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

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

Published in Journal of Official StatisticsSpecial issue on Hard to Reach Populations

Co-Authors:Kristen Himelein, World BankSiobhan Murray , World Bank

Stephanie Eckman

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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

Under pressure from development & climate change

HH based samples may not be sufficient to capture Pastoralists‐ Coverage error‐ Measurement error

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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

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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

Stratification

Definition Likelihood Radius1 Towns High 0.1 km2 Settled agricultural areas,

commercial farmsLow 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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Stratum 3 (High Likelihood)

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Stratum 5 (Low Likelihood)

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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

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Weights

Inverse of probability of selection

But what is probability of selection of Household i?

i

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All points that lead to interviewer finding Household i

If any of these points selected, i selected

Base Weights

i

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Base Weights

Probability of selection is:

1 – Pr(none of these points selected)

c is number of points selected

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

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Source: ASTER GDEM v2 (30 m)

Weight Adjustment

Viewshed analysis tells us how much of circle interviewers could see

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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

Interviewers and supervisors cited flooding, difficult terrain, weather as reasons they could not complete work

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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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Livestock Estimates: Means

RGCS (unadj weights) RGCS (adj weights) ERSS

Cattle 10.4 10.8 15.3Camels 8.1 7.7 6.2Goats 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

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Livestock Estimates: Totals

RGCS (unadj) RGCS (adj) ERSS

Cattle 153,505 186,164 1,092,752Camels 92,009 139,608 237,568Goats 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

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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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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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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”

www.iab.de

stephanie.eckman@iab.de

Website: stepheckman.com

Thanks – comments & ideas welcome

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Resulting Stratification

Description Points%

Visited HHs% Without Livestock

1 Towns 10 100% 69 40%

2Settled agri. areas, commercial farms 15 93% 113 53%

3Within 2 km of major river 60 82% 229 40%

4Within 10 km of major river 30 73% 182 20%

5 Remainder 10 70% 191 10%Total  125 82% 784 34%

Results

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Stratification Complicates Base Weights

Stratum 1 Stratum 2

X

r2

r1

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Stratification Complicates Base Weights

Stratum 1

X

Stratum 2