1Child

benefit,tax

allowances,andbehavioral

2

responses: The case of Japanese reform, 2010–2011 ☆

3
4

Shun-ichiro Bessho †5

April 20166

The reform of Japan’s child benefit system in 2010 was followed by the abolition of the tax

7

allowance for dependents in 2011. This study uses micro-level data from the Employment

8

Status Survey and an estimation of a discrete-choice model of labor supply to examine the9effects of these reforms on the labor supply, after-tax incomes, and utility of households. The

10

results show that the reforms decreased the labor supply of parents and that the funds necessary

11

to implement them were underestimated by 22% when this behavioral change was disregarded.

12

JEL: J20, H24

13

Key Words: Child benefit; tax reform; discrete-choice model; household labor supply; Japan

14☆

Micro-level data from the Employment Status Survey of 2002 were provided by the Research Centre

for Information and Statistics of Social Science at Hitotsubashi University’s Institute of Economic
Research. I am grateful to the anonymous referees, Takuro Miyamoto, Masayoshi Hayashi, and Atsuhiro
Yamada for their helpful comments and suggestions. I acknowledge the financial support fromKAKENHI (Grant-in-Aid for Young Scientists B-25780190) and the Seimei Foundation. The usual
disclaimers apply.
†

Faculty of Economics, Keio University, 2-15-45 Mita, Minato-ku, Tokyo 108-8345, Japan. E-mail:bessho@econ.keio.ac.jp. Tel.: +81-3-5427-1360.

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1. Introduction

2

Many industrial countries have introduced packages of cash benefits, tax allowances, subsidies,3

and services in kind to assist children and their parents (Bradshaw and Finch, 2002). Although

4Japan is no exception, the Japanese government spent only 1.11% of GDP on family benefits in

5cash, services, and tax breaks in 2001 (OECD Family Database). The Democratic Party of Japan

6(DPJ), which assumed office in 2009, replaced the existing child benefit, known as “jido teate,”7

with a new version called “kodomo teate” in 2010. 1 This reform abolished the income test and8raised the limitation on the eligible age and the associated benefits, as reflected in the budget

9

increase from 996 billion yen in FY 2009 to 2,285 billion yen in FY 2010. Meanwhile, the tax

10allowance for young dependents was abolished in 2011. This package of reforms increased

11

public spending on family benefits in cash, services, and tax breaks in 2011 to 1.74% of GDP,

12which was still lower than the OECD average (2.25%).

13

These reforms changed the distribution of tax payments as well as household behaviors.

14

Because the tax allowance for dependents represented a deduction from taxable income, a15

reduced allowance resulted in a relatively high tax payment for the rich compared with the poor

16

under progressive taxation. On the other hand, the amount of child benefit is fixed. Therefore,

17

this package of reforms should be pro-poor. Since the reforms alter the budget set of households,

18

their behavior could be affected, which in turn changes the tax payments. This study uses a

19

discrete-choice model of labor supply to quantify the effects of these reforms on household

20

behavior, the distribution of tax payments, and income and welfare.

21

Since child benefit is a lump-sum payment and does not require parents to be employed, it can22

have a negative effect on labor supply and a positive effect on leisure consumption because of1

Both jido and kodomo mean “children” in Japanese.

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1

the income effects generated by an increase in after-tax income, at least in the short run. 2 In fact,2

by simulating the effects of increased child benefit in Sweden, Brink et al. (2007) found that the3effects included a decrease in labor supply. Gonzales (2013) and Tamm (2010) found, in

4Spanish and German cases, respectively, that the impact of universal child benefit on labor5

supply is negative. 3 The child tax credit has been shown to exert a similar effect in Canada

6(Milligan and Stabile, 2007). 4

7

In this study, I examine the combined effects of Japan’s 2010 child benefit reform and 2011 tax

8allowance abolition on the labor supply, after-tax incomes, and utility of households, based on a9

micro-simulation comparing before and after the reform. For this purpose, I estimate the10parameters of the utility function based on a labor supply model that discretizes the hours

11

worked, pioneered by van Soest (1995). I also compute the wage elasticity of labor supply. 5 I

12further calculate the government’s requirement of public funds for a scenario involving the2

Although we focus on labor supply behavior, child benefit can affect household spending patterns (Blowet al., 2012).
3

Gonzales (2013) found that eligible mothers are less likely to be working one year after delivery. Her

results showed a positive effect on fertility and a negative effect on abortion and formal childcare.
4

Many studies have examined the effects of child-related benefits and/or tax breaks that are designed toencourage parents to participate in the labor force, such as the Earned Income Tax Credit (EITC) in the
United States, the Working Tax Credit (WTC) in the United Kingdom, the National Child Benefit in
Canada, and the French Family Tax Splitting. See, e.g., Blank (2002), Blundell (2000), Immervoll et al.
(2007), and Milligan and Stabile (2007). The effects of subsidies available for childcare, which lead to adecrease in the cost of childcare, on the labor supply of mothers are also examined (e.g., Anderson andLevine, 1999; Baker et al., 2008; Berger and Black, 1992; Havnes and Mogstad, 2011).
5

For studies of labor supply elasticity, see Blundell and MaCurdy (1999) and Evers et al. (2008). For aninvestigation in the Japanese context, see Bessho and Hayashi (2011).

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1

behavioral changes of households in response to the reform compared with a scenario in which

2such behavioral changes do not occur.

3

One reason for the use of a structural model in this study is the complicated nature of the4

Japanese tax-and-transfer system. As demonstrated in the second section of this paper, the old5child benefit was calculated according to a complex formula based on the income and structure

6of a household. The tax allowance for dependents is only one ingredient of a convoluted income

7

taxation system that complicates the computation of changes in each household’s after-tax

8income. A structural model thus seems appropriate for an analysis of the Japanese tax-and-

9transfer system. While some discrete-choice structural models explicitly account for childcare

10

options (Brink et al., 2007; Haan and Wrohlich, 2011; Kornstad and Thoresen, 2007), I employ

11

a standard consumption leisure model here, mainly because of data limitations. To validate the12results, I assume two specifications: a translog and a quadratic utility function. The estimation

13

in this study generates a number of coefficients. To interpret the estimation results, I use these14

coefficients to compute the labor supply elasticity with respect to the pretax wage rate.

15

One contribution of this study is its explicit consideration, through a micro-simulation, of the16

behavioral changes pertaining to labor supply associated with the child benefit and tax

17

allowance reforms. A number of previous studies have investigated the various effects of child18

benefit. For example, Abe (2003), while examining the corrective effects of child benefit and19

the tax allowance on income disparity, found that child benefit had a much smaller effect than20

the tax allowance. By estimating the effect of child benefit on household consumption,

21

Kobayashi (2011) showed that the child benefit provision neither decreased Engel’s coefficients22

nor increased the proportion of educational expenditure, thus implying that it did not improve23

child welfare. Unayama (2011) utilized frequent changes to the child benefit system to identify

24

its effect, finding that a major part of child benefit is used to accumulate savings rather than to25change consumption patterns. Only those households facing liquidity constraints tend to-4-

1

increase consumption when receiving child benefit. Stephens and Unayama (2015) employed a2similar identification strategy to investigate the impact on wealth accumulation. Their empirical3

results are consistent with their theoretical predictions in the sense that the cumulative benefits

4

received to date raise household assets and that increases in future benefits lower the stock of

5

wealth. The micro-simulation performed by Takayama and Shiraishi (2009), by using micro-

6

data obtained from the National Livelihood Survey, demonstrated an increase in after-tax

7

income for 38% of households, that is, for most of the households with pre-high school children.

8

However, none of these studies has considered the effects of child benefit on labor supply.

9

Although child benefit in Japan is not explicitly intended to either stimulate or depress

10

households’ labor supply, 6 it is possible that the tax-and-transfer structure affects labor supply

11

behavior, which could in turn change tax revenues and government expenditure. Therefore, a12

study of the reform’s effect on labor supply should be of economic significance.

13

The results of the present study are summarized as follows. Based on the estimated coefficients,

14

the average labor supply elasticity is found to range from −0.01 to 0.08 for husbands and from15

0.106 to 0.138 for wives, which was largely attributed to the extensive margin. The child benefit

16

reform and abolition of the tax allowance for dependents were found to jointly depress

17

household labor supply by between 0.29% and 1.41% in terms of labor income. The decrease in18

labor income was not considerable. While disregarding behavioral changes resulted in a19

decrease of net tax payments per household of 44,150 yen (i.e., the benefits increased),20

incorporating behavioral changes by means of the translog utility function raised this amount to

21

53,720 yen. That is, changes in labor supply increased the government expenditure required to22

implement the reform by 22%. In addition, the reform was shown to raise the utility level of all

23

households with incomes of less than 3 million yen (USD 30,000) and with children aged 14
6

Kita (2004) pointed out that the Japanese government, especially the Ministry of Welfare, tried to use

child benefit as a tool to promote home childcare by non-working mothers when it was first introduced.

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1

years and below, whereas it decreased the utility level of more than half of the households with2

incomes of more than 13 million yen (USD 130,000).

3

The rest of this paper is organized as follows. Section 2 introduces the institutional background4of the study. The econometric specifications are described in Section 3. Section 4 presents the5data and Section 5 explains the simulation method. The results are reported in Section 6 and the6conclusions in Section 7.

7

8

2. Institutional background

9

This section provides an overview of the child benefit and tax allowance systems for dependents

10in Japan. 7

11

2.1. Child benefit

12

Japanese child benefit, which is based on the Child Benefit Act of 1971, comprises a cash

13

benefit. The allowance is paid to the custodians of eligible children. 8 In many cases, the14recipients are fathers, considered to be the heads of the households to which children belong.

15

The government has revised the criteria for payment on several occasions based on the number

16

of children in a household, their ages, and the recipient’s income. Until FY 1999, however, total

17

child benefit expenditure did not change drastically, ranging from 140 to 180 billion yen, an

18

exceptional year being FY 1992, which reached 214 billion yen.
7

See Suzuki (2009, 2011) and Kita (2002a, 2002b) for further details of these systems and their history. I

do not discuss the revenue aspects of these systems, focusing instead on their effects on householdbehavior.
8

A “child” was defined as being 18 years or below.

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1

After a further revision in 2000, the government anticipated that child benefit would function as

2

a countermeasure to the falling birth rate through its mitigation of the economic burden of

3childcare. This revision expanded the benefit for children aged between 3 and 6 years: whereas

4

the benefit for the first or second child was fixed at 5,000 yen, that for the third child or

5subsequent children was 10,000 yen. This was followed by the raising of the upper age limit to6

the third grade (aged 9 years) in 2004 and to elementary school graduation (aged 12 years) in7

2006. The income limitation was also gradually raised, and add-ons for babies (aged 3 years and8

below) were provided for in 2007.

9

In 2009, the DPJ took office, offering reform of child benefit as an eye-catching policy. In 2010,10

the DPJ established a new child benefit system that abolished the income test, raised the upper11

age limit to junior high school graduation (aged 15 years), and increased the monthly benefit to12

13,000 yen. This amount was further revised in October 2011 to 15,000 yen for children aged 3

13

years and below, 10,000 yen for children aged between 3 and 12 years (elementary school

14

graduation) (15,000 yen in the case of the third child or subsequent children), and 10,000 yen

15

for junior high school students. However, the Liberal Democratic Party, which defeated the DPJ

16

in 2012, reintroduced the income test. 9

17

The successive expansion of child benefit since 2000 has resulted in increased government

18

expenditure from 159 billion yen in FY 1999 to 800 billion yen in FY 2006, and up to 996

19billion yen in FY 2009. The DPJ’s reform doubled total expenditure, raising this figure to 2.29

20

trillion yen in FY 2010, with a further increase to 2.26 trillion yen in FY 2013.

21

2.2. Tax allowance for dependents

9

The benefit amount is 5,000 yen if income is above the upper limit.

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1

In Japan, individual income tax includes national “income tax,” local “inhabitants’ tax,” 10 and2

the social insurance premium. The principle used to compute the amount of tax is similar for3

income tax and inhabitants’ tax. First, “employment income” is derived as the salary received

4by an individual, minus any “employment income deductions.” Second, “taxable income” is5defined as employment income minus certain allowances, including the allowance and special

6

allowance for spouses and taxable non-labor income (non-labor income minus necessary

7

expenses). The allowance for dependents, which is the focus of my study, is applied at this step8

in the process. Third, tax rates are applied to taxable incomes to compute the gross tax amounts.

9

Finally, some tax credits and breaks are subtracted from the gross tax amounts to obtain the tax

10amounts. The social insurance premium is virtually a fixed-rate payroll tax with an upper limit.

11

Child benefit is not taxable.

12

Tax credit based on the number of dependents was introduced in 1950. In 1989, the current

13

structure of tax allowances was established with allowances for “special dependents” aged

14

between 16 and 23 years. Keeping pace with inflation, the allowance amounts from taxable

15income have been steadily raised, reaching 380,000 yen for dependents and 630,000 yen for

16“special dependents” in 2010.

17

Tax allowances for dependents were revised at the same time as the child benefit reform by the18

DPJ in 2010. Because tax allowances for dependents were designed as allowances from taxable

19

income, under progressive taxation, an increase in pretax income led to a more than

20

proportionate decrease in the tax payment. The DPJ considered the reduced tax payments to be
10

In practice, the amount of inhabitants’ tax is calculated based on the income of the previous year.

However, since my dataset was not panel data, inhabitants’ tax was assumed to be computed by using
current income. In addition, while prefectures and municipalities can charge their own tax rates, I used thestandard tax rates uniformly set by national law. This should not cause a major problem since most local
governments abide by these standard rates.

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1

undesirable and replaced tax allowances with child benefit. In 2011, the allowance for2

dependents aged 15 years and below was abolished and that for “special dependents” aged

3

between 16 and 18 years was reduced from 630,000 yen to 380,000 yen, in concurrence with the4waiving of high school tuition fees.

5

The tax-and-transfer code in 2002, when the data used in this study were collected, is in Table 1.

6

The codes just before and after the reform, in 2009 and 2011, respectively, are outlined in Table

7

2. In the following sections of this paper, I quantify the effects of this package of reforms (i.e.,

8increase in child benefit and abolition of the tax allowance) on the behavior of households.

9

3. Econometric specifications

10

Following van Soest (1995), 11 I apply a structural discrete-choice household labor supply model.

11

According to this model, household i consumes numeráire xi and the leisure of husband lhi and12of wife lwi to obtain utility ui = ui(xi, lhi, lwi). I assume a unitary model in the sense of the joint

13

maximization of utility ui by household members given the pretax wage rate and tax codes. The

14

decision-making of children is not explicitly considered here. The time endowment is expressed15

as T, meaning that the husband’s working hours, denoted as hhi = T − lhi, and the wife’s working

16

hours, denoted as hwi = T − lwi. xi, are equal to the family’s income (including both the17husband’s and wife’s earnings) after tax. Denoting the parameters of the tax-and-transfer code18

as τ, the household’s income after tax is represented as𝑥𝑥𝑖𝑖 = [𝑊𝑊ℎ𝑖𝑖 ℎℎ𝑖𝑖 − 𝑇𝑇(𝑊𝑊ℎ𝑖𝑖 ℎℎ𝑖𝑖 , 𝑊𝑊𝑤𝑤𝑤𝑤 ℎ𝑤𝑤𝑤𝑤 ; 𝒁𝒁𝑖𝑖 , 𝜏𝜏)] + [𝑊𝑊𝑤𝑤𝑤𝑤 ℎ𝑤𝑤𝑤𝑤 − 𝑇𝑇(𝑊𝑊𝑤𝑤𝑤𝑤 ℎ𝑤𝑤𝑤𝑤 , 𝑊𝑊ℎ𝑖𝑖 ℎℎ𝑖𝑖 ; 𝒁𝒁𝑖𝑖 , 𝜏𝜏)],(1)19

11

See Löffler et al. (2014) for details on the assumptions and variations of discrete-choice models ofhousehold labor supply. Another type of structural model is based on Hausman’s (1979) design andassumes a linear labor supply function.

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1

where T(⋅) denotes an income tax-and-transfer function, including child benefit; Zi is a vector of2

the household’s characteristics; and Whi and Wwi are the respective pretax wage rates of the3husband and wife. Notably, in Japan, the husband’s (or wife’s) income tax depends on the

4wife’s (or husband’s) income through, for example, allowance for spouses. Because the5household’s

6ui = u(xi, lhi, lwi; Zi, τ).

7

The choice of labor supply is discretized so that each household is assumed to choose among

8

the

9

{(xij, lhij, lwij) : j = 1, 2, ..., J} to maximize u(xij, lhij, lwij; Zi, τ). Since I set up eight choice sets of

10hours worked for the wife and husband, J is set to be 8×8 = 64 here. I work with two types of

11

standard specifications of the direct utility function, namely the translog function and the12quadratic function. The translog utility function is

utilityalternatives

depends

provided

(

)

on

in

the

the

tax-and-transfer

choice

set

code,

of

incomeI

calculateleisureutilityas

combinations

u t (.;.) = V t xij , lhij , lwij Z i ,t + ε ijq
13

=β

t
x ln xij +

(

)(

)

t
(ln lhij )2 + β wwt (ln lwij )2
β wt ln lwij + β xxt (ln xij )2 + β hh()( )+ β 1(h > 0 ) + β 1(h > 0 ) + ε

(

)(

t
t
t
ln xij ln lhij + β xw
ln xij ln lwij + β hw
ln lhij ln lwij
+ β xh
t
hf

14

β

t
h ln lhij +

hij

t
wf

wij

)

,

(2)

t
ij

and the quadratic utility function isu q (.;.) = V q (xij , lhij , lwij Z i ,τ ) + ε ijqq(lwij )
= β xq xij + β hqlhij + β wqlwij + β xxq (xij ) + β hhq (lhij ) + β ww
2

15

2

q
(xij )(lwij ) + β hwq (lhij )(lwij )+ β xhq (xij )(lhij ) + β xw

2

,

(3)

+ β hfq 1(hhij > 0 ) + β wfq 1(hwij > 0 ) + ε ijq

16

where the ɛijs are additive random disturbances and βs are the coefficients to be estimated. The

17

last two terms before ɛij reflect the fixed cost associated with working. Thus, the expected signs

18of βhfq, βwfq, βhft, and βwft are negative.
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1

These specifications did not impose a priori restrictions such as the positive marginal utility of2income or quasi-concavity of preferences. If utility increases with income, the partial derivative

3of u with respect to x, ux, is positive (van Soest, 1995, Eq. (4)). If utility is quasi-concave, HC in

4

Eq. (3) in van Soest (1995) is positive definite. HC is defined as

5

HC = −𝑢𝑢𝑥𝑥−1 �6where HU denotes the matrix of the second-order partial derivatives of the utility function.

7

I assume that the household characteristics linearly affect all the coefficients, βs. The

8coefficients of terms that included a husband’s leisure lhi and a wife’s leisure lwi are assumed to9depend on their respective ages and educational backgrounds. Similarly, the coefficients of the10terms that include consumption xi are assumed to depend on the husbands and wives’ respective

11

ages, educational backgrounds, number of children, and place of residence. The coefficients of

12

fixed costs are assumed to depend on the number of children aged 6 years and below. While I

13consider other cases in which the coefficients of the quadratic terms are independent of the14household characteristics, the likelihood ratio tests emphatically reject these restricted models.

15

The random disturbances ɛijq and ɛijt are assumed to identically and independently follow the16

type I extreme value distribution as in the literature. This assumption about random disturbances

17

implies that the ratio of the probabilities of two alternatives does not depend on any other18

alternatives (independence from irrelevant alternatives assumption). If all the coefficients, βs,

19

are assumed to be functions of only the constant and the household characteristics, the20probability of each alternative is the same for all households conditional on the household

21

characteristics. One direction toward a more flexible model is to allow some of the coefficients,

22

βs, to be randomly distributed. This random coefficient model or mixed logit model is often

23

used to cope with unobserved heterogeneity (e.g., Brink et al., 2007; Haan and Wrohlich, 2011;−𝑢𝑢𝑙𝑙ℎ /𝑢𝑢𝑥𝑥−𝑢𝑢𝑙𝑙𝑙𝑙 /𝑢𝑢𝑥𝑥

100−𝑢𝑢 /𝑢𝑢� 𝐻𝐻𝐻𝐻 � 𝑙𝑙ℎ 𝑥𝑥
1−𝑢𝑢𝑙𝑙𝑙𝑙 /𝑢𝑢𝑥𝑥

10- 11 -

0 ′
�,
1

1

van Soest, 1995). 12 In this study, I assume that the coefficient of after-tax income, βx, is random2

and employ the mixed logit model to estimate the βs (Hole, 2007; van Soest, 1995). 13

3

4. Data4

4.1. Sample

5

The data sample was obtained from Syugyo Kozo Kihon Chosa (the Employment Status Survey)

6

conducted by the Japanese government’s Statistical Bureau in 2002. This survey, the most7

comprehensive labor survey in Japan, is conducted every 5 years. It produces a large dataset

8containing approximately 440,000 household observations reflecting a variety of household

9characteristics. Although the data in 2002 may be unsuitable to evaluate the reform in

10

2010/2011, this is not a major problem for estimating the parameters of the utility function,

11

which are not considered to change drastically over time. The price level does not rise or fall

12

during this period as the CPI (all items less imputed rent) was 101 in 2002 and 100 in 2010.

13

I focused on the labor supply of nuclear families with the following characteristics: male heads

14

of prime age (25–55 years) with children aged 14 years and below. I omitted the following

15

observations from the sample: (a) self-employed workers, (b) board members of private

16

companies and non-profit organizations, (c) family workers in small and medium-sized17

enterprises, (d) those who were unemployed due to illness, (e) those who had changed their
12

As Haan (2006) showed, the results do not change dramatically if random coefficients models are used.

Pacifico (2013) pointed out that the estimation results can change significantly if other types ofunobserved heterogeneity are assumed.
13

I failed to find convergence in the parameter estimates when I assumed the other coefficients to berandomly distributed in addition to the after-tax income coefficient. As Pacifico (2013) pointed out, thismay be because of the computational difficulties that arise when using gradient-based maximization
algorithms.

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1

residence or job within the last year, and (f) those who had had children within the last year.

2

These omissions reduced the sample size to 18,961 respondents.

3

The Employment Status Survey’s codes hours worked as interval data. By using these intervals,

4

I set up eight choice sets of the hours worked per year (see Table 3). Because of the significant

5variations in the observed distribution of hours worked, I applied different choice sets of hours

6

worked for husbands and wives to avoid a concentration of the distribution on particular choices.

7

In sum, the choice sets contained 8×8 = 64 alternatives of hours worked for nuclear households

8with T = 16×365 = 5, 840 hours per year.

9

The variables included in the vector of the household’s characteristics Zi were standard ones

10

cited in the literature. Dummies were included for the following variables: (1) five age groups

11(30–34, 35–39, 40–44, 45–49, and 50–54); (2) residence in one of the three major urban areas of

12Japan (the Greater Tokyo area, the Chukyo area, and the Kinki area); (3) three educational

13

backgrounds (junior high school graduates, junior college graduates, and university graduates or

14higher); and (4) numbers of each of the three groups of children; aged 6 years and below, 7–14

15

years , and 15 years and above. The sample statistics are shown in Table 4.

16

4.2. Pretax wage rate

17

I used predicted values as the pretax wage rates. 14 Given the availability of data for days worked

18(per year) and hours worked (per week) and the provision of annual labor incomes as intervals, I

19

first calculated the pretax wage rate as a quotient of the middle values of hours worked (per20year) and labor income. The predicted pretax wage rate was defined as a fitted value of a wage

14

Other wage imputation procedures include predicting wage rates only for non-workers (van Soest,1995), adding a single random draw to predicted wage rates (Bargain et al., 2012), and estimating pretax
wage rates jointly with the utility parameters (Blundell and Shephard, 2012).

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1

rate regression for each gender, where the dependent variable was the log of the pretax wage

2

rate and the explanatory variables included dummies for age, residence, education, and their3

cross-terms. Since a non-negligible proportion of wives selected zero hours worked, the wage

4

rate regression for women was estimated by using the Heckit sample selection model, wherein

5excluded instruments were the quadratic terms of residuals obtained from the regression

6

analysis for non-labor income (household income minus the husband’s labor income).

7

4.3. Income tax code

8

The household budget, based on the computation of the household’s after-tax income, xij, for9each alternative in the choice set was required to estimate the direct utility function. Once pretax

10income was known, after-tax income could be obtained by following the tax code outlined in

11

Section 2. However, the available allowances or tax credits differed according to individual

12

characteristics, and I could not account for some of them because of data limitations. The

13

categories I selected were basic allowance, allowance for spouses, special allowance for spouses,

14

allowance for dependents, employment income deduction, and deduction for social insurance

15

premiums. Necessary expenses were assumed to be subtracted from non-labor income at the rate16

of 20%. I further assumed that social insurance consisted of public pension insurance, public

17

health insurance, and public unemployment insurance. However, I could not calculate the social

18

insurance premium because it differed according to the place of work, and this information was19

not included in the data. I therefore made the following assumptions: the social insurance

20

premium was 13.3% if the firm where the individual worked employed fewer than 1,000

21

people; 14.3% if the firm employed more than 1,000 people; and 12.9% if the individual was a22

public servant. I also considered the upper limit of the social insurance premium.

23

5. Micro-simulation method- 14 -

1

5.1. Tax-and-transfer system reform2

I was able to simulate labor supply behavior when the tax-and-transfer code τ changed by using

3

a structural labor supply model. In particular, I simulated the model by using the tax-and-4transfer codes before and after the reform (see Table 2) as well as the estimated parameters of5

the utility function based on the data in 2002.

6

In 2009, before the reform, the upper age limit for child benefit was 12 years (graduation of7elementary school) and the monthly amount was 5,000 yen for the first and second children,8

which increased to 10,000 yen for the third child and subsequent children. The benefit was also

9

income-tested. For example, a parent with one eligible child could receive the benefit if his or10

her annual income minus employment income deductions amounted to less than 5 million yen.

11

Because the benefit is paid to the person who has custody of the eligible children, the income

12

test was based on the highest labor income of the parents. The tax allowance for young

13

dependents during this period was 380,000 yen and the allowance for “special dependents,”

14

aged between 16 and 23 years, was 630,000 yen.

15

After the reform was implemented in 2010/2011, monthly child benefit increased to 13,000 yen

16

for all children who were not yet junior high school graduates (aged 15 years). The reform

17

ended the income test, but it also abolished the tax allowance for young dependents and reduced

18

the allowance for “special dependents” to 380,000 yen.

19

Overall, my simulation consisted of three phases. First, I estimated the parameters of the utility20function based on the tax-and-transfer code in existence in 2002. Second, by using the estimated21

utility parameters, I simulated household behavior under the tax-and-transfer code before the22

reform (i.e., in 2009). Third, by using the same utility parameters, I simulated household

23

behavior under the tax-and-transfer code after the reform in 2010/2011 and then compared the- 15 -

1results with those in 2009. The method used to perform the simulation is explained in detail in2

the next subsection.

3

5.2. Simulation method

4

I based my calculation on a behavioral discrete-choice model micro-simulation (Creedy and5Kalb, 2006). This approach starts from the labor supply observed in the data to compute a

6

distribution after a specified reform. 15 Readers may recall that utility (Eqs. (2) and (3)) consisted

7

of a deterministic part and random components, βx, ɛijq, and ɛijt. Therefore, I first estimated the8parameters of the utility functions (2) and (3), as explained in Section 3.

9

In the second step, I calibrated the random components so that they became the observed labor

10

choices. I drew vectors of J+1 random numbers, one of which followed the normal distribution

11

and the others followed the type I extreme value distribution, for each household i. Hereafter,

12

the superscripts q and t are not noted since the same procedure was used for the quadratic and13translog utility functions. Let t02 denote the tax-and-transfer code in 2002 and βxir and1415

ɛir ≡ [ɛi1r, ɛi2r, ⋯, ɛiJr]ʹ the r-th draw for such numbers, which yields uir ≡ [ui1r, ui2r, ⋯, uiJr]ʹ

16

was stored as a “successful” draw if the labor choice from this draw (hhir*, hwir*) ≡ argmax{uir}17coincided with the observation (hhi, hwi). If not, (βxir, ɛir) was discarded and the process was

18

repeated until either a “successful” draw was obtained in which these coincided, or else the19household was excluded from the exercise. 16 Through my repetition of this process, I obtained

where uijr ≡ V(xij, lhij, lhij| Zi, τ02) + ɛijr and V(xij, lhij, lhij| Zi, τ02) is evaluated by using βxir. (βxir, ɛir)

15

There is a closed-form solution for the predicted choice probabilities under the multinomial logit modelin this study. This solution, however, cannot give a distribution after a reform conditional on the observed
pre-reform labor supply.
16

If a “successful” draw was not obtained after 1,000 trials, such a household was discarded. Less than

10% of the sample was discarded for each draw.

- 16 -

2

K successful draws βxi ≡ vec[βxi1, ⋯, βxik, ⋯, βxiK] and ɛi ≡ vec[ɛi1, ⋯, ɛik, ⋯, ɛiK], where k

3

In the third step, I calibrated the other sets of the tax parameters that effect changes for child4

benefit and tax allowances for dependents, as explained in Section 5.1. Let t09 denote the tax-

5

and-transfer code before the reform in 2009 and t11 that after the reform in 2011. t09 is shown in6

the second and third columns and t11 is in the fourth and fifth columns of Table 2. To simulate

7

the households’ choice before the reform, utility for alternative j with the k-th draw was

8

calculated as uij09k ≡ V(xij, lhij, lwij| Zi, τ09) + ɛijk evaluated by using βxik. The optimal choice9

became (hhi09*, hwi09*) ≡ argmax{ui09k}. The same procedure was applied to obtain the optimal

10

choice under the tax-and-transfer code after the reform in 2011, (hhi11*, hwi11*). Since K

11

successful draws occurred, there were two sets of, at most, K pairs, (hhi09*, hwi09*) and12(hhi11*, hwi11*) for each household. 17 These K pairs constituted the empirical distributions of the13labor supply before and after the tax change.

14

The estimation in this study generates a number of coefficients. To interpret the estimation

15

results, I calculated the labor supply elasticity with respect to the pretax wage rate for reference,

16

using the same micro-simulation method whereby I raised the pretax wage rates by 1%. I

17computed the after-tax income for each alternative, xij, under the tax-and-transfer code in 2002

18

with the increased pretax wage rates by 1% to obtain the optimal choice. Focusing on aggregate

19labor supply, I calculated the intensive and extensive margin elasticities, as defined by Kleven20

and Kreiner (2006), for K successful draws. Let superscript 0 denote a variable before the21

increase in the wage rate and superscript 1 denote a variable after the increase. Aggregate labor22

supply, H, is the product of the proportion of individuals who participate in the labor market, P,

1

indexed the draws in place of r. Here, I set the value K = 200.

17

Successful draws could not be obtained for a small proportion of the sample for each draw after thetrials. Thus, there were fewer than K pairs for many households. See also footnote 16.

- 17 -

1

and the average labor supply of the participants, h̄. The rate of change of aggregate labor supply,2(H1 − H0)/H0, is decomposed into the sum of the participation rate, (P1 − P0)/P0, corresponding3

to the extensive margin, and the average labor supply of participants (h̄1 − h̄0)/h̄0, corresponding4

to the intensive margin. As the pretax wage rates were increased by 1%, the extensive margin5

elasticity is defined as [(P1 − P0)/P0]/0.01 and the intensive margin elasticity is [(h̄1 −6h̄0)/h̄0]/0.01. Since both the husband and the wife could work in this setting, I also calculated the7“cross” elasticity, namely the husband’s labor supply elasticity with respect to the wife’s wage

8

rate and the wife’s elasticity with respect to the husband’s wage rate.

9

6. Results

10

6.1. Labor supply elasticity

11

The translog and quadratic specifications used in this study did not impose a priori restrictions

12such as the positive marginal utility of income or quasi-concavity of preferences. I used the13parameters shown in Tables A-1 and A-2 to check these two properties based on ux and HC

14

shown in Section 3, for all individual households in my sample. Hence, I report the statistics of

15

those households that satisfied these two properties herein.

16

I computed the elasticities of aggregate labor supply by using the micro-simulation method,

17

described in Section 5.2, which provided K = 200 sets of the simulated elasticities. Table 5

18

presents the averages and standard deviations of these elasticities. This table shows the19elasticities with respect to the husband’s or the wife’s own wage rate, the average being 0.075

20

for husbands and 0.138 for wives in the case of the quadratic utility function. The translog

21

specification generated smaller and even negative estimates for husbands; these did not deviate

22

significantly from zero. This negative elasticity suggests that the income effect dominated the23

substitution effect in case of the translog utility function.

- 18 -

1

The average elasticity of the intensive margin, i.e., the hours-of-work elasticities, was 0.058 for2husbands and 0.061 for wives in the case of the quadratic utility function. The respective values

3for husbands and wives were −0.016 and 0.045 in the case of the translog utility function. These

4

values were small compared with those obtained by using the discrete-choice model, which

5ranged between 0.0 and 0.3 for husbands and 0.1 and 0.7 for wives, with a few exceptions (see6

Tables 1, 2, and 4 in Bargain et al., 2011). The similarity of the results of these two

7

specifications is consistent with that provided by Löffler et al. (2014), who ran a meta-analysis

8

and found that the estimated elasticities are robust to the specification of the utility function. 18

9The average elasticity of the extensive margin, i.e., the participation elasticities, was 0.017 and

100.009 for husbands and 0.077 and 0.062 for wives, which were also modest results. Indeed, the11

discrete-choice labor supply model showed larger elasticities, ranging between 0.0 and 0.2 for

12husbands and 0.1 and 1.0 for wives (see Tables 1, 2, and 4 in Bargain et al., 2011). Although my13

results for the elasticities of the extensive margin are small in comparison with those presented

14

in the discrete-choice literature, they seem comparable with the corresponding values for the15

intensive margin mentioned above. This non-negligible magnitude of the participation

16

elasticities may have been caused by the fixed utility cost of working.

17

Because this study assumes that a couple jointly maximizes the household’s utility, a change in18

the husband’s gross wage rate can affect the wife’s labor supply behavior and vice versa. Thus,

19

cross-elasticities, i.e., those of the wife’s labor supply with respect to the husband’s wage rate20

and those of the husband’s labor supply with respect to the wife’s wage rate, should be defined.

21

These cross-elasticities, shown in Table 5, were −0.002 and −0.010 for husbands and 0.527 and

22

0.328 for wives. These results imply that a husband responded to an increase in his wife’s wage
18

The slight difference obtained in this study may come from the differences in the magnitude of theincome effect. When non-labor income is raised by 10,000 yen, the labor supply of husbands decreases by
0.1% (0.01%) in the case of the translog utility (quadratic utility) function.

- 19 -

1by reducing his working hours, whereas a wife conversely responded to a similar increase in her

2husband’s wage in the opposite manner. Although Bargain et al. (2011) did not list cross-wage

3

elasticities, van Soest (1995, Table 4) indicated negative cross-elasticities for husbands. The

4average cross-elasticity (−0.010), in terms of absolute value, was lower for husbands than that5

obtained by van Soest (1995) (−0.015), which does not necessarily seem unrealistic.

6

6.2. The effects of the reform without the inclusion of behavioral

7

responses

8

Although this study examines the effects of the reform based on the parameters shown in Tables

9

A-1 and A-2, here I briefly note the simulated effects when behavioral responses were10

disregarded. This type of micro-simulation was used by Takayama and Shiraishi (2009). Figure

11

1 shows the average changes in tax payments for each income class based on pre-reform after-

12

tax incomes. Each panel shows the average values of K = 200 successful draws in the case of13

the translog utility function. The white bars in Figure 1 show the changes in the gross tax

14

payments, excluding the changes in child benefit, while the gray bars indicate the changes in15

child benefit.

16

Because the allowance for dependents was deducted from taxable income, the effect of this17

allowance on tax payments was greater when pretax income was relatively large and was equal18

to almost zero when pretax income was sufficiently low. The white bars in Figure 1 represent

19

this situation where the changes in the tax payments were shown to be negligible for households

20with an annual income of less than 3 million yen. Changes in the tax payments for households

21

did not monotonically increase with annual income because the average number of children for

22

each income bracket differed.

- 20 -

1

The joint effects of the tax allowance, which were correspondingly larger as pretax income grew,2

and the provision of child benefit without income testing operated in favor of the lower income

3classes. The reform resulted in increased child benefit, decreasing the net tax payments of

4households with annual incomes below 12 million yen on average, ranging from the lowest to

5

the upper-middle-income classes. Note that the average decrease in the net tax payments is

6larger for the upper-middle-income classes than for the middle-income classes because the7former benefit from the abolition of the income test for child benefit, while the increase in child8

benefit for children aged 3 years and below, who are often in middle-income households, is9relatively small (36,000 yen; see Table 2). While the average changes in the net tax payments

10

shown in Figure 1 are positive for the middle-income classes, for some of these households they11

are negative since the increase in child benefit is less than the increase in income tax.

12

6.3. The effects of the reform with the inclusion of behavioral
responses

13

14

The reform of the tax-and-transfer system resulted in behavioral changes regarding labor supply

15

when households obtained utility from leisure. This effect is examined below. As described in

16

Section 6.1, both the translog and the quadratic utility specifications generate similar results.

17

Thus, I report the results based on the translog utility function only. 19

18

Given that this study included the options of both a husband and a wife being able to control

19

their labor supply, the changes in the pretax household income and labor supply of both the20husband and the wife are shown in Figure 2. As noted above, the combination of the abolition of21

the tax allowance and increased child benefit increased the after-tax incomes of the lowest to the19

The results based on the quadratic utility function are available upon request. The data were collected

in 2002, not around 2010. Nevertheless, even if the distribution is adjusted based on the household
characteristics in 2010/2011, the results are similar to those reported in this paper.

- 21 -

1

upper-middle-income households if behavioral changes were disregarded. This reform affected2

parents’ labor supply through three pathways. First, because child benefit is a lump-sum benefit

3

that only depends on the number of children, which is exogenous in a static setting, households

4

raised their leisure consumption and decreased their labor supply because of the income effect.

5

Second, the abolition of the tax allowance reduced after-tax incomes. Thus, the income effect6

decreased leisure consumption and increased labor supply. Third, the abolition of the tax

7

allowance generated bracket creep that resulted from the increase in taxable income. The

8marginal after-tax wage rate correspondingly declined and substitution effect increased leisure9

consumption, which, in turn, led to a decrease in labor supply.

10

The third effect was irrelevant for low-income households. Because institutional changes in net

11

tax payments negatively influenced them, the income effect should have decreased labor supply.

12

As Figure 2 shows, there was a decrease in parents’ labor supply in some low-income classes.

13

The magnitude of this decrease, less than 20 hours per year, is quite small considering the14

average working hours per year is more than 1,000 hours. This result is consistent with the15

aforementioned low labor supply elasticity.

16

The higher-income classes were affected by all three effects. The decrease in labor supply might17

have reflected the dominance of the third effect, i.e., the substitution effect created by bracket

18

creep. The degree of the pretax income decrease was greater for the middle-income classes than19

for the lower-income classes. The rate of income decrease, at almost 2%, was perhaps due to the20

estimated small labor elasticity discussed in Section 6.1. The average rate of the decrease was21

1.41% (translog) for households with children aged 14 years and below.

22

Figure 3 shows the simulated changes in the after-tax incomes of the different income classes,

23

their decomposition into the effects of the reform itself, and the behavioral responses to it. For24

low-income households, the reform resulted in a rise in their after-tax incomes (pretax incomes- 22 -

1minus income tax, plus child benefit), and their behavioral responses, in turn, caused a decrease2

or a slight increase in their incomes. Since this behavioral response was minimal, their resultant

3

after-tax incomes rose. For the higher-income classes with annual incomes exceeding 13 million

4yen, the abolition of the tax allowance and their behavioral responses reduced their after-tax

5incomes. Whereas the reform was advantageous for the upper-middle-income classes, the6

reduction in their labor supply because of the income effect cancelled out this advantage.

7

Figure 1 also decomposes the changes in the net tax payments for the different income classes

8

into the effect of the reform itself and of the behavioral responses to it. Notably, the increase in9

the net tax payments (including child benefit) was equal to the decrease in after-tax incomes if

10behavioral responses were disregarded. As discussed earlier, the reform reduced the net tax

11

payments for low-income households. Although the behavioral responses negatively influenced

12

net tax payments, the magnitude was small. For middle-income households, while the directions

13of the reform and labor supply change were the same as those for low-income households, the14

relative magnitude of the behavioral effect was greater. The reform resulted in higher taxes for

15

high-income households; however, the rise in the tax payments was partially cancelled out by

16

the reduction in labor supply. As Table 6 shows, while the average tax payment (including child17

benefit) decreased by 44,150 yen without considering the behavioral responses, it decreased by

18

53,720 yen when labor supply changes were considered in the case of the translog utility19function. This difference corresponded to 21.7% of the tax decrease generated by the reform. In

20

the case of the quadratic utility function, these values were 44,560 yen and 45,120 yen,21

respectively, resulting in a difference of 1.3%. In other words, the behavioral changes scaled up

22

the budget needed to implement the reform by 1.3% to 21.7%.

23

Because of my use of a structural model, I was able to simulate the changes in utility created by24

the reform. Figure 4 indicates the proportion of households whose utility did not decrease for25

each income class. Most low-income households experienced utility gain. Utility lowered for- 23 -

1

more than half of households with children aged 6 years and below (shown by white circles) for2

the middle-income classes because the increase in child benefit is less than that in income tax,

3

as explained in Section 6.1. On the other hand, utility rose for more than 90% of households

4with children aged 7 years and above (shown by black circles) for the same income classes.

5

More than half of the higher-income households were detrimentally affected by the reform, even6

considering their labor supply responses.

7

7. Concluding remarks

8

In this study, I used a micro-simulation method to investigate the combined effects of the9abolition of the tax allowance for dependents and the increase in child benefit on the labor10

supply, after-tax incomes, and utility of households. I adopted a structural discrete-choice model

11of labor supply for the presented estimations, assuming a unitary household model. Based on

12

the estimated utility parameters, the average labor supply elasticity ranged from −0.01 to 0.08

13

for husbands and from 0.106 to 0.138 for wives. These elasticities were largely attributed to the14

extensive margin. The simulation results of the tax allowance and child benefit reforms

15

suggested the following findings. The reform reduced the labor supply of households with

16

children aged 14 years and below for most income classes, which corresponded to a labor

17

income decrease of between 0.29% and 1.41%. Although this labor income decrease was not

18

drastic, the changes in the net tax payments, including child benefit, were not negligible for19

middle- and high-income households. Although the average net tax payments decreased by

20

44,150 yen when behavioral responses were disregarded, they decreased by 53,720 yen when

21

labor supply changes were considered in the case of the translog utility function. In other words,

22

the behavioral changes scaled up the budget needed to implement the reform by 22%. Further,23

the simulation of utility changes based on the structural model indicated that all low-income

24households and more than half of those with children aged 14 years and below and annual- 24 -

1

incomes of less than 12 million yen enjoyed utility gain, whereas more than half of the upper-2

income households experienced utility loss as a result of the reform.

3

This analysis has some limitations. First, labor supply was evidently static; therefore, dynamic

4

decision-making was not considered (Unayama, 2011). Second, I assumed unitary households

5

whose members jointly maximized the household’s utility. However, strategic interactions

6

within a household may also matter, as discussed in Bargain et al. (2006). Third, I assumed

7

perfect knowledge of and compliance with tax codes and did not factor in the possibility of tax

8

avoidance or evasion by households. All these are topics for future research.

910

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5- 30 -

Table 1. Outline of tax codes in 2002
(1,000 yen)
National income tax

Local inhabitants tax

Basic exemption

380

330

Exemption for spouse

380

330

Special exemption for spouse

380

330

Exemption for dependents

380

330

Exemption for special dependents

630

450

Employment income deduction≤ 1,800, 40%≤ 1,800, 40%≤ 3,600, 30%≤ 3,600, 30%≤ 6,600, 20%≤ 6,600, 20%≤ 10,000,10%≤ 10,000, 10%> 10,000, 5%> 10,000, 5%

650

650

≤ 3,300, 10%≤ 2,000, 5%≤ 3,300, 20%≤ 2,000, 10%≤ 9,000, 30%> 7,000, 13%

Lower limit
Tax rate> 18,000, 37%
Special tax break

20%

15%

Upper limit

250

40

Child benefit (income tested)
1st and 2nd children

60

3rd and subsequent children

120

Upper limit
Note: Standard rates are shown for local inhabitants’ tax.

Aged 6

Table 2. Outline of tax codes before and after the reform in 2010/2011(1,000 yen)
Before reform (2009)

After reform (2011)

National income tax Local inhabitants tax National income tax Local inhabitants tax
Basic exemption

380

330

[unchanged][unchanged]

Exemption for spouse

380

330

[unchanged][unchanged]

Special exemption for spouse

380

330

[unchanged][unchanged]

Exemption for dependents

380

330

0

0

Exemption for special dependents

630

450

380

330

Employment income deduction

Same as 2002

Same as 2002

[unchanged][unchanged]

Tax rate≤ 1,950, 5%

10%[unchanged][unchanged][unchanged][unchanged]≤ 3,300, 10%≤ 6,950, 20%≤ 9,000, 23%≤ 18,000, 33%> 18,000, 40%
Special tax breakNo

NoChild benefits
1st and 2nd children

60

156

3rd and subsequent children

120

156

Aged less than 3

120

156

Yes

No

Income test

Note: Standard rates are shown for local inhabitants’ tax.

Table 3. Discretized distribution of hours worked.
Husband
Alternatives

Wife

Annual

Lower

Upper

Relative

Annual

Lower

Upper

Relative

hours

Limit

Limit

frequency

hours

Limit

Limit

frequency

worked(hours)(hours)worked(hours)(hours)

1

0.0

0

0

3.06

0.0

0

0

46.48

2

615.2

1

900

2.15

274.2

1

500

7.82

3

1033.1

901

1,240

2.68

615.8

501

800

7.12

4

1249.2

1,241

1,400

16.35

850.2

801

1,000

6.58

5

1488.2

1,401

1,600

21.91

1034.6

1,001

1,200

4.05

6

1782.5

1,601

2,000

22.91

1278.6

1,201

1,500

12.37

7

2166.8

2,001

2,500

16.78

1644.5

1,501

2,000

12.17

8

2785.7

2,501

3,000

14.16

2338.6

2,001

3,000

3.41

Table 4. Sample statistics.
mean

s.d.

min

medianmax

Husband: aged 25–29 years [D]

0.074

0.262

0

0

1

Husband: aged 30–34 years [D]

0.179

0.383

0

0

1

Husband: aged 35–39 years [D]

0.234

0.423

0

0

1

Husband: aged 40–44 years [D]

0.249

0.432

0

0

1

Husband: aged 45–49 years [D]

0.184

0.388

0

0

1

Husband: aged 50–44 years [D]

0.081

0.272

0

0

1

Wife: aged 25–29 years [D]

0.097

0.296

0

0

1

Wife: aged 30–34 years [D]

0.244

0.429

0

0

1

Wife: aged 35–39 years [D]

0.276

0.447

0

0

1

Wife: aged 40–44 years [D]

0.253

0.435

0

0

1

Wife: aged 45–49 years [D]

0.107

0.309

0

0

1

Wife: aged 50–44 years [D]

0.016

0.124

0

0

1

Urban area [D]

0.664

0.473

0

1

1

Husband: junior high school[D]

0.065

0.247

0

0

1

Husband: high school [D]

0.443

0.497

0

0

1

Husband: junior college [D]

0.095

0.293

0

0

1

Husband: university [D]

0.396

0.489

0

0

1

Wife: junior high school [D]

0.018

0.132

0

0

1

Wife: high school [D]

0.500

0.500

0

1

1

Wife: junior college [D]

0.345

0.475

0

0

1

Wife: university [D]

0.137

0.343

0

0

1

# of children aged 0–6 years

0.711

0.775

0

1

4

# of children aged 7–14 years

0.952

0.866

0

1

5

# of children aged 15+

0.273

0.569

0

0

4

Husband: Annual hours worked

1835.985

545.520

53.571

1749.357

2785.714

Wife: Annual hours worked

1054.578

561.658

53.5711033.286

2785.714

5,833.25

3,324.01

0

5,500

30,000982.191712.500

25

30,0006,973.72

4,306.45

500

6,500

30,000Husband: Annual labor income(thousand yen)
Wife: Annual labor income(thousand yen)
Annual household income(thousand yen)

Notes: [D] represents indicator variables. The middle points of intervals are shown for annual hours worked, annuallabor incomes, and household incomes. The sample size is 18,961.

Table 5. Estimated gross wage elasticity of labor supply
A. Quadratic utility

Husband

1% change in grosswage rate
Own wage rate
Wife’s wage rate

Wife

Own wage rate
Husband’s wage rate

Total
0.0750(0.0203)-0.0018(0.0086)
0.1383(0.0531)0.5168(0.0985)

Intensive margin
0.0576(0.0184)-0.0014(0.0078)0.0609(0.0292)
0.1740(0.0547)

Extensive margin
0.0174(0.0103)-0.0004(0.0031)0.0774(0.0324)
0.3422(0.0705)

B. Translog utility
1% change in gross
Husbandwage rate
Own wage rate
Wife’s wage rate

Wife

Own wage rate
Husband’s wage rate

Note: Only for those who satisfy monotonicity.

Total-0.0073(0.0186)-0.0092(0.0081)0.1065(0.0502)0.3276(0.0816)

Intensive margin-0.0160(0.0157)-0.0038(0.0068)
0.0448(0.0268)
0.1058(0.0426)

Extensive margin
0.0086(0.0089)-0.0055(0.0058)
0.0617(0.0313)0.2216(0.0564)

Figure 1. Effects on net tax payment (translog utility function)
400(1,000yen)
Tax reform

300

Child Benefit

Response

Total

200
100
0
-100
-200
-300
> 15

< 15

< 14

< 13

< 12

< 11

< 10

<9

<8

<7

<6

<5

<4

<3

<2

<1

-400
(income class, million yen)

Figure 2. Effects on labor supply pre-tax income (translog utility function)(hours)10(1000 yen)30
Husband

Wife

Income (right axis)

20

5

10
0

0

-10
-20

-5

-30
-10

-40
-50

-15

-60
-70
> 15

< 15

< 14

< 13

< 12

< 11

< 10

<9

<8

<7

<6

<5

<4

<3

<2

<1

-20
(income class, million yen)

Figure 3. Effects on after-tax income (translog utility function)
300(1,000yen)

200
100
0
-100
-200
-300

Tax reform

Child Benefit

Response

Total
> 15

< 15

< 14

< 13

< 12

< 11

< 10

<9

<8

<7

<6

<5

<4

<3

<2

<1

-400
(income class, million yen)

Table 6. Effects on after-tax income and net tax payment
Utility

Reformlabor supply

Before-tax

After-tax income

Net tax payment

husbandwife

incomeResponse

Total

Response

Total

share

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(7)/(1)unit

1000yen

Hours

Hours

1000yen

1000yen

1000yen

1000yen

1000yen%Translog-44.15-11.47-0.83-45.55-35.97

8.17

-9.58

-53.72

21.69

Quadratic-44.56

-0.92

1.46

-3.75

-3.19

41.37

-0.56

-45.12

1.26

Note: Results are based on the specification with random effects. Only for those who satisfy monotonicity

Figure 4. Changes in utility: proportion of households whose utility do not go down
(%)
100
80
60
40
Aged 6 years and below
Aged 7–14 years
Aged 14 years and below

20

> 15

< 15

< 14

< 13

< 12

< 11

< 10

<9

<8

<7

<6

<5

<4

<3

<2

<1

0

(income class, million yen)

Table A-1. Estimation results: Quadratic utility function
βx
HusbandConstant
×(aged 30-34)
×(aged 35-39)
×(aged 40-44)
×(aged 45-49)
×(aged 50-44)
×(junior high)×(high school)
×(University)
×(children aged 0-6)×(children aged 7-14)
×(children aged 15+)×(Urban area)

3.921(0.832)-0.533(1.044)-0.367(0.777)-0.246(0.596)0.251(0.500)-0.533(0.441)1.826(1.156)-0.166(0.604)-0.352(0.338)-0.812(0.410)-0.672(0.295)-1.257(0.312)-0.930(0.382)Wife***
0.636 ***
(0.218)0.245(0.180)0.241(0.166)0.188(0.156)
0.318 **
(0.159)-0.111(0.204)-0.067(0.076)-0.180(0.120)
**
**
***
**

βh
Husband1.980(0.297)-0.244(0.337)-0.144(0.262)0.023(0.218)0.098(0.197)-0.087(0.185)0.239(0.391)-0.068(0.176)0.003(0.106)-0.331(0.132)-0.236(0.095)-0.354(0.111)-0.232(0.131)

βw
Wife

***

**
**
***
*

0.919
(0.153)0.063(0.142)-0.092(0.131)
0.021(0.127)0.021(0.126)-0.069(0.132)-0.386(0.149)-0.209(0.045)-0.243(0.063)0.184(0.066)0.070(0.045)-0.018(0.053)-0.180(0.067)

***

***
***
***
***

***

Table A-1. Estimation results: Quadratic utility function (continued)
βxx
HusbandConstant
×(aged 30-34)
×(aged 35-39)
×(aged 40-44)
×(aged 45-49)
×(aged 50-44)
×(junior high)×(high school)
×(University)
×(children aged 0-6)×(children aged 7-14)
×(children aged 15+)×(Urban area)-0.060(0.016)0.013(0.027)0.012(0.019)0.015(0.014)0.001(0.010)0.019(0.008)-0.051(0.027)0.003(0.016)0.008(0.008)0.014(0.009)0.013(0.006)0.020(0.006)0.015(0.008)

Wife

***

**
*

**
***
**

-0.011(0.007)-0.007(0.005)-0.011 ***(0.004)-0.008 **(0.004)-0.008 **(0.003)0.000(0.005)-0.002(0.002)0.002(0.003)βhh
Husband-0.016(0.002)0.003(0.003)0.002(0.002)0.000(0.002)0.000(0.002)0.001(0.001)-0.001(0.003)0.000(0.001)-0.002(0.001)
0.002(0.001)0.001(0.001)0.001(0.001)0.001(0.001)

βww
Wife

***

*
**
*
*

-0.002(0.001)-0.001(0.001)0.000(0.001)-0.001(0.001)-0.001(0.001)0.000(0.001)
0.004(0.001)
0.002(0.000)0.002(0.001)-0.003(0.001)-0.002(0.000)-0.002(0.000)0.000(0.000)

***
***
***
***
***
***

Table A-1. Estimation results: Quadratic utility function (continued)
βxh
HusbandConstant
×(aged 30-34)
×(aged 35-39)
×(aged 40-44)
×(aged 45-49)
×(aged 50-44)
×(junior high)×(high school)
×(University)
×(children aged 0-6)×(children aged 7-14)
×(children aged 15+)×(Urban area)

Constant
×(children aged 0-6)
Log likelihood-0.039(0.011)0.010(0.016)0.008(0.011)
0.004(0.009)-0.001(0.007)0.010(0.006)-0.032(0.018)0.002(0.009)0.005(0.005)0.012(0.005)0.007(0.004)0.013(0.004)0.009(0.005)

βxw
Wife

***

*

**
*
***
**

-0.031(0.003)-0.006(0.004)0.003(0.003)0.002(0.003)0.002(0.002)0.001(0.002)0.003(0.004)0.001(0.001)
0.002(0.002)0.004(0.002)0.003(0.001)0.008(0.001)0.006(0.002)

βhf

βwf

HusbandWife-2.462 ***(0.122)
0.039(0.119)-63546.3

βhw
Husband

***

**
**
***
***

-1.795 ***(0.053)-0.864 ***(0.062)-0.010(0.001)-0.001(0.000)-0.001(0.000)-0.001(0.000)0.000(0.000)0.000(0.000)0.000(0.000)0.000(0.000)0.001(0.000)0.002(0.001)0.001(0.001)
0.003(0.001)
0.002(0.001)sd(βx)-0.016(0.039)

Wife

***
***
***
***
**

***
**
***
***
***

0.001
(0.001)
0.002(0.001)0.001(0.001)0.001(0.001)
0.002(0.001)0.001(0.001)0.000(0.000)0.000(0.000)

***
**
**
***

Table A-2. Estimation results: Translog utility function
βx
HusbandConstant
×(aged 30-34)
×(aged 35-39)
×(aged 40-44)
×(aged 45-49)
×(aged 50-44)
×(junior high)×(high school)
×(University)
×(children aged 0-6)×(children aged 7-14)
×(children aged 15+)×(Urban area)-3.959(7.628)15.889(7.765)18.343(6.845)19.084(6.340)19.775(5.782)
14.536(5.791)-0.240(5.308)-1.265(3.448)-1.623(2.508)-0.411(2.397)-3.906(1.956)-5.376(2.827)-0.976(2.663)βh
Husband

Wife

**
***
***
***
**

**
*

3.608
(3.482)-2.710(2.982)1.688(2.911)
2.294(2.847)6.870(3.120)-5.335(3.130)2.859(1.315)9.790(1.985)

**
*
**
***

95.839(18.418)
12.221(20.086)
20.435(17.163)
37.605(15.610)24.655(14.215)
34.217(14.070)-11.506(14.311)-4.358(10.985)
21.382(7.246)-5.172(7.622)-15.243(6.051)
1.567(7.739)-2.791(8.307)

βw
Wife

***

**
*
**

***

**

55.386(22.385)23.306(20.999)
3.548(19.549)
22.127(19.080)19.052(18.792)
10.462(19.722)-61.709(20.890)-29.202(6.691)-23.352(9.055)49.697(9.502)
14.590(6.250)19.932(7.539)11.410(9.248)

**

***
***
***
***
**
***

Table A-2. Estimation results: Translog utility function (continued)
βxx
HusbandConstant
×(aged 30-34)
×(aged 35-39)
×(aged 40-44)
×(aged 45-49)
×(aged 50-44)
×(junior high)×(high school)
×(University)
×(children aged 0-6)×(children aged 7-14)
×(children aged 15+)×(Urban area)-0.044(0.018)0.041(0.018)0.031(0.016)0.018(0.015)0.042(0.015)0.019(0.014)-0.030(0.010)0.003(0.012)0.003(0.009)0.017(0.007)-0.002(0.005)0.008(0.008)0.015(0.007)βhh
Husband

Wife

**
**
*

***

***

**

**

0.011
(0.013)0.030(0.012)0.025(0.012)0.028(0.012)0.016(0.013)-0.001(0.013)0.014(0.006)0.018(0.010)

**
**
**

**
*

-10.820(1.952)0.158(2.371)-0.780(2.002)-3.234(1.811)-1.356(1.652)-3.212(1.620)1.352(1.682)0.382(1.413)-3.464(0.911)0.466(0.787)0.640(0.635)-2.090(0.809)0.024(0.832)

βww
Wife

***

*

**

***

***

-1.385(2.624)-3.004(2.665)-0.830(2.482)-3.021(2.419)-2.601(2.382)-1.156(2.493)7.701(2.669)3.834(0.844)3.496(1.121)-6.824(1.033)-3.059(0.619)-4.613(0.754)-2.826(0.904)

***
***
***
***
***
***
***

Table A-2. Estimation results: Translog utility function (continued)
βxh
HusbandConstant
×(aged 30-34)
×(aged 35-39)
×(aged 40-44)
×(aged 45-49)
×(aged 50-44)
×(junior high)×(high school)
×(University)
×(children aged 0-6)×(children aged 7-14)
×(children aged 15+)×(Urban area)

Constant
×(children aged 0-6)
Log likelihood

5.470(1.636)-3.807(1.883)-4.413(1.657)-4.638(1.533)-4.735(1.396)-3.527(1.397)-0.058(1.287)0.323(0.833)0.413(0.607)0.081(0.488)0.555(0.407)0.144(0.590)-0.760(0.532)

βxw
Wife

***
**
***
***
***
**

-4.625(0.863)-0.821(0.860)0.763(0.739)-0.307(0.722)-0.444(0.708)-1.611(0.775)1.303(0.785)-0.660(0.326)-2.360(0.487)0.092(0.383)0.397(0.293)1.196(0.363)1.059(0.427)

βhf

βwf

HusbandWife-2.410 ***(0.128)-0.377 ***(0.135)-63646.5

βhw
Husband

***

**
*
**
***

***
**

-1.772 ***(0.048)-0.863 ***(0.056)-8.693(1.681)-0.489(0.137)-0.453(0.119)-0.287(0.109)-0.163(0.101)-0.081(0.096)-0.112(0.074)0.101(0.071)
0.539(0.052)0.484(1.011)2.180(0.782)3.361(0.977)1.519(1.120)

Wife

***
***

0.339
(0.154)0.470(0.146)0.535(0.146)0.552(0.145)0.482(0.152)0.083(0.165)0.097(0.059)0.064(0.094)

***
***

***

***
***

sd(βx)`-0.008(0.058)

**
***
***
***
***

*