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: Updated released March 15, 2019

      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      The original version is no longer active but is available upon request.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: Philippines DHS 2017

      # of survey questions in full wealth index: 32

      # of variables in full index: 113


      # of survey questions in EquityTool: 7

      # of variables in EquityTool: 7




       QuestionOption 1Option 2
      Q1Does your household have… a refrigerator?YesNo
      Q2… a washing machine?YesNo
      Q3… a laptop/computer?YesNo
      Q4…a DVD player?YesNo
      Q5What is the main material of the exterior walls of your house?CementOther
      Q6What is the main material of the floor of your home?Ceramic TilesOther
      Q7What type of fuel does your household mainly use for cooking?LPGOther


      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.




      Level of agreement:



      National Population


      Urban only population


      % agreement85.1%84.3%
      Kappa statistic0.7670.756


      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 17% are still identified as being in the poorest quintile when we use the simplified index.  However, approximately 3% 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 7 questions produces results that are not identical to using all 32 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 1 17.3%2.6%0.2%0.0%0.0%20%
      Quintile 2 7.2%9.6%3.1%0.1%0.0%20%
      Quintile 30.4%4.2%12.3%3.1%0.0%20%
      Quintile 4 0.0%0.0%3.8%13.7%2.6%20%
      Quintile 50.0%0.0%0.0%3.9%16.1%20%
      Total 24.8%16.4%19.3%20.7%18.7%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 1Quintile 2Quintile 3Quintile 4Quintile 5Total
      Original DHS Urban QuintilesQuintile 1 16.1%3.7%0.2%0.0%0.0%20%
      Quintile 2 3.9%12.6%3.4% 0.1%0.0%20%
      Quintile 3 0.1%4.5%12.7%2.6%0.1%20%
      Quintile 4 0.0%0.0%4.6%11.1%4.2%20%
      Quintile 5 0.0%0.0%0.1%4.9%14.9%20%
      Total 20.1%20.8%21.0%18.8%19.3%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 WorldBank indicates that 7.8% of people in the Philippines live below $1.90/day, and 32.6% live below $3.20/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 the Philippines, 29% of people living in urban areas are in the richest national quintile, compared to only 13% 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 the Philippines 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 December 9, 2015 which compared user data to a benchmark of 2013. A new source survey, the 2017 DHS, 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 may 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 the Philippines, 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 2017
      # of questions in EquityTool97
      # of questions in full wealth index30 32
      Kappa statistic (EquityTool vs full wealth Index) for 3 groups

      National: 0.766

      Urban: 0.768

      National: 0.767

      Urban: 0.756


      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 estimate. 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 2017). 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 4 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 DHS data and the newer 2017 DHS data. In 2013, the proportion of households in each of the 5 quintiles is very close to 20%. The discrepancy seen is due to the use of a shorter questionnaire than used by the DHS survey originally. In 2017, the previous EquityTool does a poorer job of distinguishing between the wealthiest two quintiles in the population, but otherwise the distributions are quite similar.



      1. Keeping the same 10 questions and response options as the previous EquityTool, but calculating scores based upon the 2017 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 2017 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, 2017 scoring2017 EquityTool


      The current EquityTool has the best agreement with the full wealth index quintiles. The previous tool meets this standard but does so using more questions. The reason for this difference is because the previous 10 questions are no longer the best predictors of the overall wealth distribution.



      1. Comparing the previous 10 questions and scores, and the new EquityTool (7 questions)


      Although all of the questions in the previous EquityTool are found in the current EquityTool, we found that just 7 questions were enough to accurately predict wealth in 2017, according to our standard minimum level of agreement.


      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 116.5%7.8%0.5%0.0%0.0%24.9%
      Quintile 23.2%8.3%4.9%0.0%0.0%16.4%
      Quintile 30.3%3.3%11.4%4.3%0.0%19.3%
      Quintile 40.0%0.5%3.6%13.7%3.0%20.7%
      Quintile 50.0%0.0%0.1%3.4%15.2%18.7%


      The bottom row indicates how the previous EquityTool divides the population into 5 groups. The rightmost column shows that using the current EquityTool divides the population into 3 groups (40%, 20%, 40%) equally well, but it does so with fewer questions and variables than the previous tool. 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, 82% 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, 22% 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 2015 international prices.

      [2] From the Philippines DHS dataset household recode, available at