Panel Data Research Center, Keio University
PDRC Discussion Paper Series

Japan Household Panel Survey (JHPS/KHPS) Sampling Weights
Mateus Silva Chang, Guillaume Osier, Kayoko Ishii
28 November, 2022
DP2022-003
https://www.pdrc.keio.ac.jp/en/publications/dp/8199/

Panel Data Research Center, Keio University
2-15-45 Mita, Minato-ku, Tokyo 108-8345, Japan
info@pdrc.keio.ac.jp
28 November, 2022

Japan Household Panel Survey (JHPS/KHPS) Sampling Weights

Mateus Silva Chang, Guillaume Osier, Kayoko Ishii
PDRC Keio DP2022-003
28 November, 2022
JEL Classification: C8; C80; C83
Keywords: JHPS/KHPS; Sampling Weights; Weight calculation; Calibration; Survey

Abstract
Sampling weights are used to make inferences about the target population based on a specific sample.
Given the importance of weighting survey observations when drawing inferences about the overall
population, this paper documents the calculation of sampling weights for the Japan Household Panel
Survey (JHPS/KHPS). First, we provide an overview of the sample design and structure of the
JHPS/KHPS. This information is then used as a base to define the strategy adopted in the weightcalculation process. Next, the integrated approach used to compute sampling weights for the initial
fourteen waves (2004-2017) of the JHPS/KHPS and the different types of weights available are
introduced. Finally, we provide advice on how the weights should be used and illustrate theireffectiveness by comparing unweighted and weighted JHPS data with official statistics.

Mateus Silva Chang
Faculty of Economics, Keio University
2-15-45 Mita, Minato-ku, Tokyochang.mateus@keio.jp
Guillaume Osier
Statistics Luxembourg (STATEC)
13 Rue Erasme, 1468 Luxembourg
Kayoko Ishii
Faculty of Economics, Keio University
2-15-45 Mita, Minato-ku, Tokyo

Acknowledgement: The authors gratefully acknowledge the Japanese National Statistics Center(NSTAC) for providing the data from the Labor Force Survey from the Statistics Bureau of Japanthat was necessary for the calculation of the Japanese population benchmark used in the weightcalibration process. We also thank Akira Fukuda and Wenqing Chen for their support regarding
the preparation of the figures for Section 8. This work was supported by Grant-in-Aid for Specially
Promoted Research 17H06086 (2017/04/25 - 2022/03/31) and JSPS Program for Constructing
Data Infrastructure for the Humanities and Social Sciences (R3-R4).

Japan Household Panel Survey (JHPS/KHPS) Sampling Weights1

Mateus Silva Chang

Guillaume Osier

Kayoko Ishii

Keio University

Statistics Luxembourg (STATEC)

Keio University

Summary
Sampling weights are used to make inferences about the target population based on a
specific sample. Given the importance of weighting survey observations when drawing
inferences about the overall population, this paper documents the calculation of samplingweights for the Japan Household Panel Survey (JHPS/KHPS). First, we provide an overview
of the sample design and structure of the JHPS/KHPS. This information is then used as a baseto define the strategy adopted in the weight calculation process. Next, the integrated approach
used to compute sampling weights for the initial fourteen waves (2004-2017) of the
JHPS/KHPS and the different types of weights available are introduced. Finally, we provide
advice on how the weights should be used and illustrate their effectiveness by comparing
unweighted and weighted JHPS data with official statistics.

The authors gratefully acknowledge the Japanese National Statistics Center (NSTAC) for providing the datafrom the Labor Force Survey from the Statistics Bureau of Japan that was necessary for the calculation of theJapanese population benchmark used in the weight calibration process. We also thank Akira Fukuda and Wenqing
Chen for their support regarding the preparation of the figures for Section 8. This work was supported by Grantin-Aid for Specially Promoted Research 17H06086 (2017/04/25 - 2022/03/31) and JSPS Program for Constructing
Data Infrastructure for the Humanities and Social Sciences (R3-R4).
1

1. Introduction
Ideally, the collection of data from all individuals in a target population would be thebest option to study population characteristics, evaluate implemented policies or analyze thedynamic behavior of individuals and households. However, given time and resource constraints,
the survey is a powerful alternative to the census as a tool to quickly gain information andinsights from the target population. A survey is designed to guarantee that collecting data froma subgroup of the population, identified as a sample, will still enable us to draw inferences
about the entire target population.
Despite the development of a careful sample design and the efforts made to implement
the survey according to an established plan, unexpected problems and difficulties in collecting
the data can lead to sample selection bias. In addition, in the case of panel data surveys,
nonresponse and attrition are also problems that can affect the representativeness of thecollected data. These distortions can be adjusted by a correction technique called weighting.
Sampling weights are values attributed to each sample observation to ensure that the
metrics derived from the survey data are representative of the whole target population. The
calculation of these weights must take into consideration the main features of the survey design,
such as selection probabilities, the existence of regional clusters or unit nonresponse.
Population benchmarks taken from external sources can also be used to adjust the samplingweights to generate more stable survey estimations. This adjustment process is known ascalibration.
Given the importance of weighting survey observations when drawing inferences about
the overall population, this paper documents the calculation of sampling weights to the Japan
Household Panel Survey (JHPS/KHPS). In the first section, an overview of the sample designand structure of the JHPS/KHPS is provided. Next, the integrated approach used to compute
sampling weights for the JHPS/KHPS and the different types of weights available are
introduced. Finally, we provide advice on how the weights should be used and illustrate theireffectiveness by comparing unweighted and weighted data with official statistics.

2. Overview and structure of the Japan Household Panel Survey(JHPS/KHPS)
The Japan Household Panel Survey (JHPS/KHPS) was established in 2014 as a resultof the integration of the Keio Household Panel Survey (KHPS), a survey that has been
implemented since 2004, and the Japan Household Panel Survey (JHPS), a survey that was1introduced in 2009. The main objective of the JHPS/KHPS is to provide data that represent theJapanese population, thereby allowing the analyses of dynamic behavior by economic entities.
The survey covers comprehensive topics such as household structure, individual attributes,
academic background, employment status, time use, health condition, well-being, income,wealth and others.
2.1 Data Structure
The first wave of the KHPS was conducted in 2004 and collected information from asample of 4,005 respondents aged 20 to 69 years. The KHPS sample was extended through therecruitment of an additional 1,419 individuals in 2007 and 1,012 more individuals in 2012. The
first wave of the JHPS was conducted in 2009 and obtained data from 4,022 respondents aged
20 and over. Due to the similarity of these two surveys, the KHPS and the JHPS were combinedin 2014 and named the “Japan Household Panel Survey (JHPS/KHPS)”, thereby indicating the
adoption of a common questionnaire. After the integration, the JHPS/KHPS received a top-up
sample of 2,203 respondents aged 20 and over in 2019. Figure 1 shows the total sample sizesof the JHPS/KHPS for each survey year.
Figure 1. Sample size of the Japan Household Panel Survey (JHPS/KHPS)
8,000

Number of Respondents

7,000
6,000
5,000
4,000
3,000
2,000
1,000
0
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021
Survey YearKHPS2004

KHPS2007

KHPS2012

JHPS2009

JHPS/KHPS2019

Source: Authors, based on the JHPS/KHPS.

2.2 Sample Design
The survey subjects were restricted to individuals living in private dwellings in Japanand were selected according to a two-stage stratified random sampling method. For the case of2the KHPS, the samples were limited to individuals aged 20 to 69 years in the first wave, while
for the JHPS, the samples were limited to individuals aged 20 and over in the first wave.2
To implement the two-stage stratified random sampling method, Japan was stratified
into 24 strata following a regional and municipal classification. The number of subjects in eachstratum was allocated in proportion to the registered population according to the Basic Resident
Register of the previous year.3 Next, the districts inside each stratum were selected followinga systematic random sampling process, and an average of 10 subjects per district were selecteduntil the predefined total number of subjects per stratum was achieved. Then, these subjects
were also randomly sampled.
To guarantee the necessary number of respondents, reserve subjects were selected andused, when necessary, to replace original subjects who could not be contacted or declined toparticipate in the survey. For each original subject, between 3 and 5 reserve subjects wereselected from the same surveyed district. Although these reserve subjects were also randomly
selected, they had characteristics similar to those of the original subjects they were supposed
to replace; i.e., they were from the same sex and age group (20s, 30s, 40s, 50s, and 60s for the
KHPS sample and 20s, 30s, 40s, 50s, 60s and over for the JHPS sample).
2.2.1 Sample design of the top-up samples
In light of sample attrition problems, top-up samples were added to the KHPS in 2007
and 2012. The sample selection for the KHPS2007 sample and the KHPS2012 sample followed
the sampling method adopted for the KHPS2004 sample. Two-stage stratified random
sampling was adopted, with Japan being stratified into the same 24 strata and the number ofsubjects in each stratum being allocated in proportion to the registered population according to
the Basic Resident Register of the previous year.
2.3 Survey methods and respondent tracking
The JHPS/KHPS uses the drop-off and pick-up (DOPU) method.4 Regarding the dropoff and pick-up survey, an investigator visits the respondents, distributes the questionnaires
and then once again visits the respondents on a later date to collect the completed
questionnaires. If a subject is absent at the time of visitation, then the investigator attempts to
The age range of the respondents were defined based on the age they completed in the year the survey was
conducted and not on the age they were when they were selected.
3
For example, on the KHPS first wave it was used the Basic Resident Register from March 31, 2003.
4
In the first years of the JHPS several experiments were performed to see the effects of different survey methods
on response rates. The experiments included, for instance, providing web survey option, conducting interview
instead of paper-based questionnaire, and introducing incentive reward to investigators. Naoi, Yamamoto andMiyauchi (2010) examines the effects of each experiment on response rate of original subjects.
23contact the subject via a different means of communication (such as leaving a note); allprocedures are recorded. Usually, the survey is conducted at the beginning of February every
year.
After the first wave, each selected subject was expected to be tracked. If in the following
years a respondent has moved out, he or she is tracked to his or her new living location. In theevent that a respondent has died or disappeared, his or her spouse can participate by serving asa substitute for the respondent in the survey; in such cases, the spouse is added as a new
respondent and receives a new ID number. For cases where the respondent is not able tocooperate with the survey in a given year and has not clearly expressed his or her intention of
not cooperating in following years, the investigator tries to contact him or her in subsequent
years. For cases where such a respondent does cooperate in one of the following years, he or
she is counted as a revival case.
2.4 The nonresponse issue
In general, all sampled subjects are requested to participate in a survey. However, it iscommon to have cases where the selected subject does not participate and is considered anonrespondent. For the case of the KHPS and the JHPS, a different strategy was adopted, withboth original and reserve subjects being prepared to guarantee the scheduled sample size.
Therefore, it is not possible to calculate the response rate in a straightforward manner, as it isusually calculated in the first wave of other surveys.
An alternative way was to calculate the quasi-response rate by referring to the“Investigator Check Sheet” of each subject. First, the total number of original and reserve
subjects contacted by the investigators is identified. Next, the quasi-response rate is calculated
by using this number as the denominator and the number of total responses as the numerator.
Table 1 displays the quasi-response rate for the first wave of each panel.
Table 1. The quasi-response rate for the first wave
2004

(1)
(2)
(3)
(4)
(5)

KHPS
2007

JHPS
2012

2009

2019
Total number of subjects
13,4305,868
3,183
12,5499,465(original and reserve subjects)
Number of valid response1
4,005
1,419
1,012
4,022
2,203
Number of subjects contacted
9,665
4,223
2,425
10,0756,585
by the investigators2
In-touch rate [(3)/(1)*100]
72.0%
72.0%
76.2%
80.3%
69.6%
Quasi-response rate [(2)/(3)*100]
41.4%
33.6%
41.7%
39.9%
33.5%
Source: Authors, based on the JHPS/KHPS.
1
Include reserve subjects.
2
Given that the “Investigator Check Sheet” was not implemented in KHPS2004, the in-touch rate of
KHPS2004 is calculated based on the in-touch rate of KHPS2007.

4

From the second wave onward, the panel attrition issue refers to the loss of sample
subjects. The response rates after the second wave are calculated as the number of respondents
divided by the number of subjects who responded to the survey in the previous year. Table 2
and Table 3 show the response rates for both the KHPS and the JHPS.
Table 2. Response rate of the KHPS
(1)
(2)
(3)
(4)
(5)
(6)

(1)
(2)
(3)
(4)
(5)
(6)

(1)
(2)
(3)
(4)
(5)
(6)

2005
2006
wave 2wave 3
Number of subjects
4,005
3,342
of which responded in last wave
4,005
3,314
of which revivals
028
Number of respondents
3,314
2,887
of which revivals
00Response rate [((4)-(5))/(2)*100]
82.7%
87.1%
2011
2012
wave 8wave 9Number of subjects
3,232
3,041
of which responded in last wave
3,207
3,030
of which revivals
25
11
Number of respondents
3,030
2,865
of which revivals
1010
Response rate [((4)-(5))/(2)*100]
94.2%
94.2%
2017
2018
wave 14 wave 15
Number of subjects
2,970
2,769
of which responded in last wave
2,945
2,741
of which revivals
25
28
Number of respondents
2,741
2,549
of which revivals
11
14
Response rate [((4)-(5))/(2)*100]
92.7%
93.0%
Source: Authors, based on the JHPS/KHPS.
1
From 2008, this includes the 2007 top-up sample.
2
From 2013, this includes the 2012 top-up sample.

2007
wave 4
2,894
2,887
7
2,643
3
91.4%
20132
wave 10
3,888
3,877
11
3,568
11
91.7%
2019
wave 16
2,572
2,549
23
2,378
8
93.0%

20081
wave 5
4,067
4,062
5
3,691
0
90.9%
2014
wave 11
3,587
3,568
19
3,312
7
92.6%
2020
wave 17
2,409
2,378
31
2,244
14
93.8%

2009
wave 6
3,706
3,691
15
3,422
4
92.6%
2015
wave 12
3,353
3,312
41
3,124
16
93.8%
2021
wave 18
2,281
2,244
37
2,054
19
90.7%

2010
wave 7
3,448
3,422
26
3,207
7
93.55%
2016
wave 13
3,152
3,124
28
2,945
10
94.0%

Table 3. Response rate of the JHPS
2010
wave 2
4,022
4,022
0
3,470
0
86.3%

(1)
(2)
(3)
(4)
(5)
(6)

Number of subjectsof which responded in last waveof which revivals
Number of respondentsof which revivals
Response rate [((4)-(5))/(2)*100]

2011
wave 3
3,490
3,470
20
3,160
6
90.9%

2012
wave 4
3,170
3,160
10
2,821
4
89.1%

2013
wave 5
2,839
2,821
18
2,581
8
91.2%

2014
wave 6
2,596
2,581
15
2,358
6
91.1%

2015
wave 7
2,384
2,358
26
2,198
6
93.0%

(1)
(2)
(3)
(4)
(5)
(6)

2016
2017
wave 8 wave 9
Number of subjects
2,211
2,060
of which responded in last wave
2,198
2,048
of which revivals
13
12
Number of respondents
2,048
1,885
of which revivals
93
Response rate [((4)-(5))/(2)*100]
92.8% 91.9%
Source: Authors, based on the JHPS/KHPS.
1
From 2020, this includes the 2019 top-up sample.

2018
wave 10
1,897
1,885
12
1,742
4
92.2%

2019
wave 11
1,759
1,742
17
1,589
7
90.8%

20201
wave 12
3,824
3,792
32
3,226
14
84.7%

2021
wave 13
3,328
3,226
101
2,763
25
84.9%

5

3. Weight computation strategy
Hereafter, we describe how to develop weights for the first 14 waves (2004-2017) of
the JHPS/KHPS. The computation of sampling weights for the JHPS/KHPS relies on“individual base weights”, which are the backbone of the calculation procedure. Since thesample unit of the JHPS/KHPS is individual, the individual base weights are defined for the
first wave of each sample: KHPS2004, KHPS2007, KHPS2012, and JHPS2009. For thesewaves, the base weights are calculated as the inverse of the selection probabilities of eachindividual in the sample. Next, these probabilities are adjusted for unit nonresponse and thencalibrated to external population benchmarks. From the second wave onward, the base weights
must be corrected to address panel attrition. These adjustments aim to maintain the
representativeness of the data in the later years.
Next, the weights calculated for different waves and samples can be combined to
produce different sets of JHPS/KHPS weights. The derived groups of weights are classified
according to Table 4.
Table 4. Computed weights
Weight TypeBase
KHPS 2004
KHPS 2007
KHPS 2012
JHPS 2009

Longitudinalweights

JHPS/KHPS 2009

JHPS/KHPS 2012

KHPS
Individual andhouseholdcross-sectional
weights

JHPS
JHPS/KHPS

Working Sample
Individuals initially selected for the KHPS2004 sample
Individuals initially selected for the KHPS2004 sample
and those selected for the KHPS2007 top-up sample
Individuals initially selected for the KHPS2004 sample
and those selected for the two KHPS top-up samples(KHPS2007 and KHPS2012)
Individuals initially selected for the JHPS2009 sample
Individuals in the combined JHPS/KHPS sample,
including individuals initially selected for the KHPS2004
sample, those selected for the KHPS2007 top-up sample,
and those selected for the JHPS2009 sample
Individuals in the combined JHPS/KHPS sample,
including individuals initially selected for the KHPS2004
sample, those selected for the two KHPS top-up samples(KHPS2007 and KHPS2012), and those selected for the
JHPS2009 sample
Individuals initially selected for the KHPS2004 sample
and those selected for the two KHPS top-up samples(KHPS2007 and KHPS2012)
Individuals initially selected for the JHPS2009 sample
Individuals initially selected for the KHPS2004 sample,
those selected for the two KHPS top-up samples(KHPS2007 and KHPS2012), and those selected for the
JHPS2009 sample

Source: Authors, based on the JHPS/KHPS data structure.

6

The aforementioned strategy basically follows the general steps mentioned in Watson(2012). In the next subsections, the procedure will be described in detail.
3.1 Computation of individual base weights for the first wave of each sample
The procedure described here applies not only to the KHPS2004 sample but also to the
JHPS2009 sample and to the top-up samples (KHPS2007 and KHPS2012). The procedure forcomputing individual base weights for first-wave samples is basically a stepwise approach
whereby weighting coefficients are calculated and adjusted at each step to take into accountthe main features of the sample selection procedure.
3.1.1 Step 1: Computation of individual design weights
By definition, design weights are defined for each individual in the initial year of eachsample and are calculated as the inverse of the selection probability of each individual. The
selection probability of each individual depends on the sampling design of the survey. For the
JHPS/KHPS case, the survey subjects were selected by a two-stage stratified random sampling
method, where Japan as a whole was stratified into 24 strata following a regional and municipal
classification. The number of subjects drawn in each stratum was allocated in proportion to thesize of the stratum in the registered population. Districts inside each stratum were selectedfollowing a systematic random sampling process until the predefined total number of subjects
per stratum was achieved. These subjects were also randomly sampled.
Grounded in the aforementioned information, the design weights are calculated as theinverse of the selection probabilities of individual i in district d and stratum s as follows:

𝑃𝑖𝑑𝑠 = 𝑃𝑟(𝑑 𝑠𝑒𝑙𝑒𝑐𝑡𝑒𝑑) × 𝑃𝑟(𝑖 𝑠𝑒𝑙𝑒𝑐𝑡𝑒𝑑 𝑖𝑛 𝑑) =𝑎𝑠 𝑏𝑑𝑠×
𝐴𝑠 𝐵𝑑𝑠where 𝑎𝑠 is the total number of districts selected in stratum s; 𝐴𝑠 is the total number of districts
in stratum s; 𝑏𝑑𝑠 is the total number of individuals selected in district d localized in stratum s;and 𝐵𝑑𝑠 is the total number of individuals in district d localized in stratum s.
As the sample size of individuals in each stratum has been allocated to be proportional
to the total number of individuals in the stratum, the probability of selection is the same foreach individual.5 For this reason, the design weight (𝑑𝑤𝑖 ) is constant for individual i in each
initial sample:
Given that the survey was employed using the drop-off/pick-up system (DOPU), the assumption of equal
probability sampling is not entirely true. Nevertheless, given that the differences are marginal, we assume that theequal probability assumption is still valid.
5

7

1
𝑁
=
𝑃𝑖𝑑𝑠 𝑛𝑑𝑤𝑖 =where 𝑁 is the size of the overall population, and 𝑛 is the size of the total sample.
3.1.2 Step 2: Correction for unit nonresponse
Unit nonresponse refers to the failure to collect any survey information for a fractionof the individuals in the initial sample. There are several reasons for such a failure; for example,
the dwelling may be difficult to locate or access or members of the household may be absent,
refuse to respond to the questionnaire or suffer from health or more general incapacity
problems. Unit nonresponse is detrimental to sample representativity when nonrespondents andrespondents have different profiles with respect to the main characteristics of the survey.
To properly account for the bias caused by unit nonresponse, the traditional approachconsists of estimating the probability of response 𝜃𝑖 of the respondents through logistic
modeling and then adjusting the design weights accordingly. Let 𝑋𝑖 designate a vector of
response predictors, which must be available both on the respondents and the nonrespondents.6
The estimated probability of response of individual 𝑖 is given by the following logistic
relationship:̂𝜃̂ 𝑖 =𝑒𝐴𝑋𝑖
1 + 𝑒𝐴̂𝑋𝑖

̂ of the model coefficients is estimated from the sample units by maximum
where the vector 𝐴
likelihood. Estimating 𝜃̂𝑖 , we obtain the individual design weights adjusted for unit
nonresponse as follows:
̃𝑖 =
𝑑𝑤

𝑑𝑤𝑖
𝜃̂𝑖

If the response modeling is correct, the modified weights (𝑑̃ 𝑤𝑖 ) lead to unbiased
estimators.
Unfortunately, for the JHPS/KHPS case, this traditional approach was not employed.
The first reason for not employing this method was the lack of enough information received
from the nonrespondents. For the first wave of each sample, it is only possible to obtain someminor information about the nonrespondents, such as dwelling type and the number of times

There are several possible sources of auxiliary information for nonresponse correction, for instance the samplingframe itself, population censuses, administrative registers, ad hoc questions collected de visu by interviewers (e.g.,dwelling type or neighborhood appearance) or information obtained from the data collection process (paradata)such as the mode of contact, the number of contact attempts or general information on the profile of the interviewer(Osier, 2016). Overall, in order to be powerful in reducing nonresponse bias, the Xi needs to be correlated withboth the response propensity and the target variables of the survey.
68

the investigator visited the dwelling. Consequently, the necessary information for modelingresponse propensities is unavailable.
The second reason for not applying the traditional approach is the use of “reserve
subjects” in the first wave of each sample of the JHPS/KHPS. To avoid the possible bias caused
by nonresponsiveness in the first wave, for each original subject, between 3 and 5 reserve
subjects were randomly selected (from the same district, sex and age category of their original
subject).
3.1.3 Step 3: Calibration to external benchmarks
Although the aforementioned replacement by reserve subjects is expected to reduce
nonresponse bias to a great extent, there is still a minor possibility of bias. To address this issue,
the calibration approach can be used as an alternative to the traditional approach. Calibration(Deville, 1992) is a long-established technique that aims to incorporate external information
into an estimator, thereby improving data accuracy. In other words, the objective of thistechnique is to calibrate the weights according to external population benchmarks, thereby
reducing response bias and increasing the sample precision. In general, these external
population benchmarks are constructed based on disaggregated population-level information
obtained from auxiliary sources such as a census.
This technique also addresses the nonresponse issue (Lundström and Särndal, 1999;
Särndal and Lundström, 2005) when the calibration variables are also significant response
predictors; it has the advantage of not requiring the availability of data for both respondents
and nonrespondents.7 Given that in the case of the JHPS/KHPS, it is not possible to estimate
the response propensity to adjust for unit nonresponse, a one-step calibration method was
adopted to reduce nonresponse bias and increase data precision. The external population
benchmarks used in the calibration process were obtained from the Labor Force Survey,
Statistics Bureau of Japan.8
The weights obtained after the calibration step are the base weights (𝑏𝑤𝑖 ) for theindividuals in the sample. From the second wave of each sample onward, these base weights
are corrected yearly to address the panel attrition issue.

Traditional nonresponse correction methods require data from both respondents and nonrespondents; thus, it isimpracticable for cases where no data is available for nonrespondents.
8
A description of the data used in the calculation of yearly population benchmarks is available in Appendix A.
79

3.2 Computation of individual base weights from the second wave onward
According to the JHPS/KHPS tracing rules, individuals selected and interviewed during
the first wave become panel persons and are to be followed-up with in subsequent waves
regardless of the place where they live. Thus, an individual who has moved to another dwellingmust be recontacted at his or her new address. The follow-up of sample individuals leads to thecollection of longitudinal data that in turn allow the analysis of socioeconomic trends over time.
From the second wave of each sample onward, the individual base weights calculated
during the first wave have to be modified to compensate for panel attrition. In a panel setting,attrition refers to the loss of sample persons due to nonresponse. The main reasons for suchloss are the refusal by a sample person to continue to participate in the study (survey fatigue),the inability to contact the person in cases of long-term absence, or being unable to locate aperson when he or she has moved to another location. Attrition is different from being removed
from the survey scope, for instance, when a person physically dies, leaves the country or joinsa collective household or an institution.
Given the aforementioned issue, from the second wave of each sample onward, the baseweights are updated based on two procedures. First, a logistic model is used to correct the
problem of panel attrition in the JHPS/KHPS. Next, these adjusted weights are once again
subjected to a calibration process based on external population benchmarks, aiming to increase
the sample precision.
3.2.1 The attrition issue and the logistic model
Similar to unit nonresponse, attrition is a potential source of bias in estimates; thus, itneeds to be dealt with to keep panel samples representative. The probability for an individual
who has responded at wave t still to be responding at wave t+1 must be estimated through(𝑡)logistic modeling and then used to adjust the weights. Considering that 𝑏̃𝑖 is the base weight(𝑡)of i at t, in the first wave, 𝑏̃𝑖 = 𝑏𝑤𝑖 is the individual design weight adjusted for unit

nonresponse and calibrated to external data sources. From the second wave onward, let𝑝𝑖𝑡,𝑡+1 = 𝑃𝑟(𝑖 ∈ 𝑟̃𝑡+1 |𝑖 ∈ 𝑟𝑡 ) be the estimated probability for an individual i to respond at t+1(𝑟̃𝑡+1) given that it responded at t (𝑟𝑡 ). Assuming 𝑝𝑖𝑡,𝑡+1 > 0 for all i, the base weight at t+1 isderived from the base weight at t as follows:
(𝑡+1)
𝑏̃𝑖
=

(𝑡)
𝑏̃𝑖

𝑝𝑖𝑡,𝑡+1

10

Contrary to the first wave, a great deal of auxiliary information collected at t is now
available for correcting attrition at t+1. This information must be utilized as much as possible
to build powerful response explanatory models. Selection methods exist in the literature(Schork, 2018) to detect the most powerful predictors among a list of possible candidates.
To estimate 𝑝𝑖𝑡,𝑡+1, a traditional approach based on a logistic model is adopted. If agiven individual i participated in survey wave t, then it is possible to use the informationcollected from this individual in wave t to estimate the probability that this individual i will
participate again in wave t+1. First, a logistic regression model is employed to estimate theresponse propensity9 as follows:
𝑙𝑜𝑔𝑖𝑡 {Pr(𝑟𝑒𝑠𝑝𝑖𝑡+1 = 1|𝑋𝑖𝑡 )} = β0 + β1 𝑋𝑖𝑡1 + ⋯ + βp 𝑋𝑖𝑡𝑝
where 𝑟𝑒𝑠𝑝𝑖𝑡+1 is the probability that individual i will participate in survey wave t+1, while
𝑋𝑖𝑡 represents the auxiliary variables that are predictors of 𝑟𝑒𝑠𝑝𝑖𝑡+1 . The logistic regression
model predictors include marital status, sex, age, income group, household size, etc.10
̂ (𝑟𝑒𝑠𝑝𝑖𝑡+1 = 1|𝑋𝑖𝑡 ) obtained from the
Next, the estimated response propensities 𝑃𝑟
model are used to adjust the weights of each individual:

𝑤𝑖𝑡+1 = 𝑤𝑏𝑖𝑡 ×1
̂ (𝑟𝑒𝑠𝑝𝑖𝑡+1 = 1|𝑋𝑖𝑡 )𝑃𝑟

3.2.2 Re-entry, respondent substitution and calibration to external benchmarks
According to the JHPS/KHPS tracing rules, a respondent who does not participate in
the survey at point t can return to the panel at t+1 if he or she wishes to do so. These cases are
known as re-entry, and they also demand specific treatment. The concept of re-entry is
illustrated in Figure 2. First, consider that at time t-1, the respondents are identified by 1, 2, 3,
and 4, as shown in Figure 2. Next, the respondents at time t are identified by 1, 2, and 3(respondent 4 does not participate at t). At t+1, the respondent identified as 2 does notparticipate in the survey due to attrition, while the respondent identified by 1 does notparticipate because he or she is out of the survey scope. However, respondents identified as 3
and 4 do choose to participate in the survey. In this case, respondent 4 represents the re-entry
cases, that is, cases that participated in t-1 and t+1 but did not participate in t.
Individuals who turned “out-of-scope” (e.g., because of physical deaths) between t and t+1 must be removed
from the calculations.
10
A description of all predictors is available in Appendix B.
911

Figure 2. Sample dynamics between t and t+1
Sample of respondents at t+1

1

2

3

4

Sample of respondents at t
Source: Authors.

Given that the number of re-entry cases in the JHPS/KHPS is relatively small,individual re-entry is disallowed after the person exits the survey for the first time. In otherwords, re-entry cases receive a zero value weight after the respondent exits the survey for the
first time.
Another specificity of the JHPS/KHPS tracing rule is the existence of cases where thespouse of the respondent serves as a substitute for the respondent after the respondent’s deathand continues participating in the survey. Given that the subjects of the JHPS/KHPS are theinitially selected individuals, these cases are identified and automatically given a zero value
weight for all waves occurring after the original respondent died.
The resultant weights are then calibrated according to external population benchmarks
to increase the sample precision. After this process, the individual base weight at wave t+1 isobtained.

4. Integration of top-up samples
In the previous section, the strategy adopted in the computation of base weights fordifferent samples and their update in the subsequent waves was explained. This strategy allows
the computation of a group of sample weights for separate samples, e.g., the KHPS2004 sample,
KHPS2007 top-up sample, KHPS2012 top-up sample, and JHPS2009 sample. This section
explains how the weights from different samples can be combined to allow the possibility of
having larger data that provide more stable results.

12

4.1 General formula
The problem of integrating two samples of individuals drawn from different
populations at different points in time can be regarded as follows. Let 𝑠1 and 𝑠2 designate two
samples of individuals who are representative of the populations 𝑈1 and 𝑈2 , respectively.
Assuming there is some overlap between those two groups, Figure 3 shows the image of theintegration of two samples of individuals.
Figure 3. Integration of two samples of individuals

Source: Authors.
(1)

Each individual 𝑖 in 𝑠1 receives a sampling weight 𝑤𝑖 , and each individual 𝑖 in 𝑠2(2)

receives a sampling weight 𝑤𝑖 . The weights for the integrated sample 𝑠̃ = 𝑠1 ∪ 𝑠2 − 𝑠1 ∩ 𝑠2
are given by the following:(1)

𝑖𝑓 𝑖 ∈ [𝑠1 ∩ (𝑈1 − 𝑈𝑖𝑛𝑡𝑒𝑟 )](2)

𝑖𝑓 𝑖 ∈ [𝑠2 ∩ (𝑈2 − 𝑈𝑖𝑛𝑡𝑒𝑟 )]𝑤𝑖
𝑤𝑖𝑤𝑖𝐼𝑁𝑇 = 𝜃𝑤𝑖(1) + (1 − 𝜃)𝑤𝑖(2) 𝑖𝑓 𝑖 ∈ (𝑠1 ∩ 𝑠2 ∩ 𝑈𝑖𝑛𝑡𝑒𝑟 )
(1)

𝜃𝑤𝑖
{

𝑖𝑓 𝑖 ∈ (𝑠1 ∩ 𝑠̅2 ∩ 𝑈𝑖𝑛𝑡𝑒𝑟 )(1 − 𝜃)𝑤𝑖(2) 𝑖𝑓 𝑖 ∈ (𝑠̅1 ∩ 𝑠2 ∩ 𝑈𝑖𝑛𝑡𝑒𝑟 )where 𝑈𝑖𝑛𝑡𝑒𝑟 = 𝑈1 ∩ 𝑈2 . The sharing parameter 𝜃 lies between 0 and 1. Generally, 𝜃 = 1/2.
An alternative option is to set 𝜃 = 𝑛1 ⁄(𝑛1 + 𝑛2 ), where 𝑛1 is the size of 𝑠1 , and 𝑛2 is the sizeof 𝑠2 .
4.2 Application to the KHPS2007 top-up sample
Regarding the computation of the weights for the combination of the KHPS samples
from 2004 (𝑆𝐾04 ) and 2007 (𝑆𝐾07 ), it is first necessary to consider that each sample will
represent the population from the period in which it was drawn (𝑈K04 and 𝑈K07 ). Based on set
13

theory, the calculation of the weights for the integrated sample will consider the union of the
𝑈K04 and 𝑈K07 populations. In this case, there will be an overlap in the population of Japaneseindividuals aged between 23 and 69 years who were living in Japan in 2007 and those who
were also living in Japan in 2004.
Figure 4. Integration of KHPS2004 and KHPS2007 in 200711

Source: Authors, based on the JHPS/KHPS data structure.
(𝐾04)

If each individual i in the KHPS2004 received a weight 𝑤𝑖𝑡(𝐾07)

in the KHPS2007 received a weight 𝑤𝑖𝑡, and each individual i, then the integrated weight 𝑤𝑖𝑡𝐼𝑁𝑇 can be calculated

according to a general formula as follows:
(𝐾04)

𝑤𝑖𝑡
𝑤𝑖𝑡𝐼𝑁𝑇 =𝑖𝑓 𝑖 𝜖 [𝑆𝐾04 ∩ (𝑈𝐾04 − 𝑈𝑖𝑛𝑡𝑒𝑟 )]

(𝐾07)
𝑤𝑖𝑡

{

𝑖𝑓 𝑖 𝜖[𝑆𝐾07 ∩ (𝑈𝐾07 − 𝑈𝑖𝑛𝑡𝑒𝑟 )]
(𝐾04)
̅ )
𝜃𝑤𝑖𝑡
𝑖𝑓 𝑖 𝜖 (𝑆𝐾04 ∩ 𝑆𝐾07̅
(1 − 𝜃)𝑤𝑖𝑡(𝐾07) 𝑖𝑓 𝑖 𝜖 (𝑆𝐾04∩ 𝑆𝐾07 )where 𝑈𝑖𝑛𝑡𝑒𝑟 is the intersection of the population in KHPS2004 and KHPS2007 (𝑈𝐾04 and𝑈𝐾07 ), and the parameter θ lies between 0 and 1. 12 To illustrate this process, consider the(𝐾07)

weights for 2007: respondents aged between 20 and 22 years maintain 𝑤𝑖𝑡(𝐾04)

aged between 70 and 72 years maintain 𝑤𝑖𝑡, respondents, respondents aged between 23 and 69 years(𝐾04)

from KHPS2004 have their weights adjusted to 𝜃𝑤𝑖𝑡

, and respondents aged between 23 and(𝐾07)

69 years from KHPS2007 have their weights adjusted to (1 − 𝜃)𝑤𝑖𝑡.

The sampling procedure does not allow repetition. Consequently, there is an intersection of the populations
𝑈K04 and 𝑈K07 , but no intersection in samples 𝑆K04 and 𝑆K07 is possible.
12
The parameter θ can be calculated as the share of 𝑆𝐾04 in the combined samples or it can simply be assumed to
be 1/2. For simplicity, the second option was adopted.
1114

This approach can be extended from 2008 onward to combine the base weights for theoriginal KHPS2004 sample in year N (𝑁 ≥ 2008) and those from the KHPS2007 top-up
sample in year N, both of which are adjusted for attrition from 2008 onward. For the case of
integrating the samples, the calibration process mentioned in the previous section is performed
after the computation of the integrated weights.
4.3 Application to the KHPS2012 top-up sample
In 2012, another KHPS top-up sample was drawn from the resident population aged
between 20 and 69 years. To identify the overlapping part 𝑈𝑖𝑛𝑡𝑒𝑟 of the reference population
for the KHPS2004 sample with that of the KHPS2007 top-up sample, it is necessary to combine
information about the age of the individuals in 2004 and 2007. Basically, an individual who isin the KHPS2012 top-up sample belongs to the overlapping set if at least one of these two
conditions is met: the person was aged between 20 and 69 years in 2004, or the person wasaged between 20 and 69 years in 2007.
4.4 Application to the JHPS2009
In 2009, the JHPS was launched based on a representative sample of the resident
population aged 20 or older. Again, to combine the JHPS2009 sample with the KHPS sample(combining the KHPS2004 sample and the KHPS2007 top-up sample), we need to identify theindividuals in the overlapping part. The rule is the same as that stated before. Basically, asample individual who is in the JHPS2009 sample belongs to the overlapping sample if at least
one of these two conditions is met: the person was aged between 20 and 69 years in 2004, or
the person was aged between 20 and 69 years in 2007.
Finally, from 2012 onward, the JHPS2009 sample can also be merged with the combined
KHPS sample (the KHPS2004 sample and the two top-up samples, KHPS2007 andKHPS2012). The intersection is identified as individuals who are in the JHPS2009 sample andmeet at least one of these three conditions: the person was aged between 20 and 69 years in
2004, the person was aged between 20 and 69 years in 2007, or the person was aged between
20 and 69 years in 2012.

5. Computation of longitudinal weights
The combined base weights whose calculation was described in the previous section
can be used for longitudinal estimation. As previously indicated in Table 6, six different groupsof longitudinal weights were calculated. For the KHPS, longitudinal weights were calculated
from 2004 onward (original KHPS2004 sample), from 2007 onward (original KHPS2004
15

sample + KHPS2007 top-up sample) and from 2012 onward (original KHPS2004 sample +KHPS2007 top-up sample + KHPS2012 top-up sample). In addition, longitudinal weights were
calculated for the JHPS from 2009 onward (original JHPS2009 sample), for the combinationof the JHPS/KHPS from 2009 onward (original KHPS2004 sample + KHPS2007 top-up
sample + original JHPS2009 sample) and from 2012 onward (original KHPS2004 sample +KHPS2007 top-up sample + KHPS2012 top-up sample + original JHPS2009 sample).
These weights should be calibrated to reflect external population benchmarks for the
population of reference. However, population benchmarks need to be calculated properly,
taking into account the changes in population over time. The reference populations for thedifferent JHPS/KHPS samples are listed in Table 5.
Table 5. JHPS/KHPS reference populations
SampleOriginal KHPS sample (2004)
Original KHPS sample (2005 onward)
Original KHPS sample + KHPS2007 top-up sample
Original KHPS sample + KHPS2007 top-up sample(2008 onward)
Original KHPS sample + KHPS2007 top-up sample +KHPS2012 top-up sample
Original KHPS sample + KHPS2007 top-up sample +KHPS2012 top-up sample (2013 onward)
Original JHPS sample (2009)
Original JHPS sample (2010 onward)
Original KHPS sample + KHPS2007 top-up sample +original JHPS (2010 onward)
Original KHPS sample + KHPS2007 top-up sample +KHPS2012 top-up sample + original JHPS sample(2013 onward)

Reference population13
Resident population aged 20–69
Resident population aged 20+N – 69+N
(N = CURRENT YEAR – 2004)
Resident population aged 20–72
Resident population aged 20+M – 69+N
(N = CURRENT YEAR – 2004)(M = CURRENT YEAR – 2007)
Resident population aged 20–77(N = CURRENT YEAR – 2004)
Resident population aged 20+M – 69+N
(N = CURRENT YEAR – 2004)(M = CURRENT YEAR – 2012)
Resident population aged 20 or more
Resident population aged 20+M or more
(M = CURRENT YEAR – 2009)
Resident population aged 20+M or more
(M = CURRENT YEAR – 2009)
Resident population aged 20+M or more
(M = CURRENT YEAR – 2012)

Source: Authors, based on the JHPS/KHPS data structure.

6. Computation of individual and household cross-sectional weights
In addition to the longitudinal weights, two other types of weights are calculated:individual cross-sectional weights and household weights.

The resident population consists of the Japanese citizens living in Japan; it does not include foreigners living inJapan.
1316

6.1 Individual cross-sectional weights
Three types of individual cross-sectional weights are calculated: weights for the KHPS
that include the individuals selected for the KHPS2004 sample and the top-up samples
collected in 2007 and 2012; weights for the JHPS that comprise the individuals from
JHPS2009; and weights for the integrated JHPS/KHPS sample, including all theaforementioned samples.
Different from the longitudinal weights that apply to panel data, the individual crosssectional weights allow inference for a specific year. For this reason, the cross-sectional
weights are available for all years. They are available from 2004 to 2017 for the case of KHPS
and JHPS/KHPS cross-sectional weights, while they are available from 2009 to 2017 for the
JHPS cross-sectional weights.
As explained before, given that re-entry cases are disallowed, the organization of theindividual cross-sectional weights is based on the calculated longitudinal weights.
6.2 Household cross-sectional weights
Based on the aforementioned three types of individual cross-sectional weights, three
types of household cross-sectional weights are calculated. As the JHPS/KHPS subjects areindividuals and not households, the household cross-sectional weights had to be calculatedusing the weight share method, as presented in Lavallée (2007). This method states that thehousehold cross-sectional weight (𝑊ℎ𝑖𝑡 ) can be calculated according to the following formula:

𝑊ℎ𝑖𝑡 =∑𝑖𝜖𝑟 𝑤𝑖𝑡 1𝑖𝜖ℎ𝑁ℎ𝑖𝑡where the cross-sectional weight of individual i’s household h in year t (𝑊ℎ𝑖𝑡 ) is the result of
the sum of the cross-sectional weight of all members from this household in year t (𝑤𝑖𝑡 ) divided
by the total number of members in household h in year t (𝑁ℎ𝑡 ).14 In other words, the weightshare method determines that the household weights are the average of the weights of members
inside the survey scope. If the individual weights 𝑤𝑖𝑡 are unbiased, then the household weights
𝑊ℎ𝑖𝑡 are unbiased as well.

The first step in calculating the household cross-sectional weights is to obtain from the survey the data regarding
the number of household members and their age in each wave. After that, it is possible to calculate the number ofmembers in the household that are inside the scope of the survey.
1417

7. Selection and use of the most appropriate weight
Given the complexity of the JHPS/KHPS structure, multiple types of weights areprovided to meet the different needs of the users. The most appropriate weight for a given
analysis must reflect the survey instrument, which is the source of the data being used in theanalysis (KHPS, JHPS, or JHPS/KHPS), the combination of waves involved in the type ofanalysis and the indicators we seek to produce, and the level of analysis (household orindividual).
The weight names and labels are given to help users choose the correct weight. The
name and label of each weight reflect the combination of samples for which the weight iscalculated, the level of analysis and its nature (cross-sectional analysis weight or longitudinal
analysis weight).
Table 6. Available weights and corresponding definitions
Analysislevel
Individual
Individual
Individual
Individual
IndividualStarts from
year
2004
2009
2007
2012
2009

Individual

2012

Individual
Individual

2004
2009

Individual

2004

HouseholdHousehold

2004
2009

Household

2004

Data source
KHPS2004
JHPS2009
KHPS2004 and KHPS2007
KHPS2004, KHPS2007, and KHPS2012
KHPS2004, KHPS2007, and JHPS2009
KHPS2004, KHPS2007, JHPS2009, andKHPS2012
KHPS2004, KHPS2007, and KHPS2012
JHPS2009
KHPS2004, KHPS2007, JHPS2009, andKHPS2012
KHPS2004, KHPS2007, and KHPS2012
JHPS2009
KHPS2004, KHPS2007, JHPS2009, andKHPS2012

Analysis
WeightLong_Weight_1
Long_Weight_2
Long_Weight_3
Long_Weight_4
Long_Weight_5
Long_Weight_6
Cross_Weight_1
Cross_Weight_2
Cross_Weight_3
HH_1
HH_2
HH_3

Source: Authors, based on the JHPS/KHPS data structure.

If the analysis uses only data from a specific wave, the cross-sectional version of theweight would be the most appropriate option for this study. The cross-sectional weight iscalculated and attributed to all sample members who answered the survey questionnaire in a
specific wave, except for re-entry cases.15 If the study uses data from multiple waves, then theappropriate version of the longitudinal weights should be selected.

Another specificity of the JHPS/KHPS is the existence of cases where the spouse of the respondent served as a
substitute for the respondent after his or her death and continued participating on the survey. Given that the
subjects of the JHPS/KHPS are the initially selected individuals, these cases are identified and automaticallyreceived a zero value weight for all waves occurring after the original respondent died.
15

18

For example, the longitudinal weights for the integration of KHPS2004, KHPS2007,
and KHPS2012 are available from 2012 to 2017. If a study requires the examination of panel
data that starts in 2010, then the use of combined longitudinal weights for KHPS2004,
KHPS2007, and KHPS2012 is not possible. In this case, the most appropriate would be the useof longitudinal weights for the combination of the KHPS2004 and KHPS2007 samples, since
the weights would be available from 2007 to 2017.
7.1 Assumptions when not using weights16
As stated before, the use of weights aims to adjust possible distortions derived fromsample selection bias, nonresponse, and attrition. These weights allow us to build populationlevel estimators in a statistically proper way that keeps bias and variance as small as possible.
In general, an unweighted analysis does not correctly reflect the population structure unlesssome assumptions are true. In other words, the use of weights will not be necessary if we
assume that population estimated parameters (means, measure of dispersion, etc.) do not differ
between the following:- Individuals from different Japanese regions;- Individuals who participated in the first wave of each sample and those who did not;- Individuals who continued to participate at later waves and those who did not; and- Individuals from different samples.
For this reason, the theory suggests that researchers who publish or present unweighted
estimates make these assumptions explicit. Otherwise, the use of weights to correct the possible
distortions in estimations of population parameters is recommended. Furthermore, weights are
generally not recommended in the case of model-based analyses, such as linear or logistic
regression modeling, as they make estimates more volatile. That is why practitioners often
prefer not to use them and keep data analysis unweighted, although there might be someconcern about bias. However, there is no clear answer to this question, which actually depends
on the data available. Thus, an empirical approach comparing weighted and unweighted results
remains a sensible solution.

This section was based on: Understanding Society (2019). The UK Household Longitudinal Study: waves 1-9
User Guide. University of Essex, Colchester, Essex.
1619

7.2 Commands for using the weights in Stata17
The Japan Household Panel Survey (JHPS/KHPS) weights are available in the file
named “Weights.dta”. After merging this file with the JHPS/KHPS data, the “svy” command
can be used to obtain estimates that correctly take into account the sample design.
First, it is necessary to specify the design of the survey using the svyset command.
Suppose a study investigates the population features in 2013 using JHPS data. In this case, theappropriate weights are available in Column Cross_Weight_2 (JHPS Cross-sectional weights).
The following command can be employed to specify the use of these weights:
svyset id [pweight = Cross_Weight_2]
Next, any compatible command needs to be prefixed with “svy”. For example, a logistic
regression for the data from 2013 using the weights can be performed in the following way:
svy: logistic depvar variable1 variable2 variable3 if year==2013
Suppose a study in which a longitudinal analysis using the JHPS/KHPS data for theperiod 2013-2017 needs to be performed. In this case, the appropriate weight would be foundin Column Long_Weight_6 (JHPS/KHPS longitudinal weights). For the case of longitudinal
analysis, it is necessary to copy the longitudinal weights of the last wave used in the analysis(in our example, the weights from 2017) and apply them to each respondent’s previous waves(from 2013 to 2016 in our example).
In practice, after merging the weights file with the JHPS/KHPS data and keeping only
the data for the period 2013-2017, the following procedure can be adopted:
1- Generate a new weighting variable for the longitudinal analysis of the period 20132017:
gen weight_lg17= Long_Weight_6
2- Copy the last wave weight value over the previous year:
sort id year
bysort id (year): replace weight_lg17=weight_lg17[_N]This section was based on: Understanding Society (2017). The UK Household Longitudinal Study: wave 1-7,
2009-2016 User Guide. Ed. Knies, Gundi. University of Essex, Colchester, Essex.
1720

3- Specify the use of these weights:
svyset id [pweight = weight_lg17]4- Perform the desired analysis:
svy: reg depvar variable1 variable2 variable3
Some commands in Stata are not compatible with the prefix “svy”. For these cases, analternative would be stating the use of the weight as follows:18
bysort year: table var1 [pw=weight_lg17]
xtset id year
xtreg depvar year i.var2 [pw=weight_lg17]
Table 7 provides more examples of weight choice based on the type of analysis
employed by a given study.
Table 7. Examples of weight choice based on the analysis type
Data source
KHPS2004
KHPS2004 + KHPS2007
KHPS2004 + KHPS2007 +KHPS2012
JHPS2009
KHPS2004 + KHPS2007 +JHPS2009
KHPS2004 + KHPS2007 +JHPS2009 + KHPS2012

Longitudinal analysis of individual
respondents
Long_Weight_1 weight from latest
wave in the longitudinal analysis
Long_Weight_3 weight from latest
wave in the longitudinal analysis
Long_Weight_4 weight from latest
wave in the longitudinal analysis
Long_Weight_2 weight from latest
wave in the longitudinal analysis
Long_Weight_5 weight from latest
wave in the longitudinal analysis
Long_Weight_6 weight from latest
wave in the longitudinal analysis

Cross-sectional analysis ofindividual respondents

Cross_Weight_1 weightfrom the analyzed wave
Cross_Weight_2 weightfrom the analyzed wave

Cross_Weight_3 weightfrom the analyzed wave

Source: Authors, based on the JHPS/KHPS data structure.

8. Sampling weights and the representativeness of the data
To illustrate and confirm the representativeness of the survey data after the adoption of
the calculated sampling weights, this section presents a series of exercises in which weighted
and unweighted values from the JHPS data are compared with the official statistics. At the first
moment, individual-level characteristics are tested, while at the second moment, householdlevel information is observed. For the case of personal characteristics, the JHPS cross-sectional
18

For more information on the commands and use of weights in Stata, please refer to the Stata user guide.

21

weight (Cross_Weight_2) is employed, while for household characteristics, the JHPS
household weight (HH_2) is employed.
In the first exercise, the sex and marital status of the unweighted and weighted JHPS
data in 2017 are compared with the data from the Labor Force Survey from the Statistics Bureau
of Japan. Figure 5 illustrates the shares of the Japanese population according to sex and marital
status. Interestingly, for the case of unweighted JHPS data, it is possible to observe a distortionin the data. However, after weighting the data, we verify that the shares are similar to thoseobtained from the Labor Force Survey.
Figure 5. Share of sex and marital status in 2017
(%)
80.0
70.0

74.6
68.3 68.0

60.0
50.2
50.0
40.0

47.8 47.8

49.8

52.2 52.2

31.7 32.0
25.4

30.0
20.0
10.0
0.0
MarriedSingleUnweighted JHPS

Male
Weighted JHPS

FemaleLabor Force Survey

Source: Authors based on the JHPS data and the 2017 Employment Status Survey.

Next, a group of characteristics related to the job condition of the Japanese population
is observed. Data on labor force status (Table 6), form of employment (Table 7), form of
employment of employees only (Table 8), and firm size (Table 9) obtained from the 2017
Employment Status Survey are compared with weighted and unweighted JHPS data. In thiscase, the shares of the unweighted JHPS data are already very close to those found for thestatistical data. Consequently, after the adoption of the weights, there is a slight change in which
the shares with the weighted values get closer to the official statistics, thereby confirming thatthe use of sampling weights improves the representativeness of the JHPS data.

22

Figure 6. Labor force status in 2017
(%)
50

40

30

20

10

0
Mainly worked

Worked while mainly
attending school

Unweighted JHPS

Worked while mainly
keeping house

Weighted JHPS

Not engaged in work

Employment Status Survey

Source: Authors based on the JHPS data and the 2017 Employment Status Survey.

Figure 7. Form of employment in 2017
(%)
90
80
70
60
50
40
30
20
10
0
Self-employed worker

Family worker

Unweighted JHPS

Working at home
without an employee
relationship
Weighted JHPS

Consigned work or
subcontractor

Employee

Employment Status Survey

Source: Authors based on the JHPS data and the 2017 Employment Status Survey.

23

Figure 8. Form of employment in 2017 (only employees)
(%)
50

40

30

20

10

0
Executives ofcompany or
corporation

Regularemployee

Unweighted JHPS

Contract
employeePart-time
worker

Weighted JHPS

Dispatched workers
from temporary
labour agency

Entrusted
employees

Employment Status Survey

Source: Authors based on the JHPS data and the 2017 Employment Status Survey.

Figure 9. Employment size of enterprise in 2017
(%)
50

40

30

20

10

0
1 to 99 people
Unweighted JHPS

100 to 499people
Weighted JHPSOver 500people

Government

Employment Status Survey

Source: Authors based on the JHPS data and the 2017 Employment Status Survey.

Regarding the household-level characteristics, data on household size (Figure 10), typeof dwelling (Figure 11), and household income (Figure 12) are observed. Some of the variablesin the JHPS, such as the household size and the type of dwelling, are originally biased. Thisbias is attributed to the fact that the JHPS is conducted using the drop-off and pick-up (DOPU)
method, which tends to obtain more answers from detached-house respondents and fewer from24

single-household respondents. Thus, it is possible to observe that the weights mitigate thosebiases but cannot completely correct them.
Figure 10. Household size in 2015
(%)
35
30
25
20
15
10
5
0
1 Person

2 Persons

3 Persons

Unweighted JHPS

4 Persons

5 PersonsWeighted JHPS

6 Persons

7 Persons andover2015 Census

Source: Authors, based on the JHPS data and the 2015 Census.

Figure 11. Type of dwelling in 2017
(%)
80
70
60
50
40
30
20
10
0
Detached house
Unweighted JHPS

Semi-detached house
Weighted JHPS

Collective housing

Others2018 Housing and Land Survey

Source: Authors, based on the JHPS data and the 2018 Housing and Land Survey.

For household income, the JHPS collects less data from low-income households.
However, the weighted data correct this problem, which suggests that the collection resembles
what is observed in the official statistics.

25

Cumulative ratio (%)

Figure 12. Cumulative ratio of household income in 2016 (ten thousand yens)
100
90
80
70
60
50
40
30
20
10
0

2016 Comprehensive survey of living conditions（Large-scale survey）
Unweighted JHPS
Weighted JHPS

Source: Authors, based on the JHPS data and the 2016 Comprehensive Survey of Living Condition(Large-scale Survey).

Last, Table 8 presents data for household deposits and savings, household securities,
and household debts. Once again, we verify that the unweighted data slightly deviate from theofficial statistics; however, with the use of the sampling weights, it is possible to obtain values
for the JHPS data that are more representative of the reality of the Japanese population.
Table 8. Deposits and saving, securities and debts in 2014

Unweighted JHPS
Weighted JHPS
2014 Comprehensive
Survey of Living
Conditions

Deposits and savings
Average
Holdingamountratio(ten
thousand
(％)
yens)
912.2
79.8
931.6
78.2
973.8

71.3

Securities
Average
Holdingamountratio(ten
thousand
(％)
yens)
229.9
27.2
223.7
25.2

Debts
Average
Holdingamountratio(ten
thousand
(％)
yens)
613.1
43.9
563.5
41.8

215.9

533.3

24.7

41.8

Source: Authors, based on the JHPS data and the 2014 Comprehensive Survey of Living Condition.

The figures and table presented in this section compare the official statistics for theJapanese population at the individual and household levels with the data collected for the JHPS.
It is possible to conclude that the JHPS data provide a good representation of the Japanese
population, with individual-level data being more representative than household-level data.
The importance of the use of sampling weights is also illustrated, with weighted JHPS data
achieving values closer to those of the official statistics than unweighted JHPS data.
26

References
Deville J.C. and Särndal C.E. (1992). Calibration Estimators in Survey Sampling.
Journal of the American Statistical Association, pp. 376-382
Lavallée P. (2007). Indirect Sampling. Springer Series in Statistics.
Lundström S. and Särndal C.E. (1999). Calibration as a Standard Method forTreatment of Nonresponse. Journal of Official Statistics, Vol. 15, No. 2, pp. 305-327.
Naoi M., Yamamoto K., and Miyauchi T. (2010) JHPS Kaitoujokyo niokeru
Chosajissihouhou no Performance (in Japanese), PDRC Discussion Paper DP-2009-005.
Osier G. (2016). Unit non-response in household wealth surveys: Experience fromthe Eurosystem’s Household Finance and Consumption Survey. ECB Statistics Paper
Series, No.15 / July 2016.
Särndal C.E. and Lundström S. (2005). Estimation in Surveys with Nonresponse.
Wiley.
Schork J. (2018). Automatic Variable Selection for Imputation Models: Common
Methods Applied to EU-SILC. Statec Économie et statistiques N° 98/2018. Available at :https://statistiques.public.lu/catalogue-publications/economie-statistiques/2018/982018.pdf
Understanding Society (2017). The UK Household Longitudinal Study: wave 1-7,
2009-2016 User Guide. Ed. Knies, Gundi. Colchester: University of Essex.
Understanding Society (2019). The UK Household Longitudinal Study: Wave 1-9,
2009-2018 User Guide. Colchester: University of Essex.
Watson N. (2012) Longitudinal and Cross-sectional Weighting Methodology for the
HILDA Survey, HILDA Project Technical Paper Series. No.2/12.

27

Appendix A: Benchmarks used in weighting
The population benchmark data used in the weight calibration process were calculated
based on made-to-order aggregated data provided by the Japanese National Statistics Center(NSTAC). A tabulation of the following data was requested for the NSTAC:
1- Japanese male population by 5-year-old category;
2- Japanese female population by 5-year-old category;
3- Japanese population by 5-year category and marital status;
4- Japanese population by 5-year category and region; and5- Japanese population by 5-year category and employment status.
The tables for the aforementioned data were created based on the Labor Force Survey
from the Statistics Bureau of Japan. The NSTAC elaborated the tables using quarterly data
(January, February, and March) for 2004-2017. 19 Given that these are made-to-ordertabulations and that the Statistics Bureau of Japan periodically updates its statistics, there is apossibility that the employed data can slightly differ from the data published by the Labor Force
Survey, Statistics Bureau of Japan.
The population benchmark data are composed of the following calibration variables:- Age group: yearly data of the Japanese population calculated by 5-year-old category (e.g.,
population from 20 to 24 years old, population from 25 to 29 years old, etc.).
- Sex: yearly data of the Japanese population by male and female group.
- Marital status: yearly data of the Japanese population by married and unmarried group.
-

Region:yearly

data

of

the

Japanese

population

calculatedper

region(Hokkaido/Tohoku/Kanto/Chubu/Kinki/Chugoku/Shikoku/Kyushu and Okinawa).
- Employment status: yearly data of the Japanese population calculated by employment status(e.g., mainly working, working and studying, unemployed, etc.).

Given the Great Disaster that occurred in 2011, the collection of data in March 2011 was not possible.
Consequently, the obtained data for 2011 was calculated by the NSTAC based on the average of the data collected
in January and February instead of quarterly data. In addition to this, data for the population of Tohoku is also notavailable for that year. In this case, Tohoku’s population by 5-year-old category was calculated by employing the
average of the available data for years 2010 and 2012.
19

28

Appendix B: Logistic regression model for response propensity
Based on the information available in each wave of the survey, the logistic regression
model employed to control for attrition was defined as follows:
𝑙𝑜𝑔𝑖𝑡 {Pr(𝑟𝑒𝑠𝑝𝑖𝑡+1 = 1|𝑋𝑖𝑡 )}= β0 +𝛽1 𝑀𝑎𝑟𝑖𝑡𝑎𝑙𝑖𝑡 + 𝛽2 𝑆𝑒𝑥𝑖𝑡 + 𝛽3 𝐴𝑔𝑒𝑖𝑡 + 𝛽4 𝐻𝐻𝑠𝑖𝑧𝑒𝑖𝑡 + 𝛽5 𝐼𝑛𝑐𝑜𝑚𝑒𝑖𝑡+ 𝛽6 𝐾𝑖𝑑𝑠𝑖𝑡 + 𝛽7 𝐶ℎ𝑎𝑛𝑔𝑒1𝑖𝑡 + 𝛽8 𝐶ℎ𝑎𝑛𝑔𝑒2𝑖𝑡 + 𝛽9 𝐶ℎ𝑎𝑛𝑔𝑒3𝑖𝑡+ 𝛽10 𝐶ℎ𝑎𝑛𝑔𝑒4𝑖𝑡 + 𝛽11 𝐸𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡𝑖𝑡 + 𝛽12 𝐻𝑒𝑎𝑙𝑡ℎ𝑖𝑡 + 𝛽13 𝑅𝑒𝑔𝑖𝑜𝑛𝑖𝑡+ 𝛽14 𝐶𝑖𝑡𝑦𝑖𝑡 + 𝛽15 𝑊𝑎𝑣𝑒𝑖𝑡
where 𝑟𝑒𝑠𝑝𝑖𝑡+1 assumes the value of 1 if individual i participates in the survey in year t+1 andzero otherwise. The probability of individual i participating in the survey in year t+1 is definedby individual i’s marital status, sex, age group, household size, income, the presence of kids inthe household, changes in individual i’s household, employment status, health status, the region
where individual i lives, the size of the city where individual i lives, and the number of survey
waves individual i participated until year t.
Although the information for the auxiliary variables was collected from the survey inyear t and all individuals i participated in this survey, it is not uncommon for the existence ofsituations where the respondent unintentionally forgets or intentionally does not answer a given
question of the survey. The item nonresponse issue can lead to the deletion of the observationsof individuals during the implementation of the logistic model. To avoid the deletion of caseswith missing values, an imputation process based on multiple imputed chained equations(MICE) was performed before the implementation of the logistic model, replacing all themissing values with estimates.

29