By Cheyanne Scharbatke-Church
When programming does not fit the issue it is meant to address, creating significant change becomes highly unlikely. As Diana Chigas and I posit in our paper Taking the Blinders Off: Questioning How Development Assistance is Used to Combat Corruption, much current work assumes corruption is a “simple” problem and therefore reacts with a “simple” (e.g. predictable cause-effect based) solution. The academic debate has unintentionally contributed to this adoption of simplicity with its focus on which theory better or more fully explains corruption as a phenomenon: principal-agent or collective action. Should we follow the principal-agent model and analyze opportunities, discretion and sanctions to understand motivations? Or should we follow the collective action model and focus on group dynamics and lack of trust?
We would argue: both and neither.
Both theories are valid but…
Both theories offer valuable insights into what variables enable corruption, and thus provide useful conceptual frameworks for analysis that helps develop approaches to combat corruption. Understanding and addressing how discretion, monopoly, and accountability affect people’s incentives is important. At the same time, collective action theories bring the important insight that group dynamics also affect incentives—i.e. that there are no incentives to initiate changes in corruption practices when there is no trust that others will do the same. As Marquette and Peiffer conclude in their 2015 paper on Corruption and Collective Action, it is more useful to identify how both lenses can complement each other in analysis and strategic planning.
… neither leads to effective strategy and programming
Neither theory offers an explanation of corruption and its persistence. Marquette & Peiffer suggest that both approaches fail to recognize that corruption persists because it, in fact, solves problems.
They propose a third lens for understanding corruption: identifying the functions corruption serves and the political dynamics that underpin it.
We suggest that the lack of attention to this “third perspective” is a symptom of a larger blind spot in anti-corruption practice. It points to certain implicit but fundamental assumptions about the nature of corruption, in that both “principal-agent” and “collective action” theories treat corruption as a “simple” or “obvious” problem. In reality, corruption is a “complex” and resilient social problem that cannot be understood, or addressed, separately from the broader political, social, cultural and economic context in which it takes place. In other words, it is not only important to know what factors to look for in a corruption analysis, but also to understand how those factors interact with each other.
Why does this matter for anti-corruption strategy?
Simple and complex systems evolve and change differently. The “complexity” or “simplicity” of the situation one is trying to address will affect what kinds of strategies and programs will be effective in promoting change. Yet when a situation is understood—and analyzed—as a simple problem, the risk of adapting a “simple” strategy that cannot address the dynamics of a complex problem is high. This, we argue, sheds some light on the lack of effectiveness of many anti-corruption programs.
When “principal-agent” and “collective action” strategies treat corruption as a “simple” or “obvious” problem, they fall short when faced with the complexity of corruption. This results in a limited and narrow analysis that stops the development of effective strategies. Consequently, both models, together or separately, are unlikely to generate strategies that will succeed! Anti-corruption strategies need to be premised on the assumption that corruption in fragile states is a complex dynamic.
About the Author
Cheyanne Scharbatke-Church is Principal at Besa: Catalyzing Strategic Change, a social enterprise committed to catalyzing significant change on strategic issues in places experiencing conflict and structural or overt physical violence. As a Professor of Practice, she teaches and consults on program design, monitoring, evaluation and learning.