Estimating the Inequality of Household Incomes: A Statistical Approach to the Creation of a Dense and Consistent Global Data Set

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Estimating the Inequality of Household
IncomesA Statistical Approach to the Creation
of a Dense and Consistent Global Data Set

• A presentation prepared for the
• International Association for Research on Income
  and Wealth
• Cork, Ireland
• August 23, 2004

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by James K. Galbraith and Hyunsub Kum
The University of Texas Inequality Project
http//utip.gov.utexas.edu
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Basic Question Has Inequality been Rising or
Falling? Three ways to measure it, per
Milanovic, 2002

• Un-weighted Between-Country
• (has been rising in all studies)
• Weighted Between-Country
• (has fallen because of China)
• Within-country True(disputed territory)

?
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The Economist compares inequality types 1 and
2, 1980-2000. (from Stanley Fischer, 2003 Ely
Lecture)
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Existing studies of true world income
inequality give conflicting results, recently
surveyed by B. Milanovic
Including Sala-i-Martins claim that inequality
has been steadily decliningbased on Deininger
and Squire.
Figure borrowed from Milanovic
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Key Questionsfor comparing global data sets when
little is known about their quality in advance

• How good is the coverage?
• Are the numbers accurate and comparable?

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Comparing Coverage Deininger and Squire
Version of DS used by Dollar and Kraay, Growth
is good for the poor.
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The DS data are heterogeneous for North America
and Europe, but homogeneous for Asia
Note the low inequality registered for Indonesia
and India, comparable to Europe and Canada. The
fact that South Asia uses expenditure surveys
while Europe uses income surveys is clearly
relevant, but how to make an adjustment?
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Elementary economics suggests these differences
in inequality are implausible. Europe has an
integrated economy with free trade, free capital
flow, nearly equal average incomes (between, say,
France and Germany) and factor mobility.
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Indonesia and India have highly unequal
manufacturing pay. So how do they arrive at
highly equal DS measures more equal than
Australia or Japan? Through strong redistributive
welfare states? Probably not. Or, if low Ginis
in those countries reflect egalitarian but
impoverished agriculture as many who use these
data believe -- then why are the DS Ginis so
high in agrarian Africa?
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Inequality in Spain, as reported by DS
HGI Household Gross Income HNE Household Net
Expenditure
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Rank and Distribution of DS Gini for 20 OECD
countries
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The U.T. Inequality Project

• Measures Global Pay Inequality
• Uses Simple Techniques that Permit Up-to-Date
  Measurement at Low Cost
• Uses International Data Sets for Global
  Comparisons, especially UNIDOs Industrial
  Statistics
• Has Many Regional and National Data Sets as well,
  including for Europe, Russia, China, India, and
  the U.S.

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General Technique
We use Theils T statistic, measured across
sectors within each country, to show the
evolution of economic inequality. You can do
this with many different data sets, including at
the regional or provincial level. International
comparisons are facilitated by standardized
categories, for which sources include UNIDO and
Eurostat. Our global pay inequality data set is
calculated from UNIDOs Industrial Statistics,
and gives us 3,200 country-year Observations.
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A brief review of the Theil Statistic
The Between-Groups Component
n employment mu average income j
subscript denoting group
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The UTIP-UNIDO Data Set for Pay Inequality has
fewer gaps .
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Inequality in Income and in Manufacturing Pay, US
and UK
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Correspondence to known events
Revolution
War
Tiananmen
Banking Crisis
GATT Entry
Falklands War
Military Coup
Data for China drawn partly from State
Statistical Yearbook
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These maps rank countries by comparative measures
of inequality over a long historical period, with
red and orange indicating relatively low
inequality, yellow and green in the middle, and
light and dark blue indicating the highest values.
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Note that the UTIP-UNIDO measures are homogeneous
for Europe, North America, and South America, but
highly heterogeneous for Asia.
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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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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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Trends of Inequality in the EHII 2.2 Dataset by
Income Level
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Income Inequality in North America
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