The benefits of more detailed poverty maps

Posted:
18 December, 2015

The lenses through which we observe the appalling situation that the poor endure now have higher resolution than in the past for several countries.

At a time when the Sustainable Development Goals urge countries to Leave No One Behind, the value-added of poverty maps that have higher levels of disaggregation is considerable. Such maps make it far easier to target poverty alleviation efforts and monitor progress. Let’s have a brief look at three countries, Bangladesh, Malawi and Yemen.

Bangladesh

The winter updates of the Global MPI 2015/16 include new maps for Bangladesh which cover 69 districts. This was possible due to the data from the UNICEF Multiple Indicator Cluster Survey (MICS) 2012-13 data for Bangladesh. The map on the left hand side below 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. The table 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.

MPI Headcount ratio (H) Proportion of population in Chittagong
Comilla 0.098 22.8% 19.0%
Feni 0.105 25.4% 4.9%
Chittagong 0.138 28.9% 26.3%
Chandpur 0.167 37.7% 9.4%
Brahmanbaria 0.207 44.0% 9.3%
Noakhali 0.253 48.8% 11.4%
Rangamati 0.270 55.7% 2.1%
Lakshmipur 0.276 52.4% 6.5%
Khagrachhari 0.277 56.3% 2.2%
Cox's Bazar 0.306 59.8% 7.3%
Bandarban 0.376 70.0% 1.5%



MPI-map-Bangladesh_largeweb

Malawi

The Winter 2015/16 updates of the Global MPI include a new dataset of Malawi – UNICEF MICS 2013-14. Whereas previous decompositions for Malawi covered three regions, the current dataset allows the poverty map to cover 27 regions of the country. As a result, a sharp gradient of poverty experienced from north to south can be readily seen in the map.

Even when considering the Southern region only, important differences can be observed. The region as a whole has an MPI of 0.292, and it is – on average – poorer than the national average of 0.265. But we can see that urban areas such as Zomba and Blantyre Cities have the lowest MPI at district level. In contrast, other districts in Southern Malawi are significantly poorer, such as Chikwawa or Mangochi, whose poverty level is similar to that of Uganda.

MPI

Headcount ratio

Population in southern region

Zomba City 0.099 22% 1%
Blantyre City 0.107 25% 10%
Mulanje 0.263 57% 9%
Mwanza 0.265 56% 2%
Blantyre 0.271 61% 7%
Zomba 0.279 60% 10%
Balaka 0.281 60% 4%
Chiradzulu 0.286 64% 5%
Neno 0.301 63% 2%
Phalombe 0.304 63% 5%
Machinga 0.332 68% 10%
Nsanje 0.342 68% 4%
Thyolo 0.348 72% 11%
Chikwawa 0.355 69% 9%
Mangochi 0.368 71% 12%

  MPI map Malawi

Yemen

For Yemen, the UNICEF MICS 2013 data has enabled significant mapping improvement from the MICS 2006 for that country. The newer dataset means that Yemen’s MPI can now be disaggregated into 21 districts instead of having only a national estimate. Yemen’s multidimensional poverty is just higher than Pakistan’s, and just less poor than Cameroon or Haiti. A total of 47% of the population are multidimensionally poor. The map shows where these people live. The metropolitan capital city Sana’a and the large Eastern regions (which are very sparsely populated), are radically less poor than the other regions. For example, in Sana’a city, only 14% of the population are poor. In Hajjah, it’s 76%.

Yemen poverty map_orig

The MPI thus offers poverty maps. In addition, a vital point is that behind each one of the geographical regions we also have a policy key. The MPI can be unpacked in each region, to show how people are poor – the deprivations they experience together. For example, in Yemen, we see that Sana’a city (rightmost) has very few living standard deprivations (green) but strong deprivations in the two health indicators (red), whereas in Hajjah (leftmost), each standard of living indicator contributes visibly to poverty.

Chart-Yemen_web

The high resolution poverty maps of the Global MPI 2015/16 include a vast number of subnational areas. This body of data and its visualisation provides a user-friendly technology, a high-resolution set of lenses, to show how poverty varies. Over time it can be analysed to see who is progressing and who might be left behind. The Alkire Foster methodology (2011) proves to be a well-suited tool to carry out this analysis, as the measure of poverty can be aggregated at national level and it can also be broken down back into the subnational regions that comprise the nation. And each subnational region comes with its own deprivation key. This means that the lenses through which we observe the appalling situation that the poor endure have higher resolution than in the past. This higher resolution may better inform public policy to fight poverty.

Subscribe to the blog

About the author(s)
Gisela Robles Aguilar
Research Officer in Multidimensional Poverty, OPHI
Sabina Alkire
Director, OPHI, and Associate Professor
i