Assessing Performance of Social Sector Development Programmes Using Programme Data - Improvements on Existing Analytical Frameworks - Boniface Francis Kalanda; Asseneth Jerotich Cheboi (Archives of Current Research International, 2025)
Aims: There are no known rigorous methods for assessing performance of social human development programmes using programme data. This study aims to explore how routine programme data generated by international agencies, NGOs, and governments in low-income countries can be systematically used to assess the performance of social sector development programmes. It also aimed to evaluate existing analytical frameworks for such assessments, identify their limitations, and propose methodological improvements to enhance their effectiveness. Study Design: This was a methodological and analytical study using a case-based framework application approach, supported by qualitative and quantitative secondary data analysis. Methodology: We applied a generic analytical framework to assess programme performance across three dimensions: achievement of intended results, systemic change, and adaptive capacity. The analysis used secondary data, including service coverage, financial utilization, and beneficiary feedback. Where necessary, primary data was collected through key informant interviews and focus group discussions. Identified limitations of the framework were addressed through methodological enhancements, including deep-dive analyses, integration of external datasets, and stakeholder consultations. Results: The framework effectively assessed “what” was implemented and “how many” were reached. However, it was limited in addressing “so what,” “how well,” and “what effect,” offering minimal insight into programme quality and impact. Limitations stemmed from the original design of programme data, which is often not aligned with structured performance assessment needs. Improvements—such as defining key concepts, triangulating with administrative data (e.g., HMIS, EMIS), and conducting focused geographic analyses enhanced the framework’s utility. Conclusion: Routine programme data holds significant potential for real-time performance assessments and programme improvement. However, existing frameworks require methodological refinements to unlock this potential. The proposed improvements can guide development actors in generating actionable insights from existing data to inform adaptive management and strategic learning. These improvements can lead to a lot of value being generated from the huge investments that are made in generating routine programme data. In addition, such improvements will provide information for use in decision making and as input to have processes such as programme evaluations.