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https://doi.org/10.7807/microm:zahlindex:suf:v7:1

1

Dataset

• RWI
• microm

• RWI-GEO-GRID

2018-10-23

Essen

• 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)

German

• JEL:
  • Compensation Packages, Payment Methods (J33)
  • Regional Labor Markets, Population, Neighborhood Characteristics (R23)

payment index; payment default; raster data; RWI-GEO-Grid

• Abstract

  The variable payment index describes the statistical probability of payment default for each house in Germany. The houses are accordingly grouped in 9 risk groups. Those groups are found by a scoring procedure that is, amongst others, based on negative characteristics obtained from the Creditreform Group as well as on information on the age and family structure and the residential environment. The most important basis is the share of households with payment defaults (microm 2016 p. 42). All information is anonymised according to the rules of data protection.

• 2005
• 2009
• 2010
• 2011
• 2012
• 2013
• 2014
• 2015
• 2016

• Germany (DE)

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).

• Cross-section

• 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.

• Unit Type: Geographic Unit
  Number of Units: 1515276
  Number of Variables: 15
  Type of Data: Microdata
  
  • File Name: 107807_microm_zahlindex_suf_V7-1.dta"
    File Format: Stata
    File Size: 154172 KB
    

Besides information on the payment index, the dataset contains the geographical key of the raster point as well as the variables of the group microm-Basis. 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. The scientific use file described here differs from the original data only in its degree of anonymisation: If the variables on residents and households exhibit less than 20 observations in a square kilometre, they are not contained. In addition, purchasing power, and number of children are anonymised if there are less than 20 observations in a square kilometre.

microm Micromarketing-Systeme und Consult GmbH

• Is supplemented by
  DOI: 10.7807/microm:hstruktur:suf:v7:1 (Dataset)
  
• Is supplemented by
  DOI: 10.7807/microm:pkwseg:suf:v7:1 (Dataset)
  
• Is supplemented by
  DOI: 10.7807/microm:kaufkraft:suf:v7:1 (Dataset)
  
• Is supplemented by
  DOI: 10.7807/microm:pkwmarken:suf:v7:1 (Dataset)
  
• Is supplemented by
  DOI: 10.7807/microm:auslaender:suf:v7:1 (Dataset)
  
• Is supplemented by
  DOI: 10.7807/microm:haustyp:suf:v7:1 (Dataset)
  
• Is supplemented by
  DOI: 10.7807/microm:kinder:suf:v7:1 (Dataset)
  
• Is supplemented by
  DOI: 10.7807/microm:einwohner:suf:v7:1 (Dataset)
  
• Is supplemented by
  DOI: 10.7807/microm:einwGeAl:suf:v7:1 (Dataset)
  
• Is supplemented by
  DOI: 10.7807/microm:alq:suf:v7:1 (Dataset)
  
• Is supplemented by
  DOI: 10.7807/microm:ethno:suf:v7:1 (Dataset)
  
• Continues
  DOI: 10.7807/microm:zahlindex:suf:V6:1 (Dataset)
  
• Is variant form of
  DOI: 10.7807/microm:zahlindex:V7 (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: http://hdl.handle.net/10419/97627 (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)