A huge amount of data...

Let’s try some maths: if we multiply the number of the indicators and measures that the Multidimensional Inequality Framework includes (177) by, for instance, 6 variables of disaggregation we want to address, such as gender, socio-economic or social class, ethnicity or age, this would give us 1,416 measures. By multiplying this number by the minimum of three time periods which are required to understand trends (e.g. malnutrition by gender in 2000, 2009 and 2018), we get... 3,186 tables and/or graphs! I’m sure you cannot wait now to apply the MIF in a comprehensive way!

It’s better to prioritise, but how?

Firstly, it is important to analyse which domains of life are relevant and viable for measurement in a specific country, because it may not be possible to collect data for some domains. For example, in Guatemala, we decided to leave out the domain on inequality regarding individual, family and social life as there was no data for this domain. Given the major survival problems facing the country, this domain is not high on the agenda for statistical data generation.

Next, the social groups which are more likely to experience inequality should be defined. This also depends to a large extent on the availability of data and how the surveys are carried out. In Guatemala, for example, however hard you look, it is impossible to find information about people’s situation by religious affiliation, so we could not take this variable into account. 

Finally, we need to analyse those indicators preliminarily selected: which ones are relevant for your country or for a certain context? This step is what we called the ‘tropicalisation’ of the framework. For example, loneliness of the elderly may be a key social indicator in some Northern European countries or in the United States, but it is not so relevant in many contexts of the Global South, where family structures are stronger. Therefore, when applying the MIF, we need to ask ourselves: what indicators may be relevant for our contexts but the MIF doesn’t capture? And for which indicators there is data and information available?

In a country with little statistical data, assessing the feasibility of collecting data and having good knowledge of the existing official surveys is important, both to reject and to propose measures and indicators.

What are the steps to create a national inequality report with an impact on the public and political agenda?

There’s no magic formula, but some of the lessons we have learned are: 

    •     It is a good idea to involve experts from the beginning. They help to set priorities and provide input for specific aspects to be considered in certain contexts with limited data available. Ideally, you could build a closer collaboration with some of them at an institutional level to root the methodology in the country.

    •     Carry out an exhaustive analysis based on the information available. In Guatemala we created a small team, coordinated by an expert on inequality and public policies, with good knowledge and access to surveys and statistics not ordinarily available. Don’t try to save money on these people! They should be experts, which has an added-value and a cost.

    •     Based on this analysis, we elaborated a public summary report, in a thorough way and able to generate killer facts. To do this, we created an internal team with clear goals in terms of impact and influencing. 

What other recommendations can be gained from the Guatemala’s experience?

We always have to be careful when talking about inequality. Some indicators of this MIF and the existing data tend to refer to (average) conditions, leading us to talk about poverty or exclusion instead of inequality. However, we must keep focused on analysing gaps, contrasts and relations between groups of people.

Besides, when there is a lack of statistical data, qualitative information can help to capture how people experience inequalities in their everyday lives by the use of testimonies, interviews or summaries of previous research reports. 

Finally, we need to be creative when it comes to using data: the theoretical framework and indicators aren’t written in stone. New indicators can be added if they are relevant in a given context. 

So, good luck in this exciting process!