JOINT RESEARCH CENTER FOR PANEL STUDIESSELECTED DISCUSSION PAPER SERIESSDP2012-001
March, 2013

Overview of the Japan Household Panel Survey 2012
Hideo Akabayashi*
Chizuru Shikishima**
Kayo Nozaki**【Abstract】
The Keio University Joint Research Center for Panel Studies started the Japan
Household Panel Survey (JHPS) in 2009. The most recent survey was implemented in2012 (the JHPS 2012, fourth wave). The JHPS 2012 included 2,821 subjects, with an
89.3% retention rate from the third wave. This article provides an overview of sample
characteristics of the fourth wave and reports some basic results of selected issues of
special interest in this wave.
First, a probit analysis for panel continuation revealed that the parallel use withinterview method caused the probability of response continuation to decrease in thesecond wave. However, this did not have any effect subsequent to the third wave, and it
was determined that more visits by surveyors during previous fielding caused the rateof response continuation to decrease. Second, we report the characteristics ofintergenerational financial assistance, from parents to their children and vice versa,
using an array of variables, such as education, co-residence with parents, andemployment status. Third, we report distributions of a sense of happiness as well as its
relationship with employment, income, and health. Finally, basic statistics of thereasoning test included as an indicator of cognitive ability are presented, and spousal
correlation, age differences, and relationship with education and income from work aredescribed.

*

Faculty of Economics, Keio University**Keio Advanced Research Centers(Panel Data Research Center), Keio University

Joint Research Center for Panel Studies
Keio University

Overview of the Japan Household Panel Survey 20121
Hideo Akabayashi*

Chizuru Shikishima**

Kayo Nozaki**

Abstract
The Keio University Joint Research Center for Panel Studies started the Japan
Household Panel Survey (JHPS) in 2009. The most recent survey was implemented in2012 (the JHPS 2012, fourth wave). The JHPS 2012 included 2,821 subjects, with an
89.3% retention rate from the third wave. This article provides an overview of sample
characteristics of the fourth wave and reports some basic results of selected issues of
special interest in this wave.
First, a probit analysis for panel continuation revealed that the parallel use withinterview method caused the probability of response continuation to decrease in thesecond wave. However, this did not have any effect subsequent to the third wave, and it
was determined that more visits by surveyors during previous fielding caused the rateof response continuation to decrease. Second, we report the characteristics ofintergenerational financial assistance, from parents to their children and vice versa,
using an array of variables, such as education, co-residence with parents, andemployment status. Third, we report distributions of a sense of happiness as well as its
relationship with employment, income, and health. Finally, basic statistics of thereasoning test included as an indicator of cognitive ability are presented, and spousal
correlation, age differences, and relationship with education and income from work aredescribed.

* Faculty of Economics, Keio University** Keio Advanced Research Centers(Panel Data Research Center), Keio University

Corresponding author: H. Akabayashi (email: hakab@econ.keio.ac.jp). This paper is
an English version of DP-2012-006 of the Keio University Joint Research Center for
Panel Studies. This English version was translated into by an external translation
service supplier from the Japanese version DP2012-06 issued in March 2013. Note that
information included in this paper is as of March 2013.
1

1

Section 1 Introduction†
This study provides an overview and discussion of the summary and responses, and of
the recently completed the JHPS 2012 results conducted by the Keio University Joint
Research Center for Panel Studies.
The JHPS started in 2009, making the 2012 survey the fourth wave of its kind. The
response rates, sample characteristics, and sample representativeness of each survey
are reviewed in Naoi and Yamamoto (2010), Naoi et al. (2010), and Ishii (2012). Naoi
and Yamamoto (2011) analyzed the relationship between the differences in survey
methods and sample attrition in the JHPS second wave. However, the sample attrition
trends in the third and fourth waves have not been analyzed. In this paper, we
examined the response conditions of the JHPS 2012 and the trend of sample attrition
since the second wave.
In addition, we analyzed the characteristic items, “family assistance,” “happiness,”and “reasoning” included in the JHPS 2012. The newly introduced items of the JHPS
2012 include items concerning financial assistance between parents and children. With
the declining birth rate and aging population, discovering the realities ofintergenerational transfer; that is, the assistance from parents to their children or viceversa, is a crucial step toward enacting future social insurance policy.
Recently, Gross National Happiness (GNH) has been drawing attention as anindicator of a country’s prosperity. Items related to happiness have been introduced
since the JHPS 2011 (third wave), not only investigating happiness at the time of thesurvey completion but during multiple periods (e.g., the past year, entire life). Factors† Individual data of the Japan Household Panel Survey were offered by the Keio

University Joint Research Center for Panel Studies for this article. Also, valuablecomments were received from Dr. K. Sakamoto (Keio University). We would like toexpress our gratitude here. All remaining errors are ours. The opinions expressed inthis paper are the authors’ alone and do not in any way reflect those of the authors’affiliations or of individuals involved in the survey.
2

such as gender differences, age differences, and effects of employment status andincome on happiness were examined using descriptive statistical analysis.
Furthermore, this paper explains the results of the reasoning test as a simple
indicator of cognitive ability; this has not been previously conducted in a large-scale
panel survey on Japanese adults. The reasoning test was answered by the subjects in
the JHPS 2011 and by their spouses in the JHPS 2012. We explain the test and describe
basic statistics such as the spousal correlation of scores, age differences, and education–
income relationship.
This paper is organized as follows. Section 2 reviews the response retention rate andsample attrition in the JHPS 2012. Section 3 explains the results of “family assistance,”“happiness,” and “reasoning.” Section 4 summarizes this chapter.

Section 2 Overview and Sample Characteristics of the JHPS 2012
This section provides an overview of the JHPS 2012 and explains the sample
characteristics and sample attrition. The JHPS is an annual household panel surveythat has been conducted since January 2009 by the Keio University Joint ResearchCenter for Panel Studies, as part of the Ministry of Education, Culture, Sports, Scienceand Technology’s “The Promotion of the Joint Research Center for Human Studies andSocial Sciences.” In the first wave, the subjects included men and women at the age of
20 or older (born before January 1989) who were registered in the Basic Resident
Registry in Japan as of January 31, 2009. The first phase of sample selection comprised
the selection of survey locations, with the 2005 National Census basic districts as
selection units. In the second phase, individuals were selected at random using the
Basic Resident Registry of the selected locations. The final sample size was 4,022 (thedata include an extra 22 individuals who were surveyed as a back-up sample). The
JHPS 2012 (fourth wave) introduced in this paper was conducted on January 31, 2012,
comprising a total of 3,170 subjects; this number included a total of 3,160 valid subjects
3

from the third wave and 10 individuals who abstained from the third wave but electedto participate in the fourth.
Table 1 presents the number of respondents and the retention rates from the first(JHPS 2009) to the fourth (JHPS 2012) wave, indicating instances of respondents
among both the original subjects and the original subjects plus their spouses. In case of
married subjects, JHPS is designed to ask questions about the subject’s spouse; thesequestions are nearly identical to those about the subject. Therefore, a married subject
allows for analysis including their spouse’s information. As seen in Table 1, theretention rate in the JHPS 2012 was 89.3%, a decrease since the JHPS 2011’s 91.1%,
and sample attrition was greater in the JHPS 2012 than in the JHPS 2011. The shift in
the retention rates was the same even when including spouses. Cohort A and Cohort B
of Keio Household Panel Survey (KHPS) 2 is based on a similar sample selection.
Figure 1 displays the cumulative attrition rate (rate = (number of cumulative attritionin each wave/ number of respondents in the first wave) × 100) from the second to fourthwave of the KHPS Cohort A (since 2004) and Cohort B (since 2007). Figure 1 shows thatthe attrition rate of the JHPS is less in comparison to the KHPS Cohort A, beginning in
2004, and marginally higher (especially in the fourth wave) compared to the KHPS
Cohort B, beginning in 2007.

2

The age range of the KHPS sample selection was 20–69.
4

Table 1 Number of JHPS Respondents and Panel Retention Rate
Survey Year
2009
2010
2011
2012
2012
(Excluding returning
sample)Number of Respondents (People)
Subjects
Spouses Included
4,022
6,911
3,470
6,010
3,160
5,497
2,821
4,903
2,811

Retention Rate (%)
Subjects
Spouses Included
86.3
87.0
91.1
91.5
89.3
89.2

4,888

89.0

88.9

Note 1: “Number of respondents” represents the subjects that responded to that year’s survey.
Note 2: “Retention rate” is the number of respondents in one year divided by the number of
respondents in the previous year.
Note 3: “2012 (Excluding returning sample)” includes the subjects that responded to all waves
from 2009 to 2012.
Note 4: “Spouses included” is the sum of the number of subject respondents and spousal
respondents.
Source: Produced by the authors from JHPS2009–2012.

Figure 1 Cumulative Sample Attrition Rate of the JHPS and KHPS (%)
40
35
30
25
JHPS
20

KHPS: Cohort A

15

KHPS: Cohort B

10

50
Second Wave

Third Wave

Fourth Wave

Note 1: Cumulative attrition rate = (number of cumulative attrition in each wave / number of
respondents in the first survey) × 100
Note 2: The first surveys conducted by JHPS, KHPS Cohort A, and KHPS Cohort B were in
2009, 2004, and 2007, respectively.
Source: Produced by the authors from JHPS2009–2012 and KHPS2004–2012.

Sample attrition is a major problem when collecting panel data. Panel data are
obtained by conducting surveys with the same individuals every year; however, withtime, respondents are unable or unwilling to respond for a variety of reasons, such as

5death, health issue, and relocation, which makes continuous panel data collection withthese individuals impossible. This does not become an issue if sample attrition is
random; however, if attrition is non-random, any analysis using that panel data will
require caution because of some sort of bias.
Sakamoto (2006a) investigated the factor of sample attrition, with the JapanesePanel Survey of Consumers (JPSC), conducted by the Institute for Research on
Household Economics since 1993. The results showed high attrition rates among people
planning to get married and newlyweds. Many respondents dropped out of the sample
when they got married, due to the difficulties in understanding their spouses enough toanswer the survey’s questions regarding their spouses and family constructions, anddue to the high moving rate of newlywed samples. In addition, this attritional bias wasshown to have an effect on estimates of marriage preference functions. Moreover, the
KHPS has been analyzed by Miyauchi et al. (2006) and Naoi (2007, 2009) in this regard.
While Miyauchi et al. (2006) indicated that “moving” is a factor that reduces the panel
retention rate, “years of education” and “marital status” increase the panel retention
rate. This analysis was based on the Missing At Random (MAR) hypothesis, stated by
Fitzgerald, Gottschalk and Moffitt (1998) and Lillard and Panis (1998), who analyzed
the reason for sample attrition based on the Panel Study of Income Dynamics (PSID),
which states that attrition at any given time can be predicted by observable values up
until that point. Naoi and Yamamoto (2011) analyzed the relationship between sample
attrition and survey methods of the JHPS second wave. The results showed that theconfiguration of the bonus reward upon completion in the first wave caused theprobability of continuation to decrease in the second wave. However, they also showedthat the effects that raised the response rate in the first wave, with its bonus reward
upon completion, were greater than the effects lowering the probability of continuationin the second wave. In addition, they demonstrated that the combined use of interview

6

surveys resulted in a decreasing the probability of continuation, and that there were nodifferences in the probability of continuation between web and paper surveys.
In this section, we reviewed the trends in sample attrition in the JHPS third andfourth waves based on the MAR hypothesis. Probit analysis for panel continuation was
conducted with the sample continuing responses as 1, and the sample dropping out as 0.
Estimations were conducted in each survey with the panel continuation for the JHPS
2010 as “Continued Participation (second wave),” for the JHPS 2011 as “Continued
Participation (third wave),” and for the JHPS 2012 as “ Continued Participation (fourthwave)” 3.
The explanatory variables in this analysis regarding the subject and family are as
follows: “Gender: dummy variable with female as 1, and male as 0”; “Age: categoricalvariable with 20–29 years as reference category, 30–39 years, 40–49 years, 50–59 years,
and 60 years and above”; “Marriage: dummy variable with spouse present as 1, andspouse absent as 0”; “Level of Education: categorical variable with junior high school
graduate as the reference category, high school graduate, technical school/othergraduate, junior college/technical college graduate, and university/graduate school
graduate”; “Household Size: number of household members living together”; “Number ofChildren: number of children living together”; “Regular Employment: dummy variablewith regular employment as 1, and no regular employment as 0”; and “Health Degree:
categorical variable from healthy (1) to unhealthy (5).”Variables related to alternative survey methods were used in addition to thesevariables. During the JHPS 2009, the surveyors distributed questionnaires to thesubjects at their houses, and randomly sorted subjects between at-home surveys, where

JHPS has four cases of subjects in the sample who are dead or missing, so the survey
is continued by requesting the cooperation of their spouse. However, these subjects are
only included up to the last survey year that they themselves responded. There are also
10 cases of subjects who abstained from a certain survey and returned in the following
survey. They were excluded from this subject analysis as these samples were predicted
to have differing behavior from the other samples.
3

7

the surveyor would return to collect completed questionnaires, and combined interviews
and questionnaires, where a questionnaire component was conducted orally in aninterview format. Naoi and Yamamoto’s (2011) estimation results on the response
continuation of the second wave showed that the probability of response continuation
was significantly lower in “subjects with combined interview and questionnaire,” andthat interview surveys incur a temporal cost of visitation, as well as the possibility ofresistance to directly answer to the surveyor. Therefore, this section uses the variable“Combined Interview Dummy: dummy variable with subjects with combined interview
surveys as 1, subjects with at-home surveys as 0.” Furthermore, JHPS provides anInternet response option in addition to the usual question sheet response method. Naoi
and Yamamoto’s (2011) analysis on the second wave did not find a difference in theprobability of response continuation between web survey respondents and paper survey
respondents. To confirm whether this had an effect on the probability of response
continuation in the third and fourth waves, this section uses the variable “Web Survey
Dummy: dummy variable with web survey respondents as 1, paper survey respondents
as 0.”
Inability to follow-up with subjects due to a change of residence is a plausible reason
for discontinuing the survey. JHPS requires surveyors conducting the survey tocomplete surveyor confirmation forms, which include questions such as the number ofvisits and recovery of questionnaires. This surveyor survey includes a question
regarding the subject’s condition at the time of survey, which provides information onwhether the subject has changed residence. This section uses the variable “Change ofResidence Dummy: dummy variable with 1 if the subject has changed residence, and 0 if
the subject has not.”In addition, the surveyor’s survey form includes a multiple-choice question in case thequestionnaire was recovered from the subject; it is, however, excluded if thequestionnaire is improperly completed, or in cases where on visiting, the subject refused8to participate in the survey. Here the surveyor needed to provide the reason explaining
their refusal (Table 2). Looking at these reasons, many subjects provided the following
reasons for refusal: “too busy to fill in the survey,” “bad health,” “too many survey items,”“already cooperated enough,” and “vague reasons.” To display the effects of the mannerof refusal, this section uses the variable “Number of surveyor visits: number of times thesurveyor visited this household.” In addition, all these explanatory valuables, with the
exception of “Change of Residence Dummy,” use information from the last wave
answered before opting out.
Table 3 displays the basic statistics of the variables used in analysis for each survey
in terms of the continued response sample and attrition sample. The variables related
to household attributes showed that the tendency for people to drop out changes basedon the number of surveys. For example, with regard to marital status, the attrition
sample comprised a high percentage of unmarried subjects in the second and third
waves (p < 0.001); however, this trend was not apparent in the fourth wave. Meanwhile,
there were similar trends in number of children and highest level of education in every
wave, and the attrition sample tended to have fewer children and a lower level of
education (p < 0.05). Subjects changing their residences had a similar trend, and in eachsurvey, the attrition sample had a higher percentage of the subjects changing residence
during the wave (p < 0.001).

9

Table 2 Reasons for Survey Refusal (Multiple Answers, %)SecondWave(413 cases)
16.9
41.9
27.1
9.0
3.6
4.6
18.2
7.0
5.1
3.4
7.0
19.4
－
0.2
11.4

Physically or psychologically disease
Too busy to fill the survey
Difficult to fill in due to the number of survey items
Unwilling to participate in interview survey
Suspiciousness of surveys in general
Suspiciousness of this survey's questionnaire
Already cooperated enough, as survey items are similar to the last survey
Survey items are difficult to understandObjectionable questions infringing on privacy
Do not want to answer due to family circumstances
Family is opposed to it
Vague reasons, do not want to do it, it's an inconvenience and a bother
Have not received announcement or report afterward
Little recompensation
Other

Third
Wave
(197 cases)
19.3
47.2
20.8
7.1
1.5
3.0
18.3
5.1
3.0
8.6
7.6
13.7
－
－
18.8

FourthWave(184 cases)
19.0
51.1
25.0
6.0
0.5
1.6
32.1
4.3
0.5
6.0
6.5
13.0
－
－
13.0

Source: Produced by JHPS2009–2012 (Surveyor Forms).

Table 3 Descriptive Statistics

Variables regarding household attributes
Respondent's gender (Reference: Male)
FemaleRespondent's age (Reference: Under 30)
30–39
40–49
50–59
60 or above
Marital Status (Reference: Unmarried)
MarriedNumber of Family Members
Number of Children
Sample Size
Respondent's Highest Level of Education(Reference: Junior High School)

Second Wave (2010)
Continued Participation
Attrition
Mean
SD
Mean
SD

Third Wave (2011)
Continued Participation
Mean
SD
Mean

0.51

0.50

0.55

0.50

0.51

0.50

0.17
0.18
0.17
0.36

0.38
0.38
0.37
0.48

0.17
0.12
0.15
0.35

0.37
0.33
0.36
0.48

0.17
0.18
0.17
0.38

0.73
3.63
1.04

0.44
1.59
1.06

0.64
3.57
0.83

0.48
1.61
1.01

0.74
3.62
1.06

High School
0.45
Technical School/Other
0.06
Junior College/Technical College
0.13
University/Graduate School
0.26
Respondent's Health (Reference: Good)
Somewhat Good
0.24
Average
0.33
Not Very Good
0.09
Bad
0.02
Respondent's Regular Employment
0.33
Sample Size†
0.02
Respondent's Change of Residence
Variables Regarding Survey
Survey Mode (Reference: At-home Survey)
Combined Interview
0.49
Response Mode (Reference: Paper Survey)
Web Survey
0.02
2.82
Number of Visits By Surveyors†
Sample Size

3464

548

0.53

0.50

0.51

0.50

0.51

0.50

0.38
0.39
0.37
0.48

0.12
0.15
0.15
0.41

0.32
0.36
0.36
0.49

0.17
0.20
0.17
0.38

0.37
0.40
0.37
0.49

0.11
0.15
0.17
0.46

0.31
0.36
0.38
0.50

0.44
1.56
1.07

0.65
3.63
0.91

0.48
1.54
1.06

0.74
3.59
1.08

0.44
1.57
1.07

0.75
3.60
0.94

0.43
1.61
1.04

3152

312

2811

Attrition
SD

341

0.50
0.24
0.33
0.44

0.43
0.06
0.12
0.25

0.50
0.24
0.32
0.43

0.45
0.06
0.13
0.27

0.50
0.24
0.33
0.44

0.46
0.06
0.13
0.22

0.50
0.24
0.33
0.41

0.46
0.07
0.13
0.27

0.50
0.25
0.33
0.44

0.46
0.05
0.12
0.24

0.50
0.22
0.32
0.43

0.43
0.47
0.29
0.13
0.47

0.22
0.36
0.09
0.02
0.31

0.41
0.48
0.28
0.15
0.46

0.25
0.34
0.09
0.01
0.33

0.43
0.47
0.29
0.10
0.47

0.24
0.35
0.13
0.03
0.28

0.43
0.48
0.34
0.16
0.45

0.27
0.34
0.10
0.02
0.33

0.44
0.47
0.30
0.13
0.47

0.25
0.41
0.11
0.02
0.31

0.43
0.49
0.31
0.12
0.46

0.15

0.14

0.34

0.03

0.18

0.12

0.33

0.03

0.18

0.08

0.26

0.50

0.53

0.50

0.49

0.50

0.49

0.50

0.49

0.50

0.49

0.50

0.15
1.18

0.02
2.95

0.15
1.28

0.03
2.99

0.17
1.15

0.02
3.25

0.14
1.23

0.03
3.09

0.17
1.55

0.03
3.24

0.17
1.60

3427

3403

SD

Fourth Wave (2012)
Continued Participation
Mean
SD
Mean

Attrition

540

3107

539

3107

305

305

2747

332

2747

Note: Basic statistics are shown for the largest sample size in each estimation. † indicatesvariables produced from surveyor forms. Only “Respondent’s change of residence” uses
the current wave data, all other variables use values from the previous wave data.
Source: Produced from JHPS2009–2012 and Surveyor forms.

10

332

The survey-related variables showed that there was a higher percentage of combinedinterviews than at-home surveys among the attrition sample in the second wave (p <0.05); however, there was no difference in the percentages of at-home and combinedinterviews between the continuing sample and attrition sample from the third wave
onward. Further, the web survey made no difference between the continuing andattrition sample in any waves. The number of surveyor visits was higher in the attrition
sample in every wave (p < 0.05), and subjects who tended to be uncooperative in theprevious survey tended to opt out of subsequent waves, though the reasons for not
wanting to cooperate are unclear.
Table 4 shows the estimation results of the probit model for panel continuation. Thisshows that the “Change of Residence dummy” significantly decreases the probability ofcontinuation in each survey, and that the majority of subjects that drop out do so
because of a change of residence. As for other variables related to household attributes,
higher age and education levels were correlated with higher probability of continuationin the second wave; however, these connections were not as visible in the third andfourth waves. In addition, unmarried subjects or particularly respondents with badhealth conditions in the previous wave tended to drop out in the third wave.
Respondents with more family members and fewer children tended to drop out in the
fourth wave. Furthermore, the female dummy and the regular employment dummy(introduced as an indicator of busyness), had very little effect on the probability ofcontinuation.
Similarly to Naoi and Yamamoto (2011), variables related to the survey tended tocause attrition among subjects of the interview survey in the second wave. However, it
was not the case in the third and fourth waves, demonstrating that this did not affectthe probability of continuation for subjects who had accepted the interview survey by
the second wave. In addition, the number of surveyor visits in the previous wave tended
to lower probability of continuation in every survey, and subjects difficult for the11

surveyor to approach tended to drop out, whether for voluntary (e.g., do not want toparticipate, too much of a bother) or involuntary (e.g., too busy) reason.
Table 4 Probit Model of Response Continuation
Continued Participation (Second Wave)
Variables regarding household attributes-0.0184* -0.0167-0.0146
Female Dummy[0.0108] [0.0118][0.0118]
Age (Reference: 20–29)
0.0456*** 0.0445**
0.0527***30–39[0.0175] [0.0175][0.0169]0.0702*** 0.0702***0.0591***40–49[0.0169] [0.0170][0.0179]
0.0456** 0.0498***
0.0419**50–59[0.0187] [0.0186][0.0192]
0.0497*** 0.0649***
0.0561***60 or above[0.0179] [0.0191][0.0193]0.01530.01430.0142Married Dummy[0.0149] [0.0151][0.0150]Highest Level of Education(Reference: Junior High School)
0.0547***
0.0533***High School[0.0176][0.0175]0.0608***0.0580***Technical School/Other[0.0191][0.0189]0.0575***0.0590***Junior College/Technical College[0.0177][0.0173]
0.0590***0.0611***
University/Graduate School[0.0177][0.0173]-0.00265 -0.00458-0.00384
Number of Family Members[0.00458] [0.00471][0.00468]
0.0139* 0.0152*
0.0150*
Number of Children[0.00812] [0.00816][0.00806]
Health (Reference: Standard: Good)
0.005930.00236
Somewhat Good
[0.0146][0.0148]-0.018-0.0245*
Average[0.0140][0.0143]0.00167
0.00353
Not Very Good
[0.0206][0.0204]-0.0294-0.0246
Bad[0.0471][0.0458]
0.004170.00855
Regular Employment Dummy[0.0134][0.0135]-0.359***Change of Residence Dummy[0.0427]
Variables regarding survey-0.0216**
Combined Interview Dummy[0.0108]-0.00298
Web Survey Dummy[0.0369]-0.00962**
Number of Surveyor Visits[0.00451]
No
Yes
Yes
Residence Dummy
Yes
Yes
Yes
Regional Dummy
Yes
Yes
Yes
City and District Scale Dummy
4,012
3,967
3,942
Sample Size
-1575
-1543
-1482
Log Likelihood

Continued Participation (Third Wave)

Continued Participation (Fourth Wave)-0.00562 0.00316[0.00953] [0.0104]

0.00248[0.0100]-0.00099 0.00129[0.0109] [0.0121]-0.000486[0.0119]0.0413*** 0.0485***[0.0150] [0.0137]
0.02580.0314*[0.0175] [0.0163]
0.01650.0216[0.0185] [0.0175]
0.00596 0.0228[0.0175] [0.0183]
0.0320** 0.0286**[0.0139] [0.0137]

0.0394***[0.0143]0.0149[0.0179]0.00193[0.0198]-0.00273[0.0191]
0.0263**[0.0134]

0.0440** 0.0493***[0.0197] [0.0189]
0.02650.0275[0.0224] [0.0220]
0.00214 0.00546[0.0248] [0.0244]-0.00858 -0.00896[0.0227] [0.0241]-0.0118 -0.0167[0.0143] [0.0140]

0.0483**[0.0188]
0.021[0.0224]-0.00365[0.0250]-0.0192[0.0241]-0.0185[0.0136]0.0204[0.0157]
0.0238[0.0200]
0.012[0.0186]
0.0342**[0.0160]-0.00474 -0.00644[0.00396] [0.00393]
0.00533 0.00636[0.00700] [0.00688]

0.02[0.0153]
0.0232[0.0194]0.01[0.0183]
0.0313**[0.0156]-0.00797**[0.00375]
0.00736[0.00671]

0.0286[0.0186]
0.0315[0.0239]0.0203[0.0215]0.0302[0.0193]-0.00962**-0.0125***[0.00460] [0.00453]
0.0183** 0.0206***[0.00812] [0.00789]

0.0302[0.0184]
0.0319[0.0234]
0.0237[0.0208]
0.0334*[0.0188]-0.0131***[0.00450]
0.0218***[0.00767]-0.0123[0.0137]-0.0198[0.0130]-0.0454**[0.0217]-0.129*[0.0711]0.00718[0.0120]-0.0157[0.0138]-0.0233*[0.0131]-0.0479**[0.0218]-0.139*[0.0715]
0.0082[0.0115]-0.196***[0.0405]-0.00865[0.0159]-0.0329**[0.0153]-0.0159[0.0222]0.00815[0.0414]-0.00832[0.0139]-0.00708[0.0158]-0.0327**[0.0156]-0.016[0.0224]0.011[0.0393]-0.00918[0.0139]-0.137***[0.0429]

No
Yes
Yes
3,464
-1026

Yes
Yes
Yes
3,412
-993.6-0.00111[0.00932]
0.0179[0.0247]-0.0159***[0.00368]
Yes
Yes
Yes
3,412
-965.2

No
Yes
Yes
3,152
-1065

Yes
Yes
Yes
3,079
-1029

-0.00338[0.0110]-0.0347[0.0401]-0.00694**[0.00319]
Yes
Yes
Yes
3,079
-1019

Note: ***, **, and * indicate that the coefficient is statistically significant at 1%, 5%, and 10%
levels, respectively. Numbers in brackets are the standard error.
Source: Produced from JHPS2009–2012 and Surveyor forms.

12Section 3 Overview of the Selected Results in the JHPS 2012
3.1. Family Assistance
The JHPS 2012 included questions concerning the level of financial assistanceprovided between the subject or subject’s spouse and their parents,4 and the reasonsinvolved. Previous surveys included questions about inheritance and lifetime gifts;
however, there have been only few surveys investigating mutual financial assistance ofparents including spouses, and asking for specific details, such as the amount given or
received.5 This section presents basic findings based on the data obtained from thesequestions.6
The JHPS 2012 began with a sample of 2,821 subjects (739 single, 2,082 married).
Because the survey asked married subjects the same questions about their spouse as
about themselves, the spouse’s responses were added to the sample of married subjects(because the households of married couples show up twice in the sample due to thismanipulation, further analysis will be conducted on single and married subjects
separately). Finally, excluding respondents whose parents are deceased or who did not
respond, the sample totaled 3,014 respondents (513 single, 2,501 married).
The survey questioned the level of financial assistance that respondents provided totheir parents or that received from their parents in the previous year, and inquired
about the amount and reasons.
Table 5 shows the incidence ratio of the four patterns made by “presence of financial
assistance from parents” and “presence of financial assistance to parents” within the
Hereafter, “parent” or “parents” refers to the subject’s own biological parents, unlessstated otherwise.
5 The “Japanese Panel Survey of Consumers” (Institute for Research on Household
Economics) asks whether children provide financial assistance to their parents, inaddition to parents giving lifetime gifts to their children. KHPS asks only the total
amount of assistance given to the subjects’ and spouse’s parents.
6 Considerable literature exists regarding the motivations and policy implications offinancial assistance and remittance within the family or extended family (e.g., Lucasand Stark, 1985; Altonji et. al., 1997; Hayashi, 1995; Horioka, 2002; Yamada, 2006; andSakamoto, 2006b.
4

13

samples by marital status. First, it is evident that the ratio of financial assistance sent
to parents was higher among single respondents than married respondents. Further
analysis of the data revealed that single respondents do not tend to provide financial
assistance to their parents if they received financial assistance from their parents (p =0.06). Conversely, married respondents tended to provide financial assistance to theirparents if they received financial assistance (p < 0.01). This indicates that financial
assistance between parents and children is possibly from the motivation to ensure
earnings in the case of single respondents, but may be motivated by reciprocity in thecase of married respondents.7

Table 5 Financial Assistance between Parents and Children (According to Marital
Status)Marital Status
SingleMarried

Receive Financial Assistance from Parents
No
Yes
No
Yes

Provide Financial Assistance to Parents
No
Yes
50.9%
29.4%
14.4%
5.3%
78.3%
7.8%
11.2%
2.7%

Note: Sample size = 3,014
Source: Produced from JHPS 2012.

Next, we confirm the reasons provided for financial assistance to and from therespondents’ parents. Figure 2 shows the ratio of respondents who chose one of thefollowing reasons among those who answered “received financial assistance fromparents” or “provided financial assistance to parents”: “living expenses,” “medical
expenses,” “housing purchasing assistance,” “rent,” “other,” or “no purpose.” First, the mostcommon reason, among both single and married respondents, why respondents
provided financial assistance to their parents was for “living expenses.” This wasfollowed by “rent” and “other” for married respondents. In Japan, it is customary forsingle individuals living with their parents to pay rent to their parents as theirco-residence fees and expenses; it is likely that this is what is meant by providing rent
The economic motives of intra-family financial assistance are summarized in Cox and
Fafchamp’s (2007).
7

14

in this context. In fact, we confirm that 95% of single respondents providing monetaryassistance to their parents for rent purposes actually reside with their parents.
Alternatively, among respondents, the primary reason why parents provided
financial assistance to their children was for “living expenses”; the secondary reason
was “other,” for both single and married respondents. This indicates that these include
inter-vivos given to children with no specific purpose. 8 In addition, only minimal
assistance is provided for home purchases. This is natural, considering the low
incidence rate of purchasing a home.9
Figure 2 Reasons for Financial Assistance

LivingExpenses

MedicalExpenses

Housing
Purchasing
Assistance

Rent

Other

MarriedSingleMarriedSingleMarriedSingleMarriedSingleMarriedSingleMarried

0.90
0.80
0.70
0.60
0.50
0.40
0.30
0.20
0.10
0.00
SingleRatio(a) From Respondents to Parents

No Purpose

The questions have the following note attached to it: “Please exclude inheritanceitems, but include housing purchases, rent, land rent, living expenses and otherfinancial assistance and allowances..”
9 However, there is literature that indicates the importance of the relationship between
inter-vivos and home purchase (Yukutake et al., 2011).
8

15

(b) From Parents to Respondents
0.90
0.80
0.70

Ratio

0.60

0.50
0.40
0.30
0.20
0.10

LivingExpenses

MedicalExpenses

Housing
Purchasing
Assistance

Rent

Other

MarriedSingleMarriedSingleMarriedSingleMarriedSingleMarriedSingleMarriedSingle0.00

No Purpose

Note: Each ratio indicates the proportion of subjects and their spouses who answered “provide
assistance to parents” or “receive assistance from parents” that chose one of thesereasons.
Source: Produced from JHPS2012.

We now confirm the relationship between attributes of the respondents and theirparents, and the financial assistance between parents and children. First, we refer tothe balance of the amount of assistance provided, both to and from parents, as “netfinancial assistance.” Confirmation of net financial assistance is required owing to thefact that many households provide/receive financial assistance to/from their parents
simultaneously.

16

Table 6 Relationship between Respondent/Parent Attributes and Financial Assistance(a) Gender
Give Financial Assistance to Parents
Receive Financial Assistance from Parents
Net Financial Assistance to Parents (in units of 10,000 yen)

Male
0.37
0.17
26.56

Female
0.32
0.23
3.71

Married Give Financial Assistance to Parents
N=2462 Receive Financial Assistance from Parents
Net Financial Assistance to Parents (in units of 10,000 yen)

0.12
0.13
-3.47

0.09
0.14
-6.55

Below University
0.32
0.19
7.98

University or Higher
0.40
0.21
27.45

0.11
0.13
-3.80

0.09
0.16
-7.25

SingleN=505p-value Test Results
0.21
NS
0.09
*
0.16
NS
0.06
0.48
0.47

*
NS
NS

(b) Education
SingleN=502

Give Financial Assistance to Parents
Receive Financial Assistance from Parents
Net Financial Assistance to Parents (in units of 10,000 yen)

Married Give Financial Assistance to Parents
N=2404 Receive Financial Assistance from Parents
Net Financial Assistance to Parents (in units of 10,000 yen)p-value Test Results
0.08
*
0.49
NS
0.25
NS
0.19
0.03
0.46

NS
**
NS

(c) Living Together With or Separately From Parents
Separate
0.15
0.12
2.35

Together
0.41
0.22
19.52

0.09
0.13
-2.95

0.20
0.21
-19.12Give Financial Assistance to Parents
Receive Financial Assistance from Parents
Net Financial Assistance to Parents (in units of 10,000 yen)

No
0.30
0.22
18.54

Yes
0.40
0.17
11.85

Married Give Financial Assistance to Parents
N=2462 Receive Financial Assistance from Parents
Net Financial Assistance to Parents (in units of 10,000 yen)

0.09
0.15
-3.18

0.11
0.13
-5.96

Employed
0.39
0.17
21.36

Unemployed
0.19
0.29
-1.97

0.11
0.14
-5.48

0.05
0.05
-0.44

SingleN=505

Give Financial Assistance to Parents
Receive Financial Assistance from Parents
Net Financial Assistance to Parents (in units of 10,000 yen)

Married Give Financial Assistance to Parents
N=2462 Receive Financial Assistance from Parents
Net Financial Assistance to Parents (in units of 10,000 yen)p-value Test Results
0.00
***
0.02
**
0.36
NS
0.00
0.00
0.01

***
***
***

(d) Parents’ Health Problems
SingleN=505p-value Test Results
0.02
**
0.20
NS
0.68
NS
0.03
0.26
0.54

**
NS
NS

(e) Respondent’s Employment Status
SingleN=465

Give Financial Assistance to Parents
Receive Financial Assistance from Parents
Net Financial Assistance to Parents (in units of 10,000 yen)

Married Give Financial Assistance to Parents
N=2020 Receive Financial Assistance from Parents
Net Financial Assistance to Parents (in units of 10,000 yen)p-value Test Results
0.03
**
0.09
*
0.52
NS
0.25
0.12
0.79

NS
NS
NS

(f) Respondent’s Work Status
Single
N=412

Give Financial Assistance to Parents
Receive Financial Assistance from Parents
Net Financial Assistance to Parents (in units of 10,000 yen)

Married Give Financial Assistance to Parents
N=1889 Receive Financial Assistance from Parents
Net Financial Assistance to Parents (in units of 10,000 yen)

17

Regular Employment
0.45
0.15
30.15
0.12
0.13
-2.70

Irregular Employment p-value Test Results
0.26
0.00
***
0.24
0.03
**
4.15
0.23
NS
0.08
0.16
-13.34

0.03
0.06
0.08

**
*
*

(g) Presence of Children (Married, N = 2462)
Married Give Financial Assistance to Parents
Receive Financial Assistance from Parents
Net Financial Assistance to Parents (in units of 10,000 yen)

No Children
0.11
0.09
-4.37

Children
0.10
0.15
-5.21

p-value Test Results
0.52
NS
0.00
***
0.88
NS

Note: “Other” under Highest Level of Education was classified as not completed University
education. “Parents’ health problems” were given a value of 1 if either parent had ahistoryofcancer,cardiovascular/endocrine/metabolic
disorder,orstroke/cerebrovascular disease. “Financial assistance to parents” and “financial
assistance from parents” are dummy variables; the numbers to the right represent theratio of “assistance present.” Difference tests were conducted by chi-squared tests fordummy variables, and t-tests (2-sided) for continuous variables.
Source: Produced from JHPS2012.

Table 6 describes the effects of the attributes of the respondents and their parents on
the provision and amount of assistance for single and married respondents. Test results
are summarized below:(a) Single female respondents tended to receive more financial assistance fromtheir parents, and married male respondents tended to provide morefinancial assistance to their parents; however, these differences were
marginal.
(b) University graduates tended to provide more assistance to parents in thecase of single respondents, and receive more assistance from parents in thecase of married respondents, than non-university graduates.
(c) Respondents living together with parents provided more assistance to, andreceived more assistance from, parents than respondents not living withparents.
(d) Both single and married respondents increased the financial assistanceprovided to parents in case of health issues compared to if they did not.
(e) In the case of single respondents, those unemployed provided less assistanceto parents and received more assistance from parents than employed
respondents. In all cases, respondents with non-regular employment
provided less financial assistance to parents and received more financial
18

assistance from parents, than respondents with regular employment. The
difference in incidence of parental assistance between regularly andnon-regularly employed respondents was greater among single respondents
than married respondents.
(f) Having children resulted in increased financial assistance from parents.
Multivariate analysis methods are required to provide a more detailed explanation;
however, (d), (e), and (f) are meaningful in that they suggest financial assistance withthe motivation of risk-sharing between parents and children.

3.2. Happiness
Since the 2011 survey, JHPS has asked for a subjective rating of the respondent’s
happiness in the three periods of “past week,” “past year,” and “entire life,” on a scale of0, “having no sense of happiness at all,” to 10, “having a sense of complete happiness.”
The JHPS 2012 obtained valid answers from 4,856 individuals, including subjects andtheir spouses, on these three items. Figure 3 displays the response distribution of thesethree items as percentages. The distributions are similar for all three items, and over
one-quarter of the cases rated their happiness as 5, the central value on the scale, inevery stage of life.

19

Figure 3 Frequency Distribution of Happiness
Past Week(%)

Past Year

Entire Life

30
25
20
15
10
5
0
0
No senseofhappinessat all

1

2

3

4

5

6

7

8

9

10
A sense ofcompletehappiness

Note: The percentage indicates the ratio with the total number of each response, “past week,”“past year,” and “entire life” as 1.
Source: Produced from JHPS 2012.

Averages for all three items were determined on the whole and by gender, and thedifferences in happiness levels by time period, gender, and age were reviewed. No
differences were found in the average happiness in “past week” and “past year”;
however, average happiness over the respondents’ “entire life” was significantly higher
than the other two variables. This did not change when comparing male and female
respondents separately (p < 0.001) (Table 7). Happiness among females was
significantly higher than among males for all three time periods (p < 0.001). Happinesstended to be significantly higher as age increased in the “past week” period (r = 0.04, p <0.001); however, no age differences were found for other time periods.

20

Table 7 Gender and Age Differences in Happiness

Past Week
Past Year
Entire Life

Total
(N = 4856)

Male(n = 2385)

Female(n = 2471)

Mean

Mean

Mean

6.17
6.17
6.41

SD

2.23
2.15
1.90

6.02
6.05
6.34

SD

2.25
2.15
1.91

6.30
6.28
6.48

Gender Difference

SD

2.20
2.14
1.89

***
***
***

Effect ofAge ( r ).04 ***.02-.02

Note: *** indicates that the coefficient is statistically significant at 1% level.
Source: Produced from JHPS 2012.

The correlation of happiness for these three time periods was high, at r = 0.83
between “past week” and “past year,” r = 0.69 between “past week” and “entire life,” andr = 0.76 between “past year” and “entire life” (p < 0.001 across the three values). In
addition, highly similar correlations were observed of couples’ happiness levels from the
2,037 pairs of valid answers from both the subjects and their spouses, at r = 0.45 for“past week,” r = 0.48 for “past year,” and r = 0.45 for “entire life” (p < 0.001 across thethree values).
Recently, there has been a considerable amount of active research explaining thedetermining factors of happiness in the field of economics (Frey, 2008). This section
considers the relationship of happiness with employment, income from work, householdincome, and health conditions.
Each JHPS asks whether they were engaged in employment that earned their income
during the month prior to the survey (including family business and part-time work). If
the respondent indicated that they had worked, they were asked to choose one of thefollowing: “I worked fulltime,” “I worked besides attending school,” or “I worked besides
doing housework.” Conversely, if the respondent indicated that they had not worked,
they were asked to choose one of the following: “I was absent from work,” “I was looking
for a job,” or “I was attending school/housework/other.” This was asked to better
determine the individual’s situation. Excluding “I worked besides attending school,”which had less than 10 responses, the average happiness values in the “past week” for21each group that chose each option are shown in Figure 4. No significant difference wasfound between the happiness of people who “worked fulltime,” which comprised morethan half of the cases, and those who “worked besides attending school” and “wereabsent from work”; however, their happiness was significantly higher than the people
who did not work and “were looking for a job” (p < 0.001), and significantly lower thanpeople who did not work and “were attending school/housework/other” (p < 0.001).

Figure 4 Employment and Happiness (Past Week)(Points)

Workedfulltime

Worked besides
doing Housework

Absentfrom work

Looking
for a job

Attending school/
Housework/Others

Note: Error bars show 95% confidence interval.
Source: Produced from JHPS2012.

Among working respondents, JHPS asks the amount of income generated from theirprimary job, before taxes and social insurance deductions, over the past year. These
self-generated incomes were categorized into seven levels, and their relationship totheir individual “entire life” happiness was examined as shown in Figure 5. This showedthat happiness was proportional with income; the correlation was significant at r = 0.12
(N = 3,115, p < 0.001).

22

Figure 5 Job Income and Happiness (Entire Life)(Points)

250~

500~

750~

1000~

1250~

1500~

1750~

(In units of ten thousand yen)

Note 1: Income from work refers to the income from the respondent’s primary employment overthe past year. This is the amount before taxes and social insurance deductions.
Note 2: Error bars show 95% confidence interval.
Source: Produced from JHPS2012.

To clarify whether this happiness originates from the income obtained from therespondents’ own jobs, or from the wealth of their household on the whole, a similar
examination was conducted using the entire household income, which revealed a trendthat happiness significantly increased in relation to an increase in household income(Figure 6). The correlation was r = 0.14 (N = 4,219, p < 0.001), and the entire householdincome had a marginally higher coefficient of determination for happiness than that ofincome obtained from the respondents’ own job.

23

Figure 6 Household Income and Happiness (Entire Life)(Points)

250~

500~

750~

1000~

1250~

1500~

1750~

(in units of ten thousand yen)

Note 1: Household income refers to the take-home income for the entire household in the past
year.
Note 2: Error bars represent a 95% confidence interval.
Source: Produced from JHPS2012.

Figure 7 Health and Happiness (Entire Life)(Points)

Good

Fairly Good

Average

Note: Error bars show a 95% confidence interval.
Sorce: Produced from JHPS2012.

24

Fairly Bad

Bad

A comparison of the averages of “entire life” happiness for the groups that reported
their health conditions as “good,” “fairly good,” “fairly bad,” or “bad,” demonstrated that
better health conditions were related to higher levels of happiness, and that worse
health conditions were related to lower levels of happiness (Figure 7). The correlation ofhappiness and health conditions (reversed) is r = −0.24 (p < 0.001), which is a higher
correlation than income from work and household earnings.

3.3. Reasoning
It is rare in Japan for social surveys of adults to include cognitive ability indicators.
There have been attempts to do so through vocabulary tests for adolescents and theirparents, such as in “Occupation and Personality Research” (Kikkawa, 1998); however,in such cases the sample sizes were too small for the adults’ cognitive ability torepresent as a single variable on statistical analysis, and such studies never developed
into a positive review explaining its relationship to households.
Alternatively, the American National Longitudinal Surveys of Youth (NLSY) 10
include a cognitive test from the National Armed Forces Qualifying Test (AFQT) in its
large-scale panel surveys. The respondent’s cognitive scores are included in the data set,
and the variable is used for analysis in various fields of research as an intelligencequotient (IQ).
Furthermore, the Organisation for Economic Co-operation and Development (OECD)began the “Programme for the International Assessment of Adult Competencies”(PIAAC)11 in 2011 across 24 countries around the world, including Japan, which uses a
cognitive ability test for adults. This indicates a growing global interest focusing on therelationships between economics and adult cognitive abilities.

10
11

National Longitudinal Survey http://www.bls.gov/nls/
PIAAC http://www.nier.go.jp/04_kenkyu_annai/div03-shogai-piaac-pamph.html
25Given this background, JHPS attempted to include cognitive ability indicators intheir survey. In addition to the practical restriction that the problems would have to be
included in a self-reported questionnaire, these problems would require no strict time
limit for the participant’s answer. In addition, the answers should be difficult to look up
elsewhere, while the problems should not be of great burden to answer. Therefore,
JHPS introduced five reasoning problems12 (Shikishima et al. 2011) using syllogistic
logic questions, to ensure reliability and validity as a test of cognitive ability in a mailsurvey. Syllogisms are a form of logical deductive reasoning where one concluding
statement is deduced from two premise statements. It has been revealed that syllogism
solving is an excellent indicator of general intelligence (Shikishima et al. 2009).
Below is a sample problem similar to the five reasoning problems introduced in JHPS.
The respondent is asked to choose one of the five statements that holds true as aconclusion of the two premises.

Some cats are pets. All cats are mammals.
1. No mammal is a pet.
2. All mammals are pets.
3. Some mammals are not pets.
4. Some mammals are pets.
5. No valid conclusion.

The instructions specified that responding is voluntary in addition to responding it
independently without discussing it with anyone, and answering each question within aminute. Such specifications were made out of consideration for the participant’s
resistance to answering the extraneous reasoning questions newly introduced into the

The same problems are translated into English, and archived in the PsycTESTS®
database of the American Psychological Association.
12

26

household panel survey. As a result, valid answers were obtained from 2,545 subjects in
the JHPS 2011 (81% response rate), and 1,640 spouses in the JHPS 2012 (79% responserate), a total of 4,185 respondents (80% response rate). To review whether there wereany different attributes between respondents and non-respondents, the subjects
participating in the JHPS 2011 and the spouses participating in the JHPS 2012 weredivided into reasoning question participants and non-participants; this was used as thedependent variable for logistic regression analysis. Age, gender, education, and income
from work were used as independent variables. Only education had a significant
coefficient (p < 0.01), demonstrating that more respondents with high levels of
education were participants, and more respondents with low levels of education were
non-participants.
The reasoning score was the sum of the problems answered correctly out of five. The
mean (SD) scores of the 2,052 male and 2,133 female respondents were 2.44 (1.62) and2.35 (1.61), respectively. This indicated that the male respondents’ scores weresignificantly higher by a 10% level. The correlation obtained from the 1,429 pairs ofvalid answers from both the subjects and their spouses was r = 0.39 (p < 0.001).
Presently, there is no publicly disclosed value of the spousal correlation of cognitive
ability in Japan; however, the spousal correlation of IQ reported in Europe and North
America is generally r = 0.4–0.5 (Vandenberg, 1972). Considering the fact that theseself-administered questions have a higher possibility for measurement errors compared
to cognitive tests administered in the test setting, this value is plausible.

27

Figure 8 Age Differences of Reasoning Score(Points)

4

3

2

1

0
―29

30―39 40―49 50―59 60―69 70―79

80―

(Age)

Source: Produced from JHPS2011 and JHPS2012.

Categorizing ages by decades and determining the average reasoning score for eachgroup showed no difference in performance in individuals in their 20s–50s; however, theaverage began to decline from the 60s (Figure 8). Collective psychological researchrelying on factor analysis explains human intelligence structure as a hierarchicalframework with general intelligence at the top (Spearman, 1904). At the bottom of thishierarchy are crystallized intelligence, mainly consisting of language functions typicallymeasured by vocabulary comprehension, and fluid intelligence, which is a nonverbal
function and is reflected in new problem-solving ability (Horn & Cattell, 1966).
Research indicates that crystallized intelligence peaks at the age of 45–54 years, andgradually declines subsequently, while fluid intelligence peaks in the early 20s, later
witnessing a rapid decline (Kaufman, 2001). The age differences in syllogism-solving
level acquired from this survey can be seen as being positioned in the middle of thechanges of crystallized and fluid intelligence levels, showing that the reasoning score
has validity as an indicator of a more generalized cognitive ability (general intelligence),
combining both intelligences.
The average reasoning scores for each group of respondents to the question in the
JHPS 2009 on their highest level of schooling as “junior high school,” “high school,”28

“junior college/technical college,” “university,” or “graduate school” were recorded. This
demonstrated that the scores were notably higher as education level increased (Figure9). The correlation between education and reasoning scores was significant at r = 0.31 (p< 0.001). Interpretation of these results requires caution, because more people withhigher education levels tended to answer; however, this demonstrated that there was aclear positive correlation between levels of education and reasoning ability.

Figure 9 Education and Reasoning Score(Points)

Junior highSchoolHigh Junior/Technical
SchoolCollege

University

Graduate
SchoolNote: Error bars show a 95% confidence interval.
Source: Produced from JHPS 2011 and JHPS 2012.

To investigate the relationship between reasoning score and employment income, therespondents who worked in the month prior to the survey completion were classified
into seven levels on the basis of their income over the past year from their primary job(before taxes and social insurance deductions), and the average reasoning scores foreach group was found. From the 2.5–5.0 million yen group to the 12.5–15.0 million yen
group, performance increased with annual income; however, performance tended todecrease in the 15.0–17.5 million yen and above 17.5 million yen group (Figure 10). The
29

t-test showed that the reasoning scores significantly increased between the 5.0–7.5
million yen and 7.5–10.0 million yen groups (p < 0.01), and between the 7.5–10.0 million
yen and 10.0–12.5 million yen groups (p < 0.01); however, the 15.0–17.5 million yen
group scored significantly lower than the 12.5–15.0 million yen group (p < 0.05). With
education and reasoning as well as happiness and income from work both having alinear correlation; what is the cause of the nonlinear correlation between reasoning
score and income from work? Future detailed research will be necessary to explore thecomplex relationship between cognitive ability and income.

Figure 10 Income from Work and Reasoning Score(Points)

250~

500~

750~

1000~

1250~

1500~

1750~

(in units of ten thousand yen)

Note: Error bars display a 95% confidence interval.
Source: Produced from JHPS 2011 and JHPS 2012.

Section 4: Conclusion
In this paper, we used data obtained from the latest JHPS conducted in January 2012
to clarify panel retention rates and sample attrition, and explain the outlook of thesurvey results on a number of topics. In particular, Section 2 showed the results on“family assistance,” “happiness,” and “reasoning” as they are unique survey items
30

characteristic to JHPS. Due to page and time restraints, this has not been a detailed
analysis; however, the analysis was able to obtain findings of interest that could lead tofuture research. We sincerely expect the results from this paper to inspire future
research on these items in the broader fields including economics, sociology, andpsychology.
JHPS started in 2009, and will complete its initially-scheduled five-year surveys in
2013. JHPS will continue with the addition of revisions; however, survey continuation isa extremely difficult challenge. In particular, JHPS, which has actively included novel
survey items that have not necessarily been included in previous panel surveys,
presents a difficult problem of continuing the survey over a long term while fulfilling anappearently

opposing

demandsofconservative

continuation

andprogressive

improvement. As part of our nation’s policy research infrastructure, we must consider
what is necessary for JHPS to be both socially accepted and widely used in the future,
based on the experience that has been obtained over the past five years.
Finally, we would like to mention the many people who have been involved inplanning the JHPS, designing the questionnaires, administering the survey, data
cleaning and consolidating/providing data, and analyzing the data over the past five
years. We would like to express our heartfelt gratitude to all these people involved inthis process. High quality data is the foundation of all research, and creating them t
requires often unrewarding vast amount of hard work because of its “public good”
nature. More attention must be given to create a better academic and social
environment in Japan so that many researchers will be able to participate in building
this important research infrastracture.

31

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