Measuring Poverty in Japan from a
Multidimensional Perspective
Jun Matsuyama∗
September 2016

Preliminary and incomplete draft
Abstract
In this paper, following a methodology developed by Alkire and Foster (2011), we
define a multidimensional poverty index (MPI) consisted of three dimensions such as
consumption, wealth, and dwelling environment. By using household data from the
National Survey of Family Income and Expenditures, we compute the MPI at national
level. Using the decomposable property of the index, we also estimate the MPI by four
sub-groups such as single parent, two parents, three generation, and childless households. We analyze intertemporal changes in multidimensional poverty in Japan.
Keywords: the capability approach, poverty, multidimensional, the adjusted headcount∗Faculty of Economics, University of Toyama, 3190 Gofuku, Toyama, 930-8555, Japan.
Email: mazyama@eco.u-toyama.ac.jp Financial support of Hokuriku Bank is gratefully acknowledged. I am
also grateful to the Research Center for Information and Statistics for Social Sciences at Hitotsubashi University and the National Statistics Center for providing the raw data from the National Survey of Family Income
and Expenditures. I would like to thank Sabina Alkire, Antonio D’Agata, Kenji Mori, Oleksandr Movshuk,
and Naoki Yoshihara. Usual disclaimers apply.

1

1

Introduction

A few years ago, the Health, Labor and Welfare Ministry in Japan reported new calculations
on poverty rates since the mid-1980s. The report clarifies that both overall and child poverty
rate have been continuously increasing over the past 30 years. Especially, child poverty rate
in 2012 (16.3%) seemed to be recognized as shocking news among people in Japan. Sincea publication of the oﬃcial report, government makes related laws and launches various
policies in order to alleviate the situations.
Among almost all poverty related issues including the case mentioned above, a focal
variable is just an economic dimension such as income or consumption. Although an economic dimension is practically essential, one’s well-being might not be appropriately evaluated from a unidimensional point of view. In this context, some thinkers pay attention toa multidimensionality of a person’s well-being. For example, according to the capability
approach (Sen 1985a, 1992), a person’s well-being should be multidimensional in nature.
From such a multidimensional perspective, several approaches to poverty measurement havebeen proposed so far (Tsui 2002, Atkinson 2003, Bourguignon and Chakravarty 2003).
We employ a methodology developed by Alkire and Foster (2011) to compute a multidimensional poverty index (MPI) in Japan. They specify a class of multidimensional poverty
measures, which is an extension of a class of the mono-dimensional poverty measure by
Foster, Greer, and Thorbecke (1984). Especially, we will use the so-called M0 measures ofthis class, namely, the censored headcount H, the intensity of poverty among poor A, andthe adjusted headcount M0 . As we will explain in detail in the next section, roughly speaking, the former two indices focus on a breadth of poverty and an intensity of poverty in a
multidimensional framework, respectively. The M0 is an index combined the H with theA through the relation: M0 = H × A. By using this relation, for example, we can analyze
that intertemporal changes in M0 is a result of an increase (or a decrease) in H and/or in A.
The M0 also satisfies useful properties such as decomposability. This property allows us toanalyze that the M0 is decomposed by any subgroup of household type.
The paper will try to capture poverty profiles for Japan from a multidimensional perspective. In order to do so, the paper has two tasks. First, we will focus on intertemporal changes
in multidimensional poverty in Japan. Based on the National Survey of Family Income andExpenditure, we compute the nationwide MPI for the 1989, 1994, 1999, and 2004 survey.
Second, when households are classified as four categories such as single parent, two parents,
three generation and childless households, we calculate the MPI by household types by wayof using decomposability of the M0 .
Through the preliminary exercises, two findings are obtained. First, the M0 value for theentire Japan decreases, but the degree of intertemporal changes in the value have become2smaller through time. This is mainly due to reductions of the censored headcount H. On
the other hand, the intensity of poverty among poor A became stable around mid-1990s.
Second, through a decomposition of the MPI values into sub-groups, the MPI values for
single parent and two parents households are higher than the national level for every survey
year, indicating that the two types of households are always the most vulnerable ones.
The paper is organized as follows: Section 2 elaborates a method employed in detail.
Section 3 explains dataset used, dimensions/indicators, cutoﬀs/weights, and treatment of
missing values. Section 4 reports main results. Section 5 concludes.

2

MethodIn this section, we explain the methodology developed by Alkire and Foster (2011).1 We
consider a n-person society. Let N := {1, 2, . . . , n} be a set of persons. Similar to thecapability approach, the method employs a multidimensional informational base to assessa person’s well-being. A person’s well-being is represented by an achievement vector in
the d-dimensional nonnegative real space Rd+ , which is called a functioning vector in Sen’s
terminology. A matrix Y ∈ Rn×d
is called an achievement matrix. In order to determine+
whether each entry of a person’s achievement vector is short or not compared to a given
threshold for each dimension, let us introduce a vector z in the d-dimensional positive real
space Rd++ . This vector z is called a deprivation cutoﬀ vector. Using a deprivation cutoﬀ, anachievement matrix is transformed a matrix with a 0-1 entry, denoted as D, by the following
rule: di j = 1 if yi j < z j ; di j = 0 otherwise. We call D a deprivation matrix. Here, a word
“deprived” is introduced for each dimension as follows. Letting dimension j be consumption
for instance, if consumption for person i is lower than a given threshold z j , we say he or she
is deprived in consumption.
Now, in order to consider a relative importance among dimensions, we introduce aweight vector w ∈ Rd+ so that the sum of each entry across dimensions is unity. Froma deprivation matrix D and a weight vector w, we build a weighted deprivation matrix D̄where d̄i j = w j · di j . Let us introduce a counting vector c, which is a row-sum vector of D̄,∑that is, ci := dj=1 w j di j , i = 1, 2, . . . , n. An entry ci of c can be interpreted as a deprivation
score for person i. A ci takes a value between zero and unity: ci = 0 if a person i is notdeprived at all; ci = 1 if deprived in all dimensions.
Let us introduce a parameter k ∈ (0, 1] called a poverty cutoﬀ. Then, when a deprivation
score for i, ci , is greater or equal to a given poverty cutoﬀ k, person i is identified to be a
We just give an explanation regarding the M0 measures. The measures M1 , M2 and Mα (α ≥ 3) will not
be explained because we do not use them in the following analysis.
13multidimensional poor in terms of k. Here, two spacial cases should be noticed. If a poverty
cutoﬀ k is set as d1 , then a person is judged as poor if deprived at least one dimension, whichis called the union approach; Letting k be unity, called the intersection approach, a person is
judged as poor if deprived in all dimensions.
Let P(k) be a set of a multidimensional poor in terms of k. The censored headcount ratio
is defined as
H(k) =|P(k)|.
n

The H says the population share of the multidimensional poor.2 For clear understanging,consider a simple example. Let the number of dimension be 4 (d = 4). When H(0.3) = 53 ,the figure means 60% of the population is multidimensional poor deprived in 1 or more dimensions. Similar to the usual headcount ratio in a unidimensional framework, the censoredheadcount ratio focuses on a breadth of poverty, that is, how many persons are multidimensional poor in a society at hand. Although the H is easy to calculate and provides us a clear
interpretation, it cannot capture a depth of poverty. That is to say, the censored headcountratio does not satisfy a property of the dimensional monotonicity: when an achievement fora person to be identified as multidimensional poor worsen more, the censored headcountremains unchanged.
As explained below, the intensity of poverty A is an index to satisfy this property. For
each i ∈ N \ P by replacing a 1 × d vector d̄i for a zero vector, build a censored matrix D̄,
which is a deprivation matrix D obtained by dropping all information on the non-poor. The
intensity of poverty A is defined as the average deprivation score among the poor. Formaly,
the A is defined as∑ci
A = i∈P .
|P| · dFor example, when A = 13
for example, the figure means the poor in this society experience20
65% of the total possible deprivations the poor could experience. We can easily understandthat the A satisfies the dimension monotonicity. This is because a deprivation score ci increases and accordingly the A does when an achievement for the poor worsen.
When the H is adjusted through taking the intensity of poverty A, the adjusted headcountratio is defined as the average deprivation score among persons in a society at hand. Formaly,

2

In the following, a povety cutoﬀ k will be omitted for a simple description unless a confusion occurs. For
example, the H(k) will be shortly described as H.

4

the M0 is defined as∑
M0 =i∈N cin·d.

9For example, when M0 = 20
for example, the figure means the poor in this society experience
45% of the total possible deprivations the society could experience. A simple calculation3
leads to the following relation:

M0 = H × A.
This equation means that the adjusted headcount is a simple product of the two indices. Accordingly, the M0 also satisfies the dimension monotonicity since the A satisfies this property.
As we said earlier, the adjusted headcount ratio M0 has useful properties. First, the M0
can be break-down across dimensions as follows, which will be used in the following tocalculate percentage contributions to M0 .
M0 =

d
∑
wj

d
j=1

Hj

(1)

, where H j is the uncensored headcount ratio for dimension j.4 Second, the M0 can be
decomposed by subgroups as follows:
M0 =

l
∑
ni

n
i=1

M0i ,

(2)

i

where nn , i = 1, 2, . . . , l is a population share for subgroup i and M0i is a adjusted headcountfor subgroup i. 53∑

i∈N ci =∑i∈P ci +

∑

ci =

∑

i∈P ci .

i∈N\P| {z }
=0

∑ ∑d
∑
∑
j
i∈N
j=1 d̄i j
Let d̄ , d be n×1 vectors of matrices D̄, D, respectively. Then, M0 = i∈Nndci (k) == d1 dj=1 end̄ =nd∑jw j ed j1 ∑d= d1 dj=1 w j H j , where e := (1, . . . , 1) and H j := edn .
j=1 nd| {z }
n times] ∑∑∑∑] [ n1 ∑i∈N1 ci n2 ∑i∈N2 cii
nl i∈N l ci
1 [∑5
= li=1 nn M0i .
M0 = ndi∈N 1 ci + i∈N 2 ci + · · · + i∈N l ci = n n1 d + n n2 d + · · · + n nl d
4

j

j

5

3

Set up3.1

Data

We use as dataset the National Survey of Family Income and Expenditures (the NSFIE)
conducted by the Ministry of Internal Aﬀairs and Communications by every five years. The
unit of the survey is households in the whole area of Japan. The households are classified
by two groups, namely, single-person household and two-or-more persons household. The
collection of data takes place on autumn season and the investigation periods diﬀer by thehousehold types; 3 months (September, October, and November) for two-or-more persons
household; 2 months (September and October) for single-person household. The survey
collects a lot of items including household income and expenditure, savings and liabilities,
and amenities including house and residential land, etc. In the following, we employ the
1989, 1994, 1999 and 2004 surveys for two-or-more persons household. The whole samples
for each survey are 44,537, 44,687, 44,540 and 43,861 households, respectively.

3.2

Dimensions and Indicators

In designing a multidimensional poverty index, first of all we have to choose dimensions andindicators. We will choose as dimensions consumption, wealth, and dwelling environment.
In the following, we briefly explain indicators for measuring each dimension and their definitions.
Consumption
First, to consume something is indispensable for our daily economic activities and closely
related to our wellbeing. Although income can be one of candidates for evaluating ourwellbeing, in an aﬄuent society, consumption is often considered to be an appropriate proxy
for a person’s wellbeing compared to income (Movshuk, 2015; Peichl and Pestel, 2013). In
addition, a reason of this is that in focusing on relatively long periods (i.e., 15 years between
1989 and 2004 in our case) a disposable income, which is considered as a standard measureof household income, is largely aﬀected by changes in a taxation. As an alternative, we will
focus on a nondurable consumption and employ an equivalent nondurable consumption as
an indicator for consumption. The definitions of nondurable consumption and an equivalent
nondurable consumption are given in Appendix.
WealthOur wealth dimension consists of amenities and savings. First, amenities are a very simple

6

measure for evaluating household wealth and focus on how many amenities the household
have. The list of amenities and their diﬀusions rates for non-durable goods, car and house
are reported in Table 4.
Second, we will explain savings for household as the second indicator for wealth dimension. According to the NSFIE, savings are defined as total amount of ordinary and time
deposits for banks, life insurance, stocks, bonds and deposits for non-banking institutions.
Household savings are evaluated at the end of November of the corresponding survey year
based on the questionnaire on annual income and savings. The amounts of savings for eachhousehold are provided with a top coding where a threshold is uniformly set as ninety fivemillion yen across the survey employed. We can also calculate net savings defined as savings
minus liabilities. However, we will not use this as an alternative indicator. Since a housing
loan makes up a large fraction of household liabilities, net savings for households that own
a house tend to be negative. Intuitively, we would be hard to consider these households as
be deprived because getting a mortgage shows a long-term solvency.
Here, we have a notice on the two indicators of wealth dimension. Compared to indicators for other dimensions, a lot of missing values exist. For example, in the 2004 survey,
3,825 households out of 43,861 are identified as providing unreliable saving data. We willgo back to this point in section 3.4.
Dwelling environment
As the last dimension, we consider dwelling environment. Although dwelling environment
can be evaluated from several aspects such as sick house issues for instance, it is simply
assumed to be an increasing function of living spaces, that is, the larger living spaces are,the better our housing environment is. Living space mentioned is defined as total floor spaceminus floor space for business use.
In order to use as a cutoﬀ point, we calculate minimum living spaces with taking family
member compositions into consideration. Minimum living spaces are proposed by Ministryof Land, Infrastructure, Transport and Tourism and the formula is given in Appendix. Although we employ the oﬃcial formula in calculating minimum living space, the settingsfor child coeﬃcient need to be modified because our dataset does not contain the age ofchildren. Here, we will count a 0.75 person per child (see Appendix A.2 in detail).

3.3

Cutoﬀs and Weights

In a poverty measurement, it is important to set a poverty line to determine who is poor.
There are several ways to set a poverty line such as relative or absolute. An adjective “relative” means here that a cutoﬀ point can be diﬀerent by survey year or by household, for7

instance. The so-called one half of median criteria would be typical relative poverty line
because the median number of a focal variable would be diﬀerent by survey year. For nondurable consumption, we will use the one half of median criteria. Namely, a household isdeprived in non-durable consumption if it is less than the one half of median equivalent nondurable consumption. For living spaces, relative poverty cutoﬀs are also employed and set
as the minimum living space which is diﬀerent from compositions of household members.
On the other hand, an adjective “absolute” means that a cutoﬀ is uniformly fixed across any
survey year or any subgroup, for example. For the two indicators of wealth dimension, we
will use absolute cutoﬀs. Regarding amenities, cutoﬀs are set as 10 amenities, which isdetermined by the number of amenities that their diﬀution rates are more than 70%. One
million yen is used as a cutoﬀ point for savings.
Defining weights among dimensions is essential for computing the MPI. Similar to theissue of defining cutoﬀs, there are various procedures to assign weights to dimensions (Decancq and Logo, 2012). Here, we employ nested weights as a weighting structure (Alkire
and Foster, 2011). In our case, the nested weights assigns one third to each dimension. Next,
the weight assigned is divided by the number of indicators for each dimension. Since wealthdimension has two indicators, each indicator for this dimension is assigned one sixth. The
last column of Table 1 shows and weights assigned to each indicator.
Dimensions

Indicators

Deprived if

non-durable consumption

the half of median non-durable consumption

amenities

10 items

savings

one million yen

living space

minimum living space

Consumption

WealthDwelling environment

Weights
0.33(0.33)
0.33(0.167)(0.167)0.33(0.33)

Table 1:

3.4

Data Cleaning

In this subsection, we explain how we treat unreliable information. Second through fifthrows of Table 2 reports the number of unreliable information for each indicator. As we
said above, the two indicators of wealth dimension have much unreliable information. On
the other hand, there are no unreliable information concerning non-durable consumption
and living space. In order to compute the MPI, we have done data cleaning by dropping8

all unreliable information for related indicators. Seventh and last rows of Table 2 report
unreliable information dropped and sample size employed in the following analysis.
# of unreliable information
non-durable consumption
amenitiessavings
living spacewhole sample size
unreliable information dropped
remaining sample size

1989
0
1,384
2,030
0
44,537
3,386
41,151

1994
0
1,544
1,830
0
44,687
3,345
41,342

Table 2:

9

1999
0
2,307
1,362
0
44,5403,616
40,924

2004
0
3,033
3,825
0
43,861
4,737
39,124

4

Results

We present multidimensional poverty profiles for Japan. First, levels and changes in the
MPI indicators are discussed. Second, we report the multidimensional poverty indices forthe entire Japan and the four sub-groups mentioned above. Third, results for decomposition
the MPI into contributions by sub-groups and dimensions are reported.

4.1

Levels and changes in indicators

In this subsection, we report levels and changes in the MPI indicators except for these forliving space. Regarding non-durable consumption, Table 3 shows that the median equivalent non-durable consumption per month. As a general feature, we see that across all thehousehold types including nationwide the median non-durable consumption increase radically during the first five years, and during the subsequent 10 years it decreases gradually.

the entire nation
childlesssingle parent
two parents
three generation

1989
127,767
142,071
84,389115,008
123,928

1994
146,766
160,188
111,711
132,689
149,050

1999
145,008
157,209
101,720
129,749
147,3812004
142,911
153,515
101,539
127,480
143,390

Table 3: the median equivalent nondurable consumption per month (JPY)

Next, the lists of amenities and their diﬀusion rates are reported in Table 4. Blanks intable mean that the corresponding diﬀusion rates are not collected. Although the lists ofgoods little bit change by the survey year, we can observe that diﬀusion rates for almost allitems increase through time.6 For example, the numbers of items whose diﬀusion rates are
more than 70% are 10 items for 1989, 11 items for 1994, and 13 items for 1999 and 2004.

6

There are several notices concerning Table 4. First, the diﬀusion rate for piano was dropped suddenly
between 1999 and 2004. This is a result of a change in the questionnaire on piano. Namely, the first threesurvey collected data on piano including electric organs while in the 2004 survey electric organs were excluded.
Second, the diﬀusion rate for stereo goes up over 30% between the last two surveys, which is also the same
reason for piano. In the 2004 survey, the two items on stereo and cd/md were combined. Lastly, in 1989, thediﬀusion rate for house seems to be little bit high. This is a reason that the 1989 rate for house is computedfrom the item on a relationship of the ownership of house while other three are calculated from the item onwhether a respondent own a house or not.

10

system kitchenwater heater

1989
18.6%
46.2%

wash standcombined toilets
refrigerator
wash machine
air conditioner
piano
video recordercolor tv
video camera
cameracomputerword processor
vacuum cleaner

98.1%
98.9%
65.0%
38.2%
68.7%
98.3%
17.4%
86.8%
12.5%
25.0%
97.6%

rice cooker
microwave
dish washer
sawing machine

73.0%
4.5%
68.7%

desk
stereoradio-cas/cd/md

60.1%
76.3%

fax
golf equipment
mobile phone
phone
car
house

33.8%

1994
28.0%
51.6%
34.3%
23.5%
98.7%
99.4%
79.3%
42.0%
68.8%
99.2%
33.9%
89.7%
16.4%
43.7%
98.9%
78.1%
89.4%
5.2%
73.5%
62.3%
54.9%
68.0%
9.5%
41.1%
43.7%

1999
43.0%
55.6%
45.8%
42.0%
99.2%
99.3%
84.3%
41.4%
78.9%
99.3%
39.8%
87.5%
37.8%
45.3%
99.2%
81.7%
95.3%

2004
57.0%
61.1%
63.4%
59.2%
99.1%
99.2%
87.1%
27.5%
82.0%
97.3%
41.9%
81.0%
69.7%

99.4%
85.6%
97.6%
19.3%
70.7% 67.7%
64.9% 64.8%
50.0% 81.0%
78.1%
33.1% 51.3%
42.2% 38.7%
64.7% 85.0%

81.4%
79.3% 81.4% 85.2% 86.6%(75.5%) 72.8% 76.5% 80.4%

Table 4: List of amenities and diﬀusion rates for the entire nation

Last, Table 5 reports that the median and 25 percentile in parenthesis of equivalent household savings for each household category. For all the household types including the entire
Japan, both the median and 25 percentile went up during the first five years. During thesubsequent 10 years, the median and 25 percentile for the entire Japan and childless household still continued increasing while the degree of increases in the median for two parents
and three generation households became slowly; a hundred thousand yen up for two parents household; two hundred thousand yen up for three generation household. On the other
hand, both the median and 25 percentile for single parent household, and 25 percentile fortwo parents and three generation households decreased during the same period. From this11

observation, it is fair to say that one half of single parent household and one quarter of bothparents household (which are usually considered as the most vulnerable households) were
worse oﬀ while others were well oﬀ.
median (25 percentile)
entire Japan
childlesssingle parent
two parents
three generation

1989
310 (144)445 (202)
135 (32)204 (101)
270 (138)1994
428 (200)640 (297)
238 (92)279 (141)375 (196)1999
470 (203)707 (308)
197 (53)277 (130)387 (189)2004
517 (227)762 (333)
201 (55)289 (135)
396 (188)

Table 5: Equivalent savings for household: median and 25 percentile (ten thousand yen)

4.2 The MPI for Japan
Table 6 reports figures, standard deviations and confidence intervals for M0 , H, and A for
k = 0.3. In 2004, the censored headcount ratio H was 9.19%, which means that 9.19% of
the population were deprived in one or more indicators. When the H is adjusted throughtaking depth of poverty A into consideration, the adjusted headcount is calculated as 3.84%.
This is lower than the M0 value for 1999, which was 4.52%. The decrease in the M0 value in2004 is a consequence of reduction in the H while the intensity of poverty A seems to remainunchanged. We can also observe that the M0 value decreases between 1994 and 1999 due tothe same reason as in 2004, that is, a relatively large reduction in the H. During the first five
years, a decrease in the M0 value can be observed. This is a result of a reduction of both the
H and the A values.
A decomposition of the MPI results by sub-groups are reported in Table 7. Several
features can be observed. First, all the MPI figures for single parent household are highest
across every survey year. Two parents household have the second highest MPI values among
sub-groups except for the A in 1989 ranked as third. It is not the case for both three generation and childless household. Second, across every survey year, the MPI results for single
and two parents household are always higher than for the entire Japan, whereas regarding
three generation and childless households the converse relationship holds. Third, across all
sub-groups, there is a general tendency of decreasing the M0 values through time due tomainly reductions in the censored headcount.
To check whether intertemporal changes in the estimated values are statistically significant, we perform a simple statistical test under the null hypothesis that there are no diﬀerences in the values between adjoint two periods (i.e. ∆MPIt,t−1 := MPIt − MPIt−1 = 0)
12

1989
Estimate
M0
H
A

Estimate
M0
H
A

Estimate
M0
H
A

Estimate
M0
H
A

Value

Standard Error

Confidence Interval (95%)

7.25%
16.55%
43.8%

0.085%
0.18%
0.18%
1994

(7.08%, 7.41%)(16.2%, 16.9%)(43.5%, 44.1%)

Value

Standard Error

Confidence Interval (95%)

5.91%
14.23%
41.57%

0.076%
0.17%
0.18%
1999

(5.77%, 6.06%)(13.9%, 14.6%)(41.2%, 41.9%)

Value

Standard Error

Confidence Interval (95%)

4.52%
10.82%
41.77%

0.068%
0.15%
0.20%
2004

(4.39%, 4.65%)(10.5%, 11.1%)(41.4%, 42.6%)

Value

Standard Error

Confidence Interval (95%)

3.84%
9.19%
41.82%

0.064%
0.15%
0.21%(3.72%, 3.97%)(8.9%, 9.5%)(41.4%, 42.2%)

Table 6: The MPI for the entire nation (k = 0.3)

13

against the alternative ∆MPIt,t−1 , 0. The results are reported in Table 8. During the periods of 1989-94, the estimated values M0 , H and A have declined statistically significant at
the national level and across all four sub-groups except for a change in A for single parent
household. During the subsequent periods from 1994 to 1999, decreases in M0 as well asin H are statistically significant at the entire Japan and three sub-groups excluding single
parent household. Changes in intensity of poverty A are not statistically significant for allgroups except for three generation household. In the third period of 1999-2004, we find a
sharp contrast between changes in H and in A. Namely, all changes in H are statisticallysignificant whereas these in A are not. From the statistical test, almost all changes in both
M0 and H are statistically significant and decreases in M0 are mainly due to decreases in H.
On the other hand, we also find that almost all changes in A are not statistically significant
during the periods between 1994 and 2004 while these are significant during the first five
years. This might suggest that seriousness of poverty among the poor became stable aroundmid-1990s.

14

15

1999
M0
H
13.38 27.43
6.01 14.24
3.91
9.80
3.69
8.92
4.52 10.82
A
48.77
42.24
39.93
41.39
41.77

94-89
∆H
-17.27***-5.12***-1.90***-1.47***-2.32***

99-94
∆A
∆M0
∆H
-2.25
0.26
-0.09
-1.99*** -1.59*** -3.87***-2.59*** -0.92*** -2.60***-2.59*** -1.34*** -3.13***-2.23*** -1.39*** -3.41***

04-99
∆A
∆M0
∆H
∆A
1.11
-2.22
-5.69** 2.53
0.24 -1.35*** -3.20*** 0.06
0.98*
-0.38
-1.17** 1.01
-0.36 -0.32*** -0.72*** -0.26
0.20 -0.68*** -1.63*** 0.05

2004
M0
H
A
11.15 21.74 51.3
4.47 11.04 42.3
3.53
8.63 40.94
3.37
8.19 41.13
3.84
9.19 41.82*** statistically significant at ω = 0.01, ** statistically significant at ω = 0.05, * statistically significant at ω = 0.10.

Table 8: Subgroup decomposition of intertemporal changes in the mpi values∆M0
Single parent-9.24***
Two parents-2.61***
Three generation -1.11***
Childless-0.96***
Entire Japan-1.33***

Diﬀerence

1994
M0
H
A
13.11 27.52 47.66
7.61 18.11 42.0
4.83 12.40 38.94
5.03 12.05 41.75
5.91 14.23 41.57

Table 7: The measures M0 , H and A by sub-groups (k = 0.3)

1989
M0
H
A
Single parent
22.35 44.79 49.91
Two parents
10.22 23.23 43.99
Three generation 5.94
14.3 41.54
Childless5.99 13.52 44.34
Entire Japan
7.25 16.55 43.8

4.3

Decomposition of the M0 values

By using equation (2), we compute the percentage contributions to the M0 values by subgroups, which is shown in Figure 1. The contributions to the M0 for child bearing households is relatively higher than the corresponding population share among the four surveys,
where child bearing households refer to single, two parents, and three generation households. However, the contributions have declined through time. This reflects a smaller shareof child bearing households in the population. In other words, childless households have
had relatively larger eﬀects on the M0 value.

1989

1994

1999

childlesstwo parents

pop. Share

mpi poor

pop. Share

mpi poor

pop. Share

mpi poor

pop. Share

mpi poor

0

.2

.4

.6

.8

1

Sub−group decomposition of the M0 (k=0.3)

2004

single parent
three generation

Figure 1: the contributions to the M0 values by sub-groups

Figure 2 shows the contributions to the M0 values by dimensions, which are computedby using equation (1). We can find that the dwelling environment dimension is the largest
contributor to the M0 in 1989, whereas it becomes the least in 2004. Conversely, we also
observe that the dimension on consumption makes the largest contribution to the M0 value
in 2004, which was the least in 1989. Further, regarding the wealth dimension, after thepercentage contribution decreased between 1989 and 1994, it remains stable during the 1994
through 2004. Why the consumption dimension dominate in 2004? The primary reason isthat the degree of deprivation in non-durable consumption worsened during 1989-2004. In
fact, the usual headcount on this indicator is shown in the upper right panel of Figure 3,
where the poverty line is set as one half of the median equivalent non-durable consumption
for each survey. It is increased 0.7 points from 5.5% in 1989 to 6.2% in 2004. The secondary
reason is that situations on dwelling environment improved. This is supported by the lower
left panel of Figure 3. This is the usual headcount on living space where the cutoﬀ is set
16

as the minimum living space calculated by each household. The headcount monotonously
decreased during the 15 years. Due to continuous increases in percentage contributions, theM0 value has become more sensitive to changes in non-durable consumption.

0

.2

.4

.6

.8

1

Dimensional contributions to the M0 (k=0.3)

1989

1994

1999

non durable consumption
saving2004
amenities
living space

Figure 2: the contributions to the M0 values by dimensions

17

Head count: Amenities

0

5

5.3

5

5.6

percentage
10

percentage
5.9
6.2

15

6.5

6.8

20

7.1

Head count: non durable consumption

1995

2000

2005

1990

1995

2000
year

Head count: saving

Head count: living space

2005

2

8

9

4

10

percentage
6

percentage
11 12 13

8

14

15

10

year

16

1990

1990

1995

2000

2005

year

1990

1995

2000
year

Figure 3: the headcount ratios by indicators

18

2005

5

Concluding remarks

In this paper, we have tried to capture poverty profiles for Japan from a multidimensional
perspective. Following the methodology developed by Alkire and Foster (2011), we compute the MPI for the entire nation and four sub-groups based on the NSFIE. In the following,
we will sum up several results.
First, we find that the M0 value for the entire Japan decreases, but the degree of intertemporal changes in the value have become smaller through time. This is mainly due
to reductions of the censored headcount. It seems that this finding perhaps contradict theresult of the government oﬃcial reports where the monetary headcount goes up over the
past-30 years. However, as shown the upper left panel of Figure 3, intertemporal changes in
the headcount on non-durable consumption are consistent with that in the oﬃcially-reportedmonetary headcount. It would be natural that such diﬀerences occur because informationalbases are not same.
Second, through a decomposition of the MPI values into sub-groups, the MPI values
for single and two parents household are higher than the national level for every survey
year. Although this would be a standard result, the finding suggests that the two types ofhouseholds are always the most vulnerable ones from a multidimensional perspective.
Third, as shown in Figure 1, child bearing households in the population decrease overtime. This makes the corresponding contributions to M0 small, which indicating that compare to the past the M0 value becomes relatively insensitive to changes in child bearing
households. Given the all parameters such as poverty cutoﬀ and cutoﬀ vector etc., nondurable consumption in 2004 have become the largest contributor to the M0 value (see Fig.2).
This means that movements in M0 become approximated to changes in the indicator.
Lastly, during 1999 through 2004, we find a sharp contrast between changes in H andin A. Namely, all changes in H are statistically significant whereas these in A are not.
From the statistical test, almost all changes in both M0 and H are statistically significant anddecreases in M0 are mainly due to decreases in H. On the other hand, we also find that almost
all changes in A are not statistically significant during the periods between 1994 and 2004
while these are significant during the first five years. This might suggest that seriousness ofpoverty among the poor became stable around mid-1990s.

19References
Alkire, S., Foster, J. E., Suman, S., Santos, M. E., Roche, J. M., and Ballón, P. (2015)
Multidimensional Poverty Measurement and Analysis, Oxford University Press.
Alkire, S. and Foster, J. E. (2011) “Counting and multidimensional poverty measurement,”
Journal of Public Economics, 95 (7-8), pp. 476–487.
Alkire, S. and Santos, M. E. (2014) “Measuring Acute Poverty in the Developing World:
Robustness and Scope of Multidimensional Poverty Index,” World Development, 59, pp.
251–274.
Atkinson, A.B. (2003) “Multidimensional deprivation: contrasting social welfare andcounting approaches,”Journal of Economic Inequality, 1, pp. 51–65.
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poverty,” Journal of Economic Inequality, 1, pp. 25–49.
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An Overview,” Econometric Reviews, 32(1), pp. 7–34.
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Profile for Uganda,” Journal of Human Development and Capabilities, Vol.15, No.4, pp.
369–390.
Ministry of Land, Infrastructure, Transport and Tourism (2016) “Basic Plan for Housing(National Plan),” http://www.mlit.go.jp/common/001123474.pdf
Movshuk, O. (2015) “Child poverty in Japan: comparing the accuracy of alternative measures,” mimeo.
Peichl, P. and Pestel, N. (2013) “Multidimensional aﬄuence: theory and applications toGermany and the U.S. Applied Economics, Vol.45, No.32, pp. 4591–4601.
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Stiglitz, J., Sen, A. K., and Fitoussi, J-P. (2009) “The measurement of economic performance and social progress revisited: Reections and Overview,” HAL Id: hal-01069384
https://hal-sciencespo.archives-ouvertes.fr/hal-01069384 Submitted on 29 Sep 2014
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21

Appendix
A.1 Non-durable consumption
Following Movshuk (2015), a non-durable consumption for household is defined as follows:
Non-durable consumption =Food + (Housing - Rents for dwelling and land) + Fuel, lights and water charges+ (Furniture and household utensils - Household durables - Interior furnishings - Bedding) + Clothing and footwear + Medical care + (Transportation andcommunication - Purchase of car and bicycles) + Education + (Culture andrecreation - Recreational durable goods) + Other consumption expenditure Remittances
A equivalent non-durable consumption is defined as
Equivalent non-durable consumption =non-durable consumption
square root of the number of household .

A.2 Minimum living space
According to Ministry of Land, Infrastructure, Transport and Tourism (2016), the minimum
living space is set as


2


25m for single household10m2 × (# of household members) + 10m2 for two-or-more household
Secondly, when one calculates the number of household members, a child is counted as
0.25 person for under age 3








0.5 person for between age 3 and a age 6
0.75 person for between age 6 and age 10






1 person for over age 10
Our dataset has no information on how old children are since we only obtain the number ofchildren less than age 17. Here we will modify the child counting rule as follows. That is,
this paper counts any child less than age 17 as 0.75 person.

22

oﬃcial this paper
under 30.25
0.75
bet. 3-6
0.5
0.75
bet. 6-10
0.75
0.75
over 101
0.75
Table 9: Child counting rules

Lastly, in the case that total number of household is greater than 4, the minimum living
space is 5% deduction. For example, consider a household consisted of two adults and threechildren, the minimum living space is 49.875m2 = (10 × (2 + 0.75 ∗ 3) + 10) × 0.95. On
the other hand, when we consider a household consisted of two adults and two children, thenumber of household members is 3.5 (=2+0.75*2) according to our child counting rule. In
this case, the 5% deduction rule does not apply and accordingly the minimum living spaceis 45m2 .

23