Schmollers Jahrbuch 

About: Schmollers Jahrbuch is an academic journal. The journal publishes majorly in the area(s): German & Wage. It has an ISSN identifier of 1439-121X. Over the lifetime, 299 publications have been published receiving 4417 citations.

The IAB Establishment Panel – Things Users Should Know

Abstract: This section offers descriptions as well as discussions of data sources that are of interest to social scientists engaged in empirical research or teaching courses that include empirical investigations performed by students. The purpose is to describe the information in the data source, to give examples of questions tackled with the data and to tell how to access the data for research and teaching. We focus on data from German speaking countries that allow international comparative research. While most of the data are at the micro level (individuals, households, or firms), more aggregate data and meta data (for regions, industries, or nations) are included as well. Suggestions for data sources to be described in future columns (or comments on past columns) should be send to: Joachim Wagner, Leuphana University of Lueneburg, Institute of Economics, Campus 4.210, 21332 Lueneburg, Germany, or e-mailed to wagner@leuphana.de . Past “European Data Watch” articles can be downloaded free of charge from the homepage of the German Council for Social and Economic Data (RatSWD) at: http: // www. ratswd.de.

Quantifying the Effect of Neighbourhood on Individuals: Challenges, Alternative Approaches, and Promising Directions

Abstract: Six major challenges confront statistical researchers attempting to quantify accurately the independent effect of neighbourhood context on individuals: (1) defining the scale of neighbourhood; (2) identifying mechanisms of neighbourhood effect; (3) measuring appropriate neighbourhood characteristics; (4) measuring exposure to neighbourhood; (5) measuring appropriate individual characteristics; and (6) endogeneity. The paper describes these challenges, prior attempts to meet them, and their respective shortcomings. It notes several approaches on the horizon that offer the promise of surmounting these challenges: experiments with varied scales of bespoke neighbourhoods; databases with multi-domain measures of neighbourhood characteristics; statistical models testing for non-linear neighbourhood effects that are stratified by residential group, density of local social interactions, and duration of residency; and econometric devices involving instrumental variables and residuals. It argues that furth...

The Establishment History Panel

Abstract: Before 2007, the only establishment-related data sets available at the FDZ were the Establishment Panel (cf. Kolling, 2000) and the Linked-EmployerEmployee Data (LIAB) (cf. Alda / Bender / Gartner, 2005). Both data sets are based on disproportionately drawn samples comprising approx. 16,000 establishments. Due to sample size and disproportionate sampling, the analysis possibilities regarding smaller establishments are restricted substantially.

PanelWhiz: Efficient Data Extraction of Complex Panel Data Sets - An Example Using the German SOEP

Abstract: Applied social scientists have forever been faced with different data interfaces for different data sets. In most cases, an interface is not even available, forcing the researcher to address data files by name, and extract the information required by hand. However, the specific structure of panel data can be very complex and vary dramatically as described in Haisken-DeNew (2001). Some panel data sets provide many files per year (“wide format”), differing by their population, or level of aggregation etc., creating many obstacles for researchers. If one wants to put together variables across time (“long format”), this is typically much more difficult, but ultimately the format which is required for estimation. PanelWhiz is a collection of subroutines that allows researchers to use an intuitive “common” graphical interface for accessing many panel datasets directly within the statistical package Stata/SE 10 or better (http://www.stata. com), whereby the researcher does not select individual variables, but rather vectors of variables (items) with one mouse click. This allows for an efficient method of selecting information for a data set retrieval, especially if the panel data set contains many waves (years) of information. With one mouse-click, data can be automatically retrieved, with merging and matching done automatically. With the PanelWhiz system, the user can open data files by clicking on a browse page.