PRI Discussion Paper Series (No.23A-04)

Top Income Shares in Japan from the Survey and Tax Data in2014 and 2019:
Following the Distributional National Accounts Guidelines
Visiting Scholar, Policy Research Institute, Ministry of Finance

Masahiro Mikayama
Visiting Scholar, Policy Research Institute, Ministry of Finance

Tomotsugu Imahori
Chief Economist, Policy Research Institute, Ministry of FinanceTaro Ohno
Senior Economist, Policy Research Institute, Ministry of FinanceYasutaka Yoneta
Director-General, Policy Research Institute, Ministry of FinanceJunji Ueda

October 2023

The views expressed in this paper are those of theauthors and not those of the Ministry of Finance or
the Policy Research Institute.

Research Department Policy Research Institute, MOF
3-1-1 Kasumigaseki, Chiyoda-ku, Tokyo 100-8940, JapanTEL 03-3581-4111

Top Income Shares in Japan from the Survey and Tax Data in 2014 and 2019:
Following the Distributional National Accounts Guidelines*1
Masahiro Mikayama*2
Tomotsugu Imahori*3
Taro Ohno*4
Yasutaka Yoneta*5
Junji Ueda*6
Abstract
This paper shows the results of estimating top income shares in Japan using the 2014 and 2019
household survey dataset and the newly aggregated tax dataset from income tax microdata
following the Distributional National Accounts (DINA) Guidelines. The guidelines are presenting
the concepts, data sources, and methods to keep consistency for international comparison and used
for making data series in the World Inequality Database (WID). By following the DINA procedure,
we combine the survey dataset and the newly aggregated income tax dataset for 2014 and 2019using the method presented by Blanchet et al. (2022). In addition, we consider various types ofincomes which are not included in the survey and tax datasets but are included in the Net National
Income (NNI) concept. The main results by using the most recent Japanese survey and tax data
show that the top 1% income share in Japan was 8.44%, and the top 10% income share was 33.79%
in 2019, which are much lower than the results for the United States and France presented in theWorld Inequality Report 2022 (Chancel et al., 2021).
Keywords: Top income shares, Inequality, Distributional National Accounts (DINA), Survey data,
Tax data, Net National Income (NNI)
JEL Classification: D3, E01, H2, J3*1 The contents of this paper represent the authors’ personal views and not the official views of the organizations towhich they belong. The authors express deep gratitude to Chiaki Moriguchi (Hitotsubashi University), Ryo Ishida(Policy Research Institute, Ministry of Finance), Kentaro Asai (World Inequality Lab), Eisuke Sato (Policy ResearchInstitute, Ministry of Finance), and Sehyun Hong (World Inequality Lab) for providing various helpful insights andcomments.
*2 Visiting Scholar, Policy Research Institute, Ministry of Finance*3 Visiting Scholar, Policy Research Institute, Ministry of Finance*4 Chief Economist, Policy Research Institute, Ministry of Finance*5 Senior Economist, Policy Research Institute, Ministry of Finance*6 Director-General, Policy Research Institute, Ministry of Finance-1-

I. Introduction
Since Thomas Piketty published a book in 2013 (Piketty, 2013) focusing on time-series income andwealth inequality data in European countries and the United States, it is attracting more efforts tomeasure income and wealth inequality based on more reliable data in an internationally comparable
way. One of the most systematic efforts to measure inequality in a comparable way across different
countries is the Distributional National Accounts (DINA) project implemented by researchers at theWorld Inequality Lab (WIL). The first version of the guidelines was released in 2016 (Alvaredo etal., 2016), and the most recent version was released in 2021 (Alvaredo et al., 2021). Piketty et al.
(2018) published income distribution in the United States, Garbinti et al. (2018) published income
distribution in France, and now the WIL publishes income and asset inequality for major countries
in the World Inequality Report (WIR), to the extent possible, in accordance with the methodology in
the DINA Guidelines. In the latest WIR, World Inequality Report 2022 (Chancel et al., 2021), figures
are given for the share of income of the top 1% and the share of the top 10% of income groups inmajor countries.
The DINA Guidelines set up the concepts and the methods to make internationally harmonizedtime-series data for income and wealth inequality based on the System of National Accounts (SNA)and provide procedures to use both statistical survey data and tax microdata. This paper shows theresults of estimating top income shares in Japan using the 2014 and 2019 household survey data andthe newly aggregated tax data from income tax microdata following the DINA Guidelines.
From the perspective of research for Japan’s income distribution, there have been extensive works
using statistical household survey datasets (Tachibanaki, 1998; Ohtake, 2005; Ohtake and Kohara,2010)1). However, it has been widely recognized that the statistical survey data lack the sufficient
distributional information about the high-income groups, and it is important to utilize both statisticalsurvey datasets and tax datasets. In this context, the most important work was Moriguchi and Saez
(2008) which estimated the shares of the high-income groups from 1876 to 2005 by combining semiaggregated tax datasets for self-assessed income tax and withholding tax for wage income. After
their seminal works for estimating top income shares in Japan, there have not been further updates
in line with the DINA Guidelines. This paper can shed light on the recent situation of top income
shares in Japan using the 2014 and 2019 data, and provide insights about how the top income shareresults from datasets and methods proposed by the DINA Guidelines could be different from previous
seminal works for Japan’s income distribution.
1) Tachibanaki (1998), Ohtake (2005), and Ohtake and Kohara (2010) are representative papers measuring the Gini
coefficient. Ohtake and Kohara (2010) measured the Gini coefficient from 1980 to 2004 using the Household Survey,
the Comprehensive Survey of Living Conditions, the National Survey of Family Income and Expenditure, and theSurvey on the Redistribution of Income. The results show that the Gini coefficient has increased in all statistics, but
the slope varies.

-2-

The major contributions of this paper are the following. First, it utilizes the semi-aggregated income
tax dataset which was newly created by Kunieda and Yoneta (2023) under the Joint Research
Program (JRP) adopted by the National Tax Agency (NTA). The advantage of using the new dataset
is that the concept of income for the tax microdata can be appropriately aligned with the concept of
the SNA. The most outstanding advantage is that we can accurately remove capital gains and losses
from income in the tax microdata. In addition, the tax dataset can provide more information on the
distribution of capital income, so we can more accurately reflect the distribution of capital income
in our results.
Second, this is the first paper which utilizes the method proposed by Blanchet et al. (2022) tocombine the Japanese statistical survey dataset and the tax dataset to estimate the whole income
distribution through replacing and reweighting. This approach enables us to fully utilize theinformation in both statistical survey dataset and tax dataset in a consistent manner to estimate thewhole income distribution.
Third, this paper makes extensive efforts to cover different types of incomes which are not coveredin the statistical survey dataset and the tax dataset to estimate the whole distribution of the annual
Net National Income (NNI) following the concept of the SNA. For this purpose, this paper clarifies
the other components of the NNI which are not covered in the household statistical survey dataset
and the tax dataset, such as imputed rents, capital incomes, retained earnings by corporations, etc.,
and then considers the distributions of individual components following the DINA Guidelines.
This paper is structured as follows. Section Ⅱ describes the definition of income and thecharacteristics of the data, and Section Ⅲ explains the method of combining tax and survey data.
Section IV explains the method for distributing the income components that are not included in thesurvey and tax datasets and shows the results of top income shares following the DINA Guidelines.
We also compare the results with the estimated results without using the new income tax dataset.
Section V provides a summary and the remaining research agenda.
Ⅱ. Definitions and data
In this section, we introduce the definitions of several concepts that need to be considered as a basis
for estimating income distribution, such as population, unit of observation, and types of incomes,
which are adopted in this paper referring to the DINA Guidelines. Then we describe thecharacteristics of the data used in this paper in detail. In particular, the semi-aggregated tax dataset
derived from the income tax return data under the JRP adopted by the NTA in 2022 is the key input
to the novelty of this paper.

-3-Ⅱ-1. Definitions
Ⅱ-1-1. Distributional National Accounts Guidelines (DINA Guidelines)
Since the beginning of the 21st century, there have been active efforts to use tax microdata to lookat the historical series of the top income share in a country, and in 2011, a number of researchers
created the World Top Income Database (WTID) to share the results. Subsequently, efforts have been
made to develop guidelines on how to measure income distribution by combining tax and statistical
data based on the SNA concept, in order to enable international comparisons based on a common
concept regarding the distribution of income and assets. The first version of the DINA Guidelines
was released in 2016 (Alvaredo et al., 2016), and the first edition of the WIR, World Inequality
Report 2018, was published in 2017 (Alvaredo et al., 2017) by the WIL. The most recent version of
the DINA Guidelines was released in 2021 (Alvaredo et al., 2021), setting standards and clarifying
concepts and methods so that the estimation results are comparable over time and across countries.
Ⅱ-1-2. Unit of observation, population, and income concept
As for the unit of observation for estimating income distribution, the DINA uses “equal-split adults”
series showing the results of incomes distributed to adults and distributed equally within couples orhouseholds as its benchmark series. It is easy to estimate the series of the equal-split adults incountries where the income tax is levied on joint incomes of couples or overall household incomes,
like the United States and France. However, the income tax in Japan is separately levied on individual
incomes, and there is no reliable tax data on the overall household incomes, and it is inevitable touse the series of “individualistic adults” distributing income specifically to each individual.
Therefore, it should be noted that the results in this paper cannot be perfectly comparable to thebenchmark series of other countries following the “equal-split adults” concept.
Regarding the population, the DINA Guidelines recognize “resident and adult population aged 20
years or older” as a benchmark, and this paper also follows the same definition, using “Population
Estimates” data provided by the Ministry of Internal Affairs and Communications (MIC).
According to the DINA Guidelines, it is aiming at showing the distribution of the entire NNI, whichis equal to Gross Domestic Product (GDP) plus Net Foreign Income (NFI) corresponding to theincome generated abroad but accruing to domestic residents minus Consumption of Fixed Capital(CFC). The concept of the aggregate income is fully consistent with the SNA, and therefore,
including certain types of incomes, like the undistributed profits of corporations, while excluding
capital gains and losses. In this paper, the NNI provided by the Cabinet Office (CAO) is used as theaggregate income in Japan.
In measuring income distribution across individuals, there are four different types of individual
incomes in the DINA Guidelines: (1) pretax factor income, (2) pretax post-replacement income(which we will generally abbreviate as “pretax income”), (3) post-tax disposable income, and (4)-4-post-tax national income. Among them, the pretax income concept is most favored as the benchmark
in the DINA Guidelines, and also adopted in this paper. This is because the distribution of pretax
income is less sensitive to the age structure of the population in a country than pretax factor
income2),3).
Ⅱ-2. Data
The DINA Guidelines provide the method to estimate income distribution by combining statisticalsurvey dataset and tax dataset. The former is expected to convey sufficient information about theincome distribution of low- and middle-income groups, while the latter is expected to reflect the
distribution of high-income groups accurately.
In this paper, we use the National Survey of Family Income, Consumption and Wealth (FICW,
provided by the MIC) as the statistical survey dataset for Japan. It is the largest household survey
statistics with 56,000 sample households in 2014 and 78,000 sample households in 2019, carried out
once in five years, providing individual income data for the members of households.
Since the FICW is a household-based survey, we need to calculate individual incomes of all themembers within households. For this purpose, the sum of major income components was calculated
for each member of households as the total income 4 ). As a result, a dataset with approximately
164,000 individuals were obtained from the 2019 FICW survey5).
As for the tax dataset, we use the dataset which was newly created by the JRP carried out by the
NTA in 2023 (“JRP tax data”) using administrative tax record data from 2014 to 2020. The dataset
was provided in a tabulated form as the annex tables of Kunieda and Yoneta (2023) by aggregating
individual self-assessed tax return data. The advantage of this dataset is that it provides various
tabulations based on different concepts of incomes, including the one in line with the DINA’s income
concept, such as excluding capital gains and losses and adding non-taxable incomes and deductions.
2) In the DINA Guidelines, it is explained that pretax factor income inequality can be artificially large in economies
with large retired people, and pretax national income can satisfy the neutrality condition that, in a hypotheticalsteady-state economy with 100% replacement rates for pensioners, the cross-sectional inequality of pretax national
income should be the same whether it is measured within the entire population (including pensioners) or within theworking-age population.
3) Pretax, post-replacement income is classified into (a) pension-based pretax income, which considers replacement
related to public pension, and (b) broad-based pretax income, which considers other replacement such as
unemployment benefits. This paper uses the pension-based pretax income because the distribution of unemployment
benefits is difficult to measure from available data in Japan. The Japanese tax law stipulates that unemployment
benefits are non-taxable and therefore, it is difficult to capture the distribution of benefits from tax return data, andthe statistical survey data do not have a sufficient number of household samples receiving unemployment benefits.
4) The sum of (1) annual income from employer, (2) income from agriculture, forestry, and fishing, (3) businessincome other than agriculture, forestry, and fishing, (4) annual income from side job, (5) annual income from rent
and land rent, (6) public pension and benefits, (7) corporate pension benefits, and (8) private pension benefits wascalculated from the individual data in the FICW.
5) In the FICW, weights are also given for the sample to be assigned back to the population. These are given to eachhousehold, and the individual data were given the same weights as households. The total number of weights is
96,551,000 in the 2019 FICW, which is slightly less than the adult population of Japan. Therefore, each weight ismultiplied by a certain ratio so that the total of weights is equal to the adult population of Japan.

-5-Therefore, it is not necessary to transform the income information in the tabulation to measureincome distribution in line with the DINA. However, although the JRP dataset covers all the selfassessed tax returns (22.7 million taxpayers), it still misses a large part of high-income groups
because workers with the earnings of salaries equal to or less than 20 million yen are not required tosubmit self-assessed tax return and can complete their tax reporting and payment process under thewithholding mechanism6). So, we need to use an additional tax dataset, the Statistical Survey ofActual Status for Salary (ASS), covering workers subject to withholding taxation mechanism. The
ASS is a sample survey conducted by the NTA on payments of salaries to employees at private
business establishments.
Without the JRP dataset, previous studies on Japan’s top income shares, including Moriguchi and
Saez (2008), used the dataset of the Sample Survey for Self-Assessment Income Tax (SAI, provided
by the NTA) to see the income distribution of high-income groups. The SAI is an annual sample
survey conducted since 1951, and its sample size was 2.3 million in 2019, providing tabulations by
income groups with overall taxable incomes. Therefore, it is necessary to make adjustments whentrying to estimate the income distribution in line with the DINA Guidelines. We show the results ofusing the SAI dataset following the methods used in the previous studies, and make a comparison to
the results of using the JRP data. Table 1 describes the characteristics of the data used in this paper.

6) The main conditions for salary earners to be required to file a tax return are (1) earning salary income of more than
20 million yen or (2) earning total income of more than 200 thousand yen except for salary income and retirement
income. Based on the above conditions, the term for complete tax filing is that the total income is more than 20.2
million yen, but in this paper, the term “over 20 million yen” is used for simplicity.

-6-

Table 1. Characteristics of survey and tax data

Tax data

Statistical survey data
Name

Advantage

FICW(National Survey of Family Income,Consumption and Wealth)Including a wide range of households,
both working and non-working, as the
population.

JRP(Joint Research Program
Income Tax Data)

SAI(Sample Survey for Self-Assessment
Income Tax Data)

Covering the entire population of selfassessed income tax return data,
including workers earning wage income
with 20 million yen and more.

Covering all the self-assessed income
tax return data as the population,
including workers earning wage income
Providing various tabulations, including with 20 million yen and more.
the one in line with the DINA income
concept.

ASS(Statistical Survey of Actual Status for
Salary in the Pricate Sector)

Including all the employees receiving
wage income as the population.

Incomes subject to withholding taxation(interest income, dividend income,
Incomes subject to withholding taxation
retirement income, etc.) are not covered.
(interest income, dividend income,retirement income, etc.) are not covered.
Workers earning below 20 million yen Providing tabulations for wage income
and pensioners receiving below 4 million only.
Disadvantage Limited coverage for top income groups.
Workers earning below 20 million yen
yen are not covered, unless they submit
and pensioners receiving below 4 million
tax returns by special reasons.
Public employees are not covered.
yen are not covered, unless they submit
tax returns by special reasons.
Providing tabulations only for taxable
incomes.
Population

Approx. 50 million households

Approx. 22 million people

Approx. 6.3 million people

Approx. 60 million people

Sample size

56,352 households (2014)
89,471 households (2019)

Census

1,137,592 people (2014)
2,307,506 people (2019)298,881 people (2014)
243,018 people (2019)

Available year

1954- (every 5 year)

2014-2020

1951-

1949-

-7-

Ⅲ. Methods of estimating income distribution from survey and tax data
This section describes a method for estimating income distribution using both statistical survey
data and tax data following the DINA Guidelines and several additional treatments which arerequired to use Japan’s income data described in Section II. As described in Figure 1, the first step isto derive the income distribution from the tax datasets using the JRP dataset or the SAI dataset
merged with the ASS dataset, while preparing the income distribution from survey dataset. This
procedure requires to adjust the concepts of incomes in the datasets considering the components of
the income datasets respectively following the income concept used in the DINA Guidelines, as
explained in Section III-1. Then, merging the ASS dataset by using the method presented in
Moriguchi and Saez (2008) is necessary to widen the “trustable span” as explained in Section III-2.
Finally, it is necessary to merge the income distribution from the tax dataset and the income
distribution from the survey dataset by using the method presented in Blanchet et al. (2022) as
explained in Section III-3. The results of the estimated income distribution are presented in Section
III-4, while the total income under the procedure in this section does not match with the NNI.

Figure 1. Flowchart of estimating income distribution using Japanese data

JRP

Tabulated form

Incometax data

or
SAI

Adjusting to the
DINA income
concept

Merging by
Moriguchi
and Saez(2008)
methodIncomedistribution
fromtax data

Merging byBlanchet et al.
(2022) method

ASS

FICW

Incomedistribution
fromsurvey data

Adjusting to the DINA income concept
Making individual income from household income

Incomedistribution
from survey
and tax data

Note:
Total income
does not matchwith the NNI

Ⅲ-1. Making income data under the common definition
To measure income distribution by combining various income datasets presented in Section II, it isnecessary to make the income data under the common definition with respect to the coverage of theincome to be measured so that they can be merged. Since the datasets have their own purposes anddata collection methods, the coverage of the overall incomes and the available data for income
components in the datasets are profoundly different. In addition, the measurement of the available-8-

income data can vary concerning whether they are evaluated after subtracting costs and otherdeductible incomes for tax purposes following the “income” concept, or before subtracting these
items following the “money received” concept.
Table 2 shows the coverage and the available components data within the overall incomes in thedatasets. The “C” mark indicates that the dataset provides the income components data which are
sufficiently comprehensive, while the “NC” mark indicates that the data are not comprehensive inthe dataset. The “#NA” mark indicates that the data are not provided in the dataset.
For example, the FICW dataset does not include data on capital gains and losses, as well as
occasional incomes, because it aims to capture the overall incomes with permanent characteristics.
As for the available data components, the FICW dataset and the ASS dataset provide salary data
following the money received concept, while the SAI dataset provides salary data following thetaxable income concept only. The advantage of the JRP dataset is that it provides salary data
following both concepts.
There are several reasons why the income components data cannot be recognized as comprehensive.
As for the JRP and SAI tax datasets, the full amount of income cannot be captured if a part of theincome is subject to separate withholding taxation. Because the majority of interest and dividends
receipts are subject to the withholding taxation mechanism, the data on the interest and dividends
income in the tax datasets only reflect a small part of the total financial income. Regarding the FICW
data, it is widely recognized that the responses to the survey questions about financial incomes andassets are significantly underreported. This is evident because the total sum of responses to the survey
is significantly less than the total amount of financial incomes presented in the SNA data7).

7) In 2019, the total value of interest and dividends income derived from the FICW data was 1.4 trillion yen, which

is 10.6% of the total financial income derived from the SNA data.

-9-

Table 2. Available income components data in the datasets

Name of income

Income /Money received

Tax data

Survey
FICW

JRP

SAI

ASS

Business income

Income

C

C

C

#NA

Agricultural income

Income

C

C

C

#NA

Forestry income

Income

C

C

C

#NA

Real estate income

Income

C

C

C

#NA

Wages and salaries income

Income

#NA

C

C

#NA

Wages and salaries

Money received

C

C

#NA

C

Miscellaneous income

Income

#NA

C

C

#NA

Pension income (public and private)

Income

#NA

C

C

#NA

Public pension

Money received

C

C

#NA

#NA

Private pension

Money received

C

C

#NA

#NA

NC

NC

NC

#NA

NC

NC

NC

#NA

Interest
Dividends

Income*Capital gains

Income

#NA

NC

NC

#NA

Occasional incomes

Income

#NA

NC

NC

#NA

Retirement income

Income

#NA

NC

NC

#NA

Social benefits other than public pension

Money received

NC

#NA

#NA

#NA

Other income

Money received

NC

#NA

#NA

#NA

*Interest income is the same as the money received for receipt of interest, while dividends income is the money
received for dividends minus interest payments for borrowing to buy the stocks.

Considering the availability of data and the comprehensiveness of each income component, we use
the sum of business income, agricultural income, forestry income, real estate income, wages andsalaries, and public and private pensions to estimate income distribution by combining the survey
dataset and the JRP dataset. Alternatively, we also estimate income distribution by using the SAI
dataset to make a comparison with the results using the JRP dataset. In this case, we need to convert
the wages and salaries data and public pension data in the SAI dataset from the income concept tothe money received concept to follow the common definition with the FICW dataset 8). Also, we needto subtract other incomes, such as capital gains and occasional incomes by making assumptions about
the share of these incomes so that the dataset can be merged to the FICW dataset.
The ASS dataset is used for supplementing income data of wage earners to the JRP and SAI datasets.
In this paper, we assume that income other than wages and salaries for the samples included in the
ASS dataset is negligible because if wage earners have different types of incomes with equal to or
more than 0.2 million yen, they need to submit self-assessed tax returns and they are included in thesample of the JRP and SAI datasets.
Ⅲ-2. Merging the JRP or SAI dataset with the ASS dataset
In general, survey data are expected to accurately represent the income distribution with respect to8) Conversion methods for employment income and pension income are explained in Appendix.

- 10 -

low- and middle-income individuals, while tax data are expected to accurately represent the income
distribution with respect to high-income individuals. Therefore, in each country, the DINA
Guidelines stipulate that the distribution of survey data should be used for individuals below a certain
marginal point income, while the distribution of tax data should be used for individuals above themarginal point income9 ). To apply this method, we need to assure that the tax data can provide
sufficiently reliable distribution data above the merging point. The DINA Guidelines provide theconcept of the “trustable span” within which the distribution data can be considered reliable because
the share of the population covered in the tax data is sufficiently high.
As for Japan’s tax dataset, the lower end of the trustable span of the JRP and SAI datasets is
recognized to be 20 million yen because only workers with wages and salaries more than 20 million
yen are required to submit tax returns of their self-assessed incomes. To avoid the situation in which
the merging point is outside the trustable span of the tax dataset, we combine the JRP dataset and the
SAI dataset with the ASS dataset, which provides better data on the distribution of workers with theearnings of salaries equal to or less than 20 million yen, in order to extend the trustable span of thetax dataset. For this purpose, we refer to the method used in Moriguchi and Saez (2008).
The first step is to enlarge the population of the ASS dataset to include workers in the public sector,
whose wages and salaries are also subject to withholding tax. Because the population of the ASS
dataset is approximately 52.5 million with workers in the private sector, while the number of workers
in the public sector is approximately 4.2 million, the ASS dataset is inflated with a multiple of 1.1
by assuming that the income distributions of public and private workers are similar.
The second step is to extract and remove duplicated samples included in both the JRP or SAI
datasets and the ASS dataset. The duplicated samples are those who earn wages and salaries andsubmit tax returns for some reason, for example, with wages and salaries of more than 20 million
yen. By estimating the number of duplicates and their total income for each income group using the
JRP or SAI datasets, they can be removed from the ASS dataset.
The third step is combining the JRP or SAI datasets with the adjusted ASS dataset. Both data aretabulated data, and the income class ranges of these data do not necessarily match. Therefore, bothdata are interpolated to create a distribution and then combined.
After combining the datasets, the lower end of the trustable span of the combined data is recognized
to be 4 million yen.
Ⅲ-3. Merging tax data and survey data
The method of merging the FICW survey dataset and the tax dataset (combination of the JRP and
the ASS or the SAI and the ASS) follows the one presented in Blanchet et al. (2022), using the9) The method of calculating the merging point income will be explained in Section III-3.

- 11 -STATA’s code “bfmcorr”. This procedure tries to exploit the strengths of both the survey dataset andthe tax dataset by setting an appropriate merging point and reweighting and replacing the samples.
The method is graphically illustrated in Figure 2, where 𝑓 𝑦 is the probability density function of
the survey dataset and 𝑓 𝑦 is the probability density function of the tax dataset. The intersectionof 𝑦 ∗ is called the merging point and different correction procedures are applied below the merging
point and above the merging point. The value of the density above the merging point should be
increased to the distribution of the survey dataset by replacing it with the tax dataset, because people
in high-income groups are underrepresented or unsampled in the survey dataset. Conversely, thevalue of the density below the merging point should be decreased by reweighting, because people inlower-income groups must be overrepresented in the survey dataset.
Figure 2. Image of the procedure combining survey and tax datasets

Source: Alvaredo et al. (2021, Figure 5.2)
Note: The solid blue line represents the density of the survey dataset 𝑓 𝑦 . The dashed red line represents the densityof the tax dataset 𝑓 𝑦 , which is only available for high-income groups.

Ⅲ-4. Results of income distribution
Table 3 presents three results of estimating income distribution for 2014 and 2019. The two lefthand columns show the results using a combination of tax and survey data; the first column is theresults using the JRP dataset and the second column is the results using the SAI dataset, while therightmost column shows the results using only the FICW survey data.
- 12 -

Apparently, the results of top income shares using the tax datasets are higher than the ones using
only the survey dataset, reflecting the richer information of high-income groups provided in the tax
datasets. For example, the top 10% income share in 2019 using the JRP dataset was 33.73%, which
is higher than the result using only the FICW (32.96%) by 0.77% point. The top 1% income share in2019 using the JRP dataset was 8.06%, which is also higher than the result using only the FICW
(6.83%) by 1.23% point.
The differences between the estimation results of top income shares using the JRP dataset and the
SAI dataset do not seem to be significant. In 2019, the top 10% income share using the SAI dataset
was 33.94%, which is a little higher than the result using the JRP dataset (33.73%) by 0.21% point,
while the top 1% income share using the SAI dataset was 7.80%, which is slightly lower than the
result using the JRP dataset (8.06%) by 0.26% point. Because the SAI dataset needs to be adjustedwith several assumptions to make the dataset to be merged to the FICW dataset, it is not ideal to use
the SAI dataset to precisely estimate the income distribution following the DINA Guidelines. The
advantage of the SAI dataset is that it is available since 1951 and can be used for estimating income
distribution for the period in which the JRP dataset is not available.

- 13 -

Table 3. Income distribution of survey and tax datasets before matching to the NNI<2014>
JRP+ASS, FICW
Income group

Number of adults

Income share

SAI+ASS, FICW

Income threshold(yen)

Average income(yen)

FICW only

Income share

Income threshold(yen)

Average income(yen)Income share

Income threshold(yen)

Average income(yen)

Full population

104,865,000

100.00%-2,757,886

100.00%-2,715,072

100.00%-2,640,310

Bottom 50%

52,432,500

12.49%-

728,328

12.28%-

704,779

12.52%-

698,843

Bottom 20%(P0-P20)

20,973,000

0.34%

-

69,8700.30%-

62,0790.29%-

60,121

Next 30%(P20-P50)

31,459,500

12.15%

490,000

1,167,300

11.98%

470,000

1,133,246

12.22%

460,000

1,124,658

Middle 40%(P50-P90)

41,946,000

50.17%

1,350,000

3,559,255

50.22%

1,300,000

3,514,596

51.09%

1,300,000

3,475,575

Top 10%

10,486,500

35.17%

6,070,888

9,700,814

35.25%

6,300,000

9,569,451

34.11%

6,160,000

9,005,501

Top 1%

1,048,650

8.45%

13,688,577

23,303,869

7.87%

13,253,270

21,377,939

7.01%

12,000,000

18,496,977

Top 0.1%

104,865

2.35%

34,705,832

64,693,871

1.90%

30,476,434

51,661,914

1.59%

27,800,000

41,981,543

Income threshold(yen)

Average income(yen)<2019>
JRP+ASS, FICW

SAI+ASS, FICW

Income threshold(yen)

Average income(yen)Income share

FICW only

Income threshold(yen)

Average income(yen)Income share

Income group

Number of adults

Income share

Full population

105,134,000

100.00%-3,096,502

100.00%-3,006,271

100.00%-2,862,276

Bottom 50%

52,567,000

14.40%-

937,023

14.26%-

900,671

14.55%-

874,564

Bottom 20%(P0-P20)

21,026,800

1.06%

-

199,545

1.00%-

185,0130.98%-

174,268

Next 30%(P20-P50)

31,540,200

13.34%

720,000

1,428,675

13.26%

710,000

1,377,77713.56%

700,000

1,341,428

Middle 40%(P50-P90)

42,053,600

49.78%

1,700,000

3,959,302

49.68%

1,611,664

3,839,006

50.36%

1,560,000

3,704,069

Top 10%10,513,400

33.73%

6,586,407

10,444,330

33.94%

6,508,804

10,204,215

32.96%

6,300,000

9,432,825

Top 1%

1,051,340

8.06%

14,846,112

24,949,369

7.80%

14,538,370

23,453,367

6.83%

13,320,000

19,558,000

Top 0.1%

105,134

2.18%

36,879,859

67,380,102

1.88%

33,674,809

56,410,535

1.43%

27,180,000

40,959,563- 14 -

IV. Distributing the other NNI incomes
As explained in Section II-1-2, the DINA Guidelines aim at showing the distribution of the entire
NNI for the purpose of comparing income distribution across different countries. The income data
used in Section Ⅲ, hereafter referred to simply as “merged incomes”, were not the same as the NNI
mainly for two reasons.
First, because the merged incomes data were selectively picked up in consideration of theavailability of distributional data and the comprehensiveness of each income component in thesurvey and tax datasets. Therefore, the merged incomes were much smaller than the overallhousehold incomes. This was because some types of household incomes, including retirement
income, interest, and dividends, were excluded from the survey and tax datasets. In addition, imputed
rents of owner-occupied dwellings are not recorded in the survey and tax datasets because there areno cash transactions, while the NNI should include them.
Second, the NNI includes several types of incomes which are not directly distributed to households
and thus, are not recorded in the survey and tax datasets and are not included in the merged incomesby any means. Examples of such types of incomes include the retained earnings of corporations, andthe incomes of the General Government (GG) and the Non-Profit Organizations Serving Households(NPISH).
In addition, even for the income components included in the merged incomes, such as wages andsalaries, there can be discrepancies between the total values of the incomes recorded in the survey
and tax datasets and the values in the National Accounts due to measurement issues, such as
differences in concepts and data coverage.
To make the comprehensive income distribution of the NNI following the DINA Guidelines, it isnecessary to consider the distribution of these types of incomes. In this section, we classify the
incomes which are not included in the merged incomes and then explain how the incomes are
distributed to individuals to make the final income distribution.
IV-1. Distributing other incomes of households
First, we clarify the income components of households which are not included in the merged
incomes in Section III, following the income concept of the National Accounts. Table 4 displays theincome components of households for the allocation of primary incomes and the secondary
distribution of incomes for households. The second column shows the incomes which are alreadyincluded in the merged incomes in Section III, and the third column provides the category of otherincomes which need to be considered in this section.

- 15 -

Table 4. Income components of households
Income components
Resources
Operating surplus, net
Mixed income, net

Merged incomes

Other incomes of householdsーBusiness income,
Agricultural income,Forestry income

Imputed rents of owner-occupied dwellingsーCompensation of employees
Allocation of Primary Income(1) Wages and salaries(2) Employers' social contributions

Wages and salaries

ーー*currnet costs incurred by employers (A)Property income(1) InterestーInterest(2) DividendsーDividendsー

Investment income attributable toinsurance policyholders

ー*interest costs incurred for retirement benefit obligations (B)(3) Other investment incomea) Investment income attributable to insurance
policyholdersb) Investment income payable on pension entitlementsc) Investment income attributable to collective
investment fund share holders(4) Rents

Real estate income

Investment income attributable tocollective investment fund share holdersーーInterest (-)

ー(omitted)

Public pensionーUses(1) Interest(2) Rents
Resources
Social benefits other than social transfers in kindSecondary Distribution of Income(1) Social security benefits by cash(2) Other social insurance pension benefitsー(3) Other social insurance nonpension benefitsーーRetirement incomes form accrual entities(including private pensions)
Retirement incomes from non-accrual entities(4) Social assistance benefits in cashー

outside the scope of pretax incomeーoutside the scope of pretax incomeーoutside the scope of pretax income(1) Employers' social contributionsー(2) Households' actual social contributions

ーー*the same amount as (A)Social contributions(public pension only for the scope of pretax income)*the same amount as (B)ーoutside the scope of pretax income

Other current transfers
Uses
Current taxes on income, wealth, etc.
Net social contributions(3) Households' social contribution supplements
Other current transfers

The DINA Guidelines provide methods to be applied to each type of income for making an imputed
distribution, either based on external sources or using the same distribution as other types of incomein the datasets. If there are high-quality data, it is recommended to adopt detailed methods, while
simplified imputations can be used in more problematic contexts10).
Table 5 shows the summary of the methods mentioned in the DINA Guidelines and the actual
methods used in this paper for individual income types of households in consideration of data
availability. The individual methods applied to each type of income are briefly described below.

10) The methods of imputing missing pretax income components recommended by the DINA Guidelines areprovided in Table 5.4 of Alvaredo et al. (2021).

- 16 -

Table 5. Distribution methods for other income components of households
Estimated total
amounts in 2019
(trillion yen)

DINA allocation
methodAllocation method in this paper(a) Interest+5.6 (received)-8.7 (paid)

Pretax income(simplified)

Pretax income(b) Dividends

6.5

Pretax income(simplified)Amounts shown on tax returns: JRP
Amounts withheld: pretax income(c) Investment income attributable tocollective investment fund share holders

0.5

Capital income

Capital income(d) Investment income attributable toinsurance policyholders

8.3

Wages

Wages

(e) Imputed rents of owner-occupied dwellings

21.1

Consumption/Wages

Estimated from the survey dataset(FICW)(f) Retirement income

8.9

ー

Wages

(g) Social contributions for public pension

-20.2

Wages

Estimated from the survey dataset(FICW)(a) Interest
Most interest income is subject to separate withholding taxation under the Japanese tax system, so
there is no reliable information in the tax datasets. The FICW survey dataset does not provide reliable
information either about the distribution of financial income, which is the sum of interest anddividends, due to significant underreporting.
Therefore, in accordance with the simplified recommendation of the DINA Guidelines, thedistribution of interest is based on the proportion of pretax income. The total amount is controlled
by the net interest (received interest minus paid interest) of households in the National Accounts data.
Since the total share of net interest is quite small (0.4% of the total NNI), it is unlikely to have asignificant impact on the results of income distribution in this paper.

(b) Dividends
Under the Japanese tax system, dividends income is subject to three types of taxation: (1) declared
as a part of aggregate income subject to comprehensive taxation (for large amounts of dividends
receipts or dividends receipts by majority shareholders), (2) separately declared and subject todifferent tax rates, and (3) subject to withholding taxation (optional). Therefore, it is not possible toknow the full distribution of the overall dividends income from the JRP and SAI datasets, and the
FICW cannot provide reliable distributional data due to significant underreporting.
So, we apply the distributional data from the tax datasets to the declared part (1) and (2), while theallocation of the withheld part (3) is based on the proportion of pretax income with the assumptionthat the total amount of dividends in the National Accounts minus the amount of declared dividends
is subject to withholding tax. The annex tables provided in Kunieda and Yoneta (2023) include Case
- 17 -

2 (income data including dividends) and Case 3a (income data excluding dividends), and thedifference between these two can be seen as the declared dividends in each bracket11).

(c) Investment income attributable to collective investment fund shareholders
Investment income attributable to collective investment fund shareholders is the retained profits incollective investment vehicles. Due to the limitation of data on the distribution of such incomes, we
adopt the proportion of capital income, which is the sum of interest and dividends, to allocate theincome. The total amount is controlled by the amount in the National Accounts data.

(d) Investment income attributable to insurance policyholders
Investment income attributable to insurance policyholders is the income earned by life insurancecompanies. Because it is difficult to accurately estimate the distribution of insurance premiums paidby individual households, we adopt to allocate the income in proportion to wages and salaries byassuming that the distribution of life insurance policies depends on wages. The total amount is
controlled by the National Accounts data.
(e) Imputed rents of owner-occupied dwellings
When households reside in their own dwellings, they do not actually pay rent, but are treated as
producing and consuming services equivalent to the rent they would pay if they valued their own
housings at market prices. This is called the imputed rents of owner-occupied dwellings. Since
imputed rents are not subject to taxation in Japan, the tax dataset does not provide any information
on imputed rents.
In this paper, we first estimate the imputed rents of owner-occupied dwellings by using the FICW
survey dataset, and then use the value to allocate the total imputed rents in the National Accounts
data12). The estimates are based on the estimation formula published by the MIC, which consolidates
the FICW 13 ). For regional classifications, we used the average value for each prefecture 14 ). The
imputed rents of owner-occupied dwellings, estimated at the household level, are allocated toindividuals in proportion to the income of each household member. For the samples replaced in theprocedure of merging the datasets, the imputed rents were estimated by using the relationship
11) Since the JRP data are bracketed data, it is difficult to make a simple comparison between the two brackets
because the number of people in each bracket differs between Case 2 and Case 3a due to the difference in whether
dividends are included or not. Therefore, the interpolation method of Moriguchi and Saez (2008) is used to convert
the data into data with thresholds for each percentile and average income in each bracket for comparison. In making
the distribution, the assumption is made that the rank order of total income in the sample is not reversed before andafter the distribution of dividends.
12 ) Since imputed rents of owner-occupied houses are not subject to taxation in Japan, tax data do not provide
sufficient information on imputed rents of owner-occupied houses.
13) See Ministry of Internal Affairs and Communications (2014, 2019).
14) We confirm that the estimates from the simplified model (household average) are almost the same as the estimatesof imputed rents of owner-occupied houses published by the MIC.

- 18 -

between the original incomes and the estimated rents. Finally, the estimated imputed rents of owneroccupied dwellings for each sample are adjusted to the total amount in the National Accounts data.
(f) Retirement income
Retirement income refers to lump sum payments, such as retirement allowance or lump-sum benefit
paid at the time of retirement. In Japan, large amounts of retirement benefits are generally paid at thetime of retirement to employees of large corporations and public officials who have been
continuously employed for a long period of time. Therefore, how the amount received at the time ofretirement is treated is an important factor when considering income distribution.
The total amount of retirement income is estimated to be 8.9 trillion yen in 2019, which amounts to
1.6% of annual GDP, or 2.0% of the NNI15). However, there are no reliable data on the distributionof lump-sum cash transfers because the FICW does not survey retirement income. In addition, thetax datasets for self-assessment declaration rarely cover retirement income because it is subject toseparate withholding taxation.
So, in this paper, we adopt an approach that retirement income is distributed to individual wage
earners on an accrual basis by assuming that retirement income is a deferred payment of wages andsalaries. The total amount of retirement income derived from the NTA’s Annual Statistics Report(ASR) is distributed by using the proportion of wages and salaries in the FICW dataset. For the
samples replaced by the procedure of merging the survey and tax datasets, the amount of wages andsalaries is appropriately adjusted16).
(g) Social contributions for public pension
In this paper, we estimate the income distribution for pretax income before the redistribution of tax
systems. In the meantime, the benefits of public pension are to be included in the household’s receipts,
and the social contributions for public pension need to be subtracted. While the total amount of actualsocial contributions for public pension by households can be derived from the National Accounts
data, which amounts to 20.2 trillion yen in 2019, but there are no clear data about the distribution ofsocial contributions at the individual level.
We use a method proposed in Ohno et al. (2022) to estimate the social contributions for public
pension at the individual level from the FICW dataset. The first step is to allocate household members

15) The amount of retirement income is reported in the ASR by the NTA; 1.5 trillion yen for public workers, and 7.4

trillion yen for others.
16) In the annex tables of Kunieda and Yoneta (2023), Case 3a represents the number of people and average amountin the total income bracket, and Case 3b represents the number of people and average amount of salary earners in
the total income bracket. The total income for each bracket is obtained by multiplying the number of people andaverage income in these two brackets, and the total income and total wage are used to calculate the percentage ofwage in each bracket. For the replaced sample, wage is estimated by multiplying the sample’s total income by thepercentage of wage in the relevant bracket.

- 19 -

in the FICW samples to the categories of public pension systems by considering information on
income and relationship between household members. It is necessary to estimate the social
contributions for pension because the contribution rates are different by the categories of public
pension systems. Category Ⅰ members (under the National Pension Insurance system) are required to
pay a lump-sum contribution, and Category II members (under the Employees’ Pension Insurance
system) need to pay contribution with a certain proportion of wage income. Category Ⅲ members(dependent spouses of Category II members) are not required to pay social contributions17).
The second step is to calculate the average pension contribution ratio (pension contributions divided
by total income) for each income bracket. For the samples replaced by merging procedure, theamount is appropriately adjusted. Then, the total amount of actual social contributions is distributedby referring to the estimated individual social contribution, and then subtracted from the otherincomes.
IV-2. Distributing primary incomes of other institutional sectors
As shown in Table 6, the NNI in 2019 was 444.9 trillion yen, of which 343.5 trillion yen was
allocated to households at the stage of primary income allocation. The share of allocation to thehousehold sector was 77.2% of the total NNI, which means that the remaining 22.8% was notallocated to households. For example, 58.1 trillion yen was recorded as non-distributed corporate
income, and 43.0 trillion yen was recorded as income of the GG. To make the distribution of the NNI
as a whole, it is necessary to consider how these types of incomes should be imputed to households.
Table 6. Composition of the NNI of institutional sectors
2014

2015

2016

2017

2018

2019

2020

2021

Gross Domestic Product (GDP)
Receipts of Foreign Income (net)

518.8
19.3

538.0
21.2

544.4
18.9

553.1
20.5

556.6
21.3

557.9
21.9

539.1
19.6

549.4
26.7

Gross National Income (GNI)

538.1

559.2

563.3

573.5

577.9

579.8

558.7

576.0

-126.2-0.2-128.1-0.1-128.20.0-130.10.1-132.4-0.1-134.5-0.4-135.6-0.9-138.7
1.5

Net National Income (NNI)

411.7

431.0

435.1

443.6

445.5

444.9

422.1

438.8

[Factor income composition of NNI]
Compensation of employees
Operating surplus and mixed incomes
Net receipt of property income from abroad
Taxes on production - subsidies

257.5
97.3
19.2
37.7

260.6
107.8
21.1
41.6

267.4
107.0
18.8
41.9

272.1
108.6
20.4
42.5

281.4
100.0
21.2
43.0

286.9
92.8
21.7
43.4

283.2
74.8
19.5
44.7

288.7
76.6
26.6
46.9

[Sectoral composition of net primary income]Households
NPISH
Corporations
General Government

318.2
0.3
58.8
34.4

323.2
0.3
68.7
38.8

327.9
0.3
67.9
39.1

333.5
0.3
69.5
40.4

339.2
0.3
64.1
41.8

343.5
0.3
58.1
43.0

337.8
0.3
39.9
44.1

343.9
0.3
48.1
46.5

Consumption of Fixed Capital
Statistical Discrepancy

Source: National Accounts17) Under the pension system in Japan, anyone between the ages of 20 and 60 is covered by one of the plans.

- 20 -

The DINA Guidelines provide procedures for the imputation of these incomes to households. In
general, we follow the methods proposed by them for the GG and the NPISH to allocate the incomesin proportion to the distribution of the pretax factor income, which is equal to the sum of all pretax
income flows accruing directly or indirectly to the owners of the factors of production (labor andcapital), before the operation of the tax and transfer system, including indirect taxes, and before theoperation of the social insurance systems. When considering undistributed corporate profits, it isrecommended to distribute them in proportion to direct or indirect stock ownership. Therefore, we
carefully consider the stock ownership in Japan by using the Flow of Funds Accounts (FFA) data
provided by the Bank of Japan (BOJ).
(a) Income of the General Government
The primary incomes of the government consist of taxes on production and imports and property
income. Taxes on production and imports are distinguished from ordinary taxes imposed on income,wealth, etc. and capital taxes in that they are considered to constitute part of the cost of production
for the producer and are recorded only as the GG’s receipts in the primary income distribution
account. They are broadly divided into (1) taxes on production and (2) other taxes on production.
The former includes value-added tax, customs duties, and excise tax, the latter includes property
taxes, stamp revenue taxes, etc. Property income of the government consists of net receipts of interestand dividends by the government. A uniform proportionally scaling-up method is applied to the
distribution to pretax income for these incomes.
(b) Income of the Non-Profit Institutions Serving Households
The NPISH are organizations that provide social and public services to households without pursuing
profits, and specifically include political parties, religious organizations, labor unions, and private
schools. The DINA Guidelines recommend distributing the income of NPISH, which is usually avery small amount compared to other incomes, proportionally to the rest of factor income. In thispaper, we follow the method proposed in the DINA Guidelines.
(c) Undistributed corporate profits
Undistributed corporate profits refer to the profits that are not distributed to other sectors throughdividends etc., and are the sum of retained earnings of firms before paying corporate income taxes18).
Since undistributed corporate profits should be imputed to shareholders, the shareholding structure
in Japan needs to be carefully considered. The DINA Guidelines propose to use a method of breaking18) Since the income concept estimated in this paper is “Pretax, post-replacement income”, “Current taxes imposedon income, wealth, etc.” is assumed to be pretax for both the household and corporate sectors.

- 21 -

down the shareholders into households, government, and foreign sector. In this paper, we also
explicitly consider the shareholding structure through the BOJ, life insurance companies, andinvestment trusts as shown in Figure 3.
Figure 3. Structure of shareholding and distribution methods

Note: This figure follows the classification of the FFA.

The reason for treating the BOJ separately is that the BOJ has increased its equity holdings in recent
years through its ETF holdings. The profits generated by its equity holdings are ultimately paid into
the national treasury, and therefore, the BOJ’s holdings should be treated in the same manner as thegovernment’s holdings. The reason for treating life insurance companies separately is that the main
insurance companies in Japan are mutual companies, and the retained earnings need to be imputed
to the policyholders, which are mainly households. In addition, the ownership of investment trusts
should be carefully considered by using the FFA.
If the imputed shareholders are other than the five sectors (households, government, BOJ, life
insurance companies, and foreign sector) through cross-shareholdings, they are also repeatedly
imputed to shareholders reflecting the ratio of shareholders to ultimately eliminate the share of theother sectors.
The distribution methods of the undistributed corporate profits imputed to each sector follow themethods presented in the DINA Guidelines. As for households, they are allocated in proportion tocapital income. The imputed profits to the government are allocated in proportion to pretax income,as well as the imputed profits to the BOJ. The imputed profits to the foreign sector are not distributed.
The imputed profits to life insurance companies are allocated in proportion to wage, on theassumption that the share of insurance policies held by individuals is proportional to wage income.
IV-3. Results of income distribution
The results of the distribution of income other than the merged incomes discussed in the previous- 22 -

section are shown in Table 7.
For 2019, the results using the JRP dataset show that the shares of the higher income classes, suchas the top 10%, 1%, and 0.1%, were higher than the results shown in Table 3. For example, the top
1% share was 8.44%, which is higher by 0.38% point than the result in Table 3. This indicates thatthe distribution of incomes other than the merged incomes is more skewed toward the top income
classes than the distribution of the merged incomes. These conclusions seem to be reasonable because
the incomes recognized in this section include dividends, imputed rents, and undistributed corporate
profits, which would be expected to be skewed toward the top income classes.
Comparing the 2014 and 2019 results in Table 7, we observe that the income share of the top decile
above the top 10% in 2019 is lower than in 2014. This result is also the case for all the cases in Table
3, including the case using only the FICW survey dataset. Although a more detailed study should beneeded to explain the recent reduction of the top income shares, it should be noted that in Table 6,
the NNI increased from 411.7 trillion yen to 444.9 trillion yen between 2014 and 2019 byapproximately 8%, and compensation for employees, including wages and salaries, has risen from257.5 trillion yen to 286.9 trillion yen by approximately 11%. This means that the increase in the
NNI was largely due to the higher contribution of the increase in wages received by employees. Since
the distribution of wages is generally considered to be more equally distributed, it seems to bereasonable to understand that the reason for the recent change in the distribution of the NNI can be
attributable to the increase in the share of wages and salaries. In the meantime, it should be notedthat in this paper we obtain the distribution of income per individualistic adult, not the distributionof income per household. If the labor participation rates of women and the elderly have been
increasing in recent years, thereby increasing the incomes of individuals who have not previously
earned income, the income share of the top income groups may decline. In addition, the NNI does
not include capital gains and losses, and the results in Tables 3 and 7 do not show any distributional
impacts of changes in capital gains and losses on individuals. According to Kunieda and Yoneta(2023), the estimated results of the Pareto coefficient of individuals have been gradually increasing
in recent years, and this is not contradicting to the results in this paper, because the overall householdincomes in their paper include capital gains and losses. To derive further implications for overall
inequality and necessary policies in Japan, it is necessary to consider the distributional impacts ofcapital gains and losses.

- 23 -

Table 7. Distribution of the NNI in Japan<2014>
JRP+ASS, FICW

SAI+ASS, FICW

Income group

Number of adults

Income share

Income threshold(yen)

Average income(yen)Income share

Income threshold(yen)

Average income(yen)

Full population

104,865,000

100.00%-3,964,146

100.00%-3,964,146

Bottom 50%

52,432,500

12.77%-

1,068,660

12.80%-

1,071,555

Bottom 20%(P0-P20)

20,973,000

0.37%

-

107,164

0.33%-

98,901

Next 30%(P20-P50)

31,459,500

12.40%

728,862

1,709,657

12.47%

728,745

1,719,991

Middle 40%(P50-P90)

41,946,000

49.90%

1,989,607

5,087,668

50.08%

2,002,010

5,114,100

Top 10%

10,486,500

35.19%

8,581,738

13,948,377

34.88%

9,012,682

13,828,803

Top 1%

1,048,650

8.86%

19,466,627

35,140,438

8.07%

19,383,754

31,997,516

Top 0.1%

104,865

2.70%

53,912,742

107,119,133

2.11%

45,379,910

83,594,633<2019>
JRP+ASS, FICW

SAI+ASS, FICW

Income group

Number of adults

Income share

Income threshold(yen)

Average income(yen)Income share

Income threshold(yen)

Average income(yen)

Full population

105,134,000

100.00%-4,229,084

100.00%-4,229,084

Bottom 50%

52,567,000

14.69%-

1,304,371

14.62%-

1,297,889

Bottom 20%(P0-P20)

21,026,800

1.12%

-

286,687

1.06%-

275,325

Next 30%(P20-P50)

31,540,200

13.57%

1,032,721

1,982,827

13.55%

1,034,780

1,979,598

Middle 40%(P50-P90)

42,053,600

49.45%

2,331,566

5,369,727

49.31%

2,323,476

5,360,023

Top 10%10,513,400

33.79%

8,908,574

14,290,558

33.96%

9,106,093

14,361,826

Top 1%

1,051,340

8.44%

20,300,674

35,685,004

8.07%

20,680,559

34,119,270

Top 0.1%

105,134

2.47%

54,266,914

104,441,336

2.07%

48,745,012

87,649,594- 24 -

Furthermore, Table 8 compares the 2019 results for the top 1% and top 10% income shares withthose of other countries. As already mentioned, the results for Japan are not based on the equal-split
amount of household income, but show the distribution of income based on the individualistic adults.
Therefore, in a strict sense, they are not simply comparable to the results for the United States andFrance. In general, the distribution based on the individualistic adults is expected to have a higher
share of the upper income bracket than the equal-split distribution, since the equal-split treatment
results in an equal division of income in the higher income brackets. For Japan, the share of the top
1% is lower than that of the other countries, even though it shows the results based on theindividualistic adults. This may indicate that the share of the top 1% income bracket is clearly smallerin Japan than in other countries as for the distribution of the NNI.
Table 8. Income distribution, comparison with other countries
2014

2019

Top1%

Top10% Bottom50%

Top1%Top10% Bottom50%

Japan

8.9%

35.2%

12.8%

8.4%

33.8%

14.7%

United States

19.0%

45.6%

13.1%

19.1%

45.7%

13.6%

France

10.5%

32.6%

21.9%

10.0%

32.4%

22.6%

China

13.7%

41.6%

14.4%

14.7%

42.4%

14.0%

South Korea

9.0%

31.8%

21.3%

11.7%

34.6%

20.8%

Note: The values of the United States, France, China, and South Korea are taken from the WID 2022.

As for the top 10% income share, Japan's figure is lower than that of the United States, but higher
than that of France and South Korea. These results may be caused by the fact that the results forJapan are based on the individualistic adults rather than the equal-split amount, and need caution
when interpreting the results. If the differences in wages between male and female workers arelarge, the results for the share of income based on the individualistic adults may be much higher
than the equal-split results. We do not have sufficient data for Japan to make the estimation basedon the equal-split amount, but this is a major challenge for the future research.
V. Conclusion
This paper shows the results of estimating top income shares in Japan using the 2014 and 2019
household survey dataset and the newly aggregated tax dataset from income tax microdata, following
the Distributional National Accounts (DINA) Guidelines. By following the DINA procedure, we
combine the survey dataset and the newly aggregated income tax dataset for 2014 and 2019 using- 25 -

the method presented by Blanchet et al. (2022). In addition, we consider various types of incomes
which are not included in the survey and tax datasets, but are included in the Net National Income(NNI) concept. The main results using the most recent Japanese survey and tax data show that the
top 1% income share in Japan was 8.44%, and the top 10% income share was 33.79% in 2019, which
are much lower than the results for the United States and France presented in the World Inequality
Report 2022.
This paper contributes to the literature of the income distribution in Japan in two ways. First, byusing the newly available tax dataset, we provide the more accurate and internationally comparable
income distribution of pretax incomes in 2014 and 2019 for Japan. Second, we carefully followed
the procedures to distribute the overall NNI in Japan in addition to the merged incomes. These wereachieved through carefully following the methods provided by the DINA Guidelines.
Finally, it should be noted that several limitations of the dataset do not allow for a complete
international comparison. The estimates in this paper are based on the individualistic adults and are
not strictly comparable to the results estimated by the equal-split amounts. In addition, for someincomes subject to separate taxation, it is difficult to know their distributions. Further improvements
will be possible when additional data related to these issues become available in the future. Also,
when interpreting the results in this paper, it is necessary to consider that the analysis is limited to
the NNI, not including capital gains and losses, and that further aspects need to be considered whentrying to understand the holistic views of inequality in Japan, including the impacts of wealthinequality.

- 26 -

References
Alvaredo, F., Atkinson, A. B., Chancel, L., Piketty, T., Saez, E., and Zucman, G. (2016)“Distributional National Accounts (DINA) Guidelines: Concepts and Methods Used in
WID.world”, WID.world Working Paper, 2016/2, World Inequality Lab.
Alvaredo, F., Chancel, L., Piketty, T., Saez, E., and Zucman, G. (2017) “World Inequality Report
2018”, World Inequality Lab.
Alvaredo, F., Atkinson, A. B., Blanchet, T., Chancel, L., Bauluz, L. E. et al. (2021) “Distributional
National Accounts Guidelines: Methods and Concepts Used in the World Inequality Database”,
Research Report, World Inequality Lab.
Blanchet, T., Flores, I., and Morgan, M. (2022) “The Weight of the Rich: Improving Surveys Using
Tax Data”, The Journal of Economic Inequality, 20, pp.119-150.
Chancel, L., Piketty, T., Saez, E., and Zucman, G. (2021) “World Inequality Report 2022”, World
Inequality Lab.
Garbinti, B., Goupille-Lebret, J., and Piketty, T. (2018) “Income Inequality in France, 1900-2014:
Evidence from Distributional National Accounts (DINA)”, The Journal of Public Economics, 162,
pp.63-77.
Kunieda, S. and Yoneta, Y. (2023) “Significance of a Tax Data-Based Analysis of Japanese Income
Tax System”, NTC Joint Research Discussion Paper Series, 230100-01ST, National Tax College.
(in Japanese)
Ministry of Internal Affairs and Communications (2014) “Creating Attributable Rent Weights for
Owner-Occupied Homes” (in Japanese, last viewed on September 30, 2023)
https://www.stat.go.jp/data/cpi/2015/kaisetsu/pdf/fu3.pdf
Ministry of Internal Affairs and Communications (2019) “Methodology for Estimating Attributable
Rent of Owner-Occupied Households” (in Japanese, last viewed on September 30, 2023)
https://www.stat.go.jp/data/zenkokukakei/2019/pdf/kizoku.pdf
Moriguchi, C. and Saez, E. (2008) “The Evolution of Income Concentration in Japan, 1886-2005:
Evidence from Income Tax Statistics”, The Review of Economics and Statistics, 90(4), pp.713734.
Ohno, T., Imahori, T., and Kojima, T. (2022) “Reduction and Redistribution Effects of Increasing
Income Tax Deductions in Income Taxation and Inhabitant Taxation”, PRI Discussion Paper
Series, 22A-03, Policy Research Institute, Ministry of Finance. (in Japanese)
Ohtake F. (2005), Inequality in Japan: Illusion and Future of Inequality Society, Nihon Keizai
Shimbun, Inc. (in Japanese)
Ohtake, F. and Kohara, M. (2010) “Income Inequality”, in Higuchi, Y. (ed.), Labor Market andIncome Distribution (Series “The Japanese Economy and Economic Policy during the Bubble /- 27 -

Deflation Era”), Keio University Press. (in Japanese)
Piketty, T. (2013), Capital in the Twenty-First Century, Éditions du Seuil. (in French)
Piketty, T., Saez, E., and Zucman, G., (2018) “Distributional National Accounts: Methods andEstimates for the United States”, The Quarterly Journal of Economics, 133(2), pp.553-609.
Tachibanaki, T. (1998), Japan’s Economic Inequality: A Consideration from Income and Assets,
Iwanami Shoten, Publishers. (in Japanese)- 28 -

Appendix: Treatment of data in the Sample Survey for Self-Assessment Income (SAI)
The results of the SAI are only published in a tabulated form, and it is necessary to adjust the data
to be merged with the survey dataset under the common income concept. To make the merged income,
we need to have the income distribution of the sum of (1) business income, (2) agricultural income,(3) forestry income, (4) real estate income, (5) wages and salaries income, (6) miscellaneous income,and (7) pension income. Therefore, the other incomes need to be deleted from the bracket data. The
thresholds of the bracket also need to be revised accordingly. In addition, wages and salaries income
and pension income must be corrected to the concept of money received.
The correction method of removing the other incomes is to reduce the amounts of other incomesfor each bracket, and the threshold of each bracket is also reduced proportionally. The correction toemployment earnings is made by calculating the average employment income in each bracket andadding the estimated deductions. The threshold for each bracket is also increased by the rate ofincrease in that total income. The conversion from pension income to pension earnings is the same
method as in the case of employment income.

- 29 -