Since the third quarter of 2000, in response to an invitation from the Zambian Government, the Civil Society in Zambia has been participating in the process of formulating a Poverty Reduction Strategy Paper (PRSP) for the country. In order to enhance the effectiveness of this participation, the Civil Society for Poverty Reduction, CSPR was formed. The CSPR comprises civil society organizations (CSO's) from various backgrounds and from throughout the nation.

The CSPR identified ten thematic areas that needed to be considered in the PRSP. Position papers on these themes were prepared by expert facilitators appointed by the CSPR. These are being submitted as Civil Society's inputs to the overall PRSP through consultations with the Government's PRSP Coordinator and Government's PRSP Working Groups.

In the process of conducting its PRSP-related activities, Civil Society has become keenly cognizant of the need for a whole lot of information at all phases of the PRSP process. These phases include:

• A preliminary assessment of the process itself:



• Identifying the poverty concerns of the nation;



• Mapping out the poverty reduction strategies;



• Prioritizing issues;



• Implementing the poverty reduction strategies; and



• Monitoring and evaluating the process as well as the outcomes of the process.

For its effective participation in the PRSP at all phases, Civil Society must be able to obtain all the relevant information at national, provincial and district levels. Such information must be as current as possible and be readily accessible to all interested members and organs of Civil Society. A commendable example of such information is the statistics on macroeconomic indicators published on a monthly basis by the Ministry of Finance and Economic Development that can be easily obtained from the Ministry by any interested individual.

However, the above illustration is not necessarily typical of the general state of availability and accessibility of data. For instance, data on actual expenditures by the Government published in the annual Financial Reports ("Blue Books" as they are called) provide a counter illustration. As of now, actual expenditure data are available only for 1998. But these cannot be easily accessed by Civil Society since, reportedly, they have not yet been tabled before Parliament. So the latest data on actual public expenditures can be obtained only for 1997. Such a time lag makes virtually impossible any poverty-proofing¹ of the public budget.

In the sections that follow, we shall outline Civil Society's perceptions of the kinds of information needed in relation to poverty, the present state of such information and ways in which the current information base can be enhanced for better analyses.

Information needs on poverty

In the first place, we need data that will enable us calculate measures of poverty and inequality². In here, we need not just one measure of poverty or of inequality but several.

In the case of poverty, multiple measures are required in order to capture the multi-dimensional nature of the concept. Poverty, as we are all aware, is not just inadequacy of income or consumption but also of several other non-monetary variables. Poverty also refers to deprivation of health, of knowledge, of access to basic social services and so on. And there is enough empirical evidence from many countries to show that income poverty and poverty in non-income dimensions need not be strongly related. This is particularly the case in Zambia. For instance, Spearman's rank correlation coefficient between the incidence of income poverty and the incidence of stunting among children (a measure of nutritional deprivation) is only 0.42 for the 72 districts of Zambia³. Hence in addition to the traditional headcount index of poverty (percentage of population below a stipulated money-metric poverty line), we need measures that capture other forms of deprivation.

One of the measures that have become well known in this respect is the Human Poverty Index (HPI) of the UNDP. The HPI concentrates on deprivation in three essential elements of human life — deprivation of longevity (indicated by vulnerability to death at a relatively early age), deprivation of knowledge (implying exclusion from the world of reading and communication) and deprivation of a decent standard of living (in terms of overall economic provisioning). The HPI, therefore, encompasses the following variables: percentage of people not expected to survive to age 40; percentage of adults who are illiterate; percentage of people living without access to safe water and to health services; and the percentage of moderately and severely underweight children under 5.

Needless to say, we need data on all the above variables on a regular basis in order to monitor success in poverty reduction through an analysis of HPI trends.

In the case of inequality too, more than one measure is needed since empirical evidence again shows that different measures of inequality such as the Coefficient of Variation, the Gini Coefficient and the Kuznets Ratio need not be consistent in their portrayal of trends. Hence relying on only one measure could be misleading⁴.

The measures that we have stated so far are all based on a quantitative approach to the understanding of poverty. But there is an imperative need to understand the qualitative dimensions of poverty as well. The qualitative approach in fact involves a still broader conceptualization of poverty than even any multi-dimensional definition of poverty adopted under the quantitative approach. This is because the qualitative approach seeks to define poverty in a manner as to capture the processes and interactions between social, cultural, political and economic factors. "It includes a wider range of factors such as vulnerability, isolation, powerlessness, survival, personal dignity, security, self-respect, basic needs and ownership of assets than does the definition of poverty under the quantitative approach" (Carvalho and White, 1997).

While data for quantitative measures can be collected through nation-wide surveys such as the Living Standards Measurement Surveys (LSMS), Living Conditions Monitoring Surveys (LCMS), Household Budget Surveys (HBS) and Demographic and Health Surveys (DHS), the qualitative approach requires different data collection methods and techniques of analysis. Key informant interviews, focus groups, community meetings, structured observation, participant observation and informal surveys are some of the data collection methods used in the qualitative approach. Techniques of analysis used include social mapping and modeling, seasonality maps, oral histories, daily time use analysis, participatory linkage diagramming, ranking of wealth and well-being and use of Venn diagrams⁵.

The qualitative approach to poverty analysis in Zambia has been used in some studies notably those by PAG (Poverty Assessment Group). But with the advent of the PRSP as the bedrock of development programming and monitoring, it is Civil Society's view that both quantitative and qualitative approaches should be used on a systematic and regular basis to collect data on poverty trends in Zambia.

Information is also needed on who the poor are and where the poor live. Although poverty is high in Zambia it is not evenly distributed. Different socioeconomic groups suffer different levels of poverty. For example, small-scale farmers who account for over a million households in the country are among the poorest while large-scale farmers (there are only about 1000 households) are among the most prosperous people. Similarly, disparities exist as between rural and urban areas, among the various provinces and among the various districts within every province.

Gender is a very important consideration in developing anti-poverty strategies. Female-headed households, especially households headed by women without any financial support from outside, are distinctly poorer than male-headed households. Child poverty, reflected by the growing numbers of orphans, street children and child-headed households, constitutes yet another conspicuous facet of poverty. Child and gender mainstreaming of anti-poverty programmes is therefore important in the poverty reduction and monitoring process. But this cannot be achieved without data disaggregated by gender and age.

Information on all the preceding aspects needs to be generated on a regular basis if there is to be effective targeting and channeling of resources to the needed groups and areas.

Another vital information required is about the nature of poverty among different geographic and socioeconomic groups. For instance, one group may be income poor but not health poor. Another group may be health poor but not income poor. A third group may be prominently knowledge poor and yet another group's problem may be lack of access to input markets. The identification of the nature of poverty is hence required in order to determine what specific anti-poverty programmes and projects are required for what specific areas or groups.

In addition to the data on measures that help us monitor the poverty status at the national, regional and local levels, we also need information that will help correlate changes in poverty levels with policies and programmes. Information on the macroeconomic and sector policies is a major pre-requisite. Allied to this is accessibility to information for budget monitoring and resource and expenditure tracking. This in turn requires the maintenance of a transparent record of inflows (through domestic revenue generation, external aid, debt relief and debt cancellation) of resources, of inter- and intra-ministerial/departmental prioritization in the allocation of resources and of the actual use of resources. In this respect, the on-going exercise within the Ministry of Finance and Economic Development of transforming the format of budget presentations to Activity-Based Budgeting (ABB) is a commendable one. It is eminently desirable that the completion of this exercise, arduous as it may be, is achieved as quickly as possible. For information on the annual budgets (both the "Yellow" and the "Blue" books) presented in the ABB format would greatly facilitate Civil Society's task of verifying the poverty-proofing of the budgets. In here, one may also stress the need to narrow the time lag in the publication of the "Blue" books.

What we have discussed so far may not be exhaustive of all the information needs relating to poverty reduction strategies, but they are indicative of the wide spectrum of data that need to be collected.

Some ten years ago, it would not have been easy to formulate, implement and monitor a meaningful and comprehensive poverty-reduction programme such as the PRSP. There would have been too many lacunae in data inhibiting such a process.

The nineties brought about significant improvements in the data base. Among others, there were two Priority Surveys (1991, 1993), two Living Conditions Monitoring Surveys (1996, 1998), two Demographic and Health Surveys (1992, 1996), a Household Budget Survey (1993/94) and a Census of Population and Housing (2000, whose results are not yet out). These nation-wide surveys created a data bank that enabled us to trend the evolution of poverty during the past decade and to look at changes in some critical social indicators and in the living conditions of the people in general.

However, significant gaps in information still remain. It is not possible for us in this short paper to present an exhaustive list of all these gaps. But a few illustrations can be cited. Statistics disaggregated by gender constitute one prominent area that is deficient. It would hardly be possible to calculate any recent value for the Gender-related Development Index (GDI) or the Gender Empowerment Measure (GEM) because the data are just not there. Similarly, the HPI cannot be calculated as per the formula since, in the absence of up-to-date data on life tables especially at the provincial level, one cannot know the percentage of people not expected to live up to age 40. The Zambia Human Development Report 1997 therefore used data on Under-5 mortality rates as a proxy for this variable in order to calculate HPIs for Zambia and its nine provinces.

Statistics on inequality also need to be improved upon. In here, it is to be noted that inequality needs to be measured not only in respect of income but also of other opportunities such as education. The calculation of a useful index such as the Inequality-Adjusted Human Development Index would require values of the Gini Coefficient not only for income distribution but also for the distribution of life expectancy at birth and education. But there is no recent published data to enable the calculation of these values. Again, while conjectures have been made about the growing incidence of child-headed households, there is no concrete information on this variable.

One can also provide many more such examples of data gaps relating to various sectors such as agriculture, environment, education, health, etc. Suffice it to say, there is considerable scope for improving the information base.

Despite a lot of poverty analysis that has been done in recent years, there are still some important aspects on which information is still relatively scant. Analyses of these aspects of poverty would be very useful but would also require more data. Some of these are explained briefly below.

Hidden deprivation: One aspect is intra-household distribution of poverty. A household on the whole may not be deemed to be poor in terms of its income and yet some members within the household may experience poverty of some form or another. For example, a girl child or an orphan in such a household may not be sent to school for reasons other than income deficiency. It would be useful to gather information on such "hidden deprivation" that exists within households.

Cumulative deprivation: Another aspect that would be useful to analyze from a strategic perspective is "cumulative deprivation". Deprivation suffered by individuals or households even in one form or another would be a manifestation of poverty. For example, a child that is undernourished, a child that is not sent to school or a child that does not have a home suffers from poverty. But an undernourished street child that has no benefit of formal education would suffer far more extreme poverty due to the combination of several forms of deprivation that the child experiences. Similarly, an adult who is illiterate, unemployed and HIV positive would undergo far greater suffering than an adult who experiences only one of these forms of deprivation.

Poverty transitions: Published statistics tell us that in 1991 the incidence of poverty in Zambia in terms of the headcount index was 69.7%, in 1996 it fell to 69.2% and in 1998 it rose again to 72.9%. But these statistics do not mean that the same individuals or households that were poor in 1991 were poor in 1996 or 1998. During this period, some of these individuals/households may have left poverty, some may have remained poor and some new individuals/households who were not poor before may have subsequently entered poverty. In other words, the people who were poor in 1991, 1996 and 1998 may not have been the same individuals.

The above raises the questions: at any given time, how likely are people to enter poverty?; what is the likelihood of their remaining in poverty?; and what are their chances of getting out of poverty?

According to Bradbury, Jenkins and Micklewright (2000), in countries with higher poverty rates, one could expect the rate at which individuals enter into poverty to be higher and the rate at which they leave poverty to be lower than in countries with lower poverty rates. This hypothesis already puts Zambia at this present juncture at a disadvantage and suggests the need for more concerted efforts in devising strategies for poverty reduction here than in practically all of Zambia's neighbours where poverty rates are lower⁶.

To explain the dynamics of child poverty transitions, Bradbury et al (op. cit.) suggest four measures of poverty flows:

1. The exit rate: the number of children leaving poverty expressed as a proportion of the number of children who were poor;



2. The outflow fraction: the number of children leaving poverty expressed as a proportion of the total number of children in the population (whether they were poor or not);



3. The entry rate: the number of children entering poverty expressed as a proportion of the number of children who were not poor; and



4. The inflow fraction: the number of children entering poverty expressed as a proportion of the total number of children in the population (whether they were poor or not).

If data were available, the above kind of analysis could be carried out not only for children but for other groups as well.

In the preceding sections we have outlined some of the major categories of information needs for poverty reduction strategies as perceived by Civil Society. To summarize the salient points:

• Both quantitative and qualitative approaches must be adopted in providing a situation analysis of poverty;



• Multiple measures of both income and non-income dimensions of poverty and inequality are required to obtain a comprehensive and consistent picture of poverty across space and over time;



• Data must be available in disaggregated form in relation to geographic, gender, demographic and other socioeconomic criteria;



• A few more perceptive analyses in addition to the traditional analyses of poverty would be useful in pursuing more effective poverty reduction strategies;



• Information must be timely and readily accessible to Civil Society. References

Bradbury, B., S. Jenkins & J. Micklewright (2000): Child Poverty Dynamics in Seven Nations, Innocenti Working papers No. 78, UNICEF, Florence.

Carvalho, S. & H. White (1997): Combining the Quantitative and Qualitative Approaches to Poverty Measurement Analyses: The Practice and the Potential, World Bank Technical Paper No. 366, Washington D.C.

Chambers, R. (1994): The Origins and Practice of Participatory Rural Appraisal, World Development, Vol. 22, pp. 959-961.

Combat Poverty Agency (1999): Annual Report 1999, Dublin.

Ray, D. (1998): Development Economics, Princeton University Press, New Jersey.

Seshamani, V. (2000): Deprivation in Zambia: District-level Rankings Based on an Index of Deprivation, UNICEF-sponsored study, Lusaka.

UNDP, SADC & SAPES Trust (2000): SADC Regional Human Development Report 2000, Harare.