The preceding discussion should make the potential practitioner somewhat nervous about the choice of methodology. It is in practice difficult and impractical for all practitioners to test alternative methods. Poverty mapping is carried out by a variety of institutions and individuals, ranging from government ministries to NGOs to individual academic researchers. Each may have different ideas and analytical and financial capacity with which to carry out the exercise. Five elements or constraints taken together guide and justify the choice of a poverty-mapping methodology: the purpose or objective of the exercise, the poverty philosophy of a practitioner or institution, data availability, analytical capacity and cost.
Practitioners may have one or several objectives when planning and carrying out a poverty-mapping exercise. These may range from targeting specific small or large interventions, building a map to convey a political message or constructing inputs for a correlation or multivariate analysis. Each of these objectives may dictate a particular methodology. Interventions require greater precision and specific indicators, because the welfare of thousands, even millions, of people in a country depends on this measurement. Research and maps for communication can tolerate greater levels of error and thus do not face this particular constraint. The purpose of a poverty map is thus linked directly to the issue of bias and error, though practitioners may be unaware that they are making this decision or trade-off.
Practitioners may have a range of philosophical beliefs that influence the choice of methodology. These beliefs are often associated with professional disciplines or institutional characteristics. Economists generally prefer consumption-based welfare measures and methods based on econometric analysis; they thus prefer the two small-area estimation methods, which were developed by economists. Sociologists and anthropologists are generally suspicious of poverty characterizations generated by quantitative survey data; they prefer case studies, rapid rural appraisals and participatory approaches. NGOs tend to prefer the latter techniques, which are more suited to the work and interventions they carry out. National statistical institutes have traditionally relied on statistical measures that have little economic meaning, such as principal-component and factor analysis, which are often used to create different types of indices. This is not surprising, because national statistical institutes have traditionally been staffed by statisticians. The great variety among the methods described in Section 3 stems largely from these professional and institutional beliefs.
In poverty analysis, however, these traditional preferences have recently begun to break down. Interagency cooperation in a number of countries has fostered openness at national statistical institutes for implementing other types of methodology. This is particularly evident in the diffusion of the World Bank's small-area estimation methodology, which has been accepted in an increasing number of countries. Much of this acceptance can be attributed to a commitment by the World Bank poverty-mapping group to in-country training and production of poverty maps, and to provision of user-friendly statistical instruments designed to utilize data already collected by national statistical agencies. Conversely, given the scarcity of consistent consumption or income data in many countries, and the cost of collection, economists have experimented with asset-based poverty indices based on statistical techniques such as principal components.14
Different types of data constitute the basic inputs into poverty mapping. Data availability is thus a fundamental constraint in choosing a poverty-mapping method. This constraint has two levels: the existence of data, and access to existing data. Many methodologies depend on the existence of data derived from extremely expensive collection efforts such as a population census and national household surveys. Few poverty-mapping exercises can justify such a level of expense for this single use, so in marketing its small-area estimation technique, probably the most data-intensive methodology, the World Bank wisely argues that their method serves to utilize data that already exist. But many countries do not have contemporaneous census and household-survey data, which constitutes a major problem for small-area estimation methodologies. Such databases are expensive to collect, so they are not repeated very frequently. When combining databases, practitioners are thus faced with problems of timing between databases; at some point, after a certain number of years, the databases are no longer compatible.
Other methodologies described in Section 3 require collection of primary data, and may combine this with whatever else is available. Methodologies that combine qualitative with secondary data are relatively inexpensive to implement and thus are less constrained by data availability. Because statistical rigour is less important, practitioners take advantage of existing data and fill the gaps with their own fieldwork. Participatory methods create their own data, but the formalization of the participatory method described in Section 3 requires a more formal and thus more expensive data-collection effort.
Obtaining access to existing data constitutes a barrier for a number of poverty-mapping methodologies. The household-level unit census data required for the World Bank small-area estimation method is perhaps the most sensitive type of information, and many countries are rightly reluctant to provide it to outside institutions and researchers. The World Bank poverty-mapping group has had great success in obtaining access to this information, primarily because of its policy of conducting all analysis in-country in collaboration with national analysts. Not all international organizations or NGOs and few individuals have similar resources and influence with which to obtain the same access, however, which limits use of the World Bank small-area estimation method by other practitioners.
Community-level averages from census data are more readily available, often on the Internet, which makes the alternative small-area estimation method more attractive for general use. For the same reason, practitioners working on small budgets will be drawn to those methodologies for which data are more readily available, be they marginality indices, direct measurement of census data or other secondary sources.
Subnational accessibility data such as access to health and education facilities and infrastructure and transport and travel time have proved useful in Mexico (PROGRESA, 1998) and Burkina Faso (Bigman et al., 2000) as inputs into targeting anti-poverty programmes and visual correlation with poverty and food insecurity (see Henninger, 1998, for many examples). They can play a very important role as explanatory variables in the multivariate analysis of the determinants of poverty and food insecurity, though this has yet to become common practice. Availability of this type of data varies widely by country, however, and must be taken into consideration in terms of the design and selection of empirical studies.15
A major problem is that data are still weak on the environmental aspect, particularly for single-country subnational poverty studies. Few in-depth environmental surveys collect information typically found in household surveys - a search must be made for those notable exceptions - and although some kind of subnational poverty data are usually available, it is often not comparable with the environmental surveys, or it is not geo-referenced. Many global data sets may not be appropriate for use in subnational studies, particularly in medium-sized or small countries, because they do not capture in-country variation and are thus insufficient for establishing relations between these variables and the outcomes of interest. These include, for example, the FAO farming-systems typology and the GLASOD soil-degradation database.
Analytical capacity is another constraint. The poverty-mapping methods presented in Section 3 imply a wide range of analytical demands. The World Bank poverty-mapping group, for example, has expended great effort in making the sophisticated econometric small-area estimation model as standard and user-friendly as possible, but it still requires a certain level of statistical and econometric understanding to implement and interpret. Methods that employ statistical techniques that are more traditional in the sense that they form part of a professional statistician's training may be much more appealing; examples are principal components or factor analysis. Most statistical agencies are staffed by statisticians, as discussed earlier, and the application of econometric techniques favoured by economists is not always straightforward or easily understood. For smaller-scale practitioners such as NGOs, which depend on qualitative work, lack of basic training in statistics or econometrics tends to exclude more quantitative methods from the outset.
Cost represents the final and usually overriding constraint. Basically, the more sophisticated the analysis, and the more data to be collected, the more expensive the exercise becomes. Cost includes time spent obtaining, understanding and analysing data. Governments planning to use poverty maps to guide policy interventions should obviously invest in analytical and data infrastructure and then choose the technique which best fits their objectives and philosophical perspective. Researchers at the computer with limited funds may be restricted to what they can find on the Internet.
14 See, for example, Filmer and Pritchett (1998).
15 See Nelson et al. (2000) for a description of constructing these types of variables in Honduras, and Bigman and Deichmann (2000b) for discussion and examples relating to Madagascar.
