Household saving in different family types:
evidence from Japanese micro databyOleksandr Movshuk
University of Toyama

Research meeting at National Statistics Center andInstitute of Statistical Mathematics
November 11, 2011

What the paper does
 Examines whether different family types in Japan have distinct
age-saving profiles over the life cycle

 Uses 4 waves from the National Survey of Family Income andExpenditure (全国消費実態調査)

 Applies a semiparametric ‘varying-coefficient model’ (VCM) toestimate specific age-saving profiles for

singles without children
couples without children (such as DINKs)
couples with children
single parents with children
multi-generational households
 Applies the VCM to isolate the contribution of aged and retired
household members to the total pool of household savings

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Major findings
 Age-saving profiles are very different across family types
 Humped-shaped profiles of savings were most evident amonghouseholds with children

 In contrast, households without children reached their peaksaving rates much earlier

 Little evidence for negative savings among aged and retired
household members

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Neglect of family structure in thelife-cycle hypothesis (LCH) of saving
 The LCH postulates that the major saving motive is for
retirement

 In the stripped-down version of LCH, family structure isassumed away: individuals remain single, and raise no children Little evidence is currently available on savings in families thatraise children (apart from simple linear effects from the numberof children). We know little (a) whether the presence of children
shifts the timing for retirement saving, or (b) whether children
modify the shape of age-saving profiles

 Conventional controls for demographic effects include thenumber of adults and children (Paxson (1996), Attanasio et al.
(1999) and Fernández-Villaverde and Krueger (2007)), butdemographic effects are only linear
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At present, traditional patterns of family life are
getting more diverse
 Traditional family of a married couple and kids is no longer themajor type in many countries

 According to the U.S. Census for 2010, married couples were
only 45% of all households. The minority share was the firsttime in the history of U.S. Census

 More people decide to avoid traditional marriage, and preferpartnerships, or just stay single

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What about Japan?
単独

夫婦のみ夫婦と子

ひとり親と子

拡大家庭等

0.40
0.35
0.30
0.25
0.20
0.15
0.100.05
1990 1995 2000 2005 2010 2015 2020 2025 2030
Source: Household Projections for Japan (1995 –2030), National Institute of Population andSocial Security Research.

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Stylized view oflife-cycle model of household saving
250
Consumption
IncomeSavings
Net worth

200
150
100
50
0‐50
‐100‐150
‐200
2030

40

50

Age

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60

7080

Baseline models for household saving
 For household i at time t, saving rate sri,t is specified by a semiparametric age-period-cohort (APC) model:

sri ,t  f  age    c Dc   tDt   zi ,t   i ,t
parametricpart

nonparametricpartf(age) :
Dc:
Dt:
zi,t:

nonparametric effect from the age of household head
dummy variables for cohort effects (c = t – age)dummy variables for time effects (i.e., macro shocks)
other factors of saving (education, gender, region ofresidence, employment status, etc.)

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Why nonparametric effect for age?
 If age, cohort and period effects are specified as linear effects,
this creates a simultaneity problem between three effects
calendar time t = year of birth of cohort c + age a One of effects is no longer independent For example, once we know the period effect from t and cohort

effect from c, the age effect for a is automatically determined asa linear combination of t and c With the nonparametric specification, saving rate is a nonlinear
function of age, which solves the simultaneity problem

 The choice which effect is nonparametric is arbitrary, and could
not be tested: there is a perfect multicollinearity in thealternative hypothesis
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Specification of Models 1-3
Model 1:

only APC effects on the saving rate

Model 2:

APC effects + basic demographic effects(number of adults, children)

Model 3:

APC effects + full list of parametric effects (familytype, type of contract, region, industry, gender ofhousehold head)

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Data
 I used household data from the National Survey of Family
Income and Expenditure (NSFIE) The survey collects data from about 50,000 households.
 Survey’s data include demographic and economic characteristics
of households, such as: household structure, detailed member’sinformation, sources of income, consumption expenditures,financial assets and liabilities, etc.

 I used data from 4 waves of the NSFIE (1989, 1994, 1999, and2004)

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11/22

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Nonparametric estimates of age effect in Models 1-3(a) Model 1: Age-Cohort-Year(b) Model 2: Add demographics(c) Model 3: Additional controls
0.10

0.05

0.05

0.05

0.00

0.00

0.00

s(age,8)s(age,6.94)

0.10s(age,8)

0.10

-0.05

-0.05

-0.05

-0.10

-0.10

-0.10

-0.15

-0.15

-0.15

20304050

age

60708020304050

age

60708020304050

age

Note: zero denotes the average saving rate over the life cycle
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60

7080

Varying coefficient model The model introduces interactions between nonparametricterms and categorical variables

 Consider a general semiparametric modely   X  f  z    Let d be a dummy variable with three categories, d1, d2, d3
 The varying coefficient model isy    X  f  z   f ( z )d1  f ( z )d2  f ( z )d3   We have a general nonparametric effect f(z), like in Models 1-3
 Importantly, f(z)d1 denotes differences (differentials) from thegeneral effect f(z) for members in first category

 The model has two more alternative specifications

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Varying coefficient model (cont.)
 Second specification: the general nonparametric term is omitted,
with each of f(z)dk (k=1,…,3) denoting category-specificnonparametric effects (no more differential effects). The null
hypothesis is H0: f(z) = 0, and it can be testedy    X  f ( z )d1  f ( z )d2  f ( z )d3   Third specification: one category is used as a benchmark (say,the first one). For the remaining categories, their specificnonparametric effects are compared with the benchmark
category y    X  f (z )d2  f (z )d1  f ( z )d3  f ( z )d1   Third specification is not a different model, but a different
presentation of the second specification, to compare
nonparametric effects between categories
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Specifications of the varying-coefficient model Model 4: Uses first version, with general term f(age), anddifferentiated effects f(age)dk for specific categories

 Model 5: Uses second version with no general term f(age) Model 6: Same as Model 5, plus third version to estimate theeffect of aged and retired household members(households with no such members are thebenchmark category)

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17/22Age effects for different family types in Model 6
Type 2: Couple only

0.15

0.100.10

0.10

0.05
0.00

s(age,6.72):family3

0.15s(age,6.77):family2s(age,7.06):family1

Type 1: Single households
0.15

0.05
0.00

-0.10

-0.10

-0.15

-0.15
2030

40

50

60

70

80

0.05
0.00

-0.05

-0.05

-0.05

Type 3: Couple & child(ren)

-0.10
-0.15

2030

40

50

60

70

80

2030

4050

age
Type 4: Single parent & child(ren)Type 5: Multi-generation family

0.15s(age,6.64):family5s(age,7.1):family4

0.15
0.10
0.05
0.00

-0.05

0.10
0.05
0.00

-0.05

-0.10
-0.10
-0.152030

4050

age

60

70

80

2030

4050

age

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60

70

80

60

7080

Age effects for households withaged and retired household members(a) (age >65 & unempl) = 1(b) (age >65 & unempl) >= 2
0.15s(age,6):aged65.d.un2s(age,6.07):aged65.d.un1

0.15

0.10

0.05

0.10

0.05

0.00

0.00

-0.05
20

30

40

50

60

70

80

20

age

30

4050

age

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60

70

80

Major findings
 Different types of households had vastly different age-saving

profiles, with little evidence of a common saving pattern acrossfamily types

 There was little evidence that households save for the old age,as postulated by the life-cycle hypothesis of savings

 The best correspondence with the life-cycle theory of savings

was for couples with children, with a clear decline in the savingrate for aged households

 When households were differentiated by the number of aged

and retired members, the presence of such members almost
never had negative effect on household savings, thus providing
little support to the central prediction of the LCH of saving

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Work to be done
Household net saving rates
As a percentage of household disposable income

FranceGermanyJapanUnited States

15131197531-1
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
Source : OECD Factbook 2010; National Accounts of Japan Age-saving profiles by household types f(age)dk (k=1,…5) canbe used to estimate the effect of changing demographic (i.e.,household) structure on saving rate Preliminary results: demographic changes and population

ageing had negligible effect on Japan’s saving rate in the past.
Their contribution is likely to be minor in the future too
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Thank you for listening!

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