Posted by Ignacio Mas, Independent Consultant

The following post was originally published on the Financial Access Initiative (FAI) Blog.

The fastest growing part of the financial inclusion movement isn’t a product or even a standard, it’s data and measurement. And if there’s something experts are increasingly agreeing on, it’s that it is illusory to try to define financial inclusion in any precise, universal way. John Gitau says he’s confused, and so am I. How do you measure financial inclusion?

It’s true that you might not be able to measure financial inclusion itself, but you can still measure things that indicate either actual, or the potential for, progress. Such indicators are what we can measure, and they are very useful as long we don’t confuse them with actual measurement of financial inclusion.

There are two broad types of indicators which can be applied to fuzzy concepts like our cherished financial inclusion, and let me illustrate each with an example of an equally fuzzy concept from outside our field.

The first set of indicators measure concrete things that enable the desired, fuzzier outcome. Think of it as pieces of the puzzle that we know are important even though we never get to see the full image of the puzzle. We can call these antecedent indicators.

If we want to know how to measure the state of democracy in the world, for example, we would start by listing the specific set of ideas that we expect would form part of democratic governance. Just looking at which countries run regular elections would be seriously misleading. We’d also want to know whether there’s freedom of the press so that people have a chance to make informed decisions; how easy it is to vote, as that ensures representativeness; how parties and campaigns are financed; how transparently votes actually translate into who holds power; the levels of government at which there is democratic election; the list goes on. And then there’s of course another pile of important considerations of democratic interplay during the time between elections, like mechanisms to protect minority rights or handling recalls.

These would all be fine indicators, but each only tells part of the story and it is doubtful that all of them together could ever tell the full story of the state of democracy. The bottom line is: If you simplify the measurement then you need to nuance the interpretation, and you can’t let the data speak entirely for itself.

In the field of (formal) financial inclusion, we might deem that having an account at a bank, being able to understand some basic financial literacy concepts, and being on a convenient interoperable payment platform are essential requirements for meaningful inclusion. But it would be a tremendous stretch to argue this backwards. Having an account, knowing how to compute compound interest and having an option to pay electronically does not make you automatically (formally) included in any real sense.

The second set of indicators focuses on what concretely happens in a state where the desired fuzzy outcome is fulfilled. We can call these impact indicators.

Take the notion of gender equality. Can anyone claim to be able to measure what percent of women are equal in any particular country? No, but we can look at pay differentials between men and women and infer something about equality in the workplace. We can count how many women are battered by their husbands and infer something about equality in the home. More equality should lead to less/no pay differential and less domestic violence. But there’s an assumption of causality there, and we had better be sure that the causality is strong before assuming that the concrete indicators tell us anything about the fuzzy outcome we are trying to measure.

In the field of financial inclusion, the impact indicator logic would have to be something like this: Because people are financially included, they can enjoy smoother consumption, and hence experience more stable caloric intake and their children miss fewer school days. Because people are financially included, they can mitigate risks better, and hence they have more stable incomes and fewer serious diseases. The problem is of course that caloric intake, school attendance, income volatility, and health have so many drivers that it is hard in any given situation to disentangle how much of that was due to financial inclusion. One could in principle establish the causality through appropriately designed randomized control trials (RCTs), but that would have to be tested against so many different locations, population segments, personal circumstances, quality and scale of services, etc., as to make the basic causality proposition unprovable, in my humble opinion.

This lack of solid evidence of causal factors leading to financial inclusion and outcomes of financial inclusion means that even what we can measure is almost as fuzzy as financial inclusion itself.

As David Porteous argues, the lack of a standard definition of financial inclusion can be both a strength and a weakness. But the lack of clarity about what are reasonable causal indicators is a lot more troubling.

None of this is an argument to stop trying, but we do need to be cautious about what conclusions we draw from all the data that purportedly tells us something about financial inclusion. It also helps me understand (or maybe rationalize) why I feel like I get so little out of so many financial inclusion surveys that get conducted annually across the globe.

Ignacio Mas is an independent consultant. You can learn more about his work here.

Image Credit: FAI

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