The Liberia 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 December 7, 2022

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 The original version is no longer active but is available upon request.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: December 9, 2015


Source data: Liberia DHS 2019-20

# of survey questions in full wealth index: 31

# of variables in full index: 133

# of survey questions in EquityTool: 9

# of variables in EquityTool: 1



 QuestionOption 1Option 2Option 3
Q1Does your household have electricity that is connected?YesNo 
Q2a television?YesNo 
Q3a cupboard?YesNo 
Q4Does any member of this household have a watch?YesNo 
Q5(I don’t want to know the amount, but) does any member of this household have a bank account?YesNo 
Q6What type of fuel does your household mainly use for cooking?Fire coal / charcoalWood Other type of fuel
Q7What is the main material of the floor of your dwelling?Natural floor – earth / sand / mudOther material 
Q8What is the main material of the wall of your dwelling?Finished walls – cementOther material 
Q9What kind of toilet facility do members of your household usually use?Flush to septic tank (not shared with any other households)Other kind of toilet facility 

Technical notes:

The standard simplification process was applied to achieve high agreement with the original wealth index. Kappa was 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 released by ICF.

The EquityTool uses the same question wording whenever possible as that used by the source questionnaire. In Liberia, Q5 above was asked as written, however we note that this is unique to Liberia, and parenthesis are added around the phrase not seen in other surveys.


Level of agreement:


National Population


Urban only population


% agreement87%84.6%
Kappa statistic0.79550.7595


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 16% are still identified as being in the poorest quintile when we use the simplified index.  However, approximately 3.5% 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 9 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 1Quintile 2Quintile 3Quintile 4Quintile 5Total
Original DHS National QuintilesQuintile 116.39%3.54%0.04%0.00%0.00%19.97%
Quintile 27.04%11.23%1.75%0.00%0.00%20.03%
Quintile 30.15%5.13%12.15%2.57%0.00%20.00%
Quintile 40.00%0.00%3.30%14.03%2.65%19.98%
Quintile 50.00%0.00%0.03%2.88%17.10%20.01%


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 1Quintile 2Quintile 3Quintile 4Quintile 5Total
Original DHS Urban QuintilesQuintile 118.14%1.69%0.19%0.00%0.00%20.01%
Quintile 21.99%14.65%3.36%0.06%0.00%20.05%
Quintile 30.00%3.63%12.50%3.78%0.06%19.97%
Quintile 40.00%0.10%4.02%12.70%3.19%20.02%
Quintile 50.00%0.00%0.20%3.27%16.48%10.95%


Data interpretation considerations:

  1. This tool provides information on relative wealth – ‘ranking’ respondents within the national or urban population. The most recent available data from the WorldBank indicates that 27.62% of people in Liberia live below $2.15/day[1]. This information can be used to put relative wealth into context.
  2. People who live in urban areas are more likely to be wealthy. In Liberia, 23.2% of people living in urban areas are in the richest national quintile, compared to only 1.5% of those living in rural areas[2].
  3. 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 dwellers.
  4. 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.
  5. Some counties in Liberia are wealthier than others. It is important to understand the country context when interpreting your results.
  6. In most cases, your population of interest is not expected to be equally distributed across the five wealth quintiles. 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 November 1 2016 which compared user data to a benchmark of 2013.  A new source survey, the DHS 2019-20 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 Liberia, 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.

Source DataDHS 2013DHS 2019-20
# of questions in EquityTool119
# of questions in full wealth index 36 31
Kappa statistic (EquityTool vs full wealth Index) for 3 groups

National: 0.844

Urban: 0.782

National: 0.796

Urban: 0.760


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. Additionally, some of the questions in the wealth quintile have changed between the two rounds of survey and one of the previous EquityTool variables no longer exists in the most recent survey, further limiting the comparison between the surveys.

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 lower quintile 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 a survey 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 are found in the new source data (DHS 2019-20). 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 6-7 years between the two source data studies, more people have acquired assets that are indicative of wealth. In the graph below, the previous EquityTool, derived from the 2013 DHS, is applied to the 2013 data and the newer 2019-2020 DHS. In 2013, the proportion of households in each of the 5 quintiles is very close to 20%.  However, by 2019-2020, the distribution indicates more households would be in the 3rd quintile.

 *Note: for comparison across the two surveys, the 2013 EquityTool variable that is no longer used in DHS 2019-20 survey was also not applied in the 2013 data.

We do not use the previous questions and weights, because over time, the population has changed in its wealth profile. Thus, comparing your respondents to this old distribution becomes challenging.


  1. Keeping the same 10 questions and response options as the previous EquityTool, but calculating scores based upon the 2019-20 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 2019-20 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 EquityTool2013 questions, 2019-20 scoring2019-20 EquityTool

While the previous and current EquityTools have the same kappa statistic of 0.80, the current EquityTool has the best agreement with the full wealth index quintiles in urban population and is the only one that exceeds our minimum kappa statistic of 0.75 in urban population. The previous tool, even when the scoring is updated, falls short of this standard. The reason for this difference is because these 10 questions are no longer the best predictors of the overall wealth distribution in urban settings.


  1. Comparing the previous 10 questions and scores, and the new EquityTool (9 questions).

Although all but one of the questions in the previous EquityTool are found in the current EquityTool, we found that the 10 questions were not enough to accurately predict wealth. Because more people may own the assets predictive of wealth in 2013, we need to change questions to differentiate people and households more accurately.

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

  Previous EquityTool Quintiles
  Quintile 1Quintile 2Quintile 3Quintile 4Quintile 5Total
Current EquityTool QuintilesQuintile 118.27%5.28%0.04%0.00%0.00%23.59%
Quintile 23.83%11.42%4.63%0.02%0.00%19.91%
Quintile 30.01%1.19%14.35%1.71%0.00%17.27%
Quintile 40.00%0.00%4.22%11.99%3.27%19.48%
Quintile 50.00%0.00%0.00%3.07%16.68%19.75%


Comparing the rightmost column and the bottom row, we see that the current EquityTool does a better job of allocating the population into 5 quintiles than the older EquityTool did. 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, 77.4% of them 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, 9.9% 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 $1.90/day at 2011 international prices.

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