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RWI-GEO-GRID: Socio-economic data on grid level (wave 10)
weiterer Titel:
Sozioökonomische Daten auf Rasterebene (Welle 10)

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.

population; ethnic background; unemployment rate; household structure; family structure; single person households; purchasing power; credit default risk; cars
Rüdiger Budde; Lea Eilers; RWI; microm
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).
Geografischer Raum:
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).
Zeitliche Dimension:
  • Unit Type: Geographic Unit
    Number of Units: 2396808
    Number of Variables: 135
    Type of Data: Microdata
  • File Name: microm_panel_05-18.dta
    File Format: dta
    File Size: 2441 MB

These variables are data provided for scientific use by the FDZ Ruhr at RWI. Data on such a small regional scale (1km²) is not collected directly for all parts of Germany, which makes this dataset a valuable addition for small-scale regional analyses. A basic description on the data collection of the individual variables is found in the microm handbook (microm 2016). Details on the data generation are not publicly available, however the procedure of collecting particular data is known (cf. procedure of data collection). Screenings of the FDZ Ruhr do not indicate issues with data quality.

microm Micromarketing-Systeme und Consult GmbH
Zitation: RWI; microm (2021): RWI-GEO-GRID: Socio-economic data on grid level(wave 10). RWI-GEO-GRID. Version: 1. RWI – Leibniz Institute for Economic Research. Dataset. https://doi.org/10.7807/microm:v10
weitere Beschreibung:

DOI: 10.7807/microm:v9 (Dataset)


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

JEL Classifications:
R23; D31; O18; F22; D10; H31; J13; H31; E23; F22; J15; J33