This blog was written by Dan Brockington, SIID Director.
To what extent can development problems be solved by better data? Critics of the development data agenda argue that decision-making can be independent of evidence, and cynics that politicians prefer it that way. Other pragmatists find that data have been so flawed for so long that we have already found ways of getting by, or that data need only be good enough, for most decisions. For many, the ‘return on investment’ in terms of getting better policy from better data remain unproven.
Given that governments, and development professionals, have been coping for so long with flawed data, or have only recently begun to realize some of the flaws, these arguments have bite. Some of the best writings on development hinge on the fact that fact does not drive development policy. In different policy situations it often behooves analysts to ask not ‘how can development policy be improved by better evidence’ but to assess the extent to which development policy could or would be improved by better evidence. We cannot assume that good data are relevant or even desirable.
Indeed it is worth remembering that development agendas and data are immersed in particular modernist world views that can be insensitive to alternative life-ways and models of wellbeing. Their different ontologies and epistemologies are difficult to reconcile. From this perspective better knowledge could not result in better policy. Rather recognition of different knowledges is vital.
I would argue, however, that it is still possible to admit these arguments, to be sanguine and humble about the power of good data, and to want development data to improve. If, like me, you prefer a world in which good data matter and policies are based on both better evidence and better recognition of difference, then questions about reliable data and evidence are critical. The political challenges make the researchers’ task of exploring new ways of getting better data only more important. For the politics of data and policy can become clearer when data gaps become more obvious, when better data are sought and/or when they becomes available. The new methods being employed to acquire and manage ‘big’ data (from phones, internet, automated sensors, and satellites, for example), their potential for large mistakes and the politics just mentioned, all make a clearer vision about the relevance and potential of development data a central part of contemporary development agendas.
This is why I was so keenly looking forward to the annual gathering of the FLARE network (Forests and Livelihoods: Assessment, Research and Engagement). FLARE is a network of scientists, researchers, private sector actors and policy makers. It is in part a community that cultivates researchers and research projects, and in part a means of engaging and working with different policy makers who are interested in evidence. At FLARE, new data, their flaws and their politics are pre-eminent in the discussions and presentations. There are few better places to be if you want to understand the key development issues facing forests and forest communities.
The second FLARE meeting was held in Edinburgh from 2nd to 5th December 2016. It brought together over one hundred people from nearly thirty countries to share research and findings on trends in poverty, prosperity and environmental health in forests around the world. I had the privilege of being the conference ‘mole’, and providing the feedback of some of the content and themes at the end of the meeting.
The work of the FLARE network is characterized by a mixture of case studies, far-reaching overviews, re-use of existing data, integration of public datasets with newly acquired survey data, and innovative questions and techniques. It is hosted by the same research group that maintains the IFRI database, established by the late Lin Ostrom. The IFRI database has proven vital in advancing our understanding of the effects of different forms of forest governance, on comparisons between different forms conservation protection, and on ways to visualize data and compare case studies. Part of FLARE’s role is to expand and improve its coverage (for instructions on how to contribute see here).
FLARE’s work matters in part because it helps to make forests and forest livelihoods of poorer communities legible and visible to policy makers. FLARE network members are particularly good at examining the institutional and governance arrangements that deliver sustained healthy environmental management poverty alleviation and wellbeing. Yet it has to battle the fact that not enough attention is paid to forestry and forest-based livelihoods in policy making circles surrounding poverty alleviation. Where the economic focus and development policies are bent on improving economic growth and restructuring economies, there is a danger that the livelihoods of forest dwellers can get overlooked because they cannot yield the changes in productivity required. Observers at the FLARE meeting reported that in ODA departments, and organisations like the World Bank, forest-based livelihoods are decidedly marginal.
And, perhaps because this meeting raises the bar so well, a number of remaining data challenges become visible. There is a clear need for better longitudinal data which will allow us to trace long term paths of families and individuals out of poverty. There is much more to be made of existing data sets, and scepticism required of them. Care must be taken as to what ‘forest’ means. In drier forests, radar data may be better at measuring gradations of change.
If the above are challenges that the FLARE community presents to development researchers more broadly, then what challenges does the network face as it grows? Perhaps the main point is how FLARE copes with getting beyond its comfort zones. The private sector is still relatively absent, and working out how to engage with it will take time. Then there is the challenge that many of the studies of forests and livelihoods depend on complicated number crunching. This is the domain of economistic approaches, satellite data, forest plots, panel studies and high powered, highly revealing statistical correlations. Given the insights these are affording I am glad of that. They help us to see forests and forest peoples in new ways and a development data agenda which uses these approaches carefully will be richer for doing so. Yet, precisely because these techniques are so powerful we need to recall, as mentioned above, that there are other ways of living and being in forests. There are other ways knowing about and understanding poverty aside from poverty lines and asset bundles. Effectively representing the communities and issues in gathering like FLARE to reflect this sort of otherness requires empathy, care, patience and considerable skill.
Meeting these new challenges is a tall order. But if data are to be used sensitively and well in development, and if we are to understand the limits and constraints they face, then we need to meet them.