Developing countries commonly adopt reforms to improve their governments yet they usually fail to produce more functional and effective governments. Andrews argues that reforms often fail to make governments better because they are introduced as signals to gain short-term support. These signals introduce unrealistic best practices that do not fit developing country contexts and are not considered relevant by implementing agents. The result is a set of new forms that do not function. However, there are realistic solutions emerging from institutional reforms in some developing countries. Lessons from these experiences suggest that reform limits, although challenging to adopt, can be overcome by focusing change on problem solving through an incremental process that involves multiple agents.
Many reform initiatives in developing countries fail to achieve sustained improvements in performance because they are merely isomorphic mimicry—that is, governments and organizations pretend to reform by changing what policies or organizations look like rather than what they actually do. The flow of development resources and legitimacy without demonstrated improvements in performance, however, undermines the impetus for effective action to build state capability or improve performance. This dynamic facilitates 'capability traps' in which state capability stagnates, or even deteriorates, over long periods of time despite governments remaining engaged in developmental rhetoric and continuing to receive development resources. How can countries escape capability traps? We propose an approach, Problem-Driven Iterative Adaptation (PDIA), based on four core principles, each of which stands in sharp contrast with the standard approaches. First, PDIA focuses on solving locally nominated and defined problems in performance (as opposed to transplanting pre-conceived and packaged "best practice" solutions). Second, it seeks to create an 'authorizing environment' for decision-making that encourages 'positive deviance' and experimentation (as opposed to designing projects and programs and then requiring agents to implement them exactly as designed). Third, it embeds this experimentation in tight feedback loops that facilitate rapid experiential learning (as opposed to enduring long lag times in learning from ex post "evaluation"). Fourth, it actively engages broad sets of agents to ensure that reforms are viable, legitimate, relevant and supportable (as opposed to a narrow set of external experts promoting the "top down" diffusion of innovation).
There is an inherent tension between implementing organizations—which have specific objectives and narrow missions and mandates—and executive organizations—which provide resources to multiple implementing organizations. Ministries of finance/planning/budgeting allocate across ministries and projects/programmes within ministries, development organizations allocate across sectors (and countries), foundations or philanthropies allocate across programmes/grantees. Implementing organizations typically try to do the best they can with the funds they have and attract more resources, while executive organizations have to decide what and who to fund. Monitoring and Evaluation (M&E) has always been an element of the accountability of implementing organizations to their funders. There has been a recent trend towards much greater rigor in evaluations to isolate causal impacts of projects and programmes and more ‘evidence base’ approaches to accountability and budget allocations. Here we extend the basic idea of rigorous impact evaluation—the use of a valid counter-factual to make judgments about causality—to emphasize that the techniques of impact evaluation can be directly useful to implementing organizations (as opposed to impact evaluation being seen by implementing organizations as only an external threat to their funding). We introduce structured experiential learning (which we add to M&E to get MeE) which allows implementing agencies to actively and rigorously search across alternative project designs using the monitoring data that provides real time performance information with direct feedback into the decision loops of project design and implementation. Our argument is that within-project variations in design can serve as their own counter-factual and this dramatically reduces the incremental cost of evaluation and increases the direct usefulness of evaluation to implementing agencies. The right combination of M, e, and E provides the right space for innovation and organizational capability building while at the same time providing accountability and an evidence base for funding agencies.