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Household Production and Consumption over the Life Cycle in Japan:
NTA and NTTA summaries by gender from 1999 to 2014
Setsuya FUKUDA / Itaru SATO(National Institute of Population and Social Security Research)
Kazuyuki TERADA / Takahiro TORIYABE / Hidehiko ICHIMURA / Naohiro OGAWA(The University of Tokyo)
Rikiya Matsukura (Nihon University)
WITTGENSTEIN CENTRE CONFERENCE 2017
AGENTA FINAL CONFERENCE: ECONOMIC CONSEQUENCES OF POPULATION AGEING AND INTERGENERATIONAL EQUITY
Vienna,20 November 2017
1. Aim of the paper
1. Updating Japanese NTA in terms of data and method
2. Providing descriptive figures of generational economy in comparison with gender during the 2000s in Japan
3. Assessing various policy reform using the NTA / NTTA life cycle accounts
2
2. Background
3
2-1. Population aging and decline
0 100 2000
20
65
75
100+
1,613 (13%)
1,734 (14%)
7,028 (55%)
2,190 (17%)
Total Pop: 127.1 mil.2015
0 100 2000
20
65
75
100+
2,180 (18%)
1,497 (12%)
6,635 (54%)
1,943 (16%)
Total Pop: 122.5 mil.2025
0 100 2000
20
65
75
100+
2,248 (26%)
1,133 (13%)
4,189 (48%)
1,2370 (14%)
Total Pop: 88.1 mil.2065
0 100 2000
20
65
75
100+
597 (5%)
7,590 (61%)
Total Pop: 123.6 mil.
892 (7%)
3,249 (26%)
1990
Sources: Statistics Bureau, Population Census of Japan and NIPSSR (2017) Population Projection of Japan: 2016-2065. 4
0
5
10
15
20
25
30
35
40
45
1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 2060
% o
f age
65+
in to
tal p
op.
DEU FRA JPN KOR SWE USA
2-2. Prospects of population aging
Sources: World Bank World Development Indicators for 1960-2015. UN (2017) The World Population Prospects, The 2017 Revision for 2016-2060. NIPSSR (2017) Population Projection for Japan: 2016-2065 for Japan.
5
2-3. Population aging and social expenditure
1980
2013
1980
2013
1980 1990
2000
2010 2013
1980
2013
1980
2013
1980
2013
5
10
15
20
25
30
35
8 10 12 14 16 18 20 22 24 26
in %
of G
DP
% of pop. age 65+
Social Expenditure in % of GDP
JapanUSA
FRA SWE
DEU
UK
Sources: OECD (2016): OECD Social Expenditure Database, OECD Health Statistics 2016. 6
2-4. Social security expenditure in Japan
0
10
20
30
40
50
60
70
80
90
100
0102030405060708090
100110120130
1950 1960 1970 1980 1990 2000 2010
in 1
0,00
0 Ye
n (=
75.5
0 EU
R)
in Tr
illion
Yen
(=7.
5 Bi
llion
EUR)
Per Capita expenditure on social benefit (right axis)
Welfare and others
Medical Care
Pension
6,800EUR
90 bln. EUR
Sources: NIPSSR (2017) Financial Statistics of Social Security in Japan 2015. 7
3. Policy Reforms during the 2000s• (Formal childcare: Creating more slots in big cities, but no time-series data)
• Formal education (High school)
• Female employment
• (Long work hours / Dependent spouse privileges in tax, pension and health care: stalled…)
• Elderly care
• (Medical care: various adjustments on the price of medical services)
• (Pension: Increases in pension age and contribution fee)8
4. Focus• Focus 1: 1999 vs 2004 Introduction of the new social insurance for elderly care (2000.4)- Changes in monetary and physical costs of elderly care in the households and government
• Focus 2: 2009 vs 2014 Introduction of free tuition fee for high school + child allowances (2010.4)- Changes in monetary costs of education in the household
• Focus 3: 1999 vs 2014Gradual expansion of policy support for female employment (1999.4 -)- Changes in gender gaps in paid and unpaid work during the 2000s
9
5. DataData and Year Description
NTA• Private sector
• Public sector
• Macro controls
Family Income and Expenditure Survey(1999, 2004, 2009, 2014)
Education- Statistical abstract on education, culture,
sports, science and technology Health- Survey of medical care benefit expenditures,- Estimates of National Medical Care Expenditure Elderly care- Annual report on the long-term care
insurance
SNA2008
Around 60,000 households and 170,000 individuals
Statistics from the government reports
NTTA• Time Use Data
Survey on Time Use and Leisure Activities (1996, 2001, 2006, 2011)
Over 70,000 households and 170,000 individuals
The specialist replacement method
10
6. Limitation
• Only life cycle accounts
• Ignoring institutionalized population
• Ignoring heterogeneity other than age and sex
• Not final values
11
7. NTA / NTTA overview for 2014
12
7-1. Economic Life Cycle in 2014
0
100
200
300
400
500
Japa
nese
Yen
in 1
0,00
0
0 10 20 30 40 50 60 70 80 90Age
LI C
Per Capita value
13
7-2. Economic Life Cycle in 2014 by Sex
0
200
400
600
800Ja
pane
se Y
en in
10,
000
0 5 1015202530354045505560657075808590Age
LI C
Male
0
200
400
600
800
Japa
nese
Yen
in 1
0,00
0
0 5 1015202530354045505560657075808590Age
LI C
Female
14
7-3. NTTA in 2014 by Sex
0
100
200
300Ja
pane
se Y
en in
10,00
0
0 10 20 30 40 50 60 70 80 90Age
NTTA Prod NTTA Cons
Male
0
100
200
300
Japa
nese
Yen
in 10
,000
0 10 20 30 40 50 60 70 80 90Age
NTTA Prod NTTA Cons
Female
15
7-4. NTA + NTTA in 2014 by Sex
0
200
400
600
800Ja
pane
se Y
en in
10,
000
0 5 1015202530354045505560657075808590Age
LI + NTTA prod C + NTTA cons
Male
0
200
400
600
800
Japa
nese
Yen
in 1
0,00
0
0 5 1015202530354045505560657075808590Age
LI + NTTA prod C + NTTA cons
Female
16
7-5. NTA+NTTA: Production in 2014 by Sex
0
200
400
600
800
Japa
nese
Yen
in 1
0,00
0
0 10 20 30 40 50 60 70 80 90Age
NTTA NTA
Male
0
200
400
600
800
Japa
nese
Yen
in 1
0,00
0
0 10 20 30 40 50 60 70 80 90Age
NTTA NTA
Female
2014
17
7-6. NTA+NTTA: Consumption in 2014 by Sex
100
200
300
400
500
600
Japa
nese
Yen
in 1
0,00
0
0 10 20 30 40 50 60 70 80 90Age
NTTA NTA public NTA private
Male
100
200
300
400
500
600
Japa
nese
Yen
in 1
0,00
0
0 10 20 30 40 50 60 70 80 90Age
NTTA NTA public NTA private
Female
2014
18
7-7. Life Cycle Deficit in 2014
-300
-200
-100
0
100
200
300
400
500
600
0 10 20 30 40 50 60 70 80 90
Japa
nese
Yen
in 1
0,00
0
AgePrivate Public NTTA LCD
-600
-400
-200
0
200
400
600
800
1000
0 10 20 30 40 50 60 70 80 90
Japa
nese
Yen
in 1
0 bi
lli.
AgePrivate Public NTTA LCD
1. Per Capita 2. Population Aggregate
19
8. Examination of the Focus Points
20
8-1. Introduction of “the long-term care insurance” (2000.4)
0
50
100
150
200
Japa
nese
Yen
in 1
0,00
0
0 10 20 30 40 50 60 70 80 90Age
Public: Elderly carePublic: Medical care
NTTA CarePrivate: Health
1999
0
50
100
150
200
Japa
nese
Yen
in 1
0,00
0
0 10 20 30 40 50 60 70 80 90Age
Public: Elderly carePublic: Medical care
NTTA CarePrivate: Health
2004
Health Consumption: 1999 vs 2004
21
8-2. NTA differences before and after the LT care insurance
-20
0
20
40
60
80Ja
pane
se Y
en in
10,
000
65 70 75 80 85 90Age
Public: Elderly carePublic: Medical care
NTTA CarePrivate: Health
Male
-20
0
20
40
60
80
Japa
nese
Yen
in 1
0,00
0
65 70 75 80 85 90Age
Public: Elderly carePublic: Medical care
NTTA CarePrivate: Health
Female
Difference: (2004 - 1999)
22
8-3. NTTA differences before and after the LT care insurance
-1
0
1
2
Hour
s/Wee
k
45 50 55 60 65 70 75 80 85 90Age
NTTA: care prod NTTA: care consNTTA: hwk prod
Male
-1
0
1
2
Hour
s/Wee
k
45 50 55 60 65 70 75 80 85 90Age
NTTA: care prod NTTA: care consNTTA: hwk prod
Female
Difference: (2004 - 1999)
23
8-4. Introduction of “free tuition fee for high school” + “child allowances” (2010.4)
0
20
40
60
80Ja
pane
se Y
en in
10,
000
10 15 20 25 30Age
2009 2014
Private Coonsumption for Education in 2009 and 2014
24
8-5. Introduction of “free tuition fee for high school” + “child allowances” (2010.4)
0
20
40
60
80Ja
pane
se Y
en in
10,0
00
10 15 20 25 30Age
2009 2014
Male
0
20
40
60
80
Japa
nese
Yen
in 1
0,000
10 15 20 25 30Age
2009 2014
Female
Private Consumption for Education: 1999 vs 2004
25
8-6. Gradual expansion of policy support for female employment (1999.4 -)
0
20
40
60
Hour
s/Wee
k
0 10 20 30 40 50 60 70 80 90Age
Unpaid 1999Unpaid 2004Unpaid 2009Unpaid 2014
Paid 1999Paid 2004Paid 2009Paid 2014
Male
0
20
40
60
Hour
s/Wee
k
0 10 20 30 40 50 60 70 80 90Age
Unpaid 1999Unpaid 2004Unpaid 2009Unpaid 2014
Paid 1999Paid 2004Paid 2009Paid 2014
Female
Hours Spent for Paid and Unpaid Work by Sex
26
8-7. Period differences in time use for paid and unpaid work by sex: 1999-2014
-10
-5
0
5
10
Hour
s/Wee
k
0 10 20 30 40 50 60 70 80 90Age
Paid 2004Paid 2009Paid 2014
Unpaid 2004Unpaid 2009Unpaid 2014
Male
-10
-5
0
5
10
Hour
s/Wee
k
0 10 20 30 40 50 60 70 80 90Age
Paid 2004Paid 2009Paid 2014
Unpaid 2004Unpaid 2009Unpaid 2014
Female
Time Differences from 1999 by Sex
27
8-8. Gender differences in time use for paid and unpaid work 1999 vs 2014
-40
-20
0
20
40
Hours
/Wee
k
0 5 1015202530354045505560657075808590Age
HouseworkChildcareElderly care
Other careWork
1999
-40
-20
0
20
40
Hours
/Wee
k
0 5 1015202530354045505560657075808590Age
HouseworkChildcareElderly care
Other careWork
2014
Time Use, Female - Male Differences
28
8-9. Gender comparison of total production time
0
20
40
60
Hour
s/Wee
k
0 10 20 30 40 50 60 70 80 90Age
Male Female
1999
0
20
40
60
Hour
s/Wee
k
0 10 20 30 40 50 60 70 80 90Age
Male Female
2004
0
20
40
60
Hour
s/Wee
k
0 10 20 30 40 50 60 70 80 90Age
Male Female
2009
0
20
40
60
Hour
s/Wee
k0 10 20 30 40 50 60 70 80 90
Age
Male Female
2014
NTA+NTTA Production Hours by Sex
29
8-10. Gender differences in labor incomein 1999 and 2014
0
200
400
600
800Ja
pane
se Y
en in
10,
000
0 10 20 30 40 50 60 70 80 90Age
1999 2014
Male
0
200
400
600
800
Japa
nese
Yen
in 1
0,00
0
0 10 20 30 40 50 60 70 80 90Age
1999 2014
Female
Labor Income by Sex: 1999 vs 2014
30
8-11. Gender differences in unpaid work wagesin 1999 and 2014
0
50
100
150
200Ja
pane
se Y
en in
10,
000
0 10 20 30 40 50 60 70 80 90Age
1999 2014
Male
0
50
100
150
200
Japa
nese
Yen
in 1
0,00
0
0 10 20 30 40 50 60 70 80 90Age
1999 2014
Female
Unpaid work wage by Sex: 1999 vs 2014
31
9. Conclusion NTA/NTTA as a necessary tool for Japan to monitor & analyze the aging society!
• Focus 1: Socializing elderly care increased total public costs for elderlies, but reduced both medical costs and disabled elderlies
• Focus 2: Free high school tuition and child allowances decreased householdeducation costs for secondary education, while those of post-secondary education increased
• Focus 3: a. There is a large gender gap in paid and unpaid work in Japan both in time and monetary basis.
b. The gender gaps narrow a bit in both paid and unpaid work by age of 40.
32
10. Future work
• Examination of the shifts or changes in focused points
- Running regression models to control for demographic and socio-
economic components behind the age and sex
33