So, how do we fix the way we think about development to address the challenges of global environmental change?  Well, there are myriad answers, but in this post I propose two – we have to find ways of evaluating the impact of our current projects such that those lessons are applicable to other projects that are implemented in different places and at various points in the future . . . and we have to better evaluate just where things will be in the future as we think about the desired outcomes of development interventions.
To achieve the first of these two is relatively easy, at least conceptually: we need to fully link up the RCT4D crowd with the qualitative research/social theory crowd.  We need teams of people that can bring the randomista obsession with sampling frames and serious statistical tools – in other words, a deep appreciation for rigor in data collection – and connect it to the qualitative social theoretical emphasis on understanding causality by interrogating underlying social process – in other words, a deep appreciation for rigor in data interpretation.  Such teams work to cover the weaknesses of their members, and could bring us new and very exciting insights into development interventions and social process.
Of course, everyone says we need mixed methodologies in development (and a lot of other fields of inquiry), but we rarely see projects that take this on in a serious way.  In part, this is because very few people are trained in mixed methods – they are either very good at qualitative methods and interpretation, or very good at sampling and quantitative data analysis.  Typically, when a team gets together with these different skills, one set of skills or the other predominates (in policy circles, quant wins every time).  To see truly mixed methodologies, this cannot happen – as soon as one trumps the other, the value of the mixing declines precipitously.
For example, you need qualitative researchers to frame the initial RCT – an RCT framed around implicit, unacknowledged assumptions about society is unlikely to “work” – or to capture the various ways in which an intervention works.  At the same time, the randomista skill of setting up a sampling frame and obtaining meaningful large-scale data sets requires attention to how one frames the question, and where the RCT is to be run . . . which impose important constraints on the otherwise unfettered framings of social process coming from the qualitative side, framings that might not really be testable in a manner that can be widely understood by the policy community.  Then you need to loop back to the qualitative folks to interpret the results of the initial RCT – to move past whether or not something worked to the consideration of the various ways in which it did and did not work, and a careful consideration of WHY it worked.  Finally, these interpretations can be framed and tested by the qualitative members of the team, starting an iterative interpretive process that blends qualitative and quantitative analysis and interpretation to rigorously deepen our understanding of how development works (or does not work).
The process I have just described will require teams of grownups with enough self-confidence to accept criticism and to revise their ideas and interpretations in the face of evidence of varying sorts.  As soon as one side of this mixed method team starts denigrating the other, or the concerns of one side start trumping those of the other, the value of this mixing drops off – qualitative team members become fig leaves for “story time” analyses, or quantitative researchers become fig leaves for weak sampling strategies or overreaching interpretations of the data.  This can be done, but it will require team leaders with special skill sets – with experience in both worlds, and respect for both types of research.  There are not many of these around, but they are around.
Where are these people now?  Well, interestingly the answer to this question leads me to the second answer for how development might better answer the challenges of global environmental change: development needs to better link itself with the global environmental change community.  Despite titles that might suggest otherwise (UNEP’s Fourth Global Environment Outlook was titled Environment for Development), there is relatively little interplay between these communities right now.  Sure, development folks say the right things about sustainability and climate change these days, but they are rarely engaging the community that has been addressing these and many other challenges for decades.  At the same time, the global environmental change community has a weak connection to development, making their claims about the future human impacts of things like climate change often wildly inaccurate, as they assume current conditions will persist into the future (or they assume equally unrealistic improvements in future human conditions).
Development needs to hang out with the scenario builders of the global environmental change community to better understand the world we are trying to influence twenty years hence – the spot to which we are delivering the pass, to take up a metaphor from an earlier post on this topic.  We need to get with the biophysical scientists who can tell us about the challenges and opportunities the expect to see two or more decades hence.  And we need to find the various teams that are already integrating biophysical scientists and social scientists to address these challenges – the leaders already have to speak quant and qual, science and humanities, to succeed at their current jobs.  The members of these teams have already started to learn to respect their colleagues’ skills, and to better explain what they know to colleagues who may not come at the world with the same framings, data or interpretations.  They are not perfect, by any stretch (I voice some of my concerns in Delivering Development), but they are great models to go on.
Meanwhile, several of my colleagues and I are working on training a new generation of interdisciplinary scholars with this skill set.  All of my current Ph.D. students have taken courses in qualitative methods, and have conducted qualitative fieldwork . . . but they also have taken courses on statistics and biogeographic modeling.  They will not be statisticians or modelers, but now they know what those tools can and cannot do – and therefore how they can engage with them.  The first of this crew are finishing their degrees soon . . . the future is now.  And that gives me reason to be realistically optimistic about things . . .