* An extract from Issue 1 of the Newsletter on "Measuring the Progress of Societies".

[Oxford Poverty Human & Development Initiative (OPHI)]
By Sabina Alkire

The Project on Measuring Progress draws attention to the need for broader indicators of progress, and for better ways of using these indicators to inform policy. A new research centre in the University of Oxford, the Oxford Poverty & Human Development Initiative (OPHI) has two related research themes. One identifies 'Missing Dimensions' of data in developing countries, and crafts brief survey modules for gathering internationally comparable data on each dimension. In the other, OPHI has developed a new set of intuitive and robust Multidimensional Measures, which can identify the individuals, groups, or regions where progress has failed to materialise, using ordinal or cardinal data.

Reviews of the dimensions that have been used in several literatures reveal that there is considerable diversity in the content, name and number of categories used, and that common themes emerge repeatedly. Some common 'dimensions' of progress of well-being include Survival, Working Activities, Knowledge, Relationships, Self-direction, Expression and Harmony.

There are also some missing dimensions in international databases. The current data which shape poverty analysis in developing countries and are used to monitor the Millennium Development Goals (MDGs) come from four survey instruments. OPHI noted that in these survey instruments, it is impossible to obtain data on Violence, Empowerment, Informal work, Dignity or Shame/Humiliation, and Meaning. In May 2007, authors related to OPHI, in dialogue with interest groups in each area, drafted modules for each of those five areas to inform international and national policy.

OPHI researchers Sabina Alkire and James Foster just proposed a Multidimensional Poverty Measure that draws on the widely used 'Counting' approaches but addresses the ongoing problem of identification, and provides axiomatic foundations. It can identify the people who are poor in a range of different dimensions at the same time. Different weights can be applied to different dimensions, and ordinal, categorical, and cardinal data can all be used. The main advantage of this measure is that it is highly intuitive, easy to calculate, and can be decomposed by state/province, rural/urban etc, and, if it is calculated for individuals, by age, gender, ethnicity, educational attainment, and other variables.

* For further information, please visit www.oecd.org/progress/newsletter.