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Labor Density and Wages in Spain: Evidence from Geographically Disaggregated Data
Author(s) -
Díaz Dapena Alberto,
Fernández Vázquez Esteban,
Rubiera Morollón Fernando
Publication year - 2018
Publication title -
growth and change
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.657
H-Index - 55
eISSN - 1468-2257
pISSN - 0017-4815
DOI - 10.1111/grow.12233
Subject(s) - quantile , economics , ordinary least squares , wage , econometrics , estimator , externality , instrumental variable , urban agglomeration , quantile regression , distribution (mathematics) , economies of agglomeration , estimation , sample (material) , labour economics , economic geography , statistics , mathematics , economic growth , microeconomics , mathematical analysis , chemistry , management , chromatography
In this paper, Ciccone's ([Ciccone, A., 2002]) approach is applied to the Spanish case in 2011 but by estimating it using local labor markets (LLMs) instead of NUTS‐2 or NUTS‐3 regions. It is especially relevant in the case of Spain because the NUTS‐3 (provinces) are large regions in comparison with other cases in Europe. From a sample of income taxpayers published by the Spanish Fiscal Studies Institute, we derive figures on average wages by worker on the scale of LLMs. We argue that working at this level of spatial disaggregation is more in line with the idea of externalities from agglomerations, which are generated on a local scale. We can also observe intra‐regional heterogeneity and how the urban wage premium changes along the entire distribution of cities, including small‐medium size urban areas or rural areas. The empirical analysis is based on several estimation strategies, namely, ordinary least squares, two‐stages least squares, quantile regressions (QR), and instrumental variable quantile regressions (IVQR) estimators; they all find a significantly positive effect of agglomeration in the conditional mean of wages. This result can be estimated along the conditional distribution of wages. According to the QR and IVQR estimates, important variations are found along the distribution.