JOINT RESEARCH CENTER FOR PANEL STUDIESDISCUSSION PAPER SERIES
DP2009-003
August, 2009

Attrition Bias in Longitudinal Survey: A Validation Studywith Interviewer’s Record of Respondent Mobility

Michio Naoi*
Abstract
This paper aims to examine respondent's mobility-related non-response in thelongitudinal survey. We use the interviewer's record of respondent mobility, which canbe observed even if the respondent does not participate in the respective wave, as asource of validation data. Using the Keio Household Panel Survey (KHPS) 2004–2007
as a primary dataset, household mobility equations are estimated for selectedsubsample of non-attritors by two competing methods—an inverse probability weighted(IPW) estimator and a sample selection (SS) estimator. These two estimators arecompared with a probit estimates using complete sample including both attritor andnon-attritor. It is found that SS generally outperforms IPW in terms of coefficientestimates, suggesting that the mobility-related non-responses in the KHPS are
non-ignorable. However, the results of Hausman test cannot find any significant bias for
either IPW or SS estimator.
Key words: Attrition Bias, Validation Study, Respondent Mobility, Non-ignorable
non-response.

JEL classification: C33, C81, R23.

*Michio Naoi
Faculty of Economics, Keio University

Joint Research Center for Panel Studies
Keio University

Attrition Bias in Longitudinal Survey: A Validation Studywith Interviewer’s Record of Respondent Mobility
Michio Naoiy
Keio University
August 21, 2009

Abstract
This paper aims to examine respondent’s mobility-related non-response in the longitudinal survey. We use the interviewer’s record of respondent mobility, which can be observed
even if the respondent does not participate in the respective wave, as a source of validation
data. Using the Keio Household Panel Survey (KHPS) 2004–2007 as a primary dataset,household mobility equations are estimated for selected subsample of non-attritors by twocompeting methods — an inverse probability weighted (IPW) estimator and a sample selection (SS) estimator. These two estimators are compared with a probit estimates using
complete sample including both attritor and non-attritor. It is found that SS generally
outperforms IPW in terms of coe¢ cient estimates, suggesting that the mobility-related nonresponses in the KHPS are non-ignorable. However, the results of Hausman test cannot …nd
any signi…cant biasfor either IPW or SS estimator.
Key words: Attrition Bias, Validation Study, Respondent Mobility, Non-ignorable nonresponse.
JEL classi…cation: C33, C81, R23.

The author is grateful to the 21st Century Center of Excellence Program at Keio University for generously
providing us with the data (Keio Household Panel Survey). The author would like to thank Colin McKenzie,
Koyo Miyoshi, and Miki Seko for their helpful comments and suggestions.
y Faculty of Economics, Keio University, Mita Toho Bldg. 3rd Floor, 3-1-7 Mita, Minato-ku, Tokyo 108-0073.
Email: naoi@2001.jukuin.keio.ac.jp.

11Introduction

The primary aim of this paper is to examine respondent’s mobility-related non-response in the
Keio Household Panel Survey (KHPS) 2004–2007. There are always drop-outs from the survey
at each wave and some of them are directly or indirectly related to the respondent’s mobility. It
is often hard to keep track of the new address of the respondent who moved. Respondent’s move
pertaining to his/her marriage often leads to survey refusal by his/her new spouse. All thesefactors cause a close linkage between respondent mobility and survey non-response, and hence
potential bias in studying household mobility with longitudinal data.
In the survey sampling literature, a crucial issue in examining non-response in the longitudinalsurvey is whether or not the underlying missing data process is ignorable (Rubin, 1976; Little
and Rubin, 2002). Using s as an indicator of survey response (s = 1 if respond, 0 otherwise) andy and x as the outcome of interest and other observed characteristics, ignorable non-responseor missing-at-random (MAR) can be de…ned by Pr(s = 1jy; x) = Pr(s = 1jx).1 This implies
that, conditional on observed characteristics, survey response behavior is independent of the(possibly unobservable) behavior of interest. In our context, ignorability or MAR requires thatthe probability of non-response does not vary systematically across movers and non-movers.
Fitzgerald et al. (1998) further extend the notion of ignorability of missing data process byusing the concepts of selection-on-observables and selection-on-unobservables. Introducing an
auxiliary variable z, which is distinct from x and always observable, selection-on-observables
occurs when Pr(s = 1jy; x; z) = Pr(s = 1jx; z). They show that the inverse probability weighting(IPW) — weighting the observed data by the inverse of the probability of response — provides
consistent estimates under the selection-on-observable assumption. The selection-on-observableapproach requires z to be endogenous to y but excluded from the regression equation. Theysuggest that the lagged dependent variable can be used as an obvious candidate for z.
On the other hand, selection-on-unobservables, also termed as not-missing-at-random or nonignorable non-response, occurs when the above conditional probability assumption does not hold.
In such a case, an appropriate method to cope with non-response bias follows sample selection
model (Heckman, 1979; Hausman and Wise, 1979). In applying a sample selection (SS) model,
the identi…cation of the behavioral coe¢ cients requires an exclusion restriction, i.e., there should
be an instrument z that a¤ects non-response while being independent from the behavior ofinterest. In practice, however, …nding a suitable instrument for unobservable selection is by nomeans easy in the case of non-response.2 Parameter estimates from the observed data alone
are typically biased to an extent that depends on the strength of the relationship between theunobserved outcome of interest and the probability of non-response.
The non-ignorability assumption, however, is generally untestable because the behavior ofinterest cannot be observed when the respondent dropped out of the survey. In this paper,
1 When the underlying missing data process is completely at random, Pr(s = 1jy; x) = Pr(s = 1) will besatis…ed.
2 Naoi (2007) uses the interview process characteristics as a source of instruments. We also use the same set of
IVs in the following analysis.

2

we use the interviewer’s record of respondent mobility to test the non-ignorability assumption.
Since the interviewer’s record can be observed even if the respondent himself is dropped out fromthe survey, we can directly test the conditional probability assumption above. In the following
analysis, household mobility equations are estimated for both the entire sample including attritors
and the selected subsample of non-attritors. The performance of two competing estimators —an inverse probability weighted (IPW) estimator and a sample selection (SS) estimator — isevaluated by comparing them with a probit estimates using complete sample. It is found that SS
generally outperforms IPW in terms of coe¢ cient estimates, suggesting that the mobility-related
non-responses in the KHPS are non-ignorable. However, the results of Hausman test cannot …nd
any signi…cant bias for either IPW or SS estimator.
The paper is organized as follows. Section 2 brie‡y introduces the KHPS. Section3 summarizes
attrition pattern in the KHPS and presents descriptive analysis of attrition. Section 4 explains
the estimation method and variables. Section 5 summarizes our main empirical results. Section
6 presents the conclusion.

2

Data

The KHPS, sponsored by the Ministry of Education, Culture, Sports, Science and Technology,
is the …rst comprehensive panel survey of households in Japan, conducted annually by Keio
University since 2004. In the following analysis, we use the …rst four waves of the KHPS, whichwere conducted in 2004, 2005, 2006 and 2007, respectively. In 2004, 13,430 individuals, male andfemale, aged 20–69 years, were selected by strati…ed two-stage random sampling as a potential
respondent. Out of 13,430 individuals initially approached, 4,005 primary respondents …nallyparticipated in the …rst wave of the survey (response rate = 29.8%). Although the overall
response rate of the …rst wave was not so high (29.8%), the age and sex distribution of the initial
4,005 respondents is quite similar to that of the Japanese population.3
The questionnaire of the KHPS contains both individual and household related questions.
The former covers a wide array of questions with respect to the respondent’s demographic characteristics, education and employment activities, while the latter has basic questions pertaining
to household income, asset holdings, and housing conditions. If the primary respondent was
married at the time of survey, the questionnaire also contains virtually identical questions to beanswered by his/her spouse. The standard procedure for the KHPS was to send a pre-survey
letter to the respondent and then provide a post-interview payment of 3,000 yen (approximately$25) per household. In addition to the main dataset described above, the interviewer’s records
are provided about the respondent’s moves and the interview process such as the contact history.4
These supplemental surveys are conducted in 2005, 2006 and 2007 (waves 2 to 4).
3 Details of data description can be found in Kimura (2005) and Higuchi et al. (2008).
4 We have also used the complete list of assignments of each interviewer to the targeted respondents in thefollowing analysis. This information was made available by the Central Research Services Inc., to which theauthor is grateful.

3

By 2006, the KHPS witnessed a sample loss of approximately 28% due to cumulative attrition
from its initial 2004 sample. Compared with other longitudinal surveys, the attrition in the
KHPS is somewhat heavy; this can possibly be attributed to the fairly long and comprehensive
questionnaire used. For example, Japanese Panel Survey of Consumers (JPSC, cohort A) has
10.1% of sample loss in the initial three waves. The cumulative attrition rates for the …rst threewaves are approximately 15% in the Panel Study of Income Dynamics (PSID) and 5.7% in the
National Longitudinal Survey of Youth (NLSY). For the European Community Household Panel(ECHP), these …gures range from 12.1% (Germany) to 36.5% (Denmark). In the next section,
we will review the general attrition pattern of the KHPS, focusing on the relationship withrespondent mobility.5

3

Descriptive Analysis of Attrition in the KHPS

Table 1 presents the attrition pattern of the KHPS and its relationship with selected socioeconomic and demographic characteristics of the respondents. The overall drop-outs decrease theinitial sample size of 4,005 to 3,314, 2,884, and 2,634, in 2005, 2006, and 2007 respectively.6 The
corresponding drop-out rates for each wave becomes 17.3%, 13.0%, and 8.7%, respectively.7 Asin the most longitudinal surveys, drop-out rate is highest in the second wave (2005), and thenbecomes lower in the subsequent waves, implying that there may be some duration dependence
in the attrition process.
Table 1 also shows that the drop-out rate varies systematically with the respondent’s socioeconomic and demographic characteristics at each wave. Except for household mobility, respondent/household characteristics are all measured in wave t 1. As expected, respondent’s age has
clear U-shaped e¤ect on the drop-out probability. In 2005, drop-out rate is lowest of 14.0% for
those in their 40s, and highest at the margin, 21.5% for those aged under 30 years and 18.0% for
those aged over 60 years. Compared with overall drop-out rates in each wave, female respondents
have slightly lower drop-out probability. Respondents who are married or have some college degrees are less likely to be dropped out from the survey. It is also found that those with poor
health condition tend to have higher probability of drop-outs. With regard to the relationshipbetween respondent mobility and non-response, Table 1 suggests that the respondent’s mobility
is strongly and positively related to non-response — in the second wave, 36.3% of movers attrited from the survey as compared to a mere 17.3% of overall drop-out rate. The same pattern
can be found in the subsequent waves.8 Basically, non-response poses serious problems when it
5 For an extensive review of attrition problem in the KHPS, see Miyauchi et al. (2006), McKenzie et al. (2007)
and Naoi (2007).
6 As in other longitudinal surveys, the KHPS has some individuals who come back into the survey from nonresponse. There are 3 rejoining respondents in the fourth wave (2007). Since the number of rejoining respondents
are so small that it is almost negligible, we simply omit them from the following analysis.
7 The drop-out rate is the percentage of the number of drop-outs in wave t to the number of respondents inwave t 1.
8 Information on residential mobility used here is based on the interviewer’s record, which can be obtained even
if the respondent is dropped out from the survey. “Recent mover” in Table 1 represents those who have moved4

is not independent from the behavior of interest, suggesting that sample attrition is especially
important when we try to understand the household residential mobility.
(Table 1 around here)
Table 2 presents summary statistics of …rst-wave respondent/household characteristics bysubsequent survey responses status. To create this table, we use the same set of individuals asin the empirical analysis conducted in section 5. If the primary respondent is married and isfemale, individual characteristics (age, years of education, labor force participation, and healthcondition) are for her male spouse. The left panel shows summary statistics for two groups of
respondents — those respond and drop out in the second wave. The right panel shows summary
statistic calculated for those participate all four waves of the KHPS, and those drop out in either
waves 2–4. Generally, we can observe the same pattern of survey response as shown in Table 1.
Non-response decreases with individuals’age at the start of the panel. Non-response is greater
among those with less formal education and with poor health condition. Among others, marital
status and household type are found to be a strong predictor of survey response; those respondin the second wave are likely to be married and less likely to be in a single family household in
the …rst wave. These points will be further investigated in Table 4 below.
(Table 2 around here)
For those dropped out from the survey at each wave, Table 3 reports major reason for nonresponse. Approximately 30% of attritors in each wave reported that they did not participate
in the survey because they were too busy. Length of survey questionnaire also matters, andthe proportion of individuals choosing this reason is closely related to the actual volume ofquestionnaire. The proportion was 24.6% and 25.1% in 2005 and 2006 where the questionnaire
had 56 pages, and 21.1% in 2004 where the volume of questionnaire reduced to 47 pages. Feeling
distrustful about the KHPS/survey in general constitutes over 20% of reason for non-response
in 2005. The proportion, however, becomes smaller in the subsequent waves, suggesting that,on the one hand, individuals less willing to participate in the survey dropped out in the earlier
waves, and, on the other, ongoing participation may form some kind of “familiarity” or “trust”
to the survey itself. Proportions of individuals choosing other reasons are relatively stable acrosswaves.
(Table 3 around here)
To extend the descriptive analysis of non-response presented in Tables 1 and 2, Table 4
presents probit models for survey response. The dependent variable for these models equal 1 if
the individual responds at the wave in question and 0 otherwise. The probability of response ismodelled as a function of the wave t 1 values of regressors, which can be observed regardless ofthe survey response status in wave t, and wave dummies. Table 4 shows the marginal e¤ects of
between waves t

1 and t.

5

the regressors on the probability of response at each wave. The marginal e¤ects are evaluated at
the sample means. The marginal e¤ects for dummy variables represent a discrete change fromzero to one.
Model [1] is our baseline result. The results reveal statistically signi…cant associations between
non-response and respondent’s age. As expected, respondents who are married or have higher
educational status are likely to remain in the survey. Respondents from the single family household have signi…cantly lower retention probability. Wave dummies are included to account forpossible duration dependence, and all of them are highly signi…cant with monotonically increasing marginal e¤ects. This suggests that there is strong evidence of positive duration dependence
in the KHPS.9
In model [2], we additionally introduce two explanatory variables of household mobility inorder to test the ignorability, or selection-on-observability, assumption presented in section 1, i.e.
Pr(s = 1jy; x; z) = Pr(s = 1jx; z). These mobility indicators are obtained from the interviewer’s
record, hence they can be observable even if the respondent attrites from the respective wavesof the panel. Movet represents the behavior of interest — whether or not the respondent moves
between waves t 1 and t — which is denoted by y in the above conditional probability. Following
previous studies, we use the lagged variable of y (Movet 1 ) as the auxiliary variable z (Fitzgerald
et al., 1998; Jones et al., 2006). Selection-on-observability requires that, once controlling for z, yshould be independent of the response, thereby it does not have any direct e¤ect on the survey
response. It is found that the wave t mobility (Movet ) has strongly negative e¤ect on the wave t
response probability even after controlling for lagged mobility indicator (Movet 1 ). This implies
that, in the KHPS, the ignorability assumption does not hold for the analysis of household
mobility. Therefore we expect that an appropriate method to account for non-response bias will
be sample selection models rather than inverse probability weighting. In the next section, we
will explain our empirical methods (SS and IPW) in detail, and compare the estimated mobility
equations by these two competing methods in section 5.
(Table 4 around here)

4

Models and Estimation Methods

Consider a longitudinal survey that includes T waves. Attrition occurs if individual i leaves
the survey in period Ti
T . If information for individual i is not observed for any wave after
Ti , attrition is an absorbing state. Attrition is nonabsorbing if an individual can return to thesample after exiting. Attrition is considered to be absorbing for this analysis. Thus, the attrition
9 Positive duration dependence arises when the survey response becomes less time consuming as the respondents repeatedly participate in the survey, or the respondents feel some form of ‘familiarity’ or ‘trust’ to theinterviewer/survey. Since any unobserved heterogeneity causes spurious duration dependence, we also estimate
the model including the …rst wave values of the all time-varying variables (Zabel, 1998). This yields qualitatively
same results as in Table 4.

6

process is speci…ed as
sit = Xi;t 1 1 + Dit 1 + Zit 1 + "1iti = 1;; N; t = 1;

; Ti

(1)

where
individual i remains in wave t (sit=1) if sit > 0

leaves in wave t (sit=0) if sit

0:

Xi;t 1 is a 1 M vector of regressors, Dit is a 1 T vector of wave dummies, Zit is a 1 K
vector of auxiliary variables/instruments, and "1it is an independent and identically distributed(i.i.d.) normal random variable with mean zero and variance 21 that is independent of Xit 1 .10
Our interest of household mobility equation is modeled along with the attrition equation.
Consider the following panel data model of household mobility behavior.
yit = Xi;t 1 2 + Dit 2 + "2iti = 1;; N; t = 1;

; Ti

(2)

where
individual i moves between waves t

1 and t (yit=1) if yit > 0

does not move between waves t

1 and t (yit=0) if yit0:

We use the same set of regressors of Xi;t 1 and Dit as in equation (1). "2it is i.i.d. normal
random variable with mean zero and variance 22 . Since the observed move (yit ) takes place
between waves t 1 and t, household residential mobility is modelled as a function of the wave
t 1 values of regressors, which can be observed regardless of the survey response status in wave
t. Note that, in the usual situation, respondent moves (yit ) cannot be observed if individual
i does not rameins in the wave t, because they are surveyed at wave t panel. Ti = Ti 1 inthis case. In our current situation, however, interviewer’s record of respondent mobility can beobserved even at t = Ti . Hence Ti = Ti in our current situation.
In the following analysis, we will …rst estimate equation (2) using entire sample by probit
model. We will then estimate equation (2) for selected subsample of non-attritors (i.e. excluding
observations in t = Ti for those with Ti < T ) by two estimation methods explained below.

4.1

Inverse Probability Weighted Estimator

To compute the IPW estimator, we …rst estimate probit model for survey response (equation 2)
using lagged mobility indicator as an auxiliary variable (i.e. Zit = yi;t 1 ). Using the estimated
1 0 If the household mobility decision follows a dynamic utility maximization problem, the individual …xed e¤ects,
which is a function of all information available at t = 1, will exist (Heckman and Macurdy, 1980). This implies
that "2it is correlated with Xi;t 1 . Following Zabel (1998), we model the individual …xed e¤ects as function ofwave 1 values of time-varying variables.

7

result, the …tted response probability in wave t, conditional that the individual i is in the wave t 1
panel, can be obtained. Denoting this conditional probability as ^ it , the predicted probability
weights are constructed cumulatively using p^it = ^ i2 ^ i3
^ it . Finally, using 1=^
pit as a weightfor observations in selected subsample of non-attritors, equation (2) is estimated by probit ML.

4.2

Sample Selection Estimator

Using selected subsample of non-attritors, we employ the method proposed by Van de Ven and
Van Pragg (1981), which extends Heckman’s (1979) selection model to the case of a probit modelwith sample selection. For the identi…cation of the parameters, we assume that "1it and "2it haveunit variances and are joint normally distributed. Setting = Corr ("1it ; "2it ), the null hypothesisof interest is given by H0 : = 0, which indicates that there are no attrition biases. This can betested either by the Wald or likelihood ratio tests.
The key variable in equation (1) is the identifying instrument (Zit ) which has been excluded
from equation (2). To construct this variable, we use information about the interviewer assignment. The supplemental questionnaire of the KHPS provides detailed information to the targeted
respondents about the complete list of assignments of each interviewer. Using this information,
we construct the following three variables for zit : (1) the number of respondents (including thetargeted respondent) which the interviewer is in charge of, and (2) whether or not the assigned
interviewer is the same as the one in the previous wave, and (3) the wave 2 retention rate of thetargeted respondents for each interviewer. The …rst two variables are used in Naoi (2007), andthe third variable is used to control for the quality of each interviewer.

5

Empirical Results

The estimation results for equation (2) are presented in Table 5. In the …rst column, probit resultwith entire sample (including both attritors and non-attritors) is presented as our baseline result.
To assess the extent of the attrition bias for each parameter estimate, results of (1) probit modelusing subsample of non-attritors, (2) IPW model, and (3) sample selection model are presented
along with the baseline model.11(Table 5 around here)
Comparing the results of three alternative models with that of baseline model, we …nd thatthe regression coe¢ cients generally show similar signs although there are a number of sizabledi¤erences in magnitude and signi…cance. For example, while health condition and length of stay
appear to be signi…cant predictor of residential moves in the baseline model, estimated coe¢ cientsfor these two variables are not estimated to be signi…cant in the three alternative models. In termsof the magnitude of the estimated coe¢ cients, relative di¤erences of the estimated coe¢ cients
1 1 Results of probit models for survey response can be available upon request.

8

from probit with entire sample are presented in Table 6. Denoting parameter estimate of thebaseline model as 0 and the alternative model as ~, relative di¤erence is de…ned by j 0 ~j=j 0 j. It
is found that sample selection model generally outperforms IPW and uncorrected probit model interms of coe¢ cient estimates, which is consistent with our …ndings shown in Table 4. Although
there are sizable di¤erences for several variables, coe¢ cient estimates of the sample selection
model are virtually identical with baseline case for “single family,” “labor force participation,”and “housing tenure.”(Table 6 around here)
To examine the overall size of non-response bias, Hausman tests of the hypothesis = 0 are
conducted for three alternative models. The results of test are presented in the bottom of Table
5. Standard implementation of this test requires estimation of V( 0 ~), and obtaining thisestimate can be di¢ cult without the strong assumtions on the covariance structure. Here, we
use the paired bootstrap to obtain consistent estimate of V( 0 ~) (Cameron and Trivedi, 2005,
p.378). The results indicate that the null hypothesis of identical coe¢ cients can be rejected at
the 10% signi…cant level only for uncorrelated probit model. Although our descriptive analysis of
non-response suggest that the ignorability assumption does not hold for the analysis of household
mobility, and that we expect IPW model cannot adequately account for non-response bias,
Hausman test cannot …nd signi…cant bias in the the estimated coe¢ cients of IPW model evenat the 10% signi…cance level.

6

Conclusion

This paper aims to examine respondent’s mobility-related non-response in the longitudinal survey. We use the interviewer’s record of respondent mobility, which can be observed even if the
respondent does not participate in the respective wave, as a source of validation data. Using
the Keio Household Panel Survey (KHPS) 2004–2007 as a primary dataset, household mobility
equations are estimated for selected subsample of non-attritors by two competing methods — aninverse probability weighted (IPW) estimator and a sample selection (SS) estimator. These two
estimators are compared with a probit estimates using complete sample including both attritor
and non-attritor. It is found that SS generally outperforms IPW in terms of coe¢ cient estimates,suggesting that the mobility-related non-response in the KHPS is non-ignorable. However, theresults of Hausman test cannot …nd any signi…cant bias for either IPW or SS estimator.

References
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9Fitzgerald, J., P. Gottschalk, and R. Mo¢ t (1998). An analysis of sample attrition in panel
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Hausman, J. A. and D. A. Wise (1979). Attrition bias in experimental and panel data: The
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and purposes. In M. Yano (Ed.), The Japanese Economy — A Market Quality Perspective,
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569.
Kimura, M. (2005). Design and sample characteristics of the 2004 Keio Household Panel Survey(KHPS). In Y. Higuchi (Ed.), Dynamism of Household Behavior in Japan [I], Chapter 1.
Tokyo: Keio University Press.
Little, R. J. A. and D. B. Rubin (2002). Statistical Analysis with Missing Data (2nd ed.).
Hoboken, NJ: John Wiley & Sons.
McKenzie, C. R., M. Naoi, T. Miyauchi, and K. Kiso (2007). Attrition and individual behavior
in the labor market. In Y. Higuchi and M. Seko (Eds.), Dynamism of Household Behavior inJapan [III], Chapter 1, pp. 13–75. Tokyo: Keio University Press. (In Japanese).
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Naoi, M. (2007). Residential mobility and panel attrition: Using the interview process as identifying instruments. Keio Economic Studies 44 (1), 37–47.
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10Zabel, J. (1998). An analysis of attrition in the panel study of income dynamics and the surveyof income and program participation with an application to a model of labor market behavior.
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11

Table 1: Drop-Out Rates by Selected Socioeconomic and Demographic
Characteristics, KHPS
Wave 2 (2005) Wave 3 (2006) Wave 3 (2007)
All Data
Number of respondents%drop-outs
Age
20s
30s
40s
50s
60s+
GenderFemaleMarital Status
MarriedEducation
Some college or above
Health Condition
Poor health
Housing Tenure
Homeowner
Private renter
Public renter
Mobility
Recent mover

3,314
17.3%

2,884
13.0%

2,634
8.7%

21.5%
16.8%
14.0%
17.2%
18.0%

19.0%
10.6%
11.8%
12.2%
13.9%

10.1%
9.2%
7.7%
7.2%
10.3%

17.0%

13.0%

7.7%

15.6%

11.6%

8.3%

16.1%

12.9%

8.4%

26.7%

20.0%

13.3%

17.1%
19.1%
11.0%

12.8%
15.6%
10.6%

8.2%
10.5%
7.9%

36.3%

32.5%

26.2%

Notes : Initial number of respondents in the first wave (2004) was 4,005.
Respondent/household characteristics are all measured in wave t - 1. Mobility indicator
is obtained from the interviewer's record, where “recent mover” is those who have
moved between waves t - 1 and t .

11.7%
68.1%
20.2%
81.9%
1.99
74.1%
16.47
23.3%
57.5%
19.2%
1,072

6.8%
71.6%
21.6%
84.7%
1.95
75.9%
15.61
24.8%
57.4%
17.8%
2,235

12.5%
66.8%
20.6%
81.5%
1.94
74.1%
16.47
25.2%
56.6%
18.2%
606

7.5%
71.3%
21.3%
84.3%
1.97
75.6%
15.76
24.1%
57.6%
18.3%
2,701

Notes : Respondent/household characteristics are all measured at wave 1 (2004). If the primary respondent is married and is female, individual
characteristics (age, years of education, labor force participation, and health condition) are for her male spouse.

Variables
AgeYears of education
Marrital status (1 = married)Household Type
Single family (# of HH member = 1)
Nuclear family (1 < # of HH member < 5)
Extended family (# of HH member >5)
Labor force participation (1 = worked in the last month)
Health condition (1: good - 5: poor)
Housing tenure (1 = homeowner)
Length of stay in the current residence
Place of residence
14 major cities
Other cities
Town/village
N

Averages
Responses in Waves 2 - 4
Response in Wave 2
Out
Always in
Out
In
46.79
47.81
47.09
47.56
13.06
13.21
13.00
13.20
67.1%
77.1%
66.0%
75.6%

Table 2: First-wave Respondent/Household Characteristics by Subsequent Survey Responses

Table 3: Major Reasons of Non-response to the Survey
ReasonToo busy
Too long questionnaire
Feel meaningless to answer the samequestionnaire as in the previous wave
Distrustful to the KHPS
No specific reasons
OthersToo difficult questionnaire
Distrustful to surveys in general
Health concern
Privacy issue
Disagreement by the family members
Personal reasons
No feedback from the survey organizer
Insufficient reward

Wave 2 (2005) Wave 3 (2006) Wave 3 (2007)
28.0%
30.4%
34.0%
24.6%
25.1%
21.1%
23.7%

24.8%

30.5%

10.9%
10.5%
10.5%
9.8%
9.6%
8.7%
8.4%
8.0%
4.0%
0.3%
0.0%

4.8%
13.1%
13.5%
2.7%
6.3%
11.4%
6.8%
8.2%
4.3%
0.0%
0.0%

3.7%
11.8%
12.5%
4.5%
2.8%
11.8%
3.7%
8.7%
3.7%
0.0%
0.9%

Table 4: Probit Models for Survey Responses
Model [1]Dependent variable:

Model [2]Survey response (1 = respond in wave t )dF/dx(S.E.)

dF/dx(S.E.)

Age (× 100)0.0747(0.0352) *0.0515(0.0352)

Years of education

0.0027(0.0016) +0.0028(0.0016) +Marrital status (1 = married)

0.0948(0.0612) +0.0894(0.0602) +Single family (# of HH member = 1)

-0.0401(0.0206) *-0.0326(0.0201) +Nuclear family (1 < # of HH member < 5)

-0.0008(0.0092)0.0012(0.0092)

Extended family (# of HH member >5)(omitted category)Household Type

Labor force participation (1 = worked in the last month)0.0223(0.0113)

Health condition (1: good - 5: poor) (× 100)-0.0667(0.3521)

Housing tenure (1 = homeowner) (× 100)0.4475(0.9614)

Length of stay in the current residence (× 100)(omitted category)**

0.0192(0.0111) +-0.1713(0.3504)-0.9427(0.9397)-0.0694(0.0306) *-0.0695(0.0307)

14 major cities

0.0149(0.0109)0.0146(0.0109)

Other cities (× 100)0.0090(0.0099)

0.0089(0.0099)

Town/village(omitted category)(omitted category)

Wave 2(omitted category)(omitted category)

Wave 30.0616(0.0072) **0.0605(0.0072) **Wave 4

0.0977(0.0070) **0.0972(0.0069) **Place of residence

Wave dummies

Household mobility
Movet- 1 (1 = move between waves t - 2 and t - 1)---

0.0115(0.0178)

Movet (1 = move between waves t - 1 and t )----0.1871(0.0285) **Log likelihood
N-3093.321-3060.485

8,415

8,415

Notes : Marginal effects are evaluated at the sample means of the regressors. ** , * , and + indicate that the estimated marginal effect issignificant at the 0.01, 0.05, and 0.10 levels, respectively. Except for household mobility, all regressors are measured at wave t - 1.
Mobility indicators are obtained from the interviewer’s record. “Move t ” equals 1 if the respondent moves between waves t - 1 and t ,and “Movet- 1 ” is the lagged variable for “Move t .” The wave 1 marital status is also controlled in the model but omitted from theresults.

Marrital status (1 = married)

**

---

7,343

8,415
---

-767.808-1053.155

+

**

**

(0.1797) **(0.3200) *(0.0155)(0.0036)(S.E.)(0.1213)(0.0038)(0.0810)(0.0349)17.2(0.246)---(0.3291)(0.0904)

---

7,343

-773.208-1.0649-0.1097(0.0828)(omitted category)
0.0567****(0.1180) *-0.0109 (0.1088)(omitted category)-0.0807-0.0029-0.8000-0.0458-0.2779

0.2336 (0.1054) *(omitted category)

0.4682-0.7503

0.0251-0.0165

Coef.

Inverse Probability
Weighting(0.1588) *(0.3172) *(0.0149)(0.0033) **(S.E.)(0.1148)(0.0036)(0.0765) **(0.0337)(0.1094) *(0.400)(1.1750) *(0.3138) **(0.0878)(0.0811)

1,072

8,415

-3712.444

14.7

2.2330-1.2024-0.1149

0.0080(omitted category)-0.0039 (0.1038)(omitted category)-0.0546-0.0037-0.7963-0.0451-0.2308

0.2181 (0.1029) *(omitted category)

0.4026-0.6986

0.0245-0.0148

Coef.

Sample Selection

Notes : ** , * , and + indicate that the estimated coefficient is significant at the 0.01, 0.05, and 0.10 levels, respectively. All regressors are measured at wave t - 1. The wave 1 marital status is also
controlled in the model but omitted from the results. f(r ) = 1/2*ln[(1 + r )/(1 - r )]. Standard error for f(r ) and the variance-covariance matrix for Hausman test statistic are computed by a nonparametric bootstrap procedure with 200 replications.

Number of censored observation

Number of observation

Log likelihood(0.086)21.7---(0.3223)

Hausman test statistics / (P-value)-1.0852(0.0896)---(0.2763)-0.0945----0.7215

Constant(0.0796)f(r )-0.0666(0.0832)(omitted category)
0.0645(0.0731)(omitted category)
0.0887(0.1181)-0.0077 (0.1068)(omitted category)(0.1011)(0.0037)(0.0781)0.0041 (0.0916)(omitted category)-0.0359

Wave 4

Other cities
Town/village
Wave dummies
Wave 2
Wave 314 major cities

Place of residence-0.0645-0.0033(0.0033) +-0.0059

Length of stay in the current residence(0.0676)-0.8149-0.7923

Housing tenure (1 = homeowner)****(0.0347)-0.0465(0.0301) *-0.0720

Health condition (1: good - 5: poor)(0.1130) *-0.2283-0.2497(0.1638) **(0.0963) *0.4463**(0.3324) *(0.0153)(0.0034)(S.E.)

0.2228 (0.1052) *(omitted category)(0.1367) **0.0238-0.7709-0.0159

Coef.

(0.3107) +**(0.0133)(0.0029)(S.E.)

Probit with
Non-attritor

0.1831 (0.0889) *(omitted category)

Nuclear family (1 < # of HH member < 5)
Extended family (# of HH member >5)
Labor force participation (1 = worked in the last month)Single family (# of HH member = 1)

0.40260.0175-0.5519

Years of education

Household Type-0.0168

Coef.

Probit with
Entire Sample

AgeHousehold mobility (1 = move between waves t - 1 and t )Dependent variable:

Table 5: Estimation Results for Household Mobility

0.0175-0.5519

Years of education

Marrital status (1 = married)

43.8%

+

-0.0359
0.0041---

14 major cities

Other cities

Town/village

Place of residence----288.4%-79.5%-2.9%-0.7923 **Housing tenure (1 = homeowner)-0.005935.4%-0.0720 *Health condition (1: good - 5: poor)

Length of stay in the current residence-9.4%-0.2283

Labor force participation (1 = worked in the last month)

--*

---

21.7%

0.1831 *Nuclear family (1 < # of HH member < 5)

Extended family (# of HH member >5)

10.9%-39.7%

36.1%

5.9%

% Difference

0.4026 **+**Probit with
Non-attritor

Single family (# of HH member = 1)

Household Type-0.0168

Coef.

Probit with
Entire Sample

AgeHousehold mobility (1 = move between waves t - 1 and t )Dependent variable:

Table 6: Coefficient Differences from Probit with Entire Sample----368.3%-124.5%

51.4%

-1.0%

36.4%

-21.7%

---

27.6%

16.3%

-35.9%

43.3%

2.2%

% Difference

IPW

-1.1%

---

-195.4%-52.0%

37.8%

-0.5%

37.4%

---

19.1%

0.0%

-26.6%

39.6%

12.0%

% DifferenceSS