Monthly Archives: January 2013

A bottom up perspective on results measurement

Thanks to my engagement in the ‘Systemic M&E’ initiative of the SEEP Network (where M&E stands for monitoring and evaluation but we really have been mainly looking into monitoring), I have been  discussing quite a bit with practitioners on monitoring and results measurement and how to make monitoring systems more systemic. For me this bottom up perspective is extremely revealing in how conscious these practitioners are about the complexities of the systems they work in and how they intuitively come up with solutions that are in line with what we could propose based on complexity theory and systems thinking. Nevertheless, practitioners are often still strongly entangled in the overly formalistic and data-driven mindset of the results agenda. This mindset is based on a mechanistic view of systems with clear cause-and-effect relationships and a bias for objectively obtained data that is stripped from its context and by that rendered largely meaningless for improving implementation. Continue reading

New Year, New Theme

At the beginning of the year, I decided that my blog needed a face lifting. Finally, I made it and here it is. I’m still using a free WordPress Theme, so you might find other blogs that just look like this one. But they will not have the same content. Also in 2013, I will continue to write about complexity, systems thinking and my work in international development.

I also want to take the opportunity to thank my readers and especially the people that comment now and then. Comments are extremely important for me as a feedback on the relevance of my posts. Looking forward to more dialogue in 2013!

About reaching scale

Economic development projects often struggle when it comes to scaling up the impacts of their successful interventions in order to reach a large number of people. Questions about how scaling up is done in a successful way have been asked in connection to various types of development interventions without finding a successful and definitive answer.

More recently, it is often said that scaling up happens quasi automatically or at least with much less effort when the interventions of a project are ‘systemic’. This can happen in economic development projects by actors copying new business models or when new business models in a specific market also benefit connected markets in a positive way. In the Making Markets Work for the Poor (M4P) literature, these phenomena are called breadth and depth of crowding in, respectively. At the same time, the M4P literature acknowledges that crowding in might only in particular cases happen by itself and needs further efforts by the projects, such as for example dissemination of information about the new and successful implementation of business models. It is then anticipated that companies learn about the successes of their peers and will try to imitate them and with that the change will proliferate through the system. Again, it is often stressed that this will only happen if the introduced changes are ‘systemic’. But what does systemic mean in this regard? There are three important aspects at play here. Continue reading

Solutions For Building More Effective Market Systems

This blog post was originally published on the Website of CGAP, an independent policy and research center dedicated to advancing financial access for the world’s poor. The Blog post was part of the Systemic M&E initiative I worked on for the SEEP Network.

Screen Shot 2013-01-14 at 11.07.55There is a controversy brewing among systems and complexity thinkers. Is it useful to define a future goal towards which our initiatives strive? Or is it wiser instead to focus our attention on what we know we can change and trust that this will eventually lead us to a future that is better than any we could have anticipated? While the first feels intuitively right to many development practitioners, proponents of the latter argue that the absence of defined goals and targets may lead to future possibilities that are more sustainable and resilient (and that could not be fully anticipated). So the question is: can we know in advance what the best (or a good) outcome will be? Continue reading