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Job vacancies at APR - Arcus Policy Research

Arcus Policy Research extends research services on economic, gender, social, and technological issues related to agricultural policies. We are a data driven enterprise and use both secondary and primary data (from our own surveys) to shape and test ideas and interventions. Our partners include governments, institutions, banks, other think tanks, foundations, NGOs, and reputed foreign organizations including foreign governments. Our approach to impact can be broken down into the following:

  1. Bringing local perspectives to global issues
  2. Shaping and testing ideas through data
  3. Demystifying scalable solutions
  4. Creating public goods via our knowledge products

What we offer?

  1. Policy Evaluations: We conduct scientific evaluations for policies. This includes “looking back” on to the desired outcomes of the policy, its implementation strategies and identifying gaps to allow our clients with clear cut insights on what could have been done better and can be improved while implementing the said policies in the future.  
  2. Strategic Support: We provide on-demand and strategic support to our clients for designing interventions, policies and implementation strategies.
  3. Landscaping Studies: We conduct in-depth studies for programs and policies to landscape the both macro and micro environments. These studies allow our client to adjust their roadmaps to achieve the desired outcomes.   
  4. Data backed Evidence: Using scientific and rigorous techniques, we go to the field to bring out robust evidences for our clients. This part of our work helps us understand problems at the ground level and act as a link between different groups through which we bring local voices to the table.

Our methods

To deliver data driven insights, our team uses qualitative and quantitative research methods. In order to understand opinions and/or experiences, we use ethnographic surveys, focused group discussions (FGDs), market assessments to collect and analyse non-numerical data. To find patterns, test causal relationships and make predictions, we use methods such as surveys and secondary data collection. On the basis of these, descriptive and inferential statistics are estimated.