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Small area estimation of poverty in four West African countries by integrating survey and geospatial data

Small area estimation of poverty in four West African countries by integrating survey and geospatial data
Small area estimation of poverty in four West African countries by integrating survey and geospatial data
The paper presents methodology to generate experimental small area estimates (SAE) of
poverty in four West African countries: Chad, Guinea, Mali, and Niger. Due to the absence of
recent census data in the four countries, household level survey data are integrated with grid level geospatial data, which are used as covariates in model-based estimation. Leveraging
geospatial data enables reporting of poverty estimates more frequently at disaggregated
administrative levels and makes estimation feasible in areas for which survey data are not
available. The paper leverages the availability of a recent census in Burkina Faso for evaluation purposes. Estimates obtained with the same survey instruments and candidate geospatial covariates as the other four countries are compared against estimates obtained using recent census data and an empirical best predictor under a unit level model. For Burkina Faso, estimates obtained using geospatial data are highly correlated with the census-based ones in sampled areas but moderately correlated in non-sampled areas. The results demonstrate that in the absence of recent census data, small area estimation with publicly available geospatial covariates is feasible, can lead to large efficiency improvements compared to direct estimation and improve the timeliness of small area estimates.
0282-423X
Edochie, Ifeanyi
adb3f57b-d569-48ef-accc-93030dd096ba
Newhouse, David
bc786866-c42c-4d90-acf7-8cf2781d789f
Tzavidis, Nikos
431ec55d-c147-466d-9c65-0f377b0c1f6a
Schmid, Timo
6f0ac270-0f64-4f86-ad3c-77a722cb14a4
Foster, Elizabeth
6f009562-f9d7-42f6-b9f0-02ed8aacd5cb
Luna Hernandez, Angela
b4de50ed-b80a-4202-aaad-c97d057369ed
Ouedraogo, Aissatou
95df068a-98fd-4a25-a51f-f266e26ca1cc
Sanoh, Aly
67ff228a-4937-4ab1-92a5-7ee2419ad325
Savadogo, Aboudrahyme
f77c2917-e831-4561-876c-f246acabc148
Edochie, Ifeanyi
adb3f57b-d569-48ef-accc-93030dd096ba
Newhouse, David
bc786866-c42c-4d90-acf7-8cf2781d789f
Tzavidis, Nikos
431ec55d-c147-466d-9c65-0f377b0c1f6a
Schmid, Timo
6f0ac270-0f64-4f86-ad3c-77a722cb14a4
Foster, Elizabeth
6f009562-f9d7-42f6-b9f0-02ed8aacd5cb
Luna Hernandez, Angela
b4de50ed-b80a-4202-aaad-c97d057369ed
Ouedraogo, Aissatou
95df068a-98fd-4a25-a51f-f266e26ca1cc
Sanoh, Aly
67ff228a-4937-4ab1-92a5-7ee2419ad325
Savadogo, Aboudrahyme
f77c2917-e831-4561-876c-f246acabc148

Edochie, Ifeanyi, Newhouse, David, Tzavidis, Nikos, Schmid, Timo, Foster, Elizabeth, Luna Hernandez, Angela, Ouedraogo, Aissatou, Sanoh, Aly and Savadogo, Aboudrahyme (2024) Small area estimation of poverty in four West African countries by integrating survey and geospatial data. Journal of Official Statistics. (In Press)

Record type: Article

Abstract

The paper presents methodology to generate experimental small area estimates (SAE) of
poverty in four West African countries: Chad, Guinea, Mali, and Niger. Due to the absence of
recent census data in the four countries, household level survey data are integrated with grid level geospatial data, which are used as covariates in model-based estimation. Leveraging
geospatial data enables reporting of poverty estimates more frequently at disaggregated
administrative levels and makes estimation feasible in areas for which survey data are not
available. The paper leverages the availability of a recent census in Burkina Faso for evaluation purposes. Estimates obtained with the same survey instruments and candidate geospatial covariates as the other four countries are compared against estimates obtained using recent census data and an empirical best predictor under a unit level model. For Burkina Faso, estimates obtained using geospatial data are highly correlated with the census-based ones in sampled areas but moderately correlated in non-sampled areas. The results demonstrate that in the absence of recent census data, small area estimation with publicly available geospatial covariates is feasible, can lead to large efficiency improvements compared to direct estimation and improve the timeliness of small area estimates.

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Small area estimation of poverty in four West African countries by integrating survey and geospatial data_Final - Accepted Manuscript
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Accepted/In Press date: 8 July 2024

Identifiers

Local EPrints ID: 492511
URI: http://eprints.soton.ac.uk/id/eprint/492511
ISSN: 0282-423X
PURE UUID: 49114655-69e0-49f2-bc50-4ea696ba039d
ORCID for Nikos Tzavidis: ORCID iD orcid.org/0000-0002-8413-8095
ORCID for Angela Luna Hernandez: ORCID iD orcid.org/0000-0001-8629-1918

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Date deposited: 30 Jul 2024 16:36
Last modified: 31 Jul 2024 01:47

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Contributors

Author: Ifeanyi Edochie
Author: David Newhouse
Author: Nikos Tzavidis ORCID iD
Author: Timo Schmid
Author: Elizabeth Foster
Author: Aissatou Ouedraogo
Author: Aly Sanoh
Author: Aboudrahyme Savadogo

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