Socio-economic data on grid level (SUF 6.1). Car segments

Resource Type
  • RWI
  • microm
Collective Title
Publication Date
Publication Place
  • Budde, Rüdiger (RWI – Leibniz-Institut für Wirtschaftsforschung) (Contact Person)
  • Eilers, Lea (RWI – Leibniz-Institut für Wirtschaftsforschung) (Researcher)
  • microm Micromarketing-Systeme und Consult GmbH (Data Collector)
  • RWI – Leibniz-Institut für Wirtschaftsforschung (Editor)
  • JEL:
    • Automobiles, Other Transportation Equipment, Related Parts and Equipment (L62)
    • Regional Migration, Regional Labor Markets, Population, Neighborhood, Characteristics (R23)
Free Keywords
car segments; mini cars; compact cars; mid-range; estate cars; van; utility vehicle; cabriolets; all-terrain vehicle; ATV; raster data; RWI-GEO-Grid
  • Abstract

    Due to an increasing variety of the product lines of car manufacturers, brand manufacturers supply cars in almost every segment. The brand of one's car does not allow for conclusions about the socio-economic status. For the car segments, cars have been aggregated to classes that allow for this kind of conclusion. In addition to the car capability, car segments provide information on the intended use of the car. The dataset comprises of 12 car segments: mini cars, compact cars, lower mid-range cars, mid-range cars, upper mid-range cars, top-of-the-range cars, ATVs, cabriolets, estate cars, vans, utility vehicles, other vehicles (microm 2016, p. 96). The following classes are available.

    Mini cars: Mini cars used to be included in the segment of small cars, but not constitute an own segment due to an increased market share. These vehicles are characterized by an exceptionally small size. Sometimes they only provide two seats (examples: Renault Twingo, Ford Ka, VW Up, Peugeot 107 and smart fortwo) (microm 2016, p. 97).

    Compact cars: The definition of a compact car is controversial and has changed over time. The general idea is that of a cheap way to be mobile on four wheels with a roof, often with compromises regarding space and comfort (examples: VW Polo, Opel Corsa, Ford Fiesta, Fiat Punto, Peugeot 207 and Renault Clio) (microm 2016, p. 97). 

Temporal Coverage
  • 2005
  • 2009
  • 2010
  • 2011
  • 2012
  • 2013
  • 2014
  • 2015
Geographic Coverage
  • Germany (DE)
Sampled Universe

Microm uses more than a billion individual data points for the aggregation of the microm dataset. These are anonymised and stem from various data sources. The data points are available for all 40.9 million households in Germany, while the final data product contains information on approximately 20 million houses (microm 2016, p. 8).

Time Dimension
  • Cross-section
Collection Mode
  • Other

    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). The dataset is based on the variable group microm-Basis which is comprised of four categories: number of households, number of business enterprises, number of houses (including those purely used for business), and number of residential houses (excluding 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.

Data and File Information
  • Unit Type: Geographic Unit
    Number of Units: 1515276
    Number of Variables: 13
    Type of Data: Microdata
    • File Name: 107807_microm_pkwseg_suf_V6-1.dta
      File Format: Stata
      File Size: 182033 KB
The dataset comprises information on the car segment, the geographical key of the raster point, the variables of the group microm-Basis and a variable on car density. 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
  • Is supplemented by
    DOI: 10.7807/microm:kinder:suf:v6:1 (Dataset)
  • Is supplemented by
    DOI: 10.7807/microm:pkwmarken:suf:v6:1 (Dataset)
  • Is supplemented by
    DOI: 10.7807/microm:kaufkraft:suf:v6:1 (Dataset)
  • Is supplemented by
    DOI: 10.7807/microm:haustyp:suf:v6:1 (Dataset)
  • Is supplemented by
    DOI: 10.7807/microm:auslaender:suf:v6:1 (Dataset)
  • Is supplemented by
    DOI: 10.7807/microm:hstruktur:suf:v6:1 (Dataset)
  • Is supplemented by
    DOI: 10.7807/microm:alq:suf:v6:1 (Dataset)
  • Is supplemented by
    DOI: 10.7807/microm:einwohner:suf:v6:1 (Dataset)
  • Is supplemented by
    DOI: 10.7807/microm:einwGeAl:suf:v6:1 (Dataset)
  • Is supplemented by
    DOI: 10.7807/microm:ethno:suf:v6:1 (Dataset)
  • Is supplemented by
    DOI: 10.7807/microm:zahlindex:suf:v6:1 (Dataset)
  • Continues
    DOI: 10.7807/microm:pkwseg:suf:V5:1 (Dataset)
  • microm Consumer Marketing (2016), Datenhandbuch: Arbeitsunterlagen für microm MARKET & GEO. Neuss: microm GmbH, Neuss.

  • Budde, R.; Eilers, L. (2014): Sozioökonomische Daten auf Rasterebene – Datenbeschreibung der microm-Rasterdaten. RWI Materialien 077. Essen: RWI.

    • ID: (Handle)
  • Bauer, T. K.; Budde, R.; Micheli, M.; Neumann, U. (2015): Immobilienmarkteffekte des Emscherumbaus? Raumforschung und Raumordnung 73(4): 269-283.

    • ID: 10.1007/s13147-015-0356-5 (DOI)
  • Hentschker, C., and A. Wübker (2016). The impact of technology diffusion in health care markets: Evidence from heart attack treatment. Ruhr Economic Papers #632. Essen: RWI.

    • ID: 10.4419/86788734 (DOI)

Update Metadata: 2019-03-18 | Issue Number: 1 | Registration Date: 2019-03-18