# **1. Introduction**

<mark>This paper presents the methodology to generate experimental small area estimates (SAE) of poverty in four West African countries: Chad, Guinea, Mali, and Niger, as well as an evaluation exercise using data from another country in the region, Burkina Faso. SAE is a statistical method used to improve survey estimates by integrating survey data with geographically comprehensive auxiliary data (covariates) typically derived from census, administrative, remote sensing, or mobile phone data. Data integration is achieved with the use of statistical models to produce estimates at disaggregated geographic levels that are more accurate and precise than estimates that rely only on direct use of the survey data. More disaggregated estimates are key for a better understanding of how to target interventions for the poorest areas as well as for monitoring the impact of s</mark> uch <mark>interventions.</mark> <mark>Table 1 illustrates the issues with obtaining poverty estimates at disaggregated geographic levels solely from survey data in the countries under study in this paper. The coefficient of variation estimated (cve) is a common measure used to judge the statistical precision of an estimate.</mark><sup>3</sup> <mark>Countries often adopt a maximum threshold for the mean or median cves of the estimates that can be reported, which in practice usually ranges from 0.15 to 0.3. For the countries of focus in this paper, the most recent survey estimates of poverty can be obtained from the 2018 round of the Enquête Harmonisée sur le Conditions de Vie des Ménages (EHCVM)</mark><sup>4</sup> <mark>which is available at the regional level for each country. The median cve of regional direct estimates produced by the Horvitz-Thompson estimator ranges from</mark> 0.0 <mark>7 to 0.12, which is typically within the acceptable range for publication. However, when we examine direct estimates of the poverty rates for the set of target administrative areas, which is one or two levels below the region, the estimates are too imprecise to publish.</mark><sup>5</sup> <mark>The target geography in Chad is the latest unofficial definition of departments provided to use by the National Institute of Statistics, while in Guinea it is the Sous-prefectures, and in Mali and Niger it is Communes. At these levels, the median cve of the direct survey estimates reported in Table 1 exceeds the 0.3 threshold for each country except for Chad, where it is 0.27. In addition, not all the target areas are covered by the surveys, making direct estimation impossible for these areas. While this is the case in all countries, unsampled areas are particularly prevalent in Mali, where less than 40% of the target areas are in the sample.</mark>

> 3 <mark>The use of the cve requires care especially when reported in relation to estimates of head count ratios. This is because although in theory -for example under simple random sampling- an estimate of 0.1 or 0.9 should have the same variance, the value of the cve is affected by the point estimate leading to different relative precisions. The use of cves in Table 1 is only for illustrating that direct estimates at the required level of geography are not reliable and not for comparing the precision between direct estimates.</mark>

4 This survey is the main output from the Harmonized Surveys on Household Living Conditions Program of the World Bank and the West Africa Economic and Monetary Union (WAEMU) Commission, which resulted in 10 countries (8 WAEMU members plus Guinea and Chad) collecting household data and constructing household welfare using methodologies that were highly harmonized across all the countries and updated in line with international best practice.

5 These estimates were obtained using an approximation to the variance of the Horvitz-Thompson estimator implemented in the R SAE package (Molina and Marhuenda, 2015), which assumes that the second order inclusion probabilities are the product of first order inclusion probabilities.