RWI-GEO-GRID: Socio-economic data on grid level- Scientific Use File (wave 8)
- Sozioökonomische Daten auf Rasterebene - Scientific Use File (Welle 8) (Translated Title)
- Budde, Rüdiger (RWI - Leibniz-Institut für Wirtschaftforschung) (Contact Person)
- Eilers, Lea (RWI - Leibniz-Institut für Wirtschaftforschung) (Researcher)
- microm Micromarketing-Systeme und Consult GmbH (Data Collector)
- RWI - Leibniz-Institut für Wirtschaftforschung (Editor)
The dataset is based on the variable gourp microm-Basis which is comprised of four categories: number of households, number of business enterprises, number of houses (incl. those purely used for business), and number of residential houses (excl. those purely used for business) (cf. microm 2016, p. 26). The number of houses on the street segment level is the basis for all aggregations to other regional levels. Based on business registers, the number of enterprises in each house is determined. Moreover, the dataset contains the geographical key of the raster point als well as information on cars, purchasing power, foreigners, household structure, children, unemployment rate, population, ethnic background, credit default risk.
2005; 2009; 2010; 2011; 2012; 2013; 2014; 2015; 2016
Mircom uses more than a billion individual datapoints for the aggregation of the microm dataset. These are anonymised and stem from various data sources. The datatapoints are are available for all 40.9 million households in Germany, while thefinal data product contains information on approximately 20 million houses (microm 2016, p. 8).
For data privacy reasons, houses within a residential environment are summed up to a "virtual" micro-geographic segment (so-called micro-cell), which on average comprises eight, but at least five households. Houses in which at least five households live become a distinct micro-cell, while houses with less than five households are combined with similar houses on the same street. Combined houses are as close as possible in spatial terms. Structural indicators are aggregated on the micro cell level and subsequently computed household level averages are computed (microm 2016, p.8). If such data exist, the calculated data is made consistent with official data sources (microm 2014, p. 2). Additionally, due to the cooperation with SOEP, it is possible to validate the small scale regional data of microm (microm 2016, p. 8).
Number of Units: 223786
Number of Variables: 103
Type of Data: Microdata
File Format: Stata
File Size: 1.43 GB
- File Name: microm_panelSUFv8.dta
Budde, R. und L. Eilers (2014), Sozioökonomische Daten auf Rasterebene – Datenbeschreibung der microm-Rasterdaten. RWI Materialien 077. Essen: RWI.
Bauer, T. K., R. Budde , M. Micheli und U. Neumann (2015), Immobilienmarkteffekte des Emscherumbaus? Raumforschung und Raumordnung 73(4): 269-283.
Hentschker, C und A. Wübker (2016), The impact of technology diffusion in health care markets: Evidence from heart attack treatment. Ruhr Economic Papers #632. Essen: RWI.
microm Consumer Marketing (2016), Datenhandbuch: Arbeitsunterlagen für microm MARKET & GEO. Neuss: microm GmbH, Neuss.
Breidenbach, P. and L. Eilers (2018), RWI-GEO-Grid: Socio-economic data on grid level. Jahrbücher für Nationalökonomie und Statistik 238 (6): 609-616. DOI: 10.1515/jbnst-2017-0171
Update Metadata: 2019-07-03 | Issue Number: 11 | Registration Date: 2019-07-03