All truths are easy to understand once they are discovered; the point is to discover them – Galileo Galilei

Like many development and peacebuilding practitioners, I believe in-depth contextual understanding is essential to good programme design. But all too often I’ve struggled to translate my own conflict or political economy analyses into practice and ensure it has real-life applications. I’d be frustrated as my reports, after being read by a few individuals, ended up sitting on a shelf, providing limited help to practitioners searching for new ways to bring about positive change. I knew that if these reports were going to help practitioners they needed to be more involved in the analysis exercise, they needed to improve their ability to adapt to the context and describe the process of transformation.

This blog explores my most recent collaboration in the Democratic Republic of Congo (DRC) and our attempts to adopt a systems thinking approach to analysing the context and addressing some of these challenges. This collaboration involved studying the behaviour of the elite in Kinshasa and the provinces of Kasai Centrale, North Kivu, and South Kivu, how they exert power and the effect this has on private sector development (PSD).

 This blog outlines how we used a complexity lens to build a narrative of institutions behaviour and power dynamics. During a number of participatory analysis workshops with selected local experts we explored the following:

1. Actors most relevant to shaping the PSD’s programme objectives. These were selected based on their ability to explain the power dynamics at the provincial and national level and to meet the PSD programme objectives. They varied from formal state institutions in Kinshasa, or the Provincial Governments (N Kivu, S Kivu, Kasai) to informal actors such as local businesses or the leaders of ethnic groups.

2. The factors driving the above actors’ behaviour – we assigned numerical values to their attitudes (-5 to +5) towards taking the necessary risks to support the PSD’s programme objective and their willingness to change this attitude (0-5);

3. The underlying power structures shaping relationships between actors by examining each actor’s sources of power or how they exert power. For example authoritative, social, political, economic and coercive power. For each stakeholder we assigned numerical values to different power categories (0-5);

4. The factors that build or undermine trust between key stakeholder relationships (i.e. interests, capacities, values, regulation, communication flow). For each relationship we assigned numerical values (-5 to +5) depending on the effect of the relationship on PSD.

 Building a clear narrative of the evolving context

Due to sensitivities we are unable to disclose the specific findings from our analysis, however below are some of the key narratives around behaviour, power and relationships we were able to uncover: 

1. How power is interconnected and affects behaviour in different ways: We discovered how state institutions used their power to pursue their interests and how power is interlinked. For example, we found that authoritative power was significant. High levels of authoritative power often led to high levels of economic and political power. Correspondingly we were also found links between levels of power and attitudes towards PSD reform and their willingness to change their attitudes. For example, those with high levels of economic power would tend to exhibit a negative attitude and would have a low willingness to change their attitude. Meanwhile those with low levels of economic power overwhelmingly always exhibited a high willingness to change or to challenge the status quo and PSD.

2. How the distribution of power in the provinces has different effects: We found when analysing power amongst Provincial Governors, we found power varies depending on their ability to influence the political elite in Kinshasa (political power) and their ability to influence the local population (social power). We found that provinces where ethnic groups dominated power structures were more dependent on Kinshasa for legitimacy than provinces where power was more evenly distributed between different ethnic groups. Provinces where there was a greater distribution of power led to policies that promoted more vibrant economies. This was because the increased political competition meant the elite needed to pay greater attention to the concerns of the local population. As a consequence provincial governments from these provinces were more likely to exhibit a more positive attitude towards PSD reform.

Tracking these narratives as they evolve

So far we gained an insight into the systems affecting PSD reform, how institutions behave, the underlying elite bargains, and the different ways power is exerted. Whilst many of these narratives may seem quite intuitive, the methodology has allowed us to create a baseline of both qualitative and quantitative data.

Moving forward this data can be updated using the Dialectiq software as the context evolves. This allows users to track movements in distribution of power, changes in behaviour, and shifts in competing interests over time. This will enable programs to better understand the impact their interventions are having on the structural power dynamics that are affecting PSD. Building on this, the next challenge is to understand how these findings can help programs identify strategic entry points, design programme interventions, and adapt as they move forward. 

Translating analysis into practice

To build a narrative of the changing context we identified the key stakeholders that are affecting PSD and analysed their attitude, how open they were to change and their different forms of power. Our next step was to consider how we could turn this analysis into practical activities that could support PSD reform.

Dialectiq is based on the idea that societal change involves transforming relationships. Therefore understanding relationships is essential to creating societal change. A stakeholder’s power does not exist in isolation and only becomes real when one stakeholder is interacting with another. Therefore, only through understanding these interactions can we explore why stakeholders compete, and the spaces where agreements can be shaped.

Finding spaces where change is possible

Applying complexity thinking to this analysis, finding spaces where change is possible begins by identifying ‘low hanging fruits’. This means pinpointing relationships with the greatest potential to create positive change while also considering the relative effort needed to transform the relationship. To identify these ‘low hanging fruits’ means firstly pinpointing the stakeholders with the greatest scope for change and secondly identifying the key relationships where there is a high likelihood of transformation.

During our initial analysis, we found that stakeholder’s driven by short term interests such as personal economic gains were more open to change than those with long term beliefs or interests in maintaining the status quo. This analysis allowed us to pinpoint those open to change.

Once we identified the most relevant relationships we analysed each relationship using the analytical framework outlined above in the methodology section. Through this analysis, we are able to see how different elements of each relationship are affecting PSD in different ways.

Some relationships are having a negative effect due to high levels of competing interests, others due to a lack of flow of information whilst others due to a lack of administrative capacity. We discovered competing interests between institutions often led to a lack of coordination as they contest access to economic rent-seeking opportunities. The analysis of relationships also enabled us to understand how the status quo is maintained. At the provincial level, the administration of state resources is often separated from stakeholders with high levels of political power. As a consequence, this fortifies a culture of maintaining control of the distribution of resources at the national level.

This detailed form of analysis helped us to breakdown each relationship into discrete problems making it easier to discover different solutions and to assess the likelihood of interventions having an impact. This ability to assess the level of effort required and the likelihood will increase the capacity of programme teams to think much more strategically about where to begin their interventions and the challenges that lie ahead.

 Identifying strategic entry points

To evaluate the likelihood of transforming each key relationships or creating a positive pathway for change, we tested the following assumptions:

• Relationships where attitudinal behaviour is positive or there is willingness to change will have a higher likelihood of transformation;
• Relationships where areas of common (short and long term) interests and values outweigh conflicting (short term) interests and values are more likely to change;
• Relationships where levels of institutional, contractual or structural constraints that restrict communication between stakeholders are low will have a higher likelihood of transformation;
• Relationships that are dependent on a manageable number of stakeholders will have a higher likelihood of transformation than those dependent on a much larger number.

Based on this examination we then assign each relationship as having a High/Medium/Low likelihood of transformation. By disaggregating the relationships in this way provides a basis to identify entry points based pathways where there is a higher likelihood of success. Moving forward, these entry points provide a framework for designing operational activities and pinpointing the reform champions with the capacity and willingness to support change.

Figure 1 – Overview of the relationships between key stakeholders

Sample screenshot from the Dialectiq platform –this is for illustrative purposes only and does not use data from the study

Legend for relationship visuals – The size of each circle represents the power of the stakeholder. The colour of the circle represents a stakeholder’s attitude (Purple – Negative, Green – Positive) and each line represents the effect a relationship is having on the programme outcomes (Purple – Negative, Green – Positive). The width of the line represents the level of effect this relationship is having on the programme outcomes, the thicker the line the greater the effect.

Conclusion

So far this experience has provided some valuable insights into power dynamics across a wide variety of stakeholders. We have been able to see how power is used in different ways, how it drives attitudes, affects the willingness to take risks and shapes their relationships with others.

As we move forward our next challenge is to continue testing this people centred approach and to expand the baseline data to include more stakeholders. By improving our understanding of these stakeholders’ and their relationships will increase our capacity to comprehend where change is more likely. By doing so increasing our ability to engage more effectively with key stakeholders, design more relevant interventions and promote more precise learning that is necessary for building locally driven peaceful solutions.