Causal Design possesses unique expertise in applied political economy analysis (PEA) for program evaluation. It understands that partners and programs are embedded within complex environments, interacting with myriad forces—social, cultural and political—that are not readily observable but which may have a direct bearing on project outcomes.
A Causal Design PEA can be a stand-alone deliverable, designed in consultation with clients to address specific questions, or precede more complex evaluations, to inform the Theory of Change, assist in the design of more sensitive data collection instruments, and to ground impact, process or performance evaluations within a more detailed context, yielding more relevant and actionable recommendations. An example is a political economy analysis of a resilience program, to understand what affected participation or differential outcomes.
A PEA conducted by Causal Design is a mix of secondary literature and background interviews, with on the ground qualitative inquiry and/or surveys that more rigorously identify:
- The full spectrum of stakeholders and their driving incentives;
- Governance mechanisms including the role of informal institutions and locally-relevant non-state actors;
- Path-dependencies, including identity-based or historical grievances that continue to reverberate among target populations;
- Differential effects among sub-groups, whether between urban and rural communities, genders, livelihood types or ethno-linguistic divisions;
- The interplay between national-level political events—whether elections or legislative battles—and local outcomes, including the role of party politics or electoral violence.
- Unobservable confounders that affect implementation, uptake or impact, whether because of social, environmental or household-level dynamics.
Most often, after defining the level of analysis and extent of desired granularity with the client, Causal Design combines its in-house expertise with local knowledge and interlocutors to craft thorough, sensitive and utility-driven instruments that probe for and reveal patterns and processes. We are experts in collecting data that “shows more than tells.”
Depending on the research question, investigators choose from among a range of qualitative analysis methods, at times complemented by QDA software as appropriate, to explore for causality or more richly describe context.
These may include:
- a qualitative comparative analysis (QCA) of like villages for example
- In-depth case studies of a target district or municipality
- Process-tracing and/or outcome harvesting
- Qualitative panel data for longitudinal studies
Causal Design also works with its clients to design its reports and presentations so as to both address the most salient questions under investigation and maximize learning and the relevance of its recommendations. Clearly articulating complex results is an important component of our work, with great care underlying the delivery of our findings.