Since Roback’s seminal work (J Polit Econ 90(6):1257–1278, 1982), the literature on hedonic prices has evaluated the role of amenities in equilibrating regional differentials in nominal wages and prices. While these studies generally find evidence for traditional amenities and disamenities in developed countries, there remains little research on how characteristics such as violence affect the equilibrium in less developed countries. This article explores violence and other local characteristics as an amenity or disamenity for Mexico and employs the hedonic wage and rent theory proposed by Roback. This research uses a multilevel estimation technique using data from the Mexican Household Income and Expenditure Survey, along with other information from the municipal and state levels. This article finds evidence to suggest that illegal earning opportunities outweigh crime disamenity by inhabitants of some traditional drug-trafficking regions, because such crime appears to be the modus vivendi in those regions in a way that does not reduce economic performance.
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Figure A1 appears in the online supplementary data.
Although state-level characteristics are better attained to municipality level, information is not reported at the latter level.
There are potential spatial autocorrelation problems for the location (municipality and/or state) error term due to unobservable variables and to potential spillover effects between neighboring units (LeSage and Pace 2009). But we cannot use spatial regression techniques at household level since these are not georeferenced. To the best of our knowledge, Corrado and Fingleton (2011) claim that there is no systematic approach to deal with multilevel and spatial autocorrelation at the same time for not georeferenced observations. Dong et al. (2015) have proposed initial steps to deal with this empirical problem, but applied to georeferenced observations. Paredes (2013) has applied an indirect approach by testing autocorrelation (second stage) on the predicted error term from the multilevel regression for the location level (first stage), which provides a simple way to suspect about this problem, but still it is not accurate. We will let this issue as a pending task for a future research.
We do not report constant, dummy, and interaction coefficients, but the complete set of estimates are available from the authors upon request.
We use a 5-nearest neighbors weight matrix to estimate. (1) spatial autoregressive model and (2) spatial error model coefficient. Table 3 reports the spatial coefficient for both models under the null hypothesis of spatial autocorrelation.
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The research was supported by the project “Estudio de la Competitividad: Regiones, Economía y Sociedad” funded by the Consejo Nacional de Ciencia y Tecnología (CONACYT)—Instituto Nacional de Estadística y Geografía (INEGI) sectorial fund 2012–2014. We would like to thank Jonathan Jaime for his assistance and Dr. Michael Margolis for his valuable comments as well as the editor and the referees for their constructive comments and suggestions.
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Nuñez, H.M., Paredes, D. & Garduño-Rivera, R. Is crime in Mexico a disamenity? Evidence from a hedonic valuation approach. Ann Reg Sci 59, 171–187 (2017). https://doi.org/10.1007/s00168-017-0823-8