©2016 Asian Productivity Organization©2016 Asian Productivity Organization

Contents
Abbreviation
Forewordviii

1 Introduction

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2 Overview

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3 Economic Growth17

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

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

References

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©2016 Asian Productivity Organization

1.1 Databook 2016
1.2 List of Contributors
1.3 Map of Countries Coveredix

Contents

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

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GDP using Exchange Rate, 1970, 1980, 1990, 2000, 2010, and 2014
GDP using PPP, 1970, 1980, 1990, 2000, 2010, and 2014
GDP Growth, 1990–1995, 1995–2000, 2000–2005, 2005–2010, and 2010–2014
Population, 1970, 1980, 1990, 2000, 2010, and 2014
Per Capita GDP using Exchange Rate, 1970, 1980, 1990, 2000, 2010, and 2014
Per Capita GDP, 1970, 1980, 1990, 2000, 2010, and 2014
Country Groups Based on the Initial Economic Level and the Pace of Catching Up
Final Demand Shares in GDP, 1970, 1990, 2000, 2010, and 2014
Per-Worker Labor Productivity Levels, 1970, 1980, 1990, 2000, 2010, and 2014
Per-Worker Labor Productivity Growth, 1990–1995, 1995–2000, 2000–2005, and 2005–2014
Per-Hour Labor Productivity Levels, 1970, 1980, 1990, 2000, 2010, and 2014
Per-Hour Labor Productivity Growth, 1990–1995, 1995–2000, 2000–2005, and 2005–2014
Output Growth and Contributions of Labor, Capital, and TFP, 1970–2014
Role of TFP and Capital Deepening in Labor Productivity Growth, 1970–2014
Energy Productivity Levels, 1980, 1990, 2000, 2010, and 2013
Country Groups Based on the Current Economic Level and the Pace of Catching Up
Output Growth by Industry, 2000–2014
Labor Productivity Growth by Industry, 2000–2014
Real Income, Real GDP and Terms of Trade, 1970–2014, 1995–2000, 2000–2005, and 2005–2014
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 25iv

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GDP Growth of Asia, the EU, Japan, and the US, 1970–2014
Share of Asia in World GDP in 2014 and Projection for 2021
Price Level Indices of GDP, 2011
Regional GDP of Asia and the EU, Relative to the US, 1970–2014
GDP of China, India, and Japan, Relative to the US, 1970–2014
Regional GDP of South Asia, ASEAN, CLMV, and GCC, Relative to the US, 1970–2014
Country Contributions to Regional GDP Growth, 1970–1990, 1990–2000, and 2000–2014
Correlation of GDP Growth, 1990–2000
Correlation of GDP Growth, 2000–2014
Share of Asian Population in the World, 2014
Per Capita GDP using Exchange Rate of Japan and Australia, Relative to the US, 1970–2014
Per Capita GDP using Exchange Rate of the Asian Tigers, Relative to the US, 1970–2014
Per Capita GDP of Japan, the EU, and Australia, Relative to the US, 1970–2014
Per Capita GDP of the Asian Tigers, Relative to the US, 1970–2014
Per Capita GDP of China, India, and ASEAN, Relative to the US, 1970–2014
Per Capita Non-Mining GDP in Oil-Rich Countries and Japan, 2014
Initial Level and Growth of Per Capita GDP, 1970–2014
Labor Productivity and Employment Rate Gap Relative to the US, 1990 and 2014
Sources of Per Capita GDP Growth, 1990–2000 and 2000–2014
Share of Female Employment
Employment Rates, 1970, 1990, and 2014
Final Demand Shares in GDP of China, 1952–2014
Final Demand Shares in GDP of the US, 1929–2014
Final Demand Shares in GDP, 1995 and 2014
Ratio of Dependent Population and Consumption Share in GDP, 2014
Export and Import Shares in GDP, 1995 and 2014
Long-Term Trend of Household Consumption Share in GDP, 1970–2014
Household Consumption by Purpose, 2014
Engel Curve of Japan during 1949–2014 and Levels of Asian Countries in 2014
Long-Term Trend of Investment Share in GDP, 1970–2014
FDI Inflows, 2000–2014
FDI Inflow Ratio and Business Environment, 2000–2014
Investment Share by Type of Asset, 1970 and 2014
Long-Term Trend of Net Export Share in GDP, 1970–2014
Final Demand Contributions to Economic Growth, 1990–2000 and 2000–2014
Final Demand Contribution Shares to Economic Growth of the Asian Tigers, 2000–2014
Impacts of Global Financial Crisis and Recoveries, 2007–2012
Impacts of Asian Financial Crisis, 1997–1998
Final Demand Decomposition of Real GDP Growth, 1970–2014
Labor Productivity Level by Per-Worker GDP, 2014
Labor Productivity Trends of China and India, 1970–2014
Labor Productivity Level Relative to the US, 1970–2014
Labor Productivity Gap by Per-Worker and Per-Hour GDP Relative to the US, 2014
Labor Productivity Trends in Japan and the Asian Tigers, 1970–2014
Labor Productivity Growth, 1970–2014, 1970–1990, and 1990–2014
Labor Input Growth, 1970–2014, 1970–1990, and 1990–2014

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FigureContents

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Figure 87©2016 Asian Productivity Organization

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Labor Productivity Trends of Japan in 1885–2014 and Levels of Asian Countries in 2014
Time Durations Taken to Improve Labor Productivity by Japan and the Asian Tigers
TFP Growth, 1970–2014, 1970–1990, and 1990–2014
Sources of Economic Growth, 1970–2014
Contribution Shares of Economic Growth, 1970–2014
Sources of Economic Growth, 1970–1985, 1985–2000, and 2000–2014
Contribution Shares of Economic Growth, 1970–1985, 1985–2000, and 2000–2014
Comparison of Sources of Economic Growth with OECD Countries, 2000–2014
Comparison of TFP Contribution Shares with OECD Countries, 2000–2014
Individual Countries’ Growth Accounting Decomposition, 1970–2014
IT Capital Contribution to Capital Input Growth of Japan and the US, 1970–2014
IT Capital Contribution to Capital Input Growth of the Asian Tigers, China, and India, 1970–2014
Individual Countries’ Growth Accounting Decomposition (year-on-year), 1970–2014
Capital Deepening, 1970–2014, 1970–1990, and 1990–2014
Capital Productivity Growth, 1970–2014, 1970–1990, and 1990–2014
Capital Productivity Trends in Japan and the Asian Tigers, 1970–2014
Capital Productivity Trends in China and India, 1970–2014
Sources of Labor Productivity Growth, 1970–2014
Contribution Shares of Labor Productivity Growth, 1970–2014
Sources of Labor Productivity Growth, 1970–1985, 1985–2000, and 2000–2014
Contribution Shares of Labor Productivity Growth, 1970–1985, 1985–2000, and 2000–2014
Decomposition of Labor Productivity Growth, 1970–2014
Shares of Asia in World Energy Consumption and CO2 Emission, 2013
Labor Productivity and Energy Productivity, 2013
Sources of CO2 Emission Growth, 2000–2013
Industry Shares of Value Added, 2014
Manufacturing Share and TFP Growth, 2000–2014
Industry Shares of Value Added in Manufacturing, 2014
Industry Shares of Value Added and Employment by Country Group, 1980, 1990, 2000, and 2014
Long-Term Trends of Value-added Share in the Agriculture Sector, 1970–2014
Industry Shares of Employment, 2014
Employment Share of Agriculture in Japan during 1885–2014 and Levels of Asian Countries in 2014
Long-Term Trends of Employment Share in the Agriculture Sector, 1970–2014
Job Creation in Manufacturing, 1970–2014
Industry Origins of Economic Growth, 1990–2000 and 2000–2014
Contribution of Manufacturing to Economic Growth, 1990–2000 and 2000–2014
Contribution of Service Sector to Economic Growth, 1990–2000 and 2000–2014
Industry Origins of Output Growth in Manufacturing, 1990–2000 and 2000–2014
Industry Origins of Regional Economic Growth, 1990–2000 and 2000–2014
Industry Origins of Asian Economic Growth, 2000–2014
Industry Origins of Economic Growth, 1970–2014
Industry Origins of Labor Productivity Growth, 1990–2000 and 2000–2014
Contribution of Manufacturing to Labor Productivity Growth, 1990–2000 and 2000–2014
Contribution of Service Sector to Labor Productivity Growth, 1990–2000 and 2000–2014
Effect of Net Income Transfer on GDP, 1970–2014
Real Income and Real GDP Growth, 1970–2014
Trading Gain Effect, 2005–2014

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Figure B1
Figure B2.1
Figure B2.2
Figure B2.3
Figure B2.4
Figure B2.5
Figure B3
Figure B4
Figure B5
Figure B6
Figure B7.1
Figure B7.2
Figure B8.1
Figure B8.2
Figure B9Price of Crude Oil, 1986 January–2016 May
Decomposition of Real Income Growth, 1970–2014 and 2000–2014
Decomposition of Real Income Growth, 1973–1979 and 1996–1998
Sources of Real Income Growth, 1970–2014
Trading Gain Effect and Labor Productivity Growth, 1970–2014
Trading Gain Effect and Value-added Share in Mining Sector, 1970–2014
Adjustment of FISIMFISIM Share in GDP, 2000–2014
Software Investment Ratio and GFCF Ratio to GDP, 2005
Adjustment of R&D
R&D Share in GDP, 2014
Capital-Output Ratio, 1980 and 2014
Ex-Post Real Rate of Return in Asia, 1970–2014
Average Annual Hours Worked Per Worker Relative to the US, 2000–2014
Visualization in Asian Economy and Productivity Map

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Revisions of PPP for GDP by the 2011 ICP RoundDistribution of the World’s Population in Different Regions, 1950–2100
Asian Countries’ Population Size and Projection, 1970, 2014, and 2050
Proportion of the Dependent Population, 2014
Demographic Dividend by Country, 1950–2100
Demographic Dividend by Country Group, 1950–2100
Implementation of the 1968, the 1993, and the 2008 SNA
Employee Share and GDP Level, 2014
Price of Labor Relative to Capital in China, Japan, and the Asian Tigers, 1970–2014
Per-Worker Labor Productivity Levels of Asian Cities, 2014
Labor Income Share for Employees, 2014
Sensitivity of TFP Estimates by the Change of Income Share, 1970–2014
Contributions of Labor Quality to Growths in Japan and the US, 1955–2012
Average Schooling Years of Workers, 1970–2014
Average Wage and Per Capita GNI, 2014

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Figure 94

Abbreviation©2016 Asian Productivity Organization

ADB
AEC
AIIB
APO
APO20

Asian Development BankASEAN Economic Community
Asian Infrastructure Investment Bank
Asian Productivity Organization
20 member economies of the Asian Productivity Organization: Bangladesh,
Cambodia, the Republic of China, Fiji, Hong Kong, India, Indonesia, Islamic Republic of Iran, Japan, the Republic of Korea, the Lao PDR, Malaysia, Mongolia,
Nepal, Pakistan, the Philippines, Singapore, Sri Lanka, Thailand, and Vietnam
AEPM		 Asian economy and productivity map (see Appendix 7)
ASEAN
Association of Southeast Asian Nations: Brunei, Cambodia, Indonesia, the Lao
PDR, Malaysia, Myanmar, the Philippines, Singapore, Thailand, and Vietnam
Brunei, Indonesia, Malaysia, the Philippines, Singapore, and Thailand
ASEAN6
APO20 plus the People’s Republic of China, the Kingdom of Bhutan, Brunei,
Asia24and Myanmar
Asia24 plus GCC countries
Asia30
Cambodia, the Lao PDR, Myanmar, and Vietnam
CLMV
consumer price index
CPI
European UnionEU
15 member economies of the European Union prior to enlargement: Austria,
EU15
Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain, Sweden, and the United Kingdom
EU28
European Union: EU15 plus Bulgaria, Republic of Croatia, Cyprus, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Romania, Slovak
Republic, and Slovenia
FDI
foreign direct investment
FISIM
financial intermediation services indirectly measured
GCC
Gulf Cooperation Council: Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and theUAEgross domestic productGDPgross fixed capital formation
GFCF
gross national income
GNI
International Comparisons Program
ICP
International Labour Organization
ILO
International Monetary Fund
IMF
International Standard Industry Classification
ISIC
information technology
IT
Keio Economic Observatory, Keio University
KEO		
Lao PDR		 Lao People’s Democratic Republic
less developed countries
LDCs
non-profit institutions serving households
NPISHsOrganisation for Economic Co-operation and Development
OECDpurchasing power parity
PPP
quality adjusted labor inputs
QALI
quarterly national accounts
QNA
Regional Comprehensive Economic Partnership
RCEP
Republic of China
ROC
research and development
R&D
System of National Accounts
SNA
total factor productivity
TFP
Trans-Pacific Partnership
TPP
United Arab Emirates
UAE
United Nations
UN
United Nations Statistics Division
UNSD
United States
US

viii

Foreword
One of the biggest challenges to sustaining growth in Asia is, and will continue
to be, raising productivity. The Asian Productivity Organization (APO), as thesole organization devoted to productivity in Asia, has implemented a variety of
projects since its establishment in 1961. Last year, we successfully trained some
5,000 participants through capacity building and knowledge sharing. We haveencouraged businesses in emerging countries to grow efficiently through thedevelopment of process and product innovation. We have also cultivated newsocioeconomic opportunities for productivity growth in the Asia-Pacific region,such as service- and public-sector productivity, demographic efficiency in labor
participation, knowledge management, and agricultural innovation.
In recent years, in collaboration with multiple partners, the APO has assumed
the role of policy advisory. The primary agenda of policy advice covers challenges in SME development, science and technology, innovation, and healthcare.
These are ultimately key factors to improving national competitiveness. The
APO has focused efforts on providing evidence-based policy guidance to its
members through research on productivity measurement offering a wealthof data.
I am pleased to release this new edition of the APO Productivity Databook toreaders. The databook presents an analytical report on recent and long-term
productivity and economic performance of the region and reference economies. This publication is the result of concerted research efforts by the APO
Productivity Databook Project of the Secretariat Research and Planning Department together with Keio Economic Observatory, Keio University, Tokyo. My
gratitude goes to Professor Koji Nomura for his insightful leadership in project
management. I also thank all of the contributors for developing the productivity
database and this publication.
I hope that readers will enjoy this publication, while finding it a useful tool as areference for their own work.

Mari Amanoix©2016 Asian Productivity Organization

Secretary-General
Asian Productivity Organization
Tokyo, September 2016

1.1 Databook 2012

1 Introduction
1.1 Databook 2016

1

This is the ninth edition in the APO Productivity Databook series. The publication aims to provide acomprehensive cross-country comparison of economic growth, structural change, and productivity
performance of Asian economies in relation to global and regional economies. Productivity gains,which enable an economy to produce more for the same amount of inputs, are the only route to sustainable economic growth in the long run. Therefore, it follows that monitoring and improving national productivity capability are important aspects of public policy, especially when many countries
are facing aging population.
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 includes: Bangladesh, Cambodia, the Republic of China (ROC), Fiji,
Hong Kong, India, Indonesia, the Islamic Republic of Iran (Iran), Japan, the Republic of Korea (Korea),
the Lao People’s Democratic Republic (Lao PDR), Malaysia, Mongolia, Nepal, Pakistan, the Philippines,
Singapore, Sri Lanka, Thailand, and Vietnam. The 10 non-member economies in Asia are: the People’s
Republic of China (China), the Kingdom of Bhutan (Bhutan), Brunei, Myanmar, and the Gulf Cooperation Council (GCC) that consists of Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United ArabEmirates (UAE). In addition, Australia, the European Union (EU), Turkey, and the United States (US) are
included as reference economies. This edition covers the period from 1970 to 2014.

To analyze the overall productivity performance as well as productivity subsets (e.g., labor productivity, capital productivity, and energy productivity), this Databook project constructs estimates of capital services appropriate to the concept of capital input introduced in the 2008 SNA. The research anddevelopment (R&D) is treated as a factor of production. The energy productivity estimates are presented as a reflection of the impending need to improve energy productivity as a policy target forpursuing sustainable growth of the Asian countries. Based on the growth accounting framework, thesources of economic growth in each economy are further decomposed to factor inputs of labor andcapital and total factor productivity (TFP) for 20 Asian economies – Bangladesh, Cambodia, the ROC,
Fiji, Hong Kong, India, Indonesia, Iran, Japan, Korea, 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 the TFP estimates for Nepal and the growth accounting for the Asian
regions are newly developed in this edition. The estimates of labor input and its compensation in

1©2016 Asian Productivity Organization

Productivity can be defined simply as the ratio of an output volume measure to an input volume measure. Applying productivity is significantly more complex, especially when operationalizing this notion to suit different purposes in a world with data limitations, such as developing countries in Asia.
The productivity measures in this report are based on the official data and our estimates collated for
the APO Productivity Database project since September 2007. This is a joint research effort between
the APO and the Keio Economic Observatory (KEO), at Keio University, Tokyo. Recent significant revisions based on the System of National Accounts in 2008 (2008 SNA) have resulted in updates for Brunei, Bangladesh, the ROC, Indonesia, Korea, Mongolia, and Singapore during 2014–2015 and in Sri
Lanka as of March 2016. Additionally, Japan will publish the revised JSNA, also based on the 2008 SNA,
as of the end of 2016. While there are movements toward upgrading the SNA, some countries, such as
Cambodia, the Lao PDR, and Nepal, have yet to fully introduce the earlier version 1993 SNA. Because
the varying SNA adaptions among the member economies 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 estimatesfor international comparison. The GDP harmonization process is provided in Appendix 1.

1 Introductionsome countries are revised in this edition, reflecting our work-in-progress estimates on number ofworkers, hours worked per worker, and hourly wages. This data is cross-classified by gender, education attainment, age, and employment status, which has been developed for the past few years in our
project. In addition, this edition provides our preliminary estimates on the per-worker labor productivity for cities in Asia.
The structure of the Databook is as followed. The overview is presented in Chapter 2. In order to understand the dynamics of the long-term economic growth within Asia, Chapter 3 details countries’
diverse development efforts and achievements, through cross-country level comparisons of GDP. In
national accounts GDP is captured and measured by three approaches: production by industry, expenditure on final demand, and income to factor inputs. Decompositions of GDP are valuable in understanding the structure, and in turn the behavior, of an economy. Chapter 4 presents the compositionof countries’ expenditure (the demand side). The decomposition of output growth into factor inputs
and TFP growths (the supply side) is analyzed in Chapter 5, while the industry structure of countries ispresented in Chapter 6. Finally, Chapter 7 focuses real income to evaluate an improvement in theterms of trade.
The official national accounts and metadata information used for constructing the APO Productivity
Database 2016 have been provided by national experts in APO member economies through questionnaires designed at KEO. The names of these experts are listed in Section 1.2. The submitted data
was then examined and processed at KEO, where further information was collected on labor, production, prices, trades, and taxes as required. This edition effectively reflects the revisions to the official
national accounts and other statistical data published as of May 2016. 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 Fukunari
Kimura (Keio University), with support from the research assistants Shinyoung Oh, Hiroshi Shirane,
Naoyuki Akashi, Shiori Nakayama, Misato Hori, and Rie Kinoshita, The Databook project appreciates
Eunice Ya Ming Lau for her contribution to developing the foundation of Databook series and Trina
Ott for her review of the draft.

1.2 List of Contributors
Authors of This Report

KEO, Keio University

Dr. Koji Nomura

Ms. Shinyoung Oh

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

Dr. Fukunari Kimura©2016 Asian Productivity Organization

Professor, Department of Economics,
Keio University

2

Mr. Hiroshi Shirane
Mr. Naoyuki Akashi
Ms. Shiori Nakayama
Ms. Misato Hori
Ms. Rie Kinoshita1.2 List of Contributors

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
Mr. Wei-Jie Huang
Senior Executive Officer, Department of Statistics,
Directorate-General of Budget, Accounting, andStatistics (DGBAS), Executive Yuan, No. 2,
Guangzhou St., Zhongzheng District Taipei,
10065

Fiji
Ms. Navilini Singh
Senior Statistician, Economics Statistics Division,
Fiji Bureau of Statistics, Rata Sukuna House,
Macarthur Street, Victoria Parade, Suva

India
Dr. Kolathupadavil Philipose Sunny
Director & Group Head (Economic Services),
National Productivity Council, Lodhi Road,New Delhi, 110003

Indonesia
Ms. Harmawanti Marhaeni

Islamic Republic of Iran
Mr. Behzad Mahmoodi

1

Deputy of Auditing, Research and Technology,
National Iranian Productivity Organization, 3rd
Floor, Building No.3, Management and Planning
Organization, Daneshsara St., Baharestan Sq.
TehranJapan
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

Republic of Korea
Dr. Rhee Keun Hee
Senior Researcher, Productivity ResearchInstitute, Korea Productivity Center
32, Saemunan-ro 5Ga-gil, Jongno-gu,
Seoul 03170

Lao PDR
Ms. Salika Chanthalavong
Chief of National Account Division, Economic
Statistic Department, Lao Statistic Bureau,
Ministry of Planning and Investment, Ban
Sithanneau, Souphanouvong Road, Vientiane

Malaysia
Ms. Hezlin Suzliana Binti Abdul Halim
Assistant Director, Department of Statistics,
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,
Macro-economic Statistical Department,
National Registration and Statistics Office ofMongolia, Government Building III,
Ulaanbaatar-20a

Chief of Subdirectorate of Statistical Analysis
BADAN PUSAT STATISTIK (Statistics
Indonesia), Jl. Dr. Sutomo No.6-8, Jakarta

3©2016 Asian Productivity Organization

APO Officer

1 Introduction

Nepal
Mr. Rajesh Dhital
Director, Establishment Census and Survey
Section, Central Bureau of Statistics,
Ramshahpath, Thapathali, Kathmandu

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

Philippines
Ms. Ma. Julieta P. Soliven©2016 Asian Productivity Organization

Chief Statistical Specialists (SG 24), Philippine
Statistics Authority, 16th Floor Eton Cyberpod
Centris Three, EDSA cor Quezon Ave. Quezon
City

4

Sri Lanka
Mr. Weerasinghe Wasala Mudiyanselage Ananda
Sarath Premakumara
Additional Director General, Department ofCensus and Statistics, “Sankyana Mandiraya”,
No. 306/71, Polduwa Road, Battaramulla

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

5©2016 Asian Productivity Organization

APO member economies
Non-member Asian economies included in Databook 2016

1.3 Map of Countries Covered

1.3 Map of Countries Covered1

Overview

2 Overview
In 2015, most of the Asian developing economies successfully maintained steady economic growth.
The average annual growth of GDP at constant market prices in 2010–2014 in the Asia30 and East Asia
grew 5.4% and 5.7%, respectively. Despite the recent slowdown of the Chinese economy, the modest
performance of the ASEAN and India’s sustained rapid growth saved the downturn of the entire Asian
economy. Low food and fuel prices helped most Asian economies keep inflation low and sustain thepace of economic growth.

2

Advanced economies continued to grow slowly. Among them, the US economy performed better
than others. The average annual growth of GDP at constant market prices in 2010–2014 in the US was
1.9%. On the other hand, the European economy followed a low growth path in a slow recovery fromthe global financial crisis and the Euro Crisis. The average annual growth of GDP at constant market
prices in 2010–2014 in the EU15 and the EU28 remained as low as 0.5% and 0.7%, respectively. The
situation in some countries remains unstable, both economically and politically, being jeopardized bymassive refugee inflows from the Middle East and “Brexit.” The Japanese economy appeared to be going back to the pre-crisis normal performance but lost momentum for pulling up its potential growthrate. The average annual growth of GDP at constant market prices in 2010–2014 in Japan was 0.7%.
The recent World Economic Outlook by the IMF (2016) presented a somewhat pessimistic view forcontinued slow growth of advanced economies.
While China’s growth hit a serious slowdown, their economy had vigorous energy, achieving 7.8% in
official statistics for the average annual growth of GDP at constant market prices in 2010–2014. However, the growth of trade and tradable sectors decelerated, particularly with excess capacity in basic
metals and other material production, while services sector growth was steady. The Chinese government seemed to maintain control over short-term uncertainty in asset markets, but the adjustments
in productive sectors took time. Negative impacts on the Korean and Japanese economies were obvious. To date, the impact on the ASEAN economies is limited.

Latecomers in the ASEAN, Cambodia, the Lao PDR, and Myanmar, have rapidly grown in the past two
decades, reaching $1,140, $1,780, and $1,310 in the per capita GDP using exchange rate in 2014, respectively. However, the easy catch-up period is almost gone. To achieve sustained economic growth,
they have to engage in international production networks for tier 2. It is fortunate to see a sign of “Thaiplus one” investment by machinery parts producers, setting up fragmented satellite factories off Thailand. Vietnam presented much deeper involvement in production networks after the global financial
crisis, reaching $2,080 in the per capita GDP using exchange rate in 2014, facing a challenge for tier
1a. The Philippines and Indonesia are also struggling with the formation of efficient industrial ag­
glomeration for tier 1a with $2,890 and $3,560 in the per capita GDP using exchange rate in 2014. Thailand, Malaysia, and Singapore have successfully completed the formation of efficient industrial

7©2016 Asian Productivity Organization

Industrialization in developing East Asia has been characterized by the effective use of global value
chains (GVCs). The Economic Research Institute for ASEAN and East Asia (ERIA, 2015) proposes theconcept of the tier structure in terms of the sophistication of GVCs linkages. Tier 3 is a step to connect
with slow GVCs such as that observed in traditional operations in food processing, garment industry,
tourism, and others. Tier 2 is a stage where a country participates in fast and sophisticated international production networks (Ando and Kimura, 2005) or the second unbundling (Baldwin, 2011). Production networks in machinery industries are typical of this tier, though quick operations in otherindustries such as food processing with cold chains, cut flowers by air, call centers, and software outsourcing, are also categorized in this tier. In tier 1a, a country starts forming industrial agglomeration
while maintaining strong channels of international production networks. The last tier, tier 1b, is a stepto create an innovation hub in order to progress to a fully developed economy.

2 Overview

agglomerations with $6,100, $11,050, and $56,010 in the per capita GDP using exchange rate in 2014
and now face a challenge for tier 1b, the creation of innovation hubs.
On the other hand, the South Asian countries have not fully taken advantage of international production networks, although some of them have advocated “Look East” policies. Their engagement in GVCs
is generally still in tier 3 rather than tier 2. The per capita GDP using exchange rate in 2014 in Nepal,
Bangladesh, Pakistan, and India is $790, $1,110, $1,320, and $1,560, respectively. There is ample room
for them to learn a novel development strategy with production networks from the ASEAN member
states and China.
While overall economic growth in Asian economies seems fair, in the era of globalization, countries
must always be prepared for possible turbulence in the asset and financial markets. Since the crisis,international capital flows have substantially slowed, but relatively good economic performance, as
well as open capital markets in some of the Asian economies, attracts possible excessive footloose
capital inflows. Although the current management of macroeconomic fundamentals is much better
than that in the era of the Asian currency crisis, the financial world is also much more globalized now.
A slight sign may trigger sudden massive outflows of capital, and a speculative attack may result. The
financial authority must monitor asset and financial markets with heightened credibility.
There are numerous external events that could trigger turmoil in the region. A current prime risk is apossibly serious repercussion of the UK’s exit from the EU, which may trigger a massive overturn of thecurrent globalization trend not only in Europe but also in the rest of the world. In addition, there is acontinuous concern of the impact of a tapering policy for monetary easing in the US, though suchworry seems to have eased. The Asian economies should always be well-prepared for such possible
negative shocks.

©2016 Asian Productivity Organization

The year 2015 was a landmark year for economic integration in East Asia and the Asia-Pacific, marked
by three major events. Three big events occurred. First, the ASEAN Economic Community (AEC) completed economic integration at the end of 2015, and the ASEAN came into a new era with the AEC2025
that furthers economic development. The AEC is the most successful economic integration concludedin the developing world. The AEC Blueprint in 2007 presented a detailed work program toward 2015,
which consisted of four pillars: single market and production base; competitive economic region; equitable economic development; and integration into global economy. The first pillar corresponds tohigh-quality free trade agreements (FTAs) by which various international commercial policies are covered. The second and third pillars provide various programs for economic cooperation, which are notusually covered by FTAs. The fourth pillar sets the basis of economic diplomacy in order to integrate
the ASEAN with external regions. The AEC Blueprint 2025 stands with five pillars. Retaining four pillars
from the AEC2015, it adds another pillar by combining connectivity from the second pillar and integration priority sectors from the first pillar. Although the details will be documented in the coming
year, the effort toward deepening the ASEAN Community will assuredly continue. As for economic
integration, the link with utilizing global value chains will receive greater emphasis, rather than simply
pursuing the textbook version of “single market.” To enhance competitiveness, focus will be on productivity growth and innovation with good governance and responsive administrative practices apriority. For inclusiveness, the development of micro, small, and medium enterprises will continue.
Second, the negotiation of the Trans-Pacific Partnership (TPP) by 12 countries in the Asia-Pacific concluded in October 2015, and an agreement was signed in February 2016. There still exists uncertainty
in TPP – the US and Japan must ratify the agreement in order to make the agreement effective. However, substantial repercussions have already been observed. We see strong domino effects where anumber of non-negotiating countries reveal interest in participating in the TPP. When the presidents8Overviewof Indonesia and Korea visited the US in October 2015, they directly stated to President Obama theirinterest in entering into the TPP. In the ROC, pro-TPP government commenced in May 2016. The Philippines and Thailand also showed their interest. China, too, began studying the detailed contents of the
TPP. The draft text of the TPP, consisting of 6,000-plus pages, was disclosed on the web in November
2015. Once the TPP goes into effect, it will set a new standard of deep liberalization for goods andservices trade as well as investment. It will also move towards developing a new series of international rule including government procurement, intellectual property rights protection, competition
policy – particularly with state-owned enterprises, and a dispute settlement mechanism including
investor-state dispute settlements. Although a number of political concessions will certainly mitigate
radical aspects of the agreement, the TPP will still be a landmark agreement, particularly for
East Asia.

2

The negotiation over the Regional Comprehensive Economic Partnership (RCEP) by the ASEAN+6
countries now faces difficulties.1 It is an important initiative for the ASEAN in order to keep their“ASEAN centrality” for East Asian economic integration. It would cover all of East Asia, including China
and India, which is the extended area of international production networks, and thus should be important for Japan as well. However, India and China have continuously been reluctant in committing
themselves to higher levels of trade liberalization, with the negotiation protocol set as low as only
80% tariff removals in August 2015. With this level of liberalization, RCEP would be futile and marginalized. Countries in the region should redefine the strategic significance of the ASEAN+6 economic integration and upgrade its quality.
Third, China’s new approach toward development issues was launched in 2015. A new international
financial institution called Asian Infrastructure Investment Bank (AIIB) was officially established in December 2015. Although China has already deeply committed to infrastructure development in theregion in the form of bilateral aid or foreign direct investment as well as being an important memberof Asian Development Bank (ADB) and the World Bank, the establishment of AIIB will upgrade China’s
commitment to Asia and other developing regions. China has also been active in extending its own
initiative called “One Belt One Road” for infrastructure development and other economic cooperation
with continental and maritime Asian countries.

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.

1: The ASEAN+6 are the ASEAN and its six major trading partners: China, India, Japan, Korea, Australia, and New Zealand.

9©2016 Asian Productivity Organization

Asia’s economic vitality warrants considerable attention to the rapid and vigorous changes in its economic performance in the short run. To fully understand this economic dynamism, it is essential tograsp its growth performance, structural changes, and the advancement of its economic development within a context of its middle- and long-term performance. Asia, in particular, consists of a variety of countries at different development stages, with diversified resource endowments, and undervarious political regimes. The APO Productivity Databook provides concise information and useful
insights into the basis of growth performance and economic structure of Asian countries by presenting such long-term data analysis.

2 Overview

Bearing in mind these caveats, the main findings from our analysis are as follows:
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 43%
and 53% larger than the US and the EU15 in 2014, respectively (Table 1).
u Based on GDP adjusted for purchasing power parity (PPP),2 the weight of the world economy iseven more tilted toward Asia, with the Asia30 1.69 times and 1.91 times larger than the US and theEU15 in 2014, respectively. China has overtaken Japan as the largest Asian economy since 1999;
and its size was 106% relative to the US in 2014. India surpassed Japan, replacing it as the second
largest economy in Asia in 2009. In 2014, the total GDP of the three largest Asian economies alone
was 77% larger than the US economy (Table 2 and Figure 5).
u During the period 1990–2014, the Asia30 grew at 5.5% on average per annum, compared with

2.4% and 1.5% in the US and the EU15, respectively. Japan was the slowest growing economy
among the Asia30 at 1.0%, compared with 25 of the 30 Asian economies with over 4.0% of annual
economic growth (Table 3 and Figure 1).
u Since 2000, China and India have emerged as the driving force propelling Asia forward, accountingfor 51% and 17% of regional growth, respectively (Figure 7).
u The global financial crisis slowed Asia30’s growth significantly from a recent peak of 8.1% during

2006–2007, to 4.8% during 2007–2008 and further to 3.9% during 2008–2009, before rebounding
strongly to 8.1% 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.3 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 2014 its per capita GDP was 54% higher. In contrast, veteran Japan has fallen behind, widening its gap with the US
to 29%. In 2014, the ROC’s and Korea’s per capita GDP was 85% and 65% of the US level, respectively (Table 6 and Figure 14).

©2016 Asian Productivity Organizationu Despite their rapid growth, due to their population, per capita GDP of China and India was 25%

and 10% of the US in 2014, 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 21% of the US,
indicating that there is ample room for catch-up (Table 6 and Figure 15).
2: 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.
3: Refers to Hong Kong, Korea, Singapore, and the ROC.

10

Overviewu Asia’s huge per capita GDP gap with the US is predominantly explained by its labor productivity

gap. With the exception of the Asian Tigers, Japan, Iran, and Malaysia, all Asian countries have alabor productivity gap of 50% or higher (Figure 18).

2

u 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 Bhutan, Nepal, Singapore, and Pakistan in 2000–2014 (Figure 19).
u There is a significant variation in Asia’s employment rate from 25% to over 60% at present. The

employment rate has been rising in most Asian countries and is more than 10 percentage points
above the US in Singapore, Myanmar, Cambodia, Thailand, and Vietnam (Figure 21).
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 48.1% of GDP in 2014, largely reflecting
the trend in China. This compares to 68.4% in the US, 56.9% in the EU15, and 56.9% 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 2014, the Asia30 invested 36.0% of its GDP, compared with 19.9% for the US, 19.2% for
the EU15, and 26.6% for Australia (Table 8 and Figure 30).
u China faces huge internal and external imbalances. The investment share of GDP (at 47.2%), as thebiggest component in final demand and the household consumption share, plummeted to 36.7%
in 2014. In contrast, the weight of net exports has been rising in the past decade, although it isdeclining in recent years due to weak foreign demand (Figure 22).
u GCC economies are unusually skewed toward net exports because of their oil. Net exports ac-

counted for 19.7% of final demand in 2014, compared with Asia30’s 2.3% and China’s 2.7%. Only
the US and South Asia run trade deficits of a more significant nature, which accounted for –3.1%
and –3.9% of final demand, respectively, in 2014 (Table 8).
u Basic necessities account for a high proportion of household consumption in lower-income coun-u In the 2000s, investment recovered in the Asian economies and drove growth. For Singapore

and the ROC, however, the strength of net exports was still the dominant force behind their economic growth. The growth slowed in the US and the EU15, and the contributions of government
consumption to growth nearly tripled as contributions from investment took a plunge (Figures 35
and 39).

11©2016 Asian Productivity Organization

tries, according to the cross-country version of Engel’s Law, which states that basic necessities willaccount for a high proportion of household consumption for a lower per capita income group andvice versa. They spend 30–50% of total consumption for food, which corresponds to Japan’s experience in the 1950s and the 1960s (Figures 28 and 29).

2 Overview

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 20% of the US in 2014
(Table 9 and Figure 40).
u Growth of per-worker GDP in Asia has outstripped that in the US, allowing catch-up. In particular,

the low-income countries appeared to experience a labor productivity growth spurt in the 2000s.
China achieved the fastest labor productivity growth of 9.0% on average per year in 2005–2014,
followed by Myanmar’s 7.8% and Mongolia’s 7.2%; this compares with the US’s 1.0%. Japan’s 0.5%
growth over the same period was the weakest performance among the Asian Tigers and Japan(Table 10 and Figure 42).
u The productivity gap based on GDP per hour is generally wider between Asian countries and theUS. While the adjustments are negligible for most Asian countries, the productivity gap significantly widened by 13–32 percentage points for the Asian Tigers, suggesting that people work
much longer hours than in the US (Figure 43).
u Most Asian countries experience faster growth in GDP per hour than the US. Among them, China’s

performance is the most outstanding, with average annual productivity growth doubling from
4.5% to 8.4% between 1970–1990 and 1990–2014, compared to the US at 1.5% and 1.7% over thesame periods (Figure 45).
u Mapped onto Japan’s historical trajectory of GDP per hour, most Asian countries cluster around thelevel that Japan achieved in the 1950s and early 1970s, with the Asian Tigers being the clear frontrunners, sprinting away from the pack (Figure 47).
Total factor productivity
u Of the 20 Asian countries compared, 11 experienced faster TFP growth than the US over the period

1970–2014, with China in a league of its own. Its TFP growth was at 3.1% on average per year, compared with those of Thailand at 1.6% in second place and the US at 0.8%. With TFP growing at 0.6%
on average per year, Singapore’s productivity performance has been weak relative to its economic
counterparts (Figure 49).
u Over the past four decades, economic growth in Asia has been predominantly explained by thecontribution of capital input, but the role of TFP growth should not be underestimated. Its contribution accounted for over 25% of economic growth in eight of the 20 Asian countries compared,
with it being most prominent in China (36%), India (35%), Sri Lanka (34%), and Pakistan (31%)(Figure 51).

©2016 Asian Productivity Organization

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 56% on average, while the contribution of TFP is getting progressively
more significant, rising to a share of above 36% on average in 2000–2014 (Figures 53 and 59).
u The evident rise in the contribution of information technology (IT) capital is noteworthy. By the

2000s, it had risen to above 5% in most Asian countries compared, while accounting for around12

Overview

one-third of economic growth in Japan and the US. The allocation shift towards IT capital started
two decades earlier in the US than in any Asian country (Figures 53 and 57).

2

u Over the past decades, it has been observable that economic growth has decelerated in the earlystarters (Japan and the Asian Tigers). Their experience lends support to the likelihood of an eventual slowdown in China; the question is more likely “when,” than “if.” TFP growth slowed from its
former peaks achieved in the late 1970s or late 1980s until recent years when countries experienced TFP resurgence (Figure 56).
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 10.2% on average per year to 7.0% in Korea between 1970–1990 and 1990–2014, whereas it doubled in China from 5.5% to 10.3% (Figure 60).
u Capital deepening tends to go hand in hand with deterioration in capital productivity. China’s per-formance is particularly impressive as its acceleration in capital deepening over the past two decades did not compromise its capital productivity as much as the early starters in the early period(Figure 61).
u Over a long period – stretching four decades – a downward trend in labor productivity growth canbe seen among the early starters, but there is a step-up in China and India. Singapore’s productivity performance, albeit robust compared with other mature economies like the US, has been very
modest against its Asian counterparts (Figure 68).
Industry structureu Evidence supports the view that a country’s industry structure transforms with its economic devel-

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

economies. It is particularly prominent in Korea, China, the ROC, Thailand, Philippines, and Indonesia, in which higher TFP growths are measured in 2000–2014 (Figure 73). Asian manufacturing is
dominated by machinery and equipment in the richer Asian economies while their poorer counterparts concentrate on light manufacturing such as textiles and the food industry (Figure 74).

ment, accounting for 35% of total employment in 2014 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 75 and 78).
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 the13©2016 Asian Productivity Organizationu While Asian countries are diversifying away from agriculture, the sector still dominates employ-2 Overview

1980s. Since the 1990s, however, the manufacturing sector has no longer been an absorption sector of employment, regardless of the sound expansion of production in this sector. (Figure 80).
Industry origins of economic growthu Our results support the observation that China and India have taken different development paths,

with the former relying more on the traditional growth engine of manufacturing and the latter onservices. In the past two and a half decades China has been undergoing a slight transition, with its
growth shifting away from manufacturing-driven to more services-driven. In the period 2000–
2014, the contributions to economic growth by manufacturing and services were 34% and 46%,
respectively, compared with 42% and 35% in the 1990s (Figures 82 and 83).
u In contrast, growth in India has always been more driven by services, the contributions of whichare 61% in the 1990s and 61% in 2000–2014, while manufacturing usually contributes one-fifth orless (Figures 82 and 83).
u A total of 28% of Asia30’s regional growth originated from the expansion of manufacturing in

2000–2014, 61% of which was accounted for by China. In other words, China’s manufacturing alone
contributed 17% to regional growth (Figure 86).
u The importance of manufacturing as a contributor to overall labor productivity growth has neverwaned in Korea and the ROC. However, manufacturing has never been a major contributor in India
in its recent development process or in Hong Kong and Sri Lanka in 2000–2014 (Table 18 and
Figure 89).
Real income and terms of tradeu Real GDP could systematically underestimate (or overestimate) growth in real income if terms oftrade improve (or deteriorate). It is generally observed that the trading gain effect is more significant in the short term than in the long term. Our findings confirm this observation, with the exceptions in some oil-exporting countries such as Kuwait and Brunei, where trading gain has always
been positive and significant (Table 19 and Figure 97).
u Positive net primary income from abroad also bolsters a country’s real income. In Japan and thePhilippines, net primary income from abroad has been rising steadily, albeit at different magnitudes. In Japan, it rose from 0.6% of GDP in 1990 to 3.9% in 2014, compared with 1.5% in 1990 and
34.4% in 2014 in the Philippines. Singapore’s historical margin fluctuates within a large range whencompared with other rich economies – from +2.0% in 1997 to –7.0% in 2004, but on the whole, it
has been more negative than positive (Figure 91).
u Our results show that for most countries studied, the difference between growth of real GDP and©2016 Asian Productivity Organization

real 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 1970–2014; Kuwait and Brunei appear tobe the outliers (Figure 92).
u The five countries that have been enjoying a trading gain over 1% per annum in the past four de-cades are all oil-exporting countries. Among them, only Iran managed to achieve a positive growthin labor productivity. In contrast, export-oriented, high productivity Asian countries have been facing a deteriorating trading gain position as a price of their own success (Figure 98).

14

Overview

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.

©2016 Asian Productivity Organization

152

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 consistently has been outperforming the West. With the exception of 1997–1999, when the economy was
adversely affected by the Asian financial crisis (Figure 38 in Section 4.3, p. 56), the Asia30 has been
growing faster than the US and the EU15 by 3 to 4 percentage points on average per year.4

3

In 2009 at the height of the global financial storm, the growth differentials were 6.7 and 8.4 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 2012–2014, from 7.1% before
the global financial crisis (2002–2007). This is mainly due to the onset of deceleration in China’s
growth to 7.3% from 11.0% on average in the same periods.5 Plagued by the euro crisis, the EU15 saw
their economy shrink by 0.6% from 2011 to 2012 and their recovery to 1.2% in 2013–2014, whereas
the US economy sustained a steady growth of 2.0% in the period 2012–2014. The difference in recent
GDP growth is unchanged between Asia and the US (3.3 percentage points on average per year during 2012–2014), but expanded between Asia and the EU15 (5.1 percentage points).
%

10
8
6
4
2
0
−2
−4
−6
1970

Asia30

Japan

US

EU15

1975

1980

1985

1990

1995

2000

2005

2010

Figure 1 GDP Growth of Asia, the EU, Japan, and the US, 1970–2014_Annual growth rate of GDP at constant market prices

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

It is therefore no surprise that the center of gravity in the global economy is gradually shifting towards
Asia. In 2014, the Asian economy contributed 45% (42% for Asia30) of world output, compared with
the US and the EU28, each accounting for 16% and 17%, respectively (Figure 2). The IMF (2016) projects the Asian share in world output will continue to rise, reaching 50% (47% for Asia30) by 2021. In
contrast, the output shares of each of the US and the EU28 will shrink by a similar extent to 15%.

4: 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.
5: According to the preliminary estimation by the National Bureau of Statistics of China, the growth rate of Chinese GDP is estimatedas 6.9% in 2015 (reported on 19 January 2016), which is the weakest in a quarter century. The annualized growth for the 1st quarter of 2016 is 6.7% to the same quarter in 2015 (reported on 18 April 2016). OECD (2016b) forecasts the Chinese growth is set toedge down further, from 6.5% in 2016 to 6.2% by 2017.

17©2016 Asian Productivity Organization

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 expressed3 Economic Growthin its equivalent in a common currency unit, customarily in the US
dollar, using a set of conversion
rates between the individual national currencies. The choices forconversion rates are exchange rateand PPP.

3.1 Economic Scale and
Growth

2014

Asia
45 %

APO20
23 %

Others
22 %

EU28
17 %

2021

Asia30
42 %

EU15
15 %
US
16 %

Other Asia
3%

Others
20 %

APO20
25 %

EU15
13 %
EU28
15 %

Asia
50 %

Asia30
47 %
US
15 %

Other Asia
3%

Figure 2 Share of Asia in World GDP in 2014 and Projection for 2021_

Share of GDP using constant PPPTable 1 provides snapshot-level com-­
parisons of Asian countries, based
Source: IMF, World Economic Outlook Database, April 2016.
on GDP at current market prices
using exchange rates,6 for the six
separate years of 1970, 1980, 1990, 2000, 2010, and 2014. By this measure, 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 and reducing the US lead from five times to lessthan twice its economy. The turn of Japan’s fortune came in 1990, when the country’s excessive growthyears of the late 1980s ended and its descent began. Thereafter, stagnation in Japan combined with vibrant growth in
56 Australia
35
Japan
developing Asia has resulted in the rapid erosion of Japan’s
−23
Korea
prominence in the regional economy. The leading position−29
Singapore−30
Hong Kongof the four largest Asian economies (China, Japan, India
−31
UAE
−34
Qatar
and Korea) has been consistent, with their positions rather−38
Kuwaitsecure in the past two decades. The ASEAN as a group has−41
Turkey−42
Fijibeen demonstrating vigor in catching up since 2000. On−43Brunei−44
Bahrainthis measure, the Asia30 was 43% and 53% larger than the
−46
China
US and the EU15 in 2014, respectively.
−49
ROC

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 scale of economy rankings change dramaticallywhen international price differences are properly taken
into account.7

−50
−51
−52
−56
−58
−59
−59
−59
−64
−65
−67
−67
−67
−68
−69
−69
−71
−72

©2016 Asian Productivity Organization−80 −60 −40 −20 0

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 in

18

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 2014
_GDP at current market prices, using annual average exchange rate
1970
Japan
China
India
Iran
PakistanIndonesia
Bangladesh
Korea
ThailandPhilippines
ROC
Saudi Arabia
Malaysia
Hong Kong
KuwaitMyanmar
Sri Lanka
Singapore
Vietnam
Nepal
UAE
Cambodia
Qatar
BahrainOman
Fiji
BruneiMongolia
Bhutan(regrouped)
APO20
358 171.3
Asia24454 217.0
Asia30464 222.0
East Asia
320 153.2
87 41.7
South Asia
35 16.6
ASEAN
ASEAN630 14.3
4.7 2.2
CLMV
11 5.1
GCC(reference)
1,076 514.5
US1,247 596.5
EU15Australia
Turkey1980(%)

209 100.0
93 44.3
64 30.4
11 5.410 4.8
10 4.7
10 4.7
9.0 4.3
7.3 3.5
6.8 3.2
5.8 2.8
5.4 2.6
3.9 1.9
3.8 1.8
3.0 1.4
2.7 1.3
2.5 1.2
1.9 0.9
1.2 0.6
1.1 0.5
1.1 0.5
0.8 0.4
0.5 0.3
0.4 0.2
0.3 0.1
0.2 0.1
0.2 0.1
0.1 0.1
0.1 0.0

45
24

21.6
11.4

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

1,767
2,083
2,339
1,548
240
196
188
7.7
257

159.7
188.2
211.4
139.9
21.7
17.7
17.0
0.7
23.2

2,863 258.7
3,321 300.2
173
91

1990

(%)

Japan
1,106 100.0
305 27.6
China
190 17.2
India
Saudi Arabia 165 14.9
97 8.8Iran
80 7.2Indonesia
Korea
65 5.9UAE
44 4.0ROC
42 3.8Thailand
33 3.0Philippines
33 3.0
Kuwait30 2.7
Hong Kong
29 2.625 2.2
Malaysia
24 2.2
Pakistan
Bangladesh
19 1.7Singapore
12 1.1
7.9 0.7
Qatar
6.3 0.6
Oman
5.9 0.5
Myanmar
5.0 0.5
Brunei
Sri Lanka
4.3 0.4
3.5 0.3
Bahrain
2.6 0.2
Nepal
1.2 0.1Fiji
1.0 0.1
Vietnam
0.7 0.1
Cambodia
0.5 0.0
Mongolia
0.1 0.0
Bhutan

15.6
8.2

(%)

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

3,182 100.0
394 12.4
335 10.5
279 8.8
167 5.2
127 4.0
118 3.7
95 3.089 2.877 2.451 1.647 1.545 1.444 1.439 1.231 1.019 0.6
12 0.48.3 0.3
7.5 0.2
6.5 0.2
5.2 0.2
4.5 0.1
4.4 0.1
3.4 0.1
1.8 0.1
1.6 0.0
1.4 0.0
0.9 0.0
0.3 0.0

Australia
Turkey

324
200

4,581
4,985
5,196
4,100
424
365
350
14
212

144.0
156.7
163.3
128.9
13.3
11.5
11.0
0.5
6.7

5,980 187.9
6,387 200.8
10.2
6.3

2000
Japan
4,887
1,216
China
562
Korea
482
India
331
ROC
Saudi Arabia 190
Hong Kong
172
Indonesia
168
Thailand
127
Iran
111
UAE
106
Singapore96
Malaysia
95
81
Philippines
72
Pakistan
Bangladesh
51
Kuwait38
33
Vietnam
20
Oman
18
Qatar
17
Sri Lanka
8.4
Bahrain
7.3
Myanmar
6.3
Nepal
5.8
Brunei
3.7
Cambodia
1.7
Fiji
1.7
Lao PDR
1.4
Mongolia
0.4
Bhutan(regrouped)
7,299
APO20
8,528
Asia24
8,909
Asia307,169
East Asia
629
South Asia
618
ASEAN
572
ASEAN6
46
CLMV
381
GCC
(reference)
10,285
US
9,899
EU15
11,005EU28
409
Australia
268
Turkey

(%)

100.0
24.9
11.5
9.9
6.8
3.9
3.5
3.4
2.6
2.3
2.2
2.0
1.9
1.7
1.5
1.1
0.8
0.7
0.4
0.4
0.3
0.2
0.1
0.1
0.1
0.1
0.0
0.0
0.0
0.0
149.4
174.5
182.3
146.7
12.9
12.6
11.7
0.9
7.8
210.5
202.6
225.2
8.4
5.5

2010
China
6,144
5,697
Japan
1,671
India
1,094
Korea
756
Indonesia
Saudi Arabia 532
Iran
477
ROC
446
Thailand
342
UAE
294
Malaysia
255
Singapore
236
Hong Kong
229
200
Philippines
175
Pakistan
Qatar
128
Kuwait118
117
Vietnam
115
Bangladesh
60
Oman
50
Sri Lanka
42
Myanmar
26
Bahrain
19
Nepal
14
Brunei11
Cambodia
7.2
Mongolia
7.0
Lao PDR
3.2
Fiji
1.6
Bhutan(regrouped)
11,906
APO20
18,107
Asia24
19,264
Asia30
East Asia 13,617
South Asia 2,031
1,979
ASEAN
1,802
ASEAN6
177
CLMV
1,157
GCC
(reference)
14,964
US
14,592EU15
16,776EU28
1,293
Australia
740
Turkey

(%)

100.0
92.7
27.2
17.8
12.3
8.7
7.8
7.3
5.6
4.8
4.2
3.8
3.7
3.2
2.9
2.1
1.9
1.9
1.9
1.0
0.8
0.7
0.4
0.3
0.2
0.2
0.1
0.1
0.1
0.0
193.8
294.7
313.5
221.6
33.1
32.2
29.3
2.9
18.8
243.6
237.5
273.0
21.0
12.0

2014
China
10,561
4,774
Japan
2,021
India
1,411
Korea
891
Indonesia
Saudi Arabia 762
ROC
530
Iran
457
UAE
412
Thailand408
Malaysia
338
Singapore
306
Hong Kong
291
285
Philippines
249
Pakistan
Qatar
216
Vietnam
188
173
Bangladesh
167
Kuwait83
Oman
76
Sri Lanka
67
Myanmar
34
Bahrain
22
Nepal
18
Brunei17
Cambodia
12
Mongolia
12
Lao PDR
4.6
Fiji
2.0
Bhutan(regrouped)
12,465
APO20
23,113
Asia2424,789Asia30
East Asia 17,580
South Asia 2,542
2,531
ASEAN
2,246
ASEAN6
285
CLMV
1,675
GCC
(reference)
17,348US
16,180EU1518,758EU28
1,451
Australia
810
Turkey

(%)

100.0
45.2
19.1
13.4
8.4
7.2
5.0
4.3
3.9
3.9
3.2
2.9
2.8
2.7
2.4
2.0
1.8
1.6
1.6
0.8
0.7
0.6
0.3
0.2
0.2
0.2
0.1
0.1
0.0
0.0

3

118.0
218.9
234.7
166.5
24.1
24.0
21.3
2.7
15.9
164.3
153.2
177.6
13.7
7.7

6: 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.
7: 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.

19©2016 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 2014
_GDP at constant market prices, using the 2011 PPP, reference year 2014
1970

(%)

1980

(%)

Japan
1,586 100.0
India
718 45.3
China
413 26.0
Saudi Arabia 290 18.3
Iran
289 18.2
Indonesia
197 12.4
Kuwait148 9.3
Philippines
114 7.2
Thailand
96 6.1
Pakistan
92 5.8Korea
85 5.3
Bangladesh
84 5.3ROC
56 3.5
Malaysia
46 2.9Vietnam
42 2.7
Hong Kong
36 2.2
26 1.6
Sri Lanka
Singapore
22 1.4Myanmar19 1.2
Qatar
18 1.2
13 0.8Nepal
12 0.8BruneiUAE
11 0.7Oman
10 0.7Bahrain
8 0.5
Mongolia
4 0.2
Fiji
2 0.1
Bhutan0 0.0

Japan2,499 100.0
India
965 38.6
Saudi Arabia 767 30.7
China
755 30.2
Indonesia
440 17.6
Iran
403 16.1
UAE
207 8.3
Korea
204 8.2
Philippines
203 8.1
Thailand184 7.4
ROC
152 6.1
Pakistan
148 5.9
Kuwait119 4.8
Malaysia
101 4.1
Bangladesh
92 3.7
Hong Kong84 3.455 2.2
VietnamSingapore
52 2.1
Sri Lanka
39 1.6
Qatar
32 1.329 1.2
Brunei29 1.2Oman
28 1.1
Myanmar17 0.7Nepal
16 0.6BahrainMongolia
7 0.3
4 0.1
Fiji
1 0.0
Bhutan(regrouped)
APO20               Asia24            Asia30              East Asia           South Asia          ASEAN               ASEAN6
CLMV
GCC                 (reference)
US                  EU15                5,132 323.5
6,339 399.6(regrouped)
APO20               Asia24            Asia30              East Asia           South Asia          ASEAN               ASEAN6
CLMV
GCC                 (reference)
US                  EU15                

18.3
15.4

Australia           Turkey              Australia           Turkey              

3,686
4,234
4,738
2,346
1,001
601
530
75
523

290
244

232.4
266.9
298.7
147.9
63.1
37.9
33.4
4.7
33.0

5,863
6,835
8,033
3,922
1,338
1,171
1,072
101
1,218

234.7
273.5
321.5
157.0
53.6
46.9
42.9
4.0
48.8

7,011 280.6
8,666 346.8
387
364

15.5
14.6

1990

(%)

Japan
3,970
China
1,834
India
1,657
Indonesia
814
Saudi Arabia 722
Korea
525
Iran
522
Thailand
392
ROC
334
Pakistan
297
Philippines
247
UAE
211
Malaysia
185
Hong Kong
162
Bangladesh
137
Singapore
110
Vietnam
94
Kuwait91
Oman
64
Sri Lanka
60
37
Qatar
32
Myanmar
27
Nepal
22
Brunei19
BahrainMongolia
129
Cambodia
7
Lao PDR
5
Fiji
1
Bhutan(regrouped)
APO20                9,773
Asia24             11,902
Asia30               13,071
East Asia            7,111
South Asia           2,276
ASEAN                1,995
1,845
ASEAN6152
CLMV
GCC                  1,177(reference)
US                   9,733
EU15                 11,076

100.0
46.2
41.7
20.5
18.2
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
1.5
0.9
0.8
0.7
0.6
0.5
0.3
0.2
0.2
0.1
0.0

522
607

13.1
15.3

Australia           Turkey              

246.2
299.8
329.3
179.1
57.3
50.3
46.5
3.8
29.7
245.2
279.0

2000
China
4,956
Japan
4,453
India
2,819
Indonesia
1,236
Korea
1,027
Saudi Arabia 945
Iran
772
ROC
638
Thailand610
Pakistan
478
Malaysia
376
UAE
347
Philippines
347
Hong Kong
239
Bangladesh
227
Singapore
219
Vietnam
203
Kuwait160
Oman
103
Sri Lanka
100
71
Qatar
63
Myanmar
43
Nepal
30
Bahrain28
Brunei
Cambodia18
13
Lao PDR
Mongolia
12
Fiji
6
2
Bhutan(regrouped)
APO20                14,023
Asia24             19,367
Asia30               21,048
East Asia            11,627
South Asia           3,780
ASEAN                3,178
2,869
ASEAN6309
CLMV
GCC                  1,690(reference)
US                   13,651
EU15                 13,864
EU28
15,744Australia            739Turkey               872

(%)

100.0
89.9
56.9
24.9
20.7
19.1
15.6
12.9
12.3
9.6
7.6
7.0
7.0
4.8
4.6
4.4
4.1
3.2
2.1
2.0
1.4
1.3
0.9
0.6
0.6
0.4
0.3
0.2
0.1
0.0
283.0
390.8
424.7
234.6
76.3
64.1
57.9
6.2
34.1
275.4
279.8
317.7
14.9
17.6

2010
China
13,504
India
5,773
Japan
4,811
Indonesia
2,153
Korea
1,582
Iran
1,394
Saudi Arabia 1,318
ROC
960
Thailand956
Pakistan
772
Malaysia
624
Philippines
552
UAE
515
Vietnam
414
Bangladesh
391
Singapore
385
Hong Kong
355
Kuwait
242
Qatar
242
Myanmar198
166
Sri Lanka
144
Oman
63
Nepal
53
Bahrain38
Cambodia
Brunei32
26
Lao PDR
Mongolia
22
Fiji
7
5
Bhutan(regrouped)
APO20                21,503
Asia24             35,380
Asia30               37,908
East Asia            21,369
South Asia           7,215
ASEAN                5,405
4,724
ASEAN6681
CLMV
GCC                  2,530(reference)
US                   16,068
EU15                 15,697
EU28
18,061
Australia            1,002
Turkey               1,281

(%)

100.0
42.7
35.6
15.9
11.7
10.3
9.8
7.1
7.1
5.7
4.6
4.1
3.8
3.1
2.9
2.8
2.6
1.8
1.8
1.5
1.2
1.1
0.5
0.4
0.3
0.2
0.2
0.2
0.0
0.0
159.2
262.0
280.7
158.2
53.4
40.0
35.0
5.0
18.7
119.0
116.2
133.7
7.4
9.5

2014
China
18,428
India
7,256
Japan
4,941
Indonesia
2,687
Korea
1,784
Saudi Arabia 1,629
Iran
1,400
ROC
1,080
Thailand1,076
Pakistan
891
Malaysia
769
Philippines
693
UAE
636
Vietnam
519
Bangladesh
499
Singapore
458
Hong Kong
401
Qatar
328
Kuwait282
Myanmar266
219
Sri Lanka
167
Oman
75
Nepal
62
Bahrain51
Cambodia
Lao PDR
35
35
Mongolia
32
Brunei8
Fiji
6
Bhutan(regrouped)
APO20                24,879
Asia24             43,610
Asia30               46,715
East Asia            26,669
South Asia           8,947
ASEAN                6,587
5,716
ASEAN6871
CLMV
GCC                  3,104(reference)
US                   17,348
EU15                 16,043
EU28
18,583Australia            1,114
Turkey               1,529

(%)

100.0
39.4
26.8
14.6
9.7
8.8
7.6
5.9
5.8
4.8
4.2
3.8
3.5
2.8
2.7
2.5
2.2
1.8
1.5
1.4
1.2
0.9
0.4
0.3
0.3
0.2
0.2
0.2
0.0
0.0
135.0
236.7
253.5
144.7
48.5
35.7
31.0
4.7
16.8
94.1
87.1
100.8
6.0
8.3

©2016 Asian Productivity Organization

Unit: Billions of US dollars (as of 2014).
Sources: Official national accounts in each country, including author adjustments.
Note: See Appendix 1 for the adjustments made to harmonize GDP coverage across countries.

this report. The underestimation is substantial for some, ranging from 23% for Korea to 72% for Pakistan. Thus, the exchange-rate-based GDP considerably underestimates the economic scales in realterms for those countries. By taking into account the international price differentials, PPP rectifies thetrade sector bias, and in turn the relative size of economies can be more adequately measured.8

20

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

APO20

40

Japan

20

India

East Asia

40
1970 1975 1980 1985 1990 1995 2000 2005 201001970 1975 1980 1985 1990 1995 2000 2005 2010

Figure 4 Regional GDP of Asia and the EU,
Relative to the US, 1970–2014

Figure 5 GDP of China, India, and Japan,
Relative to the US, 1970–2014

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.

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 159%, instead of 43%, larger than the US economy in 2014, having overtaken it in 1974
(Figure 4).9 East Asia (China, the ROC, Hong Kong, Japan, Korea, and Mongolia) caught up with the US
in 2006 from a low base of 46% 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 2014. 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 2014 was 373% or about four times that of Japan, comparedwith 221% when exchange rates are used in Table 1. Considering that the Chinese economy was only
26% that of Japan and 58% that of India in 1970, represents remarkable growth. China overtook Japanafter 1999 to become the leading economy in Asia as shown in Figure 5.10 On this measure, Figure 5
also demonstrates that Chinese GDP overtook the US as the world’s largest economy in 2013–2014,
although it was only 8% that of the US in 1970. The level and the timing to overcome should not betaken as precise numbers,11 but they may provide a good basis for assessing the relative production
size of these two economies. For the first time in more than 140 years, China comes back as the largest
producer in the world.

8: 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.
9: 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.
10: 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.
11: BBC News: Is China's economy really the largest in the world?, 16 December 2014.

21©2016 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 147% in 2014, surpassing Japan and replacing it as the second largest economy

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 ofmacroeconomic 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 lastyear’s 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.

©2016 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 >22

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 inmind these circumstances.

3
in Asia in 2009. In 2014, the total GDP of thethree countries, which are counted as the largest economies in Asia, was larger than the US
economy by 77%.

US=10050
40South Asia
ASEAN

30

20
ASEAN6
Figure 6 shows the rapid expansion of therelative size of the South Asian economy (conGCC
10
sisting of Bangladesh, Bhutan, India, Nepal,
CLMV
Pakistan, and Sri Lanka), 81% of which was ac01970 1975 1980 1985 1990 1995 2000 2005 2010
counted for by India in 2014. The ASEAN also
showed vigor in their catch-up effort. They were
Figure 6 Regional GDP of South Asia, ASEAN,
on par with the South Asian economy in 1996–
CLMV, and GCC, Relative to the US, 1970–2014
1997 before the setback caused by the Asian fi_Indices of GDP at constant market prices, using 2011
nancial crisis of 1997–1998 took hold, setting
PPP
them on a lower growth path, once again openSources: Official national accounts in each country, including auing up a divergence. In contrast, the progress ofthor adjustments.
GCC12 countries lagged for more than two decades. Only in the past decade has it picked up
and brought the relative size of the country group back to its previous peak of the early 1980s.13

Performance of each country is also transformed when economic growth is used as a yardstick. Table
3 presents cross-country comparisons of real GDP growth in Asia since 1990. The ranking varies fromperiod to period and the economic giants no longer take precedence in the ranking. In fact, small
developing Asian countries, like Qatar, Myanmar, Cambodia, Vietnam, the Lao PDR, and Mongolia, areequally capable of exhibiting exuberant growth.14 As labor costs are edging up in China (see Box 5, p.
67), 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 7.6% on average per year compared with 4.9% managed by the
ASEAN6 in the period 1990–2014.

12: 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 2012, thesecountries account for about 34% of the world’s proven crude oil reserves and possess at least 21% of the proven global natural
gas reserves (GCC Secretariat General, 2014).
13: 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.
14: See footnote 14 in Section 3.2 and footnote 25 in Section 3.3 for the reliability of the data in Myanmar.

23©2016 Asian Productivity Organization

At the other end of the table, Japan consistently has been struggling at the bottom over the past two
decades (1990–2014), with an average growth of 0.9% per year, compared with Asia30’s 5.5% andEU15’s 1.5%. 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 2010–2014_Average annual growth rate of GDP at constant market prices
1990–1995

1995–2000

China
Malaysia
KuwaitSingapore
VietnamThailandKorea
Indonesia
ROC
Cambodia
Lao PDR
Oman
Myanmar
PakistanBahrain
Sri Lanka
Hong Kong
Bangladesh
India
Nepal
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
7.6
7.2
6.6
6.0
5.7
5.7
5.5
5.3
5.3
5.2
5.0
5.0
4.9
3.7
3.6
3.4
3.1
2.8
2.8
2.7
2.4
1.4
−2.8

Australia           Turkey              

3.2
3.2

4.3
5.7
5.5
5.5
5.1
7.3
7.3
7.4
3.8
2.6
1.6

Qatar
China
MyanmarVietnam
Cambodia
UAE
Lao PDR
ROC
BhutanIndia
Singapore
Korea
Bangladesh
Malaysia
Sri Lanka
Nepal
Bahrain
Iran
Pakistan
Philippines
Oman
Mongolia
Hong Kong
Saudi Arabia
KuwaitFiji
BruneiJapan
Indonesia
Thailand(regrouped)
APO20               Asia24             Asia30
East Asia           South Asia          ASEAN               ASEAN6
CLMV
GCC                 (reference)
US                  EU15                EU28               Australia           Turkey              2000–2005
10.6
8.3
8.0
7.3
7.0
6.3
6.0
5.8
5.7
5.7
5.5
5.3
5.1
4.9
4.9
4.8
4.2
4.1
4.0
3.9
3.7
2.7
2.6
2.6
2.1
2.0
1.3
0.9
0.8
0.7
3.1
4.3
4.3
4.5
5.4
2.4
2.0
7.4
3.6
4.2
2.9
2.9
3.8
4.1

Myanmar
China
Cambodia
Qatar
VietnamBhutanKuwait
Iran
India
Mongolia
Lao PDR
BahrainPakistan
UAE
Thailand
Malaysia
BangladeshIndonesia
Singapore
Korea
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
12.1
9.4
9.0
8.0
8.0
7.6
7.2
6.9
6.5
6.3
6.2
5.9
5.9
5.4
5.3
5.2
5.0
4.9
4.8
4.6
4.5
4.1
4.0
4.0
4.0
3.1
2.1
2.0
1.2
1.0
4.3
5.8
5.7
5.7
6.3
5.3
4.9
9.0
4.6
2.5
1.8
1.9
3.4
4.5

Qatar
Myanmar
China
BhutanLao PDR
India
Cambodia
Singapore
Mongolia
Indonesia
Sri Lanka
VietnamBangladesh
Oman
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              2010–2014
16.6
10.7
10.7
9.1
7.8
7.8
6.5
6.5
6.4
6.2
6.2
6.2
5.9
5.7
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
0.3
4.5
6.7
6.5
6.9
7.1
5.6
5.3
7.5
3.7
0.8
0.7
0.9
2.7
3.2

Mongolia
China
Lao PDR
Qatar
Myanmar
Sri Lanka
Cambodia
Bangladesh
India
Philippines
VietnamIndonesia
Saudi Arabia
UAE
Malaysia
BhutanSingapore
Nepal
Bahrain
KuwaitOman
PakistanFiji
Hong Kong
Korea
Thailand
ROC
Japan
Iran
Brunei(regrouped)
APO20               Asia24             Asia30
East Asia           South Asia          ASEAN               ASEAN6
CLMV
GCC                 (reference)
US                  EU15                EU28               Australia           Turkey              1990–2014
11.5
7.8
7.7
7.6
7.4
7.0
7.0
6.1
5.7
5.7
5.7
5.5
5.3
5.3
5.2
5.1
4.4
4.3
3.8
3.8
3.7
3.6
3.5
3.0
3.0
3.0
3.0
0.7
0.1
0.0
3.7
5.4
5.4
5.7
5.5
5.1
4.9
6.3
5.3
1.9
0.5
0.7
2.7
4.4

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

9.6
9.1
8.8
7.2
7.1
6.7
6.2
6.2
5.9
5.9
5.4
5.4
5.1
5.0
5.0
4.9
4.7
4.6
4.6
4.6
4.3
4.3
4.2
4.1
4.0
3.8
3.4
2.1
1.5
0.9
4.0
5.6
5.5
5.7
5.9
5.2
4.9
7.6
4.2
2.4
1.5
1.6
3.2
3.8

©2016 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 5.4% on average per annum, compared with 1.9% in the US and
0.5% in the EU15 in the period 2010–2014.
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 39% and 14% of regional growth, respectively, in24

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
Qatar
Brunei
Myanmar
Nepal
BahrainMongolia
Cambodia
Lao PDR
Fiji
BhutanKuwait−10

1990–2000
29.2

16.1
10.7
7.5
7.0
5.1
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
0.3
0.2
0.2
0.2
0.2
0.1
0.1
0.1
0.0
0.0

−0.7
0

10

20

30 %

China
India
Japan
Korea
Indonesia
ROC
Iran
ThailandSaudi Arabia
Malaysia
Pakistan
UAE
Singapore
VietnamPhilippines
Bangladesh
Hong Kong
KuwaitOman
Sri Lanka
Qatar
Myanmar
Nepal
BahrainCambodia
BruneiLao PDR
Fiji
BhutanMongolia−10

2000–2014
38.8

14.1
6.4
6.3
5.9
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
0.4
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
Korea
Iran
Saudi Arabia
Japan
ThailandROC
Pakistan
Malaysia
Philippines
Vietnam
UAE
Bangladesh
Qatar
Singapore
MyanmarHong Kong
Kuwait
Sri Lanka
Oman
Cambodia
Bahrain
Nepal
Mongolia
Lao PDR
Brunei
Bhutan
Fiji

50.6
16.8
5.5
3.1
2.8
2.6
2.1
1.9
1.7
1.7
1.5
1.3
1.2
1.2
1.0
0.9
0.9
0.8
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

10

3

20

30

40

50 %

Figure 7 Country Contributions to Regional GDP Growth, 1970–1990, 1990–2000, and
2000–2014_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 periods for the Lao PDR and Cambodia are 1981 and 1987, respectively.

the 1990s. In the period 2000–2014, the growth in China and India accounts for more than two-thirdsof regional growth (51% and 17%, respectively).15 Indonesia became the third engine of Asian growth(5.5%), followed by Korea (3.1%).

15: The growth in Chinese manufacturing sector explains about one-third of the China’s contribution to regional growth (17 percentage points of 51%) in the period 2000–2014. See Figure 86 in Section 6.2 (p. 116) for the industry origins of regional growth.

25©2016 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 will be substantially less volatile. Figures 8 and 9 compare the correlation coefficients of growth rates among countries in the 1990s and the period from 2000 to 2014,
respectively. Countries are grouped by region. Overall, the fortunes of the reference countries havebecome 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 the East Asian
countries (Group 1) has been strengthened over time and their correlation with the US, the EU15, andthe South Asian countries (Group 2) has strengthened as well. Although the inter-regional correlationin the Southeast Asian countries (Group 3) is stable, their correlation with the US and the EU15 hasgrown much stronger. Therefore, comparisons of the correlation coefficients of growth between thetwo periods lend support to an increase, not a decrease, in business cycle synchronicity.

3 Economic Growth

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

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

US

TUR

EU15

AUS

UAE

Group 5
SAU

QAT

OMN

KWT

BHR

VIE

THA

SIN

Group 4
PHL

MYA

MAL

LAO

IDN

FIJ

CAM

BRN

SRI

PAK

Group 3

NEP

IRN

IND

BTN

BAN

ROC

Group 2
MGL

KOR

JPN

HKG

CHN

Group 1

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

If less than −0.55

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

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

3.2 Catching Up in Per Capita GDP©2016 Asian Productivity Organization

Performance comparisons based on the whole-economy GDP do not take into account the population and can in turn exaggerate the wellbeing of countries with large populations. Asia is the mostpopulous region in the world. In 2014, the population of Asia accounted for 60% of the world’s population (56% for Asia30), with China and India alone accounting for more than one-third (Figure 10). In
addition, there is a significant difference in the population among Asian economies, as Table 4 shows.
Six countries’ populations were over 100 million in 2014 (in addition the Philippine population reached
100 million in 2015), but the populations are less than 20 million in 14 economies of the Asia30. Basedon per capita GDP, which adjusts for the differences in population, China and India, two rising giantsin the Asian economy, remain substantially less well-off in light of the US standard. Conversely, theAsian Tigers proliferate.
Table 5 presents cross-country comparisons of per capita current-price GDP, using exchange rates asconversion rates.16 However, given the volatile nature of exchange rates, snapshot comparisons as
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 levelin the late 1980s and peaked in 1995, reflecting the strong yen (Figure 11). Figure 12 shows comparisons26

3.2 Catching Up in Per Capita GDP

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

US

TUR

EU15

AUS

UAE

Group 5
SAU

QAT

OMN

KWT

BHR

VIE

THA

SIN

Group 4
PHL

MYA

MAL

LAO

IDN

FIJ

CAM

BRN

SRI

PAK

Group 3

NEP

IRN

IND

BTN

BAN

ROC

Group 2
MGL

KOR

JPN

HKG

CHN

Group 13

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

1.0
0.7
0.2
0.6
0.3
0.5

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

If less than −0.55

Figure 9 Correlation of GDP Growth, 2000–2014_Correlation of GDP growth at constant market prices

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

2014

Others
29 %

EU28
7%

EU15
6 % US
4 % Other Asia
4%

Asia
60 %
APO20
35 %
Asia3056 %Figure 10 Share of Asian Population in
the World, 2014
Source: IMF, World Economic Outlook Database, April 2016.

16: In Myanmar the first census in three decades was conducted between March 30, 2014 and April 10, 2014. This showed that the
total population was 51 million, which was considerably below the official estimate of 61 million. Reflecting this revision, the percapita GDP is upwardly revised, compared to the results in the Databook 2014.

27©2016 Asian Productivity Organization

among the Asian Tigers. Singapore and Hong Kong
have been moving closely with one another for three
and a half decades until the mid-2000s, when Singapore overtook Hong Kong.17 Hong Kong’s per capita
GDP peaked in 1997, the year when Hong Kong was
returned to China, and subsequently plummeted until
2004. Singapore followed a similar path to that ofHong Kong – peaking in 1996, and falling to an alltime low in 2002 before the surge from the late 2000s.
The ROC and Korea moved together but at a lower
level than Singapore and Hong Kong. In Asia, Japanand Singapore are the two countries that have income
levels almost equivalent to the US. However, this view3 Economic Growth

Table 4 Population, 1970, 1980, 1990, 2000, 2010, and 2014
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
UAE0.25 0.0
Brunei0.13 0.0(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
ASEAN
279.5 13.9
ASEAN6200.3 9.9
CLVM
79.3 3.9
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
UAE1.04 0.0
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
ASEAN
352.2 14.3
ASEAN6256.9 10.4
CLVM
95.3 3.9
14.0 0.6
GCC(reference)
227.2 9.2
US
357.3 14.5
EU15
EU28
461.6 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
Singapore
3.05
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
UAE1.77 0.1
Brunei0.25 0.0(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
ASEAN
435.2 14.6
ASEAN6316.0 10.6
CLVM
119.2 4.0
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
Singapore
4.03
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
ASEAN
511.6 14.9
ASEAN6371.2 10.8
CLVM
140.3 4.1
29.9 0.9
GCC(reference)
282.2 8.2
US
377.6 11.0
EU15486.8 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
49.4
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
Singapore
5.08
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
UAE8.26 0.2
Brunei0.39 0.0(regrouped)
2421.2 62.8
APO20
3812.9 98.8
Asia243857.8 100.0
Asia30
East Asia 1551.3 40.2
South Asia 1599.5 41.5
ASEAN
586.8 15.2
ASEAN6430.0 11.1
CLVM
156.9 4.1
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
Turkey2014
1367.8China
1295.3India
250.3
Indonesia
188.7
Pakistan
Bangladesh 155.9
127.1
Japan
Philippines
98.4
90.7
Vietnam
78.1
Iran
66.9
Thailand
51.4
Myanmar
50.6
Korea
30.6
Malaysia
27.5
Nepal
23.4
ROC
Sri Lanka
20.8
14.9
Cambodia
7.24
Hong Kong
6.73
Lao PDR
Singapore
5.47
2.96
Mongolia
0.89
Fiji
0.75
Bhutan

(%)

34.1
32.3
6.2
4.7
3.9
3.2
2.5
2.3
1.9
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

1.31 0.0
Bahrain3.41 0.1
Kuwait
4.18 0.1
Oman
2.09 0.1
Qatar
Saudi Arabia 30.9 0.8
UAE9.01 0.2
Brunei0.41 0.0(regrouped)
2542.5 63.3
APO20
3962.9 98.7
Asia244013.8 100.0
Asia30
East Asia 1579.1 39.3
South Asia 1688.8 42.1
ASEAN
615.9 15.3
ASEAN6452.1 11.3
CLVM
163.8 4.1
50.9 1.3
GCC(reference)
318.9 7.9
US402.8 10.0
EU15
506.9 12.6
EU28
23.5 0.6
Australia
77.7 1.9
TurkeyUnit: Millions of persons.
Sources: Population census and other official data in each country, including author interpolations.

©2016 Asian Productivity Organizationis considerably revised if focusing on production or real income per capita, using PPP as the conversion rates, as shown in Table 6.

17: 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%.

28

3.2 Catching Up in Per Capita GDP

Table 5 Per Capita GDP using Exchange Rate, 1970, 1980, 1990, 2000, 2010, and 2014
_GDP at current market prices per person, using annual average exchange rate
1970

1980

(%)

2.00 100.0
0.96 48.2
0.93 46.3
0.43 21.4
0.40 19.8
0.39 19.6
0.36 17.8
0.28 14.0
0.23 11.4
0.21 10.6
0.20 10.1
0.18
9.3
0.17
8.4
0.14
6.9
0.12
5.9
0.11
5.7
0.11
5.6
0.10
4.9
0.10
4.9
0.09
4.7
0.09
4.3
0.03
1.4

Japan
Hong Kong
Singapore
Iran
ROC
Fiji
Malaysia
Korea
ThailandPhilippines
Indonesia
BhutanChina
Sri Lanka
Pakistan
Mongolia
India
Bangladesh
Myanmar
Nepal
Cambodia
VietnamBahrain
KuwaitOman  
Qatar   Saudi Arabia
UAE  
Brunei(regrouped)
APO20
Asia24  
Asia30
East Asia
South Asia
ASEAN
ASEAN6
CLVM
GCC(reference)USEU15

1.88 94.3
4.00 200.4
0.40 19.8
4.97 248.6
0.92 46.2
4.28 214.4
1.43 71.5

5.25 262.6
3.65 182.5

Bahrain
10.30
Kuwait
21.82
Oman
5.79
35.31
Qatar
Saudi Arabia         16.67
UAE
42.28
26.73
Brunei  (regrouped)
1.23
APO20
0.85
Asia24
0.95
Asia301.33
East Asia
South Asia
0.27
0.56
ASEAN
0.73
ASEAN6
0.08
CLVM
18.38
GCC
(reference)
12.60
US
9.29
EU15  

Australia
Turkey3.57 178.8
0.67 33.7

Australia  
Turkey

0.31
0.23
0.23
0.32
0.12
0.12
0.15
0.06
1.36

15.6
11.3
11.5
16.2
6.2
6.2
7.5
3.0
67.9

(%)

9.45 100.0
5.70 60.3
5.00 52.9
2.51 26.5
2.37 25.0
1.92 20.3
1.78 18.8
1.70 18.0
0.74 7.9
0.69 7.3
0.54 5.7
0.34 3.6
0.31 3.3
0.29 3.1
0.29 3.1
0.29 3.0
0.27 2.9
0.22 2.3
0.19 2.0
0.18 1.9
0.11 1.2
0.02 0.2

Japan
Hong Kong
Singapore
Fiji
Iran
ROC
Malaysia
Korea
BhutanThailandSri Lanka
Philippines
Pakistan
Bangladesh
Cambodia
India
China
Myanmar
Nepal
Mongolia
Indonesia
Vietnam

108.9
230.9
61.3
373.6
176.4
447.3
282.8
13.0
9.0
10.0
14.0
2.8
5.9
7.7
0.9
194.4
133.3
98.3

11.78 124.6
2.03 21.5

1990
Japan
Hong Kong
Singapore
ROC
Korea
MalaysiaFiji
IranThailandPhilippines
Mongolia
Indonesia
BhutanSri Lanka
Pakistan
India
China
Bangladesh
Nepal
Lao PDR
Cambodia
MyanmarVietnam(%)

25.74 100.0
13.49 52.4
12.77 49.6
8.17 31.7
6.52 25.3
2.50 9.7
1.86 7.2
1.72 6.7
1.63 6.3
0.77 3.0
0.77 3.0
0.71 2.8
0.58 2.2
0.49 1.9
0.39 1.5
0.38 1.5
0.34 1.3
0.29 1.1
0.25 1.0
0.21 0.8
0.20 0.8
0.13 0.5
0.10 0.4

Bahrain              9.25 35.9
Kuwait               9.10 35.3
Oman                 7.20 28.0
Qatar                17.71 68.8
Saudi Arabia         7.19 27.9
UAE                  28.94 112.4
Brunei               13.43 52.2
(regrouped)
APO20                2.59 10.0
Asia24             1.69 6.6
Asia30             1.74 6.8
East Asia            3.06 11.9
South Asia           0.38 1.5
ASEAN                0.84 3.3
1.11 4.3
ASEAN6
0.12 0.5
CLVM
GCC                  9.30 36.1
(reference)
US                   23.95 93.1
EU15                 17.44 67.7
Australia            18.97
Turkey               3.55

73.7
13.8

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

38.50 100.0
25.76 66.9
23.79 61.8
14.88 38.6
11.95 31.0
4.04 10.5
2.11 5.5
2.09 5.4
1.72 4.5
1.06 2.8
0.96 2.5
0.89 2.3
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
0.16 0.4

Bahrain              13.18
Kuwait               20.61
Oman                 8.21
Qatar                29.33
Saudi Arabia         8.89
UAE                  35.33
Brunei               17.76(regrouped)
APO20                3.49
Asia24             2.50
Asia30             2.59
East Asia            4.87
South Asia           0.46
ASEAN                1.21
1.54
ASEAN60.32
CLVM
GCC                  12.73(reference)
US                   36.45
EU15                 26.21
EU28             22.61
Australia            21.49
Turkey               3.96

34.2
53.5
21.3
76.2
23.1
91.8
46.1
9.1
6.5
6.7
12.6
1.2
3.1
4.0
0.8
33.1
94.7
68.1
58.7
55.8
10.3

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

(%)

46.57 100.0
44.48 95.5
32.55 69.9
22.15 47.6
19.26 41.4
8.92 19.2
6.42 13.8
5.18 11.1
4.58 9.8
3.68 7.9
3.18 6.8
2.61 5.6
2.41 5.2
2.28 4.9
2.16 4.6
1.36 2.9
1.35 2.9
1.11 2.4
1.01 2.2
0.84 1.8
0.81 1.7
0.78 1.7
0.72 1.5

Bahrain              20.84 44.7
Kuwait               40.68 87.4
Oman                 21.46 46.1
Qatar                75.14 161.3
Saudi Arabia         18.92 40.6
UAE                  35.57 76.4
Brunei               35.45 76.1
(regrouped)
APO20                4.92 10.6
Asia24             4.75 10.2
Asia30             4.99 10.7
East Asia            8.78 18.8
South Asia           1.27 2.7
ASEAN                3.37 7.2
4.19 9.0
ASEAN6
1.13 2.4
CLVM
GCC                  25.72 55.2
(reference)
US                   48.37 103.9
EU15                 36.73 78.9
EU28             33.34 71.6
Australia            58.70 126.0
Turkey               10.04 21.6

2014
Singapore
Hong Kong
Japan
Korea
ROC
Malaysia
China
ThailandIran
Fiji
Mongolia
Sri Lanka
Indonesia
Philippines
BhutanVietnamLao PDR
India
Pakistan
MyanmarCambodia
Bangladesh
Nepal

(%)

56.01 100.0
40.22 71.8
37.56 67.1
27.90 49.8
22.62 40.4
11.05 19.7
7.72 13.8
6.10 10.9
5.85 10.4
5.16 9.2
4.15 7.4
3.64 6.5
3.56 6.4
2.89 5.2
2.65 4.7
2.08 3.7
1.78 3.2
1.56 2.8
1.32 2.4
1.31 2.3
1.14 2.0
1.11 2.0
0.79 1.4

3

Bahrain              25.76 46.0
Kuwait               49.08 87.6
Oman                 19.90 35.5
Qatar                103.55 184.9
Saudi Arabia         24.68 44.1
UAE                  45.72 81.6
Brunei               42.58 76.0
(regrouped)
APO20                4.90 8.8
Asia24             5.83 10.4
Asia30             6.18 11.0
East Asia            11.13 19.9
South Asia           1.51 2.7
ASEAN                4.11 7.3
4.97 8.9
ASEAN61.74 3.1
CLVM
GCC                  32.91 58.8
(reference)
US                   54.40 97.1
EU15                 40.16 71.7
EU28             37.00 66.1
Australia            61.84 110.4
Turkey               10.42 18.6

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 was 61% of the US, quite a distance aheadof 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.18 In
recent years, Japan’s level has stabilized to around 70–73% of the US (Figure 13).

29

©2016 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

120

160
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 201001970 1975 1980 1985 1990 1995 2000 2005 2010

Figure 11 Per Capita GDP using Exchange
Rate of Japan and Australia, Relative to theUS, 1970–2014

Figure 12 Per Capita GDP using Exchange
Rate of the Asian Tigers, Relative to the US,
1970–2014

annual average exchange rate, relative to the US

annual average exchange rate, relative to the US

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

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

_GDP at current market prices per person, using

Korea_GDP at current market prices per person, using

Japan’s per capita GDP was the highest among Asian countries until it was overtaken by Singapore in
1980.19 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 1993.20 In
2014, Singapore had a per capita GDP which was 54% 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 71% of the US, or around 46% of the group leader(Singapore), is similar to that of the EU15. The ROC and Korea trail behind the other two Asian Tigers
at 85% and 65% of the US, respectively.

©2016 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 24.8% and 10.3% of the US in
2014, respectively (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,21 indicating significant opportunity
for catch-up.

18: 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.
19: 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.
20: 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 92.6% of GDP (see Figure 91 in Section 7.1, p. 129). On the other hand, the US GNI never goes outside +1.6% of GDP.
However, Singapore’s lead of 54% over the US in 2014 was large enough that their relative positions would be independent of
whether GNI or GDP was used.

30

3.2 Catching Up in Per Capita GDP

Table 6 Per Capita GDP, 1970, 1980, 1990, 2000, 2010, and 2014

_GDP at constant market prices per person, using 2011 PPP, reference year 2014
1970
Japan
Singapore
IranHong Kong
Fiji
Malaysia
ROC
Philippines
Mongolia
ThailandKorea
Sri Lanka
Indonesia
PakistanIndia
BhutanBangladesh
Nepal
Vietnam
MyanmarChina(%)

15.2 100.0
10.6 69.8
10.2 67.1
9.0 59.5
4.5 29.4
4.2 27.7
3.8 25.1
3.1 20.5
3.1 20.3
2.8 18.5
2.6 17.3
2.1 13.7
1.7 11.2
1.5 10.1
1.3
8.6
1.2
8.0
1.2
7.8
1.1
7.4
1.0
6.5
0.7
4.5
0.5
3.3

Bahrain              37.6 248.1
Kuwait               199.9 1318.9
Oman                 15.1 100.0
Qatar                168.2 1109.7
Saudi Arabia         49.7 327.9
UAE                  43.3 285.6
Brunei               93.7 618.0(regrouped)
3.2 21.2
APO20               2.1 13.9
Asia24             Asia30              2.4 15.5
2.4 15.7
East Asia           South Asia           1.49.32.1 14.2
ASEAN               2.6 17.5
ASEAN60.9
6.2
CLVM
GCC                  66.9 441.3(reference)
US                   25.0 165.1
EU15                 18.5 122.3
Australia           Turkey              22.9 151.4
6.9 45.3

1980
SingaporeJapan
Hong Kong
Iran
ROC
Malaysia
Fiji
Korea
Philippines
Mongolia
ThailandIndonesia
Sri Lanka
PakistanIndia
BhutanNepal
Bangladesh
Vietnam
Myanmar
China

Bahrain              47.8
Kuwait               87.9
Oman                 26.5
Qatar                142.5
Saudi Arabia         77.4
UAE                  198.3
Brunei               157.0(regrouped)
4.1
APO20               
2.8Asia24             Asia30              
3.3
3.4East Asia           South Asia           1.53.3ASEAN               
4.2ASEAN6
1.1
CLVM
GCC                  87.2
(reference)
US                   30.9
EU15                 24.3
Australia           Turkey              

(%)

21.7 100.0
21.3 98.6
16.7 77.0
10.4 47.9
8.5 39.2
7.3 33.7
5.8 26.9
5.3 24.7
4.2 19.5
4.2 19.4
4.1 19.0
3.0 13.8
2.7 12.3
1.8 8.3
1.4 6.4
1.3 5.9
1.2 5.4
1.1 5.0
1.0 4.7
0.9 4.1
0.8 3.5

220.7
405.9
122.6
658.0
357.2
916.0
725.3
18.9
12.9
15.0
15.5
6.9
15.4
19.3
4.9
402.9
142.5
112.0

26.4 121.8
8.1 37.6

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

36.1 100.0
32.1 89.0
28.4 78.6
16.4 45.3
12.2 33.9
10.2 28.3
9.5 26.3
7.2 19.9
6.3 17.4
5.7 15.7
4.5 12.6
4.1 11.3
3.5 9.7
2.7 7.4
2.5 7.0
1.9 5.3
1.7 4.7
1.6 4.4
1.5 4.1
1.4 3.9
1.3 3.5
1.0 2.8
0.8 2.2

Bahrain              38.2
Kuwait               43.4
Oman                 39.6
Qatar                87.7
Saudi Arabia         44.1
UAE                  119.3
Brunei               88.5
(regrouped)
5.5
APO20               
4.0Asia24             Asia30              
4.4
5.3East Asia           South Asia           2.04.6ASEAN               
5.8ASEAN6
1.3
CLVM
GCC                  51.7
(reference)
US                   39.0
EU15                 30.2

105.8
120.2
109.7
242.9
122.2
330.5
245.2

30.6
10.8

84.7
29.8

Australia           Turkey              

15.3
11.2
12.2
14.7
5.6
12.7
16.2
3.5
143.3
108.1
83.8

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

54.3 100.0
35.8 65.9
35.1 64.6
28.7 52.8
21.8 40.2
16.0 29.4
12.0 22.1
10.1 18.5
7.3 13.4
6.0 11.0
5.2 9.6
4.9 9.0
4.5 8.3
3.9 7.2
3.6 6.6
3.5 6.4
2.7 4.9
2.6 4.8
2.5 4.5
1.9 3.5
1.8 3.4
1.5 2.7
1.4 2.6

Bahrain              47.5
Kuwait               85.8
Oman                 42.9
Qatar                114.8
Saudi Arabia         44.2
UAE                  115.9
Brunei               85.9
(regrouped)
6.7
APO20               
5.7Asia24             Asia30              
6.1
7.9East Asia           South Asia           2.86.2ASEAN               
7.7ASEAN62.2
CLVM
GCC                  56.5
(reference)
US                   48.4
EU15                 36.7
EU28               32.3
Australia            38.8
Turkey               12.9

87.4
158.0
79.1
211.4
81.3
213.3
158.1
12.3
10.5
11.3
14.5
5.1
11.4
14.2
4.1
104.0
89.1
67.6
59.5
71.5
23.7

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

(%)

75.8 100.0
50.6 66.8
41.4 54.7
37.6 49.6
32.0 42.3
21.8 28.8
18.7 24.7
14.5 19.1
10.1 13.3
9.1 12.0
8.0 10.6
8.0 10.6
7.8 10.3
7.1 9.3
6.0 7.9
4.8 6.3
4.7 6.2
4.4 5.9
4.2 5.5
4.0 5.3
2.8 3.6
2.7 3.5
2.4 3.2

Bahrain              43.3 57.1
Kuwait               83.4 110.1
Oman                 52.0 68.6
Qatar                142.3 187.8
Saudi Arabia         46.9 61.9
UAE                  62.3 82.3
Brunei               82.6 109.1(regrouped)
8.9 11.7
APO20               9.3 12.2
Asia24             Asia30              9.8 13.0
East Asia            13.8 18.2
South Asia           4.5 6.0
9.2 12.2
ASEAN               11.0 14.5
ASEAN6
4.3 5.7
CLVM
GCC                  56.3 74.3
(reference)
US                   51.9 68.6
EU15                 39.5 52.1
EU28               35.9 47.4
Australia            45.5 60.0
Turkey               17.4 22.9

2014
Singapore
Hong Kong
ROC
Japan
Korea
Malaysia
Iran
Thailand
China
Mongolia
Indonesia
Sri Lanka
Fiji
BhutanPhilippines
VietnamIndia
Lao PDR
Myanmar
Pakistan
Cambodia
Bangladesh
Nepal

(%)

83.7 100.0
55.4 66.2
46.1 55.1
38.9 46.5
35.3 42.2
25.1 30.0
17.9 21.4
16.1 19.2
13.5 16.1
11.8 14.1
10.7 12.8
10.6 12.6
8.7 10.4
8.1 9.7
7.0 8.4
5.7 6.8
5.6 6.7
5.2 6.3
5.2 6.2
4.7 5.6
3.4 4.1
3.2 3.8
2.7 3.3

3

Bahrain              47.3 56.6
Kuwait               82.7 98.8
Oman                 40.0 47.8
Qatar                157.0 187.6
Saudi Arabia         52.7 63.0
UAE                  70.5 84.3
Brunei               77.6 92.8
(regrouped)
9.8 11.7
APO20               Asia24              11.0 13.1
Asia30               11.6 13.9
East Asia            16.9 20.2
South Asia           5.3 6.3
ASEAN                10.7 12.8
12.6 15.1
ASEAN6
5.3 6.4
CLVM
GCC                  61.0 72.9
(reference)
US                   54.4 65.0
EU15                 39.8 47.6
EU28               36.7 43.8
Australia            47.5 56.8
Turkey               19.7 23.5

Table 6 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

21: Per capita GDP may have underestimated the welfare of people in some countries. In the ROC, Hong Kong, and Japan, for example, GNI is consistently higher than GDP although the fluctuations are within +6%. The Philippines is the exception where thedivergence between GNI and GDP has been increasing and has become significant for the past two decades, and GNI was morethan 30% higher than GDP in the 2010s (See Figure 91 in Section 7.1, p. 129).

31©2016 Asian Productivity Organization

Unit: Thousands of US dollars (as of 2014)
Sources: Official national accounts in each country, including author adjustments.
Note: See Appendix 1 for the adjustments made to harmonize GDP coverage across countries.

3 Economic Growth

US=100 in each year

100
90
80

US

Australia
EU15

US=100 in each year

160
140

Singapore

120
100

US
Hong Kong

80
70
60

60
Japan

40

ROC

20
50
1970 1975 1980 1985 1990 1995 2000 2005 2010

Korea
01970 1975 1980 1985 1990 1995 2000 2005 2010

Figure 13 Per Capita GDP of Japan, the EU,
and Australia, Relative to the US, 1970–2014

Figure 14 Per Capita GDP of the Asian Tigers,
Relative to the US, 1970–2014

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.

©2016 Asian Productivity Organization

remarkably higher per capita GDP than those ofJapan and the US. For example, in 1970, Kuwait,
Qatar, and Brunei had a per capita GDP 13.2 times,
11.1 times, and 6.2 times that of Japan, respectively. However, the measurement of GDP as anindicator of production is misleading for thesecountries, as it erroneously includes proceedsfrom the liquidation of a natural resource stock
as part of the income flow. In other words, GDPoverestimates income from the oil-exporting
economies because it does not account for depletion of their natural resource assets. To givea rough indication of the extent of distortion, Figure 16 provides comparisons of per capita GDP
excluding production of the mining 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’slevel, 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

Figure 15 Per Capita GDP of China, India, and
ASEAN, Relative to the US, 1970–2014_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.

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 level. However, as their income levels approach those of the more advanced countries, their economic growth rates are expected to gradually decline over time.22

22: The OECD (2016b) 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.

32

3.2 Catching Up in Per Capita GDP

Figure 17 plots countries’ initial per capita
64.5
85.4
149.9
Qatar
42.6
41.1
83.7
BruneiGDP levels against their respective average
UAE
37.1
28.3
65.4
growth rates per year between 1970 (or the
Kuwait
36.1
52.0
88.0
initial year data first became available for the
Japan
35.0
3.1 38.2
country in question) and 2014. If the two variBahrain
34.5
12.3 46.7
ables have a correlation coefficient of –0.5
Saudi Arabia
26.2
25.7 51.9
Non-mining GDP(i.e., a negative relationship of medium
Oman 22.1
21.0 43.1
Mining GDP
strength), the higher the initial income level
Malaysia 20.1 3.4 23.5
becomes, the more slowly the average growth
Thousands of US dollars (as of 2014)
Iran 15.9 2.1 18.0
rate per year is expected to move. However,
0
30
60
90
120
150
this is not always true. Low-income countries
like Bangladesh, Nepal, the Philippines, andFigure 16 Per Capita Non-Mining GDP in OilFiji have failed to catch up, while Thailand andRich Countries and Japan, 2014
_GDP at constant market prices per person, using 2011
Malaysia could be expected to have grown
PPP, reference year 2014
even faster, given their initial income levels.
The Asian Tigers have enjoyed robust growth
Sources: Official national accounts in each country, including author
adjustments.
in the past four decades, but Korea and theROC, with their lower initial per capita GDP,
have sustained higher growth rates than Singapore and Hong Kong. Relative to the Asian Tigers, China appears to be at the start of the catch-up
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%; Group-L3,
from 8% to under 20%; and Group-L4, below 8%. Likewise, countries are also grouped according to
the speed of their catch-up with the US: Group-C1, at 3% per annum or above; Group-C2, from 1% to

5%Growth rate of per capita GDP during 1970–2014

Cambodia
MyanmarBhutanLao PDR

4

8=1970, =1990, =2014

Growth rate of per capita GDP during 1970–2014=1970, =1990, =2014

China
7

Indonesia

Vietnam

Korea

6

ROC

Sri Lanka
India

5

3

Singapore

Mongolia
Pakistan

Hong Kong

4

Malaysia Thailand

Bangladesh
2

Nepal

3

Philippines
Fiji

Turkey
2

Japan

EU15

1

Australia

Iran

1

US

Thousands of US dollars (as of 2014)

Thousands of US dollars (as of 2014)

0

0
0

2

4

6

8

10

12

Per capita GDP at constant prices

0

10

20

30

40

50

60

70

80

90

Per capita GDP at constant prices

Figure 17 Initial Level and Growth of Per Capita GDP, 1970–2014_Level and average annual growth rate of GDP at constant market prices, using 2011 PPP, reference year 2014
Sources: Official national accounts in each country, including author adjustments.
Note: The starting periods for the Lao PDR and Cambodia are 1981 and 1987, respectively.

33©2016 Asian Productivity Organization%

3 Economic Growthunder 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.

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,(L4)
From Table 7 one can see the initial
Nepal,
Cambodia, China Indonesia, Lao PDR,
< 8%
Myanmar, Vietnam
Pakistaneconomic 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 1970–2014.
lated diagonally from the bottom
The starting years for some countries are different due to data availability: Cambodia
left corner to top right corner. Of(1987), the Lao PDR (1981).
the Asia30 countries, five achieveda very fast catch-up (over 3% peryear on average) between the respective starting years of their data series and 2014. 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.23 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.

3.3 Sources of Per Capita GDP Gap©2016 Asian Productivity Organization

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.24 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 2014.25

23: Philippines shifted from Group-C4 to Group-C3 in this edition of the Databook, with a positive catch-up rate of 0.1% on average.
24: 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.
25: 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%

MyanmarBangladesh
CLMV
Vietnam
Nepal
China
Lao PDR
India
South Asia
BhutanPakistan
Sri Lanka
Asia24
Philippines
Asia30Indonesia
ASEAN
East Asia
APO20
Mongolia
ASEAN6Fiji
ThailandIran
Malaysia
TurkeyKorea
ROC
Hong Kong
EU15
Australia
Japan
Singapore

1990

3

20
4

3

0

4

2
−7
−8 −13−20−20 0−14 −9
−33
−46
−7
−49
−86 −56 −60

−20
−40
−60

−72 −86 −85 −82 −76 −78−84−76 −81 −90 −72 −63−93 −86−96 −93−94−89−99 −88 −81 −70−80−13 −21−7−10−21−21 −3 −13 −4 −8 −6−3 −7 −9 −13−2 −13 −4 −2 −7

−100

−12
−43
−23 −9
−18

2014

Nepal
Bangladesh
Cambodia
Pakistan
MyanmarLao PDRSouth Asia
CLMV
India
VietnamPhilippines
BhutanFiji
APO20
Sri Lanka
ASEAN
Indonesia
Asia24
Asia30
Mongolia
ASEAN6
China
East Asia
ThailandIran
TurkeyMalaysia
Korea
Japan
EU15
ROC
Australia
Hong Kong
Singapore

Most of the Asian countries display a huge per capita GDP gap
with the US. This is predominantly explained by theirrelative labor productivity performance. With the exceptionof the Asian Tigers, Japan, Iran,and Malaysia, all the other
Asian countries had labor productivity gaps of more than
50% against the US in 2014. At
the top end of performance,
estimates show Singapore was
20% above while Hong Kongwas 5% below the US laborproductivity level. In Singapore, its employment rate was
34 percentage points higher,
giving an overall per capita
GDP which was 54% higher
than the US in 2014. The laborproductivity gaps of the other
two Asian Tigers are still sizableagainst the US, at 16% 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

34

20
8 3

7

0

8

8

2

−60

−87

−74

−94

−95 −101 −99

−80
−100

−7

−18

−97

−98

−87

−81

−81

−78

−9

−9

−9

Employment rate

−75

−71

−82

−72

−13 −7 −8

−78

−78

−66

−77

−84

−75

6 20

1 2
−21−16−15 −5−33 −48 −34−41 −41−6−77

9 10 8

1

−20
−40

6 5

−81

−6
−34
−23

−13
−2 −2 −2

−2

Labor productivity

Per capita GDP

Figure 18 Labor Productivity and Employment Rate Gap
Relative to the US, 1990 and 2014

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

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

China’s improvement was the most impressive, achieving per capita GDP growth of 8.9% and 8.8% peryear on average in the two periods, respectively. Improvement in labor productivity explains almost
all of that growth. According to official statistics,27 Myanmar achieved a similar performance to China
in growth terms, with per capita GDP growth of 5.6% and 9.4% per year on average in the two periods.
26: 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.

35©2016 Asian Productivity Organization

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 1990–2000 and 2000–
2014, respectively.26 For most countries, labor productivity explains a larger share of per capita GDP
growth than employment. However, this should not lead us to underestimate the role of changes inthe employment rate. The employment rate contribution, relative to labor productivity, was also highly significant in countries such as, Bhutan (39%), Nepal (40%), Singapore (44%), and Pakistan (33%).

3 Economic Growth

1990–2000

%

10

0.0

8
6
4

0.5

0.6 0.4
1.3 1.5

8.9
5.6 5.2 5.2

20.7

1.6 0.2

0.1
1.3

4.3 4.0 3.8 3.8 3.9

0.2
1.1

3.9

2.7

2.6

3.5

0

0.0

0.0

0.1
0.4 0.2
0.2 0.3
0.5
0.3
0.0 0.3
3.7
1.3
3.4
3.4
3.3
3.3
3.0
1.0
2.5 2.7 2.5 2.8 2.1
2.1 1.9 1.9 1.6 2.4 1.1
2.2
1.3
1.1
0.4 1.2 −0.3
−0.2
−0.6
−0.3
−0.30.0 −0.1
−0.2 −0.1

0.5

−2

−1.1

Mongolia
Japan
GCC
Philippines
Fiji
Turkey
EU15
APO20
US
Hong Kong
Iran
Australia
Nepal
PakistanIndonesia
ASEAN6ASEAN
South Asia
Asia30ThailandIndia
Asia24
BhutanLao PDR
Bangladesh
East Asia
Sri Lanka
Singapore
Cambodia
Malaysia
CLMV
Myanmar
ROC
Korea
Vietnam
China

However, this growth was from avery low base – even in 2014,
Myanmar’s per capita GDP wasonly 38% of China’s (see Table 6).
Like China, Myanmar’s per capita GDP growth has been predominantly explained by laborproductivity. In both periods
Japan had a waning employment rate. With an aging population (see Box 2), this pattern may
well continue. The US also experienced a declining employment
rate in the recent period, which
was a drag on per capita GDP
growth. In contrast, falling laborproductivity was the drag in
GCC countries.

2000–2014

%

10
8

0.7

6
4

0.7 1.0
8.7 8.9

0.8

5.6 5.3 4.3 3.5 4.4 5.4 4.6 5.52
0

1.6 2.3 1.1

0.0

0.0

0.4 0.6

1.5
3.6 4.7 4.6

−0.2

0.0

4.6

0.6
0.4 0.8 0.7 0.2

3.8 3.4 3.3 3.1 2.6 2.7 3.1 2.4 2.5
0.0

2.7

0.3 0.4

0.1
0.7
0.4 0.1
1.0
2.6
2.5
2.7
0.0 1.7
1.7
1.5 1.5 1.1 1.1
1.4 0.8−0.6 0.6−0.1−1.2

0.8 0.6 0.5 1.4

GCC
EU15
Japan
US
Fiji
Australia
Pakistan
Nepal
APO20
Iran
TurkeySingapore
Hong Kong
Philippines
Malaysia
Thailand
ROC
Korea
ASEAN6ASEAN
BangladeshIndonesia
Asia30South Asia
Asia24
Sri LankaIndia
Lao PDR
East Asia
VietnamBhutanCambodia
CLMV
Mongolia
China
Myanmar−0.1
In the Muslim countries like Iran,−2
Turkey, and Pakistan, the employment rate is significantly
less than the US, further reinEmployment rate
Labor productivity
Per capita GDP
forcing the poor productivity
performances of these countries
Figure 19 Sources of Per Capita GDP Growth, 1990–2000(Figure 18). It is no coincidence
and 2000–2014
they are among the countries_Decomposition of average annual growth rate of per capita GDP atwith the lowest shares of female
constant market prices, using 2011 PPP
workers in employment, at 15%,
Sources: Official national accounts in each country, including author adjustments.
30% and 23%, respectively, as
Note: The starting period for Cambodia are 1993.
shown in Figure 20. In contrast, a
handful of countries such as
Cambodia, the Lao PDR, Vietnam, and Mongolia, had higher employment rates than the US, counteracting the negative impact of their productivity performances.

©2016 Asian Productivity Organization

All other things being equal, increasing employment and improving labor productivity could present
a policy trade-off in the short term, as they cannot be achieved simultaneously. If the policy target is
to increase employment, productivity may suffer in the short term as marginal and less-productive
workers are recruited, bringing down the average productivity performance. The huge labor

27: The author would caution readers as to the reliability and quality of Myanmar’s official statistics, which have been questioned (especially the estimate in a decade from 1999, based on our observations). Researchers have suggested that this is not consistent
with other variables closely correlated with GDP, such as energy use. Non-official estimates put GDP growth at less than half of theofficial estimates (see The Economist Intelligence Unit, 2010). However, the recent growth with an expansion of mining production (in particular, natural gas and jade) may not necessarily require an increase in energy consumption. In the current edition of
the Databook, the numbers reported in official statistics are presented. In order to improve the international comparability, however, we started a project to examine the national accounts in Myanmar as of March 2016 at APO. The past numbers are expected
to be revised in the next edition of the Databook.
28: Japan is the exception where the employment rate in 2014 was lower than that in 1970. This reflects, among other things, its aging population. US employment rates also indicate weakening in the recent period, with levels in 2014 lower than that in 1990 (i.e.,
48% compared with 51%).

36

3.3 Sources of Per Capita GDP Gap

productivity gap between Asia and the US –discussed in Chapter 5 – should be considered in
the context of the generally high employment rate
in Asia.
Figure 21 shows cross-country comparisons of employment rates in 2014, based on the labor statistics
of each country. Employment consists of employees,
own-account workers, and contributing family workers. Singapore and Myanmar lead the Asian groupwith employment rates of over 60%, around 15 and18 percentage points higher than the US and theEU15, respectively, in 2014. It is clear that employment rates have been rising in most Asian countries.28 The fastest catch-up countries (i.e., those inGroup C1 in Table 7) are also countries with the largest surge in employment rates over the past four
decades: China, Korea, Cambodia and the ROC. However, China seems to have exhausted its capacity
for further improvement as its employment ratechanged little between 1990 and 2014 at 56%.
Some of the countries in Group C2 also experienced
significant improvements in employment rates (forexample, Indonesia and Vietnam). While there
are exceptions, generally countries that have%65
failed to catch up also
tend to make less vigor60
ous improvements overthe period, and in turn55
continue to have lower
50
employment rates.

Cambodia (2008)
Lao PDR (2005)
Hong Kong (2014)
Vietnam (2014)
Canada (2014)
Mongolia (2014)
US (2014)
EU28 (2014)
Australia (2014)
Thailand (2014)
Bhutan (2014)
Singapore (2014)
China (2010)
ROC (2014)
Japan (2014)
Nepal (2011)
Korea (2014)
Brunei (2011)
Philippines (2014)
Malaysia (2014)
Indonesia (2014)
Sri Lanka (2014)
Kuwait (2011)
Fiji (2007)
Turkey (2014)
Bangladesh (2013)
India (2010)
Pakistan (2014)
Bahrain (2010)
UAE (2008)
Oman (2010)
Iran (2014)
Saudi Arabia (2014)
Qatar (2013)

51
50
49
49
48
47
47
46
46
46
45
45
45
44
43
43
42
41
40
38
38

3

34
33
32
30
29
26
23
21
19
17
15
13
13

0

10

20

30

40

50

60 %

Figure 20 Share of Female Employment_Ratio of female to total employment

Sources: Population census or labor survey in each country.

45
40
35
30
25
2014

1990

1970

20

_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.

37©2016 Asian Productivity Organization

Singapore
MyanmarCambodia
VietnamThailandChina
Lao PDR
Korea
Hong KongJapan
BhutanAustralia
Nepal
ROC
US
Brunei
Inodonesia
EU15
Malaysia
Sri Lanka
Philippines
Bangladesh
India
Mongolia
Fiji
TurkeyPakistan
Iran

Figure 21 Employment Rates, 1970, 1990, and 2014

3 Economic Growth

Population and Demographic Dividend

Box 2

According to the United Nations (UN) (2015), the world’s population is estimated to reach 7.3 billion in2014, of which Asian countries account for 60.0%. The region is by far the most populous in the world.
China and India account for 18.8% and 17.8% of the world’s population, respectively. It has been observedthat falling fertility rates and rising living standards go hand in hand, although the direction of causality isless certain. The evolution of the demographic structure implies dynamics in a society that are not captured by the overall population size or growth. As people’s economic behavior, aspirations, and needs varyat different stages of life, changes in a country’s age structure can have a significant impact on its economic growth via supply-side and demand-side impacts.
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 widespreadsocial 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 and reproductive health services, all have been contributing factors to this trend. Coupled with changes in the mortality
rate, such a trend can dramatically alter the age profile of a country’s population, bringing with it economic implications.
The growth rate of the world’s population has slowed from its peak of around 2.0% in the 1970s to today’s
1.2% per year. With falling fertility rates, the UN projects the world’s population growth rate will decelerate
to 0.54% per year by 2050 and further to 0.11% by 2100. Even so, the world population will still increase by
one-third from today’s 7.3 billion to 9.7 billion in 2050 and a further 12% 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 (10.8 billion by 2050 and 16.6 billion in 2100) or
lower (8.7 billion in 2050 and 7.3 billion in 2100). Figure B2.1 depicts this shift in the distribution of theworld population with the share from the more developed regions gradually declining from 17.2% in 2014
to 13.2% in 2050 and 11.4% in 2100, compared with 32.2% in 1950. Conversely, the share of the least developed countries is depicted as rising from today’s 12.8% to a projected 19.5% in 2050 and 28.2% in 2100, up
from 7.8% in 1950.

Billion

12

BillionLeast developed countries
Less developed regions, excluding
least developed countries
More developed regions

10©2016 Asian Productivity Organization

6.5

6.8

6.8

4

4.3
0.2

0

6
5.7

0.3

4

Oceania
Northern America
Latin America and the Caribbean
EuropeAfricaOther Asia
0.4
ASEAN
0.7
China
0.7
India
0.3

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

1.3

1.3

1.3

1950

1975

2000

2025

2050

2075

2100

0

0.5
0.3
0.5
0.4

1950

0.3
0.7
0.4
0.5
0.3
0.9

0.4
0.8
0.7

0.5
0.8
0.7

3.5

0.5
0.7
0.6

4.4

2.5

0.5
0.7

1.5

0.8

1.2

0.9
0.5

0.7

0.8

0.8

1.4

1.3

1.2

1.0

1.7

1.7

1.7

2050

2075

2100

1.3

0.6

1.1

1.5

1975

2000

2025

1.5

1.5

1.5
0.7

Figure B2.1 Distribution of the World’s Population in Different Regions, 1950–2100
Source: UN (Department of Economic and Social Affairs), World Population Prospects: The 2015 Revision.
continued on next page >38

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

2014

1970

2050
scale by 200 millions

1600
1400
1200
1000

3

800
600
400

scale by 50 millions

350
300
250
200
150
100
30

scale by 5 millions

25
20
15
10
5
0

Figure B2.2 Asian Countries’ Population Size and Projection,
1970, 2014, and 2050
Source: World Bank, World Development Indicators 2015.

18.9
19.3

25.2
24.6
23.1
23.4
26.0

Age 0–14

30

Japan
Australia
Hong Kong
US
Korea
ROC
Singapore
ThailandChina
Sri Lanka
TurkeyVietnam
Iran
Myanmar
Nepal
Fiji
India
Philippines
Indonesia
MyanmarBangladesh
BhutanPakistan
Cambodia
Mongolia
BruneiLao PDR
Saudi Arabia
Oman
Bahrain
Kuwait
Qatar
UAE

11.1

14.6
14.3
16.2
18.2
16.4

33.1
28.8
29.6
31.1
28.2
26.9
30.5
29.9
35.4
31.5
27.5
24.5
36.7
29.1
21.9
21.7
22.8

40 %

12.9

15.1
14.8

20

10

0

25.1

14.4
14.2
14.1
12.2
11.5
10.5
9.7
9.7
7.9
7.7
6.6
5.7
5.5
5.5
5.4
5.4
5.2
5.1
5.1
4.9
4.7
4.5
4.4
3.9
3.8
3.7
2.8
2.5
2.4
1.9
0.9
0.9

0

5

10

15

20

25 %

Age over 65

Figure B2.3 Proportion of the Dependent Population, 2014
Sources: Population census and official national accounts in each country.
continued on next page >39©2016 Asian Productivity Organization

Figure B2.3 shows the demographic make-up of countries in2014 (the population proportions of the under-15 and over65 age groups, which together
make up the dependent population). Ranking the countries by
the share of old-age population
filters the rich economies to the
top end. These economies also
have a relatively low share of theyoung-age group compared toless developed countries. Thissuggests that demographic transition tends to run parallel witheconomic progress, although
the direction of causation is notcertain. As countries move fromhigh to low mortality and fer­tility rates, the demographictransition produces a “boom”
generation that is larger thanthose immediately before andafter it. As this boom generation
gradually works through a nation’s age structure, it produces ademographic 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 54.2% in 2050
and 43.6% in 2100, while Africa’s
share will rise from today’s 15.9%
to 25.5% and 39.1%, respectively. Figure B2.2 shows the cur­rent population size of individual
Asian countries compared withthe 1970 level and its 2050 projection. As can be seen from thechart, China’s population is expected to more or less stabilize
around the current level. China
has socially engineered thechange with its one-child policy,
which has made its current population 300–400 million lower
than it would have been otherwise. In less than two decades,
India is projected to overtake
China as the most populous
country in the world.

3 Economic Growth> continued from previous page

Using demographic data since 1950
and UN projections up to 2100, Figures B2.4 and B2.5 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.

©2016 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
53 in Section 5.3, p. 78). 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.

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
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(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 B2.4 Demographic Dividend by Country, 1950–
2100
Source: UN (Department of Economic and Social Affairs), World Population Prospects: The 2015 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 B2.5 Demographic Dividend by Country Group,
1950–2100
Source: UN (Department of Economic and Social Affairs), World Population Prospects: The 2015 Revision.

404.1 Final Demand Compositions

4 Expenditure
GDP is measured by three approaches in national accounts: 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 the long-term trends in Asian countries, respectively. The expenditure-side decomposition
of GDP growth is 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.29 Over the past
four decades, the share of household consumption for mature economies has tended to be stable,
trending upward in recent years. It is more volatile and largely trends downward in economies undergoing rapid transformation, such as the Asian Tigers in the 1970s and 1980s, and India and China in
Table 8 Final Demand Shares in GDP, 1970, 1990, 2000, 2010, and 2014
_Share of final demands with respect to GDP at current market prices

APO20

Household consumption
Government consumption
Investment
Net exports
1970 1990 2000 2010 2014 1970 1990 2000 2010 2014 1970 1990 2000 2010 2014 1970 1990 2000 2010 201459.8 56.9 58.7 57.0 58.2 11.1 11.9 12.9 13.2 13.0 29.6 31.9 25.8 29.0 28.7 −0.5 −0.6
2.7
0.8
0.0

Asia24

59.5

55.6

55.4

48.8

49.1

11.1

12.2

13.9

13.0

13.2

29.9

32.2

28.0

36.2

36.6

−0.5

0.0

2.6

1.9

1.1

Asia30

56.8

55.0

54.3

48.0

48.1

11.5

13.4

14.4

13.2

13.6

28.7

30.7

27.2

35.7

36.0

3.0

0.8

4.0

3.1

2.3

East Asia

50.9

50.9

51.1

42.9

42.8

10.8

13.1

15.8

14.4

14.7

37.7

34.4

31.1

39.5

40.5

0.6

1.6

2.0

3.2

2.1

South Asia

75.6

65.7

66.7

61.1

62.0

8.8

11.6

11.8

11.2

10.8

16.0

25.2

23.1

32.6

31.2

−0.4

−2.5

−1.5

−4.9

−3.9
3.7

ASEAN

68.9

60.6

58.0

55.4

56.1

12.5

9.6

9.4

10.7

11.1

23.4

30.7

23.7

28.9

29.2

−4.8

−0.9

8.9

5.0

ASEAN6

68.5

59.1

57.1

54.3

55.3

10.5

9.7

9.6

11.1

11.6

23.5

31.6

23.4

28.2

29.3

−2.5

−0.3

9.9

6.3

3.8

CLMV

76.7

83.3

70.8

66.5

60.9

27.3

9.0

8.4

6.3

9.7

19.3

14.2

24.3

31.4

30.0 −23.3

−6.6

−3.5

−4.1

−0.7
19.7

GCC

34.8

49.4

41.3

36.3

33.5

14.9

25.7

20.9

16.5

19.9

19.2

15.8

18.2

28.5

26.9

31.2

9.2

19.6

18.8

China

55.8

49.0

46.4

35.7

36.7

11.1

13.7

16.6

12.8

13.3

33.0

34.6

34.6

47.9

47.2

0.1

2.7

2.4

3.6

2.7

India

74.0

62.4

64.1

57.5

58.7

9.4

11.9

12.8

11.7

11.1

16.7

27.1

23.9

35.3

33.3

−0.1

−1.4

−0.9

−4.5

−3.0

Japan

47.8

51.5

54.7

57.2

58.4

10.7

13.0

16.4

19.0

19.9

40.3

34.6

27.5

22.6

24.7

1.2

0.9

1.4

1.2

−3.0

Australia

54.3

57.9

58.7

54.0

56.9

13.8

18.1

17.6

17.8

18.0

32.2

24.3

23.5

27.1

26.6

−0.3

−0.2

0.2

1.0

−1.4

US

60.2

64.0

66.0

68.2

68.4

18.1

15.9

14.0

16.9

14.7

21.4

21.5

23.6

18.4

19.9

0.4

−1.3

−3.7

−3.4

−3.1

EU15

56.8

57.1

57.7

57.3

56.9

15.9

19.3

19.1

21.6

21.0

27.8

24.2

22.8

20.3

19.2

−0.5

−0.7

0.4

0.8

2.8

29: 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 2016 on national accounts for APO member economies, Japan is
an exceptional country that estimates GDP from its expenditure side. In other countries, GDP is estimated from the production
side (value added in industries). And some countries record statistical discrepancy as the difference in the estimates between production-based GDP and the 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 2016.

41©2016 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 PPPs for GDP. Household consumption includes consumption of
NPISHs. Investment includes GFCF plus changes in inventories.

4 Expenditure

the present day, as the investment share increases for their development effort.

%

70
60

Household consumption

50

China’s household consumption has been trend40
ing downward as a share of GDP. It fell from
Investment
55.8% in 1970 to 46.4% in 2000. This compares
30
Exports
with the early communist era when household
20
consumption was more volatile and at a higher
Imports (−)
10level of over 60% of GDP (Figure 22). China was0less well-off then. Figure 22 shows how house1952
1962
1972
1982
1992
2002
2012
hold consumption share and investment sharemirror each other.30 As the decline in household
Figure 22 Final Demand Shares in GDP of
consumption share accelerated in the 2000s,
China, 1952–2014_ Share of final demands with respect to GDP atplummeting to 36.7% in 2014, the investment
current market prices
share rose rapidly to 47.2% of GDP from 34.6% in
2000. Investment has overtaken household conSources: National accounts by National Bureau Statistics of China,
including author interpolation.
sumption as the largest component in GDP expenditure since 2004, and the divide shows nosign of narrowing. The falling share of household
consumption may partially reflect the falling labor income share of GDP and/or an uneven distribution of economic gain between the rich and the poor in these countries. There also is a notably rapid
rise in exports as a share of GDP since the 1980s when China began to open its economy, from around
5.0% or below in the 1950s and 1960s to its peak of 36.3% in 2006 before softening to 21.2% in 2014.
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, welldeveloped domestic financial markets may provide a strong incentive for precautionary saving on
the part of households (Eichengreen, Park, and Shin, 2012). All of these factors suggest that there arepolicy levers available to the government to impede or rebalance the economy.

©2016 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 5, p. 67). This could be a good news for the world, as ahigher labor share of GDP will bring about higher household consumption, helping the domestic
market 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 havestarted 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 2.6% in
30: 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.

424.1 Final Demand Compositions

2014. Since the peak of 8.6% in 2007, net exports
have been shrinking.

%

80
70
60

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 68.4% of GDP in 2014. From a historical perspective as shown in Figure 23, thecurrent level is below the share of household
consumption that the US experienced during
the Great Depression, when it was over 75%,
even as high as 82% in 1932. The share of household consumption in the EU15, which is ataround 57%, has remained fairly stable over the
past four decades. The Asian average, meanwhile, has hovered around the 50% range until
recently when the gap with the EU15 widened,

75
50
25
0

−15

−6 −6

−15 −9 −9 −9 −2 −9 0 −1 −2 −1 0

−4 −1

Investment

20
Imports (−)

10

Exports0
1929 1939 1949 1959 1969 1979 1989 1999 2009

Figure 23 Final Demand Shares in GDP of theUS, 1929–2014_ Share of final demands with respect to GDP atcurrent market prices

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

−2 −2 0

−7 −1

0

−4

−4

2014

%

39
15
11 11

75

0
23 29 21 20 21
5

5 10 15 15

24

30 22

20
9 13

3

3
28

80 80 76

25
−6
−29

−4

73 72 68 68 67 65 65−7−4 −3 −4−8 −8

431 30 33 25 29 19 27

6 11 10 11 20 13 21 18

50

0

30

15
0
4
1 1
1
2
1 2
1 0 1
27
6 14 20
23 27 25
24
8
8
10 5
10 17
11
20
31
24 26 21 29
23 33
24
25
8
22
34
28
34 30
17
32 31 14 30 28 43 39
5 8 826
34 21 43 20 21 40 15
35
8 12 10 1444
9 11 11 15
7 8 8 11 13 1634
9 9 12 18 20 12 13 21 15 17
22
32
11 10
14 22 12 23 28
13
95
8 31
84 81 80 77
27
74 73 73 71 67
66 65 65 64 62 62 62 62 61 60 59
58 58 57 56 55 54 54 54 53 52
51 51 49 49 47 45 45
43 41
33 27

125
100

4

40

Brunei
Qatar
Singapore
KuwaitChina
Oman
Saudi Arabia
Malaysia
GCC
East Asia
Bhutan
Korea
Thailand
ROC
Japan
Bahrain
Asia30
Asia24EU15Australia
APO20
ASEAN6
ASEAN
Fiji
Iran
India
Hong Kong
UAE
Indonesia
US
South Asia
Mongolia
TurkeySri Lanka
Pakistan
Philippines
Vietnam
CLMV
Nepal
Bangladesh
MyanmarCambodia

−25

50

1995

%

125
100

Household consumption

36

5 51 10 975−1 −1 −52

1

22 25 25 29 37

32

36 40
25

20

27 24
47
16

7 14 15 1655 54 54 53 53 52 52 50 49 49 48

−8

3

2

19

9 13 11 12 17 9 17 15 13 17
15 13

63 62 61 59 58 58 57 57 57 56 56−4 −1 −3 −3 0

435 35 29 29

29

27

29

22

36 37 38

23
16

10 13 20

43 41 37 37

13

26 25

27

19
21

33 32 31 29

34
14

15 15

−21

−25

Government consumption

Investment

Net exports

Figure 24 Final Demand Shares in GDP, 1995 and 2014

_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.

43©2016 Asian Productivity Organization

Qatar
Brunei
KuwaitOman
Saudi Arabia
GCC
China
Singapore
BahrainEast Asia
Asia30UAE
Asia24
Korea
Thailand
Malaysia
ROC
BhutanIran
Myanmar
ASEAN6ASEAN
Mongolia
Indonesia
Australia
EU15
APO20
Japan
India
CLMV
South Asia
VietnamFiji
Sri Lanka
Hong Kong
TurkeyUS
Philippines
Bangladesh
Cambodia
Nepal
Pakistan
Household consumption

4 Expenditurelargely reflecting the trend in China (Table 8).
Within Asia, all regions display a decline inhousehold consumption ratios. South Asia
maintains the highest share, despite its fallfrom 75.6% in 1970 down to 62.0% in 2014.

% Household consumption share in GDP

90

Pakistan

80

Cambodia Nepal
Bangladesh

70

PhilippinesUSFiji
Sri Lanka

Hong Kong Turkey

VietnamOverall, Asian countries invest significantly
60
Mongolia India Japan
Thailand IranAustralia
more than the US and the EU15 as a share of
BhutanROC
GDP. Historically, the gap in the investment
UAE
Malaysia
50
Indonesia
Korea
share between the Asia30 and the EU15 never
MyanmarBahrainexceeded 10 percentage points. However,40
since the beginning of the 1990s, it has started
China Singapore
Saudi Arabia
to widen (except for the period of the Asian fiOman
30
Kuwaitnancial crisis). In 2014 the difference was over
16 percentage points. In the 1970s the EU15
20
was investing on average 3% more of their
Qatar
Brunei
GDP than the US. Thereafter, the EU15 invest10
ment share converged to the US level. They
10
15
20
25
30
35
40
45 %
Dependent population ratio
were out of synch with each other temporarily
in the late 1980s and early 1990s. For the past
Figure 25 Ratio of Dependent Population andfive years, a divergence has opened up with
Consumption Share in GDP, 2014
_Shares of dependent population (age under 14 andthe US investment share of GDP declining fastover 65) to total population and consumption share iner than that of the EU15 (Figure 30.3). In 2014
GDP at current market prices
investment accounted for 19.5% and 19.1% of
final demand in the US and the EU15, respecSources: Population data by national statistical office in each country; World Bank, World Development Indicators 2015; official national
tively, compared with 36.6% for the Asia24.
accounts in each country with author estimates.
Australia’s investment level has been closer tothe level of the APO20 than the US/EU15. In2014 it accounted for over a quarter of final
demand. The share of investment in China is the biggest final demand component of GDP since 2004.
At 47.2% in 2014, it is likely unsustainable in the long term. East Asia has the highest investment ratio
among the Asian regions. While South Asia caught up with them in 2007, since then the paths of thetwo regions diverged in opposite directions.

©2016 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
–0.0% to 2.6%, the contribution of net exports decreased to 1.1% in 2014. This compares with the oilexporting GCC countries at 9.2% in 1990, rising to 18.8% in 2010 and further to 26.1% in 2013.31 In
the US, there is an observable trend of persistent deficit between exports and imports, which has considerably expanded from the beginning of the 1980s to 5.6% in 2006 before narrowing to 3.1% in
2014. South Asia is the only Asian region that consistently has run a fluctuating trade deficit over theyears. Lately, it is historically sizable at 7.4% of GDP in 2012, narrowing to 3.9% in 2014.
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 2014.
Countries are arranged in descending order of their household consumption shares. Although most

31: The recent increase is mainly due rise in price of crude oil to over USD 100 per barrel since 2010 and held until the middle of 2014.
See Figure 94 in Section 7.1 (p. 131). In 2014 the contribution of net exports decreased to 19.7%.

44

4.1 Final Demand Compositions

1995

%

100

181

80
60

52
37

40

34

44

59

51

22

20

46
20

44

41

39

28

25
9

0

18 19

28 26
11 11 1432 34 32

10 11

22

31

4

−7

−12−12
−14
−15
−18
−17
−18
−17
−25 −19 −27−26−28 −29−29−41 −41−37
−40
−42
−43
−45 −43−46−48 −43
−49
−59
−67

−20
−40
−60
−80
−100

143

91

78

−94 −147−165

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

2014

%

100
80
60

192
67 67 66 71

95

219

85
70

58

74

69

61
50

47

54

52

41

40

36

29 28

21

24 20

−19

−21−20 −17−26−24−32−32

22

20

17

23

13

12

19 17

22
11

0
−20
−40

−17
−30 −31−30−37 −34
−44

−60
−76

−80

−45

−19
−26−25
−31

−57
−66

−60−65 −62
−82

−168

−100

−38

−62

−41

−57

−219

Nepal
BhutanSri Lanka
Fiji
MyanmarBangladesh
Pakistan
Mongolia
Cambodia
TurkeyPhilippines
US
India
Japan
Australia
Indonesia
Hong Kong
China
EU15
Vietnam
Iran
Korea
Thailand
Malaysia
ROC
Saudi Arabia
UAE
Oman
Singapore
Bahrain
KuwaitBrunei
Qatar

countries 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, and the Philippines fellto the left of the US in both yearsof comparisons. The high consumption rate in these countries
could be partly explained by thedifference in demographic conditions. Figure 25 shows thatcountries with a high proportionof dependent population (ageunder 14 and over 65) tend tohave a high household consumption share in their GDP. The
aforementioned five countries
have higher shares of dependent
population with over 36% in
2014. On the other hand, thevariation of consumption rates isalso related to the income level.
Countries with a low income will
struggle to defer consumption. It
is no coincidence that countries
clustered on the left of Figure 24
tend to be those in the bottom
income groups among the countries studied in this report (seeTable 16 in Section 6.1, p. 101).32

Exports

Imports

Net exports

Figure 26 Export and Import Shares in GDP, 1995 and 2014

_Share of exports and imports with respect to GDP at current market
prices32: The Lao PDR is also in the bottom income bracket and the share of dependent population is the highest among Asian countries(40%); it is, however, omitted from Figures 24 and 25 because of a lack of final demand data.
33: It should also be noted that the shares are calculated in current market prices. Revenues from oil exports are notoriously erratic.
It is possible that a sudden surge in export revenues relative to imports can squeeze the shares of other components of final demand without any real change in the underlying behavior in the economies. For example, Qatar has the smallest share of household consumption, which shrank from 32.5% in 1995 to 14.8% in 2014, while over the same period, net exports swung from 1.0%
to 37.5%. Similarly, net exports for GCC countries as a whole swung from 8.1% to 19.7%, squeezing household consumption from
49.1% in 1995 to 33.5% in 2014.

45©2016 Asian Productivity Organization

A deficit in net exports can be asSources: Official national accounts in each country, including author adjustments.
sociated with high household
consumption. At the other endof the spectrum, GCC and otheroil-exporting countries tend to cluster at the low end of household consumption share of GDP in both
years of comparison. The average of household consumption share for GCC countries has beensqueezed by net exports (which in turn are dominated by erratic oil revenues), from 49.1% in 1995 to
33.5% in 2014, as shown in Figure 24.33 Given that a large part of GCC countries’ GDP is not sustainable
income, it may in fact be prudent for oil-exporting countries not to consume beyond their sustainablelevels and instead purposefully invest to generate a steady income stream in the eventuality of oil

4 Expenditure

depletion, regardless of how distant this may seem now. Among the non-oil-exporting Asian countries, Singapore had the smallest household consumption share. Since 2002, however, China has replaced Singapore in that position, with a share of 36.7% in 2014.
Figure 26 presents the export and import shares in GDP as a decomposition of net exports in 1995 and2014. Net exports are particularly important in a handful of economies. In 2014 the shares in Singapore exports were at 192%, and that in Hong Kong 219%, 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.34

4.2 Consumption and Investment
Figure 27 shows the long-term trends of household consumption share of GDP for Asian economies
and some country groups. Countries are grouped according to the levels of per-capita income in2014.35 The Asian Tigers have been the consistent high performers, coming at the top for most of thelevel indicators presented in Chapter 3. As seen in Figure 27.1, Singapore and Korea showed the most
rapid relative retrenchment in household consumption as a share of GDP in their initial stage of development of the 1970s and 1980s. While the downward trend continues in Singapore, it has halted andbeen mildly reversed in Korea since the late 1980s. Between 1970 and 2014 the household consumption share of GDP fell from 69.0% of GDP to 36.8% and from 73.5% to 50.3% in Singapore andKorea, respectively.
In contrast, household consumption as a share of GDP, at 66.6% in 2014, 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 tounder 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 justunder 50% in 1970 to 58.4 in 2014. With a rapidly aging population, this rising trend can be expected
to continue. Japan’s share of dependent population stood at 38.7% in 2014 (Figure 25), nearly 60% of
which was accounted for by the over-65 age group.

©2016 Asian Productivity Organization

Figure 27.5 illustrates the observations of Table 8, plotting Asian group averages against those of thereference countries. The US household consumption share has been climbing since the mid-1980s toover 68% of GDP since 2008, from a level of around 62%. Today the US level is more than 10% higher
than that of the EU15 and the APO20.36 The share in the EU15 has been stable, fluctuating within anarrow 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.

34: 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.
35: Table 16 in Section 6.1 (p. 101) defines four levels of per capita GDP groups in 2014: 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.
36: It is worth noting that the GDP share of government consumption in the EU15 was higher than the average of the Asia24 by 7.8
percentage points in 2014 (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.

46

4.2 Consumption and Investment

%

100
90

%

Hong Kong
Korea
Singapore

100

Japan
ROC

90

%

China
Malaysia
ThailandIranMongolia
Turkey100

BhutanIndonesia

Fiji
Philippines

90

India
Sri Lanka
Vietnam

80

80

80

70

70

70

60

60

60

50

50

50

40

40

40

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

Figure 27.1

Figure 27.2

Figure 27.3

%

%

100
90

Bangladesh
Nepal

Cambodia
Pakistan

100
90

4

%

APO20
US
Australia

Asia30
EU15

100
90

East Asia
ASEAN

South Asia
ASEAN6
CLMV

80

80

80

70

70

70

60

60

60

50

50

50

40

40

40

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

Figure 27.4

Figure 27.5

Figure 27.6

Figure 27 Long-Term Trend of Household Consumption Share in GDP, 1970–2014_Share of household consumption with respect to GDP at current market prices
Sources: Official national accounts in each country, including author adjustments.

Figure 29 traces the decreasing long-term path of Japan’s Engel’s Curve during the period 1949–2014.
The countries’ levels in 2014 are mapped against Japan’s experience (as circles). Among the selected
countries, it is staggering to note that in 2014, 52.8% of Bangladesh’s household consumption was
spent on food and non-alcoholic beverages at one end of the spectrum, compared with only 6.6% in
the US at the other end. This suggests the fact that low-income countries spend 30–50% of their GDP
on food and non-alcoholic beverages corresponds to Japan’s experience in the 1950s and the 1960s.
Besides food and non-alcoholic beverages, housing/utilities and transportation are the other twolarge spending categories. In rich economies, these two categories account for bigger shares inhousehold consumption than food and non-alcoholic beverages. Idiosyncratic spending, such as education in Korea and Cambodia accounting for 5.8% and 5.0% of household consumption, respectively,
and health in the US, accounting for one-fifth of consumption, are not reflected in other countries.

47©2016 Asian Productivity Organization

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. 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. 101). The other end of the spectrum is occupied by the rich Asian countries,
namely, the Asian Tigers and Japan.

4 Expenditure

%

100
90
80
70
60
50
40
30
20
10
0

4
4
8 2
12 10 3
4
2
3
4 4 3
3 4
3 2 5 253 3
4
12 17 114
3
11 13 13 13
14 15
14
2
19
2 17
20
3
7
8 2
7
8 7
15 10 9
3
2 2
9 2
2 4
5
5
8
6
9
9 7
9
6
14
4
9
5 14 53 8 8 10
2
6 14
2
7
2
4
3
3
4 2 14
4 4
3
10
4
3
11 8
22
2 6 4
15
4
4
10
13
13
12
2
3
4
15 18
5 9
14 31
12
13 4 2
6
4
11
9
4
5
7
21
4
5 5
6
3 4
3 6 4 4
16
9 9
7
3
6 8
25
15
5
19
5 4
4 4
18
57
24
3
53
3
17
2
42
16 19
39 39
4 4
6
35
32 31 29
2
3
29
2 3
25
4 3
21
2 13
14 14 14 13
10 7
7
2
2
6
2
5

4 5 5
3
10 5
5
5
4 3
3 5 4
6

Miscellaneous goods andservices
Restaurants and hotels
Education
Recreation and culture
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 KongJapan
ROC
Malaysia
ThailandIndia
IranMongolia
Indonesia
Cambodia
Sri Lanka
BhutanPhilippines
Bangladesh
Fiji

Figure 30 compares the long-term
trend of investment share (including R&D investment) in GDP
among countries. Historically, aninvestment share in the region of
40% or above seems to be unsustainable in the long run. We seethat Japan’s investment share of
GDP steadily declined over the
past decades from 40.3% in 1970
to 24.7% in 2014 (Figure 30.1).37 In
the initial period of our observation, Singapore also sustained aninvestment share of 40% or above.
Since the mid-1980s, however, it
has seen a downward trend, inspite of its fluctuations. In 2014
it was 28.9%.38 The investment
share hit around 40% in the ROC
and Korea at different times but
these were nothing more than
temporary spikes (Figure 30.1).

Figure 28 Household Consumption by Purpose, 2014
Sources: Official national accounts in each country.
Note: For data of Hong Kong, transportation includes communication; recreation andculture includes hotels; miscellaneous goods and services include restaurants. For
data of Sri Lanka, transportation includes communication; food and non-alcoholic
beverages includes alcoholic beverages, tobacco and narcotics. For Fiji, the observation periods are 2009, respectively.

In contrast, the investment sharein China and India has been rising.
India in particular has been investing very aggressively since 2000, approaching China’s 41.7% share in 2007, with the gap of 4.2
percentage points. Since then, the gap has widened to 14.0 percentage points in 2014 as investment
in India softened (Figure 30.3). At 47.2% in 2014, China’s investment share reached a level previously
unseen in Asia, except tentative achievements in small countries.39 If history is any guide, the contribution of investment to final demand in China will drop eventually. ASEAN’s investment share was previously around 35%, but it fell sharply to the lowest point of 18.0% in 1999 in the aftermath of the Asianfinancial crisis. Since then, it has been slowly inching up, reaching 29.2% in 2014. In the past two
and a half decades, the investment share in GCC countries has fluctuated between 15–30% of GDP(Figure 30.6).

©2016 Asian Productivity Organization

The role of foreign direct investment (FDI) differs considerably among Asian countries. Figure 31
shows the FDI inflows as a percentage of GFCF during 2000–2014, for the Asian economies withsome 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 (105% of GFCF) and Singapore (69%), both recording a remarkable achievement in
37: 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.
38: Although Singapore’s investment ratio in 2014 is higher than that of Japan, it is of note that Singapore has succeeded in sustaininga higher ex-post rate of return on capital (13.5% for the period 2010–2014, based on our estimates in Table 22 in Appendix 3) thanthat of Japan (4.1% for the same period). Korea is another country which confronts the decreases in the ex-post rate of return on
capital. In 2010–2014, Korea’s rate of return reached 6.8%, which is similar to that of Japan in the early 1990s.
39: 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.

48

4.2 Consumption and Investment

economic growth in the 2000s. Nepal(0.9%) and Japan (0.6%), whose FDI
inflows are extremely small in thisperiod, should consider a domestic
reform for lowering barriers to entry, therefore encouraging interna­tional investment.

Food consumption share in 2014 relative to Japan

0.7

0.6

Japan (1949–2014)

Fiji
Bangladesh

0.5

0.4

Lao PDR

Philippines
BhutanMongolia
Cambodia

4
Sri Lanka
Indonesia

India
It is an important policy target
Iran
0.3
for low-income countries to create
Thailanda business-enabling environment,
Malaysia
just as it is important for middle0.2
Japan
ROC
income countries to improve variKorea
Hong
Kong
ous business environments. Based
0.1
SingaporeUSon the EIU’s (Economist Intelligence
Unit, The Economist) ranking 2014–
0.0
2018 (covering 82 countries in the
1940
1950
1960
1970
1980
1990
2000
2010
2020
world),40 Singapore (1st) and Hong
Figure 29 Engel Curve of Japan during 1949–2014 andKong (3rd) are in the top 10% of theLevels of Asian Countries in 2014
covered countries. In contrast, Ban_Share of food in household consumption
gladesh (69th), Pakistan (74th), andSources: Official national accounts in each country. The historical data of Japan isIran (81th) are in the bottom 10%.
based on JSNA by ESRI, Cabinet Office of Japan.
Figure 32 plots this business enviNote: Food is defined as sum of food and non-alcoholic beverages and alcoholic
beverages, tobacco and narcotics. For Fiji and the Lao PDR, the observation periods
ronment score and the FDI inflows
are 2009 and 2005, respectively.
ratio in the countries presented in
Figure 31 (excluding the countries
in which the FDI inflows ratio is over25%). 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.41 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 2014. Among the Asian Tigers, however, the two global cities (Singapore and HongKong) have a smaller share of R&D in GFCF – 9.3% and 3.1%, respectively, in 2014.

40: 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).
41: 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 20 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.

49©2016 Asian Productivity Organization

Figure 34 plots the long-term trend of net export share in GDP from 1970 to 2014. Net exports, whichwere previously a huge drag on the Asian Tigers, Singapore, and Korea in the 1970s, have improved

4 Expenditure

%

%

50

40

%

60

60

50

50

40

40

30

30

20

20

BhutanIndia
Philippines
VietnamFiji
Indonesia
Sri Lanka

30

20

10
Hong Kong
ROC

Korea

Japan
Singapore

10

China
Mongolia

Iran
Thailand

Malaysia
Turkey1001970 1975 1980 1985 1990 1995 2000 2005 201001970 1975 1980 1985 1990 1995 2000 2005 201001970 1975 1980 1985 1990 1995 2000 2005 2010

Figure 30.1

Figure 30.2

Figure 30.3

%

50

%

%

Bangladesh
Nepal

50

Cambodia
Pakistan

APO20
USAustralia

Asia30
EU15

50

40

East Asia
ASEAN
CLMV

South Asia
ASEAN6
GCC

40

40

30

30

30

20

20

20

10

10

10

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

Figure 30.4

Figure 30.5

Figure 30.6

Figure 30 Long-Term Trend of Investment Share in GDP, 1970–2014_Share of investment with respect to GDP at current market prices
Sources: Official national accounts in each country, including author adjustments.

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 24.4% in 2014, comparedwith 5.3%, 10.3%, and 0.1% 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

%

120
100
80
60
40
20

©2016 Asian Productivity Organization

105.2

69.0
55.9
45.0

38.7 37.7
24.8 24.6

19.6 19.4 18.2

15.1 14.9 13.4 12.9 12.6

12.3 11.2 8.7 8.7

8.5 8.4 8.4 6.7 6.3 6.2 5.4 5.4 5.1 5.0

3.6 3.5 3.4 3.3 3.3 3.3 3.0 0.9

Figure 31 FDI Inflows, 2000–2014_FDI inflows as a percentage of GFCF, an average of the ratios during the period 2000–2014
Source: United Nations Conference on Trade and Development (UNCTAD), World Investment Report 2015.

50

0.6

Japan
Nepal
Indonesia
Bangladesh
KuwaitIran
BhutanROC
Korea
FranceItaly
Sri Lanka
India
China
US
Philippines
TurkeyGermany
Oman
Qatar
Pakistan
Australia
Spain
ThailandSaudi Arabia
Malaysia
UAE
CanadaVietnamBahrain
UK
Myanmar
Netherlands
Mongolia
CambodiaFiji
BruneiSingapore
Hong Kong0

FDI inflows as a percentage to GFCF, an average in the period 2000–2014

4.2 Consumption and Investment

contribution to final demand. The net
export share of GDP peaked at 8.6% in
2007. Since then, it has lagged to 2.7%
in 2014.
Japan had enjoyed a trade surplus formost of the period compared, but recently its trade balance has turned
negative amounting to –0.9% in 2011
deepening to –3.0% in 2014 (Figure34.1). In the aftermath of the triple
disaster (earthquake, tsunami, andnuclear) in 2011, Japan had to in­crease the imports of natural gas andcoal to meet the increase of thermal
power generation as a result of the
shutdown of its nuclear power plants.
This trend may change in response to
its new energy policy, which will in turn
reduce imports.

% FDI inflows as a percentage to GFCF, an average in the period 2000–2014

25

Netherlands

20
BahrainUK

4

Vietnam
15

UAE
Saudi Arabia
Thailand

Malaysia
Spain
Australia

10

Turkey Qatar

Pakistan

Germany
Philippines
China
US
India
Sri Lanka
Italy France
ROC
Kuwait5
Iran0

CanadaBangladesh Indonesia

Korea

Japan
2

4

6

8

10

Business environment rankings score 2014–18

Figure 32 FDI Inflow Ratio and Business Environment,
2000–2014_ FDI inflows as a percentage of GFCF and business environFigure 34.3 illustrates the external imbalance of the world’s major economent scoremies. Both the US and the EU15 faced aSources: United Nations Conference on Trade and Development (UNCTAD),World Investment Report 2015; The Economist Intelligence Unit (2014)
trade deficit at the beginning of this period. While the EU15 managed to recover, being in surplus since the early 1990s(within a range of 0–2% of GDP), the US
position has significantly deteriorated since the middle of the 1990s, despite a tremendous effort torestore its trade balance in the late 1980s. In 2014 the size of the US trade deficit stood at 3.1% of its
GDP, compared to its recent dip to 5.6% of GDP in 2006. Australia has been running a trade deficit formost of this period. Only in the past few years has its trade balance been in surplus. In contrast, Asia30’s

80
60
40
20

Dwellings
Transport equipment

100
80
60
40
20
0

4
9
9
11
18 14 241822 26
522 26 22
22 10 19 2227
30 28 3 25
10
34
4 16
6
5
40
4
3
3 3
4 7
10
17
6
3
13 7 438 16 8 316
7
9 12 42 7 20
3
12 9
15 11
12
6
5 13 164
14 10 30
23
3
22
3
15
9 7
22
8
23 34
9 9
8 19
24
6 27
27
14 16
30
19 25
9
19 13 32
18
15 9
29
27
26
32
9 7 10
18 14
22
34
19 12
11
33 27
19
7
26 24 23 19
18 16 16 15 14 14 13 13 12 11
13
10 9 6 5 2

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

China
PhilippinesROC
Sri Lanka
Thailand
Malaysia
PakistanIndonesia
Japan
Singapore
US
Fiji
Hong Kong
Korea
Vietnam
Iran
Nepal
India
Mongolia
Bangladesh

0

2014

%

11
16
16
24 18
24
25
25 28 4
30
32 4
2 29
35 30 35
7
2
41
40
43
4
17
2 12
4
12 6
13 2
2
4
5
10
12
8
4
12
11
28
12
10
13
5 4
16 5
54 11 21
23 13
8 5
6
11 24 1622
23
8
11 20 32
24
14
8 19
18
22
34 15 918
25
8 15 20 2114
15 13 16 13
18
46 40
27
13
38
17
14 19 27
29 26 23 23 23
22 19 19 19 18 16 15
11 9 95 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 2014
Sources: Official national accounts in each country, including author adjustments based on input–output tables and trade data.

51©2016 Asian Productivity Organization

1970

%

100

4 Expenditure

%

%

32
24

Hong Kong

Japan

Korea

ROC

30

%

China
Mongolia

Iran
Thailand20

Malaysia
TurkeyBhutanIndonesia
VietnamFiji
Philippines

India
Sri Lanka

20

10

10

0

0

−10

−10

−20

−20

−30

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

Figure 34.1

Figure 34.2

Figure 34.3

Singapore

16
8
0
−8
−16

%

30
20

%

%

Bangladesh

Cambodia

Nepal

Pakistan

10

10
8

APO20
US
Australia12

Asia30
EU15

6

6

0

4

−6

0
2

−12

−10
0
−20

−2

−18
East Asia
ASEAN
CLMV

South Asia
ASEAN6

−30

−4

−24

−40
1970 1975 1980 1985 1990 1995 2000 2005 2010

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

Figure 34.4

Figure 34.5

Figure 34.6

Figure 34 Long-Term Trend of Net Export Share in GDP, 1970–2014_Share of net exports with respect to GDP at current market prices
Sources: Official national accounts in each country, including author adjustments.

trade has been in surplus continuously and a near mirror-image of the US. Asia30’s net exports shareof GDP was 2.3%, compared to the recent peak of 5.5% in 2006. Addressing this external imbalancehas been highlighted as a necessary step to healthy and sustained growth in the world economy.

©2016 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 10.0%, up from
–0.1% in the previous year. Trade balance moderated over time to the more normal level of 3.7% in
2014. In recent years the trade balance of CLMV is in surplus for the first time during these four decades.42 Its improvement has been rapid, from a deficit of 7.5% in 2008 to a surplus of 1.8% in 2012.
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 years to come.

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

52

4.3 Expenditure-Side Growth Decomposition

4.3 Expenditure-Side
Growth Decomposition

0.3
0.1

8
6
4

3.9

3.2

1.3 1.1

0−2

2.1

1.2 1.5

0.2 0.4
1.5 1.1
0.3 4.2
1.0 0.7 0.0
0.7 0.7
0.5 1.4
0.1
1.6
0.7 0.0
1.6 1.4 1.0 0.6
0.3 1.1 1.2
0.6
0.8 2.7
0.7 0.2 0.3 0.5 0.1 0.5 0.2 0.2 0.1
2.7 3.6
4.3
3.8 3.6 0.5
0.5
3.2
3.6 3.4
3.5
3.0
3.0 0.3 1.3 0.2
2.9 2.7
2.5
2.5 2.4 2.7 2.0 2.4
2.3 2.7
0.4
1.8 0.7 2.2 2.2 2.2
1.3 0.7 0.7
0.8
0.6
0.4
0.1
0.0
−0.1−0.3−0.1−0.3−0.1
−0.1
−0.3
−0.3 −0.2−0.4 −0.7−0.4 −0.4
−2.1
1.5

0.4

0.6 0.8

2

0.2

1.0

2.7 2.8 2.0 3.1

0.7

0.6

2.0
0.5

0.8 1.3

2.4

1.4
0.7
4.5 0.4

4

−4
Japan
Brunei
EU15
Philippines
US
Australia
Indonesia
TurkeyHong Kong
Bangladesh
Thailand
BhutanSaudi Arabia
Bahrain
Iran
Pakistan
Oman
India
Sri Lanka
KuwaitUAE
Cambodia
Korea
ROC
Qatar
Singapore
Myanmar
Malaysia
VietnamChina

2000–2014

%

12
10

1.7

2.0

8
6
4
2
0
−2
−4

0.1
3.7

5.7

1.0 5.5 0.5

3.5

3.0
1.9
0.1
2.5
2.1
1.1 0.4 2.1 0.8 2.1
1.2 1.2 0.2
0.2
2.3
0.4
1.9 2.7
0.6
1.3
0.9 0.4 0.6 1.9 0.5 1.1 0.9 0.9
0.3 0.6
1.4
1.9 1.4
6.8
0.6
6.5
1.1
0.0
1.6
0.5
0.6
1.2
2.2
0.2
0.8 1.2
0.7
1.5
0.2
0.8
0.5 0.2 0.2
3.9 3.4
3.8 4.2 4.2
1.0
0.6
0.3
3.4
3.4
3.3
3.3
3.2
0.4
2.9
2.6
2.6
2.6
0.1 0.0 0.1 0.8
2.5 0.2 2.2
2.2
2.1
1.9
1.8
1.6
1.7
1.4
1.3
1.4
1.4 0.3 0.3 0.7
0.5 0.5
−0.5
−0.1
−0.2
0.0
−0.2−0.2−1.5−0.2−0.3−0.1
−0.3
−0.5
−1.7
−0.8
−2.5−0.7
−0.9
−2.6
−2.5
3.2
0.6

Brunei
Japan
EU15
US
Australia
Nepal
Hong Kong
ROC
Oman
KuwaitSaudi Arabia
Korea
TurkeyThailandUAE
IranPakistan
Philippines
Malaysia
Singapore
Indonesia
Bangladesh
Sri Lanka
Bahrain
VietnamBhutanIndia
Cambodia
China
Myanmar
Qatar

Figure 35 shows the decomposition of the average annual economic growth by final demand forthe periods 1990–2000 and 2000–
2014, respectively. Here, theAsia30 grew faster in the latter period than in the former (at 5.3% on
average per annum comparedwith 4.8%, as presented in Table2).43 The earlier period embodied
the economically atypical event of
the Asian financial crisis, whichcaused some erratic contributions
by the final demand components
observed in some countries in thelate 1990s. In the 1990s overall,the engine of growth for mostcountries in Asia was household
consumption, while investment
growth was more subdued.44

1990–2000%

10

On the back of the Asian financial
Household consumption
Government consumption
crisis, investment growth surged
Investment
Net exports
Real GDP
strongly. Its impact on real GDP
growth became more significant
Figure 35 Final Demand Contributions to Economic
in Asia in the 2000s, especially in
Growth, 1990–2000 and 2000–2014_ Decomposition: Average annual growth rate of GDP at constantthe fast-growing economies. For
marketpricesexample, investment contributed
5.5 percentage points in China,
Sources: Official national accounts in each country, including author adjustments.
Note: The starting period for Cambodia is 1993.
3.7 percentage points in Myanmar, 3.2 percentage points in India, and 3.0 percentage points in
Vietnam. China grew by 10.1% on average per year in the latter period. The role of investment became
significant with its contribution to economic growth expanding between the two periods from 42.5%
to 55.0%, while squeezing the contribution of net exports from 3.3% to 0.7% and that of household
consumption from 37.6% to 31.6%. However, for Singapore and the ROC, the strength of net exports

Real GDP growth

Contribution of final demand ifinal demand i ­ in period t. Thus, the real GDP growth may diverge from the official estimates or those presented in Table 3 inSection 3.1 (p. 24).
44: The exceptions are some of the oil-producing countries, which enjoyed a positive contribution from net exports higher than mostcountries, and China, which experienced the fastest economic growth among the countries studied, averaging 9.2% per year,
42.5% of which was driven by investment, compared with 37.6% by household consumption. This compares with average annual
growths of 3.4% in the US and 2.3% in the EU15. The contribution from household consumption was 70.3% and 56.7%, whereas
investment growth accounted for 35.2% and 23.0% of overall growth in the US and the EU15, respectively.

53©2016 Asian Productivity Organization

43: 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 of

4 Expenditure

Box 3

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 2015 and March 2016, the APO Productivity Database project conducted the Metadata Survey 2016 on the national accounts and otherstatistical data required for international comparisons of productivity among the APO member economies.
Broadly speaking, cross-country data inconsistency can arise from variations in one or more of the three
aspects of a statistic: definitions, coverage, and methodology. The international definitions and guidelines
work to standardize countries’ measurement efforts. However, country data can deviate from the international best practice and vary in terms of omissions and coverage achieved. Countries can also vary in theirestimation methodology and assumptions in benchmark and/or annual revisions. This may account forpart of the differences observable in the data, as well as interfere with comparisons of countries’ underlying economic performance.
Most of the economic performance indicators in this report are GDP-related. The surveys therefore put
much emphasis on discerning countries’ GDP compilation practices. In the Databook 2016, 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 B3 presents the current situation in compilations
and data availability of the backward estimates based on the 1968 SNA, the 1993 SNA, and the 2008 SNA(including the future plan for introducing the 2008 SNA), based on our Metadata Survey 2016. 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
93
08
68
93
08
68
93
08
68
93
08
68
93
08
68
93
08
68
93
68
93
08
68
93
08
68
93
08
68
93
08
68
93
08
68
93
68
93
08
68
93
08
68
93
08
68
93
68
93
08

1973

2000

1996
1993
1993

2014
2009

2017

1988

1951
1951
1951

2005
1968

1974

1995

2003
2005

1978

1950
1960

1999

1981

2000

2002

1960
1975
(mixture of 1953 SNA until 1968)

2000

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

2005

1988

2000

1998

1975
1986

1990
1989

2012
2015

2006
2004
2006

1998
1998

1975

2016

2007

2010
2000

1972

1972

2014

2005

1995

1975
1981

2016
2004

1990

1946

2015

2006

1986

1970

2010

2010

1991

1978
1980

1953

2008
2007

2000

1955
1953

2014

2004

1970

1959

Introduction year

©2016 Asian Productivity Organization

1980

2013
2011
2011

2001

2010

2016
2012

1993

2018

Backward estimates and implementation

Figure B3 Implementation of the 1968, the 1993, and the 2008 SNA
Source: APO Metadata Survey 2016.
continued on next page >54

4.3 Expenditure-Side Growth Decomposition> continued from previous page

Japan started to publish national accounts based on the 1968 SNA in 1978 (backward estimates based on
the 1968 SNA are available from 1955 at present) and national accounts based on the 1993 SNA in 2000
(backward estimates based on the 1993 SNA are available from 1980 at present), and plans to introduce
the 2008 SNA as of the end of 2016.
As Figure B3 suggests, countries differ in their year of introduction, the extent of implementation, andbackward estimates available. According to the survey response, 12 countries are currently 2008 SNA compliant (partly 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 the framework for their national accounts,
but the extent of compliance in terms of coverage may also vary. The APO Productivity Database tries toreconcile the national accounts variations based on the metadata information and our investigation, andprovide harmonized estimates for international comparison. See Appendix 1 for details of the adjustments.

%

120
100
80

16
34

16

16
6

42

60

24

18

Korea

Hong Kong

64

20
0

8

18

Government consumption

26

60
40

Household consumption

5

Investment

50

Net exports−3SingaporeROC

was the real economic story, accounting for 50.7%
and 60.5% of their economic growth on average per
year between 2000 and 2014, respectively.45 Even in
the other two Asian Tigers, net exports accounted
for 23.2% and 16.9% of Korea’s and Hong Kong’s
economic growth, respectively (Figure 36). In contrast, net exports have been a drag on economic
growth in India over both periods, making a negative contribution of –2.2% and –3.3%, respectively.

4

Figure 37 shows the impacts of the global financial crisis and countries’ path of recovery from theviewpoint of final demand between 2007 and 2012. The adverse impact of the crisis was felt throughinvestment in most countries, and to a lesser extent, through net 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 thecrisis on the economy, and shrinking net exports. Hong Kong and Japan also suffered from the negative impact of net exports on growth. Investment rebounded strongly in 2009–2010 with favorable
policy levers, but moderated in the subsequent years when the effects of policy faded out. Only China
and Singapore sustained their robust investment growth.

45: In the real income term, the trading gain effect ran counter to welfare for those countries. See Figure 93 in Section 7.1 (p. 131).

55©2016 Asian Productivity Organization

In some of these economies, the contribution ofhousehold consumption to economic growth wassqueezed – for example, from 37.6% in 1990–2000
Figure 36 Final Demand Contribution
to 31.6% in 2000–2014 in China, from 36.7% toShares to Economic Growth of the AsianTigers, 2000–2014
26.3% in Singapore, and from 54.1% to 34.8% in the_Shares of final demand contributions to growth
ROC. In contrast, the role played by household conrate of GDP at constant market prices
sumption in economic growth increased in the US
Sources: Official national accounts in each country, including
and Japan, from 70.3% to 81.1% and from 61.1% toauthor adjustments.
68.1%. Overall economic growth in Japan slowedfrom 1.2% to 0.7% between the two periods compared. This was a sluggish performance, especially relative to the acceleration that most Asian economies experienced. Also, in the latter period net exports made negative contributions in more countries than previously, with its impact in certain oil-exporting countries particularly large.

4 Expenditure%

2007–2008

2008–2009

%

15

15

10

10

5

5

%

2009–20102010–2011

%

10

10

12

8

8

6

6

4

4

2

2

0

0

0

−2

−2

−3

−4

−4

9

2011–2012

%

15

6
0

0

3
−5

−5

−10

−10

EU15

Net exports

US

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 37 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.

Figure 39 shows how the contribution of economic growth by final
demand varies across countries
and over time for the period 1970–2014. The immediate impact of the
global financial crisis in 2007–2008
is represented in the data. Most
countries felt an adverse impact in2008 and 2009, with the exceptionof India where in 2009 growth
rebounded strongly from a slowdown in the previous year. The impact on the Asian countries variedboth in magnitude and nature. Japan’s recession was particularly
deep with the economy falling by
1.1% and 5.6% in 2008 and 2009,
respectively, compared with 2.2%
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,

56

%

20
15
10
5
0
−5
−10
−15
−20
−25
Australia
EU15
US
China
VietnamBahrainOman
Cambodia
Saudi Arabia
India
Sri Lanka
Bangladesh
Fiji
Myanmar
ROC
Qatar
Pakistan
TurkeyKuwaitUAE
BhutanIranJapan
Singapore
Malaysia
Brunei
Korea
Philippines
Hong Kong
ThailandIndonesia©2016 Asian Productivity Organization

In comparison, the impact of the Asian financial crisis was more contained. Figure 38 suggests that the
impact was contained within Asia, except for the handful of countries affected, it marked an exceptional time. In 1998, investment took 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.

Household consumption
Investment

Government consumption
Net exports

GDP

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

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

4.3 Expenditure-Side Growth Decomposition

other Asian countries either experienced a mild recession or a growth slowdown. Moreover, relative
to their rapid growth the magnitude of the impact could still be substantial. For example, the growthin 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

The channels through which economic growth was adversely impacted also varied across countries.
Japan’s recession in 2009 was largely accounted for by a sharp fall in investment (4.0 percentage
points) and, to a lesser extent, a fall in net exports (1.6 percentage points). Meanwhile the 0.4% growthof government spending canceled out the 0.4% fall in household consumption. Similarly, in the ROC,
investment fell by 5.2% in 2009, while household consumption and net exports grew, albeit more
slowly than previously. 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 in 2009 to 0.1 percentage
point before bouncing back to its normal range of 3–5%.

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

57©2016 Asian Productivity Organization

It is difficult to understand the oil-exporting economies fully without analyzing the oil market in parallel. Its volatility can be observed clearly from Figure 39, with huge peaks and valleys, particularly in the
1970s. The oil booms of the 1970s brought benefits, but the downturn was a detriment. Net exports
remain erratic, but overall volatility seems to have reduced in the past two decades. Qatar experienced the fastest GDP growth among the oil-exporting countries in recent years with very strong investment growth. However, its economy remains very dependent on oil and gas and related industries,
which accounted for 49.3% of its GDP in 2014 (Figure 72 in Section 6.1, p. 102) – roughly 80% of its
export earnings, and 70% of government revenues in the 2000s.46 In contrast, Bahrain has diversified
into a regional banking and financial center and benefited from the regional boom in recent years.
Petroleum production and processing accounted for 25% of its GDP in 2014 (Figure 72) – about 60%
of export earnings, and 75% of government revenues in the 2000s.47

4 Expenditure%
60

Bahrain%
15

Bangladesh

10

45

20

5

30

Bhutan

%
30

10

0

15

−10

−10

−15

−15

−30

−20

%
30

0

−5

0

1971 1976 1981 1986 1991 1996 2001 2006 2011

Brunei−20

1971 1976 1981 1986 1991 1996 2001 2006 2011%20

Cambodia

−30

16

12

12

10

8

8

0

4

4

−10
−20

%
18

1971 1976 1981 1986 1991 1996 2001 2006 2011

ROC

0

0

−4

−4

−8

%
15

12

10

6

5

0

0

−6

−5

−12

−10

China

%
20

16

20

1971 1976 1981 1986 1991 1996 2001 2006 2011

1971 1976 1981 1986 1991 1996 2001 2006 2011

Hong Kong−8

1971 1976 1981 1986 1991 1996 2001 2006 2011

India

%
12
9
6
3

%
18

1971 1976 1981 1986 1991 1996 2001 2006 2011

Indonesia

%
24

12

16

6

8

0

0

−6

−8

−12

−16

−18

−24

−24

−32

%
18

1971 1976 1981 1986 1991 1996 2001 2006 2011

Korea

%
200

3
1971 1976 1981 1986 1991 1996 2001 2006 2011

Iran

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

Kuwait

−150

−24

−200

1971 1976 1981 1986 1991 1996 2001 2006 2011

Mongolia

%
25

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

Myanmar

10

5
0

0

0

−5
−10

©2016 Asian Productivity Organization−5−101971 1976 1981 1986 1991 1996 2001 2006 2011

Household consumption

58

Nepal

5

10

−20

1971 1976 1981 1986 1991 1996 2001 2006 2011

10

15
20

−24

%
15

20

30

Malaysia

6

−100

−18

1971 1976 1981 1986 1991 1996 2001 2006 2011

12

−50

−12

−6

%
18

0

−6

Japan

6

50

0

1971 1976 1981 1986 1991 1996 2001 2006 2011

8

100

6

−6

%
10

150

12

%
40

0

−15

1971 1976 1981 1986 1991 1996 2001 2006 2011

Government consumption

Investment−101971 1976 1981 1986 1991 1996 2001 2006 2011

Net exports

Real GDP

4.3 Expenditure-Side Growth Decomposition

%
40

Oman

%
15

30

12

20

9

10

6

0

3

−10

0

−20

−3

−30

%
30

1971 1976 1981 1986 1991 1996 2001 2006 2011

Qatar

20
10

−6

%
60

Pakistan

Philippines

%
15
10
5
0
−5
−10

1971 1976 1981 1986 1991 1996 2001 2006 2011

Saudi Arabia−15

1971 1976 1981 1986 1991 1996 2001 2006 2011

Singapore

%
18

40

12

20

6

0

0

−20

−6

4

0
−10
−20
−30

%
18

1971 1976 1981 1986 1991 1996 2001 2006 2011

Sri Lanka

−40

%
20

12

10

6

0

0

−10

−6

−20

1971 1976 1981 1986 1991 1996 2001 2006 2011

Thailand−121971 1976 1981 1986 1991 1996 2001 2006 2011

UAE

%
90

60

30

−12

%
30

1971 1976 1981 1986 1991 1996 2001 2006 2011

Vietnam

−30

%
9

20

6

10

3

0

0

−10

−3

0

1971 1976 1981 1986 1991 1996 2001 2006 2011

US

−30

1971 1976 1981 1986 1991 1996 2001 2006 2011

EU15

%
6
4
2
0

−20

%
8

1971 1976 1981 1986 1991 1996 2001 2006 2011

Australia

6

−6

%
15

−2
−4

1971 1976 1981 1986 1991 1996 2001 2006 2011

Turkey

APO20

8
6

5

2

4

0

2

0

0

−5

−2

−2
−10

−4

%
12

1971 1976 1981 1986 1991 1996 2001 2006 2011

%
10

10

4

−6

−6

1971 1976 1981 1986 1991 1996 2001 2006 2011

−15

−4

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

Asia30

10
8
6
4
2
0

−4

1971 1976 1981 1986 1991 1996 2001 2006 2011

Household consumption

Government consumption

Investment

Net exports

Real GDP

Figure 39 Final Demand Decomposition of Real GDP Growth, 1970–2014
Sources: Official national accounts in each country, including author adjustments.

59©2016 Asian Productivity Organization−2

4 Expenditure

Box 4

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 huge. Compared with workers in the formal sector, those in the informal sector are typically paid poorly and supply labor in low-quality working conditions without legal protection or official social protection. Some part of the informal sector exists for the
purpose of tax evasion, but the dominant portion in LDCs provides “the only opportunity for many poor
people to secure their basic needs for survival” (ILO, 2013, p.3). Encouraging labor movements from theinformal sector to the formal sector is one of the most important developmental issues in many LDCs.

©2016 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 seem to be drawn fromofficial labor surveys and thus arelikely to exclude most of theemployment in the informal sector.
Therefore, a difference between thenumber of employment and thenumber of employees is loosely regarded as employers/self-employedworkers in the formal sector andworkers in the informal sector. Although statistical problems are evident, particularly for the treatmentof the employment data in the agricultural sector, we can still clearly
see that the number of employees issubstantially lower than the numberof employment in LDCs.

% Share of employee to employment in 2014

100
90

US
Australia
Hong Kong
Japan
EU15

China

80

Malaysia Korea

Singapore

ROC

Turkey70

Philippines
Sri Lanka
60
Iran
Fiji
Mongolia
50
ThailandBangaldesh Indonesia
40
PakistanBhutan
Vietnam
30

Nepal

20

Cambodia
India

10
0

Thousands of US dollars (as of 2014)

0

10

20

30

40

50

60

70

80

90

Per capita GDP in 2014 (using 2011 PPP, reference year 2014)

Figure B4 Employee Share and GDP Level, 2014
Sources: Official national accounts in each country, including author adjustments;
APO Productivity Database 2016.

continued on next page >60

4.3 Expenditure-Side Growth Decomposition> continued from previous page

Figure B4 plots the ratio of the number of employees to the number of employment (the vertical axis)
against PPP-adjusted per capita GDP (the horizontal axis) in 2014 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.

461©2016 Asian Productivity Organization

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

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.48 As workers in high-performing Asian countries tend to work longer hours on average than those in the US, the worker-based labor productivity gaps, in this instance, cast the Asian
countries in a particularly favorable light. Section 5.2 sees the focus shift to alternative estimates oflabor productivity measure, namely GDP per hour worked.

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.49 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).50 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 40 presents the cross-country comparisons of per-worker labor productivity levels in 2014,
measured as GDP per worker in US dollars. The countries naturally bundle into groups. On this measure, Singapore is the leading economy, more than 10% larger than the US level.51 Hong Kong and the
ROC follow at some distance. While Iran is close to the top, it is worth noting that it has the lowest
employment rate in Asia (Figure 21 in Section 3.3, p. 37). Japan took the fourth place, with productivity levels at 34% below the US. Iran, Korea, and Malaysia followed.52 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, 21% 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.53

48: 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.
49: 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.
50: In this edition of the Databook, the TFP estimates were newly developed for Nepal and some country groups such as the ASEAN6and South Asia. Another important improvement in this edition is that the estimates of labor input and its compensation wererevised in some countries, reflecting our work-in-progress estimates on number of workers, hours worked per worker, and hourly
wages, cross-classified by gender, education attainment, age, and employment status, which has been developed for the past few
years at KEO. In Bangladesh, Pakistan, Sri Lanka, and Vietnam, the COE (compensation of employees) data is not available in theirnational accounts in some years. These were interpolated/extrapolated based on our estimates of COE.
51: Cross-country level productivity comparisons are notoriously difficult to make and hence subject to much data uncertainty. Estimates should therefore be taken as indicative for broad groupings rather than precise ranking. The level of labor productivity in
Singapore was slightly lower than the US level in 2011, in the Databook 2013, which was based on the 2005 benchmark PPP. However, in this Databook, it was upwardly revised by 16% due to the use of the new 2011 benchmark PPP (See Box 1, p. 22).
52: Note that the workers aged over 65 are excluded from labor input in Malaysia, due to the definition in labor survey in Malaysia.
This edition of the Databook does not adjust the difference in coverage of workers, which can be defined differently among countries. Thus readers should mind that our estimates of the labor productivity for Malaysia in Figure 40 would be underestimated atleast by 1%, if the omitted workers were included.
53: 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 6, p. 68).

63

©2016 Asian Productivity Organization

Table 9 presents the comparison of the per-worker labor productivity level. In 2000–2014, the APO20
as a group achieved little change in its labor productivity relative to the US, stagnating at around 20%,
while Asia24’s rose from 12% to 20%. In 2000, Hong Kong sustained a productivity gap of 20% with
the US, but by 2014 the gap narrowed to around 5%. In contrast, the relative productivity level of5 Productivity

Japan against the US has deteriorated over the lasttwo decades.
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 41. China’s relative performance against the
US moved up from 2% in 1970 to 7% in 2000 and
19% in 2014, compared with the corresponding figures of 5%, 7%, and 12% for India.54©2016 Asian Productivity Organization

The figures for GCC countries and Brunei are uncharacteristically high, especially in 1970. There are noticeable variations within the country group. The
atypically high figures in the early period reflect thenatural 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 thelargest proven oil reserves in the world and is thelargest world oil exporter. Kuwait has the fourthlargest oil reserves in the world. In addition, Qatar
has become the fourth-largest exporter of liquefied
natural gas. In contrast, Bahrain has the smallest oil
reserves compared to its peers. Its dependence on
oil is therefore considerably lower. Consequently, it
has worked to diversify its economy over the
past decade (see Figure 87 in Section 6.2, p. 118).55
Table 10 presents the growth comparison of perworker labor productivity. When labor productivity growth is compared, the ranking ofcountries is substantially reshuffled. In the 2000s
there was a surge in labor productivity growthamong low-income countries. While they were
scattered in the earlier periods, by 2000–2005
the seven countries with the fastest labor productivity growth were all from Group-L4 (as defined in Table 7 in Section 3.2, p. 34). In the latest
period 2005–2014, five out of the top six werefrom Group-L4 and one from Group-L3. Amongthem, China has sustained rapid productivity
growth in the past two decades.56 Its growth
accelerated to an average of 9.0% per year in
2005–2014 from 7.1% per year in 1995–2000 and
8.7% per year in 2000–2005. This compares with
India at 6.0%, 4.2%, and 4.7% over the same
periods. Labor productivity growth among the64

Singapore
US
Hong Kong
ROC
Australia
EU15
Japan
Iran
Korea
Malaysia
TurkeyMongolia
East Asia
ASEAN6
ThailandSri Lanka
Asia30Indonesia
APO20
Asia24
China
ASEAN
Fiji
Philippines
Pakistan
BhutanIndia
South Asia
Lao PDR
Vietnam
Myanmar
CLMV
Bangladesh
Cambodia
Nepal

125.4
109.4
104.2
92.5
90.6
79.4
72.2
65.1
61.8
54.4
52.4
27.6
27.2
26.2
24.9
24.2
23.0
23.0
22.5
21.6
21.0
20.5
20.5
16.9
15.4
15.2
13.5
12.9
9.0
8.9
8.4
8.4
7.4
5.4
5.1

0

20

Thousands of US dollars (as of 2014)

40

60

80

100

120

Figure 40 Labor Productivity Level by
Per-Worker GDP, 2014

_GDP at constant basic prices per worker, using
2011 PPP, reference year 2014

Source: APO Productivity Database 2016.

Thousands of US dollars (as of 2014)

21
18

China
15
12
9
6
3

India

0
1970 1975 1980 1985 1990 1995 2000 2005 2010

Figure 41 Labor Productivity Trends of China
and India, 1970–2014_GDP at constant basic prices per worker, using
2011 PPP, reference year 2014

Source: APO Productivity Database 2016.

5.1 Per-Worker Labor Productivity

Table 9 Per-Worker Labor Productivity Levels, 1970, 1980, 1990, 2000, 2010, and 2014
_GDP at constant basic prices per worker, using 2011 PPP, reference year 2014
1970

1980

(%)

1990

(%)

(%)

2000

(%)

2010

(%)

2014

(%)

Iran
SingaporeJapan
Hong Kong
Fiji
Malaysia
ROC
Philippines
Korea
Mongolia
Sri Lanka
ThailandIndonesia
Pakistan
Bangladesh
BhutanIndia
VietnamChina

38.3 100.0
31.7 82.8
28.1 73.4
23.1 60.4
14.7 38.4
12.8 33.3
11.0 28.7
9.4 24.6
7.8 20.3
7.4 19.3
6.8 17.7
5.7 14.8
5.3 13.7
5.1 13.3
3.5 9.2
3.1 8.2
2.9 7.6
2.1 5.5
1.0 2.6

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

44.8 100.0
41.3 92.1
40.2 89.8
36.5 81.4
21.5 48.0
20.3 45.3
17.2 38.5
13.0 29.0
11.4 25.4
11.1 24.9
8.8 19.6
8.4 18.7
7.6 17.1
6.0 13.4
3.3 7.4
3.2 7.2
3.0 6.8
2.4 5.3
2.1 4.8
1.6 3.7
1.4 3.1

SingaporeJapan
Hong Kong
Iran
ROC
Malaysia
Korea
Fiji
Mongolia
ThailandSri Lanka
Indonesia
Philippines
Pakistan
BhutanIndia
Bangladesh
Nepal
Lao PDR
Vietnam
China
Myanmar66.8 100.0
58.4 87.4
58.1 87.0
41.3 61.8
37.5 56.2
26.5 39.6
25.1 37.6
16.7 25.1
13.1 19.6
11.7 17.5
11.2 16.8
10.5 15.8
10.5 15.7
9.4 14.1
6.9 10.3
4.3 6.5
3.5 5.3
3.4 5.1
3.3 4.9
2.7 4.1
2.5 3.7
1.6 2.4

Singapore
Hong Kong
Japan
ROC
Iran
Korea
Malaysia
Fiji
ThailandSri Lanka
Indonesia
Mongolia
Pakistan
Philippines
BhutanIndia
China
VietnamLao PDR
Bangladesh
Nepal
Cambodia
Myanmar98.5 100.0
71.9 73.0
64.6 65.6
63.0 64.0
46.0 46.7
42.2 42.9
38.8 39.4
17.4 17.7
16.4 16.6
14.7 14.9
13.5 13.7
12.6 12.8
12.5 12.7
11.9 12.1
9.3 9.4
6.2 6.3
6.1 6.2
4.8 4.8
4.7 4.8
4.6 4.7
4.2 4.3
2.9 3.0
2.5 2.5

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

119.0 100.0
98.9 83.2
86.7 72.9
71.0 59.7
66.9 56.2
58.8 49.4
50.2 42.2
22.4 18.9
20.0 16.8
19.5 16.4
18.7 15.7
18.3 15.4
15.6 13.1
14.5 12.2
14.4 12.1
13.5 11.3
11.2 9.4
7.6 6.4
7.4 6.2
6.7 5.6
6.3 5.3
4.8 4.0
4.4 3.7

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

125.4 100.0
104.2 83.1
92.5 73.8
72.2 57.5
65.1 51.9
61.8 49.3
54.4 43.4
27.6 22.0
25.5 20.3
24.2 19.3
23.0 18.3
21.0 16.8
20.5 16.3
16.9 13.5
15.4 12.3
15.2 12.1
13.5 10.8
9.0 7.2
8.9 7.1
8.4 6.7
7.4 5.9
5.4 4.3
5.1 4.1

Bahrain
KuwaitOman
Qatar
Saudi Arabia
UAE

134.4 350.9
675.9 1765.1
121.7 317.8
311.6 813.8
250.2 653.4
99.2 259.1

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

121.8 271.7
264.9 591.0
166.0 370.4
264.0 589.0
251.6 561.5
365.8 816.1
457.9 1021.6

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

90.3
108.2
177.4
169.8
138.9
227.3
219.9

135.2
162.0
265.5
254.1
207.9
340.2
329.2

107.5
208.9
153.4
227.2
155.1
196.1
197.4

109.2
212.2
155.8
230.8
157.5
199.2
200.6

62.7
130.9
91.8
159.0
124.5
122.2
149.1

Australia
Turkey62.2
28.3

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

82.9
156.4
76.9
220.3
146.2
158.1
168.8

57.8 128.9

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

74.5
155.8
109.2
189.2
148.2
145.4
177.3

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)
7.8 20.4
APO20
4.8 12.6
Asia245.4 14.1
Asia304.9 12.7
East Asia
3.3 8.7
South Asia
6.6 17.1
ASEAN
7.5 19.6
ASEAN6
3.6 9.3
CLMV
309.4 807.8
GCC(reference)
57.2 149.4
US
40.4 105.5
EU15Australia

50.4 131.5

9.7 21.6
5.9 13.1
6.9 15.4
6.3 13.9
3.5 7.8
8.1 18.1
10.7 23.8
2.3 5.0
271.7 606.2
63.4 141.5
52.2 116.4

12.9 19.3
7.9 11.8
8.7 13.0
8.6 12.9
4.8 7.3
10.0 15.0
12.9 19.3
2.7 4.0
151.9 227.4
74.1 110.9
62.4 93.5
93.1
42.4

15.6 15.9
11.1 11.3
12.1 12.3
12.7 12.9
6.8 6.9
12.9 13.1
16.9 17.1
4.0 4.1
171.3 173.9
89.6
73.4
65.6
77.8
35.8

91.0
74.5
66.7
79.0
36.4

20.5 17.2
18.2 15.3
19.4 16.3
22.2 18.6
11.0 9.3
17.9 15.0
22.8 19.2
7.1 5.9
146.4 123.1
107.6
78.0
71.7
85.2
50.3

90.4
65.6
60.2
71.6
42.3

5

66.1
124.7
61.3
175.7
116.6
126.1
134.6

22.5 17.9
21.6 17.2
23.0 18.4
27.2 21.7
12.9 10.3
20.6 16.4
26.2 20.9
8.4 6.7
145.3 115.9
109.4
79.4
73.4
90.6
52.4

87.3
63.3
58.6
72.2
41.7

54: 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.
55: The GCC countries have also been experiencing high population growth, especially in the late 1970s and the early 1980s. In
2000–2014, this has somewhat stabilized at around 3.8% per year, except in the Qatar and the UAE where the population grew at
8.8% and 7.9%, 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.
56: See footnote 27 in Section 3.3 (p. 36) for the reliability of the data in Myanmar.

65©2016 Asian Productivity Organization

Unit: Thousands of US dollars (as of 2014).
Source: APO Productivity Database 2016.

5 Productivity

Table 10 Per-Worker Labor Productivity Growth, 1990–1995, 1995–2000, 2000–2005, and 2005–2014_Average annual growth rate of GDP at constant basic prices per worker, using 2011 PPP
1990–1995

1995–2000

KuwaitChina
Malaysia
Indonesia
Thailand
Vietnam
Korea
ROC
Cambodia
BhutanSingapore
Pakistan
Hong Kong
Sri Lanka
MyanmarIndia
Lao PDR
Bahrain
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
6.5
5.8
5.8
5.5
5.0
4.8
4.2
4.0
3.8
3.7
3.3
3.1
3.1
2.9
2.5
2.2
1.4
0.6
0.6
0.3
−0.1
−0.2
−0.2
−2.3
−3.7
−9.3

Australia
Turkey

2.4
1.3

2.3
4.0
3.9
4.4
3.0
4.8
5.4
3.2
0.5
1.5
1.9

China
Oman
Qatar
Vietnam
Myanmar
ROC
Korea
India
Lao PDR
Singapore
Cambodia
Bangladesh
Philippines
Nepal
Pakistan
Mongolia
Sri Lanka
Saudi Arabia
Japan
Bhutan
Malaysia
Fiji
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
5.5
5.4
5.3
4.8
4.6
4.2
3.9
3.5
3.2
3.0
2.6
1.7
1.7
1.7
1.6
1.6
1.4
1.3
1.1
1.0
0.7
0.7
0.6
0.4
0.3
0.2
−1.5
−2.0
1.5
2.8
2.8
3.4
3.6
0.3
0.0
4.8
1.9
2.3
1.3
1.8
2.1
3.4

Myanmar
China
Vietnam
Cambodia
India
Lao PDR
Indonesia
Thailand
Malaysia
IranHong Kong
Korea
ROC
Singapore
Pakistan
Bangladesh
Mongolia
Sri Lanka
BhutanFiji
Japan
Philippines
Oman
KuwaitNepal
Saudi Arabia
Brunei
Qatar
UAE
Bahrain(regrouped)
APO20
Asia24
Asia30
East Asia
South Asia
ASEAN
ASEAN6
CLMV
GCC(reference)
US
EU15
EU28
Australia
Turkey2005–2014

10.4
8.7
5.6
4.9
4.7
4.1
4.0
3.8
3.6
3.5
3.3
3.2
3.2
3.2
3.0
2.8
2.7
2.5
1.6
1.3
1.3
1.2
1.1
0.8
0.5
0.0
−0.7
−0.8
−1.8
−4.8
2.4
4.3
4.2
4.8
4.1
3.5
3.3
6.2
−0.4
2.2
0.9
1.3
1.3
5.9

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

9.0
7.8
7.2
6.0
4.9
4.6
4.2
4.0
3.8
3.8
3.7
3.2
2.8
2.5
2.4
2.3
1.9
1.8
1.8
1.1
0.9
0.7
0.5
0.1
−0.2
−0.7
−1.4
−1.4
−3.7
−8.3
2.7
5.0
4.8
5.8
5.0
3.2
3.0
4.8
−1.6
1.0
0.4
0.5
1.0
0.9

China
KuwaitVietnam
Korea
ROC
MyanmarSingapore
Malaysia
India
Cambodia
Lao PDR
Thailand
BhutanQatar
Pakistan
Sri Lanka
BangladeshIndonesia
Hong Kong
Nepal
Bahrain
Philippines
Saudi Arabia
Iran
Japan
Fiji
Mongolia
BruneiOman
UAE(regrouped)
APO20
Asia24
Asia30
East Asia
South Asia
ASEAN
ASEAN6
CLMV
GCC(reference)
US
EU15
EU28
Australia
Turkey2000–2014

8.9
6.6
5.6
5.2
5.2
4.3
3.9
3.8
3.7
3.7
3.5
3.4
3.0
2.9
2.8
2.7
2.6
2.5
2.1
2.1
1.7
1.3
1.1
1.1
1.0
0.4
−0.3
−1.1
−1.5
−1.5
1.9
3.4
3.3
3.9
3.3
2.5
2.7
4.0
1.2
1.9
1.6
1.8
2.2
2.4

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

8.9
8.7
5.6
5.5
4.6
4.4
4.3
3.8
3.6
3.5
3.4
3.1
2.7
2.7
2.7
2.5
2.5
2.4
1.7
1.5
1.3
1.1
0.8
−0.2
−0.4
−1.1
−1.5
−1.9
−2.1
−4.9
2.6
4.7
4.6
5.4
4.6
3.3
3.1
5.3
−1.2
1.4
0.6
0.8
1.1
2.7

©2016 Asian Productivity Organization

Unit: Percentage.
Source: APO Productivity Database 2016.

Asian Tigers was steady, ranging from 3.1% to 3.3% on average per year in 2000–2005. This performance was sustained in 2005–2014, except in Singapore. While Singapore’s average annual productivity growth slowed significantly to 0.9%, the others experienced growth of about 2.4% in 2005–2014.
The 2000s were an era when labor productivity deteriorated in GCC countries. The decline accelerated
from –0.4% to –1.6% between the two halves of the 2000s.

665.1 Per-Worker Labor Productivity

Box 5

Turning Point in China

The Lewis model (Lewis, 1954) or the Fei-Ranis model (Fei and Ranis, 1964), which established development economics as a respectable academic discipline in the late 1950s and 1960s, proposed the conceptof a turning point, where a developing economy transforms itself from an unskilled-labor-abundant economy with seemingly unlimited supply of labor, to a labor-scarce industrial economy. The Chinese economy
seems to have reached its turning point in the latter half of the 2000s, based on the APO Productivity Database 2016.
Figure B5 presents the price of labor, relative to capital in China, Japan, and the Asian Tigers. The price oflabor is defined as the average wage (total labor compensation, including our estimates of wages for selfemployed and family workers) over total hours worked. The price of capital is estimated by the ex-post
approach for measuring user cost of capital (see Appendix 3). The relative price index of labor on capital isnormalized as 1.0 in 1970 in each country.
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 wasaround 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.

5

1970=1.0

60
50
40

China
ROC
Hong Kong
Korea
Singapore
Japan

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

Figure B5 Price of Labor Relative to Capitalin China, Japan, and the Asian Tigers, 1970–
2014
Source: APO Productivity Database 2016.

Figure 42 shows labor productivity levels relative to the US (=100) for Asian countries. The samegrouping, as in Table 7 in Section 3.2 (p. 34), based on the speed of catch-up with the US in per capita
GDP, is used here. Broadly speaking, countries that are catching up faster with the US in per capita GDP(Group-C1) are also faster catching up in labor productivity (Figure 42.1). Similarly, countries with deteriorating relative per capita GDP (Group-C4) also present signs of deterioration of or little change
against the US in terms of labor productivity (Figure 42.4).

67©2016 Asian Productivity Organization

As a group, the Asia24 achieved the highest labor productivity growth in recent years, reaching 4.7%
on average per year in 2005–2014, up from 4.3% in 2000–2005. Within Asia, labor productivity growthhas been accelerating in both South Asia and East Asia, to 5.0% and 5.8% in 2005–2014, respectively.
South Asia displayed a newfound vigor in recent years. In contrast, average annual productivity
growth in the US slowed abruptly to 1.0% between 2005 and 2014, after a decade of over 2.0% growth
per year. The EU15 shows signs of weakening as well, slowing in every successive period from 1.9% in
the first half of the 1990s to 0.4% in the most recent period of 2005–2014. Japan’s labor productivity
growth performed closer to that of other mature economies. Having managed to grow at 1.4% on
average per year for a decade in 1995–2000, labor productivity growth in Japan has slowed to 0.5%
per year on average since 2005.

5 Productivity

Box 6

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. 31) and per-worker labor productivity (Figure 40 and Table 9 in Section 5.1, pp.
64–65), this may 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 4.3% of that in Japan, 10.8% of
Korea, and 0.4% of China. It may be more comparable to Tokyo metropolitan (13.4 million), Seoul city (10.0
million), Beijing (21.5 million) and Shanghai (24.3 million). Comparing productivity among cities may provide a better picture for understanding a productivity gap among countries, which consist of a number ofcities with different scales.
Our project began developing a database on productivities of cities in Asia. Figure B6 gives a first look of
our preliminary estimates on the per-worker labor productivities in 2014 among Asian cities with populations of more than 3 million, compared to some large cities in non-Asian countries. The average per-worker
labor productivity level in Tokyo, which is defined as Tokyo metropolitan with population of 13.4 million(not as the greater metropolitan area with 37.0 million), is 118,000 US dollars, which is 63% higher than the
country average of Japan (72,000). This indicates that Tokyo’s productivity has an almost equivalent (6%lower) to that in Singapore (125,000), although the per capita GDP in Tokyo is smaller by 18% than that in
Singapore, which has 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 10.0 million (not as the greater metro area with 24.6 million), is on the 6th position in thischart. The gap in labor productivity between Seoul and the country average of Korea is only 10%, which
may indicate relatively less concentration to the capital in Korea.

Thousands of US dollars (as of 2014)

160

Miliions of persons

Labor productivity

152

Per capita GDP

30

Population (right-axis)

140
129

118
20.1

21.5

20

104

100

96

92
86

80

78.8
70.913.3 13.4
12.5

60

64.3
60.2

20

84 83

15.6
14.4

80

45.2 8.9

7.1 44.9

41.0
40.8
41.1

7.5 39.8
6.1

2.7

2.7

35.0
3.0

63 60
49 47

44

8.3
39 38 37

12.8

11.9

10.8
53 53

15

14.2

13.5

13.1

54.1
10.0

16.2

15.4

9.0

10.1
8.9

8.1 9.9

37 36
30.3 32 32 31

7.1 26.3
4.6 22.4
21.0 21.8
20.0
26.1 21.6
22.2 22.0
1.7 18.9
3.5
11.8

8.7

8.5

10

7.3

30 28 28
27 26
6.4
24 22
20.9 22.1
21
17 17 16 16
14.8
12.3
14 12
11.6
20.4
9.2 2.3 1.6
11
6.5 6.0
7.0 4.7
3.8 3.8
3.7
12.0
10.7
9.8
1.1
3.8

Kolkata (India)
Bangalore (India)
Hanoi (Vietnam)
Karachi (Pakistan)
Lahore (Pakistan)
Chennai (India)
Mumbai (India)
Jakarta (Indonesia)
Haerbin (China)
Ho Chi Minh (Vietnam)
Chengdu (China)
Peshawar (Pakistan)
Colombo (Sri Lanka)
Ulaanbaatar (Mongolia)
Hangzhou (China)
Manila (Philippines)
Qingdao (China)
Wuhan (China)
Shenzhen (China)
Delhi (India)
Tianjin (China)
Shanghai (China)
Beijing (China)
Guangzhou (China)
Busan (Korea)
Ankara (Turkey)
Kuala Lumpur (Malaysia)
Riyadh (Saudi Arabia)
Bangkok (Thailand)
Istanbul (Turkey)
Seoul (Korea)
Kuwait City (Kuwait)
Moscow (Russia)
Toronto (Canada)
Taichung (ROC)
Osaka (Japan)
Nagoya (Japan)
Taipei City (ROC)
Hong Kong (Hong Kong)
Paris (France)
Tokyo (Japan)
Singapore (Singapore)
Los Angeles (US)New York (US)

0

5.5

18.4

74
12.1 69 68

50.4

51.3

40

25

24.3

125

120

5

0

Figure B6 Per-Worker Labor Productivity Levels of Asian Cities, 2014©2016 Asian Productivity Organization_GDP at constant basic prices per worker, using 2011 PPP, reference year 2014

Unit: Thousands of US dollars (as of 2014).
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 and Ulaanbaatar, the observation periods are 2011 and 2010, respectively.

continued on next page >68

5.1 Per-Worker Labor 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 30–35% of the Singapore level, regardless of these cities’ larger populations, whichare 13.1, 21.5, 24.3, and 15.4 million, respectively. These Chinese cities are flowed by Delhi, Manila, Ulaanbaatar, Colombo, and Peshawar. For better policies to foster nation-wide productivity growth, observing
the improvement in city’s productivity may play a key role. Our database is scheduled to be expanded toobserve the changes over periods and to include smaller cities in Asia.

5
Among the countries that are catching up with the US in per capita GDP (Group-C1 and Group-C2), theAsian Tigers have made a tremendous effort in improving their relative labor productivity over the past
four decades. Singapore passed the US in the middle of the 1990s and Hong Kong closed the gap from
60% in 1970 to 5% in 2014 (Figures 42.1 and 42.2). Similarly, the ROC and Korea reduced a gap of around
80% initially to 15% and 44% by 2014, respectively (Figure 42.1). Malaysia is making steady progress,
raising its relative productivity level from 22% of the US in 1970 to 50% in 2014 (Figure 42.2). The rest of
the countries in these two groups all display an initial relative labor productivity level of below 15%, buthave shown signs of a strong and promising start in their catch-up process in the past decade.
Countries that have managed a modest catch-up with the US (Group-C3) or have a declining per
capita GDP against the US (Group-C4) are also those with stagnant or deteriorating relative labor

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

US=100100

Hong Kong
Lao PDR
Mongolia
SingagoreIndia

01970 1975 1980 1985 1990 1995 2000 2005 2010

Figure 42.1: Group-C1 Countries
120

BhutanIndonesia
Malaysia
Myanmar
Sri Lanka
Thailand
VietnamFigure 42.2: Group-C2 Countries
US=100

Bangladesh

Japan

Nepal

Pakistan

Philippines

Turkey

120
100

Fiji

Iran

Australia

EU15

80

80

60

60

40

40

20

20

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

Figure 42.3: Group-C3 Countries

Figure 42.4: Group-C4 Countries

Figure 42 Labor Productivity Level Relative to the US, 1970–2014_Indices of GDP at constant basic prices per worker, using 2011 PPP
Source: APO Productivity Database 2016.
Note: Countries are grouped based on Table 7 in Section 3.2 (p. 34).

69

©2016 Asian Productivity Organization120

5 Productivity

productivity. Japan is the only high-income Asian country in this group, while the rest (except Iran) are
all low-income countries with per capita GDP less than 30% of the US. Japan showed strong catch-up
behaviors in the earlier period, with relative labor productivity peaking at 79% of the US in 1991. Since
then, the gap has widened again to over 30% in 2014. Similarly the EU15, a reference economy withhigh income, has seen its productivity gap double against the US since 1995, from 14% to 27% in
2014; whereas the low-income countries have managed little catch-up (Figure 42.3) or a declining
relative productivity level (Figure 42.4). Iran (a Group-L2 country) experienced a drastic decline in its
relative labor productivity from its former peak of 98% in 1976 to 48% in 1988, before recovering to
64% in 2011. As a result of the strengthened sanctions against Iran, however, labor productivity todate declined drastically.

5.2 Per-Hour Labor Productivity©2016 Asian Productivity Organization

Australia

Singapore

EU15

Hong Kong

ROC

Japan

Turkey

Korea

Iran

Malaysia

Mongolia

Sri Lanka

ThailandIndonesia

Fiji

China

Philippines

PakistanIndia

BhutanVietnamBangladesh

Nepal

Cambodia

The per-worker-based labor productivity gaps presented in Section 5.1 are most likely conservative
estimates, since workers in high-performing Asian countries tend to work longer hours than those in
the US on average. To adjust for this discrepancy, total hours worked are constructed in our databasefor the 21 Asian countries, although the quality of the estimates may vary considerably across countries.57 Figure 43 shows how the
productivity gap against the US
in 2014 varies depending on
which measure of labor productivity is used.58 The productivity%
20
15
gap with the US widens for all
Asian countries when the differ0−5
ences in working hours are tak−15−17
en into account. However, for 14−27 −17 −17−20of these countries, the adjust−34 −30 −27 −23−40−40−44 −37
ments are within 1–5 percent−50−52
age points, and hence are not−60−58 −57 −57
Per-worker based labor−63
deemed as statistically signifiproductivity−77 −79 −78 −75−81cant. In contrast, the choice of
−81
−80
−78
−86 −88 −86 −85−83 −83 −83 −82−92labor productivity measure makes−88 −85−89−95 −95 −93−91 −91−94−100 −96 −96 −95
a significant difference for the
Per-hour based laborproductivity
previously high-performing countries in their relative performance.
Figure 43 Labor Productivity Gap by Per-Worker and PerOn a per-hour GDP basis, theHour GDP Relative to the US, 2014
labor productivity gap with the US_GDP at constant basic prices per worker and hour, using 2011 PPP
widens by 13–32 percentage points
Source: APO Productivity Database 2016.
for the Asian Tigers. Europeans
Note: Light green is used for the countries in which per-hour labor productivity is lower
generally work fewer hours. This
than per-worker labor productivity.
57: 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.
58: The labor productivity gap for country x is country x’s labor productivity divided by the US’s labor productivity in Figure 43.

70

5.2 Per-Hour Labor Productivity

Table 11 Per-Hour Labor Productivity Levels, 1970, 1980, 1990, 2000, 2010, and 2014
_GDP at constant basic prices per hour, using 2011 PPP, reference year 2014
1970

1980

(%)

1990

(%)

(%)

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

14.9 100.0
14.5 97.2
12.7 85.4
8.2 55.2
8.2 55.0
5.6 37.6
5.2 34.6
4.1 27.6
3.9 26.5
3.7 24.7
2.9 19.4
2.9 19.1
2.3 15.5
2.3 15.4
1.7 11.6
1.3 8.9
1.2 8.4
1.1 7.4
1.1 7.3
0.5 3.4

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

21.1 100.0
19.2 91.0
16.4 78.0
14.3 67.9
9.7 46.2
9.6 45.8
8.9 42.2
5.9 28.2
5.1 24.0
4.8 22.9
4.6 21.8
4.4 21.0
2.8 13.4
2.8 13.3
1.6 7.5
1.4 6.6
1.2 5.8
1.2 5.5
1.1 5.2
0.7 3.3

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

29.5 100.0
28.9 98.0
25.5 86.6
17.6 59.7
16.3 55.2
11.6 39.3
9.6 32.7
9.4 31.8
7.0 23.6
5.9 20.0
5.5 18.6
4.8 16.2
4.5 15.3
4.3 14.7
2.4 8.2
2.0 6.7
1.8 6.1
1.7 5.7
1.3 4.3
1.3 4.3(reference)US32.2 215.8(reference)US37.2 176.5(reference)US

43.8 148.6

Australia

31.5 149.4

Australia
Turkey35.0 118.6
15.2 51.5

2000

2010

(%)

41.0 100.0
35.3 86.1
30.8 75.1
30.0 73.2
18.2 44.5
16.8 41.0
16.8 40.8
9.6 23.3
7.6 18.6
7.1 17.2
6.7 16.4
6.5 16.0
5.7 14.0
5.5 13.4
3.3 7.9
2.9 7.1
2.8 6.9
2.1 5.2
2.1 5.1
2.0 4.9
1.3 3.1

SingaporeJapan
Hong Kong
ROC
Iran
Malaysia
Korea
Fiji
Sri Lanka
Indonesia
Mongolia
Thailand
Pakistan
Philippines
BhutanChina
India
Vietnam
Nepal
Bangladesh
Cambodia(reference)
US
EU15
Australia
Turkey53.0 129.1
44.6 108.7
43.7 106.6
18.5 45.1

Singapore
ROC
Hong Kong
Japan
Iran
Korea
MalaysiaFiji
Sri Lanka
Mongolia
ThailandIndonesia
China
Philippines
PakistanIndia
BhutanVietnamBangladesh
Nepal
Cambodia(reference)
US
EU15
Australia
Turkey(%)

51.2 100.0
43.2 84.3
43.0 83.9
40.3 78.6
28.1 54.9
26.2 51.1
21.9 42.8
10.2 19.9
10.2 19.8
9.9 19.3
9.5 18.6
9.5 18.6
7.2 14.1
6.8 13.4
6.8 13.3
5.0 9.8
4.9 9.7
3.4 6.6
2.8 5.5
2.4 4.6
1.8 3.6
65.0 126.9
49.0 95.6
50.4 98.4
26.8 52.4

2014
Singapore
Hong Kong
ROC
Japan
Korea
Iran
Malaysia
Mongolia
Sri Lanka
Indonesia
ThailandFiji
China
Philippines
PakistanIndia
BhutanVietnamBangladesh
Nepal
Cambodia(reference)
US
EU15
Australia
Turkey(%)

54.4 100.0
47.9 88.0
46.0 84.6
41.4 76.0
28.4 52.2
27.2 50.0
24.2 44.5
14.6 26.8
12.0 22.1
11.5 21.0
11.3 20.8
11.3 20.8
9.6 17.6
8.2 15.0
7.4 13.5
6.0 11.1
6.0 11.0
4.2 7.7
3.3 6.1
2.5 4.6
2.3 4.3

5

65.5 120.4
50.4 92.5
54.5 100.1
28.1 51.7

Unit: US dollar (as of 2014).
Source: APO Productivity Database 2016.

is reflected in comparisons of hourly labor productivity showing the EU15 in a more favorable lightagainst the US, albeit only marginally.
Thousands of US dollars (as of 2014)

60
50
40

ROC
Hong Kong
Japan
Korea
Singapore

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

Figure 44 Labor Productivity Trends in Japanand the Asian Tigers, 1970–2014_GDP at constant basic prices per hour, using 2011
PPP, reference year 2014

Unit: Thousands of US dollars (as of 2014).
Source: APO Productivity Database 2016.

59: 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.

71©2016 Asian Productivity Organization

Based on GDP at constant basic prices per hour
worked, the US labor productivity has been able
to sustain a big lead over even the Asian highperformers (Table 11).59 In 1970, the US productivity level was nearly 2.5 times that of Japan.
This gap was reduced to around 34% in 1990.
Since 1990, Japan’s pace in closing the gap has
slowed. By 2014, a sizable gap of 37% 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 2014. This is in contrast with the picture
painted by the per-worker productivity measure,in which the Asian leaders have overcome or almost closed the gap with the US (Table 9 and
Figure 42).

5 Productivity

The levels of labor productivity for the top five economies – Japan and the four Asian Tigers – maintained their relative positions for almost four decades. The progress of labor productivity in thesecountries during 1970–2014 is shown in Figure 44. Within four decades, GDP per hour has more thantripled for Japan and Singapore. Hong Kong and the ROC have improved by six and nine times in thisperiod and have overcome Japan in 2006 and 2008, respectively. They were ahead of Korea, despite
the effort in catching up with Japan by 2.5% per year on average over the past four decades (1970–2014). If they were to maintain this effort at the same pace, it would take Korea 15 years to finally draw
level with Japan.
Over the entire observation period (1970–2014), hourly labor productivity growth ranged from 0.7%
(Fiji) to 6.7% (China) on average per year, compared with the US at 1.6%, as shown in the left chart of
Figure 45. Among the 21 Asian countries compared, only Bangladesh, Fiji, Iran, Nepal, and the Philippines grew slower than the US. Between the two sub-periods (1970–1990 and 1990–2014), there is anotable deceleration in the hourly productivity growth for 10 of 20 Asian countries (excluding Cambodia). For example, 3.0 percentage points and 2.6 percentage points were shaved off productivity

1970−2014%10

1970−1990

1990−2014
8.4

8
6.7
6.1

6

5.2 5.0
4.03.9 3.84

5.9 5.7
4.5

3.6 3.4

3.3 3.2

3.1 3.0 3.0

2.7 2.7 2.62

5.0
4.1 3.9

3.6 3.5

3.4 3.3 3.2

2.4

4.0 3.9 3.8 3.8
2.8

1.6 1.6 1.6 1.6 1.5 1.4

3.1 3.1 3.1 3.0
2.4

2.0 1.9

1.5

0.7

2.2 2.2 2.1

1.8 1.7 1.6

1.5

Fiji
Iran
Bangladesh
Australia
Philippines
Nepal
US
TurkeyPakistanJapan
Sri Lanka
Mongolin
Singapore
VietnamIndonesia
Malaysia
India
Thailand
BhutanCambodiaHong Kong
ROC
Korea
China

0.4 0.3 0.2

0.7

Fiji
Japan
Nepal
US
Australia
Iran
Pakistan
Philippines
Bangladesh
Singapore
TurkeyHong Kong
Sri Lanka
Malaysia
Indonesia
Mongolia
BhutanThailandCambodia
ROC
Korea
India
VietnamChina

1.0 0.8 0.8

2.6 2.6 2.6 2.5Bangladesh
TurkeyIranVietnamPhilippinesFiji
Australia
US
India
Nepal
Sri Lanka
Mongolia
PakistanIndonesia
ThailandSingapore
Malaysia
BhutanJapan
China
Hong Kong
Korea
ROC

0.7

0

4.7 4.6

Figure 45 Labor Productivity Growth, 1970–2014, 1970–1990, and 1990–2014_Average annual growth rate of GDP at constant basic prices per hour

Source: APO Productivity Database 2016.
Note: The starting periods for Australia, Cambodia, and Turkey are 1978, 1993, and 1988, respectively.

1970−2014

%

5

4
3
2
1

1970−1990

1990−2014

4.5
3.9

3.8 3.7

3.6
3.1

2.8 2.6

2.6 2.5 2.5

2.4 2.2

2.1 2.1 2.0 2.0

2.0 1.8

1.7 1.7

3.4 3.3

3.6
3.1 3.0

2.9 2.8 2.7

1.5

2.7 2.6 2.5

3.4
2.9 2.8

2.2 2.2 2.2

1.9 1.8

2.5 2.5 2.4

1.7

0.9 0.7

0.6 0.5

−0.6

Japan
Korea
ThailandUS
ROC
Hong Kong
China
Fiji
Mongolia
India
Indonesia
Iran
Philippines
Vietnam
Pakistan
Sri Lanka
Nepal
Bangladesh
Malaysia
Singapore
CambodiaJapan
US
Sri Lanka
Hong Kong
Nepal
Bangladesh
India
Iran
Fiji
Pakistan
Mongolia
ROC
China
Korea
Philippines
Vietnam
Malaysia
ThailandIndonesia
SingaporeJapan
US
Hong Kong
Korea
ROC
India
China
ThailandFiji
Mongolia
Sri Lanka
Iran
Nepal
Pakistan
Philippines
Bangladesh
VietnamIndonesia
Malaysia
Cambodia
Singapore©2016 Asian Productivity Organization

1.2 1.2−0.1−1

Figure 46 Labor Input Growth, 1970–2014, 1970–1990, and 1990–2014_Average annual growth rate of total hours worked
Source: APO Productivity Database 2016.
Note: The starting period for Cambodia is 1993.

72

1.9
1.5 1.5 1.5

1.2
0.4

0

2.1 2.1 2.0

5.2 Per-Hour Labor Productivity

Table 12 Per-Hour Labor Productivity Growth, 1990–1995, 1995–2000, 2000–2005, and 2005–2014_Average annual growth rate of GDP at constant basic prices per hour, using 2011 PPP
1990–1995

1995–2000

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

10.3
6.4
6.2
6.2
6.2
5.7
5.5
5.0
4.8
4.0
4.0
3.6
3.6
3.1
2.4
2.0
1.5
0.8
0.5
−0.7
−2.5

Australia
Turkey

2.2
1.2

1.5

China
Korea
ROC
Vietnam
India
Singapore
Philippines
CambodiaJapan
Pakistan
Mongolia
Nepal
Bangladesh
BhutanSri Lanka
Thailand
Malaysia
Iran
Fiji
Hong Kong
Indonesia(reference)
US
EU15
Australia
Turkey2000–2005

6.3
5.3
5.2
4.9
4.1
3.1
2.3
2.2
2.1
1.9
1.7
1.7
1.6
1.2
1.2
1.2
1.0
0.8
0.5
−0.2
−1.2
2.3
1.7
2.3
2.8

China
VietnamThailand
India
Korea
Cambodia
Iran
Singapore
Indonesia
ROC
Pakistan
BhutanHong Kong
Malaysia
Mongolia
Bangladesh
Sri Lanka
Philippines
Japan
Fiji
Nepal
(reference)
US
EU15
Australia
Turkey2005–2014
7.7
6.7
5.2
4.7
4.3
4.2
3.7
3.7
3.6
3.5
3.2
3.1
3.1
3.1
2.8
2.7
1.8
1.8
1.4
0.8
0.6
2.5
1.2
1.9
6.0

China
Mongolia
India
BhutanCambodia
Sri Lanka
Bangladesh
Vietnam
Korea
Philippines
Indonesia
ThailandHong Kong
ROC
Iran
Malaysia
Nepal
Fiji
Singapore
PakistanJapan(reference)
US
EU15
Australia
Turkey1990–2000
9.0
7.0
5.8
5.0
4.4
4.1
3.9
3.8
3.5
3.4
3.4
3.2
3.2
2.8
2.4
2.3
1.7
1.5
1.1
1.0
1.0
1.0
0.7
1.4
1.3

China
Korea
ROC
Vietnam
Malaysia
ThailandIndia
Singapore
Cambodia
BhutanPakistan
Sri Lanka
Indonesia
Nepal
Japan
Hong Kong
Philippines
Bangladesh
Iran
Fiji
Mongolia(reference)
US
EU15
Australia
Turkey2000–2014

8.3
5.8
5.4
5.3
3.7
3.7
3.6
3.3
3.0
3.0
2.8
2.6
2.5
2.1
2.0
1.9
1.4
1.2
1.1
−0.1
−0.4
1.9
1.7
2.2
2.0

China
Mongolia
India
VietnamBhutanCambodia
Thailand
Korea
BangladeshIndonesia
Sri Lanka
Hong Kong
ROC
IranPhilippines
Malaysia
Singapore
Pakistan
Nepal
Fiji
Japan
(reference)
US
EU15
Australia
Turkey

8.6
5.5
5.4
4.8
4.3
4.3
3.9
3.8
3.5
3.5
3.3
3.1
3.0
2.9
2.8
2.6
2.0
1.8
1.3
1.2
1.1

5

1.5
0.9
1.6
3.0

Unit: Percentage.
Source: APO Productivity Database 2016.
Note: The annual average growth rate for Cambodia during 1990–1995 replicates their annual average growth rates of
1993–1995 due to absent data.

The deceleration of labor productivity growth between the two sub-periods reflects weaknesses in
output growth in most countries. Figure 46 shows all countries except three South Asian countries, as
Bangladesh, Nepal, and Sri Lanka, experienced a slowdown in hours-worked growth between the
sub-periods, which should have worked to boost labor productivity growth, all other things being
equal.60 For labor productivity growth to slow implies that output growth must have been decelerating
more than labor input in percentage points. In China, output growth was reinforced by the slower
pace of labor input growth to result in an extraordinary surge in labor productivity growth in Figure
45. Labor input growth slowed to 1.2% 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 in labor input in the period from 1990 to 2014. This served to compensate for a sluggish output growth during said period;and sustain a positive labor productivity growth of 1.5% per year on average.

60: By definition, positive labor productivity growth occurs when output grows faster than labor input. Figures 45 and 46 therefore
tend to have an inverse relationship, namely that the higher the labor input growth, the lower the labor productivity growth,
other things being equal.

73©2016 Asian Productivity Organization

growth in the earlier period in Hong Kong and Japan, respectively. Nine Asian countries managed toaccelerate their productivity improvement after 1990. Among these, the performances in China andVietnam are outstanding, with a productivity acceleration from 4.5% to 8.4% in China and from 0.7%
to 5.0% in Vietnam, respectively, between the two sub-periods.

5 Productivity©2016 Asian Productivity Organization

Table 12 more closely examines
the sub-period from 1990–2014,providing the growth rates of perhour-based labor productivity
since 1990. The growth patternsof individual countries generally
follow their counterparts closely in per-worker productivity
growth, as illustrated in Table 10.
In some countries the two measures diverge greatly and are notat all consistent through the periods compared.61 This contrast wasparticularly stark in the first half ofthe 1990s, when Japan’s hourly
productivity growth was 2.0% compared with 0.6% in per-worker
productivity growth. However, thedivergence narrowed to 0.3
percentage points in the period
2000–2014. Korea is anothercountry in which hourly productivity growth was consistently
higher than its per-worker counterpart. Instead of narrowing, thedivergence widened to 1.1 percentage points after 2000. Hours
worked in the ROC have also
grown at a slower rate than number of workers.

Labor productivity gap in 2014 relative to Japan(Japan's level in 2014=1.0)

Singapore

1.3
1.2

Japan (1885−2014)

Hong Kong
ROC

1.1
1.0
0.9
0.8
0.7

Korea
Iran

0.6

Malaysia

0.5
0.4
Sri Lanka

0.3

China
Philippines

0.2
0.1

India

Mongolia
Indonesia
Fiji
Thailand
Pakistan

Cambodia Nepal

Vietnam Bhutan
Bangladesh
0.01880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

Figure 47 Labor Productivity Trends of Japan in 1885–2014
and Levels of Asian Countries in 2014
_GDP at constant basic prices per hour, using 2011 PPP

Sources: For historical data of Japan, the sources of GDP are Long-Term Economic Statistics by Ohkawa, Takamatsu, and Yamamoto (1974) during 1885–1954 and the JSNA
by ESRI, Cabinet Office of Japan, during 1955–2014 (including author adjustments).
Hours worked data is based on KEO Database, Keio University, during 1955–2014.
During 1885–1954, the average hours worked per person are assumed to be constant.
For the labor productivity level of Asian countries in 2014, it is based on the APO Productivity Database 2016.

1970

1980

1990

2000

2010

1970
1991
Japan (21)One can identify where countries
are today in terms of their hourly
1978
1992
Hong Kong (14)productivity performance against
1985
2000
ROC (15)
a backdrop of Japan’s historical
1995
2014
Korea (19)experience. Figure 47 traces thelong-term path of Japan’s perhour labor productivity for the
Figure 48 Time Durations Taken to Improve Labor Producperiod 1885–2014 along thetivity by Japan and the Asian Tigers
green line, expressed as relative
Source: See Figure 47.
to Japan’s 2014 level (set equal to1.0).62 A structural break is observed during World War II when output collapsed. Countries’ relative hourly productivity levels
against Japan in 2014 are then mapped against Japan’s growth experience (as circles). By so doing, acorresponding year can be located when Japan’s hourly productivity level was the closest to the61: 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.
62: 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.

74

5.3 Total Factor Productivity

country in question. The two countries with the lowest hourly productivity in 2014 (Cambodia and
Nepal) see levels corresponding to Japan’s in the 1920s. 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. Among them, China has
been leading the catch-up effort, with productivity growing three times faster than Japan’s long-term
average (Table 12), followed by Mongolia, India, and Vietnam.

5

In pole position are the Asian Tigers, of which Singapore, Hong Kong, and the ROC have already surpassed Japan. Figure 48 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 47). What Japan had
achieved in the 21 years from 1970 to 1991, Hong Kong, the ROC, and Korea managed to achieve in
14, 15, and 19 years, respectively (Figure 48). 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 47.

5.3 Total Factor Productivity
Labor productivity in the previous sections is only a one-factor or partial-factor productivity measureand does not provide a full perspective of production efficiency. An observation of low labor productivity could suggest production inefficiency, but it could also reflect different capital intensities in 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 abundant in low-skilled labor, production lines may
be deliberately organized in a way to utilize this abundant, and hence relatively cheap, resource. It
follows that the chosen production method is most likely to be (low-skilled) 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.63

63: Different types of inputs and outputs are aggregated by using index numbers, and TFP is calculated as the output quantity index
divided by the input quantity index. In this edition of the Databook, the Törnqvist index is used for aggregating labor and 11 typesof capital inputs (the classification is provided in Table 20 in Appendix 2).
64: 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.
65: 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).

75©2016 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.64 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.65 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.66 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

1970−2014

1990−2014

1970−1990
4.1

%

4.0
3.0
2.0
1.0
0.0

3.1

1.9 1.8

2.8

2.5

2.2
1.71.7 1.71.6 1.6 1.61.6

1.4

1.9 1.8 1.7
1.2

1.3
0.90.9 0.9 0.8

0.7 0.7 0.6 0.6

0.4 0.3 0.3−0.1 −0.2
−0.2

−1.0

1.1 1.1 1.1 1.1 1.1

0.9 0.8

2.12.1 2.1 2.0

1.9

1.71.7

1.5 1.5 1.4

0.8 0.7 0.7 0.6
0.1 0.1 0.1−0.1−0.6−0.7−0.7

−2.0

1.3

1.1 1.01.0

0.8 0.8 0.7

0.7 0.6 0.6

0.5 0.4

0.1
−0.3

−1.9

Bangladesh
Nepal
Japan
Malaysia
Indonesia
FijiHong Kong
Vietnam
ASEAN6
US
Philippines
Singapore
APO20
Thailand
IranPakistan
Korea
ROC
CambodiaAsia24
East Asia
Sri Lanka
South Asia
Mongolia
India
China

Iran
Mongolia
Philippines
Nepal
Bangladesh
Fiji
Malaysia
Singapore
ASEAN6
Indonesia
APO20
US
Korea
Asia24South Asia
Sri Lanka
India
Japan
Vietnam
East Asia
PakistanThailandChina
ROC
Hong Kong

Nepal
Bangladesh
Iran
Philippines
Malaysia
Fiji
Singapore
Indonesia
Japan
ASEAN6
US
Mongolia
Vietnam
APO20
Korea
Asia24Thailand
Pakistan
Hong Kong
Sri Lanka
South Asia
Cambodia
East Asia
India
ROC
China

Figure 49 TFP Growth, 1970–2014, 1970–1990, and 1990–2014
Source: APO Productivity Database 2016.
Note: The starting period for Cambodia is 1993. See footnote 70 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 2016 estimates capital services and TFP for the 20 Asian economies forwhich long-time investment data by type of asset are available or estimated.67 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.68 Labor input is measured by total hours worked,
without adjustments for changes in labor quality.69©2016 Asian Productivity Organization

Cross-country comparisons of TFP growth for the 20 Asian countries and the US are shown in Figure
49 for the period 1970–2014, and the two sub-periods 1970–1990 and 1990–2014. In addition, theregional growth accounts are developed for some country groups: the Asia24, the APO20, East Asia,
South Asia, and the ASEAN6.70 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 highgrowth of TFP over 3%. Taking the US as the reference economy, with TFP growth of 0.8% on average
per year, 11 Asian economies achieved higher TFP growth than the US.

66: 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, 2014) and ourestimates for the period of 1970–2014 are high; they are 89.1% and 99.3% 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 20 Asian countries compared.
67: 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; compensation forthe self-employed is not separately estimated but is combined with returns to capital in mixed income, except China, where labor
remuneration in the national accounts includes labor income for the self-employed (Holz, 2006). In the Databook, it is assumed
that the per-worker wages for self-employed and contributing family workers are 5% to 70% of the per-worker wage for employees in the countries where the appropriate wage data is not available, in order to measure total labor compensation. See Box 7 forsensitivity of our assumptions to the TFP results.
68: IT capital is defined as a composite asset of IT hardware (computers and copying machines), communications equipment, andcomputer software.
69: The hours worked were newly estimated for Fiji and Mongolia in this edition. The failure to take into account improvements in labor quality leads to TFP overestimation. The harmonized estimates of labor quality changes are planned to be incorporated in thenext edition of the Databook (See Box 8, p. 123).
70: In sections 5.3 and 5.4 the Asia24 does not include Bhutan, Brunei, Myanmar, and the Lao PDR, the APO20 does not include the
Lao PDR, South Asia, and the ASEAN6 does not include Brunei.

765.3 Total Factor Productivity

%

%

9

TFP

8
7

1.7 0.6

4.2

3.9

0.6

0.2
0.2
1.1

4.6
3.4

3.9 3.5

3.1
0.4 0.4

0.9

16

29

2.7 2.7 3.0 2.1 2.2 2.4 3.2

1.6 0.9 0.3

2.0 2.6

2.6

56

60
53
3.0 2.8
3.0

0.8 0.4
0.9

40

1.1
0.1 1.6

72

IT Capital

31 30 29 26 35 32 31

Labor
6

17

14

21

29

34

65

62
65 62 58

51

56

49

0.7

0.1
0.1 0.1 0.1
0.1
0.2 0.1
0.4 0.3 0.3
0.4
0.2
1.2 1.2 1.2 0.3
1.2
1.2 1.2 1.1
1.1 0.2 1.0 1.2 1.4 0.1
0.8 1.0 0.7
0.8
0.6
0.6 0.7
0.4
0.3
–0.1
–0.1
–0.2 –0.2

0.3 0.2 0.3 0.3

0.8 1.0 1.1

1736

Non-IT Capital
11 13

74 69
1.8 1.7 1.6

3.3

0.6

1.6 1.8

5

0.7 0.9
1.7 1.6 1.6 1.4

4.4

9
23

80

3.9

2

−1

TFP
100

0.3

1.2
1.9

4

0

120

Labor

Output5

1

IT Capital

3.1

6

3

Non-IT Capital

49 49 56 40 43 47

65

20
0

3
12

22 26

6 6

5 5
5 3

18
12 15

2 2

39

83
31

41

9
2

28

2

59

3
6 3

2

1
7 5
35 3
3
6
5
25
25 29
23
23 23 21
21 21 21 617
15
15 17 13
14
7
−6 −6

5

64

14

−3

45
11
−3

Japan
Fiji
US
Iran
Nepal
Bangladesh
Philippines
APO20
Sri Lanka
Mongolia
PakistanSouth Asia
India
Asia24ThailandHong Kong
East Asia
Vietnam
ASEAN6
Indonesia
Malaysia
ROC
Korea
Singapore
Cambodia
China

Japan
Fiji
US
Iran
Nepal
Bangladesh
Philippines
APO20
Sri Lanka
Mongolia
PakistanSouth Asia
India
Asia24ThailandHong Kong
East Asia
Vietnam
ASEAN6
Indonesia
Malaysia
ROC
Korea
Singapore
Cambodia
China

Figure 50 Sources of Economic Growth,
1970–2014

Figure 51 Contribution Shares of Economic
Growth, 1970–2014

Source: APO Productivity Database 2016.
Note: The starting period for Cambodia is 1993. See footnote 70
for the country-exception in the country groups.

Source: APO Productivity Database 2016.
Note: The starting period for Cambodia is 1993. See footnote 70
for the country-exception in the country groups.

Looking at the sub-periods (1970–1990 and 1990–2014), one can discern that the two were not identical and, in fact, had quite significant differences in terms of the magnitude of growth and countries’
relative performance. Eleven of the 20 Asian countries experienced acceleration in TFP growth. Iranand Mongolia achieved considerable recoveries from negative TFP growths: from –1.9% to 1.4% andfrom –0.7% to 2.1%, respectively.71 More modestly, the TFP growths in China and India improved from
1.9% on average per year in the earlier period to 4.1% since 1990 and from 1.1% to 2.5%, respectively.
The three countries that saw their productivity growth more than halved are Hong Kong and Japan.

71: 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.
72: 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.

77©2016 Asian Productivity Organization

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 50 and 51 present the sources of economic growth
and those contribution shares, respectively, for the entire observation period 1970–2014. 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 Singapore and Korea, growing at 6.9%
on average per year. From these GDP growths, the TFP contribution accounted for over 25% of economic growth in eight of the 20 Asian economies compared. Among them, TFP contribution was thelargest in China (36%), India (35%), Sri Lanka (34%), Pakistan (31%), and Hong Kong (30%) with over
30%, followed by the ROC (29%), Thailand (29%), and Japan (28%). In contrast, TFP performance wasvery modest in Singapore, resulting in its relatively small contribution of only 9% to economic growthover the same period (0.6% on average per year as the TFP growth rate). In Korea the TFP contribution
in GDP growth was 17% (1.2% on average per year), which was outperformed by the whole Asia of
26% (1.4% on average per year).72

5 Productivity

1970−1985%

9

1985−2000

2000−2014

0.2
1.5
0.4

4.0

2.4 1.9

6

6.0
6.2

1.8
4.9 4.5

3.6 3.9

1.2

0.3
3.4

1.3 0.8

5.6 3.1

1.4

3.4 3.4

0.6 0.5

5.3

3.7
4.0

2.7 2.2

2.4 2.1 1.9

5.2

4.4
3.2

4.1

2.3

4.2

0.4

3.7

1.1

3.8

3.4 2.1

4.0
3.3

1.1

4.8
1.0
1.0

3.1 3.1

0.7

1.4 0.6
1.8 2.7
0.8 2.4 2.0 0.8 0.7 1.6
0.1
0.1
0.3 0.3 0.3 0.1
0.2 0.1 0.1
0.5 0.9 0.1
0.1 0.6 1.8 0.4
0.4 0.1
0.1 0.3 0.5
0.2 0.1 1.6
1.8
1.1 0.9 0.9 1.3 1.1 0.9 1.1 1.2 1.2 0.3 0.9 0.7 1.2 0.7 1.2 1.6 1.5 0.3 0.7 1.0 0.7 1.4 0.1
0.6
0.4 0.6
0.4
−0.1
−0.4
−0.2
−1.1
2.1

2.3

2.7

2.2 2.2 1.5

5.2

4.9

0.8
2.7 3.5
1.6 1.5
0.5
0.7
0.2
2.3 3.1 0.1
0.4
0.2 0.1 0.1 1.1 0.9
0.4 0.2
0.1 0.2
0.1
0.3
0.2
1.7 1.4 2.1 1.4 1.6 1.2 1.5 1.6 0.1 1.9 0.2 0.2 0.1 1.7 1.5 1.6 0.1 1.6 1.8 1.7
1.0 1.0
0.8 1.1 0.6 0.9 0.9 0.7 0.3
0.8
0.2
−0.4
−0.5
−0.7 −0.2
−0.6
−1.2
−2.0
−2.6

0.8

2.6

0.7 0.5

1.7

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

0

0.5

Bangladesh
US
Fiji
Nepal
Philippines
Iran
India
South Asia
VietnamJapan
Sri Lanka
APO20
Asia24
East Asia
PakistanThailandMongolia
ASEAN6
Indonesia
Malaysia
China
Hong Kong
Singapore
Korea
ROC

3

1.0

0.2

0.9
1.1

2.8 3.0

0.9

5.3 6.6

3.5

0.3
1.8 1.3

3.1

3.2

1.7 1.2 1.2
4.7

2.8

2.7

2.1

1.8 1.9
1.9

1.0

1.5
2.6

2.4

1.5 1.5

1.8

2.5
0.6
2.8 2.0
2.1 1.7 1.2 1.3 0.6
0.1
0.1
0.3
0.5 0.2
0.3 0.4
1.1
0.2
0.7
0.5 1.8 0.2
1.4
0.4 1.2 0.9 1.0 0.9.0.8 0.7 1.2 0.2 0.3 0.3 0.3 0.3 0.4 1.2 0.1
0.1
0.5 0.9 0.8 0.8 0.9 0.4
0.2 0.5
0.3 0.4 0.1 0.1 0.4 0.3 0.5 0.4
0.2
0.1−0.2
−0.2
2.4

0.2

3.1 3.0

0.2 0.2 0.2

0.6

2.2 1.9 1.3

0.6

−3

Non-IT Capital

IT Capital

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

TFP

Labor

OutputFigure 52 Sources of Economic Growth, 1970–1985, 1985–2000, and 2000–2014
Source: APO Productivity Database 2016.
Note: See footnote 70 for the country-exception in the country groups.

1970−1985

%

120
100

2

6

17
32

80

59

88

77

71

72
91

−8

−7

30

68

72

4

17 14

18

25

35

19
53 71 1

52

1

3
45 40
3 4
1
2 31 4
19
19
6
18
16
13
11
5
−6

8

38 41

43
43

17 20

24 18

20
45

38 35

28

14 10

25 25

31

30

47

4

1

9
52
45 2 47 54
22

73

47

2 2 35 2

62

57
43

55

44
11

67

75

59

33

32

61

39
51

2

51
19 55

51

3
4

1

1

37

45

23

22 23
35 39 42

16

29

84
66
52

3 60

38 39

35

48

64
62

9

3

78
47

49
48

52 50

31

39
48

47
43 39 29

11

34
67 64

2

65

38
48

52

45

33

3 8
4

17

2
12
4
27
6 7
3 4
21
28
4
3 7 3
27 6
7 11 3127
25
6 24 12 12 14 7 6 21 16 18 18 16 15 4 10 7
12
8
8
9
8
8
5
6
3
4
1
−1
−3

−88

−29

5 1 2
6
5 5
10
2
6
34 32 7 30
524
24 18 16 14 20 22 22 6 17 23 24
18
14
12
13
7 13
−8
−3

−60 −21−75 −35

TFP

31

49

12 12
−9

26

26

1 7 25 630

36 37 36

38
54

23

62 64

3

12

22

71

40

84

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

2

10 6

Bangladesh
US
Fiji
Nepal
Philippines
Iran
India
South Asia
VietnamJapan
Sri Lanka
APO20
Asia24
East Asia
PakistanThailandMongolia
ASEAN6
Indonesia
Malaysia
China
Hong Kong
Singapore
Korea
ROC

−20

2000−2014
130

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

3 1 1

2

19 16 27 18 21 18 21 25

0

19

48 65

6
5 4

33

31

47 53

40
20

15

26

75

60

1985−2000
151108 79 66

Non-IT Capital

IT Capital

15
−9

Labor

Figure 53 Contribution Shares of Economic Growth, 1970–1985, 1985–2000, and 2000–2014©2016 Asian Productivity Organization

Source: APO Productivity Database 2016.
Note: See footnote 70 for the country-exception in the country groups.

China’s productivity performance was outstanding in this period. The average TFP growth was 3.1%
per year during 1970–2014 (Figure 49). This compares with the long-run estimates of 3.8% during
1978–2005 in Holz (2006) and also 3.8% during 1978–2004 in Bosworth and Collins (2008). The Chineseexperience of long-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 in labor quality had been taken into account in the estimation of labor growth (and, assuch, eliminating overestimation in TFP).73

78

5.3 Total Factor Productivity

Looking at the breakdown of the period in Figure 52, one can see Iran, the Philippines, Nepal, and Fiji
were running an overall negative TFP growth in the period 1970–1985, at –2.6%, –2.0%, –1.2%, and–0.7% on average per annum, respectively.74 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-onyear changes of growth decomposition in each country (Figure 59), the Philippine’s TFP fell severely inthe beginning of the 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 thetransition to market economy in 1992 in Figure 59, which induced a negative TFP growth in the period
1985–2000 (–1.1% on average), as shown in the center chart of Figure 52.

5

It is obvious in Figure 51 that economic growth was predominantly explained by the contribution ofcapital input in most of the Asian countries, which ranged from 41% in Fiji to 86% in Iran. Among the
Asian Tigers, the contribution of capital services ranged from 56% in Hong Kong to 65% in Singapore,
whereas in China and India, it accounted for 52% and 42% of economic growth, respectively. This
compares with 45% in the US, of which 14 percentage points were contributed by IT capital, a shareunmatched by the Asian countries. Japan has been leading Asian countries in terms of contribution
from IT capital (11% of economic growth) whereas in other Asian countries it has been 1–9%, with
China and India trailing behind.
One prevalent characteristic of the Asian countries is their investment intensity as a share of GDP (Figure 30 in Section 4.2, p. 50), and in turn its contribution to economic growth (Figures 51 and 53). Thereis policy significance in identifying the driver(s) 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 52 and 53), 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 toward driving growth.

73: In the same period 1960–1973, the average annual contribution rate of labor quality improvement to growth is measured as 0.54%
in Jorgenson and Nomura (2005). As a measure of the TFP contribution that is comparable with the estimates in this Databook,their estimate can be recognized as 3.6% per year during the same period.
74: 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.
75: The estimates of TFP growth in Vietnam were revised downwardly in this edition; from 2.6% (in the previous edition of the Databook) to 1.1% (in this edition) in 1985–2000 and from 1.3% (in 2000–2013) to 0.2% in 2000–2014. It is mainly due to the downward
revisions on the estimates for compensation of employees (COE) over the whole observation periods by more than 20%. In the
Vietnamese system of national accounts only the totals of COE and mixed income are published during 1995–2002. In this edition
these were separated using our time-series estimates of COE, based on the newly developed labor data on hours worked andhourly wages.

79©2016 Asian Productivity Organization

As show in Figure 53, capital accumulation was the dominant factor in the early period 1970–1985,
typically explaining two-thirds to three-quarters of economic growth achieved. In Vietnam, Pakistan,Hong Kong, Sri Lanka, and China, however, the contribution of TFP growth was still significant, accounting for 26–35% of their respective economic growth.75 In the subsequent periods, the contribution of capital input became progressively smaller, falling to a share of below 56% on average in 2000–
2014 from 71% in 1970–1985 in the whole Asia, while the contribution of TFP became progressively
more significant, rising to a share of above 36% from 10%. Reflecting on these results, capital
accumulation appears to be a necessary step to economic growth, especially in the early period
of development. Although a prerequisite, capital accumulation does not guarantee TFP growth.

5 Productivity

%

10

9.4

8

3.5

6
4

7.8

TFP
0.9 6.8 6.7
0.2 6.4

4.2

2.6

0

2.3

6.0 6.0

5.6 5.6 5.5

2.2 2.2 1.5

5.7

5.3 5.1 5.1

1.7 1.2 1.2

4.9

Labor

Output

4.9

5.0

4.4

1.8 1.9
1.9

4.3 4.2 4.1 4.0
1.0

1.5

3.8 3.7 3.7

2.9
2.7 2.6
0.5
0.3
0.9 2.0 2.0 1.9 1.8 1.7 1.7
1.3 1.1
0.4
2.3
0.4 0.6 0.6 1.4 1.4 1.3 1.2 1.1
2.4
1.3 0.7
1.1 1.0 0.7
1.9 1.6
0.2 0.5
1.3 0.8
0.6 0.6
0.8
1.8
1.4
0.6 0.2
0.1
1.0 0.5 0.7 0.7 1.1 0.6 0.4
1.2 1.1 1.2
0.2 0.8
1.2 0.9 1.0 1.4 0.9 0.8
1.2
0.9
0.9
0.8
0.8
0.8
0.7
0.5 0.4
0.5
0.4 0.3
0.3 0.4
0.5 0.4 0.6
0.3
0.3 0.4 0.6 0.4 0.6 0.3 0.7 0.5
0.2 0.5
0.2
0.1
0.1
0.1
0.1
0.1
0.1
–0.1
–0.1
–0.2
–0.2
–0.5
5.4

2

Capital

7.6

2.7

3.4

3.2

3.0

3.6 3.3

2.9

2.7

3.0

1.5

2.6

2.5 2.3 1.4 2.9

1.5 1.8

2.6

Italy
DenmarkJapan
Netherlands
Germany
FranceFinland
Spain
Austria
Belgium
UK
US
Switzerland
SwedenFiji
CanadaIreland
New ZealandAustralia
Nepal
Hong Kong
ROC
Korea
ThailandAPO20
IranPakistan
Philippines
ASEAN6
Malaysia
Singapore
Indonesia
Bangladesh
Sri Lanka
Asia24
East Asia
South Asia
Vietnam
India
Cambodia
Mongolia
China

Figure 54 Comparison of Sources of Economic Growth with OECD Countries, 2000–2014
Sources: APO Productivity Database 2016 for APO member economies and China and the US. OECD Stat (Dataset: Multi-Factor Productivity)
and OECD (2016a) for OECD countries (except Japan and Korea).
Note: The ending year for Ireland, Portugal, and Spain is 2013. See footnote 70 for the country-exception in the country groups.

Some countries may be more capable than others in reaping the benefits through assimilationof technologies.
Figure 54 places our estimates among those of OECD for 17 other OECD countries to give readers awider perspective.76 Countries are arranged according to their average economic growth per annum
for the period 2000–2014, 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 2000–2014:
4.2% in Mongolia, 3.5% in China, 2.6% in Thailand77 and India, 1.9% in the Philippines and Pakistan,and 1.8% in Hong Kong.

©2016 Asian Productivity Organization

Though growing at a more subdued pace, the contribution made by TFP in the slower-growing, mature economies should not be underestimated. Figure 55 plots per capita GDP levels in 2014 andthe TFP contribution shares in the period 2000–2014, for 20 Asian countries with comparison ofOECD countries (as circles). The roles of TFP contribution are also large in some mature economies,
rather than some low-income countries in CLMV and South Asia. TFP accounted for more than onethird of economic growth in Japan, Germany, Finland, Austria, Sweden, the UK, France, and the US in
this period.

76: The multi-factor productivity in the OECD Productivity Database (OECD, 2016a), 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.
77: 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 2.7%, –9.3%, and 2.7%, 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 ourestimate of TFP growth of 1.2% in 1990–2010.

80

5.3 Total Factor Productivity% TFP contribution share, 2000–2014
Table 13 and Figure 56 show the90
Japan
growth accounting decomposition
80
for individual countries in five-year
70
intervals covering the period 1970–
ThailandFiji
2014. The relative importance ofGermany
60
drivers behind economic growth
Mongolia
50
FinlandHong Kongchanges over time. It is a common
Pakistan
ROC Austria
Korea
experience in most countries that a
Philippines
40
UK
China
India
large part of the vibrant growth in
US
SwedenIndonesia
France30
Irelandthe initial period is driven by input
Singapore
Switzerland
CanadaSri Lanka
Iran Malaysia
Netherlands
20
growth. TFP growth becomes more
Belgium
Cambodia
Australia
prominent and makes a steady con10
New Zealandtribution in the later periods. Hong
Nepal Vietnam
Denmark
0
Kong’s TFP growth peaked at 5.4%
Bangladesh
in 1975–1980, and was robust at
−10
Spain
3.8% in 1985–1990, when TFP(Thousands of US dollars)−20
growth also peaked in the ROC,78
0
10
20
30
40
50
60
70
80
90
Per capita GDP, 2014
Korea, Singapore, and Japan, at
4.3%, 2.7%, 2.3%, and 2.1%, respecFigure 55 Comparison of TFP Contribution Shares withtively. Thereafter, TFP growth slow­
OECD Countries, 2000–2014
ed until recent years when countries
Sources: APO Productivity Database 2016 for APO member economies and China
experienced productivity growth
and the US. OECD Stat (Dataset: Multi-Factor Productivity) and OECD (2016a) forOECD countries (except Japan and Korea).
resurgence. This resurgence is
Note: The ending year for Ireland and Spain are 2013.
also shared by Malaysia and thePhilippines. TFP growth in Mongolia has been particularly strong
since 1995. It also has bounced back in Indonesia79 and Thailand80 from a negative standing, following
the Asian financial crisis of the late 1990s, but has lapsed again since 2005. In contrast, the US experienced a surge in TFP growth in the second half of the 1990s, which was sustained into the early 2000s
before the adverse cyclical effect hit in 2005–2014.

5

78: 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 0.82 for this period. For 1985–1999, ourestimate is 2.8%, compared to their estimate of 3.6%.
79: 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.
80: 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.

81©2016 Asian Productivity Organization

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 that has more bearing in determining
the overall economic growth over time. For example, the low economic growth that China experienced in 1985–1990 was explained largely by the lack of TFP growth. Similarly, when output growthslowed from its peak in the latter half of the 1990s, it was due to the slowdown in TFP growth from
7.2% on average per annum in the previous period to 2.9%. Thereafter, output growth has accelerated
to reflect the pickup in TFP growth in the 2000s. In India, TFP growth was a drag in the 1970s. Since
then, it has accelerated and has increasingly accounted for a greater proportion of economic growth.
In 2005–2010, India achieved TFP growth of 3.5% – its highest in the past four decades. Through trial
and error, China and India invested first and then learned how to combine inputs efficiently. Both have

5 Productivity

82

Cambodia
ROC
Hong Kong

0.0
0.0
0.1
0.1
0.1
0.2
0.2
0.2
0.2
0.1
0.0
0.0
0.0
0.1
0.1
0.2
0.6
0.5
0.4
0.2
0.1
0.1
0.0
0.1
0.1
0.0
0.1
0.1
0.0
0.1
0.0
0.0
0.0
0.0
0.1
0.1
0.1
0.2
0.2
0.1
0.1
0.1
0.0
0.1
0.1
0.1
0.2
0.2
0.1
0.1
0.2
0.5
0.4
0.6
0.5
0.6
0.6
0.2
0.1
0.4
0.1
0.1
0.1
0.1
0.1
0.1
0.2
0.4
0.3
0.2
0.0
0.0
0.0
0.1
0.1
0.0
0.1
0.1
0.1
0.1

Indonesia

−0.2
(8)
1.9
(51)
1.2
(33)
1.4
(33)
1.9
(38)
1.5
(30)
1.0
(20)
0.9
(16)
0.8
(12)
1.2
(29)
1.4
(24)
1.4
(22)
1.9
(19)
1.3
(17)
0.8
(7)
1.3
(15)
1.0
(10)
0.1
(1)
0.4
(5)
1.1
(12)
2.1
(37)
1.5
(39)
1.7 (243)
1.2
(33)
1.9
(71)
1.1
(53)
0.7
(36)
0.2
(22)
0.6
(17)
1.2
(45)
1.8
(62)
1.7
(56)
1.4
(29)
1.3
(23)
1.2
(25)
1.0
(17)
1.1
(17)
0.5
(7)
0.7
(12)
1.2
(23)
0.4
(5)
1.0 (−36)
0.8
(21)
0.8
(62)
0.5
(15)
0.8
(20)
0.8
(11)
0.0 (−1)
0.2 (191)
0.6
(17)
1.6
(17)
1.4
(19)
1.2
(13)
1.7
(17)
1.0
(13)
0.0 (−1)0.2(5)−0.3 (−6)
0.5
(17)
0.8
(12)
0.6
(9)
0.9
(17)
0.9
(13)
1.6
(43)
−0.1
(4)
0.3
(12)
1.3
(20)
0.5
(8)
0.9
(8)
0.8
(15)
1.4
(38)
0.9
(16)
0.9
(13)
1.0
(15)
0.8
(14)
1.0
(26)
1.3
(21)
1.5
(40)
0.8
(21)
1.1
(21)

Japan

−2.0
3.7
3.7
4.4
5.0
5.1
5.0
5.9
6.1
4.0
5.8
6.3
10.1
7.6
11.6
8.3
9.4
10.7
7.8
8.6
5.6
3.7
0.7
3.7
2.7
2.0
2.0
0.7
3.5
2.7
2.8
3.1
5.0
5.8
5.0
5.7
6.5
7.8
5.7
5.3
9.5
−2.8
3.8
1.3
3.7
4.1
6.9
5.0
0.1
3.6
9.4
7.5
8.9
9.8
8.1
5.3
4.7
4.2
3.0
6.9
6.5
5.4
6.6
3.8
−2.8
2.7
6.3
6.4
11.5
5.0
3.6
5.8
7.4
6.6
5.5
4.0
5.9
3.7
3.6
5.1

CapitalTFP
Non-IT(−1) 0.2 (−10) −2.1 (104)(1) 0.7 (20) 1.0 (28)(2) 1.8 (50) 0.6 (16)(2) 2.9 (66) 0.0 (−1)(3) 3.2 (64) −0.3 (−5)(3) 4.2 (83) −0.9 (−17)(3) 4.6 (93) −0.8 (−16)(3) 4.8 (81) 0.0(0)(4) 4.8 (79) 0.3(5)(3) 3.0 (74) −0.2 (−6)(1) 4.2 (73) 0.1(2)(1) 4.0 (64) 0.8 (13)(0) 3.4 (34) 4.8 (47)(1) 4.4 (58) 1.8 (24)(1) 3.6 (31) 7.2 (62)(2) 4.0 (48) 2.9 (35)(7) 4.3 (46) 3.5 (37)(5) 5.6 (52) 4.4 (41)(5) 4.8 (62) 2.2 (29)(3) 4.2 (49) 3.1 (36)(2) 2.5 (45) 0.9 (16)(2) 2.4 (66) −0.3 (−7)(7) 1.7 (237) −2.7 (−387)(2) 0.1(2) 2.3 (62)(5) 1.2 (43) −0.5 (−20)(−1) 0.9 (46) 0.0(2)(5) 0.5 (25) 0.7 (34)(14) 0.1 (14) 0.4 (51)(0) −0.2 (−6) 3.1 (88)(3) 1.1 (39) 0.4 (14)(1) 1.3 (47) −0.3 (−10)(1) 1.6 (51) −0.2 (−7)(1) 1.5 (29) 2.1 (41)(1) 1.6 (28) 2.8 (49)(1) 1.7 (35) 1.9 (38)(2) 2.0 (34) 2.6 (46)(2) 2.1 (32) 3.2 (49)(3) 3.6 (46) 3.5 (44)(3) 4.1 (71) 0.8 (13)(2) 2.1 (40) 1.8 (35)(1) 6.0 (63) 3.0 (31)(−2) 7.1 (−248) −11.0 (386)(1) 2.8 (73) 0.2(4)(4) 0.3 (25) 0.1(9)(2) 1.0 (28) 2.0 (55)(3) 1.3 (31) 1.9 (47)(3) 2.8 (41) 3.0 (44)(3) 3.4 (68) 1.4 (29)(101) 1.9 ( - ) −2.1 ( - )(3) 3.0 (83) −0.1 (−3)(3) 7.0 (75) 0.5(6)(6) 8.1 (107) −2.4 (−32)(4) 4.8 (55) 2.5 (28)(6) 4.8 (49) 2.7 (27)(6) 5.0 (62) 1.6 (20)(11) 3.5 (66) 1.2 (23)(12) 2.4 (51) 1.5 (32)(4) 2.0 (48) 2.3 (55)(3) 1.8 (60) 0.6 (20)(6) 4.4 (65) 1.2 (17)(1) 5.2 (81) 0.6 (10)(3) 5.7 (105) −1.3 (−25)(2) 6.0 (91) −0.4 (−6)(3) 3.8 (99) −1.7 (−45)(−3) 0.9 (−32) −3.6 (131)(5) 0.2(8) 2.0 (75)(4) 0.2(3) 4.6 (73)(6) 3.0 (47) 2.5 (39)(2) 4.4 (38) 6.0 (52)(3) 3.2 (65) 0.9 (17)(1) 2.4 (66) −0.2 (−4)(0) 2.4 (41) 2.5 (43)(0) 3.3 (44) 3.2 (43)(2) 4.1 (63) 1.4 (21)(2) 3.5 (63) 1.2 (21)(1) 2.3 (57) 0.6 (15)(2) 1.5 (25) 3.1 (52)(3) 1.7 (47) 0.4 (10)(1) 0.5 (13) 2.3 (64)(1) 2.4 (47) 1.6 (31)IT

Malaysia

Labor

Nepal

1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2014
1970–2014
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2014
1970–2014
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2014
1970–2014
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2014
1970–2014
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2014
1970–2014
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2014
1970–2014
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2014
1970–2014
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2014
1970–2014

OutputPhilippines

Pakistan©2016 Asian Productivity Organization

Mongolia

Korea

Iran

India

Fiji

China

Bangladesh

Table 13 Output Growth and Contributions of Labor, Capital, and TFP, 1970–2014

1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2014
1970–2014
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2014
1970–2014
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2014
1970–2014
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2014
1970–2014
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2014
1970–2014
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2014
1970–2014
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2014
1970–2014
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2014
1970–2014

Output

Labor

7.6
7.0
9.0
6.5
7.0
7.4
9.3
10.6
6.9
8.9
7.2
5.8
4.0
4.2
3.0
6.7
6.3
10.9
5.6
7.4
5.2
2.6
4.1
3.8
3.0
5.5
8.3
7.8
4.8
7.5
7.6
0.8
4.9
6.2
5.5
5.9
4.4
4.3
4.3
4.9
1.4
0.9
1.2
0.3
0.7
2.5
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
4.0
4.0
5.7
5.9
−1.4
5.3
2.8
3.9
4.5
4.8
5.7
4.1

1.1
(15)
2.0
(28)
2.3
(25)
1.8
(28)
0.5
(7)
1.6
(22)
2.0
(22)
1.9
(18)
1.1
(16)
1.2
(14)
1.0
(14)
0.3
(5)
0.2
(5)
0.2
(6)
0.7
(23)
1.0
(15)
2.0
(32)
1.6
(14)
0.6
(10)
0.2
(2)
0.6
(11)
1.4
(55)
0.5
(13)
0.2
(5)
0.2
(6)
0.8
(15)
1.6
(19)
1.3
(17)
1.5
(32)
2.1
(28)
0.7
(9)
0.9 (122)
0.6
(13)
1.8
(29)
0.4
(8)
1.2
(21)
−0.3 (−7)
0.6
(15)
0.3
(7)
0.4
(7)
−0.3 (−21)−0.7 (−77)−0.1 (−11)−0.5 (−141)
0.0 (−2)−0.1 (−3)
1.2
(16)
1.2
(15)
1.3
(25)
1.3
(19)
1.1
(11)
1.4
(28)
0.7
(14)
1.0
(20)
1.0
(20)
1.1
(18)
2.1
(72)
2.4
(77)
0.9
(23)
0.6
(11)
1.3
(27)
1.7
(35)
1.3
(41)
1.0
(23)
1.3
(32)
1.4
(35)
2.0
(36)
1.4
(24)
1.4 (−102)
0.8
(14)
0.9
(33)
0.5
(13)
0.9
(19)
0.8
(17)
0.6
(10)
1.0
(25)

IT

CapitalNon-IT

TFP

0.1(1) 1.1(15) 5.3 (69)0.1
(2) 2.8(40) 2.1 (29)0.2
(2) 4.0(44) 2.6 (29)0.2
(3) 5.5(84) −0.9 (−14)0.2(3) 4.7(68) 1.6 (23)0.2
(2) 3.9(53) 1.7 (23)0.5
(5) 6.7 (72) 0.1(1)
0.4(4) 5.2 (49) 3.0 (28)0.4
(5) 3.9 (57) 1.5 (22)0.3
(4) 3.1 (35) 4.3 (48)0.3
(4) 3.5 (48) 2.4 (33)
0.7 (12) 3.2 (55) 1.6 (28)
0.6 (14) 2.2 (55) 1.0 (26)0.1
(3) 1.6 (39) 2.2 (53)0.1
(3) 1.1 (37) 1.1 (37)0.4
(6) 3.4 (51) 1.9 (29)0.2
(3) 2.8 (44) 1.4 (22)0.2
(2) 3.7 (34) 5.4 (50)0.3
(5) 4.2 (76) 0.5(9)
0.4(6) 3.1 (41) 3.8 (51)0.4
(9) 3.4 (67) 0.7 (14)0.7 (27) 2.9 (111) −2.4 (-93)
0.5 (13) 1.4 (34) 1.7 (41)0.3(8) 1.2 (32) 2.1 (55)
0.3 (10) 1.0 (34) 1.5 (51)0.4
(7) 2.7 (49) 1.6 (30)0.0(0) 3.8 (46) 2.9 (35)0.2(2) 5.5 (70) 0.9 (11)0.2
(3) 5.5 (115) −2.4 (−50)0.2(2) 3.7 (50) 1.5 (20)0.3
(3) 4.4 (58) 2.3 (30)
0.2 (22) 3.9 (498) −4.2 (−542)
0.2(3) 2.3 (47) 1.8 (37)0.2
(4) 2.8 (45) 1.4 (23)0.2
(4) 3.0 (54) 1.9 (34)0.2
(3) 3.9 (65) 0.6 (11)0.4
(8) 4.8 (110) −0.5 (−11)0.2(5) 2.5 (58) 1.0 (22)0.2
(5) 1.9 (44) 1.9 (44)0.4
(9) 2.0 (41) 2.1 (43)0.3 (22) 1.8 (125) −0.4 (−26)
0.3 (34) 1.0 (108) 0.3 (35)
0.4 (31) 0.4 (31) 0.6 (49)
0.2 (52) 0.1 (39) 0.5 (150)0.0(7) −0.2 (−27) 0.8 (123)
0.3 (11) 1.6 (64) 0.7 (28)0.1
(1) 5.7 (73) 0.8 (10)0.1
(1) 5.8 (70) 1.1 (14)0.1
(2) 7.0 (139) −3.4 (−67)0.2(3) 3.5 (51) 1.9 (27)0.3
(3) 6.5 (70) 1.4 (15)0.5 (11) 5.5 (112) −2.5 (−51)
0.7 (14) 2.4 (47) 1.3 (25)
0.7 (13) 2.1 (43) 1.2 (25)0.4
(8) 2.8 (54) 1.0 (19)0.3
(5) 4.6 (72) 0.3(5)0.1(2) 2.0 (69) −1.3 (−43)0.1(3) 2.6 (87) −2.0 (−67)0.1(2) 3.3 (81) −0.2 (−5)0.1(1) 3.2 (66) 1.1 (22)0.0(1) 3.2 (66) 0.3(6)
0.1(2) 2.9 (62) 0.1(2)
0.1(2) 2.4 (80) −0.7 (−24)0.1(2) 2.6 (65) 0.4(9)0.1(3) 2.3 (57) 0.3(7)0.1(2) 2.8 (69) −0.2 (−6)0.2(3) 3.0 (53) 0.5(8)0.1(2) 4.4 (75) −0.1 (−1)
0.2 (−13) 3.6 (−265) −6.5 (480)0.1(3) 1.0 (18) 3.4 (65)0.1
(3) 2.3 (79) −0.4 (−15)
0.5 (12) 2.9 (75) 0.0(0)0.6 (14) 2.0 (45) 1.0 (22)0.3
(6) 1.8 (37) 2.0 (41)0.1
(3) 2.0 (35) 3.0 (53)0.2
(6) 2.6 (62) 0.3
(6)

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

Output

Sri Lanka

2.6
(29)
2.4
(29)
1.4
(21)
2.2
(26)
2.1
(26)
1.1
(20)
0.5
(11)
2.5
(38)
1.2
(28)
1.8
(26)
1.0
(19)
3.2
(43)
1.3
(25)
1.9
(20)
0.9
(11)
−0.2 (−28)
0.1
(1)
0.6
(16)
−0.6 (−20)
1.0
(17)
0.5
(18)
1.6
(44)
0.8
(25)
1.2
(37)
0.7
(26)
1.2
(27)
0.0 (−1)−0.5 (−63)
1.0
(52)
0.7
(25)
0.7
(14)
1.2
(25)
1.0
(18)
1.0
(17)
0.5
(9)
0.5
(12)
0.6
(11)
0.3
(5)
0.4
(7)
0.7
(13)
1.6
(61)
1.6
(46)
1.3
(25)
1.3
(22)
1.2
(24)
1.1
(20)
1.1
(18)
0.7
(10)
0.7
(13)
1.2
(23)

CapitalTFP
Non-IT(6) 7.0 (78) −1.1 (−12)(5) 5.1 (62) 0.4(5)(9) 5.7 (86) −1.1 (−17)(10) 3.0 (36) 2.3 (28)(10) 3.4 (41) 2.0 (24)(13) 3.9 (72) −0.3 (−5)(14) 2.0 (42) 1.6 (33)(7) 2.0 (30) 1.6 (24)(14) 2.3 (53) 0.2(5)(9) 3.9 (56) 0.6(9)(2) 3.6 (65) 0.8 (15)(3) 3.1 (41) 1.0 (13)(4) 2.8 (52) 1.0 (19)(3) 3.0 (30) 4.6 (47)(7) 5.4 (66) 1.2 (15)(42) 2.8 (369) −2.1 (−283)(4) 0.7 (13) 4.3 (82)(11) 1.3 (34) 1.4 (39)(11) 1.4 (45) 1.9 (64)(5) 2.7 (49) 1.6 (29)(8) 1.3 (49) 0.6 (24)(7) 1.1 (31) 0.6 (17)(13) 0.9 (28) 1.1 (34)(15) 0.9 (29) 0.6 (20)(16) 0.6 (24) 0.9 (35)(17) 0.8 (19) 1.5 (36)(22) 0.9 (34) 1.1 (45)(42) 0.7 (97) 0.2 (23)(9) 0.3 (16) 0.4 (23)(14) 0.9 (31) 0.8 (29)(4) 4.0 (79) 0.2(4)(3) 3.5 (76) −0.2 (−4)(3) 2.8 (51) 1.5 (28)(5) 2.7 (44) 2.1 (35)(4) 2.8 (50) 2.1 (36)(7) 2.6 (61) 0.9 (20)(7) 2.4 (42) 2.3 (40)(5) 3.3 (50) 2.6 (39)(5) 3.2 (61) 1.4 (27)(5) 3.0 (56) 1.4 (26)(1) 1.4 (55) −0.4 (−17)(1) 1.6 (47) 0.2(6)(1) 1.8 (34) 2.1 (40)(1) 2.0 (36) 2.4 (41)(2) 2.1 (41) 1.7 (33)(2) 2.1 (39) 2.1 (39)(2) 2.1 (34) 2.8 (45)(3) 3.4 (48) 2.8 (40)(3) 3.7 (67) 0.9 (17)(2) 2.2 (43) 1.6 (32)

Vietnam

9.1
8.3
6.6
8.3
8.3
5.5
4.8
6.5
4.4
6.9
5.5
7.4
5.3
9.8
8.1
0.7
5.3
3.7
3.0
5.5
2.6
3.6
3.3
3.3
2.6
4.2
2.5
0.8
1.9
2.8
5.1
4.6
5.4
6.0
5.7
4.3
5.8
6.6
5.3
5.4
2.5
3.5
5.2
5.7
5.1
5.4
6.3
7.1
5.5
5.1

IT

APO20

Labor

East Asia

1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2014
1970–2014
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2014
1970–2014
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2014
1970–2014
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2014
1970–2014
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2014
1970–2014

OutputASEAN6South Asia

Asia24

US

ThailandSingapore

5.3 Total Factor Productivity

1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2014
1970–2014
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2014
1970–2014
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2014
1970–2014
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2014
1970–2014
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2014
1970–2014

2.9
5.4
5.0
3.3
5.3
4.9
4.0
6.2
7.0
4.8
1.8
3.5
6.2
4.4
8.1
7.3
8.0
6.2
5.7
5.7
5.0
4.3
4.7
5.7
4.3
3.0
4.3
4.5
3.7
4.4
5.0
5.2
6.0
6.2
5.5
4.5
5.6
6.8
5.6
5.6
7.4
7.7
3.8
7.6
7.4
2.0
5.0
5.4
4.9
5.7

Labor
0.8
1.0
0.3
1.5
0.8
2.1
1.1
1.2
1.3
1.1
1.6
1.2
2.4
1.2
1.1
1.0
0.5
1.6
0.0
1.2
0.6
1.1
0.8
0.9
0.5
0.3
0.5
0.4
0.4
0.6
0.2
0.9
0.7
0.7
0.2
0.2
0.4
0.0
0.3
0.4
1.6
1.7
1.4
1.8
0.9
0.7
0.5
1.4
0.4
1.2

(30)
(19)
(6)
(46)
(14)
(43)
(29)
(19)
(18)
(23)
(90)
(34)
(38)
(27)
(13)
(14)
(7)
(26)
(1)
(21)
(12)
(26)
(18)
(16)
(11)
(9)
(11)
(9)
(11)
(14)
(4)
(17)
(12)
(12)
(4)
(5)
(8)
(0)
(5)
(7)
(22)
(22)
(38)
(24)
(12)
(36)
(11)
(25)
(8)
(21)

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

CapitalTFP
Non-IT(1) 1.8 (63) 0.2(7)(1) 2.2 (40) 2.1 (39)(2) 3.0 (60) 1.6 (32)(1) 1.3 (40) 0.4 (13)(1) 0.8 (14) 3.8 (71)(3) 0.4(9) 2.2 (46)(6) 1.6 (40) 1.0 (25)(5) 2.8 (45) 1.9 (31)(1) 4.2 (60) 1.5 (21)(2) 2.0 (41) 1.6 (34)(0) 0.4 (25) −0.3 (−15)(2) 1.2 (34) 1.1 (30)(1) 0.5(8) 3.3 (53)(6) 2.5 (56) 0.5 (11)(3) 3.8 (47) 3.0 (36)(6) 6.0 (82) −0.1 (−1)(5) 5.6 (70) 1.5 (18)(10) 5.5 (89) −1.6 (−25)(8) 4.3 (76) 0.9 (16)(5) 3.3 (58) 0.9 (16)(4) 4.0 (80) 0.2(4)(4) 3.4 (79) −0.4 (−9)(4) 2.7 (57) 1.0 (22)(5) 2.3 (40) 2.2 (38)(6) 2.6 (62) 0.9 (20)(10) 2.2 (73) 0.2(8)(8) 1.7 (39) 1.8 (42)(5) 2.2 (49) 1.7 (37)(5) 2.2 (60) 0.9 (23)(6) 2.6 (59) 0.9 (21)(6) 4.8 (96) −0.3 (−5)(4) 3.2 (62) 0.9 (17)(4) 2.6 (43) 2.5 (42)(6) 2.9 (48) 2.1 (35)(5) 2.8 (50) 2.3 (41)(7) 2.6 (57) 1.3 (30)(10) 2.6 (46) 2.0 (36)(6) 3.5 (52) 2.9 (42)(5) 3.3 (59) 1.7 (30)(6) 3.1 (56) 1.7 (31)(1) 3.9 (53) 1.7 (23)(2) 4.8 (62) 1.0 (13)(5) 4.8 (127) −2.7 (−70)(3) 3.1 (41) 2.5 (33)(5) 4.5 (61) 1.6 (22)(16) 3.7 (187) −2.8 (−139)(7) 1.9 (38) 2.2 (44)(6) 2.2 (41) 1.5 (27)(6) 2.5 (50) 1.8 (36)(5) 3.5 (62) 0.7 (13)5

Unit: Average annual growth rate (percentage), contribution share in parentheses.
Source: APO Productivity Database 2016.
Note: See footnote 70 for the country-exception in the country groups.

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,
which were largely confined to manufacturing, IT is a technology that can permeate the economy
and bring about significant production gains in, for example, wholesale and retail, banking and finance, and transportation and telecommunications (service sectors that have traditionally struggledwith slow productivity growth). Given the share of the service sector in the economy (Figure 75 in Section 6.1, p. 104), 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 a83©2016 Asian Productivity Organization

reaped the benefits of their efforts in robust TFP growth, while the contribution from labor input
growth dwindles over time in the two countries.

5 Productivity

Bangladesh

%

6
4
2
0
–2

1.9

2010–2014
2010–2014
2010–2014

2005–2010

2000–2005

1995–2000

1990–1995

1985–1990

0–2

Philippines

5.7 5.9
5.7
5.3
1.4
0.5
4.5 4.8
3.9
1.4
3.0
2.0
2.8
1.0 2.0
0.5 0.9
3.4
0.8 0.6
4.4 3.6
0.9
3.0
0.2 0.8 2.3 2.9 2.0
1.8 2.0
1.0 0.1 0.5 0.6 0.3 0.1
0.2 0.1
–0.1
0.1
–0.4
–1.4
–6.5

–4
–6

2010–2014

2005–2010

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980–8

1970–1975

2010–2014

2005–2010

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980

Labor

2

1980–1985

1.0 1.2 4.0
3.1 3.7
0.8
3.6
0.9
0.6
3.6
0.4
1.0
1.4 0.9
1.5 2.3
1.3
4.1
3.5
3.3
2.4 2.4
2.3
1.5 1.7 0.8
0.5
0.1 0.1
0.1 0.1 0.1
–0.2
2.5

2.1 2.8

–2.5

–3.4

1975–1980

4

5.0 5.2
1.2 1.0
1.0
1.0

0.1 0.1 0.1 0.2 0.3 0.5 0.7 0.7 0.4

1970–1975

2010–2014

2005–2010

2000–2005

1995–2000

1990–1995

6

5.9

9.3
8.2
7.7
1.3 6.9 1.4
0.8 1.1
1.1
1.4
1.2 1.2
1.9
4.9 5.2
5.1 1.3
1.3
7.0
6.5 5.5 0.7
5.7 5.8
3.5
2.4

–2
–4

2005–20100

2005–20102

2000–2005

4

%

6.6
3.2 1.4 5.5

6

1995–20008

2.7 8.1

1990–1995

1985–1990

1980–1985

1975–1980

1970–1975

2010–2014

Malaysia%

9.8

2000–2005

0.2 0.2 0.2 0.3 0.8 0.2 0.2 0.2
0.2
–2.4
–4.2

–3

10

1995–2000

Indonesia

8.3 7.8
7.5 7.6
0.9
6.2 5.5
2.9
1.3 1.5 1.5 2.3
4.9 1.4
2.1 0.7
1.6
1.9
0.9
4.8
1.8 1.8 0.4
5.5 5.5
3.9 0.6
3.7 4.4
3.8
2.3 2.8 3.0

0–6

1990–1995

3

5.8

5.2

1985–1990

6

7.2
2.4

1.6 4.0 4.2
1.2 1.0 0.3
3.0
1.0 2.2
0.2
1.1
3.9
3.2
3.1 3.5
2.2 0.2 0.7
1.6 1.1
0.7
0.6 0.1 0.1
0.5 0.4 0.4 0.3 0.3

6.7

1980–1985

0

4.3

1.5
1.1

1975–1980

5.7

Pakistan
5.8

2

9

0.8
0.7

2005–2010

2000–2005

1995–2000

2.5

8.9
0.1 3.0
2.0
6.9

1970–1975

2010–2014

2005–2010

2000–2005

1995–2000

3.5
3.2

9.3

%

7.4

1970–1975

2010–2014

2005–2010

2000–2005

1995–2000

IT capital

5.7

10.6

6
4

4.8

7.8

2.6

1990–1995

0–1

1990–1995

7.5

8.9

1985–19901

1985–1990

1.4

1980–1985

2

5.6

0.4

1.6 5.3
1.2 1.7 1.0
4.7 4.2
1.2
8.1
1.5
3.0
7.0
2.3
4.8 4.8 5.0 3.5 0.2
0.6
0.5
2.4
2.0 1.8
0.2 0.5 0.4 0.6 0.5 0.6 0.6 0.2 0.1
–0.3
–2.4

6

3

1985–1990

1.6

7

4

4.3

2.2

Korea
9.4
0.5

8

5

1.0

8

0.5
1.9
2.1
2.8 3.1
1.0 1.1
1.8 1.7 1.4 1.3 1.2
3.6 4.1
2.0 2.1
1.7
1.6
1.6
1.5
1.3
0.1 0.1 0.1 0.2 0.2–0.3 –0.2

1975–1980

–3

5.0
2.8

1980–1985

0

1.3

0.1

7.8

6.5

5.8

1970–1975

2010–2014

2005–2010

2.4 2.6 2.3

0.1 0.1 0.1 0.1–0.7

1990–1995

1985–1990

1980–1985

1975–1980

Non-IT capital

3

0.8

5.0

1975–1980

–2

1.3

India

1970–1975

2010–2014

2005–20102.0

0.1 0.1 0.1 0.1
–0.2
–1.3
–2.0

1.9

1980–1985

–0

2.9

3.5

10
4.4

0.1 0.1 0.2 0.6 0.5 0.4
1975–1980

2

7.2

4.4
4.2 4.0
3.6 4.0
3.4
1970–1975

2010–2014

2005–2010

3.3 3.2 3.2 2.90

1970–1975

2010–2014

2005–2010

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980

1970–1975

TFP

2.6

8.3

1.8

%

4.8
4.9 4.9 0.1
4.1 1.1 0.3
4.1 4.0
2.4
1.3 1.7 1.3
0.4 0.3
0.9 0.6
2.1
1.0 1.3
3.0
2.9 3.1

1–2

2000–2005–1

2000–2005

–3.6
–2.8

2

2000–20053

2.0

1995–2000

2.5 0.9
1.6
5.4
3.8
2.7 4.6 0.5
5.2 5.7 6.0
4.4
0.9
3.8 0.1 2.0 1.3 3.0
0.1 0.1 0.1 0.1 –0.1 0.3 0.2 0.4 0.3
0.2
0.2–1.3 –0.4 –1.7
0.1

0.6

1995–2000

4

4

0.4

1990–1995

5

0

6

Nepal

%

6

2.1

1985–1990

6.4 6.0

1980–1985

6.3

1.9

2

9

1.4 0.3 1.2
1.8 0.9 0.6 0.3 0.7
1.0 0.4 0.5
0.4 0.2 0.2 0.4 0.3 0.3 0.4 0.1 0.8–0.3–0.3 –0.7 –0.1 0.2 –0.2
–0.5
–0.4
–0.5
1975–1980

11.5

0.3
2.5

1970–1975

2010–2014

2005–2010

2000–2005

0.9

6.6
0.9

–3
–6

1995–2000

0

1990–1995

3

1985–1990

1980–1985

1975–1980–1

1970–1975

–12

4.8

1.9

4

6.3
5.8
0.1 0.8
1.4 1.4

%

4.9

9.4

4.8

12

4.3 4.3
0.6

1

Mongolia

4.4

1.0

0

6

Japan

3
2

1990–1995

–2.8
–11.0

6.5
0.6

–2.4

4

–9

9

0.5 3.8
1.6 0.6
1.4 4.1 3.8
3.0
0.2 0.6 2.6 1.7
2.1
3.7 4.2 3.1 3.4 2.9 0.5 0.2 1.5
2.8
1.4 1.2 0.2
1.0
0.2 0.2 0.3 0.4 0.4 0.7 0.5 0.3 0.3

1985–1990

5.0
3.0
0.1
0.8 1.4 0.2
2.8 3.4 1.9
0.2 0.2
0.1
–2.1

6

6

2.0

1980–1985

5

6.9

8

12

10.7

7.6

8

5.2
0.7

1.4

10

ROC

%

11.6

10.1

%

7.4
5.6

1975–1980

3.8
0.2
4.1
0.8 1.3 3.7
6.0 7.1
0.1 2.0 1.9
0.8
0.5
0.1 2.8 0.8 1.0 1.3
0.3
0.1
0.1 0.1 0.1%

6.3 5.4

6

3.0 1.0

12

Hong Kong

%

0.4

2.6

10.9

1970–1975

2010–2014

Iran

–3

1995–2000

3

1990–1995

6

0

9.5

–6

9

1985–1990

2.0
3.1
0.7 0.7
0.7
0.6
0.5 0.4
0.2
0.1 0.1 –0.2
0.1

2005–20100–3

0
1980–1985

3.5

2000–2005

3

1995–2000

6

1990–1995

1985–1990

1980–1985

1975–1980

1970–1975

92%

0.9 3.7
3.7
2.1
2.7
1.5 1.7 2.3
2.0
1.9
0.7
1.1
2.5 2.4
1.2 1.2
0.1 0.1 1.7 0.1 0.1 0.9
0.1 –0.5
–0.3
–2.7

%

4

12

–4

12

6

–2

7.0

6.5 7.0
1.8 1.6
2.1
2.3
0.5
5.3
2.0
5.5
4.7
4.0
1.1 2.8
1.1
0.1 0.1 0.2 0.2 0.2–0.9
1975–1980

Fiji

5.6

7.6

8

China

%

12

9.0

1970–1975

2010–2014

2005–2010

2000–2005–2

1995–2000

0

1990–19952

1985–1990

4–2.0
1980–1985

6

5.9 6.1
5.0 1.5 1.0 0.9 0.3
0.8
4.4
5.1
5.0
3.7 3.7 1.4 1.9
1.0 0.6
1.2
4.2 4.6 4.8 4.8
1.9
2.9 3.2
1.8
0.2 0.7 0.1
0.1 0.2 0.2 0.2 0.2–0.2–0.3 –0.9 –0.8
–2.1

1975–1980%

10

1970–1975–4

Cambodia%8

Output©2016 Asian Productivity Organization

process of accumulation and assimilation. IT capability becomes a factor which determines an economy’s long-term growth prospects.81
Japan has been leading Asian countries in terms of IT capital contribution to economic growth (Figures 51 and 53). Japan’s shift in capital allocation took off in earnest in the mid-1990s, with the
81: 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.

84

5.3 Total Factor Productivity

Singapore

%

12
9
6
3
0

1.8 2.2

0.3 0.4

0.8

1.3

6
4
2

2.8

2.1

8

1.2

1.1

4.2

2
0

2010–2014

0

5.6
5.6
5.5
5.0 5.2
2.9
0.2
2.1
4.5
0.9 2.5
1.7
2.0
2.3
0.9
1.3
0.3
0.7
0.7
0.2 0.2 0.4
4.8
3.2

2.6

2.9 2.8 2.6 2.6

3.5 3.3

0.3 0.2 0.2 0.4 0.3 0.3 0.6 0.4 0.3–0.3
2010–2014

2005–2010

1995–2000

2000–2005

1990–1995

1985–1990

1980–1985

1975–1980

1970–1975

2010–2014

2005–2010

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980

1970–1975

2010–2014

2005–2010

–1

5

6.8

6.0 6.21

0.2 0.2 0.2 0.3 0.2 0.3 0.4 0.3 0.3–0.2

2005–20103

2000–2005

3.3 3.2

1995–2000

2

2.8 2.7 2.8 2.6 2.4

4

1990–1995

4.0

5

1985–1990

East Asia

7
6

4.4

8.0
7.3
1.5 1.6
3.0 1.0 0.5
5.7
6.2
0.9
1.1

0.5
6.0 5.6 5.5
1.2
4.3
1.8 1.1
3.8
1.2 2.4
2.5
1.6
0.4 1.2 0.5 0.2 0.2 0.4 0.4 0.6 0.5–0.3 0.1 0.1
–0.1
–1.6
1980–1985

–2

3.3

3.5

1975–1980

0

4

%
6.6

1
–1

6

5.8
5.7
5.3
5.4
5.1
0.2 4.6
2.6
2.1
4.3
1.5
2.1
1.4
2.3
0.7
1.2
0.9
0.3 0.4
1.0 1.0 0.5 0.5
0.6
3.5

6.2

1970–1975

2010–2014

2005–20102

2000–2005

1995–2000

1990–1995

3

2.2 2.2

1985–1990

4

0.9
0.4 0.4

1980–1985

6.0

5

3.7

1975–1980

Asia2478.1

8ASEAN6
7.4
1.7

7.7

7.6 7.4

1.0
1.7 1.4 2.5 1.6
0.9

5.4 4.9
5.0
1.5
0.7
1.8
2.0 2.2
1.4 0.4
4.5
0.5
3.9
3.1
3.7 1.9 2.2 2.5
0.1 0.2 0.2 0.2 0.3 0.3 0.3 0.3 0.3
1.6

3.8
4.8 4.8

1.8

–2.7

–2

–2.8

2010–2014

2000–2005

2005–2010

1995–2000

Non-IT capital

1990–1995

1985–1990

1980–1985

1975–1980

1970–1975

2010–2014

2005–2010

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980–4

2000–20050

1995–2000

2

1990–1995

4

1985–1990

1980–1985

6

4.5
1.7

7.4
4.6 1.2
5.3
5.5 1.0
5.3
0.9
0.8 3.2 1.0
3.7
1.9
1.0
0.7
3.0
1.3
5.4 2.8 4.3 1.4
1.9
0.6
3.6 3.1 2.8 3.0
0.1
0.7 1.3 1.4
0.1 0.2 0.2 0.3 0.6 0.3 0.2 0.4 0.3
–0.6
–0.2
–2.1

1970–1975

2010–2014

2005–2010

2000–2005

1995–2000

4.3 4.7

0.2 0.2 0.2 0.3 0.3 0.3 0.3 0.2 0.2
–0.4

–1

0.6

10

8.1

%

6

0

2

–4

5.7

1

3

0
–2

1.6

0.4
0.8 0.1 0.2 0.3 0.1
1990–1995

1985–1990

1980–1985

0.9

0.7
1.7 2.1
3.5 2.1
0.7
2.5 0.2
1.2 1.1 1.1
1.3
1.6 1.3
1.6
3.4 3.7
2.0 2.1 2.1 2.1
1.4 1.6 1.8
0.1 0.1 0.1 0.1 0.2 0.2
–0.4

TFP

5.0
0.2

1975–1980

2.8

1.0

1.5

2.2 4.3
4.3
1.1 1.0
0.9 3.0 1.8
0.8 0.9 0.5
0.2
0.3 0.5
4.0
3.4
2.6
2.7 2.3
2.2 1.7

4

8

5.5

3.8

2.2

1.9

Vietnam

%

9.8

10

APO20

1970–1975

2.8

1.3

4.9
4.0

1.5
3.0
0.1

1975–1980

6.3
5.4

2.4

1970–1975–10

0.2 1.0
0.8

1970–19750

2010–20141

2010–20142

2005–20103

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980

1970–1975

7
5.1

1

2.9

%
7.1

5.7

2

5
2.5

South Asia

5.2

3

5.3

5.0

2.1 1.6 3.3

4

6

0.6 1.6
1.9
1.2 0.9 1.2
0.8
0.5
1.1 0.8
0.7
0.2 0.4
0.8 0.9
1.0
1.3 1.1 0.9 0.9 0.6
0.7 0.3
0.7 0.5
0.3 0.2
0.2 0.2 0.4 0.5 0.4
–0.5

6

5.4

5

7

3.3 3.3
1.5
2.6
1.1 0.6

8

4

2005–2010

0.6

%

5

2000–2005

0
–1

3.6
2.6

6

Thailand

%

7.0
6.2

%
4.2

4

1

1995–2000

52

1990–1995

1985–1990

1980–1985

1975–1980

US

%

3

7

9.1
8.3
8.3 8.3
2.6
0.4 1.4 2.3 2.0
6.5
2.4 6.6
5.5
4.8 1.6 4.4
2.2 2.1 1.1
7.0
1.6 2.5 0.2
5.7
1.2
5.1
0.5
3.0 3.4 3.9
2.0 2.0 2.3
0.5 0.4 0.6 0.8 0.8 0.7 0.7 0.5 0.6
–0.3
–1.1
–1.1

1970–1975–3

Sri Lanka%

IT capital

Labor

OutputFigure 56 Individual Countries’ Growth Accounting Decomposition, 1970–2014

contribution of IT capital to capital input growth rising from a low of 12% in 1995 to a peak of 64% in2009 (Figure 57).82 It took place in a period when Japan’s overall investment growth slowed significantly after the economic collapse of the early 1990s (Figure 39 in Section 4.3, p. 58). After years ofexcesses, 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 oftime. 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 in total capital growth has concentrated more on non-IT capital, resulting in spikes in the contribution of IT capital in both Japan and the US. The findings here are in accordance with Jorgenson,
Ho, and Stiroh (2005). Based on their measurement, IT capital in the 1980s contributed 31.9% of thegrowth of total capital inputs in the US, but only 13.5% in Japan.83 Since 1995, the Japanese economy
82: Japan’s capital services recorded negative growth in 2009–2014, for the first time after World War II, although IT capital services
increased. This period has been omitted from our calculations of the IT capital contribution share in total capital input in Figure 57.

85©2016 Asian Productivity Organization

Source: APO Productivity Database 2016.
Note: See footnote 70 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

IT capital (right-axis)

0.0
1976

1981

1986

1991

1996

2001

2006

2011

IT capital contribution to capital input growth

Figure 57 IT Capital Contribution to Capital Input Growth of Japan and the US, 1970–2014
Source: APO Productivity Database 2016.

%

%

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 58 IT Capital Contribution to Capital Input Growth of the Asian Tigers, China, and India,
1970–2014
Source: APO Productivity Database 2016.

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.

©2016 Asian Productivity Organization

A similar allocation shift to IT capital is also found in the Asian Tigers (Figure 58).84 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
83: Our estimates in the same period show that IT capital contributes 32.7% in the US and 14.1% in Japan to the growth of total capital input.
84: 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.

865.3 Total Factor Productivity

Bangladesh

8

9
9

0

6
3

6

–4

0

3

–8

–3

–12

0

–16

–3
1971 1976 1981 1986 1991 1996 2001 2006 2011

ROC

–6

9
6
3
0

Fiji

1971 1976 1981 1986 1991 1996 2001 2006 2011

12

16

9

12

6

8

3

4

0

0

–3

–4

–6

–8

–6

–9

–12

1971 1976 1981 1986 1991 1996 2001 2006 2011

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

2

4

0

0

–2

0
–4
–8

–8

–6
1971 1976 1981 1986 1991 1996 2001 2006 2011

Mongolia

–12

–12
–16
1971 1976 1981 1986 1991 1996 2001 2006 2011

Nepal

%
9

4

0
0

0

–6

–2

–9

–12
1971 1976 1981 1986 1991 1996 2001 2006 2011
%
12

2

–3

–6

Philippines–41971 1976 1981 1986 1991 1996 2001 2006 2011

Singapore

%
15

8
4
0
–4
–8

1971 1976 1981 1986 1991 1996 2001 2006 2011

TFP

1971 1976 1981 1986 1991 1996 2001 2006 2011

Sri Lanka

%
10

12

8

9

6

6

4

3

2

0

0

–3

–2

–6

–4
–6

–9

–12

Pakistan

%
10

6

3

6

1971 1976 1981 1986 1991 1996 2001 2006 2011

8

6

12

Malaysia

%
16

4

–4

–4

1971 1976 1981 1986 1991 1996 2001 2006 2011

8

8

4

–21

12

12

6

%
18

–7

–10

–8

–8

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

–3

India

5

–9
1971 1976 1981 1986 1991 1996 2001 2006 2011

%
15

12

%
12

China

%
15
12

12

4

%
15

Cambodia%15

1971 1976 1981 1986 1991 1996 2001 2006 2011

Non–IT capital

IT capital

1971 1976 1981 1986 1991 1996 2001 2006 2011

Labor

Output87©2016 Asian Productivity Organization%
12

5 Productivity%
16

Thailand

12

12

8

8

4

4

0

0

–4

–4

–8

–8

–12

–12

1971 1976 1981 1986 1991 1996 2001 2006 2011
%
8

APO20

4
2
0
–2

1971 1976 1981 1986 1991 1996 2001 2006 2011

Asia24

2

2

0

0

–2

–2

–4

–4

1971 1976 1981 1986 1991 1996 2001 2006 2011

South Asia

–4
–6

9
6

4

3

2

0

0

–3

–2

–6

–4

–9

–6

–12

1971 1976 1981 1986 1991 1996 2001 2006 2011

1971 1976 1981 1986 1991 1996 2001 2006 2011

ASEAN6

%
12

6

TFP

–2

1971 1976 1981 1986 1991 1996 2001 2006 20118

East Asia

%
10

4

4

0

1971 1976 1981 1986 1991 1996 2001 2006 2011

6

6

2

–4

8

8

4

US

%
8
6

%
10

6

%
10

Vietnam%161971 1976 1981 1986 1991 1996 2001 2006 2011

Non–IT capital

IT capital

Labor

OutputFigure 59 Individual Countries’ Growth Accounting Decomposition (year-on-year), 1970–2014
Source: APO Productivity Database 2016.
Note: See footnote 70 for the country-exception in the country groups.

– 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 with asurge in its contributions only taking off around 2000 and peaking at 15% in the early 2000s. There has
not been as big a drive in IT pickups in India as in other Asian countries. Rather, the process has been
gradual with a clear step-up in effort from a minimal level in the early 1990s. The share of IT capitalreached 8% in the early 2000s before recently decreasing.

©2016 Asian Productivity Organization

5.4 Sources of Labor Productivity Growth
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 in all of the countries compared, albeit to various degrees(Figure 60). Experience of countries suggests that capital deepening is an accompanying process of88

5.4 Sources of Labor Productivity Growth

1970−2014%

1970−1990

1990−2014128

8.4 8.1

7.3

7.1

6

4.9 4.8 4.8 4.7 4.6

4

10.3

10.2
9.1

10

4.5 4.2 4.1 4.1

3.6 3.5

2

6.8

7.4
6.1 5.9

7.0

5.6 5.6 5.5

5.6 5.5
4.4

3.0

3.4 3.2
2.2 2.1 2.1 2.1

1.8

2.7 2.7 2.6 2.4

5.0 4.9 4.94.2 4.1 3.9

2.1 1.8

0.9

3.3

2.9 2.7

2.1 1.9 1.8

1.7

0.6

0

1.3 1.0

5

0.1

–0.9

–2

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

VietnamBangladesh
Fiji
US
Pakistan
Philippines
Sri Lanka
India
ThailandIranIndonesia
China
Mongolia
Malaysia
Nepal
Hong Kong
Singapore
Japan
ROC
Korea

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

Figure 60 Capital Deepening, 1970–2014, 1970–1990, and 1990–2014
Source: APO Productivity Database 2016.
Note: The starting period for Cambodia is 1993.

1970−2014%

1970−1990

2
1
0

–1
–2
–3

1990−2014

1.6
0.9

1.3 1.3 1.2

0.8

0.6

0.2
–0.1
–0.4 –0.5–0.5–0.5–0.6–0.6–0.7–0.8–0.9–1.4–1.5–1.5–1.6–1.7–2.1–2.2
–2.9
–3.2

0.9

0.6

0.4
–0.3–0.4–0.5–0.9–1.0–1.1–1.2–1.3–1.5–1.6–1.8–1.9–0.3–0.4–0.4
–0.6
–0.8
–1.0–1.0–1.1–1.8 –2.0–2.4–2.4–2.5–2.8–3.0–3.0–3.2–3.3–4–4.3–4.4–5Bangladesh
Vietnam
Korea
China
Indonesia
Nepal
ROC
Hong KongJapan
Thailand
Malaysia
India
US
Cambodia
Singapore
Philippines
Fiji
Pakistan
Iran
Sri Lanka
Mongolia

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

Korea
Nepal
ROC
Japan
Singapore
BangladeshIndonesia
Malaysia
China
Hong Kong
India
Iran
Philippines
VietnamMongolia
ThailandUS
CambodiaFiji
Sri Lanka
Pakistan

Figure 61 Capital Productivity Growth, 1970–2014, 1970–1990, and 1990–2014
Source: APO Productivity Database 2016.
Note: The starting period for Cambodia is 1993.

While labor productivity steadily improved for all countries as shown in Figure 45 in Section 5.2, thegrowth rate of capital productivity (as the other measure of partial productivity) remains negative foralmost all countries during 1970–2014, shown in the left chart of Figure 61. Although rates of capital
deepening in Korea and the ROC were outstanding, at 8.4% and 7.1% per year, on average during

89©2016 Asian Productivity Organization

rapid economic development. The relatively early starters (Japan and the Asian Tigers) underwent
more rapid capital deepening than the other countries compared; and in the earlier rather than thelatter period. The reverse is true for the emerging Asian economies, where concerted efforts weremade to increase capital intensity in the latter period. In 1990–2014, China, Vietnam, India, Bangladesh, Thailand, and Indonesia moved up to occupy the top spots among the Asian Tigers, while Singapore and Japan stepped down in the rankings. In 1970–1990, the capital–labor ratio was rising by
10.2% and 9.1% on average per year in Korea and the ROC, respectively. Over the subsequent two
decades it slowed to 7.0% and 5.5% respectively. Meanwhile, China’s pace doubled between the two
periods, from 5.5% to 10.3% on average per year. In Vietnam, it has accelerated to 7.4% since 1990. In
the US, the pace of capital deepening was stable at 2.1% between the two sub-periods.

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

Figure 62 Capital Productivity Trends inJapan and the Asian Tigers, 1970–2014
Source: APO Productivity Database 2016.

TFP
IT capital deepening

6

TFP
Non-IT capital deepening
IT capital deepening

100

9

20 23

23
1.2
1.9

3.6

1.7 1.7

1.6 1.447

45 43 41

30

21

16

26

29

51

54

38
60

61

50

82
1.6

0.6 0.7

0.9 0.6 0.9

2.0

2.7

2.4 2.2 1.9

1.9

1.6

50

0.9
0.3

0.8
1.7
1.8
1.6
1.3
1.1 1.4
0.4
1.0 1.0 0.4
0.5
0.4
0.4
0.4
0.3
0.3
0.3
0.3
0.2 0.1
0.1 0.2 0.1 0.1 0.3
0.2
0.1
0.1 0.1 0.3
0.1 0.1 0.2 0.2
0.2−0.2 −0.2−0.1
2.0 1.8 1.8 1.8

1.5

1.5

70

40

0.7

20

2.0

0

51
110
102

112

0.3
1.8

1.6
2.7

39 41 44

60
1.6

3.4

120

80

3

0

Non-IT capital deepening
Labor productivity

3.1

4

1

Figure 63 Capital Productivity Trends in
China and India, 1970–2014

%

7

2

0.0
1970 1975 1980 1985 1990 1995 2000 2005 2010

Source: APO Productivity Database 2016.

%

5

China
India

0.2

75 70
54 50

52
53 48 49

44

64
62

46

9 3 8 7 7 3 9 3 5 7 8
3 7 7

5 3

54 28
42

36
16

72

63

67

38
11

23
2 8

4

12 7

–16

–17

6 7
–8

–20

Fiji
Iran
Bangladesh
Philippines
Nepal
US
APO20
PakistanJapan
Sri Lanka
Mongolia
Singapore
Vietnam
ASEAN6
Indonesia
South Asia
Malaysia
India
Asia24ThailandEast Asia
CambodiaHong Kong
ROC
Korea
China

Fiji
Iran
Bangladesh
Philippines
Nepal
US
APO20
PakistanJapan
Sri Lanka
Mongolia
Singapore
Vietnam
ASEAN6
Indonesia
South Asia
Malaysia
India
Asia24ThailandEast Asia
CambodiaHong Kong
ROC
Korea
China

Figure 64 Sources of Labor Productivity
Growth, 1970–2014

Figure 65 Contribution Shares of Labor
Productivity Growth, 1970–2014

Source: APO Productivity Database 2016.
Note: The starting period for Cambodia is 1993. See footnote
70 for the country-exception in the country groups.

Source: APO Productivity Database 2016.
Note: The starting period for Cambodia is 1993. See footnote
70 for the country-exception in the country groups.

©2016 Asian Productivity Organization

this period, their capital productivity experienced the sharpest decline of 3.2 and 2.2% per year,
respectively. Figure 62 presents the declining trends in Japan and the Asian Tigers. They are pretty
similar except in Hong Kong.
In contrast, the deterioration of capital productivity (by 1.4%) was relatively mild in China as shownin Figure 61, despite its fast capital deepening of 8.1% shown in Figure 60. Looking at the two subperiods of 1970–1990 and 1990–2014, overall the rate of deterioration in capital productivity for all
countries was slower in the latter period. China’s performance is particularly impressive. Its accelerationin capital deepening in the latter period did not compromise its capital productivity as much as theearly starters (Figure 63). In 1990–2014, China’s capital-labor ratio rose by 10.3% whereas its capital
productivity fell by 1.9%. This compares with Korea’s performance in 1970–1990 when its capitallabor ratio rose by 10.2% while capital productivity fell by 4.3%.

90

5.4 Sources of Labor Productivity Growth

1970−1985%

2000−2014

1985−2000

10
8
3.5

6

0.2
4.0 1.8

1.5

0.2
2.8

4

2.4

0.8

1.9
4.0

0.4 0.3 0.2

0

2.4

3.0

4.1 3.8

1.8

1.3 1.0

2.7

0.1 0.3 0.1 0.4 0.1−0.4 −0.5−0.6

1.2

0.9 0.5

0.3

3.7

3.6

2.5

1.8 0.8 0.6 0.5 1.8
1.8
1.3 1.7
1.1 1.4
0.6
0.1
0.2 0.1 0.1 0.1 0.2 0.3 0.8 0.7 0.1 0.1−0.7−1.2 −1.0−0.2−2.0

1.6 1.4

0.3
2.4

0.7
1.9

0.4
2.0 1.7

2.3 2.2 0.9

4.2
1.3 1.1

4.6

0.7 0.5
1.1

2.7 3.0 2.7

2.6

4.2

1.5

1.7 1.8 1.5
3.55
1.8 1.5 1.0

1.9 1.5 1.2

3.1 2.5
1.0
2.7
3.0 2.8
1.4
1.7 2.2
2.1 1.0
2.0
1.5
1.5
1.1 0.9 0.2 1.5 1.4
1.3
1.2
0.5
0.1 0.5 0.4 0.4 0.3 0.1 0.3 0.3 0.5 0.1 0.2 0.3 0.6 0.1 0.1 0.2 0.4 0.3 0.2 0.5 0.1 0.6 0.1
0.1−0.1−0.1−0.3 −0.4−0.1−1.2
−1.1

2.6
2.9
2.7
1.2 0.6
2.4 2.3
2.0
1.6 1.3 1.6 1.0 1.4 1.7
0.9 1.9
0.6
1.0
1.1
0.6 1.1 0.4 0.6 1.3 1.3 0.3
0.5 0.2 0.2 0.4 0.4 0.2 0.3 0.1 0.3 0.3 0.2 0.2 0.3 0.2 0.4 0.2 0.2 0.3 0.2 0.5 0.4 0.1 0.4 0.1 0.1 0.2
−0.1
−0.2
−0.2

Japan
Fiji
Nepal
US
Pakistan
Singapore
Malaysia
APO20
Philippines
Iran
ROC
Hong Kong
Sri Lanka
ASEAN6
Indonesia
Bangladesh
Korea
ThailandCambodiaAsia24South Asia
Vietnam
East Asia
India
Mongolia
China

4.3
2.5

1.1

0.4

5.1

Mongolia
IranFiji
Bangladesh
US
Sri Lanka
Philippines
APO20
Nepal
Japan
Indonesia
Cambodia
Pakistan
Singapore
ASEAN6
Asia24South Asia
Hong Kong
East Asia
Malaysia
India
VietnamThailand
ROC
Korea
China

2

1.2

2.6 2.2

3.2−2.6

Philippines
Bangladesh
Vietnam
Fiji
Nepal
Iran
India
South AsiaUS
APO20
Asia24Thailand
ASEAN6
East Asia
Sri Lanka
PakistanIndonesia
Singapore
Malaysia
Japan
Mongolia
China
Hong Kong
Korea
ROC

–2

TFP

Non-IT capital deepening

IT capital deepening

Labor productivity

Figure 66 Sources of Labor Productivity Growth, 1970–1985, 1985–2000, and 2000–2014

Source: APO Productivity Database 2016.
Note: See footnote 70 for the country-exception in the country groups.

338

100
80

3

14

20

22
44 39

47 44

60

74

68

0

49 44

95
72
54

54

55

8
41

47

55

52

85

81
35

44

38
16
16
12
9 5
6 5 4 1 3 7 2
3 0 2 8 4 5 6 7
2 2 5 8−9 −13−17−160−20 −50
−243
−274

46

44

40
38

32

2 8 7 8 6 2 8 8

13

41

45

48

60
100

79

68

23

25 18

34

2 7 8

99

42

57

12

53

49

50

49

52

48

31 44

46

28
13

2

10 9

2
−15

8 9

20

−21 −18−32 −431

22
39 50

6 4 3 8 9 3 7 4 8 7 5 5 8 6
−5

12 8

39

5 10 897 104

105
38

25

22
23

19 20
3
−9

20
6 5
−3 −8

Japan
Fiji
Nepal
US
Pakistan
Singapore
Malaysia
APO20
Philippines
Iran
ROC
Hong Kong
Sri Lanka
ASEAN6
Indonesia
Bangladesh
Korea
ThailandCambodiaAsia24South Asia
Vietnam
East Asia
India
Mongolia
China

Non-IT capital deepening

19

35

60

55

58

53

26
2 3 6
−3 −5

54
68
46

74
45 43

39

48

45

101

86

43
18

49

66

62 80
64

34

40

48 45

77

48 113

Mongolia
IranFiji
Bangladesh
US
Sri Lanka
Philippines
APO20
Nepal
Japan
Indonesia
Cambodia
Pakistan
Singapore
ASEAN6
Asia24South Asia
Hong Kong
East Asia
Malaysia
India
VietnamThailand
ROC
Korea
China

TFP

40

49

65 66

61
54 57

60

66

5

13

19

27 27

30

Philippines
Bangladesh
Vietnam
Fiji
Nepal
Iran
India
South AsiaUS
APO20
Asia24Thailand
ASEAN6
East Asia
Sri Lanka
PakistanIndonesia
Singapore
Malaysia
Japan
Mongolia
China
Hong Kong
Korea
ROC

−20

39

83

49 56

20

112 215124 511

12

61

105
111

106

91

40

241
1119
252 369 104126

2000−2014

1985−2000

1970−1985%
120

IT capital deepening

Figure 67 Contribution Shares of Labor Productivity Growth, 1970–1985, 1985–2000, and 2000–
2014

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 motor of labor productivity growth, generally explaining 50% of it. Taking the US as the reference economy, with contribution share of capital deepening to labor productivity growth of 50.2% on
average in 1970–2014, it has been a main engine to enhance labor productivity in 12 Asian countries (Figure 65). The exceptions to this observation are Fiji, India, Sri Lanka, and Pakistan, in which therole of TFP has been more significant.

91©2016 Asian Productivity Organization

Source: APO Productivity Database 2016.
Note: See footnote 70 for the country-exception in the country groups.

5 Productivity

Bangladesh

%

5
4
3
1

2.3
1.0 0.6 1.3 1.0
0.1 0.1 0.1 0.1 0.2 0.2

0.3

–1

–2.1 –1.1–2–0.3 –0.9 –0.8

2010–2014

2005–20102010–2014

2005–2010

2000–2005

1995–2000

1990–1995

Philippines
4.4

3.6

2.8

2.6 3.0
2.3 1.8
1.4
0.5
1.9 1.0 2.0 1.3
0.5 2.7 1.7 3.4
0.8 0.1 0.1 0.1 0.9 0.4 0.3
0.5 0.5
0.2 0.1–0.4
0.1 –0.1 –6.5

–2
–4
–6
–4.7
2010–2014

2005–2010

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980–8

1970–1975

2010–2014

2005–2010

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980

IT capital deepening

–2.5

–3.4

1985–1990

0.1 0.1

0.2
–0.2

0

5.0 1.9 4.7

1980–1985

0.1 0.4
0.1
0.1 –0.2–0.5

2000–2005

2.2 2.5
1.3
1.0
3.1
1.2
1.1
1.5
0.5 1.2
0.1 0.1 0.1 0.1 0.3 0.4 0.6 0.5 0.3

1975–1980

0.3 2.3
1.3

1995–2000

3.6 3.7

1970–1975

2.2 2.4

0.6 3.1

1990–1995

2

2
1.8

1985–1990

6.4

6

1.9

1.8

2010–2014

2005–2010

1.4 1.2

1.9

–1.2
–4.2

4

–4

2.4

Malaysia

%

3.2

3.6

4.9 1.7
4.4
1.4
3.5
0.8 1.1
1.0 3.1

0

2010–2014–2.4

1980–1985

2.2

4.7

2.3

1.4 2.6
0.1 1.6 0.8
1.0 1.7
1.6
0.1 0.1 0.1 0.2
0.1 0.2 0.2

1975–1980

2.0

3.3

1.5 3.7 2.9 1.8

3.3

1970–1975

64

3.7 3.6

3.3

8

2000–2005

1.4

1995–2000

1985–1990

1990–1995

1980–1985

Pakistan

2.5

0.1

2.9 0.9%

4.7
1.2 4.3
1.5 2.3

2005–2010

6.2

–2
–4

2000–2005

0

5.3

5.0

3.2

1.6

2.0

1995–20002

2010–2014

2005–2010

2000–2005

6.2

4

2.8

1990–1995

1985–1990

1980–1985

1975–1980

1970–1975

0.8

2.6 3.2

1995–2000

1990–1995

1985–1990

1980–1985©2016 Asian Productivity Organization

2.3

4.5 4.3–2

3.9

1970–1975–1

2010–2014

4.1
3.1

2

0

2005–2010

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980

1970–1975

2010–2014

2005–2010

2000–2005

1995–2000

1990–1995

Non-IT capital deepening

3.9

4.7 3.5 4.5

–2.4

3

1

3.2

8

2.2 0.6
1.4
0.2 0.4 0.3 0.5 0.4 0.6 0.6 0.2 0.1

4

2.4
0.3

2.2

Indonesia

3.9 3.4 4.2 3.5

5

2.9

%

6.5

–2
–4

3.8

1.1
1.4
0.4 0.3 0.3 0.3 0.3 0.7 0.5 0.1 0.5

2.5 2.7 1.6

5.1

3.5 3.7
1.0

0

Korea
5.7

5.2

5.0 4.3
2.4 1.6
1.5

0.1 0.1 0.2 0.6 0.5 0.4

4.5 6.7 6.6%

1.1

1985–1990

1980–1985

1975–1980–2

1975–1980

–1

0

5.5

3.0

2

3.6

2.8
1.9
2.1
3.2 3.6
0.3 0.6
1.4 1.4
0.8 1.0 1.1
0.8
0.1 0.1 0.1 0.2 0.2
0.6–0.3 –0.2

1970–1975

2010–2014

2005–2010

2000–2005

0

1975–1980

2010–2014

2005–2010

2000–2005

1995–2000

1

1970–1975

2010–2014

2005–2010

2000–20052

2.0 2.1

2.0
1.7
1.4
1.3 0.4
0.1
0.3
2.1
0.6
1.7
1.5
1.5
1.0
1.1 0.1
0.1 0.1
0.1 0.1 0.1 0.1
–0.2
–0.7
–0.1 –0.3
–1.3
–2.0

1970–1975

2010–2014

2005–2010

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980

TFP

1995–2000

2.1
0.9 1.0 2.0
0.1 0.1 0.1 0.1 0.1 0.1 0.2 0.3 0.2–3.6 –0.4–1.3 –0.4
–1.9
–1.7
–0.8
–2.5
1970–1975–4

1995–2000

–2

3.5

2.9

4

2.1

0.1

6

0.5

1990–1995

0

1.7 4.6

2.6

3

–2

6

3.9

4

–0

5.6

4.6

3.2 3.0 3.2

6.9

4
2

6

6.8

5.0

India

8

2.8 2.8

2.5

1.8

7.2

4

2.2

3.5
5.5

%

Nepal

%

2

1990–1995

4.3 4.3 4.2

2.8

1990–1995

2

4.9

3.1 3.9

2.7

4.3

1985–1990

0.6

1985–1990

5.1

4

1985–1990

6.0

1980–19858

1975–1980

9.7

3.1 3.5

0.3 1.4 1.2
2.1 1.8 1.8 2.0
0.7
1.4 0.6
0.5
0.4 0.5
0.8
0.4 0.2 0.2 0.4 0.3 0.3 0.4 0.2
–0.2
–0.4
–0.5
1970–1975

2010–2014

2005–2010

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980

1970–1975–1

6

1980–1985

3.3

–12

Mongolia

3.9

5.0 1.0 1.9

0

4.0
0.7

Japan

3
2

2.8 2.6

4.4 7.2

2.9

4.8 1.8

8

4.9

4

–9

%

0.5

5

1

–6.0

10

1980–1985–6

5.4 4.4 3.8

1975–1980

5.0
1.4
3.7
1.3
1.5
0.8
4.9 4.9
2.0 1.9 3.0 3.4 1.2
0.2
0.1
0.1
0.1 0.1 0.1 0.5 0.2–11.0 1.1 –1.6 –0.6 –1.2 0.2
–2.1
–1.5
–0.8
3.0

0

2.9
0.1 0.8

7.2

5.1

7.1

%

6

2

3.5

%

2.1
1.4
3.6 2.9 2.9
1.5
1.7
1.6
2.3
1.0 1.1 0.9
1.5
0.2 0.2 0.3 0.4 0.4
0.5 0.3 0.3
0.6
–0.2
–2.4
1970–1975

2010–2014

2005–2010

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

–3

–3

6

6.3

6
3.0

3

1.5

8

7.7
6.6

ROC

%
10.5

10.3

10

4

Hong Kong
8.0

6

–1.9

Iran

2.6
3.9 4.2

2.1

China

%

12

8

0.1 0.1 0.1 0.2 0.2
–0.3
–0.9

9

0

7.9

3.1

2

1975–1980

–0.5
–0.7

1975–1980

0

5.3

1970–1975

3.1

0.5 0.8 0.5
1.1 1.4 0.9 2.3 0.1
0.7 0.4
0.1
0.1 –0.3
0.1 0.1
0.5
0.1 –0.4–0.3 –2.7 –0.5

1970–19753

2010–2014

0.9 1.2%

6

2005–2010

2.0

–2

9

2.2

3

–1

1.6

4.2

%
2.7

1.9

–1

–3

2000–20050

1995–2000

1

1990–1995

1985–1990

1980–1985

1975–1980

Fiji

3
2

5

0

%

6.0

5.0

1

–1.7
1970–1975–3

3.3 3.6 3.7

1.3
0.6 1.3 0.8

0

6

4

1.6

2

Cambodia%

3.7 4.2
0.3
2.7

Labor productivity

Within this long period, the composition of labor productivity growth has seen substantial shifts (Figures 66 and 67). In the earlier period 1970–1985, TFP growth was enjoyed by 11 out of the 19 Asian
countries compared (excluding Cambodia). It was a significant drag on labor productivity growth ineight countries (Iran, the Philippines, Nepal, Fiji, Singapore, Malaysia, Mongolia, and Bangladesh). During the middle period 1985–2000, all countries (except Bangladesh, Indonesia, and Mongolia)
achieved positive TFP growth to bolster labor productivity growth. By 2000–2014, TFP growth had
become the dominant driver of labor productivity growth in 7 of the 19 countries compared. At thesame time, the contribution from IT capital deepening was also strengthening, from a range of 0–12%

92

5.4 Sources of Labor Productivity GrowthSingapore

%

6

5

0

3.4 3.6 3.1 3.7

3.2
4.9 0.42

3.9 2.3 2.0

2.6

1.6
0.8 1.5
0.2

2.8

1.5
0.9
0.5 1.0
1.6
0.4 0.3 0.5 0.6 0.6 0.6 0.6 0.1 0.5
–
0.3
–0.9
–1.1
–1.1

3.0
0.8

3.1
1.0

4.6
4.5

1.0

4.3
1.2

4.3

2.4 1.9

3.0

2

4.9

5.5
0.9

1.5
2.4

4.7 4.9

4.2
1.7
3.4
1.2 3.3
0.5 2.5
1.1 1.2 0.9–1.0 0.1
0.2 0.2 0.4 0.4 0.5 0.4
–0.1
–2.1
–1.6
–0.3
–1.3

2.6

2.6

2

0.2

1

2.2 1.9

2.1

4.1 2.6

4.0
2.1

2.6

4.5
1.4

2.3

2010–2014

2005–2010

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980

1970–1975

2010–2014

7

6.7

6
3.6

5
3.8

4

2.9

2.9

4.4
2.3

4.1
2.8

5.0
1.7

3

2.6 2.6

2.8 2.82

1.0
0.2 0.1 0.1 0.2 0.2 0.2 0.4 0.3 0.2
–0.2

1

1.3
3.4
3.1
1.6
1.4 1.9 1.2
0.2
0.3 0.2 0.2 0.3 0.3 0.3 0.5 0.4 0.3
–0.3

1.9

0.9

1.5

1.3 1.8 1.4 1.4

0

0.9

2.6

2.1

2.0

2.5

1.5

2010–2014

2005–2010

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980

1970–1975

2010–2014

2005–2010

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980

1970–1975

2010–2014

2005–2010

2000–2005–1

5

East Aisa

%
5.7

3

–1

2005–2010

Asia 24

4

0

2000–2005

1995–2000–2

1990–1995–4

1985–19900

1980–1985–2
1975–1980

1.7 0.9
1.8

4

6.7

1.4 2.0
1.9 1.5
2.1
3.0 0.6 0.6
1.0
0.1 0.1 0.2 0.3 0.5 0.3 0.2 0.4 0.4
0.1
–2.1

1970–1975

2010–2014

2005–2010

2000–2005

2.6

5.7

5.6
3.8

4.0
3.5

1.6
0.6

0.6

1.0

2.5

4.3

3.6

2.5 1.8

3.1

3.7 3.1 2.2

–2.7

–2.8

1.5
2.2
2.0 2.3
1.2 0.7
1.4
0.1 0.1 0.2 0.2 0.3 0.3 0.3 0.3 0.3

2010–2014

2005–2010

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980

1970–1975

2010–2014

2005–2010

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980

1970–1975

TFP–4

1995–2000–2

1995–2000

00.9

1990–1995

2.8 3.1

0.2 0.2
1.3 1.2 1.4 1.4
0.6 0.7 1.0 0.1 0.1 0.1 0.1 0.2 0.2–0.4

1990–1995

2.8

0

6

ASEAN6

1.7

2

3.0

1.0 1.2 1.0

1985–1990

4

2

Vietnam

8

5.2

1.2

5

1.6
1.1
0.6
0.2 0.1 0.2 0.3 0.2 0.3 0.3 0.2 0.1
–0.4
1.3

6

4.2

1985–1990

2.1

0.2

1.9

6
4

%

6.3 6.2

6

2.2
0.9 1.5

8

%

2.6

2.3

1.0

1980–1985

1.7

–1

0.9

3.0
2.4

0

2.3

1975–1980

2.1

2

1

2.4
1.6

1970–19753

2010–2014

2005–2010

3.1

2.8

3.6

2.7
0.2

%

5.9
4.3

4

–1

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980

South Asia

3.7

APO20
3.5

2

0.2
0.9 1.0

5

0

3
1.6

1.0
1.0 0.6 0.4
0.1
0.2
0.3 0.2
0.6 0.5
0.2
0.4 0.4
0.2 0.2 0.4 0.4 0.4
0.1
–0.3

6

–2

1980–1985

1.5

0.6 0.9

1.9

–1.1

4

2.5
1.1

1.3 1.50

1.5–1
1975–1980

1.1

1

3.9

4.0

4.3

2.1
0.6
3.8 2.2 1.8
1.1
2.8
2.7
1.0
1.7
0.2
1.2
0.6
0.9 1.2
0.4
0.1 0.1 0.2 0.1 0.2 0.3 0.1

1970–1975

0.6

%

1

2010–2014

1.9

1970–1975

–1

1.9

2

Thailand

%

1.6

%
2.3

0

2005–20103

1

2000–2005

1995–2000

1990–1995

1985–1990

1980–1985

1975–1980

1970–1975

US

%

3.4

3

–2

2

4.4

4

3.3

4

Sri Lanka%

4.3

Non-IT capital deepening

IT capital deepening

Labor productivity

Figure 68 Decomposition of Labor Productivity Growth, 1970–2014
Source: APO Productivity Database 2016.
Note: See footnote 70 for the country-exception in the country groups.

Figure 68 and Table 14 show the decomposition of labor productivity growth for individual countries
in five-year intervals covering the period 1970–2014. 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 heights of the late 1980s. In both93©2016 Asian Productivity Organizationin 1970–1985, to 2–20% in 1985–2000, and 3–20% in 2000–2014. This may have accounted for a boostof countries’ TFP performance. In the mid period 1985–2000, the contribution of IT capital deepening
in the US was ahead of Asian countries accounting for 28% of labor productivity growth. Coincidentally, this was also the period when the share of TFP growth was the largest, at 60%.

5 Productivity

Table 14 Role of TFP and Capital Deepening in Labor Productivity Growth, 1970–2014

94

Cambodia
ROC
Hong Kong
Indonesia

(121)
(-)
(45)
(−3)
(−33)
(−56)
(−29)
(0)
(7)
(−17)
(4)
(23)
(72)
(35)
(70)
(46)
(45)
(42)
(31)
(46)
(45)
(−22)
(148)
(126)
(78)
(9)
(89)
(71)
(117)
(51)
(−82)
(71)
(71)
(73)
(62)
(63)
(68)
(50)
(17)
(54)
(38)
(184)
(12)
(−8)
(133)
(253)
(83)
(29)
(274)
(−8)
(9)
(−54)
(37)
(41)
(26)
(23)
(35)
(49)
(29)
(23)
(13)
(−42)
(−10)
(217)
(145)
(116)
(162)
(51)
(62)
(29)
(−148)
(64)
(63)
(38)
(32)
(31)
(94)
(128)
(125)
(60)

Japan

−2.1
1.0
0.6
0.0
−0.3
−0.9
−0.8
0.0
0.3
−0.2
0.1
0.8
4.8
1.8
7.2
2.9
3.5
4.4
2.2
3.1
0.9
−0.3
−2.7
2.3
−0.5
0.0
0.7
0.4
3.1
0.4
−0.3
−0.2
2.1
2.8
1.9
2.6
3.2
3.5
0.8
1.8
3.0
−11.0
0.2
0.1
2.0
1.9
3.0
1.4
−2.1
−0.1
0.5
−2.4
2.5
2.7
1.6
1.2
1.5
2.3
0.6
1.2
0.6
−1.3
−0.4
−1.7
−3.6
2.0
4.6
2.5
6.0
0.9
−0.2
2.5
3.2
1.4
1.2
0.6
3.1
0.4
2.3
1.6

Malaysia

−1.7
0.0
1.3
1.3
0.8
1.6
2.7
3.7
4.2
1.5
2.9
3.5
6.6
5.1
10.3
6.3
7.7
10.5
7.2
6.7
2.0
1.2
−1.9
1.9
−0.7
0.5
0.8
0.5
2.7
0.7
0.3
0.6
2.9
3.9
3.1
4.1
4.7
6.9
4.5
3.4
7.9
−6.0
1.3
−1.5
1.5
0.8
3.7
5.0
−0.8
1.4
5.7
4.5
6.7
6.6
6.2
5.3
4.3
4.7
2.0
5.2
5.1
3.1
3.9
−0.8
−2.5
1.7
2.8
4.9
9.7
3.0
0.1
3.9
5.0
3.7
3.6
1.9
3.2
0.3
1.8
2.6

Labor
Productivity

TFP

Nepal

1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2014
1970–2014
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2014
1970–2014
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2014
1970–2014
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2014
1970–2014
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2014
1970–2014
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2014
1970–2014
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2014
1970–2014
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2014
1970–2014

Capital deepeningIT
Non-IT
0.0 (−1)
0.3 (−20)
0.0
(-)
−1.1
(-)
0.0
(4)
0.6
(51)
0.1
(5)
1.3
(98)
0.1
(13)
1.0 (120)0.1(9)2.3 (147)0.1(5)3.3 (124)
0.2
(4)
3.6
(96)
0.2
(5)
3.7
(88)
0.1
(7)
1.6 (110)
0.0
(1)
2.8
(95)
0.0
(1)
2.6
(76)
0.0
(1)
1.8
(27)
0.1
(2)
3.2
(63)
0.1
(1)
3.0
(29)
0.2
(3)
3.2
(51)
0.6
(8)
3.6
(47)
0.5
(5)
5.5
(53)
0.4
(5)
4.6
(64)
0.2
(3)
3.4
(50)
0.1
(3)
1.1
(52)
0.0
(3)
1.4 (119)
0.0 (−2)
0.9 (−46)0.1(3)−0.5 (−30)
0.1 (−18)−0.3(40)0.0 (−7)
0.5
(98)
0.1
(9)
0.0
(2)
0.1
(18)
0.1
(10)
0.0 (−1)−0.4 (−16)
0.0
(7)
0.3
(42)
0.0
(4)
0.6 (178)
0.0
(1)
0.8
(28)
0.0
(1)
0.8
(28)
0.0
(1)
1.0
(26)
0.1
(2)
1.1
(36)
0.1
(3)
1.4
(34)
0.1
(2)
1.4
(30)
0.2
(3)
3.2
(47)
0.2
(4)
3.6
(79)
0.1
(2)
1.5
(44)
0.1
(1)
4.9
(61)
0.0 (−1)
4.9 (−83)
0.0
(3)
1.1
(85)
0.0 (−2)−1.6 (110)0.1(3)−0.6 (−37)0.1(9)−1.2 (−163)
0.2
(4)
0.5
(13)
0.2
(3)
3.4
(68)
0.1 (−14)
1.2 (−160)0.1(6)1.4 (102)
0.2
(3)
5.1
(88)
0.4
(9)
6.5 (145)
0.3
(5)
3.9
(58)
0.5
(8)
3.4
(51)
0.4
(6)
4.2
(68)
0.6
(11)
3.5
(66)
0.6
(13)
2.2
(52)
0.2
(4)
2.2
(47)
0.1
(3)
1.4
(68)
0.4
(7)
3.6
(70)
0.1
(1)
4.3
(86)
0.1
(4)
4.3 (138)0.1(3)4.2 (107)
0.1 (−10)
0.9 (−107)
0.1 (−3)
1.0 (−42)0.1(7)−0.4 (−22)0.2(6)−1.9 (−68)
0.3
(7)
2.1
(42)
0.2
(2)
3.5
(36)
0.1
(5)
2.0
(66)
0.0
(14)
0.2 (234)
0.0
(0)
1.4
(36)
0.0
(0)
1.8
(37)
0.1
(2)
2.2
(60)
0.1
(2)
2.4
(65)
0.0
(1)
1.3
(67)
0.1
(2)
0.1
(3)
0.1
(25)
−0.2 (−53)0.0(2)−0.5 (−27)
0.0
(2)
1.0
(38)

Philippines

Pakistan©2016 Asian Productivity Organization

Mongolia

Korea

Iran

India

Fiji

China

Bangladesh

Labor
Productivity

1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2014
1970–2014
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2014
1970–2014
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2014
1970–2014
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2014
1970–2014
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2014
1970–2014
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2014
1970–2014
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2014
1970–2014
1970–1975
1975–1980
1980–1985
1985–1990
1990–1995
1995–2000
2000–2005
2005–20102010–2014
1970–2014

5.0
2.2
4.2
3.1
6.0
3.9
5.6
7.2
5.0
6.8
5.5
5.2
3.5
3.7
1.6
5.0
3.0
8.0
4.4
7.1
4.0
−0.2
3.1
3.5
2.7
4.0
4.5
4.3
1.0
3.3
6.2
−1.2
3.6
2.4
4.7
3.2
4.9
3.3
3.9
4.3
2.0
2.1
1.4
1.2
0.7
2.7
4.4
4.9
1.7
3.5
6.4
1.0
3.1
2.2
2.5
3.3
−0.1
−0.3
2.6
3.9
2.4
1.7
0.6
2.0
1.4
1.6
1.4
2.8
−4.7
3.6
0.5
2.3
1.8
2.6
4.4
1.6

Capital deepeningIT
Non-IT

0.1
(1)
0.1
(5)
0.1
(3)
0.2
(5)
0.2
(3)
0.1
(3)
0.4
(8)
0.3
(5)
0.3
(6)
0.3
(4)
0.3
(5)
0.7
(13)
0.5
(15)
0.1
(2)
0.0
(3)
0.3
(7)
0.2
(5)
0.2
(3)
0.3
(7)
0.4
(6)
0.4
(10)
0.6 (−267)
0.5
(16)
0.3
(9)
0.3
(11)
0.4
(9)
0.0
(0)
0.1
(12)
0.1
(12)
0.1
(4)
0.2
(4)
0.1 (−12)
0.1
(4)
0.2
(7)
0.2
(4)
0.1
(5)
0.4
(7)
0.2
(6)
0.2
(6)
0.4
(10)
0.3
(17)
0.3
(17)
0.4
(27)
0.2
(18)
0.0
(7)
0.3
(11)
0.1
(1)
0.1
(2)
0.1
(5)
0.1
(4)
0.3
(4)
0.4
(42)
0.6
(21)
0.5
(24)
0.3
(12)
0.3
(8)
0.0 (−33)
0.1 (−24)
0.1
(3)
0.1
(1)
0.0
(1)
0.1
(4)
0.1
(10)
0.1
(4)
0.1
(7)
0.1
(4)
0.1
(5)
0.1
(3)
0.1 (−3)
0.1
(3)
0.0
(8)
0.4
(18)
0.5
(29)
0.2
(7)
0.1
(2)
0.2
(12)

−0.3
0.0
1.5
3.9
4.2
2.0
5.0
3.8
3.2
2.3
2.8
2.9
2.0
1.4
0.5
2.7
1.5
2.3
3.6
2.9
2.9
1.6
1.0
1.1
0.9
2.0
1.6
3.3
3.3
1.7
3.7
2.9
1.6
0.8
2.6
2.4
5.0
2.1
1.8
1.8
2.0
1.4
0.4
0.5
−0.2
1.7
3.6
3.7
5.0
1.5
4.7
3.1
1.1
0.5
1.2
2.7
1.1
1.7
2.8
2.8
2.1
1.5
1.3
1.5
1.0
1.8
0.8
2.7
1.7
0.0
0.9
1.9
0.3
0.5
1.3
1.1

(−7)
(1)
(36)
(125)
(70)
(53)
(90)
(53)
(63)
(34)
(51)
(57)
(56)
(39)
(30)
(54)
(50)
(29)
(83)
(41)
(71)
(−719)
(31)
(31)
(33)
(50)
(35)
(328)
(328)
(50)
(60)
(−250)
(45)
(34)
(55)
(75)
(102)
(65)
(45)
(42)
(102)
(68)
(31)
(40)
(−24)
(63)
(82)
(75)
(290)
(42)
(73)
(297)
(37)
(21)
(49)
(82)
(−867)(−550)
(106)
(72)
(87)
(91)
(209)
(77)
(73)
(112)
(62)
(99)
(−37)
(0)
(174)
(81)
(15)
(18)
(29)
(72)

TFP

5.3
2.1
2.6
−0.9
1.6
1.7
0.1
3.0
1.5
4.3
2.4
1.6
1.0
2.2
1.1
1.9
1.4
5.4
0.5
3.8
0.7
−2.4
1.7
2.1
1.5
1.6
2.9
0.9
−2.4
1.5
2.3
−4.2
1.8
1.4
1.9
0.6
−0.5
1.0
1.9
2.1
−0.4
0.3
0.6
0.5
0.8
0.7
0.8
1.1
−3.4
1.9
1.4
−2.5
1.3
1.2
1.0
0.3
−1.3
−2.0
−0.2
1.1
0.3
0.1
−0.7
0.4
0.3
−0.2
0.5
−0.1
−6.5
3.4
−0.4
0.0
1.0
2.0
3.0
0.3

(105)
(94)
(62)
(−30)
(27)
(44)
(2)
(42)
(30)
(62)
(44)
(31)
(29)
(59)
(68)
(39)
(45)
(68)
(11)
(53)
(18)
(1086)
(54)
(60)
(57)
(41)
(64)
(−240)(−240)
(46)
(36)
(362)
(51)
(60)
(41)
(20)
(−10)
(29)
(49)
(49)
(−19)
(15)
(42)
(42)
(117)
(26)
(17)
(23)
(−195)(54)(22)(−239)
(43)
(56)
(39)
(9)
(1000)
(674)
(−8)
(27)
(12)
(6)
(−119)
(19)
(21)
(−16)
(33)
(−2)
(140)
(97)
(−82)
(0)
(55)
(75)
(68)
(16)

(−26)
(12)
(−33)
(68)
(55)
(−9)
(43)
(201)
(14)
(21)
(27)
(101)
(32)
(73)
(20)
(−184)
(84)
(60)
(45)
(43)
(34)
(61)
(57)
(49)
(60)
(66)
(45)
(11)
(201)
(50)
(8)
(−11)
(58)
(58)
(51)
(34)
(56)
(46)
(32)
(41)
(−283)
(23)
(67)
(64)
(56)
(58)
(66)
(48)
(22)
(51)

Vietnam

−1.1
0.4
−1.1
2.3
2.0
−0.3
1.6
1.6
0.2
0.6
0.8
1.0
1.0
4.6
1.2
−2.1
4.3
1.4
1.9
1.6
0.6
0.6
1.1
0.6
0.9
1.5
1.1
0.2
0.4
0.8
0.2
−0.2
1.5
2.1
2.1
0.9
2.3
2.6
1.4
1.4
−0.4
0.2
2.1
2.4
1.7
2.1
2.8
2.8
0.9
1.6

Sri Lanka

TFP

APO20

Capital deepeningIT
Non-IT
0.4(10)
4.9 (115)
0.3
(9)
2.6
(79)
0.5
(16)
3.9 (117)
0.6
(19)
0.5
(14)
0.6
(16)
1.0
(29)
0.6
(18)
2.8
(90)
0.6
(17)
1.5
(40)
0.1
(17)
−0.9 (−118)
0.5
(30)
0.9
(56)
0.5
(16)
1.9
(64)
0.1
(3)
2.1
(71)
0.1
(12)
−0.1 (−13)
0.2
(6)
1.9
(62)
0.3
(4)
1.5
(23)
0.5
(8)
4.5
(72)
0.3
(29)
3.0 (255)
0.2
(4)
0.6
(12)
0.4
(15)
0.6
(25)
0.4
(9)
2.0
(46)
0.3
(7)
1.8
(49)
0.2
(11)
1.0
(55)
0.2
(20)
0.2
(19)
0.4
(20)
0.4
(23)
0.4
(32)
0.3
(19)
0.4
(25)
0.2
(15)
0.6
(28)
0.1
(6)
0.5
(22)
0.9
(34)
0.4
(23)
1.0
(66)
0.1
(48)
−0.3 (−149)
0.4
(23)
0.4
(28)
0.2
(7)
2.2
(85)
0.1
(7)
1.9 (104)
0.1
(5)
1.0
(36)
0.2
(6)
1.3
(36)
0.2
(5)
1.8
(44)
0.2
(10)
1.4
(56)
0.4
(9)
1.4
(35)
0.3
(6)
2.8
(49)
0.2
(5)
2.8
(62)
0.2
(7)
1.8
(52)
0.0
(9)
0.6 (374)
0.0
(2)
0.7
(75)
0.0
(1)
1.0
(32)
0.1
(1)
1.3
(35)
0.1
(2)
1.2
(41)
0.1
(3)
1.4
(39)
0.1
(3)
1.4
(31)
0.2
(3)
2.8
(49)
0.2
(4)
3.1
(74)
0.1
(3)
1.5
(46)

East Asia

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
2010–2014
1.5
1970–2014
3.0
1970–1975
3.0
1975–1980
1.0
1980–1985
3.1
1985–1990
6.3
1990–1995
6.2
1995–2000
1.2
2000–2005
5.2
2005–2010
2.4
2010–2014
4.3
1970–2014
3.6
1970–1975
1.9
1975–1980
1.0
1980–1985
1.9
1985–1990
1.3
1990–1995
1.5
1995–2000
2.3
2000–2005
2.5
2005–2010
1.6
2010–2014
0.2
1970–2014
1.6
1970–1975
2.6
1975–1980
1.9
1980–1985
2.6
1985–1990
3.6
1990–1995
4.0
1995–2000
2.6
2000–2005
4.1
2005–2010
5.7
2010–2014
4.5
1970–2014
3.5
1970–1975
0.2
1975–1980
0.9
1980–1985
3.1
1985–1990
3.7
1990–1995
3.0
1995–2000
3.6
2000–2005
4.3
2005–2010
5.9
2010–2014
4.2
1970–2014
3.2

ASEAN6South Asia

Asia24

US

ThailandSingapore

5.5 Energy Productivity

Labor
Productivity
1970–1975
1.1
1975–1980
3.4
1980–1985
4.4
1985–1990
0.6
1990–1995
4.0
1995–2000
1.2
2000–2005
1.8
2005–2010
3.9
2010–2014
4.3
1970–2014
2.7
1970–1975−1.3
1975–1980
1.2
1980–1985
1.2
1985–1990
1.7
1990–1995
5.7
1995–2000
4.9
2000–2005
6.7
2005–2010
2.4
2010–2014
5.5
1970–2014
3.1
1970–1975
2.6
1975–1980
1.9
1980–1985
2.6
1985–1990
3.6
1990–1995
4.0
1995–2000
2.6
2000–2005
4.1
2005–2010
5.7
2010–2014
4.5
1970–2014
3.5
1970–1975
2.6
1975–1980
2.6
1980–1985
2.9
1985–1990
3.8
1990–1995
4.4
1995–2000
2.8
2000–2005
4.1
2005–2010
6.7
2010–2014
5.0
1970–2014
3.8
1970–1975
3.8
1975–1980
3.5
1985–1990
0.6
1985–1990
4.0
1990–1995
5.6
1995–2000
0.6
2000–2005
3.6
2005–2010
2.5
2010–2014
4.3
1970–2014
3.1

Capital deepeningIT
Non-IT
0.0
(2)
0.9
(80)
0.0
(1)
1.2
(36)
0.1
(2)
2.8
(62)
0.0
(0)
0.1
(24)
0.0
(1)
0.2
(4)
0.1
(7)
−1.1 (−98)
0.2
(12)
0.6
(35)
0.3
(7)
1.7
(44)
0.1
(1)
2.7
(64)
0.1
(3)
1.0
(36)
0.0
(2)
−1.0
(77)
0.0
(4)
0.1
(9)
0.0
(4)
−2.1 (−180)
0.2
(13)
0.9
(57)
0.2
(4)
2.5
(44)
0.4
(7)
4.7
(95)
0.4
(5)
4.9
(73)
0.5
(22)
3.4 (144)
0.4
(8)
4.2
(76)
0.2
(8)
1.9
(62)
0.2
(7)
2.2
(85)
0.1
(7)
1.9 (104)
0.1
(5)
1.0
(36)
0.2
(6)
1.3
(36)
0.2
(5)
1.8
(44)
0.2
(10)
1.4
(56)
0.4
(9)
1.4
(35)
0.3
(6)
2.8
(49)
0.2
(5)
2.8
(62)
0.2
(7)
1.8
(52)
0.3
(10)
2.6 (100)
0.2
(7)
1.5
(59)
0.2
(6)
0.2
(8)
0.3
(8)
1.4
(36)
0.3
(6)
1.9
(43)
0.3
(10)
1.2
(41)
0.5
(13)
1.6
(38)
0.4
(6)
3.4
(51)
0.3
(5)
3.1
(61)
0.3
(8)
1.8
(48)
0.1
(2)
2.0
(53)
0.1
(4)
2.3
(67)
0.2
(27)
3.1 (525)
0.2
(5)
1.4
(34)
0.3
(6)
3.7
(65)
0.3
(48)
3.1 (515)
0.3
(9)
1.2
(32)
0.3
(10)
0.7
(30)
0.3
(6)
2.2
(52)
0.2
(7)
2.2
(70)

TFP
0.2
2.1
1.6
0.4
3.8
2.2
1.0
1.9
1.5
1.6
−0.3
1.1
3.3
0.5
3.0
−0.1
1.5
−1.6
0.9
0.9
0.2
−0.2
1.5
2.1
2.1
0.9
2.3
2.6
1.4
1.4
−0.3
0.9
2.5
2.1
2.3
1.3
2.0
2.9
1.7
1.7
1.7
1.0
−2.7
2.5
1.6
−2.8
2.2
1.5
1.8
0.7

(18)
(63)
(36)
(76)
(95)
(191)
(54)
(49)
(34)
(61)
(21)
(87)
(277)
(30)
(52)
(−2)
(22)
(−66)
(16)
(30)
(8)
(−11)
(58)
(58)
(51)
(34)
(56)
(46)
(32)
(41)
(−11)
(35)
(86)
(56)
(52)
(48)
(49)
(43)
(34)
(45)
(45)
(29)
(−451)(61)(29)(−463)
(60)
(60)
(41)
(23)

5

Unit: Average annual growth rate (percentage), contribution share in parentheses.
Source: APO Productivity Database 2016.
Note: See footnote 70 for the country-exception in the country groups.

5.5 Energy Productivity
In 2013, 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%

95©2016 Asian Productivity Organization

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

5 Productivity

and 15.6%) of world CO2 wasemitted, as shown in Figure 69.
This implies that Asia has lower
energy productivity (defined as aratio of output per energy consumption) and higher carbon
intensity of energy at the aggregate level. It is key to improveenergy productivity and carbon
intensity in the growing economies of Asia in order to reduce
CO2 emissions in the world in thelong run.

Energy Consumption

CO2 Emission

Japan

Japan

India

APO20
19 %

Others
26 %

Asia
46 %

Asia 30
43%
EU28
12 %

EU15
10 %

China
US
16 %

APO20
18 %

Others
21 %
EU28
10 %

India

Asia 30
50 %

EU15
8%
US
16 %

Other
Asia
3%

Asia
53 %

China

Other
Asia
3%

Figure 69 Shares of Asia in World Energy Consumption and
CO2 Emission, 2013
Sources: IEA, CO2 Emissions from Fuel Combustion 2015; IEA, Energy Balances of OECDCountries 2015; IEA, Energy Balances of non-OECD Countries 2015.
The average level of energy productivity in Asia was inferior to
the EU28 and the US by 32% and
4%, respectively, in 2013. There is considerable diversity in Asia however, reflecting the differences inindustrial 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 37% and 87% higher than the US andChina, respectively, in 2013.

Table 15 Energy Productivity Levels, 1980, 1990, 2000, 2010, and 2013

_GDP at constant basic prices per energy consumption, using 2011 PPP, reference year 2014©2016 Asian Productivity Organization

1980

1990

(%)

Hong Kong
Singapore
Iran
Malaysia
Philippines
ThailandBangladeshJapan
Indonesia
Sri Lanka
ROC
Pakistan
Korea
India
Vietnam
Nepal
China

28.0 100.0
23.1 82.6
14.5 51.7
13.4 47.9
11.5 41.2
11.0 39.2
10.7 38.1
10.2 36.5
8.7 31.0
8.6 30.5
8.0 28.4
6.4 23.0
5.9 21.1
5.1 18.3
3.8 13.7
3.5 12.4
1.3 4.8(reference)
US
EU15

5.2
8.9

Australia
Turkey

7.8
12.3

(%)

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

29.9 100.0
20.7 69.2
12.7 42.4
12.6 42.3
12.3 41.2
11.8 39.6
11.4 38.2
11.0 36.9
10.4 34.9
10.0 33.5
9.5 31.7
8.0 26.9
7.3 24.3
6.3 21.1
5.3 17.8
4.3 14.4
3.7 12.3
2.4 8.1

18.5
31.8(reference)
US
EU15

7.3
11.0

24.4
36.7

27.7
43.8

Australia
Turkey

8.6
13.4

28.9
45.0

2000
Singapore
Hong Kong
Philippines
Bangladesh
ROC
Sri Lanka
Japan
Malaysia
ThailandIndonesia
Pakistan
India
Iran
Vietnam
Korea
Mongolia
Cambodia
China
Nepal
(reference)
US
EU15
EU28
Australia
Turkey(%)

24.8 100.0
24.5 98.8
13.6 54.9
13.6 54.8
12.8 51.5
12.6 50.6
12.4 50.0
12.0 48.5
10.9 44.1
10.0 40.3
9.1 36.5
8.3 33.4
8.1 32.6
7.4 29.7
7.3 29.2
7.0 28.1
5.6 22.5
5.4 21.6
5.0 20.2
8.5
12.3
12.0
10.0
13.4

34.4
49.8
48.3
40.1
53.9

2010
Hong Kong
Singapore
Philippines
Sri Lanka
BangladeshJapan
Indonesia
Malaysia
ROC
India
PakistanThailand
Korea
Iran
Cambodia
Vietnam
China
Mongolia
Nepal(reference)
US
EU15
EU28
Australia
Turkey(%)

41.8 100.0
23.6 56.3
21.9 52.3
17.5 41.8
15.6 37.4
14.2 34.0
14.1 33.8
14.0 33.4
13.8 32.9
11.3 26.9
10.7 25.7
10.2 24.4
9.1 21.8
8.8 21.1
7.9 18.8
7.8 18.7
7.8 18.7
7.2 17.2
5.7 13.6
10.4
13.8
13.4
12.3
14.6

24.8
33.1
32.2
29.4
35.0

2013
Hong Kong
Philippines
Singapore
Sri Lanka
BangladeshIndonesia
Japan
ROC
Malaysia
India
PakistanThailand
Korea
Vietnam
Cambodia
Mongolia
Iran
China
Nepal
(reference)
US
EU15
EU28
Australia
Turkey(%)

43.6 100.0
23.8 54.6
21.2 48.7
21.1 48.5
17.3 39.7
15.5 35.6
15.1 34.6
15.0 34.3
12.9 29.5
11.9 27.2
11.5 26.5
10.1 23.2
9.4 21.5
8.7 20.1
8.5 19.5
8.1 18.6
8.1 18.5
7.8 17.9
6.5 14.9
11.0
14.8
14.4
12.6
15.3

25.2
33.9
33.1
29.0
35.2

Unit: Thousands of US dollars per toe (tonne of oil equivalent) (as of 2014).
Sources: Official national accounts in each country, including author adjustments; IEA, Energy Balances of OECD Countries 2015; IEA, Energy Balances of non-OECD Countries 2015; APO Productivity Database 2016.

96

5.5 Energy Productivity

Figure 70 placed countries on the two partial
productivity indicators of labor and energy,
measured in 2013. Less-developed countries
with lower labor productivity (such as the Philippines, Sri Lanka, and Bangladesh) tend tohave higher energy productivity. One of theeffective strategies to improve labor productivity in such countries is to expand the manufacturing sector (as shown in Figure 73 in
Section 6.1 (p. 103), there is a positive correlation between the TFP growth and the manufacturing share). This frequently follows thedeterioration in energy productivity.

Labor productivity(US dollar/hours worked)

70

US
60
Australia

Singapore

50

EU15

40

Japan

ROC

30

Korea
Iran

5

TurkeyMalaysia

20
Mongolia
10

As a next stage of economic growth, welldeveloped countries will be able to pay more
attention to improving energy productivity byabolishing implicit or explicit subsidies on energy prices, especially in electricity prices, andlevying 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 (at the x-axis).

Nepal

0
0

Thailand
Pakistan
China
India
Vietnam
Cambodia

5

10

15

Sri Lanka

Indonesia

Philippines
Bangladesh
20

25

Energy productivity(Thousands of US dollars/toe)

Figure 70 Labor Productivity and Energy Productivity, 2013
Sources: Official national accounts in each country, including author
adjustments; IEA, Energy Balances of OECD Countries 2015; IEA, Energy Balances of non-OECD Countries 2015; APO Productivity Database
2016.

%

GDP

Carbon intensity of energy

Energy intensity of GDP

CO2

Italy
UK
EU15
EU28
US
CanadaGermany
FranceJapan
ROC
Singapore
Hong Kong
Australia
Korea
Sri Lanka
Philippines
Pakistan
TurkeyIndonesia
Nepal
Iran
Malaysia
ThailandMongolia
India
Bangladesh
Cambodia
China
VietnamFigure 71 decomposes the12sources of CO2 emission
1.0
10
growth (from fuel combustion)
2.6
3.8
8
in the Asian countries during
2.7 1.0
2000–2013, based on the so1.1
1.2
6
1.5 0.6
9.7
0.0 2.0
called Kaya identity. The
1.8
0.6
4
7.6
7.6
6.9
6.7
growth in CO2 emissions is de0.1
5.6
5.6
5.5
4.7 5.4
5.1 4.8
4.4
4.1 4.52
3.9
3.7
3.0 3.9
composed to three compo4.2
0.9
0.7 1.1 1.0 2.0 1.7 1.3 1.1 1.6−0.2−0.4
nents: changes in real GDP;0−0.1
−0.1−0.1−0.4−0.2−1.0−0.5−0.6−0.4−0.7−0.9−0.9
−1.3
−1.4
−0.5
−1.8
−1.9−1.5−1.4−1.2−1.0−1.9−2.0−2.0−1.8−2.8−2.3
carbon intensity of energy; and−2.9−3.2−1.9−1.4−1.4−2.8
−3.4
−1.6
−2
−4.3−4.0
−0.4
−4.4
−1.2
−5.5
−1.2
energy intensity of GDP (the in−4verse of energy productivity).
−6
In many countries, the production expansion (real GDP
growth) is the most significant
factor to explain the growth of
CO2 emissions. With an
Figure 71 Sources of CO2 Emission Growth, 2000–2013
exception of Thailand, Iran, andSources: Official national accounts in each country, including author adjustments; IEA,
Singapore, energy productivity
Energy Balances of OECD Countries 2015; IEA, Energy Balances of non-OECD Countries 2015;
IEA, CO2 Emissions from Fuel Combustion 2015.
has been improved in many
Asian countries in this period,
but these improvements are not enough to offset an expansion of energy consumption (except in thePhilippines, Hong Kong, and Japan).

97©2016 Asian Productivity Organization

14

5 Productivity©2016 Asian Productivity Organization

On 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 ofnuclear power plants after the Fukushima Daiichi nuclear disaster in March 2011.85 Singapore realized
a large improvement in carbon intensity of energy by the shift from oil to LNG in electricity power
generation.86 This helped to offset the increases in CO2 emission accompanied by strong economic
growth, regardless of some deterioration in energy productivity. In this period, a decoupling in thegrowths 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 the same output. For
sustainable growth of the world economy, improvements in energy productivity and carbon intensityof energy are recognized as one of the important policy targets in Asia.

85: 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 2.8%.
86: In Singapore, the share of natural gas in electricity power generation reached to 91.5% in 2013 from 18.5% in 2000, compared tothe decrease in the share of oil in power generation from 80.0% in 2000 to 4.9% in 2013 (IEA, Energy Balances of non-OECD Countries 2015.).

98

5.5 Energy Productivity

Box 7

Sensitivity of TFP Estimates

TFP computations, based on the growth accounting framework, depends on data that is sometimes difficult to observe. One difficult data point is calculating the wages for the self-employed and unpaid family
workers. As a crude approximation in this report, it is assumed that per-worker wages for the self-employed and contributing family workers are 5–70% of the per-worker wage for employee in the countries
where the appropriate wage data is not available. This approximation is made in order to estimate the labor compensation for total employment. The future review on this assumption affects TFP estimates directly through the revision of factor income shares and indirectly through the estimates of the ex-post rateof return and thus the aggregate measure of capital services.

5

The right panel of Figure B7.1 presents the labor income share (the ratio of compensation of employees tothe basic-price GDP) based on the official national accounts (including author adjustments in basic-priceGDP for some countries) in 20 Asian countries and the US in 2014. The left panel of the figure illustrates theemployee share to total employment. There is a large divergence in labor income share for employees
among the Asian countries.
This does not necessarilyreflect differences in the num100 % 80
60
40
20
0
0
10 20 30 40 50 60 %
86
China
56
ber of employees in total em94
US55
ployment. Although Malaysia
94
HongKong
53
87
Japan
53
and the Philippines have a
80
Korea
49
high employee share of 79%
55
Fiji
46
94
Singapore45
and 61%, the labor income
30
Nepal
45
share is only 36% and 37% in
80
ROC
45
59
Sri Lanka
39
2014, respectively.
46
Indonesia
38
Pakistan
Philippines
Malaysia
Thailand
India
Bangladesh
VietnamMongolia
Cambodia
Iran

40
61
79
48
20
39
37
51
22
56

Share of employees to total employment

37
37
36
35
34
31
30
29
22
20

Labor income share for employees

Figure B7.1 Labor Income Share for Employees, 2014
Sources: Official national accounts in each country, including author adjustments.

%

3.9

3

3.1

2

2.3

TFP(vL+10%)
TFP
TFP(vL−10%)

2.6

2.3
1.9 1.8
1.4

1.2

1

2.1
1.7
1.3

2.1
1.9
1.6 1.6
1.4 1.1

1.8 2.0 2.0
1.6 1.6
1.4 1.2 1.2
0.3

0

1.3
0.9
0.5

1.2 1.0 1.2 1.1
0.9 0.8 0.7 0.6
0.5 0.6
0.2 0.2

1.1
0.6 0.5
0.4
0.2 0.3

−1

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

Nepal

Bangladesh

Iran

Philippines

Malaysia

Fiji

Singapore

Indonesia

Japan

US

Mongolia

Vietnam

Korea

Thailand

Pakistan

Hong Kong

Sri Lanka

India

Cambodia

ROC

Figure B7.2 Sensitivity of TFP Estimates by the Change ofIncome Share, 1970–2014
Source: APO Productivity Database 2016.

99©2016 Asian Productivity Organization

4

China

Figure B7.2 illustrates the sensitivity of TFP estimates bychanging the factor income
share during the period from1970 to 2014. In general, thegrowth rate of capital input ishigher than that of labor input, therefore the higher income share of labor resultsin higher estimates of TFP
growth. In other words, laborproductivity is improved much
faster over a given period
than capital productivity, thegrowth of which tends tobe frequently negative (seeFigures 45 and 61). The TFP estimate reflects the improvement of labor productivity
more when the labor income
share increases. In Malaysia,
with TFP growth of 0.3% on
average during the period
1970–2014, the true estimate
could be 0.8% if the current
labor income share is underestimated by 10%.

6 Industry Perspective
This chapter provides an overview of the industry structure of countries. Industry decomposition allows an insight into the source of a country’s economic dynamics, which, in turn, determines its overall performance and characteristics, its strengths, and its vulnerabilities. On one hand, a broad industry
base reflects diversification and sophistication in the economy, and in turn is more resourceful inweathering economic shocks. On the other hand, reliance on a narrow industry base leaves economies more vulnerable to shocks and more susceptible to volatility. Furthermore, the different composition of economic activities among countries is also one of the main sources of the huge gap in
average labor productivity at the aggregate level, as observed in Chapter 5. By analyzing the industry
structure of the Asian economies, one can clearly trace the path of economic development and identify countries’ respective stages based on their characteristics.87

6

6.1 Output and Employment
Table 7 in Section 3.2 (p. 34) 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 2014 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 first recorded data.
Countries at the lower rungs of the development ladder tend to have a bigger agriculture sector as ashare of value added.88 Figure 72
shows the industry composition89
Table 16 Country Groups Based on the Current Economicof the Asian economies in 2014,
Level and the Pace of Catching Up
and indicates a broad, negative_Level and average annual growth rate of per capita GDP at constantmarket prices, using 2011 PPP
Per capita
GDP level to
the US in 2014
(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

Malaysia, Mongolia,
ThailandTurkeyIran

Bhutan, India,
Indonesia, Sri Lanka,
VietnamPhilippinesFiji

Lao PDR, Myanmar

Bangladesh,
Nepal,
Pakistan(L3)
10% <–< 20%
(L4)
< 10%

Cambodia

Sources: Official national accounts in each country, including author adjustments.
Note: The annual catch-up rates are based on the data during 1970–2014. The starting
years for some countries are different due to data availability: Cambodia (1987–) andthe Lao PDR (1981–).

87: Constructing the industry origins of labor productivity growth requires confronting a large volume of data from different sources.
Issues of data inconsistency arising from fragmentation of national statistical frameworks can present enormous hurdles toresearchers in this field. The industry data in this chapter is mainly based on official national accounts. Where back data is notavailable, series are spliced together using different benchmarks and growth rates. Data inconsistencies in terms of concepts,
coverage, and data sources have not been fully treated although levels of breakdown are deliberately chosen to minimize the potential impact of these data inconsistencies. In this sense, the industry data in the APO Productivity Database should be treated asa work in progress and it is difficult to advise on data uncertainty. These data will be further developed and examined in the near
future. Readers should bear these caveats in mind in interpreting the results.

101©2016 Asian Productivity Organization

correlation between the share of
the agriculture sector and the relative per capita GDP against the
US. Half of the Asian countries
compared have an agriculture
sector accounting for over 10% of
total value added. They all have arelative per capita GDP that is30% below the US (except Iran).
Among them, the five countries
with the biggest agriculturalshare are all in the lowest income
group in Table 16 (with a per capita GDP less than 10% of the US).
Note also how finance, real estate,and business activities grow inimportance as one moves up

6 Industry Perspective

income levels. The finance sector
is especially prominent in Hong
Kong (38%), Singapore (32%), andthe US (32%). Mining appears
to be what defines oil-exporting
countries, typically accounting forover 40% of total value added,
except in Bahrain (25%), Iran(12%), and the UAE (33%), which
are countries that have managed
to diversify mining. Finance isthe biggest sector in Bahrain, accounting for 21% of total value
added, whereas it is the second
largest sector (18%) in the UAE,
following mining.

Nepal
Cambodia
Myanmar
Pakistan
Lao PDR
VietnamBhutanBangladesh
India
Mongolia
Indonesia
Fiji
Philippines
IranThailandSri Lanka
China
Malaysia
TurkeyAustralia
Korea
Saudi Arabia
ROC
US
Oman
Japan
BruneiUAE
KuwaitBahrain
Qatar
17
Hong Kong
12
Singapore90 100 %32
15
17
1 6 1 7
8
13
31
16
10
1 9
15
8
9
1
28
20
5
1 6
19
7
13
25
14
16
22
20
3
10
8
25
8 4 7
11
19
15
4 7
20
15
12
4 6
15
12
4
13
18
16
12
15
9
3 9
10
8
16
18
21
1 7
14
2
11
11
16
15
12
2 8
18
2
6
19
15
10 2 5
16
14
18
8
13
13
20
9
1 10
16
10
11
11
13 1
15
15
23
18
17
17
11 1
21
12
3 7
19
6
20
11
15
5
12
7
16
12
8
14
10 4
28
14
3 3
18
7
14
10 2
18
12
9
2
20
13
14
9
29
16
2 7
12
4
7
13
9
23
13
3 4
18
10
9
11
8 2
3 5
16
18
19
16
15
2 7
7 3 9
30
12
8
22
3 5
11
2
30
20
8
21
2
9
11 1 5
16
40
5
10
2
23
20
31
20
4
19
12
13
12 2 4
27
8
32
7 5
1
9 1 6
18
44
8
1
3 6
14
21
30
10
15
1
12 5 3 7
16
17
48
13
1
33
9 2 9
8
8
18
53
5 22 4 4
17
13
25
1 6
6
15
19
7
21
10 1 5 7 3
11
49
14

Group-L4

Group-L3(except for
Bangladesh
and Mongolia)

Group-L2(except for
Sri Lanka)

Group-L1

For fostering productivity in the
28
12 4
10
38
19
1 5
19
12
32
less-developed countries, it is
0
10
20
30
40
50
60
70
80
important to adopt existing tech1. Agriculture
2. Mining
nologies from the advanced econ3. Manufacturing
4. Electricity, gas, and water supply
5. Construction
6. Wholesale and retail trade, hotels, and restaurantsomies. In this view of assimilation,
7. Transport, storage, and communications
8. Finance, real estate, and business activities
manufacturing is a key sector in
9. Community, social, and personal services
propelling countries to make aleap in economic development. It
Figure 72 Industry Shares of Value Added, 2014
accounts for around 20% or more
Sources: Official national accounts in each country, including author adjustments.
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 73
shows there is a positive correlation between our estimates of TFP growth in Chapter 5 during 2000–
2014 and the shares of manufacturing in 2014. Outlier countries are Hong Kong and Mongolia,90 who
have a higher share of services and mining, respectively.

©2016 Asian Productivity Organization

Figure 74 shows the breakdown of the manufacturing sector, comprising nine sub-industries, for 17
selected Asian countries and the US.91 The dominance of machinery and equipment in Asian manufacturing can be clearly seen, particularly in Singapore (61% of manufacturing’s total value added) and
the ROC (66%), Korea (52%), and Japan (49%). These compare with 47% in the US. At the other end are
88: In Chapter 5, GDP is adjusted to be valued at basic prices (including our estimates, if the official estimates at basic prices are notavailable). However, the definition of GDP by industry differs among countries in this chapter due to data availability. GDP is valued at factor cost for Fiji and Pakistan; at basic prices for Cambodia, Hong Kong, India, Korea, the Lao PDR, Mongolia, Nepal, andSingapore; at producers’ prices for Iran, the ROC and the Philippines; and at market prices for Bangladesh, Indonesia, Japan, Malaysia, Sri Lanka, Thailand, and Vietnam.
89: The nine industries are 1–agriculture; 2–mining; 3–manufacturing; 4–electricity, gas, and water supply; 5–construction; 6–wholesale and retail trade, hotels, and restaurants; 7–transport, storage, and communications; 8–finance, real estate, and business activities; and 9–community, social, and personal services. See Appendix 6 for the concordance with the ISIC, Revision 3.
90: 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
91: Manufacturing consists of nine sub-industries: 3.1–food products, beverages, and tobacco products; 3.2–textiles, wearing apparel,and leather products; 3.3–wood and wood products; 3.4–paper, paper products, printing, and publishing; 3.5–coke, refined petroleum products, chemicals, rubber, and plastic products; 3.6–other non-metallic mineral products; 3.7–basic metals; 3.8–machineryand equipment; and 3.9–other manufacturing. See Appendix 3 for the concordance with ISIC, Revision 3.

102

6.1 Output and Employment

countries dominated by light manufacturing; e.g., the food products,
beverages, and tobacco products
sector in Sri Lanka (51%), the Philippines (50%), Fiji (49%), and Mongolia (41%); and the textiles, wearing
apparel, and leather products
sector in Cambodia (65%) and Bangladesh (54%). Coke, refined petroleum products, chemicals, rubber,and plastic products are also a
prominent subsector, not least in
Kuwait (69%), where they accountfor two-thirds of the country’s manufacturing value added.

%

4.5

Mongolia
4.0
3.5

China

3.0

India

2.5
2.0

ThailandPhilippines
Indonesia
Sri Lanka
Singapore
Fiji
Malaysia
Iran
Cambodia

6

Pakistan

Hong Kong
1.5
1.0

Korea
ROC

Japan

US

0.5

Vietnam
0.0
−0.5

Nepal
0

5

Bangladesh
10

15

20

25

30

35 %

Manufacturing share, 2014

Figure 73 Manufacturing Share and TFP Growth, 2000–
2014
Sources: Official national accounts in each country, including author adjustments;
APO Productivity Database 2016.

51
50
49

19

41

14

30
30
24

7

5
8
12

13
4 4

20

40

1
18
6

7
21

3
35
4 2

22

15
3 21
41
15
65
13
2 3 2
33
13
11 4
9
3
15
10
12 4
22
26
2 3
14
10
22
9
54
3 3
9
69
5 2
1
25
5 2
15
4
3
16
4 3 2
2 5
13

0

5 12
11
12
12

9
4 3
49
47
6

10
31 4

7

61
52
66

60

4 Sri Lanka
7
19
3 Philippines
16
3 2
6
4 1 9
7
Fiji
4 3 Mongolia
87729
Hong Kong
1 Indonesia
11
4 4
5 Thailand
33
1 Iran15
14
12 3 3 2 8
Cambodia
1 Malaysia
40
5 Japan
4 US
6
27
India
10
11
3
Bangladesh
6 1 7 1 Kuwait
5 Singapore
2 Korea
4 ROC

80

100 %

3-1. Food products, beverages, and tobacco products
3-2. Textiles, wearing apparel, and leather products
3-3. Wood and wood products
3-4. Paper, paper products, printing, and publishing
3-5. Coke, refined petroluem products, chemicals, rubber, and plastic products
3-6. Other non-metallic mineral products
3-7. Basic metals
3-8. Machinery and equipment
3-9. Other manufacturing

Figure 74 Industry Shares of Value Added in Manufacturing, 2014
Sources: Official national accounts in each country, including author adjustments.

92: The group averages as industry share of value added are based on a country’s industry GDP, using exchange rates for the whole
economy without consideration of the differences in relative prices of industry GDP among countries.
93: 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.

103©2016 Asian Productivity Organization

Figure 75 shows the industry sharesof value added and employment by
the four country groups based on
2014 income levels, compared with
the Asia30 average and the US for
the years 1980, 1990, 2000, and2014.92 The first thing to note is thatin 2014, the service sector accounted for the largest share of total value added in all country groups,
independent of their economic development.93 That said, Group-L1
has always had the biggest service
sector among all Asian countries.
This has become much more distinctive as over time the bulk of theeconomy in this group continues toshift heavily toward services. By
2014, the service sector accounted
for 61% of total value added inGroup-L1, compared to 79% in the
US and 48% in Group-L2.94 The
weight of the service sector is similar in Group-L3 and Group-L4 at
53% to 52%. This reflects the relative importance of manufacturing
to the former, and agriculture forthe latter, at their particular stagesof development.

TFP growth during 2000–2014

6 Industry Perspective

The second 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 35% in 2014. In the past three
decades, the value-added share of agriculture in Group-L3 has more than halved from 28% in
1980 to 15% in 2014, with the most rapid shift taking place in the 1990s. Employment in the sector
also was cut by one-third over the same period. In contrast, the least well-off countries have not beenas successful in diversifying away from agriculture, which accounted for 23% of total value added
and 47% of employment in 2014, compared with 36% and 64%, respectively, in 1980. In the meantime,
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 2014, 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.95 Thus,
countries with a big agriculture sector often have low per capita GDP. In these cases, shifting out ofagriculture 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 in this
sector is higher than that experienced in Asian countries. The reverse is true for the sector of finance,
1980

%

100
90
80
70
60
50

17

21

13 4

8

40

2

30

21

10 9
1
3

6 14

3

7

8

18

6
2

7
12

8
13 1
2
4
3
7 1

12 9
6

13 1

28

16

1
68
24

10

11
7

9 10
21

24

7

36

4
1

7
2

7

22
13

13

21
61

6
15

5
2 6
1

13
21

28

3

13 15

8 9

15 9
3
8 10

9
12
8

15

19

5
2

4
2

28

5

3

63

58

22

23

27

31
38

4
14
26
17
7 5
13
57

6

4
2 6
1
18

13

3

13 20
6
3 9

19
8
2

7

15

8

12
4
13

15 1

1

16 15

1
5

2
4

9
9

14
4
1

17 10
4
2

5
2

14

14 14

18
49

2
54

30

13 15

19

6

19
11

1
1 2

3

16 17
27

14

14

15

5
1

8

15

14

11
8
9
18

18
13
1

8

4
2

9

28 1

2

15
4

17

5

29

10

3

43

14

9

32

1

2

46

1

2
5

20

7 16
1

16

11
1

19

1
6
8

17

7 1
20

11

47

4

35
23

15

8
12

22

6

4
2

5

15
6

12

Emp.
V.A.

Emp.
V.A.

Emp.
V.A.

9

9
3 1
1 1

Emp.
V.A.

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

15 13

10

16

Emp.
V.A.

Emp.
V.A.

Emp.
V.A.

Emp.
V.A.

Emp.
V.A.

US

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

4
2 6

5
2

13

12

8

13
8 6

48

5

6
14

53

8
19

1

22

7
7

3
5

22

13 1
4
17

30

12 10

36

38

8 6
1

6
3

15

22

26

11

11

30

1
21

16

2014

15 13

Emp.
V.A.

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

3

15

1 1
2 2

6

5

2
4

8

12 1

7

12 10

Emp.
V.A.

Emp.
V.A.

Emp.
V.A.

Emp.
V.A.

Emp.
V.A.

Emp.
V.A.

US

15

8

6

16

1
10 3
8

16

23

7 10

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

12

8

7
2

32
22

7

1
3

13 1

2

5

14

10

11

17
59

16

1
4

1
5 1
2

2 1

23 24

14

11

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

4
7 1

12

20

3 1
2 3

1
3
6

2000
11

Emp.
V.A.

14

10

11 9
1
3

Emp.
V.A.

16

8
18

8

10
64

5

15

13

12 1

1

25

3

7

24

17

10
14
1
3

17

36

Emp.
V.A.

0

2
4

8 2

7 63

20
10

9

9 10

11

2

5
1
9

1990

13

US

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

US

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

Figure 75 Industry Shares of Value Added and Employment by Country Group, 1980, 1990,
2000, and 2014©2016 Asian Productivity Organization

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

94: If Figure 72 were to rank countries by the size of the service sector, Hong Kong would top the table at 92.7%, followed by the US(78.6%), and other Group-L1 countries, namely the ROC (62.5%), Japan (69.8%), and Singapore (74.5%). Fiji is an exception, with alarge service sector share (66.9%) relative to its per capita GDP level.
95: 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.

104

6.1 Output and Employment

%

%

30

Australia
BruneiHong Kong
Korea
Oman
Saudi Arabia
UAE

25
20

Bahrain
ROC
Japan
Kuwait
Qatar
Singapore

50

China
Mongolia

Iran
Thailand

Malaysia
Turkey

40

30

15
20
10

0

0

1970 1975 1980 1985 1990 1995 2000 2005 2010

1970 1975 1980 1985 1990 1995 2000 2005 2010

Group-L2

Group-L1
%

60

6

10

5

%

BhutanIndonesia

50

Fiji
Philippines

India
Sri Lanka
Vietnam

70
60

40

50

30

40

20

30

10

20

0

10
1970 1975 1980 1985 1990 1995 2000 2005 2010

Group-L3

Bangladesh
Lao PDR
Nepal

Cambodia
Myanmar
Pakistan1970 1975 1980 1985 1990 1995 2000 2005 2010

Group-L4

Figure 76 Long-Term Trends of Value-added Share in the Agriculture Sector, 1970–2014
Sources: Official national accounts in each country, including author adjustments.

The third point to note is that the industry structure in Asian countries differs from that in the US
regarding the relative importance of manufacturing, even in Group-L1 countries, where manufacturing accounts for 19% of the economies’ value added, compared with 12% in the US in 2014. The US
economy is highly skewed toward the service sector, accounting for 79% of the total value added,
compared with an average of 61% in Group-L1 countries. Certainly, its share of finance, real estate,and business activities at 32% was much larger than the share in Group-L1 countries, at 17%. 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 as a whole is reflected in the fact that income groups are not filtered out by thesize of a country’s manufacturing sector.96 In Asia, the manufacturing employment share is typically
96: If Figure 72 were to rank countries by the size of the manufacturing sector, the ROC would lead with a share of 30.7%, followed by
Korea and China at 30.3% and 29.2%, respectively.

105©2016 Asian Productivity Organization

real estate, and business activities, which often generate a much greater value-added share thansuggested by its employment share. In 2014, the sector accounted for 32% of total value added generated by 20% of employment in the US, and 15% and 2%, respectively, in the Asia30. While the valueadded share of the sector has grown by 10 percentage points in the US over the past three decades, it
has only grown by 4 percentage points in the Asia30.

6 Industry Perspective©2016 Asian Productivity Organization

smaller than the value-added
share it generates. Furthermore the value-added share of
the sector has been shrinking
in the high-income groups(i.e., Group-L1 and Group-L2)
whereas in Group-L3 countries
it has been relatively stable,
and slowly expanding inGroup-L4, reflecting their different developmental stage.
Figure 76 shows how the shareof the agriculture industry intotal value added shrank overtime in the Asian economies.
This could reflect the actu­al decline in agricultural out­put and/or the relatively rapid
expansion in other sectors.
Despite the broad spread,the downward trend is unmistakable, even for Group-L4
countries. The share of the agriculture sector displays a longterm declining trend in all
countries, albeit at different
paces and at different startingtimes. Looking at the available
data, the share of agriculture in
most Asian countries (excluding the oil-exporting countries)
clustered around the 30–50%
band in the 1970s, trending
down to the 10–20% band by
2014. Vietnam and Mongolia
are two countries where theagriculture sector experienced
similar declines but within amuch shorter period (from thelate 1980s and mid-1990s, respectively). The relative declineof agriculture was most rapid in
Korea, from 28.9% of total value added in 1970 to 2.3% in
2014. In many countries, theshare of the agriculture sector
more than halved between
1970 and 2014: for example,
from 37% to 13% in Indonesia,

106

Nepal
BhutanVietnam
50
1
15
1 7
India
49
1 13
10
12
5 3 7
Cambodia
45
9
5
22
41
1410
10
Bangladesh
44
18
3
14
6 2
11
Pakistan
44
14
1 7
16
5
12
Thailand
33
17
6
23
3 4
13
Indonesia
33
14
7
21
4 2
15
1
Philippines
30
6
22
8
6
20
1 8
China
29
19
1 10
12
41
23
1
Sri Lanka
28
1
18
7
16
7 4
19
Mongolia28
4 8 2 719
8 2
22
Turkey
21
1
19
1 7
20
5
8
17
Iran
18
1
17
1
15
16
11
4
18
Malaysia
12 1
16
1 10
26
5
9
20
Fiji
8 1
17
7
6
20
16
3
22
Korea
6
17
7
23
8
14
25
Saudi Arabia
5 1 7 2
13
18
5 2
47
Oman
5 1 11
36
17
2 5
23
ROC
5
27
8
25
5
9
20
Japan
5
15
1 8
18
8
4
41
Australia
33 8 1 9217644
3 3 6 2
Brunei16
21
5 5
38
2 2 7 1 11
UAE
17
7
10
42
11 9
US
6
15
5
20
43
12 4 1 12
10 3 10
Kuwait56
1 5 7 1
40
13
6 2
26
Qatar
1 12
20
Bahrain
14
9 3
41
15
14
20
10
8
33
Singapore
3
8
30
12
20
26
Hong Kong
0
10
20
30
40
50
60
70
80
90 100 %
71

51

1 6 1

12

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

7 1 3 9 21 6
9 32
15
14
3 1 88Group-L4(except for
Bhutan, India,
and Vietnam)
Group-L3(except for
Thailand)Group-L2(except for Fijiand Sri Lanka)

Group-L1

2. Mining
4. Electricity, gas, and water supply
6. Wholesale and retail trade, hotels, and restaurants
8. Finance, real estate, and business activities

Figure 77 Industry Shares of Employment, 2014
Sources: Official national accounts in each country, including author adjustments.

Agriculture share in total employment

0.8

Nepal

Japan (1885–2014)

0.7
0.6
0.5
0.4
0.3
0.2

BhutanIndia

Vietnam
Cambodia
Bangladesh
PakistanThailandIndonesia
Sri Lanka
Mongolia

Philippines
China

Iran
Malaysia

0.1

Korea ROC
BruneiSingapore
0.0
Hong Kong
1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010
Fiji

Figure 78 Employment Share of Agriculture in Japan during
1885–2014 and Levels of Asian Countries in 2014
Sources: Official national accounts in each country, including author adjustments. The
sources of historical data of Japan are Long-Term Economic Statistics by Ohkawa, Takamatsu, and Yamamoto (1974) during 1885–1954 and population censuses since 1920.

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
Malaysia
ThailandIranMongolia
Turkey

60
50

30
40
20

30

10

6

20

0
1970 1975 1980 1985 1990 1995 2000 2005 2010101970 1975 1980 1985 1990 1995 2000 2005 2010

Group-L1

Group-L2

%

%

90

100

80

90

Bangladesh
MyanmarCambodia
Nepal
Pakistan

70
80

60
50

70

40

60

30
20
10

50
BhutanFiji
Indonesia
Sri Lanka

India
Philippines
Vietnam40

01970 1975 1980 1985 1990 1995 2000 2005 201030
1970 1975 1980 1985 1990 1995 2000 2005 2010

Group-L3

Group-L4

Figure 79 Long-Term Trends of Employment Share in the Agriculture Sector, 1970–2014
Sources: Official national accounts in each country, including author adjustments.

from 42% to 16% in India, and from 39% in 1972 to 16% in Bangladesh. In China, the share of this sector also significantly declined, from 36% in 1970 to 10% in 2014.

The trend of employment share over time (Figure 79) 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.98 This trend is unmistakable in most of the countries plotted in Figure 79.99 Between 1970
and 2014, the employment share in agriculture dropped from 50% to 6% in Korea and from 20% to 5%

97: Data for the Lao PDR and Myanmar are unavailable for Figure 77.

107©2016 Asian Productivity Organization

Despite the relative decline of agriculture’s share in total value added, employment in the sector for
Asia as a whole still accounted for 35% of total employment in 2014. Figure 77 shows countries’ industry shares in total employment, and ranks them by size of employment in the agriculture sector.97
Figure 78 traces the historical trajectory of Japan’s employment share of agriculture for the period
1885–2014 and the countries’ levels in 2014, mapped against Japan’s experience (as circles). Largeshares of agriculture employment over 30% in 10 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.

6 Industry Perspective

%

2

%

10

Growth of
Employment in Mnf

1

6

1970s

−2

0

1990s

0

2

−1

Growth of
Employment in Mnf

8Growth of
GDP in Mnf

4

6%

2

4

−8

1980s

−4

0
−2

1970s
Growth of
GDP in Mnf

0

4

8

12

16 %

1980s

−4

−2

−6
−3

2000s

2000s

−8

Japan
US
Australia

ROC
Hong Kong
Korea

−10

1990s

−4

−12
%

%

10

6

Growth of
Employment in Mnf
1970s

Growth of
Employment in Mnf

5

8
China
Indonesia
Singapore
Thailand

6

India
Iran
Pakistan
Sri Lanka

4

2000s

3
4
2

1980s

2

1

2000s

0

Growth of
GDP in Mnf

1990s

0

5

1970s

10

15

20

25 %

1990s

1980s
Growth of
GDP in Mnf

0
0

2

4

6

8

10 %

Figure 80 Job Creation in Manufacturing, 1970–2014_Average annual growth rates of GDP at constant prices and number of employment
Sources: Official national accounts in each country, including author adjustments.
Note: Each dot represents the average annual growth rate in manufacturing (mnf ) in the 1970s, 1980s, 1990s, and 2000s (2000–
2014). The white dots indicate the rate in the latest decade.

in Japan. Employment in agriculture also fell rapidly in the ROC, from 25% in 1978 to 5% in 2014. In
China, the share has declined from 63% in 1978 to 29% in 2014.

©2016 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 80 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, and 2000s (2000–2014). The growth rate in the 2000s is illustrated by a white dot.
98: Nepal’s employment-by-industry figures are constructed by interpolating benchmark data taken from its labor force survey as wellas its population census. Figure 79 indicates that its share of agriculture has increased since 2001. This reflects the employment
share of agriculture at 66% in the population census of 2001 and its share of 74% in the labor force survey of 2008.
99: 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.

1086.2 Industry Growth

If manufacturing GDP and employment grow at the same rate, a dot will be on a 45-degree line
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.
In Korea and the ROC, expansions of manufacturing output could allow for increases of employment
in the 1970s and the 1980s, but since the 1990s manufacturing has not been an absorption sector of
employment, regardless of the sound expansion of production in this sector (Figure 80). The experiences of Singapore, Indonesia, and Thailand are closer to the 45-degree line through the origin, which
implies well-balanced growth of output and employment in the manufacturing sector. The job creation role of manufacturing remains effective or increasingly more important in Indonesia and Pakistan, but it is diminishing rapidly in India and Iran.

6

6.2 Industry Growth
In Section 3.1, it can be seen that as a region growth in the Asia30 accelerated in the period 2005–
2010, averaging 6.5% per annum, up from 5.7% in 2000–2005. China and India have been the twomain drivers among the Asian economies, accounting for 46% and 16% of the region’s growth during
1990–2014, respectively (Figure 7 in Section 3.1, p. 25). However, looking at the industry composition,
the origins of economic growth in China and India are quite different. Bosworth and Collins (2008)

1990−2000

2000−2014

%

10
0.7
2.4

2.9 0.5
0.5

0.6
4.2

8
1.2
0.5 4.2

6

1.2

2

0

0.3

0.2 0.2
0.3

0.7 2.4

3.2

1.3 1.7 1.8
0.6

2.4

0.8

1.7

0.7

1.4

3.5 0.2

2.2

2.3

1.0 0.6 0.5
1.4 1.3

1.2
0.6

0.4

0.5 0.5
0.6
0.3
0.2
0.9
3.9
0.3
1.0 0.8
0.4
1.3
0.3
2.1
0.7 1.0
0.2
0.
2
0.9
1.3
0.2
1.2
0.6
2.
0
0.3
0.6
1.5 1.3 0.3
1.9
0.2 0.2
0.
7
1.3
0.2
0.2
0.5 0.7 0.9 0.7 1.5
1.3 1.0 0.4
0.9
0.2 0.70.
6
1.3
0.7 0.3
1.0
1.1 1.2 0.2
0.4 1.0
0.4
0.3
0.5
0.7
0.4 0.2 0.3 0.5 0.2
0.
5
0.5 0.7
0.6 0.8
1.4
0.8 0.2
0.2
0.8
0.5
0.7 0.6
0.3
0.9 0.2
0.4 0.4 0.6
0.7 0.5 0.7
0.4
0.6 1.0 0.7
1.4
0.2
0.3
0.5
0.6
0.3
0.3
0.5
0.6
0.3 0.7
0.6
0.5 1.5 1.4
1.3
0.4
0.6
0.5 0.4 0.5
0.2
1.0 0.5
0.4
1.
4
0.
7
0.
4
0.
8
2.5 0.7 1.2
1.4 0.3
0.4
0.2
0.6
1.0
1.0 0.5 0.5
0.2 0.2 0.3 0.4 0.3
0.6
1.0
0.4
0.5 0.5
0.7 0.7
0.5 0.3
0.2
0.2
0.
4
0.
6
1.0
1.0 0.8 0.8
1.0 0.7 0.8 0.4 1.1
0.
7
1.0
0.8
0.4
0.5 0.4
0.5
0.4 0.6 0.6 0.6 0.7 0.5 0.6 0.7 0.6 0.2 0.2
0.4
0.4 0.6 0.6 0.2 0.5 0.3
0.2
0.2−0.2 −0.3 −0.2
−0.4
−0.5
−0.3
−0.3
0.6

4

1.1

1.0

0.8 0.6

0.4
0.2
0.7

0.7
1.4 0.2

0.4

0.7 0.4 0.3
0.2 0.2

0.9 1.2

0.3
0.5 0.3
0.2
0.6 0.2
1.0 0.3 1.0 0.8 1.1
0.2
0.5 0.7 0.4
0.9
1.1
0.8 0.6 0.6
0.3
2.1 1.2
1.
2
0.5
0.7
1.1
0.5
1.6
2.
0
0.3
0.
9
0.7
0.6
0.4
0.5
0.3
0.2 0.7
0.2 0.7
1.2 1.5
0.6
1.8
0.7 1.8 0.8
0.4
1.2
0.6
0.9
0.8
0.3
0.6 1.0 0.8 1.0 0.5 0.2
0.3
1.0 1.1
0.4
0.6
1.6
0.4 0.4
0.7 0.7 0.3
0.7 0.3
1.3
0.8
0.6
1.0
1.7
0.
6
1.2 0.8
0.7
0.
7
0.5
0.5
0.3
0.5 0.6
0.4 1.6
1.0
0.5 0.3
0.5 0.5 0.4
0.4 0.2
0.4
0.6
1.1
1.7
1.7
0.6
1.7 0.6 0.2
0.9 0.5
0.8 0.6
0.6
0.7 0.6 0.7 0.5 0.9
0.5
1.1 1.0
0.6 0.8 0.7
0.
8
0.2
0.
7
1.4
1.4
0.6 0.7
1.0
0.
0.9 0.8 0.7 0.9 0.6 0.6
0.9 4 0.7
0.8 0.7
0.8 0.7 0.9
0.8
0.7
0.5 0.4 0.5 0.7
0.4 0.3
0.4 0.4
0.4
0.4 0.7
0.4 0.4
0.3 0.3 0.3−0.2
0.6 0.7

0.5 1.3

1.5

1.7

1.3

1.0 0.9

−1.3

−0.8

−2

2. Mining

3. Manufacturing

5. Construction

6. Wholesale and retail trade, hotels, and restaurants

7. Transport, storage, and communications

8. Finance, real estate, and business activities

9. Community, social, and personal services

Real GDP

Figure 81 Industry Origins of Economic Growth, 1990–2000 and 2000–2014_Industry decomposition: Average annual growth rate of GDP at constant prices
Sources: Official national accounts in each country, including author adjustments.

109©2016 Asian Productivity Organization

Brunei
Japan
US
Fiji
Australia
Iran
ROC
Nepal
Hong Kong
Thailand
Korea
Saudi Arabia
Pakistan
TurkeyKuwaitOman
Malaysia
UAE
Philippines
Singapore
Bahrain
Sri Lanka
Indonesia
Bangladesh
Vietnam
Cambodia
BhutanMongolia
India
Lao PDR
China
Myanmar
Qatar

MongoliaJapan
Fiji
Brunei
Saudi Arabia
Philippines
Hong Kong
US
Australia
TurkeyIndonesia
Pakistan
Oman
ThailandBahrain
Bangladesh
Nepal
Iran
BhutanSri LankaIndia
Lao PDR
Qatar
ROC
Myanmar
Korea
Singapore
Cambodia
UAE
Malaysia
Vietnam
China
Kuwait
1. Agriculture
4. Electricity, gas, and water supply

6 Industry Perspective

1990−2000
5.0 4.0 3.0 2.0 1.0 0.0 −1.0 %
4.2
2.4
2.4
2.1
1.8
1.7
1.5
1.4
1.3
1.3
1.3
1.2
1.2
1.1
1.0
0.9
0.9
0.7
0.7
0.7
0.7
0.6
0.5
0.5
0.5
0.4
0.4
0.3
0.2
0.2
0.1

2000−2014
−30

China
Malaysia
Korea
Cambodia
Singapore
ROC
Thailand
VietnamLao PDR
UAE
Sri Lanka
KuwaitTurkeyIndonesia
Iran
India
Bangladesh
Bahrain
Nepal
BhutanUS
Philippines
MyanmarOman
FijiPakistan
Brunei
Saudi Arabia
Japan
Australia
Qatar
Hong Kong−0.4
Mongolia−0.8

Contribution

0

30

60 %
42

4.0 3.0 2.0 1.0 0.0 −1.0 %
3.2
2.3

33

1.8

36
30

1.7

26

1.6

26

1.5
1.3

34
19

1.3

22

1.2

18

1.2
1.0

25

1.0

12
33

1.0

30

0.9

19

0.9

16

0.8

19

0.8

17

0.8

15
14

0.8
0.7

21

0.7

21

0.7
0.6

9

0.6

12

0.3

28

0.3

10

0.3

18

0.2

13

0.1

19

0.1

5

0.1

1

0.0

−14

−0.1

52

Contribution share

Contribution

−30
China
Myanmar
ROC
Vietnam
Korea
Cambodia
India
Bangladesh
ThailandSingapore
Philippines
Malaysia
Indonesia
Sri Lanka
Bahrain
Qatar
Lao PDR
Oman
TurkeyIranPakistan
BhutanSaudi Arabia
Mongolia
Kuwait
UAE
Japan
US
Fiji
Nepal
Australia
BruneiHong Kong

0

30

60 %
34

22
50
27
40
24
18
22
30
21
21
22
17
16
16
8
11
18
19
21
18
9
16
9
8
7
52
11
7
3
2
187
−2

Contribution share

Figure 82 Contribution of Manufacturing to Economic Growth, 1990–2000 and 2000–2014
Sources: Official national accounts in each country, including author adjustments.

indicate that China’s economic growth has been fueled by industry sector expansion;100 whereas India’s economic growth has been led by service sector expansion, based on their observation during
1978–2004. Although the findings broadly support their conclusion, it also discerns that the nature of
growth in China may have started shifting more toward services in recent years.

©2016 Asian Productivity Organization

Our results show that manufacturing was the biggest contributor to economic growth in China until
the 2000s when the service sector overtook manufacturing in this respect (Figure 81).101 The gap between contributions of manufacturing and services was the widest in the early 1990s; narrowing in
the late 1990s until a redress in 2000–2014, with manufacturing and services accounting for 34% (Figure 82) and 46% (Figure 83) of economic growth, respectively. In contrast, economic growth in India
always has been dominated by services. Its growth has only become more pronounced over time. The
contribution of manufacturing and services to economic growth were 18% (Figure 82) versus 61%(Figure 83) in 2000–2014, compared with 16% and 61% in 1990–2000. The increased prominence ofthe service sector has weakened, not so much manufacturing’s hold, but agriculture’s, where the contribution fell from 14% in the late 1990s to 8% in the latest period of comparisons.

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

110

6.2 Industry Growth

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
3.7
3.4
3.4
3.3
3.1
2.9
2.9
2.6
2.5
2.5
2.5
2.3
2.3
2.2
2.2
2.2
2.1
2.0
2.0
1.9
1.8
1.7
1.7
1.2
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
Bangladesh
BhutanOman
MyanmarIndonesia
TurkeyQatar
Lao PDR
Philippines
Japan
Saudi Arabia
Fiji
Brunei−0.3 Mongolia

2000−2014
0

30

60

90 %

5.0

4.0

3.0

2.0

1.0

4.5

61

4.5

64

4.3

70

4.2

64
55

4.1

56

4.0
3.9

35

3.8

61

3.7

44
103

3.6

56

3.3

59

3.1
3.0

78

3.0

37

3.0

53

3.0

75
53

2.9

52

2.9

53

2.8

49

2.7

45

2.6
2.5

49

2.4

32
53

2.4

54

2.3
2.2

29

2.2

28

2.1

57
96

1.8
1.6

40

1.4

52

1.1

39

0.4

23

Contribution share

Contribution

0.0 %
India
Mongolia
China
Qatar
MyanmarSingapore
Hong Kong
Bahrain
Lao PDR
Indonesia
Sri Lanka
Bangladesh
Philippines
UAE
TurkeyMalaysia
BhutanVietnam
Cambodia
KuwaitOman
Pakistan
Iran
Saudi Arabia
Nepal
Thailand
Korea
Australia
ROC
Fiji
US
Brunei
Japan

−30

0

30

60

90 %

61
62
46
41
40
73
100
71
48
63

6

59
53
62
65
70
65
42
46
44
61
59
61
68
59
59
56
55
71
49
83
91
−9680
75

Contribution share

Figure 83 Contribution of Service Sector to Economic Growth, 1990–2000 and 2000–2014
Sources: Official national accounts in each country, including author adjustments.

Manufacturing has sustained its prominence in Thailand, Korea, and the ROC, contributing 30%, 40%,
and 50% to economic growth in 2000–2014, respectively. Its importance is modest in Singapore at
21% (Figure 82). In Hong Kong, it has been a drag on economic growth in the past decade or so. During the Asian crisis, the most impaired economies were Thailand and Indonesia, and the sectors whichbore the brunt were construction, wholesale and retail trade, hotels, and restaurants, and finance, realestate, and business activities. In contrast, manufacturing played a significant role in bolstering the
economy at the time (Figure 82).

102: The computer software industry in India depends considerably on export demands. According to India’s Input–Output Table
2006–2007, 82% of the output in computer and related activities is exported. This export is equivalent to 14.8% of total exports in
India and is the second-largest export product (among 130 products in this table).
103: Of the total motor vehicles produced in 2015 (90.8 million), India was the 6th largest producer (4.1), following Korea (4.6), Germany(6.0), Japan (9.3), the US (12.1), and China (24.5), 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.

111©2016 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 the ROC and Qatar(Figure 83). The story behind India’s recent growth has been one of services. Modern information andcommunication technology have allowed India to take an unusual path in its economic development,
bypassing a stage when manufacturing steers growth.102 Within the service sector, contribution isquite evenly spread among the sub-sectors, more recently the iron/steel and motor vehicle sectors
have been intensively developed.103 For further improvement in per capita GDP and to capitalize on
the demographic dividend (see Box 2, p. 38), expansion of labor-intensive manufacturing may be required in India for greater job creation.104

6 Industry Perspective

Economic growth in the Asian Tigers was also dominated by the service sector, albeit more so in Singapore and Hong Kong than in the ROC and Korea, where manufacturing remained a significant force.
The service sector accounted for 49% of growth in the ROC for the period 2000–2014, 55% in Korea,
73% in Singapore, and 100% in Hong Kong, counterbalancing the negative contribution of 2% by
manufacturing (Figures 82 and 83). These compare with 91% in the US, to counterbalance the negative contribution of 8% by construction. In the 2000s, growth in Hong Kong was highly skewed toward
wholesale and retail trade, hotels, and restaurants, accounting for 39% of growth. This compares with
22% in Singapore and 17% in the ROC. In contrast, the sector contributed only 8% to Korea’s growthover the same period. Finance, real estate, and business activities also played an important role, contributing 42% to growth in Hong Kong, 31% in Singapore, and 16% in the ROC.
The oil-exporting countries have different industry structures from other countries, with their relianceon mining for growth. The sector is volatile in nature and could in turn give rise to big swings in its
economies from one period to another. In 2000–2014, mining accounted for 41% of economic growthin Qatar, 26% in Kuwait, and 15% in Saudi Arabia (Figure 81). Still, it has been a drain on growth, insome cases a quite significant one. Its contribution was negligible in Bahrain (0.1%) and negative inBrunei and Iran. Bahrain has been successful in branching into finance, real estate, and business activities, which accounted for 29% of the 5.4% 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 dismal growth of –0.01% on average per year between 2000 and 2014.
Oil and gas production activities are also reflected in Mongolia and the Lao PDR, where mining accounted for 17% and 18% of overall economic growth, respectively, in the 2000s.
For some Asian countries, agriculture is still the biggest sector. The seven countries in which the agriculture sector has the largest share in total value added are Nepal, Cambodia, Myanmar, Pakistan, theLao PDR, Vietnam, and Bhutan (Figure 72). For the period 2000–2014, agriculture in Myanmar, Nepal,
and Cambodia had the highest contribution to economic growth among all Asian countries, accounting for 28%, 28%, and 22% of growth, respectively.105 In the latest period, agricultural output continued expanding in the majority of Asian countries, suggesting that the shrinkage in its value-added
share (Figure 76) over the recent period is more a result of rapid growth in other sectors than any actual contraction of the sector.
Comparisons across the country groups in Table 17 reveal that Asia enjoyed more vibrant growth thanthe 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 retrenched in the US in the 2000s, while growth has beenstrongest in CLMV and GCC countries at 9.5% and 7.9% per year on average, respectively. Apart fromconstruction, the other fast-growing sectors in CLMV and GCC countries were transportation, storage,
and communications (at over 10% per year on average), presumably reflecting their effort in building
and upgrading infrastructure for their development needs. Finance, real estate, and business activities
also enjoyed robust expansion at 9.0% per year on average in South Asia. Manufacturing has been
growing at 10.9% per year on average in CLMV, compared with 4.4% in the ASEAN6.

©2016 Asian Productivity Organization

Figure 84 presents the sub-industry origins of average annual growth of manufacturing GDP for selected Asian countries for the periods 1990–2000 and 2000–2014.106 Manufacturing in Asia has been
104: 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.
105: In Myanmar, agriculture accounted for over 27.9% of GDP in 2014. Since 1988, the government has continued its modest steps toliberalize the sector and marketing controls have been made less onerous. As a result, farm production has increased. According
to official statistics, the quality of which has been questionable, this sector accounted for 27.7% of GDP growth in 2000–2014.

112

6.2 Industry Growth

Table 17 Output Growth by Industry, 2000–2014

7. Transport,
storage, andcommunications

8. Finance, realestate, andbusiness activities

9. Community,social, andpersonal services

Total economy−0.6 (−0.0)
3.7 (0.7)
2.0 (0.4)
2.6 (0.0)
4.2 (1.4)
4.1 (0.5)
0.5 (0.0)
0.1 (0.0)−2.8 (−0.0)
3.1 (0.6)
3.6 (0.4)
3.1 (0.3)−1.3 (−0.0)
1.2 (0.0)
4.2 (0.0)
2.9 (1.0)
2.9 (0.3)
4.0 (0.5)
6.2 (2.9)
3.1 (1.1)
2.5 (0.0)
2.6 (0.6)
2.6 (0.3)
5.6 (0.0)
1.3 (0.0)−0.8 (−0.0)
2.8 (0.3)
2.2 (0.2)−2.8 (−0.0)
3.5 (0.7)−0.2 (−0.0) 6.9 (0.9) 11.0 (0.1) 7.2 (0.6)
7.1 (0.1) 7.9 (1.3) 8.0 (0.1) 7.4 (0.5)
11.3 (0.2) 8.0 (0.7) 10.3 (1.5) 7.6 (1.2)−2.2 (−1.3) −0.2 (−0.0) 3.2 (0.0) 2.5 (0.1)
18.2 (0.1) 9.9 (1.5) 10.5 (0.0) 9.1 (0.5)
10.3 (0.5) 10.0 (3.2) 8.7 (0.3) 11.1 (0.7)−5.4 (−0.0) 6.4 (1.8) 4.2 (0.1) −0.3 (0.0)−2.1 (−0.0) 1.0 (0.1) 2.9 (0.1) 2.6 (0.1)−2.8 (−0.0) −2.8 (−0.1) 1.1 (0.0) 1.4 (0.1)
5.0 (0.1) 8.7 (1.3) 8.6 (0.2) 8.0 (0.6)
1.5 (0.2) 4.6 (1.0) 7.3 (0.1) 6.8 (0.5)−2.1 (−0.2) 5.6 (0.7) 7.3 (0.3) 1.5 (0.1)−6.8 (0.0) 1.4 (0.3) −1.8 (−0.0) −1.5 (−0.1)−0.6 (−0.0) 5.6 (1.6) 4.1 (0.1) 0.9 (0.1)
2.5 (1.1) 5.2 (0.3) 4.5 (0.1) 4.1 (0.1)
32.6 (1.4) 8.7 (0.8) 6.0 (0.2) 9.3 (0.5)
0.3 (0.0) 3.9 (1.0) 4.6 (0.1) 5.4 (0.2)
7.4 (1.2) 7.7 (0.6) 4.1 (0.1) 9.8 (0.4)
14.0 (0.3) 16.8 (2.3) 11.4 (0.1) 16.6 (0.6)
4.6 (0.0) 1.9 (0.1) 4.8 (0.1) 4.1 (0.3)
0.2 (0.1) 8.9 (0.8) 8.9 (0.1) 18.1 (0.7)
4.2 (0.1) 5.2 (0.7) 2.3 (0.0) 2.6 (0.1)
8.7 (0.1) 4.6 (1.0) 4.0 (0.1) 5.0 (0.3)
8.1 (4.2) 9.4 (0.8) 7.9 (0.1) 19.4 (1.0)
1.5 (0.6) 6.3 (0.6) 6.1 (0.1) 6.2 (0.3)
0.0 (0.0) 4.8 (1.2) 3.8 (0.1) 5.1 (0.2)
12.6 (0.2) 4.9 (0.9) 6.4 (0.1) 9.0 (0.7)
4.5 (0.1) 4.1 (1.2) 4.9 (0.1) 2.6 (0.1)
1.4 (0.5) 3.2 (0.3) 8.8 (0.2) 7.5 (0.7)
2.3 (0.2) 9.3 (1.7) 10.1 (0.3) 7.7 (0.4)

5.0
6.5
11.7
4.5
7.4
10.9
3.3
2.0
5.5
8.1
5.9
4.8
0.0
2.8
3.2
9.6
6.5
10.4
9.1
2.5
5.8
3.3
6.0
14.5
9.5
6.3
5.4
3.4
4.7
7.3

(0.4)
(0.9)
(0.8)
(0.2)
(1.0)
(1.1)
(0.6)
(0.4)
(1.5)
(1.3)
(0.9)
(0.7)
(0.0)
(0.3)
(0.2)
(1.8)
(1.0)
(2.1)
(2.0)
(0.4)
(0.5)
(0.7)
(1.0)
(0.9)
(0.7)
(1.2)
(1.2)
(0.6)
(0.8)
(1.1)

8.4
7.3
7.9
3.5
7.3
9.0
3.2
3.4
4.0
10.1
11.1
7.8
1.3
4.7
11.0
8.6
6.1
12.2
16.2
6.1
12.1
4.2
6.5
19.0
12.0
4.7
8.2
5.5
7.4
7.6

(0.5)
(0.7)
(0.8)
(0.1)
(0.5)
(0.7)
(0.2)
(0.5)
(0.4)
(0.8)
(1.3)
(0.6)
(0.1)
(0.4)
(0.6)
(0.4)
(0.5)
(1.2)
(1.7)
(0.5)
(0.7)
(0.5)
(0.5)
(0.6)
(0.5)
(0.6)
(1.0)
(0.4)
(0.6)
(0.3)

6.2
4.5
8.8
3.7
8.6
10.0
3.0
2.3
4.7
10.2
6.7
5.0
0.3
4.0
5.5
8.9
6.6
8.7
27.1
4.4
6.4
4.6
6.7
15.0
6.1
6.2
5.5
6.0
6.5
5.3

(1.6)
(0.5)
(0.7)
(0.2)
(0.6)
(1.0)
(0.6)
(0.4)
(1.7)
(1.6)
(0.9)
(0.7)
(0.0)
(0.8)
(1.1)
(0.6)
(0.8)
(0.8)
(0.0)
(0.6)
(0.6)
(0.5)
(1.1)
(1.7)
(0.6)
(1.7)
(0.7)
(0.7)
(1.0)
(0.7)

8.4
4.7
5.3
3.7
8.6
10.0
1.9
1.7
1.8
5.5
5.3
2.6
0.9
3.4
5.5
11.8
5.8
3.2
12.1
6.8
6.1
6.2
2.6
10.6
3.8
4.5
3.8
3.3
8.0
6.9

(1.4)
(0.9)
(0.7)
(0.7)
(0.8)
(1.4)
(0.4)
(0.3)
(0.3)
(0.7)
(0.4)
(0.4)
(0.3)
(0.6)
(0.8)
(0.8)
(0.7)
(0.4)
(0.4)
(0.8)
(0.9)
(0.9)
(0.4)
(1.0)
(0.6)
(0.5)
(0.4)
(0.4)
(0.5)
(0.7)

5.4
5.8
7.0
0.0
6.4
9.4
3.7
1.9
3.9
7.3
5.7
3.5
0.6
4.0
4.4
7.6
4.6
7.3
10.4
3.9
4.4
4.1
4.9
10.4
4.1
5.4
5.6
4.0
4.6
6.3

2.9
3.4
3.4
3.7
3.0
3.9
3.1
4.6
1.0

(0.3)
(0.3)
(0.3)
(0.3)
(0.6)
(0.4)
(0.3)
(1.3)
(0.0)

1.6
5.1
3.7
10.0
5.2
1.8
1.5
4.0
2.0

(0.1)
(0.2)
(0.2)
(0.3)
(0.1)
(0.1)
(0.1)
(0.3)
(0.8)

4.9
7.2
7.1
7.6
8.2
5.0
4.4
10.9
5.9

(0.9)
(1.7)
(1.6)
(2.1)
(1.2)
(1.1)
(1.0)
(1.8)
(0.6)

(0.2)
(0.4)
(0.4)
(0.4)
(0.5)
(0.3)
(0.3)
(0.5)
(0.4)

4.6
6.2
6.3
6.1
7.3
5.9
5.4
8.0
7.4

(0.7)
(0.9)
(0.8)
(0.7)
(1.2)
(1.0)
(0.9)
(0.7)
(0.6)

6.0
7.0
7.1
5.7
8.7
9.3
8.8
11.6
10.6

(0.5)
(0.6)
(0.6)
(0.5)
(0.7)
(0.8)
(0.8)
(0.7)
(0.5)

5.2
6.3
6.4
5.2
9.5
6.4
6.5
5.7
6.8

(0.8)
(0.9)
(0.9)
(0.7)
(1.4)
(0.9)
(0.9)
(0.5)
(0.8)

2.9
5.0
5.0
5.0
5.4
4.7
4.4
7.8
5.1

(0.5)
(0.8)
(0.8)
(0.9)
(0.7)
(0.5)
(0.5)
(0.6)
(0.7)

4.1
5.9
5.8
6.0
6.6
5.4
5.0
6.6
4.6

US
Australia
Turkey

2.4
2.0
1.6

(0.0)
(0.1)
(0.2)

2.2
4.4
1.6

(0.1)
(0.4)
(0.0)

1.0
3.1
4.2

(0.2) −0.5 (−0.0) −2.5 (−0.1)(0.1) 1.0 (0.0) 4.7 (0.4)(0.8) 4.7 (0.1) 4.1 (0.2)

1.3
2.7
3.5

(0.2)
(0.3)
(0.6)

3.4
3.3
5.3

(0.3)
(0.3)
(0.8)

2.1
3.1
5.9

(0.7)
(0.6)
(0.9)

1.0
3.1
4.2

(0.3)
(0.9)
(0.7)

1.5
3.0
4.2

4.0
5.8
5.8
5.2
7.7
5.9
5.2
10.1
7.0

(0.1)
(0.1)
(0.1)
(0.2)
(0.1)
(0.1)
(0.1)
(0.2)
(0.1)

5. Construction

2. Mining

4. Electricity, gas,
and water supply

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)

1. Agriculture

6. Wholesale andretail trade, hotels,
and restaurants

3. Manufacturing___Average annual growth rate of industry GDP at constant prices

3.8
6.4
6.5
6.2
7.8
6.4
5.8
9.5
7.9

6

106: The Törnqvist quantity index is adopted for calculating the growth of real GDP of manufacturing. Using this index, the growth ofreal GDP of manufacturing into the products of contributions by sub-industries of manufacturing can be decomposed:
In (GDPt / GDPt−1)= ∑ j (1/2) (sjt + sjt−1) In (Qjt / Qjt−1)
where Qjt is real GDP of a sub-industry j in period t and sjt is the nomReal GDP growth of manufacturing

Contribution of a sub-industry jinal GDP share of a sub-industry j in period t.

113©2016 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

1990–2000

%

18

9

1.3

16
14
12
10

8

0.8

7

1.0

5

0.6
0.4

4

2.0 1.5

6

0.4 0.9

4

0.4

2

0.6

−2

0.6

0.8

6
12.6

8

0

2000–2014

%

10

20

1.7 5.6
1.3

1.0
3.6

5.1

1.7 0.8
0.7
0.5 0.9 1.2
1.7 1.6

1.5 0.4 2.3
0.6 0.3 0.3
1.0 0.7
0.3
0.5
0.4 0.9 1.8 0.6 2.9 0.3
0.4
1.3
0.7 1.2 1.4 0.7
0.5 1.0−0.4−1.0
−0.3
−0.3

3

0.5
8.0

2.0

4.1
2.2

0.4

0.6
0.4

0.7

2.1
0.5
0.5 0.7 5.0

2

2.6 0.51

0.8 2.0
0.3
0.4 0.6 0.7

0

0.7
0.3

2.2

0.6

4.0
4.2

1.3 0.8
0.5

0.9
1.0
0.6 0.3

1.9

1.7

2.1

0.6
1.4

1.5

1.8

−1
Japan
Malaysia
ThailandIndonesia
Singapore
Philippines
Kuwait
Korea
ROC
Iran
Bangladesh
India
CambodiaJapan
US
Philippines
Indonesia
ROC
India
ThailandIran
Bangladesh
Korea
Malaysia
Cambodiadominated by 3-8 (machinery andequipment) accounting for 40% ormore of overall manufacturing
growth in half of the Asian countries compared. In the ROC andKorea, it was about 80%. The
sub-sector 3-1 (food products, beverages, and tobacco products) isthe largest contributor in thePhilippines for 2000–2014, accounting for 48% of manufacturing
output growth. In Bangladesh andCambodia, manufacturing growthhas been dominated by the subsector of 3-2 (textiles, wearing
apparel, and leather products),
whereas in Kuwait, and to a lesser
extent Singapore and Iran, it is 3-5(coke, petroleum, chemicals, rubber, and plastic products).

3-1. Food products, beverages, and tobacco products
3-2. Textiles, wearing apparel, and leather products
3-3. Wood and wood products
3-4. Paper, paper products, printing, and publishing
3-5. Coke, petroluem, chemicals, rubber, and plastic products
3-6. Other non-metallic mineral products
3-7. Basic metals
3-8. Machinery and equipment
3-9. Other manufacturing
3. Manufacturing©2016 Asian Productivity Organization

Figure 85 contrasts industry contributions to economic growth for theperiods 1990–2000 and 2000–2014,
as well as between the US and
Figure 84 Industry Origins of Output Growth in ManufacAsian averages.107 Even within suchturing, 1990–2000 and 2000–2014
a short period, one can see that the___Sub-industry decomposition: Average annual growth rate of GDP
industry structure of growth isat constant prices of manufacturingchanging. The first striking feature
Sources: Official national accounts in each country, including author adjustments.
is the dominance of manufacturingin Asian countries. Between 1990
and 2000, its contribution to economic growth in the Asia24 was 31% compared to 21% in the US. Although its significance has fallen
in recent years, it still accounted for 29% of economic growth in the Asia24 between 2000 and 2014,
compared with 11% in the US. This, however, masks a divergence within Asia. In the earlier period,
manufacturing accounted for 37% of growth in East Asia but only 16% in South Asia. The corresponding figures were 35% and 18% in 2000–2014. The differential is somewhat narrowing.
In the ASEAN, manufacturing’s contribution was reduced to 21% in 2000–2014 from 29% in the 1990s,
while wholesale and retail trade, hotels, and restaurants increased from 16% to 18%. In the US, thefinance, 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 45% in 2000–2014. In contrast, its
contribution in the Asia24 was 15% in the period 2000–2014. 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.
107: 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.

114

6.2 Industry Growth

1990−2000%

120

2000−2014100
16 14 14 15 1580

13 13 10
16
11 10

60

14 14
18

40

9

9

10

3
4

3
4

16 16

3

3
3

19

17
3
3

14

15

4
8

8
11

16

17

17

3

9
3
1

5
2

6
3

7
3

6
3

2
29 28

4

1

3

4

6

7

6

6

3

1
0

6

12

6
2

4
18

6
2

27

14

9

12

18 21 21

4

19 18

5
4

2

2

9

8

2
6

18

2

35

26
21

18 18

8

23

45

10
8

East Asia

4
5

15

11 18

South Asia

5
7

9

17 19

12

US

3
8

9

15 15

10

GCC

2

10 10
13

6

26

9

30
6

23

7

11
12 14 1416

20 15 15 12 21

14 23

2

0

7
7

31 29 37 16 29
30

20

12

8

4

5
3

2
3

15

13
12 13

13
2
4

18
10

16 16 14
16

11

13

0

10
11
4
2
−1
−8

−20
US

GCC

CLMV

ASEAN

ASEAN6

Asia30APO20

1. Agriculture
3. Manufacturing
5. Construction
6. Wholesale and retail trade,hotels, and restaurants
8. Finance, real estate, andbusiness activities

Asia24

CLMV

ASEAN

ASEAN6

East Asia

South Asia

Asia30

APO20

Asia24

Figure 86 presents industry contributions
to regional economic growth in the
Asia30 during 2000–2014, decomposing
Figure 7 in Section 3.1 (p. 25) into countries’ industry origins.108 In each industry
contribution, the top eight countries arepresented. The top four industries interms of contributions to regional growth
were manufacturing (27.8%), finance, realestate, and business activities (15.1%),
wholesale and retail trade (14.6%), andcommunity, social, and personal services(13.6%). A total of 28% of Asian economic
growth originated from the expansion ofits manufacturing sector, two-thirds ofwhich was accounted for by China. In other words, China’s manufacturing sector
alone accounted for nearly 17% of theregion’s economic growth. This wasfollowed by China’s community, social,
and personal services (7.5%) and wholesale and retail trade, hotels, and restaurants (6.2%).

2. Mining
4. Electricity, gas, andwater supply
7. Transport, storage, andcommunications
9. Community, social, andpersonal services

108: The average growth rate of the Asian economy for 2000–2014 is set at 100%. Asian economic growth is calculated as the sum of
the contributions over countries and industries:∑ x (1/2) (Sxt + Sxt−1) ∑ j (1/2) (sx,t j + sx,t−1j ) In (Qx,t j / Qx,t−1j )where Qx,t j is real GDP of an industry j in a country x in period t, sx,t j is GDP share ofContribution of an industry j in a country x

an industry j in a country x with respect to GDP of a country x in period t and sxt is GDP share of a country x with respect to the regional GDP in period t. All the industries whose contribution is more than 0.25% are shown in Figure 86.

115©2016 Asian Productivity Organization

Over a period of four decades there has
been a noticeable shift in the industry origins of economic growth (Figure 87). For
the ROC and Korea, manufacturing has
Figure 85 Industry Origins of Regional Economic
been a clear driving force behind ecoGrowth, 1990–2000 and 2000–2014
nomic growth as a whole. In the decade_Contribution sharebetween the mid-1980s and the midSources: Official national accounts in each country, including author ad1990s, however, the importance of manujustments.
facturing retreated in the ROC temporarily
while the economy developed its service
sector. Since the mid-1990s, the role of manufacturing in the ROC has increased again, although compared to its heyday of the 1970s and 1980s its impact in terms of percentage points is much reduced.
In Singapore, finance, real estate, and business activities, as well as wholesale and retail trade, hotels,
and restaurants are important drivers alongside the manufacturing sector. Working within 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 volatility of these countries. In recent years the GCC countries
have been making efforts in diversifying, especially into the service sector, with different degrees ofsuccess. Bahrain and Oman are leading the way and have yielded results. The largely agricultural

6 Industry Perspective

3. Manufacturing (27.8%)
17.0

0

5

10

China

1.1 1.1 0.8

3.2

15
Korea

India

4.5

20

25
ROC

Indonesia

30 %Rest of the world

8. Finance, real estate, and business activities (15.1%)
5.6

0

4.0

2

4

China

6

India

1.1

8

0.6 0.5

10

Indonesia

3.4

12

Korea

14

Iran

16 %

Rest of the world

6. Wholesale and retail trade, hotels, and restaurants (14.6%)
6.2

0

3.2

2

4

China

6

India

1.1

8

Indonesia

0.5 0.5

3.2

10

12

Iran

14

Saudi Arabia

16 %Rest of the world

9. Community, social, and personal services (13.6%)
7.5

0

2

1.7

4

China

6

India

0.8 0.5 0.4

8

Japan

2.7

10

12

Indonesia

Korea

14

16 %

Rest of the world

7. Transport, storage, and communications (10.0 %)
3.7

0

1.8

2

4

China

India

0.4 0.4

1.4

2.3

6

8

Indonesia

10

Japan

Iran

12 %

Rest of the world

5. Construction (6.5%)
3.7

0

1

1.4

2

China

India

3

4

Indonesia

0.2 0.2

0.5

0.5

5

6

UAE

Rest of the world

Saudi Arabia

7%

1. Agriculture (5.6%)
2.5

0

1.4

1
China

India

0.2 0.2

0.5

2

3

Indonesia

Pakistan0.8

45Myanmar

6%
Rest of the world

2. Mining (4.3%)
2.7

0
China

0.4

1

2

Saudi Arabia

Qatar

0.4

0.3

0.2

3

0.4

4

India

Indonesia

5%
Rest of the world

4. Electricity, gas, and water supply (2.5%)©2016 Asian Productivity Organization

1.4

0

0.5
China

India

0.4

1

1.5
Iran

Indonesia

0.2

0.1 0.1

0.4

2

2.5
Korea

Figure 86 Industry Origins of Asian Economic Growth, 2000–2014_Contribution to regional growth of GDP at constant prices, using 2011 PPP
Sources: Official national accounts in each country, including author adjustments.

116

3%

Rest of the world

6.3 Labor Productivity by Industry

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 theperiod, but it never recovered its dominance after its crash in the mid-1980s. In the second half, economic growth was better balanced, with the development of finance, real estate, and business activities in particular.

6.3 Labor Productivity by Industry

6

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 2014, Singapore and Hong Kong were thecountries that had labor productivity levels comparable to the US. Besides these two, the best performers in Asia achieved productivity levels that were at least 40% of the US. However, Asia collectively was dragged down by a long tail of countries with labor productivity of less than 25% of the US
level. This pulled down the average performance to 21% of the US for the APO20 and 20% for the
Asia24 (Table 9 in Section 5.1, p. 65). In growth terms, however, Asia’s performance far exceeded theUS, allowing the countries to gradually close the gap with the US over time. Labor productivity growthin the Asia24 was 5.0% per year on average between 2005 and 2014, compared to 1.0% in the US(Table 10 in Section 5.1, p. 66).
Table 18 presents cross-country comparisons in labor productivity growth by industry109 for the period 2000–2014.110 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 some
services in the Asia24; i.e., manufacturing (at 4.8% on average per year), electricity (4.5%), agriculture(4.4%), and transport, storage, and communications (4.3%). Construction was the sector with theslowest productivity growth at 1.4%.
Figure 88 shows the industry origins of average labor productivity growth per year in two periods:
1990–2000 and 2000–2014.111 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, the ROC, Korea, and Thailand). In India, although it has notbeen a major contributor in the 1990s, its contribution has expanded recently.

109: Labor productivity growth in Table 18 is defined simply as per-worker GDP at constant prices by industry (vj). The industry decomposition of labor productivity growth for the whole economy (v) in Figure 88 (industry contribution in Table 18) is based on
the equation v = ∑ jwjvj* where the weight is the two-period average of value-added shares. In this decomposition, the number ofworkers as a denominator of labor productivity (vj*) is adjusted, weighting the reciprocal of the ratio of real per-worker GDP by industry to its industry average. Thus, the industry contribution (wjvj*) is emphasized more in industries in which the per-worker GDP
is higher than the industry average, in comparison with the impact (wjvj) of using the non-adjusted measure of labor productivity.
110: 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.
111: Not all Asian countries are included, as employment by industry sector is not available for some countries.

117©2016 Asian Productivity Organization

The manufacturing sector has been a major driving force behind productivity growth in most Asian
countries, as shown in Figure 89. In the 1990s, manufacturing accounted for a significant part of laborproductivity growth in Hong Kong (98%), Indonesia (32%), and China (46%). Nevertheless, its role
has lessened in 2000–2014 to 7%, 17%, and 31%, respectively. In contrast, contributions from

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
12
9

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

Hong Kong

%
12

China

%
15

12

−3

1971 1976 1981 1986 1991 1996 2001 2006 2011−3

1971 1976 1981 1986 1991 1996 2001 2006 2011

India

%
12

8

8

4

4

0

0

−4

−4

6
3
0
−3

1971 1976 1981 1986 1991 1996 2001 2006 2011

Indonesia

%
12

−8

1971 1976 1981 1986 1991 1996 2001 2006 2011

Iran

%
20

−8

1971 1976 1981 1986 1991 1996 2001 2006 2011

Japan

%
10
8

6

10

0

0

−6

−10

−12

−20

6
4
2
0
−2
−4
−6

−18

1971 1976 1981 1986 1991 1996 2001 2006 2011

Korea

%
15
12

−30

1971 1976 1981 1986 1991 1996 2001 2006 2011

Kuwait

%
60
40

9

5
0

−20

0

−5

−40

−3
1971 1976 1981 1986 1991 1996 2001 2006 2011

Malaysia

%
12

−60

1971 1976 1981 1986 1991 1996 2001 2006 2011

Mongollia

%
12
8

8
4

0

0

−4

−16

−5
−10

1971 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

118

Myanmar

%
15

0

−12
1971 1976 1981 1986 1991 1996 2001 2006 2011

1971 1976 1981 1986 1991 1996 2001 2006 2011

5

−8
−4

−10

10

4

©2016 Asian Productivity Organization

Lao PDR

%
15

0

3

−8

1971 1976 1981 1986 1991 1996 2001 2006 2011

10

20

6

−6

−8

−15

1971 1976 1981 1986 1991 1996 2001 2006 2011

3. Manufacturing
7. Transport, storage, and communications
Real GDP

4. Electricity, gas, and water supply
8. Finance, real estate, and business activities

6.3 Labor Productivity by Industry

%
8

Nepal

6

Oman

%
30

8

20

4

6

10

4

2
0

0

%
9

1971 1976 1981 1986 1991 1996 2001 2006 2011

Philippines

6
3
0
−3
−6
−9

%
15

2

−10

−2
−4

1971 1976 1981 1986 1991 1996 2001 2006 2011

Singapore−2001971 1976 1981 1986 1991 1996 2001 2006 2011

Qatar

%
25

−2

20

18

15

12

10

6

5

0

0

−6

−5

−12

−10

−18

−15

1971 1976 1981 1986 1991 1996 2001 2006 2011

Sri Lanka

%
10

−24

8

12

6

8

6

4

4

3

2

0

0

0

−4

−3

−2

−6

−4

UAE

%
8

1971 1976 1981 1986 1991 1996 2001 2006 2011

Australia−121971 1976 1981 1986 1991 1996 2001 2006 2011

US

%
8

2
0

0

0

Thailand42201971 1976 1981 1986 1991 1996 2001 2006 2011

6

4

40

−20

Vietnam

%
10

6

60

6

−8
1971 1976 1981 1986 1991 1996 2001 2006 2011

8

80

Saudi Arabia

%
16

9

1971 1976 1981 1986 1991 1996 2001 2006 2011

1971 1976 1981 1986 1991 1996 2001 2006 2011

%
24

12

%
100

Pakistan

%
10

−2

−2

−4
1971 1976 1981 1986 1991 1996 2001 2006 2011−41971 1976 1981 1986 1991 1996 2001 2006 2011

Turkey

%
10
8

6

6
4

4

2

2
0

0

−2
−2
−4

−4
1971 1976 1981 1986 1991 1996 2001 2006 2011−61971 1976 1981 1986 1991 1996 2001 2006 2011

1. Agriculture
2. Mining
5. Construction
6. Wholesale and retail trade, hotels, and restaurants
9. Community, social, and personal services

3. Manufacturing
7. Transport, storage, and communications
Real GDP

4. Electricity, gas, and water supply
8. Finance, real estate, and business activities

Figure 87 Industry Origins of Economic Growth, 1970–2014
Sources: Official national accounts in each country, including author adjustments.

119©2016 Asian Productivity Organization_Industry decomposition: Average annual growth rate of GDP at constant prices

6 Industry Perspective

Table 18 Labor Productivity Growth by Industry, 2000–2014

2. Mining
2.9
4.8
4.8
5.3
5.8
2.1
2.4
4.1
1.2

(0.7)
(1.4)
(1.3)
(1.7)
(1.0)
(0.8)
(0.8)
(1.2)
(0.2)

(0.1) −2.2 (−0.2)(0.1) 1.4 (0.0)(0.1) 1.5 (0.0)(0.2) 2.9 (0.1)(0.1) −0.6 (0.0)(0.1) 0.9 (0.0)(0.1) 1.1 (0.0)(0.2) 0.5 (0.2)(0.0) −0.6 (−0.8)−1.6 (0.1)−5.5 (0.2)−1.7 (−0.0)

4.0
1.6
1.9

(0.4) 0.4 (0.0) −1.7 (−0.1)(0.2) −2.8 (−0.0) 1.7 (0.1)(0.3) −0.8 (0.1) 2.0 (0.0)

Total economy

2.3
4.5
4.5
4.4
5.7
2.6
3.6
1.0
1.0

(0.0)
(0.2)
(0.2)
(0.3)
(0.1)
(0.1)
(0.1)
(0.3)
(0.8)

9. Community,social, andpersonal services

−0.7
3.3
2.0
7.9
3.8
−2.8
−3.5
1.2
−2.3

8. Finance, realestate, andbusiness activities−5.8 (−1.4)
3.4 (0.4)−5.0 (−1.0)−4.6 (0.3)
7.4 (0.4)−0.6 (−0.0)−6.4 (−0.3)
0.4 (0.0)−0.9 (0.0)
0.7 (0.1)−3.3 (−0.4)−0.1 (0.0)
0.2 (0.0)
1.1 (−0.5)
0.8 (−0.2)
3.1 (0.0)−0.5 (0.1)−0.8 (−4.1)−2.0 (−0.2)
1.4 (0.1)
2.8 (−4.8)−2.7 (−0.6)
1.8 (−0.2)
4.8 (0.5)−0.4 (−0.1)
6.0 (−0.1)−1.0 (0.0)

7. Transport,
storage, andcommunications

5. Construction

18.4 (0.2)
6.1 (0.1)
0.6 (0.0)−4.9 (0.0)
8.3 (0.3)
4.7 (0.1)−2.8 (−0.2)
2.2 (0.0)
8.1 (0.2)
5.3 (0.1)
4.1 (0.2)−2.1 (−0.0)
1.6 (0.1)−0.4 (0.1)
1.5 (0.1)−3.1 (−0.0)
5.2 (0.1)
16.0 (0.1)−2.6 (0.0)
3.3 (0.1)
2.8 (0.1)
2.4 (0.0)
1.3 (0.1)
9.8 (0.2)
6.5 (0.1)
1.5 (0.1)
1.5 (0.3)

6. Wholesale andretail trade, hotels,
and restaurants

4. Electricity,gas, and watersupply4.5 (0.0) 2.5 (0.2)
2.0 (0.1) 1.5 (0.4)−4.2 (−1.3) 0.8 (0.0)
11.4 (0.1) 4.6 (1.3)
9.3 (0.5) 7.6 (2.8)
2.4 (0.0) 5.5 (1.7)−0.2 (0.1) 1.6 (0.3)
0.0 (0.0) 4.6 (0.2)
4.8 (0.1) 8.2 (1.2)−3.2 (0.1) 2.1 (0.6)−4.6 (−0.1) 4.1 (0.5)−1.1 (−0.0) 3.2 (0.6)−0.1 (−0.0) 5.7 (1.6)−1.6 (1.2) 0.3 (0.1)−9.5 (−0.0) 3.8 (1.0)−3.8 (1.1) 7.8 (0.6)−2.2 (0.0) 1.4 (0.1)−0.7 (0.1) 0.8 (−0.1)−3.9 (0.1) 1.1 (0.1)
1.3 (0.1) 3.5 (1.0)−5.3 (3.7) 1.9 (0.1)
1.1 (0.7) 3.7 (0.3)
0.0 (0.0) 3.6 (0.9)
9.9 (0.2) 2.0 (0.4)
2.5 (0.1) 3.7 (0.9)−3.9 (0.5) −0.1 (0.1)−0.7 (0.2) 2.7 (0.9)

1. Agriculture
Bahrain−4.0 (−0.1)
Bangladesh 2.4 (0.1)
Brunei−3.9 (−0.1)
Cambodia
4.5 (1.5)China
7.3 (1.7)ROC
2.9 (0.1)Fiji
2.0 (0.2)
Hong Kong −1.9 (−0.0)
India
2.5 (0.1)
Indonesia
4.0 (0.6)
Iran
2.8 (0.2)Japan
0.7 (0.1)
Korea
4.1 (0.3)
Kuwait1.8 (0.0)
Malaysia
2.8 (0.3)
Mongolia
3.8 (0.8)
Nepal
0.2 (−1.0)
Oman−2.0 (−0.3)
Pakistan
0.2 (−0.3)
Philippines
1.3 (0.0)
Qatar−1.0 (−0.2)
Saudi Arabia −2.1 (−0.1)
Singapore −8.2 (−0.0)
Sri Lanka
2.5 (0.2)
Thailand2.0 (0.6)
UAE−0.5 (0.1)
Vietnam
3.0 (0.5)(regrouped)
APO20
2.1 (0.0)
Asia244.4 (0.7)
Asia304.3 (0.7)
East Asia
7.0 (1.4)
South Asia
1.9 (0.0)
ASEAN
3.9 (0.5)
ASEAN6
3.4 (0.4)CLMV
4.1 (1.1)
GCC−1.5 (−0.1)(reference)US
3.1 (0.0)
Australia 4.2 (0.1)
4.7 (0.9)
Turkey3. Manufacturing_Average annual growth rate of industry labor productivity

0.8 (−1.3)
4.1 (0.9)
4.8 (0.9)−1.0 (−0.1)
7.3 (0.8)
0.5 (0.1)
1.5 (0.3)−0.2 (−0.2)
6.1 (0.7)
0.7 (-0.2)
2.8 (0.3)
0.1 (−0.1)−0.5 (−0.2)−2.4 (−3.1)
3.6 (0.3)
0.2 (−0.1)
7.7 (0.8)
3.3 (0.1)
4.4 (0.6)−0.2 (−0.3)
2.2 (−1.0)−0.3 (−1.3)
0.0 (−0.9)
1.9 (−0.1)
0.6 (0.1)−0.7 (−2.2)
3.4 (0.4)

−1.7
3.5
−2.3
3.8
8.9
2.7
1.1
2.5
5.5
3.8
1.9
0.7
2.7
−1.6
1.8
5.1
1.5
−4.4
1.2
2.4
−1.4
−0.4
1.9
3.3
3.0
−1.6
3.9

−1.7 (−0.7) −2.8 (−0.3) 6.4
4.9 (0.7) 4.6 (0.5) −4.6
−0.5 (−0.8) −0.3 (−0.1) 3.4−1.6 (−0.3) 0.2 (0.3) −0.6
7.4 (0.8) 7.1 (0.6) 7.7
1.6 (0.2) 3.1 (0.1) 0.4
0.1 (0.0) 1.7 (0.3) −2.2
4.8 (1.1) 2.4 (0.2) 1.3
4.6 (1.0) 7.1 (0.6) 5.6
3.9 (0.5) 10.5 (1.2) −1.9
2.4 (0.4) 4.4 (0.2) 0.4−0.1 (−0.0) 0.8 (0.1) 0.5
3.0 (0.3) 2.7 (0.2) 0.1
1.4 (−0.2) 7.7 (0.5) −5.1
1.4 (−0.2) 2.1 (0.3) −0.2
6.0 (1.2) 9.0 (0.9) −2.2
1.9 (0.3) 1.8 (0.5) 1.3−2.0 (−1.0) 8.0 (0.6) −4.3
−0.9 (0.1) 0.1 (0.3) 6.3
2.7 (0.4) 3.8 (0.2) −1.3
3.3 (−0.6) 3.3 (0.0) 8.8
6.0 (−0.1) 8.5 (0.3) 5.8
2.9 (0.5) 1.4 (0.3) 1.1
1.5 (0.7) 3.7 (0.7) −0.8
2.0 (0.2) 6.3 (0.4) 2.9−1.5 (−0.3) 1.0 (0.1) −7.1
4.0 (0.7) 6.7 (0.3) −5.4

(1.6)
(0.4)
(0.2)
(0.7)
(1.0)
(0.4)
(0.4)
(1.1)
(1.5)
(0.8)
(0.6)
(0.1)
(0.4)
(0.3)
(0.2)
(0.7)
(0.5)
(0.1)
(0.4)
(0.8)
(1.4)
(0.4)
(1.3)
(0.5)
(0.6)
(0.1)
(0.6)

1.7 (0.3)
2.9 (0.5)
2.9 (0.5)
2.8 (0.4)
3.4 (0.8)
3.1 (0.4)
2.9 (0.4)
3.5 (0.2)
2.3 (−0.2)

3.2
4.3
4.4
3.6
4.9
7.2
6.7
9.0
4.0

(0.4) 0.5(0.5) 1.9(0.5) 1.9(0.4) 2.8(0.6) 3.8(0.8) −0.8(0.7) −0.5(0.6) 3.3(0.2) −0.9(0.7) 1.3 (0.3)
2.4(0.8) 2.5 (0.4)4.7(0.8) 2.4 (0.4)
4.5(0.7) 2.1 (0.4)
5.5
4.7(1.3) 5.0 (0.7)(0.8) 0.7 (0.0)3.4(0.7) 0.5 (−0.1)
3.2(0.1) 3.0 (0.3)
4.3(0.5) −0.4 (−1.5) −0.9

1.3
1.4
1.3

4.3
2.2
3.1

(0.3) 1.4
(0.2) 0.9(0.7) −1.6(0.5) −0.1 (−0.2)(0.5) 0.3 (−0.3)(0.5) 1.0 (0.2)

(0.1)
(0.0)
(0.1)

1.1
1.0
2.9

©2016 Asian Productivity Organization

Unit: Average annual growth rate (percentage), contribution in parentheses.
Source: APO Productivity Database 2016.

manufacturing strengthened from 54% to 59% in Korea, from 33% to 61% in the ROC, and from 56%
to 91% in Japan between the two periods. In other economies, however, like Sri Lanka, Nepal, andMongolia, manufacturing played a negligible role in the 2000s.
Traditionally, it has been difficult for the service sector to realize productivity growth, but modern
advancements in information and communication technology have changed this. Many IT-intensive
users are located in this sector, which is capable of capturing the productivity benefits arising from IT
utilization. The growing importance of these services has been observed in explaining the productivity

120

6.3 Labor Productivity by Industry

1990−2000

%

10

8

2000−2014

1.1
0.6

1.7
0.5

6
4.1 0.3 0.3

0.6

4
1.1

2

0

0.3

1.7

0.2
2.7

1.7

0.3 0.2

1.7

0.4
0.6 1.4
0.5
0.5
0.4 0.4
0.5
0.7
0.8
0.6
0.5
1.9
0.3
0.5
0.4
0.3 0.7 0.2
0.2
0.2 0.6 0.4 0.4 0.3 0.5 0.8
0.3 0.2
0.2
0.3
0.2 0.2 0.3
0.7
0.7
0.6
0.7 0.6
0.7 0.3
0.8 0.2 0.5
1.2 1.0 0.6 0.4 0.5
0.4 0.2
0.3 0.6
0.9
0.8 0.2 0.3
0.3 0.4
0.6
0.3
0.4
0.4
0.6
0.7
0.2 0.4
0.4
0.8 0.3
0.2
0.9 1.0 1.0
0.5
0.3
0.3
0.7
0.3
0.5 0.4 0.6 0.4
0.5 0.4 0.5 0.4 0.7 0.3 0.7 0.5 0.5 0.2 0.5 0.2
0.3 0.3
0.3 0.6
0.2 0.2
0.2
−0.2
−0.2
−0.2 −0.2 −0.4
−0.3
−0.2
−0.4
−0.2
−0.4
−0.4
−0.5
−0.2
−0.5
−0.2
−0.4
−0.2 −0.5
−0.3
−0.3
−0.2
0.2
0.4 0.2
0.7
0.3 0.5
0.5 0.4 0.6

0.8

2.8
1.2

0.8

1.3

6

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

−1.2

−2

Japan
Australia
Fiji
US
Pakistan
Nepal
Malaysia
Iran
Singapore
Philippines
Hong Kong
Korea
ROC
TurkeyThailandSri Lanka
Bangladesh
Cambodia
Indonesia
VietnamBhutanMongolia
India
China

Mongolia
Fiji
Philippines
Hong Kong
Japan
US
Nepal
Indonesia
TurkeyAustralia
Bangladesh
IranPakistan
Sri Lanka
MyanmarThailandCambodia
BhutanIndia
Singapore
Malaysia
Korea
ROC
Vietnam
China

1. Agriculture

2. Mining

4. Electricity, gas, and water supply

5. Construction

6. Wholesale and retail trade, hotels, and restaurants

7. Transport, storage, and communications

8. Finance, realestate, and business activities

9. Community, social, and personal services

3. Manufacturing

Real GDP

Figure 88 Industry Origins of Labor Productivity Growth, 1990–2000 and 2000–2014_Industry decomposition: Average annual growth rate of GDP at constant prices
Source: APO Productivity Database 2016.

growth in Western economies of recent decades. In Asia, the contribution from services matches thatof manufacturing. Among the four industries in the service sector, three are potentially IT-employing
industries: wholesale and retail trade, hotels, and restaurants; transport, storage, and communications; and finance, real estate, and business activities.

121©2016 Asian Productivity Organization

Figure 90 presents the contribution of services in labor productivity growth by country. In 2000–2014,services were contributing at least one-third or more to labor productivity growth in most Asian countries. The contribution was predominant in Hong Kong and India, accounting for 90% and 70% 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 2000–2014.

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
1.1
0.8
0.8
0.7
0.7
0.7
0.6
0.6
0.6
0.5
0.4
0.4
0.3
0.3
0.3
0.0
−0.2
−0.2
−0.5

2000−2014
−20 0

China
Korea
Singapore
ROC
Cambodia
Malaysia
VietnamThailandHong Kong
US
TurkeyBangladeshIndonesia
BhutanJapan
Sri Lanka
India
Philippines
Pakistan
IranAustralia
Myanmar
Nepal
Fiji
Mongolia

20 40

3.0 2.5 2.0 1.5 1.0 0.5 0.0 %60 80 100 %
54

1.7

46

1.6

33

1.3

39

1.2

33

1.0

22

1.0

27

0.9

98

0.9

39

0.9

35

0.6

32

0.6

32

0.6

17

0.5

56

0.4

21

0.4

11

0.4

47
13

0.4

12

0.3

13

0.3
0.2

−1

0.2

−9

0.1

81

0.1

31

Contribution

Contribution share toaggrergate labor productivity

0

China
ROC
Korea
Cambodia
India
Malaysia
Philippines
Singapore
VietnamThailandIndonesia
Japan
Mongolia
Iran
Sri Lanka
BangladeshUS
BhutanTurkeyFijiHong Kong
Australia
Pakistan
Nepal

2.8

46

Contribution

20

40

80 100 %

60

31
61
59
35
22
57
40
49
24
31
17
91
11
27
13
11
35
9
12
27
7
16
13
5

Contribution share toaggrergate labor productivity

Figure 89 Contribution of Manufacturing to Labor Productivity Growth, 1990–2000 and 2000–
2014
Source: APO Productivity Database 2016.

1990−2000
3.0

2.0

1.0

0.0 −1.0 %

2.9
2.6
2.3
2.3
2.0
1.9
1.8
1.4
1.4
1.3
1.3
1.3
1.2
1.2
1.1
1.0
0.7
0.7
0.6
0.5
0.4
0.2
0.1
0.1

2000−2014

−20

ROC
India
China
Vietnam
Malaysia
BhutanCambodia
Singapore
Korea
Pakistan
Iran
Sri Lanka
Nepal
Australia
Bangladesh
US
TurkeyIndonesia
ThailandMyanmarJapan
Hong Kong
Philippines
Mongolia−0.8
Fiji

Contribution

0

20

40

60

80 %

58

4.0 3.0 2.0 1.0 0.0 −1.0 %
3.9

70
26

3.2
2.7

44

2.5

50

2.3

54

2.3

52

2.2

39

2.0

28

1.8

60

1.5

61

1.5

53
66
60

1.4
1.3
1.2

55

1.2

58
38

1.1

34

1.0
0.8

18

0.7

20

0.7

40

0.6

19

0.6

13
−5

0.4
314

Contribution share toaggrergate labor produtcivity

0.0

Contribution

India
China
Mongolia
BangladeshIndonesia
Hong Kong
Nepal
Vietnam
Sri Lanka
TurkeyIranPakistanThailand
BhutanPhilippines
Singapore
Fiji
ROC
Korea
US
Cambodia
Malaysia
Australia
Japan

−20 0

20 40 60 80 100 %
70
36
53
70
60
90
146
50
54
53
78
121
43
30
48
59
88
31
27
60
16
33
45

−2

Contribution share toaggrergate labor productivity©2016 Asian Productivity Organization

Figure 90 Contribution of Service Sector to Labor Productivity Growth, 1990–2000 and 2000–
2014
Source: APO Productivity Database 2016.

1226.3 Labor Productivity by Industry

Box 8

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 (see Appendix 4 for the details). In productivity analysis, however, labor inputs are expected to be quality adjusted
in order to reflect workforce heterogeneity, as recommended in the SNA 2008 (United Nations, 2009).
In the stage of high economic growth, labor quality growth can be a significant factor as well as the increase in hours worked, improvement in education attainment of workers, and a shift from the self-employed (e.g., in agriculture or informal service sectors) to the employees (e.g. in manufacturing or formal
service sectors).

6

Figure B8.1 shows the contributions of labor quality and hours worked to economic growths in Japan andthe US since 1955, by Jorgenson, Nomura, and Samuels (2016). Although the US sustained a steady pace oflabor quality contribution of 0.1–0.3% on average per year to economic growth over a half century, thecontributions 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 B8.2 presents the time-series comparisons of the average schooling years observed in terms ofworkers since 1970, based on our work-in-progress estimates. In terms of persons aged 25 years and over,published in the Human Development Index by the United Nations Development Programme (UNDP)(2015), Korea has the longest years of schoolings (11.9 years) among the Asian countries, followed by Japan(11.5 years) and Sri Lanka (10.8 years) in 2014. In our focus on employment shown in Figure B8.2, however,Japan is the leading country (13.2 years), followed by Korea (12.9 years), the ROC (12.9 years), Hong Kong
(12.1 years) and Sri Lanka (11.3 years). The reverse reflects the differences in employment rate of highly%

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 B8.1 Contributions of Labor Quality to Growths in Japan and theUS, 1955–2012
Source: Jorgenson, Nomura, and Samuels (2016).
continued on next page >123©2016 Asian Productivity Organization

1.8

6 Industry Perspective> continued from previous pageeducated persons, e.g. higher rate of unemployment of educated persons in Korea. Although there is asignificant range in 2014 from 4.3 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 are planned to be published in 2016/2017.

Years

14

Japan 13.2
Korea 12.9

13

ROC 12.9
Hong Kong 12.1
Sri Lanka 11.3
Malaysia 11.3
Mongolia 10.9
Fiji 10.9
Singapore 10.7
Philippines 10.1

12
11
10

Iran 9.6
China 9.6

9Thailand 8.5
Indonesia 8.3

8

Vietnam 8.3

7
India 6.2
6Bangladesh 5.6
Nepal 4.8
Pakistan 4.7
Cambodia 4.7
Bhutan 4.3

5
4
3
2
1
0
1970

1975

1980

1985

1990

1995

2000

2005

2010

Figure B8.2 Average Schooling Years of Workers, 1970–2014©2016 Asian Productivity Organization

Sources: Population census and labor survey in each country, including author adjustments

124

6.3 Labor Productivity by Industry

Box 9

Per-Worker Wage and Income Level

Figure B9 plots per-worker average wages for employees against per capita GNI, using annual average exchange rates for selected countries in 2014 (taking the logarithms). The overall trend is a positive association; the higher average wages, the higher the per capita income. Of course, average wages are not equal
to GNI per capita. First, some adjustments are needed for the number of workers in one family. Second,
income from capital must be counted. If you inspect Figure B9, some countries are off the simple regression line. One outlier is Singapore, which is below the regression line. This likely reflects a large proportionof foreign workers out of total labor force who are paid lower than local workers.
Other off-lines are the ASEAN member states including Cambodia, 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.

6

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 2014 (using 2014 exchange rate)

2.0

Australia
EU15
Hong Kong
Japan

1.6

US

Singapore

Korea
ROC
1.2
TurkeyMalaysia

Fiji
India

0.8

China
Iran
ThailandMongolia
Philippines

Indonesia
Sri Lanka

Pakistan

0.4

Nepal

VietnamBangaldesh
Cambodia

0.0
–0.4

0.0

0.4

0.8

1.2

1.6

2.0

Log of per capita GNI in 2014 (using 2014 exchage rate)

Figure B9 Average Wage and Per Capita GNI, 2014125©2016 Asian Productivity Organization

Sources: Official national accounts in each country, including author adjustments; APO Productivity Database 2016.

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.112 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 per se, the real GDP concept does not capture this beneficial effect of the
improvement in the terms of trade.113 In contrast, real income focuses on an economy’s consumption
possibilities, and in turn captures the impact of a change in the relative price of exports to imports.
Real income growth attributed to changes in the terms of trade can be significant when there arelarge fluctuations in import and export prices and the economy under concern is highly exposed tointernational trade, as many Asian economies are (see Figure 26 in Section 4.1, p. 45). For example, real
income growth for oil-exporting countries was more than double that of real GDP growth in recent
years (as in Saudi Arabia and Brunei). Meanwhile, there has been no significant difference between
real income growth and real GDP growth in Myanmar, which is a relatively closed economy (see Figure
34 in Section 4.2, (p. 52) and Figure 97 for the expenditure-side and the income-side, respectively). In
the 2000s, the trading gain has also driven a significant wedge between real income and real GDP in
Australia. That is partly due to a fall in import prices, but owes more to the rising prices of its commodity exports.

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,114 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.115 As such, real income captures the purchasing
power of the income flow. Furthermore, the Databook adopts the concept of gross national income(GNI) instead of GDP in its estimation of real income, to take into account net income transfer fromabroad. Applying the method proposed by Diewert and Morrison (1986), the annual growth rate ofreal income can be fully attributed to three components: annual growth rate of real GDP, real income
growth attributed to changes in prices of exports and imports (referred to as the trading gain),116 andthe effect of net income transfer.117

112: See Diewert and Morrison (1986) and Kohli (2004).
113: 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.”
114: The weight for import price changes is negative. Thus, if import prices decrease, this tends to raise the GDP deflator.
115: 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).
116: The term “trading gain” is used by some authors (Kohli, 2006). This term is adopted in this report.
117: 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/GDP t

D

Real income growth(

Income transfer effect)

Real GDP growth()(1/2) (sXt + sXt−1) In ( PXt / PXt−1 ) − In ( PDt / PDt−1 ) − (1/2) (sMt + sMt−1) In ( PMt / PMt−1 ) − In ( PDt / PDt−1 )Real income growth attributed to changes in the terms of trade (=trading gain)

where Pit is price of final demand i in period t and sit is expenditure share of final demand i in period t. D is domestic expenditure,
X is export, and M is import. Note that the real GDP growth based on this formulation may differ from that used in other chapters,
since the implicit Törnqvist quantity index is adopted for calculating it.

127©2016 Asian Productivity Organization

GNIt

7 Real Income

A general observation is that over a long period of time the trading gain effect is, on average, small,but over a shorter period could be very significant.118 The findings presented in Table 19 confirm thisobservation. Excluding the oil-exporting countries, the trading gain effect in 17 out of 22 economies
compared, fell within the margin of ±10% of real GDP growth on average for the long period of
Table 19 Real Income, Real GDP and Terms of Trade, 1970–2014, 1995–2000, 2000–2005, and
2005–2014_Average annual growth rate of real income, real GDP and trading gain, and net primary income transfer fromabroad©2016 Asian Productivity Organization

3.51
1.65
4.04
8.62
3.13
6.56
1.69

2.87
4.41
3.90
5.80
2.06
1.87
3.15

−0.35
0.34
−0.38
−0.97
−0.34
−0.42
0.00

2.72 2.76 −0.06
0.02
2.06 2.09 −0.02 −0.01

Bahrain
6.04
Kuwait
6.39
Oman
7.54
Qatar
13.46
Saudi Arabia 4.86
UAE
8.01
Brunei4.83(reference)
US
4.28
EU15
2.92

4.19 0.09
2.90 −0.09

0.00
0.11

3.41 3.27 0.17 −0.03
4.15 4.29 −0.12 −0.02

Australia
Turkey3.78 0.12
0.15
4.36 −0.31 −0.08

Bahrain
7.85
Kuwait
10.59
Oman
8.23
Qatar
12.12
Saudi Arabia 9.14
UAE
6.37
Brunei
8.19(reference)
US
2.53
EU15
1.93
EU28
1.77
Australia
4.30
Turkey4.68(reference)
US
EU15
Australia
Turkey

4.05
3.98

6.53
7.20
3.60
9.85
3.97
4.71
3.58

1.33
4.56
4.43
4.57
5.20
1.74
4.61

−0.02
−1.17
0.21
−2.30
−0.02
−0.08
0.00

2.49 −0.03
0.07
1.76 0.07
0.11
1.63 0.06
0.08
3.36 1.17 −0.22
4.61 0.27 −0.19

Trading gain

4.88 1.08 −0.42
0.81 4.05
0.44
6.28 1.49
0.12
6.26 0.48
0.01
3.89 0.63
0.61
9.83 −0.22
0.31

Net primary income
from aboad

5.54
5.29
7.89
6.75
5.08
9.93

China
MyanmarCambodia
Mongolia
India
VietnamBhutanSri Lanka
Philippines
BangladeshIndonesia
Malaysia
Singapore
Nepal
PakistanThailandHong Kong
Korea
Iran
ROC
Fiji
Japan

Real GDP

Bahrain
KuwaitOman
Qatar
Saudi Arabia
UAE

15.58 14.65 0.93
0.00
11.54 6.22 5.54 −0.24
11.04 9.94 0.99
0.10
10.11 10.37 0.00 −0.26
8.90 7.18 2.02 −0.30
8.15 7.68 0.57 −0.09
7.25 5.30 1.19
0.77
7.01 6.91 0.36 −0.26
6.99 7.24 −0.32
0.07
5.70 5.85 −0.78
0.63
5.54 4.71 0.72
0.11
5.40 4.25 −0.28
1.44
5.35 5.18 −0.05
0.23
4.62 5.15 −0.01 −0.52
3.92 4.96 0.20 −1.25
3.82 4.53 −0.70 −0.01
3.67 4.28 −0.99
0.38
3.06 4.09 −0.98 −0.05
2.65 3.17 −0.79
0.07
2.57 3.81 −1.44
0.21
2.09 2.29 0.34 −0.54
1.07 1.21 −0.33
0.20Real income

MyanmarMongolia
China
Cambodia
Iran
Vietnam
Malaysia
Bhutan
India
Pakistan
Sri Lanka
Philippines
Bangladesh
ThailandSingapore
Korea
Indonesia
Hong Kong
Nepal
ROC
Fiji
Japan

Trading gain

2.49
−0.26
0.08
−0.27
1.66
−0.03
0.01
−0.75
0.15
−0.31
−0.15
0.10
−0.61
−0.38
−0.05
−0.01
1.40
0.09
−0.81
−0.01

2005–2014
Net primary income
from aboad

Bhutan8.48 6.07 −0.08
Vietnam
7.37 7.41 0.21
China
7.26 7.32 −0.14
Singapore
6.17 6.30 0.14
Philippines
5.84 3.04 1.14
ROC
5.73 5.90 −0.14
India
5.32 5.48 −0.17
Malaysia
5.27 5.59 0.43
Iran
5.20 2.74 2.30
Cambodia5.16 5.42 0.04
Sri Lanka
4.85 5.07 −0.07
Bangladesh 4.15 4.02 0.02
Myanmar4.14 9.62 −4.90
Pakistan
3.59 3.99 −0.02
Korea
3.15 5.16 −1.95
Hong Kong
2.75 2.39 0.37
Fiji
2.73 2.43 −1.12
Japan
0.81 0.88 −0.16
Indonesia −0.60 −0.39 0.62
Thailand−0.93 0.27 −1.20

Real GDP

−0.01
−0.14
−0.04
−0.03
0.07
0.29
−0.04
−0.07
0.05
−0.01
−0.09
−0.12
0.11
0.13
0.71
0.24
0.14
0.09

Real income

Net primary income
from aboad

8.63 −0.05
7.22 −0.22
6.44 0.43
6.96 −0.41
7.03 −0.83
5.75 0.12
5.20 0.59
6.15 −0.34
5.54 −0.07
5.42 −0.04
5.30 −0.07
5.59 −0.37
3.51 1.37
5.10 −0.29
3.78 −0.01
2.74 0.41
3.36 −0.20
2.59 −0.31

2000–2005
Trading gain

Net primary income
from aboad

8.58
6.86
6.83
6.52
6.27
6.16
5.75
5.74
5.53
5.37
5.13
5.10
5.00
4.94
4.48
3.38
3.29
2.37

Real GDP

Trading gain

China
Singapore
Malaysia
Korea
ROC
BhutanIndonesia
MyanmarHong Kong
India
Sri Lanka
Thailand
IranPakistan
Philippines
Fiji
Bangladesh
Japan

Real income

Real GDP

1995–2000

Real income

1970–201410.25 10.15 0.05
0.04
9.31 8.46 0.86 −0.01
8.36 8.23 0.17 −0.04
8.12 9.06 −0.18 −0.77
7.27 7.39 −0.07 −0.05
6.88 6.35 0.87 −0.35
6.59 7.32 −0.10 −0.63
6.53 6.43 0.24 −0.14
5.76 5.24 −0.19
0.72
5.74 5.89 −0.39
0.24
5.60 6.04 −0.59
0.16
5.44 5.00 0.33
0.12
4.77 5.57 −1.00
0.20
4.48 3.61 0.78
0.09
3.53 3.80 −0.67
0.40
3.29 3.50 −0.16 −0.05
3.06 3.44 −0.55
0.17
3.05 3.44 −0.51
0.12
2.23 2.89 −0.84
0.18
2.18 3.63 −1.50
0.06
1.85 2.06 −0.34
0.13
0.17 0.47 −0.48
0.18

Bahrain5.69 5.44 1.98 −1.72
Kuwait4.32 1.84 2.68 −0.20
Oman
7.09 3.79 3.45 −0.15
Qatar
13.03 11.19 0.83
1.01
Saudi Arabia 5.59 3.66 1.75
0.18
UAE
3.76 3.77 0.15 −0.17
Brunei
4.08 −1.21 5.40 −0.14(reference)US
1.30 1.26 −0.04
0.09
EU15
0.53 0.61 −0.06 −0.02
EU28
0.66 0.74 −0.06 −0.02
Australia
3.19 2.74 0.23
0.21
Turkey3.37 3.72 −0.36
0.01

Unit: Percentage.
Sources: Official national accounts in each country, including author adjustments.
Note: See footnote 117 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 1970–2014: Brunei (1989–), Cambodia (1993–), Mongolia (2000–), Nepal (2000–), andVietnam (1989–).

118: 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.

1287.1 Real Income and Terms of Trade

1970–2014. Movements in terms of trade have been consistently unfavorable to the ROC and Korea.
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 of trade
have been turning strongly in its favor. The trading gain effect in Australia has therefore been risingfrom 3% on average per year in 1995–2000, to 35% in 2000–2005, and 8% in 2005–2014 of its real GDP
growth. In terms of percentage points, the trading gain added 0.12, 1.17, and 0.23 percentage points
to real GDP growth in the three consecutive periods. For the oil-exporting countries, the trading gain
effect is almost always positive and significant, making it possible to sustain a rise in purchasing power with little real GDP growth in countries, such as Brunei, Kuwait, and the UAE in 2005–2014.

7

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 Qatar. Net primary income from abroad has been a long-term significant contribution to the purchasing power of the Philippines, with remittances from a large number of overseas workers. When its
real GDP growth slowed (during the late 1990s), net primary income from abroad played an evengreater role in cushioning the real income of Filipinos. Over the past four decades, net primary income
from abroad augmented real GDP growth by 3.5% on average per year in Japan. This has grown to be
more significant at 38%, as real GDP growth slowed from 2005–2014.
Figure 91 plots the time series of net primary income from abroad as a percentage of GDP. The role ofnet primary income from abroad has been shifting from negative to positive in Hong Kong, with thetransition taking place in the mid-1990s leading up to the handover of Hong Kong from British rule to
China in 1997. Since then, net primary income from abroad has been positive. A shift in the role of net
primary income from abroad has also taken place in Korea from negative to a more or less neutral
position in the 2000s. It has held positive in the ROC, oscillating around +2% of GDP, since 1980. Singapore’s net primary income from abroad displayed the largest fluctuations, ranging from +2.0% in
1997 to –7.0% in 2004, but on the whole, it has been more negative than positive. In Japan and thePhilippines, net primary income from abroad has risen strongly, albeit at different magnitudes. In Japan, it rose from 0.6% of GDP in 1990 to 3.9% in 2014, compared with 1.5% in 1990 and 34.4% in 2014
in the Philippines. In the US, it has always been positive, fluctuating within +1.6% of GDP, whereas in
the EU15 it was marginally negative for the three decades between 1975 and 2005 before turning
mildly positive.

%

6
4

%

ROC
Korea

Hong Kong
Singapore

4

3

2

%

China
Japan
US

India
EU15

40

30

2

20

1

10

0

0

−1

−10

Bangladesh
Fiji
Malaysia
Pakistan

Cambodia
Indonesia
Mongolia
Philippines

0

−4
−6
−8

1970 1975 1980 1985 1990 1995 2000 2005 2010−21970 1975 1980 1985 1990 1995 2000 2005 2010−20

1970 1975 1980 1985 1990 1995 2000 2005 2010

Figure 91 Effect of Net Income Transfer on GDP, 1970–2014
Sources: Official national accounts in each country, including author adjustments.

129©2016 Asian Productivity Organization−2

7 Real Income

Combining both the trading gain
effect and net primary income fromabroad, real income growth formost of the countries compared fellwithin the margin of ±20% of real
GDP growth (Figure 92). Kuwait andBrunei appear to be the outliers,
with real income growth being 6.5
times and 3.8 times their respective
long-term dismal real GDP growthof 0.8% and 1.1%.119

%

12

10

8

6

4

Real income growth(+20%)

UAE
Mongolia
China
Cambodia
Oman
Vietnam
Malaysia
Singapore
Qatar
Korea
Bhutan
ROC
Indonesia
MyanmarBahrain
KuwaitHong Kong
Saudi Arabia India
Thailand
IranPakistan Sri Lanka
Philippines
BruneiTurkeyAustralia Nepal(−20%)©2016 Asian Productivity Organization

Unlike the oil-exporting countries,
BangladeshFijiUSat any one time roughly half of theJapan
2Asian countries compared susEU15
tained a negative trading gain effect, albeit to variable extents,
0
whereas the impact from net pri0
2
4
6
8
10
12 %
Real GDP growthmary income from abroad was relatively less pronounced. The period
Figure 92 Real Income and Real GDP Growth, 1970–2014
of 1995–2000 reflects the impact of_Average annual growth rate of GDP at constant prices and real
the Asian financial crisis. For Thaiincome
land, the trading gain effect more
Sources: Official national accounts in each country, including author adjustments.
than outweighed the small positive
Note: The starting years for some countries are different due to data availability
during 1970–2014: Brunei (1989–), Cambodia (1993–), Mongolia (2000–), Nepal
average real GDP growth per year
(2000–), and Vietnam (1989–).
(0.3%), giving rise to a marginal fallin real income of –0.9%. In Korea,
the negative trading gain also shaved 38% off real GDP growth of 5.2%, producing real income growthof 3.2%. At the start of the 2000s, the Asian economy recovered from the financial crisis, but the trading gain effect ran counter to welfare for some countries, with a negative impact that only intensified
after 2005. For example, in the ROC, the trading gain effect caused real income growth to be 38%
lower than real GDP growth in the period 2000–2005. However, in the period 2005–2014 it wiped out
41% of the attractive 3.6% real GDP growth on average per year, leaving real income to grow at 2.2%.
Similarly, in Korea the trading gain effect caused real GDP growth to overestimate real income growthby 16% in the first half of the 2000s, which increased to 15% in the years 2005–2014 (Table 19 and
Figure 93). In Japan, the negative trading gain effect more than wiped out the 0.5 percentage pointsof real GDP growth, leaving real income to actually fall by 0.2% per year on average in the period
2005–2014.
In contrast, the trading gain worked to counterbalance falling real GDP in Brunei, leaving it with a robust, real income growth of 4.1%, despite its contracting real GDP of 1.2% in the period 2005–2014(Table 19). In Saudi Arabia, real income growth increased more than 153% faster than its real GDP
growth. This takes place against the backdrop of strong oil prices, which spiked in mid-July 2008 to
USD 145 per barrel. After dropping sharply to USD 30 per barrel by the end of 2008 (reflecting the fallin demand by the global financial crisis), oil has steadily risen to, and held at, over USD 100 per barrel

119: 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.

1307.1 Real Income and Terms of Tradesince 2010 through the middle of 2014 (Figure 94). In theUS, the trading gain effect has been unfavorable more often than not, but its positive net primary income fromabroad has worked to counterbalance this and the difference between real GDP and real income growth is reduced. For example, in the latest period 2005–2014, thetrading gain effect shaved 0.04 percentage points off real
GDP growth of 1.26%. It was counterbalanced by the positive effect from net primary income from abroad, which
added 0.09 percentage points to real GDP growth, leaving
real income growth slightly higher than real GDP.

5.4
3.5
2.7
2.0
1.8
0.9
0.9
0.8
0.8
0.3
0.2
0.2
0.2
0.2
0.1
0.0
−0.1
−0.1
−0.1

BruneiOman
KuwaitBahrainSaudi Arabia
Vietnam
Myanmar
Qatar
Nepal
Malaysia
Sri Lanka
Australia
Cambodia
UAE
China
US
EU28
EU15
India
BhutanThailandMongolia
Philippines
Fiji
TurkeyBangladesh
Japan
Korea
Hong Kong
Indonesia
Pakistan
IranSingapore
ROC

7

−0.1
Figure 95 provides the results of further decomposition of−0.2
the trading gain into the terms-of-trade effect and the real
−0.2
−0.2
exchange rate effect in Asian countries for the period−0.3
1970–2014.120 The terms-of-trade effect is the part of real
−0.4
−0.4
income growth attributed to the change in the relative−0.5
price between exports and imports. The real exchange
−0.5
−0.6
rate effect refers to the part of real income growth attrib−0.6−0.7
uted to changes in the relative prices of traded goods and−0.8
domestically consumed goods. By applying this result, real
−1.0
−1.5
income growth can be decomposed into real GDP growth,
−2
0
2
4
6%
terms-of-trade effect, real exchange rate effect, and net
primary income from abroad. The first chart in Figure 95
Figure 93 Trading Gain Effect, 2005–
applies this break down to Asian countries for the period
2014
1970–2014. It shows that the real exchange rate effect is_Average annual contribution to real
generally much smaller than the terms-of-trade effect, imincome growthplying that the relative prices of traded versus domestiSources: Official national accounts in each country,
cally consumed goods have been largely stable in mostincluding author adjustments.
countries. The exception is Kuwait where the real exchange rate effect accounted for 32% 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 2000–2014. It shows that the trading
gain, particularly the terms-of-trade effect, is highly significant and favorable for the oil-exporting

Dollars per barrel

145.3

150
125

113.4

109.4

110.2 106.8

100
77.1

75

68.0

50

48.0

41.1

43.4
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 94 Price of Crude Oil, 1986 January–2016 May
Source: US Energy Information Administration, WTI spot prices FOB (Cushing, Oklahoma).

131©2016 Asian Productivity Organization

30.3

25
0
Jan 02,
1986

59.2

75.4

7 Real Income

countries, but is significant and negative in a handful of Asian economies such as the ROC, Hong Kong,
Pakistan, Korea, Indonesia, and Japan.

EU15
Japan
US
Bangladesh
Fiji
Australia
TurkeyPhilippines
Pakistan
Iran
Saudi Arabia
ThailandSri Lanka
KuwaitIndia
Hong Kong
Bahrain
MyanmarIndonesia
Bhutan
ROC
Korea
Qatar
Malaysia
Singapore
Oman
China
UAE

Figure 96 shows the decomposition of average annual real income growth covering two periods ofmajor economic shocks faced by the Asian economies: during 1973–1979, which includes the two oil
price hikes in 1974 and 1979; and 1996–1998 to capture the impact of the Asian financial crisis. Highoil prices improved the terms foroil-exporting countries, such as
1970–2014%
12
Iran and Indonesia, and worsened the terms of trade for oil9
importing countries. During the
1.2
Asian financial crisis, the terms6
0.7
1.0
9.8
1.0 1.5
8.6
of-trade effect was still the pre1.7
0.7
7.2
7.0 7.0
6.3
6.4
6.3
3
5.7 5.2 6.2
5.6
5.5
5.4
5.3
5.1
4.9
dominant factor in determining
2.4
3.9 3.5
3.8 4.3 3.3
2.7 3.4 2.8 2.6 2.1
0.8
the difference between real in0−0.8
come growth and real GDP−3growth. In Brunei, the terms-oftrade effect further reinforced
the negative real GDP growth of–7.3%, reducing its real income
2000–2014%
growth a further 7.4 percentage
15
points. In Iran, the negative
12 1.90.9
terms-of-trade effect counteract9
1.3
ed the 1.0% real GDP growth, giv0.7 0.8
0.9 1.7
10.7
0.7
ing real income growth of –1.5%.
6 10.7
0.8 1.0
3.2
10.1
2.5
2.4
0.7
1.4
9.0
0.9
8.0
In Indonesia, the trading gain ef7.2
6.8 7.3
5.8
5.8
3
5.4 4.5
5.1 4.9 5.6 3.8 5.4
4.4
4.1
3.8
3.8
3.7
3.6 4.0 4.1 3.0 3.8
fect worked to counterbalance
3.7 3.7 2.1 1.7
1.0 0.7
0
−0.7
−1.1
−0.8
the contraction in real GDP,
−1.5
−3whereas in Thailand, it reinforced
the negative real GDP growth. In
the Philippines, although thestrong favorable terms-of-trade
Real exchange rate effectTerms-of-trade effect
Real GDPeffect was moderated by the
Net primary income from abroad
Real income
negative real exchange rate effect, the resulting real income
Figure 95 Decomposition of Real Income Growth, 1970–
growth more than tripled the real
2014 and 2000–2014
GDP growth.121
Sources: Official national accounts in each country, including author adjustments.

Japan
EU15
US
Fiji
ROC
Hong Kong
Korea
Australia
ThailandTurkeyNepal
Pakistan
Singapore
Iran
UAE
Indonesia
Brunei
Bangladesh
Philippines
Malaysia
Sri Lanka
Bahrain
KuwaitBhutanSaudi Arabia
India
Vietnam
Oman
Cambodia
Mongolia
China
Myanmar
Qatar

120: Following Kohli (2006), trading gain can be decomposed into two components as follows:

(

)

(

)

(1/2) (sXt + sXt−1) In ( PXt / PXt−1 ) − In ( PDt / PDt−1 ) − (1/2) (sMt + sMt−1) In ( PMt / PMt−1 ) − In ( PDt / PDt−1 ) =Real income growth attributed to changes in the terms of trade (=trading gain)()©2016 Asian Productivity Organization(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 effect121: 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%.

1327.1 Real Income and Terms of Trade

1973–1979

%

5

3.3

7.5

5.7

1.1 1.0

5
2.2

9.7
6.6

1.4

0.5
1.2 1.2

3.7

7.9

8.7 8.5

7.3
4.2

0

1996–1998

%

10

10

0.6

1.1
0.7

0
5.8

4.1

5.1

5.0 5.1 4.4 4.4 4.8

1.5−0.9 −1.3

−1.1

−0.7

−0.5

3.5 3.7 3.6 2.9 2.8
2.7−0.5 −0.8

7.3 6.8−0.7

5.2 5.1 4.6

1.2
6.0
−0.8

7.6
4.4 4.6−2.5

3.0 1.5

1.1

1.4
1.0
1.5 2.5 2.6 2.9 2.9 1.3 1.3
−0.9
−0.7
−1.7

1.0
−2.6 −2.2−5

1.2 5.6
2.2−6.2 −5.4 −5.1 −7.3
−1.4

−0.4

−0.3

−0.9
−0.7
−5.4

−10

−7.4
−1.6

−15

−5

Terms-of-trade effectBruneiThailandMyanmarIndonesia
Korea
Iran
Fiji
Japan
Malaysia
Hong Kong
EU15
Bangladesh
Singapore
Pakistan
Philippines
Australia
US
BhutanCambodia
India
ROC
TurkeyChina
Vietnam
Sri Lanka

Bangladesh
EU15
US
Japan
Australia
India
TurkeyPhilippines
MyanmarBhutanSri Lanka
China
PakistanThailandFiji
Singapore
ROC
Hong Kong
Iran
Malaysia
Korea
Indonesia
Real exchange rate effect

7

Real GDPNet primary income from abroad

Real income

Figure 96 Decomposition of Real Income Growth, 1973–1979 and 1996–1998_Decomposition: Average annual growth rate of real income

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

122: 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.

133©2016 Asian Productivity Organization

Figure 97 shows this decomposition of real income in each Asian country, along with the US, the EU15,
Australia, and Turkey122 from 1970, or the year of first data collection for the country in question. The
trading gain can be positive 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 some oil-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 truein the EU15, where the negative trading gain effect counterbalanced real GDP growth, leaving virtually no growth to real income in the period 1974–1975. The effect of the second oil spike can be seenin 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 formost of the period 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

%
21

%
18

18

18

15

12

15

12

6

12

9

0

9

6

−6

6

3

−12

3

0

−18

0

−3

−24

1971 1976 1981 1986 1991 1996 2001 2006 2011

ROC

%
18

−3

Hong Kong

%
16

15

−6

1971 1976 1981 1986 1991 1996 2001 2006 2011

%
12

12

9

8

6

4

3

1971 1976 1981 1986 1991 1996 2001 2006 2011

China

1971 1976 1981 1986 1991 1996 2001 2006 2011

India

12
9
6
3

0

0

−4

−3

0
−3
−6

1971 1976 1981 1986 1991 1996 2001 2006 2011

Indonesia

%
20

−8

36

10

27

5

18

0

9

−5

0

−10

−9

−15

−18

−20

−27

1971 1976 1981 1986 1991 1996 2001 2006 2011

Korea

%
20
15

Iran

%
45

15

−6

1971 1976 1981 1986 1991 1996 2001 2006 2011

%
8

4
2
0
−2
−4
−6

1971 1976 1981 1986 1991 1996 2001 2006 2011

Kuwait

%
90

%
20

5
0

0

0

−30

1971 1976 1981 1986 1991 1996 2001 2006 2011

Mongolia

%
25
20

−60

−5

1971 1976 1981 1986 1991 1996 2001 2006 2011−10

Myanmar

%
24

4

6

5
0

2

0

0

−5
−6

−10
1971 1976 1981 1986 1991 1996 2001 2006 2011

Real GDP

134

−12

Nepal

6

12

10

%
10

1971 1976 1981 1986 1991 1996 2001 2006 2011

8

18

15

©2016 Asian Productivity Organization

Malaysia

10

30

−5

−15

1971 1976 1981 1986 1991 1996 2001 2006 2011

15

5

−10

Japan

6

60

10

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

Trading gain

Net primary income from abroad−41971 1976 1981 1986 1991 1996 2001 2006 2011

Real income

7.1 Real Income and Terms of Trade

%
50

Oman

40

Pakistan

%
12

%
15
10

9

30

5

6

20
10

0

3

−5

0

0

−10

−10
−3

−20
−30

%
40

1971 1976 1981 1986 1991 1996 2001 2006 2011

Qatar

30

−6

−15
1971 1976 1981 1986 1991 1996 2001 2006 2011−20

Saudi Arabia

%
45

%
24

12

0

8

0

4

−15

−10

0
−30

−20
1971 1976 1981 1986 1991 1996 2001 2006 2011

Sri Lanka

15

−45

−8

1971 1976 1981 1986 1991 1996 2001 2006 2011

Thailand

%
15

%
100

40

0

20
−5

−5

0

−10

−10
1971 1976 1981 1986 1991 1996 2001 2006 2011

Vietnam−15−20
1971 1976 1981 1986 1991 1996 2001 2006 2011

−40

US

%
8

%
6

9

6

4

6

4

2

3

2

0

0

0

−2

−3

−2

−4

−6

−4

%
8

UAE

60

0

%
12

1971 1976 1981 1986 1991 1996 2001 2006 2011

80

5

5

7

−4

10

10

−15

Singapore

16
15

10

%
20

1971 1976 1981 1986 1991 1996 2001 2006 2011

20

30

20

−30

Philippines1971 1976 1981 1986 1991 1996 2001 2006 2011

Australia

6

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

EU15

1971 1976 1981 1986 1991 1996 2001 2006 2011

Turkey

%
12
8

4

4

2
0

0

−4

−2
−4

1971 1976 1981 1986 1991 1996 2001 2006 2011

Real GDP−81971 1976 1981 1986 1991 1996 2001 2006 2011

Trading gain

Net primary income from abroad

Real income

Figure 97 Sources of Real Income Growth, 1970–2014135©2016 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% Trading gain effect3

KuwaitWhen 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.

Brunei

2

Iran
Oman
1

Saudi Arabia
BahrainIndonesia
Vietnam Malaysia

Fiji

Qatar
0

Singapore
Turkey Hong Kong
China
BhutanEU15
Cambodia
US
Philippines
India
Myanmar
Sri Lanka
UAE
Korea
Japan
Bangladesh
Thailand
Pakistan
Australia

ROC
−1
−4.5

−3.0

−1.5

0.0

1.5

3.0

4.5

6.0

7.5 %

Labor productivity growth©2016 Asian Productivity Organization

Figure 98 plots the labor producFigure 98 Trading Gain Effect and Labor Productivity
tivity growth and the trading gain
Growth, 1970–2014
effect for the whole observation
Sources: Official national accounts in each country, including author adjustments;
period. Over the past four deAPO Productivity Database 2016.
Note: The starting years for some countries are different due to data availability duringcades, only five countries have
1970–2014: Brunei (1989–), Cambodia (1993–), Myanmar (1977–), and Turkey (1988–).
enjoyed a favorable trading gain
effect of over 1% per year. They
are Kuwait, Brunei, Iran, Oman,
Bahrain, and Saudi Arabia (all oil-exporting countries). Only Iran among them could achieve a significant positive growth in labor productivity. A resource-rich country can suffer from “Dutch disease,”
which is a phenomenon in which a country’s currency is pushed up by the commodity boom, making
other parts of its economy less competitive and potentially increasing the country’s dependence onnatural resources.123 This is how resource abundance can easily lead to resource dependence.
Figure 99 illustrates trading gain effects and value-added shares of the mining sector in 1970 and2014 in select Asian economies. It indicates that large trade gainers typically have dominant mining
sectors, petroleum and natural gas in particular. Provided resource prices continuously rise, thesecountries continue to gain from the positive terms-of-trade effects. However, if resource prices fell, or
natural reserves were 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 tocounteract Dutch disease is broad-based, robust productivity growth and industry diversification. Figure 99 shows some of the trading gainers (i.e. Brunei, Oman, Qatar, and UAE) actively reduced theirshare of the mining sector over time, which could reflect the intention of developing industries otherthan mining. However, Figure 98 shows that labor productivity growth rates in these countries after
1990 remained low, or even negative. Even if they wanted to start industrialization, their high income and strong local currency would not easily allow them to develop a manufacturing sector or

123: 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.

136

7.2 Trading Gain and Productivity Growth

an internationally competitive service industry. Another concern is their heavy dependence on foreign workers, both skilled
and unskilled.

% Trading gain effect, 1970−201454=1970 (starting year), =2014

KuwaitOn the other side of coin are the resource/
Brunei3energy-importing economies. Most of thesesuffered from negative trading gain effects,
losing a part of their economic growth due
2
to resource price hikes, particularly in the
Oman
Iran
2000s (Table 19). However, it has actually
Bahrainstrengthened their competitiveness in man1
Indonesia
ufacturing and other productive activities
Qatar
Malaysia
Saudi Arabia
for the future. Figure 98 also shows that
Vietnam0Australia
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 2014
are typically resource importers whose voracious demand for commodities pushes up
Figure 99 Trading Gain Effect and Value-added
their import prices. Meanwhile, export pricShare in Mining Sector, 1970–2014
es tend to fall as a result of their achievement
Sources: Official national accounts in each country, including author
in productivity improvement, resulting inadjustments; APO Productivity Database 2016.
Note: The starting years for some countries are different due to data
unfavorable movements in terms of trade.
availability during 1970–2014: Brunei (1989–) for trading gain effect,
This is particularly the case in countries
Brunei (1974–), UAE (1972–), Bahrain (1975–), Malaysia (1987–), andVietnam (1986–) for value-added share of mining sector.
where economic growth is highly dependent on export promotion. In such instances,a negative trading gain is partially a sideeffect 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.

©2016 Asian Productivity Organization

137

7

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 Bangladesh, Brunei, the ROC,
Indonesia, Korea, Mongolia, and Singapore during 2014–2015 and in Sri Lanka as of March 2016. Based on
our Metadata Survey 2016 for the APO member economies (see Box 3, p. 54), 11 economies are already2008 SNA-compliant in Asia and others (Cambodia, Iran, Japan, 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 statuses of SNA adaptions among economies are responsible for the huge variations of data definitions and coverage 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).124 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 until the 1950s or 1960.
In addition, some extra 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).

124: 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 thenational experts in our project. In March 2016, Sri Lanka published the new national accounts based on the 2008 SNA and somelarge differences are found in comparison with the past estimates based on the 1993 SNA. However, this edition used the pastestimates, since the sources of the difference between the two estimates are not clear and the latest data covers only the period
since 2010.

139©2016 Asian Productivity Organization

Among the 20 APO member economies, four countries – Cambodia, the Lao PDR, Nepal, and Sri Lanka– do not allocate FISIM to final demands in their official national accounts, as a result of them still not
following the 1993/2008 SNA recommendation. Thus, the GDP values in these countries are smaller
than others by definition. In addition, even in the countries whose national accounts follow the
1993/2008 SNA’s recommendation on FISIM, the available data sometimes does not cover 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

Appendix

on FISIM allocated to final demand
are assumed to be identical withthe average ratios observed in the
countries in which data is available.
Figure 100 describes the countries,
years, and methods to adjust FISIM
in the official national accounts. As
described, in instances where both
value added data are not available,
the trend of the FISIM share on GDP
is applied to extrapolate past estimates (although the impacts on
GDP are minor).
Figure 101 plots per capita GDP levels in 2014 and the FISIM share in
GDP in the 2000–2014 (including
both of the original estimates in theofficial national accounts and ourestimates). In countries where GDPs
are adjusted, the proportions bywhich author adjustments for FISIM
increases GDP stand at 0.6–1.2% for
Nepal, Brunei, and the Lao PDR andless than 0.4% GDP in others.125

1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
Bangladesh
Cambodia
Fiji
India
Indonesia
Iran
Japan
Lao PDR
Malaysia
Mongolia
Nepal
Pakistan
Philippines
Sri Lanka
Thailand
VietnamBhutanBrunei
MyanmarBahrain
KuwaitOman
Qatar
Saudi Arabia
UAE
Turkey

1970
1970
1970
1970

1950
1951

1980

1995
1993
1994

1977

2014
2003

1960
1959

2009
1990

1955

1979
1981 1985

2014

1970

1955

1999

1960

1988
1974

1964

2014

1960

2005

1946

1967
1959

1997

1972
1970

1951

2014
1989
1986

1955
1970
1970 1974

2004

1980

1999

1958

2014
2014

1990
1970

1975

2005
1982

1962

2014
2014
2014
2014
2014

1970
1970 19751970
1970 1975
1960

1997

Adjustment using value added of imputed bank service charge
Adjustment using value added of financial intermediation
Adjustment using the average trend of FISIM share in GDP

Figure 100 Adjustment of FISIM
Source: APO Productivity Database 2016.

% FISIM share in GDP (average in the 2000−2014)

3.5

Hong Kong
3.0©2016 Asian Productivity Organization

2) Software
The 2008 SNA recommends thecapitalization of intellectual property products (IPP), which changes
not only the size of GDP but also 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, 11 economies have capitalized all three
types of software. Another three
countries exclude own-accountsoftware in their capitalizationand in one country only custom
software is capitalized. For the APO
Productivity Database, tentative adjustments have been made to harmonize data to include all software.

2.5
Australia
India
Philippines
Fiji China

2.0

SingaporeJapan

Thailand

1.5

BhutanIndonesia

Malaysia
Pakistan
Nepal
Vietnam Turkey
1.0
Iran
Bangladesh Mongolia

US

Korea

BruneiROC

Lao PDR

0.5

Cambodia
Sri Lanka
Myanmar

0.0
0

10

20

Thousands of US dollars (as of 2014)

30

40

50

60

70

80

90

Per capita GDP in 2014 (using 2011 PPP, reference year 2014)

Offical national accounts in each country, including author adjustment
Our estimates using value added in financial intermediation
Our estimates using value added in imputed bank service charge

Figure 101 FISIM Share in GDP, 2000–2014
Sources: Official national accounts in each country and author estimates.

125: In this edition of the Databook, our estimates of FISIM are replaced to the official estimates for Bangladesh (revised from 0.5% to
0.9%, as the average share of FISIM on GDP during the period 2000–2014) and Indonesia (from 1.1% to 1.3%).

140

A.1 GDP Harmonization

Among the countries studied, thedata for software investment isavailable for the ROC, Japan, Korea,
Mongolia, the Philippines, Singapore, Thailand, and China. To harmonize data, a country’s GDP isadjusted to include software investment (through its software industry)by using the ratio between software
investment and GDP (software ratio) and the tangible GFCF to GDP
ratio (GFCF ratio). Data from theOECD Productivity Database andthe APO Productivity Database suggest an inverse relationship between these two ratios (Figure 102).
Countries with a low GFCF ratio
tend to be those with high per capita GDP, and the observed data suggest that IT tends to play a more
important role in these countries
than in less developed countries.

%

2.5

Software Investment/GDP

Sweden
2.0

US
Japan
Denmark
Finland
Korea
Hong Kong
France Switzerland New Zealand
UK
ROC
Netherlands
Canada1.5

1.0
Germany

Singapore
Austria
Italy

Australia
Spain

App.

Vietnam

0.5

Cambodia Fiji
Philippines Nepal
Pakistan Malaysia

0.0
13

18

Iran

Indonesia
Sri Lanka Ireland
India
Portugal
ThailandBangladesh Mongolia

23

28

China

33

38

43 %

Tangible GFCF/GDPCountries in which software is included
Countries in which software is excluded
Countries in which pre-package and own account software are excluded
Countries in which own account is excluded

Figure 102 Software Investment Ratio and GFCF Ratio toGDP, 2005
Sources: OECD Productivity Database and author estimates.

The Databook applies the inverse
relationship between these two ratios observed from the OECD countries and national accounts in Asian non-OECD countries to estimate the software ratio in 2006 forthose APO member economies that do not capitalize software investment. The estimated ratios forindividual countries in 2006 gradually taper off as one moves back in time. However, there is an exception. Countries at the very early stage of economic growth are found to have a GFCF ratio as low as
countries with high per capita GDP, but for a different reason. The low GFCF ratio is explained by thefact that these countries have not experienced economic development yet, and in turn this does not
imply an important role for software investment. In this report, Cambodia, the Lao PDR, and Nepal are
regarded as countries at the very early stage of economic development, and are assigned Vietnam’s
software ratio accordingly, which is the lowest of all APO member economies.
Another problem arises from partial software capitalization. There are three types of software: prepackaged software, custom software, and own-account software. Countries may have capitalized one
or two types of software, but software investment data is often not available separately. The Databook
attempt’s to adjust for the varied level of capitalization across countries by adding the type of software not capitalized to countries’ GDP.

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, 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,

141©2016 Asian Productivity Organization

3) Valuables

Appendix

accounting for 1.4% of GDP for India
on average during 1999–2014. The
current decision is to harmonize thedata by excluding net acquisition ofvaluables from GDP in this editionof the Databook.

4) Consumption of Fixed
Capital of Assets Owned byGovernment

1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
Bangladesh
Cambodia
Fiji
India
Indonesia
Iran
Japan
Lao PDR
Malaysia
Mongolia
Nepal
Pakistan
Philippines
Sri Lanka
Thailand
VietnamBhutanBrunei
China
MyanmarBahrain
KuwaitOman
Qatar
Saudi Arabia
UAE
Turkey

1995
1995
1993

1970
1970
1970
1950
1951

2002
2010
2003
2003
2000
2001

1958
1970
1959

2009

1955
2002
1996 2004
2004
1997

1981
1955
1960

1970
1964

2009

1974
1990

1960
1946 1950

1997
1996

1959
1990

1951

2002

1955

2011

1970

2002 2005
1970
1989
At the end of 2011, Thailand official2013
1952
1996
ly switched to the 1993 SNA, and its
1997
1952
1958
2003
1970
national accounts became compat1962
1997
1970
2011
ible with the 1993 framework for
1970
1970
2003
the first time. In this series, govern1970
2011
ment consumption includes the
1960
1970
1996
consumption of fixed capital (CFC)
Adjustment using R&D expenditure
Adjustment using the average trend of R&D share in GFCF
owned by the government since
Adjustment using the average trend of R&D share in GDP
1990. In order to construct the longtime-series data in the Databook seFigure 103 Adjustment of R&D
ries, the past data based on the
Source: APO Productivity Database 2016.
1968 SNA has been adjusted to beconsistent with the new series. In
the Databook, government capital stock and its CFC for the period 1970–1989 are estimated and thepast government consumption and GDP are adjusted accordingly. A similar adjustment on the CFC of
the assets owned by government was conducted for Bangladesh (for the period 1970–1995), Malaysia(1970–1999), and Mongolia (1970–2004).

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.126
Figure 103 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 101 plots the per capita GDP and the R&D investment share in GDP in 2014. The impacts
on GDP by our adjustment of the additional R&D investment are less than 1.0% of GDP for all countries
in 2014.

©2016 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’
126: In the case of Japan, in which the official estimates of the R&D investment data are not published yet, the R&D investment series
are developed based on the related expenditures in Survey on the Research and Development by the Statistics Bureau of Japan,
covering the period of 1952–2014 by Nomura.

142

A.1 GDP Harmonization

prices), but this is not true for
GDP at factor cost and GDP
at basic prices. International
comparisons in Chapter 3 (oneconomic scale and growth)and Chapter 4 (on final demand) are based on GDP atmarket prices. However, by
valuing output and input at
the prices that producers actually pay and receive, GDP atbasic prices is a more appropriate measure of countries’
output than GDP at market
prices for international comparisons of TFP and industry
performance, as it is a mea­sure from the producers’ perspective. Hence, Chapter 5
on whole-economy productivity performance is based on
GDP at basic prices, including
our estimates.

% R&D investment share in GDP, 2014

4.5

Korea

4.0
Japan

3.5

ROC

3.0
USA

2.5
Australia
2.0

Singapore

China

App.

1.5
MyanmarThailand Turkey
1.0
BhutanPhilippines
Malaysia
Hong Kong
India
Pakistan
Iran
0.5
Mongolia
Nepal
Thousands of US dollars (as of 2014)
Sri Lanka
Cambodia
Indonesia
Brunei0.0
FijiBangladesh 0
10
20
30
40
50
60
70
80
90
Per capita GDP in 2014 (using 2011 PPP, reference year 2014)
Lao PDR Vietnam
Offical estimates of R&D investment
Our estimates based on R&D expenditure, including author adjustments
Our estimates using the average trend of R&D share in GFCF
Our estimates using the average trend of R&D share in GDP

Figure 104 R&D Share in GDP, 2014
Sources: Official national accounts; Surveys on R&D in each country; World Bank, World

Readers should bear these caveats in mind in interpreting the results in Chapter 6, since the definition
of GDP by industry differs among countries due to data availability. GDP is valued at factor cost for Fiji,and Pakistan, at basic prices for Cambodia, Hong Kong, India, Korea, the Lao PDR, Mongolia, Nepal,
and Singapore, at producers’ prices for Iran, the ROC, and the Philippines, and at market prices for
Bangladesh, Indonesia, Japan, Malaysia, Sri Lanka, Thailand, and Vietnam. In this sense, APO industry
data should be treated as a work in progress as it is difficult to advise on data uncertainty. These issues
will be developed and examined in the future.

143©2016 Asian Productivity Organization

Development Indicators 2015, including author adjustments.
These concepts of GDP differ
in the treatment of indirect tax
and subsidies (and import duties). The difference between GDP at basic prices and GDP at market prices is “taxes on products” minus “subsidies on products.” “Taxes on products” are the indirect taxes payable on goods and services
mainly when they are produced, sold, and imported, and “subsidies on products” are subsidies payable on goods and services mainly when they are produced, sold, and imported. Since GDP at basic
prices is available for some economies, such as Hong Kong, India, Korea, Mongolia, Nepal, Singapore,and Sri Lanka, a GDP at basic prices calculation, needs to be constructed for all other countries. In
order to obtain 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.

Appendix

A.2

Capital Stock
Table 20 Asset Classification andParameters in Hyperbolic Function

At present, half of APO member economies publish estimates of capital stocks in their systems of national
accounts. Even where estimates are available, users must
be mindful of differences in methodologies and assumptions used to estimate capital stock, as well as a large diversity in the treatment of quality adjustment in pricestatistics among countries. In the APO Productivity Database2016, a harmonized methodology has been applied inestimating capital stock and capital services, covering
20 Asian economies: Bangladesh, Cambodia, China, theROC, Fiji, Hong Kong, India, Indonesia, Iran, Japan, Korea,
Malaysia, Mongolia, Nepal, Pakistan, the Philippines, Singapore, Sri Lanka, Thailand, and Vietnam, and the US as areference country.

©2016 Asian Productivity Organization

Quality changes in the aggregate measure ofcapital input can originate from two kinds ofsources, namely the composition change bytype of asset, and the quality change in eachtype of asset. To take the composition changeof assets into account, the current databaseclassifies 11 types of assets (shown in Table
20). For countries in which detailed investment data is not available from national accounts, the 11 types of investment data are
estimated based on the benchmark input–
output tables and our estimates of the commodity flow data of domestic production andexport/import of assets. The input-output tables and supply and use tables are listed inTable 21. The starting years for estimating
capital stock based on the perpetual inventory method is 1901 for the US, 1951 for the
ROC, 1952 for China, 1953 for Korea, 1955 forJapan, 1960 for Singapore, 1961 for Hong
Kong, and 1970 for other countries.

Tβ

1. IT hardware

7

0.50

2. Communications equipment

15

0.50

3. Transportation equipment

15

0.50

4. Other machinery and equipment

15

0.50

5. Residential buildings

30

0.75

6. Non-residential buildings

30

0.75

7. Other construction

40

0.75

8. Cultivated assets

10

0.50

9. R&D100.50

10. Computer software

3

0.50

11. Other intangible assets

7

0.50

Source: APO Productivity Database 2016.

Table 21 Input-Output Tables and Supply and
Use Tables
Input-Output Tables and Supply and Use Tables
Bangladesh 1981/1982, 1993/1994, 2000
Benchmark (1981, 1984, 1986, 1989, 1991, 1994, 1996, 1999,
ROC
2001, 2004, 2006, 2011) Annual (2006–2014)
Fiji
1972, 1982, 2005
India

1993/1994, 1998/1999, 2003/2004, 2006/2007, 2007/2008

Indonesia

1971, 1975, 1980, 1985, 1990, 1995, 2000, 2005

Iran

1999, 2001

Japan

1960, 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005, 2011
Benchmark (1960, 1963, 1966, 1970, 1975, 1980, 1985, 1990,

Korea

1995, 2000, 2005, 2010) Updated (1973, 1978, 1983, 1986–1988,

Malaysia

1993, 1998, 2003, 2006–2013)
1978, 1983, 1987, 1991, 2000, 2005, 2010

Mongolia

1970, 1977, 1983, 1987, 2000, 2005, 2010

Pakistan

1975/1976, 1984/1985, 1989/1990, 1990/1991, 1999/2000

Philippines 1961, 1965, 1969, 1974, 1979, 1985, 1988, 1994, 2000, 2006
Singapore

1973, 1978, 1983, 1988, 2000, 2005, 2007, 2010

Sri Lanka

2006

Thailand

1975, 1980, 1985, 1990, 1995, 1998, 2000, 2005

Vietnam

1996, 2000, 2007

China

1987, 1992, 1997, 2002, 2007

Brunei2005, 2010

Turkey1973, 1979, 1985, 1990, 1996, 1998, 2002

It is well known that prices of constant-quality IT capital have been falling rapidly. For cross-country
comparisons, it has been noted that there is great diversity in the treatment of quality adjustment inprice statistics among countries. Cross-country comparisons will be significantly biased if some countries adjust their deflators for quality change while others do not. Price harmonization is sometimes
used in an attempt to control for methodological differences in the compilation of price indexes, under the assumption that individual countries’ price data fails to capture quality improvements. Assuming that the relative price of IT to non-IT capital in the countries compared is set equal to the IT to
X
non-IT prices relative in the reference country, the harmonized price is formulated as: ∆ ln P̃ITX = ∆ ln PnlT
refref+ (∆ ln PIT − ∆ ln PnlT ), where the superscript X denotes the country included in the comparisons, PIT isthe price of IT capital, and PnIT is the price of non-IT capital. The price of IT capital in country X, P̃ITX , is144

A.2 Capital Stock

GDP=1.0

4.5

3.7

3.1

2.9

2.5

3.1

3.0

3.0

2.3

2.8 2.7

2.7

2.6

2.6

2.6

2.2

2.4 2.4

2.3

2.3

2.3
1.9

2.2 2.3 2.2

2.2
1.9

1.8

2.0

1.7

1.7

Cambodia

1.9

Pakistan

1.7

Sri Lanka

2.1

India

1.6

1.8

Malaysia

2.1

1.7

1.5

2.7

Fiji

2.0

2014

3.5

3.5
3.0

1980

4.0

4.0

1.3

1.2

1.0
0.5
0.0
Philippines

VietnamUS

Mongolia

Bangladesh

Singapor

Hong Kong

ROC

Thailand

Iran

Nepal

China

Indonesia

Korea

Japan

computed by the observed
refin the refprices PITref and PnlTX
in X.
erence country and PnlT
Schreyer (2002) and Schreyer, Bignon, and Dupont(2003) applied price harmonization to OECD capital
services, with the US as areference country, since thepossible error due to using aharmonized price index
would be smaller than thebias arising from comparing
capital services based onnational deflators.

App.

Figure 105 Capital-Output Ratio, 1980 and 2014___Ratio of the beginning-of-period net capital stock to basic-price GDP atcurrent prices

Source: APO Productivity Database 2016.

In this Databook, the same
Note: The estimate for Cambodia is not available for 1980.
price harmonization method is applied to adjust thequality 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 applied in cases where the prices for some assets were not available, to estimate missing data based on the relative price of these assets to total GFCF. In measuring capital services, thisDatabook largely follows the framework of the OECD Productivity Database.127 The OECD assumes thetruncated 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 until T) when β=1 and linear when β=0. These two parameters are set, asshown in Table 20. The estimates of productive capital stock by type of asset are used in measuring
capital services (see Appendix 3).

127: See OECD (2016a) 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.

145©2016 Asian Productivity Organization

Figure 105 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 3.7. 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 Iran, Mongolia, Pakistan,and the Philippines have an increasing trend of capital-output ratio, unlike the ratio in the US, whichis stable.

Appendix

Rate of Return and Capital Services

A.3

In the analysis of production and productivity, capital service provides an appropriate concept ofcapital as a factor of production. The fundamental assumption in measuring capital services is proportionality between the (productive) capital stock and capital services in each type of asset. Thus, thegrowth rates of capital services can differ from that of capital stock only at the aggregate level. For
aggregating different types of capital, the user costs of capital by type of asset should be estimated.
This Appendix outlines the methodology of the user cost of capital estimation and presents the estimated results of endogenous rate of return for Asian countries in the APO Productivity Database 2016.
%
50

Bangladesh%
50

Cambodia

%
50

China

%
50

ROC

%
50

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

%
50

Indonesia

%
50

Iran

%
50

Japan

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

Korea

%
50

Malaysia%
50

Mongolia

%
50

Nepal

%
50

Pakistan

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

Philippines%
50

Singapore%50

Sri Lanka%
50

Thailand%60

Vietnam

50

40

40

40

40

30

30

30

30

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

40
30

%
50

US

40
30

©2016 Asian Productivity Organization20
10
01970 1980 1990 2000 2010

Figure 106 Ex-Post Real Rate of Return in Asia, 1970–2014
Source: APO Productivity Database 2016.

146

20

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 defined
k
k
k
k
k
k
as qt−1,0 {rt + (1 + π t ) P,t,0 − π t }, where rt, P,t,0, and qt,0 are the expected nominal rate of return, crosssection 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 20 Asian
countries and the US are shown in Figure 106. Although there are large fluctuations in countries likeThailand, Mongolia, and Vietnam, many Asian countries may exhibit decreasing trends in the (endogenous) real rate of return, while the US holds a stable rate of around 10%. Table 22 presents the fiveyear averages of the estimated rates for ex-post real rate of return during 1970–2014. In 2010–2014,
the real rate of return ranged from 4.1% for Japan and 6.8% for Korea to 21.7% in Malaysia and 37.3%
for Cambodia. Using these ex-post estimates, the aggregate capital services are measured in this report. The difference caused by the ex-ante and ex-post approaches may provide a modest differencein the growth measure of capital services, regardless of the substantial differences in the rates of return and capital compensations (Nomura, 2004).
Table 22 Average Ex-Post Real Rate of Return in Asia
19.1

18.8

15.9

23.5

21.7
30.3
15.9
20.5
9.8
37.8
32.5
13.9
35.8
32.6
13.0
31.6
26.3
17.6
23.8
22.4
22.4
8.0
10.9

16.5
20.2
16.1
24.2
14.6
40.4
27.6
6.5
28.4
32.5
11.0
21.3
24.4
19.4
17.9
32.9
17.3
14.4
9.3

15.2
14.4
8.4
17.6
10.7
41.3
14.3
5.8
15.6
20.9
10.6
19.4
24.5
15.3
15.2
16.9
10.1
35.1
7.0

11.8
18.8
4.8
22.5
9.9
23.0
11.1
8.1
18.0
17.2
13.3
22.1
33.8
14.1
13.2
12.0
13.6
18.4
8.4

25.2
22.8
14.7
15.8
17.1
13.7
8.3
20.2
23.9
6.4
14.4
17.6
−4.0
19.1
27.1
12.9
14.7
10.2
13.1
41.4
7.9

21.5
36.5
9.9
14.1
10.1
11.7
7.8
22.5
15.9
4.2
10.5
17.4
23.9
13.0
22.1
16.9
12.2
8.7
10.5
30.2
10.0

20.6
30.8
12.5
15.9
9.4
10.1
11.4
21.2
27.9
4.6
9.7
18.3
2.9
17.6
27.1
22.5
10.7
10.9
9.9
27.1
10.9

18.8
22.7
14.4
14.2
5.5
13.8
12.0
24.7
32.0
5.1
8.7
22.3
14.0
19.4
24.5
20.1
16.7
14.3
11.2
17.4
10.1

19.2
37.3
8.6
10.5
2.1
10.2
3.6
14.3
21.7
4.1
6.8
21.7
14.8
13.2
24.9
18.6
13.5
19.3
11.4
15.8
9.5

Unit : Percentage
Source: APO Productivity Database 2016.
Note: The starting year is 1993 for Cambodia.

147©2016 Asian Productivity Organization

1970–1974 1975–1979 1980–1984 1985–1989 1990–1994 1995–1999 2000–2004 2005–2009 2010–2014
Bangladesh
Cambodia
China
ROC
Fiji
Hong Kong
India
Indonesia
Iran
Japan
Korea
Malaysia
Mongolia
Nepal
Pakistan
Philippines
Singapore
Sri Lanka
Thailand
VietnamUSAppendix

A.4

Hours Worked

Labor volume can be measured in three units: number of persons in employment, number of filledjobs, 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 large variety ofdata sources, definitions, and methodologies available in estimating these labor market variables often leads to a fragmentation of labor market statistics of an individual country concerned, dubious
data quality, and incomparability across countries. Here follows an attempt to outline some of these
intricate measuring issues.

©2016 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 oferrors originates from proxy response, which relies on the proxy’s perception and knowledge of another household’s member. A high level of proxy responses could, therefore, reduce the reliability ofdata collected.

148

A.4 Hours Worked

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 so as to distinguish 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.

149©2016 Asian Productivity Organization

The APO Productivity Database 2016 defines labor inputs as the simple sum of hours worked. Hours
worked are defined as the economywide hours worked by employees,
the self-employed, and contributing
Table 23 Sources of Labor Data
family workers. Japanese and US’s
Sources of Labor Datanational accounts publish estimates
Bangladesh Population and Housing Census, Labour Force Surveyof the total hours worked, as recomPopulation Census, Labor Force Survey, Labour Market Information Bulletin,
BhutanADB Key Indicators for Asia and the Pacificmended by the SNA. Other Asian
General Population Census, Inter-Censal Population Survey, Labor Force Survey,
Cambodiacountries do not publish hours
Socio-Economic Survey
China Statistical Yearbook, China Labor Statistical Yearbook, Population Census,
Chinaworked in their national accounts.
1% National Population Sample Survey
Population and Housing Census, Yearbook of Manpower Survey Statistics in
For these countries the procedure of
ROC
Taiwan Area, Manpower Utilization Survey
constructing economy-wide annual
Census of Population and Housing, Employment and Unemployment Survey,
Fiji
Annual Employment Survey
hours worked consists of two steps;
Population Census, Population By-Census, General Household Survey, Annual
for many Asian countries first, an avHong Kong Earnings and Hours Survey, Wage Survey, Women and Men in Hong Kong Key
Statistics
erage weekly hours worked is obIndia
Census of India, Employment and Unemployment Survey
tained and the number of workers
Indonesia
Population and Housing Census, Labor Force Situation in Indonesia
collated from official statistics, such
IranNational Population and Housing Census, Labour Force Survey
Japan
Labor Force Survey, National Accounts
as LFS. The data used in this edition
Population and Housing Census, Economically Active Population Survey,
is listed in Table 23. Some countries
Korea
Employment Structure Survey, Monthly Labor Survey, Survey Report on Wage
Structure
do not fully provide the time-series
Lao PDR
Population Census, ADB Key Indicators for Asia and the Pacificdata of average weekly hours
Population and Housing Census, Labour Force Survey, Economic Report
Malaysia
worked. This edition of the DataVarious issues, Malaysia Economic Statistics-Time Series
Population and Housing Census, Labour Force Survey, Mongolian Statisticalbook considered the changes in the
Mongolia
Yearbook
composition of workforces (deterNepal
Population and Housing Census, Labor Force Survey
Population Census, Labor Force Survey, Pakistan Statistical Yearbook, Pakistan
mined by four kinds of attributes;
Pakistan
Economic Survey
gender, education, age, and status
Philippines Labor Force Survey, Yearbook of Labor Statistics
Population Census, Labor Force Survey, Singapore Yearbook of Manpowerof employment) in the estimation
Singapore
Statistics, General Household Survey
process of the time-series average
Population and Housing Census, Labor Force Survey, Central Bank of Sri Lanka
Sri Lanka
Annual Report
hours, although the previous ediThailand
Population and Housing Census, Labor Force Survey
tions have used a linear interpolaPopulation and Housing Census, Labor Force and Employment Survey, Statistical
Vietnam
Yearbooktion or a fixed average hour for theApp.

Appendix

periods in which the data is not available without considering the changes in the composition.128 Multiplying the average hours workedby the number of workers gives economy-wide
averages of weekly hours worked. Second, thenumber of weeks worked is obtained, by counting the number of national holidays in eachcountry as a crude approximation. Multiplying
economy-wide average weekly hours workedby the number of weeks worked gives economywide annual hours worked. For the Lao PDR
total hours worked are not estimated due todata constraints.

Iran
Cambodia
Thailand
Korea
Singapore
Malaysia
Hong Kong
Bangladesh
India
Vietnam
China
Pakistan
Philippines
ROC
Nepal
Indonesia
Sri Lanka
TurkeyMongolia
Fiji
Japan
Australia
EU15 −3

47
42
41
40
40
38
38
35
33
32
29
28
25
22
22
19
17
14
13
10
7
3

©2016 Asian Productivity Organization

Figure 107 presents a cross-country comparison−50
5 10 15 20 25 30 35 40 45 50 %of average annual hours worked per worker for
2000–2014, relative to the level of the US. It indiFigure 107 Average Annual Hours Worked Per
cates that workers in Asian countries tend toWorker Relative to the US, 2000–2014
work much longer hours than those in the US
Sources: Official national accounts and labor force survey in eachand Europe. In many of the countries sampled,country, including author adjustments.
the difference in annual hours worked per person relative to the US is more than 20% of the
US level. Prolonged working hours are observed in Asian 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 to work much
shorter hours than those in other Asian countries. However, compared with the EU15, hours worked
by workers in Japan are still about 10% longer.

128: The project is developing the QALI database covering the Asian countries. The first set of the QALI estimates is planned to becompleted in 2016 and 2017.

150

A.5 Other Data

A.5

Other Data

For China, multiple data sources have been used; GDP for the whole economy, industry GDP, final
demands, employment, and income data are taken from China Statistical Yearbook and China National
Income 1952–1995; time-series data of GFCF during 1952–2014 at current and constant prices are constructed at KEO; the main references for GFCF construction are drawn from Statistics on Investment in
Fixed Assets of China 1950–2000, China Statistical Yearbook, and 1987, 1992, 1997, 2002, and 2007 Input–
Output Tables of China; and multiple data sources for manufacturing, electrics, and trade data from
China’s Customs Statistics are also utilized.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.

151©2016 Asian Productivity Organization

Tax data of member economies are supplemented by the IMF’s Government Finance Statistics. From
its tax revenue data, “taxes on goods and services” and “taxes on imports” are used for calculatingtaxes on products. From its expenditure data, “subsidies” are taken. Data taken from Government Finance Statistics play a key role in adjusting GDP at market prices to GDP at basic prices. The data forenergy consumptions and CO2 emissions is based on IEA’s CO2 Emissions from Fuel Combustion, Energy
Balances of OECD Countries, and Energy Balances of non-OECD Countries.

Appendix

A.6

Industry Classification

Cambodia, Iran, the Lao PDR, Malaysia, Nepal, the Philippines, Sri Lanka, and China use the International Standard Industry Classification of All Economic Activities (ISIC) Rev.3. Other Asian economies
already have switched to the ISIC Rev.4. The concordances between the industry classification used in
the Databook and the ISIC Rev.3 and Rev.4 are shown in Tables 24 and 25, respectively.
Table 24 Industry Classification – Concordance with ISIC Rev.3
ISIC Rev. 3
SectionDivision

Databook
1st 2ndA - Agriculture, hunting, and forestry

01
02

Agriculture, hunting, and related service activities
Forestry, logging, and related service activities

1
1

B - Fishing05

Fishing, operation of fish hatcheries, and fish farms; service activities incidental to fishing

1

C - Mining and quarrying

10
11

2
2

21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
40
41

Mining of coal and lignite; extraction of peat
Extraction of crude petroleum and natural gas; service activities
incidental to oil and gas extraction excluding surveying
Mining of uranium and thorium ores
Mining of metal ores
Other mining and quarrying
Manufacture of food products and beverages
Manufacture of tobacco products
Manufacture of textiles
Manufacture of wearing apparel; dressing and dyeing of fur
Tanning and dressing of leather; manufacture of luggage, handbags, saddlery, harness, and footwear
Manufacture of wood and of products of wood and cork, except furniture; manufacture of articles ofstraw and plaiting materials
Manufacture of paper and paper products
Publishing, printing, and reproduction of recorded media
Manufacture of coke, refined petroleum products, and nuclear fuelManufacture of chemicals and chemical products
Manufacture of rubber and plastics products
Manufacture of other non-metallic mineral products
Manufacture of basic metals
Manufacture of fabricated metal products, except machinery and equipment
Manufacture of machinery and equipment n.e.c.
Manufacture of office, accounting, and computing machinery
Manufacture of electrical machinery and apparatus n.e.c.
Manufacture of radio, television, and communication equipment and apparatus
Manufacture of medical, precision, and optical instruments, watches, and clocks
Manufacture of motor vehicles, trailers, and semi-trailers
Manufacture of other transport equipment
Manufacture of furniture; manufacturing n.e.c.
Recycling
Electricity, gas, steam, and hot water supply
Collection, purification, and distribution of water

F - Construction

45

Construction

5

G - Wholesale and retail trade; repair ofmotor vehicles, motorcycles, andpersonal and household goods

50
51
52

Sale, maintenance, and repair of motor vehicles and motorcycles; retail sale of automotive fuelWholesale trade and commission trade, except of motor vehicles and motorcycles
Retail trade, except of motor vehicles and motorcycles; repair of personal and household goods

666

H - Hotels and restaurants

55

Hotels and restaurants

6

I - Transport, storage, andcommunications

60
61
62
63
64

Land transport; transport via pipelines
Water transport
Air transport
Supporting and auxiliary transport activities; activities of travel agencies
Post and telecommunications

7
7
7
7
7

J - Financial intermediation

65
66
67

Financial intermediation, except insurance and pension funding
Insurance and pension funding, except compulsory social security
Activities auxiliary to financial intermediation

88
8

K - Real estate, renting, and businessactivities

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 - Public 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

O - Other community, social, andpersonal 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 - Private households with employedpersons

95

Private households with employed persons

9Q - Extra-territorial organizations andbodies99

Extra-territorial organizations and bodies

9

12
13
14
D - Manufacturing©2016 Asian Productivity Organization

E - Electricity, gas, and water supply

15
16
17
18
19
20

Note: “n.e.c.” represents “not elsewhere classified.”

152

2
2
2
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
4
4

3.1
3.1
3.2
3.2
3.2
3.3
3.4
3.4
3.5
3.5
3.5
3.6
3.7
3.8
3.8
3.8
3.8
3.8
3.8
3.8
3.8
3.9
3.9

A.6 Industry Classification

Table 25 Industry Classification – Concordance with ISIC Rev.4
Division

Databook
1st2nd(a) (b)A - Agriculture, forestry, and fishing1
2
3

Crop and animal production, hunting, and related service activities
Forestry and logging
Fishing and aquaculture

1

1
1
1

B - Mining and quarrying

5
6
7
8
9

Mining of coal and lignite
Extraction of crude petroleum and natural gas
Mining of metal ores
Other mining and quarrying
Mining support service activities

2

2
2
2
2
2

C - Manufacturing

10
11
12
13
14
15
16

3

3
3
3
3
3
3
3

3.1
3.1
3.1
3.2
3.2
3.2
3.3

17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33

Manufacture of food products
Manufacture of beverages
Manufacture of tobacco products
Manufacture of textiles
Manufacture of wearing apparel
Manufacture of leather and related products
Manufacture of wood and of products of wood and cork, except furniture; manufacture of articles ofstraw and plaiting materials
Manufacture of paper and paper products
Printing and reproduction of recorded media
Manufacture of coke and refined petroleum products
Manufacture of chemicals and chemical products
Manufacture of basic pharmaceutical products and pharmaceutical preparations
Manufacture of rubber and plastics products
Manufacture of other non-metallic mineral products
Manufacture of basic metals
Manufacture of fabricated metal products, except machinery and equipment
Manufacture of computer, electronic and optical products
Manufacture of electrical equipment
Manufacture of machinery and equipment n.e.c.
Manufacture of motor vehicles, trailers and semi-trailers
Manufacture of other transport equipment
Manufacture of furniture
Other manufacturing
Repair and installation of machinery and equipment

3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3

3.4
3.4
3.5
3.5
3.5
3.5
3.6
3.7
3.8
3.8
3.8
3.8
3.8
3.8
3.8
3.9
3.9

D - Electricity, 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

I - Accommodation and food service
activities

55
56

Accommodation
Food and beverage service activities

6J - Information 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 - Administrative 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

99Q - Human 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

999

U - Activities 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.

153

App.

©2016 Asian Productivity Organization

ISIC Rev. 4
SectionAppendix

A.7

Data Publication and Visualization©2016 Asian Productivity Organization

The productivity data used in this Databook is based on the APO Productivity Database 2016, which
provides the annual productivity accounts covering Asian countries for the period 1970–2014. 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 until last year. This project attempted to renew and upgrade the AQGM, by expanding its scope on data visualization, and newly
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 2016.

154

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