autoregressive parameter and the spatial error terms are statistically significant, I present
the results only for the SAC model (for both inequality and poverty).
Column (1) shows that counties with positive growth rate of real per capita
income between 1979 and 1999 experienced low income inequality in 1999. Ten-
percentage point increase in the real growth rate of income is associated with 0.85
percentage point fall in income inequality and the coefficient estimate is statistically
significant at less than the 1% level. Column (1) also shows that counties with a higher
level of income inequality and poverty in 1979 experienced higher income inequality in
1999 and the coefficient estimates are once again statistically significant.
Column (2) in Table 3 has percentage of people living in poverty in 1999 as the
dependent variable. As with inequality, average growth rate between 1979 and 1999
reduces end of the period poverty and initial levels of inequality and poverty increases
end of the period poverty. The models explain 84% of the variation in income inequality
and 82% of the variation in poverty, respectively.
While initial levels of income inequality and poverty reduce subsequent economic
growth (Table 2), economic growth reduces end of the period income inequality and
poverty. These two results together indicate that a virtuous cycle of economic growth,
poverty reduction and inequality reduction may exist in the U.S. counties. Thus, for the
U.S. counties, policies aimed at increasing economic growth need not necessarily face a
trade-off in increasing income inequality.
Barro (1999) finds that the relationship between inequality and economic growth
depends on whether a country is rich. I test this hypothesis for the U.S. counties by
classifying rich counties as ones having per capita income of more than $17,084 and poor