SOEPcompanion (v37), V.3

Kara, Selin; Zimmermann, Stefan Research Report SOEPcompanion (v37), V.3 SOEP Survey Papers, No. 1192 Provided in Cooperation with: German Institute for Economic Research (DIW Berlin) Suggested Citation: Kara, Selin; Zimmermann, Stefan (2022) : SOEPcompanion (v37), V.3, SOEP Survey Papers, No. 1192, Deutsches Institut für Wirtschaftsforschung (DIW), Berlin This Version is available at: https://hdl.handle.net/10419/265516 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. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. 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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/ Selin Kara, Stefan Zimmermann, and SOEP Group Series G - General Issues and Teaching Materials SOEPcompanion (v37), V.3 1192 2022 SOEPcompanion Release 2022, v.3 Selin Kara, Stefan Zimmermann, and SOEP Group Sep 26, 2022 CONTENTS 1 Preface 1 2 Topics of SOEP-Core 2 2.1 Demography and Population ....................................... 3 2.2 Work and Employment .......................................... 4 2.3 Income, Taxes, and Social Security ................................... 12 2.4 Family and Social Networks ....................................... 22 2.5 Health and Care ............................................. 27 2.6 Home, Amenities, and Contributions of Private HH ........................... 29 2.7 Education and Qualification ....................................... 36 2.8 Attitudes, Values, and Personality .................................... 45 2.9 Time Use and Environmental Behavior ................................. 49 2.10 Integration, Migration, Transnationalization ............................... 54 2.11 Survey Methodology ........................................... 56 3 Survey Design 58 3.1 SOEP Questionnaires .......................................... 58 3.1.1 Overview of the Questionnaires ................................ 60 3.1.2 Household Questionnaire .................................... 60 3.1.3 Individual Questionnaire .................................... 62 3.1.4 Biography Questionnaire .................................... 65 3.1.5 Mother and Child Instruments ................................. 66 3.1.6 Youth Instruments ........................................ 68 3.1.7 Additional Instruments ..................................... 71 3.2 Survey Concepts and Modes ....................................... 73 3.3 Panel Care ................................................ 74 4 Target Population and Samples 76 4.1 The SOEP Samples in Detail ....................................... 77 4.1.1 Sample-Specific Questionnaires ................................ 80 4.2 Eligibility and Follow-up ......................................... 88 4.3 Development of Sample Sizes ...................................... 89 5 Data Structure of SOEP-Core 92 5.1 Data Editions of SOEP-Core ....................................... 92 5.1.1 Teaching, International, and EU Edition ............................ 93 5.1.2 Add-ons: Area Types and Planning Regions .......................... 93 5.1.3 Remote Edition ......................................... 93 5.1.4 Onsite Edition .......................................... 94 5.2 Principles of Data Analysis ....................................... 94 i 5.2.1 Cross-Sectional Data Structure (CS) .............................. 95 5.2.2 Data Structure in “Wide” Format (wide) ............................ 95 5.2.3 Data Structure in “Long” Format (long) ............................ 95 5.2.4 Data Structure in Spell Format (spell) ............................. 96 5.3 Data Distribution File .......................................... 96 5.3.1 Core Datasets .......................................... 98 5.3.2 Raw Datasets .......................................... 99 5.3.3 eu-silc-like-panel ........................................ 101 5.4 Datasets SOEP-Core ........................................... 102 5.4.1 Tracking Data .......................................... 103 5.4.2 Original Data .......................................... 105 5.4.3 Survey Data ........................................... 107 5.4.4 Generated Data ......................................... 107 5.4.5 Spell Data ............................................ 112 5.5 Data Processing ............................................. 113 5.6 Dataset Identifiers ............................................ 114 5.6.1 Partner Identifier ........................................ 114 5.6.2 Interviewer Identifier ...................................... 117 5.7 Versioning and Harmonization ...................................... 117 5.8 Missing Conventions ........................................... 118 6 Working with SOEP Data 120 6.1 Working with Tracking Data (PPATHL) ................................. 120 6.2 Generating a Cross-Sectional Dataset .................................. 132 6.3 Syntax Generator on paneldata.org ................................... 140 6.4 Generating a Longitudinal Dataset .................................... 149 6.5 Working with harmonized Variables ................................... 162 6.6 Longitudinal Data Analysis ....................................... 178 6.7 Working with Migration Data (BIOIMMIG) .............................. 191 6.8 Fixed Effects Estimation ......................................... 200 6.9 Working with SOEP Regional Data ................................... 214 6.10 Working with spatial data in R ...................................... 221 6.10.1 Prerequisites .......................................... 221 6.10.2 Reading data .......................................... 223 6.10.3 Transformations ......................................... 227 6.10.4 Plotting Spatial Data ...................................... 228 6.10.5 Frequently Used Operations .................................. 229 6.10.6 Complete Example ....................................... 235 6.10.7 Appendix ............................................ 239 6.11 How to Use SOEP IGEL ......................................... 240 6.11.1 IGEL Workstation ........................................ 240 6.11.2 Logging in ........................................... 240 6.11.3 Working with SOEP DATA ................................... 245 6.11.4 Importing Scripts or External Data ............................... 246 6.11.5 Instructions for exporting from Hauser to user ......................... 246 6.11.6 Data transfer from Moran to Hauser .............................. 248 6.12 Working with SOEP data in csv format ................................. 249 7 Working with SOEP Documentation 253 7.1 Variable Search with Questionnaires ................................... 253 7.2 Variable Search with paneldata.org ................................... 255 7.3 Topic Search with paneldata.org ..................................... 263 7.4 Documentation on Generated Data ................................... 270 7.5 Working with SOEPhelp ......................................... 276 ii 7.5.1 Working with SOEPhelp in R .................................. 276 7.5.2 Working with SOEPhelp in STATA .............................. 278 7.6 Working with Metadata-based Questionnaires .............................. 285 8 Contact Information 287 iii CHAPTER ONE PREFACE SOEP-Core is the centerpiece of the Socio-Economic Panel, a wide-ranging representative longitudinal study of private households in Germany, based at the German Institute for Economic Research, DIW Berlin. SOEP-Core was started in 1984, and in 1990—shortly after German reunification—it was enlarged to include a representative sample from East Germany. This feature makes the SOEP unique among household panel surveys worldwide. Every year since 1984, individuals in households have been surveyed by the SOEP’s fieldwork organization, infas Institut für angewandte Sozialwissenschaften GmbH. The data provide information on every member of every household taking part in the survey. Respondents include Germans living in both the former East and West Germany, foreign citizens residing in Germany, recent immigrants, and a new sample of refugees added in 2016. Some of the many topics include household composition, education, occupational biographies, employment, earnings, health, and satisfaction indicators. The SOEPcompanion describes the current version of the SOEP-Core data (v37) and introduces users to the different SOEP-Core data structures. It also provides applications in Stata as well as instructions on how to use our various documentation services. We plan to revise the information in the SOEPcompanion annually to continue providing users a comprehensive, up-to-date introductory understanding of the SOEP. We know that starting to use any new dataset is difficult, and this is especially true of panel data given their complexity. We hope that this introduction will help. We always welcome any feedback or tips on how to improve our documentation. •Recommendation of our most recent version of a general short description of SOEP study: The German Socio- Economic Panel Study (SOEP) •To the information system for efficient working with complex datasets: paneldata.org 1 CHAPTER TWO TOPICS OF SOEP-CORE The topics of the SOEP questionnaires and the various modules they contain can be grouped into 11 areas. Some of the modules deal with aspects of life that tend to change from one year to the next, and are therefore repeated annually, while other modules are repeated at intervals of several years. How often a module is repeated is stated in the “module” column of our topic tables. Some SOEP modules are also adapted in different ways to the different questionnaires. The questions in the “Big Five” personality traits module, for instance, are formulated differently in the mother-child questionnaires than they are in the individual questionnaire. Overview of Modules in Different SOEP Questionnaires Individual Youth Mother- Child A Mother- Child B Mother- Child C Parents D Mother- Child E Affective well-being x x Big Five personality traits x x x x x Birth history x x x x x x Childcare x x x x x Educational aspirations x x x Health of child x x x x Height and weight of child x x x Height and weight x x Language ability German / native language x x Leisure and activities (with child) x x Life satisfaction x x Language use x x Locus of control x x Family background x x Parental interest in school performance x x Allowance x x Political orientation x x Risk aversion x x State of health x x Strengths and difficulties x x Temperament x x The SOEP Scales Manual briefly describes the theoretical background and development of all of the scales used in the Socio-Economic Panel (SOEP) study. It also provides the relevant citations as well as the items belonging to the scales and the answer format, including the verbal anchors. 2 SOEPcompanion, Release 2022, v.3 Table 2 – continued from previous page Questionnaire Module Years Variables Short-time compensation (Kurzarbeitergeld) [1984-2001,2003- 2005,2010-2011], [1984- 2001,2003-2005], [2010- 2011], [1984], [1985- 2001,2003-2005,2010- 2011] plc0057_h,plc0057_v1, plc0057_v2,plc0058_v1, plc0058_v2 Side jobs [1998-2007], [1998] plb0382_h,plb0382_v1 Side jobs, Agriculture [1999-2007] plb0382_v2 Side jobs, Construction [1999-2007] plb0382_v3 Side jobs, Days [1985-2016] plb0396 Side jobs, Gross Income [1995-2016], [1995-2001], [2002-2016] plc0062_h,plc0062_v1, plc0062_v2 Side jobs, Helping Family Members out [1986-2016] plb0392 Side jobs, Hours per Month [1985-2014] plb0397 Side jobs, Hours per Week [2015-2016] plb0573 Side jobs, Industrial Sector [1999-2007] plb0382_v4 Side jobs, Iregual [1985-2016] plb0395 Side jobs, Months [2000-2013] plb0398 Side jobs, Occupational Classification ISCO08 [2013-2016] p_isco08_sidejob, p_isco08_sidejob1, p_isco08_sidejob2, p_isco08_sidejob3 Side jobs, Occupational Classification ISCO88 [1991-2016] p_isco88_sidejob, p_isco88_sidejob1, p_isco88_sidejob2, p_isco88_sidejob3 Side jobs, Other [1985-2016] plb0393 Side jobs, Regular [1985-2016] plb0394 Side jobs, Service Sector [1999-2007] plb0382_v5 Standby duty [2011,2014-2019] plb0212,plb0213,plb0214, plb0215 Start of working hours (irregular) [2002-2019] plb0180,plb0181,plb0182 Starting a new job, Acceptable Position [1984-2020] plb0423 Starting a new job, Active Job Search [1994-1998], [1999-2020] plb0424_v1,plb0424_v2 Starting a new job, Desired Employment Type [1984-2020] plb0240 Starting a new job, Expected Minimum Income [1987-1989,1992- 1994,1996-2001], [2002- 2020] plb0420_v1,plb0420_v2 Starting a new job, Intention [1984-1993], [1994-2020] plb0417_v1,plb0417_v2 Starting a new job, Nonresponse Salary [1987-1989,1992- 1994,1996-2020] plb0421 Starting a new job, Number of Hours [2007-2020] plb0422 Starting a new job, Suitable Job [1987-2020], [1987-2002], [2003-2020] plb0419_h,plb0419_v1, plb0419_v2 Starting a new job, Timing [1984-2020] plb0418 continues on next page 2.2. Work and Employment 9 SOEPcompanion, Release 2022, v.3 Table 2 – continued from previous page Questionnaire Module Years Variables Supervisory position (irregular) [2007-2019], [2007,2009,2011,2013,2015,2017] plb0067,plb0068,plb0069 Use of professional skills in job [1985-2007,2009] plb0357 Vacation entitlement, Carried over Vaccation [2005,2010] plb0275,plb0276 Vacation entitlement, Contracted Days [2000,2005,2010] plb0269 Vacation entitlement, Days on Vaccation [1985- 1990,2000,2005,2010] plb0265 Vacation entitlement, Expired Vaccation [2005,2010] plb0273,plb0274 Vacation entitlement, Not specified [2005,2010] plb0270,plb0272 Work council (Betriebsrat) [2001,2006,2011,2016,2019] plb0050 Work from home [1997,1999,2002,2009- 2014,2020], (irregular) [1997-2020], [2012], [2013] plb0095,plb0096_v1, plb0096_v2,plb0096_v3 Work from home, Possibility [1997,1999,2009-2014] plb0097 Work from home, Possibility in Contract [2020] plb0697 Work in black economy [2015-2016], [2015] plb0571,plb0572 Work time regulations [2003,2005,2007,2009- 2019] plb0211 Work, last 7 days [1999-2020] plb0018 Working hours, October 2014 [2015] plb0579,plb0579_h, plb0580,plb0581, plb0581_h Working overtime [1997-2020] plb0193 Working overtime, Compensation Period [2002-2020], [2020] plb0194_v1,plb0194_v2 Working overtime, Compensation period [2002-2020], [2002-2017], [2018-2020] plb0220_h,plb0220_v1, plb0220_v2 Working overtime, Financial Compensation [2015-2020] plb0605 Working overtime, Hours Last Month [1986,1988-2020] plb0197 Working overtime, Last Month [1986,1988-2020], [1986,1988-1996], [1997- 2001], [2002-2020] plb0196_h,plb0196_v1, plb0196_v2,plb0196_v3 Working overtime, Paid Hours Last Month [2001-2020] plb0198 Working overtime, Time taken off [2013-2020] plb0483,plb0484 Workload (effort-reward imbalance), Career Prospects [2006,2011-2012,2016] plb0134,plb0135 Workload (effort-reward imbalance), Interruptions [2006,2011-2012,2016] plb0120,plb0121 continues on next page 10 Chapter 2. Topics of SOEP-Core SOEPcompanion, Release 2022, v.3 Table 2 – continued from previous page Questionnaire Module Years Variables Workload (effort-reward imbalance), Job at risk [2006,2011-2012,2016] plb0128,plb0129 Workload (effort-reward imbalance), Poor Career Prospects [2006,2011-2012,2016] plb0124,plb0125 Workload (effort-reward imbalance), Poor Working Conditions [2006,2011-2012,2016] plb0126,plb0127 Workload (effort-reward imbalance), Problems Sleeping [2006,2011-2012,2016] plb0117 Workload (effort-reward imbalance), Recognition by Superiors [2006,2011-2012,2016] plb0130,plb0131 Workload (effort-reward imbalance), Recognition for Performance [2006,2011-2012,2016] plb0132,plb0133 Workload (effort-reward imbalance), Sacrifices for Career [2006,2011-2012,2016] plb0115 Workload (effort-reward imbalance), Salary [2006,2011-2012,2016] plb0136,plb0137 Workload (effort-reward imbalance), Thinking about Work [2006,2011-2012,2016] plb0113,plb0114,plb0116 Workload (effort-reward imbalance), Time Pressure [2006,2011-2012,2016] plb0112,plb0118,plb0119 Workload (effort-reward imbalance), Work Volume [2006,2011-2012,2016] plb0122,plb0123 Youth Questionnaire Jobs and money, Employment Form [2000-2020] jl0014 Jobs and money, First Job [2000-2020] jl0017,jl0018 Jobs and money, Job Search [2006-2020] jl0386 Jobs and money, Own Earnings [2000-2020] jl0013 Jobs and money, Paid Work [2006-2020] jl0385 Jobs and money, Reason for Working [2001-2020] jl0019 Jobs and money, Savings [2000-2020], [2000-2001], [2002-2020], [2000-2020] jl0023,jl0024_h, jl0024_v1,jl0024_v2, jl0025 Jobs and money, Unemployment [2006-2020] jl0387 2.2. Work and Employment 11 SOEPcompanion, Release 2022, v.3 2.3 Income, Taxes, and Social Security The income, taxes, and social security modules collect wide-ranging financial information from earnings and spending to public benefits, pensions, inheritances, taxes, and debts. They also cover assets such as real estate and other property. Questionnaire Module Years Variables Individual Questionnaire Additional questions for employed people, 13th month payment prev. year [1984-2020], [1984-2001], [2002-2020] plc0042,plc0043_h, plc0043_v1,plc0043_v2 Additional questions for employed people, 14th month payment prev. year [1984-2020], [1984-2001], [2002-2020] plc0044,plc0045_h, plc0045_v1,plc0045_v2 Additional questions for employed people, Christmas Bonus prev. year [1984-2020], [1984-2001], [2002-2020] plc0046,plc0047_h, plc0047_v1,plc0047_v2 Additional questions for employed people, No Bonus prev. year [1984-2020] plc0054 Additional questions for employed people, Other Bonus prev. year [1984-2020], [1984-2001], [2002-2020] plc0052,plc0053_h, plc0053_v1,plc0053_v2 Additional questions for employed people, Profitsharing Bonus prev. year [1985-2020], [1985-2001], [2002-2020] plc0050,plc0051_h, plc0051_v1,plc0051_v2 Additional questions for employed people, Vacation Bonus prev. year [1984-2020], [1984-2001], [2002-2020] plc0048,plc0049_h, plc0049_v1,plc0049_v2 Additional questions for retirees / pensioners, Accident Insurance Retirement Pension [1986-2001,2003-2020], [1986- 2001], [2003-2020] plc0243_h,plc0243_v1, plc0243_v2 Additional questions for retirees / pensioners, Accident Insurance Widow’s Pension [1986-2001,2003-2020], [1986- 2001], [2003-2020] plc0286_h,plc0286_v1, plc0286_v2 Additional questions for retirees / pensioners, Company Retirement Pension [1986-2001,2003-2020], [1986- 2001], [2003-2020] plc0240_h,plc0240_v1, plc0240_v2 continues on next page 12 Chapter 2. Topics of SOEP-Core SOEPcompanion, Release 2022, v.3 Table 3 – continued from previous page Questionnaire Module Years Variables Additional questions for retirees / pensioners, Company Widow’s Pension [1986-2001,2003-2020], [1986- 2001], [2003-2020] plc0283_h,plc0283_v1, plc0283_v2 Additional questions for retirees / pensioners, Invalid Pension non-response [2003-2020] plc0251 Additional questions for retirees / pensioners, Orphan Benefit non-response [2003-2020] plc0290 Additional questions for retirees / pensioners, Other Retirement Pensions [1986-2001,2003-2020], [1986- 2001], [2003-2020] plc0249_h,plc0249_v1, plc0249_v2 Additional questions for retirees / pensioners, Other Widow’s Pensions [1986-2001,2003-2020], [1986- 2001], [2003-2020] plc0288_h,plc0288_v1, plc0288_v2 Additional questions for retirees / pensioners, Private Retirement Pension [2003-2020], [2018-2020] plc0242_v1,plc0242_v2 Additional questions for retirees / pensioners, Private Widow’s Pension [2003-2020] plc0285 Additional questions for retirees / pensioners, Retirement Pension Civil Servants [1986-2001,2003-2020], [1986- 2001], [2003-2020] plc0236_h,plc0236_v1, plc0236_v2 Additional questions for retirees / pensioners, Riester Pension [2015-2020] plc0516,plc0517 Additional questions for retirees / pensioners, Shareholder Company [2019] plc0572 Additional questions for retirees / pensioners, Supplementary Pension Civil Servants [1986-2001,2003-2020], [1986- 2001], [2003-2020] plc0238_h,plc0238_v1, plc0238_v2 Additional questions for retirees / pensioners, Supplementary Widow’s Pension Civil Servants [1986-2001,2003-2020], [1986- 2001], [2003-2020] plc0281_h,plc0281_v1, plc0281_v2 Additional questions for retirees / pensioners, War Victims Pension [1986-2001,2003-2016], [1986- 2001], [2003-2016] plc0245_h,plc0245_v1, plc0245_v2 Additional questions for retirees / pensioners, War Victims Widow’s pension [1986-2001,2003-2016], [1986- 2001], [2003-2016] plc0247_h,plc0247_v1, plc0247_v2 continues on next page 2.3. Income, Taxes, and Social Security 13 SOEPcompanion, Release 2022, v.3 Table 3 – continued from previous page Questionnaire Module Years Variables Additional questions for retirees / pensioners, Widow’s pension [1986-2001,2003-2020], [2003- 2020] plc0268_h,plc0268_v1, plc0268_v2,plc0268_v3 Additional questions for retirees / pensioners, Widow’s pension Civil Servants [1986-2001,2003-2020], [1986- 2001], [2003-2020] plc0279_h,plc0279_v1, plc0279_v2 Asset balance [2002] plc0340 Asset balance, Building Loan Contract (Bausparvertrag) [2007,2012], [2017,2019], [2007,2012], [2017,2019] plc0317_v1,plc0317_v2, plc0318_v1,plc0318_v2 Asset balance, Building Society Savings [2007,2012,2017,2019] plc0315,plc0316, plc0319 Asset balance, Cash Surrender [2002] plc0327,plc0335, plc0336,plc0337, plc0338 Asset balance, Enterprise [2002] plc0341,plc0364, plc0365,plc0366, plc0367,plc0368, plc0369 Asset balance, Financial Assets [2002], [2002,2007,2012], [2002,2007,2012,2017,2019] plc0314,plc0326, plc0328,plc0329, plc0330,plc0331, plc0332,plc0333, plc0334 Asset balance, Financial Burden [2002], [2002,2007,2012] plc0408,plc0409, plc0411,plc0412, plc0413,plc0414, plc0415,plc0416, plc0417,plc0418, plc0419,plc0420 Asset balance, Life Insurance [2002,2007,2012,2017,2019] plc0363 Asset balance, Non self used Property [2002,2007,2012,2017,2019], [2002,2007,2012], [2002,2007,2012,2017,2019] plc0356,plc0357, plc0358,plc0359, plc0360,plc0361, plc0362 Asset balance, Other Property [2002,2007,2012,2017,2019] plc0354 Asset balance, Property non-response [2007,2012,2017,2019] plc0355 Asset balance, Remaining Debt [2002] plc0410,plc0421, plc0422,plc0423, plc0424,plc0425 Asset balance, Residential property [2002] plc0339,plc0342, plc0343,plc0344, plc0345,plc0346, plc0347,plc0348, plc0349 continues on next page 14 Chapter 2. Topics of SOEP-Core SOEPcompanion, Release 2022, v.3 Table 3 – continued from previous page Questionnaire Module Years Variables Asset balance, Residential property Usage [2002,2007,2012,2017,2019] plc0350,plc0351, plc0352 Asset balance, Tangible Assets [2002,2007,2012,2017,2019] plc0370,plc0371, plc0372,plc0373, plc0374 Asset balance, Undeveloped Land [2002,2007,2012] plc0353 Asset development [2019] plc0570i01,plc0570i02, plc0570i03,plc0570i04, plc0570i05,plc0570i06, plc0570i07,plc0570i08 Benefits and bonuses from employer (irregular) [2006-2020] plc0026,plc0027, plc0028,plc0029, plc0030,plc0031, plc0032,plc0033, plc0034,plc0035, plc0036,plc0037, plc0038,plc0039 Financial advantages from use of company car [2016-2018,2020] plc0532 Financial support received, Children [2009-2013] plj0156,plj0157,plj0158, plj0159 Financial support received, No Payments [2009-2013] plj0172 Financial support received, Other Relatives [2009-2013] plj0164,plj0165,plj0166, plj0167 Financial support received, Parents [2009-2013] plj0152,plj0153,plj0154, plj0155 Financial support received, Spouse [2009-2013] plj0160,plj0161,plj0162, plj0163 Financial support received, Unrelated Persons [2009-2013] plj0168,plj0169,plj0170, plj0171 Financial support to relatives or others, Children [1984-1991,1993,1995-2020] plj0135,plj0136_h, plj0136_v1,plj0136_v2, plj0137_h,plj0137_v1, plj0137_v2,plj0137_v3 Financial support to relatives or others, No Payments [1984-1991,1993,1995-2020] plj0151 Financial support to relatives or others, Other Relatives [1984-1991,1993,1995-2020] plj0143,plj0144_h, plj0144_v1,plj0144_v2, plj0145_h,plj0145_v1, plj0145_v2,plj0145_v3 Financial support to relatives or others, Parents [1984-1991,1993,1995-2020] plj0131,plj0132_h, plj0132_v1,plj0132_v2, plj0133_h,plj0133_v1, plj0133_v2,plj0133_v3 continues on next page 2.3. Income, Taxes, and Social Security 15 SOEPcompanion, Release 2022, v.3 Table 3 – continued from previous page Questionnaire Module Years Variables Financial support to relatives or others, Spouse [1984-1991,1993,1995-2020] plj0139,plj0140_h, plj0140_v1,plj0140_v2, plj0142_h,plj0142_v1, plj0142_v2,plj0142_v3 Financial support to relatives or others, Unrelated Persons [1984-1991,1993,1995-2020] plj0147,plj0148_h, plj0148_v1,plj0148_v2, plj0149_h,plj0149_v1, plj0149_v2,plj0149_v3 Gross / net income, collective wage agreements [1984-2020] plc0013_h,plc0013_v1, plc0013_v2,plc0014_h, plc0014_v1,plc0014_v2, plc0502_v1,plc0502_v2, plc0507,plc0508, plc0509 Income, Alimony [2010-2015] plc0181,plc0182, plc0183_v1,plc0183_v2, plc0184,plc0188_v1, plc0188_v2,plc0190_v1, plc0190_v2,plc0494, plc0496 Income, Alimony Months [2010-2017], [2012,2018-2020], [2010-2013,2015] plc0189_v1,plc0189_v2, plc0495 Income, Child Support [2010-2015] plc0177,plc0178, plc0488,plc0490 Income, Child Support Months [2010-2013,2015] plc0489 Income, Gross Selfemployed prev year [1990-2020], [1990-2001], [2002-2020], [1995-2020], [2019-2020], [2000-2020], [2000-2001], [2002-2020] plb0474_h,plb0474_v1, plb0474_v2,plc0073_v1, plc0073_v2,plc0075_h, plc0075_v1,plc0075_v2 Income, Gross prev year [1990-2020], [2017-2020] plb0471_h,plb0471_v1, plb0471_v2,plc0015_h, plc0015_v1,plc0015_v2 Income, Maternity benefit [1995-2020], [2019-2020], [1995-2020], [2019-2020], [1995-2020], [1995-2001], [2002-2020], [1990-2020], [1990-2001], [2002-2020] plc0126_v1,plc0126_v2, plc0152_v1,plc0152_v2, plc0153_h,plc0153_v1, plc0153_v2,plc0155_h, plc0155_v1,plc0155_v2 Income, Maternity benefit Months [1995-2020] plc0154 Income, Months of Second Job Income [1995-2020] plc0065 Income, Months of Self- Employed Income [1995-2020] plc0074 Income, Months of Wages [1995-2020], [2000-2020], [2000-2001], [2002-2020] plc0016,plc0017_h, plc0017_v1,plc0017_v2 Income, No Other Income [1995-2020], [2001-2020] plc0116,plc0117 continues on next page 16 Chapter 2. Topics of SOEP-Core SOEPcompanion, Release 2022, v.3 Table 3 – continued from previous page Questionnaire Module Years Variables Income, Retirement pension [1995-2020], [2017-2020], [1995-2020], [1995-2001], [2002-2020], [1995-2020], [2017-2020] plc0232_v1,plc0232_v2, plc0233_h,plc0233_v1, plc0233_v2,plc0234_v1, plc0234_v2 Income, Retirement pension Months [1995-2020] plc0235 Income, Second Job [2015,2019-2020] plc0515 Income, Second Job prev year [1990-2020], [1990-2001], [2002-2020] plb0477_h,plb0477_v1, plb0477_v2 Income, Self- Employment [2015,2019-2020] plc0514 Income, Sideline Job prev year [1995-2020], [2019-2020] plc0064_v1,plc0064_v2 Income, Student loans [1995-2020], [1995-2001], [2002-2020], [1995-2020], [2017-2020], [1990-2020], [1990-2001], [2002-2020] plc0168_h,plc0168_v1, plc0168_v2,plc0169_v1, plc0169_v2,plc0171_h, plc0171_v1,plc0171_v2 Income, Student loans Months [1995-2020] plc0170 Income, Support from outside the household [1990-2020], [1990-2001], [2002-2020], [1995-2020], [2019-2020], [1995-2020], [1995-2001], [2002-2020], [1995-2020], [2019-2020] plc0198_h,plc0198_v1, plc0198_v2,plc0202_v1, plc0202_v2,plc0203_h, plc0203_v1,plc0203_v2, plc0204_v1,plc0204_v2 Income, Support from outside the household Months [1995-2020], [2018] plc0205_v1,plc0205_v2 Income, Unemployment benefit [1995-2020], [2017-2020], [1995-2020], [1995-2001], [2002-2020], [1995-2001], [2002-2020], [2018-2020], [1995-2020], [2017-2020], [1995-2020], [1990-2020], [1990-2001], [2002-2020] plc0130_v1,plc0130_v2, plc0131_h,plc0131_v1, plc0131_v2,plc0132_v1, plc0132_v2,plc0132_v3, plc0135_v1,plc0135_v2, plc0136,plc0137_h, plc0137_v1,plc0137_v2 Income, Unemployment benefit II [1995-2020], [2018-2020], [1995-2020] plc0138_v1,plc0138_v2, plc0139 Income, Wages [2015,2019-2020] plc0513 Income, Widow’s pension [1995-2020], [2019-2020], [1995-2020], [1995-2001], [2002-2020], [1995-2020], [2019-2020] plc0273_v1,plc0273_v2, plc0274_h,plc0274_v1, plc0274_v2,plc0275_v1, plc0275_v2 Income, Widow’s pension Months [1995-2020] plc0276 Inheritances [2001,2019], [2017] plc0375_v1,plc0375_v2 Inheritances, First Inheritance, amount [2001], [2017], [2001,2019], [2017] plc0383_v1,plc0383_v2, plc0384_v1,plc0384_v2 Inheritances, First Inheritance, last 15 years [2017] plc0376_v2,plc0377_v2, plc0378_v2,plc0379_v2, plc0380_v2,plc0381_v2, plc0382_v2 continues on next page 2.3. Income, Taxes, and Social Security 17 SOEPcompanion, Release 2022, v.3 Table 3 – continued from previous page Questionnaire Module Years Variables Inheritances, First Inheritance, once/ever [2001,2019] plc0376_v1,plc0377_v1, plc0378_v1,plc0379_v1, plc0380_v1,plc0381_v1, plc0382_v1,plc0383_h Inheritances, From Whom [2001] plc0385,plc0395, plc0405 Inheritances, Future [2001] plc0406,plc0407 Inheritances, Second Inheritance, last 15 years [2017] plc0386_v2,plc0387_v2, plc0388_v2,plc0389_v2, plc0390_v2,plc0391_v2, plc0392_v2,plc0393_v2, plc0394_v2 Inheritances, Second Inheritance, once/ever [2001,2019], [2001], [2001,2019] plc0386_v1,plc0387_v1, plc0388_v1,plc0389_v1, plc0390_v1,plc0391_v1, plc0392_v1,plc0393_v1, plc0394_v1 Inheritances, Third Inheritance, amount [2001], [2017], [2019], [2001,2019], [2017] plc0403_v1,plc0403_v2, plc0403_v3,plc0404_v1, plc0404_v2 Inheritances, Third Inheritance, last 15 years [2017] plc0396_v2,plc0397_v2, plc0398_v2,plc0399_v2, plc0400_v2,plc0401_v2, plc0402_v2 Inheritances, Third Inheritance, once/ever [2001,2019] plc0396_v1,plc0397_v1, plc0398_v1,plc0399_v1, plc0400_v1,plc0401_v1, plc0402_v1,plc0403_h Pension entitlements, company [2013,2018], [2013], [2018], [2013,2018] plc0432,plc0433, plc0434_v1,plc0434_v2, plc0435,plc0441, plc0442,plc0443, plc0444_v1,plc0444_v2, plc0445 Pension payments [2013,2018], [2013], [2018] plc0437,plc0438, plc0439_v1,plc0439_v2 Riester / Ruerup pension plans (irregular) [2004-2020] plc0313_h,plc0313_v1, plc0313_v2,plc0430, plc0431 Social security, Don’t know [2002,2007,2012,2017] plc0009 Social security, Financial Security [2002,2007,2012,2017] plc0111,plc0112, plc0113,plc0114 Social security, Minimum Household Income [1992,2002,2007,2012,2017], [1992], [2002,2007,2012,2017] plc0001_h,plc0001_v1, plc0001_v2 Wage tax classification [1991,1993,2004,2016], [2004,2016] plc0091_h,plc0091_v1, plc0091_v2,plc0091_v3, plc0091_v4,plc0091_v5, plc0091_v6,plc0091_v7, plc0091_v8,plc0091_v9 continues on next page 18 Chapter 2. Topics of SOEP-Core SOEPcompanion, Release 2022, v.3 Table 4 – continued from previous page Questionnaire Module Years Variables Youth Questionnaire Allowance (Pocket money) , [2000-2020], [2002-2020] ,jl0022_h,jl0022_v2 Allowance (Pocket money, Deutschmark) [2000-2001] jl0021_v1,jl0022_v1 Childhood and parental home [2000-2018] jl0273,jl0279 Childhood and parental home (parent’s education, ISCO-08) [2013-2018] j_isco08_jobfather, j_isco08_jobmother Childhood and parental home (parent’s education, ISCO-88) [2000-2017] j_isco88_jobfather, j_isco88_jobmother Childhood and parental home (parent’s education, KldB 2010) [2013-2018] j_kldb2010_jobfather, j_kldb2010_jobmother Childhood and parental home (parent’s education, KldB 92) [2000-2017] j_kldb92_jobfather, j_kldb92_jobmother Childhood and parental home, father [2000-2018], [2014- 2017], [2014-2018], [2015-2018] jl0307,jl0309,jl0311,jl0313_v1, jl0313_v2,jl0315,jl0327_h, jl0327_v1,jl0327_v2,jl0506, jl0508,jl0510,jl0512,jl0514, jl0516,jl0518,jl0520,jl0522 Childhood and parental home, mother [2000-2018], [2014- 2017], [2014-2018], [2015-2018] jl0304,jl0308,jl0310,jl0312, jl0314_v1,jl0314_v2,jl0316, jl0328_h,jl0328_v1,jl0328_v2, jl0507,jl0509,jl0511,jl0513, jl0515,jl0517,jl0519,jl0521, jl0523 Childhood and parental home, siblings [2004-2012] jl0274,jl0275,jl0276,jl0277, jl0278,jl0446,jl0447,jl0454, jl0455,jl0456,jl0457,jl0458, jl0459,jl0460,jl0461,jl0462, jl0463,jl0464,jl0465,jl0466, jl0467,jl0468,jl0469,jl0470, jl0471,jl0472,jl0473,jl0474, jl0475,jl0476,jl0477,jl0478, jl0479,jl0480,jl0481,jl0482, jl0483,jl0484,jl0485,jl0486, jl0487,jl0488,jl0489,jl0490, jl0491,jl0492,jl0493,jl0494, jl0495,jl1406,jl1407,jl1408, jl1409,jl1410,jl1411 Parental interest in child’s performance in school [2000-2018] jl0167,jl0168,jl0169,jl0170, jl0171,jl0172,jl0173,jl0174 continues on next page 2.4. Family and Social Networks 25 SOEPcompanion, Release 2022, v.3 Table 4 – continued from previous page Questionnaire Module Years Variables Relationship to family members [2001-2020] jl0026,jl0027,jl0028,jl0029, jl0030,jl0031,jl0032,jl0033, jl0034,jl0040,jl0041,jl0043, jl0044,jl0045,jl0046,jl0047, jl0048,jl0049,jl0050,jl0051, jl0052,jl0053,jl0054,jl0055, jl1043 Relationship to family members, conflicts [2001-2018], [2019-2020], [2001- 2018], [2019-2020], [2001-2018], [2019- 2020], [2001-2018], [2019], [2001- 2018], [2019] jl0035_v1,jl0035_v2,jl0036_v1, jl0036_v2,jl0037_v1,jl0037_v2, jl0038_v1,jl0038_v2,jl0039_v1, jl0039_v2 Mother and Child Instruments Allowance (Pocket money) [2003-2020] allow,allowpm,allowpw Attitude toward parental role [2003-2020] bepar1,bepar10,bepar2,bepar3, bepar4,bepar5,bepar6,bepar8, bepar9 Attitude towards maternal role [2003-2020] change1,change2,change3, change4,change5,change6, change7,change8,health Breastfeeding [2003-2020] breastf,breastfc,breastfm Childcare [2003-2020] care10h,care11h,care12h,care19, care1h,care3h,care4h,care5h, care6h,care7h,care8h,care9h, maincare Eating behavior (child) [2003-2020] eatsat1,eatsat2,eatsat3,eatson1,eatson2,eatson3,eatweek1, eatweek3 Frequency of leisure and other activities (child) [2003-2020] freqact1,freqact10,freqact11,freqact12,freqact13,freqact14,freqact15,freqact16,freqact17,freqact18,freqact19,freqact2,freqact20,freqact3,freqact4,freqact5, freqact6,freqact7,freqact8,freqact9 Friends (child) [2003-2020] frndadlt,frndchld Language use [2003-2020] language Leisure and activities (with child) [2003-2020] activ1,activ2,activ3,activ4,activ5, activ6,activ7,activ8,activ9 Parental interest in child’s performance in school [2003-2020] conscho1,conscho2,conscho3,conscho4,conscho5,conscho6,conscho7 Parenting goals [2003-2020] edgoal1,edgoal10,edgoal11,edgoal12,edgoal13,edgoal14,edgoal15,edgoal16,edgoal17,edgoal18,edgoal2,edgoal3,edgoal4, edgoal5,edgoal6,edgoal7,edgoal8, edgoal9 continues on next page 26 Chapter 2. Topics of SOEP-Core SOEPcompanion, Release 2022, v.3 Table 4 – continued from previous page Questionnaire Module Years Variables Parenting style [2003-2020] edbeh1,edbeh10,edbeh11,edbeh12,edbeh13,edbeh14,edbeh15, edbeh16,edbeh17,edbeh18,edbeh2,edbeh3,edbeh4,edbeh5,edbeh6,edbeh7,edbeh8,edbeh9 Pregnancy and childbirth [2003-2020] birthpw,delivpl,nchild Relationship to other parent or child [2003-2020] biochild 2.5 Health and Care The modules on health and care cover doctor visits, sports and fitness, alcohol consumption, health insurance, health status, and grip strength, both on respondents themselves and on other individuals in the household, such as children and deceased household members. Questionnaire Module Years Variables Individual Questionnaire Additional private insurance (irregular) [1999-2020] ple0128_h,ple0128_v1, ple0128_v2,ple0129, ple0130,ple0131, ple0132,ple0133, ple0134 Alcohol consumption [2006,2008,2010] ple0090,ple0091, ple0092,ple0093, ple0177,ple0178 Disabilities in everyday life (SF-12) (irregular) [2002-2020] ple0004,ple0005 Electronic cigarette: liquid [2020] ple0195 Health insurance [1999-2020], [1999], [2000- 2009], [1999-2020] ple0097,ple0099_h, ple0099_v1,ple0099_v2, ple0099_v3,ple0099_v4, ple0099_v5,ple0104_h, ple0104_v1,ple0104_v2, ple0104_v3,ple0104_v4, ple0104_v5,ple0104_v6, ple0104_v7,ple0160 Health insurance debts [2017] plc0567 continues on next page 2.5. Health and Care 27 SOEPcompanion, Release 2022, v.3 Table 5 – continued from previous page Questionnaire Module Years Variables Health insurance, private [1984-1986], (irregular) [1999- 2020] ple0098_v1,ple0098_v2, ple0098_v3,ple0098_v4, ple0098_v5 Health restrictions [2011-2013,2015-2020], [2012- 2013,2015-2020] ple0009,ple0162 Height and weight (irregular) [2002-2020] ple0006,ple0007 Hospital stays [1984-1989,1991-1992,1994- 2020] ple0053,ple0055, ple0056 Illnes [2011,2013,2015,2017,2019], [2019] ple0011,ple0012, ple0013,ple0014, ple0015,ple0016, ple0017,ple0018, ple0019,ple0020, ple0021,ple0022, ple0023,ple0024, ple0189 Individual health services [2016,2018,2020] ple0186 Nutritional awareness [2016,2018,2020] ple0179,ple0180, ple0181,ple0182 Private supplementary care insurance [2016,2018,2020] ple0183,ple0184, ple0185 Qualification for additional benefits [1999-2011] ple0121 Reduced ability to work [1984-1989,1991-1992,1994- 2020] ple0040,ple0041 Sickness notifications to employer [1985-1989,1991-1992,1994- 2020] plb0024_h,plb0024_v1, plb0024_v2,plb0024_v3, ple0044_h,ple0044_v1, ple0044_v2,ple0046, ple0048,ple0049, ple0050,ple0051, ple0052,ple0174, ple0175 Smoking [1998], (irregular) [2006-2020], [2016,2018,2020] ple0080_v1,ple0080_v2, ple0080_v3,ple0081_h, ple0081_v1,ple0081_v2, ple0082,ple0083, ple0084,ple0085, ple0086_v1,ple0086_v2, ple0086_v3,ple0086_v4, ple0089,ple0176 State of health [1992,1994-2020] ple0008 Stress and exhaustion (SF- 12) (irregular) [2002-2020], (irregular) [1984-2020] ple0026,ple0027, ple0028,ple0029, ple0030,ple0031, ple0032,ple0033, ple0034,ple0035, ple0036 continues on next page 28 Chapter 2. Topics of SOEP-Core SOEPcompanion, Release 2022, v.3 Table 5 – continued from previous page Questionnaire Module Years Variables Visits to the doctor [1988-1989,1991-1992,1995- 2020], [1984-1989,1991- 1992,1994-2020] ple0072,ple0073 Youth Questionnaire Height and weight [2006-2020] jl0219,jl0220 State of health [2006-2020] jl0218 Household Questionnaire Satisfaction with availability of care [1997,2002,2008] hlf0318 Mother and Child Instruments Health of child [2003-2020] chhealth,lstmedex, medaid3mb Health of child, disorders [2003-2020] disord,disord1,disord2, disord3,disord4,disord5, disord6,disord7,disord8, disord9 Health of child, hospital stays [2003-2020] hospital12m,hospital3mb Health of child, illnesses [2003-2020] ill0,ill10,ill11,ill12, ill13,ill14,ill15,ill2, ill31,ill32,ill4,ill5,ill6, ill7,ill8,ill9,illno Height and weight of child [2003-2020] height,weight,weightb Physical and mental health of mother [2003-2020] feeling1,feeling2,feeling3,feeling4 2.6 Home, Amenities, and Contributions of Private HH The housing, amenities, and household expenses modules provide wide-ranging information on everyday life including the type of dwelling and whether it is a rental property or owner-occupied; expenditures on personal hygiene, transportation, and vacations; and the division of household labor. Questionnaire Module Years Variables Household Questionnaire Childcare costs [2010-2013,2015,2017- 2019], [2010-2012], [2013,2015,2017-2019] ks_cost_h,ks_cost_v1, ks_cost_v2 continues on next page 2.6. Home, Amenities, and Contributions of Private HH 29 SOEPcompanion, Release 2022, v.3 Table 6 – continued from previous page Questionnaire Module Years Variables Childcare provider [1987,1995,1997,2002,2005,2007], [2002,2005,2007] kd_insta_h,kd_insta_v1, kd_insta_v2,kd_insta_v3, kd_insta_v4,kd_insta_v5, kd_insta_v6,kd_insta_v7 Childcare situation [1987,1997,1999- 2002,2004-2020] kc_care_h,kc_care_v1, kc_care_v2,kc_care_v3, kc_care_v4,kc_care_v5, kc_care_v6,kc_care_v7 Dependence on childcare hours [2002] kd_rely Leisure activities, children (unregelmaessig) [2006- 2020] ka06_art,ka06_mus,ka06_non, ka06_oth,ka06_spo,ka16_art, ka16_ctr,ka16_mus,ka16_non, ka16_org,ka16_sar,ka16_smu, ka16_sot,ka16_spo,ka16_ssp, ka16_sth,ka16_yth Leisure costs, children (unregelmaessig) [2002-2019], [2002,2005,2007], [2010- 2013,2015,2017,2019], [2017-2018], [2010- 2013,2015,2017- 2019], [2010-2012], [2013,2015,2017,2019], [2017-2018] kk_amtp_h,kk_amtp_v1, kk_amtp_v2,kk_amtp_v3, kk_cost_h,kk_cost_v1, kk_cost_v2,kk_cost_v3 Lunch, childcare (unregelmaessig) [1997-2019], [1997,2002,2005,2007], [2010- 2013,2015,2017,2019], [2017-2018] kd_lunch_h,kd_lunch_v1, kd_lunch_v2,kd_lunch_v3 Lunch, school [2010-2013,2015,2017- 2019] ks_lunch School attendance by child [1984-2020], [1984- 1994], [1990], [1991], [1995-2020], [2017], [2016-2019] ks_gen_h,ks_gen_v1, ks_gen_v2,ks_gen_v3, ks_gen_v4,ks_gen_v5,ks_spe School provider and costs (unregelmaessig) [1987-2019], [1987,1996], [2010- 2012], [2013,2015,2017- 2019] ks_amtp_h,ks_amtp_v1, ks_amtp_v2,ks_amtp_v3 Household Questionnaire Change in residential situation [1991-2020], [1991- 1998], [1999-2020], [2015-2020] hlf0106,hlf0107_h,hlf0107_v1, hlf0107_v2,hlf0523 continues on next page 30 Chapter 2. Topics of SOEP-Core SOEPcompanion, Release 2022, v.3 Table 6 – continued from previous page Questionnaire Module Years Variables Changes in home fixtures and furnishings since last year [2004,2006,2008,2010- 2013] hlc0116,hlc0117,hlf0159, hlf0164,hlf0165_h,hlf0165_v1, hlf0165_v2,hlf0166,hlf0167, hlf0223,hlf0224,hlf0225, hlf0226,hlf0227,hlf0228, hlf0229,hlf0230,hlf0231, hlf0232,hlf0233,hlf0234, hlf0235,hlf0236,hlf0237, hlf0238,hlf0244,hlf0245, hlf0246,hlf0247,hlf0248, hlf0249,hlf0250,hlf0251, hlf0252 Changes in home fixtures and furnishings since last year: Internet [2000,2002,2004,2006], (unregelmaessig) [2000-2013], [2000], [2002,2004,2006,2008,2010- 2013] hlf0169_v1,hlf0169_v2, hlf0169_v3,hlf0169_v4, hlf0169_v5,hlf0169_v6, hlf0169_v7,hlf0170_h, hlf0170_v1,hlf0170_v2 Changes in home fixtures and furnishings since last year: car (unregelmaessig) [2000-2013], [2000], [2002,2004,2006,2008,2010- 2013], [2010-2013], [2010-2011] hlf0209_h,hlf0209_v1, hlf0209_v2,hlf0210,hlf0211 Changes in home fixtures and furnishings since last year: cell phone (unregelmaessig) [2000- 2020], (unregelmaessig) [2000-2013], [2010-2013] hlf0241_h,hlf0241_v1, hlf0241_v2,hlf0241_v3, hlf0241_v4,hlf0241_v5, hlf0241_v6,hlf0241_v7, hlf0241_v8,hlf0242,hlf0243 Changes in home fixtures and furnishings since last year: kitchen appliances (unregelmaessig) [1998- 2013] hlf0214,hlf0215,hlf0216, hlf0217,hlf0218,hlf0219, hlf0220,hlf0221,hlf0222 Changes in home fixtures and furnishings since last year: motorcycle, moped (unregelmaessig) [2000-2013], [2000], [2002,2004,2006,2008,2010- 2013], [2010-2013] hlf0212_h,hlf0212_v1, hlf0212_v2,hlf0213 Changes in home fixtures and furnishings since last year: phone (unregelmaessig) [1990- 2020], (unregelmaessig) [1990-2013], [2013,2015], [2014], [2016-2020], (unregelmaessig) [1998-2013] hlf0239_h,hlf0239_v1, hlf0239_v2,hlf0239_v3, hlf0239_v4,hlf0240 Cleaning or household help [1991,1994,1999-2020], [2010-2020] hlf0261,hlf0262 Comparison of old and new home [1985- 2013,2015,2017,2019- 2020] hlf0126,hlf0127,hlf0128, hlf0129,hlf0130,hlf0131, hlf0132 Consumption Module [2010-2013] hlf0172 Consumption Module: Cars (unregelmaessig) [1990- 2013], [1991] hlf0163_h,hlf0163_v1, hlf0163_v2 Consumption Module: Clothes and Shoes [2010-2013] hlf0379,hlf0380,hlf0381, hlf0382 continues on next page 2.6. Home, Amenities, and Contributions of Private HH 31 SOEPcompanion, Release 2022, v.3 Table 6 – continued from previous page Questionnaire Module Years Variables Consumption Module: Cosmetics [2010-2013] hlf0383,hlf0384,hlf0385, hlf0386 Consumption Module: Culture [2010-2013] hlf0399,hlf0400,hlf0401, hlf0402 Consumption Module: Education [2010-2013] hlf0395,hlf0396,hlf0397, hlf0398 Consumption Module: Food and Drinks [2010-2013] hlf0371,hlf0372,hlf0373, hlf0374,hlf0375,hlf0376, hlf0377,hlf0378 Consumption Module: Furniture [2010-2013] hlf0427,hlf0428,hlf0429, hlf0430 Consumption Module: Health [2010-2013] hlf0387,hlf0388,hlf0389, hlf0390 Consumption Module: Hobby [2010-2013] hlf0403,hlf0404,hlf0405, hlf0406 Consumption Module: Holiday [2010-2013] hlf0407,hlf0408,hlf0409, hlf0410 Consumption Module: Insurance [2010-2013] hlf0411,hlf0412,hlf0413, hlf0414,hlf0415,hlf0416, hlf0417,hlf0418 Consumption Module: Internet [2010-2013] hlf0168,hlf0171 Consumption Module: Other [2010-2013] hlf0431,hlf0432,hlf0433, hlf0434 Consumption Module: Repair [2010-2013] hlf0419,hlf0420,hlf0421, hlf0422 Consumption Module: Telecommunication [2010-2013] hlf0391,hlf0392,hlf0393, hlf0394 Consumption Module: Transportation [2010-2013] hlf0423,hlf0424,hlf0425, hlf0426 Costs of comparable rental homes [1984-2002,2005-2014] hlf0094 Costs of home ownership [2010-2014,2016-2020] hlf0084,hlf0090_h,hlf0090_v1, hlf0090_v2,hlf0601,hlf0602, hlf0603,hlf0604,hlf0605 Dwelling / building type [1986-2020], [1986-1990, 1991-2008, 2010-2018], [2009], [2016-2020] hlf0155_h,hlf0155_v1, hlf0155_v2,hlf0596 Financial burden of home ownership [2016] hlf0606 Financial burden of home rental [2016] hlf0611 Government-subsidized housing [1984-2020], [1998- 2015], [1986-2002,2008- 2020] hlf0011_h,hlf0011_v1, hlf0011_v2,hlf0011_v3, hlf0011_v4,hlf0073 Hereditary lease interest [1984-2013,2015-2020], [1986-2020], [1986- 1987], [1988-1992], [1993-2020] hlf0016,hlf0154_h,hlf0154_v1, hlf0154_v2,hlf0154_v3 continues on next page 32 Chapter 2. Topics of SOEP-Core SOEPcompanion, Release 2022, v.3 Table 6 – continued from previous page Questionnaire Module Years Variables Home fixtures and furnishings [1991], [2015-2020] hlf0023,hlf0024,hlf0025, hlf0026,hlf0027,hlf0028, hlf0029,hlf0030,hlf0031, hlf0032,hlf0033,hlf0034, hlf0035,hlf0036,hlf0037, hlf0529,hlf0530,hlf0531 Home ownership / rental [1984-2020], [2003-2020] hlf0001_h,hlf0001_v1, hlf0001_v2,hlf0001_v3, hlf0006,hlf0015 Home ownership / rental, ownership acquisition [1984-2020], [1984- 1990,1999-2001], [1991- 1998], [1991-1997] hlf0007_h,hlf0007_v1, hlf0007_v2,hlf0007_v3 Home ownership / rental, ownership transfer [1999-2020] hlf0007_v4,hlf0009 Homeowner [1990-2020], [1990- 2002,2005-2012], [2003- 2004], [2013-2020] hlf0013_h,hlf0013_v1, hlf0013_v2,hlf0013_v3 Loans, mortgages, building loan agreements [1985-2020], [1985-1990, 1991-1998], [1999-2020], [1984-2020], [1984-1990, 1991-2001], [2002-2020] hlf0087_h,hlf0087_v1, hlf0087_v2,hlf0088_h, hlf0088_v1,hlf0088_v2 Material deprivation (unregelmaessig) [2001- 2015] hlf0175,hlf0177,hlf0178_h, hlf0178_v1,hlf0178_v2, hlf0178_v3,hlf0178_v4, hlf0178_v5,hlf0179,hlf0180, hlf0181,hlf0183,hlf0185, hlf0186,hlf0187,hlf0188, hlf0189,hlf0190,hlf0191, hlf0192,hlf0193,hlf0194, hlf0195,hlf0444,hlf0613, hlf0622 Modernization costs [2016-2020] hlf0599 Monthly rent, heating, other expenses [2016-2020] hlf0607,hlf0608,hlf0610 Monthly rent, heating, other expenses (Deutschmark) [2002-2014,2016-2020] hlf0081_v2 Monthly rent, heating, other expenses: electricity [2010-2014,2016-2020] hlf0078,hlf0079 Monthly rent, heating, other expenses: heating [1986-2014,2016-2020], [1986-1990,1997-2001], [2002-2014,2016-2020] hlf0069_h,hlf0069_v1, hlf0069_v5 Monthly rent, heating, other expenses: heating and hot water [1990,1996], [1991-1995] hlf0069_v2,hlf0069_v3, hlf0069_v4 Monthly rent, heating, other expenses: other [1991-2014,2016-2020], [1991-2001], [1996- 2014,2016-2020] hlf0081_h,hlf0081_v1,hlf0082 Monthly rent, heating, other expenses: rent [1984-2020], [1984-2001] hlf0074_h,hlf0074_v1 continues on next page 2.6. Home, Amenities, and Contributions of Private HH 33 SOEPcompanion, Release 2022, v.3 Table 6 – continued from previous page Questionnaire Module Years Variables Monthly rent, heating, other expenses: rent (Deutschmark) [2002-2020] hlf0074_v2 Name and birth of children [1984-2020], [2017,2020] hlk0044_v1,hlk0044_v2 Number of books in household [2001,2006,2011,2016] hlf0197 Persons in household in need of care [1984-2020], [2015- 2020], [2016-2020] hlf0291,hlf0292,hlf0300, hlf0301,hlf0302,hlf0303, hlf0304,hlf0315_h,hlf0315_v1, hlf0315_v2,hlf0315_v3, hlf0317_h,hlf0317_v1, hlf0317_v2,hlf0317_v3, hlf0319,hlf0320,hlf0321, hlf0322,hlf0331,hlf0332, hlf0369,hlf0370_h,hlf0370_v1, hlf0370_v2,hlf0446,hlf0448, hlf0595,hlf0631 Pets [2006,2011,2016], [2006,2011] hlf0196,hlf0254,hlf0255, hlf0256,hlf0257,hlf0258, hlf0259 Photovoltaic and solar thermal system [2015-2016,2020] hlf0532,hlf0535,hlf0536, hlf0537,hlf0538,hlf0539 Reasons for moving [1985-2013,2015,2017- 2020] hlf0108_h,hlf0108_v1, hlf0108_v10,hlf0108_v11, hlf0108_v12,hlf0108_v13, hlf0108_v14,hlf0108_v15, hlf0108_v2,hlf0108_v3, hlf0108_v4,hlf0108_v5, hlf0108_v6,hlf0108_v7, hlf0108_v8,hlf0108_v9, hlf0109,hlf0124,hlf0125 Reasons for moving, comparison of old and new home [2015,2017-2020], [2015,2017], [2015,2017,2019-2020] hlf0524,hlf0525,hlf0526 Residential area (unregelmaessig) [1986-2019], [1994,1999,2004,2009,2014,2019], [2004,2009,2014,2016- 2020], [1994,1999,2004,2009,2014,2019], [1986-2020], [1986- 1987], [1988- 1992], [1993-2020], [1986,1994,1999,2004,2009], [2014, 2019] hlf0148,hlf0149,hlf0150, hlf0151,hlf0152,hlf0153_h, hlf0153_v1,hlf0153_v2, hlf0153_v3,hlj0004_v1, hlj0004_v2 Residential area, distances (unregelmaessig) [1986- 2019] hlf0135,hlf0136,hlf0137, hlf0138,hlf0139,hlf0140, hlf0141,hlf0142,hlf0143, hlf0144,hlf0145,hlf0146, hlf0147 continues on next page 34 Chapter 2. Topics of SOEP-Core SOEPcompanion, Release 2022, v.3 Table 7 – continued from previous page Questionnaire Module Years Variables Further education, course details and motives for participation, Course 3 Telecourse [1989,1993,2000,2004,2008]plg0134 Further education, course details and motives for participation, Course 3 pay off [2004,2008] plg0116 Further education, course details and motives for participation, Course Subject / Content [1989,1993] plg0153 Further education, course details and motives for participation, Financial Support [1989,1993] plg0167,plg0168 Further education, course details and motives for participation, Initiative for taking Course [1989,1993] plg0166 Further education, course details and motives for participation, Pay Off [1989] plg0187,plg0188,plg0189 Further training measures, Further professional training [2014-2015], [2016-2020] plg0269_v1,plg0269_v2 Further training measures, Trainig measures prev year [2014-2020] plg0270,plg0271 Further training, financing [2015-2018,2020] plg0285,plg0286,plg0287, plg0288,plg0289,plg0290, plg0291 Further training, reasons for not taking part [2014] plg0277,plg0278,plg0279, plg0280,plg0281 Further training, suggested / provided by employer [2014] plg0274 Further training, suggested / provided by employere [2014] plg0273 Lifelong learning [2014] plg0266 Vocational training, Currently in education / training [1984-2020], [2020] plg0012_v1,plg0012_v2 Vocational training, General school [1984-2015], [2016-2020] plg0013_v1,plg0013_v3 Vocational training, Scholarship [2007-2020] plg0015_h,plg0015_v1, plg0015_v2,plg0015_v3, plg0015_v4 continues on next page 2.7. Education and Qualification 41 SOEPcompanion, Release 2022, v.3 Table 7 – continued from previous page Questionnaire Module Years Variables Vocational training, University [1984-1995], [1999- 2008], [2009-2012], [2013-2020] plg0014_v1,plg0014_v2, plg0014_v3,plg0014_v4, plg0014_v5,plg0014_v6, plg0014_v7 Youth Questionnaire Education and career plans [2013-2019], [2000- 2017], [2013-2019], [2000-2017] j_isco08_jobwish, j_isco88_jobwish, j_kldb2010_jobwish, j_kldb92_jobwish Education and career plans, Apprenticeship [2000-2020], [2001-2020] jl0177,jl0182,jl0183,jl0203 Education and career plans, Career Training [2014-2020] jl0438,jl0439 Education and career plans, Engineering school [2013-2020] jl0440,jl0441 Education and career plans, Exploring Skills [2001-2020] jl0205 Education and career plans, Financial Independence [2000-2020] jl0197,jl0198 Education and career plans, Informed about Future Occupation [2001-2020] jl0201 Education and career plans, No Particular Plans [2001-2020] jl0204 Education and career plans, Occupational Foundation [2000-2020] jl0179 Education and career plans, Occupational Integration [2000-2020] jl0178,jl0180,jl0181 Education and career plans, Parents Suggestions [2001-2020] jl0202 Education and career plans, Preferred Occupation [2000-2020] jl0199 Education and career plans, Vocational School [2000-2020] jl0184,jl0185 Education and career plans, Volunteering [2000-2020] jl0186,jl0187 Educational aspirations, Apprenticeship [2014-2020] jl0504 Educational aspirations, Aspired school-leaving qualification [2000-2020], [2000], [2001-2020], [2000-2020] jl0130_h,jl0130_v1,jl0130_v2, jl0131 Educational aspirations, Career Training [2000-2020] jl0193 Educational aspirations, Civil Servant Training [2000-2020] jl0192 continues on next page 42 Chapter 2. Topics of SOEP-Core SOEPcompanion, Release 2022, v.3 Table 7 – continued from previous page Questionnaire Module Years Variables Educational aspirations, Completed Apprenticeship [2000-2020] jl0189 Educational aspirations, Engineering school [2000-2020] jl0194 Educational aspirations, Future Apprenticeship [2000-2020], [2003-2020] jl0188,jl0196 Educational aspirations, Trade and Technical School [2000-2020] jl0191 Educational aspirations, University [2000-2020] jl0195 Educational aspirations, Vocational School [2000-2020] jl0190 School, attendance & homework, Class Representative [2000-2020] jl0139 School, attendance & homework, Course type [2000-2020] jl0162,jl0163 School, attendance & homework, Extracurricular activities [2000-2020] jl0141,jl0142,jl0143,jl0144, jl0145,jl0146 School, attendance & homework, First Foreign Language [2000-2020], [2000], [2001-2005], [2006-2020] jl0132_h,jl0132_v1,jl0132_v2, jl0132_v3 School, attendance & homework, Grade / Year [2014-2020] jl0434 School, attendance & homework, Grades / Points [2000-2020] jl0152,jl0153,jl0154,jl0155, jl0156,jl0157 School, attendance & homework, Number classmates [2000-2020], [2000], [2001-2018] jl0176_h,jl0176_v1,jl0176_v2 School, attendance & homework, Private School [2001-2018] jl0138 School, attendance & homework, Satisfaction with grades [2000-2020] jl0147,jl0148,jl0149,jl0150 School, attendance & homework, School attendance abroad [2000-2020] jl0137_h,jl0137_v1,jl0137_v2, jl0435,jl0436 School, attendance & homework, School recommendation [2001-2018] jl0151 School, attendance & homework, Schoolleaving certificate [2000-2020], [2000- 2011], [2012-2020] jl0127_h,jl0127_v1,jl0127_v2 School, attendance & homework, Second Foreign Language [2000-2020], [2000], [2001-2005], [2006-2020] jl0133_h,jl0133_v1,jl0133_v2, jl0133_v3 continues on next page 2.7. Education and Qualification 43 SOEPcompanion, Release 2022, v.3 Table 7 – continued from previous page Questionnaire Module Years Variables School, attendance & homework, Still in School [2000-2020], [2000- 2002], [2003-2005], [2006-2020] jl0125_h,jl0125_v1,jl0125_v2, jl0125_v3 School, attendance & homework, Student Body President [2000-2020] jl0140 School, attendance & homework, Year of leaving school [2000-2020] jl0126 School, attendance & homework, Year repeated [2000-2020] jl0164,jl0165,jl0166 Mother and Child Instruments Educational aspirations, Ideal school completion [2003-2020] idegrad1,idegrad2,idegrad3 Educational aspirations, intermediate secondary [2003-2020] probgra2 Educational aspirations, lower secondary [2003-2020] probgra1 Educational aspirations, upper secondary [2003-2020] probgra3 School and homework [2003-2020] scolcon1,scolcon2,scolcon3, scolcon4,scolcon5,scolcon6, scolcon7 School and homework, Comprehensive school [2003-2020] curscol7 School and homework, Grammar secondary class [2003-2020] curscol6 School and homework, Intermediae secondary schol [2003-2020] curscol5 School and homework, Last report mark [2003-2020] lamark,matmark,nomark School and homework, Other schoool [2003-2020] curscol8 School and homework, Place [2003-2020] hwplace School and homework, Primary school [2003-2020] curscol1 School and homework, Second general school [2003-2020] curscol4 School and homework, Special pedagogic concept [2003-2020] curscol2 School and homework, Special school [2003-2020] curscol3 School and homework, Support [2003-2020] hwsupprt School enrollment [2003-2020] sclenrolm,sclenroln,sclenroly 44 Chapter 2. Topics of SOEP-Core SOEPcompanion, Release 2022, v.3 2.8 Attitudes, Values, and Personality The attitudes, values, and personality modules provide extensive information on respondents’ personality traits, political orientations, concerns, satisfaction with different aspects of life, willingness to take risks, and much more. Questionnaire Module Years Variables Individual Questionnaire 10,000-euro question [2010,2017] plh0134,plh0135,plh0136 Affective well-being [2007-2020] plh0184,plh0185,plh0186, plh0187 Anomie (irregular) [1990- 2018] plh0188,plh0189,plh0190, plh0191 Attitudes towards genders [2019] plh0395i01,plh0395i02, plh0395i03,plh0395i04, plh0395i05,plh0395i06 Attitudes towards refugees [2016,2018,2020] plj0433,plj0434,plj0435,plj0436, plj0437,plj0438,plj0439,plj0440, plj0441,plj0442,plj0443 Big Five personality traits [2005,2009,2012- 2013,2017,2019], [2009,2012- 2013,2017,2019] plh0212,plh0213,plh0214, plh0215,plh0216,plh0217, plh0218,plh0219,plh0220, plh0221,plh0222,plh0223, plh0224,plh0225,plh0226, plh0255 Bundestag election [2014,2018] plh0333 Depressive traits [2016,2019] plh0339,plh0340,plh0341, plh0342 Discrimination [2019] plh0387i01,plh0387i02, plh0387i03,plh0387i04, plh0387i05,plh0387i06, plh0387i07,plh0387i08, plh0387i09,plh0387i10, plh0387i11 Donation of blood [2010] plh0131_v1,plh0131_v2,plh0132, plh0133 Donations [2010,2015,2018,2020]plh0129,plh0130 Donations of goods [2010,2020] plj0108,plj0109,plj0110,plj0111, plj0112,plj0113,plj0114,plj0115 Flourishing [2015-2020] plh0334 continues on next page 2.8. Attitudes, Values, and Personality 45 SOEPcompanion, Release 2022, v.3 Table 8 – continued from previous page Questionnaire Module Years Variables Goals in life (Kluckhohn) (irregular) [1990- 2016], [2013,2017- 2019], [2016] plh0104,plh0105,plh0106, plh0107,plh0108,plh0109, plh0110,plh0111,plh0112, plh0343_v1,plh0343_v2 Impulsivity, patience [2008,2013,2018] plh0253,plh0254 Income justice, general [2005] plh0116,plh0117,plh0118, plh0119,plh0120,plh0121, plh0122,plh0123,plh0124, plh0125,plh0126,plh0127 Life satisfaction [1984-2020] plh0182 Locus of control [1994-1996] plh0369,plh0370,plh0371, plh0372,plh0373,plh0374, plh0375,plh0376,plh0377_v1, plh0378_v1,plh0379_v1, plh0380_v1,plh0381_v1, plh0382_v1,plh0383_v1, plh0384_v1,plh0385_v1, plh0386_v1 Locus of control, rephrased [2005,2010,2015- 2016,2020] plh0377_v2,plh0378_v2, plh0379_v2,plh0380_v2, plh0381_v2,plh0382_v2, plh0383_v2,plh0384_v2, plh0385_v2,plh0386_v2 Loneliness [2013,2016-2019], [2013,2016-2020] plj0587,plj0588,plj0589 Lottery question [2004,2009,2014] plh0203 Money and account balance [2016,2018] plh0344,plh0345,plh0346 Optimism/pessimism [1999,2005,2009,2014,2019]plh0244 Organisational and community membership (irregular) [1985- 2019], (irregular) [1990-2019], (irregular) [1985-2019], [1985,1989,1993], (irregular) [1990- 2019], (irregular) [2001-2019], [1998,2001,2003,2007,2011,2019], [2003,2007,2011] plh0263_h,plh0263_v2, plh0264_h,plh0264_v1, plh0264_v2,plh0265,plh0266, plh0267 Organizational and community membership [1985,1989,1993] plh0263_v1 Policy objectives (Inglehart Index) [1984- 1986,1996,2006,2016] plh0054,plh0056,plh0058, plh0061 Political Tendency, Left- Right [2005,2009,2014,2019]plh0004 Political influence [2019] plh0397i01,plh0397i02, plh0397i03,plh0397i04, plh0397i05 Political orientation [1985-2020] plh0007 continues on next page 46 Chapter 2. Topics of SOEP-Core SOEPcompanion, Release 2022, v.3 Table 8 – continued from previous page Questionnaire Module Years Variables Political orientation (Party Affiliation) [1984-2020], [1984- 1989], [1990], [1991], [1992], [1993], [1994- 2020], [1987-1988] plh0012_h,plh0012_v1, plh0012_v2,plh0012_v3, plh0012_v4,plh0012_v5, plh0012_v6,plh0013_v1 Political orientation (Party Preference) [1984-2020] plh0011_h,plh0011_v1, plh0011_v2,plh0013_h, plh0013_v2 Reciprocity [2005,2010,2015- 2020] plh0206i01,plh0206i02, plh0206i03,plh0206i04, plh0206i05,plh0206i06 Religious affiliation (irregular) [1990- 2020], [1990- 1991,1997], [1990], [1991,1997], [1990- 1991,1997], [2003], [2007,2011], [2015], [2013,2016- 2020] plh0258_h,plh0258_v1, plh0258_v10,plh0258_v11, plh0258_v12,plh0258_v13, plh0258_v2,plh0258_v3, plh0258_v4,plh0258_v5, plh0258_v6,plh0258_v7, plh0258_v8,plh0258_v9 Risk aversion in different domains [2004,2009,2014] plh0197,plh0198,plh0199, plh0200,plh0201,plh0202 Risk aversion in general [2004,2006,2008- 2020], [2013], [2004,2006,2008- 2020] plh0204_h,plh0204_v1, plh0204_v2 Satisfaction with various aspects (irregular) [1989- 2019], [1984-2020], [2008-2020], [1984-2020], [1984- 1990,1993-2020], [1984-2020], [2004- 2020], [1984-2020], [1984-1989,1991- 1994,1996-2020], [1990,1997-2020], [2006-2020], [2006,2011- 2013,2016] plh0164,plh0171,plh0172, plh0173,plh0174,plh0175, plh0176,plh0177,plh0178, plh0179,plh0180,plh0181 Self-esteem [2010,2015-2020] plh0206i11 Social justice [2019] plh0396i01,plh0396i02, plh0396i03,plh0396i04 Social responsibility [1997,2002,2017] plh0016,plh0017,plh0018, plh0019,plh0020,plh0021, plh0022,plh0023,plh0024, plh0025,plh0026 Tendency to forgive [2010,2015- 2016,2020] plh0206i07,plh0206i08, plh0206i09,plh0206i10 Trust, trustworthiness and fairness [2003,2008,2013,2018]pld0043,pld0044,pld0045, plh0192,plh0193,plh0194, plh0195,plh0196 continues on next page 2.8. Attitudes, Values, and Personality 47 SOEPcompanion, Release 2022, v.3 Table 8 – continued from previous page Questionnaire Module Years Variables Wage justice [2009,2011,2013,2015,2017,2019], [2015], [2017,2019], [2015], [2017-2019] plh0138,plh0139,plh0140, plh0141,plh0337_v1,plh0337_v2, plh0338_v1,plh0338_v2 Well-being aspects [1994,1998-1999] plh0091_v2,plh0092_v2, plh0093_v2,plh0094_v2, plh0095_v2,plh0096_v2, plh0097_v2,plh0098_v2, plh0099_v2,plh0100_v2,plh0101, plh0102,plh0103 Well-being aspects, East Germany [1990-1991] plh0091_v1,plh0092_v1, plh0093_v1,plh0094_v1, plh0095_v1,plh0096_v1, plh0097_v1,plh0098_v1, plh0099_v1,plh0100_v1 Worries [1984-2020] plh0032,plh0033,plh0034, plh0035,plh0038,plh0040, plh0042,plh0043,plh0046, plh0047,plh0335,plh0336 Youth Questionnaire Affective well-being [2007-2020] jl0381,jl0382,jl0383,jl0384 Attitudes and opinions [2000-2020] jl0329,jl0330,jl0360,jl0364 Big Five personality traits [2006-2020] jl0365,jl0366,jl0367,jl0368, jl0369,jl0370,jl0371,jl0372, jl0373,jl0374,jl0375,jl0376, jl0377,jl0378,jl0379,jl0380 Future [2000-2020] jl0222,jl0223,jl0224,jl0225, jl0226,jl0227,jl0228,jl0229, jl0230,jl0231,jl0232 Life satisfaction [2006-2020] jl0392 Locus of control [2006-2020] jl0350_v1,jl0351_v1,jl0352_v1, jl0353_v1,jl0354_v1,jl0355_v1, jl0356_v1,jl0357_v1,jl0358_v1, jl0359_v1 Locus of control, rephrased [2001-2005] jl0350_v2,jl0351_v2,jl0352_v2, jl0353_v2,jl0354_v2,jl0355_v2, jl0356_v2,jl0357_v2,jl0358_v2, jl0359_v2 Political orientation [2006-2020] jl0388,jl0389,jl0390,jl0391 Risk aversion in general [2006-2020] jl0349 Social justice [2019-2020] jl1909,jl1910,jl1911,jl1912 Sources of social inequality [2000-2020] jl0337,jl0338,jl0339,jl0340, jl0341,jl0342,jl0343,jl0344, jl0345,jl0346,jl0347,jl0348 Trust [2006-2020] jl0361,jl0362,jl0363 Mother and Child Instruments Big Five personality traits [2003-2020] char10,char1a,char1b,char2, char3,char4,char5,char6,char7, char8,char9 continues on next page 48 Chapter 2. Topics of SOEP-Core SOEPcompanion, Release 2022, v.3 Table 8 – continued from previous page Questionnaire Module Years Variables Strengths and difficulties questionnaire [2003-2020] behav1,behav10,behav11,behav12,behav13,behav14,behav15, behav16,behav17,behav18,behav2,behav3,behav4,behav5, behav6,behav7,behav8,behav9 Temperament [2003-2020] temp1,temp2,temp3,temp4, temp5,temp6,temp7 Vineland adaptive behavior scales (Movements) [2003-2020] mvmn1,mvmn3,mvmn4,mvmn5, mvmn6 Vineland adaptive behavior scales (Playing) [2003-2020] sclr2,sclr3,sclr4,sclr5,sclr6 Vineland adaptive behavior scales (Skills) [2003-2020] skll1,skll2,skll3,skll4,skll5 Vineland adaptive behavior scales (Speaking and Listening) [2003-2020] spch3,spch5,spch6,spch7,spch8 2.9 Time Use and Environmental Behavior The modules on time use and environmental behavior give information on time commitments, free time, and time planning as well as environmental awareness, for instance, the use of public transport and different energy sources, as well as what respondents think about renewable energies. Questionnaire Module Years Variables Individual Questionnaire Computer usage: Private [1997,2000-2001], [1997,2001], [2000], [1997,2001], [1997], [2001] pli0066_h,pli0066_v1, pli0066_v2,pli0067_h, pli0067_v1,pli0067_v2 Computer usage: Private (Internet) [2001] pli0068,pli0069 Computer usage: Work [1997,1999,2001], [2000], [1997,2001], [1997], [2001] pli0070_v1,pli0070_v2, pli0071_h,pli0071_v1, pli0071_v2 Computer usage: Work (Internet) [2001] pli0072,pli0073 continues on next page 2.9. Time Use and Environmental Behavior 49 SOEPcompanion, Release 2022, v.3 Table 9 – continued from previous page Questionnaire Module Years Variables Leisure activities (long) [2019] plh0390,plh0391,plh0392, plh0393,plh0394,pli0083, pli0084,pli0085_v1,pli0085_v2, pli0087,pli0088,pli0089, pli0090_v1,pli0090_v2, pli0090_v3,pli0091_h, pli0091_v1,pli0091_v2,pli0165, pli0178,pli0182 Leisure activities (long): Art and Music (unregelmaessig) [1990- 2019], (unregelmaessig) [2001-2017], (unregelmaessig) [1990-2019] pli0093_h,pli0093_v1, pli0093_v2 Leisure activities (long): Politics (unregelmaessig) [1984- 2019], [1984], (unregelmaessig) [1985-2017], (unregelmaessig) [1990- 2019] pli0097_h,pli0097_v1, pli0097_v2,pli0097_v3 Leisure activities (long): Religion (unregelmaessig) [1990- 2019], (unregelmaessig) [1990-2017], (unregelmaessig) [1990-2019] pli0098_h,pli0098_v1, pli0098_v2 Leisure activities (long): Socializing (unregelmaessig) [1990- 2019], [1984], (unregelmaessig) [1985-2017], [1984], (unregelmaessig) [1985-2017] pli0079,pli0080,pli0081, pli0082,pli0094_v1,pli0094_v2, pli0095_v1,pli0095_v2 Leisure activities (long): Sports (unregelmaessig) [1984- 2019], [1984], (unregelmaessig) [1985-2017], (unregelmaessig) [1990- 2019] pli0092_h,pli0092_v1, pli0092_v2,pli0092_v3 Leisure activities (long): Voluntary work (unregelmaessig) [1984- 2019], [1984], (unregelmaessig) [1985-2017], (unregelmaessig) [1990- 2019] pli0096_h,pli0096_v1, pli0096_v2,pli0096_v3 Time use for different activities (Saturdays) (unregelmaessig) [1990- 2019], (unregelmaessig) [2001-2019], (unregelmaessig) [2003-2019], [2008- 2013,2015,2017,2019- 2020] pli0003_h,pli0003_v1, pli0003_v2,pli0003_v3, pli0003_v4,pli0005,pli0036, pli0054,pli0055,pli0056, pli0060 Time use for different activities (Saturdays): Childcare (unregelmaessig) [1990- 2019], (unregelmaessig) [1993-2019] pli0019_h,pli0019_v1, pli0019_v2,pli0019_v3, pli0019_v4 Time use for different activities (Saturdays): Chores (unregelmaessig) [1990- 2019], (unregelmaessig) [1990-2019] pli0012_h,pli0012_v1, pli0012_v2,pli0012_v3 continues on next page 50 Chapter 2. Topics of SOEP-Core SOEPcompanion, Release 2022, v.3 Table 11 – continued from previous page Questionnaire Module Variables Foreign language skills ispr01 -ispr10 ,ibspre01 -ibspre10 Flags (conflicts) genderconfl ,birthconfl ,maritalconfl ,educconfl ,startintconfl ,ista1confl Important documents regarding this Topic are available here Last change: May 12, 2022 2.11. Survey Methodology 57 CHAPTER THREE SURVEY DESIGN 3.1 SOEP Questionnaires The interview methodology of the SOEP is based on a set of pre-tested questionnaires for households and individuals. Interviewers try to obtain face-to-face interviews with all members of a given survey household aged 16 and over. Thus, there are no proxy interviews for adult household members. Additionally, one person (the “head of household”) is asked to answer a household-related questionnaire covering information on housing, housing costs, and different sources of income (e.g., social transfers such as social assistance or housing allowances). This questionnaire also includes questions on children up to the age of 16 in the household, mainly concerning daycare, kindergarten, and school attendance. The questions in the SOEP are largely identical for all participants of the survey to ensure comparability across the participants within a given year, but of course there are differences across years. There are a few exceptions to this rule, which are due to different requirements in the target population. Up to 1996, the questionnaires for the sample of foreigners (B) and the immigrant sample (D) covered additional measures of integration or information on re-migration behavior. Between 1990 and 1992, i.e., during the first years of the German reunification process, the questionnaire for the East German sample (C) also contained some additional specific variables. From 1996 to 2012, all questionnaires were uniform and completely integrated for all of the main SOEP samples. For the IAB-SOEP Migration Sample, which was launched in 2013, specific questions were added to the SOEP questionnaires. The same is true of the IABBAMF-SOEP Survey of Refugees, which was launched in 2016. Another special questionnaire is used for first-time respondents since some questions do not have to be repeated every year. Each respondent is asked to fill out a biographical questionnaire covering information on the life course up to the first SOEP interview (e.g., marital history, social background, and employment biography). Additional information not provided directly by the respondent can be obtained from the “address logs”, which are stored for every year in the $PBRUTTO and $HBRUTTO files. Every address log is filled in by the interviewer even in the case of non-response, thus providing very valuable information, e.g. for attrition analysis. For researchers interested in methodological issues, these data also contain information on the fieldwork process such as the number of contacts, reasons for drop-outs, and interview mode. For households that were contacted successfully, the address logs cover the size of the household, some regional information, survey status, etc. The individual data for all household members include the relationship to the household head, survey status of the individual, and some demographic information. Life History 58 SOEPcompanion, Release 2022, v.3 The SOEP questionnaires are designed so that people in a SOEP household can be analyzed from birth to adulthood and throughout the rest of their lives. In addition to the Youth Questionnaire, which was conducted for the first time in 2000/01, a series of questionnaires for specific cohorts of children living in SOEP households have been introduced since 2003. These have been completed annually since their year of introduction by mothers (in exceptional cases by fathers) with children of the appropriate age. In 2003, a questionnaire was developed for the mothers of newborn children, Mother and Child Questionnaire (Newborns). The following instruments were developed in such a way that this starting cohort (born 2002/2003) can be followed up in their development and analyzed longitudinally. This was followed in 2005 by a questionnaire for mothers of 2-3-year-old children, Mother and Child Questionnaire (2-3-year- olds) and in 2008 by a questionnaire for 5-6-year-olds, Mother and Child Questionnaire (5-6-year-olds). In 2010, the questionnaire for 7-8-year-old children, Parents and Child Questionnaire (7-8-year-olds), completed by both mothers and fathers, was launched. In 2012, the questionnaire for 9-10-year-old children, Mother and Child Questionnaire (9-10-year-olds) was added as the last questionnaire to be answered by the mothers. This was followed by two youth instruments in which the children, aged 12, Pre-Teen Questionnaire and 14, Early Youth Questionnaire, answered questions about their own lives for the first time. These were introduced in 2014 and 2016, respectively. In 2018, the first cohort completed the entire battery of age-specific instruments and from then on, they will complete the annual questionnaires of the long-term SOEP study. Each person in a SOEP household receives the Individual Questionnaire as soon as they reach the age of 18, and the head of the household also receives the Household Questionnaire. If a respondent states in their interview that someone has died in the last year, regardless of whether the deceased person was part of a SOEP household, the Deceased Individual Questionnaire is given to the respondent providing the information. 3.1. SOEP Questionnaires 59 SOEPcompanion, Release 2022, v.3 3.1.1 Overview of the Questionnaires 3.1.2 Household Questionnaire The household questionnaire in its basic form has been an important part of the SOEP surveys since 1984 and has been improved and expanded continuously. The data collected and the questionnaire itself have become so complex that the original topics are no longer sufficient. Between 1984 and 2016, the number of questions more than doubled from 46 to 97. The multitude of questions offer users many options for analysis. Each year, the number of questions varies because new innovative question modules are added or because some questions are not asked every year. An overview of the modules included at different intervals can be found in the section Topics of SOEP-Core. The questions provide diverse information about the respondents’ households that is stored in several hundred variables. Child-specific questions asked in the household questionnaire are found in the separate dataset $kind. Availability: Since 1984 Dataset: $h (CS), hl (long) Respondent: Head of household The following question modules are part of the core program of the Household Questionnaire: •Change of living situation •Neighborhood •Building type •Size and condition of dwelling •Amenities •Type of dwelling •Loans, mortgages, building-society loans •Hereditary lease interest •Modernization costs •Ownership costs 60 Chapter 3. Survey Design SOEPcompanion, Release 2022, v.3 •Photovoltaic and solar thermal system •Owner debt •Government-subsidized housing •Home ownership •Rental and expenses •Tenant debt •Cleaning or household assistance •Persons in need of care •Names and birth dates of children •Child’s school attendance •Childcare situation •Income and expenses from renting/leasing •Loan repayment •Debt •Inheritances, gifts, winnings •Investments •Income/expenses household •Savings •Material deprevation •Number of books •Pets •Cause of moving where applicable: +migration-specific modules for the IAB-SOEP Migration Sample •distinguishing repayment of loans, debt, income / expenses between Germany and foreign country or where applicable: +refugee-specific modules for the IAB-BAMF-SOEP Sample of Refugees •Information on shared accomodations •Location preferences 3.1. SOEP Questionnaires 61 SOEPcompanion, Release 2022, v.3 3.1.3 Individual Questionnaire The individual questionnaire has been a standard instrument since the beginning of the SOEP. In order to enable analysis over time, the individual questionnaire has a large number of question modules that are asked every year. There are also questions that do not have to be asked every year, as short-term changes are unlikely. In order to be able to react to current social changes, new topics are added to the individual questionnaire and repeated at intervals of more than one year. Availability: Since 1984 Dataset: $p (CS), pl (long) Respondent: Persons over 18 years in the household The following question modules are part of the core program of the Individual Questionnaire: •Satisfaction with various live aspects •Satisfaction with current life situation •Feelings •Flourishing •Risk aversion •Political orientation •Worrying •Life satisfaction overall •Ethnic/national origins •Vocational training •Completed level of education •Higher education •Family situation •Family changes •State of health •Disability or severe disability •Visits to the doctor •Hospital stays •Sick leave •Health insurance •Wages and collective wage agreements •Additional questions for employees •Additional questions for retirees/pensioners •Government transfers •Calendar •Time use •Second jobs 62 Chapter 3. Survey Design SOEPcompanion, Release 2022, v.3 •Income •Work, last 7 days •Maternity/ parental leave •Care period (Pflegezeit) •Registered unemployed •Quitting a job •Employment status •Start of job •Change of job •Job search •Current profession •Current job •Working hours •Overtime •Optimism •Religion •Organization and Association membership •Personality traits (Big Five) •Anomie •Life goals •Locus of control •Reciprocity •Trust and Fairness •Narcissism •Lonelisness •Impulsiveness and Patience •Political Goals (Ingelhart-Index) •Attitude towards refugees •Just society •Discriminatiom •Bundestag election •Social responsibility •Influence on public decisions •Friends •LGBT-Status •Child wish 3.1. SOEP Questionnaires 63 SOEPcompanion, Release 2022, v.3 •Gender stereotypes •Attitudes towards gender •On-Call occupation •Commuting •Home-Office •Short-Time work payment •Work council •Payment equity •Workload •Occupational expectations •Depressive traits •Smoking and drinking •Integration indicators •Free time •Leisure activities •Donation where applicable: +migration specific modules for the IAB-SOEP-Migrationsample •First Job in Germany •Job before immigration •Language proficiency before and since immigration •Partnership during immigration •Living situation since immigration •Religion and faith of parents •Satisfaction in various areas of life before and after immigration or where applicable: +refugee specific modules for the IAB-BAMF-SOEP-Sample of Refugees •Legal status •Religion and faith •Language proficiency •Integration courses and government measures •Special questions for interviewers concerning language •Recognition of qualifications Re-Interviewed •Cultural and political participation •Application for recognition 64 Chapter 3. Survey Design SOEPcompanion, Release 2022, v.3 •Trauma screener •OK (Judgement of different actions) •Citizenship (inkl. connection with country of origin/ Germany) •Disadvantages •Location preferences •Willingness to participate in a tandem program •Satisfaction in various areas before and after fleeing New respondents •Obtaining help and knowledge about advice services •Assessment of current situation in country of origin •Government, democracy and woman’s position 3.1.4 Biography Questionnaire Availability: Since 1987 Dataset: $lela (CS), biol (long) Respondent: Supplementary, one-time data from the personal questionnaire of all persons aged 18 and over in the household. Content: •Nationality •Country of Origin •Childhood •Parents •Life course since the age of 15 •Education •Occupation •Partnership/marriage •Information on children •Siblings where applicable: +migration specific modules for the IAB-SOEP-Migrationsample •Travel to Germany •Stays Abroad •Citizenship •Language proficiency •Work before moving to Germany •First job in Germany 3.1. SOEP Questionnaires 65 SOEPcompanion, Release 2022, v.3 •Relationship at the time of moving to Germany or where applicable: +refugee-specific modules for the IAB-BAMF-SOEP Sample of Refugees •Travel to Germany •Questions concerning parents of respondent •Lodging and living situation •Language proficiency before moving to Germany 3.1.5 Mother and Child Instruments Mother and Child Questionnaire (Newborns) Mothers of newborn children answer questions dealing primarily with pregnancy, birth, breastfeeding, and the health of the newborn child. The questionnaire also asks to what extent the mother feels that her living situation changed after the birth of the child, how childcare is handled, and how mothers assess their baby’s temperament (as a precursor to personality). Availability: Since 2003 Dataset: $muki (CS), bioagel (long) Respondent: Mother in household (child age 0-1) Content: •Course of pregnancy •Childbirth •Health screening •Well-being •Childcare •Living situation Mother and Child Questionnaire (2-3-year-olds) Mothers of 2-3-year-old children answer questions about their child’s health and how long they have been breastfeeding. The questionnaire asks again about the childcare situation and the child’s temperament and includes a short scale on personality (the dimensions of agreeableness, extraversion, openness, and conscientiousness from the “Big Five”; McCrae and Costa 1987). In addition, it asks what language is spoken with the child and what activities they or the main caregiver engages in with their child (e.g., going to the playground, reading or telling stories, visiting other families with children). Mothers are asked to assess their children’s adaptive behavior in the areas of communication, everyday skills, social relationships, and motor skills. This is based on a translated version of the Vineland Adpative Behavior Scale, which was reduced to 20 items for the SOEP to provide data on the child’s stage of development in everyday life. Availability: Since 2005 Dataset: $muki2 (CS), bioagel (long) Respondent: Mother in household (child age 2-3) Content: 66 Chapter 3. Survey Design SOEPcompanion, Release 2022, v.3 3.2 Survey Concepts and Modes Measuring stability and detecting changes means repeating (almost) identical measures over time. Furthermore, the SOEP questions capture stability and change by varying with regard to the time dimension, that is, asking about events in the past, the present, and the future. Conceptually, different measurements of time are used: •Questions about a point in time (present), e.g., current employment status or current levels of satisfaction •Retrospective questions about certain events in the past, e.g., how often have you changed jobs in the last ten years? •Retrospective life event history since the age of 15 (in the past), e.g., employment or marital history •Monthly calendar information on income and labor market participation (in the past), e.g., employment status January through December of last year •Questions about a period of time (in the past), e.g., demographic changes since the last interview such as marriage or death of spouse •Questions about the future, e.g., expected satisfaction with life five years from now, or job expectations Survey Modes The SOEP uses several different modes to collect the data. Originally, the respondent’s answers were always recorded by an interviewer who filled in the answers in a paper questionnaire, the “pen-and-paper interview” or PAPI. The personal contact between interviewer and respondent is important for the success of the survey; however, before losing a respondent due to a scheduling conflict between interviewer and respondent, the SOEP has allowed respondents to mail in the questionnaire since the second wave of subsamples A-I. This is not the same as the concept of a regular mail survey, because the interviewer still maintains personal contact with the household and schedules appointments with respondents if possible. Starting with subsample J, only “computer-assisted personal interviews” (CAPI) are allowed, and thus it is no longer possible to mail in the questionnaires. When visiting a household, the interviewer interviews household members one at a time and can also give questionnaires to other household members to complete without the interviewer’s assistance (self-administered questionnaires, SAQ). This is a time-efficient approach because it allows different household members to complete their questionnaires at the same time. In 1998, computers were used for the first time in the SOEP for computer-assisted personal interviews (CAPI). Compared to PAPI, the CAPI mode is much more efficient in converting the data into an electronic format, which was an important asset especially with the extensions to the panel starting in the year 2000. The CAPI mode was first used parallel with PAPI, meaning that interviewers and respondents were free to chose how they wanted to do the interview. This was important for the “older” sample members (respondents as well as interviewers), who were used to the PAPI concept. Only in the most recent samples (starting in subsample J) is CAPI the sole interview mode. The figure depicts the development of modes up to 2011, showing that the CAPI mode has gained importance since its implementation. Since the questionnaires have to be identical in both modes, CAPI is implemented in a relatively simple way in the SOEP and does not utilize all the technical possibilities of this interview mode. For example, the SOEP basically does not use any form of dependent interviewing (i.e., referring to respondent data from previous waves), because this cannot be easily implemented in the PAPI mode. Also, the filtering structure is very simple in the SOEP, because a respondent must be able to follow the interview path on paperon her/his own. Still, some technical features like the control of value ranges (e.g. month of birth, year of first marriage) or the randomization of scale items are implemented in the CAPI version of the questionnaire. In the future, new modes will be introduced into the SOEP as they develop. The computer-assisted web interview (CAWI) is close to implementation, but will not be used as a replacement of the current CAPI and PAPI modes, but rather as an extension the respondents may use, similar to the mail-in or self-administered questionnaires. The core interview concept of the SOEP survey, the personal contact between respondent and interviewer, will not change. 3.2. Survey Concepts and Modes 73 SOEPcompanion, Release 2022, v.3 Download STATA Code to create figure. Last change: May 12, 2022 3.3 Panel Care To cope with panel attrition and to keep longitudinal response rates high, the SOEP has implemented “panel care” efforts to maintain personal contact between respondents and the survey. Panel care can be divided into incentives given directly to the respondent and other measures undertaken to keep the respondent in the study. Respondents have been given gifts as tokens of appreciation since the very beginning of the study. Most of these gifts are small in-kind incentives like flowers, for which the interviewers have their own budget. In addition, the interviewers are asked to hand out a brochure with recent results from the study. Up to 2007, respondents also received a lottery ticket as a thank-you upon completion of their interview. Proceeds from the lottery benefit social projects in Germany. Since 2008, the lottery ticket has been included with the contact letter that is sent out about two weeks prior to the interview. It is thus given unconditionally, as long as the person participated in the previous wave. After a successful interview, the respondent receives a thank-you letter from survey institute along with one postage stamp as a small additional gift. In 2009, different incentive schemes were tested in the new subsample I to increase the first-wave response rates. The basic experiment included four randomized groups of households: (1) those with the default setup of the conditional lottery ticket; (2) those with a “low” cash incentive of 5 euros per household and 5 euros per adult respondent; (3) those with a “high” cash incentive of 5 euros per household and 10 euros per adult respondent; and (4) those with a choice between a “low” cash incentive and a lottery ticket. The results showed slightly higher response rates in the cash groups, although the extra money in group (3) did not pay off. The current incentive strategy for the different SOEP samples is shown here: 74 Chapter 3. Survey Design SOEPcompanion, Release 2022, v.3 Samples A-H J,K, L1, N, O, Q L2, L3 P M1/M2 Incen- tives for adults Lottery ticket (5,427 households) and cash (636 households) 5 euros (households) and 10 euros (adults) 5 euros (households), 5 euros (adults), 10 euros (bonus payment) Lottery Ticket 5 euros (households) and 10 euros (adults) Incen- tives for other respon- dents Power bank (Youth Questionnaire), Bicycle repair kit (Early Youth Questionnaire), Small clock (Pupils Questionnaire CAPI/PAPI), Puzzle (Pupils Questionnaire MAIL) Power bank (Youth Questionnaire), Bicycle repair kit (Early Youth Questionnaire), Small clock (Pupils Questionnaire CAPI/PAPI), Puzzle (Pupils Questionnaire MAIL) 5 euros (Youth Questionnaire), (Youth Questionnaire,Early Youth Questionnaire, Pupils Questionnaire) and 5 euros (Mother-Child Instruments) Power bank (Youth Questionnaire), USB-Stick (Early Youth Questionnaire), Bicycle repair kit (Pupils Questionnaire ) The survey institute also does additional work to keep response rates high. Addresses are checked throughout the year to ensure that current addresses are on file. This is done, for instance, by sending out brochures about recent research based on the SOEP data and seasonal greeting cards. Face-to-face interviews also ensure a personal relationship between interviewer and respondent, which increases the likelihood that respondents will stay in the survey. Keeping the same interviewer over time is therefore an important goal of the survey. Some SOEP respondents have in fact had the same interviewer since the beginning in 1984. Last change: Sep 26, 2022 3.3. Panel Care 75 CHAPTER FOUR TARGET POPULATION AND SAMPLES The target population covered in the SOEP is defined as the population of private households residing within the current boundaries of the Federal Republic of Germany (FRG). Because of changes in these boundaries (in 1990) and changes in the population due to migration, various adaptations have been made to the initial sampling structure to maintain the sample’s representativity. In addition, certain groups have been oversampled to increase the statistical power. The different SOEP-Core subsamples constitute the centerpiece of the SOEP. 1. Within SOEP-Core, samples A-Q form the heart of the SOEP. They contain the oldest samples, beginning with the founding sample in A from 1984 and the highest number of participating households. Fieldwork traditionally starts at the beginning of February, and its questionnaires serve as a master for the other SOEP-Core subsamples. 2. The SOEP migration sample with it`ssamples M1 and M2 was established in 2013 and is designed to improve the representation of migrants living in Germany. Fieldwork started in April, using the questionnaires from samples A-Q, supplemented by translated questionnaires for five different languages. 3. In order to map recent migration and integration dynamics, SOEP refugee samples M3 to M5 were installed beginning in the year 2016. In 2020, fieldwork began in August with a questionnaire that was tailored to issues of recent refugees while containing many questions from the SOEP samples A-Q as well. 4. Sample M6 – a boost sample of refugees targeted the same population as the older refugee sample M5 - adult refugees who have applied for asylum in Germany since 1 January 2013 and are currently living in Germany – and the same sample design and sample frame were used. 5. The two boost samples, samples M7 and M8a, were added the SOEP migration sample system. Like the older migration samples M1 and M2, the Integrated Employment Biographies Sample (IEBS) of the Federal Employment Agency (BA) served as the sampling frame for both boost samples. Boost sample M7’s goal was to capture migration dynamics and processes from 2016 to 2018 with a focus on EU migration. To ensure that statistically significant group comparisons can be made, sampling was restricted to the three most significant countries of origin in that time period: Romania, Bulgaria, and Poland. M8a, on the other hand, was designed to help evaluate the skilled worker immigration law (Fachkräfteeinwanderungsgesetz), which came into effect March 1, 2020, and targeted migrants from third countries that came to Germany between 2017 and 2018, sampling them as a control group for a treatment group that will be sampled at a later date. In 1984, the survey started with a sample covering the entire population of then West Germany (FRG), where the five biggest groups of foreigners (“guest workers”) were oversampled. The SOEP was expanded to the territory of the German Democratic Republic in June 1990, only six months after the fall of the Berlin Wall. In 1994/95, a boost sample of migrants who came to Germany after 1984 was added to take the influx of ethnic Germans from former Soviet countries into account. In 2013 another sample of migrants which includes individuals who immigrated to Germany after 1995 or second-generation immigrants was added. Since then, multiple migration or refugees samples were added in cooperation with the IAB (Institut für Arbeitsmarkt- und Berufsforschung) or the BAMF (Bundesamt für Migration und Flüchtlinge) Now and then samples that were representative of the entire population in Germany were added to counter effects of panel attrition and to increase the overall sample size. 76 SOEPcompanion, Release 2022, v.3 The different samples in the SOEP are identified by letters: sample “A” refers to the German sample drawn in 1984, “C” to the East Germans from 1990, and so on. Even though these samples are kept separate, the respondents have received identical questionnaires for the most part, and distinctions by sample are usually not necessary in an analysis. However, one of the ideas of the SOEP is that the users have full information available about survey methodological issues and survey design, which in this case means that you can identify the corresponding sample for each observation. In the following section, we present details on each of the samples, which unless stated otherwise are multi-stage random samples with regional clusters. The households are selected by random-walk routines. For an extensive discussion on sampling (and weighting), see: Survey methods. 4.1 The SOEP Samples in Detail Sample A “Residents of the Federal Republic of Germany” covers individuals in private households with a household head who does not belong to one of the main groups of “guest workers” (i.e., Turkish, Greek, Yugoslavian, Spanish, or Italian households). Because only a few foreigners are in Sample A, it is often called the “West German Sample” of the SOEP. In 1984 it covered 4,528 households with a sampling probability of about 0.0002. Sample B “Foreigners in the Federal Republic of Germany” adds individuals in private households with a Turkish, Greek, Yugoslavian, Spanish, or Italian household head, who in 1984 constituted the main groups of foreigners in the FRG. Compared to Sample A, the population of Sample B is oversampled with a sampling probability of about 0.002. In the first wave, Sample B included 1,393 households. Sample C “German Residents of the German Democratic Republic (GDR)” consists of individuals in private households in which the household head was a citizen of the German Democratic Republic (GDR). This meant that approximately 1.7% of the residential population of the GDR in June 1990 was excluded from the sample as foreigners (most of whom were living in “institutionalized” housing). In total, the sample started with 2,179 households with a sampling probability of about 0.0005. Sample D “Immigrants” started in 1994/95 with two different samples. In 1994, the first sample, D1, had 236 households and in 1995, the second sample, D2, had 295 households, leading to a total of 531 households (D1 and D2) in 4.1. The SOEP Samples in Detail 77 SOEPcompanion, Release 2022, v.3 1995. This sample consisted of households in which at least one household member had moved from abroad to West Germany after 1984. The sampling probability is about 0.0002. Sample E “Refresher” was added in 1998, selected from the entire population of private households in Germany. The households were chosen independently of the ongoing panel and its subsamples A through D. The aim was to increase the number of observations of the general population and to preserve its representativity. The selection scheme used for sample E essentially resembles the one used in subsample A. The number of households in the first wave of subsample E was $1,060$, with a sampling probability of about 0.00005. With the 2012 data release, parts of subsample E were extracted into the SOEP Innovation Sample. It is also the first sample in which Computer-Assisted Personal Interview (CAPI) was used. At that time, interviews in Samples A-D were being conducted entirely using Paper-and-Pencil- Interviews (PAPI). To study mode effects, households from sample E were randomly allocated to either CAPI or PAPI. Sample F “Refresher” was selected independently of all other subsamples from the population of private households in 2000. The selection scheme was slightly altered compared to the previous addition in Sampl’ E: while the “German” households (all adults aged 16 or older in the household have German nationality) were selected with a sampling probability of $0.00028$, the ’non-German’ households (at least one adult does not have German nationality) were oversampled with a probability of 0.0005. Overall, the number of added households in subsample F’s first wave amounts to 6,043. Sample G “High-Income” entered the SOEP in 2002 independently from all other subsamples. The original selection scheme required that the responding households had a monthly income of at least DM 7,500 (EUR 3,835), which - due to the lack of an adequate sampling frame - were identified using a screening procedure. This sample of a total of 1,224 households increased the potential for analysis in the high-income bracket, which was previously difficult to study because of the low case numbers. The derived sampling probability is about 0.0014. Starting with Wave 2 in 2003, the selection scheme for this subsample was changed such that only households with a net monthly income of at least EUR 4,500 were followed. Sample H “Refresher” started in 2006 as a random sample, again independently of all previous subsamples, covering all residential households in Germany. The added 1,506 households were sampled with a probability of 0.0001. Sample I “Incentive Sample” started in 2009, where in the first wave, a new incentive scheme was tested to increase participation rates (see also [sec:PanelCare]. The sampling was independent of all other SOEP samples, adding a total number of 1,531 households to the SOEP. The sampling probability was 0.00013. This sample remained in the main data release for its first two waves (2010 and 2011, or waves Z and BA). With the 2012 data release, subsample I was extracted into the SOEP Innovation Sample. Sample J “Refresher Sample” started in 2011 as a random sample, independently of all previous subsamples, covering residential households in Germany. The added3,136 households were sampled with a probability of 0.0002. Sample K “Refresher Sample” started in 2012 as a random sample, drawn independently of all previous subsamples, covering the residential households in Germany. The added 1,526 households were sampled with a probability of 0.0001. Sample L1 “Cohort Sample” covers private households in Germany in which at least one household member was born between January 2007 and March 2010 and was therefore a child at that time. Again, migrants identified were oversampled using an onomastic procedure. Sample L1 (as well as L2 and L3) was part of the SOEP-related study “Families in Germany” (FiD), which was integrated into the SOEP in 2014. As part of an evaluation project by the Federal Ministry for Family Affairs, Senior Citizens, Women and Youth (BMFSFJ) and the Federal Ministry of Finance (BMF), the study focused on public benefits in Germany for married people and families. Therefore, the survey instruments used in waves BA to BD differ in some respects from those used in the other samples. Sample L2 “Family Types I” covers private households in Germany that meet at least one of the following criteria for household composition: single parents, low-income families, and large families with three or more children. Similar to Sample G, we face the problem that the eligible sub-population is relatively small and an adequate sampling frame is lacking. So again, a preceding telephone screening procedure identifies eligible households. Sample L3 “Family Types II” covers private households in Germany that meet at least one of the following criteria for household composition: single parents or large families with three or more children. It is conducted analogously to Sample L2 to increase the number of cases in these sub-populations. 78 Chapter 4. Target Population and Samples SOEPcompanion, Release 2022, v.3 Sample M1 “Migration Sample” is a new migration sample added in 2013 with around 2,700 households drawn using register information from the German Federal Employment Agency. It includes individuals who immigrated to Germany after 1995 or second-generation immigrants. Sample M2 “Migration Sample” was another migration sample added in 2015 with around 1,100 households drawn using register information from the German Federal Employment Agency. It includes individuals who immigrated to Germany between 2010 and 2013. Sample M3 “Refugee Sample” was a new refugee sample added in 2016 for the IAB-BAMF-SOEP Refugee Survey in which roughly 1,769 refugee households were interviewed repeatedly. Respondents aged 18 and older who entered Germany between January 2013 and December 2016 and who had filed an asylum application by April 2016 (regardless of their current legal status) were interviewed along with the other members of their households. Sample M4 “Refugee Family Sample”: the 2016 “IAB-BAMF-SOEP Survey of Refugees” (Samples M3 and M4) is a joint project of the Institute for Employment Research (IAB), the Research Center of the Federal Office for Migration and Refugees (BAMF-FZ) and the Socio-Economic Panel (SOEP). The target population of the samples consists of 1,769 households with individuals who arrived in Germany between January 2013 and January 2016 and had applied for asylum by June 2016 or were hosted as part of specific programs of the federal states (irrespective of their asylum procedure and their current legal status). The first part of the sample (M3) was financed with funds allocated to the IAB from the research budget of the Federal Employment Agency (BA) . Sample M4 was funded by the Federal Ministry of Education and Research (BMBF) and has a focus on refugee families. Sample M5 “Refugee Sample” M5 is the third boost sample of refugee households. The population of M5 covers adult refugees who applied for asylum in Germany between January 1, 2013, and December 31, 2016, and are currently living in Germany. The first wave of M5 was conducted in 2017. M5 added another 1,519 households of refugees who have migrated to Germany since 2013 to the SOEP framework. Sample N “Refresher Sample (PIAAC-L)”: Sample N integrated 2,314 households of former participants in the Program for the International Assessment of Adult Competencies (PIAAC and PIAAC-L) in 2017. This is the most recent addition to the SOEP-Core samples. Fieldwork in sample N was conducted between mid-March and mid-August and thus slightly later than the majority of samples A–L1. Sample O “Social City Sample”: Sample O includes 935 households located primarily in bigger cities. It was designed to enhance the potential of the data for analysis by incorporating more city-specific environments. The sample was selected in cooperation with BBSR using a new sampling design based on regional data in areas where the “Soziale Stadt” (social city) urban development project is being carried out. Based on the digital data available on the boundaries of the “Soziale Stadt” areas, it was possible to create a new variable going back to the year 2000 that shows whether or not a household’s address is within an area covered by the project. Sample P “Top Shareholder Sample”: Sample P was conceptualized as a sample of highly affluent households in Germany. Against the backdrop of increasing income and wealth inequality in Germany, despite economic growth in recent decades, a lack of data on wealthy populations has become increasingly evident in the social sciences. Goals to be accomplished with sample P were to improve the empirical basis of the poverty and wealth report of the German government as well as laying the foundation for medium and long-term cross-sectional and longitudinal analyses. The gross sample of sample P consisted of 23,259 households. Sample Q “LGB*”: Sample Q is a boost sample of a hard-to-survey population: lesbians, gays, bisexuals, transgender people, and those who identify as non-binary. While the actual percentage of LGBTQ+ people in the general population is unknown, this population was too scarcely represented in the SOEP to meaningfully analyze this group. 835 households were recruited via an approximately 9-month long telephone screening process. Of these households 477 participated between April and November. Sample M6 “Refugee Sample”: M6 is the acronym for the fourth top-up sample for households that represents refugees. The population of M6 covers two groups: firstly, adult refugees who arrived in Germany between January 1, 2013 and December 2016 (“Refreshment”) and secondly adult refugees who came to Germany between January 1, 2017 and June 2019 (“Enlargement”) with a strongly disproportionate oversampling of refugees from East- and West-Africa. Sample M7 “Migration Sample”: Like the older migration samples M1 and M2, the Integrated Employment Biographies Sample (IEBS) of the Federal Employment Agency (BA) served as the sampling frame for both boost samples. 4.1. The SOEP Samples in Detail 79 SOEPcompanion, Release 2022, v.3 Boost sample M7’s goal was to capture migration dynamics and processes from 2016 to 2018 with a focus on EU migration. To ensure that statistically significant group comparisons can be made, sampling was restricted to the three most significant countries of origin in that time period: Romania, Bulgaria, and Poland. Sample M8a “Migration Sample”: Like the older migration samples M1 and M2, the Integrated Employment Biographies Sample (IEBS) of the Federal Employment Agency (BA) served as the sampling frame for both boost samples. Boost sample M8a was designed to help evaluate the skilled worker immigration law (Fachkräfteeinwanderungsgesetz), which came into effect March 1, 2020, and targeted migrants from third countries that came to Germany between 2017 and 2018, sampling them as a control group for a treatment group that will be sampled at a later date. More information about “Sample Sizes and Panel Attrition” can be found here 4.1.1 Sample-Specific Questionnaires In SOEP it is common for special samples to receive extended, adapted, and/or integrated questionnaires in the first few years. This ensures that sample-specific questions that do not play a role in the main SOEP can also be included. In the following tables you can see which questionnaires the respective samples received, which years they ran, which raw data set they were included in, and which “long” data set they went into. From the start of Sample B (foreigners), respondents could complete the individual questionnaire in German or in the respective foreign language. Starting with wave 2 of the panel, there were “old” and “new” survey units (households, persons), and there were survey units with or without certain changes (e.g., households that had or had not moved; individuals who had or had not changed careers). The questionnaires took these changes into account for all subgroups. Survey procedures and tools were designed to ensure that each subgroup received the right questionnaire for them. This technique as well as the bilingual design of the foreigner questionnaires was retained for waves 3-6. In addition, retrospective information and missing information on temporary drop outs was collected. The “financial statement”, which is now a survey module, was a separate questionnaire in the year 1988. 80 Chapter 4. Target Population and Samples SOEPcompanion, Release 2022, v.3 SOEP researchers were determined to seize the historic opportunity of German reunification to obtain a first baseline measurement of incomes in the “old” GDR currency. The questionnaire was prepared by an East-West working group including DIW Berlin, WZB, Collaborative Research Centre 3, and the ISS at the Academy of Sciences in the GDR, with the participation of Infratest and its partner organization in the GDR. The result was a questionnaire that covered many of the same themes and questions and was structured similarly to the West SOEP questionnaire, but which focused more on the specific situation in the GDR (e.g., the housing situation). 4.1. The SOEP Samples in Detail 81 SOEPcompanion, Release 2022, v.3 A major shift in the design of SOEP questionnaires took place with Sample J. Due to the increased panel mortality from wave 1 to wave 2 that was observed for the refresher samples F (2000- 2001), H (2006-2007), and I (2009-2010), the biographical module, with an average interview length of 17 minutes, was integrated into wave 1. If this had not been done, no biographical data would have been collected for approximately 20% of all SOEP respondents who would probably not have participated in wave 2. In comparison to the longitudinal samples, data collection in the first wave was focused on the main three questionnaires: the household, the individual, and the youth questionnaire. As the fieldwork in these refresher samples was conducted exclusively by CAPI, it was feasible to include complex modules with event-triggered question loops. 82 Chapter 4. Target Population and Samples SOEPcompanion, Release 2022, v.3 Download R Code to create figure Last change: May 12, 2022 4.3 Development of Sample Sizes Individuals who decline to take part in the survey or are not available for an interview are kept in the so-called “gross” sample of the study as long as they continue to live in households with at least one participating respondent. If the entire household declines to participate in two consecutive waves, all individuals in the household are removed from the SOEP. The table shows the starting sample sizes of samples A through M4, the years when the samples were first collected, as well as the percentage of those persons who were eligible for an interview but declined participation (“partial unit non-response”, PUNR) in the first wave. The figure illustrates the development of the number of successful person interviews since 1984. The reduction in the population size for all individual samples is mainly the result of individual-level drop-outs, refusals, moving abroad, etc. However, due to new persons moving into already existing households and children reaching the age of 16 and thereby increasing the sample size, this negative development is offset somewhat. Starting Sample Size of the SOEP Samples 4.3. Development of Sample Sizes 89 SOEPcompanion, Release 2022, v.3 Cross-Sectional Development of Sample Size (Respondents) Download Stata Code to create figure This cross-sectional view is insufficient when examining the longitudinal development of the sample, which is influenced by different demographic and fieldwork-related factors. As already shown, demographic reasons for entering the panel are birth and residential mobility. Analogously, the demographic reasons for a panel exit are death and moving abroad. Fieldwork-related reasons are different, in that they relate to the interaction between the interviewer and the responding household. Respondents are either not reached for an interview (non-contact) or they decline to participate 90 Chapter 4. Target Population and Samples SOEPcompanion, Release 2022, v.3 for the current year. The figure illustrates the longitudinal development of first-wave respondents in 1984, as well as their children, of samples A and B. Longitudinal Development of the 1984 Population Download Stata Code to create figure Last change: May 12, 2022 4.3. Development of Sample Sizes 91 CHAPTER FIVE DATA STRUCTURE OF SOEP-CORE 5.1 Data Editions of SOEP-Core Access to SOEP data is provided in compliance with the highest security standards to protect respondents’ confidentiality and maintain their trust in the survey. The data are also provided solely for scientific research purposes, that is, they are only made available to members of the scientific community. This means that researchers are only given access to SOEP data after they have signed a data distribution contract with DIW Berlin. Different data packages, called “editions”, reflect these requirements and can be differentiated by the amount of information contained in them, the level of data protection, and the mode of data access. The EU Edition is considered the standard edition. More restricted editions provide less information; less restricted editions provide more information but are only available under more restrictive conditions. The Teaching, International, and EU Editions Teaching, International, and EU Edition are made available as downloads under the standard data distribution contract, while the two add-ons Area Types and Planning Regions Add-ons: Area Types and Planning Regions require additional contracts. The Remote Edition Remote Edition can only be accessed through remote execution, and the Onsite Edition Onsite Edition can only be accessed on site at the SOEP Research Data Center at DIW Berlin. In this figure, “more restrictive” means that existing variables from the EU Edition are left blank for reasons of data protection or not all cases are included. For example, variables that provide information at the federal state level are 92 SOEPcompanion, Release 2022, v.3 not available in the International or Teaching Editions (which only distinguish between East and West Germany). A higher level of data protection makes it possible to provide more information with fewer restrictions. This makes the editions less restrictive in terms of the information available. In most cases, as more sensitive information is added to an edition, access to the data edition changes and the requirements for its use also change. 5.1.1 Teaching, International, and EU Edition Only the standard data distribution contract is required for the EU Edition and the International Edition. The EU Edition includes 100% of all observations, the German federal states, and the urban/rural variable. This edition is only available to users from research institutions in the EU and countries with an “adequacy decision” (Angemessenheitsbeschluss)—Switzerland, Japan, Canada, Israel, and a few others. The International Edition is available to users from research institutions in all other countries than those listed above. This edition contains 95% of all households from the first wave of each SOEP subsample based on a random sampling of the original households in each subsample and only the East/West versions of variables normally containing the federal states. The original variables (with information on the federal states) remain in the edition but they are assigned the missing code -7 “Only available in less restricted edition” if a variable cannot be made accessible in a specific edition. For more information on the missing codes in SOEP-Core, see the chapter Missing Conventions. The least restrictive edition of the data but the one containing the least information is the Teaching Edition. Here, a data distribution contract is required for teaching staff; students only need to sign the data protection declaration, which the contract holder must keep on file. The contract holder is responsible for ensuring strict adherence to data protection. German data protection laws stipulate that a maximum of 50% of all cases in the original dataset may be used for teaching purposes. The Teaching Edition has the same data structure as the International Edition (with the exception of the EU-SILC Clone) but contains half the number of cases in the EU Edition. The Teaching Edition provided to students must be stored in a separate hard drive area to which the user guarantees controlled access. Students may under no circumstances take data home with them or transfer the data to any other device at the university. 5.1.2 Add-ons: Area Types and Planning Regions In addition to the EU Edition, the SOEP offers additional datasets that can extend the standard file to include municipality size classes (add-on: Area Types) or even spatial planning units (add-on: Planning Regions). Access to these files is more restricted because they provide users with more sensitive information about the respondents. For the add-on Area Types, a regional data contract is required in addition to the data distribution contract. This requires that the user submit a data protection concept to the SOEP. There is no template for this; users must develop this concept specifically for the workplace in which they want to use the data. For the add-on Planning Regions, a regional data contract is also necessary, and the SOEP requires that users submit a data protection concept that they have developed themselves. For this add-on, however, the requirements for the data protection concept are significantly higher. 5.1.3 Remote Edition Further information such as official county codes (KKZ), identifying administrative districts (Landkreise) and urban districts (kreisfreie Städte) can be accessed through remote execution using the Remote Edition (or on site). For this edition, users are required to submit an application to use SOEPremote in addition to the data distribution contract. For the remote execution contract, no separate data protection concept is required, as users will only access the information remotely and no files are transmitted to computers outside of the Research Data Center of the SOEP (RDC SOEP). To access the Remote Edition, there are two options available: •SOEPremote execution (e-mail processing) •SOEPremote access (on-site processing at special workstations) 5.1. Data Editions of SOEP-Core 93 SOEPcompanion, Release 2022, v.3 With SOEPremote execution, users can email their Stata syntax to a remote server, which processes the syntax and returns the results to users by email. With SOEPremote access, users can use IGEL clients at RDC SOEP in Berlin. By using the IGEL clients, onsite users have the advantage of working directly with the Remote Edition instead of having to go through the email procedure. The disadvantage is having to plan and book a visit to the RDC SOEP in Berlin. 5.1.4 Onsite Edition The Onsite Edition is the edition with all available information. Guests using RDC SOEP IGEL clients (How to Use SOEP IGEL) can access the additional information about the municipalities or postal codes of the SOEP households or data from microm GmbH on households’ neighborhoods. Users can even analyze geocoded data. To access these data, researchers are first required to sign a data protection agreement, and a complete record is kept of all data access. The concept for providing the geo-coordinates of SOEP households is that the point coordinates are kept separate from the actual survey information throughout the entire process of analysis by data users due to privacy concerns. Researchers therefore never have simultaneous access to the SOEP survey data and the geo-coordinates of SOEP households. The results may only be published in completely anonymous form and are checked before they are transmitted from the secure server to the user. •To apply to use a guest work station, click here: •For more information about your workplace at the SOEP Research Data Center see the section How to Use SOEP IGEL •For more information about how to work with SOEP’s spatial data see the section Working with spatial data in R Last change: May 12, 2022 5.2 Principles of Data Analysis All SOEPtutorials can be found on our YouTube Channel The structure of panel data has three dimensions. First, the respective examination units (n) and a matrix of dependent and independent variables (y,x) are completely analogous to a cross-sectional design. Second, the dimension of time (t), whereby a distinction is made between two data formats for panel data structures - “wide” or “long” (with wide format the variable matrix is indexed with the dimension of time and with long format the respective examination units). Regardless of the selected data format, when using panel data with several survey waves, the data matrices often do not contain complete information due to the panel mortality of individual survey units or because data from new panel members are only collected at a later point in time. In both cases, the term “unbalanced panel data” is used. In contrast, the classical panel data structure, on the other hand, is “balanced”, i.e., as many observations of dependent and independent variables are available for all study units as there are waves of data collection. Social science panel data often show a data structure characterized by many investigation units (large n) as well as, in relation to it, few waves and therefore measuring time (small t). When data from a panel study are available, even descriptive forms of data analysis are often of particular interest, since the identification of changes in a variable over time and the corresponding separation of interindividual and intraindividual changes can represent important social facts, particularly in the case of generalizable samples. It is of social scientific interest whether a constant 15% proportion of people whose income is below the poverty risk level is repeatedly found in the same person over time, or whether there was a even balance of increases and decreases in poverty risks and only half of the population was permanently exposed to the risk. The choice of complex analysis methods for panel data depends first and foremost on the respective measurement level of the dependent and independent variables, but also on whether they are time-constant variables (such as gender or migration background) or time-invariant variables. The statistical analysis models of panel data range from structural equation models, various regression models, event analysis, sequence data analysis, latent growth models to causal analyses using matching methods. A particular advantage of panel data is that the chronological sequence of changes can be modelled and calculated and the problem of unobserved heterogeneity, which is often encountered in the social sciences, can be significantly reduced, at least in comparison with cross-sectional data. 94 Chapter 5. Data Structure of SOEP-Core SOEPcompanion, Release 2022, v.3 5.2.1 Cross-Sectional Data Structure (CS) Cross-sectional data is a type of data that observes many subjects at the same point in time. Each person is assigned a row in the dataset and is only included once in such a dataset. By merging cross-sectional SOEP data across waves, you obtain a dataset in wide-format. Row ID wave sex income 1 1 2015 m 1500 2 2 2015 m 1000 3 6 2015 f 2000 4 8 2015 m 5500 5.2.2 Data Structure in “Wide” Format (wide) The SOEP data are available with different data structures. In the wide format, a respondent’s repeated responses are displayed in a single row and each response in a separate column. Each column represents a variable. We provide four datasets in the wide format: ppath, phrf, hpath, hhrf. Row ID sex income2015 income2016 income2017 1 1 m 1500 1500 2000 2 2 m 1000 1200 1200 3 6 f 2000 2000 2000 4 8 m 5500 6000 6500 5.2.3 Data Structure in “Long” Format (long) The long format is a condensed and user-friendly dataset structure for longitudinal section analysis. Here, each person has one line per survey year. This means that you do not have several datasets for the different waves, but one dataset in which all survey waves are represented. A person can appear more than once in such a dataset. In the long format, one line describes a person-year combination. Row ID syear sex income 1 1 2015 m 1500 2 1 2016 m 1500 3 1 2017 m 2000 4 2 2015 m 1000 5 2 2016 m 1200 6 2 2017 m 1200 7 6 2015 f 2000 8 6 2016 f 2000 9 6 2017 f 2000 In addition to the classic long format where one row in the dataset describes a person-year combination, there are also datasets that describe a longer period or a whole life, but only appear uniquely in the dataset without a survey year. These data sets can contain longitudinal information, but are constant over time. These time-constant data sets may include, for example, information on biological parents or employment history up to a certain age. 5.2. Principles of Data Analysis 95 SOEPcompanion, Release 2022, v.3 Row ID father_id mother_id 1 20 18 19 2 21 18 19 3 35 34 35 4 36 34 35 5 37 34 35 5.2.4 Data Structure in Spell Format (spell) In the strict sense of the word, spell data are about time periods with a defined start and end. When handling spell data it is necessary to take potential censoring into account. Censoring denotes that the beginning (left censored) or ending (right censored) of a spell is imprecise because of missing information or the beginning or ending of a spell is outside of the period of observation. It is quite conceivable that a person has only one spell over a given period, such as a male who is full-time employed. For a ten year period, there may be just the one spell “full-time employed”. In panel data, the same person would have 10 observations, one per year. A person may have many spells over a time period, and even have overlapping spells, like working part-time and receiving a disability pension. Spell data are useful for looking at stays in a certain state, and transitions in and out of that state. Row ID spellnr spelltype begin end censored 1 1 1 Retired 1983 2007 left and right censored 2 1 2 Housewife/husband 1983 1984 left censored 3 1 3 Housewife/husband 1994 1994 uncensored 4 1 4 Housewife/husband 1998 1998 uncensored 5 2 1 Full-Time Employment 1984 1984 left censored 6 2 2 Full-Time Employment 1985 1985 uncensored Last change: May 12, 2022 5.3 Data Distribution File In the SOEP, each survey year is allocated to a data wave, which is abbreviated using the letters of the alphabet. One data wave may be released in several versions, which are displayed in SOEP with a “v” for version and the respective version number. The version number represents the survey years since the beginning of the survey. The SOEP has recently published the 34th version since the survey began in 1984. Within a data wave, updates may be made over time, such as v34.1. If updates have been made, users will be informed through various channels and be asked to order the data again. After ordering the data, the data will be sent to you in a zip file. Within this zip file you will find various datasets, a “raw” subdirectory and the “eu-silc-like-panel” subdirectory. The datasets in the top-level folder are a highly compressed and easy-to-analyze version of the SOEP data. 96 Chapter 5. Data Structure of SOEP-Core SOEPcompanion, Release 2022, v.3 Note: SOEP strongly recommends that users use the top-level folder. 5.3. Data Distribution File 97 SOEPcompanion, Release 2022, v.3 The data in SOEP-Core are no longer provided only as wave-specific individual files but are now pooled across all available years (in “long” format). In some cases, variables are harmonized to ensure that they are defined consistently over time. For example, the income information provided up to 2001 is given in euros, and categories are modified over time when versions of the questionnaire have been changed. The longitudinal nature of the data is one of the biggest assets of the SOEP. This is why we provide longitudinal datasets such as PL or HL. The advantage of such a dataset is that longitudinal analyses can be carried out without great effort. If you need more information about the “long” data structure, see chapter Data Structure in “Long” Format (long). 5.3.1 Core Datasets The datasets in the top-level folder: Tracking Data Original Data Survey Data Generated Data Spell Data ppathl pl design pgen artkalen hpathl hl exit hgen biocouplm pbrutto biol cov_contact bioagel biocouply hbrutto jugendl kidlong biomarsm hbrutt plueckel pequiv biomarsy pbr_exit abroad biobirth einkalen cov_brutto vpl bioedu lifespell cov bioimmig migspell biojob pbiospe bioparen refugspell biopupil sozkalen biosib biotwin camces cogdj cognit cog_refu gripstr hconsum health hwealth interviewer mihinc pflege pkal pwealth timepref trust 98 Chapter 5. Data Structure of SOEP-Core SOEPcompanion, Release 2022, v.3 covers all respondents who were either interviewed for the first time or contacted for the purpose of being interviewed again in a given wave. The dataset provides gross information on all SOEP respondents’ interviews as well as their positions in the panel framework. hbrutto “Gross Household Data” (long): HBRUTTO consists of all waves of the raw datasets $HBRUTTO. HBRUTTO covers all households that were successfully interviewed for the first time in a wave or were contacted for the purpose of being interviewed again. The datasets provide gross information on all SOEP households’ interviews as well as their positions in the panel framework. hbrutt “Original gross population of a first wave sample” (long): The dataset HBRUTT contains demographic information and data on the interviews of all newly surveyed samples in the respective survey year that were successfully interviewed or contacted for the first time. All cross-sectional variables from HBRUTT$$ are used for hbrutt. pbr_exit “Cumulated Exit” (long): The dataset pbr_exit is a supplement of pbrutto for individual dropouts. Individual dropouts are removed from the original pbrutto population, so that pbrutto covers all current household members. Pbr_exit contains the corresponding register information on individual dropouts from households. cov_brutto “Gross Household Data SOEP-COV” (long): COV_BRUTTO contains the brutto information of SOEP- CoV study. This datset is associated with the 9 tranches of the SOEP-CoV in 2020, the SOEP-CoV wave in 2021 and COVID-19-special interviews 2020 from the IAB-BAMF-SOEP Survey of Refugees in Germany. More information about the project can be found online: 5.4.2 Original Data These datasets contain respondents’ direct information. The contents of these variables mirror the contents of the survey instruments. By searching the questionnaires, you can determine the exact wording of the question and obtain possible filter guidance. Dataset Label Format Identifier (ID) Additional Identifier pl Individual questionnaire long pid, syear hid, cid, intid hl Household questionnaire long hid, syear cid, intid biol Biographical data long pid, syear hid, cid, intid jugendl Youth questionnaire for first-time respondents at age 17 long pid, syear hid, cid, intid more_docu Dataset on the Mentoring of Refugees (MORE) Project long pid, syear hid, cid more_local Dataset on the Mentoring of Refugees (MORE) Project long pid, syear hid, cid plueckel Follow-up questionnaire long pid, syear hid, cid, intid abroad Questionnaire for respondents who have moved abroad long pid, syear hid, cid vpl Deceased individual long vpid, syear hid, cid, intid cov SOEP-COV questionnaire long pid, syear hid, cid, intid pl “Individual questionnaire” (long): The PL dataset contains all waves of the $P datasets from SOEP-Core. In addition, the PL file includes all variables of all waves of the datasets $POST and $PAUSL. This means that the PL dataset contains all variables from the individual questionnaire for all waves. In addition, the individual-specific data from the IAB-SOEP Migration Survey and IAB-BAMF-SOEP Refugee Survey are integrated into the PL dataset. Attention: For large datasets we recommend the use of Stata/MP or Stata/SE on a computer with an internal memory of 16GB. Users can still work with the data in Stata/IC or on less powerful computers, but some modifications allow users to work effectively with even the largest datasets while placing low demands on their hard- and software. 5.4. Datasets SOEP-Core 105 SOEPcompanion, Release 2022, v.3 clear global data ="\\hume\rdc-prod\complete\soep-core\soep.v35" *Search all available pl variables on paneldata.org:https://paneldata.org/soep-core/ ˓→data/pl describe using "$data/pl.dta" use pid hid cid syear plh0149-plh0151 using "$data/pl.dta" hl “Household questionnaire” (long): HL contains all waves of the datasets $H from SOEP-Core. This means that the HL dataset includes all questions of the household questionnaire. In addition, the household-specific data from the IAB-SOEP Migration Survey and IAB-BAMF-SOEP Refugee Survey are integrated into the original HL dataset. biol “Biographical data” (long): BIOL contains cumulated individual-level raw data from the biographical questionnaire and from wave-specific biographical modules of the individual questionnaire. BIOL is intended to be used in addition to the generated biographical files (by advanced users) to complete (or modify) generated biographical variables. jugendl “Youth questionnaire for first-time respondents at age 17” (long): JUGENDL contains the waves q (2000) up to the current wave of $JUGEND in SOEP-Core. Since 2000 (wave Q), first-time respondents between the age of 16 and 17 have received a separate biographical questionnaire with additional age-group-specific questions, for instance, about their relationship to their parents or about what they do in their free time. MORE_Docu “Dataset on the Mentoring of Refugees Projekt”: A dataset on the Mentoring of Refugees (MORE) project. Carried out in partnership with Start with a Friend (SWAF), this project aimed at bringing refugees and locals together to form friendships. This dataset contains information on German contacts provided to refugees. Information about the federal state of the SWAF location is includes in the EU data edition. More localized information (county or municipality) is only available remotely or on site. MORE_Local “Dataset on the Mentoring of Refugees Projekt”: A new dataset on the Mentoring of Refugees (MORE) project. Carried out in partnership with Start with a Friend (SWAF), this project aimed at bringing refugees and locals together to form friendships. This dataset contains information from the surveys of the locals in the project. plueckel “Catch-up questionnaire” (long): The PLUECKEL dataset contains all waves of the $PLUECKE datasets in SOEP-Core. Temporary drop-outs (“gaps”) can cause problems for longitudinal analyses. This has especially negative consequences for the employment and income data. That is why the SOEP tries to fill in at least some of the key missing information. PLUECKEL is a small questionnaire covering information on the year previous to which the temporary drop-out occurred. It covers questions on job-related changes, employment calendar, income, education, and qualifications. abroad “Questionnaire for respondents who have moved abroad” (long): With the pilot study “Life outside Germany” in 2008, the longitudinal SOEP study ventured into completely uncharted methodological territory by attempting to locate the addresses of former SOEP respondents who have since moved abroad and to survey these individuals with the help of a specially developed written questionnaire on the reasons for their move. The project was discontinued due to insufficient case numbers in 2014. vpl “Questionnaire on the deceased individual” (long): The VPL dataset contains all waves of the $VP datasets of SOEP-Core. The VPL file contains information about respondents who lost a relative in the previous year. It provides information about the deceased individual and the respondent who reported the death. cov “SOEP-COV questionnaire” (long): COV contains the survey content of SOEP-CoV study. This datset is associated with the 9 tranches of the SOEP-CoV in 2020, the SOEP-CoV wave in 2021 and COVID-19-special interviews 2020 from the IAB-BAMF-SOEP Survey of Refugees in Germany. More information about the project can be found online: 106 Chapter 5. Data Structure of SOEP-Core SOEPcompanion, Release 2022, v.3 5.4.3 Survey Data These datasets contain information on survey methodologies used in SOEP-Core. The various datasets contain detailed exit information provided by respondents and the household weighting factors that users need for representative analysis. Dataset Label Format Identifier (ID) Special Identifier design Survey design long (timeconstant) cid intid exit Cumulative drop-outs long (timeconstant) pid cid, syear pbr_hhch PBR_HHCH long pid, syear hid, cid, pnralt, pnrneu, hhnrold cirdef Randomized survey file long (timeconstant) cid cov_contact Contact Data SOEP-COV long hid ContactDate tranche design “Survey design”: The dataset DESIGN provides information on the stratified sampling of the SOEP in the form of two variables. The variable STRAT identifies each of the discrete sampling groups described above. Altogether, the SOEP consists of 40 strata: one stratum in sample A, twenty-seven in sample B, one in sample C, three in sample D, one in sample E, two in sample F, four in sample G, and one in sample H. Each of these strata have unique inclusion probabilities. The variable design contains the inverse of this probability, i.e., the design weight. exit “Follow-up study [Verbleibstudie]”: The dataset EXIT delivers the results from the follow-up study [Verbleibstudie] conducted by Kantar Public (formerly: TNS Infratest) in 2008/2009. This study has been used to identify reasons for (demographic) dropouts. Deceased individuals identified through the follow-up study are included in the corresponding variables in PPATH/L [todjahr, todinfo]. pbr_hhch “PBR_HHCH”: The dataset pbr_hhch is a subfile of pbrutto that was used from 1984 to 2009 to identify individuals from households that underwent split-offs in subsamples A-H. cirdef “Randomized survey file”: This dataset includes randomized groups of original sample households [rgroup] for selection of representative shares across all subsamples with full representation of any cross-sectional and longitudinal information (variables) at all levels (case, households, individuals, spells) for the entire SOEP population across all waves. cov_contact “Contact Data SOEP-COV”: COV_CONTACT contains the contact information of SOEP-CoV study. This datset is associated with the 9 tranches of the SOEP-CoV in 2020, the SOEP-CoV wave in 2021 and COVID-19-special interviews 2020 from the IAB-BAMF-SOEP Survey of Refugees in Germany. More information about the project can be found online: 5.4.4 Generated Data The SOEP team has prepared these datasets for easy use and subjects them to additional plausibility checks and quality controls prior to data release. In most cases, they consist of several variables and different survey instruments and are described in the documentation provided. As a result, these datasets cannot be assigned 1:1 to a single survey instrument. Dataset Label Format Identifier (ID) Additional Identifier pgen Generated individual data long pid, syear hid, cid, pgpartnr continues on next page 5.4. Datasets SOEP-Core 107 SOEPcompanion, Release 2022, v.3 Table 1 – continued from previous page Dataset Label Format Identifier (ID) Additional Identifier hgen Generated household data long hid, syear cid bioagel Generated biographical information long pid, syear, persnre hid, cid, biopupil Generated biographical information long pid, syear hid, cid kidlong Data on children long pid, syear hid, cid pequiv Cross National Equivalent File long pid, syear hid, cid biobirth Generated biographical information wide pid cid, kidpnr01- kidpnr15 bioedu Generated biographical information long (timeconstant) pid cid bioimmig Generated biographical information long pid, syear hid, cid biojob Generated biographical information long (timeconstant) pid cid bioparen Generated biographical information long (timeconstant) pid cid, fnr, mnr bioregion Generated biographical information long (timeconstant) pid, syear cid bioresidrefinG Generated biographical information wide pid biosib Generated biographical information wide pid cid, sibpnr1- sibpnr11 biotwin Generated biographical information wide pid cid, pnrtwin, pnrtrip, pnrquad camces Highest educational qualification, migrants sample M1 and M2 long pid hid, syear, cid cogdj Data on cognitive tests (Youth) long pid syear, cid cognit Data on cognitive potential long pid syear, cid, intid cog_refu Data on cognitive tests (Refugees) CS pid syear, cid, hid gripstr Grip Strength Measures long pid, syear cid, intid hconsum Household Consumption Module CS hid syear, cid health Data on health indicators long pid, syear cid hwealth Wealth module long hid, syear cid interviewer Data on the SOEP interviewer long intid, syear cid mihinc Multiple imputed data on monthly household income long hid, syear cid continues on next page 108 Chapter 5. Data Structure of SOEP-Core SOEPcompanion, Release 2022, v.3 Table 1 – continued from previous page Dataset Label Format Identifier (ID) Additional Identifier pflege Persons needing care within the household long pid, syear cid pkal Individual calendar long pid, syear hid, cid pwealth Wealth module long pid, syear hid timepref Experiment on time preferences CS pid hid, syear, cid trust Experiment on trust long pid hid, syear, cid pgen “Generated individual data” (long): PGEN contains all waves of the $PGEN datasets in SOEP-Core. The PGEN- file contains user-friendly data on the individual level that are consolidated from different sources. The plausibility is validated longitudinally in many respects, making the data superior to those in PL in most situations. The file contains one row for each individual (pid is unique) with a completed individual or youth questionnaire. hgen “Generated household data” (long): HGEN contains all waves of the $HGEN datasets in SOEP-Core. In order to minimize computational effort for the user, the SOEP provides yearly status variables on the household level. The HGEN data provide a set of time-invariant variables generated from the SOEP household questionnaire. They only include households that participated in the respective year. bioagel “Generated biographical information” (long): The BIOAGEL data files are generated using information collected in the “Mother & Child” and “Parent” questionnaires. BIOAGEL is now provided in one dataset. biopupil “Generated biographical information” (long): The BIOPUPIL data files are generated using information collected in the “Pre-Teen” and “Early-Youth” questionnaires. BIOPUPIL is provided in one dataset. bioregion “Generated biographical information” (long): A dataset on places in Germany that are of biographical importance to respondents (place of birth, first place of residence). Information about the federal state of these important places is includes in the EU data edition. More localized information (county or municipality) is only available remotely or on site. bioresidrefinG “Generated biographical information” (long): A dataset on refugees’ place(s) of residence in Germany (Wohnorthistorie). Information about the federal state in which refugees reside is included in the EU data edition. More localized information (county or municipality) is only available remotely or on site. kidlong “Data on children” (long): The variables stored in the KIDLONG file are based on the information collected annually and contained in the wave-specific $KIND files. The relevant information is not provided by children themselves but is obtained from answers to questions in the household questionnaire provided by the respondent within the household (usually the head of the household). This data is reaggregated at the individual level and stored as child-specific entries in the file $KIND. pequiv “Cross-National Equivalent File” (long): PEQUIV contains all waves of the $PEQUIV datasets in SOEP-Core. The PEQUV-File is based on the Cross-National Equivalent File (CNEF) with extended income information for the SOEP. This file comprises not only the aggregated income figures from CNEF but also additional separate income components. pkal “Individual calendar” (long): PKAL contains all waves of the $PKAL datasets in SOEP-Core. The PKAL datasets contain calendar variables from the individual questionnaire. The dataset includes the individual’s employment or educational status on a monthly basis as well as the individual’ income status. biobirth “Generated biographical information” (wide): The file BIOBIRTH provides information on fertility histories of adult respondents in the SOEP. Up to 2014 (version 30, wave BD), the data were stored in two separate files: BIOBIRTH containing female fertility histories, and BIOBRTHM providing male fertility histories. Fertility histories in BIOBIRTH provide information on every woman (as well as every man with panel entry since 2001) who has ever completed at least one SOEP interview. 5.4. Datasets SOEP-Core 109 SOEPcompanion, Release 2022, v.3 Outdated bioedu “Generated biographical information” (long time constant): The SOEP contains a broad range of variables on early childhood education and care, educational participation, educational degrees, and related topics. The BIOEDU dataset is designed to provide ready-made variables on educational transitions and related topics for use in longitudinal analysis. bioimmig “Generated biographical information” (long): The variables contained in BIOIMMIG relate to foreigners in (and migrants to) Germany. Questions deal with the desire to return to the home country, the presence of relatives in the home country, reasons for coming to Germany, and conditions upon initial arrival in Germany. Outdated biojob “Generated biographical information” (long time constant): The purpose of BIOJOB is to provide a file that offers the user convenient access to biographical information on past job activities. BIOJOB consists of generated variables as well as plain questionnaire information. Up to now, all but two variables in BIOJOB are timeinvariant. Information on occupational changes and on the age at the most recent change of occupation refer to the date of the respondent’s biography interview. Outdated bioparen “Generated biographical information” (long time constant): The dataset BIOPAREN contains biographical entries on the parents’ and respondent’s background. The information in BIOPAREN is obtained from two sources: from proxy entries by children on their parents in the biography questionnaire and youth questionnaire, and from direct entries by parents when the respondent lives in the same household as the parents. Please note that BIOPAREN focuses on the social parent. Biological parent identifiers can be found in BIOBIRTH. Outdated biosib “Generated biographical information” (wide): BIOSIB provides information on siblings living within SOEP households. The dataset contains the individual identifiers of all siblings in a SOEP household. It includes information on the individual sibling’s sex, year of birth, number of siblings, position in birth order, and relationship between siblings. Outdated biotwin “Generated biographical information” (wide): The file BIOTWIN contains all twins that were ever identified within the SOEP. To be classified as a twin, a individual is required to have exactly the same age as his or her sibling (year & month of birth), have a relationship to the head of the household that indicates that he or her and a second individuals are siblings, and have the same mother (as far as a pointer to the mother is available). Furthermore, it is not only twins that are recorded in the BIOTWIN dataset, but also triplets or quadruple siblings. camces “Highest educational qualification, migrant samples M1 and M2” (CS): The CAMCES-File provides information about computer-assisted measurement and coding of educational qualifications in surveys. cogdj “Data from cognitive tests (Youth)” (CS): In SOEP 2006, a separate questionnaire with cognitive tests for adolescents was used for the first time: “Lust auf DJ”. The acronym “DJ” stands for “Denksport und Jugend” (mind sports and youth)”, but it was named for its more common association with “disc jockey”. The questionnaire “Lust auf DJ” was created for all respondents aged 16-17. cognit “Data on cognitive potential” (long): In the 2006 survey year, for the first time, short cognitive tests were carried out with a subsample of the SOEP. The goal was to employ a robust set of instruments that could be administered easily by trained interviewers within just a few minutes. COGNIT06 provides the aggregated sum scores (total values for three time packages, so-called “parcels” of 30, 60 and 90 seconds). cog_refu “Data on cognitive tests (refugees)” (CS): The dataset contains sum scores for two competence measurements (previous school knowledge and basic cognitive skills) of youths born in 2000, 2003 and 2005 surveyed in 2017. gripstr “Measures of grip strength (left and right hand)” (long): The data on grip strength from the survey year 2012 is now included in the GRIPSTR dataset. hconsum “HH consumption module” (CS)“: We were faced with three methodological challenges in generating the final consumption data. First, due to the design of the consumption module, inconsistent answers arose between the amounts give for monthly and annual consumption. Second, there was the common problem of missing data, here in particular item nonresponse. And third, consumption data are usually blurred by heaping. For researchers who do not want their consumption variables to include changes from all steps of data preparation, the new dataset “HCONSUM” contains not only the prepared consumption variables but also flag variables providing researchers the opportunity to select individual solutions. 110 Chapter 5. Data Structure of SOEP-Core SOEPcompanion, Release 2022, v.3 health “Data on health indicators” (long): Starting in 2002, the SOEP health module in the individual questionnaire has been revised and replicated at two-year intervals. In the HEALTH file, users find, for instance, the generated variables on height and weight with imputation flags and a user-friendly longitudinal checked generated variable for Body Mass Index (BMI). hwealth “Wealth module” (long): The generated SOEP wealth data is stored in two separate data files called PWEALTH for information at the individual level and HWEALTH for correspondingly aggregated data at the household level. HWEALTH contains all information on the household level; it is purely the result of aggregating the individual-level information in PWEALTH. However, for all individuals with valid household-level information who did not respond to the individual questionnaire (partial unit non-response), imputations have been carried out and the results are included in HWEALTH. interviewer “Data on the SOEP interviewer” (long): The SOEP aims not only to collect high-quality data on the living conditions and well-being of households, but also to provide a valuable empirical source for survey research. The INTERVIEWER file provides users with easy access to all available longitudinal information on the SOEP interviewers. mihinc “Multiple imputed data on monthly household income” (long): The dataset MIHINC contains the complete imputation results and is available separately. To be compatible with methods for analyzing multiply imputed data, MIHINC is constructed in the “stacked” or MIM data format. It contains the following variables: HHNRAKT, SVYYEAR, MJ, MI, IHINC and IMPFLAG. Since 1995 for every survey household in all survey years, there are ten imputed values for current household income. pflege “Persons needing care within the household” (long): Since wave B (1985), the SOEP household questionnaire includes questions on household members in need of care. In order to support individual-level analysis, this information has been restructured and is stored in the cumulative file PFLEGE. pwealth “Wealth module” (long): For the first time in 2002, the individual questionnaire included a special module focusing on wealth. It included questions on seven different wealth components: owner-occupied property (including debt), other property (including debt), financial assets, private pensions (including life insurance and building savings contracts), business assets, tangible assets, and consumer credit. The generated SOEP wealth data are stored in two separate data files called PWEALTH for information at the individual level and HWEALTH for correspondingly aggregated data at the household level. Wealth-related variable names in the file PWEALTH consist of six digits. The first digit tells the user which wealth component is referred to, and the second to sixth digits provide more detailed information about possible filter information, the personal share, the gross amount, and the amount of any outstanding debt. In principle, a digit is coded “1” if a given variable does indeed contain this specific piece of information and “0” otherwise. The wealth information in the SOEP questionnaire is surveyed at the individual level and thus also imputed or edited at the individual level (although checked against household information for consistency). timepref “Experiment on time preferences” (CS): Following the behavioral experiment on trust and trustworthiness carried out in the 2003, 2004, and 2005 SOEP surveys, the experiment “time preferences” was run in 2006. In this experiment on economic behavior, respondents were asked to decide how they would want to receive €200 in prize money: if they would want to receive it immediately by check or if they would want to wait and receive a larger amount later, that is, with interest. trust “Experiment on trust” (long): The economic behavior experiment on trust and trustworthiness from survey years 2003, 2004, and 2005 served to measure trust based on an investment game, a one-off game for two players who interact anonymously. The first player receives a credit of ten points and can overwrite any number of points of the second player. Each overwritten point is doubled. The second player also receives a credit of ten points. After receiving the (doubled) points from the first player, the second player decides how much of her own credit she will transfer to the first player (zero to ten points). As with the first transfer, the recipient’s points are doubled. After the decision of the second player, the game ends and the other players are paid (one point corresponds to one euro, the total is paid by check a few days later). The trust dataset thus contains the information from all three waves in which the behavioral experiment was conducted. 5.4. Datasets SOEP-Core 111 SOEPcompanion, Release 2022, v.3 5.4.5 Spell Data Spell, duration, and event history data are used frequently in the social sciences. In the strict sense of the word, spell data are about time periods with a defined start and end. General information about the data structure of spell data can be found in the chapter Data Structure in Spell Format (spell) Working with spell data: Working with spell data (pdf): Working with spell data (do-files): How to generate spell data from data in wide format: Based on the migration biographies in the IAB-SOEP Migration Sample: Generating spell data: Dataset Label Format Identifier (ID) Additional Identifier artkalen Spell data from the activity calendar spell pid cid biocouplm Generated biographical information spell pid cid, coupid biocouply Generated biographical information spell pid cid biomarsm Generated biographical information spell pid cid biomarsy Generated biographical information spell pid cid einkalen [deprecated] Spell data on income spell pid cid lifespell Spell Information on the pre- and post-survey history of SOEP respondents spell pid cid migspell Migration history spell pid cid pbiospe Generated biographical information spell pid cid refugspell Migration history spell pid cid sozkalen [deprecated] Spell data on social benefits spell hid, cid artkalen “Spell data from the activity calendar” (long): The ARTKALEN contains spells (monthly) for events starting in January 1983. This is in contrast to PBIOSPE, where spells were in yearly durations, and events previous to 1983 were included. The information on activity status is collected on a monthly basis in the yearly individual questionnaire and stored in the file ARTKALEN. biocouplm “Generated biographical information” (long): With the BIOCOUPLM the SOEP provides consistent and continuous partnership histories for nearly all adult respondents. BIOCOUPLM is built on the prospective information at the time of each interview. The relationsship histories are collected on a monthly basis from all adult SOEP participants since their entry into the SOEP. biocouply “Generated biographical information” (long): With the BIOCOUPLY, the SOEP provides consistent and continuous partnership histories for nearly all adult respondents. BIOCOUPLY is built on retrospective and prospective information at the time of each interview. The relationship histories are provided on an annual basis. biomarsm “Generated biographical information” (long): With BIOMARSM the SOEP provides consistent and continuous marital histories for nearly all adult respondents. BIOMARSM is built on the prospective information at the time of each interview. The martial histories are collected on a monthly basis from all adult SOEP participants since their entry into the SOEP. biomarsy “Generated biographical information” (long): With BIOMARSY the SOEP provides consistent and continuous marital histories for nearly all adult respondents. BIOMARSY is built on retrospective and prospective information at the time of each interview. The marital histories are provided on an annual basis. Outdated einkalen “Spell data on income” (long) The income calendar is used to gain information about sources of income throughout the year. The respondent checks off for each month all appropriate sources of income. 112 Chapter 5. Data Structure of SOEP-Core SOEPcompanion, Release 2022, v.3 lifespell “Spell information on the pre- and post-survey history of SOEP respondents” The SOEP team regularly conducts follow-up studies to relocate attritors. These studies draw on official register data and allow us to determine whether a individual is still living in Germany, is deceased, or has moved abroad since the last SOEP interview. The information is combined in a spell file LIFESPELL. This dataset reports all available information on the pre- and post-survey history of all individuals who have ever been a member of a SOEP household. Outdated migspell “Migration history” (long): MIGSPELL is derived from the migration biographies, which are collected from each new respondent of the IAB-SOEP migration samples M1 and M2. It contains data on moves by foreign-born migrants as well as on stays abroad by German-born respondents. pbiospe “Generated biographical information” (long): 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 who completes the biographical questionnaire. The observations start at the age of 15 and end at the current age (up to age 65). To update ongoing employment information in PBIOSPE, information from the yearly individual questionnaire is also used. Outdated refugspell “Migration history” (long): For migration biographies in the refugee samples, we created the spell dataset REFUGSPELL. The variables in MIGSPELL and REFUGSPELL are derived from different instruments and only partially overlap. The data structure allows the dataset to be linked with MIGSPELL if desired. Outdated sozkalen “Spell data on social benefits“: The file SOZKALEN provides spell data on households receiving social assistance, defining the beginning, end, and censoring status of any period of receiving 3 different types of assistance. This file is set up using information from the calendar that is collected for the previous year (between 1992-2000). Thus, it contains information on a monthly basis. Last change: May 12, 2022 5.5 Data Processing The following overview shows which datasets form the basis of each questionnaire, and the respective data processing process, from the questionnaire to the wave-specific datasets, to the prepared “long” datasets. Please note that not all datasets are based on questionnaires but that many have been prepared meticulously by our staff. The table therefore does not show the full range of datasets available. In addition to the classic SOEP survey instruments, there are also a large number of sample-specific questionnaires whose information flows into other unlisted raw datasets (e.g. $pausl, $post, $pkalost etc.). The chapter Sample- Specific Questionnaires explains why such special survey instruments exist, how they become raw datasets and in which long datasets these variables can be found. Last change: May 12, 2022 5.5. Data Processing 113 SOEPcompanion, Release 2022, v.3 5.6 Dataset Identifiers Because of the overall data structure with data on different observational levels, any analysis requires the combination of data using matching or merging procedures. These merging procedures need identifiers such that a combination of datasets becomes feasible. The central individual identifier across time is pid, which is fixed over time (and of course datasets). Since a person might change the household in which he or she lives at any point in time, yearly household identifiers called hid are necessary, facilitating matching depending on the dataset used. Finally, each individual (respondents as well as children) can be traced back to be a member of or a split-off from an original household from the very first wave. This household’s ID, which is fixed no matter how often a person changes households over time, is called cid. In addition, respondents in long data can be differentiated by survey year. The syear variable can be used to identify a respondent’s survey year. The SOEP provides additional identifiers in the various datasets in order to identify respondents and to provide further possibilities for merging datasets. A excerpt of these additional identifiers can be found here: Please note that these are not all identifier variables. The name of the identifier variable can change depending on the dataset used. •parid “Unchanging Individual identifier of Partner (PID)” •pgpartnr “Individual Identifier of Partner” •coupid “Couple Identifier” •intid “Interviewer Identifier” •intid1 “Identifier of First Interviewer” •vpid “Individual Identifier of Deceased Indivdiual” •mnr “Individual Identifier Mother” •fnr “Individual Identifier Father” •kidpnr01-kidpnr19 “Individual Identifier nth Child” •sibpnr1-sibpnr11 “Individual Identifier, nth Sibling” •pnrtwin “Individual Identifier 2nd Sibling” •pnrtrip “Individual Identifier 3rd Sibling” •pnrquad “Individual Identifier 4th Sibling” 5.6.1 Partner Identifier Partner identification (parid and pgpartnr) Partner indicators (parid from ppathl and pgpartnr from pgen) have the purpose of defining couples in SOEP households and thus to make possible analyses on the dyadic level. Persons without spouse and (cohabitating) partner receive a missing code “-2” (=does not apply). The assignment of the partner ID within households is based on four sources of information: A question in the person-file, that asks (unmarried) respondents to identify their partner in the household, the household matrix reported by the head of household at the beginning of the interview (stell from pbrutto), the partnership biography in the lifehistory calendar reported by new respondents, and self-reports on marital status and life events, such as marriage, move in with partner, separation, etc. In unclear cases, due to temporal non-response for instance, we also consider longitudinal information from previous and prospective waves. Moreover, parid is selfconsistent between two individuals. For analyses of partner relationships, this information can be used to link all persons with their respective partners, and all information on both partners can also be stored in a common dataset. parid includes all persons that have ever participated in the SOEP. pgpartnr from the pgen dataset contains the same information, but is restricted to the pgen population and includes only persons with persons interview. 114 Chapter 5. Data Structure of SOEP-Core SOEPcompanion, Release 2022, v.3 Example: •H:/material/exercises/do •H:/material/exercises/log •H:/material/exercises/output •H:/material/exercises/temp These are used to store your script, log files, datasets and temporary datasets. Open an empty do-file and define your paths with globals: 1*********************************************** 2*Set relative paths to the working directory 3*********************************************** 4global AVZ "H:/material/exercises" 5global MY_IN_PATH "//hume/rdc-prod/distribution/soep-core/soep.v37/eu/Stata/" 6global MY_DO_FILES "$AVZ/do/" 7global MY_LOG_OUT "$AVZ/log/" 8global MY_OUT_DATA "$AVZ/output/" 9global MY_OUT_TEMP "$AVZ/temp/" Attention: Please note that until version 33 (v33), PPATH was called PPFAD. The following exercises are done with version 37 (v37). The global „AVZ“ defines the main path. The main paths are subdivided using the globals “MY_IN_PATH”, “MY_DO_FILES”, “MY_LOG_OUT”, “MY_OUT_DATA”, “MY_OUT_TEMP”. The global “MY_IN_PATH” contains the path to the data you ordered. Based on the data in PPATHL, answer the following questions: 1. Look at the two people with the Person IDs (pid) 2102 and 19202 a) What is their gender? When were they born and when (if applicable) did they die? Open the PPATHL dataset. Search the dataset for variables that describe survey year, sex, year of birth and year of death. Display the information from the variables for individuals 2102 and 19202. 1use "${MY_IN_PATH}ppathl.dta", clear 2list pid syear sex gebjahr todjahr if pid == 2102 |pid == 19202 6.1. Working with Tracking Data (PPATHL) 121 SOEPcompanion, Release 2022, v.3 Individual 2102 is female, was born in 1927 and died in 1999. She has participated annually since 1984 until 1998. Individual 19202 is male, was born in 1960 and participated twice, in 1985 and 1986. The value “-2” for the variable year of death (todjahr) stands for “Does not apply”. For more information on the values, see the Missing Conventions. b) Were these people and their parents born in Germany? In the dataset, search for a variable that describes the migration background and the survey year. Display the information from the variables for indivIduals 2102 and 19202. 1list pid syear migback if pid == 2102 |pid == 19202 122 Chapter 6. Working with SOEP Data SOEPcompanion, Release 2022, v.3 Individual 2102 has no migration background. Individual 19202 has a direct migration background which means that he was not born in Germany. c) If they immigrated to Germany, in which year and from what country? Search the dataset for a variable that describes the country of birth, the year of moving to Germany and the survey year. Display the information from the variables on individuals 2102 and 19202. 1list pid syear immiyear corigin if pid == 2102 |pid == 19202 6.1. Working with Tracking Data (PPATHL) 123 SOEPcompanion, Release 2022, v.3 Individual 2102 is born in Germany and has therefore no immigration year. Individual 19202 immigrated from Turkey in 1980. d) Are these people from East or West Germany? Search the dataset for a variable that tells whether respondents are from the East or West, the survey year and sample. Display the information from the variables for individuals 2102 and 19202. 1list pid syear loc1989 psample if pid == 2102 |pid == 19202 124 Chapter 6. Working with SOEP Data SOEPcompanion, Release 2022, v.3 The variable loc1989 shows where the individual lived in 1989. Individuals 2102 and 19202 lived in West Germany in 1989 and, accordingly, were from Sample A (West). e) What sources provide the information on the migration background and year of death Search the data set for variables that give you the sources of information for year of death and migration background. The variable miginfo contains the information about the usage of (grand-)parents’ migration history in the SOEP. The variable todinfo gives the source of the information for all persons who have been identified as deceased over the course of SOEP. Display the information from the variables for individuals 2102 and 19202. 1list pid syear miginfo todinfo if pid == 2102 |pid == 19202 6.1. Working with Tracking Data (PPATHL) 125 SOEPcompanion, Release 2022, v.3 The information on the migration background for both individulas come from the respondents themselves. No further indicators are provided. For individual 2102, the information for the year of death comes from an Infratest Follow-Up Study of drop-outs in 2001. For individual 19202 the year of death is not provided. 2. How many people lived in a private household that was interviewed in 2016 and completed the individual questionnaire? Search the dataset for variable that describe the population in the 2016 survey year. Display the characteristics of the population variable. 1tab pop if syear==2016 Values 1 and 2 are relevant to answer the question because they describe private households with completed interview. 126 Chapter 6. Working with SOEP Data SOEPcompanion, Release 2022, v.3 Search the dataset for variable that describe the survey status in the 2016 survey year. Display the characteristics of the survey status: 1fre netto if syear==2016 Respondents with survey status between 10 and 15 or survey status 19 completed the individual questionnaire. These are all individuals 18 years and older. Cross-tabulate the variables netto and pop with an appropriate restricting condition to answer the question. 6.1. Working with Tracking Data (PPATHL) 127 SOEPcompanion, Release 2022, v.3 1tab netto pop if ((netto>=10 &netto<=15)|netto==19)&(pop==1|pop==2)&␣ ˓→(syear==2016) In 2016, a total of 27,401 respondents completed the individual questionnaire for Sample Membership 1 and 2. 3. PPATHL allows you to see which populations can be viewed from a longitudinal perspective: a) How many people who answered the individual questionnaire in 2000 also took part in the survey in 2014? Generate a variable and limit the survey status to individuals who answered an individual questionnaire in 2000 and 2014. Note that the values 10,12,13,14,15,16,18,19 of the netto variable mean realized interviews. Sort the dataset by the variable pid and generate a second variable to calculate the sum of the first generated variable. Display the characteristics of the survey status under the condition that the individual questionnaire has been answered. 1gen v1 =1if (netto>=10 &netto<=19 &syear==2000)|(netto>=10 &netto<=19 &␣ ˓→syear==2014) 2bysort pid : egen v2 =sum(v1) if netto>=10 &netto<=19 3tab netto syear if v2==2&(syear==2014|syear==2000) A total of 7,639 respondents completed the individual questionnaire in 2000 and 2014. b) How many people answered the individual questionnaire every year from 2000 to 2014? Generate a variable that counts the number of waves of completed individual interviews and limit it to the years 2000 until 2014. If the generated variable takes the value 15, a person has completed a personal interview 15 years in a row. Display the survey status and the survey year with the newly created variable. 1egen h1 =count(syear) if netto>=10 &netto<=19 &syear>=2000 &syear<=2014, by(pid) 2tab netto syear if h1==15 &syear>=2000 &syear<=2014 128 Chapter 6. Working with SOEP Data SOEPcompanion, Release 2022, v.3 A total of 6,665 people completed the individual questionnaire every year from 2000-2014. c) How many people who turned 15 in 2011 and spent at least part of their childhood in a SOEP household took part in the survey in 2016? Generate a variable with people who turned 15 in 2011 and had lived in a survey household as a child. The age of the respondent can be determined with the year of birth, and you can limit children using the net code. Display the new generated variable and the year of birth. 1gen a15kind =1if 2011-gebjahr==15 &netto>=20 &netto<30 &syear==2011 2tab a15kind gebjahr A total of 741 people were 15 years old in 2011 and lived as children in a survey household. To find out if these 741 people filled out a person questionnaire in 2016, we generate a second variable that fills up the value of one person for all remaining available years. Limit the net code and survey year to narrow down the cases appropriately. 1bysort pid : egen a1 =max(a15kind) 2tab netto if a1==1&netto>=10 &netto<20 &syear==2016 6.1. Working with Tracking Data (PPATHL) 129 SOEPcompanion, Release 2022, v.3 A total of 309 people who were 15 years old at the time of the survey and had been part of a survey household as a child in 2011 completed an individual interview in 2016. d) The individual with pid=588010 was born in 1984 in a panel household and was still part of the sample in 2009. The individual changed households twice during this time. In which years? To identify how often and when a individual changed households, you must display all available household numbers in PPATHL for individual 588010. 1list pid hid syear gebjahr if pid==588010 130 Chapter 6. Working with SOEP Data SOEPcompanion, Release 2022, v.3 weighting for a representative analysis. 1*weighted* 2tabstat yp0101 [aw=yphrf], by(emplst08) c) Age Since you do not have a variable that represents age, you must generate a suitable age variable using the birth year variable. The year of birth is metric and should be categorized for analysis. Define categories for your age variable and assign suitable labels. 1*c) by age in 2008 (<30,30-64,65+) 2 3gen age=2008-gebjahr 4gen age_3=age 5recode age_3 (17/29=1) (30/64=2) (65/120=3) 6label define age_3 1"17-29" 2"30-64" 3"65+" 7label values age_3 age_3 Create a mean value comparison with your age variable and health satisfaction in weighted and unweighted form. 1*unweighted* 2tabstat yp0101, by(age_3) 6.2. Generating a Cross-Sectional Dataset 137 SOEPcompanion, Release 2022, v.3 1*weighted* 2tabstat yp0101 [aw=yphrf], by(age_3) d) Income As with age, generate a categorized version of income for household net income: 1*d) by monthly houshold net income (-1.999,2.000-3.999,4000+Euro) 2gen hinc08_3 =hinc08 3recode hinc08_3 (0/1999=1) (2000/3999=2) (4000/99999=3) 4label define hinc08_3 1"<2000 Euro" 2"2000-<4000 Euro" 3"4000+ Euro" 5label values hinc08_3 hinc08_3 Display the mean values in weighted and unweighted form: 1*unweighted* 2tabstat yp0101, by(hinc08_3) 138 Chapter 6. Working with SOEP Data SOEPcompanion, Release 2022, v.3 1*weighted* 2tabstat yp0101 [aw=yphrf], by(hinc08_3) e) Smoking Since this variable is nominal, adjustments to this variable are not necessary. Display average satisfaction with health for smokers and non-smokers in weighted and unweighted form: 1*e) by smoking yes/no 2 3*unweighted* 4tabstat yp0101, by(yp10601) 6.2. Generating a Cross-Sectional Dataset 139 SOEPcompanion, Release 2022, v.3 1*weighted* 2tabstat yp0101 [aw=yphrf], by(yp10601) Last change: Jul 20, 2022 6.3 Syntax Generator on paneldata.org Paneldata allows registered users to collect and save variables relevant to their research in a variable basket. These variables can be simply written into a single dataset with the script generator. The script generator helps you with data management and can save valuable working time. Open Paneldata For our experienced users, we have temporarily equipped the old soepinfo with the current data so that the variable basket function and the script generator can also be used there. Open soepinfo 140 Chapter 6. Working with SOEP Data SOEPcompanion, Release 2022, v.3 Click on “Register / login” to log in to paneldata.org. 6.3. Syntax Generator on paneldata.org 141 SOEPcompanion, Release 2022, v.3 If you have already registered, go to “user login”. As a new user, you can register under “register here”. Once you have logged in, you have access to the variable basket and the syntax generator. 142 Chapter 6. Working with SOEP Data SOEPcompanion, Release 2022, v.3 To access the activated functions, click on the button “my baskets”. You will be taken to your personal workspace on paneldata.org. “My baskets” displays your variable baskets. If you click on “create basket”, you can create a new basket. When creating the basket, first define the name of the variable basket. The name must be lower case to be accepted by 6.3. Syntax Generator on paneldata.org 143 SOEPcompanion, Release 2022, v.3 Paneldata. Optionally, you can assign a label and enter a description. Finally, you select the study that you want to use as a database for your research. Now click on “Create basket” and your newly created variable basket appears in the interface. Now search for the relevant variables on paneldata.org and add them to your individual basket. For example, you are interested in monthly net household income. If you do not know the variable name, you can find the overarching concept using the topic search. Click on “paneldata.org” to get to the main page. Select the study SOEP-Core and click on “topics” at the top of the page. Check the different topics for income-relevant concepts and select “income, taxes, and social security”. 144 Chapter 6. Working with SOEP Data SOEPcompanion, Release 2022, v.3 Browse the topic list and you will reach the sub-topic “income” –> “household income” –> “monthly income”. There you will find the variables you are looking for. Click on “show all related variables” and you will see the history of variables. Select the variable of your desired study SOEP-Core and you will reach the variable overview with important information about the variable. In the variable overview, you should make sure that the variable also meets your requirements. 6.3. Syntax Generator on paneldata.org 145 SOEPcompanion, Release 2022, v.3 When logged in, the basket area appears in the overview of variables. Your baskets are listed there. If you want to add the variable to a basket, click on “add to basket”. If the variable is already in the basket and you want to remove it, select “remove from basket”. If you want to create a new basket within the overview of variables, click on “create a new basket” and your variable will automatically be placed in the new basket. You can access the basket overview by clicking on the name of your basket in the “basket” section. Alternatively, you can click on the button “my baskets” and you will also return to the basket overview. 146 Chapter 6. Working with SOEP Data SOEPcompanion, Release 2022, v.3 •What is the export file format? (.rds, .shp, .csv are permitted) (to save in dataframe in rds format please use saveRDS()) •What are the unique identifiers for the dataset? (e.g., ID & syear) Formal criteria for data transfer: The following criteria apply to exports: •The README.csv is a two-column .csv table –$name: column containing the variable names of the indicators to be exported (e.g., distance) –$description: short description of the respective variable (e.g., distance in meters to the next flood point for household i in year t (for the flood in 2002)) •The following applies to the dataset containing the indicators to be exported –Dataset must have the column/variable ID from the input dataset used –Permissible file formats: rds, shp, csv –Dataset otherwise only contains the indicators described in the README.csv file Additional notes on data transfer: •After the data transfer has taken place, the output (datasets, transfer scripts) will be stored in your transfer folder on Hauser, in a subdirectory of your import folder that is identified by date (/home/USER/transfer/import/fromMoran/yyyy-mm-dd) Section author: Jan Goebel <[email protected]> Last change: Sep 26, 2022 If you want to import the SOEP data as csv files with an older version of Stata, this exercise will help you. 6.12 Working with SOEP data in csv format SOEP offers the data in statistical program specific file formats (e.g.: Stata .dta) and also as comma-separated values FIle (csv). With these csvs you can read the non-formatted information directly into a statistical program of your choice. This example shows how to open SOEP data of data version v.36 in csv format with an old Stata version (12) and how to prepare the data in an efficient way. Create an exercise path with four subfolders: Example: •H:/material/exercises/do •H:/material/exercises/output •H:/material/exercises/temp •H:/material/exercises/log 6.12. Working with SOEP data in csv format 249 SOEPcompanion, Release 2022, v.3 These are used to store your script, log files, datasets, and temporary datasets. Open an empty do-file and define the paths you created with globals: 1*********************************************** 2*Set relative paths to the working directory 3*********************************************** 4global AVZ "H:\material\exercises" 5global MY_IN_PATH "\\hume\rdc-prod\distribution\soep-core\soep.v35\csv" 6global MY_DO_FILES "$AVZ\do\" 7global MY_LOG_OUT "$AVZ\log\" 8global MY_OUT_DATA "$AVZ\output\" 9global MY_OUT_TEMP "$AVZ\temp\" The global “AVZ” defines the main path. The main paths are subdivided using the globals “MY_IN_PATH”, “MY_DO_FILES”, “MY_LOG_OUT”, “MY_OUT_DATA”, “MY_OUT_TEMP”. The global “MY_IN_PATH” contains the path to your ordered data. For the following script to work, the global “MY_IN_PATH” must contain the folder path to the SOEP csv files of all datasets. The csv files for each data set should always consist of three csvs. If we want to import and prepare the dataset jugendl in csv format, we need the following csv Files: •jugendl.csv •jugendl_variables.csv •jugendl_values.csv In the SOEP, the csv of each data set contains the variables as columns and their numerical values. Variables and Values csvs contain the variable labels and the value labels for the data set. First some packages for Stata have to be installed so that the process can start. 1* Import and Labeling of SOEP csv-Files 2clear 3set more off 4 5* Load ados 6capture which adolist 7if _rc==111{ 8ssc install adolist 9} 10 quietly adolist list 11 local allAdos `r(names)' 12 foreach package in fre labutil2 chardef labundef saveascii useold { 13 if !regexm("`r(names)'", " `package'") { 14 display as result "Paket " as error "`package'" as result " wird␣ ˓→versucht über SSC-Server zu installieren" 15 ssc install `package' 16 } 17 } Once the packages are installed, you will need to define the following functions to be able to label your dataset later. We define the function soeplabelsvars for linking the variables to the variable labels. 1* Assign German variable labels from *_variables.csv 2capture program drop soeplabelsvars 3program soeplabelsvars (continues on next page) 250 Chapter 6. Working with SOEP Data SOEPcompanion, Release 2022, v.3 (continued from previous page) 4version 12 5syntax , varlabels(string) 6preserve 7insheet using "`varlabels'", clear names 8putmata varLab = (variable label_de) ,replace 9restore 10 foreach variable of varlist * { 11 label variable `variable'"" 12 } 13 14 mata: st_local("n", strofreal(rows(varLab))) 15 forvalues i = 1/`n'{ 16 mata: st_local("varName",varLab[`i',1]) 17 mata: st_local("varLabel",varLab[`i',2]) 18 capture confirm variable `varName' 19 if !_rc { 20 di "Variable: `varName'mit -`varLabel'- gelabelt" 21 label variable `varName'"`varLabel'" 22 } 23 else di "Variable " as error "`varName'" as result " nicht vorhanden" 24 } 25 end The soeplabelvals function links the information in the data set with valuelabels. 1* Assign German value labels from *_values.csv 2capture program drop soeplabelsvals 3program soeplabelsvals 4version 12 5syntax , vallabels(string) 6quietly label drop _all 7quietly labundef , detach 8preserve 9insheet using "`vallabels'", clear names 10 quietly tostring value, replace 11 putmata valLab = (variable value label_de) ,replace 12 quietly levelsof variable, local(variables) clean 13 restore 14 foreach variable in `variables'{ 15 di "------------" 16 di "Variable `variable'wird gelabelt" 17 mata: valLabVar= select(valLab, valLab[.,1]:=="`variable'") 18 mata: st_vlmodify("`variable'", strtoreal(valLabVar[.,2]) , ˓→valLabVar[.,3]) 19 capture confirm variable `variable' 20 if !_rc label value `variable' `variable', nofix 21 else di "Variable " as error "`variable'" as result " nicht vorhanden" 22 } 23 end After both functions have been loaded we can define in a local the dataset we want to import and prepare as csv. We load the csv via the insheet command. Then we use the defined functions and use the variables.csv and values.csv provided by SOEP to label the data. 6.12. Working with SOEP data in csv format 251 SOEPcompanion, Release 2022, v.3 1*import and label dataset 2local dataset ="jugendl" 3insheet using "$MY_IN_PATH/`dataset'.csv", clear names 4soeplabelsvars, varlabels("$MY_IN_PATH/`dataset'_variables.csv") 5soeplabelsvals, vallabels("$MY_IN_PATH/`dataset'_values.csv") Congratulations you should now have a fully labeled dataset! Last change: Sep 26, 2022 252 Chapter 6. Working with SOEP Data CHAPTER SEVEN WORKING WITH SOEP DOCUMENTATION 7.1 Variable Search with Questionnaires If you come across a variable in the dataset whose variable content is unclear, you should always check whether there is a suitable questionnaire for the dataset. Under Original Core Data you can see whether the datasets correspond to a survey instrument. The related questionnaires can be found here: Questionnaires Example: Working on a research project, you come across the variable bjh_16_04 with the German label “Auto: Gründe” (Car: Reasons) and the English label “Reason for No Car in Household” Unfortunately, it is unclear what exactly this variable represents. You should refer to the questionnaires for the complete question and possible filter instructions. Example Variable: bjh_16_04: Wave “bj” (Survey Year 2011); household questionnaire (“h”), question number 16, item 4 Open Questionnaires The variable “bjh_16_04” can be found in the questionnaires for 2019. Select the survey year and questionnaire by using the filter “Year” and “Type of Questionnaire” and download the household questionnaire. 253 SOEPcompanion, Release 2022, v.3 Search the variable “bjh_16_04” in the questionnaire. Since you are already in the correct questionnaire, you must now search for question 16. To understand which information the variable “bjh_16_04” contains, you have to deal with the question. For each answer category, respondents should indicate whether or not the shown items apply to the household. If the item does not apply, respondents must answer an additional question about the reasons. Both questions should be understood as separate variables. E.g. the variable “bjh_16_01” indicates whether an internet connection is available in the household. The reasons why there is no internet in the household can be found in the variable “bjh_16_02”. The variable “bjh_16_03” shows whether a car is present in the household and the variable “bjh_16_04” shows reasons why no car is present in the 254 Chapter 7. Working with SOEP Documentation SOEPcompanion, Release 2022, v.3 household. By looking into the questionnaire, the variable is now easier to understand. The variable “bjh_16_04” only contains people who do not have a car in their household and shows the reasons given. Last change: May 12, 2022 7.2 Variable Search with paneldata.org Paneldata.org also allows you to search for variables and to find more information about generated variables. It offers comprehensive frequency counts, chronologies of variables, cross-study variable linkage via concepts, a syntax generator, and a topic list for content search in the SOEP. Example Variable: bbh5508: Wave “bb” (Survey Year 2011); household questionnaire (“h”), question number 55, item 8 Open Paneldata Select the study SOEP-Core. The SOEP-Core overview contains important general information about the study, e.g., data access, survey method, questionnaires, themes, terms for missing codes, all available datasets in the study and metadata-based questionnaires. To search for a variable, a dataset, or a publication, simply enter the desired search term in the search bar. 7.2. Variable Search with paneldata.org 255 SOEPcompanion, Release 2022, v.3 To obtain the desired results, you will need to input specific information. The results window displays all search results. You will see that the variable “bbh5508” originates from SOEP-Core data and can be found in the dataset “bbh” (survey year 2011). If your search is not so specific, you can also search by keywords. We are still interested in the topic “car”. To better limit the 10000 results, the filter options on the left and on the top should be used. We are looking for variables from the “SOEP-Core” datasets. The search results should be limited with the filter options. Which survey years are of interest to me, do I want to work with original data or generated data? For more information about the different datasets in SOEP-Core, see the section Data Distribution File. Should the variable I am looking for be at household level or at individual level? 256 Chapter 7. Working with SOEP Documentation SOEPcompanion, Release 2022, v.3 By filtering, the search result is limited to 18 hits, which also shows the variable we searched for. If you click on the variable “bbh5508”, you will find additional information about the variable. First you see the weighted absolute frequencies for the variable. It is possible to remove the missing codes from the analysis and/or to display the relative frequencies. Even without opening the dataset, paneldata obgain a good overview of the frequencies of a variable. 7.2. Variable Search with paneldata.org 257 SOEPcompanion, Release 2022, v.3 In the Related Variables section you will also find the chronology of the variable you are looking for. The sample variable was collected in 2001, 2003, 2005, 2007, 2011, 2013. Below the survey year, the name of the variable in the respective year is displayed and can be clicked to access the respective variable page. You can see at a glance when the variable was measured, how often it was measured, and what its name is in the respective survey year. In addition, by clicking on “Output variables”, you will find a variable forwarding you to the variable in “long” format. For a more detailed understanding of the long format, read the section Data Structure in “Long” Format (long). 258 Chapter 7. Working with SOEP Documentation SOEPcompanion, Release 2022, v.3 The paneldata topic list has three possible functions for each sub-topic. You can display all variables that belong to a sub-topic. In the future, paneldata will also display the texts of the questions from the SOEP questionnaires in which the variables in that sub-topic appear. Paneldata also allows you to keep variables from a sub-topic in a variable basket. The chapter Syntax Generator on paneldata.org explains in detail how to use the basket in your research and what possibilities this offers. Click on one of the variables to see the variable overview. 7.3. Topic Search with paneldata.org 265 SOEPcompanion, Release 2022, v.3 If you click on the concept of a variable, you will get to the concept overview. Concepts in SOEP are used to link variables with the same content. The concepts can even be used to link variables with the same content across studies. 266 Chapter 7. Working with SOEP Documentation SOEPcompanion, Release 2022, v.3 The concept overview displays the study- and wave-specific variables with this concept. The concept allows you to determine whether the variable you are looking for is also available and comparable across studies. In the column “Study” you can see which studies have the same variable linked by concept. The label of the respective variable is also displayed in the “Label” column. The column “path” shows the wave name of the variable. By clicking on the label, you will get to the overview of variables with all of the relevant information. The “Object” column in the concept overview shows you the type of information displayed. 7.3. Topic Search with paneldata.org 267 SOEPcompanion, Release 2022, v.3 In addition to the variables linked by concept, you can find the relevant questions in the concept overview. Questions are displayed in the “Object” column with question. Without having to open the questionnaire, you can read the question and identify possible differences. Click on the desired question and you will be taken to the question display. 268 Chapter 7. Working with SOEP Documentation SOEPcompanion, Release 2022, v.3 Attention: To find out the exact wording of the question and possible filter structures, a variable search in the questionnaires is necessary. The question display in Paneldata only provides a quick overview. In the question overview, you can navigate through the questionnaire using the “next question” and “previous question” buttons. The “Instrument” section shows the position of the question in the questionnaire, the survey year, and links to the metadata-based survey instrument. Click on the survey instrument “Questionnaire 2011”. 7.3. Topic Search with paneldata.org 269 SOEPcompanion, Release 2022, v.3 The survey instrument used in the SOEP-IS study in 2011 is now displayed. You can navigate through the questionnaire in this overview. The search bar allows you to search for research-relevant terms. Click on the question to access the question display. Last change: May 12, 2022 7.4 Documentation on Generated Data SOEP-Core contains a wide range of generated variables and datasets. To facilitate data use, we generate a large number of variables in the process of data preparation and release them with the SOEP-Core data. To make the generation process transparent to users, we provide comprehensive documentation on the numerous generated datasets and variables. For an overview, see our Documentation on Generated Data Example: A number of frequently used variables are provided in SOEP as “generated variables” (e.g., the datasets $PGEN and $HGEN). These variables are checked for consistency across waves. The documentation can be used to answer the following questions: a) Which variable gives the highest school-leaving certificate attained by individuals surveyed in 2007? To search for the variable that provides this information, open Paneldata , click on the search button and the tab “Variables”, then enter “school leaving degree” in the search bar. Specify your search by adjusting the filter settings as follows: •study: soep-core •Conceptual dataset: Generated (raw folder) 270 Chapter 7. Working with SOEP Documentation SOEPcompanion, Release 2022, v.3 •analysis unit: individual •period: 2007 All variables could contain the information you are looking for. Since almost all variables in the search result come from the generated “xpgen” dataset, the documentation for the $pgen dataset should be used. Visit the Documentation of SOEP-Core Page and enter the search term pgen in the search field. Alternatively, you can also use the filters and select “Data Documentations”: 7.4. Documentation on Generated Data 271 SOEPcompanion, Release 2022, v.3 Now select the documentation of the required version of pgen The table of contents on the left gives you a classification of the dataset by topics. To find the variable you are looking for, select topic area 10. 272 Chapter 7. Working with SOEP Documentation SOEPcompanion, Release 2022, v.3 After a few searches, you will find the variable you are looking for. The documentation provides useful information about the generated variable: it comes from the biography questionnaire, which was introduced in 1994 and is administered only once per respondent. The documentation also explains the two additional variables $psbila and $psbilo in more detail: the $psbil variable is updated regularly to take into account possible changes in the respondent’s highest school-leaving certificate. For this reason, the generated variable is useful in providing the most up-to-date information on completed secondary schooling. The variable we are looking for is xpsbil and describes the highest degree in certificate attained by individuals surveyed since 2007. b) What values do individuals with an upper secondary school-leaving certificate (Abitur) have for this variable?? Since you now know the variable you are looking for, you can use the extensive functions of paneldata.org in addition to the information from the documentation. If you search for the variable “xpsbil” in paneldata.org and click on it, the frequency counts are displayed. 7.4. Documentation on Generated Data 273 SOEPcompanion, Release 2022, v.3 In addition to the absolute and relative frequencies, you can also read the value codes of specific response categories. A translation of the answer categories can be found in the “Label translations” section: 274 Chapter 7. Working with SOEP Documentation SOEPcompanion, Release 2022, v.3 If you enter the command soephelp <variable> in a wave-specific data set, you will get detailed information about the variable in question. The question asked in the questionnaire is displayed as well as the samples and instruments in which the question was asked. Additionally, the command offers the corresponding long variable as well as the link of 7.5. Working with SOEPhelp 281 SOEPcompanion, Release 2022, v.3 the displayed variable to the documentation at paneldata.org. Conversely, with long data, you receive the wave-specific input variables and datasets used to generate the long-variable. 282 Chapter 7. Working with SOEP Documentation SOEPcompanion, Release 2022, v.3 Since our recent wave (v35) a new stata command option is being introduced. With soephelp, search (string) you reveive a list of variables that contain the respective word or label you are looking for. 7.5. Working with SOEPhelp 283 SOEPcompanion, Release 2022, v.3 For example, you are interestet in variables regarding children in a household. With soephelp, search (child) you are able to see all variables having the word child in their label. To receive more details on the list of variables, use soephelp, search (child) verbose. Now you have the possibility to click on a variable and a new window opens up with details on the variable, like the question asked in the latest questionnaire, the question`s source, the long or core variable, depending on the data format. To use this option in english, add en at the end of the option. For example, soephelp, search (child) verbose en. SOEPhelp is directly linked to the SOEPcompanion. Contributions Contributions of all sorts are very welcome. Issues and requests can be reported to: Hans Walter Steinhauer for R and Marvin Petrenz for STATA Last change: Jun 23, 2022 284 Chapter 7. Working with SOEP Documentation SOEPcompanion, Release 2022, v.3 7.6 Working with Metadata-based Questionnaires Metadata-based questionnaires make it considerably easier to find the variables of interest from the perspective of the questionnaire. Each of the generated PDFs reflects a questionnaire. With the help of these documents the user learns which questions have been asked in the respective sample and in which sequence. In addition, the documents make it clear what the question variable is called and which dataset it can be found in. The example shows question 5 from the individual questionnaire of SOEP-Core, which can be found in the data set bhp under the variable name bhp_05. 1. Example: Integrated Variable Let’s say you’re interested in finding out about refugees’ general life satisfaction. Search the questionnaire to find which refugees were surveyed for a second time in 2017. You’ll find what you’re looking for under question Q518. Below the question is the information on the name of the variable and the dataset where it is found. 7.6. Working with Metadata-based Questionnaires 285 SOEPcompanion, Release 2022, v.3 The general satisfaction with life can be found in the dataset bhp under the name bhp_205. 2. Example: Additional Variable Let’s say you’re interested in finding out about countries or origin. You want to know specifically how connected respondents feel to their country of origin. You’ll find the question in the questionniare given to refugees participating in the survey for the second time or more under question number Q480. The information on the question is stored in the data file bhp under the name bhp_480_q57. The name indicates that the question is not in the samples A-M2 because it has the suffix _q57. This does not preclude the question from being further down the integration hierarchy in questionnaires. Last change: May 12, 2022 286 Chapter 7. Working with SOEP Documentation CHAPTER EIGHT CONTACT INFORMATION The first version of the SOEPcompanion (formerly Desktop Companion) was published as a PDF document by John P. Haisken-DeNew and Joachim R. Frick in September 1996. It was originally intended to give novice users a broad introduction in understanding the SOEP, its structure, depth, and research potential. The Desktop Companion was updated several times between 1996 and 2005. The first major change came in 2014, when Jan Goebel and Mathis Schröder decided to shorten the Desktop Companion to its most important content and make it web-based. The new, completely edited version of the SOEPcompanion (formerly Desktop Companion) has a strong focus on the use of the SOEP-Core data from the perspective of a data user who has received our most recent data release from the SOEP Research Data Center. This new version is not only a web-based documentation, we also offer it as a download. Address: SOEP, DIW Berlin, Mohrenstraße 58, 10117 Berlin, Germany Homepage: http://www.diw.de/soep E-Mail: [email protected] SOEPhotline: +49 30 89789-292 Developers: Selin Kara, Stefan Zimmermann 287