The IAB establishment panel : from sample to survey to projection

"The IAB Establishment Panel is an annual survey of establishments and is unique in Germany, as it represents all industries and establishment sizes nationwide and can also be analysed on a longitudinal basis. The design of the IAB Establishment Panel was developed in the early 1990s and subjected to a wide range of tests. This process also involved parallel development activities taking place on the Hannover Firm Panel, which were carried out on behalf of the "Forschungsstelle Firmenpanel" at the University of Hanover (Gerlach et al.: 1998) and the Institute for Applied Economic Research in Tübingen (IAW). The survey began in West Germany in 1993, with the aim of building up a representative information system for continuous analysis of labour demand. It has been carried out in East Germany since 1996, making it a nationwide survey. The IAB Establishment Panel is conceived as a longitudinal survey, i.e. a large majority of the same establishments are interviewed every year. Consequently, it enables both analysis of developments across time through comparison of cross-sectional data on different points in time, and also longitudinal studies of individual establishments. Now in the IAB Establishment Panel approx. 16,000 establishments are surveyed on a large number of employment policy-related subjects, including employment development, business policy and business development, investment activities, innovations in the establishment, public funding, personnel structure, vocational training and apprenticeships, new and exiting personnel, recruitment, wages and salaries, working times in the establishment, further training and general data on the establishment. The survey also includes varying focal topics every year. With the exception of Hamburg, all the German federal states (Bundesländer) currently contribute regional extension samples to the IAB Establishment Panel. This firstly enables evaluations on the federal state level, and secondly results in a total range of samples that significantly widens the evaluation options on the nationwide level. The IAB Establishment Panel contains high data quality, achieved by means of the high-quality sample, the high exploitation level and the sophisticated process of data monitoring and error correction. The survey is carried out by TNS Infratest Sozialforschung GmbH on behalf of the IAB. A general introduction to the IAB Establishment Panel is contained in German in Bellmann (2002) or in English in Kölling (2000). The IAB Establishment Panel is based on a complex study design, which also presents challenges for users of the dataset. This paper provides an overview of the methodology of the IAB Establishment Panel. It goes into detail on the design of the samples and survey, the weighting process, and data access at the Research Data Centre (FDZ) of the Federal Employment Agency (BA) at the Institute for Employment Research (IAB). The most important points are presented at the beginning of each chapter. It is intended for users of the IAB Establishment Panel, firstly as a collection of methodological aspects of the IAB Establishment Panel, and secondly to make it easier for first-time users in particular to start using the data. This paper is also aimed at users of the IAB Linked-Employer-Employee Dataset (LIAB1), in which the IAB Establishment Panel is an important component." (Author's abstract, IAB-Doku) ((en)) Additional Information Here you can find the German version of the report.IAB-Betriebspanel, Datengewinnung, Stichprobenverfahren, Stichprobe, Erhebungsmethode, Befragung, Fragebogen, Antwortverhalten, Datenaufbereitung, Datenanalyse, Hochrechnung, Querschnittuntersuchung, Längsschnittuntersuchung

Visual Integration of Data and Model Space in Ensemble Learning

Ensembles of classifier models typically deliver superior performance and can outperform single classifier models given a dataset and classification task at hand. However, the gain in performance comes together with the lack in comprehensibility, posing a challenge to understand how each model affects the classification outputs and where the errors come from. We propose a tight visual integration of the data and the model space for exploring and combining classifier models. We introduce a workflow that builds upon the visual integration and enables the effective exploration of classification outputs and models. We then present a use case in which we start with an ensemble automatically selected by a standard ensemble selection algorithm, and show how we can manipulate models and alternative combinations.Comment: 8 pages, 7 picture

Higgs Pair Production: Choosing Benchmarks With Cluster Analysis

New physics theories often depend on a large number of free parameters. The precise values of those parameters in some cases drastically affect the resulting phenomenology of fundamental physics processes, while in others finite variations can leave it basically invariant at the level of detail experimentally accessible. When designing a strategy for the analysis of experimental data in the search for a signal predicted by a new physics model, it appears advantageous to categorize the parameter space describing the model according to the corresponding kinematical features of the final state. A multi-dimensional test statistic can be used to gauge the degree of similarity in the kinematics of different models; a clustering algorithm using that metric may then allow the division of the space into homogeneous regions, each of which can be successfully represented by a benchmark point. Searches targeting those benchmark points are then guaranteed to be sensitive to a large area of the parameter space. In this document we show a practical implementation of the above strategy for the study of non-resonant production of Higgs boson pairs in the context of extensions of the standard model with anomalous couplings of the Higgs bosons. A non-standard value of those couplings may significantly enhance the Higgs pair production cross section, such that the process could be detectable with the data that the Large Hadron Collider will collect in Run 2.Comment: Editorial changes, improvements in figures and changes in the appendi

Führt Zufütterung von Esparsette zu einer höheren Effizienz bei eiweissreicher Herbstweide?

Schlussfolgerung: Esparsette … • erhöht die Proteineffizienz bei graslandbasierter Fütterung. • kann kurzzeitig zu einer Verbesserung der Leistung in Bezug auf Milch-, Eiweiss- und Fettmenge führen. • führt dauerhaft aber zu einer geringeren Milchleistung. • beeinflusst keine weiteren Milchparameter

Study of forces in a 2T9R robot mechanism

The paper presents in detail a method of calculating the forces acting on a 2T9R type robot. In order to determine the reactions (forces in the kinematic couples), one must first determine the inertial forces in the mechanism to which one or more useful loads of the robot can be added. The torsor of the inertia forces is calculated with the help of the masses of the machine elements and the accelerations from the centers of mass of the mechanism elements, so the positions, velocities, and accelerations acting on it will be determined, i.e. its complete kinematics. The calculation method applied by a MathCad program intelligently uses data entry through the IFLOG logic function so that the calculations can be automated. So the effective automation of the calculation program is done exclusively through the IFLOG functions originally used in the paper