SOEP-Core v35 - activity biography in the files PBIOSPE and ARTKALEN

Schmelzer, Paul; Hamjediers, Maik Research Report SOEP-Core v35 - activity biography in the files PBIOSPE and ARTKALEN SOEP Survey Papers, No. 877 Provided in Cooperation with: German Institute for Economic Research (DIW Berlin) Suggested Citation: Schmelzer, Paul; Hamjediers, Maik (2020) : SOEP-Core v35 - activity biography in the files PBIOSPE and ARTKALEN, SOEP Survey Papers, No. 877, Deutsches Institut für Wirtschaftsforschung (DIW), Berlin This Version is available at: https://hdl.handle.net/10419/222841 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-sa/4.0/ SOEP Survey Papers Series D – Variable Descriptions and Coding SOEP-Core v35 – Activity Biography in the Files PBIOSPE and ARTKALEN 877 SOEP — The German Socio-Economic Panel at DIW Berlin 2020 Paul Schmelzer, Maik Hamjediers, and SOEP Group Paul Schmelzer, Maik Hamjediers, and SOEP Group Activity Biography in the Files PBIOSPE and ARTKALEN by Paul Schmelzer and Maik Hamjediers (based on earlier work by Rainer Pischner, Henning Lohmann, Marco Giesselmann, and Mila Staneva) PBIOSPE and ARTKALEN encompass activities over the life course and distinguish between spells in education, employment (full- and part-time employment as well as minor employment and registered unemployment), retirement, housekeeping, parental leave and others (see Table 2 for an overview). Please note that these spells do not capture transitions between educational institutions or jobs and employers; spells reflect a continuous status for instance in full-time employment regardless of potential job changes. In the yearly Individual Questionnaire the respondents are also asked to report job changes between the previous and current years and this information can be added to ARTKALEN using the $PGEN data files.1 The spell file ARTKALEN is collected from Individual Questionnaires as a calendar-matrix of months of the previous year and respective statuses of 15 categories (for example Question 118 in 2015).2 The information from each annual Individual Questionnaire is attached to the information of previous surveys in a way that same statuses in consecutive months were treated as one continuous spell. Thus, being for instance unemployed in the December of one year and being still unemployed in the following January is treated as one continuous spell. The generated spell file starts with the year before the entrance into the sample and ends with a respondent’s last observation. The spell file PBIOSPE is based on the information on activity status over the life course, which is collected as a matrix from every respondent answering the Biography Questionnaire (for example Question 45 in 2015).3 The observations start at the age of 15 and end at the current age (up to age 65). Information on activity status covers only the period up to the time the biography is collected. To update the ongoing occupational career in PBIOSPE, information from the yearly Individual Questionnaire is also used by aggregating the recorded spells from ARTKALEN into yearly values.4 In the following, the method of combining the data is described. There have been no changes how the data is generated since the previous version, distributed in 2016.5 But if you have been working with older versions of the dataset (versions 1 To add this information it is necessary to split each spell at the time point of an interview. Afterwards, reports of job changes can be merged at each respective time point. 2 For persons who were temporarily unavailable for interviewing, it is sometimes possible to fill in the gaps in their occupational status. If these persons fill out the additional questionnaire for temporary drop-outs later on, we can use the information collected there (see files $PLUECKE). 3 See Chapter 1 for general information on the collection of biography information. 4 For more information, see Haisken-DeNew, John and Joachim R. Frick (2005): DTC - Desktop Companion to the German Socio-Economic Panel Study (SOEP), Chapter 3. 5 The only exception is the reconsideration of information on short work hours (spelltyp ‘2’ in ARTKALEN), which was omitted in the questionnaire for 2016. SOEP Survey Papers 877 1 v35 distributed in 2008 and earlier) you should check the section at the end of the chapter, where you will find information on previous changes. But before we move on to the details, we provide a brief overview of the contents of ARTKALEN and PBIOSPE. Table 1 contains a list of all the variables in the datasets. The variables BEGIN and END indicate the beginning and the end of a spell. These variables are age entries in PBIOSPE and month entries in ARTKALEN starting with January 1983 as the first month. There are also variables in PBIOSPE that refer to calendar years: BEGINY and ENDY. The SPELLNR is a serial identifier of spells of each activity status of a given person. The variable SPELLTYP contains information on the activity status during the spell, e.g., employed full-time or unemployed. Note, for refugees (samples M3/M4) the category “engaged in active military combat, in captivity” was merged with category “Military/Civilian service” as it is accordingly surveyed for in the Biography Questionnaires for the whole sample since 2017. Table 1: Contents of ARTKALEN and PBIOSPE (variables) Variables Information in ARTKALEN PBIOSPE HHNR Original Household Number Original Household Number PERSNR Never Changing Person ID Never Changing Person ID SPELLNR Serial Number of the Spell per Person Serial Number of the Spell per Person SPELLTYP Type of Spell Type of Spell BEGIN Month Spell Begins Age Spell Begins END Month Spell Ends Age Spell Ends BEGINY Year Spell Begins ENDY Year Spell Ends ZENSOR Censor Variable Censor Variable SPELLINF Spell Construction Information ERHEBJ Survey Year Biography Data KALYEAR First Observation in Calendar Data BEGINB1 Age Spell Begins, 1st Initial Biography Spell ENDB1 Age Spell Ends, 1st Initial Biography Spell BEGINK1 Age Spell Begins, 1st Initial Calendar Spell ENDK1 Age Spell Ends, 1st Initial Calendar Spell BEGINB2 Age Spell Begins, 2ndInitial Biography Spell ENDB2 Age Spell Ends, 2nd Initial Biography Spell BEGINK2 Age Spell Begins, 2nd Initial Calendar Spell ENDK2 Age Spell Ends, 2nd Initial Calendar Spell BEGINB3 Age Spell Begins, 3rd Initial Biography Spell ENDB3 Age Spell Ends, 3rd Initial Biography Spell BEGINK3 Age Spell Begins, 3rd Initial Calendar Spell ENDK3 Age Spell Ends, 3rd Initial Calendar Spell BEGINB4 Age Spell Begins, 4th Initial Biography Spell ENDB4 Age Spell Ends, 4th Initial Biography Spell BEGINK4 Age Spell Begins, 4th Initial Calendar Spell ENDK4 Age Spell Ends, 4th Initial Calendar Spell SOEP Survey Papers 877 2 v35 As mentioned above, PBIOSPE combines information collected in the biography questionnaire and the calendar matrix of the individual questionnaire stored in ARTKALEN. The two types of information are merged into PBIOSPE following a number of rules. First of all, it is important to acknowledge that the Biography Questionnaire Matrix as well as the Individual Questionnaire Matrix allow for multiple activity statuses for a given year or month. No concept of main activity is used. A common combination is, for instance, “housewife/-husband” and “working part-time”. There are a number of other plausible combinations, but also combinations that are less plausible. However, a list of valid combinations of activity statuses defined according to legal or similar constructs would need to be based on very strong assumptions. In addition—in particular in case of the yearly matrix in the Biography Questionnaire—activities are reported that took place in a calendar year in consecutive months, which makes it impossible to exclude combinations of activities. Therefore, no data cleaning is performed at this stage. As a consequence, the data may contain information on more than one activity for a given point in time. This also defines the rules for aggregating the monthly ARTKALEN data into yearly values. Take, for example, a person who was in full-time employment from January to November 2007, and unemployed in December 2007. The exact months are recorded in the dataset ARTKALEN. In the aggregated data, which is merged with the yearly data from the Biography Questionnaire, you find the information that the person worked full-time and was also unemployed in the year 2007. There is a second level of aggregation of ARTKALEN information as the data on type of activity, which is recorded in the variable SPELLTYP is more detailed than in PBIOSPE. The respective information is aggregated as described in Table 2. Table 2: Aggregation of ARTKALEN spell information into PBIOSPE PBIOSPE ARTKALEN 1 School/University School, College (1) 2 Apprenticeship/Training Vocational Training (4), First Job Training, Apprenticeship (13), Continuing Education, Retraining (14) 3 Military/Civilian service Military, Community Service (9) 4 Full-time employed Full-Time Employment (1), Short Work Hrs (2) 5 Part-time employed Part-Time Employment (3), Second Job (11), Mini-job (up to 400 euros) (15) 6 Unemployed Unemployed (5) 7 House-Husband/Wife Housewife, Husband (10) 8 Retired Retired (6) 9 Other Maternity Leave (7), Other (12) 99 Gap Information on gaps in ARTKALEN is not used. Gaps are calculated on the basis of the merged dataset. SOEP Survey Papers 877 3 v35 The category 3 “military / civilian service” denotes several activities, such as being imprisonment, serving in the military, doing former community service or any voluntary service (FSJ or Bundesfreiwilligendienst). If you are more interested in distinguishing these, it is necessary to trace down the information in the person data $P (e.g. variable pkal09a in bhp) and to merge them onto ARTKALEN or PBIOSPE. Missing information on the beginning or end of a spell causes what is known as censoring problems. There are two types of missing data. First, data can be missing on periods outside the observation window (before the age of 15 and after the age of 65 in the case of PBIOSPE or before and after the panel participation in ARTKALEN). Second, data can be missing on years within the observation window due to item non-response in particular years or due to temporary drop-outs (the latter applies to calendar information only). In this case, we speak of “gaps.” There are nine different patterns (see Table 3). As stated above, the calendar information is used to update the biography information. However, there is also a certain overlap of the periods covered by the two types of data. This is shown in Table 4. It indicates, for persons included in PBIOSPE, the year in which the biography information was collected (variable ERHEBJ). This year is usually also the last year for which biography information is available.6 The table also shows the first year recorded in the calendar data (variable KALYEAR). In the majority of cases (58.7 percent), the earliest calendar information is available for the year before the biography interview. This is the case for persons who answered the Biography Questionnaire in their first year as survey respondents. The calendar in the Individual Questionnaire refers to the year before the survey. There are, however, changes over time. In 1998, it was decided that first-time respondents from new samples would not be given the Biography Questionnaire in the first wave but in the second in order to reduce the entry threshold for these new respondents. Consequently, for the majority of persons in years after new samples were integrated (1999, 2001, 2003, 2007, 2010/11 – Samples E to I), the earliest calendar information is available two years before the biography information was collected. This was once again changed in 2011 and in 2013: respondents from samples J and K were given both questionnaires in their first year in SOEP and 2013 the sample 6 Please note that some biographies were collected in 2011 although they are part of Wave 27. This results from the fact that some members of Sample I were interviewed in early 2011 instead of 2010. Table 3: Coding of the variable ZENSOR Right: Left: not censored censored missing censored before gap not censored 1 2 3 censored missing 4 5 6 censored after gap 7 8 9 Note: ‘(99) Gap’ spells are all markes as (-2) SOEP Survey Papers 877 4 v35 M was introduced via a special Biography Questionnaire about individual migration histories without surveying the calendar data. The same applies to first-time respondents who are members of an old sample (e.g., persons who moved into a panel household) - they answer the Biography Questionnaire at the time of their first interview. The pattern is quite stable for most Table 4: Overlap between biography and calendar information First observation in ARTKALEN (compared to erhebj*) same year or later % earlier erhebj* 1 year % 2 years % 3 years % 4+ years % Total n 1984 0,1 99,9 0,0 0,0 0,0 10993 1987 0,0 36,4 33,5 30,1 0,0 505 1988 0,0 100,0 0,0 0,0 0,0 164 1989 0,5 99,5 0,0 0,0 0,0 193 1990 0,0 100,0 0,0 0,0 0,0 180 1991 0,0 100,0 0,0 0,0 0,0 157 1992 0,0 8,4 3,6 88,0 0,0 3930 1993 0,0 76,6 0,3 2,3 20,7 304 1994 0,2 97,8 0,3 0,2 1,4 922 1995 0,2 99,0 0,0 0,1 0,7 1037 1996 0,2 97,5 0,2 0,0 2,1 482 1997 0,0 98,5 0,0 0,0 1,5 477 1998 0,7 97,8 0,0 0,2 1,2 416 1999 0,1 26,6 72,8 0,0 0,5 1821 2000 0,0 90,2 0,9 7,7 1,3 235 2001 0,0 6,3 93,6 0,0 0,1 7530 2002 0,2 48,1 0,4 39,0 12,4 526 2003 0,1 16,9 81,3 0,1 1,6 2193 2004 0,0 68,6 4,2 20,1 7,2 433 2005 0,0 89,0 3,4 0,7 6,8 292 2006 0,0 92,2 4,1 0,0 3,7 217 2007 0,0 16,1 83,4 0,1 0,3 1858 2008 0,0 68,9 2,9 26,9 1,3 309 2009 0,0 89,5 2,1 0,5 7,9 190 2010 4,1 79,2 16,7 0,0 0,0 7887 2011 2,5 91,7 5,7 0,0 0,1 5670 2012 3,9 95,6 0,3 0,2 0,0 2205 2013 92,7 7,0 0,2 0,1 0,0 3882 2014 41,3 57,9 0,7 0,0 0,2 443 2015 81,5 14,7 2,2 1,4 0,2 1434 2016 92,1 7,1 0,6 0,2 0,1 2904 2017 0,0 99,3 0,4 0,1 0,2 3691 Total 9,5 58,7 24,2 7,1 0,5 56998 Notes: *) Year of biography data collection (variable ERHEBJ). Source: SOEP v33 (PBIOSPE). SOEP Survey Papers 877 5 v35