Reputable Mining Investors Wanted in Sierra Leone: How to Improve Integrity Due Diligence in Mining License Management

On the first anniversary of the Panama Papers, the National Minerals Agency (NMA) of Sierra Leone wants to know who they are doing business with. The NMA is the government agency responsible for issuing mining licenses. Beny Steinmetz of Simandou fame, is one such mining investor who has prompted the NMA to strengthen integrity due diligence. The Panama Papers implicated Steinmetz in a complex chain of ownership of Koidu Holdings, Sierra Leone’s largest diamond mine. By checking the reputation and track record of applicants, the NMA hopes to weed out shady investors, and instead attract reputable companies to transform Sierra Leone’s mineral wealth. This ambition is echoed by the EITI’s call for disclosure of the ultimate owners of extractive companies, as well as the launch of a global register of beneficial owners by OpenOwnership.

But, is it realistic to expect a resource constrained country such as Sierra Leone to be able to reliably test the ‘integrity’ of mining investors?

The short answer is yes. From October 2016 to March 2017, OpenOil was contracted by the Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH to work with the NMA to strengthen integrity due diligence in mining license management. We found that prioritisation of resources, smart use of commercial products, and the ability to judge risk, enable the NMA to build a reasonably comprehensive picture of the integrity (or not) of potential investors. The expectation should not be that the NMA will always uncover the ultimate owner, or chase down the last dollar. The goal is to make informed decisions about who gets access to Sierra Leone’s natural resources.

Here are four lessons we learned that may be useful to other countries.

1. Prioritise mining license holders for ongoing integrity checks.

Developing country governments may not have enough staff to run integrity checks on all mining license holders. The NMA has four compliance staff to oversee 175 industrial mining licenses, and 2,078 trading licenses. Thankfully, not all license holders pose the same level of risk to the sector, or the wider economy. With the NMA, we developed a way of ‘triaging’ high-risk license holders for regular integrity checks. Regular integrity checks are important because the business profile of a license holder may change over time (e.g. sale of asset).

Here’s what we did:

a) First, we identified a set of risk indicators to measure all license holders against, based on data the NMA already collects via its Mining Cadastre System (e.g. contribution to total non-tax mining revenue);

b) We gave each indicator a weight based on level of risk, and reliability. For example, variation in sale price has been given less weight than license type, or presence in a low-tax jurisdiction, this is because there may be numerous legitimate reasons why a company’s sale price would be lower than the market price (e.g. differences in quality), making this indicator less highly correlated to risk;

c) Finally, we proposed an algorithm and scoring system for each indicator. The NMA is currently embedding these rules into its data analytics system. The result will be an automated list of 10-12 ‘watch list’ companies that requiring ongoing monitoring by NMA compliance staff.

Our aim was to establish an automated system that can generate a reasonable assessment of whether an existing mining license holder should be subject to ongoing integrity checks. Where the results appear incorrect, NMA staff can add or subtract ‘watch list’ companies.


Integrity Due Diligence Risk Matrix


2. Commercial databases must be evaluated based on their specific application to the mining industry in the source country.

There may be limits to what open source data can deduce about an investor (e.g. where an investor is wholly owned by a company in a financial secrecy jurisdiction). The response to this challenge is usually that countries should invest in a commercial database from Reuters, or Bureau Van Djik. Yet, for many developing countries, including Sierra Leone, the cost of a database (anywhere from £5000 to £25,000 per year) is hard to justify. To decide whether to invest in a subscription, government agencies should test the specific application of the database, to the mining industry in their country. In doing so, they should consider the following issues:

  • The volume of mining companies that need monitoring. It may be that government would use only a fraction of the information on the database, despite paying for the whole resource;
  • How much information the database contains on privately held companies, that is not publicly available from the local registry. The main reason to subscribe to a database is to aid due diligence of privately held companies. Yet, if the database is so scant that government would need to purchase extra data elsewhere (e.g. from a consultancy), it may not be a good investment;
  • The extent to which the database uses public information to identify Politically Exposed Persons. It may be that this information comes from media and news websites, company websites, and sanctions lists, all of which are publicly available;
  • If the mining regulatory agency can establish the corporate ownership structure of mining investors i.e. identify all legal entities, directors, officers, shareholders etc. Some databases rely on users first knowing who the legal entities, and individuals involved are, to be able to search them. Developing countries should avoid databases that need a high level of prior knowledge of corporate structures.

A commercial database might be feasible if the EITI Secretariat and donor partners were to buy a collective subscription to license out to EITI implementing countries. This would be more cost effective, as well as help countries to implement the EITI standard on beneficial ownership.

3. Countries with a small number of mining investors may find purchasing one-off due diligence reports more cost effective than subscribing to a database.

If a government agency finds closed data necessary to conduct due diligence, a more cost effective and targeted solution is to buy one-off due diligence reports for specific mining license applicants. However, there are costs involved with this approach, therefore it should only be done for high-risk privately held companies (e.g. investors linked to State Owned Enterprises), once the compliance team has exhausted all possible means of researching the company. An advantage to this approach is that it ‘kills two birds with one stone’ by getting a report, as well as translation of local registry documents and media.

Depending on the level of reporting, standard reports range between £100-£800. Very sophisticated reports can go up to $30,000. The cost comes down to how much source commentary, or ‘on-the-ground’ investigation is involved. In most cases, a standard due diligence report (company registration, adverse media results, sanctions and enforcements, basic information on directors and shareholders) should be enough for integrity checks, unless there are very serious concerns, or challenges to accessing company records.

For a country like Sierra Leone, where there is a handful of significant mining operations, buying one-off reports is undoubtedly more cost effective than subscribing to a database. This may not be the case for other countries with a more developed mining industry. However, even these countries may find it strategic to purchase one-off reports given databases can be variable on privately held companies.

4. Countries seeking disclosure of ultimate owners require a legal framework for beneficial ownership. In the meantime, government agencies can use other data points to inform integrity due diligence.

Whilst the NMA has ambitions to know who the ultimate owners of companies are, as it stands, the law only requires disclosure of shareholders who own 5% or more of the issued share capital. There is a legal review currently taking place, which will likely result in comprehensive beneficial ownership legislation. However, integrity due diligence does not have to wait until legal reform takes place. A lot can be deduced from information already collected by government agencies. For example, the NMA can already detect some PEPs issues based on the shareholder and related party data it collects from license applicants. Bearing in mind their current legal framework, government agencies should update existing disclosure requirements to maximise the due diligence relevant information they are already entitled to.

* * *

Effective due diligence of investors is critical to countering corruption, tax abuse, and criminal activity in the mining sector. Due diligence solutions should be simple and cost effective, considering the resources and capabilities of the government agency responsible for administering them. The mark of success is not a government agency knowing every legal and natural person involved in an investment. But, whether that agency can put forward an informed and reasoned view of the level of risk associated with an investor, such that a decision to grant a license (or not) can be made, and areas of concern flagged for ongoing monitoring.

Alexandra Readhead was the OpenOil Team Leader for the NMA due diligence project. She is an independent consultant in international taxation and extractive industries.

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Is Tanzania’s export ban a high stakes game of poker with Acacia?

OpenOil’s Excel model on the Bulyanhulu mine  is available to view the workings of the conclusions in this post. 

Tanzania’s decision to impose a ban on concentrate exports from its mining industry has gained wide coverage among industry watchers. The question is: what does the government hope to achieve with it, and what will companies’ reaction be to it?

In this piece, we explore the possibility that the ban particularly targets Acacia Mining’s gold production in Tanzania and that the objective is to pursue re-negotiation to increase revenues to government from Acacia’s three large gold mines, which generated turnover of over a billion dollars in 2016 but have yet to pay any corporate income tax.

We further consider the impact of the ban on Acacia, and on government revenues. Financial analysis suggests the following preliminary conclusions:

  • The government will lose $1 million or more per month in revenues, as falling sales translate into lower royalties.
  • The economics of the Bulyanhulu mine, which were marginal anyway, could be pushed over into operating loss (depending on gold prices), leaving the company with a tough decision about whether to shutter the mine, and that decision point could come in the next few months. Of Acacia’s three operating mines, Buzwagi could also face difficulties while North Mara lies at the other end of the spectrum and could continue to run at operating profit.
  • Acacia’s overall financial position could be materially affected as lower revenues and profits affect its leverage, and erode investor confidence. Shares dropped by 20 percent in the week following the government’s announcement of the closure and have only partially recovered.

Each of these points is dealt with in detail below.

The background to the ban on concentrates

Exports of concentrates and ores of all metallic minerals were banned on Tanzania on March 3rd.

Thomas Scurfield from the Natural Resource Governance Institute has rigorously laid out how the ban on mineral concentrates fits schematically into a broader push by many producing countries to increase beneficiation. In theory, more processing of raw commodities in-country helps add value and convert the mining industry into a broader engine of wealth creation. Against that has to be balanced the amount of capital and time needed to create such facilities in Tanzania from scratch and estimates of how much extra value would be created in this instance – from these mines, and with these commodities.

Scurfield’s article shows that in the Tanzanian case the specifics look challenging: it would take two to three years to establish a facility, a previous feasibility study suggested the economies of scale were not there to make a copper smelting plant viable, and the level of value added in the case of copper is also debatable.

At least according to media reports, the government’s decision seemed related to another issue, that Tanzania was not receiving enough income from its minerals sector, and that better physical auditing was needed as part of measures to redress this.

Acacia’s mines are the only scaled operations which are immediately affected by the ban. The other main producer of gold, Anglo-Gold Ashanti, does not export concentrates. There are plans for several large nickel projects in the future, which could also be affected, but these are several years away from execution and actual export.

Separately to the ban, the government of Tanzania has had a long-standing tax dispute with Acacia over payment of corporate income tax. The Tanzanian Revenue Authority raised a case against Acacia for $41 million withholding tax on dividends paid out to shareholders of the UK-based holding company. OpenOil issued an analysis of the tax dispute last June. Acacia’s last public comment on the case was in October 2016, when they said they would refile an appeal in the Tanzanian court system against the ruling. Because the Bulyanhulu contract has not been published, it is not immediately clear if the Tanzanian courts are the ultimate arbitrator in the dispute, or if some other arbitration or adjudication procedure is also specified.

Tanzanian loss of income: over a million dollar a month

The government of Tanzania is losing $1-1.2 million a month in royalty payments as a result of the export ban.

Acacia has stated that the export ban on concentrates affects 30% of its gold production. In 2016, the company reported paying royalties of $47 million. The simplest approximate calculation of the effect of the ban, then, is to assume a straightforward correlation between the percentage of production shut in by the ban and value in the market: $14.1 million a year, or just over a million dollars a month at current market prices.

If the ban continues for some time this revenue is unlikely to be recoverable as and when it is lifted, since there is a limit to how much concentrate Acacia can store at the mines, so it is likely to curtail production.

Will Bulyanhulu tip into operating losses?

Of Acacia’s three mines, the ban has the most drastic effect on Bulyanhulu where, according to Acacia, concentrates account for 45% of the mine’s production. The ban could push the mine over the economic limit, where Acacia would be running operating losses in order to keep producing at all.

To some extent the marginal economics of the mine, relative to Acacia’s other holdings, has become clearer this month with the publication of the company’s 2016 annual report. These have led us to revise near term future forecasts for revenues and positive cash flows down from our first publication of financial analysis of Bulyanhulu, which were based on an investor presentation published by the company in March 2015.

Acacia has for some time been advertising capital investment and management overhaul strategies to make the mine more profitable. In the 2015 investor presentation, still the company’s last public statement of forward looking estimates, it anticipated the pay off from these efforts to be a rise in production from 195,000 ounces in 2013 to 380,000 ounces by 2017, and a drop in cash costs from $890 per ounce produced in 2013 to just $480 per ounce in 2017. It was these two effects combined which would push the mine, at long last, into free cash flow.

But the company’s annual reporting since has given a different picture (see table below).

Bulyanhulu report vs estimated

Production has risen, and costs have dropped, but by considerably less than was predicted two years ago. In particular 2016 cash costs at the mine were $722 per ounce, substantially higher than the $540 per ounce anticipated in the 2015 investor review (we assume here that the reporting basis of these metrics remains constant, since the company has provided no information to suggest the contrary, as would be expected under normal reporting practices).

Accordingly, OpenOil has revised down revenue and earnings forecasts from Bulyanhulu. We still model the general trend of the company’s assertions about future performance at the mine – that production will go up and costs will come down – but by less and over a longer timeline than originally assumed (see table below).

Bulyahnulu financial model assumptions

If Acacia’s 2015 forward estimates had held the mine could have expected to generate around $150 million in 2017 in cash flow for the company at current market prices. But under revised assumptions this drops to just $25 million. This could rise to $75 million by 2019 and go over $130 million in 2020. In terms of the tax issue, these lower assumptions would push back the point at which Acacia would pay income tax from 2020, projected in last year’s iteration of the model, to 2022.

And that is before the concentrates export ban.

If we assume that Acacia’s statement that 45% of gold at Bulyanhulu is in concentrate form means the mine will simply produce and sell 45% less (something which remains to be confirmed from a more precise understanding of the geology of the mine), Bulanhulu’s cash flows turn negative. At current market prices, the company could run an operating loss of $40 million in 2017 from the mine. It seems unlikely the export ban is going to remain in place for years without movement one way or another – either a partial or total lifting of it, or, by contrast, the move to build facilities and therefore unlock the concentrate-based production. However, theoretically, if it did, Bulyanhulu might not get out from under operating losses until the next decade.

Interestingly, under the same assumptions about the impact of the ban on production, but keeping to Acacia’s original 2015 forward looking estimates on production and costs, Bulyanhulu could stay cash positive over the next few years with a ban – but only just. Positive cash flows could be in the region of $30 million a year for the next three years. The company would therefore not face a question of whether it needed to shutter the mine as it approached the Economic Limit.

Bulyanhulu Cash flow scenarios

North Mara and Buzwagi

We have not conducted full financial analysis on Acacia’s other two mines. One unresolved question here is how much production from concentrates come from these two mines, since Acacia has not specified this for them, unlike Bulyanhulu. Piecing the company’s statements together, it would seem as though an additional 115,000 ounces of gold production at the two mines could be locked in by the ban (since according to the company 30% of overall production is affected – about 245,000 ounces in 2016, of which Bulyanhulu, with gold production from concentrates at 45%, accounts for 130,000 ounces).

Nevertheless, North Mara’s clear status as the group’s cash cow, with the highest production and a materially lower cost base, makes it unlikely Acacia would face the same issue of operating losses there.

Buzwagi at first sight also could be precarious. There is concentrate production there but the proportion was not immediately known. The mine is approaching end of life, but has the highest all-in costs of the three mines – although its vulnerability to price volatility may be lower since the company locked in pricing agreements which apparently covered all of first quarter 2017’s production at a floor price of $1,150 per ounce.

Nevertheless further analysis needs to be done to determine the vulnerability of both these mines to a continued concentrates export ban.

Bulyanhulu rev scenarios

Acacia’s overall position

Clearly the concentrates export ban, if it shuts in 240,000 ounces or more of gold production in 2017, could materially affect Acacia’s financials as a whole. At current market prices it would lead to a loss in gross revenues of $300 million.

One of the reasons Bulyanhulu could be pushed into net operating losses is because the mine operation is still leveraged. Acacia’s 2014 annual report includes a reference to a $142 million loan. Terms of that loan are not available, but it is possible Bulyanhulu Gold Mine Limited, the Tanzanian entity operating the mine, could be repaying between $15 million and $30 million in interest and principal. Further research would need to be done to determine the company’s overall leverage. Acacia reported a net cash position of $218 million at the end of 2016, considerably better than 2015’s $105 million, so it is better placed to face loss of cash flows than it was a year ago.

Despite Acacia’s stronger 2016 performance, the ban still comes at a delicate time for the company. Market rumours persisted throughout 2016 of talks with South African firms of a possible sale of the company, whose majority position (63%) is held by Barrick Gold. When news of the ban first broke, shares in Acacia dropped 20% within a week. In the following three weeks they recovered half of that. But the impact of the ban itself is likely to make investors skittish, both in and of itself and in relation to what it might portend in terms of ongoing relations between the company and the Tanzanian government.

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How to build a robust model with almost no data

After doing the OpenOil training I had to choose a project I was interested in modeling. The joint venture between YPF and Chevron to exploit shale gas and oil in Vaca Muerta (South West of Argentina) was the obvious option. It had been all over the news, since the U.S. Energy Information Agency (EIA) had announced in 2013 that Argentina had one of the world’s largest reserves of shale gas and oil.

When I started to gather data, I realized that there was very little information, compared to the OpenOil online trainings I had watched. In the videos you could see how easy it was to find almost all relevant facts by looking into financial statements or corporate presentations. My case was nothing of the sort. Both YPF and Chevron published very little information relevant for the model, usually not more than a brief paragraph in the 20F submitted to the U.S. Securities Exchange Commission (SEC). The Energy Ministry of Argentina and Neuquen (the province where Vaca Muerta is located) didn’t help much either. I could only find aggregate production by month and year, but the rest was either too confusing for me to make sense of it, or it related to the whole of the Vaca Muerta region (instead of specifically to the project I was modeling). In essence, after a lot of time spent looking for data, I realized that other than the actual annual gas and oil production for years 2014 and 2015, my only really useful piece of information was just one slide of a corporate presentation of the project.


Only useful piece of information? A slide of a YPF presentation on Vaca Muerte

The slide merely gave totals for the project that would last 35 years: total investment of USD 16,000 million; total operating cost of USD 9,000 million; 1,500 new wells; 750 million barrels of oil equivalent (BOE); USD 8,500 million royalties for the province, and the peak rates for oil (50,000 barrels per day) and for gas (3 million cubic meters per day). After that, with the help of Iliusi Vega from OpenOil and Aleph (see her blog), I was able to find local prices for gas and oil in some financial statements, although I then had to understand the details and calculations for the subsidies for both gas and oil.

With the help of OpenOil’s expert Alistair Watson, we constructed a first sketch of the model, trying to simulate the total gas and oil production to reach a total of 750 million BOEs based on the peak rates and the actual production for years 2014 and 2015. With this data and the value of royalties and the price of gas, we were able to estimate the oil price envisioned by the company when the joint venture took place. As for wells and development costs, there was little (and contradictory) information about the number of drilled wells, and how many were vertical and horizontal. Therefore, we only used bulk numbers of development costs based on some company press releases about the investment during the 2013-2014 pilot project and the investment expectations within the next years.

It was then time to construct the model, for which I used a template from another oil project. Being a lawyer it was indeed challenging to understand the model, its different sheets, relationships, formulas, and to adapt the template to the specifics of my project: adding gas production, special gas subsidies, different oil subsidies, and preparing different scenarios (depending on the time the peak was reached, the availability of subsidies, gas and oil prices, etc).

When I got to Berlin’s Sprint, my stage 1 model (pre fiscal cash flows) was almost done, and I was half way through stage 2 (the fiscal regime). After really intense – but fun – days of work, feedback from experts and peer reviews with other learning-modelers, my model was finally done.


Click on the chart to download a copy of the full model for your own use, under CC-BY-SA 4.0 license

Seeing the dashboard, which summarized all of the model’s results and allowed me to instantly see the impact on production, cash flows or internal rate of return upon any change to parameters… it was simply magical. Given the many factors influencing my model (peak rate and duration, availability of subsidies, prices, operating and development costs, all applicable both to gas and oil), OpenOil’s expert Alistair Watson taught me how to create scenario and sensitivity analyses, where we could model changes to many of the parameters all at once. This way we could see how results changed from the base scenario, to the price and production reality of 2016 and then to predict how results would change under more favourable scenarios, or upon changes to the fiscal regime (including the repeal of subsidies).

Upon returning from Berlin, I got in touch with people from YPF who informally agreed to look at my model and let me know if any of my parameters was way out of line. I was happy to find out that they broadly validated the model, in spite of the development cost assumptions not related to the drilling of wells (I didn’t have enough information to model this). They suggested to fix some parameters related to the fiscal regime and the peak duration, both of which took a few minutes to change. They suggested different parameters for cost reduction (in an optimistic future scenario). It took me just a few seconds to change those values.

All in all, this was a great learning experience, that proved the usefulness of modeling, which goes beyond the extractives industry. It allowed me to ask the right questions, to question basic assumptions or claims which contradict the model, and I hope that it will provide a useful tool now that the fiscal regime and the cost of subsidies of the gas and oil sectors seems to be under discussion.

The financial model and narrative report are available here.

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Vaca Muerta-Loma Campana model

The Vaca Muerta-Loma Campana project is an unconventional oil & gas development located in the southwest of Argentina. It started operations in 2013.

Developing the latent Stage 2 model from solely total figures was a challenge Andres Knobel successfully went through.

This page contains the Vaca Muerta-Loma Campana Fiscal Model, the accompanying narrative report, and a short-form presentation.

Main findings:

● With current low prices the project is not viable without significant subsidies from government (for gas) and Argentinian consumers (for oil).

● If cost savings exceed 25% (versus 2013 predictions), the project may be viable without subsidies.

● Government should not contemplate fiscal regime concessions unless it is clear these are necessary to make an otherwise unviable project stay in business.


Click the file to download the narrative report.

For media inquiries:
Johnny West, Director,
Olumide Abimbola, Head of R&D,

Vaca Muerta-Loma Campana Project Fiscal Model


Click on the chart to download a copy of the full model for your own use, under CC-BY-SA 4.0 license.

Supporting documents:

Interview with Andres Knobel, the modeler

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El proyecto estrella de shale en Argentina implicaría miles de millones de dólares en subvenciones

Fuera de Estados Unidos, el mayor proyecto de shale en el mundo se ubica en la cuenca de Vaca Muerta, en Argentina. Este no es viable bajo las condiciones de mercado y suposiciones vigentes, a menos que el gobierno de Argentina esté dispuesto a mantener subvenciones en petróleo y gas de hasta 9 mil millones de dólares americanos (mmd).

Este es el principal resultado del modelo desarrollado por OpenOil y Andres Knobel, abogado y representante de la Tax Justice Network (Red para la Justicia Fiscal) en Buenos Aires.

El proyecto fue desarrollado por Chevron y YPF en 2013, cuando el precio del petróleo estaba por encima de los 100 dólares. Tres años después, a menos de que haya enormes subvenciones, el proyecto parece inviable bajo las suposiciones originales.

El panorama de este enorme proyecto de shale en Argentina necesariamente afectará la percepción del mercado en cuanto a otros yacimientos petrolíferos fuera de los Estados Unidos. Argentina posee las segundas reservas de gas shale más grandes del mundo y es el único país, además de China, con una producción significativa de shale fuera de América del Norte.

Vaca Muerta es un caso fascinante de cómo las políticas energéticas y la viabilidad de un proyecto pueden coincidir en un punto y chocar en otro.

Sucesivos gobiernos en Argentina han intentado garantizar la seguridad energética. Resulta interesante que, en un país que repetidamente ha sufrido severas divisiones ideológicas, esta política haya sido mantenida tanto por grupos de izquierdas como de derechas. Las importaciones de energía –incluyendo gran cantidad de gas del vecino Bolivia– han impactado de forma significativa al peso, debido a las grandes cantidades de dólares necesarios para su compra.

El anuncio de la Administración de Información Energética de Estados Unidos (EIA) sobre los recursos estimados de gas y crudo shale en Vaca Muerta galvanizó la política energética. Como indica el reporte que acompaña a este modelo, Chevron y YPF llevaron a cabo un proyecto piloto en la región, con un coste por encima de los mil millones de dólares. Con este, declararon el inicio de un masivo proyecto que implicaba una inversión de 16 mmd, la excavación de 1500 nuevos pozos, la producción de 750 millones de barriles equivalentes de petróleo (bep) y regalías de 8.5 mmd para la provincia de Neuquén.

Pero entonces colapsó el mercado.

El acuerdo original había fijado precios para gas y petróleo. En el caso del petróleo, en 65 dólares por barril. Esto ahora implicaría una subvención de 15 dólares por barril producido fuera del campo petrolífero. Si a esto se añade una subvención al gas por 4 mmd, las subvenciones conformarían la mayor parte de los flujos de caja positivos del proyecto.

Por supuesto hay múltiples fuentes de incertidumbre. Los precios pueden subir –aunque nadie está a la expectativa–, pero estos necesitarían subir considerablemente. Se habla de que las compañías podrían alcanzar los mismos niveles de producción que en 2013, recortando los costes operativos y el coste de capital un 25%, o más. También se argumenta que la tecnología para la extracción de shale mejora constantemente. Desde el punto de vista del inversionista, la tasa de descuento aplicable siempre es debatible. Con base en normas generales de la industria, hemos considerado una tasa de descuento de 10% en la mayor parte del análisis, pero Wood Mackenzie recientemente usó una tasa de descuento de 12.5% en un proyecto similar en la región. Mientras mayor sea la tasa de descuento en los ingresos futuros –para explicar los riesgos políticos y del mercado–, menor será la rentabilidad garantizada por los flujos de caja futuros del proyecto.

Como Andres indica en su blog, el modelo ha sido producido con muy pocos datos: un comunicado de prensa de una página, publicado por las compañías en 2013. Pero este ya ha sido presentado –dos veces– a YPF, la compañía petrolera estatal de Argentina, quien no ha refutado sus conclusiones. Creemos que este modelo es suficientemente robusto, pero por supuesto invitamos a cualquiera a comentar el modelo y sus conclusiones, así como a proveer mejores fuentes de información.

Por encima de todo, esperamos que este trabajo contribuya a la discusión sobre las subvenciones a los combustibles fósiles en Argentina. El trabajo de OpenOil es técnico, no político. No es nuestra función apoyar u oponernos a una política como la de las subvenciones en el sector energético, la cual está cimentada en las prioridades del gobierno electo y de la sociedad argentina en general. Pero sí es el trabajo de los analistas financieros intentar estimar el impacto de las políticas y negociaciones de contratos: ponerles cifras. Actualmente, en Argentina hay un animado debate sobre todas las dimensiones de las subvenciones en el sector energético: qué tan asequibles son, su relación con el cambio climático, la liberación del mercado o el proteccionismo. Pero, hasta donde logramos ver, no se habla de números duros. Esperamos que el modelo de Vaca Muerta sea el primero de una serie de análisis que ayuden a fundar e informar dicho debate.

El modelo financiero y el reporte narrativo se encuentran disponibles aquí (en inglés).

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