KEO Discussion Paper No. 114

A Survey of National Accounts in Asia
for Cross-country Productivity Comparisons

Koji Nomura,
Eunice Y. M. Lau
andHideyuki Mizobuchi
Keio Economic Observatory, Keio University
November 2008

Abstract
This paper collates country practices on compilation of national accounts in 18 Asian countries, basedon the metadata gathered from a survey conducted by the Asian Productivity Organization (APO) in
joint research with Keio Economic Observatory (KEO), Keio University, between April and July 2008.
The paper was prepared as a first step towards the construction of an international database for
productivity comparisons among Asian countries.
Our observations are summed up as follows. First, although most countries are 1993 SNA
compliant, the extent of compliance in terms of coverage still varies. The treatment of FISIM andsoftware is far from standard in this country group, and methods to align GDP coverage need beconsidered for any cross-country comparisons. Second, employment data are more fragmented andcountries are more likely to have a manufacturing census than a census on services. Third, mostcountries have detailed benchmark supply-use/input-output tables which can be used to supplement
their national accounts. Our judgment is that GDP per worker as a labor productivity volume should
be feasible for this country group, whereas GDP per hour worked will be more of a challenge due todata limitations. Industry comparative analysis of labor productivity should be feasible; but comparing
total factor productivity performance, which requires capital stocks and services estimates, is feasible
for only a handful of countries at the whole-economy level.

Contents
1

Introduction ......................................................................................................................... 1

2

System of National Accounts .............................................................................................. 3

2.1 1993 SNA Compliance ....................................................................................................... 3
2.2 Coverage of GDP ................................................................................................................ 6
2.3 Industry and Institutional Sectors ..................................................................................... 10
2.4 Valuation, Estimation and Aggregation Methods of GDP ................................................ 12
2.5 GFCF and Capital Stock ................................................................................................... 15
2.6 Labor ................................................................................................................................. 18
3

Non-NA Statistics ............................................................................................................. 21

3.1 Benchmark Input-Output Table......................................................................................... 21
3.2 Population Census............................................................................................................. 23
3.3 Business Survey ................................................................................................................ 23
3.4 CPI and PPI ....................................................................................................................... 25
3.5 Productivity Statistics ....................................................................................................... 26
4

Conclusion ........................................................................................................................ 27

Appendix 2: IMF Data Quality Assessment for Asian Countries ............................................ 29
References ............................................................................................................................... 33
Tables and Figures
Table 1: 1968 and 1993 SNA Compliance of Annual National Accounts ................................. 4
Table 2: Coverage of GDP......................................................................................................... 7
Table 3: Level of Industry Detail ............................................................................................. 11
Table 4: GDP Estimates ........................................................................................................... 13
Table 5: GFCF and Capital Stock ............................................................................................ 17
Table 6: Number of Jobs/Persons and Hours Worked in the National Accounts ..................... 20
Table 7: Labor Statistics .......................................................................................................... 21
Table 8: Benchmark Input-Output Table ................................................................................. 22
Table 9: Population Census ..................................................................................................... 23
Table 10: Survey of Manufacturing ......................................................................................... 24
Table 11: CPI and PPI.............................................................................................................. 25
Table 12: Productivity Statistics .............................................................................................. 26
Table 13: Quarterly National Accounts ................................................................................... 28
Figure 1: Implementation of 1968 SNA .................................................................................... 5
Figure 2: Implementation of 1993 SNA .................................................................................... 6
Figure 3: Comparison of the Impact of FISIM Inclusion on GDP ............................................ 8
Figure 4: Comparison of Imputed Services of Owner-Occupied Dwellings ............................. 8
Figure 5: Comparison of Fixed-based and Chain-linked Real GDP in Japan ......................... 15
Figure 6: IMF Assessment of Six Datasets for Asian Countries ............................................. 31
Figure 7: IMF Assessment of Data Quality for Asian Countries ............................................. 32

A Survey of National Accounts in Asia for Cross-country Productivity Comparisons
Koji Nomura, Eunice Y. M. Lau and Hideyuki Mizobuchi †November 2008
1

Introduction

Understanding data comparability is essential for the construction of an international database.
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, but country data
can deviate from international best practice and vary in terms of omissions and coverageachieved. Last but not least, countries can also vary in their estimation methodology andassumptions, which may account for part of the differences we observe in the data and interferewith comparisons of countries’ underlying economic performance. The metadata of data series
therefore help illuminate these data inconsistencies and their potential impact on international
comparisons, and highlight areas where adjustments may be needed.
The Asian Productivity Organization (APO) aspires to create an international productivity
database of its 20 member countries, with non-member countries in Asia like the People’s
Republic of China serving as the reference countries. In September 2007 APO launched theProductivity Database Project as a joint research effort with Keio Economic Observatory (KEO),
Keio University, Tokyo. In this project, we constructed an APO questionnaire on national†

This study was implemented as a part of the APO Productivity Database Project, a joint project of
APO and KEO, Keio University. The authors thank Mukesh D. Bhattarai and Yasuko Asano (Researchand Planning Department, APO) for their support and Soyuen Myung (Graduate School of Keio
University) for her assistance. We would like to give special thanks to the national experts in our
project, who provided the metadata information on the System of National Accounts and otherstatistics for the APO member countries. The details of data comparability were discussed at acoordination meeting held in Dhaka, Bangladesh, on 20–23 May 2008, and examined at Keio
University after the meeting. The national experts who provided metadata information are:
Sabila Khatun, Bangladesh Bureau of Statistics
Keo Chettra, National Institute of Statistics, Ministry of Planning, Cambodia
Jia-yuan Mei, Bureau of Statistics, Directorate-General of Budget, Accounting and Statistics, ROC
Nilima Usharani Lal, Fiji Islands Bureau of Statistics
Kolathupadavil Philipose Sunny, National Productivity Council, India
Wachyu Winarsih, Indonesian Statistic/Analysis and Development Statistic Directorate
Hamid Azarmand, Central Bank of Islamic Republic of Iran
Geonwoo Lee, Korea Institute for Industrial Economics and Trade
Salika Chanthalavong, Department of Statistics, Ministry of Planning and Investment, Lao PDR
Syahron Helmy Binti Abdullah Halim, Department of Statistics, Malaysia
Bibish Oyunsuren, National Statistics Office, Mongolia
Rajesh Dhital, Central Bureau of Statistics, Nepal
Noor Shahid, Federal Bureau of Statistics, Pakistan
Elsie B. Solidum, National Statistics Office, the Philippines
Patabendige Gunasena Jayasooriya, Central Bank of Sri Lanka
Wannapa Khlaisuan, National Economic and Social Development Board, Thailand
Nguyen Thi Viet Hong,General Statistics Office, Vietnam.
1

accounts and conducted a survey on the national accounts and other statistical data required forinternational comparisons of productivity among the APO member countries between April andJuly 2008 (hereafter the APO NA Survey 2008).
The aim of this paper is to present the first results of these metadata gathered from the survey ofthe APO member countries and to draw preliminary implications for international comparisons of
productivity, which should be useful to general users. To ensure quality, country responses to thequestionnaire have been cross-checked with other national and international references. Any
inconsistencies have been followed up with the national experts involved. The results presented
in this paper are judged to be reasonably reliable.
There are some international studies on metadata of countries’ national accounts. For example,
the Asian Development Bank (ADB) and UN Economic and Social Commission for Asia and thePacific (ESCAP) organized workshops for linking and rebasing national accounts data in the Asia
Pacific region in 2000 and 2001. The OECD, ADB and ESCAP also organized a meeting for
improving quarterly gross domestic product in Asia in 2001. Conference volumes such as ADB(2002) and OECD (2001) provide useful information on Asian countries’ national accounts.
Furthermore, the IMF's Dissemination Standards Bulletin Board administers the Special Data
Dissemination Standard (SDDS) and the General Data Dissemination System (GDDS) sites. In
their websites, metadata information on official statistics of the countries participating in SDDS
or GDDS is provided. 1 These references are used to supplement official documents andpublications provided by national statistical agencies, which give more detailed information on
country practices, constraints and limitations.
The main observations from the APO NA Survey 2008 are as follows.
1. Most APO member countries are 1993 SNA compliant; the extent of compliance in terms ofcoverage may still vary, and the challenge is in splicing series to form a long, consistent timeseries.
2. The treatment of FISIM (financial intermediation services indirectly measured) is still less thanstandard in Asian countries.
3. In comparing the coverage of GDP, most Asian countries make provisions to include
agricultural production by households for their own final consumption.
4. Most countries have detailed industry and commodity data from their supply-use

1

See SDDS’s website (http://dsbb.imf.org/Applications/web/sddshome) and GDDS’s website (linked
from the SDDS website). SDDS was established in 1996 to guide countries that have access tointernational capital markets in disseminating economic and financial data to the public. GDDS wasestablished in 1997 to guide countries in the provision to the public of comprehensive, timely,accessible and reliable economic, financial and socio-demographic data. Its site provides information
on data produced and disseminated by member countries that participate in GDDS. The appendix of
this paper provides a summary assessment by the IMF’s Reports on the Observance of Standards andCodes (ROSC) based on the Data Quality Assessment Framework (DQAF) for Asian countries.
2

tables/input-output tables (SUT/IOT). Comprehensive data on institutional units are not as
readily available, however.
5. Most countries based their GDP estimates on the production side, and fixed-based Laspeyres isstill the most common aggregation method used.
6. Within the national accounts, GFCF (Gross Fixed Capital Formation) by asset type is available
for the whole economy but not for industry or institutional sectors. Only a handful of countries
offer GFCF by industry in the national accounts. 2
7. Countries which have capitalized software are in the minority. Price indices with quality
adjustment are also not the common practice in the countries surveyed.
8. Labor volume is denominated in number of persons worked rather than jobs in nearly all thecountries surveyed. Gross value added (GVA) per person should therefore be feasible for all
countries. GVA per hour worked is likely to be available for only a sub-set of the countries,
however.
9. Most countries have a comprehensive survey for manufacturing but not for the service sector,
confirming the discrepancy of data quality and availability between manufacturing and services.
10. All countries have a population census, which can be a potential source of socio-economic
data needed to measure labor quality.
The rest of the paper is divided into three sections. Section 2 presents information from ourmetadata survey on the Systems of National Accounts in Asian countries, focusing on statistics
required for productivity measurement. Section 3 covers other related statistics, e.g. benchmark
supply-use and input-output tables, population censuses, price statistics and so on, some of which
are not directly used for productivity calculations at this stage but are useful as backgroundinformation to help judge data quality. Section 4 concludes.
2

System of National Accounts

2.1

1993 SNA Compliance

The current international standard for the framework of compiling country national accounts isprovided in 1993 SNA recommended by the United Nations (1993). Since there are differences
between the 1993 SNA and its predecessor (1968 SNA) in some concepts and coverage, it isimportant to know in which year in the data series definitions and classification started to switch
over, so as to identify breaks in time series. Countries can differ in their year of implementation,
the extent of compliance and backward estimates available.

2

Note that, in countries’ benchmark SUT/IOTs, estimates by more detailed types of asset areavailable (based on product classification).
3

Table 1: 1968 and 1993 SNA Compliance of Annual National Accounts
Year of firstFirst year national
Last year nationalYear of firstFirst year national
implementation of 1968 accounts based on 1968 accounts based on 1968 implementation of 1993 accounts based on 1993
SNA
SNA are available
SNA are available
SNA
SNA are available(including backward(including backward
estimates)
estimates)
1972/73

1997/98

2000

Cambodia

Bangladesh 1973
1993

1993

2007

ROC

1988

1951

2005

NA (Some definitions ofNA
1993 SNA are introduced.)
2005
1951

Fiji

1982

1968

2002

2003

1995

India

1978

1950/51

2007/082007

1999/2000

Indonesia

1970

1950

2007

Iran

1981

1959

2007

NA (Some definitions ofNA
1993 SNA are introduced.)
2006
1988

Japan

1978

1955

1998

2000

Korea

1986

1970

1997

Laos

NA (Before 1993 SNA is introduced, country's own system of national accounts isavailable for 1990–2006.)

Malaysia

1969

Mongolia

NA (Before 1993 SNA is introduced, Material Product System was used.)

1999

1995

Nepal

1975

1974/75

2004/052006

2000/01

Pakistan

1981

1987

2001

2000

1999/2000

Philippines 1985

1946

2006

Planned for 20081975 (Statistics based on
the guidance of SNA (not1968 SNA) is available
since 1968)
1972

2001

2001

1991 (Not officiallyreleased.)
1998

Planned for 2009

2000

1996

1986

1960

2004

2006

2002

2007

Sri Lanka

1975

Thailand

1975

Vietnam

NA (Before 1993 SNA is introduced, Material Product System was used.)2007

1979/80

1980 (Some data are
available since 1996.)
1970
2006 (It is planned to issue
backward estimates since1997.)
2000

Table 1 3 summarizes the timing of the switchover in each country and the first year when
consistent time series start in the annual national accounts (ANA). As seen in this table, most
APO member countries are currently 1993 SNA compliant, although for some this has only beena recent development. The exceptions are the Philippines 4 and Thailand, which will switch overin 2008 and 2009 respectively. Although Cambodia and Indonesia are the only two countries thathave not adopted 1993 SNA as the basic framework for ANA, they follow 1993 SNA in someareas. In Cambodia COICOP, which is the international classification of individual consumption
recommended by 1993 SNA, has been implemented. In Indonesia the production and asset
boundaries have been influenced by 1993 SNA. 5
The starting year of the official 1993 SNA-compliant time series varies a great deal acrosscountries, reflecting the difference in availability of backwards estimates. The longest consistent3

Figures 1 and 2 show when countries introduced 1968 SNA and 1993 SNA in more intuitive way.
The National Statistical Coordination Board (NSCB) is currently undertaking an overall revision of
the Philippine system of national accounts (PSNA) for the period 2000–2006.
5
IMF (2005b) indicates “The production boundary is generally in line with the 1968 SNA. However,the 1993 SNA concepts of own-account production of all goods for own final consumption, and outputof goods for own-account fixed capital formation have already been implemented. The asset boundaryis also generally in line with the 1968 SNA. However, the 1993 SNA concepts of defense-related
assets that could be used for civilian purposes and valuables have already been implemented. The
other 1993 SNA changes will be implemented in due course.”
44

time series are available for the Republic of China (ROC) from 1951, followed by Korea from1970. Countries which have consistent time series from the 1980s are Japan, 6 Iran, Bangladesh
and Vietnam, and from the 1990s Fiji, Mongolia, Sri Lanka and the Philippines (targeted). The
remaining six countries, with a starting year from 2000 onwards, are India, Lao PDR, Malaysia,
Nepal, Pakistan and Thailand (targeted).
However, depending on the data series, it is possible that time series which go back further may
be available but are not released officially. For Korea, data based on 1968 SNA or 1993 SNA are
available only after 1970. Korea implemented 1953 SNA before 1968 SNA. The Korean systemof national accounts (KSNA) based on 1953 SNA is available for the years 1953–1970. Before
the introduction of 1993 SNA, Mongolia and Vietnam had adopted Material Product System(MPS), which was theoretical manuals prepared in the Soviet Union in 1940. This was the system
which socialist countries used to follow. The biggest difference between MPS and SNA is that
MPS excludes production and sales of many services from its production boundary. 7
Year
Country

1946

1950

1955

1960

1965

1970

Bangladesh

1975

1980

1985

1990

1993

1950

Indonesia

1950

1978
1970

Iran

Mongolia

1981

1959

1978

1955

Korea
Malaysia

1982

1968

India

Laos

1986

1970

N.A.(Before 1993 SNA is introduced, country's own system of national accounts is available for 1990–2006.)
1960

1969

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

Nepal

1975

Pakistan

1981

Philippines 1946

Vietnam

1987

1985

Sri Lanka
Thailand

2005

1988

1951

Fiji

Japan

2000

1973

Cambodia
ROC

1995

1975
1972

1975

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

Figure 1: Implementation of 1968 SNA

6The Economic and Social Research Institute (ESRI), Cabinet Office, Japan is currently undertaking
the time-series revision of the Japan’s system of national accounts (JSNA) from 1955.
7
See Jansen (1973) for the differences between MPS and SNA.
5

Year

1951

Country

1955

1960

1965

1970

1975

Bangladesh

1985

1990

1995

2000

2005

2000

1979

N.A.(Some definitions of 1993 SNA are introduced.)

Cambodia
ROC

1980

2005

1951

Fiji

2003

1995

India

2007

1999

NA (Some definitions of 1993 SNA are introduced.)Indonesia
Iran

2006

1988

Japan

2000

1980

Korea

2004

1970

Laos

2002

1997

Malaysia

2000

Mongolia
Nepal

2000

Pakistan

2000

Philippines

1998

Thailand

2001
2000

Vietnam

1986

2006
2008

1991

Sri Lanka

2007

1999

1995

2009

1996

Figure 2: Implementation of 1993 SNA

2.2

Coverage of GDP

Countries may have adopted 1993 SNA as the framework for their national accounts, but theextent of compliance in terms of coverage may still vary. This section focuses on six areas wherecompliance may be less than standard.
Five of these areas stem from own-account production of goods and services by households,
which according to 1993 SNA (paragraph 4.148) are part of GDP for the whole economy andthus should be included in GDP. These areas are construction of dwellings by households fortheir own use; production of agricultural goods by households for their own use; production ofhousehold goods by households for their own use; imputed service of owner-occupied dwellings;
and production of domestic services by employing paid staff for their own consumption. Since
the goods and services produced by households for their own use are not traded in the market,
their prices have to be imputed in order to calculate their contribution to GDP. This gives rise tomeasurement difficulties, and may hamper their inclusion in country national accounts.
Another area which gives rise to divergent practice is the treatment of FISIM (financial
intermediation services indirectly measured). FISIM captures the wedge between the interest
rates charged to borrowers and those paid to depositors by banks and other financial institutions.
It represents a significant part of value added in the financial sector. The divergence is in the1993 SNA recommendations that FISIM should be allocated to users (individual industry,6households and overseas). This is in contrast to the 1968 SNA, where the imputed banking
services were allocated exclusively to the business sector. Instead of allocating the imputedbanking services to each industry, the common practice has been to create a notional industry
which buys the entire service as an intermediate expense and generates an equivalent negativevalue added. As such, the imputed banking services have no impact on GDP.
If the 1993 SNA recommendation is fully implemented, the allocation of FISIM to its users will
impact industry GDP and the overall GDP for the total economy (by the part of FISIM allocatedto households, government and overseas). It is therefore important for analysts to note thedivergent practice with regards to FISIM in countries’ national accounts.
Table 2 shows the extent to which countries have incorporated these six areas in their national
accounts. The treatment of FISIM is less standard in this group of countries.

Table 2: Coverage of GDP
Construction of
Production of
dwellings by
agricultural goods
households for their by households forown usetheir own final
consumption

Imputed service ofProduction ofhousehold goods by owner-occupied
households for their dwellings
own use (e.g. cloth,furniture)

Production ofdomestic services
by employing paid
staff for their own
consumption

Financialintermediation
services indirectly
measured (FISIM)

Bangladesh

Yes

Yes

No

Yes

Yes

No

Cambodia
ROC

Yes

Yes

Yes

Yes

Yes

No

Yes

Yes

Yes

Yes

Yes

Yes

Fiji

Yes

Yes

Yes

Yes

No

No

India

Yes

Yes

No

Yes

Yes

Yes

Indonesia
Iran

Yes

Yes

Yes

Yes

Yes

No

No

No

No

Yes

No

Yes

Japan

No

Yes

No

Yes

No

Korea

Yes

No

No

Yes

Yes

No (Trial estimates areavailable.)
Yes

Laos

Yes

Yes

Yes

Yes

Yes

Malaysia

No

No

No

Yes

No

No (Trial estimates areavailable.)
Yes

Mongolia

Yes

Yes

Yes

Yes

No

No

Nepal
Pakistan

Yes

Yes

No

Yes

No

Yes

Yes

No

No

Yes

Yes

Yes

Philippines

Yes

Yes

Yes

Yes

No

No

Sri Lanka
Thailand

Yes

Yes

Yes

Yes

Yes

No

Yes

Yes

Yes

Yes

No

Vietnam

Yes

Yes

Yes

Yes

No

No (Trial estimates areavailable.)No

Most have not allocated FISIM to final demands,8 although three (Japan, Lao PDR and Thailand)
have trial estimates available. The impact of the allocation of FISIM to final demands is always
to raise GDP. For those countries which have implemented the 1993 SNA recommendation, theimpact on GDP ranges from 1.3 percent to 3.8 percent. Figure 3 compares the time trend of the

8

Although seven APO member countries incorporated FISIM, only three counties allocated it to final
demands. Other countries, like Iran, allocate it only to intermediate demands, thus it has no impact onGDP.
7

impact of FISIM in ROC, India, Japan, Korea and the U.S. 9
4.0%(% of GDP)
3.5%
3.0%
2.5%
2.0%
1.5%
1.0%
0.5%

ROC

India

Korea

U.S.

Japan

0.0%
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Figure 3: Comparison of the Impact of FISIM Inclusion on GDP

All countries have included the imputed service of owner-occupied dwellings. In many countries
its volume is significant. Figure 4 compares its contribution to GDP in India, Iran and Japanover time. The shares of the imputed service of owner-occupied dwellings in GDP are significant
in these countries. For international comparisons of productivity in the business economy, thevalue added in the imputed service of owner-occupied dwellings should be excluded from GDP.

14%(% of GDP)
12%
10%
8%
6%
4%
2%

India

Iran

Japan

0%
1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

Figure 4: Comparison of Imputed Services of Owner-Occupied Dwellings

9

For ROC and Korea, the allocations of FISIM estimates are not available in their official national
accounts. In Figure 1, the impact of FISIM inclusion on GDP for these two countries is tentatively
estimated by using the average of Japan's proportions of FISIM allocated to final demand.
8

Compared with the imputed service of owner-occupied dwellings, country experience is morevaried in other areas of own-account production by households. Construction of dwellings byhouseholds for their own use has been included in all the countries surveyed except Japan, Iranand Malaysia. Most countries have also incorporated production of agricultural goods byhouseholds for their own consumption. For instance, “The estimate of the gross domestic productof Nepal is based on production approach. Agricultural GDP is estimated on the basis ofproduction flow. Therefore, the estimates of agriculture GDP in principle, covers all types of
production. In case of non-agriculture part, production of own account goods produced byhouseholds are not properly included in the estimates because of data gaps on such types of
production” (Nepal Central Bureau of Statistics, 2007). Given that agricultural employment still
averages 45 percent of total employment in Asia, this is encouraging, as otherwise
underestimation of a country’s GDP could be significant.
It should be noted that the informal sector poses another problem for GDP coverage. Not only is
it by definition difficult to measure, but its nature and boundary keep evolving with the wider
economy, making it even more elusive to capture. For example, Mohammad (2007) observes thatin Malaysia, globalization has impacted on the informal sector in two ways. First, as in the formal
sector, the existing informal sector is experiencing technological deepening to diversify thecommodities and services offered as a response to globalization. The traditional formal/informal
demarcation with respect to the level of technology employed is being challenged. Secondly, inorder to utilize global changes to expand production and lower labor costs, certain forms of
subcontracting and outworking have become important informal activities. As such, the informal
sector is expanding beyond the traditional confines of the non-agricultural activities of those whohave migrated to escape from poverty in rural areas.
Lack of coverage of the informal sector and the non-observed economy in general would result inbiases in levels and trends of GDP and cause imbalances in the internal consistency of economictransactions. Depending on the relative size of the informal sector, it could ultimately pose a real
challenge to the credibility of national account estimates. The Department of Statistics, Malaysia(DOSM) has invested in studying the issue. Having identified and gathered some information on
informal activities, DOSM launched a pilot survey for this sector in August 2006. As a start, theinformal activities covered include those associated with street vendors, shops opened during
festival seasons and night-market operators. The information from the survey will be used in thecompilation of GDP in Malaysia. 10 While some countries are making an effort to improve GDP
coverage insofar as the informal sector is concerned, researchers should beware of the different
progress achieved by countries – which is a source of cross-country data incomparability in itself.

10

See Mohammad (2007) for details.
92.3

Industry and Institutional Sectors

In order to understand the dynamics in an economy, it is informative to investigate the changes instructure and productivity performance at industry level. It is at the industry level that we can see
more clearly where the strengths and weaknesses of an economy lie. Countries may have similar
overall economic growth rates and productivity growth performance, but can differ a great deal intheir compositions, reflecting different growth paths and implying a different set of risk factors.
This is why there is always a huge interest in industry productivity analysis, and the demand formore and more detailed analysis is relentless. In this section we investigate the comparability ofcountries’ industry GDP data and compare the level of detail at which they are available
officially.
Production units (i.e. establishments) are classified by their production activities. The
classification of production activities used by 1993 SNA is ISIC revision 3. There are four levelsof ISIC, referred to as 1-digit to 4-digit levels, with the 1-digit level giving the broadest
categories, rising up to the most detailed in the 4-digit level. At 1-digit level there are around 17
industry sectors. ISIC further breaks down manufacturing into 23 sub-sectors at 2-digit level.
This framework enables more robust and detailed data to be collected for manufacturing. In
comparison, the breakdown for service industries is far less comprehensive. The different
treatment of manufacturing and services in ISIC does not reflect the relative importance of thesectors, but the relative difficulty in measuring and defining the sectors. 11
Table 3 shows the level of industry detail that appears on countries’ national accounts. It by nomeans rules out that more detailed information can be available outside the national accounts, forexample in the input-output tables. As shown in Table 3, industry data are available at close to
1-digit level for most countries, and manufacturing data are also comprehensive.

11

The current framework of the APO Productivity Database has a breakdown to 10 sectors and 6
manufacturing sub-sectors.
10

Table 3: Level of Industry Detail
Number of mainindustries

Number of subindustries inmanufacturing

Industry classification (relashionship with ISIC)

Bangladesh
Cambodia
ROC
Fiji
India
Indonesia

15

39

BSIC (ISIC Rev. 2 up to the 4-igit level)

16

12

ISIC Rev. 3

15

24

SIC. 2001 version (ISIC Rev. 3 up to the 1-digit level)

17

21

FSIC. (ISIC Rev. 3 up to the 4-digit level)

17

23

NIC. 1998 (ISIC Rev. 3 up to the 4-digit level.) and NIC 2004 (ISIC Rev. 3.1.)

9

11

ISIC. Revision 2005 (ISIC Rev. 3)

Iran
Japan
Korea
Laos
Malaysia

920

ISIC Rev. 3

10

13

JSIC. 2002 version (ISIC Rev. 3)

13

11

KSIC. 2000 version (ISIC Rev. 3)

14

23

ISIC Rev. 3 (Data available in the 1-digit level only)

10

24

MSIC. 2000 version (ISIC Rev. 3)

Mongolia
Nepal
Pakistan
Philippines
Sri Lanka

14

3

ISIC Rev. 3.1

15

1

NSIC (ISIC Rev. 3)

14

3

PSIC. 1970 version (ISIC Rev. 2)

12201994 PSIC (ISIC Rev. 3)

11

4

ISIC Rev. 3

Thailand
Vietnam

16

22

TSIC (It corresponds to ISIC Rev. 3)

20

1

VSIC. 1993 version (ISIC Rev. 3) VSIC. 2007 version 3 (ISIC Rev. 4)

Besides the industry breakdown, it is not uncommon that productivity analysis sometimes
focuses on the performance of the market sector. The reasons for the interest in this sector are
twofold: by excluding the public sector, which produces largely non-marketed services, output
and inputs of the market sector can be more accurately and independently measured, and in turngive rise to better-quality productivity estimates; and, arguably, it is the sector which holds the
key to the dynamics of productivity growth in any economy.
Strictly speaking, the market sector should be defined in terms of institutional units, which 1993
SNA defines as units that are capable of owning goods and assets, incurring liabilities andengaging in economic activities and transactions with other units in their own right. SNA
provides a classification of five mutually exclusive sectors: non-financial corporations; financial
corporations; government units, including social security funds; non-profit institutions serving
households (NPISHs); and households. The outputs of government units and NPISHs are mainly
non-market outputs that are provided free or at economically insignificant prices. SNA makes
provision for a complete set of flow accounts and balance sheets to be compiled for each sector
as well as for the total economy. Separate flow accounts and balance sheets for each sector arevery useful in our attempt at productivity analysis.
Our survey includes a question on data availability by institutional sector. However, we feel that asimple yes/no answer does not capture the variation across countries in the extent to which eachcountry introduces these sectors into its national accounts. For example, in Korea, Nepal and thePhilippines, NPISHs are included in the household sector. In the Republic of China, for11

non-financial corporations and households, only outlay and income account is implemented. On
the other hand, Vietnam implements production accounts and outlay and income accounts for all
the five mutually exclusive institutional sectors. 12 The survey results are deemed insufficient indistinguishing between these practices, and thus are not presented in this paper.
2.4

Valuation, Estimation and Aggregation Methods of GDP

GDP can be valued using different price concepts: market prices, factor cost and basic prices.
Valuation concerns the decision to include or exclude indirect taxes and subsidies in the prices of
output. The nominal GDP can differ in size depending on the price concept used. If countries areusing different price concepts to value their GDP, this in turn will interfere with the levelcomparisons of GDP-related indicators across countries.
For analysis at the whole-economy level, GDP at market prices can be used and is available for
all APO member countries, as shown in Table 4. In comparing GDP by industry, a more
appropriate concept is one based on factor cost, which excludes all indirect taxes on production
and includes all subsidies, and has been used in many countries. However, factor cost is not
explicitly used in 1993 SNA; rather it recommends using the concept of basic price, which isintended to measure the amount actually retained by the producer. 13 Bangladesh, Cambodia,
Malaysia and Vietnam are the only four countries which have no other GDP measures except at
market prices. Other countries have GDP available at either basic prices or factor cost in addition
to market prices. GDP at basic prices is available in four countries, while GDP at factor cost isfound in nine countries. Nepal switched over to basic prices in 2005, with backwards estimatesfrom 2000/01.

12

See ADB (2002).
Market price measures the amount actually expended by the purchaser to acquire a particular goodor service at a specific time and place; basic price excludes “taxes on products” payable on goods andservices when they are produced, delivered, sold, transferred or otherwise disposed of by theirproducers, but includes “other taxes on production”, consisting mainly of taxes on the ownership or
use of land, buildings or other assets used in production or on the labor employed, or compensation of
employees paid.
13

12

Table 4: GDP Estimates
Approaches to GDP Estimation
Production approach Expenditure
Income approach
approach

Price Concept
GDP at market GDP at basic GDP at factor
price
price
cost

Index
Number forReal GDP

Bangladesh

Yes (Base estimate)

Yes

No

Yes

No

No

Cambodia

Yes (Base estimate)

Yes

No

Yes

No

No

ROC

Yes (Base estimate)

Yes

No

Yes

No

Yes

Fiji

Yes (Base estimate)

Yes

No

Yes

No

Yes

India

Yes

No

Yes

Yes

Yes (Base estimate,depending on sectors)
No

Yes

Indonesia

Yes (Base estimate,depending on sectors)
Yes (Base estimate)

Yes

No

Yes

Iran

Yes (Base estimate)

Yes

No

Yes

Yes

No

Japan

Yes

Yes (Base estimate)

No

Yes

No

Yes

Korea

Yes (Base estimate)

Yes

No

Yes

Yes

No

Laos

Yes (Base estimate)

Yes

No

Yes

Yes

No

Malaysia

Yes (Base estimate)

Yes

No

Yes

No

No

Mongolia

Yes (Base estimate)

Yes

No

Yes

Yes

No

Nepal

Yes (Base estimate)

Yes

No

Yes

PakistanYes (Base estimate)

Yes

No

Yes

Yes (Since
2000/01)No

Yes (Before
2000/01)
Yes

Philippines

Yes (Base estimate)

Yes

No

Yes

No

Yes

Sri Lanka

Yes (Base estimate)

Yes

No

Yes

No

Yes

Thailand

Yes

Yes

Yes

Yes

No

Yes

VietnamYes (Base estimate)

Yes

No

Yes

No

No

Fixed-baseLaspeyres
Fixed-baseLaspeyres
Fixed-baseLaspeyres
Fixed-baseLaspeyres
Fixed-baseLaspeyres
Fixed-baseLaspeyres
Fixed-baseLaspeyres
Chain-linked
Laspeyres
Fixed-baseLaspeyres
Fixed-baseLaspeyres
Fixed-baseLaspeyres
Fixed-baseLaspeyres
Fixed-baseLaspeyres
Fixed-baseLaspeyres
Fixed-baseLaspeyres
Fixed-baseLaspeyres
Fixed-baseLaspeyres
Fixed-baseLaspeyres

Besides valuation, another aspect of GDP is how it is estimated. There are three approaches used
to measure GDP: production, expenditure and income. In theory, they are accounting identities
and should sum up to the same GDP level. But in reality we do not have perfect information, andGDP estimates based on different approaches do not necessarily converge. Choosing between
these estimates, one approach may have more reliable and accurate data sources than the others
and may become the “base estimate” of GDP in some countries. Yet in other countries thepractice may be to confront the three approaches with each other as a standard procedure, giving
rise to one consolidated GDP estimate after balancing adjustments have been made. 14
Our survey results in Table 4 show that the base estimates of GDP for the APO member countries
are predominantly derived from the production approach. In Fiji, for example, “The production
approach plays a lead role in the estimation of the GDP. The expenditure approach is comparedwith the production approach and adjusted as far as possible, mainly on private final consumption,
14

In the Australian system of national accounts (ASNA), GDP estimates based on three approaches
have been integrated with annual balanced supply and use tables. As integration with these tables
ensures that the same estimate of GDP is obtained from the three approaches, annual estimates using
the income, expenditure and production approaches are identical for the years for which these tables
are available (Trewin, 2000).
13

and then on gross fixed capital formation. However, there is a residual discrepancy between the
two approaches, which is separately identified beside the final expenditures. The income
approach is adjusted to the same total as the GDP from the production approach. The adjustment
is imputed to the net operating surplus” (IMF, 2007a).
Japan is the only country which relies on the expenditure side to give its GDP base estimate.15
India is another exception: it utilizes information from different approaches to estimate GDP ofdifferent sectors. Industry GDP is estimated from either the production approach or the income
approach based on industries’ characteristics. 16
Only two out of the 18 countries have independent GDP estimates based on the income approachin their national accounts, although this approach is highly valued as a direct method toestimating a time series of value added by industry in the counties that have high-quality source
income data like the USA. 17 For most countries in Asia, net operating surplus is notindependently estimated, but derived as residuals. 18
Another aspect of GDP estimation where countries can have diverse practices is the method of
aggregation. Indices are required in aggregating heterogeneous goods and services in GDP. The
results are sensitive to the specific index number formula chosen. The most widely used index
number formulae are the Laspeyres and Paasche indices (the former uses based-period weights
while the latter uses the current-period weights), the Fisher index (a geometric average of theLaspeyres and Paasche indices) and the Törnqvist index (a weighted geometric average of its
components).
An important distinction between index numbers is whether they draw chain- or fixed-basecomparisons. Fixed-based Laspeyres indices, for example, tend to overestimate growth by

15

Based on the first draft of the Basic Plan published in October 2008 by Statistics Commission in
Japan, Economic and Social Research Institute (ESRI), the Cabinet Office of Japan plans to integrate
two measures of GDP estimates based on expenditure and production approaches under theframework of supply and use tables until 2015. Development of income approaches to measure GDP
will be another challenge for ESRI, to be investigated until 2015.
16
ADB (2002) explains “GDP estimates for agriculture, forestry and logging; fishing; mining andquarrying; organized manufacturing (establishments registered under Factories Act/ Workers act); andconstruction are based on production approach. For electricity, gas and water supply; trade, hotels andrestaurants; transport, storage, and communication; banking, insurance, real estate, ownership ofdwellings and business services; public administration and defense; and other services; the GDP isestimated following the income approach.”17
In the national income and product accounts (NIPAs) estimated by the Bureau of Economic
Analysis, the measures of value added in its GDP-by-industry accounts are derived from the industry
distributions of the components of GDI from NIPAs, which in turn are based on establishment-based
data from the Bureau of Labor Statistics and enterprise-based annual tax return and administrative
record data from the Internal Revenue Service. For more information see Lawson et al. (2006).
18
TFP measurement requires information from the income account to derive weights for the factor
inputs. It is not estimated in two countries, Bangladesh and Malaysia (in progress).
14

placing too much weight on items for which relative prices have fallen and too little weight on
items for which relative prices have risen. The reverse is true for fixed-base Paasche indices. The
further away the current year is from the base year, the bigger this substitution bias. But by
updating the weights to last year’s prices as the base for comparisons each year, chain indicesminimize the substitution bias found in fixed-base indices. In so doing, chain indices also reduce
the Laspeyres-Paasche spread, making the choice of index formula less consequential than in thecase of fixed-base indices.

115(100 in 2000)
110

Fix-based Real GDP
Chain-linked Real GDP

105
100
95
90
85
1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Figure 5: Comparison of Fixed-based and Chain-linked Real GDP in Japan

The international standard has now moved on from fixed-base Laspeyres to chain-linked indices.
But, as shown in Table 4, most countries surveyed are still using fixed-base Laspeyres. Japan isthe only country which has moved to chain-linked indices. In the Japanese system of national
accounts published by the Economic and Social Research Institute (2008), there are two series ofreal GDP: one based on a fixed-base Laspeyres quantity index and the other on a chain-linked
Laspeyres quantity index. Figure 5 compares the two series, and the upward bias of the
fixed-base GDP can be seen to have accumulated to 1.5 percent over six years. In 2006 thefixed-base real GDP had grown by 10.7 percent and the chain-linked real GDP by 9.2 percent
compared with their levels in 2000. In comparing countries’ economic performance, researchers
should be mindful of this divergence if countries are using different index numbers to aggregate
their real GDP estimates.
2.5

GFCF and Capital Stock

Investment enables capital upgrade, and in turn technological transfer to technologically
backward economies to spur growth. Investment data are collected as gross fixed capitalformation (GFCF) in the national accounts, based on which capital services will be estimated for
productivity analysis. Investment is not homogeneous, but differentiated by asset type. In thecapital services methodology, the heterogeneity of different assets is accounted for. Breakdown
15

by asset type will allow quality change in capital through changes in its composition to be
adjusted for properly. As such, the finer the breakdown by asset type, the better we can take careof asset heterogeneity and the more accurate the capital services estimates will be. Capital
services measurement is not yet a standard requirement of the 1993 SNA and hence is not readily
estimated in the national accounts. Some international databases, however, publish their own
estimates of capital services for countries.
According to the SNA classification, there are 15 types of produced fixed assets (seven types oftangible fixed assets, four of intangible assets and four of inventories) and 10 types of
non-produced assets (six types of tangible non-produced assets and four of intangible
non-produced assets). Each country adopts different asset classification. It is useful for
researchers to know the practice in each country should they want to compile their own estimatesand need to draw up the detailed methodology for capital services.
The 1993 SNA recommends the capitalization of intangible assets, which are classified into four
categories: mineral exploration, computer software, entertainment, literary or artistic originals,
and other intangible fixed assets. The purchase of these intangible assets used to be considered asintermediate consumption, and thus GDP does not properly reflect their characteristic of a capitalwith production capacity beyond the current period.
In capitalizing intangible assets, each country faces different data limitation problems. Further
complications surface for the capitalization of computer software, which includes custom
software, own-account software and pre-packaged software. There may be great variations in the
degree of implementation across countries. The capitalization of intangible assets changes not
only the size of capital input but also the size of GDP. Information on the capitalization of
intangible assets is required in order to standardize output and input concepts for our analysis.
Table 5 presents the detail level available for GFCF and capital stock in the national accounts. All
countries collect GFCF data for the whole economy but the number of asset type available varies
from one to 13. Six countries have three asset types or fewer; six countries have six asset types.
Mongolia has nine while Indonesia has 13. Nine countries have GFCF available also by
institutional units and six have GFCF by industry; most of them do not further disaggregate into
asset type. Of course, more detailed breakdown may be found outside the national accounts, forexample in the input-output tables (see section 3.1).
Table 5 also shows a great variation in the capitalization of intangible assets. Ten countries
capitalize mineral exploration, but only the ROC, Korea and Mongolia capitalize all three typesof software. In India, Japan, Malaysia, Mongolia and Pakistan, software capitalization excludes
own-account software, whereas in Indonesia only custom software is capitalized. This variationin coverage can pose problems for international comparisons of productivity. Before drawing up
16

a solution, an understanding of the order of magnitude of the problem will be helpful.

Table 5: GFCF and Capital Stock
Gross Fixed Capital Formation
Number of Number of Number oftypes oftypes oftypes ofasssets for assets byassets by
the wholeinstitutional industy
economysectorCapitalization of Intangible Assets
MineralComputerComputerComputer
exploitation software
softwaresoftware(custom)(pre(ownpackaged) account)

Net

Capital Stock
Gross
Number oftypes ofassetsBangladesh 8

NA

NA

Yes

No

No

No

NA

NA

NA

Cambodia

3

NA

NA

No

No

No

No

Yes

NA

NA

ROC

8

NA

6

Yes

Yes

Yes

Yes

NA

NA

NA

Fiji

6

NA

6

Yes

No

No

No

Yes

Yes

6

India

2

2

2

Yes

Yes

Yes

No

Yes

Yes

2

Indonesia

13

4

4

Yes

Yes

No

No

Yes

NA

6

Iran

2

1

2

No

No

No

No

NA

Yes

2

Japan

6

1

NA

Yes

Yes

Yes

No

Yes

NA

6

Korea

6

1

1

Yes

Yes

Yes

Yes

No (National Wealth Survey) NA

Laos

3

3

NA

No

No

No

No

NA

NA

NA

Malaysia

6

Yes

NA

Yes

Yes

Yes

No

Yes

Yes

5

Mongolia

9

NA

Yes

Yes

Yes

No

NA

NA

NA

Nepal

1

No (Plannedfor 2008)
NA

NA

No

Yes

Yes

No

NA

NA

NA

Pakistan

5

1

NA

Yes

Yes

Yes

No

NA

NA

NA

Philippines 4

NA

NA

No

No

No

No

Yes

Yes

4

Sri Lanka

6

NA

NA

No

No

No

No

NA

NA

NA

Thailand

6

2

NA

No

No

No

Yes

Yes

6

Vietnam

1 (4 for 1996– NA
1999)

NA

No

No

No

Yes (Only
publicinstitutes)
No

NA

NA

NA

Methods of GFCF estimation can be based on supply-side or demand-side information, or a
mixture of both. In Nepal, for example, only one aggregate GFCF is available, and it is a case ofusing a mixture of information. Nepal largely uses a supply-side approach, except for the value ofsoftware which is based on expenditure data reported by establishments/industries in benchmark
surveys. Annual estimates of intangible assets/computer software are derived using the growthrate of corporate sector value added (for details, see Nepal Central Bureau of Statistics, 2007).
Besides the changes in capital composition, another source of quality change is within the asset
group, which needs to be reflected in the deflator for the asset group under concern. For example,
prices for computers of constant quality have been falling rapidly. If computers are not properly
deflated to reflect the quality improvement, the volume of computers will be underestimated. In
the case of computers it makes a huge difference in the volume index depending on whether one
uses a deflator with quality adjustment or not. If countries are treating computers differently fromeach other, we may have to standardize the treatment. Quality adjustment by hedonics is an
exception rather than the norm among the countries studied. Only three countries havequality-adjusted price indices: Iran, Japan and Korea. Prices are deflated for personal computers
but not for software (see Table 11).

17

Capital services are derived from the productive capital stock, which is the stock of assets
surviving from past investment and corrected for its loss in productive efficiency (deterioration).
The figure is directly related to the capacity aspect of capital. Estimates constitute anintermediate step towards the measurement of capital services to provide an appropriate measureof capital as a factor of production. It is therefore important to distinguish the concept of capitalstock as measured in countries’ national accounts. The information will guide in deciding what
adjustments are needed for which country.
The concepts used in the national accounts are gross capital stock and net (or wealth) capitalstock. Gross capital stock is the stock of assets surviving from past investment and revalued at
the purchaser’s prices of new assets in the current period. Net (or wealth) capital stock is thestock of assets surviving from past investment and corrected for depreciation. It is valued as if theassets were acquired on the date to which a balance sheet relates, reflecting the value of capitalstock.
Gross capital stock can be estimated either by survey (National Wealth Survey: NWS) 19 or using
the perpetual inventory method (PIM) based on investment data, assumptions about asset
life-lengths and an initial stock. The latter is the more frequently used approach in the national
accounts. Table 5 shows that measuring capital stock is not a common practice among thecountries studied. Out of the 18 countries, eight estimate net capital stock and six estimate grosscapital stock, of which five estimate both measures: Fiji, India, Malaysia, the Philippines andThailand. Cambodia, Indonesia and Japan estimate only net capital stock while Iran estimates
only gross capital stock. For Korea, a comprehensive national wealth survey has been conducted
every 10 years from 1968 to 1997. Thus, gross and net capital stock are available outside national
accounts. 20
2.6

Labor

The 1993 SNA recommends the inclusion of labor input variables in national accounts “in order
to examine productivity” (paragraph 17.1). Here the consideration is not just whether labor input
variables appear in the published national accounts but also if they are suitable for confronting
with the value-added data to produce productivity estimates. Given the diverse data sources forlabor volume, it is possible that even if labor data are published alongside the national accounts,
they are not necessarily integrated. In other words, one should not automatically assume thesuitability of labor data in national accounts for productivity estimates.
19

The NWS directly investigates the past investment (gross book values) of the assets surviving at
the period of investigation, owned by corporations, government and households. It was implemented
in Japan 12 times from 1905 to 1970 by different ministries of the Government of Japan and the Bankof Japan. In particular, two large-scale surveys were conducted in 1955 and 1970 by the Economic
Planning Agency (the predecessor of ESRI). The current JSNA use the 1970 NWS as their benchmark.
20
In Korea, net and gross capital stock are available for the years 1968, 1977, 1987 and 1997–2006.
The number of assets is 20 in 1968, 21 in 1977, 22 in 1987 and 20 in 1997–2006.
18

Data on labor volume come from two main statistical sources: business surveys and/or the Labor
Force Survey (LFS). Each has its strengths and weaknesses. The key strength of the former is in
their accuracy and consistency in industry classification with national accounts, but coverage islimited by what companies are required to gather for administrative purposes. Labor volume
based on business surveys therefore normally needs a raft of adjustments to improve its coverage.
In contrast, the key strength of the LFS is its coverage (including, for example, information about
the self-employed and multiple job holders, and a direct measure of average actual hours worked).
Its structure is based on ILO definitions. As such, its definitions are independent of changes inbusiness administrative requirements. It also offers rich socio-economic data about the workforce,
which are very valuable. But its weakness is in its data accuracy and inconsistent industry
classification with national accounts, as answers are based on respondents’ recollections andperceptions. The fragmentation of labor market statistics has meant that it is difficult for
researchers to make good judgments on which to use for productivity calculations without expert
advice from the country concerned.
In some of the countries surveyed, the LFS has only been recently implemented. Sri Lanka is an
example of how a country makes the best use of its data sources to enrich and extend
employment statistics. In Sri Lanka employment has traditionally been estimated based on the
Annual Employment Survey, which is an establishment survey conducted by the Department ofLabor since 1971 using postal questionnaires. This survey is designed to collect information on
employment by industry and occupation, covering establishments with five or more paid
employees engaged in production, distribution and commercial activities in both the private andpublic sectors. More specifically, the survey provides information on the distribution, change andgender distribution of employment among major industries. The reference period is 30 June of
the survey year. The non-availability of a complete list of establishments and a high non-response
rate are the main drawbacks in its coverage.
In 1990 Sri Lanka started collecting employment data through the quarterly LFS. The data arederived from household interviews obtained from a sample of the population 10 years of age andolder. The survey provides comprehensive information on the labor force, the employed and theunemployed, and includes such characteristics as age, sex, occupation and industry attachment. It
also provides information on hours worked, and reasons for unemployment and non-participationin the labor force.
Data sources aside, standardization of the definition of the labor input measure is another aspect
which is very important in international comparisons of productivity performance. Labor input
can be measured in three counting units: number of persons in employment, number of filled jobs
and total hours actually worked. Total actual hours worked is seldom observed directly but
derived from multiplying the first concept of labor input by average hours worked per person or
19the second concept by average hours worked per job. Given the variations in working patterns
and employment legislation both over time and across countries, total hours worked, if accurately
measured, offers the most time-consistent and internationally comparable concept of labor input.
The breakdown of total employment into employees, self-employed and family workers isneeded for imputing the compensation to self-employed labor in order to split the mixed incomein the national accounts into return to labor and capital respectively. This is required in thecalculation of total factor productivity as weights.
Two-thirds of the countries we surveyed do not publish any labor data in their national accounts,
as shown in Table 6. Of the six countries which do, all have labor volume denominated in
number of persons rather than jobs, except Japan. All six countries cover employees (in terms ofeither jobs or persons). Mongolia has no data on the self-employed, meaning that it has
incomplete coverage. India and Mongolia have no hours worked data presented in the national
accounts, whereas Malaysia, Thailand, Japan and Bangladesh have no hours worked for the
self-employed.

Table 6: Number of Jobs/Persons and Hours Worked in the National Accounts
Labor data as a Number ofpart of national jobs/persons
accounts

Number of
Number of
Number of
Hours worked
jobs/persons for jobs/persons for jobs/persons by for employees
employees
self-employed industry
and family
workers

Hours worked Hours worked
for selfby industry
employed andfamily workers

Bangladesh
Cambodia

No

NA

NA

NA

NA

NA

NA

NA

Yes

Persons

Yes

Yes

Yes

ROC

No

NA

NA

NA

NA

Yes (Average
hours worked)
NA

Yes (Average
hours worked)
NA

Yes (Average
hours worked)
NA

Fiji
India

No

NA

NA

NA

NA

NA

NA

NA

Yes

Persons

Yes

Yes

No

No

No

Indonesia

No

NA

NA

Yes (Only selfemployed)
NA

NA

NA

NA

NA

Iran
Japan

No

NA

NA

NA

NA

NA

NA

NA

Yes

Jobs

Yes

Yes

Yes

No

Korea

No

NA

NA

NA

Yes (Only
employees)
NA

NA

NA

Yes (Only
employees)
NA

Laos
Malaysia

No

NA

NA

NA

NA

NA

NA

NA

Yes

Persons

Yes

Yes

Yes

Yes

No

Mongolia

Yes

Persons

Yes

No

No

No

Nepal

No

NA

NA

NA

Yes (Only
employees)NA

Yes (Only
employees)
No

NA

NA

NA

Pakistan
Philippines

No

NA

NA

NA

NA

NA

NA

NA

No

NA

NA

NA

NA

NA

NA

NA

Sri Lanka
Thailand
Vietnam

No

NA

NA

NA

NA

NA

NA

NA

Yes

Persons

Yes

Yes

Yes

No

No

No

No

NA

NA

NA

NA

NA

NA

NA

Table 7 shows what data are available for each country from the LFS. All countries have labor
data from their LFS. The challenge for researchers is to decide how best to use all available
information on labor data. One could argue that labor data in the national accounts could still be20

superior to single-sourced data from the LFS, on the assumption that the former would
incorporate the strengths of all available data sources on labor statistics.

If the target is to measure labor volume in terms of total actual hours worked, combining
information from Tables 6 and 7, the following data gaps are identified: no self-employed data
for Mongolia; no hours worked data (both employees and self-employed) for India, Iran, Lao
PDR, Mongolia, Sri Lanka and Vietnam; and no self-employed hours for Korea and Thailand.
The basic labor productivity measure of gross value added (GVA) per person worked should beavailable for all countries, whereas GVA per hour worked will be feasible only for a sub-set ofthe countries.

Table 7: Labor Statistics
Number of jobs/persons
Employees SelfNumber by
employed and industry
familyworkers

Employees

Hours worked
SelfHours
employed and worked byfamilyindustry
workers

Frequency

Bangladesh

Yes

Yes

Yes

Yes

Yes

No

Cambodia

Yes

Yes

Yes

Yes

Yes

Yes

1997, 2000, 2001

ROC

Yes

Yes

Yes

Yes

Yes

Yes

Monthly

Fiji

Yes

Yes

Yes

Yes

Yes

Yes

India

Yes

Yes

Yes

No

No

No

Indonesia

Yes

Yes

Yes

Yes

Yes

Yes

Occasional (So far
1982, 2004/05)
Every 5 years (Large
sample), annual (Small
sample)Annual

Iran

No

No

No

No

No

No

Quarterly

Japan

Yes

Yes

Yes

Yes

Yes

Yes

Monthly

Korea

Yes

Yes

Yes

No

Yes

Monthly

Laos

No

No

No

Yes (Only
regularemployees)
No

No

No

No

Malaysia

Yes

Yes

Yes

Yes

Yes

Yes

Monthly

Mongolia

Yes

No

No

No

No

Every 4–5 years

Nepal

Yes

Yes

Yes (Only
employees)
Yes

Yes

Yes

Yes

Pakistan

Yes

Yes

Yes

Yes

Yes

Yes

Every 5 years (So far
1998/99, 2007/08)
AnnualPhilippines

Yes

Yes

Yes

Yes

Yes

Yes

Quarterly

Sri Lanka

Yes

Yes

No

No

No

Quarterly

ThailandYes

Yes

Yes (Only
employees)
Yes

Yes

No

Yes

Vietnam

Yes

Yes

Yes

No

No

No

Annual, monthly (First
month started fromJanuary 2002)
Annual3

Non-NA Statistics

3.1

Benchmark Input-Output Table

Every 2 years

Supply-use tables and/or input-output tables (SUT/IOT) are derived as an analytical frameworkof tables describing the interrelationships among producers and users in a market economy. The
21

rich information and data in SUT/IOT make it possible for us to understand the interdependence
of industry sectors better and further investigate how the structure of an economy changes overtime. Furthermore, more detailed information than in the national accounts is often available in
the SUT/IOT, providing an excellent alternative raw data source to refine capital services
estimates. As they have been found to be powerful tools for compiling production accounts innational accounts, they have been integrated into SNA since 1968.
Table 8 shows the availability of SUT/IOT and their dimensions in the countries surveyed, andoffers promising results. 21 Among the 18 countries surveyed, only four do not construct SUT or
IOT: Cambodia, Lao PDR, Nepal and Sri Lanka. For most countries, the details are far richer
than those available in the national accounts and the feasibility of extracting investment data byasset from these tables for capital services measurement looks promising. The tables areconstructed at five-year intervals.

Table 8: Benchmark Input-Output Table
Use Table

Supply Table

Commodity-commodity Table

Starting year

Bangladesh

For 1993-94, 79 (Commodity) ×
79 (Industry), for 2002, 94(Commodity) × 86 (Industry)

For 2002, 86 (Industry) × 94
(Commodity)NA

1986/87

Cambodia

NA

NA

NA

NA

ROC

No

No

Fiji

For 2002, 37 (Commodity) × 35
(Industry)
For 1989/90 and 1993/94, 115(Commodity)×115 (Industry)
Exercise

For 2002, 35 (Industry) × 37
(Commodity)
For 1989/90 and 1993/94, 115(Industry) × 115 (Commodity)
Exercise

For 2001, 610 (Commodity) ×
162 (Commodity)
NA

1972

India
Indonesia
Iran
Japan

Korea
Laos

For 2001, 119 (Commodity) × 58 For 2001, 58 (Industry) × 119(Industry)(Commodity)NA
For 2000, 121 (Industry) × 121(Commodity)
For 2000 and 2003, 21(Commodity) × 22 (Industry)
NA

1961

For 1989/90 and 1993/94, 115
1968/69(Commodity) × 115 (Commodity)
For 1995, 172 (Commodity) ×1971
172 (Commodity)No
1962

For 1995, 519 (Commodity) ×
1951
405 (Commodity), for 2000, 517(Commodity) × 405 (Commodity)
For 2000 and 2003, 21 (Industry) For 2000 and 2003, 404
1960
× 22 (Commodity)(Commodity) × 404 (Commodity)
NA
NA
NA

Mongolia

For 1983 and 1987, 60(Commodity) × 60 (Industry), for1991, 92 (Commodity) × 92
(Industry), and for 2000, 94(Commodity) × 94 (Industry)NA

For 1983 and 1987, 60 (Industry) For 1983 and 1987, 60
1960
× 60 (Commodity), for 1991, 92 (Commodity) × 60 (Commodity),(Industry) × 92 (Commodity), and for 1991, 92 (Commodity) × 92
for 2000, 94 (Industry) × 94
(Commodity), and for 2000, 94(Commodity)(Commodity) × 94 (Commodity)
127 (Industry) × 298
NA
1963(Commodity)

Nepal

NA

NA

PakistanNA

For 1999/2000, 80 (Industry) × 80 For 1999/2000, 80 (Commodity)(Commodity)
× 80 (Commodity)

Philippines

For 2000, 240 (Commodity) ×240 (Industry)

For 2000, 240 (Industry) × 240(Commodity)

Sri Lanka

NA

NA

ThailandNo (It has trial estimates)

No (It has trial estimates)

VietnamNA

NA

Malaysia

NA

NA
1974/75

For 1988, 230 (Commodity) ×1961
230 (Commodity), for 1994, 229(Commodity) × 229(Commodity), for 2000, 240(Commodity) × 240 (Commodity)
NA
NA
For 1995 and 2000, 180
1975(Commodity) × 180 (Commodity)
For 1989, 54 (Commodity) × 54 1989
(Commodity), for 1996, 97(Commodity) × 97 (Commodity),for 2000, 112 (Commodity) × 112(Commodity)

21

Frequency
Every 5 years, but this is notstrictly followed due to resource
constraints (1986/87, 1992/93,
1993/94, 2002)
NA
Every 5 years (For years ending in
1 and 6)
1972, 1981, 2002
Every 5 years
Every 5 years (Since 1975)
Every 5 years
Every 5 years (Since 1960)

Every 5 years
NA
Every 5 years

1963, 1966, 1970, 1977, 1983,
1987 (Material Product System),
1997, 2000, 2005NA
1974/75, 1989/90, 1999/2000(Work on 1999/2000 is inprogress)
Every census year or depending
on availability of budgetary
resources (Recently, 2000 IO)

NA
Every 5 years
1989, 1996, 2000. (Planned to beconstructed every 5 years from2008)

In Japan the Director-General for Policy Planning (Statistical Standards) (2006) conducted a survey
on the compilation of SUT/IOT all over the world.
22

3.2

Population Census

A population census is a valuable source of socio-economic data on the population in its own
right. It also provides the benchmark for the LFS, which is seldom revised except whenbenchmarked to the latest census results. A population census is often conducted at an interval of
every five or 10 years. Mid-year population estimates are projections of census results based on acontinuous population register or on the balance of births, deaths and migration. Census years
give researchers an anticipation of when labor statistics based on the LFS may be revised, in turnaffecting productivity estimates.
If countries have not developed the sophisticated socio-economic profiles of their workforce
needed for the construction of a quality-adjusted labor input measure, data from the population
census can be used as an alternative. Table 9 shows that all participating countries have a
population census. It is a decennial exercise except in Japan, Indonesia and Korea, where it isquinquennial.

Table 9: Population Census
Year of the first census Year of the last census

3.3

Frequency

Bangladesh

1974

2001

Every 10 years

Cambodia

1998

2008

Every 10 years

ROC

1956

2000

Every 10 years

Fiji

1881

2007

Every 10 years

India

1901

2001

Every 10 years

Indonesia

1961

2005

Every 5 years

Iran

1956

2006

Every 10 years

Japan

1920

2005

Every 5 years

Korea

1925

2005

Every 5 years

Laos

1985

2005

Every 10 years

Malaysia
Mongolia

1911

2000

Every 10 years

1918

2000

Every 10 years

Nepal

1911

2001

Every 10 years

Pakistan

1951

1998

Every 10 years

Philippines

1903

2007

Every 10 years

Sri Lanka

1891

2001

Every 10 years

Thailand

1909

2000

Every 10 years

Vietnam

1979

Planned for 2009

Every 10 years

Business Survey

If an industry sector is covered by a census, this implies quality data and the availability ofcomprehensive and reliable data for that sector. Countries vary in their practices. Some may havea consolidated economic census, covering both manufacturing and services, and usuallyconducted at a frequency of every five years. Alternatively, countries like the UK may have anannual consolidated business survey covering both manufacturing and services for benchmarking
purposes. A unified approach has the advantage of consistency and coherence in the23

survey/census framework, covering all sectors. A few countries implement a comprehensive
census/survey covering all sectors: Lao PDR (economic census), Mongolia (census ofestablishment), Pakistan (economic census) and Vietnam (establishment census).

Table 10: Survey of Manufacturing
Year of the first survey

Year of the last survey

Frequency

Bangladesh

1972/73

2004/05

Every 2 years

Cambodia

1993

2006

Depends on available budget (mostly from donors)

ROC

1979

2005

Fiji

1969

2004

Annual (except the years ending in 1 and 6, when Industry, Commerce andService Census (ICSs) were conducted)
AnnualIndia

1951/52 (Annual Survey ofIndustries)

Indonesia

1975

2004/05 (Annual Survey ofIndustries), 2007 (Survey on
Unorganized ManufacturingEnterprises)
2006

Iran

1972

2007

Annual (Census), quarterly (Survey)

Japan

1909

2005

Korea

1955 (Census), 1968 (Survey)

2003 (Census), 2006 (Survey)

Laos

2006

2006

Annual (surveys in years whose last number is 0, 3, 5 and 8 have larger
scale than in other years)
Every 5 years whose last number is 3 and 8 (Census), annual except years
whose last number is 3 and 8 (Survey)
Plan to conduct the 2nd Economic Census in 2011

Malaysia

1959 (Census), 1960 (Survey)

2005 (Census), 2006 (Survey)

Every 5 years (Census), annual (Survey)

Mongolia

1991 (Census)

2006 (Census), 2007 (Survey)

Nepal
Pakistan

1964/65 (Census), 1972/73 (Survey) 2006/07 (Census), 1999/2000(Survey)
1959/60
2005/06

Philippines

1961 (Census), 1976 (Survey)

Sri Lanka

1946

2003/04

Every 5 years or depending on availability of budgetary resources (Census
of Philippine Business and Industry), in between censal year or depending
on availability of resources (Annual Survey of Philippine Business andIndustry– covering all sectors)
Scheduled to be held in every 10 years

Thailand1998 (Survey)

2006 (Survey)

Every year

Vietnam1995 (Census), 1998 (Survey)

2007

Every 5 years (Establishment Census since 2002), annual (Industrial
Enterprise Survey)

2006 (Census), 2005 (Survey)

Annual (Annual Survey of Industries (for the registered)), every 5 years(Survey on Unorganized Manufacturing Enterprises (for the unregistered))

Annual (1975–2006), quarterly (1986–2006), monthly (2000–2006)

1991, 1994, 1998, 2006 (Enterprise Census), annual (Enterprise Survey)
Every 5 years (Census), every 10 years (Survey)
Every 5 years

Yet another group of countries may not have consolidated their benchmarking surveys or census
into one, but have separate surveys/census for different segments of the economy; this is thecurrent practice in Japan, although the first economic census is planned for 2010.
As can be seen in Table 10, a manufacturing census covering all establishments in this sector ismore common than a census covering the service sector; 14 out of the 18 countries surveyed havea manufacturing census, and half of these countries have an annual census. This suggests a richdata source on manufacturing, and offers potential for further and more in-depth productivity
analysis of manufacturing and its sub-sectors.
The same cannot be said about the service sector. Few countries implement a unified,comprehensive survey, but many conduct several surveys covering different parts of the service
sector. For example, Fiji Islands Bureau of Statistics conducts a number of separate surveys
covering social and related community services, restaurants and hotels, real estate and businessservices, and distributive trades. For benchmarking purposes, the National Accounts section of
the Nepal Central Bureau of Statistics has been conducting various census/surveys from 2003/04
24

onwards: restaurant and catering services, land transportation services, travel, trekking andrafting services, freight services, postal services, cable TV activities, real estate activities,
communication services, renting services, Internet service activities, legal activities, auditing
services, trade warehouses, private health services, and private education activities and similar. 22
3.4

CPI and PPIPrice indices underlie the construction of volume indices in the national accounts. Their methodsof construction directly affect national accounts statistics in real terms. The consumer price index(CPI) is the representative indicator of cost of living. It aggregates prices of different
commodities using weights. Knowing the price of each commodity is indispensable forcalculating the GDP deflator. Among the issues related to measuring CPI, the serious problem ofbias induced by quality change and new goods is widely recognized. Hedonics is one of theuseful tools for controlling quality change. It has been used for commodities which experience
rapid technological improvement.

Table 11: CPI and PPIYear of the firstsurveyConsumer Price Index (CPI)
Frequency Index number

Qualityadjustment byhedonics

Producer Price Index (PPI) or Wholesale Price Index
Year of the first
Frequency Index number
Qualitysurveyadjustment byhedonics

Bangladesh

1973

Monthly

Fixed-base Laspeyres No

1988/89

Quarterly

Fixed-base Laspeyres No

Cambodia

Fixed-base Laspeyres No

2003

2003, 2007

Fixed-base Laspeyres No

ROC

1994 (CPI for Phnom MonthlyPenh) and 2000
(Urban Cambodia
1959
Monthly

Fixed-base Laspeyres No

Fixed-base Laspeyres No

Fiji

1968

Monthly

Fixed-base Laspeyres No

1952 (Wholesale Price Monthly
Index)
NA
NA

India

1949

Monthly

Fixed-base Laspeyres No

Fixed-base Laspeyres No

Indonesia

1953

Monthly

Fixed-base Laspeyres No

1939 (Wholesale Price Weekly
Index)
NA
NA

Iran

1936

Monthly

Fixed-base Laspeyres Yes

1990

Fixed-base Laspeyres Yes

Japan

1946

Monthly

1897 (Wholesale Price Monthly
Index)

Korea

1949

Monthly

Fixed-base Laspeyres Yes (PC and(Chain-linked
Camera)Laspeyres is prepared
as a reference)
Fixed-base Laspeyres Yes (PC)

Laos

1987

Monthly

Malaysia

1959

Monthly

Mongolia

1991

Monthly

Nepal

1972

Monthly

Fixed-base Laspeyres No(Chain-linked
Laspeyres is prepared
as a reference)
Fixed-base Laspeyres No

Pakistan

1956/57

Monthly

Fixed-base Laspeyres No

Philippines

1957

Monthly

Fixed-base Laspeyres No

Sri Lanka

1952

Monthly

NA (Wholesale Price Monthly
Index)

Thailand

1988

Monthly

Fixed-base Laspeyres No(Monthly chainlinked)
Fixed-base Laspeyres No

1988

MontlyFixed-base Laspeyres No

Vietnam1998 (Before 1998 a Monthly
detailed sales index isavailable)

Fixed-base Laspeyres No

1995

Quarterly

Fixed-base Laspeyres No

Fixed-base Laspeyres No(Its weight is updated
every 5 years)
Fixed-base Laspeyres No

22Monthly

NA

NA

NA

NA

1910

Monthly

Fixed-base Laspeyres(Chain-linked
Laspeyres is prepared
as a reference)
Fixed-base Laspeyres

Yes (PC, digital
camera, video
camera, copy
machine, printer)
Yes (PC)

NA

NA

NA

NA

1973

Monthly

Fixed-base Laspeyres No

No (Planned for 2008) No

No

1982

Quarterly

Fixed-base Laspeyres No

1960 (Wholesale Price Monthly
Index)
1980
Monthly

Fixed-base Laspeyres No
Fixed-base Paasche

No

No

Fixed-base Laspeyres No

Even countries which conduct comprehensive surveys for service industries are likely to conduct
supplementary surveys in order to capture service sectors which are not covered by the comprehensive
survey.
25

Our survey results show that quality adjustment is still not commonplace (Table 11). Only threeof the countries surveyed have quality adjustment in their price indices: Japan, Korea and Iran.
Chain linking is not standard, either; Japan is one of the few to adopt chain linking in its priceindices.
3.5

Productivity Statistics

Table 12 presents our survey results regarding the availability of official productivity statistics,
which indicates if the national statistical offices house expertise on productivity estimates andtheir data requirements. Only the ROC, Iran, Malaysia, Mongolia and Thailand are publishingproductivity estimates at least annually. This, of course, does not mean that other productivity
estimates from other sources or research do not exist.

Table 12: Productivity Statistics
Labor Productivity
Starting year
Bangladesh
Cambodia
ROC

Fiji
India

Total Factor Productivity

Frequency

Starting year

NA

NA

NA

NA

NA

NA

Labor Productivity by Value Quarterly (Labor
1981
Added started in 1986,
Productivity by Value
Labor Productivity byAdded), Monthly (Labor
Production started in 1972 Productivity by Production)
NA
NA
NA

Frequency
NA
NA
Annual

NA

NA

NA

NA

NA

Indonesia
Iran
Japan
Korea

NA

NA

2001

Incidental

1996

Annual1996

Annual

NA

NA

NA

NA

NA

NA

NA

NA

Laos
Malaysia

NA

NA

NA

NA

1980 (Using 1987 basedyear), 2000 (Using 2000
based year)
2000

AnnualAnnualAnnual1990 (Using 1987 basedyear), 2000 (Using 2000
based year)
2000

Annual

NA

NA

NA

NA

NA

NA

NA

NA

Philippines

NA (Some estimates by
NA
NSCB (Philippine National
Statistical Coordination
Board), and NWPC
(National Wages andProductivity Commission))NA

NA

Sri Lanka
Thailand

NA

NA

NA

NA

1982 (by the National
Economic and Social
Development Board)NA

Annual1982

AnnualNA

NA

NA

Mongolia
Nepal
Pakistan

Vietnam

In the ROC two types of labor productivity are released on a regular basis. The
Directorate-General of Budget, Accounting and Statistics (DGBAS) began to prepare laborproductivity indices for manufacturing and electricity, gas and water industries in 1972, theso-called Labor Productivity by Production. This indicator is constructed by dividing theindustrial production index by hours worked. Since comparison of this type of productivity
between industries is impossible, a new indicator, the so-called Labor Productivity by Value
26

Added, has been released since 1986. This indicator is constructed by dividing industry GDP byhours worked; by using it, we can compare labor productivity across industries.
4

Conclusion

This paper collates countries’ data availability and practices in data compilation to shed light on
the extent of cross-country data comparability for productivity analysis. The information
presented is based on metadata gathered in a survey on national accounts and other required
statistical data, conducted by the Asian Productivity Organization (APO) between April and July
2008, covering APO member countries. As the statistical systems are still maturing in many of
the countries studied, while others seek to improve theirs, the systems reported on in this paper
are far from stable but subject to continual incremental upgrades. In light of this, the metadata
survey will be updated annually under the APO Productivity Database Project. Likewise, thispaper will be updated with new changes and improvements on an annual basis to reflect
countries’ effort.
Furthermore, we look to extend the scope of the paper in the near future to include additional
measures which are relevant to more sophisticated productivity analysis. Two such measures in
the pipeline of the APO Productivity Database Project are on land as a capital and aquality-adjusted labor input measure. The APO metadata survey will be adapted to provide
information on their data availability and feasibility.

27

Appendix 1: Quarterly National Accounts (QNA)
Up to this point, the focus of this paper has been on data availability and feasibility for annual
productivity estimates. To extend to short-term estimates, we asked a couple of questions on
countries’ quarterly national accounts (QNA). QNA face more data limitations than annual
estimates. It is not possible to implement the same comprehensive approach used in the annual
estimates for the quarterly estimates. As a result, the short-term indicators are likely to be basedon whichever of the three approaches has timelier data.
Short-term indicators are used for their timeliness. Subsequent to their first release (usuallyaround one month after the end of the reference period), they are subject to a short-term revision
cycle to incorporate new data as they emerge. Ultimately they will be benchmarked to, andbrought consistent with, the annual estimates when they are available. The project team isparticularly interested in the timing of the first release of the quarterly estimates and the time
when the set of quarterly estimates consistent with the annual accounts is first made available.
As can be seen from Table 13, 13 out of 18 countries have constructed QNAs and most countries
base them on the production approach, with the expenditure approach available alongside. The
General Statistical Office of Vietnam recently compiled quarterly estimates of GDP by the
expenditure approach at current and constant prices. This is one of the current improvements ofVietnam’s national accounts.
Table 13: Quarterly National Accounts
Implementation of QNA

Approaches to GDP Estimation
Production approach

Expenditure approach

Income approach

Bangladesh No

NA

NA

NA

Cambodia

Yes

Yes (Base estimate)

Yes

No

ROC

Yes

Yes (Base estimate)

Yes

No

Fiji

No

NA

NA

NA

India

Yes

Yes (Base estimate,depending on sectors)

Yes

Yes (Base estimate,depending on sectors)

Indonesia

Yes

Yes (Base estimate)

Yes

No

Iran

Yes

Yes (Base estimate)

Yes

No

Japan

Yes

No

Yes

No

Korea

Yes

Yes (Base estimate)

Yes

No

Laos

No

NA

NA

NA

Malaysia

Yes

Yes (Base estimate)

Yes

No

Mongolia

Yes

Yes (Base estimate)

No

No

Nepal

No

NA

NA

NA

Pakistan

No

NA

NA

NA

Philippines Yes

Yes (Base estimate)

Yes

No

Sri Lanka

Yes

Yes

No

No

ThailandYes

Yes (Base estimate)

Yes

No

VietnamYes

Yes (Base estimate)

Yes

No

28

Appendix 2: IMF Data Quality Assessment for Asian Countries
In this paper, our focus is on countries’ data compilation practices and data availability. We do
not assess data quality. Thus we believe it is informative to introduce readers to a data quality
assessment framework and summarize how some of the participating countries in our survey fareagainst it.
The IMF has developed its own comprehensive data quality assessment framework (DQAF) in
order to assess data quality of countries participating in its General Data Dissemination System(GDDS). IMF assessments of individual countries are published in its series of Reports on the
Observance of Standards and Codes (ROSC). In this appendix we attempt to summarize IMF
data quality assessments for 10 Asian countries which overlap with the participating countries inour survey: Bangladesh (evaluation in 2003), India (2002), Indonesia (2003), Japan (2003),
Korea (2001), Mongolia (2003), Pakistan (2003), the Philippines (2003), Sri Lanka (2001) and
Thailand (2003).
The IMF’s DQAF is defined by five dimensions. They are:Quality Dimensions
0. Prerequisites of quality

Elements
0.1 Legal and institutional environment
0.2 Resources
0.3 Relevance
0.4 Other quality management

1. Assurances of integrity

1.1 Professionalism.
1.2 Transparency
1.3 Ethical standards

2. Methodological soundness(if elements are in accord withinternational standards,
guidelines, or good practices)

2.1 Concepts and definitions
2.2 Scope
2.3 Classification/ sectorization
2.4 Basis for recording

3. Accuracy and reliability

3.1 Source data
3.2 Assessment of source data
3.3 Statistical techniques
3.4 Assessment and validation of intermediate data and statistical outputs
3.5 Revision studies

4. Serviceability

4.1 Periodicity and timeliness
4.2 Consistency
4.3 Revision policy and practice

5. Accessibility

5.1 Data accessibility
5.2 Metadata accessibility
5.3 Assistance to users

IMF country assessment is based on six datasets against these five dimensions of data quality:
System of National Accounts (SNA); Consumer Price Index (CPI); Producer Price Index (PPI);29

Government Finance Statistics (GFS); Monetary Statistics; and Balance of Payments Statistics(BOP).
The IMF scoring system is made up of four grades: O (practice observed), LO (practice largely
observed), LNO (practice largely not observed) and NO (practice not observed). To aggregate
IMF scoring, we assign numerical scores of 1, 2/3, 1/3 and 0 to IMF scoring of O, LO, LNO andNO, respectively. The aggregated scores for each dataset and each data quality dimensions arepresented in Figures 6 and 7 respectively.
For our purpose, the data quality of national accounts for most countries shown is judged as high,with scores ranging from 0.72 to 0.93. Sri Lanka is the exception, with a score of 0.58. Japan isthe country with the highest average score for all six datasets, followed by Korea. For countries
which have price indices, the quality is quite high, except for Sri Lanka. In terms of the data
quality dimensions (Figure 7), Japan has the highest data quality overall, followed by Korea,
Thailand and the Philippines. India, Indonesia, Pakistan and Mongolia have similar data quality,
while Sri Lanka has the lowest overall score. For these 10 countries as a group on average,
assurance of integrity is their strength, whereas data quality dimensions of methodologicalsoundness and accuracy and reliability prove to be more challenging for them.

30

Bangladesh (2003)

India (2002)

National Accounts

National Accounts

1.00

1.00

0.80 0.72

Balance ofPayments Statistics

Balance ofPayments Statistics

0.60
0.40

0.85

Index

0.40
0.20

0.00

0.00

Wholesale Price
Index

Monetary Statistics

0.88

0.96
0.79

0.76

Government
Finance Statistics

Government
Finance Statistics

Indonesia (2003)

Japan (2003)

National Accounts

National Accounts

1.00 0.81

1.00

0.93

0.80

0.80
0.91

Balance ofPayments Statistics

0.60
0.40

0.85

0.93 Consumer Price

0.60

0.20

Monetary Statistics 0.81

Balance ofPayments Statistics

0.79

0.80
0.92

0.40

0.20

0.20

0.00

0.00

Monetary Statistics

0.97

0.60

Producer Price
0.97 Index

Monetary Statistics

0.94

Consumer Price
Index

0.94

0.62
0.91

Government
Finance Statistics

Government
Finance Statistics

Korea (2001)

Mongolia (2003)

National Accounts

National Accounts
1.00

1.00 0.88

Balance of
0.84
Payments Statistics

Monetary Statistics

0.75

0.80

0.80

Consumer Price
0.98 Index

0.60
0.40

Balance of
0.80
Payments Statistics

0.20

0.00

0.00

Producer Price
Index

0.80

0.40

0.20

0.90

Consumer Price
Index

0.60

Monetary Statistics0.75

0.94

0.71
0.81

Government
Finance Statistics

Government
Finance Statistics

Pakistan (2003)

Philippines (2003)

National Accounts

National Accounts
1.00

1.00 0.77

Balance of 0.89
Payments Statistics

0.88

0.80

0.80
0.88 Consumer Price

0.60

Balance ofPayments Statistics 0.77

Index

0.40

0.40

0.20

0.20

0.00

0.00

Wholesale Price
0.95 Index

Monetary Statistics 0.78

Consumer Price
Index

0.60

0.94

Monetary Statistics0.80

0.87

Producer Price
Index

0.65
0.75

Government
Finance Statistics

Government
Finance Statistics

Sri Lanka (2001)

Thailand (2003)

National Accounts

National Accounts
1.00 0.88

1.00

0.80

0.80

Balance ofPayments Statistics 0.70

0.58
0.60
0.40

Consumer Price
0.70
Index

Balance ofPayments Statistics
0.89

0.60
0.40
0.20

0.20

0.00

0.00

0.51

0.78

Monetary Statistics

Producer Price
Index

Monetary Statistics
0.95
0.92

0.79

Government
Finance Statistics

Government
Finance Statistics

Figure 6: IMF Assessment of Six Datasets for Asian Countries
31

Bangladesh (2003)

India (2002)

Prerequisites ofquality

Prerequisites ofquality
1.00 0.89

1.00

0.80

0.80
0.50

0.83 Assurances of

0.60

Accessibility 0.83

0.40

0.67

0.20

0.00

0.00

0.56

0.56

Methodologicalsoundness

1.00

Assurances ofintegrity

0.40

0.20

Serviceability

1.00

0.60

Accessibility

integrity

Methodologicalsoundness

Serviceability
0.89

0.58
0.75

Accuracy andreliability

Accuracy andreliability

Indonesia (2003)

Japan (2003)

Prerequisites ofquality
1.00

Prerequisites ofquality
1.00 0.83

1.00

0.80

0.80
1.00

0.83 Assurances of

0.60

Accessibility

Serviceability

1.00

0.20

0.00

0.00

0.78

Assurances ofintegrity

0.40

0.20

0.67

1.00

0.60

Accessibility

integrity

0.40

Methodologicalsoundness

Methodological

Serviceability

1.00 soundness

1.00

0.58
0.75

Accuracy andreliability

Accuracy andreliability

Korea (2001)

Mongolia (2003)

Prerequisites ofquality
1.00

Prerequisites ofquality
1.00

1.00

0.40

1.00

1.00

Assurances of
0.83 integrity

0.60

Accessibility

0.75

0.80

0.80

Accessibility

0.20

0.00

0.00

Methodologicalsoundness

0.67

Serviceability

0.78

0.89

0.83

0.40

0.20

Serviceability

Assurances ofintegrity

0.60

Methodologicalsoundness

0.67

0.58
0.78

Accuracy andreliability

Accuracy andreliability

Pakistan (2003)

Philippines (2003)

Prerequisites ofquality

Prerequisites ofquality
1.00

1.00 0.78
1.00

0.60

Accessibility

0.40

0.83

1.00

Assurances ofintegrity

Accessibility

Assurances ofintegrity

0.20
0.00

0.00

0.56

Serviceability0.78

0.78

Methodologicalsoundness

0.78 Methodological

Serviceability

soundness

0.67

0.75

Accuracy andreliability

Accuracy andreliability

Sri Lanka (2001)

Thailand (2003)

Prerequisites ofquality

Prerequisites ofquality

1.00

1.00

0.80 0.56

0.92

0.80
0.83Assurances of

0.60

0.83

Accessibility

integrity

0.40
0.33

1.00

0.60
0.40

0.20

Accessibility

1.00

0.80

0.80

1.00

Assurances ofintegrity

0.60
0.40

0.20

0.20

0.00

0.00
0.44

Serviceability

Methodologicalsoundness

0.67

Serviceability

0.78

0.83

Methodologicalsoundness

0.67
0.92

Accuracy andreliability

Accuracy andreliability

Figure 7: IMF Assessment of Data Quality for Asian Countries
32References
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