POVERTY IS ‘MULTIDIMENSIONAL'—AND WE NEED TO MEASURE IT THAT WAY

Nobel laureate Amartya Sen's argument that poverty is more than a simple lack of income is widely accepted in theory. But according to a new study by Professor Jean-Yves Duclos and colleagues, published in the October 2006 Economic Journal, it is time for it to be properly implemented through poverty comparisons that take account of deprivation in a range of different dimensions as well as correlation between the dimensions.

Most people think of poverty as a lack of income and for most of the twentieth century, economists interested in poverty accepted that intuitive idea. Beginning in the 1980s, however, Amartya Sen challenged economists, ethicists and social theorists to think of poverty more broadly as an inability to be or do certain intrinsically important things, which Sen called ‘functionings'.

While a lack of income certainly limits our ability to function in certain ways, it is not the only thing that may limit our functionings. Political repression, physical handicaps or a lack of social services like schools or hospitals all limit us as well.

An important implication of Sen's argument is that poverty is ‘multidimensional', involving more than a simple lack of income. Sen won a Nobel Prize for his ideas, which are now widely accepted among economists, in theory. In practice, empirical research on poverty continues to study lack of income, with very few exceptions.

Duclos and his colleagues think that this should change, so that empirical studies of poverty are aligned more closely with Sen's poverty theory. Their study shows that it is feasible to make multidimensional poverty comparisons, and it provides examples in which these comparisons differ from standard poverty comparisons based on incomes alone.

The research team's methods are more general than two apparently intuitive methods. The first is the approach taken by the United Nations' Human Development Index (HDI). This is a weighted average of three dimensions of well-being: incomes, life expectancy and literacy. But the weights are arbitrary, and changing them may well change the poverty estimates. The methods proposed in the new study are valid for any set of weights.

The second approach is to compare each dimension of well-being ‘one-at-a-time'. For example, we can check to see if income poverty is lower in country A than country B; then compare life expectancy and then literacy. If country A shows less poverty in each dimension than country B, then we can conclude that poverty is lower… or can we?

As it turns out, a genuinely multidimensional poverty comparison should take into account not only the individual dimensions ‘one-at-a-time', but also the correlation between the various dimensions. This is because, ethically, it is worse for someone who is poor in one dimension, say health, to be poor in a second dimension, say education, than it is for someone who is not ‘health poor'.

As an example, the researchers find that the correlation of consumption and health status for children in Uganda is higher in urban than rural areas. As a result, even though consumption is higher in urban areas and health problems are fewer, it is not necessarily the case that poverty is lower in urban areas, because those that have low consumption in urban areas are more likely to also suffer health problems compared with rural areas.

This higher correlation means that at least some poverty measures—those that put greatest emphasis on multiple deprivations—will judge that poverty is higher in urban areas, despite the comparisons based on one variable.

ENDS

Notes for editors: ‘Robust Multidimensional Poverty Comparisons' by Jean-Yves Duclos , David Sahn and Stephen Younger is published in the October 2006 issue of the Economic Journal.

Jean-Yves Duclos is at Universite Laval. David Sahn and Stephen Younger are at Cornell University.

For further information: contact Romesh Vaitilingam on 07768-661095 (email: romesh@compuserve.com); or the authors via email: Jean-Yves Duclos ( Jean-Yves.Duclos@ecn.ulaval.ca), David Sahn ( des16@cornell.edu) and Stephen D. Younger (sdy1@cornell.edu).