Abstract
I analyze the effects of sub-city-level density of economic activity on wages. Using a geocoded dataset on employment and wages in the city areas of Sweden, the analysis is based on squares representing “neighborhoods” (\(0.0625\,\hbox {km}^{2})\), “districts” (\(1\,\hbox {km}^{2})\), and “agglomerations” (\(10\,\hbox {km}^{2})\). The wage-density elasticity depends on spatial resolution, with the elasticity being highest in neighborhood squares, where a doubling of density is associated with wage increases of 1.2 %, or roughly the size of the elasticity for region density. Moving from a mean-density neighborhood to the densest neighborhood would on average increase wages by 9 %. The results are consistent with (i) the existence of a localized density spillover effect and (ii) quite sharp attenuation of human capital spillovers. An implication of the findings is that if the data source is not sufficiently disaggregated, analyses of the density–wage link risk understating the benefits of working in dense parts of regions, such as the central business districts.
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Notes
Wages and productivity are not equalized, but the underlying identifying assumption is that firms that incur higher costs (wages) must be more productive to stay competitive and survive.
Technically, each square originates from a coordinate associated with an active employer. By construction, the square grid does not cover the entire geography, but only places with registered economic activity.
The fact that all squares are of the same size within regressions means that no normalization is needed to obtain an exact measure of density. This feature makes interpretation of the coefficients particularly straightforward.
Sweden has three metropolitan regions: Stockholm (population 2.2 m), Gothenburg (population 1 m), and Malmö (population 0.7 m), the city areas of which are sparser than most of the metropolitan areas of the United States. The Stockholm city area has about 3,500 inhabitants per square kilometer, or just short of 10,000 inhabitants per square mile, which would put it somewhere outside of the top 100 MSAs in the United States in terms of population density.
The neighborhood squares are used as a base, from which higher-level resolutions are obtained by spatial aggregation.
Incidentally, Combes et al. (2008) truncate their data for the same reason, using a 3 % cutoff. Adopting that cutoff would not change anything substantial in practice, and nothing in terms of conclusions.
Technically, using yearly wages may be a source of bias in an OLS setting under the assumption that workers in dense areas work longer hours than workers in sparsely populated areas, where a wage-differential unmatched by productivity differences would be observed. In a fixed effects setting, this is a smaller problem, since bias would only be introduced in the parameters to the extent that workers in dense areas work increasingly longer hours, relative to workers in sparse areas during the reporting period, and to the extent that such a phenomenon is not picked up by any of the time-variant variables.
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This research has been supported by a financial grant from the Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning (FORMAS).
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Larsson, J.P. The neighborhood or the region? Reassessing the density–wage relationship using geocoded data. Ann Reg Sci 52, 367–384 (2014). https://doi.org/10.1007/s00168-014-0590-8
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DOI: https://doi.org/10.1007/s00168-014-0590-8