Andreas Berg Federal Statistical Office of Germany C 1 - Mathematical-statistical methods

© FSO, Unit C 1, Division Mathematical Statistical Methods, Research Data Center

Federal Statistical Office of Germany

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First steps of the Federal Statistical Office of Germany working with small area methods:

An attempt to provide more reliable results for publishing data in smaller subgroupswith application to labor force data in

North Rhine-Westphalia

Andreas BergFederal Statistical Office of GermanyC 1 - Mathematical-statistical [email protected]

1st of September 2013Bangkok



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NRW

Germany



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Outline:I. Problem descriptionII. The Data

Microcensus data Data from the German Federal Employment

AgencyIII. Matching processIV. ModelV. ResultsVI. Outlook

Problem specific In General



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Description of the problem:

Exemplary for the largest German Land with 18 Mio inhabitants we would like to analyze via small area methods NUTS3-level estimates for labor force data

Starting point is estimation of number of unemployed persons

Estimates for NUTS3-level based on classical methods exist but have not been published



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Description of the problem:

Due to restriction to the use of aggregated data only area level models can be analyzed

Comparison of the estimates will be done (and therefore the politically-induced decision of publishing) mainly on the base of a hopefully smaller MSE which should also not touch a certain barrier



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Microcensus:

Annual survey of 1% of the German private households

Sampling units are clusters of about 8 to 10 households

Includes the German Labour Force Survey

MSE of estimated results acceptable only for regions with at least inhabitants (here: only NUTS2-level Data will be published => Bezirke)



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Microcensus:

Here: data from 5 NUTS2-areas comprising 53 NUT3-areas (Kreise) available for 2009

Variable of interest: number of unemployed persons according the ILO definition



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Data from the German Federal Employment Agency:

Number of people registered of being unemployed as auxiliary variable

Data from 395 labor office areas averaged over several time points during the year 2009

Problem: this variable differs from the ILO definition,

Not all jobless persons are recorded by the German Federal Employment Agency, there are additional community based institutions recording jobless people



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Data from Microcensus and German Federal Employment Agency differ markedly even on high-aggregated levels, but they are highly correlated



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Matching process:Regional administrative structures are different between microcensus and labor office data collection

First attempt: splitting overlapping labor office areas proportional to number of inhabitants involved

Cooperation with microcensus and labour office experts highly recommended



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Model:

Area level model according to Fay/Herriot as a combination of a synthetic and a HT estimator

Covariates: As unemployed registered persons

according to Federal Employment Agency NUTS2 data NUTS1 data



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Model:

MSE estimation according to Ghosh and Rao



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Software:

Tools developed for the ESSNET Project on SAE 2010-2012.

Public deliverables available on CROS webportal at EU



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Results:

Estimation carried out in SAS



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Outlook 1: Extend analyses to all Länder and additional

variables of interest Further refinements, for instance regarding

sex/age groups Cooperation matching Different/Refined small area models

especially with hindsight towards survey design

MSE of MSE: how to explain to users and deal with this “unknown” concept



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Outlook 1:

Balance between “easy” calculation and loss of accuracy

long way to go until production of results based on small area techniques can be

established



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Outlook 2 – in general:

Small area estimation is on the agenda at the federal Statistical office of Germany.

At the methodological unit we are currently trying to anticipate future demands regarding the development of a new system of household statistics which might start off with issues in the field of



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Microcensus Labour force survey European Union statistics on

Income and Living Conditions Information and communication

technology surveys



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I would like to thank the Statistical office of the Land of North Rhine Westphalia (“Information und Technik Nordrhein-Westfalen”) for their cooperation



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Reference: Körner, T. and Puch,K: “Coherence of German Labour Market Statistics”, in Statistics and Science, Vol. 19.



Thank You for your Great Deal of Attention

Khorb khun khrab

Andreas Berg, Unit C 1Federal Statistical Office of Germany,WiesbadenPhone: +49 (0)611 / 75-4362Mail: [email protected]

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Andreas Berg Federal Statistical Office of Germany C 1 - Mathematical-statistical methods

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