New ESRI Working Paper No.44

Responses of household expenditure tochanges in discretionary income:Micro data study using two episodes of changes
in tuition payment in Japan

Masahiro Hori
December 2017

Economic and Social Research InstituteCabinet Office
Tokyo, JapanThe views expressed in “New ESRI Working Paper” are those of the authors and not those of
the Economic and Social Research Institute, the Cabinet Office, or the Government of Japan.
(Contact us: https://form.cao.go.jp/esri/en_opinion-0002.html)

New ESRI Working Paper No.44
Responses of household expenditure to changes in discretionary income:Micro data study focusing on tuition payments
to examine consumption smoothing by Japanese households

Responses of household expenditure to changes in discretionary income:Micro data study focusing on tuition payments to examine consumption smoothing by
Japanese households †Masahiro Hori
Economic and Social Research Institute, Cabinet Office, Japan

December 2017

Abstract
This paper focuses on tuition fees in Japan to examine consumption smoothing by Japanesehouseholds. The analysis shows that a 100 yen decrease in discretionary income due to the paymentof university tuition fees is associated with a decrease of about 15 yen in discretionary spending in themonth that tuition payments are made. It is also found that the free high school tuition program
introduced in 2010 significantly stimulated household spending, especially in the case of poorer
households. Moreover, it is found that the free tuition program significantly altered the compositionof household expenditure of wealthier households, with a surge in spending shares observed in nontuition education expenses, clothing, and recreational goods. These findings indicate that Japanesehouseholds responded to predictable income changes and did not fully engage in consumption
smoothing.

Keywords: consumption smoothing, life-cycle/permanent income hypothesis, college tuition payment,
free high school tuition program, Family Income and Expenditure Survey
JEL Classification: D12, I21

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New ESRI Working Paper No.44
Responses of household expenditure to changes in discretionary income:Micro data study focusing on tuition payments
to examine consumption smoothing by Japanese households

1. Introduction
This paper focuses on tuition fees in Japan to examine consumption smoothing by Japanesehouseholds. In Japan, tuition fees make up a large proportion of household expenses of householdswith children attending school or university. Therefore, changes in discretionary income due tochanges in tuition fee expenditures are likely to have a considerable impact on household welfare.
Against this background, the aim of this paper is to examine consumption smoothing by householdswith children attending university or high school to grasp the welfare implications of the tuition burden
for such households
Changes in tuition payments due to external factors often cause predictable large changes indiscretionary income in predetermined months. The standard life cycle/permanent income hypothesis(LC/PIH) predicts that anticipated changes in individuals’ income should have no impact on theirexpenditure patterns unless they are liquidity constrained. Therefore, examining the effect ofanticipated changes in tuition payment flows on household spending patterns makes it possible toexamine whether the LC/PIH holds in practice and households engage in consumption smoothing.
Investigating the impact of college tuition fees on household expenditure patterns in the United States,
Souleles (2000), for example, found that households appear to do a relatively good job in smoothing
their consumption over the year, despite the large expense that tuition payments represent. Several
other studies similarly found no or little link between large fluctuations in household income andhousehold spending when the fluctuations were anticipated (Paxson, 1993; Browning and Collado,
2001; and Hsieh, 2003). On the other hand, a substantial number of empirical studies on (relatively
small) policy-induced income changes have found that predictable income changes caused statisticallysignificant changes in spending, known as “excess sensitivity,” rejecting the LC/PIH. 1 Meanwhile,studies on Japan suggest that households display a high degree of “excess sensitivity” in the sense thatsignificant consumption responses are reported not only when the anticipated income changes are
small but also when they are large. 2
This paper focuses on tuition fee payments to examine the issue of excess sensitivity inJapan. In Japan, it is mandatory for parents to send their children to elementary school to receive2New ESRI Working Paper No.44
Responses of household expenditure to changes in discretionary income:Micro data study focusing on tuition payments
to examine consumption smoothing by Japanese households

primary education for 6 years and to junior high school from age 12 to receive lower secondary
education for 3 years. On the other hand, attendance of senior high school (higher secondary
education) is not mandatory, and both public and private high schools charge tuition fees. Similarly ,two-year junior colleges as well as national and private universities offering four-year undergraduate
degree and post-graduate degrees charge tuition fees. (In the remainder of this study, these will besummarily referred to as “universities.”) These high school and university tuition fees are widely
regarded as major household expenses.
In order to examine excess sensitivity in Japan, this study conducts two different analyses
focusing on tuition fee payments. The first analysis relies on the fact that university tuition payments
are made in certain months of the year and hence represent large and clearly predictable changes inmonthly discretionary income. One would therefore expect households that act according to the
LC/PIH to smooth their consumption over the year to cover tuition payments.
The second analysis focuses on the reaction of household expenditures to a program to makehigh school tuition-free launched in FY2010. The program (1) makes public high school tuition freeand (2) provides a subsidy of 118,800 yen (about 1,200 dollars) per year to students at private highschools. The subsidy is not paid directly to parents; instead, high schools receive the money on behalfof students or parents and use it to offset part of the tuition. According to the LC/PIH, rational
households do not change their consumption path unless their permanent income is altered. Since theimpact of the free tuition program on household budgets, though not negligible, is too small to resultin a substantial increase in permanent income, one would expect that it would not have a notable effecton household spending patterns unless households are liquidity constrained.
Contrary to this expectation, the results of the analysis indicate that the change in income
due to the free tuition program did have statistically significant effects on household consumption
patterns. It is also found that the free tuition program stimulated poorer households’ spending
significantly more than richer households’ spending; in addition, the program significantly altered thecomposition of wealthier households’ expenditure, leading to a surge in the share of spending on
education other than tuition fees as well as on clothing and recreational goods. The findings of this3New ESRI Working Paper No.44
Responses of household expenditure to changes in discretionary income:Micro data study focusing on tuition payments
to examine consumption smoothing by Japanese households

paper therefore indicate that the consumption of Japanese households is excessively sensitive toanticipated (large as well as small) changes in household income, and that in this sense it is fair to
broadly assume that Japanese households do not fully engage in consumption smoothing.
The remainder of this paper is organized as follows. The next section describes the data used
for the analysis and identifies the university tuition payments and the 2010 high school tuition
reduction in the dataset. Section 3 presents the results of regression analyses (i) measuring the size ofthe consumption response to changes in discretionary income due to university tuition payments, and(ii) investigating whether households with high school students altered the amount and compositionof their spending after the introduction of the free tuition program. Section 4 summarizes the findings
and concludes the paper.

2. Data description
The data used in this study are micro-level data from the Family Income and Expenditure Survey(FIES) conducted by the Statistics Bureau, Ministry of Internal Affairs and Communications. The
survey covers approximately 9,000 non-single households which are randomly chosen from all regionsof Japan. Survey units are randomly chosen from the selected municipalities and six two-or-moreperson households are randomly chosen in each survey unit from 170 municipalities.
In the FIES, each household is surveyed for six months and one-sixth of the households are
replaced by new households every month. The survey provides detailed information on income andexpenditures as well as on household characteristics including the type of school which children in thehousehold are attending. The monthly expenditure data are compiled from a diary.
For the first analysis, consisting of the influence of the payment of university tuition fees,
monthly household panel data from 1984 to 2008 are used, while for the second analysis measuring
the impact of the free high school tuition program, repeated semi-annual cross-section data from 2002
to 2012 covering the period before and after the introduction of the free tuition program are used. Inorder to improve the reliability of the estimates, certain criteria to limit the data used for each of thetwo analyses are set. 3 Summary statistics of the main variables from the two data sets used for the

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New ESRI Working Paper No.44
Responses of household expenditure to changes in discretionary income:Micro data study focusing on tuition payments
to examine consumption smoothing by Japanese households

two analyses are shown in Tables 1(a) and (b).
Table 1(a) reports the summary statistics of the data used in the analysis of the link between
university tuition fee payments and expenditure patterns. In this table, sample households are restricted
to those with one university student. The average monthly total spending (excluding university tuition)
is about 320,000 yen. The average monthly tuition payment (among households with positive tuition
payments) is 317,000 yen; more specifically, 182,000 yen is paid for public universities/colleges and
361,000 yen for private universities/colleges. The average head of household age is 51.9 years, andthe average family size is 4.1 members. Note that tuition fee payments are concentrated in certain
specific months. The monthly pattern of tuition payments over the year is shown in Figure 1. The
average monthly tuition amount for tuition-paying households is higher in March and April reachingaround 500,000 yen.
Table 1(b) reports the summary statistics for the analysis focusing on the free high school
tuition program. The table presents the summary statistics for all households in the sample, forhouseholds above and below the 25th percentile of households in terms to their spending level, andfor households before and after the introduction of the program. The figures indicate that the annual
income, disposable income, and household living expenditures of households above the 25th
percentile, as expected, are larger than those below the 25th percentile. Moreover, households above
the 25th percentile, again as expected, also spend more on each of the 15 disaggregated expenditure
items than households below the 25th percentile. On the other hand, dividing households into thosesurveyed before and after April 2010 shows that the annual income, disposable income, and householdliving expenditure of the two groups are quite similar, and no clear pattern in terms of differences inindividual expenditure items can be discerned.
The FIES also provides information on households’ high school tuition fee payments,
distinguishing between public and private high schools. Figure 2 presents the average high school
tuition fee payments per high school student from FY2002 to FY2011. Given that these figures show
a clear drop in FY2010, it can be said that the FIES data capture the change in the tuition policy
effective since FY2010 well.

5New ESRI Working Paper No.44
Responses of household expenditure to changes in discretionary income:Micro data study focusing on tuition payments
to examine consumption smoothing by Japanese households

3. Regression analyses
This section reports the results of regression analyses examining households’ response to discretionary
income changes related to tuition payments. Specifically, the first analysis deals with consumption
smoothing in response to large seasonal payments of university tuition fees, while the second examineshouseholds’ response to the introduction of the free high school tuition program.

3.1 Consumption responses to university tuition fee payments
This subsection presents the results of regression analyses to measure the size of the consumption
response to university tuition fee payments. For the regression, tuition payments are included in a
Euler equation, where the coefficient on the tuition payment variable should be zero if the LC/PIH
holds. The specification used for the estimation is as follows:1

2008∆Yi ,t = cons + ∑ α j × ∆X i ,t − j + ∑ β y × Year( y )j =0+

y =1984

December

6m = January

n =1∑ γ 1m × Month( m ) + ∑ γ 2 n × SurveyMonth( n )(1)+ δZ i ,t + ε i ,twhere i indexes households and t indexes the month. The dependent variable is the change in the levelof household h’s consumption in month t from the previous month. Consumption variables used aretotal household spending as well as various individual components, with tuition fees excluded fromtotal household spending and service spending (under which they fall). The key independent variable
is ∆X , which represents the month-on-month change in the amount paid for tuition. The estimatedcoefficients are interpreted as marginal propensities to consume out of a change in discretionary
income due to the tuition payment. Other independent variables included are year dummies (Year),month dummies (Month), and dummies indicating the position (1st, 2nd,..., 6th) of the observation
during the six month examination period for an individual household (SurveyMonth), 4 as well as
demographic variables (Zi,t ) that contain the age of the household head and changes in family size. All
regressions are estimated using ordinary least squares (OLS) and the standard errors are clustered at
the household and robust to heteroscedasticity.
6New ESRI Working Paper No.44
Responses of household expenditure to changes in discretionary income:Micro data study focusing on tuition payments
to examine consumption smoothing by Japanese households

Table 2 shows the results for households with only one university student. Panel (a) reports theregression results for households, referred to as “new student group,” which have only one child who
was not a university student in the first month surveyed but who was in the sixth month. The coefficienton ∆X t for total spending is negative and statistically significant. Specifically, households in thisgroup reduce spending by 15 yen for every 100 yen in tuition fees paid and do not smooth consumption,
which sharply contrasts with Souleles’s (2000) finding for the United States that households appear to
be engaged in consumption smoothing. The result remains unchanged even if a one-month lag of ∆X t
is added to the regression. The remaining columns in panel (a) show that the consumption response totuition payments varies across spending categories; however, the results generally indicate thathouseholds decrease their spending in the payment month and increase it in the next month. Next,
panel (b) reports the results when combining households with a new student and households with a“continuing student,” defined as households with one university student in both the first and the sixth
month of the survey. The results indicate that the pattern of the estimated coefficients does not changeeven after “continuing student households” are included.
Next, Table 3 reports the results for the same specification but using not only households with
one university student but also households with more than one university student (panel (a) and (b))
as well as households with no university students (panel (c)). The basic pattern of the coefficients on
tuition fee payments in panels (a) and (b) is similar to that reported in Table 2. Meanwhile, panel (c)reports the coefficients on tuition fee payments if all households in the sample regardless of whether
they have a university student are included in order to separate month effects from tuition payment
effects. The results show a similar pattern to the previous results.
In sum, households with university students respond to tuition fee payments by reducing
spending by about 15 yen for every 100 yen in tuition fees paid in the same month, implying that thetuition burden lowers households’ welfare in terms of consumption. Moreover, households’ spending
in the following month does not fully recover from the tuition fee payment, implying that Japanesehouseholds do not fully engage in consumption smoothing.

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New ESRI Working Paper No.44
Responses of household expenditure to changes in discretionary income:Micro data study focusing on tuition payments
to examine consumption smoothing by Japanese households

3.2 Reactions of household expenditures to the tuition-free high school programThis subsection presents the results of regression analyses to examine whether the 2010 tuition-freeprogram in Japan altered the amount and composition of household spending using observations fromthe FIES.

3.2.1 Effects on the amount of household consumption
The introduction of the program can be regarded as an exogenous increase in income forhouseholds that benefited from the program, i.e., households with a high school student. However,households’ financial gain through the free tuition program is too small to represent a substantial
increase in their permanent income, so that the natural response for such households would be to leave
their spending level unchanged unless they are liquidity constrained. On the other hand, a priori no
particular change in spending patterns is expected. Any changes in the composition of spending as aresult of the free tuition program therefore would be an empirical matter.
The basic specification for the estimation is as follows:

Yi ,t = α + β 1 * DI i ,t + β 2 * FS i ,t + β 3 * Studentsi ,t + β 4 * FreeTuitiont ++ β 5 * [ Studentsi ,t * FreeTuitiont ] + δ * Z i ,t + ε i ,t(2)where i refers to a household (unit of observation) and t refers to the timing of the survey (year andmonth). The dependent variable is defined in two ways. The first is (unadjusted) household living
expenditure, which is the sum of monthly expenditures over the six month survey period for eachhousehold in the FIES. The second is household living expenditure adjusted for high school tuition byadding 59,400 yen (600 dollars), which corresponds to half of the annual tuition payment subsidy. The
reason for using the second definition is that households sending their child or children to high school
continue to consume a flow of services provided by the school even if they no longer pay for thoseservices out of pocket. The explanatory variables include a constant, household disposable income
over the six month survey period (DI), the number of household members (FS), the number of highschool students in the household (Students), as well as an indicator (FreeTuition) which takes 1 forhouseholds that were surveyed after April 2010 when the free tuition program became effective, and8

New ESRI Working Paper No.44
Responses of household expenditure to changes in discretionary income:Micro data study focusing on tuition payments
to examine consumption smoothing by Japanese households

0 otherwise.
The main variable of interest is the interaction term between the number of high school students
in the household (Students) and the dummy for the free tuition program (FreeTuition). The aim ofincluding this term is to capture any changes in household spending by distinguishing households withhigh school students before and after implementation of the free tuition program. If households
decreased their spending by an amount equivalent to the tuition fee exemption, the coefficient on theinteraction term should be negative and significant and close to 59,400 yen (600 dollars) in size when
the unadjusted dependent variable is used. On the other hand, when the dependent variable is adjusted
by adding 59,400 yen (600 dollars), the coefficient should no longer be significant. The other control
variables are expressed as vector Z, which includes the age of the household head, the shares of family
members in each age group in a household, indicators for the month when the survey on eachhousehold was completed, and year dummies. The last term is a well-behaved error term. 5 In the
regression analysis below, households that benefited from the free tuition program – the treatmentgroup – are compared with two alternative control groups: households with elementary school or
junior high school students, and households without children aged 6 to 18.
Table 4 reports the estimated coefficients. Let us focus on the coefficients on the interaction
term between the number of high school students in the household and the indicator for the period inwhich the free tuition program was in effect. Panel (a) shows that the coefficients are negative andsignificant but the size differs between households above and below the 25th percentile. The coefficientfor households above the 25th percentile is minus 46,427 yen (464 dollars), while that for households
below the 25th percentile is minus 22,462 yen (225 dollars), indicating that poorer households’
spending fell by less than the high school tuition fee amount and less than the spending of wealthierhouseholds. The next column shows the coefficients on the same interaction term but replaces theunadjusted with the adjusted dependent variable where 59,400 yen (600 dollars) are added. The
coefficients are now positive but not statistically significant in the estimation for all sample households
as well as for households above the 25th percentile. In contrast, the coefficients are positive andsignificant for poorer households, indicating that those households did indeed increase their spending

9New ESRI Working Paper No.44
Responses of household expenditure to changes in discretionary income:Micro data study focusing on tuition payments
to examine consumption smoothing by Japanese households

after the introduction of the free tuition program. The implied marginal propensity to consume (MPC)
is 62.2 (36,938 yen / 59,400 yen). The canonical LC/PIH predicts that less well-off households are
more likely to be liquidity constrained and that for such households an increase in current economic
resources through a windfall is likely to be associated with an increase in current spending. Next, panel(b) of Table 4 reports the coefficients using households without children aged 6 to 18 as the control
group. The observed patterns for the coefficients are similar to those in the upper panel.
In sum, the free tuition program had a larger effect on poorer than wealthier households withchildren enrolled in high school. In fact, poorer households responded to the “windfall” provided by
the scrapping of high school tuition fees by significantly increasing their spending. On the other hand,
while some increase for richer households can be observed, the effect is not statistically significant.

3.2.2 Effects on the composition of household consumption
Next, how households’ composition of spending was affected is examined. The specification used forthe estimation is as follows:

S i , j ,t = α + β 1 * DI i ,t + β 2 * FS i ,t + β 3 * Student i ,t + β 4 * Tuitionfreet ++ β 5 * [ Student i ,t * Tuitionfreet ] + δ * Z i ,t + ε i ,t(3)

The notation here is the same as in the regressions above except for the dependent variable. The
dependent variable is now the share of spending on item j over the six month survey period out of total
household living expenditure over the six months. The consumption categories are: food, alcohol,
dining out, clothing for men, clothing for women, clothing for children, compulsory school expenses,
high school expenses, university expenses, cram school expenses, text and reference books,
recreational goods, recreational services, spending money, remittances, and other living expenditures.
Since the shares are dependent on each other, we perform Zellner’s seemingly unrelated regression(SUR) to obtain the coefficients.
Table 5 reports the estimated coefficients on the interaction term between the number of highschool students in the household and the dummy taking a value of 1 for the period since April 2010,
when the free tuition program became effective. Panel (a), which presents the results for unadjusted

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New ESRI Working Paper No.44
Responses of household expenditure to changes in discretionary income:Micro data study focusing on tuition payments
to examine consumption smoothing by Japanese households

household living expenditure, shows that the coefficient on high school expenses is negative andsignificant. This result simply reflects that households no longer incur high school expenses under thefree tuition program. Positive and significant coefficients are also found for some other categories,
namely, alcohol, clothing for children, cram/supplementary school expenses, text and reference books,
and recreational goods. These coefficients show that households’ spending shares on these
consumption categories increased after introduction of the free tuition program. Meanwhile, panel (b)shows the results when adjusted expenditure is used as the denominator. The pattern of the coefficients
is similar to that in panel (a). The only major difference is the coefficient on high school tuition
expenses, which is positive and significant once the adjustment for the tuition subsidy is made.
The remaining columns report the estimated coefficients for wealthier and poorer households.
First, the pattern of the estimated coefficients for households above the 25th percentile, i.e., wealthierhouseholds, is very similar to that for all households: in panel (a) for unadjusted household expenditure,
positive and significant coefficients are found for alcohol, clothing, cram/supplementary school
expenses, text and reference books, and recreational goods, while the coefficient for high school
expenses is negative and significant. The coefficient on other living expenditures is positive but notsignificant. The pattern in panel (b) looks basically the same.
In contrast, the coefficients for households below the 25th percentile are mostly insignificant. In
panel (a) using unadjusted household living expenditure, the exceptions are positive and significant
coefficients on alcohol, recreational goods, and spending money for family members and a negativeand significant coefficient on high school expenses. In panel (b), positive and significant coefficientsare found for high school tuition fees and spending money for family members and negative andsignificant coefficients for dining out, college tuition fees, and other living expenditures.
In sum, the free tuition program had a more substantial impact on the spending composition of
wealthier than of poorer households. Wealthier households responded to the increase in discretionary
income by reallocating spending to some specific items, such as clothing and/or educational spending.
In contrast, a shift in spending composition to those items is not found for poorer households.

11New ESRI Working Paper No.44
Responses of household expenditure to changes in discretionary income:Micro data study focusing on tuition payments
to examine consumption smoothing by Japanese households

4. Summary and conclusion
This paper focused on tuition fees to examine consumption smoothing by Japanese households.
Specifically, two analyses were conducted. The first focused on expenditure patterns of householdswith a university student. The second focused on households’ expenditure response to the introductionof the free high school tuition program in 2010. The findings can be summarized as follows:
1) The estimation results suggest that a 100 yen decrease in discretionary income due to the paymentof university tuition fees is associated with a decrease of about 15 yen in discretionary spending in
the month that tuition payments are made.
2) The results also suggest that the free high school tuition program significantly stimulated householdspending, especially in the case of poorer households.
3) The free tuition program significantly altered the composition of household expenditure of wealthierhouseholds, with a surge in spending shares observed in non-tuition education expenses, clothing,
and recreational goods.
These findings indicate that Japanese households responded to predictable income changes and did
not fully engage in consumption smoothing. While the fact that the consumption response looks larger
for poorer households provides evidence of the presence of liquidity constraints, the finding regarding
the composition effect for wealthier households probably means that changes in tuition fees have somewelfare impact even for households that are not liquidity constrained.

† This paper forms part of the author’s microdata-based research on household consumption in Japan

at the Economic and Social Research Institute (ESRI). The author is grateful to Takashi Oshio for his
discussion in a seminar at ESRI and would also like to thank ESRI colleagues for their comments andsupport. Special thanks go to Satoshi Shimizutani for his insightful discussion (as a coauthor in
substance) and to the Statistics Bureau of Japan for providing the microdata from the Family Income
and Expenditure Survey (FIES). The views expressed in this paper are personal and do not represent
those of ESRI.
1

Most recent work using micro-level data has found that household consumption reacts to predictable
income changes when they materialize (Shea (1995), Shapiro and Slemrod (1995, 2003), Parker(1999), Souleles (1999, 2000), Stephens (2003), Johnson, Parker, and Souleles (2006)).

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New ESRI Working Paper No.44
Responses of household expenditure to changes in discretionary income:Micro data study focusing on tuition payments
to examine consumption smoothing by Japanese households

2

Examples of such studies include Hori and Shimizutani (2009), Hsieh, Shimizutani, and Hori (2010),
Hori and Shimizutani (2012), and Stephens and Unayama (2011).

3

For the university tuition analysis, households were removed if the reported age of the head ofhousehold decreases or increases by more than 1 year during the 6 months. Households were also
excluded if the household’s tenancy status changed from owner to renter or vice versa. For the freetuition program analysis, the sample is restricted to wage earner households, which account forroughly half of all survey households, and then further confined to those with children aged 6 to 18.
4 For example, For example, for a household surveyed from April to September, observations for May take the second
position in the six month period.
5

Another possible approach to examine the impact of the free tuition program on households would
be to use the panel structure of the FIES and focus on households that were surveyed before and after
the introduction of the program as the treatment group. However, the sample size of the treatmentgroup is too small to obtain meaningful estimates.

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New ESRI Working Paper No.44
Responses of household expenditure to changes in discretionary income:Micro data study focusing on tuition payments
to examine consumption smoothing by Japanese households

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to examine consumption smoothing by Japanese households

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Responses of household expenditure to changes in discretionary income:Micro data study focusing on tuition payments
to examine consumption smoothing by Japanese households

Figure 1. Distribution of university tuition payments over the year
60000018
Average tuition amount (yen; left scale)16
Share of households paying university tuition in the month (%; right scale)

5000001440000012103000008200000

6
41000002
00

Source: Constructed from the FIES microdata from 1984 to 2008.

Figure 2. High school tuition payments per student (in yen)
600,000
Public high school
500,000

Private high school

400,000
300,000
200,000
100,000
02002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Source: Author's calculation using FIES microdata.

16

New ESRI Working Paper No.44
Responses of household expenditure to changes in discretionary income:Micro data study focusing on tuition payments
to examine consumption smoothing by Japanese households

Table 1(a). Summary statistics of the data used for the university tuition analysis
Mean

Std. Dev.

Tuition payments
Tuition payments (excluding observations with zero expenditure)
Public university
Public university (excluding observations with zero expenditure)
Private university
Private university (excluding observations with zero expenditure)
Education expenditures
Education expenditures (excluding observations with zero expenditure)

56,617.1
317,162.4
9,297.8
182,125.1
47,319.3
361,486.4
66,558.0
145,172.4

197,162.1
367,605.3
56,584.4
176,770.8
190,393.2
404,187.5
187,691.6
255,784.6

Total spending (excluding university tuition payments)
Durable consumption
Semi-durable consumption
Non-durable consumption
Service consumption (excluding university tuition payments)
Food expenditures
Clothing expenditures

319,013.4
14,075.3
42,239.5
137,732.1
124,966.4
105,154.7
30,553.8

239,395.9
79,805.8
71,082.9
51,614.5
174,329.6
45,585.3
58,981.5

51.9
2,741.1
4.1

6.6
733.1
1.0

Age of household head
Square of the age of household head
Family size
Notes: The number of observations is 73,788 (12,298 households).
The sample is confined to households with only one university student.

17

18

Source: Author's calculation using FIES microdata.

Number of households

Number of household members
Number of high school students
Age of the household head
Share of household members aged 0-5
Share of household members aged 6-14
Share of household members aged 15-18
Share of household members aged 19-24
Share of household members aged 25-39
Share of household members aged 40-59
Share of household members aged 60-74
Share of household members aged 75 or overAnnual income of the previous year / 2
Household disposable income
Program adjusted household disposable income
Household living expenditures
Program adjusted household living expenditures

Food
AlcoholDining out
Clothing for men
Clothing for women
Clothing for children
Compulsory school expenses
High school expenses
Program adjusted high school expenses
University expenses
Cram/Supplementary school expenses
Text and reference books
Recreational goods
Recreational services
Spending money for family members
Remittances
Other living expenditures

Variable |

(
(
(
(
(
(
(
(
(
(
(

(
(
(
(
(

(
(
(
(
(
(
(
(
(
(
(
(
(
(
(
(
(

0.956 )
0.557 )
8.920 )
0.108 )
0.184 )
0.151 )
0.089 )
0.201 )
0.221 )
0.094 )
0.063 )

1,973,031 )
1,315,205 )1,315,671 )
1,012,361 )
1,013,500 )

145,343 )
34,951 )69,501 )
33,673 )62,523 )25,395 )87,563 )101,868 )106,348 )
191,962 )
111,530 )17,064 )92,499 )
130,176 )
167,849 )263,216 )528,603 )Std. Dev.

41,8694.255
0.373
45.474
0.055
0.275
0.113
0.033
0.152
0.324
0.032
0.017

3,659,493
2,828,842
2,833,653
2,057,838
2,062,683

375,656
28,951
94,19823,18041,105
18,77950,64348,80953,65045,92657,164
5,334
101,226
116,186
113,081
57,967869,320

Mean

Sample households

144,208 )37,474 )72,864 )36,981 )69,350 )27,558 )96,314 )112,475 )117,030 )219,137 )
123,431 )18,949 )98,922 )140,254 )
182,623 )
301,128 )544,818 )

(
(
(
(
(
(
(
(
(
(
(

0.958 )
0.575 )
8.384 )
0.106 )
0.185 )
0.153 )
0.091 )
0.193 )
0.216 )
0.090 )
0.063 )

( 2,003,604 )( 1,292,461 )( 1,292,769 )(
957,726 )(
958,806 )

(
(
(
(
(
(
(
(
(
(
(
(
(
(
(
(
(

31,4664.301
0.412
45.727
0.052
0.266
0.123
0.035
0.136
0.342
0.030
0.017

3,981,436
3,119,350
3,124,524
2,386,839
2,392,043

408,058
32,994106,214
27,90949,09321,06956,55259,47164,66560,51970,8796,486
118,386
139,151
140,552
76,542
998,057

Above 25th percentile
Mean
Std. Dev.
96,636 )21,679 )40,310 )12,436 )20,236 )15,314 )48,733 )46,018 )51,148 )21,590 )41,126 )8,343 )
35,395 )47,777 )57,667 )19,228 )
158,181 )

(
(
(
(
(
(
(
(
(
(
(

0.936 )
0.476 )
10.411 )0.114 )
0.179 )
0.140 )
0.084 )
0.216 )
0.229 )
0.107 )
0.063 )

( 1,453,062 )(
864,714 )(864,529 )(
220,654 )(
221,077 )

(
(
(
(
(
(
(
(
(
(
(
(
(
(
(
(
(

10,4034.108
0.248
44.661
0.064
0.303
0.082
0.027
0.204
0.266
0.038
0.015

2,629,417
1,880,057
1,883,682
1,070,738
1,074,507

277,648
16,72357,854
8,877
16,943
11,85432,76916,562
20,331
1,786
15,6801,848
49,32446,72429,9891,785
479,928

Below 25th percentile
Mean
Std. Dev.

By spending level

Table 1(b). Summary statistics of the data used for the tuition-free high school program analysis

(
(
(
(
(
(
(
(
(
(
(

(
(
(
(
(

33,2014.273
0.376
45.372
0.055
0.274
0.114
0.034
0.154
0.321
0.032
0.017

3,693,048
2,846,936
2,847,445
2,077,161
2,077,686

(
(
(
(
(
(
(
(
(
(
(
(
(
(
(
(
(

0.962 )
0.559 )
8.813 )
0.108 )
0.185 )
0.151 )
0.090 )
0.202 )
0.221 )
0.094 )
0.063 )

1,977,231 )
1,327,269 )
1,327,275 )
1,022,734 )
1,022,886 )

146,020 )35,303 )69,870 )34,827 )63,234 )25,899 )87,068 )105,340 )
105,920 )
193,905 )109,355 )
17,411 )93,916 )132,038 )
172,904 )272,311 )528,832 )BeforeStd. Dev.

379,954
29,415
94,18623,80941,83019,19150,44852,52353,05046,77756,5495,503
102,944
116,563
118,359
60,838867,888Mean

141,527 )
33,512 )68,075 )28,702 )59,645 )23,294 )89,433 )85,837 )107,948 )184,305 )119,470 )
15,646 )86,545 )122,778 )
145,131 )
224,677 )
527,718 )

(
(
(
(
(
(
(
(
(
(
(

0.927 )
0.549 )
9.328 )
0.109 )
0.180 )
0.152 )
0.084 )
0.197 )
0.224 )
0.097 )
0.065 )

( 1,950,216 )( 1,267,319 )( 1,270,511 )(
968,198 )(974,722 )

(
(
(
(
(
(
(
(
(
(
(
(
(
(
(
(
(

After
Std. Dev.

8,668

4.184
0.360
45.884
0.055
0.279
0.111
0.029
0.144
0.334
0.031
0.016

3,523,925
2,761,768
2,782,526
1,983,971
2,005,331

359,193
27,173
94,241
20,76938,32817,204
51,392
34,58555,94542,66859,5194,683
94,647
114,743
92,866
46,970874,803

Mean

Before and after introduction of the program

New ESRI Working Paper No.44
Responses of household expenditure to changes in discretionary income:Micro data study focusing on tuition payments
to examine consumption smoothing by Japanese households

New ESRI Working Paper No.44
Responses of household expenditure to changes in discretionary income:Micro data study focusing on tuition payments
to examine consumption smoothing by Japanese households

Table 2. Regression results using households with one university student
Total spending
except tuition

Durables-0.1529(0.012)-0.0066(0.004)Services
Semi-durables Non-durables except tuition

Food

Clothing

0.0018(0.001)0.0012(0.003)0.25406,395

0.04336,395

0.0018(0.001)0.0015(0.001)0.0042(0.004)0.0086(0.003)(a) "New student group" only
(1) No lag included
ΔX (α(1)）

R-squared
Number of observations(2) One month lag included
ΔX (α(1)）
ΔX (1month lag) （α(2)）

F test : (α1)+(α2)=0R-squared
Number of observations***

0.06006,395-0.1547(0.015)-0.0107(0.012)***

F=47.42
Prob>F=0.00
0.054
5,116

-0.0010(0.003)0.0008(0.001)-0.1460(0.010)0.0085
6,395

0.03776,395

0.2065
6,395

0.0556
6,395-0.0080(0.005)-0.0008(0.004)0.0034(0.005)0.0112(0.004)-0.0006(0.002)-0.0019(0.002)-0.1496(0.014)-0.0192(0.010)F=1.19Prob>F=0.28
0.009
5,116

F=1.97
F=3.58F=3.95
F=0.60F=66.93
Prob>F=0.05 Prob>F=0.44 Prob>F=0.00 Prob>F=0.16 Prob>F=0.06
0.043
0.169
0.047
0.225
0.044
5,116
5,116
5,116
5,116
5,116

*

***

***

***

*

**

(b) "New student group" and "Consecutive student group"(1) No lag included
ΔX (α(1)）

R-squared
Number of observations(2) One month lag included
ΔX (α(1)）
ΔX (1month lag) （α(2)）

F test : (α1)+(α2)=0R-squared
Number of observations-0.1281(0.007)***0.041554,755-0.1239(0.010)-0.0015(0.007)***

F=72.00
Prob>F=0.00
0.042
43,8040.0003(0.002)0.0004(0.002)0.0025(0.001)0.001554,7550.015854,7550.240154,7550.0031(0.002)0.0021(0.002)0.0035(0.003)0.0051(0.002)0.0019(0.001)-0.0009(0.001)F=1.60Prob>F=0.21
0.002
43,804

F=3.95
F=0.52
F=115.98
F=0.13
F=1.93Prob>F=0.05 Prob>F=0.47 Prob>F=0.00 Prob>F=0.72 Prob>F=0.17
0.016
0.248
0.031
0.285
0.012
43,80443,80443,80443,80443,804*****-0.1312(0.006)***

0.031654,755**-0.1325(0.009)-0.0078(0.006)***0.0011(0.001)0.0008(0.002)0.274254,7550.012354,7550.0003(0.0007)-0.0008(0.001)0.0025(0.002)0.0032(0.002)

Notes: The results are obtained using OLS. ***, ** and * indicate significance at the 1%, 5%, and 10% level, respectively.
Numbers in paretheses are robust standard errors.

19**New ESRI Working Paper No.44
Responses of household expenditure to changes in discretionary income:Micro data study focusing on tuition payments
to examine consumption smoothing by Japanese households

Table 3. Regression results using households with more than one university student or no university student
Total spending
except tuition

DurablesSemi-durables Non-durables

Services
except tuition-0.1544(0.010)-0.0044(0.003)-0.0009(0.003)0.0015(0.001)-0.1506 *** 0.0016(0.001)(0.009)

0.008
7,350

0.038
7,350

0.212
7,350

0.0046(0.004)0.0102(0.003)0.0006(0.002)-0.0018(0.002)-0.1512 *** 0.0023(0.001)(0.011)-0.0174 *** 0.0014(0.008)(0.001)

F=5.49Prob>F=0.02
0.043
5,880

F=0.19Prob>F=0.66
0.169
5,880

F=101.26
Prob>F=0.00
0.055
5,880

-0.0007(0.001)-0.0007(0.001)0.0021(0.001)0.001460,8100.016260,8100.239260,8100.0002(0.002)0.0002(0.002)0.0022(0.002)0.0041(0.002)0.0020(0.001)-0.0003(0.001)

F=117.15
Prob>F=0.00
0.044
48,648F=0.02Prob>F=0.90
0.002
48,648F=2.85
Prob>F=0.09
0.016
48,648F=1.59Prob>F=0.21
0.246
48,648-0.1572(0.009)-0.0007(0.001)-0.0004(0.001)0.0029 *** -0.1590 *** 0.0011 *** 0.0004(0.0005)(0.008)(0.0004)(0.001)0.00101,683,497

0.0128
1,683,497

0.2311
1,683,497-0.0008(0.001)-0.0008(0.002)0.0021(0.001)0.0042(0.001)0.0027(0.001)-0.0001(0.001)

F=0.36
Prob>F=0.55
0.001
1,346,859

F=7.41
Prob>F=0.01
0.013
1,346,859

Food

Clothing(a) "New student group" only
(1) No lag included
ΔX (α(1)）

R-squared
Number of observations(2) One month lag included
ΔX (α(1)）
ΔX (1month lag) （α(2)）

F test : (α1)+(α2)=0R-squared
Number of observations

***

0.068
7,350

-0.1534(0.013)-0.0112(0.010)***F=70.51
Prob>F=0.00
0.059
5,880

-0.0073(0.004)-0.0022(0.003)*F=2.13Prob>F=0.14
0.009
5,880

***

0.067
7,350

*

0.255
7,350

0.0011(0.002)
0.042
7,350

*

0.0049(0.004)0.0080 ***(0.003)F=3.17F=5.01
Prob>F=0.07 Prob>F=0.03
0.221
0.044
5,880
5,880

(b) "New student group" and "Consecutive student group"(1) No lag included
ΔX (α(1)）

R-squared
Number of observations(2) One month lag included
ΔX (α(1)）
ΔX (1month lag) （α(2)）

F test : (α1)+(α2)=0R-squared
Number of observations-0.1329(0.006)***0.044260,810-0.1291(0.008)-0.0032(0.006)******** -0.1336 *** 0.0008(0.0005)(0.005)0.035860,810*0.0001(0.001)

0.272960,8100.012560,810*** -0.1335 *** 0.0006(0.007)(0.001)-0.0072-0.0003(0.005)(0.001)0.0020(0.002)0.0029(0.002)

F=172.01
Prob>F=0.00
0.034
48,648F=0.09F=1.91
Prob>F=0.77 Prob>F=0.17
0.283
0.013
48,64848,648(c) All households(1) No lag included
ΔX (α(1)）

R-squared
Number of observations(2) One month lag included
ΔX (α(1)）
ΔX (1month lag) （α(2)）

F test : (α1)+(α2)=0R-squared
Number of observations***

0.0275
1,683,497-0.1588(0.011)0.0013(0.008)***

F=102.10
Prob>F=0.00
0.028
1,346,859***

0.0076
1,683,497

0.2452
1,683,497*** -0.1622 *** 0.0006(0.011)(0.0005)-0.0020-0.0006(0.007)(0.0004)

F=7.15
F=124.96
Prob>F=0.01 Prob>F=0.00
0.242
0.008
1,346,859
1,346,859

0.0016(0.001)0.0026(0.001)**

F=0.01
F=3.90Prob>F=0.93 Prob>F=0.05
0.254
0.009
1,346,859
1,346,859

Notes: The results are obtained using OLS. ***, ** and * indicate significance at the 1%, 5%, and 10% level, respectively.
Numbers in paretheses are robust standard errors.

20

0.0091
1,693,497

(b)

(a)

Number ofhigh school
students in thehousehold(c)Free-tuitionhigh school
program
dummy
(d)
(e)

Interaction
term

(

0.063
0.003

0.336
0.004

0.410
0.003

***
)
(

***
)
(

***
)
(

19,267
3,187

22,891
8,033

58,341
6,433

***
)
(

***
)
(

***
)
(

34,6599,571-52,749
17,028

8,128
15,210
(

***
)
(

***
)
(

)

-28,077
19,40449,34041,301

31,53935,719

) (

) (

) (

21

(

0.046
0.002

0.298
0.003

0.366
0.003

***
)
(

***
)
(

***
)
(

11,022
3,083

52,808
6,863

75,160
6,046

***
)
(

***
)
(

***
)
(

19,324
18,816-26,088
31,53438,70629,170

)

)

)

(

(

(

-32,482
16,706

48,611
39,14818,70934,446

*
) (

) (

) (

-12,391
12,280-41,884
22,032-43,314
20,077-22,462
11,756-46,427
20,819-43,729
18,576

)

*
)

**
)

*
)

**
)

**
)

(

(

(

(

(

(

47,00912,28017,51622,032

16,086
20,07736,93811,756

12,973
20,81915,671
18,576(e')(Dep: Adjusted
expenditures)

Interaction
term

***
)

)

)

***
)

)

)

10,52036,117

46,637

8,090

26,421

34,511

Number ofobservations

Notes: Coefficients are obtained from OLS regressions. Standard errors of coefficients are shown in parentheses. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% level, respectively.
Regressions also include the other control variables described in the text as well as a constant.
The coefficients reported in column (e ') are those on the interaction term when adjusted expenditure is used as the dependent variable.

Households below the 25th percentile

By spending level
Households above the 25th percentile

All sample households(b) Households with high school student(s) (treatment group) vs. Households without child(ren) aged 6-18 (control group)Households below the 25th percentile

By spending level
Households above the 25th percentile

All sample households(a) Households with high school student(s) (treatment group) vs. Households with child(ren) aged 6-18 not in high school (control group)

Number ofhouseholdmembers

Householddisposable
incomeTable 4. Impact of free-tuition high school program on household living expenditures

0.059

0.241

0.309

0.080

0.296

0.383

Adj Rsquared

200,000

890,000

880,000

210,000

780,000

770,000Root
MSE

New ESRI Working Paper No.44
Responses of household expenditure to changes in discretionary income:Micro data study focusing on tuition payments
to examine consumption smoothing by Japanese households

22

0.005
0.005 ***
0.000
0.002 *
0.004 **
0.003 ***
0.000
-0.070 ***0.002
0.013 ***
0.002 ***
0.014 ***
0.005
0.004
-0.0060.017 *

(
(
(
(
(
(
(
(
(
(
(
(
(
(
(
(

0.005 )
0.002 )
0.003 )
0.001 )
0.002 )
0.001 )
0.003 )
0.003 )
0.005 )
0.004 )
0.001 )
0.003 )
0.004 )
0.005 )
0.006 )
0.010 )

-0.0070.004 **
-0.0040.000
0.001
0.002 **
-0.001
0.046 ***
-0.0040.008 **
0.001 *
0.008 ***
-0.002-0.005-0.013 **-0.035 ***

(
(
(
(
(
(
(
(
(
(
(
(
(
(
(
(

0.005 )
0.002 )
0.003 )
0.001 )
0.002 )
0.001 )
0.003 )
0.003 )
0.005 )
0.004 )
0.001 )
0.003 )
0.004 )
0.005 )
0.006 )
0.010 )

0.482
0.017
0.164
0.034
0.038
0.197
0.260
0.546
0.227
0.101
0.045
0.048
0.108
0.068
0.164
0.085

0.483
0.017
0.162
0.034
0.038
0.196
0.259
0.494
0.226
0.101
0.046
0.047
0.106
0.068
0.164
0.083

0.059
0.017
0.029
0.012
0.020
0.011
0.034
0.031
0.051
0.042
0.007
0.034
0.044
0.061
0.063
0.111

0.060
0.017
0.029
0.012
0.020
0.011
0.034
0.031
0.051
0.042
0.007
0.034
0.044
0.061
0.063
0.112

R-squared Root MSE-0.0040.003 *
0.000
0.001
0.002
0.003 ***
-0.0020.032 ***
-0.0020.010 **
0.001 *
0.009 ***
-0.001-0.008-0.009-0.036 ***0.005
0.004 **
0.003
0.002 *
0.004 **
0.003 ***
-0.001-0.064 ***0.004
0.015 ***
0.002 **
0.014 ***
0.005
-0.002-0.001
0.006

(
(
(
(
(
(
(
(
(
(
(
(
(
(
(
(

(
(
(
(
(
(
(
(
(
(
(
(
(
(
(
(

0.005 )
0.002 )
0.003 )
0.001 )
0.002 )
0.001 )
0.003 )
0.003 )
0.006 )
0.004 )
0.001 )
0.004 )
0.005 )
0.007 )
0.007 )
0.011 )

0.005 )
0.002 )
0.003 )
0.001 )
0.002 )
0.001 )
0.003 )
0.003 )
0.006 )
0.004 )
0.001 )
0.004 )
0.005 )
0.007 )
0.007 )
0.011 )

Interaction term

0.371
0.018
0.171
0.023
0.032
0.218
0.263
0.549
0.260
0.095
0.056
0.051
0.092
0.031
0.188
0.086

0.372
0.018
0.169
0.023
0.032
0.217
0.262
0.507
0.259
0.095
0.057
0.050
0.090
0.030
0.188
0.084

0.051
0.016
0.026
0.012
0.021
0.011
0.033
0.031
0.057
0.044
0.007
0.035
0.046
0.065
0.070
0.112

0.051
0.016
0.026
0.012
0.021
0.011
0.033
0.031
0.057
0.044
0.007
0.035
0.046
0.065
0.070
0.112

-0.0210.006-0.015 **-0.002-0.0010.001-0.0030.095 ***
-0.006 *-0.0020.001
0.002-0.0050.016 *
0.000
-0.065 ***0.001
0.008 **
-0.008
0.001
0.004
0.002
0.001
-0.099 ***-0.0050.005
0.002
0.012 **
0.007
0.030 ***
0.001
0.039 *

(
(
(
(
(
(
(
(
(
(
(
(
(
(
(
(

(
(
(
(
(
(
(
(
(
(
(
(
(
(
(
(

0.014 )
0.004 )
0.006 )
0.002 )
0.003 )
0.002 )
0.007 )
0.006 )
0.003 )
0.006 )
0.001 )
0.006 )
0.007 )
0.009 )
0.003 )
0.020 )

0.014 )
0.004 )
0.007 )
0.002 )
0.003 )
0.002 )
0.007 )
0.006 )
0.003 )
0.007 )
0.001 )
0.006 )
0.007 )
0.009 )
0.003 )
0.020 )

Interaction term

0.385
0.018
0.137
0.024
0.029
0.155
0.260
0.554
0.045
0.060
0.020
0.047
0.110
0.053
0.015
0.103

0.387
0.018
0.134
0.025
0.030
0.154
0.260
0.454
0.045
0.060
0.021
0.045
0.108
0.053
0.015
0.102

0.076
0.020
0.035
0.011
0.018
0.013
0.037
0.031
0.017
0.034
0.007
0.029
0.039
0.048
0.016
0.107

0.077
0.020
0.035
0.011
0.018
0.013
0.037
0.031
0.017
0.035
0.007
0.029
0.039
0.048
0.016
0.107

R-squared Root MSE

By spending level
Households below the 25th percentile(Number of observations = 10,391)

R-squared Root MSE

Households above the 25th percentile(Number of observations = 31,174)

Notes: Coefficients are obtained from Zellner's seemingly unrelated regressions. Standard errors of coefficients are shown in parentheses. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% level, respectively.
Regressions in this table include the same control variables as those included in the regressions in Table 4.

Food
AlcoholDining out
Clothing for men
Clothing for women
Clothing for children
Compulsory school expenses
High school expenses
College expenses
Cram/Supplementary school expenses
Text and refrence books
Recreational goods
Recreational services
Spending money for family menbers
Remittances
Other living expenditures(b) Share in program-adjusted total household expenditures

Food
AlcoholDining out
Clothing for men
Clothing for women
Clothing for children
Compulsory school expenses
High school expenses
College expenses
Cram/Supplementary school expenses
Text and refrence books
Recreational goods
Recreational services
Spending money for family menbers
Remittances
Other living expenditures(a) Share in total household expenditures

Interaction term

(Number of observations = 41,565)

All sample households

Households with high school student(s) (treatment group) vs. Households with child(ren) aged 6-18 not in high school (control group)

Table 5. Impact of free-tuition high school program on shares of consumption categories in total household expenditures

New ESRI Working Paper No.44
Responses of household expenditure to changes in discretionary income:Micro data study focusing on tuition payments
to examine consumption smoothing by Japanese households

New ESRI Working Paper No.44
Responses of household expenditure to changes in discretionary income:Micro data study focusing on tuition payments
to examine consumption smoothing by Japanese households

Appendix 1. Robustness check (1): University tuition analysis
This appendix reports the results using alternative empirical procedures as a robustness check for theuniversity tuition analysis. Table A.1(a) reports the coefficients on the main variable [when ∆X t isdecomposed into public and private universities. – Is this accurate? Is ∆X t really decomposed? Or doyou mean: “on the main variable, ∆X t , when distinguishing between students attending public andprivate universities.”] The patterns of the coefficients on ∆X t are basically unchanged in both thespecifications with and without lagged terms. Table A.1(b) reports the results using educational
expenditure including both tuition and non-tuition payments as the main independent variable insteadof tuition payments only. The pattern of the coefficients on total or service spending looks similar tothat using tuition only, but the size of the coefficients looks much smaller. These findings suggest thatstudies using total educational spending including non-tuition payments such as Souleles (2002) arelikely to be biased.
Table A.1. Regression results using alternative specifications
Total spending
except tuition

DurablesSemi-durables

Non-durables

Services
except tuition

Food

Clothing(a) Students at public (national or prefectural) universities and private universities(1) No lag included
Public [β1]Private [γ1]R-squared
Number of observations(2) One month lag included
Public [β1]Public (1 month lag) [β2]F test : [β1]+[β2]=0Private [γ1]Private (1 month lag) [γ2]F test : [γ1]+[γ2]=0
R-squared
Number of observations-0.0002(0.006)0.0004(0.002)0.013(0.004)-0.001(0.002)

0.00
54755

0.02
54755

-0.002(0.009)-0.003(0.006)0.018(0.005)0.005(0.006)

F=21.51
Prob>F=0.00

F=0.15
Prob>F=0.70

F=6.18Prob>F=0.01

F=6.84
Prob>F=0.01-0.122(0.010)-0.002(0.008)0.004(0.002)0.003(0.002)0.002(0.003)0.005(0.002)0.002(0.001)-0.001(0.001)F=1.97
Prob>F=0.16
0.00
43804

F=2.67
Prob>F=0.10
0.02
43804

F=0.01
Prob>F=0.91
0.25
43804

0.0004(0.002)0.002(0.002)0.004(0.001)

0.00
54755

0.02
54755

0.24
54755

0.003
(0.003)0.002(0.003)0.004(0.003)0.004(0.002)0.004(0.001)-0.0005(0.001)

F=0.85
Prob>F=0.36
0.00
43804

F=3.15
Prob>F=0.08
0.02
43804

-0.156(0.015)-0.126(0.008)

***
***

0.04
54755

-0.147(0.020)-0.0002(0.016)******

F=62.27
Prob>F=0.00
0.04
43804

***

0.004
(0.002)0.002(0.001)

**
***

0.24
54755

***

**

0.007
(0.003)0.005(0.003)-0.173(0.012)-0.128(0.007)***0.002(0.002)*** 0.0010(0.0006)

*

*

***

0.27
54755

0.01
54755

0.002
(0.002)0.0002(0.002)0.018(0.004)0.001(0.004)

F=49.34
Prob>F=0.00

F=0.44
Prob>F=0.51

F=6.20Prob>F=0.01-0.129(0.009)-0.008(0.006)0.0002(0.001)-0.001(0.001)0.001(0.003)0.003(0.002)

F=97.25
Prob>F=0.00
0.03
43804

F=0.30Prob>F=0.59
0.29
43804

F=1.16
Prob>F=0.28
0.01
43804

-0.040(0.010)0.001(0.0006)

0.002(0.002)

0.03
54755

**

*

0.015
(0.004)-0.0005(0.002)-0.170(0.016)-0.008(0.013)

***

***

***

(b) Educational expenditure instead of tuition payments(1) No lag included
Education

R-squared
Number of observations(2) One month lag included
Education [A1]
Education (1month lag) [A2

F test : [A1]+[A2]=0
R-squared
Number of observations-0.034(0.010)

***

0.03
54755

-0.040(0.012)-0.004(0.009)***F=5.79Prob>F=0.02
0.03
43804

*

***

***

0.01
54755

***

F=4.10Prob>F=0.04
0.25
43804

-0.050(0.011)-0.010(0.008)***F=13.23
Prob>F=0.00
0.01
43804

0.27
54755

0.01
54755

0.0005(0.0007)-0.0005(0.0007)

0.003(0.002)0.003(0.002)

F=0.00Prob>F=1.00
0.29
43804

F=1.52Prob>F=0.22
0.01
43804

Notes: The results are obtained using OLS. ***, ** and * indicate significance at the 1%, 5%, and 10% level, respectively.
Numbers in paretheses are robust standard errors.

23**

New ESRI Working Paper No.44
Responses of household expenditure to changes in discretionary income:Micro data study focusing on tuition payments
to examine consumption smoothing by Japanese households

Appendix 2. Robustness check (2): Tuition-free high school program analysis
This appendix provides robustness checks of the free-tuition program analysis. Three alternative
approaches are taken to confirm the robustness of the findings. First, observations for 2008 and 2009
are removed from the sample, since rational households will have anticipated the introduction of thefree-tuition program in advance and altered their behavior prior to its implementation in April 2010.
Table A.2.1 confirms that the basic results remain unchanged.
Second, the indicator of households’ wealth is changed from households’ spending level to theirincome level. Table A.2.2 indicates that the finding that the effect of the free-tuition program on the
spending amount is larger for poorer than wealthier households is reconfirmed by this exercise,although for some reason no significant (or precise coefficient) estimates are obtained in the
regressions with households below the 25th percentile,.
Third, a “placebo” analysis is conducted to confirm whether the identifying assumption in the
DID strategy used in this paper is fulfilled or not; the treatment and control groups would have thesame outcomes in the absence of treatment (the common trend assumption). To conduct this analysis,
the model in equation (1) is estimated assuming that the reform was implemented in years other than2010. Concretely, the regression is run assuming that the program was implemented in 2005, 2007, or2009, and the significance of the interaction term is examined. Table A.2.3 reports the results for thecoefficients and confirms that the coefficients on the interaction term are statistically insignificant in
the “placebo” regressions.

24

(b)

(a)

(c)

Number ofhigh school
students in
the household(d)Free-tuitionhigh school
programdummy(e)Interaction
term

(

(

(

0.060
0.003

0.330
0.004

0.404
0.004

***
)
(

***
)
(

***
)
(

18,527
3,624

24,8809,023

59,544
7,258

***
)
(

***
)
(

***
)
(

35,955
11,008-25,521
19,382

27,37817,350

(

(

***
)
(

)

)

-19,028
51,335

125,336
114,968

107,757
98,14925

Notes: See notes for Table 4.

Households below the 25th percentile

By spending level
Households above the 25th percentile

All sample households

(

(

(

0.046
0.003

0.296
0.004

0.363
0.003

***
)
(

***
)
(

***
)
(

11,5793,455

51,197
7,707

71,993
6,792

***
)
(

***
)
(

***
)
(

17,634
20,971

11,44335,52677,87732,793

)

)

**
)

(

(

(

-13,80944,91861,750113,587

69,28398,369(b) Households with high school student(s) (treatment group) vs. Households without child(ren) aged 6-18 (control group)Households below the 25th percentile

By spending level
Households above the 25th percentile

All sample households

) (

) (

) (

) (

) (

) (

-18,047
13,017-39,474
22,872-37,438
20,906-25,393
12,379-51,740
21,586-43,606
19,324(a) Households with high school student(s) (treatment group) vs. Households with child(ren) aged 6-18 not in high school (control group)

Number ofhouseholdmembers

Householddisposable
incomeTable A.2.1. Robustness check I: Results after removing households surveyed in 2008 & 2009

)

*
)

*
)

**
)

**
)

**
)

(

(

(

(

(

(

41,35313,017

19,92622,872

21,962
20,90634,007
12,3797,660
21,586

15,794
19,324(e')(Dep: Adjusted
expenditures)

Interaction
term

***
)

)

)

***
)

)

)

8,185

28,11836,303

6,271

20,571

26,842

Number ofobservations

0.059

0.243

0.311

0.073

0.293

0.380

Adj Rsquared

200,000

890,000

880,000

210,000

780,000

770,000Root
MSE

New ESRI Working Paper No.44
Responses of household expenditure to changes in discretionary income:Micro data study focusing on tuition payments
to examine consumption smoothing by Japanese households

(b)

(a)

Number of highschool students
in thehousehold(c)Free-tuitionhigh school
program
dummy
(d)

(

(
0.123
0.014

0.417
0.005
***
)
(

***
)
(
79,172
7,936

37,991
10,388
***
)
(

***
)
(
66,52023,142-35,539
18,897
(

***
)
(

*
)
33,44455,737

37,121
43,690Notes: See notes for Table 4.

Households below the 25th percentile

By income level
Households above the 25th percentile((-0.062
0.015

0.370
0.004
***
)
(

***
)
(
90,272
10,58959,304
7,273
***
)
(

***
)
(
123,323
55,291-1,65734,066
**
)

)

(

(
-38,866
55,58549,216
41,812(b) Households with high school student(s) (treatment group) vs. Households without child(ren) aged 6-18 (control group)Households below the 25th percentile

By income level
Households above the 25th percentile

) (

) (

) (

) (

(a) Households with high school student(s) (treatment group) vs. Households with child(ren) aged 6-18 not in high school (control group)

Number ofhouseholdmembers

Householddisposable
income6,466
40,815-50,782
23,231-33,996
29,960-40,772
22,485(e)Interaction term

)

**
)

)

*
)

Table A.2.2. Robustness check II: Impact of the free-tuition high school program on household living expenditures by income level

(

(

(

(

65,86740,815

8,618
23,231

25,40429,96018,62822,485(e')(Dep: Adjusted
expenditures)

Interaction term

)

)

)

)

11,65334,984

8,631

25,880

Number ofobservations

0.026

0.277

0.074

0.325

Adj Rsquared

730,000

920,000

610,000

820,000Root
MSE

New ESRI Working Paper No.44
Responses of household expenditure to changes in discretionary income:Micro data study focusing on tuition payments
to examine consumption smoothing by Japanese households

26

New ESRI Working Paper No.44
Responses of household expenditure to changes in discretionary income:Micro data study focusing on tuition payments
to examine consumption smoothing by Japanese households

Table A.2.3. Robustness check III: Placebo test results to examine the significance of the interaction term coefficient
Placebo (2005)

Placebo (2007)

Placebo (2009)

(a)

(b)

(c)

Free-tuition
program(2010)(e)(a) Households with high school student(s) (treatment group) vs. Households with child(ren) aged 6-18 not in high school (control group)
All sample households([-9,472
18,23925,792)]([-12,271
19,14525,792

) (
] [

4,039
31,96025,792)]([-43,729
18,576
34,511

**
)
]

(
[

-15,216
20,215
19,776)]([-29,283
21,241
19,776) (] [-22,192
35,481
19,776)]([-46,427
20,81926,421

**
)
]

(
[

-144
12,332
6,016

)
]

(
[

5,210
12,937
6,016

) (
] [

8,768
21,881
6,016

)
]

(
[

-22,462
11,756
8,090

*
)
]

(
[

-13,245
21,93819,338)]([-12,102
23,094
19,338

) (
] [

4,253
39,287
19,338)]([-40,772
22,48525,880

*
)
]

(
[

6,583
30,252
6,454

)
]

(
[

-9,82931,451
6,454

) (
] [

16,99849,579
6,454

)
]

(
[

-33,996
29,9608,631)]

By spending level
Households above the 25th percentile

Households below the 25th percentile

By income level
Households above the 25th percentile

Households below the 25th percentile(b) Households with high school student(s) (treatment group) vs. Households without child(ren) aged 6-18 (control group)
All sample households
(
[

3,612
19,917
34,813)]([-1,76920,931
34,813) (] [16,432
34,847
34,813)]([-43,314
20,077
46,637

**
)
]

(
[

-231
21,63827,012)]([-8,90822,73127,012) (] [-1,24737,71927,012)]([-41,884
22,032
36,117*)]([-2,49313,024
7,801

)
]

(
[

11,04113,809
7,801

) (
] [

-6,388
24,104
7,801

)
]

(
[

-12,391
12,28010,520

)
]

(
[

1,136
22,951
26,115

)
]

(
[

5,036
24,245
26,115) (] [37,72341,025
26,115)]([-50,782
23,231
34,984

**
)
]

(
[

30,411
41,084
8,698

)
]

(
[

-4,24442,334
8,698

) (
] [

-27,747
65,886
8,698

)
]

(
[

6,466
40,815
11,653)]

By spending level
Households above the 25th percentile

Households below the 25th percentile

By income level
Households above the 25th percentile

Households below the 25th percentile

Notes: Coefficients are those on the interaction term between the number of high school students in the household and the placebo dummy assuming that the program was
implemented in 2005, 2007, or 2009 instead of 2010. Standard errors of coefficients are shown in parentheses, while the numbers of observations are shown in brackets.
Observations after April 2010 are excluded from the regressions in columns (a), (b), and (c).
The coefficients reported in column (e ) are the corresponding coefficients taken from Table 4.
***, **, and * indicate statistical significance at the 1%, 5%, and 10% level, respectively.

27

New ESRI Working Paper No.44
Responses of household expenditure to changes in discretionary income:Micro data study focusing on tuition payments
to examine consumption smoothing by Japanese households

Appendix 3. Robustness check (3): Changes in the composition of spending by income level

To check the robustness of the findings on changes in the composition of spending reported in Table
5, this appendix reports the results when the income level instead of the spending level is used as aproxy to identify (or separate) wealthier and poorer households.
Table A.3. Impact on shares of consumption categories in the total household expenditures by income level
Households with high school student(s) (treatment group) vs. Households with child(ren) aged 6-18 not in high school (control group)
By income level
Households below the 25th percentile
Households above the 25th percentile(Number of observations = 25,880)(Number of observations = 8,631)
Interaction term

Interaction term
R-squared Root MSE

R-squared Root MSE(a) Share in total household expenditures
Food
AlcoholDining out
Clothing for men
Clothing for women
Clothing for children
Compulsory school expenses
High school expenses
College expenses
Cram/Supplementary school expenses
Text and refrence books
Recreational goods
Recreational services
Spending money for family menbers
Remittances
Other living expenditures

0.008
0.007 ***
0.001
0.004 ***
0.003
0.002 *
-0.004-0.067 ***0.001
0.014 ***
0.002 **
0.018 ***
0.006
0.005
-0.010
0.011

(
(
(
(
(
(
(
(
(
(
(
(
(
(
(
(

0.006
0.002
0.003
0.001
0.002
0.001
0.004
0.003
0.006
0.005
0.001
0.004
0.005
0.007
0.007
0.012

)
)
)
)
)
)
)
)
)
)
)
)
)
)
)
)

0.416
0.018
0.169
0.023
0.032
0.217
0.262
0.507
0.259
0.095
0.057
0.050
0.090
0.030
0.188
0.084

0.050
0.016
0.026
0.012
0.021
0.011
0.033
0.031
0.057
0.044
0.007
0.035
0.046
0.065
0.070
0.112

0.002
0.001
-0.009
0.000
0.006
0.001
0.007
-0.067 ***-0.006
0.003
0.002
0.005
0.005
0.007
-0.0020.047 **

(
(
(
(
(
(
(
(
(
(
(
(
(
(
(
(

0.013
0.003
0.006
0.002
0.004
0.002
0.006
0.006
0.008
0.007
0.002
0.006
0.007
0.010
0.007
0.021

)
)
)
)
)
)
)
)
)
)
)
)
)
)
)
)

0.444
0.025
0.149
0.036
0.042
0.175
0.242
0.445
0.148
0.082
0.030
0.046
0.090
0.054
0.074
0.091

0.070
0.018
0.030
0.011
0.019
0.012
0.033
0.032
0.045
0.037
0.008
0.032
0.040
0.052
0.039
0.114

0.415
0.013
0.205
0.022
0.033
0.211
0.283
0.573
0.267
0.100
0.055
0.050
0.102
0.051
0.190
0.079

0.050
0.016
0.026
0.012
0.019
0.011
0.034
0.028
0.050
0.043
0.006
0.034
0.044
0.066
0.065
0.106

-0.016-0.001-0.013 **-0.002
0.002
0.000
0.005
0.080 ***
-0.012-0.0020.001-0.003-0.004-0.001-0.006-0.029

(
(
(
(
(
(
(
(
(
(
(
(
(
(
(
(

0.013
0.003
0.006
0.002
0.004
0.002
0.006
0.006
0.008
0.007
0.002
0.006
0.007
0.010
0.007
0.021

)
)
)
)
)
)
)
)
)
)
)
)
)
)
)
)

0.442
0.026
0.151
0.036
0.042
0.176
0.243
0.530
0.149
0.082
0.029
0.048
0.092
0.055
0.073
0.093

0.070
0.018
0.030
0.010
0.019
0.012
0.033
0.032
0.045
0.037
0.008
0.032
0.040
0.051
0.039
0.114

(b) Share in program adjusted total household expenditures
Food
AlcoholDining out
Clothing for men
Clothing for women
Clothing for children
Compulsory school expenses
High school expenses
College expenses
Cram/Supplementary school expenses
Text and refrence books
Recreational goods
Recreational services
Spending money for family menbers
Remittances
Other living expenditures-0.0020.005 ***
-0.001
0.002
0.000
0.002
-0.0040.034 ***
-0.0040.009 *
0.001 *
0.013 ***
0.000
-0.003-0.018 **-0.034 ***

(
(
(
(
(
(
(
(
(
(
(
(
(
(
(
(

0.006
0.002
0.003
0.001
0.002
0.001
0.004
0.003
0.006
0.005
0.001
0.004
0.005
0.007
0.007
0.012

)
)
)
)
)
)
)
)
)
)
)
)
)
)
)
)

Notes: See notes for Table 5.

Key findings:-

The findings regarding the composition of household spending after the introduction of the freetuition program appear to be robust, regardless of the criterion used to distinguish wealthier andpoorer households.

-

The program altered the composition of household expenditure significantly for richer
households, while the effects on the expenditure composition of poorer households are mostly
insignificant.

28

New ESRI Working Paper No.44
Responses of household expenditure to changes in discretionary income:Micro data study focusing on tuition payments
to examine consumption smoothing by Japanese households

Appendix 4: Effects of the free-tuition program on the high school enrollment rate
This appendix examines the effect of the free-tuition program on the high school enrollment rate. Since
the aim is to explore changes following the introduction of the free-tuition program, the enrollment
rate of households surveyed before April 2010 and households surveyed from April 2010 onward arecompared.
Figure A.4.1 presents the enrollment rates for [all high schools, - What does “all high schools”
mean? All high schools in Japan? I suspect you mean “all types of high schools together” or “public
and private high schools together…”] comparing enrollment before and after the introduction of thefree-tuition program by sex. The figure shows that enrollment rates increased after the introduction of
the free-tuition program. Conducting Welch’s t-test (one-sided; the test statistics are shown below thefigure) to test whether the averages in the period before the program (FY2002 to FY2009) and theperiod following the introduction of the program (FY2010 and FY2011) are equal shows that the
average student enrollment rate for [all high schools – See my question above.] is significantly higher
in the latter period (the p-value is less than 5 percent).
Moreover, the effect of the free-tuition program may depend on households’ wealth: one would
expect the enrollment rate before the program to be higher among wealthier households, so that there
should be more room for improvement in the enrollment rate among poorer households. Figure A.4.
2, which uses households’ spending level as indicator of households’ wealth, confirms this prediction.
For the figure, households are divided into those whose six-month spending total is above or below
the 25th percentile and those above or below the 10th percentile. The figure shows an increase inenrollment rates in three groups: above the 25th percentile, below the 25th percentile, and below the10th percentile. A closer look, however, shows a more pronounced increase in the enrollment rate
among poorer households. While the enrollment rate remained almost unchanged among households
above the 25th percentile (the [increase/change] was only 0.03 percentage points), among households
below the 25th percentile the rate increased by 3.4 percentage points from 95.5 percent to 98.9 percent,and among households below the 10th percentile it increased by 2.7 percentage points from 93.3
percent to 96.0 percent. In fact, the Welch test (shown below the figure) confirms that the average
enrollment rates in the two periods are not significantly different among households above the 25th
percentile, but are significantly different (at the 5 percent level) among households below the 25th
percentile.
In sum, the high school enrollment rate among lower income households increased significantly
following the introduction of the free-tuition program. In other words, by making high school tuitionfree, the program appears to have removed a financial barrier that may have prevented somehouseholds from sending their child(ren) to high school.

29

New ESRI Working Paper No.44
Responses of household expenditure to changes in discretionary income:Micro data study focusing on tuition payments
to examine consumption smoothing by Japanese households

Figure A.4.1. High school enrollment rates before and after the tuition-fee high school program

Before (2002-2009)

1.000

After (2010-2012)

0.995

0.990

0.985

0.980
High school enrollment rate
(total)

Boys

Girls

High school enrollment rate
sample av.

[95% Conf. Interval]

Before(20022009)

Total
Boys
Girls

0.989
0.989
0.990

0.986 0.993
0.984 0.994
0.985 0.995

After(20102012)

Total
Boys
Girls

0.996
0.996
0.995

0.991 1.000
0.990 1.002
0.989 1.002

p-value
H0:
Before=Afte

Total
Boys
Girls

0.018
0.045
0.100

Source:Author's calculations using FIES data.

Figure A.4.2. High school enrollment rates by household spending level
1.00

Before (2002-2009)

After (2010-2012)

0.99
0.98
0.97
0.96
0.95
0.94
0.93
0.92
0.91
0.90
25p or above

Below 25p

Below 10p

Before(2002-2009)

25p or above
Below 25p
Below 10p

Average rate
0.993
0.955
0.933

[
[
[

After
(2010-2012)

25p or above
Below 25p
Below 10p

0.996
0.989
0.960

[
[
[

p-value
25p or above
0.094
Below 25p
0.024
Below 10p
0.296
Source: Author's calculation using FIES data.

H0: Before<=After30[ 95% conf. interval ]0.990
0.996 ]
0.930
0.980 ]
0.876
0.991 ]
0.992
0.966
0.877

1.000
1.011
1.043

]
]
]