Inequality
Measurement: Individuals differ in
countless ways. And groups are characterized by different levels of inequality
in all the ways in which individuals differ. Several important examples of
difference among individuals include income, wealth, power, life expectancy,
and mathematical ability. There are inequalities among individuals in each of
these dimensions, and different populations have different distributions of
various characteristics across individuals. It is often the case that there are
also inequalities among subgroups within a given population: men and women,
rural and urban, black and white. It is often important to identify and measure
various dimensions of inequality within a society. For the purposes of public
policy we are particularly interested in social inequalities that represent or
influence differences in individuals’ well-being, rights, or life prospects.
How do we measure the degree of inequalities within a
population? There are several important alternative approaches, depending on
whether the distribution of the factor is approximately normal across the
population. If the factor is distributed approximately normally, then the
measurement of inequality is accomplished through analysis of the mean and
standard deviation of the factor over the population. On this approach, we can
focus on the descriptive statistics of the population with respect to the
feature (income, life expectancy, weight, test scores): the shape of the distribution
of scores, the mean and median scores for the population, and the standard
deviation of scores around the mean. The standard deviation of the variable
across the population provides an objective measure of the degree of dispersion
of the feature across the population. We can use such statistical measures to
draw conclusions about inequalities across groups within a single population
(“female engineers have a mean salary only 70% of male engineers”), conclusions
about the extent of inequality in separate populations (“the variance of adult
body weight in the United States is 20% of the mean, whereas the variance in
France is only 12% of the mean”), and so forth.
A different approach to measurement is required for an
important class of inequalities—those having to do with the distribution
of resources across a population. The distribution of wealth and income as well
as other important social resources is typically not statistically normal;
instead, it is common to find distributions that are heavily skewed to the low
end. Here we are explicitly interested in estimating the degree of difference
in holdings across the population from bottom to top. Suppose we have a
population of size N, and each individual i has wealth Wi, and consider the graph that results from
rank ordering the population by wealth. We can now standardize the
representation of the distribution by computing the percent of total wealth
owned by each percent of population. And we can graph the percent of cumulative
wealth against percentiles of population. The resulting graph is the Lorenz
distribution of wealth for the population. Different societies will have Lorenz
curves with different shapes; in general, greater wealth inequality creates a
graph that extends further to the southeast. Several measures of inequalities
result from the technique of organizing a population in rank order with respect
to ownership of a resource. The Gini coefficient and the ratio of bottom quintile to top quintile of
property ownership are common measures of inequality used in comparative
economic development.
Encyclopedia
of Social Science Research Methods, edited
by Michael Lewis-Beck (University of Iowa), Alan Bryman (Loughborough
University), and Tim Futing Liao.
Sage Publications.
Gini Coefficient: A measure of inequality of wealth or income within a
population between 0 and 1. The coefficient is a measure of the graph that
results from the Lorenz distribution of income or wealth across the full
population (see Inequality Measurement).
The graph plots “percent of ownership” against “percentile of population”.
Perfect equality of distribution would be a straight line at 45 degrees. Common
distributions have the shape represented in the figure. The Gini coefficient is
the ratio of the area bounded by the curve and the 45 degree line divided by
the area of the surface bounded by the 45 degree line and the x axis.
Percent of income
Encyclopedia
of Social Science Research Methods, edited
by Michael Lewis-Beck (University of Iowa), Alan Bryman (Loughborough
University), and Tim Futing Liao.
Sage Publications.