By David Mihalyi, Natural Resource Governance Institute, and Jim Cust, the World Bank
This post originally appeared on resourcegovernance.org on November 29, 2017
Resource-rich countries tend to experience slower economic growth and more social problems than do less-endowed countries—a phenomenon dubbed the “resource curse.” But it turns out that in many cases, economic growth begins to underperform long before the first drop of oil is produced; this we call the “presource curse.”
In a recent research paper, we found that, following oil discoveries, growth systematically underperforms the forecasts made by the International Monetary Fund. For certain countries with weak institutions, the discoveries have even led to significant growth disappointments, compared with pre-discovery trends.
We propose that the presource curse is driven by elevated expectations. Expectations can in turn drive suboptimal behavior. For example, governments may be pressured by voters to embark on risky borrowing on the back of overly rosy projections.
To find out more, read our new article published in Finance and Development.
The underlying World Bank research paper provides econometric evidence of this phenomenon.
Elsewhere, we discuss how expectations of resource wealth drove policy making in Ghana, Lebanon, Mongolia, Mozambique and Sierra Leone. On the other hand, Tanzania is an example of a country that so far has avoided the presource curse.
Our brief on premature funds discusses the risks of governments creating sovereign wealth funds in countries when resource revenues are small, distant or uncertain.
David Mihalyi is an economic analyst with the Natural Resource Governance Institute.
Jim Cust is an economist in the Office of the Chief Economist, Africa, at the World Bank.
By Johnny West, OpenOil
This post originally appeared on OpenOil.net on March 15, 2018
This analysis was referenced in an April 8, 2018 Bloomberg article "Exxon Sparks IMF Concern With Weighty Returns in Tiny Guyana."
Guyana’s first and major oil deal, with ExxonMobil, produces results for the government which are outlier low, an OpenOil financial model reveals. Over the life of the project the government should expect to see from 52% to 54% of profits, compared to well over 60% in a cluster of comparable projects signed in other frontier countries.
The gap could cost the small South American country billions of dollars, as successful drilling continues apace in the Stabroek field, and recoverable reserves figures climb into the billions of barrels.
The relatively low performance of the Stabroek terms, first signed in 1999 and renegotiated in 2016, following the first significant discovery the previous year, holds under a wide variety of market and field size conditions.
There is also a significant possibility, as reserves growth gathers pace, that Exxon and its partners Hess and Nexen could achieve “super profits”, rates of return of over 25% and edging considerably higher under certain conditions, as this profit map of the project shows.
The agreement has become controversial in Guyana in the past year or so and the contract was published by the government at the end of 2017 to allow public scrutiny. The financial model and this accompanying narrative are based on that contract, as well as public statements and media reports giving details of reserves, development lead time and costs.
Even under conservative assumptions, Stabroek will transform Guyana. Government revenues could hit a billion dollars a year by 2024 – more than the entire current government budget.
The 52% Average Effective Tax Rate (also known as “the government take”) is lower than a general rule of thumb of 60% to 80% government take in oil projects, and also from a range of frontier projects in Ghana, Senegal, Papua New Guinea, Mauritania and Guinea, which were comparable at the time of signature. A more detailed description of the comparison methodology is laid out in the Annex to the narrative report.
What is significant here is to understand the role reserves growth scenarios could play in increasing company rates of return. At the currently stated field size of 450 million barrels in Stabroek, for example, the company does not reach “super profits”, defined here as an Internal Rate of Return (IRR) of 25% or more, until a price point of $75 per barrel for oil. But this field size relates only to the first stage of development of the field now underway, with first oil anticipated for 2020. A second phase is now under active consideration by the companies, with a projected production plateau which could be twice as high as in the first phase. If the amount of oil produced rose only modestly, compared to Exxon’s declared reserves, to 750 million barrels, the superprofit level (25% IRR) could be reached at $50 per barrel – below today’s prices. At a billion barrels, that stage could be reached with prices in the $40s per barrel.
The FAST-compliant financial model and accompanying report are part of OpenOil’s public interesting financial modelling library, part of a practise as commercial and financial analysts to governments and public policy makers.
A second stage of the model will be published in the coming weeks, to incorporate feedback from interested parties, and quantify how revenue streams could play for both investors and the government under a modified fiscal regime.
For further enquiries contact firstname.lastname@example.org
By Alexander Malden, Governance Associate; Toyin Akinniyi, Media Capacity Development Associate; Zira John Quaghe, Nigeria officer - Natural Resource Governance Institute (NRGI)
This post originally appeared on resource governance.org on April 10, 2018
On Monday, Shell released its payments to governments report for 2017, the company’s third year of reporting under the U.K.’s Reports on Payments to Governments Regulations. Nigeria national media closely covered the disclosure.
In the 24 hours since Shell published its report, six national media outlets in Nigeria--Punch, Vanguard, This Day, The Cable, World Stage and Leadership—have analyzed the news.
This level of immediate national press coverage reaffirms the importance of payments to government data to citizens in resource-rich countries, and how it is increasingly informing national debates on countries’ natural resources management.
Similar laws to the U.K.’s in Europe and Canada have come into force over the last few years to shed light on billions paid to governments around the world by oil, gas and mining companies. Greater disclosure of these financial flows can deter corruption and mismanagement in the natural resource sector.
Shell reported payments made to governments in 29 countries amounting to USD 22.4 billion in 2017. Nigeria is the largest payment recipient at USD 4.3 billion (NGN 1.5 trillion), a USD 700 million increase on the amount Shell paid to Nigerian government entities in 2016. As analysis by Vanguardnoted, this NGN 1.5 trillion figure represents 15 percent of Nigeria’s NGN 10.6 trillion total government revenue for 2017. (This total includes both oil and non-oil sources.)
Shell is the third international oil company to disclose payments to Nigerian government entities for 2017, with Statoil (USD 469 million) and Total (USD 1.15 billion) having already reported in March. Chevron, CNOOC, Eni and Seplat are all expected to disclose payments for 2017 by the end of May.
The stories written include analysis breaking Shell’s Nigeria payments up by recipient government entity (USD 3.2 billion to the Nigerian National Petroleum Corporation; USD 80 million to the Niger Delta Development Commission; USD 280 million to the Department of Petroleum Resources; and USD 765.5 million to the Federal Inland Revenue Service) and by payment type (USD 3.2 billion to production entitlement; USD 765.5 million in taxes; USD 245.7 million in royalties; and USD 114 million in fees).
NRGI has been working to promote the use of this data in Nigeria. In December, NRGI published Nigeria’s Oil and Gas Revenues: Insights from New Company Disclosures. The briefing explores how payments to governments data from Shell and six other international oil companies operating in Nigeria could be used to hold the government accountable for revenues generated from the sector.
NRGI is co-hosting a meeting with nongovernmental organization Connected Development in Abuja on Wednesday with around 20 Nigerian civil society organizations to launch the briefing. We aim to explore new ways in which civil society within the country can use this data as an accountability tool. NRGI has already been working to support Nigeria civil society organizations to use this data, including working with BudgIT, which produced infographics analyzing Shell’s 2016 payments to governments report.
NRGI also works with Nigeria-based journalists through the Media for Oil Reform Fellowship to promote the use of extractives industries data. Program fellow and Punch senior correspondent ‘Femi Asu was among the journalists who reported on the Shell disclosure.
As a new data source—most companies have reported for only the second or third time this year—it is exciting to see stakeholders in Nigeria engaging with payments to governments data as an informative and accountability tool.
By Kate Vang, Data Scientist, The ONE Campaign, and Joseph Kraus, Director, Transparency and Accountability, The ONE Campaign
This post originally appeared on www.ONE.org on January 11, 2018.
Over the weekend of November 25-26, ONE had the unique privilege of partnering up with dozens of data scientists at DataKind UK's Autumn DataDive. After years of advocating with our partners for extractive companies to publish information about their payments to governments, we finally had a large set of data on these financial flows at a granular, project-by-project level. This data is important because it helps enable citizens to demand that their country's natural resource wealth goes towards things like education, health, infrastructure and poverty eradication.
Our challenge for the weekend was to dive into this new mandatory disclosure data, alongside voluntary payment data from the Extractive Industries Transparency Initiative (EITI), and answer a set of complex questions â with the help of our volunteer, expert data wranglers. This wouldn't have been possible without the heavy lifting of the data team from the Natural Resource Governance Institute, who have scraped thousands of pages of pdf documents to build a tidy data set of mandatory disclosure payment information. Part of our objective for the weekend was to put this groundbreaking data to use, and to explore ways of making it actionable for researchers and advocates.
After 19 hours of intensive work, presentations, camaraderie and pizza-eating, here's a look at what we learned.
The data set we analysed contained details on $292bn of payments made by 499 companies, related to projects located in 135 different countries. Most of the data is for payments made in 2015 and 2016, although a smaller number are for 2014 and 2017. $44bn (15%) of the payments reported were made to governments in Africa, with the majority to Angola ($17bn) and Nigeria ($15bn). The payments relate to approximately 3,400 different extractives projects around the world, although companies vary in how they define a "project". But this was just a snapshot â the data is regularly updated by NRGI as companies make more disclosures and as new PDFs are ingested.
DEBUNKING INDUSTRY CLAIMS WITH DATA
As we push for this data to be made available by all companies around the world, we often run into resistance and push-back from the oil, gas and mining industries. What we found in the data sheds new light on long-running debates.
One industry group, the American Petroleum Institute (API), has staunchly opposed efforts to require this payment information to be published in the US, in part because it claims that doing so would be too burdensome. But our volunteers found that several API member companies are already publishing this information in other jurisdictions. In fact, we found that API members â such as Shell, Chevron, BP, and others â have already disclosed at least $145 billion of payments to governments, many through subsidiaries. This represents nearly half of the total payments reported so far. That undermines the API'a assertions that publishing these reports under US law would be burdensome, since many are already doing it anyways as required by EU and Canadian laws.
Our analysis of the data also fully debunks another evidence-free claim advanced by the API, namely, that four countries (Angola, Cameroon, China, and Qatar) would prohibit them from disclosing payments and punish them if they did. Guess what? Five of the largest API members we identified in the data have collectively reported payments of nearly $20 billion to those countries, without experiencing any negative effects. The data undermines their claims that publishing this information was prohibited or would cause them harm.
Some opponents of this data also claim that publishing the information would put them at a competitive disadvantage to state-owned competitors, on the assumption that state-owned companies would not need to report their payments to their own or other governments. However, the data shows that this is simply not the case: state-owned companies account for 2 in 5 of the total payments reported to date. These include several large state-owned companies from countries like Russia (e.g. Gazprom and Rosneft) and China (e.g. CNOOC) that are hardly models of transparency, as well as Norway (Statoil). (See Figure 1).
Figure 1: Many of the companies with the largest reported payments are state-owned.
VISUALISING PAYMENT FLOWS
A team of volunteers also set out to visualise the data in ways that would make it more actionable for activists and journalists. In doing this, we found that the mix of payment types varies widely across company and recipient government â and that there were differences in the mix of payments made to African governments vs. non-African governments.
One team focused on payments to Africa and visualised the payments from the top 20 companies in an interactive Sankey diagram (Figure 2). Doing so revealed that production entitlements were the largest payment type made by these companies to African governments, and that the majority of payments unsurprisingly went to Angola and Nigeria, the continent's two largest oil producing countries.
STATOILProduction entitlementsROYAL DUTCH SHELL PLCTOTAL SATaxesBP PLCENIRoyaltiesSEPLAT PETROLEUMANGLO AMERICAN PLCCHEVRON CANADA LIMITEDBG GROUP LTDFeesGLENCORE PLCPayments for infrastructure improvementsRIO TINTO PLCRANDGOLD RESOURCESCHINA PETROLEUM CHEMICAL CORPORATIONFIRST QUANTUM MINERALS LTDCEPSABonusesIVANHOE MINES LTDNORD GOLD SETULLOW OIL PLCACACIA MINING PLCLUCARA DIAMOND CORPCould not be identifiedDividendsAngolaNigeriaLibyaAlgeriaCongoEgyptSouth AfricaGabonEquatorial GuineaDemocratic Republic of the CongoZambiaMaliTunisiaChadBotswanaTanzaniaBurkina FasoGhanaUgandaGuineaCÃ´te d'IvoireNamibiaMauritaniaZimbabweMoroccoKenyaMadagascar
Figure 2: A Sankey diagram showing payments made to African governments from the 20 largest paying companies.
When we plotted the same companies' payments to governments outside of Africa, the picture looked different: taxes represent a larger share of the payment mix (see Figure 3). This reveals an interesting issue that merits further exploration. Production entitlements often flow to state-owned entities in the form of in-kind payments (e.g. barrels of oil). While this can be a legitimate arrangement, state-owned entities can be notoriously opaque, particularly in Africa, where several such companies have come under scrutiny in recent years for misplacing or mismanaging billions in revenues. The revelation that these types of payments are more extensively used in countries like Angola and Nigeria, where state-owned oil companies are particularly secretive and scandal-prone, highlights the importance of more closely examining these types of payments to ensure that they are handled appropriately.
STATOILTaxesROYAL DUTCH SHELL PLCProduction entitlementsBP PLCTOTAL SARoyaltiesRIO TINTO PLCFeesBonusesCHEVRON CANADA LIMITEDCould not be identifiedGLENCORE PLCBG GROUP LTDCHINA PETROLEUM CHEMICAL CORPORATIONENIANGLO AMERICAN PLCNORD GOLD SEFIRST QUANTUM MINERALS LTDPayments for infrastructure improvementsCEPSATULLOW OIL PLCACACIA MINING PLCNorwayAzerbaijanUnited Arab EmiratesMalaysiaAustraliaOmanIraqUnited States of AmericaIndonesiaQatarBrazilThailandKazakhstanPhilippinesDenmarkChina (People's Republic of)CanadaRussiaTrinidad and TobagoArgentinaBoliviaIndiaItalyBrunei DarussalamColombiaMyanmarMongoliaIranNew ZealandPakistanViet NamPeruTurkmenistanUnited Kingdom of Great Britain and Northern IrelandSpainYemenTimor-LesteNetherlandsChileEcuadorCyprusFranceIrelandBulgariaVenezuelaTurkeyGermanyJordanPanamaFinlandFees -> United Arab EmiratesPayments ($bn):2.222
Figure 3: A Sankey diagram that shows the same companies as the previous, but now reflecting the payments they made to governments outside of Africa.
Maps also featured at the Data Dive as the data experts attempted to link project-level data to individual concessions using OpenOil's Concession Map. In time, this could be a great way for activists to explore this new payment data. However, more work will be needed in cleaning the project names so that we can cleanly link them to individual concessions.
The volunteers also tried using machine learning techniques such as clustering to identify patterns in the types of payments that companies make to governments. Clear patterns emerged (see Figure 4), so we think that this approach could eventually become a tool to help researchers to spot "red flags" in the data.
Figure 4: An example of clustering analysis of the payments data.
Another team of volunteers worked with Alex Malden of NRGI to explore in-kind payments. This partially included the production entitlements described earlier, but also meant analysing the free-text notes and annotations that companies use to describe the payments data.
For example, ENI's 2016 Report on Payments to Governments shows taxes and royalties paid to Libya's National Oil Corporation (see Figure 5). Footnotes on these payments explain that at least part of these payments were made as direct transfers of oil instead of cash.
Figure 5: Excerpt from ENI's 2016 Report on Payments to Governments (PDF) showing notes about in-kind payments.
Over the weekend, volunteers developed a provisional methodology to flag line items in the data that refer to in-kind payments. Using this methodology, they estimated that roughly 20% of all payments reported in the mandatory disclosures are made in-kind. This equates to roughly $80 billion of value â a huge number that highlights the urgent need for more transparency on the volumes and transfer pricing of non-cash payments. We also found that the share of in-kind payments varied significantly from one receiving government to the next. Doing further work to perfect this methodology will allow investigators to target their investigations to the areas most susceptible to corruption.
LINKING THE DATA
While this new data tells us a great deal, we think its real potential will be realised when it is combined with other available data â such as data from the Extractives Industry Transparency Initiative (EITI), commodity data, budget data, financial statements, corporate ownership data, contracts, and more. So part of our exploratory work at the Data Dive involved trying to build methodologies to link the information from the mandatory disclosures to these other data sources. This proved to be difficult, but in the process we learned a lot about the specific challenges we face and the next steps to overcome them as a community.
One team focused on the EITI data, with the aim of linking individual companies between it and the mandatory disclosures data. Why did we hone in on this data? In short, we think that finding a way to combine them could result in a more comprehensive picture of extractives payments. EITI member countries submit annual reports that detail the payments their governments receive from extractive companies. In essence, the information provided through this process is similar to what companies are supposed to report in the mandatory disclosures, particularly going forward since EITI countries will soon begin reporting project level data. But since neither EITI nor the mandatory disclosures are yet implemented globally, the two data sets each reflect a different, overlapping patchwork of countries and companies. Linking them together would enable us to compare two different accounts of the same underlying system.
âUsing text matching tools on company names, the team was able to find 35 companies from the mandatory disclosures in the EITI data. While this number was small, the matched companies accounted for over 40% of the total financial flows in the mandatory disclosures. These overlaps can now be analysed in further detail to check for validity and consistency.
But we also saw very clearly what we were missing: a tidy dataset of company ownership information. The entities reporting the mandatory disclosures were predominately large parent companies while the entities reported in the EITI data were usually smaller, local operating subsidiaries. We used text analysis of the company names to link some of these together, but we know this method left a lot of connections uncovered. The next step would be to locate information from the larger parent companies about their subsidiaries and build a comprehensive ownership dataset, which could then be used to decisively connect the two data sets. We look forward to continuing this work with OpenOwnership and the wider community of partners.
Linking companies solely through text matching proved to be a messy and time-consuming process. So a team also worked on connecting the EITI data to OpenCorporates, which maintains a vast data set of corporate entity information organised with unique corporate IDs. At first we were able to make exact matches on 230 names, which represented c.23% of the total flows reported in the EITI data. After a lot of cleaning and fuzzy matching the volunteers found matches for 600 names, which correspond to c.28% of the financial flows. We would love to share our code and learning from this work with others who are keen to help take it forward. This work also highlighted the value of Legal Entity Identifiers being incorporated in all company reporting, as our research would have been much easier if we could easily identify and link unique corporate entities.
As a community, we are still at the beginning of a journey to maximise the potential of data about governments' natural resource revenues. But the DataDive was an energetic, whistle-stop tour of a groundbreaking data set: we left with a deeper understanding of what the data meant and feel inspired by the new questions and possibilities that volunteers unlocked. In coming weeks we will publish further detailed documentation of the work done at the dive, along with links to code. Please contact Kate Vang or Joseph Kraus with questions, contributions, or to discuss anything in more detail.
âAll of us at ONE give huge thanks to DataKind UK, to NRGI and to all of the DataDive volunteers. This project would not have been possible without the incredible volunteer Data Ambassadors: Victoria Bauer, Stephen Gaw and Nick Jewell. And a special thanks to the Elsevier Foundation and University College London for sponsoring the event.
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