Economic Growth and Income Inequality in Indiana Counties

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. 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. 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. Real per capita income – maps 1980 1970 2000 1990

5. Real per capita income – space 1980 1970 2000 1990

6. Real per capita income – space-time • The Moran’s I statistic is similar to a correlation coefficient, and measures spatial clustering

7. Real per capita income – outliers 1980 1970 2000 1990

8. Real per capita income – inequality • The Gini coefficient measures income inequality between counties

9. 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. 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. Midwest, 2003

12. 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. Solow model • Standard neoclassical model • correcting for growth of capital and labor • note: lacking data for investments

14. Human capital in Indiana and Midwest Low, 2000 Low, 2000 High, 2000 High, 2000

15. 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. 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