SOEP-IS 2015 - PGEN: Person-related status and generated variables

DIW Berlin / SOEP (Ed.) Research Report SOEP-IS 2015 - PGEN: Person-related status and generated variables SOEP Survey Papers, No. 444 Provided in Cooperation with: German Institute for Economic Research (DIW Berlin) Suggested Citation: DIW Berlin / SOEP (Ed.) (2017) : SOEP-IS 2015 - PGEN: Person-related status and generated variables, SOEP Survey Papers, No. 444, Deutsches Institut für Wirtschaftsforschung (DIW), Berlin This Version is available at: https://hdl.handle.net/10419/161652 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. http://creativecommons.org/licenses/by-sa/4.0/ The German Socio-Economic Panel study 444 SOEP Survey Papers Series D – Variable Descriptions and Coding SOEP — The German Socio-Economic Panel study at DIW Berlin 2017 SOEP-IS 2015—PGEN: Person-related Status and Generated Variables SOEP-IS Group Running since 1984, the German Socio-Economic Panel study (SOEP) is a wide-ranging representative longitudinal study of private households, located at the German Institute for Economic Research, DIW Berlin. The aim of the SOEP Survey Papers Series is to thoroughly document the survey’s data collection and data processing. The SOEP Survey Papers is comprised of the following series: Series A – Survey Instruments (Erhebungsinstrumente) Series B – Survey Reports (Methodenberichte) Series C – Data Documentation (Datendokumentationen) Series D – Variable Descriptions and Coding Series E – SOEPmonitors Series F – SOEP Newsletters Series G – General Issues and Teaching Materials The SOEP Survey Papers are available at http://www.diw.de/soepsurveypapers Editors: Dr. Jan Goebel, DIW Berlin Prof. Dr. Martin Kroh, DIW Berlin and Humboldt Universität Berlin Prof. Dr. Carsten Schröder, DIW Berlin and Freie Universität Berlin Prof. Dr. Jürgen Schupp, DIW Berlin and Freie Universität Berlin Please cite this paper as follows: SOEP-IS Group, 2017. SOEP-IS 2015—PGEN: Person-related Status and Generated Variables. SOEP Survey Papers 444: Series D – Variable Descriptions and Coding. Berlin: DIW Berlin/SOEP This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. © 2017 by SOEP ISSN: 2193-5580 (online) DIW Berlin German Socio-Economic Panel (SOEP) Mohrenstr. 58 10117 Berlin Germany [email protected] The German Socio Economic Panel study at DIW Berlin SOEP-IS 2015—PGEN: Person-related Status and Generated Variables SOEP-IS Group 2017 The file pgen is part of a collection, which is released with doi:10.5684/soep.is.2015. SOEP Innovation Sample (2015) pgen (2015) Contents 1 Overview 4 2 Variables in file pgen 4 cid – Case-ID, Original Household Number .......................... 4 hid – Current Wave HH Number ................................ 4 pid – Never Changing Person ID ................................ 4 syear – Survey Year ....................................... 4 pgerwtyp – Type of occupation ................................. 4 pgerljob – Working in Occupation Trained for ......................... 5 pgbetr – Size of the Company ................................. 5 pgoeffd – Civil Service ..................................... 6 pgausb – Required Training for Job .............................. 6 pgpartz – Partner Identicator .................................. 7 pgpartnr – Person ID number of partner ............................ 8 pgnation – Citizenship - nationality .............................. 8 pgsbil – Diplomas/degrees from secondary/tertiary ..................... 9 pgbbil01 – Vocational degree attained ............................ 10 pgbbil02 – Completed college education ........................... 10 pgbbil03 – No vocational degree ............................... 11 pgsbila – Secondary school degrees/diplomas abroad .................... 11 pgbbila – Occupational Training in abroad .......................... 12 pgsbilo – Secondary school degree/diploma - East Germany ................. 12 pgbbilo – Vocational degree attained - East Germany .................... 13 pgfamstd – Marital status in survey year ........................... 13 pgbilzt – Amount of education or training (in years) ..................... 14 pgerwzt – Length Of Time With Firm .............................. 15 pgtatzt – Actual Weekly Work Time .............................. 16 pgvebzt – Agreed Upon Weekly Work Time .......................... 17 pguebstd – Overtime per Week ................................ 18 pglfs – Labor Force Status ................................... 19 pgis88 – 4-digit ISCO-88 Occupation Code .......................... 21 pgisei – International Socio-Economic Index of Occupatioal Status ............. 23 pgmps – Magnitude Prestige Scale KLAS ........................... 23 pgnace – Two-digit NACE Industry – Sector .......................... 24 pgsiops – Treimans Standard Int. Occupation Prestige Score ................. 25 pgegp – Erikson and Goldthorpe Class Category ....................... 26 pgklas – StaBuA 1992 Job Classification ........................... 27 pgautono – Autonomy in occupational activity ........................ 30 pgisced – Highest degree/diploma attained, ISCED-1997 .................. 30 pgcasmin – Highest degree/diploma according to CASMIN ................. 31 pgstib – Occupational Position ................................. 32 pgmonth – Month of interview ................................. 33 pgmode – Interview method .................................. 34 pglabgro – Current gross labor income in euros (generated) ................. 34 pgi1labgro – 1. Imput. Akt. Bruttoerwerbseink.(gen) in Euro [1/5] ............. 36 pgi2labgro – 2. Imput. Akt. Bruttoerwerbseink.(gen) in Euro [2/5] ............. 36 pgi3labgro – 3. Imput. Akt. Bruttoerwerbseink.(gen) in Euro [3/5] ............. 37 pgi4labgro – 4. Imput. Akt. Bruttoerwerbseink.(gen) in Euro [4/5] ............. 38 pgi5labgro – 5. Imput. Akt. Bruttoerwerbseink.(gen) in Euro [5/5] ............. 39 SOEP Survey Papers 444 2 SOEP Innovation Sample (2015) pgen (2015) pgimpgro – Imputation flag for LABGROxx .......................... 40 pglabnet – Current net labor income (generated) in euros .................. 40 pgi1labnet – 1. Imput. Akt. Nettoerwerbseink.(gen) in Euro [1/5] .............. 41 pgi2labnet – 2. Imput. Akt. Nettoerwerbseink.(gen) in Euro [2/5] ............. 42 pgi3labnet – 3. Imput. Akt. Nettoerwerbseink.(gen) in Euro [3/5] ............. 43 pgi4labnet – 4. Imput. Akt. Nettoerwerbseink.(gen) in Euro [4/5] ............. 44 pgi5labnet – 5. Imput. Akt. Nettoerwerbseink.(gen) in Euro [5/5] ............. 45 pgimpnet – Imputation flag for LABNETxx .......................... 46 pgallbet – Core size category of the company ......................... 46 pgemplst – Employment status ................................ 46 pgexpft – Working experience full-time employment ..................... 47 pgexppt – Working experience part-time employment .................... 48 pgexpue – Unemployment experience ............................. 49 pgjobch – Occupational Change ................................ 50 pgfield – Field of tertiary education .............................. 51 pgdegree – Type of tertiary degree ............................... 53 pgtraina – Apprenticeship - two-digit occupation KldB92 .................. 54 pgtrainb – Vocational school – two-digit occupation KldB92 ................. 55 pgtrainc – Higher vocational school - two-digit occupation KldB92 ............. 56 pgtraind – Civil servant training – two-digit occupation KldB92 ............... 57 pgfdt_f – Data source FIELD, DEGREE, TRAIN ........................ 58 pgbilztch – Change in Education since last survey / last year ................ 58 pgbilztev – Change in Education, total observed period ................... 59 pgsndjob – Current gross secondary income in euros ..................... 59 pgimpsnd – Imputation flag for SNDJOBxx .......................... 60 SOEP Survey Papers 444 3 SOEP Innovation Sample (2015) pgen (2015) 1 Overview Variables in the file pgen documented here are generated mostly from the answers in the personal questionnaire. There is one row for each wave (syear) a person (pid) participated in the survey. 2 Variables in file pgen cid – Case-ID, Original Household Number hid – Current Wave HH Number pid – Never Changing Person ID syear – Survey Year 1998 1998 724 1999 1999 750 2000 2000 755 2001 2001 766 2002 2002 780 2003 2003 795 2004 2004 792 2005 2005 799 2006 2006 797 2007 2007 797 2008 2008 794 2009 2009 3226 2010 2010 2745 2011 2011 2506 2012 2012 3696 2013 2013 5141 2014 2014 6638 2015 2015 5897 -1 [-1] No Answer 0 -2 [-2] Does Not Apply 0 -3 [-3] Answer Improbable 0 -4 [-4] Inadmissible Multiple Answer 0 -5 [-5] Not Contained In Questionnaire 0 -6 [-6] Questionnaire Version With Modified Filtering 0 pgerwtyp – Type of occupation ?Are you currently employed? Which one of the following applies best to your status? (from: soep-is/soep-is-2015/Q271;perw[3133]) 1[1] Not Employed, Green 16918 2[2] Not Employed (First Surveyed) Not Applicable Since 94 0 3[3] Employed (First Surveyed) Not Applicable Since 94 0 4[4] Empl. Exc Change 14293 5[5] Empl. No Info If Change 3382 6[6] Empl. With Change, Also First Time Employment 3650 SOEP Survey Papers 444 4 SOEP Innovation Sample (2015) pgen (2015) 7[7] Empl. With Near-Retirement Part-time 155 -1 [-1] No Answer 0 -2 [-2] Does Not Apply 0 -3 [-3] Answer Improbable 0 -4 [-4] Inadmissible Multiple Answer 0 -5 [-5] Not Contained In Questionnaire 0 -6 [-6] Questionnaire Version With Modified Filtering 0 This variable is generated from the question on whether a respondent has changed jobs since the beginning of the previous year, which is a central filter variable in the questionnaire. Not all employed persons are asked the relevant input questions on an annual basis. Only those employed persons who changed jobs and first-time respondents were asked to provide up-to-date information. An alternative variable is PGJOBCH (see below), which is an improved version of PGERWTYP, as it is generated in a longitudinally consistent way and contains an additional category for first-time employed persons. Respondents from the supplementary samples are not being asked about the information on job change; hence, in the year when these samples enter the SOEP-IS, the majority of the employed persons fall into the category [5] (Employed, no info if change). pgerljob – Working in Occupation Trained for 1[1] Yes 4660 2[2] no 2593 3[3] In Education 412 4[4] has No Job Training 397 -1 [-1] No Answer 354 -2 [-2] Does Not Apply 6909 -3 [-3] Answer Improbable 0 -4 [-4] Inadmissible Multiple Answer 0 -5 [-5] Not Contained In Questionnaire 23073 -6 [-6] Questionnaire Version With Modified Filtering 0 This variable is designed to offer annual data on all employed persons, indicating whether they are working in the occupation they were trained for. Not all employed persons are asked the relevant input questions on an annual basis. Only those employed persons who changed jobs and first-time respondents were asked to provide up-to-date information. Because detailed information on working in occupation trained for is not assessed in the Questionnaire of the SOEP Innovation Sample, PGERLJOB is not generated for Sample E (since 2012), I (since 2011) and the supplementary samples (since 2012). For this purpose, PGERLJOB is coded to “-5” (not contained in questionnaire). pgbetr – Size of the Company ?How many employees does your enterprise have approximately in total? This question is not just about your local commercial unit. (from: soep-is/soep-is-2015/Q305;pgesunt[3162]) 1[1] LT 5 1338 2[2] GE 5 LT 10 1287 SOEP Survey Papers 444 5 SOEP Innovation Sample (2015) pgen (2015) 3[3] GE 11 LT 20 1237 4[4] Until 90: LT 20 0 5[5] 91-04: GE 5 LT 20 553 6[6] GE 20 and LT 100 3552 7[7] From 100 To Les Than 200 1602 8[8] Until 98: GE 20 LT 200 229 9[9] 200 Up To 2000 3753 10 [10] 2000 And More 4506 11 [11] Self-Employed Without Coworkers 0 12 [12] Do not know 0 -1 [-1] No Answer 1129 -2 [-2] Does Not Apply 19206 -3 [-3] Answer Improbable 6 -4 [-4] Inadmissible Multiple Answer 0 -5 [-5] Not Contained In Questionnaire 0 -6 [-6] Questionnaire Version With Modified Filtering 0 This variable is designed to offer annual data on company size for all employed persons. Please pay attention to special codes 4, 5, and 8! These codes were necessary due to the differentiation of items for small and medium-sized companies over the years. Not all employed persons are asked the question on firm size on an annual basis. Only those employed persons who changed jobs and first-time respondents were asked to provide upto-date information. Please also see PGALLBET for a broader categorization of the firm size, which is appropriate for analyses that include all sample years. Self-employed are not included in this variable. Detailed information about the company size of self-employed is included in the variable PGSTIB. pgoeffd – Civil Service ?Does the company in which you are employed belong to the public sector? (from: soep-is/ soep-is-2015/Q293;poed[3152]) 1[1] Yes 5005 2[2] no 15925 -1 [-1] No Answer 532 -2 [-2] Does Not Apply 16936 -3 [-3] Answer Improbable 0 -4 [-4] Inadmissible Multiple Answer 0 -5 [-5] Not Contained In Questionnaire 0 -6 [-6] Questionnaire Version With Modified Filtering 0 This variable is designed to provide annual data on employment in the civil service for all employed persons. Not all employed persons are asked the relevant input questions on an annual basis. Only those employed persons who changed jobs and first-time respondents were asked to provide up-to-date information. pgausb – Required Training for Job 1[1] Yes 63 SOEP Survey Papers 444 6 SOEP Innovation Sample (2015) pgen (2015) As a supplement to the variable PGSBIL the highest secondary school degree/diploma in East Germany is provided as a separate variable. New SOEP respondents are also asked about secondary degrees/diplomas obtained in the former GDR; and for old respondents, the same codes are carried forward. As SOEP-IS does not include a youth questionnaire, since 2012 information usually coming from the youth questionnaire was not included in the generation of PGSBILO. pgbbilo – Vocational degree attained - East Germany ?What type of vocational training or university degree is this about? (from: soep-is/soep-is2015/Q258;[3122]) 1[1] Vocational Training 2768 2[2] Master Craftsman 296 3[3] Engineering, Technical Degree 668 4[4] Other Degree 56 -1 [-1] No Answer 0 -2 [-2] Does Not Apply 33886 -3 [-3] Answer Improbable 0 -4 [-4] Inadmissible Multiple Answer 0 -5 [-5] Not Contained In Questionnaire 724 -6 [-6] Questionnaire Version With Modified Filtering 0 To supplement the variable PGBBIL01 the highest secondary school degree/diploma in East Germany is provided as a separate variable and updated if necessary for 1991. Since 1992 only the West German version has been used for new vocational degrees. For new SOEP respondents, vocational degrees attained in the former GDR are asked as well; for old respondents, the same codes are carried forward. From 2002 on, the questionnaire was expanded and revised, but this led to an operationalization involving more assumptions on the vocational degrees attained in the GDR; (from 2002 on, Code 3 is also listed as the additional category Code 4 in the integrated variables PGBBIL03 if this degree has not been replaced by a more recently attained, higher-level university or college degree). Since the SOEP-IS does not include a youth questionnaire, the information usually coming from the youth questionnaire was not included in the generation of PGBBILO. pgfamstd – Marital status in survey year ?What is your family status? (from: soep-is/soep-is-2015/Q149;pfamst[3019]) 1[1] Married 22268 2[2] Married, But Separated 743 3[3] Single 9176 4[4] Divorced 3524 5[5] Widowed 2571 6[6] registered same sex partnership 53 7[7] registered same sex partnership, seperated 10 -1 [-1] No Answer 49 -2 [-2] Does Not Apply 0 -3 [-3] Answer Improbable 4 -4 [-4] Inadmissible Multiple Answer 0 -5 [-5] Not Contained In Questionnaire 0 -6 [-6] Questionnaire Version With Modified Filtering 0 SOEP Survey Papers 444 13 SOEP Innovation Sample (2015) pgen (2015) Marital status is describing the institutional status of marriage at the time of the person interview. Marital status is based on information given by the respective person on his or her current relationship as well as on retrospective information about previous relationships asked in the biography questionnaire. For those whose partner was identified within the household, marital status is counter-checked with the information given by the partner. Where contradictions can be found, indication of the person information is compiled if reasonable. If no information is available, the indication by position related to head of household is deferred. Remaining contradictions are solved using information on marriage status when a child was born as well as future reports on a given relationship. Marital status is only available for people, who were interviewed. Note that the partner indicator PGPARTZ supplied in the PGEN data files as well might not match the information provided in PGFAMSTD in its entirety. pgbilzt – Amount of education or training (in years) 7 530 8.5 71 9 3394 10 1718 10.5 8568 11 1375 11.5 6808 12 3450 13 1918 13.5 610 14 751 14.5 1040 15 2091 16 962 17 133 18 3866 -1 [-1] No Answer 582 -2 [-2] Does Not Apply 531 -3 [-3] Answer Improbable 0 -4 [-4] Inadmissible Multiple Answer 0 -5 [-5] Not Contained In Questionnaire 0 -6 [-6] Questionnaire Version With Modified Filtering 0 The following statements describe the standard computation for amount of education or training (in years). years of education =years of schooling + years of occupational training Schooling: •no degree <= 7 years •lower school degree = 9 years •intermediary school = 10 years •degree for a professional coll. = 12 years •high school degree = 13 years SOEP Survey Papers 444 14 SOEP Innovation Sample (2015) pgen (2015) •other = 10 years additional occupational training (includes universities): •apprenticeship = 1.5 years • technical schools (incl. health) = 2 years •civil servants apprenticeship = 1.5 years •higher technical college = 3 years •university degree = 5 years Note that for a high school degree 13 years of education are being taken into account, despite the changes of reducing high school by one year in most German federal states in the period 2001-2007. Furthermore, the introduction of a Bachelor/Master System in the German higher education system in the early 2000´s is not yet reflected in the calculation of years in (higher) education. Hence, 5 years of university education is taken into account although the respondents could have finished in 3 years with the Bachelor’s degree. Helberger, Christof (1988): Eine Überprüfung der Linearitätsannahme der Humankapitaltheorie. In: H.-J. Bodenhöfer (ed.): Bildung, Beruf, Arbeitsmarkt, pp. 151-170, Berlin. Schwarze, Johannes (1991): Ausbildung und Einkommen von Männern - Einkommensfunktionsschätzungen für die ehemalige DDR und die Bundesrepublik Deutschland. In: Mitteilungen aus der Arbeitsmarktund Berufsforschung, (24), pp. 63-69. pgerwzt – Length Of Time With Firm ?Since when are you working for your current employer? // Year (from: soep-is/soep-is-2015/ Q301:PSEITJ[3160]) ?Since when are you working for your current employer? // Month (from: soep-is/soep-is2015/Q301:PSEITM[3160]) -1 [-1] No Answer 64 -2 [-2] Does Not Apply 17166 -3 [-3] Answer Improbable 5 -4 [-4] Inadmissible Multiple Answer 0 -5 [-5] Not Contained In Questionnaire 0 -6 [-6] Questionnaire Version With Modified Filtering 0 The variable PGERWZT is designed to offer data on the length of time with the firm at the point in time of the interview for all employed persons. This variable is generated from the respondent’s start date with the current employer. In the case of a job change within the firm, the full length of time with the firm is calculated. Hence, the variable describes the length of time with the same firm and not the length of time in the same position. The variable provides consistent longitudinal information on the length of time with the same employer. Data that show longitudinal inconsistencies are corrected. 1. In case of no job change, the information on the start date with the current employer given in the earliest interview available is treated as dominant and carried forward to the subsequent years. SOEP Survey Papers 444 15 SOEP Innovation Sample (2015) pgen (2015) 2. In case of a job change between firms, the information on the start of the current position is used and carried forward to the subsequent years. 3. Up to wave Z (2009), a respondent who starts working again after a period of nonemployment is assumed to have returned to the former employer if the indicated start date with the current employer was before the previous interview date. In this case, the start date with the current employer given in the previous interview is treated as dominant. Otherwise, the present information on the start date with the current employer is used and carried forward to the subsequent years. For respondents who are assumed to have returned to their former employer, the full length of time with the firm is calculated. There is no deduction for the time during which the respondent was not employed. 4. Since wave BA (2010), there is a modified answer category in the questionnaire which indicates that a respondent returns to his/her former employer after a period of nonemployment. If a respondent indicates to have started working again at a former employer, the present information on the start date with the current employer is used and carried forward to the subsequent years. Unlike before wave BA (2010), the present information is treated as dominant even if the indicated start date with the current employer was before the previous interview date. Hence, the full length of time with the firm is calculated, and there is no deduction for the time during which the respondent was not employed or employed in another firm. 5. The length of time with the firm is also provided for the East German sample since its start in 1990. Due to the massive restructuring of the economy that took place in East Germany after reunification, this variable should be dealt with cautiously in the first transition years. Both monthly and annual information is used in the variables and rounded off as length of time in years (with months in decimal form). If the month was not available a random month is used. pgtatzt – Actual Weekly Work Time ?And how many hours do you generally work every week, including any overtime? / [if self-employed:] How many hours do you usually work per week? // Hours per week (from: soep-is/soep-is-2015/Q310:PAZ10[1903]) 0.4 1 1 21 1.5 4 2 47 2.5 4 3 58 3.5 8 4 79 4.5 6 5 128 5.5 10 6 145 6.5 2 7 67 SOEP Survey Papers 444 16 SOEP Innovation Sample (2015) pgen (2015) 7.5 11 ... (147 rows omitted) 19044 69 1 70 142 71 1 72 15 75 27 76 3 77 1 78 2 80 14 -1 [-1] No Answer 641 -2 [-2] Does Not Apply 16194 -3 [-3] Answer Improbable 78 -4 [-4] Inadmissible Multiple Answer 0 -5 [-5] Not Contained In Questionnaire 1644 -6 [-6] Questionnaire Version With Modified Filtering 0 This variable is designed to offer annual data on actual weekly working hours (including overtime) for all persons employed at the time of the survey (including the self-employed). The data are obtained by asking respondents how many hours they work on average per week. For implausible answers (actual weekly working hours of more than 80 per week), we assign the value (-3). The variable is rounded off and gives the number of working hours as a decimal number. Please also see PGVEBZT and PGUEBSTD. pgvebzt – Agreed Upon Weekly Work Time ?How many hours per week are stipulated in your contract (excluding overtime)? // Hours per week (from: soep-is/soep-is-2015/Q309:PAZ08[2205]) 0.6 1 1 3 1.5 4 2 26 2.5 3 3 37 3.5 7 4 53 4.5 4 5 82 5.5 7 6 96 6.5 2 7 43 7.5 7 ... (153 rows omitted) 16377 57 1 57.5 1 60 27 SOEP Survey Papers 444 17 SOEP Innovation Sample (2015) pgen (2015) 62.5 1 65 4 70 3 72 1 75 1 78 1 -1 [-1] No Answer 1278 -2 [-2] Does Not Apply 18681 -3 [-3] Answer Improbable 3 -4 [-4] Inadmissible Multiple Answer 0 -5 [-5] Not Contained In Questionnaire 1644 -6 [-6] Questionnaire Version With Modified Filtering 0 This variable is designed to offer annual data on agreed weekly working hours. The variable takes into account only those persons who were in dependent employment (not selfemployed) at the time of the survey. Agreed weekly working hours were asked up to 1989 only in full hours, and from 1990 on in three-digit form (counting the first digit after the decimal point). The value (-2) is assigned to non-employed people, employees without set hours and to selfemployed people, including self-employed farmers, freelancers, and other self-employed persons. In 2012, the value (-2) was assigned only to non-employed people and to self-employed people, including self-employed farmers, freelancers, and other self-employed persons. If persons helping out in family businesses report agreed weekly working hours, we assign a non-missing value. For implausible answers (agreed weekly working time of more than 80 hours per week) we assign the value (-3). The variable is rounded off and gives the number of working hours as a decimal number. Please also see PGTATZT and PGUEBSTD. pguebstd – Overtime per Week 0 9004 0.1 1 0.2 20 0.4 3 0.5 109 0.7 75 0.8 1 0.9 130 1 300 1.1 1 1.2 195 1.4 132 1.5 78 1.6 45 1.7 1 ... (109 rows omitted) 7065 27.5 1 28 4 29 4 SOEP Survey Papers 444 18 SOEP Innovation Sample (2015) pgen (2015) 30 22 30.5 1 31 1 34 1 35 5 40 1 -1 [-1] No Answer 1159 -2 [-2] Does Not Apply 18393 -3 [-3] Answer Improbable 2 -4 [-4] Inadmissible Multiple Answer 0 -5 [-5] Not Contained In Questionnaire 1644 -6 [-6] Questionnaire Version With Modified Filtering 0 This variable is designed to offer annual data on overtime per week for all persons in dependent employment at the time of the survey. The data is obtained by asking respondents how many overtime hours they worked in the month before the survey. The number of monthly overtime hours is then converted into weekly overtime by dividing the number given by 4.3. Since PGUEBSTD refers to weekly overtime during the last month, the number may deviate from the difference between average actual weekly working hours and the agreed weekly working hours. In the year 2012, respondents were not asked about the number of hours of overtime per week. PGUEBSTD was therefore generated using the difference between average actual weekly working hours and agreed weekly working hours. The value (-2) is assigned to non-employed people, employees without set hours and to selfemployed people, including self-employed farmers, freelancers, and other self-employed persons. If persons helping out in family businesses report overtime hours, we assign a nonmissing value. For implausible answers (agreed-upon weekly working time or actual weekly working time of more than 80 hours per week AND weekly overtime of more than 10 hours we assign the value (-3). The variable is rounded down and gives the number of overtime hours as a decimal. Please also see PGVEBZT and PGTATZT. pglfs – Labor Force Status ?Have you done paid work during the last 7 days, even if only for an hour or a few hours? (from: soep-is/soep-is-2015/Q240;p7tag[3110]) ?Are you currently on maternity leave (Mutterschutz) or on statutory parental leave (Elternzeit)? (from: soep-is/soep-is-2015/Q241;perz[3111]) ?Are you currently enrolled in an educational or training program? In other words: are you in school or higher education, working on a doctor’s degree, completing vocational training, or taking part in further training? (from: soep-is/soep-is-2015/Q260;paus1[3123]) ?Are you currently employed? Which one of the following applies best to your status? (from: soep-is/soep-is-2015/Q271;perw[3133]) 1[1] Non-Working 4059 2[2] NW-Age 65 And Older 8314 3[3] NW-In Education-Training 1077 4[4] NW-Maternity Leave 540 5[5] NW-Military-Community Service 23 6[6] NW-Unemployed 1848 8[8] NW-But Sometimes Sec. Job 216 SOEP Survey Papers 444 19 SOEP Innovation Sample (2015) pgen (2015) 9[9] NW-Work But Past 7 Days 322 10 [10] NW-But Reg. Sec. Job 540 11 [11] Working 21156 12 [12] Working But NW Past 7 Days 302 -1 [-1] No Answer 1 -2 [-2] Does Not Apply 0 -3 [-3] Answer Improbable 0 -4 [-4] Inadmissible Multiple Answer 0 -5 [-5] Not Contained In Questionnaire 0 -6 [-6] Questionnaire Version With Modified Filtering 0 This variable is based on the annual question on current employment status, combined with additional information on activities of non-working individuals. Since the beginning of the SOEP in the year 1984, the number of values assigned has been based on a large number of highly differentiated answer categories. It is designed to provide consistent longitudinal data on labor force participation across all waves. PGLFS provides a differentiation between “working” (Code 11-12) and “non-working” (Code 1-10), categories which are constant over all waves. Non-employment is subdivided further in order to make it possible to efficiently apply different labor market concepts in studying the data. To calculate this variable, the variables on employment status, age, maternity leave, second jobs, registration at the employment office, participation in paid work during the past 7 days and training status are used. Code (12) was added in 2000. For respondents who have multiple status codes and different values for this variable, the following hierarchy was used to determine which of the values would play the determining role (increasing dominance): 11 - working 1 - non-working without further information 2 - non-working, and older than 65 3 - non-working, and currently in a training program 6 - non-working, and registered unemployed 4 - non-working, on maternity leave 5 - non-working, in military/community service 9 - non-working, but working past 7 days 10 - non-working, but regular second job 8 - non-working, but occasional second job 12 - working, but non-working past 7 days PGLFS supplements the variable PGEMPLST, which differentiates among persons who are employed. SOEP Survey Papers 444 20 SOEP Innovation Sample (2015) pgen (2015) pgis88 – 4-digit ISCO-88 Occupation Code 100 [100] Soldiers 63 1000 [1000] Legislators, Senior Officials and Managers 0 1100 [1100] Legislators and Senior Government Officials 0 1110 [1110] Legislators and Senior Government Officials 0 1140 [1140] Senior Officials of Special-Interest Organisations 4 1141 [1141] Senior Officials of Political Party Organisations 0 1142 [1142] Senior Officials of Employers’, Workers’ and Other Economic-Interest Organisations 22 1143 [1143] Senior Officials of Humanitarian and Other Special-Interest Organisations 0 1200 [1200] Corporate Managers 71 1210 [1210] Directors and Chief Executives 208 1220 [1220] Production and Operations Managers 0 1221 [1221] Production and Operations Managers in Agriculture, Hunting, Forestry and Fishing 0 1222 [1222] Production and Operations Managers in Manufacturing 65 1223 [1223] Production and Operations Managers in Construction 0 1224 [1224] Production and Operations Managers in Wholesale and Retail Trade 0 ... (468 rows omitted) 19895 9213 [9213] Fishery, Hunting and Trapping Labourers 0 9300 [9300] Labourers in Mining, Construction, Manufacturing and Transport 0 9310 [9310] Mining and Construction Labourers 0 9311 [9311] Mining and Quarrying Labourers 2 9312 [9312] Construction and Maintenance Labourers: Roads, Dams and Similar Constructions 3 9313 [9313] Building Construction Laborer 35 9320 [9320] Manufacturing Laborer 334 9330 [9330] Transport Lab., Freight Handler 156 -1 [-1] No Answer 293 -2 [-2] Does Not Apply 17247 -3 [-3] Answer Improbable 0 -4 [-4] Inadmissible Multiple Answer 0 -5 [-5] Not Contained In Questionnaire 0 -6 [-6] Questionnaire Version With Modified Filtering 0 -8 [-8] Question this year not part of Survey program 0 Code name (Main group, group): (1000) Legislators, senior officials, and managers (1001) Legislators and senior officials (1002) Corporate managers (1003) Managers of small enterprises (1004) Professionals (1005) Physical, mathematical, and engineering science professionals (1006) Life science and health professionals (1007) Teaching professionals (1008) Other professionals SOEP Survey Papers 444 21 SOEP Innovation Sample (2015) pgen (2015) (1009) Technicians and associate professionals (1010) Physical and engineering science associate professionals (1011) Life science and health associate professionals (1012) Teaching associate professionals (1013) Other associate professionals (1014) Clerks (1015) Office clerks (1016) Customer services clerks (1017) Service Workers and shop and market sales workers (1018) Personal and protective services workers (1019) Models, salespersons, and demonstrators (1020) Skilled agricultural and fishery Workers (1021) Skilled agricultural and fishery workers (1022) Craft and related trades workers (1023) Extraction and building trades workers (1024) Metal, machinery, and related trades workers (1025) Precision, handicraft, craft printing and related trades workers (1026) Other craft and related trades workers (1027) Plant and machine operators and assemblers (1028) Stationary plant and related operators (1029) Machine operators and assemblers (1030) Drivers and mobile plant operators (1031) Elementary occupations (1032) Sales and services elementary occupations (1033) Agricultural, fishery, and related laborers (1034) Laborers in mining, construction, manufacturing, and transport This variable is designed to provide annual data on occupational activity for all employed persons according to the International Standard Classification of Occupations ISCO-88. Respondents answer the question on their current occupational title in their own words, and this response is entered into a blank in the questionnaire. ISCO-88 is a strictly four-digit classification, and this variable is therefore coded in four-digit form. In contrast to the previous version of the classification system, ISCO-68, ISCO-88 does not use blanks if there is no adequate information for specific coding, but uses zeros instead. Thus 4000 stands for an unspecified office job; 2300 stands for teachers and 2000 stands SOEP Survey Papers 444 22 SOEP Innovation Sample (2015) pgen (2015) IIId KLAS-Codes 1600-1799 Berufe in der Papierherstellung, -verarbeitung und im Druck IIIe KLAS-Codes 1800-1859 Berufe in der Holzverarbeitung, Holzund Flechtwarenherstellung IIIf KLAS-Codes 1900-2459 Berufe in der Metallerzeugung und –bearbeitung IIIg KLAS-Codes 2500-3099 Metall-, Maschinenbauund verwandte Berufe IIIh KLAS-Codes 3100-3189 Elektroberufe IIIi KLAS-Codes 3200-3239 MontiererInnen und Metallberufe, a.n.g. IIIk KLAS-Codes 3300-3619 Textilund Bekleidungsberufe IIIl KLAS-Codes 3700-3789 Berufe in der Lederherstellung, Lederund Fellverarbeitung IIIm KLAS-Codes 3900-4359 Ernährungsberufe IIIn KLAS-Codes 4400-4729 Hoch-, Tiefbauberufe IIIo KLAS-Codes 4800-4929 Ausbauberufe, PolsterInnen IIIp KLAS-Codes 5000-5069 Berufe in der Holzund Kunststoffverarbeitung IIIq KLAS-Codes 5100-5149 MalerInnen, LackiererInnen und verwandte Berufe IIIr KLAS-Codes 5200-5239 WarenprüferInnen, VersandfertigmacherInnen IIIs KLAS-Codes 5300-5319 HilfsarbeiterInnen ohne nähere Tätigkeitsangabe IIIt KLAS-Codes 5400-5509 MaschinistInnen und zugehörige Berufe IV Technische Berufe IVa KLAS-Codes 6000-6129 IngenieurInnen, ChemikerInnen, PhysikerInnen, MathematikerInnen IVb KLAS-Codes 6200-6529 TechnikerInnen, Technische Sonderfachkräfte V Dienstleistungsberufe Va KLAS-Codes 6600-6899 Warenkaufleute Vb KLAS-Codes 6900-7069 Dienstleistungskaufleute und zugehörige Berufe Vc KLAS-Codes 7100-7449 Verkehrsberufe Vd KLAS-Codes 7500-7899 Organisations-, Verwaltungs-, Büroberufe Ve KLAS-Codes 7900-8149 Ordnungsund Sicherheitsberufe Vf KLAS-Codes 8200-8399 Schriftwerkschaffende, -ordnende und künstlerische Berufe Vg KLAS-Codes 8400-8599 Gesundheitsdienstberufe Vh KLAS-Codes 8600-8949 Sozialund Erziehungsberufe, anderweitig nicht genannte geistesund sozialwissenschaftliche Berufe Vi KLAS-Codes 9000-9379 Sonstige Dienstleistungsberufe VI KLAS-Codes 9700-9979 Sonstige Arbeitskräfte Because of gaps in the answers provided by respondents, the following “new” codes were created: 9711 - Mithelfende Familienangehörige außerhalb der Landwirtschaft, anderweitig nicht genannt 9811 - Auszubildende mit (noch) nicht feststehendem Ausbildungs-beruf 9821 - Praktikanten/Praktikantinnen, Volontäre/ Volontärinnen mit (noch) nicht feststehendem Beruf 9911 - Facharbeiter/innen, ohne nähere Tätigkeitsangabe 9921 - Heimarbeiter/innen, ohne nähere Tätigkeitsangabe 9931 - Vorarbeiter/innen, Gruppenleiter/innen, ohne nähere Tätigkeitsangabe 9971 - Sonstige Arbeitskräfte, ohne nähere Tätigkeitsangabe Statistisches Bundesamt (1996): Bevölkerung und Erwerbstätigkeit, Fachserie 1, Reihe 4.1.2., Beruf, Ausbildung und Arbeitsbedingung der Erwerbstätigen 1995 (Ergebnisse des Mikrozensus). Stuttgart: Metzler-Poeschel. pp. 317-323. Hartmann/Schütz (2002): Die Klassifikation der Berufe und der Wirtschaftzweige im Soziooekonomischen Panel – Neuvercodung der Daten 1984 – 2001. Infratest Sozialforschung, München. SOEP Survey Papers 444 29 SOEP Innovation Sample (2015) pgen (2015) pgautono – Autonomy in occupational activity ?In your position at work, do you supervise others? In other words, do people work under your direction? (from: soep-is/soep-is-2015/Q296;pvor1[3155]) 0[0] Apprentice 885 1[1] Low Autonomy 2343 2[2] [2/5] 4993 3[3] [3/5] 7621 4[4] [4/5] 4433 5[5] High Autonomy 768 -1 [-1] No Answer 441 -2 [-2] Does Not Apply 16914 -3 [-3] Answer Improbable 0 -4 [-4] Inadmissible Multiple Answer 0 -5 [-5] Not Contained In Questionnaire 0 -6 [-6] Questionnaire Version With Modified Filtering 0 This variable gives the occupational autonomy for all employed persons. It offers an alternative to the ISCO-based scales on occupational status (PGISEI), class (PGEGP), or prestige (PGSIOPS). PGAUTONO is the simplest variable based on the scales of “occupational position” in terms of its construction, and strongly correlated with the Treiman Prestige Scale (PGSIOPS). The basis for the “autonomy in occupational activity” scale is the classification of occupational position. Self-employed persons are categorized according to the size of the company (with the exception of farmers, who are all classified within the same category of autonomy, independent of farm size in hectares). Civil servants are differentiated according to the civil service laws defining each kind of activity and the amount of autonomy connected to it. Workers are differentiated according to their vocational training, and thus categorized hierarchically according to the different tasks they can be expected to carry out and the different amounts of responsibility associated with each task. Similarly, salaried employees are classified according to how differentiated their tasks are and how much responsibility is associated with each. The value “1” is assigned mainly to manual workers with a low level of status and a low level of autonomy. Group 2 encompasses work in production, services demanding a minimal level of specialization, and farm work. Activities that require completion of the middle track of secondary education and entail a limited amount of responsibility are classified in Group 3. Group 4 includes activities carried out either with or without supervision that require a degree from a college of applied sciences or university, but are not very high in prestige. Managers and freelance academics are both placed in Group 5 (highest autonomy). Depending on the number of employees, self-employed are categorized in Group 3, Group 4, or Group 5. Hoffmeyer-Zlotnik, Jürgen H.P., and Alfons J. Geis (2003) Berufs-klassifikation und Messung des beruflichen Status/ Prestige. In: ZUMA-Nachrichten 52, Jg. 27, Mai 2003. pp. 125-138. pgisced – Highest degree/diploma attained, ISCED-1997 0[0] (0) in school 524 1[1] (1) inadequately 650 2[2] (2) general elemantary 5339 3[3] (3) middle vocational 18995 SOEP Survey Papers 444 30 SOEP Innovation Sample (2015) pgen (2015) 4[4] (4) vocational + Abi 2656 5[5] (5) higher vocational 2272 6[6] (6) higher education 7490 -1 [-1] No Answer 472 -2 [-2] Does Not Apply 0 -3 [-3] Answer Improbable 0 -4 [-4] Inadmissible Multiple Answer 0 -5 [-5] Not Contained In Questionnaire 0 -6 [-6] Questionnaire Version With Modified Filtering 0 To make the educational degrees and diplomas attained in different countries comparable, for all respondents an educational variable (PGISCED) is generated retroactively using the international classification scheme ISCED-1997 (International Standard Classification of Education). It creates the highest degree/diploma attained, taking into account degrees and diplomas attained in both general schooling and in vocational and university education. Here the higher-level vocational and university override lower-level school diplomas. Persons who, for example, have no values for the variables on secondary school degrees/diplomas but state that they have a university degree are placed in the highest ISCED category. Please note that, due to a lack of more detailed information on tertiary degrees – in particular on doctoral degrees – we include all tertiary degrees in our ISCED category 6. Thus, the ISCED variable provided here is not comparable one-to-one with the ISCED levels as defined by the OECD, since we have included the original ISCED level 5A in our ISCED category 6. See below for more details. Since the SOEP-IS does not include a youth questionnaire, the information usually coming from the youth questionnaire was not included in the generation of PGBBILA. Furthermore, since the year 2012, input information from PGBBILA is not being used as PGBBILA itself is not being generated. OECD (1999): Classifying Educational Programmes: Manual for ISCED-97 Implementation in OECD Countries. Paris 1999. pgcasmin – Highest degree/diploma according to CASMIN 0[0] (0) In School 531 1[1] (1a) Inadequately Completed 647 2[2] (1b) General Elementary School 3336 3[3] (1c) Basic Vocational Qualification 10177 4[4] (2b) Intermediate General Qualification 1563 5[5] (2a) Intermediate Vocational 9775 6[6] (2c_gen) General Maturity Certificate 1482 7[7] (2c_voc) Vocational Maturity Certificate 2850 8[8] (3a) Lower Tertiary Education 2127 9[9] (3b) Higher Tertiary Education 5363 -1 [-1] No Answer 547 -2 [-2] Does Not Apply 0 -3 [-3] Answer Improbable 0 -4 [-4] Inadmissible Multiple Answer 0 -5 [-5] Not Contained In Questionnaire 0 -6 [-6] Questionnaire Version With Modified Filtering 0 SOEP Survey Papers 444 31 SOEP Innovation Sample (2015) pgen (2015) As an alternative to PGISCED, a second educational variable is generated (PGCASMIN) that also enables comparison with international educational degrees/diplomas. Based on the modified CASMIN classification scheme (Comparative Analysis of Social Mobility in Industrial Nations), this variable has been computed retroactively from 1984 on for all respondents. Taken into account are both secondary-level and university/college-level degrees and diplomas. As with PGISCED, the higher-level occupational degrees override the lower-level secondary school degrees. The original version is described in: König, W./Lüttinger, P./Müller, W. (1988): A Comparative Analysis of the Development and Structure of Educational Systems. Methodological Foundations and the Construction of a Comparative Educational Scale. CASMIN Working Paper No. 12. Mannheim: Universität Mannheim. For the modified version see: Brauns, H./Steinmann, (1999): Educational Reform in France, West-Germany and the United Kingdom: Updating the CASMIN Educational Classification. In: ZUMA Nachrichten, Jg. 23, H. 44, pp. 7-44. pgstib – Occupational Position ?Are you currently enrolled in an educational or training program? In other words: are you in school or higher education, working on a doctor’s degree, completing vocational training, or taking part in further training? (from: soep-is/soep-is-2015/Q260;paus1[3123]) ?What kind of general education / secondary school is it about? (from: soep-is/soep-is-2015/ Q262;paus2[3125]) ?What is your current position/ occupation? (from: soep-is/soep-is-2015/Q292;pber[3151]) ?What is your current occupational status? (from: soep-is/soep-is-2015/Q295;pstell[3154]) ?What is your current occupational status? // Other position, in fact: (from: soep-is/soep-is2015/Q295:PSTELLSO[3154]) ?In your position at work, do you supervise others? In other words, do people work under your direction? (from: soep-is/soep-is-2015/Q296;pvor1[3155]) ?What is your current occupational status as blue-collar worker? (from: soep-is/soep-is-2015/ Q297;parb[3156]) ?What is your current occupational status as white-collar worker? (from: soep-is/soep-is2015/Q298;pang[3157]) ?What is your current occupational status as civil servant? (from: soep-is/soep-is-2015/Q299;pamt [3158]) ?What is your current occupational status as apprentice / trainee or intern? (from: soep-is/ soep-is-2015/Q300;pazubi[3159]) ?In what occupational position are you currently self-employed? (from: soep-is/soep-is-2015/ Q307;psst[3164]) ?Please specify the number of your employees and staff. (from: soep-is/soep-is-2015/Q308;psstanz [2567]) 0[0] Do Not Know 0 10 [10] Not Employed 2867 11 [11] In Education 1348 12 [12] Unemployed, Not Employer 2122 13 [13] Pensioner 10499 15 [15] Military, Community Service 78 110 [110] Apprentice 43 120 [120] Apprentice,Trainee Industry Technology 436 130 [130] Apprentice, Trainee Trade And Commerce 296 SOEP Survey Papers 444 32 SOEP Innovation Sample (2015) pgen (2015) 140 [140] Trainee, Intern 110 150 [150] Aspirant 0 210 [210] Untrained Worker 653 220 [220] Semi-Trained Worker 1690 230 [230] Trained Worker 1821 240 [240] Foreman, Team Leader 254 ... (21 rows omitted) 3737 522 [522] Trained Employee With Simple Tasks 1799 530 [530] Qualified Professional 5608 540 [540] H. Qualified Professional 2946 550 [550] Managerial 296 610 [610] Low-Level Civil Service 48 620 [620] Middle-Level Civil Service 400 630 [630] High-Level Civil Service 582 640 [640] Executive Civil Service 330 999 [999] Employed Without StiB Info 0 -1 [-1] No Answer 434 -2 [-2] Does Not Apply 1 -3 [-3] Answer Improbable 0 -4 [-4] Inadmissible Multiple Answer 0 -5 [-5] Not Contained In Questionnaire 0 -6 [-6] Questionnaire Version With Modified Filtering 0 The variable represents a compilation of all relevant information on current occupational position. It is generated by combining information on “occupational group”, “unemployed (yes/no)”, “military/community service”, “in education (yes/no)”, and “pensioner”. A hierarchical scheme is used to determine which data is given precedence when a variety of divergent information exists (increasing dominance): 10 – not employed 13 – pensioner 11 – currently in education 15 – military / community service 12 – registered unemployed 110-150 - apprentice 410-440 – self-employed 210-250 – manual laborer 510-550 - employee 610-640 – civil service In PGSTIB, non-working persons are only assigned to the category (13) “pensioner” if they are recipients of retirement pension or if they are recipients of widow’s pension AND are older than 60 years. pgmonth – Month of interview 1[1] January 2782 2[2] February 4049 3[3] March 2835 4[4] April 1268 5[5] May 1003 6[6] June 726 SOEP Survey Papers 444 33 SOEP Innovation Sample (2015) pgen (2015) 7[7] July 1266 8[8] August 689 9[9] September 6792 10 [10] October 10395 11 [11] November 4600 12 [12] December 1992 -1 [-1] No Answer 0 -2 [-2] Does Not Apply 0 -3 [-3] Answer Improbable 1 -4 [-4] Inadmissible Multiple Answer 0 -5 [-5] Not Contained In Questionnaire 0 -6 [-6] Questionnaire Version With Modified Filtering 0 Month of interview is generated using the answers to the individual questionnaire. Missing answers are filled in using data from the HBRUTTO files. pgmode – Interview method 100 [100] With Interviewer Assistance 30 110 [110] Oral Interview 2511 120 [120] Written Ques. Interviewer 2038 130 [130] Mix Between With/Without Interviewer 0 131 [131] Written Ques. No Interviewer 306 132 [132] Oral And Written 202 133 [133] Proxy 2 134 [134] With Interpreter 0 135 [135] Exc Interpreter 0 140 [140] CAPI - Wave O Onwards 33109 200 [200] Telephone Assistance 0 210 [210] Written, By Mail 181 220 [220] phone interview 0 300 [300] CAWI 18 -1 [-1] No Answer 0 -2 [-2] Does Not Apply 0 -3 [-3] Answer Improbable 1 -4 [-4] Inadmissible Multiple Answer 0 -5 [-5] Not Contained In Questionnaire 0 -6 [-6] Questionnaire Version With Modified Filtering 0 The interview method is generated via the answers to the questions in the individual questionnaire. Missing answers are filled in from the PBRUTTO files. pglabgro – Current gross labor income in euros (generated) ?How much did you earn from your work last month? If you had extra income in the last month, e.g.: vacation pay or subsequent payments, please do not accout that. But please include overtime payments. If you are self-employed, please estimate your monthly income before and after tax. If possible, please state both: your gross income, which means income before tax and social security deductions and SOEP Survey Papers 444 34 SOEP Innovation Sample (2015) pgen (2015) your net income, which means income after tax, social security, and unemployment and health insurance deductions. // My gross income was ___ euros (from: soep-is/soep-is-2014a/Q249:PBRUT[2795]) ?How large was your income in the last month? // My gross income was ... euros (from: soep-is/soep-is-2014-f/Q249:PBRUT[2570]) 0 177 1 1 14 1 15 1 20 1 22 1 25 2 30 2 35 1 40 7 48 1 50 14 51 1 55 2 60 9 ... (2655 rows omitted) 20053 18000 3 19000 2 19500 1 20000 7 21000 1 22000 3 24000 1 30000 1 50000 1 -1 [-1] No Answer 1063 -2 [-2] Does Not Apply 17023 -3 [-3] Answer Improbable 18 -4 [-4] Inadmissible Multiple Answer 0 -5 [-5] Not Contained In Questionnaire 0 -6 [-6] Questionnaire Version With Modified Filtering 0 The variable PGLABGRO represents the current gross labor income of all SOEP respondents who are employed in each respective wave. The variable contains both generated and imputed values for Sample E (until 2011) & Sample I (until 2010); since then, the variable contains only generated values for all samples. The imputed values are available in the variables PGI1-PGI5LABGRO. Income details are consistently provided in euros for all waves. Item nonresponse is imputed in a twostage procedure: first, with the “Row-and-Column” method of Little und Su (1989) using individual longitudinal data as well as cross-sectional trend data (cf. Joachim R. Frick and Markus M. Grabka (2005): Item-Non-Response on Income Questions in Panel surveys: Incidence, Imputation and the Impact on the Income Distribution. Allgemeines Statistisches Archiv (ASTA) 89, 49-61). Alternatively, if no individual longitudinal information is available, we base the imputation on a regression using different Mincer covariates, also taking into account current net labor income. If both types of income information are lacking, first we impute current net labor income and then current gross labor SOEP Survey Papers 444 35 SOEP Innovation Sample (2015) pgen (2015) income. Imputed values are flagged (PGIMPGRO). pgi1labgro – 1. Imput. Akt. Bruttoerwerbseink.(gen) in Euro [1/5] -1 [-1] No Answer 0 -2 [-2] Does Not Apply 10922 -3 [-3] Answer Improbable 0 -4 [-4] Inadmissible Multiple Answer 0 -5 [-5] Not Contained In Questionnaire 14520 -6 [-6] Questionnaire Version With Modified Filtering 0 Multiple imputation procedures provide a way to deal with missing values on the variable current net labor income in Euros by using information about determinants of the household income and replacing item-nonresponse with multiply imputed data. Five imputations are available within the $PGEN datasets: the variables pgi1labnet-pgi5labnet. The imputations were calculated using the method of chained equations predictive mean matching in STATA. The procedures were written by Patrick Royston (see Royston 2004, 2005a, 2005b, 2007, 2009) and Ian White (see White, Daniel and Royston 2010; White, Royston and Wood 2011). Predicted mean matching means that for each missing observation on income, the particular non-missing observation is found whose prediction on observed data is closest. This closest observation is used to impute the missing value. The most important variable for modelling the current net labor income is the gross labor income of the previous year. A complete list of the variables used for modelling is available upon request. The missing observations were assumed to be missing at random. We set the number of imputations m=5 and get 5 multiple imputed values for pglabnet. The number of iterations carried out in each prediction model was specified to be 2000. Sample E&I and the supplementary sample S1 were imputed separately. Analysing multiply imputed data: For analysing multiple imputed data, one does not necessarily need special methods; however, such tools exist and simplify the use of multiply imputed data. Below is given a short overview of some useful tools for various statistical packages. These tools estimate the parameters of a regression model by combining the estimates across the several replicates of imputation. Point estimates from multiple imputations are then the arithmetic mean of the several point estimates obtained from analysis on each imputed data. Standard errors are obtained by combining the average of the squared standard errors of the several (m) estimates with the within-and between-imputation variance. · STATA provides a built-in functionality called mi. · Within SAS, PROC MIANALYZE combines the results of analyses on the data sets. · IVEware is a set of routines that can be launched from SAS or run independently using data from many sources. You can use the IVEware module regress to perform multiple imputation analysis. Royston, P. 2004. Multiple imputation of missing values. Stata Journal 4: 227–241. Royston, P. 2005a. Multiple imputation of missing values: Update. Stata Journal 5: 188–201. Royston, P. 2005b. Multiple imputation of missing values: Update of ice. Stata Journal 5: 527–536. Royston, P. 2007. Multiple imputation of missing values: Further update of ice, with an emphasis on interval censoring. Stata Journal 7: 445–464. Royston, P. 2009. Multiple imputation of missing values: Further update of ice, with an emphasis on categorical variables. Stata Journal 9: 466–477. pgi2labgro – 2. Imput. Akt. Bruttoerwerbseink.(gen) in Euro [2/5] SOEP Survey Papers 444 36 SOEP Innovation Sample (2015) pgen (2015) -1 [-1] No Answer 0 -2 [-2] Does Not Apply 10922 -3 [-3] Answer Improbable 0 -4 [-4] Inadmissible Multiple Answer 0 -5 [-5] Not Contained In Questionnaire 14520 -6 [-6] Questionnaire Version With Modified Filtering 0 Multiple imputation procedures provide a way to deal with missing values on the variable current net labor income in Euros by using information about determinants of the household income and replacing item-nonresponse with multiply imputed data. Five imputations are available within the $PGEN datasets: the variables pgi1labnet-pgi5labnet. The imputations were calculated using the method of chained equations predictive mean matching in STATA. The procedures were written by Patrick Royston (see Royston 2004, 2005a, 2005b, 2007, 2009) and Ian White (see White, Daniel and Royston 2010; White, Royston and Wood 2011). Predicted mean matching means that for each missing observation on income, the particular non-missing observation is found whose prediction on observed data is closest. This closest observation is used to impute the missing value. The most important variable for modelling the current net labor income is the gross labor income of the previous year. A complete list of the variables used for modelling is available upon request. The missing observations were assumed to be missing at random. We set the number of imputations m=5 and get 5 multiple imputed values for pglabnet. The number of iterations carried out in each prediction model was specified to be 2000. Sample E&I and the supplementary sample S1 were imputed separately. Analysing multiply imputed data: For analysing multiple imputed data, one does not necessarily need special methods; however, such tools exist and simplify the use of multiply imputed data. Below is given a short overview of some useful tools for various statistical packages. These tools estimate the parameters of a regression model by combining the estimates across the several replicates of imputation. Point estimates from multiple imputations are then the arithmetic mean of the several point estimates obtained from analysis on each imputed data. Standard errors are obtained by combining the average of the squared standard errors of the several (m) estimates with the within-and between-imputation variance. · STATA provides a built-in functionality called mi. · Within SAS, PROC MIANALYZE combines the results of analyses on the data sets. · IVEware is a set of routines that can be launched from SAS or run independently using data from many sources. You can use the IVEware module regress to perform multiple imputation analysis. Royston, P. 2004. Multiple imputation of missing values. Stata Journal 4: 227–241. Royston, P. 2005a. Multiple imputation of missing values: Update. Stata Journal 5: 188–201. Royston, P. 2005b. Multiple imputation of missing values: Update of ice. Stata Journal 5: 527–536. Royston, P. 2007. Multiple imputation of missing values: Further update of ice, with an emphasis on interval censoring. Stata Journal 7: 445–464. Royston, P. 2009. Multiple imputation of missing values: Further update of ice, with an emphasis on categorical variables. Stata Journal 9: 466–477. pgi3labgro – 3. Imput. Akt. Bruttoerwerbseink.(gen) in Euro [3/5] -1 [-1] No Answer 0 -2 [-2] Does Not Apply 10922 -3 [-3] Answer Improbable 0 -4 [-4] Inadmissible Multiple Answer 0 SOEP Survey Papers 444 37 SOEP Innovation Sample (2015) pgen (2015) -5 [-5] Not Contained In Questionnaire 14520 -6 [-6] Questionnaire Version With Modified Filtering 0 Multiple imputation procedures provide a way to deal with missing values on the variable current net labor income in Euros by using information about determinants of the household income and replacing item-nonresponse with multiply imputed data. Five imputations are available within the $PGEN datasets: the variables pgi1labnet-pgi5labnet. The imputations were calculated using the method of chained equations predictive mean matching in STATA. The procedures were written by Patrick Royston (see Royston 2004, 2005a, 2005b, 2007, 2009) and Ian White (see White, Daniel and Royston 2010; White, Royston and Wood 2011). Predicted mean matching means that for each missing observation on income, the particular non-missing observation is found whose prediction on observed data is closest. This closest observation is used to impute the missing value. The most important variable for modelling the current net labor income is the gross labor income of the previous year. A complete list of the variables used for modelling is available upon request. The missing observations were assumed to be missing at random. We set the number of imputations m=5 and get 5 multiple imputed values for pglabnet. The number of iterations carried out in each prediction model was specified to be 2000. Sample E&I and the supplementary sample S1 were imputed separately. Analysing multiply imputed data: For analysing multiple imputed data, one does not necessarily need special methods; however, such tools exist and simplify the use of multiply imputed data. Below is given a short overview of some useful tools for various statistical packages. These tools estimate the parameters of a regression model by combining the estimates across the several replicates of imputation. Point estimates from multiple imputations are then the arithmetic mean of the several point estimates obtained from analysis on each imputed data. Standard errors are obtained by combining the average of the squared standard errors of the several (m) estimates with the within-and between-imputation variance. · STATA provides a built-in functionality called mi. · Within SAS, PROC MIANALYZE combines the results of analyses on the data sets. · IVEware is a set of routines that can be launched from SAS or run independently using data from many sources. You can use the IVEware module regress to perform multiple imputation analysis. Royston, P. 2004. Multiple imputation of missing values. Stata Journal 4: 227–241. Royston, P. 2005a. Multiple imputation of missing values: Update. Stata Journal 5: 188–201. Royston, P. 2005b. Multiple imputation of missing values: Update of ice. Stata Journal 5: 527–536. Royston, P. 2007. Multiple imputation of missing values: Further update of ice, with an emphasis on interval censoring. Stata Journal 7: 445–464. Royston, P. 2009. Multiple imputation of missing values: Further update of ice, with an emphasis on categorical variables. Stata Journal 9: 466–477. pgi4labgro – 4. Imput. Akt. Bruttoerwerbseink.(gen) in Euro [4/5] -1 [-1] No Answer 0 -2 [-2] Does Not Apply 10922 -3 [-3] Answer Improbable 0 -4 [-4] Inadmissible Multiple Answer 0 -5 [-5] Not Contained In Questionnaire 14520 -6 [-6] Questionnaire Version With Modified Filtering 0 SOEP Survey Papers 444 38 SOEP Innovation Sample (2015) pgen (2015) dard errors of the several (m) estimates with the within-and between-imputation variance. · STATA provides a built-in functionality called mi. · Within SAS, PROC MIANALYZE combines the results of analyses on the data sets. · IVEware is a set of routines that can be launched from SAS or run independently using data from many sources. You can use the IVEware module regress to perform multiple imputation analysis. Royston, P. 2004. Multiple imputation of missing values. Stata Journal 4: 227–241. Royston, P. 2005a. Multiple imputation of missing values: Update. Stata Journal 5: 188–201. Royston, P. 2005b. Multiple imputation of missing values: Update of ice. Stata Journal 5: 527–536. Royston, P. 2007. Multiple imputation of missing values: Further update of ice, with an emphasis on interval censoring. Stata Journal 7: 445–464. Royston, P. 2009. Multiple imputation of missing values: Further update of ice, with an emphasis on categorical variables. Stata Journal 9: 466–477. pgi5labnet – 5. Imput. Akt. Nettoerwerbseink.(gen) in Euro [5/5] -1 [-1] No Answer 0 -2 [-2] Does Not Apply 10922 -3 [-3] Answer Improbable 0 -4 [-4] Inadmissible Multiple Answer 0 -5 [-5] Not Contained In Questionnaire 14520 -6 [-6] Questionnaire Version With Modified Filtering 0 Multiple imputation procedures provide a way to deal with missing values on the variable current net labor income in Euros by using information about determinants of the household income and replacing item-nonresponse with multiply imputed data. Five imputations are available within the $PGEN datasets: the variables pgi1labnet-pgi5labnet. The imputations were calculated using the method of chained equations predictive mean matching in STATA. The procedures were written by Patrick Royston (see Royston 2004, 2005a, 2005b, 2007, 2009) and Ian White (see White, Daniel and Royston 2010; White, Royston and Wood 2011). Predicted mean matching means that for each missing observation on income, the particular non-missing observation is found whose prediction on observed data is closest. This closest observation is used to impute the missing value. The most important variable for modelling the current net labor income is the gross labor income of the previous year. A complete list of the variables used for modelling is available upon request. The missing observations were assumed to be missing at random. We set the number of imputations m=5 and get 5 multiple imputed values for pglabnet. The number of iterations carried out in each prediction model was specified to be 2000. Sample E&I and the supplementary sample S1 were imputed separately. Analysing multiply imputed data: For analysing multiple imputed data, one does not necessarily need special methods; however, such tools exist and simplify the use of multiply imputed data. Below is given a short overview of some useful tools for various statistical packages. These tools estimate the parameters of a regression model by combining the estimates across the several replicates of imputation. Point estimates from multiple imputations are then the arithmetic mean of the several point estimates obtained from analysis on each imputed data. Standard errors are obtained by combining the average of the squared standard errors of the several (m) estimates with the within-and between-imputation variance. · STATA provides a built-in functionality called mi. · Within SAS, PROC MIANALYZE combines the results of analyses on the data sets. · IVEware is a set of routines that can be launched from SAS or run independently SOEP Survey Papers 444 45 SOEP Innovation Sample (2015) pgen (2015) using data from many sources. You can use the IVEware module regress to perform multiple imputation analysis. Royston, P. 2004. Multiple imputation of missing values. Stata Journal 4: 227–241. Royston, P. 2005a. Multiple imputation of missing values: Update. Stata Journal 5: 188–201. Royston, P. 2005b. Multiple imputation of missing values: Update of ice. Stata Journal 5: 527–536. Royston, P. 2007. Multiple imputation of missing values: Further update of ice, with an emphasis on interval censoring. Stata Journal 7: 445–464. Royston, P. 2009. Multiple imputation of missing values: Further update of ice, with an emphasis on categorical variables. Stata Journal 9: 466–477. pgimpnet – Imputation flag for LABNETxx 0[0] not imputed 19233 1[1] Imputed 2142 -1 [-1] No Answer 0 -2 [-2] Does Not Apply 17023 -3 [-3] Answer Improbable 0 -4 [-4] Inadmissible Multiple Answer 0 -5 [-5] Not Contained In Questionnaire 0 -6 [-6] Questionnaire Version With Modified Filtering 0 The variable PGIMPNET designates imputations of item-nonresponse in the variable PGLABNET (current net labor income). pgallbet – Core size category of the company 1[1] Less Than 20 4415 2[2] 20 Up To 200 5383 3[3] 200 Up To 2000 3753 4[4] 2000 And More 4506 5[5] Self-Employed Without Coworkers 0 -1 [-1] No Answer 1129 -2 [-2] Does Not Apply 19206 -3 [-3] Answer Improbable 6 -4 [-4] Inadmissible Multiple Answer 0 -5 [-5] Not Contained In Questionnaire 0 -6 [-6] Questionnaire Version With Modified Filtering 0 This variable is designed to provide annual data on the core size category of the company for all employed persons. Not all employed persons are asked the relevant input questions on an annual basis. Only those employed persons who changed jobs and first-time respondents were asked to provide up-to-date information. Self-employed are not included in this variable. Information about the company size is included in the variable pgstib. pgemplst – Employment status ?Are you currently employed? Which one of the following applies best to your status? (from: soep-is/soep-is-2015/Q271;perw[3133]) SOEP Survey Papers 444 46 SOEP Innovation Sample (2015) pgen (2015) 1[1] Full-Time Employment 13781 2[2] Regular Part-Time 4555 3[3] Vocational Training 806 4[4] Marginal, Irregular Part-Time Employment 2114 5[5] Not Employed 17072 6[6] Sheltered workshop 69 -1 [-1] No Answer 1 -2 [-2] Does Not Apply 0 -3 [-3] Answer Improbable 0 -4 [-4] Inadmissible Multiple Answer 0 -5 [-5] Not Contained In Questionnaire 0 -6 [-6] Questionnaire Version With Modified Filtering 0 This variable is generated from the annual question on current employment status, which has a central filter function in the questionnaire to separate employed people from nonemployed people for further questions. It is designed to provide consistent longitudinal data on employment status across all waves. The category “not employed” comprises non-working individuals, those in military/community service, those on maternity leave, and employed persons in a phased retirement scheme (Altersteilzeit) whose current actual working hours are zero. PGEMPLST supplements the variable PGLFS, which differentiates among persons who are not employed. pgexpft – Working experience full-time employment 0 1675 0.1 18 0.2 28 0.3 49 0.4 20 0.5 196 0.6 21 0.7 20 0.8 36 0.9 36 1 144 1.1 20 1.2 22 1.3 22 1.4 26 ... (448 rows omitted) 12106 47.4 1 47.8 11 48 11 48.1 2 48.2 1 48.3 1 48.6 11 49.3 2 50 6 SOEP Survey Papers 444 47 SOEP Innovation Sample (2015) pgen (2015) -1 [-1] No Answer 840 -2 [-2] Does Not Apply 0 -3 [-3] Answer Improbable 0 -4 [-4] Inadmissible Multiple Answer 0 -5 [-5] Not Contained In Questionnaire 23073 -6 [-6] Questionnaire Version With Modified Filtering 0 This variable reflects the total length of full-time employment in the respondent’s career. The variable is created by combining monthly information on employment status from the calendar dataset ARTKALEN (which provides monthly information on activity status since an individual entered the SOEP) and annual information from the biographical dataset PBIOSPE (which provides information on activity status over the individual’s life course). PGEXPFT uses calendar information up to December of the previous year and gives the length of time in years with months in decimal form. If there is no monthly calendar data available in a given year of a respondent’s career, the annual data from PBIOSPE is used for that year. In the most current wave the variable only uses up-to-date information from the newly answered Biography Questionnaires. If the year in which a spell started and ended is the same, and if there is no monthly data, a spell of 0.5 years is assumed. Persons whose life course has been observed completely but with no spell of full-time employment are assigned the code (0). The code (-1) is assigned to all persons whose life course has not been observed completely. Persons with inconsistent information receive a (-3). Because detailed information on the acitivity status of the respondents is not assessed in the Questionnaire of the SOEP Innovation Sample, PGEXPFT is not generated for Sample E (since 2012), I (since 2011) and the supplementary samples (since 2012). For this purpose, PGEXPFT is coded to “-5” (not contained in questionnaire). Please also see PGEXPPT and PGEXPUE. pgexppt – Working experience part-time employment 0 9031 0.1 70 0.2 117 0.3 112 0.4 76 0.5 249 0.6 67 0.7 67 0.8 91 0.9 28 1 436 1.1 57 1.2 62 1.3 97 1.4 37 ... (276 rows omitted) 3862 38 4 38.2 1 39.2 1 40 14 SOEP Survey Papers 444 48 SOEP Innovation Sample (2015) pgen (2015) 40.2 1 41.3 1 42 2 42.2 1 43 1 -1 [-1] No Answer 840 -2 [-2] Does Not Apply 0 -3 [-3] Answer Improbable 0 -4 [-4] Inadmissible Multiple Answer 0 -5 [-5] Not Contained In Questionnaire 23073 -6 [-6] Questionnaire Version With Modified Filtering 0 This variable reflects the total length of part-time employment in the respondent’s career. The variable is created by combining monthly information on employment status from the calendar dataset ARTKALEN (which provides monthly information on activity status since an individual entered the SOEP) and annual information from the biographical dataset PBIOSPE (which provides information on activity status over the life course of an individual). PGEXPPT uses calendar information up to December of the previous year and gives the length of time in years with months in decimal form. If there is no monthly calendar data available in a given year of a respondent’s career, the annual data from PBIOSPE is used for that year. In the most current wave the variable only uses up-to-date information from the newly answered Biography Questionnaires. If the year in which a spell started and ended is the same, and if there is no monthly data, a spell of 0.5 years is assumed. Persons whose life course has been observed completely but with no spell of full-time employment are assigned the code (0). The code (-1) is assigned to all persons whose life course has not been observed completely. Persons with inconsistent information receive a (-3). Because detailed information on the activity status of the respondents is not assessed in the Questionnaire of the SOEP Innovation Sample, PGEXPPT is not generated for Sample E (since 2012), I (since 2011) and the supplementary samples (since 2012). For this purpose, PGEXPPT is coded to “-5” (not contained in questionnaire). Please also see PGEXPFT and PGEXPUE. pgexpue – Unemployment experience 0 10635 0.1 149 0.2 123 0.3 173 0.4 83 0.5 531 0.6 72 0.7 91 0.8 128 0.9 66 1 409 1.1 103 1.2 39 1.3 59 1.4 102 SOEP Survey Papers 444 49 SOEP Innovation Sample (2015) pgen (2015) ... (146 rows omitted) 1712 22.5 1 23 1 23.5 1 24 1 24.5 1 25.3 1 26.4 2 32 1 33 1 -1 [-1] No Answer 840 -2 [-2] Does Not Apply 0 -3 [-3] Answer Improbable 0 -4 [-4] Inadmissible Multiple Answer 0 -5 [-5] Not Contained In Questionnaire 23073 -6 [-6] Questionnaire Version With Modified Filtering 0 This variable reflects the total length of unemployment in the respondent’s career. The variable is created by combining monthly information on employment status from the calendar dataset ARTKALEN (which provides monthly information on activity status since an individual entered the SOEP) and annual information from the biographical dataset PBIOSPE (which provides information on activity status over the life course of an individual). PGEXPUE uses calendar information up to December of the previous year and gives the length of time in years with months in decimal form. If there is no monthly calendar data available on a given year in a respondent’s career, the annual data from PBIOSPE is used for that year. In the most current wave the variable only uses up-to-date information from the newly answered Biography Questionnaires. If the year in which a spell started and ended is the same, and if there is no monthly data, a spell of 0.5 years is assumed. Persons whose life course has been observed completely but with no spell of full-time employment are assigned the code (0). The code (-1) is assigned to all persons whose life course has not been observed completely. Persons with inconsistent information receive a (-3). Because detailed information on the activity status of the respondents is not assessed in the Questionnaire of the SOEP Innovation Sample, PGEXPUE is not generated for Sample E (since 2012), I (since 2011) and the supplementary samples (since 2012). For this purpose, PGEXPUE is coded to “-5” (not contained in questionnaire). Please also see PGEXPFT. pgjobch – Occupational Change 1[1] Not Employed 16914 2[2] Employed No Change 14570 3[3] Employed No Info If Change 2685 4[4] Employed With Change 3167 5[5] First Job 426 -1 [-1] No Answer 636 -2 [-2] Does Not Apply 0 -3 [-3] Answer Improbable 0 -4 [-4] Inadmissible Multiple Answer 0 -5 [-5] Not Contained In Questionnaire 0 -6 [-6] Questionnaire Version With Modified Filtering 0 SOEP Survey Papers 444 50 SOEP Innovation Sample (2015) pgen (2015) This variable indicates a change of job since the previous interview for respondents with a follow-up interview, whereas for first-time respondents, the information refers to a change of job since the beginning of the previous year. PGJOBCH is generated based on the central filter variable, which indicates whether a respondent has changed jobs since the beginning of the previous year. Information on the date of job change is then combined with interview month of the previous year’s interview to identify whether a new job change has taken place since the previous interview. Hence, PGJOBCH is a modified version of the variable PGERWTYP which indicates whether a respondent has changed jobs since the beginning of the previous year. Unlike PGERWTYP, the variable is calculated for all waves, and the codes are assigned independently of the respondent being a first-time or follow-up respondent. In addition to PGERWTYP, the variable is also designed to identify respondents who have entered employment for the first time. In addition to PGERWTYP, the variable is designed to provide consistent longitudinal information on job changes. The PGJOBCH variable is generated by correcting the original job change information in various ways: 1. We check whether the job changes stated by a respondent in two consecutives interviews refer to one and the same job change. The date of the job change and the interview month are used to correct double entries. 2. If the respondent indicates a job change with a date before the previous interview but did not state a job change in the previous interview, this is coded as a job change in the current interview. 3. If a respondent indicates no job change and was not employed at the time of the previous interview, this is coded as “no job change” despite the seeming implausibility, since there are possible explanations how this information could be plausible, e.g. if there were short-term employment spells between two interview dates. 4. Respondents can be “first-time employed” only once. If a respondent states being “firsttime employed” for a second time, this is coded as “employed, with change”. pgfield – Field of tertiary education 1[1] Applied Linguistics and Cultural Studies 0 2[2] Protestant Theology 3 3[3] Catholic Theology 0 4[4] Philosophy 0 5[5] History 2 6[6] Library Science, Archival Studies, Journalism 12 7[7] Literary Studies, Linguistics 4 8[8] Classical Philology, Modern Greek 0 9[9] German Philology 12 10 [10] English Studies 12 11 [11] Roman Studies 0 12 [12] Slavonic Studies 0 13 [13] Non-European Languages and Cultural Studies 0 14 [14] Cultural Studies 2 15 [15] Psychology 5 ... (36 rows omitted) 537 SOEP Survey Papers 444 51 SOEP Innovation Sample (2015) pgen (2015) 68 [68] Civil Engineering 19 69 [69] Surveying and Mapping 0 74 [74] Art, Aesthetics 0 75 [75] Fine Arts 0 76 [76] Design 0 77 [77] Performance, Film and Television, Theater 0 78 [78] Music, Musicology 7 83 [83] Outside the structure of the university system 0 98 [98] Not categorizable 57 -1 [-1] No Answer 70 -2 [-2] Does Not Apply 14582 -3 [-3] Answer Improbable 1 -4 [-4] Inadmissible Multiple Answer 0 -5 [-5] Not Contained In Questionnaire 23073 -6 [-6] Questionnaire Version With Modified Filtering 0 The variable is designed to provide information on the field of education of tertiary degrees which adds details to the information recorded in the variable PGBBIL02. While the latter variable records if a person holds a degree PGFIELD contains more detailed information on the type of the degree. The data of the generated variable PGFIELD stem from two sources: 1. Person questionnaire: Each year since 1985 respondents are asked if they have left education since the beginning of the year prior to the survey and which degrees they have obtained. This part of the questionnaire contains an open question on the type and the field of newly obtained tertiary degrees. This information is coded and used for the generation of the variables PGFIELD. 2. Biography questionnaire: Since 2001 similar information is collected from respondents who fill in the biography questionnaire (usually during the first two years of participation in the panel). In contrast to the information from the person questionnaire the questions do not refer to currently obtained degrees but to degrees obtained during the time before being part of the SOEP sample. In the variable PGFIELD we combine these two types of information. Each year the variable contains the most recently collected information. If you want to take into account that a person holds two degrees you have to combine the information from all available years. However, only a minority of the population holds more than one tertiary degree. In very few cases we encounter the problem that a respondent provides information on two different degrees in one survey year. This only happens in years when respondents fill in the person as well as the biography questionnaire. In these cases we prioritize the information from the person questionnaire as it refers to the current situation while the biography questionnaire contains retrospective information. Furthermore, there are cases who report an applied university degree and a university degree in the biography questionnaire. In these cases, the variable contains information on the university degree only. The variable is coded according to the classification on fields of education (“Fächergruppen”) provided by the Statistisches Bundesamt (2009). Until 2009 data from the person questionnaire were coded using an earlier version of this classification (1982). In the variable PGFIELD we recoded the original values. As the newer version is more precise this could be done with hardly any loss of information. Some categories are collapsed. Category 3 is coded as 2 (no distinction between catholic and protestant theology), 14 as 13, 17 as 16, 24 as 23, 25 as 26 and 48 as 49. Because detailed information on the field of tertiary education is not assessed in the Questionnaire of the SOEP Innovation Sample, PGFIELD is not generated for Sample E (since SOEP Survey Papers 444 52 SOEP Innovation Sample (2015) pgen (2015) 2012), I (since 2011) and the supplementary samples (since 2012). For this purpose, PGFIELD is coded to “-5” (not contained in questionnaire). Stat. Bundesamt (2009): Bildung und Kultur. Studierende an Hochschulen, Fachserie 11 Reihe 4.1, Wiesbaden: 446ff, Übersicht 1: „Fächergruppen, Studienbereiche und Studienfächer“. pgdegree – Type of tertiary degree 11 [11] Magister 12 12 [12] Diplom (University) 164 13 [13] Bachelor 6 14 [14] Master 2 15 [15] 1st State Examination 13 16 [16] Other state examination 14 21 [21] Diplom (at technical college, technical college for administration) 184 22 [22] Bachelor (at technical college, technical college for administration) 8 23 [23] Master (at technical college, technical college for administration) 1 31 [31] Teacher training,BA,MA at elementary, lower secondary schools/primary level 36 32 [32] Teacher training,BA,MA at 2ndary level 1/elementary schools/primary level 0 33 [33] Teacher training,BA,MA at intermediate scndry schools/scndry level I 5 34 [34] Teacher training, BA, MA at secondary level II and I 0 35 [35] Teacher training,BA,MA at academic 2ndry schools,2ndry levl 2,genrl school 15 36 [36] Teacher training, BA, MA at special needs schools 3 37 [37] Teacher training, BA, MA at vocational schools 5 38 [38] Teacher training, other 28 41 [41] Academic degree in the arts 1 42 [42] Doctorate 19 43 [43] Post-doctoral dissertation (Habilitation) 0 44 [44] Other Degree 8 98 [98] Not categorizable 98 -1 [-1] No Answer 120 -2 [-2] Does Not Apply 14582 -3 [-3] Answer Improbable 1 -4 [-4] Inadmissible Multiple Answer 0 -5 [-5] Not Contained In Questionnaire 23073 -6 [-6] Questionnaire Version With Modified Filtering 0 The variable is designed to provide information on the type of tertiary degree (e.g., Diploma, Bachelor, Master) which adds details to the information recorded in the variable PGBBIL02. While the latter variable records if a person holds a degree PGDEGREE contains more detailed information on the type of the degree. The data of the generated variable PGDEGREE stem from two sources: 1. Person questionnaire: Each year since 1985 respondents are asked if they have left education since the beginning of the year prior to the survey and which degrees they have obtained. This part of the questionnaire contains an open question on the type and the field of newly obtained tertiary degrees. This information is coded and used for the generation of the variables PGDEGREE. 2. Biography questionnaire: Since 2001 similar information is collected from respondents who fill in the biography questionnaire (usually during the first two years of participation in the panel). In contrast to the information from the person questionnaire the questions do not refer to currently obtained degrees but to degrees obtained during the time before being part of the SOEP sample. In the variable PGDEGREE we combine these two types of information. However, since the SOEP Survey Papers 444 53 SOEP Innovation Sample (2015) pgen (2015) retrospective information was not collected before 2001 the variable covers until 2000 only persons for whom we have prospectively observed the end of study. This explains why the number of valid observations is rather small in these years. Each year the variable contains the most recently collected information. If you want to take into account that a person holds two degrees you have to combine the information from all available years. However, only a minority of the population holds more than one tertiary degree. In very few cases we encounter the problem that a respondent provides information on two different degrees in one survey year. This only happens in years when respondents fill in the person as well as the biography questionnaire. In these cases we prioritize the information from the person questionnaire as it refers to the current situation while the biography questionnaire contains retrospective information. Furthermore, there are cases who report an applied university degree and a university degree in the biography questionnaire. In these cases, the variables contain information on the university degree only. The variable is coded according to a slightly collapsed version of the classification on types of tertiary degrees (“Prüfungsgruppen und Abschlussprüfungen”) provided by the Statistisches Bundesamt (2009). Since 2010 the data were coded according to the classification presented here. In the variable PGDEGREE we recoded the original values from years 2009 and earlier. As the newer version is more precise this could be done with hardly any loss of information. Some categories are collapsed. Category 16 was mostly likely coded as 15 in earlier years, 34 as 35 and 43 as 44. The original values of the data collected from the person questionnaire of 2009 are stored in the respective variables in the dataset P. Because detailed information on the type of tertiary degree is not assessed in the Questionnaire of the SOEP Innovation Sample, PGDEGREE is not generated for Sample E (since 2012), I (since 2011) and the supplementary samples (since 2012). For this purpose, PGDEGREE is coded to “-5” (not contained in questionnaire). Stat. Bundesamt (2009): Bildung und Kultur. Studierende an Hochschulen, Fachserie 11 Reihe 4.1, Wiesbaden: 449ff, Übersicht 2: „Prüfungsgruppen und Abschlussprüfungen“. pgtraina – Apprenticeship - two-digit occupation KldB92 1[1] Agricultural Occupations (Crops) 21 2[2] Agricultural Occupations (Livestock) 15 3[3] Administrative/Advisory/Technical Specialist In Agriculture 1 5[5] Horticultural Occupations 19 6[6] Forestry and Hunting Occupations 0 7[7] Mineworkers 3 8[8] Mineral Exploitation and Processing 1 10 [10] Stonemasons 0 11 [11] Manufacturers of Construction Materials 0 12 [12] Ceramicists 0 13 [13] Glass Manufacturing Occupations 1 14 [14] Chemical Industry Occupations 4 15 [15] Plastics Manufacturing Occupations 1 16 [16] Paper Manufacturing and Processing 3 17 [17] Printing Occupations 14 ... (65 rows omitted) 1516 89 [89] Pastoral Occupations 0 90 [90] Personal Care Occupations 46 91 [91] Occupations in Hotels and Hospitality 15 92 [92] Occupations in Domestic and Nutritional Science 16 SOEP Survey Papers 444 54