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User Guide "Panel Study Labour Market and Social Security
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1. 37 Characteristics of the person dataset PENDDAT 38 Characteristics of the children dataset KINDER 39 Characteristics of the person dataset on retirement provision PAVDAT 40 Characteristics of the person weights pweights 41 Characteristics of the Biography spells bio_spells 42 Characteristics of the measure spells mn_spells 43 Characteristics of the Unemployment Benefit Spells wave 1 only a g1_ SDEIS zu a darn awe Gee ae Gea 2 AE e a Se eee ee ee 44 Characteristics of the measure spells wave 1 only massnahmespells 45 Characteristics of the 1 euro job spells ee_spells 46 Characteristics of the vignette dataset VIGDAT 47 List of subject related indicators used in the variable names cross sections 50 List of subject related indicators used in the variable names spells 51 Overview of the steps involved in editing the dataof PASS 53 Interviews at least required for a household to be regarded as successfully Surveyed M PASS s se gd kad wu be am ee ee Fe a LE 54 Overview of standardised codes used in PASS 54 Overview of the variables in the household weights data file hweights 66 Overview of the variables in the person weights data file pweights 66 Overview of key variables in the scientific use file 70 Key variables in the datasets of the scient
2. Dataset Household dataset File name HHENDDAT Level household Type cross section Format long Data collected in 1 6 waves Integration of data from new waves 1 Each newly recorded household interview is added as new observa tion in the dataset 2 The newly recorded information is assigned to existing variables for this new observation New variables are added if they were surveyed for the first time Key variables Pointer variables hnr Household number welle Indicator for survey wave uhnr Original household number One obs row in data matrix One obs row in data matrix uniquely identified by Topics Cross sectional information regarding a certain household in a certain wave hnr welle 1 Housing 2 General 2 Standard of living 3 Demography 4 Income 5 Child care 6 6 Education and inclusion subsidies Explanatory notes Only household interviews of households which were successfully sur veyed according to the definition of PASS were included in the Dataset see chapter 7 1 for definition The dataset contains variables that are required to specify the survey set command in STATA psu strata The dataset includes as many observations for a certain household as the number of waves this household was successfully interviewed FDZ Datenreport 07 2013 Household dataset on retirement provision HAVDAT Table 7 Character
3. the one that is reached first This household is defined as the successor here This means that households which have split off from original households are not included in the analyses above This could be one explanation for the finding that there are more households which have acquired a car than households that no longer have one Households that were merged were included in the analysis In contrast of the households that have split up only the half which remained at the old address or which was reached first was counted FDZ Datenreport 07 2013 Pa It is possible to incorporate split off households into the longitudinal analysis too For this each split off household has to be allocated the cross sectional weight of the original household from wave 1 and a re participation probability The eight lines which are kept from being executed above starting with and ending with have to be included for this For that please execute the code above again deleting both and The split off households are projected to about 200 000 additional households and increase the percentage of households that had a car in wave 1 but no longer had one in wave 2 to 1 9 12 3 5 Longitudinal analyses utilizing more than two waves Allthe examples above used only two subsequent PASS waves In many applications more than two waves are used For a longitudinal survey of n waves 2 1 different combinations of waves are possible cf Lynn and Kaminska
4. FDZ Datenreport 07 2013 l Unemployment Benefit Il receipt of the household alg2_spells was continued in the 2nd wave the survey concept for the other two spell datasets Unemployment Benefit receipt alg1_spells and participation in employment and training measures massnahmespells was thoroughly revised In the course of this revision process it was decided not to continue the data structure of the spell datasets on employment and training measures and Unem ployment Benefit receipt used in the 1st wave but to create new datasets see chapter 4 4 and 4 5 in Gebhardt et al 2009 Periods when the respondent received Unemployment Benefit are surveyed from the 2nd wave onwards as part of the periods of registered unemployment in the biography module For every period when the respondent was regis tered as unemployed information is gathered as to whether he she received Unemployment Benefit and if so from which start date and to which end date Periods of Unemployment Benefit receipt are therefore embedded in a period of registered unemployment and are no longer surveyed directly as they were in the 1st wave The way in which participation in employment and training measures is surveyed was revised because it had emerged that in some cases it was not possible to identify the type of measure clearly with the concept used in the 1st wave Starting with wave 2 the type of measure is identified right at the beginning of the module using
5. Research Data Centre FDZ of the German Federal Employment Agency BA at the Institute for Employment Research IAB FDZ Datenreport 07 2013 EN User Guide Panel Study Labour Market and Social Security PASS Wave 6 Arne Bethmann Benjamin Fuchs Anja Wurdack A Bundesagentur f r Arbeit User Guide Panel Study Labour Market and Social Security PASS Wave 6 Arne Bethmann Benjamin Fuchs Anja Wurdack Eds Die FDZ Datenreporte beschreiben die Daten des FDZ im Detail Diese Reihe hat somit eine doppelte Funktion zum einen stellen Nutzerinnen und Nutzer fest ob die angebotenen Daten f r das Forschungsvorhaben geeignet sind zum anderen dienen sie zur Vorbereitung der Auswertungen FDZ Datenreporte FDZ data reports describe FDZ data in detail As a result this series has a dual function on the one hand users can ascertain whether the data are suitable for their research task on the other the reports can be used to prepare the analyses FDZ Datenreport 07 2013 Es Contents I Introduction to the PASS data 1 Getting started with PASS Arne Bethmann Benjamin Fuchs and Anja Wurdack 1 1 The user guides and other working tools 1 2 Data access u a cu ee eo ee Be ee we Se SE SS 2 PASS background Mark Trappmann 2 2 Hmmm nn 2 1 Objectives and research questions of the panel study Labour Market and SOCIal SECUFIy aan a ac ac HBR Soe Bee Oe he bd 2 2 How
6. Sarndal Swensson Wretman 1992 144 pp If the sampling rate is very low the variance estimation for sampling without replacement can be approximated very well using the formulae for sampling with replacement This is the case for PASS only approximately 3 6 of the postcodes in Germany were selected for the survey There is therefore no need to indicate finite population corrections or further clusters FDZ Datenreport 07 2013 here households However the recommended surveyset then takes neither calibration nor pps sampling into account nor the low finite population correction for sampling without replacement The resulting standard errors are too large and thus should be considered conservative estimates From wave 2 onwards there are rare cases where strata defined by the variable strosu only contain one single primary sampling unit because all of the respondents in the other PSU belonging to the stratum have dropped out When a stratum consists of only one PSU Stata cannot estimate standard errors The easiest way to circumvent this problem is to retain the cases from all waves even if only one wave is being analysed and to declare the wave of interest to be a subpopulation using the subpop option of the survey commands see Stata Corp 2007 53 pp If one works solely with the dataset of a single wave after wave instead e g witha refreshment sample Stata from Version 10 onwards provides various approximation procedu
7. 1999 are being used in conjunction Prior to the beginning of each wave s fieldwork attempts are made to update address and contact information In PASS this happens primarily on the basis of the thank you letter mailing to previous wave s respondents and the mail out of advance letters to all sample members of the current wave i e including temporary dropouts and newly issued cases from refreshment samples In both instances the returned mail identifies addresses with need for tracking prior to the beginning of the actual fieldwork In wave 1 and 2 movers were attempted to be traced through address information provided by the Deutsche Post on the return mail or by a request to the residents registration office Einwohnermeldeamter at a household s last known address As of wave 3 additional resources have been committed to tracking First a specialised tracking FDZ Datenreport 07 2013 E service of Deutsche Post called Addressfactory was used as a supplementary source to update and search for addresses Second an additional update of address information and telephone numbers was conducted on the basis of administrative records available at the BA before the respective wave s fieldwork period The proactive tracking procedures just described remained largely the same in waves 4 6 i e after the change in fieldwork agency Retrospective tracking sets in during fieldwork when interviewers discover that a sample member does
8. Christoph Bernhard Achatz Juliane Wenzig Claudia M ller Gerrit Gebhardt Daniel 2009 Design and stratification of PASS A New Panel Study for Research on Long Term Unemployment IAB Discussion Paper 05 2009 Institut f r Arbeitsmarkt und Berufsforschung N rnberg Trappmann Mark Gundert Stefanie Wenzig Claudia Gebhardt Daniel 2010 PASS a household panel survey for research on unemployment and poverty forthcoming In Schmollers Jahrbuch Zeitschrift f r Wirtschafts und Sozialwissenschaften Vol 130 No 4 p 609 622 Wagner Gert G Frick Joachim R Schupp J rgen 2007 The German Socio Economic Panel Study SOEP Scope Evolution and Enhancements In Schmollers Jahrbuch Zeitschrift f r Wirtschafts und Sozialwissenschaften Vol 127 No 1 p 139 169 FDZ Datenreport 07 2013 a Watson Nicole Wooden Mark 2009 Identifying Factors Affecting Longitudinal Survey Response In Lynn Peter Ed Methodology of Longitudinal Surveys Chichester John Wiley amp Sons p 157 181 FDZ Datenreport 07 2013 En Imprint FDZ Datenreport 07 2013 Publisher The Research Data Centre FDZ of the Federal Employment Agency in the Institute for Employment Research Regensburger Str 104 D 90478 Nuremberg Editorial staff Stefan Bender Heiner Frank Technical production Heiner Frank All rights reserved Reproduction and distribution in any form also in parts requires the permission of FDZ
9. Dataset Biography Spells File name bio_spells Level individual Type spells Format spell Data collected in 2 6 integration since wave 4 waves Integration of data from new waves Key variables 1 New episodes that were reported in the last interview are added as new observations to the dataset 2 Current spells from the time of the last interview were updated if the person has been inter viewed 3 The newly recorded information is assigned to existing variables New Variables are added if they were surveyed for the first time or if they refer to a certain wave cross sectional information as part of a biographical episode pnr Constant personal ID number spellnr Spell number Pointer variables One obs row in data matrix One biographical episode One obs row in data matrix uniquely identi fied by pnr spellnr Topics 1 Information on employment with an income of more than 400 euros start date end date occupa tional status number of staff cultivated area supervisor function number of supervised employees temporary fixed term contract and conversion working hours temporary work public sector no of employees in establishment local office reason for termination of contract way of taking notice of the job offer income various measures of occupational status and prestige sector 2 Information on registered unemployment employment or training measure p
10. Information on measure start date end date duration for completed and current measures type of measure subject of measure reason for ending measure prematurely initiative for participation assessment of measure hours per week requirements identical work as permanent employees social education worker present sector Explanatory notes In wave 2 the concept for surveying participation in employment and training measures was reworked because in the concept of wave 1 it proved difficult to identify clearly the exact type of the measure with the exception of the one Euro jobs which were recorded directly In wave 2 the type of measure in which a person had participated was first recorded directly using multiple choice questions Then further information was collected in the form of looped sequences of questions about the reported measure types As a special characteristic different types of end dates durations were asked for the measure episodes For measures that were already completed the real end date duration was recorded For current measures in which the respondent still participated the intended end date duration was recorded The later were marked as right censored using the variable zensiert In contrast to the employment unemployment and UB II spells the current measure spells were not updated in the following interview Instead spells that had not been completed at the time of the interview stay right censored
11. Microm wqbap Projection factor person BA ppbleib Reciprocal re participation pro bability person wn Wn 1 Remarks Used together with welle for linking the datasets Used together with pnr for linking the datasets Indicates whether BA or Microm weights are used Projection factor for the cross section of the re spective wave total Projection factor for the cross section of the re spective wave Microm Projection factor for the cross section of the re spective wave BA Reciprocal value of the probability of the individual participating in the survey again in the following wave as predicted by means of a logit model FDZ Datenreport 07 2013 Part Il Using the PASS data FDZ Datenreport 07 2013 E 9 Key variables Daniel Gebhardt and Arne Bethmann Key variables are used to identify units and observations and to establish links between different datasets These variables are essential whenever a certain research question requires information from different datasets which must therefore be combined before analyses can be carried out This section aims to explain the key variables of PASS and how they are putto use Ina first step this section will explain how the key variables are connected to the structure of the scientific use file SUF and its datasets which were discussed in section 5 Secondly these variables are described in more detail Also an overview of the key variables included in the
12. Olena 2010 Criteria for developing non response weight adjust ments for secondary users of complex longitudinal surveys Paper presented at the XXI International Workshop on Household Survey Nonresponse Nurnberg M ller Gerrit 2011 Wechsel des Erhebungsinstituts und Adressweitergabe mit Wider spruchsverfahren im IAB Haushaltspanel PASS In Methoden Daten Analysen Zeitschrift f r Empirische Sozialforschung Vol 5 No 2 p 207 226 Peytchev Andy Riley Sarah Rosen Jeff Murphy Joe Lindblad Mark 2010 Reduction of Nonresponse Bias through Case Prioritization In Survey Research Methods Vol 4 No 1 p 21 29 Rudolph Helmut Trappmann Mark 2007 Design und Stichprobe des Panels Arbeits markt und Soziale Sicherung PASS In Promberger Markus Ed Neue Daten f r die Sozialstaatsforschung Zur Konzeption der IAB Panelerhebung Arbeitsmarkt und Soziale Sicherung Vol 12 2007 of IAB Forschungsbericht N rnberg p 60 101 FDZ Datenreport 07 2013 m Schnell R Dietz C 2006 CATI RAT Multimediale Interviewerschulung f r CATI In terviewer Center for Quantitative Methods and Survey Research Universit t Konstanz unpublished Schnell Rainer 2007 Alternative Verfahren zur Stichprobengewinnung f r ein Haushaltspanelsurvey mit Schwerpunkt im Niedrigeinkommens und Transferleistungs bezug In Promberger Markus Ed Neue Daten f r die Sozialstaatsforschung Zur Konzeption der IAB Panel
13. RDC of the Federal Employment Agency at the IAB and PASS a combined survey and administrative dataset named PASS ADIAB comprising all PASS respondents and variables plus administrative information from the SIAB dataset Dorner et al 2010 is currently in preparation This integrated file will be available to researchers for onsite use at the RDC in Nuremberg or one of the RDC locations at Berlin Bremen Dresden Dusseldorf Mannheim or Ann Arbor see http fdz iab de en FDZ_Scope_of_Services aspx for details and locations This combined dataset will allow for both substantive research that treats the administrative data as a supplement with additional information and methodological research that uses administrative records as a validation source e g in studies of measurement error in survey responses e g Kreuter M ller Trappmann 2010 FDZ Datenreport 07 2013 BE 3 3 10 Callrecords fieldwork monitoring and the development towards responsive design In recent years survey agencies have started to collect basic call history data on a routine basis Call history data contain for example information about the day and time of the callto asample unit and the outcome of that call In an interviewer administered survey like PASS with both face to face and telephone components a call is referred to as either a visit to a household or a telephone call depending on the assigned mode Survey researchers hope to employ such call records t
14. Therefore the meaning of a right censored spell differs from other spell datasets Here a spell that is right censored does not mean that it is current at the time of the respondents last interview but that it was current at the time of the interview when it was reported The wave a measure spell was reported in can be identified using the wave indicator spwelle included in the dataset Therefore a right censored measure spell was current in the wave indicated by spwelle Persons who have never reported an episode of measure participation are not represented by an observation in the dataset The dataset includes as many observations for a certain person as the number of episodes this person reported over the waves This dataset has not been continued since wave 4 ever since information about measure participation is stored in the ee_spells dataset FDZ Datenreport 07 2013 Unemployment Benefit I spells alg1_spells Table 17 Characteristics of the Unemployment Benefit I Spells wave 1 only alg1_ spells Dataset Unemployment Benefit Spells wave 1 only File name alg1_spells Level individual Type spells Format spell Data collected in waves 1 Integration of data from new waves 1 Episodes of UB recipiency were only recorded directly in wave 1 Therefore no data from new waves need to be integrated Key variables Pointer variables pnr Constant personal ID number sp
15. researchers will wish to carry out many analyses especially on fast changing characteristics using the latest available data to which many characteristics refer such as employment status income or working hours The survey date of the first wave is between 6 and 13 months after the sampling date that of the second wave is even 18 to 25 months later When working on the latest available data exclusively with the BA sample researchers can only make statements about so called stayers those who continued to receive benefits from the sampling date until the survey date In view of a considerable turnover 37 of people receiving benefits under SGB II in January 2005 were no longer doing so by December 2006 Graf 2007 this group may differ significantly in its makeup from the current benefit recipients The refreshment of the benefit recipient sample cannot solve this problem It can be solved however by merging the benefit recipient sample with the population sample The price for this is however a substantial loss of statistical power Analyses of benefit recipients at the latest interview date on the household level Representative results for current benefit recipients can therefore only be obtained using the total weights The variable for whether the household is currently receiving benefits alg2abez is contained in the household dataset HHENDDAT Estimations are therefore relatively simple for analyses at the household leve
16. the result is a sample made up of households receiving benefits at any of the reference dates in July 2006 July 2007 July 2008 July 2009 July 2010 or July 2011 admittedly an unusual population However if this combined population is restricted to households that were also still in receipt of benefits in accordance with Social Code Book II at the reference date for the most recent wave currently 2011 for wave 6 then these cases can be projected to all households with Unemployment Benefit II recipiency at this last reference date The annual refreshment of the sample thus enables us to remain representative for the benefit recipients in July of the previous year using the integrated benefit recipient samples The indicator for benefit recipiency as of the sampling date of the respective wave at the household level is the variable alg2abez in HHENDDAT which is available for each household in every wave At the person level it is the variable bgbezs in p_register Here is a placeholder for the respective wave bgbezs7 in wave 1 bgbezs2 in wave 2 and so on We take up the examples from section 12 2 1 again in the following when we calculate the percentage of households with a car and the percentage of individuals with a migration background as of the interview date of the 6nd wave but restricted this time to all benefit recipients as of July 2011 We do not have to tell Stata which samples to use as the BA sample weights are defined
17. 000 March 2012 according to the BA statistics This benchmark value is thus not quite reached The underreporting arises from the fact that unlike in the figures referring to the sampling date information on benefit recipiency at the time of the survey is not available from the register data for all 3 In the sample code recode bgbezb6 5 0 is used to treat all benefit units for which current recipiency of benefits is unclear on the basis of the survey data as non recipients FDZ Datenreport 07 2013 na respondents Thus the underreporting of benefit recipiency using the latest available data is not corrected by means of calibration Analyses on benefit recipients at the latest interview date at the person level Analyses can be transferred to the person level in much the same way as was done when using data referring to the sampling date To start with the person weights and the information for the surveyset should again be merged with the individual dataset For analyses on individuals from households currently receiving benefits the frequency counts should be limited to individuals with alg2abez 1 This variable has to be merged from the household dataset use HHENDDAT dta clear keep hnr welle psu strpsu alg2abez save psu_alg2_info replace use PENDDAT dta clear merge 1 1 pnr welle using pweights dta drop _m merge 1 m hnr welle using psu_alg2_info drop _m svyset psu pw wap strat
18. 2 2 acquired one 76 2 kept one 19 7 still do not have one Instead of again distinguishing now between households receiving benefits and those not receiving benefits we wish to discuss something more fundamental here The result produced above applies to all households of the resident population at the end of 2006 and their successor households existing as of the survey date in wave 2 As households are not units that are stable over time a longitudinal analysis of households always requires a definition of what is to be regarded as the successor of a household in cases where the household composition changes If the estimation is done as in this example then the rules applied by PASS when allocating household numbers are used a If individuals move into a household the household number does not change The new larger household is the successor of the smaller household from the previous wave If household members die or move abroad the household number does not change The new smaller household is the successor of the larger household from the previous wave If parts of the old household form a new household within Germany then the house hold that retains the household number and is therefore defined as the successor household is the one that is reached via the original contact information depending on the field this is either the telephone number or the address or if this does not apply to either of the new households
19. Download http doku iab de fdz reporte 2013 DR_07 13 pdf Internet http fdz iab de
20. Employment Agency not in employment and not registered as unemployed since wave 2 dataset covers period from January 2005 date of in terview in the most recent panel wave ee_spells Information on periods when the re spondent was participating in a One Euro Job since wave 4 dataset covers period from January 2009 date of in terview in the most recent panel wave Datasets which are not continued mn_spells Information on periods when the re spondent was participating in an em ployment or training measure waves 2 and 3 only dataset covers period from January 2006 date of in terview in wave 3 massnahmespells Information on periods when the re spondent was participating in an em ployment or training measure wave 1 only dataset covers period from January 2005 date of in terview in wave 1 alg1_spells Information on periods when the re spondent was receiving Unemploy ment Benefit wave 1 only dataset covers period from January 2005 date of in terview in wave 1 FDZ Datenreport 07 2013 The beginning of the covered period depends not only on the wave of the first interview but also on additional characteristics e g if there was a later change in the household composition or when the person who answered the household questionnaire in the last interview moved out The end of the period depends on the wave in which the respective module was
21. Kies 2010 Senior citizens interviews were calibrated to population statistics in the same way as the standard personal interviews The BA statistics however do not contain figures on the number of senior citizens in households receiving benefits Nor do they identify individuals living in households receiving benefits who are not part of a benefit unit It was therefore impossible to obtain the BA person weights for these individuals by means of calibration The participation probability of these individuals given that their household takes part in the survey was estimated using a logit model with the following covariates number of individuals aged 15 and over in the household interview mode age and gender The modified design weight was subsequently divided by this value The calibrated person weights are contained in the pweights dataset wqbap calibrated person weight of the BA sample wqmip calibrated person weight of the Microm sample wggesp calibrated person weight of the total sample 8 2 Construction of the weights from wave 2 onwards The starting points for the weighting procedure for the second wave and for the longitudinal section from wave 1 to wave 2 are the cross sectional weights from wave 1 for households and individuals More generally the starting points for the weighting procedure for the n 1 th wave and for the longitudinal section from wave n to wave n 1 are the cross sectional weights from wave n for households
22. analysis FDZ Datenreport 07 2013 u pronouncedly decreased compared to the first wave It is highly plausible that deprivation has increased for this selective group that is still or again on benefits 5 to 6 years after the reference date for sampling The corresponding confidence intervals are displayed using the option ci svy subpop if alg2abez 1 amp welle 6 tab HLS0800a if sample 1 cell ci format 9 0q 24 3 31 8 is reported as the 95 confidence interval This confidence interval lies entirely outside the corresponding interval for 2006 If the 6th wave was selected using an if condition instead of the subpop option in other words by entering the following command svy subpop if alg2abez 1 tab HLS0800a if welle 6 amp sample 1 cell ci format 9 0gq then the message Note missing standard errors because of stratum with single sampling unit would appear As described above there are three approximation procedures for this case available which can be applied modifying the svyset command svyset psu pw wgbahh strata strpsu singleunit certainty svy subpop if alg2abez 1 tab HLS0800a if welle 6 amp sample 1 cell ci format 9 0g produces anti conservative estimates i e smaller confidence intervals However in this case results only differ only on the second position after the decimal point when the proportion is displayed as a percentage svyset psu pw wgbah
23. and individuals In wave n n gt 1 each household had two weights wghh calibrated total weight and depending on the sample wqbahh calibrated BA weight or wqmihh calibrated general population sample weight and each individual also had two weights wgp and depending on the sample wgbap calibrated BA weight or wqmip calibrated general population sample weight All four weights are updated for the following wave wave n 1 Figure 2 shows the steps of the weighting procedure which are explained below This section is meant as a comprehensive overview For waves 3 6 chapter 6 of each wave specific data report Berg et al 2011 2013a b c contains details about the exact models and variables used and on model coefficients FDZ Datenreport 07 2013 Ps For details on wave 2 the reader is referred to B ngeler et al 2009 This section does not include a description of the integration of weights for replenishment samples with the ongoing panel samples Section 8 3 is devoted to this special case Figure 2 Generation of the weights for wave n 1 given the weights of wave n 5 Propensity models for temporary dropouts 3 6 ris reset 1 Propensity logit Integration of 9 Design weight models for contact modified wave n gt gt ieee o a households wave with h hold and cross sectional wgbahh wqmih
24. condition Questions that were asked even though they should not have been were corrected to 3 too 2 While in this case falsely recorded information could be corrected that is set to 3 easily information could not be added to correct missing answers If an item was not surveyed although it should have been according to the relevant filter condition the missing code 4 question mistakenly not asked was allocated to mark these cases The codes 1 and 2 were assigned as standard values for Don t know and Details refused recorded during the interview The codes 5 to 7 are question specific codes These can either be specific missing codes e g Not applicable not available for the labour market or special categories for valid values e g a category for an income above 99 999 in the open question on income These codes were only allocated as required The missing codes for items that were not included in a specific questionnaire or wave were allocated The code 9 was assigned if a certain item was not surveyed in a specific wave Due to the dataset being prepared in long format see section 5 1 3 variables that were not surveyed in a specific wave were given the value 9 for the observations in that wave The code 10 can be used to account for differences between the questionnaire versions in other words between the standard questionnaire and the senior c
25. different datasets of the scientific use file is given The section concludes with several practical examples illustrating the use of the key variables 9 1 Key variables and their connection to the structure of the scientific use file The structure ofthe SUF and its datasets were illustrated in chapter 5 There it was shown that the datasets of the SUF can be classified by their eve household or individual their type register cross section weight or spell and which formats they are prepared wide long spell in Which key variables can be used to identify units and their respective observations depends on the level and format of the dataset On the household as well as on the individual level PASS uses specific identification numbers ID that are constant across waves These ID numbers can be used to identify certain units households or persons in all datasets of the SUF and across all waves A certain household can be identified via the current household number hnr and can be related to its household of origin via the original household number uhnr Households keep their hnr across waves If a part of an already surveyed household splits off the newly formed household gets a new hnr and keeps it for future waves Individuals are assigned a constant personal ID number pnr when they are a member of a successfully surveyed household in PASS for the first time Persons keep their pnr across waves even if they change between hou
26. due to dependent interviewing New or updated episodes since the last interview were used to update the respective spell datasets Detailed information on how information that was recorded using dependent interviewing was combined with information from previous waves can be found in the wave specific data reports see e g chapters 4 3 5 6 5 7 and 5 8 in Berg et al 2013c for wave 6 The so called constant characteristics see section 14 are to be distinguished from this type of generated variable as it is assumed that they do not change over time Therefore they are only surveyed once for each household person although corrections in a later wave are possible 13 3 Simple generated variables This type of variable covers for example variables for which different items of one construct that were surveyed separately for technical reasons were aggregated or for which information from the current wave was combined with information from the previous wave such as the highest educational qualification or for which important information was merged from other partial datasets e g indicators for current receipt of Unemployment Benefit or Unemployment Benefit II For households persons that were asked for the first time regarding a certain topic the simple generated variables can be created using only the information from this wave For households persons that were already asked in the past regarding a certain topic the simple generated
27. efficacy beliefs xX xX X X x Personality Big Five x Work orientations x xX X X X Gender role attitude X X X Attitudes towards handling money and partnership x x Aspiration for children s education x X X Education training Employment employment history since January 2005 first last job pooled measures on the entire employment biography and currently earned income Unemployment and receipt of Unemployment Benefit history since January 2005 pooled measures on the entire unemployment history Mini jobs Job search Participation in employment and training measures Participation in 1 euro jobs Contact to social security institutions Perceptions about justice x x X x x x x X X x x x x Leisure time activities for respondents younger than 25 Social integration Social integration special focus x x Social Media x Health Xx XXX XxX X Health special focus x x Sports X Care EEK X x Pensions KK X X Pensions special focus FDZ Datenreport 07 2013 5 Structure of the scientific use file and its datasets Benjamin Fuchs The information collected in PASS is available as scientific use file SUF This chapter will give an introduction on how it is organised the different types of datasets it includes on the individual and household level and the links between them Therefore the first section of this chapter will deduce the SUF s basic logic from the way households and its members are questi
28. for the total population a relatively balanced picture emerges here 38 8 with increased satisfaction face 36 4 with a reduction in satisfaction This preliminary work now also makes it possible to analyse rapidly the change in the satisfaction levels of people entering FDZ Datenreport 07 2013 ES and leaving benefit recipiency svy subpop if bgbezb2 0 amp bgbezbl 1 tab rel_zufr count cell format 10 0g svy subpop if bgbezb2 1 amp bgbezbl 0 tab rel_zufr count cell format 10 0g Of the individuals leaving benefit recipiency 55 0 are more satisfied but 27 7 are less satisfied of the individuals entering benefit recipiency 46 4 are less satisfied but 26 8 are more satisfied This of course leads to the question as to whether the relatively large proportions of people who are less satisfied than they were in the previous year despite leaving benefit recipiency or are more satisfied despite entering benefit recipiency are associated with the fact that their income has hardly changed This would go too far here however 12 3 4 Longitudinal weighting at the household level First we present a simple example and then we address some of its problematic aspects We answer the question as to how many households of the resident population acquired or gave up acar between wave 1 and wave 2 We use the same procedure as in the example for individuals described above first the dataset is crea
29. from previous waves Therefore the date of the last interview was displayed as part of the question text to narrow down the reference period In other cases particularly where episodes were updated answers given in the last interview were integrated directly in the wording of a question to remind the respondent of the statements in the last interview In doing so the reporting of changes that did not really happen in the reference period should be prevented These kinds of changes would be artifacts that result from recall errors or imprecise reports 235 ogebland country of birth ostaatan nationality ozulanda f parents grandparents country of residence before migration FDZ Datenreport 07 2013 Due to the use of dependent interviewing the information for certain households persons in the datasets can be incomplete if only a certain wave specific observation is considered as it may only reflect the changes since the last interview On the other hand the infor mation of a certain observation can also be complete up to the time of the interview ifthe household person was interviewed for the first time about the topic in question Inthe course of data editing the changes between two waves were combined with informa tion from previous waves to provide generated variables with complete information for the cross sectional datasets HHENDDAT PENDDAT although only changes since the last interview were reported in the interview
30. households that were interviewed in all of the waves can be selected 10 2 Person register The person register dataset contains all individuals who were a member of a PASS survey household in at least one wave irrespective of whether an interview at the individual level has already been conducted with them or not In addition to the constant personal ID number as the identifier and details regarding e g the person s gender sex and wave specific age alter the person register dataset contains information about which household the person belonged to in the survey waves hnr and what position he she occupied in the structure of these households zp fd The person register thus makes it possible to allocate individuals to households in specific waves Furthermore the person register dataset contains information regarding the individuals survey status in the individual survey waves pnettok pnetto 1 which can be used for example to identify fully surveyed households to distinguish between reasons for non response and to clarify people s whereabouts In addition to the person related information the person register dataset also contains information on the benefit unit to which the individual was assigned These benefit units are so called synthetic benefit units created on the basis of the current legal situation at the particular time and based on information about the ages of the households members and relationships betwe
31. in this form directly in the questionnaire These variables are given entirely new abstract variable names The concept behind this naming process is illustrated in Figure 1 using an example The 1st letter of the variable name indicates the questionnaire level i e household or individual dataset by means of the letter H or P upper case respectively This is followed by one or two upper case letters which indicate the subject area to which the variable belongs see Tables 21 and 22 for a complete list In the datasets which are processed in spell form there is no introductory P or H Instead the variables in these datasets are given a uniform subject based name consisting of two or three letters or two letters and one number The introductory letter combination is then followed by two consecutively allocated numbers which indicate the number of the question within the subject area These two numbers are followed by two zeros which are intended to permit the addition of further variables in later waves Also this option has been used in cases where a second variant including coded information from an open ended survey question or response category has been made available in addition to the original version of the variable The final zero is changed to 1 for these variables e g PA0101a instead of PAO100a In the case of variables for items from multi item batteries or in a looped sequence of questions a further lower case
32. interviewed in wave 1 No Except the first repeated in terview for senior citizens first interviewed in wave 1 No Table 36 Information on constant characteristics generated variables on migra tion background Variable ogebland ozulanda f migration Description Target person s country of birth if not Germany incl responses to open ended questions cate gorised Country from which parent grandparent migrated to Germany incl responses to open ended questions categorised Target person s migration background generated Dataset PENDDAT PENDDAT PENDDAT Filled in for wave the first interview Yes Yes of Filled in for wave s of repeated inter views Yes Yes Not surveyed for se nior citizens in wave 1 Yes Yes Not generated for se nior citizens in wave 1 The country from which the parents grandparents migrated to Germany was surveyed for senior citizens for the first time in the 2nd wave 5 Not generated for senior citizens interviews FDZ Datenreport 07 2013 Ka Table 37 Information on constant characteristics social origin Filled in for wave of Filled in for wave s of Variable Description Dat set the first interview repeated interviews PSH0200 Target persons mothers PENDDAT Yes No highest general school qual ification PSH0300a i Target person s mother s vo PENDDAT Yes No cational qualifications PSH0310 Mother s occup sta
33. it is repeatedly collected in different panel waves is only one column Changes in the way a question is asked can lead to the decision that a new variable has to be integrated in the dataset Variables surveyed only for certain waves are assigned the missing code 9 for waves in which they were not surveyed Therefore the observations in the cross sectional and the weighting datasets represent certain units in certain waves and can be identified using a combination of key variables for the unit and the wave The spell datasets of PASS are prepared in spell format Each episode that was recorded for a unit is represented by another observation in the dataset as many rows in the data matrix as episodes reported by the unit An episode can include information that was recorded in more than one wave when a current episode was updated in a following wave Units that never reported an episode although they were successfully surveyed are not represented by an observation in the spell dataset Units that reported more than one episode are represented by one observation per reported episode Therefore the observations in the spell datasets represent certain episodes of certain units and can be identified using a combination of key variables for the unit and the number of the spell 5 2 Datasets of the scientific use file The scientific use file of PASS consists of several datasets As described above these can be grouped by three criteria lev
34. last asked of the household person If a household person missed a wave temporary drop out the resulting gap in the spells was filled in the next interview if the household person had been asked the respective module before If a person was not asked a certain module due to a filter the resulting gap was not necessarily filled in the next interview Before using the spell datasets it is reasonable to take a look into the questionnaires and to trace the way the spells were recorded This will help to interpret times were no spell data is available for a household person The spell datasets of PASS have a comparable structure In addition to an identifier household or personal ID number they also contain a spell number which numbers the individual spells within a household alg2_spells or a person bio_spells ee_spells mn_spells massnahmespells alg1_spells consecutively in chronological order and makes it possible to identify them clearly together with the household or personal ID number Furthermore generated date variables for the beginning bmonat bjahr and the end emonat ejahr of the respective spell can be found in the datasets These variables were recoded e g information on seasons was recoded into definite months and cleansed e g missing codes were set for implausible values In addition if these variables contained censored spells the interview date was entered for the end of the spell In contrast the date variables
35. letter may be added to identify the item or the current cycle within the loop 6 2 3 Generated variables The generated variables in the strict sense are aggregated from various other variables e g from open ended and categorical income measures or they are even more complex FDZ Datenreport 07 2013 ES Table 21 List of subject related indicators used in the variable names cross sec tions Individual level Household level Code Subject area Code Subject area PD Demography HW Housing PA General HA General PSM Social Media HLS Standard of living PB Education HD Demography PEO Attitudes and orientations HEK Income PGR PPG Perceptions about justice HKI Child care PET Employment HT Social participation PEK Income HBT Education and inclusion sub PTK Contact to social security insti sidies tutions PEE 1 euro jobs PAS Job search PLS Standard of living PSK Social relations PG Health PSB Sports PP Care PMI Migration PSH Social origin constructs such as equivalised household income or classifications for education such as ISCED or Casmin or status e g EGP ESEC Generated variables in this strict sense are allocated individual names that are as clear and memorable as possible in lower case letters For an overview of the generated variables see chapter 13 Another group of generated variables includes those in which information from open ended survey questions or response categories was added to another closed
36. next section 12 2 Use of the cross sectional weights In this section examples are given on how to use the cross sectional weights for different purposes For all examples code in Stata 12 1 is given All Stata code is printed in separate lines in Courier New and can be copied from this User Guide and pasted right into your Stata do file editor Please replace PATH_TO_DIRECTORY_OF_ORIGINAL_PASS_DATA FDZ Datenreport 07 2013 a by the name of the path where the original PASS data are on your computer and replace PATH_TO_DIRECTORY_FOR_WEIGHTING_EXERCISES by the name of the path where you want to store the results of this training session In case you are using any later version of Stata than version 12 1 all you have to do in order to ensure getting the same results is precede the code by version 12 1 All of the cross sectional weights are projection factors Dividing these weights by their mean value results in weights that add up to the sample size Design weights dw_mi dw_ba dw and the estimated propensities propensities for the initial wave prop_tO are provided in PASS however we recommend using the calibrated weights Researchers who nevertheless wish to do without calibration should bear in mind that although division of the household weights by the adequate participation propensities estimated for the respective subsample does yield modified household design weights these weights cannot simply be transferre
37. observation By linking via the personal ID number the respective interview dates of each individual wave are added to each of a person s spells and are available for further calculations The biography spell dataset consists of different spell types employment unemployment as well as other times out of employment e g retirement housewife husband and military or civil service You can keep certain types of spells by using the variable spelltyp In the example only the employment spells are kept in the dataset use PENDDAT dta clear keep pnr welle pintdat reshape wide pintdat i pnr j welle FDZ Datenreport 07 2013 Pe la var pintdatl Datum des Personeninterviews in Welle 1 la var pintdat2 Datum des Personeninterviews in Welle 2 la var pintdat3 Datum des Personeninterviews in Welle 3 la var pintdat4 Datum des Personeninterviews in Welle 4 la var pintdat5 Datum des Personeninterviews in Welle 5 la var pintdat Datum des Personeninterviews in Welle 6 save PINTDAT dta use bio_spells dta keep if spelltyp merge m 1 pnr using PINTDAT dta tab _m drop if _m 2 The tabulation of the _merge variable shows that no employment spell is available for over 19 000 individuals Some of these individuals were only interviewed in the 1st wave some had not reported any employment spells since and some were not asked about the
38. of sampling for wave 1 and the date of sampling for one of the refreshments the mean selection probability of a household in the refreshment sample in the respective postcode sector and the average participation probability in that sample are assumed The two weights from 4 and 6 are then integrated to form a new total weight 8 2 9 Calibration to the household weight wave n 1 cross section The steps described above are followed by another calibration of the weights from step 6 At the household level raking wave 3 or GREG all other waves is used to calibrate the weights to the benchmark statistics of the Federal Statistical Office for the respective year FDZ Datenreport 07 2013 2007 in wave 2 to 2011 in wave 6 and for households in receipt of benefits the weights are adjusted to the statistics of the Federal Employment Agency for July of the respective year The calibration process is described in detail in Kies 2010 for wave 1 and 2 and in the data reports of infas for wave 3 to 6 Berg et al 2011 2013a b c 8 2 10 Calibration to the person weight wave n 1 cross section As in wave 1 the person weights were calibrated under the restriction that they differ as little as possible from the calibrated household weights The calibration is therefore not based directly on the person weights of the previous wave The calibration process is described in detail in Kies 2010 for wave 1 and 2 and in the data reports of infas f
39. of the periods may cover the time of interview and others may not This kind of information is organised in spell datasets where each episode of the respondent forms a single observation 5 1 3 Wide format long format and spell format As described above the SUF contains four types of datasets register cross sectional weights spells on two levels household individual These four types of datasets are prepared in three formats wide format long format spell format The register datasets of PASS are prepared in wide format This means that each unit is represented by exactly one observation in the dataset one row in the data matrix Wave specific information is allocated to these units in wave specific variables For waves where no information is available for one unit the wave specific variables are filled with specific missing value codes Therefore the observations of the register datasets uniquely present certain units and can be identified using a single key variable FDZ Datenreport 07 2013 The cross sectional and weighting datasets of PASS are both prepared in long format and not as can be found in some other panel surveys in separate annual files Each wave a unit was surveyed is represented by another observation in the dataset as many rows in the data matrix as waves the unit was surveyed in Thus the wave specific information can be found in wave specific observations for the unit Each variable even if
40. off household to its household of origin FDZ Datenreport 07 2013 Pe 5 2 1 Household level datasets Household register hh_register Table 5 Characteristics of the household register dataset hh_register Dataset Household register File name hh_register Level household Type register Format wide Data collected in 1 6 waves Integration of data from new waves 1 Households that were surveyed for the first time are added as new observations 2 New wave specific variables are added They include the information recorded in the last wave Key variables Pointer variables 1 hnr Household number 2 hnr Household number in wave 1 uhnr Original household number 2 pnrzp Constant personal ID number of person who gave the house hold interview in wave One obs row in data matrix One household that was at least once successfully surveyed in PASS One obs row hnr in data matrix uniquely identified by Topics 1 Constant sampling information Explanatory notes 2 Wave specific household information households survey status size of household number of synthetic benefit units pointers Only households that were successfully surveyed at least once are in cluded in the household register FDZ Datenreport 07 2013 Household dataset HHENDDAT Table 6 Characteristics of the household dataset HHENDDAT
41. standard table shown in Table 4 will be used The meaning of the different categories was included in italic font and should be self explanatory FDZ Datenreport 07 2013 Eu Table 4 Standard table for information on the characteristics of the dataset Dataset Full name of the dataset e g Household register File name Filename of the dataset in the scientific use file e g hh_register Level Level of the dataset e g household Type Type of the dataset e g register Format Format of the dataset e g wide Data collected in Wave from which the dataset includes information e g 1 6 waves Integration of data from new waves Logic used to integrate information from new waves e g 1 Households that were surveyed for the first time are added as new observations 2 New wave specific variables are added They include the information recorded in the lastwave Key variables All key variables included in the dataset e g 1 Anr Household number 2 hnr Household number in wave Pointer variables All pointer variables included in the dataset e g 1 uhnr Original household number 2 pnrzp Constant personal ID number of person who gave the house hold interview in wave One obs row in data matrix What exactly is represented by one observation e g One household that was at least once succ
42. the cross section a refreshment sample for this group is drawn in every wave on the concept of the refreshment sample see Trappmann et al 2009 11 pp Therefore the sample in the 1st wave of PASS consisted of two subsamples These two otherwise independent samples are connected on the first sampling stage via the selection of identical primary sampling units for detailed information about the sampling design of the 1st wave see Rudolph Trappmann 2007 65 pp The first subsample BA sample is a random sample of benefit units Bedarfsgemeinschaften in which at least one person was receiving UB II in July 2006 This sample was drawn from administrative data of the federal employment agency BA As PASS is a household survey the entire household in which a benefit unit was living was targeted for the survey The second subsample is a sample of private households in Germany general population sample In wave 1 a random sample of addresses was drawn from the MOSAIC database of addresses held by the commercial provider Microm The sample was stratified disproportionately by status in such a way that households with a low social status and thus a greater risk of entry into benefit receipt had a higher probability of inclusion on the results of the stratification see Trappmann et al 2007 On the first sampling stage 300 postcodes were drawn from the postcode register These postcodes serve as primary sampling units in PASS on the selectio
43. the start date as well as infor mation that refers to a certain wave e g the simple classification of the occupational status in wave 6 These cross sectional information are valid only for a certain point in time and can change while the episode continues Therefore the dataset contains cross sectional variables referring to a certain wave They are filled if the episode covers the respective wave and are otherwise assigned the missing code 9 The wave a cross sectional variable in the spells refers to can be read from the variable labels FDZ Datenreport 07 2013 Measure spells from wave 2 mn_spells Table 16 Characteristics of the measure spells mn_spells Dataset Measure Spells from wave 2 File name mn_spells Level individual Type spells Format spell Data collected in 2 3 waves Integration of data from new waves Key variables 1 New episodes that were reported in the last interview are added as new observations to the dataset 2 Current spells from the time of the last interview were not updated 3 The newly recorded information is assigned to existing variables pnr Constant personal ID number spellnr Spell number Pointer variables One obs row in data matrix Episode during which a certain person participated in a certain employment training measure One obs row pnr spellnr in data matrix uniquely identified by Topics 1
44. 009 for all respondents 1 euro job episodes from the datasets massnahmespells_spells or mn_spells have not been integrated into the ee_spells dataset Persons who have never reported an offer or an episode of an 1 euro job are not represented by an observation in the dataset The dataset includes as many observations for a certain person as the number of offers or episodes this person reported over the waves Consequently there are also spells contained if the person did not participate in an offered 1 euro job FDZ Datenreport 07 2013 Vignette dataset VIGDAT Table 20 Characteristics of the vignette dataset VIGDAT Dataset Vignette dataset File name VIGDAT Level individual Type factorial Format long Data collected in 5 waves Integration of data from new waves The factorial survey module was only questioned in wave 5 Therefore no data from new waves need to be integrated Key variables pnr Constant personal ID number vignr vignette number Pointer variables One obs row in data matrix Factorial survey information regarding a certain person with a certain vignette One obs row pnr vignr in data matrix uniquely identified by Topics 1 Characteristics of the job offer vignette dimensions Explanatory notes 2 Assessment of the job offer FDZ Datenreport 07 2013 ca 6 Variable types and their names Arne Bethmann 6 1 General iss
45. 2010 It is impossible for survey data producers to supply weights for all of these combinations PASS longitudinal weights thus refer to the balanced panel only i e to all those households who participated in all waves between the first and last wave of an analysis Thus if a researcher uses data from waves 3 to 6 a longitudinal weight can only be constructed for those respondents who continually participated in wave 3 4 5 and 6 If a researcher only uses waves 3 and 6 e g because she analyses the extended health module the same applies Those who did not participate in wave 4 or wave 5 cannot be included in a weighted longitudinal analysis of waves 3 and 6 This implies a loss of power Thus a methodologically advanced researcher could try to generate a more general longitudinal weight for her specific longitudinal analysis by estimating a participation probability of a person in wave 6 conditioned on participation in wave 3 and multiplying its reciprocal value with the wave 3 cross sectional weight The specification of the PASS propensity models is documented in the wave specific data reports Berg et al 2011 2013a b c and did not change much from wave 3 Most variables are taken from the previous observed wave and are available to users from the Scientific Use Files Additional variables from the paradata of the survey like number of contact attempts can be supplied to users upon request 13 Generated variables Daniel Gebhardt an
46. 75 9 95 confidence interval of 73 9 to 77 7 and in the second case 75 6 95 confidence interval of 73 5 to 77 6 The confidence interval is slightly narrower when the total weights are used as in this case the part of the population receiving benefits under SGB II is represented much more precisely which is why we prefer to use these weights The same applies to the person weights 12 2 3 Analyses on benefit recipients at different points in time Section 12 2 1 explained how the data can be projected onto the total population of the BA register data sample of the 1st wave households with at least one benefit unit that was in receipt of benefits in accordance with Social Code Book II in July 2006 As a result of its design however PASS is more flexible and makes it possible in principle to make projections onto the benefit recipients at any point in time since the benefit was introduced in January 2005 Analyses on benefit recipients in July 2011 PASS takes a first step in this direction with the annual refreshment samples of the register data sample The refreshment samples samples 3 4 5 8 9 consist of households in which there was at least one benefit unit FDZ Datenreport 07 2013 e receiving benefits at the reference date in July of the respective wave but of which no member was living in a household with at least one benefit unit in receipt of benefits at any previous reference date When all BA samples are taken together
47. B Il see chapter 2 1 on the objectives and questions of PASS and in more detail Achatz Hirseland Promberger 2007 17 pp An adequate survey design has to be tailored to the research demands and the population to be surveyed The strategies employed in PASS are described in subsection 3 3 They are further detailed in Schnell 2007 and Rudolph Trappmann 2007 The most important decisions that were taken in PASS are those for a prospective longi tudinal design and for conducting it as a household survey The main research questions require longitudinal data They ask for determinants of inflows into and outflows from benefit receipt or for changes in attitudes action taken or the material situation before and after the beginning of benefit receipt The only adequate design to answer such questions is the panel design where the same units of observation are asked to answer the same questions in repeated waves In PASS the period of time between two consecutive waves was based on expectations on how quickly important target variables change devised to be one year When examining research questions in the context of the SGB II the respondents action con text and in particular here their household context is of importance for two different reasons First because individuals make decisions against the background of their household specific circumstances Second because the SGB II also examines the household context when activating benef
48. BA person weight which is obtained by multiplying their BA person weight from the previous wave by the reciprocal re participation probability ppbleib Individuals in these households who did not provide a personal interview in the previous wave are given a new BA person weight calculated by dividing the BA household weight of their household for wave n 1 by the proportion of such individuals who participate provided that their household is taking part 3 Individuals who are not members of a benefit unit in panel households that are still in receipt of Unemployment Benefit II at the current reference date Individuals in these FDZ Datenreport 07 2013 households with interviews in both waves are given a new BA person weight which is obtained by multiplying their BA person weight of the previous wave by the reciprocal re participation probability ppbleib 8 3 Integration of the weights of the replenishment samples with the ongo ing panel samples The following section is adapted from for the purposes of this User Guide from the wave 5 data report by Berg et al 2013b Integrating the replenishment samples with the ongoing panel samples in terms of weighting is not trivial since weights must be integrated several times This integration which only became necessary in wave 5 was performed between steps 7 8 2 7 and 8 8 2 8 Ina first step cases in the ongoing panel that are not or no longer part of the population of the refreshments ar
49. Christine 2008 Codebook and Documentation of the Panel Study Labour Market and Social Security PASS Volume Introduction and Overview Wave 1 2006 2007 FDZ Datenreport 05 2008 EN Institut f r Arbeitsmarkt und Berufsforschung N rnberg Couper M P Ofstedal Mary Beth 2009 Keeping in Contact with Mobile Sample Members In Lynn Peter Ed Methodology of Longitudinal Surveys Chichester Wiley Couper Mick P 1998 Measuring Survey Quality in a CASIC Environment In Proceedings ofthe Survey Research Methods Section American Statistical Association p 41 49 Dorner Matthias Heining J rg Jacobebbinghaus Peter Seth Stefan 2010 The Sample of Integrated Labour Market Biographies In Schmollers Jahrbuch Zeitschrift f r Wirtschafts und Sozialwissenschaften Vol 130 No 4 p 599 608 Felderer Barbara M ller Gerrit Kreuter Frauke Winter Joachim 2012 The Effect of Monetary Incentives on Attrition Bias in a Household Panel institut f r Arbeitsmarkt und Berufsforschung N rnberg unpublished manuskript Gebhardt Daniel M ller Gerrit Bethmann Arne Trappmann Mark Christoph Bernhard Gayer Christine M ller Bettina Tisch Anita Siflinger Bettina Kies Hans Huyer May Bernadette Achatz Juliane Wenzig Claudia Rudolph Helmut Graf Tobias Biedermann Anika 2009 Codebook and Documentation of the Panel Study Labour Market and Social Security PASS Volume I Introdu
50. SS Additions to existing data Survey and Sampling Design Instruments and interview programme Structure of the scien tific use file and its datasets General logic of data editing Weighting concept Examples on how to use the datasets For each wave the respective data report provides wave specific information on the data editing and tabulations of the surveyed variables in the differ ent datasets of the scientific use file Because the user guide was first introduced in wave 3 the data reports of wave 1 and 2 include some of the user guides general information as well The following wave specific topics are covered Key statistics Generated variables Data editing Weighting Tabulations of the surveyed variables Language English English excluding the tabula tions of the surveyed variables German Waves covered 1 6 integrated 1 6 wave specific Methods and Field Reports Questionnaires For each wave the methods and field report de scribes the work of the field institute for the respec tive wave The following wave specific topics are covered Objectives and design of PASS Pretest Detailed information on the steps of the field work Data editing by the field institute Weighting modeling of non response For each wave the different questionnaires doc ument which items have been surveyed in the respective wave Furthermore they make trans parent in which variables th
51. UB II receipts and un employment history on individual and household level PASS provides excellent data to answer the pathways which lead into and out of UB II receipt and how sustainable and enduring these exits are see core questions 1 and 5 Starting in wave 6 information on the Educational Package Bildungs und Teilhabepaket which was introduced in 2011 for needy children from low income families is collected The new benefit provides financial means to enable children to take part in sports music and cultural activities as well as to go on school trips have lunch in day care centres schools or receive learning support if they are at risk of having to repeat a school year Respondents are asked whether they know about the new benefit and if so to name the sources of information Furthermore data is available about the non take up of the new benefit separately for each activity reasons for non take up and the evaluation of the new benefits Information on participation in the respective activities is not exclusively collected for children who are eligible for benefits of the Educational Package but for all children and youths below the age of 25 Thus participation rates can be compared The study s second main research question refers to the living conditions of individuals and households Therefore PASS includes a broad range of questions concerning the households financial situation the standard of living degree of m
52. a detailed two digit variant hnettod1 hnettod2 The two digit net variables differentiate the single digit codes further The single digit code 2 in hnettok2 household not successfully surveyed only in gross sample is further differentiated in hnettod2 in the codes beginning with 2 This makes it possible to establish why the household could not be successfully surveyed in the 2nd wave for example because the household could not be reached hnettod2 20 or because it refused to participate hnettod2 21 As only households that were successfully surveyed are to be selected here the information in hnettok1 and hnettok2 is sufficient After retaining only the cases that were successfully surveyed in both the 1st and the 2nd waves and were receiving Unemployment Benefit II on the sampling date only the relevant variables hnr alg2samp are retained the dataset is sorted by household number stored temporarily and merged with the observations from the first two waves of the household dataset which has also been sorted according to hnr use hh_register dta clear keep if hnettokl 1 amp hnettok2 1 amp alg2samp 1 keep hnr alg2samp save hh_register_vorbl dta replace use HHENDDAT dta clear keep if welle welle merge m 1 hnr using hh_register_vorbl dta tab _merge alg2samp m An examination of the _ merge variable indicates that 6210 observations from 3105 house holds from the household
53. a multiple choice question Due to low numbers of reported spells for other training measures only One Euro Jobs are recorded from wave 4 onwards ee_spells Another important innovation regarding the spell datasets results from the fact that the concept for surveying periods of employment unemployment and economic inactivity was altered in the 2nd wave Instead of only asking about the status as of the interview date as was done in the 1st wave a biography module is used since wave 2 to record spells of employment and registered unemployment retrospectively for a certain period In wave 2 episodes since January 2005 up to the date of the interview were recorded In wave 3 persons who already answered questions about their employment and unemployment biography in wave 2 were asked about the period since the interview in wave 2 Persons who were not interviewed in wave 2 or were not asked about this topic reported about the periods since January 2006 up to the date of the interview In wave 2 as well as in wave 3 gaps as of the date of the interview date or periods of more than three months duration for which the respondent reported neither employment nor unemployment are caught by a gap module Ifthe gap existed due to the omission of a period of employment or unemployment or if the dating of a reported spell was incorrect this could be corrected here If the gap represented a time of economic inactivity this could also be specified in the g
54. a reference date for sampling to persons in hypothetical benefit units not re ceiving Unemployment Benefit II at the same reference date As this variable was used for the weighting process a decision was made for every unclear case a Households persons in households receiving Unemployment Benefit II on the survey date of a wave alg2abez 1 to households persons in households not receiving Unemployment Bene fit II at the survey date of the respective wave Persons in benefit units receiving Unemploy ment Benefit II on the survey date of a wave to persons in benefit units not receiving Unem ployment Benefit II on the survey date of that wave FDZ Datenreport 07 2013 E In a second step the weights from the first wave and the re participation probabilities from wave 1 to wave 2 are stored use pweights dta clear keep if welle save pweightsl dta replace Now the individual dataset is retrieved We have decided to run the analyses in wide format and therefore have to re sort the dataset so that the variables PA03001 satisfaction with the standard of living in wave 1 and PA03002 satisfaction with the standard of living in wave 2 are retrieved We only retain the variables that we require later use PENDDAT dta clear keep pnr hnr welle PAO300 reshape wide PA0300 hnr i pnr j welle Now the three datasets are merged rename hnrl hnr merge m 1 hnr using psu_strpsu_wl dta keep if _m 3 drop _m merge m 1 pnr us
55. a strpsu svy subpop if alg2abez 1 amp welle 6 amp fb_vers 1 tab migration count cell format 9 0g According to this of the individuals in households currently receiving Unemployment Benefit Il 57 8 have no migration background 28 1 migrated to Germany themselves 8 6 have at least one parent who migrated and 2 8 one grandparent who migrated In most cases however analyses will not be limited to individuals in households receiving benefits but to individuals in benefit units receiving benefits This characteristic is contained in the person register The following series of commands produces the percentage of migrants among individuals in benefit units aged between 15 and 64 drop if welle lt 6 merge 1 1 pnr using p_register dta svy subpop if bgbezb6 1 amp fb_vers 1 tab migration count cell format 9 0g Analyses on benefit recipients at other points in time The biographical data on Un employment Benefit II recipiency at the household level also make it possible in principle to perform analyses referring to other points in time which are between the sampling date and the date when the first wave of the survey was administered However variables such as bgbezs1 bgbezb1 or nbgbezug are only provided for the two dates described above 24 As recipiency of Unemployment Benefit Il is a socially undesirable characteristic a certain amount of underre porting is not surprising Compare Kreuter M ller Tra
56. ad a car at the time of the interview in the 6th wave The fact that the value increased compared with that of the first wave is likely to be associated with the fact that a considerable number of these households have managed to end benefit recipiency between the first and sixth wave If researchers are solely interested in those households that are still in receipt of benefits at the time of the most recent interview then the command has to be restricted to this set As it is not a separate sample a restriction with if would result in an underestimation of the variances in this case The restriction is to be carried out using subpop see Stata Corp 2007 53 pp The information as to whether a household is receiving benefits on the survey date is contained in the variable alg2abez in HHENDDAT Here the value 1 means that the household was drawing benefits the value 2 means that it was not in receipt of benefits and 5 means that it is not possible to establish from the information available whether the household was receiving benefits The command is therefore svy subpop if alg2abez 1 amp welle 6 tab HLS0800a if sample 1 count cell format 9 0q Of the households which were receiving benefits in July 2006 and were also still in receipt of benefits at the survey date in the 6th wave only 28 0 have a car This value has 19 Households that had split off from wave 1 households since then by moving out are included in this
57. aining measures necessitated the revision Since the last revision wave 4 the module exclusively relates to one euro jobs start and end of the programme evaluation of institutional support during the programme perceived success of the programme Information on unemployment insurance payments so called UB is also collected on the personal level Questions about the start and end date of the benefit receipt and reasons for ceasing unemployment are integrated in the biography module where the employment history of all participants is gathered All episodes of employment unemployment and non employment e g education maternity leave retirement are recorded beginning approximately two years before individuals first interview and are updated in each panel wave The start and end date month year of each episode is surveyed to ensure a FDZ Datenreport 07 2013 chronological and complete employment history furthermore parallel activities should be reported and a control mechanism is developed to show gaps of more than one month between the reported activities For each job episode detailed information is obtained like occupation job level type of contract working hours income industrial sector firm size Similar characteristics are also collected for the first job ever For mini jobs only cross sectional data is collected working hours and income of the current mini job With the combination and linkage between the history of
58. ap module Starting with wave 4 spells of employment unemployment and economic inactivity are surveyed in chronological order in an integrated questionnaire module These spell data are provided as a single dataset in the scientific use file bio_spells The period of time covered by a spell dataset differs between households and between persons The beginning of the period depends on the wave in which the respective module in the questionnaire was first asked of the household person and additional characteristics With each wave the year starting from which the respondents were asked to report episodes was increased by one year to keep the length of the first retrospective period constant 15 E g in wave 2 the employment spells were recorded for the first time in the personal interviews The FDZ Datenreport 07 2013 Bi Table 30 Overview of the spell datasets in the scientific use Dataset Contents Data collection in waves Household level alg2_spells Information on periods when the household received Unemployment Benefit Il periods of cuts in Unem ployment Benefit II since wave 1 dataset covers period from January 2005 date of in terview in the most recent panel wave Individual level bio_spells Information on periods when the re spondent was employed with a monthly in come of more than 400 registered as unemployed or was participating in a employ ment or training measure run by the
59. arents highest school qualification and their vocational qualifications If FDZ Datenreport 07 2013 Ka Table 35 Information on constant characteristics migration background Variable PMIO100 PMIO200 PMIO300a b PMIO700 PMIO800a f PMIO900a f PMI1000a f PMI1200 Description Target person born in Germany Target person s country of birth if not Germany Date of migration to Ger many Parents grandparents born outside Germany Which parent grand parent not born in Germany Which parent grand parent migrated to Germany Country from which parent grandparent migrated to Germany Is German your mother tongue Dataset PENDDAT PENDDAT PENDDAT PENDDAT PENDDAT PENDDAT PENDDAT PENDDAT Filled in for wave of the first interview Yes Yes Yes Yes Yes Yes Yes Yes Filled in for wave s of re peated interviews No Except the first repeated in terview for senior citizens first interviewed in wave 1 No Except the first repeated in terview for senior citizens first interviewed in wave 1 No Except the first repeated in terview for senior citizens first interviewed in wave 1 No Except the first repeated in terview for senior citizens first interviewed in wave 1 No Except the first repeated in terview for senior citizens first interviewed in wave 1 No Except the first repeated in terview for senior citizens first
60. articipation start date end date reason for exit and recipiency of UB during an episode of registered unemployment start date end date total amount of benefits per month 3 Information on other biographical episodes unregistered unemployment educational training military alternative service housewife househusband maternity protection parental leave retire ment sickness job related incapacity self employment other activities Explanatory notes Employment with an income of more than 400 euros was recorded as part of the persons question naires biography module Since wave 4 in addition to episodes of employment the respondent was asked for episodes of registered unemployment and other biographical episodes as mentioned above This is different to the procedure in wave 2 3 where employment spells unemployment spells and other biographical episodes were questioned seperately Also they were stored seper ately in the datasets et_spells al_spells and lu_spells These datasets were integrated into the bio_spells dataset in wave four and are not contained in the SUF anymore Persons who have never reported an episode of employment unemployment or other biographical episodes are not represented by an observation in the dataset The dataset includes as many observations for a certain person as the number of episodes this person reported over the waves An episode includes information that refers to the spell itself e g
61. as the respective hnr In the case of households which have split off from panel households split off households the uhnr corresponds to the hnr of the household from which the split off household originated Household number in wave Eight digit constant ID number of the household in wave of PASS This variable is only contained in the register datasets processed in wide format Constant personal ID number Ten digit constant ID number of the individual The pnr is allocated when a person first joins a PASS survey household The first eight figures consist of the household number of the household to which the person belonged when he or she joined PASS and the last two figures are the serial number that the person had within this household E g 1001000801 person joined the PASS in household 10010008 and had the serial number 01 in this household Serial number of the target person in the household in wave Two digit serial number within the household in wave which indicates the person s position in the household structure Within a particular household the zpifd is constant in principle If a person moves to a different household between the waves then a new zpifd is allocated in the new household in this case zplfd1 and zplfd2 differ Serial numbers that were already used for a certain household in one of the previous waves are not allocated to anyone else The numbering of new people in a household begins at N 1 N highes
62. as they were reported by the respondent e g BIO0200 BIO0300 BIO0400 BIO0500 in the bio_spells which are also included were not altered Following content related information on the various spell types all of the spell datasets contain a censoring indicator zensiert for spells that were still ongoing on the respective last interview date in other words right censored spells Generated variables e g ISCO 88 coding of occupational activities can be found at the end of each list of variables in the spell datasets Finally some important peculiarities of the spell data in PASS should be pointed out Due to the orientation towards actual spells here it is generally not easy to relate the spells to specific waves as spells may span more than one survey date Furthermore observations are not available for all households or individuals in the spell data This may be the case if there were no relevant spells or if the corresponding questions were not asked due to the filters For identifying individual spells the identifier variable hnr or pnr and the spell number are respondents were asked to report episodes since January 2005 In wave 3 this date was altered to January 2006 In this case UBll episodes in the first interview were asked since the date of the last change of the household composition In this case the former household was asked for episodes of UBll recipiency since the move out while the new household split off
63. ased construct variables 2 2 02004 101 14 Constant characteristics Daniel Gebhardt and Arne Bethmann 103 14 1 Gender o s esada we Od ea aoa Be ee 103 14 2 Half year of birth 22 2 2 Co nn 103 14 3 Migration background 2 028 Es ee eee kamen wa eR ee 104 14 4 Parents education vocational training occupational status and activity 104 14 5 Sample indicator sampling year and receipt of Unemployment Benefit II of the household on the sampling date 0 4 107 FREIGNENCOS o e xea A Rra ars Gee Sete se a ee ee EO ey ee 108 FDZ Datenreport 07 2013 Bi List of Tables ooNO OT POD1 Oe 6022020040460 aM G NO TV fF WDM O 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 Overview of the working tools available in wave3 10 Overview of modules 2 2 26 Overview of the datasets of the scientific use file 30 Standard table for information on the characteristics of the dataset 31 Characteristics of the household register dataset hh_register 32 Characteristics of the household dataset HHENDDAT 33 Characteristics of the household dataset on retirement provision HAVDAT 34 Characteristics of the household weights hweights 35 Characteristics of the Unemployment Benefit II spells alg2_spells 36 Characteristics of the person register dataset p_register
64. ased for every wave inform in detail about key statistics data editing generated variables and the weighting of a certain wave the User Guide offers comprehensive information that is not specific for a single wave Part I gives an introduction to the PASS data starting in chapter 1 with a first overview of the topics covered by the User Guide and the other working tools that will help users to work with PASS Information on how to obtain the data can be found there as well Subsequently the main research questions which influenced the development of the study will be presented in chapter 2 and it will be pointed out which addition to existing data is made by PASS In chapter 3 the design sampling procedure and several special characteristics of the survey design will be described Chapter 4 deals with the topics of the survey and gives an overview of the subjects of the household and personal interview since wave 1 Thereafter the structure ofthe SUF and the datasets included will be presented in chapter 5 Not only does this chapter give essential information on the levels types and formats of the datasets in the SUF but also on their topics key variables and special characteristics After this overview of the SUF and its datasets chapter 6 focuses on the types of variables that can be found in these datasets and their naming conventions Subsequently the general logic of data editing and its most important steps will be discussed Herein th
65. at the reference date for sampling of a given wave ii no benefit recipiency at all previous reference dates iii currently living in a household with a former reference date benefit recipient iv not having lived with such a person at any previous reference date As the frames are disjunctive under this assumption the weights of the register data sample alone remain unaffected by the integration of the refreshment sample those of the general population sample on its own of course too The new design weights of the benefit recipient sample project in the cross section to all individuals who were living in a household containing at least one benefit unit at one of the reference dates in either 7 2006 7 2007 7 2008 7 2009 7 2010 or 7 2011 It is only when calculating new weights for the total sample that it becomes necessary to adjust the weights for all households in receipt of benefits at prior reference dates For this adjustment the inclusion probability in the respective other sample is estimated for cases from the general population sample and the refreshment samples For cases from the refreshment sample the mean selection probability in the general population sample in the respective postcode sector and the average participation probability in that sample are assumed For cases from the general population sample if they are according to survey data new recipients of Unemployment Benefit II who first received the benefit between the date
66. ata collected in 2 6 waves Integration of data from new waves Key variables 1 New episodes that were reported in the last interview are added as new observations to the dataset 2 Current spells from the time of the last interview were updated if the person has been interviewed 3 The newly recorded information is assigned to existing variables New variables are added if they were surveyed for the first time or if they refer to a certain wave cross sectional information as part of an 1 euro job episode pnr Constant personal ID number spellnr Spell number Pointer variables One obs row in data matrix Episode during which a certain person participated in or got an offer for an 1 euro job One obs row pnr spellnr in data matrix uniquely identified by Topics 1 Information on the 1 euro job date of the offer start date and end date duration for finished and current 1 euro jobs non participation reasons for non participation UB Il cuts due to non participation premature ending and reasons for it hours per week identical work as permanent employees requirements subjective assessments of the 1 euro job Explanatory notes In wave 4 the concept for surveying participation in employment and training measures was reworked and refers exclusively to 1 euro jobs since then Therefore the mn_ spells dataset is not continued The starting point in wave 4 was January 2
67. ater wave can be identified via the respective net variables In addition in these cases the wave specific household number hnr is allocated the code 6 In the following sections the structure and contents of the household register dataset and the person register dataset are presented and their use demonstrated using two examples 14 These are described later in this chapter FDZ Datenreport 07 2013 E 10 1 Household register All of the households which have been successfully surveyed at least once in the sense of PASS see section 7 1 for the definition are contained in the household register Accordingly households from the gross samples of the individual waves which were not successfully surveyed and households that have split off from panel households and have not been interviewed are not contained in the household register In addition to the identifiers the register dataset contains in particular wave specific information on the survey status of the households hnettok hnettod on the sample sample the sampling year jahrsamp the Unemployment Benefit II receipt of the household on the sampling date alg2samp and on the number of benefit units in the household The household register therefore makes it possible to establish in which waves a household was interviewed in PASS and why no interview is available for certain waves In this way a preliminary selection of households can be conducted for example all of the
68. aterial deprivation residential conditions the domestic situation individual health care for elderly persons social networks and social integration In the following two dimensions should be described in detail financial situation and health There are several income measures to assess the material situation of the households resource approach At the household level there is information on the total disposable household income saving loans and debts as wells as security benefits and transfers between households Individual income components are gathered mainly within the personal questionnaire They refer to income from work including earnings from self employment or special payments unemployment insurance payments as well as statutory or civil servants pensions As income measures are prone to short term fluctuations e g due to changes in household composition or household members employment status standard of living is measured in a more comprehensive way by accounting for economic deprivation Deprivation can be defined as the non availability of basic goods considered essential for an appropriate standard of living in a society PASS includes a weighted deprivation index of 26 items covering basic goods and technical equipment like having an apartment with separate bathroom or balcony television or DVD player activities satisfying basic needs such as having a hot meal per day or buying necessary medication and l
69. being in receipt of Unemployment Benefit Il on the sampling date The decisions upon which the weighting is based can be explained as follows At the household level it was decided that 1 All households from the BA sample sample T were in receipt of benefits as of the sampling date even if they denied this provided at least one person aged between 15 and 64 lives in the household 2 Households from the general population sample for which benefit receipt can not be clearly established on the basis of the survey data are regarded as households receiving Unemployment Benefit II for the purpose of weighting if they report ever having received Unemployment Benefit II HA0300 1 and if the start or end date of at least one observation lies in 2006 in cases of an undetermined end or start Transferring from the household to the benefit unit level is wrought with even greater uncertainty The reason for this is that it is not possible to obtain reliable retrospective information on which parts of the household received benefits in July 2006 In most cases the entire household consists of only one benefit unit making the question redundant as the benefit unit receives benefits precisely when the household does so In cases where the household consists of more than one benefit unit the following approach was selected The information as to which individuals the household is currently receiving benefits for AL20600 and AL20700a 0 was used A benefi
70. c household structures or on the integration of spell datasets were made after consulting the IAB In addition the IAB was open for discussion and requests during the whole process Third after the SUF of wave 3 was finished the final datasets were subject to a final check by the IAB regarding their structure and content Besides this the logic and succession of the data editing process stayed the same over the waves It can be divided into the following steps 5 The contract with the former field institute TNS Infratest was initially limited for three waves As a consequence the field work from wave 4 on had to be put out on a request for proposals in which the IAB decided to include the data editing starting with wave 3 Therefore infas as the new field institute of PASS from wave 4 on also carried out the data editing of wave 3 FDZ Datenreport 07 2013 Table 23 Overview of the steps involved in editing the data of PASS No Step of the procedure Check of the household structure of re interviewed households Removal of problematic incomplete interviews household and or individual level Integration of individual dataset and senior citizens dataset Correction of the household structure of re interviewed households Filter checks at the household level Construction of a household grid dataset and plausibility checks Generation of the synthetic benefit units see description of variables in wave specific data reports 8 Gen
71. cannot be traced in the SUF e g removed first time interviews of households from the refreshment sample or individual interviews in these households Therefore the datasets of the SUF do not include interviews that were canceled before the respondent finished the questionnaire Because the definition of successfully surveyed differs between the types of households the SUF contains households without interviews at the individual level in certain waves FDZ Datenreport 07 2013 The standardised codes shown above can be divided into the following groups Missing values due to direct answers of the respondent 1 2 Missing values due to filters or problems with filters 3 4 Question specific codes 5 6 7 Missing values due to implausible answers of the respondent 8 Missing values due to questions not included in the questionnaire wave 9 10 With the exception of implausible answers which were identified later see later on in this section the other groups were treated during this step of data editing The correct operation of the filters was checked and the system missings were replaced Therefore the variables of the raw datasets were examined step by step in the order in which they were recorded Hereby the codes 3 and 4 were assigned A variable was set to 3 not applicable if the question had not to be asked due to a filter
72. coded to the corresponding categories where possible Moreover in some cases new categories were created on the basis of the information from open ended questions The naming of these additional variables differs from that of the original variable in the last digit only where the 0 was replaced by a 1 The items on country of birth nationality and the parents grandparents country of residence before migration were also anonymised and given eloquent variable names Information about the variables generated during the coding of open ended survey questions in the different waves can be found in the wave specific data reports see e g chapter 4 1 in Berg et al 2013c for wave 6 13 2 Variables generated due to dependent interviewing In various parts of both the household and the person interviews information was gathered depending on responses given in previous waves Information from the last interview was used in filter conditions to display alternative texts or displayed directly in the current interview Two primary objectives were pursued with the use of information from previous waves First in some modules only the changes since the last interview should be recorded depending on whether information on a certain topic was already recorded in a previous wave In these cases information from previous waves was used in filter conditions Second in some parts of the interview the respondent was provided with information
73. ction and Overview Wave 2 2007 2008 FDZ Datenreport 06 2009 EN Institut f r Arbeitsmarkt und Berufsforschung N rnberg Graf Tobias 2007 Bedarfsgemeinschaften 2005 und 2006 Die H lfte war zwei Jahre lang durchgehend bed rftig IAB Kurzbericht 17 2007 Institut f r Arbeitsmarkt und Berufs forschung N rnberg Groves Robert M Heeringa Steven G 2006 Responsive design for household surveys tools for actively controlling survey errors and costs In Journal of the Royal Statistical Society Series A Statistics in Society Vol 169 No 3 p 439 457 Groves Robert M McGonagle Katherine A 2001 A Theory Guided Interviewing Training Protocol Regarding Survey Participation In Journal of Official Statistics Vol 17 No 2 p 249 265 Hartmann Josef Brink Kathrin J ckle Robert Tschersich Nikolai 2008 IAB Haushaltspanel im Niedrigeinkommensbereich Methoden und Feldbericht FDZ Methodenreport 07 2008 Institut f r Arbeitsmarkt und Berufsforschung N rnberg Jesske Birgit Quandt Sylvia 2011 Methodenbericht Panel Arbeitsmarkt und Soziale Sicherung PASS 4 Erhebungswelle 2010 Haupterhebung FDZ Methodenreport 08 2011 Institut f r Arbeitsmarkt und Berufsforschung N rnberg FDZ Datenreport 07 2013 Ka Jesske Birgit Schulz Sabine 2013 Methodenbericht Panel Arbeitsmarkt und Soziale Sicherung PASS 6 Erhebungswelle 2012 FDZ Methodenreport 10 2013 Institut f r Arbeitsmark
74. d Arne Bethmann The datasets of the scientific use file SUF of PASS include different types of variables This section focuses on the generated variables which were created during the data editing process They are meant to provide users a quick start or information that could not be included directly in the datasets of the scientific use file e g information on the relationships between the household members Detailed information about the generated variables can be found in the wave specific data reports e g an overview of the variables generated for a certain wave or the source variables they are based on e g see chapter 4 in Berg et al 2013c for wave 6 This chapter of the user guide will give a general introduction to the different types of generated variables and some notes on their use The datasets of the SUF contain six different types of generated variables FDZ Datenreport 07 2013 Variables generated due to coding of open ended survey questions Variables generated due to dependent interviewing Constant characteristics Simple generated variables Theory based construct variables 13 1 Coding of responses to open ended survey questions Some items of the survey were gathered as closed items with an open residual category or as open ended items In such cases additional variables were usually generated which differed from the original variable only insofar as the information from the open ended responses was
75. d based only on the information that was recorded in this wave E g hhincome Household income per month FDZ Datenreport 07 2013 Ka Table 33 Information on constant characteristics gender Variable Description Dataset Filled in for wave s of the first and repeated interview s HD0100a o Gender of individuals 1 to 15 in HHENDDAT Yes if person lived in household the household zpsex Gender of target person PENDDAT Yes sex Gender of target person p_register Information not wave specific but contains KINDER the respective last correction elsewhere and have a foundation in theoretical concepts Moreover some of them are standardized instruments used in social sciences or economics Examples of such standard ized instruments are the European Socio economic Classification ESeC the International Standard Classification of Education ISCED or the equivalized household income Detailed information on these variables in the different waves can be found in the wave specific data reports see e g chapter 4 5 in Berg et al 2013c for wave 6 14 Constant characteristics Daniel Gebhardt and Arne Bethmann Variables which are assumed not to change over time are only surveyed once in PASS However despite the constant nature of the characteristics in reality changes in these variables are sometimes possible since for example incorrect entries may be corrected in subsequent interviews e g in the case of gender The following sec
76. d to all responding persons in the households as they do not take into account person non response within participating households partial unit nonrepsonse Use of design weights at the person level thus aditionally requires an estimation of the person s participation propensity given participation of the household The following sections provide examples showing how to use the cross sectional weights for different research questions 12 2 1 Analyses of benefit recipients in July 2006 The most efficient way to obtain findings on the population of the BA sample in the 1st wave households in which there was at least one benefit unit receiving benefits in accordance with SGB II as of July 2006 referred to below as households receiving benefits in July 2006 is to use only the BA sample and the relevant weights Proceeding in this way is more efficient than using the total sample as the weights in the BA sample have less variance Furthermore the analyses have to be restricted to sample 1 as cases from the refreshment samples are otherwise taken into account too Analyses at the household level To make analyses of households receiving benefits in July 2006 researchers should use wgbahh The example below demonstrates its use in Stata 12 1 It is intended to calculate the number or percentage of households receiving benefits which are in possession of a car variable HLS0800a To start with the household weights have to be merged with
77. de in a given wave the respective variable is assigned the missing code 9 The wave a given variable in the spell refers to can be read from the variable labels The following example demonstrates the generation of a variable containing the latest information about the amount of benefits received per month for each Unemployment Benefit II spell Variables for the other cross sectional information can be generated in the same way 11 1 Example Using the cross sectional information included in the spell datasets First a new variable is created hoehebez which is assigned code 3 not applicable as details about the amount of benefit received are only available for Unemployment Benefit Il spells that were still ongoing at the interview date in at least one wave Then the generated variable is filled with the information from AL20800 amount of benefit received per month in wave 1 AL20801 amount of benefit received per month in wave 2 and the respective variables for the following waves using a loop Information is only incorporated into hoehebez however when it does not involve the values 3 not applicable or 9 item not surveyed in wave A cross sectional variable on the amount of benefit received is given the value 3 if information about the spell was gathered in the respective wave new details surveyed or previous details updated but the spell was not ongoing on the interview date The variable is assigned th
78. does PASS fit in the German microdata landscape 3 Design of the study Mark Trappmann Gerrit M ller and Arne Bethmann 3 1 Iniroduelloni z 2 amp ag a Boa aR SRP Ta Bee ey ee G 3 2 Sampling procedure 0 000 eee 3 3 Other survey design features 2 oaa a 4 Instruments and interview programme Jonas Beste Johannes Eggs Stefanie Gundert and Claudia Wenzig ooo aaa a 5 Structure of the scientific use file and its datasets Benjamin Fuchs 5 1 Introduction to the scientific use file 0 0 00 5 2 Datasets of the scientific use fille 0 0 2 0 0 0004 6 Variable types and their names Arne Bethmann 4 6 1 General issues ss s ccc waa p bo todan ga wok ea g i e e 6 2 Variabletypes 22 ann 7 Data editing Daniel Gebhardt 2 2 2 Hm nn n GA Structure checks lt 24 20 deed wird aaa Cad 22H ES 7 2 Filter checks and assignment of standardised codes 8 Weighting Mark Trappmann ooa 8 1 Initial Weights edd Bo 2 oe ee 2 Swe ee a nur ee 8 2 Construction of the weights from wave 2 onwards 8 3 Integration of the weights of the replenishment samples with the ongoing panelsamples cpi si a aa i aa a a a ank ea ak E a a e a 8 4 Datasets and variables oaoa ooa eee eee II Using the PASS data 9 Key variables Daniel Gebhardt and Arne Bethmann 2 222200 9 1 Key variables and their connection to th
79. e 3 BA refreshment sample wave 2 4 BA refreshment sample wave 3 5 BA refreshment sample wave 4 6 general popula tion replenishment sample 7 BA replenishment sample 8 BA refreshment sample wave 5 9 BA refreshment sample wave 6 0 no benefit recipi ency 1 benefit recipiency 2 no benefit recipi ency acc to survey BA sample 3 benefit recipiency unclear acc to survey BA sample 4 benefit recipiency unclear acc to survey Microm sample 1 UB Il recipiency as of sampling date O no UB Il recipi ency as of sampling date 1 HH currently re ceiving UB II 2 HH currently not receiving UB II 5 No generation poss missing val ues 1 UB II recipiency in wave 1 0 No UB II recipi ency in wave 1 5 No generation poss missing val ues Suitable for comparing households persons in households with Unem ployment Benefit II receipt at a certain reference date to households persons of the general population and to households persons in households who are new entries to benefit receipt households persons in households with Unem ployment Benefit II receipt at a certain reference date for sampling to households persons with no benefit receipt at the same reference date Users may choose how to deal with cases that were receiving Unemployment Benefit II accord ing to register information but not according to the survey Persons in benefit units receiving Unemploy ment Benefit II at
80. e 3 FDZ Datenreport 06 2010 EN Institut f r Arbeitsmarkt und Berufsforschung N rnberg Beste Jonas 2011 Selektivit tsprozesse bei der Verkn pfung von Befragungs mit Prozessdaten Record Linkage mit Daten des Panels Arbeitsmarkt und soziale Sicherung und administrativen Daten der Bundesagentur f r Arbeit FDZ Methodenreport 09 2011 Institut f r Arbeitsmarkt und Berufsforschung N rnberg Beste Jonas Eggs Johannes Gebhardt Daniel Gundert Stefanie Hess Doris Jesske Birgit Quandt Sylvia Trappmann Mark Wenzig Claudia 2011 IAB Haushaltspanel Lebensqualit t und soziale Sicherung Interviewerhandbuch Welle 5 FDZ Methodenreport 03 2011 Institut f r Arbeitsmarkt und Berufsforschung N rnberg B ngeler Kathrin Gensicke Miriam Hartmann Josef J ckle Robert Tschersich Nikolai 2010 IAB Haushaltspanel im Niedrigeinkommensbereich Welle 3 2008 09 Methoden und Feldbericht FDZ Methodenreport 10 2010 Institut f r Arbeitsmarkt und Berufs forschung N rnberg FDZ Datenreport 07 2013 Ka B ngeler Kathrin Gensicke Miriam Hartmann Josef J ckle Robert Tschersich Nikolai 2009 IAB Haushaltspanel im Niedrigeinkommensbereich Welle 2 2007 08 Methoden und Feldbericht FDZ Methodenreport 08 2009 Institut f r Arbeitsmarkt und Berufs forschung N rnberg Christoph Bernhard M ller Gerrit Gebhardt Daniel Wenzig Claudia Trappmann Mark Achatz Juliane Tisch Anita and Gayer
81. e code 9 if no information was collected about this spell in the respective wave First hoehebez is filled with the information on the amount of benefits received as recorde in the cross sectional variable for wave 1 AL20800 and then in the consecutive loop runs is replaced by the values of the cross sectional variables referring to the respective waves Thus hoehebez is replaced with the latest available information for this spell use alg2_spells dta clear gen hoehebez 3 forvalues i 0 5 FDZ Datenreport 07 2013 Ey replace hoehebez AL2080 i if AL2080 i 3 amp AL2080 i 12 Weights Mark Trappmann 12 1 Recommendations for the use of surveyset in Stata All of the weights in PASS are so called probability weights the weight of a household or a person is equivalent to the reciprocal value of its his her inclusion probability adjusted by non response modelling and calibration In Stata starting with version 9 probability weights have to be set using the surveyset command see Stata Corp 2007 However surveyset not only has the purpose of defining the weights to be used but also of defining the aspects of the survey design that have an impact on the standard errors There are two different options for doing this in Stata by specifying the design or by using replication weights In the first option the aspects of the survey design that influence the standard error have to be entered in t
82. e collected answers to the items can be found in the scientific use file Therefore they establish the correspondence be tween question numbers and constant variables names The following questionnaires are available Household questionnaire for new and split households household questionnaire for panel households first introduced in wave 2 person s questionnaire senior citizens questionnaire German English German 1 6 wave specific 1 6 wave specific FDZ Datenreport 07 2013 w 2 PASS background Mark Trappmann 2 1 Objectives and research questions of the panel study Labour Market and Social Security The panel study Labour Market and Social Security PASS established by the Institute for Employment Research IAB is a dataset for labour market welfare state and poverty research in Germany creating an empirical basis for the scientific community and for policy advice The study is carried out as part of the IAB s research into the German Social Code Book II SGB II The IAB has the statutory mandate to study the effects of benefits and services under SGB II aimed at integration into the labour market and subsistence benefits However due to its complex sample design the study also enables researchers to answer questions far beyond this scope Five core questions influenced the development of the new study which are detailed in Achatz Hirseland Promberger 2007 1 Which pathways
83. e identified The weights of this subset are not affected by the replenishment samples After this step the remaining panel sample and the panel supplements are now two random samples of the same population with known inclusion probabilities Thus the concept of convex combination Spiess Rendtel 2000 was applied to the fusion of these two samples and the combination with minimum variance was chosen The weights of the combined population samples are designed to project the initial general population sample sampled from the Microm database and the replenishment from munici pal registers to all households in Germany Thus separate weights were calculated initially for the general population panel sample and the general population replenishment sample following the steps described in sections 8 1 and 8 2 1 8 2 7 Then the general population panel was integrated with the general population replenishment Sample 6 via a convex combination to obtain the population weight before calibration The weights of the combined BA samples are designed to project the original BA sample the four refreshment samples with new entries to unemployment benefit II and the new BA replenishment sample to all households which received benefits at one of the reference days in July of the years 2006 2010 The subset still receiving unemployment benefit Il at the reference date in July 2010 is to be projected to all recipients at that date Initially separate weights
84. e standardised missing value codes and special codes that are used in all datasets of PASS are described as part of the section on filter checks chapter 7 Chapter 8 provides information on the weighting concept e g on the creation of the design weights the weighting datasets and the variables included The second part of the User Guide provides advice and hands on examples for working with the data Chapter 9 discusses the use of key variables when merging information from different datasets The specific handling of the register and spell datasets is intro duced in chapters 10 and 11 respectively An in depth treatment of the use of weights in cross sectional and longitudinal analyses is given in chapter 12 The last two chapters discuss specific issues when working with generated variables chapter 13 and constant characteristics chapter 14 This part is particularly helpful for new users It demonstrates certain standard procedures of the work with the PASS datasets The User Guide will evolve over time as it is planed that new topics will be included and already included chapters will be updated in future waves For this process feedback from the users of PASS is essential as it can give evidence where the User Guide should go into more detail which new topics should be considered and where a chapter should be revised or updated Therefore we appreciate any feedback be it positive or negative Feedback can be addressed direct
85. e structure of the scientific use file 9 2 Example Merging household data with the individual dataset 9 3 Example Merging the household weights with the household dataset 9 4 Example Merging information from the individual dataset with the person spece speldata r sa 24 Yow a a a ee oe a ae a a 10 Register data Daniel Gebhardt and Arne Bethmann 73 11 10 1 Household register 2 2 Hm nn nn 74 10 2 Person register 2 2 Hmm nn 74 10 3 Example Selection of the households that were successfully surveyed in the 1st and 2nd wave and were receiving Unemployment Benefit II on the sampling dat soss sa a n nn 75 10 4 Example Identification of the personal interviews with the heads of households 75 Spell data Daniel Gebhardt and Arne Bethmann 2 22 76 11 1 Example Using the cross sectional information included in the spell datasets 80 12 Weights Mark Trappmann gt s s cs soa sro satsaa sadesa i erau 81 12 1 Recommendations for the use of surveysetin Stata 81 12 2 Use of the cross sectional weights 2 204 82 12 3 Use of the longitudinal weights 2 2 2 204 93 13 Generated variables Daniel Gebhardt and Arne Bethmann 99 13 1 Coding of responses to open ended survey questions 100 13 2 Variables generated due to dependentinterviewing 100 13 3 Simple generated variables 00 0 000022 eae 101 13 4 Theory b
86. e using HHENDDAT dta keepusing hhtyp tab _m welle drop if _m 2 The tabulation of the _ merge variable shows that information from the household dataset was merged for some cases from wave 2 N 140 and wave 3 N 190 for which no personal interviews were available These are re interviewed households without personal interviews in the respective wave These cases are dropped for the example 9 3 Example Merging the household weights with the household dataset The household dataset and the household weights are available in the same format and on the same level Accordingly the datasets can be merged directly The same procedure is used for merging the individual dataset and the person weights FDZ Datenreport 07 2013 Ka Table 28 Overview of key variables in the scientific use file Key variable hnr uhnr hnr pnr zplfd welle spellnr Description Current household number Eight digit constant ID number of a household which is allocated when the household joins the panel The first digit indicates the wave in which the house hold was first part of the gross sample of PASS E g 10010008 household in gross sample for first time in 1st wave 21011685 household in gross sample for first time in 2nd wave Original household number Eight digit constant ID number that points to the original household In the case of households that were drawn directly for one of the subsamples the uhnr is the same
87. eisure activities like going to the movies or inviting friends for dinner at home once in a while The index refers to FDZ Datenreport 07 2013 fl the number of items respondents miss for financial reasons Each item is weighted by the proportion of respondents regarding it essential Health related questions are gathered by a basic module in each wave and by extended questionnaire modules in selected waves The basic module includes self reported health indicators referring to mental and physical well being number of consultations hospital stays in the last twelve months having chronic diseases and disabilities mental health problems subjective health status and health satisfaction In addition to the basic set questions about health related behavior such as drinking and smoking obesity and health related limitations of employment are integrated in selected waves Furthermore in this extended module wave 3 and 6 the standardized SF 12 item battery is used as a global measure of mental and physical health Besides new questions about sports activities types of sports intensity of physical excercise social networks related to sports activities sports activities in youth were developed for another focus module referring to health wave 6 and 7 Referring to the third core dimension of the study coping strategies attitudes and behaviour a variety of subjective indicators are gathered at individual level such as global aspect
88. el household individual type register cross sectional weights spells factorial and format wide format long format spell format Table 3 provides an overview of the datasets that are part of the SUF in wave 3 as well as their level type and format Each dataset will be described in more detail in the following sections starting with the datasets on the household level followed by those on the individual level FDZ Datenreport 07 2013 f Table 3 Overview of the datasets of the scientific use file Type Format Name of dataset information on waves and filenames in brackets on Household level Individual level Register wide Household register Person register Cross section long hh_register Household dataset HHENDDAT Household dataset on retirement provision wave 3 only HAVDAT p_register Person dataset PENDDAT Children Dataset KINDER Person dataset on retirement provision wave 3 only PAVDAT Weights long Household weights Person weights hweights pweights Spells spell Unemployment Benefit Il spells Biography spells alg2_spells from wave 2 bio_spells 1 euro job spells from wave 2 ee_spells Measure spells wave 2 and 3 only mn_spells Unemployment Benefit spells wave 1 only alg1_spells Measure spells wave 1 only massnahmespells Factorial long Vignette dataset wave 5 only VIGDAT To describe the datasets in a layout that is easy to read a
89. el respondents the information is updated every wave Information on the highest level of education and vocational qualification is available also for respondents parents Furthermore the module on social background covers parents occupational status and the jobs they held when the respondent her himself was 15 years old Migration background is recorded dating back to the third generation For migrants additional information on their residential permit is collected Furthermore the language spoken in the household and German language skills are surveyed in selected waves Table 2 gives a detailed overview over wave specific modules of the household and personal questionnaires waves 1 6 FDZ Datenreport 07 2013 Table 2 Overview of modules Modules on household level Wave Household composition X X X XXX Standard of living deprivation X XxXxX X X Language spoken in household x XxX xX Housing x X xX xX Xx Receipt of Unemployment Benefit II UB II xXx X XXX X Income x X X X 2G Child care Xx xX xX XXX Participation of children and youths x Education and inclusion subsidies x Modules on individual level Date of birth Xx xX xX XXX Religion xX xX xX xX X Migration Xx X X X XX Language spoken in circle of friends x X X X Knowledge of German language Xx xX xX Social origin X x XxxX Satisfaction with life in general health and living circumstances Perceived integration in the society x x x x x x Self
90. elinr Spell number One obs row in data matrix Episode during which a certain person received UB I One obs row pnr spellnr in data matrix uniquely identi fied by Topics 1 Information on UB I recipiency start date end date total amount of benefits per month Explanatory notes Episodes of UB I recipiency were only recorded directly in wave 1 Starting with wave 2 the information on times when the respondent received this benefit was recorded as part of the episodes of registered unemployment From wave 2 on information on UB I recipiency can be found in the unemployment spell dataset Persons who have not reported an episode of UB I recipiency in wave 1 are not represented by an observation in the dataset The dataset includes as many observations for a certain person as the number of episodes this person reported in wave 1 FDZ Datenreport 07 2013 u Measure Spells massnahmespells Table 18 Characteristics of the measure spells wave 1 only massnahmespells Dataset Measure spells wave 1 only File name massnahmespells Level individual Type spells Format spell Data collected in waves 1 Integration of data from new waves The concept of wave 1 to survey measure participation was reworked in wave 2 Therefore no data from new waves need to be integrated Key variables pnr Constant personal ID number spelinr Spell number Pointer va
91. empts by telephone towards the end of the fieldwork period for households or persons who initially refused to participate for the following reasons lack of interest in the topic length of the interview lack of time when someone immediately hung up the phone or when someone that was not the target respondent refused on behalf This follow up on reluctant hard to interview sample cases was conducted by selected CATI interviewers with above average performance during the regular fieldwork and special FDZ Datenreport 07 2013 Bu training in refusal conversion see e g Hartmann et al 2008 54 56 for waves 1 3 and e g Jesske Quandt 2011 26 for waves 4 6 Indicator variables for interview mode are contained in the PASS scientific use file in order to control for potential mode effects in empirical analyses 3 3 4 Advance letter and other survey notification material In wave 1 each household in the gross sample was notified with an advance letter about upcoming calls or personal visits by interviewers approximately one week prior to the first scheduled contact attempt The letter introduced the name and purpose of the survey the involved research institutes IAB fieldwork agencies and the sponsor Federal Ministry of Labour and Social Affairs It explained how the respective household was selected into the sample and that all data protection laws would be strictly adhered to Respondents were given a promise of confidentiality which g
92. en them irrespective of whether they are currently receiving Unem ployment Benefit II see chapter 3 2 The information about the benefit units is available as wave specific information It must be taken into account that this information was generated each time on the basis of the information available for the individual waves Via the benefit unit ID number bgnr it is possible to identify the individuals who together constitute a benefit unit Here it must be taken into consideration that new numbers are allocated in each wave and that there is no continuation in the longitudinal section Furthermore the dataset contains information on the type of benefit unit bgtyp and on the benefit receipt of the benefit unit on the sampling date bgbezs and the survey date of the current wave bgbezb FDZ Datenreport 07 2013 us The person register dataset also contains pointer variables referring to the mother living in the household zmhh the father living in the household zvhh and the partner living in the household zparthh These pointers each contain the ten digit personal ID number of the person who is the target person s mother father partner 10 3 Example Selection of the households that were successfully surveyed in the 1st and 2nd wave and were receiving Unemployment Benefit Il on the sampling date The net variables are available in two levels of detail in a short single digit variant hnettok1 hnettok2 and
93. eration of new control variables on the basis of the household data following filter checks and the household grid dataset after plausibility checks 9 Filter checks at the individual level 10 Coding of information from open ended survey questions 11 Plausibility checks of the household and individual level data excluding spell data 12 Preparation plausibility checks and construction of the spell datasets 13 Simple variable generations 14 Complex variable generations 15 Generation of the data structure for the scientific use file household dataset individual dataset register dataset 16 Anonymisation 17 Final check of the SUF datasets NOOR WD 7 1 Structure checks First the household structure of re interviewed households was compared to the structure reported in the previous interview in order to identify and if necessary correct implausible or problematic changes in the household composition and errors in the allocation of the individual interviews to their respective position in the household For observing the house holds in the longitudinal section it is essential that the individuals are assigned consistently to their position in the household and that the respondents can be identified clearly across the waves The same personal identification number must not be allocated to different individuals in different waves If the correct household composition was unclear all of the interviews conducted with the household in
94. erhebung Arbeitsmarkt und Soziale Sicherung Vol 12 2007 of IAB Forschungsbericht N rnberg p 33 59 Schnell Rainer Gramlich Tobias Mosthaf Alexander Bender Stefan 2010 Using complete administration data for nonresponse analysis The PASS survey of low income households in Germany Proceedings of Statistics Canada Symposium 2010 Social Statis tics The Interplay among Censuses Surveys and Administrative Data Schouten Barry Cobben Fannie Bethlehem Jelke 2009 Indicators for the representa tiveness of survey response In Survey Methodology Vol 35 No 1 p 101 113 Spiess Martin Rendtel Ulrich 2000 Combining an Ongoing Panel with a New Cross Sectional Sample Diskussionspapiere Discussion Papers 198 Deutsches Institut f r Wirtschaftsforschung Berlin S rndal Carl Erik Swensson Bengt Wretman Jan 1992 Model Assisted Survey Sam pling New York Springer Stata Corp 2007 Survey Data Reference Manual Release 10 Stata Press College Station Trappmann Mark Beste Jonas Bethmann Arne M ller Gerrit 2013 The PASS Panel Survey After Six Waves In Journal for Labour Market Research online first Trappmann Mark Christoph Bernhard Achatz Juliane Wenzig Claudia 2007 Labour Market and Social Security A New Panel Study for Research on Long Term Unemployment Paper presented at the International Conference of the German Association of Political Economy Trappmann Mark
95. erns Households who drop out for one wave and return in the next wave cannot be treated this way The treatment of those temporary dropouts is specified in section 8 2 5 8 2 4 Non response weighting for households from the wave n refreshment sample For the households in the refreshment samples non response was modelled in a two step procedure as was done for the first wave The full lists of variables in the models and FDZ Datenreport 07 2013 es coefficients are described in the wave specific data reports cited above The participation probability derived from this can be found in variable prop_tO 8 2 5 Propensity models for temporary dropouts From wave 3 on there are households in the PASS dataset that have returned after temporar iliy dropping out of the panel The longitudinal weights cannot be applied to this group of households which means that weighted longitudinal analyses can only be performed with the balanced panel of households who participated in all waves within the period considered for the longitudinal analysis Allowing for non monotonous patterns would result in an exponentially growing number of weights by wave Lynn Kaminska 2010 For temporary dropouts first the probability of dropping out in wave n given participation in wave n 1 is derived from the propensity models for the transition from wave n 1 to wave n 1 Then a simple propensity model containing only final disposition code of the previous wave mode sam
96. erson weights are available for children under the age of 15 this dataset cannot completely and should not be merged with the person weights This dataset in combination with the personal dataset does not yield representative results for children of different age groups Examples for the usage of the children dataset are given in XXX FDZ Datenreport 07 2013 Person dataset on retire provision PAVDAT Table 13 Characteristics of the person dataset on retirement provision PAVDAT Dataset Person dataset on retirement provision File name PAVDAT Level individual Type cross section Format long Data collected in wave 3 only waves Integration of data from new waves In depth information on retirement provisions was only collected in wave 3 Therefore no data from new waves need to be integrated Key variables pnr Constant personal ID number welle Indicator for survey wave Pointer variables One obs row in Cross sectional information regarding a certain person in wave 3 data matrix One obs row pnr welle in data matrix uniquely identified by Topics 1 In depth individual information on retirement provisions Explanatory notes In depth information on retirement provision was only collected in wave 3 The respective module of the persons questionnaire was only asked for persons who were 40 to 64 years old or had a partner of this age The dataset c
97. es for example on the impact of SGB II on certain target groups like young adults migrants single parents supplemental benefit recipients Aufs tocker and to obtain more precise estimates of statistics and model coefficients than from datasets in which benefit recipients are only included in proportion to their share of the population 2 Collecting additional characteristics such as the intensity and type of contact to institutions providing basic social security or participation in employment and training measures makes it possible to analyse the significance of institutional assistance for the population below the poverty line Linking the survey data with the administrative data of the BA enables validating the characteristics surveyed and also conducting analyses in which the higher measurement precision of the process generated data can be combined with further variables and the household context from the survey FDZ Datenreport 07 2013 3 Design of the study Mark Trappmann Gerrit M ller and Arne Bethmann 3 1 Introduction By establishing the panel study Labour Market and Social Security PASS the Institute for Employment Research IAB is setting up a database that creates a new empirical basis for research into the labour market the welfare state and poverty in Germany The survey pays particular attention to the dynamics of households in receipt of benefits in accordance with the Social Code Book II SG
98. es as of wave 4 see Jesske Quandt 2011 28 41 wave 4 Jesske Schulz 2012 41 54 wave 5 Jesske Schulz 2013 38 54 wave 6 3 3 6 Respondent incentives As in many other household surveys PASS distributes incentives for respondents in order to increase response rates and potentially bound the scope for nonresponse and attrition bias In wave 1 all sampled households received a special postage stamp as a small token of appreciation together with the advance letter In the advance letter it was stated that respondents to the survey would receive a ticket for the lottery Aktion Mensch The ticket had a value of about 1 50 EUR and was mailed to each individual respondent after the interview together with the thank you letter In wave 2 the type of incentive strategy was left unaltered with the exception that the ticket was now for the lottery ARD Fernsehlotterie and had increased in value to about 5 00 EUR Flanking the other measures to increase survey participation as described e g extended field period increased tracking efforts in wave 3 there was also a shift in incentive strategy towards the usage of monetary incentives A new incentive scheme was introduced that consisted of a 10 00 EUR note distributed at the household level in advance of the interview i e unconditional on participation It was sent to each panel household that had participated at least once together with the advance letter A split sample experime
99. es or who had passed a mandatory two day general interviewer training by the fieldwork agency were admitted to PASS The study specific training program provided an introduction to the survey topic and target population followed by an overview of the questionnaire modules and some hands on exercises with the programmed instrument In waves 1 3 all CATI interviewers were directly trained by IAB researchers and programme directors at the fieldwork agency For CAPI interviewers the training was organised as a train the trainers program Multiplikatorenkonzept That is a small group of experienced interviewers Kontaktinterviewer was trained centrally and went out to instruct other interviewers in the various geographic areas sampling points for details see Hartmann et al 2008 34 37 wave 1 B ngeler et al 2009 22 24 wave 2 and B ngeler et al 2010 33 35 wave 3 As of wave 4 the train the trainers program was abandoned and all CATI and CAPI interviewers were trained centrally by IAB researchers and trainers of the responsible fieldwork agency see Jesske Quandt 2011 66 68 Jesske Schulz 2012 79 81 Jesske Schulz 2013 83 85 In all waves interviewers could keep the training materials and additionally received an interviewer project manual as a comprehensive reference for later for an example see the training material for wave 5 which has been published as FDZ Methodenreport Beste et al 2011 In order to keep survey non c
100. essfully surveyed in PASS One obs row Key variable that uniquely identifies an observation e g in data matrix hnr uniquely identified by Topics Information on the topics covered by the dataset e g 1 Constant sampling information 2 Wave specific household information households survey status size of household number of synthetic benefit units pointers Explanatory notes Notes that point out special characteristics or give additional information on the dataset e g Only households that were successfully surveyed at least once are included in the household register All datasets include key variables which are used to identify units and observations and to establish links to other datasets of the SUF The key variables included in the dataset are listed in the corresponding tables see Key variables Further information about their meaning and on how to use them can be found in chapter 9 We strongly request the users of PASS to make themselves familiar with the structure of the datasets their meaning and the key variables before combining different datasets A second group close to the key variables is the pointer variables While the key variables are used to identify the same unit and link it between datasets the pointer variables are used to establish links between different units e g the variable uhnr original household number can be used to link a split
101. f an intensive data editing process In its course the raw data collected by the field institute in a certain wave is checked answers to open ended survey questions are coded variables are generated and the data is integrated into the datasets of the SUF Although this process is improved and adjusted for each wave its basic logic and the succession of its steps stay the same over time While the wave specific procedures are described in the data reports see for example Berg et al 2013c for the data editing of wave 6 this section will focus on giving an overview of the important steps and their succession The data editing of the first two waves was performed at the Institute for Employment Research IAB With wave 3 the Institut fur Angewandte Sozialwissenschaft infas the new field institute of PASS took over this task To ensure that this change in who edits the data would not result in a change in procedures and inconsistency in the datasets of the SUF several precautions were taken First the new contract with infas stated as a condition that all steps of the data editing process had to be carried out in the same order and in an analogue way as in the previous waves infas was therefore provided with the relevant syntax files and datasets of wave 2 as well as with a documentation of each step Second the process of data editing was accompanied by continuous coordination between infas and the IAB Important decisions e g on problemati
102. for a household to be regarded as success fully surveyed in PASS Type of household Household level Individual level interwiew interwiew s new household yes completed yes at least one com pleted household was interviewed for the first time and drawn for the initial sample or a refreshment sample re interviewed household yes completed none required household was already interviewed in a previous wave of PASS new split off household yes completed none required household was interviewed for the first time and is a split off from an other household in PASS 7 2 Filter checks and assignment of standardised codes Every surveyed variable in the SUF datasets was filter checked During these checks filter errors were marked and standardised missing codes were assigned Table 25 gives an overview of the standardised codes used in PASS Table 25 Overview of standardised codes used in PASS Code Explanation 1 Don t know 2 Details refused 3 Not applicable filter question not asked due to filter 4 Question mistakenly not asked question should have been asked 5 Question specific code No 1 only allocated as required 6 Question specific code No 2 only allocated as required 7 Question specific code No 3 only allocated as required 8 Implausible value 9 Item not administered in wave 10 Item not administered in questionnaire version register datasets
103. g the start date as well as information that refers to a certain wave e g the amount of benefits the household received in wave 3 These cross sectional information are valid only for a certain point in time and can change while the episode continues Therefore the dataset contains cross sectional variables referring to a certain wave They are filled if the episode covers the respective wave and are otherwise assigned the missing code 9 The wave a cross sectional variable in the spells refers to can be read from the variable labels FDZ Datenreport 07 2013 Eu 5 2 2 Individual level datasets Person register p_register Table 10 Characteristics of the person register dataset p_register Dataset Person register File name p_register Level individual Type register Format wide Data collected in 1 6 waves Integration of data from new waves 1 Persons that are members of a surveyed household for the first time are added as new observations 2 New wave specific variables are added They include the information recorded in the last wave Key variables 1 pnr Constant personal ID number 2 hnr Household number in wave 3 zplfd Serial number of the target person in the household in wave Pointer variables One obs row in data matrix 1 uhnr Original household number 2 zmhh Constant personal ID number of target persons mother living in
104. g year of the subsample wghh Projection factor household total wqmihh Projection factor household Microm wgbahh Projection factor household BA hpbleib Reciprocal re participation probabil ity household wn Wn Remarks Used together with welle for linking the datasets Used together with hnr for linking the datasets Indicates whether BA or Microm weights are used Is the selection probability during sampling in the respective subsample gross Is the selection probability during sampling in the respective subsample gross Is the selection probability during sampling in the total sample gross Is the probability of the household taking part in the year when the subsample was drawn as predicted by means of a logit model Projection factor for the cross section of the re spective wave total Projection factor for the cross section of the re spective wave Microm Projection factor for the cross section of the re spective wave BA Reciprocal value of the probability of the house hold participating in the survey again in the follow ing wave as predicted by means of a logit model The file pweights contains the following variables Table 27 Overview of the variables in the person weights data file pweights Name Label pnr Unchanging personal ID number welle Indicator for survey wave sample Subsample wqp Projection factor person total wgmip Projection factor person
105. h strata strpsu singleunit scaled svy subpop if alg2abez 1 tab HLS0800a if welle 6 amp sample 1 cell ci format 9 0g in this case produces slightly larger confidence intervals 24 2 31 9 than the more exact estimation using subpop Finally svyset psu pw wgbahh strata strpsu singleunit centered svy subpop if alg2abez 1 tab HLS0800a if welle 6 amp sample 1 cell ci format 9 0g again leads to results that only differ from the on the second position after the decimal point from the more exact procedure These examples indicate that when using PASS due to a small number of strata with only one PSU differences between the estimation procedures are negligible FDZ Datenreport 07 2013 a Analyses at the benefit unit level Researchers working on recipiency of Unemployment Benefit Il are often not interested in households but in benefit units If the above question on the percentage of households receiving benefits in July 2006 which are in possession of a car is to be transferred to benefit units the PASS data can be used to answer the question as to how many benefit units live in a household that has a car as the benefit units were identified retrospectively there are no questions in the questionnaire relating directly to benefit units it is therefore not possible to identify which benefit unit owns the car in a household consisting of several benefit units This question is relatively easy to a
106. h h n 1 weight share response contact weights by convex wqhh ae gt hpbleib combination 4 2 Non response 9 F Gar Calibration of Design weight weighting 8 A i cross section n 1 for refreshment sample households logit Integration of the y households wave n 1 gt dw models for contact weights ea wgbahh wqmihh dw_ba and wqhh response contact Cross sectional 7 A 1 BERN i g Calibration of weight individuals Propensity logit cross section n 1 for wave n models for contact Uren r H individuals gt qwbap wqmi with P and response 3 in waqbap wqmip wages contact gt ppbleib wap 8 2 1 Design weights for the wave n households in the n 1 th wave New household design weights were generated for the n 1 th wave from the cross sectional weights for households of wave n taking into account people moving into house holds from within Germany This is done using a weight share procedure Births deaths or moves out of households have no influence on the weight moves into households from within Germany on the other hand increase the inclusion probability of a household as the individuals who have moved into the household also had the chance of being included in the sample in all previous waves Thus for the weighting if individuals had moved into the household from within Germany the previous inclusion probability was increased by the mean inclu
107. he command line Besides the weights these aspects are clusters stratification characteristics and finite population corrections for sampling without replacement The effect of calibration on the standard error and other factors such as pps sampling cannot be taken into account The second option on the other hand makes use of a set of replication weights which are calculated for all units of the study using processes such as jackknifing BRR or bootstrapping These procedures also potentially permit the calibration to be taken into account There are no replication weights available for PASS to date so researchers will have to use the first variant for the surveyset for PASS However the complex sample design of PASS cannot be used for variance estimation with the surveyset command in all details We recommend the following approach svyset psu pw wqaX strata strpsu Here wqX stands for the adequate weight for the intended analyses An indicator for the primary sampling units which are the same for both subsamples is the variable psu in the household dataset HHENDDAT The strata for the selection of the primary sampling units are represented by the variable strpsu in the same dataset Strata with fewer than two units in the sample were collapsed In the case of sampling with replacement neglecting strata and clusters from the second level onwards in PASS these would be households only leads to an unimportant underestimation
108. hese groups are for example their tendency to relocate more frequently than the general population difficulties in contacting them by phone due to low landline coverage or changes in mobile phone numbers The sequential mixed mode design ensures that target persons who cannot be contacted and interviewed by phone are visited by an interviewer at their home to conduct the interview in CAPI mode Initiated by acall for tenders PASS changed the fieldwork agency responsible for data collection and preparation after wave 3 see Muller 2011 In the course of that change there has also been a shift towards CAPI as the default mode for cases of all refreshment samples from wave 4 onwards For details on contact routines and mode switches please see subsection 3 3 3 3 3 2 Foreign language interviews The design anticipates that a considerable proportion of the target population has a migrant background and may not have sufficient knowledge of German to participate Therefore the survey instrument was translated into Turkish and Russian the most frequent first languages of immigrants to Germany In wave 1 there was an additional English language version as a fall back for all other nonnative speakers Since only a small number of cases was realised using the English version of the instrument 9 household level interviews it was FDZ Datenreport 07 2013 a dropped after wave 1 In the CATI telephone survey the foreign language instruments were admi
109. household of this person was asked for episodes since the interview date of the former household Exceptions to this are the merging of two spells and the spells of Unemployment Benefit II receipt surveyed in the first wave FDZ Datenreport 07 2013 fies D N always required for a clear selection as there are often several observations available per household or person This also has to be taken into account when linking spell data and the household and individual datasets As several spells are frequently available and there is also no wave indicator for the individual observations in the spell data a wave specific reference is not possible without further work A spell can include several pieces of information of the same kind that refer to different points in time These are recorded in individual variables within the same observation in the spell dataset e g the amount of benefits the household received AL20800 if the information was recorded in wave 1 AL20801 for wave 2 etc As long as a reported episode has not ended the information from the last interview always corresponds to that interview date However if an episode has ended the information from the last interview corresponds to the reported end date If there are several pieces of information recorded in different waves the ones which were reported while the episode had not ended correspond to the respective interview date If there is no information recorded for an episo
110. household dataset contains two observations for this household one for each wave with an interview Therefore the wave indicator welle is required in addition to the household or personal ID number in order to identify an observation clearly In spell format datasets the spell number spellnr has to be taken into account when identifying an observation The spell datasets contain as many observations as there are episodes reported by the household or person e g the employment spells contain two observations for a person if this person reported two episodes of employment All datasets include key variables which are used to identify units and observations and to establish links to other datasets of the SUF The key variables included in the dataset are listed in tables 28 and 29 For further information about their meaning and on how to use them see the corresponding chapter in Berg et al 2013c We strongly recommend PASS users to make themselves familiar with the structure of the datasets their meaning and the key variables before combining different datasets 9 2 Example Merging household data with the individual dataset If household data are to be merged with the individual dataset e g the information on the type of the household which is contained in the variable hhtyp then the two relevant key variables the household number hnr and the wave indicator welle must be used use PENDDAT dta clear merge m 1 hnr well
111. ications of longitudinal weights can be imagined depending on restrictions to certain waves subsamples or cases with certain characteristics or analyses at different levels household benefit unit individual We would like to demonstrate the use of the longitudinal weights for some typical applications 12 3 1 Persons of the resident population One possible research question involving the longitudinal section could be how many persons from the age of 15 of the resident population reported greater satisfaction with their standard of living in wave 2 than they did in wave 1 variable PAO300 The population for such a question is all individuals who belonged to the resident population of Germany in wave 1 and wave 2 Some preparations have to be made before but they can also be used for the subsequent analyses In a first step wave 1 and the variables psu and strpsu are extracted from the household dataset use HHENDDAT dta clear keep hnr welle psu strpsu keep if welle drop welle save psu_strpsu_wl dta replace FDZ Datenreport 07 2013 Pe Table 31 Variables and their possible usage for comparing SGB Il benefit recipients with the general population Variable sample Dataset PENDDAT HHENDDAT alg2samp hh_register bgbezs1 bgbezs2 bgbezs3 bgbezs4 bgbezs5 bgbezs6 alg2abez bgbezb1 bgbezb2 bgbezb3 bgbezb4 bgbezb5 bgbezb6 p_register HHENDDAT p_register Values 1 BA sample 2 Microm sampl
112. icial call windows daytimes and weekdays in order to reduce the number of attempts needed to reach a sample unit and to increase cooperation given contact was established Kreuter M ller forthcoming FDZ Datenreport 07 2013 Pe 4 Instruments and interview programme Jonas Beste Johannes Eggs Stefanie Gundert and Claudia Wenzig To address the study s key research questions cf section 2 the PASS questionnaire covers a broad range of information on individuals and their households Therefore information is collected by means of separate questionnaires at the household level and the individual level First the head of each household answers a household questionnaire In this interview information referring to the entire household is gathered In addition for each household member aged 15 years or older there is a personal interview about the personal situation of the particular household member Household members from the age of 65 are interviewed on the basis of a so called senior citizens questionnaire This is a short version of the individual questionnaire and excludes questions that are less relevant for this age group Below referring to the main questions of PASS the issues of the different questionnaires are described see also Trappmann et al 2013 2010 In order to analyse the duration and dynamics of receiving welfare benefits a core part of the survey is composed of data on Unemployment Benefit II UB II receipt of the h
113. idual interviews each person in PASS is linked to a specific household in every single wave Due to the logic of the survey to collect information on the household level and on individuals living in these households the SUF contains these levels as well Therefore each dataset of the SUF can be assigned to the household or the individual level 5 1 2 Types of datasets in the scientific use file The second criterion by which the datasets of the SUF can be classified is their type The types of datasets that can be found on either level are attached to the contents of the survey while the levels are attached to the surveys basic logic On each level the SUF contains four different types of datasets register cross sectional weight spell FDZ Datenreport 07 2013 La The SUF contains register datasets The household register contains a list of all households that have ever been surveyed in PASS while the person register contains a full list of all persons in these households These register datasets provide basic information about the survey status of the household or person in every wave as well as additional wave specific information While the register datasets contain only basic information about the household their mem bers and the respective survey status the cross sectional datasets of PASS contain most of the survey data collected during the interviews at the household and individual level excluding the parts where the res
114. ific use file 71 Overview of the spell datasets in the scientific use 78 Variables and their possible usage for comparing SGB II benefit recipients with the general population 2 2 0 eee ee 94 Types of simple generated variables in the cross sectional datasets HHEND DAT PENDDAT for household persons that were already asked in the past regarding a certain topic 0 0 ee ee 102 Information on constant characteristics gender 103 Information on constant characteristics half year of birth 104 Information on constant characteristics migration background 105 Information on constant characteristics generated variables on migration DACKGFOUMG 2 8 2 dude be a ed GS ee Pe at a Ge 105 Information on constant characteristics social origin 106 38 Information on constant characteristics sample information List of Figures 1 The variable naming scheme 2 Generation of the weights for wave n 1 given the weights of wave n Part Introduction to the PASS data FDZ Datenreport 07 2013 23 1 Getting started with PASS Arne Bethmann Benjamin Fuchs and Anja Wurdack This User Guide is meant to give information on general issues of the panel study Labour Market and Social Security PASS and to offer assistance for the work with the datasets of the scientific use file SUF While the data reports which are rele
115. ights Level household Type cross section Format long Data collected in 1 6 waves Integration of data from new waves 1 Each wave a household is successfully interviewed is added as new observation in the dataset 2 New weights are assigned to existing variables for this new observa tion Key variables hnr Household number welle Indicator for survey wave Pointer variables One obs row in data matrix Cross sectional information regarding a certain household in a certain wave One obs row hnr welle in data matrix uniquely identified by Topics 1 Information on sample Explanatory notes 2 Design weights for the total sample and the subsamples 3 Households participation probability in year of sampling 4 Projection factors for households of the total sample and the subsam ples 5 Households reciprocal re participation probability Only household interviews of households which were successfully sur veyed according to the definition of PASS were included in the dataset see chapter 7 1 for definition The dataset includes as many observations for a certain household as the number of waves this household was successfully interviewed FDZ Datenreport 07 2013 Pe Unemployment Benefit II spells alg2_spells Table 9 Characteristics of the Unemployment Benefit II spells a g2_spells Dataset Unemployment Benefit II spel
116. in a long term panel study Moreover the decision in favour of organising the data in long format as described above requires the use of uniform variable names 6 2 Variable types The codebook distinguishes between three different types of variables 6 2 1 System variables System variables are variables created in the course of the survey process They can be used firstly to comprehend the filters documented in the questionnaire At least some of the system variables can also be of interest from a content related or methodological point of view for example the interview mode or the number of children of a certain age group living in the household System variables are allocated individual names for which lower case letters and numbers are combined in some cases The system variables also include the weights FDZ Datenreport 07 2013 Figure 1 The variable naming scheme Survey type 1 letter Number If required code let or code for the spell da 2 figure ter e g for item in taset AL ET LU MN battery or for num ALM AL2 ber of cycle PA0100a 2 zeros or 2 figure num Subject area of the hou ber for items added after sehold or individual data the 1st wave for the coding set e g demography Un of responses to open ended employment Benefit II etc questions or variables cor 1 2 letters rected on the basis of spell data 6 2 2 Surveyed variables Surveyed variables are variables that were collected
117. ing pweightsl dta drop _m In order to make the tables clearer a variable is created that indicates the relative level of satisfaction in wave 2 compared with wave 1 gen rel_zufr 2 if PA03002 gt PA03001 amp PA03001 gt 0 amp PA03002 gt 0 replace rel_zufr 1 if PA03002 PA03001 amp PA03001 gt 0 amp PA03002 gt 0 replace rel_zufr 0 if PA03002 lt PA03001 amp PA03001 gt 0 amp PA03002 gt 0 replace rel_zufr 1 if PA03001 lt 0 PA03002 lt 0 label define rel_zufr_lb 2 W2 zufriedener als W1 1 W1 und W2 gleich zufrieden 0 W2 weniger zufrieden als W1 1 in mind 1 Welle keine Angabe label values rel_zufr rel_zufr_lb Finally the longitudinal weight is constructed and the weighted analysis follows gen wpl_2 wqp ppbleib svyset psu pw wp1_2 strata strpsu svy tab rel_zufr count cell format 10 0g FDZ Datenreport 07 2013 A It refers to 67 7 million individuals who were at least 15 years old in wave 1 and were still resident in Germany on the survey date in wave 2 Of this group 34 6 were less satisfied in wave 2 than they were in wave 1 In contrast 32 1 were more satisfied For 33 1 the assessment had not changed 12 3 2 Individuals in households receiving Unemployment Benefit Il in July 2006 Now the same question can also be asked for the individuals in the benefit recipient sample of the first wave How satisfied are these individuals in
118. ir employment owing to a filter These cases are dropped 10 Register data Daniel Gebhardt and Arne Bethmann In addition to the cross sectional datasets at the household and the individual levels HHENDDAT and PENDDAT respectively the various spell datasets alg2_spells bio_spells ee_spells and the weighting datasets hweights pweights the scientific use file of PASS also contains a household register dataset and a person register dataset hh_register p_register In contrast to the other datasets these two files are processed in wide format i e there is exactly one observation available per household or individual Information referring to individual survey waves is stored in wave specific variables The wave to which a piece of information refers is indicated by a counter at the end of the respective variable thus the variable alterT in the person register for example contains the person s age in the 1st wave and alter2 is accordingly the person s age in the 2nd wave and so on The register datasets are prepared in such a way that they can easily be converted from wide format to long format for example using the reshape command in Stata Subsequently the register information can be merged with the survey datasets which are available in long format Households which are not interviewed in certain waves individuals in households which are not interviewed and individuals who no longer belong to a sample household in a l
119. is modelled in the second stage Finally in the third stage the weights are calibrated 8 1 1 Stage 1 design weighting The design weights are reciprocal selection probabilities for the gross sample Which proce dure has been used to generate the weights is described in detail in Rudolph Trappmann 2007 and in section 6 1 of Berg et al 2013b for the wave 5 replenishment sample of the general population The design weights are contained in the dataset hweights The individual design weights supplied are dw_ba Design weight of a household in the BA sample population households in which there was at least one benefit unit in joint receipt of benefits in accordance with Social Code Book II in any July since 2006 dw_mi Design weight of a household in the general population sample population households in the Federal Republic of Germany dw Design weight of a household in the total sample population households in the Federal Republic of Germany 8 1 2 Stage 2 modelling of nonresponse With the aid of two logit models the participation probability is estimated for all households in the gross sample The first logit model explains the probability of a contact The second logit model explains the participation at least the household interview and one complete personal interview conditional on a successful contact These logit models are estimated separately for each subsample The set of variables used in these propensity models ha
120. istics of the household dataset on retirement provision HAV waves DAT Dataset Household dataset on retirement provision File name HAVDAT Level household Type cross section Format long Data collected in wave 3 only Integration of data from new waves In depth information on retirement provisions was only collected in wave 3 Therefore no data from new waves need to be integrated Key variables Pointer variables hnr Household number welle Indicator for survey wave uhnr Original household number One obs row in Cross sectional information regarding a certain household in wave 3 data matrix One obs row hnr welle in data matrix uniquely identified by Topics 1 In depth household information on retirement provisions Explanatory notes In depth information on retirement provision was only collected in wave 3 The respective module of the household questionnaire was only asked for households where at least one person was 40 to 64 years old The dataset contains observations for each household interviewed success fully in wave 3 In households for which no in depth information on retirement provisions were collected the survey variables were assigned the missing code 3 FDZ Datenreport 07 2013 f Household weights hweights Table 8 Characteristics of the household weights hweights Dataset Household weights File name hwe
121. it recipients in the context of support and demand see Achatz Hirse land Promberger 2007 Therefore PASS is designed as a household survey within a household all members aged 15 or above are to be interviewed with a person level ques tionnaire The personal interviews are always preceded by a household interview in which general household related information is gathered In section 3 3 the reader will find information on the sampling design while section 3 3 contains other design aspects like mode interview languages interviewer trainings etc 3 2 Sampling procedure The two main features of the sampling design are the dual frame Unemployment Benefit Il recipients UB II recipients and general population and the yearly refreshment of the UB II sample by new entries to the population Analyses of inflows into receipt of UB Il comparisons of households in receipt of benefits with households not receiving benefits the investigation into hidden poverty FDZ Datenreport 07 2013 S and the formation of control groups require a comparison of benefit recipients with the rest of the population For this reason PASS combines a sample of benefit recipient households with a sample of the general population disproportionately stratified according to status In order to be able to analyse inflows into receipt of UB II already after a short time and to guarantee the representativeness of the sample of benefit recipient households in
122. it unit was receiving Unemployment Benefit Il as of the sampling date After calibration multiplying the characteristics of all benefit units in receipt of benefits as of the sampling date by the projection factors for FDZ Datenreport 07 2013 ea households yields the benchmark figures Separate benefit units in receipt of benefits within one household are therefore always given the same projection factors It is not always possible to determine accurately the benefit receipt of a household or even of a benefit unit As much data as possible is therefore provided in order to enable users to make independent decisions Thus for instance the variable alg2samp at the household level is supplied in the hh_register dataset This variable contains the benefit receipt as of the sampling date for all households in the categories O no receipt 1 receipt 2 no receipt according to survey but included in BA sample and thus receipt according to register data 3 receipt unclear from survey but included in BA sample and thus receipt according to register data 4 receipt unclear from survey general population sample In addition every user can generate this variable him herself using the unemployment benefit II spell data alg2_spells dataset Other useful variables are AL20600 and AL20700a 0 for which members does the household receive benefits To generate the weights however a clear decision is needed on which benefit units should be regarded as
123. itizens questionnaire or between the two versions of the household questionnaire only from wave 1 to 3 That is they were set to the value they would have received if there had not been a problem with the filter condition e g detailed information on vocational training should only be recorded if the respondent stated that he she has a vocational qualification If it was recorded anyway the variable was set to 3 In this case falsely recorded information was replaced by 3 0 For example variables that were surveyed for the first time in the 2nd wave were retroactively coded 9 for observations of wave 1 On the other hand variables only surveyed in the 1st wave were set to 9 for the observations of the following waves FDZ Datenreport 07 2013 8 Weighting Mark Trappmann This chapter contains information on the concept and process of constructing and calculating the weights Information on how to use the weights can be found in section 12 8 1 Initial weights PASS consists of multiple subsamples compare section 3 2 An initial recipient sample a population sample a refreshment sample for the recipient sample in each wave from wave 2 and two replenishment samples introduced in wave 5 The weighting process for each sample in the wave that the sample was first included consists of three stages In the first stage design weights are produced for the gross sample Subsequently non response
124. l Person register r X X x X p_register Person dataset M x z x PENDDAT Children dataset X x KINDER Person weights x x pweights Factorial survey job offer acceptance x VIGDAT wave 5 only Person dataset on retirement provision x x PAVDAT wave 3 only Employment biographies X bio_spells from wave 2 One Euro Job spells x x ee_spells from wave 4 Measure spells 7 x mn_spells wave 2 and 3 only Measure spells x x massnahmespells wave 1 only Unemployment Benefit I spells s alg1_spells wave 1 only represents the number of a certain wave and indicates a wave specific variable e g hnr represents the household number in wave therefore the variable name for wave 1 is hnr1 FDZ Datenreport 07 2013 E use HHENDDAT dta clear merge 1 1 hnr welle using hweights dta tab _m welle The tabulation of the _merge variable shows a perfect match of the household dataset and the household weights For each household that was interviewed in a certain wave an observation from the weighting dataset was merged See chapter 12 on the use of the weights 9 4 Example Merging information from the individual dataset with the person specific spell data When merging spell data and the household or individual dataset it is always necessary to take the different logics of the datasets into account Whilst the household and individual datasets contain wave specific observations of the study units the spel
125. l The example below shows this again using the question of car ownership use HHENDDAT dta clear merge 1 1 hnr welle using hweights dta svyset psu pw wghh strata strpsu svy subpop if alg2abez 1 amp welle 6 tab HLS0800a cell ci format 9 0g Of the households currently receiving benefits 31 9 had a car on the survey date of the 6th wave Ifthis were estimated using the BA weights and the BA sample svyset psu pw wgba strata strpsu svy subpop if alg2abez 1 amp welle 6 tab HLS0800a cell ci format 9 0g a proportion of 30 5 would be estimated However as these data only include stayers in other words households that were receiving benefits both on one of the sampling dates in July 2006 2011 and on the survey date it is plausible that fewer of these households FDZ Datenreport 07 2013 ES have cars than those that started receiving benefits between the last reference date and the survey date One consequence of using the total weights rather than the BA weights is the substantial increase in the confidence intervals The variance of the total weights is significantly larger due to the very different sampling rates in the two subsamples The analyses on car ownership in households receiving Unemployment Benefit II in July 2011 for which we can only work with the BA register data sample result in a 95 confidence interval of 28 2 to 32 9 For the survey date we obtain a substantially la
126. le at the household level and for the telephone field only As of wave 4 the call record data also contain any call at the person level individual respondents within households and from the face to face field Initially the call record data have mainly been used for purposes of monitoring key indicators e g contact and cooperation rates number of fixed and vague appointments nonworked cases cases with need for tracking independently of the respective fieldwork agency and for communication with responsible field managers Since wave 4 information from the call history file has been linked with variables from the administrative sampling frame for BA refreshment samples in order to monitor the development of nonresponse bias along a number of dimensions Since wave 5 this has been further extended to estimating response propensity models repeatedly during the field period and to monitoring the development of the sample composition by means of R indicators Schouten Cobben Bethlehem 2009 www risq project eu In wave 6 towards the end of the field period this information was used to initiate a case prioritization of likely nonrespondents in combination with increased interviewer incentives similar to Peytchev et al 2010 in an effort to reduce the risk of nonresponse bias Wave 6 also saw the first attempt in PASS to actively use the available call record information to enhance call scheduling algorithms i e to assign call attempts to benef
127. lead out of receipt of Unemployment Benefit II UB II Which factors facilitate or impede those exits and how do former recipients gain subsistence after having overcome UB II receipt 2 How does the social situation of a household change when it receives benefits Apart from the financial situation and the standard of living the impact on health or social exclusion is of interest here 3 How do the individuals concerned cope with their situation Is there a change of attitudes or behaviour over time 4 In what form does contact between benefit recipients and institutions providing basic social security take place What are the actual institutional procedures applied in practice 5 What employment history patterns or household dynamics lead to receipt of UB II 2 2 How does PASS fit in the German microdata landscape When PASS was designed German labour market poverty and welfare state research already had access to various excellent micro data In particular there were a number of longitudinal datasets available which already covered relatively long survey periods A particularly important survey data source is the German Socio Economic Panel Study SOEP Wagner Frick Schupp 2007 which provides annual data at the individual and household level dating back to 1984 In addition administrative data from the Federal 4 Social Code Book II Basic Social Security for Jobseekers Sozialgesetzbuch SGB Zweites Buch II Gru
128. ls File name alg2_spells Level household Type spells Format long Data collected in 1 6 waves Integration of data from new waves Key variables 1 New episodes that were reported in the last interview are added as new observations to the dataset 2 Current spells from the time of the last interview were updated if the household has been interviewed 3 The newly recorded information is assigned to existing variables New variables are added if they were surveyed for the first time or if they refer to a certain wave cross sectional information as part of an UB Il episode hnr Household number spellnr Spell number Pointer variables One obs row in data matrix Episode during which a certain household received UB Il One obs row hnr spellnr in data matrix uniquely identified by Topics 1 Information on UB Il recipiency start date end date total amount of benefits per month reason for beginning end of recipiency 2 Identification of household members receiving benefits 3 Benefit cuts start date end date duration reasons Explanatory notes Households that have never reported an episode UB Il recipiency are not represented by an observation in the dataset The dataset includes as many observations for a certain household as the number of episodes this household reported over the waves An episode includes information that refers to the spell itself e
129. ls cannot be assigned clearly to one particular wave A spell of employment for example can span several survey dates This spell is then visible in the data structure as a single observation with its respective start and end dates If for instance individual level information is to be merged with the person specific spell data spells of employment unemployment gaps employment and training measures then these different data structures have to be taken into consideration As it is not straight forward to assign every spell clearly to a particular survey wave only the personal ID number can be used as a key variable The information from the individual dataset therefore has to be converted to wide format first and then merged with all of a person s spells This is demonstrated below using the example of the date of the personal interview which is available in the individual dataset and is to be merged with the employment spells First the individual dataset reduced to the relevant variables is converted to wide format For this the information on the interview date which has been stored in wave specific observations so far is restructured Instead of there being one observation per survey wave there is now only one single observation for each individual in the dataset The information on the interview date is now stored in the wave specific variables pintdat1 pintdat2 et cetera For many individuals the spell dataset contains more than one
130. ly via E Mail to iab fdz iab de FDZ Datenreport 07 2013 fi 1 1 The user guides and other working tools In addition to this User Guide several other working tools provide information about PASS and its SUF Table 1 gives an overview of the working tools that are currently available via download from the Homepage of the Research Data Centre FDZ of the German Federal Employment Agency BA at the Institute for Employment Research IAB and its contents 1 2 Data access Currently the six waves of PASS are available as weakly anonymised SUF The last version of the SUF includes information on all waves that have been released before e g the SUF of wave 6 includes all information from wave 1 to 6 as well Wave 7 is expected to be available in autumn 2014 The SUF can be used by researchers at scientific institutions for non commercial research Data access is provided by the FDZ of the BA at the IAB The homepage of the FDZ offers further information on requirements and how to apply for the data 2 http fdz iab de en FDZ_Individual_Data PASS Working_Tools aspx 5 http fdz iab de en FDZ_Data_Access FDZ_Scientific_Use_Files aspx FDZ Datenreport 07 2013 Fal Table 1 Overview of the working tools available in wave 3 Name User Guide Data Reports Content The User Guide offers general information on PASS that is not specific to certain waves The following topics are covered Objectives and research questions of PA
131. n of the primary sampling units see Rudolph Trappmann 2007 77 pp The selection probability of a postcode sector was dependent on the number of households in the particular sector according to the MOSAIC database probability proportional to size Within each sampling point benefit units BA sample or addresses general population sample were drawn The number of benefit units to be drawn for the BA sample depended on the rate of benefit recipients number of benefit units in the sampling point according to BA process data divided by the number of households in the sampling point according to the MOSAIC database On average 100 benefit units were selected into the gross sample per sampling point As the number of selected benefit units is proportional to the benefit recipient rate in the sampling points a uniform selection probability is also guaranteed in the BA sample Rudolph Trappmann 2007 78 pp All members of each household in which a benefit unit was living were surveyed In the initial wave 6 804 households from this sample were interviewed For the general population sample 100 addresses were drawn within each sampling point In order to obtain an overrepresentation of the lower status classes addresses of lower status classes had a higher inclusion probability The addresses drawn in this way were visited by employees of the field institute conducting the survey who copied all names on the doorbell panels At the field institute a
132. ndsicherung f r Arbeitsuchende FDZ Datenreport 07 2013 ral Employment Agency BA is processed at the IAB and provided for research use by the Research Data Centre FDZ of the BA at the IAB for example in the form of the Integrated Employment Biographies IEBS the AB Employment Samples IABS or the Linked Employer Employee Dataset LIAB The spectrum of questions and the design of PASS are intended to close gaps in the existing stock of data PASS has three main characteristics that extend analysis potential beyond that of the Federal Employment Agency s administrative data 1 The panel takes the household context into account including the situation before and after receipt of UB Il 2 The panel is complete in that it covers all groups of persons and all employment biographies not only people in dependent employment unemployed people and those in need of assistance The dataset also provides information on the status during phases of economic inactivity self employment or employment as civil servants 3 The panel collects additional or significantly more detailed data on relevant character istics such as attitudes employment potential or job search behaviour Compared to the existing surveys of individuals or households PASS aims to improve the data situation in particular with regard to the following points 1 The high case numbers of UB II recipients cf section 3 make it possible to conduct more detailed analys
133. nistered by interviewers who were native speakers of the respective language As a cost saving measure the strategy employed in CAPI mode was to transfer respondents back to the telephone field whenever possible Where this could not be done the CAPI interviewers used a written foreign language version of the respective questionnaire as translation aid For wave specific information see Hartmann et al 2008 19 20 wave 1 B ngeler et al 2009 12 14 wave 2 B ngeler et al 2010 17 wave 3 These procedures remained largely unchanged in waves 4 6 except that no written translation aides were used anymore in combination with a German language version in face to face interviewing All foreign language interviews were done by native speakers of the respective language with a fully translated survey instrument In practice as before most foreign language interviews were conducted in CATI For details see Jesske Quandt 2011 19 25 44 46 wave 4 Jesske Schulz 2012 29 37 57 61 wave 5 Jesske Schulz 2013 25 34 58 60 wave 6 3 3 3 Fieldwork procedures contact routines mode switches and refusal conver sion By default in waves 1 3 contact was first attempted by telephone whenever a number was known to exist for a particular address either because it was part of the information on the sample frame or because it could be traced by phone number search prior to the beginning of fieldwork Cases for which no valid teleph
134. nkage IEB PASS Tech Rep Institut f r Soziologie Universit t Duisburg Essen unpublished Berg Marco Cramer Ralph Dickmann Christian Gilberg Reiner Jesske Birgit Kleudgen Martin Bethmann Arne Fuchs Benjamin Gebhardt Daniel 2013a Codebook and Documentation ofthe Panel Study Labour Market and Social Security PASS Datenreport Wave 4 FDZ Datenreport 08 2011 EN Institut f r Arbeitsmarkt und Berufsforschung N rnberg Berg Marco Cramer Ralph Dickmann Christian Gilberg Reiner Jesske Birgit Kleudgen Martin Bethmann Arne Fuchs Benjamin Trappmann Mark Wurdack Anja 2013b Codebook and Documentation of the Panel Study Labour Market and Social Security PASS Datenreport Wave 5 FDZ Datenreport 06 2012 EN Institut f r Arbeitsmarkt und Berufsforschung N rnberg Berg Marco Cramer Ralph Dickmann Christian Gilberg Reiner Jesske Birgit Kleudgen Martin Bethmann Arne Fuchs Benjamin Trappmann Mark Wurdack Anja 2013c Code buch und Dokumentation des Panel Arbeitsmarkt und soziale Sicherung PASS Daten report Welle 6 FDZ Datenreport 06 2013 Institut f r Arbeitsmarkt und Berufsforschung N rnberg Berg Marco Cramer Ralph Dickmann Christian Gilberg Reiner Jesske Birgit Mar winski Karen Gebhardt Daniel Wenzig Claudia Wetzel Martin 2011 Codebook and Documentation ofthe Panel Study Labour Market and Social Security PASS Datenreport Wav
135. not live at the designated address anymore or the telephone number is not no longer valid In CAPI mode interviewers would try to obtain address and phone information from neighbours or present occupants at the respondent s former address If unsuccessful these cases are forwarded to the centralised tracking system and searched for in the various databases and registers described above The same procedure is applied to CATI cases with invalid telephone numbers In waves 1 3 centralised tracking was not yet performed on a continuous basis for each individual address mover but only at a few designated points during the fieldwork period in batches of addresses However tracking efforts during fieldwork were continuously intensified From wave 1 to 3 the number of time points at which searches via residents registration offices were initiated was increased from three to five For details on the tracking procedures in each of the first three waves please see Hartmann et al 2008 22 23 31 33 wave 1 B ngeler et al 2009 15 16 20 22 wave 2 B ngeler et al 2010 24 25 28 33 wave 3 As of wave 4 tracking happened on an almost continuous basis and using all search channels e g residents registration offices Addressfactory of Deutsche Post telephone registers administrative records as well as information from tracking efforts by face to face interviewers concurrently For further details on the tracking procedur
136. nswer using the variable nbgbezug which states how many benefit units in joint receipt of Unemployment Benefit Il a household contains as of the sampling date The fastest way to do this is to multiply the household weights by this value use HHENDDAT dta clear merge 1 1 hnr welle using hweights dta gen bgweight wqbahh nbgbezug svyset psu pw bgweight strata strpsu svy subpop if welle 1 tab HLS0800a if sample 1 count cell format 9 0g The percentages deviate slightly from those in the analysis presented above 37 9 of households receiving benefits but 38 2 of the benefit units receiving benefits had a car in their household in wave 1 Above all however the absolute numbers are different the sum of all households receiving benefits was 3 882 013 whereas the sum of all benefit units receiving benefits is 4 011 889 and matches the BA benchmark statistics due to the calibration In contrast with PASS it is not possible to calculate the percentage of car owners as of the survey date of the 2nd or any later wave for the benefit units of the first wave As the compositions of benefit units are constantly changing due to deaths births moves into and out ofthe household and also due to members reaching certain age limits 25 and 65 years of age this kind of analysis across waves should be conducted at the level of more stable units Analyses at the person level Analyses at the person level are similarly sim
137. ntal design was used in order to be able to evaluate the effects on response rates sample composition and bias afterwards see Felderer et al 2012 Households new entrants of the wave 3 refreshment sample were not part ofthe FDZ Datenreport 07 2013 a experiment and kept receiving the unchanged incentive i e the lottery ticket conditional upon participation for each responding household member individually B ngeler et al 2010 18 20 In waves 4 6 monetary incentives were maintained and further extended in two ways First for panel households the unconditional monetary incentive 10 00 EUR was now distributed at the person level i e to each individual household member eligible for an interview Second also cases of the respective wave s refreshment sample s started receiving the same monetary incentive after the interview i e conditional on response Jesske Quandt 2011 31 32 Across all waves and in addition to incentives distributed centrally by mail face to face interviewers were equipped with doorstep incentives e g small gifts which they could deploy at discretion in order to gain cooperation 3 3 7 Interviewer training Shortly before the beginning of each wave s fieldwork a one day as of wave 4 two day intensive training programme was offered to interviewers in order to familiarise them with the specific survey requirements Only experienced interviewers who had previously worked on comparable studi
138. o inform and improve responsive and adaptive survey designs Groves Heeringa 2006 Laflamme Maydan Miller 2008 that is to monitor the survey data collection process and to respond to changing survey conditions in real time For example the available information may be used to identify more difficult cases with regard to establishing contact or cooperation and to flag sample units that require further follow ups or a different calling strategy Call record data may also help to evaluate interviewer performance and to inform calling strategies and interviewer training longer term Another advantage is that such data are typically available for both respondents and nonrespondents In particular in combination with variables from the sampling frame and other auxiliary information such survey process data paradata Couper 1998 Kreuter 2013 may be of help in nonresponse modeling and in designing effective interventions to reduce nonresponse and nonresponse In PASS detailed interviewer call record data have been made available by the respective fieldwork agency only as of wave 3 but have since been delivered on a regular basis throughout any subsequent field period The data are at the call level and include for any PASS sample member the time and day of each call the outcome of the call the mode whether a call was a regular or a refusal conversion call or part of the Turkish or Russian language fieldwork For wave 3 call records are availab
139. one number was available started off in CAPI mode As noted above from wave 4 onwards there has been a shift towards CAPI as the default mode for cases of all refreshment samples with panel cases being first contacted in the mode of the previous interview Across all waves a mode switch from CATI to CAPI took place if at least twelve consecutive contact attempts by telephone were unsuccessful or if the household explicitly asked for being interviewed face to face Similarly cases could also be switched from CAPI to CATI mode This happened automatically if six consecutive contact attempts were unsuccessful or if a household requested to be interviewed by phone Contact attempts in both survey modes were varied across weekdays and daytimes in order to minimise household nonresponse due to noncontact For further details on the organisation of fieldwork in each wave please see the survey agencies field reports Hartmann et al 2008 20 44 B ngeler et al 2009 14 29 B ngeler et al 2010 22 40 waves 1 3 and Jesske Quandt 2011 23 28 Jesske Schulz 2012 36 41 Jesske Schulz 2013 33 38 waves 4 6 Note that in waves 1 3 the interview mode was determined at the household level that is all respondents within a given household were interviewed in the same mode As of wave 4 this could be handled more flexibly with interview mode being assigned individually that is at the person level In each wave there were refusal conversion att
140. oned in PASS In doing so it will be shown how the datasets of the SUF can be classified by their level household or individual and their type register cross section weight or spell and in which formats they are prepared wide long spell Subsequently in the second section we will focus on the datasets themselves After a brief overview of the content of the SUF the datasets will be presented in more detail starting with the different types of datasets on the household level followed by the individual level 5 1 Introduction to the scientific use file 5 1 1 Levels in the scientific use file To understand the structure of the SUF it is crucial to know that PASS collects information on the household as well as on the individual level and that these two levels are linked due to the survey design see section 3 PASS surveys specific households and then questions the persons aged 15 and over living in these households at the time of the interview The questioning of a household and its members starts by recording or updating the structure and other information concerning the whole household using the household questionnaire After the household level information is collected the household members suitable for individual interviews are known PASS tries to question all persons up from the age of 15 with individual interviews Because of this succession where the household gives information about its members who are then targeted for indiv
141. only for cases from the BA samples Households in receipt of benefits in July 2011 use HHENDDAT dta clear merge 1 1 hnr welle using hweights dta svyset psu pw wgbahh strata strpsu svy subpop if alg2abez 1 amp welle 6 tab HLS0800a cell ci format 9 0g 30 5 of all households in receipt of benefits in July 2011 had a car on the interview date of the 6th wave A 95 confidence interval of 28 2 to 32 9 is obtained Individuals in receipt of benefits in July 2011 use PENDDAT dta clear merge 1 1 pnr welle using pweights dta drop _m merge m 1 hnr welle using psuinfo drop _m merge m 1 pnr using p_register dta svyset psu pw wgbap strata strpsu svy subpop if bgbezs6 1 amp welle 6 amp fb_vers 1 tab migration count cell format 9 0g Of all the individuals in receipt of benefits in accordance with Social Code Book II in July 2011 25 6 migrated to Germany themselves a further 8 7 have at least one parent who FDZ Datenreport 07 2013 ES migrated to Germany and another 2 0 have at least one grandparent who migrated Analyses on benefit recipients at the latest interview date When working with the original BA sample only sample T and the appropriate weights the results refer to recipients in July 2006 For analyses of this population this approach achieves the greatest statistical power as the BA weights have a relatively low variance However
142. ontains observations for each person interviewed successfully in wave 3 For persons for whom no in depth information on retirement provisions were collected the survey variables were assigned the missing code 3 FDZ Datenreport 07 2013 Person weights pweights Table 14 Characteristics of the person weights pweights Dataset Person weights File name pweights Level individual Type cross section Format long Data collected in 1 6 waves Integration of data from new waves 1 Each wave a person is successfully interviewed is added as new observation in the dataset 2 New weights are assigned to existing variables for this new observa tion Key variables pnr Constant personal ID number welle Indicator for survey wave Pointer variables One obs row in data matrix Cross sectional information regarding a certain person in a certain wave One obs row pnr welle in data matrix uniquely identified by Topics 1 Information on sample 2 Projection factors for persons of the total sample and the subsamples 3 Persons reciprocal re participation probability Explanatory notes The dataset includes as many observations for a certain person as the number of waves this person was successfully interviewed FDZ Datenreport 07 2013 a Biography spells bio_spells Table 15 Characteristics of the Biography spells bio_spells
143. ooperation low the IAB requires the survey agencies to employ a special training course for interviewers the refusal avoidance training RAT by Schnell Schnell Dietz 2006 which is based on Groves McGonagle 2001 It instructs interviewers how to deal with typical arguments of designated respondents who are reluctant to participate in the survey Interviewers who have already participated in a prior wave receive a somewhat shorter training focussing only on changes to recruitment protocols and instrument changes Interviewers new to the survey in each wave always receive the full initial PASS training program as described above FDZ Datenreport 07 2013 3 3 8 Sampling frame and auxiliary data for nonresponse analyses and post survey adjustments In addition to the survey design characteristics that were adopted to reduce nonresponse and panel attrition ex ante an unusually good database is available for nonresponse analyses and post survey adjustments First the population samples were either drawn directly from the database MOSAIC by Microm Consumer Marketing wave 1 or later linked to it using address information wave 5 MOSAIC contains a number of auxiliary variables available at the address level of a sampled unit that can potentially be used for example to predict survey non cooperation e g by indicators of social status or of privacy concerns or whether a sampled unit can be localized contacted successfully e g by
144. or wave 3 to 6 Berg et al 2011 2013a bc 8 2 11 Estimating the BA cross sectional weights for households and individuals not in receipt of Unemployment Benefit Il Finally some households and individuals remain that can not be assigned a BA cross sectional household weight or a BA cross sectional person weight by means of calibration They belong to one of the following three groups which did not receive benefits at any of the reference dates after wave 1 but which belong to the population of the BA sample households with receipt of Unemployment Benefit Il at the reference date in 7 2006 7 2007 7 2008 7 2009 7 2010 or 7 2011 and individuals in households with receipt of Unemployment Benefit II at the reference date in 7 2006 7 2007 7 2008 7 2009 7 2010 or 7 2011 1 From the refreshment sample individuals in the household who are not members of a benefit unit here the person weight is obtained from the BA household weight of the respective wave after calibration wqbahh by dividing it by the proportion of these individuals who gave a personal or senior citizens interview provided that their household was participating 2 Panel households in which nobody was in receipt of Unemployment Benefit II any longer at the reference date of the current wave The household retains the BA weight before calibration from step 8 Individuals in these households with interviews in both the previous and the current wave are given a new
145. ousehold The data are gathered retrospectively in the household questionnaire For newly interviewed households e g of the refreshment sample information on benefit receipt is collected for a period of about two years before the respective wave For example in wave 6 2012 all episodes since January 2010 were relevant For re interviewed households dependent interviewing is used and benefit receipt is always recorded for the period since the last interview date For each episode there is information about its beginning and end month and year and if applicable about reasons for and duration of UB II cuts Furthermore we ask about reasons which lead into and out of receiving benefits since wave 4 The household level information about UB II receipt is complemented by two questionnaire modules on the personal level which all persons living in households that receive UB II should answer The first one includes questions about individuals contact to the agencies responsible for the provision of UB II e g contact frequency perceived quality and intensity of support The second module consists of retrospective questions about individual participation in labour market policy programmes The concept of surveying participation in active labour market programmes ALMP was thoroughly reworked in the first waves for further information see Gebhardt et al 2009 Especially difficulties in identifying clearly the exact type of employment and tr
146. ovides information on a person s affiliation with a benefit unit in receipt of benefits at the sampling date for wave 1 drop _m merge m 1 pnr using p_register dta keep if pnettol 2 pnettol svy subpop if bgbezsl 1 amp fb_vers 1 amp welle 1 tab migration count cell format 9 0g 22 For a further 1 2 the variable cannot be formed due to missing information FDZ Datenreport 07 2013 a The percentage of individuals who migrated to Germany themselves is therefore marginally higher among the people who are members of a benefit unit at 25 1 than among people living in a household receiving benefits 24 8 12 2 2 Analyses on the resident population of Germany Analyses on the resident population of Germany can be carried out both with the total weights and with the population sample weights In most cases the results will differ only slightly The percentage of households with a car in the total population in wave 1 in this case is therefore estimated either with the following commands using the total weights use HHENDDAT dta clear merge 1 1 hnr welle using hweights dta svyset psu pw wghh strata strpsu svy subpop if welle 1 tab HLSO800a cell ci format 10 0g or alternatively with the population sample weights svyset psu pw wgqmihh strata strpsu svy subpop if welle 1 tab HLSO800a cell ci format 10 0g In the first case the percentage of households with a car is
147. pensities of the models are again multiplied 1 In PASS households which do not participate in two consecutive waves are no longer contacted 2 It can simply be calculated as 1 hpbleib for that wave FDZ Datenreport 07 2013 Pe The reciprocal value of this product can be found in variable ppbleib The longitudinal weight of an individual for the period wave_n wave_n k between waves can then be calculated as the product of the cross sectional weight for wave_n and all ppbleib for wave n to wave n k 1 The full lists of variables in the models and coefficients are described in the field and method report of TNS Infratest for wave 2 and in the data report of infas for wave 3 Again temporary dropouts must be treated separately 8 2 8 Integration of the weights to yield the total weight before calibration This step involves combining the household weights of the latest refreshment sample and the panel households which have been modified by the non response modelling steps 3 and 4 and the integration of temporary dropouts step 6 The one time integration of replenishment samples is described in section 8 3 The double selection probability of a newly sampled benefit recipient who was living in the same household as benefit recipients in the previous year but without being a member of the benefit unit him herself is ignored This is likely to be a rare population as four conditions have to be fulfilled simultaneously i benefit recipiency
148. ple The person dataset PENDDAT dta and the weight wqbap should be used in this case An intermediate step becomes necessary as the variables psu and strpsu are only contained in the household dataset The following example estimates the number of individuals aged 15 and above in households receiving benefits who have a migration background variable migration For this variable the decisions required when the statements do not clearly identify how many benefit units are receiving Unemployment Benefit II in the household were made in the same way as for the calibration process Every user is of course free to make his or her own decisions on the basis of the Unemployment Benefit Il spells As younger people are not interviewed in person the PASS data can only be used to establish characteristics about them which are surveyed in the household questionnaires e g age gender The household weights should be used in this case FDZ Datenreport 07 2013 Eu N use HHENDDAT dta clear keep hnr welle psu strpsu save psuinfo replace use PENDDAT dta clear merge 1 1 pnr welle using pweights dta drop _m merge m 1 hnr welle using psuinfo svyset psu pw wgbap strata strpsu svy subpop if welle 1 tab migration count cell format 9 0g According to this calculation about 59 7 do not have a migration background 24 0 migrated to Germany themselves at least one parent migrated to Germany for a fu
149. ple and whether it is a split household is specified predicting the probability of returning in wave n 1 given a dropout in wave n The reciprocal value of the product of the predicted probabilities of these two models is multiplied with the calibrated household weight of wave n 1 to calculate a modified cross sectional weight which is used as a base for calculating a new cross sectional weight for wave n 1 8 2 6 Integration of weights by convex combination The temporary dropouts are from the same population as that for which new base weights have been calculated in step 3 8 2 3 Thus integrated weights can be calculated as a convex combination of the modified cross sectional weights for the two subsamples cf Spiess Rendtel 2000 Formulae for this can be found in chapter 6 of Berg et al 2011 2013ab c 8 2 7 Response propensity models for panel persons The most important longitudinal weight is not the one at the household level but the one at the individual level as the units here are stable over time Participation propensities for individuals with monotonous dropout patterns are modelled in the same way as the model for households shown in step 3 8 2 3 As the participation of the household is a precondition for the participation of the individual the models contain similar variables In addition characteristics of the respective individual e g age item missings in the previous wave are taken into account The predicted pro
150. pondent was asked to report episodes e g on the receipt of Unemployment Benefit II UB II The cross sectional data refers to the date of the interview it was collected it represents the situation at a certain point in time PASS has a complex sample design which does not allow descriptive analyses without using weights Therefore the SUF contains weighting datasets on the household and the individual level These datasets correspond to the cross sectional datasets in their structure they contain weights for each wave a household or person was surveyed in PASS that can be used to project the samples on the different populations See section 12 on how to use the weights and section 8 on the weighting concept In addition the SUF includes several spell datasets for information recorded in form of episodes This way to collect data differs strongly from the cross sectional concept described above Therefore it cannot be integrated directly in the cross sectional datasets When asked to report activities or events in form of episodes the respondent had to fill a certain time period starting in the past and reporting all relevant activities or events up to the date of the interview For each single episode the respondent had to report the begin date and end date and to give further information about its content In each wave several episodes can be recorded each of which refers to the period between its reported begin date and end date Some
151. ppmann 2010 for a discussion of this underreporting FDZ Datenreport 07 2013 Users who would like to run projections referring to other points in time will therefore have to generate analogous variables themselves When doing this both imprecision and the problem of benefit recipiency being under reported will always have to be taken into account 12 2 4 Comparison of benefit recipients with the general population The large variety of options for studying benefit recipients and their households or benefit units benefit units shown above results in an equally large variety of options for comparing benefit recipients with the general population Table 31 provides an overview The total weights are to be used in all cases 12 3 Use of the longitudinal weights The basic principle of the longitudinal weighting is simple Estimated reciprocal re participation probabilities hpbleib and ppbleib are used for longitudinal weighting of households and persons respectively The longitudinal weight for a household or an individual for the longitudinal section from wave 1 to wave 2 is obtained by multiplying the cross sectional weight of the household or person for wave 1 by the reciprocal re participation probabil ity The reciprocal re participation probability is provided in the dataset hweights dta or pweights dta for all households and persons that participated in a given wave and in the subsequent wave A large variety of different appl
152. r citizens in the same way as in the standard personal interviews because information was only available about whether the respondent him herself was born outside Germany From the 2nd wave onwards the migration of parents and grandparents and their respective countries of origin are also surveyed in the senior citizens interviews In the first repeated interview after wave 1 all senior citizens were asked the questions In subsequent waves this information will also be surveyed in the senior citizens questionnaire in the first interview only The respondent s country of birth and information about the countries from which his her individual parents and grandparents migrated to Germany are also made available in generated variables in which the information that was collected once is also taken over into subsequent waves These variables are shown in Table 36 Furthermore for a wave in which a person was not interviewed for the first time the generated variable migration see description e g for wave 6 in chapter 4 of Berg et al 2013c contains the information as to whether this person has a migration background and if so what this background is Starting with wave 4 respondents are also asked for their first language PMI1200 on the first interview 14 4 Parents education vocational training occupational status and activ ity In wave 1 individuals whose mother and or father did not live in the same household were asked about their p
153. random selection of these doorbells was made If a doorbell panel had more than one name on it one of these names was selected Each FDZ Datenreport 07 2013 K selected person s entire household was surveyed In the initial wave 5 990 households from this sample were interviewed All households in the two samples of the 1st wave were to be re interviewed in the 2nd and all consecutive waves see the corresponding data report for response rates e g Berg et al 2013c for the 6th wave In addition to this households that had split off from the households interviewed in one of the preceding waves were also surveyed They were each assigned to the subsample from which their original household had been drawn either of the two subsamples in the 1st wave or a refreshment sample in one of the later waves In addition starting with the 2nd wave for each wave a refreshment sample was drawn from benefit units that had begun receipt of UB II These are benefit units which were receiving UB II at the refrence date for sampling of a specific wave i e July of the year prior to the respective survey wave e g 2007 for the 2nd wave up to July 2011 for the 6th but not at the sampling date of all preceding waves The sample was drawn within the primary sampling units of the 1st wave following the procedure used in the first wave The size of these yearly refreshments is about 1 000 households The aggregate of all households from all BA subsamples who s
154. register dataset which were interviewed in both waves and were in receipt of Unemployment Benefit Il on the sampling date were merged with the individual dataset 10 4 Example Identification of the personal interviews with the heads of households The household register dataset contains the wave specific information about which person the household interview was conducted with In order to mark the personal interviews of FDZ Datenreport 07 2013 P these heads of households it is first necessary to prepare the household register and convert it into long format First of all only the required variables are retained the household number and the wave specific pointer to the target persons of the household interview Then the dataset is reshaped from wide format to long format For this the household number serves as an ID variable that identifies an observation In the course of the reshaping process a wave indicator welle is created which is needed for merging with the individual dataset However before the register which has been converted into long format can be merged with the individual dataset some observations have to be deleted If a household was not interviewed in one wave then the pointer variable referring to the head of the household was given the value 6 household not interviewed in wave or not in gross sample for this wave A household that was interviewed for the first time in the 2nd wave for example in the con
155. res for cases of strata with only one PSU the singleunit option of the svyset command see Stata Corp 2007 but none of them solve the problem entirely satisfactorily Singleunit certainly assumes that the single PSU in the sample is also the only one in the population and that the variance between PSUs in this stratum is therefore zero As there are several PSUs in every stratum in the population of PASS the basic assumption is not correct This setting thus results in the variance being underestimated In the case of singleunit scaled the stratum with missing variance is assumed to have a variance equal to the mean variance in the other strata As these are rather small strata however the variance is likely to be larger in reality With singleunit centered a variance within the stratum with only one PSU is estimated by assuming that the unknown stratum mean is equal to the grand mean The variance of the stratum is then estimated from the mean of the single PSU in the stratum and the grand mean Another general remark is in place on this subject restrictions to subpopulations using if or keep if can hinder the estimation of standard errors if the restriction results in strata with only one PSU We recommend to implement restrictions using subpop and not if or keep if The only exception is the restriction to one of the PASS subsamples Here the restriction with if is appropriate Examples are given in the
156. rger 95 confidence interval of 28 6 to 35 5 Analyses on benefit recipients at the latest interview date at the benefit unit level In comparison to the analyses referring to the sampling date in the previous section an additional step has to be taken as there is no variable equivalent to nbgbezug for recipiency of benefits as of the survey date This variable first has to be generated using the variable bgbezb6 in p_register which indicates for each benefit unit whether this particular community was receiving Unemployment Benefit II on the survey date of wave 6 use p_register dta clear collapse mean hnr6 bgbezb6 by bgnr6 recode bgbezb6 5 0 by hnr6 sort egen nbgbezak sum bgbezb6 collapse nbgbezak by hnr6 rename hnr6 hnr save hnr_nbgbezak dta replace use HHENDDAT dta clear merge 1 1 hnr welle using hweights dta drop _m keep if welle merge hnr using hnr_nbgbezak dta gen bgw_akt wgqhh nbgbezak svyset psu pw bgw_akt strata strpsu svy subpop if alg2abez 1 tab HLS0800a cell ci format 9 0g The estimated value of 31 8 differs only marginally from that obtained in the analysis at the household level However the value no longer refers to a sub population of just over 3 185 000 households as in the section above but to just over 3 212 000 benefit units receiving benefits as of the survey date During the survey period the number of benefit units varied between 3 288 000 September 2012 and 3 377
157. riables One obs row in data matrix Episode during which a certain person participated in a certain employment training measure One obs row pnr spellnr in data matrix uniquely identi fied by Topics 1 Information on measure start date duration for completed and current measures type of measure reason for ending measure prematurely initiative for participation assessment of measure hours per week requirements identical work as permanent employees social education worker present Explanatory notes In wave 2 the concept for surveying participation in employment and training measures was reworked because in the concept of wave 1 it proved difficult to identify clearly the exact type ofthe measure with the exception of the one Euro jobs which were recorded directly Because of the extent of the changes the information recorded from wave 2 on could not be integrated in the measure spell dataset of wave 1 Persons who have not reported an episode of measure participation in wave 1 are not represented by an observation in the dataset The dataset includes as many observations for a certain person as the number of episodes this person reported in wave 1 FDZ Datenreport 07 2013 1 euro job spells ee_spells Table 19 Characteristics of the 1 euro job spells ee_spells Dataset 1 euro job spells File name ee_spells Level individual Type spells Format spell D
158. rmation is also collected only once After the first interview however information is also available on the mother s school and vocational qualifications It is contained in the generated variables mschul1 mschul2 mother s highest general school qualification without with coding of responses to open ended survey questions and mberuf 1 mberuf2 mother s highest vocational qualification without with coding of responses to open ended survey questions Corresponding infor mation for the target person s father can be found in vschul1 vschul2 and vberuf1 vberuf2 The information on mother s and father s occupational status which was first gathered in wave 2 are available in mstib and vstib in the individual dataset also as generated variables The generated variables cited here are described in the list of variables in the wave specific data reports Moreover the information on the parents occupational activity at the time when the target person was 15 years old was coded by Gesis ZUMA misco visco according to the 1988 International Standard Classification of Occupations ISCO 88 published by the International Labour Office ILO FDZ Datenreport 07 2013 Ka Table 38 Information on constant characteristics sample information A Pe Filled in for wave of Filled in for wave s of Variable Description Dataset none s H the first interview repeated interviews sample Sample indicator HHENDDAT Information not wave
159. rom refreshment samples 8 2 3 Response propensity models for panel households In this step the probability of re participating is estimated for each household which par ticipated in the previous wave on the basis of logit models for willingness to participate in a panel loss of contact and refusal The models contain survey design features e g mode number of call attempts aspects of the previous wave interview situation e g item nonresponse or partial unit nonresponse household respondent characteristics e g gender age education country of birth labour force status house ownership household size and area characteristics e g municipal size as is state of the art in longitudinal studies cf Watson Wooden 2009 The predicted propensities of all three models are multiplied The reciprocal value of this product can be found in the variable hpbleib This variable serves a double purpose a The longitudinal weight of a household for the period wave_n wave_n k between waves can then be calculated as the product of the cross sectional weight for wave_n and the product of all hpbleib for wave n to wave n k 1 b The product of the updated household design weight from step 1 cf section 8 2 1 multiplied by hpbleib which we call modified cross sectional weight serves as a base for calculating a new cross sectional weight for wave n 1 Note that this procedure works only for households with monotonous drop out patt
160. rther 7 6 and at least one grandparent for another 1 8 The code Item not surveyed in questionnaire applies to 3 6 This is due to the fact that the data from the short questionnaire for people aged 65 and above are stored in the same dataset as data from the standard personal questionnaire People aged 65 and above are assigned this code for questions that are not asked in the senior citizens questionnaire In order to run analyses excluding these individuals researchers can limit the frequency count to data from the standard questionnaires fb_vers 1 svy subpop if welle 1 amp fb_vers 1 tab migration count cell format 9 0g In much the same way as shown above for households the analyses for individuals from households receiving benefits in July 2006 can be run for the survey date of later waves e g welle 6 and restricted to those people who were still living in a household in receipt of benefits on the survey date in a later wave e g welle 6 amp alg2abez 1 The person weights of the BA sample project to all individuals in households receiving benefits Some households however consist of several synthetic benefit units not all of which receive benefits Researchers wishing to project only to persons who are members of benefit units according to Social Code Book II have to exclude individuals who did not belong to a benefit unit on the sampling date The variable bgbezsT from the dataset p_register pr
161. s of personality traits and situation specific behavior or attitudes The basic questionnaire programme includes items on perceived self efficacy general life satisfaction satisfaction with health and living conditions religiosity and confession employment orientations as well as the respondents subjective perception of social integration The module on job search behavior is another basic element of the questionnaire Here the following information is available search duration activities to find a new job reasons for not looking for a job preferences regarding job attributes e g working hours willingness to make concessions e g regarding wages commuting distance qualification mismatches and reservation wages Some subjective indicators are not included in each wave see table 1 e g gender role attitudes opinions on handling money and partnership or aspirations for children s education and leisure activities Furthermore the questionnaire of wave 5 covers the so called Big Five personality traits a standardized 21 item battery covering five broad domains of personality Finally PASS includes a broad range of important socio demographic background variables like household size and household composition In addition questions about child care and school type of children under 15 years are integrated in the household questionnaire At the personal level questions on education and vocational qualifications are asked for pan
162. s changed over the first five waves In the wave 1 general population sample micro geographical variables supplied by Microm as well as regional indicators like state and FDZ Datenreport 07 2013 pe municipal size were used For details the user is referred to the wave specific reports by Hartmann et al 2008 B ngeler et al 2009 and Berg et al 2011 2013a bc In the case of the models for the BA samples additional characteristics on the level of the benefit unit from the sampling frames A2LL or XSozial could be used From wave 3 on only variables on Microm variables were dropped and only variables on the level of the benfit unit regional variables and the number of contact attempts were used for these samples For the wave 5 population replenishment sample age gender and nationality were used in addition to regional predictors The dataset hweights contains the variable prop_tO This is the product of the predicted probabilities of the two models Dividing the design weights by the estimated participation probabilities yields the modified design weights which formed the starting point for the third stage calibration 8 1 3 Stage 3 calibration A detailed documentation of the calibration process of waves 1 and 2 can be found in Kiesl 2010 The calibration procedures and results reported by TNS Infratest in the method and field reports Hartmann et al 2008 B ngeler et al 2009 are not the ones used for the weights in the
163. scientific use file The calibration of waves 3 to 6 is detailed in the data reports by infas Berg et al 2011 2013a bc We therefore merely outline the basic procedure here This section will deal with the calibration of the initial samples in wave 1 only because at later waves refreshment samples are not calibrated separately but only within the calibration of the complete samples see section 8 2 7 and 8 2 8 Household level wave 1 In an initial step the two subsamples and the total sample were Calibrated to official statistics at the household level The total and BA weights for benefit recipients in the two samples were calibrated to benchmark statistics from the Federal Employment Agency reporting month July 2006 The total and Microm weights were additionally calibrated to benchmark statistics on private households in Germany for 2007 from the Federal Statistical Office The benchmark figures used are detailed in Kies 2010 All weights are household weights The BA statistics however are based on values at the level of benefit units The link is created using the synthetic benefit units generated as described in the data report for wave 1 Christoph et al 2008 49 variable bgnr7 in the p_register dataset Households are initially broken down into synthetic benefit units The characteristics used for the calibration process are then generated at the benefit unit level This also includes the characteristic of whether the benef
164. seholds e g when they leave their household of origin and form a new split off household Using only the ID numbers hnr on the household and pnr on the individual level one can clearly identify a unit in each of the different datasets but not necessarily a certain observation Additional information is required to clearly identify an observation which depends on the format of the specific dataset in question 13 For households that have been drawn directly for one of the samples the uhnr is identical to the hnr Households that have split off from another household in PASS carry an uhnr representing the Anr of the household of origin FDZ Datenreport 07 2013 Ka Datasets that are prepared in wide format the register datasets contain only one observa tion per unit while the wave specific information is stored in wave specific variables e g age for a persons age in wave 1 age2 for the age in wave 2 and so on In these datasets each unit has exactly one observation and therefore can be clearly identified using the ID variables Datasets that are prepared in long format the cross sectional datasets and the weights as well as the datasets that are prepared in spell format the different spell datasets can contain more than one observation per unit Datasets in long format contain as many wave specific observations for each unit as there are waves this unit was interviewed in e g if a household was interviewed twice the
165. sion probability in the respective subsample as it is not possible to reconstruct precisely what inclusion probability the new household members households had in all previous waves The new design weight for subsample i dwjhhn 1 is therefore calculated from the old cross sectional weight wqihhn 1 1 1 dwihhn 1 u wgihhn sample ij Npopulation i The new design weight is only an intermediate step and is therefore not included in the data FDZ Datenreport 07 2013 8 2 2 Design weights for the wave n 1 refreshment sample From wave 2 on there is a yearly refreshment of the BA sample selecting only benefit units Bedarfsgemeinschaften in which no member was receiving benefits in July of the previous years The refreshment sample is drawn in the same sampling points as the initial samples From wave 5 on it is extended to the 97 additional sampling points of the replenishment samples Analogous to the special pps procedure used to draw the first register data sample which is described in Rudolph Trappmann 2007 the sample size is proportional to the share of new benefit recipients in the population in the sampling point at the time when the sampling points were selected The calculation of the design weights is described in the same article However from wave 2 on the number of benefit units in a household was no longer taken into account The design weight of the refreshment sample is included in the variable dw_ba for all cases f
166. specific PENDDAT hh_register p_register hweights pweights jahrsamp Sampling year hh_register Information not wave specific alg2samp Receipt of Unemployment hh_register Information not wave specific Benefit II of the household on sampling date 14 5 Sample indicator sampling year and receipt of Unemployment Benefit ll of the household on the sampling date The sample indicator sample the sampling year jahrsamp and the receipt of Unemploy ment Benefit Il of a household on the sampling date alg2samp are constant characteristics of the household which are defined once when the household joins the PASS sample Individuals are assigned the sample indicator sample of the household to which they belong when they first become part of the PASS sample Households which have split off from households already surveyed in the previous wave and are now surveyed as separate households in PASS take over the values of their original household in the variables sample jahrsamp and alg2samp FDZ Datenreport 07 2013 K References Achatz Juliane Hirseland Andreas Promberger Markus 2007 IAB Panelbefragung von Haushalten im Niedrigeinkommensbereich Entwurf f r ein Rahmenkonzept In Promberger Markus Ed Neue Daten f r die Sozialstaatsforschung Zur Konzep tion der IAB Panelerhebung Arbeitsmarkt und Soziale Sicherung No 12 2007 in IAB Forschungsbericht N rnberg p 11 32 Bachteler Tobias 2008 Dokumentation Record Li
167. sting variables for this new observation New variables are added if they were surveyed for the first time Key variables pnr Constant person ID number hnr Household number welle Indicator for survey wave Pointer variables uhnr Original household number One obs row in data matrix One obs row in data matrix uniquely identified by Topics Cross sectional information regarding a certain person in a certain wave pnr welle 1 Demography 2 General 3 Social Media 4 Education 5 Attitudes and orientations 6 Perceptions about justice 7 Employment 8 Income 9 Contact to social security insitutions 1 euro jobs Job search Standard of living Social relations Health Sports Care Migration 0 1 2 3 4 5 6 7 8 Social origin 1 11 12 13 14 15 16 17 18 Explanatory notes The dataset includes as many observations for a certain person as the number of waves this person was successfully interviewed FDZ Datenreport 07 2013 Children dataset KINDER Table 12 Characteristics of the children dataset KINDER Dataset Children dataset File name KINDER Level individual Type cross section Format long Data collected in 1 6 waves Integration of data from new waves 1 Each wave for which at least one of the person related ques
168. t und Berufsforschung N rnberg Jesske Birgit Schulz Sabine 2012 Methodenbericht Panel Arbeitsmarkt und Soziale Sicherung PASS 5 Erhebungswelle 2011 FDZ Methodenreport 11 2012 Institut f r Arbeitsmarkt und Berufsforschung N rnberg Kiesl Hans 2010 Kalibrierte Hochrechnung f r das Panel Arbeitsmarkt und soziale Sicherung unpublished N rnberg Kreuter Frauke 2013 Improving Surveys with Paradata Analytic Uses of Process Infor mation New Jersey Wiley Kreuter Frauke M ller Gerrit forthcoming A Note on Improving Process Efficiency in Panel Surveys with Paradata In Field Methods Kreuter Frauke M ller Gerrit Trappmann Mark 2010 Nonresponse and Measurement Error in Employment Research Making Use of Administrative Data In Public Opinion Quarterly Vol 74 No 5 p 880 906 Kueppers Rolf 2005 MOSAIC von microm In Gr zinger G Matiaske W Eds Deutschland Regional Sozialwissenschaftliche Daten im Forschungsverbund M nchen Hampp p 95 104 Laflamme Francois Maydan Mike Miller Andrew 2008 Using Paradata to Actively Manage tge Survey Data Collection Process In Proceedings of the Survey Research Methods Section American Statistical Association p 630 637 Laurie Heather Smith Rachel Scott Lynne 1999 Strategies for Reducing Nonresponse in a Longitudinal Panel Survey In Journal of Official Statistics Vol 15 No 2 p 269 282 Lynn Peter Kaminska
169. t unit was regarded as receiving benefits if at least one of its members was reported as a benefit recipient In a household with more than one benefit unit and with no information as to which individuals the household is receiving benefits for e g because the questionnaire responses state that no benefits are being claimed all of the synthetic benefit units were regarded as being in receipt of benefits The result of this generation is contained in the variable bgbezsT in the p_register dataset The weights following calibration at the household level are also contained in the hweights dataset FDZ Datenreport 07 2013 Pe wqbahh calibrated household weight of the BA sample wqmihh calibrated household weight of the general population sample wghh calibrated household weight of the total sample Person level Following the calibration at the household level the individuals who gave a personal or senior citizen s interview were calibrated to benchmark statistics at the individual level The calibrated household weights were the starting point for this step The total and BA weights for benefit recipients in both subsamples were calibrated to benchmark statistics from the Federal Employment Agency reporting month July 2006 The total and general population sample weights were additionally calibrated to benchmark statistics from the Federal Statistical Office on private households in Germany for 2007 The benchmark figures used are detailed in
170. t zplfd ever allocated in that household Indicator for survey wave Both the household and individual datasets as well as the corresponding weight ing files of PASS are processed in long format For every interview that was conducted with a household or a person there is a row in the data matrix By means of a wave indicator welle it is possible to assign these different observa tions for a household or a person to the respective survey wave Spell number As in the datasets processed in long format another variable is necessary in addition to the household and personal ID numbers in order to identify observa tions clearly in the spell datasets In the different subject related datasets the spells were put into chronological order and then each one was given a serial number the spell number within the household or the person It is not easily possible to relate spell information clearly to a survey wave as the spells contain cross wave information FDZ Datenreport 07 2013 n Table 29 Key variables in the datasets of the scientific use file Dataset Key variables contained u Ea S r amp S s 3 f F 8 lt 3 S Q R z D Household level Household register hh_register i 5 x Household dataset x x HHENDDAT Household weights x X hweights Household dataset on retirement provision x x x HAVDAT wave 3 only Unemployment Benefit II spells alg2_spells 5 Individual leve
171. ted use hweights dta clear keep if welle save hweightsl dta replace use HHENDDAT dta clear keep hnr uhnr welle HLSO800a psu strpsu reshape wide HLSO800a psu strpsu i hnr j welle gen split 1 if hnr uhnr replace hnr uhnr if uhnr hnr by hnr sort egen psulx mean psul replace psul psulx if psul by hnr sort egen strpsulx mean strpsul replace strpsul strpsulx if strpsul by hnr sort egen HLS0800alx mean HLS0800al replace HLS0O800al HLS0800alx if HLSO800al x merge m 1 hnr using hweightsl dta keep if _m 3 drop _m Then a variable is generated which expresses the change with regard to car ownership gen auto_neu 3 if HLS0800al 2 amp HLSO800a2 replace auto_neu 2 if HLS0800al 1 amp HLSO800a2 replace auto_neu 1 if HLS0800al 2 amp HLSO800a2 FDZ Datenreport 07 2013 La rep rep lace auto_neu 0 if HLS0800al 1 amp HLS0800a2 lace auto_neu 1 if HLSO800a1 lt 0 HLSO800a2 lt 0 label define auto_neu_lb 3 2 1 Auto angeschafft Auto behalten weiterhin kein Auto 0 Auto abgeschafft in mind 1 Welle keine Angabe label values auto_neu auto_neu_lb Finally the weight is constructed and the table produced gen whl_2 wqhh xhpbleib svyset psul pw wh1_2 strata strpsul svy tab auto_neu count cell format 10 0g 1 7 of the households gave up a car
172. text of the refreshment sample has the value 6 for the observation referring to the 1st wave These observations cannot be merged with the individual dataset and should therefore be deleted After this the pointer variable pnrzp is renamed pnr as the data is to be merged via the constant personal ID number After the register dataset has been prepared it is stored temporarily and merged with the individual dataset use hh_register dta clear keep hnr pnrzpl pnrzp2 pnrzp3 reshape long pnrzp i hnr j welle drop if pnrzp 6 ren pnrzp pnr drop hnr save hh_register_vorb2 dta replace use PENDDAT dta clear merge 1 1 pnr welle using hh_register_vorb2 dta tab _merge drop if _merge gen hhvorst _merge 3 The tabulation of the _merge variable shows that in 1028 cases there is no personal interview available with the person who completed the household interview in that wave As there is no information about them from personal interviews these observations which were merged from the person register can be deleted All of the cases for which the merging was successful _merge 3 were the head of the household in the particular wave and are flagged via the variable hhvorst 11 Spell data Daniel Gebhardt and Arne Bethmann In all waves the scientific use file of PASS included spell datasets on the household level as well as on the individual level see table 30 for an overview Whereas the dataset on
173. the following values 1 wave 1 BA sample 2 wave 1 general population sample from Microm addresses 3 wave 2 refreshment of BA sample new entries 4 wave 3 refreshment of BA sample new entries FDZ Datenreport 07 2013 Es 5 wave 4 refreshment of BA sample new entries 6 wave 5 general population replenishment from municipal registers 7 wave 5 replenishment of BA sample from municipal registers 8 wave 5 refreshment of BA sample new entries 9 wave 6 refreshment of BA sample new entries 3 3 Other survey design features PASS is administered to a particularly difficult survey population that is usually underrep resented in surveys A substantial part of the sample consists of benefit recipients and low income households with on average a rather poor level of formal education and low social status A number of survey design characteristics and fieldwork procedures have been adopted to reduce initial nonresponse and panel attrition as well as selectivity of nonresponse with respect to important target variables 3 3 1 Sequential Mixed mode design PASS uses a mix of computer assisted telephone interviews CATI and computer assisted personal interviews CAPI with CATI as the default mode in waves 1 3 The mixed mode design was chosen as a cost effective way of addressing various issues related to low income and welfare populations Rudolph Trappmann 2007 91 92 Particular problems faced when trying to interview t
174. the household dataset then the surveyset command has to be carried out and then the projected value can be calculated FDZ Datenreport 07 2013 use HHENDDAT dta clear merge 1 1 hnr welle using hweights dta svyset psu pw wgbahh strata strpsu svy subpop if welle 1 tab HLS0800a if sample z ALF count cell format 9 0g svy subpop if welle 1 tab HLS0800a if sample z LT cell ci format 9 0g Approximately 37 9 of the households receiving benefits in July 2006 had a car at the time of the survey in the 1st wave 62 1 did not have a car and the percentage with no valid response is extremely low Whilst the first tabulation command shows the projected number and percentages of individuals with and without a car the second tabulation gives the percentage and the corresponding 95 confidence intervals with the option ci The confidence interval is 36 0 39 7 It would also be possible to dispense with the restriction if sample 1 as the weight wqbahh in wave 1 is only defined for the cases from sample 1 BA register data sample as of the reference date in July 2006 The values for the number and percentage of car owners in the same population at the time of the survey in the 6th wave in the relevant population are obtained as follows svy subpop if welle 6 tab HLS0800a if sample 1 count cell format 9 0g Approximately 52 4 of the households receiving benefits in July 2006 h
175. the proportion of households moving away from a designated area in the course of a year and which have been used in post survey adjustment for the wave 1 population sample A detailed description of the MOSAIC database can be found in Kueppers 2005 Second the administrative record data on benefit recipiency used for drawing the register samples offers an even richer database in that regard It contains information e g level of schooling age current employment status at the individual level that can be used to analyse and correct for initial nonresponse of UB II recipient sample cases e g Schnell et al 2010 3 3 9 Record linkage to administrative data In order to further enhance PASS survey data individual survey responses have been linked to administrative data of the Federal Employment Agency with additional information on episodes of UB and II receipt employment job search and participation in active labour market programmes for respondents who gave their consent to record linkage For the wave 6 data release about 87 2 of the individuals responding to the person level questionnaire could be linked successfully Trappmann et al 2013 For technical issues regarding the linkage methodology please see the report by Bachteler 2008 Empirical analyses of the determinants of consent to record linkage and or potential selectivity biases it may introduce can be found in Beste 2011 In cooperation with the Research Data Center
176. the same household in wave 3 zvhh Constant personal ID number of target persons father living in the same household in wave 4 zparthh Constant personal ID number of target persons partner living in the same household in wave One person that was at least once a member of a successfully surveyed household in PASS One obs row pnr in data matrix uniquely identified by Topics 1 Constant sampling information information on persons sex and entry in the panel study 2 Wave specific household information household the person is a member of serial number in the household 3 Wave specific individual information persons survey status age inclusion in the children dataset 4 Wave specific synthetic benefit unit information number type and recipiency of the persons synthetic benefit unit 4 Wave specific pointers Explanatory notes Only persons that were at least once members of a successfully surveyed household are included in the person register FDZ Datenreport 07 2013 Person dataset PENDDAT Table 11 Characteristics of the person dataset PENDDAT Dataset Person dataset File name PENDDAT Level individual Type cross section Format long Data collected in 1 6 waves Integration of data from new waves 1 Each wave a person is successfully interviewed is added as new observation in the dataset 2 The newly recorded information is assigned to exi
177. this wave were removed from the SUF If one of the individual interviews was conducted with the wrong person but without any further problems emerging in the household composition then just the individual interview was removed The wave specific data reports give an overview of the checks carried out to identify problematic cases e g see Berg et al 2013c for wave 6 The net variables in the household register hnettok hnettod and person register datasets pnettok pnettod provide information about removed interviews over the waves Please note that not all deleted interviews can be identified in the SUF due to the logic of the register files 6 In PASS the register files of the SUF are net files Therefore the household register contains all households that have ever been successfully surveyed The person register contains all persons living in the households at the time of the interview Removed interviews from households or persons that are not included in the FDZ Datenreport 07 2013 Second incomplete interviews at the household and individual level were not included in the SUF as well as interviews from households which were regarded as not successfully surveyed according to the definition of PASS see Table 24 2 These cases were not documented in the register datasets because they were not regarded eligible in the first place in contrast to the removed interviews described above Table 24 Interviews at least required
178. till receive benefits at the most recent reference date can be projected to all households with at least one recipient of Unemployment Benefit II in Germany at that time In the course of a panel former respondents attrite reducing the number of units households persons left for analysis To counteract this process PASS replenished both samples before wave 5 To distinguish these additional samples from the refreshment samples above which focus on new entries to the population we will refer to them as replenishment samples 100 new primary sampling units were selected for this replenishment again with probability proportional to size This time the general population sample was however drawn from municipal registers without stratification Details of the procedure can be found in chapter 6 of Berg et al 2013b The Replesnishment of the BA sample was drawn according to the same procedure as all previous BA samples The population of the refreshment was designed to be all households with at least one benfit unit in receipt at the wave 5 reference date for sampling July 2010 The Methodology used to estimate weights that allow for an integrated analysis of the ongoing panel samples with the replenishments was described in Spiess Rendtel 2000 For details on weighting the reader is referred to chapter 8 of this User Guide In the PASS dataset all subsamples can be identified by the variable sample in the household dataset HHENDDAT It uses
179. tions ofthe HKI HT or HBT modules was asked for the respective person is added as new observation in the dataset 2 The newly recorded information is assigned to existing variables for this new observation New variables are added if they were surveyed for the first time Key variables pnr Constant person ID number hnr Household number welle Indicator for survey wave Pointer variables zmhh Constant personal ID number of target persons mother living in the same household in the respective wave zvhh Constant personal ID number of target persons father living in the same household in the respective wave One obs row in Cross sectional information regarding a certain person in a certain wave data matrix One obs row pnr welle in data matrix uniquely identified by Topics 1 Demography 2 Child care 3 Social participation 4 Education and inclusion subsidies Explanatory notes This dataset is included in the SUF since wave 6 It contains variables with information about children living in the household which was stored in the household dataset until wave 5 The respective variables have been deleted in the household dataset Only persons for whom one of the questions of the HKI HT or HBT modules was asked within the household interview are contained in the children dataset Consequently it contains also persons above the age of 14 Due to the fact that no p
180. tions provide a brief overview of the constant characteristics that are available in PASS The intention here is to show the conditions under which the variable was surveyed for the first time and to indicate the dataset in which it can be found The key variables are disregarded here 14 1 Gender Information on a person s sex is gathered at the household level either when the household in which the individual is living is first interviewed in the context of PASS or when the individual joins a sample household as a new member e g when new individuals move into the household In re interviewed households the interviewers had the opportunity to correct details regarding gender which had been recorded incorrectly in the previous wave During the plausibility checks of the household structure too changes were occasionally made to the gender variables in households that attracted attention as a result of implausible relationships between the household members Here the gender data was checked on the basis of the first names No retrospective changes of the data collected in earlier waves were made in either the household or the individual dataset 14 2 Half year of birth A person s half year of birth was generated from the date of birth reported in the personal interview Although it is a constant characteristic the date of birth is asked for in every FDZ Datenreport 07 2013 Ka Table 34 Information on constant characteristics half
181. tus and PENDDAT Yes No PSH0380 type of occup activity when Not surveyed in Except the first repeated in target person was aged 15 wave 1 terview for persons first in terviewed in wave 1 PSH0500 Target persons fathers PENDDAT Yes No highest general school qualification PSH0600a i Target person s father s vo PENDDAT Yes No cational qualifications PSH0610 Father s occup status and PENDDAT Yes No PSH0680 type of occup activity when Not surveyed in Except the first repeated in target person was aged 15 wave 1 terview for persons first in terviewed in wave 1 the mother or father was living in the household the information they provided in their own personal interviews was assigned to the target person For individuals interviewed for the first time after wave 1 the parents highest school qualifications and vocational qualifications were recorded as proxy information irrespective of whether the mother and or father was living in the same household Details about the qualifications which the parents may have given in their own personal interviews were thus no longer assigned to the children living in the household People who had already been interviewed in the previous wave were not asked questions on this topic again Furthermore in wave 2 additional questions were incorporated about the mother and father s occupational status and occupational activity at the time when the target person him herself was 15 years old This info
182. uaranteed that their names and addresses would be kept separately from any of the information they provided in the survey and would not be passed on to third parties The letters were tailored to the two subsamples register vs population sample stressing the importance of response to the survey request yet emphasizing that participation was voluntary Hartmann et al 2008 43 78 83 From wave 2 onwards additional versions of the advance letter were developed tailored to panel households that had already participated in the previous wave s see e g B ngeler et al 2009 29 62 65 for waves 2 3 and e g Jesske Quandt 2011 90 134 for waves 4 6 New entrants to the study such as cases of the yearly refreshment samples received a revised version of primary notification letter In all waves a thank you letter was mailed out to each respondent shortly after the interview in order to increase the propensity to participate in future waves In addition a newsletter was mailed out to respondents between waves providing them with some results from prior waves with the main objective to build rapport with respondents through means other than the annual interview itself 3 3 5 Tracking One of the top priorities in an ongoing panel survey is to maintain up to date and accurate records of the whereabouts of each sample member In PASS both prospective proactive and retrospective tracking procedures Couper Ofstedal 2009 Laurie Smith Scott
183. ues For naming the variables of the dataset we considered two main alternatives from which we had to choose one The first option is naming the variables in accordance with their respective order in the questionnaire as is done in the German Socio Economic Panel GSOEP for example The advantage of this type of naming convention is that the items corresponding to the variables are easy to find in the questionnaire which significantly enhances the value of the questionnaire as a documentation instrument The central disadvantage of this approach is that identical items are given different names due to changes in the order of questions in the questionnaire resulting in considerable preparation being required for compiling and if necessary renaming the required variables even for simple trend analyses as more and more panel waves become available The second main alternative is allocating independent variable names which are kept constant across waves apart from a wave indicator if necessary The advantages and disadvantages of this strategy are opposite to those of the first alternative identifying the variables corresponding to an item across waves is unproblematic whereas using the questionnaire as a documentation instrument becomes more difficult as it is no longer possible to derive the position of an item in the questionnaire from the variable name In our opinion the advantages of fixed variable names clearly outweigh the disadvantages
184. variable Although these variables are strictly speaking also generated variables and are classified as such in the frequency tables of the codebook they are not given clear names Instead their names are based on those of the original variable but with a 1 as the final number rather than a 0 FDZ Datenreport 07 2013 pe Table 22 List of subject related indicators used in the variable names spells Individual level Code Subject area Household level Code Subject area ET AL LU EE MN ALM AL wave Employment with earnings of more than 400 per month since January 2005 spell data individual level data from wave 2 onwards Spells of registered unemploy ment and receipt of Unem ployment Benefit since Jan uary 2005 spell data individ ual level from 2nd wave on wards Other activities since January 2005 spell data individual level data from wave 2 on wards 1 euro jobs spell data indi vidual level from wave 4 on wards Employment and training mea sures spell data individual level waves 2 and 3 only Employment and training mea sures spell data individual level wave 1 only Receipt of Unemployment Benefit spell data individual level wave 1 only AL2 Receipt of Unemployment Benefit Il spell data house hold level FDZ Datenreport 07 2013 P 7 Dataediting Daniel Gebhardt The Scientific Use File SUF of PASS is the product o
185. variables can be distinguished by the origin of the source information for their creation The three different types of simple generated variables are displayed in table 32 Detailed information on the variables generated in the different waves and their respective source variables can be found in the wave specific data reports see e g chapter 4 4 in Berg et al 2013c for wave 6 13 4 Theory based construct variables Theory based construct variables are variables whose generation requires more extensive re coding and or coding In most cases these variables have been empirically tested FDZ Datenreport 07 2013 Ka Table 32 Types of simple generated variables in the cross sectional datasets HHENDDAT PENDDAT for household persons that were already asked in the past regarding a certain topic Type Source variables for generation from Description wave of house current hold s person s wave first interview re garding the topic constant yes no uv updated yes yes fs independent no yes neu In general information from the first interview regarding the topic was carried forward except for cases where falsely entered data was corrected in the cur rent wave E g zpsex Gender of target person The latest information from the previous wave was updated with the information recorded in the current wave E g schul1 Highest general school qualification In each wave the variable was newly generate
186. wave 2 compared with wave 1 The only difference to the previous analysis is that the BA weight has to be used instead of the total weight gen wbapl_2 wgbap ppbleib svyset psu pw wbapl_2 strata strpsu svy tab rel_zufr count cell format 10 0g Here 33 7 are less satisfied than in the previous wave whereas 42 7 are more satisfied The result refers to 6 045 000 individuals from the age of 15 who were living in a household which was receiving benefits in July 2006 and belonged to the resident population irrespec tive of receipt in wave 2 In this respect it is not surprising the majority is more satisfied than in wave 1 as some of them should have managed to leave benefit recipiency in the meantime Researchers will therefore perhaps be more interested in how the satisfaction levels changed for those people who were receiving benefits on both survey dates 12 3 3 Individuals in receipt of Unemployment Benefit Il on two subsequent waves As was the case in the analyses described above for the question as to changes in the satisfaction levels of people who are still in receipt of benefits the variables that indicate benefit recipiency on the survey date are required These variables are contained in the person register which is merged here merge m 1 pnr using p_register dta keep if _m 3 svyset psu pw wpl_2 strata strpsu svy subpop if bgbezb2 1 amp bgbezbl 1 tab rel_zufr count cell format 10 0g As
187. were calculated according to the steps described in sections 8 1 and 8 2 1 8 2 7 for the ongoing BA panel sample and the BA replenishment The BA replenishment sample 7 then had to be integrated with recipients of UB II from all BA samples of waves 1 4 convex combination Cases from the BA samples from waves 1 4 which did no longer receive UB II maintain their weight in this case This resulted in the new BA weight before calibration Since the new BA refreshment sample sample 8 and the BA panel samples are disjoint all cases maintain their weights during the integration A fusion of the samples for the calculation of the BA weight before calibration was not necessary Following this the general population weights wwmihh and BA weights wqbahh were integrated to generate the total weights as described in section 8 2 8 FDZ Datenreport 07 2013 pe 8 4 Datasets and variables Like the individual and household datasets the weighting datasets hweights household weights and pweights person weights are organised as long files The file hweights therefore now contains the following variables Table 26 Overview of the variables in the household weights data file hweights Name Label hnr Household number current welle Indicator for survey wave sample Subsample dw_mi Design weight Microm sample dw_ba Design weight BA sample dw Design weight total sample prop_tO Participation probability in the sam plin
188. year of birth FAR Filled in for wave of Filled in for wave s of Variable Description Dataset en s z the first interview repeated interviews gebhalbj Target person s half year of PENDDAT Yes Yes birth generated personal interview conducted Among other things it serves to check whether the correct person is being interviewed For re interviewed persons the interviewers had the opportunity to correct details which had been entered incorrectly in the previous wave If the half year of birth differs from that in the previous wave as a result of the date of birth being corrected in the personal interview this was understood as the correction of an incorrect entry No retrospective changes were made to the information collected in the previous wave 14 3 Migration background A person s migration background is also understood as a constant characteristic and is only surveyed in the personal questionnaire in the first interview conducted with a person The information on nationality PMIO400 PMIO500 on temporary residence permits PM 0600 and the type of residence settlement permit PM 0650 on the other hand is gathered in every wave as changeable characteristics In the senior citizens interviews of the 1st wave no information was collected about whether the respondent s parents and or grandparents migrated to Germany and if so from where they migrated It was therefore not possible to establish the migration background for senio
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