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The following questions and answers should help you with your research based on the SHP data. Since 2013, the SHP is also involved in the related studies SHP – Vaud as well as LIVES Cohort. The following information applies to all three studies.

Acquiring the SHP Data

How can I obtain the data?

You just have to sign this contract and send it including all the pages via mail or email to the Swiss Household Panel. One copy will be countersigned by the SHP and will be sent back to you. Subsequently you receive your personal login and a password, which you need to download the datasets from our web site (you need to accept pop-ups for this page).

If you want to analyse the SHP – Vaud data, you have to sign an additional user contract.

To acquire the CNEF data please find all information here.

How much do the data cost?

You get the data free of charge, but you need to sign the contract.

What are the conditions to use the SHP data?

The SHP data are free of charge for the whole research community. However, you are required to cite the study correctly in all publications based on the SHP. Instructions regarding citation can be found here.

In addition, you need to send a copy of your research output to the SHP (

Am I allowed to share the data with other people?

No part of the data may be shared with any other person unless that person is an authorized user. An authorized user is someone who has signed a contract and has been granted access to the SHP. Individuals working on a research project together need to apply for the dataset independently.

I am teaching a course/seminar. Am I allowed to share the data with the students of my seminar?

Yes, under certain conditions. You are responsible to ensure that your student(s) adhere to our data protection policy. You must also make sure that the dataset is appropriately disposed of once your course/seminar is over. In order to use the SHP data in a course/seminar, you must sign the „Student data set“ in addition to the usual user contract. If you are in any doubt about the use of these data in a teaching context, please e-mail

Where can I download the data from the SHP, the SHP - Vaud, and the LIVES Cohort?

Once you logged in with your personal password, the data can be downloaded here. You need to accept pop-ups for this page.

Why can I not log in?

To log in, you need to accept pop-ups for this page. If you do not accept pop-ups, the login window will not show up.

Merging data files

What are Master Files (SHP_MP and SHP_MH)?

Master Files are data files, which contain time-invariant information such as for example the year of birth or the sex of the respondents. In addition, you find information on participation for each wave. You can match these data files with other data files using the identifiers idpers or idhous$$.

Despite its time-invariant character, the variable sex can be found not only in the individual Master File (SHP_MP) but also in the annual individual data file (sex99, sex00, sex01 etc. in SHP$$_P_USER).

How can I merge different data files?

If you want to merge different data files (for example merging data from several years, combining individual and household information or combining data from couples/families), you can use model syntaxes.

By clicking on the name of your preferred statistical program, you can find examples of syntaxes to construct the mentioned data files: Stata, SAS, and SPSS.

If you want to merge vertical biographic data files with horizontal individual data files, you find examples of syntaxes here: Stata, SAS, and SPSS.

You find further assistance for your data management with SPSS and Stata here.

Data analysis

How can I best get an overview of the SHP?

Besides our website, we strongly encourage you to consider the questionnaires and the User Guide. The latter gives a good overview of the design and the implementation of the study and also documents the data extensively and describes the quality of the data.

Which method is most appropriate to analyse the data?

Our longitudinal analysis guide contains short introductions of different methods to analyse longitudinal data. It also gives coding examples and recommendations for further readings.

How can the three samples of the SHP be differentiated?

The three samples can simply be distinguished using the variable FILTER$$ (SHP_I = 1; SHP_II = 0; SHP_III = 4).

Why do I have so many missing values in my data?

The data files contain always all individuals who are part of the samples and do not consider if they took part in the respective wave. Most of the missing values come from household members who did not take part in the respective wave or who are children under 14 years and are not yet eligible for an individual interview. Valid cases can simply be selected with the variables:

  • STATHH$$ at the household level (where 1 = household questionnaire completed) and
  • STATUS$$ at the individual level (where 0 = individual questionnaire completed).

Can I compare the data from the Swiss Household Panel with other countries?

The SHP is part of the Cross-National Equivalent Files (CNEF). The CNEF aims to provide harmonized data for comparative analysis. Besides the SHP, the CNEF includes data from the US – Panel Study of Income Dynamics (PSID), the German Socio-Economic Panel (GSOEP), the British Household Panel Study (BHPS) / Understanding Society (UKHLS), the Household Income and Labour Dynamics in Australia (HILDA), the Canadian Survey of Labour and Income Dynamics (SLID), the Korean Labour and Income Panel survey (KLIPS) and the Russia Longitudinal Monitoring Survey-Higher School of Economics (RLMS-HSE). The harmonized data in the CNEF cover the domains of income, employment and health.


Information on the access to the CNEF-data can be found here.


Where can I best get information about the SHP survey weights?

Here you can find different documents on the weighting procedures. We have a special FAQ on how to use the weights with the SHP data. If you only use a subsample of the SHP or if you want to merge different subsamples, you find information on how to construct the respective weights here.

What is the difference between cross-sectional and longitudinal weights?

If you use cross-sectional weights (on the individual or on the household level), the weighted data refer to the respective year of the survey. Cross-sectional weights correct the data to reflect the distribution in the Swiss population with regard to characteristics such as age, sex and region in the respective year. If you use longitudinal weights (only on the individual level), weighted data refer to the Swiss population of the year when the sample was drawn, i.e. 1999 for the SHP_I, 2004 for the SHP_II and 2013 for the SHP_III. Further information regarding the use of weights can be found here and in the weights section.

Why do some cases have a weight of 0 or -3?

There are cases that have a weight of 0 or -3. This means that these individuals are disregarded when the data are weighted. This happens when an individual does not belong to the respective sample or does not have the necessary characteristics to get a positive weight (for example if the respondent is too young).

What is the weighting scheme for the SHP-Vaud and the LIVES Cohort data?

Information can be found here for the SHP-Vaud and here for the LIVES Cohort.


Why do I sometimes obtain contradictory results when comparing different variables concerning for example the educational level of the respondents?

Contradictory results can occur when you compare information from different SHP questionnaires. For example, both the household grid questionnaire and the individual questionnaire collect information such as the educational attainment of household members. Both sources of information are included in the individual files. Mostly, this concerns the variables edugr$$, edgr$$ and occupa$$ that are gathered via the reference person of the household. Once you compare this information with the information from the individual interview (educat$$, edcat$$, edu_1_$$, wstat$$, etc.) differences can occur. Information from the individual interview should always be preferred.

Which education variable should I use?

There are seven variables related to the level of education in the individual data file. This leads sometimes to confusion about which variables should be used for analysis. We generally advise to use the constructed variables that combine data from the individual and the grid questionnaire: educat$$, edcat$$, isced$$, or edyear$$.  For information about how these variables are constructed, please refer to the user guide.

Variables with one source of information:

  • edgr$$: The information in this variable stems from the grid questionnaire. The references person of the household gives the highest level of education achieved for every member of the household (17 categories).
  • edugr$$: Same as edgr$$, but only 11 categories.
  • edu_1_$$: The information in this variable stems from the individual questionnaire. The individual gives the highest level of education achieved for him or herself. This information is considered more reliable than the information of the reference person (grid questionnaire)

Constructed variables  (Two sources of information):

  • edcat$$: This variable combines information from the individual (ed_1_$$) and the grid questionnaire (edgr$$) whereas the individual interview is considered more reliable than the grid (17 categories).
  • educat$$:  This variable combines information from the individual (edu_1_$$) and the grid questionnaire (edugr$$) whereas the individual interview is considered more reliable than the grid (11 categories).
  • isced$$: Based on edcat$$, this variable re-constructs the International Standard Classification of Education (ISCED, 10 categories)
  • edyear$$: Based on isced$$, this variable estimates the number of years relative to the highest finished type of education.

Why does the SHP often use questions with answers on an 11-point scale?

The 11-point scale has proved successful in survey research because it provides participants with neither too many nor too few response categories. It allows differentiating between individuals and nobody is forced into a category due to an insufficient availability of response alternatives. Therefore this type of scale improves the data quality. Moreover, such variables can be treated as continuous rather than ordinal in statistical analyses.

Further information can be found in Annette Scherpenzeel’s working paper.

Why is the SHP conducted annually?

The SHP is conducted annually for several reasons. First, respondents are better able to remember an event if it happened more recently. Second, an annual survey reduces the risk of confusion of dates in between two waves of data collection. Third, it is important for panel surveys to have frequent contact with sample members to avoid attrition.

Further reasons can be found here.

Is it possible to add questions to the SHP questionnaire?

Under certain conditions, scholars have the opportunity to propose questions for the SHP.  Each year, the deadline for submission is the end of January. The time available is very limited. Proposals have to be submitted in English, the wording of the proposed question(s)/item(s) can be in English, German, French, or Italian. The proposal has to include: (1) an explanation of the relevance and theoretical foundation of the proposal, (2) an outline of the proposed concept(s) and question(s) and (3) a plan of publication and dissemination of research findings. As a first step, proposals will be evaluated by the SHP team. At least two experts of the field will review the proposition in a second phase. The question(s) of the accepted proposal(s) will be developed in collaboration with the SHP team. Please send your proposal in electronic form to