Livestock are an important component of rural livelihoods in developing countries, but data about this source of income and wealth are difficult to collect due to the nomadic and seminomadic nature of many pastoralist populations. Most household surveys exclude those without permanent dwellings, leading to undercoverage. In this study, we explore the use of a random geographic cluster sample (RGCS) as an alternative to the household-based sample. In this design, points are randomly selected and all eligible respondents found inside circles drawn around the selected points are interviewed. This approach should eliminate undercoverage of mobile populations. We present results of an RGCS survey with a total sample size of 784 households to measure livestock ownership in the Afar region of Ethiopia in 2012. We explore the RGCS data quality relative to a recent household survey, and discuss the implementation challenges.

1. Sampling Nomads: A New Technique for Remote,
Hard-to-Reach, and Mobile Populations
Published in Journal of Official Statistics
Special issue on Hard to Reach Populations
Co-Authors:
Kristen Himelein, World Bank
Siobhan Murray , World Bank
Stephanie Eckman

2. Background
 Livestock play integral role in livelihoods of vulnerable populations
‐ Main source of food and transportation
‐ Store of wealth
‐ Coping mechanism in response to shocks
 Under pressure from development & climate change
 HH based samples may not be sufficient to capture Pastoralists
‐ Coverage error
‐ Measurement error
2

3. Our Approach
 We propose an alternative sampling approach to reach Pastoralists
 Random Geographic Cluster Sampling (RGCS)
‐ 1st stage: select random points
‐ 2nd stage: survey all eligible respondents within given radius
 Similar designs used:
‐ Agricultural statistics agencies (ex: USDA)
‐ Livestock studies in developing world
‐ Surveys of forests
3

4. Location: Afar, Ethiopia
 More than 40 percent of population owns 10 or more cattle
‐ 2009 Agricultural Sample Survey
‐ Camels, goats
 Bounded by
‐ National borders north & east
‐ Mountains to the west
‐ Ethnic differences
4

5. Stratification
Definition Likelihood Radius
1 Towns High 0.1 km
2 Settled agricultural areas,
commercial farms
Low 0.5 km
3 Within 2 km of major river or
swamps
High 1 km
4 Within 10 km of major river or
swamps
Medium 2 km
5 Remainder Low 5 km
5

6. Stratum 3 (High Likelihood)
6

7. Stratum 5 (Low Likelihood)
7

8. Field Work
 Selected points pre-loaded on GPS
‐ Alarm indicated when interviewer inside radius
 Interview all eligible respondents within radius
‐ Only HHs with livestock eligible
‐ Questions about ownership, vaccination, theft, death, etc
8

9. Weights
 Inverse of probability of selection
 But what is probability of selection of Household i?
9
i

10.  All points that lead to interviewer finding Household i
 If any of these points selected, i selected
Base Weights
10
i

11. Base Weights
 Probability of selection is:
1 – Pr(none of these points selected)
𝜋𝑖 = 1 − 1 −
𝜋𝑟2
𝑡𝑜𝑡𝑎𝑙 𝑎𝑟𝑒𝑎
𝑐
c is number of points selected
11

12. Weight Adjustment
 Teams did not always visit
entire circle
 GPS recorded path as they
worked
 Most relevant is what they
could see from their path

13. 13
Source: ASTER GDEM v2 (30 m)
Weight Adjustment
 Viewshed analysis tells us how much of circle interviewers could see
13

14. Implementation Challenges
 Field workers unaccustomed to technique
 Unexpected challenges
‐ Early start to rainy season
‐ Ethnic conflict / kidnapping
‐ Volcanoes
‐ River crossings, trouble with vehicles
 Interviewers and supervisors cited flooding, difficult terrain,
weather as reasons they could not complete work
14

15. Results of Data Collection
 102 circles canvassed
‐ From 125 selected
‐ 59% contained at least 1 HH with livestock
 784 households with livestock interviewed
‐ 9 excluded for being outside radius
‐ 3,698 individuals
 3.4% of persons reported having no permanent dwelling
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16. Livestock Estimates: Means
16
RGCS (unadj weights) RGCS (adj weights) ERSS
Cattle 10.4 10.8 15.3
Camels 8.1 7.7 6.2
Goats 20.2 19.7 20.7
Mean number owned (conditional on ownership)
ERSS: Ethiopian Rural Socioeconomic Survey
Adjusted weights: include adjustment for % of circle observed

17. Livestock Estimates: Totals
17
RGCS (unadj) RGCS (adj) ERSS
Cattle 153,505 186,164 1,092,752
Camels 92,009 139,608 237,568
Goats 566,139 815,310 2,095,876
Total number owned (conditional on ownership)
ERSS: Ethiopian Rural Socioeconomic Survey
Adjusted weights: include adjustment for % of circle observed

18. What Explains Discrepancies in Totals?
 Interviewer Effort hypothesis
‐ Why were some circles not visited?
‐ Far from roads
‐ Why were some circles not entirely observed?
‐ Larger circles
‐ Seem unrelated to flooding
‐ Strong supervisor effects
 ERSS Quality hypothesis
‐ Suggestions of problems with weights and missing data imputation
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19. Conclusions
 RGCS can be implemented in a low capacity environment with
inexpensive hardware – though not without some difficulties
‐ Does capture nomadic populations
 RGCS likely under-estimated the total livestock population
‐ May be more accurate than census-frame ERSS survey
‐ 3rd comparison (in paper) suggests RGCS closer to truth
 More on incentivizing interviewers to elicit a high effort response
‐ In published paper
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20. Papers in Progress
 Himelein, Eckman & Murray “Second Stage Sampling for
Conflict Areas: Methods and Implications”
 Eckman, Himelein & Dever “New Ideas in Sampling for Surveys
in the Developing World”
20

21. www.iab.de
stephanie.eckman@iab.de
Website: stepheckman.com
Thanks – comments & ideas welcome

22. 22

23. Resulting Stratification
23

24. Description Points
%
Visited HHs
% Without
Livestock
1 Towns 10 100% 69 40%
2
Settled agri. areas,
commercial farms
15 93% 113 53%
3
Within 2 km of
major river
60 82% 229 40%
4
Within 10 km of
major river
30 73% 182 20%
5 Remainder 10 70% 191 10%
Total 125 82% 784 34%
Results
24

25. Stratification Complicates Base Weights
25
Stratum 1 Stratum 2
X
r2
r1

26. Stratification Complicates Base Weights
26
Stratum 1
X
Stratum 2