Measuring the value of data governance in agricultural investments: A case study

Abstract

Summary The study at hand measures the value of improving data governance and access in the Supporting Soil Health Interventions (SSHI) project in Ethiopia. We applied two separate but interlinked models, one qualitative and one quantitative, to create a new framework enhancing the traditional cost–benefit analysis. The qualitative analysis provided novel insights into the specific types of value and the mechanisms through which they are generated. These results underpinned the development of an innovative framework to measure this perceived value quantitatively. By combining the quantitative and qualitative framework, the study demonstrated that it is possible to generate plausible and credible quantitative estimates of both costs and benefits of data governance and access. While acknowledging that the estimates are only illustrative, the case study results suggested on a direct cost measure, at a particular point in time, the SSHI data governance activities yielded a negative return. However, indirect social and public benefits are rarely quantified, but this paper shows that relatively few “indirect” benefits (current but unmeasured, or measurable but in the future) are necessary to reverse that view, at least from the point of the economy more generally.

Using the Five Safes to structure economic evaluations of data governance

“…Baker (2000) suggests the minimum is a simple counterfactual: "doing X" versus "not doing X." Whittard et al (2021) summarize the guidelines on EE as follows:…”

“…Wdowin and Diepeveen (2020) summarize both the issues and the wide range of resulting estimates arising from different assumptions, but even these are just for the valuation of data investments. As noted in the introduction, data are not the same as data governance, and there appear to be no evaluations of investments in the latter prior to Whittard et al (2021).…”

“…In other words, if data governance is well done, the outcome of it is unobservable by design. Whittard et al (2021) throw an additional problem into the mix: evaluation in LMICs. While there have been a number of evaluation on projects in LMICs (including agriculture), evaluation methods designed for HICs may not be appropriate in places where discount rates, financing methods, outside options, and institutions differ significantly from the models envisaged.…”

“…Table 5. Lessons learned (matrix; extract based on Whittard and Ritchie, 2021) learned in respect of… …organization of projects and investment in setting up processes…”

“…The complete report can be found at Gates Open Research (Whittard et al, 2021). A summary of the EE, which is not our focus here, is given by Whittard et al (2022).…”