Growth and Convergence Across the Us: Evidence from County-Level Data

54 Pages Posted: 24 Apr 2003

See all articles by Matthew John Higgins

Matthew John Higgins

University of Utah - Department of Entrepreneurship & Strategy; National Bureau of Economic Research (NBER); Max Planck Institute for Innovation and Competition

Daniel Levy

Bar-Ilan University - Department of Economics; Emory University - Department of Economics; Rimini Center for Economic Analysis

Andrew Young

Emory University - Department of Economics

Abstract

We use U.S. county-level data consisting of 3,058 observations, to study growth determination and measure the speed of income convergence. County-level data are particularly valuable for studying convergence because they allow us to study a sample with substantial homogeneity and exceptional mobility of capital, labor and technology without sacrificing the benefits of a large number of cross-sectional units. Our data set allows us to include nearly 40 different conditioning variables to study their effect on the counties' balanced growth paths. We report estimates using a 2SLS instrumental variables method which yields consistent estimates, as well as estimates from standard OLS. In order to explore possible heterogeneity in the conditional convergence rates, we report the estimates for the entire data set as well as for subsets including metro counties, non-metro counties, and five regional groupings. Our findings include: (i) while OLS yields convergence rates around 2 percent, the 2SLS method yields rates between 6 and 8 percent; (ii) the estimated convergence rates are not constant across the U.S., for example, the counties in the Southern states converge at a rate that is more than two and half times faster than the counties located in the New England states; (iii) the extent of the public sector at all levels (federal, state and local) negatively affects growth and there is no evidence of the public sector becoming more productive at more decentralized levels; (iv) the relationship between a population's educational attainment and economic growth is nonlinear depending on the years of education considered; and (v) large presences of both finance, insurance and real estate industry and entertainment industry are positively correlated with growth while the percent of a county's population employed in the education industry is negatively correlated with economic growth.

Keywords: Economic Growth, Conditional Convergence, County-Level Data

JEL Classification: O40, O11, O18, O51, R11, H50, H70

Suggested Citation

Higgins, Matthew John and Levy, Daniel and Young, Andrew T., Growth and Convergence Across the Us: Evidence from County-Level Data. Review of Economics and Statistics, 2006, Available at SSRN: https://ssrn.com/abstract=389680 or http://dx.doi.org/10.2139/ssrn.389680

Matthew John Higgins

University of Utah - Department of Entrepreneurship & Strategy ( email )

1655 East Campus Center Dr.
Salt Lake City, UT 84112
United States

National Bureau of Economic Research (NBER) ( email )

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Max Planck Institute for Innovation and Competition ( email )

Marstallplatz 1
Munich, Bayern 80539
Germany

Daniel Levy (Contact Author)

Bar-Ilan University - Department of Economics ( email )

Ramat-Gan, 5290002
Israel
+972 3 531-8345 (Phone)
+972 3 738-4034 (Fax)

HOME PAGE: http://econ.biu.ac.il/en/levy

Emory University - Department of Economics ( email )

1602 Fishburne Drive, Suite 306
Rich Building
Atlanta, GA 30322-0001
United States

HOME PAGE: http://economics.emory.edu/home/people/faculty/Levydaniel.html

Rimini Center for Economic Analysis ( email )

Wilfrid Laurier University
75 University Ave W.
Waterloo, Ontario N2L3C5
Canada

HOME PAGE: http://rcea.org/

Andrew T. Young

Emory University - Department of Economics ( email )

1602 Fishburne Drive
Atlanta, GA 30322
United States
404-727-1022 (Phone)

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