Zur Missy-Homepage

Metadata for Official Statistics

Expand / Collapse

Use the - and -icon left of the headlines to open and close the different sections of the content area.

The - and -icon open / close all sections at once.



Explanatory notes are shown when the mouse cursor is moved over the field headlines.



The myMetadata Box collects variables which can be used in the functions of the work with myMetadata section.

Add variables
via drag & drop into the box in the top right corner or by clicking the -icon which appears when you hover over the variables name or headline.

Add variable lists
On pages displaying variable lists you can add complete lists of variables as well by using the -icon which is placed next to the headline.

Remove a selected variable
with the -icon which appears on the right when hovering the variable in the list.


Study: MZ 2016

Additional Programs

  • Additional Programs
    • Biological number of children for all women aged 15 to 75 years (Sampling Fraction: 1% )
    • Commuting characteristics of employed persons, pupils and students (Sampling Fraction: 1% )
  • Ad-hoc-module EU Labour Force Survey
    • Labour market entry of young people (Sampling Fraction: 0.1% )

Specific Features

Changes In Questionnaire

  • New variables of the additional program commuting characteristics of pupils, students (EF297-EF303) and employed persons (EF191-EF194), which were last surveyed in 2012, are: Workplace: Way from current residence (EF191, F45), Workplace: Distance (EF192, F46), Workplace: Time expenditure for the way from current residence (EF193, F47), Workplace: Mode of transport for the way from current residence (EF194, F48), School/Higher education: Way from current residence (EF297, F131), School/Higher education: Location in the same federal state (EF298, F130), School/Higher education: Location in the same municipality (EF299, F129), School/Higher education: Federal state (EF300, F130), School/Higher education: Distance (EF301, F132), School/Higher education: Time expenditure for the way from current residence (EF302, F133), School/Higher education: Mode of transport for the way from current residence (EF303, F134).
  • New variable included: Workplace: Location in the same municipality (EF195, F41)
  • New variables of the additional program biological number of children for all women aged 15 to 75 years (EF618-EF621), which were last surveyed in 2012, are: Births (EF618, F11), Births: Number (EF619, F12), Births (Imputation id) (EF620), Births: Number (Imputation id) (EF621).
  • In comparison with the Ad-hoc-Modul "Labour market entry of young people" (EF1080 – EF1106) from the year 2009 there are modifications regarding questions and response categories. Therefore the results for 2016 are comparable only to a limited extent; see also Eurostat (2018).

Methodical Notes

  • A new procedure for the subsampling and time-consistent identifiers allow researchers to independently create panel files from the Scientific Use File 2012 onwards. Since a new sample was drawn in 2016, panel files can be created only from 2012 up to 2015. Furthermore, panel files from 2016 onwards are limited to the period up to 2019, as the Microcencus and other household statistics will be converted to a fundamentally new system from 2020 onward. For easier longitudinal analyses, the following identifiers have been included since 2015: idpers (longitudinal personal identifier), idpersx (cross-sectional personal identifier), idhh (longitudinal household identifier) and idhhx (cross-sectional household identifier).
  • In order to improve the measurement of ILO labour status, in particular, the fieldwork and a number of questions have undergone changes. Although no significant changes have taken place since 2011, some deviations between the results of the Microcensus and other labour market statistics are still to be found in 2016. This applies, for example, to small and marginal jobs. In particular, changes due to methodological factors must be taken into account when comparing with previous surveys of the Microcensus (see Körner/Puch 2009; Körner/Marder-Puch 2015).

Changes In Typification

  • To simplify data preparation a number of typifications are no longer included since 2015. The report "Einführung in die eigenständige Erstellung von Typisierungen am Beispiel des Mikrozensus Scientific Use Files 2014" (Börlin 2020) shows how these typifications can be created using the data available in the data, using the example of the Microcensus SUF 2014.


  • The regional details federal state (Bundesland) and a rough classification of the community size classes (Gemeindegrößenklassen) – the latter is not shown for the small federal states Bremen and Saarland – are included in the Scientific Use File (SUF). With the help of a special code of the variable community size class, it is possible to distinguish between West and East Berlin (EF563 = 9 Berlin-East).
  • The other variables in the SUF are also coarsened if necessary, so that each value in the univariate distributions comprises at least 5,000 persons from the target population.
  • The values of the variable nationality are aggregated in such a way that each group of nationalities in the target population comprises at least 50,000 inhabitants. In cases where variables are coarsened, the most strongly populated category is shown in the SUF.
  • The SUF is a de-facto anonymised 70% sample. Until 2011, the sampling units were households or apartments where all persons in a selected household or apartment were included in the subsample. From 2012 onwards, the sampling districts within a rotational quarter will be used as sampling units for the subsample. This, together with longitudinal consistent identifiers, makes it possible to generate panel data sets with the Scientific Use Files.

Year Specific Documentation

Statistisches Bundesamt / GESIS (2020): Wichtige Informationen zur Nutzung des Mikrozensus Scientific Use Files 2016.

This document contains the information on this website as well as further details on the Microcensus SUF 2016.

Target Sample Size

Cross-sectional Data
Sampling Units -
Achieved Sample Size 546785


Units Of Observation

  • Persons (in private households and collective dwellings)
  • Households
  • Dwellings

Units Of Analysis

  • Persons
  • Living arrangements
  • Families
  • Households
  • Dwellings

Data Collection

Date of Data Collection

01.01.2016 - 31.12.2016

Participation Mandatory



Design Weight: Target

  • Dwelling, Household, Persons

Non-response Weight: Method

  • The Microcensus is a single-stage, stratified cluster sample with a uniform sampling fraction for all strata. As a rule, the sampling fraction is 1%; this applies to all sampling units (dwellings, households, persons).
  • Because the selection probability of the Scientific Use File (SUF) basically remains constant at 70%, design weights can be created on the basis of the inverse of the sampling fraction of the Microcensus (1%) and the Scientific Use File (70%): w = 1 / (0.01 * 0.7).

Final Weighting: Method

  • For the final weight a two-stage procedure is employed: in a first step (compensation) the net sample of the successfully interviewed households is adjusted to the gross sample of all households to be interviewed. In a second step (adjustment) this gross sample is benchmarked to the update of the current population figures (the extrapolation is based on the results of the current population update, as updated by the Census in 2011). The final weights of the Microcensus result from a combination of this two-stage procedure of compensation and adjustment.
  • The standard extrapolation factors of the full Microcensus (EF951 and EF952) are adjusted quarterly at different regional levels. In order to improve the adjustment of the SUF distributions to the published results, the results of the SUF weighted with the extrapolation factors of the original data (EF951, EF952) were subsequently adjusted to the distributions of the weighted original data according to gender, age groups (under 15 years, 15 to under 45 years, 45 to under 65 years, 65 years or older) and employment status (gainfully employed, inactive) within each federal state. These poststratified extrapolation factors of the SUF were then modified to be the same for all persons within a household.
  • The extrapolation factor for the ad hoc module (EF956) has been adjusted at the personal level because (1) analyses in the household context are of secondary importance to the ad hoc module and (2) personal factors improve the quality of the extrapolation. In the construction of the extrapolation factor EF956 used for the characteristics of the ad hoc module, the survey method (EF7), the type of employment (EF29) differentiated by gender (EF46) and age groups (EF44) at the federal level, the type of employment (EF29) differentiated by gender (EF46) at the state level, age groups (EF44) also at the state level and the two nationality groups German/foreigners in the East/West regions were used in 2016 for estimating the answer probabilities.