World Bank: Estimation of Poverty in Somalia Using Innovative Methodologies

This is a 2019 paper that used GIS and probability modelling to estimate poverty and population distribution across Somalia & Somaliland. This is unlike previous reports that used human estimates for poverty and population that were often unreliable. Very interesting findings.


Abstract

Somalia is highly data-deprived, leaving policy makers to operate in a statistical vacuum. To overcome this challenge, the World Bank implemented wave 2 of the Somali High Frequency Survey to better understand livelihoods and vulnerabilities and, especially, to estimate national poverty indicators. The specific context of insecurity and lack of statistical infrastructure in Somalia posed several challenges for implementing a household survey and measuring poverty. This paper outlines how these challenges were overcome in wave 2 of the Somali High Frequency Survey through methodological and technological adaptations in four areas. First, in the absence of a recent census, no exhaustive lists of census enumeration areas along with population estimates existed, creating challenges to derive a probability-based representative sample. Therefore, geospatial techniques and high-resolution imagery were used to model the spatial population distribution, build a probability-based population sampling frame, and generate enumeration areas to overcome the lack of a recent population census. Second, although some areas remained completely inaccessible due to insecurity, even most accessible areas held potential risks to the safety of field staff and survey respondents, so that time spent in these areas had to be minimized. To address security concerns, the survey adapted logistical arrangements, sampling strategy using micro-listing, and questionnaire design to limit time on the ground based on the Rapid Consumption Methodology. Third, poverty in completely inaccessible areas had to be estimated by other means. Therefore, the Somali High Frequency Survey relies on correlates derived from satellite imagery and other geo-spatial data to estimate poverty in such areas. Finally, the nonstationary nature of the nomadic population required special sampling strategies.

Results

mMxHfw0.png


Poverty is somewhat heterogeneous between different population types and regions. Urban areas have a lower poverty headcount rate (60 percent), than the rest of the Somali population (Figure 6; p<0.01 vs. Mogadishu, p<0.05 vs. IDPs in settlements and nomads, and p<0.10 vs. rural areas).34 This comparison excludes the capital, Mogadishu, whose residents are poorer than in other urban areas (between 72 and 76 percent). This higher poverty rate in Mogadishu compared to other urban areas is likely the result of a larger concentration of the displaced population and the challenges associated with the displacement crisis, which the 2016/17 drought recently exacerbated.35

Poverty is also heterogeneous across space. Based on estimates from satellite imputation, the highest levels of poverty are clustered in south‐western Somalia, and several districts in northern Somalia (Figure 7).

Ka fikir walaalayaal :yacadiim: Let's keep it qabyaalad free during this blessed month :silanyosmile:

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Interesting. Awdal and Puntland are looking a bit too red :yacadiim:never knew many parts of the south and Mudug were that uninhabited
 
This is ganna be tasty. The fake awdal and puntland statistics are going out of the window. Also check out jamaame walle dad badan ba ka qaylindoona
910A73B1-189C-4BFE-AFD4-EAEA0FB19141.jpeg

95CA8DEF-FD2A-4982-8DED-9D209ABF9CE1.jpeg
 

Thegoodshepherd

Galkacyo iyo Calula dhexdood
VIP
@JohnQ that is "imputed" poverty not a map of the "actual" poverty rate. They are testing to see how well satellite data can predict their poverty data from my famous 2017 study written by the same researcher as this paper.
https://www.somalispot.com/threads/huge-world-bank-poverty-survey.31811/

They actually just released a new UN poverty survey today, but it is useless because they don't disaggregate the data by region. NN must have seen my thread from 2 years ago, and instructed UNFPA to not release regional data.

@Awbarkhadle I find it endearing that the first thing that comes to your mind is to compare your region with Puntland. It is nice to see Puntland is the yard stick Somalis measure themselves against.
:obama:
 
@JohnQ that is "imputed" poverty not a map of the "actual" poverty rate. They are testing to see how well satellite data can predict their poverty data from my famous 2017 study written by the same researcher as this paper.
https://www.somalispot.com/threads/huge-world-bank-poverty-survey.31811/

They actually just released a new UN poverty survey today, but it is useless because they don't disaggregate the data by region. NN must have seen my thread from 2 years ago, and instructed UNFPA to not release regional data.

@Awdalia Rising I find it endearing that the first thing that comes to your mind is to compare your region with Puntland. It is nice to see Puntland is the yard stick Somalis measure themselves against.
:obama:
@Awdalia Rising didn’t mention Puntland sxb I think you confused my post with his
 
@JohnQ that is "imputed" poverty not a map of the "actual" poverty rate. They are testing to see how well satellite data can predict their poverty data from my famous 2017 study written by the same researcher as this paper.
https://www.somalispot.com/threads/huge-world-bank-poverty-survey.31811/

They actually just released a new UN poverty survey today, but it is useless because they don't disaggregate the data by region. NN must have seen my thread from 2 years ago, and instructed UNFPA to not release regional data.

@Awbarkhadle I find it endearing that the first thing that comes to your mind is to compare your region with Puntland. It is nice to see Puntland is the yard stick Somalis measure themselves against.
:obama:
The reason they're using multiple imputation is because they found previous sampling methods to estimate poverty to be unreliable in wave 1 (the survey you linked).

First, in the absence of a recent census, no exhaustive lists of census enumeration areas along with population estimates existed, creating challenges to derive a probability‐based representative sample. Second, while some areas remained completely inaccessible due to insecurity, even most accessible areas held potential risks to the safety of field staff and survey respondents, so that time spent in these areas had to be minimized. Third, poverty in completely inaccessible areas had to be estimated by other means. Finally, the non‐stationary nature of the nomadic population required special sampling strategies. This paper outlines how these challenges were overcome in wave 2 of the SHFS through methodological and technological adaptations in four areas: sampling strategy, survey design, fieldwork implementation, and poverty measurement.

Do you have a link to the new UN poverty survey? I'm interested to see how that looks.
 

Thegoodshepherd

Galkacyo iyo Calula dhexdood
VIP
The reason they're using multiple imputation is because they found previous sampling methods to estimate poverty to be unreliable in wave 1 (the survey you linked).



Do you have a link to the new UN poverty survey? I'm interested to see how that looks.

Do you have any evidence for this claim?
 

Awdalia Rising

SSpot Special Correspondent
Doesn’t it seem pointless to argue over all these estimates? The last on the ground census took place in 1975. Let’s wait for the next one before we start insulting eachother
 
Do you have any evidence for this claim?
See the quote in my last post. They listed various challenges in generating a population estimate and measuring poverty. The purpose of this paper was to outline their adaptations in methodology for wave 2 and present their findings.

Do you have a link to the new UN poverty survey you mentioned?
 

Thegoodshepherd

Galkacyo iyo Calula dhexdood
VIP
See the quote in my last post. They listed various challenges in generating a population estimate and measuring poverty. The purpose of this paper was to outline their adaptations in methodology for wave 2 and present their findings.

Do you have a link to the new UN poverty survey you mentioned?

Coverage in areas outside of Alshabab control was above 92% in places like Puntland. They had no problems whatsoever collecting poverty information anywhere north of Galkacyo in Somalia. The satellite data is vastly inferior in those regions to the actual survey. You can use the satelitte data for places like KG and Middle Juba where there is no survey data at all.

 
Coverage in areas outside of Alshabab control was above 92% in places like Puntland. They had no problems whatsoever collecting poverty information anywhere north of Galkacyo in Somalia. The satellite data is vastly inferior in those regions to the actual survey. You can use the satelitte data for places like KG and Middle Juba where there is no survey data at all.

Interestingly enough, the paper specifically noted issues in data collection in Puntland.

Rural North‐East

The implementation of the survey also experienced some constraints in the recruitment of field teams in the rural North‐East regions. The access of some areas in this region is possible only for team members from certain clans. Thus, enumerators had to be selected and replaced based on this criterion. Some of these candidates might not otherwise have been selected given their performance during training, the pilot, and data collection. This was judged to have affected the quality especially of the consumption data collected.

Moreover, the EAs sampled were spread across a vast territory and mostly in remote areas. They were far from each other, and far from urban centers. NE teams who covered rural areas had to travel up to two days to reach some EAs, longer than teams in any other region. Team leader reports from the field indicate that these large distances and conditions created fatigue among enumerators. Further, direct monitoring of field teams by supervisors was limited due to poor connectivity, and thus sending frequent and timely feedback was more challenging that for other teams. As a result, the performance of teams did not improve as in other regions.

Thanks for the link.
 
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Thegoodshepherd

Galkacyo iyo Calula dhexdood
VIP
You do realize that the problem with data collection that the author is discussing took place during Wave 2? It does not change the findings in Wave 1. Even after the adjustment using the imputed remote sensing data, North East urban&rural have a lower poverty rate than anywhere in Somalia other than Jubaland Urban.
 
You do realize that the problem with data collection that the author is discussing took place during Wave 2? It does not change the findings in Wave 1. Even after the adjustment using the imputed remote sensing data, North East urban&rural have a lower poverty rate than anywhere in Somalia other than Jubaland Urban.
Even with the new implementations they still couldn’t overcome issues with data collection in Puntland in Wave 2.
 

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