| The poverty profile focuses on presenting the poverty characteristics of various household groups. The choice of the types of groups will be driven by some ex-ante knowledge of important dimensions (where qualitative data can help) or by dimensions which are relevant for policies. For instance, geographic location, age or gender might be dimensions along which policies can be developed. Another dimension which can provide useful insights for policy elaboration is the link between employment and poverty. This could indicate which sectoral pattern of growth would have the highest impact on poverty. There are three main ways to present a poverty profile. Poverty measures according to household groups: The first and most common way to present poverty data is to give poverty measures for various household groups. For example, Table A below, shows that households without education have higher poverty incidence than those with higher levels of education in Malawi. Another example is provided below in Table B which shows that households living in Barisal in Bangladesh had a poverty incidence of 60 percent in 1996, as compared to 53 percent for the country as a whole. Contribution of various household groups to poverty measures: An alternative way to present a poverty profile consists in assessing how various household groups contribute to the overall poverty in the country. The contribution of a household group to overall poverty is a function of that group’s population share and the incidence of poverty in the group. Table B shows that the population living in the Barisal division represents 7 percent of the population, and the headcount index is 60%, against a national average of 53%. Therefore, the share of all the poor living there is 8 percent (8=7*60/53). In the case of Madagascar, Table 3 shows that 14 of the country’s poor live in urban areas (14 = 21 * 47/70). Relative risk: Poverty measures can be translated into relative risks of being poor for different household groups. These risks provide the probability that the members of a given group will be poor in relation to the corresponding probability for all other households of society (all those not belonging to the group). In Madagascar, Table B indicates that urban households are 39 percent less likely to be poor than non-urban (i.e. rural) households (0.39 = 1 – 47/77), while rural households are 63 percent more likely to be poor than non-rural (i.e. urban) households (0.63 = 1 – 77/47). Similar calculations could be carried out relative to the entire population or to a specifically selected group.
Table A: 
Table B:
While certain variables like education, health, and access to service will almost always be part of a poverty profile, the relevance of many variables will depend on country circumstances and on the data source available. The profile should, if possible, identify the major production and consumption characteristics of the poor: whether the rural poor farm their land, are agricultural wage laborers, or work in various non-farm activities; whether the urban poor work as wage employees or as micro-entrepreneurs in the informal sector. Data on asset holdings by the poor are also relevant, as are their production technologies, use of inputs, and access to social and infrastructure services. Information on the composition of poor people’s consumption, including their access to public goods, is also valuable. Cross-links to other forms of poverty, such as lack of education, health care, and security, can also be established. The extent to which a detailed poverty profile can be constructed depends on the type of data available. (See Data for measurement for more information.) If the survey is designed to be representative at the level of relatively small geographic areas (e.g. the district level), the various measures could also be presented graphically on a poverty map. Poverty comparisons across countries are difficult for several reasons. For a discussion of the issues, see “Poverty Debate.”
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