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A spatial model of growth relationships and Latino-owned business

  • Craig Wesley CarpenterEmail author
  • Scott Loveridge
Original Paper
  • 16 Downloads

Abstract

The expansion of ethnic minorities evokes policy debate about their impact on the local economy, driving a need to measure their effects. This article introduces a spatial econometrics approach to Deller et al.’s expansion of the Carlino-Mills growth model. We employ the confidential US Census data to investigate drivers of local economic performance with emphasis on the role of Latino-owned businesses (LOB) on convergence. The model also includes a number of controls. The model produces direct, indirect, and total impact estimates, and expected values for the non-LOB controls. The estimated total impact of LOB employment on county-level average annual growth rates is significant and positive, but a rurality interaction carries the opposite sign, such that the total impact in rural areas is negative. The negative effect in rural areas is due to negative spatial spillovers captured by the model. The spatial Durbin error model empirical results indicate that although LOB employment interacted with rurality significantly impacts county-level growth rates of population, employment, and income, they do not change the equilibrium relationship between these variables captured by the speed of convergence.

JEL classification

R1 R11 R12 L26 

Notes

Acknowledgements

Thanks to Anil Rupasingha, Myriam Quispe-Agnoli, Julie L. Hotchkiss, Melissa Banzhaf, Margaret C. Leventsin, J. Clint Carter, Mark Fossett, Bethany DeSalvo, the Interuniversity Consortium for Political and Social Research, the MRDC, the TXRDC Consortium, numerous conference participants, and an anonymous referee. Support for this research from the North Central Regional Center for Rural Development and the Department of Agricultural, Food, and Resource Economics at Michigan State University is also gratefully acknowledged. The project was supported by the Agricultural and Food Research Initiative Competitive Program of the USDA National Institute of Food and Agriculture (NIFA), Award Number 2017-67023-26242, and by Hatch project 1014691. Michigan State University Institutional Review Board reviewed this work and declared it to have exempt status. Any opinions and conclusions expressed herein are those of the author and do not necessarily represent the views of the US Census Bureau. All results have been reviewed to ensure that no confidential information is disclosed.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Agricultural EconomicsTexas A&M UniversityCollege StationUSA
  2. 2.Department of Agricultural, Food, and Resource EconomicsMichigan State UniversityEast LansingUSA

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