Sierra Leone

The Sierra Leone EquityTool country factsheet and file downloads on this page are licensed under CC BY-NC 4.0

 The simplest method of collecting EquityTool data is to sign up to our web app. To use the EquityTool in DHIS2 or another data collection platform, you will need to download the supporting file. Click on your preferred data collection method and complete the form to receive the file via email. Please check your junkmail folder if you do not receive an email from us.

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EquityTool: Update released 27 November 2023

The EquityTool has been updated based upon new source data. The original version is no longer active but is available upon request.

Previous version released: August 30, 2017 Source data: Sierra Leone DHS 2019

# of survey questions in full wealth index: 31

# of variables in full index: 120

# of survey questions in EquityTool: 8

# of variables in EquityTool: 9

Question Option 1 Option 2 Option 3
Q1 Does your household have electricity? Yes No
Q2 Does your household have television? Yes No
Q3 Does any member of your household have a bank account? Yes No
Q4 Does any member of your household own a watch? Yes No
Q5 Does any member of your household own any agriculture land? Yes No
Q6 What type of fuel does your household mainly used for cooking?
Other cooking fuel
Q7 What is the main material of your dwelling’s floor?
Earth and Sand floor
Other floor material
Q8  What is the main material of your dwelling’s exterior walls?
Other wall material
  Technical notes: 

The standard simplification process was applied to achieve high agreement with the original wealth index. Kappawas greater than 0.75 for the national and urban indices. Details on the standard process can be found in this article. The data used to identify important variables comes from the factor weights [link] released by ICF.

  Level of agreement:
National Population (n=13399) Urban only population (n=4976)
% agreement
Kappa statistic 0.836 0.750
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 DHS. 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 DHS wealth index. The graph below illustrates the difference between the EquityTool generated index and the full DHS wealth index. Among all of those people (20% of the population) originally identified as being in the poorest quintile, approximately 78.1% are still identified as being in the poorest quintile when we use the simplified index. However, approximately 20.4% 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 8 questions produces results that are not identical to using all 31 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 DHS wealth index:

EquityTool National Quintiles
Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Total
Original DHS National Quintiles Quintile 1 15.62% 4.08% 0.26% 0.00% 0.00% 20%
Quintile 2 6.21% 11.22% 2.58% 0.02% 0.00% 20%
Quintile 3 0.28% 2.61% 15.22% 1.88% 0.00% 20%
Quintile 4 0.00% 0.11% 2.71% 15.10% 2.09% 20%
Quintile 5 0.00% 0.00% 0.00% 2.09% 17.91% 20%
Total 22.12% 18.02% 20.78% 19.09% 20.00% 100%

The following graph provides information on the movement between urban quintiles when using the EquityTool versus the original DHS 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 DHS Urban Quintiles Quintile 1 17.65% 2.25% 0.10% 0.00% 0.00% 20%
Quintile 2 2.23% 14.39% 3.39% 0.01% 0.00% 20%
Quintile 3 0.16% 3.14% 12.48% 3.78% 0.45% 20%
Quintile 4 0.00% 0.34% 4.07% 9.99% 5.60% 20%
Quintile 5 0.00% 0.01% 0.57% 5.48% 13.92% 20%
Total 20.05% 20.11% 20.61% 19.26% 19.97% 100%
  Data interpretation considerations:
  1. This tool provides information on relative wealth – ‘ranking’ respondents within the national or urban The most recent available data from the WorldBank indicates that 26.06% of people in Sierra Leone live below $2.15/day[1]. This information can be used to put relative wealth into context.
  1. People who live in urban areas are more likely to be wealthy. In Sierra Leone, 46% of people living in urbanareas are in the richest national quintile, compared to only 2% of those living in rural areas[2].
    1. If your population of interest is predominantly urban, we recommend you look at the urban results to understand how relatively wealthy or poor they are, in comparison to other urban
    2. If the people you interviewed using the EquityTool live in rural areas, or a mix of urban and rural areas, we recommend using the national results to understand how relatively wealthy or poor they are, in comparison to the whole country.
  2. Some districts in Sierra Leone are wealthier than It is important to understand the country context when interpreting your results.
  3. In most cases, your population of interest is not expected to be equally distributed across the five wealth For example, if your survey interviewed people exiting a shopping mall, you would probably expect most of them to be relatively wealthy.


Changes from the previous EquityTool

We released an EquityTool on August 20, 2017 which compared user data to a benchmark of 2013. A new sourcesurvey, the DHS 2019 was recently released, and allows us to benchmark results to a more recent population.This is important, because wealth generally increases over time, and comparing your respondents to an old benchmark population will lead to over- estimating the relatively wealthy in your survey. The new EquityTool was generated using the exact same methodology as the previous version, and in generating the new EquityTool, no attempt was made to account for the fact that a previous version existed. In other words, we did not explicitly try to keep the same questions or response options as the previous tool.

For those who have not previously conducted an EquityTool based study in Sierra Leone, the remainder of this section is not particularly relevant. For those who have used the previous EquityTool, you may be interested to know how the two versions compare.

Previous Current
Source Data DHS 2013 DHS 2019
# of questions in EquityTool 10 8
# of questions in full wealth index 34 31
Kappa statistic (EquityTool vs full wealth Index) for 3 groups National: 0.774 Urban: 0.756 National: 0.836 Urban: 0.750
  Practical considerations for users of the previous EquityTool Comparing the results of surveys that used the previous EquityTool against those that use the current EquityTool is difficult. It will not always be clear whether any difference is because of actual differences in the wealth level of the respondents or because the EquityTool has changed.

The technical comparison section below, particularly the 3rd comparison, illustrates how quintile results compare when using the previous EquityTool and the current one. Generally, there is a partial shift down in quintiles when using a more recent EquityTool. In other words, the current EquityTool will usually put some respondents into a lowerquintile than the previous one would.

It is generally best to use the current version of the EquityTool, since it will give a more accurate quintile estimates. If you are currently collecting data, it is best to continue to use the previous tool. Note that if you have created asurvey in the EquityTool web application using the previous EquityTool, that survey will continue to use the previous EquityTool.

If conducting a follow-up survey to a baseline that used the previous EquityTool, and the most important result is change from the baseline, it may be preferable to continue to use the previous EquityTool for comparability. If you need to do this, please contact us at


Technical comparison between the current and previous EquityTool

All of the questions and response options for the previous EquityTool (DHS2013) are found in the new source data (DHS2019). This makes comparison between the two versions of the EquityTool, and two different data sources, easier.

The comparison will be assessed in 3 different ways, described below.

  1. Using the same 10 questions and response options, and scoring system as in the previous EquityTool, with two different benchmark populations.

This analysis simulates results if the only thing which changes is the benchmark against which respondents are compared. In the six years between the two source data studies, fewer people have acquired assets that are indicative of wealth. In the graph below, the previous EquityTool, derived from the DHS2013, is applied to the DHS2013 data and the newer DHS2019. In 2013, the proportion of households in each of the 5 quintiles is very close to 20%. In the newer survey, there is a shift from the 4th to the 3rd quintile, suggesting that there are morehouseholds in the middle of the wealth distribution.

We do not recommend the use of the previous questions and weights, because over time, there is a shift toward the middle of the wealth distribution. Thus, comparing your respondents to this skewed distribution becomes challenging.

  1. Keeping the same 10 questions and response options as the previous EquityTool, but calculating scores based upon the 2019 data.

As an alternative, one might wish to use the same questions as the previous tool, but update the weighting. This seems reasonable, as the relative contribution of each asset towards overall wealth may have changed over time. Using new weights, but the same variables as the previous tool, we can see how well the resulting quintiles compare to the quintiles based on the full wealth index created by ICF.

The table below presents the agreement between the quintiles created from the full wealth index in the DHS 2019 dataset and the quintiles created by the previous EquityTool, the previous EquityTool variables with updated weighting, and the current EquityTool. As with the agreement statistics above, these figures are for the bottom 2 quintiles, middle quintile and top 2 quintiles.

2013 EquityTool 2013 questions, 2019 scoring 2019 EquityTool
Agreement 90.1% 90.1% 89.9%
Kappa 0.85 0.85 0.84

Both the old and current EquityTool have strong agreement with the full wealth index and exceed our standardminimum kappa statistic of 0.75. Updating the scoring while using the old tool is a reasonable step to take.

  1. Comparing the previous 10 questions and scores, and the new EquityTool (8 questions)

The table below shows how the previous and current EquityTool compare, using the same population. This isanalogous to a comparison of the two versions of the EquityTool on the population you surveyed using our previous EquityTool.

Previous EquityTool Quintiles
Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Total
Current EquityTool Quintiles Quintile 1 17.31% 4.80% 0.01% 0.00% 0.00% 22.12%
Quintile 2 4.22% 12.62% 1.09% 0.00% 0.00% 17.93%
Quintile 3 0.00% 1.48% 19.26% 0.09% 0.00% 20.82%
Quintile 4 0.00% 0.00% 2.40% 16.04% 0.71% 19.15%
Quintile 5 0.00% 0.00% 0.00% 1.12% 18.85% 19.98%
Total 21.54% 18.90% 22.76% 17.25% 19.56% 100.00%

The rightmost column indicates that the current EquityTool does in fact evenly divide the population into 5 groups. Similarly, the bottom row shows that using the older EquityTool also divides the population into equal quintiles, with a slight skew towards the 3rd quintile. The cells within the table indicate how respondents are categorized, if measured using the two different tools. Of those who are categorized as quintile 1 using the current tool, 78% ofthem would have been considered in the poorest quintile in the previous tool (see the first row). Similarly, for those currently categorized as in the third quintile, 0.4% would have previously been categorized as being in the fourth quintile. If you had used the previous EquityTool, you can expect that with the current version, your respondents will look slightly more poor. This is not incorrect, but rather reflects the reality that we are measuring them against a more accurate benchmark.

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 and we will assist you.

[1] From, reporting Poverty headcount ratio at $2.15/day at 2017 international prices.

[2] From the [citation] dataset household recode, available at