Previous version released: January 24, 2017
Source data: MIS 2020
# of survey questions in full wealth index: 21
# of variables in full index: 139
# of survey questions in EquityTool: 10
# of variables in EquityTool: 11
|Question||Option 1||Option 2||Option 3|
Does this house household have electricity?
a DVD player?
Does any member of your household own a watch?
Does any member of your household have an account in a bank or other financial institution?
In your household, what type of fuel or energy source is mainly used for cooking?
Liquified petroleum gas (LPG)/ cooking gas stove
What is the main material of the floor of your dwelling?
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.
Data used in the construction of this EquityTool comes from Kenya’s 2020 Malaria Indicator Survey (MIS), a survey periodically undertaken by countries to assess malaria control efforts over time. MIS assessments are more focused and require smaller sample sizes than larger demographic health surveys (DHS) that assess the burden and risk factors of numerous communicable and noncommunicable diseases. For this reason, this updated Kenya EquityTool is based on survey data from 7,952 households, while its predecessor was based on 2014 DHS data from over 36,000 households. Despite a smaller sample, the survey data is nationally representative and was more than sufficient to construct a simplified EquityTool that exhibited strong agreement with the survey’s original wealth index. The advantage to using this updated survey data is that it allows us to provide an EquityTool that more accurately estimates the relative wealth of contemporary Kenyan households than older survey data can do.
Level of agreement:
Urban only population
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 16.5% are still identified as being in the poorest quintile when we use the simplified index. However, approximately 3.2% 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 10 questions produces results that are not identical to using all 21 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 1||Quintile 2||Quintile 3||Quintile 4||Quintile 5||Total|
|Original MIS National Quintiles||Quintile 1||16.59%||3.19%||0.23%||0%||0%||20%|
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 DHS wealth index:
|EquityTool Urban Quintiles|
|Quintile 1||Quintile 2||Quintile 3||Quintile 4||Quintile 5||Total|
|Original DHS Urban Quintiles||Quintile 1||17.19%||2.72%||0.10%||0.00%||0.00%||20%|
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 37.1% of people in Kenya live below $1.90/day . 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 Kenya, 31.7% of people living in urban areas are in the richest national quintile, compared to only 4.5% of those living in rural areas .
a.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.
b.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.
3.Some counties in Kenya are wealthier than others. It is important to understand the country context when interpreting your results.
4.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 January 24, 2017, which compared user data to a benchmark of 2014. A new source survey, the Kenya MIS 2020 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 Kenya, 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 Data||DHS 2014||MIS 2020|
|# of questions in EquityTool||13||10|
|# of questions in full wealth index||38||21|
|Kappa statistic (EquityTool vs full wealth Index) for 3 groups|
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 email@example.com.
Technical comparison between the current and previous EquityTool
All of the questions and response options for the previous EquityTool are found in the new MIS 2020 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 13 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, more people have acquired assets that are indicative of wealth. In the graph below, the previous EquityTool, derived from the 2014 DHS, is applied to both the older 2014 and newer 2020 data. In both 2014 and 2020, the old EquityTool performs well in classifying roughly equal numbers of participants into each of the quintiles.
We do not use the previous questions and weights, because over time, the population has become wealthier. Thus, comparing your respondents to this skewed distribution becomes challenging.
2.Keeping the same 13 questions and response options as the previous EquityTool, but calculating scores based upon the 2020 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 2020 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.
|2014 EquityTool||2014 questions, 2020 scoring||2020 EquityTool|
The current 2020 EquityTool has marginally lower agreement with the full wealth index quintiles than the 2014 EquityTools with 2014 or 2020 weights. However, all three alternatives exceed our minimum kappa statistic of 0.75. While the new EquityTool demonstrates slightly lower agreement at present, its shorter list of questions and inclusion of new consumer goods suggestive of wealth mean that the new Kenya EquityTool will be quicker to deploy and better suited to a Kenyan population that continues to grow in wealth. As time goes on and Kenyans acquire more material assets, using the older 2014 DHS-derived EquityTool will result in greater numbers of respondents being classified as belonging to wealthier quintiles.
3.Comparing the previous 13 questions and scores, and the new EquityTool (10 questions)
Although some of the questions in the previous EquityTool are found in the current EquityTool, we found that the previous EquityTool’s 13 questions would not be accurate predictor of wealth in the future. Because more people may own the assets predictive of wealth in 2020, we need to adjust survey 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 1||Quintile 2||Quintile 3||Quintile 4||Quintile 5||Total|
|Current EquityTool Quintiles||Quintile 1||18.04%||4.31%||0.22%||0.00%||0.00%||22.57%|
The rightmost column indicates that the current EquityTool does closely evenly divide the population into 5 groups. The bottom row shows that using the older EquityTool similarly divides the population into fairly even 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, 18.04% 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, 3.05% would have previously been categorized as being in the fourth quintile. The previous EquityTool and this updated version share a number of questions and perform comparatively well against both the old 2014 and newer 2020 benchmarks. However, we have also shown that there is a slow, but significant increasing trend in Kenyan’s material wealth. With time, this steady accumulation in assets will result in the older EquityTool performing increasingly poorly when deployed in urban and rural Kenyan communities.
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 Kenya 2020 MIS dataset household recode, available at http://dhsprogram.com/