Background
Decisions on how to invest scarce resources in CGIAR-NARES genetic innovation systems have historically been predominantly supply-driven and therefore potentially out-of-sync with the demands of smallholders, consumers, and agroindustry. The turnover of improved crop varieties developed by CGIAR and its NARES (National Agricultural Research and Extension Services) and SME partners has been slow Little is known about the drivers of varietal replacement and product substitution, and which strategies are most effective to increase the adoption of improved crop varieties.
Within CGIARโs Breeding for Tomorrow program, the Area of Work on Market Intelligence aims to maximize returns on investment in breeding and seed systems based on reliable and timely market intelligence that enables stronger demand orientation and strengthens co-ownership and co-implementation by CGIAR and partners. To achieve these objectives, the area of work will generate and synthesize evidence on how to increase the adoption and turnover of improved varieties by farmers, consumers, and private sector.
An important question in doing so is whether the cost-effectiveness of these strategies depends on the traits of promoted varieties. For instance, it may be more expensive to promote nutritious varieties given that it is difficult to observe their benefits in the short term, whereas high-yielding varieties might be easier to promote. Another question is whether it is more cost-effective to focus on promoting a varietyโs production- versus consumption-related traits, given that many smallholder farmers grow for subsistence, and production and consumption decisions cannot be separated from one another.
In this context, the International Food Policy Research Institute (IFPRI) is hiring a consultant to conduct two meta-analyses over the remainder of 2025 to quantify the cost-effectiveness of various interventions to accelerate the use of quality seed of improved varieties. One meta-analysis is based on a systematic literature review of seed promotion interventions, for which some data curation will still need to be completed, and a second meta-analysis will use data from a set of recently completed coordinated trials quantifying effects of producer- and consumer-targeted interventions. For both studies, the consultant will deliver a harmonized database, analysis code, and presentation slide deck, and will contribute to a paper that is ready for submission to peer-reviewed journals.
In applying to this consultancy, please include a writing sample (research paper, report or similar output), a sample of data analysis code (preferably in R), an estimated number of days for the consultancy (with a maximum of 120 days), and daily rate.
Scope of work
Conduct a meta-analysis based on a systematic literature review.
Review and validate database produced by the systematic literature review team (incl. supporting the literature review team in following up with authors on missing details, e.g. intervention costs or other missing variables).
Analyze cost-effectiveness of different types of interventions, for different types of traits, using Bayesian meta-analysis methods for which an R package is available.
Prepare a replication package (database and well-documented analysis code) that will be published in a public, online repository.
Produce a slide deck based on the dataset with descriptive statistics and other analyses that may not appear in the analysis code.
Conduct a meta-analysis based on a set of six coordinated trials on promoting new varieties through producer- and consumer-oriented approaches.
Work closely with a research analyst to generate the database that harmonizes data from baseline and endline surveys for the six different study teams.
Meta-analyze the effects of producer-oriented and consumer-oriented approaches, as well as the effect of the two when bundled together, using R.
Prepare a replication package (database and well-documented analysis code) that will be published at a later stage in a public, online.
Produce a slide deck based on the dataset with descriptive statistics and other analyses that may not appear in the analysis code.
Required qualifications
Masterโs degree in development economics, agricultural economics, or related field, either completed or close to completion.
Demonstrated knowledge of and experience with applied econometrics and statistical analysis within the past 2 years.
Excellent writing skills as demonstrated by a writing sample (research paper, report, or similar), with an explanation of the candidateโs contribution if the sample is co-authored
Proficiency in software for econometrics and statistical analysis (e.g., R and/or STATA), as demonstrated by including a sample of code that the candidate has written.
Value for money based on the financial proposal (specifying number of days and daily rate).
Excellent communication and interpersonal skills.
Preferred qualifications
Demonstrated experience with impact evaluations related to behavioral change and/or technology adoption
Demonstrated experience with systematic literature reviews
Experience with project management and coordination
Proficiency in R for statistical analysis
Experience with Bayesian methods for econometric analysis and/or meta-analysis
Application Deadline: March 31, 2025