The Measurement and Macroeconomics of Income Inequality and work in progress on political outcomes. The University of Utah Department of Political Science February 15, 2019 James K. Galbraith The University of Texas at Austin

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)

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 1996. Here is a sampling of that data set, in its complete and “high quality” versions.

Samples from the World Bank High Quality Data Set

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.

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

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

1987

1997

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.

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

New Evidence from Data on Industrial Pay: The UTIP-UNIDO Data Set 1963-2011 Calculated as the Between-Groups Component of Theil's T Statistic Across Industrial Categories from UNIDO Industrial Statistics

Estimated Household Income Inequality Levels and Changes in the World 1963-2014 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.

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.

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.

Maps by Aleksandra Malinowska Research supported by INET

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

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

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

Wealthy Countries (The model works)

“Transition Economies” (Again, the model works)

“Developing Countries” (Sometimes the model works)

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

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

Correlation Coefficients between Pay Inequality and Exchange Rates Country Years Corr. Coef Australia 1973-2011 0.7839 Singapore 1971-2011 0.8137 Canada 0.8034 Chile 0.686 Mexico 1994-2011 0.9443 Croatia 1992-2011 0.7293 Hungary 0.9262 Poland 1991-2011 0.8587 Romania 0.9341 India 0.7665 Russia 1993-2011 -0.1805 South Africa 1999-2011 0.0917 U.K. 0.5264

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

The US Vote 2016 Source: Magog the Ogre via Wikimedia

Changing Inequality and the US Election Outcome, 2016

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

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

Calculations and Charts by http://utip.gov.utexas.edu Calculations and Charts by Jaehee Choi Beatrice Halbach Aleksandra Malinowska Delfina Rossi Amin Shams Wenjie Zhang Support from INET gratefully acknowledged.