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Food Systems Trade-offs Research Assistant

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Date: Mar 6, 2023

Location: Washington, DC, US, 20016

Company: American University

Summary/Objective

Food systems consist of diverse actors along the supply chain from production to consumption and are shaped by environment, technological, political, economic, socio-cultural, and demographic factors. Well-functioning food systems are expected to contribute several outcomes, including human health and nutrition as well as other social, economic, and environmental outcomes. However, food systems are complex, and trade-offs can arise where improving one outcome occurs at the detriment of other target outcome(s). Identifying and quantifying trade-offs in food systems is critical for informing policy decisions where one outcome must be weighed against another.

Essential Functions

This research assistant will conduct a scoping review of food systems trade-offs in the peer-reviewed literature. The review will focus on recent literature covering the full supply chain and across social, economic, environmental, and health domains. Additional tasks related to identifying and collating potential indicators related to food system trade-offs may be required, as needed.

Supervisory Responsibility

  • The research assistant will work closely with Jessica Gephart (AU Environmental Science) and Christophe Béné (CGIAR, Alliance Bioversity-CIAT) over the course of the project.

Position Type/Expected Hours of Work

  • The research assistant will be expected to work part time during the Spring semester, with an opportunity to  be hired half-time during the summer.

Salary Range

  • The research assistant will be paid at the AU student rate.

Required Education and Experience

  • An interest in food systems
  • Experience with formal literature review (e.g., scoping and/or systematic review)
  • Experience managing and entering spreadsheet data
  • Strong written communication skills
  • Strong organizational skills

Preferred Education and Experience

  • Familiarity with statistics and modeling, ideally related to causal identification


Nearest Major Market: Washington DC