Comparing ethnic segregation across cities measurement issues matter

Abstract

"This paper investigates how important measurement issues such as the modifiable areal unit problem (MAUP), random unevenness and spatial autocorrelation affect cross-sectional studies of ethnic segregation. We use geocoded data for German cities to investigate the impact of these measurement problems on the average level of segregation and on the ranking of cities. The findings on the average level of residential segregation turn out to be rather robust. The ranking of cities is, however, sensitive to the assumptions regarding reallocation of population across neighbourhoods that the use of different segregation measures involves. Moreover, the results suggest that standard aspatial approaches tend to underrate the degree of segregation because they ignore the spatial clustering of ethnic groups. In contrast, non-consideration of random unevenness gives rise to a moderate upward bias of the mean segregation level and involves minor changes in the ranking of cities if the minority group is large. However, the importance of random segregation significantly increases as the size of the minority group declines. If the size of specific ethnic groups differs across regions, this may also affect the ranking of regions. Thus, the necessity to properly account for measurement issues increases as segregation analyses become more detailed and consider specific (small) minority groups." (Author's abstract, IAB-Doku) ((en))

Cite article

Meister, M. & Niebuhr, A. (2021): Comparing ethnic segregation across cities measurement issues matter. In: Review of regional research, Vol. 41, No. 1, p. 33-54. DOI:10.1007/s10037-020-00145-4