1. The Measurement and Macroeconomics of Income Inequality and work in progress on political outcomes. The University of Utah Department of Political Science February 15, James K. Galbraith The University of Texas at Austin

2. A Credo
“Kepler undertook to draw a curve through the places of Mars, and his greatest service to science was in impressing on men's minds that this was the thing to be done if they wished to improve astronomy; that they were not to content themselves with inquiring whether one system of epicycles was better than another, but that they were to sit down to the figures and find out what the curve, in truth was.”
-- Charles Sanders Peirce (1877)

3. To Begin with a Comparison
At the time we began our work in the area of inequality measurement, the principal data source was the Deininger and Squire compilation of past inequality measures, done at the World Bank and published around Here is a sampling of that data set, in its complete and “high quality” versions.

5. Samples from the World Bank High Quality Data Set

6. Our method uses administrative data, usually payrolls and employment by industry or sector, and relies on the Theil Statistic, or more specifically its “Between-Groups Component.” Data are widely available, cost is minimal and information is usually published on a timely basis. There are national, regional and global data sets that can be drawn on.

7. A brief review of the Theil Statistic:
The “Between-Groups Component”
n ~ employment; mu ~ average income; j ~ subscript denoting group

8. Decomposing Inequality in China Note the downward trend after 2008
Kuznets
Schumpeter
Data from the State Statistical Yearbook
Calculations by UTIP

9. 1987

10. 1997

11. Contribution of Each County to Income Inequality, Late 2000s
Contribution to inequality is presented as shading and as height above or below the zero plane.

12. Decomposing Europe by Region or, Compared to Everything Else, London and Paris are Really Rich

13. New Evidence from Data on Industrial Pay: The UTIP-UNIDO Data Set Calculated as the Between-Groups Component of Theil's T Statistic Across Industrial Categories from UNIDO Industrial Statistics

14. Estimated Household Income Inequality Levels and Changes in the World With 4550 observations for 154 countries, we believe EHII is the largest single-concept income inequality data set not using any interpolation across years or countries. The remaining slides however use the previous version, through 2008.

15. A Brief Summary
The EHII data set is a panel of estimated Gini coefficients for gross household income, derived from measures of cross-sector industrial pay inequality and other information, especially the share of manufacturing in total output. It is calibrated to standard Gini coefficients by a simple regression analysis.

16. Extending EHII
EHII is calculated by regressing the original Deininger Squire “High Qualitÿ”
data set against UTIP-UNIDO, with controls for the share of manufacturing in
total employment and dummies for the various income/expenditure concepts
present in the original DS data set. The coefficient estimates are then used to
generate the EHII values. Here is the new regression underlying EHII 2013.

21. Maps by Aleksandra Malinowska
Research supported by INET

22. Global Mean Values EHII Gini Coefficients`
Inequality within countries surged from 1981 to 2000.
Adding new transition countries lowers the mean here.

23. Global Turning Points Match Monetary Upheavals Global Mean Values of EHII Gini Coefficients`
NASDAQ & 9/11
End of Bretton Woods
Debt Crisis

24. Are these estimates any good?
But...
Are these estimates any good?
Charts to Follow by Beatrice Halbach

25. Wealthy Countries (The model works)

38. “Transition Economies” (Again, the model works)

44. “Developing Countries” (Sometimes the model works)

54. “Developing Countries” (Sometimes the model undershoots...)

59. Pay Inequality and the Exchange Rate
A Neglected Link
With implications for understanding the direction of causality in the world economy between financial power and inequality

81. Correlation Coefficients between Pay Inequality and Exchange Rates
Country
Years
Corr. Coef
Australia
0.7839
Singapore
0.8137
Canada
0.8034
Chile
0.686
Mexico
0.9443
Croatia
0.7293
Hungary
0.9262
Poland
0.8587
Romania
0.9341
India
0.7665
Russia
South Africa
0.0917
U.K.
0.5264

82. Inequality and the 2016 Election
Change in Inequality by state is closely associated with political outcomes in the Electoral College in the United States
4/9/12

83. The US Vote 2016
Source: Magog the Ogre via Wikimedia

84. Changing Inequality and the US Election Outcome, 2016

85. Changing Inequality and the US Election Outcome, 2016
Calculations by UTIP

86. Changing Inequality and the US Election Outcome, 2016
Calculations by UTIP

87. Calculations and Charts by
Calculations and Charts by
Jaehee Choi
Beatrice Halbach
Aleksandra Malinowska
Delfina Rossi
Amin Shams
Wenjie Zhang
Support from INET gratefully acknowledged.