1. Ben Kriechel Economix Research & Consulting München
Revision in JVS methodology Germany: Introduction of the GREG Estimator
Item 4.: Job Vacancy Statistics
LAbour MArket Statistics working group Eurostat/F3/LAMAS/52/16
5th-6th October, Luxembourg
Ben Kriechel
Economix Research & Consulting
München

2. Contents Introduction: German Job Vacancy Survey
A first comparison of the different methods
Weighting Methodology (GREG)
Anchor variables
Dimensions
Adjustments
Non-response analysis

3. Project Team – Revised Methodology
Hanna Brenzel, Judith Czepek, Alexander Kubis, Andreas Moczall, Martina Rebien, Christof Röttger, Jörg Szameitat, Anja Warning, Enzo Weber IAB Nürnberg Hans Kiesl Ostfränkische Technische Hochschule Regensburg Ben Kriechel Economix Research & Consulting

4. Process of development
Several ways to specify the GREG estimator were tested:
Non-Response inclusion (and the specification of the non- response estimation) or not
Inclusion of additional anchor variables
Mechanisms to insure stability and convergence (possibility to combine cells if necessary by size class or across economic activities)
Final ‘choice’ of estimator was judged along the original specifications and wishes:
Simplest form of a stable methodology
Inclusion of NR (also in light of future additional variables)
Stability across the time series (2010q4 onwards)
Development time:
2014 – 2015; with first implementation in 2015q4.

5. Overview over differences
New
Old
Method
GREG regression estimator
Horvitz-Thomsen/RAS method
Anchor variables
Companies, employees subject to social insurance contributions
Companies, notified vacancies, employees subject to social insurance contributions
Dimensions
East/West, 24 economic activities, 6 company size classes
East/West, 23 economic activities, 7 company size classes
Adjustment variables
Extrapolation along the cell frequencies of the matrix
Extrapolation along the benchmark figures / marginal totals of the matrix
Non-response correction
Yes No
Empirical special effects
Algorithm (first choice)
Individual case decision
Transparency of the syntax
Open
Proprietary (Economix)

6. Methodology New weighting methodology GREG regression estimator
𝑡 𝑌,𝐺𝑅𝐸𝐺 = 𝑘=1 𝑛 𝑤 𝑘 𝑦 𝑘
Old methdology Horvitz-Thomsen/RAS method 𝑡 𝑌,𝐻𝑇 = 𝑖=1 𝑛 𝑦 𝑖 𝜋 𝑖 = 𝑖=1 𝑛 𝑑 𝑖 𝑦 𝑖

7. GREG estimation 1 ,with weights 2
The calibrated weights now result as noted here.

8. GREG estimation with Non-response1
,with weights 2
The calibrated weights now result as noted here.

9. GREG estimation with Non-response1
,with weights 2
The calibrated weights now result as noted here.

10. Requirements weights
GREG estimators determine the weights along the entire range of values if unrestricted;
For useful statistics, we need to restrict the values of w towards economically reasonable values:
There is a upper limit and lower limit to the values of w, which so far has been implemented as w ∈[1, 10000].
An iterative process ensures the upper and lower limit of the range, usually implemented to be between 1 and (Note that reflects the experience about the overall size of the population and the response, if smaller samples out of a population were to be weighted this upper limit needs to be increased)
Observations reaching the upper or lower limit will be fixed to the limits and are removed for the further calculation of the weights for the remaining sample.
The process is repeated until all w are within the limits set.

11. Anchor variables
Using the administrative social security data, the following anchor variables are used by combined sector and size class:
Number of companies
Number of employees (subject to social security contributions)
(obsolete in new methodology): administrative (PES) count of vacancies
New method:
GREG estimators satisfies both anchor variables simultaneously
Old method
HT-RAS estimator satisfies either company count or employee count together with the administrative count of vacancies.

12. Anchor variables - Challenges
There is a time lag after which ‘final’ administrative counts of companies and employees of a quarter become available
Solution: We use last available information on anchor variable in combination with the official short term forecast of the development into the current quarter of the survey. This allows timely weighting and data delivery.
Problem: There will be deviations in the number of employees and firms in the final administrative data relative to our anchor variable used in the weighting.
Revision after one year: To overcome these differences, we suggest to revise the series backward after (approximately) one year.

13. Non-Response
Non-Response uses administrative information on the companies and organisations in order to correct for potential non-response.
Formally, a probability model of response is estimated based on the following variables:
Economic activity
Size class
Average pay in company (in quintiles)
Age of workforce in company
The nonresponse analysis of the quarters 1 – 3 (based on Q4) includes also information on the rapidness of response as an explanatory variable.

14. Dimensions Minor changes to the dimensions:
24 instead of 23 economic activities. Sector N is split into activities that do not involve manpower / temporary work agencies and those that do involve these activities.
6 company size classes (final size classes were combined)
Regional split East / West as before (however slight adjustment in relative sample size towards West to reflect overall size and ‘normalization’ of East German economic situation)

15. Adjustments
We try to minimize influencing the outcomes. In the process of generating weights, some adjustments remain necessary (as an option).
Under the new methodology these adjustments are attempted to keep the observations unchanged in any of its variables.
Adjustments are included in the quarterly programs to ensure reproducibility.

16. … for those that would like to read in their own time …
Brenzel, H et al. (2016). Revision of the IAB job vacancy survey: backgrounds, methods and results (No _en). Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].