In any economy, the design of development programs and policies are typically tailored towards changing outcomes of interest. In most cases, such outcomes may include identified economic parameters, the absence of which undermines the development of the economy. As various legitimate economic activities should contribute to the development of the society, low productivity limit the potential of such activities to boost job creation, income generation, and the overall livelihood of the people in the society. Hence, among other things, increasing productivity may define the thrust and objectives of any development programs and policies. In any case, the crucial question, unfortunately not often examined, has to do with whether or not these changes are actually achieved. For a development-minded and goal-oriented government or administrator, impact evaluation is a sine-qua-non. Asides that it provides a clear definition of the implementation pathways that lead to output and how these outputs can be sustained, It forms a crucial part of evidence-based policy making .
During implementation of an intervention project, programmes must be implemented with a clear definition of the impact pathways that lead to output and how these outputs can be sustained. Evaluation, alongside its complement- Monitoring- forms the essence of developing a project management dash board to allow an on-the-spot review of pertinent information related to the project implementation. With both, stakeholders can verify and improve efficiency, quality, effectiveness and other Key Performance Indicators (KPIs) of policies and programs at any stages of implementation [1,2]. It is also possible to identify the most-cost effective program schedule, project performance, budget allocation and accountability of allocated budget, especially when public fund is involved. Hence, performance evidence provided by a robust impact evaluation may provide platform for greater accountability, innovation, and learning. With an impact evaluation assessment, the changes in the target outcome traceable to the particular project can be obtained as the causal relationship between the program and the outcomes of interest.
In the Agricultural sector for instance, intervention projects are basically targeted at farmers and other stakeholders along agricultural produce value chain. Given the peculiarities of challenges that actors face at each node of the chain, certain programs are implemented by both public and private concerns to improve productivity, profitability, income and livelihood of actors along the chain. Prominent in the recent agricultural development efforts in countries of sub-Saharan Africa are the input subsidy initiatives of the governments which aims at enhancing access to agro-inputs for productive agriculture sector and reducing poverty . A productive agricultural sector is in turn, believe to bring about more income, food security, employment generation and after attainment of food self-sufficiency, agro-export promotion. thus, with impact evaluation studies, policy maker and researchers have been able to document the mile stone achieved, challenges, and areas for improvement in development project implementation.
In spite of the importance, the major obstacle to the success of this exercise is the dearth of data describing the characteristics of the target populations at the beginning and/or throughout the period of project implementation through the baseline, mid-line and end-line surveys. To ensure availability of data for evaluation exercise, data collection efforts like extensive quantitative survey, Semi-structured Personal Interviews (SPIs) and Focus Group Discussions (FGDs) are implemented. The quantitative survey produces quantitative data using structured questionnaire designed in both hard and online soft copy while SPIs and FGDs produce qualitative data. With availability of quantitative data, a project’s impacts can be assessed by analyzing the data using descriptive and inferential data mining techniques. On the other hand, qualitative data from FGDs and SPIs can help us to systematically compare and validate any quantitative evidences obtained .
 Gertler, P. J., Martinez, S., Premand, P., Rawlings, L. B., & Vermeersch, C. M. (2016). Impact evaluation in practice. The World Bank.
 Your Guide to Project Management Dashboards – Examples & Templates
 Covadonga Canteli (2013) A proposal of mixed methods approach to impact evaluations. https://daraint.org/wp-content/uploads/2013/07/DARA-Impact-Methods.pdf
 Kato, T. and Greeley, M. (2016) Agricultural Input Subsidies in Sub-Saharan Africa. International Development Strategies (IDS) bulletin 47(2). https://bulletin.ids.ac.uk/idsbo/article/view/2716/html.