Making Power Analysis Useful To Anti-Corruption Programming

By Cheyanne Scharbatke-Church

The power that is abused through a corrupt transaction or that enables a corrupt system to become entrenched is at the heart of understanding corruption dynamics. How to generate nuanced and useful analysis is of significant concern to both the work on CAASDI and The Fletcher School’s research on corruption and legitimacy. In particular, how to generate useful programming insights around power flows, and the connection between the political center and the local context.

In societies where the ruling regime keeps the reins of power tightly held, it is rare that the systems that drive corruption are limited to the area in which an implementing actor can conduct programming e.g. a city, district.

One must determine how the systems that drive or enable corruption connect to different levels of power beyond the immediate programming area. Otherwise, how can one truly understand the entire system of corruption and thus potential levers for change or forces that will work against change?

For instance, in analyzing how political interference in court cases functions at the city or district level, one must commonly look to the power-centre or ruling elite (often found in the country’s capital) to understand how power is being used to direct decisions.

Information needed

It is easy to understand why one needs to look at the connection between levels, but how does one analyze these connections in a way that informs programming? We find the following information to be helpful:

  • Identifying who are key versus more people in both geographic spheres. Whereby ‘key’ people are those that if not engaged with, the issue at hand can not be changed. Conversely ‘more’ people are those who are involved in a system, but are not central to affecting change.  Sometimes this involves simply numbers of people, but not always.
  • Understanding the relationships and the lines of influence between actors pertinent to corruption locally and where and how these tie to the political centre.
  • Mapping how and where power is used appropriately and inappropriately in roles, whether this is driven by local or national level forces, and why this is the case.
  • Determining where there is energy for change (sometimes called tension in the system) and if there is a relationship between these and the centre. This could be actors behaving differently (e.g. positive deviance), actors opting out of the current patterns or actors with alternative power bases.
  • Identifying how the political centre would react to changes in the corrupt system at the local level.

Have we missed anything on this list? Are there things that are interesting but not useful in terms of understanding how power connections flow from the local to the power-centre?

Options considered

At first glance a political economy analysis (PEA) would seem to fit well to respond to these questions. According to the OECD-DAC, “political economy analysis is concerned with the interaction of political and economic processes in a society; including the distribution of power and wealth between groups and individuals, and the processes that create, sustain and transform these relationships over time.”  Yet, when we used a PEA, it was challenging to move from the national analysis perspective to a perspective that looks at the connections that matter between the national and the local.

Our work found that the national level analysis provided important background information.  However it did not provide sufficiently granular local level information to enable programming that would target local conditions.   Conversely if the team would have only conducted a local level analysis, we would most likely miss critical drivers that must be accounted for if one is to change the system dynamics.

The team is therefore working through the following alternative analytic models that could generate more useful programmatic insights:

Systems map

One option under consideration is to develop a systems map from the classic PEA.  With this national picture in hand, one could create submaps around each national factor that linked to the local context.

Conversely the opposite might be more valuable, whereby the local system’s dynamics are mapped and then linked to similar dynamics at the centre, as a separate submap.  For instance, if impunity was deemed to be a key local issue that enabled corruption, one would ask what is the connection between impunity locally and the political power centre?   It could be that the power centre openly turns a blind eye to corruption and this feeds the perception that no one is watching at the local level either.



Another idea being discussed is to use netmapping.  This is a mapping tool that helps people understand who has power and their relationships to each other and then to discuss who has influence to change the issue at hand.  The tool is based on social network analysis, but developed in a purposefully low-tech manner. The idea is that one might be able do a netmap that specifically tries to visualize the power relationships between the power centers and the local context in relation to corruption issues.

It is also possible that multiple maps asking different questions and with different boundaries are actually what is needed.  Alternatively, it may be as simple as doing a local map and including the factors from the centre that drive the local issues.

How does a center of power interface with local power dynamics?

Use the comment section below to share feedback or suggestions from your experience analyzing how the center of power interfaces with local power dynamics. Help us draw input by sharing this post through your networks!

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.

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