How We Measure Poverty Influences How We Fight It

How do we measure who is poor, who escapes poverty and who moves into it? The most widely used indicator of poverty, the headcount measure, is simple but deficient, according to Martin Ravallion of the World Bank in an article in the September 1996 issue of the Economic Journal. Since poverty is multi-faceted, he argues, multiple indicators are required, including measures of the distribution of real expenditure per adult, access to non-market goods like health and education, distribution within households and the personal characteristics of the poor. We need to move away from narrow money metric measures of poverty if we are to measure the phenomenon more effectively, understand its causes more fully and formulate better policies to combat it.

Ravallion notes that for over a century, sample surveys of household living conditions have been used to address public concerns about poverty and inform public action. Once rare, nationally representative living standards surveys are now common in both rich and poor countries. Poverty measures produced from these data are keenly watched and debated. They are also increasingly relied on in policy discussions.Ravallion reviews the state of current thinking on these issues. Every step between the primary survey data and the final pronouncements on poverty headcounts and so on has been contentious, he reveals, finding that:

The headcount index of poverty, the percentage of people deemed to be living below the poverty line, has remained popular, despite trenchant critiques by economists. But policy analysis has started to be more aware of the need to consider impacts below the poverty line and allow a potentially wide range above and below. This is evident in the widespread use of headcount indices for multiple poverty lines, echoing concerns about the ‘ultra-poor’ and ‘vulnerable’ households just above the line.

The existence of a ‘jump’ at the poverty line has been an issue. From the point of view of anti-poverty policy, a jump attaches a premium to gains for the least poor among the poor. Starting instead from the value judgement that the poorest in terms of the agreed welfare measure should always get highest priority, then jumps are ruled out.

For policy purposes, the method of setting poverty lines can matter greatly to the interpersonal welfare comparisons being made and hence the structure of the resulting poverty profile. Alas, looking closely at the ‘rule of thumb’ methods used in practice can often leave one sceptical as to whether the outcome will guide policies in the right direction. For example, a worrying problem in much current practice is that the poverty lines used as deflators do not account well for the actual cost of living differences facing the poor partly due to differences in the prices they face. This can bias both the structure of the poverty profile and the aggregate measure.

‘Non-income’ indicators may help in identifying omitted aspects of welfare in standard poverty measures. For example, in the treatment of inequalities within households, standard practice has been to assume that all family members are equal. The inadequacy of this has long been recognised. But data are typically for the household's total consumption, though often with some individual level data on labour supply and some ‘non-income’ welfare indicators. Despite recent advances, there will remain an important role for supplementary data, such as indicators of child nutritional status.

Recognising the limitations of conventional money metrics of welfare does not mean that one should aggregate the multiple indicators into a single metric when there is no obvious basis for setting the trade-offs. It can be important to know that region A is doing well in incomes but not in basic health and schooling, while in region B it is the reverse. What is needed is a genuinely multi-dimensional approach in which expenditure on market goods sits side-by-side with ‘non-income’ indicators of access to non-market goods and indicators of intra-household distribution.

Given the pervasive uncertainties in measurement, there is a compelling case for greater future effort in testing the robustness of key conclusions to changes in measurement assumptions. Recent research has illustrated ways in which changes in measurement assumptions can radically alter policy relevant conclusions.

Poverty analysis has traditionally relied heavily on single household surveys of consumption or incomes, with a somewhat minimal set of other relevant variables. Such data were once only used to inform a rather narrow range of policy issues, notably targeted interventions. Now there is a much wider range of applications in all aspects of poverty policy, including macroeconomic policies, pricing policies, and public spending allocations. This is creating a demand for different types of data.

Conventional cross-sectional data sets are less than ideal for analysing the dynamics of poverty - who escapes poverty and who moves into it? There is a potentially high return to longitudinal data.

ENDS

Note for Editors: ‘Issues in Measuring and Modelling Poverty’ by Martin Ravallion is published in the September 1996 issue of the Economic Journal. Ravallion is at the World Bank.

For Further Information: contact Martin Ravallion on 001-202-473-6859 (email: mravallion@worldbank.org); or RES/ESRC Media Consultant for Economics Romesh Vaitilingam on 0171-878-2919 or mobile 0468-661095.