income distribution worldwide: the VMIR
http://www.countdownnet.info/archivio/analisi/world_economy/530.pdf
An International Comparison of the Incomes of the Vast Majority Anwar Shaikh and Amr Ragab April 12, 2007
Introduction
Income levels and income inequality are two major dimensions of
national and international
well-being. The last two decades have witnessed a growing concern
about both issues, from
policy makers, social scientists, and the media. But the two
dimensions are generally treated
separately, with GDP per capita (GDPpc) as the paramount measure of
national income and
the Gini coefficient (G) as the central measure of inequality.
Sometimes these are implicitly
combined, as in the case of poverty measures which count the number
of people in each nation
who live on less than one dollar a day (World Bank, 2000/2001, p. 3)
There are well-known problems with these traditional approaches.
First of all, average income
per capita also tells us nothing about income variations within a
population. For instance, if
four people with an average income of $50,000 are joined by one more
with an income of
$300,000, the per capita income of the group doubles1. Knowing that
the Gini coefficient of
the group is “high” alerts us to the fact that the average is
unrepresentative, but does little to
help us understand its real magnitude.
A simple alternative would be to measure the income per capita of the
vast majority of this
group, say the first 80 percent in the income ranking. Such a measure
would combine the
average level of income and its distribution into an intuitively
useful statistic. Moreover, it
would have obvious political resonance in any modern political
system. There is evidence, for
instance, that state-wide voting preferences in the US are correlated
with changes in average
local incomes (Altman, 2006) and it would be interesting to see if
this relationship is stronger
with vast majority incomes.
This paper is part of an ongoing project to analyze international
inequality. International
comparisons tend to focus on either GDP per capita or the incomes of
the very poor (e.g. those
living on less that $2 per day). The VMI adds a new dimension,
because it combines
information on income levels and their distribution into a single
measure which is the average
income of the vast majority of the population. We believe that this
broadens the discussion of
international inequality, and will ultimately shed new light on
several important issues in the
development literature such as the relationships between development
and inequality, growth
and inequality, trade liberalization and living standards, and
political instability and inequality.
In this paper we focus on the first issue by exploring the links
between development and the
incomes of the vast majority of the world’s population.
In this paper we develop the preceding measure on an international
scale. We begin by
calculating the ratio of the disposable income per capita of the
first 80 percent (the vast
majority) of the population to the average income per capita, in any
given nation. We call this
the Vast Majority Income Ratio (VMIR), and show how it can be derived
from a Lorenz curve.
Multiplying this ratio by an appropriate average real per capita
income measure (Net National
Income per capita) then gives us the real income per capita of the
vast majority of the
population across nations, regions and/or time. It is shown that the
VMIR varies considerably
across countries. This implies that average per capita income
measures such as GDP or Net
National Income are not reliable proxies for the per capita incomes
of international vast
majorities. Indeed, we show that ranking countries by their vast
majority incomes (VMIs) gives
different results than ranking them by average per capita income. In
next section of the paper
we demonstrate that the VMIR, which is itself an equality measure,
bears a constant ratio to
(1- G) across countries and across time. This unexpected internal
relation, which we call “The
1.1 Rule”, also leads us to a new interpretation of the Gini
Coefficient as the relative per capita
income of the first seventy percent of the population in any given
country. The theoretical
foundations of these two empirical rules are addressed in a section
on distribution theory and
“econophysics”. Policy implications are outlined next, as are
connections to an earlier literature
in which (1-G) was proposed as a discount factor for various measures
of well-being. The
paper ends with a summary and a discussion of potential areas of
future research. The Data
Appendix explains our sources and methods.
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