The Uganda 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 October 13, 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 The original version is no longer active but is available upon request.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: November 1, 2016

Source data: Malaria Indicator Survey (MIS) 2018-19


# of survey questions in full wealth index: 38

# of variables in full index: 139

# of survey questions in EquityTool: 12

# of variables in EquityTool: 13



 QuestionOption 1Option 2Option 3
Q1Does your household have electricity?YesNo 
Q2…a cassette/CD/DVD player?YesNo 
Q3…a radio?YesNo 
Q4…a television?YesNo 
Q5…a cupboard?YesNo 
Q6…a sofa set?YesNo 
Q7Does any member of your household own a watch?YesNo 
Q8Does any member of your household have a bank account?YesNo 
Q9What type of fuel does your household mainly use for cooking?WoodCharcoalOther fuel type
Q10What is the main material of the floor of your dwelling?CementOther material 
Q11What is the main material of the roof of your dwelling?Thatch/palm leafOther material 
Q12What is the main material of the exterior walls of your dwelling?Burnt bricks with cementOther material 


Technical notes:

During the initial steps of creating this EquityTool it was discovered that the urban and rural sample sizes provided within this MIS’s supporting documentation did not correspond to those found within the dataset itself. These discrepancies limited our ability to validate many of the descriptive statistics we consider for inclusion during the EquityTool creation process and also raised questions about how the wealth index provided in the dataset was produced. For these reasons, we chose to recreate the wealth index and to use our newly recreated wealth index as the standard against which we would later compare interrater reliability of the simplified EquityTool wealth index.

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 recreated wealth index factor weights.


Level of agreement:


National household sample


Urban household only sample


% agreement87.8%84.4%
Kappa statistic0.8090.757


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 MIS. 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 MIS wealth index.

The graph below illustrates the difference between the EquityTool generated index and the full MIS wealth index. Among all of those people (20% of the population) originally identified as being in the poorest quintile, approximately 87% are still identified as being in the poorest quintile when we use the simplified index.  However, 12.5% 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 12 questions produces results that are not identical to using all 38 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 MIS wealth index:

  EquityTool National Quintiles
  Quintile 1Quintile 2Quintile 3Quintile 4Quintile 5Total
Original MIS National QuintilesQuintile 1 17.47%  2.50%0.04% 0.00% 0.00%20%
Quintile 2 4.96% 12.59% 2.41% 0.13% 0.00%20%
Quintile 3 0.11% 3.90% 13.28% 2.62% 0.00%20%
Quintile 4 0.00% 0.08% 2.88%14.98% 2.06%20%
Quintile 5 0.00%0.00% 0.01% 2.14% 17.84%20%
Total 22.54%19.07%18.62%19.86%19.90%100%


The following graph provides information on the movement between urban quintiles when using the EquityTool versus the original MIS wealth index:



The following table provides the same information on the movement between urban quintiles when using the EquityTool versus the original MIS wealth index:

  EquityTool Urban Quintiles
  Quintile 1Quintile 2Quintile 3Quintile 4Quintile 5Total
Original MIS Urban QuintilesQuintile 1 17.78% 2.45%0.00%0.00% 0.00%20%
Quintile 2 2.30% 13.50% 4.07%0.00% 0.00%20%
Quintile 3 0.02% 3.85% 12.25% 3.58% 0.20%20%
Quintile 4 0.00% 0.10% 3.38% 12.01% 4.61%20%
Quintile 5 0.00% 0.05% 0.31% 4.83% 14.71%20%
Total 20.10%19.95%20.01%20.42%19.51%100%

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 World Bank indicates that 41.3% of people in Uganda live below $1.90/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 Uganda, 56% of people living in urban areas are in the richest national quintile, compared to only 8% 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 regions in Uganda 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 2016.  A new source survey, the Uganda MIS 2018-19 his since then been 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 Uganda, 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 2016MIS 2018-19
# of questions in EquityTool1112
# of questions in full wealth index1313
Kappa statistic (EquityTool vs full wealth Index) for 3 groups

National: 0.753

Urban: 0.772

National: 0.809

Urban: 0.757


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 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. 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 11 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 graph below, the previous EquityTool, derived from the 2016 DHS, is applied to the 2016 data and the newer 2019 data. In 2016, the proportion of households in each of the 5 quintiles is very close to 20%. In 2019, more respondents are classified in the first and second wealth quintiles and fewer in the third. While this inter-quintile shift toward the lower quintiles are not indicative of a systematic increase in Ugandan wealth, it does suggest that the previous 11 questions are no longer the best measures of relative wealth in Uganda


  1. Keeping the same 11 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 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 two quintiles, middle quintile, and top two quintiles.

  2016 DHS EquityTool2016 questions, 2018-19 scoring2018-19 MIS EquityTool

The current EquityTool has the best agreement with the full wealth index quintiles and is the only one that exceeds our minimum kappa statistic of 0.75. The previous tool, even when the scoring is updated, falls short of this standard. The reason for this difference is because these 11 questions are no longer the best predictors of the overall wealth distribution.


  1. Comparing the previous 11 questions and scores, and the new EquityTool (12 questions)

Although all of the questions in the previous EquityTool are found in the current EquityTool, we found that some of the questions used in the 2016 DHS EquityTool no longer accurately predicted wealth. Because more people may own the assets predictive of wealth in 2016, we need to add 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 1 18.73% 3.78% 0.04% 0.00% 0.00%22.54%
Quintile 2 3.09% 12.03% 3.83% 0.12% 0.00%19.07%
Quintile 3 0.25% 4.24% 10.66% 3.47% 0.00%18.62%
Quintile 4 0.00% 0.02% 3.33% 14.75% 1.76%19.86%
Quintile 5 0.00% 0.00% 0.00% 1.68% 18.23%19.90%
Total 22.07% 20.06% 17.86% 20.01% 19.99%100%


The rightmost column indicates that the current EquityTool more evenly divides the population into 5 groups. The bottom row shows that using the older EquityTool does not divide the population into equal quintiles – respondents tend to drift from higher to lower quintiles. 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, 83% 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, 18.6% would have previously been categorized as being in the fourth quintile. If you had used the previous EquityTool, you could expect that with the current version, your respondents will look slightly poorer.  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 Uganda MIS 2018-19 dataset household recode, available at