Poverty Mapping

Poverty, income inequality and natural resource degradation are among the important disturbing factors on the way to development in many developing countries. The incidence of poverty defined here is the proportion of households whose per capita expenditure is below poverty line for the respective area. Poverty maps are drawn to develop various kinds of decisions for poverty alleviation. However, like the poverty profile, the use of poverty maps does not provide an estimate of the causal linkage between poverty and the variables that influence it; such maps provide mainly .visual advice, hence, leaving researchers to look for empirical relationships between poverty and socio-economic indicators. There are significant differences in poverty and welfare levels between communities living in different geographical areas.

The major problems to this kind of analysis have been data deficiency and the correct application of analytical tools. However, using a combination of geo-referenced environmental information and household expenditure explore the statistical relation between poverty and the environment at a fine resolution. Cross sectional data can be used to explain the relationship between the location of the poor and the environment and how changes in levels of poverty relate to changes in selected environmental indicators. Specifically, they analyse how incorporation of the biomas information helps improve the precision of poverty estimates. A major strength of the poverty mapping method and inclusion of biomass data is that it calculates the standard errors, a measure of the accuracy of the estimate.

As such, an analytical link between the people and their local environments can be established, that is between the economic data and the Geographical Information Systems (GIS) based environmental data. Information on all dimensions of well-being; household and socio-economic characteristics including education, assets, employment and household consumption expenditure can be aggregated with data from the population and housing census, housing characteristics, location of residences and access to basic utilities to map the spatial distribution of poverty. In order to understand the relationship between poverty and the environment, some additional environmental variables must be combined describing land cover and land use, and roads, as well as rainfall, amount of arable land, distance to nearest towns with varying population densities, slope, rangelands, distance to hospitals, travel times to different towns and roads, and flooding areas.

The small area estimation method requires a minimum of 2 data sets: usually household survey data and census data corresponding to almost the same time period. This approach is useful in estimating poverty estimates for administrative levels (districts, counties and sub counties) on the basis of the census and survey data. The poverty estimates derived from this method are based on household consumption expenditures and a series of household characteristics common to both the survey and census. This method however does not measure any linkages between poverty and environmental variables.

The depth of poverty (or the poverty gap) takes into account not just how many people are poor, but how poor they are on average. It is equal to the head count index multiplied by the poverty gap ratio. This index gives a good measure of the extent or intensity of poverty as it reflects how far the poor are from the poverty line. It can therefore be used to calculate the amount of income that needs to be transferred to the poor in order to eradicate poverty under perfect targeting. However the poverty gap ratio is insensitive to income distribution among the poor.

The severity of poverty (or squared poverty gap) takes into account not just how many people are poor and how poor they are, but also the degree of income inequality among poor households. It is equal to the head count index multiplied by the average squared percentage gap between the poverty line and the income of the poor. It therefore attaches greater weights to the poorest of the poor. The poverty gap squared reflects the degree of inequality among the poor in the sense that the greater the inequality of distribution among the poor, the higher the severity of poverty.

The choice of environmental variables estimates can make a difference in terms of the level of poverty and thus the targeting of poverty alleviation programs. Methodological differences can result in different estimates of the incidence of poverty at district, county and sub county levels. It is interesting that the inclusion of the additional land use data can change the poverty estimates at all levels. The poor are actually using the ecological resources to improve their welfare but in the process they degrade the natural environment as well. Portraying less or more poverty could also be explained by the different land tenure systems in the different regions of the country, such as being communal or private. The tenure system could have productivity impacts and therefore household welfare.

Environmental factors have a significant relationship to a sub counties probability of being poor, such as, better soil will provide better levels of welfare for the communities and therefore result in lower poverty rates. Obviously this justifies the need for interventions to improve soil conditions through better soil conservation practices and possible use of fertilizers in areas with poor soils. Where areas with larger slopes are more affected by poverty does explain effects of erosion. Income inequality has significant negative impacts on poverty. This result is consistent with other findings (see for example CBS, 2005) that areas with higher poverty generally tend to have lower inequality levels. The other significant impact on poverty is representing the potential of the area to flood. A higher potential to flood implies greater vulnerability of the communities, other things being equal and has the expected positive impact on living standards. Communities nearer to the wetlands could be deriving some benefits (water, fish, papyrus and wetland farming) from the wetlands. Interestingly, sub counties with larger income inequality have a low probability of being poor. The importance of soil quality, wetlands, roads, hospitals, grasslands, farmland, built areas, slopes and rainfall are factors that affect the probability of areas being poor. Different factors account for different effects on probability of being poor and therefore the need to devise specific interventions for particular areas.

Evidently environmental factors are important in any poverty eradication effort. Such factors should therefore be considered in the design and implementation of any poverty reduction strategies and used as a guide for resource allocation. This analysis identify where the poor are, under what environmental conditions they live and why the poor are where they are, but there is a need to refine and extend this analysis, including more disaggregate analysis at the agro ecological zone level, as well incorporating supplementary information from other data sources such as the livestock and agricultural census, national agricultural survey, demography and health survey, and service delivery survey.


Oxfam Poverty Assessment,PRSP: A Guideline, www.oxfam.org.uk

CBS and ILIRI (2003). Geographic dimensions of Well-being in Kenya. Where are the Poor? Volume 1. Central Bureau of Statistics, Kenya

World Bank (2002), World Development Report. New York: Oxford University Press

P. Birungi, P. Okiira Okwi, D. Isoke1, (2005), Incorporating Environmental Factors in Poverty Analysis, the Poverty and Economic Policy (PEP) Network,


Elbers C., Lanjouw, J.O and Lanjouw, P. (2002). .Welfare in Villages and Towns: Micro level estimation of Poverty and Inequality.. Policy Research Working paper, World Bank

Jalan, J. and Ravallion, M. (1998). Geographic poverty traps. World Bank