## that a random shock in an omitted variable (or variables) in the model affects growth rate

in the neighboring county as well. Thus, the results provide evidence that economic

growth exhibits spatial dependence in the U.S. counties. The model explains about 65%

of variation in economic growth.

## To test whether there is a trade-off between long-run economic growth and

income inequality and poverty, I follow Bruno et al. (1999) and Bhatta (2001) in

regressing inequality and poverty on economic growth as one of the independent

variables.^{12 }

## Thus, I estimate the following equations:

I n e q u a l E V P V G V S F e i i i i i 0 1 2 3

i

-----

(4)

P o v E V P V G V S F e i i i i i 0 1 2 3

i

-----

(5)

I n e q u a t i o n ( 4 ) , I n e q u a l i m e a s u r e s i n c o m e i n e q u a l i t y i n 1 9 9 9 i n c o u n t y i a n d E V i

measures the economic variables that include the growth rate of income between 1979

a n d 1 9 9 9 , i n e q u a l i t y a n d p o v e r t y i n 1 9 7 9 . A s i n e q u a t i o n ( 3 ) , P V i m e a s u r e s t h e

p o p u l a t i o n c h a r a c t e r i s t i c s , G V i m e a s u r e s t h e p e r c a p i t a g o v e r n m e n t e x p e n d i t u r e , S F

capture the unobserved state-specific characteristics and e is a random error term. In

e q u a t i o n ( 5 ) , P o v i m e a s u r e s p o v e r t y i n 1 9 9 9 i n c o u n t y i a n d t h e o t h e r v a r i a b l e s a r e t h e

same as in equation (4). I estimate equations (4) and (5) employing the three spatial

models that I used to estimate equation (3).

## Table 3 has end of the period income inequality and poverty as the dependent

variables. While the first column shows the results for income inequality, column (2)

shows the results for poverty from the spatial regression. Since both the spatial

12 I use the other controls in equations (2) and (3) following Rupasingha and Goetz (2007), Partridge and Rickman (2005),Levernier et al. (2000).

22