I have written things you wouldn’t believe. Country assessment frameworks for social accountability organizations. I watched donors try to coordinate in a small Central American country. All those reports will be lost in time, like tears in rain.
It’s Part Deux of PEA confessions! This time I want to discuss one of my favourite pet peeves: PEA reports. In this case I will refer back to some of the themes covered in Why We Lie About Aid, and in particular to a 2015 ESID briefing that I wrote: “Making political analysis useful: Adjusting and scaling”. Again, the goal here is to see if prior insights hold true in light of more practical experience as a PEA consultant. Continue reading PEA Confessions, part II: Report rapport
Four years ago I published a research paper and policy briefing at ESID that focused on the barriers to political-economy analysis (PEA) in donor agencies. I thought our research gave me a pretty good grasp of the promises and pitfalls of PEA in the aid community. After two-and-a-half years of working as a PEA consultant, the time has come for some self-imposed accountability. This is part I of a new series of posts dramatically called “PEA Confessions”.
I want to begin with ESID Briefing Paper 5: “Mainstreaming political economy analysis (PEA) in donor agencies”. It is not my most inspired writing, but at the time it felt like a very clever contribution. Having found – with David Hulme – how organizational dynamics made the use of political analysis by DFID and the World Bank very inconsistent, I thought I needed to devote some thinking to the “so what” question and come up with some semi-coherent recommendations. Continue reading PEA Confessions, part I: Mainstreaming woes
This has been my third year teaching political analysis of development policy at Manchester GDI. Strangely, I have never used any of the donor-produced PEA frameworks in my course materials or lectures. The reason lies partly in the fact that commonly employed PEA frameworks – like most social science – are better at identifying structures than theorizing change; to my mind, this was true of Drivers of Change, SGACA, and the World Bank’s Problem-Driven PEA. So if you are interested in change – which is what development actors do – then you need a different set of tools. With that in mind, here are three intuitive but subversive conceptual tools that I introduce in Why We Lie About Aid. Continue reading 3 concepts that should change how we do political analysis in development
These days I am writing a paper on how the STAAC programme that I work on in Ghana has managed to chart a bespoke adaptive programming course to anti-corruption. There’s a lot in there about embedding PEA into everyday practice, about being smart and adaptive, about doing things differently. And you would expect as much from a DFID programme designed with TWP/PDIA buzzwords in mind. But the exercise of reflecting on two years of experimentation with the approach and evolving relations with partners has also made me ask questions about the broader dilemmas of anti-corruption programming.
The challenges of combatting corruption are well known: informal institutions, social norms, principal-agent and collective-action poblems (I have written about some of these in my book). In fact, corruption tends to be one of the most difficult components of the broader public sector agenda. Naturally the anti-corruption community – such as it is – appears to be developing a new consensus that challenges conventional approaches to anti-corruption, compiling evidence of what works, and asking for a smarter way of tackling an intrinsically difficult problem: we need to “move away from thinking of anti-corruption as a blueprint”, finding solutions that are “localised and adapted to individual country contexts”.
All of this sounds great, and is very much in line with DFID’s own approach to chain-link, politically-smart, adaptive approaches to anti-corruption. There is just one minor glitch: local partners do not necessarily share this view. Continue reading Adaptive anti-corruption: Best-fit methods for best-practice goals