survey
worked is obtained, by counting the number
Estimates by General Statistics Office, Labor Force andVietnam
Employment Surveyof national holidays in each country as a crude

App.

Appendix

approximation. Multiplying economy-wide average weekly hours worked by the number ofweeks worked gives economy-wide annual
hours worked. For Fiji, the Lao PDR, and Mongolia, total hours worked are not estimated due todata constraint.

Cambodia
Iran
Korea
Singapore
Thailand
Malaysia
Vietnam
Pakistan
Bangladesh
Hong Kong
ROC
China
India
Philippines
Indonesia
Sri Lanka
TurkeyNepal
Japan
Australia
EU15 −4

52
44
43
40
40
39
39
38
35
35

©2015 Asian Productivity Organization

29
Figure 106 presents a cross-country comparison
29
of average annual hours worked per worker for
29
26
2000–2013, relative to the level of the US. It indi21
cates that workers in Asian countries tend to21
work much longer hours than those in the US
14
10
and Europe. In many of the countries sampled,6
the difference in annual hours worked per per3
son relative to the US is more than 20% of the−5 0
5 10 15 20 25 30 35 40 45 50 55 %US level. Prolonged working hours are observedin Asian countries regardless of their stage of
Figure 106 Average Annual Hours Worked Per
development, spanning low-income countries
Worker Relative to the US, 2000–2013
such as Bangladesh and Cambodia to high-inSources: Official national accounts and labor force survey in eachcome countries such as the ROC and Singapore.
country, including author adjustments.
An exception is Japan. Workers in Japan are likely to work much shorter hours than those inother Asian countries. However, compared with the EU15, hours worked by workers in Japan are still
about 10% longer.

144

A.5 Other Data

A.5

Other Data

For China, multiple data sources have been used; GDP for the whole economy, industry GDP, final
demands, employment, and income data are taken from China Statistical Yearbook and China National
Income 1952–1995; time-series data of GFCF during 1952–2013 at current and constant prices are constructed at KEO; the main references for GFCF construction are drawn from Statistics on Investment in
Fixed Assets of China 1950–2000, China Statistical Yearbook, and 1987, 1992, 1997, 2002, and 2007 Input–
Output Tables of China; and multiple data sources for manufacturing, electrics, and trade data from
China’s Customs Statistics are also utilized.122

App.

The data source for the EU15 and EU28 is the OECD.Stat (http://stats.oecd.org/), the data of the Eurostat (http://ec.europa.eu/), and OECD (2015). The data for the US, Australia, Bhutan, and Turkey aretaken from the website of the US Bureau of Economic Analysis (http://www.bea.gov), the Australian
Bureau of Statistics (http://www.abs.gov.au/), the National Statistics Bureau of Bhutan (http://www.
nsb.gov.bt/), and the Turkish Statistical Institute (http://www.turkstat.gov.tr), respectively.
The exchange rates used in this edition are adjusted rates, called the Analysis of Main Aggregate(UNSD database) rates, in the UNSD National Accounts Main Aggregate Database. The AMA rates coincide with IMF rates except for some periods in countries with official fixed exchange rates and highinflation, when there could be a serious disparity between real GDP growth and growth converted to
US dollars based on IMF rates. In such cases, the AMA adjusts the IMF-based rates by multiplying the
growth rate of the GDP deflator relative to the US.

122: Holz (2006) provides a useful reference on Chinese official statistics. The project appreciates Meng Ruoyan (Keio University) for
her supports.

145©2015 Asian Productivity Organization

Tax data of member economies are supplemented by the IMF’s Government Finance Statistics. From
its tax revenue data, “taxes on goods and services” and “taxes on imports” are used for calculatingtaxes on products. From its expenditure data, “subsidies” are taken. Data taken from Government Finance Statistics play a key role in adjusting GDP at market prices to GDP at basic prices. The data forenergy consumptions and CO2 emissions is based on IEA’s CO2 Emissions from Fuel Combustion, Energy
Balances of OECD Countries, and Energy Balances of non-OECD Countries.

Appendix

A.6

Industry Classification

Cambodia, Iran, the Lao PDR, Malaysia, Nepal, the Philippines, Sri Lanka, and China use the International Standard Industry Classification of All Economic Activities (ISIC) Rev.3. Other Asian economies
already have switched to the ISIC Rev.4. The concordances between the industry classification used in
the Databook and the ISIC Rev.3 and Rev.4 are shown in Tables 24 and 25, respectively.
Table 24 Industry Classification – Concordance with ISIC Rev.3
ISIC Rev. 3
SectionDivision

Databook
1st 2ndA - Agriculture, hunting, and forestry

01
02

Agriculture, hunting, and related service activities
Forestry, logging, and related service activities

1
1

B - Fishing05

Fishing, operation of fish hatcheries, and fish farms; service activities incidental to fishing

1

C - Mining and quarrying

10
11

2
2

21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
40
41

Mining of coal and lignite; extraction of peat
Extraction of crude petroleum and natural gas; service activities
incidental to oil and gas extraction excluding surveying
Mining of uranium and thorium ores
Mining of metal ores
Other mining and quarrying
Manufacture of food products and beverages
Manufacture of tobacco products
Manufacture of textiles
Manufacture of wearing apparel; dressing and dyeing of fur
Tanning and dressing of leather; manufacture of luggage, handbags, saddlery, harness, and footwear
Manufacture of wood and of products of wood and cork, except furniture; manufacture of articles ofstraw and plaiting materials
Manufacture of paper and paper products
Publishing, printing, and reproduction of recorded media
Manufacture of coke, refined petroleum products, and nuclear fuelManufacture of chemicals and chemical products
Manufacture of rubber and plastics products
Manufacture of other non-metallic mineral products
Manufacture of basic metals
Manufacture of fabricated metal products, except machinery and equipment
Manufacture of machinery and equipment n.e.c.
Manufacture of office, accounting, and computing machinery
Manufacture of electrical machinery and apparatus n.e.c.
Manufacture of radio, television, and communication equipment and apparatus
Manufacture of medical, precision, and optical instruments, watches, and clocks
Manufacture of motor vehicles, trailers, and semi-trailers
Manufacture of other transport equipment
Manufacture of furniture; manufacturing n.e.c.
Recycling
Electricity, gas, steam, and hot water supply
Collection, purification, and distribution of water

F - Construction

45

Construction

5

G - Wholesale and retail trade; repair ofmotor vehicles, motorcycles, andpersonal and household goods

50
51
52

Sale, maintenance, and repair of motor vehicles and motorcycles; retail sale of automotive fuelWholesale trade and commission trade, except of motor vehicles and motorcycles
Retail trade, except of motor vehicles and motorcycles; repair of personal and household goods

666

H - Hotels and restaurants

55

Hotels and restaurants

6

I - Transport, storage, andcommunications

60
61
62
63
64

Land transport; transport via pipelines
Water transport
Air transport
Supporting and auxiliary transport activities; activities of travel agencies
Post and telecommunications

7
7
7
7
7

J - Financial intermediation

65
66
67

Financial intermediation, except insurance and pension funding
Insurance and pension funding, except compulsory social security
Activities auxiliary to financial intermediation

88
8

 eal estate, renting, and business
K-R
activities

70
71
72
73
74

Real estate activities
Renting of machinery and equipment without operator and of personal and household goods
Computer and related activities
Research and development
Other business activities

8
8
8
8
8

L-P
 ublic administration and defence;compulsory social security

75

Public administration and defence; compulsory social security

9

M - Education80

Education

9

N - Health and social work

85

Health and social work9

 ther community, social, andO-O
personal service activities

90
91
92
93

Sewage and refuse disposal, sanitation, and similar activities
Activities of membership organizations n.e.c.
Recreational, cultural, and sporting activities
Other service activities

9
9
9
9

P-P
 rivate households with employedpersons

95

Private households with employed persons

9Q - Extra-territorial organizations andbodies99

Extra-territorial organizations and bodies

9

12
13
14
D - Manufacturing©2015 Asian Productivity Organization

E - Electricity, gas, and water supply

15
16
17
18
19
20

Note: “n.e.c.” represents “not elsewhere classified.”

146

2
2
2
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
4
4

3.1
3.1
3.2
3.2
3.2
3.3
3.4
3.4
3.5
3.5
3.5
3.6
3.7
3.8
3.8
3.8
3.8
3.8
3.8
3.8
3.8
3.9
3.9

A.6 Industry Classification

Table 25 Industry Classification – Concordance with ISIC Rev.4
Division

Databook
1st2nd(a) (b)A - Agriculture, forestry, and fishing1
2
3

Crop and animal production, hunting, and related service activities
Forestry and logging
Fishing and aquaculture

1

1
1
1

B - Mining and quarrying

5
6
7
8
9

Mining of coal and lignite
Extraction of crude petroleum and natural gas
Mining of metal ores
Other mining and quarrying
Mining support service activities

2

2
2
2
2
2

C - Manufacturing

10
11
12
13
14
15
16

3

3
3
3
3
3
3
3

3.1
3.1
3.1
3.2
3.2
3.2
3.3

17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33

Manufacture of food products
Manufacture of beverages
Manufacture of tobacco products
Manufacture of textiles
Manufacture of wearing apparel
Manufacture of leather and related products
Manufacture of wood and of products of wood and cork, except furniture; manufacture of articles ofstraw and plaiting materials
Manufacture of paper and paper products
Printing and reproduction of recorded media
Manufacture of coke and refined petroleum products
Manufacture of chemicals and chemical products
Manufacture of basic pharmaceutical products and pharmaceutical preparations
Manufacture of rubber and plastics products
Manufacture of other non-metallic mineral products
Manufacture of basic metals
Manufacture of fabricated metal products, except machinery and equipment
Manufacture of computer, electronic and optical products
Manufacture of electrical equipment
Manufacture of machinery and equipment n.e.c.
Manufacture of motor vehicles, trailers and semi-trailers
Manufacture of other transport equipment
Manufacture of furniture
Other manufacturing
Repair and installation of machinery and equipment

3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3

3.4
3.4
3.5
3.5
3.5
3.5
3.6
3.7
3.8
3.8
3.8
3.8
3.8
3.8
3.8
3.9
3.9

D - E lectricity, gas, steam, and air
conditioning supply

35

Electricity, gas, steam, and air conditioning supply

44

E - Water supply; sewerage, waste
management, and remediation
activities

36
37
38
39

Water collection, treatment, and supply
Sewerage
Waste collection, treatment, and disposal activities; materials recovery
Remediation activities and other waste management services

4

4
9
9
9

F - Construction

41
42
43

Construction of buildings
Civil engineering
Specialized construction activities

5

5
5
5

G - Wholesale and retail trade; repair ofmotor vehicles and motorcycles

45
46
47

Wholesale and retail trade and repair of motor vehicles and motorcycles
Wholesale trade, except of motor vehicles and motorcycles
Retail trade, except of motor vehicles and motorcycles

6

6
6
6

H - Transportation and storage

49
50
51
52
53

Land transport and transport via pipelines
Water transport
Air transport
Warehousing and support activities for transportation
Postal and courier activities

7

7
7
7
7
7

 ccommodation and food service
I-A
activities

55
56

Accommodation
Food and beverage service activities

6

J - Information and communication

58
59
60
61
62
63
64
65
66

Publishing activities
Motion picture, video, and television programme production, sound recording and music publishing activities
Programming and broadcasting activities
Telecommunications
Computer programming, consultancy, and related activities
Information service activities

7

6
6
3
9
9
7
8
8

Financial service activities, except insurance and pension funding
Insurance, reinsurance, and pension funding, except compulsory social security
Activities auxiliary to financial service and insurance activities

8

8
8
8

L - Real estate activities

68

Real estate activities

88

M - Professional, scientific, and technicalactivities

69
70
71
72
73
74
75

Legal and accounting activities
Activities of head offices; management consultancy activities
Architectural and engineering activities; technical testing and analysis
Scientific research and development
Advertising and market research
Other professional, scientific, and technical activities
Veterinary activities

8

8
8
8
8
8
8
9

N-A
 dministrative and support service
activities

77
78
79
80
81
82

Rental and leasing activities
Employment activities
Travel agency, tour operator, reservation service, and related activities
Security and investigation activities
Services to buildings and landscape activities
Office administrative, office support, and other business support activities

9

9
9
7
9
9
9

O - Public administration and defence;compulsory social security

84

Public administration and defence; compulsory social security

99

P - Education

85

Education

9

9

Q-H
 uman health and social work
activities

86
87
88

Human health activities
Residential care activities
Social work activities without accommodation

9

9
9
9

R - Arts, entertainment, and recreation

90
91
92
93

Creative, arts, and entertainment activities
Libraries, archives, museums, and other cultural activities
Gambling and betting activities
Sports activities and amusement and recreation activities

9

9
9
9
9

S - Other service activities

94
95
96

Activities of membership organizations
Repair of computers and personal and household goods
Other personal service activities

9

9
6
9

T - Activities of households as
employers; undifferentiatedgoods- and services-producing
activities of households for own use

97
98

Activities of households as employers of domestic personnel
Undifferentiated goods- and services-producing activities of private households for own use

9

9
9

U-A
 ctivities of extraterritorialorganizations and bodies

99

Activities of extraterritorial organizations and bodies

99

K - Financial and insurance activities

Note: The concordance (b) is used if the division-level data is available. The concordance (a) is used if only the section-level data is available.

147

App.

©2015 Asian Productivity Organization

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