We compare results from our multidimensional poverty estimates at the district (Zambia) and county (Kenya) levels with those obtained using traditional small-area income poverty mapping. exercises in both countries. The main objection to using income alone as an indicator of well-being is that the ability of individuals or groups to convert income or other resources into valuable functions depends on several other factors including personal characteristics (e.g. physical conditions and mental), social norms (eg the role of women), and environmental factors (eg the availability of public goods) (Sen 1992b). Likewise, the population ratio figure shows that the incidence of multidimensional poverty is 54.6% in Kenya with the figures being relatively higher in rural areas (60.5%) and peri-urban areas (52.2%) compared to major urban areas. (38%).
Looking beyond the rural and urban averages, Table 2 and Figure 1 show the existence of large within-country differences in the extent of multidimensional poverty in Kenya. The poverty estimates indicate that the percentage of people who are multi-dimensionally poor is 81.5% in the Northeast Province, while this figure is 41.2% and 27.4% in Central and Nairobi Provinces respectively. The patterns of regional differences are also reflected in Table 1A in the Appendix, which provides income and multidimensional poverty and inequality estimates by province.
This scatterplot reinforces the fact that there is a strong positive relationship between the extent of income poverty and multidimensional poverty scores. For example, while MP turnout is only 27% in Nairobi County, the intra-county figure varies from 20.7% in Westland Constituency to 33.2% in Kamukunji and 41.2% in Langata Constituency. Similarly, in Mombasa County, the MP incidence is 25.6% in Mvita Constituency, while the figure is more than 40% in the other three constituencies.
Similarly, the incidence of multidimensional poverty in the poorest location in Mombasa is about eight times that of the richest location.
Multidimensional poverty and inequality in Zambia
11 Overall, poverty estimates at the county level suggest that both income levels and multidimensional poverty are relatively lower in predominantly urban counties, such as Nairobi, Kiambu, Nyeri and Mombasa (Figure 1A in the Appendix). When we further disaggregate the poverty estimates by constituencies, larger differences in poverty levels emerge (Table 2A in the Appendix). Of the five richest districts, four are in the Copperbelt Province (Livingstone is in the Southern Province), while four of the five poorest districts are in the Western Province.
Income poverty estimates at the district level were obtained from a small area poverty mapping exercise (De la Fuente et al., 2015). Source: Multidimensional self-reports and income poverty estimates obtained from De la Fuente et al. The incidence of MP is 42% and 43% in the two largest urban districts of Kitwe and Lusaka respectively.
The figure varies between 28%-33% in the nine relatively less income poor districts (Kabwe, Luanshya, Chingola, Ndola, Kalulushi, Mufulira, Chililabomb, Kitwe and Livingstone). In contrast, the incidence of income poverty is greater than 60% in 57 of the 72 districts, and the figure ranges from 88% to 95% in five of the income-poor districts (Milenge, Kalabo, Kabompo, Samfya and Shang'ombo). Looking at the relationship between the incidence of income poverty and multidimensional poverty, estimates suggest a high correlation between measures of income and multidimensional poverty at district levels (Figure 4), with the Spearman correlation coefficient being 0.8 (p<0.000).
Based on both income and multidimensional poverty measures, urban districts are generally less poor than rural districts in Zambia. Moreover, further disaggregation of the poverty estimates by consistencies within each district reveals large differences in poverty levels across both urban and rural districts (Table 2B in the Appendix). Similarly, poverty in Kitwe District ranges from 28% in Wusakile Constituency to 52% in Chimawemw Constituency.
Overall, the poverty estimates at the constituency level show that although urban districts are on average less poor than rural areas in Zambia, there are also large differences in poverty rates within large urban districts. Comparison of the poverty rate in Zambia with that of Kenya indicates that the level of multidimensional poverty in Zambia is higher than that of Kenya. For example, the MPI in the poorest province (Northeastern) in Kenya is three times higher than in the richest province (Nairobi), while the MPI in the poorest province (Western) in Zambia is about twice as high as in the richest province (Lusaka or Copperbelt ).
Change over time in access to basic services
Looking at access to improved sanitation services, levels of deprivation increased in 33 of the districts, with the figure increasing by 10-40% in Luanshya, Chililabomb, Kalulushi, Ndola Mufulira, Kitwe, Chingola and Livingstone districts, while the figure increased by 5.2% in Lusaka and by 9.7% in Kabwe districts. These results show that although the level of deprivation from access to safe drinking water and access to sanitation services in many of the major urban districts is lower than in rural districts, the change over time shows that the major urban districts are the areas where levels of deprivation have increased significantly. Educational deprivation is calculated for those aged 18 and over (deprived if they have not completed lower secondary primary education, which is eight years of schooling).
The use of piped water may not be appropriate in defining the level of deprivation in rural areas where there is also access to safe drinking water. To capture changes in rural areas, we calculate another variable indicating water scarcity, considering water obtained from wells, springs and boreholes as safe (whether protected or not) in 2009 and 1999.9. A comparison of the scatterplots from 1999 and 2009 shows a clear, large persistence in the rate of development across counties in Kenya.
Results from Figure 6 and 2A (in the appendix) show that in all the provinces deprivation in education has decreased, although with significantly varying degrees. 9 Thus, only access to water from a dam, pond, lake, river and Jabias is considered unsafe. We could not compare deprivation levels in access to improved sanitation because the variable is not comparable in the two censuses.
Likewise, levels of deprivation of access to electricity increased in only four counties: Samburu, Marsabit, Mandera and Lamu (increasing by less than 2%). Large declines in the level of deprivation of access to electricity are observed in Nairobi counties (43%) followed by Kiambu (37%) and Mombasa counties (26%). Unlike access to electricity and education, the level of deprivation from access to piped water increased in many of the counties.
In contrast, piped water deprivation levels decreased by at least 10% in nine other counties (Tharaka Nithi, Nyeri, Kirinyaga, Garissa, Kiambu, Kilifi, Isiolo and Murang'a). Among the counties where deprivation of access to water increased between 1999 and 2009, five of them (Wajir, Marsabit, Samburu, Mandera and Turkana) are also among the seven poorest counties in multidimensional concepts of poverty in 2009. In addition, the results show that although access to clean drinking water is higher in large urban centers such as Mombasa and Nairobi, higher increases in deprivation are observed in both Mombasa and Nairobi counties.
Conclusions
19 sanitation does not correspond to the scale required to accommodate the rapid urban growth in large urban centers in both countries. In conclusion, we return to some of the framework questions that were raised in the introduction. Our work has shown that progress has been made in improving access to basic services in both countries.
Similarly, wide disparities in economic and social indicators have been identified as one of the potential risk factors for conflict in Zambia (Smith-Hohn, 2009). In recent years, both countries have started to implement various measures to reduce poverty and regional disparities. Roots and Trajectories of Political Violence in Kenyan Civil and Political Society: A Case Study of Marsabit District (IDS Evidence Report No; 71).
Appendix A: Kenya
Appendix B: Zambia
