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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@example.com, firstname.lastname@example.org Website: http://web.ics.purdue.edu/~rflorax/
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
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)
Real per capita income – maps 1980 1970 2000 1990
Real per capita income – space 1980 1970 2000 1990
Real per capita income – space-time • The Moran’s I statistic is similar to a correlation coefficient, and measures spatial clustering
Real per capita income – outliers 1980 1970 2000 1990
Real per capita income – inequality • The Gini coefficient measures income inequality between counties
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
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
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
Solow model • Standard neoclassical model • correcting for growth of capital and labor • note: lacking data for investments
Human capital in Indiana and Midwest Low, 2000 Low, 2000 High, 2000 High, 2000
MRW model with human capital • Mankiw, Romer and Weil model • accounting for human capital as well • educational level of the population in 4 categories
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