sharing in governance of extractive industries
Models are powerful tools. So powerful in fact that the discussion of whether they should be unleashed into the public space has sometimes been accompanied by a discussion of the “dangers” of doing so – reminiscent of the first debates around the idea of transparency itself.
General thinking has been that models are a stage to aspire to, to work up towards - perhaps because they involve a degree of technical complexity, and because they have traditionally been built by experts for experts.
But I'd like to suggest this should be inverted. Models are the natural entry point, not the grand finale, of work on governance in extractive industries. Not because they are easy – although we definitely believe they can be made more accessible than they have been. But because, brutally speaking, it is not possible to get system-level understanding of how the oil and mining industries work without them. They are not nice-to-haves, in other words. They are essential.
We see this in “single term” debates in many countries: demands by the public for a royalty rate, or income tax, to be raised to gain more income, and company, and sometimes government resistance to those demands. And nowhere in the debate any projection of how much money this or that measure would raise.
Economic nationalism only sharpens the need for modeling because we see the headline terms of contracts adapted to political window-dressing without an overview of how it all actually plays out in reality. In Iraq, the Baghdad government claims its service contracts are superior to the production sharing contracts of the Kurdish region because companies don't get to own a single drop of the oil – but that tells us nothing about how investor metrics in the two fiscal regimes stack up against each other. Libya's last round of PSCs fixed profit share at 92 percent to the government and a mere eight percent to the companies, a supposed triumph for the government. But how does that fit into the broader picture of total costs and revenues allocated to the companies?
In other words, models don't create the complexity – don't shoot the messenger! They simply make the complexity inherent in the arrangements and render it transparent. So much so that it becomes hard to imagine any credible analysis of any financial aspect of the extractive industries which is not supported by a model of some kind.
But just how possible is it that all this weft of interlocking terms can be laid out neatly, and made accessible to non-specialists?
To test this proposition, I recently traveled to Chad for a week of training with local civil society groups on a model developed around an oil project there run by Glencore. At the end of two days, half the group had assimilated what a model could do and what the main features of that project were. At the end of five days there were several members of the group who could themselves present the model to each other.
Although there were aspects in the prototype which didn't work I was convinced that public interest models can work and be adopted by civil society and others in the independent space like journalists. In order to achieve this pedagogical function, a couple of simple things need to happen.
A certain ruthless and meritocratic elitism in selection of participants for training. Modeling isn't for everyone and doesn't need to be for everyone. Think of this like the layperson's interaction with the many complex issues of science. Few of us know much about the science of genetically modified crops, climate change or stem cell research. But we don't feel a need to all go and become experts in such fields because we trust (perhaps naively!) that we live in a free enough society that there are genuinely independent experts who will explain the issues to us when they touch the public interest. This doesn't mean that all the experts will agree on everything, or that there is only one possible position to adopt even in the face of scientific consensus. If in any country there are several genuinely independent people able to model, that is enough.
The development of friendly messaging is key here. One thing which worked well in Chad was to try and differentiate the various possible relationships people can have with a financial model by analogy with a car. A car is a stupendously complex machine. But are you seeking to create a new one (“Designer”)? Maintain or fix an existing one (“Mechanic”)? Or simply use it to go places (“Driver”)? This in turn should lead to much more adapted materials for each of those roles, since, continuing the analogy, nobody would expect driving lessons and the exam to include how to replace the engine.
Concentration on the interface. If we assume that the open publication of models quickly (within a year) allows competent engineering of the guts of models to be established, the need in training and pedagogy will be to concentrate on delivering interfaces which work for new and vastly expanded audiences. One approach is to constantly evolve and modify a User Dashboard on a single spreadsheet and to clearly demarcate between Input areas – boxes where you change stuff – and Output areas – where you see what happens when you change stuff.
And in turn, because there will be no single right or wrong answer to this question, experimentation and a healthy diversity of approaches will be the order of the day in building public interest models. To assume that technical robustness requires a one-size-fits-all approach would be like assuming that safety standards required any other form of central planning and production.
There are also clearly benefits to spreading modeling as a training mechanism above and beyond specific knowledge of individual projects. Generic features of the industry such as the Investor Curve (the long lead-time of investments and the need to structure revenues so investor recover costs), or State Participation (the complex arrangements of national oil companies inside the producing consortium) become embedded in understanding through models in a way they simply don't with words and explanation. Using a model, you can actually see these features play out. They are no longer abstract principles. The model gives them a specific shape and contours.
How would a higher public understanding of the revenue flows play out in the governance and politics of managing these industries? We can only speculate because it hasn't happened yet but, for example:
Analysis of mining contracts in the 2000s would have clearly shown governments and their publics how “un-progressive” many mining contracts were – how incapable they were, in other words, of capturing an increased share of profitability as a commodities boom took hold. This earlier awareness might have played into the renegotiation debate very differently. As it was, awareness of the issue only reached a critical mass in decision-forming circles round about 2010-11, just when mineral prices started to come off historic highs, making potential conflict with companies around demands for renegotiation at that time that much more intense.
In the coming period, how would any debate on sweetening terms of oil contracts for producers play out if there was a general awareness, through models, of the difference to investors between revenues and profits over the lifetime of a project, and operating cash flows?
To sum up: public interest models are the key to an independent understanding of the economics of these industries to a new level. As such, they are an essential part of the transparency and governance armoury. It would be great to see how models, for example, could be integrated into the EITI process.
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