Over the last three decades the supply of economic statistics has vastly improved. Unfortunately, statistics on regional price levels (sub-national purchasing power parities) have been exempt from this positive trend, even though they are indispensable for meaningful spatial comparisons of regional output, income, wages, productivity, standards of living, and poverty. To improve the situation, our paper demonstrates that a highly disaggregated and reliable regional price index can be compiled from data that already exist. We use the micro price data that have been collected for Germany’s Consumer
Price Index in May 2016. For the computation we introduce a multi-stage version of the CountryProduct-Dummy method. The unique quality of our price data set allows us to depart from previous spatial price comparisons and to compare only exactly identical products. We find that the price levels of the 402 counties and cities of Germany are largely driven by the cost of housing and to a much lesser degree by the prices of goods and services. The overall price level in the most expensive region, Munich, is about 27 percent higher than in the cheapest region. Our results also reveal strong spatial autocorrelation.
spatial price comparison, regional price index, PPP, CPD-method, hedonic regression,
consumer price data.