Sometimes, standard social definitions of income and consumption definitions of poverty just aren't enough to accurately measure and describe what poverty is. Many less-common methods can be also used to help determine the extent of poverty in a specific country. Measuring Poverty I and Measuring Poverty II examine some of these alternatives.

Top

One alternative method sometimes used to measure poverty is the measure of inequality in income distribution. This can help show the difference between the richest percentiles of a society and the poorest. While this does not actually show who in the society is truly in poverty by international standards, it can show who is considered poor compared to others in the same society. This is similar to the poverty measures used by the European Union (EU) and the Organization for Economic Cooperation and Development (OECD). In the EU, the income poverty line is set at 60% of the median household income. This would mean that in Britain, for example, where the median household income is about $33,734, any household making less than $20,240 would be considered poor. This is obviously a far cry from people making less than a dollar a day in Africa, but poverty lines based on inequality can be readily used in richer nations, because they show who would be considered ‘in poverty’ in that nation.

There are two major tools used to help determine the inequality in incomes in a group of people – the Gini-coefficient and the decile dispersion ratio:

Top

Gini-coefficients are used in conjunction with a graphical device called a Lorenz curve (see sample curve below) and are the most common method used to evaluate a country’s income inequality. A Lorenz curve graphs the share (in percentile form) of the total income the bottom x% of the population receive, ranging from 0% to 100%. For example, if the bottom 10% of the population received only 2% of the total income, it would be represented on a Lorenz curve with a coordinate point of (10, 2). Once data for all percentiles is graphed (the more the better), a ‘45-degree line’ is drawn. This line represents perfect equality – income is equally distributed across the entire population, and can be represented algebraically by the line y=x (this is because in a perfect equality situation, the bottom x% will always have x% of the income). To determine the Gini coefficient using this data, a ratio of the regions formed by a Lorenz curve is used. For example, if the area of the region between the line of equality and the Lorenz curve is called A (represented in red on the sample graph) and the area of the region between the curve and graph’s bounds is called B (represented in blue on the sample graph), then the Gini-coefficient would be A/(A+B). For a visual of this information, see the our graph of a Lorenz curve based on U.S. inequality statistics from 1978 here or see our interactive demonstration.

Lorenz curve of U.S. inequality statistics, 1978. The Gini-coefficient is calculated by dividing A by A+B.

A typical Gini-coefficient can vary between around 0.24 and 0.71. Often, when countries’ inequality measures are compared using Ginis, the coefficient is multiplied by 100 (effectively converting it to a percentage form) and is called a Gini Index. Currently, Denmark has the lowest inequality going by Gini-coefficient – 0.247. Namibia has the highest, with a coefficient of 0.707. The U.S. falls somewhere in the middle, with 0.408, though this puts it behind many other industrialized nations, which typically have coefficients around 0.3.

The world by income inequality: this map color-codes countries based on their Gini Index. Green is good, red is bad.

Top

The decile dispersion ratio is another common method to determine inequality, and in many ways, a much simpler one. In the words of the World Bank, it “expresses the income of the rich as a multiple of that of the poor.” To calculate a decile dispersion ratio, the average income of the top 10% of income makers is divided by the average income of the bottom 10% of income makers. This can be computed for any percentile – 5%, 10%, 30%... and is very easy in interpret. If the top 10% of the population make an average of $80,000 a year and the bottom 10% make an average of $15,000 a year, the dispersion ratio is equal to about 5.33 – the rich make about 5.33 times as much as the poor.

Top

A Bengali carpenter working. Poor workers in developing countries can lead very insecure lives - a single unforseen event can drive them into poverty.

One major result of poverty is the inability of a household to control their situation in any meaningful way – they are helpless to any political, natural, or health-related disasters that befall them. If the primary wage-earner falls sick, or a drought destroys their crops, they have no safety net to fall on, and are driven into deeper poverty. Because of this, measuring vulnerability - the probability or risk today of being in poverty or falling into deeper poverty in the future – is an important dimension in any measurement of poverty. It allows for people who may be above the poverty line but are at a high risk of falling below it to be more easily identified and helped. In traditional measurements, laborers in an urban setting with a steady income just below the poverty line would be considered worse off than someone making slightly more income in an agricultural job, even if the income in the agricultural area fluctuated wildly based on other factors – such as weather. In vulnerability measures, especially those focusing on income fluctuations, the agricultural worker would not be ignored as much, since he would have a higher risk of falling deeper into poverty than the laborer. This allows for governments to create better support systems, where some people receive small amounts of benefits very frequently (the urban laborer), while others receive large benefits only at certain times, when a large change in income demands it (the agricultural worker).

There are four different ‘levels’ of vulnerability someone can have:

Non-poor: This person is not in poverty and has a very low risk of falling into poverty.

Transiently poor: This person’s income is usually above the poverty line, but has sometimes fallen under it.

Chronically poor: This person’s income is usually below the poverty line, but has sometimes been over it.

Persistently poor: This person’s income is always below the poverty line.

Also, comparing data from different years can yield valuable information on the vulnerability of people in and around the poverty line in a certain country. A sample of this data, comparing poor and non-poor people in rural Ethiopia in 1989 and 1995, appears below.

'Transition matrix' showing flows in and out of poverty in Rural Ethiopia from 1989 to 1995.

This ‘transition matrix’ can be hard to read at first, but is really quite simple. The totals on the far right and bottom of the table refer to the percentage of the population in poverty (poor) and the percentage not in poverty (non-poor) in each year. Therefore, in 1989, 61% were in poverty, and 39% were not. In 1995, only 46% were in poverty, and 54% were not. The inner numbers, however, reveal that the vulnerability of many people has remained high. 31% of the population remained in poverty from 1989 to 1995 – more than half of all those who were in poverty in ’89 (the other half, the 30% on the graph, escaped poverty by 1995). 15% of the population that was ‘not poor’ in 1989 also fell into poverty by 1995 (while the other 24% of the population that was also ‘not poor’ remained so in 1995). This shows a high movement ‘in and out’ of poverty, which indicates a high level of vulnerability in the area.

Top

2005 HHS Poverty Guidelines

Gilbert, Geoffry. World Poverty. Santa Barbara: ABC-CLIO 2004

How the Census Bureau Measures Poverty

Measuring Income Distribution

Wikipedia: Gini Coefficient

Wikipedia: Lorenz Curve

Wikipedia: Poverty

Wikipedia: Poverty in the U.S.

World Bank: PovertyNet: Measuring Poverty

Top

Copyright © 2006 ThinkQuest Team 00282