Discussion Paper No.350

One-Person Households and Public Assistancein Japanese Elderly:An Analysis Using Prefectural Data

Tsukasa Matsuura
Chuo University

June

2021

INSTITUTE OF ECONOMIC RESEARCH
Chuo University
Tokyo, JapanOne-Person Households and Public Assistance in Japanese Elderly:
An Analysis Using Prefectural Data

AbstractThis study explores the effects of regional differences in elderly one-person households
on regional differences in the public assistance rate for the elderly using data by prefecture.
We show that a positive correlation between elderly single-male households and the
public assistance rate for the elderly exists even when prefectural fixed effects are
considered. However, no significant effect was found between elderly single-female
households and the public assistance rate. We also confirmed that there a significantly
positive relationship between these variables after 2000 or in large cities exist, while noeffect was found before 1995 or in rural areas. From these results, since the late 1990s,
elderly one-person households are increasing due to further urbanization in Japanesesociety, cohabitation is decreasing, and family ties are weakening for risk sharing. Thissuggests that the increase in elderly one-person households may be directly linked to the
public assistance rate for the elderly.

Key words: Elderly one-person households, Public Assistance, Gender Difference
JEL code: I30 J12

 This research was supported by JSPS (Japan Society for the Promotion Science) KAKENHI: Grant-

in-Aid for Young Scientists (B) and Grant-in-Aid for Scientific Research (B) from the Japanese
Ministry of Education, Culture, Sports, Sciences and Technology (MEXT) (Grand number: 17K13754,
17KT0037).
Tsukasa Matsuura: Department of Economics, Chuo University. t-matsu@tamacc.chuo-u.ac.jp
11. Introduction
Japanese society is aging rapidly and is now the fastest aging country in the world.
Population aging is also expected to continue increasing in the future. Table 1
demonstrates that the aging rate was 26.5% for Japan in 2019, while it will reach 38.1%
for Japan in 2060.
In contrast, poverty is another problem of Japanese society, along with aging. The
Japanese regarded Japan as an equal society that it was “100 million total middle class”
as it in the 1980s. For its people to focus on poverty in the 1980s was rare. However, theissue of income distribution began to reappear after the collapse of the bubble economy
in the 1990s. In the 2000s, although poverty became a critical issue in Japan, the problem
was not resolved. As Figure 1 shows, the relative poverty rate in Japan is one of thehighest among developed countries. Considering this, the number of households receiving
public assistance started increasing since the latter part of the 1990s. Figure 2 presents
the trend of households receiving public assistance. Although the number of households
receiving public assistance was stable at around 600,000 until the middle of the 1990s,
the number started increasing rapidly since then, and it exceeded 1.6 million in 2015. The
number of elderly households receiving public assistance is 800,000, which accounts forhalf of the total.
Since the 1990s, aging and poverty have become major problems in Japan. Therefore,
in this study, we explore the relationship between aging and poverty, which is a major
issue in Japan, by focusing on elderly one-person households. In a study that examined
the relationship between elderly one-person households and the poverty problem,
Tachibanaki and Urakawa (2006) used individual data to measure the percentage of
people below the poverty line based on livelihood protection standards. Thus, they2

demonstrated that one out of every four elderly one-person households lives below
minimum living standards.
Thus, previous studies have shown that elderly one-person households and poverty
are closely related. However, on elderly one-person households and public assistance,
many previous studies have examined individual data, and only a few studies havefocused on gender differences using prefecture-level data. Therefore, in this study, we
analyze the effect of prefectural differences in elderly one-person households on
prefectural differences in the public assistance rate for the elderly, focusing on genderdifferences. As a result of the analysis, we confirm a correlation between prefecturaldifferences in elderly single-male households and those in the public assistance rate forthe elderly, even while considering prefectural fixed effects. No significant relationshipexists between elderly single-female households and public assistance for the elderly.
Moreover, while single-male households have significant effects on public assistance forthe elderly after 2000 and in city areas, no relation exists between them before 1995 andin rural areas.
Beck (1986) states that the perseverance of modernization freed females from thetraditional division of sexual roles and effected the personalization of the family structure.
This dramatically increased the number of one-person households and single mothers. In
addition to the decrease in living together due to further urbanization around the late
1990s in Japan, the function of risk sharing by families has weakened. This suggests thatthe increase in elderly one-person households may have been directly linked to the public
assistance rate for the elderly1.
1

One of the evidences family structure has changed in 1990s is that nursing care insurance system was

implemented in 2000. The system intends to share the burden with the whole in the country, instead ofeach family.

3

Aging populations are present not only in Japan but all over the world. As Beck(1986) highlights, changes in family structure due to urbanization and personalization are
observed in many countries transitioning from traditional to modern societies. Therefore,
our study is useful examining the future of countries around the world, especially Asian
countries, which have changed from traditional to modern societies and have become
more urbanized and personalized.

2. The current and future situation of elderly single households
In this section, we explain the present condition of elderly one-person households inJapan. Figures 3–1 and 3–2 show the trend of one-person households by age. In the 1980s,
the percentage of elderly single-male households was 4%–5%. Although it was slightly
lower for 65 to 69 years and 70 to 74 years old, there was no large difference for each age
group. Since then, it has risen sharply in all age groups, although the growth in the 65–69
age group is particularly remarkable. As a result, the 65–69 age group was the highest at
15%, and 85 years old and over was the second highest at 13.3% in 2015.
In contrast, the percentage of elderly single-female households aged 85 and over was
the lowest (at 5.4% in the 1980s). After that, the 65–69 and the 70–74 age groups have
hardly increased since 2000, but other generations have an upward trend. In particular,
the growth of people aged 85 and over was remarkable and reached 21.8% in 2015. In
1980, no difference was found comparing the number of males and females over the ageof 85, but females were significantly higher in other age groups. In 2015, almost no
difference was found between the ages of 65 and 69 years; however, it was significantly
higher in females in other age groups.

4Figure 4 presents the forecast of one-person households by the National Institute ofPopulation and Social Security Research. This indicates that in 2005, the rate of elderly
one-person households was 19% and 9.6% for females and men, respectively, which was
almost double the difference. The rate of one-person households increases slightly for
elderly females. In contrast, the rate of elderly single-male households is predicted to rise
sharply. As a result, the difference will narrow to 20.9% for females and 17.8% for males
in 2030. Inagaki (2013) simulates the trend of the rate of multiple-person households andlow income, and insist that the number of unmarried and divorced elderly females withlow pensions is expected to increase.
Furthermore, Figure 5 shows the prefectural differences in the rate of the elderly oneperson households by gender. It increased in all regions from 1980 to 2015. Focusing on
prefectural differences, Western Japan tends to be higher than eastern Japan. While therate is higher in metropolitan areas such as Tokyo and Osaka, it is also high in some rural
areas such as Kagoshima, Kochi, and Wakayama prefectures in 1980 and 2015.

3.

Literature Review
Marchand and Smeeding (2016) surveyed previous studies focusing on therelationship between ageing and poverty. This article shows that the relationship ofpoverty rate over age distribution is U-shaped both in the US and OECD countries. In
addition, the right-hand side of the U-shaped, which indicates the poverty rate for theelderly, has shifted downward. This means that the poverty rate among the elderly has
decreased. They also point out the feminization of poverty that poverty among females
has steadily increased relative to males.

5Several studies in Japan have focused on prefectural differences in aging and public
assistance (Ushizawa and Suzuki, 2004; Suzuki and Zhou, 2007; Sekine, 2007; Sekine,
2009; Hayashi, 2012; Suzuki and Tanabe, 2018). Ushizawa and Suzuki (2004) found thatthe aging, unemployment, and divorce rates have a strong effect on the public assistance
rate using prefectural data. Sekine (2007) defined elderly one-person households as thepercentage of one-person households aged 65 and above divided by the households withrelatives aged 65 and above (one-person household with aged 65 and above/householdswith relatives aged 65 and above), and shows that it has a significant effect on public
assistance rate. Sekine (2009) demonstrated that elderly one-person households tend tobe households receiving public assistance in metropolitan areas. Suzuki and Zhou (2007)
estimated using prefecture-level longitudinal data and indicated that the financial
capability index of the local governments and minimum wage both impose negative
effects on the public assistance rate, and the aging rate has a positive effect on it. Hayashi(2012) found that the rate of elderly one-person households had a positive effect on public
assistance rates using municipal data. Seki (2012) demonstrated that the rate of elderly
one-person households has a significant effect on public assistance rate using
government‐ordinance‐designated city’ data between 2002 and 2008. Suzuki and Tanabe(2018) predicted the poverty rate after nonlinear multiple regression analysis of thedeterminants of single elderly households.
In addition, although not focused on elderly one-person households, Abe andTamada (2007) examined the relationship between the relative level of public assistanceover low-wage income and the employment of junior high school graduate men. Theyshow that a relationship exists between regional differences in the ratio of part-time wages
to the amount of public assistance and those of the employment rate in junior high school
6

graduate men. Furthermore, Yugami et al. (2017) analyzed the impact of changes in thelevel of public assistance on individual employment by using an exogenous shock inwhich the changes in these standards were caused by the merger of cities, towns, andvillages as a natural experiment.
In contrast, many studies focusing on the determinants of the poverty rate or the
probability of receiving public assistance for elder single persons used individual data.
Tachibanaki and Urakawa (2006) analyzed the determinants of disposable income below
the standard of public assistance and showed that the marginal effect of elderly oneperson households on public assistance increased to 0.109 in 1995 and 0.205 in 2001. Abe(2008) found that the poverty rate of the elderly is high, and it is even higher in oneperson households. In particular, the poverty rate of elderly single-female households is
51.7%, which is more than half. In addition, Inagaki (2013) used a microsimulation model
to predict the future of elderly one-person households and the poverty rate. The results
show that the poverty rate of the elderly will rise rapidly until 2040. Yamada and Shikata(2016) demonstrated a decrease in the poverty among elderly one-person households wasoffset by an increased share of this households. Since they are unlikely to receive any
help by surroundings, they rely on public assistance. Therefore, the beneficiary rate of
public assistance increased regardless of the decrease in poverty rate among elderly oneperson households.
In addition, many studies in sociology and demography focused on elderly single
households. Klinenberg (2012) described the positive aspect of living alone, criticizing
traditional views that emphasize the negative aspects of living alone. In Japan, Ueno
(2007) focused on the positive aspects of elderly one-person households. Ishida (2018)
analyzed the determinants of isolation and demonstrates the tendency of people with low
7socioeconomic status to become isolated.
Previous studies have explored the impact on the subjective well-being of elderly
single households (Raymo et al., 2008; Oshio, 2012). Raymo et al. (2008) found theeffects of mating and living patterns on subjective health and well-being in Japan over
the age of 60. As a result, marriage increases subjective health and well-being in males,
but these effects cannot be confirmed in females. Raymo et al. (2008) analyzed the effects
of marital status and living patterns on subjective well-being in Japan over the age of 60.
As a result, marriage increases subjective well-being in males, but such effects cannot be
confirmed in females. Oshio (2012) also focused on gender differences in marital statuson subjective well-being by dividing the sample into males and females for the elderly
and shows that in males, having a spouse significantly increases life satisfaction, but infemales, having a spouse does not have the effect of significantly increasing satisfaction.
Furthermore, Ishikawa (1999) analyzed the mortality rate by marital relationship using alife table and found that spouses have a longer life expectancy than unmarried individuals(including those who never married and those widowed and divorced), especially in males.
He states that the reason for this is the physical and mental stability of the marriage, suchas eating habits and maintaining good health2.
4. Data, Hypothesis, and Method
This study explores the effects of prefectural differences of elderly single householdson the public assistance rate for the elderly. Although previous studies have used
individual data, only a few studies have examined gender differences of elderly single

2

For females, the difference between married (married, divorced, bereavement) and unmarried is large, not thedifference between married and unmarried. However, recently, the divergence between unmarried and marriedmortality rates is narrowing.

8

household on the public assistance rate for the elderly using prefecture-level data. In
addition, we will examine whether differences exist by areas and periods. Specifically,
we estimate the following model.

𝑌 = 𝑎 + 𝛽 𝐴𝑙𝑜𝑛𝑒_𝑚𝑎𝑙𝑒 + 𝛽 𝐴𝑙𝑜𝑛𝑒_𝑓𝑒𝑚𝑎𝑙𝑒 + 𝛾𝑋 + 𝑐 + 𝑢

Yit is the public assistance rate for the elderly, Alone indicates the rate of elderly oneperson households for each gender. The coefficient β indicates the effect of the elderly
one-person households on the public assistance rate for the elderly, which we focus on
the most. Xit is a vector of independent variables, and ci is an unobserved time-constantvariable. To control for an unobserved effect, we use a fixed effect model with clusteredrobust standard error. We use independent variables such as age structure, life expectancy
at age 65, prefectural income per capita, the labor force among the elderly, theunemployment rate, the owned-house ratio, and the financial capability index. In addition,
we divide the sample by period and area to consider the effect of urbanization andpersonalization.
We can deal with endogeneity caused by prefectural individual effects using afixed effect. However, we must also consider the endogeneity caused by inverse causality
because single elderly people might move to the prefectures where public assistance iswell developed. Thus, whether the rate of elderly single households is exogenous isunclear. However, these are not plausible for the following reasons. First, although thelevel of public assistance differs by prefecture, the national government is decided by ageneral standard considering lifestyle and living standards in areas. There is little room
for prefectures to arbitrarily decide the level of public assistance. Second, as the central
government owes 75% of the expenditure for public assistance, the prefecture has little
9incentive to lower the expenditure. Third, considering the supplementary principle oflivelihood protection, rather than making one-person households move to other areas inanticipation of support from livelihood protection, they are classified as one-personhouseholds because of exogenous factors such as bereavement of a spouse and individual
income. Thus, the cause of relying on public assistance when there is no support frompeople, assets, family, or community is more persuasive.
The definitions of the variables are shown in Table 2, and a descriptive statistic is also
shown in Table 3. The average public assistance rate for the elderly is 1.95%. The averagerate of elderly one-person households is 6.9% for males and 15.6% for females. The rates
of age 65-74, and aged 75 and over are 10% and 8.1%, respectively. Life expectancy atage 65 is 17.2 for males and 21.6 for females. Prefectural income per capita is 2.52 million
yen on average. The average rate of regular workers is 1.4% on average. The average
unemployment rate was 4.2%. The owner-occupied ratio is 67.6%. The financial
capability index is 0.47 on average.
Figure 6 shows the correlation between the rates of elderly single households and the
public assistance for the elderly in 1990 and 2010. The proportion of elderly single
households is higher in 2010 than in 1990 for both genders. The slope is steeper in males
than in females for both periods. This indicates that the correlation between elderly single
households and elderly receiving public assistance is stronger in males than in females,
regardless of years. Focusing on periods, these correlations for females are stronger in2010 than in 1990, while there is no difference in the correlation by years for males. These
relations are focused on only two variables, without considering other effects. Thus, weestimate the effect of elderly single households on public assistance for the elderly.

105. Results
5.1. Basic Model
The results are shown in Table 4–1. Columns (1)–(4) are the results of pooled OLS.
The coefficients of elderly single-male households are positively significant in columns(1)–(3). This indicates that the higher the rate of elderly single-male households, thegreater the public assistance rate for the elderly. For females, the coefficients of elderly
single households are not significant except for column (1). This indicates that an increasein elderly single-female households does not affect the rate of public assistance for theelderly.
From the results of pooled OLS, we confirm a positive correlation between elderly
single-male households and the public assistance rate for the elderly, after controlling forcovariates such as demographic and economic conditions. However, these results might
be spurious, as we do not control for fixed effects. For example, the elderly who facepoverty do not need to rely on public assistance in areas where neighborhoods could help
elderly single households. In contrast, the elderly could receive public assistance fromneighborhoods in these areas. When variables affect dependent and independent variablesat the same time, we arrive at a spurious correlation.
To deal with spurious correlations, we use a fixed effect model to control unobserved
time-consistent effects. We also use a clustered robust standard error to consider thecorrelation of the error term among prefectures. The results in Table 4–1 (5)–(8).
Columns (5)–(8) show that the coefficients of elderly single-male households are
significant for all columns. This indicates a positive correlation between elderly singlemale households, and the public assistance rate for the elderly is confirmed, even if
prefectural fixed effects are considered. However, the coefficients of elderly single11

female households are not significant for all columns in fixed models. This indicates thatthe increase in elderly single-female households does not affect the rate of public
assistance for the elderly.
The effects of elderly one-person households on the rate of public assistance for theelderly are quite different between genders. Elderly one-person households facilitate
public assistance for the elderly, while elderly single-female households suppress it. As
females are more sociable and apt at establishing connections with neighborhoods andcommunities, we believe they can rely on the support from communities or relatives wholive near them. However, as elderly males can only rely on governmental support, the rateof elderly single-male households directly affects the rate of public assistance for theelderly.
The results of other variables are as follows. Demographic variables such as
population structure and life expectancy are not significant in most cases considering
prefectural fixed effects, except for the rate of aged 65–74 in columns (7) – (8). Thissuggests that the rate of elderly single-male households is more important in determining
the prefectural difference of public assistance than other demographic factors, although
the rate of those aged 65–74 has a positive correlation with public assistance for theelderly. The prefectural income per capita is positively significant, and the labor
participant rate among the elderly is negatively significant.

5.2. Estimation by Period
As 5.1 suggests, the relationship between elderly single-male households and public
assistance for the elderly is confirmed even if the prefectural fixed effect is considered.
Next, we examine whether the relationship between one-person households and the public
12assistance rate for the elderly is different by period. To examine this hypothesis, we divide
the sample by year. Tables 4–2 (1)–(4) show the results using the sample after 2000. As
columns (1)–(4) show, the coefficients of elderly single-male households are positively
significant. In contrast, the coefficients of elderly single-female households are negatively
significant at the 10% level in columns (3) – (4). These results suggest that the higher therate of elderly single-male households, the more the elderly receive public assistance after
2000. However, the higher the rate of elderly single-female households, the less theelderly receive public assistance after 2000 with controlled covariates. In contrast, Tables
4–2 (5) – (8) show that the coefficients of elderly one-person households are insignificant
for both genders at all columns. These results suggest that the effects of one-personhouseholds on the public assistance rate for the elderly are not observed before 1995 forboth genders.
We interpret these results in the following way: At the stage when the aging society
was not advanced and one-person households were not prominent, the increase in theelderly one-person households did not accelerate the rate of public assistance for theelderly with the support of neighboring residents and relatives living nearby. However,after the 2000s, the personalization of risks, as highlighted by Beck (1986), hasprogressed in Japan as well, and the household structure, family relationships, andregional support systems have changed. As a result, elderly individuals rely on public
assistance from governments rather than nearby families and neighbors. For this reason,
the increase in elderly one-person households has directly affected the rate of the elderly
receiving public assistance.

5.3. Estimation by Area
13

We proceed to examine the personalization of risks caused by urbanization in Japan,
dividing the sample into rural areas and city areas, wherein it is not relatively difficult for
the elderly to rely on neighborhoods in the former but difficult in the latter. These results
are shown in Tables 4–3. Columns (1)–(4) show the results for large city areas, andcolumns (5)–(8) show the results in rural areas. In city areas, the coefficients of elderly
single-male households are positively significant in all columns, but in rural areas, it isinsignificant in all columns. In other words, in city areas, many elderly single-malehouseholds facilitate the increase in public assistance rate for the elderly, but in rural areas,
it cannot be said that the increase in elderly single-male households accelerates theincrease in public assistance rate for the elderly.
We conclude these results in the following way. In areas where close interaction withfamily members and neighbors occurs, the large number of elderly single-malehouseholds does not lead to an increase in the public assistance rate for the elderly by
being supported by family members and neighbors. This is shown by the fact that thecoefficient of elderly single-male households is significantly positive in city areas, but
not in rural areas. In contrast, since the late 1990s, the personalization of risks has
progressed, and the large number of elderly single-male households has affected the highrate of receiving public assistance for the elderly. This was shown by the fact that thecoefficient of elderly single-male households has a positive effect on the public assistance
rate for the elderly after 2000.
6. Conclusion
In this study, we explore the factors behind the rapid increase in the number and ratioof the elderly receiving public assistance since the late 1990s while focusing on the rapid
increase in elderly one-person households. Specifically, we tested the hypothesis that a14

large number of elderly one-person households would affect the increase in the proportionof the elderly receiving public assistance. We found a relationship between prefecturaldifference in the rate of one-person households and the one in the rate of the elderly
receiving public assistance, despite controlling for the prefectural fixed effect.
Next, we divide the sample by period and area and find that the effect is observed incity areas, not in rural areas, and after 2000, not before 1995. We interpret these results
as follows. Since an elderly single male has difficulty obtaining support from theneighborhood and local communities in large cities, he can rely on public assistance fromthe government. As a result, the large number of elderly single men leads to an increasein the number of elderly people receiving public assistance. In addition, a stronger
correlation between the number of elderly single-male households and the elderly
receiving public assistance has been observed after the late 1990s and in city areas. In
other words, this study shows that increase in elderly single-male households directly
contributes to the increase in the elderly receiving public assistance because of changes
in family and regional relationships. As a result, the number of households receiving
public assistance has risen rapidly since the late 1990s. Although the economic downturncaused by the collapse of the bubble economy since the 1990s also contributes to theincrease in the number of households receiving public assistance, the increase in thenumber of the elderly receiving public assistance cannot be explained by the economic
recession alone. We show the support from families and neighbors has changed since late
1990s and “personalization” has become apparent, especially in city areas. These results
are consistent with Yamada and Shikata (2016), because they demonstrated the increasein the share of elderly one-person households who can rely on only public assistance leads

15

to the rising in the public assistance rate regardless of the decrease in poverty rate among
elderly one-person households.
From these results, we derive two opposite implications. From a conservative
standpoint, by providing community and family support as before the 1990s, curbing theincrease in elderly single-male households, we should ensure that the increase in elderly
single-male households does not affect the proportion of the elderly receiving public
assistance. The position is that welfare by families and communities should beemphasized again, as in the so-called “Japanese social welfare theory,” which was popularfrom the latter half of the 1970s to the 1980s. However, it is unrealistic seeking a solution
by family support, as we cannot imagine family care for the elderly without a long-termcare insurance system.
In contrast, on the premise of the personalization of Japanese society, one positionemphasizes the direction of enhancing public support without expecting excessive
support from the community and family. Furthermore, individuals who once had to relyon their community and family support could make their own decisions by living alone
and receiving public support. This trend has a positive aspect, as the elderly want to decideon their life by themselves, as Klinenberg (2012) emphasized.
Which views are more appropriate? It depends not only on the individual opinion of
how much should each entity such as among nations, communities, and families andindividual are responsible for preventing poverty among the elderly, as it is said in the
aspects of “self-, mutual-, and public-help,” but also on self-determination by the elderly.
Evaluating the welfare of the elderly from various perspectives is a possible further
research direction.

16

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19

Table１ Countries with a high proportion of the population aged 65 and over
1950

Rank

2015

CountryRate

2050(Predicted)

Country

Rate

Country

Rate

1

France11.40 Japan

26.02 Japan

36.37

2

Belgium

11.00 Italy

22.36 Spain

36.31

3

Island10.97 Germany

21.12 Portugal

35.59

4

UK

10.83 Portugal

20.74 Greece

35.45

5

Estonia10.60 Finland

20.26 Korea

35.27

6

Austria10.42 Bulgaria

20.08 Italy

34.62

7

Sweden10.19 Greece

19.95 (Taiwan)

34.50
1)

8

Georgia10.10 Sweden

19.60 Hong Kong

9

Latvia10.10 Latvia

19.28 Singapore

33.58

10

Germany

9.67 Denmark19.05 Slovenia

32.52

…

…

…

4.91

Japan

Source: UN, World Population Prospects：The 2017 Revision

Figure 1

International comparison of relative poverty rates：2015

0.25
0.2
0.15
0.1
0.05

Source:OECD Factbook 201820

IsraelTurkeyAmerica

Lithuania

Chili

LatviaEstonia

Spain

Japan

Italy

Greece

Korea

CanadaPortugal

UK

PolandGermany

Luxembourg

Ireland

Belgium

Slovenia

Swiss

SwedenAustria

Slovakia

FranceNorwayHollandCzech

Finland

Iceland0
Denmark60

33.91

Figure 2

Annual trend of the number of household receiving public assistance

1,800,000
1,600,000
1,400,000
1,200,000
1,000,000
800,000
600,000
400,000
200,000

Elderly

Single Parent

Disable

2008
2009
2010
2011
2012
2013
2014
2015

1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007

0

Others

Source: Before 2011, Statistics Information Department “Social welfare administrative work report”
After 2012, Social Welfare and War Victims' Relief Bureau “National Survey on Public Assistance
Recipients” Monthly Survey

Figure 3 1

Trend in one-person households by age：Male

16
14
12
10
8
6
4
2

65～69

70～74

75～79

80～84

Aged 85 and over
0
1980

1990

2000

2005

Source: Census

21

2010

2015

Figure 3 2

Trend in one-person households by age：Female

30

25

20

15

10

5

65～69

70～74

75～79

80～84

Aged 85 and over
0
1980

1990

2000

2005

2010

2015

Source: Census

Figure 4 Trend and Forecast in one-person households
25
20
15
10
5
0
2005

2010

2015

2020

Male

2025

2030

FemaleSource：National Institute of Population and Social Security Research ”Forecast of the living conditions of theelderly”

22

Hokkaido
Aomori
Iwate
Miyagi
Akita
Yamagata
Fukushima
IbarakiTochigi
Gumma
Saitama
Chiba
Tokyo
Kanagawa
NiigataToyamaIshikawa
Fukui
Yamanashi
NaganoGifu
Shizuoka
Aichi
Mie
Shiga
Kyoto
Osaka
Hyogo
Nara
Wakayama
Tottori
Shimane
Okayama
Hiroshima
Yamaguchi
Tokushima
KagawaEhime
Kochi
Fukuoka
Saga
Nagasaki
Kumamoto
Oita
MiyazakiKagoshima
Okinawa

Figure 5
The Rate of Single elderly household: by Prefecture

35

30

25

20

15

10

5

0

2015_male
2015_female
1980_male

Source: Census

23
1980_female

Table 2

Definition of Variables

Public Assistance Rate for the elderly: Monthly

Number of aged 65 and over receiving public assistance

National Survey on Public

average over 65 years old per 1,000 people／Population aged 65 and over

Assistance Recipients

The rate of Elderly single-male(female)The number of elderly single-male(or female) households

households／Male’s(or Female’s) Aged 65 and above

Life Expectancy
Prefectural Income per capita
Elderly Labor Participant Rate

CensusLife Table

Prefectural income／Population
Percentage of elderly general workers aged 65 and over／Population aged 65 and over

Unemployment Rate

Prefectural Accounts
Basic Survey on Wage Structure
CensusOwned House ratio

Number of homes／Number of residential households

Housing and Land Survey of Japan

Financial Capability Index

Basic Amount Income／Basic Fiscal Demand

Local Financial Situation Survey

24

0

20

40

60

Figure 6. Relationship between single elderly household rate and the elderly receiving
welfare rate

0

10

20

Male2010
Male1990

30

Female2010
Female1990

Table 3. Descriptive Statistics
N

Mean

S.D.

Min

Max

Public Assistance Rate for the elderly376

19.53411.9284.300

75.100Elderly single-male households

376

6.923

3.346

1.564

19.676

Elderly single-female households

376

15.672

5.412

4.493

29.812Rate of Aged 65-74

376

10.005

2.689

4.240

15.800Rate of Aged 75 and over

376

8.125

3.874

1.919

18.292

Male’s Life Expectancy at Aged 65376

17.226

1.540

13.83020.270Female’s Life Expectancy at Aged 65

376

21.6492.227

17.22025.190Prefectural Income per capita

376

25.1686.021

11.99453.780Elderly Labor Participant Rate

376

1.402

0.604

0.370

5.140

Unemployment Rate

376

4.244

1.706

1.200

11.900Owned House Ratio

376

67.5758.284

39.60085.700Financial Capability Index

376

0.472

0.214

0.205

1.622

25

１ Determinants of public assistance rate for the elderly：Basic Model

Table 4
VARIABLES

Elderly single-male households

Elderly single-female households

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

POLS

POLS

POLS

POLS

FE

FE

FE

FE

2.711***

2.512***

1.774**

1.059

2.118***

2.103***

1.760***

1.638**(0.939)(0.922)(0.736)(0.746)(0.553)(0.616)(0.654)(0.658)0.744*

0.642

0.494

0.318

0.157

-0.273-0.896-0.866(0.422)(0.457)(0.336)(0.264)(0.326)(0.560)(0.700)(0.667)0.122

1.067**

1.961***

0.567

1.472*

1.501*(0.450)(0.441)(0.570)(0.545)(0.739)(0.777)-1.194**-0.961**-1.254***-0.853-0.563-0.554(0.535)(0.374)(0.365)(0.537)(0.567)(0.582)-15.63***-8.034***-6.568**

0.160

0.789

0.900

(4.252)(2.868)(2.499)(1.850)(1.642)(1.664)12.34***

4.777**

2.469

2.989

2.091

1.766

(3.118)(2.194)(1.891)(1.972)(1.763)(1.639)-0.0587

0.379**

0.578**

0.604**(0.207)(0.174)(0.241)(0.254)0.561-0.400-2.382***-2.315***(1.404)(1.006)(0.718)(0.698)4.357***

3.667***

1.060

1.078

(1.247)(0.963)(1.276)(1.279)Rate of Aged 65-74

Rate of Aged 75 and over

Male’s Life Expectancy at Aged 65

Female’s Life Expectancy at Aged 65

Prefectural Income per capita

Elderly Labor Participant Rate

Unemployment Rate

Owned House Ratio

Financial Capability Index-0.628***-0.0198(0.179)(0.179)-19.18***-3.708(5.236)(5.648)Year Dummy

yes

yes

yes

yes

yes

yes

yes

yes

Observations

376

376

376

376

376

376

376

376

R-squared

0.615

0.695

0.776

0.814

0.663

0.683

0.720

0.720

47

47

47

47

Number of id

Note:

１．Clustered robust standard errors in parentheses
２．*** p<0.01, ** p<0.05, * p<0.1

26

Table 4

2

Determinants of public assistance rate for the elderly：By Year
(1)

VARIABLES

(2)

(3)

(4)

(5)

After 2000

Elderly single-male households

Elderly single-female households

(6)

(7)

(8)

Before 1995

FE

FE

FE

FE

FE

FE

FE

FE

2.093*

2.429**

2.040***

2.043***

2.821

2.339

-0.902-0.927(1.117)(1.011)(0.708)(0.632)(2.403)(1.772)(1.803)(2.020)-0.537-1.024-0.796*-0.645*-1.031-0.634-0.218-0.386(0.584)(0.636)(0.419)(0.349)(1.633)(1.396)(0.846)(0.803)0.907

0.957**

0.931**-4.879***-1.230-1.549(0.741)(0.451)(0.424)(1.561)(1.518)(1.598)

0.249

0.380

0.240

5.024**

4.925***

5.091***(1.153)(0.680)(0.643)(2.214)(1.553)(1.573)-5.808***-5.381***8.645***

9.248***(1.859)(1.787)(2.723)(2.770)

3.418

2.836

-4.284*-4.326*(2.331)(1.918)(2.132)(2.190)0.0292-0.00899

1.734***

1.776***(0.172)(0.153)(0.408)(0.471)-1.944*-1.669*

0.555

0.677

(1.014)(0.977)(0.878)(0.823)-1.742***-1.815***

3.699***

3.712***(0.553)(0.546)(1.333)(1.321)

Rate of Aged 65-74

Rate of Aged 75 and over

Male’s Life Expectancy at Aged 65

Female’s Life Expectancy at Aged 65

Prefectural Income per capita

Elderly Labor Participant Rate

Unemployment Rate

Owned House Ratio

Financial Capability Index

0.144

0.250*(0.237)(0.139)-14.20*-3.632(7.099)(6.185)

Year Dummy

yes

yes

yes

yes

yes

yes

yes

yes

Observations

188

188

188

188

188

188

188

188

R-squared

0.808

0.820

0.874

0.881

0.728

0.755

0.852

0.854

Number of id

47

47

47

47

47

47

47

47

Note:

１．Clustered robust standard errors in parentheses
２．*** p<0.01, ** p<0.05, * p<0.1

27

Table 4

3

VARIABLES

Determinants of public assistance rate for the elderly：By Area
(1)

(2)

(3)

(4)

(5)

(6)

City Area

Elderly single-male households

Elderly single-female households

(7)

(8)

Rural Area

FE

FE

FE

FE

FE

FE

FE

FE

3.426**

3.613***

3.707***

2.583**

0.217

0.432

0.525

0.661

(1.361)(0.774)(0.794)(0.915)(1.257)(1.094)(1.059)(1.033)-2.221-2.677**-2.764**-2.196-0.0936-0.232-0.621-0.529(1.455)(1.122)(1.084)(1.333)(0.370)(0.672)(0.684)(0.654)2.221***

2.384**

2.905***

0.183

0.718

1.008

(0.590)(0.755)(0.786)(0.692)(0.718)(0.777)0.654-0.184-1.587-0.496-0.317-0.208(1.549)(1.190)(1.563)(0.638)(0.651)(0.634)-11.19**-9.701*-5.754

1.155

0.533

0.259

(4.030)(4.578)(4.927)(1.948)(1.898)(1.933)9.855**

9.329*

6.823

0.989

0.526

0.701

(3.819)(4.416)(4.370)(1.957)(1.589)(1.591)-0.07860.107

0.853***

0.827**(0.128)(0.123)(0.295)(0.310)-0.589-0.522-1.995***-2.451***(0.707)(0.506)(0.606)(0.701)-1.170-1.426

1.209

1.239

(0.970)(0.993)(1.513)(1.464)Rate of Aged 65-74

Rate of Aged 75 and over

Male’s Life Expectancy at Aged 65

Female’s Life Expectancy at Aged 65

Prefectural Income per capita

Elderly Labor Participant Rate

Unemployment Rate

Owned House Ratio

Financial Capability Index

0.466-0.243(0.301)(0.226)-8.1396.697(5.658)(11.41)

Year Dummy

yes

yes

yes

yes

yes

yes

yes

yes

Observations

64

64

64

64

312

312

312

312

R-squared

0.875

0.912

0.915

0.926

0.669

0.674

0.714

0.718

Number of id

8

8

8

8

39

39

39

39

Note:

１．Clustered robust standard errors in parentheses
２．*** p<0.01, ** p<0.05, * p<0.1

28

Appendix A Determinants of public assistance rate for the elderly：Basic Model
VARIABLES

Elderly single-male households

Elderly single-female households

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

POLS

POLS

POLS

POLS

FE

FE

FE

FE

4.142***

3.925***

3.012***

1.758

3.665***

4.075***

3.325***

3.103***(1.160)(1.316)(1.087)(1.085)(0.711)(0.818)(0.805)(0.848)1.052***

0.896*

0.594*

0.388-0.506-1.120-1.898**-1.988**(0.389)(0.476)(0.349)(0.275)(0.558)(0.742)(0.787)(0.792)-0.1440.944**

1.900***

0.707

1.736***

1.616**(0.411)(0.432)(0.570)(0.506)(0.624)(0.680)-1.192*-0.839*-1.192***-0.0844

0.392

0.365

(0.627)(0.420)(0.421)(0.706)(0.692)(0.703)-16.07***-8.533***-6.858***-1.418-0.854-0.766(4.336)(2.937)(2.522)(1.796)(1.496)(1.501)12.36***

4.982**

2.600

4.929**

3.788**

3.609**(3.250)(2.205)(1.903)(1.986)(1.620)(1.548)-0.00949

0.407**

0.631***

0.644***(0.199)(0.162)(0.202)(0.209)0.549-0.408-2.807***-2.632***(1.368)(1.010)(0.667)(0.656)4.465***

3.759***

1.117

1.102

(1.188)(0.923)(1.099)(1.128)Rate of Aged 65-74

Rate of Aged 75 and over

Male’s Life Expectancy at Aged 65

Female’s Life Expectancy at Aged 65

Prefectural Income per capita

Elderly Labor Participant Rate

Unemployment Rate

Owned House Ratio

Financial Capability Index-0.625***0.128(0.177)(0.150)-18.92***-1.883(5.120)(4.596)

Year Dummy

yes

yes

yes

yes

yes

yes

yes

yes

Observations

376

376

376

376

376

376

376

376

R-squared

0.610

0.695

0.777

0.813

0.678

0.701

0.749

0.751

47

47

47

47

Number of id

Note:

１．Clustered robust standard errors in parentheses
２．*** p<0.01, ** p<0.05, * p<0.129

Appendix B

Determinants of public assistance rate for the elderly：By Year
(1)

VARIABLES

(2)

(3)

(4)

(5)

(6)

After 2000

Elderly single-male households

Elderly single-female households

(7)

(8)

Before 1995

FE

FE

FE

FE

FE

FE

FE

FE

2.883*

3.021**

2.375**

2.520***

5.461

6.015**

1.774

1.661

(1.512)(1.298)(0.975)(0.912)(3.298)(2.768)(2.684)(3.012)-0.240-0.857-0.493-0.369-2.705*-1.859-1.930*-2.127**(0.685)(0.666)(0.437)(0.386)(1.600)(1.707)(1.043)(0.976)0.733

0.754*

0.736-3.863***-0.949-1.393(0.601)(0.446)(0.451)(1.289)(1.546)(1.607)

0.391

0.629

0.474

5.040***

5.840***

6.108***(0.997)(0.703)(0.644)(1.746)(1.666)(1.705)-7.128***-5.972***-5.498***

8.468

5.187

5.907*(2.578)(1.881)(1.780)(5.141)(3.252)(3.298)

3.917

3.279

2.652

-3.890*-2.665-2.701(2.963)(2.426)(1.975)(2.235)(2.177)(2.208)0.05880.0162

1.677***

1.688***(0.179)(0.156)(0.420)(0.483)-1.854*-1.550-0.09540.0950(1.054)(1.017)(0.876)(0.799)-1.763***-1.826***

3.158**

3.174**(0.482)(0.484)(1.194)(1.192)Rate of Aged 65-74

Rate of Aged 75 and over

Male’s Life Expectancy at Aged 65

Female’s Life Expectancy at Aged 65

Prefectural Income per capita

Elderly Labor Participant Rate

Unemployment Rate

Owned House Ratio

Financial Capability Index

0.156

0.319**(0.246)(0.135)-15.90**-3.417(7.495)(6.336)

Year Dummy

yes

yes

yes

yes

yes

yes

yes

yes

Observations

188

188

188

188

188

188

188

188

R-squared

0.808

0.836

0.869

0.877

0.758

0.792

0.862

0.865

Number of id

47

47

47

47

47

47

47

47

Note:

１．Clustered robust standard errors in parentheses
２．*** p<0.01, ** p<0.05, * p<0.130Appendix C
VARIABLES

Determinants of public assistance rate for the elderly：By Area
(1)

(2)

(3)

(4)

(5)

(6)

City Area

Elderly single-male households

Elderly single-female households

(7)

(8)

Rural Area

FE

FE

FE

FE

FE

FE

FE

FE

5.591**

5.859***

5.892***

4.244**

0.610

1.822

1.703

1.780

(1.853)(1.208)(1.260)(1.256)(1.447)(1.228)(1.317)(1.304)-2.114-2.928**-3.113**-2.728**-0.907-1.594-1.878**-1.817**(1.146)(1.035)(0.981)(1.098)(0.588)(0.977)(0.907)(0.891)0.694*

0.942

1.757**

1.012

1.456**

1.535*(0.348)(0.641)(0.664)(0.751)(0.714)(0.779)

3.100

2.401

0.544

0.210

0.452

0.479

(1.911)(1.399)(1.840)(0.788)(0.756)(0.752)-12.56**-10.67*-6.698-0.562-0.981-1.180(4.161)(4.545)(4.844)(1.936)(1.868)(1.890)10.58**

9.898*

7.204

3.649*

2.672

2.887*(3.774)(4.351)(4.092)(2.066)(1.729)(1.712)-0.03270.127

0.820***

0.793***(0.127)(0.120)(0.266)(0.279)-0.817-0.750-2.478***-2.763***(0.750)(0.507)(0.694)(0.769)-1.161-1.433

1.173

1.148

(1.001)(1.017)(1.381)(1.365)Rate of Aged 65-74

Rate of Aged 75 and over

Male’s Life Expectancy at Aged 65

Female’s Life Expectancy at Aged 65

Prefectural Income per capita

Elderly Labor Participant Rate

Unemployment Rate

Owned House Ratio

Financial Capability Index

0.478*-0.0795(0.226)(0.187)-8.0498.340(4.799)(10.38)Year Dummy

yes

yes

yes

yes

yes

yes

yes

yes

Observations

64

64

64

64

312

312

312

312

R-squared

0.890

0.919

0.922

0.934

0.683

0.697

0.740

0.742

Number of id

8

8

8

8

39

39

39

39

Note:

１．Clustered robust standard errors in parentheses
２．*** p<0.01, ** p<0.05, * p<0.131