Glencore has a 79% stake in an iron ore project at Askaf North in Mauritania, via its 88% owned subsidiary Sphere Resources, which is incorporated in Australia. Sphere states in its 2014 annual report that it took a loan from Glencore at an interest rate of 12% per annum for the project. Separately, in March 2014 Sphere announced the loan facility was for $186 million.
We don’t have access to Glencore’s agreement with the government so it is not possible to tell if these interest charges are tax deductible in Mauritania. This note addresses the question of under what circumstances loans at 12% have been made to extractives companies in recent years, when generally low interest rates have prevailed.
Here are some examples from Aleph, Open Oil’s extractives corporate filings database holding 1.2 million filings from companies in 11 jurisdictions around the world, of 12% interest rate instruments – loans, promissory notes, convertible bonds – issued by companies.
Glencore has a 79% stake in an iron ore project at Askaf North in Mauritania, via its 88% owned subsidiary Sphere Resources, which is incorporated in Australia. Sphere states in its 2014 annual report that it took a loan from Glencore at an interest rate of 12% per annum for the project. Separately, in March 2014 Sphere announced the loan facility was for $186 million.
We don’t have access to Glencore’s agreement with the government so it is not possible to tell if these interest charges are tax deductible in Mauritania. This note addresses the question of under what circumstances loans at 12% have been made to extractives companies in recent years, when generally low interest rates have prevailed.
Interest of 12% per annum does seem used in recent years when general interest rates (LIBOR and associated) are much lower.
But the pattern seems to be early stage companies with very high risk – and in smaller amounts ($5 million or less) relation to raising funds from external investors. The question then is would these conditions apply to Sphere in Mauritania – and at nearly $200 million? In other words, is Glencore treating Sphere Resources as though it were an “arms length” investor and effectively ensuring lower risk… while already having committed to a risk-reward ratio through its initial investment in the project – when the risk-reward ratio should already have been factored into its own original calculation to invest? Is there a possibility, in other words, that Glencore seeks to make a project which is in a marginal space on the cost production curve more viable by locking in higher revenue flows much earlier in a cycle (interest rate repayments coming under cost recovery rather than share of profits made after all costs are recovered)… and with less exposure to tax?
Here are some examples from Aleph, Open Oil’s extractives corporate filings database holding 1.2 million filings from companies in 11 jurisdictions around the world, of 12% interest rate instruments – loans, promissory notes, convertible bonds – issued by companies
Indus Resources in the Jambi coal field in Indonesia: issues a convertible bond of about $4 million with an applicable interest rate of 12%
Australian company with copper gold projects in Western Australia, mid-2015: http://rninl.com.au/projects/the-grosvenor-gold-project/overview/
Company has unsecured loans which are turned into convertible bonds at 12% per annum?
Listed in Australia, 2010 report apparently lists work in Galveston (Texas?)
Loan to Kilgore Oil and Gas Limited which was originally 15% up to 2010 and then became 2012. No immediate info on relationship of Kilgore to Odin.
Alloy Resources Limited
Australia, mainly active in Australia but exploration license in Laos
$400k issued in note convertible at 10%. The interesting thing is company seems to be in trouble – laying off staff etc. So if 10% under these circs, what would justify 12% (in other loans)?
Wetar Copper Project in West Papua, Indonesia: http://findersresources.com/wetar-copper-project/project-description/
EGM May 2009 mentions Convertible Note facility – several tranches with “coupon” repayable at 12% – maybe $10 million USD.
Registered in Delaware, active in Israel since the 1980s.
Promissory note in which 12% is one of the interest rates mentioned. Isramco seems to have been connected to Tamar, which was discovered 2008-9 – so presumably would have good prospects? Not sure of context…
America Sands Energy Corp
Loan at six percent for 12 months; if not repaid by then move to higher rate of 12%
Daybreak Oil and Gas
Originally Daybreak Uranium, now with exploration assets in the Appalachians. Promissory notes in annual accounts at 12% – $5 million.
Placer del Mar
BBridge Notes and Investor Notes will generate 12% interest upon default of repayment schedules
Listed on NASDAQ where it states under risks it has generated “minimal revenues” http://biz.yahoo.com/e/140604/urhy10-q.html
Magellan Gold Corporation
Promises to pay John Power $50k at 12% – individual investor.
Balaton Power Inc
Listed in the US, mining in the state of Orissa in India
“During the period ended March 31, 2010, the Company received a
loan in the amount of $15,000 from three shareholders of the Company for the
purposes of financing the CompanyÂ’s trip to India that occurred during the
period. The loan bears interest at a rate of 12% per annum and is payable at
maturity on January 28, 2010.”
OpenOil is happy to announce the second stage of the world’s first open data map of oil concessions – the Middle East and North Africa. The addition includes 18 jurisdictions across the region, from Oman and Iraqi Kurdistan in the east to Algeria and Morocco in the West, making a total now of 53 jurisdictions and 3,400-odd blocks in the map as a whole. We are on course to add another 50 countries around the world by the end of the year.
We have also upgraded the functionality:
Share zoom in views of any part of the map, for instance Moroccan oil blocks extending into the disputed Western Sahara
Switch between a map base layer and satellite, to look for how the concession blocks relate to activity on the ground. For instance, in South Sudan, the satellite layer up close shows produced water reservoirs apparently lying outside the designated production area 1B in Unity State, in the broader exploration area. It would be interesting to check against the contract if such operations were allowed.
Or just type in a placename. For instance, “Kirkuk” will show the precise boundaries of fields allocated by the Kurdish Regional Government around the city.
The real value of the data comes when people take it and combine it with other layers. Imagine, for example, overlaying this view of Syria and Iraq with maps which show the frontline in the war there – showing which blocks are now controlled by ISIS and which companies therefore have historical information which could help assess the current and future potential, based on knowledge of reservoirs and engineering, of ISIS as an oil producer (the answer seems to include currently Shell, the Croatian national oil company INA, but most of all the Syrian state oil company SPC. One wonders whether Bashar al-Assad has ordered his technicians to share the information with any of his allies, like the Russians and Iranians, for example).
Or the Eastern Mediterranean region: as knowledge of the oil and gas in the ground develops, known reservoirs of interest to companies like Nobel and Eni are abutting each other in the waters of Israel, Cyprus and Egypt. No wonder, then, that Eni CEO Claudio Descalzi met Israeli Prime Minister Binyamin Netanyahu last week to discuss possible integration of new finds in Egyptian waters with existing and planned infrastructure in the Israeli offshore – even though, of course, such connections are highly politically charged.
The map is the best that currently be done with maps published openly on the Internet. But the Eastern Med is an example of the limitations of that. The Egyptian portion is based on a 2012 map issued by the government, predating the Eni discoveries. We will be updating it in the next week with more recent data around these specific fields, and producing a deeper analysis of this story, a classic interplay between geography, politics and the oil business.
The map is powered on the back end by a unified editing system which we will open to structured user contributions, to get to the goal of a global and updated map, folding in all other significant information in the open data space.
OpenOil is pleased to announce an online training for financial modeling of extractives projects.
Sign up now to learn the basics of how to model revenue flows, investor returns and fiscal regimes of oil and gas projects!
Beginning on Friday November 13th, there will be five weekly Google Hangouts which walk through models of a mining and an oil project, adapted for training purposes to teach the basics of project economics, supply and demand curves, investor metrics such as NPV and IRR and discount rates. Each 90-minute session will build on the last and work against presentation materials and exercises provided online. Participants are encouraged to devote 3-4 hours a week between sessions to the exercises and materials, and post questions, and answers, on message boards. For more information, email Daniel Gilbert who is managing assistance to the course.
The sessions will be led by Alistair Watson, who was the principal developer of the FARI modeling tool for the IMF and has modeled for governments, industry and civil society for 20 years. They explore the public interest financial modeling paradigm developed by OpenOil, and will also discuss full project models already published on the Internet of oil projects in Chad (Glencore), Afghanistan (CNPC) and the Bulyanhulu gold mine in Tanzania (Acacia Resources).
Each Friday for five weeks (1400 GMT, 1500 CET, 0900 EST), Alistair, together with OpenOil director Johnny West, will lead a 90-minute video presentation of modules in the training course and answer questions.
(Note: we may need to restrict numbers to create a viable learning environment. Applicants will be informed by Tuesday November 10 of if a place is available on the first course. Familiarity with Excel is advised (for example, the ability to carry out this exercise in 30 minutes or less). No prior experience of extractives project modeling is necessary.
Priority on the first course will be given to applicants from extractives dependent economies. All applicants from extractives dependent countries will be offered a place in a subsequent course. All places offered are free of charge.
A French-language version of the same online training will be offered in January 2016).
For the past year, OpenOil has collected oil concession data from all continents as a contribution to what we believe holds the potential to revolutionise natural resource management across the globe: an open data framework around the extractive industries. Today, we are pleased to announce its next evolution – the first open oil concession map of the world – starting with Sub-Saharan Africa.
Today’s release covers 33 Sub-Saharan countries and includes the shapes of over 1,750 oil blocks, as well as references to 200 corporations that were given the licenses for these blocks, and finally – as highlighted in the map – links to more than 120 of these full-text contracts. All of the data comes from concession maps, such as governments frequently publish them to provide basic information about oil rights in particular areas, but which so far have mostly remained hidden on ministry or other websites, five clicks away from being accessible… and analogue… All we did was to use GIS and digital tracing methodologies to make these maps available as open data, and by that, to give our concession database a graphical interface.
So why do we think this is important? Well… for a simple reason: because such open data maps make information about the extraction of natural resources tangible. 800 million people in Sub-Saharan Africa can now see – at a click – if they live within the bounds of an oil exploration or production area, and if so, who owns the rights to operate. This will become four billion people in the various phases of extension of the map between now and the end of 2015. Of course there are many qualifications to this statement, such as level of internet access, etc, and it’s only the starting point… but I will get back to that later.
First, let me review what our previous iterations of our concession database offered, and what they didn’t. When we started to collect concession data, we made the data accessible in our contract repository as a simple index. This, we believe, provides context to the norm of contract transparency: our estimate of roughly 2,000 assigned oil blocks across all of Africa led us to the realisation that less than ten percent of Africa’s oil contracts are in public domain. In return, we suggested, it is now possible to move away from a “n+1” to a “1-n” approach to this norm: why not measure what percentage of the total has been released, rather than how many contracts were published this year?
The second version of our concession database came in the form of our oil rights API. The idea for it was twofold: rather than having to deal with vast lists, such as published with our first iteration (after all we have collected data on over 20,000 oil blocks from 70 countries in the meantime), the API allows you to frame smarter queries of the data with questions like “What concession blocks exist in Uganda”, or “Who holds exploration licenses in Brazil”. Also, forward looking, API’s offer automatic updates. 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’s 1,200 affiliate companies farm into or out of operations anywhere in the world?
All of these questions, of course, are very technical and are only really relevant to specialists. Also, we still only have the level of detail in the data for a few rather “rich information” countries, that would allow to answer such questions in the first place. As we have stressed in our previous releases, so we stress also today that this is only a start. Yet, there is much more information available in the public domain already, as we displayed with our Open Data Tour of Tanzania. And once you put these datasets together, a lot of things become possible, ranging from Public Interest Financial Modeling of oil contracts to addressing the local impact of a particular mining site. All of this, we hope, will contribute to a ‘real’ and editorially independent costs and benefits analysis of the extractive industries, but it needs to be build piece by piece and concessions provide a framework for it.
This brings me back to what it is, that we hope to offer today, besides the framework to gather in further data: we believe that such a graphical interface, and maps in particular, make information about the extractives accessible. Imagine you can type in your address and see which companies holds the rights to extract resources in your immediate neighbourhood? It is possible… The extraction of natural resources is something that directly touches the lives of many people in many countries, and it is through maps – we think – that we or anybody else can best provide access to surrounding information.
This is why we are happy today to publish the data for the concession maps for 34 of these countries in the form of shapefiles. In a few weeks, we hope to expand this to the whole MENA region, so that in a couple of months we have what will be an interactive and truly global concession map for the 70-80 countries across the globe, for which such oil block maps exist in public domain. So do we plan to add more substance to the data itself, such as in our Tanzania pilot, just like we will further develop the functionality of our API to make the data more useful to specialists. Each of our previous releases offer their advantages, but it is through interactive maps that we hope to be able to reach a broader constituency. Who are, after all, the legal owners of sub-soil resources in every country in the world bar one?
OpenOil today releases the fourth version of the contracts repository. As my colleague Anton Rühling writes, there are another 83 contracts, from 29 countries, and some interesting material around the Brazilian state oil company Petrobras, currently at the middle of major corruption scandals.
But along with the new contracts comes a feature we hope will be useful to researchers and extractives governance professionals around the planet – the ability to comb all the contracts word by word for phrases, combinations of phrases, date ranges and other functions.
So what can you do with it? A short tour…
Use case 1: royalties for gas
There are many reasons to support the norm of contract transparency. While a lot of attention has been on the ability to scrutinise the terms of particular deals, there is also huge value in having a large body of contracts in public domain to provide a broad basis for comparison. The repository is still small relative to the total universe of contracts – 800 out of… 50,000? 100,000? Nevertheless…
Type the word “royalties” in to the search box and you get 216 results. But you may have a more specific focus of enquiry. For example, you’re interested in the royalty rates applied on gas in contracts, you’ll get 49 contracts from six countries (including India and Israel, as well as the more usual “transparency” jurisdictions). Still a fair piece of work to decide how to approach this document base… but the starting point came quick.
Use case 2: when is a benchmark a benchmark?
Or, to go a little more oil geek: one of many contentious issues in contracts is how oil and gas are valued and how much they are sold for. Of the thousands of grades of crude oil in the world, only a few dozen have their own standing price. The rest are valued against them, the benchmarks. But which benchmarks, and at what premium? What’s reasonable, what isn’t? How do contracts deal with this?
One important factor is the technical quality of the crude governed in the contract compared to the benchmarks. Are they similar grades, or, in the parlance, do they have similar “gravity”? So, run a search for “API” and “gravity” across the repository and you find there are 32 published contracts which reference this. Many references to cut off points along the API scale – above 30 degrees, below 10, above 15 degrees. These are triggering specific sales and valuation conditions, using the API index as proxy for market value.
And among them… two contracts from Sierra Leone which show some interesting language… “the Basket shall differ less than four (4) degrees API”. It suggests an external, perhaps even vaguely objective, parameter to the limits of comparison. Benchmarks shouldn’t be more than four API degrees lighter or heavier than the crude in the contract – an indication of outliers. Insisting on less than one degree would be unnecessarily restrictive. Ten degrees? Exploitative.
But it’s only two contracts from one country, and Sierra Leone is not even a producer. Can we strengthen it? Well, zoom out from just the contracts repository to the whole of the corporate filings database, currently 1.3 million records from half a dozen financial regulators in the major jurisdictions for the industry around the world… and… we find two more descriptions of Kosmos contracts in Suriname, filed with the SEC in the United States, and another from Albania filed with the Canadian authorities. And these also mention four degrees API as the reasonable limit of comparison. Still not definitive… but a starting point to examine any specific set of terms defining benchmarks by technical quality, in less than ten minutes looking.
Use case 3: local training programs
How oil companies bring on local staff and their counterparts in state oil companies and even ministries is another issue of keen interest. But what’s the norm? Run a search for just the word “training” and you find 250 contracts which reference it. But maybe that’s too many… Maybe you’re in a country that is debating and wants to put a figure on it. But the company is shying away from specifying an amount. So how normal is it for a contract to specify an amount? Adapt the search to say: find the contracts which mention “dollars” within 25 words of “training” and you get 98 results. So, inconclusive. But informed inconclusive.
Use case 4: Finding that troublesome phrase
You might actually use the repository to search one contract for a specific phrase. Outsiders would be amazed at how much time lawyers and others spend combing contracts they didn’t write for where the standard provisions governing this or that must be.
Simple example: Ghana’s Deepwater Tano contract does not mention “decommissioning “in its index of articles. But it must be there. “Tano” and “decommissioning” gives you a host of references to it – in the Accounting Procedure, leading back to the clause in the main contract (headed “Taxation and Other Imposts”) which specifies a decommissioning mechanism.
Use case 5: confidentiality
Over 300 contracts (out of 800 remember) reference “confidentiality”. So yes it’s a big deal. But companies and governments often cite just the existence of language relating to confidentiality as the end to a discussion about whether contracts can be published. Does that stand up?
Read through some of the results and you find words like “unless” “without” and “however” – implying that generic confidentiality clauses have lists of exceptions. The transparency lobby has long claimed that this list of exceptions often entitles the government to publish contract under a wide range of circumstances found. How common? “Without” appears in the same paragraph in 134 contracts, “unless” in 42, and “except” or “exceptions” in 150. Those are pretty high proportions of the 309 total – so clearly the presence of a confidentiality clause is the beginning of a debate about contract transparency, as the advocates say, rather than the end of it.
Use case 6: Non-English contracts
First, our corporate filings search engine Aleph has taken PDF versions of the contracts (along with hundreds of thousands of other filings) and crunched them through a reader which renders them into text, which is then indexed in the system. Which is what makes the search possible. But it’s far from perfect.
Second, a lot of the power in searching lies in what data geeks call “regular expressions” – give me word A in the same sentence as word B… in a document which does not mention word C. Give me this phrase in contacts signed between 2000 and 2005. Now between 2005 and 2010. This functionality is built into the repository – but we haven’t had the space to design a front end interface which allows it all to be laid out for the casual user intuitively. For now, either read up on regular expressions, or ask us.
It’s almost been a year since we have first launched the OpenOil contracts repository and we are happy to today announce our fourth update: 83 new full-text agreements, including new host government contracts from countries such as Colombia, Egypt, Libya, Mongolia, Portugal and most notably Brazil, taking the total past the 800 mark.
As we explain in a separate blog, this release also now enables full-text searches across the 30 million words of the contract document base. But there are also some notable additions to the repository.
Let’s start with Petrobras. The Brazilian state owned company has been constantly in the news for various corruption allegations. We are adding 11 Petrobras contracts into the repository today, as well as welcoming Brazil to the list of countries covered. These had been published previously to investors, so we scraped them from financial regulator websites, as we have done before.
But the Petrobras contracts are noteworthy for two reasons: first, we have now managed to acquire contracts for most major oil companies, including Chevron, ExxonMobil, Shell, Total, BP, BG, Anadarko, Tullow, Statoil - and now also Petrobras.
This is relevant in so far, as it is much harder to get access to the terms in the contracts signed by these companies. Materiality regulations, requiring listed companies to release contracts to stock markets, are connected to the individual weight that agreements hold for a particular company. Only if the business of a company is considered dependent on a particular contract, the contract will be classified as material and released to investors. So given the sheer size of oil majors with their many assets across the globe, most published contracts come from smaller or medium sized oil companies where it is more likely for a company to depend on them. The Petrobras contracts therefore form a rare exception.
The second reason for why the Petrobras contracts are interesting, is that they include the main host government agreement, signed in 2013, on Brazil’s biggest oil field to date, Libra. The contract assigns Petrobras, Shell, Total and CNOOC the rights to develop what has been classified as a supergiant oil field, that is expected to produce over a million barrels of oil per day and has been the prestige project driving the whole of Brazil’s deep offshore development.
It regulates what is likely to become a significant income stream to the Brazilian government, and it is now possible to extract the relevant terms for the project such as the cost oil provisions, royalties, etc, and to use these terms to create a public interest financial model.
The contract repository now stands at 806 contracts from 73 countries, and we are happy to have more than doubled the number since our initial release. This of course still represents a small proportion of the total number of contracts in the world. But it is enough of a base to allow norms and best practices to be much more findable. Need a dozen references for gas royalty rates? Trying to figure out how contracts might price local grades of crude against benchmarks? Or simply trying to find language on any issue in Spanish and French? All are now a short search process away in the full-text search set up on Aleph, our corporate filings database.
We are even happier that there is ongoing demand. The last month has seen steady traffic of about 200 contract downloads a day – and a full mirror of the repository downloaded every other day. We estimate that there are now over 200 complete copies of the repository saved on a harddrive somewhere around the globe.
This is particularly important to us, since it was one of our aims to work against contracts ‘disappearing’. By disappearing contracts I mean those documents that had been released somewhere before, but then taken down again. It happens. But with 300 complete copies spread across the globe, the repository effectively ensures that “once public, always public”.
Finally, the list of countries from which we have logged downloads is growing. Although most downloads still come from the global north, our logs show downloads from 50 countries over the last 30 days only. Recent spikes of interest include in Greece, where natural resource management has become a hot issue under the Syriza government, Yemen, Tanzania, where contract transparency continues to be an issue of major public interest, and Egypt, where companies have recently declared new finds on a scale they say will transform the local energy sector.
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?