2017 Asian Productivity Organization

2017 Asian Productivity Organization

Contents
Abbreviation
Forewordviii
ix

1 Introduction

1

2 Overview
2.1 Global and Regional Economic Trends
2.2 Summary Findings

3 Economic Growth
3.1 Economic Scale and Growth
3.2 Catching Up in Per Capita GDP
3.3 Sources of Per Capita GDP Gap

4 Expenditure
4.1 Final Demand Compositions
4.2 Consumption and Investment
4.3 Expenditure-Side Growth Decomposition

5 Productivity
5.1 Per-Worker Labor Productivity
5.2 Per-Hour Labor Productivity
5.3 Total Factor Productivity
5.4 Sources of Labor Productivity Growth
5.5 Energy Productivity

6 Industry Perspective
6.1 Output and Employment
6.2 Industry Growth
6.3 Labor Productivity by Industry

7 Real Income
7.1 Real Income and Terms of Trade
7.2 Trading Gain and Productivity Growth

8 Development Policy
8.1 National Development Strategies

Appendix
A.1 GDP Harmonization
A.2 Capital Stock
A.3 Rate of Return and Capital Services
A.4 Hours Worked and Labor Compensation
A.5 Other Data
A.6 Industry Classification
A.7 Data Publication and Visualization

References

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2017 Asian Productivity Organization

1.1 Databook 2017
1.2 List of Contributors
1.3 Map of Countries Covered

Contents

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PPP in the 2011 ICP Round
System of National Accounts in Asia
Size of the Informal Sector
Turning Point in China
Revising Myanmar’s Growths
Sensitivity of TFP Estimates
Labor Quality Changes
Productivity of City
Premature Deindustrialization
Per-Worker Wage and Income Level
Population and Demographic Dividend

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GDP using Exchange Rate, 1970, 1980, 1990, 2000, 2010, and 2015
GDP using PPP, 1970, 1980, 1990, 2000, 2010, and 2015
GDP Growth, 1990–1995, 1995–2000, 2000–2005, 2005–2010, and 2010–2015
Population, 1970, 1980, 1990, 2000, 2010, and 2015
Per Capita GDP using Exchange Rate, 1970, 1980, 1990, 2000, 2010, and 2015
Per Capita GDP, 1970, 1980, 1990, 2000, 2010, and 2015
Country Groups Based on the Initial Economic Level and the Pace of Catching Up
Final Demand Shares in GDP, 1970, 1990, 2000, 2010, and 2015
Per-Worker Labor Productivity Levels, 1970, 1980, 1990, 2000, 2010, and 2015
Per-Worker Labor Productivity Growth, 1990–1995, 1995–2000, 2000–2005, 2005–2010, and 2010–2015
Per-Hour Labor Productivity Levels, 1970, 1980, 1990, 2000, 2010, and 2015
Per-Hour Labor Productivity Growth, 1990–1995, 1995–2000, 2000–2005, 2005–2010, and 2010–2015
Output Growth and Contributions of Labor, Capital, and TFP, 1970–2015
Role of TFP and Capital Deepening in Labor Productivity Growth, 1970–2015
Energy Productivity Levels, 1980, 1990, 2000, 2010, and 2014
Country Groups Based on the Current Economic Level and the Pace of Catching Up
Output Growth by Industry, 2000–2015
Labor Productivity Growth by Industry, 2000–2015
Real Income and Terms of Trade, 1995–2000, 2000–2005, 2005–2010, and 2010–2015
National Development Strategies
Asset Classification and Parameters in Hyperbolic Function
Input-Output Tables and Supply and Use Tables
Average Ex-Post Real Rate of Return in Asia
Sources of Labor Data
Industry Classification – Concordance with ISIC Rev.3
Industry Classification – Concordance with ISIC Rev.4

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Table
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Table 1252017 Asian Productivity Organization

Table 126iv

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GDP Growth of Asia, the EU, Japan, and the US, 1970–2015
Share of Asia in World GDP in 2015 and Projection for 2022
Price Level Indices of GDP, 2011
Regional GDP of Asia and the EU, Relative to the US, 1970–2015
GDP of China, India, and Japan, Relative to the US, 1970–2015
Regional GDP of South Asia, ASEAN, CLMV, and GCC, Relative to the US, 1970–2015
Country Contributions to Regional GDP Growth, 1970–1990, 1990–2000, 2000–2010, and 2010–2015
Correlation of GDP Growth, 1990–2000
Correlation of GDP Growth, 2000–2015
Share of Asian Population in the World, 2015
Per Capita GDP using Exchange Rate of Japan and Australia, Relative to the US, 1970–2015
Per Capita GDP using Exchange Rate of the Asian Tigers, Relative to the US, 1970–2015
Per Capita GDP of Japan, the EU, and Australia, Relative to the US, 1970–2015
Per Capita GDP of the Asian Tigers, Relative to the US, 1970–2015
Per Capita GDP of China, India, and ASEAN, Relative to the US, 1970–2015
Per Capita Non-Mining GDP in Oil-Rich Countries and Japan, 2015
Initial Level and Growth of Per Capita GDP, 1970–2015
Labor Productivity and Employment Rate Gap Relative to the US, 1990 and 2015
Sources of Per Capita GDP Growth, 1990–2000 and 2000–2015
Share of Female Employment
Employment Rates, 1970, 1990, and 2015
Final Demand Shares in GDP of China, 1952–2015
Final Demand Shares in GDP of the US, 1929–2015
Final Demand Shares in GDP, 1995 and 2015
Ratio of Dependent Population and Consumption Share in GDP, 2015
Export and Import Shares in GDP, 1995 and 2015
Long-Term Trend of Household Consumption Share in GDP, 1970–2015
Household Consumption by Purpose, 2015
Engel Curve of Japan during 1949–2015 and Levels of Asian Countries in 2015
Long-Term Trend of Investment Share in GDP, 1970–2015
FDI Inflows, 2000–2015
FDI Inflow Ratio and Business Environment, 2000–2015
Investment Share by Type of Asset, 1970 and 2015
Long-Term Trend of Net Export Share in GDP, 1970–2015
Final Demand Contributions to Economic Growth, 1990–2010 and 2010–2015
Impacts of Global Financial Crisis and Recoveries, 2007–2012
Impacts of Asian Financial Crisis, 1997–1998
Final Demand Decomposition of Real GDP Growth, 1970–2015
Labor Productivity Level by Per-Worker GDP, 2015
Labor Productivity Trends of China and India, 1970–2015
Labor Productivity Level Relative to the US, 1970–2015
Labor Productivity Gap by Per-Worker and Per-Hour GDP Relative to the US, 2015
Labor Productivity Trends in Japan and the Asian Tigers, 1970–2015
Labor Productivity Growth, 1970–2015, 1970–1990, and 1990–2015
Labor Input Growth, 1970–2015, 1970–1990, and 1990–2015
Labor Productivity Trends of Japan in 1885–2015 and Levels of Asian Countries in 2015
Time Durations Taken to Improve Labor Productivity by Japan and the Asian Tigers
TFP Growth, 1970–2015, 1970–1990, and 1990–2015
Sources of Economic Growth, 1970–2015
Contribution Shares of Economic Growth, 1970–2015
Sources of Economic Growth, 1970–1985, 1985–2000, and 2000–2015
Contribution Shares of Economic Growth, 1970–1985, 1985–2000, and 2000–2015

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2017 Asian Productivity Organization

FigureFigure 111

Contents

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2017 Asian Productivity Organization

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Figure 105vi

Comparison of Sources of Economic Growth with OECD Countries, 2000–2015
Comparison of TFP Contribution Shares with OECD Countries, 2000–2015
Individual Countries’ Growth Accounting Decomposition, 1970–2015
IT Capital Contribution to Capital Input Growth of Japan and the US, 1970–2015
IT Capital Contribution to Capital Input Growth of the Asian Tigers, China, and India, 1970–2015
Individual Countries’ Growth Accounting Decomposition (year-on-year), 1970–2015
Capital Deepening, 1970–2015, 1970–1990, and 1990–2015
Capital Productivity Growth, 1970–2015, 1970–1990, and 1990–2015
Capital Productivity Trends in Japan and the Asian Tigers, 1970–2015
Capital Productivity Trends in China and India, 1970–2015
Sources of Labor Productivity Growth, 1970–2015
Contribution Shares of Labor Productivity Growth, 1970–2015
Sources of Labor Productivity Growth, 1970–1985, 1985–2000, and 2000–2015
Contribution Shares of Labor Productivity Growth, 1970–1985, 1985–2000, and 2000–2015
Decomposition of Labor Productivity Growth, 1970–2015
Shares of Asia in World Energy Consumption and CO2 Emission, 2014
Labor Productivity and Energy Productivity, 2014
Sources of CO2 Emission Growth, 2000–2014
Industry Shares of Value Added, 2015
Manufacturing Share and TFP Growth, 2000–2015
Industry Shares of Value Added in Manufacturing, 2015
Industry Shares of Value Added and Employment by Country Group, 1980, 1990, 2000, and 2015
Long-Term Trends of Value-added Share in the Agriculture Sector, 1970–2015
Industry Shares of Employment, 2015
Employment Share of Agriculture in Japan during 1885–2015 and Levels of Asian Countries in 2015
Long-Term Trends of Employment Share in the Agriculture Sector, 1970–2015
Job Creation in Manufacturing, 1970–2015
Industry Origins of Economic Growth, 1990–2000 and 2000–2015
Contribution of Manufacturing to Economic Growth, 1990–2000 and 2000–2015
Contribution of Service Sector to Economic Growth, 1990–2000 and 2000–2015
Industry Origins of Output Growth in Manufacturing, 1990–2000 and 2000–2015
Industry Origins of Regional Economic Growth, 1990–2000 and 2000–2015
Industry Origins of Asian Economic Growth, 2000–2015
Industry Origins of Economic Growth, 1970–2015
Industry Origins of Labor Productivity Growth, 1990–2000 and 2000–2015
Contribution of Manufacturing to Labor Productivity Growth, 1990–2000 and 2000–2015
Contribution of Service Sector to Labor Productivity Growth, 1990–2000 and 2000–2015
Effect of Net Income Transfer on GDP, 1970–2015
Price of Crude Oil, 1986 January–2017 May
Trading Gain Effect, 1990–2000 and 2010–2015
Real Income and Real GDP Growth, 1970–2015
Decomposition of Real Income Growth, 1970–2015 and 2000–2015
Decomposition of Real Income Growth, 1973–1979 and 1996–1998
Sources of Real Income Growth, 1970–2015
Trading Gain Effect and Labor Productivity Growth, 1970–2015
Trading Gain Effect and Value-added Share in Mining Sector, 1970–2015
Adjustment of FISIMFISIM Share in GDP, 2000–2015
Software Investment Ratio and GFCF Ratio to GDP, 2005
Adjustment of R&D
R&D Share in GDP, 2015
Capital-Output Ratio, 1980 and 2015
Ex-Post Real Rate of Return in Asia, 1970–2015

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Revisions of PPP for GDP by the 2011 ICP Round
Implementation of the 1968, the 1993, and the 2008 SNA
Figure B3
Employee Share and GDP Level, 2015
Figure B4Price of Labor Relative to Capital in China, Japan, and the Asian Tigers, 1970–2015
Figure B5
Official and Revised Estimates of Real GDP Growths in Myanmar
Figure B6.1 Labor Income Share for Employees, 2015
Figure B6.2 Sensitivity of TFP Estimates by the Change of Income Share, 1970–2015
Figure B7.1 Contributions of Labor Quality to Growths in Japan and the US, 1955–2012
Figure B7.2 Average Schooling Years of Workers, 1970–2015
Figure B8
Per-Worker Labor Productivity Levels of Cities in PDB-City, 2015
Figure B9Country Peaks of GDP Shares of Manufacturing
Figure B10 Average Wage and Per Capita GNI, 2015
Figure B11.1 Distribution of the World’s Population in Different Regions, 1950–2100
Figure B11.2 Asian Countries’ Population Size and Projection, 1970, 2015, and 2050
Figure B11.3 Proportion of the Dependent Population, 2015
Figure B11.4 Demographic Dividend by Country, 1950–2100
Figure B11.5 Demographic Dividend by Country Group, 1950–2100

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Figure B1
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2017 Asian Productivity Organization

Average Annual Hours Worked Per Worker Relative to the US, 2010–2015
Availability of COE Estimates
Visualization in Asian Economy and Productivity Map

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Abbreviation
ADB
APO
APO20

AEPM		ASEAN
ASEAN6
Asia24
Asia30
CLMV
CPI
COE
ESRI
EU
EU15

EU28

2017 Asian Productivity Organization

FDI
FISIM
GCC
GDP
GFCF
GNI
ICP
ILO
IMF
ISIC
IT		
KEO		
Lao PDR		
LDCs		
NPISHs		
OECD
PPP
QALI
QNA
RCEP
ROC
R&D
SNA
TFP
TPP
UAE
UN
UNSD
US

viii

Asian Development 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
Asian economy and productivity map (see Appendix 7)
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
APO20 plus the People’s Republic of China, the Kingdom of Bhutan, Brunei,and Myanmar
Asia24 plus GCC countries
Cambodia, the Lao PDR, Myanmar, and Vietnam
consumer price index
compensation of employees
Economic and Social Research Institute, Cabinet Office of Japan
European Union
15 member economies of the European Union prior to enlargement:
Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy,
Luxembourg, Netherlands, Portugal, Spain, Sweden, and the United Kingdom
European Union: EU15 plus Bulgaria, Republic of Croatia, Cyprus, Czech
Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Romania, Slovak
Republic, and Slovenia
foreign direct investment
financial intermediation services indirectly measured
Gulf Cooperation Council: Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and theUAEgross domestic productgross fixed capital formation
gross national income
International Comparisons Program
International Labour Organization
International Monetary FundInternational Standard Industry Classification of All Economic Activities
information technology
Keio Economic Observatory, Keio University
Lao People’s Democratic Republic
less developed countries
non-profit institutions serving households
Organisation for Economic Co-operation and Development
purchasing power parity
quality adjusted labor inputs
quarterly national accounts
Regional Comprehensive Economic Partnership
Republic of China
research and development
System of National Accounts
total factor productivity
Trans-Pacific Partnership
United Arab Emirates
United Nations
United Nations Statistics Division
United States

Foreword
A major challenge to sustaining growth continues to be raising productivity.
The Asian Productivity Organization (APO), as the sole organization devoted to
productivity in the Asia-Pacific, has endeavored to offer innovative solutions
and assistance to its member economies not only for enhancing productivity
but also for effectively dealing with the uncertain global business environment
driven by fast-changing, emerging technologies that are drastically altering ourlives and the business environment.
Measuring productivity is an important part of the APO project portfolio, as it istasked with monitoring productivity gaps for member economies. At the sametime, monitoring social, technological, economic, environmental, and politicalchanges by governments to foresee trends is equally pivotal, so that they canalign the most needed policies with national development blueprints, as well as
create a favorable environment for industries to adapt and prepare quickly for
new opportunities and challenges.
I am pleased to invite readers to utilize this new edition of the APO Productivity
Databook. It presents an analytical report on recent and long-term productivity and economic performance in the Asia-Pacific and reference economies. My
gratitude goes to the chief expert of this project, Professor Koji Nomura of Keio
University, for his contributions to developing methods for the comprehensive
analyses of productivity. I hope that readers will find this a useful reference on
the productivity status of countries in the APO region.

Santhi Kanoktanapornix

2017 Asian Productivity Organization

Secretary-General
Asian Productivity Organization
Tokyo, September 2017

1.1 Databook 2012

1 Introduction
1.1 Databook 2017

1

This is the tenth edition in the APO Productivity Databook series. The Databook aims to provide a useful reference for the quality of economic growth in Asia. It presents authoritative estimates of productivity and its decomposition, which are comparable across countries at different development stages
in the middle and long run. Productivity gains, which enable an economy to produce more for thesame amount of inputs or to consume less to produce the same amount of outputs, are the only routeto sustainable economic growth in the long run. Thus it follows that monitoring and improving national productivity capability are important targets of public policy.
In this edition of the Databook, baseline indicators on economic growth and productivity are calculated for 30 Asian economies, representing the 20 Asian Productivity Organization member economies (APO20) and the 10 non-member economies in Asia. The APO20 consists of 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 nonmember economies in Asia are: the People’s Republic of China (China), the Kingdom of Bhutan (Bhutan), 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. This edition covers theperiod from 1970 to 2015.

Based on the growth accounting framework in PDB, the sources of economic growth in each economy
are further decomposed to factor inputs of labor and capital and total factor productivity (TFP) for 21
Asian economies – Bangladesh, Cambodia, the ROC, Fiji, Hong Kong, India, Indonesia, Iran, Japan, Korea, the Lao PDR, Malaysia, Mongolia, Nepal, Pakistan, the Philippines, Singapore, Sri Lanka, Thailand,
Vietnam, and China – along with the US as a reference economy. It is a notable achievement that theestimates on TFP for the Lao PDR are newly developed in this edition of the Databook. In addition, themain aggregates for the Lao PDR are backwardly estimated from 1970 (the starting year was 1981 in
Databook 2016). This edition also attempts to revise the official estimates of the economic growth inMyanmar, which might have been significantly overstated since the latter half of the 1990s, as indicatedby The Economist Intelligence Unit (2010) and ADB (2017). The revision process is described in Box 5.
To analyze the overall productivity performance as well as productivity subsets (e.g., labor productivity and capital productivity), the Databook constructs the estimates of capital services, which provides

1

2017 Asian Productivity Organization

The productivity measures in the Databook are based on the official data and our own estimates collated for the APO Productivity Database (PDB). This is a joint research effort between the APO and the
Keio Economic Observatory (KEO), at Keio University, Tokyo, since September 2007. In Asian countries,
recent significant revisions based on the System of National Accounts 2008 (2008 SNA), which is thelatest version of the international statistical standard for the national accounts by the United Nations(2009), have resulted in updates for Sri Lanka as of March 2016 and Japan and Turkey as of December
2016. While there are movements to upgrade the national accounts, some countries such as Cambodia, the Lao PDR, and Nepal, have yet to fully introduce the earlier version 1993 SNA. Because thevarying SNA adaptions among the economies can result in discrepancies between data definitions
and coverage, data harmonization is necessary for comparative productivity analyses. The 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 better international comparison. The GDP harmonization process including capitalization of softwareand research and development (R&D) is provided in Appendix 1.

1 Introduction

an appropriate concept of capital as a factor of production, as recommended in the 2008 SNA. The
fundamental assumption in measuring capital services is proportionality between the (productive)
capital stock and capital services in each type of asset. Thus, the growth rates of capital services candiffer from that of capital stock only at the aggregate level. The assumption and data in measuring
capital stock is presented in Appendix 2. For aggregating different types of capital, the user cost ofcapital by type of asset is required. The outline of the methodology to measure price and volume of
capital service is presented in Appendix 3.
The labor share is one of the key factors to determine the TFP growth. However, the estimates on thecompensation of employees (COE) are not fully available in the official national accounts in Asian
countries (i.e., Bangladesh, the Lao PDR, Pakistan, and Vietnam). At KEO, the comprehensive database(PDB-L) on number of workers, hours worked per worker, and hourly wages, which are cross-classified
by gender, education attainment, age, and employment status, has been developed for the past few
years. The COE are estimated based on this work-in-progress database and used for the countries inwhich the official estimates are not available. In addition, the compensation of self-employed andcontributing family workers, which tend to have a larger share in total employment in less developed
countries, have to be estimated to determine the total labor cost. In this edition of the Databook, theharmonized assumption on the hourly-wage differentials between employees and self-employed andcontributing family workers in the most detailed category of labor in PDB-L is newly applied. The
methodology to measure labor input is presented in Appendix 4.

2017 Asian Productivity Organization

The structure of the Databook is as followed. The recent trends in global and regional economic
growth and the summary findings are presented in Chapter 2. In order to understand the dynamics ofthe long-term economic growth within Asia, Chapter 3 details countries’ diverse development efforts
and achievements, through cross-country level comparisons of GDP. Decompositions of GDP, which isdefined by three approaches in SNA: production by industry, expenditure on final demand, and income to factor inputs, are valuable in understanding the structure, and in turn the behavior, of an
economy. Chapter 4 presents the demand side decomposition analyzing the sources of countries’
expenditure growths. The estimates of final demands are newly added for the Lao PDR and Myanmar
in this edition of the Databook. In Chapter 5, the supply side decompositions of economic growth andlabor productivity improvement are analyzed in each country and region. In this edition, the country
aggregations of capital and labor inputs are newly based on the estimates of PPP for capital and labor
inputs, respectively. This chapter also provides the energy productivity performance to reflect theimpending need to improve energy efficiency as a policy target for pursuing sustainable growth. The
preliminary digest of our work-in-progress database on productivity of a city (PDB-City) is presented
in Box 8. The different composition of economic activities among countries is one of the main sourcesof the huge gap in average labor productivity at the aggregate level. The industry structure is presented in Chapter 6. Chapter 7 focuses on real income to evaluate an improvement in the terms oftrade. Finally, Chapter 8 was newly added in this edition of the Databook to present the summary of
the national development strategies in the APO member economies.
The official national accounts and metadata information used for constructing the APO Productivity
Database 2017 has been collected by the national experts in APO member economies and researchmembers at KEO. The names of these contributors are listed in Section 1.2. The submitted data wasthen examined and compiled at KEO, where further information was collected on labor, production,
prices, trades, and taxes as required. This edition effectively reflects the revisions to the official national accounts and other statistical data published through May 2017. The project was managed by
Koji Nomura (Keio University), under the consultancy of Professor Dale W. Jorgenson (Harvard University) and Professor W. Erwin Diewert (University of British Columbia), and with coordination by Yasuko
Asano (APO). The text, tables, and figures of this edition were authored by Koji Nomura and Fukunari21.2 List of Contributors

Kimura (Keio University), with support from the research assistants Shinyoung Oh, Naoyuki Akashi,
Hiroshi Shirane, Shiori Nakayama, Daisuke Matsuoka, Kei Iwai, and Yurika Katayama. The Databook
project appreciates Eunice Ya Ming Lau for her contribution to developing the foundation of the Databook series during her stay at KEO and Trina Ott for her review of the draft. In particular, we express
our heartfelt condolences on the death of our colleague, Ms. Navilini Singh. She served as the national expert for Fiji for the APO Productivity Databook since 2011. May she rest in peace.

1

1.2 List of Contributors

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

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

Research Members at KEO
Ms. Shinyoung Oh
Mr. Naoyuki Akashi
Mr. Hiroshi Shirane
Ms. Shiori Nakayama
Mr. Daisuke Matsuoka
Mr. Kei Iwai
Ms. Yurika Katayama
APO Officer
Ms. Yasuko Asano
Program Officer, Research and Planning
Department, Asian Productivity Organization,
1-24-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan

National Experts
Bangladesh
Mr. Ziauddin Ahmed
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, #386 Preah Monivong Blvd,
Phnom Penh

Republic of China
Ms. Ming-Chun Yu
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, Economics Statistics, Fiji
Bureau of Statistics, Rata Sukuna House, PO box
2221, Government Building, Suva

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

Indonesia
Ms. Ema Tusianti
Head of Cross Sector Statistical Analysis Section,
Statistics Indonesia,
Jl. Dr. Sutomo No.6-8, Jakarta

Islamic Republic of Iran
Mr. Behzad Mahmoodi
Head of Goods and Services Analyzing Section(GSA), Central Bank of IR Iran, Economic Statistics
Department, Ferdousi Ave., Tehran

32017 Asian Productivity Organization

Authors of This Report1 Introduction

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
Ms. Salika Chanthalavong
Head, National Account Division, Economic
Statistics Department, Lao Statistics Bureau,
Ministry of Planning and Investment, Vientiane

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

Mongolia
Ms. Bayarmaa Baatarsuren
Director of National Accounts Division, Economic
Statistical Department, National Statistics Officeof Mongolia, Government Building III,
Ulaanbaatar-20a

Nepal
Mr. Rajesh Dhital2017 Asian Productivity Organization

Director, Central Bureau of Statistics,
Ramshahpath, Thapathali, Kathmandu

4Pakistan
Mr. Fazil Mahmood Baig
Director, National Accounts Wing, Statistics
Division, Pakistan Bureau of Statistics, 21 Mauve
Area, Statistics House, G-9/1, Islamabad

Philippines
Ms. Ma. Julieta P. Soliven
Statistician E, Philippine Statistics Authority, 16th
Floor Eton Cyberpod Centris Three, EDSA cor
Quezon Ave., Quezon City

Sri Lanka
Mr. Weerasinghe Wasala Mudiyanselage Ananda
Sarath Premakumara
Additional Director General (Statistics I),
Department of Census and Statistics, 5th Floor,
Rotunda Tower, No. 109, Galle Road, Colombo 03

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

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

52017 Asian Productivity Organization

APO member economies
Non-member Asian economies included in Databook 2017

1.3 Map of Countries Covered

1.3 Map of Countries Covered12.1 Global and Regional Economic Trends

2 Overview
2.1 Global and Regional Economic Trends

2

The year 2016 would be remembered as the year when anti-globalism rose in advanced economies,
marked by the vote for “Brexit” in the UK, and the election of US President Donald Trump. However,
the overall economic situation was fair, with a continued recovery from a long recession in developed
economies, and steady economic growth in most of the Asian developing economies.
In Asia30 and East Asia, the average annual growth of GDP at constant market prices in 2010–2015
was 5.3% and 5.6%, respectively (Table 3 in Section 3.1). The growth slowdown in China and the decline of world trade seemed to stabilize. Latecomers in ASEAN, India, and other Asian developing
countries sustained rapid growth. Prolonged low food and fuel prices helped most of the Asian economies keep inflation low and sustain the pace of economic growth.
Advanced economies maintained a path of slow recovery. Among them, the US economy performed
better than others. The average annual growth of GDP at constant market prices in 2010–2015 in the
US was 2.1%. The unemployment rate dropped to 4.7% in December 2016, which was low by US standards, and continued to decline. The European economy also presented some sign of recovery. There,
the average annual growth rate of GDP at constant market prices in 2010–2015 in the EU15 and theEU28 were 0.9% and 1.0%, respectively. The Japanese economy was also on the course of recovery
though its potential growth rate was low. The annual growth of GDP at constant market prices in thesame period in Japan was 1.0%. The unemployment neared 3%. The recent World Economic Outlook
by the IMF (2017) shows growth forecasts for the year of 2017 better than the previous year, particularly for the US and Japan.
Although the growth slowdown in China continued for three years, it seems to have stabilized as a“new normal,” achieving 7.6% in the average annual growth of GDP at constant market prices in 2010–2015. There, drastic reform in the domestic economy continues. Along with the Chinese economy,
Korea also slowed, having 3.0% average annual growth in the same period.

The South Asian countries have not taken full advantage of international production networks,
though some have been successful in connecting with slow global value chains in labor-intensive industries. The per capita GDP using exchange rate in 2015 in Nepal, Bangladesh, Pakistan, and India
was $790, $1,230, $1,390, and $1,610, respectively.
The growth perspectives of the Asian economies are fair, though the slowdown of China seems tocontinue. However, there are both internal and external factors with which steady economic growth
could be jeopardized. The prime concern is on protectionism in advanced economies. Results in the72017 Asian Productivity Organization

Latecomers in ASEAN, Cambodia, the Lao PDR, and Myanmar, have steadily grown in the past two
decades, reaching $1,210, $1,870, and $930 in the per capita GDP using exchange rate in 2015, respectively (Table 5 in Section 3.1). However, the easy catch-up period is almost over. To achieve sustained
economic growth, they have to engage in international production networks. “Thai plus one” investment by machinery parts producers that set up fragmented satellite factories off Thailand appear tohave slowed recently. Vietnam achieved deeper involvement in international production networks
and had $2,130 per capita GDP. However, the ratio of manufacturing value added to GDP is still 15% in2015 (Figure 71 in Section 6.1 and Figure B.9 in Box 9). Growth of supporting industry and industrial
agglomeration is a hopeful anticipation. The Philippines and Indonesia are also in the process of forming an efficient industrial agglomeration with $2,900 and $3,400 in per capita GDP. Thailand, Malaysia,
and Singapore reached $6,000, $9,560, and $53,600 in per capita GDP though they struggled with theindustrial upgrading and the creation of innovation hubs.

2 Overview

UK EU membership referendum and the US presidential election point to a rise of anti-globalization
sentiment. Brexit will certainly work as a headwind against deepening economic integration in
Europe. Immediately after his inauguration, President Trump announced that the US would step outof the Trans-Pacific Partnership Agreement (TPP). The Trump administration may further extend
protective measures in international trade. The world, in particular East Asia, is tightly connectedby global value chains. Therefore any trade deterrent may disturb the functions of international
production networks.
The root of anti-globalization sentiment is complex. Some literature assumes that globalization or
freer trade and investment worsens income distribution in advanced economies. The logical basis of
the argument is the so-called Stolper-Samuelson (S-S) theorem in the Heckscher-Ohlin (HO) model; itclaims that freer trade makes capitalists or skilled labor better off, while unskilled labor is worse off inadvanced economies. However, the real world may not meet some of the basic settings and assumptions in the model. First, while the HO model assumes the perfect domestic mobility of capital/skilledlabor and unskilled labor between industries, the reality seems to suffer from slow industrial adjustments and labor replacements. Rather than adjusting for income distribution between different factor
holders, smoothing labor replacements across industries and firms as well as regions may be a moreurgent policy agenda. Second, although the benchmark HO model has only two productive factors,
the real world seems to be proxied by a model with three or more factors. If so, the effect of freer trade
may be much more complicated. For example, demand for labor with the least human capital does
not seem to decline; rather, some sort of the mid-class labor may face less demand. Third, the default
HO model does not include productivity growth over time. It does not take into account changes inhuman capital either. These factors may also cause a departure from the S-S theorem.
It is true that technology has become internationally mobile and the great convergence of income
levels between advanced economies and newly developed economies has occurred since the 1980s
as Baldwin (2016) claims. Therefore, advanced economies should accelerate industrial adjustments.
However, it does not necessarily mean that income distribution issues become aggravated. Indeed,
while the US and the UK have experienced increases in the income shares of the top 1% population
since the 1980s, Japan and Germany have not.

2017 Asian Productivity Organization

Although the fear of protectionism remains, some moves to counter this have emerged. Because President Trump is embroiled in domestic politics, the formation of a trade team may be delayed. Additionally, the administration has significant room for discretion in security and trade issues, making it
difficult to predict what will happen with US diplomacy. In time, US diplomacy may become morepredictable as the White House staff involvement increases. A cascading win by Mr. Emmanuel Macron in the French presidential election mitigated the fear of expanding extreme right power in Europe. However, some noneconomic factors such as terrorism may change the political atmosphere atany time. Asian countries are not immune from terrorism and therefore the containment of terrorism
is an essential element of the political agenda for all countries in the world.
On a positive note, at the G20 Hamburg Summit in July 2017, the Japan-EU Economic Partnership
Agreement (EPA) was announced. This advocates the intention of both sides to maintain the momentum of a free trade agenda, resisting the possible wind of protectionism. Because US exports will become relatively disadvantageous in the Japanese and EU markets, the policy demand for freer trade
may be strengthened in US politics.
East Asia can also join the freer trade initiative. Although the US stepped out of TPP, the remaining 11 negotiating countries began consideration of making TPP effective among 11 or 11 minus alpha countries.
This is a meaningful attempt because the text of TPP has a high value as a model free trade agreement,8

2.2 Summary Findings

even without the US, in terms of the level of liberalization and international rule making. The key is whether the countries can agree and validate TPP without changing much text, in addition to Article 30.5 which
specifies the condition of validating TPP. Each country may have a compromise in the text of TPP. However, changing the text may lead to negotiations and complications. The question is whether some slowmoving countries can enter in the second round and validate TPP with 11 minus alpha countries quickly.
Once TPP is in effect, an anticipated domino effect will attract new applicants, possibly even the US.

2

The Regional Comprehensive Economic Partnership (RCEP) also poses a significant challenge. Although ASEAN plus six countries started negotiating RCEP in 2013, progress was slow. However, there
are signs of accelerating negotiations from early 2017. Because 2017 marks the 50th anniversary of
the ASEAN, the need for ASEAN member states create some memorable achievements can aid in the
progress of RCEP negotiations and potentially a good outcome for presenting ASEAN centrality. Thusly, the negotiation team of ASEAN began aggressive work for concluding RCEP. Of concern is a tradeoff between the speed of negotiation and the quality of agreement. At some point in time, RCEP
should be designed as multi-layered with the less-complex segments concluded quickly.
In the globalizing world, careful macroeconomic management is essential. The tapering of the US
from long-lasting monetary easing will soon be realized if the US economy continues to strengthen.
Although the current management of macroeconomic fundamentals is much better than that in theera of Asian currency crisis, the financial world is also much more globalized now. A slight shift maytrigger sudden massive outflows of capital, resulting in a speculative attack. The financial authority
must monitor asset and financial markets with scrutiny.

2.2 Summary Findings
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 is intended to be a useful reference for thequality of economic growth. It provides authoritative estimates of productivity and its decomposition,
which are comparable across countries at different development stages in the middle and long run.
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.

Recent economic growth of Asia
u In terms of exchange-rate-based GDP, China overtook Japan in 2010 as the largest economy in Asiaand the second largest economy in the world, after the US. On this measure, the Asia30 was 36%
and 48% larger than the US and the EU15 in 2015, respectively (Table 1).

92017 Asian Productivity Organization

Bearing in mind these caveats, the main findings from our analysis are as follows:

2 Overview

u Based on GDP adjusted for purchasing power parity (PPP),1 the weight of the world economy iseven more tilted toward Asia, with the Asia30 1.75 times and 1.98 times larger than the US and theEU15 in 2015, respectively. China has overtaken Japan as the largest Asian economy since 1999. In
2013 China overtook the US as the largest economy in the world, 10% larger relative to the US in
2015. India surpassed Japan, replacing it as the second largest economy in Asia in 2009. In 2015,
the total GDP of the three largest Asian economies alone was 82% larger than the US economy(Table 2 and Figure 5).
u During the period 1990–2015, the Asia30 grew at 5.4% on average per annum, compared with

2.4% and 1.6% in the US and the EU15, respectively. Japan was the slowest growing economy
among the Asia30 at 1.0%, compared with 24 of the 30 Asian economies with over 4.0% of annual
economic growth (Table 3 and Figure 1).
u In the period 2010–2015, China and India have emerged as the driving forces propelling Asia for-ward, accounting for 55% and 18% of regional growth, respectively (Figure 7).
u The global financial crisis slowed Asia30’s growth significantly from a recent peak of 8.0% during

2006–2007, to 4.8% during 2007–2008 and further to 3.9% during 2008–2009, before rebounding
strongly to 8.0% during 2009–2010. This is in comparison to the deep recession of –2.8% and –4.5%
experienced by the US and the EU15, respectively, during 2008–2009 (Figure 1).
u The correlation coefficients between China and other Asian economies strengthened between thetwo decades. This suggests that China has become more integrated within the Asian economy. For
most Asian countries, the correlation with the US and the 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 2015 its per capita GDP was 53% higher. In contrast, veteran Japan has fallen behind, widening its gap with the US
to 28%. In 2015, the ROC’s and Korea’s per capita GDP was 83% and 65% of the US level, respectively (Table 6 and Figure 14).
u Despite their rapid growth, due to their population, per capita GDP of China and India was 26%

and 11% of the US in 2015, respectively. However, this represents a tenfold increase in China’s relative per capita GDP over the last four decades. The level achieved by the Asia30 was 22% 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, GCC, Japan, and Iran, all Asian countries have a laborproductivity gap of 50% or higher (Figure 18).

2017 Asian Productivity Organizationu For most countries in Asia, the majority of per capita GDP growth can be explained by improve-

ment in labor productivity. However, the employment rate contribution relative to labor productivity was also highly significant in Singapore, Malaysia, Korea, and the ROC in 2010–2015 (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.

10

2.2 Summary Findingsu 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, Vietnam, and Thailand (Figure 21).

2

Changes in demand compositionu With a few exceptions, household consumption is the biggest component of final demand. In

recent years, Asia30’s consumption ratio has dropped to 48.4% of GDP in 2015, largely reflecting
the trend in China. This compares to 68.1% in the US, 56.4% in the EU15, and 57.7% 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 2015, the Asia30 invested 35.4% of its GDP, compared with 20.3% for the US, 19.5% for
the EU15, and 25.7% for Australia (Table 8 and Figure 30).
u China faces huge internal and external imbalances. The investment share of GDP (at 45.7%), as thebiggest component in final demand and the household consumption share, plummeted to 37.0%
in 2015. 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 towards net exports because of their oil. Net exports ac-

counted for 18.7% of final demand in 2010, compared with Asia30’s 3.3% and China’s 3.6%. Only
the US and South Asia run trade deficits of a more significant nature, which accounted for –3.4%
and –4.9% of final demand, respectively, in 2010 (Table 8).
u According to the cross-country version of Engel’s Law, basic necessities will account for a high pro-portion of household consumption for a lower per capita income group and vice versa. Lowerincome countries spend 30–50% of total consumption for food, which corresponds to Japan’s
experience in the 1950s and the 1960s (Figures 28 and 29).
Labor productivity
u For most Asian countries, the per capita GDP gap with the US is largely explained by labor produc-

tivity shortfalls of 80% or more against the US level. Only Singapore and Hong Kong have effectively closed that gap. The relative labor productivity of the Asia24 was 21% of the US in 2015
(Table 9 and Figure 39).

the low-income countries appeared to experience a labor productivity growth spurt in 2010–2015.
Mongolia achieved the fastest labor productivity growth of 7.7% on average per year in this period,
followed by China’s 7.2%, the Lao PDR’s 6.9%, Sri Lanka’s 5.7%, India’s 5.0%, and Cambodia’s 4.9%;
this compares with 1.2–1.3% in the Asian Tigers, 0.7% in Japan and the US, and 0.6% in the EU15
(Table 10 and Figure 41).

112017 Asian Productivity Organizationu Growth of per-worker GDP in Asia has outstripped that in the US, allowing catch-up. In particular,

2 Overview

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 9–25 percentage points for the Asian Tigers, suggesting that people work much
longer hours than in the US (Figure 42).
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
4.5% to 8.4% between 1970–1990 and 1990–2015, compared to the US at 1.5% and 1.7% over thesame periods (Figure 44).
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 46).
Total factor productivity
u Of the 21 Asian countries compared, 11 experienced faster TFP growth than the US over the period

1970–2015, with China in a league of its own. Its TFP growth was at 3.0% on average per year, compared with those of Thailand at 1.3% in second place and the US at 0.7%. With TFP growing at 0.4%
on average per year, Singapore’s productivity performance has been weak relative to its economic
counterparts (Figure 48).
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 25% of economic growth in seven of the 21 Asian countries compared, with
it being most prominent in India (35%), China (34%), Sri Lanka (33%), and Japan (31%) (Figure 50).
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 52% on average, while the contribution of TFP is getting progressively
more significant, rising to a share of above 40% on average in 2000–2015 (Figures 52 and 58).
u The evident rise in the contribution of information technology (IT) capital is noteworthy. By the

2000s, it had risen to above 4% 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 52 and 56).
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 55).

2017 Asian Productivity Organization

Capital deepening and capital productivity
u 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 10.1% on average per year to 6.7% in Korea between 1970–1990 and 1990–2015, whereas it doubled in China from 5.5% to 10.5% (Figure 59).

12

2.2 Summary Findingsu 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 60).

2

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 67).
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 71).
u Manufacturing is a significant sector, accounting for over 20% of total value added in eight Asian

countries in 2015. It is particularly prominent in China, Thailand, and the Philippines, where over
1.5% of annual TFP growths are measured in 2000–2015 (Figure 72). 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 73).
u While Asian countries are diversifying away from agriculture, the sector still dominates employ-ment, accounting for 33% of total employment in 2015 for the Asia30, down from 61% in 1980. Its
share in total value added decreased more moderately, from 14% to 9% over the same period.
Shifting out of agriculture into more efficient sectors will boost economy-wide productivity (Figures 74 and 77).
u Manufacturing is a main absorption sector for workers who have been displaced from the agricul-

ture 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. In the 1990s and 2000s, however, the manufacturing sector was no longer an absorption
sector of employment, regardless of the sound expansion of production in this sector. Since 2010,
the output growths in manufacturing deteriorated, but they had a positive impact on increasing
jobs. (Figure 79).
Industry origins of economic growthu Our results support the observation that China and India have taken different development paths,

u In contrast, growth in India has always been more driven by services, the contributions of whichare 61% in the 1990s and 64% in 2000–2015, while manufacturing usually contributes one-fifth orless (Figures 81 and 82).

132017 Asian Productivity Organization

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–
2015, the contributions to economic growth by manufacturing and services were 34% and 46%,
respectively, compared with 42% and 35% in the 1990s (Figures 81 and 82).

2 Overview

u A total of 29% of Asia30’s regional growth originated from the expansion of manufacturing in

2000–2015, 77% of which was accounted for by China. China’s manufacturing alone contributed
22% to regional growth (Figure 85).
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 2000–2015 (Table 18 and Figure 88).
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 96).
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 0.8% of GDP in 1990 to 3.8% in 2015, compared with 1.5% in 1990 and
41.8% in 2015 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 90).
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 1970–2015; Kuwait and Brunei appear tobe the outliers (Figure 93).
u The eight countries that have been enjoying a trading gain over 0.5% per annum in the past four

decades are all resource-rich countries. Among them, only Indonesia, Myanmar, and Iran managed
to achieve a positive growth in 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 97).

2017 Asian Productivity Organization

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.

14

3.1 Economic Scale and Growth

3 Economic Growth
In the past quarter of a century, the story of the world economy belonged to Asia, featuring its steadyrise in economic prowess. Before the mid-1980s, the fortune of Asia closely followed that of Japan, but
1988 marked the start of their paths decoupling (Figure 1). Since the early 1990s, Asian growth has
been outperforming the West consistently. With the exception of 1997–1999, when the economy was
adversely affected by the Asian financial crisis (Figure 37 in Section 4.3, p. 52), the Asia30 has been
growing faster than the US and the EU15 by 3 to 4 percentage points on average per year.3

3

%

10
8
6
4
2
0
−2
−4
−6
1970

Asia30

Japan

US

EU15

1975

1980

1985

1990

1995

2000

2005

2010

2015

Figure 1 GDP Growth of Asia, the EU, Japan, and the US, 1970–2015_Annual growth rate of GDP at constant market prices

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

In 2009, at the height of the global financial storm, the growth differentials were 6.6 and 8.3 percentage points with the US and the EU15, respectively. In 2010, simultaneous large-scale fiscal stimulus
packages helped major economies rebound strongly, before growth slowed again in 2011. The Asian
growth rate thereafter decreased to 5.3% on average per year during 2013–2015, from 7.0% before
the global financial crisis (2002–2007). This is mainly due to the onset of deceleration in China’s growth
to 7.1% from 11.0% on average in the same periods.4 Plagued by the euro crisis, the EU15 saw theireconomy shrink by 0.6% from 2011
to 2012 and their recovery to 1.8%
2015
2022
in 2013–2015, whereas the US economy sustained a steady growth of
2.5% in the period 2013–2015.
Asia
Asia
Others21 %Asia30
43 %

EU15
15 %
EU28
17 %

US
16 %

Other Asia
4%

Others
19 %

47 %

52 %

Asia30
48 %

EU15
13 %
EU28
15 %

APO20
25 %

US
14 %

Other Asia
4%

Figure 2 Share of Asia in World GDP in 2015 and Projection for 2022
_Share of GDP using constant PPP

Source: IMF, World Economic Outlook Database, April 2017.

3: The data 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.
4: According to the preliminary estimation by the National Bureau of Statistics of China, the growth rate of Chinese GDP is estimatedas 6.7% in 2016 (reported on 24 January 2017), which is the weakest in a quarter century. The annualized growth for the 1st quarter of 2017 is 6.9% to the same quarter in 2016 (reported on 18 April 2017). OECD (2017b) forecasts the Chinese growth is set toedge down further, from 6.6% in 2017 to 6.4% by 2018.

152017 Asian Productivity Organization

It is therefore no surprise that thecenter of gravity in the global economy is gradually shifting towards
Asia. In 2015, the Asian economy
contributed 47% (43% for Asia30) ofworld output, compared with the
US and the EU28, each accountingfor 16% and 17%, respectively(Figure 2). The IMF (2017) projects
the Asian share in world output will

APO20
23 %

3 Economic Growthcontinue to rise, reaching 52% (48% for Asia30) by 2022. In contrast, the output shares of each of the
US and the EU28 will shrink by a similar extent to 14–15%.
To better understand the dynamics of the long-term economic growth within the region, the remainder of this chapter details countries’ diverse development efforts and achievements since 1970,
through cross-country level comparisons of GDP and other related performance indicators. To facilitate international level comparisons, harmonized GDP for each of the individual countries is expressedin 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 for conversion rates are exchange rateand PPP.

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,5 for the six separate years of 1970, 1980, 1990, 2000, 2010, and 2015. By thismeasure, Japan was the largest economy in Asia until 2010 when China finally overtook Japan’s position to become the second-largest economy in the world next to the US. Japan clearly surged ahead
between the 1970 and 1990 comparisons; dwarfing the relative size of all other Asian economies andreducing the US lead from five times to less than twice its economy. The turn of Japan’s fortune cameat of the beginning of the 1990s, when the country’s excessive growth years of the late 1980s ended
and its descent began. Thereafter, stagnation in Japancombined with vibrant growth in developing Asia result56 Australia
35
Japan
ed in the rapid erosion of Japan’s prominence in the re−23
Korea
gional economy. The leading position of the four largest−29
Singapore−30
Hong Kong
Asian economies (China, Japan, India and Korea) has been
−31
UAE
−34
Qatar
consistent, with their positions rather secure in the past
−38
Kuwaitthree decades. On this measure, the Asia30 was 36% and−41
Turkey−42Fiji
48% larger than the US and the EU15 in 2015, respectively.
−43
Brunei2017 Asian Productivity Organization

Comparisons based on exchange rates, however, appear
arbitrary as movements in exchange rates can be volatileand subject to short-term or substantial fluctuations ofspeculative 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 scaleof economy ranking changes dramatically when international price differences are properly taken into account.6
Figure 3 shows the extent to which the exchange rates
have failed to reflect countries’ price differentials properly,
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 and Australia, exchange rates systematically under-represent therelative purchasing power for all the countries covered inthis report. The underestimation is substantial for some,ranging from 23% for Korea to 72% for Pakistan. Thus, theexchange-rate-based GDP considerably underestimates

16

−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

Bahrain
China
ROC
Oman
Saudi Arabia
Malaysia
Iran
Mongolia
Philippines
Indonesia
Thailand
BhutanSri Lanka
Nepal
Cambodia
Vietnam
India
Bangladesh
Lao PDR
Myanmar
Pakistan
20 40 60 %Figure 3 Price Level Indices of GDP,
2011

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

Sources: Analysis of Main Aggregate rates by United
Nations Statistics Division (UNSD) and PPP by WorldBank (2014).

3.1 Economic Scale and Growth

Table 1 GDP using Exchange Rate, 1970, 1980, 1990, 2000, 2010, and 2015
_GDP at current market prices, using annual average exchange rate
1970

Australia
Turkey1980(%)

208 100.0
Japan
93 44.7
China
64 30.5
India
11 5.4Iran
10 4.9Pakistan
10 4.8Indonesia
Bangladesh
10 4.7
9.0 4.3
Korea
7.3 3.5
Thailand6.8 3.3
Philippines
5.8 2.8
ROC
5.4 2.6
Saudi Arabia
3.9 1.9
Malaysia
3.8 1.8
Hong Kong
3.0 1.4
Kuwait
Sri Lanka
2.8 1.4
2.7 1.3
Myanmar
1.9 0.9
Singapore
1.2 0.6
Vietnam
1.1 0.5
Nepal
1.1 0.5UAE
0.8 0.4
Cambodia
0.5 0.3
Qatar
0.4 0.2
Bahrain
0.3 0.1
Oman
0.2 0.1
Fiji
0.2 0.1
Brunei0.2 0.1
Lao PDR
0.1 0.1
Mongolia
0.1 0.0
Bhutan(regrouped)
358 171.9
APO20
454 218.0
Asia24464 223.1
Asia30320 153.7
East Asia
88 42.1
South Asia
35 16.7
ASEAN
30 14.4
ASEAN6
4.8 2.3
CLMV
11 5.1
GCC(reference)
1,076 517.0
US1,248 599.5
EU15
45
24

21.7
11.7

1,087 100.0
306 28.2
190 17.5
165 15.2
97 9.080 7.365 6.044 4.142 3.933 3.133 3.030 2.729 2.725 2.324 2.2
19 1.7
12 1.1
7.9 0.7
6.3 0.6
5.9 0.5
5.0 0.5
4.9 0.5
3.5 0.3
2.6 0.2
1.2 0.1
1.0 0.1
0.7 0.1
0.5 0.0
0.5 0.0
0.1 0.0

Australia
Turkey

173
92

1,748
2,065
2,322
1,530
241
196
188
8.1
257

1990

(%)

Japan
China
India
Saudi Arabia
IranIndonesia
Korea
UAE
ROC
ThailandPhilippines
KuwaitHong Kong
Malaysia
Pakistan
Bangladesh
Singapore
Qatar
Oman
MyanmarBrunei
Sri Lanka
Bahrain
Nepal
Fiji
Vietnam
Cambodia
Mongolia
Lao PDR
Bhutan(regrouped)
APO20
Asia24
Asia30
East Asia
South Asia
ASEAN
ASEAN6
CLMV
GCC(reference)
US
EU15

160.8
190.0
213.6
140.7
22.2
18.0
17.3
0.7
23.6

2,863 263.3
3,321 305.5
16.0
8.5

(%)

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

3,128 100.0
395 12.6
335 10.7
279 8.9
167 5.3
127 4.1
118 3.8
95 3.089 2.877 2.551 1.647 1.545 1.444 1.439 1.231 1.019 0.6
12 0.4
9.4 0.3
7.5 0.2
6.5 0.2
5.6 0.2
4.5 0.1
4.4 0.1
3.4 0.1
1.8 0.1
1.6 0.1
1.4 0.0
1.2 0.0
0.3 0.0

Australia
Turkey

324
204

4,530
4,933
5,145
4,047
425
366
350
15
212

144.8
157.7
164.5
129.4
13.6
11.7
11.2
0.5
6.8

5,980 191.1
6,387 204.2
10.4
6.5

2000
4,888
Japan
1,211
China
562
Korea
482
India
331
ROC
Saudi Arabia 190
Hong Kong
172
168
Indonesia
127
Thailand
111
Iran
106
UAE
96
Singapore95
Malaysia
81
Philippines
72
Pakistan
Bangladesh
51
38
Kuwait33
Vietnam
20
Oman
19
Sri Lanka
18
Qatar
8.4
Bahrain
7.8
Myanmar
6.3
Nepal
5.8
Brunei
3.7
Cambodia
1.7
Fiji
1.6
Lao PDR
1.4
Mongolia
0.4
Bhutan(regrouped)
7,302
APO20
8,527
Asia24
8,908
Asia307,165
East Asia
631
South Asia
618
ASEAN
572
ASEAN6
46
CLMV
381
GCC
(reference)
10,285
US
9,982
EU15
11,105EU28
409
Australia
273
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
0.4
0.2
0.2
0.1
0.1
0.1
0.0
0.0
0.0
0.0
149.4
174.5
182.3
146.6
12.9
12.7
11.7
0.9
7.8
210.4
204.2
227.2
8.4
5.6

2010
6,101
China
5,700
Japan
1,671
India
1,094
Korea
756
Indonesia
Saudi Arabia 532
Iran
477
446
ROC
342
Thailand
294
UAE
255
Malaysia
236
Singapore
229
Hong Kong
200
Philippines
175
Pakistan
Qatar
128
118
Kuwait117
Vietnam
115
Bangladesh
60
Oman
56
Sri Lanka
37
Myanmar
26
Bahrain
19
Nepal
14
Brunei11
Cambodia
7.2
Mongolia
6.9
Lao PDR
3.2
Fiji
1.6
Bhutan(regrouped)
11,916
APO20
18,070
Asia24
19,227
Asia30
East Asia 13,577
South Asia 2,037
1,975
ASEAN
1,802
ASEAN6
173
CLMV
1,157
GCC
(reference)
14,964
US
14,619EU15
16,803EU28
1,294
Australia
772
Turkey

(%)

100.0
93.4
27.4
17.9
12.4
8.7
7.8
7.3
5.6
4.8
4.2
3.9
3.7
3.3
2.9
2.1
1.9
1.9
1.9
1.0
0.9
0.6
0.4
0.3
0.2
0.2
0.1
0.1
0.1
0.0
195.3
296.2
315.2
222.6
33.4
32.4
29.5
2.8
19.0
245.3
239.6
275.4
21.2
12.7

2015
11,008
China
4,383
Japan
2,108
India
1,383
Korea
862
Indonesia
Saudi Arabia 660
ROC
525
403
Thailand
395
Iran
384
UAE
309
Hong Kong
297
Singapore
296
Malaysia
293
Philippines
269
Pakistan
Vietnam196
194
Bangladesh
169
Qatar
117
Kuwait80
Sri Lanka
71
Oman
48
Myanmar
31
Bahrain
22
Nepal
18
Cambodia
13
Brunei13
Lao PDR
12
Mongolia
4.4
Fiji
2.1
Bhutan(regrouped)
12,062
APO20
23,133
Asia2424,565
Asia30
East Asia 17,620
South Asia 2,675
2,439
ASEAN
2,164
ASEAN6
275
CLMV
1,432
GCC
(reference)
18,037
US
16,624EU15
19,280EU28
1,243Australia
859
Turkey

(%)

100.0
39.8
19.1
12.6
7.8
6.0
4.8
3.7
3.6
3.5
2.8
2.7
2.7
2.7
2.4
1.8
1.8
1.5
1.1
0.7
0.6
0.4
0.3
0.2
0.2
0.1
0.1
0.1
0.0
0.0

3

109.6
210.2
223.2
160.1
24.3
22.2
19.7
2.5
13.0
163.9
151.0
175.1
11.3
7.8

5: 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.
6: 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.

172017 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 Economic Growth

Table 2 GDP using PPP, 1970, 1980, 1990, 2000, 2010, and 2015
_GDP at constant market prices, using 2011 PPP, reference year 2015
1970

1980

(%)

2,547 100.0Japan
977 38.3
India
Saudi Arabia 774 30.4
761 29.9
China
472 18.5
Indonesia
408 16.0
Iran
UAE
209 8.2
206 8.1
Korea
206 8.1
Philippines
186 7.3
Thailand153 6.0
ROC
150 5.9
Pakistan
121 4.7
Kuwait103 4.0
Malaysia
93 3.6
Bangladesh
Hong Kong85 3.456 2.2
Vietnam
53 2.1
Singapore
52 2.1
Myanmar43 1.7
Sri Lanka
32 1.3
Qatar
30 1.2
Brunei29 1.1Oman
17 0.7Nepal
16 0.6Bahrain
6.0 0.3
Mongolia
4.0 0.2
Lao PDR
4.0 0.1Fiji
1.0 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

3,582
4,046
4,413
2,214
947
588
505
85
487

221.7
250.4
273.1
137.0
58.6
36.4
31.3
5.2
30.1

5,194 321.5
6,422 397.5
293
257

18.2
15.9

Australia
Turkey

5,775
6,619
7,733
3,760
1,280
1,167
1,049
118
1,182

226.7
259.9
303.6
147.6
50.3
45.8
41.2
4.6
46.4

7,095 278.6
8,778 344.6
392
382

1990

(%)

1,616 100.0
Japan
727 45.0
India
416 25.8
China
293 18.1
Iran
Saudi Arabia 293 18.1
211 13.1
Indonesia
Kuwait149 9.2
115 7.1
Philippines
97 6.0Thailand
94 5.8
Pakistan
86 5.3Korea
85 5.3
Bangladesh
57 3.5ROC
46 2.9
Malaysia
43 2.7
VietnamHong Kong
36 2.235 2.2Myanmar29 1.8
Sri Lanka
22 1.4Singapore
18 1.1
Qatar
13 0.8Nepal
12 0.8Brunei11 0.7UAE
10 0.6Oman
8.0 0.5
Bahrain
4.0 0.2
Mongolia
3.0 0.2
Lao PDR
2.0 0.1
Fiji
0.0 0.0
Bhutan

15.4
15.0

(%)

4,020
Japan
1,849
China
1,677
India
866
Indonesia
Saudi Arabia 728
531
Korea
Iran
528
397
Thailand
338
ROC
301
Pakistan
250
Philippines
214
UAE
187
Malaysia
164
Hong Kong
139
Bangladesh
Singapore
111
95
Vietnam
92
Kuwait66
Sri Lanka
65
Oman
59
Myanmar
37
Qatar
27
Nepal
23
Brunei19
Bahrain11
Mongolia
9.0
Cambodia
7.0
Lao PDR
5.0
Fiji
1.0
Bhutan(regrouped)
9,727
APO20
11,660
Asia24
12,811
Asia306,912
East Asia
South Asia 2,210
2,005
ASEAN
1,834
ASEAN6
171
CLMV
1,155
GCC
(reference)
9,850
US
11,220
EU15

100.0
46.0
41.7
21.5
18.1
13.2
13.1
9.9
8.4
7.5
6.2
5.3
4.7
4.1
3.5
2.8
2.4
2.3
1.6
1.6
1.5
0.9
0.7
0.6
0.5
0.3
0.2
0.2
0.1
0.0

528
635

13.1
15.8

Australia
Turkey

242.0
290.1
318.7
172.0
55.0
49.9
45.6
4.2
28.7
245.1
279.1

2000
4,984
China
4,575
Japan
2,853
India
1,309
Indonesia
1,039
Korea
Saudi Arabia 954
Iran
781
646
ROC
618
Thailand484
Pakistan
380
Malaysia
351
UAE
351
Philippines
241
Hong Kong
230
Bangladesh
Singapore
222
206
Vietnam
162
Kuwait110
Sri Lanka
104
Oman
102
Myanmar
71
Qatar
44
Nepal
30
Bahrain28
Brunei18
Cambodia13
Lao PDR
12
Mongolia
6.0
Fiji
2.0
Bhutan(regrouped)
14,136
APO20
19,253
Asia2420,907
Asia30
East Asia 11,497
South Asia 3,723
3,246
ASEAN
2,907
ASEAN6
339
CLMV
1,673
GCC
(reference)
13,815
US
14,045EU15
15,946EU28
748
Australia
909
Turkey

(%)

100.0
91.8
57.2
26.3
20.8
19.1
15.7
13.0
12.4
9.7
7.6
7.0
7.0
4.8
4.6
4.4
4.1
3.2
2.2
2.1
2.1
1.4
0.9
0.6
0.6
0.4
0.3
0.2
0.1
0.0
283.6
386.3
419.5
230.7
74.7
65.1
58.3
6.8
33.6
277.2
281.8
320.0
15.0
18.2

2010
13,572
China
5,842
India
4,875
Japan
2,179
Indonesia
1,601
Korea
1,411
Iran
Saudi Arabia 1,330
971
ROC
968
Thailand781
Pakistan
632
Malaysia
559
Philippines
521
UAE
419
Vietnam
396
Bangladesh
Singapore
390
360
Hong Kong
245
Kuwait
242
Qatar
196
Myanmar182
Sri Lanka
146
Oman
64
Nepal
54
Bahrain39
Cambodia32
Brunei26
Lao PDR
22
Mongolia
7.0
Fiji
5.0
Bhutan(regrouped)
21,723
APO20
35,529Asia2438,083
Asia30
East Asia 21,402
South Asia 7,270
5,439
ASEAN
4,759
ASEAN6
680
CLMV
2,538
GCC
(reference)
16,262
US
15,901EU15
18,293EU28
1,014
Australia
1,347
Turkey

(%)

100.0
43.0
35.9
16.1
11.8
10.4
9.8
7.2
7.1
5.8
4.7
4.1
3.8
3.1
2.9
2.9
2.7
1.8
1.8
1.4
1.3
1.1
0.5
0.4
0.3
0.2
0.2
0.2
0.0
0.0
160.1
261.8
280.6
157.7
53.6
40.1
35.1
5.0
18.7
119.8
117.2
134.8
7.5
9.9

2015
19,828
China
7,915
India
5,119
Japan
2,852
Indonesia
1,856
Korea
Saudi Arabia 1,716
Iran
1,368
1,124
Thailand
1,102
ROC
954
Pakistan
817
Malaysia
745
Philippines
661
UAE
560
Vietnam
538
Bangladesh
Singapore
477
416
Hong Kong
323
Qatar
296
Kuwait246
Sri Lanka
180
Myanmar
178
Oman
77
Nepal
64
Bahrain55
Cambodia40
Lao PDR
36
Mongolia
32
Brunei8.0
Fiji
7.0
Bhutan(regrouped)
26,305
APO20
46,352
Asia24
49,613
Asia30
East Asia 28,358
South Asia 9,735
6,882
ASEAN
6,047
ASEAN6
835
CLMV
3,237
GCC
(reference)
18,037
US
16,651EU15
19,184EU28
1,163
Australia
1,898
Turkey

(%)

100.0
39.9
25.8
14.4
9.4
8.7
6.9
5.7
5.6
4.8
4.1
3.8
3.3
2.8
2.7
2.4
2.1
1.6
1.5
1.2
0.9
0.9
0.4
0.3
0.3
0.2
0.2
0.2
0.0
0.0
132.7
233.8
250.2
143.0
49.1
34.7
30.5
4.2
16.3
91.0
84.0
96.8
5.9
9.6

2017 Asian Productivity Organization

Unit: Billions of US dollars (as of 2015).
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 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.7
Table 2 repeats the same snapshot level comparisons on real GDP for Asian countries in Table 1, using
PPP as conversion rates. By correcting international price differentials, the Asia30 has been expanding
rapidly. It was 175%, instead of 36%, larger than the US economy in 2015, having overtaken it in 1974

18

3.1 Economic Scale and Growth

US=100

US=100

280

120

250

100

220

Asia30

China

80

190
160
130

US

3

60
EU15
US

100
70

40

Japan

20

APO20

India

East Asia

40
1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

01970 1975 1980 1985 1990 1995 2000 2005 2010 2015

Figure 4 Regional GDP of Asia and the EU,
Relative to the US, 1970–2015

Figure 5 GDP of China, India, and Japan,
Relative to the US, 1970–2015

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.

(Figure 4).8 East Asia (China, the ROC, Hong Kong, Japan, Korea, and Mongolia) caught up with the US
in 2006 from a low base of 43% in 1970. In contrast, the EU15 has been experiencing a gradual relative
decline in economic size, from 124% of the US economy in 1970 to a low of 92% in 2015. Based on GDP
using constant PPP, the weight of the world economy is even more tilted toward Asia than portrayedby GDP using exchange rates. This reflects the fact that nearly 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 2015 was 3.9 times that of Japan, compared with 2.5 times
when exchange rates are used in Table 1. Considering that the Chinese economy was only 26% that ofJapan and 57% that of India in 1970, represents remarkable growth. China overtook Japan after 1999
to become the leading economy in Asia as shown in Figure 5.9 On this measure, Figure 5 also demonstrates that Chinese GDP overtook the US as the world’s largest economy in 2013, although it was only
8% that of the US in 1970. The level and the timing to overcome should not be taken as precise numbers,10 but they may provide a good basis for assessing the relative production size of these two economies. Based on the estimates in Maddison (2007), China was the largest producer in the world as of
1880. For the first time in more than 130 years, China comes back to this position.

7: 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.
8: This compares with the findings in Databook 2013, which were based on the 2005 benchmark PPP, that the economic size of the
Asia30 overtook the US in 1988.
9: 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.
10: BBC News: Is China's economy really the largest in the world?, 16 December 2014.

192017 Asian Productivity Organization

Given that PPP for India has been revised by –24% in the 2011 ICP round (see Box 1), the effects havebeen to raise the relative size of India. Compared to Japan, the Indian economy has been increasingfrom 45% in 1970 to 155% in 2015, surpassing Japan and replacing it as the second largest economy
in Asia in 2009. In 2015, the total GDP of the three countries, which are counted as the largest economies in Asia, was larger than the US economy by 82%.

3 Economic Growth

Box 1

PPP in 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 conversion of macroeconomic measures, estimated in national currencies and price levels, into comparable cross-country
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 single or composite goods andservices 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 by holding
worldwide surveys at regular intervals (currently, every six years) to collect comparable price and expenditure 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 a number ofmethodological improvements, see Eurostat-OECD (2012) and World Bank (2014).
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 the2015 publication. The use of this approach creates national series for volumes at the prices of a common reference
year (i.e., 2014), and deflates these by the PPP for a fixed
year (i.e., 2011).
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 is 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 greater 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.

2017 Asian Productivity Organization

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. 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 been used in the Databook 2008. For example, the PPP
estimates were upwardly revised by 55% and 65% (thus the internationally comparable measures of GDP
in 2005 were reduced by 36% and 40%) for India and China, 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 revisions of the ICP 2005 and 2011 rounds. The
PPP for Singaporean GDP was revised by –29% and by –16% in the ICP 2005 and 2011 rounds, respectively.
continued on next page >20

3.1 Economic Scale and Growth> continued from previous page

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 US 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
an appropriate 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 in mind
these circumstances.

3
Figure 6 shows the rapid expansion of the relative size of the South Asian economy (consistingof Bangladesh, Bhutan, India, Nepal, Pakistan,and Sri Lanka), 81% of which was accounted forby India in 2015. The ASEAN also showed vigor
in their catch-up effort. They were on par with
the South Asian economy in 1996–1997 before
the setback caused by the Asian financial crisisof 1997–1998 took hold, setting them on a lower
growth path, once again opening up a divergence. In contrast, the progress of GCC11 countries lagged for more than 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.12

US=1006050South Asia

40

ASEAN

30
20

ASEAN6
GCC

10
CLMV

0
1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

Figure 6 Regional GDP of South Asia, ASEAN,
CLMV, and GCC, Relative to the US, 1970–2015_Indices of GDP at constant market prices, using 2011
PPP

Sources: Official national accounts in each country, including auPerformance of each country is also transthor adjustments.
formed when economic growth is used as ayardstick. Table 3 presents cross-country comparisons of real GDP growth in Asia since 1990.
The ranking varies from period to period and the economic giants no longer take precedence in the
ranking. In fact, small developing Asian countries are equally capable of exhibiting exuberant growth.13
As labor costs are edging up in China (see Box 4, p. 53), the workshop of the world has started shifting
its location to the neighboring countries such as Cambodia, the Lao PDR, Myanmar, and Vietnam,
called CLMV. They are clearly the faster growing group among the ASEAN countries, at 6.4% on average per year compared with 4.8% managed by the ASEAN6 in the period 1990–2015.

11: 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 profound reliance on the oil and energy sector. In 2015, thesecountries account for about 33% of the world’s crude oil reserves and possess at least 21% of the global natural gas reserves (GCC
Secretariat General, 2017).
12: 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.
13: In comparison of economic growths among Asian countries, Myanmar was ranked as the top position (12.1%) and the second
position (10.7%) in the periods of the first and the second half of the 2000s, respectively, in Databook 2016. However, some questions have been raised about the reliability of Myanmar’s official system of national accounts since the late 1990s. This edition of
Databook attempts to revise the past economic performance based on the industry-level examinations in Nomura and Shirane(2016). See Box 5 (p. 56) for the details of this revision.

21

2017 Asian Productivity Organization

At the other end of the table, over the past two decades (1990–2015) Japan has been struggling consistently at the bottom with an average growth of 1.0% per year, compared with Asia30’s 5.4% andEU15’s 1.6%. During this period, only three Asian countries – Brunei, Fiji, and Japan – grew slower thanthe US (2.4%). The divergence of growth performance between the Asian countries on the one hand

3 Economic Growth

Table 3 GDP Growth, 1990–1995, 1995–2000, 2000–2005, 2005–2010, and 2010–2015_Average annual growth rate of GDP at constant market prices
1990–1995

1995–2000

China
Malaysia
KuwaitSingapore
VietnamThailandKorea
Indonesia
ROC
Cambodia
Lao PDR
Oman
Pakistan
Sri Lanka
BahrainHong Kong
Bangladesh
India
Myanmar
Nepal
Iran
UAE
BhutanBrunei
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
6.6
6.2
5.7
5.5
5.3
5.3
5.2
5.0
5.0
4.9
4.9
3.7
3.6
3.4
3.1
2.8
2.8
2.7
2.3
1.5
−1.8

Australia           Turkey              

3.2
3.2

4.4
5.7
5.5
5.6
5.1
7.2
7.3
6.9
3.8
2.6
1.6

Qatar
China
Vietnam
Cambodia
UAE
Myanmar
ROC
BhutanIndia
Lao PDR
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           Turkey              2000–2005
10.6
8.3
7.3
7.2
6.3
6.0
5.8
5.7
5.7
5.5
5.5
5.3
5.1
4.9
4.9
4.8
4.2
4.1
4.0
3.9
3.7
3.6
2.6
2.6
2.1
2.0
1.3
1.1
0.7
0.7
3.1
4.3
4.3
4.6
5.4
2.4
1.9
6.8
3.6
4.2
2.9
2.9
3.8
4.0

China
Cambodia
Vietnam
Qatar
BhutanKuwait
Iran
India
MyanmarMongolia
Lao PDR
BahrainPakistan
UAE
Thailand
Malaysia
Bangladesh
Singapore
Korea
Indonesia
Philippines
Hong Kong
Saudi Arabia
Sri Lanka
ROC
Nepal
Brunei
Fiji
Japan
Oman
(regrouped)
APO20               Asia24             Asia30
East Asia           South Asia          ASEAN               ASEAN6
CLMV
GCC                 (reference)
US                  EU15                EU28               Australia           Turkey              2005–2010
9.3
8.8
8.0
8.0
7.6
7.2
6.9
6.5
6.4
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
4.0
3.1
2.1
2.0
1.2
1.0
4.2
5.7
5.6
5.6
6.2
5.1
4.8
7.5
4.6
2.5
1.8
1.9
3.4
4.7

Qatar
China
BhutanLao PDR
India
MyanmarCambodia
Singapore
Mongolia
Sri Lanka
VietnamBangladesh
Oman
Indonesia
Bahrain
Malaysia
Iran
Philippines
Nepal
ROC
Korea
Hong Kong
Thailand
Pakistan
Saudi Arabia
UAE
KuwaitFiji
BruneiJapan(regrouped)
APO20               Asia24             Asia30
East Asia           South Asia          ASEAN               ASEAN6
CLMV
GCC                 (reference)
US                  EU15                EU28               Australia           Turkey              2010–2015
16.6
10.7
9.1
7.8
7.8
6.5
6.5
6.5
6.4
6.2
6.2
5.9
5.7
5.6
5.4
5.0
5.0
4.8
4.4
4.2
4.0
3.8
3.7
3.7
2.7
2.5
1.2
0.7
0.7
0.1
4.4
6.6
6.4
6.8
7.1
5.2
5.0
6.4
3.7
0.8
0.7
0.9
2.7
3.2

Mongolia
Lao PDR
China
Cambodia
Bangladesh
India
Sri Lanka
Vietnam
Qatar
Philippines
BhutanIndonesia
Malaysia
Saudi Arabia
UAE
Singapore
Pakistan
Oman
KuwaitNepal
BahrainFiji
Thailand
Korea
Hong Kong
ROC
Japan
Brunei
Iran
Myanmar(regrouped)
APO20               Asia24             Asia30
East Asia           South Asia          ASEAN               ASEAN6
CLMV
GCC                 (reference)
US                  EU15                EU28               Australia           Turkey              1990–2015

9.8
8.4
7.6
7.0
6.1
6.1
6.0
5.8
5.8
5.7
5.4
5.4
5.2
5.1
4.7
4.0
4.0
4.0
3.7
3.6
3.6
3.6
3.0
3.0
2.9
2.5
1.0
−0.1
−0.6
−1.7
3.8
5.3
5.3
5.6
5.8
4.7
4.8
4.1
4.9
2.1
0.9
1.0
2.7
6.9

China
Qatar
Cambodia
VietnamLao PDR
BhutanIndia
Malaysia
Singapore
Bangladesh
Sri Lanka
Korea
BahrainMongolia
Indonesia
ROC
KuwaitPakistan
UAE
MyanmarPhilippines
Thailand
Nepal
Oman
Iran
Hong Kong
Saudi Arabia
Fiji
BruneiJapan(regrouped)
APO20               Asia24             Asia30
East Asia           South Asia          ASEAN               ASEAN6
CLMV
GCC                 (reference)
US                  EU15                EU28               Australia           Turkey              

9.5
8.7
7.2
7.1
6.8
6.3
6.2
5.9
5.8
5.4
5.3
5.0
4.9
4.9
4.8
4.7
4.7
4.6
4.5
4.4
4.4
4.2
4.2
4.0
3.8
3.7
3.4
2.2
1.4
1.0
4.0
5.5
5.4
5.6
5.9
4.9
4.8
6.4
4.1
2.4
1.6
1.6
3.2
4.4

2017 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.

and the US and the EU15 on the other was even more pronounced if focusing on the most recent
years, with the Asia30 growing at 5.3% on average per annum, compared with 2.1% in the US and
0.9% in the EU15 in the period 2010–2015.
The change of guards in Asia is clearly illustrated in Figure 7. While Japan was the standard-bearer inyesteryears in the left chart of Figure 7, China and India have emerged as the driving force propelling
Asia forward since 1990. Their growth accounts for 38% and 14% of regional growth, respectively, in22

3.1 Economic Scale and Growth

1970–1990
Japan
China
India
Indonesia
Saudi Arabia
Korea
Iran
Thailand
ROC
UAE
Pakistan
Philippines
Malaysia
Hong Kong
Singapore
Oman
VietnamBangladesh
Sri Lanka
Myanmar
Qatar
Brunei
Nepal
BahrainMongolia
Cambodia
Lao PDR
Fiji
BhutanKuwait1990–2000
29.0

16.0
10.6
7.8
6.9
5.0
3.8
3.3
3.3
3.0
2.3
1.8
1.7
1.5
1.0
0.7
0.6
0.5
0.4
0.4
0.3
0.2
0.2
0.2
0.1
0.1
0.1
0.0
0.0
−0.7

−10

0

10

20

30 %

China
India
Japan
Korea
Indonesia
ROC
Iran
ThailandSaudi Arabia
Malaysia
Pakistan
UAE
Singapore
VietnamPhilippines
Bangladesh
Hong Kong
Kuwait
Sri Lanka
Myanmar
Oman
Qatar
Nepal
BahrainCambodia
BruneiLao PDR
Fiji
BhutanMongolia

2000–2010
38.2

14.0
7.1
6.3
6.1
3.8
3.1
3.0
2.9
2.4
2.3
1.6
1.4
1.3
1.2
1.1
1.0
0.9
0.5
0.5
0.5
0.4
0.2
0.1
0.1
0.1
0.1
0.0
0.0
0.0

0

10

20

30

40 %

China
India
Indonesia
Iran
Korea
Saudi Arabia
ThailandJapan
ROC
Pakistan
Malaysia
VietnamPhilippines
UAE
Bangladesh
Singapore
Qatar
Hong Kong
KuwaitMyanmar
Sri Lanka
Oman
BahrainCambodia
Nepal
Lao PDR
Mongolia
Brunei
BhutanFiji

2010–2015
49.3

17.2
5.1
3.9
3.4
2.2
2.2
2.0
1.9
1.8
1.5
1.3
1.2
1.1
1.0
1.0
0.9
0.7
0.6
0.5
0.4
0.2
0.1
0.1
0.1
0.1
0.1
0.0
0.0
0.0

0

China
India
17.6
Indonesia
5.9
Saudi Arabia
3.4
Korea
2.2
Japan
2.1
1.6
Malaysia
1.6
Philippines
1.5
Pakistan
1.4
ThailandBangladesh
1.2
UAE
1.2
Vietnam
1.2
ROC
1.2
Singapore
0.8
0.7
Qatar
0.6
Sri Lanka
0.5
Hong Kong
0.5
KuwaitOman
0.3
0.1
Cambodia
0.1
Mongolia
0.1
Lao PDR
0.1
Nepal
0.1
Bahrain
0.0
Bhutan0.0
Fiji
0.0
Brunei
Myanmar −0.2
Iran −0.3

10 20 30 40 50 %

54.5

3

−10 0 10 20 30 40 50 60 %Figure 7 Country Contributions to Regional GDP Growth, 1970–1990, 1990–2000, 20002010, and 2010–2015_Contribution share to the growth of gross regional products (growth rate of Asia30=100)
Sources: Official national accounts in each country, including author adjustments.
Note: The starting period for Cambodia is 1987.

the 1990s. In the recent period 2010–2015, the growth in China and India accounts for more than twothirds of regional growth (55% and 18%, respectively).14 Indonesia became the third engine of Asian
growth (5.9%), followed by Saudi Arabia (3.4%).

14: The growth in Chinese manufacturing sector explains about one-third of the China’s contribution to regional growth (22 percentage points of 64%) in the period 2000–2015. See Figure 85 in Section 6.2 (p. 111) for the industry origins of regional growth.

232017 Asian Productivity Organization

It has been a subject of much debate whether the Asian economy has decoupled from the US and theEU15. If it has, the world economy would be substantially less volatile. Figures 8 and 9 compare thecorrelation coefficients of growth rates among countries in the 1990s and the period from 2000 to2015, respectively. Countries are grouped by region. Overall, the fortunes of the reference countries
have become increasingly tied to Asia in a pro-cyclical manner. It is interesting to note that China’s
correlation with the US and the EU15 has moved from negative to positive. Correlation among theEast Asian countries (Group 1) has been strengthened over time and their correlation with the US,
the EU15, and the ASEAN countries (Group 3) has strengthened as well. In the South Asian countries (Group 2), their correlation with the US and the EU15 has weakened, although the correlation
with ASEAN has grown stronger. Therefore, comparisons of the correlation coefficients of growth between the two periods lend support to an increase in business cycle synchronicity, but in the South
Asian countries.

3 Economic Growth

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

Group 3
PAK

NEP

IRN

IND

BTN

BAN

ROC

Group 2
MGL

KOR

JPN

HKG

CHN

Group 1China (CHN)
1.0
Hong Kong (HKG) 0.5 1.0
Japan (JPN)−0.1 0.5 1.0
0.2 0.8 0.6 1.0
Korea (KOR)
Mongolia (MGL) −0.6 −0.5 −0.2 −0.2 1.0
0.5 0.8 0.4 0.7 −0.7 1.0
ROC (ROC)
Bangladesh (BAN) 0.4 −0.0 0.1 −0.1 0.1 −0.2 1.0
Bhutan (BTN)−0.3 −0.3 −0.3 −0.1 0.8 −0.5 0.3 1.0
India (IND)
0.1 −0.4 −0.3 −0.1 0.6 −0.4 0.6 0.8 1.0
Iran (IRN)
0.0 0.1 0.4 −0.1 −0.4 0.0 0.5 −0.4 −0.1 1.0
Nepal (NEP)
0.1 0.1 −0.1 0.2 −0.2 −0.1 0.3 −0.1 0.2 0.4 1.0
0.4 0.3 0.4 0.4 −0.5 0.7 0.4 −0.0 0.1 0.3 −0.1 1.0
Pakistan (PAK)
0.3 0.3 −0.1 0.1 −0.1 −0.0 −0.3 −0.2 −0.3 −0.3 0.2 −0.6 1.0
Sri Lanka (SRI)
Brunei (BRN)
0.3 0.4 0.5 0.6 −0.3 0.6 0.3 0.0 0.2 0.3 0.0 0.9 −0.5 1.0
Cambodia (CAM) −0.2 0.4 0.2 0.7 0.2 0.4 −0.3 0.3 0.0 −0.1 −0.0 0.3 −0.1 0.5 1.0
Fiji (FIJ)
0.3 −0.1 −0.4 0.1 0.1 0.1 0.4 0.5 0.8 −0.0 0.2 0.4 −0.5 0.4 0.2 1.0
Indonesia (IDN)
0.5 0.9 0.7 0.9 −0.4 0.8 0.1 −0.3 −0.2 0.0 0.1 0.5 0.2 0.5 0.3 −0.0 1.0
Lao PDR (LAO)
0.6 0.4 0.3 0.2 0.1 0.2 0.5 0.3 0.4 −0.1 −0.2 0.2 0.3 0.3 −0.1 0.1 0.5 1.0
Malaysia (MAL)
0.5 0.9 0.7 0.9 −0.4 0.7 0.1 −0.3 −0.2 0.1 0.1 0.4 0.2 0.5 0.3 0.0 1.0 0.5 1.0
Myanmar (MYA)
0.2 0.3 −0.1 0.1 0.1 −0.0 0.5 0.5 0.3 0.3 0.2 0.2 0.1 0.3 0.4 0.3 0.1 0.5 0.1 1.0
Philippines (PHL) 0.1 0.4 0.3 0.4 0.5 −0.1 0.3 0.3 0.4 −0.1 0.2 −0.2 0.3 −0.1 0.1 0.1 0.4 0.6 0.5 0.3 1.0
Singapore (SIN)
0.6 0.9 0.4 0.8 −0.3 0.6 0.0 −0.2 −0.1 −0.0 0.2 0.1 0.5 0.2 0.3 0.0 0.9 0.5 0.9 0.3 0.6 1.0
Thailand (THA)
0.6 0.7 0.5 0.8 −0.6 0.8 0.2 −0.3 −0.0 0.2 0.3 0.7 −0.0 0.8 0.3 0.3 0.8 0.3 0.8 0.1 0.1 0.7 1.0
Vietnam (VIE)
0.5 0.3 0.4 0.1 0.1 0.0 0.7 0.2 0.4 −0.0 −0.1 0.2 0.2 0.2 −0.3 0.1 0.5 0.9 0.4 0.2 0.7 0.4 0.3 1.0
Bahrain (BHR)
0.3 0.1 −0.2 −0.1 −0.6 0.0 0.3 −0.3 −0.2 0.4 0.8 −0.0 0.3 −0.2 −0.4 −0.0 0.0 −0.2 0.1 0.1 −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 0.4 0.4 0.0 0.1 0.3 0.1 0.1 −0.2 0.5 −0.0 0.5 0.0 0.7 0.1 0.2 0.1 0.4 0.4 1.0
Oman (OMN)
0.5 0.5 0.3 0.1 −0.7 0.5 0.1 −0.4 −0.6 0.1 −0.2 0.3 0.4 0.1 −0.2 −0.5 0.4 0.4 0.4 0.3 −0.1 0.4 0.3 0.3 0.3 0.3 1.0
Qatar (QAT)−0.3 −0.1 −0.2 −0.4 0.4 −0.4 −0.1 0.4 −0.0 −0.4 −0.5 −0.3 0.1 −0.5 −0.1 −0.3 −0.3 0.2 −0.2 0.2 0.1 −0.2 −0.7 0.1 −0.2 0.2 0.2 1.0
Saudi Arabia (SAU) −0.2 0.2 0.5 −0.0 −0.5 0.3 −0.3 −0.6 −0.8 0.5 −0.4 0.3 −0.2 0.1 −0.1 −0.6 0.1 −0.3 0.1 −0.2 −0.5 −0.2 0.0 −0.3 −0.0 −0.4 0.5 0.0 1.0
UAE (UAE)−0.2 0.3 0.4 0.3 0.6 −0.2 0.1 0.5 0.3 −0.1 −0.3 −0.1 0.2 0.1 0.5 −0.1 0.2 0.6 0.3 0.5 0.6 0.3 −0.1 0.4 −0.6 0.0 0.0 0.4 −0.2 1.0
Australia (AUS)
0.2 −0.5 −0.7 −0.5 0.5 −0.6 0.4 0.7 0.7 −0.4 0.0 −0.3 0.0 −0.4 −0.4 0.5 −0.4 0.3 −0.4 0.2 0.3 −0.2 −0.4 0.4 0.0 0.6 −0.3 0.4 −0.7 0.0 1.0
Turkey (TUR)
0.1 0.1 0.3 −0.2 −0.2 −0.2 0.5 −0.1 −0.1 0.3 0.2 −0.0 0.3 −0.2 −0.5 −0.4 0.1 0.3 0.1 0.2 0.2 0.0 −0.1 0.5 0.6 0.3 0.6 0.3 0.1 0.0 0.1 1.0−0.7 −0.3 0.1 −0.0 0.7 −0.4 −0.3 0.5 0.1 −0.2 −0.4 −0.2 −0.2 0.0 0.5 −0.2 −0.4 −0.1 −0.3 0.2 0.0 −0.3 −0.5 −0.3 −0.7 −0.4 −0.4 0.3 0.1 0.7 −0.1 −0.3 1.0
EU15−0.2 −0.3 −0.5 −0.3 0.7 −0.6 0.3 0.8 0.6 −0.2 −0.1 −0.3 −0.0 −0.3 0.1 0.4 −0.4 0.3 −0.4 0.6 0.4 −0.2 −0.5 0.1 −0.2 0.5 −0.4 0.5 −0.6 0.5 0.7 −0.1 0.4 1.0
US
If greater than 0.55

If less than −0.55

Figure 8 Correlation of GDP Growth, 1990–2000_Correlation of GDP growth at constant market prices

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

3.2 Catching Up in Per Capita GDP

2017 Asian Productivity Organization

Asia is the most populous region in the world. In 2015, the population of Asia accounted for 60% of
the world’s population (56% for Asia30), with China and India alone accounting for more than onethird (Figure 10). In addition, there is a significant difference in the population among Asian economies, as Table 4 shows. Seven countries’ populations were over 100 million in 2015 (the Philippine
population reached 100 million in 2015), but the populations are less than 10 million in 12 economiesof the Asia30.15 Performance comparisons based on the whole-economy GDP in Section 3.1 do nottake into account the population and can in turn exaggerate the wellbeing of countries with large
populations. Based on per capita GDP, which adjusts for the differences in population, China and India, two rising giants in the Asian economy, remain substantially less well-off in light of the US standard. Conversely, the Asian Tigers proliferate.
Table 5 presents cross-country comparisons of per capita current-price GDP, using exchange rates asconversion rates. However, given the volatile nature of exchange rates, snapshot comparisons as
15: In Myanmar the first census in three decades was conducted between March 30 and April 10, 2014. This showed that the total
population was 51 million, which was considerably below the official estimate of 61 million.

24

3.2 Catching Up in Per Capita GDP

1.0
China (CHN)
Hong Kong (HKG) 0.6 1.0
0.2 0.7 1.0
Japan (JPN)
0.4 0.5 0.6 1.0
Korea (KOR)
0.1 0.5 0.4 0.1 1.0
Mongolia (MGL)
0.5 0.8 0.7 0.7 0.4 1.0
ROC (ROC)
Bangladesh (BAN) 0.3 0.4 0.2 −0.2 0.4 0.1 1.0
Bhutan (BTN)
0.6 0.2 0.1 0.6 −0.2 0.5 0.0 1.0
India (IND)
0.4 0.4 0.4 0.1 −0.2 0.4 0.3 0.2 1.0
0.7 0.4 0.1 0.7 −0.2 0.5 −0.4 0.6 0.2 1.0
Iran (IRN)
Nepal (NEP)
0.4 0.1 −0.1 −0.1 0.4 0.2 0.1 0.1 0.0 0.0 1.0
0.3 0.6 0.3 0.2 0.2 0.3 0.1 −0.1 0.4 0.3 −0.1 1.0
Pakistan (PAK)
0.3 0.5 0.3 0.0 0.5 0.5 0.5 0.1 0.2 −0.2 0.4 0.1 1.0
Sri Lanka (SRI)
Brunei (BRN)
0.3 0.3 0.3 0.6 0.2 0.4 −0.2 0.4 −0.2 0.6 −0.1 0.1 0.1 1.0
Cambodia (CAM) 0.5 0.8 0.7 0.4 0.5 0.5 0.4 0.1 0.3 0.3 −0.1 0.7 0.2 0.3 1.0
Fiji (FIJ)−0.5 0.2 0.4 0.2 0.3 0.2 −0.1 −0.4 −0.2 −0.2 −0.3 0.0 −0.1 −0.1 0.0 1.0
Indonesia (IDN)
0.4 0.5 0.3 0.1 0.6 0.5 0.7 0.2 0.2 −0.2 0.6 −0.1 0.8 −0.1 0.3 −0.2 1.0
Lao PDR (LAO)−0.1 0.0 0.0 −0.2 −0.1 −0.0 0.5 0.1 0.2 −0.4 0.1 −0.1 0.4 −0.4 −0.1 0.1 0.4 1.0
Malaysia (MAL)
0.3 0.7 0.8 0.6 0.5 0.9 0.2 0.2 0.3 0.2 0.1 0.2 0.6 0.3 0.6 0.3 0.6 0.1 1.0
Myanmar (MYA)
0.3 0.3 0.2 0.5 −0.3 0.5 −0.2 0.4 0.3 0.6 −0.2 0.1 −0.2 0.1 0.2 0.0 −0.1 −0.0 0.4 1.0
Philippines (PHL)
0.1 0.6 0.8 0.3 0.4 0.6 0.4 0.0 0.5 −0.2 0.1 0.2 0.5 −0.1 0.5 0.5 0.5 0.3 0.8 0.0 1.0
Singapore (SIN)
0.6 0.9 0.7 0.6 0.4 0.9 0.2 0.4 0.5 0.4 0.3 0.3 0.6 0.3 0.5 0.1 0.6 0.1 0.8 0.4 0.7 1.0
Thailand (THA)
0.3 0.5 0.7 0.6 0.2 0.6 −0.1 0.3 0.3 0.3 0.0 0.3 0.4 0.5 0.5 0.0 0.2 −0.2 0.6 −0.0 0.5 0.6 1.0
Vietnam (VIE)
0.4 0.5 0.4 0.5 −0.1 0.4 −0.2 0.3 0.3 0.7 −0.2 0.7 −0.1 0.4 0.7 −0.2 −0.2 −0.3 0.3 0.5 −0.0 0.3 0.4 1.0
Bahrain (BHR)
0.7 0.5 0.4 0.4 0.1 0.4 0.1 0.3 0.4 0.5 0.3 0.6 0.1 0.1 0.7 −0.3 0.2 −0.3 0.4 0.3 0.3 0.3 0.4 0.6 1.0
Kuwait (KWT)
0.3 0.5 0.4 0.1 0.6 0.3 0.2 0.1 −0.0 0.3 0.2 0.6 0.4 0.4 0.7 −0.1 0.2 −0.2 0.4 −0.1 0.2 0.3 0.5 0.6 0.5 1.0
Oman (OMN)−0.0 −0.2 −0.2 −0.3 −0.1 −0.4 0.5 −0.2 0.1 −0.6 −0.1 −0.4 0.1 −0.4 −0.2 −0.2 0.3 0.4 −0.3 −0.3 0.1 −0.2 −0.3 −0.5 −0.2 −0.5 1.0
Qatar (QAT)
0.7 0.5 0.0 0.3 0.3 0.4 0.2 0.3 0.2 0.4 0.3 0.1 0.4 0.2 0.2 −0.1 0.5 0.0 0.2 0.2 0.1 0.6 0.1 0.0 0.3 0.0 0.3 1.0
Saudi Arabia (SAU) 0.1 0.5 0.4 −0.2 0.6 0.3 0.3 −0.2 −0.0 −0.1 0.2 0.2 0.5 0.1 0.4 0.1 0.5 0.1 0.5 −0.1 0.4 0.4 0.2 0.2 0.1 0.7 −0.3 0.1
UAE (UAE)
0.2 0.7 0.6 0.2 0.7 0.4 0.3 −0.2 0.1 0.1 0.1 0.6 0.4 0.4 0.8 0.3 0.2 −0.1 0.5 −0.2 0.5 0.4 0.5 0.4 0.4 0.8 −0.3 0.2
Australia (AUS)
0.4 0.4 0.3 0.4 0.3 0.2 −0.0 0.4 0.0 0.6 −0.1 0.4 −0.1 0.7 0.5 −0.1 −0.2 −0.4 0.1 0.0 −0.0 0.2 0.4 0.5 0.4 0.7 −0.6 0.1
Turkey (TUR)
0.2 0.8 0.7 0.4 0.7 0.7 0.3 0.0 0.2 0.1 0.3 0.4 0.6 0.2 0.6 0.4 0.6 0.3 0.8 0.1 0.6 0.7 0.4 0.3 0.2 0.5 −0.4 0.2
EU15
0.3 0.7 0.8 0.7 0.4 0.6 0.3 0.4 0.2 0.4 −0.3 0.4 0.1 0.5 0.8 0.3 0.1 −0.0 0.6 0.4 0.5 0.5 0.4 0.5 0.4 0.5 −0.3 0.2
US0.1 0.8 0.9 0.5 0.5 0.7 0.2 0.0 0.3 0.2 −0.2 0.6 0.3 0.3 0.8 0.4 0.2 −0.0 0.8 0.2 0.7 0.6 0.6 0.6 0.4 0.6 −0.4 −0.0
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
0.2 0.5
0.6 0.6
0.3 0.6
0.5 0.8

1.0
0.2 1.0
0.6 0.5 1.0
0.5 0.7 0.8 1.0

If less than −0.55

Figure 9 Correlation of GDP Growth, 2000–2015_Correlation of GDP growth at constant market prices

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

2015

Others
29 %

EU28
7%

Asia
60 %
APO20
36 %

Asia30EU15
56 %
6 % US
4 % Other Asia
4%

Figure 10 Share of Asian Population in
the World, 2015
Source: IMF, World Economic Outlook Database, April 2017.

16: 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%.

25

2017 Asian Productivity Organization

those presented in Table 5 can appear arbitrary. Rather, long-term trends of nominal per capita GDP provide a better guide of relative movements. Based on
this measure, Japan closed in on the US level in thelate 1980s and peaked in 1995, reflecting the strong
yen of 94.1 yen per dollar, as shown in Figure 11. However, it is 40% below the US level in 2015, in which theaverage annual exchange rate is 121.0 yen per dollar.
Figure 12 shows comparisons among the Asian Tigers.
Singapore and Hong Kong have been moving closelywith one another for three and a half decades until themid-2000s, when Singapore overtook Hong Kong.16
Hong Kong’s per capita GDP peaked in 1997, the year

3 Economic Growth

Table 4 Population, 1970, 1980, 1990, 2000, 2010, and 2015
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
553.9
116.1
104.7
71.2
60.6
42.7
36.7
34.4
32.2
28.4
27.3
14.8
12.5
11.3
10.9
6.77
3.96
2.50
2.07
1.25
0.52
0.29

(%)

41.2
27.5
5.8
5.2
3.5
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
0.74 0.0
Kuwait0.68 0.0
Oman
0.11 0.0
Qatar
Saudi Arabia 5.84 0.3
0.25 0.0
UAE
0.13 0.0
Brunei(regrouped)
1147.5 57.0
APO20
2005.1 99.6
Asia242012.9 100.0
Asia30986.8 49.0
East Asia
South Asia 709.8 35.3
279.5 13.9
ASEAN
200.3 9.9
ASEAN679.3 3.9
CLVM
7.82 0.4
GCC(reference)
205.1 10.2
US342.1 17.0
EU15
EU28
439.9 21.9
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
697.2
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

(%)

40.0
28.3
6.0
4.7
3.5
3.3
2.2
1.9
1.8
1.6
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

0.34 0.0
Bahrain1.36 0.1
Kuwait1.09 0.0
Oman
0.22 0.0
Qatar
Saudi Arabia 9.91 0.4
1.04 0.0
UAE
0.19 0.0
Brunei(regrouped)
1434.0 58.1
APO20
2453.5 99.4
Asia242467.4 100.0
Asia30
East Asia 1166.8 47.3
South Asia 895.0 36.3
352.2 14.3
ASEAN
256.9 10.4
ASEAN695.3 3.9
CLVM
14.0 0.6
GCC(reference)
227.2 9.2
US
357.3 14.5
EU15
EU28
461.8 18.7
14.7 0.6
Australia
44.7 1.8
Turkey1990
1143.3
China
870.6
India
179.4
Indonesia
123.6
Japan
112.1
Pakistan
Bangladesh 109.0
Vietnam
66.0
60.7
Philippines
55.1
Iran
54.5
Thailand
42.9
Korea
40.2
Myanmar
20.4
ROC
18.1
Malaysia
18.1
Nepal
Sri Lanka
17.0
8.84
Cambodia
5.70
Hong Kong
4.14
Lao PDR
3.05
Singapore
2.07
Mongolia
0.74
Fiji
0.54
Bhutan

(%)

38.4
29.2
6.0
4.1
3.8
3.7
2.2
2.0
1.8
1.8
1.4
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
Bahrain2.10 0.1
Kuwait1.63 0.1
Oman
0.42 0.0
Qatar
Saudi Arabia 16.4 0.5
1.77 0.1
UAE
0.25 0.0
Brunei(regrouped)
1772.0 59.5
APO202956.3 99.2
Asia242979.0 100.0
Asia30
East Asia 1338.0 44.9
South Asia 1127.3 37.8
435.2 14.6
ASEAN
316.0 10.6
ASEAN6119.2 4.0
CLVM
22.8 0.8
GCC(reference)
249.6 8.4
US366.3 12.3
EU15475.2 16.0
EU28
17.1 0.6
Australia
56.5 1.9
Turkey2000
1267.4
China
1053.5
India
206.3
Indonesia
137.9
Pakistan
126.9
Japan
Bangladesh 124.1
Vietnam
77.6
76.5
Philippines
64.2
Iran
60.6
Thailand
47.0
Korea
45.6
Myanmar23.5
Malaysia
22.8
Nepal
22.3
ROC
Sri Lanka
19.1
11.9
Cambodia
6.67
Hong Kong
5.22
Lao PDR
4.03
Singapore
2.39
Mongolia
0.80
Fiji
0.60
Bhutan

2010

(%)

36.9
30.7
6.0
4.0
3.7
3.6
2.3
2.2
1.9
1.8
1.4
1.3
0.7
0.7
0.6
0.6
0.3
0.2
0.2
0.1
0.1
0.0
0.0

0.64 0.0
Bahrain1.86 0.1
Kuwait2.40 0.1
Oman
0.61 0.0
Qatar
Saudi Arabia 21.4 0.6
3.00 0.1
UAE
0.32 0.0
Brunei(regrouped)
2093.3 60.9
APO20
3407.2 99.1
Asia243437.1 100.0
Asia30
East Asia 1472.7 42.8
South Asia 1357.9 39.5
511.6 14.9
ASEAN
371.2 10.8
ASEAN6140.3 4.1
CLVM
29.9 0.9
GCC(reference)
282.2 8.2
US
377.6 11.0
EU15487.3 14.2
EU28
19.0 0.6
Australia
67.8 2.0
Turkey1340.9China
1231.0India
237.6
Indonesia
173.5
Pakistan
Bangladesh 147.3
128.1
Japan
Philippines
92.3
86.9
Vietnam
74.3
Iran
65.9
Thailand
49.7
Myanmar
49.6
Korea
28.6
Malaysia
26.4
Nepal
23.2
ROC
Sri Lanka
20.7
14.0
Cambodia
7.02
Hong Kong
6.26
Lao PDR
5.08
Singapore
2.76
Mongolia
0.86
Fiji
0.70
Bhutan

(%)

34.8
31.9
6.2
4.5
3.8
3.3
2.4
2.3
1.9
1.7
1.3
1.3
0.7
0.7
0.6
0.5
0.4
0.2
0.2
0.1
0.1
0.0
0.0

1.23 0.0
Bahrain2.91 0.1
Kuwait2.77 0.1
Oman
1.70 0.0
Qatar
Saudi Arabia 28.1 0.7
8.26 0.2
UAE
0.39 0.0
Brunei(regrouped)
2421.3 62.8
APO20
3813.0 98.8
Asia243858.0 100.0
Asia30
East Asia 1551.5 40.2
South Asia 1599.5 41.5
586.8 15.2
ASEAN
430.0 11.1
ASEAN6156.9 4.1
CLVM
45.0 1.2
GCC(reference)
309.3 8.0
US397.3 10.3
EU15
503.2 13.0
EU28
22.0 0.6
Australia
73.7 1.9
Turkey20151374.6
China
1311.1
India
253.3
Indonesia
192.7
Pakistan
Bangladesh 158.0
127.1
Japan
Philippines 101.0
91.7
Vietnam
79.1
Iran
67.2
Thailand
51.9
Myanmar
51.0
Korea
31.0
Malaysia
27.8
Nepal
23.5
ROC
Sri Lanka
21.0
15.2
Cambodia
7.31
Hong Kong
6.85
Lao PDR
5.54
Singapore
3.01
Mongolia
0.89
Fiji
0.76
Bhutan

(%)

33.9
32.3
6.2
4.8
3.9
3.1
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

1.37 0.0
Bahrain3.54 0.1
Kuwait4.43 0.1
Oman
2.15 0.1
Qatar
Saudi Arabia 31.5 0.8
9.09 0.2
UAE
0.42 0.0
Brunei(regrouped)
2574.1 63.5
APO20
4001.7 98.7
Asia244053.9 100.0
Asia30
East Asia 1586.6 39.1
South Asia 1711.2 42.2
624.0 15.4
ASEAN
458.4 11.3
ASEAN6
165.6 4.1
CLVM
52.1 1.3
GCC(reference)
320.9 7.9
US404.6 10.0
EU15508.4 12.5
EU28
23.8 0.6
Australia
78.7 1.9
Turkey2017 Asian Productivity Organization

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

when Hong Kong was returned to China, and subsequently plummeted until 2004. Singapore followed a similar path to that of Hong Kong – peaking in 1996, and falling to an all-time low in 2002
before the surge from the late 2000s. The ROC and Korea moved together but at a lower level thanSingapore and Hong Kong.
The views found in Table 5 are considerably revised if focusing on production or real income per capita, using PPP as the conversion rates. In terms of per capita GDP at constant prices using PPP in Table
6, Japan was the first country in Asia to start catching up with the US. By 1970, its per capita GDP was26

3.2 Catching Up in Per Capita GDP

Table 5 Per Capita GDP using Exchange Rate, 1970, 1980, 1990, 2000, 2010, and 2015

_GDP at current market prices per person, using annual average exchange rate
1970

1980

(%)

(%)

1990

(%)

2000

(%)

2010

(%)

2015

(%)

Japan
Hong Kong
Singapore
Fiji
Iran
ROC
Malaysia
Korea
BhutanSri Lanka
ThailandPhilippines
Pakistan
Bangladesh
Cambodia
India
China
Myanmar
Nepal
Mongolia
Indonesia
Lao PDR
Vietnam

1.99 100.0
0.96 48.4
0.93 46.5
0.43 21.5
0.40 19.9
0.39 19.7
0.36 17.9
0.28 14.0
0.23 11.5
0.23 11.4
0.21 10.7
0.18
9.3
0.17
8.4
0.14
7.0
0.12
6.0
0.11
5.8
0.11
5.6
0.10
5.0
0.10
5.0
0.09
4.7
0.09
4.3
0.07
3.3
0.03
1.4

Japan
Hong Kong
Singapore
Iran
ROC
Fiji
Malaysia
Korea
ThailandPhilippines
Indonesia
BhutanSri Lanka
China
Pakistan
Mongolia
India
Bangladesh
MyanmarNepal
Lao PDR
Cambodia
Vietnam

9.29 100.0
5.70 61.4
5.00 53.9
2.51 27.0
2.37 25.5
1.92 20.7
1.78 19.1
1.70 18.4
0.74 8.0
0.69 7.4
0.54 5.8
0.34 3.6
0.33 3.6
0.31 3.3
0.29 3.1
0.29 3.1
0.27 2.9
0.22 2.4
0.19 2.0
0.18 1.9
0.14 1.6
0.11 1.2
0.02 0.2

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

25.3 100.0
13.5 53.3
12.8 50.4
8.17 32.3
6.52 25.7
2.50 9.9
1.86 7.3
1.72 6.8
1.63 6.4
0.77 3.0
0.77 3.0
0.71 2.8
0.58 2.3
0.55 2.2
0.39 1.5
0.38 1.5
0.35 1.4
0.30 1.2
0.29 1.1
0.25 1.0
0.20 0.8
0.14 0.6
0.10 0.4

Japan
Hong Kong
Singapore
ROC
Korea
Malaysia
Fiji
ThailandIranPhilippines
Sri Lanka
China
Indonesia
BhutanMongolia
Pakistan
India
VietnamBangladesh
Lao PDR
Cambodia
Nepal
Myanmar38.5 100.0
25.8 66.9
23.8 61.8
14.9 38.6
11.9 31.0
4.04 10.5
2.11 5.5
2.09 5.4
1.72 4.5
1.06 2.8
1.01 2.6
0.96 2.5
0.82 2.1
0.74 1.9
0.60 1.6
0.52 1.4
0.46 1.2
0.42 1.1
0.42 1.1
0.32 0.8
0.31 0.8
0.28 0.7
0.17 0.4

SingaporeJapan
Hong Kong
Korea
ROC
Malaysia
Iran
ThailandChina
Fiji
Indonesia
Sri Lanka
Mongolia
BhutanPhilippines
India
VietnamLao PDR
Pakistan
Cambodia
Bangladesh
MyanmarNepal

46.6 100.0
44.5 95.6
32.6 69.9
22.1 47.4
19.3 41.4
8.92 19.2
6.42 13.8
5.19 11.1
4.55 9.8
3.68 7.9
3.18 6.8
2.72 5.8
2.61 5.6
2.28 4.9
2.16 4.6
1.36 2.9
1.35 2.9
1.10 2.4
1.01 2.2
0.81 1.7
0.78 1.7
0.75 1.6
0.72 1.5

Singapore
Hong Kong
Japan
Korea
ROC
Malaysia
China
ThailandIran
Fiji
Mongolia
Sri Lanka
Indonesia
Philippines
BhutanVietnamLao PDR
India
Pakistan
Bangladesh
Cambodia
MyanmarNepal

53.6 100.0
42.4 79.0
34.5 64.3
27.1 50.5
22.4 41.7
9.56 17.8
8.01 14.9
6.00 11.2
5.00 9.3
4.97 9.3
3.92 7.3
3.83 7.1
3.40 6.3
2.90 5.4
2.74 5.1
2.13 4.0
1.87 3.5
1.61 3.0
1.39 2.6
1.23 2.3
1.21 2.3
0.93 1.7
0.79 1.5

Bahrain
KuwaitOman
Qatar
Saudi Arabia
UAE
Brunei(regrouped)
APO20
Asia24  
Asia30
East Asia
South Asia
ASEAN
ASEAN6
CLVM
GCC(reference)USEU15

1.88 94.7
4.00 201.2
0.40 19.9
4.97 250.0
0.92 46.4
4.28 215.4
1.43 71.9

10.3
21.8
5.79
35.4
16.7
42.3
26.7

110.9
234.9
62.4
381.4
179.5
455.3
287.9

34.2
53.5
21.3
76.7
23.1
91.8
46.1
9.1
6.5
6.7
12.6
1.2
3.1
4.0
0.9
33.1

3.58 179.9
0.68 34.4

Australia  
Turkey11.8 127.1
2.06 22.2

Australia           Turkey              

36.4
26.4
22.8
21.5
4.03

94.7
68.7
59.2
55.9
10.5

Bahrain
KuwaitOman
Qatar
Saudi Arabia
UAE
Brunei(regrouped)
APO20               Asia24            Asia30            East Asia           South Asia          ASEAN               ASEAN6
CLVM
GCC                 (reference)
US                  EU15                EU28            Australia           Turkey              22.7 42.4
33.2 61.9
16.1 29.9
78.7 146.7
20.9 39.0
42.2 78.8
30.9 57.6

3.49
2.50
2.59
4.87
0.46
1.21
1.54
0.33
12.7

Bahrain
KuwaitOman
Qatar
Saudi Arabia
UAE
Brunei(regrouped)
APO20               Asia24            Asia30            East Asia           South Asia          ASEAN               ASEAN6
CLVM
GCC                 (reference)
US                  EU15                EU28            Australia           Turkey              20.8 44.7
40.7 87.4
21.5 46.1
75.2 161.6
18.9 40.6
35.6 76.4
35.5 76.1

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

13.2
20.6
8.22
29.5
8.89
35.3
17.8

12.6 135.7
9.29 100.1

Bahrain
KuwaitOman
Qatar
Saudi Arabia
UAE
Brunei(regrouped)
APO20               Asia24            Asia30            East Asia           South Asia          ASEAN               ASEAN6
CLVM
GCC                 (reference)
US                  EU15                9.25 36.5
9.10 35.9
7.21 28.5
17.8 70.4
7.19 28.4
28.9 114.4
13.4 53.1

5.25 263.9
3.65 183.4

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

0.31
0.23
0.23
0.32
0.12
0.12
0.15
0.06
1.36

15.7
11.4
11.6
16.3
6.2
6.3
7.5
3.1
68.2

1.22 13.1
0.84 9.1
0.94 10.1
1.31 14.1
0.27 2.9
0.56 6.0
0.73 7.9
0.09 0.9
18.4 197.9

2.56
1.67
1.73
3.02
0.38
0.84
1.11
0.13
9.30

10.1
6.6
6.8
12.0
1.5
3.3
4.4
0.5
36.8

24.0
17.4

94.7
68.9

19.0
3.61

75.1
14.3

4.92
4.74
4.98
8.75
1.27
3.37
4.19
1.10
25.7

10.6
10.2
10.7
18.8
2.7
7.2
9.0
2.4
55.2

48.4 103.9
36.8 79.0
33.4 71.7
58.7 126.1
10.5 22.5

4.69
5.78
6.07
11.1
1.56
3.91
4.72
1.66
27.5

3

8.7
10.8
11.3
20.7
2.9
7.3
8.8
3.1
51.2

56.2 104.8
41.1 76.6
37.9 70.7
52.3 97.5
10.9 20.4

61% of the US, quite a distance ahead of other 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.17 In recent years, Japan’s level has stabilized to around 70–73% of
the US, as shown in Figure 13.

272017 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
40

60
Australia

ROC

40

20

20
1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

01970 1975 1980 1985 1990 1995 2000 2005 2010 2015

Figure 11 Per Capita GDP using Exchange
Rate of Japan and Australia, Relative to theUS, 1970–2015

Figure 12 Per Capita GDP using Exchange
Rate of the Asian Tigers, Relative to the US,
1970–2015

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.

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

Japan’s per capita GDP was the highest among Asian countries until it was overtaken by Singapore in
1980.18 The result highlights the outcome of the dramatic development effort made by the Asian Tigers, 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 1993.19 In
2015, Singapore had a per capita GDP which was 53% above the US. It became the richest economy
in Asia, representing a remarkable achievement. Hong Kong holds the second place, with a per capita
GDP similar to the US. Japan’s per capita GDP, at 72% of the US, or around 47% of the group leader(Singapore), is similar to that of the EU15. The ROC and Korea trail behind the other two Asian Tigers
at 83% and 65% of the US, respectively.

2017 Asian Productivity Organization

The relative performance of China and India, the two most populous countries in the world, is diminished in this measure due to their population. Their per capita GDP is 25.7% and 10.7% of the US in
2015, respectively, as shown in Figure 15. However, this should not taint the remarkable progress
made over the past decades, especially by China where the per capita GDP was less than 2.0% of the
US in 1970. China’s relative per capita GDP has increased more than tenfold in these four decades. The
income gap between the US and the majority of Asian countries is still sizable,20 indicating significant
opportunity for catch-up.

17: Jorgenson, Nomura, and Samuels (2016) 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.
18: 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.
19: 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 90 in Section 7.1, p. 121). On the other hand, the US GNI never goes outside +1.6% of GDP.
However, Singapore’s lead of 53% over the US in 2015 was large enough that their relative positions would be independent of
whether GNI or GDP was used. Based on the comparison among cities in Box 8 (p. 93), the per capita GDP in Singapore was 11%
above New York City and 22% above Tokyo in 2015.

28

3.2 Catching Up in Per Capita GDP

Table 6 Per Capita GDP, 1970, 1980, 1990, 2000, 2010, and 2015

_GDP at constant market prices per person, using 2011 PPP, reference year 2015
1970
Japan
Singapore
IranHong Kong
Fiji
Malaysia
ROC
Philippines
Mongolia
ThailandKorea
Sri Lanka
Indonesia
Pakistan
India
MyanmarBhutanBangladesh
Nepal
Lao PDR
VietnamChina(%)

15.4 100.0
10.7 69.5
10.3 66.7
9.12 59.1
4.51 29.2
4.25 27.5
3.85 24.9
3.14 20.3
2.84 18.4
2.83 18.4
2.66 17.2
2.29 14.8
1.82 11.8
1.54 10.0
1.31
8.5
1.28
8.3
1.22
7.9
1.20
7.8
1.14
7.4
1.14
7.4
1.00
6.5
0.50
3.3

Bahrain37.8 244.6
202.1 1309.1
Kuwait15.3 99.3
Oman
168.1 1089.2
Qatar
Saudi Arabia 50.2 324.9
43.8 283.8
UAE
94.8 614.1
Brunei(regrouped)
APO20                3.12 20.2
Asia24              2.02 13.1
Asia30               2.19 14.2
East Asia            2.24 14.5
South Asia           1.33
8.6
ASEAN                2.11 13.6
2.52 16.3
ASEAN6
1.07
6.9
CLVM
GCC                  62.2 403.2(reference)
US                   25.3 164.1
EU15                 18.8 121.6
Australia           Turkey              23.2 150.5
7.21 46.7

1980
SingaporeJapan
Hong Kong
Iran
ROC
Malaysia
Fiji
Korea
Philippines
ThailandMongolia
Indonesia
Sri Lanka
Pakistan
MyanmarIndia
BhutanLao PDR
Nepal
Bangladesh
VietnamChina(%)

21.9 100.0
21.8 99.2
16.9 76.9
10.5 47.8
8.58 39.1
7.40 33.7
5.90 26.9
5.41 24.7
4.27 19.5
4.16 18.9
3.87 17.7
3.20 14.6
2.94 13.4
1.81 8.3
1.65 7.5
1.40 6.4
1.29 5.9
1.27 5.8
1.18 5.4
1.09 5.0
1.04 4.7
0.77 3.5

Bahrain48.0 218.6
Kuwait88.9 405.0
Oman
26.8 122.3
Qatar
142.8 651.1
Saudi Arabia 78.1 355.8
200.7 914.8
UAE
158.9 724.4
Brunei(regrouped)
APO20                4.03 18.4
Asia24              2.70 12.3
Asia30               3.13 14.3
East Asia            3.22 14.7
South Asia           1.43 6.5
ASEAN                3.31 15.1
4.08 18.6
ASEAN6
1.24 5.6
CLVM
GCC                  84.6 385.6(reference)
US                   31.2 142.3
EU15                 24.6 112.0
Australia           Turkey              26.7 121.6
8.54 38.9

1990
SingaporeJapan
Hong Kong
ROC
Korea
Malaysia
Iran
ThailandFiji
Mongolia
Indonesia
Philippines
Sri Lanka
Pakistan
BhutanIndia
Lao PDR
China
Nepal
MyanmarVietnamBangladesh
Cambodia(%)

36.6 100.0
32.5 88.9
28.7 78.6
16.6 45.3
12.4 33.9
10.3 28.2
9.59 26.2
7.28 19.9
6.34 17.4
5.23 14.3
4.83 13.2
4.13 11.3
3.86 10.5
2.69 7.3
2.55 7.0
1.93 5.3
1.75 4.8
1.62 4.4
1.50 4.1
1.48 4.0
1.44 3.9
1.27 3.5
1.02 2.8

38.3 104.9
Bahrain
43.9 120.0
Kuwait40.1 109.6
Oman
Qatar
88.1 240.8
Saudi Arabia 44.5 121.7
120.7 330.1
UAE
89.5 244.9
Brunei(regrouped)
APO20                5.49 15.0
Asia24              3.94 10.8
Asia30               4.30 11.8
East Asia            5.17 14.1
South Asia           1.96 5.4
ASEAN                4.61 12.6
5.80 15.9
ASEAN6
1.43 3.9
CLVM
GCC                  50.7 138.7(reference)
US                   39.5 107.9
EU15                 30.6 83.8
Australia           Turkey              

30.9
11.2

84.6
30.8

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

55.0 100.0
36.2 65.8
36.0 65.5
29.0 52.7
22.1 40.1
16.2 29.4
12.2 22.1
10.2 18.5
7.38 13.4
6.34 11.5
5.73 10.4
4.95 9.0
4.58 8.3
3.93 7.1
3.64 6.6
3.51 6.4
2.71 4.9
2.65 4.8
2.50 4.5
2.25 4.1
1.93 3.5
1.85 3.4
1.51 2.8

47.6 86.5
Bahrain
86.9 157.8
Kuwait43.5 79.0
Oman
115.5 209.8
Qatar
Saudi Arabia 44.6 81.0
117.3 213.0
UAE
86.9 157.9
Brunei(regrouped)
APO20                6.75 12.3
Asia24              5.65 10.3
Asia30               6.08 11.1
East Asia            7.81 14.2
South Asia           2.74 5.0
ASEAN                6.35 11.5
7.83 14.2
ASEAN62.42 4.4
CLVM
GCC                  55.9 101.6(reference)
US                   49.0 88.9
EU15                 37.2 67.6
EU28               32.7 59.5
Australia            39.3 71.5
Turkey               13.4 24.4

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

(%)

76.8 100.0
51.2 66.7
41.9 54.6
38.1 49.6
32.3 42.1
22.1 28.8
19.0 24.7
14.7 19.1
10.1 13.2
9.17 11.9
8.81 11.5
8.09 10.5
7.88 10.3
7.15 9.3
6.05 7.9
4.82 6.3
4.75 6.2
4.50 5.9
4.20 5.5
3.94 5.1
2.78 3.6
2.69 3.5
2.43 3.2

43.4 56.6
Bahrain84.4 109.9
Kuwait52.6 68.5
Oman
142.5 185.6
Qatar
Saudi Arabia 47.3 61.7
63.1 82.2
UAE
83.6 108.9
Brunei(regrouped)
APO20                8.97 11.7
Asia24              9.32 12.1
Asia30               9.87 12.9
East Asia            13.79 18.0
South Asia           4.55 5.9
ASEAN                9.27 12.1
11.07 14.4
ASEAN6
4.34 5.6
CLVM
GCC                  56.4 73.5
(reference)
US                   52.6 68.5
EU15                 40.0 52.1
EU28               36.4 47.4
Australia            46.0 60.0
Turkey               18.3 23.8

2015
Singapore
Hong Kong
ROC
Japan
Korea
Malaysia
Iran
Thailand
China
Mongolia
Sri Lanka
Indonesia
Fiji
BhutanPhilippines
VietnamIndia
Lao PDR
Pakistan
Cambodia
MyanmarBangladesh
Nepal

(%)

86.1 100.0
56.9 66.1
46.9 54.5
40.3 46.8
36.4 42.3
26.4 30.6
17.3 20.1
16.7 19.4
14.4 16.7
12.1 14.1
11.7 13.6
11.3 13.1
9.09 10.5
8.63 10.0
7.37 8.6
6.11 7.1
6.04 7.0
5.85 6.8
4.95 5.7
3.63 4.2
3.47 4.0
3.40 4.0
2.76 3.2

346.8 54.4
Bahrain83.7 97.1
Kuwait40.1 46.6
Oman
Qatar
150.3 174.5
Saudi Arabia 54.4 63.2
72.7 84.4
UAE
77.1 89.6
Brunei(regrouped)
APO20                10.2 11.9
Asia24              11.6 13.4
Asia30               12.2 14.2
East Asia            17.9 20.8
South Asia           5.69 6.6
ASEAN                11.0 12.8
13.2 15.3
ASEAN65.05 5.9
CLVM
GCC                  62.1 72.1
(reference)
US                   56.2 65.3
EU15                 41.2 47.8
EU28               37.7 43.8
Australia            48.9 56.8
Turkey               24.1 28.0

Table 6 also presents individual figures for seven oil-rich economies (Brunei and the six GCC countries). At first glance, figures in 1970, and those to a lesser extent in 1990, suggest these economies
had remarkably higher per capita GDP than those of Japan and the US. For example, in 1970, Kuwait,
20: 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 90 in Section 7.1).

29

2017 Asian Productivity Organization

Unit: Thousands of US dollars (as of 2015)
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

US

US=100 in each year

160
140

90
80

Australia
EU15

Singapore

120
100

US
Hong Kong

80
70
60

60
Japan

40

ROC

20
50
1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

Korea
01970 1975 1980 1985 1990 1995 2000 2005 2010 2015

Figure 13 Per Capita GDP of Japan, the EU,
and Australia, Relative to the US, 1970–2015

Figure 14 Per Capita GDP of the Asian Tigers,
Relative to the US, 1970–2015

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.

Qatar, and Brunei had a per capita GDP 13.1
times, 10.9 times, and 6.1 times that of Japan,
respectively. However, the 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 of the income flow. In otherwords, GDP overestimates income from the oilexporting 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 comparisonsof per capita GDP excluding production of themining sector (e.g., crude oil and natural gas). The
non-mining GDP per person in GCC economies,
such as the UAE, Bahrain, and Kuwait, is almost
similar to Japan’s level, although total GDP per
capita is much larger.

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

US=100 in each year

25
20
15
10
5

ASEAN

India

China
0
1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

Figure 15 Per Capita GDP of China, India, and
ASEAN, Relative to the US, 1970–2015_Ratio of per capita GDP at constant market prices,using 2011 PPP, relative to the US

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

2017 Asian Productivity Organization

Catching up with the per capita GDP level of advanced economies is a long-term process that 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 themselves tocatch up to average income levels. However, as their income levels approach those of the more advanced countries, their economic growth rates are expected to gradually decline over time.21

21: The OECD (2017b) 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.

30

3.2 Catching Up in Per Capita GDP

Figure 17 plots countries’ initial per capita
Qatar
78.1
150.3
72.2
Brunei
42.9
34.3
77.1
GDP levels against their respective average
UAE
31.1
72.7
41.6
growth rates per year between 1970 (or the
Japan
38.5
1.7
40.3
initial year data first became available for the
Kuwait
46.0
83.7
37.7
country in question) and 2015. If the two
Bahrain
34.1
12.8 46.8
variables have a correlation coefficient of
Saudi Arabia
29.3
25.1 54.4
Non-mining GDP–0.5 (i.e., a negative relationship of medium
Malaysia 22.8 3.6 26.4
Mining GDP
strength), the higher the initial income level
Oman 21.7
18.4 40.1
becomes, the more slowly the average growth
Iran 15.2 2.1 17.3
Thousands of US dollars (as of 2015)
Myanmar 2.2 1.3 3.5
rate per year is expected to move. However,this is not always true. Low-income countries
0
30
60
90
120
150
like Bangladesh, Nepal, the Philippines, andFiji have failed to catch up, while Thailand andFigure 16 Per Capita Non-Mining GDP in OilRich Countries and Japan, 2015
Malaysia could be expected to have grown_GDP at constant market prices per person, using 2011even faster, given their initial income levels.
PPP, reference year 2015
The Asian Tigers have experienced robust
Sources: Official national accounts in each country, including author
growth in the past four decades, but Korea
adjustments.
and the ROC, with their lower initial per capita
GDP, have sustained higher growth rates thanSingapore and Hong Kong. Relative to the Asian Tigers, China appears to be at the start of the catchup process. Mature economies like the US, the EU15, and Japan shared similar growth experiences(around 2% on average per year, in the past four decades).

3

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%; GroupL3, 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%

5%Growth rate of per capita GDP during 1970–2015

Cambodia

Bhutan4

8=1970, =1990, =2015

7

Indonesia

Mongolia

3
Pakistan
Bangladesh
Myanmar

2

ROC

5

Singapore
Malaysia

4

Hong Kong

ThailandPhilippines

Nepal

Korea

6

Sri Lanka
India=1970, =1990, =2015

China

VietnamLao PDR

Growth rate of per capita GDP during 1970–2015

3

Turkey

Fiji

Japan

2

EU15

1

Australia

Iran

1
Thousands of US dollars (as of 2015)

US

Thousands of US dollars (as of 2015)

0

0
0

2

4

6

8

10

12

14

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, 1970–2015_Level and average annual growth rate of GDP at constant market prices, using 2011 PPP, reference year 2015
Sources: Official national accounts in each country, including author adjustments.
Note: The starting period for Cambodia is 1987.

31

2017 Asian Productivity Organization%

3 Economic Growth

Box 2

System of National Accounts in Asia

Understanding data comparability is essential for the construction of an international database, and requires continuous effort and expert knowledge. Between December 2016 and March 2017, the APO Productivity Database project conducted the Metadata Survey 2017 on the national accounts and other statistical
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 2017, 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 the data series definitions and classification started to switch over. This
allows identification in breaks in the time series. Figure B2 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 2017. 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
Vietnam

68
1973
93
1980
2000
08
1996
2014
68
1993
93
1993
2009
08
2019
68
1951
1988
93
1951
2005
08
1951
2014
68
1968
1974
93
1995
2003
08
2005 2008
68
1950
1978
93
1999
2007
08
2004
2010
68
1960
1970
93
2000
08
2010
2015
68
1959
1981
93
1991
2006
68
1955
1978
93
1980
2000
08
1994
2016
68
1953
1986
93
1970
2004
08
1953
2014
68
1990
93
2002 2005
68
1975
93
2000
2007
1960(mixture of 1953 SNA until 1968)
08
2005
2012
68 N.A.(Before 1993 SNA is introduced, Material Product System was used.)
93
1980
1995
08
2010
2015
68
1975
93
2000
2006
68
1981
1988
93
2000
2004
08
2006
2013
68 1946
1972
93
1998
2011
08
1998
2011
68
1975
93
1998 2001
08
2010
2016
68
1972 1975
93
1990
2012
08
2018
68
1989
93
1986
1993
08
2018

2017 Asian Productivity Organization

Introduction year

Backward estimates and implementation

Figure B2 Implementation of the 1968, 1993, and 2008 SNA
Source: APO Metadata Survey 2017.
continued on next page >32

3.2 Catching Up in Per Capita GDP> continued from previous page

Japan started to publish national accounts based on the 1968 SNA in 1978 (at present, backward estimates
based on the 1968 SNA are available from 1955), national accounts based on the 1993 SNA in 2000 (backward estimates based on the 1993 SNA are available from 1980 at present), and national accounts basedon the 2008 SNA in 2016 (backward estimates based on the 2008 SNA are available from 1994 at present).
As Figure B2 suggests, countries differ in their year of introduction, the extent of implementation, andbackward estimates available. According to the survey response and our investigation, 15 countries ofAsia24 are currently 2008 SNA compliant (partially or fully). While there are movements toward upgrading
the SNA, Cambodia, the Lao PDR, and Nepal have yet to fully introduce the 1993 SNA. 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 theframework for their national accounts, but the extent of compliance in terms of coverage 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.

to under 3%; Group-C3, from 0% tounder 1%; and Group-C4, under
0%. The speed of catch-up with the
US is defined as the difference inthe average annual growth rate of
per capita real GDP between eachcountry and the US. Table 7 showsthat 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, although some are
more successful than others.

3

Table 7 Country Groups Based on the Initial Economic
Level and the Pace of Catching Up_Level and average annual growth rate of per capita GDP atconstant market prices, using 2011 PPPInitial GDP
level
to the US

Annual rate of catch-up to the US
(C1)
> 3%

(C2)
1% <–< 3%

(L1)
60% <

Japan, Oman

(L2)
20% <–< 60%(L3)
8% <–< 20%(C3)0% <–< 1%

ROC, Korea

(C4)
< 0%
Australia, Bahrain,
Brunei, EU15,
Kuwait, Qatar,
Saudi Arabia, UAE

Hong Kong,
Singapore

TurkeyIranMalaysia, Mongolia,
Sri Lanka, Thailand

PhilippinesFiji

Bhutan, India,

Bangladesh,

22: Myanmar shifted from Group-C2 to Group-C3 in this edition of Databook, with a positive catch-up rate of 0.5% on average, due toour revision in the official GDP estimates (see Box 5, p. 56).

33

2017 Asian Productivity Organization(L4)
From Table 7 one can see the initial
Myanmar,Cambodia, China Indonesia, Lao PDR,
< 8%
Vietnam
Nepal, Pakistan
economic level does not fully explain the catch-up process. If it did,
Sources: Official national accounts in each country, including author adjustments.
the table would have been popuNote: The annual catch-up rates are based on the difference in the growths of percapita GDP at constant prices between each country and the US during 1970–2015.
lated diagonally from the bottom
The starting year for Cambodia is 1987.
left corner to top right corner. Of
the Asia30 countries, four achieveda very fast catch-up (over 3% peryear on average) between the respective starting years of their data series and 2015. Their initial per
capita GDP level classifies them into the three groups: Singapore from Group-L2, the ROC and Korea
from Group-L3, and Cambodia and China from Group-L4. Eleven countries in Group-C4 experienced
deterioration in their relative income level against the US with low-income countries like Fiji failing totake off.22 The seven high-income Asian countries in Group-C4 are all oil exporting countries, which
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 the EU15, it
has since failed to achieve further parity with the US.

3 Economic Growth

3.3 Sources of Per Capita GDP Gap
To further understand the diverse performance in the Asian group, per capita GDP can be simply broken into two components: labor productivity (defined as real GDP per worker in this section) and theemployment rate.23 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 and 2015.24

2017 Asian Productivity Organization

Bangladesh
CLMV
Vietnam
Myanmar
Nepal
China
Lao PDR
India
South Asia
BhutanPakistan
Sri Lanka
Asia24
Philippines
Asia30ASEAN
Indonesia
East Asia
Mongolia
APO20
ASEAN6Fiji
ThailandIran
Malaysia
TurkeyKorea
ROC
Hong Kong
EU15
Australia
Japan
Singapore
GCC

1990

%

40

66

20
5

3

0

8

6

−20

−37

−40 −87−60−83−95−96−99 −89−101−95−90 −88

−80
−100

−10

−5 −7

−7
−1 0

−10

−66
−74 −71 −79−77−78−81−88−85−83−93−89−89−19 −19

−10
−1

−1

−1 −3 −5

−10 −8−26−60−52−64

−50

−20

−8

−39
−14

−18

−1

4
6
−14−19 −22−14
−8
−38
−3

−5

−4

2015
Nepal
Bangladesh
MyanmarCambodia
Pakistan
CLMVSouth Asia
Lao PDR
India
VietnamPhilippines
Bhutan
Fiji
APO20
ASEAN
Indonesia
Asia24
Sri Lanka
Mongolia
Aisa30ASEAN6
China
Thailand
Iran
East Asia
TurkeyMalaysia
Korea
Japan
EU15
ROC
Australia
Hong Kong
GCC
Singapore

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, GCC, Japan, and Iran, all
the other Asian countries hadlabor productivity gaps ofmore than 50% against the US
in 2015. At the top end of performance, estimates show Singapore was 20% above while
Hong Kong was 4% below the
US labor productivity level.
In Singapore, its employment
rate was 33 percentage points
higher, giving an overall per
capita GDP which was 53%
higher than the US in 2015. The
labor productivity gaps of theother two Asian Tigers are stillsizable against the US, at 17%
and 41% for the ROC and Korea,
respectively. In most countries,
the effect of the employment
rate was to widen the per capita
GDP gap in 1990. However, in
recent years more Asian countries have employment rates
higher than the US, with the effect of narrowing the gap.

%

40

39

20
1

9

8 8

0

9

3

6

11 12

−20
−40
−60

−39

−3

−6

−16

−8

−8

−8

Employment rate

−13 −65 6 11
−9 −9

9 6
−36

−75 −82 −82 −79 −71−75 −79−79−68−69−78−76 −82 −79−83−85−91 −98 −91−92−88−21−102−102 −100
−30

−80
−100

11

20 14

−52−45−25−21−19
−35
−2

−2

−11−10−1 0

0 −1

Labor productivity

Per capita GDP

Figure 18 Labor Productivity and Employment Rate Gap
Relative to the US, 1990 and 2015

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

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

23: 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.
24: 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.

34

3.3 Sources of Per Capita GDP Gap

Figure 19 focuses on explaining a country’s per capita GDP growth by its components: namely laborproductivity growth and the change in the employment rate for the periods 1990–2010 and 2010–2015, respectively.25 For most countries, labor productivity explains a larger share of per capita GDP
growth than employment. China’s improvement was the most impressive, achieving per capita GDP
growth of 9.2% and 7.1% per year on average in the two periods, respectively. Improvement in laborproductivity explains almost all of that growth. However, this should not lead us to underestimate therole of changes in the employment rate. The employment rate contribution, relative to labor productivity, was also highly significant in countries such as, Singapore (49%), Malaysia (49%), Korea (44%),and the ROC (44%).

3

GCC
Japan
Fiji
EU15
US
Philippines
Australia
Mongolia
Nepal
TurkeyAPO20
Pakistan
Hong Kong
Indonesia
ASEAN6Iran
ASEAN
ThailandSingapore
Bangladesh
Malaysia
Sri Lanka
Aisa30South Asia
Asia24
Lao PDR
India
ROC
Korea
MyanmarEast Asia
BhutanCambodia
CLMV
VietnamChina

In Muslim countries like Iran, Turkey, and Pakistan, the employment rate is significantly less than theUS, further reinforcing the poor
productivity performances of%
1990–2010
these countries (Figure 18). It is
100.0
no coincidence they are among8
the countries with the lowest
6
0.9
0.0
shares of female workers in em0.5
1.2 2.0
0.2
0.6 0.6
9.2
1.7
4
0.2
0.3 0.4 0.2
ployment, at 16%, 30% and
1.2 0.6 0.7 0.8
0.6 0.9
0.5 0.1
0.3
5.1 4.4 4.0
4.9 4.3 4.2 4.2 4.8 4.1 4.3 4.3 4.2
0.4 0.3
223%, respectively, as shown in
3.4
0.2
3.3
3.2
3.1 2.92.9 2.5 2.9 2.8 2.7
2.9
2.9
2.1 2.4
2.1 2.2 1.6 1.6 1.7
0.5
1.1 0.5
0.9 0.6
Figure 20. In contrast, a handful
0
0.0
−0.1
−0.3 0.0−0.10.0
−0.3
−0.1
−0.5
of countries such as the Lao−2
PDR, Vietnam, Mongolia, andCambodia had higher employment rates than the US, counteracting the negative impact of2010–2015%
their productivity performances.
9
0.4

0.1

6
3

7.7 7.2

0.4
2.0

6.9

5.7
3.6

0

−0.1
−0.3

0.4

4.9 5.2 5.0 4.3
0.0−0.2

0.6
1.3
4.1 4.6 4.5 4.4 4.24.3

0.0 0.4
1.7

2.5 1.8−0.1 −0.1−0.1 −0.1 −0.3

3.6 3.5

−0.1

0.1
0.4
0.6
0.6
2.0
1.8
0.3
1.2 1.3 1.2 1.3 1.2
0.7 1.5 0.7 0.1
0.0−0.1−0.2 0.0
−0.3
−1.9
−0.4
−2.9

2.7 3.1 3.12.6

1.3 1.1 1.1 1.0 0.9−3Myanmar
Iran
EU15
Japan
Australia
US
Pakistan
GCC
Hong Kong
ROC
Singapore
Korea
Nepal
APO20
Thailand
Fiji
CLMV
ASEAN
ASEAN6
Malaysia
BhutanPhilippines
Indonesia
Aisa30
Asia24South Asia
Bangladesh
Vietnam
India
East Asia
Cambodia
TurkeySri Lanka
Lao PDR
China
Mongolia
Employment rate

Labor productivity

Per capita GDP

Figure 19 Sources of Per Capita GDP Growth, 1990–2010
and 2010–2015_Decomposition of average annual growth rate of per capita GDP atconstant market prices, using 2011 PPP

Sources: Official national accounts in each country, including author adjustments.
Note: The starting period for Cambodia is 1993.

25: 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 x in period t.
26: Japan is one of the exceptional countries where the employment rate in 2015 was not increased from that in 1970. This reflects,
among other things, its aging population. US employment rates indicate weakening in the recent period, with levels in 2015 lower
than that in 1990 (i.e., 49% compared with 51%).

35

2017 Asian Productivity Organization

Figure 21 shows cross-country
comparisons of employment
rates in 2015, based on the labor
statistics of each country. Employment consists of employees, own-account workers, andcontributing family workers.
Singapore, Myanmar, and Cambodia lead the Asian group with
employment rates of around60%, more than 10 percentage
points higher than the US in
2015. It is clear that employment rates have been rising inmost Asian countries.26 The fastest catch-up countries (i.e.,3 Economic Growth

those in Group C1 in Table 7) are also countries withthe 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 ratechanged little between 1990 and 2015 at 56%. Someof the countries in Group C2 also experienced significant improvements in employment rates (forexample, Indonesia and Vietnam). While there are
exceptions, generally countries that have failed tocatch up also tend to make less vigorous improvements over the period, and in turn continue to havelower employment rates.

Lao PDR (2010)
Hong Kong (2015)
Vietnam (2015)
Mongolia (2015)
Canada (2015)
Cambodia (2012)
US (2015)
Bhutan (2015)
Australia (2015)
EU28 (2015)
Thailand (2015)
Singapore (2015)
China (2010)
ROC (2015)
Japan (2015)
Nepal (2011)
Brunei (2014)
Korea (2015)
Philippines (2015)
Malaysia (2015)
Indonesia (2015)
Sri Lanka (2015)
Kuwait (2011)
Fiji (2007)
Turkey (2015)
Bangladesh (2013)
India (2012)
Pakistan (2015)
Bahrain (2010)
UAE (2008)
Oman (2010)
Iran (2015)
Saudi Arabia (2015)
Qatar (2015)

50
49
48
48
47
47
47
47
46
46
46
45
45
44
43
43
43
42
40
38
37
35
33
32
30
29
25
23
21
19
17
16
14
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.
%

65
60
55
50
45
40
35
30
25
2015

1990

1970

20

2017 Asian Productivity Organization

Singapore
MyanmarCambodia
VietnamThailand
China
Bhutan
Korea
Hong Kong
Japan
Australia
ROC
Lao PDR
US
Brunei
EU15
Malaysia
Indonesia
Nepal
Bangladesh
Philippines
India
Mongolia
Sri Lanka
Fiji
TurkeyPakistan
Iran

Figure 21 Employment Rates, 1970, 1990, and 2015
_Ratio of employment to total population

Sources: Employment and population data by national statistical offices in each country, including
author adjustments.
Note: The starting period for Cambodia is 1993.

36

4.1 Final Demand Compositions

4 Expenditure
GDP is defined by three approaches in SNA: production by industry; expenditure on final demand;and income to factor inputs. In this chapter, the economic insights are drawn from analyzing the expenditure side of GDP. Sections 4.1 and 4.2 present the composition of countries’ expenditure and thelong-term trends in Asian countries, respectively. The expenditure-side decomposition of GDP growthis provided in Section 4.3.

4

4.1 Final Demand Compositions
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). One can see thatcountry 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.
For most countries, household consumption is by far the biggest component of GDP.27 Over the past
four decades, the share of household consumption for mature economies has been stable, trending
upward in recent years. It is more volatile and largely trends downward in economies undergoing
Table 8 Final Demand Shares in GDP, 1970, 1990, 2000, 2010, and 2015
_Share of final demands with respect to GDP at current market prices

APO20

Household consumption
Government consumption
Investment
Net exports
1970 1990 2000 2010 2015 1970 1990 2000 2010 2015 1970 1990 2000 2010 2015 1970 1990 2000 2010 201559.6 56.7 58.6 57.2 58.0 11.2 12.1 12.9 13.2 13.0 29.7 31.8 25.7 28.8 27.9 −0.5 −0.7
2.7
0.9
1.1

Asia24

59.4

55.7

55.6

48.9

49.0

11.2

12.3

13.9

13.0

13.3

29.9

32.2

27.9

35.9

35.5

−0.5

−0.2

2.7

2.1

2.2

Asia30

56.8

55.1

54.4

48.1

48.4

11.6

13.5

14.5

13.3

14.0

28.8

30.7

27.1

35.4

35.3

2.9

0.7

4.0

3.3

2.2

East Asia

50.3

50.5

51.1

43.2

42.4

11.1

13.5

16.0

14.5

14.9

38.0

34.4

30.9

39.0

39.3

0.6

1.5

2.0

3.3

3.4

South Asia

75.8

65.9

66.8

61.2

63.0

8.6

11.5

11.6

11.1

10.2

16.0

25.2

23.1

32.7

30.2

−0.4

−2.5

−1.5

−4.9

−3.4

ASEAN

70.4

62.0

59.0

55.1

55.8

12.0

9.3

9.1

10.5

11.3

22.4

29.9

23.3

28.4

28.6

−4.9

−1.2

8.6

6.1

4.3

ASEAN6

68.6

59.6

57.2

54.3

54.6

10.5

9.4

9.6

11.1

11.9

23.4

31.5

23.4

28.3

28.7

−2.4

−0.6

9.9

6.3

4.8

CLMV

81.8

88.4

74.5

60.8

64.0

21.5

7.4

5.3

5.8

7.0

16.5

12.4

22.7

29.2

28.0 −19.8

−8.3

−2.5

4.2

1.0

GCC

34.8

49.1

41.0

35.8

39.6

14.9

25.9

21.1

16.8

24.3

19.2

15.8

18.2

28.7

33.0

31.2

9.1

19.7

18.7

3.2

China

55.5

49.0

46.6

35.9

37.0

11.0

13.6

16.6

12.8

13.8

33.3

34.7

34.4

47.6

45.7

0.1

2.7

2.4

3.6

3.5

India

74.0

62.4

64.1

57.5

59.9

9.4

11.9

12.8

11.7

10.4

16.7

27.1

23.9

35.3

32.0

−0.1

−1.4

−0.9

−4.5

−2.3

Japan

47.2

50.9

54.4

57.8

56.6

11.1

13.6

16.9

19.5

19.9

40.6

34.7

27.3

21.3

23.9

1.1

0.8

1.4

1.5

−0.3

Australia

54.2

57.8

58.7

54.0

57.7

13.9

18.2

17.7

17.8

18.9

32.1

24.2

23.4

27.1

25.7

−0.3

−0.2

0.2

1.0

−2.2

US

60.2

64.0

66.0

68.2

68.1

18.1

15.9

14.0

16.9

14.4

21.4

21.5

23.6

18.4

20.3

0.4

−1.3

−3.7

−3.4

−2.9

EU15

56.9

57.2

58.1

57.4

56.4

15.9

19.3

18.9

21.6

20.7

27.7

24.2

22.6

20.2

19.5

−0.5

−0.7

0.4

0.8

3.5

27: In theory, three approaches to measure GDP are accounting identities and should yield the same result, but in practice, they differ
by statistical discrepancies. Based on our Metadata Survey 2017 on national accounts for APO member economies, Japan is an exceptional country that determine GDP from its expenditure side (the expenditure-side estimate is based on the commodity flow
data, in which production/shipment data provide the controlled totals. The gap between the expenditure-side and the production-side GDP has been reduced.). 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 sum of
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 impact on the share of final demand: e.g., it accounts for 2.5% of GDP in 1990
in the Thailand SNA published in February 2017.

37

2017 Asian Productivity Organization

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.

4 Expenditure

rapid transformation, such as the Asian Tigers in
the 1970s and 1980s, and India and China in thepresent day, as the investment share increases
for their development effort.

%

70
60

Household consumption

50
40

Investment

China’s household consumption has been trend30
Exports
ing downward as a share of GDP. It fell from20
55.5% in 1970 to 35.9% in 2010. This compares
Imports (−)
10with the early communist era when household
0
consumption was more volatile and at a higher
1952
1962
1972
1982
1992
2002
2012
level of over 60% of GDP (Figure 22). China wasless well-off then. Figure 22 shows how houseFigure 22 Final Demand Shares in GDP ofhold consumption share and investment share
China, 1952–2015_ Share of final demands with respect to GDP at
mirror each other.28 As the decline in household
current market prices
consumption share accelerated in the 2000s,
plummeting to 37.0% in 2015, the investment
Sources: National accounts by National Bureau Statistics of China,
including author interpolation.
share rose rapidly to 45.7% of GDP from 34.4% in
2000. Investment has overtaken household consumption as the largest component in GDP expenditure since 2004, and the divide shows no considerable sign of narrowing. The falling share ofhousehold 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 as a share of GDP since the 1980s when China began to open its economy, fromaround 5.0% or below in the 1950s and 1960s to its peak of 36.5% in 2006 before softening to 18.7%
in 2015.
With a low consumption ratio, coupled with an unsustainable rise in investment and an overdependence on exports, China faces huge internal and 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, 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 governance of state-owned enterprises is not conducive to distribution of dividends and therefore, in effect,
may act to subsidize investment. Additionally, in the absence of a social safety net, well-developed
domestic financial markets may provide a strong incentive for precautionary saving on the part ofhouseholds (Eichengreen, Park, and Shin, 2012). The share of investment in China is the biggest final
demand component of GDP since 2004. At 45.7% in 2015, it is likely unsustainable in the long term. Allof these factors suggest that there are policy levers available to the government to impede or rebalance the economy.

2017 Asian Productivity Organization

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 4). This could be a good news for the world, as a higher
labor share of GDP will bring about higher household consumption, helping the domestic market
28: The Chinese official statistics on household consumption could be misleading. Zhang and Tian (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.

38

4.1 Final Demand Compositions

fulfill its potential. This will make China less dependent on foreign demand; at the same time,
China will generate demand for foreign products. Early signs that the Chinese economy may
have started moving in the right 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 3.5% in 2015, decreased from thepeak of 8.7% in 2007.

%

80
70
60
50

30

15
12
27
6 4 2022 27 25
5

75
95 94

8

6

20

81 80 80 74−15 −9−6

8

24 24 26 21 29 34 28

Exports0
1929 1939 1949 1959 1969 1979 1989 1999 2009

50
25
0

_ Share of final demands with respect to GDP atcurrent market prices

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

9 11 10 15773 73 72 67

0

1

1

23 34 30 21 25
17 26 33
32 31

1

1

66 65 65 64 62 62 62 61 61 61 59−15 −9 −9 −9 −2 −8 0 −1 −2 −1 0 −4 −1

−3

1

11
14 28 30 43 39

8 11 13 10 9 16 9 12 19 18
12 13 21 17 15

11 10

58 58 58 56 55 54 54 53 53 52

−2 0

0

−7 −1

31

22

4

8

2

34 21 43 20
14 22 12 23

21

40

28
13

51 51 49 49 47 47 45

−4

1

2

10 17

10

15
32

36

44

34

8 31
43 41

27

32 27

−4

2015

%

75

Figure 23 Final Demand Shares in GDP of theUS, 1929–2015

0

4

125
100

Imports (−)

10

Brunei
Qatar
Singapore
KuwaitChina
Oman
Saudi Arabia
Malaysia
GCC
East Asia
Bhutan
Korea
ThailandJapan
ROC
Bahrain
Asia30
Asia24Australia
EU15
APO20
ASEAN6
Fiji
ASEAN
UAE
Iran
India
Hong Kong
Indonesia
US
South Asia
Mongolia
TurkeySri Lanka
Pakistan
Philippines
Vietnam
CLMV
Nepal
Bangladesh
MyanmarCambodia

−25

20 28

8 12 10 8250Investment

1995

%

125

50

4

40

In contrast to China, the share of household consumption was relatively stable in the US ataround 60–64% for the 1970s and 1980s before
edging up to 68.1% of GDP in 2015. From a historical perspective as shown in Figure 23, thecurrent level is below the share of household

100

Household consumption

34

2 1
1
1 1
2 2
3
3 4
4
3
4 0
52
16
5 8 6
9
9
18
11 14 1313 7
23 21 29 28 20
30 28 32
26
35
20 20
11 11
24
26
19
22 29
27 28
28
29 34 29 25 29
33
5 11 5 9 14
36
28
39 25
21
21 29 35 35 23
46
10
28 24
7 10 14 10 136
19 18 20 21
13
39 35
27
7 11 10 12 13 13 14 15
13 14 17 13 18
24 30 24
15
27
14
11
84 80
76 74 73 70
68 66 64 64 64 63
19 25
60 60 58 58 58 57 57 56 56 56 56 55 54
52 52 49 49 48 48 46 45
42 42 40 40 37 37
33
23 20
−6
−28

−4

−6 −7 −8−3

−3

−3 −3 −2

−2

−27

0

−5

−25

Government consumption

Investment

Net exports

Figure 24 Final Demand Shares in GDP, 1995 and 2015

_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.

39

2017 Asian Productivity Organization

Brunei
Qatar
Oman
Singapore
China
GCC
Saudi Arabia
KuwaitEast Asia
Bahrain
UAE
ThailandAsia30
Asia24
Korea
ROC
Iran
Malaysia
ASEAN6
Indonesia
ASEAN
Myanmar
EU15
Japan
BhutanAustralia
APO20
Mongolia
India
TurkeySouth Asia
CLMV
Fiji
VietnamHong KongUS
Sri Lanka
Bangladesh
Philippines
Cambodia
Pakistan
Nepal
Household consumption

4 Expenditureconsumption that the US experienced during
the Great Depression, when it was over 75%,
even as high as 82% in 1932. The share ofhousehold consumption in the EU15, which is
at around 57%, has remained fairly stable overthe past four decades. The Asian average,
meanwhile, has hovered around the 50%
range until recently when the gap with the
EU15 widened, largely reflecting the trend in
China (Table 8). 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 63.0%
in 2015.

% Household consumption share in GDP

90

Nepal Pakistan

80

Cambodia
Bangladesh
Sri Lanka

70

Hong Kong
Vietnam

60

50
UAE

40

TurkeyPhilippines
US
Fiji

India
Mongolia
Bhutan Japan
Myanmar
Australia
ROC Iran Malaysia Indonesia
Thailand
Korea
Bahrain
KuwaitSaudi Arabia
China Singapore
Oman

30
Qatar

20

Brunei

Overall, Asian countries invest significantly
more than the US and the EU15 as a share of
10
GDP. Historically, the gap in the investment
10
15
20
25
30
35
40
45 %
Dependent population ratio
share between the Asia30 and the EU15 neverexceeded 10 percentage points. However,
Figure 25 Ratio of Dependent Population andsince the beginning of the 1990s, it has started
Consumption Share in GDP, 2015
_Shares of dependent population (age under 14 andto widen (except for the period of the Asian fiover 65) to total population and consumption share innancial crisis). In 2015 the difference was over
GDP at current market prices
15 percentage points. In the 1970s the EU15
was investing on average 3% more of their
Sources: Population data by national statistical office in each country; World Bank, World Development Indicators 2016; official national
GDP than the US. Thereafter, the EU15 investaccounts in each country with author estimates.
ment 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 thatof the EU15 (Figure 30.3). In 2015 investment accounted for 20.3% and 19.5% of final demand in the
US and the EU15, respectively, compared with 35.5% for the Asia24. East Asia has the highest investment ratio among the Asian regions. While South Asia caught up with them in 2007, since then thepaths of the two regions diverged in opposite directions.

2017 Asian Productivity Organization

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 –0.1%
to 2.7%, the contribution of net exports slightly decreased to 2.2% in 2015. This compares with theoil-exporting GCC countries at 9.1% in 1990, rising to 18.7% in 2010, and drastically decreased to 3.2%
in 2015, reflecting the rapid decline of oil prices as of the end of 2014.29 In the US, there is an observable trend of persistent deficit between exports and imports, which has considerably expanded fromthe beginning of the 1980s to 5.6% in 2006 before narrowing to 2.9% in 2015. South Asia is the only
Asian region that consistently has run a fluctuating trade deficit over the years. Lately, it is historicallysizable at 7.4% of GDP in 2012, narrowing to 3.4% in 2015.
The regional averages disguise the great variation displayed by individual countries. Figure 24 shows
the cross-country comparisons of final demand shares in current-price GDP in 1995 and 2015. Countries
29: The price of crude oil has been over USD 100 per barrel since 2010 and held until the middle of 2014, before drastic declining. See
Figure 91 in Section 7.1 (p. 122).

40

4.1 Final Demand Compositions

1995

%

100

181

80
60

126

91

78
52

44 38

37

40

22

20

59

51

46
20

43

41

39

27

25
9

17 19

28 26

11 11 14

30 32

32

10

31

22

15

4

0
−8
−12−12
−14
−17
−17
−18
−25 −19 −27
−26
−28
−29
−34
−40
−41
−43
−45 −42
−49
−59
−67

−20
−40
−60
−80
−100

−15

−24

−38
−48 −41−37−41 −46−94 −131−165

Cambodia
Nepal
Philippines
MyanmarVietnam
Sri Lanka
ThailandBangladesh
Hong Kong
Bhutan
Malaysia
Pakistan
US
India
Korea
Mongolia
Australia
TurkeyIndonesia
Fiji
Qatar
Japan
ROC
EU15
China
Brunei
Oman
UAE
Iran
Saudi Arabia
KuwaitBahrain
Singapore2015%

100 178

94

80
60

52 55

40

196

85
64 68 59

53

61

55

45

42
19

20

89

71

19

48

46
21 18 19 20 23

33 28
13

33
11

17 21

10

0
−13 −15

−20
−33
−35

−40
−60
−80
−100

−152

−15
−18
−21 −21−23−26−39 −45−44 −38−52−51 −50
−57
−63
−72
−80
−193 −88−51−17−25−29

−34
−38

−65

−39

−59

Nepal
BhutanSri Lanka
Bangladesh
Pakistan
Philippines
Saudi Arabia
CambodiaUS
Fiji
TurkeyIndia
Australia
Japan
Indonesia
VietnamMongolia
Hong Kong
EU15
China
Oman
Iran
Korea
Malaysia
KuwaitMyanmarThailandROC
Bahrain
UAE
Qatar
BruneiSingaporeare arranged in descending order of their household consumption shares. Although mostcountries fall to the right of theUS, there are a handful of Asian
countries that have a higher consumption ratio than the US.
Bangladesh, Cambodia, Nepal,
Pakistan, Sri Lanka, and the Philippines fell to the left of the US in
both years of comparisons. The
high consumption rate in thesecountries could be partly explained by the difference in demographic conditions. Figure 25
shows that countries with a highproportion 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
35% in 2015. 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 clusteredon the left of Figure 24 tend tobe those in the bottom income
groups among the countries
studied in this report (see Table
16 in Section 6.1, p. 95).

Exports

Imports

Net exports

Figure 26 Export and Import Shares in GDP, 1995 and 2015

_Share of exports and imports with respect to GDP at current market
prices

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

30: 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.

41

2017 Asian Productivity Organization

Figure 26 presents the export and import shares in GDP as a decomposition of net exports in 1995 and2015. Net exports are particularly important in a handful of economies. In 2015 the shares in Singapore exports were at 178%, and that in Hong Kong 196%, 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.30

4 Expenditure

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.31 Countries are grouped according to the levels of per-capita income in2015.32 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
and been mildly reversed in Korea since the late 1980s. Between 1970 and 2015 the household consumption share of GDP fell from 69.0% of GDP to 36.7% and from 73.5% to 49.1% in Singapore andKorea, respectively.
In contrast, household consumption as a share of GDP, at 66.4% in 2015, has been rising in Hong Kong
since the mid-2000s. It did fall from 66.2% in 1970 to nearly 55% in the late 1980s, but it was subsequently reversed. Similarly, the relative household consumption in the ROC fell from 55.9% in 1970 to

90

%

%

%

100

Hong Kong
Korea
Singapore

Japan
ROC

100
90

China
Iran
Mongolia

Indonesia
Malaysia
Sri Lanka
Thailand

100
90

80

80

80

70

70

70

60

60

60

50

50

50

40

40

40

30
1970 1975 1980 1985 1990 1995 2000 2005 2010 201530
1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

Figure 27.1

Figure 27.2

Figure 27.3

%

%

%

BhutanFiji
India
Lao PDR
Philippines
Vietnam30
1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

100

100

100

90

90

80

80

80

70

70

70

60

60

60

50

50

50

40

40

40

Bangladesh
Myanmar
Pakistan

Cambodia
Nepal

APO20
US
Australia

Asia30EU15
Turkey90

East Asia
ASEAN

South Asia
ASEAN6
CLMV

301970 1975 1980 1985 1990 1995 2000 2005 2010 201530
30
1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

Figure 27.4

Figure 27.5

Figure 27.6

Figure 27 Long-Term Trend of Household Consumption Share in GDP, 1970–2015_Share of household consumption with respect to GDP at current market prices
2017 Asian Productivity Organization

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

31: This edition of Databook newly covers the Lao PDR and Myanmar (based on our revised estimates, presented in Box 5). In addition,
the final demands are estimated backwardly until 1970 in Nepal.
32: Table 16 in Section 6.1 (p. 95) defines four levels of per capita GDP groups in 2015: Group-L1, with per capita GDP above 60% of
the US; Group-L2, from 20% to under 60%; Group-L3, from 10% to under 20%; and Group-L4, below 10%. They are presented in
Figure 27.1, Figure 27.2, Figure 27.3, and Figure 27.4, respectively. The same country groups are applied in Figures 30 and 34.

42

4.2 Consumption and Investment

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 56.6 in 2015. With a rapidly
aging population, this rising trend
can be expected to continue.
Japan’s share of dependent population stood at 38.8% in 2015
(Figure 25), nearly 60% of which
was accounted for by the over-65
age group.

%

100
90
80
70
60
50
40
30
20
10

Miscellaneous goods andservices
Restaurants and hotels
Education
Recreation and culture

4

Communication
Transport
HealthFurnishings, household
equipment, and routine
household maintenance
Housing, water, electricity,
gas and other fuels
Clothing and footwear
Alcoholic beverages,
tobacco and narcotics
Food and non-alcoholic
beverages

US
Singapore
Australia
Korea
Hong Kong
ROC
Japan
Malaysia
ThailandSri Lanka
Iran
India
China
Indonesia
Mongolia
Cambodia
BhutanPhilippines
Lao PDR
VietnamBangladeshFiji0

4 5 5 2
4 4 3
4
8 2
3
10
3
12 12 14 14 14
5 3 136 3
15 17 14
11 18 2
2 4
10
3
2
21
3 5 5
24
3
4
3
4
4 3
6
4
10 8 93 14 11 47
8 7
4 5 13 2 8
2 3
16
9 2
2 5 3
3
5
2 2 4 2 6 5
3 3 3 9 12 5
8 4
6
5 3
6 8
4
4 9
2
7 15
11
9
8 10
12 17
5
4
6 3 14
2
7
8
4
8
2
13 3
6
3 2 12
4
4 2
3
9
10
7
23
4
4
8
2 6
4
2
22 13
13 12 12 10
4
15 4
2 4 3
4
2 4 12 18
9
22 32
6 14
4 3 4
12
9
4
5
22
3 4 5
7
7
3 4
5
4
3 4
7
16
9
11
3 8 6
8
7
15 25
5 4
4 5 4 4
18 18
24
57
2 3
53 50
49
3
15
2
42
18 19
4 4
38 35
6
33 33 31
2 2
3 3
28 27 26
25
3 2
21
2 3
15 15 14 13
10
7 6

Figure 27.5 illustrates the observations of Table 8, plotting Asiangroup averages against those of
Figure 28 Household Consumption by Purpose, 2015
the reference countries. The US
Sources: Official national accounts in each country.
household consumption share
Note: For data of Hong Kong, transportation includes communication; recreation andhas been climbing since the midculture includes hotels; miscellaneous goods and services include restaurants. For
data of China, food and non-alcoholic beverages includes alcoholic beverages, to1980s to over 68% of GDP since
bacco and narcotics; transportation includes communication; recreation and culture
2008, from a level of around 62%.
includes education. For data of Vietnam, transportation includes communication.
For Fiji, the Lao PDR, and Vietnam, the observation periods are 2009, 2005, and 2014,
Today the US level is more thanrespectively.
10% higher than that of the EU15
and the APO20.33 The share in the
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 the 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.
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 illustrates the cross-country version of Engel’s Law, which states thatbasic necessities will account for a high proportion of household consumption for a lower per capita
income group and vice versa.34 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 in Section 6.1, p. 95). The other end of the spectrum is occupied by the rich Asian countries,
namely, the Asian Tigers and Japan.

33: It is worth noting that the GDP share of government consumption in the EU15 was higher than the average of the Asia24 by 7.3
percentage points in 2015 (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.
34: The estimates for China, the Lao PDR, and Vietnam are newly added in this edition of Databook.
35: The estimates for China and Vietnam are newly added in this edition of Databook.

43

2017 Asian Productivity Organization

Figure 29 traces the decreasing long-term path of Japan’s Engel’s Curve during the period 1949–2015.
The countries’ levels in 2015 are mapped against Japan’s experience (as circles).35 Among the selected4 Expenditurecountries, it is staggering to notethat in 2015, 52.8% of Bangladesh’s
household consumption was spent
on food and non-alcoholic beverages at one end of the spectrum,compared with only 6.4% in the US
at the other end. This suggests thefact that low-income countries
spend 30–50% of their GDP on foodand non-alcoholic beverages corresponds to Japan’s experience in the
1950s and the 1960s. Besides foodand non-alcoholic beverages, housing/utilities and transportation are
the other two large spending categories. In rich economies, these twocategories account for bigger sharesin household consumption thanfood and non-alcoholic beverages.
Idiosyncratic spending, such as education in Korea, Vietnam, and Cambodia accounting for 5.5%, 5.4%,
and 5.1% of household consumption, respectively, and health in
the US, accounting for one-fifth of
consumption, are not reflected inother countries.

Food consumption share in 2015

0.7

Japan (1949–2015)

Fiji

0.6

Bangladesh
Lao PDR
Vietnam

0.5

Philippines
Bhutan Mongolia
Cambodia Indonesia

0.4

India
China Sri Lanka
IranThailand

0.3

Malaysia
Japan
ROC
Korea
HongKong

0.2

0.1

Singapore

US

0.0
1940

1950

1960

1970

1980

1990

2000

2010

2020

Figure 29 Engel Curve of Japan during 1949–2015 andLevels of Asian Countries in 2015
_Share of food in household consumption

Sources: Official national accounts in each country. The historical data of Japan isbased on JSNA by ESRI, Cabinet Office of Japan.
Note: Food is defined as sum of food and non-alcoholic beverages and alcoholic
beverages, tobacco and narcotics. For Fiji, the Lao PDR, and Vietnam, the observation periods are 2009, 2005, and 2014, respectively.

Figure 30 compares the long-term trend of investment share (including R&D investment) in GDP
among countries. 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 40.6% in 1970 to 23.9% in 2015 (Figure 30.1).36 In the initial period of our observation,
Singapore also sustained an investment share of 40% or above. Since the mid-1980s, however, it has
seen a downward trend, in spite of its fluctuations. In 2015 it was 26.8%.37 The investment share hitaround 40% in the ROC and Korea at different times but these were nothing more than temporary
spikes (Figure 30.1).

2017 Asian Productivity Organization

In contrast, the investment share in China and India has been rising. India in particular has been investing very aggressively since 2000, approaching China’s 41.5% share in 2007, with the gap of 4.0
percentage points. Since then, the gap has widened to 13.7 percentage points in 2015 as investment
in India softened (Figure 30.3). At 45.7% in 2015, China’s investment share reached a level previously
unseen in Asia, except tentative achievements in small countries.38 If history is any guide, the contribution of investment to final demand in China will drop eventually, as seen in 2014–2015. ASEAN’s
investment share was previously around 35%, but it fell sharply to the lowest point of 17.8% in 1999
36: Japan’s current share of gross investment is almost equivalent to the share of consumption of fixed capital (CFC) in GDP. Thus thenet investment is close to zero.
37: Although Singapore’s investment ratio in 2015 is higher than that of Japan, it is of note that Singapore has succeeded in sustaininga higher ex-post rate of return on capital (14.0% for the period 2010–2015, based on our estimates in Table 23 in Appendix 3) thanthat of Japan (4.9% for the same period). Korea is another country which confronts the decreases in the ex-post rate of return on
capital. In 2010–2015, Korea’s rate of return reached 7.2%, which is similar to that of Japan in the late 1990s.

444.2 Consumption and Investment

%

%

50

40

%

60

60

50

50

40

40

30

30

20

20

BhutanIndia
PhilippinesFiji
Lao PDR
Vietnam

30

4

20

10
Hong Kong
ROC

Japan
Singapore

Korea

10

China
Malaysia
ThailandIndonesia
Mongolia

Iran
Sri Lanka

1001970 1975 1980 1985 1990 1995 2000 2005 2010 2015

01970 1975 1980 1985 1990 1995 2000 2005 2010 2015

01970 1975 1980 1985 1990 1995 2000 2005 2010 2015

Figure 30.1

Figure 30.2

Figure 30.3

%

50

40

%

%

Bangladesh
Myanmar
Pakistan

50

Cambodia
Nepal

40

APO20
US
Australia50

Asia30EU15
Turkey40

East Asia
ASEAN
CLMV

South Asia
ASEAN6
GCC

30
30

30

20

20

20
10
10

10

0
1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

01970 1975 1980 1985 1990 1995 2000 2005 2010 2015

–101970 1975 1980 1985 1990 1995 2000 2005 2010 2015

Figure 30.4

Figure 30.5

Figure 30.6

0Figure 30 Long-Term Trend of Investment Share in GDP, 1970–2015_Share of investment with respect to GDP at current market prices
Sources: Official national accounts in each country, including author adjustments.

%

120

FDI inflows as a percentage to GFCF, an average in the period 2000–2015
115.3

100
80

69.9

60
40
20

39.1

35.6

27.9 24.2

19.7 18.7 18.6 18.2

15.1 13.9 12.8 12.7 12.4 11.6

10.9 8.8 8.6 8.3 8.2 8.0

6.7 6.6 6.1 5.4 5.4 5.2 5.0 3.8 3.4 3.4 3.3 3.2 3.2 3.1

0.9 0.6

Japan
Nepal
Indonesia
Iran
Bangladesh
Kuwait
Korea
ROC
BhutanSri Lanka
France
India
Italy
China
US
Philippines
Pakistan
Germany
Qatar
TurkeyOman
Australia
Spain
Saudi Arabia
Thailand
Malaysia
UAE
CanadaUK
Vietnam
BruneiBahrain
Myanmar
Netherlands
Mongolia
Cambodia
Fiji
Singapore
Hong Kong0

45.1

Figure 31 FDI Inflows, 2000–2015_FDI inflows as a percentage of GFCF, an average of the ratios during the period 2000–2015

38: In Mongolia the two world-class large mines (coal and copper) started production in 2010, sparking a resources boom. The country’s capital investment ratio jumped from 30% of GDP in 2009 to 58% in 2011 (Figure 30.2). In Bhutan the investment booms
shown in Figure 30.2 reflect the construction of large-scale hydropower plants, i.e., Tala hydropower plant (1020 MW) has operated since 2006 and other plants to be commissioned by 2017–2019.

45

2017 Asian Productivity Organization

Source: United Nations Conference on Trade and Development (UNCTAD), World Investment Report 2016.

4 Expenditurein the aftermath of the Asian financial crisis. Since then, it has been slowly inching up, reaching 28.6%
in 2015. In the past two and a half decades, the investment share in GCC countries has fluctuated between 15–35% of GDP (Figure 30.6).
The role of foreign direct investment (FDI) differs considerably among Asian countries. Figure 31
shows the FDI inflows as a percentage of GFCF during 2000–2015, for the Asian economies with some
EU countries for comparison. In almost half of the Asia30 (13 countries), the FDI inflows are over a 10%
share of GFCF. In particular, they are outstanding in the two global cities of the Asian Tigers, Hong
Kong (115% of GFCF) and Singapore (70%), both recording a remarkable achievement in economic
growth in the 2000s. Japan (0.6%) and Nepal (0.9%), whose FDI inflows are extremely small in thisperiod, should consider a domestic reform for lowering barriers to entry, therefore encouraging international investment.
It is an important policy target for low-income countries to create a business-enabling environment,
just as it is important for middle-income countries to improve various business environments. Basedon the EIU’s (Economist Intelligence Unit, The Economist) ranking 2014–2018 (covering 82 countries in
the world),39 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 thisbusiness environment score and the FDI inflows ratio in the countries presented in Figure 31 (excluding the countries in which the FDI inflows ratio is over 25%). There is a positive correlation between
these two. Improving business environment is a necessary condition for attracting FDI.
Figure 33 focuses on investment components, showing the nominal investment share of seven typesof assets for some selected countries.40 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
the current price comparisons. The real-term comparisons are conducted at the flow 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 2015. Among the Asian Tigers, however, the two global cities (Singapore and HongKong) have a smaller share of R&D in GFCF – 9.5% and 3.1%, respectively, in 2015.
Figure 34 plots the long-term trend of net export share in GDP from 1970 to 2015. Net exports, whichwere previously a huge 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 Asian Tigers. The share of net exports in Singapore is particularly large, at 25.9% in 2015, comparedwith 7.0%, 12.8%, and 2.4% for Korea, the ROC, 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 8.6% in 2007. Since then, it has
lagged to 3.5% in 2015.

2017 Asian Productivity Organization

Japan had enjoyed a trade surplus for most of the period compared, but recently its trade balance hasturned negative amounting to –0.5% in 2011 deepening to –2.5% in 2014 (Figure 34.1). In the aftermath
39: The EIU’s business rankings model examines 10 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).
40: The investment data by type of assets includes our own estimates for the countries where data is not available. Although ourestimates are constructed based on 11 classifications of assets (see Table 21 in Appendix 2), including the R&D investment (seeAppendix 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.

46

4.2 Consumption and Investmentof the triple disaster (earthquake, tsunami, and nuclear power plant accident) in 2011, Japan had to increase theimports of natural gas and coal to meetthe increase of thermal power generation as a result of the shutdown of its
nuclear power plants. This trend changed
in 2015, easing to –0.3%, thanks to thedecline in fossil fuel prices. In June 2017,42 reactors are operable and potentiallyable to restart, and 24 of these are in theprocess of restart approvals. The restartis expected to reduce imports.

% FDI inflows as a percentage to GFCF, an average in the period 2000–2015

30

Netherlands
25

20

4

BahrainUK
Vietnam

15
ThailandSaudi Arabia
10

TurkeyPakistan

Germany

Philippines
Sri Lanka

5

CanadaUAE
Malaysia
Spain
Australia

China
Italy
India

US
FranceKorea
ROC

Figure 34.3 illustrates the external imIran BangladeshIndonesia Kuwaitbalance of the world’s major econoJapan0
mies. Both the US and the EU15 faced a
2
4
6
8
10
trade deficit at the beginning of this peBusiness environment rankings score 2014–18
riod. While the EU15 managed to recover, being in surplus since the early 1990s
Figure 32 FDI Inflow Ratio and Business Environment,
2000–2015(within a range of 0–3% of GDP), the US_ FDI inflows as a percentage of GFCF and business environposition has significantly deteriorated
ment scoresince the middle of the 1990s, despite a
Sources: United Nations Conference on Trade and Development (UNCTAD),
tremendous effort to restore its trade
World Investment Report 2016; The Economist Intelligence Unit (2014).
balance in the late 1980s. In 2015 thesize of the US trade deficit stood at 2.9%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 past
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 2.2%, compared to therecent peak of 5.3% in 2006. Addressing this external imbalance has been highlighted as a necessary
step to healthy and sustained growth in the world economy.

80
60
40
20

Dwellings
Transport equipment

2015

%

100
80
60
40
20
0

6
3
10
17 21
17 16 3 14 17 23
18 22 25 23
20
10
3
28 21
28 5 26
31
33 4
3
4 32
41 7
15 7 37
4
7
3 13 710 12
4
537 9 14 11 4 16
9
3
15 14
11
8
3
3
18
7
13 16
10
42
49
12 13 4
4
8
15 10
30 16
10 3 23
3
9 27 3224
6
6 20
10 9 27
25
23
7
14 17
26
30
26 24
18
11 19
9 9
21
11 9
28
18
27
16
32
14
22
15
9 11 731
19 18
1032 26 26 2619924 22 18 18 18
17 16 15 13 13 13 12 11
9 9 9 6 5

Philippines
India
Indonesia
ThailandFiji
ROC
Cambodia
Malaysia
Japan
China
Hong Kong
Korea
Lao PDR
PakistanUS
Iran
Sri Lanka
VietnamSingapore
Nepal
Mongolia
Bangladesh

China
PhilippinesROC
Sri Lanka
Thailand
Malaysia
PakistanIndonesia
Japan
SingaporeUS
Lao PDR
Fiji
Hong Kong
Korea
Vietnam
Iran
Nepal
India
Mongolia
Bangladesh

0

1970
5
11
16 5
16
23 18
24
25 28 425
2 29
35 30 35 40 30 41
2 32 4743
417
12 35 6513 2
213 12 558
4
10 28 11
12
12
10
5 4
11
16 3
54 11 21
5
23 12
16
8
24
6
11
5
23
8
11 20 32
24 27
14
18
8
21
34 33 15 91918
25
8 11 20 2114
15 13 15 13
46 40
13 18 17 27
38
14 19 26
29 26 25 23 23
22 22 19 19 19 18 16 15
11 9 9 5 3

15 15

Non-residential buildings
R&D

Other structures
IT capital

Other non-IT

Figure 33 Investment Share by Type of Asset, 1970 and 2015
Sources: Official national accounts in each country, including author adjustments based on input–output tables and trade data.

47

2017 Asian Productivity Organization%100

4 Expenditure

%

%

32
24

Hong Kong

Japan

Korea

ROC

30

%

China
Malaysia

20

Singapore

16

Indonesia
Mongolia
ThailandIran
Sri Lanka

20

BhutanLao PDR

Fiji
Philippines

India
Vietnam

10

10

0

0

−10

−10

−20

−20

−30

8
0
−8
−16

−30
−24
1970 1975 1980 1985 1990 1995 2000 2005 2010 2015
1970 1975 1980 1985 1990 1995 2000 2005 2010 2015−40
1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

Figure 34.1

Figure 34.3

Figure 34.2

%

40
30

%

%

Bangladesh
Myanmar
Pakistan

Cambodia
Nepal

9

6

APO20
US
Australia

Asia30EU15
Turkey

20
10
0

3

20
10
0
−10

0
−20

−10

−3

−20

−30

−6

−40

−40
1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

−91970 1975 1980 1985 1990 1995 2000 2005 2010 2015−50
1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

Figure 34.4

Figure 34.5

Figure 34.6

−30

East Asia
ASEAN
CLMV

South Asia
ASEAN6

Figure 34 Long-Term Trend of Net Export Share in GDP, 1970–2015_Share of net exports with respect to GDP at current market prices
Sources: Official national accounts in each country, including author adjustments.

2017 Asian Productivity Organization

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 9.1%, up from
–0.4% in the previous year. Trade balance moderated over time to the more normal level of 4.3% in
2015. In recent years the trade balance of CLMV is in surplus for the first time during these four decades.41 Its improvement has been rapid, from a deficit of 5.1% in 2008 to a surplus of 7.6% in 2014.
This should not be a surprise when CLMV is picking up the slack from China as the workshop of theworld. If the time series of China’s net exports is any guide, CLMV’s trade surplus could continue toexpand for a decade to come.

41: The huge deficit of CLMV in the 1970s was due to the impact of the Vietnam War.

48

4.3 Expenditure-Side Growth Decomposition

4.3 Expenditure-Side
Growth Decomposition8

1990–2010

0.2

2.1
5.0

1.9

6
4
2
0
−2

3.6

4.0

0.6

2.1 2.4

0.6

4.6

0.1

0.4
0.3 0.1
0.6 2.2
0.6
0.7 1.9 1.8 0.8
1.0 1.8 0.3
1.6
1.4 0.5 2.7
1.1 0.4 0.3
0.2 0.6
1.5 0.9 0.2 0.5
0.3
1.0
5.7
1.4 0.6
0.7 0.6
0.4
0.7
0.4
0.5 0.3
0.5 0.2 0.1
4.5 4.1 0.7
3.7
3.6
3.5
0.6 0.3
3.4 3.1
0.7 3.1
3.4 1.0
3.0 2.8
0.3
2.8
2.8
2.5 1.8 2.5 2.9 2.3 2.3 2.1
2.4
2.4 2.4
2.1
2.0 1.9 0.4 0.8 0.4 0.2
1.4
1.3
1.0 0.6 0.6 0.2
0.6−0.6 −0.2
−0.1
−0.7
−0.7
−0.9 −0.4−0.9−1.0 −0.2−0.2−0.3 −0.5
−0.3
−0.5
−0.6
−0.1
−2.1
−2.3
−2.4

1.5

0.4 1.1

4.6

1.7

0.7

1.9

0.8 0.8

1.0 2.1

2.3

1.4 1.8

1.5 0.5

4

−4
Fiji
Japan
BruneiEU15
USAustralia
Nepal
Philippines
Saudi Arabia
Hong Kong
TurkeyThailandMyanmar
Oman
KuwaitUAE
Pakistan
BangladeshIndonesia
Iran
ROC
Sri Lanka
Korea
Bahrain
Bhutan
Malaysia
India
Singapore
Mongolia
Vietnam
Cambodia
Qatar
China

2010–2015

%

10
8
6
4
2
0

2.2

3.1

1.4 0.2

3.1

1.2 0.4 2.3
0.5
0.0
1.7
2.0
2.3 1.0
1.5
2.5
0.8
0.3 1.9
0.5 0.3
0.7
1.6
2.4
3.2 0.6
0.3 1.0 1.8
0.3
0.9
0.4
1.1
0.4
0.9
0.4
6.0
1.5
5.9
1.8
4.8
4.3 1.0
2.0
1.3 3.8
3.8 4.2 3.8
3.5
3.5
3.2 2.9
0.8
3.0
2.2
1.7
1.3
1.2
1.1
−0.5
−0.7 −0.6−0.7 −0.1−0.2 −1.3−0.2 −0.9 −0.9
−0.3
−1.7
3.8

−2

0.7

2.5
0.4

0.1
0.3
1.3 1.0
0.6
0.6
0.1
5.4
0.8
0.8
1.0 0.6
3.1
0.2
0.8 1.1 3.7 0.6
1.9
0.6 3.2 0.4 1.5 0.2 0.3
0.7
0.3
0.5
0.4
0.3
0.3
1.3 1.0 1.4 1.1 1.5
1.3
1.3 1.4
0.5 0.3 0.1 0.6−0.6 −0.1−0.7−0.2 −0.1 −0.3 0.1
0.2−9.8−0.1 −2.3
−2.9
−2.9

−4
MyanmarBrunei
EU15
Japan
Iran
US
Korea
ROC
Fiji
Australia
Hong Kong
Thailand
Nepal
Bahrain
Singapore
Kuwait
Sri Lanka
Pakistan
Oman
UAE
Malaysia
Saudi Arabia
Indonesia
VietnamBangladesh
Philippines
India
Cambodia
TurkeyQatar
China
BhutanMongolia

Figure 35 shows the decomposition of the average annual
economic growth by final demand for the periods 1990–
2010 and 2010–2015, respectively. Here, the Asia30 grewfaster in the latter period thanin the former (at 5.3% on average per annum compared with
4.8%, as presented in Table 2,
p. 18).42 The earlier period embodied the economically atypical event of the Asian financial
crisis, which caused some erratic contributions by the final
demand components observedin some countries in the late
1990s. In general terms, theengine of growth for mostcountries in Asia was household
consumption, while investment
growth was more subdued.43%

10Household consumption
Investment

Government consumption
Net exports

Real GDP

Figure 36 shows the impacts ofthe global financial crisis andFigure 35 Final Demand Contributions to Economic Growth,countries’ path of recovery
1990–2010 and 2010–2015_Decomposition: Average annual growth rate of GDP at constant market
from the viewpoint of final deprices
mand between 2007 and2012. The adverse impact ofSources: Official national accounts in each country, including author adjustments.
Note: The starting periods for Cambodia, Nepal, and Mongolia are 1993, 2000, 2000, rethe crisis was felt through inspectively.
vestment in most countries,
and to a lesser extent, throughnet exports. Drastic contraction in investment became commonplace in countries from 2008–2009. China’s robust growth in investment was a result of prompt active policy intervention in the face of the potential detrimental
effects of the crisis on the economy, and shrinking net exports. Hong Kong and Japan also suffered

42: 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 ofContribution of final demand i

49

2017 Asian Productivity Organization

Real GDP growth

final demand i ­ in period t. Thus, the real GDP growth may diverge from the official estimates or those presented in Table 3 inSection 3.1 (p. 22).
43: The exceptions are some of the oil-producing countries, which enjoyed a positive contribution from net exports higher than mostcountries. In the , and China, which experienced the fastest economic growth among the countries studied, averaging 10.0% per
year, 49.9% of which was driven by investment, compared with 33.5% by household consumption. This compares with averageannual growths of 2.5% in the US and 1.8% in the EU15. The contribution from household consumption was 75.2% and 58.3%,
whereas investment growth accounted for 22.1% and 15.4% of overall growth in the US and the EU15, respectively.

4 Expenditure

Box 3

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).
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 vast. 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 purposeof tax evasion, but the dominant portion in LDCs provides “the only opportunity for many poor people tosecure their basic needs for survival” (ILO, 2013, p.3). Encouraging labor movements from the informal sector to the formal sector is one of the most important developmental issues in many LDCs.

2017 Asian Productivity Organization

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.”
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 seems to be drawn fromofficial labor surveys and thus is likely
to exclude most of the employment
in the informal sector. Therefore, adifference between the number of
employment and the number of employees is loosely regarded as employers/self-employed workers in theformal sector and workers in the informal sector. Although statisticalproblems are evident, particularly
for the treatment of the employment data in the agricultural sector,
we can still clearly see that the number of employees is substantiallylower than the number of employment in LDCs.

% Share of employee to employment in 2015100

Singapore

US

90

Japan

China

80

Malaysia

70

Australia

Hong Kong

EU15
ROC
Korea

TurkeyPhilippines
Fiji

Iran
60
Sri Lanka
Bangaldesh
Mongolia
50
Pakistan Thailand
Indonesia
Bhutan40
Vietnam
Nepal
30
Cambodia
20

India

10
0

Thousands of US dollars (as of 2015)

0

10

20

30

40

50

60

70

80

90

Per capita GDP in 2015 (using 2011 PPP, reference year 2015)

Figure B3 Employee Share and GDP Level, 2015
Sources: Official national accounts in each country, including author adjustments;
APO Productivity Database 2017.

continued on next page >50

4.3 Expenditure-Side Growth Decomposition> continued from previous page

Figure B3 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 2015 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.

4

Second, raising minimum wage is a recently 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.

from the negative impact of net exports on growth. Investment rebounded strongly in 2009–2010with favorable policy levers, but moderated in the subsequent years when the effects of policy fadedout. Only China and Singapore sustained their robust investment growth.
In comparison, the impact of the Asian financial crisis was more contained. Figure 37 suggests that the
impact was contained within Asia, except for the handful of countries affected, it marked an exceptional time. In 1998, investment took a nosedive in Indonesia, Korea, Malaysia, Singapore, and Thailand. Household consumption also fell, albeit to a lesser extent. The crisis however, greatly boosted
these countries’ net exports, likely to have benefitted from the rapid devaluation of the Asian currencies, except the Japanese yen at the time of the crisis. This helped bolster the impacted economies
against the retrenchment in other components of final demand.

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 (1.5 percentage points). Meanwhile the 0.4% growthof government spending canceled out the 0.4% fall in household consumption. Similarly, in the ROC,
44: The figure for ASEAN are newly added in this edition of Databook.

51

2017 Asian Productivity Organization

Figure 38 shows how the contribution of economic growth by final demand varies across countries
and over time for the period 1970–2015.44 The immediate impact of the global financial crisis in 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 1.2% and 5.6% in 2008 and 2009, respectively, compared with
1.6% growth in 2007. The economic retrenchment in Japan was deeper than the –2.9% in the US and–4.9% in the EU15 in 2009. Besides Japan, other Asian countries either experienced a mild recession ora growth slowdown. Moreover, relative to their rapid growth the magnitude of the impact could still
be substantial. For example, the growth in the ROC slowed from 6.7% in 2007 to 0.8% in 2008 before
moving into the negative zone of –2.2% in 2009.

4 Expenditure%

2007–2008

2008–2009

%

15

15

10

10

5

5

%

2009–20102010–2011

%

%

15

10

10

12

8

8

6

6

4

4

2

2

0

0

0

−2

−2

−3

−4

−4

9

2011–2012

6
0

0

3
−5

−5

−10

−10

EU15

US

Net exports

Japan
India
China
Hong Kong
ROC
Korea
Singapore

EU15

Investment

US

Japan
India
China
Hong Kong
ROC
Korea
Singapore

Government consumption

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

GDP

Figure 36 Impacts of Global Financial Crisis and Recoveries, 2007–2012_Annual growth rate of GDP at constant market prices and contributions of final demands
Sources: Official national accounts in each country, including author adjustments.

2017 Asian Productivity Organization

%

25
20
15
10
5
0
−5
−10
−15
−20
−25
Australia
EU15
US
China
VietnamBahrain
MyanmarCambodiaOman
Sri Lanka
Saudi Arabia
India
Fiji
Bangladesh
TurkeyROC
Qatar
BhutanPakistan
Kuwait
UAE
Iran
Japan
Malaysia
BruneiSingapore
Korea
Philippines
Hong Kong
ThailandIndonesia

investment fell by 5.2% in 2009,
while household consumption andnet exports grew, albeit more slowly than previously. Hong Kong took
a hard hit in terms of net exports
in 2009, which fell by 3.0 percentage points. Household consumption growth slowed considerably in2009 to 0.1 percentage point before bouncing back to its normal
range of 3–5%.

It is difficult to understand the oilexporting economies fully without
Household consumption
Government consumption
analyzing the oil market in parallel.
Investment
Net exportsGDP
Its volatility can be observed clearly
from Figure 38, with huge peaks
Figure 37 Impacts of Asian Financial Crisis, 1997–1998_Annual growth rate of GDP at constant market prices and contriand valleys, particularly in the
butions of final demands
1970s. The oil booms of the 1970s
brought benefits, but the downSources: Official national accounts in each country, including author adjustments.
turn 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 andgas and related industries, which accounted for 42.7% of its GDP in 2015 (Figure 71 in Section 6.1,
p. 96) – roughly 80% of its export earnings, and 70% of government revenues in the 2000s.45 In contrast,

45: Data from the series of Annual Statistical Abstract, State of Qatar.

52

4.3 Expenditure-Side Growth Decomposition

Box 4Turning 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 2017.

4

Figure B4 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.
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 laborintensive 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 on 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 tomove beyond the upper middle-income plateau.

1970=1.0

70
60
50
40

China
ROC
Hong Kong
Korea
Singapore
Japan

30
20
10
0
1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

Figure B4 Price of Labor Relative to Capitalin China, Japan, and the Asian Tigers, 1970–
2015
Source: APO Productivity Database 2017.

46: Data from the Ministry of Finance, Kingdom of Bahrain.

53

2017 Asian Productivity Organization

Bahrain has diversified into a regional banking and financial center and benefited from the regionalboom in recent years. Petroleum production and processing accounted for 14% of its GDP in 2015
(Figure 71 in Section 6.1) – about 60% of export earnings, and 75% of government revenues in
the 2000s.46

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

%
20

1971 1976 1981 1986 1991 1996 2001 2006 2011

Fiji

−8

1971 1976 1981 1986 1991 1996 2001 2006 2011

Hong Kong

%
15

15

12

China

%
20

16

20

1971 1976 1981 1986 1991 1996 2001 2006 2011

10

10
6
0

5

5

0

0

−5

−6
−12

%
12

1971 1976 1981 1986 1991 1996 2001 2006 2011

India

9
6
3
0

−15

%
18

1971 1976 1981 1986 1991 1996 2001 2006 2011

Indonesia

−10

12

16

6

8

0

0

−6

−8

−12

−16

−18

−24

−6

−24

1971 1976 1981 1986 1991 1996 2001 2006 2011

Japan

8

%
18

−4

−18

−6

−24

1971 1976 1981 1986 1991 1996 2001 2006 2011

Malaysia

12
6
0
−6
−12
−18
1971 1976 1981 1986 1991 1996 2001 2006 2011

Household consumption

54

%
40

Kuwait

50

0
−50
−100
−150

1971 1976 1981 1986 1991 1996 2001 2006 2011

Mongolia

−200

20

20

10

10

0

0

−10

−10

−20
1971 1976 1981 1986 1991 1996 2001 2006 2011

Government consumption

1971 1976 1981 1986 1991 1996 2001 2006 2011

Myanmar

%
30

30

−20

1971 1976 1981 1986 1991 1996 2001 2006 2011

100

−12

−2

−32

150

−6

0

Iran

%
200

0

2

−24

Korea

6

4

%
18

1971 1976 1981 1986 1991 1996 2001 2006 20111261971 1976 1981 1986 1991 1996 2001 2006 2011

%
24

3

%
10

2017 Asian Productivity Organization

−5

−10

Investment−30

1971 1976 1981 1986 1991 1996 2001 2006 2011

Net exports

Real GDP

4.3 Expenditure-Side Growth Decomposition

%
15

Nepal

%
40

Oman

30

12

20

9

5

10

6

0

0

3

10

−5
−10

%
15

1971 1976 1981 1986 1991 1996 2001 2006 2011

Philippines

−10

0

−20

−3

−30

%
30

10

20

5

10

0

0

−5

−10

−10

−20

−15

%
18

−30

1971 1976 1981 1986 1991 1996 2001 2006 2011

Singapore%18

Pakistan%
15

1971 1976 1981 1986 1991 1996 2001 2006 2011

Qatar

−6

1971 1976 1981 1986 1991 1996 2001 2006 2011

20
0
−20

1971 1976 1981 1986 1991 1996 2001 2006 2011

Sri Lanka

−40

10

6

6

0

0

0

−10

−6

−6

−20

%
90

UAE

60

−12

%
30

1971 1976 1981 1986 1991 1996 2001 2006 2011

Thailand%20

121971 1976 1981 1986 1991 1996 2001 2006 2011

4

40

12

−12

Saudi Arabia%
60

1971 1976 1981 1986 1991 1996 2001 2006 2011

Vietnam−30

1971 1976 1981 1986 1991 1996 2001 2006 2011

US

%
9

20

6

10

3

0

0

−10

−3

30

0

%
6

1971 1976 1981 1986 1991 1996 2001 2006 2011

EU15

−20

%
8

1971 1976 1981 1986 1991 1996 2001 2006 2011

Australia

5

2
0

−10

−4
1971 1976 1981 1986 1991 1996 2001 2006 2011

APO20

−6

%
12

8

10

6

8

4

6

2

4

0

2

−2

0

−4

−2

−6

−5

−2

−4

%
10

0

0

−2

−6

Turkey10421971 1976 1981 1986 1991 1996 2001 2006 2011

%
15

6

4

−6

1971 1976 1981 1986 1991 1996 2001 2006 2011

Household consumption−41971 1976 1981 1986 1991 1996 2001 2006 2011

Asia30−151971 1976 1981 1986 1991 1996 2001 2006 2011

ASEAN

%
12
8
4
0
−4
−8
−12

1971 1976 1981 1986 1991 1996 2001 2006 2011

Government consumption

Investment−16

1971 1976 1981 1986 1991 1996 2001 2006 2011

Net exports

Real GDP

Figure 38 Final Demand Decomposition of Real GDP Growth, 1970–2015
Sources: Official national accounts in each country, including author adjustments.

55

2017 Asian Productivity Organization−30

4 Expenditure

Box 5

Revising Myanmar’s Growths

The economic potential of Myanmar is attracting significant attention. However, some questions havebeen raised about the reliability of Myanmar’s official system of national accounts (MMSNA). First, it is suspected that under the military regime, economic growth might have been significantly overstated since
the latter half of the 1990s, by The Economist Intelligence Unit (EIU) (2010) and the ADB (2017). The second
problem is that until the shift to the managed floating exchange rate system in April 2012, the official exchange rate had been used in the MMSNA in converting international trade into the national currency.
Under the official exchange rate, which set the value of the Myanmar kyat at a level far above the market
exchange rate, the amounts of exports and imports were significantly undervalued, resulting in a significant underestimation of GDP. The third problem is extensive illegal trade. In recent years, Global Witness(2015a and 2015b) and Dapice et al. (2010) pointed out that illegal exports of jade, whose prices began tosurge in the latter half of the 2000s, have not been properly reflected in the MMSNA. According to thoserecent research findings, the total transaction value of jade is estimated at 48% of Myanmar’s GDP in 2014.
In a bid to respond to those problems, Nomura and Shirane (2016) tried to develop new estimates ofGDP for Myanmar. Figure B5 presents the revised estimates in comparison with the official estimates
and the revised estimates in EIU and ADB, which do not include the jade trade. The revised estimatesshow that Myanmar’s real GDP growth turned negative twice; first in 2003–2004, reflecting the impactof the economic sanctions by the U.S. and Europe, and second in 2007–2008, reflecting the impacts of
the Cyclone Nargis that hit Myanmar in May 2008, the damage of which was estimated as more than 15%
of GDP, and the fallouts of the global financial crisis. In terms of the average growth rate for the period
1998–2010, the revised estimate of 4.9% represents a downward revision of 7.0 percentage points
compared with the MMSNA estimate of 11.9%. Although Myanmar’s productivity performance seemedsuperior to those of other Asian countries in the past Databook series, the downward revision to economic
growth in 1998–2010 brings Myanmar’s GDP growth and labor productivity growth closer to those of Thailand and Bangladesh.
Meanwhile, the impact of revaluing jade transactions on macroeconomic growth is observed from themid-2000s, for instance, turning negative growth estimated for 2004 before reflecting the reassessed values of jade transactions to positive growth. The impact of revaluation of jade is even more conspicuous in2008 and thereafter with jade production accounting for more than 10% of Myanmar’s GDP. Notably, therevaluation of jade results in a significant upward revision in 2009–2010, from 3.2% to 17.9%. On the other
hand, real GDP dropped 21.5% in 2012 as jade production decreased by half following the transfer of
power to the civilian government. Based on the revised estimates reflecting the revaluation of jade, Myanmar was comparable to India and Vietnam—both in real GDP growth and labor productivity growth—in
the period 1998–2010. However, Myanmar dropped off from the other two countries when it fell into negative growth in the period 2010–2015. Although subject to a certain degree of data uncertainty, this edition of the Databook follows the updated estimates based on the revisions in Nomura and Shirane (2016).

average annual growth rate (%)

annual growth rate (%)

12

20
15

10

10

8

5

6

0
−5
−10
−15

2017 Asian Productivity Organization

−20
1990

Official estimate in MMSNAADB estimate
EIU estimate
Databook (without jade trades)
Databook (with jade trades)

11.4

7.2
5.6

5.9
4.7 4.7

4
Official estimate in MMSNAADB estimate
EIU estimate
Databook (without jade trades)
Databook (with jade trades)
1995

2000

2005

5.0

4.7

5.7

6.4

3.0

2
0
2010

2015

−1.3

−2
1990−1998

1998−2010

Figure B5 Official and Revised Estimates of Real GDP Growths in Myanmar
Sources: Official national accounts in Myanmar; Nomura and Shirane (2016); APO Productivity Database 2017.

56

7.1 7.3

2010−2015

5.1 Per-Worker Labor Productivity

5 Productivity
Labor productivity can be measured in a number of ways, depending on the definitions of output andlabor input measures. In this chapter Section 5.1 presents the labor productivity measure in terms of
GDP per worker.47 As workers in high-performing Asian countries tend to work longer hours on average than those in the US (Figure 106 in Appendix 4), the worker-based labor productivity gaps in thisinstance cast the Asian countries in a particularly favorable light. Section 5.2 shifts the focus to alternative estimates of labor productivity measure, namely GDP per hour worked.

5

The sources of economic growth in each economy are further decomposed to factor inputs of laborand capital and total factor productivity (TFP), based on the growth accounting framework.48 In Sections 5.3 and 5.4, capital input is included as another key factor of production and the TFP estimates
are presented for the 20 Asian countries and the US, based on the estimates of capital services (seeAppendix 3).49 Section 5.5 presents the estimates of energy productivity, which is becoming an important policy target for pursuing sustainable growth of the Asian countries.

5.1 Per-Worker Labor Productivity
Figure 39 presents the cross-country comparisons of per-worker labor productivity levels in 2015,
measured as GDP per worker in US dollars. The countries naturally bundle into groups. On this measure, Singapore is the leading economy, more than 5% larger than the US level.50 Hong Kong and the
ROC follow at some distance. Japan took the fourth place, with productivity levels at 35% below the
US. Korea, Turkey, Iran, and Malaysia followed.51 It is worth noting that Iran has the lowest employment rate in Asia (Figure 21 in Section 3.3, p. 36). Thereafter, a number of countries from among
the Asia group followed with labor productivity levels at less than 25% of the US, pulling down the
average performance of the group to 21% for the APO20, 22% for the Asia30, and 19% for the ASEAN.
Bringing up the rear were China and India, with productivity levels that were 19% and 12% of the US
level, respectively.52

47: 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.
48: 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.
49: In this edition of Databook, the preliminary estimate of the growth accountings for the Lao PDR was newly developed. Anotherimportant improvement in this edition is that the estimates of labor input and its compensation were revised in some countries,
reflecting our work-in-progress estimates on number of workers, hours worked per worker, and hourly wages, cross-classified bygender, education attainment, age, and employment status, which has been developed for the past few years at KEO. In Bangladesh, Pakistan, and Vietnam, the COE (compensation of employees) data is not available in their national accounts in some years.
These were interpolated/extrapolated based on our estimates of COE.
50: 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 15.3% due to the use of the new 2011 benchmark PPP (See Box 1, p. 20).
51: 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 39 would be underestimated atleast by 1%, if the omitted workers were included.
52: Comparing productivity among cities may provide a better picture for understanding a productivity gap among countries, which
consists of a number of cities with different scales (See Box 8, p. 93).
53: The Lao PDR and Myanmar is newly added in this edition of Databook.

57

2017 Asian Productivity Organization

Table 9 presents the comparison of the per-worker labor productivity levels from 1970 to 2015, evaluated at the US price as of 2015.53 The figures for GCC countries and Brunei are uncharacteristically high,5 Productivity

especially in 1970. There are noticeable variations
within the country group. The atypically high figuresin the early period reflect the natural resource rents(the value of 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 in the group. Its dependence on oil is therefore considerably lower. Consequently, it has worked to diversify its economy overthe past decade (see Figure 86 in Section 6.2, p. 113).54

2017 Asian Productivity Organization

China and India are the two giant and fast-emerging
economies in Asia. China began with one-third ofIndia’s productivity levels in 1970. Four decades laterit shows signs of pulling ahead of India, as shown in
Figure 40. China’s relative performance against the
US moved up from 2% in 1970 to 6% in 2000 and
19% in 2015, compared with the corresponding figures of 5%, 6%, and 12% for India.55
Table 10 presents the growth comparison of perworker labor productivity. When labor productivity
growth is compared, the ranking of countries issubstantially reshuffled. In the 2000s there was asurge in labor productivity growth among lowincome countries. In the latest period 2010–2015, five out of the top seven countries with thefastest labor productivity growth were fromGroup-L4 and two from Group-L3 (as defined inTable 7 in Section 3.2, p. 33). Among them, China
has sustained rapid productivity growth in thepast two decades. Its growth accelerated to an
average of 10.3% per year in 2005–2010 from
7.1% per year in 1995–2000 and 8.6% per year in
2000–2005, and slowed down to 7.2% in 2010–2015. This compares with India at 7.0%, 4.2%,
4.7%, and 4.8% over the same periods. Labor
productivity growth among the Asian Tigers wassteady, ranging from 3.1% to 3.3% on average
per year in 2000–2005. This performance wassustained in the late 2000s, except in Singapore,
where the average annual productivity growthslowed significantly to 0.6%. In 2010–2015 laborproductivity growth in the Asian Tigers slowed

58

Singapore
US
Hong Kong
ROC
Australia
EU15
Japan
Korea
TurkeyIranMalaysia
East Asia
Sri Lanka
ASEAN6
Mongolia
ThailandAsia30
APO20
Asia24
Indonesia
China
Fiji
ASEAN
Philippines
Pakistan
BhutanSouth Asia
India
Lao PDR
Vietnam
CLMV
Bangladesh
Nepal
Cambodia
Myanmar127.8 (108)117.9 (100)106.5 (90)93.4 (79)92.7 (79)81.3 (69)77.2 (65)63.4 (54)62.9 (53)61.8 (52)55.7 (47)31.3 (27)29.5 (25)28.1 (24)27.7 (23)26.5 (22)25.8 (22)24.5 (21)24.4 (21)24.3 (21)22.3 (19)22.1 (19)21.9 (19)18.1 (15)16.1 (14)15.4 (13)14.8 (13)14.6 (12)11.1 (9)9.6 (8)8.4 (7)7.9 (7)5.8 (5)5.7 (5)
Thousands of US dollars (as of 2015),
US=100 in parentheses
5.7 (5)

0

20

40

60

80

100

120

140

Figure 39 Labor Productivity Level by
Per-Worker GDP, 2015

_GDP at constant basic prices per worker, using
2011 PPP, reference year 2015

Source: APO Productivity Database 2017.

Thousands of US dollars (as of 2015)

24
20

China
16
12
8
4

India

0
1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

Figure 40 Labor Productivity Trends of China
and India, 1970–2015_GDP at constant basic prices per worker, using
2011 PPP, reference year 2015

Source: APO Productivity Database 2017.

5.1 Per-Worker Labor Productivity

Table 9 Per-Worker Labor Productivity Levels, 1970, 1980, 1990, 2000, 2010, and 2015
_GDP at constant basic prices per worker, using 2011 PPP, reference year 2015
1970

1980

(%)

1990

(%)

(%)

2000

(%)

2010

(%)

2015

(%)

Iran
SingaporeJapan
Hong Kong
Fiji
Malaysia
ROC
Philippines
Korea
Sri Lanka
Mongolia
ThailandIndonesia
Pakistan
Bangladesh
BhutanIndia
Nepal
MyanmarVietnamLao PDR
China

37.9 100.0
32.1 84.8
29.7 78.3
23.4 61.7
14.9 39.3
12.7 33.6
11.1 29.4
9.6 25.2
7.8 20.7
7.3 19.1
6.8 18.0
5.7 15.2
5.6 14.9
5.2 13.6
3.1 8.3
3.1 8.3
2.9 7.7
2.6 6.8
2.5 6.6
2.2 5.8
2.0 5.3
1.0 2.6

Singapore
Japan
Iran
Hong Kong
ROC
Malaysia
Fiji
Korea
Philippines
Mongolia
Sri Lanka
Indonesia
Thailand
Pakistan
BhutanIndia
Bangladesh
MyanmarNepal
Lao PDR
VietnamChina

45.4 100.0
43.7 96.3
40.2 88.4
36.9 81.3
21.8 48.0
20.6 45.3
17.4 38.3
13.1 28.9
11.5 25.4
10.3 22.6
9.5 20.9
9.0 19.7
7.7 17.0
6.2 13.5
3.3 7.3
3.1 6.8
3.0 6.5
2.8 6.2
2.6 5.6
2.5 5.4
2.2 4.9
1.4 3.0

SingaporeJapan
Hong Kong
Iran
ROC
Malaysia
Korea
Fiji
Sri Lanka
Mongolia
ThailandIndonesia
Philippines
Pakistan
BhutanIndia
Nepal
Bangladesh
Lao PDR
MyanmarVietnamChina

67.7 100.0
62.4 92.1
58.8 86.9
41.3 60.9
38.0 56.1
26.8 39.6
25.4 37.6
16.9 25.0
12.5 18.4
12.1 17.8
11.8 17.4
11.2 16.5
10.6 15.7
9.6 14.2
6.9 10.2
4.4 6.5
3.7 5.5
3.5 5.1
3.4 5.0
2.8 4.1
2.8 4.1
2.5 3.7

Singapore
Hong Kong
Japan
ROC
Iran
Korea
Malaysia
Fiji
ThailandSri Lanka
Indonesia
Mongolia
Pakistan
Philippines
BhutanIndia
China
VietnamNepal
Lao PDR
Bangladesh
MyanmarCambodia99.8 100.0
72.7 72.9
68.8 69.0
63.8 63.9
46.1 46.2
42.7 42.8
39.3 39.4
17.8 17.9
16.6 16.6
16.3 16.4
14.3 14.3
12.8 12.8
12.7 12.7
12.0 12.1
9.4 9.4
6.3 6.3
6.0 6.0
4.8 4.8
4.8 4.8
4.6 4.6
4.6 4.6
3.9 3.9
3.0 3.0

Singapore
Hong Kong
ROC
Japan
Iran
Korea
Malaysia
ThailandSri Lanka
Indonesia
Fiji
Mongolia
China
Pakistan
Philippines
BhutanIndia
Lao PDR
Vietnam
MyanmarBangladesh
Nepal
Cambodia120.6 100.0
100.1 83.0
87.7 72.8
74.4 61.7
68.0 56.4
59.5 49.3
50.9 42.2
22.7 18.8
22.2 18.4
19.7 16.4
18.9 15.7
18.9 15.7
15.5 12.9
14.7 12.2
14.6 12.1
13.6 11.3
11.4 9.4
7.8 6.5
7.7 6.4
6.5 5.4
6.4 5.3
5.6 4.6
4.5 3.7

Singapore
Hong Kong
ROC
Japan
Korea
Iran
Malaysia
Sri Lanka
Mongolia
ThailandIndonesia
China
Fiji
Philippines
Pakistan
BhutanIndia
Lao PDR
VietnamBangladesh
Nepal
Cambodia
Myanmar127.8 100.0
106.5 83.4
93.4 73.1
77.2 60.4
63.4 49.6
61.8 48.3
55.7 43.6
29.5 23.1
27.7 21.7
26.5 20.7
24.3 19.0
22.3 17.4
22.1 17.3
18.1 14.2
16.1 12.6
15.4 12.1
14.6 11.4
11.1 8.6
9.6 7.5
7.9 6.2
5.8 4.6
5.7 4.5
5.7 4.4

Bahrain
KuwaitOman
Qatar
Saudi Arabia
UAE

135.0 356.2
683.6 1804.1
123.1 324.9
311.5 822.1
252.5 666.4
100.4 265.0

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

122.3 269.4
267.9 589.9
167.9 369.6
264.7 582.8
253.9 559.1
370.2 815.1
463.4 1020.3

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

90.8
109.6
179.5
170.5
140.1
230.0
222.6

134.0
161.8
265.1
251.8
206.9
339.6
328.6

108.0
211.5
155.3
228.5
156.5
198.5
199.8

108.3
212.0
155.7
229.0
156.9
198.9
200.3

62.1
130.8
91.7
157.2
124.0
122.2
146.5

Australia
Turkey62.9
29.4

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

80.9
156.5
84.2
164.5
148.4
171.4
167.3

58.5 128.7

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

74.9
157.7
110.5
189.5
149.5
147.3
176.6

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)
7.9 20.8
APO20
4.8 12.7
Asia245.4 14.2
Asia305.0 13.2
East Asia
3.3 8.7
South Asia
5.7 15.1
ASEAN
7.3 19.2
ASEAN6
2.5 6.7
CLMV
279.8 738.3
GCC(reference)
64.2 169.5
US40.9 108.0
EU15Australia

50.9 134.5

9.9 21.8
6.0 13.3
7.1 15.6
6.5 14.4
3.5 7.7
8.2 18.1
10.6 23.4
2.7 6.0
256.0 563.7
69.5 153.1
52.9 116.4

13.3 19.7
8.2 12.2
9.0 13.3
9.1 13.4
5.0 7.3
10.2 15.1
13.0 19.3
3.1 4.5
144.8 213.8
80.7 119.1
63.2 93.4
92.9
43.5

16.3 16.4
11.8 11.8
12.7 12.8
13.6 13.7
7.0 7.0
13.4 13.4
17.4 17.4
4.5 4.5
164.7 165.1
98.2
74.3
66.5
78.8
37.2

98.4
74.5
66.6
79.0
37.2

21.5 17.8
19.5 16.2
20.7 17.2
24.1 20.0
11.7 9.7
18.4 15.3
23.4 19.4
7.4 6.1
142.8 118.4
113.7
79.0
72.6
86.2
52.6

94.3
65.6
60.2
71.5
43.6

5

63.3
122.5
65.9
128.7
116.1
134.1
130.9

24.5 19.2
24.4 19.1
25.8 20.2
31.3 24.5
14.8 11.6
21.9 17.1
28.1 21.9
8.4 6.6
142.0 111.1
117.9
81.3
74.9
92.7
62.9

92.2
63.6
58.6
72.5
49.2

54: The GCC countries have also been experiencing high population growth, especially in the late 1970s and the early 1980s. In
2000–2015, this has somewhat stabilized at around 3.8% per year, except in the Qatar and the UAE where the population grew at
8.4% and 7.4%, 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.
55: 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.

59

2017 Asian Productivity Organization

Unit: Thousands of US dollars (as of 2015).
Source: APO Productivity Database 2017.

5 Productivity

Table 10 Per-Worker Labor Productivity Growth, 1990–1995, 1995–2000, 2000–2005,
2005–2010, and 2010–2015_Average annual growth rate of GDP at constant basic prices per worker, using 2011 PPP
1990–1995

1995–2000

KuwaitChina
Malaysia
ThailandIndonesia
Vietnam
Korea
ROC
Cambodia
BhutanSingapore
Pakistan
Hong Kong
Sri Lanka
Lao PDR
India
Bahrain
Myanmar
Nepal
Bangladesh
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

13.0
10.6
6.6
6.5
6.4
5.8
5.8
5.5
4.9
4.9
4.2
3.9
3.8
3.8
3.6
3.1
2.9
2.8
2.4
2.3
1.4
0.7
0.6
0.3
−0.1
−0.2
−0.2
−1.4
−3.7
−9.3

Australia
Turkey

2.4
1.3

2.4
4.2
4.0
4.5
3.2
5.0
5.6
3.1
0.6
1.6
1.9

China
Oman
Qatar
Vietnam
ROC
Korea
India
MyanmarSingapore
Cambodia
Bangladesh
Nepal
Philippines
Mongolia
Lao PDR
Sri Lanka
Pakistan
Saudi Arabia
BhutanJapan
Fiji
Malaysia
Iran
UAE
BahrainHong Kong
Thailand
KuwaitIndonesia
Brunei(regrouped)
APO20
Asia24
Asia30
East Asia
South Asia
ASEAN
ASEAN6
CLMV
GCC(reference)
US
EU15
EU28
Australia
Turkey2000–2005

7.1
6.4
5.6
5.4
4.8
4.6
4.2
3.8
3.5
3.3
3.3
2.8
2.6
2.5
2.3
1.6
1.6
1.6
1.3
1.3
1.2
1.1
0.8
0.7
0.6
0.4
0.3
0.2
−1.6
−2.0
1.6
3.0
2.9
3.6
3.8
0.5
0.1
4.6
1.9
2.4
1.3
1.8
2.1
3.4

China
Vietnam
MyanmarLao PDR
Cambodia
India
ThailandIndonesia
Malaysia
Hong Kong
Bangladesh
Korea
ROC
Singapore
Pakistan
Iran
Mongolia
Sri Lanka
BhutanJapan
Philippines
Oman
KuwaitFiji
Nepal
Saudi Arabia
Brunei
Qatar
UAE
Bahrain(regrouped)
APO20
Asia24
Asia30
East Asia
South Asia
ASEAN
ASEAN6
CLMV
GCC(reference)
US
EU15
EU28
Australia
Turkey2005–2010

8.6
5.6
5.0
4.8
4.7
4.7
3.8
3.7
3.6
3.3
3.2
3.2
3.2
3.2
3.1
2.9
2.7
2.5
1.6
1.4
1.2
1.1
0.8
0.7
0.7
0.0
−0.7
−0.8
−1.8
−4.8
2.5
4.4
4.3
4.9
4.3
3.4
3.3
5.3
−0.2
1.8
0.9
1.3
1.3
6.1

China
India
BhutanLao PDR
MyanmarMongolia
Iran
Vietnam
Sri Lanka
Bangladesh
Korea
Cambodia
ROC
Hong Kong
Indonesia
Philippines
Thailand
Nepal
Malaysia
Singapore
Fiji
Japan
Pakistan
Saudi Arabia
BruneiBahrain
Qatar
UAE
KuwaitOman(regrouped)
APO20
Asia24
Asia30
East Asia
South Asia
ASEAN
ASEAN6
CLMV
GCC(reference)
US
EU15
EU28
Australia
Turkey2010–2015

10.3
7.0
5.9
5.9
5.6
5.1
4.8
3.8
3.6
3.4
3.4
3.2
3.2
3.1
2.8
2.6
2.5
2.1
1.6
0.6
0.4
0.2
−0.1
−0.9
−1.8
−2.6
−2.9
−4.2
−6.7
−7.9
3.0
5.6
5.4
6.5
5.9
2.9
2.6
4.4
−2.6
1.1
0.3
0.4
0.5
0.8

Mongolia
China
Lao PDR
Sri Lanka
India
Cambodia
VietnamPhilippines
Indonesia
Bangladesh
Fiji
Thailand
UAE
Bhutan
Malaysia
PakistanBahrain
Korea
ROC
Hong Kong
Singapore
Nepal
Japan
KuwaitSaudi Arabia
Brunei
Iran
Qatar
MyanmarOman(regrouped)
APO20
Asia24
Asia30
East Asia
South Asia
ASEAN
ASEAN6
CLMV
GCC(reference)
US
EU15
EU28
Australia
Turkey1990–2015

7.7
7.2
6.9
5.7
5.0
4.9
4.3
4.3
4.2
4.1
3.1
3.1
3.0
2.5
1.8
1.8
1.6
1.3
1.3
1.2
1.2
1.0
0.7
−0.1
−0.2
−1.1
−1.9
−2.8
−2.9
−5.4
2.6
4.5
4.4
5.2
4.6
3.5
3.6
2.7
−0.1
0.7
0.6
0.6
1.5
3.6

China
VietnamIndia
Lao PDR
Cambodia
Korea
ROC
Sri Lanka
Mongolia
Bangladesh
BhutanThailandIndonesia
Malaysia
MyanmarSingapore
Hong Kong
Philippines
Pakistan
Nepal
Iran
KuwaitFiji
Japan
Saudi Arabia
Qatar
BahrainBruneiUAE
Oman(regrouped)
APO20
Asia24
Asia30
East Asia
South Asia
ASEAN
ASEAN6
CLMV
GCC(reference)
US
EU15
EU28
Australia
Turkey

8.8
5.0
4.8
4.7
4.1
3.7
3.6
3.4
3.3
3.3
3.2
3.2
3.1
2.9
2.9
2.5
2.4
2.1
2.1
1.8
1.6
1.4
1.1
0.9
0.2
−0.1
−0.5
−1.1
−1.2
−3.0
2.4
4.3
4.2
5.0
4.4
3.0
3.1
4.0
−0.1
1.5
1.0
1.1
1.5
3.0

2017 Asian Productivity Organization

Unit: Percentage.
Source: APO Productivity Database 2017.
Note: The starting period for Cambodia is 1993.

down to 1.2–1.3% per year. The exception is Japan, where they succeeded to improve the laborproductivity growth to 0.7% in 2010–2015, from 0.2% in 2005–2010.
Figure 41 shows labor productivity levels relative to the US (=100) for Asian countries. The samegrouping, as in Table 7 in Section 3.2 (p. 33), 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

60

5.1 Per-Worker Labor Productivity(Group-C1) are also faster catching up in labor productivity (Figure 41.1). Similarly, countries with declining relative per capita GDP (Group-C4) also show signs of deterioration of or little change against
the US in terms of labor productivity (Figure 41.4).
Among the countries that are catching up with the US in per capita GDP (Group-C1 and Group-C2),
the Asian Tigers have made tremendous headway in improving their relative labor productivity overthe past four decades. Singapore passed the US in the middle of the 1990s and Hong Kong closed thegap from 64% in 1970 to 10% in 2015 (Figures 41.1 and 41.2). Similarly, the ROC and Korea reduced
a gap of 80–90% initially to 21% and 46% by 2015, respectively (Figure 41.1). Malaysia is making
steady progress, raising its relative productivity level from 20% of the US in 1970 to 47% in 2015 (Figure41.2). The rest of the countries in these two groups all display an initial relative labor productivity
level of below 15%, but have shown signs of a strong and promising start in their catch-up process inthe past decade.

5Countries 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 labor 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 78% of the US in 1991. Since

US=100100

US=100

Cambodia

China

ROC

Korea

120
100

80

80

60

60

40

40

20

20

0
1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

India

Figure 41.2: Group-C2 Countries

US=100100

Hong Kong
Lao PDR
Mongolia
Sri Lanka

01970 1975 1980 1985 1990 1995 2000 2005 2010 2015

Figure 41.1: Group-C1 Countries
120

BhutanIndonesia
Malaysia
Singapore
Thailand
Vietnam

US=100

Bangladesh

Japan

Myanmar

Pakistan

Philippines

Turkey

Nepal

120
100

Fiji

Iran

Australia

EU15

80

80

60

60

40

40

20

20

0
1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

01970 1975 1980 1985 1990 1995 2000 2005 2010 2015

Figure 41.3: Group-C3 Countries

Figure 41.4: Group-C4 Countries

Figure 41 Labor Productivity Level Relative to the US, 1970–2015_Indices of GDP at constant basic prices per worker, using 2011 PPP
Source: APO Productivity Database 2017.
Note: Countries are grouped based on Table 7 in Section 3.2 (p. 33).

61

2017 Asian Productivity Organization120

5 Productivity

then, the gap has widened again to over 30% in 2015. Similarly the EU15, a reference economy withhigh income, has seen its productivity gap widened against the US since 1995, from 20% to 31% in
2015; whereas the low-income countries have managed little catch-up (Figure 41.3) or a declining
relative productivity level (Figure 41.4). Iran (a Group-L2 country) experienced a drastic decline in its
relative labor productivity from its former peak of 91% in 1976 to 44% in 1988, before recovering to
61% in 2011. As a result of the strengthened sanctions against Iran, however, labor productivity todate declined drastically.

5.2 Per-Hour Labor Productivity

2017 Asian Productivity Organization

MyanmarCambodia
Nepal
Bangladesh
VietnamLao PDR
Bhutan
India
Pakistan
Philippines
China
Fiji
Indonesia
ThailandMongolia
Sri Lanka
Malaysia
Iran
Korea
TurkeyJapan
ROC
Hong Kong
EU15
Singapore
Australia

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 the 23 Asian countries, although the quality of the estimates may vary considerably across countries.56 Figure 42 shows how the productivity gap against the US in 2015 varies depending on whichmeasure of labor productivity is used.57 The productivity gap with the US widens for all Asian countries
when the differences in working hours are taken into account. However, for 17 of these countries, theadjustments are within 1–5 percentage points, and hence are
not deemed as statistically significant. In contrast, the choiceof labor productivity measure%20
makes a significant difference for8
the previously high-performing
0−10
countries in their relative perfor−21
−22
−21
−17
mance. On a per-hour GDP basis,
−20
−16
−26
−35
the labor productivity gap with
−31
−31
−34
−40
the US widens by 9–25 percent−48 −46 −47−53−50
age points for the Asian Tigers.
−55−60−62 −60
Per-worker based
Europeans generally work fewer−75
labor productivity−78 −77−81 −81 −79
hours. This is reflected in compar−78 −77−80−85−87 −88 −86−82 −82 −82−84−92 −91−87
isons of hourly labor productiv−95 −95 −95 −93−90 −90 −88−93 −92−95−95−100 −96 −96
ity showing the EU15 in a more
Per-hour based laborproductivity
favorable light against the US,
albeit only marginally.
Figure 42 Labor Productivity Gap by Per-Worker and PerHour GDP Relative to the US, 2015
Based on GDP at constant basic_GDP at constant basic prices per worker and hour, using 2011 PPP
prices per hour worked, the US
Source: APO Productivity Database 2017.
labor productivity has been able
Note: Light green is used for the countries in which per-hour labor productivity is lower
to sustain a sizeable lead overthan per-worker labor productivity.
56: 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 required 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 caseof China and Thailand. In reading the results, it is therefore important to bear in mind the data limitations. This edition of the Databook uses new and improved time-series estimates of average hours worked, considering the changes in the compositions ofworkforces. See Appendix 4 for an explanation of the estimation procedure of total hours worked.
57: The labor productivity gap for country x is country x’s labor productivity divided by the US’s labor productivity in Figure 42.

625.2 Per-Hour Labor Productivity

Table 11 Per-Hour Labor Productivity Levels, 1970, 1980, 1990, 2000, 2010, and 2015
_GDP at constant basic prices per hour, using 2011 PPP, reference year 2015
1970

1980

(%)

1990

(%)

(%)

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

15.1 100.0
14.7 97.5
13.1 87.1
9.3 61.4
8.0 53.0
5.6 37.4
4.8 31.7
4.5 30.0
3.8 25.4
3.6 23.9
3.0 20.2
2.8 18.8
2.3 15.5
2.3 15.4
1.5 10.1
1.5 9.9
1.4 9.4
1.1 7.3
1.0 6.8
0.9 6.1
0.8 5.6
0.5 3.4

Singapore
Japan
Iran
Hong Kong
ROC
Fiji
Malaysia
Mongolia
Philippines
Korea
Sri Lanka
Indonesia
PakistanThailand
India
Nepal
Bangladesh
MyanmarBhutanLao PDR
VietnamChina

21.4 100.0
20.3 95.2
15.9 74.5
14.9 69.7
9.4 44.1
9.2 43.2
9.0 42.3
5.4 25.5
5.4 25.3
5.1 23.9
4.8 22.7
4.5 21.0
2.9 13.4
2.9 13.4
1.5 7.0
1.5 6.9
1.4 6.6
1.2 5.4
1.2 5.4
1.1 5.4
0.9 4.0
0.7 3.3

SingaporeJapan
Hong Kong
ROC
Iran
Malaysia
Korea
Fiji
Mongolia
Sri Lanka
Indonesia
Philippines
Thailand
Pakistan
BhutanIndia
Nepal
Bangladesh
Lao PDR
China
MyanmarVietnam

29.9 100.0
29.8 99.8
25.8 86.4
17.0 57.0
16.2 54.3
11.7 39.3
9.8 32.99.4 31.6
6.4 21.5
6.3 21.0
5.9 19.6
4.8 16.2
4.6 15.3
4.4 14.9
2.4 8.1
2.1 7.0
2.1 6.9
1.6 5.5
1.6 5.3
1.3 4.2
1.1 3.8
1.0 3.5(reference)US

31.9 211.4(reference)US36.6 171.3(reference)US

43.3 144.9

Australia

31.9 149.5

Australia
Turkey35.4 118.4
15.8 52.8

2000
SingaporeJapan
Hong Kong
ROC
Iran
Korea
MalaysiaFiji
Sri Lanka
Indonesia
Mongolia
Thailand
Pakistan
Philippines
Bhutan
India
China
Nepal
Lao PDR
Bangladesh
Vietnam
MyanmarCambodia(reference)
US
EU15
Australia
Turkey2010(%)

41.6 100.0
36.4 87.6
31.2 75.0
29.3 70.4
18.3 44.1
17.5 42.1
17.1 41.0
9.6 23.1
8.2 19.6
7.5 17.9
6.8 16.3
6.6 15.9
5.8 13.9
5.8 13.9
3.3 7.9
3.0 7.3
2.9 6.9
2.7 6.4
2.1 5.1
2.1 5.0
2.0 4.8
1.6 3.8
1.3 3.2
53.5 128.6
45.2 108.6
44.5 107.1
19.2 46.1

Singapore
Hong Kong
ROC
Japan
Iran
Korea
Malaysia
Sri Lanka
Fiji
Mongolia
ThailandIndonesia
China
Pakistan
Philippines
India
BhutanLao PDR
Vietnam
Nepal
Bangladesh
MyanmarCambodia(reference)
US
EU15
Australia
Turkey(%)

51.9 100.0
43.5 83.8
42.5 82.0
41.3 79.7
28.6 55.0
27.2 52.5
22.2 42.8
11.9 22.9
10.4 20.0
10.0 19.3
9.7 18.6
9.6 18.5
7.1 13.8
6.9 13.3
6.9 13.3
5.4 10.4
5.0 9.6
3.6 7.0
3.3 6.5
3.0 5.9
2.8 5.5
2.7 5.2
1.9 3.6
64.0 123.3
49.6 95.7
51.0 98.3
28.0 54.0

2015
Singapore
Hong Kong
ROC
Japan
Korea
Iran
Malaysia
Sri Lanka
Mongolia
ThailandIndonesia
Fiji
China
Philippines
PakistanIndia
BhutanLao PDR
VietnamBangladesh
Nepal
Cambodia
Myanmar(reference)
US
EU15
Australia
Turkey(%)

54.9 100.0
48.7 88.9
45.4 82.8
43.6 79.5
29.5 53.8
26.2 47.7
24.9 45.4
15.3 27.9
14.7 26.7
12.1 22.1
12.0 21.9
11.8 21.5
10.2 18.7
8.4 15.4
7.7 14.0
6.9 12.6
6.3 11.5
5.1 9.4
4.4 8.0
3.5 6.4
3.2 5.8
2.3 4.3
2.3 4.2

5

65.9 120.1
51.7 94.2
55.2 100.6
32.9 60.0

Unit: US dollar (as of 2015).
Source: APO Productivity Database 2017.

The levels of labor productivity for the top fiveeconomies – Japan and the four Asian Tigers –maintained their relative positions for almost

Thousands of US dollars (as of 2015)

60
50
40

ROC
Hong Kong
Japan
Korea
Singapore

30
20
10
0
1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

Figure 43 Labor Productivity Trends in Japanand the Asian Tigers, 1970–2015_GDP at constant basic prices per hour, using 2011
PPP, reference year 2015

Unit: Thousands of US dollars (as of 2015).
Source: APO Productivity Database 2017.

58: 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. The estimates for Bhutan, Fiji, andMongolia are newly added in this edition of the Databook.

63

2017 Asian Productivity Organizationeven the Asian high performers (Table 11).58 In
1970, the US productivity level was nearly 2.5
times that of Japan. This gap was reduced toaround 31% in 1990. Since 1990, Japan’s pace inclosing the gap has slowed. By 2015, a considerable gap of 34% remained. The gap between the
US and the Asian leader, Singapore, has been
narrowing with a very slow pace and the productivity gap of 17% still remains in 2015. This is
in contrast with the picture painted by the perworker productivity measure, in which the Asianleaders have overcome, or almost closed, thegap with the US (Table 9 and Figure 41).

5 Productivity

four decades. The progress of labor productivity in these countries during 1970–2015 is shown in
Figure 43. Within four decades, GDP per hour has more than tripled for Japan and Singapore. Hong
Kong and the ROC have improved by five and ten times in this period and have overcome Japan in2007 and 2010, respectively. They were ahead of Korea, despite the effort in catching up with Japanby 2.4% per year on average over the past four decades (1970–2015). If they were to maintain this effort at the same pace, it would take Korea 16 years to finally draw level with Japan.
Over the entire observation period (1970–2015), hourly labor productivity growth ranged from 0.9%
(Fiji) to 6.7% (China) on average per year, compared with the US at 1.6%, as shown in the left chart of
Figure 44. Among the 23 Asian countries compared, only Fiji, Iran, and the Philippines grew slower
than the US. Between the two sub-periods (1970–1990 and 1990–2015), there is a notable deceleration in the hourly productivity growth for 9 of 22 Asian countries (excluding Cambodia). For example,
2.6 percentage points were shaved off productivity growth in the earlier period in both Hong Kongand Japan. A total of 13 Asian countries managed to accelerate their productivity improvement after
1990. Among these, the performances in China and Vietnam are outstanding, with a productivity

1970−2015%10

1970−1990

1990−2015
8.4

8
6.7

6.4

6
3.93.83.73.73.7

4

3.63.5

3.33.23.13.1

2.92.75.8
4.5

4.1 4.0

4.8 4.7
3.7 3.6 3.6

2.72.7
1.91.81.7

2

1.61.5

1.41.2

3.4 3.2

2.9 2.7

2.5

2.0

1.6 1.5

0.9

1.1 1.0

0.8

4.4

3.9 3.9 3.8

3.6 3.6

3.3 3.1

3.0 2.9 2.9 2.9

2.5 2.4

2.2 2.2

1.9 1.8 1.8 1.7

1.5
0.9

0.50.4 0.4 0.3 0.3

Fiji
Japan
US
Nepal
Australia
Iran
Pakistan
Philippines
Singapore
Hong Kong
Indonesia
MyanmarTurkeyMalaysia
Bangladesh
Mongolia
Sri Lanka
Cambodia
BhutanThailandROC
Korea
Lao PDR
India
Vietnam
China

TurkeyPhilippines
Iran
Bangladesh
MyanmarFiji
Australia
Vietnam
US
Nepal
India
Sri Lanka
Lao PDR
Mongolia
PakistanThailandSingapore
Indonesia
Malaysia
BhutanJapan
China
Hong Kong
Korea
ROC

Fiji
Iran
Philippines
Australia
US
Nepal
MyanmarBangladesh
PakistanJapan
TurkeySingapore
Sri Lanka
Mongolia
Indonesia
Malaysia
India
Cambodia
Thailand
VietnamHong Kong
Lao PDR
Bhutan
ROC
Korea
China

0

5.9
5.1

5.05.0Figure 44 Labor Productivity Growth, 1970–2015, 1970–1990, and 1990–2015_Average annual growth rate of GDP at constant basic prices per hour

Source: APO Productivity Database 2017.
Note: The starting periods for Australia, Cambodia, and Turkey are 1978, 1993, and 1988, respectively.

1970−2015

%

5

4
3
2
1

1970−1990

1990−2015

4.5
3.93.8

3.7 3.5
3.1

2.8

2.62.62.5

2.22.22.2

2.12.12.01.91.9

1.81.81.81.71.7

3.4 3.3

3.8
3.0 2.9 2.93.4
2.8 2.7 2.6

1.71.62.6 2.6 2.6

2.9
2.4

2.2 2.2 2.1 2.1

2.0

2.42.42.32.3
1.7 1.7

1.2
0.5

0

1.61.51.31.31.2

1.1

0.8 0.7
0.7

−0.6

Japan
Thailand
Korea
US
ROC
China
Hong Kong
Fiji
Vietnam
India
MyanmarMongolia
Sri Lanka
IranIndonesia
Philippines
Lao PDR
Nepal
Bangladesh
Pakistan
Bhutan
Malaysia
Singapore
CambodiaJapan
Sri LankaUS
Lao PDR
Bangladesh
Nepal
Myanmar
India
Hong Kong
ROC
Iran
PakistanFiji
Mongolia
BhutanVietnam
China
Korea
Malaysia
Indonesia
Philippines
ThailandSingapore2017 Asian Productivity Organization

1.71.6

0.3

Figure 45 Labor Input Growth, 1970–2015, 1970–1990, and 1990–2015_Average annual growth rate of total hours worked
Source: APO Productivity Database 2017.
Note: The starting period for Cambodia is 1993.

64

1.91.9

−0.1

Japan
US
ROC
Sri Lanka
Korea
Hong Kong
ThailandIndia
MyanmarFiji
China
VietnamMongolia
Lao PDR
Iran
Nepal
Bangladesh
PakistanIndonesia
BhutanPhilippines
Malaysia
Cambodia
Singapore−1

2.12.1

5.2 Per-Hour Labor Productivity

Table 12 Per-Hour Labor Productivity Growth, 1990–1995, 1995–2000, 2000–2005, 2005–2010,
and 2010-2015_Average annual growth rate of GDP at constant basic prices per hour, using 2011 PPP
1990–1995

1995–2000

China
Malaysia
Indonesia
Thailand
Korea
Vietnam
ROC
Cambodia
BhutanSri Lanka
Hong Kong
Lao PDR
Singapore
Pakistan
India
MyanmarBangladesh
Nepal
Japan
Iran
PhilippinesFiji
Mongolia(reference)
US

10.3
6.4
6.3
6.2
6.2
5.7
5.6
5.0
4.9
4.2
4.0
3.6
3.6
3.4
3.2
2.8
2.2
2.2
1.9
1.6
0.5
−0.7
−1.5

Australia
Turkey2000–2005

7.4
6.3
5.4
5.2
4.1
3.8
3.1
3.0
2.9
2.6
2.5
2.4
2.3
2.1
1.9
1.2
1.2
1.1
1.1
1.0
0.9
−0.2
−1.4

1.7

Vietnam
China
Korea
ROC
India
MyanmarSingapore
Philippines
Nepal
Mongolia
Bangladesh
Cambodia
Lao PDRJapan
Pakistan
BhutanThailandSri Lanka
Fiji
Malaysia
IranHong Kong
Indonesia(reference)
US

2.2
1.2

Australia
Turkey

2.4
2.7

2.5

China
VietnamThailandMyanmarLao PDR
India
Korea
Cambodia
Bangladesh
ROC
Sri Lanka
Singapore
PakistanIndonesia
BhutanHong Kong
Malaysia
Iran
Mongolia
Japan
Philippines
Nepal
Fiji
(reference)
US
EU15
Australia
Turkey2005–2010
7.7
7.3
5.2
5.0
4.8
4.6
4.2
4.1
3.7
3.7
3.7
3.7
3.3
3.3
3.1
3.1
3.1
3.0
2.8
1.8
1.1
0.8
0.5
2.2
1.2
1.8
6.1

China
India
Lao PDR
Iran
MyanmarBhutanMongolia
Korea
Sri Lanka
ROC
Hong Kong
Cambodia
VietnamBangladesh
Philippines
Thailand
Malaysia
Nepal
Indonesia
Fiji
SingaporeJapan
Pakistan(reference)
US
EU15
Australia
Turkey2010–2015
10.5
6.9
5.9
5.9
5.7
5.2
4.9
4.6
3.8
3.8
3.5
3.1
2.8
2.6
2.4
2.4
2.2
2.0
1.8
1.0
0.8
0.8
0.2
1.4
0.7
0.9
1.4

Mongolia
China
Lao PDR
Vietnam
Sri Lanka
India
BhutanThailandIndonesia
Cambodia
Bangladesh
PhilippinesFiji
Hong Kong
Malaysia
Pakistan
Korea
ROC
Singapore
Japan
Nepal
Iran
Myanmar(reference)
US
EU15
Australia
Turkey1990–2015

7.7
7.2
6.9
5.5
5.1
5.0
4.6
4.5
4.5
4.4
4.3
4.1
2.5
2.3
2.3
2.1
1.6
1.3
1.1
1.1
1.0
−1.7
−2.9
0.6
0.8
1.6
3.2

China
VietnamIndia
Lao PDR
Korea
ROC
Thailand
BhutanCambodia
Sri Lanka
Mongolia
Bangladesh
Malaysia
MyanmarIndonesia
Hong Kong
Singapore
Philippines
Pakistan
Iran
Nepal
Japan
Fiji
(reference)
US
EU15
Australia
Turkey

8.4
5.8
4.8
4.7
4.4
3.9
3.9
3.8
3.6
3.6
3.3
3.1
3.0
2.9
2.9
2.5
2.4
2.2
2.2
1.9
1.8
1.5
0.9

5

1.7
0.9
1.8
2.9

Unit: Percentage.
Source: APO Productivity Database 2017.
Note: The annual average growth rate for Cambodia during 1990–1995 replicates their annual average growth rates of
1993–1995 because of the lack of hours-worked data.

acceleration from 4.5% to 8.4% in China and from 1.1% to 5.8% in Vietnam, respectively, between thetwo sub-periods.

59: By definition, positive labor productivity growth occurs when output grows faster than labor input. Figures 44 and 45 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.

65

2017 Asian Productivity Organization

The deceleration of labor productivity growth between the two sub-periods reflects weaknesses in
output growth in most countries. Figure 45 shows all countries except three South Asian countries(Bangladesh, Nepal, and Sri Lanka) and the Lao PDR experienced a slowdown in hours-worked growth
between the sub-periods, which should have worked to boost labor productivity growth, all otherthings being equal.59 For labor productivity growth to slow implies that output growth must havebeen 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 in Figure 44. Labor input growth slowed to 1.1% per year on average in the latter period,
from 2.9% in the previous period. Japan was the only economy in Asia to experience an actual fall inlabor input in the period from 1990 to 2015. This served to compensate for a sluggish output growthduring said period; and sustain a positive labor productivity growth of 1.5% per year on average.

5 Productivity

Labor productivity gap in 2015 relative to Japan
Table 12 more closely exam(Japan's level in 2015=1.0)
1.3
ines the sub-period from 1990–
Singapore
2015, providing the growth
1.2Japan (1885−2015)
rates of per-hour-based labor
Hong Kong
1.1
productivity since 1990. The
ROC
growth patterns of individu1.0
al countries generally follow
0.9
their counterparts closely in
per-worker productivity growth,
0.8
as illustrated in Table 10. In
0.7
Korea
some countries the two measures diverge greatly and are
Iran
0.6
Malaysia
not at all consistent through0.5
the periods compared.60 This
contrast was particularly stark
0.4
Sri Lanka
in the first half of the 1990s,
Mongolia
0.3
Thailand
when Japan’s hourly producFiji
China
Indonesia
Philippines
tivity growth was 1.9% com0.2
India
Pakistanpared with 0.7% in per-worker
Lao PDR
Bangladesh
Bhutan0.1
Cambodia
productivity growth. HowevVietnam
Myanmar Nepal
er, the divergence narrowed
0.01880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010
to 0.3 percentage points
in the period 2010–2015. KoFigure 46 Labor Productivity Trends of Japan in 1885–2015 andrea is another country in which
Levels of Asian Countries in 2015
hourly productivity growth was_GDP at constant basic prices per hour, using 2011 PPP
consistently higher than its
Sources: For historical data of Japan, the sources of GDP are Ohkawa, Takamatsu, and Yamaper-worker counterpart. Almoto (1974) during 1885–1954 and the JSNA by ESRI, Cabinet Office of Japan, during 1955–
though the divergence wid2015 (including author adjustments). Hours worked data is based on KEO Database, Keio
University, during 1955–2015. During 1885–1954, the average hours worked per person areened to 1.2 percentage points
assumed to be constant. For the labor productivity level of Asian countries in 2015, it isin 2005–2010, it narrowed
based on the APO Productivity Database 2017.
to 0.3 percentage points in2010–2015. Hours worked in
1970
1980
1990
2000
2010
2020
the ROC have also grown at a slower rate than
1970
1991
Japan (21)number of workers.
1978
1994
Hong Kong (16)2017 Asian Productivity Organization

1987

2002

ROC (15)

One can identify where countries are today in
19952015 Korea (20)terms of their hourly productivity performanceagainst a backdrop of Japan’s historical experiFigure 47 Time Durations Taken to Improveence. Figure 46 traces the long-term path of JaLabor Productivity by Japan and the Asian
pan’s per-hour labor productivity for the period
Tigers1885–2015 along the green line, expressed as
Source: See Figure 46.
relative to Japan’s 2015 level (set equal to 1.0).61 A
structural break is observed during World War II
when output collapsed. Countries’ relative hourly
productivity levels against Japan in 2015 are then mapped against Japan’s growth experience (as circles). By so doing, a corresponding year can be located when Japan’s hourly productivity level was the
60: 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.
61: 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.

66

5.3 Total Factor Productivity

closest to the country in question. The two countries with the lowest hourly productivity in 2015
(Cambodia and Myanmar) see levels corresponding to Japan in the 1910s. Even if they manage Japan’s long-term productivity growth of 2.7% on average per year, this means it will take them over a
century to catch up with the Asian leader’s current position (Singapore, Hong Kong, the ROC, and Japan). Most Asian countries are clustered around Japan’s level in the 1960s and early 1970s. Amongthem, China has been leading the catch-up effort, with productivity growing over five times faster thanJapan’s long-term average (Table 12), followed by Vietnam, India, and the Lao PDR.

5

In pole position are the Asian Tigers, of which Singapore, Hong Kong, and the ROC have already surpassed Japan. Figure 47 compares the time spans 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 46). What Japan had
achieved in the 21 years from 1970 to 1991, Hong Kong, the ROC, and Korea managed to achieve in
16, 15, and 20 years, respectively (Figure 47). Although the speed of catch-up for latecomers is increasing somewhat, most Asian countries will take a long time to catch up with the leaders, currently clustered near Japan’s 1970 levels in Figure 46.

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 thechosen 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 the case. In
populous Asian economies, which are relatively plentiful in low-skilled labor, production lines may bedeliberately 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) labor-intensive and withlittle capital, manifested in low labor productivity and high capital productivity. This is why economists analyze TFP, which is GDP per unit of combined inputs, to arrive at an overall efficiency of acountry’s production.62

62: 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 the Databook, the Törnqvist index is used for aggregating labor and 11 types of capital inputs (the classification is provided in Table 21 in Appendix 2).
63: 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.
64: 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).

672017 Asian Productivity Organization

Measuring capital input is a key factor for determined TFP. It is defined by capital services – the flow ofservices from productive capital stock, as recommended in the 2008 SNA.63 The required basis for estimating capital services is the appropriate measure of (productive) capital stock. The SNA recommends constructing the national balance sheet accounts for official national accounts. However, thisis not a common practice in the national accounts of many Asian countries.64 Even where estimates ofnet capital stocks are available for the entire economy, assumptions and methodologies can differ
considerably among countries. In response to this challenge, harmonized estimates for productive
capital stocks and capital services have been constructed and compiled within the APO Productivity
Database, built on the same methodology and assumptions.65 In this methodology, changes in thequality of capital are incorporated into the measurement of capital services in two ways: changes in
the composition are captured by explicitly differentiating assets into 11 types and; an appropriate and5 Productivity

1970−2015

1990−2015

1970−1990

%

3.8

4.0
3.0
2.0
1.0
0.0
−1.0

3.0
2.0 1.9

2.2
1.71.6 1.61.6

1.5 1.31.31.3

0.90.90.9 0.80.8 0.8

0.6 0.40.4

2.0 1.9

2.42.4
1.7 1.6
1.21.11.1 1.01.0

0.30.3 0.2

0.1
0.0−0.3−0.3

2.2 2.12.02.0

2.0
1.41.40.8 0.70.7

0.5 0.50.50.50.5

0.1 0.0
0.0−0.1

1.21.2 1.1

1.01.0 0.80.8

0.70.7 0.6 0.5

0.40.4 0.4

0.3

0.0
−0.1

−0.7
−0.9 −1.0

−2.0
−2.3

−3.0
Indonesia
Bangladesh
Nepal
ASEAN6
Lao PDR
Malaysia
Japan
Hong Kong
Fiji
VietnamSingapore
US
Philippines
Thailand
Korea
Iran
APO20
Pakistan
ROC
Asia24
CambodiaSouth Asia
Sri Lanka
Mongolia
East Asia
India
China

Iran
Philippines
Nepal
Mongolia
FijiSingapore
Bangladesh
Malaysia
Korea
ASEAN6
APO20
Asia24
Indonesia
East AsiaUS
Lao PDR
Sri Lanka
South Asia
Japan
India
Vietnam
PakistanThailandChina
ROC
Hong Kong

Iran
Nepal
Bangladesh
Philippines
Indonesia
MalaysiaFiji
ASEAN6Singapore
Lao PDR
US
Japan
Korea
APO20
Mongolia
VietnamThailandHong Kong
Asia24
Pakistan
Sri Lanka
South Asia
East Asia
ROC
India
Cambodia
China

Figure 48 TFP Growth, 1970–2015, 1970–1990, and 1990–2015
Source: APO Productivity Database 2017.
Note: The starting period for Cambodia is 1993. See footnote 69 for the country-exception in the country groups.

harmonized deflator is used for IT capital to reflect the rapid quality change embodied in IT-related
assets (see Appendix 2).
The APO Productivity Database 2017 estimates capital services and TFP for the 21 Asian economies forwhich long-time investment data by type of asset are available or estimated.66 Their economic growthis decomposed into sources from factor inputs and TFP, based on the methodology developed byJorgenson and Griliches (1967). The Databook defines output as GDP at constant basic prices, andfactors inputs as labor, IT capital, and non-IT capital.67 Labor input is measured by total hours worked,
without adjustments for changes in labor quality.68

2017 Asian Productivity Organization

Cross-country comparisons of TFP growth for the 21 Asian countries and the US are shown in Figure
48 for the period 1970–2015, and the two sub-periods 1970–1990 and 1990–2015. In addition, theregional growth accounts are developed for some country groups: the Asia24, the APO20, East Asia,
South Asia, and the ASEAN6.69 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 high65: 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, 2016) and ourestimates for the period of 1970–2015 are high; they are 89.2 % and 99.8% for the growth rates of net and productive capital stock,
respectively. In the Databook, 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 21 Asian countries compared.
66: In measuring TFP, income generated from domestic production should be separated into labor and capital compensations. The
national accounts readily provide the estimates of compensation of employees as a component of value added in many countries; 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). The
assumption on wages for self-employed and contributing family workers are presented in Appendix 4. See Box 6 for sensitivity of
our assumptions to the TFP results.
67: IT capital is defined as a composite asset of IT hardware (computers and copying machines), communications equipment, andcomputer software.
68: The hours worked were newly estimated for the Lao PDR and Myanmar in this edition of Databook. The failure to take into accountimprovements in labor quality leads to TFP overestimation. The harmonized estimates of labor quality changes are planned to beincorporated in the next edition of Databook (See Box 7).
69: In this edition of Databook, the country aggregations of capital and labor inputs are newly based on the estimates of PPP for
capital and labor inputs, respectively, which are developed in Nomura (2017). He indicates that the PPP for output (Box 1, p. 20)
underestimates the PPP for capital input, indicating the capital prices are higher than the output prices, in most Asian countries
and overestimates the PPP for labor inputs, indicating the labor prices are lower than the output prices, in all Asian countries. Note
that, in sections 5.3 and 5.4, Bhutan, Brunei, and Myanmar are not included in the country groups: the Asia24, South Asia, and theASEAN6.

68

5.3 Total Factor Productivity

%

%

9

TFP

8
7

Non-IT Capital

2.0

0.4

0.8
1.7

0.3
0.6 0.2

2.9

3.7
0.1

0.2 2.4
1.0

4.6

4.7

4.4

2

0

80
0.9

1.6

0.7

4.4

4.4
3.8

1.3 1.3 1.3

1.9

0.9

1.6 1.5

34

10

3

IT Capital

24 24 24

29

Labor
2

7

16

26

27

3.0 2.9 2.8

2.2 3.5 2.3 2.6

1.6 0.9 0.1

2.3

2.4

2.7 2.9 2.9

58

60
39

3.9
2.8

5

7 12

Non-IT Capital

18
35

31 29 33

11

20

0.3
3.0 0.8
0.8
0.9

1.4

0.1
0.3
0.1
0.2 0.2 0.3 0.3 0.3 0.2 0.4 0.1
0.1 0.1 0.2 0.3 0.1
0.4 0.1 1.5
1.7 0.4 0.4
0.2
1.2 0.1
1.2 0.2 1.1 1.0
0.8 0.9 1.0 1.1
0.8 0.8 1.1 0.7 1.0 0.9 0.9 1.0 0.8 1.0 0.9
0.6
0.6 0.7 0.9 0.3
0.0 –0.3–0.3
0.0

−1

40

77
74

56
2

70
66

49

55 54 52

41

3
12

32

67

45 51

10
2

20
0

70

51

66 72

73

2

31

28

0.4

4.0

4

TFP

100

3.0

5

1

120

Labor

Output6

3

IT Capital

73

88

52
33

55
47

5

59
6 3

2

15

4

3
5
1 2 5
3 3 5 5 6 4 83332
425 6 6
4
25 26
24
22 19
18
18 15 18 16 22
17
17 19
17
17
15
13
12
12
12
11
−1 −7 −10

−2

Japan
Fiji
US
Iran
Nepal
Bangladesh
Philippines
APO20
Sri Lanka
PakistanSouth Asia
Mongolia
India
Hong Kong
Asia24Thailand
ASEAN6
East Asia
VietnamIndonesia
Lao PDR
Malaysia
ROC
Korea
Singapore
Cambodia
China

Japan
Fiji
US
Iran
Nepal
Bangladesh
Philippines
APO20
Sri Lanka
PakistanSouth Asia
Mongolia
India
Hong Kong
Asia24Thailand
ASEAN6
East Asia
VietnamIndonesia
Lao PDR
Malaysia
ROC
Korea
Singapore
Cambodia
China

Figure 49 Sources of Economic Growth,
1970–2015

Figure 50 Contribution Shares of Economic
Growth, 1970–2015

Source: APO Productivity Database 2017.
Note: The starting period for Cambodia is 1993. See footnote 69
for the country-exception in the country groups.

Source: APO Productivity Database 2017.
Note: The starting period for Cambodia is 1993. See footnote 69
for the country-exception in the country groups.

growth of TFP of around 3%. Taking the US as the reference economy, with TFP growth of 0.7% on
average per year, 12 Asian economies achieved higher TFP growth than the US.

In terms of its contribution to economic growth, TFP has played a significant role in some fast-growing
Asian economies over the past decades. Figures 49 and 50 present the sources of economic growth
and those contribution shares, respectively, for the entire observation period 1970–2015. Countries
are arranged according to their long-run economic growth. In this period, China achieved the fastest
output growth of 8.6% on average per year. This is followed by Cambodia, Singapore, Korea, the ROC,
and Malaysia, growing at over 6% on average per year. From these GDP growths, the TFP contribution
accounted for over 25% of economic growth in seven of the 21 Asian economies compared. Amongthem, TFP contribution was the largest in India (35%), China (34%), Sri Lanka (33%), and Japan (31%),
all with over 30%, followed by Pakistan (29%), Cambodia (27%), and the ROC (26%). In contrast, TFP
performance was very modest in Singapore, resulting in its relatively small contribution (only 7%) to
economic growth over the same period (0.4% on average per year as the TFP growth rate). In Korea
the TFP contribution in GDP growth was 12% (0.8% on average per year), which was surpassed by thewhole Asia at 24% (1.3% on average per year).71

70: In Iran and Mongolia, subsoil assets may have a significant impact on the TFP growth. Note that they are omitted in our measuresof capital inputs.

69

2017 Asian Productivity Organization

Looking at the sub-periods (1970–1990 and 1990–2015), 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. Ten of the 21 Asian countries experienced acceleration in TFP growth. In particular Iran, Mongolia, and the Philippines achieved considerable recoveries from negative TFP
growths: from –2.5% to 1.1%, from –1.1% to 2.5%, and from –1.1% to 1.1%, respectively.70 More modestly, the TFP growths in China and India improved from 2.1% on average per year in the earlier period
to 3.6% since 1990 and from 1.1% to 2.5%, respectively. The three countries that saw their TFP growthdecline by more than 0.5 percentage points are Hong Kong, Indonesia, and Japan.

5 Productivity

1970−1985%

1985−2000

2000−2015

9

1.3

6

5.7

7.1

6.2
6.2

5.3

1.7
3.4

0.0

3.4 3.4

1.3

0.8 1.3

5.5 4.0

4.8

3.1

0.2
3.0 2.5

3.6

2.4 1.9 2.1 1.6

5.3
1.7
3.6
0.1

4.4 4.3

0.9

0.6
5.4

4.7

0.9

3.6

0.2
2.1

1.0

0.9

1.6 0.7
0.8
0.7
1.0 2.3 2.2 0.8
1.0 1.7
0.1 0.6 1.8 2.5 0.5 0.3 0.4 0.3 0.1 0.3 0.1 0.2 0.1 0.2 0.2 0.6 0.1 0.1 0.1 0.3 0.6 0.7 0.1 1.5
0.2
0.1 0.7 0.8 1.2 0.9 1.1 0.9 0.9 0.9 0.8 0.7 1.1 1.2 1.4 0.8 0.8 0.1 1.0 0.1
1.1 0.9
0.7 1.2 0.5
0.7 0.8 0.4 0.5
−0.5
−0.1
−0.5
−1.1

4.5

1.8 2.3 2.1

2.6 3.6

3.2

2.8

3.3

1.1

2.6
3.5

5.1

1.2 0.6 0.6
3.4
3.5 2.8 0.7
3.0
0.5
2.8 3.0 2.6 0.9 1.7 1.4
0.2
0.4 0.4 0.3 0.0
0.3 0.2
0.0 0.0 0.0 0.2 0.1 0.1 1.2 0.9
0.1 0.2
0.2
0.1
0.3
1.5 1.3 2.0 1.5 1.6 1.2 1.3 0.1 1.5 1.7 0.0 1.5 1.4 0.0 1.2 0.1 1.6 1.5 1.6 0.1 1.6 1.4 1.5 1.0 0.0
0.4 0.5
0.9
0.8
1.1
0.7
0.7 0.2−0.2−0.8
−0.2
−0.5
0.0
−1.0 −0.2
−0.3
−1.7
−2.3
−2.8

4.1

0.0

1.7 1.6

1.5 0.6 0.2

Mongolia
Japan
Fiji
Iran
US
Philippines
APO20
Sri Lanka
Bangladesh
Nepal
Hong Kong
Indonesia
Lao PDR
Pakistan
Asia24South Asia
East Asia
India
ASEAN6
Thailand
Vietnam
Malaysia
ROC
Cambodia
Singapore
Korea
China

5.6 4.7

1.2

Bangladesh
US
Fiji
Nepal
Philippines
Iran
India
South Asia
Vietnam
Sri Lanka
Japan
APO20
Lao PDR
Asia24
East Asia
PakistanThailand
ASEAN6
Mongolia
Indonesia
Malaysia
China
Hong Kong
Singapore
Korea
ROC

0

0.3

0.8
4.0 3.93

3.8 1.5

0.2

1.8 1.9

2.1

1.4 0.9 1.2 0.9

2.8 2.4

5.9

4.9

4.7

1.8 1.3

2.2

1.9 1.7

1.2

0.3

1.6 1.3
2.2
3.0
1.4
1.7 2.2 1.3 1.7 0.1 1.3 0.5
2.3 0.2 0.2
0.2 0.2 0.2 0.6 1.4 0.4 0.4 0.1 1.2
0.4 0.7 0.8
0.3
0.5 0.5
0.3 0.3 0.2 0.9 0.8 0.9 0.8 1.1 0.2 0.3 0.2 0.4 0.3 1.3 0.1
0.4 0.0
0.7 0.6 0.4
0.4 0.6 0.4 0.8 0.5 0.8 0.3
0.5 0.5
0.3 0.3 0.3 0.3 0.2 0.2
0.0 0.2
−0.1
−0.3
−0.1
3.0

3.2

0.1 3.3

3.2

2.6 2.8

3.3 2.5

0.6

2.1 2.6

2.4−3

Non-IT Capital

IT Capital

Japan
US
Fiji
Nepal
ROC
Hong Kong
Iran
Korea
ThailandAPO20
Pakistan
ASEAN6
Philippines
Singapore
Malaysia
Indonesia
Sri Lanka
Bangladesh
Asia24
East Asia
South Asia
Vietnam
India
Cambodia
Lao PDR
Mongolia
China

TFP

Labor

OutputFigure 51 Sources of Economic Growth, 1970–1985, 1985–2000, and 2000–2015
Source: APO Productivity Database 2017.
Note: See footnote 69 for the country-exception in the country groups.

1970−1985

%

120
100

2

15

88

82
64

53 53

0

77

62 67

55

71
24

53

59

68

58

1

15 15

5 5

41

42

1

22

34

10

3
44

34 39 30

38 2
5

75

49
62

3 3

4 1

45
1 8

12 11

23
11
24

1
34

71

76

68

49
5

33
7 6

43 39

48

2 6 2 5

106

5

1

18

23 22
46

90
63

66

67
58

1

53 54

3

30
34

51

6 6

46

21 3

6

3
34
16

28

−6

−10

56

34

38

47

59
17

1

23 21

37

25

22

12
2 3 4

26

33

30
6
21 17 21 18 16 17 16 22 25 18 18 25 25
17 2
14
10 7 12 15

65
40

6 4 6
5 8 6

2

46

17 23 17

63 48

30

42 42

41 52 31

59
44

27

44

29

35
63 59

43

82
50

7
35

61

88
48

27

40

48

12

80
40

57

39
36 49 3

19
2 31
20 6
8 7
36 6
5
4
11
3
28
7
22
4 13 4 4 16 4 15 18 16 14 25 14 8 5
16 12
12 7
9 6 8 9 8
6 8 10
0
−1
−2

31 3

4

12

−26

−33

Mongolia
Japan
Fiji
Iran
US
Philippines
APO20
Sri Lanka
Bangladesh
Nepal
Hong Kong
Indonesia
Lao PDR
Pakistan
Asia24South Asia
East Asia
India
ASEAN6
Thailand
Vietnam
Malaysia
ROC
Cambodia
Singapore
Korea
China

Japan
US
Fiji
Nepal
ROC
Hong Kong
Iran
Korea
ThailandAPO20
Pakistan
ASEAN6
Philippines
Singapore
Malaysia
Indonesia
Sri Lanka
Bangladesh
Asia24
East Asia
South Asia
Vietnam
India
Cambodia
Lao PDR
Mongolia
China−69 −31−81 −49

TFP

11

16
102

45 40

1

3

19 16

22 48

Bangladesh
US
Fiji
Nepal
Philippines
Iran
India
South Asia
Vietnam
Sri Lanka
Japan
APO20
Lao PDR
Asia24
East Asia
PakistanThailand
ASEAN6
Mongolia
Indonesia
Malaysia
China
Hong Kong
Singapore
Korea
ROC

−20

77 92

29

32

10
60
5 4
3
5
3
43 39 44 2 47 42 44
1 2
030
2 24 28 27 28 1 25 1
26 20 2123
17 19 1217 15
1714 8 125−2−7 −6 −4
−1
−11
−10
7

20

18
27

30

77

40

2000−2015
91

5 0

14

23 26

80
60

1985−2000
157117105 84−21

Non-IT Capital

IT Capital

Labor

Figure 52 Contribution Shares of Economic Growth, 1970–1985, 1985–2000, and 2000–2015
Source: APO Productivity Database 2017.
Note: See footnote 69 for the country-exception in the country groups.

2017 Asian Productivity Organization

China’s productivity performance was outstanding. The average TFP growth was 3.0% per year during
1970–2015 (Figure 48). 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 oflong-term TFP growth of about 3.0% is not unprecedented in Asia. According to Jorgenson and Nomura (2005), Japan achieved an annual TFP growth of 3.1% during 1960–1973, even after improvements

71: Compared to preceding studies on measuring TFP in Korea, it should be noted that economic growth in 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 KSNA. As a result, Korea’s GDP growth at constant market prices has been revised up from 7.0% to 8.6%
on average in 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 the 2008 SNA, these are further revised to 8.8%, 9.4%, and 6.7%, respectively.

70

5.3 Total Factor Productivity

in labor quality had been taken into account in the estimation of labor growth (and, as such, eliminating overestimation in TFP).72
Looking at the breakdown of the period in Figure 51, one can see Iran, the Philippines, Nepal, and Fiji
were running an overall negative TFP growth in the period 1970–1985, at –2.8%, –2.3%, –1.7%, and–1.0% on average per annum, respectively.73 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 is unlikely to be sustainable in the long run. As shown in the year-on-yearchanges of growth decomposition in each country (Figure 58), the Philippine’s TFP fell severely in theearly 1980s, in which the economy declined by 15.6% for two years, from 1983–1985, under the regime of Ferdinand Marcos. In Mongolia, negative TFP growths are observed before the transition to amarket economy in 1992 in Figure 58, which induced a negative TFP growth in the period 1985–2000(–0.5% on average), as shown in the center chart of Figure 51.

5

It is obvious in the long run (Figure 50) that economic growth was predominantly explained by thecontribution of capital input in most of the Asian countries, which ranged from 41% in Cambodia to
92% in Iran. Among the Asian Tigers, the contribution of capital services ranged from 60% in Hong
Kong to 76% in Korea, whereas in China and India, it accounted for 54% and 43% of economic growth,
respectively. This compares with 48% in the US, of which 15 percentage points were contributed by IT
capital, a share unmatched by the Asian countries. Japan and Singapore have been leading Asian
countries in terms of contribution from IT capital (12% and 10% of economic growths, respectively)
whereas in other Asian countries it has been 1–8%, with China and India trailing behind.
One prevalent characteristic of the Asian countries is their investment intensity as a share of GDP (Figure 30 in Section 4.2, p. 45), and in turn its contribution to economic growth (Figures 50 and 52). Thereis policy significance in identifying the drivers behind the rapid economic growth in the Asian countries. If growth has been driven by capital accumulation more than assimilation of existing technologies from the advanced economies, the Asian model may prove to be too expensive for many lesswell-off countries to emulate. According to our findings (Figures 51 and 52), 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 havebeen periods when (and in some countries where) assimilation as reflected in TFP growth also contributed significantly to driving growth.

72: 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.
73: Negative TFP growth for these 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.

71

2017 Asian Productivity Organization

As shown in Figure 52, capital accumulation was the dominant factor in the early period 1970–1985,
typically explaining two-thirds to three-quarters of economic growth achieved. In Vietnam, Pakistan,
Sri Lanka, the Lao PDR, and China, however, the contribution of TFP growth was still significant, accounting for more than 25% of their respective economic growth. In the subsequent periods, thecontribution of capital input became progressively smaller, falling to a share of below 52% on averagein 2000–2015 from 71% in 1970–1985 in the whole Asia, while the contribution of TFP became progressively more significant, rising to a share of above 40% from 0%. Reflecting on these results, capital
accumulation appears to be a necessary step to economic growth, especially in the early period ofdevelopment. Although a prerequisite, capital accumulation does not guarantee TFP growth. Somecountries may be more capable than others of reaping the benefits through assimilation of technologies.

5 Productivity

%

10
8

9.2

3.1

TFP
7.5 7.5 7.4
1.7 1.66

3.6

6.8 6.7
0.0

6.4

6.0 5.9

CapitalLabor

Output5.7

5.4 5.2
5.1 5.1 5.0 4.9
4.5
1.4 0.9 1.2 0.9
4.1 4.0 3.9
1.8 1.3
3.6
3.7 3.6 3.6
3.5
1.9
6.2
1.2 0.3
4.9
2.9
1.7
5.6
5.4
2.7 2.7
1.6 1.3
2.8
2.4
0.4 0.3
3.4
3.5 3.1
3.4
2.3
0.8 2.1 2.1 1.9
3.3
3.4
2.4 2.9 1.5
1.8 1.7 1.7 1.4
3.0
3.2
1.4 1.1
2.9
1.8
2.5
1.3 0.8 0.3 0.5 0.4 0.6 0.1 1.3 1.3 1.1 1.1 1.1 1.1
2.0
0.2 0.9
1.7
0.9
2.3
0.2 0.8
1.7
0.7 0.5 0.9 0.5
0.9
1.6
1.1 0.6 0.5 0.4 0.7
1.0
1.4
1.3 1.1 1.2
0.5
1.1
0.8 0.0
0.7
0.9 0.5 0.8 0.9
0.6
0.8
0.8
0.8
0.8
0.7 0.2
0.7
0.5
0.7
0.6
0.6
0.4
0.5
0.5
0.5 0.4
0.5
0.4
0.4
0.3
0.3 0.4
0.3
0.3 0.3 0.3
0.2
0.2
0.1 –0.1 0.1 0.1 0.1 0.2 0.0
0.0 0.2
0.0
–0.1
–0.1
–0.3 –0.4
2.6

2.1

2.8 2.4

4
2
0

Italy
Japan
Denmark
Netherlands
FranceFinlandGermany
Spain
Austria
Belgium
UK
Switzerland
US
CanadaSwedenFiji
Ireland
New ZealandAustralia
Nepal
ROC
Hong Kong
Iran
Korea
ThailandAPO20
Pakistan
ASEAN6
Philippines
Singapore
Malaysia
Indonesia
Sri Lanka
Bangladesh
Asia24
East Asia
South Asia
Vietnam
India
Cambodia
Lao PDR
Mongolia
China

Figure 53 Comparison of Sources of Economic Growth with OECD Countries, 2000–2015
Sources: APO Productivity Database 2017 for APO member economies and China and the US. OECD Stat (Dataset: Multi-Factor Productivity)
and OECD (2017a) for OECD countries (except Japan and Korea).
Note: The ending year for Ireland, Portugal, and Spain is 2014. See footnote 69 for the country-exception in the country groups.

Figure 53 places our estimates among those of OECD for 17 other OECD countries to give readers awider perspective.74 Countries are arranged according to their average economic growth per annum
for the period 2000–2015, in descending order. In so doing, the wedge in economic growth is clearly
displayed, with all Asian countries (barring Fiji and Japan) having been filtered out to occupy the top
end. Asian countries are also among those that experienced the fastest TFP growth in 2000–2015:
3.6% in Mongolia, 3.1% in China, 2.6% in India, 2.4% in Thailand,75 1.8% in the Philippines, and 1.7% in
the Lao PDR.
Though growing at a more subdued pace, the contribution made by TFP in the slower-growing, mature economies should not be underestimated. Figure 54 plots per capita GDP levels in 2015 and the
TFP contribution shares in the period 2000–2015, for the 21 Asian countries (as dots) with comparisonof OECD countries (as white circles). There are no large differences in the roles of TFP contribution toeconomic growth between the mature OECD economies and the middle-income Asian countries. TFP
accounted for more than one-third of economic growth in Japan, Germany, Finland, Austria, Sweden,
the UK, and France in this period.

2017 Asian Productivity Organization

Table 13 and Figure 55 show the growth accounting decomposition for individual countries in five-year
intervals covering the period 1970–2015. The relative importance of drivers behind economic growth
74: The multi-factor productivity in the OECD Productivity Database (OECD, 2017a), referred to 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.
75: 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 2.0%, –10.0%, and 3.7%, 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
our estimate of TFP growth of 0.8% in 1990–2010.

72

5.3 Total Factor Productivity

changes over time. It is a common
experience in most countries that alarge part of the vibrant growth in
the initial period is driven by input
growth. TFP growth becomes more
prominent and makes a steadycontribution in the later periods.
Hong Kong’s TFP growth peaked at
4.6% in 1975–1980, and was robust
at 3.6% in 1985–1990, when TFP
growth also peaked in the ROC,76
Korea, Singapore, and Japan, at
4.1%, 2.4%, 2.2%, and 2.0%, respectively. Thereafter, TFP growthslowed until the second half of the
2000s when countries experienced
productivity growth resurgence.
TFP growth in Mongolia has been
particularly strong since 1995. It
also has bounced back in Indonesia77 and Thailand78 from a negative
standing, following the Asian financial crisis of the late 1990s.

%

110

TFP contribution share, 2000–2015
Japan

100
90
80
70
60

ThailandFiji

50

Pakistan

40

Mongolia

Philippines
India
China

30

Cambodia
Sri Lanka
Lao PDR
20
Indonesia
100

Nepal Vietnam

UK
FranceKorea

Finland
Austria
ROC

Malaysia
CanadaNew Zealand

IranHong Kong

SwedenIrelandUS
Denmark
Switzerland
Netherlands
Australia

Singapore

Belgium

Bangladesh

−10
−20

5

Germany

Spain
0

10

20

30

40

(Thousands of US dollars)
50

60

70

80

90

Per capita GDP, 2015

Figure 54 Comparison of TFP Contribution Shares with
OECD Countries, 2000–2015
Sources: APO Productivity Database 2017 for APO member economies and China
and the US. OECD Stat (Dataset: Multi-Factor Productivity) and OECD (2017a) forOECD countries (except Japan and Korea).
Note: The ending year for Ireland and Spain are 2014.

Looking 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 thathas had 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 slowed from its peak in the latter half of the 1990s, it was dueto the slowdown in TFP growth from 7.1% on average per annum in the previous period to 2.6%. In the
recent period 2010–2015, the slowdown in Chinese economic growth was mainly explained by thelower TFP growth (2.0%). 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 2005–2010, India
achieved TFP growth of 3.5% – its highest in the past four decades. China and India have reaped thebenefits of their efforts in robust TFP growth, while the contribution from labor input growth dwindles over time in the two countries.

76: The National Statistics, Republic of China, published the TFP estimates for the period 1982–1999, although it is not updated (http://
eng.stat.gov.tw/). The correlation of TFP growth rates between their estimates and ours is 0.81 for this period. For 1985–1999, ourestimate is 2.6%, compared to their estimate of 3.6%.
77: 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.
78: 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.

73

2017 Asian Productivity Organization

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 bring5 Productivity

Table 13 Output Growth and Contributions of Labor, Capital, and TFP, 1970–2015

74

Cambodia
ROC
Hong Kong
Indonesia

(−13)
(35)
(46)
(23)
(27)
(24)
(11)
(24)
(12)
(26)
(24)
(22)
(19)
(17)
(6)
(15)
(10)
(1)
(3)
(12)
(31)
(36)
(188)
(22)
(56)
(27)
(35)
(−17)
(11)
(33)
(60)
(55)
(29)
(23)
(24)
(18)
(18)
(6)
(11)
(22)
(6)
(−42)
(16)
(79)
(13)
(17)
(12)
(−4)
(−54)
(18)
(16)
(17)
(13)
(16)
(13)
(−1)
(5)
(−5)
(22)
(12)
(16)
(14)
(25)
(19)
(11)
(26)
(13)
(19)
(20)
(17)
(53)
(58)
(21)
(11)
(32)
(24)
(44)
(26)
(40)
(32)

Japan

0.3
1.3
1.7
1.0
1.4
1.2
0.5
1.4
0.8
1.1
1.4
1.4
1.9
1.3
0.7
1.2
0.9
0.1
0.2
1.0
1.8
1.3
1.3
0.8
1.5
0.5
0.7
−0.1
0.4
0.9
1.7
1.7
1.4
1.3
1.2
1.0
1.2
0.5
0.7
1.2
0.6
1.2
0.6
1.1
0.5
0.7
0.8
−0.2
0.3
0.6
1.5
1.3
1.1
1.6
1.0
0.0
0.3
−0.2
0.7
0.8
1.2
1.2
1.2
1.3
1.0
1.3
0.7
0.9
1.0
1.1
1.6
1.8
0.9
0.5
1.6
1.1
1.3
1.1
1.4
1.2

Lao PDR

−2.0
3.7
3.7
4.4
5.0
5.1
5.0
5.9
6.1
4.1
5.7
6.3
10.1
7.6
11.6
8.3
9.3
10.7
7.6
8.6
5.6
3.7
0.7
3.7
2.7
2.0
2.0
0.7
3.6
2.8
2.8
3.1
5.0
5.8
5.0
5.7
6.5
7.8
6.1
5.3
9.5
−2.8
3.8
1.3
3.7
4.1
6.9
5.0
−0.6
3.4
9.4
7.5
8.9
9.8
8.1
5.3
4.7
4.2
2.9
6.8
7.7
8.2
5.1
6.9
9.3
4.9
5.2
5.0
5.2
6.4
2.9
3.1
4.1
4.9
4.9
4.8
3.0
4.1
3.4
3.9

CapitalTFP
Non-IT
0.0 (−1) 0.2 (−12) −2.5 (126)0.0(1) 0.7 (20) 1.6 (44)0.1
(2) 1.6 (44) 0.3(8)0.1(2) 2.7 (63) 0.5 (12)0.1
(3) 3.0 (60) 0.5 (10)0.2
(3) 4.0 (79) −0.3 (−6)0.2(3) 4.5 (90) −0.2 (−4)0.2(4) 4.7 (81) −0.5 (−8)0.2(4) 4.8 (78) 0.3(6)
0.1(3) 2.9 (72) 0.0 (−1)0.0(1) 4.2 (73) 0.1(2)
0.0(1) 4.1 (64) 0.8 (13)0.0(0) 3.4 (34) 4.8 (47)0.1
(1) 4.4 (58) 1.8 (23)0.1
(1) 3.6 (31) 7.1 (61)0.2
(2) 4.2 (51) 2.6 (32)0.7
(7) 4.5 (48) 3.2 (35)0.5
(5) 5.9 (55) 4.2 (39)0.3
(4) 5.1 (67) 2.0 (26)0.2
(3) 4.4 (51) 3.0 (34)0.1
(2) 3.2 (57) 0.5(9)0.1(2) 3.0 (80) −0.7 (−18)0.1(9) 2.3 (321) −3.0 (−419)
0.1(3) 0.3(7) 2.5 (68)0.2
(6) 1.5 (55) −0.5 (−17)0.0(0) 1.4 (69) 0.1(5)
0.1(6) 0.8 (39) 0.4 (19)0.2 (22) 0.4 (57) 0.3 (38)0.1
(2) 0.0(1) 3.1 (86)0.1
(4) 1.4 (52) 0.3 (11)0.0(1) 1.4 (48) −0.2 (−8)0.0(1) 1.5 (49) −0.2 (−5)0.0(1) 1.5 (29) 2.1 (41)0.0(1) 1.6 (27) 2.9 (49)0.1
(1) 1.8 (36) 1.9 (39)0.1
(2) 2.1 (37) 2.5 (44)0.1
(2) 2.1 (33) 3.1 (47)0.2
(3) 3.6 (46) 3.5 (44)0.2
(3) 4.0 (66) 1.2 (20)0.1
(2) 2.2 (41) 1.9 (35)0.1
(1) 6.2 (66) 2.6 (27)
0.1 (−2) 7.3 (−256) −11.4 (399)0.1(1) 2.9 (75) 0.3(8)0.1(5) 0.8 (58) −0.6 (−42)0.1(2) 0.4 (11) 2.7 (73)0.1
(2) 0.8 (19) 2.6 (62)0.3
(4) 3.1 (45) 2.7 (40)0.2
(4) 3.8 (77) 1.1 (22)0.1 (−16) 2.0 (−337) −3.1 (507)0.1(3) 3.0 (88) −0.3 (−10)0.2(3) 7.4 (79) 0.3(3)
0.5(7) 8.6 (114) −2.9 (−38)0.4(5) 5.3 (59) 2.1 (23)0.7
(7) 5.2 (53) 2.4 (24)0.5
(6) 5.4 (67) 1.2 (15)0.6 (12) 3.8 (72) 0.9 (17)0.6 (13) 2.6 (56) 1.2 (26)0.2
(4) 2.2 (52) 2.0 (49)0.1
(4) 1.9 (65) 0.3(9)
0.4(6) 4.7 (70) 0.8 (12)0.1
(1) 5.6 (73) 0.8 (10)0.1
(1) 5.8 (71) 1.1 (14)0.1
(2) 7.1 (141) −3.4 (−67)0.2(3) 3.5 (51) 1.9 (28)0.3
(3) 6.5 (71) 1.4 (15)0.5 (11) 5.7 (116) −2.6 (−53)
0.7 (14) 2.3 (44) 1.5 (28)0.7 (14) 2.1 (42) 1.2 (25)0.4
(8) 3.0 (58) 0.7 (14)0.3
(5) 4.6 (73) 0.3(5)0.1(2) 3.0 (104) −1.8 (−60)0.1(4) 3.9 (127) −2.7 (−89)0.1(2) 3.6 (89) −0.5 (−12)0.1(1) 3.1 (62) 1.3 (26)0.0(1) 3.0 (60) 0.3(7)0.1(1) 2.5 (52) 1.1 (23)0.1
(2) 2.1 (69) −0.4 (−15)0.1(2) 2.4 (58) 0.5 (13)0.1
(4) 2.2 (63) −0.2 (−7)0.1(2) 2.9 (73) −0.3 (−7)
IT

Mongolia

1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2015
1970–2015
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2015
1970–2015
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2015
1970–2015
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2015
1970–2015
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2015
1970–2015
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2015
1970–2015
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2015
1970–2015
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2015
1970–2015

Labor

Pakistan

Nepal2017 Asian Productivity Organization

Malaysia

Korea

Iran

India

Fiji

China

Bangladesh

Output1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2015
1970–2015
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2015
1970–2015
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2015
1970–2015
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2015
1970–2015
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2015
1970–2015
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2015
1970–2015
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2015
1970–2015
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2015
1970–2015

Output

Labor

7.6
7.2
8.8
6.5
7.0
7.4
9.3
10.6
6.9
8.9
7.2
5.8
4.0
4.2
2.5
6.6
6.3
10.9
5.6
7.4
5.2
2.6
4.1
3.8
2.9
5.4
8.3
7.8
4.7
7.5
7.5
0.7
4.6
5.6
5.4
5.8
4.4
4.7
4.3
4.9
1.5
1.1
1.2
0.1
1.0
2.6
5.3
1.8
7.4
4.2
6.2
5.5
6.2
7.8
8.4
5.9
6.5
5.4
6.6
3.8
−1.8
3.6
6.3
6.4
9.8
5.2
3.6
5.8
7.4
6.6
5.5
4.0
5.9
3.7
4.0
5.2

1.4
(18)
2.9
(41)
3.2
(36)
2.1
(32)
1.6
(23)
2.4
(32)
1.6
(18)
1.6
(15)
1.2
(17)
1.0
(11)
0.9
(13)
0.3
(5)
0.1
(3)
0.2
(5)
0.6
(23)
0.8
(13)
1.9
(30)
1.8
(17)
0.8
(15)
0.2
(2)
0.5
(11)
1.4
(54)
0.5
(12)
0.2
(4)
0.3
(11)
0.9
(16)
1.5
(18)
1.0
(13)
1.4
(31)
1.0
(14)
0.6
(8)
0.9 (121)
0.5
(11)
1.4
(25)
0.4
(7)
1.0
(17)
−0.4 (−10)
0.7
(15)
0.5
(11)
0.4
(8)
−0.3 (−17)−0.6 (−52)−0.3 (−29)−0.4 (−372)−0.1 (−5)
0.0 (−2)
1.1
(20)
0.2
(12)
0.8
(10)
0.9
(21)
0.8
(13)
1.0
(18)
0.4
(7)
0.5
(6)
0.4
(4)
0.7
(11)
0.5
(8)
0.9
(16)
0.8
(12)
1.5
(39)
−0.1
(6)
0.2
(6)
0.8
(12)
0.3
(5)
0.7
(7)
0.6
(12)
1.2
(34)
0.7
(12)
0.9
(12)
0.8
(12)
0.8
(15)
1.0
(24)
1.2
(20)
1.4
(38)
0.8
(19)
1.0
(19)

IT

CapitalNon-IT

TFP

0.1(1) 0.9(12) 5.2 (68)0.1
(1) 2.0(28) 2.1 (30)0.1
(1) 2.7(30) 2.9 (33)0.2
(3) 4.5(70) −0.2 (−4)0.1(2) 3.1(45) 2.1 (30)0.1(2) 2.9(39) 2.0 (27)0.5
(6) 7.2 (78) −0.1 (−1)0.4(4) 5.6 (53) 2.9 (27)0.4
(5) 4.3 (62) 1.1 (16)0.3
(4) 3.4 (39) 4.1 (47)0.3
(5) 3.9 (54) 2.1 (29)0.7 (12) 3.5 (61) 1.3 (22)0.6 (14) 2.3 (59) 0.9 (24)0.1
(3) 1.8 (42) 2.1 (50)0.1
(4) 1.1 (45) 0.7 (28)0.4
(6) 3.7 (56) 1.7 (26)0.3
(4) 3.6 (56) 0.6(9)0.3(3) 4.2 (38) 4.6 (42)0.4
(6) 4.3 (77) 0.1(2)
0.5(7) 3.1 (42) 3.6 (49)0.5
(9) 3.5 (68) 0.6 (12)0.8 (30) 3.0 (115) −2.6 (−99)
0.5 (13) 1.5 (37) 1.6 (38)0.4
(9) 1.3 (35) 2.0 (51)0.3 (11) 1.1 (36) 1.2 (41)0.4
(8) 2.8 (52) 1.3 (24)0.0(0) 4.2 (51) 2.6 (31)0.2
(2) 5.8 (74) 0.9 (11)0.2
(4) 6.0 (130) −3.0 (−64)0.2(3) 4.6 (62) 1.6 (22)0.3
(4) 4.9 (65) 1.8 (23)0.2 (27) 4.6 (632) −5.0 (−680)
0.2(4) 2.7 (58) 1.2 (26)0.3
(5) 3.4 (61) 0.5(9)0.3(5) 3.8 (70) 1.0 (18)0.2
(3) 4.4 (77) 0.2(3)0.5 (11) 4.9 (111) −0.5 (−11)0.3(6) 2.4 (52) 1.3 (28)0.3
(7) 1.8 (43) 1.6 (38)0.5 (11) 1.9 (39) 2.0 (42)0.3 (22) 1.7 (111) −0.2 (−15)
0.3 (32) 0.9 (81) 0.4 (39)0.3 (25) 0.3 (27) 0.9 (77)0.1 (140) 0.1 (57) 0.3 (274)0.1(6) −0.2 (−25) 1.2 (124)
0.3 (12) 1.5 (59) 0.8 (31)0.0(0) 2.6 (50) 1.5 (29)0.0(1) 2.6 (145) −1.1 (−59)0.0(1) 3.3 (44) 3.4 (45)0.1
(2) 3.7 (89) −0.5 (−12)0.2(3) 5.4 (87) −0.2 (−3)0.2(4) 7.1 (128) −2.8 (−50)0.3(5) 4.3 (70) 1.1 (18)0.4
(5) 4.1 (52) 2.9 (37)0.7
(8) 6.3 (74) 1.1 (13)0.2
(4) 4.4 (74) 0.6 (10)0.1
(1) 5.2 (80) 0.7 (10)0.1
(3) 5.7 (106) −1.3 (−24)0.1(2) 6.0 (91) −0.4 (−6)0.1(3) 3.9 (103) −1.7 (−46)
0.1 (−4) 0.9 (−53) −2.7 (151)0.1(4) 0.2(7) 3.0 (83)0.3
(4) 0.2(4) 5.0 (80)0.4
(7) 3.7 (58) 1.9 (30)0.3(3) 5.1 (52) 3.7 (37)0.2
(4) 3.5 (67) 0.9 (18)0.0(1) 2.5 (70) −0.2 (−4)0.0(0) 2.7 (47) 2.4 (41)0.0(0) 3.7 (49) 2.8 (38)0.1
(2) 4.4 (68) 1.2 (19)0.1
(2) 3.7 (67) 0.9 (16)0.1
(1) 2.5 (64) 0.4 (11)0.1
(2) 1.7 (28) 2.9 (50)0.1
(4) 1.9 (53) 0.2(5)0.1(2) 0.6 (15) 2.6 (64)0.1
(1) 2.6 (51) 1.5 (29)

ASEAN6

0.2
0.1
0.2
0.2
0.1
0.5
0.7
0.3
0.2
0.3
0.0
0.0
0.1
0.0
0.0
0.1
0.2
0.3
0.1
0.1
0.0
0.0
0.1
0.1
0.1
0.2
0.2
0.3
0.3
0.2
0.3
0.2
0.2
0.3
0.2
0.3
0.2
0.1
0.1
0.2
0.4
0.2
0.3
0.4
0.2
0.3
0.3
0.2
0.2
0.3
0.1
0.2
0.2
0.3
0.4
0.3
0.3
0.4
0.3
0.3

Singapore

2.0
(35)
1.4
(24)
1.4 (−100)
1.0
(19)
0.9
(30)
0.3
(7)
1.0
(22)
0.8
(17)
0.7
(12)
1.0
(25)
0.8
(27)
0.8
(15)
−0.1 (−1)
1.6
(48)
0.6
(11)
1.9
(39)
0.1
(3)
1.1
(18)
0.4
(6)
0.8
(17)
2.0 (113)
1.4
(39)
1.5
(24)
0.8
(18)
0.7
(8)
0.0
(0)
0.2
(3)
1.1
(18)
0.1
(2)
0.9
(15)
1.0
(20)
1.3
(30)
1.2
(25)
1.1
(19)
0.8
(18)
0.5
(17)
0.7
(16)
0.6
(14)
0.5
(13)
0.9
(19)
1.2
(25)
1.5
(28)
1.8
(30)
1.3
(21)
0.6
(11)
0.9
(20)
0.8
(13)
0.1
(1)
0.2
(4)
0.9
(17)
1.5
(21)
1.6
(21)
1.4
(35)
1.2
(16)
0.8
(11)
0.6
(28)
0.6
(13)
1.1
(21)
0.4
(8)
1.0
(18)

Thailand

5.7
5.9
−1.4
5.3
2.8
3.9
4.5
4.8
5.7
4.1
2.9
5.4
5.0
3.3
5.3
4.9
4.0
6.2
6.0
4.8
1.8
3.5
6.2
4.4
8.1
7.3
8.0
6.2
5.8
5.7
5.0
4.5
4.7
5.7
4.4
3.1
4.2
4.4
3.8
4.4
5.1
5.5
6.0
6.2
5.6
4.6
5.6
6.8
5.6
5.7
7.1
7.4
3.9
7.6
7.3
2.0
4.8
5.1
4.8
5.6

CapitalTFP
Non-IT(3) 3.2 (57) 0.3(5)(2) 4.8 (81) −0.5 (−8)(−14) 3.9 (−287) −6.8 (502)(3) 1.1 (21) 3.0 (57)(3) 2.4 (85) −0.5 (−18)(12) 3.0 (78) 0.1(3)(15) 2.1 (47) 0.8 (17)(7) 1.8 (38) 1.9 (39)(3) 2.2 (39) 2.7 (46)(6) 2.7 (66) 0.1(2)(1) 2.0 (69) 0.1(4)(1) 2.4 (45) 2.1 (39)(2) 3.4 (67) 1.6 (32)(1) 1.5 (46) 0.2(5)(1) 0.9 (17) 3.8 (71)(3) 0.6 (13) 2.2 (45)(6) 1.8 (46) 1.8 (45)(5) 3.1 (50) 1.7 (27)(1) 4.7 (78) 0.9 (14)(2) 2.3 (47) 1.6 (33)(0) 0.2 (12) −0.5 (−26)(1) 1.0 (28) 1.1 (32)(1) 1.6 (26) 3.0 (49)(3) 2.5 (57) 1.0 (22)(2) 4.1 (50) 3.2 (40)(3) 7.0 (95) 0.2(2)(2) 6.7 (84) 0.9 (11)(5) 6.5 (104) −1.7 (−28)(6) 4.5 (76) 0.9 (16)(3) 3.8 (66) 0.9 (16)(6) 4.4 (87) −0.6 (−13)(4) 3.2 (71) −0.2 (−4)(5) 2.6 (55) 0.8 (16)(6) 2.3 (41) 2.0 (35)(5) 2.6 (59) 0.8 (17)(9) 2.1 (66) 0.2(8)(6) 1.4 (33) 1.9 (46)(3) 1.7 (39) 1.9 (44)(3) 1.9 (49) 1.3 (35)(5) 2.4 (55) 0.9 (20)(7) 5.0 (99) −1.6 (−31)(4) 3.0 (54) 0.8 (14)(4) 2.3 (39) 1.6 (27)(7) 2.5 (41) 1.9 (31)(4) 2.4 (43) 2.3 (41)(6) 2.1 (45) 1.3 (29)(6) 1.9 (35) 2.6 (46)(4) 2.8 (41) 3.7 (55)(3) 3.2 (56) 2.1 (37)(5) 2.8 (49) 1.6 (29)(2) 4.1 (58) 1.4 (20)(2) 4.9 (67) 0.8 (10)(5) 5.1 (133) −2.8 (−73)(3) 3.7 (48) 2.5 (33)(5) 5.1 (69) 1.1 (15)(16) 4.2 (216) −3.1 (−160)(7) 2.1 (44) 1.7 (36)(8) 2.6 (51) 1.0 (20)(7) 3.0 (62) 1.1 (23)(5) 3.9 (70) 0.4
(7)

IT

US

Labor

Asia241970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2015
1970–2015
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2015
1970–2015
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2015
1970–2015
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2015
1970–2015
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2015
1970–2015
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2015
1970–2015

OutputSouth Asia

East Asia

APO20

Vietnam

Sri Lanka

Philippines

5.3 Total Factor Productivity

1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2015
1970–2015
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2015
1970–2015
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2015
1970–2015
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2015
1970–2015
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2015
1970–2015

Output

Labor

9.1
8.3
6.6
8.3
8.3
5.5
4.8
6.5
4.0
6.8
5.5
7.4
5.3
9.8
8.1
0.7
5.3
3.7
3.0
5.4
2.6
3.6
3.3
3.3
2.6
4.2
2.5
0.8
2.1
2.8
5.1
4.7
5.4
6.0
5.7
4.3
5.7
6.6
5.4
5.4
2.5
3.5
5.2
5.7
5.1
5.4
6.2
7.1
5.8
5.2

2.5
(28)
2.3
(27)
1.4
(20)
2.1
(25)
2.1
(25)
1.1
(20)
0.5
(11)
2.4
(37)
1.3
(32)
1.7
(25)
1.0
(18)
3.0
(41)
1.1
(22)
1.6
(16)
0.8
(9)
−0.2 (−23)
0.1
(1)
0.5
(14)
−0.6 (−20)
0.8
(15)
0.6
(23)
1.5
(41)
0.8
(25)
1.0
(31)
0.5
(20)
1.0
(23)
0.2
(7)
−0.4 (−47)
0.8
(40)
0.7
(24)
1.3
(25)
1.5
(31)
1.6
(29)
1.2
(20)
0.7
(13)
0.9
(20)
0.8
(14)
0.3
(5)
0.3
(6)
1.0
(18)
1.5
(59)
1.5
(44)
1.3
(26)
1.2
(21)
1.2
(23)
1.0
(19)
1.1
(18)
0.7
(10)
0.7
(12)
1.1
(22)

IT
0.5
0.4
0.6
0.9
0.9
0.7
0.7
0.5
0.6
0.7
0.1
0.2
0.2
0.4
0.6
0.3
0.2
0.5
0.4
0.3
0.2
0.3
0.4
0.5
0.4
0.7
0.6
0.3
0.2
0.4
0.2
0.2
0.2
0.3
0.2
0.2
0.3
0.2
0.2
0.2
0.0
0.0
0.0
0.1
0.1
0.1
0.1
0.2
0.2
0.1

CapitalTFP
Non-IT(6) 7.3 (80) −1.3 (−14)(5) 5.3 (64) 0.3(3)(10) 5.9 (89) −1.3 (−19)(10) 3.1 (38) 2.2 (26)(10) 3.5 (42) 1.9 (23)(13) 4.2 (76) −0.5 (−9)(15) 2.1 (45) 1.4 (30)(8) 2.1 (32) 1.5 (23)(15) 2.3 (58) −0.2 (−5)(10) 4.0 (58) 0.4(7)(2) 3.4 (62) 1.0 (19)(3) 3.3 (44) 0.9 (13)(5) 3.4 (64) 0.5 (10)(4) 3.6 (37) 4.2 (43)(8) 6.4 (79) 0.3(4)(46) 3.3 (436) −2.7 (−358)(4) 0.8 (15) 4.2 (79)(12) 1.4 (38) 1.3 (36)(13) 1.5 (49) 1.8 (59)(6) 3.0 (55) 1.3 (24)(8) 1.4 (53) 0.4 (16)(7) 1.2 (34) 0.7 (19)(13) 1.0 (31) 1.0 (30)(15) 1.0 (31) 0.7 (22)(17) 0.7 (26) 1.0 (38)(18) 0.9 (21) 1.6 (39)(23) 0.9 (37) 0.9 (34)(44) 0.8 (104) 0.0 (−1)(9) 0.4 (17) 0.7 (34)(15) 0.9 (33) 0.8 (28)(5) 4.4 (86) −0.8 (−15)(3) 3.3 (69) −0.2 (−3)(3) 2.6 (49) 1.0 (18)(5) 2.6 (42) 1.9 (32)(4) 2.7 (48) 2.0 (36)(5) 2.4 (56) 0.8 (19)(5) 2.1 (37) 2.5 (44)(4) 3.0 (45) 3.0 (46)(3) 3.3 (62) 1.5 (28)(4) 2.9 (54) 1.3 (24)(1) 1.5 (60) −0.5 (−20)(1) 1.7 (49) 0.2(7)(1) 1.8 (35) 2.0 (38)(1) 2.0 (35) 2.4 (43)(2) 2.0 (40) 1.8 (35)(2) 2.2 (41) 2.0 (38)(2) 2.2 (35) 2.8 (45)(3) 3.6 (50) 2.6 (37)(3) 3.9 (67) 1.0 (17)(2) 2.3 (45) 1.6 (31)5about 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 74 in Section 6.1, 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 ofaccumulation and assimilation. IT capability becomes a factor which determines an economy’s longterm growth prospects.79

75

2017 Asian Productivity Organization

Unit: Average annual growth rate (percentage), contribution share in parentheses.
Source: APO Productivity Database 2017.
Note: See footnote 69 for the country-exception in the country groups.

5 Productivity

Bangladesh

%

6
4
2
0
–2

Cambodia%

810
5.9 6.1
5.0 1.2 0.5 1.4 0.3
0.8
4.4 0.5 5.1 5.0
3.7 3.7 0.5 1.4
0.3 1.0
1.6 1.7
4.7 4.8
4.0 4.5
2.7 3.0
1.3
1.6
0.3
0.2 0.7 0.1 0.1 0.1 0.2 0.2 0.2 0.2–0.3 –0.2 –0.5
–2.5

8

7.6 7.2

6

2.1
5.2

4

2.9

0

–2.0

8

1.6

6
4
2

6

5.9

4.5

2010–2015

2010–2015
2010–2015

2005–2010

6.6
5.8

2.8 1.2

5.9

5.5

0.8 0.9 4.0
2.4
2.9 3.7 4.0
3.6
0.8
0.9
0.4
0.2
1.2 0.7
1.0
1.4 2.6
1.2
4.4
3.7
3.7
2.5
2.5 2.7
0.8
1.7 1.9 0.6
0.1 0.1 0.1 0.1 0.1 0.1–0.2
2010–2015

2005–2010

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980

1970–1975

2010–2015

2005–2010

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980–1

2005–20101

2005–20102

2000–2005

3

1995–2000

7.4

6
4

1990–1995

Pakistan

85

1985–1990

7

1980–1985%

1975–1980

Nepal
4.1 4.9 4.9 4.8
1.8 0.9
0.3
4.1
1.3
1.1
1.6
3.0 0.5 3.4
3.1
0.5 1.6
2.9
1.1
1.3 1.1 1.4
3.9 3.6
3.0
3.1 3.0
2.5 2.1 2.4 2.2
0.1 0.1 0.1 0.1
0.1 0.1 0.1 0.1
–0.2
–0.4
–0.5
–1.8
–2.7

8.4
7.8
1.1
6.2 1.0
0.8 5.5 6.2 2.9 0.4
4.2
5.3
1.1
3.4 0.9
0.4 0.5
1.5
7.1
6.3
1.1 0.2 0.8
5.4
4.3 4.1
3.7
2.6 1.8 3.3
2.6
0.1 0.2 0.2 0.3 0.4 0.7–1.1–0.5 –0.2–2.8
7.4

1970–1975

2010–2015

2005–2010

2000–2005

1995–2000

1990–1995–4

2000–2005

0–2

2000–20052

1995–2000

4

1990–1995

6

3.4 3.8
0.3 0.3

Lao PDR%8

5.6 5.4
0.5 1.0
1.4 0.4–5.0

1985–1990

1980–1985

1975–1980–6

1995–2000

1990–1995

1985–1990

Indonesia

8.3 7.8
7.5 7.5
2.6 0.9 1.4
4.6
1.0 4.7 1.6 1.8
1.0 0.6 0.9 1.2
1.5
4.6
5.8 6.0
0.5
4.6 4.9 0.7
4.2
2.7
0.2 0.2 0.2 0.3 0.2 0.2
–3.0

10

Labor

7.2
2.1 5.8

5.6

1980–1985

3

4.1

1.3 4.0
1.0 0.9 0.3 0.9 4.2
0.1 2.1 2.5
4.3
3.4 3.9 3.5
0.2 0.7
2.3
1.8 0.6
1.1
0.5–0.1 0.4 0.4 0.3 0.3 0.7 0.6 0.1 0.1

1970–1975

Korea

1970–1975

2010–2015

2005–2010

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

IT capital

1.1
1.2

7.2

1975–1980

6

0
–4

6.9
1.6

1970–1975

2010–2015

2005–2010

2000–2005

1995–2000

1990–1995

–2

0.7

9.4
9.8
0.3 1.3 8.9
2.4 8.1
1.5 7.5
2.1
1.6 1.2
1.1
1.0 5.3
4.7
8.6
0.9 1.2 4.2 2.9
7.4
0.3 2.0 0.3
5.3 5.2 5.4
3.8
0.7
2.6
2.2 1.9
0.2 0.5 0.4 0.7 0.5 0.6 0.6 0.2 0.1
–0.2
–2.9
1985–19900

1985–1990

2

0.5

1.0 1.2

6.1
1.2

–3

1980–1985

3.6
5.0
5.1
0.9
3.7
3.9 0.1 3.0 0.8–0.1 0.2 0.3
0.1 0.1 0.1 0.1
0.4 0.3
0.2 0.2–1.3 –0.4 –1.7
0.1
–1.8
–2.7

4

2.5

3.5
3.1

9

1.8 2.1 2.1
1.4 1.5 1.5 1.6
0.1 0.1 0.1 0.2 0.2

%

6

1.9

5.7

0

8.9

2.9

%

7.8

–0.2 –0.2

1980–1985

1.9 0.7
0.3

–3

2.9

5.0

2

0

1975–1980

3.7

0

5.1

3.6 4.0

1970–1975

6.3 6.4

3

4

2010–2015

2005–2010

2.1

5.8

1.7 1.7 1.4 1.3 1.2

1975–1980

2010–2015

2005–2010

2000–2005

9.8

6.0
5.2 5.7

1975–19802017 Asian Productivity Organization

0.1 2.0
0.2
0.9

2000–2005

2.8 3.1

1970–1975

2010–2015

2005–2010

2000–2005

1.5

6.6
0.8
6.5
0.9
1.5
0.7
0.5 5.4
3.8

Non-IT capital

–2

5.0

%

Mongolia

1970–1975

2010–2015

2005–2010

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980

1970–1975–4

1.2

1995–2000–0

6

1995–2000

–2

1.6

0.7

1990–1995

2

9

1990–1995

0–2.6

1985–1990

0.1 0.1 0.1 0.2 0.3 0.5 0.7 0.7 0.4

TFP

1980–1985

4
2

–3.4

1975–1980

6

8

3.2

India

12

0.4 1.2
2.4
1.9
1.1 0.9 0.3 1.0
1.8
0.1
1.7 0.9
0.3 0.1 1.2
0.5 0.3 0.3 0.5 0.3 0.3 0.3
0.1
0.1
–0.3
–0.2
–0.6 –0.3–0.4–0.4 –0.1–0.2–0.5
1970–19758

1.3

1985–1990

3.8
2.9
2.0
1.2
0.2 0.3
1.3 1.1
0.4 0.3

2.0

0.5 0.4

10

2.3 2.1 3.0

3.5

1.6

%

9.3
7.7 8.2
1.4
1.2
6.9 1.0 1.3
0.8 1.1
1.2 1.2 5.1 1.9
4.9 5.2
5.0 5.2
1.5 1.2 0.7
1.3
7.1
6.5 5.7 0.7 0.9 1.0
5.6 5.8–2–4

2010–20150

2005–20102

2000–2005

4

1995–2000

6

1990–1995

Malaysia%

8

1985–1990

1980–1985–1

1975–1980–12

1970–1975

0

1.3
4.9 0.7

1980–19854

1995–2000

1

1990–19952

1985–1990

3

9.3

0.1 0.1 0.2 0.7 0.5 0.3
1975–1980

5.2
0.6

4.9
4.4 4.7 4.3

4

–9

10

1980–1985–6

1975–1980

–3.1

–2.8
–11.4

5

1.9

8

Japan

6

1.2 3.8
6.9 5.0
1.1
0.6
0.3
4.1 2.7
0.6 1.3 3.7
0.3
7.3
0.8
1.1
6.2
0.8 2.7 2.6 3.1 3.8 2.0
0.7
0.1
0.1 0.1 2.9 0.1 0.5 0.8 0.3 0.2
0.1 –0.6 0.4 0.1–0.2 –0.6
0.1

2.6

4.2 4.1 3.4 4.4 3.6 4.2

6

1.4 4.1
0.5 2.6
1.6
3.5 3.0 0.5
1.5
0.3 0.3 0.4 0.5 0.5 0.8 0.5

%

9.5
2.6

1.4 1.4

10
7.6

4.2

6.5

4.6
7.4
6.3
5.6
0.6
1.8 0.1 3.6
1.9
0.8 0.2
3.6 4.2 4.3 3.1

1970–1975–3

2010–20150

2005–20103

2000–2005

6

1995–2000

9

1990–1995

1985–1990

1980–1985

1975–1980

1970–1975

Iran

%

12

–3

7.1

1.8

%

10.9

–2.6

–4

5.7 6.3
0.1 0.8

10.6

9.3
8.3

4.8

1970–1975

2010–2015

2005–2010

0

10.7
7.6

ROC

%

12

11.6

10.1

10

2.1 2.1
3.2

2000–20050

1995–2000

3

1990–1995

6

1985–1990

9

6.5 7.0

Hong Kong

%

12

3.7
3.6
2.7
1.3
2.0 2.0
2.3 2.5 1.5 0.1 0.4
0.7 3.1
3.2 3.0 0.7
0.5
0.7 0.3
0.8 1.5 1.4 0.8
0.4
0.1 0.4 0.1
0.1 0.1 0.1 0.3 0.2
0.1
0.2
–0.5
–0.1
–0.7
–3.0

1980–1985

1975–1980

Fiji

5.6
0.5 3.7
1.3
1.8

–2

1970–1975

2010–2015

2005–2010

2000–2005–2

1995–2000

0

1990–19952

1985–1990

4

1980–1985

6

1975–1980%

1970–1975

–4

2.9

4.5
3.1
1.4
2.7
0.9 2.0
0.1 0.1 0.1 0.2 0.1
–0.2

2

China

%

12

8.8

OutputJapan has been leading Asian countries in terms of IT capital contribution to economic growth(Figures 50 and 52). Japan’s shift in capital allocation took off in earnest in the mid-1990s, with the contribution of IT capital to capital input growth rising from a low of 12% in 1995 to a peak of 64% in 2009
(Figure 56).80 It took place in a period when Japan’s overall investment growth slowed significantly
79: The 2008 SNA formally acknowledges the IT sector’s importance to the modern economy and has made it more identifiable andseparable in industry classification and asset type.
80: Japan’s capital services recorded negative growth in 2010–2015, 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 56.

76

5.3 Total Factor Productivity

%

6
4
2
0

–2

5.9
Philippines
5.7 1.4
5.3
3.9 4.5
0.3
1.4
0.1 0.8
2.0
2.8
0.3 1.0
4.8 3.9 3.0 0.9
1.0
3.0 2.1
3.2
0.2 1.1 2.4

1.9

5.7
2.7

0.8 0.7
1.8 2.2
0.2 0.1 0.5 0.7 0.3 0.2–0.5

0.2 0.1
–0.5

–4

4.8

10
8
6

–1.4

4

–6.8

2

5.5 0.9 5.3

0
–2

0.8
5.3
1.0 3.0 0.5 1.6
3.7
3.0
1.1
1.0
6.4 3.3
4.2
1.3 1.8
0.7
3.6
0.5
3.4
3.3
3.4
0.1 1.4 1.5
0.1 0.2 0.2 0.4 0.6 0.3 0.8 0.5 0.4–0.2 0.2
–0.6
–2.7

6
5

1.3
5.1

4
3
2

4.4

0

6.6
5.7

5.4
3.0
1.0 1.9 2.0 4.3
1.5
2.5
1.5
0.8
0.3
1.6 1.2
0.3
0.7 0.9
0.8
5.4

4.7

3.3
2.6 2.6 2.7 2.4 2.1 3.0

3.3

10.2 0.2 0.2 0.3 0.2 0.2 0.3 0.2 0.2–0.8 –0.2
2010–2015

2005–2010

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980

1970–1975

2010–2015

2005–2010

2000–2005–1

5

2010–2015

Asia24
6.0 5.7

2005–2010%

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980

1970–1975

2010–2015

2005–2010

2000–2005

–4

8.1
0.3

ASEAN6–2.8–3.1

2010–2015

2000–2005

2005–2010

1995–2000

1990–1995

1985–1990

1980–1985

IT capital

1975–1980

1970–1975

–4

6

4.2

7.6 7.3
7.1 7.4
0.8
1.1
1.4
1.6 1.4 2.5
0.8
4.8 5.1 4.8
1.5
0.6
1.0
1.2
1.1
3.9
2.0 1.7 1.1 0.4
5.1
0.6
4.9 5.1
4.1
3.7
3.0
4.2 2.1 2.6
0.1 0.2 0.2 0.3 0.4 0.3 0.3 0.4 0.3–2

2010–2015

2005–2010

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980

Non-IT capital0

1995–2000

2

2.5
0.2
1.0 1.1
1.3 1.2 1.2
1.5 1.5
3.6 3.9
2.0 2.0 2.2 2.2
1.5 1.7 1.8
0.1 0.1 0.1 0.1 0.2 0.2–0.5

1990–1995

4

0.7

0.3 0.2 0.2 0.3 0.2 0.3 0.2 0.1 0.1–0.6 –0.2
1985–1990

0.7

6

5.8
1.0

1.0
4.5 4.7 2.0
5.0
4.4
4.2 4.4
0.8
3.8
1.3
0.8
3.1
1.2 1.1 0.8
1.9 1.9 1.3
0.2
4.4
0.5
0.5
0.7 0.6
3.2
2.6 2.3 2.6 2.1
1.4 1.7 1.9

1980–1985

2.0 2.4 1.8 2.0

2.8

7.4

7

5.7

1975–1980

2.6

5.4

4.7

APO20

1970–19758

1995–2000

0
–1

8

2

1.8

9.8

10

4

1.1

2.2

0.1

1990–1995

1

3.8

1.7 0.9
0.4

3.1
1.9
0.6
1.8
1.5 0.9 0.6
0.1
0.1 0.2 0.3 0.1–0.1
1985–1990

2

4.0

3.4

1980–1985

3

%

6.2
5.1

2.4

1975–1980

4

2010–2015

2005–20105.7

1970–1975

2010–2015

2005–2010

3.5

1970–1975

2010–2015

2005–2010

1995–2000

2000–2005

1990–1995

1985–1990

1980–1985

1975–1980

1970–1975–1

2000–2005

05

1995–2000

41

2000–2005

1995–2000

1990–1995

1985–1990–1.6

1990–1995

1985–1990–1

TFP

1980–1985

0.4 0.2 0.3 0.4 0.2 0.3 0.3 0.2 0.2–2

1980–1985

0

5.2

2.0

4.9

1.6

6

7.1

5

0.1
0.8

0.2

0.8

7

South Asia

6

2

0

2.9

%

7

3

1

3.6
3.3 3.3
1.6
0.7
2.6
1.0 0.7

1975–1980

3.2
2.3 2.5 2.4 2.1 1.9 2.8

2

2.5
0.4
2.1
1.5
1.0
0.6
0.8 1.0 1.0
0.9
0.7
0.5 0.9 0.2 0.8
0.9
0.8
1.4 1.2 1.0 1.0 0.7
0.8 0.4
0.2 0.3 0.4 0.5 0.4 0.7 0.6 0.3 0.2–0.4
1970–1975

1.2 5.5
5.6
5.6
4.6
0.8 1.6 1.9
3.7
2.1
2.3
2.6
1.5
1.3
1.8 1.3
0.2
0.1
0.6 0.9
5.0
0.8
3.0

0
–1

2.6

8

5.6

5.1

1

1

2.4 1.3

4.2

%

6.8

6.0 6.2

2

3

5.3

5.0

2.1 1.6
3.3

4
4.0

US

4
3

1.5

5.4

Thailand%6.2 6.05

6.5

2.1 2.3
0.5 0.4 0.6 0.9 0.9 0.7 0.7 0.5 0.6
–1.3
–0.2
–1.3
–0.5
1975–1980

2010–2015

East Asia

2005–2010

2000–2005

1995–2000

1990–1995

1985–1990

2

1980–1985

3

8.3 8.3
1.4 2.2 1.9
5.5
6.6
4.8
2.1 2.1 1.1
7.3
1.4
5.9
5.3
0.5
3.1 3.5 4.2
2.1
2.3

1970–1975–1.7
1975–1980

1970–1975

4

2010–2015

–0.5

7
5

2005–2010

1.8 1.1
1.4
2.0
0.2 1.0%

6

2000–2005

3.5

4

0

1995–2000

6

7
6

5

8.0
7.3
0.9 1.1
0.2 0.2
6.2
3.2
5.8
6.2 0.9
4.4
0.1
3.0
0.7
1.0
7.0 6.7 6.5
0.8
4.5
1.5
4.1
1.6 2.5
0.1 0.1 0.1 0.2 0.2 0.3 0.3

Sri Lanka%

9.1 8.3
0.3
2.5

%

8.1

8

–2

1990–1995

Vietnam%2

1985–1990–2

1980–1985–8

1975–1980

0
1970–1975–6

10

Singapore

%

12

Labor

OutputFigure 55 Individual Countries’ Growth Accounting Decomposition, 1970–2015

after the economic collapse of the early 1990s (Figure 38 in Section 4.3, p. 55). 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 capital much 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 of over 50% in the late-1990s and the late-2000s. In recent years, the slowdown intotal 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.81 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 to 50.4%, which is higher than the 44.0% for the US.
81: Our estimates in the same period show that IT capital contributes 31.7% in the US and 18.4% in Japan to the growth of total capital input.

77

2017 Asian Productivity Organization

Source: APO Productivity Database 2017.
Note: See footnote 69 for the country-exception in the country groups.

5 Productivity

Japan

%

%

US

%

7.0

70

60

6.0

60

50

5.0

40

4.0

30

3.0

70

2.5

50

2.0

40
1.5
30

20

2.0

10

1.0

0

0.0

10

–1.0

0
1971

–10
1971

1976

1981

1986

1991

1996

2001

2006

2011

Non-IT capital (right-axis)

%

3.0

1.0

20

0.5
0.0
1976

IT capital (right-axis)

1981

1986

1991

1996

2001

2006

2011

IT capital contribution to capital input growth

Figure 56 IT Capital Contribution to Capital Input Growth of Japan and the US, 1970–2015
Source: APO Productivity Database 2017.

%

%

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

1976

1981

1986

1991

1996

2001

2006

2011

Figure 57 IT Capital Contribution to Capital Input Growth of the Asian Tigers, China, and India,
1970–2015
Source: APO Productivity Database 2017.

2017 Asian Productivity Organization

A similar allocation shift to IT capital is also found in the Asian Tigers (Figure 57).82 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 29%; the second in
2003–2004 when it peaked again at 31%. 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

82: 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.

785.3 Total Factor Productivity

Bangladesh

Cambodia

%
15

8
4
0
–4

12

12

9

9

6

6

3

3

0

0

–3

–3

–12

–6

–6

–16

–9

–8

1971 1976 1981 1986 1991 1996 2001 2006 2011
%
15

ROC

1971 1976 1981 1986 1991 1996 2001 2006 2011

Fiji

%
15
12

16

9

9

12

6

6

8

3

3

4

0

0

0

–3

–3

–4

–6

–6

–8

–9

–9

–12

India

1971 1976 1981 1986 1991 1996 2001 2006 2011

10

8

–14

–15

–12

–20
1971 1976 1981 1986 1991 1996 2001 2006 2011

1971 1976 1981 1986 1991 1996 2001 2006 2011

Japan

Korea

%
16

8

12

6
2

4

0

0

–2
–6

–8

–8

–12

1971 1976 1981 1986 1991 1996 2001 2006 2011

Malaysia

0–41971 1976 1981 1986 1991 1996 2001 2006 2011

Mongolia

15

6

0

–3

0

–8

–3

–12

–6

–16

–6
–9

–9
1971 1976 1981 1986 1991 1996 2001 2006 2011

1971 1976 1981 1986 1991 1996 2001 2006 2011

Pakistan

Philippines

%
12

8
6

12
9

0

2

–3

6
3
0

–6

–2
–4

–9

–3

–12

–6

–15
1971 1976 1981 1986 1991 1996 2001 2006 2011

TFP

Singapore

%
15

6
3

0

1971 1976 1981 1986 1991 1996 2001 2006 2011

9

4

Nepal

%
9

0

3

–4

1971 1976 1981 1986 1991 1996 2001 2006 2011

3

9

4

–8

6

12

8

Lao PDR

%
16

4

%
18

12

1971 1976 1981 1986 1991 1996 2001 2006 2011

8

–4

–4

–21

12

8

4

%
10

–7

–10

–8

%
16

0

–5

–4

Iran

%
21

7

0

0

1971 1976 1981 1986 1991 1996 2001 2006 2011

14

5

4

%
10

Indonesia%
15

Hong Kong

%
20

12

%
12

5

–9

1971 1976 1981 1986 1991 1996 2001 2006 2011

1971 1976 1981 1986 1991 1996 2001 2006 2011

China

%
15

–9
1971 1976 1981 1986 1991 1996 2001 2006 2011

Non–IT capital

IT capital

1971 1976 1981 1986 1991 1996 2001 2006 2011

Labor

Output79

2017 Asian Productivity Organization%
12

5 Productivity%
10

Sri Lanka

Thailand

%
16

8

12

12

6

8

8

4

4

4

2

0

0

0

–4

–4

–2

–8

–8

–4

–12

–12

1971 1976 1981 1986 1991 1996 2001 2006 2011
%
8

US

6
4
2
0
–2
–4

%
10

1971 1976 1981 1986 1991 1996 2001 2006 2011

APO20

%
8

8

4

6

2

4

0

2

–2

0

–4

–2

–6

East Asia

1971 1976 1981 1986 1991 1996 2001 2006 2011

South Asia%
101971 1976 1981 1986 1991 1996 2001 2006 2011

8

9

6

6

4

4

3

2

2

0

0

0

–3

–2

–2

–6

–4

–4

–9

–6

–6

–12

TFP

–8

1971 1976 1981 1986 1991 1996 2001 2006 2011

Non–IT capital

IT capital

ASEAN6%12

61971 1976 1981 1986 1991 1996 2001 2006 2011

Asia24–4

8–81971 1976 1981 1986 1991 1996 2001 2006 2011

%
10

6

1971 1976 1981 1986 1991 1996 2001 2006 2011

Vietnam%16–15

1971 1976 1981 1986 1991 1996 2001 2006 2011

Labor

OutputFigure 58 Individual Countries’ Growth Accounting Decomposition (year-on-year), 1970–2015
Source: APO Productivity Database 2017.
Note: See footnote 69 for the country-exception in the country groups.

has not been as big a drive in IT pickups in India as in other Asian countries. Rather, the process has
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 recently decreasing.

5.4 Sources of Labor Productivity Growth2017 Asian Productivity Organization

Although TFP more accurately measures how efficiently an economy utilizes its factor inputs, laborproductivity and its drivers are of interest because of the close link to GDP per capita. Within the same
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.
Capital deepening has been taking place – albeit to various degrees – in all of the countries compared(Figure 59). 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 were80

5.4 Sources of Labor Productivity Growth

1970−2015%

1970−1990

1990−2015

12

10.1

10
8

10.5
9.3

8.3 8.2

7.4

7.1

6

4.9 4.7 4.7 4.7 4.6 4.6 4.6 4.6

4

7.4

6.9
6.0 5.7 5.7 5.7

4.2 4.1 3.9

3.4 3.4

5.5

4.8
3.8

2.7

2

2.2 2.0 2.0 1.8

3.1 3.0 2.9 2.8

1.1

2.5 2.3

5.9 5.9 5.6

5.3 5.0

4.9

2.1 1.9

4.2 3.9 3.93.5

2.9 2.7

0.8

02.4 2.0

1.9 1.6

1.3

0.9

5

0.5

–0.4

Fiji
Iran
Pakistan
Mongolia
PhilippinesUSJapan
Sri Lanka
Singapore
Nepal
Malaysia
Hong Kong
Cambodia
ThailandIndonesia
ROC
Bangladesh
India
Lao PDR
Korea
Vietnam
China

VietnamBangladesh
Fiji
USPhilippines
Pakistan
Sri LankaIndia
Lao PDR
ThailandIranIndonesia
China
Malaysia
Hong Kong
Mongolia
Nepal
Singapore
Japan
ROC
Korea

Fiji
Pakistan
US
Philippines
Iran
Sri Lanka
Mongolia
Bangladesh
VietnamThailandCambodia
India
Japan
Nepal
Lao PDR
Malaysia
Hong Kong
Singapore
Indonesia
ROC
Korea
China

–2

6.7

Figure 59 Capital Deepening, 1970–2015, 1970–1990, and 1990–2015
Source: APO Productivity Database 2017.
Note: The starting period for Cambodia is 1993.

1970−2015%

1970−1990

2
1

0.8

–3

–0.3 –0.3 –0.3
–0.4 –0.5 –0.6

0.4 0.3

0.2

0

–2

1.0 0.9 0.9

0.8
0.4

–1

1990−2015
1.7

1.5–0.7–0.8–1.0 –1.0 –1.0
–1.4
–1.6 –1.6–1.7–0.3–0.3–0.4–0.5–0.5–0.9 –0.9–1.8–1.9
–2.1
–2.9
–3.2

–4

–0.3
–1.1–1.2–1.9 –2.0–2.8–3.0–3.3–3.4–0.5 –0.6–0.9 –0.9–1.1–1.2–1.2–1.4–1.4–1.6–1.8–2.1 –2.1
–2.3
–2.5

–3.4
–4.3 –4.3–5Bangladesh
Korea
Indonesia
China
Nepal
Vietnam
ROC
Hong Kong
Lao PDR
India
Thailand
MalaysiaJapan
Cambodia
Singapore
US
Philippines
Fiji
Pakistan
Sri Lanka
Iran
Mongolia

Nepal
Korea
Iran
SingaporeJapan
ROC
Mongolia
Malaysia
Philippines
Indonesia
Fiji
China
India
Hong Kong
US
Bangladesh
Lao PDR
Sri Lanka
Thailand
Pakistan
Vietnam

Korea
Nepal
ROC
Japan
Singapore
Indonesia
China
Bangladesh
Malaysia
India
Hong Kong
Iran
Lao PDR
Philippines
Cambodia
ThailandUS
Mongolia
Fiji
Vietnam
Sri Lanka
Pakistan

Figure 60 Capital Productivity Growth, 1970–2015, 1970–1990, and 1990–2015
Source: APO Productivity Database 2017.
Note: The starting period for Cambodia is 1993.

While labor productivity steadily improved for all countries as shown in Figure 44 in Section 5.2,
the growth rate of capital productivity (as the other measure of partial productivity) remains negative
for almost all countries during 1970–2015, shown in the left chart of Figure 60. Although rates ofcapital deepening in Korea and the ROC were outstanding, at 8.2% and 7.1% per year, on average during this period, their capital productivity experienced the sharpest decline of 3.2 and 2.1% per year,
respectively. Figure 61 presents the declining trends in Japan and the Asian Tigers. They are similar
except in Hong Kong.

81

2017 Asian Productivity Organization

made to increase capital intensity in the latter period. In 1990–2015, China, Vietnam, the Lao PDR, India,
Bangladesh, 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 risingby 10.1% and 9.3% on average per year in Korea and the ROC, respectively. Over the subsequent two
decades it slowed to 6.7% and 5.3% respectively. Meanwhile, China’s pace doubled between the two
periods, from 5.5% to 10.5% on average per year. In Vietnam, it has accelerated to 7.4% since 1990, acontrast to capital shallowing in 1970–1990.

5 Productivity

1970=1.0

1970=1.0

1.2

1.2

1.0

1.0

0.8

0.8

0.6

0.6
ROC
Hong Kong
Korea
Singapore
Japan

0.4
0.2

0.4

0.0
1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

Figure 61 Capital Productivity Trends inJapan and the Asian Tigers, 1970–2015
Source: APO Productivity Database 2017.

TFP
IT capital deepening

6

120

Non-IT capital deepening
Labor productivity

80

4

1.6

3

0

3.5

1.3

0.9

0.3

1.3 1.3
2.0 1.9

3.0
2.1

44.2

35.4

40.9

24.8

36.2 35.6

48.5

56.3 52.5

7.2

13.8 15.1
29.1

32.1 29.5
51.7

0.2

1.5 1.6

0.9

1.6
2.7

2.0 2.6 2.1 2.1

0.4 0.4

2.4 2.0

2.1

1.7 1.6

1.8 1.9

0.8 0.1

1.5 0.3
0.5 1.1
0.4 0.5
0.2 0.4 0.3 0.2 0.2 0.4 0.1 0.2 0.3 0.1 0.1 0.3 0.1 0.2 0.2 0.1 0.2 0.5 0.2 0.3 0.1 0.1 0.1 0.4 0.2 0.1 0.1
1.4

1.1

−0.3

−0.3

78.7
59.3

52.5

1.5

2.9
1.6

112.1
76.0

40

0.9 0.8

1.6

52.9

82.2
54.0 71.4–20

3.3 7.6 7.0 6.2 5.2

10.6

89.3

78.5

48.9

61.1
45.2

3.8 5.4 8.2 2.7 2.6 8.7 2.7 5.2 5.2 3.1 7.7

78.6

95.6

68.2

65.7

58.4 56.2
41.0 44.9

20
0

36.8

48.0

55.9

60

0.6

3.8
3.0

33.8

5.5

9.1

16.0

16.4

1.7

119.8

TFP
Non-IT capital deepening
IT capital deepening

100

3.0
0.8

1

Figure 62 Capital Productivity Trends in
China and India, 1970–2015

%

7

2

0.0
1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

Source: APO Productivity Database 2017.

%

5

China
India

0.2

58.7

29.0

54.7

42.1
16.7

23.0

6.9 11.7 2.0 5.6 3.9
–1.2
–16.0

14.2

7.8 8.5

–27.6

Fiji
Iran
Philippines
US
Nepal
Bangladesh
Pakistan
Japan
APO20
Singapore
ASEAN6
Sri Lanka
Mongolia
Indonesia
South Asia
Malaysia
India
Cambodia
ThailandAsia24
VietnamHong Kong
Lao PDR
East Asia
ROC
Korea
China

Fiji
Iran
Philippines
US
Nepal
Bangladesh
Pakistan
Japan
APO20
Singapore
ASEAN6
Sri Lanka
Mongolia
Indonesia
South Asia
Malaysia
India
Cambodia
ThailandAsia24
VietnamHong Kong
Lao PDR
East Asia
ROC
Korea
China

Figure 63 Sources of Labor Productivity
Growth, 1970–2015

Figure 64 Contribution Shares of Labor
Productivity Growth, 1970–2015

Source: APO Productivity Database 2017.
Note: The starting period for Cambodia is 1993. See footnote
69 for the country-exception in the country groups.

Source: APO Productivity Database 2017.
Note: The starting period for Cambodia is 1993. See footnote
69 for the country-exception in the country groups.

2017 Asian Productivity Organization

In contrast, the deterioration of capital productivity (by 1.6%) was relatively mild in China as shownin Figure 60, despite its fast capital deepening of 8.3% shown in Figure 59. Looking at the two subperiods of 1970–1990 and 1990–2015, 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 muchas the early starters (Figure 62). In 1990–2015, China’s capital-labor ratio rose by 10.5% whereas its
capital productivity fell by 2.1%. This compares with Korea’s performance in 1970–1990 when its capitallabor ratio rose by 10.1% while capital productivity fell by 4.3%.
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 theprime engine 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 52.0% on

82

5.4 Sources of Labor Productivity Growth

1970−1985%

2000−2015

1985−2000

10
8
3.1

6

1.3

3.8 1.5

4
4.4

2

5.5

4.4
2.4 2.4

0.4 0.3 0.3
−0.2

0

2.9

4.1

3.9 1.3
1.9

0.2

1.3

3.0
1.8

0.1 0.3 0.1 0.4 0.1 0.1−0.3 −0.5−0.8

–2

1.7

0.3
2.4 2.4 2.3 2.5

4.0

3.9

0.8

2.4
0.7 0.6 0.6
1.8
1.4
1.3
1.2 1.4
0.6 0.9
0.2 0.2 0.2 0.1 0.1 0.3 0.8 0.1 0.1 0.1 0.1−0.3 −1.0−0.1
−0.2
−0.2
−1.7
−2.3
−2.8

2.6 2.8
1.9

3.0 3.3

3.5

0.2 0.6
2.4

1.6

3.1 2.5

0.2

2.1 1.2

3.1

1.5

0.9 0.9
3.5

1.0 0.7

3.0

3.5 0.2 0.9

2.4 2.1

1.4
2.4

4.9

1.6

5

1.2 0.9

1.3 1.6 1.3
1.7
1.2 0.3
1.8
2.0 0.9
1.2 2.2
1.3
1.0 1.1 1.4 0.9 1.9 0.5 0.5 1.3 1.2 0.8
0.5
0.2
0.5
0.5 0.4 0.2 0.2 0.3 0.3 0.2 0.2 0.2 0.4 0.1 0.2 0.3 0.2 0.3 0.4 0.1 0.2 0.3 0.5 0.2 0.1 0.4 0.3 0.1 0.1 0.2
−0.1
−0.1
−0.1
3.7

2.2 2.1 1.6 1.6 2.0 1.1 2.1
1.8
1.7
1.4
1.0 0.3 0.7 1.6 0.5
1.2
0.1 0.5 0.4 0.1 0.4 0.3 0.1 0.3 0.5 0.2 0.3 0.1 0.6 0.2 0.1 0.1 0.2 0.4 0.2 0.1 0.2 0.5 0.1 0.1 0.1
−0.5
−0.3
−0.5
−1.1
−1.4

Non-IT capital deepening

3.6

4.8

2.7 2.4

3.5

2.4 2.6 2.6

2.0 2.1 1.5

Japan
Nepal
Fiji
US
Singapore
Pakistan
Iran
Malaysia
Philippines
ROC
APO20
Hong Kong
ASEAN6
Indonesia
Korea
Bangladesh
Cambodia
ThailandSri Lanka
South Asia
Asia24
Mongolia
Vietnam
East Asia
India
Lao PDR
China

TFP

3.3

1.7
1.5 0.6

Mongolia
IranFiji
Sri Lanka
US
Philippines
Bangladesh
Lao PDR
Japan
APO20
Nepal
Pakistan
Cambodia
Indonesia
Singapore
South Asia
ASEAN6
Asia24
Hong Kong
Malaysia
India
East Asia
Thailand
Vietnam
ROC
Korea
China

Philippines
Bangladesh
Fiji
Nepal
Vietnam
Iran
India
South Asia
US
Thailand
ASEAN6
Asia24
APO20
East Asia
Pakistan
Lao PDR
Indonesia
Sri Lanka
Singapore
Malaysia
Japan
Mongolia
China
Hong Kong
Korea
ROC

–4

2.5

0.8

1.8 1.9

IT capital deepening

Labor productivity

Figure 65 Sources of Labor Productivity Growth, 1970–1985, 1985–2000, and 2000–2015

Source: APO Productivity Database 2017.
Note: See footnote 69 for the country-exception in the country groups.

109

5

10

20

80

39

40 44

35

41
57

60
40

54

82

92

71

72

59

56

42

72

42

53

5

16

28 29

36

44

49

1

10

30

34

27

37

42

59
43

6 6 6 1 3 8 2−3−8 −13

12

−21

2 3 1

51

60 57
39

8 7 7 5 6 18 1 2 5 3 9 13
−1

−9

45

87

86 70

77

66

37

70 69 57

50

44

15

6 8 2

18

−11

57

48 16

−16

6 28
2 8

28
42

61

53

43
59

46

64
48 53
25

56

37
66
93 98

101
82
58 66

61

48

69

48

31

6 7 3 4 5 6 4 3 5 9 3 5 8 6 10
−3

49

34
36
12

5 8

38

35

29

20

23 24

18

19
12

7 4
−5

7 7
−4

15

−629
−455

Japan
Nepal
Fiji
US
Singapore
Pakistan
Iran
Malaysia
Philippines
ROC
APO20
Hong Kong
ASEAN6
Indonesia
Korea
Bangladesh
Cambodia
ThailandSri Lanka
South Asia
Asia24
Mongolia
Vietnam
East Asia
India
Lao PDR
China

Mongolia
IranFiji
Sri Lanka
US
Philippines
Bangladesh
Lao PDR
Japan
APO20
Nepal
Pakistan
Cambodia
Indonesia
Singapore
South Asia
ASEAN6
Asia24
Hong Kong
Malaysia
India
East Asia
Thailand
Vietnam
ROC
Korea
China

Non-IT capital deepening

34

41

98

50 44

−18
−46

34

94
88

15
1
6 2 2 1 9 6 5 10−262 −30−30 −198 −20

TFP

47 52

86

57

26

48 44
69

39

33

2 9 7 3 9 7 2 8

85

97
45

67

116

11

29

58

59

65

104

Philippines
Bangladesh
Fiji
Nepal
Vietnam
Iran
India
South Asia
US
Thailand
ASEAN6
Asia24
APO20
East Asia
Pakistan
Lao PDR
Indonesia
Sri Lanka
Singapore
Malaysia
Japan
Mongolia
China
Hong Kong
Korea
ROC

−20

93 92

58

20
0

45

39

7

14

25

467
702

110

106 111

97

140

499
357 289
127 355

100
21

2000−2015

1985−2000

1970−1985%
120

IT capital deepening

Figure 66 Contribution Shares of Labor Productivity Growth, 1970–1985, 1985–2000, and 2000–2015

average in 1970–2015, it has been a main driver to enhance labor productivity in 17 Asian countries(Figure 64). The exceptions to this observation are Cambodia, India, Sri Lanka, and Pakistan, 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 65 and 66). In the earlier period 1970–1985, TFP growth was experienced by 11 out of the 20
Asian countries compared (excluding Cambodia). It was a significant drag on labor productivity

832017 Asian Productivity Organization

Source: APO Productivity Database 2017.
Note: See footnote 69 for the country-exception in the country groups.

5 Productivity

Bangladesh

%

3
2

1.1
1.6 0.61

2.3 2.2
0.5 0.5

2.6

2.5
3.8

2.9

0.3
3.7

2.7
1.7 1.6
0.3 0.1 0.1 0.1 0.1 0.2 0.2
0.3–0.3 –0.2 –0.5

0
–1

4.3

3.7

4

–2.5 –0.6

2.5

2010–2015

2005–2010

2000–2005

2010–2015

2005–2010

2000–2005

1995–2000

Pakistan
3.9

2.8 1.2

3.4
0.9

0.4 2.9

0.2 2.6

1.5
0.3 0.2
0.1 0.1 –0.5
–0.1

0.1 0.1

1995–2000

1990–1995

1985–1990

2010–2015

1980–1985

2.6 2.4

2.1

1.9

2005–2010

1.9

3.3

2000–2005

0.1 1.7
0.2–0.2
1975–19802017 Asian Productivity Organization

2010–2015

2005–2010

2000–2005

1995–2000

2.4

1990–1995

4.8
4.1

1985–1990

1980–1985

2010–2015

2005–2010

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980–1

1995–2000

1990–1995

1985–1990

1980–1985
3.4

1.5
3.4 2.7
3.6
2.3 1.7 1.9
0.1 0.2 0.2 0.3 0.3 0.7
1.4–1.1–0.5 –0.2 –2.8
1.3

1970–1975

0
–2.7

6.9
5.9 1.1
4.8
3.6 4.9 1.1 2.9
2.3
5.2

2
1

3.4

1.5

1975–1980

0.8 0.5 1.0
2.5 3.0 2.7
1.7
1.9 1.8
1.1 1.4 1.1
0.1 0.1 0.1 0.1
0.1 0.1 0.1 0.1
–0.1
–0.2
–0.4
–0.5
–1.8

1990–1995

1985–1990

1980–1985

3.0

3

2.0

4.2

–1.4

5.1

1970–1975

4

3.0

Lao PDR

5

2.9

3.3

–5.0
1975–1980

0

4.5

1.8
1.6

–3.0

1970–1975

2

1.3

1.0
1.8
1.2
0.5 3.3
1.9
1.1
0.2
1.6
0.1 0.1 0.2 0.3
0.2 0.2 0.2
3.4
3.9 0.6%

0.3 1.1

IT capital deepening

0.9

6

–4

2010–2015

2005–2010

2000–2005

1995–2000

2.2

1975–1980

2010–2015

2005–2010

2000–2005

1995–2000

1990–1995

2.6 1.3

4.8

8

4

2.1

2.2

6.3
4.9

4.2

%

Nepal

–2
–3

1985–1990

0
–1

–4

3.3

2.7 3.2

Indonesia–2

4.0

1970–1975

2010–2015

2005–2010

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980

Non-IT capital deepening

1

0

–2
1980–1985

1.9

2.8

2

–2.9

1975–1980

2.6

2

3.1

2.1

3

0.9
3.3

1.6 0.7
0.5 0.4 0.3 0.3 0.3 0.7 0.5 0.1 0.5
0.1
–0.1

2.6

6.6 6.5

4

4.3

6
4

1.2

6.2
1.2 5.4
4.6
2.4
0.9 4.2
7.0
1.2 2.0
5.3
4.6 3.8
4.2
1.6
3.6
2.4
2.4 0.3
1.3
0.2 0.4 0.4 0.6 0.4 0.6 0.6 0.2
0.1

5.8
0.3

1970–1975

2010–2015

2005–2010

2000–2005

1995–2000

4.9

2.5

5.0

Korea

–2
–4

1990–1995

0

1985–1990

1980–1985

1975–1980

2

4.6
4.1

3.5

5.2

2.1 1.3 3.7 3.8

5.6

8

2.1
3.3 3.6
0.4 0.6
1.2 1.5 1.4
1.0
0.8
0.8
0.1
0.1
0.1
0.2
0.2
0.6
–0.2 –0.2

4.6

5.6

1.1

%

3.2
1.9

7.1

2.9 4.7

1970–1975

2.9

1970–1975

–2

–2

6.9

3.0

4

0

2010–2015

2005–2010

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980

2010–2015

2005–2010

2000–2005

1995–2000

5.0

3.7
2.5
0.9 1.1 3.0
0.1 0.1 0.1 0.1 0.1 0.1 0.2 0.4 0.3–1.3 –0.4–0.5 –2.4–0.8–1.7 –1.5–2.7
1970–1975

2010–2015

2005–2010

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980

1970–1975–4

1970–1975

2010–2015

2005–2010

2000–2005

1995–2000

1990–1995–2

TFP

1990–1995

–2.6

3.7

0

3.8

4.9

0.1 0.1 0.2 0.6 0.5 0.3

3.9

6.0

2
5.8

3.3 3.1 3.4

7.5

4.1

2.0

3.2
2.6

India

5

7.7

2

1.8

%

6

4.4 4.4 4.3

–0

4

2.0

8

4

2

6

Mongolia

5.1
3.2 4.1
0.7

2.8 2.7

8

4.2

1985–1990

1980–1985

1975–1980

3.2 3.1
2.2 2.3
4.8 1.0
1.9
1.5
3.6 3.7 1.7
1.2 0.7
1.0 0.4 1.2
1.5
0.1 0.1 0.1 0.1 0.3 0.4 0.7 0.6 0.3
5.0

1990–1995

1.6

2.9
0.8
0.1

%

1.9 2.1
1.8
0.4
0.8 1.1
2.0 1.6 1.6 1.8
1.3 0.9 0.3
0.5 0.3 1.2
0.5 0.3 0.3 0.5 0.3 0.4 0.3 0.2 0.1
–0.5
–0.2
–0.2
1970–1975

2010–2015

2005–2010

2000–2005

1995–2000–1

1985–1990

1980–1985

3.6
3.5

0

7.1

5.1

3.5 4.8 1.84

3.1 3.5

Japan

%

3.5

–3.4

1975–1980

2

4.0

5.1

5.2 1.32

6.3

4.2 7.26–2.6
1970–1975

2010–2015

3

6.6

8

0.6
2.3
2.0
3.4 3.0 2.9 1.7 1.6
1.2
2.2
0.7 1.1 1.2 0.8
1.6
0.2 0.3 0.3 0.5 0.4
0.5 0.3 0.3
–0.2

10

6.4
1.4

5.0
1.1

–2
–4

1990–1995

0

1985–1990

1980–1985

1975–1980

1970–1975

Malaysia

4.5
0.8

0.1

6

8

4

8

7.7

%

0.6

4

0

–11.4

8

2

5

1

%

2.4

3.9 3.6

6

–6.2

6

–3

2.2

7.1

%
5.9
1.1

1.6
2.1
0.9 3.0
4.7 5.1 0.3
2.7 2.6 2.7 4.5 1.3–
1.3
0.2 0.2 0.1
0.1
0.1 0.1 0.1
1.8
–1.2
1.7
–1.8
–1.8
–3.1
–0.6

–9

4

2005–2010

2000–2005

1995–2000

1990–1995

1985–1990

Iran

7.3
2.6

4.6

3

1.1

Hong Kong

6

10

ROC

%
10.5

10.3

6
3.2

0.1 0.1 0.1 0.1 0.1
0.1
–0.2
–0.3

9

0

–1.7
–3.0

–6

–12

1

–1

2.1

2.9
2.1

1985–1990

–0.7
–0.5

1980–1985

–3

5.2

2

1980–1985

1.0 3.1

0.5
1.2 2.5 0.1–0.3 1.0 0.4 0.3
0.5
0.1
0.1 0.2 –0.6
–0.5
–0.7

1975–1980

03

1975–1980

1.1
0.1

3.1

2.4

1970–1975

1.3
0.1

1.0
1.6

4.4

4.1

4

7.1

6
3

2010–2015

0.5

1970–1975

9

5

China

%

12

5.0

%

2.5

2.2

–2

%

2005–2010

1.9

–1

–3

2000–20050

1995–2000

3

1

1990–1995

1985–1990

1980–1985

1975–1980

1970–1975

Fiji

%

2

6

0

–2
–3

Cambodia%5

Labor productivity

growth in nine countries (Iran, the Philippines, Nepal, Fiji, Singapore, Malaysia, Mongolia, Bangladesh,
and Korea). During the middle period 1985–2000, all countries (except Lao PDR, Indonesia, and Mongolia) achieved positive TFP growth to bolster labor productivity growth. By 2000–2015, TFP growth
had become the dominant driver of labor productivity growth in 7 of the 21 countries compared. At
the same time, the contribution from IT capital deepening was also strengthening, from a range of
0–13% in 1970–1985, to 2–18% in 1985–2000, and 3–24% in 2000–2015 (except the countries experiencing negative growth). This may have accounted for a boost of countries’ TFP performance. In themid period 1985–2000, the contribution of IT capital deepening in the US was ahead of Asian countries845.4 Sources of Labor Productivity GrowthPhilippines

%

6
4
2
0

–2

6

2.8
2.4
3.0
3.0
0.1
2.8
1.1 2.4 2.7
1.2
0.5 2.4 0.8 1.9
1.7
1.3
0.3
0.1 0.1 0.1 1.0 0.5 0.5 0.4 0.1
0.8–0.2 0.2–0.3 –0.5
0.1 –0.5

–4

–8

0

0.9

1.8 1.7
4.2
1.6 1.8

1.1
0.3 0.1 0.2 0.3 0.1

0.2

–1.2

4.5

5.3 3.5 4.2 2.4 1.8

0.5
1.0 0.9
1.3 2.3
2.4 1.8
2.0
1.2 0.7 0.7
0.1 0.9 0.2 0.3 0.6 0.4 0.2 0.4 0.4
0.1
–0.1
–2.7

5

2.7

2

3.3

2.1
2.1

1

2.5 1.9

2010–2015

3

5.9

4.2

3.7

2005–2010

2000–2005

4.3

4

2.0 2.7

3.0 4.7
1.5

2.5

0.8

1.0

2.1 1.7
1.4
1.4 1.5

2.7

3.0

0.2 0.1 0.2 0.3 0.2 0.2 0.2 0.2 0.2–0.8 –0.2
2010–2015

2005–2010

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980

1970–1975

2010–2015

2005–2010

2000–2005

1995–2000–1

1995–2000

Asia 24

5

0

1990–1995

1985–1990

1980–1985

1975–1980

1970–1975

2010–2015

2005–2010

2000–2005

1995–2000

3.4
3.4 0.8

5.4
1.1

3.9

3.4
3.1 2.5
4.0 0.5
0.4

3.2

2.2 1.1

1.4
1.7 1.0 2.5
1.9
1.9 2.5
1.2 0.8
0.1 0.1 0.2 0.2 0.3 0.3 0.3 0.3 0.3
–2.8

–2

–3.1

2010–2015

IT capital deepening

2005–2010

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980

–4

3.1

4.2

ASEAN61970–1975

2010–2015

2005–2010

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980

Non-IT capital deepening0

1990–1995

1.8
2.0
3.0 3.4
1.1
0.2
1.3 1.3 1.5 1.4
0.9 1.1 0.1 0.1 0.1 0.1 0.2 0.2
–0.5
0.7
0.2

1985–1990

4

1.4

0.3 0.2 0.2 0.3 0.2 0.2 0.2 0.1 0.1–0.6 –0.2

4.7

2

2.8

1990–1995

1.6 1.4 1.9 1.6

1980–1985

1.0

1985–1990

1980–1985

2.0

2.4

2.2

6

4.6

3.1

6

0.7 1.1

–1

0

5.2

%

2.8
2.9
2.9
2.6 2.0
2.1
0.8 2.1
0.8
1.9 1.3
3.5
0.2 1.9

1
0

2

–2

3.1

1975–1980

3.2

3.1

2.6

3.7

2

6.2
6.3 0.3

6
4

3.7

%

5.8

4.3
3.8

1970–1975

2010–2015

2005–2010

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980

–1

2.2

3.8

APO20
3.1

1970–1975

0

3.7

8

–4
1975–1980

1.4

2010–2015

–1.6

1

3.8

3.4 0.1

0.1 1.5
0.9
0.1

1970–19753

2005–2010

South Asia

3
2

2000–2005

4

2.1
1.1

–1

2010–2015

2005–2010

0.9

1.6

1995–2000

2.1

1990–1995

5

1985–1990

1980–1985–1

2.7 3.0
2.0 1.4 1.4
1.1 1.6
0.3 0.2 0.2 0.3 0.2 0.2 0.3 0.2 0.2

TFP

1.5 1.7

–0.2

5.2

2.0

1970–1975–20

1975–1980

0
–1

1

4

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

1970–1975

1

3.0
2.3
2.8 2.9 3.0
2.6
1.9
4.0 0.8 1.6
1.3

1

6

3.7

3.9

1.8

1.6

0.4 1.1 1.0

2010–2015

2

2

4.2
1.6

%

2.2

2

%

6.7

4.2

4.5

2000–2005

4.3 4.3

6
5

1995–2000

5.5

2005–2010

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980

1970–1975

East Aisa

7

1990–1995

6.2

3

2.5

1.3 3.0
2.4
1.0–0.5 1.1–0.5 0.1 0.1 0.4 0.1 0.2 0.2 0.3 0.3
0.1
–0.5
–1.7
–1.0

%

1985–1990

7.0

1.4

1980–1985

2.6

US

3

2.8 0.9

3.6

5.1

–2

%

7.4 7.3
0.2
0.9

4

Thailand

%

5.1

5

3.7
3.2 3.3 3.4 3.6 3.1
5.1 0.3
1.9
4.1
1.4 0.8
2.2
2.9
1.5 1.1
2.7
1.6
1.1
0.5
0.9
0.4 0.3 0.5 0.6 0.6 0.6 0.6 0.2 0.5–0.5–0.9 –0.2
–1.3
–1.3

1975–1980

3.2

4

3

6

4.3

1970–1975

5.7

6

4

2010–2015

2005–2010

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980

1970–1975

Vietnam8–22

Sri Lanka

%

–2

%

0

4

0

–5.0
–6.8

–6

2

Singapore%
4.1

Labor productivity

Figure 67 Decomposition of Labor Productivity Growth, 1970–2015
Source: APO Productivity Database 2017.
Note: See footnote 69 for the country-exception in the country groups.

Figure 67 and Table 14 show the decomposition of labor productivity growth for individual countries
in five-year intervals covering the period 1970–2015. 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
3% 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 6.9% in 2005–2010. In contrast, the early starters (Japan and the Asian Tigers) have been
experiencing a slowdown in labor productivity growth since their height in the late 1980s. In bothHong 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 mature85

2017 Asian Productivity Organizationaccounting for 26% of labor productivity growth. Coincidentally, this was also the period when theshare of TFP growth was the largest, at 58%.

5 Productivity

Table 14 Role of TFP and Capital Deepening in Labor Productivity Growth, 1970–2015

86

Cambodia
ROC
Hong Kong

−2.5
(100)
1.6
(149)
0.3
(49)
0.5
(23)
0.5
(22)
−0.3
(−12)
−0.2
(−6)
−0.5
(−18)
0.3
(8)
0.0
(−1)
0.1
(4)
0.8
(23)
4.8
(72)
1.8
(35)
7.1
(69)
2.6
(42)
3.2
(42)
4.2
(40)
2.0
(27)
3.0
(44)
0.5
(28)
−0.7
(−66)
−3.0
(172)
2.5
(118)
−0.5
(70)
0.1
(10)
0.4
(76)
0.3
(28)
3.1
(122)
0.3
(37)
−0.2
(−56)
−0.2
(−24)
2.1
(70)
2.9
(72)
1.9
(61)
2.5
(60)
3.1
(67)
3.5
(50)
1.2
(24)
1.9
(53)
2.6
(35)
−11.4
(182)
0.3
(15)
−0.6
(32)
2.7
(170)
2.6
(295)
2.7
(91)
1.1
(19)
−3.1
(177)
−0.3
(−28)
0.3
(4)
−2.9
(−63)
2.1
(31)
2.4
(36)
1.2
(19)
0.9
(17)
1.2
(29)
2.0
(44)
0.3
(16)
0.8
(16)
0.8
(18)
1.1
(23)
−3.4 (−200)
1.9
(55)
1.4
(21)
−2.6 (−248)
1.5
(47)
1.2
(57)
0.7
(33)
0.3
(9)
−1.8 (−42598)−2.7 (2904)
−0.5
(−19)
1.3
(31)
0.3
(15)
1.1
(38)
−0.4
(−59)
0.5
(27)
−0.2
(−26)
−0.3
(−16)

Indonesia

0.0(0)−0.6 (−52)
0.3
(44)
1.7
(74)
1.6
(73)
2.7 (106)
3.8 (102)
2.9 (112)
3.7
(87)
1.8
(96)
2.8
(95)
2.7
(76)
1.8
(27)
3.3
(64)
3.1
(30)
3.4
(55)
3.8
(49)
5.8
(55)
4.9
(68)
3.5
(52)
1.3
(68)
1.6 (162)
1.2 (−70)−0.5 (−22)
−0.3
(52)
1.0
(92)
0.0
(7)
0.5
(55)
−0.6 (−23)0.5(55)
0.6 (153)
0.8 (121)
0.8
(29)
1.0
(27)
1.2
(37)
1.5
(37)
1.4
(31)
3.3
(47)
3.6
(72)
1.6
(45)
4.7
(64)
5.1 (−82)
1.8
(83)
−1.3
(71)
−1.2 (−75)−1.8 (−203)
0.1
(3)
4.5
(77)
1.3 (−72)
1.5 (120)
5.3(93)
7.0 (153)
4.2
(63)
3.6
(55)
4.6
(74)
3.8
(71)
2.4
(57)
2.4
(51)
1.3
(80)
3.8
(76)
3.6
(81)
3.7
(75)
5.0 (295)
1.5
(41)
4.8
(74)
3.2 (306)
1.0
(31)
0.4
(18)
1.2
(55)
2.7
(82)
1.7 (41348)
2.5 (−2704)
3.0 (116)
2.7
(67)
1.9
(84)
1.8
(60)
1.1 (152)
1.4(69)
1.1 (114)
1.9 (112)Japan

0.0 (−1)
0.0
(3)
0.0
(7)
0.1
(3)
0.1
(5)
0.1
(6)
0.1
(4)
0.2
(6)
0.2
(5)
0.1
(6)
0.0
(1)
0.0
(1)
0.0
(1)
0.1
(2)
0.1
(1)
0.2
(3)
0.6
(8)
0.5
(5)
0.3
(4)
0.2
(3)
0.1
(4)
0.0
(4)
0.0 (−2)0.1(4)0.1 (−21)
0.0 (−2)
0.1
(17)
0.2
(17)
0.0
(2)
0.1
(9)
0.0
(3)
0.0
(3)
0.0
(1)
0.0
(1)
0.1
(2)
0.1
(3)
0.1
(3)
0.2
(3)
0.2
(4)
0.1
(3)
0.1
(1)
0.0 (−1)0.0(2)0.0 (−3)
0.1
(5)
0.1
(8)
0.2
(7)
0.2
(4)
0.1 (−5)
0.1
(8)
0.2
(3)
0.4
(10)
0.4
(6)
0.6
(9)
0.4
(7)
0.6
(12)
0.6
(14)
0.2
(4)
0.1
(4)
0.4
(8)
0.1
(1)
0.1
(2)
0.1
(5)
0.1
(4)
0.3
(4)
0.4
(43)
0.7
(21)
0.6
(26)
0.3
(12)
0.3
(9)
0.1 (1350)
0.1 (−100)
0.1
(3)
0.1
(1)
0.0
(1)
0.1
(2)
0.1
(7)
0.1
(4)
0.1
(11)
0.1
(4)

Lao PDR

−2.5
1.1
0.6
2.3
2.2
2.5
3.7
2.6
4.3
1.9
2.9
3.5
6.6
5.1
10.3
6.3
7.7
10.5
7.2
6.7
1.9
1.0
−1.7
2.2
−0.7
1.1
0.5
1.0
2.5
0.9
0.4
0.6
3.0
3.9
3.2
4.1
4.6
6.9
5.0
3.5
7.3
−6.2
2.1
−1.8
1.6
0.9
3.0
5.9
−1.7
1.2
5.8
4.6
6.6
6.5
6.2
5.4
4.2
4.6
1.6
5.0
4.5
5.0
1.7
3.5
6.4
1.0
3.1
2.2
2.3
3.3
0.0
−0.1
2.6
4.0
2.2
2.9
0.8
2.0
1.0
1.7

Labor
Productivity

TFP

Mongolia

1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2015
1970–2015
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2015
1970–2015
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2015
1970–2015
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2015
1970–2015
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2015
1970–2015
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2015
1970–2015
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2015
1970–2015
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2015
1970–2015

Capital deepeningIT
Non-IT

Pakistan

Nepal2017 Asian Productivity Organization

Malaysia

Korea

Iran

India

Fiji

China

Bangladesh

Labor
Productivity

1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2015
1970–2015
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2015
1970–2015
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2015
1970–2015
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2015
1970–2015
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2015
1970–2015
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2015
1970–2015
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2015
1970–2015
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2015
1970–2015

5.0
2.4
4.1
3.1
4.4
3.6
6.0
7.5
4.7
7.1
5.6
5.2
3.7
3.8
1.3
5.0
2.4
7.1
3.9
7.1
4.0
−0.2
3.1
3.5
2.3
3.7
4.2
4.9
0.6
4.8
6.3
−1.4
3.3
1.8
4.5
3.2
5.1
3.6
3.5
4.2
1.9
2.1
1.8
0.8
1.1
2.7
3.0
1.3
5.1
1.5
3.6
2.3
4.8
5.9
6.9
3.8
5.1
3.2
4.1
−0.8
−1.5
2.6
2.8
4.9
7.7
3.1
0.1
4.1
4.8
3.9
3.4
1.9
3.3
0.2
2.1
2.7

Capital deepeningIT
Non-IT

0.1
(1)
0.1
(3)
0.1
(2)
0.1
(4)
0.1
(3)
0.1
(3)
0.5
(8)
0.4
(5)
0.3
(7)
0.3
(4)
0.3
(5)
0.7
(13)
0.5
(15)
0.1
(3)
0.1
(5)
0.3
(7)
0.2
(9)
0.3
(4)
0.3
(9)
0.5
(7)
0.4
(11)
0.7 (−299)
0.5
(15)
0.3
(10)
0.3
(13)
0.4
(11)
0.0
(0)
0.1
(3)
0.1
(24)
0.2
(3)
0.3
(4)
0.2 (−12)
0.2
(5)
0.2
(11)
0.2
(5)
0.2
(5)
0.5
(9)
0.3
(7)
0.3
(8)
0.5
(12)
0.3
(17)
0.4
(19)
0.3
(18)
0.2
(22)
0.1
(6)
0.3
(12)
0.0
(0)
0.0
(2)
0.0
(1)
0.1
(5)
0.2
(5)
0.2
(8)
0.3
(6)
0.3
(6)
0.7
(10)
0.2
(5)
0.1
(1)
0.1
(4)
0.1
(3)
0.1 (−10)
0.1 (−5)
0.1
(5)
0.2
(7)
0.4
(8)
0.3
(3)
0.2
(5)
0.0
(17)
0.0
(0)
0.0
(0)
0.1
(2)
0.1
(3)
0.0
(2)
0.1
(3)
0.1
(40)
0.0
(2)
0.1
(2)

−0.3
(−5)
0.1
(6)
1.1
(27)
3.2 (103)
2.2
(50)
1.5
(41)
5.6
(94)
4.3
(56)
3.3
(70)
2.7
(38)
3.2
(57)
3.3
(62)
2.2
(60)
1.6
(41)
0.5
(41)
3.0
(59)
1.6
(67)
2.2
(31)
3.4
(89)
3.0
(42)
2.9
(73)
1.7 (−760)
1.1
(34)
1.2
(34)
0.8
(34)
2.0
(54)
1.6
(38)
3.9
(79)
3.4 (606)
3.0
(62)
4.2
(68)
3.4 (−240)
1.9
(58)
1.1
(61)
3.3
(73)
2.9
(89)
5.2 (100)
2.0
(57)
1.6
(45)
1.6
(39)
1.8
(94)
1.3
(61)
0.5
(30)
0.3
(43)
−0.2 (−19)
1.6
(59)
1.4
(47)
2.3 (181)
1.7(33)
1.9 (129)
3.6 (100)
4.9 (212)
3.4
(71)
2.7
(45)
5.2
(75)
3.0
(79)
4.4
(86)
4.4 (137)
4.3 (106)
0.9 (−110)
1.1 (−74)−0.5 (−19)−2.4 (−85)
2.5
(52)
3.7
(49)
2.1
(66)
0.2 (244)
1.7
(42)
1.9
(40)
2.6
(66)
2.4
(71)
1.5
(77)
0.3
(9)
−0.1 (−29)−0.5 (−24)
1.1
(42)

TFP

5.2
2.1
2.9
−0.2
2.1
2.0
−0.1
2.9
1.1
4.1
2.1
1.3
0.9
2.1
0.7
1.7
0.6
4.6
0.1
3.6
0.6
−2.6
1.6
2.0
1.2
1.3
2.6
0.9
−3.0
1.6
1.8
−5.0
1.2
0.5
1.0
0.2
−0.5
1.3
1.6
2.0
−0.2
0.4
0.9
0.3
1.2
0.8
1.5
−1.1
3.4
−0.5
−0.2
−2.8
1.1
2.9
1.1
0.6
0.7
−1.3
−0.4
−1.7
−2.7
3.0
5.0
1.9
3.7
0.9
−0.2
2.4
2.8
1.2
0.9
0.4
2.9
0.2
2.6
1.5

(104)
(91)
(71)
(−8)
(47)
(56)
(−1)
(39)
(23)
(58)
(37)
(25)
(25)
(56)
(54)
(34)
(24)
(65)
(3)
(51)
(16)
(1160)
(51)
(56)
(52)
(35)
(61)
(18)
(−530)
(34)
(28)
(352)
(36)
(28)
(22)
(6)
(−10)
(36)
(47)
(49)
(−12)
(21)
(51)
(35)
(113)
(30)
(52)
(−83)
(66)
(−34)
(−5)
(−120)
(23)
(49)
(16)
(16)
(13)
(−41)
(−9)
(220)
(179)
(114)
(178)
(40)
(48)
(29)
(−161)
(58)
(59)
(31)
(27)
(22)
(89)
(89)
(122)
(56)

ASEAN6

(23)
(−19)
(137)
(109)
(−97)
(4)
(71)
(76)
(65)
(7)
(10)
(58)
(31)
(134)
(92)
(207)
(49)
(44)
(17)
(52)
(47)
(89)
(116)
(67)
(56)
(2)
(13)
(−61)
(17)
(25)
(−20)
(−9)
(29)
(53)
(26)
(12)
(67)
(62)
(47)
(32)
(−56)
(26)
(55)
(50)
(51)
(44)
(61)
(56)
(40)
(41)
(42)
(22)
(−633)(54)(20)(−586)
(55)
(48)
(29)
(14)

Thailand

0.3
−0.5
−6.8
3.0
−0.5
0.1
0.8
1.9
2.7
0.1
0.1
2.1
1.6
0.2
3.8
2.2
1.8
1.7
0.9
1.6
−0.5
1.1
3.0
1.0
3.2
0.2
0.9
−1.7
0.9
0.9
−0.6
−0.2
0.8
2.0
0.8
0.2
1.9
1.9
1.3
0.9
−1.6
0.8
1.6
1.9
2.3
1.3
2.6
3.7
2.1
1.6
1.4
0.8
−2.8
2.5
1.1
−3.1
1.7
1.0
1.1
0.4

Singapore

TFPUS

Capital deepeningIT
Non-IT
0.1
(6)
0.8
(71)
0.1
(3)
2.8 (115)
0.1 (−3)
1.7 (−35)0.1(4)−0.3 (−12)0.0(9)
1.0 (188)
0.5
(15)
2.4
(81)
0.5
(48)
−0.2 (−19)
0.2
(9)
0.4
(14)
0.1
(3)
1.3
(32)
0.2
(14)
1.1
(79)
0.0
(2)
0.9
(88)
0.0
(1)
1.5
(40)
0.1
(2)
3.4
(67)
0.0 (−1)
0.0 (−33)
0.0
(1)
0.3
(7)
0.1
(7)
−1.2 (−115)
0.2
(7)
1.6
(44)
0.3
(7)
1.8
(49)
0.1
(1)
4.2
(82)
0.1
(3)
1.4
(45)
0.0
(0)
−0.5
(53)
0.0
(2)
0.1
(9)
0.1
(2)
−0.5 (−18)
0.1
(6)
0.4
(26)
0.1
(2)
2.4
(42)
0.2
(3)
7.0
(95)
0.2
(2)
6.2
(85)
0.3
(10)
4.3 (151)
0.3
(6)
4.3
(77)
0.1
(4)
2.6
(71)
0.3
(8)
3.5 (112)0.2(7)2.2 (102)
0.2
(7)
1.6
(64)
0.3
(8)
1.4
(39)
0.2
(7)
1.9
(67)
0.2
(12)
1.6
(77)
0.2
(7)
0.7
(26)
0.1
(4)
1.1
(34)
0.1
(3)
1.4
(49)
0.2
(7)
1.7
(61)
0.3
(12)
4.0 (144)
0.2
(7)
2.0
(67)
0.2
(7)
1.1
(38)
0.3
(9)
1.6
(41)
0.2
(5)
2.0
(44)
0.2
(8)
1.4
(48)
0.3
(7)
1.4
(32)
0.2
(4)
2.7
(41)
0.2
(3)
3.0
(57)
0.2
(6)
2.1
(53)
0.1
(2)
1.9
(56)
0.1
(4)
2.5
(74)
0.2
(38)
3.1 (696)
0.2
(5)
1.9
(41)
0.3
(6)
4.0
(74)
0.3
(53)
3.4 (632)
0.3
(9)
1.2
(36)
0.3
(13)
0.8
(39)
0.3
(8)
2.5
(63)
0.2
(8)
2.4
(79)

Asia24

Labor
Productivity
1970–1975
1.2
1975–1980
2.4
1980–1985−5.0
1985–1990
2.8
1990–1995
0.5
1995–2000
3.0
2000–2005
1.1
2005–2010
2.4
2010–2015
4.1
1970–2015
1.4
1970–1975
1.1
1975–1980
3.6
1980–1985
5.1
1985–1990
0.1
1990–1995
4.2
1995–2000
1.1
2000–2005
3.7
2005–2010
3.8
2010–2015
5.1
1970–2015
3.1
1970–1975−1.0
1975–1980
1.3
1980–1985
2.6
1985–1990
1.4
1990–1995
5.7
1995–2000
7.4
2000–2005
7.3
2005–2010
2.8
2010–2015
5.5
1970–2015
3.7
1970–1975
3.1
1975–1980
2.1
1980–1985
2.6
1985–1990
3.7
1990–1995
2.9
1995–2000
2.1
2000–2005
2.9
2005–2010
3.1
2010–2015
2.8
1970–2015
2.8
1970–1975
2.8
1975–1980
2.9
1980–1985
3.0
1985–1990
3.9
1990–1995
4.5
1995–2000
3.0
2000–2005
4.2
2005–2010
6.7
2010–2015
5.2
1970–2015
4.0
1970–1975
3.4
1975–1980
3.4
1980–1985
0.4
1985–1990
4.7
1990–1995
5.4
1995–2000
0.5
2000–2005
3.2
2005–2010
2.2
2010–2015
3.9
1970–2015
3.0

South Asia

East Asia

APO20

Vietnam

Sri Lanka

Philippines

5.4 Sources of Labor Productivity Growth

Labor
Productivity
1970–1975
4.3
1975–1980
3.2
1980–1985
3.3
1985–1990
3.4
1990–1995
3.6
1995–2000
3.1
2000–2005
3.7
2005–2010
0.8
2010–2015
1.1
1970–2015
2.9
1970–1975
3.1
1975–1980
0.9
1980–1985
3.1
1985–1990
6.3
1990–1995
6.2
1995–2000
1.2
2000–2005
5.2
2005–2010
2.4
2010–2015
4.5
1970–2015
3.7
1970–1975
1.6
1975–1980
1.1
1980–1985
1.8
1985–1990
1.5
1990–1995
1.7
1995–2000
2.5
2000–2005
2.2
2005–2010
1.4
2010–2015
0.6
1970–2015
1.6
1970–1975
2.7
1975–1980
2.1
1980–1985
2.5
1985–1990
3.7
1990–1995
4.3
1995–2000
2.7
2000–2005
4.2
2005–2010
5.9
2010–2015
4.7
1970–2015
3.7
1970–1975
0.2
1975–1980
1.1
1980–1985
3.1
1985–1990
3.8
1990–1995
3.2
1995–2000
3.7
2000–2005
4.3
2005–2010
5.8
2010–2015
4.6
1970–2015
3.3

Capital deepeningIT
Non-IT
0.4(11)
5.1 (120)
0.3
(9)
2.7
(83)
0.5
(16)
4.1 (122)
0.6
(19)
0.5
(16)
0.6
(17)
1.1
(30)
0.6
(19)
2.9
(96)
0.6
(17)
1.6
(44)
0.2
(22)
−0.9 (−113)
0.5
(42)
0.9
(78)
0.5
(17)
2.0
(68)
0.1
(2)
2.0
(64)
0.1
(13)
−0.1 (−15)
0.2
(7)
2.4
(76)
0.3
(5)
1.8
(28)
0.6
(9)
5.3
(85)
0.4
(31)
3.5 (301)
0.2
(4)
0.7
(14)
0.4
(17)
0.7
(27)
0.4
(10)
2.3
(51)
0.3
(8)
2.1
(56)
0.2
(12)
1.0
(63)
0.2
(18)
0.3
(22)
0.4
(22)
0.4
(24)
0.4
(29)
0.3
(23)
0.4
(23)
0.3
(19)
0.7
(26)
0.2
(9)
0.5
(25)
0.8
(36)
0.4
(26)
1.0
(75)
0.1
(19)
−0.2 (−41)
0.4
(23)
0.5
(29)
0.2
(8)
3.3 (120)0.1(6)2.1 (101)
0.2
(6)
1.4
(55)
0.3
(7)
1.5
(41)
0.2
(4)
2.1
(49)
0.2
(7)
1.7
(63)
0.2
(6)
1.4
(34)
0.2
(4)
2.7
(45)
0.2
(4)
3.0
(65)
0.2
(5)
2.1
(58)
0.0
(9)
0.7 (381)
0.0
(2)
0.9
(78)
0.0
(1)
1.1
(35)
0.1
(1)
1.3
(34)
0.1
(2)
1.3
(42)
0.1
(3)
1.5
(42)
0.1
(3)
1.4
(33)
0.2
(3)
3.0
(51)
0.2
(4)
3.4
(74)
0.1
(3)
1.6
(49)

TFP
−1.3
0.3
−1.3
2.2
1.9
−0.5
1.4
1.5
−0.2
0.4
1.0
0.9
0.5
4.2
0.3
−2.7
4.2
1.3
1.8
1.3
0.4
0.7
1.0
0.7
1.0
1.6
0.9
0.0
0.7
0.8
−0.8
−0.2
1.0
1.9
2.0
0.8
2.5
3.0
1.5
1.3
−0.5
0.2
2.0
2.4
1.8
2.0
2.8
2.6
1.0
1.6

(−30)
(8)
(−38)
(65)
(53)
(−15)
(39)
(191)
(−20)
(15)
(33)
(103)
(17)
(67)
(5)
(−232)
(82)
(56)
(39)
(36)
(25)
(59)
(54)
(48)
(58)
(64)
(39)
(0)
(122)
(48)
(−29)
(−8)
(39)
(52)
(47)
(29)
(61)
(51)
(32)
(36)
(−290)
(21)
(64)
(65)
(56)
(55)
(64)
(45)
(22)
(48)

5

economies 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 (2.5%) and early 2000s (2.2%), theorigin of which attracted much research attention at the time. In recent years, it has returned to its
long-term average of under 2%.

87

2017 Asian Productivity Organization

Unit: Average annual growth rate (percentage), contribution share in parentheses.
Source: APO Productivity Database 2017.
Note: See footnote 69 for the country-exception in the country groups.

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 difficulty is calculating the compensations for the self-employed and unpaid family
workers. Appendix 4 presents the assumption on measuring the labor compensation for total employment, which was revised in this edition of the Databook. The future review on this assumption affects TFP
estimates directly through the revision of factor income shares and indirectly through the estimates of the
ex-post rate of return and thus the aggregate measure of capital services.

62
40
19

Share of employees to total employment

35
34
34
32
29
28
27

20

Labor income share for employees

Figure B6.1 Labor Income Share for Employees, 2015
Sources: Official national accounts in each country, including author adjustments.

%

4

3.8

3

3.0

2
1
0
−1

TFP(vL+10%)
TFP
TFP(vL−10%)

2.5
2.4
2.1 2.0 2.3
1.7 1.8 1.7
1.6
1.6 1.9 1.7 1.9
1.6 1.5
1.4
1.3 1.3
1.0 1.1
1.0 1.3 1.3 1.3 1.3 1.3
0.8 0.9 0.9 0.9 0.9 0.8 0.8 0.6 0.9 0.4 0.8 0.7
0.5 0.5 0.6
0.4
0.6
0.3 0.3 0.2
0.3 0.2 0.1
0.1 0.0 0.3
0.0
0.2
−0.1
−0.2 −0.3 −0.1 0.0 −0.3 −0.3
−0.4 −0.7
−0.6

Iran

Nepal

Bangladesh

Philippines

Indonesia

Malaysia

Fiji

Singapore

Lao PDR

US

Korea

MongoliaJapan

VietnamThailandHong Kong

Pakistan

Sri Lanka

ROC

India

88

21
48
50

CambodiaFigure B6.2 illustrates the sensitivity of TFP estimates bychanging the factor income
share during the period from1970 to 2015. In general, thegrowth rate of capital input ishigher than that of labor input, therefore the higher income share of labor results inhigher 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 44 and60). The TFP estimate reflects
the improvement of laborproductivity more when thelabor income share increases.
In Malaysia, with TFP growthof 0.3% on average during
the period 1970–2015, thetrue estimate could be 0.8%
if the current labor income
share were underestimatedby 10%.

Myanmar
Pakistan
India
Indonesia
Mongolia
Iran
Bangladesh
Lao PDR

44
40

China

2017 Asian Productivity Organization

The right panel of Figure B6.1 presents the labor income share (the ratio of compensation of employees
to the basic-price GDP) based on the official national accounts (including author adjustments in basicprice GDP for some countries) in 23 Asian countries and the US in 2015. The left panel of the figure illustrates
the employee share to total
employment. There is a significant divergence in labor
income share for employees
100 % 80
60
40
20
0
0
10 20 30 40 50 60 %
85
58
China
among the Asian countries.
94
55
US
This does not necessarily reflect
88
52
Japan
94
51
HongKong
differences in the number
81
49
Korea
27
46
Cambodiaof employees in total em95
46
Singaporeployment. Although Malaysia
30
46
Nepal
40
45
Bhutanand the Philippines have a
83
45
ROC
59
38
high employee share of 78%
Sri Lanka
62
37
Philippines
and 62%, the labor income
78
36
Malaysia
49
36
Thailandshare is only 36% and 37% in
4336
Vietnam
63
35
2015, respectively.
Fiji

Figure B6.2 Sensitivity of TFP Estimates by the Change ofIncome Share, 1970–2015
Source: APO Productivity Database 2017.

5.4 Sources of Labor Productivity Growth

Box 7

Labor Quality Changes

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 (seeAppendix 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 the stage of high economic growth, labor quality growth can be a significant factor as well as theincrease in hours worked, improvement in education attainment of workers, and a shift from the selfemployed (e.g., in agriculture or informal service sectors) to the employees (e.g. in manufacturing or formal service sectors).

5

Figure B7.1 shows the contributions of labor quality and hours worked, to economic growths in Japan andthe US from 1955 to 2012, by Jorgenson, Nomura, and Samuels (2016). Although the US sustained a steadypace of labor quality contribution of 0.1–0.3% on average per year to economic growth over a half century,
the contributions of labor quality were substantially changed in 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
factors that enhanced the growths by 0.3–0.4% for two decades after 1990 even when Japan’s hours
worked began to decrease.
The TFP growth measured in Chapter 5 includes the contributions of labor quality improvements by definition. On the analogy of the experiences of the Japanese economy, it may be reasonable that the current
estimates of TFP growth includes the contributions of labor quality improvements, about 0.5–1.0% peryear in the Asian economies. Although it is a very data-demanding exercise, our 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 growth.
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. Japan is the leading country (13.2 years),followed by Korea (13.0 years), the ROC (12.9 years), Hong Kong (12.2 years) and Mongolia (12.0 years). The
reverse reflects the differences in employment rate of highly educated persons, e.g. higher rate of unemployment of educated persons in Korea. Although there is a significant range in 2015 from 3.8 years (Bhutan) to 13.2 years (Japan), the average years have been increased since 1970 in almost all economies in
Asia. This improvement in labor quality should be measured in QALI. A first set of the QALI estimates areplanned to be published in 2017/2018.

%

2.4

Labor quality
Hours worked

1.7

0.6

0.6
0.9

0.0
0.3

1.0 0.7

1955–60

1.2
0.7 0.6

1.1
0.2
0.2

0.2
0.0

US
Japan

0.0

1.3

0.6

1.2

1960–65

1965–70

0.4
0.6

0.8

0.6

0.2

0.2

0.4 1.0
0.5

0.1

1970– 75

0.4 0.2−0.3−0.4

1975–80

1980–85

1985–90

1990–95

0.8

1.0

0.4
0.4

0.3 0.2−0.3 −0.1

0.3 0.3

1995–2000 2000–05

2005–10−0.6

0.1−0.4 −0.3

0.2 0.3
0.0

2010–12Figure B7.1 Contributions of Labor Quality to Growths in Japan and theUS, 1955–2012
Source: Jorgenson, Nomura, and Samuels (2016).
continued on next page >892017 Asian Productivity Organization

1.8

5 Productivity> continued from previous page

Years

14

Japan, 13.2
Korea, 13.0
ROC, 12.9

13

Hong Kong, 12.2
Mongolia, 12.0
Sri Lanka, 11.3
Malaysia, 11.3
Singapore, 10.8
Fiji, 10.6
Philippines, 10.1
China, 9.8
Iran, 9.6

12
11
10
9

Thailand, 8.7
Vietnam, 8.5
Indonesia, 8.4

8
7

India, 6.2

6

Nepal, 5.4
Bangladesh, 5.1

5Pakistan, 4.7
Cambodia, 4.7

4

Bhutan, 3.8

3
2
1
0
1970

1975

1980

1985

1990

1995

2000

2005

2010

2015

Figure B7.2 Average Schooling Years of Workers, 1970–2015
Sources: Population census and labor survey in each country, including author adjustments.

2017 Asian Productivity Organization

5.5 Energy Productivity
In 2014, in order to produce 41.2% of the world output in the Asia30 (17.3% and 16.0% in the EU28
and the US, respectively), 42.8% (12.2% and 16.1%) of world energy was consumed and 49.8% (10.3%
and 15.6%) of world CO2 wasemitted, as shown in Figure 68.
Energy Consumption
CO2 Emission
This implies that Asia has lower
energy productivity (defined
Japan
Japan
India
India
as a ratio of output per energy
Asia
APO20
APO20
consumption) and higher car46 %Others
Asia
19%
19 %
Others21 %53 %bon intensity of energy at the
26 %aggregate level. It is key to imEU15
Asia 30
Asia 30
8%prove energy productivity and
EU28
43%
50 %
EU15
%
10
10 %
US
EU28
Other
carbon intensity in the growChina
Other
US
16
%
China
12 %
Asia
Asia
16 %
3%
ing economies of Asia in order3%
to reduce CO2 emissions in theworld in the long run.
The average level of energy
productivity in Asia24 is almost
equivalent to the US level and90

Figure 68 Shares of Asia in World Energy Consumption and
CO2 Emission, 2014
Sources: IEA, CO2 Emissions from Fuel Combustion 2016; IEA, Energy Balances of OECDCountries 2016; IEA, Energy Balances of Non-OECD Countries 2016.

5.5 Energy Productivity

Table 15 Energy Productivity Levels, 1980, 1990, 2000, 2010, and 2014

_GDP at constant basic prices per energy consumption, using 2011 PPP, reference year 2015
1980
Hong Kong
Singapore
Iran
Malaysia
Philippines
ThailandBangladeshJapan
Sri Lanka
Indonesia
ROC
Pakistan
Korea
India
Vietnam
Nepal
China

(regrouped)
APO20
Asia24
Asia30
East Asia
South Asia
ASEAN
ASEAN6
CLMV
GCC(reference)
US
EU15
Australia
Turkey1990(%)

28.4 100.0
23.5 82.7
14.7 51.7
13.7 48.5
11.7 41.3
11.1 39.3
10.8 38.1
10.5 37.1
9.5 33.5
9.3 32.9
8.1 28.4
6.5 23.0
6.0 21.1
5.2 18.4
3.9 13.7
3.5 12.4
1.4 4.8

Hong Kong
SingaporeJapan
Malaysia
ThailandPhilippines
Sri Lanka
Bangladesh
ROC
Indonesia
IranPakistan
Korea
India
Vietnam
Nepal
Mongolia
China

5.3
9.0

18.6
31.8

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

7.9
12.8

27.7
45.2

Australia
Turkey8.9 31.5
5.7 20.2
6.5 23.1
4.7 16.7
5.9 20.7
10.1 35.7
11.2 39.6
5.4 19.0
31.3 110.5(%)

30.3 100.0
21.0 69.3
13.4 44.3
12.9 42.6
12.5 41.2
12.0 39.7
11.6 38.4
11.6 38.2
11.2 36.9
10.6 35.1
9.6 31.7
8.1 26.9
7.3 24.3
6.4 21.1
5.4 17.8
4.4 14.4
3.4 11.2
2.5 8.1

10.4
7.2
7.6
6.4
7.1
11.3
12.1
6.5
16.7

34.4
23.8
25.1
21.3
23.6
37.2
40.0
21.6
55.2

7.4
11.1

24.5
36.8

8.7
14.0

28.9
46.2

2000
Singapore
Hong Kong
Sri Lanka
Philippines
Bangladesh
Japan
ROC
Malaysia
ThailandIndonesia
Pakistan
India
Iran
Vietnam
Korea
Mongolia
Cambodia
China
Nepal
(regrouped)
APO20
Asia24
Asia30
East Asia
South Asia
ASEAN
ASEAN6
CLMV
GCC(reference)
US
EU15
EU28
Australia
Turkey(%)

25.1 100.0
24.8 98.6
14.0 55.5
13.8 55.0
13.8 54.7
13.3 53.1
12.9 51.4
12.2 48.7
11.1 44.1
10.7 42.4
9.2 36.5
8.4 33.3
8.2 32.5
7.5 29.7
7.3 29.2
7.1 28.1
5.7 22.7
5.5 21.9
5.1 20.2
10.8
9.1
9.4
8.6
9.1
11.6
12.1
8.4
15.3

43.1
36.1
37.3
34.2
36.3
46.1
48.2
33.2
60.7

8.7
12.5
12.1
10.1
13.9

34.6
49.6
48.1
40.1
55.1

2010
Hong Kong
Singapore
Philippines
Sri Lanka
BangladeshJapan
Indonesia
Malaysia
ROC
India
PakistanThailand
Korea
Iran
Cambodia
VietnamMongolia
China
Nepal
(regrouped)
APO20
Asia24
Asia30
East Asia
South Asia
ASEAN
ASEAN6
CLMV
GCC(reference)
US
EU15
EU28
Australia
Turkey(%)

42.3 100.0
23.8 56.2
22.2 52.5
19.4 45.9
15.8 37.2
15.1 35.7
14.3 33.8
14.3 33.7
13.9 32.9
11.3 26.6
10.8 25.5
10.3 24.4
9.2 21.8
8.9 21.1
8.0 18.8
7.9 18.7
7.3 17.2
7.1 16.8
5.8 13.6
12.7
10.4
10.4
9.5
11.9
13.8
14.6
10.1
11.7

30.0
24.5
24.7
22.3
28.2
32.7
34.5
23.8
27.7

10.5
14.0
13.6
12.3
15.3

24.7
33.0
32.0
29.2
36.1

2014
Hong Kong
Singapore
Philippines
Sri Lanka
BangladeshJapan
Indonesia
ROC
Malaysia
India
PakistanThailand
Korea
Mongolia
Cambodia
Vietnam
China
Iran
Nepal
(regrouped)
APO20
Asia24
Asia30
East Asia
South Asia
ASEAN
ASEAN6
CLMV
GCC(reference)
US
EU15
EU28
Australia
Turkey(%)

43.3 100.0
25.4 58.7
24.5 56.5
24.2 55.9
17.8 41.2
16.4 37.9
16.1 37.3
15.7 36.2
14.0 32.4
12.2 28.2
11.9 27.6
10.3 23.9
9.6 22.3
9.3 21.4
8.7 20.2
8.6 19.9
8.1 18.8
7.6 17.6
6.0 13.8
13.5
11.2
11.2
10.4
13.0
14.6
15.7
9.8
12.1

31.1
25.8
26.0
23.9
30.1
33.8
36.2
22.6
28.0

11.1
15.9
15.4
13.1
18.4

25.7
36.8
35.6
30.3
42.6

5

Unit: Thousands of US dollars per toe (tonne of oil equivalent) (as of 2015).
Sources: Official national accounts in each country, including author adjustments; IEA, Energy Balances of OECD Countries 2016; IEA, Energy Balances of Non-OECD Countries 2016; APO Productivity Database 2017.

Figure 69 placed countries on the two partial productivity indicators of labor and energy, measured in
2014. 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 72 in Section
6.1 (p. 97), there is a positive correlation between the TFP growth and the manufacturing share). Thisfrequently follows the deterioration in energy productivity.
As a next stage of economic growth, well-developed countries will be able to pay more attention to
improving energy productivity by abolishing implicit or explicit subsidies on energy prices, especially912017 Asian Productivity Organization

inferior to the EU15 by 30% in 2014. There is considerable diversity in Asia however, reflecting the differences in industrial structure and energy efficiencies of industries and households 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 47% and 102%
higher than the US and China, respectively, in 2014.

5 Productivity

in electricity prices, and levying heavier taxes
on energy consumptions. The C-shape dynamics found between labor and energy productivities corresponds to the so-called Environmental Kuznets curve, as an inversed U-shape
relationship between environmental quality(at the y-axis) and economic development (atthe x-axis).

Labor productivity(US dollar/hours worked)

Figure 70 decomposes the sources of CO2
emission growth (from fuel combustion) in the
Asian countries during 2000–2014, based on
the so-called Kaya identity. The growth in CO2emissions is decomposed to three components: changes in real GDP; carbon intensity ofenergy; and energy intensity of GDP (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 and Iran, energy productivity has been improved in many Asian
countries in this period, but these improvements are not enough to offset
an expansion of energy con%14
sumption (except in Hong Kong
GDP
and Japan).
12302017 Asian Productivity Organization

10
8

US
60
EU15

50

ROC
Japan

40

TurkeyMalaysia

20

Mongolia−2

Sri Lanka
ThailandIndonesia
China Pakistan
India
Philippines
VietnamBangladesh
Cambodia

10
Nepal
0
0

5

10

15

20

25

30

Energy productivity(Thousands of US dollars/toe)

Figure 69 Labor Productivity and Energy Productivity, 2014
Sources: Official national accounts in each country, including author
adjustments; IEA, Energy Balances of OECD Countries 2016; IEA, Energy Balances of Non-OECD Countries 2016; APO Productivity Database2017.

Carbon intensity of energy

Energy intensity of GDP

CO2

1.1
3.2

3.6

1.8

4.0

1.8

1.6
2.0

9.4
6.7

2
0

Korea

Iran

6
4

Singapore

Australia

7.5
5.6

−1.8

5.1

5.2

4.1

−1.1 −1.0 −0.7
−2.8 −3.0 −2.7

0.2

1.6

0.5

1.6

4.1

5.4 4.8 5.0
4.5 4.0 3.7

7.9

6.7
3.7

−1.0

0.5

−2.1
−1.9

−0.8
−3.0

−0.3 −0.3

−2.0
−3.9

3.8

5.3 0.2
2.0−0.2−1.9 −1.7−1.9 −1.9
−4.1

3.0

−4.0

1.0
0.7

−1.7 −1.5−1.4 −4.7

1.7
1.1 1.1 1.2 1.1 1.7−0.6 −0.3 −0.7 −1.0 −1.0 −0.3 −0.1
−1.2
−1.6
−0.7
−1.8
−1.7 −1.7−1.8 −3.1

−4
−0.1

−6
Italy
UK
FranceEU28
EU15
Germany
US
Japan
CanadaSingapore
ROC
Australia
Hong Kong
Korea
Pakistan
Philippines
TurkeySri Lanka
ThailandIndonesia
Mongolia
Iran
Malaysia
Nepal
India
Cambodia
China
Bangladesh
VietnamOn the other hand, in many
Asian economies, the carbon
intensity of energy has increased, mainly due to an expansion of coal consumption.
Japan achieved some improvement in energy efficiency inthis period, but the carbon
intensity of energy had to be
increased due to a very low operation rate of nuclear power
plants after the Fukushima Daiichi nuclear disaster in March
2011.83 Singapore realized alarge improvement in carbon
intensity of energy by theshift from oil to LNG in electricity

70

Figure 70 Sources of CO2 Emission Growth, 2000–2014
Sources: Official national accounts in each country, including author adjustments; IEA,
Energy Balances of OECD Countries 2016; IEA, Energy Balances of Non-OECD Countries 2016;
IEA, CO2 Emissions from Fuel Combustion 2016.

83: 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, 0.0% in 2014. A few plants were reactivated in 2015 and the utilization rate was slightly recovered to 2.5%.

92

5.5 Energy Productivity

Box 8

Productivity of City

International comparison provided in the Databook is based on an economic territory of each country.
Although the two global cities in Asia, Singapore and Hong Kong, achieved much higher per capita GDP(Table 6 in Section 3.2, p. 29) and per-worker labor productivity (Figure 39 and Table 9 in Section 5.1), thismay be a result of the cities fully incorporating benefits of an urban environment, e.g., economies of agglomeration. Singapore’s population is 5.5 million, which is only 4.4% of that in Japan, 10.8% of Korea, and
0.4% of China. It may be more comparable to Tokyo metropolitan (13.7 million), Seoul city (9.9 million),
Beijing (21.7 million) and Shanghai (24.2 million). Comparing productivity among cities may provide a better picture for understanding a productivity gap among countries, which consist of a number of cities withdifferent scales.

5

The KEO began developing a database on productivity of city in Asia (PDB-City). Figure B8 gives our preliminary estimates on the per-worker labor productivities in 2014/2015 among Asian cities with populations of more than 3 million, compared to some large cities in non-Asian countries. The current PDB-City
covers 51 cities in total, increased by 7 cities from last year’s version. The average per-worker labor productivity level in Tokyo, which is defined as Tokyo metropolitan with population of 15.9 million (not as thegreater metropolitan area with 37.0 million), is 109,000 US dollars, which is 42% higher than the country
average of Japan (77,000). This indicates that Tokyo’s productivity is 15% lower than that in Singapore(128,000). But the productivity gap is smaller than the per capita GDP gap (32% lower) between Tokyo andSingapore, which has a higher employment rate. The productivity in Osaka, which is the largest city of WestJapan, is behind that in Nagoya in terms of labor productivity. Seoul, which is defined as Seoul city with apopulation of 9.9 million (not as the greater metro area with 24.6 million), is in the 19th position on thischart. The gap in labor productivity between Seoul and the country average of Korea is only 3.5%, which
may indicate relatively less concentration to the capital in Korea.

Thousands of US dollars (as of 2015)

180
160
140

40
20

25
21.7

128

24.2

18.1

109
104
15.9

14.4 83.0
81.5

16.7

96
86 85 84

12.5

20

18.4

83 82 81 80

78

15.6
14.8
13.5
75 74

15.5

15
12.8

69 69

12.9
66
10.6
12.3
12.1
10.1
9.9 61 59 59
9.6
11.4
9.1
55 54 53 53
54.8
54.1
9.0
8.5
51.3
8.2
50 48 4849.4
47 46 46 45
7.7
45.0 43.2
44.2
7.4
7.2
41.1
40.6
39.6 39.540.8
6.4
39 38 38
38.2
35.2
5.5
34.536 32 31
7.330.6 32.5
34.0
33.3
28 28 28 26
6.1
26.6
26.3 25.526.3 4.625.8 26.6 25.4
3.5
21.6 19.0
20.3
21.8
3.4
21
21.320.0
3.0
17.9
2.7
2.6 2.8 2.7
2.313.0 17 17 17 16 15
12.0
2.0
12 11
11.5 14.91.7
1.7
3.5
9.8
1.6
1.4
18.6
7.6
1.1
6.5 6.0
4.7 3.7
2.5
3.7 3.7
1.7
3.7 10.7

10.1

Kolkata (IND)
Bangalore (IND)
Hanoi (VIE)
Karachi (PAK)
Lahore (PAK)
Chennai (IND)
Mumbai (IND)
Jakarta (IDN)
Ho Chi Minh (VIE)
Colombo (SRI)Peshawar (PAK)
Haerbin (CHN)
Manila (PHL)
Chengdu (CHN)
Delhi (IND)
Hangzhou (CHN)
Daegu (KOR)
Qingdao (CHN)
Busan (KOR)
Tianjin (CHN)
Shanghai (CHN)
Ankara (TUR)
Shenzhen (CHN)
Beijing (CHN)
Wuhan (CHN)
Kuala Lumpur (MAL)
Riyadh (SAU)
Ulaanbaatar (MGL)
Guangzhou (CHN)
Bangkok (THA)
Istanbul (TUR)
Sapporo (JPN)
Seoul (KOR)
Kuwait City (KWT)
Fukuoka (JPN)
Moscow (RUS)
Yokohama (JPN)
Hong Kong (HKG)
Toronto (CAN)
Osaka (JPN)
Kobe (JPN)
Taichung (ROC)
Kaohsiung (ROC)
Nagoya (JPN)
Sendai (JPN)
Taipei City (ROC)
Paris (FRA)
Tokyo (JPN)
Singapore (SIN)
Los Angeles (US)New York (US)030

Population (right-axis)

141

100 98.3

60

Per capita GDP

150

120

80

Miliions of persons

Labor productivity

10

5

0

Figure B8 Per-Worker Labor Productivity Levels of Cities in PDB-City, 2015
Unit: Thousands of US dollars (as of 2015).
Sources: Official national accounts, Population census and Labor force survey in each country, including author adjustments; The
Brookings Institution, Global Metro Monitor 2014.
Note: For Colombo, Kolkata, and Mumbai, the observation period is 2011. For Bangalore, Bangkok, Cairo, Chennai, Giza, Istanbul,
Jakarta, Kaohsiung, Kuala Lumpur, Kuwait City, Manila, Moscow, Paris, Riyadh, Taichung, Toronto, and Yangon the observation periods are 2014.
continued on next page >932017 Asian Productivity Organization_GDP at constant basic prices per worker, using 2011 PPP, reference year 2015

5 Productivity> continued from previous pageIn this ranking, a number of Chinese cities emerged to the middle class of the chart. Compared to thecountry average of Chinese per-worker labor productivity which is only 17% of the Singapore level, theproductivities in Guangzhou, Beijing, Shanghai, and Tianjin are twice larger than the country average ofChina and reached to 35–45% of the Singapore level, regardless of these cities’ larger populations, whichare 13.5, 21.7, 24.2, and 15.5 million, respectively. These Chinese cities are followed by Daegu, Delhi, Manila,
Peshawar, and Colombo. For better policies to foster nation-wide productivity growth, observing the improvement in city’s productivity may play a key role. The PDB-City is planned to be expanded to observe
the changes over periods and to include smaller cities in Asia.

2017 Asian Productivity Organization

power generation.84 This helped to offset the increases in CO2 emission accompanied by strong economic growth, regardless of very minor improvement in energy productivity. In this period, a decoupling in the growths of GDP and CO2 emission seems to be realized in a few developed countries,
especially in the EU. However, this may be mainly due to the shift of the energy-consuming production to the Asian countries, in which more energy is required and more CO2 is emitted to produce thesame output. For sustainable growth of the world economy, improvements in energy productivity
and carbon intensity of energy are recognized as one of the important policy targets in Asia.

84: In Singapore, the share of natural gas in electricity power generation reached to 95.3% in 2014 from 18.5% in 2000, compared tothe decrease in the share of oil in power generation from 80.0% in 2000 to 0.7% in 2014 (IEA, Energy Balances of Non-OECD Countries 2016).

94

6 Industry Perspective
Industry decomposition gives insight into the source of a country’s economic dynamics, which, inturn, determines its overall performance and characteristics, its strengths, and its vulnerabilities. On
one hand, a broad industry base reflects diversification and sophistication in the economy, and in turnis more resourceful in weathering economic shocks. On the other hand, reliance on a narrow industry
base leaves economy more vulnerable to shocks and more susceptible to volatility. 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 the Asian economies, one can clearly trace the path of economic development and identify countries’ respective stages based on their characteristics.85

6

6.1 Output and Employment
Table 7 in Section 3.2 (p. 29) introduces a country grouping according to stages of development (asmeasured by per capita GDP relative to the US). Table 16 regroups countries based on the same set ofcriteria as in Table 7, but applies it to 2015 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.

Table 16 Country Groups Based on the Current Economic
Level and the Pace of Catching Up_Level and average annual growth rate of per capita GDP at constantmarket prices, using 2011 PPP
Per capita
GDP level to
the US in 2015
(L1)
60% <
(L2)
20% <–< 60%

Annual rate to catch-up to the US
(C1)
>3%

(C2)
1% <–< 3%(C3)
0% <–< 1%

(C4)
< 0%

ROC, Korea

Hong Kong,
Singapore

Japan, Oman

Australia, Bahrain,
Brunei, EU15,
Kuwait, Qatar,
Saudi Arabia, UAE

China

Indonesia, Malaysia,
Mongolia,
Sri Lanka, Thailand

TurkeyIran

Bhutan, India,
Lao PDR, Vietnam

Philippines

Fiji

(L3)
10% <–< 20%
(L4)
< 10%

Cambodia

Bangladesh,
Myanmar,
Nepal, Pakistan

Sources: Official national accounts in each country, including author adjustments.
Note: The annual catch-up rates are based on the data during 1970–2015. The starting
years for Cambodia is 1987.

85: 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 to researchers in this field. The industry data in this chapter is mainly based on official national accounts. Where back data is not available, 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 the potential
impact of these data inconsistencies. In this sense, the industry data in the APO Productivity Database should be treated as a workin 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.
86: 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, Singapore and Vietnam; at producers’ prices for Iran, the ROC and the Philippines; and at market prices for Bangladesh, Indonesia, Japan,
Malaysia, Sri Lanka, and Thailand.

95

2017 Asian Productivity Organization

Countries at the lower rungs ofthe development ladder tend tohave a bigger agriculture sector
as a share of value added.86 Figure
71 shows the industry composition87 of the Asian economies in
2015, and indicates a broad, negative correlation between the shareof the agriculture sector and therelative per capita GDP against the
US. About half of the Asian countries compared have an agriculture
sector accounting for over 10% of
total value added. They all havea relative per capita GDP that is
below 30% of the US level (exceptIran). Among them, the four countries with the biggest agricultural share are all in the lowest
income group in Table 16 (with6 Industry Perspective

a per capita GDP less than 10%of the US). Note also how finance,
real estate, and business activities
grow in importance as one moves
up income levels. The financesector is especially prominent
in Hong Kong (39%), Singapore(33%), and the US (33%). Mining
appears to be what defines oilexporting countries, typically accounting for over 30% of total value
added, except in Bahrain (14%),
Iran (6%), and the UAE (22%),
which are countries that havemanaged to diversify mining. Finance is the biggest sector in Bahrain, accounting for 23% of total
value added, whereas it is the second largest sector (22%) in the
UAE, following mining.

16
15
14
1 6 1 7
32
8
15
1 10
10
17
9
1
28
9
19
22
16
13
8
3
25
12
15
8
6
13
9
13
4 8
24
9
8
6 2 8
41
19
6
15
4 6
12
15
13
11
19
4
10
16
15
12
8
4 8
17
9
18
2 7
14
14
18
2
17
8
14
1 8
21
18
11
2
16
10
14
14
8 2 5
16
19
15
8
16
8 3
15
14
16
6
9
13
16
1 10
10
21
10
7
13
12
20
23
14
15
16
12 1
18
18
3 7
14
20
21
10 1
6
11
2 7
17
29
14
3
9
7
19
3 3
16
27
12
9 3
8
13
1 8
22
20
12
9 3
13
19
3 5
13
23
11
9
9
9
16
3 9
18
19
15
8 1
12
11
1 7
22
12
13
25
3
6
12
24
7 3 9
29
3 6
8
11
3 5
20
30
21
2
8
19
33
21
31
17
2
5
9
23
9 2 7
10
33
2
5
12 6 3
20
14
9
43
1
17
3 6
20
21
23
1
10
12
27
12 2 4
33
12
8
15
3 10
9
9
22
22
1
9
22
6 33 6 6
17
38
1
7
1 7
21
18
14
23
8
10
13
9 1 9
37
16
4
10
26
17
11 5
39
12
17
1 5
12
19
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

Nepal
Cambodia
Pakistan
Lao PDR
MyanmarVietnamBhutanIndia
Bangladesh
Mongolia
Iran
Indonesia
Fiji
Philippines
China
ThailandSri Lanka
Malaysia
TurkeySaudi Arabia
Australia
Korea
ROC
Oman
BruneiJapan
US
UAE
KuwaitBahrain
Qatar
Hong Kong
Singapore
100 %Group-L4(except for
Lao PDR)Group-L3(except for
Bangladesh,Indonesia, Iranand Mongolia)

Group-L2

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

To foster productivity in the lessdeveloped countries, it is important to adopt existing technologies
from the advanced economies. In
Figure 71 Industry Shares of Value Added, 2015
this view of assimilation, manufacSources: Official national accounts in each country, including author adjustments.
turing is a key sector in driving
countries to make a leap in economic development. It accounts for around 20% or more of total value added in nine of the 30 Asian
countries compared. Among these, manufacturing is the largest sector in the ROC, Korea, and China
equivalent to around 30% of total value added, while in Thailand and Malaysia it accounts for a quarter or more. Figure 72 shows a positive correlation between our estimates of TFP growth in Chapter 5
during 2000–2015 and the shares of manufacturing in 2015.88 Outlier countries are Hong Kong andMongolia,89 who have a higher share of services and mining, respectively.

2017 Asian Productivity Organization

Figure 73 shows the breakdown of the manufacturing sector, comprising nine sub-industries, for 17
selected Asian countries and the US.90 The dominance of machinery and equipment in Asian manufacturing is apparent, particularly in Singapore (55% of manufacturing’s total value added), the ROC(65%), Korea (52%), and Japan (49%). These compare with 43% in the US. At the other end are countries dominated by light manufacturing; e.g., the food products, beverages, and tobacco products
sector in Mongolia (55%), Sri Lanka (54%), the Philippines (50%), and Fiji (49%); and the textiles, wearing
87: 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, Revisions 3 and 4.
88: The estimates for the Lao PDR is newly added in this edition of Databook.
89: 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.
90: 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, Revisions 3 and 4.

966.1 Output and Employment

apparel, and leather products sector
in Cambodia (66%) and Bangladesh(49%). Coke, refined petroleum
products, chemicals, rubber, andplastic products are also a prominent subsector. They account for
two-thirds of Kuwait’s manufacturing value added (62%).

%

4.0

TFP growth during 2000–2015

Mongolia

3.5

China
3.0

India

2.5

Thailand2.0
Lao PDR

Hong Kong

Another noteworthy point is that
the 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 the Asia30 dropped
from 61% in 1980 to 33% in 2015.

Philippines

Cambodia

1.5

Sri Lanka

Malaysia

Singapore

Indonesia
Japan

Fiji
1.0
US

0.5

Iran
0.0
−0.5

Nepal
0

5

6
ROC

Korea

VietnamBangladesh

10

15

20

25

30

35 %

Manufacturing share, 2015

Figure 72 Manufacturing Share and TFP Growth, 2000–
2015
Sources: Official national accounts in each country, including author adjustments;
APO Productivity Database 2017.

3 2 4 510
5 1 Mongolia
21
10
6 2 4 2 Sri Lanka
3 316
2 Philippines
6
7
7
4 1 9
6
Fiji
34
8
4 4
31
4 4
12
1 Indonesia
31
5
12
12
3
30
7
Hong Kong
23
6 12
25
4 2
33
4 Thailand
15
3 21
41
9
14
15
1 Iran1566
12 3 3 2 8
Cambodia
13
2 4 2
31
4 3
42
1 Malaysia
12
13 5
28
2 3
43
4 US
12
11 4
15
3
9
49
6
Japan
10
3 4
62
7 2
9
2 Kuwait
10
14
2 3
22
6
10
27
6
India
10
49
21 3
9
3
13
10
Bangladesh
5 2
31
1
55
5 Singapore
5 5 2
16
3
15
52
2 Korea
4 3 2
15
2 4
65
5 ROC
55
54
50
49

0

20

16
19

5 12
11

40

18

60

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 73 Industry Shares of Value Added in Manufacturing, 2015
Sources: Official national accounts in each country, including author adjustments.

91: The group averages as industry share of value added are based on a country’s industry GDP, using the PPPs for GDP for the whole
economy without consideration of the differences in relative prices of industry GDP among countries.
92: 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.

972017 Asian Productivity Organization

Figure 74 shows the industry sharesof value added and employment by
the four country groups based on2015 income levels, compared with
the Asia30 average and the US for
the years 1980, 1990, 2000, and2015.91 In 2015, the service sector
accounted for the largest share of
total value added in all countrygroups, independent of their economic development.92 That said,
among all Asian countries, GroupL1 has always had the biggest service sector. This has become much
more distinctive over time as thebulk of the economy in this group
continues to shift heavily toward
services. By 2015, the service sector
accounted for 64% of total value
added in Group-L1, compared to 80%
in the US and 50% in Group-L2.93
The weight of the service sector issimilar in Group-L3 and Group-L4 at
58% to 50%. This reflects the relative importance of manufacturing
to the former, and agriculture forthe latter, at their particular stagesof development.

Pakistan6 Industry Perspective

In the past three decades, the value-added share of agriculture in Group-L3 has more than halved
from 33% in 1980 to 16% in 2015, 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 havenot been as successful in diversifying away from agriculture, which accounted for 25% of total value
added and 46% of employment in 2015, compared with 37% and 63%, respectively, in 1980. In themeantime, the richest economies continued to squeeze agriculture even though it had a share of only
3% in total value added and 16% in total employment in 1980. By 2015, 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.94 Thus,
countries with a sizeable agriculture sector often have low per capita GDP. In these cases, shifting out
of agriculture will help boost economy-wide labor productivity. The US is an exception, where its agricultural value-added share and employment share are similar; suggesting that labor productivity inthis sector is higher 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 greater value-added sharethan suggested by its employment share. In 2015, the sector accounted for 33% of total value added
generated by 20% of employment in the US, and 16% and 2%, respectively, in the Asia30. While the
1980

%

100
90
80
70
60
50

17

21

13 4
7
7
12
8

40

2

30

21

18

8
12
1
2
5
7 3
1
7
13
11 1

13

11 8

6

3

8

4
12

12

9
2
4
10

9
19

20

67

2

16

21

24

7

25

36

3
1

10

13

1
3
6

15 5
7

22

13

8

6

6
14

8
12

5
1

1
3

1

21
63

63

13
61

10
37

15

5
2 6
1

12
21

23
14

3

8 9

6

3

8 12

11

3
20 10

2 1

23 24

11
1
3

13

3

7
2

11

28

2
58

23

13
57

34

6

15 20
6
3 9
21
19

4
1

14

14 1

28

14

48

10 14

18

4
1
10

2

7

8 6
1

14 2
4

18 14

14 14

8

19

52

3
32

8

15
8 6
13 15
48

19

9
4
2 6

19

11

15 13
1

1 2

18

18 13
1

11

18

2
16

16 5

7
1

8

13

14

4
2

27

1

1
15

8

1 4

4

28

10

15

9

33

1
20

8
46

45

43

15

6 16
2
1

12

7

27

7

2

33
25

16

8
12

22

5
9

4
2

5
14
6

12

9
2 1

1 1

US

Group Group Group Group Asia30
L-1
L-2
L-3
L-4

Emp.
V.A.

Emp.
V.A.

Emp.
V.A.

Emp.
V.A.

Emp.
V.A.

Emp.
V.A.

Group Group Group Group Asia30
L-1
L-2
L-3
L-4

9
1

16 18

Emp.
V.A.

Emp.
V.A.

Emp.
V.A.

Emp.
V.A.

Emp.
V.A.

Emp.
V.A.

US

1

11
1
5

7 9

13
7
2

15

2

6
2

6

14

8

1

23

14

7

30

2
55

2. Mining
5. Construction
8. Finance, real estate, and business activities

9
3
6

36

38

11

6
3

11

7

14
22

21

22
8

2
4

16

19

26

14

4
2

6
3
16

15

1 1

7

15 15

1
5

14 10

7

5
3 1

15 13

2
4

6

13 1

1

15

2015

Emp.
V.A.

Emp.
V.A.

Emp.
V.A.

Emp.
V.A.

Group Group Group Group Asia30
L-1
L-2
L-3
L-4

1. Agriculture
4. Electricity, gas, and water supply
7. Transport, storage, and communications

15

2 2

Emp.
V.A.

Emp.
V.A.
US

3

Emp.
V.A.

Emp.
V.A.

Emp.
V.A.

Emp.
V.A.

Emp.
V.A.

Group Group Group Group Asia30
L-1
L-2
L-3
L-4

14

7 10

10 14

8 7

4
2 6
1
18

20
3 1
2 3

26

7 5

6

20

6

14

15

9 10

19

17

4
2

63

31

38

4
8

12

18
27

8

13 1

18
59

16

1
4

8

6
2

25

14 12

13 1

14
1

18

7

2000

9

8 4

13

2

10

1990

11

33
17

7

1
3

13

1

23

11

9 11

9

12 1

6
2

23

15

2

4
1
9

Emp.
V.A.

0

1
3

1

6
1

20
10

12 9

US

3. Manufacturing
6. Wholesale and retail trade, hotels, and restaurants
9. Community, social, and personal services

Figure 74 Industry Shares of Value Added and Employment by Country Group, 1980, 1990,
2000, and 20152017 Asian Productivity Organization

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

93: If Figure 71 were to rank countries by the size of the service sector, Hong Kong would top the table at 92.6%, followed by the US(79.5%), and other Group-L1 countries, namely the ROC (62.1%), Japan (70.0%), and Singapore (73.8%). Fiji is an exception, with alarge service sector share (68.0%) relative to its per capita GDP level.
94: 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.

986.1 Output and Employment

%

%

30

Australia
BruneiHong Kong
Korea
Oman
Saudi Arabia
UAE

25
20

Bahrain
ROC
Japan
Kuwait
Qatar
Singapore

50

40

China
Malaysia
ThailandIndonesia
Mongolia
TurkeyIran
Sri Lanka

30

15
20
10

6

10

5

0

0

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

Group-L2

Group-L1%%

80

BhutanLao PDR

70

Fiji
Philippines

India
Vietnam

70
60

60
50

50

40

40
30

30

20
20

10
0

10
1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

Group-L3

Bangladesh
Nepal

Cambodia
Pakistan

Myanmar1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

Group-L4

Figure 75 Long-Term Trends of Value-added Share in the Agriculture Sector, 1970–2015
Sources: Official national accounts in each country, including author adjustments.

A 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 manufacturing accounts
for 19% of the economies’ value added, compared with 12% in the US in 2015. The US economy ishighly skewed toward the service sector, accounting for 80% of the total value added, compared withan average of 64% in Group-L1 countries. Certainly, its share of finance, real estate, and business activities at 33% was much larger than the share in Group-L1 countries, at 21%. This suggests that 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 asa whole is reflected in the fact that income groups are not filtered out by the size of a country’s manufacturing sector.95 In Asia, the manufacturing employment share is typically smaller than the valueadded share it generates. Furthermore the value-added share of the sector has been shrinking in thehigh-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.
95: If Figure 71 were to rank countries by the size of the manufacturing sector, the ROC would lead with a share of 31.0%, followed by
Korea and China at 29.8% and 28.7%, respectively.

99

2017 Asian Productivity Organization

value-added share of the sector has grown by 11 percentage points in the US over the past three decades, it has only grown by 5 percentage points in the Asia30.

6 Industry Perspective

Figure 75 shows how the shareof the agriculture industry intotal value added shrank overtime in the Asian economies.96
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 for Group-L4 countries.
The share of the agriculture
sector displays a long-term declining trend in all countries,
albeit at different paces and atdifferent starting times. Looking at the available data, theshare of agriculture in mostAsian countries (excluding theoil-exporting countries) clustered around the 30–50%
band in the 1970s, trending
down to the 10–20% band by
2015. Vietnam and Mongolia
are two countries where theagriculture sector experienced
similar declines but within amuch shorter period (fromthe 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 to2.3% in 2015. In many countries, the share of the agriculture sector more than halved
between 1970 and 2015–
from 39% to 13% in Indonesia, from 42% to 17% in India,
and from 43% in 1972 to 16%
in Bangladesh. In China, theshare of this sector also significantly declined, from 36%
in 1970 to 9% in 2015.

0

10

2017 Asian Productivity Organization

100

20

1

30

40

50

60

70

1. Agriculture
3. Manufacturing
5. Construction
7. Transport, storage, and communications
9. Community, social, and personal services

80

90Group-L4(except for
Bhutan, India,
and Vietnam)
Group-L3
except for Indonesia,
Sri Lanka, Mongolia,
and Thailand)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 76 Industry Shares of Employment, 2015
Sources: Official national accounts in each country, including author adjustments.

Agriculture share in total employment

0.8Japan (1885–2015)

0.7

Nepal

0.6
Bhutan0.5
India
Cambodia

0.4

VietnamBangladesh
PakistanIndonesia
ThailandSri Lanka
China

0.3

0.2

Mongolia
Philippines

Iran
Malaysia

0.1

Fiji

Korea ROC
BruneiSingapore

0.0

Hong Kong
1880

Despite the relative decline of
agriculture’s share in total
value added, employment in
the sector for Asia as a wholestill accounted for 33% of total

Nepal
BhutanIndia
Vietnam
Cambodia
Bangladesh
PakistanIndonesia
ThailandSri Lanka
Mongolia
Philippines
China
TurkeyIranMalaysia
Fiji
Saudi Arabia
Korea
Oman
ROC
Japan
BruneiUAE
Australia
US
Qatar
KuwaitBahrain
Singapore
Hong Kong100 %7 1 4 10 2 1 76
9 32
14
1 6 1 1213
13 1 10
6 3 8
17
1 6
15
4 1 117
22
5
15
9
41
15
6
12
11
14
8 2
16
1 7
15
5 1 12
24
7
15
14
4 2
1
33
23
6
14
17
4 3
32
16
7
20
18
7 3
1
29
19
22
8 2
4 7 2 8
29
23
7
20
6
8
1 8
28
12
24
1 10
19
41
1
28
20
18
1 7
9
19
5
21
15
14
18
1
5
17
10
1
18
26
10
20
9
16
5
12 1
20
6
22
7
3
17
16
9 1
4 2
17
48
6 1 8 1 12
8
23
7
25
14
17
5
3 5
17
34
26
5 1 10
6
26
19
1 8
8
27
5
9
17
42
1 8
4
15
4
5 5
22
17
37
3 3 6 281840
11
3 2 7 1 11
7
21
44
5
23 7 1 9
5
14
6
43
20
11 9
5 1
12
45
24
14 6 19 3 1059
12 4 1 12
9
13
21
41
3
1 12
10
20
14
34
8
14
12
29
27
20
3 8
68

54

47
44
43
43
42

1890

1900

1910

1920

1930

1940

1950

1960

1970

1980

1990

2000

2010

Figure 77 Employment Share of Agriculture in Japan during
1885–2015 and Levels of Asian Countries in 2015
Sources: Official national accounts in each country, including author adjustments. The
sources of historical data of Japan are Ohkawa, Takamatsu, and Yamamoto (1974) during
1885–1954 and population censuses since 1920.

6.1 Output and Employment

%

%

60

Australia
BruneiHong Kong
Korea
Oman
Saudi Arabia
UAE

50
40

Bahrain
ROC
Japan
Kuwait
Qatar
Singapore

80
70

China
Iran
Mongolia

60

Indonesia
Malaysia
Sri Lanka
ThailandTurkey

50

30
40
20

30

10

6

20

0
1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

101970 1975 1980 1985 1990 1995 2000 2005 2010 2015

Group-L1

Group-L2

%

%

90

100

80

90

Bangladesh
MyanmarCambodia
Nepal
Pakistan

70
80

60
50

70

40

60

30

50

20
10

BhutanIndia
VietnamFiji
Philippines40

01970 1975 1980 1985 1990 1995 2000 2005 2010 201530
1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

Group-L3

Group-L4

Figure 78 Long-Term Trends of Employment Share in the Agriculture Sector, 1970–2015
Sources: Official national accounts in each country, including author adjustments.

employment in 2015. Figure 76 shows countries’ industry shares in total employment, and ranks themby size of employment in the agriculture sector.97 Figure 77 traces the historical trajectory of Japan’s
employment share of agriculture for the period 1885–2015 and the countries’ levels in 2015, mapped
against Japan’s experience (as circles).98 Large shares of agriculture employment – over 30% in 9 countries – correspond to Japan’s level at the end of the 1950s and the onset of high economic growth. This
may indicate there is much room for improving labor productivity and per capita income.

96: The estimates for the Lao PDR and Malaysia are backwardly estimated until 1970 in this edition of Databook.
97: Data for the Lao PDR and Myanmar are unavailable for Figure 76.
98: In Nepal the employment share of agriculture was revised to 68% in this edition, by considering the property of the extended
definition of employment, from 71% in Databook 2016.
99: Nepal’s employment-by-industry figures are constructed by interpolating benchmark data taken from its labor force survey as wellas its population census. Figure 78 indicates that its share of agriculture has increased since 2001. This reflects the employment
share of agriculture at 61% in the population census of 2001 and its share of 70% in the labor force survey of 2008.

101

2017 Asian Productivity Organization

The trend of employment share over time (Figure 78) suggests that 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.99 This trend is unmistakable in most of the countries plotted in Figure 78.100 Between 1970
and 2015, the employment share in agriculture dropped from 50% to 5% in Korea and from 21% to 4%
in Japan. Employment in agriculture also fell rapidly in the ROC, from 25% in 1978 to 5% in 2015. In
China, the share has declined from 71% in 1978 to 28% in 2015.

6 Industry Perspective

%

2

%

−2

6

1970s

1990s

0

2

−1

Growth of
Employment in Mnf

8

2010s

1

0

10

Growth of
Employment in Mnf

Growth of
GDP in Mnf

4

6%

2

4

−8

1980s

−4

0
−2
−4

−2

1970s
Growth of
GDP in Mnf

0

4

8

12

16 %

1980s
2010s

−6
−3

Japan
US
Australia

2000s

−4

−8

1990s

ROC
Hong Kong
Korea

2000s

−10
−12

%

%

10

6

Growth of
Employment in Mnf

Growth of
Employment in Mnf

1970s

5

8

4

China
Indonesia
Singapore
Thailand

6

India
Iran
Pakistan
Sri Lanka

1990s

3
1980s

2

4

2010s
1980s

2

2000s

1

2000s

1970s

0
0

−2

−2

1990s

0

2010s

5

10

15

Growth of
GDP in Mnf

20 %

0

2

4

6

8

10 %

−1
−2

Growth of
GDP in Mnf

Figure 79 Job Creation in Manufacturing, 1970–2015_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, 2000s, and 2010s(2010–2015). The white dots indicate the rate in the latest decade.

2017 Asian Productivity Organization

It is the manufacturing sector that largely absorbs workers who have been displaced from the agriculture sector, especially in the initial stages of economic development. Figure 79 traces the trajectory ofgrowth 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, 2000s, and 2010s (2010–2015). The growth rate in the 2010s is illustrated by awhite dot. If manufacturing GDP and employment grow at the same rate, a dot will be on a 45-degreeline through the origin running from the lower left to upper right quadrants. Despite positive gains in
manufacturing GDP for the US and Japan, the overall growth in manufacturing employment was
negative – except during the 1970s for the US, and the 1980s for Japan.

100: 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

In Korea and the ROC, expansions of manufacturing output could allow for increases of employment
in the 1970s and the 1980s. However, since the 1990s manufacturing has not been an absorption sector of employment, regardless of the sound expansion of production in this sector (Figure 79). 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 andPakistan, but it is diminishing rapidly in India and Iran.

6

6.2 Industry Growth
As seen in Section 3.1, growth in the Asia30 region accelerated in the period 2005–2010, averaging
6.4% per annum, up from 5.6% in 2000–2005. China and India have been the two main drivers among
the Asian economies, accounting for 49% and 17% of the region’s growth during 2000–2010, and 55%
and 18% during 2010–2015, respectively (Figure 7 in Section 3.1, p. 23). However, looking at the industry composition, the origins of economic growth in China and India are quite different. Bosworth andCollins (2008) indicate that China’s economic growth has been fueled by industry sector expansion;101
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 recognizes
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 80).102 The gap between contributions of manufacturing and services was the widest in the late 1990s until a redress in
2000–2015, with manufacturing and services accounting for 34% (Figure 81) and 46% (Figure 82) of
economic growth, respectively. In contrast, economic growth in India always has been dominated byservices. Its growth has only become more pronounced over time. The contribution of manufacturing
and services to economic growth were 16% (Figure 81) versus 64% (Figure 82) in 2000–2015, compared with 15% and 64% 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 14% in thelate 1990s to 8% in the latest period of comparisons.
Manufacturing has sustained its significance in Thailand, Korea, and the ROC, contributing 29%, 39%,
and 49% to economic growth in 2000–2015, respectively. Its importance is modest in Singapore at
19% (Figure 81). 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 81).

101: The industry sector in Bosworth and Collins (2008) is equivalent to industry groups 2–5 in this report.
102: 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.

103

2017 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 82). The
story behind India’s recent growth has been one of services. Modern information and communication

6 Industry Perspective

1990−2000

2000−2015

%

10
0.7
2.4

0.5
0.5

0.6

8

4.0
1.2
0.5 4.26

1.2

0.2 0.2
0.3
2.4

0.9
3.2

1.3

1.3

1.7 1.8
0.8

2

0

2.4

1.7

−0.4
−0.3

0.6 0.5
0.3
1.1 0.7

1.3 0.8 0.6
0.7
0.5 0.3
0.3
0.2
0.3 1.1
1.
5
1.1
0.2 0.9
0.6
1.0 0.3
1.1 0.7
1.0 0.8
1.5
0.3 0.5
0.2
0.4 1.4
1.5
0.2 0.2
0.6 0.2 1.0
0.5 1.4
0.2
0.3
1.0 0.0.32
1.1
1.0 0.7 1.0 0.6 0.7
0.5 0.5 0.3
1.2
0.9
0.2
0.4
1.2 0.3
0.6
0.9
0.6 0.5 1.3 0.5
0.8 0.5
0.4
0.2
0.7 0.6 1.1 1.5
0.3
1.5 1.0
0.7
1.
0
0.6 0.7 1.8 0.9
0.2
0.9
1.7 0.7 0.4
0.3
0.8
0.8 0.6
0.9
0.
2
0.3
0.
6
1.
0
0.
9
0.9
0.2
0.4
0.5
0.9 1.5
0.4 0.5 0.6 0.7
0.2
0.7
0.4 0.3
0.5
0.7 0.6 0.3
1.0
0.6 0.6 0.6
0.6 0.0.33
1.5
0.5
0.4 1.5 0.8 0.5 0.3
0.8 1.3
0.5
0.5 0.5 0.5 0.4 0.7
0.
6
0.4 0.2
0.
8
1.
0
1.
7
0.7
1.1 0.8
0.5
0.6
0.3
0.5
0.4 1.5 0.6 0.2 0.5 0.8 0.5 0.3
1.1 0.7
0.7 0.6 0.8
0.5 0.6 0.8
0.6
0.5 0.6
1.4
0.4
1.4
0.2
0.4 0.7
0.9
1.0
0.9 0.8 0.9 0.6
0.9 0.8 0.7 0.8 0.8 0.9 1.0
0.6 0.6 0.5 0.3 0.8 0.5 0.3 0.6 0.3 0.3 0.2 0.6
0.5 0.6 0.7 0.5 0.7
0.5
−0.3
−1.2
0.8

0.7 0.3

0.6
0.5 0.5
2.0
0.2
0.6
0.9
0.3
0.2
4.0
1.0 0.9 0.3
0.4 0.3
2.8 0.8 1.5 1.0 0.7
0.2
0.2 2.1
0.9
1.3
0.3
1.2
0.6
0.2
2.0 0.6
1.5 1.3 0.2 0.2
0.
7
1.9
0.3
0.8
0.2
1.3
1.5
0.7
0.2 0.2
1.0
0.3 0.5
0.7
0.9
0.7
0.7 0.7
1.3
1.0
0.3
0.4 1.0 0.9 0.6
0.4
0.4 1.3
0.3
0.6 1.1
1.0 0.7
0.5
0.2
0.
4
0.
3
0.5 0.7 0.3
0.
2
0.5
0.6
0.6 0.8
0.8
0.8
0.4
0.9 0.2
0.6 0.5 0.5 0.7 0.2 0.5 0.2 0.7 0.6 0.2
0.5 0.5
0.6 0.3 1.0
1.4
0.5
0.5
0.7
0.3
1.4 0.5
0.3
0.5 1.1 0.2
0.6
1.3
0.4 0.5 0.7 1.0 0.6 0.3 0.7 0.6
1.5 1.4
1.0 0.7
0.4 0.4 0.5
2.5 0.7
1.4
0.2 0.4 0.4 0.3
0.4
1.
1
0.3
1.0
0.2 0.2 0.3
0.7 0.5 0.2 0.5 1.0 0.5 0.4 0.7
1.0
0.
6
0.2
0.
2
0.5
0.6 0.3 0.4
1.0 0.4 0.2
1.0 0.8
0.4
0.9 1.0 0.7 0.8 1.2
0.9 0.8 0.6 0.6 0.6 0.8 0.6
0.3 0.8 0.3 0.4
0.5 0.7 0.6 0.2
0.5 0.3 0.0.23
0.5
0.4 0.6 0.6 0.2 0.4 0.3
0.2−0.2 −0.3 0.2
−0.4
−0.5
0.6

4

1.3

−0.8

−2

Brunei
Japan
US
Fiji
Australia
Iran
ROC
Nepal
Hong Kong
Myanmar
Korea
ThailandSaudi Arabia
Pakistan
KuwaitOman
TurkeyUAE
Malaysia
Philippines
Singapore
Bahrain
Sri Lanka
Indonesia
Bangladesh
Mongolia
Vietnam
Cambodia
BhutanIndia
Lao PDR
China
Qatar

MongoliaJapan
Fiji
Brunei
Saudi Arabia
Philippines
Hong Kong
US
TurkeyAustralia
Indonesia
Pakistan
Oman
BahrainThailandBangladesh
Nepal
BhutanIran
Sri Lanka
India
MyanmarLao PDR
Qatar
ROC
Korea
Singapore
Cambodia
Vietnam
UAE
Malaysia
China
Kuwait
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 80 Industry Origins of Economic Growth, 1990–2000 and 2000–2015_Industry decomposition: Average annual growth rate of GDP at constant prices
Sources: Official national accounts in each country, including author adjustments.

technology have allowed India to take an unusual path in its economic development, bypassing a
stage when manufacturing steers growth.103 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.104 For further improvement in per capita GDP and to capitalize on the demographic dividend (see Box 11, p. 145), expansion of labor-intensive manufacturing may be required inIndia for greater job creation.105

2017 Asian Productivity Organization

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 50% of growth in the ROC for the period 2000–2015, 55% in Korea,
75% in Singapore, and 100% in Hong Kong, counterbalancing the negative contribution of 2% by

103: The computer software industry in India depends considerably on export demands. According to India’s Input–Output Table
2006–2007 and 2007–2008, 82% and 89% of the outputs in computer and related activities are exported, respectively. These exports are equivalent to 14.4% and 15.5% of total exports in India, respectively, as the second-largest export product (among 130
products in these tables).
104: Of the total motor vehicles produced in the world in 2016 (95.0 million), India overtook Korea (4.2) and became the 5th largest
producer (4.5), following Germany (6.1), Japan (9.2), the US (12.2), and China (28.1), 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, 7th (3.5) in
2010, and 6th (4.2) in 2015.
105: 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“Make in India” initiative in September 2014 with an aim to give the Indian economy global recognition.

1046.2 Industry Growth

1990−2000
5.0 4.0 3.0 2.0 1.0 0.0 −1.0 %
China
2.4
Malaysia
2.4
Korea
2.1
Cambodia
1.8
Singapore
1.7
ROC
1.5
Sri Lanka
1.5
Thailand
1.3
UAE
1.3
Indonesia
1.2
KuwaitTurkey1.1
Bangladesh
1.0
Iran
1.0
Vietnam
0.9
India
0.8
Lao PDR
0.8
Bahrain
0.7
Nepal
0.7
Bhutan0.7
US0.7
Philippines
0.6
Oman
0.5
Myanmar
0.5
Fiji
0.5
Pakistan
0.4
Brunei
0.4
Saudi Arabia
0.3
Australia
0.2
Qatar
0.1
Japan
0.0
Hong Kong−0.4
Mongolia−0.8
Contribution

2000−2015
−30

0

30

60 %

4.2

4.0 3.0 2.0 1.0 0.0 −1.0 %
3.2

42
33

1.7

36

1.5

30

1.5

26

1.5

26

1.4

29

1.1
1.1

34

1.1

18

1.0

33

1.0

12

1.0

34
21

1.0

19

1.0

13

0.9

15

0.8

13

0.8

17

0.7

15

0.7

14

0.7

21

0.7

21

0.6

12

0.5

9

0.4
0.3

28

0.3

10

0.2

18

0.1

13

0.1

7

0.0

1

0.0

0

−0.1

−14

−0.1

62

Contribution share

Contribution

−30
China
ROC
Korea
Cambodia
VietnamBangladesh
ThailandLao PDR
India
TurkeyPhilippines
Malaysia
Indonesia
Singapore
Bahrain
Qatar
Sri Lanka
Oman
Pakistan
Iran
BhutanSaudi Arabia
MyanmarMongolia
UAE
Kuwait
US
Fiji
Nepal
Australia
BruneiHong Kong
Japan

0

30

60 %
34
49
39

24
24
23
29
15
16
24

6

20
22
18
19
16
8
14
17
18
22
9
16
13
6
6
6
10
6
2
1
−7
−2
619

Contribution share

Figure 81 Contribution of Manufacturing to Economic Growth, 1990–2000 and 2000–2015
Sources: Official national accounts in each country, including author adjustments.

manufacturing (Figures 81 and 82). These compare with 90% in the US, to counterbalance the negative contribution of 6% 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
23% 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 40% to growth in Hong Kong, 33% in Singapore, and 15% in the ROC.

For some Asian countries, agriculture is still the principal sector. The seven countries in which the agriculture sector has the largest share in total value added are Nepal, Cambodia, Pakistan, Lao PDR,
Myanmar, Vietnam, and Bhutan (as shown in Figure 71). For the period 2000–2015, agriculture in Nepal, Myanmar, and Cambodia had the highest contribution to economic growth among all Asian

105

2017 Asian Productivity Organization

The oil-exporting countries have different industry structures from other countries, with a reliance on
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 2000–2015, mining accounted for 40% of economic growth in
Qatar, 25% in Kuwait, and 16% in Saudi Arabia (Figure 80). Still, it has been a drain on growth, in somecases a quite significant one. Its contribution was negative in Brunei, Iran, and Bahrain. Bahrain has
been successful in branching into finance, real estate, and business activities, which accounted for
29% of the 5.3% overall growth over the same period. Oman also sustained growth of 4.4% on average
per year, 59% of which originated from the service sector. Brunei has not managed as well, with anegative growth of –0.05% on average per year between 2000 and 2015. Oil and gas production activities are also reflected in Mongolia and the Lao PDR, where mining accounted for 18% and 17% of
overall economic growth, respectively, in the 2000s.

6 Industry Perspective

1990−2000
6.0 5.0 4.0 3.0 2.0 1.0 0.0 −1.0 %
6.2
4.6
4.4
4.3
4.0
3.8
3.4
3.3
3.3
3.1
3.0
2.9
2.8
2.5
2.5
2.5
2.3
2.3
2.2
2.2
2.1
2.1
2.0
1.9
1.8
1.8
1.7
1.5
1.3
1.1
1.0
0.9

Contribution

KuwaitUAE
ROC
Singapore
Malaysia
Korea
China
India
VietnamHong Kong
Sri Lanka
Iran
Australia
Cambodia
Nepal
US
BahrainThailand
Pakistan
BhutanOman
Bangladesh
Lao PDR
Indonesia
Qatar
TurkeyPhilippines
MyanmarJapan
Saudi Arabia
Fiji
Brunei−0.3 Mongolia

2000−2015
0

30

60

90 %

5.0

4.0

3.0

2.0

1.0

4.6

61

4.2

64

4.0

69

3.9

64
55

3.7

57

3.7
3.7

35

3.7

61

3.5

46
103

3.4
3.1

57

3.0

59

3.0

79

2.9

37

2.9

53

2.8

75

2.8

53
52

2.6

53

2.6

45

2.5

49

2.4

46

2.3
2.3

34

2.2

47

2.2

29
52

2.1

57

2.0
1.8

27

1.7

115

1.4

40

1.2

52

1.0

39

0.3

20

Contribution share

Contribution

0.0 %
India
China
Qatar
Singapore
Bahrain
Lao PDR
Mongolia
Hong Kong
Sri Lanka
Indonesia
Philippines
Bangladesh
UAE
Bhutan
Malaysia
Vietnam
Cambodia
KuwaitOman
Pakistan
TurkeySaudi Arabia
Thailand
Nepal
Korea
Australia
Iran
ROC
Fiji
US
MyanmarBrunei
Japan

−30

0

30

60

90 %
64

46
40
75
70
47
61
100
65
61
62
51
65
42
64
46
44
63
59
61
55
57
58
61
55
71
69
50
81
90
33
−2197
−1,212Contribution share

Figure 82 Contribution of Service Sector to Economic Growth, 1990–2000 and 2000–2015
Sources: Official national accounts in each country, including author adjustments.

countries, accounting for 28%, 24%, and 21% of growth, respectively. In the latest period, agricultural
output continued expanding in the majority of Asian countries, suggesting that the reduction in its
value-added share (Figure 75) over the recent period is more a result of rapid growth in other sectors
than any actual decline of the sector.

2017 Asian Productivity Organization

Comparisons across the country groups in Table 17 reveal that Asia achieved more vibrant growththan the US in all sectors. It is notable that the US was more directly affected by the global financial
crisis of 2008–2009 than Asia. Overall, construction slowed in the US in the 2000s, while growth wasstrongest in CLMV and the GCC countries at 9.8% and 7.8% per year on average, respectively. Apartfrom construction, the other fast-growing sectors in CLMV were electricity, gas, and water supply (atover 10% per year on average), presumably reflecting their effort in building industry infrastructure
for their development needs.106 Finance, real estate, and business activities also experienced robust
expansion at 9.6% per year on average in South Asia. Manufacturing has been growing at 9.4% peryear on average in CLMV, compared with 4.3% in the ASEAN6.
Figure 83 presents the sub-industry origins of average annual growth of manufacturing GDP for selected Asian countries for the periods 1990–2000 and 2000–2015.107 Manufacturing in Asia has been
dominated by 3-8 (machinery and equipment) accounting for 35% 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

106: See Chapter 8 for the national development strategies in Asian countries.

1066.2 Industry Growth

Table 17 Output Growth by Industry, 2000–2015

6. Wholesale andretail trade, hotels,
and restaurants

7. Transport,
storage, andcommunications

8. Finance, realestate, andbusiness activities

9. Community,social, andpersonal services

Total economy

(0.0)
(0.7)
(0.5)
(0.0)
(1.3)
(0.5)
(0.0)
(0.1)
(0.0)
(0.6)
(0.5)
(0.4)
(0.0)
(0.0)
(0.0)
(0.9)
(0.3)
(0.6)
(0.9)
(1.0)
(0.0)
(0.6)
(0.3)
(0.0)
(0.1)
(0.0)
(0.3)
(0.2)
(0.0)
(0.8)

−0.2 (0.0) 6.7 (0.9) 11.0
7.3 (0.1) 8.0 (1.4) 7.9
11.4 (0.3) 7.9 (0.7) 10.1
−2.1 (−1.2) 0.0 (0.0) 3.2
18.2 (0.1) 9.8 (1.5) 10.4
9.1 (0.5) 10.0 (3.2) 8.1−5.5 (0.0) 5.9 (1.7) 3.0−2.4 (0.0) 0.9 (0.1) 3.0−2.7 (0.0) −2.7 (−0.1) 1.1
5.0 (0.1) 7.6 (1.1) 9.6
1.2 (0.1) 4.6 (1.0) 6.9−2.4 (−0.3) 5.0 (0.7) 6.5−4.2 (0.0) −0.6 (−0.1) −1.2
−0.6 (0.0) 5.4 (1.5) 4.2
2.3 (1.0) 3.8 (0.3) 6.8
31.0 (1.3) 12.8 (1.1) 6.3
0.6 (0.0) 4.0 (1.0) 4.6
7.8 (1.1) 7.3 (0.4) 4.0
3.6 (0.3) 6.9 (0.5) 11.1
3.8 (0.0) 1.1 (0.1) 4.4
0.4 (0.2) 8.4 (0.7) 8.8
4.2 (0.1) 5.2 (0.7) 2.5
8.5 (0.1) 4.7 (1.0) 4.2
7.5 (4.0) 9.0 (0.8) 10.1
1.7 (0.7) 6.3 (0.6) 6.0
0.0 (0.0) 4.1 (1.0) 3.6
11.2 (0.2) 3.7 (0.8) 6.1
4.4 (0.1) 3.9 (1.1) 4.9
1.4 (0.5) 2.9 (0.3) 9.3
2.6 (0.3) 9.7 (1.5) 10.2(0.1) 7.1 (0.6)(0.1) 7.5 (0.5)(1.5) 7.7 (1.2)(0.0) 2.6 (0.1)(0.0) 9.7 (0.6)(0.3) 10.8 (0.7)(0.1) −0.3 (0.0)(0.1) 2.6 (0.1)(0.0) 1.4 (0.1)(0.2) 7.4 (0.6)(0.1) 6.7 (0.5)(0.2) −1.0 (0.0)(0.0) −1.4 (−0.1)(0.1) 1.2 (0.1)(0.2) 4.2 (0.1)(0.2) 9.4 (0.5)(0.1) 5.6 (0.2)(0.1) 9.2 (0.3)(0.1) 16.0 (0.7)(0.1) 3.6 (0.2)(0.1) 17.2 (0.7)(0.0) 2.8 (0.1)(0.2) 5.4 (0.3)(0.0) 19.9 (1.1)(0.1) 6.1 (0.3)(0.1) 5.1 (0.2)(0.1) 7.8 (0.5)(0.1) 3.6 (0.1)(0.2) 7.2 (0.6)(0.4) 7.9 (0.5)

4.7
6.5
11.7
4.2
7.3
10.6
3.2
2.3
5.3
8.3
5.7
3.7
0.1
2.7
3.4
9.4
6.5
9.2
2.3
2.2
5.5
3.3
5.4
14.2
9.1
6.2
5.2
3.5
4.7
7.7

(0.3)
(0.9)
(0.8)
(0.1)
(0.9)
(1.1)
(0.6)
(0.4)
(1.5)
(1.4)
(1.0)
(0.6)
(0.0)
(0.3)
(0.2)
(1.8)
(1.0)
(1.3)
(0.4)
(0.3)
(0.5)
(0.7)
(0.9)
(0.9)
(0.7)
(1.2)
(0.8)
(0.6)
(0.8)
(0.9)

8.3
7.2
7.9
3.2
7.3
8.9
3.3
3.4
3.9
10.9
10.9
7.4
0.5
4.6
10.6
8.5
6.2
11.5
5.4
5.8
11.5
4.2
6.5
17.4
11.5
4.5
7.8
5.7
7.1
7.7

(0.5)
(0.8)
(0.8)
(0.1)
(0.5)
(0.7)
(0.2)
(0.5)
(0.4)
(0.9)
(1.3)
(0.5)
(0.0)
(0.4)
(0.6)
(0.4)
(0.5)
(0.7)
(0.4)
(0.5)
(0.6)
(0.5)
(0.5)
(0.6)
(0.5)
(0.6)
(0.9)
(0.5)
(0.6)
(0.3)

6.0
4.5
8.5
3.4
8.8
10.0
2.7
2.3
4.2
9.4
6.7
4.2
0.7
4.0
5.1
9.4
6.3
8.5
28.2
4.3
6.3
4.5
6.6
14.2
5.8
6.1
6.8
5.7
6.8
5.4

(1.5)
(0.3)
(0.7)
(0.2)
(0.6)
(1.0)
(0.5)
(0.4)
(1.5)
(1.5)
(0.6)
(0.6)
(0.2)
(0.8)
(1.0)
(0.6)
(0.8)
(0.7)
(0.0)
(0.6)
(0.5)
(0.5)
(1.1)
(1.5)
(0.5)
(1.7)
(0.8)
(0.6)
(1.1)
(0.8)

8.2
4.7
5.3
3.2
8.4
10.0
2.2
1.9
1.8
6.0
5.3
2.5
0.2
3.3
5.4
9.6
5.7
2.3
12.0
6.7
6.2
6.1
4.6
10.1
3.7
4.4
4.5
3.7
7.3
6.8

(1.4)
(1.0)
(0.7)
(0.6)
(0.8)
(1.4)
(0.5)
(0.3)
(0.3)
(0.8)
(0.5)
(0.3)
(0.0)
(0.6)
(0.8)
(0.9)
(0.7)
(0.9)
(0.5)
(0.8)
(0.9)
(0.9)
(0.6)
(0.9)
(0.6)
(0.5)
(1.0)
(0.6)
(0.5)
(0.8)

5.3
5.9
7.1
0.0
6.4
9.3
3.5
2.0
3.7
7.1
5.5
3.0
0.0
3.9
4.2
7.9
4.6
6.1
3.7
3.7
4.4
4.1
5.0
10.0
4.1
5.2
5.4
4.0
4.5
6.2

2.8
3.3
3.3
3.7
3.1
3.0
3.0
3.1
1.9

(0.3)
(0.3)
(0.3)
(0.3)
(0.6)
(0.4)
(0.3)
(0.8)
(0.0)

1.5
4.6
3.5
8.9
5.2
1.7
1.3
3.6
2.1

(0.0)
(0.2)
(0.2)
(0.3)
(0.1)
(0.1)
(0.1)
(0.4)
(0.8)

3.9
6.8
6.8
7.2
7.2
4.6
4.3
9.4
5.7

(0.8)
(1.6)
(1.6)
(2.0)
(1.1)
(1.1)
(1.0)
(1.2)
(0.6)

(0.1)
(0.1)
(0.1)
(0.1)
(0.2)
(0.1)
(0.1)
(0.3)
(0.1)

(0.2)
(0.4)
(0.4)
(0.4)
(0.5)
(0.4)
(0.3)
(0.6)
(0.4)

4.4
6.0
6.1
5.7
7.5
5.4
5.3
6.2
7.2

(0.7)
(0.9)
(0.8)
(0.7)
(1.3)
(0.9)
(0.9)
(0.8)
(0.6)

5.9
6.8
7.0
5.5
9.3
8.5
8.6
6.7
10.2

(0.5)
(0.6)
(0.6)
(0.5)
(0.8)
(0.8)
(0.9)
(0.3)
(0.5)

4.6
5.9
6.0
5.1
8.9
6.3
6.4
5.7
6.6

(0.8)
(0.9)
(0.9)
(0.8)
(1.3)
(0.8)
(0.8)
(0.6)
(0.8)

3.2
5.4
5.4
5.6
5.8
5.0
4.7
7.7
4.9

(0.5)
(0.8)
(0.8)
(0.9)
(0.8)
(0.6)
(0.5)
(0.7)
(0.7)

3.9
5.8
5.8
6.0
6.6
5.0
5.0
5.7
4.6

2.1
0.9
2.5

(0.0)
(0.0)
(0.2)

2.7
4.5
0.4

(0.1)
(0.4)
(0.0)

1.0
2.7
4.3

(0.2) −0.5(0.0) 1.1
(1.0) 5.0(0.0) −1.9 (−0.1)(0.0) 5.6 (0.4)(0.1) 7.9 (0.6)

1.4
2.7
2.4

(0.2)
(0.3)
(0.4)

3.4
3.2
5.0

(0.3)
(0.3)
(0.6)

2.2
3.3
4.5

(0.7)
(0.8)
(0.7)

1.0
2.7
4.3

(0.3)
(0.6)
(0.7)

1.6
2.9
4.4

4.0
5.5
5.6
4.9
8.5
5.8
5.2
10.1
7.5

5. Construction

2. Mining

4. Electricity, gas,
and water supply

−0.3
3.7
2.2
2.8
3.9
4.1
−0.1
0.8
−2.7
3.1
3.6
3.7
−2.4
1.1
4.2
2.9
2.8
4.4
2.5
2.9
2.6
2.6
2.4
6.7
2.4
−0.9
3.3
1.6
−2.3
3.4

1. Agriculture
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
Turkey3. Manufacturing___Average annual growth rate of industry GDP at constant prices

3.6
6.3
6.4
6.3
7.2
6.4
5.9
9.8
7.8

6

107: 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 theReal GDP growth of manufacturing

Contribution of a sub-industry j

nominal GDP share of a sub-industry j in period t.

107

2017 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

for 2000–2015, accounting for 47%
of manufacturing output growth. In
Bangladesh and Cambodia, manufacturing growth has been dominatedby the sub-sector of 3-2 (textiles,
wearing apparel, and leather products), whereas in Kuwait, and to alesser extent Singapore and Iran,it is 3-5 (coke, petroleum, chemicals,
rubber, and plastic products).

1990–2000

%

18

9

1.3

16

8

14

7

12
10
8

4

0.6

0.9
0.7
0.8

6
12.6

5

0.6
0.4
1.5

4

0.4 0.9 1.0

3

2.0

6

2000–2015

%

10

20

1.7 0.8 0.7
0.5 0.9 1.2
0.4
3.6
1.7 1.6
1.7 5.6 5.6
1.5 0.4 2.3
0.6 0.3 0.3
1.0 0.7
0.6
0.3 0.9
0.6 2.9 0.3 0.4
0.4
0.4
1.8 1.3
1.3
0.5 1.0
0.7 1.2 1.4 0.5−0.4−1.0
−0.3
−0.3

2

7.9

0.7 0.5

3.9

0.6

1.7

0.4

0.4
0.6

2.3

0.4
2.1

4.7

0.7
1.0

1.9
0.5
0.6

0.6

0.8

1.4

1.3 0.7
4.0 0.8

1.8
0.4 2.7

2.0

JapanSingapore
KuwaitIndonesia
Thailand
Malaysia
Philippines
Korea
Iran
ROC
India
Bangladesh
CambodiaJapan
US
Philippines
Indonesia
ROC
India
ThailandIran
Bangladesh
Korea
Malaysia
Cambodia

1.9
2
1 0.3
1.2
1.1 1.6
1.5
Figure 84 contrasts industry contri1.0
0.6
0.4 0.7 0.5
0.3 0.3
00butions to economic growth for the−2−1
periods 1990–2000 and 2000–2015,
as well as between the US andAsian averages.108 Even within sucha short period, one can see that the
3-1. Food products, beverages, and tobacco products
industry structure of growth is
3-2. Textiles, wearing apparel, and leather products
changing. The first striking feature
3-3. Wood and wood products
3-4. Paper, paper products, printing, and publishingis the dominance of manufacturing3-5. Coke, petroluem, chemicals, rubber, and plastic products
in Asian countries. Between 1990
3-6. Other non-metallic mineral products
3-7. Basic metals
and 2000, its contribution to eco3-8. Machinery and equipment
nomic growth in the Asia24 was
3-9. Other manufacturing
3. Manufacturing
30% compared to 21% in the US.
Although its significance has fallen
Figure 83 Industry Origins of Output Growth in Manufacin recent years, it still accounted forturing, 1990–2000 and 2000–2015
28% of economic growth in the___Sub-industry decomposition: Average annual growth rate of GDP
Asia24 between 2000 and 2015,at constant prices of manufacturing
compared with 10% in the US. This,
Sources: Official national accounts in each country, including author adjustments.
however, masks a divergence within Asia. In the earlier period, manufacturing accounted for 35% ofgrowth in East Asia but only 15% in South Asia. The corresponding figures were 34% and 19% in
2000–2015. The differential is somewhat narrowing.

2017 Asian Productivity Organization

In the ASEAN, manufacturing’s contribution was reduced to 21% in 2000–2015 from 30% in the 1990s,
while wholesale and retail trade, hotels, and restaurants increased from 16% to 18%. In the US, the finance, real estate, and business activities sub-sector made the biggest contribution in both periods,
accounting for 30% of economic growth in 1990–2000 and rising to 44% in 2000–2015. In contrast, its
contribution in the Asia24 was 15% in the period 2000–2015. Mining in GCC countries took a hit in2008–2009 due to the downturn in the world economy. Consequently, the contribution of mining fell
from 26% to 18% between the two periods while construction’s share increased from 6% to 9%. Finance, real estate, and business activities became the biggest contributors of economic growth in
GCC countries, with its share rising from 13% to 18% between the two periods.
Figure 85 presents industry contributions to regional economic growth in the Asia30 during 2000–2015,
decomposing Figure 7 in Section 3.1 (p. 23) into countries’ industry origins.109 In each industry contribution,
108: 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.

1086.2 Industry Growth

the top eight countries are presented. The
top four industries in terms of contributions
to regional growth were manufacturing(28.7%), community, social, and personal services (14.3%), wholesale and retail trade(14.0%), and finance, real estate, and business activities (13.8%). A total of 29% of Asian
economic growth originated from the expansion of its manufacturing sector, more than
two-thirds of which was accounted for by
China. In other words, China’s manufacturingsector alone accounted for 22% of the region’s
economic growth. This was followed by China’s community, social, and personal services(9.9%) and wholesale and retail trade, hotels,
and restaurants (7.6%).

1990−2000%

120

2000−2015100
16 14 14 15 16

13 13 12
17

13 14

18 4
4

4
4

2
4

0

12

13 14 14 15 13 11 11 12 14

12

1
7

2

6
3
3 13 3

5
3

9

3

5
7

5
4 15
9
5

4
3

8
12

18

6

1

16 17
15

6
2

7
2

6
2

19

7

5
2
28 27

1
7

18
44

6

11

12

34

3

4

6

6

18

7
2

2

10
18

14

5

9
16
7
2
2 21
12

16 21 21 7

20
21

18

15 16 10

15 15

10

3
1
28 26

6

10 10
13

14 23

32

1

8

6

3 18 44
330

20 15 15 13 1930

16 17 7 1430 28 35 15
21

4
13

10

13
14 14

20

15

13

16

11 10 1040

9 10

13 12 13

80

60

9

4
4

2

2

9

7

2 15
6

11

18 10
1

5
1
−1
−6

109: The average growth rate of the Asian economy for 2000–2015 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.

109

2017 Asian Productivity Organization

US
GCC
CLMV
ASEAN6ASEAN
South Asia
East Asia
Asia30
Asia24
APO20

US
GCC
CLMV
ASEAN6ASEAN
South Asia
East Asia
Asia30
Asia24
APO20

Over a period of four decades there has beena noticeable shift in the industry origins of−20
economic growth (Figure 86). For the ROC
and Korea, manufacturing has been a clear
driving force behind economic growth as awhole. In the decade between the mid-1980s
1. Agriculture
2. Mining
3. Manufacturing
4. Electricity, gas, andand the mid-1990s, however, the importancewater supply
5. Constructionof manufacturing in the ROC retreated tem6. Wholesale and retail trade,
7. Transport, storage, andhotels, and restaurants
communications
porarily while the economy developed its
9. Community, social, and
8. Finance, real estate, andpersonal services
business activities
service sector. Since the mid-1990s, the roleof manufacturing in the ROC has increased
Figure 84 Industry Origins of Regional Economic
again, although compared to its heyday ofGrowth, 1990–2000 and 2000–2015
the 1970s and 1980s its impact in terms of_Contribution sharepercentage points is much reduced. In SingaSources: Official national accounts in each country, including author
pore, finance, real estate, and business activiadjustments.
ties, as well as wholesale and retail trade,hotels, and restaurants are important drivers
alongside the manufacturing sector. Working within the data constraints, Hong Kong appears a clear
service-driven economy in recent years. While the lack of diversification of the oil-exporting countries
cannot be missed; historically, the dominance of the mining sector influenced the economic volatilityof these countries. In recent years the GCC countries have been making efforts in diversifying, especially into the service sector, with different degrees of success. Bahrain and Oman are leading the way
and have yielded results. The largely agricultural countries 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 the period, 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 Industry Perspective

Box 9Premature Deindustrialization

Deindustrialization, or the shrinkage of the manufacturing sector, has been a major concern in advanced
economies for reasons, Rodrik (2015) calls “premature deindustrialization.” He claims that many developing
economies in recent periods are starting to have a declining share of the manufacturing sector without
experiencing full industrialization.
Premature deindustrialization may harm developing economies in the course of its economic development because the manufacturing sector is a dynamic sector typically at the center of sustained economic
growth and technological progress. The sector also creates massive jobs for relatively poor people. Additionally, it generates flows of labor from rural to urban, and from informal to formal sectors, as well as
nurturing human capital. Early servicification of the economy without a mature manufacturing sector may
jeopardize a smooth transition from developing to developed economies.
Rodrik points out that premature deindustrialization is serious particularly in Latin America and SubSaharan Africa. How about in Asia? Figure B9 plots GDP shares of the manufacturing sector in Asian economies, placing the peak of each country’s inverse U shape at the center. A typical image of the up anddown is drawn by the US and Japan with peaks above 30% in 1953 and 1971 respectively. China, the ROC,
and Korea also reach and remain high. Thailand, Malaysia, and Singapore show a similar pattern.

%

40

Manufacturing share in value added

China, 1980 (1952–2015)
ROC, 1987 (1951–2015)35
Japan, 1971 (1955–2015)

Thailand, 2008 (1970–2015)
USA, 1953 (1929–2015)
Philippines, 1973 (1970–2015)
Korea, 2013 (1953–2015)
Malaysia, 2005 (1970–2015)
Singapore, 2006 (1960–2015)
Sri Lanka, 1977 (1971–2015)
Indonesia, 2001 (1960–2015)

30

25

Hong Kong, 1980 (1980–2015)
Mongolia, 1988 (1970–2015)
Iran, 1992 (1970–2015)
Cambodia, 2006 (1993–2015)
Bangladesh, 2009 (1972–2015)
India, 1980 (1950–2015)20

15

Vietnam, 1986 (1986–2015)
Pakistan, 2013 (1970–2015)
Fiji, 1999 (1977–2015)
Bhutan, 1997 (1970–2015)
Myanmar, 1970 (1970–2015)10

Nepal, 1995 (1974–2015)

5

0
−50

−40

−30

−20

−10

peak

+10

+20

+30

+40

+50

2017 Asian Productivity Organization

Elapsed years from the peak year in which each country

Figure B9 Country Peaks of GDP Shares of Manufacturing
Sources: Official national accounts in each country, including author adjustments; APO Productivity Database 2017.
continued on next page >110

6.2 Industry Growth> continued from previous page

The Philippines somehow reaches its peak in 1973 and recently holds around 20%. Indonesia is also justabove 20%. Although these are respectable figures, some more room for industrialization may be suggested. Cambodia, Bangladesh, India, and Vietnam are struggling somewhere below 20%. Obviously thesecountries are not fully industrialized yet, needing further effort to promote the sector.

3. Manufacturing (28.7%)

9. Community, social, and personal services (14.3%)
0.4 2.2
2.7 0.70.6

22.1

0

5

10

China

India

15

20

Indonesia

Korea

25

ROC

9.9

30 %

6. Wholesale and retail trade, hotels, and restaurants (14.0%)
7.6

0

2
China

0.2
0.70.2

3.3

4

6

India

8

Indonesia

10

0

2
China

4

India

14

16 %

Iran

1

1.4

2
India

10

Korea

SaudiArabia

12

14

16 %

6

Rest of the world

4
India

0.5 0.2
0.4

3.5

6

8

Indonesia

10

2.1

12

Korea

14

Iran

16 %

Rest of the world

4.5

10 %

0

1
China

Rest of the world

2
India

0.1 0.6
0.4 0.1

1.3

3

4

5

Indonesia

Qatar

Saudi Arabia

6

7

8%

Rest of the world

2. Mining (4.6%)

3.2

China

2
China

1.2

1. Agriculture (5.7%)

0

8

1.6

5. Construction (7.0%)

8SaudiArabia

0

Rest of the world

0.9 0.20.2

6Indonesia

6
Indonesia

7.2

Malaysia

2.0

4
India

0.3 0.2
0.3

8. Finance, real estate, and business activities (13.8%)

7. Transport, storage, and communications (9.3%)
4.8

2
China

1.9

12

Saudi Arabia

0

Rest of the world

1.9

3

Indonesia

0.3 0.10.1

4
Pakistan

5
Iran

0.5

3.2

6%

Rest of the world

0

1
China

Qatar

0.5

2
India

0.3 0.2 0.1 0.3

3
Saudi Arabia

4
Indonesia

5%
Rest of the world

4. Electricity, gas, and water supply (2.6%)
1.7

0

0.5
China

India

0.4

0.1
0.1 0.1 0.3

1

1.5

2

Iran

VietnamIndonesia

2.5

3%Rest of the world

Figure 85 Industry Origins of Asian Economic Growth, 2000–2015_Contribution to regional growth of GDP at constant prices, using 2011 PPP

111

2017 Asian Productivity Organization

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

6 Industry Perspective

Bahrain

%
50
40
30
20

Bangladesh%
12

Bhutan

%
20

9

15

6

10

3

5

0

0

10
0
−10

−3

−5

−20

−6

−10

1971 1976 1981 1986 1991 1996 2001 2006 2011

Brunei

%
20
10
0
−10
−20
−30

1971 1976 1981 1986 1991 1996 2001 2006 2011

ROC

%
15

1971 1976 1981 1986 1991 1996 2001 2006 2011

Cambodia

%
15

12

9

9

6

6

3

3

0

0

1971 1976 1981 1986 1991 1996 2001 2006 2011

Fiji

%
12
9

12

China

%
15

12

−3

1971 1976 1981 1986 1991 1996 2001 2006 2011−3

1971 1976 1981 1986 1991 1996 2001 2006 2011

Hong Kong

%
12
8

6

9

3

4

0

0

6
3

−3

0
−3

−4

−6
1971 1976 1981 1986 1991 1996 2001 2006 2011

India

%
12

−9

1971 1976 1981 1986 1991 1996 2001 2006 2011

Indonesia

%
12
6

10

4

0

0

0

−6

−10

−4

−12

−20

1971 1976 1981 1986 1991 1996 2001 2006 2011

Japan

%
10
8
6
4

−18

1971 1976 1981 1986 1991 1996 2001 2006 2011

Korea

%
15

−30

60

9

40

6

20

3

0

0

−20

−4

−3

−40

−6

−6

0
−2

1971 1976 1981 1986 1991 1996 2001 2006 2011

Lao PDR

%
20
15
10
5
0
−5
−10

1971 1976 1981 1986 1991 1996 2001 2006 2011

1971 1976 1981 1986 1991 1996 2001 2006 2011

Malaysia

%
20

8
4

8

0

4

−4

0

−8

−4

−12
1971 1976 1981 1986 1991 1996 2001 2006 2011

1971 1976 1981 1986 1991 1996 2001 2006 2011

Kuwait1971 1976 1981 1986 1991 1996 2001 2006 2011

Mongollia

%
12

12

1. Agriculture
2. Mining
5. Construction
6. Wholesale and retail trade, hotels, and restaurants
9. Community, social, and personal services

112

−60

16

−8

Iran

%
80

12

2

1971 1976 1981 1986 1991 1996 2001 2006 2011

%
20

8

−8

2017 Asian Productivity Organization

−8

−16

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

6.2 Industry Growth%18

Myanmar

12

6

6
0

10

2
0

0

−12

−10

−2

−18

%
10

Oman

%
30
20

4

−6

−24

Nepal

%
8

1971 1976 1981 1986 1991 1996 2001 2006 2011

Pakistan−41971 1976 1981 1986 1991 1996 2001 2006 2011

Philippines

%
9

8

6

6

3

4

0

2

−3

−20

1971 1976 1981 1986 1991 1996 2001 2006 2011

Qatar

%
25
20
15
10

6

5
0
−5

0

−6

−2

−9
1971

%
24

1971 1976 1981 1986 1991 1996 2001 2006 2011

Saudi Arabia−101976 1981 1986 1991 1996 2001 2006 2011

Singapore

%
15

−15

12

8

12

9

6

0
−6
−12
−18
−24

%
16

1971 1976 1981 1986 1991 1996 2001 2006 2011

Thailand

12

6

4

3

2

0

0

−3

−2

−6

1971 1976 1981 1986 1991 1996 2001 2006 2011

UAE

%
100

2

20

−12

%
8

0

0

−8
1971 1976 1981 1986 1991 1996 2001 2006 2011

US

−20

−2
1971 1976 1981 1986 1991 1996 2001 2006 2011

Australia

%
8

6

6

4

4

2

2

0

0

Vietnam

%
10

4

40

0
−4

1971 1976 1981 1986 1991 1996 2001 2006 2011

6

60

4

−4

8

80

8

Sri Lanka

%
10

18

6

1971 1976 1981 1986 1991 1996 2001 2006 2011−41971 1976 1981 1986 1991 1996 2001 2006 2011

Turkey

%
10
8
6
4
2
0
−2

−2

−2

−4

−4

1971 1976 1981 1986 1991 1996 2001 2006 2011−41971 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−61971 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

Figure 86 Industry Origins of Economic Growth, 1970–2015
Sources: Official national accounts in each country, including author adjustments.

113

2017 Asian Productivity Organization_Industry decomposition: Average annual growth rate of GDP at constant prices

6 Industry Perspective

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 2015, Singapore and Hong Kong were thecountries that had labor productivity levels comparable to the US, as shown in Figure 39 (in Section
5.1, p. 58). Besides these two, the best performers in Asia achieved productivity levels that were atleast 50% of the US. However, Asia collectively was dragged down by a long tail of countries with laborproductivity of less than 30% of the US level. This pulled down the average performance to 21% of the
US for the APO20 and 21% for the Asia24 (Table 9 in Section 5.1, p. 59). In growth terms, however,Table 18 Labor Productivity Growth by Industry, 2000–2015
6. Wholesale andretail trade, hotels,
and restaurants

7. Transport,
storage, andcommunications

8. Finance, realestate, andbusiness activities

9. Community,social, andpersonal services

Total economy

3. Manufacturing

2. Mining

5. Construction

Bahrain−3.2 (−0.1)
Bangladesh 2.8 (0.3)
Brunei−3.9 (−0.1)
Cambodia
4.3 (1.4)China
7.6 (1.7)ROC
1.9 (0.1)Fiji
2.4 (0.3)
Hong Kong −2.6 (−0.0)
India
3.0 (0.5)
Indonesia
4.0 (0.6)
Iran
2.9 (0.2)Japan
0.4 (0.1)
Korea
4.6 (0.3)
Kuwait2.1 (0.0)
Malaysia
2.3 (0.2)
Mongolia
4.9 (0.7)
Nepal
0.1 (−0.9)
Oman−1.8 (−0.3)
Pakistan
0.5 (−0.3)
Philippines
1.8 (0.0)
Qatar−1.3 (−0.2)
Saudi Arabia −1.9 (−0.2)
Singapore −8.3 (−0.0)
Sri Lanka
3.6 (0.3)
Thailand2.9 (0.6)
UAE
0.2 (0.1)
Vietnam
3.9 (0.9)(regrouped)
APO20
2.7 (0.2)
Asia244.7 (0.9)
Asia304.7 (0.8)
East Asia
7.1 (1.4)
South Asia
2.7 (0.4)
ASEAN
3.4 (0.5)
ASEAN6
3.4 (0.4)
CLMV
3.6 (0.9)
GCC−1.3 (−0.1)(reference)US
2.3 (0.0)
Australia
3.5 (0.1)
Turkey4.8 (0.9)

4. Electricity,gas, and watersupply2017 Asian Productivity Organization

1. Agriculture_Average annual growth rate of industry labor productivity

16.3 (0.2)
7.3 (0.1)
0.0 (0.0)−4.2 (−0.0)
8.3 (0.3)
1.1 (0.0)−1.8 (−0.2)
1.6 (0.0)
7.8 (0.2)
5.4 (0.1)
4.2 (0.2)−0.9 (−0.0)
1.7 (0.1)
3.3 (0.1)
2.3 (0.1)−2.1 (−0.0)
4.4 (0.1)
14.5 (0.1)−2.1 (−0.0)
4.6 (0.1)
4.0 (0.0)
1.0 (0.0)
2.2 (0.1)
9.3 (0.1)
5.7 (0.1)
1.9 (0.1)
2.7 (0.3)−4.8 (−1.4)
1.1 (0.3)−4.8 (−1.0)−3.2 (0.3)
7.6 (0.4)−0.8 (−0.0)−4.9 (−0.2)
1.1 (0.0)
0.0 (0.1)
0.9 (0.2)−4.6 (−0.5)
0.5 (0.1)
0.3 (0.0)
0.4 (−0.5)
1.6 (−0.2)
1.1 (−0.1)−0.5 (0.1)
0.2 (−3.6)−1.0 (−0.2)
1.6 (0.1)
1.9 (−5.4)
0.0 (−0.5)
1.8 (−0.2)
5.2 (0.3)
1.2 (0.0)
5.9 (0.0)
0.7 (0.1)−1.1 (−0.7)
4.6 (0.6)−0.9 (−0.8)−1.4 (−0.3)
6.8 (0.7)
1.4 (0.2)
0.7 (0.1)
4.5 (1.1)
4.9 (1.0)
3.1 (0.4)
1.7 (0.2)
0.3 (0.1)
2.5 (0.3)
2.0 (−0.1)
1.6 (−0.1)
3.9 (0.5)
1.5 (0.3)−2.0 (−0.9)−0.6 (0.1)
2.5 (0.3)
2.6 (−0.7)
5.5 (0.0)
2.8 (0.5)
2.3 (0.3)
1.7 (0.2)−0.6 (−0.2)
3.1 (0.3)−2.2 (−0.2)
3.7 (0.5)−0.7 (−0.1)−0.5 (0.3)
6.6 (0.6)
2.7 (0.1)
2.1 (0.4)
2.3 (0.2)
7.4 (0.7)
10.7 (1.3)
3.8 (0.2)−0.1 (−0.0)
2.6 (0.2)
7.1 (0.5)
2.7 (0.3)
8.1 (0.5)
1.5 (0.4)
5.9 (0.5)
0.6 (0.3)
3.5 (0.2)
2.3 (−0.1)
7.1 (0.3)
1.1 (0.2)
4.3 (0.7)
3.4 (0.4)
1.5 (0.1)
5.7 (0.3)

5.8
−2.5
2.5
−0.2
7.0
−0.3
−1.2
0.8
5.6
−3.3
−0.8
1.3
0.5
−4.6
−0.6
−1.4
1.0
−3.6
4.5
−0.2
7.1
4.1
1.3
3.9
0.8
−5.3
−4.0

(1.5)
(0.2)
(0.1)
(0.7)
(1.0)
(0.3)
(0.4)
(0.9)
(1.4)
(0.5)
(0.4)
(0.2)
(0.4)
(0.2)
(0.2)
(0.6)
(0.5)
(0.2)
(0.4)
(0.9)
(1.3)
(0.4)
(1.4)
(0.7)
(0.5)
(0.2)
(0.7)

1.0 (−1.4)
3.3 (0.8)
3.7 (0.8)−1.0 (−0.1)
6.8 (0.8)
1.1 (0.2)
2.0 (0.4)−0.1 (−0.2)
6.1 (0.8)
1.6 (0.0)
2.2 (0.3)−0.7 (−0.3)−0.3 (−0.2)−2.4 (−3.2)
3.4 (0.2)−0.3 (0.4)
6.6 (0.8)
2.6 (0.1)
4.5 (0.6)
1.0 (0.0)
1.3 (−1.3)−0.6 (−1.4)−0.3 (−1.0)
2.4 (0.5)
0.7 (0.2)
0.2 (−1.8)
1.6 (0.2)

−1.8
3.7
−2.3
3.8
8.8
2.5
1.4
2.3
5.9
3.8
1.3
0.2
2.6
−1.8
1.7
3.8
1.4
−3.6
1.2
2.7
−2.8
−0.4
1.8
3.6
3.0
−1.0
3.9

0.6
2.2
2.2
2.7
5.0
−1.0
−0.8
−3.6
−0.2

(0.7) 1.7 (0.3)2.5(0.8) 3.0 (0.4)4.8(0.8) 2.9 (0.4)4.7(0.8) 2.7 (0.3)
5.5
5.2(1.2) 5.6 (0.8)(0.6) 1.1 (0.1)
3.4(0.6) 1.2 (0.0)3.2(0.5) 1.9 (0.4)
4.3(0.5) −0.4 (−1.5) −0.8

4.2 (0.0) 2.4
8.9 (0.1) 3.2−4.1 (−1.2) 0.2
11.6 (0.1) 5.1
9.2 (0.5) 7.7
1.3 (0.0) 5.1
0.5 (0.1) 2.0
0.0 (0.0) 4.5
4.7 (0.1) 7.0−2.0 (0.1) 2.5−3.6 (−0.2) 3.6−0.2 (−0.0) 0.9
0.7 (0.0) 4.9−1.5 (1.1) −0.5
−8.9 (−0.0) 3.7−0.1 (0.9) 7.1−2.0 (−0.0) 1.1−0.6 (0.2) 0.4−4.1 (0.1) 0.9
2.6 (0.1) 4.1−5.0 (3.4) 1.2
0.0 (0.7) 1.6
0.0 (0.0) 3.3
12.1 (0.2) 2.0
1.0 (0.1) 2.2−2.7 (0.5) 0.6
1.4 (0.3) 3.7

(0.2)
(0.8)
(0.0)
(1.3)
(2.8)
(1.5)
(0.3)
(0.2)
(1.1)
(0.7)
(0.4)
(0.2)
(1.5)
(0.1)
(1.0)
(0.4)
(0.1)
(0.0)
(0.1)
(1.0)
(0.1)
(0.3)
(0.8)
(0.4)
(0.9)
(0.1)
(0.8)

0.2
4.1
3.0
8.9
4.8
−1.7
−2.4
1.9
−1.6

2.3
4.8
4.8
5.1
6.7
3.8
4.8
2.1
2.3

(0.0)
(0.2)
(0.2)
(0.3)
(0.1)
(0.1)
(0.1)
(0.5)
(0.9)

2.7
5.0
5.0
5.3
6.0
2.2
2.7
3.5
1.0

(0.6)
(1.4)
(1.3)
(1.7)
(0.9)
(0.8)
(0.8)
(0.8)
(0.2)

(0.1) −1.6 (−0.1)(0.1) 2.0 (0.1)(0.1) 2.1 (0.1)(0.1) 3.5 (0.1)(0.1) 0.2 (0.1)(0.1) 1.3 (0.1)(0.1) 1.4 (0.1)(0.2) 2.0 (0.3)(0.1) −0.1 (−0.8)

1.6 (0.3)
2.9 (0.5)
2.9 (0.5)
2.7 (0.4)
4.2 (0.9)
2.3 (0.3)
2.7 (0.3)
1.0 (0.3)
2.8 (−0.2)

3.2
4.3
4.4
3.5
5.9
6.8
7.1
4.1
4.6

(0.4)
(0.5)
(0.5)
(0.4)
(0.6)
(0.7)
(0.8)
(0.3)
(0.3)

0.0 (0.1)−4.5 (0.2)−2.6 (−0.0)

3.3
1.1
3.4

(0.4) −0.1 (−0.0) −1.7 (−0.1)(0.1) −2.3 (−0.0) 2.8 (0.2)(0.6) −0.4 (0.1) 5.7 (0.4)

1.2 (0.1)
1.4 (0.1)
0.1 (−0.1)

3.5
1.9
3.2

(0.3) 1.2(0.2) 1.2(0.5) −3.3

Unit: Average annual growth rate (percentage), contribution in parentheses.
Source: APO Productivity Database 2017.

114(0.5) −0.3 (−0.3)(0.7) −0.1 (−0.5)(0.2) 1.3 (0.2)

1.0
1.0
2.9

6.3 Labor Productivity by Industry

1990−2000%

10

2000−2015

8 1.1

1.7

0.6
0.5

6

0.5
4.1

4

2

0

0.3 0.5

2.8

0.6

1.1

0.8 0.3
0.2
0.6
1.7
0.7
1.7 0.3
2.7
0.2 0.2 1.3 0.3
0.6 1.4
0.4
0.2
0.7 0.5
0.4
0.5
0.2
0.3
0.4
0.3
0.5
0.2
0.3 0.7 0.5 0.6
0.4
0.5
0.4
0.3 1.4 0.3
0.2
0.2 0.7
0.8 0.7 2.0 0.8
0.6 0.4 0.4 0.3
0.2 0.3
0.5
0.2
0.2
0.2
0.7 0.6 1.2 1.4
0.5 0.6 0.4 0.2 0.3
0.4 0.3 0.3 0.2 0.3 0.8
0.4
0.2
0.7
0.4 0.8
0.6
0.8
0.3
0.9 0.4
0.5 0.4
0.4 0.3
0.3
0.6
0.4
0.7 0.9
0.4 0.2 0.5 0.3 0.2 0.4
1.0
1.0
1.0
0.2 0.3
0.2
0.5 0.5 0.5 0.5
0.7 0.7
0.6
0.5 0.5 0.4 0.4 0.5 0.2 0.3 0.5 0.3 0.3 0.6
0.4 0.6 0.2
0.3 0.2
0.2−0.2 0.2 −0.3 −0.2 −0.2 −0.3 −0.4
−0.2
−0.4
−0.4
−0.5
−0.5 −0.3−0.2 −0.2−0.2 −0.4−0.2 −0.5−0.3−0.3 −0.4
−0.2
1.7

6

0.3
0.2
0.3 0.4 0.3
0.3
0.7 0.6
0.4 1.0 0.9 1.4
0.2
0.3
0.3
0.9 0.7 0.8 1.7 0.4 0.6
0.7
0.2 0.8
0.7
0.9
0.3
0.8
0.4 0.2 0.3
0.2
0.6
0.2
1.0
0.3
1.3
0.4 0.6
0.5
0.3 0.3
0.9
0.6
0.3
1.5 1.5 1.1
0.5
0.7
0.4
1.4
0.7
0.4
0.2
0.2
0.3
1.0
1.0
0.5
0.3
0.2 0.3 0.2
0.5
0.3
0.2
0.4
1.3
0.3 0.3
0.5
0.2
0.2
0.2
0.2 0.7 0.7 0.4
0.4 0.2
0.4 0.2 0.4
0.3
0.3 0.2
0.5
0.6
1.4 0.3
0.7
0.2
0.2
0.4
0.4
0.5
0.8 0.6
0.2
0.8 0.8
0.7
0.2 0.8
0.7
0.2 0.9 0.4 0.3 0.9
0.4 0.2
0.5 0.2 0.2
0.4 0.5
0.4 0.3 0.6
0.2
0.2
0.2
−0.2
−0.2 −0.5 −0.2 −0.5 −0.2 −0.3
−0.2
−0.3
−0.2
−0.3
−0.7
−0.9 −0.2
−1.0
−0.2
−0.2

−1.0

−2

Japan
US
Australia
Pakistan
Iran
Fiji
Nepal
Malaysia
Singapore
Hong Kong
ROC
Korea
Philippines
TurkeyThailandSri Lanka
BhutanBangladeshIndonesia
Mongolia
Cambodia
Vietnam
India
China

Mongolia
Fiji
Philippines
Hong KongJapan
TurkeyUS
Indonesia
Pakistan
Iran
Nepal
Australia
Bangladesh
Myanmar
Sri Lanka
ThailandCambodia
BhutanSingapore
India
Malaysia
Vietnam
ROC
Korea
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, realestate, and business activities

9. Community, social, and personal services

3. Manufacturing

Real GDP

Figure 87 Industry Origins of Labor Productivity Growth, 1990–2000 and 2000–2015_Industry decomposition: Average annual growth rate of GDP at constant prices
Source: APO Productivity Database 2017.

Asia’s performance far exceeded the US, allowing the countries to gradually close the gap with the US
over time. Labor productivity growth in the Asia24 was 4.5% per year on average between 2010 and2015, compared to 0.7% in the US (Table 10 in Section 5.1, p. 60).

110: 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 87 (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.
111: The data presented in this chapter is subject to greater 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.

1152017 Asian Productivity Organization

Table 18 presents cross-country comparisons in labor productivity growth by industry110 for the period 2000–2015.111 Positive labor productivity growth was achieved across all sectors for the Asia24. If
one 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6 Industry Perspective

1990−2000
4.0 3.0 2.0 1.0 0.0 −1.0 %
4.1
2.7
1.7
1.7
1.4
1.3
0.8
0.8
0.8
0.8
0.7
0.7
0.7
0.7
0.6
0.4
0.4
0.3
0.3
0.3
0.3
0.0
−0.2
−0.2
−0.4

2000−2015
−20 0

China
Korea
Singapore
ROC
Cambodia
Malaysia
ThailandHong Kong
Indonesia
Bangladesh
Sri LankaUS
VietnamTurkeyBhutanPhilippines
Japan
India
Australia
Pakistan
Iran
MyanmarFiji
Nepal
Mongolia

Contribution

60 80 100 %3.0 2.5 2.0 1.5 1.0 0.5 0.0 %

46

2.8

20 40

53

1.5

46

1.5

33

1.3

39

1.1

33

1.0

26

1.0

98

0.9

38

0.8

33

0.8

29

0.8

38

0.7

13

0.6

37

0.4

17

0.4

47

0.4

36

0.4

9

0.4

14
13

0.3

12

0.2
0.2

1

0.1

117

0.1

−11

0.1

42

Contribution share toaggrergate labor productivity

Contribution

0

China
ROC
Korea
Cambodia
India
Malaysia
Philippines
ThailandSingapore
Bangladesh
VietnamIndonesia
TurkeyBhutanSri Lanka
Iran
US
Mongolia
Fiji
Hong Kong
Japan
Australia
Pakistan
Nepal

20

40

80 %

60

32
61
56
34
18
58
36
29
46
21
19
19
21
12
12
31
37
9
24
9
67
12
7
5

Contribution share toaggrergate labor productivity

Figure 88 Contribution of Manufacturing to Labor Productivity Growth, 1990–2000 and 2000–
2015
Source: APO Productivity Database 2017.

non-manufacturing sectors in the Asia24; i.e., manufacturing (at 5.0% on average per year), electricity(4.8%), agriculture (4.7%), and transport, storage, and communications (4.3%). Construction was thesector with the slowest productivity growth at 2.0%.
Figure 87 shows the industry origins of average labor productivity growth per year in two periods:
1990–2000 and 2000–2015.112 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., China, Korea, the ROC, and Malaysia). In India its contribution has
expanded recently, although it was not a major contributor in the 1990s.

2017 Asian Productivity Organization

The manufacturing sector has been a major driving force behind productivity growth in most Asian
countries, as shown in Figure 88. In the 1990s, manufacturing accounted for a significant part of laborproductivity growth in Hong Kong (98%), Indonesia (38%), and China (46%). Nevertheless, its role haslessened in 2000–2015 to 9%, 19%, and 32%, respectively. In contrast, contributions from manufacturing strengthened from 33% to 61% in the ROC and from 36% to 67% in Japan between the two periods.
In other economies, like Pakistan, Nepal, and Mongolia, manufacturing played a negligible role inthe 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 matches

112: Not all Asian countries are included, as employment by industry sector is not available for some countries.

1166.3 Labor Productivity by Industry

1990−2000
3.0

2.0

1.0

0.0 −1.0 %

3.0
2.5
2.3
2.3
2.0
1.9
1.8
1.5
1.5
1.4
1.4
1.3
1.3
1.1
1.1
1.0
0.7
0.6
0.6
0.5
0.2
0.2
0.2
0.1

ROC
India
Vietnam
China
Malaysia
BhutanCambodia
Korea
Australia
Singapore
Iran
Sri Lanka
Pakistan
Nepal
US
Bangladesh
ThailandTurkeyIndonesia
Japan
Mongolia
Hong Kong
MyanmarPhilippines
Fiji
−0.7

2000−2015

−20

0

20

40

60

80 %

58

4.0 3.0 2.0 1.0 0.0 −1.0 %
4.0

66
46

3.1
2.2

26

2.2
50

2.1

53

2.1

52

2.0

30
63
39
61
52
61
50

2.0
1.5
1.4
1.3
1.3
1.3
1.2

59

1.1

45

1.1

21

0.9

33

0.9

27

0.7

55

0.7

−22

Contribution

0.6

19
6

0.6

13

0.4
469

Contribution share toaggrergate labor produtcivity−0.1

Contribution

India
China
Sri Lanka
BangladeshIndonesia
Nepal
Hong Kong
Mongolia
Vietnam
Pakistan
Philippines
Thailand
BhutanFiji
IranSingapore
TurkeyROC
Korea
Cambodia
Malaysia
US
Australia
Japan

−20 0

20 40 60 80 100 %
67
36
61
59
57
145
89
52
38
120

6

50
44
36
81
87
62
30

34
29
17
35
58
42
−37

Contribution share toaggrergate labor productivity

Figure 89 Contribution of Service Sector to Labor Productivity Growth, 1990–2000 and 2000–
2015
Source: APO Productivity Database 2017.

that of manufacturing. Among the four industries in the service sector, three are potentially ITemploying industries: wholesale and retail trade, hotels, and restaurants; transport, storage, andcommunications; and finance, real estate, and business activities.

117

2017 Asian Productivity Organization

Figure 89 presents the contribution of services in labor productivity growth by country. In 2000–2015,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 89% and 67% oflabor productivity growth, respectively. It also accounted for around two-thirds or more of labor productivity growth in Fiji and some South Asian countries like Bangladesh, India, Nepal, and Pakistan.
There is an expansion of the role played by services in China between these two periods, from 26% to36%. Only Japan failed to improve the labor productivity in the period 2000–2015.

7 Real Income
The constant-price GDP captures real production, not real income. An improvement in the terms oftrade, which is defined as the relative prices of a country’s exports to imports, explicitly raises real income and in turn welfare.113 In many ways, a favorable change in the terms of trade is synonymouswith 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 Trade
By focusing on production, the real GDP concept does not capture the beneficial effect of the improvement in the terms of trade.114 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 is the case with many Asian economies (shown in Figure 26 in Section 4.1, p. 41).
For example, real income growth for resource-rich countries was more than double that of real GDP
growth in recent years in Oman, Saudi Arabia, and Brunei during 2000–2005 (due to oil price hikes)
and in Myanmar during 2005–2010 (due to price hike of jade). 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 inimport prices, but owes more to the rising prices of its commodity exports.

7

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,115 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. As such, real income captures the purchasing
power of the income flow.116 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),117 andthe effect of net income transfer.118
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.119 The findings presented in Table 19 confirm this113: See Diewert and Morrison (1986) and Kohli (2004).
114: 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.”
115: The weight for import price changes is negative. Thus, if import prices decrease, this tends to raise the GDP deflator.
116: 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).
117: The term “trading gain” is used by some authors (Kohli, 2006). This term is adopted in this report.
118: Real income growth can be decomposed into two components as follows:( )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

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.

1192017 Asian Productivity Organization

D7 Real Income

Table 19 Real Income and Terms of Trade, 1995–2000, 2000–2005, 2005–2010, and 2010-2015_Average annual growth rate of real income, real GDP, trading gain, and net primary income transfer from abroad

Bhutan8.6 6.2 −0.1 2.5
Vietnam
7.4 7.4 0.2 −0.3
China
7.2 7.3 −0.1 0.1
Singapore
6.2 6.4 0.1 −0.3
Philippines 5.8 3.0 1.1 1.7
ROC
5.7 5.9 −0.1 0.0
India
5.3 5.5 −0.2 0.0
Malaysia
5.3 5.6 0.4 −0.8
Iran
5.2 2.7 2.3 0.2
Cambodia
5.0 5.3 0.1 −0.3
Sri Lanka
4.7 4.9 −0.1 −0.1
Myanmar
4.3 3.3 1.5 −0.6
Bangladesh 4.1 4.0 0.0 0.1
Pakistan
3.6 4.0 0.0 −0.4
Korea
3.2 5.2 −2.0 −0.1
Hong Kong 2.8 2.4 0.4 0.0
Fiji
2.1 2.1 −0.9 1.0
Indonesia
1.4 1.4 0.8 −0.8Japan
1.0 1.1 −0.2 0.1
Thailand −0.9 0.3 −1.2 0.0

Mongolia
China
Cambodia
Iran
MyanmarVietnamBhutan
Malaysia
India
Pakistan
Sri Lanka
Philippines
Bangladesh
ThailandSingapore
Korea
Indonesia
Hong Kong
Nepal
ROC
Japan
Fiji

Bahrain
6.0
Kuwait
6.4
Oman
8.2
Qatar
13.5
Saudi Arabia 4.9
UAE
8.0
Brunei
4.9(reference)
US
4.3
EU15
2.9

3.5
1.6
4.2
8.7
3.1
6.6
1.8

Australia
Turkey3.8 0.1 0.2
4.5 −0.3 −0.1

Bahrain
7.8 6.5 1.3 0.0
Kuwait10.6 7.2 4.6 −1.2
Oman
8.1 3.0 4.9 0.2
Qatar
12.1 9.8 4.6 −2.3
Saudi Arabia 9.1 4.0 5.2 0.0
UAE
6.6 4.9 1.8 −0.1
Brunei8.2 3.6 4.6 0.0(reference)US
2.5 2.5 0.0 0.1
EU15
1.9 1.8 0.1 0.1
EU28
1.9 1.7 0.1 0.1
Australia
4.3 3.4 1.2 −0.2
Turkey4.7 4.6 0.3 −0.2

4.1
4.1

2.9
4.4
4.4
5.8
2.1
1.9
3.2

−0.3
0.3
−0.4
−1.0
−0.3
−0.4
0.0

4.2 0.1
2.9 −0.1

0.0
0.1

11.5 6.2 5.5 −0.2
11.0 10.0 0.9 0.1
10.2 10.5 0.0 −0.3
8.9 7.2 2.0 −0.3
8.6 5.8 2.8 0.0
8.2 7.7 0.6 −0.1
7.5 7.5 0.2 −0.3
7.3 5.3 1.2 0.8
7.0 7.2 −0.3 0.1
5.7 5.8 −0.8 0.6
5.6 4.9 0.6 0.1
5.4 4.2 −0.3 1.4
5.4 5.2 −0.1 0.2
4.6 5.2 0.0 −0.5
4.0 5.0 0.2 −1.2
3.8 4.5 −0.7 0.0
3.6 4.2 −1.0 0.4
3.1 4.1 −1.0 −0.1
2.7 3.2 −0.8 0.1
2.6 3.8 −1.4 0.2
1.0 1.2 −0.3 0.1−1.9 −2.3 1.0 −0.6

Myanmar
China
India
Cambodia
VietnamSingapore
Bangladesh
BhutanSri Lanka
Philippines
Malaysia
Indonesia
IranMongolia
Nepal
Thailand
Korea
Hong Kong
Pakistan
ROC
Japan
Fiji

12.9 5.7 7.1 0.0
11.9 11.7 0.2 0.1
8.6 8.4 0.3 −0.1
7.7 6.7 1.1 0.0
7.3 6.6 1.1 −0.4
7.1 6.7 −0.9 1.3
6.3 6.2 −0.6 0.7
6.2 7.0 0.1 −0.9
6.0 5.7 0.3 0.0
5.9 4.8 −0.1 1.1
5.8 4.9 0.6 0.3
5.7 6.0 −0.7 0.4
5.2 3.3 1.7 0.2
4.8 7.3 −1.1 −1.4
4.1 3.2 0.9 0.0
4.1 4.0 0.0 0.1
3.7 4.1 −0.6 0.2
3.3 3.8 −0.8 0.3
3.1 3.7 −0.9 0.4
1.9 4.2 −2.4 0.1−0.3 0.1 −0.5 0.1−0.8 0.0 −0.6 −0.2

Bahrain
8.5 6.4 3.5
Kuwait3.2 0.4 3.3
Oman
7.1 5.0 2.7
Qatar
15.0 13.4 1.0
Saudi Arabia 5.5 2.6 2.7
UAE
2.8 2.7 0.5
Brunei4.1 −2.2 6.4(reference)US
0.8 0.7 −0.1
EU15
0.6 0.7 −0.1
EU28
0.8 0.8 −0.1
Australia
4.1 2.8 1.4
Turkey3.5 3.9 −0.3

−1.4
−0.5
−0.6
0.6
0.2
−0.3
−0.2
0.1
0.0
0.0
0.0
0.0

Net primary income
from aboad

Real GDP

Trading gain

Real income

Trading gain

1970–2015
Net primary income
from aboad

Real GDP

Real income

2010–2015
Net primary income
from aboad

Real GDP

Trading gain

Real income

Trading gain

2005–2010
Net primary income
from aboad

Real GDP

Real income

Net primary income
from aboad

Real GDP

2000–2005

Trading gain

Real income

1995–2000

Mongolia 10.0 9.3 0.7 0.0
China
8.1 7.8 0.3 0.0
Bhutan7.0 8.6 −0.9 −0.7
Cambodia
7.0 6.8 0.4 −0.2
Philippines 6.8 5.9 −0.3 1.2
India
6.1 6.5 −0.4 0.0
Vietnam
6.1 5.6 0.8 −0.3
Bangladesh 5.3 5.8 −0.1 −0.3
Myanmar
4.9 −0.6 5.5 0.0
Malaysia
4.8 4.9 −0.2 0.1
Nepal
4.7 3.6 0.8 0.3
Indonesia
4.7 5.1 −0.3 −0.1
Sri Lanka
4.5 4.0 0.8 −0.3
Pakistan
4.4 4.2 −0.2 0.4
Thailand3.7 3.1 0.7 −0.2
Fiji
3.2 2.5 1.1 −0.4
ROC
3.0 2.5 0.5 0.1
Hong Kong 2.9 2.9 0.1 −0.1
Korea
2.7 2.4 0.3 0.0
Singapore
2.3 3.7 −0.6 −0.8
Japan
1.2 1.0 0.0 0.2
Iran−1.9 1.3 −3.3 0.1

China
Singapore
Malaysia
Korea
Bhutan
ROC
Indonesia
Hong Kong
India
ThailandMyanmar
Sri Lanka
Pakistan
IranPhilippines
Bangladesh
Japan
Fiji

8.6 8.6 0.0 0.0
6.8 7.1 −0.2 −0.1
6.8 6.4 0.4 0.0
6.5 6.8 −0.3 0.0
6.3 6.0 0.0 0.2
6.2 6.9 −0.7 0.1
5.6 5.1 0.6 0.0
5.5 5.5 0.0 0.0
5.4 5.5 0.0 0.0
5.2 5.6 −0.2 −0.1
5.1 3.2 2.0 −0.1
5.0 5.1 0.0 −0.1
5.0 5.1 −0.3 0.1
4.8 3.4 1.3 0.1
4.6 3.8 0.0 0.8
3.4 3.4 −0.2 0.1
2.4 2.6 −0.3 0.1
2.2 2.1 0.1 0.0

Bahrain
2.8 3.7 −1.6 0.8
Kuwait−1.4 3.7 −5.8 0.7
Oman
4.0 4.5 −1.1 0.6
Qatar
6.2 7.0 −2.5 1.7
Saudi Arabia 1.9 4.9 −3.2 0.2
UAE
4.5 4.6 −0.1 0.0
Brunei−1.1 −0.1 −0.9 −0.1(reference)US
2.2 2.1 0.1 0.0
EU15
0.8 0.8 0.1 −0.1
EU28
1.0 1.0 0.1 −0.1
Australia
1.8 2.8 −1.4 0.4
Turkey6.5 6.8 −0.3 0.0

Bahrain
KuwaitOman
Qatar
Saudi Arabia
UAE

5.5
4.5
7.6
6.4
4.7
9.8

4.8 0.9 −0.1
0.9 3.2 0.5
6.4 1.0 0.2
6.2 0.1 0.1
3.9 0.2 0.6
9.7 −0.2 0.3(reference)
US
EU15

2.7
2.1

2.8
2.1

0.0
0.0

0.0
0.0

Australia
Turkey3.4
4.4

3.3 0.1
4.6 −0.1

0.0
0.0

2017 Asian Productivity Organization

Unit: Percentage.
Sources: Official national accounts in each country, including author adjustments.
Note: See footnote 118 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 1970–2015: Brunei (1989–), Cambodia (1993–), EU28 (1999–), Mongolia (2000–), Nepal(2000–), and Vietnam (1989–).

observation. 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 1970–2015. In the short 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 oftrade have been 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 52% in 2005–2010 of its
119: 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.

120

7.1 Real Income and Terms of Tradereal GDP growth. In terms of percentage points, the trading gain added 0.1, 1.2, and 1.4 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 in this observation period, making it possible
to sustain a rise in purchasing power with little real GDP growth in countries, until the middle of 2014.
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 28 countries compared, except for the Philippines, Kuwait,
and Saudi Arabia. Figure 90 plots the time series of net primary income from abroad as a percentage
of GDP. The role of net primary income from abroad has been shifting from negative to positive inHong Kong, with the transition 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 the early 1980s. 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. Net primary income from abroad has risen strongly in Japan and the Philippines, albeit
at different magnitudes. In Japan, it rose from 0.8% of GDP in 1990 to 3.8% in 2015, compared with
1.5% in 1990 and 41.8% in 2015 in the Philippines, providing a long-term significant contribution to
the purchasing power of Filipinos, with remittances from a large number of overseas workers.

7

Unlike the oil-exporting countries, at any one time roughly half of the Asian countries compared sustained a negative trading gain effect, albeit to variable extents, whereas the impact from net primary
income from abroad was relatively less pronounced. The period of 1995–2000 reflects the impact of
the Asian financial crisis. For Thailand, the trading gain effect more than outweighed the small positive average real GDP growth per year (0.3%), giving rise to a marginal fall in real income of –0.9%. In
Korea, the negative trading gain also shaved 38% off real GDP growth of 5.2%, producing real income
growth of 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 tobe 32% lower than real GDP growth in the period 2000–2005. However, in the period 2005–2010 it
wiped out 57% of the attractive 4.2% real GDP growth on average per year, leaving real income togrow at 1.9%.

%

6
4

%

ROC
Korea

Hong Kong
Singapore

4

3

2

%

China
Japan
US

India
EU15

50
40

Bangladesh
Fiji
Malaysia
Pakistan

Cambodia
Indonesia
Mongolia
Philippines

30

2

0

20
1

−2

10
0

−6
−8

0

−1

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

−2

−10

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015−20

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

Figure 90 Effect of Net Income Transfer on GDP, 1970–2015
Sources: Official national accounts in each country, including author adjustments.

1212017 Asian Productivity Organization−4

7 Real Income

Dollars per barrel

150

145.3

125

113.4

106.8

110.2

109.4

100
77.1

75

59.2

75.4

68.0

50

41.1

43.4

25
0
Jan 02,
1986

26.2
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

Jan 02,2016

Figure 91 Price of Crude Oil, 1986 January–2017 May
Source: US Energy Information Administration, WTI spot prices FOB (Cushing, Oklahoma).

2017 Asian Productivity Organization

In contrast, the trading gain
worked to counterbalancefalling real GDP in Brunei, leaving it with a robust, real income growth of 4.1%, despite
its contracting real GDP of–2.2% in the period 2005–2010 (Table 19). In Saudi Arabia, real income growth increased more than 212%
faster than its real GDP growthin the same period. This takes
place against the backdrop ofstrong oil prices, which spiked
in mid-July 2008 to USD 145
per barrel. Figure 91 presents
the prices of crude oil fromJanuary 1986 to May 2017. After dropping sharply to USD
30 per barrel by the end of2008 (reflecting the fall in demand by the global financial
crisis), oil has steadily risen to,and held at, over USD 100 per
barrel since 2010 through the
middle of 2014, and dropped
to an average of USD 50 per
barrel in the recent 3 years.

1990−2000
−2 −1

0

1

2

3

2010−2015
4

5

6%
5.5

5.0
3.9
3.9
3.8
2.8
2.4
2.2
1.8
1.3
1.1
0.9
0.8
0.6
0.5
0.4
0.2
0.2
0.1
0.0
0.0
0.0
0.0
0.0
−0.1
−0.2
−0.3
−0.3
−0.4
−0.7
−0.8
−0.9
−0.9
−1.9

−6
Brunei
Myanmar
Kuwait −5.8
Saudi Arabia
Oman
Qatar
BahrainMongolia
Iran
Australia
UAE
Malaysia
Vietnam
China
Cambodia
Sri Lanka
Fiji
Bhutan
Nepal
EU28
EU15
ThailandIndia
TurkeyUS
Philippines
Bangladesh
Singapore
Japan
Korea
Indonesia
Pakistan
Hong Kong
ROC

−4

−2

0

2

4

6%

−0.9
5.5
−3.2
−1.1
−2.5
−1.6
0.7
−3.3
−1.4
−0.1
−0.2
0.8
0.3
0.4
0.8
1.1
−0.9
0.8
0.1
0.1
0.7
−0.4
−0.3
0.1
−0.3
−0.1
−0.6
0.0
0.3
−0.3
−0.2
0.1
0.5

Figure 92 Trading Gain Effect, 1990–2000 and 2010–2015_Average annual contribution to real income growth

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

The price changes of crude oil in the recent decade have a great impact in trading gains in Asian countries. Figure 92 compares the trading gain effects from the 2000s and the period 2010–2015. The longsustained trading gain effects in Thailand, the ROC and Korea turned to be positive as 0.7, 0.5, and 0.3
percentage points per year, respectively. In contrast, the positive trading gain effects which oil-richcountries experienced in the 2000s were negative in the period 2010–2015: e.g., –5.8 percentage
points in Kuwait and –3.2 percentage points in Saudi Arabia. The exception is Myanmar. Myanmar

1227.1 Real Income and Terms of Trade

expanded a production of natural
gas since the late 1990s and has exported it mainly to Thailand. The
positive trading gains have beenbrought about not only by the pricehike in natural gas in the 2000s, butalso by the price hike in jade since
the middle 2000s (see Box 5). The
impact by the rapid price jump injade was large enough to offset theprice decline in natural gas in theearly 2010s.

%

12Real income growth(+20%)

10

UAE
Mongolia
China

8

6

4

(−20%)

Oman Cambodia
Vietnam
Malaysia
Singapore
Qatar
Korea
BhutanSaudi Arabia
ROC
Indonesia
Hong Kong
Bahrain
Myanmar
India
Thailand
Pakistan
Iran
Kuwait
Sri Lanka
Philippines
TurkeyNepal
Australia
Brunei
Bangladesh

7

US
Combining both the trading gain
Fiji
Japan
2
effect and net primary income fromEU15
abroad, real income growth formost of the countries compared fell0
within the margin of ±20% of real
0
2
4
6
8
10
12 %
Real GDP growth
GDP growth in the long run, as
shown in Figure 93. In particular in
Figure 93 Real Income and Real GDP Growth, 1970–2015
larger economies, as the US, the_Average annual growth rate of GDP at constant prices and real
EU15, China, India, and Japan, theincomereal income growths were almost
Sources: Official national accounts in each country, including author adjustments.
equivalent to the real GDP growthNote: The starting years for some countries are different due to data availability
during 1970–2015: Brunei (1989–), Cambodia (1993–), Mongolia (2000–), Nepal
on average over the past four de(2000–), and Vietnam (1989–).
cades. Kuwait and Brunei appear tobe the outliers, with real income
growth being 5.1 times and 3.1 times their respective long-term dismal real GDP growth of 0.9%
and 1.0%.120

Figure 94 provides the results of further decomposition of the trading gain into the terms-of-tradeeffect and the real exchange rate effect in Asian countries for the period 1970–2015.121 The terms-oftrade effect is the part of real income growth attributed to the change in the relative price between
exports and imports. The real exchange rate effect refers to the part of real income growth attributed
to changes in the relative prices of traded goods and domestically consumed goods. By applying thisresult, real income growth can be decomposed into real GDP growth, terms-of-trade effect, realexchange rate effect, and net primary income from abroad. The first chart in Figure 94 applies thisbreak-down to Asian countries for the period 1970–2015. It shows that the real exchange rate effect isgenerally much smaller than the terms-of-trade effect, implying that the relative prices of traded

120: 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.
121:Following Kohli (2006), trading gain can be decomposed into two components as follows:

(

)

(

)

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 effect1232017 Asian Productivity Organization(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 ) =7 Real Income

versus domestically consumed goods have been largely stable in most countries. The exception is
Kuwait where the real exchange rate effect accounted for 33% of real income growth. This might
have reflected the weight of oil in the composition of their traded goods. The second chart shows
the decomposition for the most recent period 2000–2015. 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 the ROC, Hong Kong, Pakistan, Korea,
Indonesia, the Philippines, and Japan.

EU15
Fiji
Japan
US
Australia
Bangladesh
TurkeyKuwaitPhilippines
Saudi Arabia
IranPakistan
Sri Lanka
MyanmarThailandIndia
Hong Kong
BahrainIndonesia
ROC
Bhutan
Qatar
Korea
Malaysia
Singapore
Oman
China
UAE

Figure 95 shows the decomposition of average annual real income growth covering two periodsof major 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.
High oil prices improved the terms for oil-exporting countries, such as Iran and Indonesia, and worsened the terms of trade for oilimporting countries. During the
1970–2015%
Asian financial crisis, the terms12
of-trade effect was still the predom9
inant factor in determining the
0.7
difference between real income
6
0.9
9.7
growth and real GDP growth. In
8.6
0.8
1.4
1.5
7.1 6.4 6.8
6.9
1.5
6.4
6.2
6.0
3
Brunei, the terms-of-trade effect
5.1 4.8 5.5 5.5 5.6
5.1 5.1
4.6
3.9
3.8
1.7
3.4 3.3 2.8
3.4
3.2
2.6 2.1 2.1
further reinforced the negative
0.9
0
−0.7
real GDP growth of –7.3%, reduc−3
ing its real income growth a further 7.4 percentage points. In
Iran, the negative terms-of-tradeeffect counteracted the 1.0% real
2000–2015
GDP growth, giving real income%
12
growth of –1.5%. In Indonesia,
1.0
the trading gain effect worked to
9
1.1
0.8
counterbalance the contraction
4.9
1.4
6
1.3 0.7
0.8
in real GDP, whereas in Thailand,
10.1 9.8
1.7
1.1
7.7
7.6 8.0 7.4
6.7
1.0
it reinforced the negative real3
5.5
5.1 4.6
5.0 5.0 5.7
4.9 5.1 5.1
4.2
4.0
3.8
3.8 4.1 3.9 3.5 2.5
3.7
3.0 3.7 3.6 3.5
GDP growth. In the Philippines,
1.8 1.1
0.7
0−0.7
although the strong favorable−1.1
terms-of-trade effect was moder−3
ated by the negative real exchange rate effect, the resulting
real income growth more thanReal exchange rate effectTerms-of-trade effect
Real GDP
tripled the real GDP growth.122
Fiji
Japan
EU15
US
ROC
Hong Kong
Korea
Australia
Brunei
Nepal
IranThailand
KuwaitPakistan
Singapore
UAE
Indonesia
TurkeySri Lanka
Saudi Arabia
Bangladesh
Malaysia
Philippines
Oman
Bahrain
BhutanVietnam
India
Cambodia
Mongolia
Myanmar
China
Qatar
Net primary income from abroad2017 Asian Productivity Organization

Figure 96 shows this decomposition of real income in each Asian
country, along with the US, theEU15, Australia, and Turkey123

Real income

Figure 94 Decomposition of Real Income Growth, 1970–2015 and 2000–2015
Sources: Official national accounts in each country, including author adjustments.

122: 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 the Asian economies.
However, the trading gain later became significant, especially for the three years 2002–2005. Over these years, the average trading
gain is 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%.
123: 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.

124

7.1 Real Income and Terms of Trade

1973–1979%

1996–1998

%

12

10

9

5

2.9
3.7 1.2 1.2

5.2

6

1.4

9.8

3
0

6.1

7.5

5.7

7.9

8.7 8.6

7.3
4.8

0

1.1
0.7
5.8

1.5
−0.9 −1.3 −1.1

−0.5 −0.5

3.2
1.1 1.0

7.3

5.9 6.7 5.7 5.1

−0.7

−1.3
−1.4

8.3

4.6
−2.5

1.2
5.9
−0.8

4.4 4.6

1.1
3.0 1.5
1.4 1.0
2.9 2.9 2.1
1.5 2.5
1.3 1.3−0.9 −0.7
−1.7

−1.1

1.0
−2.6 −2.2−5

5.3 5.0 5.1
4.4
4.1
3.5 3.7 3.6 2.9 3.4 2.8
2.7
−0.7

1.4

−5.3 −5.1−7.3−1.4 −0.9
−0.7

−10

−0.8

1.2
2.2

−7.4

−15

−3

Terms-of-trade effectBruneiThailandIndonesia
Korea
Iran
Fiji
Japan
Malaysia
Hong Kong
Singapore
EU15
Bangladesh
Pakistan
Philippines
Australia
US
Cambodia
BhutanIndia
ROC
Myanmar
China
TurkeyVietnam
Sri Lanka

Bangladesh
EU15
Japan
US
TurkeyAustralia
India
Philippines
BhutanMyanmar
Sri Lanka
Pakistan
China
Fiji
ThailandSingapore
ROC
Hong Kong
Iran
Malaysia
Indonesia
Korea
Real exchange rate effect

7

Real GDPNet primary income from abroad

Real income

Figure 95 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.

125

2017 Asian Productivity Organizationfrom 1970, or the year of first data collection for the country in question. The trading gain can bepositive or negative, depending on the direction of change in the terms of trade. Its impact is modest
for most countries, adding less than ±1 percentage point to annual real GDP growth, except for someoil-rich 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 the EU15, where
the negative trading gain effect counterbalanced real GDP growth, leaving virtually no growth toreal 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

40

5

20

0

0

−5

−20

−10

−40

−15

%
30

Bhutan

24
18
12
6

−60

1971 1976 1981 1986 1991 1996 2001 2006 2011

Brunei

%
24

−20

0
−6
1971 1976 1981 1986 1991 1996 2001 2006 2011

−12

Cambodia

%
18

%
18

18

15

15

12

12

12

6

9

9

0

6

6

−6

3

3

−12

0

0

−18

−3

−3

−24

1971 1976 1981 1986 1991 1996 2001 2006 2011

ROC

%
18
15

−6

−6

1971 1976 1981 1986 1991 1996 2001 2006 2011

Fiji

%
20

%
16

15

12

10

8

5

4

1971 1976 1981 1986 1991 1996 2001 2006 2011

China

1971 1976 1981 1986 1991 1996 2001 2006 2011

Hong Kong

12
9
6
3

0

0

−5

−4

0
−3
−6

1971 1976 1981 1986 1991 1996 2001 2006 2011

India

%
12
9
6
3
0
−3
−6

1971 1976 1981 1986 1991 1996 2001 2006 2011

Japan

%
10
8

−10

−8

1971 1976 1981 1986 1991 1996 2001 2006 2011

Indonesia

%
20

%
45

15

36

10

27

5

18

0

9

−5

0

−10

−9

−15

−18

−20

1971 1976 1981 1986 1991 1996 2001 2006 2011

−27

Korea

%
20

%
90

15
10

2
0

0
−30

−5

1971 1976 1981 1986 1991 1996 2001 2006 2011

Malaysia

%
20
15
10
5
0

2017 Asian Productivity Organization

Kuwait

30

0

−4

−5
−10

1971 1976 1981 1986 1991 1996 2001 2006 2011

5

−2

−6

Iran

60

6
4

1971 1976 1981 1986 1991 1996 2001 2006 2011−101971 1976 1981 1986 1991 1996 2001 2006 2011

Mongolia%
30

Real GDP

126

%
50

25

40

20

30

15

20

10

10

5

0

0

−10

−5

−20

−15

1971 1976 1981 1986 1991 1996 2001 2006 2011

Myanmar−30−101971 1976 1981 1986 1991 1996 2001 2006 2011−60

1971 1976 1981 1986 1991 1996 2001 2006 2011

Trading gain

−40

Net primary income from abroad

1971 1976 1981 1986 1991 1996 2001 2006 2011

Real income

7.1 Real Income and Terms of Trade

%
10

Nepal

8

40

6

30

4

20

2

10

0

0

−2

−10

%
15

1971 1976 1981 1986 1991 1996 2001 2006 2011

Philippines

−30

30

5

20

0

10

−5

0

−10

−10

−15

−20

−20

−30

1971 1976 1981 1986 1991 1996 2001 2006 2011

Singapore

20

0
−3
−6

1971 1976 1981 1986 1991 1996 2001 2006 2011

Qatar

%
45

1971 1976 1981 1986 1991 1996 2001 2006 2011

Saudi Arabia

30
15
0
−15

7

−30

1971 1976 1981 1986 1991 1996 2001 2006 2011−45

Sri Lanka

%
20

%
15

1971 1976 1981 1986 1991 1996 2001 2006 2011

Thailand

10
5

5

8

0

0

4

−5

−5

0

−10

−10

−4

%
100

3

10

12

−8

6

15

16

Pakistan

9

%
40

10

%
24

%
12

−20

−4
−6

Oman

%
50

1971 1976 1981 1986 1991 1996 2001 2006 2011

UAE

−15

1971 1976 1981 1986 1991 1996 2001 2006 2011

Vietnam

%
12

80
60

−15

%
8

9

6

6

4

1971 1976 1981 1986 1991 1996 2001 2006 2011

US

40
3

2

0

0

−20

−3

−2

−40

−6

20
0

%
6

1971 1976 1981 1986 1991 1996 2001 2006 2011

EU15

−4

1971 1976 1981 1986 1991 1996 2001 2006 2011

Australia

%
8

%
12

4

6

8

2

4

4

0

2

0

−2

0

−4

−4

−2

−8

−6

1971 1976 1981 1986 1991 1996 2001 2006 2011

Real GDP−41971 1976 1981 1986 1991 1996 2001 2006 2011

Trading gain

−12

Net primary income from abroad

1971 1976 1981 1986 1991 1996 2001 2006 2011

Turkey1971 1976 1981 1986 1991 1996 2001 2006 2011

Real income

Figure 96 Sources of Real Income Growth, 1970–2015

1272017 Asian Productivity Organization

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

7 Real Income

7.2 Trading Gain and Productivity Growth
When the trading gain is highly
favorable, it can breed a sense ofcomplacency with productivity
performances suffering as a result. Resource-rich economies are
susceptible to this pitfall because
they are poised to reap some extremely positive trading gains
when commodity prices turn intheir favor over a sustained periodof time. While commodity prices
can rise, they can also fall. This iswhen countries’ real income
growth could suffer if fundamentals for real GDP growth are weak.

% Trading gain effect

2.5

2.0
KuwaitBrunei
Myanmar

1.5

Iran

1.0

BahrainSaudi Arabia

Oman

0.5

0

Qatar

Vietnam
Malaysia
Philippines
Cambodia
Australia Hong Kong
Singapore
BhutanFiji
TurkeyUS EU15
India
Thailand

Korea

Pakistan Sri Lanka

ROC

UAE
Japan
Bangladesh

−0.5

−1.0
−4.5

Indonesia

−3.0

−1.5

0.0

1.5

3.0

4.5

6.0

China

7.5 %

Labor productivity growth2017 Asian Productivity Organization

Figure 97 plots the labor producFigure 97 Trading Gain Effect and Labor Productivity
tivity growth and the trading gain
Growth, 1970–2015
effect for the whole observation
Sources: Official national accounts in each country, including author adjustments;
period. Over the past four deAPO Productivity Database 2017.
Note: The starting years for some countries are different due to data availability durcades, eight countries have ening 1970–2015: Brunei (1989–), Cambodia (1993–), and Turkey (1988–).
joyed a favorable trading gain
effect of over 0.5% per year. They
are Kuwait, Brunei, Myanmar, Iran,
Bahrain, Oman, Indonesia, and Saudi Arabia. Only Indonesia, Myanmar, and Iran among them could
achieve a significant positive growth in labor productivity. In general, a resource-rich country can suffer from “Dutch disease,” which is a phenomenon in which a country’s currency is pushed up by thecommodity boom, making other parts of its economy less competitive and potentially increasing thecountry’s dependence on natural resources.124 This is how resource abundance can easily lead to resource dependence.
Figure 98 illustrates trading gain effects and value-added shares of the mining sector in 1970 and2015 in select Asian economies. It indicates that large trade gainers typically have dominant mining
sectors, petroleum and natural gas in particular. Provided resource prices continually rise, these countries continue to gain from the positive terms-of-trade effects. However, if resource prices fall, or
natural reserves are depleted, then the story of the Dutch disease may appear. Richness in natural resources may become a curse if they do not have competitive industries other than mining. A way
to counteract Dutch disease is broad-based, robust productivity growth and industry diversification.
Figure 98 shows some of the trading gainers (i.e., Brunei and the GCC countries) actively reduced
their share of the mining sector over time, which could reflect the intention of developing industries
other than mining. However, Figure 97 shows that labor productivity growth rates in these countries
after 1990 remained low, or even negative. Even if they wanted to start industrialization, their highincome and strong local currency would not easily allow them to develop a manufacturing sector
124: 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.

128

7.2 Trading Gain and Productivity Growthor an internationally competitive service
industry. Another concern is their heavy dependence on foreign workers, both skilled
and unskilled.

% Trading gain effect, 1970−2015

4=1970 (starting year), =2015

Kuwait3

On the other side of coin are the resource/
Bruneienergy-importing economies. Most of these2
suffered from negative trading gain effects,
losing a part of their economic growth due
Iranto resource price hikes, particularly in the
Oman
2000s (Table 19). However, it has actually
1
Bahrainstrengthened their competitiveness in manIndonesia
Malaysia
ufacturing and other productive activities
Saudi Arabia Qatar
Vietnamfor the future. Figure 97 also shows that0Australia
many Asian countries have succeeded in
UAE
achieving high growth of labor productivity
while having to accept a deteriorating trad−1
60
0
10
20
30
40
50
70
80
90 %
ing gain over the long run. These countries
Value-added shares in Mining, 1970 and 2015
are typically resource importers whose voracious demand for commodities pushes up
Figure 98 Trading Gain Effect and Value-added
their import prices. Meanwhile, export
Share in Mining Sector, 1970–2015
prices tend to fall as a result of their achieveSources: Official national accounts in each country, including author
ment in productivity improvement, resultadjustments; APO Productivity Database 2017.
Note: The starting years for some countries are different due to data
ing in unfavorable movements in terms ofavailability during 1970–2015: Brunei (1989–) for trading gain effect,
trade. This is particularly the case in counBahrain (1975–), Brunei (1974–), Vietnam (1986–), and UAE (1972–) forvalue-added share of mining sector.
tries where economic growth is highly
dependent on export promotion. In such
instances, a negative trading gain is partiallya 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.

2017 Asian Productivity Organization

129

7

7 Real Income

Box 10Per-Worker Wage and Income Level

Figure B10 plots per-worker average wages for employees against per capita GNI, using annual averageexchange rates for selected countries in 2015 (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 notequal 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 B10, some countries are off the simple
regression line. One outlier is Singapore, which is below the regression line. This likely reflects a large proportion of foreign workers out of total labor force who are paid lower than local workers.
Other off-lines are the ASEAN member states including Myanmar, Vietnam, the Philippines, Thailand, andMalaysia. 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 or from 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.
In contrast, the South Asian countries including India, Pakistan, and Nepal are above the regression line
perhaps because they face a difficulty in labor movements from informal to formal or from rural to urban.
The reasons may reside in both labor supply and demand. Presumably, education gaps between rural andurban 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 the manufacturing sector.

Log of per-worker average wage in 2015 (using 2015 exchange rate)

2.0

Australia
US
EU15
Japan

1.6

Korea

Singapore
Hong Kong

ROC
1.2
TurkeyMalaysia
China
ThailandMongolia
Indonesia
Philippines

Iran
Fiji
India Sri Lanka

0.8

Pakistan

0.4

Cambodia
Nepal

Lao PDR
VietnamBangaldesh

Myanmar
0.0
–0.4

0.0

0.4

0.8

1.2

1.6

2.0

Log of per capita GNI in 2015 (using 2015 exchage rate)

Figure B10 Average Wage and Per Capita GNI, 20152017 Asian Productivity Organization

Sources: Official national accounts in each country, including author adjustments; APO Productivity Database 2017.

130

8 Development Policy
National development strategies are instrumental in encouraging development efforts and making
them consistent. An important role of political leaders is to demonstrate a broad scope of economic
development and government policies to the public, while fostering engagement in economic activities. National development strategies should provide a long-term vision and goal, as well as generate coordinated efforts among ministries and other government agencies. As the world moves from
central planning to market economies, national development plans are also changing from directive
to indicative. The effectiveness of plans depends on the strength of the development agency, theirbudgetary controls – including foreign aid – and the coordination among government agencies.

8.1 National Development Strategies

8

The elements of national development strategies depend on each country’s development stage. In
cases of low-income and lower middle-income countries, development of industrial infrastructure(e.g., infrastructure for transportation, logistics, and electricity generation and distribution), creationof a business-enabling environment, and job creation for sustainable poverty alleviation are typicallyprioritized. All other things being equal, if the policy target is to create jobs, labor productivity may
suffer in the short term, since marginal and less-productive workers are recruited, bringing down the
average productivity performance. In the early stage of development, however, it is reasonable to givepriority to expanding production and employment, rather than improving economic efficiency.
On the other hand, upper middle-income and high-income countries emphasize development of social and cultural infrastructure such as well-being (e.g., infrastructure for higher education, healthcare,and recreation), improvement on efficiency in resource allocations, fostering productivity in the service sector, promotion of technological innovation, environmentally-friendly growth, and so on. The
format varies across countries, therefore national development strategies reflect country-specific interests and aspirations.

125: The summaries in the tables were reconfirmed as of April 2017 by the national productivity organizations of Bangladesh, Cambodia, the ROC, Fiji, India, Malaysia, Mongolia, Pakistan, and Vietnam, while the rest are based on previous feedback from the othernational productivity organizations as well as the APO country officers in line with available information.

1312017 Asian Productivity Organization

Table 20 summarizes the gist of major national development policies pertinent to productivity enhancement in APO member economies.125 From an economics stand-point, several check points exist
in evaluating each country’s strategies. First, targeted economic growth rates should be reviewed
carefully. Political leaders often have incentives to announce a high growth rate as a target. However,one must consider whether such a target is achievable. Otherwise, it may jeopardize macroeconomic balances. One way to check whether or not a targeted growth rate is reasonable is to utilize
a-la-Harrod-Domar formula as g = ΔY/Y = (ΔY/I)(I/Y) = (1/ICOR)(I/Y) where Y is the gross domestic
product (GDP), ΔY is the increment of GDP, I is gross (or net) investment, and ICOR is incremental capital output ratio. In cases of less developed countries, steady growth rates are typically coupled withICOR between three and six. If the corresponding ICOR is too low, the target growth rate is regarded as too ambitious. An alternative way is to check the decomposition of economic growth into thegrowth of factor inputs (capital and labor) and the total factor productivity (TFP) growth as shown in
Chapter 5. If the expected contribution of TFP growth is too high, the target growth rate would be
unlikely. Too high target growth rates are dangerous. It may distort the balance of government revenue and expenditure, matching energy supply to demand, monetary and fiscal policies, and businessexpectation for the expansion of the market.

8 Development Policy

Secondly, it must be determined whether any strategic sectors are set. Industrial development should
result from market forces – too much control by the government might not be desirable. On the other
hand, listing too many industries would weaken the strategic nature of national development. Alongthe development path, certainly, some sectors are in the critical path, and others are not. It is important to identify critical sectors and provide concrete ideas of promoting them.
Surprisingly, most of the national development strategies in Asia do not consider global value chains(GVCs). In the era of the second unbundling (Baldwin, 2011), participation in international production
networks is crucial to economic development. To do so, conscious efforts for reducing service linkcosts and improving investment climate is essential. However, these policy demands are not explicitly
stated. One possible reason is the incentive for policymakers to avoid placing too heavy emphasis on
foreign players vis-a-vis domestic companies. The commitment to GVCs requires aggressive invitationof foreign-owned firms in the form of international trade and inward foreign direct investment.
The emphasis on small and medium enterprises (SMEs) development is common to most of the countries. This is a means of strengthening the sense of ownership on economic development by promoting
domestic players. The contents are diversified with SMEs including cottage industry, primary exporters, supporting industries, venture business, and others. Sectors expand not only manufacturing but
other industries. The corresponding policies are also naturally diversified. Some policies are to promote SMEs as economic policy while others are primarily social policy to work for equity and poverty
issues. The details are important for SME development.
A third consideration is how social sectors are addressed. Education, health, social protection, andgender issues are typical factors, as is environmental sustainability. Are they reasonable, achievabletargets? Are they politically-correct ideals or serious and achievable goals? The hidden intention could
be difficult to identify.

2017 Asian Productivity Organization

Half a century ago, Wassily Leontief, who won the Nobel Prize in economics in 1973 for the development of the input-output method, indicated that “the issue that confronts top management is notthat of how to choose between unrestricted competition and all-pervasive planning, but rather of
how to choose an effective combination of the two. Despite what the professional debates on bothsides would have us believe, it is not an eternal conflict between two incompatible philosophical principles which we face today, but rather a practical question of efficient working arrangements (Leontief, 1966).” In a quest for better policies to combine competition and planning effectively, it is also
important to improve observation of the economy. Better measurement must run parallel with national development strategies in many Asian countries.

1328.1 National Development Strategies

Table 20 National Development Strategies
Bangladesh
Seventh Five Year Plan, 2016-2020(General Economics Division, Planning Commission, Govt. of Bangladesh, December 2015)
Key Numerical Targets Relevant for Productivity- Raise the GDP growth rate progressively from 6.5% in FY15 to 8% by FY20
- Reduce poverty rate to 18.6% and extreme poverty to around 8.9% by FY20
- 12.9 million additional jobs will be available during next five years- Increase the contribution of the manufacturing sector to 21% of GDP by FY20
- Substantial improvement of exports to $54.1 billion by FY20
- Electricity coverage to be increased to 96 percent with uninterrupted supply to industries
Focus Sectors of the Development Plana) General Public Services and Public Or- - Special focus on reforms to improve development (i) Judiciary (ii) Public administration capacity (iii) Financial
der and Safety
sector (iv) Public order and safetyb) Industrial and Economic Services- (i) Modernizing the service sector with emphasis on export of non-factor services (ii) Improve the incentive policies for boosting private investment in services (iii) Increasing public investment in key service sector infrastructure (iv) Strengthening the skills base for the service industry (v) Strengthening implementation of prudential
regulations to boost service quality, increase public safety, improve compliance and ensure accountability ofservice providers (vi) Strengthen monitoring and enforcement services on overseas employment related recruitment services (vii) Strengthening public institutions to support the growth of services sector and improve service
quality, safety and accountabilityc) Agriculture- Productivity gains, diversification, value addition and agro-processing commensurate with national environmental protection and climate change adaptation- Comprehensive long term water resource management plan under the umbrella of the Bangladesh Delta Pland) Power and Energy- The total power generation capacity in 2015 stands around 14000 MW which will be increased to 23000 MW by 2020.
- Rooppur Nuclear Power Project's first plant will be commissioned immediately after 2020.

e) Transport and Communication- Modern transportation and communication for achieving the target growth of 8%

8f ) L ocal Government and Rural Develop- - (i) A Local Government Legal Framework (LGFL) (ii) Building the capacity of local governments (iii) Developing planment
ning and budgeting capacities at the local level (iv) Link local level plan to the national medium to long term planningg) Environment and Climate Change- Ensure environmental sustainability through conservation of natural resources and reduce air and water pollution- Natural conservation with increased forest coverage, Environment, climate change adaptation and mitigation- Alternate livelihoodh) Health- Service delivery and utilize the vast health network- Reduce the fertility rate to 2.0 by the end of the plani) Education and Technology- Scientific education- Attention to disadvantaged groups, women, children and persons with disabilities

Cambodia
National Strategic Development Plan (NSDP), 2014-2018(Royal Government of Cambodia, September 2014)
Key Numerical Targets Relevant for Productivity- Annual GDP growth rate: 7.0% (4.0-4.2% for Agriculture, 8.8-9.9% for Industry, and 6.8-7.2% for Service)
Focus Sectors of the Development Plana) Promotion of Agriculture Sector- Improved productivity, diversification, commercialization- Promotion of livestock farming and aquaculture- Land reform, and clearnce of mines and UXO (Unexploded ordance)- Sustainable mabagement of natural resourcesb) D evelopment of Physical Infrastructure - Development of transport and urban infrastructure- Water resources and irrigation system management- Electrical power development- Development of information and communication technologyc) P rivate Sector Development and Em- - Strengthening the private sector and promoting investment and businessployment- Development of industry and small and medium enterprises (SMEs)- Development of labor market- Banking and financial sector developmentd) Capacity Building and Human Re- - Strengthening and enhancing eduction, science and technology, and technical training
source Development- Promotion of health and nutrition- Development of social protection system- Enhanced implementation of population policy and gender equity

ROC
Four year national development Plan 2017-2020 & National Development in 2017, 2013-2016(The new government is still drafting the new long-term development plan; CPC's source is based on the latest directions of the new government rather than the existing development plan.)(ROC country paper for WSM 2016 (by CPC), November 3, 2016)
Key Numerical Targets Relevant for Productivity- Economic growth rate: 4.5%; CPI less than 2%; umemployment rate 3.9%a) Industrial Upgrading and Innovative - Develop innovative industries
Economy- Recruit professional talent- Push for linkage between industries, academics and research institutions- Develop innovative finance- Develop sustainable tourism- Push for overall well-being of financial laws and regulations

133

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Focus Sectors of the Development Plan

8 Development Policy> ROC (continued from previous page)b) Worry-Free Living and Just Society- Create a safe homeland- Strenghten food safety management- Promote long term care
- Ensure implementation of pension reform- Secure safety of the society- Push for overall well-being of the general public and sports development- Protect labour rights- Push for youth employmnet- Strenghen child care services- Push for “Transitional Justice“c) Inter-Regional Balance and Sustain- - Push for overall well-being of land development plans and relevant disaster prevention measuresable Development- Balance regional development- Develop convenient transportation projects- Promote “Greenhouse Gas Reduction“ regulations and air pollution prevention measures- Supply sustainable energy- Develop and conservate water resources- Establish and develop ocean resourcesd) Government Effectiveness and Sound - Emhance the effectiveness of public sectors
Finances- Amplify the benefits of public infrastructure- Create digital service of public sectors- Push for overall well-being of national financee) Education, Culture and Diverse Ethnic - Push for transformation of higher education
Groups- Accelerate the transformation of vocational and technical edcuation- Strenghen general public education- Promote digital learning- Educate disadvantaged children- Cultivate cultural power- Develop cultural economy- Respect multiple ethnic groupsf) N ational Security and International - Secure national security
and Cross-Strait Relations- Promote “Pragmatic Diplomacy“- Strenghen international ties with other nations- Stable development of Cross-Strait Relations- Promote the “New Southbound Policy“Fiji
2nd Draft: Fiji’s 20-year and 5-year National Development Plan, 2016-2035(Ministry of Economy: Department of Strategic Planning & National Development, March 14, 2016)
Key Numerical Targets Relevant for Productivity- Continue to achieve annual GDP growth rates of between 4 to 5 % in some years over the medium to long term,with an annual average GDP growth rate of 3.5%
- By the end of the Plan period with the right enabling and productive environment, the country should reach areal GDP per capita of over $15,000 compared to about $7,500 today.
- Government will target an increase in the share of private sector investment of 2% over the next five years and tosustain private sector investment at an annual average rate of 15% of GDP for the duration of the plan period.
- The Government’s target in the medium term is to ensure that public revenue is maintained at more than 27 % ofGDP
- Promoting user pay principle where appropriate in the public sector.
- Main drivers of growth in 2017 and 2018 are expected to be manufacturing, transport and storage, financial andinsurance and accommodation and food service sectors.
- Tourism Industry: A $2.2 billion industry by 2020 with sustainably growing visitor arrivals and a sustainable, highly
developed and globally competitive tourism industry.

2017 Asian Productivity Organization

Focus Sectors of the Development Plan

134a) Agriculture- Competitive, sustainable and value-adding agriculture sector adapted to the impacts of climate change- Public-private partnerships and other innovative ways in partnering with large commercial enterprises will be an
avenue to boost large scale production as well as a means of introducing modern technologies, technology
transfer, knowledge and expertise.
- Strengthening the linkages along the agriculture sector value-chain from production, distribution, storage, marketing and value addition with the view to improve efficiency and lay a platform for the agriculture sector to be a
key driver of Fiji’s future economic growth- Revitalize the sugar industry: Review of the master award to reflect need for modernization, mechanization andimproved productivity in the sugar industry.
- Support growth of Aquaculture industries- Encourage the growth of timber product development: Sustainable management and development of forestry
resources that positions Fiji to capture better returns along global value chains for timber products thus will leadto the industry including more value-added products by incorporating more down-stream processing.

b) Tourism Sector- Infrastructure and utilities will need maintenance and expansion to keep up with growing tourism demand, whileat the same time care will be needed to minimise any negative impact on the natural environment of Fiji.
- Innovative Tourism Packages- Improve human resources capacity and quality of hospitality services- Increase value addition by developing market linkages between tourism and other local sectorsc) Industry Sector- Value added tax reduced from 15% to 9% while zero duty is imposed on plant and machineries, raw materials, andother inputs for manufacturing.
- National Branding-to help MSMEs develop their businesses and find opportunities for growth through the Fijian
Made and buy Fijian Campaign that looks to promote and raise the profile of locally produced goods.
- Identify tourism products for further development and foster sector innovation for increased value addition.

d) Mining Sector- Encourage and accelerate growth through Foreign Direct Investment (FDI)- Ensure sustainable development of groundwater resources

8.1 National Development Strategies> Fiji (continued from previous page)e) Energy and Infrastructure sector- Private engagement infrastructure service provision- Further development of the full road network to international standards with a greater emphasis on maintenance,
rehabilitation and upgrading- Maintain a strong independent, autonomous and efficient civil aviation oversight system that meets ICAO standards and international best practices- Increase share of electricity generation through renewable energy resources- Improve energy efficiency in the electricity sectorf ) Favorable Business Environment- Ensure efficient management and strengthening of international relations- Enhance Fiji’s trade base and economic interests in the global community- Strengthen ICT capabilities in the workforce- Develop a national framework for Innovation and R&D- Significant investment to be undertaken in various sectors including infrastructure development, education,
health, housing, water, and energy to improve overall economic activity and raise living standardsg) Sustainable Social Development- GDP growth of above 3% per annual- Sound regulatory policy to support inclusive and sustainable private sector-led growth- Investments in health to ensure a productive, motivated health workforce with a focus on patient rights andcustomer satisfaction- Improving Fiji’s labour market standards to meet accredited international best practices- Enhance technical, vocational and lifelong skills training at all levels- Empowering youth to be critical agents of change and development by promoting the educating and training ofyoung people to secure decent employment- Empowering women so that they reach their full development potential through full participation in businessand decision – making processes and national development- Improving Regulatory Performance: Labor Market Information System (FMIS linked to ITC Data)- Developing the smaller urban centres in the first 5 years in order to relieve the pressure currently exerted on thebasic infrastructure and the environment in the major urban centres due to high population densityh) Environmental Sustainability

8- To supply safe drinking water and improved sanitation services to every Fijian household- To promote Green Manufacturing- Strengthen all partnerships at all levels for building resilience for climate change and disaster

India
Five Year Plan, 2012-2017(2012-: Planning Commission, 2014-: NITI Aayog, 2012-2017)
Key Numerical Targets Relevant for Productivity- Faster, sustainable and more inclusive growth. GDP gowth target 7-7.5% in 2016-17; Long-term growth: 8%- To achieve the Gross National Income of around USD6,000-7,000 to become a middle-income country. Presently,
India is classified as a lower middle-income country with a per capita GNI of around $1,500.
- Train 400 million youth in different industry-ready skills by 2022
- Reduce poverty by 10% through a systematic 2% annual reduction on a sustainable basis- Achieve HDI threshold of 0.9 for parity with a number of EU countries. India HDI of 0.609 is below the average of
0.630 for countries in the medium human development group. It climbed 5 ranks, to reach 130 among 188 countries in 2014 in Human Development Report 2015.
- To achieve universal access to affordable, reliable and modern energy services by 2019- Achieve higher levels of economic productivity through diversification, technological upgrading and innovation,
including through a focus on high-value-added and labour-intensive sectors
Focus Sectors of the Development Plan
- To double farmers income by 2022 (as announced in Union Budget 2016-17). The SDG target is to double the agricultural productivity and incomes of small-scale food producers (women, indigenous peoples, family farmers, pastoralists, and fishers) by 2030. This includes providing secure and equal access to land, other productive resources
and inputs, knowledge, financial services, markets and opportunities for value addition and non-farm employment.
- Create infrastructure for Food Processing and modern warehousing- Irrigation Funding mechanism for agriculture infrastructure and to execute various programmes related to sustainable management of ground water resources- Farm Mechanisation: Increasing reach to the regions where availability of farm power is low, and to make it accesible to small and marginal farmers- Improve irrigation facilities: It aims to complete 99 major and medium irrigation projects by 2019. These projects aretargeted to bring 7.6 million hectares of land under irrigation in some of the most drought-prone regions of India.
- Improve soil fertility on a sustainable basis through the soil health card scheme and to support the organic farming scheme Paramparagat Krishi Vikas Yojana- Fish and fish products: Mordenization and mechanization of fishing and processing for value add to ensure India meets
its target of increasing its export earnings by three times to USD14.88 billion by year 2020. Promoting sustainable marine fishing and aquaculture. (India has taken up the ambitious Blue Revolution program for integrated development
and management of fisheries with total financial outlay US440.15 million for a period of five years to achieve it.)- Food Safety (as per SDG): Ensure sustainable food production systems and implement resilient agricultural practices by 2030 to increase productivity and production that can help maintain ecosystems, strengthen capacity foradaptation to climate change, extreme weather, drought, flooding and other disasters and that progressively
improve land and soil quality.

135

2017 Asian Productivity Organizationa) Agriculture8 Development Policy

2017 Asian Productivity Organization> India (continued from previous page)136b) Manufacturing- Target: Increasing the contribution of manufacturing output to 25% of GDP by 2025, up from the existing 16%.
Expects to become world's top three manufacturing destinations by 2020.
- Build best-in-class manufacturing infrastructure to boost industrial growth and make the country a global manufacturing hub. The government's flagship program “Make in India“ aims in achieving this. The initiative is designed
to facilitate investment, foster innovation, protect intellectual property and motivate designed in Indian products.
- 25 focus sectors for Make in India: Automobiles, aviation, chemicals, IT & BPM, pharmaceuticals, construction, defence manufacturing, electrical machinery, food processing, textiles and garments, ports, leather, media and entertainment, wellness, mining, tourism and hospitality, railways, automobile components, renewable energy,
biotechnology, space, thermal power, roads and highways and electronics systems.
- Focus on “Zero Defect Zero Effect“ which signifies production mechanisms wherein products have no defects andthe process through which product is made has zero adverse environmental and ecological effects. This is aimed
to ensure that products developed from India does not get rejected by the global market.
- Facilitate acquisition of clean, green and energy efficient technologies by Micro, Small & Medium Enterprises(MSMEs). The government has already set up Technology Acquisition and Development Fund (TADF) under the
National Manufacturing Policy (NMP) for this.

c) Skill Development- 400 million youth to be trained in different skills by 2022. The governments Skill India Program is a major initiative
launched in July 2015. It includes initiatives like National Skill Development Mission, National Policy for Skill Development and Entrepreneurship, Pradhan Mantri Kaushal Vikas Yojana (PMKVY) and the Skill Loan scheme.

d) Urban Development- Create adequate urban infrasstructure to house 40% of India’s population by 2030, which is expected to contribute over 75% of India’s GDP. India’s urban growth is primarily concentrated in large cities with a population of
100,000 or more. The number of cities with a population exceeding 1 million is expected to go up from 53 (in2011) to 87 by 2030.
- Develop 100 Smart Cities by 2020. The government's SmartCity Mission is expected to improve the efficiency of
cities and enable local area development, thereby driving economic growth and improving the quality of life. The
urban transformation is expected to be driven by adopting technology-based interventions.
- Transformation and rejuvnation of 500 cities through its Atal Mission for Rejuvenation and Urban Transformation(AMRUT) program. It also includes achieving the target of housing for all by 2022. The program also aims to ensurethat every household has access to a tap with assured supply of water and a sewerage connection. It also aims toincrease the amenity value of cities by developing greenery and well-maintained open spaces and to reduce
pollution by switching to public transport or constructing facilities for non-motorised transport.

e) Energy- Provide universal energy access by 2019. The Power for All programme aims to generate two trillion units (kilowatt
hours) of quality, reliable and affordable energy by 2019.
- Set up over 10,000 solar, wind and biomass power projects in next five years, with an average capacity of 50 kilowatt per project, thereby adding 500 megawatt to the total installed capacity.
- Create infrastructure to achieve annual renewable energy target of 175GW by 2020. This includes 100,000 MW fromsolar power, 60,000 MW from wind energy, 10,000 MW from biomass, and 5,000 MW from small hydro power projects.

f ) Digital Infrastructure & Inclusion- Transform India into a digitally empowered society and knowledge economy. To achieve this the Government ofIndia has launched the Digital India initiative that is driven primarily by the national e-governance plan. The overall objective is to create ICT Infrastructure, including high speed internet access for all. It also includes use of ICT
to improve government sector processes and online delivery of citizen services.
- Digital empowerment of citizens: This is one of the most important factor of the Digital India initiative to provide
universal digital literacy and make digital sources easily accessible. The services are also provided in Indian languages for active participation.
- Achiving financial inclusion for all. The government has taken the JAM Number Trinity – the Jan Dhan Yojana (ano-frill bank account), Aadhar (unique citizen number) and mobile number – for direct subsidy transfers in orderto enable the government to provide targeted subsidies, reducing distortion and subsidy leakages while expanding financial inclusion to meet this challenge.

g) Infrastructure- Develop four key areas of public infrastructure in order to attract investments and facilitate overall economic
growth. These include Railways, Roads and Highways, Sagarmala project (for ports and coastal development) andInland waterways.
- Prioritise decongestion of heavy haul routes, speed up trains and provide better passenger amenities, safety, andimproving railway systems through sustainable measures.
- Develop new road infrastructure and modernise the existing road network, in addition to developing interstate
highways/expressways.
- Coastline development: To transform the existing ports and for creating new ones with world-class technology
and infrastructure. India's Sagarmala Project is also expected to integrate them with industrial clusters and thehinterland through rail, road, inland and coastal waterways.
- Developing Inland Waterways Transport (IWT) to help enhance an alternative mode for transportation of goods,
to decongest existing models as well as realising advantages in terms of fuel and cost savings.

h) Favorable business environment- Reduce bureaucratic process and red tape to create a conducive business environment by streamlining regulatory structures for an investor-friendly business climate. (India ranks 130th out of 189 countries in the World Bank's
2016 ease of doing business index, covering the period from June 2014 and June 2015. It improved its rank from
134 in the 2015 index.)i) Hygiene Infrastructure- Making India 100% free of open defecation by 2019. The initiative is driven under the Swachh Bharat Abhiyan(Clean India Mission) that also aims at adopting modern and scientific municipal solid waste management, effectbehavioural change for healthy sanitation practices, generate awareness about sanitation and its linkage withpublic health, augment capacity of Urban Local Bodies (ULBs).
- Abatement of pollution and rejuvenation of the river Ganga by adopting a river basin approach to promote
coordination within different sectors for comprehensive planning and management. The program also includes
interception and diversion, and treatment of waste water flowing through open drains via bio- remediation/ appropriate in-situ treatment/ use of innovative technologies/ sewage treatment plants (STPs)/ effluent treatment
plant (ETPs).

j) Sustainable Development Goal

- Achieve broader social objectives to achieve UNDP 2030 sustainable goals that have 169 sub-targets

8.1 National Development Strategies

Indonesia
National Development Plan, 2015-2019(Ministry of National Development Planning, January 8, 2015)
Key Numerical Targets Relevant for Productivity- SMEs productivity increase of 5-7% per annum
Focus Sectors of the Development Plana) Education- Connectiveness between higher education, research institution and industry- Incentive for industry and SMEs that is able to train their employess (matching fund)b) Youth, Culture and Sport- These three focuses are seen as an integrated approach to develop and manage human resources as stated in
Govt. Regulation, UU 3/2005.
- Facilitating productivity enhanement for creative industryc) A ccelerating Growth in Industry Sector - Increasing efficiency and productivity of State Owned Entreprises- Creating relatively high value added industry- Industry-wide focuses: (1) strategic industry, (2) maritime-based industry, (3) labour intensive- Three emphasis: (1) technical efficiency enhancement, (2) Innovation and technology transfer, (3) new productdevelopment, (4) input factor development- Improving level of human development indicator- Improving efficiency for industrial cluster area through connectivity, human resources, infrastructure development and good governance8d) Productivity and Competitiveness En- - Focusing on agriculture, fisheries and cooperatives
hancement for SMEse) Labour Productivity Human Resources - Creating a gradual transformation of structure of labour from low productivity sector/sub-sector to the higher
ones- Creating conducive industrial relation- Developing rural economy- Implementation of wage-productivity incentice schemef ) International Trade- Increasing productivity for domestic market player vis-à-vis their foreign counterpart- Product creationg) Investment (mining sector)- Investment on old and existing mine field to increase its productivityh) Domestic Trade- Revitalization of distribution network to enhance productivity of the economyi) International Affairs- The use of diplomacy channels to achieve “growth with equity“ to maintain the desired level of productivityj) Public Sector- Creating an efficient and productive governmentk) Under-Developed Areas- Enhancing overall productivity for the under-developed areas- Resources management for productivity enhancementl) Infrastructure- Infrastructure investment and revitalization- Reliable and affordable housing for people- Reliable transportation for easy flow of good, services and humanm) Agriculture- Increasing land productivity- Productivity and competitiveness enhancement of primary commodities- Enhancing efficiency in the agribusiness sector through revitalization on produc quality, land, and technique of
production- Capacity optimazation of the existing production mode

Iran
The Sixth Five-Year Development Plan, 2016-2021(Iranian Government, 2017)
Key Numerical Targets Relevant for Productivity- Average 8% economic growth during the five-year period
Focus Sectors of the Development Plana) Mining, Information Technology, En- - Promotion of value-added activities in industry and mining
ergy, Tourism, Agriculture and Trans- - Greater export orientation and the prioritization of strategic industrial sectors, including oil, gas, petrochemicals,
portationtransportation, construction, information technology and agriculture- Increasing the penetration rate of high-end technologies in these sectorsb) Government- Encourage the government to improve the business environment to address unemployment and poverty- Improving Iran’s ranking in the ease of doing business index to 70 from its current place of 118c) P roductivity, Cyberspace, Educational
Reforms and Academic Development

Japan
Japan Revitalization Strategy 2016, 2016-2020(The Japanese government, June 2, 2016)
Key Numerical Targets Relevant for Productivity- Achieve nominal GDP JPY 600 trilliona) The fourth industrial revolution- Establishment of the “Public-Private Council for the fourth industrial revolution“- Specifying research and development and the strategy of industrialization in the “The Artificial Intelligence Technology Strategy Council“- Regulation and system reform, promotion of projects etc. for data use beyond the framework of companies andorganizations and ensuring security- Promotion of business metabolism and streamlining of business restructuring toward the fourth industrial revolution- Substantiation of human resources development and education-related measures in the “Council for Promoting
Human Resource Development to respond to the Fourth Industrial Revolution“- Dissemination of the fourth industrial revolution in medium-sized and small and medium-sized companiesb) Healthcare- Providing diagnosis support and innovative new drugs and medical device by using Big Data, etc.
- Providing personalized healthcare services using IoT, etc.
- Improving the quality and productivity of nursing care by utilizing technologies such as robot, sensor, etc.

1372017 Asian Productivity Organization

Focus Sectors of the Development Plan

8 Development Policy> Japan (continued from previous page)c) Environment-energy- Promotion of investment of distribution and service industries and small and medium-sized companies in energy
efficiency- Promotion of introduction of renewable energy and development of new energy systems- Strengthening of resource securityd) Sports/Culture- Improvement the attractiveness and profitability of sports facilities- Development and use of sports management human resources and creation of a platform- Integration of sports with IT, healthcare, tourism, fashion, culture and arts, etc. and its expansion- Establishment of an existing housing transaction market in which houses are evaluated as assetse) Service- Creation of businesses leading the service industry's productivity improvement- Productivity improvement by each business field- Community-based productivity improvement through utilization of SME support providers, etc.

f ) SME- Support for the growth of local core companies with an eye to the global market- Support for local SME's expansion to overseas by taking advantage of TPP- Support for improvement of productivity of SMEs including IT utilization- Promotion of provision of growth funds which do not need collateral or survey by using a “local benchmark“, etc.,
enhancement of financing functions and acceleration of business revitalization/successiong) Agriculture- Enhancement of functions of the Public Corporations for Farmland Consolidation to Core Farmers through Renting
and Subleasing- Cost reduction of production materials and realization of distribution/processing structures favorable to producers- Development of human resources- Reinforcement of export- Promotion of “Smart agriculture“ (realizing unmanned automatic operation by remote monitoring by 2020)- Building a system for cooperation between the agricultural and business communitiesh) Tourism- Enhancement of the attractiveness of tourism resources- Revision of tourism-related regulations and systems- Promotion of establishing and developing Destination Management/Marketing Organization (DMOs)- Development of tourism human resources- Improvement of regional tourism environment to accept an increasing number of foreign visitors to Japan- Promotion of taking leaves/staggered holidays

Korea
National Strategy Project(Ministry of Science, ICT and Future Planning, August 10, 2016)
Focus Sectors of the Development Plana) Power for new growth engine- Developing core technology for self-driving cars
- Develop lightweight materials- Establish smart city to lead world- Develop AI (Artificial Intelligece) to lead intelligent information society- Build Vitural Reality ecosystemb) Quality improvement for life

- Technology development for precise medical treatment based on bio information- Develop bio new medicine to overcome severe disease- Technology development for carbon resourses- Technology development for super fine dust

Lao PDR
Five Year National Socio-economic Development Plan VIII, 2016-2020(Ministry of Planning and Investment, February 25, 2015)
Key Numerical Targets Relevant for Productivity1) S ustained, inclusive economic growthwith economic vulnerability (EVI) reduced to levels required for LDC
graduation and consolidated financial, legal and human resources tosupport growth.

- Sustained and inclusive economic growth- Integrated development planning and budgeting- Balanced regional and local development- Improved public /private labor force capacity- Local entrepreneurs are competitive in domestic and global markets- Regional and international cooperation and integration2) H uman resources development
achieved to LDC graduation criteria
level and achievement of off-track
MDGs through the provision and useof services which are balanced geographically and distributed equitably
between social groups.

- Improved living standards through poverty reduction- Food security ensured and incidence of malnutrition reduced- Access to high quality education- Access to high quality health care and preventative medicine- Enhanced social welfare- Protection of traditions and culture- Political stability, order, justice, gender equality3) R educed effects of natural shocks as - Environmental protection and sustainable natural resources management
required for LDC graduation and sus- - Preparedness for natural disasters and risk mitigation
tainable management of natural re- - Reduced instability of agricultural production
sources exploitation.

2017 Asian Productivity Organization

Focus Sectors of the Development Plana) Ensuring Sustained and Inclusive Eco- - Industrial Sector: Develop the industrial sector to be a high growth sector quantitatively and qualitatively, to dinomic Growthversify in its commercial and modern goods production to be able to meet the domestic consumption as well as
for exports’ needs and transforming it into a main sector to support the sustainable economic growth.
- Service Sector: Develop the service sector firmly linked to production enabling high revenue for the country,
creating jobs to the people; enhance land transit points in the region and provide diversified goods and services
to meet domestic and international markets demand.
- Agriculture and Forest Sectors: Develop the agriculture-forest sector so to ensure stabile and sustainable productionof food and commercial goods; expand agriculture production according each local area potential in the orientationof clean and modern and qualitative intensive agriculture; apply modern techniques and technology into production to ensure the agriculture goods supply to industrial processing and service sectors quantitatively and qualitatively linking it to the goods processing for value added enhancement; and sustainably manage the forest.

1388.1 National Development Strategies> Lao PDR (continued from previous page)b) Improved Public / Private Labor force - Develop workforce in both quantity and quality of various areas at different levels according to the economic
Capacity
structure by focusing on agriculture, industry and services areas in respect to the national development demandand build capacity for them to compete in the region especially in the ASEAN region. Build workforce to be national conscious, be a good citizen, respect laws and regulations, highly discipline, ethical, tolerant and active totheir work and self-development and thereby become healthy workforce which is significantly essential for eachperiod of the socioeconomic development.
c) L ocal Entrepreneurs are Competitive - Human development is a key factor of the national development especially in the generation of advanced sciin Domestic and International Markets ence, technology and integration. Therefore, it is essential to build human resources to be knowledgeable, competent and proficient in various areas of education and professions, build experts in specific fields to employ themin public and private organizations and thereby contribute in the national development and construction as they
are capable to run private business most efficiently and effectively. At the same time, it is essential to concentrate
in developing and managing technical services to build experts who specialized in each profession that can support the national development in each period.
d) Food security Ensured and Incidence - Ensure food security and dietary intake of essential nutrients and safe for health which count from the productionof Malnutrition Reduced
to the consumption processes, aiming at contributing to poverty reduction and livelihood improvement for thepeople in a sustainable manner.
e) Universal Access to Quality Education- Improve and modify curriculum for vocational and university educations in the fields that suit the socio-economic
development such as electrical, mining, processing, handicraft, mechanics and services. This is to help ensuring
higher quality of the education and sufficient labor force to employ in production and services sectors that supporting the future national development, and enhance capacity to integrate and compete at the regional andinternational level.

8f ) E nvironmental protection and sustain- - To ensure sustainable development, initiatives on environmental protection and disaster risks management areable natural resources management
essential. Develop green, clean and beautiful economy for the people’s well-being, health and pollution free bysustainable urbanization. Increase capacity on climate change resilience and impact mitigation.
g) Prepare to cope with the disaster risks - Further develop sustainable ownership to protect the environment, ready to cope and reduce the risk of various
and climate change
disasters, economic green people live a good, healthy, strong and safe from pollution of water, sound and air. Increase and improve the ability to adapt, to reduce the impact of climate change by reducing greenhouse gas
emissions to a minimum.
h) Reducing the instability of agricultural - To meet the sustainable development and stable and continued economic growth, reducing the instability of
production caused by the impact of agriculture production is essential to foster economic growth and promoting stable jobs for farmer. In connection
disasters(ensure about markets and to this, preparing for and anticipating the environment condition and climate change are the main factors for
agriculture. Moreover, it is also important to ensure stable supply, markets and prices for the agricultural products.
prices for the agricultural products

Malaysia
11TH MALAYSIA PLAN, 2016-2020(Economic Planning Unit, Prime Minister's Department, May 21, 2015)
Key Numerical Targets Relevant for Productivity- Labour productivity will reached USD21,000 in the year 2020 from USD17,500 in 2015
Focus Sectors of the Development Plana) National Level- Formulating a five-year Malaysia Productivity Blueprint- Enhancing public sector productivity by introducing productivity, enhancement KPIs, accelerating regulatory
reforms and rationalizing government institutions- Encouraging up-skilling and re-skilling as well as research through increased industry-academia collaboration,
more targetted training programs and increased support for industrial and scoial innovation activitiesb) Industry Level- Appointing productivity champions and customising industry-level productivity programmesc) Entreprise Level- Setting up enterprise-level productivity assessments and targets by promoting productivity performance targets,
introducing firm level interventions, promoting health check mechanisms and fostering productivity-based culture

Mongolia
Mongolia Sustainable Development Vision 2030, 2016-2030 (Phase I (2016-2020) Phase II (2021-2025) and Phase III (2026-2030)(State Great Hural of Mongolia, February 5, 2016)
Key Numerical Targets Relevant for Productivity- Increase GNI per capita to USD 17,500 to become an upper middle-income country- Average economic growth rate of no less than 6.6%
- Become among first 40 countries by the Doing Business Index and among 70 countries by the Global Competitiveness Index- Adopt advanced technologies with high productivtiy in each sector and encourage new products, production
and services adopting innovationsa) Agriculture- Increase productivity through sustainable agribusiness to promote agriculture infrastructure and rural development- Adopt economical and efficient advanced clean agro technology- Develop intensified farmingb) Tourism Sector- Improve business, law and economic environment for sustainable production- Improve the inftrastructure and service quality- Eco-tourism developmentc) Industry Sector- Employ advanced methods, technology and innovations to increase productivity and competitiveness- Food security- Mongol branding for international market/export- Develop the chemical industrial sectord) Mining Sector- Potential mineral resource exploration- Develop environment friendly infrastructure- Create a favorable environment for investment in mining sectore) Energy and Infrastructure Sector- Become energy export country- Increase the share of renewable energy consumption- Expand information technology and telecommunication coverage- Improve the urben planning and development- Expand and develop roads and transportation logistics- Improve trade and services; develop transportation and logistics networok for import/export of goods

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Focus Sectors of the Development Plan

8 Development Policy> Mongolia (continued from previous page)f ) Public Sector- Accountable and transparent governance- Transfarency in administration Ethics in public sector- Leadership of public organizations PPPg) Sustainable Social Development- Improve quality and access too health care services- Introduction of labor market system that values workers' productivity, focusing on human development- Ensure gender equality in social development- Support youth employment by training to provide proper knowledge and skills- Coherence of science and industry to promote and adopt innovation- Improve quality of general education system and build science technology clusterh) Environmental Sustainability- Resource efficiency- Support clean technology and encourage low-waste and sustainable production and consumption- Improve city planning and waste management system- Develop the green development standardi) Governance for Sustainable Develop- - Establish and strengthen an accountable and transparent governancement- Public Private Partnership (PPP)- Improve the leadership in public organizations

Nepal
14th National Development Plan 2073-2076, 2016-2019(National Planning Commission (NPC), February 16, 2017)
Focus Sectors of the Development Plana) reducing absolute povertyb) sharing economic prosperityc) post-earthquake reconstruction and rehabilitationd) development of physical infrastructuree) good governancePakistan
Pakistan2025 One Nation - One Vision, -2025(Planning Commission, Ministry of Planning, Development, and Reform, Government of Pakistan, August 1, 2014)
Key Numerical Targets Relevant for Productivity- 2014-2025: Strengthening Pakistan's development foundations to become top 25 global economy and and aupper middle income country by 2025
- Double the share of total factor productivity (from the level of 2013) by 2025
- 2025-2035: Attaining regional and global leadership in ket target sectors- 2035-2047: Putting Pakistan on a fast track of development with the ultimate goal of transforming it to become
one of top ten economies in the world by 2047 (The centennial year of our independence)
Focus Sectors of the Development Plan
- The seven pillars of Vision 2025 are based on the imperatives of embracing change and transformation, and tocreate new opportunities based on the country's innate strengths.
a) People First: Developing social and - Population managementhuman capital and empowering - Basic and college education
women- Health- Labour, employment and skill development- Poverty alleviation and sustainable development goals- Social welfare- Gender and women empowerment- Youth and sports- Religious pluralism and interfaith harmony- Mass media, culture and national heritage
 rowth: Achieving sustained, indige- - Fiscal, monetary and capital market developmentb) Gnous and inclusive growth- Trade and commerce – Balance of payments- Balanced development – Focus on the less developed regions- Physical planning and housing- Improvement in existing industrial zones with focus on - resource efficiency, technical skills development, Marketing skills development, improve quantity and quality of raw material and supply chainc) G overnance: Democratic governance, - Institutional reforms, good governance, and modernization of the public sector
institutional reform and modernization of the public sector

2017 Asian Productivity Organizationd) Security: Energy, water and food

- Energy: double power generation to over 45,000 MW ; development of indigenous energy resources, such as coal,hydro, alternative and renewable sources; energy efficiency.
- Water: increase storage capacity, improve efficiency of usage in agriculture; construction of small and medium
dams, and modernisation and improvement of existing irrigation system- Food: Reduce food insecure population from 60% to 30% and agricultural development- Nutrition: Launching policies, programs, and projects to improve the nutrition situation- Environment and climate change: institutional capacity-building to combat disasters; Strategy to develop forest
cover and along with conservation and restoration of the natural resources; The COP 21agreement will be implemented to stabilize the climate and avoid its worst impacts.
- Establishment of new green industrial parks for Mining and Precious Stones sector, Agro-based, exhibition center/
facilitating centers and capacity building centers in different provinces, easy and direct excess (Road) to airports
and dry ports for industrial clusters- Promoting joint venture leading to clean technology transfere) Entrepreneurship: Private sector and - Manufacturing, commerce and mineral sectors; create at least 5 global Pakistani brands (having more than 50%
entrepreneurship led growthsales coming from consumers outside Pakistan), and make ‘Made in Pakistan’ a symbol of quality- Establishment of Venture Capital Fund for Start Ups and Innovation supports.
- Encourage micro businesses and promote entrepreneurship & innovation

1408.1 National Development Strategies> Pakistan (continued from previous page)f ) Knowledge economy: Developing a - Higher educationcompetitive knowledge economy - Science and technology
through value addition- Information and communications technologyg) Connectivity: Modernizing transpor- - Road connectivity, transport, and logistics
tation infrastructure and greater re- - Increase in annual exports
gional connectivity

Philippines
Philippine Development Plan, 2011-2016(National Economic and Development Authority (NEDA))Key Numerical Targets Relevant for Productivity- Improved global competitiveness to top 30% in global universe- Annual average real GDP growth increased by 7-8%
- Agri, fishery & forestry output increased by 4.6-5.7%; industry by 8.1-9.1% increase; service output increased by
7.1-8%.
- Generated employment for industry & service sectors by additional 4.67 million (other target)
Focus Sectors of the Development Plana) Industry and Services

8- Business environment improved (e.g., public and business satisfaction with public services improved)- Sectors made globally competitive and innovative- Productivity increased (e.g., merchandise and service exports increased; total approved investments increased)b) C ompetitive & Sustainable Agriculture - Food security improved& Fisheries Sectors- Incomes in agriculture & fiehsry sector increased- Sector resilience to climate change risks increased- Growth in agriculture & fishery sector increasedc) G ood Governance and the Rule of law - Improved scores on the indices relating to corruption and ruling(Effective & transparent governance - Established and operationalized Integrity Infrastructure Development and Centralised Case Monitoring System
practices, Enhanced access to justice) - Improved government accountability, transparency and efficiency- Increased constructive engagement between CSO/private groups and governmentd) Infrastruture Development- Performance of tourism, agriculture and industries improved- Access to goods and services improved (education, health, housing, etc.)- Environmental quality improved- Resilience to climate change and natural disasters increasede) Social Development (Health, Nutrition - Improved access to quality health and nutrition services
and Population management, Educa- - Improved access to quality education, training and culturetion, Training and Culture)- Improved access to asset reformf) C onservation, Protection & Rehabilita- - Natural resources conserved, protected and rehabilitatedtion of the Environment and Natural - Environmental quality for a cleaner and healthier environment improved
Resources- Waste generated and waste disposal improved- Resilience of natural systems enhanced with improved adaptive capacities of human communities- Water poullution reducedg) Peace and Secutiry- All armed conflicts brought to a permanent and peaceful closure- Safer and more secured environment conducive to national development created and sustained

Singapore
1. Plans from the Future Economy Council , 2. Research, Innovation & Enterprise 2020 Plan(RIE2020), 3. Singapore Budget 2016,(1. Future Economy Council, May 2017, 2. National Research Foundation, January 2016, 3. Ministry of Trade and Industry, March 2016)
Key Numerical Targets Relevant for Productivity- From 2009 to 2020, target productivity growth is 2% to 3% per annum
Focus Sectors of the Development Plana) Develop skills for the future and sup- - Advancing SkillsFuture: Developing an integrated system of education, training, and career progression for all
port productivity-led economic growth Singaporeans - Driving industry transformation: Overseeing implementation of plans for key clusters throughskills development, innovation, productivity and internationalisation strategies - Fostering a culture of innovationand lifelong learning in Singaporeb) A dvanced Manufacturing and Engi- - Eight key industry verticals based on the potential of Singapore to achieve global leadership, the presence of newneering Vision: Innovative, Competi- opportunities for growth and the ability to generate good jobs for Singaporeans are: Aerospace, Electronics,
tive Economy
Chemicals, Machinery & Systems, Marine & Offshore, Precision Modules & components, Biologics & Pharmaceutical Manufacturing, Medical Technology Manufacturing- Four cross-cutting technology areas have also been identified as essential enablers to support the eight key industry verticals: Robotics and Automation, Digital Manufacturing, Additive Manufacturing, Advanced Materials
 ealth & Biomedical Sciences Vision: - Healthcare is a huge and rapidly growing global market, with the biomedical sector remaining an importantc) H
Biomedical Innovations, Better Health- contributor to Singapore manufacturing economy. Singapore also aims to develop healthcare services, drugs orcare
devices that will deliver better health outcomes for Singaporeas and enable a sustainable healthcare system.

e) Services & Digital Economy Vision: - Use digital innovation as a force multiplier to meet national priorities and enhance productivity in services sector
Connected City, Smart Nation- T hree critical focus areas are: (E1) Urban Mobility: fusion of traditional transport engineering with autonomous
technologies, real-time analytics, modelling and simulation to manage real-time traffic events; (E2) Healthcare ICT:
predictive analytics and machine learning based on real-time data collected from IoT healthcare devices to meetdemands of ageing society; (E3) Services Productivity: automation of knowledge work, discovery of insight
through data mining and creation of iinovative digital applications to improve delivery of government and private
sector services.
f ) Transforming Enterprises- Provide grants on capability building, training and internatl expansion- Support automation to scale up- Provide financing and tax incentives to support scale-ups e.g. through M&A- Support internationalization of SMEs

141

2017 Asian Productivity Organizationd) Urban Solutions & Sustainability Vi- - Enhance living environment to address resource constraints through an interdisciplinary approach including
sion: Green City, Best Home
devising new urban mobility solutions, optimizing liveable space, building the next generation smart grid, lowering energy consumption of used water treatment and seawater desalination.

8 Development Policy> Singapore (continued from previous page)g) Transforming Industries- National Trade Platform through internet- Leveraging new technologies to support robotic deployment across sectors such as Healthcare, Construction,
Manufacturing and Logistics- Increasing outreach to SMEs through trade associations, chamber of commerces, etch) Transforming through Innovation- Deepening innovation capabilities through industry-research collaboration- SG-Innovate: promote start-ups and entrepreneurship- Jurong Innovation District: create open innovate urban environment to bring together elements for the futuresocietyi) SupportingchangeSingaporeansthrough - Adapt and Grow: help people adapt to changing job demands and grow their skills- TechSkills Accelerator: help people learn new ICT skills quickly though skills development and job placements

Sri Lanka
National Development Plan, - 2020
(Sri Lanka Country paper of WSM and various web sites, October 19-21, 2016)
Key Numerical Targets Relevant for Productivity- Enhancing productivity of major sectors contributing to the GDP by 1% annually- Placing Sri Lanka within the first 50 countries of World Innovative Index by 2020
- Elevating all the government institutions and schools to “A” Grade level of their performance by 2020
- Upgrading 3000 Small Entrepreneurs up to Medium level and 1000 Medium Entrepreneurs up to Large Scale by2020- Developing 3000 community productivity villages by 2020
- Productivity for all by 2020
- Generating of one million employment opportunities- Enhancing income levels- Development of rural economies- Ensuring land ownership to rural and estate sectors, the middle class and government employees- Creating a wide and a strong middle class2017 Asian Productivity Organization

Focus Sectors of the Development Plan

142a) Agriculture- Labour is exiting the agriculture sector: but slowly- Overall have grown and become more diverse in nature, compared to those of the plantation dominated era- Increase support for success in existing farms- Increase mobility towards more productive farming- Increase opportunities for mobility to the industrial sectorb) Tourism Sector- To achieve 2.0 million tourist arrivals by the year 2016
- Target the upscale Free Independent Travellers (FITS), who are comparatively high spenders.
- Make tourism Sri Lanka’s third largest foreign exchange earner- Transform tourism to become the fastest job creator and help reduce the unemployment levelsc) Industry Sector- Not Availabled) Energy and Infrastructure Sector- Development of the Electricity Master Plan through 2040 including the development of the plan for power generation, power transmission and distributione) Favorable Business Environment- Not Availablef ) Sustainable Social Development- Ambitious plan to develop the country's Western Province, which includes Colombo, into a massive urban
agglomerationg) Environmental Sustainability- Not Availableh) Key Sectors of Investment- Tourism and Leisure- Agriculture- Export Manufacturing- Export Services- Apparel Industry- Infrastructure- Knowledge Services- Utilities- Educationj) SME Sector- Promoting high potential, viable and promising sectors- Focus on cluster approach for SME Development. Under the cluster approach, the entire value chain from input
supply to processing and export will be supported and promoted- Promote enterprises with high value addition and encourage enterprises that use the local raw materials as they
offer strong comparative advantages due to factor endowments- Motivate and encourage export oriented or import substitution enterprises and industries- Encourage and promote flagship SME sectors with high potential spillover effects to facilitate job creation or
employment generation- Promotion and relocation of industries in the backward regions to reduce urban-rural imbalances- Transforming the landscape of the SMEs away from mere trade and commerce towards production and industry
based with special focus on high value addition, innovative and usage of modern appropriate technology- Strengthening the SME sector in order to enhance inclusive economic development and thus provide opportunities for better employment and higher income- Achieve regionally balanced growth across Sri Lanka- Promote resource efficiency at all levels including the use of Green Technologyk) National Road Masterplan- Increase Economic Growth to 8.5% by 2010
- Socially Inclusive Development- Economy and Efficiency in the Provision of Roads- Safety- Asset Sustainability- Sustainable institutional capacities- Environment- Private Sector Participation

8.1 National Development Strategies

ThailandNational Development Plan, 2017-2021(Office of The National Economic and Social Development Board, September 22, 2016)
Key Numerical Targets Relevant for Productivity- 40% of lowest income group will get more income at least 15%- GDP increases average 5% per annum- Incomer per capita not less than USD 8,200- Reduce green house gas from energy sector and transportation at least 7% within 2020
- Growth rate of Agricutural (3%), industrial (4.5%) and service sector (6%)
Focus Sectors of the Development Plana) Agriculture and Biomass- Promote Sufficiency Economy for agriculture production- Achieve global safety and quality standard- Utilize Bio Base
- Promote organic agri-products, production zoning, value addition and non-chemical supply chainb) Industry- Innovation Development with latest technology i.e. industry 4.0, robotic and IOT- Develop key enablers for ease of doing business- Promote Industrial Ecology- Develop marketing for niche groups such as elderly, creativity products and health care
- Develop workforce competency for hi-tech and future industry- Logistic management using Natioanl Single Window

8c) Services and Tourism- Develop capability to adopt and adapt for change- Sustain and balance growth among stakeholders- Utilize latest technology (such as cloud) to leverage new service sector (digital services, education and content
businesses)- Promote Cluster management- Expand eco-tourismd) Eco-Friendly (Environment)- Water conservation and utilization- Reduce pollution for better health and eco-system- Disaster prevention- Biodiversity conservation- Provide eco-friendly infrastructure for urban growth- Promote eco-friendly consumptione) Public Sector- Enhance public sector productivity and good governance for central and local government- Integrate and share ICT infrastructure

VietnamEnhancing the productivity and quality of products and goods of Vietnamese enterprises until 2020, 2010-2020(Primer Minister, May 21, 2010)
Key Numerical Targets Relevant for Productivity
Period 1: 2010 to 2015:- Build newly 4.000 national standards (TCVN), ensuring synchronous TCVN for major products and goods of theeconomy, 45% of TCVN of system of national standards harmonized with international standards, regional
standards- Management by national technical standards for 100% of the group of products or goods likely to endanger thesafety, sanitation, environmental pollution- Establish a organization network of assessing conformity with technical regulations, conformity of national
standards for key products, goods- Build capacity and quality movement in 40 provinces and cities throughout the country- Build a team of specialists, consultant staff on productivity and quality; organization or individual operating professionally on productivity and quality of products and commodities at the ministries, branches, localities andenterprises produce key products and commodities- 40,000 enterprises are guided application of advanced scientific and technological innovation, application management systems, models and tools to improve productivity and quality- 40% of enterprises manufacturing key products and commodities construct and implement projects on improving productivity and quality- To contribute to raising the proportion of the productivity of general factors (TFP) in gross domestic product(GDP) growth to 30% in 2015
Period 2: From 2016 to 2020:- Develop new 2,000 Vietnam Standard (TCVN); and 60% TCVN under the national standards harmonized withinternational standards, regional standards- 100% laboratory of quality of key products, commodities reaching international level- 60,000 enterprises are guided to apply advanced scientific and technology and technological innovation, applying management systems, models and tools of improving productivity and quality- Build capacity and quality movement in all provinces and cities throughout the country- 100% of enterprises producing key products and goods develop and implement key projects of improving productivity and quality- To contribute to raise the proportion of the productivity to general factors (TFP) in gross domestic product growth(GDP) to at least 35% by 2020
- Build 1000 specialists, consultant staffs, trainers on productivity and qualitya) Establish and apply the standards, - Establish and popularize the application of standards and technical regulations
technical regulations- Establish a organization network of assessing conformity with technical regulations, conformity of national standards for key products, goods- Invest the testing laboratories of product and goods quality

143

2017 Asian Productivity Organization

Focus Sectors of the Development Plan

8 Development Policy

2017 Asian Productivity Organization> Vietnam (continued from previous page)144b) Productivity and Quality Promotion- Propaganda, train and enhance knowledge on productivity and quality- Popularize the application of management systems, models, tools to improve productivity and quality- Promote the application of scientific and technological advances and technological renewal in enterprises- Evaluate the quality of products and goods- Measure the productivity of the economy, industry, enterprisesc) Industry- Increase the productivity and quality of key products and commodities in the priority and spearhead industry
sectors by applying approciate productivity and quality measures/tools- Renovate the technologies and apply the advanced technologies in manufacturing in order to build up the quality and content of science and technology, the proportion of value added and the domestic value in industrial
productsd) Agriculture- Increase the productivity and quality of key agricultural products and commodities by applying approciate
productivity and quality measures/tools- Apply and transfer the scientific and technological advances in manufacturing, cultivation techniques and application of new plants and animal breeds for high productivity and quality- Apply and renovate teh technology in the exploitation, preliminary processing, preservation and deep processing; Enhance the the value of key agricultural, forestry and fishery products and export commoditiese) Information and Communications- Increase the productivity and quality of key products and commodities in the field of information and communication equipment production by applying approciate productivity and quality measures/tools- Strengthen the testing capacity of the quality of information and communication equipment- Enhance the capability of research and development, apply the renovate the technology in order to improve thequality of switchboards and terminals' products compliance with the national standards, international standards
and export market standardsf ) Construction- Increase the productivity and quality of key products and commodities in the field of construction materials
production by applying approciate productivity and quality measures/tools- Use the advanced technologies and select the suitable equipment to ensure the production of high-quality
products compliance with the national standards, international standards and export market standards- Toward the mineral resources efficiency, reduce fuel costs and environmentally friendlyg) Health- Increase the productivity and quality of key products and commodities in the fields of vaccines and medical bioproducts, pharmaceuticals, medical equipment and supplies by applying approciate productivity and quality
measures/tools- Apply and develop the advanced technologies in the production of traditional medicines, vaccines, bio-products,
essential medical equipments- Invest and increase the testing capability the quality of drugs and pharmaceutical materials compliance withinternational standards in service of production, import and export of products and goodsh) Transport- Increase the productivity and quality of key products and goods in the manufacture of means and transport
equipment by applying approciate productivity and quality measures/tools- Invest and strengthen the testing capacity of specialized products- Technological innovation- Apply the advanced synchronous technologies in the production of key products and goods with high localization
ratios compliance with export standards or equivalent to the quality of the same products of foreign countriesl) Vietnamese SMEs

- Enhance the productivity and quality of key local products and goods by applying approciate productivity andquality measures/tools compliance with the characteristics of localities and enterprises- Guide enterprises on exploiting information on quality standards, technical regulations, technologies and information on industrial property for application in production and business practices- Replace the backward technology, apply the advanced technology, master the transferred technology fromabroad8.1 National Development Strategies

Population and Demographic Dividend

Box 11

According to the United Nations (2017), the world’s population is estimated to reach 7.4 billion in 2015, of
which Asian countries account for 59.9%. The region is by far the most populous in the world. China andIndia account for 18.9% and 17.7% of the world’s population, respectively. It has been observed that falling
fertility rates and rising living standards go hand in hand, although the direction of causality is less certain.
The evolution of the demographic structure implies dynamics in a society that are not captured by theoverall population size or growth. As people’s economic behavior, aspirations, and needs vary at 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.
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 2.2 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 6.8 children to 2.4 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.

8

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 0.53% per year by 2050 and further to 0.09% by 2100. Even so, the world population will still increase by
one-third from today’s 7.4 billion to 9.8 billion in 2050 and a further 14% to 11.2 billion by 2100. These estimates are based on the medium-fertility variant, but with only a small variation in fertility, particularly inthe more populous countries, the total could be higher (10.9 billion by 2050 and 16.5 billion in 2100) or
lower (8.8 billion in 2050 and 7.3 billion in 2100). Figure B11.1 depicts this shift in the distribution of theworld population with the share from the more developed regions gradually declining from 17.0% in 2015
to 13.3% in 2050 and 11.5% in 2100, compared with 32.1% in 1950. Conversely, the share of the least developed countries is depicted as rising from today’s 13.0% to a projected 19.6% in 2050 and 28.6% in 2100, up
from 7.7% in 1950.

BillionLeast developed countries
Less developed regions, excluding
least developed countries
More developed regions

10

4

6.6

6.8

6.7

4

4.3
0.2

0

6
5.7

0.3

Oceania
Northern America
Latin America and the Caribbean
EuropeAfricaOther Asia
0.4
ASEAN
0.7
China
0.7
India

8

0.7

6

12
10

1.9

1.2

8

2

2.6

3.2

2.7

2

1.5
0.8

1.0

1.2

1.3

1950

1975

2000

2025

1.3

1.3

1.3

2050

2075

2100

0

0.5
0.3
0.6
0.4

1950

0.3
0.7
0.4
0.3
0.6
0.9

0.4
0.8

0.5
0.8
0.7

0.5
0.7
0.7

0.7
3.4
2.3

4.3

0.3
0.5
0.7

1.4

0.8
0.4
1.0

1.3

1.5

1.4

1.4

1.2

1.0

1.6

1.7

1.5

2050

2075

2100

1.3

0.5

0.6

1.0

1.4

1975

2000

2025

0.6

0.7
1.6

0.7
1.5

Figure B11.1 Distribution of the World’s Population in Different Regions, 1950–2100
Source: UN (Department of Economic and Social Affairs), World Population Prospects: The 2017 Revision.
continued on next page >1452017 Asian Productivity Organization

Billion12

8 Development Policy> continued from previous page

1970

2050

1600
1400
1200
1000
800
600
400

scale by 50 millions

350
300
250
200
150
100
30
25
20
15
10
5
0

scale by 5 millions

2017 Asian Productivity Organization2015

scale by 200 millions

Figure B11.3 shows the demographic make-up of countries in2015 (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 thedirection of causation is not certain. As countries move from highto low mortality and fertility
rates, the demographic transition
produces a “boom” generationthat is larger than those immediately before and after it. As
this boom generation graduallyworks through a nation’s age structure, it produces a demographic dividend of economic growth
as people reach their prime.

Million

1800

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
59.9% today to 53.8% in 2050
and 42.7% in 2100, while Africa’s
share will rise from today’s 16.2%
to 25.9% and 39.9%, respectively. Figure B11.2 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.

Figure B11.2 Asian Countries’ Population Size andProjection, 1970, 2015, and 2050
Source: World Bank, World Development Indicators 2016.

18.8
19.1

Japan
Hong Kong
Australia
US
Korea
ROC
Singapore
ThailandChina
TurkeySri Lanka
BruneiVietnam
Malaysia
Iran
Fiji
Nepal
India
Philippines
MyanmarIndonesia
Bangladesh
BhutanCambodia
Pakistan
Mongolia
Lao PDR
Saudi Arabia
Oman
Bahrain
Kuwait
UAE
Qatar

12.5
11.3

13.8
13.6
15.5
17.7
16.5

24.0
25.2
23.6
23.1
25.3
23.6
28.7
31.3
28.8
30.5
25.9
27.7
29.4
29.7
31.0
35.0
28.1
35.9
28.6
20.5
22.3
22.3

40 %
Age 0–14

30

15.0
15.7

20

10

0

8.2
7.9
7.2
6.7
5.9
5.9
5.8
5.8
5.6
5.4
5.2
5.2
5.0
4.8
4.6
4.5
4.0
3.8
2.9
2.6
2.5
2.0
1.0
0.9

0

5

26.3

15.3
15.0
14.9
12.8
12.5
11.7
10.5
10.5

10

15

20

25 %

Age over 65

Figure B11.3 Proportion of the Dependent Population, 2015
Sources: Population census and official national accounts in each country.
continued on next page >146

8.1 National Development Strategies> continued from previous page

Using demographic data since 1950
and UN projections up to 2100, Figures B11.4 and B11.5 track changes in
the ratio of the working population(aged 15-64) to dependent population (aged under 14 and over 65) bycountry and by country group, respectively. The higher the ratio, themore favorable its demography foreconomic growth. Japan could havecapitalized on the demographic dividend in the 1960s, when its GDP
growth was over 10% on average per
year for ten years. Similarly, China,
Hong Kong, Korea, Singapore, andThailand are poised for the prospectof such demographic dividend in the
2000s and 2010s, whereas, based on
projections, some ASEAN counties as
Myanmar and Indonesia will have to
wait for such opportunity until the2020s and 2030s, and South Asian
countries (except Sri Lanka) until thelate 2030s and 2040s.

Dependent population (age under 14 and over 65)=1.0(East Asia)

3.0
2.5
2.0

China
Hong Kong
Japan
Korea
Mongolia

1.5
1.01950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
Dependent population (age under 14 and over 65)=1.0(South Asia)

3.0
2.5
2.0

Bangladesh
BhutanIndia
Nepal
Pakistan
Sri Lanka

1.5

8

1.0
1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
Dependent population (age under 14 and over 65)=1.0(ASEAN6)

3.0
2.5
2.0

BruneiIndonesia
Malaysia
Philippines
Singapore
Thailand

1.5
1.0
1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
Dependent population (age under 14 and over 65)=1.0(CLMV)

3.0
2.5

Cambodia
Lao PDR
MyanmarVietnam

2.0
1.5
1.0
1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

Figure B11.4 Demographic Dividend by Country, 1950–
2100
Source: UN (Department of Economic and Social Affairs), World Population Prospects: The 2017 Revision.

Dependent population (age under 14 and over 65)=1.0

3.0
2.5

East Asia
South Asia
ASEAN6
CLMV

2.0
1.5
1.0
1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

Figure B11.5 Demographic Dividend by Country Group,
1950–2100
Source: UN (Department of Economic and Social Affairs), World Population Prospects: The 2017 Revision.

147

2017 Asian Productivity Organization

The reaping of this dividend, however, is far from automatic. A favorable
demography can work wonders to
produce a virtuous cycle of wealthcreation only if it is combined withappropriate health, labor, financial, human capital, and growth-enhancing
economic policies. The presence of
these complementary factors cannot be taken for granted, but needs
to be cultivated in order to earn the
demographic dividend. As the analysis of the Databook shows, the contribution of labor to economic growthhas been smaller than those of capital and TFP for most countries (Figure
52 in Section 5.3, p. 70). This meansthat countries should not be afraidof aging too much as long as fairly
high growth rates of capital and TFP
are maintained. Nevertheless, understanding the demographic shift andits implications is highly relevant
for economic projections, providing
valuable foresight for economic
policy making.

Appendix
A.1

GDP Harmonization

The Databook incorporates some significant revisions to the national accounts. New developments
for upgrading their national accounts based on the 2008 SNA have resulted in Sri Lanka as of March
2016 and Japan and Turkey as of December 2016. Based on our Metadata Survey 2017 for the APO
member economies (see Box 2, p. 32), 11 economies are already 2008 SNA-compliant in Asia and others (Cambodia, Iran, the Lao PDR, Nepal, Thailand, and Vietnam) are 1993 SNA-compliant, although it
should be noted that the extent of compliance in terms of coverage may vary. The different statusesof SNA adaptions among economies are responsible for the huge variations of data definitions andcoverage in 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 systems, software investment, and financial intermediation services indirectly measured (FISIM).126 In order to create long-time series data for the Databook, it is necessary
to use the past estimates based on the 1968/1993 SNA, with exceptions in the ROC, Korea, and Singapore, who already published the backward estimates based on the 2008 SNA from the 1950s or 1960.
In addition, some additional adjustments are necessary to harmonize the long-term estimates of GDP.
Procedures for these 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 industry GDP
and the overall GDP for the total economy (by the part of FISIM allocated to final demands).

126: 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 revisions 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 the national experts in our project.

149

2017 Asian Productivity Organization

Among the 20 APO member economies, four countries – Cambodia, the Lao PDR, and Nepal – do notallocate FISIM to final demands in their official national accounts, as a result of them not following the
1993/2008 SNA recommendation. Thus, the GDP values in these countries are smaller than others bydefinition. In addition, in the countries whose national accounts follow the 1993/2008 SNA’s recommendation on FISIM, the available data sometimes does not cover the entire 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 intermediation on FISIM allocatedto final demand are assumed to be 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 theofficial national accounts. As described, in instances where both value added data are not available,

Appendix

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 2015 and the FISIM share in
GDP in 2000–2015 (including bothof the original estimates in the official national accounts and our estimates). In countries where GDPs areadjusted, the proportions by whichauthor adjustments for FISIM increases GDP stand at 0.6–1.2% for
Nepal and the Lao PDR and lessthan 0.4% GDP in others.

1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015
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

1950
1951

1980
1993
1977

1960
1959
1955
1981 1985
1970

1955
1960

1974

1964
1960
1946

1967
1959
1951

1972
1970

1955

1986
1970
1970 1974

1980

1958

1990
1970 1975
1982

1962
1970
1970 19751970
1970 1975
1960

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

2) Software
The 2008 SNA recommends thecapitalization of intellectual property products (IPP), which changes
not only the size of GDP but also thesize of capital input. One of the IPP
capitalized in the Databook is computer software, which includes prepackaged software, custom software,and own-account software. Among
APO member economies, 13 economies have capitalized all three
types of software. Another three
countries exclude own-account software in their capitalization and in
one country only custom software
is capitalized. For the APO Productivity Database, tentative adjustments
have been made to harmonize data
to include all software.

1970
1970
1970
1970

Figure 99 Adjustment of FISIM
Source: APO Productivity Database 2017.

% FISIM share in GDP (average in the 2000−2015)

3.5

Hong Kong
3.0

2.5
Australia
2.0

1.5

India
Philippines
China
Fiji

Thailand

BhutanIndonesia

Pakistan
Nepal
Vietnam
1.0
IranBangladesh Mongolia
0.5Singapore

Korea
Malaysia
Turkey

Japan
US
ROC

BruneiLao PDR
Cambodia2017 Asian Productivity Organization

Sri Lanka

Thousands of US dollars (as of 2015)
MyanmarAmong the countries studied, the
0.0
0
10
20
30
40
50
60
70
80
90
data for software investment is
Per capita GDP in 2015 (using 2011 PPP, reference year 2015)
available for Bangladesh, the ROC,
Offical national accounts in each country, including author adjustment
Our estimates using value added in financial intermediation
Japan, Korea, Malaysia, Mongolia,
Our estimates using value added in imputed bank service charge
Pakistan, the Philippines, Singapore,
Thailand, and China. To harmonize
Figure 100 FISIM Share in GDP, 2000–2015
data, a country’s GDP is adjusted to
Sources: Official national accounts in each country and author estimates.
include software investment (throughits software industry) by using theratio between software investment
and GDP (software ratio) and the tangible GFCF to GDP ratio (GFCF ratio). Data from the OECD Productivity Database and the APO Productivity Database suggest an inverse relationship between these150A.1 GDP Harmonization

two ratios (Figure 101). Countries
with a low GFCF ratio tend to be
those with high per capita GDP, andthe observed data suggest that IT
tends to play a more important rolein these countries than in lessdeveloped countries.

%

2.5

Software Investment/GDP

Sweden
2.0

1.5

US
Japan
Denmark
Finland
Korea
Hong Kong
France Switzerland New Zealand
UK
ROC
Netherlands
CanadaThe Databook applies the inverse
1.0
relationship between these two raSingapore
Germany
AustriaSpaintios observed from the OECD counItaly
tries and national accounts in Asian
Lao PDR
Vietnam
0.5
non-OECD countries to estimate theCambodia Fiji
Indonesia
Chinasoftware ratio in 2006 for those APO
Philippines Nepal Sri Lanka Ireland
India
Portugal
Thailandmember economies that do not
Bangladesh
Pakistan Malaysia
Iran
0.0
Mongolia
capitalize software investment. The
13
18
23
28
33
38
43 %
Tangible GFCF/GDP
estimated ratios for individual counCountries in which software is included
tries in 2006 gradually taper off asCountries in which software is excluded
Countries in which pre-package and own account software are excluded
one moves back in time. However,Countries in which own account is excluded
there is an exception. Countries atthe very early stage of economic
Figure 101 Software Investment Ratio and GFCF Ratio to
growth are found to have a GFCF raGDP, 2005
tio as low as countries with high per
Sources: OECD Productivity Database, including author adjustments.
capita GDP, but for a different reason. The low GFCF ratio is explainedby the fact that these countries havenot experienced economic development yet, and in turn this does not play an important role for software investment. In this report, Cambodia, the Lao PDR, and Nepal are regarded as countries at thevery early stage of economic development, and are assigned Vietnam’s software ratio accordingly,
which is the lowest of all APO member economies.

App.

Another challenge 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
attempts to adjust for the varied level of capitalization across countries by adding the type of software
not capitalized to countries’ GDP.

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; artwork such as paintings andsculptures; and other valuables such as jewelry made from stones and metals. In a small number ofcountries, such as India, Iran, Mongolia, Sri Lanka, 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 1999–2015. The current decision is toharmonize the data by excluding net acquisition of valuables from GDP in the Databook.

151

2017 Asian Productivity Organization

3) Valuables

Appendix

4) Consumption of Fixed
Capital of Assets Owned byGovernment

1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015
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
1950
1951

1993

2002
2010

1958
2000
2001

At the end of 2011, Thailand official1959
2009
1955
ly switched to the 1993 SNA, and its
1981
2002
1955
1996
national accounts became compat1960
1970
1997
1964
1974
2009
ible with the 1993 framework for
1960
1990
the first time. In this series, govern1946 1950
1959
1996
ment consumption includes the
1951
1990
1955
2002
2011
consumption of fixed capital (CFC)
1970
1970
1989
2002 2005
owned by the government since
1952
1952
1958
1997 2003
1990. In order to construct the long1970
time-series data in the Databook se1962
1997
1970
2011
ries, the past data based on the
1970
1970
2003
1968 SNA has been adjusted to be
1970
2011
1960
1970
consistent with the new series. In
Adjustment using R&D expenditure
the Databook, government capital
Adjustment using the average trend of R&D share in GFCF
stock and its CFC for the period
Adjustment using the average trend of R&D share in GDP
1970–1989 are estimated and thepast government consumption and
Figure 102 Adjustment of R&D
GDP are adjusted accordingly. A
Source: APO Productivity Database 2017.
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).
1970

5) R&DThe Databook capitalizes the R&D by following the 2008 SNA recommendations. In the countries thatstill do not follow the 2008 SNA, the R&D expenditures are not allocated to GFCF (but to intermediateuses). As a result the GDP values in these countries are smaller than others by definition. To harmonize
the GDP concept among countries and over periods, the R&D investment is estimated for those countries in the APO Productivity Database. As a preferable approach, the data on the R&D expenditure arecollected based on the official surveys in each country, in order to estimate the R&D investment. 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 not available, 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 2015. The impacts on GDP by
our adjustment of the additional R&D investment are less than 1.0% of GDP for all countries in 2015.

2017 Asian Productivity Organization

6) GDP at basic prices
GDP can be valued using different price concepts: factor cost, basic prices, and market prices. If theprice concept is not standardized across countries, it will interfere with the international comparisons.
All the countries covered in this Databook officially report GDP at market prices (or at purchasers’
prices), but this is not true for GDP at factor cost and GDP at basic prices. International comparisons in
Chapter 3 (on economic scale and growth) and Chapter 4 (on final demand) are based on GDP at market prices. However, by valuing output and input at the prices that producers actually pay and receive,
GDP at basic prices is a more appropriate measure of countries’ output than GDP at market prices forinternational 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.

152A.1 GDP Harmonization% R&D share in GDP, 2015
These concepts of GDP differ
4.5
in the treatment of indirect tax
Korea
4.0
and subsidies (and import duJapanties). The difference between
3.5
ROC
GDP at basic prices and GDP atmarket prices is “taxes on
3.0
products” minus “subsidies on
USAproducts.” “Taxes on products”
2.5
Singapore
Australia
are the indirect taxes payable
2.0
on goods and services mainly
when they are produced, sold,
China
1.5
and imported, and “subsidies
Thailandon products” are subsidies
1.0
TurkeyIndia
Malaysia
Bhutanpayable on goods and services
Philippines
Hong Kong
MyanmarIranmainly when they are pro0.5
Nepal
Mongolia
PakistanThousands of US dollars (as of 2015)
duced, sold, and imported.
Cambodia
Sri Lanka
BruneiIndonesia
0.0
Since GDP at basic prices is
Bangladesh 0
10 Fiji 20
30
40
50
60
70
80
90
Per capita GDP in 2015 (using 2011 PPP, reference year 2015)
Lao PDR Vietnam
available for some economies,
Offical estimates of R&D investment
such as Hong Kong, India, KoOur estimates based on official data of R&D expenditure, including author adjustments
rea, Mongolia, Nepal, SingaOur estimates using the average trend of R&D share in GFCF
Our estimates using the average trend of R&D share in GDP
pore, and Sri Lanka, a GDP atbasic prices calculation, needs
Figure 103 R&D Share in GDP, 2015
to be constructed for all other
Sources: Official national accounts, including author adjustments; Surveys on R&D in eachcountries. In order to obtaincountry; World Bank (2016).
GDP at basic prices, “taxes on
products” and “duties on imports” are subtracted from
GDP at market prices, which are available for all the countries studied, and “subsidies on products” isadded. 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 ineach country.

App.

153

2017 Asian Productivity Organization

Readers should bear these caveats in mind when 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 costfor Fiji, and Pakistan; at basic prices for Cambodia, Hong Kong, India, Korea, the Lao PDR, Mongolia,
Nepal, Singapore, and Vietnam; at producers’ prices for Iran, the ROC, and the Philippines; and at market prices for Bangladesh, Indonesia, Japan, Malaysia, Sri Lanka, and Thailand. In this sense, APO industry data should be treated as a work in progress as it is difficult to advise on data uncertainty. Theseissues will be developed and examined in the future.

Appendix

A.2

Capital Stock2017 Asian Productivity Organization

Table 21 Asset Classification and
At present, about half of APO member economies publish
Parameters in Hyperbolic Function
estimates of capital stocks in their systems of national accounts. Even where estimates are available, users must beTβ
1. Computer hardware
7
0.50
mindful of differences in methodologies and assumptions
2. Telecommunications equipment
15
0.50
used to estimate capital stock and its consumption, as well
3. Transportation equipment
15
0.50
as a large diversity in the treatment of quality adjustment
4. O ther machinery and equipment and
15
0.50
weapon systems
in price statistics among countries. In the APO Productivity
5. Residential buildings30
0.75
Database 2017, a harmonized framework is applied in esti6. Non-residential buildings30
0.75
7. Other construction
40
0.75
mating capital stock and capital services, covering 21
8. Cultivated biological resources
10
0.50
Asian economies: Bangladesh, Cambodia, China, the ROC,
9. Research and development (R&D)
10
0.50
Fiji, Hong Kong, India, Indonesia, Iran, Japan, Korea, the10. Computer software
30.50
Lao PDR, Malaysia, Mongolia, Nepal, Pakistan, the Philip11. Other intellectual property products
70.50
pines, Singapore, Sri Lanka, Thailand, andSource: APO Productivity Database 2017.
Vietnam, and the US as a reference country.
Although the main data in the Databook basically covers the period from 1970, our stock
Table 22 Input-Output Tables and Supply and
Use Tables
estimates have the different initial periods inthe perpetual inventory method (PIM), toInput-Output Tables and Supply and Use Tables
1981/1982, 1986/1987, 1992/1993, 1993/1994, 2000, 2005/2006,
ease the errors in our assumptions on the iniBangladesh
2010/2011
tial capital stock levels. The starting years for
Benchmark (1981, 1986, 1991, 1996, 2001, 2004, 2006, 2011)
ROC
estimating capital stock based on the PIM is:
Extended (1984, 1989, 1994, 1999, 2004) Annual (2006–2015)
Fiji
1972, 1981, 2002, 2005, 2008
1901 for the US; 1951 for the ROC; 1952 forIndia
1993/1994, 1998/1999, 2003/2004, 2006/2007, 2007/2008
China; 1953 for Korea; 1955 for Japan; 1960 for
Indonesia 1971, 1975, 1980, 1985, 1990, 1995, 2000, 2005, 2010
Singapore; 1961 for Hong Kong; and 1970 for
Iran1962, 1973, 1974, 1986, 1988, 1991, 1999, 2001, 2004, 2011
Japan
1960, 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005, 2011
other countries. The hyperbolic function is
Benchmark (1960, 1963, 1966, 1970, 1975, 1980, 1985, 1990,
used to measure capital stock and the same
Korea
1995, 2000, 2005, 2010) Updated (1973, 1978, 1983, 1986-1988,
1993, 1998, 2003, 2006–2014)
parameters have been applied for all counMalaysia
1978, 1983, 1987, 1991, 2000, 2005, 2010
tries in the Databook, as shown in Table 21.
Mongolia

1963, 1966, 1970, 1977, 1983, 1987, 1997, 2000, 2005, 2010

Pakistan

1975/1976, 1984/1985, 1989/1990, 1999/2000

Quality changes in the aggregate measure ofPhilippines 1961, 1965, 1969, 1974, 1979, 1985, 1988, 1994, 2000, 2006
Singapore 1973, 1978, 1983, 1988, 2000, 2005, 2007, 2010, 2012, 2013
capital input can originate from two kinds of
Sri Lanka
2006
sources, namely the composition change by
Thailand
1975, 1980, 1985, 1990, 1995, 1998, 2000, 2005, 2007, 2010
type of asset, and the quality improvement in
Vietnam
1989, 1996, 2000, 2007, 2012
China
1987, 1992, 1997, 2002, 2007, 2012
each type of asset. To take the composition
Brunei2005, 2010change of assets into account, the current daTurkey
1973, 1979, 1985, 1990, 1996, 1998, 2002, 2012
tabase classifies 11 types of assets (as shown
Note: This edition of the Databook newly reflects China’s table inin Table 21). For countries in which detailed
2012, Vietnam’s table in 2012, and the ROC’s table in 2015.
investment data is not available from national
accounts, the 11 types of investment data are
estimated based on the benchmark and/or
annual input–output tables (IOT) or supply-use table (SUT) and our own estimates on the commodity
flow of domestic production and export/import of assets. The IOT and SUT used in our measurement
are listed in Table 22. This edition of the Databook newly reflects China’s table in 2012, Vietnam’s tablein 2012, and the ROC’s table in 2015.
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 in154

A.2 Capital Stock

Pakistan

Cambodia

India

Philippines

US

Vietnam

Malaysia

Sri Lanka

Bangladesh

FijiSingapore

Lao PDR

Hong Kong

ROC

Mongolia

Iran

ThailandIndonesia

Nepal

China

Korea

Japan

GDP=1.0
price statistics among coun4.5
1980
2015
4.0
4.0
tries. Cross-country com3.6 3.5
3.5
3.3
parisons will be significantly
3.1 3.1
3.1
3.0 2.9 3.0 2.9
3.0
2.7 2.7 2.7 2.6
biased if some countries ad2.6 2.6 2.5
2.5 2.4 2.4 2.4
2.3
2.5
2.3 2.2 2.2
2.2
2.1
just their deflators for quali2.0
1.9
1.9 2.0
1.8
1.8
1.8 1.8
2.0
1.7
1.7
1.7
1.6
ty change while others do
1.3
1.5
1.2
not. Price harmonization is
1.0
0.7
sometimes used in an at0.5
tempt to control for meth0.0
odological differences in thecompilation of price indexes,
under the assumption that
individual countries’ price
Figure 104 Capital-Output Ratio, 1980 and 2015___Ratio of the beginning-of-period net capital stock to basic-price GDP atdata fails to capture quality
improvements. Assuming thatcurrent prices
the relative price of IT to
Source: APO Productivity Database 2017.
Note: The estimate for Cambodia is not available for 1980.
non-IT capital in the countries compared is set equal
to the IT to non-IT prices
X+ (∆ ln PITref −relative in the reference country, the harmonized price is formulated as: ∆ ln P̃ITX = ∆ ln PnlT
ref∆ ln PnlT ), where the superscript X denotes the country included in the comparisons, PIT is the price of
IT capital, and PnIT is the price of non-IT capital. The price of IT capital in country X, P̃ITX , is computed byrefX
in the reference country and PnlTin X. Schreyer (2002) and Schreyer,
the observed prices PITref and PnlT
Bignon, and Dupont (2003) applied price harmonization to OECD capital services, with the US as areference country, since the possible error due to using a harmonized price index would be smaller
than the bias arising from comparing capital services based on national deflators.

App.

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 3.6. 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 calculated.
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.
127: See OECD (2017a) 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.

155

2017 Asian Productivity Organization

In this Databook, the same price harmonization method is applied to adjust the quality improvement
for IT hardware and communications equipment in countries where the appropriate quality-adjusted
price data is not available, with Japan’s prices as a reference country. A similar procedure was appliedin cases where the prices for some assets were not available, to estimate missing data based on therelative price of these assets to total GFCF. In measuring capital services, this Databook largely follows
the framework of the OECD Productivity Database.127 The OECD assumes the truncated normal 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 and decay 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 untilT) when β=1 and linear when β=0. These two parameters are set, as shown in Table 21. The estimatesof productive capital stock by type of asset are used in measuring capital services (see Appendix 3).

Appendix

A.3

Rate of Return and Capital Services

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 are required. This Appendix outlines the methodology of the user cost of capital estimation and presents the estimatedresults of endogenous rate of return for Asian countries in the APO Productivity Database 2017.
%50

Bangladesh%
50

Cambodia

China

%
50

%
50

ROC

Fiji

40

40

40

40

40

30

30

30

30

30

20

20

20

20

20

10

10

10

10

10

0
1970 1980 1990 2000 201001970 1980 1990 2000 201001970 1980 1990 2000 201001970 1980 1990 2000 201001970 1980 1990 2000 2010%
50

Hong Kong

%
50

India

Indonesia

%
50

%
50

Iran

%
50

40

40

40

40

40

30

30

30

30

30

20

20

20

20

20

10

10

10

10

10

0
1970 1980 1990 2000 201001970 1980 1990 2000 2010001970 1980 1990 2000 2010

0

%
50

Korea

%
60

Lao PDR

50

40

40

30

1970 1980 1990 2000 2010%
50

Malaysia

%
50

40

40

30

30

20

20

Mongolia

20

Japan

1970 1980 1990 2000 2010
%
60

Nepal

50
40

30

30

20

20

10

10

10

10

10

0
1970 1980 1990 2000 201001970 1980 1990 2000 201001970 1980 1990 2000 201001970 1980 1990 2000 201001970 1980 1990 2000 2010%50

Pakistan%
50

Philippines%
50

Singapore%50

Sri Lanka%
50

Thailand

40

40

40

40

40

30

30

30

30

30

20

20

20

20

20

10

10

10

10

10

0
1970 1980 1990 2000 201001970 1980 1990 2000 201001970 1980 1990 2000 201001970 1980 1990 2000 201001970 1980 1990 2000 2010%100

2017 Asian Productivity Organization%
50

Vietnam

%
50

US

80

40

60

30

40

20

20

10

0
1970 1980 1990 2000 201001970 1980 1990 2000 2010

Figure 105 Ex-Post Real Rate of Return in Asia, 1970–2015
Source: APO Productivity Database 2017.

156

A.3 Rate of Return and Capital Servicesk
The user cost of capital of a new asset (with type of asset denoted as k of the period t), ut,0
, is definedkkkk{rt + (1 + π tk ) P,t,0− π tk }, where rt, P,t,0, and qt,0
are the expected nominal rate of return, crossas qt−1,0section depreciation rate, and asset price, respectively. The asset-specific inflation rate π tk is definedkk/ qt−1,0−1). The OECD assumes the country-specific ex-ante real rate of return r * that is constantas (qt,0
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 rateof change of the CPI (see Schreyer, Bignon, and Dupont, 2003).

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.

App.

The estimated results of the ex-post real rate of return based on rt* = (1 + rt) / (1 + ρt)−1 for 21 Asian
countries and the US are shown in Figure 105. Although there are large fluctuations in countries likethe Lao PDR, 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 23 presents the
five-year averages of the estimated rates for ex-post real rate of return during 1970–2015. In 2010–2015, the real rate of return ranged from 4.9% for Japan and 7.2% for Korea to 21.4% in Malaysia and24.3% for Cambodia. Using these ex-post estimates, the aggregate capital services are measured inthis report. The difference caused by the ex-ante and ex-post approaches may provide a modest difference in the growth measure of capital services, regardless of the substantial differences in the rates
of return and capital compensations (Nomura, 2004).

Table 23 Average Ex-Post Real Rate of Return in Asia
20.7

18.2

12.2

20.7

21.4
33.2
21.0
26.2
10.0
41.7
32.0
13.5
37.6
25.6
32.5
13.0
48.4
27.8
19.2
24.7
27.1
22.1
2.0
12.0

16.4
22.1
20.0
27.3
13.7
42.5
30.1
6.0
29.8
27.2
32.7
11.0
32.8
27.2
21.0
18.7
38.5
18.6
7.1
10.3

14.0
16.2
12.9
17.5
10.3
44.6
12.3
5.0
16.5
32.0
21.4
10.6
22.5
27.4
16.7
15.7
21.1
13.2
42.5
8.0

13.0
21.3
10.2
22.7
9.5
28.4
12.7
7.4
18.7
33.8
17.1
13.7
20.9
36.1
15.4
13.8
15.8
17.3
28.2
9.4

22.8
16.6
15.0
18.2
22.3
13.7
8.0
23.1
26.7
5.9
14.9
29.4
18.0
−2.0
17.5
28.8
14.1
15.1
13.8
16.0
45.2
9.0

19.4
25.5
10.7
16.2
15.8
11.7
7.9
26.7
12.8
4.3
11.0
11.5
18.8
26.5
10.3
23.3
17.8
12.8
12.3
12.6
34.8
11.2

19.2
18.7
13.2
17.6
13.0
10.6
11.4
24.1
24.1
4.9
10.2
27.7
18.7
5.7
15.0
28.2
23.3
11.3
14.3
11.5
31.4
12.0

17.7
17.1
15.0
15.4
13.7
14.3
11.9
28.2
28.0
5.7
9.2
31.3
21.7
19.0
17.5
26.1
21.0
17.2
17.4
12.4
20.3
11.1

19.9
24.3
8.4
11.4
13.6
10.6
4.4
13.0
20.1
4.9
7.2
25.8
21.4
19.4
10.7
26.5
19.7
14.3
21.0
12.1
17.4
10.7

Unit: Percentage
Source: APO Productivity Database 2017.
Note: The starting year is 1993 for Cambodia.

157

2017 Asian Productivity Organization

1970–1974 1975–1979 1980–1984 1985–1989 1990–1994 1995–1999 2000–2004 2005–2009 2010–2015
Bangladesh
Cambodia
China
ROC
Fiji
Hong Kong
India
Indonesia
Iran
Japan
Korea
Lao PDR
Malaysia
Mongolia
Nepal
Pakistan
Philippines
Singapore
Sri Lanka
Thailand
VietnamUSAppendix

A.4

Hours Worked and Labor Compensation

Labor volume can be measured in three 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 timeconsistent 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 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 ofthese intricate measuring issues.

2017 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 a 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 oferror 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.

158

A.4 Hours Worked and Labor Compensation

The common practice of statistical offices has been to combine information from both establishment
and household surveys, with a view of 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/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 in order to differentiate the workforce into different types,
which are then weighed by their marginal productivity and approximated by their respective sharesof total compensation. Deriving a quality adjusted labor input (QALI) measure is a data-demanding
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 intofine categories.

App.

Figure 106 presents a cross-country
comparison of average annual hours
worked per worker for 2010–2015,
relative to the level of the US. It indicates that workers in Asian countries
tend to work much longer hours
than those in the US and Europe. In
many of the countries sampled, thedifference in annual hours worked
per person relative to the US is morethan 10% of the US level.128 Prolonged
working hours are observed in Asian

Korea

Population and Housing Census, Economically Active Population Survey,
Employment Structure Survey, Monthly Labor Survey, Wage Structure Survey

Lao PDR

Population Census, Labour Force and Using Child Labour Survey, ADB Key
Indicators for Asia and the Pacific

Malaysia

Population and Housing Census, Labour Force Survey, Salaries & Wages Survey

Mongolia

Population and Housing Census, Labour Force Survey, Mongolian Statistical
Yearbook, Pilot Time Use Survey

Nepal

Population and Housing Census, Labor Force Survey

Pakistan

Population Census, Labor Force Survey, Pakistan Statistical Yearbook, Pakistan
Economic Survey

Philippines

Labor Force Survey

Singapore

Population Census, Labor Force Survey, Singapore Yearbook of Manpower
Statistics, General Household Survey

Sri Lanka

Population and Housing Census, Labor Force Survey, Central Bank of Sri Lanka
Annual Report

Thailand

Population and Housing Census, Labor Force Survey

Vietnam

Population and Housing Census, Labor Force and Employment Survey, Statistical
Yearbook, Vietnam Economy

159

2017 Asian Productivity Organization

In the growth accounting frameworks in this edition of the Databook, labor input is defined as thesimple sum of hours worked. Hours
worked are defined as the economyTable 24 Sources of Labor Data
wide hours worked by employees,
Sources of Labor Datathe self-employed, and contributing
Bangladesh Population and Housing Census, Labour Force Survey
family workers. At KEO, the comprePopulation Census, Labor Force Survey, Labour Market Information Bulletin,
BhutanADB Key Indicators for Asia and the Pacifichensive database on the price and volGeneral Population Census, Inter-Censal Population Survey, Labor Force Survey,
ume of labor inputs (PDB-L) has been
Cambodia
Socio-Economic Survey
developed for the past few years,
China Statistical Yearbook, China Labor Statistical Yearbook, Population Census,
China
1% National Population Sample Survey
based on official statistics, such as
Population and Housing Census, Yearbook of Manpower Survey Statistics in
ROC
LFS and Population Census, as listed
Taiwan Area, Manpower Utilization Survey
Census of Population and Housing, Employment and Unemployment Survey,
in Table 24. This data consists of numFiji
Annual Employment Survey
ber of workers, hours worked per
Population Census, Population By-Census, General Household Survey, Annual
worker, and hourly wages, which are
Hong Kong Earnings and Hours Survey, Wage Survey, Women and Men in Hong Kong Key
Statistics
cross-classified by gender, education
Census of India, Employment and Unemployment Survey, National Sample
India
attainment, age, and employment
SurveyPopulation and Housing Census, Labor Force Situation in Indonesia, Laborer
status. Although it is still a work inIndonesia
Situation in Indonesia
progress, the estimates of total hours
Iran
National Population and Housing Census, Labour Force Survey
worked in this edition of the DataPopulation Census, Labor Force Survey, Basic Survey on Wage Structure,
Japan
Japan's System of National Accounts
book depends on this database.

Appendix

countries regardless of their stage of development, spanning low-income countries such as
Bangladesh and Cambodia to high-income
countries such as the ROC and Singapore. An
exception is Japan. Workers in Japan are likely towork much shorter hours than those in other
Asian countries. However, compared with the
EU15, hours worked by workers in Japan are still
about 12% greater.

MyanmarCambodia
Iran
Singapore
Thailand
Malaysia
Bangladesh
VietnamHong Kong
China
Lao PDR
Korea
Philippines
India
Pakistan
ROC
Indonesia
Sri Lanka
Mongolia
Turkey
Fiji
Nepal
Japan
Australia
EU15

37
36
31
30
28
27
26
26
25
22
20
20
20
18
18

2017 Asian Productivity Organization

15
The labor share, which is defined as the ratio of
13
9
labor compensation of total employment to
6
GDP at basic prices, is one of the key factors to
4
4determine TFP growth. The estimates on the20compensation of employees (COE), however,−6
are not fully available in the official national ac−12
counts in Asian countries. Figure 107 summa−12
−6
0
6
12
18
24
30
36 %
rizes the availability of the COE estimates in the
Figure 106 Average Annual Hours Worked Per
official national accounts and the input-output
Worker Relative to the US, 2010–2015
tables in each country. Currently the national
accounts in Bangladesh, the Lao PDR, Pakistan,
Sources: Official national accounts and labor force survey in eachcountry, including author adjustments.
and Vietnam still do not publish the
COE estimates. In addition, in some1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015
countries like Cambodia and Iran,
1998
Bangladesh
1993
Cambodia
the estimates are not fully available
1977
Fiji
1980
Hong Kongfor the whole period of our obser1980
India
vation (1970–2015). In such cases,
1971 1975 1980 1985 1990 1995 2000 2003 2005
Indonesia
1991
Iran
the COE is estimated or extrapolat1955
Japan
1953
Korea
ed by the estimates based on our
Lao PDR
1999
Malaysia
work-in-progress PDB-L, which is
1970
Mongolia
2000
Nepal
described above.

The compensation for the selfemployed and contributing family
workers is not separately estimatedin the national accounts, but iscombined with returns to capital in
mixed income. This edition of the
Databook newly assumes the hourly wages for self-employed andcontributing family workers as 20%of the hourly wages for employees
in the most detailed category of labor with the same gender, age, andeducation in PDB-L. This harmonized assumption is applied for all

Pakistan
Philippines
Sri Lanka
Thailand
VietnamBhutanBrunei
China
MyanmarBahrain
KuwaitOman
Qatar
Saudi Arabia
UAE
Turkey1985 1990

2000

1980
2010
1960
2007 2012

1989
1980

2010
2009

1978
1984
1976
1992 1998
1989 1993

2011
2002

1993
1987

Data from National Accounts
Estimates by National Experts

Data from Input-Output Table

Figure 107 Availability of COE Estimates
Sources: Official national accounts and IOT/SUT in each country.
Note: Hatched areas show the periods in which only the data mingled with operating surplus or mixed income is available.

128: Shorter hours worked in Nepal is due to frequent general strikes called “Banda”, which are mainly lead by some political parties.
According to the Nepal Human Rights Commission, Banda were called 821 times in various regions in 2009, and economic activities were closed during Banda.

160

A.5 Other Data

countries and periods. In many countries, the labor-share estimates based on this assumption could
finely approximate those assumed in the last edition of the Databook.

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 1952–2015 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, 2007, and 2012
Input–Output Tables of China; and multiple data sources for manufacturing, electrics, and trade datafrom China’s Customs Statistics are also utilized.129

App.

The data source for the EU15 and the EU28 is the OECD.Stat (http://stats.oecd.org/) and the Eurostat(http://ec.europa.eu/). The data for the US, Australia, Bhutan, and Turkey are taken 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.

129: Holz (2006) provides a useful reference on Chinese official statistics. The project appreciates Meng Ruoyan (Keio University) for
her supports on Chinese data.

161

2017 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, Nepal, and China use the International Standard Industry Classificationof 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 andRev.4 are shown in Tables 25 and 26, respectively.
Table 25 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 - Extraterritorial organizations andbodies99

Extraterritorial organizations and bodies

9

12
13
14
D - Manufacturing

2017 Asian Productivity Organization

E - Electricity, gas, and water supply

15
16
17
18
19
20

Note: “n.e.c.” represents “not elsewhere classified.”

162

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 26 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.

163

App.

2017 Asian Productivity Organization

ISIC Rev. 4
SectionAppendix

A.7

Data Publication and Visualization2017 Asian Productivity Organization

The productivity data used in this Databook is based on the APO Productivity Database 2017, which
provides the annual productivity accounts covering Asian countries for the period 1970–2015. The
data set is available at the APO website (www.apo-tokyo.org). Timely analysis of the current economic
situation is beyond the scope of this Databook. In the meantime, for an insight into the current economic growth, one has to rely on quarterly national accounts (QNA) from each country. Although they
are timelier, the QNA are often less precise and subject to frequent revisions as more reliable data
become available in their normal estimation cycle. With this trade-off between timeliness and data
quality in mind, the APO recognizes the complementary benefits of collating and presenting a country’s QNA alongside its database of annual data. As result, the APO developed the Asian Quarterly
Growth Map (AQGM) to offer a quarterly growth data map from 2007 through 2015. This project
attempted to renew and upgrade the AQGM, by expanding its scope on data visualization, and developed the Asian Economy and Productivity Map (AEPM) in September 2016. Shown in Figure 108, the
AEPM provides an instinctive understanding of recent economic growth, as well as the long-term
productivity performances described in this Databook. This is also available at the APO website.

Figure 108 Visualization in Asian Economy and Productivity Map
Source: Asian Economy and Productivity Map, September 2017.

164

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