home ABOUT KLIPS FAQ
ABOUT KLIPS

FAQ

a total of  32  Currently page on 1 / 4
  • question How to Estimate the Number of Irregular Workers Using the KLIPS Data
    answer
    How to Estimate the Number of Irregular Workers Using the KLIPS Data

     

     

    1. Irregular workers as defined by the tripartite committee 

     

     The classification of irregular workers in Korea is based on the agreement reached in July
    2002 by the tripartite committee of the National Assembly. According to this definition,
    workers under temporary, part-time, or atypical forms of employment are classified as
    being irregularly employed. 'Atypical' workers here include temporary agency workers,
    contract-based workers, workers in special forms of employment, home-based workers, and
    daily(short-term) workers.
     Since its 5th wave (2002), KLIPS has included questions for identifying alternative forms
    of employment (temporary agency work, contract-based work, independent subcontracting,
    and home-based work) in addition to temporary workers as frequently surveyed by studies
    in the U.S. Since its 10th wave (2007), the relevant questions were further expanded to
    allow for the identification of a wide variety of irregular workers. (For a full description of
    the additional survey on Forms of Employment, please refer to "Outline and Key Findings
    from the KLIPS 10th Wave Additional Survey on Forms of Employment" by Lee Sang-ho,
    Labor Review, KLI, July 2008 issue (in Korean)).
     According to the Supplemental Survey of the Economically Active Population Survey by
    Statistics Korea, the share of irregular workers among wage earners in Korea has fallen
    steadily from 36.6% in August, 2005 to 32.5% in August, 2015. The share based on KLIPS
    data is somewhat different. Using the same set of definitions as the EAPS, the share of
    irregular workers among wage earners calculated from KLIPS data has risen from 31.4% in
    2009 to 35.8% in 2014. While the absolute levels are comparable between the two studies,
    they follow different trends. These differences are believed to arise due to differences in
    how the studies are conducted.(See more details ; KLIPS Data Analysis Section 3)
    However, most of the core indices based on KLIPS data including the employment rate,
    unemployment rate, composition of workers by industry and occupation, labor time, and
    average wages do not exhibit substantial differences compared to the values based on
    official reference statistics. This indicates that the KLIPS data retains its reliability.

     


    2. Irregular workers based on work status


     In the KLIPS data, it is also possible to estimate the number of irregular workers based
    on the work status variable. However, whereas work status in KLIPS is determined based
    solely on the duration of the work contract, the EAPS Supplemental Survey defines work
    status under stricter criteria. This leads to some differences between the studies. To
    elaborate, the latter study first determines the work status (regular / temporary / daily)
    based on the duration of the work contract, and then re-classifies regular workers
    experiencing discrimination in terms the applicability of equal regulations at the workplace,
    eligibility for retirement and bonus payment, and eligibility for fringe benefits as temporary
    or daily workers.

     


    3. Self-declared irregular workers


     The KLIPS questionnaire also includes a question that asks respondents whether they
    regard themselves as regular or irregular workers. This 'self-declared' criterion presents
    only a minimal set of definitions about irregular work, leaving the respondent to judge for
    oneself whether one is regularly or irregularly employed. Therefore, the majority of
    respondents whose work status is 'temporary' or 'daily' tend to declare themselves as
    irregular workers. In fact, more than 80% of self-declared irregular workers in each year
    were found to be temporary or daily workers. Please note that the self-declared variable
    was not included in the 3rd wave (2000) of the study.

     


    *====================================;
    * Generating forms of employment (SAS) ;
    *====================================;


    * EAPS Supplemental Survey Criteria ;


    * Temporary workers ;


    IF p180501 = 1 THEN foe11 = 1 ; * fixed contract workers > temporary workers ;
    ELSE IF p180501 = 2 AND p180601 = 2 AND p180605 in (1,2,3,4,5,6) THEN foe11 = 1 ; * non-fixed
    contract workers > temporary workers ;
    ELSE IF p180501 = 2 AND p180602 = 2 THEN foe11 = 210 ; * non-fixed contract workers > temporary
    workers ;
    ELSE foe11 = 0 ;


    * part-time workers ;


    IF p180315 = 1 THEN foe12 = 1 ;
    ELSE foe12 = 0 ;


    * temporary agency workers > atypical workers ;


    IF p180611 = 2 THEN foe13 = 1 ;
    ELSE foe13 = 0 ;


    * subcontracted workers > atypical workers ;


    IF p180611 = 3 THEN foe14 = 1 ;
    ELSE foe14 = 0 ;


    * workers in special forms of employment > atypical workers ;


    IF p180612 = 1 THEN foe15 = 1 ;
    ELSE foe15 = 0 ;


    * home-based workers > atypical workers ;


    IF p180613 = 1 THEN foe16 = 1 ;
    ELSE foe16 = 0 ;


    * daily (short-term) workers > atypical workers ;


    IF p180508 = 1 THEN foe17 = 1 ;
    ELSE foe17 = 0 ;


    * irregular workers ;


    IF foe11 + foe12 + foe13 + foe14 + foe15 + foe16 + foe17 > 0 THEN foe10 = 1 ;
    ELSE foe10 = 0 ;


    * based on work status ;


    IF p180314 = 1 THEN foe2 = 0 ;
    IF p180314 in (2,3) THEN foe2 = 1 ;


    * self-declared ;


    IF p180317 = 1 THEN foe3 = 0 ;
    IF p180314 = 2 THEN foe3 = 1 ;

     


    *====================================;
    * Generating forms of employment (SPSS) ;
    *====================================;


    compute foe11=0.
    if (p180501=1) foe11=1.
    if (p180501=2 and p180601=1 and p180602=2) foe11=1.
    if (p180501=2 and p180601=2 and (p180605=1 or p180605=2 or p180605=3 or p180605=4 or p180605=5 or
    p180605=6)) foe11=1.
    rename variables (foe11=temporary workers).
    execute.


    compute foe12=0.
    if (p180315=1) foe12=1.
    rename variables (foe12=part-time workers).
    execute.


    compute foe13=0.
    if (p180611=2) foe13=1.
    rename variables (foe13=temporary agency workers).
    execute.


    compute foe14=0.
    if (p180611=3) foe14=1.
    rename variables (foe14=subcontracted workers).
    execute.


    compute foe15=0.
    if (p180612=1) foe15=1.
    rename variables (foe15=workers in special forms of employment).
    execute.


    compute foe16=0.
    if (p180613=1) foe16=1.
    rename variables (foe16=home-based workers).
    execute.


    compute foe17=0.
    if (p180508=1) foe17=1.
    rename variables (foe17=daily (short-term) workers).
    execute.


    compute foe1=0.
    if (foe11=1 or foe12=1 or foe13=1 or foe14=1 or foe15=1 or foe16=1 or foe17=1) foe17=1.
    rename variables (foe1=irregular workers).
    execute.


    compute foe2=0.
    if (p180317=2 or p180317=3) foe2=1.
    rename variables (foe2=irregular workers based on work status).
    execute.


    compute foe3=0.
    if (p180317=2) foe3=1.
    rename variables (foe3=self-declared irregular workers).
    execute.

     


    *====================================;
    * Generating forms of employment (Stata) ;
    *====================================;


    qui gen foe11=0
    qui replace foe11=1 if p180501=1
    qui replace foe11=1 if p180501=2 & p180601=1 & p180602=2
    qui replace foe11=1 if p180501=2 & p180601=2 & (p180605=1 | p180605=2 | p180605=3 | p180605=4 |
    p180605=5 | p180605=6)
    label var foe11 “temporary workers”


    qui gen foe12=0
    qui replace foe12=1 if p180315=1
    label var foe12 “part-time workers”


    qui gen foe13=0
    qui replace foe13=1 if p180611=2
    label var foe13 “temporary agency workers”


    qui gen foe14=0
    qui replace foe14=1 if p180611=3
    label var foe14 “subcontracted workers”


    qui gen foe15=0
    qui replace foe15=1 if p180612=1
    label var foe15 “workers in special forms of employment”


    qui gen foe16=0
    qui replace foe16=1 if p180613=1
    label var foe16 “home-based workers”


    qui gen foe17=0
    qui replace foe17=1 if p180508=1
    label var foe17 “daily (short-term) workers”


    qui gen foe1=0
    qui replace if (foe11+foe12+foe13+foe14+foe15+foe16+foe17>0) foe17=1
    label var foe1 “irregular workers”


    qui gen foe2=0
    qui replace foe2=1 if p180317=2 or p180317=3
    label var foe2 “irregular workers based on work status”


    qui gen foe3=0
    qui replace foe3=1 if p180317=2
    label var foe3 “self-declared irregular workers”​

     

     

     

    <KLIPS User's Guide (Wave 1~18th) FAQ Q27>

     

  • question Merging Individual data with side job data
    answer
    Merging Individual data with side job data

     

     

     The Individual dataset includes only information about the main job. In the event that
    information related to the side job is needed, such data must be identified using the
    Career History data then merged with the Individual dataset.


     One example could be finding data about the side job of an individual surveyed in Wave
    18. In the Career History data, the jobs that meet all of the following criteria are the
    individual's side job(s).


    ① Jobwave variable is 18 (indicating the survey year),
    ② Mainjob variable is 0 (indicating the main job),
    ③ Jobclass variable is 1, 3, 5 or 7 (indicating the continuation of the job to the present).
    (Only 1, 3, 5 and 7 are left because these jobs are the ones that have continued into the
    time of the survey.)


     The data filtered out using such process should then be merged to the Individual data
    using pid.​

     

     

    <KLIPS User's Guide (Wave 1~18th) FAQ Q26>

     

  • question When the mean values for wage and income variables fluctuate, or are too large
    answer
    When the mean values for wage and income variables fluctuate, or are too large

     

     

     When calculating the mean value for wage or income variable, the biggest attention
    should be given to handling non-responses. Wage and income variables in KLIPS are all
    top-coded in 6 digits, and non-responses are marked ‘-1’. Thus, if non-responses are not
    handled as missing values, it could lead to problems such as the values appearing bigger
    or the trends being invisible.


     It might be more convenient for the data user if the KLIPS team treated all
    non-responses as missing values across the board. But to ensure better research accuracy,
    '-1' is not re-processed as missing values in the data being made available, because
    non-response and missing value completely different from each other. In addition, there
    has been no actual case of top-coding yet, as no one reported a monthly average wage
    higher than 10 billion.


     When using wage and income variables, it is recommended to first obtain the
    elementary statistics, check if they are consistent with the missing values listed in the
    codebook then process the non-response values.​

     

     

     

    <KLIPS User's Guide (Wave 1~18th) FAQ Q25>

     

  • question If there are too many missing values in Wage Workers' work hour variable
    answer
    If there are too many missing values in Wage Workers' work hour variable

     

     

     This is an error that is caused when the KLIPS work hour variable has not been built
    properly. It usually occurs when only the variable “p181004 : (main job) average weekly
    work hours (wage)” was used.
     KLIPS surveys work hours using the type survey. Questions on work hours are different
    between wage workers and non-wage workers.


    For wage workers, they ask:
     ① If formal work hours are defined,
     ② If not, what are the average hours worked per week, excluding meal time,
     ③ If yes, what are the formal work hours per week, excluding meal time; if there are any
         hours worked in addition to the formal work hours; if yes, how many additional hours have
         been worked per week.


     For non-wage workers, there is only one question simply asking them about how many
    hours they work per week.
     Following is an example of generating the work hour variable for wage workers.

     


    *===========================;
    * SAS Generating Working Hours Variable
    *===========================;

    data p18; set a.klips18p;
    /* variable description
    p181003 : fixed work hours at main job y/n =1/2
    p181004 : avg. weekly work hours at main job
    p181006 : regular weekly work hours at main job
    p181011 : whether working overtime at main job y/n = 2/1
    p181012 : avg. weekly overtime hours at main job */
    array w[3] p181004 p181006 p181012; /* discard missing values */
    do i=1 to 3
    if w[i] =-1 then w[i]=.
    end
    if p181003=2 then worktime=p181004; /* avg. work hours */
    if p181003=1 and p181011=1 then worktime=p1871006;
    if p181003=1 and p181011=2 then worktime=sum(of p181006, p181012); /* regular +
    overtime hours */
    /* checking for impossible work hours */
    proc freq table p181004 p181006 p181012;
    proc print where p181012>168 var pid p181012;
    run
    data hours; set p18(keep=worktime); /* plotting graph of work hours */
    proc chart data=hours; vbar worktime; title 'Hours worked per week'
    run

     


    *===========================.
    * Generating working hour variable from SPSS .
    *===========================.


    get file='D:\18차\users guide\18차년도\klips18p.sav'.
    recode p181004 p181006 p181012(-1=sysmis).
    if (p181003=2) worktime=p181004.
    if (p181003=1 and p181011=1) worktime=p181006.
    if (p181003=1 and p181011=2) worktime=sum(p181006, p181012).
    GRAPH /HISTOGRAM=worktime.
    /* checking for impossible work hours */
    select if (p181012>168).
    list pid p181012.

     


    /*======================================*/
    /* Generating working hour variable from stata */
    /*======================================*/


    clear
    use klips18p, clear
    recode p181004 p181006 p181012(-1=.)
    egen worktime=rowtotal(p181006 p181012) if p181003==1 & p181011==2
    replace worktime=p181004 if p181003==2
    replace worktime=p181006 if p181003==1 & p181011==1
    histogram worktime
    /* checking for impossible work hours */
    keep if p181012>168
    tab pid p181012​

     

     

     

    <KLIPS User's Guide (Wave 1~18th) FAQ Q24>

     

     

  • question Why is the proportion of regular/irregular workers different KLIPS from the number announced by the NSO?
    answer
    Why is the proportion of regular/irregular workers different KLIPS from the number announced by the NSO?

     

     

     Some researchers have questioned the reliability of the KLIPS data, pointing out that the
    proportion of irregular workers seems to be too low. This point is not entirely without
    grounds. The Economically Active Population Survey conducted by Statistics Korea serves
    as the standard when discussing the share of irregular workers. And in this survey the
    percentage of non-farming temporary/daily workers surpassed 50% since 1999 then fell to
    44% in 2008. But in KLIPS, it remains at only 21~22%.


     Meanwhile, KLIPS also asks about the respondent’s subjective judgment regarding his/
    her regular or irregular status, which is rarely seen in other surveys. More specifically, it
    asks "What is your regular/ irregular status in this job?" to determine the regular/irregular
    status. Again, only 21~22% responded that they are irregular workers.


     At first glance, it is hard to understand why the KLIPS reports less than half of the
    figures in the Economically Active Population Survey, even when accounting for the
    difference in the survey sample. Despite the significantly smaller sample, KLIPS shows
    similar results with benchmark surveys in most of the core variables such as employment
    rate, unemployment rate, employment rate by industry and by job type, working hours and
    average wage. In light of this, it may be unfair to consider the KLIPS as unreliable solely on
    the basis of low proportion of irregular workers. The KLIPS team offers the following
    explanations for this issue. 


    1. Work status


     The KLIPS determines the work status solely based on the duration of the employment
    contract (temporary is less than one year, daily is less than one month). In comparison, the
    Economically Active Population Survey uses a stricter set of criteria. It first provisionally
    defines permanent, temporary and daily positions based on the duration of employment
    contract. Even when the response is "permanent," it raises further questions for workers
    who do not have a specified duration of employment contract. The questions concern
    whether the same rules applied to others at the workplace apply to themselves, whether
    they are entitled to severance pay, bonuses and other additional pay. If such rules are
    applied with discriminatory treatment, these respondents are re-classified as temporary or
    daily positions.


     The disparity in the criteria explains the significant differences in outcome between the
    KLIPS and the Economically Active Population Survey, despite the fact that the variables
    being surveyed have the same name (‘work status’). Needless to say, others factors, such as
    differences in the sample and the skill-levels of the interviewers may have partially played a
    part.


    2. Subjective Judgement of Irregular Status


     In all waves of the KLIPS except the 3rd, irregular status is identified through
    ‘self-declarative’ questions in addition to the questions on job type. Self-declarative
    questions, like the questions on work status, ask the respondent to subjectively judge his/
    her form of employment after providing a minimal set of guidelines: "An irregular worker is
    someone who is employed for a limited duration in short-term contract positions,
    temporary or daily positions." It is highly likely that those who responded as ‘temporary/
    daily’ under job type would respond as "irregular" in this section. In fact, analysis of the
    KLIPS data shows that 80% of those who responded as "irregular" also claimed to be
    temporary or daily workers by job type. (See KLIPS homepage : Research brief No 1. “ The
    Proportion and Status of Irregular workers from KLIPS (wave 5) survey “)


    3. Different Types of Irregular Positions


     The KLIPS introduced a new set of questions in the 5th Wave to measure the proportion
    of irregular workers more accurately, objectively, and comparably. Drawing from the
    National Statistical Office’s Additional Survey for Irregular Workers, newly included were
    the concepts of ‘contingent workers’ mostly used in the U.S., as well as workers of
    alternative employment arrangements such as temporary, project, independent contract
    and work at home. Analysis of the responses to the new set of questions yields results that
    are similar to the NSO’s Additional Survey. Furthermore, the Form of Employment
    Additional Survey was added in Wave 10, allowing for the collection of much more
    detailed information on the specific type of irregular work. (For the specifics on the
    composition and content of the Additional Survey on Employment Types, please see
    Sang-ho Lee's "Supplementary Survey on Employment Types for KLIPS Wave 10: Overview
    and Main Results," Labor Review, July 2008.)


    4. Characteristics of the KLIPS Survey of Irregular Workers


     Therefore, the sizeable gap in the proportion of irregular workers between the KLIPS and
    the NSO survey may be explained by differences in terminology rather than any design
    errors. The KLIPS asks detailed questions about job characteristics such as severance pay
    and additional pay. Therefore, if these factors are controlled for, the resulting proportion of
    job type would be similar to the NSO data, although probably not completely identical. In
    addition, if the kinds of criteria described above are applied with greater diversity, it would
    produce more fruitful results and new implications.


     To support the aforementioned explanation, the proportion of irregular workers was
    calculated from the 18th Wave KLIPS data, using different sets of criteria. First, when the
    only criterion is the work status, the proportion of irregular workers is 26.8% (see table
    below). Second, keeping the cases of daily/ on-call + dispatched work + temporary help +
    independent contractors + work-at-home + part-time + short-term contracts results in a
    share of 21.3%. Third, if any of the two previous criteria apply, the share rises to 32.0%.
    Finally, including long-term temporary workers (identified through eligibility to receive
    severance pay) results in a share of 38.6%. Although these results are not derived from the
    exact same criteria as that of the NSO survey, they do allow for a rough comparison.​


     

     

    <KLIPS User's Guide (Wave 1~18th) FAQ Q23>

     

  • question Creating variables for state of economic activity.
    answer
    Creating variables for state of economic activity.

     

     

     Defining the state of economic activity was already covered in the Subject Guide
    (Chapter IV) in the User Guide. This section illustrates how to construct different states of
    economic activity. The examples below show how to create states of economic activity
    using the 18th Wave Individual dataset.


     It should be remembered that the individual’s job type and state of economic activity
    do not necessarily match. The job type is asked in the Job Class Questionnaire while
    employment status and state of economic activity are asked in the Working Persons and
    Non-working Persons Questionnaires (see FAQ Q20). If this is the case, researchers are
    advised to construct an individual’s state of economic activity using the variables from the
    Working Persons and Non-working Persons Questionnaires.

     


    *====================================;
    * Constructing the status of economic activeness using SAS;
    *====================================;
    data p18; set a.klips18p;
    /* work status of current job
    wage earners: 1=regular, 2=temporary, 3=daily
    4= employer/self-employed, 5=unpaid family worker */
    empst=p180314;
    /* current economic activity status */
    econst=3 /* not economically active */
    if p180201=1then econst =1 /* working */
    if p182801=1 and p182806=1then econst=2 /* unemployed (ILO) */
    proc freq table empst econst;
    run

    ====================================.
    * Constructing the status of economic activeness using SPSS.
    *====================================.
    get file='D:\18차\users guide\18차년도\klips18p.sav'.
    /* work status of current job
    wage earners: 1=regular, 2=temporary, 3=daily
    4= employer/self-employed, 5=unpaid family worker */
    compute empst=p180314.
    /* current economic activity status */
    compute econst=3. /* not economically active */
    if (p180201=1) econst =1. /* working */
    if (p182801=1 and p182806=1) econst=2. /* unemployed (ILO) */


    fre empst econst.


    /*======================================*/
    /* Constructing the status of economic activeness using stata*/
    /*======================================*/
    clear
    use klips18p, clear
    /* work status of current job
    wage earners: 1=regular, 2=temporary, 3=daily
    4= employer/self-employed, 5=unpaid family worker */
    gen empst=p180314
    /* current economic activity status */
    gen econst=3 /* not economically active */
    replace econst=1 if p180201==1 /* working */
    replace econst=2 if p182801==1 & p182806==1 /* unemployed (ILO) */


    tab1 empst econst​

     

     

     

    <KLIPS User's Guide (Wave 1~18th) FAQ Q22>

     

  • question Criteria for determining job type, wage-earning/non wage-earning work
    answer
    Criteria for determining job type, wage-earning/non wage-earning work

     

     

    1. Determining the Employment Type


     In the KLIPS, the job type (i.e., permanent, temporary, daily, self-employed/ employer or
    family worker) is asked in the Job Class Questionnaire. The questions are structured slightly
    differently by Class.


    Job Class 1 & 2 inquire whether the job type (permanent, temporary or daily employment)
    has changed since the previous survey.
    Job Class 3 & 4 inquire whether the job type (permanent, temporary or daily employment)
    has changed since the previous survey.
    Job Class 5 & 6 ask about the current job type.
    Job Class 7 & 8 also ask about the current job type.


     Because Classes 1~4 ask about jobs that have been maintained since the previous
    survey, it is sufficient to inquire only about "whether there have been any changes since
    then." However, because Classes 5~8 deal with new jobs, they need to ask about the
    current employment type. If the employment type variable is required for the purpose of
    analysis, “p180314 : (main job) work status - current (or final)” variable should be used. Or
    to use data from Work History, “j150 : employment type-current (or final)” may be used. If
    the respondent answered between Classes 1~4, relevant data has been carried over from
    the previous year.

     


    2. Determining wage earners/ non-wage workers


     When trying to distinguish between wage earners and non-wage workers, the “p180211 :
    (all respondents) employment type” variable from the Individual Working Persons
    Questionnaire can be used in addition to the employment type variable obtained from the
    Job Class Questionnaire. This variable is the response to the question: “ [Q4] Then, to
    which of the following categories does your current work correspond?” In this case, the
    employment type variable should match between the Individual survey for Working
    Persons and the Job Class survey, but unfortunately this is not always the case. This is an
    example of the so-called ‘inconsistent response’ problem that often plagues micro-data
    such as KLIPS. Actually, it is often the case that quite a number of jobs have ambivalent
    types (cannot be unambiguously distinguished into self-employed or wage-earning), and
    this reality seems to be reflected in the survey. Attempts have been made to eliminate the
    inconsistency through cleaning processes, but with only partial success. In light of this,
    researchers are advised to use the work status variable (p180314) under the Individual
    survey when trying to differentiate between wage-earning and non wage-earning jobs,
    rather than the employment type variable (p180211) under the Job Class survey. This is
    one way of securing as many job-related variables as possible and reducing missing values.​

     

     

    <KLIPS User's Guide (Wave 1~18th) FAQ Q21>

     

  • question When I need demographic characteristics variables (gender, age, education)
    answer
    When I need demographic characteristics variables (gender, age, education)

     

     

     Individual demographic data such as education, gender, age and relationship with the

    household head is surveyed using the Household Questionnaire. But this set of data is
    merged with and provided together with Individual, New Respondent and Additional
    Survey data. Users of these datasets do not have to spend extra time extracting individuals'
    demographic data from Household dataset and merging them with other datasets.


     It should be noted that the Individual and New Respondent questionnaires are
    distributed only to household members aged 15 or older. Users interested in
    demographics of children of school age under 15 must extract the individuals' data using
    Household data (see FAQ Q11). Variables related to individual data are from h180201 to
    h180775 (in Wave 18).​

     

     

    <KLIPS User's Guide (Wave 1~18th) FAQ Q20>

     

  • question The housing type and size in Waves 2 and 3 Household
    answer
    The housing type and size in Waves 2 and 3 Household dataset

     

     

    In the 2nd and 3rd Waves, questions on the type of accommodation, housing type,
    housing size, market price and duration of residence were answered only in the event that
    the family had moved or any other changes have occurred since the previous survey.
    Therefore, households who experienced no changes in the housing environment since the
    1st Wave would have missing values for housing type and size in the 2nd and 3rd Waves.


    To analyze the housing environment using the Household data from the 2nd and 3rd
    Waves, the necessary variables values must be linked from the previous survey.


    But from Wave 8 onward, the released data includes variables which are available for
    immediate use without undergoing additional processing.​



    <KLIPS User's Guide (Wave 1~18th) FAQ Q19>


     

  • question Structure of the questions on the market value of real estate
    answer
    Structure of the questions on the market value of real estate

     

     

     Regarding real estate assets, the questions are made up of items concerning: ① Real
    estate not including the current house of residence ② Leased real estate including the
    current house of residence ③ Leased real estate not including the current house of
    residence. (The “current house of residence for the family only” or “deposit for monthly or
    long-term lease” can be identified in the section under housing.)


     The questions are structured as follows: the respondent head of household is first
    asked whether he/she owns real estate; if the answer is yes, the type and market value of
    the real estate in question are asked. But more often than not, the respondent is not aware
    of the exact market value of the real estate under his/her ownership, and those responding
    "Don’t know" are requested to respond in intervals. In fact, a good number respond in
    intervals, meaning that if the "Don’t know" responses are all excluded from analysis, the
    outcome may be significantly different from reality. Therefore, we advise researchers to
    transform continuous real estate market price values into intervals, or transform real estate
    market price values recorded in intervals into continuous values.​

     

     

    <KLIPS User's Guide (Wave 1~18th) FAQ Q18>

     

posts search