Measuring Inequality

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Measuring Inequality

• A practical workshop
• On theory and technique

San Jose, Costa Rica August 4 -5, 2004
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• Panel Session on
• Econometric Analysis
• Using
• Inequality Measures

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by James K. Galbraith and Enrique Garcilazo
The University of Texas Inequality Project
http//utip.gov.utexas.edu
Session 5
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A Global Coup?

• Looking Beyond Technology and Trade at the Causes
  of Rising Inequality in the Age of Globalization

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With the UTIP data, we can review changes in
global inequality both across countries and
through time. Nothing comparable can be done
with the Deininger and Squire data set, for the
measurements are too sparse and too inconsistent.
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The Scale 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. h
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1963 to 1969
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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.
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1977 to 1983
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1981 to 1987
the Age of Debt
Note the exceptions to rising inequality are
mainly India and China, neither affected by the
debt crisis
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1984 to 1990
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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
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Simon Kuznets in 1955 argued that while
inequality could rise in the early stages of
industrialization, in the later stages it should
be expected to decline. This is the famous
inverted U hypothesis. Recent studies based on
Deininger Squire find almost no support for any
relationship between inequality and income
levels. We believe, however, that in the modern
developing world the downward sloping
relationship should predominate, particularly in
data drawn from the industrial sector.
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A regression of pay inequality on GDP per capita
and time, 1963-1998.
The downward sloping income-inequality relation
holds, but with an upward shift over time
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Milanovic Unweighted Inequality Between Countries
The time effect from a two-way fixed effects
panel data analysis of inequality on GDP per
capita, with time and country effects.
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This pattern resembles the general pattern we
associate, within countries, with the coup detat
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Unemployment, Inequality and the Policy of
Europe1984-2000

• A Presentation to the
• European Commission Lectures Program
• Brussels
• June 29, 2004

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The Standard View

• Employment is determined in a labor market.
• Labor markets are national.
• Flexibility reduces unemployment.
• The United States has more jobs than Europe, but
  only at the expense of more inequality.
• Is this good or bad? A political question

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The U.S. Case

• In the American case, we have measured
  inequalities of pay (weekly earnings) in the
  manufacturing sector on a monthly basis going
  back to January, 1947, for sectors that are
  continuously measured since that time. The
  result gives us a time series of pay inequalities
  in a key part of the American industrial economy.

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Wage Inequality and Some Historical Events
Recession
Vietnam War
Recession
Recession
Korean War
Recession
Recession
JFK
LBJ
NIXON
FORD
CARTER
REAGAN
BUSH
CLINTON
TRUMAN
EISENHOWER
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Wage Inequality and Unemployment
Open UnemploymentRate
A strong positive correlation between the
unemployment rate and wage inequality in the US
is exhibited here.
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The U.S and Europe

• First, lets compare U.S. inequality to that in
  each European country.
• Then, lets compare U.S. inequality to that in
  Europe-as-a-whole
• Finally, we ask, what is the relationship between
  unemployment and inequality in Europe?

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EHII -- Estimated Household Income Inequality for
OECD Countries
Low
High
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Now, is pay inequality in Europe really lower
than in the U.S.? It depends on how you count
The value for the U.S. on this scale is about
0.29, or roughly the height of the blue bar.
Overall European manufacturing pay inequality
including differences between countries is
higher than in the US.
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• Data! Data! Data!
• I cant make bricks without clay.
• Sherlock Holmes
• The Adventure of the Copper Beeches

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European Regional Panel Data Set

• Pay across Sectors by European Region
• From Eurostats REGIO
• Annual 1984-2000, up to 159 Regions
• Enables us to compute measures of inequality
  within and between regions.
• Permits construction of a panel with which we can
  isolate regional, national and continental effects

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Contribution of European Provinces in Inequality
Across the European continent, late 1990s.
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A Simple Theory of European Unemployment

• Demand Factors
• GDP Growth and Investment
• Wealth and Demand for Services
• Supply Factors
• Inequalities of Pay
• Transition to Work for Youth

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Hypotheses

• Growth reduces unemployment. (-)
• Higher incomes mean fewer unemployed. (-)
• Inequality increases unemployment ()
• More younger workers means more unemployed. ()

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Regression analysis of European unemployment
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Country Fixed Effects Show the Differences
Between Countries Not Explained by the
Explanatory Variables.
Centralized wage bargains?
Emigration?
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Time Fixed Effects Show the Movements of
Unemployment Across All Regions, After Taking
Account of the Regressors
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Conclusions

• Labor markets are not national.
• Macroeconomic conditions matter.
• Youth is a problem.
• Equality of pay helps.
• Flexibility does not.
• Small countries have an advantage.
• EU policies started off very poorly.
• But there is hope for the future.

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Beating the Bank at its Own GameEstimating
Income Inequalityfrom measures ofpay
inequalityand other economic information
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Estimating the DS Gini Coefficients from Pay
Inequality and other variables.
Dependent variable is log(DSGini)
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EHII -- Estimated Household Income Inequality for
OECD Countries
Low
High
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Mean Value and Confidence Interval of Differences
eap East Asia and Pacific eca Eastern Europe
and Central Asia lac Latin and Central
America mena Middle East and North Africa na
North America sas South Asia ssa Sub Saharan
Africa we Western Europe
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Major Differences Between DS Gini and EHII Gini
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Trends of Inequality in the DS Data
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Trends of Inequality in subset of EHII 2.2 Data
matched to DS
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Trends of Inequality in Full EHII 2.2 Dataset
(N3,179)
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Income Inequality in North America
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