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Efforts for Statistical Reform
By Nariyasu Yamasawa

Criticism of Government Statistics& Statistical Reform
This article provides an overview of the current status and outlook
for government statistical reform. Increased criticism of government
statistics was triggered by a comment made by Finance Minister TaroAso at the Council on Economic and Fiscal Policy meeting in October
2015. Aso pointed out that the Wage Index for the Monthly Labor
Statistics fluctuates widely when the sample data is switched, andthat the movements in Consumption Expenditure of the Family
Income and Expenditure Survey and the movements in Retail Salesof the Current Survey of Commerce were different.
The Statistics Commission of Japan which leads improvement
efforts for government statistics has presented improvement plansfor each statistic. But doubts cast on the statistics were noteliminated, and in August 2016, when Minister Kozo Yamamoto
stated that “Gross Domestic Product (GDP) cannot be trusted”,
momentum to reform government statistics rose within the entire
government.
The low level of trust towards GDP can be highlighted by the
degree of revision that needs to be made after the preliminary results
are released. According to an analysis made by an OECD economist,
the width of revision made after the preliminary results of Japan’squarterly GDP (compared to the previous year) is the second largest
after Norway. Looking at the actual examples of the revisions, thereal GDP growth rate for July-September 2008, during the Lehman
Shock, compared to the previous quarter was a negative 0.4% in the
preliminary results, a small negative figure. But as time went on, thenegative range grew larger and the final figure was revised
downwards to a negative 6.5%.
In December 2016, the Statistical Reform Promotion Council wasestablished with Chief Cabinet Secretary Yoshihide Suga serving as
its chairman, and the Final Report of the Statistical Reform wasannounced in June 2017. In the final report, the policy to “improve
economic statistics around GDP statistics” was put forward, and thepolicy aims to enhance and improve various basic statistics, and also
to improve the accuracy of GDP statistics by reviewing themethodology of generating GDP. Revision to the statistical method
based on the policy of the final report is scheduled for 2018. In thepast, the Statistics Commission has only discussed statistics when achange needed to be made. It was, in other words, passive in nature,but once a revision is made to the Statistics Law, the committee can
56 Japan SPOTLIGHT • July / August 2018

AuthorNariyasu Yamasawa

implement improvements to statistics based on its own awareness,and can also recommend improvements to each ministry.
Measures to address the challenge raised by Aso in 2016 also
progressed. The method for data sample shuffle for the Monthly
Labor Statistics was changed from shuffling and changing the entire
data sample to switching one third (half during the transitional
period) every time and choosing to release numerical data forcontinued sampling where the switch has not been made. The new
method began with the statistics for January 2018. For the Family
Income and Expenditure Survey, the Consumption Trend Index (CTI),
which is closer to the concept of consumption in the GDP statistic,
has been calculated and released since January 2018.

Improvements to Inappropriate Statistics
Just as the Statistical Reform Promotion Council was establishedand government efforts were to begin addressing statistical reform,
inappropriate handling of data was discovered in the Survey ofTextile Distribution in December 2016. It was discovered that eventhough there was no response reported from the respondent, thesame numerical data as previous months had been used for many
years. Since then, various issues unfolded, such as errors in figures
being discovered in government statistics other than the Survey ofTextile Distribution, inspection systems for figures were discovered
to be not strict, or data collection was not meeting the standard.
It is the role of the Statistics Commission to address these issues
systematically. In order to improve the statistical accuracy, theStatistics Commission conducted a cross-examination of all of thefundamental statistics in fiscal 2017, and in March 2018 the Reporton the Final Deliberation Results to Improve Statistical Accuracy was
released. The report picks up the issue of complementing missing
values. It is on how to fill data using statistical methods when there
is a no response category. Various statistical approaches wereconsidered, and a desirable case study was introduced.
There are also various technical challenges to government
statistics. One example is how to handle consumption tax in thestatistical surveys. The Economic Census for Business Activity whichlooks thoroughly at enterprises released its first results for the 2012
Survey (looking at 2011 figures), and the second results for the 2016
Survey (looking at 2015 figures). Sales went up by around 20% (5%annually) from 1,336 trillion yen in 2011 to 1,603 trillion yen in 2017.
The value-added amount which is close to the concept of GDP also
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went up by around 20% (5% annually) from 245 trillion yen to 295
trillion yen. The corresponding growth in nominal GDP was anannual rate of 2%, quite a high growth rate. This is because thesurvey method changed between the first survey of “including
consumption tax”, and the second survey of “select method of either
including or excluding consumption tax” which greatly impacted theresults. The first survey should have been marked as including
income tax, but since calculations are done excluding consumption
tax for accounting purposes, it was pointed out that many
enterprises may have responded by excluding consumption tax.
Thus, the first survey underestimated the growth of sales. Therefore,
the second survey including consumption tax showed higher growthof sales than in the first survey.
This may occur in a variety of statistics and a change in theeconomic environment may give rise to new challenges, andtherefore there is a need to constantly verify the validity of thestatistics.

Expansion of Coverage
The next challenge is the enrichment of the information amount.
The basis for various business statistics is the actual number ofbusiness enterprises that exist across Japan (population). Grasping
the number of these enterprises has so far been conducted as the
Economic Census for Business Activity. Enterprises are identified one
by one by an investigator on foot. But nowadays, there are instanceswhere the enterprise looks like a normal house from the outside andthus it is difficult to determine whether there is a business activity
taking place inside or not. In addition, Internet-related enterprises
can do business without any staff on site if the location is regularly
maintained.
Such cases have led to concerns that the number of enterprises
has not been properly covered for statistical population purposes.
Indeed, the sample number for the Financial Statements Statistics ofCorporations by Industry Survey is 2.81 million enterprises, but it is1.75 million enterprises for the Economic Census for Business
Activity and there is a deviation. It may be possible that corporations
that are not operating are included in the number, and the figure of
2.55 million companies for the Taxation Statistics is closer to reality.
Even so, it means that the Economic Census for Business Activity is
unable to account for close to 800,000 enterprises (Chart 1).
Looking forward, the number of business enterprises will not be
accounted for through the Economic Census for Business Activity,
and the number of sample enterprises will be periodically grasped
through the business enterprises population database. Profiling will
be conducted for large companies, and various kinds of informationsuch as financial statements and news will be collected tounderstand corporate activities. For other companies as well, theplan is to switch to a method where for each of the regions, each andevery company can be identified and recognized.
For companies that are not readily visible, such as Internetcompanies, taxation information is useful. Taxation statistics are
currently not available for statistics, but it is planned to check the
population information by using the business information as
population information.

Accurate Understanding of Input-Output Structure
In the past, GDP statistics have been created using an InputOutput Table. Because GDP can be built by compiling the productionof each industry, ideally it is better to create GDP using the InputOutput Table. But since GDP is value-added, there is a need tocomprehend the production amount as well as the cost structure. It
is difficult to grasp the cost structure every year and thus the valueadded amount is estimated assuming that the input ratio is constant.
Even the Input-Output Table which comes out every five years relies
greatly on estimations. This is because as industry became more
subdivided, the harder it was to collect statistics and hence
estimations were used for sections where statistics were notobtained.
The Input-Output Table is an ideal tool, but since it is difficult tomeasure, the G7 countries excluding Japan have been using the
Supply and Use Table (SUT) as a basis for creating GDP statistics,
which is more easily measurable. With the planned reform, Japan
will now also change and use the SUT. In addition, the SUT will becreated once every five years, several business statistics will berenamed and enhanced as the Business Survey, and this survey isplanned to be compiled every year to resemble something close to
CHART 1

Difference in the list of sample
population for the Statistical Survey(business enterprises & companies)
Financial Statement
Statistics of Corporations:2.81 million companies

260,000
companies

800,000
companies

Companies on hiatus

Taxation Statistics(sample survey of businesses):
2.55 million companies

Economic Census:
1.75 million companies

Companies that are active
and reporting corporate taxes

Source: Created by the author from reference materials of the Statistics Commission
Office of the Ministry of Internal Affairs and Communications, as of 2014.

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CHART 2

Supply Use Table<SUT①: Use table>(U Table)

Industry

Financial DemandkiHow muchof eachproduct used
for output ofindustry k

How much of
product i used byeach industry

How much of
product i used as
final demand

Valueadded ofindustry k

Price of Service & Output<SUT②: Supply table>

Industry(S table)

input cannot be accounted for as an annual statistic. Since themonetary amount involved is large, improvements are desirable. In
education, the total cost of public schools is currently unaccounted
for, and again, improvements are desirable.
A shift from the Input-Output Table to an SUT-system will takelonger than anticipated. The basis for the SUT is the Economic
Census which is a complete census, and to change that is a huge
undertaking. To reconsider classifications of all of the industries inJapan, and to create questionnaires according to each industry type
without creating disruptions for the businesses is an enormous task,different in so many levels compared to a regular change instatistical surveys. The next economic census is expected to create
an SUT for the service industry, and the one after that will create an
SUT for all industries, but that alone can take 10 years. A long-term
outlook is necessary in carrying out statistical reform.

Importk
How much of
product i
produce by eachindustry

Supply fromimport of iproducti
How much of kindustry produce
for each produce

Source: Compiled by the author

the SUT. By doing so, the accuracy of Japanese GDP is expected to
improve (Chart 2).
It was also decided that when creating GDP, for those sectors in
the fundamental statistics where increased accuracy was expected,
improvements were to be prioritized. These sectors are termed as
the five sectors: construction and real estate, medical care andnursing, and education. In the construction industry, construction
maintenance and repair (home improvement and renovation) havenot been included, but Building Renovation and Renewal Statistics
have been created, and with the revision to the standard being
planned for 2020 it will be included in GDP. In real estate, brokeragecommissions for sales and purchases of real estate or profit margins
on sales and purchases of used houses or non-residential houses are
not included in GDP. In the medical care and nursing sector, thechallenge is how to grasp GDP for pharmaceuticals. Currently, theoutput amount for pharmaceuticals can be obtained, but intermediate
58 Japan SPOTLIGHT • July / August 2018

One important theme when creating GDP statistics is the Price
Index. This is because Real GDP is calculated by dividing Nominal
GDP by the Price Index.
Statistics for price are not just about surveying how much things
cost. The Price Index needs to indicate “given a constant quality, howmuch prices have changed”. Prices of personal computers do notappear to show much fluctuation, but capabilities and performance
keep going up as computing speed and memory storage capacity
continue to increase. What is required for the Price Index are theprices of computers with the same processing capabilities, and given
the quality, prices seem to have dropped considerably. Changes inprices of computers may appear to be flat, but there is a need toexpress this situation as being, higher quality = lower prices.
There is a need to adjust for the changes in quality for all
products, not just for computers. Consideration has been delayed so
far for quality adjustment in the service sector. For example, rents.
The GDP Statistics is supposed to use “imputation” where even if ahouse is self-owned, the owner pays rent to himself or herself.
Hence the percentage of rent in overall consumption is considerablylarge. Rent for a property built 10 years ago should be less expensive
than rent for that property newly reformed because of degradation
with the passing of years. The increase in the number of properties
with degradation over time indicates a fall in quality, and the Price
Index should rise. This needs to be embedded in the statistics.
According to a paper released by the Ministry of Internal Affairs andCommunications in 2018, the effect of degradation over time isaround 1%. Looking ahead, there is a need to calculate the overall
impact on consumer prices. Quality adjustment needs to be reviewed
in various sectors including education and medical care.

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Fundamental Revision of QE
The most closely watched statistic amongst the private sector
economists is the Quarterly Estimates of GDP (QE). But there havebeen many criticisms of the QE statistics. The fundamental problem
is the fact that the numerical values are revised quite dramaticallyafter the initial announcement. This is because the statistics used forthe estimations are different. This is not the same as changing thevalue after careful examination of the content of the statistics.
Revision of the standard year every five years will be based on the
Input-Output Table, but annual estimations are created around the
Census of Manufacturers. But since the Census of Manufacturers is
not ready yet for the first year of the annual estimation, the Current
Survey of Production is used.
On the other hand, QE uses the Current Survey of Production as
well as statistics from the demand side. Of those statistics, theFamily Income and Expenditure Survey by the Ministry of Internal
Affairs and Communications is influential for household financestatistics, as is the Financial Statements Statistics of Corporations byIndustry by the Ministry of Finance for business statistics.
The Family Income and Expenditure Survey is a tabulation ofsurveys conducted on around 8,000 households. The households
need to keep a household expenditure book and it is therefore said tobe a burdensome survey. Respondents to the survey tend to be theelderly or civil servants, and hence the sample is said to be biased.
Households who do not mind keeping books for their expenditures
tend to also be mindful of saving, and as a result consumption
expenditures may tend to be lower than the actual situation. Anotherproblem that is pointed out is the low number of samples for singleperson households often found in the younger generation and theelderly generation.
The problem with the Financial Statements Statistics ofCorporations is the time lag in when it is released. The statistics do
not make the release of the First QE which comes 90 days later
where the estimations are done using Current Production Statistics,
and the Financial Statements Statistics of Corporations is not useduntil the Second QE. Proposals from the Statistical Reform
Promotion Committee and others have prompted early data
contributions from businesses. While it will increase the burden on
businesses, being able to use data from Financial Statements
Statistics of Corporations for the First QE will lessen revisions being
made to the Second QE.

Sharing Economy
When creating statistics, we need to be careful not to miss riding
the new trends of economic society. Integrating “sharing economy”
into statistics is one such example.
Sharing economy is business intended to utilize idle assets ofindividuals, and the use of matching functions on the Internet is also
one of the characteristics. Examples include vacation rentals or
utilizing unused cars, buying and selling of unwanted goods (sharing
goods), lending costumes, and crowdfunding. In the case of vacation
rentals, self-employed owners register the vacation rentals with thebusinesses undertaking the matching, and users of the rentals makereservations through the website.
The problem with the statistics is private individuals running alodging service business. For business enterprises, there is already asystem established to account for the statistics for both private
enterprises and corporate enterprises, but there is no way tounderstand to what degree the individuals are engaged in sharing
economy-related activities. If an individual were to run a lodging
business, added-value will increase and therefore there is a need toinclude that in the GDP statistics. Looking ahead, there is a need tochange the statistics so that businesses run by individuals, not justlodging, are included. The size of sharing economy is not that large
for now, but it is fast expanding. There is a possibility that GDP will
be undervalued unless the statistics are fine-tuned.
Another challenge is how to account for free services on Social
Networking Service (SNS) in GDP. By using SNS, one can casuallycommunicate with acquaintances and exchange opinions. The
service provides enrichment. But if it is free, expenditure is zero andhence consumption does not increase. If consumption goes up via
SNS advertisements, the same amount should equal the increase in
GDP. There are also views that stating the difference in the amountthat one is willing to pay and the actual price (consumer surplus)
should be calculated and incorporated into GDP. It is still in the
research process, but it will be an endeavor that will greatly change
the way we view GDP (Chart 3).

Cooperation with Local Governments
The issue of national versus local is also important. Government
statistics are created by the central government, but the actual
survey relies much on regional statistics organizations such asprefectures.
For example, the Family Income and Expenditure Survey is under
the auspices of the Ministry of Internal Affairs and Communication,
but in reality the investigators that go around each household aremanaged by the local municipalities. The recent situation
surrounding the investigators is looking worse. The number of fulltime statistics personnel has consistently been declining inaccordance with the national capacity rationalization plan. Capacity
for fiscal 2017 was 1,702 staff, down 24% from fiscal 2004 (Chart 4).
The ageing of the investigators also continues and the percentage ofinvestigators who are 61 years or older rose from around 40% infiscal 2005 to 60% in 2015.
With the rise in people’s awareness of personal information
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CHART 3

Framework for sharing economy & free service on the Internet
Chart: Image of Sharing Economy
Payment ofcommission<Provider>
Service provider(individuals)
Skills and idle assets(bicycles, houses, etc.)
of individuals<Intermediary>
Platform formatching services(companies such as
Uber, Airbnb)

Payment of commission

Chart: Image of Free Service on the Internet

Payment ofcompensation

Advertiser
Sales ofbanners<Customer>
Service users(individuals)<Provider>

Providing services(car transportation,
lodging, etc.)

Payment ofadvertisement fee

Provide services etc.
(search services,
video image distribution,
apps, etc.)<Customer>
Service users(individuals)<Problem with understanding statistical information>• It is difficult to grasp the transaction since it is primarily a service transaction between individuals.
• It is difficult to grasp the value of the transaction since it is a service transaction that does not involve giving and receiving cash.
Source: Report on Research and Study of Rebuilding Service Statistics, Mitsubishi Research Institute, Inc., March 2017

CHART 4

Trends in appointed number of fulltime statistics staff in prefectures
Number of staff
3,000

2,242 2,219

2,000

0

2,146 2,103

2,060 2,017

1,956

1,839 1,839 1,811 1,811

1,775 1,739

1,702

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Fiscal year

Source: Director-General for Policy Planning, Ministry of Internal Affairs and Communications(officer in charge of statistics standard)

protection, surveys conducted by investigators have become
difficult. Entrances to apartment buildings have been locked andautomated, and thus the investigators are unable to go inside, which
has led to a situation where an entire building missed being surveyed
for the statistics.
In response, the Basic Plan created by the government went on todemonstrate support for improvement efforts and has begun
60 Japan SPOTLIGHT • July / August 2018measures to allocate more statistics staff accordingly. Pilot efforts
conducted so far include coordination with universities and others toutilize university student statistical investigators (Aomori Prefecture,
Chiba Prefecture), consultations with management associations formeasures on automatically locked buildings (Saitama Prefecture,
Okayama Prefecture), setting up an investigator development system(Aomori Prefecture), and convening study meetings to improvestatistical research (Nagasaki Prefecture).
Statistical representation of prefectures is also a major challenge.
Merely publishing the national total value after all the efforts
investigators have made in collecting the prefectural data
undermines the morale of the individual prefectures. Statistics byprefecture also become important when a major natural disaster,such as the Great East Japan Earthquake or the Kumamoto
Earthquake, strikes a region. Understanding the magnitude of thedisaster and the situation around reconstruction simply from figureson national value is impossible. The number of samples byprefecture is limited and there may be issues around the accuracy ofthe statistics, but it is hoped that efforts to create statistics byprefecture will be sought.

Nariyasu Yamasawa is a professor of the Faculty of Management at Atomi
University. He has worked for the Nihon Keizai Shimbun (Nikkei) and the Japan
Center for Economic Research, and is a former director of the Statistics
Commission Office at the Ministry of Internal Affairs and Communications.