1
A Study on the Characteristics of the Asset Item Nonrespondents in a Panel
Survey
Youngshil Park (Researcher, Statistics Korea)
-
Statistical Research Institute, Statistical Center, 713 Hanbatdaero, Seo-gu, Daejeon,
302-847, Korea
-
82-42-366-7210 , youngshil@korea.kr
Sunghee Lee (Assistant Research Scientist, University of Michiga)
-
Survey Research Center, 426 Thomson St., Ann Arbor, MI 48104, the United States
-
1-734-615-5264, sungheel@isr.umich.edu
Abstract
Assets and income questions that ask respondents to report exact value with an
open-ended question are typically associated with high nonresponse in surveys in western
countries. However, there is little research on assets and income nonresponse in Korea
because nonresponse rate is not as high comparing with western countries. Interestingly, there
are high nonresponse rates in specific asset items of the KLIPS, and the KLIPS has tried to
reduce nonresponse rate by applying with a follow-up range question, asking categorical
information, among those who did not provide an exact amount. In this study, we show the
asset nonresponse trends in Korea over a decade, and evaluate the effectiveness of the range
question toward nonresponse. Also, we examine whether or not there is a persistent tendency
in nonrespondents
Key Words: nonresponse rate, panel survey, range questions, nonresponse tendency
2
A Study on the Characteristics of the Asset Item Nonrespondents in a Panel
Survey
RESEARCH BACKGROUND
Assets and income questions that ask respondents to report an exact value with an
open-ended question are typically associated with high nonresponse rate in surveys (Moore,
Stinson, and Welniak, 2000; Riphahn and Serfling, 2005; Schrapler, 2006), and this high
nonresponse rate can cause bias when estimating of assets and income values as well as other
statistics in models utilizing those variables (Yan, Curtin, and Jans, 2010). There have been
efforts to reduce the bias through the improving of imputation. Also, some survey designers
have tried to diminish the bias by employing various question types such as “range cards” or
“unfolding brackets”. Both are follow- up questions that ask respondents who did not answer
an exact value to place assets and income amounts in a specific range (Juster and Smith,
1997).
Unfolding brackets ask nonrespondents and to place an amount above or below of a
specific range. Surveys including the Health and Retirement Survey (HRS) and the study of
Asset and Health Dynamics Among the Oldest Old(AHEAD) have adopted this technique
since it originated from the wealth module of the Panel Study of Income Dynamics (PSID) in
1984, and it has been proven to substantially decrease nonresponse rates (Heeringa, Hill, and
Howell, 1995; Juster, Cao, Perry, and Couper, 2006). On the other hand, the range card
technique is a range of letters that correspond to quantitative intervals to show for
nonrespondents. This is used effectively in face-to-face interviewing rather than telephone
3
interviewing (Juster and Smith, 1997).
Unlike in western countries, there are few researches on assets and income
nonresponse rates in Korea. While the income nonresponse rate is between 20~40% in
western countries according to survey literature (Moore, Stinson, and Welniak, 1999), the
data analysis from the Korean General Social Survey (KGSS) and the Korean Labor and
Income Panel Survey (KLIPS) shows that the income nonresponse rates in both surveys are
less than 5%. We guess the reason why the nonresponse rate is low is that income is not as
sensitive to Korean comparing with western people. Another possibility is that interviewers
are trained to obtain at least categorical information when respondents refuse to answer.
However, interestingly, the nornesponse rates are high in specific asset questions of the
KLIPS, and the KLIPS has tried to mitigate the nonresponse rate by adding follow-up range
questions. These questions ask about categorical information for those who did not provide
an exact amount. The range question strategy in the KLIPS is different from the range cards.
Whereas range cards are separated from the main questionnaire and shown to respondents to
answer after interviewers ask questions in other surveys like the HRS, range cards in the
KLIPS are included in the questionnaire. The category may be sometimes shown, but
interviewers usually read a question and the response categories. This technique has been
used for over a decade.
In this study, by using the KLIPS data, we show the asset nonresponse trends in
Korea for over a decade. Also, we evaluate the effectiveness of the range questions toward
nonresponses. In addition, we examine whether or not there is a persistent tendency in
nonrespondents.
RESEARCH QUESTIONS
4
This study investigates the following specific questions. First, what is the nonresponse pattern of the asset items applied to the range questions in a panel survey? We focus
on examining how the use of the range question reduces the nonresponse rate and how the
amount of the reduction has changed over a decade in the panel survey. The effectiveness of
range questions has been proven to work only for short periods like one or two waves (Juster
and Smith, 1997; Juster, Cao, Perry, and Couper, 2006; Heeringa, Hill, and Howell, 1995), so
we don’t know the trend of its effectiveness over a longer period of time. Although some
studies (Kennickell, 1997; Yan, Curtin, and Jans, 2010) examined the trends for the longer
period, the data used in the studies were not a panel survey.
A second question has to do with the characteristics of respondents and
nonrespondents, especially focusing on the range data respondents among the
nonrespondents. If the characteristics of respondents differed from the characteristics of
nonrespondents, we can expect that the asset estimates between the two groups are
statistically different. Moreover, if it proved, developing a proper imputation program would
be quite important (Juster and Smith, 1997; Juster, Cao, Perry, and Couper, 2006). Some
surveys have utilized range data gathered from the bracket questions to impute assets or
income in to the nonrespondents of the surveys. Even though the range data are not be used
for imputation purposes in the KLIPS, we need to find out whether or not there is a
systematic difference between the data of two groups for further developmental studies.
Third, do the range data respondents tend to not respond to an open-ended question
in next waves? Kennickell (1997) showed that there was a substantial persistence in the use
of ranges by individual respondents, but that result was only for questions from the same
wave. We are not sure whether or not this persistency is valid over waves. We attempt to
investigate this nonresponse tendency by showing the effect of range data experience over a
10 year period. If the propensity is consistent, interviewers can anticipate the probability of
5
that the range data respondents don’t answer to the open-ended question in future waves.
DATA
This study used data from the KLIPS, a panel survey of the labor market, income
activities of households, and individuals residing in urban areas. It has been conducted every
year with face-to-face interviewing by the Korean Labor Institute since 1998. The KLIPS
sampling method is an equal probability of households from urban areas. It started with 5,000
household samples which contain 13,000 people (aged 15 and older). Individuals that have
blood or other legal ties to the original panel members are also recruited to the original
sample from wave 2. For example, if a panel member gets married and forms an independent
household with his/her spouse, then, his/her spouse becomes a ‘new respondent’ for the
original panel (www.kli.re.kr).
The household response rate of the KLIPS from wave 2 to wave 11 are as follows:
87.9%(wave 2), 81.2%(wave 3), 78.1%(wave 4), 77.0%(wave 5), 78.7%(wave
6),78.6%(wave 7), 77.6%(wave 8), 77.5%(wave 9), 76.5%(wave 10), 75.2%(wave 11). It is
also important how many original samples remain in the current wave because the same
households and individuals are interviewed repeatedly. Household retain rates are as follows:
87.6% (wave 2), 80.9%(wave 3), 77.3%(wave 4), 76.0%(wave 5), 77.2%(wave
6),77.3%(wave 7), 76.5%(wave 8), 76.5%(wave 9), 75.5%(wave 10), 74.2%(wave 11).
Overall, the response rates and retain rates are stable after wave 4.
The questionnaire is divided into two parts: household and individual characteristics.
The asset module is included in the household section. For the household questions,
interviewers were trained to collect data from the heads of household or their spouses to gain
6
more exact information (KLI, 2010). The asset module is composed of three real estate items,
six financial items, and several items related to cars and other possessions. Real estate and
financial asset items have been surveyed from wave 2, but the items related to cars and other
asset information was started since wave 10 and wave 11 respectively. The range question for
reducing nonresponse rates is adopted by only real estate assets which consist of three items:
total real estate other than housing, real estate leasing including housing, and real estate
renting other than housing. <Figure 1> shows a flow chart of these questions. First,
ownership status is obtained with a yes or no question. If an asset is owned, its exact amount
is asked with an open-ended question. Then, if the exact amount is not answered, a range
question is asked with a closed-ended question. There are eleven categories in the range
question, and these categories are all the same for each nonrespondent and the three real
estate items. Meanwhile, the KLIPS has requested whether or not an owner knows the exact
information if an asset is owned since wave 6. After that, the question asks exact information
only for the respondents who know that information.
Figure 1 here
RESULTS
1. Nonresponse Trends of Real Estate Asset Items
1) Nonresponse Rates by Item
The final response disposition codes of a real estate asset item are followed by: the
continuous data response to the open-ended question which requests an exact value (the exact
data response), the categorical data response to the range question which asks rough
information among nonrespondents (the range data response), and the final nonresponse. The
7
range data response plus the final nonresponse is the initial nonresponse to the open-ended
question.
Table 1 here
<Table 1> presents response patterns for the three kinds of real estate asset items
from wave 2 to wave 11. The initial nonresponse rate are different according to asset items.
For the total real estate value other than housing item, 70.5% of the owners didn’t give exact
values in wave 2. Comparable figures in the other two items were 11.2% and 36.5%. The
difference of the initial nonresponse rates by item has been consistent through wave 11
although the gap between the other two items about leasing and renting real estate
disappeared. The reason why nonresponse rates of the total real estate other than housing
item is higher than the other two asset items is that the market value of total real estate other
than housing is subject to more uncertainty than deposits for leasing and renting. This
interpretation is supported by the result of <table 2>. We also see that item nonresponse rates
are different according to housing asset items. While the KILIPS asks housing market value
to people who own with using the first item, it asks about deposit amounts to people who rent
house monthly and yearly through the second and the third item. The latter information is
more easily accessible than housing market value of owners. Consequently, the nonresponse
rates of the latter items are lower than the first housing asset item.
2) The Response Trends of the Range Question
<Table 1> shows the exact data response rate and the initial nonresponse rate (range
data response rate + final nonresponse rate). Overall, the exact data response rate have
increased, but the initial nonresponse rates have declined. Specifically, it reveals that large
8
amounts of the initial nonresponse rate are substantially reduced if the questions ask
categorical information. In wave 2, the reduced nonresponse rate of the range question, that is
range data response rate, is 65.6% for the total real estate other than housing item, 8.6% for
the leasing real estate including housing item, and 32.2% for the renting real estate other than
housing item. Over a decade, the range data response rates from wave 2 to wave 11 report
range from 65.6% to 46.3% for the first item, 8.6% to 0.7% for the second item, and 32.2% to
1.1% for the third item. We find that the reduction rates have decreased for 10 years, and the
rate for the latter two items is close to 0% in the most recent wave.1
Also, the final nonresponse rate has decreased gradually. It has started with low
nonresponse rate from wave 2, and it has decreased much slower than the initial nonresponse
rate. Thus it looks stable compared with the initial nonresponse rate, but the final
nonresponse rate became near 0% in wave 11. For the first asset item, the final nonrespons
rates from wave 2 to wave 11 range from 4.9% to 0.2%.
The reduced nonresponse rate in the range question has decreased over the last
decade, yet a dramatic change happened in wave 7. We plotted the initial nonresponse rate
and the final nonresponse rate of the total real estate other than housing item dividing into
two parts from wave 2 to wave 6 and from wave 7 to wave 11 in <figure 2>. It shows the
trends of the initial nonresponse rate and the reduced nonresponse rate in each part are steady.
Interestingly, the final nonresponse rate in wave 7 is higher than the waves before and after it
(wave 6 and wave 8) in <table 1>. We found that the discontinuity between wave 6 and wave
7 is only for real estate asset items by comparing with other housing asset items in <Table 2>.
Yan, Curtin, and Jans (2010) examined the trends in income nonresponse for about
20 years from the Survey of Consumers from 1986 to 2005. This survey adopted for
unfolding brackets technique like the range questions. They found that the nonresponse
reduction rate by the unfolding brackets decrease after a peak in 1990.
1
9
There is no fluctuation in the nonresponse rate between wave 6 and wave 7 in the other items.
Figure 2 here
We have talked several times with the KLIPS manager, and there was no change
between wave 6 and wave 7 in the survey protocol, wording in the asset items, the training
manual and so on. We can only assume that interviewers may have persuaded respondents to
answer continuous data instead of allowing them to accept ‘Don’t Know(DK)’ answer.
Usually, interviewers were trained to obtain at least categorical information rather than DK.
2. The Characteristics of Respondents to the Range Question
Here, we compare how different the range data respondents among nonrespondents
are from the exact data respondents for total real estate other than housing item. Generally,
the variables including respondent’s sex, age, race, education, and marital status are
considered to be correlated with the income nonresponse (Yan, Curtin, and Jans, 2010).
Table 3 here
<Table 3> shows the result of the mean difference between the exact data
respondents and the range data respondents by the heads of household’s sex, age, level of
education, marital status, and employment status, the number of household member, and the
total household income. It displays only three waves selectively (wave 2, 7, 11), but we could
read the consistent pattern for 10 waves. The characteristics of the range data respondents are
placed in the middle between the exact data respondents and the final nonrespondents,
although we can not be sure of sure of this pattern because the number of the final
nonrespondents is low. However, there is a distinct pattern between the characteristics of the
exact data respondents and the range data respondents. On the whole, the mean difference of
these two groups is statistically significant on the heads of household’s age and the level of
10
education and household income variables. Those who answer to the range question are likely
to be older, and less educated. Also, they are likely to have a lower income level than those
who answer to the open-ended question. The result suggests that this fairly distinct pattern
will be potentially useful in a model-based imputation procedure (Hoynes, Hurd, and Chand ,
1998). Also, it could be a good reason to redesign the question for people whose cognitive
skill is lower than average.2
This selectivity in group characteristics may cause a systematic difference in asset
distribution. <Table 4> presents that there is a clearly different asset distribution pattern
between the exact data respondents and the range data respondents. Whereas 2.1% of those
who answered exact data placed in the category of less than 10 million Korean won (krw),
8.0% of those who answered range data placed in the same category in wave 3. Until about
less than 100 million krw, the proportion of range data respondents is higher than the
proportion of exact data respondents. This tendency is consistent over 10 waves. It means that
the assets of the range question respondent is relatively distributed lower level compared to
the asset of the exact data respondents. Furthermore, it suggests that the asset is likely to be
overestimated without including range data response. It coincides with the range data
characteristics. The range data respondents are less likely to be higher status in aspects of
2
Several studies shows the similar results (Juster and Smith, 1997; Hoynes, Hurd,
and Chand ,1998; Yan, Curtin, and Jans, 2010). Even though these studies focused on all
kinds of assets, bracketed respondents who self-select in HRS or AHEAD have different
characteristics from the respondents who gave actual dollar values. For example, in AHEAD
wave 1 those who gave a DK response when asked about asset values were less likely to be
married, and more likely to be old than those who gave actual dollar values (Hoynes, Hurd,
and Chand 1998). The variables having an effect on the nonresponses are related to the
cognitive ability to give correct income or asset information. Among respondents who have
lived in their current home for many years, unwillingness to report housing values may
reflect on uncertainty rather than sensitivity about values, especially among less-educated and
lower-income respondents (Juster and Smith, 1997).
11
education and income than exact data respondents.3
Table 4 here
3. Nonresponse Tendency of the Range Data Respondents
We found that the exact data response rate and the range data response rate have been
changed over waves. Does it mean that individual respondents changed their attitude to
respond exact data in later wave? However, distinct characteristics of the two groups have
been constant. How can we explain this pattern?
Table 5 here
<Table 5> presents that the response rate in the next wave depend on response or
nonresponse type in current wave. The range data respondents in wave 2 are likely to not
respond to the open-ended question in next wave than the exact data respondents do. While
59.1% of owners didn’t answer to the exact data question in wave 3 among exact data
respondents in wave 2, 77.0% of owners didn’t answer to exact data question in wave 3
among the range data respondents in wave 2. Moreover, 71.7% of owners didn’t answer to
exact data question in wave 3 among the nonowner of asset in wave 2. Nonresponse rates to
the open-ended question in next wave are larger in order of range data respondents, nonowner,
and exact data respondents. This tendency has been uniform for 10 waves, although the
nonresponse rates to the open-ended question rapidly changed between wave 6 and wave 7.
We plotted the nonresponse rates to the open-ended question splitting into wave 6 and wave 7
According to the first wave of AHEAD data, for real estate other than housing, respondents
who provide brackets have lower real estate assets than respondents who give continuous
amounts (Hoynes, Hurd, and Chand, 1998).
3
12
in <figure 3>. As a result, we see the nonresponse rate of the range data group has been more
stable than the exact data and nonowner group since wave 7. It may be interpreted people
have persistent nonrespons tendency of range data group rather than exact data respondents
and nonowner group.
Figure 3 here
Unlike range data group, the response rate to the open-ended question of exact data
respondents increases gradually. In addition, response rates to the open-ended question of
nonowner excess nonresponse rate to the open-ended question since wave 7. We propose that
encouraging the exact data and nonowner group is more effective way to increase item
response rate.
Table 6 here
<Table 6> shows the result of logistic analysis whether or not the experience of the
range data response affects to later nonresponse to the open-ended question asking exact
amount. There are two models according to the kind of independent variables. In the first
model in left side of the table 6, an independent variable is whether or not respondents
answer to the range question one wave ago. In the other model, an independent variable is
cumulative frequency of range data response for all previous waves. We examine the effect of
independent variables while controlling socio-demographic variables of same wave of an
independent variable. The logistic regressions suggest that the experience of the range data
response is related to the nonresponse in next wave, and this result is consistent for all waves
regardless of the kind of independent variable. However, odds ratio in the first model is larger
than it in the second model. It implies that the effect of range data experience one wave ago is
larger than the number of range data response experience.
13
CONCLUSION
This study shows the nonresponse trends of the real estate asset items and the
characterisicts of the nonrespondents, in particular, respondents to the range question among
nonrespondents in the panel survey of Korea. First, it appeared that nonresponse rates has
decreased over waves and the range questions have contributed to reduce nonresponse rate
dramatically. However, the reduced nonresponse rate by the range question has declined over
a decade and for even some items (real estate leasing including housing and renting other
than housing items), the rate disappeared in recent waves. Second, we found that the
characteristics of the exact data respondents and the range data respondents are different and
it leads to systematic differences in the asset distribution between two groups. Range data
respondents relative to exact data respondents are placed lower asset level than the exact data
respondents. Third, it suggested that the range data respondents have a persistent tendency
toward nonresponse in next wave comparing with other groups (the exact data respondents
and nonowner).
These findings have important implications for survey quality and practical aspects.
First, the fact that the range data response rate has decreased means the range question may
not be needed in specific time in a panel survey. The KLIPS should test this effect to save
questionnaire space and cost. Second, it should be considered imputation of the real estate
asset data value with range data information. If we use only exact data in analyzing, it causes
bias in asset estimates. The KLIPS should consider Imputation based on covariates like the
head of household’s age, education, and income may provide an important gain in assigning
assets at the individual level even though the effect on the population may not be large
(Hoynes, Hurd, and Chand, 1998). Third, by showing range data respondent’s persistent
tendency toward the nonresponse, we can predict probability of respondent’s behavior in the
next wave and prepare the effective way to reduce item nonresponse. For example, it will be
14
efficient to persuade the people who don’t experience nonresponse in previous wave to
increase response rate. At the same time, we also pay attention to a nonrespondents group,
because this group has differential characteristics from the respondents group.
15
REFERENCE
Heeringa, S.
G., D.
H.
Hill, D.
A.
Howell, 1995, “Unfolding Brackets for Reducing
Item Nonresponse in Economic Surveys,” Panel Study of Income Dynamics Technical
Paper Series #95-01, University of Michigan.
Hoynes, H., M. Hurd, and H. Chand, 1998. “Household Wealth of the Elderly Under
Alternative Imputation Procedures.” In David Wise (ed.), Inquiries in the Economics of
Aging, PP. 229-254. Chicago: University of Chicago Press.
Juster, F.
T.
and J. P.
Smith, 1997, “Improving the Quality of Economic Data: Lessons
from the HRS and AHEAD,” Journal of the American Statistical Association 92(440):
1268-1278
Juster, T.
F., H.
Cao, M.
Perry, and M.
Couper, 2006, “The Effect of Unfolding
Brackets on the Quality of Wealth Data in HRS,” Working Paper 2006-113.
University of Michigan.
Kennickell, A. B. 1997. Using Range Techniques with CAPI in the 1995 Survey of Consumer
Finance.
KLI, 2010, User’s Guide, Korean Labor Institute. (www. Kil.re.kr)
Moore, J.C., L.L. Stinson, and E. J. Welniak, 1999, “Income Reporting in Surveys:
Cognitive Issues and Measurement Error,” In Cognition and Survey Research, M.
Sirken, D.
J.
Heermann, S.
Schechter, N.
Schwartz, J.
M.
G.
Tanur, and R.
Tourangeau (eds). New York: Wiley.
Moore, J.C., L.L.
Stinson, and E. J. Welniak, 2000, “Income Measurement Error in
Surveys: A Review,” Journal of Official Statistics 16(4): 331-361.
Riphahan, R.
T.
and O.
Serfling, 2005, “Item Nonresponse on Income and Wealth
Questions,” Empirical Economics 30(2): 521-538.
Schrapler, J.
2006, “Explaining Income Nonresponse - A Case Study by Means of the
British Household Panel Survey,” Quality and Quantity 40 : 1013-1036.
Yan, T., R.
Curtin, and M.
Jans, 2010, “Trends in Income Nonresponse Over Two
Decades,” Journal of Official Statistics 26(1): 145-164.
16
<Table 1> Response Patterns of Real Estate Asset Items by Wave
Real estate other than housing
Owners
Initial Nonresponse
Wave
Owners
Exact Data
Range Data
Response
Response
Final Nonresponse
Freq.
%
Freq.
%
Freq.
%
Freq.
%
2
947
21.0
279
29.5
622
65.6
46
4.9
3
833
19.5
238
28.6
586
70.3
9
1.1
4
931
21.9
316
33.9
597
64.2
18
1.9
5
942
21.9
283
30.0
649
68.9
10
1.1
6
941
20.5
238
25.3
693
73.6
10
1.1
7
988
20.7
439
44.4
515
52.1
34
3.4
8
1096
22.6
600
54.7
481
43.9
15
1.4
9
1143
22.9
654
57.2
475
41.6
14
1.2
10
1193
23.5
700
58.7
484
40.5
9
0.8
11
1202
23.5
643
53.5
557
46.3
2
0.2
Real estate leasing including housing
2
430
9.5
382
88.8
37
8.6
11
2.6
3
550
12.9
458
83.3
82
14.9
10
1.8
4
597
14.1
503
84.3
84
14.0
10
1.7
5
618
14.4
518
83.8
97
15.7
3
0.5
6
604
13.2
535
88.6
65
10.7
4
0.7
7
623
13.1
598
96.0
15
2.4
10
1.6
8
738
15.2
719
97.4
11
1.5
8
1.1
9
801
16.0
784
97.9
13
1.6
4
0.5
10
812
16.0
796
98.0
15
1.9
1
0.1
11
804
15.7
798
99.3
6
0.7
0
0.0
Real estate renting other than housing
2
605
13.4
384
63.5
195
32.2
26
4.3
3
790
18.5
551
69.7
221
28.0
18
2.3
4
584
13.8
425
72.8
127
21.7
32
5.5
5
632
14.7
535
84.7
81
12.8
16
2.5
6
620
13.5
476
76.8
125
20.2
19
3.1
7
660
13.9
554
83.9
63
9.5
43
6.5
8
748
15.4
668
89.3
59
7.9
21
2.8
9
659
13.2
630
95.6
26
3.9
3
0.5
10
713
14.1
686
96.2
18
2.5
9
1.3
11
626
12.2
619
98.9
7
1.1
0
0.0
<Table 2> Nonresponse Rate of the Housing Asset Items in the KLIPS (percent)
w2 w3
w4
w5
w6
w7
w8
w9
w 10 w 11
Item1
12.2 12.3
9.6
9.3
5.9
4.5
4.8
3.7
3.3
0.9
Item2
4.6
4.2
4.2
3.8
2.0
1.0
1.2
0.9
0.5
0.2
Item3
5.7
5.0
3.2
3.6
2.0
0.9
0.7
0.9
0.5
0.1
Note: The KLIPS asks Item 1 to housing owner, Item 2 to yearly renter, and Item 3 to
monthly renter.
17
<Table 3> The Characteristics of the Respondents and Nonrespondents : Total Real Estate other than Housing Item
Wave2
Exact Data
Range Data
Nonrespondents
Exact Data - Range Data Respondents
Respondents
Respondents
N Mean
S.E
N Mean
S.E
N Mean
S.E
t
d.f.
sig.
Diff.
S.E.
Male
278
1.0
0.012 617
0.9
0.012 46
0.8
0.057 3.496 767.8 0.000
0.1
0.017
Age
278
49.6
0.705 617
51.6
0.498 46
57.4
1.960 -2.266 893.0 0.024
-2.0
0.880
Education
278
3.1
0.094 617
2.7
0.066 46
2.2
0.281 3.424 893.0 0.001
0.4
0.117
Married
278
0.9
0.016 617
0.9
0.013 46
0.8
0.061 2.450 660.8 0.015
0.1
0.021
Employed
279
0.8
0.025 622
0.8
0.016 46
0.7
0.071 -0.833 899.0 0.405
0.0
0.029
Number of HH
279
3.6
0.074 622
3.6
0.053 46
3.3
0.234 0.624 570.6 0.533
0.1
0.091
Total Income
278 2922.8 183.147 619 2458.8 96.806 45 2108.7
336.937 2.448 895.0 0.015
464.0 189.534
Wave7
Male
432
0.9
0.013 506
0.9
0.014 34
0.8
0.070 1.222 933.9 0.222
0.0
0.019
Age
432
52.0
0.581 506
54.6
0.557 34
56.6
2.031 -3.169 936.0 0.002
-2.6
0.807
Education
432
3.1
0.076 506
2.7
0.076 34
2.3
0.258 3.556 930.2 0.000
0.4
0.108
Married
432
0.9
0.015 506
0.8
0.016 34
0.8
0.070 2.004 935.7 0.045
0.0
0.022
Employed
439
0.8
0.020 515
0.7
0.019 34
0.7
0.079 1.157 940.0 0.247
0.0
0.027
Number of HH
439
3.3
0.056 515
3.2
0.059 34
3.0
0.223 1.171 951.2 0.242
0.1
0.082
Total Income
430 4398.5 222.202 506 3829.5 233.909 34 2836.0
438.417 1.745 934.0 0.081
568.9 326.104
Wave11
Male
639
0.9
0.013 548
0.9
0.015 2
0.5
0.500 1.349 1119.8 0.178
0.0
0.019
Age
639
53.4
0.505 548
56.8
0.571 2
42.5
5.500 -4.491 1185.0 0.000
-3.4
0.760
Education
639
3.3
0.062 548
2.9
0.073 2
4.5
1.500 3.556 1185.0 0.000
0.3
0.095
Married
639
0.9
0.014 548
0.8
0.016 2
0.5
0.500 1.921 1109.8 0.055
0.0
0.021
Employed
643
0.8
0.016 557
0.7
0.019 2
1.0
0.000 2.189 1141.7 0.029
0.1
0.025
Number of HH
643
3.1
0.047 557
3.0
0.054 2
3.0
1.000 2.085 1198.0 0.037
0.1
0.071
Total Income
643 5287.7 213.076 557 4534.3 185.584 2 2754.0 1014.000 2.627 1198.0 0.009
753.4 286.790
Note: Education: 0 no schooling, 1 elementary school, 2 middle school, 3 high school 4 college 5 university, 6 master degree, 7 doctoral degree
18
<Table 4> Asset Distribution of the Exact Data Respondents and the Range Data Respondents: Real Estate Asset other than Housing Item
1
2
3
4
5
6
7
8
9
10
11
total
mean
Exact
2.1
10.1
14.3
20.6
4.2
27.3
9.2
5.5
2.9
2.1
1.7
100.0 13,940.3
Range
8.0
14.8
18.3
10.8
13.0
20.5
7.5
2.2
1.7
2.4
.9
100.0 9,453.5
w3 Total
6.3
13.5
17.1
13.6
10.4
22.5
8.0
3.2
2.1
2.3
1.1
100.0 10,749.4
Exact
4.4
6.0
13.3
17.1
6.0
32.0
7.6
3.8
2.2
4.4
3.2
100.0 16,910.5
w4 Range
11.2
13.6
19.6
15.7
14.7
14.2
5.0
2.2
1.2
1.7
.8
100.0 10,077.9
Total
8.9
11.0
17.4
16.2
11.7
20.4
5.9
2.7
1.5
2.6
1.6
100.0 12,442.7
Exact
2.8
4.9
11.3
18.0
7.8
25.4
15.5
4.2
3.5
3.9
2.5
100.0 17,885.6
w5 Range
7.6
10.8
17.1
16.5
11.9
19.1
7.2
2.8
2.3
3.5
1.2
100.0 13,514.3
Total
6.1
9.0
15.3
17.0
10.6
21.0
9.8
3.2
2.7
3.6
1.6
100.0 14,841.6
Exact
1.3
8.0
11.8
13.4
7.1
25.2
14.3
7.6
3.4
6.3
1.7
100.0 18,459.7
w6 Range
9.4
12.1
19.3
10.7
9.4
17.2
7.8
3.0
3.2
4.9
3.0
100.0 16,195.2
Total
7.3
11.1
17.4
11.4
8.8
19.2
9.5
4.2
3.2
5.3
2.7
100.0 16,774.1
Exact
1.8
7.3
12.1
11.6
6.2
22.8
15.5
7.5
4.1
8.2
3.0
100.0 20,736.7
w7 Range
11.1
11.7
14.0
11.1
10.9
17.9
7.6
4.7
3.1
5.0
3.1
100.0 16,921.8
Total
6.8
9.6
13.1
11.3
8.7
20.1
11.2
6.0
3.6
6.5
3.0
100.0 18,677.3
Exact
1.3
5.7
13.0
14.2
4.3
25.2
13.7
6.7
4.7
8.0
3.3
100.0 21,843.7
w8 Range
9.1
12.9
14.3
10.0
9.1
16.2
9.4
6.4
1.7
5.8
5.0
100.0 19,364.3
Total
4.8
8.9
13.6
12.3
6.5
21.2
11.7
6.6
3.3
7.0
4.1
100.0 20,740.5
Exact
2.1
4.4
8.3
12.4
4.9
23.5
15.1
8.4
5.4
11.2
4.3
100.0 28,029.3
w9 Range
5.1
13.7
17.7
13.3
9.9
19.6
6.7
2.7
2.9
4.0
4.4
100.0 16,948.9
Total
3.4
8.3
12.2
12.8
7.0
21.9
11.6
6.0
4.3
8.1
4.3
100.0 23,367.5
Exact
2.4
4.9
7.9
11.7
5.0
24.6
12.3
7.1
5.1
11.4
7.6
100.0 30,885.5
w Range
5.8
7.0
17.1
12.8
14.5
15.7
8.9
4.8
3.3
4.8
5.4
100.0 19,524.3
10 Total
3.8
5.7
11.7
12.2
8.9
20.9
10.9
6.2
4.4
8.7
6.7
100.0 26,241.2
Exact
.2
4.5
8.6
10.4
5.3
23.5
12.0
9.3
5.3
14.3
6.7
100.0 33,512.6
w Range
6.5
8.4
13.1
12.4
11.5
19.2
9.0
4.5
4.8
5.6
5.0
100.0 20,530.1
11 Total
3.1
6.3
10.7
11.3
8.2
21.5
10.6
7.1
5.1
10.3
5.9
100.0 27,486.5
Note: The response category of range question is followed. 1: less than 10 million KRW, 2: 10 to less than 25, 3: 25 to less than 50, 4: 50 to
less than 75, 5: 75 to less than 100, 6: 100 to less than 200, 7: 200 to less than 300 , 8: 300 to less than 400, 9: 400 to less than 500, 10: 500
million to less than 1 billion KRW, 11: 1 billion KRW or more
19
<Table 5> Response Tendency in the Next Wave: Total Real Estate other than Housing Item
(n+1) wave
Owner(100%)
Respondent (100%)
w2
w3
w4
w5
w6
w7
w8
w9
w 10
Exact Data Respondents
Range Data Respondents
Nonowner
Exact Data Respondents
Range Data Respondents
Nonowner
Exact Data Respondents
Range Data Respondents
Nonowner
Exact Data Respondents
Range Data Respondents
Nonowner
Exact Data Respondents
Range Data Respondents
Nonowner
Exact Data Respondents
Range Data Respondents
Nonowner
Exact Data Respondents
Range Data Respondents
Nonowner
Exact Data Respondents
Range Data Respondents
Nonowner
Exact Data Respondents
Range Data Respondents
Nonowner
Size
279
622
3,561
238
586
3,433
316
597
3,316
283
649
3,356
238
693
3,651
439
515
3,774
600
481
3,753
654
475
3,858
700
484
3,876
Response
251
90.0
563
90.5
3,139
88.1
92.9
221
92.8
544
88.1
3,025
91.5
289
95.1
568
91.6
3,039
266
94.0
630
97.1
3,073
91.6
233
97.9
670
96.7
3,380
92.6
427
97.3
493
95.7
3,512
93.1
575
95.8
467
97.1
3,528
94.0
637
97.4
461
97.1
3,640
94.3
672
96.0
466
96.3
3,640
93.9
Nonown
87
34.7
206
36.6
2,867
91.3
26.7
59
30.0
163
89.7
2,712
36.3
105
26.6
151
91.0
2,764
108
40.6
185
29.4
2,822
91.8
81
34.8
174
26.0
3,095
91.6
108
25.3
110
22.3
3,176
90.4
136
23.7
123
26.3
3,234
91.7
144
22.6
123
26.7
3,332
91.5
139
20.7
121
26.0
3,349
92.0
Own
164
65.3
357
63.4
272
8.7
73.3
162
70.0
381
10.3
313
63.7
184
73.4
417
9.0
275
158
59.4
445
70.6
251
8.2
152
65.2
496
74.0
285
8.4
319
74.7
383
77.7
336
9.6
439
76.3
344
73.7
294
8.3
493
77.4
338
73.3
308
8.5
533
79.3
345
74.0
291
8.0
Exact Data
Response
67
40.9
82
23.0
77
28.3
53.7
87
24.7
94
31.0
97
46.2
85
18.9
79
36.0
99
80
50.6
67
15.1
67
26.7
105
69.1
166
33.5
147
51.6
235
73.7
143
37.3
197
58.6
321
73.1
122
35.5
176
59.9
369
74.8
112
33.1
186
60.4
357
67.0
125
36.2
146
50.2
Initial Nonresponse
Range Data
Final
Response
Nonresponse
97
59.1
0
0.0
271
75.9
4
1.1
192
70.6
3
1.1
45.7
74
1
0.6
73.8
281
6
1.6
66.5
208
8
2.6
52.7
97
2
1.1
80.1
334
4
1.0
63.3
174
2
0.7
76
48.1
2
1.3
376
84.5
2
0.4
180
71.7
4
1.6
43
28.3
4
2.6
312
62.9
18
3.6
127
44.6
11
3.9
84
26.3
0
0.0
229
59.8
11
2.9
136
40.5
3
0.9
116
26.4
2
0.5
218
63.4
4
1.2
113
38.4
5
1.7
123
24.9
1
0.2
221
65.4
5
1.5
120
39.0
2
0.6
176
33.0
0
0.0
220
63.8
0
0.0
143
49.1
2
0.7
20
<Table 6> Logistic Regression: Total Real Estate other than Housing Item
Independent var.:
Experience of range data response ahead of
Independent var.:
Time of
one wave
Cumulative frequency of range data response
Dependent
Variable
B
S.E.
Sig.
Exp(B)
B
S.E.
Sig.
Exp(B)
w3
0.696
0.204
0.001
2.006
0.696
0.204
0.001
2.006
w4
1.199
0.206
0.000
3.315
0.616
0.139
0.000
1.852
w5
1.308
0.198
0.000
3.698
0.576
0.106
0.000
1.779
w6
1.680
0.212
0.000
5.364
0.518
0.088
0.000
1.679
w7
1.282
0.200
0.000
3.605
0.118
0.060
0.048
1.125
w8
1.391
0.165
0.000
4.021
0.345
0.052
0.000
1.412
w9
1.542
0.160
0.000
4.673
0.252
0.043
0.000
1.286
w 10
1.746
0.161
0.000
5.733
0.281
0.040
0.000
1.324
w 11
1.224
0.149
0.000
3.402
0.207
0.034
0.000
1.230
Note: Socio-demographic variables of same wave with an independent variable were controlled.
A dependent variable is nonresponse to open-ended questions asking exact value in a current wave.
21
<Figure 1> A Flow of the Asset Question
22
<Figure 2> Reduced Nonresponse Rate by the Range Question: Real Estate other than
Housing Item
80.0
70.0
60.0
%
50.0
40.0
30.0
20.0
10.0
0.0
2
3
4
5
6
7
8
9
10
11
Wave
Initial nonresponse
Final nonresponse
<Figure 3> Nonresponse Rate to the Open-ended question in the Next Wave by Response
Group Type
90.0
Range Data
Respondents
80.0
70.0
nonowner
60.0
%
50.0
40.0
Exact Data
Respondents
30.0
20.0
10.0
0.0
w3
w4
w5
w6
w7
w8
w9
w10
w11