Inequality in the United States:

A brief tour of some facts

James K. Galbraith

Lyndon B. Johnson School of Public Affairs

The University of Texas at Austin

McCormick Tribune Foundation Conference Series

Chicago Federal Reserve Bank

April 2, 2008

The University of Texas Inequality Project

http://utip.gov.utexas.edu

The Official Story

Second, however, there has been, as we know and discussed over the years, a significant opening up of income spreads, largely as a function of technology and of education with the increased premium of college education over high school, and high school over high school dropouts becoming stronger. The whole spread goes right through the basic system. It is a development which I feel uncomfortable with. There is nothing monetary policy can do to address that, and it is outside the scope, so far as I am concerned, of the issues with which we deal.

Alan GreenspanTestimony to CongressMarch 5, 1997

The Official Story

Second, however, there has been, as we know and discussed over the years, a significant opening up of income spreads, largely as a function of technology and of education with the increased premium of college education over high school, and high school over high school dropouts becoming stronger. The whole spread goes right through the basic system. It is a development which I feel uncomfortable with. There is nothing monetary policy can do to address that, and it is outside the scope, so far as I am concerned, of the issues with which we deal.

Alan GreenspanTestimony to CongressMarch 5, 1997

The idea that inequality in the structure of manufacturing pay has increased systematically is

a myth. It has risen and fallen.

Inequality in manufacturing pay can be measured directly, easily and accurately. It closely tracks the unemployment rate.

This measure peaked in the early 1990s and declined sharply as the economy moved toward full employment

If technology and trade affect anything, they would affect manufacturing pay

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Unemployment rate (left)Inequality of manufacturing pay (Theil index, right)

Inequality in Manufacturing Pay and Unemployment in the U.S.1953-2005, Monthly Data

Shaded areas show recessions.

Inequality

Unemployment

The best explanation for inequality in manufacturing pay is, it is almost exactly the same thing as unemployment.

Looking beyond manufacturing, inequality in pay more generally, including in services, depends mainly on the participation rate. As the proportion of workers in the population has risen, so has inequality.

Overall pay inequality is a combination of two factors: the effect of participation rates and the effect of unemployment rates.

Inequality and the participation rateInequality and the participation rate

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Inequality for 203 sectorsparticipation rate

source: BLS data and author's calculations

Participation rates also determine the famous “stagnating median wage”

Classic argument: **stagnating** median wage

Source: CEPR report, April 2007, p.10

But, not for women …

Real median income by gender2001 Dollars, GDP deflator

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Between 1965 and 2000, labor force participation increased by nine percent, creating about nine million jobs, or fifteen percent of total job creation. The share of women in the labor force rose eleven percentage points. That of Hispanics rose ten percentage points. That of African-Americans rose three percentage points. That of white non-Hispanic males fell eighteen percentage points.

To be clear, much of this was the consequence of disruptive economic events – including especially vast macroeconomic disruptions in the 1970s and 1980s, and institutional change, including the attack on unions. Many older, white, non-Hispanic male workers were forced from work.

Nevertheless, the transition in the structure of the workforce is an essential component of the rise of measured inequality in the structure of pay.

Thus, when you break out the workforce by race, the stagnation goes away

Real median earnings Full Time 50-52 workweek, year-round,

2001 Dollars, GDP deflator

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Conclusion: real median incomes rose for all groups in the late 1990s.

Full employment is good for median wages.

They were stagnant for a period starting around 1971 and ending in 1983 for whites, 1992 for blacks and around 1995 for Hispanics.

The stagnation of aggregate median incomes through 1997 is a composition effect. The hourglass phenomenon has much to do with the rising labor force role of women and minorities.

And especially with the rising role of new immigrants in the Hispanic workforce.

The problem is not whether people start at the bottom. It is whether they end there. This depends very much on how we treat those groups, as they move into jobs previously held by unionized male, Anglo workers.

Inequality in INCOME, on the other hand, has risen substantially. This too can be measured quite precisely, from income tax and other data sources.

It is obvious that the explanation for rising income inequality must come from some other source, than rising inequalities in the structure of pay.

How about the stock market?

That works fine.

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U.S. Income Inequality Between Counties 1969 – 2005 Plotted Against the NASDAQ Composite, with Three Counterfactual Scenarios of Inequality Growth from 1994 – 2000

Piketty-Saez data would give essentially the same answer.

Inequality

It’s the stock market, s&%#*d

If you remove a handful of counties, related to information technology and finance, most of the rise in income inequality in the late 1990s would not have occurred.

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1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005

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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 ValleyWithoutTop 15

Counties with the largest positive changes in Theil Elements 1994 - 2000

Counties with the largest negative changes in Theil Elements 1994 – 2000

County, State Theil Element

Change 1994 - 2000 County, State Theil Element

Change 1994 - 2000

New York, New York 0.00517211 Los Angeles, California -0.00089362

Santa Clara, California 0.00468738 Queens, New York -0.00070519

San Mateo, California 0.00208153 Honolulu, Hawaii -0.00065515

King, Washington 0.00169613 Broward, Florida -0.00056938

San Francisco, California 0.00148821 Cuyahoga, Ohio -0.00036473

Harris, Texas 0.00147724 Kings, New York -0.00034178

Middlesex, Massachusetts 0.00131529 Miami-Dade, Florida -0.00032742

Fairfield, Connecticut 0.00099520 San Bernardino, California -0.00031665

Alameda, California 0.00088503 Genesee, Michigan -0.00031147

Westchester, New York 0.00086216 Clark, Nevada -0.00030658

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New York Nassau Westchester Suffolk Rockland Richmond Albany MonroePutnam Dutchess Saratoga Schenectady Hamilton Schuyler Columbia WarrenSchoharie Yates Lewis Montgomery Seneca Ontario Essex GreeneFulton Genesee Tioga Sullivan Delaware Wyoming Madison OrleansCortland Rensselaer Chenango Onondaga Livingston Steuben Otsego CayugaFranklin Allegany Herkimer Washington Clinton Wayne Tompkins CattaraugusChemung Jefferson Ulster Oswego St. Lawrence Orange Erie BroomeChautauqua Niagara Oneida Queens Bronx Kings

Contribution of New York Counties to U.S. Income Inequality, 1969-2004

Bar-height is the contribution of the county to the Theil T-Statistic

Manhattan

No good jobs for the unskilled?

“The spiraling crisis in the credit and housing markets has kept [Phil] Gramm in focus, fairly or not. His employer, UBS, revealed yesterday that investment losses tied to the U.S. housing market reached $37 billion over the last six months. For the last three months, UBS posted a $12 billion loss.

“Gramm, UBS's vice chairman, said yesterday he was "totally unaware" of his bank's massive holdings of securities tied to subprime mortgages, but, he added, "I'm confident we'll recover."

Washington Post, April 2. 2008

Per Capita Income Inequality Across US Counties Over Time

1969 – 2004

Contribution to Inequality between Counties (Components of the Theil T Statistic)

Relatively Impoverished

Neutral

Prosperous (income above national mean)

1969

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1973Nixon’s Soviet Wheat Deal

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Watch The West

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1981The Big Recession

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The OilBust

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1998The Tech Bubble

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BeltwayBubble

2003CheneyDoes Wyoming?

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Does monetary policy influence pay inequality?

It is obvious that monetary policy influences income inequality: any policy that affects the stock market will affect income inequality.

The Federal Reserve denies any effect, blaming technological change.

Let’s test it

The VAR modelThe VAR model

The VAR model is a very standard model to analyze covariances and “causality;” our

approach is entirely conventional. Like all VAR analysis, it makes no theoretical

prediction in advance.

Our model features the yield curve, manufacturing pay inequality, unemployment and inflation

The yield curve is an attractive, stable measure of monetary policy stance, well established in the

literature. It’s also a good predictor of recessions.

The yield curveThe yield curve

Source: U.S. Department of Commerce. 30-day T-Bill vs. 10-year bond rate

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After accounting for breaks and outliers, and having checked for stability:

1. The term structure is the most causal force

2. The term structure does affect inequality

3. The term structure is affected by unemployment but not by inflation

Inequality

Dummy regressions on the Taylor rule

• Unemployment target set at 5.5% • Inflation target set at 2%• Several cases considered:

– Inflation/Unemployment above or below target– Inflation/Unemployment rising or falling– Above and rising or below and falling– …

• Dummy regressions done before and after break in 1983.

log log *, *t t tTS f CPI CPI U U

Results for 1969- 1983

Shows the working of the Taylor rule:

-Tightening if inflation is above target (but not rising)

-Easing if inflation is below target (but not falling)

-But the explanatory power is very low (5% at best)

Results for 1984-2006 for 1984-2006

Contradicts the Taylor rule:

-The Fed does NOT react to inflation (rising or falling, above or below)

-The Fed reacts only to low (and falling) unemployment by tightening… and inviting recessions.

-The explanatory power has improved dramatically.

Politics and the Fed

• There is a well respected theory of the political cycle (Hibbs 1974, Nordhaus 1975, Tufte 1978, Alesina and Sachs 1988, Greider 1988, Abrams and Iossifov 2006, Hellerstein 2007).

• To test our version, we define separate dummy variables for the four quarters preceding a presidential election, depending on which party holds the presidency: REPUP and DEMUP

• Do those two political dummies affect our the yield curve (and therefore monetary policy)?

Tests of a political monetary policyTests of a political monetary policyPartial results

Inequality in pay is a macroeconomic phenomenon. It is strongly influenced by monetary policy, as well as by

other policies affecting unemployment and the participation rate.

Inequality in income is largely a financial phenomenon. It is mainly driven by the stock market.

Conclusions

Monetary policy appears to be driven mainly by fear of low unemployment, and by political considerations.

Reference: UTIP Working Paper No. 42

The Fed’s Real Reaction Function: Monetary Policy, Inflation, Unemployment, Inequality – and Presidential Politics

By James K. Galbraith, Olivier Giovannoni and Ann J. Russo

July 17, 2007

http://utip.gov.utexas.edu/papers/utip_42.pdf

For more information:

The University of Texas Inequality Project

http://utip.gov.utexas.edu

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