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.

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Identifying genuine underpayment of minimum wages is not straightforward. Some well-known statistical issues affect the measurement of compliance rates, but factors such as processing or behavioural influences amongst respondents can also have an impact. We study the quantitative measurement of non-compliance with the minimum wage, using UK apprentices (who have particularly high non-compliance rates) as a case study. We show that understanding the institutional and behavioural context can be invaluable, as can triangulation of different sources. While the binary nature of compliance makes such problems easier to identify and evaluate, this analysis holds wider lessons for the understanding of the characteristics of large and complex datasets.

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