A presentation at the International Labor Organization United Nations Geneva, Switzerland April 16, 2012 by James K. Galbraith Inequality and Instability A Study of the World Economy Just Before the Great Crisis

“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)

 A research initiative since ~1996  Emphasis on accurate measurement  Global and national coverage  60 working papers; six books  15 data sets on-line  Highly ranked in Google under “Inequality”  Supported by the Ford Foundation The University of Texas Inequality Project

Deininger and Squire: Data Set of Choice? Version of D&S used by Dollar and Kraay, “Growth is good for the poor.”

Deininger & Squire: Inequality Measures for the OECD Countries ranked by average value, first and last dates shown

Trends of Inequality in the Deininger-Squire Data Set

A brief review of the Theil Statistic: n ~ employment; ~ average income; j ~ subscript denoting group The “Between-Groups Component” Within-Group Inequality Weighted Sum of Within-Groups Components

The UTIP-UNIDO Theil Measure of Inequality

China: Contributions to inequality by Province Beijing Contributions to a Theil T-Statistic, measured across provinces

4/16/12 Contribution of European Provinces in Inequality Across the European continent, late 1990s.

A Stylized “Augmented Kuznets Curve”

 Inequality in the world economy is dominated by a common trend, with turning points around 1971, 1980, 2000  The most-likely explanation is changing global financial regimes. Key Global Finding

 Brown: Very large decreases in inequality; more than 8 percent per year.  Red Moderate decreases in inequality.  Pink: Slight Decreases.  Light Blue: No Change or Slight increases  Medium Blue: Large Increases -- Greater than 3 percent per year.  Dark Blue: Very Large Increases -- Greater than 20 percent per year. Global Movement of Inequality

1963 to 1969

1970 to 1976 The oil boom: inequality declines in the producing states, but rises in the industrial oil-consuming countries, led by the United States.

1977 to 1983

1981 to 1987 … the Age of Debt Note the exceptions to rising inequality are mainly India and China, neither affected by the debt crisis…

1984 to 1990

1988 to 1994 The age of globalization… Now the largest increases in inequality in are the post-communist states; an exception is in booming Southeast Asia, before 1997…

Debt Crisis End of Bretton Woods 9/11 “Concept 4” Inequality: The Common Movement of Inequality Measured within Countries, Across Time Note: The vertical axis represents the time element in a two-way fixed effects panel regression, across the panel of country-year observations. Vertical scale is log(T) units. Source: Kum Milanovic Concept 1 Profit Share in OECD The Super Bubble

Note: Bands indicate two standard deviations of country observations within each year shown. OECD and non-OECD countries shown separately. Vertical scale is log(T) units. Source: Galbraith and Kum, What would have happened without the Global Element?

This broad picture of the world economy from the standpoint of inequality measure suggests that the “super-bubble” was also, for most of the world’s population, a “super-crisis.” The super-bubble came to a peak in The period since then was marked in the United States by efforts to keep the bubble going, in part through aggressive efforts to relax standards, which may be described as the growth of a “predator state.” This led to the corruption of the financial markets whose collapse produced the great crisis.

 Pay inequality in the US depends mainly on economic conditions, especially the unemployment rate.  Income inequality in the US depends mainly on the stock market, and is driven by top- level incomes in highly concentrated small areas.  For example: Manhattan, Silicon Valley in the 1990s, Washington DC in the 2000s Key US Findings

US Pay Inequality and Unemployment, Inequality measured on earnings across industries in manufacturing, monthly data; recessions entered in gray.

US Income Inequality and the NASDAQ, Income inequality measured between counties, from tax data Tax Reform Act Internet Bubble Inequality Log of NASDAQ

U.S. Income Inequality Between Counties 1969 – 2005 Plotted Against the NASDAQ Composite, with Three Counterfactual Scenarios of Inequality Growth from 1994 – 2000 Without Manhattan Without Silicon Valley Without Top 15

Income Inequality in the United States, Measured Between Counties Calculation by Amin Shams

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. Calculation and map by Amin Shams.

Contribution of each Municipality to Inequality in Brazil Contribution to inequality is presented as shading and as height above or below the zero plane. Calculation and map by Laura Spagnolo.

Further Research Results Inequality and Unemployment Inequality and Political Regime Types Inequality and Voting: Participation Inequality and Voting: Outcomes Extent of wage flexibility in Europe Declining Inequality in Latin America Contribution of Financial Sectors to Inequality

Needs Going Forward... Staff regular updating of data sets Fill historical and geographic gaps Extend the record back in time Mapping Software Tools Spreading the Word.

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