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When you need to know more than just a general average, you need an Equity Gap Score. If you have a mandate that includes equity between any category of people, whether race, sex, income, education, geography, or whether or not they like sugar in their tea, you need an Equity Gap Score.


An Equity Gap Score is simply a number that helps to contextualize a statistic. It will help add meaning to any data result and can help you track equity issues in any data project. At We All Count, we think they should be included as standard practice any time you see an average involving people.


They look like this: 


“The city has an average yearly income of $54,000, with an equity gap score of 3.7 between the women and men”




“The rate of death by drowning has fallen by 17% in the last three years, while the equity gap score has worsened from 1.4 to 1.8 between poor neighbourhoods and rich neighbourhoods”. 


The first number tells you the overall information, and the second number tells you how the equity between categories of people is doing. An equity gap ratio of 1 is a perfect equity score, getting worse the higher the score is. 


Let’s take a step back and make an Equity Gap Score from scratch:


Imagine we’re looking at literacy rates at a state level. Let’s say the adult literacy rate is 82% in this state. Only 18% of adults in this state have real trouble reading and writing. While it’s useful to know the average, that’s not enough information if we care about equity. Let’s say we’re funding a literacy pilot project and we want to understand if there is a literacy gap between different districts in the state.


We can easily calculate an Equity Gap Score from the data tables. In the data tables, we can see what district each respondent lives in. All we have to do is average each district and we’ve got some very useful information. To make an Equity Gap Score we just have to divide the rate from the worst district by the rate in the best district. 


Worst in category / Best in category = Equity Gap Score for the category. 


Let’s say the 5 districts looked like this: 



Illiteracy Rate












District 5 (46%)/ District 3 (3%) =15.3


15.3 means that the rate of people struggling to read and write is over 15 times higher in district 5 than in district 3. It shows the gap in equity between the highest and lowest categories. You can make an equity gap score for anything you care about as long as you can get your hands on the data. 


Ok, those are the basics, and maybe that’s enough info for you right now, that’s cool. We encourage you to ask about the Equity Gap Score the next time you encounter an average. If you want more concrete examples of how to use this tool or how this can help you, say, supercharge the UN’s Sustainable Development Goals, read on!

Equity Gap Scores For Trends


I’m currently working on a project with BRAC in Bangladesh supporting Rohingya refugees. We’re collecting data on food programs and looking at the number of children who only receive one meal a day, versus two or three meals. At the start of the project, 11% of all children in the study were receiving only one meal per day, while at the end that was reduced to 4%. A great improvement which meant fewer hungry kids. However, we also wanted to see how equitable the improvement was between families with different levels of income. We needed an Equity Gap Score to accompany the change in averages!


We divided the participants’ data into five quintiles of income and then calculated the percent of children receiving one meal per day in each. Turns out there was a big difference between the richest and poorest families: 


All Families Average

Highest Wealth 

Lowest Wealth 

Equity Gap Score












Yes, there was a reduction from 11% to 4% overall in the number of kids getting only one meal per day, but there was a significant increase in the Equity Gap Score between the rich and poor. This additional information helped us to see that the interventions of the program were more effective for one group than another and if we wanted to even things out, we’d need to do more work with the lower-income families if we wanted to bring the Equity Gap Score closer to 1.


It’s important to note that we didn’t assume that the greatest difference in meal amounts would be between the richest and poorest, we let the data speak for itself in determining how to calculate our EGS. In many cases, you won’t know what or where the equity gaps will look like and always calculating an Equity Gap Score to your averages will help you keep track of them! 


The Big Picture – Looking for Equity in the SDGs


I was recently listening to a conversation between Winnie Byanyima, the Executive Director of Oxfam International and author Anand Giridharadas. Ms. Byanyima brought up the issue of equity – or the lack thereof – in the data being used to measure progress in the Sustainable Development Goals (the SDGs). She mentioned that while the infant mortality rate in the United States is lower than the infant mortality rate in Libya, a black infant in the United States has less of a chance of reaching their first birthday than a child in Libya. 


Reducing infant mortality among children is a goal that UN countries have agreed to work towards and measure with data. The specific goal they have agreed to is:


“By 2030, end preventable deaths of newborns and children under 5 years of age, with all countries aiming to reduce neonatal mortality to at least as low as 12 per 1,000 live births and under-5 mortality to at least as low as 25 per 1,000 live births”


However, there is nothing in the SDG data indicators that track equity in this category – or most other issues that are being measured. Ms. Byanyima, who was part of the group of people at the SDG negotiations, attributes at least part of this to the fact that many people simply are not willing to include measures of equity in their goals. (This is specifically discussed around income inequality at around the 15-minute mark in the video.)


If the SDGs released their national averages with Equity Gap Scores across the more common areas of inequity – race, income, sex, urbanness, education level, etc. We could easily see that Libya has a higher infant mortality rate (11 per 1000 in 2016) than the US (5.6 per 1000 in 2016), but the Equity Gap Score between ethnicities/races in the US is very high! 


Equity Gap Scores are critical information to understanding standalone averages and averages over time. They can be crafted to reflect any area of equity you care about. They can show progress when overall averages are moving slowly; “yes the average is the same, but look how much better the equity is!”. They can also be red flags when results look good, but certain groups are getting left behind. We want to see them everywhere that data is published. If we were donors, we’d demand them in every report and if we were CEOs, we’d expect them with every average. 


If you want to talk about how to start using them in your work feel free to contact us!