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Robust correlates of county-level growth in the United States

Author

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  • Matthew J. Higgins
  • Andrew T. Young
  • Daniel Levy

Abstract

Higgins et al . (2006), report several statistically significant partial correlates with US per capita income growth. However, Levine and Renelt (1992) demonstrate that such correlations are hardly ever robust to changing the combination of conditioning variables included. We ask, whether the same is true for the variables identified as important by Higgins et al . Using the extreme bounds analysis of Levine and Renelt, we find that the majority of the partial correlations can be accepted as robust. The variables associated with those partial correlations stand solidly as variables of interest for future studies of US growth.

Suggested Citation

  • Matthew J. Higgins & Andrew T. Young & Daniel Levy, 2010. "Robust correlates of county-level growth in the United States," Applied Economics Letters, Taylor & Francis Journals, vol. 17(3), pages 293-296, February.
  • Handle: RePEc:taf:apeclt:v:17:y:2010:i:3:p:293-296
    DOI: 10.1080/13504850701720254
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    5. Andrew T. Young & Matthew J. Higgins & Daniel Levy, 2008. "Sigma Convergence versus Beta Convergence: Evidence from U.S. County-Level Data," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 40(5), pages 1083-1093, August.
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    Cited by:

    1. Bologna, Jamie & Young, Andrew T. & Lacombe, Donald J., 2016. "A spatial analysis of incomes and institutional quality: evidence from US metropolitan areas," Journal of Institutional Economics, Cambridge University Press, vol. 12(1), pages 191-216, March.
    2. Young, Andrew T. & Higgins, Matthew J. & Levy, Daniel, 2013. "Heterogeneous Convergence," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 120(2), pages 238-241.
    3. repec:wvu:wpaper:11-03 is not listed on IDEAS

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