economic growth and income inequality in indiana counties
DESCRIPTION
Economic Growth and Income Inequality in Indiana Counties. Valerien O. Pede Raymond J.G.M. Florax Dept. of Agricultural Economics Purdue Center for Regional Development Purdue University, West Lafayette, USA. E-mail: [email protected], [email protected] - PowerPoint PPT PresentationTRANSCRIPT
Economic Growth and Income Inequality in Indiana Counties
Valerien O. PedeRaymond J.G.M. Florax
Dept. of Agricultural EconomicsPurdue Center for Regional Development Purdue University, West Lafayette, USA
E-mail: [email protected], [email protected]: http://web.ics.purdue.edu/~rflorax/
2 © 2006 rjgm florax, vo pede
Outline
GIScience and spatial modeling
Background income inequality knowledge and human capital Indiana, the Midwest, and US counties
Simple economic growth models convergence Solow Model Mankiw, Romer and Weil Model
Conclusions
3 © 2006 rjgm florax, vo pede
Linking GIScience and modeling
Availability of space and place characteristics technology driven (GPS, RS) georeferenced data deduct information on distance and accessibility
spatial “sorting”, spatial mismatch
Approaches to spatial data analysis visualize and find spatial characteristics
use of GIS explore spatial distribution (spatial statistics approach)
explain spatial dimension with theory and modeling many issues are inherently spatial social interaction, copycatting, spatial spillovers, etc. explain spatial distribution (spatial econometric approach)
4 © 2006 rjgm florax, vo pede
Real per capita income – maps
1970
1990
1980
2000
5 © 2006 rjgm florax, vo pede
Real per capita income – space
20001990
19801970
6 © 2006 rjgm florax, vo pede
Real per capita income – space-time
The Moran’s I statistic is similar to a correlation coefficient, and measures spatial clustering
7 © 2006 rjgm florax, vo pede
Real per capita income – outliers
1970
1990
1980
2000
8 © 2006 rjgm florax, vo pede
Real per capita income – inequality
The Gini coefficient measures income inequality between counties
9 © 2006 rjgm florax, vo pede
Real per capita income – dynamics
STARS Space-Time Analysis of Regional Systems Serge Rey, San Diego State University freeware website http://stars-py.sourceforge.net/
Spatio-temporal dynamics county level 1969 – 2003 weights matrix
provides information on spatial neighborhood structure direct neighbors with a common border
Stars.lnk
10 © 2006 rjgm florax, vo pede
Real per capita income – Indiana
Developments over space and time
dominance North and Central Indiana 1970s replaced by Central and South Indiana by the early 2000s
less spatially integrated spatial clustering of similar per capita income levels declines
Indianapolis stands out as an “island”
income inequality increases over time especially due to some counties around Indianapolis
11 © 2006 rjgm florax, vo pede
Midwest, 2003
12 © 2006 rjgm florax, vo pede
A simple model
Unconditional convergence model income growth is a function of the initial income level convergence of per capita income
poor counties grow faster, richer counties slower
OLS ML OLS ML OLS MLconstant 1.87* 1.5 3.99* 3.48* 4.62* 4.40*RPCI69 -0.15 -0.11 -0.36* -0.31* -0.43* -0.41*INVPOPEDLOWEDMEDEDHIGHlambda 0.51* 0.63* 0.57*
convergence 0.5 0.3 1.3 1.1 1.6 1.5
R2 0.02 0.22 0.26 0.50 0.34 0.51
LMERR 20.78* 322.92* 1022.96*LMLAG 21.20* 307.52* 851.82*
IN MIDWEST US
13 © 2006 rjgm florax, vo pede
Solow model
Standard neoclassical model correcting for growth of capital and labor note: lacking data for investments
OLS ML OLS ML OLS MLconstant 4.87* 4.38* 5.33* 4.68* 5.77* 5.35*RPCI69 -0.31* -0.28* -0.44* -0.35* -0.50* -0.45*INV 0.20* 0.15* 0.12* 0.16* 0.18* 0.20*POP 0.37* 0.34* 0.15* 0.19* 0.04* 0.07*EDLOWEDMEDEDHIGHlambda 0.26* 0.80* 0.77*
convergence 1.1 0.9 1.7 1.2 2.0 1.7
R2 0.26 0.31 0.32 0.52 0.37 0.52
LMERR 4.24 594.89 2562.55LMLAG 6.94 411.04 1336.36
IN MIDWEST US
14 © 2006 rjgm florax, vo pede
Human capital in Indiana and Midwest
High,
2000
High,
2000
Low,
2000
Low,
2000
15 © 2006 rjgm florax, vo pede
MRW model with human capital
Mankiw, Romer and Weil model accounting for human capital as well educational level of the population in 4 categories
OLS ML OLS ML OLS MLconstant 5.55* 5.03* 6.09* 5.36* 6.30* 5.86*RPCI69 -0.42* -0.44* -0.56* -0.49* -0.62* -0.61*INV 0.09* 0.04* 0.05* 0.05* 0.10* 0.091*POP 0.23* 0.20* 0.07* 0.10* -0.03* -0.022EDLOW -0.16 -0.05 -0.10* -0.04 -0.01 0.02EDMED 0.07 0.10* 0.01 -0.01 -0.12* 0.02EDHIGH 0.09* 0.11* 0.17* 0.15* 0.24* 0.20*lambda 0.39* 0.75* 0.79*
convergence 1.6 1.7 2.3 1.9 2.7 2.7
R2 0.41 0.50 0.5 0.61 0.49 0.61
LMERR 0.01 25.99 2280.86LMLAG 10.63 154.73 1063.86
IN MIDWEST US
16 © 2006 rjgm florax, vo pede
Conclusions
Evidence for strong spatial clustering across counties extent of spatial clustering diminishes over time
Income inequality is increasing in Indiana mainly due to metropolitan effect of Indianapolis trend not observed for the Midwest
Development of new outliers
Significance investment and human capital needs further detail in future work production of knowledge by universities and R&D labs also incorporation of agglomeration effects