Prior to the International Open Data Conference in Madrid, I was invited by the Natural Resource Governance Institute (NRGI) to participate in the extractives data deep-dive. During this event data users and experts discussed how to best visualise an emerging data story, investigated the nitty-gritty detail of complex datasets and shared their latest analyses. Participants from all over the world were able to discuss different experiences and lessons learnt from their latest work. It was in this environment that the comparison between mandatory disclosure data and Extractive Industries Transparency Initiative (EITI) figures was brought to the group’s attention by Anton Rühling from Open Oil and David Mihalyi from NRGI.
My own understanding of the issue is that the EITI standard data represents a country’s own reporting of extractives activities, such as tax and legal frameworks, production and revenues. Mandatory disclosure of payments to government in France and Great Britain, on the other hand, provides a wealth of reports on companies’ payments over US$100,000 to the government where they operate in this sector. The two reporting systems should complement each other – tracking companies’ activities in a given country and tracking what that country is reporting. As such, these two data sources should allow for double-checking of payments to governments. In EITI’s own words “The EITI and mandatory disclosure are not in an “either/or” relationship. Good management of natural resources needs both these processes and more.”.
Below we re-publish a blog by Paul Dziedzic, originally hosted on the OpenOil webpage, “How do mandatory disclosures relate to EITI figure?” (this analysis spurred out of Publish What You Pay’s “Data Extractors” programme). Here we ponder if the problems that emerge from his data analysis stem from data standards interoperability. As the author writes in his conclusions:
“A comparison, however, proves to be challenging. Important factors to consider range from simple basics such as reporting cycles and comparable currencies up to complex issues of interpretations of what a project is, what can be considered as ‘payments to government’, or the influence of company shares on payment figures.”
It becomes apparent then that the issues encountered are not too far removed from the results of our research.
In our work we advocate for a joining-up of the classifications to conserve the meaning and make contextual translations between data standards and explore how data can be joined-up. For example, the disparities between the definition of a project in the EITI or the currencies reported, as highlighted in the blog, are just two of the problems that stem from not speaking the same language across data standards. It seems that extractives provide a prime example of how necessary joining-up of data, definitions and classifications is.
Fundamentally, the aim of the EITI is to promote national debate and inform policy. However, at the heart of the data problem in the extractives sector is that similar data is being provided by a number of different data publishers in varying formats: between countries and across subsectors. If there is data available now that essentially describes the same thing it should be comparable in one way or another.
Comparative analyses are currently challenging and very time-consuming even for experienced users and this problem is not unique to extractives data. Imagine though if these data standards were linked in a machine-readable format. What if getting the meaning and translations between definitions would take you seconds rather than days? If you could investigate information even with limited technical capacity at your disposal, what advances are waiting to be discovered?
The tools are out there and clearly there is a need to link-up data standards, all we now need is the will from data standards setters themselves to help make this a reality.
How do mandatory disclosures relate to EITI figures?