One of the the strengths of measuring multidimensional poverty levels is that the results can be broken down and used to generate poverty maps. Such maps give an immediate, at-a-glance impression of where poverty is most acute, and where interventions are most urgently needed.
The most common way of decomposing the information is by region or other local government unit. Constructed in this way, poverty maps illustrate which areas within a country are more or less poor. In Yemen, for example, the new Global MPI estimations for that country show that in the capital Sana’a, poverty levels are relatively low, similar to those found in Indonesia. In Hajjah, northwestern Yemen, however, poverty is higher than in the Central African Republic.
Although region or administrative unit is the most common way, the information breakdown and mapping can also be done according to, for example, whether locations are urban or rural, or the ethnic background of most of the inhabitants. Maps overlaying poverty and infrastructure-related information could also be produced; particularly helpful for government planners. The different data layers can also be combined, which can yield fascinating insights into poverty and deprivation not easily conveyed by mere numbers on a page.
Properly constructed, poverty maps based on multidimensional poverty measures provide a detailed picture of the location as well as the interlinked deprivations of the poorest, so that policies can be effectively designed and targeted.
Read more in our briefing Poverty Maps (pdf).
Case study: Bangladesh
The lenses through which we observe the appalling situation that the poor endure have higher resolution than in the past. The winter updates of the Global MPI 2015/16 include new maps for Bangladesh which cover 69 districts.
The MICS 2012-13 data for Bangladesh is representative both at 7 regional divisions, and also at the 64 zilas or districts of the country. At a time when the Sustainable Development Goals urge all to Leave No One Behind, Bangladesh data allows us to explore the value-added of poverty maps that have higher levels of disaggregation. The map on the left hand side depicts the average poverty level for Bangladesh’s 7 major regions in 2012-13. We see that the central regions of Dhaka and Khulna had lowest poverty, while the northern region of Sylhet and the coastal region of Barisal had the highest multidimensional poverty. The map on the right hand side presents MPI at district level, and reveal a more nuanced picture. We see that the northern districts of Netrokon, Mymensingh, Sherpur, Jamalpur, Rangpur and Nilphaman show higher poverty than we were able to see before.
Take, for example, the fascinating case of Chittagong. As a region it has a ‘medium’ level of MPI. But inside, its districts have either high or low levels of poverty. Table 1 below shows the distribution – with poverty rates ranging from 23% to 70% across districts in this region. The MPI values in Chittagong range from that of Bhutan (0.119) to Liberia (0.374) – a span of 37 countries’ national MPI values! In this case, the higher level of disaggregation is surely worthwhile for improving internal poverty policies.
|TABLE 1||MPI||Headcount ratio (H)||Proportion of population in Chittagong|