Sampling Nomad: A New Technique for Remote, Hard-to-Reach, and - - PowerPoint PPT Presentation

Sampling Nomad: A New Technique for Remote, Hard-to-Reach, and Mobile Populations

Kristen Himelein DC-AAPOR/WSS July 23, 2014

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Paper

• With co-authors Stephanie Eckman

(Institute of Employment Research, Nuremberg, Germany) and Siobhan Murray (World Bank Development Economics Research Group)

• Published in the Journal of Official

Statistics (June 2014)

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Research Objectives

• Use an alternative sampling approach to reach

pastoralists

• Are we able to capture populations missed by

a dwelling based sampling frame?

• How do our figures compare with other

sources of information?

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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

• Populations difficult to survey

– Often pastoralist or semi-pastoralist

• HH based samples may not be sufficient

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Location: Afar, Ethiopia

• Afar, Ethiopia highly pastoralist
• More than 40 percent of respondents

reported owning 10 or more cattle in 2009 Agricultural Sample Survey.

– Cattle, camels, goats

• Bounded by

– national borders north & east – mountains to the west – ethnic differences

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Random Geographic Cluster Sampling [RGCS]

• 1st stage: select random geographic points
• 2nd stage: survey all eligible respondents

within given radius

• Similar designs used:

– Agricultural statistics agencies (ex: USDA) – Livestock studies in developing world (Cameron, 1997;

Soumarea et al, 2007; von Hagen, 2002)

– Surveys of forests (Husch 1982; Roesch et al 1993)

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Stratification

• Strata Inputs: land cover (towns and settled

agriculture), distance to water, vegetation index

Strata

Definition Expected Radius 1 Towns High 0.1 km 2 Settled agri. 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

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Stratification

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Stratum 3 – High

Stratum 5 – Low

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 – Livestock questions related to cattle, camels, goats

• Ownership, vaccination, theft, death, etc.

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Base Weights (1)

• Inverse of probability of selection
• But what is probability of selection of unit i?

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i

• All points that lead to interviewer finding i
• If any of these points selected, i selected

Base Weights (2)

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i

Base Weights (3)

• Probability of selection is

1 – (prob. that none of these points selected) 𝜌𝑗 = 1 − 1 − 𝜌𝑠2 𝑢𝑝𝑢𝑏𝑚 𝑏𝑠𝑓𝑏

𝑑

c is number of points selected

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Base Weights (4)

• Stratification complicates probabilities

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Stratum 1 Stratum 2 X r2 r1

Base Weights (5)

• Stratification complicates probabilities

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Stratum 1 Stratum 2 X

Weight Adjustment (1)

• Teams did not always

visit entire circle

• Two measures

– Supervisor report – Viewshed measures

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Weight Adjustment (2)

• Viewshed: High resolution elevation data

needed to look at terrain effects on complete site visualization

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Source: ASTER GDEM v2 (30 m)

Weight Adjustment (3)

• Should we adjust weights?

– No if we think area not covered because flooded

• r too thickly covered in vegetation for animals

– Yes if we think that areas were not covered because of time limitations / laziness

• Adjustment: 𝑥𝑗 = 𝑐𝑥𝑗 ∗

1 % 𝑝𝑐𝑡𝑓𝑠𝑤𝑓𝑒

• 2 sets of weights: baseweight, adj weight

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(ABRIDGED) RESULTS

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Implementation Challenges

• Field workers unaccustomed to technique
• Unexpected challenges

– Early start to rainy season – Ethnic conflict / kidnapping – Volcanoes – River crossings – Trouble with vehicles

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Results of Data Collection (1)

• 102 circles canvassed

– 59% contained at least 1 HH with livestock

• 784 households with

livestock interviewed (9 excluded for being

• utside radius)
• Total livestock found

per circle represented

• n map

Results of Data Collection (2)

• 3,698 individuals living in households owning

livestock

• 127 reported having no permanent dwelling,

(approximately two percent weighted estimate of the livestock-holding population in Zones 1, 3, 4, and 5).

• All but five of the individuals without a

permanent dwelling lived in households in which all members are completely nomadic.

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Conclusions

• RGCS can be implemented in a low capacity

environment with inexpensive hardware – though not without some difficulties.

• RGCS does in fact capture nomadic populations
• Necessary to incentivize interviewers to elicit a

‘high effort’ response.

• It is likely that RGCS has under-estimated the

total livestock population in Afar, but this still may be more accurate than those produced by the census-frame ERSS survey.

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Contact: Kristen Himelein khimelein@worldbank.org Stephanie Eckman stephanie.eckman@iab.de Siobhan Murray smurray@worldbank.org

Results of Data Collection

Description Points Visited Circles HHs Circles without Livestock 1 Towns 10 10 69 4 2 Settled agri. areas, commercial farms 15 14 113 8 3 within 2 km of major river 60 49 229 24 4 within 10 km of major river 30 22 182 6 5 Remainder 10 7 191 1 Total 125 102 784 43

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