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Within the EITI process, the issue of beneficial ownership has gained momentum. After a successful pilot phase to which 11 countries voluntarily signed up, the 2016 EITI standard now requires all 51 implementing countries to ensure that companies disclose their beneficial owners. These are early days, however, and so far reporting on beneficial ownership is showing significant gaps, as well as a high degree of variance in the information that is disclosed.
So what can you do to find out about the owners of a company, if the information is not yet available in an EITI report? And even if it is, how can you verify that the information provided is actually correct? Financial regulators in many countries already require companies to disclose information on their shareholders and subsidiaries, so that our corporate filings database Aleph can help you to find it – and here is how. 1) Who is controlling Kansanshi Mining PLC? Let’s have a look at Zambia’s 2014 EITI report. On p.15, we find a chart with the top 5 payments to government by operating company. We see that combined, they make up for 70% of all payments to the government of Zambia. Of special interest is Kansanshi Mining Plc, which alone accounts for 32.86% of the payments. So what is the parent company of Kansanshi Mining Plc? Since we are dealing with the 2014 report, we will try to find a filing from 2014. Example Aleph search term “Kansanshi mining Subsidiaries 2014″~50
The search leads us to First Quantum’s annual information form for 2014, filed to the Canadian Stock exchange authority’s filing system SEDAR. On page 5, the report includes a hierarchical table with its subsidiaries. Here, we learn that First Quantum has an 80% interest in Kansanshi Mining Plc. Other top-players from the EITI report are also included in the list. Kalumbila Mines Ltd belongs to First Quantum, which has a 100% interest in the operation, as well as First Quantum Mining and Operations Ltd. But to whom do the other 20% of the Kansanshi operation belong? Further down on p.15, the project has its own section. First Quantum states that the other 20% of Kansanshi are owned by a subsidiary of Zambia’s state-owned ZCCM.
In this case, we have been able to confirm the information provided by the ZEITI report, which includes a list of beneficial ownership structures on p.128.
2) Mopani Copper Mines Plc Things become more interesting, however, if we look at another company in the Zambia EITI report: Mopani Copper Mines Plc, the third largest contributor to government payments. Trying the exact same search terms in Aleph will lead to a miss. So we adjust them: Example Aleph search term “Mopani Copper Mines Subsidiaries”~100
The new search leads us to reports from mainly two companies; Glencore Plc and Katanga mining Ltd. Since we are looking for the ultimate owner, Glencore is a more likely candidate, because it is a multinational enterprise. A 2014 report is included, with a listing of ownership structures. On p. 186, we read that Glencore has a 73.1% interest in the Mopani Copper Mines Plc. This time, the information provided by the Glencore report does not match ZEITI’s information. According to the latter, Mopani copper Mines Plc is owned by 73.1% by First Quantum, 16.9% by Glencore Xtrata and 10% by a ZCCM subsidiary. Strikingly, we have the exact same figure for the majority owner, namely 73.1%. It looks as if the ZEITI report confused both companies.
To summarise: using our Aleph database helped validating the figures stated in the EITI report, and in the second case, it even helped identifying mistakes. In other cases, the database can help filling out missing information about stakeholders. However, it is also important to note that the availability of information depends on the respective financial regulations. Keeping this in mind, the Aleph database proves to be a powerful tool to complement existing research as well as to support access to publicly available data on intercompany ownership structures. Of course, all this is Aleph working networks of corporate affiliation structures – so it is not yet leading to the ultimate beneficial owners, which will always be a natural person. We will follow that up in a separate post.
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By Miles Litvinoff, Publish What You Pay - United Kingdom This post originally appeared on www.PublishWhatYouPay.org on June 3, 2016 In April 2016 Royal Dutch Shell became the first United Kingdom-incorporated oil, gas or mining company to submit its report to the UK company registrar Companies House under the UK’s 2014 Reports on Payments to Governments Regulations (as amended 2015). Shell’s report discloses details of payments made in 2015 to government bodies in 24 countries amounting to $21.8 billion. Shell’s first mandatory payments report marks a major new stage in the global campaign for extractive industry transparency and accountability, and an important step forward for open data corporate disclosure. Shell’s global footprint It is encouraging for transparency campaigners to see Shell report under the new UK mandatory transparency regime. Shell’s 2015 report differs from its previous voluntary disclosures by including for the first time, as required by EU and UK law, disaggregated project-level data (which it previously argued against) and by reporting on payments of €100,000 or more made in every country where it operated during the year, including China and Qatar (where it formerly claimed reporting might be problematic for legal reasons). Initial analysis of the size and distribution of Shell’s payments by country helps reveal the company’s global footprint: The rest of this blog considers the importance of open data in making payment reports such as Shell’s more accessible and useful to citizens and civil society, and suggests a few questions potentially worth pursuing.
Open data Besides reporting in a conventional pdf format document on its own website, Shell has provided the payment information in open data format using the UK company registrar’s XML schema. Open and machine-readable data reporting under the UK regulations enables the public to export the data in four zipped CSV files for analysis (more on CSV/Comma Separated Values files here). Shell is the first extractive company to disclose its payments to governments in compliance with the UK’s open and machine-readable data reporting requirements. Statoil (Norway), Total (France) and Tullow (UK) all published their payments to governments in March 2016 but not as open data. The UK Department for Business, Innovation & Skills (BIS) imposed the open data reporting requirement for UK-incorporated companies as a result of advocacy led by PWYP. The rationale for open, machine-readable extractive industry data was recognised by the G8 countries in their 2013 Open Data Charter: “Open data sit[s] at the heart of this global movement [for “more accountable, efficient, responsive, and effective governments and businesses”] … [P]eople can use open data to generate insights, ideas, and services to create a better world for all … Open data can increase transparency about what government and business are doing … about how countries’ natural resources are used, how extractives revenues are spent … All of which promotes accountability and good governance, enhances public debate, and helps to combat corruption.” London Stock Exchange-listed extractive companies that are not UK incorporated, such as Gazprom, Glencore, Rosneft, Sinopec, Statoil and Total, are currently exempt from the UK’s open data reporting requirement. But this will change for financial years starting in 2016, in line with the UK’s current consultation (see PWYP UK’s response here) and third OGP National Action Plan (2016-18). Colleagues at the Natural Resource Governance Institute (NRGI) have, however, rendered Total’s payment report on 2015 into open data here and provided a handy visualisation. Using the data files Three of Shell’s four open data CSV files – “government payments”, “government totals” and “project payments” – identify the country where payments were made (using World Bank country codes). After uploading any of these three files into a Google spreadsheet, as done with “project payments” here (Insert – New sheet – File – Import – Upload), a pivot table can be produced (Data – Pivot table – Report Editor – Rows – Add field — CountryCodeList – Values – Add field – Amount) to highlight the total amount of payments for each of the 24 countries where Shell has reported: see Pivot table 1 (full country names added in column C). The pivot table can be copied and sorted to produce a ranked list of where Shell made its total government payments in 2015 (Report editor – Order: Descending – Sort by: SUM of Amount). Pivot table 2 clearly shows that Shell’s largest total payments in 2015 were made to Nigeria (US $4.95 billion), Malaysia ($4.41 billion) and Norway ($4.15 billion), followed by Oman and Iraq. The UK, at the foot of the table, was the only country where government bodies made an overall repayment to the company ($122.9 million). The table can be turned into a visual chart like the one above (in this case by exporting the data into https://public.tableau.com/s). Payments can be further analysed, for example to reveal which government body received most payments during the year. Shell’s “government payments” CSV file results in Pivot table 3, where the state-owned Nigerian National Petroleum Corporation tops the list, having received $3.607 billion or 16.52% of Shell’s total payments to governments in 2015. The US Internal Revenue Service comes at the foot of the list, having repaid $237.7 million to Shell in taxes. We can also rank projects by size of payments. Shell’s “project totals” CSV file produces Pivot table 4, where MY15001 / Sarawak Oil and Gas in Malaysia turns out to be the project generating most payments ($3.611 billion or 16.54% of the total). Further questions
This year’s introduction of mandatory disclosures in France and the UK will bring about a considerable amount of reports listing extractive companies’ payments to governments. The mandatory disclosures promise increased transparency, however, we are only at the beginning of a debate on how to best make use of the new data. One idea has already become apparent: comparing the mandatory disclosure data to EITI figures in order to find irregularities. In the context of our involvement in Publish What You Pays “Data Extractors” programme, we have simulated how such a comparison could look like and formulated a few first thoughts, as detailed in this document. In this, we try to assess how these different sources referring to the same project relate to each other, in fact, how comparable they are after all. In the following, we would like to highlight a few aspects one has to take into account when comparing the two datasets. Since the most actual EITI reports date from 2014, we needed to make sure the company reports were also covering that same year. This limited our comparison to the four companies in the Oil & Gas sector, that had both published payments to the government reports and that are operating in one of the few countries for which there already is a 2014 EITI report available. In total, we had six cases. The graph above represents the comparison between the EITI data (in blue) and the figures put forward by the companies (in red) on payments to government. In all cases, we found that the two reports had diverging figures. Deviations range from 0.84% (Mnazi Bay) up to almost 200% (Tullow in Rovuma Area 2&5). This begs the question as to why both reports fail to show the same results:
In this post, we will take an exploratory look at the data from the United States Extractive Industries Transparency Initiative (USEITI) data portal. Part of the global Extractives Industries Transparency Initiative, USEITI is an effort led by a multistakeholder group of government, industry, and civil society organizations to increase public trust through increased transparency and accountability for the extraction of publicly-owned natural resources. More information can be found at useiti.doi.gov. We will explore two datasets available on the USEITI data portal, provided by the Department of the Interior’s Office of Natural Resources Revenue (ONRR), with details on federal revenue by location. These two comprehensive datasets (onshore and offshore) contain data on the amount of federal revenue earned in every county or offshore planning area with extractive operations on federal land. The revenue data is further broken down by commodity, product, and revenue type for each year between 2006 and 2015. The datasets can be downloaded here as .xlsx or .tsv files, along with a number of other datasets related to the extractive industries in the US. Additional documentation and explanations of the data and methodology are provided as well. The analyses and visualizations in this post were done using the open-source statistical programming language R. Interactive visualizations were created using R and Plotly. Federal Revenue by Location: There are two sets of revenue by location datasets available on the USEITI data portal, one covering 2004-2013 and another for 2006-2015. In this post we are using the more recent data. According to the data, the Federal Government collected $7,499,581,883 in 2015 from extractive operations in 557 counties in 37 states and offshore operations in the Gulf of Mexico, the Atlantic and Pacific oceans, and off the coast of Alaska. The graph below shows annual federal revenue from the extractive industries both offshore (blue) and onshore (green). Annual revenues were at their lowest in years in 2015, down from a peak of nearly $23 billion in 2008.
The Federal Government collects revenues from distinct activities relating to oil, gas, and mineral extraction on federal land. These revenues fall into four categories as defined by USEITI. Additional details can be found in the USEITI 2015 Executive Summary, linked in the descriptions:
Notably, tax payments, an important source of revenue for both Federal and state governments, were not reported by all companies for the years described. Limited 2013 tax data is available in the USEITI Executive Summary document for companies that voluntarily disclosed. Additional details on tax payments can be found in the USEITI 2015 Executive Summary on page 45. Three federal agencies collect and disburse the above revenue: the Office of Natural Resources Revenue, the Bureau of Land Management, and the Office of Surface Mine Reclamation and Enforcement. Tax revenue is collected by the Internal Revenue Service. The graph below shows the fraction of total revenue that each revenue category accounted for between 2006 and 2015. Royalties account for the majority of revenue every year, usually between 70 and 90 percent. However, bonuses can also be a major source of revenue, as they were in 2008 when they accounted for over 40 percent of the total revenues collected. Use the legend to toggle which bars are visible and your cursor to see the exact amount from each revenue category in a given year (note that holding the cursor over the "Royalties" bar segment will display the total revenue for that year)
Federal revenue from the extractive industries is overwhelmingly driven by oil, gas, and coal operations: over 96% of onshore revenue comes from oil, gas, NGL (natural gas liquids), and coal operations and over 99.5% of offshore revenue comes from oil, gas and NGL operations. Royalty revenues are nearly evenly split between oil, gas, and coal production onshore in any given year. Offshore, oil production dominates the revenue stream. Check out the interactive plots below to see how royalty revenues varied between these commodities year to year. Use the legend to toggle which commodities are visible. The USEITI data on federal revenue by location includes state, county, and FIPS codes (5-digit unique county identifiers) for each onshore observation as well as detailed geographic information for offshore production areas. Using this data, we can determine which extractive commodity provided the most federal revenue from each state over the ten years from 2006 to 2015. Some states (in grey) have no extractive operations on federal land.
Going further, we can use the county information to look at the gross federal revenue from each county from 2006 to 2015. There is a wide range in these values depending on the extent of operations in a given county; a handful of counties have extractive operations that were responsible for over $1 billion in federal revenues in these ten years. Certain counties have negative revenues because, as explained on the USEITI data portal, companies can adjust and correct payments for up to seven years after a transaction takes place. If a company overpays its royalty, rent, or bonus, it is entitled to recoup its overpayment. If the overpayment and recoupment happen in different years, the recoupment will appear as a negative amount in the Office of Natural Resource Revenue’s revenue summaries.
These visualizations present some of the many ways we can use the USEITI data portal to better understand the U.S. natural resources sector. However, this federal-level data is only one piece of the puzzle. Extractive operations on federal lands accounted for only 40% of coal, 23% of crude oil, and 16% of natural gas production in the country, with the remainder occurring on state, tribal, or private lands (page 6). The data for state-level payments is often difficult to find or unavailable. We also lack project-level detail and company tax payment disclosures, invaluable data for holding companies accountable for how they profit from citizen-owned natural resources.
However, thanks to legal requirements in the European Union and Norway, companies listed or incorporated in those territories are required to report project-level payments to all government where they operate - including U.S. Federal and state governments. “Supermajor” oil and gas companiesTotal S.A., Royal Dutch Shell andBP have all reported payments to governments for 2015. They are also required to report their tax payments. We will explore these disclosures in a future post. We would also like to hear from you - so let us know in the comments if there are any visualizations or specific areas of the data you would like to see explored further. Thank you and check back again soon! |
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