Economic growth and income inequality in indiana counties
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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 protected], [email protected]

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Economic Growth and Income Inequality in Indiana Counties

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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 protected], [email protected]

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


Midwest, 2003


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


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