This paper reconsiders the evolution of the growth of American cities since 1790 in the light of new theories of urban growth. Our null hypothesis for long-term growth is random growth. We obtain evidence supporting random growth against the alternative of mean reversion (convergence) in city...

We use US county level data (3,058 observations) from 1970 to 1998 to explore the relationship between economic growth and the extent of government employment at three levels: federal, state and local. We find that increases in federal, state and local government employments are all negatively...

Higgins et al. (2006) report several statistically significant partial correlates with U.S. 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...

We use Mississippi county-level data on (per capita) income and the percentages of populations that are Black (henceforth "Black") to examine the relationship between race and economic growth. The analysis is also conditioned on 40 other economic and socio-demographic variables. Given a negative...

In this paper we outline (i) why sigma-convergence may not accompany beta-convergence, (ii) discuss evidence of beta-convergence in the U.S., and (iii) use U.S. county-level data containing over 3,000 cross-sectional observations to demonstrate that sigma-convergence cannot be detected at the...

This paper uses un-truncated city population data from six countries (the United States, Spain, Italy, France, England and Japan) to illustrate how parametric growth regressions can lead to biased results when testing for Gibrat’s law in city size distributions. The OLS results show...

The aim of this work is to test empirically the validity of Gibrat’s Law in the growth of cities, using data for all the twentieth century of the complete distribution of cities (without any size restrictions) in three countries: the US, Spain and Italy. On considering the distribution of...