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A Decision Tree for Digital Financial Inclusion Policymaking
The problem: insufficient analytical tools hinder optimal decision making
There is a general consensus among policymakers and experts about the benefits of better financial inclusion. Accordingly, in recent years ambitious national strategies have incorporated significant efforts to advance financial inclusion—especially using digital means. Yet, as confirmed by the latest Global Findex (2017) database and nationally available data, while some countries are achieving impressive inclusion gains driven by digital financial services (DFS), in others the take-up of DFS has been lagging behind expectations.
A reason for this imbalance is that many policymakers lack the necessary analytical tools for identifying the most pressing constraints to financial inclusion—these impediments are where they should be focusing their efforts and where solutions should be prioritized, given limited resources, political space, and implementation capacity.
Without comprehensive and country-specific diagnostics of the causes of low financial inclusion, policy solutions may be both ineffective and inefficient.
What is CGD doing to address the problem?
To fill this gap, under the leadership of senior fellow Liliana Rojas-Suarez, CGD is implementing a project to offer policymakers this much needed tool. The first output of this project, developed by Rojas-Suarez and Stijn Claessens, from the Bank of International Settlements, is a new analytical framework, A Decision Tree for Digital Financial Inclusion Policymaking. The tree is adecision-making tool focused on digital financial services (DFS) that will help to diagnose country-specific binding constraints that impede significant improvements in the usage of DFS by large segments of populations.
Previous CGD work in this area, including the Task Force on Regulatory Standards for Financial Inclusion, has given CGD a privileged perspective on the strengths and shortcomings of the financial inclusion policymaking environment. Building on this knowledge, and informed by the work of Hausmann et al. (2005 and 2008), CGD has developed this new tool with the following features:
The tree follows a top-down approach. After identifying inadequate financial inclusion (the usage of DFS is below its socially optimal level), the methodology starts by asking for the potential causes of this outcome (the constraints to inclusion). Each of the potential causes (the upper branches of the tree) in turn can be explained by additional causes (the next set of branches), and so on.
Constraints are divided between those affecting the provision of DFS (supply-side) or the willingness of customers to use these services (demand-side). This distinction is central because the policy response varies significantly in the face of supply versus demand binding constraints. Examples of supply-side constraints include limited competition or a regulatory environment that discriminates against some providers. Examples on the demand side include customers’ perceived low or no benefits of usage of services or low trust in providers.
Prices are key indicators to determine whether a constraint comes from the demand or the supply side. Observing low quantities, such as the percentage of the adult population that use a service, is not enough since low usage is consistent with either low supply of or low demand for the service. As a guiding rule, one can identify the low usage of a DFS with constraints on the supply side if its price is high relative to either another similar service or the (properly adjusted) customary price charged in other countries with similar levels of development. In this case, providers are only willing to supply the service at a high price (due to the presence of supply-side constraints identified in the tree), therefore excluding large segments of the population from its use.
At these workshops, participants assessed the Decision Tree on a strategic and technical level. The workshops reinforced our view about the usefulness of policymaker and researcher collaboration to find practical solutions to key development challenges such as financial inclusion. To that end, policymakers with expertise in implementing DFS inclusion strategies from a selected group of countries from around the world (Ecuador, Egypt, Ghana, Jordan, Mexico, Nigeria, Pakistan, Peru, and the Philippines) participated in these workshops, learning about the methodology and providing valuable feedback.
Applying the Decision Tree: in-depth case studies
Now that we’ve released the final version of the Decision Tree, we are working on the application of this analytical framework. We are coordinating with external experts to develop in-depth, country-specific studies that will further advance the goal of equipping policymakers with needed decision-making tools for improving financial inclusion.
We will continue to publish these materials on this page.
You can read an introduction to the methodology and more about the workshops in the following blog posts: