Scott Karr & Yadu Chittampalli

1 Synoposis

Income inequality is a global phenomenon that is viewed in relation to the geographic, economic and political boundaries that define its dispersion. The GINI index is an internationally recognized measure of income dispersion within a specified geographic area. Income inequality has also become an increasingly topical discussion as a matter of development and growth both within OECD and developing countries. GINI’s advantage is that it captures dispresion in a singular index as a function of the Lorenz Curve which has been in use since the early 20th century.

Developing countries such as India will analyzed in this study in contrast to the U.S.–an OECD country. Income dispersion in India will be by province a rough equivalent to states in the U.S. as a basis for comparison.

We plan to use publshed statistics from a variety of sources on GINI including . . .

Data will be sourced from .csv files using the U.S. Census website and the Reserve Bank of India website. ETL transformations and clean-ups will be performed in R and stored in MySQL/NoSQL for persistence, and the graphs and plots will be rendered. Scatterplots will be used to illustrate the correlation between income dispersion and other factors such as geographic boundaries. The graphics will be displayed using color gradient to depict GINI relationships using ggmap.

The implications of income dispersion on growth and governance has implications to countries development. Testing such correlations and their effect is beyond the scope of this exercise, however, showing the contrasts in itself we believe will be informative.

2 Data

The Census Bureau publishes data sets which track the GINI index at different levels of geographic granularity including region, state, congressional district and metropolitan statical area. This study analyzes the income dispersion within the United States using Census data and in particular, income data collected for the American Community Survey. The Census Bureau provides the followng tool for acquiring data sets . . .

http://factfinder.census.gov/faces/nav/jsf/pages/guided_search.xhtml

* Gini Indicies by Global
--INDIA
* Gini Indicies by Province    
--US
* Gini Indicies by Region
* Gini Indicies by State
* Gini Indicies by Congressional District
* Gini Indicies by Metropolitan Statistical Area

The following cases correspond to each geographic level data set above: * World Data - as available * Province Data - as available * 4 Regions: Northeast, Midwest, South and West + US Oveall * 50 States * 436 Congressional Districts * 916 Gini Indicies by Metropolitan Statistical Area

The following Variables will be collected for analysis within each data set:

* Catagorical:  Geographic Level
* Numeric:      Gini Index