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Measuring Regional Inequality by Internet Car Price Advertisements: Evidence for Germany


  • Boriss Siliverstovs


  • Konstantin Kholodilin


We suggest an alternative indicator based on the car sales price placed on the Internet for measuring economic inequality among regions. The regional data on car prices in Germany were downloaded from two specialised websites and in December 2011. The corresponding number of unique car price observations downloaded from each website is 914,105 and 802,047. The following information was recorded: make, model, ZIP code, mileage, engine volume in liters and cubic centimeters, type of transmission (manual, automatic, etc.), year of the first registration, and offer price. The ZIP code information was used to find the geographical coordinates (latitude and longitude) of each car’s seller. Then, the price data were assigned to the respective NUTS1 and NUTS2 regions, given the information on their borders. The shapefile containing the geographical information on the regional borders was taken from the Eurostat. Using Germany as an example we illustrate that our estimates of regional income levels as well as of Gini indices display high, positive correlation with the official figures. This implies that the observed car prices can serve as a reasonably good proxy for income levels. In contrast to the traditional measures, our data can be fast and inexpensively retrieved from the web, and more importantly allow to estimate Gini indices at the NUTS2 level—something that has never been done for Germany before. We conclude that our approach to measuring regional inequality is a useful alternative source of information that could complement officially available measures.

Suggested Citation

  • Boriss Siliverstovs & Konstantin Kholodilin, 2012. "Measuring Regional Inequality by Internet Car Price Advertisements: Evidence for Germany," ERSA conference papers ersa12p911, European Regional Science Association.
  • Handle: RePEc:wiw:wiwrsa:ersa12p911

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    References listed on IDEAS

    1. Konstantin A. Kholodilin & Maximilian Podstawski & Boriss Siliverstovs, 2010. "Do Google Searches Help in Nowcasting Private Consumption?: A Real-Time Evidence for the US," Discussion Papers of DIW Berlin 997, DIW Berlin, German Institute for Economic Research.
    2. Nikolaos Askitas & Klaus F. Zimmermann, 2009. "Google Econometrics and Unemployment Forecasting," Applied Economics Quarterly (formerly: Konjunkturpolitik), Duncker & Humblot, Berlin, vol. 55(2), pages 107-120.
    3. D'Amuri, Francesco & Marcucci, Juri, 2009. "'Google it!' Forecasting the US unemployment rate with a Google job search index," ISER Working Paper Series 2009-32, Institute for Social and Economic Research.
    4. Peter Krause & Andrea Schäfer, 2005. "Verteilung von Vermögen und Einkommen in Deutschland: große Unterschiede nach Geschlecht und Alter," DIW Wochenbericht, DIW Berlin, German Institute for Economic Research, vol. 72(11), pages 199-207.
    5. Konstantin Kholodilin & Maximilian Podstawski & Boriss Siliverstovs, 2010. "Do Google Searches Help in Nowcasting Private Consumption?," KOF Working papers 10-256, KOF Swiss Economic Institute, ETH Zurich.
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    Cited by:

    1. David Iselin & Boriss Siliverstovs, 2013. "Using Newspapers for Tracking the Business Cycle," KOF Working papers 13-337, KOF Swiss Economic Institute, ETH Zurich.
    2. Boriss Siliverstovs & Konstantin A. Kholodilin & Vyacheslav Dombrovsky, 2014. "Using Personal Car Register for Measuring Economic Inequality in Countries with a Large Share of Shadow Economy: Evidence for Latvia," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 60(4), pages 948-966, December.

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    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence
    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes

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