Enhancing the understanding of urban economics by refining the spatial dimension

Abstract

Empirical research in the field of urban economics benefits more from accurate spatial data than any other field of economics. Exploiting the spatial relationship between economic entities allows for generating comprehensive data sets which in turn allow for more comprehensive modelling and hypotheses testing. This disseration presents three essays on different aspects of urban economics which all largely benefit from the incorporation of highly disaggregated spatial data. The first chapter (co-authored with Rainer Schulz, Martin Wersing and Axel Werwatz) deals with accurate estimation of land values by using an adaptive nonparametric procedure called Adaptive Weights Smoothing (AWS). Next to a detailed view on the AWS algorithm, the estimator is applied to transaction data of land sales in Berlin. Results prove the applicability of AWS in the context of housing markets. The second chapter (co-authored with Christian Krekel and Henry Wüstemann) evaluates the impact of different land use categories on residential well-being. The study is based on an urban subsample of the German Socio-Economic Panel (GSOEP) comprising over 4,000 individuals and geographical data on land use from the Urban Atlas (UA) provided by the European Environmental Agency (EEA). Results show a positive relationship between urban green and well-being as well as a negative link between abandoned areas ans satisfaction with life. In the third chapter, the potential access to primary health care in Berlin and Brandenburg is estimated using a gravity model. Findings illustrate a significant difference in accessibility comparing rural and urban areas. Furthermore, results suggest a considerable influence of the age of residents on potential access to health care. Namely, spatial units in Brandenburg with a higher share of elderly are facing a significant poorer supply level.