EquityTool: Released October 13, 2022
Source data: Djibouti Household Survey 2017
# of survey questions in full wealth index: 41
# of variables in full index: 105
# of survey questions in EquityTool: 11
# of variables in EquityTool: 16
|Question||Option 1||Option 2||Option 3||Option 4|
|DETERMINE IF THE RESPONDENT LIVES IN AN URBAN OR RURAL AREA||Urban||Rural|
|Q1||In this household, do you have a television?||Yes||No|
|Q3||…an air conditioner?||Yes||No|
|Q4||What is the primary material used in the construction of the roof of your dwelling?||Sheet metal||Other material|
|Q5||What is the primary material used in the construction of the floor of your dwelling?||Floor tile||Cement||Earth||Other material|
|Q6||What is the main source of light for your dwelling?||Wood||Other source|
|Q7||What is the main source of water used by members of your household?||Running water indoors (ONEAD)*||Traditional well||Other source|
|Q8||What kind of toilet do members of your household usually use?||No facility/nature||Other|
|Q9||In your household, what type of cookstove is mainly used for cooking?||Wood cookstove||Kerosene cookstove||Other type of cookstove|
|Q10||What type of fuel does your household use for cooking?||Wood||Other fuel type|
|Q11||How many goats does this household own?||None||1 to 9||10 or more|
*Office National de l’Eau et de l’Assainissement de Djibouti (ONEAD)
Creating a full wealth index
To produce this EquityTool, we used the World Bank’s 2017 Djibouti Household Survey. While the survey’s final report contained indicator data stratified by wealth quintile, a wealth quintile variable was not included in the underlying data. Since a survey’s full wealth index is the comparator against which we evaluate the performance of an EquityTool, it was necessary to first create a full wealth index from the available household asset variables.
We were unable to achieve sufficient agreement at the national and urban levels between the full wealth index and a simplified index using our standard simplification process (detailed in this article). Using a revised approach, detailed below, we achieved high agreement (kappa ≥ 0.75) for the national, urban, and rural indices.
The national factor weights used in the standard approach come from an analysis of the national population and contain only those variables that are related to the construct of wealth in the same way in both rural and urban areas. The national factor weights are usually used in EquityTools to calculate national quintiles, as they reduce some known areas of respondent error in the survey.
However, to overcome the problem of low agreement using the standard simplification approach, we instead used factor weights from the rural and urban analyses, which select variables that related to wealth differently in urban and rural areas. For example, in an urban area, ownership of goats may be more strongly associated with being poor than in rural areas. This is the case in Djibouti. A concise list of variables, common to both urban and rural areas, were iteratively selected to find those which result in high agreement (kappa ≥ 0.75) against the full wealth index quintiles for national, urban, and rural populations.
A score from the simplified index for urban residents (Uscore) was regressed against the wealth index score variable created for the corrected full wealth index analysis (Nscore), the same was done for rural residents (Rscore), and the resulting coefficients are used to create a single national score (NatScore).
Nscore=b1Uscore + a1
Nscore=b2Rscore + a2
Where Urban=1 if respondent lives in an urban area and 0 if otherwise, and Rural =1 if respondent lives in a rural area and 0 if otherwise.
Respondents’ quintile assignments resulting from NatScore, the national wealth index score created from a simplified list of questions were compared to the quintile assignments resulting from the full wealth index with 41 variables using the kappa statistic.
The questions in the simplified index which resulted from this process differ from EquityTools that are created using our standard approach. Notably, we need to know whether the respondent lives in an urban or rural area, thus an additional question has been added to the EquityTool for Djibouti: ‘Determine if the respondent lives in an urban or rural area’. In principle, the definition of ‘urban’ and ‘rural’ should match the definition used in the Djibouti household survey. Typically, this definition is defined by the country. In practice, the user needs to decide how to determine if each respondent lives in an urban or rural area. Three approaches are presented below, with some notes on each. Whichever method is chosen, it should be uniformly applied across all surveys conducted.
Level of agreement:
National household sample
Urban household only sample
Respondents in the original dataset were divided into three groups for analysis – those in the 1st and 2nd quintiles (poorest 40%), those in the 3rd quintile, and those in the 4th and 5th quintiles (richest 40%). After calculating their wealth using the simplified index, they were again divided into the same three groups for analysis against the original data in the full Djibouti Household Survey. Agreement between the original data and our simplified index is presented above.
What does this mean?
When shortening and simplifying the index to make it easier for programs to use to assess equity, it no longer matches the original index with 100% accuracy. At an aggregate level, this error is minimal, and this methodology was deemed acceptable for programmatic use by an expert panel. However, for any given individual, especially those already at a boundary between two quintiles, the quintile the EquityTool assigns them to may differ to their quintile according to the original Djibouti Household Survey wealth index.
The graph below illustrates the difference between the EquityTool generated index and the full Djibouti Household Survey wealth index. Among all of those people (20% of the population) originally identified as being in the poorest quintile, approximately 94% are still identified as being in the poorest quintile when we use the simplified index. However, approximately 6% of people are now classified as being in Quintile 2. From a practical standpoint, all of these people are relatively poor. Yet, it is worthwhile to understand that the simplified index of 11 questions produces results that are not identical to using all 41 questions in the original survey.
The following table provides the same information on the movement between national quintiles when using the EquityTool versus the original Djibouti Household Survey wealth index:
|EquityTool National Quintiles|
|Quintile 1||Quintile 2||Quintile 3||Quintile 4||Quintile 5||Total|
|Original Djibouti Household Survey National Quintiles||Quintile 1||18.8%||1.2%||0.0%||0.0%||0.0%||20%|
The following graph provides information on the movement between urban quintiles when using the EquityTool versus the original Djibouti Household Survey wealth index:
The following table provides the same information on the movement between urban quintiles when using the EquityTool versus the original DHS wealth index:
|EquityTool Urban Quintiles|
|Quintile 1||Quintile 2||Quintile 3||Quintile 4||Quintile 5||Total|
|Original Djibouti Household Survey Urban Quintiles||Quintile 1||16.3%||3.8%||0.0%||0.0%||0.0%||20%|
Data interpretation considerations:
Metrics for Management provides technical assistance services to those using the EquityTool or wanting to collect data on the wealth of their program beneficiaries. Please contact firstname.lastname@example.org and we will assist you.
 From povertydata.worldbank.org, reporting Poverty headcount ratio at $1.90/day at 2011 international prices.
 From the Djibouti Household Survey 2017 dataset, available with permission at https://microdata.worldbank.org/index.php/catalog/3463/study-description