Martin Korpi (), Daniel Halvarsson (), Özge Öner, William A.V. Clark (), Oana Mihaescu (), John Östh () and Olof Bäckman ()
Martin Korpi: The Ratio Institute, Postal: The Ratio Institute, P.O. Box 5095, SE-102 42 Stockholm, Sweden
Daniel Halvarsson: The Ratio Institute, Postal: The Ratio Institute, P.O. Box 5095, SE-102 42 Stockholm, Sweden
Özge Öner: Institutet för näringslivsforskning, Postal: The Ratio Institute, P.O. Box 5095, SE-102 42 Stockholm, Sweden
William A.V. Clark: California Center for Population Research, Postal: The Ratio Institute, P.O. Box 5095, SE-102 42 Stockholm, Sweden
Oana Mihaescu: Institute of retail economics (HUI), Postal: The Ratio Institute, P.O. Box 5095, SE-102 42 Stockholm, Sweden
John Östh: Uppsala University, Postal: The Ratio Institute, P.O. Box 5095, SE-102 42 Stockholm, Sweden
Olof Bäckman: Stockholm university, Postal: The Ratio Institute, P.O. Box 5095, SE-102 42 Stockholm, Sweden
Abstract: Using geo-coded full-population grid-level data for the three largest metropolitan areas in Sweden, 1993-2016, this paper i) estimates the level and pace of ethnic segregation, ii) examines possible tipping points in this development, and iii) gauges the importance of several mitigating or exacerbating factors (such as the mix of housing area tenure type, different types of amenities, and crime). We use OLS and 2SLS to estimate outcomes at two different geographic levels; 250 x 250 square meter grids and SAMS areas (roughly equivalent to US census tracts), respectively. On average, we find that for every 1 percentage point increase in immigration, native growth is reduced by around -0.3 percentage points. Crime levels exacerbate developments and factors such as housing area tenure-type mix and access to various amenities slows it down, but only marginally so. Using repeated and single random sampling for cross-validation, and the twin common methodological approaches as suggested in the literature, we estimate possible tipping points in these segregation developments. In contrast to most other studies in the literature, none of our potential tipping points are however statistically significant when probing their relevance in explaining factual population developments, suggesting a rather more continuous – albeit steeply so – segregation process rather than a structural brake. In terms of tipping point methodology, our main findings are that fixed-point estimation is less robust than R-square maximization for small geographical units, and that the former consistently selects for lower tipping-point candidates than the latter.
54 pages, June 12, 2022
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