Measuring Regional Inequality by Internet Car Price Advertisements: Evidence for Germany
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 http://www.mobile.de and http://www.autoscout24.de 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 carâ€™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 levelâ€”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.
|Date of creation:||Oct 2012|
|Date of revision:|
|Contact details of provider:|| Postal: |
Web page: http://www.ersa.org
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Francesco D’Amuri & Juri Marcucci, 2010.
"“Google it!”Forecasting the US Unemployment Rate with a Google Job Search index,"
2010.31, Fondazione Eni Enrico Mattei.
- D'Amuri, Francesco/FD & Marcucci, Juri/JM, 2009. ""Google it!" Forecasting the US unemployment rate with a Google job search index," MPRA Paper 18248, University Library of Munich, Germany.
- 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.
- 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.
- Konstantin A. Kholodilin & Maximilian Podstawski & Boriss Siliverstovs, 2010. "Do Google Searches Help in Nowcasting Private Consumption? A Real-Time Evidence for the US," KOF Working papers 10-256, KOF Swiss Economic Institute, ETH Zurich.
- Nikos Askitas & Klaus F. Zimmermann, 2009.
"Google Econometrics and Unemployment Forecasting,"
Research Notes of the German Council for Social and Economic Data
41, German Council for Social and Economic Data (RatSWD).
- 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.
- Askitas, Nikos & Zimmermann, Klaus F., 2009. "Google Econometrics and Unemployment Forecasting," IZA Discussion Papers 4201, Institute for the Study of Labor (IZA).
- Nikos Askitas & Klaus F. Zimmermann, 2009. "Google Econometrics and Unemployment Forecasting," Discussion Papers of DIW Berlin 899, DIW Berlin, German Institute for Economic Research.
- 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.
When requesting a correction, please mention this item's handle: RePEc:wiw:wiwrsa:ersa12p911. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Gunther Maier)
If references are entirely missing, you can add them using this form.