1. 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: vpede@purdue.edu, rflorax@purdue.edu Website: http://web.ics.purdue.edu/~rflorax/

2. 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. 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. 4 © 2006 rjgm florax, vo pede Real per capita income – maps 1970 1990 1980 2000

5. 5 © 2006 rjgm florax, vo pede Real per capita income – space 2000 1990 1980 1970

6. 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. 7 © 2006 rjgm florax, vo pede Real per capita income – outliers 1970 1990 1980 2000

8. 8 © 2006 rjgm florax, vo pede Real per capita income – inequality  The Gini coefficient measures income inequality between counties

9. 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

10. 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. 11 © 2006 rjgm florax, vo pede Midwest, 2003

12. 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

13. 13 © 2006 rjgm florax, vo pede Solow model  Standard neoclassical model correcting for growth of capital and labor note: lacking data for investments

14. 14 © 2006 rjgm florax, vo pede Human capital in Indiana and Midwest High, 2000 High, 2000 Low, 2000 Low, 2000

15. 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

16. 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