The impacts of parents on the concert attendance of their children:
behavior, education, and household income

Miyuki Taniguchi

Working Paper Series Vol. FY2022-01
May 2022

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Faculty of Economics

Saga University
1 Honjo-machi, Saga, 840-8502 JAPANThe impacts of parents on the concert attendance of their children:
behavior, education, and household income¶Miyuki Taniguchi*
Faculty of Economics, Saga University

Abstract
This study investigates how parents influence the consumption of music concerts by adolescentchildren between the age of 10-19. We attempt to reveal the differences in consumption of music concertsby adolescent children between high-income families and low-income families, identifying children with
their parents. First, we employ logistic regression analysis to determine the impact of parents' behavior,educational background, and household income on their adolescent children's concert attendance. Second,
we employ ordered logistic regression analysis to determine the impact of parents' behavior, educational
background, and household income on the frequency of the concert attendance of their adolescent children.
We utilize the individual anonymous microdata from the Survey on Time Use and Leisure Activities
conducted by the Japanese Ministry of Internal Affairs and Communications in 1991, 1996, 2001, and 2006.
Our results suggest that a child was more likely to attend a concert when their parent(s) attended together.
Generally, it is well-known that low-income families prefer popular music, while high-income families preferclassical music. However, our results suggest that children from low-income families were less likely toattend classical music concerts and that children from high-income families were more likely to attend
popular music concerts.
Key Words: inequal opportunity, education, concert attendance, logit model, ordered logit model
JEL Classification Codes: H32, H41, H42¶ Acknowledgement: The author would like to thank Yukiko Abe, Susumu Imai, Masahiro Arima, and Colin McKenzie for

their helpful comments and suggestions. This study uses microdata obtained from the Survey on Time Use and Leisure
Activities (STULA, Shakaiseikatsu-kihon-chousa), which is provided by the National Statistics Center, through Hitotsubashi
University, in accordance with the Statistics Act.
* Corresponding to tanigchi@cc.saga-u.ac.jp

11. Introduction
Since Thomas Piketty’s (2014) book Capital in the Twenty-First Century became a bestseller, income
inequality has become one of the hot issues around the world.

Recently, income inequality has also become

one of the big issues in Japan. According to the report published by OECD (2015) (P. 29, L.L. 12-14), Japan
had the sixth highest relative poverty rate among the OECD countries in 2012, and the report suggested thatthe large income inequality of Japan might result from fiscal consolidation and regulatory reform (OECD).
Figure 1 reports the relative poverty rates in OECD countries in 2012 and indicates that Japan’s relative
poverty rate is 16.1 and is far higher than the OECD average of 11.5. The OECD Economic Survey ofJapan in 2013 (OECD 2013) reported that Japan is the only OECD country where the poverty rate for all
working households and all households with children increases when account is taken of the redistribution
policies.
In Japan, the relative poverty rate among children is a serious issue.

Figure 2 shows Japan’s relative

poverty rate by age group. In figure 2, the relative poverty rate among elderly is the highest among three
age groups in 2009 and 2012. However, the relative poverty rate among elderly decreased from 2009 to
2012, while the relative poverty rate among children increased notably. Figure 3 shows the changes in therelative poverty rate among all generation and that among adolescent children. We calculate the relative
poverty rate, using the microdata of the Survey on Time Use and Leisure Activities (STULA, Shakaiseikatsukihon-chousa).*

For a reference, we report the relative poverty rate calculated by the Ministry of Health,Labour and Welfare (MHLW), Japan. Our relative poverty rates have smaller values than the governmental
ones. In our calculations, children’s relative poverty rate is not worse than that of all generations. Sincechildren’s relative poverty rate became higher after 2009, our calculations are not inconsistent with the OECD
data. In figure 3, our calculations and the governmental ones show that the relative poverty rates have kept
increasing since 1996.
Maintaining a cultured life is difficult for children who fall into relative poverty though it is essential
for human capital development. The relationship between income inequality and consumption inequalityof art and culture is also worth discussing. There are many empirical studies concerning income inequality
and consumption inequality (Kruger and Perri, 2006; Aguiar and Bils, 2015; and Goetzmann et al., 2011).
Kruger and Perri (2006) investigate whether income inequality is accompanied by consumption inequality,
using data from a U.S. consumption survey. Goetzmann et al. (2011) investigates that the expanding ofincome inequalities has affected the art auction price, using time series data on the average auction price inLondon and data on inequalities constructed by Piketty (2010).

Aquiar and Bils (2015) estimate

consumption inequality to figure out how consumption inequality has mirrored income inequality in the U.S.,
controlling for mismeasurements in the consumer expenditure survey. Aquiar and Bils (2015) found that
estimate elasticities of entertainment fees which includes the consumption of art and culture is the highest.
This suggests that among all goods, art and culture are affected the most easily by the economy. We focus
on music among art and culture because almost all adolescent children listen to music in daily life and because
whether adolescent children could attend any live music concerts depends on their parents’ behavior,*

See section 4 for the detail of the microdata of the STULA.
2

educational background and family income.
This study focuses on the impacts of parents’ behavior, educational background, and household incomeon their adolescent children’s consumption of music concerts because music is an important factor for humancapital development.

In sociology, Southgate and Roscigno (2009) empirically show that music

involvement link academic achievement. Aschaffenburg and Maas (1997) show that cultural capital plays
a strong role in determining school success, focusing on cultural participation including music.

Thomas(1994) find that youth music participation is associated with a higher acceptance rate to medical school.
Texas Commission on Drug and Alcohol Abuse (1999) show that children who involved in music were lesslikely to prefer alcohol, tobacco, and drug abuse.

In psychology, many existing studies attempt to prove therelationship between listening to music and children’s cognitive skills like intellectual development (forexample, Shaw and Brothers, 1989; and Leng and Shaw, 1991). For example, Rauscher (1998) argues that
listening to music has a positive effect on cognitive skills. Music trainings raises both mathematical skills(Bilhartz et al, 2000; Brothers et al., 1996; Costa-Giomi et al., 1999; Graziano et al., 1999; Gromko andPoorman, 1998; and Rauscher at al., 1997) and language skills (Douglas and Willatts, 1994; and Ho et al.,2003).

Therefore, the key contribution of this paper is to determine the concert attendance that could

improve adolescent children’s cognitive skills, using Japanese data.
The rest of this paper is organized as follows.

Section 2 discusses the existing papers on therelationship between income and cultural consumption.

Section 3 explains the logistic model which is

employed to examine the impacts of family income on the consumption of music concerts by their teenage
children.

Section 4 explains the source of the study data.

Section 5 discusses the estimated results.

Section 6 contains some belief summaries and the idea of future research.

3

Figure 1: Relative Poverty Rates of OECD countries
Relative poverty rate (%)
25

20

15

10

5

CZE
ISL
DNK
FIN
NOR
NLD
FRA
LUX
SVK
DEU
SVN
AUT
BEL
GBR
IRL
SWE
NZL
CHE
HUN
POL
OECD
EST
CAN
PRT
ITA
AUS
ESP
GRC
KOR
JPN
USA
CHL
TUR
ISR
MEX

0

[1] The relative poverty rate is the share of the population with an income below half the median equivalent disposable income which defines household disposable
income divided by the square root of the number of household members.
[2] The Japanese data is based on the Comprehensive Survey of Living Conditions, which is submitted to the OECD by Japan. Another survey, the National
Survey of Family Income and Expenditure, shows a much lower relative poverty rate of 10.1%.
Source: OECD Income Distribution and Poverty Database; Ministry of Health, Labor and Welfare (MHLW, Japan), Comprehensive Survey of Living Condition.

4

Figure 2: Relative Poverty Rates of Japan by Age Group

20.0%

19.0%

19.4%

19.0%
18.0%

17.0%
16.3%
16.0%
16.0%

16.1%
15.7%

15.0%
14.5%

14.4%
14.0%
2009

JPN, Age 0-17

2012

JPN, Age 18-65

JPN, Age over 66

JPN, All[1] The relative poverty rate is the share of the population with an income below half the “median equivalentdisposable income”, which defines household disposable income divided by the square root of the numberof household members.
[2] The relative poverty rate is also available by broad age groups: child poverty (0-17 years old), working-age
poverty (18-65 years old), and elderly poverty (over 66 years old).
Source: OECD (2018), the Poverty rate indicator, DOI: 10.1787/0fe1315d-en (Accessed on 07/03/2018).

5

Figure 3: The Change in Gini Coefficient

0.526

0.532

0.498
0.472
0.439

0.433

0.350

0.441
0.359

0.367

0.315

0.322

0.343

0.343
0.306

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
Age 10-19 (STULA)

All (STULA)

All (MHLW)[1] The STULA has twelve classes for annual household income. Each class interval is one million yen for
the 1st-10th lowest classes, five million yen for the second-highest one, and the top-coded for the firsthighest one. We assume uniform distribution and set 1-million-yen class intervals for the 1st and 2nd
highest classes to calculate the Gini coefficient.

We also assume that the highest class is between 24 to

24.99 million yen.
[2] Figure 3 reports the Gini coefficient before redistribution calculated by the MHLW for a reference.

Data

is available from the following webpage:(https://www.mhlw.go.jp/wp/hakusyo/kousei/19/backdata/xls/01-01-08-09.xls) (Accessed on 07/03/2018)

2. The Relationship Between Income and Cultural Consumption
One of the most notable literatures which is concerning income and consumption of art and culture isGoetzmann et al. (2011) that focuses on the impacts of income inequality on the consumption of art by thehigh-income group.

Since art in auction is sometimes used for speculation, Goetzmann et al. (2011)

considers not only the impacts of income and income inequality on art price in auction but also the impactsof equities, using Ordinary Least Square (OLS) regression.
6

Goetzmann et al. (2011) shows that theexpanding of income inequality has affected the art auction price and that the growth of personal income has
not affected the art auction price, using time series data on the average auction price in London during theperiod from 1830 to 2007. Goetzmann et al. (2011) employs the indicator of income inequality proposedby Piketty (2010), where income inequality is defined by how much wealth is concentrated in the top 0.1
percent earners.
In contrast to Goetzmann et al. (2011), this paper focuses on the impacts of income inequality on the
consumption of art by the low-income group because an increase in the lower-income population has become
a hot issue in Japan. Komuro (2015, P.39) reports that the poverty rate had rapidly increased to 18% from2007 to 2012 in Japan. In 2012, 900 million households received welfare payments. Thus, in Japan, theexpansion of income inequality has accompanied an increase in lower-income group. Especially, this study
examines the impacts of low family-income on their teenage children’s consumption of music concerts.
Southgate and Roscigno (2009) is the literature that examines the impacts of parents on their children’sconcert attendance, using U.S. data. Southgate and Roscigno (2009) found that the music attendance ofchildren and adolescents had been affected by race, gender, and parents and that the music attendance had
been related to the academic achievement, empirically.

There are some literatures that focus on thedeterminants of performing arts (see, for example, Baumol and Bowen, 1966; Lange et al., 1986; Luksetichand Lange, 1995; Ekelund and Ritenour, 1999; Prieto-Rodriguez and Fernandez-Blanco, 2000; and Tomaand Meads, 2007).

For example, Toma and Meads (2007) focuses on the determinants of the annual

attendance of the mid-sized orchestra concerts, using aggregated data. Toma and Meads (2007) employed the
reduced form equation which is driven from demand function and supply function. Prieto-Rodriguez andFernandez-Blanco (2000) examine if the characteristics of average attendee of classical music concerts are
the same as those of popular music concerts in Spain, estimating a bivariate probit model. Their estimatedresults suggest that educational status has positive effects on the consumption of classical music concerts andpopular music concerts. Therefore, this paper could be the first paper to examine the impacts of family
income on the consumption of music concerts of children, using microdata.

3. Models
Many existing studies employ binary discrete choice model to determine cultural participation (forexample, Palma, M. L. et al., 2013). In order to model the probability of a child in the 10-19 years age
attending at least one music concert in a year, this study employs the logit model as follows:
𝑍𝑖𝑡∗ = 𝑙𝑜𝑔𝑖𝑡(𝑝𝑖𝑡 ) = 𝛼 + ∑𝑡𝑠=1 𝑋𝑠𝑖𝑡 𝛽𝑠 + 𝛾1 𝐷1996 + 𝛾2 𝐷2001 + 𝛾3 𝐷2006 ,(1)

𝑝𝑖𝑡 = exp(𝑍𝑖𝑡 ) /(1 + exp(𝑍𝑖𝑡 )),where 𝑍𝑖𝑡 takes the value 1 if child 𝑖 attends at least one music concert in year 𝑡, and 0 otherwise, 𝑝𝑖𝑡 is
the probability that child 𝑖 attends at least one music concert in year 𝑡, 𝑋𝑠𝑖𝑡 is a set of control variables
which possibly affect child 𝑖’s concert attendance in year 𝑡, 𝐷1996, 𝐷2001 , and 𝐷2006 are 0-1 dummies
which take the value 1 in the year 1996, 2001, and 2006, respectively, and 0 other wise, 𝛼 is a constant, and7𝛽𝑠 , 𝛾1 , 𝛾2 , and 𝛾3 are coefficients to be estimated.
Moreover, in order to examine the impact of income level on the frequency of concert attendance, thisstudy employs the ordered logit model as follows:𝑦𝑖𝑡∗ = 𝑎 + ∑𝑡𝑠=1 𝑋𝑠𝑖𝑡 𝑏𝑠 + 𝑐1 𝐷1996 + 𝑐2 𝐷2001 + 𝑐3 𝐷2006,
if

𝑦𝑖𝑡 = 0(2)𝑦𝑖𝑡∗ < 𝜇0 ,=1

if 𝜇0 < 𝑦𝑖𝑡∗ < 𝜇1 ,=2

if 𝜇1 < 𝑦𝑖𝑡∗ < 𝜇2 ,=3

if 𝜇2 < 𝑦𝑖𝑡∗ < 𝜇3 ,=4

if 𝜇3 < 𝑦𝑖𝑡∗ < 𝜇4 ,=5

if 𝜇4 < 𝑦𝑖𝑡∗ < 𝜇5 ,=6

if 𝜇5 < 𝑦𝑖𝑡∗ < 𝜇6 ,=7

if 𝜇6 < 𝑦𝑖𝑡∗ < 𝜇7 ,where 𝑦𝑖𝑡∗ s not observed, 𝑦𝑖𝑡 is the frequency of child 𝑖’s concert attendance in year 𝑡 and the observedcounterpart to 𝑦𝑖𝑡∗ , 𝑋𝑖𝑡 is a set of control variables which possibly affect the frequency of child 𝑖’s concert
attendance in year 𝑡, 𝐷1996, 𝐷2001 , and 𝐷2006 are 0-1 dummies which take the value 1 in the year 1996,
2001, and 2006, respectively, 0 otherwise, a is a constant, and 𝑏𝑠 , 𝑐1 , 𝑐2 , and 𝑐3 are coefficients to be
estimated. In equation (1), 𝑦𝑖𝑡 takes 0 if the number of a child 𝑖’s concert attendance in year 𝑡 is 0, 𝑦𝑖𝑡
takes 1 if the number of a child 𝑖’s concert attendance in year 𝑡 is 1 - 4 days, 𝑦𝑖𝑡 takes 2 if the number ofa child 𝑖 ’s concert attendance in year 𝑡 is 5 – 9 days, 𝑦𝑖𝑡 takes 3 if the number of a child 𝑖 ’s concert
attendance in year 𝑡 is 10 – 19 days (1 day per month), 𝑦𝑖𝑡 takes 4 if the number of a child 𝑖’s concert
attendance in year 𝑡 is 40 - 99 days (1 day per week), 𝑦𝑖𝑡 takes 5 if the number of child 𝑖 ’s concert
attendance in year 𝑡 is 100 – 199 days (2 – 3 days per week), 𝑦𝑖𝑡 takes 6 if the number of a child 𝑖’s concert
attendance in year 𝑡 is 200 days or more. As for the frequency of concert attendance, we generate theordered discrete variable, following the classification in the STULA.

4. Data and Variable Construction
4.1. The Survey on Time Use and Leisure Activities
To examines the impacts of parents’ behavior and household income on their children’s concert
attendance, we utilize the microdata obtained from the Survey on Time Use and Leisure Activities (STULA,
Shakaiseikatsu-kihon-chousa) which is provided by the National Statistics Center, through Hitotsubashi
University, in accordance with the Statistics Act.

In addition to family, education, and income, the STULA

asked how many days over the year a responder engaged in the hobbies and amusement in the list, for example,
watching works of art (excluding TV/smartphone/PC, etc.), watching vaudevilles, plays and dances(excluding TV/smartphone/PC, etc.), listening to classical music at concerts, etc., listening to popular musicat concerts, etc., listening to music on CD or smartphone, etc., playing musical instruments, and so on.
Activities done as a lesson, work or household work are excluded while activities within or outside school8are included. We focus on adolescent children’s activities on listening to music because parents’ behavior,education, and household income could influence what kind of music their children prefer.

It is a well-known fact that educational background and parents’ art appreciation behavior also affect someone’s art
appreciation behavior. The microdata of the STULA in 1991 and 1996 includes information on whether
their family member had attended live music concert together.

The microdata of the STULA in 2006

includes information on whether their parent had attended live music concert together.
The anonymous microdata in 1991, 1996, 2001, and 2006 is available from the STULA and the sample
size of original microdata is 680,000 in total.
chosen, excluding missing data.

Then, the samples of children who are in the age 10-19 are

As a result, 92,649 samples remain in our dataset. Table 1 presents thedefinition and the descriptive statistics of the variables.
We categorize the households into four groups by annual household income: low family-income groupin which household income is less than 3 million yen; middle family-income group in which householdincome is between 3 million yen and 5.99 million yen; high family-income group in which household income
is between 6 million yen and 8.99 million yen; and very high family-income group in which householdincome is 9 million yen and more. We investigate the impact of household income on the concert attendanceof adolescent children, using the dummy variables of three family income groups, except for the middle
family-income group as a baseline.

9

Table 1: Definitions and Descriptive Statistics for the Variables

Variable

Definition

Mean

S.D.

Min

Max

Obs

d_1996

A 0-1 dummy variable which takes 1 in 1996, and 0 otherwise

0.306

0.461

0

1

92,649d_2001

A 0-1 dummy variable which takes 1 in 2001, and 0 otherwise

0.188

0.390

0

1

92,649d_2006

A 0-1 dummy variable which takes 1 in 2006, and 0 otherwise

0.161

0.368

0

1

92,649metro

A 0-1 dummy variable which takes 1 if the child lives in the Tokyo, Osaka, or Nagoya metropolitan area, and 0 otherwise

0.304

0.460

0

1

92,649femaleA 0-1 dummy variable which takes 1 if the child is a female sample, and 0 otherwise

0.498

0.500

0

1

92,649married

A 0-1 dummy variable which takes 1 if the child is married, and 0 otherwise.

0.018

0.133

0

1

92,649work

A 0-1 dummy variable which takes 1 if the child was engaged in any employment for the year, and 0 otherwise

0.220

0.414

0

1

92,649student_jhs

A 0-1 dummy variable which takes 1 if the child is a junior high school student, and 0 otherwise

0.222

0.416

0

1

92,649student_hs

A 0-1 dummy variable which takes 1 if the child is a high school student, and 0 otherwise

0.329

0.470

0

1

92,649student_ts

A 0-1 dummy variable which takes 1 if the child is a technical college student, and 0 otherwise

0.045

0.207

0

1

92,649student_us

A 0-1 dummy variable which takes 1 if the child is a university student, and 0 otherwise

0.052

0.222

0

1

92,649graduate_jhs

A 0-1 dummy variable which takes 1 if the child does not go to any school after junior high school graduation for the year, and 0 otherwise

0.027

0.162

0

1

92,649graduate_hs

A 0-1 dummy variable which takes 1 if the child does not go to any school after high school graduation for the year, and 0 otherwise

0.124

0.330

0

1

92,649income1

A 0-1 dummy variable which takes 1 if the annual family income is less than 3 million yen, and 0 otherwise

0.165

0.371

0

1

92,649income3

A 0-1 dummy variable which takes 1 if the annual family income is between 6 million yen and 8.99 million yen, and 0 otherwise

0.293

0.455

0

1

92,649income4

A 0-1 dummy variable which takes 1 if the annual family income is over 9 million yen, and 0 otherwise

0.206

0.404

0

1

92,649

Education

Income10

Table 1 (Continued)

Variable

Definition

Mean

S.D.

Min

Max

Obs

Concert attendance (children)d_cla

A 0-1 dummy variable which takes 1 if the child attended any classical music concert in the year, and 0 otherwise

0.117

0.322

0

1

92,649freq_cla

An ordered discrete variable which takes 1, 2, 3, 4, 5, 6, and 7 if the child attended use 0, 1-4, 5-9, 10-19, 20-39, 40-99, 100-199, and 200
and more days in 2006 to attend classical music concerts, respectively

0.170

0.598

0

7

92,616d_cla_pare [1]A 0-1 dummy variable which takes 1 if the child attended any classical music concert with his/her parent in the year, and 0 otherwise

0.147

0.354

0

1

14,932d_cla_fami [2]A 0-1 dummy variable which takes 1 if the child attended any classical music concert with any family member in the year, and 0 otherwise

0.035

0.183

0

1

58,164d_pop

A 0-1 dummy variable which takes 1 if the child attended any popular music concert in the year, and 0 otherwise.

0.155

0.362

0

1

92,649freq_pop

An ordered discrete variable which takes 1, 2, 3, 4, 5, 6, and 7 if the child attended use 0, 1-4, 5-9, 10-19, 20-39, 40-99, 100-199, and 200
and more days in 2006 to attend popular music concerts, respectively

0.249

0.987

0

7

88,120d_pop_pare [1]A 0-1 dummy variable which takes 1 if the child attended any popular music concert with his/her parent in the year, and 0 otherwise

0.161

0.368

0

1

14,932d_pop_fami [2]A 0-1 dummy variable which takes 1 if the child attended any popular music concert with any family member in the year, and 0 otherwise

0.025

0.157

0

1

57,735

A 0-1 dummy variable which takes 1 if a child listened to any recorded classical music like a CD or DVD in the year, otherwise 0.

0.806

0.395

0

1

75,259Recorded musicd_rec [3][1] Only the survey in 2006 asked the question item.
[2] Only the survey in 1991 and 1996 asked the question item.
[3] The survey in 1991, 1996, and 2006 asked the question item.

11

Figures 4 and 5 depict how many days adolescent children enjoyed classical music concerts
and popular music concerts in a year.

In four waves, classical music concerts and popular music

concerts have the same tendency that most of children never listen to music at concerts.

Ninetypercent of children did not listen to classical music at concerts in a year, while eighty-five percent
of children did not listen to popular music in a year. As the frequency of concert attendance
increases, the number of children who listened to music at concerts declines.
Figure 4: The Frequency of Teenage Children’s Classical Music Concert Attendance

100.0%
90.0%
80.0%
70.0%
60.0%
50.0%
40.0%
30.0%
20.0%
10.0%
0.0%
1991

1996

2001

2006

No attendance

1 - 4 days per year

5 - 9 days per year

10 - 19 days per year (1 day per month)

40 - 99 days per year (1 day per week)

100 - 199 days per year (2-3 days per week)

200 days and more per year

[1] The sample size in 1991, 1996, 2001, and 2006 is 31,906, 28,388, 17,390, and 14,932, respectively.

12

Figure 5: The Frequency of Teenage Children’s Popular Music Concert Attendance

100.0%

90.0%
80.0%
70.0%
60.0%
50.0%
40.0%
30.0%
20.0%
10.0%
0.0%
1991

1996

2001

2006

No attendance

1 - 4 days per year

5 - 9 days per year

10 - 19 days per year (1 day per month)

20 - 39 days per year (2-3 days per month)

40 - 99 days per year (1 day per week)

100 - 199 days per year (2-3 days per week)

200 days and more per year

[1] The sample size in 1991, 1996, 2001, and 2006 is 27,410, 28,388, 17,390 and 14,932, respectively.

4.2. How to Identify an Adolescent Child with Their Parent(s)To examine the impacts of parents’ behavior and education on their children’s concert
attendance, we attempted to identify each adolescent child with their parent(s). The questionnaireof the STULA asks about the relationship between each household member and household head
and all household members. The microdata of the STULA assigns the household type to eachsample, using the information on all household members. We employed the information on therelationships between each household member to the household head and the household type toidentify each adolescent child with their parents.
At first, we chose the samples of adolescent children with whom we could identify theirparents. We dropped the children whose parents were not the respondents of the STULA. We
kept an adolescent child who lived with their parents, one who lived with their single parent, one
who lived with their parents and their grandparents, one who lived with their parents and their
grandparent in a dataset. We dropped single household samples like university students who live
apart from their parents because we have no clue to find their parents in the dataset. Children in
our dataset could live with their brother(s) or sister(s).

13Then, we focused on the relationship between an adolescent sample and their household headto identify their father and their mother. If an adolescent sample is a child of their household head,
we judged their household head was their parent. If an adolescent sample is a grandchild of theirhousehold head, we judged that their household head’s child was their parent. We judged thatmale parent was father and that his female partner was mother.

Similarly, we judged that female

parent was mother and that her male partner was father. Here, we did not judge if a child had ablood relationship with their parent(s).

As a result, our dataset keeps five family types: (1)adolescent children and their parents; (2) adolescent children, their parents, and their grandparent;(3) adolescent children, their parents, and their grandparents; (4) adolescent children and theirmother; and (5) adolescent children and their father. Figure 6 shows the percentage of each family
types in our dataset. The most frequent family type is adolescent children and their parents. Its
percentage is 67 percent. The percentage of a child who lives with a single parent is 5 percent.
Figure 6: The Family Types and Their Percentages in Our Dataset

children (age 10-19) and their parents
children (age 10-19), their parents, and their grandparent
children (age 10-19), their parents, and their grandparents
children (age 10-19) and their motherchildren (age 10-19) and their father
1%
4%
12%

16%
67%

[1] Figure 6 shows the family types and their percentages in the dataset, where we matched thesamples of teenage children to their parents.
[2] We also exclude the samples that we have no clue to match adolescent children to their parents.
For example, we exclude the children who live apart from their parents from the dataset
because we have no clue to find their parents' samples.

Table 2 presents the definition and the descriptive statistics of variables when the dataset
includes an adolescent child with whom we could identify their parent(s). Our dataset has 76,489

14

samples of the children with their parents’ data. The mean value of each variable in table 2 is not
far from that in table 1.

In table 2, the percentage of children whose household income was 6

million yen and more is 0.544 (= 0.320 + 0.224) in the dataset excluded the children who lived
apart from their parents. This is 0.045 percent higher than that of 0.499 (= 0.293 + 0.206) in table
1. Thus, when we investigate the impacts of parents’ behavior and education on their adolescentchildren, we need to interpret the estimated results, considering the selection bias that thepercentage of high-income households is too large.

15

Table 2: Definitions and Descriptive Statistics for the Variables
Variable

Definition

Mean

S.D.

Min

Max

Obs

d_1996

A 0-1 dummy variable which takes 1 in 1996, and 0 otherwise

0.328

0.469

0

1

76,489d_2001

A 0-1 dummy variable which takes 1 in 2001, and 0 otherwise

0.199

0.400

0

1

76,489d_2006

A 0-1 dummy variable which takes 1 in 2006, and 0 otherwise

0.157

0.364

0

1

76,489metro

A 0-1 dummy variable which takes 1 if the child lives in the Tokyo, Osaka, or Nagoya metropolitan area, and 0 otherwise

0.311

0.463

0

1

76,489femaleA 0-1 dummy variable which takes 1 if the child is a female sample, and 0 otherwise

0.499

0.500

0

1

76,489married

A 0-1 dummy variable which takes 1 if the child is married, and 0 otherwise

0.010

0.100

0

1

76,489work

A 0-1 dummy variable which takes 1 if the child was engaged in any employment for the year, and 0 otherwise

0.188

0.391

0

1

76,489student_jhs

A 0-1 dummy variable which takes 1 if the child is a junior high school student, and 0 otherwise

0.239

0.427

0

1

76,489student_hs

A 0-1 dummy variable which takes 1 if the child is a high school student, and 0 otherwise

0.347

0.476

0

1

76,489student_ts

A 0-1 dummy variable which takes 1 if the child is a technical school student, and 0 otherwise

0.040

0.196

0

1

76,489student_us

A 0-1 dummy variable which takes 1 if the child is a university student, and 0 otherwise
A 0-1 dummy variable which takes 1 if the child does not go to any school after junior high school graduation for the year, and 0
otherwise
A 0-1 dummy variable which takes 1 if the child does not go to any school after high school graduation for the year, and 0 otherwise

0.035

0.185

0

1

76,489

0.022

0.148

0

1

76,489

0.108

0.310

0

1

76,489

0.475

0.499

0

1

76,489

0.049

0.215

0

1

76,489

0.241

0.428

0

1

76,489

0.578

0.494

0

1

76,489

0.183

0.387

0

1

76,489

0.067

0.250

0

1

76,489

Education (children)

graduate_jhs
graduate_hs
Education (parents)
fa_edu_hs
fa_edu_ts
fa_edu_u
mo_edu_hs
mo_edu_ts
mo_edu_u

A 0-1 dummy variable which takes 1 if the child’s father does not go to any school after high school graduation for the year, and 0
otherwise.
A 0-1 dummy variable which takes 1 if the child’s father does not go to any school after technical school graduation for the year, and 0
otherwise.
A 0-1 dummy variable which takes 1 if the child’s father graduated from university, and 0 otherwise
A 0-1 dummy variable which takes 1 if the child’s mother does not go to any school after high school graduation for the year, and 0
otherwise
A 0-1 dummy variable which takes 1 if the child’s mother does not go to any school after technical school graduation for the year, and0 otherwise
A 0-1 dummy variable which takes 1 if the child’s mother graduated from university, and 0 otherwise

16

Table 2 (Continued)
Variable

Definition

Mean

S.D.

Min

Max

Obs

Incomeincome1

A 0-1 dummy variable which takes 1 if the annual family income is less than 3 million yen, and 0 otherwise

0.109

0.312

0

1

76,489income3

A 0-1 dummy variable which takes 1 if the annual family income is between 6 million yen and 8.99 million yen, and 0 otherwise

0.320

0.466

0

1

76,489income4

A 0-1 dummy variable which takes 1 if the annual family income is over 9 million yen, and 0 otherwise

0.224

0.417

0

1

76,489Concert attendance (children)d_cla

A 0-1 dummy variable which takes 1 if the child attended any classical music concert in the year, and 0 otherwise

0.117

0.322

0

1

76,489freq_cla

An ordered discrete variable which takes 1, 2, 3, 4, 5, 6, and 7 if the child attended use 0, 1-4, 5-9, 10-19, 20-39, 40-99, 100-199, and 200
and more days in 2006 to attend classical music concerts, respectively

0.169

0.593

0

7

76,462

d_pop

A 0-1 dummy variable which takes 1 if the child attended any popular music concert in the year, and 0 otherwise.

0.151

0.358

0

1

76,489freq_pop

An ordered discrete variable which takes 1, 2, 3, 4, 5, 6, and 7 if the child attended use 0, 1-4, 5-9, 10-19, 20-39, 40-99, 100-199, and 200
and more days in 2006 to attend popular music concerts, respectively

0.242

0.966

0

7

73,020Concert attendance (parents)
pare_d_cla

A 0-1 dummy variable which takes 1 if the child’s father or mother attended any classical music concert in the year, and 0 otherwise

0.144

0.351

0

1

76,489mo_d_cla

A 0-1 dummy variable which takes 1 if the child’s mother attended any classical music concert in the year, and 0 otherwise

0.123

0.329

0

1

76,489fa_d_cla

A 0-1 dummy variable which takes 1 if the child’s father attended any classical music concert in the year, and 0 otherwise

0.054

0.226

0

1

76,489pare_d_pop

A 0-1 dummy variable which takes 1 if the child’s father or mother attended any popular music concert in the year, and 0 otherwise

0.178

0.382

0

1

76,489mo_d_pop

A 0-1 dummy variable which takes 1 if the child’s mother attended any popular music concert in the year, and 0 otherwise

0.148

0.355

0

1

76,489fa_d_pop

A 0-1 dummy variable which takes 1 if the child’s father attended any popular music concert in the year, and 0 otherwise

0.065

0.247

0

1

76,489

A 0-1 dummy variable which takes 1 if a child listened to any recorded classical music like a CD or DVD in the year, otherwise 0.

0.806

0.395

0

1

75,259Recorded musicd_rec [3][1] See Table 1 for footnotes175. Results
5.1. Determinants of the Concert Attendance (All Adolescent Children)
We used Stata 16 for the variable construction and the estimation. Since we assume thatthe determinants of concert attendance could be different between classical music concerts andpopular music concerts, classical music concerts and popular music concerts are analyzed
separately. Since the data on whether the child attended any music concert with their parents isavailable only from the survey in 2006, we pooled the data in 2006 to examine parents’ behavior
on their children’s concert attendance.
At first, we discuss on the impacts of parents’ behavior on their children’s concert attendance.
Table 3 shows the estimated results of the logit model to investigate the determinants of theadolescent children’s attending classical music concerts. On the other hand, table 4 reports thoseof the logit model to investigate the causes of attending popular music concerts.

In table 3, theestimated coefficient of cla_pare is positive and significant in column (3-3). It suggests thatchildren were more likely to attend the classical music concerts if their parents attended inclassical music concerts together.

The estimated coefficient of cla_fami is positive andsignificant in column (3-2). It suggests that children were more likely to attend the classicalmusic concerts if their family members attended in classical music concerts together. In table 4,
the estimated coefficient of pop_pare is positive and significant in column (3-3).

It suggests thatchildren were more likely to attend the popular music concerts if their parents attended in popular
music concerts together. STATA could not calculate the estimated results when the logit model
includes pop_fami as an explanatory variable. Thus, adolescent children were more likely tolisten to music at concerts if their parents attended music concerts together.
Second, we discuss on the impacts of household income on children’s concert attendance.
Since the estimated coefficient of income1 shows different signs and significance among thecolumns in table 3, it was not sure if children who came from low-income household were lesslikely to attend classical music concerts. The estimated coefficient of income3 is positive in allcolumns and significant in columns (3-1) and (3-2). The estimated coefficient of income4 is
positive and significant in all columns. As for classical music concerts, children who came fromrich households were more likely to listen to music at concerts. The estimated coefficient ofincome1 is negative and not significant in column (4-1) while that is positive and significant at
10% level in column (4-2). The estimated coefficient of income3 is positive and significant incolumn (4-1) while that is negative and not significant in column (4-2). The estimated coefficientof income4 is positive and significant in column (4-1) while that is negative and not significant incolumn (4-2).

As for popular music concerts, children’s concert attendance did not seem todepend on household income.
Thirdly, we discuss on the impacts of the potential substitute goods of music concerts on thechildren’s concert attendance: They are the recorded music like CD and the concerts of other music18

genre. The estimated coefficient of d_rec is positive and significant in columns (3-2) and (3-3).
The estimated coefficient of d_rec is positive and significant in column (4-2). It suggests thatrecorded music like CD had not been a substitute for classical music concerts and popular music
concerts. On the contrary, listening to recorded music increased children’s concert attendance.
The estimated coefficient of d_pop is positive and significant in all columns of table 3. It suggeststhat the popular music concerts had not been the substitutes for the classical music concerts.
Similarly, the estimated coefficient of d_cla is positive and significant in all columns of table 4.
It suggests that the classical music concerts had not been the substitutes for the popular music
concerts.
Fourthly, we discuss of the impact of some demographic factors on the children’s concert
attendance. In tables 3 and 4, the estimated coefficient of female is positive and significant in allmodels.

It suggests that females were more likely to listen to music at concerts. It is consistent

with other literatures that attempt to determine concert attendance. The estimated coefficients ofengaging in daily work have different signs and significance among columns in table 3. Engaged
in daily work did not affect the children’s attending classical music concerts.

The estimatedcoefficient of engaging in daily work (work) is positive and significant in all columns of table 4.
The children who engaged in daily work were more likely to listen to popular music at concerts.
The estimated coefficients of educational status suggest the different tendency between theclassical music concerts and the popular music concerts.

The estimated coefficients ofgraduate_jhs and graduate_hs are negative and significant in all columns of table 3. It suggeststhat children who did not go any school after junior high school graduation or high school
graduation were less likely to attend the classical music concerts. The estimated coefficients ofgraduate_jhs show different signs and significance between columns (4-1) and (4-2).

Theestimated coefficient of graduate_hs is positive and not significant in columns (4-1) while it ispositive and significant in column (4-2). It suggests that children with shorter schooling years
were more likely to listen to music at concerts than elementary school students. Children withshorter schooling years were less like to attend the classical music concerts, while they were notless likely to attend the popular ones.

In summary, our estimated results suggest that thedeterminants of concert attendance are different between classical music and popular music.

19

Table 3: Determinants of Attending Classical Music Concerts
Variable / Modeld_cla_pare

(3-1)

(3-2)

d_cla_fami

3.138(0.062)
0.972(0.068)0.688(0.043)0.008(0.059)0.133(0.046)0.358(0.048)-0.103(0.040)0.813(0.039)-0.062(0.157)0.163(0.054)-0.278(0.052)0.130(0.044)-0.399(0.092)0.472(0.075)-1.135(0.180)-0.998(0.083)

d_rec
d_pop
income1
income3
income4
metrofemalemarriedwork
student_jhs
student_hs
student_ts
student_us
graduate_jhs
graduate_hs
d_1996d_2001d_2006
Constant
Pseudo 𝑅2
Wald test
Log pseudolikelihood
Number of obs

1.413(0.025)-0.109(0.036)0.179(0.027)0.377(0.029)-0.081(0.024)0.813(0.023)-0.582(0.138)-0.413(0.040)0.022(0.031)-0.319(0.030)-0.764(0.062)-0.022(0.053)-1.386(0.133)-1.121(0.058)-0.126(0.031)
0.041(0.033)-0.054(0.035)-2.561(0.040)0.116577.91 (17)-29977.48
92,649

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

***
***

***
***

(3-3)
1.995 ***
(0.063)
***
***

1.137
(0.091)1.634(0.072)-0.075(0.098)0.073(0.072)0.069(0.083)0.000(0.065)0.813(0.062)0.731(0.605)-0.199(0.153)-0.102(0.074)-0.522(0.079)-1.474(0.254)-0.942(0.209)-1.037(0.367)-1.367(0.270)

***

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

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

***
***

***

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

***

***

-4.306 ***(0.075)0.214533.75 (16) ***-12553.30
58,164***-3.939 ***(0.105)0.25
2039.88 (16) ***-4110.60
14,932[1] For each explanatory variable, the first line reports the estimated coefficient, and the second line
reports the estimated robust standard error.
[2] For the Wald test, the Chi-squared value is reported as a test statistic, and the degree of thefreedom of the Wald test is reported in parenthesis.
[3] *, ** and *** denote significance at the 10%, 5% and 1% levels, respectively.

20

Table 4: Determinants of Attending Popular Music Concerts [1]Variable / Model

(4-1)

(4-2)

d_pop_pare

1.255 ***
(0.065)d_pop_fami
d_rec
d_cla
income1
income3
income4
metrofemalemarriedwork
student_jhs
student_hs
student_ts
student_us
graduate_jhs
graduate_hs
d_1996d_2001d_2006
Constant
Pseudo 𝑅2
Wald test
Log pseudolikelihood
Number of obs

1.410(0.024)-0.012(0.030)0.130(0.025)0.222(0.027)0.114(0.021)
0.682(0.020)-0.463(0.083)0.496(0.030)0.149(0.033)0.579(0.029)0.604(0.047)0.577(0.047)-0.347(0.077)0.067(0.041)-0.555(0.026)-0.360(0.029)-0.683(0.032)-2.545(0.037)0.10
7196.29 (17)-36192.737
92,649

***

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

1.752
(0.122)1.596(0.067)0.156(0.091)-0.066(0.073)-0.010(0.082)0.144(0.064)0.578(0.061)-0.302(0.609)0.227(0.117)0.201(0.086)0.491(0.085)0.615(0.170)0.445(0.169)0.156(0.281)
0.472(0.188)***-4.942(0.140)0.19
1482.56 (16)-4152.706
14,932

***

***
*

**
***

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

**

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

***

[1] See Table 3 for footnotes.

21

5.2. Determinants of the Frequency of Attending Concerts (All AdolescentChildren)At first, we discuss on the impacts of parents’ behavior on the frequency of their children’sconcert attendance. Table 5 shows the estimated results of the ordered logit model to determine
the frequency of the adolescent children’s attending classical music concerts. On the other hand,table 6 reports those of the ordered logit model to determine the frequency of the adolescentchildren’s attending popular music concerts. In table 5, the estimated coefficient of cla_pare ispositive and significant in column (5-3). It suggests that children attended the classical music
concerts more frequently if their parents attended in classical music concerts together.
estimated coefficient of cla_fami is positive and significant in column (3-2).

The

It suggests thatchildren attended the classical music concerts more frequently if their family members attended
in classical music concerts together. In table 6, the estimated coefficient of pop_pare is positiveand significant in column (6-3). It suggests that children attended the popular music concerts
more frequently if their parents attended in popular music concerts together.

The estimatedcoefficient of pop_fami is positive and significant in column (6-2). It suggests that children
attended the popular music concerts more frequently if their family members attended in popular
music concerts together.

Thus, adolescent children were more likely to listen to music atconcerts if their parents attended music concerts together.
Second, we discuss on the impacts of household income on the frequency of children’sconcert attendance.

Since the estimated coefficient of income1 shows different signs andsignificance among the columns in table 5, it was not sure if children who came from low-income
household attended classical music concerts less frequently.

The estimated coefficient ofincome3 is positive in all columns and significant in columns (5-1) and (5-2). The estimatedcoefficient of income4 is positive and significant in in columns (5-1) and (5-2). As for classicalmusic concerts, children who came from rich households tended to listen to music at concerts more
frequently. The estimated coefficients of income1 are not significant in columns (6-1) and (6-2)
while that is positive and significant at 10% level in column (6-3). The estimated coefficient ofincome3 is positive and significant in column (6-1) while that is negative and not significant incolumns (6-2) and (6-3).

The estimated coefficient of income4 is positive and significant incolumn (6-1) while that is negative in columns (6-2) and (6-3). As for popular music concerts,
the frequency of children’s concert attendance did not seem to depend on household income.
Thirdly, we discuss on the impacts of the potential substitute goods of music concerts on thefrequency of the children’s concert attendance. The estimated coefficient of d_rec is positive andsignificant in columns (5-2) and (5-3).

The estimated coefficient of d_rec is positive andsignificant in columns (6-2) and (6-3). It suggests that recorded music like CD had not been asubstitute for classical music concerts and popular music concerts. On the contrary, listening torecorded music increased the frequency of children’s concert attendance.

22

The estimatedcoefficient of d_pop is positive and significant in all columns of table 5.

It suggests that the

popular music concerts had not been the substitutes for the classical music concerts.

Similarly,

the estimated coefficient of d_cla is positive and significant in all columns of table 6. It suggeststhat the classical music concerts had not been the substitutes for the popular music concerts.
Fourthly, we discuss of the impact of some demographic factors on the frequency of thechildren’s concert attendance. In tables 5 and 6, the estimated coefficient of female is positiveand significant in all columns.

It suggests that females listened to music at concerts more

frequently. It is consistent with other literatures that attempt to determine concert attendance.
The estimated coefficients of engaging in daily work have different signs and significance amongcolumns in table 5. Engaged in daily work did not affect the children’s attending classical music
concerts. The estimated coefficient of engaging in daily work is positive and significant in allcolumns of table 6. The children who engaged in daily work listened to popular music at concerts
more frequently. The estimated coefficients of educational status suggest the different tendency
between the classical music concerts and the popular music concerts. The estimated coefficientsof graduate_jhs and graduate_hs are negative and significant in all columns of table 5. It suggeststhat children who did not go any school after junior high school graduation or high school
graduation attended the classical music concerts less frequently. The estimated coefficients ofgraduate_jhs is not significant in all columns of table 6. The estimated coefficient of graduate_hs
is positive and significant in all columns of table 6. It suggests that children who did not go any
school after high school graduation listened to music at concerts more frequently than elementary
school students. Children with shorter schooling years attended the classical music concerts lessfrequently, while they attended the popular ones more frequently. In summary, our estimatedresults suggest that the determinants of the frequency of concert attendance are different between
classical music and popular music.

23

Table 5: Determinants of the Frequency of Classical Music Concert Attendance [1]Variable / Modeld_cla_pare

(5-1)

(5-2)

d_cla_fami

2.850(0.050)0.940(0.066)0.728(0.041)
0.004(0.058)0.112(0.044)0.310(0.046)-0.097(0.038)0.766(0.037)-0.061(0.155)0.191(0.052)-0.234(0.048)0.158(0.041)-0.356(0.089)0.496(0.073)-1.075(0.178)-0.942(0.080)

d_rec
d_pop
income1
income3
income4
metrofemalemarriedwork
student_jhs
student_hs
student_ts
student_us
graduate_jhs
graduate_hs
d_1996d_2001d_2006
Pseudo 𝑅2
Wald test
Log pseudolikelihood
Number of obs

1.415(0.024)-0.110(0.036)0.167(0.027)0.356(0.029)-0.084(0.024)0.805(0.023)-0.572(0.139)-0.401(0.040)0.042(0.031)-0.295(0.030)-0.737(0.062)0.011(0.053)-1.356(0.135)-1.107(0.059)-0.115(0.031)
0.037(0.033)-0.042(0.035)0.08
6772.1 (17)-38914.43
92,616

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

***
***

***
***

(5-3)
1.906 ***
(0.060)
***
***
***

**
***
***
***

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

1.107
(0.090)1.549(0.067)-0.058(0.096)0.071(0.069)0.055(0.079)-0.006(0.061)0.801(0.060)1.043(0.750)-0.244(0.152)-0.035(0.069)-0.401(0.075)-1.324(0.253)-0.759(0.210)-0.972(0.362)-1.224(0.268)

***
***

***

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

***

***

0.17
6186.64 (16) ***-16385.23
58,131[1] See Table 3 for footnotes.

24

0.19
2395.61 (16) ***-5610.34
14,932

Table 6: Determinants of the Frequency of Popular Music Concert Attendance [1]
(6-1)

(6-2)

(6-3)
1.176 ***
(0.062)d_cla_pare
d_pop_fami

5.816(0.065)1.128(0.087)0.922(0.066)0.100(0.066)-0.075(0.060)-0.109(0.066)0.245(0.049)0.432(0.047)0.116(0.094)1.288(0.066)-0.474(0.086)0.966(0.072)0.865(0.114)0.837(0.108)0.119(0.147)0.391(0.090)

d_rec
d_cla
income1
income3
income4
metrofemalemarriedwork
student_jhs
student_hs
student_ts
student_us
graduate_jhs
graduate_hs
d_1996d_2001d_2006
Pseudo 𝑅2
Wald test
Log pseudolikelihood
Number of obs

1.491(0.026)-0.009(0.036)0.107(0.028)0.202(0.031)
0.123(0.024)0.661(0.023)0.179(0.110)0.434(0.039)0.289(0.035)0.717(0.033)0.715(0.056)0.640(0.059)-0.087(0.100)0.229(0.057)0.507(0.033)0.676(0.035)0.374(0.038)0.07
6283.33 (17)-40084.42
88,120

***

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

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

***

***
***
***

*
***
***

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

***

1.740
(0.122)
1.552(0.065)0.156(0.091)-0.077(0.071)-0.022(0.079)0.143(0.062)0.568(0.060)-0.065(0.700)0.250(0.115)0.234(0.082)0.515(0.081)
0.651(0.168)0.482(0.167)0.101(0.269)0.481(0.183)

***
***
*

**
***

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

***

***
***
***
0.31
10198.51 (16) ***-11314.22
53,206***[1] See Table 3 for footnotes.

25

0.14
1599 (16) ***-5739.85
14,932

5.3. The Impacts of Parents’ Behavior, Education, and Household Income on
Their Children’s Concert Attendance
To investigate the impacts of parents’ concert attendance and educational status on theiradolescent children’s concert attendance, we identified parents with their adolescent children,using the data on the relationship to household head. We regressed the logit model and theordered logit model, using the children’s samples identified with their parents. Here, we use thesamples of only the children who lived with their parents.
At first, we discuss on the impacts of parents’ behavior on their children’s concert attendance.
Table 7 shows the estimated results of the logit model to investigate the determinants of theadolescent children’s attending classical music concerts and popular music concerts. In table 7,
the estimated coefficient of pare_cla is positive and significant in column (7-1), and the estimatedcoefficient of pare_pop is positive and significant in column (7-3). It suggests that children weremore likely to attend the music concerts regardless of music genre if their parents attended music
concerts.

Moreover, the estimated coefficient of mo_cla is positive and significant in column(7-1), and the estimated coefficient of mo_pop is positive and significant in column (7-3). It
suggests that children were more likely to attend the music concerts regardless of music genre if
their mothers attended music concerts. Similarly, the estimated coefficient of fa_cla is positiveand significant in column (7-1), and the estimated coefficient of fa_pop is positive and significant
in column (7-3).

It suggests that children were more likely to attend the music concerts

regardless of music genre if their fathers attended music concerts. Thus, adolescent children
were more likely to listen to music at concerts if their parents attended music concerts.
Second, we discuss on the impacts of parents’ educational background on children’s concert
attendance. The estimated coefficient of fa_edu_hs is positive and significant at 10% level incolumns (7-1) and (7-2) of table 7. The estimated coefficient of fa_edu_ts is positive and not
significant in columns (7-1) and (7-2). The estimated coefficient of fa_edu_u is positive andsignificant at 1% level in columns (7-1) and (7-2). Children were more likely to attend classicalmusic concerts as their fathers’ final educational backgrounds were higher.

The estimatedcoefficient of mo_edu_hs is positive and not significant in columns (7-1) and (7-2).

Theestimated coefficient of mo_edu_ts is positive and significant in columns (7-1) and (7-2) at 1%
level. The estimated coefficient of mo_edu_u is positive and significant in columns (7-1) and (72) at 1%level. Children were more likely to attend classical music concerts as their mothers’ final
educational backgrounds were higher.

As for classical music concerts, mothers’ educational

background affected their children’s concert attendance more strongly than fathers.

Theestimated coefficient of fa_edu_hs is not significant in columns (7-3) and (7-4). The estimatedcoefficient of fa_edu_ts is positive and not significant in columns (7-3) and (7-4). The estimatedcoefficient of fa_edu_u is positive and not significant in columns (7-3) and (7-4).
educational backgrounds did not affect their children’s concert attendance.

26

Fathers’

The estimatedcoefficient of mo_edu_hs is positive and significant in columns (7-3) and (7-4) at 5% level. The
estimated coefficient of mo_edu_ts is positive and significant in columns (7-3) and (7-4) at 5%
level. The estimated coefficient of mo_edu_u is positive and not significant in columns (7-3) and(7-4).

Children were more likely to attend popular music concerts if their mothers’ final

educational backgrounds are high school graduate or technical school graduate. As for popular
music concerts, mothers’ educational background affected their children’s concert attendance whilefathers’ ones did not.
Thirdly, we discuss on the impacts of household income on children’s concert attendance.
Since the estimated coefficient of income1 is negative and significant in columns (7-1) and (7-2),
it was sure that children who came from low-income household were less likely to attend classicalmusic concerts. The estimated coefficient of income3 is positive and not significant in columns(7-1) and (7-2). The estimated coefficient of income4 is negative and not significant in columns(7-1) and (7-2). As for classical music concerts, children who came from low-income households
were less likely to listen to music at concerts. The estimated coefficient of income1 is negativeand not significant in columns (7-3) and (7-4). The estimated coefficient of income3 is positiveand significant in columns (7-3) and (7-4). The estimated coefficient of income4 is positive andsignificant in columns (7-3) and (7-4). As for popular music concerts, children who came from
low-income households were more likely to listen to music at concerts.
Next, we discuss on the impacts of the potential substitute goods of music concerts on thechildren’s concert attendance. The estimated coefficient of d_pop is positive and significant incolumns (7-1) and (7-2). It suggests that the popular music concerts had not been the substitutes
for the classical music concerts.

Similarly, the estimated coefficient of d_cla is positive andsignificant in all columns (7-3) and (7-4). It suggests that the classical music concerts had notbeen the substitutes for the popular music concerts.
Then, we discuss of the impact of some demographic factors on the children’s concert
attendance.

In table 7, the estimated coefficient of female is positive and significant in allcolumns. It suggests that females were more likely to listen to music at concerts. The estimatedcoefficient of engaging in daily work is negative and significant in columns (7-1) and (7-2).
Engaging in daily work did not affect the children’s attending classical music concerts.

The

estimated coefficient of engaging in daily work is positive and significant in columns (7-3) and (74).

The children who engaged in daily work were more likely to listen to popular music atconcerts. The estimated coefficients of educational status suggest the different tendency between
the classical music concerts and the popular music concerts.

The estimated coefficients ofgraduate_jhs and graduate_hs are negative and significant in all columns. It suggests that children
who did not go any school after junior high school graduation or high school graduation were lesslikely to attend the classical music concerts, and that children who did not go any school after highschool graduation were less likely to attend the popular music concerts. The estimated coefficient27

of graduate_hs is positive and significant in columns (7-3) and (7-4). It suggests that children
who did not go any school after high school graduation were more likely to listen to popular musicat concerts than elementary school students. Our estimated results also show that children wereless likely to attend classical music concerts after elementary school graduation and that children
were more likely to attend popular music concerts after elementary school graduation.

Insummary, our estimated results suggest that the determinants of concert attendance are different
between classical music and popular music.

28

Table 7: Determinants of Attending Music Concerts (Children Living with Their Parent(s) [1]

pare_d_cla

Classical music concert(7-1)
1.745 ***
(0.028)mo_d_cla
fa_d_cla
d_pop

1.331
(0.029)-0.137(0.049)0.014(0.031)-0.013(0.036)-0.102(0.028)0.857(0.027)-0.735(0.257)-0.314(0.049)0.045(0.035)-0.270(0.034)-0.766(0.077)-0.204(0.072)-1.020(0.152)-0.945(0.070)0.064(0.039)0.078(0.064)0.138(0.046)0.063(0.044)0.177(0.052)0.251(0.063)-0.150(0.036)-0.090(0.039)-0.192(0.043)-3.030(0.059)0.18***

8323.68 (24)***income1
income3
income4
metrofemalemarriedwork
student_jhs
student_hs
student_ts
student_us
graduate_jhs
graduate_hs
fa_edu_hs
fa_edut_ts
fa_edu_u
mo_edu_hs
mo_edu_ts
mo_edu_u
d_1996d_2001d_2006
Constant
Pseudo 𝑅2
Wald test
log pseudolikelihood

Number of obs-22666.47
76,489

***

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

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

***

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

(7-2)
pare_d_pop
1.624(0.031)0.802(0.043)1.343(0.029)-0.129(0.049)0.014(0.032)-0.014(0.036)-0.104(0.028)0.860(0.027)-0.740(0.259)-0.310(0.049)0.053(0.035)-0.266(0.035)-0.753(0.078)-0.211(0.074)-1.004(0.151)-0.944(0.071)
0.067(0.039)0.079(0.065)0.135(0.046)0.048(0.044)0.153(0.052)
0.195(0.064)-0.135(0.036)-0.073(0.039)-0.176(0.044)-3.027(0.060)0.19***

mo_d_pop***

fa_d_pop

***

d_cla

***

income18515.88 (25)income3
income4
***

metro

***

female***married***work
student_jhs***

student_hs***

student_ts***

student_us***

graduate_jhs***

graduate_hs*

fa_edu_hs
fa_edu_ts***

fa_edu_u
mo_edu_hs***

mo_edu_ts***

mo_edu_u***

d_1996*d_2001***d_2006***

Constant***

Pseudo R2
Wald test-22486.14
76,489log pseudolikelihood

Number of obs[1] See Table 3 for footnotes.

29

Popular music concert(7-3)
0.963 ***
(0.025)1.313(0.028)-0.048(0.041)
0.087(0.028)0.140(0.031)
0.096(0.024)0.729(0.023)-0.420(0.138)0.532(0.036)0.207(0.037)0.675(0.033)0.678(0.055)0.578(0.060)-0.259(0.096)0.146(0.050)-0.002(0.032)0.085(0.057)0.032(0.040)0.081(0.034)0.100(0.044)0.064(0.058)-0.545(0.030)-0.382(0.032)-0.778(0.039)-2.905(0.050)0.12***7436.14 (24)***-28465.10
76,489

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

**
**

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

(7-4)
0.918
(0.027)0.479(0.039)1.313(0.028)-0.045(0.041)
0.085(0.028)0.135(0.031)
0.097(0.024)0.732(0.023)-0.428(0.138)0.531(0.036)0.208(0.037)0.675(0.033)0.680(0.055)0.578(0.061)-0.261(0.097)0.145(0.050)0.002(0.032)0.087(0.057)0.037(0.040)0.071(0.034)0.087(0.044)0.051(0.058)-0.540(0.030)-0.380(0.032)-0.776(0.039)-2.900(0.050)0.13***7533.54 (25)***-28385.58
76,489

***
***

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

**
**

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

5.4. The Impacts of Parents’ Behavior, Education, and Household Income on the
Frequency of Their Children’s Attending Concerts
At first, we discuss on the impacts of parents’ behavior on the frequency of their children’sconcert attendance.

Table 8 shows the estimated results of the logit model to investigate thedeterminants of the frequency of the adolescent children’s attending classical music concerts andpopular music concerts. In table 8, the estimated coefficient of pare_cla is positive and significant
in column (8-1), and the estimated coefficient of pare_pop is positive and significant in column (83). It suggests that children attended the music concerts more frequently regardless of music genre
if their parents attended music concerts. Moreover, the estimated coefficient of mo_cla is positiveand significant in column (8-1), and the estimated coefficient of mo_pop is positive and significant
in column (8-3). It suggests that children attended the music concerts more frequently regardlessof music genre if their mothers attended music concerts. Similarly, the estimated coefficient of
fa_cla is positive and significant in column (8-1), and the estimated coefficient of fa_pop is positiveand significant in column (8-3).

It suggests that children attended the music concerts more

frequently regardless of music genre if their fathers attended music concerts.

Thus, adolescentchildren listened to music at concerts more frequently if their parents attended music concerts.
Second, we discuss on the impacts of parents’ educational background on the frequency ofchildren’s concert attendance. The estimated coefficient of fa_edu_hs is positive and significant at
10% level in columns (8-2). The estimated coefficient of fa_edu_ts is positive and not significant incolumns (8-1) and (8-2). The estimated coefficient of fa_edu_u is positive and significant at 1%
level in columns (8-1) and (8-2). Children attended classical music concerts more frequently if theirfathers’ final educational backgrounds were college graduate or university graduate. The estimatedcoefficient of mo_edu_hs is positive and not significant in columns (8-1) and (8-2). The estimatedcoefficient of mo_edu_ts is positive and significant in columns (8-1) and (8-2) at 1% level. The
estimated coefficient of mo_edu_u is positive and significant in columns (8-1) and (8-2) at 1%level.
Children attended classical music concerts more frequently if their mothers’ final educational
backgrounds were technical school graduate or college graduate or university graduate.

As forclassical music concerts, mothers’ educational background affected their children’s concert attendance
more strongly than fathers. The estimated coefficient of fa_edu_hs is not significant in columns (83) and (8-4). The estimated coefficient of fa_edu_ts is positive and not significant in columns (8-3
and (8-4). The estimated coefficient of fa_edu_u is positive and not significant in columns (8-3) and(8-4).

Fathers’ educational backgrounds did not affect the frequency of their children’s concert

attendance. The estimated coefficient of mo_edu_hs is positive and significant at 5% level in column(8-3). The estimated coefficient of mo_edu_ts is positive and significant at 5% level in column (83). The estimated coefficient of mo_edu_u is positive and not significant in columns (8-3) and (8-4).
Children attended popular music concerts more frequently if their mothers’ final educational
backgrounds are high school graduate or technical school graduate. As for popular music concerts,
30mothers’ educational background affected the frequency of their children’s concert attendance whilefathers’ ones did not.
Thirdly, we discuss on the impacts of household income on children’s concert attendance.
Since the estimated coefficient of income1 is negative and significant in columns (8-1) and (8-2), it
was sure that children who came from low-income household attended classical music concerts lessfrequently. The estimated coefficient of income3 is positive and not significant in columns (8-1) and(8-2). The estimated coefficient of income4 is negative and not significant in columns (8-1) and (82). As for classical music concerts, children who came from low-income households listened tomusic at concerts less frequently. The estimated coefficient of income1 is positive and not significant
in columns (8-3) and (8-4). The estimated coefficient of income3 is positive and significant at 10%
level in columns (8-3). The estimated coefficient of income4 is positive and significant at 5% levelin columns (8-3) and (8-4). As for popular music concerts, children who came from high-income
households listened to music at concerts more frequently.
Next, we discuss on the impacts of the potential substitute goods of music concerts on thefrequency of the children’s concert attendance. The estimated coefficient of d_pop is positive andsignificant in columns (8-1) and (8-2). It suggests that the popular music concerts had not been thesubstitutes for the classical music concerts. Similarly, the estimated coefficient of d_cla is positiveand significant in all columns (8-3) and (8-4). It suggests that the classical music concerts had notbeen the substitutes for the popular music concerts.
Then, we discuss of the impact of some demographic factors on the children’s concert
attendance. In table 8, the estimated coefficient of female is positive and significant in all columns.
It suggests that females listened to music at concerts more frequently. The estimated coefficient ofengaging in daily work is negative and significant in columns (8-1) and (8-2). Engaging in daily
work reduces the frequency of children’s attending classical music concerts.

The estimatedcoefficient of engaging in daily work is positive and significant in columns (7-3) and (7-4). The
children who engaged in daily work listened to popular music at concerts more frequently.

Theestimated coefficients of educational status suggest the different tendency between the classical music
concerts and the popular music concerts. The estimated coefficients of graduate_jhs and graduate_hs
are negative and significant in all columns. It suggests that children who did not go any school after
junior high school graduation or high school graduation attended the classical music concerts lessfrequently, and that children who did not go any school after high school graduation attended the
popular music concerts less frequently.

The estimated coefficient of graduate_hs is positive andsignificant in columns (7-3) and (7-4). It suggests that children who did not go any school after highschool graduation listened to popular music at concerts more frequently than elementary school
students.

Our estimated results also show that children attended classical music concerts afterelementary school graduation less frequently and that children were more likely to attended popular
music concerts more frequently after elementary school graduation.
31

In summary, our estimatedresults suggest that the determinants of the frequency of concert attendance are different between
classical music and popular music.

It is consistent with the results in tables 5 and 6.

32

Table 8: Determinants of Frequency of Concert Attendance (Children Living with Their Parent(s) [1]

pare_d_cla

Classical music concert(8-1)
1.704 ***
(0.027)mo_d_cla
Fa_d_cla
d_pop

1.301
(0.028)-0.144(0.049)0.004(0.031)-0.030(0.035)-0.104(0.027)0.839(0.026)-0.719(0.260)-0.288(0.048)0.074(0.033)-0.221(0.033)-0.699(0.077)-0.129(0.072)-0.977(0.154)-0.917(0.071)
0.061(0.039)0.065(0.063)0.128(0.045)0.071(0.044)0.173(0.052)0.255(0.062)-0.146(0.035)-0.098(0.038)-0.173(0.043)0.14***

9271.73 (24)***income1
income3
income4
metrofemalemarriedwork
student_jhs
student_hs
student_ts
student_us
graduate_jhs
graduate_hs
fa_edu_hs
fa_edu_ts
fa_edu_u
mo_edu_hs
mo_edu_ts
mo_edu_u
d_1996d_2001d_2006
Pseudo 𝑅2
Wald test
Log pseudolikelihood

Number of obs-29957.77
76,462

***

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

***

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

(8-2)
pare_d_pop
1.570(0.030)0.753(0.039)1.302(0.028)-0.137(0.049)0.005(0.031)-0.031(0.035)-0.103(0.027)0.839(0.026)-0.724(0.262)-0.282(0.049)0.082(0.033)-0.211(0.033)-0.681(0.077)-0.125(0.073)-0.961(0.153)-0.913(0.071)
0.065(0.039)0.063(0.063)0.125(0.046)0.057(0.044)0.151(0.052)0.203(0.063)-0.131(0.035)-0.082(0.038)-0.159(0.043)0.15***

mo_d_pop***

fa_d_pop

***

d_cla

***

income1

9785.85 (25)***income3
income4
***

metro

***

female***married

***

work

**

student_jhs***

student_hs***

student_ts*

student_us***

graduate_jhs***

graduate_hs*

fa_edu_hs
fa_edu_ts***

fa_edu_u
mo_edu_hs***

mo_edu_ts***

mo_edu_u***

d_1996**d_2001***d_2006
Pseudo R2-29795.71
76,462

Wald test
Log pseudolikelihood

Number of obs[1] See Table 3 for footnotes.

33

Popular music concert(8-3)
1.062 ***
(0.027)1.355(0.030)0.007(0.047)0.042(0.031)
0.085(0.035)0.108(0.027)0.694(0.026)0.221(0.181)0.496(0.046)0.328(0.038)0.785(0.036)0.786(0.065)0.634(0.074)-0.087(0.123)0.305(0.067)-0.002(0.037)0.076(0.063)0.030(0.045)0.072(0.042)0.089(0.050)0.067(0.063)0.520(0.038)0.646(0.040)0.296(0.045)0.09***

6640.56 (24)***-32102.23
73,020

*
**
***
***

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

***

**
**

***
***
***

(8-4)

0.995
(0.029)0.523(0.040)1.352(0.030)0.009(0.047)0.041(0.032)0.080(0.036)0.111(0.027)0.695(0.026)0.204(0.181)
0.492(0.046)0.331(0.038)0.787(0.036)0.790(0.065)0.637(0.074)-0.083(0.124)0.306(0.067)0.000(0.037)0.073(0.063)0.033(0.045)0.058(0.042)0.071(0.050)0.044(0.064)0.525(0.038)0.647(0.040)0.300(0.045)0.09***

6828.27 (25)***-32032.70
73,020

***
***

**
***
***

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

***

***
***
***

6. Concluding Remarks
This study focuses on the impacts of parents’ behavior, educational background, and householdincome on their adolescent children’s music concert attendance because the consumption of music
concerts could reflect income inequality well. Almost all teenagers listen to music in daily life but
whether a teenager could attend any live music concerts depends on their household income. We
regressed the logit model to investigate the impacts of parents’ behavior, educational background, andhousehold income on the likelihood of their adolescent children’s concert attendance. Additionally,
we regressed the ordered logit model to investigate the impacts of parents’ behavior, educational
background, and household income on the frequency of their adolescent children’s concert attendance.
Using data is the individual anonymous microdata from the Survey on Time Use and Leisure Activities
in 1991, 1996, 2001, and 2006. To investigate the impacts of parents’ behavior and education on
children’s concert attendance, we identified adolescent children to their parents using the informationon the relationship between each family member and their household head.

To investigate theimpacts of household income on children’s concert attendance, we divide children into four family
income group: These are; (1) low family-income-group of which family income is less than 3 million
yen; (2) middle family-income group of which family income is between 3 million yen and 5.99
million yen; (3) high family-income group of which family income is between 6 million yen and 8.99
million yen; and (4) very high family-income group of which family income is 9 million yen and more.
Our findings are the following three. First, our estimated result shows that adolescent children
were more likely to attend music concerts regardless of genre if their parents attended music concerts
together. Also, our estimated result shows that adolescent children were more likely to attend music
concerts regardless of genre if their parents attended music concerts. Moreover, parents’ concert
attendance raised the frequency of their adolescent children’s music concert attendance regardless ofgenre.

Second, our results suggest that parents’ educational background affect their children’sconcert attendance and frequency of attending concert rather than their children’s education.
When we control parents’ concert attendance, our estimated results show that parents’ educational
background has different effect on the concert attendance between classical music and popular music:
Children were more likely to attend the classical music concert if their fathers educational backgroundis a university graduate; Children were more likely to attend classical music concerts if their mothers
educational background is a technical college graduate or a university graduate; Fathers’ educational
background never affect their children’s concert attendance of popular music; And children were more
likely to attend popular music concerts if their mothers educational background is a high school
graduate or a technical college graduate. Similarly, when we control parents’ concert attendance,
our estimated results show that parents’ educational background has different effect on the frequency
of attending concerts between classical music and popular music: Children attended classical music
concerts more frequently if their fathers educational background is a university graduate; Children
attended classical music concerts more frequently if their mothers educational background is atechnical school graduate or a university graduate; Fathers’ educational background never affect their34

children’s concert attendance of popular music; And children were attended popular music concerts
more frequently if their mothers educational background is a high school graduate or a technicalcollege graduate.

Third, our results suggest that adolescent children who belong to low family-

income group were less likely to attend classical music concerts and that adolescent children who
belong to very high family-income group were more likely to attend popular music concerts.
This study shows the strong relationship between parents’ behavior, educational background,and household income and their adolescent children’s concert attendance.

Our estimated results

suggest that children were likely to attend both classical music concerts and popular music concerts
when their parents attended concerts together. Our estimated also results suggest the possibility thathousehold income has determined the preference for classical music concerts.

These results imply

that the opportunity to access classical music concerts is not equal among adolescent children. The
cultural policy to realize the equal opportunity to attend classical music concerts is needed.

For

example, the government need to supply more free classical music concerts of which targets areparents and their children.
We still have two main remaining problems. The first one is the sample period. We should
add the microdata in 2011 and 2016 to our dataset. The second one is the identification of adolescentchildren to their parents. In this study, we eliminated the single parent families, where adolescentchildren live with their single parents, their grandparent(s). If an adolescent child lived with theirsingle parent, their uncle(s) or aunt(s), and their grandparent(s), we cannot judge who is their parent
in a dataset. If an adolescent child lives with their single parent and their grandparent(s) only, we
can judge who is their parent.
to the dataset.

In future research, we need to reconsider if we should add such a child

It would be better to control the family size to investigate the impact of householdincome on the children’s concert attendance.

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