For the past year, OpenOil has researched various aspects of open data on extractive industries. First we looked at corporate mapping – creating a network map of BP’s 1,200 subsidiaries across 84 countries with our colleagues at OpenCorporates. Then, with PWYP in Canada, we searched systematically for contracts disclosed by companies, which led to the contract repository, now in its third edition with over 700 full text oil contracts. We worked with Global Witness to develop a concept of open source financial models which has now been deployed in three countries.
But open data works on a kind of “network effect” – each new layer enhances the value of the others. So we thought it was time to try and put different layers together.
The result is the Tanzania prototype – what open extractives data might look like when it’s all put together.
These are early days and the prototype is firmly in the open data ethos of release early and often. So there will be lacks and bugs and mis-structurings – we beg your indulgence.
But the basic concept demonstrates how much is possible from data already in the public domain. Starting from a country survey, we find some 50 oil and mining operations which have significant data around them, whether it is reserves, production, contractual details or other. We have “normalised” some of this into a database, and left other parts of the data just as they appear in investor documents. What we have found is that government and even compliance-level company documents are far from perfect – but they are authoritative enough to be of interest to people trying to piece the different pieces together.
From a country-level overview, we zoom in on one mine – Bulyanhulu gold mine. At the center of the data tour is a financial model of the project, which has produced over three million ounces of gold since it started up in 2001 – but is yet to pay income tax.
Why is that? And on what basis do we predict revenue flows to government and investors? As usual, all documentation and sources are included, according to the principles of public interest financial modeling.
The model is the fulcrum of the data tour. We zoom in geographically, from country, to district, to mine, then swivel from geography towards money and zoom out again. First, to the corporate network of Acacia Resources, a spin off of Barrick Gold, which is spread across Africa, and finally to the corporate networks that operate the other mines and oil fields of Tanzania, together with their inter-connections and affiliate networks around the world. All documented, all sources one click away.
There are no earth shattering surprises in all this and no smoking guns. But what putting all the different layers together promises, when it becomes fuller, is: full and editorially independent cost-benefit analysis of these industries. Without fear or favour. There may be some vested interests that will be ruffled by this. Ultimately, we believe the balance of stakeholder interest, whether it is investors trying to understand the risks in their portfolio or local communities weighing disruption against economic advantage, is served by rich, interwoven, quality checked system-level data. That is what we and our colleagues hope to offer.
If you’re interested in public governance of the extractive industries, why bother with so-called “auxiliary” contracts, those between two private entities? In a word, because they may be useful.
The third edition of OpenOil’s contract repository contains over 100 contracts relating to oil production in 30 countries. They include sales agreements for oil and gas, joint operating arrangements between two commercial entities and all kinds of asset transfers – sales of existing stakes, options to buy a stake in the future, terms under which a gas project will supply electricity to the grid and so on.
There are two issues here which ought to be separated. The first is that as agreements between two private parties there aren’t the same arguments around whether they should be published. There probably is a maximalist position which says all contracts relating to every aspect of the industry should be published regardless of the nature of the entities in the agreement. But that’s not our position. We are releasing these contracts simply because they are there – companies publish them on financial markets, just as they do Host Government Contracts (HGCs) – contracts where one of the signatories is a state or one of its representative bodies, such as a national oil company.
But they are worth curating because they add insight. If you’re grappling with the complex issue of gas pricing, term contracts and whether gas prices are linked to oil or separate, why not take a look at this gas sales contract from Israel? Or the same for crude oil from Albania? If you want some idea of how upstream gas production ties into local electricity production, try this distribution agreement from Tanzania.
Joint Operating Agreements provide useful insights into the arrangements companies in a consortium make among themselves. And farm-ins, -outs, and -downs show the structure and value of the sale of stakes by one company to another, a crucial block of analysis in industries where a project is moving from exploration to production, since this often involves an exploration company “flipping” the asset to a better capitalised production-based company.
There are literally thousands of such contracts out in public domain – they are common for projects in North America, where a competitive oil industry in transparent financial markets means investors demand these kinds of disclosures, which have a direct bearing on the value of their shares. The repository’s foray into this area is modest. There might at some later stage be an argument to collect a wider range of such contracts because some aspects of best practice are international, and so there is something to learn even from contracts relating to very different operational contexts. But for the moment, it includes only auxiliary contracts which relate to upstream operations in governance countries of interest.
It’s all part of the long, slow process of raising the transparency game. The fact is that HGCs are simply one document of what might easily be 50 to 100 contractual arrangements around a major oil project: transport agreements, lifting agreements, measuring agreements, all kinds of sub-agreements between principal parties. To be confident of building system-level analysis in the public domain, we need it all.
We are happy to announce the third edition of the contracts repository, with 200 new contracts and 18 new countries. The repository now totals 723 contracts from 72 countries.
One feature of the new release we are excited by is the inclusion of what we call “auxiliary” contracts – those signed by two private oil companies. The new edition features 100 of these from 31 countries. There are strong, technical reasons to be interested in gathering and curating these contracts as well, which I go into in a separate blog.
But here I wanted to take a chance to review the usage and impact of the repository since it was first published last year. The general picture is: there is consistent demand for contracts, and many spikes of interest coming from within particular countries – even those with low bandwidth and supposed lack of technical capacity.
Since last November, there have been about 20,000 individual downloads, or a 100 per day. The number of contract downloads is several times higher, since the country-level archives and even the four gigabytes zip-file – containing a complete copy of the repository – have been downloaded several times per day on average. This is important since the function of the repository is to provide wide bases of comparison at the click of a mouse.
While it is true that over half that traffic has come from within high bandwidth countries, there has been considerable use – and spikes of interest – originating in about 20 countries around the world.
The most spectacular was Tanzania, where opposition parliamentarian Zitto Kabwe tweeted out the Songo Songo contract to his 186,000 Twitter followers, which led to 5,000 download requests of that contract inside Tanzania in 24 hours.
But there are spikes every week. There was a run on Kurdish contracts in February-March, when a new agreement between Erbil and Baghdad reached a critical stage. There was a peak in interest in Yemeni contracts in the days after the Houthis took Sana’a. The last month has seen peaks on contracts from Indonesia, Kenya, Congo Brazzaville, Peru, and Uganda.
In most cases, this is coming from in-country. About 80 people have also registered to receive the entire archive through the post, in regions such as Africa, where low bandwidth may be an issue, to Iran and Russia, where there may also be issues of Internet control.
We have not obliged registration so we have no further data on precisely who is downloading. But in broad terms it looks like the repository is serving the purpose of stock reference. A contract-related issue comes up somewhere, there is an awareness that some data to address it, and then comes a local spike.
None of this is hyper Web traffic. Nor would anyone reasonably claim that increased contract availability will have instant and dramatic effects, or that all or even most of these downloaded contracts are being systematically analysed – yet.
But it is clear evidence that large constituencies (relative to the governance community as a whole) see the value of contracts and want them. In our view, the “what’s the point?” argument used against contract transparency is empirically disproved.
What’s next? We and others are beginning to use the easy access to build applications. There is the whole rise of the public interest financial model, where five organisations at least have various projects in play. There is the Bloomberg reporter who sought quick comparison for terms of a proposed contract in Somalia and used the repository to compare ten early stage offshore African projects. There is the academic researching how gas contracts typically differ from oil.
There is also, interestingly, demonstration of the value of transparency in proving a negative. We have received dozens of queries from investigative journalists, parliamentarians and civil society groups convinced that the contracts must show widespread malfeasance and corruption. To which we can only answer: not to our knowledge. This of course speaks to two different realities. One is that most contracts are unlikely to contain glaring illegalities because they don’t actually exist. The other, subtler point is that where corruption happens it is now better hidden. Transparency has created an arms race of sophistication with arrangements now more than ever parked in side deals, unmarked back handers and hidden ownerships.
So the goalposts keep moving. It turns out that now we have a wide range of contracts, it still needs a lot of work to make them useful. But there is no question that if transparency is going to be serious and credible, contract transparency is necessary, but not sufficient, to it.
OpenOil is pleased to announce what we believe is the first open API applied to oil extraction rights around the world: the 20,000 “blocks”, “fields” and “concession areas” (we’ll get into naming problems a little lower down) from some 69 countries involving some 2,142 unique corporate structures as operators and contractors.
We hope this Application Programming Interface is a significant step towards a formal, open data standard around extraction rights.
So what exactly is an API and why do I care?
I’m glad you asked!
APIs are popping up all over the Web. As our developer Dan says in his documentation, the OpenOil API will then allow smarter queries of the data with questions like “What concession blocks exist in Uganda”, or “Who holds exploration licenses in Brazil”.
All OpenOil data is open – you can already download the entire data set. But with large data sets – and we hope to grow this data set in depth and breadth fairly rapidly – you may not want to be overwhelmed with it all at once, and be left poking through vast spreadsheets. Using the API allows you to frame a question to get just the data you want.
The second thing an API offers then is automatic updating. Say you want to include, for your members, clients, citizens on your own website, a list of all offshore oilfields known to be active in West Africa. You can build a query which retrieves the current data, embed it in your web page, and it will retrieve all the (oil)fields in the database. And when we update, your page will update automatically.
These are fairly simple queries. But the API function achieves more and more value the more data you add to the system. Imagine, for example, you want to be automatically informed three months before any designated exploration period is coming to an end in Brazil, and the company either has to declare commerciality or relinquish the field? Or that you would like to know when any of BP 1,200 affiliate companies farm into or out of operations anywhere in the world?
But our purpose in publishing the API, supported by the Shuttleworth Foundation, is not just to make our own data easier to access. It’s to suggest a step towards an open data standard around extraction rights. And the point of that in turn is to enable automatic information exchange between any two parties.
This is hugely important. No one organisation can – or should – be responsible for gathering and updating data around extractive industries. But if we have agreed standards, agreed ways of referring to how extraction rights are granted, then all interested parties can exchange information with each other quickly and painlessly.
When Web developers can easily set up mechanisms by which the Nigerian government can exchange information with the UN Environmental Programme, ExxonMobil, and EITI, – without the need to refer to us or any other institution, that is when we will have an open data standard.
And it’s that frictionless exchange of data which will be really necessary to allow the growth of analysis and understanding at a higher level of extractive industries as entire systems at a global level. To fulfill the why of transparency initiatives, in other words, and create independent analysis in the public space which genuinely empowers everyone touched by these industries.
We have based this API, then, on the idea that we can jointly create a namespace which uniquely identifies every spot on the planet as it relates to extractive industries, and in a way which does not rely on OpenOil or any other party as an authoritative reference point or information provider.
This follows the simplest possible logic of units and measurements which are uncontested and runs as follows: all extractive rights are awarded by sovereign governments. Each government uses unique naming procedures within its own jurisdiction to demarcate areas, and all rights have a start and end date (if current, the end date is some point in the future). Put those elements together and you can uniquely identify any rights granted to the smallest level.
Once you have a way to unambiguously define these lower-level “atoms”, anyone can take them and layer them into composite and more interpretative structures – such as implementation of EITI’s 2013 data standard to make license registries available, for example, or NRGI’s initiative to define extractives projects. In fact, if a data framework is successful, it tends to “disappear into the furniture” of higher level applications which make use of it.
There are a few methodological wrinkles to our approach – which is why this API and blog are also an invitation to join a public debate about how best to build a formal data standard. The sovereign state principle is not absolute, for instance. Iraq’s central government in Baghdad challenges the right of the Kurdish regional government to demarcate areas and award contracts. And although we haven’t found a case in practice in the 20,000 records we currently have, it would be possible in theory for there to be two blocks with the same name in the same country.
Geospatial co-ordinates, of course, are in theory unambiguous. But in practice there is no way of predicting when such data will become very broadly available, so to make a naming convention depend on it would effectively be to delay its implementation indefinitely. And there are also complications around cases where the physical space allocated for rights changes, even while its name remains the same.
The oil industry itself has had a data sharing project for almost two decades called Energistics. But this has concentrated on standards for exchange of geological information on reservoirs, and drilling and production data. We were not able to find an open standard referring to the granting of rights.
Just as last year, the draft convention on naming contracts was proposed – and a decision taken to adopt and evolve the standard together by colleagues such as NRGI, we hope this API can also serve as the start of a discussion around how the governance community can agree and implement the open data standards which we all need to take our work to the next level.
What it would be really interesting to know is: how far is Tunisia’s government aware today that it will shortly have a new business partner in keeping the lights on in the country?
The announcement that Shell is acquiring the BG Group is certainly global business news. A result of falling oil prices and higher costs, the pressure on some companies with less capital and the highest profile example of consolidation that market analysts in London, New York and who knows where else have been predicting for months now.
But it’s also interesting to think of it from the viewpoint of the hundreds of millions of people whose lives have just been touched by the deal. The people who are not shareholders but whose connection to both companies has led to the coining of the word “stakeholder” in governance parlance, precisely because the connection is indirect, sometimes even fuzzy, but no less real.
At one level, there’s the general interconnectness of the globalised world thing. So Tunisia, for example, where BG Group’s production of 60 percent of domestic gas will be handed over to Shell. Egypt, where a few weeks ago BG announced a multi-billion dollar plan to increase gas production, and before that was a lead party to difficult negotiations around back payment of billions of dollars to foreign companies by the state oil company EGPC. Offshore and deep offshore exploration plays in Myanmar, Kenya, India, China and Brazil, where BG says its pre-salt holdings should eventually add 2.6 million barrels a day equivalent of new capacity – big enough to be countable in global debates around carbon emissions.
This is the nature of global business. And in theory there should be no impact straightaway on these operations since they are governed by contracts and laws which are already in place.
But we talk a lot in the governance world about the asymmetry of information in extractive industries. This is often thought of in terms of direct negotiations with governments, and wrangles over tax payments such as Zambia and Timor Leste are going through right now.
It applies just as much to the furious pace of deal making between global businesses thousands of miles away from operations, in boardrooms where the government isn’t at the table at all. It is sobering to reflect on what information decision makers like Tunisia’s President Essebsi and Prime Minister Essid may have had when about Shell’s bid for the BG Group.
Of course it’s not as if the Tunisian government doesn’t have other pressing issues: its fragile democracy, the still desperate economy, thousands of its citizens gone to join IS in Syria and some of them now launching attacks at home. This isn’t in any sense to point a finger at the Tunisian government. And we have no data either way about what consultations either company has had with its government partners around the world. But try even to imagine the possibility that a deal with this kind of domestic impact could be sudden news to the government in Germany, or Australia, or Spain. It’s a kind of thought experiment on the concept of asymmetry of information. That’s what it looks like in the flesh.
And there are two big practical governance questions around this deal – actionable, if you like.
The first is capital gains tax or its equivalent. According to Ernst and Young, Brazil, China, Colombia, Egypt, Myanmar and Tanzania are among jurisdictions where a capital gains tax or its equivalent might be levied on this deal since it involves at least an implicit valuation of assets being transferred. In these countries it’s a workflow issue – but are the civil services and governments geared up for it? For others, it may be a policy debate. Tunisia, in this context, might want to reflect on the fact that (according to a first reading of the Ernst and Young guide) its fiscal regime does not allow it to realise any share of gains in the value of assets which produce 60 percent of the gas it needs to feed its power plants. In Uruguay, it will depend on whether the BG Group has been operating as a locally incorporated subsidiary or not.
The second “workflow” issue is ringfencing, or its opposite, consolidation – provisions in the law that allow a company to deduct expenses in one contract area against taxable profits in another. In China, for example, Shell may be able to write down future exploration expenses in BG-held assets against its own profits in other fields. That may be true in other countries too.
Governance focuses on management of direct relations between companies and governments, different arms of government, and, increasingly, on whether civil society is free to poke around, investigate, and voice dissent. These are all right and proper.
But the global market is the elephant in the room. Relations between one private company and another, and between both of them and their shareholders and financiers, are critical to the way it all plays out. Even if knock-on effects are often delayed and indirect, we need to do far more to sift developments in the global marketplace, deal by deal, for how they affect the political economy, and therefore the governance structures, of scores of countries in the Global South.
Let’s start with this one.
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.
I opened this series by saying that public interest models of extractive industry projects did not serve only the purposes financial models have been used for to date but had, in addition to their analytical function, the three other main functions of pedagogy, advocacy and strengthening government support.
In this post I will focus on how public interest models – published on the Internet and relying on public domain data – measure up purely in the traditional modeling role of financial analysis, and at the project level. I will make two main arguments:
- Public interest models can put useful system-level analysis of the upstream oil and gas industry into public understanding now.
- That using public domain data alone is viable in many cases, even purely from an analytical point of view.
In a follow-up post I will deal with the analytical power that open project models can deliver cumulatively.
Who is Your Model For?
But first it would be good to get a bit of context: what do financial models do, anyway? What don’t they do? What are the strengths and limitations of models? How would any of that change under a public interest modelling approach?
What a model does depends on what you want it to do – which depends on who you are. This might seem like a truism but actually isn’t. There can be a tendency for outsiders to be so in awe of the very process of modeling – all those numbers and charts! – that modeling can take on a kind of aura of transcendent knowledge. Wisdom from on high which defines its own terms. It is that which it is!
But there are differences of economic sector. Extractives modeling, for example, is more focused on the assets on its balance sheet like, say, banking, compared with other industries such as consumer goods. The cyclicality and volatility within commodities are more crucial aspects of financial modeling than in other sectors. And even within the oil and gas industry, there are at least two different schools of thought over how to account for exploration costs, “full cost” or “successful effort”, and assets tend to deplete rather than acquire value as they do in many other industries.
A large oil company might need modeling to compare the relative attractiveness of one potential investment project with another one on the other side of the world. Or the juggling act of how to manage operations simultaneously in many producing fields to maximise profit, while maintaining both reserves and cash flow, against constantly shifting price and cost bases. A company focused on exploration and production can stop its modeling where the crude has been shipped to market, which is just where a model for a refining and processing project starts.
In the market at large, the oil company itself is often the unit of analysis. Analysts look at the market valuation of listed companies and try to work out whether to buy or sell – and you can bet there is a lot of that happening at the moment with falling prices. These models can look very different to models of projects that might predominate in the upstream – check out this one of Occidental which ranges from exploration right through to petrochemicals, or watch this video about how to model the significant hedging used to trade gas in the continental US.
As we are focused on the governance aspect of the oil industry, it might seem safe to say we can focus on the government side of things. But of course that will also vary from country to country. Some countries have active national oil companies and some don’t. Some countries are invested in every part of the value chain, others are not. Gas is different to oil in many aspects – term contracts, regional markets – and mining is different again.
The point here is not, oh the mind-blowing complexity of so many considerations. Precisely because you don’t have to consider them all at once. The point is specificity.
The definition of a successful model is one which reliably serves a particular audience for a particular purpose. Every successful model was created by some specific people for themselves, or for other specific people. It doesn’t get simpler than that. Any model maker should be able to answer with an individual name the question: who is this model for?
The Governance World – welcome to FARI
If we zoom in on the upstream model for governance purposes – which is probably where public interest type models though probably not where they will end – we can make a few observations about what these kinds of models do.
Probably the largest practitioner of this kind of model is the International Monetary Fund, which regularly advises governments on this. In the 2000s, as the pace of technical missions stepped up, the IMF developed a tool to evaluate upstream economics known as the Fiscal Analysis of Resource Industries, or FARI for short. Originally used in-house to inform macroeconomic advice to governments, FARI quickly caught the interest of governments, and there is talk now of FARI being open sourced, as a contribution to public interest modeling.
FARI has developed a number of uses, such evaluation of negotiations, bid reviews, revenue forecasting, and tax gap analysis. But it is perhaps best known for its evaluation of fiscal regimes. By having built a library of terms across a range of projects, and a number of different “fiscal regimes” – all the interlocking terms of a contract and its surrounding legal environment – FARI can run numerical comparisons on different contracts against the same oil field or mine. This comes with a health warning, of course. Quantitative comparability tells us nothing about investor perceptions of risk, which dominate what returns a company is seeking and therefore influence project economics, or the market strength of a particular government. Be careful, therefore, to compare apples to apples. But FARI is characterised by this ability to create such evaluations, built on top of a bottom-up, project-specific approach to the modeling itself.
What Models Don’t Do
Given the lack of familiarity with modeling at the moment, we should also lay down a few things models, including FARI, don’t do.
- They don’t allow you by themselves to conclude a deal was good or bad, since this question cannot be settled by financial analysis alone, as noted above.
- Although they compare different fiscal regimes, they don’t pay much attention to the different legal modes of contracts per se, that is to say, differences between a production sharing contract, a concession agreement, or a service agreement, which are often hot political topics in producing countries. That makes sense, in fact, because despite what is commonly misunderstood is that the differences between such legal approaches are deceptively small. In economic terms it is possible to produce the same financial results in a project from any contract mode.
- Nor do these models concentrate on giving direct analysis of headline terms, such as two different royalty rates, or the impact of a rise in corporate income tax. Such analytical capacity is certainly implicit – you can enter one of these models and change any of these inputs and see what happens. But the driving emphasis of the model as a whole is the project as a whole. It is there to represent all the interactions between multiple terms, not single terms standing alone.
- Last but not least, such models rarely provide a direct line to “actuals”, the real world payments made by companies to governments. They certainly provide a useful start. But even with good data (the inputs) and accurate characterisation of the fiscal regime (the engine in the middle) there are so many artefacts in the process that such a match to actuals, when it happens, requires a great deal of reconciliation, somewhat similar to some of the more challenging EITI reports.
In fact one of the characteristics of modeling large upstream oil projects compared to other economic sectors is that there is such large variation of terms (the number and configuration of possible rules inside the model) over a relatively small data set (the number of projects modeled). The literature describes dozens of individual fiscal tools, each capable of being implemented in several ways at least and most of them combinable with most of the others. This tends to confirm that idea that direct comparison will always be an art rather than a science, and that, even if harder, project-specific modeling (bottom up) is what needs to happen to build the foundations of solid understanding of the money flows in extractives, rather than a top-down approach led from, for example, trying to run numbers across a whole sector without its constituent projects.
“All Models Are Wrong But Some Are Useful”
A quote yes but from?… George Box, professor of statistics at Princeton and one time president of the American Statistical Association.
We are faced with the paradox that we are proposing to introduce models into the public domain because they can create greater certainty around the massive volatility of the oil and mining industries. And yet at the same time we must expose their limitations.
The imperfection of modeling has been openly acknowledged by leading economists since it was first deployed.
Alfred Marshall spearheaded the quantification of economics at Cambridge in the late nineteenth century which led to economics as we know it today, a social science (none of Smith, Ricardo or Mill were economists by today’s criteria). His view: “The laws of economics are to be compared with the laws of the tides, rather than with the simple and exact law of gravitation. For the actions of men are so various and uncertain, that the best statement of tendencies, which we can make in a science of human conduct, must needs be inexact and faulty.”
John Maynard Keynes wrote that “Economics is a science of thinking in terms of models joined to the art of choosing models which are relevant”. But this art of choosing is scarce, he added, “because the gift for using ‘vigilant observation’ to choose good models, although it does not require a highly specialised intellectual technique, appears to be a very rare one”.
Both Marshall and Keynes attributed uncertainty to the fact that economics seeks to describe the actions of humans. This is undoubtedly true. One way in which all extractives models must necessarily fail is in predicting the agency of human management, for example. Will a consortium continue production through a period of operating losses to keep a project running? Will they revise the risk premium they attach to a project or country over time? Will they invest in secondary enhancement techniques to boost or extend production?
But there is another generic class of errors in models which we are more familiar with in modern times: the understanding, as George Soros says, that human economic activity involves feedback loops across networks which have the potential to amplify. System complexity. And that therefore the classical assumption of equilibrium as a natural underlying state, against which all disturbances are local and to which they will always ultimately return, is an illusion.
The most obvious impact of this is on future pricing. Everyone now knows that anyone who predicts the price of oil beyond a year out is either a knave or a fool. And in an age of financialisation this volatility cannot only be taken as the extreme but measurable sensitivity of the market to fluctuations in supply of a highly inelastic commodity. Not when trading volumes of derivatives and options are maybe 30 times larger than physical oil.
This is not just a philosophical nod at human imperfection. These two general principles together – any model is only for a defined purpose or set of purposes, and all models are flawed – have specific implications when we consider the implications of public interest models.
“In modelling there is God, Exxon, and everybody else”
Not all margins of error are equal. Once we are comfortable with the idea that all models are flawed, the question, or rather questions that occur about any given margin of error is: first, how big is it? And second, how material is it to the specific purpose in hand?
That is the wisdom behind the old saw I heard from a seasoned analyst in the Middle East. “God” (or choose other culturally appropriate expression of omniscience) alone knows the future. “Exxon”, or the incumbent large integrated company, alone knows the geological prospectivity and cost basis of the project. Everyone else is left guessing from the outside. And the potential scale of margin of error at each level, in absolute terms, approaches an order of magnitude.
This might seem like bad news for public interest models since they are, by definition, looking in from the outside.
But there is still cause to stay hopeful. The first is that there are plenty of models out there already in a not dissimilar situation. Governments in theory of course have access to full information about geological prospectivity and costs, so they should – in theory again – be up there with Exxon. Globally, some are, in some aspects of both these key data inputs. But there are dozens of governments around the world, including those the governance community is trying the hardest to work with, who are effectively nowhere near either, whatever their contractual rights of access. Where governments don’t have good access to project level data, then neither does anyone else downstream of them such as international financial institutions.
So in these cases, modeling relying on public data will not necessarily be worse off, and the distinction between public interest and other kinds of models should not be placed into a paradigm of “more accurate/less accurate” so much as “more openly imperfect/less openly imperfect”.
The perfect as the enemy of the good?
To demonstrate the relative materiality of margins of error, let’s take an example oil project and some data input estimates going into it and try to see what margin of error may be generated by what estimates – and how these margins of error relate to a range of different purposes the model might have.
A project in Africa is run under a Production Sharing Contract. There are some documents the company issued to investors which have entered the public domain and the full text of the contract has been published. In order to make a model, we have to extract the terms from the contract and combine them with other legislation to produce a fiscal regime analysis. Then we have to estimate various inputs to feed the model in order to get outputs.
Our default scenario assumes a $68 per barrel price, total lifetime production of 115 million barrels, exploration costs of $200 million, capital costs of just under $1.1 billion and operating costs at $34 million a year fixed plus $2.50 per barrel variable.
If we have a model, we can test what the impact of margins of error in various inputs might be on the various things that we are using the model to test. The table below shows the sensitivity of the model to variations in each of the major classes of inputs
Illustration 1: Variation in results relative to variation in inputs: Glencore PSC of Mangara-Badila fields in Chad (illustrative)
From this it quickly becomes clear that the accuracy of some classes of input matter more than others when related to possible variations in results. Exploration, operating costs and capital expenditures produce variations of 3% or less in an assessment of the government take. Price is more significant at 5%. But it is production which makes a huge difference in determining government take. At a lower production figure for the project (1P) there is hardly any profit left to be distributed, whereas at the highest of three production scenarios (3P) six times more oil is produced, the company reaches positive cash flow sooner, and the government graduates to a higher share of profits quicker.
In estimating government revenues there is a similar differentiation in the impact of variation in data inputs. Exploration costs again have the lowest impact. Operating costs and capex both have considerable impact. But using a lower or higher price estimate can create a difference of double, and the difference between the lowest and highest production profiles available represent almost an order of magnitude.
This is a purely illustrative example. Some of the differentials relate to the particular terms of this contract and would vary in projects which were structured differently.
But it demonstrates the basic principle: the perfect risks being the enemy of the good in public interest modeling. There is reason to believe that public domain data can be used to create models, and we are the beginning of a debate about which data can reliably be used for what purpose, not the end of it.
We, and others, have made the case before that open financial models is the natural next stage of extractives governance. First, because you don’t know what you’ve got til its modeled, and secondly, the models themselves must be open to allow the cumulative learning that is really important for independent expertise to take hold.
In fact, I would go so far as to say that modeling is the prerequisite for any work on the numbers around extractives. An African colleague asked me the other day if we could train the members of his organisation in how to write a financial analysis of a contract but without using a model – just the written report. A moment’s reflection brought the realisation that no, this was not possible. Any analysis not based on a model would have no validity. It would be like reviewing a book you haven’t read.
But what I’d like to do in this series of blogs is consider the role of financial models in the public interest. Because the more we at OpenOil have got into this area, the more it has become clear that the kinds of models needed are both different to, and have more uses than models traditionally used by industry, and governments (when they do use models).
Models have traditionally served companies and the specialist arms of government who negotiate with them.
Companies run sophisticated analysis of potential profitability before and during their negotiations with governments. The metrics which dominate here are the Expected Monetary Value of a range of different outcomes, Net Present Value and its twin sister the Internal Rate of Return, adjusted to fine-tuned estimates of risk of all kinds – geological prospectivity, fiscal and political stability, and so on.
Oil ministries and national oil companies run many of the same metrics, assessing both the position of the companies and themselves. They may also look at what level of guaranteed income they will get from a project under a range of price and production scenarios, how “front-ended” payments are (what proportion of payments might come early in the lifetime of these generation-long projects), the specific implications for the national oil company if it is going to have a role in the project, and above all the “government take” – what proportion of profits relative to project turnover will come back to the state.
To be sure, these metrics will play a role in the analytical function of public interest modeling. But such analysis is only one of four major functions we see for open models (that is to say, published on the Internet), based on public domain information. Below is a headline summary of these functions, each of will be expanded in a dedicated post.
Public interest models will deal with many of the same questions as traditional modeling, but with an emphasis on ease of access and understanding, and a responsiveness to local issues and attention. To borrow a horrible phrase from IT marketing, public interest models will be “user centric”. So for instance, the falsifiable test of whether a model succeeds or not might be if it could be used to explain the three or four major characteristics of an oil project to a non-specialist audience within 30 minutes, not whether it had modeled all possible variables, or used a sophisticated future pricing scenario which simulates the volatility of the market. In a country where a defined revenue stream is allocated sub-nationally, to a district administration or the communities around the project, these revenue flows might be given major prominence in the model even if their calculation (5% of state dividends) might be considered trivial from a purely technical point of view.
There is a clear pedagogical role for public interest modeling. EITI has achieved tremendous success in process, opening up extractive industries to public debate and legitimising the right to know. But EITI is a means to an end – the systematic understanding of how these industries work, available for public understanding and informed by expertise which is independent of any vested interest, whether companies, governments or international institutions. Modeling is part of that EITI end goal – a big part. Civil society cannot assess questions such as dependency on extractives, public financial planning or whether a particular contract represents a fair deal, without having its own embedded expertise to analyse the many moving parts of finance in an oil or mining project. Public interest modeling can be said to have succeeded when every EITI national secretariat has access to models from their country which they trust and understand, and when at least five people in every EITI country outside business and the government have enough expertise to manipulate and adapt them.
Models based on public domain information have enormous potential to guide advocacy campaigns for more transparency. They are a potent illustration of the value of contract transparency, of course. But beyond that, the many data inputs and estimates needed to make each model run, when they come from public domain, are necessarily imperfect, heterogeneous, and all too often generic. The paradoxical beauty of such a model is that, to exactly the extent that it has wide margins of error from an analytical point of view because of the imperfections of its data inputs, it serves as the basis for a targeted campaign to get better data. Think of this as “keyhole surgery transparency”.
Generic transparency dialogue:
Activists: The government and industry should publish everything.
Government and Industry: Why?
Activists: Because you should! Because it’s the right thing to do!
Keyhole surgery transparency dialogue:
Activists: The government should publish the historic posted prices of crude from this field since the start of production.
Activists: Because it will close a $300 million margin of error in predicting revenues to the government from this oilfield, caused by having to model between two different equally authoritative estimates, which is the best we have right now. But you have the data to close this gap and create greater certainty.
Strengthening Government Capacity
The first and most obvious implication of a model published on the Internet is that it will strengthen the public’s ability to get a handle on projects and contracts. It is natural to assume then that such models might challenge governments, since it will open past negotiations and current management of projects to greater scrutiny.
That assumption is not wholly without foundation. But it is important to understand that public interest models will strengthen government capacity in at least two significant ways – whether they acknowledge it or not.
First, governments have access to better data than is in the public domain. So they can download models and put their own data in, whether or not they publish the results.
Second, the model is equally open to all parts of government. Experience suggests contracts and all related information are often a close hold by line ministries and specialist agencies, and indeed this secrecy within government has significant impact both on capacity to manage and in enabling corruption. But with an open model anyone in the finance ministry, tax authorities, audit agency, investment board, regional governments, prime minister’s office, ministry of the environment – or anyone anywhere in fact – can achieve a basic understanding of the economics of the project, and factor it into their workflows. Inconspicuously if necessary.
The Whole Picture
We need to look at the whole picture when it comes to public interest modeling, because although it will be based on the core concepts of project economics, which have been refined over decades by industry and governments, it will have different emphases – and many additional functions.
And a whole bunch of new constituencies. The role of the public interest model could be defined as offering expertise for the non-expert. As such, we will also need to refine our understanding of the various constituencies interested in these financial aspects of governance.
Civil society has had virtually no access to or capacity to deal with financial modeling, so may be considered a new constituency. But there is vital nuance in the way we should think about the other constituencies.
It will be too simplistic, for example, to talk about how “government” uses financial models. Are we talking two or three experts in the line ministry or national oil company, who may already have complex models based on their specific projects, or much broader circles of civil servants in a wider range of institutions – central bank, finance ministry, tax authorities, audit agencies, regional administrations – who could benefit from independent insight into project economics?
Even in the private sector, public interest models are likely to attract interest among sectors who are not themselves the primary deal makers – the integrated oil companies – whether it is local compliance companies involved in the accounting and legal aspects of the industry or service companies downstream of the main contracts.
There is growing interest and advocacy for open financial modeling to bring about the next stage of transparency. But unless we think through the full potential and implications, we could waste a couple of years producing models exactly like they always have been – for very different audiences and needs. Like the first years of TV, when producers broadcast audio with a static image and called it “television”.
Oil geek question – how many concession blocks are there in Africa? (Answer below)
Transparency has won big victories in its 15 years or so of life as a movement. EITI got going and spread to 50 countries. Companies and governments agreed some transparency was a good thing, and the national security argument that prevailed in dozens of states, which said talking about these things was treason, was swept away.
But the paradigm for most of that time has been what geeks would call “n + 1” – progress is measured by starting from zero and counting up to see how many additions have been made. Now two countries have published their contracts, now five have, now seven. We have n number of reports this year, which is so many more than last year, and that was so many more than the year before that.
In this context, there were debates about how the information set free by this work has been put to use – or if it has been put to use at all.
But I’d like to suggest it’s time to consider a different approach. Not continuing to count up from zero, but counting down from the assumption that our common goal is full disclosure. In geek speak, “1-n”, not “n+1”. It’s a logic which flows naturally from the wave of new disclosure requirements in Europe, Canada and the USA, which are universal in scope. Under the EC directives, and Dodd-Frank if it ever makes it to being enforced, all listed extractives companies have to disclose all significant payments, in all projects, in all countries.
So why not texts of all contracts, and details of all concession blocks? And if we can’t achieve everything immediately, why not measure how we are doing, not by how much more we have than we did, but how much less we have than we should have – “1-n”? Not how much more have we got than before but what percentage of the total?
In that spirit, OpenOil would like to announce a modest contribution: today we are publishing an index of most the data we could find publicly available regarding the world’s oil concessions outside the United States. About 12,500 concession blocks in 41 countries, including references to about 8,000 licenses or contracts involving some 2000 companies. It is designed as the sister to the repository published last month, which aims to provide one-click access to every contract that has entered public domain.
Of course it is only a subset of the total. But how much of a subset? That brings us back to the question: how many oil concession blocks are there in Africa?
At this point I have to admit we don’t know – exactly. We have counted 2,855 in the 47 countries we could find any data for. But the data are very dirty – a lot have been pulled from low resolution concession maps, and it turns out that even government agencies can be quite loose in their terminologies so it is not always clear even looking at official sources, what is a block and what isn’t. There is probably a five percent margin of error within the countries counted, and let’s say the eight missing countries could add another 20%. So let’s say somewhere between 2,800 and 3,300 oil concession blocks currently on the African continent. Of which maybe 1,000 are currently unassigned.http://repository.openoil.net/wiki/Downloads
This gives us our first 1-n measurement. Because there are about 100 main (host government) oil contracts published from the African continent. In the n+1 world, a couple of years ago even, that would have sounded like a lot. But, even while we have to introduce more margins of error to account for old data, unclear statuses and the like, we can safely say n to take away from 1 is over 90%. Less than ten percent of Africa’s oil contracts are in public domain.
Of course we knew that anyway. But the point is we have now reached a stage where we should index this, and work towards ubiquity.
Grabbing this data has thrown up a few interesting questions. Like:
1) Does a more comprehensive mapping exist anywhere? Our guess: almost certainly, in commercial databases like Wood Mackenzie, Rystad or Deloitte. But it is only a guess because we have not spent the many thousands of dollars it would cost to access them.
2) Is a more comprehensive mapping held anywhere by public institutions, such as the IMF or World Bank?
3) How fast can developments like the new EITI standard, which requires full license registries, or the Open Contracting Partnership, take us towards comprehensive information? Because we would like to think that the contracts repository and this concession library offer some value for now. But if they were still the only attempt to build global level data frameworks in public domain in two years time, that would be sad.
Finally, back to that gnawing question – what is the point? And there are three things to say.
First is that the “zombie transparency” so ably outlined by NRGI’s Dani Kaufman is a very real danger of partial transparency. Datasets sit unused because they can’t be made to yield any insight, and a big part of that is they are too scattered to be connected one to another – and it is above all the “network effect” of converging and meshing different data sets which allow rich insight to emerge.
Second is that oil is an industry with global markets, prices and projects competing for investment capital. Framing it as such has to be a step up for governance.
And third, a push towards universality – all contracts and all concessions in public domain – is only what is due to the public. Who are, after all, the legal owners of sub-soil resources in every country in the world bar one. What management of a company would be taken seriously if they published a partial profit-and-loss, or balance sheets from only some departments?
BERLIN, Tuesday, November 11 – OpenOil and its partners on Monday launched the world’s first comprehensive archive of oil contracts. Some 385 host government contracts from 54 countries are now available with one click.
The repository includes contracts which govern oil production in many countries where disclosure has largely been unknown, such as Algeria, Angola, Chad, China, Egypt, India, Israel, Kazakhstan, Syria, Tajikistan, Tunisia, Ukraine and Yemen. All contracts had previously been put in public domain but were scattered across scores of websites and buried in corporate filings. PWYP Canada outline how corporate disclosures were mined in a release announcing the project’s joint findings.
Over 100 contracts in the repository were filed by oil companies to Canadian and US financial regulators. In an effort funded by the Shuttleworth Foundation, the OpenOil team led by Don Hubert and its partners, PWYP Canada and the African Network for Centers of Investigative Journalism (ANCIR), unearthed them by text mining several million documents across the regulator websites going back as far as 1995.
“It is time to turn the way we look at contract transparency on its head”, said Johnny West, Director of OpenOil. “Instead of thinking of a small number of published contracts which is gradually increasing, we should map the total universe of contracts signed by governments, and work backwards from there. The repository is a small step towards contract disclosure as standard practice.”
Along with the repository itself, OpenOil has proposed a draft convention to name contract files so they can be easily exchanged between hundreds of organisations working on governance of the extractive industries.
“All contracts that govern publicly owned natural resources – and the livelihoods of hundreds of millions of people – should be open to everyone to examine,” said Helen Turvey, Executive Director of the Shuttleworth Foundation. “This curation is important not just for its immediate practical value. It proposes a system-level approach under an open data standard, both of which are key to real transformation.”
The newly exposed contracts are particularly promising in Africa, where projects from Angola, Chad and Tanzania are among those newly brought to light.
“Our network of more than one dozen newsrooms across Africa are constantly grappling to understand how the oil economy operates at a political, financial and other level,” said Khadija Sharife, investigations editor at ANCIR. “These newly unearthed contracts from countries like Angola, Egypt and Chad will be invaluable for investigations.
The repository includes contracts relating to other developments in the global oil industry, such as coal bed methane projects in China, offshore gas in India, and Israel’s rapid transition to major gas producer in the Eastern Mediterranean. Contracts from Egypt, Yemen, Algeria, Kurdistan and Syria also shed rare light on the Middle Eastern oil economy.
OpenOil collected the contracts as part of a broader initiative to publish financial models of oil projects in Chad, Cambodia and Kurdistan, scheduled for later in the month. A three-day hackathon to mine corporate disclosures for richer information about oil reserves, production and costs will take place in Berlin in January with OpenCorporates, ContentMine and the Natural Resource Governance Institute.
OpenOil publishes open data on the world’s extractive industries, including sourced maps of multinational affiliate structures. Its book How to Read and Understand Oil Contracts is at over 100,000 downloads, and it has published 19 country-level reference guides in five languages.
Shuttleworth Foundation invests in social innovators who are open at heart, have a fresh perspective on addressing challenges and have a very clear idea of their role in bringing about positive change.
PWYP-Canada is the Canadian coalition of Publish What You Pay, a global network of over 800 civil society organizations united in their call for oil, gas and mining revenues to form the basis for development and improve the lives of citizens in resource-rich countries.
The African Network of Centers for Investigative Reporting is a coalition of the continent’s best muckraking newsrooms and centers.
For more information contact:
at johnny dot west at openoil dot net
How Can Economic Modeling Improve Extractive Sector Governance?
Who is Building Economic Models?
Although economic modeling has not been a significant part of the debate on greater transparency in the extractive sector, these techniques are at the heart of behind-the-scenes decision-making for both companies and governments.
For companies, project economic models are routinely used for formulating negotiating positions and making investment decisions. These economic models (often called discounted cash flow models) allow companies to generate two core metrics such as “net present value” and “internal rate of return” in order to compare the value of one project against another. According to the a 2011 survey by the Society of Petroleum Evaluation Engineers, 89% of respondents used discounted cash flow models as their principal method for valuing projects.
Economic models are also widely used by governments seeking to more effectively manage the extractive sector. A good overview of the various uses for modeling from a government perspective can be found in the very useful new book on Administering Fiscal Regimes for Extractive Industries written for the IMF by UK tax expert Jack Calder (unfortunately not freely available). From this volume and the wider literature on economic models, five common uses can be identified.
1. Fiscal Regime Design and Revision: When designing fiscal regimes and establishing general contract terms, governments are engaged in a balancing act of attracting inward investment while at the same time maximizing revenue generation. Economic models are commonly used to assess the impact of proposed taxes under varying production, price and expense scenarios.
2. Support for Contract Negotiations: A similar but more specialized use of economic models is in direct support for contract negotiations. Companies normally come to the negotiating table with detailed models to bolster their negotiating positions. Increasingly, resource rich developing countries are developing their own economic models in an attempt to level the playing field.
3. Monitoring Incoming Revenues: Governments, particularly Ministries of Finance, develop economic models in order to evaluate incoming revenues in order to ensure that the results, taking into account real production volumes, market prices and actual company expenses, are consistent with the policy expectations when the fiscal terms were set.
4. Risk Assessment for Tax Audits: Revenue inflows are also assessed for the more specialized purpose of aiding risks assessment by tax authorities. Differences between model projects and actual revenue can highlight issues worthy of further investigation including, where appropriate, subsequent audits.
5. Budget Forecasting and Revenue Management: For many resource rich developing countries, extractive sector revenues constitute a major portion of overall revenue. Forward-looking economic models, based on hypothetical scenarios, are used to generate potential revenue forecasts to be used both in longer-term budget planning and also as a way to anticipate revenue management challenges.
The reasons for economic modeling listed above all assume a high degree of technical expertise. They build on extensive knowledge of the sector and the specific fiscal regime. We believe that economic models can also provide a good entry point for understanding the economic implications of contract terms that are in the public domain but not well understood. Thus we suggest an additional purpose:
6. Understand Economic Implications of Contract Terms: Contract monitoring workshops are often a first step in introducing new audiences to contract fiscal terms. This approach generates a bottom up perspective based on an analysis of individual fiscal instruments (i.e. signature bonuses, royalty rates, cost recovery limits, production-sharing splits, corporate income tax, and government participation). Economic models, when combined with a user-friendly dashboard, can provide a valuable top down perspective revealing the interrelationship between various fiscal instruments.
Building Off Existing Efforts?
An industry exists for developing extractive sector economic models. Take the petroleum sector for example. Models are developed by consultants (e.g. Daniel Johnston or Pedro Van Meurs) and larger firms (e.g. Wood Mackenzie or IHS). Other companies including Palantir, Ceasar, and Petrocash sell generic models that clients can develop and adapt to their specific circumstances. Resource rich developing countries often draw directly on this kind of expertise, often with funding through organizations like the World Bank.
One modeling effort of particular interest to resource rich developing countries is the FARI model developed by the Fiscal Affairs Department at the IMF. The model was initially developed to assist in fiscal regime design and allow for comparisons among contracts and countries. It has since been expanded to assess the full economic impact of projects. FARI is unusual in that it was designed from the outset to accommodate both mining and petroleum sectors.
The broad range of actors engaged in economic modeling of extractive sector projects might suggest that the tool can be easily adapted to serve the wider interest of extractive sector good governance. Unfortunately there are two recurring challenges with existing modeling efforts: they are normally confidential with extremely restricted access, and they are usually custom-built at the level of a country, a sector or even a specific project.
Problem 1 – Restricted Access
For those interested in strengthened transparency in the extractive sector this story will sound remarkably familiar. As was traditionally the case with revenue data or extractive sector contracts, economic models are nearly always confidential.
Companies view their project economics models as proprietary and never make them available for public scrutiny. The same seems to be true for governments. Even among developing countries with strong transparency credentials we know of no examples where revenue projection models have been made available for public scrutiny. And the same approach to transparency seems to characterize international development institutions including the IMF and World Bank. Although the results generated by these models are sometimes included in public reports, the models themselves are closely guarded. The only exceptions to confidentiality seem to come from CSOs, with public models recently published for Timor Leste and Uganda.
Confidentiality undermines the utility of economic modeling far beyond the obvious problem of excluding those outside of government. It also constitutes a major barrier within government circles. Knowledge is power, and power is often not shared. An underappreciated benefit of contract disclosure is ensuring easy access across all relevant government ministries to documents that are often closely guarded. The same challenges of limited access exist for economic models and the input data on which they are based. Confidentiality also stands in the way of peer review undermining the reliability of the results and obscuring the visibility of potentially flawed assumptions.
There should be a presumption that all models developed with public funds (donor or developing country) should be open rather than closed. What would this mean in practice? The principle of transparency in extractive sector good governance is now widely accepted. Our definition of a meaningful implementation, in this context, is that economic models should be accessible, at the very least, to EITI Multi-Stakeholder groups.
As has been the case in the past, commercial sensitivity will be raised as a reason why this cannot be done. But where contracts have been disclosed, the bulk of the necessary information should already be in the public domain. This includes not only fiscal terms and tax laws, but also production volumes and price data (required by the 2013 EITI Standard). The exception may be company-specific cost data. But this is not a reason to keep models confidential. Specific cost data can be replaced by information already in the public domain or industry averages.
Problem 2 – No Cumulative Learning
The underlying objective of project economic models is the same. A series of inputs (e.g. production volumes, sale price, production costs and fiscal terms) are manipulated in order to generate a series of outputs (e.g. project profits, government take, the company rate of return). And there are clear industry norms on how this should be done (See Upstream Petroleum by Kasriel and Wood and Guidelines for Economic Evaluation of Mineral Projects). But there are many different ways to design spreadsheets, structure input data, perform calculations and convey results. In spite of the common underlying purpose and logic, therefore, custom-built models look and function very differently.
These differences between custom-built models are a major barrier expanding the use of modeling, as there is little if any cumulative learning. The problem is much larger than one approach used in the petroleum sector and another for mining. It is not uncommon for donors to fund multiple models focused on the same project, each built to custom specifications allowing no interoperability. Training on one model provides no insight into the operation of another.
From the perspective of model developers, the custom-build approach makes good sense, and may even generate a competitive advantage. But for resource rich developing countries, the outcome is highly counter-productive. At the outset, a new model looks promising and is accompanied by extensive documentation and training sessions for officials. The early utility declines rapidly however because the model is seldom shared with the right people, because input data is not updated, and because attention is diverted when a newer model is commissioned.
For economic modeling to play a greater role in extractive sector good governance, design should be driven by the needs of analysts in developing countries, inside and outside government. This suggests a move away from custom-built models and towards open-source software combined with openly accessible data.
Is a Generic Public Model Needed?
Can existing modeling efforts overcome the problems of confidentiality and a lack of cumulative learning? Unfortunately for the most part the answer seems to be no. Most efforts by companies and consultants, even when working in the public policy space, have based their business model on confidentiality. Their economic interests are directly in conflict with greater openness and broader utility.
The IMF FARI model may be an exception. Designed to accommodate both petroleum and mining sectors, it addresses at least part of the cumulative learning problem. And although the model currently remains confidential, there have been indications in the last six months that the IMF may be considering making a version of the model, excluding confidential data, more widely available. NRGI recently reiterated the call for the FARI model to be released into the public domain.
Given the importance of the FARI model in fiscal regime design for resources rich developing countries, putting FARI in the public domain is an obvious next step. If the model is made public, it will be possible to assess whether FARI is a good starting point for an open source model. There are reasons however to be skeptical. By the IMF’s own admission, use of the model requires “strong economics and Excel skills.” “Unwieldy” would probably be a fair characterization, particularly when multiple projects and jurisdictions are added.
This overview of the existing landscape of economic modeling in the extractive sector suggests the there would be value in developing an open-source economic model complemented by open-source data. Initial thoughts on how that might be done will be the subject of our next article.
More contract disclosure will not necessarily result in greater understanding of the economic implications of fiscal terms. The terms only become meaningful when their interactions are understood alongside relevant national tax laws and regulations. So to make real sense of the economic implications, the fiscal terms must be considered under varying scenarios of production, price and costs. In other words, they must be modeled. Economic modeling is currently considered an advanced and esoteric pursuit, but we believe that this can be turned on its head: methodologically sound models starting from a pedagogical viewpoint can actually be the entry point to understanding the economic implications of extractives contracts. And, to put the converse case, the transparency community will not succeed in spreading public understanding of what these contracts mean unless this modeling does take place because in the case of complex extractives projects “you don’t know what you’ve got until its modeled”.
The norm of contract transparency is gaining ground and more contracts governing extractive sector projects are becoming public – through several channels. Some governments have disclosed contracts as a matter of policy. International lenders, including the International Finance Corporation, are encouraging contract disclosure. Smaller, publicly listed companies have long been required to disclose contracts in the United States and Canada if those contracts could affect their share price. And, of course, sometimes contracts end up in circulation after having been informally disclosed, as is the case with the recent Statoil contract amendment in Tanzania. Many of these contracts can be found on sites including resourcecontracts.
Increased information in the public domain can only be a good thing. But more information does not necessarily mean more understanding. In fact, there is a risk it can result in more confusion, as Michael Jarvis of the World Bank pointed out in a blog post last year.
In the petroleum sector, public “model” contracts are commonly available. But what is often missing is not the general structure of the contract but the economic terms. Against this background, and coming from a low base of what we might call “contract literacy”, the first response to publication of contracts has been to focus on royalty rates, income tax rates, and possibly the level of government equity participation.
But what do such headline terms really tell us by themselves? Not much.
From Contract Terms to Fiscal Regimes
First, percentages only count against a “tax base.” If a contract contains a 10% royalty, the real question is: 10% of what?
Is it 10% of the final sale price, and is the sale price linked to an existing international benchmark?
Does the contract allow sales to affiliated companies where the agreed price might be well below market value? If so, is any formula used to establish an “arms length formula” to calculate a notional market value for tax purposes anyway?
Is the royalty assessed on a “net-back” price where deductions for transportation and other fees are deducted before the 10% is assessed?
It quickly becomes clear the 10% royalty alone does not tell us very much at all.
Second, individual terms are like inert chemical elements. We only understand that “compound” that is the contract as a whole when we put them together and watch how they “react” to each other. Tough terms in one part of the contract can easily be offset by big concessions in another. This means reading the fine print in the main contract as well as the annexes. Take just one example: are royalty payments allowed as a deductible cost against taxable income? Sometimes yes, sometimes no. But the difference matters. When a 10 percent royalty is deductible and the income tax rate is 40 percent, the effective tax rate is considerably lower than the stated 40%. In terms of the sums involved in these contracts, that’s big money, enough to spark a public debate if it were explicit. But if it sits quietly as the impact of one term on another inside an obscure contract it can pass unnoticed.
Third, most contracts are only the final part of the economic picture. They are built on top of other legislation such as corporate income tax laws and broader foreign investment laws. So the contract’s positioning in this larger body of national laws and regulations must be understood. And it is often not just a question of drawing on current legislation. Contract stabilization clauses can mean the tax law back when the contract was signed is relevant, irrespective of subsequent changes. We therefore also need a timeline of all legislation and regulation in force at all stages in the long lifetime of these projects, as Jack Calder points out in the IMF’s new handbook on fiscal administration of extractives.
So the main contract terms are just a first step. We must also consider, at the very least, the tax base, the interaction of the terms, and the broader skein of regulations and laws. Combined, these three elements represent the overall “fiscal regime” governing a project.
From Static Analysis to Scenario Modeling
Even then, the real economic implications can only be understood in the context of overall project economics.
Of course, full knowledge is only possible when a project is over and the books are closed. But no one wants to wait for, potentially, decades. Politicians need policy options. Publics need answers. The solution is to model a range of future hypotheticals in terms of production, price and project costs.
This inevitably means speculating. In fact, once contracts have been disclosed and the broader tax analysis has been completed, the fiscal terms are the most stable part of any model. Other inputs include estimates of the overall resource base, year-by-year production, future prices, and expenses including exploration, development, and operating costs.
As many of these inputs are often based on educated guesses, economic modeling does not provide a very reliable way of projecting future revenues. But modeling a broad range of scenarios does provide a good sense of how a fiscal regime will work in whatever future scenario ultimately unfolds.
Modeling as an Essential Component of Sector Good Governance
Economic models are in widespread use in behind-the-scenes decision-making by companies and governments. All too often, governments rely on the company’s model during a contract negotiation. Curiously, economic models are not yet part of the extractive sector transparency agenda. In part, this is because the models themselves are almost never publicly accessible. But we suspect that it is also because economic modeling based on elaborate spreadsheets is seen as technical and esoteric – beyond the reach of many of those interested in better management of the extractive sector.
We want to change this. In our next post, we will review the common uses for project economic modeling in the extractive sector as well as some of the private and public organizations that build models. In the current environment, the characterization of models as highly technical and esoteric certainly rings true. These models are forbidding rather than user friendly.
It is easy to build models that are intelligible only to a very few technical experts. But we contend that this complexity is not inherent to modeling. Rather it is because, not surprisingly, existing models have been built by economists for economists.
There’s no doubt that it is easy to get lost when you combine 100+ page contracts with a model spread across multiple sheets in an Excel file. But if we make pedagogy a leading requirement in model design, it should be possible for models to be the best way to see the forest of extractive economics rather than just lots of trees.
Modeling needs to be mainstreamed. Currently, fiscal regime analysis is the precursor to building project models. We want to test the opposite hypothesis: that models can empower, and provide the best entry point to understand the economic implications of contract terms that are increasingly in the public domain but not often well understood.
In fact, we suggest there really is no alternative. Because when it comes to understanding the economic implications of extractive sector contracts, “you don’t know what you’ve got until its modeled.”
When OpenOil approached me with the proposal to contribute to their partnership with OpenCorporates and map out the global corporate structure and subsidiary hierarchy of BP plc, one of the world’s largest oil and gas company, I was enthusiastic. To attempt such a project with a small group of people was as much an methodological experiment as it was an attempt to set standards that would provide not only civil society and its socio-political activists, but also governments and corporations, with a tool that could enable more transparency and accountability – what OpenOil founder Johnny West refers to as “systematic public oversight of big companies”.
We were successful at our BP plc corporate cartography project, since we mapped out 1180 subsidiaries in over 80 countries in only matter of weeks, which evidences the immense potential of open data in ensuring corporate transparency.
What may not be clear to you, the reader, is why does mapping out a corporation matter. The first point to consider is that there are no doubts that some corporations have immense economic clout, not only the at national, but also at the global level. Mapping out a corporation clarifies how it interacts with the world’s economy. I intentionally shy away from the term ‘international’, especially in relation to trade and commerce, and prefer to employ the term ‘global’ instead because, although it is incontestable that commerce and trade occurs between nation-states and their companies, business nowadays has quickly spread beyond the territorial and regulatory limitations and oversight of singular nation-states. What I mean by that is whereas we once spoke about multinational corporations (MNC), contemporary corporations have continue to adopt and develop legal embodiments that move beyond that the multinational in order to reach a truly global scope. Global corporations spread their structure like mangrove trees, which drop their roots from their branches, latching on to national territorialities because they are fertile grounds that allow them to optimize their accumulation of capital.
The second point why mapping out corporations is important is the fact that, despite the efforts in recent years by international organizations, non-governmental organizations, and civil society and consumer groups to render corporations more and more transparent, these have intensified their political clout as well. Mapping out a corporation clarifies how it influences the world’s politics. Consider that in the United States, the Supreme Court ruled in 2010 that corporations and trade unions can employ treasury funds for direct advocacy – to be clear, to spend as much as they wished on advertising, but not direct contributions to candidates or parties – in order to endorse and/or call out civil society to vote for or against specific candidates in federal elections. More recently, in 2014, the Supreme Court ruled that no limit should apply to corporations’ campaign contributions, although there continue to be limitations on how many funds an individual candidate may receive from corporations. This allows corporations to spend as much as they want across a wide spread of candidates in parallel, since they are not allowed to spend as much as they want on a specific candidate yet. It is particularly interest that the Supreme Court’s majority decisions, in both cases, were established upon the understanding that to continue to impose such limitations would infringe upon constitutional protections for freedom of speech of corporations.
The third point, which can already be concluded from the two prior points, is that state and corporate structures are profoundly intermeshed, both economically and politically, and mostly at the level of individuals, similar to two buildings that are internally connected by some “revolving doors” of sorts. Mapping out corporations clarifies who are the economo-political elites that, many times in a conflict of interest, decide politics so as to benefit certain economic interests and direct certain economic interests to decide politics. Close scrutiny evidence how politicians become lobbyists and lobbyists become businesspeople, in an endless cycle that is global in scope.
For these reasons (i.e., economic and political clout, lobbying efforts, contributions to the political campaigns, and continuous moves back-and-forth between governments and industry)), the attempt to render corporations and their activities more transparent needs to very seriously consider their actual legal embodiment. When there is an attempt to create public awareness about a particular corporation, let us say, the Acme Corporation featured in the Road Runner and Wile E. Coyote cartoons, we tend to represent Acme as a monolithic and indivisible entity, to ensure that the general public can easily identify the company that should concern us. However, to be able to act against the Acme Corporation’s history of defective products, transfer mispricing, tax evasion, governmental bribes, social and environmental negligence, and other practices of corruption, we have to understand it and act on it in terms of what it actually is: a corporate hierarchical structure, global in its extension, legally embodied by hundreds of subsidiaries, crisscrossing through national jurisdictions, tax regimes, and banking systems, simultaneously investing millions upon millions in the stocks, bonds, and other securities of corporations across the world by way of the global stock market.
I was enthusiastic about the project because of my own research, which focuses on state and corporate policies and practices to exert control over information and communications. The project represented an opportunity to better understand the nature of the legal structures of corporations in their entirety. After we finished it successfully, I became even more convinced that – in order to strengthen our political agency as civil society as well as our economic agency as consumers – we need to strengthen our public oversight over big companies. If we are to do so, we have to understand how to render corporations transparent in systematic detail. More so than many other industries, corporations dedicated to the extractive industry are secretive, but the potential of collecting and establishing relations between publicly available information they have published themselves cannot be underestimated.
The methodological success of our project – its most experimental – was the fact that we did not have to be experts on BP plc, its activities, and/or economic and political interests, in order to thoroughly map it out. OpenOil’s presentation at Berlin’s re:publica 2014, a conference focused on social media, blogging, and digital, demonstrated with a practical example that members of the audience could map out a smaller corporate branch of BP in under an hour – sixty minutes, which included the time required for explanations on how to do so. The methodological standard our project was able to set evidences, without reservations, that mapping out a corporate giant like BP plc, with its more than twelve hundred subsidiaries, only requires a small group of committed individuals and some weeks of methodologically sound collection and analysis of readily available public data.
OpenOil and OpenCorporates are trailblazing in directions of transparency and accountability that everybody who is interested can also venture forward into, and that matters very much, especially as more and more civil societies have access to open data and public records on the Internet.
I have no doubts that Wile E. Coyote is absolutely thrilled.
The decision to try and map BP’s global network was based on the fact that the company makes many public filings. We didn’t really appreciate quite how many until we had finished.
In the UK jurisdiction alone, the BP network we constructed shows 182 affiliate companies. Of these, the 26 which were second tier companies made 157 filings in 2012 and 143 in 2011. The group probably then submits something in the region of 600 to 800 filings a year in the United Kingdom alone. Then we were able to access filings in probably a dozen other jurisdictions.
All in all, the BP group probably averages between 50 and 100 A4 pages of public disclosures per day to public authorities around the world, under normal conditions. That is, not counting investigations, legal proceedings, or any other event-driven process.
We didn’t know that when we started because we were at the start of the learning curve. So we signed up to the Companies House filings service, at a pound a time, and started pulling records.
We had come to the project ever so slightly armed with some ideas of where to look. Because of its profile, there are stacks of books about BP. We set ourselves a BP Primer out of three of them: Tom Bergin’s Spills and Spin, John Browne’s autobiography Beyond Business, clearly the founding declaration of the man to rehabilitate his reputation after his 2007 downfall, and the Evolution of a Corporate Idealist, by Christine Bader, mostly an account of her time at BP working on corporate social responsibility.
Through these we understood broad trends within the company in the past twenty years which we hoped to find traces of in the corporate hierarchies. Bergin, who had held the energy beat at Thomson Reuters for years and had huge experience and clearly great access, offered several major themes. Browne’s rampant acquisition strategy of the 1990s and all the Amoco and Castrol entities that must have adhered to the group. Since BP had moved aggressively into commodity trading suggested there should be prominent corporate investment and trading vehicles. As to individual theatres of operations, would there be any affiliates clearly linked to BP’s play with TNK in Russia, for example, or its expanded presence in Libya post rehabilitation of Muammar Gaddafi in 2004-5? Also, given that many of the vertically integrated companies seemed to manage retail operations in very atomised fashion, would we see companies that were essentially a group of petrol stations in Dorset, and another one for Cumbria?
We quickly found a huge variation of size and activity between one company and another. Some seemed not to have any turnover at all, while others, such as BP Exploration Company, reported a profit of 840 million pounds in 2012. So we began to identify which affiliates seemed to be the “monsters” – BP Global Investments, BP International, BP Holdings North America – and home in on them.
Each of these companies were showing direct subsidiaries in a list at the end of their annual accounts. So we began to build lists of these affiliates and, if they were in a jurisdiction we could reach online, to pull their filings too, to see if they in turn had any subsidiaries. As we found each new company, we recorded its jurisdiction of incorporation, full legal name and number of incorporation, and then stored all documents in directories which combined these into one string.
This was a key understanding from OpenCorporates – the need to identify individual company structures. Not “BP”, or “BP Global Investments” but “BP Global Investments Limited, incorporated in the UK on March 4th, 1932, company number 00263889”.
The entire play in the way multinationals operate is in the interplay between the group as a co-ordinated whole, making a decision about how to invest in exploration in the Arctic, how to react to the US shale gas boom, or how to allocate this year’s profits, on the one hand, and the fact that this unified strategy is played out across over a thousand affiliate companies who each exist as a separate legal “person”. The company naturally seeks to maximise advantage across jurisdictions by combining these different legal persons in the most profitable and least liable way for any given business problem. But even if the group does act with one mind, the price of being able to maintain the affiliate structure as separate legal persons is a bare minimum of autonomous reporting by each of them.
It was as if the BP group is a superorganism and its affiliates were the constituent organisms included in the whole, like individual ants or coral. None of those companies had any purpose or would even survive without being integrated into the colony. Nevertheless, each of them has a unique footprint and what we were doing was studying the traces of their uniqueness, their “genetic code”, to see if significant information was stored there which could tell us something about the internal functioning of the colony.
We kept tabs day by day on this grinding process of building the network by attrition. By the end of the first week we had found about 400 companies. That seemed like a lot of companies, although we knew from an earlier study that in 2010 BP was thought to have had about 1500 companies. So we thought, gosh, well since we understand which files to pull down and where to look within them, it might only take another two weeks, three weeks max.
The team was made up of smart people with no domain experience. Part necessity and part choice. The necessity was that we were on a tight budget and couldn’t afford people who had experience of corporate accounting, for example, or international tax structures, even if we could have persuaded them to stare at annual accounts until they went cross-eyed. The choice was, this was an ideal group for the proposition that anyone with some brains and determination to rub together could grok enough of the basics to build these corporate maps. As well as Anton Rühling and myself, who belong to OpenOil, Claire, Avner and Miguel were grad students based in Berlin. They were intrigued by the hypothesis, clearly had a solid basis of research skills and it didn’t harm that two of them came from countries where hydrocarbons were either already a major feature of the political economy (Venezuela) or might shortly be about to become one (Israel).
Then we had a couple of breakthroughs.
Sixty years after Shell and BP first struck oil in the Niger Delta, multinational companies still produce more petroleum than local Nigerian companies do. But the long, fitful process of indigenizing the industry – a national priority since the 1970s – has finally, unmistakably, taken hold.
In Port Harcourt, the steamy Delta oil hub, I saw more filling stations with local brand names than international ones: shiny signs emblazoned with Conoil and Oando logos, to name just two. Local companies are more prevalent than before in the upstream as well: the most recent NEITI report includes information on 23 indigenous producers and nine international ones. It’s safe to say that indigenization is the biggest trend in the Nigerian oil sector today. Now, the oil governance movement must adapt to this new reality.
Many questions remain unanswered about how local companies handle environmental issues and security. There are also concerns about the transparency of the various licenses awarded in oil blocks where local companies are moving in as foreign companies divest.
Higher ‘local content’ has obvious benefits, from jobs and skills building to local reinvestment of revenues with multiplier effects in other areas of the economy – as well as the hard to define but essential sense of local ownership of a vital national resource. But in governance terms, what works (and doesn’t work) when dealing with multinational companies may not always apply to locally run firms. Foreign firms often have decades-old reputations to defend in international consumer markets; many are bound by membership in industry associations that assess compliance with international health, safety and environmental standards. Local companies are rarely subject to the same scrutiny.
One industry analyst I spoke to in Port Harcourt said that environmental protection in oil areas could become more challenging as more locally-operated fields come online. Nigeria has modern laws regulating industry environmental standards for local companies and foreign ones alike, but these laws tend to be weakly enforced. Perhaps the biggest incentive for companies to operate responsibly is the reputational risk of not doing so: for all the true stories about big international producers polluting the creeks and mangrove forests of the Delta, Shell itself is among the most proactive companies in reporting its own oil spills in Nigeria. Whether or not this is a cynical effort to play nice with the environmental lobby back home in the Netherlands and UK, the outcome, in the form of detailed reporting on the location and size of new oil spills, has been positive.
If the laws won’t enforce it, how can Nigerians get a collective commitment from local companies to adhere to global environmental best practice? One solution may be beefing up the national oil and gas associations to set environmental standards. The Nigerian Association of Petroleum Explorationists is more of a business club than a standard-setter; the Petroleum Technology Association of Nigeria is mainly a local content promoter. A robust system of checks and balances through an industry association with the remit of monitoring environmental compliance – I have to reference the Norwegians here – would go a long way towards environmental responsibility being something normal, not exceptional, about companies doing business in Nigeria.
This is not to pretend that international companies have all done a great job. In fact, their own environmental failures in Nigeria have contributed to the recent surge in indigenization. The militancy in the Niger Delta that peaked in 2007-8 – and which compelled some international firms to sell their assets to local firms – grew out of a marginalized population angry that local communities are deprived of the benefits of their oil, yet must cope with the mess. Local companies have moved in as international producers sell risky, attack-prone onshore assets and focus on safer offshore plays.
The question remains, though, how local operators deal with security issues in the Delta. The euphemism is that as locals they “know” the scene and can handle conflict better. But will that really matter if they add to the mess in the swamps? How exactly do they handle conflict? If the strategy is to fund counterinsurgent militias and thugs to intimidate fractious communities, as Shell has, it is no improvement.
Finally, the allocation of new contracts that come out of the indigenization process must be subject to the same transparency standards as the contracts they replace. A lot of oil acreage has come on the market in recent years, and as a rule, local companies have snatched it up. The new commercial opportunities have brought in a range of new players, and when put under the microscope not all of them look pretty.
Contracts called Strategic Alliance Agreements (SAAs) are one example. One of the biggest gaps in the reporting of the Nigeria Extractive Industries Transparency Initiative (NEITI), the local chapter of the international transparency benchmark, has been its non-inclusion of SAAs. The best known of these contracts are the ones entered into by Seven Energy and Atlantic Energy, who took on operator status for the the state-owned NPDC after the Shell Petroleum Development Company divested from several oil blocks between 2010 and 2012. Both firms were co-founded by Nigerian business tycoon Kola Aluko – Seven Energy as a local subsidiary of Septa Energy (UK), and Atlantic Energy as a local private company. The disclosures made by suspended central bank governor Sanusi Lamido Sanusi included concerns that the government may have been deprived of some $6 billion through these SAAs.
More transparent license allocation is needed in all aspects of the indigenization of the industry – not just the allocation of production licenses, which are already awarded based on competitive bidding, but also the allocation of SAAs and other contracts under newly open acreage. Who exactly is being awarded these contracts, and what are their technical qualifications? Is the success of new operators based more on their performance or their political or social connections? These questions must be asked as standard protocol for all new producer or operator agreements in the country.
Many Nigerians are proud of the accelerating indigenization of their industry, and rightly so. But without governance mechanisms in place that recognize the difference between local and foreign-run firms, the Nigerian oil sector as a whole risks repeating many of the failures of the past.