A series of blogs and technical briefs on the implications of alternative cleaning and variable construction decisions when constructing agricultural indicators.
Outliers are a common feature of survey data, but opinions on the best way to handle them can differ. We present a summary of common outlier detection and treatment strategies and a tool for comparing the outcomes of different choices.
What
A series of blogs and technical briefs on the implications of alternative cleaning and variable construction decisions when constructing agricultural indicators. In this series, we will cover topics such as winsorization choices, representing women farmers and challenges constructing gender productivity gaps, inconsistencies interpreting the oft used phrase “improved seed,” and how the choice of prices affects estimates of value.
Why
Our work has surfaced differences in estimates for the same indicators, sometimes across the same data sources. Differences can arise from reasonable debates over proxies, for example, how to define rural, or identify a small-scale producer. Others arise from processing “shortcuts” that may be harmless and expedite the use of novel data, while others may have more serious consequences (e.g. calculating yields on intercropped plots without apportioning the area planted with each crop and how misidentification of genetically improved seed is associated with other plot management choices). Our goal is understanding when shortcuts are efficient, and when they can mislead decision-makers.
Even in top journals and even with access to the same raw data, without documenting rules or access to the underlying code, it can be difficult to understand construction choices that can nonetheless have important consequences. The practical application of this work is to understand when quick and cost-effective proxies are “fit for purpose” and when more precision and resources may be necessary.
Stay tuned for upcoming posts on this series!