Tim [user:field_middlename] Njagi

Tim Njagi

Research Fellow
Tegemeo Institute

Timothy Njagi is a seasoned Development Economist with a wealth of 15 years of experience in the fields of development planning, policy implementation and research. He holds a PhD in Development Economics and Master’s Degree in International Development from the National Graduate Institute for Policy Studies (GRIPS), Japan.

He has experience working in the public sector having worked with the National Treasury and Planning in Kenya and is currently a Fellow with Tegemeo Institute of Agricultural Policy and Development of Egerton University.

His current research focus is on farm productivity, technology adoption, irrigation, governance, resilience and impact evaluation., irrigation, credit, governance, land issues, and resilience, where he has a number of publications.

He is also a member of the International Association of Agricultural Economists (IAAE), African Association of Agricultural Economics (AAAE), African Evaluation Association (AfREA), Evaluation Society of Kenya (ESK), and the Institute of Economic Affairs (EIA) in Kenya.

He aspires to make a significant contribution towards addressing food insecurity and poverty in developing countries.

My contributions

    • Dear Daniel,

      Thanks for starting this discussion. From my experience, the development partners continue to use long-term impact indicators that are unlikely to be attained after the project's life and are used to inform decisions. The challenge is of course the true impact takes time, the interventions may provide building blocks on which the impact will be realized at some future date. We cannot model shocks that tend to affect long-term indicators. as you have correctly identified, the methods and data to credibly measure this are expensive. Many partners are clearly not willing to meet the costs. The fallback is less credible evaluations that are mainly undertaken in a BAU (business as usual) model to tickmark processes.

      A key thing that works especially if you track people for a long time is to share both data and knowledge. Our Institution share data in an effort to enhance learning over time, especially as key indicators such as knowledge acquisition and behaviour change cannot be observed in the short term.

    • Dear David,

      Thanks for initiating this useful discussion. I want to share experiences from our organisation on how we have navigated some of the points you raised.

      To minimise the time burden on respondents, we try to be strict during questionnaire development. This generates some payback that all the questions we ask relate to some indicator that we will analyse. It also helps us remove some useful and desirable questions to only relevant questions without which our assessment would not be complete.

      We have found engaging farmers to tell their stories as we present our findings very useful. It also helps ground-truth our findings. in addition, they can enumerate lessons learnt during the assessment.

      For us at Tegemeo Institute, we try and have forums with farmers where we discuss our findings and how they can use the findings for their benefit. in addition, we have found the use of infographics handy with farmers as utilising local information networks to disseminate information. Furthermore, when we have compared farmers, we have found their approaches to make comparisons and deductions quite informative. I definitely recommend participatory approaches.

    • At the onset of this century, North-South and South-South partnerships were encouraged. whereas this has happened in some places, it did not work in others. The COVID pandemic challenges have not been felt where partnerships were built, and local actors' capacities strengthened. In the case of the first question, there has been no shift in such areas as a result of the pandemic. This is because institutions have been working towards strengthening inclusion and partnerships. Our local, regional and international evaluation associations should encourage collaborations (South-South & North-South) that aim to form sustainable partnerships, in which the capacity of local evaluators and their participation in both design and implementation of evaluation studies is enhanced.


    • Thank you, Jean, for initiating a very insightful discussion.


      In my view, the effectiveness of agriculture programs and projects is heavily influenced by their design, lessons from what has gone on and ability to adjust to suit local situations. Because data and M&E are low, and the appreciation has been low in the past, many of the changes that could be made in real-time have not happened. At the policy level, it is important to evaluate why we have a relatively low impact on overall is attained in the sector. for instance, why is adoption low despite promotion and campaign for good agricultural practices? In our experience, policy incoherence explains this. Our experience was that although the promotion of technologies was well done, most of these technologies required inputs which were imported. a counter policy on taxation ensure input costs remained high and thus farmers, who are very rational opted to use local technologies because it makes economic sense.

      in setting up unifying frameworks for intervention, it is important to expand the reach beyond traditional agricultural stakeholders. bring on board people in the trade, finance, and so on to ensure that the policy support and levers required are in place, or at least there is not counteracting policy that negates the gains that can be attained in agricultural policy. this also calls for understanding the broader policy environment that we operate in.

      I have attached a link on some of the examples of policy incoherence for further contextualization. please see https://theconversation.com/how-incoherent-farm-policies-undermine-keny…;

    • Dear EvalForward members,

      Thank you all for posting beneficial innovations and experiences on maintaining data quality during this period.

      The experiences and lessons on how different actors and organisations are adapting their data collection plans are essential in maintaining the gains for utilising evidence to inform policy.

      I am thrilled that we can continue to learn how to work around challenges while maintaining the credibility of the data we collect. The next step is to share this data as widely as possible and share our experiences as wide as possible to ensure that the region benefits significantly from policies informed using credible and reliable data.

      Kindly keep sharing your experiences through this platform!

      Kind regards,


    • Thanks very much Talent. Yes, the adaptation of the food insecurity experience to the COVID-19 pandemic is a great innovation especially as to attribute the insecurity that was already existing and that due to the pandemic. we look forward to the early results from these innovations. please share some of the experiences from what you are seeing so far.

      Thank you very much Ethel for your contributions. I could not agree with you more. the utilisation of past data at this time is very useful. in addition, many organisations have made their past data available for utilisation. Analysis of the data, including being able to match and combine data is an essential skill to put into use at this time. Expert opinions are also important for contextual interpretation. There are a few models at this time such as those by IFPRI, EU, AfDB for analysis and evaluating scenarios, however, there is need to enhance capacity for modelling among evaluators.


    • Dear Diagne,

      Thank you very much for sharing your feedback on rapid response. we look forward to learning more about your study and approach and experience from Senegal. On a related note, I came across high-frequency rapid response data collection being undertaken by the World Bank on the COVID-19 in a number of countries, see more information here http://surveys.worldbank.org/covid-19.

    • Great suggestions Emile. The use of scientifically established samples greatly makes the data credible. The common practice of talking to a few people is biased and not credible. however, a key challenge remains to reach these respondents. Phone surveys are now the common approach, however, the desired information should be collected should be very short because it is not possible to keep a respondent for a long period over the phone. The other means you have proposed are ideal but may be unsuitable for people in rural areas without access to the internet and electricity. SMS is also a great option but the information collected through this method is also very short. in addition, incentives must be provided to enhance participation. This also needs careful consideration so as not to bias response.


    • The Covid-19 pandemic has disrupted the norm in all sectors and professions. The lockdown and other measures to ensure health and safety of citizens have meant that collection of data, especially that which was collected through person to person interactions was not possible.  To the credit of many researchers, evaluators, data collectors, firms and institutions, there have been many and great innovations over the past two months to bridge the gap and ensure that there is data that can inform policy and decision-makers. However, it is important to ensure that the data that comes through is credible and reliable. Wrong, imprecise, incredible, unreliable data can undo the gains to have evidence-based policymaking. Also, the pressure to have data can lead to "bending" of procedures and protocols for data collection, analysis and inference. How then can we ensure the quality of the available data that is coming in? How can we evaluate the quality of analysis and subsequent inference to ensure that we influence the correct policy prescriptions?  How can we promote collaboration and lesson sharing during these times?

    • Hi Aurelie,

      our experience shows that although we have gained some ground with policymakers to make them recognise the need for investment in data, we need more traction to get them to commit resources. An ideal strategy in my view is to get a partner willing to put up investments for a pilot. once we demonstrate the effectiveness of such a system, it would be easier to attract more resources especially when demonstrating value for investment.




    • In Kenya, like other developing countries, data within the agriculture sector remains a challenge. This has constrained the RBM model where a lot of focus remains on the processes rather than moving on to outcomes. Many of the ministry's reports give a lot of prominence to the processes and targets achieved for process indicators, and use this to claim success even when independent studies have pointed to little or no progress has been achieved in outputs and outcomes of these processes. At Tegemeo Institute, we have been tracking key indicators and trends within the agriculture sector and have learned some key useful lessons. First, there is need to have a unified data system. A unified system becomes more relevant in devolved systems like Kenya's where the devolved units are semi-autonomous. Second, there is need for continuous capacity development within the systems to ensure that quality data is continuously collected, managed, analysed and shared. Third, leveraging on technology ensures to ensure that credible and quality data is collected and used to inform decisions. 

      The Ministry of Agriculture should strengthen the linkages with evaluators within the public and private sectors to improve the quality of evaluations while maintaining the independence of evaluators to make assessment from the evaluation studies and recommend action to be taken to maximise impacts. In addition, identifying change agents who disrupt the BAU position is necessary to be outcome-oriented and use lessons from evaluation to design and implement impactful policies and programs

    • Hi Christine,

      Only a few countries in the region have been able to meet the Malabo declaration target. As such, the clarion call since Maputo days has been to always call for increased public spending in the sector. However, beyond the call for increased spending in the sector by both the public and private sectors, they is need to also pay attention to the efficiency of expenditures in the sector.

      For a start,  the key question should focus on the policies that guide spending across the various sectors. for instance, what are the key priority sectors for Uganda's Government? If the focus in on extractive industries, education, or health sectors, the trends on allocation across sectors can easily confirm the picture.

      I believe its time we go back and start talking about the efficiency of the expenditures in terms of returns to investment rather than focus on the potential of the sector. This can convince both the government and the private sector to increase investments in agriculture.