The Building State Capability (BSC) program at the Center for International Development (CID) at Harvard University researches new strategies and tactics to build the capability of public organizations to execute and implement.

The BSC program is exploring the potential of a Problem Driven Iterative Adaptation (PDIA) approach, which rests on four core principles:

local solutions for local problems
pushing problem driven positive deviance
try, learn iterate, adapt
scale through diffusion

Recent Publications

Scaling PDIA Solutions through Broad Agency, and Your Role

Andrews, Matt, Lant Pritchett, and Michael Woolcock. 2016. “Scaling PDIA Solutions through Broad Agency, and Your Role”.Abstract

Many development challenges are complex, involving a lot of different agents and with unknown dimensions. Solutions to these challenges are often unknown, and contextually dependent. At the same time, there are political imperatives at play in many contexts which create pressure to ‘find the solution now…and then scale it up.’ Such pressure raises a question: how does a policy entrepreneur or reformer find a new solution and scale it up when dealing with complexity? This is the subject we address in the current paper, which is the fifth in a series on ‘how to’ do problem driven iterative adaptation (PDIA) (Andrews et al. 2015, 2016a, 2016b, 2016c).

The paper focuses on building broad agency solutions in the process of identifying problems and finding and fitting contextually appropriate solutions. The broad agency is, in our opinion, a most effective mechanism to ensure scaling and dynamic sustainability in the change process. As with other working papers on this topic, the contents here do not offer all answers to those asking questions about how to do development effectively. It closes by reflecting on the importance of ‘you’ (the reader, and ostensibly part of a policy change or reform team somewhere) using this and the other ideas as heuristics to rethink and reorient how you work—but with your own signature on each idea.

The Big Stuck in State Capability for Policy Implementation

Andrews, Matt, Lant Pritchett, and Michael Woolcock. 2016. “The Big Stuck in State Capability for Policy Implementation”.Abstract

We divide the 102 historically developing countries (HDCs) into those with ‘very weak’, ‘weak’, ‘middle’, and ‘strong’ state capability. Analyzing the levels and recent growth rates of the HDCs’ capability for policy implementation reveals how pervasively “stuck” most of them are.

Only eight HDCs have attained strong capability, and since most of these are small (e.g., Singapore, UAE), less than 100 million (or 1.7%) of the roughly 5.8 billion people in HDCs currently live in high capability states.

Almost half (49) of these countries have very weak or weak capability, and thus their long-run pace of acquiring capability is also very slow.

Alarmingly, three quarters of these countries (36 of 49) have experienced negative growth in state capability in recent decades, while more than a third of all countries (36 of 102) have low and (in the medium run at least) deteriorating state capability.

At current rates, the ‘time to high capability’ of the 49 currently weak capability states and the 36 with negative growth is obviously “forever”. But even for the 13 with positive growth, only three would reach strong capability by the end of the 21st century at their current medium run growth.

Doing Iterative and Adaptive Work

Andrews, Matt, Lant Pritchett, and Michael Woolcock. 2016. “Doing Iterative and Adaptive Work”.Abstract

Many of the challenges in international development are complex in nature. They involve many actors in uncertain contexts and with unclear solutions. Our work has proposed an approach to addressing such challenges, called Problem Driven Iterative Adaptation (PDIA). This paper is the most recent in a series intended to show how one can do PDIA, building on the first paper, "Doing Problem Driven Work.” The current paper addresses a key part of the approach one moves to once a problem has been identified, performing real-time experimental iterations. This is intended as a practical paper that builds on experience and embeds exercises for readers who are actually involved in this kind of work.



The "PDIA: Notes from the Real World" blog series

We're sharing our lessons from our PDIA experiments over the past five years.