Data quality and the Pro-Poor Principles

Pro-Poor Principles series
On 15 May 2013 we announced our Pro-Poor Principles in a blog post, found here. In this continuing series of blog posts, we will elaborate on the path that brought us to these Pro-Poor Principles of microfinance. The principles will inform both the learning environment in our community of practice, as well as our methodology for determining organizations that will be recognized by the Pro-Poor Seal of Excellence. We appreciate any thoughts you have on the Pro-Poor Principles and how best to apply them to practice. If you would like more information, please contact MeasureLearnChange[at]gmail.com.

The Beta Tests and Data quality
In partnership with technical experts in microfinance, we recently concluded a beta testing phase for the Seal of Excellence. The 7 microfinance institutions evaluated in the beta tests represent a variety of regions, as well as organization sizes and legal forms. You can view some of the broader findings of these beta tests here. In today’s post we will be discussing data collection and disaggregation issues encountered in the beta tests, and how these experiences have helped to inform our process moving forward.

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Data quality
The Seal beta tests utilized a number of indicators evaluating a variety of data collected by the beta test participants. In looking at how a practitioner performs according to the three Pro-Poor Principles, we rely heavily on the data collected by the practitioner. A key finding in our beta tests was the need for close examination of quality issues – from sampling, data collection, data checks and data entry, to later data analysis and reporting. Moving forward, the beta test findings have highlighted the importance of developing guidelines for quality issues with data on poor clients.

Data disaggregation
The Seal beta tests brought to light one of the biggest challenges in measuring the poverty-focus of microfinance practitioners – disaggregation of data on poor clients. As is typical in most services providers to the poor, resources are scarce and data collection can be de-prioritized as quite a costly endeavor. Without quality data and its analysis, however, pro-poor institutions are left without a means to measure the impact they are having on their mission and whether or not they are, in fact, reaching who they intend to be reaching. The beta tests showed that even institutions with a poverty-focused mission were not often disaggregating data on poor clients, highlighting the need for pro-poor microfinance to promote these activities as part of the continued improvement of pro-poor practices across the globe.

Where did these findings lead us
The beta tests revealed a range of different methods, tools, and yield for collecting, tracking and analyzing data that were used by participants, all with varying results. These varied approaches used by practitioners led us towards several conclusions for the Pro-Poor Principles, as well as the final methodology.

First of all, as mentioned above, data quality issues highlight a need to promote quality data collection in pro-poor microfinance. Thus one of the 4 sub-categories under each of the 3 Pro-Poor Principles is “Measurement and Data Quality” (read more about the Pro-Poor Principles and sub-categories here).

At the same time, while encouraging quality data collection is essential to the pro-poor movement, the varied approaches seen in the beta tests also call attention to the need for some flexibility in this area. For this reason, the methodology surrounding the Pro-Poor Principles and the Seal of Excellence recognizes many tools and methods of data collection when evaluating practitioners.

While the beta tests and technical expertise have helped greatly to develop a robust methodology with the Pro-Poor Principles at the forefront, we understand that a robust methodology will be adaptable over time as well. We look forward to hearing your feedback on our work and learning together as we move forward in the pro-poor movement.

What is the current status of data disaggregation on the poor?

What are the barriers to achieving more robust data collection?

Do you see issues with using data collected by
different means to evaluate pro-poor organizations?

Is there more a need for regional flexibility or for industry-wide standardization?

Leave your thoughts in the comments section below

 


Some thoughts from Technical Committee member Lucia Spaggiari:

lucia-spaggiari_official“Microfinance promises to deliver financial inclusion to the poor and the excluded. Every single pro-poor mission raises expectations, and rightly so. Yet, very little is known about the poverty profile of the clients reached. What is known is that microfinance is not always the same: differences are there, and it is important to recognize them as a first step to align the expectations. Microfinance can be implemented in very different ways, with different poverty outreach and alleviation results.

Poverty measurement is necessary to understand if microfinance is delivering on its promise and to enhance the strategies and products’ effectiveness to reach the poor and contribute to improving their lives. To be successful in doing this, decision makers need reliable information to base their choices. It is difficult to use the information to inform management decisions and to make the good decisions if the quality of the information itself is poor. Many measurement systems exist and can be created to deliver quality information.

There is not one single measuring strategy which is the best in all cases. Identifying the tracking and monitoring system option with the cost and accuracy characteristics most appropriate to the objectives and stage of development of the microfinance institution is the way to translate the heart of a mission into good sense management. By promoting measurement and transparency around poverty outreach, products adaptation to clients’ needs and poverty alleviation, we offer an important toolkit available to pro-poor microfinance players to consolidate their reputation and avoid related risks. The microfinance mission is attractive; let’s avoid the risk of being victims of our own success.”

– Lucia Spaggiari
Social Rating Director, MicroFinanza Rating

2 thoughts on “Data quality and the Pro-Poor Principles

  1. Bonjour,

    Je suis bloqué par la langue car je suis francophone de la RDC

    Freddy NUMBI NGOIE DG ADEKOR IMF Tél.: + 243816047349  –  +243994799527

  2. Reblogged this on 100 Million Ideas and commented:

    HIGHLIGHTS: * Data quality — Moving forward, the beta test findings have highlighted the importance of developing guidelines for quality issues with data on poor clients. /// * Data disaggregation — need for pro-poor microfinance promote activities like disaggregating data on poor clients as part of the continued improvement of pro-poor practices across the globe /// * Where did these findings lead us — one of the 4 sub-categories under each of the 3 Pro-Poor Principles is “Measurement and Data Quality”

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