Strengthening existing information systems to provide improved analysis to support the design of cash transfer programmes John Seaman Evidence for Development

Planning a large scale e.g. national, CT programme will require information to estimate the resources required to reach a stated objective: The number of poor/ eligible households and people. The severity of poverty, however this is defined. Variation in the rate and severity of poverty between years e.g. with variation in crop production. + information to define appropriate, practical targeting criteria i.e. the relationship between household poverty, and household/ personal characteristics. (+ market information)

The information needed to plan a large cash transfer (CT) programme will depend on the programme objective Possible objectives include: the relief of destitution and/ or assisting specific groups e.g. the very poorest households, the elderly, the ill. poverty reduction e.g. to bring all households up to a defined standard of living. giving households sufficient money to allow them to invest and save, to increase their income and security in the long-term.

The difficulty in getting this information will vary with the objective: CT targeted at the poorest 10% of the population, or specific easily identified groups e.g. the elderly could be planned on the basis of a reasonably reliable census. More ambitious objectives will require much more information i.e. The number of poor/ eligible households and people; the severity of poverty; variation in the rate and severity of poverty between years + targeting information + market information Information on poverty is not currently available in most poorer African countries. National Household budget/ income/ expenditure surveys are infrequently updated, usually not available and there are serious questions about data quality.

The household economy approach (HEA) is a possible source of information: HEA is cheap to use, has relatively low skill requirements and is defensibly accurate. Some national data sets already exist. However: HEA provides ‘averaged’ information on income and household characteristics and this is not sufficient to meet the requirements for planning CT.

HEA, Zambezi West Bank, Zambia Data: Household economy profiles. FEWSNET/FEG + assets & contextual information e.g. on market use. Sources of food (% requirement) by wealth group Sources of cash income (ZK) by wealth group

Good quality household income/ expenditure data can also be obtained on small samples of households...

Salima, Malawi, one village, cash income/ adult equivalent after household food energy needs met.

A pilot was recently conducted tested in Zambia * to test a proposed method which would: -retain the practical advantages of HEA i.e. low cost, ease of use on large geographical areas. -while extending the range of information obtained to include that required for CT planning. * Supported by RHVP/DfID & CARE

The proposed method (‘HEA+’) an additional ‘very poor’ wealth group is added to the HEA data set. This should allow an approximation of the shape of the complete wealth distribution. Assuming that individual households can be placed (by village informants) in their correct HEA wealth group: additional information can be obtained from individual households within each wealth group by rapid interview e.g. on demography, assets, illness.

HEA 60% 30% 10% PoorMiddleBetter-off Percent in wealth group Income/MK Person/year

A B C D The ‘very poorest’ wealth group

HEA + Additional wealth groups allow an approximation of the complete income distribution HEA+ Model

The pilot study Two parallel surveys were conducted in the same village. 1.Using HEA, with the addition of a ‘very poorest’ wealth group. 2.Information on individual household income was gathered from every household. i.e. the study used two independent methods to estimate household income in the same reference period (February 2005 – March 2006)

What can HEA+ do? Area (+ census) gives cost of increasing income to SOLT or other threshold HH characteristics within wealth group e.g. number of elderly etc Modelled income change e.g. following crop failure, price change