ASSESSING THE EXTENT AND IMPACT OF ILLICIT FINANCIAL FLOWS IN THE WILDLIFE AND TOURISM ECONOMIC SECTORS IN SOUTHERN AFRICA Rowan Martin and Daniel Stiles
2017
ASSESSING THE EXTENT AND IMPACT OF ILLICIT FINANCIAL FLOWS IN THE WILDLIFE AND TOURISM SECTORS IN SOUTHERN AFRICA
Volume 1 EXECUTIVE SUMMARY Rowan Martin and Daniel Stiles Resource Africa ___________________________________________________________________________ TABLE OF CONTENTS Acknowledgements. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii Acronyms and Glossary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv INTRODUCTION .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 OBJECTIVES OF THE STUDY.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Illegal Wildlife Trade (IWT) .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wildlife Tourism. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wildlife Ranching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3 3 3 4
METHODOLOGY.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 RESULTS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Trade in wildlife products. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Wildlife Tourism. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Magnitude of IFFs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Mitigation of IFFs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 DISCUSSION. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Overview. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dynamics of IFFs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IFF Actors and Channels. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Specific points arising from the Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Initiatives to control IFF associated with illegal wildlife trade. . . . . . . . . . . . . . . . . . . . . Controlling IFFs associated with wildlife tourism and legal wildlife trade.. . . . . . . . . . .
14 14 15 17 18 21 22
CONCLUSIONS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 RECOMMENDATIONS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 __________________________ i
List of Tables 1. 2. 3. 4.
Illicit Financial Flows Arising from Trade in Wild Species Products 2006-2014 . . . . . . . 7 Illicit Financial Flows in Wildlife Tourism 2006-2015 . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Wildlife Tourism 2015 – Relationship to GDP in 2015 . . . . . . . . . . . . . . . . . . . . . . . . . 10 Mitigation of IFFs Arising from Trade in Wild Species Products 2006-2014 . . . . . . . . . 13 List of Figures
1. Total tourism spending in 8 Southern African countries . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2. Wildlife tourism income and IFFs – Relationship to Gross Domestic Product in 2015. . 11
REPORT STRUCTURE Because of the complexity of the economic sub-sectors under review and the substantially different nature of the methodologies employed, the Final Report has been divided into four quasi-independent stand-alone reports – Volume 1: Executive Summary (this volume) Volume 2: Illegal Wildlife Trade in Ivory from Southern Africa Volume 3: Illegal Wildlife Trade in Rhino Horn from South Africa Volume 4: Illegal Wildlife Trade in Other Selected Wildlife Species and Illicit Financial Flows in Wildlife Tourism
Acknowledgements The authors would like to thank the following people for their inputs and/or hospitality during our field trip in Zimbabwe, South Africa and Swaziland: Briggs Bomba (Trust Africa), Masego Madzwamuse (OSISA), Zimbabwe: Jeremy Brooke, Clive Stockil, Tom Milliken (TRAFFIC), Marshall Murphree, David Cumming, Patrick Mavros, Alistair Pole (AWF), Vernon Booth, Russell Taylor (WWF); Swaziland: Ted and Liz Reilly (Swaziland Big Game Parks), South Africa: Danie Pienaar and Sam Ferreira (SANParks), Jeremy and Liz Anderson, John Ilsely, Keith Madders, Julian Sturgeon and Hector Magome (Resource Africa), Michael Eustace, John and Albina Hume, Clara Boccino and Michael Murphree, Peter Oberem and Wiaan van der Linde (WRSA), Pelham Jones (PROA). Disclaimer: The findings in this report do not necessarily reflect the views of TA and OSISA.
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ii
Acronyms and Glossary Arm’s-length principle – The arm’s length principle is defined in the UN Model Double Taxation Convention between Developed and Developing Countries and the OECD Model Tax Convention. The principle states that “profits attributable to a permanent establishment are those which would be earned by the establishment if it were a wholly independent entity dealing with its head office as if it were a distinct and separate enterprise operating under conditions and selling at prices prevailing in the regular market”. This means that no artificial pricing favouring a related establishment should be made. The application of this principle is further supported by the OECD Transfer Pricing Guidelines. ATAF - African Tax Administration Forum – an OECD-sponsored initiative seeking to develop best practices among African tax administrations. It includes a Transfer Pricing Project aimed at a more effective application of the arm’s-length principle. The application of this principle is further supported by the OECD Transfer Pricing Guidelines. Automatic exchange of tax information – The sharing of tax information between countries in which individuals and corporations hold accounts. This exchange of information should be automatic and not require a request from tax or law enforcement officials in one jurisdiction to those in the jurisdiction where the account is held. Also referred to as “routine exchange,” automatic exchange of tax information is one of the five recommendations of the Financial Transparency Coalition (FTC). Beneficial owner – The real person or group of people who control(s) and benefit(s) from a corporation, trust, or account. The FTC advocates that beneficial ownership information be collected and made publicly accessible. Transparency of beneficial ownership is one of the five FTC recommendations. Capital flight – Capital flight is the residual difference between capital inflows and recorded foreign-exchange outflows. Capital inflows consist of net external borrowing plus net foreign direct investment. Recorded foreign-exchange outflows comprise the current account deficit and net additions to reserves and related items. The difference between the two constitutes the measure of capital flight. The outflows are unrecorded and may include licitly or illicitly acquired funds. Capital Flight takes two forms: the legal component stays on the books of the entity or individual making the outward transfer. The illegal component is intended to disappear from records in the country from which it originates. By far the greatest part of unrecorded flows are indeed illicit (IFFs), violating the national criminal and civil codes, tax laws, customs regulations, VAT assessments, exchange control requirements, or banking regulations of the countries out of which the unrecorded/illicit flows occur. CITES – Convention on International Trade in Endangered Species of Fauna and Flora – is an international agreement between governments. Its aim is to ensure that international trade in specimens of wild animals and plants does not threaten their survival. It accords varying degrees of protection to more than 35,000 species of animals and plants, whether they are traded as live specimens or products derived from them.
iii
CTD – CITES Trade Database – The CITES Trade Database (http://trade.cites.org), managed by the UNEP World Conservation Monitoring Centre (UNEP-WCMC) on behalf of the CITES Secretariat, is unique and currently holds over 13 million records of trade in wildlife and over 34,000 scientific names of taxa listed in the CITES Appendices. Around a million records of trade in CITES-listed species of wildlife are currently reported annually and these data are entered into the CITES Trade Database as they are received by UNEP-WCMC. CITES annual reports are the only available means of monitoring the implementation of the Convention and the level of international trade in specimens of species included in the CITES Appendices. Country-by-country reporting – A proposed form of financial reporting in which multinational corporations report certain financial data – such as sales, profits, losses, number of employees, taxes paid and tax obligations – for each country in which they operate. Currently, consolidated financial statements are the norm. This is also one of the FTC recommendations. DAFF – Department of Agriculture, Forestry and Fisheries (South Africa) ... has responsibility for the abalone fishery. FATCA - Foreign Account Tax Compliance Act – is a 2010 United States federal law to enforce the requirement for United States persons including those living outside the USA to file yearly reports on their non-U.S. financial accounts to the Financial Crimes Enforcement Network (FINCEN). It requires all non-U.S. (foreign) financial institutions (FFIs) to search their records for U.S. person-status and to report the assets and identities of such persons to the U.S. Department of the Treasury. FATF - Financial Action Task Force – An intergovernmental body housed at the OECD whose purpose is the development and promotion of international standards to combat money laundering, terrorist financing and the proliferation of weapons of mass destruction. FATF has published 40 recommendations on terrorist financing and related guidance documentation in order to meet this objective. GFI - Global Financial Integrity – is a non-profit, Washington DC-based research and advisory organization, which produces analytical reports of illicit financial flows, advises developing country governments on effective policy solutions, and promotes pragmatic transparency measures in the international financial system as a means to global development and security. IFF - Illicit financial flows – Illicit movements of money or capital that is illegally earned from one country to another. These funds typically originate from three sources in the private sector: commercial tax evasion, trade misinvoicing and abusive transfer pricing. However, other types of criminal activity can produce IFFs, which in this study include the trafficking of live animals and plants and their products, illegal international arms dealing for use in poaching, and corruption (bribery and theft by corrupt government officials) in which the proceeds end up in another country. IWT - Illegal wildlife trade – IWT consists of the buying or selling of any wild animal or plant, or its product, that was illegally acquired or, if legally acquired, was illegally sold to a third party. iv
Laundering – The term is normally associated with money, in which the proceeds of crime and corruption are transformed into ostensibly legitimate assets. Money laundering involves three steps: the first involves introducing cash into the financial system by some means ("placement"); the second involves carrying out complex financial transactions to camouflage the illegal source of the cash ("layering"); and, finally, acquiring wealth generated from the transactions of the illicit funds ("integration"). Some of these steps may be omitted, depending on the circumstances. For example, non-cash proceeds that are already in the financial system would not need to be placed. Wildlife products such as ivory could be laundered by mixing the illegally exported ivory with legal ivory held in the country of import. OECD - Organisation for Economic Co-operation and Development – is an international economic organisation of 34 countries, founded in 1961 to stimulate economic progress and world trade. It is a forum of countries describing themselves as committed to democracy and the market economy, providing a platform to compare policy experiences, seeking answers to common problems, identify good practices and coordinate domestic and international policies of its members. Rent-seeking – Rent-seeking is the extraction of uncompensated value from others without making any contribution to productivity. In the case of wildlife, this refers to poachers and traffickers who benefit by paying nothing for the land, protection and other care for wild animals (the ‘rent’), while reaping profits for themselves from the wildlife they kill (e.g. selling ivory and rhino horn). Round-tripping – Round tripping involves getting the money out of one country, say South Africa, sending it to a place like Mauritius and then, dressed up to look like foreign capital, sending it back home to earn tax-favoured profits. The problem for the home country is that native profits escape taxation this way. And instead of foreign capital flowing into the country, local untaxed capital is simply returned. Tax avoidance – The legal practice of seeking to minimize a tax bill by taking advantage of a loophole or exception to tax regulations or adopting an unintended interpretation of the tax code. Such practices can be prevented through statutory anti-avoidance rules; where such rules do not exist or are not effective, tax avoidance can be a major component of IFFs. Tax evasion - Actions by a taxpayer to escape a tax liability by concealing from the revenue authority the income on which the tax liability has arisen. Tax evasion can be a major component of IFFs and entails criminal or civil penalties. Tax havens - Jurisdictions whose legal regime is exploited by non-residents to avoid or evade taxes. A tax haven usually has low or zero tax rates on accounts held or transactions by foreign persons or corporations. This is in combination with one or more other factors, including the lack of effective exchange of tax information with other countries, lack of transparency in the tax system and no requirement to have substantial activities in the jurisdiction to qualify for tax residence. Tax havens are the main channel for laundering the proceeds of tax evasion and routing funds to avoid taxes. Also see Secrecy jurisdictions.
v
Trade misinvoicing – Trade misinvoicing refers to the intentional misstating of the value, quantity, or composition of goods on customs declaration forms and invoices, usually for the purpose of evading taxes, avoiding customs duties or laundering money. All forms of trade misinvoicing directly exploit the lack of communication between governments when goods are exported from one country and then imported into another country. The only evidence of manipulation may be a wire transfer in another country that the importing country has no hope of discovering. Transfer pricing - The price of transactions occurring between related companies, in particular companies within the same multinational group. A transfer price may be manipulated to shift profits from one jurisdiction to another, usually from a higher-tax to a lower-tax jurisdiction. This is a well-known source of IFFs, although not all forms of transfer pricing abuse that result in IFFs rely on manipulating the price of the transaction. Governments set rules to determine how transfer pricing should be undertaken for tax purposes (since, for example, the level of transfer pricing affects the taxable profits of the different branches or subsidiaries of the firm), predominantly based on the arm’s-length principle (see above). Much of the debate on tax-motivated IFFs revolves around the formulation and enforcement of transfer pricing regulations, their shortcomings and the way in which they are abused for tax evasion and tax avoidance purposes. _______________
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INTRODUCTION This report, sponsored by TrustAfrica and the Open Society for Southern Africa (OSISA), addresses the problem of substantial knowledge gaps on illicit financial flows (IFFs) in Southern Africa in the wildlife trade and wildlife tourism sectors. Specifically, it addresses the lack of indepth sectorial research including data collection, analysis of the overall financial value, patterns, actors, channels, IFF magnitude and impact of illicit flows. The explosive Panama Papers leak (ICIJ 2016; Obermayer & Obermaier 2016) that exposed the financial dealings of just one Panamanian law firm illustrated how corporations and wealthy individuals, including public officials and royal families, are able to transfer funds offshore and keep personal financial information private. While offshore business entities are often not illegal, reporters found that some of the Mossack Fonseca shell corporations were used for illegal purposes including fraud, kleptocracy, tax evasion and dodging international sanctions (Vasilyeva & Anderson 2016). The leaked documents contain identity information about the shareholders and directors of 214,000 shell companies set up by Mossack Fonseca, as well as some of their financial transactions. It is generally not against the law to own an offshore shell company, although offshore shell companies may sometimes be used for IFFs. Several African presidents and other high-level government officials and business people are included in the leaked documents (Pegg 2016) and at least 30 wildlife safari companies in Africa used offshore companies created by Mossack Fonseca (Fitzgibbon 2016). It is through the use of offshore shell companies that illegal wildlife dealers and those involved in legal tourism activities can move illicit money in seemingly legal ways. Very little previous work on IFFs has been carried out at the economic private sectorial level, and none in the wildlife and tourism sectors. This study therefore constitutes the first such attempt to develop a methodology on the gathering and analysis of data on the value of wildliferelated economic activity in Southern Africa, which includes both legal and illegal trade of live animals and plants and their respective products, trophy hunting and wildlife photographic tourism. Non-wildlife tourism is not included in the study. Over the last 30 years, Africa is estimated to have lost in excess of USD1 trillion in illicit financial flows (Kar & Cartwright-Smith 2010; Kar & Leblanc 2013). This sum is roughly equivalent to all of the official development assistance received by Africa during the same period. South Africa ranks seventh in the world with IFF losses estimated at over USD209 billion for the 2004-2013 period (Kar & Spanjers 2015). Africa would be in the position of being able to dispense with foreign aid if it could retain all of its economic production for reinvestment. Reducing IFFs may become even more important for some African countries in future years, as a recent study found that official development assistance to low-income African countries is declining (AfDB 2015). Currently, Africa is estimated to be losing more than USD50 billion annually in IFFs (AU/ECA 2016). But these estimates may fall short of reality because accurate data do not exist for all African countries and these estimates often exclude some forms of IFFs that by nature are clandestine and cannot be properly estimated, such as proceeds of bribery and trafficking of wildlife products, drugs and firearms. The amount lost annually by Africa through IFFs is therefore likely to exceed $50 billion by a considerable amount. This study aims to estimate selected examples of the wildlife and tourism component of these IFFs. 1
These outflows are of serious concern given insufficient economic growth, high levels of poverty, capital needs and the changing global landscape of official development assistance. Although African economies have been growing at an average rate of about 5 per cent a year since the year 2000, this rate is considered encouraging but inadequate. It is, for example, below the double-digit growth that has driven transformation in most of Asia. Furthermore, the benefits of this growth have mostly been limited to those at the top of the income pyramid and it has not been accompanied by a significant increase in jobs. Aside from the equity issues that this raises, it also means that this growth may not be sustainable due to social unrest. Signs of unrest are already manifesting themselves in several African countries. The global commodity surge that has contributed to Africa’s growth appears to be coming to an end (World Bank 2016), while positive macroeconomic factors such as debt-forgiveness may have a once-off effect. The resource needs of African countries for social services, infrastructure and investment also underscore the importance of stemming IFFs from the continent. At current population trends, Africa is set to have the largest youth population in the world. By 2050 the median age for Africa will be 25 years, while the average for the world as whole will be about 36 years (United Nations Population Division 2015). Infrastructure constraints also act as a brake on growth, just as do the low savings and investment rates of the continent, exacerbated by IFFs. Africa is estimated to need an additional USD30–USD50 billion annually to fund infrastructure projects (African Development Bank 2015) which could be met entirely by IFF losses if they could be stemmed with funds remaining for other development goals. Africa needs the resources from its economic output in order to finance education, health, infrastructure development and industrialization that are all necessary to produce jobs that can be filled by a healthy and well-educated population. Industrial agriculture must be developed to replace subsistence agriculture so that people can move to the cities to find work, preserving land for ecosystem services and biodiversity that are so critical for the long-term future of African economies and the wellbeing of its citizens. Wildlife and tourism depend on relatively undisturbed habitats, thus the current trend of human encroachment on wildlife territories in search of farm and grazing lands must be curtailed. IFFs are also of concern because of their impact on governance. Transferring these resources usually involves the corrupting of government officials and can contribute to undermining state institutions, since concerned actors have the resources to prevent the proper functioning of regulatory agencies. The latest Transparency International Global Corruption Report (2009) ranks 180 countries on perceived corruption level, the private sectors in the ten Southern Africa nations1 did not fare very well. Six fell below the halfway mark (Zimbabwe, Angola, Mozambique, Zambia, Malawi, Lesotho) and only four were above it (Swaziland, Namibia, South Africa, Botswana), with Zimbabwe at the bottom (170th) and Botswana at the top (36th). ________________
1.
Although there are 15 countries in the Southern Africa Development Community (SADC), we have included only Angola, Botswana, Malawi, Mozambique, Namibia, South Africa, Zambia and Zimbabwe. https://en.wikipedia.org/wiki/Southern_Africa
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OBJECTIVES OF THE STUDY We have divided the Wildlife Trade and Tourism sector into three parts, each with its own objectives and methodology –
Illegal Wildlife Trade (IWT) Wildlife is the iconic natural resource of Sub-Saharan Africa. Unfortunately, wild animals and products derived from them have become a largely illegal multi-billion dollar business in Africa. There are hundreds of different species of animals and plants that are trafficked live or in derived product form. This short-term initial study cannot possibly be comprehensive in its scope, therefore certain high-value species have been selected for data collection and analysis to provide case study examples of what would be needed to be done for a longer term inclusive study. The case study species are – Elephant (Loxodonta africa) – Live and the product ivory in the form of tusks Rhinoceros (Ceratotherium simum simum and Diceros bicornis) – Horn Lion (Panthera leo) – Live, bones, teeth, claws and skin Pangolin (Manis spp.) – Meat and scales Crocodiles (Crocodylus nilotica) – Skins and meat Abalone (Haliotis midae) – Meat and shell Sharks and Rays (members of the Class Elasmobranchii) – Meat and fins Cycads (Encephalartos spp.) – Live shoots, bulbs and seeds With each of these species the objectives are to – 1. Approximate the total production value in USD arising from the annual offtake 2. Attempt to determine the illegal portion value of the estimated production 3. Assess the quantitative value that might have been lost through IFFs 4. Identify the transfer methods, channels and actors involved in the IFFs and 5. Assess the impact on Southern African economies of the IFFs.
Wildlife Tourism Wildlife tourism includes both non-consumptive and consumptive uses of wildlife and, in many southern African localities, both activities involve local or international visitors and take place on the same categories of land – State Protected Areas, Conservancies and Game Ranches. Photographic and Recreational Wildlife Tourism This is includes all non-consumptive wildlife activities. Trophy Hunting Entails hunting primarily wild animals. The purpose of this sub-sector study is not to evaluate hunting’s economic effectiveness as a conservation tool, which has become increasingly controversial (IUCN 2016; Cruise 2016), but rather to investigate what economic component of it might be involved in IFFs. This component of the Tourism Study has not been completed due to time constraints. If additional funding is available we would like to finish the work. 3
The objectives for the two components of this subsector study are to – 1. Estimate the income generated by wildlife tourism in Southern Africa 2006 to 2015 2. Assess the quantitative value that might have been lost through IFFs 3. Identify the transfer methods, channels and actors involved in the IFFs and 4. Assess the impact on Southern African economies of the IFFs
Wildlife Ranching Wildlife ranching and the capture of wild animals for breeding and export has become big business in South Africa, exceeding USD1 billion annually (Child et al. 2012; Reilly 2014; Taylor et al. 2015) and to a lesser extent in Namibia, Zimbabwe and Zambia. Subsidiary industries of game meat and skins are associated with this economic sub-sector. Several studies have found that in semi-arid lands wildlife outperforms livestock in production (Child 1988; Kreuter & Workman 1997; Muir-Leresche & Nelson 2001). The land use value that could be generated by non-lethal farming of rhinos for their horns exceeds most agricultural returns (Martin 2013). The objectives of this sub-sector study are to – 1. Estimate the total annual USD value produced by live animal, game meat and skin sales in South Africa, Namibia, Zimbabwe and Zambia 2006-2015; 2. Assess the quantitative value that might have been lost through IFFs; 3. Identify the transfer methods, channels and actors involved in the IFFs; and 4. Assess the impact on Southern African economies of the IFFs. The Wildlife Ranching component of the study has not been completed due to time constraints. If additional funding is available we would like to finish the work.
The overall objectives of the wildlife and tourism study are to – 1. Develop methodologies for collecting and analysing data at the private sector level to estimate IFFs. This has never been attempted for the wildlife and tourism sector. 2. Combine the values of the three sub-sectors to arrive at a global value of the economic contribution of the wildlife and tourism sector to the Southern Africa economy. This will not be a complete valuation because only a relatively small sample of species is included in the IWT sub-sector and not all countries are included in the analysis of the other two sub-sectors. 3. Estimate the loss to the Southern African economy from IFFs in the wildlife and tourism sector, and 4. Assess the impact that this loss has on the Southern African economy. __________________
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METHODOLOGY Several attempts have been made to quantify the illicit financial flows (IFFs) that leave African countries and others. These include Kar & Cartwright-Smith (2008, 2010); Kar & Freitas (2011), Ndikumana & Boyce (2012) and Kar & Spanjers (2015). However, no analysis has been conducted that disaggregates IFFs from Africa by subsector and by destination country. The two main methodologies that have been employed in previous country-level IFF studies by Global Financial Integrity (GFI) and others are the World Bank Residual model and the Trade Misinvoicing model based on the International Monetary Fund’s (IMF) Direction of Trade Statistics (Kar & Cartright-Smith 2011). These methods were recently revised resulting in quite different estimates from previous studies of IFFs deriving from trade misinvoicing and leakages from the balance of payments, the two main conduits of IFFs from developing countries (Kar & Spanjers 2015). None of these methodologies are applicable to the analysis of IFFs in the Wildlife and Tourism sector, as is explained in Volumes 2-4 of this study. A variety of different methodologies must be developed to arrive at economic valuations of the various constituents of the wildlife trade, tourism and wildlife ranching sub-sectors. Likewise, different methodologies need to be employed to assess the value of the IFF component of the sub-sectors and to identify the patterns, actors, channels, and impact of illicit flows. Due to the variation in the methodologies employed, each sub-sector and topic within it includes the methodology at the respective section beginning.
Illegal WildlifeTrade
Laundering
Rent-seeking
Round-tripping
Tax avoidance
Tax evasion
Tax havens
Trade misinvoicing
Transfer pricing
At the beginning of this volume we have given a glossary of the various types of IFFs. It is necessary to examine each of the wildlife industries individually to assess how vulnerable they are to different types of IFFs. An indicative table is given below –
3
1
Wildlife Tourism
0
0
0
1
1
1
1
1
1
6
2
Abalone
1
1
1
0
1
1
1
1
1
8
3
Rhinos
1
0
1
0
0
0
0
0
0
2
4
Elephants
1
0
1
0
0
0
0
0
0
2
5
Crocodiles
1
0
0
1
1
1
1
1
1
7
6
Sharks & Rays
1
0
1
0
1
1
0
1
0
5
7
Cycads
1
0
1
0
1
1
0
1
1
6
8
Lion parts
1
0
0
0
1
1
0
1
0
4
5
RESULTS There are two main components in our analysis – 1. The trade in wildlife products from selected species; and 2. The income from tourism that can be attributed to wildlife. For both of these, we have attempted to estimate the overall value of the industry and the Illicit Financial Flows (IFFs) that may be taking place in the industry. The two components differ significantly – the first realises its income (legal and illegal) from trade in commodities and the second derives its income from the provision of services. For the first component there are two types of IFFs – IFF1 is the value of the product that is illegally taken and exported and IFF2 is the part of the legal trade where tax evasion methods give rise to IFFs. In the non-consumptive tourism component, only IFF2 applies – there is no trade in wildlife products. Trade in wildlife products We examined the commodity trade in seven species – all of which are of international conservation concern. For one of these species, our results indicated that the overall value of the industry and the IFFs were too small in Southern Africa to warrant inclusion in the results. The main trade in pangolin parts and derivatives takes place from East, Central and West Africa and very little is recorded from Southern Africa. The annual exports of live elephants are worth less than $0.25million and, of this, only $80,000 is for commercial trade. There is no illegal trade in live elephants. The results for the seven species over the period 2006-2014 are shown in Table 1 on the next page. Where there are no reliable data on the amount of the relevant product illegally exported, we have used 15% of the value of the legal exports to estimate this value (IFF1). Where there are no reliable data on the amount of the income from the legal trade which is being diverted into IFFs, we have used 10% of the value of the legal exports to estimate the value (IFF2). The sum of IFF1 + IFF2 is the amount appearing in the column IFFs. The total export trade in the products of the seven species over the period 2006-2014 was $3.412 billion of which $1.931 billion was legal and $1.481billion was illegal. From this trade, IFFs estimated at $1.643 billion were generated. The annual export trade in the products of the seven species was $379 million of which $215 million was legal and $165 million was illegal. The annual value of the IFFs generated was estimated at $183 million. Abalone meat was the highest-valued commodity with a total trade annual value of $217 million and an IFF of $94 million. The trade in rhino horn had an annual value of $48 million and an IFF of $43 million. For all the species examined this showed the highest proportion of IFF to total trade value at 89%. Annual trade in elephant ivory amounted to $67 million with an IFF of $38 million (57% of the total trade value). The table shows very clearly that where there is legal trade in the products of a species, the IFF component is considerably lower than it is for species such as elephants and rhinos where all legal trade in their commodities is banned. The illegal trade in abalone is relatively high (IFF=43% of total trade value) and we will explore this further in the Discussion and Recommendations sections. __________________
6
Table 1: ILLICIT FINANCIAL FLOWS ARISING FROM TRADE IN WILD SPECIES PRODUCTS The table is ranked in descending order of IFFs
TRADE US$
IFFs Countries included
#
Period
Species
Product
1
2006-2014
Abalone
Meat
2
2006-2014
Rhinos
3
2006-2014
4
LEGAL
TOTAL
VALUE US$
% Total
720,000,000
1,230,000,000
1,950,000,000
843,000,000
43.2
ZA NA
Horn
384,832,037
47,750,000
432,582,037
384,832,037
89.0
ZA
Elephants
Ivory
342,535,167
257,665,741
600,200,908
342,535,167
57.1
BW, MZ, NA, ZA, ZM, ZW
2006-2014
Sharks & Rays
Fins & Meat
22,125,618
147,504,117
169,629,735
36,876,030
21.7
NA, ZA
5
2006-2014
Crocodiles
Skins & Meat
8,700,000
230,000,000
238,700,000
31,700,000
13.3
BW, MW, MZ, NA, ZA, ZM, ZW
6
2006-2014
Cycads
Plants & Seeds
1,611,167
10,741,112
12,352,279
2,685,278
21.7
ZA
7
2006-2014
Lion
Body parts
1,050,000
7,000,000
8,050,000
1,750,000
21.7
ZA, BW, NA, ZW
1,480,853,989
1,930,660,970
3,411,514,959
1,643,378,512
48.2
TOTALS . . .
ILLEGAL
Trade in pangolins and live elephants were also examined but the resulting IFFs were too small for inclusion in the table Notes on the table #1
The figures given above are at the end of the section on abalone on page 27 of IFF Volume 4.
#2
The illegal trade in rhino horn from 2000-2016 was estimated at $644 million and the result given in the table was obtained by summing the years 2006-2014. The only legal trade is that derived from ‘pseudo-hunting’ of trophy rhino for the same period (IFF Volume 3, Table 8).
#3
The illegal ivory production (including worked ivory) from 2001-2015 was $622 million and the total trade was $1.535 billion (IFF Volume 2, Table 9). After adjustments for leakages, seizures and illegal worked ivory, the Illicit Financial Flow was $793 million. The result shown in the table above was obtained by summing illegal ivory production for the years 2006-2014 ($341 million) and scaling down to obtain the corresponding IFF and the legal component.
#4
The estimates for legal and illegal crocodile skin production and IFFS are given on page 20 of IFF Volume 4.
#5
The estimates for legal and illegal production of fins and meat from sharks and rays and the IFFS are given on page 31 of IFF Volume 4.
#6
Although cycad species occur in most of the southern African States (except Botswana and Namibia), the only available export data are from South Africa. The estimates for legal and illegal exports of cycads and the IFFS are given on page 35 of IFF Volume 4.
#7
Legal exports of lion body parts are estimated at about $7 million for the period 2006-2014 (IFF Volume 4 p13) and illegal exports have been set conservatively at 15% of this. An allowance of 10% of the legal trade ($700,000) has been included to cover possible tax evasion activities amongst the legal exporters.
__________________________ 7
Wildlife Tourism The total income from tourism in eight Southern Africa countries for the period 2006-2015 has been assembled from a number of sources (given below Table 11 on page 38 of Volume 4). The proportion of this overall income that can be attributed to wildlife is estimated at about 50% (Table 13 on page 38 of Volume 4). From the data it is clear that total income from tourism is increasing (Fig.1 below) and this has implications for any definitive statement of potential income and IFFs after 2015.
Fig.1: Total tourism spending in 8 Southern African countries
8
A best-fit polynomial has been calculated for each of the eight countries to obtain a predicted value for wildlife tourism income in 2016. The Illicit Financial Flows that follow from this are given in Table 2 below. Table 2: ILLICIT FINANCIAL FLOWS IN WILDLIFE TOURISM 2006-2015 LEISURE TRAVEL & TOURISM SPENDING Country
Total $bn
Wildlife $bn
IFFs $bn
Predicted Wildlife Income in 2016 $
Predicted IFFs in 2016 $
Angola
20.47
10.24
2.05
1,743,000,000
348,600,000
Botswana
10.85
5.43
1.09
752,000,000
150,400,000
Malawi
1.02
0.51
0.10
43,000,000
8,600,000
Mozambique
3.80
1.90
0.38
277,000,000
55,400,000
Namibia
11.17
5.59
1.12
972,000,000
194,400,000
South Africa
164.58
82.29
16.46
12,476,000,000
2,495,200,000
Zambia
3.49
1.75
0.35
272,000,000
54,400,000
Zimbabwe
7.00
3.50
0.70
481,000,000
96,200,000
TOTALS . . .
222.38
111.19
22.24
17,016,000,000
3,403,200,000
Notes on the table 1.
The total income from Leisure Travel and Tourism Spending over the period 2006-2015 for each of the countries shown in the first column of the table is given below Table 12 on page 39 of IFF Volume 4 and appears in the second column of this table.
2.
The wildlife income appears in the next column and is estimated at 50% of the total income (Table 13 on page 41 of IFF Volume 4).
3.
The Illicit Financial Flow over the period 2006-2015 (next column) is estimated at 20% of the wildlife income (Volume 4 page 41).
4.
The estimates for wildlife income in 2016 take into account the increasing trend in wildlife tourism income (top of this page).
5.
The Illicit Financial Flow in the year 2016 has been estimated at 20% of the wildlife income for 2016. ______________________
The total tourism earnings based on wildlife for the eight Southern African countries shown in Table 2 over the period 2006-2015 is estimated at $111.2 billion.2 Some 74% of this amount was produced by South Africa. The IFFs generated by tax evasion mechanisms were estimated at 20% of the gross income to the industry, i.e. about $22.2 billion. In the year 2016 wildlife-based tourism receipts are predicted to be about $17 billion and the IFF component of the industry will be about $3.4 billion. The wildlife tourism earnings this study over the period 2006-2014 are $95 billion 2 – an amount that is much greater than the earnings from the species examined in the wildlife commodity trade (Table 1) over the same period ($3.4 billion).
2.
This estimate does not include the income generated from trophy hunting (see last para, page 3).
9
In Table 3 below we calculate the percentages that wildlife tourism in each country contributes to the Gross Domestic Product for that country and the percentages which each country contributes to the total GDP for the eight countries ($500 billion). The results are shown graphically in Fig. 2 (next page). Table 3: WILDLIFE TOURISM 2015 – RELATIONSHIP TO GDP in 2015 GDP 2015
Leisure Travel & Tourism Spending
Percentage of Gross Domestic Product
$ billions
National GDP
SnA GDP
Country
$bn
Total
Wildlife
IFFs
Total
Wildlife
IFFs
Wildlife
Angola
102.6
3.192
1.596
0.319
3.11
1.56
0.31
0.319
Botswana
14.4
1.415
0.708
0.142
9.83
4.91
0.98
0.142
Malawi
6.4
0.096
0.048
0.010
1.50
0.75
0.15
0.010
Mozambique
14.8
0.521
0.261
0.052
3.52
1.76
0.35
0.052
Namibia
11.5
1.740
0.870
0.174
15.13
7.57
1.51
0.174
South Africa
314.6
22.767
11.384
2.277
7.24
3.62
0.72
2.277
Zambia
21.2
0.500
0.250
0.050
2.36
1.18
0.24
0.050
Zimbabwe
14.4
0.919
0.460
0.092
6.38
3.19
0.64
0.092
TOTALS . . .
499.9
31.150
15.576
3.115
6.23
3.12
0.62
3.116
Notes on the table – 1.
The figures for Gross Domestic Product are from World Bank (2015).
2.
The Total earnings for Leisure Travel and Tourism Spending in 2015 shown in the third column of the table are derived from a best-fit polynomial calculated for each of the eight countries over the period 2006-2015 to obtain a predicted value for wildlife tourism income in 2015. The data for the best-fit polynomials are given in Table 11 on page 38 of IFF Volume 4.
3.
The wildlife income is estimated at 50% of the total income (Table 13 on page 41 of IFF Volume 4).
4.
The Illicit Financial Flows (IFFs) are estimated at 20% of the wildlife income.
5.
The abbreviation “SnA” in the last column means Southern Africa. ______________________
Wildlife tourism income ($15.6 billion) is some 3.1% of the GDP for the Southern African region in 2015. The Illicit Financial Flows ($3.1 billion) are 0.6% of the regional GDP. Wildlife tourism in South Africa is 2.3% of the GDP for Southern Africa and 3.6% of its own GDP. The contribution of wildlife tourism is most significant for the arid countries in the region. In Namibia, wildlife tourism comprises 7.2% of its own GDP and, in Botswana, wildlife tourism comprises 4.9% of its GDP. Among the semi-arid countries, South Africa (3.6%) and Zimbabwe (3.2%) come next in the ranking. The statistic that wildlife tourism contributes 3.1% of Southern Africa’s GDP has to be seen as highly significant. It should influence development planning in future years. ______________________
10
Figure 2: Wildlife income and IFFs – Relationship to Gross Domestic Product in 2015 11
Magnitude of IFFs The estimates for the legal and illegal total export values of the different wildlife trade commodities covered in this study for the period 2006-2014 are summarised in Table 1. The column of the total IFF values is the sum of the illegal trade (IWT) values plus the estimated IFF arising from tax evasion in the legal trade values. Perhaps surprisingly, given the media and NGO attention given to other species, abalone turns out to produce the largest illegal trade and total IFF losses, an estimated $843 million 2006-2014. Rhino horn is second with $384.8 million and ivory third with $345.5 million. In the subsection below we examine the reductions in illegal hunting needed to bring the IFFs for these key species to an acceptable level. The other four wildlife species products operate on a much smaller scale, and it is interesting to note that not only the IFF quantity, but also their IFF proportion is much smaller than those of the much larger scale three species. Crocodiles stand out with only 13.3% IFF of total export value, considerably below the average of 48.2% for all of the studied commodities. We estimate that Southern Africa lost approximately $1.64 billion IFF during this period from a combination of smuggled products and financial manipulation to evade paying taxes of various sorts. The average is $182.2 million lost annually. This should be considered as a minimum figure. The IFF portion constitutes over 48% of the combined legal and illegal trade total, a clear signal that the current system of wildlife trade is not functioning properly. If this proportion of IFF to legal trade is maintained for all wildlife trade from Southern Africa, which includes hundreds of species products not examined in this report, the IFF loss could amount to billions of US dollars per annum. Table 2 above shows that we estimated that approximately $22.24 billion was lost in IFF in the non-consumptive Wildlife Tourism sector 2006-2015, assuming that 20% of income is lost through IFF financial manipulation involving misinvoicing and the other methods described above. The IFF losses in 2016 were estimated at $6.8 billion, and will grow in future years as tourism grows (see Figure 1). About 74% of the IFF is found in South Africa, which has by far the largest wildlife tourism industry in Southern Africa. Mitigation of IFFs We have carried out an exercise in Table 4 (two tables, next page) where the IFFs for all species are reduced to 15% of the total trade (legal + illegal).3 The first table contains the data shown in Table 1 summarised in US$millions and separated into the two components of the Total IFF. In the second table we have reduced the illegal wildlife trade (IFF1) by a fraction (F) applied to each species to give the end result that the percentage the Total IFF (IFF1 +IFF2) makes up of the Total Trade is 15%. The column showing the Total Trade value is the same in both tables. The illegal trade (IFF1) in the second table is reduced by the fraction F and this amount is added to the Legal trade. The second component (IFF2) is the assumed IFF resulting from tax evasion mechanisms and this increases because the Legal Trade increases.4 3.
In the case of crocodiles the present IFF is below 15%, so we have reduced it to 11%.
4.
... except for rhinos and elephants because all sales are conducted by government and it is assumed that there are no tax evasion IFFs in this process.
12
Table 4: Mitigation of IFFs Arising from Trade in Wild Species Products 2006-2014 Table 1 figures TRADE US$ millions
IFFs
Species
(IFF1) ILLEGAL
Abalone
720
1,230
1,950
Rhinos
385
48
Elephants
343
Sharks & Rays
LEGAL
IFF2 IFF1+IFF2 US$m US$m
TOTAL
% Total
123
843
43.2
433
–
385
88.9
258
600
–
343
57.1
22
148
170
15
37
21.7
Crocodiles
9
230
239
23
32
13.3
Cycads
2
11
12
1
3
21.8
Lion
1
7
8
1
2
22.2
TOTALS . . .
1,481
1,931
3,412
163
1,643
48.2
IFF1 adjusted by Fraction F TRADE US$ millions Fraction F
IFFs
Species
(IFF1) ILLEGAL
Abalone
109
0.849
1,841
1,950
Rhinos
65
0.831
368
Elephants
90
0.737
Sharks & Rays
10
Crocodiles
LEGAL
TOTAL
IFF2 IFF1+IFF2 US$m US$m
% Total
184
293
15.0
433
–
65
15.0
510
600
–
90
15.0
0.570
160
170
16
26
15.0
3
0.690
236
239
24
26
11.0
Cycads
1
0.575
12
12
1
2
15.0
Lion
0
0.590
8
8
1
1
15.0
TOTALS . . .
277
3,135
3,412
226
503
14.7
The significant outcome of reducing the IFF percentage to 15% is that the total IFF for all species is reduced from $1.643 billion to $503 million. The total IFFs for the three key species (abalone, rhinos and elephants) is reduced from $1.571 billion to $448 million – a saving of more than $1 billion. In the Discussion, Conclusions and Recommendation sections which follow, we argue that the requirements to achieve this reduction are, in the case of abalone industry, the development of the appropriate institutions that devolve full authority to local coastal communities and, in the case of rhinos and elephants, the lifting of the bans that are causing the illegal trade. _______________
13
DISCUSSION Overview This study on IFFs in the Wildlife and Tourism sector in Southern Africa emanated from the TrustAfrica and OSISA partnership project “Assessing the extent and impact of illicit financial flows in key economic sectors in Southern Africa”. The project seeks to address the problem of substantial knowledge gaps on illicit financial flows in Southern Africa, specifically in terms of addressing the lack of in-depth sectoral research, data and analysis on the patterns, dynamics, actors, channels, magnitude, and impact of illicit flows in the sub-region. The project focuses on the following economic sectors – 1. Mining 2. Agriculture 3. Wildlife and Tourism The initiative also seeks to contribute to the conceptual understanding of IFFs in the context of the political economy of Southern Africa. In addition, the initiative seeks to strengthen the methodology and capacity of researchers studying illicit financial flows in Southern Africa. The ultimate goal of the project is to expand the data, knowledge and analysis available to advocates and policymakers for effective responses to curb illicit financial flows from Southern Africa. Illicit financial flows are now widely acknowledged as harmful to economies all over Africa. This is particularly confirmed by the recently released Final Report of the High Level Panel on Illicit Financial Flows from Africa (AU/ECA 2016). The findings from the High Level Panel (HLP) echo calls made by civil society organizations from across the continent to view illicit financial flows as a serious threat to development in Africa and to take urgent practical policy action to stop the financial haemorrhage. An IFF ‘Research–Methodology and Project Inception’ workshop was held in Harare, Zimbabwe, 3-4 August 2015 in which the concept and workings of the study were presented and discussed by project participants and specialists in the field. It became apparent that there were serious gaps in knowledge and experience in measuring IFF at the economic sectoral level. Several attempts have been made to quantify the IFFs that leave African and other countries. These include Kar & Cartwright-Smith (2008, 2010), Kar & Freitas (2011), Ndikumana & Boyce (2012) and Kar & Spanjers (2015). However, no analysis has been conducted that disaggregates IFFs from Africa by subsector and by destination country. The two main methodologies that have been employed in previous country-level IFF studies conducted by Global Financial Integrity (GFI) and others are the World Bank Residual model and the Trade Misinvoicing model based on the International Monetary Fund’s (IMF) Direction of Trade Statistics (Kar & Cartright-Smith 2011). “GFI makes no estimate as to how much of IFFs arising from trade misinvoicing are attributable to multinational corporations or locally owned businesses. None of our data sources reveal such information” (Kar & Spanjers 2015). 14
Since the GFI and World Bank methodologies are not applicable to the analysis of IFFs in the Wildlife and Tourism sector, we developed our own methods that are able to calculate wildlife product values at a much finer level than those methods mentioned above which are employed at the gross country level. It should be understood that these methods are still in the development phase. However, we can state that assessments of wildlife commodity production quantity and value, both legal and illegal, are fairly straightforward if weights and price by unit weight are available or can be modelled. We have made production value estimates for a number of wildlife commodities, presented in Volumes 2-4 of this report. Two other methodological problems present themselves after the gross annual production valuations have been achieved. The first involves estimating what proportion of total production has been exported. An IFF by definition involves cross-border movement. For legal trade, we used statistics found in various online databases (e.g. CITES Trade, UN Comtrade, FAO, etc.) and various published reports. For illegal trade, we made assumptions, informed by research reports and expert opinion, which were incorporated into statistical models. These are all subject to refinement as additional data and information become available. We made the assumption that illegally exported wildlife commodities such as ivory, rhino horn, abalone and so on were IFFs because their respective financial values were lost to Southern African economies. The second methodological problem involves trying to estimate what proportion of legal trade exports might be subject to various types of manipulation that result in IFFs – misinvoicing, transfer pricing, round-tripping, the use of offshore tax havens and so on. These are impossible to estimate accurately in the absence of forensic examination of company records, including related companies held by the same beneficial owners, some of which might be offshore. Where we found ourselves with no measures of these illicit financial flows, we applied a fixed percentage of the value of the legal trade exports to obtain an estimate of funds lost to IFFs. This second methodological constraint is particularly relevant to estimating IFF in the Wildlife Tourism sector. We assessed the value of wildlife tourism in Southern African countries using various published sources, but there is no way to ascertain the value of the IFF proportion because this is clandestine and hidden away in thousands of company records unavailable without court orders. One can only guess at the IFF value by assuming a proportion of income that might have been unreported and sent or kept offshore. Dynamics of IFFs IFFs typically originate from three sources in the private sector: commercial tax evasion, trade misinvoicing and abusive transfer pricing. However, other types of criminal activity can produce IFFs, which in this study include illegal wildlife trade of live animals and plants and their products and corruption (bribery and theft by corrupt government officials) in which the proceeds end up in another country. Tax evasion consists of actions by a taxpayer to escape a tax liability by concealing from the revenue authority the income on which the tax liability has arisen. Tax evasion can be a major component of IFFs and incurs criminal or civil penalties. Evasion is facilitated by tax havens, which are jurisdictions whose legal regime is exploited by non-residents to avoid or evade taxes. 15
A tax haven usually has low or zero tax rates on accounts held or transactions made by foreign persons or corporations. This is in combination with one or more other factors, including the lack of effective exchange of tax information with other countries, lack of transparency in the tax system and no requirement to have substantial activities in the jurisdiction to qualify for tax residence. Beneficial owners are usually kept hidden by an offshore law firm setting up shell. Laundering is normally associated with money in which the proceeds of crime and corruption are transformed into ostensibly legitimate assets. Money laundering involves three steps: the first involves introducing cash into the financial system by some means (‘placement’); the second involves carrying out complex financial transactions to camouflage the illegal source of the cash (‘layering’); and finally, acquiring wealth generated from the transactions of the illicit funds (‘integration’). Some of these steps may be omitted, depending on the circumstances. For example, non-cash proceeds that are already in the financial system would not need to be placed. Wildlife products such as ivory or abalone could be laundered by mixing the illegal product (from poaching or government storeroom leakage) with legal ivory or abalone (trophies, preConvention or farmed) held by the exporter. Trade misinvoicing refers to the intentional misstating of the value, quantity, or composition of goods on customs declaration forms and invoices, usually for the purpose of evading taxes, avoiding customs duties or laundering money. All forms of trade misinvoicing directly exploit the lack of communication between governments when goods are exported from one country and then imported into another country. The only evidence of manipulation may be a wire transfer in another country that the importing country has no hope of discovering. Fraudulent transfer pricing refers to trade between related companies at prices meant to manipulate markets or to deceive tax authorities. For example, company A, an abalone grower in Africa, might process its produce through three subsidiaries: X (in South Africa), Y (in a tax haven, such as Mauritius) and Z (in Hong Kong). Now, Company X sells its product to Company Y at an artificially low price, resulting in a low profit and a low tax for Company X based in South Africa. Company Y then sells the product to Company Z at an artificially high price, almost as high as the retail price at which Company Z would sell the final product in Hong Kong. Company Z, as a result, would report a low profit and, therefore, a low tax. About 60% of IFF from Africa is from improper transfer pricing (Sharife 2011) but the proportion in the wildlife sector is unknown. Illegal wildlife trade consists of the concealed or mislabelled export of illegal wildlife commodities (i.e. smuggling), including live specimens. The commodities are usually acquired illegally through illicit harvesting from the wild. These commodities are equivalent to cash funds, as they are paid for by the foreign buyers. Because the income derived from these exports in Africa is not reported to the authorities and no taxes or duties are paid, they qualify as IFFs. The last form of IFF, bribery and corruption, probably is quite small. This is not because large sums of money are not generated from it, but rather because the proceeds of bribery in the wildlife sector in Southern Africa remain in the country of origin. Corrupt Parks, CITES and Customs and Excise officials and workers in the transport sector who take bribes to facilitate illegal wildlife exports most likely use the money for personal purposes in-country.
16
Under certain circumstances, particularly when high-level politicians are involved, the returns from bribery and corruption may qualify as significant IFFs. Valuable commodities such as ivory and rhino horn may be sold at low prices to foreign buyers in the country of origin and exported legally under national legislation. The buyer arranges for the true value of the export to be paid into an offshore tax haven account owned by the corrupt person. Two other dynamics that are involved in IFF are ‘rent-seeking’ and ‘round-tripping’. Rent-seeking is the extraction of uncompensated value from others without making any contribution to productivity. In the case of wildlife, this refers to illegal hunters and exporters who benefit by paying nothing for the land, protection and other care for wild animals (the ‘rent’), while reaping profits for themselves from the wildlife they kill (e.g. selling ivory and rhino horn). Southern African governments by maximising the numbers of elephants and rhinos on State lands are in effect subsidising the illegal traders. Round-tripping involves getting the money out of one country, say South Africa, sending it to a place like Mauritius and then, dressed up to look like foreign capital, sending it back home to earn tax-favoured profits. The problem for the home country is that native profits escape taxation this way. And instead of foreign capital flowing into the country, local untaxed capital is simply returned. The money could be got out of the country by misinvoicing, transfer pricing or ‘purchasing’ something that is never imported. The cash could even be carried in a suitcase or shipped in a diplomatic bag. The money is brought back in as foreign capital for investment and is untaxed. IFF Actors and Channels The dynamics described in the previous section that produce or facilitate IFFs operate both in the legal business sector and in the clandestine illegal sector, or ‘black market’, depending on whether the commodity sold is legal and legally acquired, or not. The legal sector actors consist mainly of business people, those involved in producing and selling wildlife products that are legal to sell and export, such as those reported on here deriving from lions, crocodiles, sharks and rays, abalone and cycads. There are dozens more of such products. In the wildlife tourism sector, the actors are hotel and safari camp operators, safari tour companies, private zoo, safari park and marine park owners, professional hunting companies, wildlife breeders and ranch owners, scuba diving companies and so on. The entrepreneurs commonly belong to professional associations of one sort or another and conduct business openly, ostensibly obeying rules and regulations and paying taxes. In the illegal sector, the actors do not advertise themselves and certainly do not operate openly nor do they obey the law or pay taxes on the illegal wildlife commodities. Illegal hunters (‘poachers’) do not normally themselves engage in creating IFFs – they simply provide the commodities to the IFF actors who do the exporting. Since few of these actors have been arrested in Southern Africa, it is difficult to generalize. The actors who have been caught are mainly in South Africa and consist of wildlife professionals like the ‘Groenewald Gang’, East Asian members of the Lao Xaysavang network, European ‘white knights’, independent Vietnamese and Chinese smugglers and North Korean diplomats (Hübschle 2016; Rademeyer 2016; UNODC 2016). Government officials and foreign diplomats have also been implicated in Zambia, Zimbabwe and elsewhere (Zambianwatchdog 2013a and 2013b; Nkala 2016). 17
How much vertical integration there is in these networks is not fully known. It is theoretically possible that an East Asian ‘kingpin’, as the media are fond of calling them, controls a network that operates from field poachers right up through middlemen and facilitator government officials and workers in the transport sector up to him. In the illegal sector, there are basically three channels used to export the commodity: (1) straightforward poaching and smuggled export, (2) legal sales from government or private owners (e.g. ivory or rhino horn) and the issue of export permits that are illegal under national laws5 and (3) legal trophy hunts (including pseudo-hunts)6 and exports using proper permits. In the legal wildlife sector, tax havens are the main channel for laundering the proceeds of tax evasion and routing funds as IFF to avoid taxes. The wildlife commodity producers (lion bones, crocodile skins, abalone, etc.) which engage in IFF would establish an offshore company and using the methods described above would channel funds to those offshore companies. There are now well-developed procedures for doing this. The Panama Papers revealed that at least 30 wildlife safari companies in Africa used offshore companies created by Mossack Fonseca (Fitzgibbon 2016). Since Mossack Fonseca is only one of hundreds of law firms operating in several tax haven jurisdictions, these 30 companies no doubt represent only the tip of the iceberg. We point out that it is very human behaviour to attempt to avoid or minimise taxes and, provided no national laws are broken, it is difficult to condemn the culprits. The obvious financial incentives to avoid taxes are augmented when taxpayers see their hard-earned tax payments being mismanaged by corrupt or incompetent government officials. People are much more likely to pay taxes if they see their contributions used wisely and for the good of society. The more recent global tightening of offshore transactions largely inspired by FATCA (Foreign Account Tax Compliance Act of the USA) has changed the ‘rules of the game’. Specific points arising from the Results We observed (page 6) that, where legal trade in a species was provided for in national laws and under the CITES Treaty, the illegal component of trade was generally low. The best example is provided by the trade in crocodile skins where the legal trade has virtually destroyed the illegal trade (IUCN 2016, Hutton & Webb 2002). In contrast, where trade has been banned under CITES, the illegal trade flourishes. The proportion that IFFs make up of the total trade in rhino horn is 89% and for ivory it is 57%. Far from the international trade ban improving the situation for rhinos and elephants, it is exacerbating the problem.
5.
Four of the Southern African countries are listed on CITES Appendix II and should be able to trade legally in ivory. However, constraints under CITES prevent this. If any of the four countries export ivory under permits which are legal under their national laws and the same holds true for the importing country, this study considers the transactions as legal trade. The fact that the respective governments have reported the exports/imports to the CITES Trade Database indicates that they considered the trade legal.
6.
A ‘pseudo-hunt’ describes a practice that originated with legal rhino trophy hunts carried out by some foreign hunters in South Africa. Many of the rhino horn trophies entered the illegal commodity market once they had been legally exported. However, we do not regard the export of a legal trophy paid for in South Africa as an IFF from South Africa.
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It will come as a surprise to many readers that the trade in abalone meat from South Africa is the highest-valued export of all the species products analysed (the annual value of exports – legal and illegal – is about $217 million and the estimated IFF is about $94 million). Unlike the success story shown by the crocodile trade, the abalone IFF is about 43% of the total trade – which gives rise to the question “what is South Africa doing wrong that is preventing the legal trade from reducing the illegal trade”? We do not underestimate the difficulties involved in transforming the abalone trade into a well-regulated sustainable industry benefitting the local people. De Greef & Raemaekers (2014) give an excellent analysis of the historical and current situation affecting the illegal trade – The exploitation of abalone is mired in “... violence, opportunism and plunder. The evolution of a potent criminal economy in coastal working class settlements has introduced gangsterism and drug abuse, among other social ills ... Criminal organizations exploit a range of vulnerabilities (from community to State level and above) to operate an extraordinarily organized system of exports that has thus far defied all attempts to bring it under regulatory control. A deeper set of problems – entrenched structural inequality, weak governance, and widespread institutional failure – allow this particular illicit trade, like many others, to continue to flourish. Part of the reason for the resilience of the illegal abalone fishery ... is that poaching has filled a socio-economic void left behind by apartheid, offering historically disadvantaged small scale fishers an unprecedented opportunity to earn good money from the sea. The final, crucial factor ... is the widespread frustration and disappointment at slow fisheries reform felt by residents of South African fishing communities. With the end of apartheid in 1994 came widespread optimism – encouraged by the African National Congress, which took office spreading a message of social justice and societal change – that South African fisheries would reform for the benefit of the poor. But the transformation process that began shortly afterwards proved cumbersome, constrained by economic and environmental objectives and hamstrung by a lack of capacity in the national fisheries authority. As a consequence, the expectations of many formerly disadvantaged fishers were not met, leaving a void for criminal groups to exploit (Hauck 1997; Steinberg 2005).” De Greef & Raemaekers (2014 p23) give their own prognostication for the future – “One promising area of current fisheries policy development is the recent promulgation of the small scale fisheries policy in June 2012. This policy aims to recognize traditional small scale fishers along the South Africa coast, by allocating collective use rights to identified communities, and delineating specific areas for their preferential or exclusive use. The policy is centred on the need to establish comanagement committees, whereby DAFF and fishers jointly make management decisions regarding harvesting levels, law enforcement and sanctions, and participatory research. While implementation will require improved capacity at both DAFF and local level, it is believed that devolved decision making power will instil a greater sense of legitimacy for the fisheries governance framework, and with this an increased ownership of local marine resources. The abalone resource will most likely be aggregated with other identified resources available to relevant local communities. Assistance will nevertheless be required to develop local co-management plans with DAFF and the fisher entities holding ownership rights.” 19
We are concerned that this prescription does not go far enough to address the problem. The ownership rights that local communities will enjoy under a co-management plan may not be sufficient to overcome their inherent mistrust of the government agency DAFF. Murphree (2000) has strong views on aborted devolution – a failing that is evident in most South African community institutional development. Under the South African government, devolution tends to be seen as a step-by-step process in which authority is conferred incrementally as local competencies in management are progressively demonstrated. “Show us that you can manage responsibly and then we will give you the authority to do so” is the watch phrase. However well intentioned, this places communities in a “Catch-22” position – authority is a pre-requisite for responsible management. Status provides the essential motivation for such development: clearly defined rights and responsibilities (including rights of exclusion) should be recognised as the basis for institutional evolution rather than being held out as its reward. Institutional evolution always involves experiment and, without authority, such experiments are both methodologically and substantively defective. “Experiment” means more than simple trial and error. It is a process of adaptive management which defines objectives, identifies options, selects and implements approaches, monitors results and adapts objective and action on the basis of these results in a continuous and iterative process. Rural peoples have, of course, been doing this for millennia and in doing so have provided the basis for much of what we now know about agricultural production and the uses of flora and fauna. Where local use is constrained by government regulation, communities have little room for experiment and their rôle is confined to being the providers of “indigenous technical knowledge” as an informational adjunct to “professional science.” Full devolution of authority opens up experimental space for local jurisdictions and provides a new basis for collaboration between civil and professional science. Experimental freedom conferred by devolution refers, however, to far more than the use of environmental goods for human consumption. Devolution is not simply about resources, it is about facilitating resourcefulness. It carries with it the responsibility for the organisation of management, control and self-sufficiency, and the necessity of discharging this responsibility in an adaptable manner. These attributes cannot be imposed: they must be developed experimentally in a local context and the initiating dynamic for this arises not from the anticipation of future entitlement but from the imperatives of immediate empowerment. Sequencing devolution in this manner also has the advantage of immediately incorporating considerations of time scale into the considerations of the local jurisdiction. Systemic ecologists are concerned with scale mismatches between short-term practice and management and long-term ecological processes. Temporal scale also features in debates about inter-generational equity and sustainability.” Applying this approach to a local coastal community to manage their abalone resource, would imply acceptance of the fact that a system of “Total Allowable Catch” where the quota is set by external scientists has not worked. There is very little risk in allowing local communities to set their own harvest quotas and develop monitoring systems that will assist them to modify harvests based on the hard data derived from the catch. Should a local community be unable to enforce their exclusion rights or resist pressure from illegal underworld gangs, they must be able to call on government agencies for assistance in law enforcement. 20
Initiatives to control IFF associated with illegal wildlife trade To date the major international initiatives to control IFF generated through trade have focused on the mineral and timber extractive industry sectors. Governments and international institutions have not considered wildlife sensu lato, which includes all non-domesticated animal and nontimber plants, as a resource class along the lines of minerals, petroleum and timber7. Consequently, no international initiatives similar to the Extractive Industries Transparency Initiative (EITI), the International Accounting Standards Board (IASB) and its Publish What You Pay offspring (http://www.publishwhatyoupay.org/en/activities/advocacy/accounting-standardsregulations), or the Forest Law Enforcement, Governance and Trade (FLEGT) scheme, to name just a few, have been devised for wildlife. CITES is the only international instrument that has been conceived to address trade in wildlife. Its inception principles, Articles and subsequent Resolutions and Decisions are almost totally devoid of any economic, financial or accounting standards that apply to other trade commodity classes. In fact, CITES over the years has been perverted from a Convention to prevent overexploitation of trade in wildlife species to one which aims to prevent any trade at all in wildlife species. This current anti-trade stance by CITES, crafted by an increasingly powerful coalition of animal rights NGOs, has led to the loss of billions of dollars in IFFs to Southern African economies through illegal trade, with the ivory and rhino horn trade bans being two highprofile examples. One approach to put wildlife trade on a rational footing in which the resource could be managed sustainably would be to include it under the Natural Resource Charter (NRC) (see www.naturalresourcecharter.org), adapting its Precepts to account for a renewable, biological natural resource. Currently the NRC applies only to non-renewable resources found in the mining and petroleum industries. The NRC comprises 12 precepts, most of which are highly relevant to wildlife commodities. Sustainable utilisation is a key precept to add to the current 12. Wildlife enjoys an advantage not found with the current natural resources included in the Charter – renewability. Wildlife can not only persist in its quantity, it can increase if managed properly. Of great importance, the NRC understands that there are many stakeholders concerned with a nation’s natural resources, perhaps most importantly consisting of a variety of civil society groups. All of these stakeholders – government, community groups, business interests, conservationists, NGOs, even consumers – must reach a consensus if wildlife trade is to be managed sustainably. Top down authoritarian approaches such as those employed by CITES, which issues trade bans without consulting most stakeholders, have proven disastrous for both financial flows and wildlife conservation. The Southern African states should re-examine their relationship with CITES. Participation in the Treaty is not enhancing the status of valuable species and, indeed, is the cause of Illicit Financial Flows that are depriving the rightful owners of the resource (the State, Private Sector and Local Communities) of their potential income. This is “Rent-Seeking” by the illegal users on a grand scale. Rhinos and elephants could provide the highest-valued land use for the semiarid savannas of Southern Africa but their value is being captured by criminal syndicates. 7.
Timber is derived both from natural forest and industrial plantations. The former source could be included in ‘wildlife’, but the manner in which it is controlled and managed by large multinational corporations places it structurally more in line with other mineral extractive industries. Certainly international institutions have treated timber as such.
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Controlling IFFs associated with wildlife tourism and legal wildlife trade The IFFs generated in the wildlife tourism and legal trade industries were outlined in the Dynamics of IFFs subsection (page 15). Though policy environments vary from country to country, there are best practices that all countries should adopt and promote. Examples are: Beneficial Ownership of Legal Entities - Countries and international institutions should require meaningful authentication of beneficial ownership in all banking and securities accounts in order to address the problems posed by anonymous companies and other legal entities. Information on the ultimate, true, human owner(s) of all corporations and other legal entities should be disclosed upon formation, updated regularly, and made freely available to the public in central registries. Country-by-Country Reporting - All countries should require businesses that operate both in a Southern African country and in one or more outside the sub-region to publicly disclose the revenues, profits, losses, sales, taxes paid, subsidiaries and staff levels on a country-bycountry basis as a means of detecting and deterring tax avoidance and evasion practices. Curbing Trade Misinvoicing - Trade misinvoicing accounts for a substantial majority of IFFs (Kar and Spanjers 2015). Governments should significantly boost customs enforcement by providing appropriate training and equipment to better detect the intentional misinvoicing of trade transactions. One particularly important tool for stopping trade misinvoicing is access to real-time, commodity-level world market pricing information at the point of export. This would allow customs officials to tell whether a good is significantly under- or over-priced in comparison to its prevailing world market norm price. This variance could then trigger an audit or another form of further review for the transaction. Given the greater potential for abuse, trade transactions with countries that are known tax havens should be treated with the highest level of scrutiny by customs, tax and law enforcement officials. Curtailing Abusive Transfer Pricing - The African Tax Administration Forum (ATAF) is an OECD-sponsored initiative seeking to develop best practices among African tax administrations. It includes a Transfer Pricing Project aimed at a more effective application of the arm’s length principle. All business entities, whether they are connected or independent, must sell and buy products at prices prevailing in the regular market. Money Laundering - all countries should comply with the Financial Action Task Force (FATF) standards on information sharing to combat money laundering (FATF 2016). Automatic Exchange of Tax Information – This is a G20 initiative (OECD 2012) in which automatic exchange arrangements establish a system for governments to collect information on account holders at banks under their jurisdiction and exchange such information with those account holders’ home countries, where tax may be due on income deposited in those accounts. Such a system can be used to investigate tax cheats at home and also allow authorities to determine whether individuals located in their jurisdictions have unexplained income abroad that could be the proceeds of trade misinvoicing or money laundering. When used in combination with beneficial ownership information on companies active in international trade, this information would be a powerful tool for ensuring business owners are not illicitly stashing cash overseas.
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While automatic exchange to date has occurred largely through bilateral agreements, the G20 and OECD have begun establishing a multilateral system of information exchange. We encourage the countries studied here to sign the OECD Convention on Mutual Administrative Assistance in Tax Matters, a precursor to fully automatic exchange, and encourage the countries’ revenue authorities to involve themselves in the process of establishing the new multilateral system of automatic exchange called for in the G20’s declaration and embodied in the OECD’s recently released ‘Common Reporting Standard’ for automatic exchange. Currently, only Mauritius and South Africa in SADC are included in the 108 participating countries (OECD 2017). ______________
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CONCLUSIONS IFF losses make a severe impact on the national economies of Southern Africa. The selected case studies of wildlife trade of the products of seven species groups suggests that close to 50% of the total export value constitutes IFF. If birds, reptiles, insects and other mammal and plant species are included, the losses annually would be in the billions of dollars. Add to this the forecast IFF losses of $7 billion or more per annum in Wildlife Tourism sector, we predict that at least $10 billion could be lost in wildlife-related IFF in 2017 in the eight countries covered in this study. The World Bank (2017) calculated that the total GDP in 2015 for the eight countries used in this study (Angola, Botswana, Malawi, Mozambique, Namibia, South Africa, Zambia and Zimbabwe) was $499.9 billion, or rounded off, $500 billion. If $10 billion is lost to IFF, that makes up 2% of total GDP. This exceeds the total that the Southern African sub-region receives in foreign assistance annually. It is important to the economies of Southern Africa that this high level of IFF in the Wildlife and Tourism sector be reduced substantially. The IFF in the legal wildlife trade and the wildlife tourism components can be addressed through initiatives that have already been launched, discussed above, such as the FAFT standards on information exchange and the Automatic Exchange of Tax Information. The solutions are basically financial accounting ones accompanied by effective government oversight. The losses due to illegal wildlife trade are in a sense more complicated because the causes are not universally agreed upon. IFFs in illegal trade consist of the commodities themselves. We would conclude that the only way to mitigate these losses would be to do away with trade bans, bring most species into the legal sector, and establish supply and demand regulatory systems that would ensure conservation of the species while concomitantly satisfying legitimate stakeholder interests, primarily those of communities and enterprises that live in association with the wildlife and which share common habitats. For example, the abalone trade IFF revealed in this study, averaging $80 million annually, is caused largely by institutional problems that result in the exclusion of South African Cape communities from the decision-making process in how the resource should be managed. The imposed abalone TAC (Total Allowable Catch) is made without involving the communities. With no sense of ownership they consider abalone – and other marine resources – as common property. Chinese triads have exploited this ‘tragedy of the commons’ situation to institute a well-organized illegal harvest that over-exploits the resource base, but which gains them tens of millions of dollars of illicit income annually. The gangland style turf wars, crime and payment in drugs leave the local communities broken and destitute. The international wildlife trade sector has been extremely poorly managed, due in large part to government and CITES interference in bottom-up trade management. The lost income from top down trade restrictions and outright bans has been very costly to Southern African communities and governments. We conclude that wildlife commodities should be managed along rational economic principles, using the same precepts as those embodied in the Natural Resource Charter, with the added precept of sustainable utilisation.
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Abalone could provide the shining example of a legal trade in a high-valued product managed sustainably and benefitting coastal communities but, due to institutional problems, it has not solved rights-of-access to the resource. Moreover, it now has powerful illegal syndicates and gangland wars to cope with. The principle of subsidiarity8 provides guidance for development of appropriate levels of decision-making and management of abalone (and other species) at regional, national, sub-national and local levels. We point out the diversity existing amongst the six ivory-producing southern African countries. Each country has its own unique system of management and source of ivory production and no two are identical. In some countries (e.g. South Africa) the illegal component is very small and in others it varies from moderate (e.g. Namibia) to extreme (e.g. Mozambique). Schneider (2002 p25-33) analysed the determinants that cause informal (illegal) economies to increase. The intensity of regulations (often measured in the numbers of laws and regulations) is an important factor that reduces the freedom of choice for individuals engaged in the official economy. It is particularly relevant to the influence of CITES on illegal trade. A plethora of regulations (such as CITES has developed) provide a strong incentive to operate in the illegal economy, where they can be avoided. Every measure of regulation is significantly correlated with the share of the illegal economy: more regulation is correlated with a larger illegal economy. The imposition of trade bans (to which CITES is particularly prone) actually causes an increase in the illegal economy. Governments should put more emphasis on improving enforcement of laws and regulations, rather than increasing their number. Some governments, however, prefer this policy option (more regulations and laws) when trying to reduce the informal economy, mostly because it leads to an increase in power of the bureaucrats and to a higher rate of employment in the public sector. It also gives the impression that they are doing something about the problem when they are not. The difficulties that assail the wildlife sector in southern Africa are very different from those affecting the mining sector and the agricultural sector. The bans on legal trade in ivory and rhino horn are both the cause of the illegal trade and the corruption that is associated with it. The limited successes which CITES has had in reducing illegal trade have been those where species are not listed on Appendix I of the Treaty and sustainability is achieved through self-imposed trade quotas by the individual Parties. Wildlife use has become a highly emotive issue and Western animal rights organisations are at the forefront in (a) persuading African governments to support banning of consumptive use of wildlife (e.g. trophy hunting) regardless of the effect it has on the national income and local community livelihoods and (b) persuading their own governments to support trade bans. There is a lack of scientific objectivity in this process. It appears that few of the advocates of trade bans are examining them in a comparative manner, i.e. whether they work or don’t work. The CITES record since its inception in 1975 is that they don’t work. Few species that have been listed on Appendix I have been removed from Appendix I. The United States Endangered Species Act shows the same lessons. By their very nature, trade bans exclude the possibility of sustainable use and provide the perverse incentives for overexploitation of wild resources. ____________________ 8.
First enunciated by Pope Leo X (1475-1521), the Principal of Subsidiarity holds that ‘it is an injustice, a grave evil and a disturbance of right order for a larger and higher organization to arrogate to itself functions which can be performed efficiently by smaller and lower bodies’.
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RECOMMENDATIONS IFFs in the legal wildlife trade and tourism component All Southern African countries should: !
comply with the Financial Action Task Force (FATF) standards on information sharing to combat money laundering;
!
join the Convention on Mutual Administrative Assistance in Tax Matters and establish the new multilateral system of automatic exchange embodied in the OECD’s ‘Common Reporting Standard’ for automatic exchange; and
!
require their companies to provide public country-by-country reporting so that the information can be analysed by legislators and auditors responsible for resolving the funds transfer and profit-shifting problems that such reporting will help identify.
IFF in the illegal wildlife trade component !
Southern African countries should develop policies and practices that assign ownership in some form to wildlife in order to confer tangible economic value to owners and preclude a ‘tragedy of the commons’ situation, which has proven so detrimental to wildlife conservation;
!
In cases of wildlife on State or Communal land, and where community rights can be determined, full devolution of decision-making authority and management involving quota-setting and other trade matters should be accorded to defined community groups;
!
Wildlife in its broadest sense should be recognized in the same manner as other extractive industry commodity classes such as minerals and timber, taking into consideration conservation concerns;
!
The rôle of government is to ensure a level playing field so that all trade entities can compete fairly and to provide the infrastructure and technical support needed;
!
Southern African countries should consider adopting the Natural Resource Charter as its guiding principles regarding management of wildlife trade;
!
If the NRC is adopted, Southern African Parties to CITES should communicate their common position clearly to the Convention through its Conference of the Parties and make a concerted effort for the NRC precepts to be incorporated into an amended Articles of the Convention. ____________________
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Kar D & B Leblanc (2013). Illicit Financial Flows from Developing Countries: 2002–2011. Washington, DC: Global Financial Integrity. http://iff.gfintegrity.org/iff2013/Illicit_Financial_ Flows_from_ Developing_Countries_2002-2011-HighRes.pdf Kar D & J Spanjers (2015). Illicit Financial Flows from Developing Countries: 2004-2013. Washington, DC: Global Financial Integrity. http://www.gfintegrity.org/report/illicit-financial-flowsfrom-developing-countries-2004-2013/. Kreuter U & J Workman (1997). Comparative profitability of cattle and wildlife ranches in semi-arid Zimbabwe. Journal of Arid Environments 35(1):171-187. https://www.researchgate.net/publication/ 222491335_Comparative_profitability_of_cattle_and_ wildlife_ranches_in_semi-arid_Zimbabwe. Martin R (2013). Community Rhino Farms. In: Proceedings of a workshop Community Rhino Farms held in Skukuza, Kruger National Park (KNP), 17-19 September 2013 jointly organised by SANParks and Resource Africa. Eds. Martin R, K Madders & J Sturgeon. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2851914 Muir-Leresche K & R Nelson (2001). Managing Africa’s wildlife. PERC Report 19:3. http://www.perc.org/articles/managing-africas-wildlife. Murphree MW (2000). Boundaries and Borders: The Question of Scale in the Theory and Practice of Common Property Management. Paper presented at the Eighth Biennial Conference of the International Association for the Study of Common Property (IASCP), Bloomington, Indiana, USA 31 May-4 June 2000. Nkala O (2016). Zim babwe: Hwange - How to Steal an Ivory Stockpile. 27 April. The Standard Oxpeckers Investigative Environmental Journalism. http://allafrica.com/stories/201605020353.html. Ndikumana L & JK Boyce (2012). Capital flight from sub-Saharan African countries. PERI Research Report, Amherst, MA, University of Massachusetts Amherst, Political Economy Research Institute. ttp://ayyaantuu.com/wpcontent/uploads/2012/10/ SSAfrica_capitalflight_Oct23_2012.pdf Obermayer B & F Obermaier (2016). The Panama Papers: Breaking the Story of How the Rich and Powerful Hide Their Money. Oneworld Publications, London. Organization for Economic Co-operation and Development (OECD) (2012). Automatic Exchange of Information: What it is, how it works, benefits, what remains to be done. OECD, Paris, France. http://www.oecd.org/ctp/exchange-of-tax-information/AEOI_FINAL_with%20cover_WEB.pdf Organization for Economic Co-operation and Development (OECD) (2017). Jurisdictions Participating in The Convention On Mutual Administrative Assistance in Tax Matters: Status – 3 January 2017. http://www.oecd.org/tax/exchange-of-tax-information/Status_of_convention.pdf . Pegg D (2016). Panama Papers reveal scale of offshore firms' African operations. The Guardian 25 July. https://www.theguardian.com/world/2016/jul/25/panama-papers-reveal-scale-of-offshore-firmsafrican-operations. Rademeyer J (2016). Tipping Point: Transnational organised crim e and the ‘war’ on poaching. Global Initiative against Transnational Organized Crime, Geneva. Reilly B (2014). Game Ranching in South Africa: Biodiversity Conservation or Agriculture? Presentation at the 8th International Congress for Wildlife and Livelihoods on Private and Communal Lands: Livestock, Tourism, and Spirit, Estes Park, Colorado, September 7-12, 2014. Sharife K (2011). 'Transparency' hides Zambia's lost billions. Aljazeera. http://www.aljazeera.com/indepth/opinion/2011/06/20116188244589715.html
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Schneider F (2002). Size and measurem ent of the informal economy in 110 countries around the world. Paper presented at Workshop of Australian National Tax Centre, ANU, Canberra, Australia, July 17, 2002. Steinberg J (2005). The illicit abalone trade in South Africa. Occasional Paper No. 105. Institute for Security Studies, South Africa. Taylor A, P Lindsey & H Davies-Mostert (2015). An Assessment of the Economic, Social and Conservation Value of the Wildlife Ranching Industry and its Potential to Support the Green Economy in South Africa. Endangered Wildlife Trust, Johannesburg. Transparency International (2009). Global Corruption Report. http://www.transparency.org/research/gcr/. UNODC (2016). World Wildlife Crime Report: Trafficking in protected species. Vienna, UNODC. https://www.unodc.org/documents/data-and-analysis/wildlife/W orld_W ildlife_Crime_Report_2016_final.pdf
United Nations Population Division (2015). World Population Prospects 2015 Revision. United Nations Population Division. https://esa.un.org/unpd/wpp/Publications/Files/WPP2015_DataBooklet.pdf Vasilyeva N & M Anderson (2016). News Group Claims Huge Trove of Data on Offshore Accounts. The New York Times, 3 April. http://www.nytimes.com/aponline/2016/04/03/world/europe/appanama-papers.html. World Bank (2016). Africa: Low Commodity Prices continue to impede growth. Press release, 16 April. http://www.worldbank.org/en/news/press-release/2016/04/11/africa-low-commodity-pricescontinue-to-impede-growth World Bank (2017). http://data.worldbank.org/indicator/NY.GDP.MKTP.CD . Zambianwatchdog (2013a). Masebo tells police to probe ivory diplomats but say nothing about army commander. 11 June https://www.zambianwatchdog.com/?s=ivory&x=-1025&y=-163 Zambianwatchdog (2013b). GBM linked to elephant tusks confiscated at airport. 13 June. https://www.zambianwatchdog.com/gbm-linked-to-elephant-tusks-confiscated-at-airport/.
________________
29
ASSESSING THE EXTENT AND IMPACT OF ILLICIT FINANCIAL FLOWS IN THE WILDLIFE AND TOURISM SECTORS IN SOUTHERN AFRICA
Volume 2 Legal and Illegal Wildlife Trade and Illicit Financial Flows in Ivory and Live Elephants Rowan Martin Resource Africa ___________________________________________________________________________ TABLE OF CONTENTS INTRODUCTION. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 ELEPHANTS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Continental Numbers and Distribution. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Approach to the Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Ivory Prices. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Trade in raw ivory for the individual southern African countries. . . . . . . . . . . . . . . . . . Trade in worked ivory. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Worked Ivory Seizures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Export of live elephants. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
10 13 19 21
Illicit Financial Flows. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 Possible Illicit Financial Flows detected by forensic auditing. . . . . . . . . . . . . . . . . . . . . . 30 Conclusions.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
_______________
Appendices 1. 2. 3. 4. 5. 6. 7.
Ivory Prices. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Zimbabwe Elephant Population. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Botswana Elephant Population. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Mozambique Elephant Population. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Namibia Elephant Population. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The South Africa Elephant Population. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Zambian Elephant Population . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
33 36 47 50 53 56 59
List of Tables 1. 2. 3. 4. 5. 5A. 5B. 6. 7. 8. 9. 10.
Changes in elephant numbers and range in Africa 1995-2013 . . . . . . . . . . . . . . . . . . . . . 3 Regional human population numbers and densities 2013 . . . . . . . . . . . . . . . . . . . . . . . . . 3 Comparison of key statistics at a continental and regional level .. . . . . . . . . . . . . . . . . . . 6 Elephant Deaths, Ivory Production and Ivory Value 2001-2015 .. . . . . . . . . . . . . . . . . . 10 Worked ivory exports from southern Africa 1990-2015 . . . . . . . . . . . . . . . . . . . . . . . . . 13 Worked ivory value corrected for likely raw ivory exports .. . . . . . . . . . . . . . . . . . . . . . 18 Raw and Worked Ivory seizures 2001-2014 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 Exports of live elephants from southern African States 1990-2014 . . . . . . . . . . . . . . . . 22 Imports of live elephant by country and region .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 CTD ‘Purposes’ for imported live elephant and their values . . . . . . . . . . . . . . . . . . . . . 23 Ivory Flow Balance Sheet 2001-2015 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 One-Off CITES Ivory Sale 2008 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
A1.1 A2.1 A2.2 A4.1
Mean tusk weights, ivory prices and tusk values .. . . . . . . . . . . . . . . . . . . . . . . . . . . . Elephant regional populations and densities in Zimbabwe . . . . . . . . . . . . . . . . . . . . . Deaths predicted in the Zimbabwe elephant population in 2015 . . . . . . . . . . . . . . . . Simulation of the Mozambique Elephant Population 2001-2015 . . . . . . . . . . . . . . . . ________________
ii
33 36 44 50
List of Figures 1.
African Elephant: Continental and Regional Populations . . . . . . . . . . . . . . . . . . . . . . . . 4
2.
Changes in the price of ivory 2006-2016 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
3.
Prices of ivory 2016 .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
4.
Regional Elephant Populations 2001-2015 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
5.
Worked ivory exports from Southern Africa 1990-2014 . . . . . . . . . . . . . . . . . . . . . . . . 14
6.
Southern Africa worked ivory imports and exports from 1990-2015 . . . . . . . . . . . . . . . 15
7.
Exports to China 1990-2014 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
8.
Numbers of exports and weights of shipments organised by weight classes . . . . . . . . . 17
9.
Exports of live elephants from Southern Africa 1990-2014 . . . . . . . . . . . . . . . . . . . . . . 24
10. Licit and Illicit Financial Flows of Ivory in and from Southern Africa .. . . . . . . . . . . . . 26 A2.1 Zimbabwe: Regional Populations (Map) .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 A2.2 Zimbabwe elephants: Total Population and Regional Subpopulations . . . . . . . . . . . . 39 A2.3 Matabeleland North Elephant Population . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 A2.4 Zambezi Valley Elephant Population . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 A2.5 Sebungwe Elephant Population . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 A2.6 Gonarezhou Elephant Population . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 A2.7 Zimbabwe: Elephant Deaths, Ivory Production and Ivory Value 2001-2015 .. . . . . . 46 A3.1 Botswana Elephant Population Numbers, Emigration and Ivory Value . . . . . . . . . . . 48 A3.2 Botswana: Elephant Deaths, Ivory Production and Ivory Value 2001-2015. . . . . . . . 49 A4.1 Mozambique Elephant Population Numbers and Ivory Value . . . . . . . . . . . . . . . . . . 51 A4.2 Mozambique: Elephant Deaths, Ivory Production and Ivory Value 2001-2015 . . . . . 52 A5.1 Increase in Namibian elephant population 2001-2015 . . . . . . . . . . . . . . . . . . . . . . . . 54 A5.2 Namibia: Elephant Deaths, Ivory Production and Ivory Value 2001-2015 . . . . . . . . . 55 A6.1 Simulation model of the South African Elephant Population 2001-2015 . . . . . . . . . . 57 A6.2 South Africa: Elephant Deaths, Ivory Production and Ivory Value 2001-2015 . . . . . 58 A7.1 Simulation model of the Zambian Elephant Population 2001-2015 . . . . . . . . . . . . . . 60 A7.2 Zambia: Elephant Deaths, Ivory Production and Ivory Value 2001-2015 . . . . . . . . . 61 _______________
iii
INTRODUCTION Definition of Illicit Financial Flows In their Report to the AU/ECA Conference of Ministers of Finance, Planning and Economic Development, the High Level Panel on Illicit Financial Flows from Africa (IFF HLP 2014) stated that – “We felt that it was important to distinguish IFFs from capital flight because capital flight, which is sometimes driven by macroeconomic and governance factors, could be entirely licit. For the purposes of our work, we agreed on a definition of IFFs as money illegally earned, transferred or used. This definition avoids complicated explanations of what qualifies as IFFs and debates about whether investors should be allowed to respond rationally to economic and political risk. Moreover, we believe that our preferred definition addresses the issue of IFFs across the entire breadth of financial transactions.” This definition is important to us because it justifies treating valuable commodities such as ivory and rhino horn as a form of money. Indeed, the current value of both of these commodities may be worth more than gold.1 The illegal hunting of elephants and rhinos is effectively theft of resources belonging to a nation’s citizens and, when the commodity is transferred to another country where it is used, the activity qualifies as an Illicit Financial Flow. Illicit Financial Flows out of Africa The Panel summarises the magnitude, scope and impact of IFFs affecting Africa – “Over the last 50 years, Africa is estimated to have lost in excess of $1 trillion in illicit financial flows (IFFs) (Kar & Cartwright-Smith 2010; Kar & Leblanc 2013). This sum is roughly equivalent to all of the official development assistance received by Africa during the same timeframe.2 Currently, Africa is estimated to be losing more than $50 billion annually in IFFs. But these estimates may well fall short of reality because accurate data do not exist for all African countries, and these estimates often exclude some forms of IFFs that by nature are secret and cannot be properly estimated, such as proceeds of bribery and trafficking of drugs, people and firearms. The amount lost annually by Africa through IFFs is therefore likely to exceed $50 billion by a significant amount.” ...
1.
The current price of gold is about $40,000/kg ($38,754/kg on 16 Jan 2016) . Biggs et al. (2013) reported rhino horn prices exceeding US$65,000/kg in Asia. The price of raw ivory used in this analysis is $500/kg in Africa and $1,000 in Asia for a 10kg tusk, making a 10kg tusk in Asia worth $10,000. Four such tusks would be worth $40,000. The abundance of ivory is far greater than that of rhino horn so that, taking into account the potential supply of ivory, its value is in the same league as rhino horn.
2.
Some $1.07 trillion of official development assistance was received by Africa between 1970 and 2008 (OECD 2012).
1
“Poverty remains of serious concern in Africa in absolute and relative terms. The number of people living on less than $1.25 a day in Africa is estimated to have increased from 290 million in 1990 to 414 million in 2010 (United Nations 2013). This is because population growth outweighs the number of people rising out of poverty. Moreover, GDP per African was around $2,000 in 2013, which is around one-fifth of the level worldwide (IMF 2014). Poverty in Africa is also multidimensional, in the sense of limited access to education, healthcare, housing, potable water and sanitation. This situation puts the loss of more than $50 billion a year in IFFs in better perspective.” ... “IFFs are also of concern because of their impact on governance. Successfully taking out these resources usually involves suborning of state officials and can contribute to undermining state structures, since concerned actors may have the resources to prevent the proper functioning of regulatory institutions.” ________________
2
ELEPHANTS Continental Numbers and Distribution The estimates from the African Elephant Database (http://www.elephantdatabase.org/) for the period 1995-2013 have been used throughout this study as the baseline reference. The most recent AED status report for 2016 appeared after this study was completed and the new information is not included.3 The elephant range in Africa was estimated by Blanc et al. (2014) at 3.4 million km2. Said et al. (1995) estimated it at 5.8 million km2 (Table1 below). Over the 28 years since 1995 the range has decreased by some 42% with largest decrease being in the Central Region (64%). Table 1: Changes in elephant numbers and range in Africa 1995-2013 Elephant range (km 2)
Elephant population Regions
1995
2013
W est
14,725
17,487
Central
225,219
East
Increase %
1995
2013
Decrease %
18.8
227,088
175,554
22.7
148,921
-33.9
2,769,550
1,002,398
63.8
128,273
125,832
-1.9
1,075,362
872,758
18.8
Southern
229,682
354,312
54.3
1,725,798
1,312,311
24.0
TOTALS
568,317
590,511
3.9
5,797,798
3,366,406
41.9
There were more elephants in Africa in 2013 than there were in 1995 (Fig.1 next page). The population of the Central Region has decreased by about one-third since 1995 but the deficit has been made up by the doubling of the Southern Africa population. The shrinkage in elephant range is not surprising given the increase in human populations in Africa (Table 2, below). The present human population in the countries making up the elephant range is some 855 million people of which 546 million live in the rural areas (World Bank 2015). Elephants generally cannot co-exist with people when the human population density exceeds 20/km2 (Parker & Graham 1989). This density has been exceeded in 21 of the 37 countries in the range. The continental elephant population is becoming increasingly fragmented (Blanc et al. 2013) – it has become “a group of elephant islands in a sea of humans” (Parker & Amin 1983). Table 2: Regional human population numbers and densities 2013 HUM AN POPULATION
3.
NUMBERS
DENSITY
Num ber of countries
Area of Region
Total
Rural
Overall
Rural
Regions
N
km 2
m illions
m illions
/km 2
/km 2
Num ber of countries D>20/km 2
W est
13
5,100,200
325
184
64
36
10
Central
7
5,365,100
114
73
21
14
1
East
8
4,299,500
265
205
62
48
6
Southern
9
5,950,500
151
84
25
14
4
TOTALS
37
20,715,300
855
546
41
26
21
There are a number of questionable estimates for southern African countries in this 2016 report which, if used, would result in considerably larger amounts of illegal ivory in the IFFs.
3
Figure 1: AFRICAN ELEPHANT: CONTINENTAL AND REGIONAL POPULATIONS The figure is constructed from the African Elephant Status Reports of the African Elephant Database over the period from 1995-2013. 1995 – Said et al. (1995); 1998 – Barnes et al. (1999); 2002 – Blanc et al. (2003); 2007 – Blanc et al. (2007); 2013 – Blanc et al. (2013)
4
Approach to the Analysis The African Elephant Database (Blanc et al. (2014) lists nine countries that make up the elephant range in southern Africa. Of these, 99% of the elephants occur in six countries – Botswana, Mozambique, Namibia, South Africa, Zambia and Zimbabwe – each of whose populations have exceeded 20,000 elephants until recently.4 These six countries form the basis for our examination of the ivory trade out of southern Africa. Stiles et al. (2015) estimated the volume of ivory leaving Africa legally and illegally for the years 2002-2014 using an earlier version of the population simulation model of Martin (2016). The major findings were – 1. The number of elephants killed illegally was estimated to be 362,940. The average killed annually from 2002-2006 was well below 20,000, jumping to over 30,000 a year after 2007. 2. The total ivory production from illegal elephant killing for the period 2002-2014 was 2,747,977kg (an average of 211,383kg a year). 3. Average tusk weight for all age groups and both sexes combined was estimated to have fallen from 7.8 kg in 2007 to 3.5 kg in 2014. It therefore required more than double the number of elephants killed in 2014 to achieve the same total weight of ivory as in 2007, assuming the illegal hunters were unselective. Hunting trophy weight declines reported in recent years in southern Africa indicate that poaching selection for the larger tuskers has occurred. 4. The total legal ivory produced from elephant deaths was estimated to be 1,138,749 kg. 5. The total ivory produced from all forms of elephant mortality was 3,886,726 kg, with 70% of it produced from illegal killing. 6. African government storerooms accumulated an estimated 500-543.1 metric tons (MT) of ivory from all sources, after assumed field losses of 174.8 MT and leakages of 279.5 MT. The accumulated ivory was added to stocks existing from before 2002. At the end of 2014 total stocks were estimated to amount to approximately 690 MT (759 U.S. tons) for all of sub-Saharan Africa, taking into account the legal 2008 ivory sales, other legal sales and the destruction of ivory stockpiles. 7. Illegal ivory exports from Africa were estimated to total 2,402,236 kg, after deductions made for law enforcement confiscations (local), seizures (import/export) made in Africa and 560,000 kg used in local African ivory markets. The figure assumes no stockpiling in Africa; therefore, it should be considered a maximum quantity. Stiles et al. (2015) study was based on the four regional elephant populations in Africa (West, Eastern, Central and Southern). In this study of southern Africa we have been able to examine the legal and illegal ivory production at the scale of the individual countries because the record of elephant population estimates is good enough to allow more detailed calculations. Stiles’ study covered the 13 years from 2002-2014: in this study we have selected the 15 years from 20012015. For comparative purposes, it is not mathematically valid either to reduce our estimates or increase Stiles’ estimates in proportion to the number of years involved because, as Stiles observes in the first paragraph above, the illegal hunting has not been uniform over the period involved. 4.
The Mozambique population has undergone a dramatic reduction since 2010 to about 10,000 animals.
5
Gross comparisons of the numbers, deaths and ivory production from Stiles et al. (2015) study at the continental level5 and this study at the regional level are given in Table 3 below. Table 3: Comparison of key statistics at a continental and regional level
AFRICA
Elephant Population 2013
Legal
Illegal
Total
Legal
Illegal
Total
590,511
207,542
309,118
516,570
1,700,342
2,678,650
4,378,992
40.2
59.8
100.0
38.8
61.2
100.0
% of total
SOUTHERN AFRICA
Legal
Illegal
Total
Legal
Illegal
Total
381,836
104,034
117,060
221,094
1,029,010
1,097,366
2,126,376
47.1
52.9
100.0
48.4
51.6
100.0
50.1
37.9
42.8
60.5
41.0
48.6
64.7
DEATHS 2001-2015
IVORY PRODUCTION 2002-2014
Elephant Population 2015
% of total % of Africa
DEATHS 2002-2014
IVORY PRODUCTION 2001-2015
If we ignore for the moment the longer time span being considered for the Southern African elephant population, several observations can be made – a. The elephant population in southern Africa is more than half of the continental population; b. The percentage of legal deaths in southern Africa is higher than that of the continental population (47% versus 40%) ... some of these may be attributable to trophy hunting;6 c. The percentage of illegal deaths in southern Africa is lower than that of the continental population (53% versus 60%); d. Southern Africa is responsible for 49% of the total ivory production from Africa; e. Of this production, 48% is legal and 52% is illegal; f.
Of the total illegal ivory production from Africa, southern Africa contributes 41%. Noting that Southern Africa contributes 38% of the illegal deaths, the conclusion must be drawn that illegal ivory production from southern Africa is at an earlier stage where the mean tusk weight compared to the rest of Africa is relatively high (5.1kg versus 3.5kg – see paragraph 3 on the previous page). It must be kept in mind the statistics for Africa as a whole include southern Africa. ________________
5.
Stiles’ results have been slightly adjusted to include the deaths from natural mortality, problem animal control and trophy hunting
6.
Trophy hunting of elephants is not included in this analysis. However, the value of large elephant tusks as a commodity should guide price-setting for trophy hunting. The trophy fee for an elephant carrying large tusks should not be less than the commodity value of the ivory (Martin 2007) – otherwise it would pay the safari operator to kill the elephant himself and sell the ivory.
6
Ivory Prices The price used for the value of raw ivory is obviously critical to the estimates of illicit financial flows. The ivory price has undergone some dramatic changes over the period 20062016 (Fig.2, next page). Prior to 2006, the average price/kg in Asia was lower than $350/kg. After 2006, it rose rapidly to over $2,000/kg in 2011, remained above this level until 2014 and then fell to about US$1,000/kg from 2015 onwards. The prices shown in Fig.2 are average end-market prices for raw ivory in Asia and it cannot be expected that the price realised at the point of export from Africa would be as high. Although Zimbabwe realised export prices before the ivory trade ban in1989 that were close to the endmarket price,7 this was generally not the case for most African range states exporting ivory. We have assumed that the export price from Africa (if there were a legal trade) would be half of the current price in Asia. Ivory prices are dependent on the size of the tusk (larger tusks are worth more per kilogramme than smaller tusks). A scatter diagram of the prices in China in mid-2016 for 100 different tusks ranging from 3-25kg in weight is shown in Fig.3 (page 9). Although the scatter is wide, it is clear that the price/kg increases with tusk size. The curve that we have used for the ivory price in China passes centrally through the scatter of points and the curve for the ivory price in Africa used in the elephant population simulation model passes below 98% of the points. Details on the relationship between elephant age and tusk size and the relationship between tusk size and ivory price are given in Appendix 1 (p 33). The analysis of the individual country elephant populations in southern Africa (Appendices 2-7) cover the time span 2000-2016. Because we have a used a constant relationship between tusk weight and ivory price,8 our results will have underestimated the value of ivory production between 2010 and 2015 when the peak in ivory prices occurred and underestimated the production from 2000-2006 (hopefully the overestimate and the underestimate will cancel out each other!). It is necessary to remark again on the variability of prices for the same weight of tusk as shown in Fig.3. This suggests that a number of other factors influence the price that might be paid by any particular buyer on any given day. These factors could include the appearance of the tusk, the state of the economy, upward or downward trends in the retail ivory carving market, the speculators’ valuation of raw ivory as an investment and how desperate he is for cash ... it could even be influenced by the severity of dyspepsia the buyer is suffering on the day of purchase! ________________
7.
For the period 1979-1987 Princen (2003) observes: “Of the ivory-producing countries, only Zimbabwe brought in a level of revenue ($63-$76/kg) close to the value of raw ivory earned in Japan ($85-$99kg). For other producer states, the revenues ranged from $6-$15/kg. Zimbabwe, unlike the other states, had actively managed elephants during the 1980s, marketing ivory in such a manner to gain the largest proportion of rents possible.”
8.
We predict that a 10kg tusk is worth about US$1,000/kg in China and worth about US$500/kg at the point of export in Africa today. The price chosen is closely representative of the prices in 2016.
7
Figure 2: Changes in the price of ivory 2006-2016
8
Figure 3: Price of ivory 2016 Tusk data collected by Wei Ji in 2016 (Daniel Stiles pers. comm.)
9
Trade in raw ivory for the individual southern African countries Individual population simulations have been carried out for each of the six southern African countries – Botswana (Appendix 3 p47), Mozambique (Appendix 4 p50), Namibia (Appendix 5 p53), South Africa (Appendix 6 p56), Zambia (Appendix 7 p59) and Zimbabwe (Appendix 2 p36). In the case of Zimbabwe, the four elephant subpopulations in the country have been divided into two parts – the National Parks (where there is no trophy hunting) and the State Safari Areas and adjacent communal lands where trophy hunting takes place – and separate simulations have been done for each of these areas.9 The results of the simulations for all of these countries are presented in Table 4 below. Table 4: Elephant Deaths, Ivory Production and Ivory Value 2001-2015 NM - Natural Mortality LH - Legal Harvesting PAC - Problem Animal Control TH - Trophy Hunting POPULATION COUNTRY
DEATHS
2001
2015
NM
LH
PAC
Botswana
134,895
221,948
36,589
2,751
7,140
2,630
49,110
7,824
56,934
Zimbabwe
88,749
80,371
18,725
5,862
4,809
2,632
32,028
63,827
95,855
Namibia
9,735
22,231
3,755
0
155
737
4,647
5,319
9,966
Mozambique
16,705
8,097
3,713
0
590
890
5,193
18,950
24,143
South Africa
14,423
27,428
4,681
304
229
309
5,523
347
5,870
Zambia
26,655
21,761
5,502
1,943
1,466
460
9,371
18,955
28,326
TOTALS
291,162
381,836
72,965
10,860
14,389
7,658
105,872
115,222
221,094
POPULATION
TH
Legal
Illegal
TOTAL
IVORY PRODUCTION (kg)
COUNTRY
2001
2015
NM
LH
PAC
TH
Legal
Illegal
TOTAL
Botswana
134,895
221,948
105,107
54,610
112,065
283,257
555,039
155,461
710,500
Zimbabwe
88,749
80,371
24,138
48,393
55,316
90,972
218,819
533,741
752,560
Namibia
9,735
22,231
9,063
0
2,112
53,444
64,619
49,675
114,294
Mozambique
16,705
8,097
4,804
0
8,394
36,470
49,668
168,475
218,143
South Africa
14,423
27,428
33,401
2,285
3,517
39,415
78,618
3,204
81,822
Zambia
26,655
21,761
6,449
10,969
19,272
36,470
73,160
176,166
249,326
TOTALS
291,162
381,836
182,962
116,257
200,676
540,028
1,039,923
1,086,722
2,126,645
NM
LH
PAC
Illegal
TOTAL
POPULATION
IVORY VALUE (US$millions)
COUNTRY
2001
2015
TH
Legal
Botswana
134,895
150,718
69.8
40.1
65.5
331.2
506.6
114.4
621.0
Zimbabwe
88,749
80,371
12.0
26.5
27.6
64.4
130.5
301.8
432.3
Namibia
9,735
22,231
7.0
0.0
1.1
64.2
72.3
30.4
102.7
Mozambique
16,705
8,097
2.4
0.0
4.6
30.2
37.2
91.8
129.0
South Africa
14,423
27,428
39.3
1.2
2.0
55.0
97.5
1.7
99.2
Zambia
26,655
21,761
2.9
8.9
10.2
15.2
37.2
82.6
119.8
TOTALS
291,162
310,606
133.4
76.7
111.0
560.2
881.3
622.7
1,504.0
The individual country populations over the period 2001-2015 are shown in Fig.4 (next page)
9.
This is justified by the fact that survey data exists for different years for the four subpopulations.
10
Figure 4: Regional Elephant Populations 2001-2015 11
Several observations can be made about the data presented in Table 4 and Fig.4 – a. The southern Africa elephant population should reach 386,000 elephants in 2015; b. Three-quarters of the southern African elephant population occurs in Botswana and Zimbabwe; c. The elephant populations of Mozambique, Zambia and Zimbabwe suffered a marked escalation in illegal hunting after 2006. Stiles et al. (2015 para 1 p5) observed the same phenomenon in the continental population.10 Botswana, Namibia and South Africa have been less harshly affected. d. The most severe decline has taken place in Mozambique where the population declined from 22,000 animals in 2008 to 10,000 in 2014.11 e. Zambia has suffered a similar but less severe decline from 28,418 elephants in 2006 to 21,760 elephants in 2015 (DNPW 2016).12 ____________________
10. This escalation is largely dependent on the AED estimates for 2006. However, in all cases the AED estimate for 2013 is lower still so that the moment of the exact onset of these declines is secondary. 11. This observation is critically dependent on the 2014 estimate of the Mozambique population being 10,438 elephants obtained from the Great Elephant Census of 2014 (MLERD 2015). The AED figure of 26,017 for 2013 (Blanc et al. 2014) is considerably higher. 12. This observation is also critically dependent on the 2015 estimate of the Zambian population being 21,760 elephants obtained from the Great Elephant Census of 2015 (DNPW 2016). The AED estimate for 2014 is 15,113 elephants (Blanc et al. 2014) which would make the decline much worse.
12
Trade in worked ivory The CITES Trade Database (CTD) has been used to assess legal exports of ivory carvings from Southern Africa13 from 1990-2015 (Table 5 below) and Fig.5 (next page). Although seven southern African countries are listed in the table, the ‘big players’ are limited to South Africa and Zimbabwe. The assumption in Table 5 is that all the ivory is genuine carved ivory. This may not be correct and in Table 5A (page 18) I present a scenario corrected for likely raw ivory exports. Table 5. Worked ivory exports from southern Africa 1990-2015 BW
MW
MZ
NA
ZA
ZM
ZW
Total
No. of exports
24
93
32
30
588
38
810
1,615
No. of carvings
331
6,247
600
329
11,520
683
200,802
220,512
33
625
60
33
1,152
68
20,080
22,051
29,980
16,450
575,993
34,150
Total weight Total value
kg
US$ 16,550 312,352 n
10,040,111 11,025,586
Personal possess %
0.1
2.3
0.1
0.1
4.1
0.1
86.5
93.3
Commercial Trade %
0.0
0.5
0.0
0.0
0.7
0.0
2.0
3.3
BW - Botswana, MW - Malawi, MZ - Mozambique, NA - Namibia, ZA - South Africa, ZM - Zambia, ZW - Zimbabwe Notes on the table – • Out of 1,615 exports of worked ivory from southern Africa, weights are only given for 119 exports • It has been assumed that the average weight for a carving is 0.1kg for the entries where no weights are given • The number of carvings has been estimated from the total weights assuming a weight of 0.1kg for a carving • To obtain the total value of the exports, the average value for a carving is assumed to be US$50
The most striking feature of this analysis is the sudden escalation in exports in 2011 caused by large shipments from Zimbabwe to China over the period 2011-2014. Details of these exports are given in Fig.5. The ‘Purpose’ for the large shipments to China is given in the CTD as “Personal Possessions” – however, it is seems highly unlikely that an export of 8 tonnes of carved ivory in a single shipment from Zimbabwe in 2014 fits the bill for personal effects. Even in the heyday of ivory carving in Zimbabwe14 when there were several large ivory carving enterprises, the amounts exported are implausibly large. The listing of Zimbabwe’s elephant population on Appendix II of CITES is constrained by a clause in the annotation that prevents the export of worked ivory for commercial purposes. However, Zimbabwe is permitted to export ivory carvings fitting the description ‘personal possessions’ under CITES. It seems more likely that raw ivory was being moved under this heading in order to circumvent the ban on trade in raw ivory. Ideally, each shipment in the CTD should be reported by both the exporting country and the importing country. In practice, this is not happening (Fig.6 p15). This is particularly noticeable in the case of the four large shipments exported from Zimbabwe to China (Fig.5). The exports were reported by Zimbabwe but the imports were not reported by China. 13. The countries appearing in the CTD as carved ivory exporters are Botswana, Malawi, Mozambique, Namibia, South Africa, Zambia and Zimbabwe. Malawi is not one of the major raw ivory exporters but has a significant domestic worked ivory industry. 14. Large ivory carving enterprises such as ‘Space Age Products” in Harare would have had difficulty in assembling a shipment of 7 tonnes of ivory even for commercial purposes.
13
Figure 5: Worked ivory exports from Southern Africa 1990-2014 14
Figure 6: Southern Africa worked ivory imports and exports from 1990-2015 15
Out of 1615 shipments 67% were reported by the southern African exporting countries, 30% by the importing countries and only 3% of exports were reported by both the exporting and importing countries. I have carried out a simple test where 67% of the imports and 30% of the exports were selected randomly from the data set and then examined for the expected number of pairings (i.e. when both exporter and importer report the transaction). It could reasonably be expected that 34% of the shipments would have been reported by both exporter and importer – not the 3% shown. From this I conclude that there are other mechanisms at work. It is noticeable that certain importing countries are more meticulous than others in their reporting, eg. China reported only one third of the total number of imports it received (according to the reports of the exporting countries). The increase in the number of shipments of worked ivory from southern Africa to China over the period 19902014 is remarkable. The cumulative curve of imports is shown in Fig.7 opposite. Up until 1998 there had only been 3 shipments. In 1999 the number of shipments began to increase and in 2007 the rate of increase entered a very steep phase. The majority of the shipments were from Zimbabwe (80%) and the remainder were from South Africa (12%), Malawi (5%) and Botswana (3%).
Figure 7: Exports to China 1990-2014
The distribution of shipments in weight classes from less than 0.1kg to over 1,000kg is shown in the lower diagram in Fig. 8 on the next page. The weight classes are arranged logarithmically. As might be expected for personal effects15, most of the shipments fall in the weight classes below 5kg. Of 1,611 shipments, the number weighing more than 50 kg is only 17. In the upper diagram, ignoring for the moment the weight classes above 100kg, it appears that the bulk of the weight is located in the weight classes from 2-50kg. This is not the case when the weight classes greater than 100kg are considered. Of the total weight of ‘worked ivory’ estimated to have been exported (22 tonnes) only 2.2 tonnes occur in the shipments that are less than 100kg. Nearly 20 tonnes (90% of the total) of carved ivory recorded in the CTD occured in 12 large shipments that took place after 2009. Of these, the four large shipments totalling 17 tonnes (shown in Fig.5 p14) that were made between 2011 and 2014 account for 77% of the total amount. These gross departures from the pattern in exports established from 1990-2008 make it very difficult to accept ‘average figures’ for any given year.
15. The declared purposes for the total number of 1615 exports were – P (personal) 53%, T (trade) 22%, H (hunting) 2%, Blank (No Purpose Given) 23%
16
Figure 8: Numbers of exports and weights of shipments organised by weight classes 17
The weaknesses in the data make it difficult to derive an average figure for use in the illicit financial flows analysis for the year 2015. There are a large number of imponderables. Firstly, is Zimbabwe likely to continue exporting amounts of “worked ivory” in excess of 3 tonnes per year? With the recent announcement by China that it is closing down its domestic worked ivory markets in 2017, it seems likely that the legal market for carved ivory will decrease – although general experience with trade bans is that they result in an increase in ivory prices and stimulate illegal markets: such markets will be fed by smuggling and nothing will be reported to the CTD. With the ivory prices we have used at the exporting point in Africa, a tonne (1,000kg) of raw ivory is worth about US$0.5 million if the average tusk weight is 10kg. The assumptions made in this carved ivory analysis are that a carving has an average weight of 0.1kg and is worth US$50. With these assumptions a tonne of carved ivory has the same value as a tonne of raw ivory, i.e. about $0.5m. I have doubled this value to take into account the value added by carving. Our estimate for the value of the raw ivory used in the ivory carving industry in southern Africa was about US$11.0 million over the period 1990-201516 (Table 5, p13). However, I surmise that most of the weight of ivory estimated from the CTD (22,051kg) was in fact raw ivory (17,057kg) exported as worked ivory by Zimbabwe between 2011-2014. Corrections for this have been made in Table 5A below. The corrections have also been made for two other time spans: the period used in Table 9 (p28) is 2001-2015 and the period used in other volumes of this IFF study is 2006-2014.17 Table 5A. Worked ivory value corrected for likely raw ivory exports BW, MW, MZ, NA, ZA, ZM, ZW
ZIMBABWE 2011-2014
Years
Carved Ivory kg
Value @$1,000/kg
Raw Ivory kg
Value @$500/kg
TOTAL VALUE US$
1990-2015
4,994
4,994,000
17,057
17,057,000
22,051,000
2001-2015
3,621
3,621,000
17,057
17,057,000
20,678,000
2006-2014
2,708
2,708,000
17,057
17,057,000
19,765,000
The total value of around $20 million arising from worked ivory is relatively small compared to trade in other items in this study. Little of it gives rise to Illicit Financial Flows. Although Zimbabwe’s probable export of raw ivory as worked ivory might contravene certain CITES provisions, it is totally legal under national laws and the income generated was probably all returned to the Zimbabwe treasury. On the next page we attempt to estimate the value of the illegal exports that don’t find their way into the CTD (at both the exporting and importing ends of the transaction). ________________
16. Noting that the CITES CTD data are incomplete for 2015. 17. The very large exports from Zimbabwe between 2011-2014 explain the lack of change of the amounts in the different time periods.
18
Worked Ivory Seizures I have used the CITES record of worked ivory seizures to estimate the illegal exports of worked ivory from Southern Africa over the period 2001-2014 (Table 5B). Data for 2001-2006 are from Milliken et al. (2013) and for 2007-2014 are from Milliken et al. (2016). Illegal ivory production from Africa from 2002-2014 was Table 5B. Raw and Worked Ivory Seizures (All) 2001-2014 (kg) estimated at 2,678,850kg (Table 3 p6). The total seizures in Table 5B amount to 14.3% of this amount of which raw Raw Worked TOTAL Year ivory seizures make up 12.2% and worked ivory seizures 2001 12,891 3,482 16,373 make up 2.1%. Illegal ivory production from Southern Africa 2002-2014 is estimated in this study at 1,086,722kg (Table 4 p10). Using the above percentage (2.1%), the expected weight of worked ivory seizures would be 22,402kg with a value of US$22,402,000. This our first estimate of the value of the worked ivory seizures from the 6 Southern African countries given in Table 4.
2002
24,150
6,582
30,732
2003
10,503
2,385
12,888
2004
6,714
1,617
8,331
2005
13,672
1,211
14,883
2006
23,648
1,980
25,628
2007
8,549
1,604
10,153
5,549 1,426 6,975 An alternative calculation can be done. Southern Africa 2008 5,273 32,683 contributed 41% of the illegal ivory originating from Africa 2009 27,410 as a whole (Table 3). If we assume that it would have 2010 22,935 3,409 26,344 contributed the same proportion of Africa’s worked ivory 2011 45,285 6,168 51,453 seizures, the estimated amount is 0.41 x 55,223kg (Table 5B 2012 36,130 5,168 41,298 opposite), i.e. 22,641kg with a value of $22,641,430. The 7,104 65,171 two results are more or less identical and give us some 2013 58,067 7,814 39,470 confidence in assuming a figure of US$22.5 million for the 2014 31,656 seizures of illegal worked ivory from Botswana, Malawi, 3 327,159 55,223 382,382 Mozambique, Namibia, South Africa, Zambia and Zimbabwe. None of this ivory would have been recorded in the CITES Trade Database. Because the shipments of illegal worked ivory would have been paid for before they were seized and it is unlikely that the payments would have been recovered after they were seized, this amount can be treated as an Illicit Financial Flow.
This gives us an opportunity to estimate the size of the illegal worked ivory industry in Southern Africa from 2001-2015 (which is required in Table 9 p28) – T = W + R kg where – T is the total weight of illegally produced ivory in Table 3(p6) W is the total weight of illegal raw ivory entering the worked ivory industry and R is the total weight of illegal raw ivory reaching middlemen in Fig.10 (p26) From Table 5B above, the relationship between the total seizures of raw and worked ivory is W/R = 18.7%. If we assume that the relationship between the illegal worked ivory industry and the illegal raw ivory industry is in the same proportions, then – R = W / 0.187 and, substituting for R in the equation above, gives T = W + W / 0.187 and the relationship reduces to W = T / 6.35. For T = 1,086,722kg (see above), W = 171,202kg. After deducting the worked ivory seizures (22,500kg), W reduces to 148,800kg with a value of $149m. 19
It is not the full amount of illegal worked ivory leaving Southern Africa. Angola is not included in the 6 countries listed above and it is known to have one of the largest illegal worked ivory markets in Africa. I have estimated the possible amount involved as follows – a. Martin & Vigne (2014) found 10,888 ivory carvings in Luanda, Angola, most of which were at the Benfica market (10,026). They estimated the weight of the carvings at 1,573 tonnes. b. In addition to the worked ivory on display were more carvings stored in trunks belonging to each vendor. We have doubled the weight of ivory to allow for this, i.e. 3,146kg. c. There was also carving taking place in artisanal workshops and we assume the volume of this was 50% of the ivory calculated in b. above, i.e. an additional 1,573 tonnes, total now 4,719kg. d. The mean weight of the items was 1,573/10,888kg, i.e. 0.1444kg. e. At the prices assumed for this study (Fig.A1.2 p35) each piece would be worth about US$70/kg and the raw ivory value of 4,719kg of such pieces would be $330,000. f.
This does not allow for a mark-up added by the artistic work in the carving or for a profit by the vendors. To account for this we have added a 100% mark-up, i.e. the value is now $660,000.
g. Martin & Vigne’s study was a snapshot taken in 2014. The data analysed in Table 9 (p28) pertains to the period 2001-2015, i.e. 15 years. If we assume that the annual turnover in the ivory market is at least 75%, then the total earnings would be $660,000 x 15years x 0.75, i.e. $7,425,000. h. Mozambique had (has?) a carving industry of the same magnitude as the Angola industry (Milliken et al. 2006 do not bely this impression), however I have assumed that its contribution to the illegal ivory flow is captured in the calculations on the previous page. Adding the value of the Angolan ivory carving industry to the figure of $149 million at the bottom of the previous page, increases the estimated IFF in worked ivory for the period 20012014 to US$156 million and this is the figure that appears in Table 9. Using the same method as on the previous page, the total seizures of ivory for Southern Africa are estimated at – Raw ivory: 132,718kg valued at $500/kg = $77,559,948 and Worked ivory: 22,402kg valued at $1,000/kg = $22,402,168 – totalling 155,120kg valued at $99,962,116 ... rounded to $100,000,000 Not all of these seizures would have been made in Southern Africa. For the purposes of completing the Balance Sheet in Table 9 (p28) I have assumed 50% of the seized ivory would enter Government Ivory Stores in Southern Africa.18 For the raw ivory this would be $39,000,000 and for the worked ivory it would be $11,000,000, i.e. a total $50,000,000. ________________
18. The seizures made within the country of origin would not qualify as IFFs.
20
Export of live elephants The CITES Trade Database (CTD) has been used to estimate the numbers of live elephants exported from southern African countries from 1990-2014.19 The annotation on Appendix II of CITES provides for Zimbabwe and Botswana to export live elephants to acceptable destinations and for South Africa and Namibia to export live elephants for in situ conservation programmes.20 Other southern African countries listed on Appendix I may export live elephants provided the export is not for “primarily commercial purposes”. The CTD dataset for live elephants is not easy to work with. Firstly, the expected complimentary records of reporting by exporting and importing countries is highly erratic: some shipments of live elephants are reported only by the exporter or importer but not both. In some cases there are significant delays between the reporting by the exporting country and the importing country so that the database must be searched for individual cases where the exporter has reported the export and the importer has reported the same shipment up to 5 entries further down in the rows of the database. Secondly, in those cases where both the exporter and the importer have reported the transaction there is often poor correspondence between the numbers reported by each. For example, if South Africa reports exporting 10 elephants to the United States the number of reports by the United States which follow the South African export seldom adds up to 10. If the US reports 8 elephants received it might be reasonable to assume that 2 elephants did not survive the translocation. But in a case where the US reports receiving 12 elephants and South Africa reports exporting 10, it has to be assumed that one or other of them has made an error! Thirdly, it is evident that there is significant movement of trained domesticated elephants within the southern African countries.21 All such movements are governed by export permits. South Africa may grant a permit for (say) 2 such elephants to be exported to Botswana. The same 2 elephants may be back again in South Africa within a week with an export permit issued by Botswana. It is unlikely that Botswana would import 2 trained elephant from South Africa on a given date and a week later export 2 different trained elephants to South Africa. Fourthly, there are a few incidents where a shipment of elephants has crossed the border into South Africa with a valid export permit from (say) Zimbabwe and within a short period a similar number has been returned to Zimbabwe with an export permit from South Africa. My diagnosis in such cases is that the original import may have satisfied the customs officers at the point of entry but when the staff of the Department of the Environment later discovered the presence of these elephants in South Africa they insisted that they be repatriated! Almost all records have to be individually scrutinised and it is not possible to simply sum all the entries in any column of the database either to arrive at the total number of exports or the total number of elephants exported. 19. The CTD contains only one record for 2014 and none for 2015 or 2016. 20. Out of 538 records of exports in the CTD for South Africa, 30 do not meet the criterion of in situ conservation and out of 86 records of exports for Namibia, 6 do not meet the criterion. 21. In the CTD, the most frequent way of describing the ‘source’ of a domesticated elephant is ‘C’ meaning captive-bred. The ‘purpose’ of the same export is ‘Q’ i.e. it is to be used in ‘circuses or travelling shows’. Neither of these adequately describe the uses to which domesticated elephants are put, e.g. they may be used for anti-poaching operations or for landmine detection.
21
The southern African countries that exported live elephants are shown in Table 6 below. Table 6. Exports of live elephants from southern African States 1990-2014 Botswana
Nam ibia
S Africa
Zam bia
Zim babwe
TOTALS
Shipm ents
16
19
92
1
33
161
Elephants
97
86
538
10
386
1,117
Notes The num bers of shipm ents are derived from the num ber of occurrences of each country in the list of exporters in the CTD. W hen the two colum ns ‘Im porter reported quantity’ and ‘Exporter reported quantity’ are sum m ed for the period 1990-2014 they give num bers of 624 and 637 elephants respectively, i.e. a total of 1,261 elephants. However, this m ethod does not take into account the entries in the data base where both the exporting country and the im porting country report the sam e elephants for the sam e shipm ent in the CTD. W hen the redundancies and duplications pointed out on the previous page are rem oved, this total reduces to 1,117 elephants. W hen the elephants are allocated to individual im porting countries there are further reductions.
The individual importing countries are shown in Table 7 below. The countries have been grouped by region and are identified by their CITES 2-letter isocodes. Because of a significant number of exports within the countries of the southern African region, most of the countries appearing in Table 6 appear again amongst the importing countries. Table 7. Imports of live elephant by country and region AFRICA
ASIA
EUROPE
NORTH AMERICA
AO
BW
KE
LS
MZ
NA
SZ
ZA
ZM
ZW
TOTALS
30
42
4
4
144
37
32
407
14
12
726
AE
CN
IN
JP
KR
LK
MY
SA
TH
3
56
2
21
2
6
8
2
2
BE
CH
CZ
DE
DK
ES
FR
GB
PL
PT
RU
SE
3
3
3
15
1
7
3
14
3
9
8
2
AR
BR
CL
CU
MX
3
AU
3
6
2
6
22
39
1
US 36
C & S America + Cbn
102
GRAND TOTAL . . .
71 Oceania 76 975
‘C & S America + Cbn” = CENTRAL AND SOUTH AMERICA AND THE CARIBBEAN
The total number of elephants is further reduced to 975 animals. Although being the largest exporter of elephants, South Africa (ZA) also emerges as the largest importer (407 elephants). China (CN) has come under considerable criticism in the last three years for importing live elephants from Africa but its imports are not alarmingly large. Not shown in the CTD are 23 elephants imported to China from Zimbabwe in 2014, 24 in July 2015 and 30 in December 2016 bringing the total to 133. 22
The CITES Trade Database (CTD) designates “Purpose codes” for exports – “T” refers to trade for commercial puposes; “N” indicates that the purpose of the export was to re-introduce elephants into the wild; “Z” refers to elephants whose destination is a zoo; and “Q” refers to elephants destined for circuses or travelling menageries. There are various other purposes such as “S” for scientific purposes, “E” for educational objectives and “B” for breeding functions: all of these I have lumped together under category “Z” because it is inevitable that such animals will be held in captivity at their destination. The purpose system fails to capture adequately a category for domesticated and trained elephants. I have placed all such cases in category “Q” although their activities include a far wider scope than elephants in circuses (see Footnote 20). All of the elephants listed in Table 7 have been assigned to one of the purpose categories given above (Table 8 below). In the 161 records in the database, 16 of them have no purpose assigned. I have taken the liberty of assigning any elephant that is imported into a non-range state into the ‘zoo’ category when no purpose is assigned. The value of elephants in each purpose category differs markedly. By far the most valuable are fully-trained domesticated elephants22 which are unlikely to sell for less than $100,000 each. However, very few owners of such elephants sell them, preferring either to hire them to tourism enterprises or engage them in special activities. Table 8. CTD ‘Purposes’ for imported live elephant and their values “PURPOSE” CODES
Nos. of elephants Price/elephant US$
T - Trade
N - reintroduction
Z - Zoos
Q - Trained
103
544
248
75
20,000
1,000
10,000
100,000
Percent for sale Value US$
TOTALS 970
10% 2,060,000
544,000
2,480,000
750,000
5,834,000
It is difficult to estimate a value for the expected annual income from the exports because of the variability in the data from 1990-2014 (Fig.9 next page). The average over the 25 year period is $233,360 per year but it is clear from the figure that there has been a progressive decline in exports since 1990. Since all of the exports have been legal under national laws, there is little scope for Illicit Financial Flows. The only likely IFFs are bribes paid to senior government officials in order to secure export permits ... which may well arise because of the opprobrium attached to live elephant exports by animal rights organisations. The annual value of trade for commercial purposes in live elephants is about $82,000 and, if this entailed bribes amounting to 10% of the value, the resulting figure of $8,000 is too small to have place in the IFF analysis. ____________________ 22. Martin (2000) estimated that the land use value of a fully trained elephant in the tourism industry is over US$1,000/ha which far exceeds any income that could be made from agriculture.
23
Figure 9: Exports of live elephants from Southern Africa 1990-2014
24
Illicit Financial Flows We examine several types of illicit financial flows involving ivory out of southern Africa – (1) The direct loss that results from illegal hunting and export of raw ivory; (2) The losses from legally obtained ivory sources through leakages that prevent the full amount of legal ivory from reaching official government ivory stores; (3) The losses that take place through official trade in contravention of the CITES Treaty; and (4) Losses from illegal international trade in domestically produced worked ivory.23 Ian Parker (2004, page 307) has likened the trade in raw ivory to a great river that has multiple sources in the hinterland of the continent and develops into a huge sluggish flow as it makes its way to the coast, overcoming any small barriers that may be in its way. This allegory suits the flow diagram in Fig.10 (next page) that attempts to capture the processes taking place at the individual country level in southern Africa. Partly to clarify my own thinking on the system, I give an interpretation of the flow diagram – a. The total ivory production in any southern African country (which we have attempted to estimate in Table 4 p10) appears at the top of the diagram. At the outset, this can be divided into an illegal component 1 and a legal component 2. The illegal component remains illegal throughout the process although it may be subdivided into several pathways. Some illegal ivory may enter the legal component as a result of seizures. The legal component suffers from various leakages where legal ivory finds its way into the illegal component. b. The legal ivory production in any southern African country arises from natural mortality 3, problem animal control 4, legal harvesting 5,24 confiscation or seizure of ivory from illegal hunters and traffickers 6,25 and from national and international trophy hunting 7.26 c. The illegal ivory production generally finds its way to ‘middlemen’ in the country of origin but some ivory may go directly to local buyers 15 who also purchase from middlemen 16. The local buyers sell ivory to local carvers some of whose products are bought oth locally and some by international tourists – who export the worked ivory illegally and this becomes part of the illicit financial flows. The middlemen arrange for the export of the raw illegal ivory 17 but some of this ivory may be seized by government authorities before it leaves the country 18.27 It then enters the ‘legal’ part of the flow diagram where it is usually kept in the main government ivory store separately from the legal ivory (see footnote 24). 23. Such losses may occur in Zimbabwe where the listing of its elephants on Appendix II is constrained by an annotation that precludes the export of worked ivory for commercial purposes. The solution for any large scale ivory carving business wanting to export carved ivory in commercial quantities is to export the carvings illegally and collect the payments in another country. 24. e.g. Zimbabwe has a domestic quota of elephants that may be used for staff rations or political celebrations. 25. This ivory is kept separately from other ivory collected in the field and, for no good reason, is regarded by CITES as ivory which should not be sold. 26. International trophies are not kept in the official ivory store and are usually exported directly. 27. Stiles et al. (2015) estimated that this type of seizure amounted to less than 10% of the illegal ivory leaving the country.
25
Figure 10: Licit and Illicit Financial Flows of Ivory in and from Southern Africa
26
d. Legal ivory movement begins in the field with the collection of the ivory defined in para a. on the previous page. However, some of the ivory from (say) natural mortality may be collected by local people before the authorities find it or, alternatively, corrupt government staff may not transfer all of the ivory originating in the field to the local repository. This becomes the first source of leakage 8 from legal ivory to illegal middlemen. The second source of leakage occurs when ivory is corruptly transferred from low security field repositories to illegal middlemen 9. After these the leakages, the remaining balance of ivory reaches the national ivory store 10. In corrupt situations ivory may even leak from the main ivory store to the illegal middlemen 11 and this requires the collusion of senior officials. e. Legal flows from national ivory stores may begin with some stockpile destruction 12. Stiles et al. (2015) estimated that such stockpile destruction was usually less than the total stock in any national ivory store. Most southern African countries refuse to destroy ivory.28 Those southern African countries whose elephant populations are listed on Appendix II of CITES are theoretically able to sell their ivory legally but, in practice, CITES has made it impossible for them to conduct regular annual sales 13.29 CITES cannot prevent the sale of ivory within its country of origin and most of the Appendix II countries sell small amounts of ivory to local legal carving industries 14.30 On the next page we attempt a balance sheet for the total ivory production from 2001-2015 estimated for the six countries – Botswana, Mozambique, Namibia, South Africa, Zambia and Zimbabwe – in which 99% of the southern African elephants occur. The final balance is our estimate of the Illicit Financial Flow out of southern Africa due to illegal ivory. ______________
28. Malawi and Mozambique have recently destroyed part of their ivory stockpiles: however, interviews with their wildlife department officials suggest that these are likely to be “once-off” events. 29. Two one-off sales of raw ivory have taken place since 1989. The first was in 1999 when Botswana, Namibia and Zimbabwe sold ivory to a single buyer (Japan) and the second took place in 2008 when Botswana, South Africa, Namibia and Zimbabwe sold to China and Japan. 30. However, these local carvings may only be sold to tourists for “non-commercial” purposes. In Zimbabwe commercial ivory carving industries were provided with receipt books in the form of CITES export permits which they could issue to any tourist (or local) to enable them to export their ivory carvings. In 1998, the Zimbabwe government ceased this practice and insisted that any tourist wanting an export permit could only obtain it from the Parks and Wildlife Authority. This caused a crash in sales of carved ivory. Tourists were not prepared to humour the bureaucracy. Ivory carvings were a thriving business in duty-free shops at airports but the requirement for an export permit (which entailed leaving the airport and returning to Harare to obtain a permit) killed the industry.
27
Table 9: Ivory Flow Balance Sheet 2001-2015 To be read together with Figure 10 and Table 4. Numbers in the column “#” are in Fig.10 Combined data: Botsw ana, M ozambique, Namibia, South Africa, Zambia and Zimbabwe IVORY PRODUCTION IN SOUTHERN AFRICA
#
ILLEGAL
Illegal Hunting
1
622,791,212
Natural mortality
3
133,384,575
Problem Animal Control
4
111,021,304
Legal harvesting
5
76,719,770
Confiscations
6
31,139,561
Trophy hunting
7
560,242,121
TOTAL LEGAL IVORY
2
912,507,331
LEGAL
TOTAL IVORY (legal and illegal) LEAKAGES
1+2
Value US$
352,265,210 8
70,453,042
Ivory reaching local repositories
Sum of 3,4,5,6 Ivory not reaching local repositories
281,812,168
Leakage from local repositories
9
56,362,434
Input to Government Ivory Stores
10
225,449,734
Leakage from Government Ivory Stores
11
22,544,973
TOTAL LEAKAGES IVORY REACHING MIDDLEMEN
See Note 1 below
1,535,298,543
Potential Field losses – 20%
Notes
Assumed 20% of stocks
Assumed 10% of input
149,360,449 622,791,212
1
less direct sales to local buyers
85,601,100
Net illegal ivory
537,190,112
plus total leakages
149,360,449
Total illegal Ivory from Illegal hunting See Note 2 below
Ivory leaving middlemen
17
686,550,561
less ivory seizures
18
50,000,000
See Note 3 below
156,225,202
See Note 4 below
792,775,763
Raw and worked ivory 2001-2015
plus illegal Worked Ivory ILLICIT FINANCIAL FLOW GOVT IVORY STORES
19
Existing stocks 10-11
202,904,761
plus Seizures
18
50,000,000
less Stockpile Destructions
12
600,000
See Note 5 below
less 2008 One-off sale under CITES
13
15,430,777
See Note 6 below
less Sales to local buyers
14
3,000,000
See Note 7 below
233,873,984
See Note 8 below
PRESENT BALANCE IN STORES
LEGAL IVORY EXPORTS FROM SOUTHERN AFRICA 2001-2015 2008 One-off sale under CITES
13
15,430,777
Legal worked ivory exports
14
20,678,000
Sport hunted trophies
7
560,242,121
TOTAL LEGAL EXPORTS
596,350,898
28
See Note 9 below
Sum of 13, 14, 7
Notes on Table 9 1.
Assum ed 5% of Illegal Hunting. The Southern African States disagree with the CITES position of not selling confiscated ivory and it has been included here a part of the tradeable legal ivory stocks.
2.
Based on proportion of raw ivory entering the worked ivory industry (Table 5B p19).
3.
The total seizures of raw and worked ivory over the period 2001-2015 are estim ated at $100 m illion of which $50 m illion (50%) rem ains in the Southern African States (last paragraph on page 20).
4.
After deduction of the value of the illegal worked ivory seizures and adding the value of the large illegal worked ivory m arket in Angola ($7.5 m illion) the total value for the illegal ivory m arket is $156 m illion.
5.
The only stockpile destruction between 2001-2015 was 2.4 tonnes in Mozam bique valued at US$0.6m .
6.
This sale is reported in W ijnstekers (2011 p 636).
7.
Assum es that the total legal sale to local buyers is about 20 tonnes valued at US$150/kg.
8.
Assum es seizures appear in stocks the year after they were acquired.
9.
Uses the result shown in Table 5A (p18).
______________ The final estimate for the illicit financial flow in ivory out of southern Africa (Table 9) is US$793 million for the period 2001-2015. We believe that this flow falls within the definition of an IFF given on the first page of this report, particularly because it involves either the illegally acquired commodity or money crossing international boundaries. Illegal trade, by its very nature, offers very little data that allow cross-checking of financial accounts. However, we have found one opportunity for a limited “forensic” analysis of possible IFFs that may have taken place in the course of an ostensibly legal sale of ivory under CITES (next page).
29
Possible Illicit Financial Flows detected by forensic auditing There have only been two ‘one-off’ sales of raw ivory since the CITES ivory trade ban was imposed in 1989. The first was held in 1999 and the second in 2008. The only countries eligible to participate in both of these sales were Botswana, Namibia and Zimbabwe whose elephant populations were listed on Appendix II at the 10th Conference of the Parties to CITES in 1997. South Africa’s elephant population was transferred to Appendix II at the 11th Conference of the Parties to CITES in 2000 and they were eligible to participate in the second ivory sale in 2008. The amounts of ivory sold and its value in the 2008 sale are shown in Table 10 below. Table 10: One-Off CITES Ivory Sale 2008 #
Botswana
Namibia
South Africa
Zimbabwe
1
Stock available kg
43,683
9,210
51,122
3,756
2
Ivory sold kg
43,153
7,503
50,945
3,764
3
Value of sales US$
7,093,551
1,147,369
6,702,695
487,162
4
Ivory Production 1999-2008 kg
145,401
7,649
20,818
98,406
5
Value of Production 1999-2008 US$
9,388,953
7,378,158
23,482,305
55,309,235
6
Difference (#4 - #2) kg
102,248
146
(30,127)
94,642
7
% Difference Estimated IFF US$
70.3
1.9
(144.7)
96.2
6,602,442
140,830
(33,982,679)
53,193,673
Notes Rows 1-3: The 2008 Ivory Sales – Records of the CITES Secretariat (Wijnstekers 2011 p636) Rows 4-5: Results from the Elephant Population Simulation Model (Martin 2016)
Interpretation It is assumed that Botswana, Namibia and Zimbabwe had emptied their government ivory stores at the 1999 ivory sales so that the ivory offered at the 2008 sales represented ivory production from April 1999 to October 2008. It would not have included any seized or confiscated ivory. The value of the sales in 2008 (Row 3) are not relevant to this calculation.31 The ivory value in 2008 (Row 5) is that shown in Fig.A1.2 (p 35). 1. Botswana is estimated to have produced 145 tonnes of ivory after the 1999 one-off sale and up to the 2008 one-off sale yet it was only able to offer 43 tonnes of ivory at the 2008 sale (about 30% of the estimated production). Some 100 tonnes of ivory went missing between 1999 and 2008 and the value of this ivory (US$6.6 million) can be construed as an Illicit Financial Flow. However, it would not be fair to attribute the loss to the keepers of the keys of the ivory store. The Botswana Wildlife Department had very low field coverage at this time (Martin 2008) and a large amount of ivory from natural mortality (Fig. A3.2 p49) would not have been found by government staff. It is more likely that local communities collected it and disposed of it ... which still constitutes an illicit financial flow. Legal citizen hunting could also have accounted for much of the loss.
31. Martin et al. (2012) estimated that the southern African countries lost more than 50% of the true value of their ivory at these sales because CITES had limited the sales to only two buyers (Japan and China).
30
2. The amount of ivory that Namibia put on the 2008 ivory sale matches almost exactly the estimated production from 1999 to 2008. There is no evidence of any illicit financial flow. 3. South Africa did not participate in the 1999 one-off sale so that it would not have cleared its ivory stocks at that time. The amount of 51 tonnes which South Africa sold on the 2008 sale far exceeds the estimated production from 1999-2008 (Row 4) and it must be concluded that they had a starting stock of 30 tonnes in 1999. No illicit financial flows can be inferred from the data. 4. Zimbabwe’s sale of only 4 tonnes of ivory in 2008 must cause eyebrows to be raised. The legal ivory estimated to have been produced from 1999-2008 is 98 tonnes. Where did it go? In the Zimbabwe elephant proposal to CITES CoP17 (PWMA 2016) data are presented from the CITES Trade Database (CITES cfm 2016) that show that raw ivory exports have been taking place from Zimbabwe since the date that it surrendered its Reservation against the Appendix I listing of elephants in 1998.32 This is an example of official trade in contravention of the rules of CITES (para (3) page 25). The fact that CITES rules are unworkable is mentioned in PWMA (2016) and the fact that Zimbabwe has breached none of its own legislation is relevant.33 However, the export of 95 tonnes of ivory (Row 6) worth US$53 million is bound to be treated by most of the world as an illicit financial flow. It certainly would be an illicit financial flow if the proceeds from the sale had not been repatriated to Zimbabwe and used by the PWMA to meet increasing demands on its recurrent expenditure to protect elephants. Without a detailed examination of the PWMA expenditure over the period 1999-2008 (which would require the Auditor-General’s blessing) this question cannot be answered. _______________ Conclusions We have estimated the illicit financial flows out of southern Africa for the period 2001-2015 as about US$793 million (Fig.10 page 26 and Table 9, page 28). This is the illegal component arising from illegal hunting and illegal trafficking. That there is collusion in the activities from both government staff and private individuals is almost certain: however, we are unable to identify the culprits at this stage (and may never be able to). We have also presented evidence to suggest that illicit financial flows are taking place in the legal component of ivory production. Firstly, we have estimated the leakages from legal component to the illegal component (ivory not entering or being illegally taken from government stores) at about US$150 million (Table 9, page 28). Secondly, we have speculated about possible illegal flows from the export of ivory in government hands (Table 10, previous page). These latter flows relate only to the four southern African countries who are legally able to trade in ivory and, at this stage, involve only Botswana and Zimbabwe for the period 1999-2008. The notional amounts involved are small compared to the illegal hunting component (Botswana US$7 million and Zimbabwe US$53 million). 32. The surprising thing about this record of trade is that Zimbabwe did not attempt to conceal it. 33. The Zimbabwe legislation provides for adherence to the CITES treaty but only in respect of the Articles of CITES. The current annotation to the listing of Zimbabwe’s elephant population on Appendix II that limits trade in raw ivory is, in Zimbabwe’s view, ultra vires since such an annotation it is not provided for in the Articles.
31
We point out the diversity existing amongst the six ivory-producing southern African countries. The pie-charts and tables on the last page of each of the six Appendices show that each country has its own unique system of management and sources of ivory production and no two are identical. In some countries (e.g. South Africa) the illegal component is very small and in others it varies from moderate (e.g. Namibia) to extreme (e.g. Mozambique). Schneider (2002 p25-33) analyses the determinants that cause informal (illegal) economies to increase. The intensity of regulations (often measured in the numbers of laws and regulations) is an important factor that reduces the freedom (of choice) for individuals engaged in the official economy. It is particularly relevant to the influence of CITES on illegal trade. A plethora of regulations (such as CITES has developed) lead to a substantial increase in labour costs in the official economy. These costs provide a strong incentive to operate in the illegal economy, where they can be avoided. Every measure of regulation is significantly correlated with the share of the illegal economy: more regulation is correlated with a larger illegal economy. The imposition of trade bans (to which CITES is particularly prone) actually results in an increase in the illegal economy. Governments should put more emphasis on improving enforcement of laws and regulations, rather than increasing their number. Some governments, however, prefer this policy option (more regulations and laws) when trying to reduce the informal economy, mostly because it leads to an increase in power of the bureaucrats and to a higher rate of employment in the public sector. It also gives the impression that they are doing something about the problem when they are not. The difficulties that assail the wildlife sector in southern Africa are very different from those affecting the mining sector and the agricultural sector. The ban on legal ivory trade is both the cause of the illegal trade and the corruption that is associated with it. The limited successes which CITES has had in reducing illegal trade have been those where species are not listed on Appendix I of the Treaty and sustainability is achieved through self-imposed trade quotas by the individual Parties. Wildlife use has become a highly emotive issue and western animal rights organisations are at the forefront in (a) persuading African governments to support banning of consumptive use of wildlife (e.g. trophy hunting) regardless of the effect it has on the national income and local community livelihoods and (b) persuading their own governments to support trade bans. It is necessary to be critical of the lack of scientific objectivity in this process. It appears that few of the advocates of bans are examining them in a comparative manner, i.e. whether they work or don’t work. The record from CITES performance since its inception in 1975 is that they don’t work. Few species that have been listed on Appendix I have been removed from Appendix I. The United States Endangered Species Act shows the same lessons. By their very nature, trade bans exclude the possibility of sustainable use and provide the perverse incentives for overexploitation of wild resources. __________
32
Appendix 1 Ivory Prices In order to derive the net income from an elephant population in any given year, two relationships are essential. The first is the relationship between elephant age and tusk weight and the second is the relationship between tusk weight and the price of ivory. The model uses a slightly modified version of Pilgram & Western’s (1986) formulae for age-specific tusk weights of male and female elephants (Fig. A1.1 next page). The price of ivory increases in a non-linear manner with increasing tusk size. The formula used is – Ivory price = A + B.( Tusk weight in kg)C US$/kg – where A, B and C are constants with the values A = 50, B = 80 and C = 0.75 The prices are shown in TableA1.1 below and Fig.A1.2 (page 35). The prices shown in the table are those that might be expected “at the farm gate” in southern Africa. The table shows the marked difference between male and female mean tusk weights. Male tusk weights may reach 45kg and would be worth close to US$100,000 for a single tusk. Few female tusks reach 10kg and a single tusk would be worth less than US$4,000. Table A1.1: Mean tusk weights, Ivory prices and Tusk values CONSTANTS FOR FORMULAE
CONSTANTS FOR FORMULAE
A
0.0453
A
0.8
B
1.731
B
1.53
C
50
C
50
D
80
D
80
E
0.75
E
0.75
F
0.00054
MALES
FEMALES
AGE
Mean tusk
Ivory Price
Single Tusk
AGE
Mean tusk
Ivory Price
Single Tusk
years
weight (kg)
US$/kg
value US$
years
weight (kg)
US$/kg
value US$
5
0.7
113
83
5
0.5
94
43
10
2.4
206
503
10
1.2
144
178
15
4.9
314
1,546
15
2.2
193
419
20
8.1
434
3,512
20
3.2
240
760
25
11.9
563
6,705
25
4.2
284
1,183
30
16.3
700
11,429
30
5.1
323
1,658
35
21.3
844
17,995
35
6.0
357
2,152
40
26.9
994
26,712
40
6.8
387
2,626
45
32.9
1,150
37,893
45
7.4
410
3,043
50
39.5
1,311
51,850
50
7.9
427
3,364
55
46.6
1,478
68,898
55
8.2
436
3,556
60
54.2
1,648
89,352
60
8.2
438
3,592
33
Figure A1.1: Relationship between elephant age and tusk weight
34
Figure A1.2: Relationship between elephant tusk weight and ivory prices
35
Appendix 2 The Zimbabwe Elephant Population34 Zimbabwe has four main elephant subpopulations located in the regions shown in Fig.A2.1 (p38). The habitats in all of these regions fall in the category of semi-arid savannas (White 1983) and, as such, are vulnerable to the impact of elephants. Despite significant illegal hunting in the Sebungwe and Zambezi Valley regions in recent years, elephant densities in 3 of the 4 regions exceeds 0.5 animals/km2 (Table A2.1 below). Table A2.1. Elephant regional populations and densities in Zimbabwe35 ZIMBABWE REGIONS Matabeleland North
Zambezi Valley
Sebungwe
Gonarezhou
TOTALS
Area (km )
24,959
17,003
15,527
5,339
62,828
Elephant Numbers 2014
53,991
11,657
3,407
11,452
80,507
Elephant Density (/km2)
2.2
0.7
0.2
2.1
1.3
2
The impact of elephants on the vegetation in these regions has been severe since the 1970s and is described in Martin et al. (2015, Appendices, p54-55). Population size The estimated numbers of elephants in the four regions are shown in Table A2.1 above and Fig.A2.2 (p39). Including Save Conservancy and various small populations outside the survey areas, the total number for Zimbabwe rises to 84,512 elephants. Population dynamics The parameters that determine the population dynamics of elephants36 are summarised below – Longevity: Elephants are generally assumed to live to about 60 years old (Laws 1966). Moss (2001) recorded the death of an adult female whose age was over 60 years. Gestation: The gestation period for elephants is well-established as 22 months (Smithers 1983). This together with the lactational anoestrus period which follows parturition determines the intercalving interval. Seasonal breeding: Although elephants may produce calves in any month of the year, most populations have a distinct breeding peak during the rains. Sex ratio: Sex ratio at birth is 1:1 with minor variations recorded in the literature, usually in small populations. The overall sex ratio in the population may vary slightly in favour of females depending on the history of management and illegal hunting. Moss (2001) recorded significantly higher mortalities for males (which included anthropogenic mortality) than for females over their entire lifetime.
34. The data given in this Appendix are largely taken from Zimbabwe’s elephant proposal to CITES CoP17. 35. These are figures for the surveyed areas. The figures for Gonarezhou do not include Save Conservancy. 36. These parameters have been used in the population simulation models of Martin (2004), Martin (2006), Craig et al. (2011), Stiles et al. (2015) and (Martin 2016).
36
The next four parameters are the main determinants of the rate of increase of elephant populations and they are typical of the large savanna populations in southern Africa. Age at first parturition: A range of values have been recorded in the scientific literature (8-20 years old). In the population simulation models referred to in the footnote below, 12 years is chosen as the typical age of first parturition for a population below carrying capacity. The lower end of the range for age at first parturition is about 10 years and the upper end is about 20 years.37 Intercalving interval: Female elephants generally produce a calf every four years throughout their main breeding lifetime (i.e. a fecundity of 0.25 including calves of both sexes). Freeman et al. (2008) found considerable variation in this parameter (2.3-5.3 years) over the years 19761995 Kruger National Park. The highest recorded mean calving interval is that of 9.1 years reported by Laws et al. (1970) for Murchison Falls Park North, Uganda. Fecundity declines in the last 10-20 years of life.38 Mortality: Both juvenile and adult mortality are ‘open-ended’ variables. There is no limit as to how high they can get. Because of this open-ended nature of mortality as a variable, it is capable of exerting a far greater influence on population growth than either fecundity or age at first conception. Data on adult mortality are scant. Craig (1992) gives perhaps the most insightful analysis of the rôle of mortality in large increasing elephant populations (the Sebungwe region in Zimbabwe) and shows that it must be about 0.5% between 10 and 40 years of age. Juvenile mortality refers to mortality in the first 9 years of life. A ‘typical’ value for the first year of life is 8% pa (Moss 2001) decreasing to 0.5% at 10 years old. All of the Zimbabwe subpopulations are depleted in the upper age classes to a variable extent dependent on the past history of illegal hunting, problem animal control, legal harvesting and trophy hunting. Details of these offtakes are given in the captions to the figures listed in the next subsection. Population trends A population simulation model (Footnote 35) has been used to approximate and explain the trends in the four regions over the period 2001-2014 (Figs.A2.3 p40, A2.4 p41, A2.5 p42, A2.6 p43). In each region the population has been split into two parts – the “Parks population” which is not subject to trophy hunting and the “Hunted population” where trophy hunting is permitted. The key results from this simulation are that (a) the Hunted part of the Sebungwe population will go extinct this year and the Parks part will go extinct next year, and (b) the Hunted part of the Zambezi Valley population will go extinct in 2021 and the Parks part will go extinct a few years later. 37. Laws et al. (1975) recorded conception being delayed until about 20 years of age in a high density population in Uganda (Murchison Falls Park South). 38. Over the last 20 years of a female’s lifetime the population simulation model reduces fecundity from 0.25 to 0.01.
37
Figure A2.1: ZIMBABWE: REGIONAL POPULATIONS The map shows the four national aerial survey regions and the smaller populations outside the survey areas based on Map 6 in Dunham (2015)
38
Figure A2.2: ZIMBABWE ELEPHANTS: Total Population and Regional Subpopulations The figure is constructed from Zimbabwe survey data over the period from 2001-2014. These are: 2001 – (Dunham 2002a, 2002b, 2002c), Dunham & Mackie (2002), Mackie (2002a, 2002b); 2003 – Dunham (2004); 2006 – Dunham et al. (2007); 2007 – Dunham et al. (2007); 2009 – Dunham et al. (2009); 2013 – Dunham et al. (2013); 2014 – Dunham et al. (2015), Dunham & van der Westhuizen (2015).
39
Figure A2.3: MATABELELAND NORTH ELEPHANT POPULATION (Population simulation) PAC was fixed at 30 animals (24 males and 6 females) for the entire simulation period from 2001-2014. The Trophy Hunting quota was set at 0.5% of the Hunted population over the same period. During the period 2000-2007 the Parks population declined at about 4% pa and the hunted population increased at about 1% pa. Estimates from the simulation model indicate that this would have resulted from 7.9% illegal hunting in the Parks area and 3.2% in the Hunted area during this period. From 2007 onwards, illegal hunting was set at 0.5% of the Hunted population. Between 2008-2014 the Parks population increased to about 44,500 animals which required that the illegal hunting remained below 1.36% for the period concerned. The Hunted population, however, increased from 6,000 animals to 9,500 animals which required a rate of increase well in excess of normal growth rates. It is assumed some animals must have moved from the Parks population to the Hunted area during this period. The immigration needed to achieve the increase in the Hunted population is about 0.6% pa of the Parks population (bars in figure). After providing the immigration required to enable the Hunted area population to reach 9,500 animals in 2014, the Parks population required the illegal hunting to be set at 0.8% of the population to achieve the match with the population estimate.
40
Figure A2.4: ZAMBEZI VALLEY ELEPHANT POPULATION (Population simulation) PAC was set at 25 animals/year for the Parks population and 50 animals/year for the hunted population from 2001-2014. The Trophy Hunting quota was set at 0.5% of the Hunted population over the same period. Between 2001 and 2003 both the Parks population and the Hunted population increased at a rate exceeding normal growth rates. The 2001 estimates were increased slightly (remaining well within the confidence intervals) to enable a match to be achieved using normal growth rates during this period. From 2004-2014 both the Parks population and the Hunted population declined significantly, the decline in the Hunted population being the more severe (from 15,700 to 8,700 animals). A fixed population offtake was used to simulate the decline during this period and in the Hunted Area the annual offtake that achieves a match with the population estimates is about 1,500 animals per year. At this rate the population will be extinct in 2021.
41
Figure A2.5: SEBUNGWE ELEPHANT POPULATION (Population simulation) Illegal hunting is set at 1% pa for both the Parks population and the Hunted population from 2000-2006. PAC is fixed at 40 males and 8 females (about 0.5% of the total population in 2001) and the Trophy Hunting quota is set at 0.5% of the Hunted population throughout the simulation period from 2000-2016. During the period 2000-2006 the Parks population declined at about 6% pa and the hunted population increased at about 6-8% pa – which exceeds any normal rate of population increase. It is assumed that animals moved from the Parks population to the Hunted area during this period. The immigration needed to achieve the increase in the hunted population amounts to 5.34%pa of the Parks population (bars at the bottom of the figure). From 2006 onwards, illegal hunting is assumed to be a constant annual harvest. In the Parks areas this harvest is 660 animals per year which reduces the population to 1,413 elephants in 2014 and results in extinction in 2017. In the Hunted Areas the harvest is 1,216 animals per year which reduces the population to 1,998 elephants in 2014 and results in extinction in 2016.
42
Figure A2.6: GONAREZHOU ELEPHANT POPULATION Population estimates and 95% confidence intervals for the Gonarezhou NP elephant population – 1991-1998: data contained in Dunham (2012); 2001 – Dunham (2002); 2007 – Dunham et al. (2007); 2009 – Dunham et al. (2009); 2013 – Dunham et al. (2013); 2014 – Dunham & van der Westhuizen (2015). The population simulation model is based on a decline from 1991 to 1996 caused by drought mortality and illegal hunting at 12.89% of the population followed by a rapid increase after 1996 caused by an age structure depleted in animals younger than 10 years combined with a reduction in intercalving interval (45 months) and age at first parturition (10 years). After 1996 the model includes Problem Animal Control (~0.5%), trophy hunting (0.1%) and illegal hunting (0.1%).
43
Threats Illegal hunting is by far the biggest proximate threat to elephants in the Sebungwe and Zambezi Valley but, in the longer term, the high densities in Matabeleland North and the Gonarezhou ultimately pose an equally serious threat. The overabundance of elephants could result in whole-scale population die-offs39 and, at the same time, the destruction of habitats will jeopardise the survival of other species. Far from these alarming prognostications being arguments for increased law enforcement effort and renewed efforts to enforce the ivory trade ban, the opposite is true. Unless the ivory trade ban is lifted, these populations almost certainly will go extinct (Stiles 2014). The population simulation model has been used to predict the expected offtakes from Zimbabwe’s four regional populations in 2015. Table A2.2: Deaths predicted in the Zimbabwe elephant population in 2015 NM = Natural Mortality, PAC = Problem Animal Control LH = Legal harvesting, IH = Illegal hunting, TH = Trophy hunting Population
NM
PAC
LH
IH
TH
Total deaths
MATABELELAND NORTH Parks
48,041
738
228
240
86
Hunted Area
8,426
127
45
42
0
57
271
Subtotals ...
56,467
865
273
282
86
57
1,563
Parks
2,911
44
6
15
224
Hunted Area
7,522
96
50
38
1,437
38
1,659
Subtotals ...
10,433
140
56
53
1,661
38
1,948
Parks
839
11
30
4
640
Hunted Area
845
11
48
4
1,212
0
1,275
Subtotals ...
1,684
22
78
8
1,852
0
1,960
Park & Hunted Area
11,787
185
13
19
0
13
230
TOTALS . . .
80,371
1,212
420
362
3,599
108
5,701
% of population
1.5
0.5
0.5
4.5
0.1
7.1
% of deaths
21.3
7.4
6.4
63.1
1.9
100.0
1,292
ZAMBEZI VALLEY 289
SEBUNGWE 685
GONAREZHOU
The “Parks” areas include all the National Parks within the region and it is assum ed that there is no trophy hunting in them . The “Hunted Area” includes all State Safari Areas in the region and som e Forest Land and Com m unal Land where hunting occurs.
The data are not yet available to confirm these predictions. The correct data for the number of elephants killed illegally (the largest part of the deaths) and the numbers dying naturally may never be available. 39. In Hwange National Park small-scale die-offs occurred in 2005 and 2012.
44
With the pressures on these four regional elephant populations, the national ivory production is less than would be expected from an unexploited population. Using the population simulation model referred to on the previous page, the legal ivory production in 2015 is estimated as slightly over 6 tonnes with a value of about US$3 million. The illegal production is nearly double this amount (11.5 tonnes) but its value is not much greater (about US$3.2 million).40 The price of ivory has risen since the ban on international trade came into place in 1989 and Bradley-Martin & Vigne (2014) noted that it had increased three-fold in China since 2010.41 The prices assumed for this proposal are shown in Fig.A1.2 (p35). The deaths, ivory production and ivory value for the period 2001-2015 are shown in Fig.A2.7 (page 46). Zimbabwe presently holds about 90 tonnes of raw ivory in the government ivory store estimated to be worth about US$50 million if it were sold on open auctions in the manner done by Zimbabwe from 1977 to 1989. The merits of this method of sale are described by Child (1995) and it is Zimbabwe’s chosen way of disposing of raw ivory.42 The extinction projected by the simulation model has resulted in calls for increased law enforcement and strengthening of the ivory trade ban as possible solutions. It is actually the lifting of the ivory trade ban that will assist in halting the population decline. Lifting the trade ban will provide an opportunity to explore and manage a well-regulated trade in elephant and elephant products. In addition, the ivory trade will generate income for rural communities thereby providing further incentives for elephant conservation. __________________
40. Because the ivory is coming mainly from two regions where the populations are rapidly approaching extinction, the mean tusk weight is low and, hence, the ivory value is low. 41. The prices given by Bradley-Martin & Vigne (2014) are end-market prices for raw ivory and it cannot be expected that the price realised at the point of export from Africa would be as high. Although Zimbabwe managed to realise export prices before the ivory trade ban in1989 that were close to the end-market price, this was generally not the case for most African range states exporting ivory. We have assumed that the export price from Africa (if there were a legal market) would be half of the price reported by Bradley-Martin & Vigne (2014). 42. For the period 1979-1987 Princen (2003) observes: “Of the ivory-producing countries, only Zimbabwe brought in a level of revenue ($63-$76/kg) close to the value of raw ivory earned in Japan ($85-$99kg). For other producer states, the revenues ranged from $6-$15/kg. Zimbabwe, unlike the other states, had actively managed elephants during the 1980s, marketing ivory in such a manner to gain the largest proportion of rents possible.”
45
Figure A2.7: Elephant Deaths, Ivory Production and Ivory Value in Zimbabwe for the period 2001-2015
46
Appendix 3 The Botswana Elephant Population This population has been the most difficult to analyse – or, rather, to decide which population estimates are the most likely to be correct. To perform the simulation over the period 2001-2015 requires a minimum of three population estimates. The first two are no problem – the estimates from the African Elephant Database for 2002 and 200643 have been used. When the best-fit line through the dry season estimates for these years is projected to the year 2015 it produces an estimate for over 200,000 animals in 2010. The DWNP estimate for 2012 (DWNP 2012) is 207,545 animals. Chase (2011) produced a population estimate for Northern Botswana in 2010 of 128,340 animals ... which lies a long way below the predicted value of about 204,000 for this year (Craig et al. 2011). His estimate for the upper 95% confidence interval was 138,277 animals which is still woefully short of the expected 200,000 animals. The “minimum estimate” for DWNP (2012) is 202,176 animals. In a more recent survey, Chase (2014) estimated the population at 129,939 animals – which is a lot less than the DWNP (2012) estimate. If Chase’s estimates are correct, then there would have had to have been total carnage taking place in northern Botswana – a scenario that is not consistent with field reports. Chase himself remarks on how few carcases were found in the 2014 survey. The DWNP estimate of 207,545 animals for 2012 has been used as the third data point for the population simulation. Craig et al. (2011) noted the very high growth rate of the Botswana population in the years after 1996 (over 6% per annum) and found that a depletion of males in the older age classes accounted for the phenomenon. This has been built into the simulation model used in this analysis. Also included in the simulation model is emigration from the northern Botswana population beginning in 2004 when the population exceeded 160,000 animals (a density of 0.7 elephants/km2). The number of animals emigrating was set at 5% of the annual surplus above this threshold. The number of elephants killed under Problem Animal Control was set at about 0.25% of the population and it was also assumed that an annual legal offtake of 0.1% of the population took place. Fecundity was set at 0.28 calves per adult female for the period 2000-2006 and reduced to 0.25 from 2007 onwards. The trophy hunting quota was set at 142 for the period 2000-2005, 235 for the period 2006-2013 and 20 for the years 2014-2015 following the domestic hunting ban in 2013. The level of illegal hunting was adjusted so that the population numbers in the year 2002, 2006 and 2012 coincided with the estimates discussed above. This entailed using a starting population of 127,013 animals in the year 2000 with the illegal hunting set at 0.1%, adjusting the illegal hunting to 0.425% in 2003 and setting it at 0.395% for the remaining years from 20072015. The performance of the population under these assumptions is shown in Fig. A3.1 and Fig.A3.2 (next two pages). The total ivory produced from 2001-2015 was 711 tonnes with a total value of $621 million of which 82% was legal and 18% was illegal. Trophy hunting contributed more than half of this value. 43. Blanc et al. (2003) – 143,103 animals in 2002; Blanc et al. (2007) – 175,487 animals in 2006.
47
__________________ Figure A3.1: Botswana Elephant Population numbers, Emigration and Ivory Value
48
Figure A3.2: Botswana Elephant Deaths, Ivory Production and Ivory Value 49
Appendix 4 The Mozambique Elephant Population Four population estimates have been used to simulate the Mozambique population over the period 2001-2015 (Table A4.1 below - red font). The levels of illegal hunting required to obtain a match with the population estimates is shown in the table. The number of elephants killed under Problem Animal Control was set at about 0.25% of the population and it was also assumed that there was no legal an annual legal offtake of 0.1% of the population took place. The trophy hunting quota for each year of the simulation was taken from the CITES website.44 Fecundity was set at 0.25 calves per adult female for the entire period. Table A4.1: Simulation of the Mozambique Elephant Population 2001-2015 IH = Illegal Hunting, AEDSR = African Elephant Database Status Report Year
Estim ate
2000
15,947
2002
17,506
2003
17,506
2006
20,492
2008
22,147
2013
25,997
2014
10,438
IH
NOTES
0.2%
IH set at 0.2%, starting population of 15,947 in 2000 required to achieve 17,506 anim als in 2002 (AEDSR estim ate [D+P+P]).
0.825%
IH increased to 0.825% in 2003 in order to achieve 22,147 anim als in 2008 (AGRECO 2008 estim ate). AEDSR estim ate for 2006 is 19,108 and this has been adjusted to 20,492.
Fixed 2,555
Illegal hunting changed to a fixed offtake of 2,555 elephants/year from 2009 onwards to give 10,438 elephants in 2014 (W CS Great Elephant Census). AEDSR estim ate for 2013 is 25,997. This estim ate was not used.
As with the Great Elephant 2014 census estimate for Botswana (page 36), we have concerns about 2014 estimate for Mozambique (10,438 elephants). The apparent loss of some 12,000 elephants from 2008-2014 is difficult to accept. However, carcase ratios were high and certain strata in the survey appeared to have been ‘cleaned out’ of elephants so the estimate has been allowed to stand. By accepting it, the AEDSR estimate for 2013 (26,000 elephants) is called into question. The performance of the population under these assumptions is shown in Fig. A4.1 and Fig.A4.2 (next two pages). The total ivory produced from 2001-2015 was 218 tonnes with a total value of $129 million of which 29% was legal and 71% was illegal. Trophy hunting contributed 23% of the total value. __________________
44. Annual trophy hunting quotas notified to CITES: 2000-2003 – 10; 2004-2008 – 40; 2009 – 60; 20102015 – 100. From 2011 onwards (during the population ‘crash’), the male sector of the population was unable to support a quota of 100 and, in theory, a significant number of females would have been taken.
50
Figure A4.1: Mozambique Elephant Population Numbers and Ivory Value
51
Figure A4.2: Mozambique Elephant Deaths, Ivory Production and Ivory Value 52
Appendix 5 The Namibia Elephant Population Namibia (2016) gives 12 estimates for its elephant population over the period 2000-2016 and 10 of these have been used in the population simulation model. The rate of increase of the population over the period 2001-2015 exceeds that expected from normal breeding parameters (4.7%) and we have taken into account the emigration from the Botswana elephant population over this period (Craig et al. 2011, Appendix 3 this study, page 47) which is very likely to have given rise to high growth rate of the Namibian population.45 The modelled immigration into Namibia is shown in Fig.A5.1 on the next page. Fecundity was set at 0.25 calves per adult female for the entire simulation period. The number of elephants killed under Problem Animal Control was set at about 0.1% of the population and it was assumed that the only other legal offtake from the population was trophy hunting. The trophy hunting quotas used are those given in Namibia (2016, section 3.1 Sport Hunting, p5). The simulation was done as follows. Illegal hunting was set at 1% of the population from 2001-2008 and increased to 3.1% from 2009-2015.46 This timing combined with these percentages gave the closest fit to the full set of population estimates, measured by the sum of squared differences. The Namibian results for deaths, ivory production and ivory value are shown in Fig.A5.2 (p55).
___________________
45. The high numbers of elephants in Khaudom National Park (4,150) and Nyae-Nyae Conservancy (2,263) (Namibia 2016, page 4), both of which border onto Botswana reinforce this assumption. 46. The illegal hunting data provided by Namibia (2016, Annex 2) indicates an increase in illegal hunting after 2009.
53
Figure A5.1: Increase in Namibian elephant population 2001-2015
54
Figure A5.2. Namibia: Deaths, Ivory Production and Ivory Value 2001-2015 55
Appendix 6 The South Africa Elephant Population The South Africa elephant population was relatively easy to simulate using the three population estimates from the African Elephant Database that lie in the time span 2001-2015. The estimates used were 2002 – 14,926 (Blanc et al. 2003, Definite, Probable and Possibles), 2006 – 18,485 (Blanc et al. 2007, D+P+P) and 2013 – 25,027 (Blanc et al. 2014, D+P+P). The fecundity of the population was increased very slightly to achieve a closer match with the estimates.47 Problem Animal Control was set at about 0.1% of the population, the trophy hunting quota was fixed at 0.1% of the population and provision was made for an additional legal offtake of 0.1% of the population. The starting population in the year 2000 was set at 13,750 animals and with illegal hunting set at 0.1% this resulted in a population of 15,126 elephants in 2002 and 18,287 elephants in 2006. Illegal hunting was increased to 0.26% of the population in 2007 and this gave a close match with the 2013 estimate of 25,027 elephants (Fig.A6.1 next page). The number of deaths, ivory production and ivory value generated over the period 2001-2015 is shown in (Fig.A6.2 p58). A feature of the South African population (compared to most of the other southern African countries) is the very low rate of illegal hunting. The ivory production comes almost entirely from natural mortality and trophy hunting. The low trophy hunting quota of 0.1% (which results in the offtake from 2001-2015 increasing from 15 to 28 animals) gives a very high mean tusk weight (over 100lbs per tusk) and the ivory value associated with this is extremely high (Fig.A1.2 p35) ... which should be reflected in the income from the trophy hunts.
_____________________
47. Average age at first parturition was reduced to 11.5 years and intercalving interval was set at 46 months.
56
Figure A6.1: Simulation model of the South African Elephant Population 2001-2015
57
Figure A6.2: Elephant deaths, Ivory production and Ivory value for the South African Elephant population 2001-2015 58
Appendix 7 The Zambian Elephant Population The estimates used to simulate the Zambian population were 2002 – 27,049 (Blanc et al. 2003, Definite, Probable and Possibles), 2006 – 28,418 (Blanc et al. 2007, D+P+P) and the estimate of 21,760 elephants in 2015 by DNPW (2016). This last estimate is considerably higher than the estimate from the African Elephant Database in 2013 – 15,113 (Blanc et al. 2014, D+P+P). Problem Animal Control was set at about 0.4% of the population, the trophy hunting quotas were taken from the CITES database48 and provision was made for an additional legal offtake of 0.5% of the population. The starting age structure for the population was that of a population that had been subjected to 3% illegal hunting for a number of years. The starting population in the year 2000 was set at 26,268 animals and with illegal hunting set at 3% this resulted in a population of 27,048 elephants in 2002. Increasing the illegal hunting to 3.12% resulted in a population of 28,419 in 2006. Illegal hunting was increased to 6.405% of the population in 2007 and this gave an exact match with the 2015 estimate of 21,760 elephants (Fig.A7.1 next page). A feature of the population decline shown in the figure is the matching decline in the value of ivory derived from an unsustainable offtake from the population. The number of deaths, ivory production and ivory value generated over the period 2001-2015 is shown in (Fig.A7.2 p61). The ivory production is dominated by the illegal hunting. In 2007 the illegal offtake was over 17 tonnes and, as the population declined, it fell to less than 10 tonnes in 2015.49 The relatively low value of the trophy hunting quota is due to a decline in the mean tusk weight of the trophies from 33kg/tusk in 2006 to less than 16kg/tusk in 2015. _______________
48. The trophy hunting quotas between 2001 and 2015 were as follows: 2001-2002 – zero, 2003-2009 – 20, 2010-2011 – 80, 2012-2013 – zero, 2014-2015 – 80. The years in which there was no trophy hunting show up clearly in Fig. A7.1 on the next page. 49. This a consequence of using an offtake which is a percentage of the population. Using a fixed offtake for a population in decline (as was done in the Mozambique simulation – Table A4.1 p50) gives a more severe impact of the illegal hunting.
59
Figure A7.1: Simulation model of the Zambian Elephant Population 2001-2015
60
Figure A7.2. Zambia: Deaths, Ivory Production and Ivory Value 2001-2015 61
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PW MA (2016). Proposal to delete the annotation for the Zimbabwe population of Loxodonta africana on CITES Appendix II. https://cites.org/sites/default/files/eng/cop/17/prop/ZW_African_Elephant.pdf . 19pp + references, figures and annexes. Resource Africa (2016). The Development of a SADC Common Position for CITES COP17. Consultancy for the SADC Secretariat, funded by GIZ. 31pp Said MY, RN Chunge, GC Craig, CR Thouless, RFW Barnes & HT Dublin (1995). African Elephant Database. Occasional Paper No.11 of the Species Survival Commission, IUCN, Gland, Switzerland. 225pp Schneider F (2002). Size and measurement of the informal economy in 110 countries around the world. Paper presented at a W orkshop of Australian National Tax Centre, ANU, Canberra, Australia, July 17, 2002. 50pp Schwarz M (2016). In: “Link Between Ivory Price Drop and China’s Trade Ban Questioned”. A Voice for Elephants, January 2016. http://voices.nationalgeographic.com/2016/01/26/link-between-ivory-price-dropand-chinas-trade-ban- questioned/ Smithers, RHN (1983). The M ammals of the Southern African Subregion. University of Pretoria, Pretoria, RSA. 736pp Stiles D (2014). Can elephants survive a continued ivory trade ban? ELEPHANTS: A Forum for Discussion, September 15, 2014
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ASSESSING THE EXTENT AND IMPACT OF ILLICIT FINANCIAL FLOWS IN THE WILDLIFE AND TOURISM SECTORS IN SOUTHERN AFRICA
Volume 3 Illicit Financial Flows in Rhino Horn from South Africa between 2000-2016 Rowan Martin Resource Africa ___________________________________________________________________________ TABLE OF CONTENTS PREFACE. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii INTRODUCTION. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 I. RHINO NUMBERS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1. The official record of the numbers of rhino killed illegally. . . . . . . . . . . . . . . . . . . . 3 2. Sustainable offtake from the Kruger National Park white rhino population. . . . . . . 4 II. SUPPLY AND DEMAND. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. Supply. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Horn weight from illegal hunting. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Horn weight from ‘pseudo-hunting’. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Privately-held stock of rhino horn. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. Demand.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Demand and the Asian economy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5 5 5 5 6 8 8
III. SIMULATION MODEL. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. Hypothetical Demand. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. Adjusted Demand. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. Available stock of horn.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. Volume of private sector horn traded. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. Goodness-of-fit. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
10 10 12 12 12 12
IV. RESULTS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. Goodness-of-fit. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. The relationship between supply and demand. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. Rate of change of demand.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. Effects of the moratorium on private trade. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. Carcase finding factor in Kruger National Park. . . . . . . . . . . . . . . . . . . . . . . . . . . . 6. Illicit Financial Flows. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
13 13 14 16 16 16 17
VI. DISCUSSION. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
List of Tables 1.
Official record of rhinos illegally killed in South Africa . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.
Sustainability of illegal hunting in Kruger National Park 2008-2016 .. . . . . . . . . . . . . . . 4
3.
Weight of rhino horn generated by illegal killing 2000-2016. . . . . . . . . . . . . . . . . . . . . . 5
4.
Weight of rhino horn originating from pseudo-trophy hunting 2000-2015 . . . . . . . . . . . 6
5.
Rhino horn stock on private land 2000-2013 including dehorning production .. . . . . . . . 7
6.
Best fit from the simulation model.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
7.
Rate of change of demand 2001-2016. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
8.
Illicit Financial Flows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
9.
Financial Flows under a legal trade scenario .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
List of Figures 1.
Official record of rhinos killed illegally in South Africa since 2000 . . . . . . . . . . . . . . . . 3
2.
Rate of increase of Chinese per capita income and rate of illegal hunting of rhinos . . . . 9
3.
Schematic diagram of the simulation model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
4.
Estimated and predicted numbers of rhino killed 2000-2016 . . . . . . . . . . . . . . . . . . . . . 14
5.
Demand and supply of rhino horn 2000-2016. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
6.
Best fit for Kruger National Park carcase finding factor .. . . . . . . . . . . . . . . . . . . . . . . . 16
7.
Types and Prices of rhino horn items. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
______________
ii
Illicit Financial Flows in Rhino Horn from South Africa between 2000-2016 Rowan B. Martin PREFACE With some 18,000 white rhinos and 2,000 black rhinos South Africa holds 83% of all the rhinos in Southern Africa. For this reason we have limited our study to the South African population. The dramatic escalation in illegal killing of these rhinos for their horn since 2008 has raised worldwide concern and led to debate on appropriate control measures (Biggs et al. 2012). An underlying problem is the scarcity of data available to understand the processes involved and on which to base decisions. A simulation model has been used to reconstruct the history of rhino population numbers, illegal hunting and illegal trade in South Africa from 2000-2016 and a numeric optimiser has been used to find the most likely values of the key parameters determining the levels of illegal hunting and trade. By comparing the numbers of rhino known to have been killed with the numbers predicted to be killed by the model and using a goodnessof-fit index based on the sum of squared differences between the two, it has been possible to make some definitive statements about these parameters. The original purpose of this investigation was to examine the impact of the moratorium introduced in 2008-9 on domestic trade in rhino horn. A cursory inspection of the graph showing numbers of rhinos illegally killed from 2000-2012 suggests that the moratorium had been responsible for an upsurge in illegal hunting starting in 2008 (Jacobsen 2013). When analysed with the simulation model, a more likely explanation for the escalation in illegal hunting emerged. The informal trade of rhino horn within the private sector (that obviously resulted in illegal exports) had been able to meet the demand from Asia up until 2008 (assuming the demand is a monotonic function over the period 2000-2013). Our diagnosis is that from 2008 onwards the supply of horn was unable to meet the demand and this, rather than any legal constraint, caused the spectacular rise in numbers of rhino killed illegally. The original work on this topic was done in 2013 (Martin 2014). Since then three additional years of data have become available. Most importantly, the annual numbers of rhino killed in South Africa appears to have levelled off ... albeit at a level that is still unsustainable. As a result of this the shape of the demand curve has been altered to resemble a logistic curve. The earlier model predicted that nearly half of the illicit flow of rhino horn would be provided by (illegal) trade from dehorning rhino on private sector rhino farms, the balance coming from illegal hunting and, to a lesser extent, from so-called “pseudo-hunting”. This conclusion has altered significantly. The illicit financial flow from rhino horn over the period 2000-2016 amounts to US$702 million - 93% of this amount occurred from 2008 onwards. The contributions from three components are 1) illegal hunting – $351 million (50%); 2) illegal private sector trade – $293 million (42%) and 3) ‘pseudo-hunting’ – $59 million (8%). This last component should not be treated as an illicit financial flow and, by removing it, the IFF is reduced to $644 million. With demand showing signs of levelling off, the sustainable production of rhino horn from dehorning rhino on private land and community rhino farms should be able to meet the hypothetical demand by 2021 if a legal export trade is allowed. Law enforcement problems will decrease and the present illicit financial flows should be replaced by the generation of legal wealth for government, private and communal landholders. It may be possible to achieve this in a true market situation where price, supply and demand are able to interact with each other to realise sustainability and stability. _____________
iii
INTRODUCTION A plethora of models relating to trade in rhino horn are available in the literature. Most of them address the burning question of whether or not there should be a legal trade in horn (e.g. Martin (2010), Hall (2012), Jacobsen (2013), Di Minin et al. (2015), Crookes & Blignaut (2015)) and most of them are designed by economists. Ruitenbeek & Cartier (2001, Chapter 6) give an insightful discussion of the various types of models employed by economists and assess the extent to which, firstly, they adequately describe the system being studied, secondly, they provide a greater understanding of the system and, thirdly, they are capable of predicting the future performance of the system. They remark “Most conventional economic modelling is deterministic.” and “Such conventional modelling cannot address many of the attributes of complex systems”. The current illegal trade in rhino horn is undoubtedly a complex system. Crookes & Blignaut (2015) claim to have designed such a model but their resulting design is what Ruitenbeek & Cartier (2001, Chapter 2) would describe as a “complicated system” – one with many elements that once understood still behave in a predictable manner. What is missing from the many models that attempt to state whether a legal trade in rhino horn would work is any consideration of adaptive management (Holling 1976). Adaptive management is the required research methodology for understanding complex systems. The only use of an economic model is to provide a starting hypothesis at the inception of any project based on adaptive management (Martin 2016). The decision to trade in rhino horn should be taken on the grounds that the present trade ban is not working – rather than on predictions from a model. Having decided to trade, the research consists of monitoring the outcome of the decision and making changes to the management system, the hypothesis and, if necessary, the original objective ... as the project progresses (Bell 1986). At this stage the reader will be asking the question “what has all the above discussion got to do with the illicit financial flows out of southern Africa in rhino horn?”. Everything. At the end of this volume we will argue that the CITES trade ban on rhino horn is the cause of the illicit financial flows. _______________ This work is based on the record of rhinos killed illegally in South Africa since the year 2000 and the timing and extent of interventions aimed at reducing illegal trade in rhino horn. The initial motivation for the paper arose from work done by Tanya Jacobsen that suggested that the introduction of a moratorium in domestic trade in rhino horn in 2009 cut off the supply of (mildly) illegal horn coming from the private sector and caused the surge in illegal hunting which then followed (Jacobsen 2013). Some early attempts to simulate the Jacobsen’s hypothesis using the available data on stocks of rhino horn held by the private sector, the numbers of rhino that had been ‘pseudo-hunted’ and the record of illegally hunted rhino ran into difficulties. It became evident that the situation was not as simple as Jacobsen’s (2013) table portrayed. 1
Any attempt to reconstruct the history of the rhino horn trade (legal and illegal) out of South Africa from the year 2000 to the present date is bedevilled by an absence of data. The only 'hard data' are the numbers of rhino recorded as illegally hunted in each year since 2000 and the numbers of rhino killed in legal trophy hunts (including "pseudo-hunts" where the horn enters the Asian market). However, the 'true' number of rhino killed over this period may be higher since a significant number of carcases in Kruger National Park (which accounts for 60-70% of the total number killed) are not found. This effect is less pronounced for the remainder of the rhino range since properties are smaller and monitoring is more intense. The stocks of rhino horn held by the private sector are another unknown and there has been considerable reluctance by private rhino owners to disclose this information (Hall-Martin et al 2008). Thus any simulation model constructed to analyse the dynamics of trade over the period 2000-2013 is an edifice built on shaky foundations. Modern computing tools allow a large number of variables to be examined simultaneously and, given crude estimates of numbers, the potential exists to explore the values that can be taken up by a number of variables in order to satisfy the conditions imposed by the few reliable data. This analysis sets out to establish the bounds within which definitive statements can be made about trade and illegal killing of rhinos. The exercise allows an unlimited uncertainty at the outset to be reduced by several orders of magnitude given a few reasonable assumptions. As such it is a ‘detective story’ based on plausible inference. Having established some ‘best-fit’ values for the key variables, the implications for any future trade in rhino horn are considered. ____________
2
Analysis I. RHINO NUMBERS In 2013 the South African rhino population numbered some 21,000 animals of which 19,000 were white rhino and 2,000 were black rhino (Emslie et al 2012). Of the 19,000 white rhino, 13,000 are in State Protected Areas and 6,000 are on private land (Rademeyer 2015). The numbers in 2016 are probably slightly less than this as a result of continued unsustainable illegal killing. In the analysis which follows, I have not distinguished between black and white rhino. In calculating horn weights the data for white rhino have been used since they make up 91% of the South African rhino population. The steps in the analysis are described below. 1.
The official record of the numbers of rhino killed illegally from 2000-2016 in South Africa is shown in Fig.1 below and Table 1 on the next page –
Figure 1: Official record of rhinos killed illegally in South Africa since 2000 3
Table 1. Official record of rhinos illegally killed in South Africa (A) and adjusted numbers assuming a finding factor of 60% for carcases in KNP (B)1 Year 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
A
7
6
25
22
10
13
24
13
83
122
333
448
668 1,004 1,215 1,175 1,172
B
10
9
35
31
14
18
33
18
116
171
430
616
951 1,403 1,766 1,726 1,641
Data from Jacobsen (2013), Condon (2013), Rademeyer (2016), Mike Knight (pers.comm.)
The best-fit curve (Fig.1) predicts that the loss will level off at 1,761 deaths in 2018. ____________ 2.
Sustainable offtake from the Kruger National Park white rhino population. A full population survey of the KNP white rhino population took place in 2010 and yielded an estimate of 10,621 animals (Ferreira et al 2012 in DEA 2013). The sustainable offtake from the population would be roughly equal to the rate of population increase. Owen Smith (1988) found typical population growth rates for the Hluhluwe-Umfolozi white rhino population of more than 9%. Martin (2012) used the white rhino population data from the HluhluweUmfolozi complex (long term average rainfall close to 900mm) and the Waterberg Plateau National Park in Namibia (400mm average rainfall) to construct a relationship between rhino population growth rates and mean annual rainfall – Rate of population increase (%) = 7.188 + 0.002116 x Mean annual rainfall (mm) This curve has very flat characteristic so that even when the mean annual rainfall is under 400mm the rate of population increase is close to 8% pa. Assuming that a population growth rate of 8% would apply to white rhino throughout most KNP, I have constructed a simple table (Table 2 below) that shows the population growth expected in the years 2010-2015 and deducts the illegal harvest (IH ) for those years. The population increases slightly up to the start of year 2013 but declines by the end of 2013, i.e. according to this model the present level of illegal hunting is now unsustainable.2 Table 2: Sustainability of illegal hunting in Kruger National Park 2008-2016 Rate of population increase 2010
2011
8
%
2012
2013
2014
Start
IH
Start
IH
Start
IH
Start
IH
10,059
243
10,621
420
11,050
708
11,226
997
Start
2015 IH
Start
IH
End
11,128 1,377 10,641 1,377 10,115
Notes The population estimate at the start of 2010) has been adjusted to give an estimate 10,621 animals at the end of 2010 (start of 2011) – which coincides with estimate of 10,621 animals of Ferreira (et al 2012). The annual offtakes for each year in KNP from 2010-2013 are those shown given by Condon (2013) after correcting for a finding factor of 0.6 for carcases.
1.
Martin (2014) derived the finding factor of 60% by iterating for a best fit to the official deaths.
2.
At a workshop held in Skukuza, KNP in September 2013, Danie Pienaar (Head, KNP Scientific Services) gave a short presentation on the status of the KNP white rhino. His presentation indicated that the inception of the decline may have been as early as 2009 (Madders et al. 2014).
4
II. SUPPLY AND DEMAND 1.
Supply
The supply of horn to the export market from South Africa has come from (a) animals illegally hunted, (b) animals legally hunted on "pseudo-hunts"3 and (c) unrecorded trade from stocks of horn belonging to private landholders with rhino. Although the law up until 2008 provided for the legal transfer of ownership of such horn from one person to another in South Africa, very few farmers availed themselves of permits for these transactions.4 The 'informal' unrecorded export trade from private stocks was extensive although a proportion of private landholders did not engage in this trade (John Hume, pers.comm). The proportion of private landholders engaging in the unrecorded trade is a variable in the model. 1. Horn weight from illegal hunting: The estimated weight of horn entering the illegal trade as a result of these killings has been calculated in Table 3 using a mean weight of 3.9kg/animal (both horns). This is the average weight for all animals in the population and its use implies that there is no selectivity by illegal hunters for large horns. Table 3. Weight of rhino horn generated by illegal killing 2000-2016 The scenario shown below assumes a finding factor of 0.6 for rhino carcases in KNP Year
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
2011
2012
2013
2014
2015
2016
616
951 1,403 1,766 1,726 1,641
Deaths
10
9
35
31
14
18
33
18
116 171
Horn kg
39
35
137 121
55
70
129
70
452 667 1,677 2,402 3,709 5,472 6,887 6,731 6,400
430
2. Horn weight from ‘pseudo-hunting’: To the total weights shown in Table 3 must be added the weight of horn exported as ‘trophies’ in the course of ‘pseudo-hunting’. The total number of trophy hunts in South Africa from 2000-2015 is shown in Table 4 (next page) together with the maximum number of trophies and weight of horn that may have entered the illegal trade from ‘pseudo-hunting’. The combined supply of horn to the Asian market from illegal hunting and ‘pseudo’ trophy hunting appears in the second-last row of Table 4. The supply of pseudo-hunted rhino horn reached its highest proportion (56%) of the total combined weight entering the export market (715kg out of 1,311kg) in 2007 when illegal hunting was relatively low. The peak amount from pseudo-hunting was 900kg (17% of the total) in 2011 when illegal hunting was high (>2,400kg). The contribution from pseudohunting dropped to less than 10% from 2013-2015 (last row of Table 4). Some might see this as a success for law enforcement: a more sanguine approach would view it as merely shifting the source of supply from pseudo-hunting to illegal killing. 3.
I use the term “pseudo-hunt” to describe a hunt which is aimed at obtaining horn for the Asian market rather than as a trophy to be retained and displayed by the hunter. There is nothing illegal about a “pseudo-hunt” – it merely offends the value-systems of some Western conservationists and conflicts with Norms and Standards regulations for trophy hunting. I have retained it as an IFF during the modelling process but deduct it in Table 8 (page 18).
4.
Early in 2013 Tanya Jacobsen contacted all of the DEA Provincial Departments in South Africa requesting the permit data. Most did not respond to the request and a second mailing took place on 15 April 2013. Northern Cape province and Kwa-Zulu Natal responded that no permits had been issued in their provinces over the period 2002-2009. Mpumalanga province reported 10 permit transactions over this period and Cape Nature Conservation reported 4 transactions.
5
Table 4. Weight of rhino horn originating from pseudo-trophy hunting 2000-2015 The scenario shown below assumes a finding factor of 0.6 for rhino carcases in KNP Year 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 All trophies
48
60
38
45
73
90
176
248
160
105
160
205
105
105
105
105
Pseudo No
0
0
0
0
13
22
61
143
66
90
145
180
90
90
90
90
Ps Horn kg
0
0
0
0
65
110
305
715
330
450
725
900
450
450
450
450
+Table 3 kg 39
35
137
121
120
180
434
785
782 1,117 2,402 3,302 4,159 5,922 7,337 7,181
0.0
0.0
0.0
54.3 61.0 70.3 91.1 42.2 40.3 30.2 27.3 10.8
Pseudo %
0.0
7.6
6.1
6.3
Notes: 1.
Data for 2000-2007 from Hall-Martin (et al 2008). Data for 2008-2011 from Jacobsen (2012). The category ‘Pseudo’ includes all non-traditional hunting safaris and, as such, is the maximum amount of horn which could have entered the trade through trophy hunting.
2.
No data were available from DEA for 2008 or 2012. I have used the same figures as those for 2009 for both these years and for 2013-2015.
3.
An average horn weight of 5kg has been used for pseudo hunts.
3. Privately-held stock of rhino horn: The metapopulation of white rhino on some 400 private properties in South Africa in 2008 was estimated at about 4,000 animals by Hall-Martin (et al 2008, Table 1.1, p10). This number had risen to 5,505 by 2014 (Mike Knight pers.comm. July 2016) and to 6,141 by 2015 (Rademeyer 2016, p36). I have estimated the stock of horn in private hands from 2000-2013 taking into account the starting stock in the year 2000, the increasing number of animals being dehorned and the expected contribution from natural mortality (Table 5 next page). In the scenario shown in the table, the stock in the year 2013 is some 8 tonnes. Hall-Martin (et al 2008, p17) had difficulty in estimating the stock of rhino horn held by the private sector and surmised that it lay between 2.5 - 3.3 tonnes in 2008. To a large extent this was caused by a reluctance of private landholders to disclose their stocks and an absence of information on the contribution which dehorning made to the overall stock. Dehorning is by far the most important contributor to rhino horn stocks. John Hume (pers.comm.) advises that there was very little, if any, dehorning of rhinos on private land before 2002. Thereafter, the proportion of rhinos being dehorned (legally and illegally) escalated sharply so that by the end of year 2013 most rhinos were dehorned (95%). Martin (2014) tested a range of scenarios for the proportion of the total stock of rhino horn actually available for trade. Percentages ranging from 10% to 90% during the period 2000-2007 were assumed when domestic trade was permitted and changes in these percentages after the moratorium in domestic trade came into effect in 2008 were also tested.5 In this study, the best fit to the available data is when the percentage of private rhino horn available for trade is 20.5% for the entire period from 2000-2016.
5.
The moratorium on domestic trade was announced in the Government Gazette (No.31301) on 8 August 2008 and it was officially gazetted in Government Notice 148 on 13 February 2009. The first spurt in illegal hunting occurred in 2008.
6
Table 5. Rhino horn stock on private land 2000-2013 including dehorning production Rate of population increase up to 2004 Rate of increase in dehorning after 2002
4.55
% per year
24.58
% per year
Production figures apply to animals older than 6-7 years
78
% of population
Additional production from dehorning animals < 6 yrs old
18
% of wt derived from above
Horn production from natural mortality (NM) RHINO NUMBERS Sex ratio % '
0.05
kg/rhino/year
DEHORNING PRODUCTION (kg)
PRIVATE
DRAW
STOCK
DOWN
kg
kg
kg
NM 42
58
2 kg/yr
1 kg/yr
Total
Males
Females
kg
Year
Total
% IH
Males
Females
Dehorned Fraction
2000
2,789
–
1,171
1,618
0.000
0
0
0
139
2,000
321
2001
2,916
–
1,225
1,691
0.000
0
0
0
146
1,818
325
2002
3,048
–
1,280
1,768
0.100
200
138
398
152
1,639
337
2003
3,187
–
1,339
1,848
0.125
260
180
519
159
1,853
367
2004
3,332
–
1,399
1,932
0.155
339
234
676
167
2,164
366
2005
3,416
–
1,435
1,981
0.193
433
299
863
171
2,641
451
2006
3,614
–
1,518
2,096
0.241
570
394
1,138
181
3,224
495
2007
3,823
–
1,606
2,217
0.300
752
519
1,499
191
4,047
487
2008
4,044
–
1,698
2,346
0.374
990
684
1,976
202
5,251
1,078
2009
4,277
31
1,796
2,481
0.466
1,305
901
2,603
214
6,351
1,304
2010
4,525
45
1,901
2,625
0.580
1,720
1,188
3,431
226
7,864
1,615
2011
4,786
35
2,010
2,776
0.723
2,266
1,565
4,521
239
9,906
2,034
2012
5,063
26
2,126
2,937
0.900
2,987
2,062
5,958
253
12,633
2,594
2013
5,355
21
2,249
3,106
0.950
3,333
2,301
6,649
268
16,250
3,337
2014
5,665
?
2,379
3,286
0.950
3,526
2,435
7,034
283
19,830
4,072
2015
5,992
?
2,517
3,475
0.950
3,730
2,575
7,440
300
23,075
4,739
2016
6,338
?
2,662
3,676
0.950
3,945
2,724
7,869
317
26,075
5,355
Notes on the table 1.
Population num bers: Hall-Martin (et al 2008, Table 1.3, p11) give estim ates for the years 2005-2008 of 3,472, 3,666, 3,791 and 3,983 respectively. I have used a best-fit curve starting with 2,789 anim als in the year 2000 and increasing at a rate of 4.55% to approxim ate these num bers. The authors estim ated the sex ratio of the population at 42 m ales: 58 fem ales. From 2005 onwards I have used a best-fit curve derived from data provided by Mike Knight (pers.com m ). The data takes into account illegal hunting on private land.
2.
Dehorning: In the analysis above I m ake the assum ption that 10% of rhino were dehorned in 2002 and that the rate of escalation of dehorning was about 25% per annum . The production of horn from dehorning averages 2kg per year for m ales over 6 years old and 1kg per year for fem ales. Anim als over 6 years old m ake up about 78% of the population (Martin 2010, population m odel). The total horn weight obtained from dehorning m ales and fem ales older than 6 years has been increased by an additional 18% to allow for the weight of horn obtained from dehorning anim als 2-6 years old – a practice which is becom ing m ore prevalent as illegal hunting increases.
3.
The horn expected from Natural Mortality is 0.05kg/living rhino/year (Martin 2010). This figure is m ultiplied by the population num bers in each year to obtain the weights in colum n NM.
7
4.
Total stocks In estim ating the running total stock of horn in the table I have assum ed that – (a) the weight of horn was 2,000kg in the year 2000; (b) in each following year the stock increases by the weight of horn generated from dehorning and the contribution from natural m ortality; (c) the ‘draw-down’ (the weight of horn illegally exported) is deducted from the stock. The ‘draw-down’ (final colum n) is based on a scenario where 20.5% of the available stock in private ownership is available for trade. This percentage is determ ined by the num eric optim iser. The am ount drawn down in any particular year is dependent on the level of dem and. ___________
2.
Demand
1. Demand and the Asian economy : In the debates which have taken place in the CITES forum over the years, the growth in the illegal trade in rhino horn has been blamed on the demand for horn from Asian countries.6 The problem has been linked to the increase in the per capita wealth of China and Viet Nam7 and the increased levels of disposable income in these countries (t’Sas-Rolfes 2012). The ‘best fit’ curve for the Chinese per capita income since the year 2000 is compared with the ‘best fit’ curve for the number of rhino illegally killed over the same period in Fig.2 (next page). There is little resemblance between the two curves. The Chinese per capita income (corrected for ppp – ‘purchasing power parity’) increased at a rate of about 14% per annum over the period 2001-2015. The illegal hunting curve is that shown in Fig.1 (page 3). It would be unwarranted to impute any cause-and-effect relationship between them.8 In an ideal world, data would exist for the price of rhino horn over a reasonable time span and the amounts of horn which had entered the market over the same period. From these data a demand curve might be constructed. A consequence of the CITES ban on rhino horn trade is that the market is entirely illegal and such data are not available for the period being examined.
6.
“The delegation of South Africa introduced their proposal for a legal trade in the horn of Ceratotherium simum simum , drawing attention to the complete failure of the Appendix I listing, and to the fact that poaching had not diminished. The introduction of a legal trade was urgently needed.” ... The delegation of Botswana supported the proposal. The delegations of Kenya, the United Republic of Tanzania and the United Kingdom considered that the problem lay in the insatiable markets in importing countries. They feared the extinction of African and Asian species was at stake if the proposal were accepted now.” (10th Session of Committee I: Excerpt from the Summary Record of COP8, 10 March 1992, my emphasis)
7.
Although recent publications (e.g. Milliken & Shaw 2012) have drawn attention to the expanding rôle of Viet Nam in the illegal rhino horn trade, China remains a major player. Recent changes in the illegal industry in Viet Nam are being reflected by similar changes in southern China.
8.
The Chinese per capita income statistics exhibit the ‘King Effect’, i.e. dividing the total GDP by the numbers of people in China is not a true measure of wealth distribution throughout the population. More of the per capita wealth is concentrated in the hands of the already rich. However, it would require an extreme correction to the data to make the curve resemble the illegal hunting curve.
8
Figure 2: Comparison of the rate of increase of Chinese per capita income with rate of escalation of illegal hunting of rhinos In recent years, the market for rhino horn has diversified significantly (Amman 2013). Uses of horn range from prestige decorative status symbols to the traditional medicinal uses that have been in place for centuries. Large amounts of fake horn play a rôle in determining prices. Although the demand for rhino horn will ultimately depend on the end-users, in the short term, the activities of speculators may have as much to do with controlling demand as the underlying supply-side economics. One thing is clear – “If we examine the global market for rhino horn as a single commodity (and ignore the specific changes in local markets) we see a clear overall trend between 1977 and today: a dramatic increase in market price. The message here is clear: rhino horn is a commodity with increasing scarcity value. Growth in market demand threatens to outpace the potential rate of supply under a trade ban regime that appears unlikely to change, so market prices should continue to rise.” (t’Sas-Rolfes 2012 p8) _______________ 9
III. SIMULATION MODEL A schematic diagram of the simulation model is shown in Fig.3 on the next page. Since the operations which take place in this model are central to the findings of this paper, it is described in some detail. All of the pale blue boxes in the diagram are vector arrays containing parameter values for the years 2000-2013. The pale purple boxes are the variables being tested by the model. A numeric optimiser is used to select values for seven variables to give the best fit (lowest sum of squared differences) between the actual rhino deaths (Fig.1 p3 and Table 1 p4) and the rhino deaths predicted by the model. The key assumption in the model is that when the supply of rhino horn falls below the demand (including demand by speculators), it results in rhinos being killed and the number killed is directly related to the extent of the shortfall. The extent to which ‘fake’ rhino horn is sold in the Asian market is irrelevant to the demand as measured in South Africa. 1.
Hypothetical Demand
This is the first of the key variables that make up the simulation model (DF in Fig.3). In Martin (2014) it was treated it as a simple exponential variable controlled by a scaling constant and an exponent.9 The new data available for 2014-2016 (Table 1, p4) suggest that illegal hunting is beginning to level off and so a logistic curve was used to approximate demand over the period 2000-2016.10 Demand D = A + B. NORMDIST (t, Mean, Std Dev, 1) kg where – A is a constant (0.1); [D1] B is a multiplier constant that scales the logistic function (12,000) [D2] t is time in years; Mean is the mean value of the cumulative normal distribution (11.76 years) [D3] Std Dev is the standard deviation of the distribution When t is less than the mean (2000-2011) the Std Dev is 3.77 [D4] When t is greater than the mean (2012-2016) the Std Dev is 1.69. [D5] Variables D1-D5 are determined by the numeric optimiser. Of course, it is unlikely that the demand has increased uniformly over the study period as the function above indicates.11 If it had, it would have been possible to find exact fits between the number of rhino predicted to be killed and the number actually killed. In real life many more variables influence the outcome – including changes in law enforcement effort and changes in the markets in Asia. 9.
Demand (weight of horn) = Ae Bt kg where t is time and A and B are constants
10. Crookes & Blignaut (2015, p13) used a logistic curve to define income. They state “Demand is a simple combination of price effects and income effects” and, as they assume that price is inelastic, their demand thus becomes a logistic function. 11. t’Sas-Rolfes (2012 p9) observes “During the last 35 years, price changes have not been consistent. Sharp increases immediately following the 1977 ban appeared to have steadied by the early 1990s. It is most likely that the 1977 ban initiated a market panic in East Asian markets – prompting a speculative scramble to accumulate stockpiles. ... Stronger domestic measures in key consumer countries in the early 1990s probably succeeded in suppressing consumer demand to some extent, leading to a slow-down in consumption. This may have had the reverse effect of the initial ban, prompting some speculators to exit the market.”
10
Figure 3: Schematic diagram of the simulation model
11
2.
Adjusted Demand
In each year, the demand is adjusted by deducting the weight of horn generated by pseudohunting (Table 4, page 6). This adjusted weight represents the amount of horn that needs to be met by (illegal) trade from the private sector if rhinos are not to be killed by illegal hunting. The value of the horn generated by pseudo-hunting is treated as an illicit financial flow (IFF3) at this stage but corrected in Table 8 (page 18). 3.
Available stock of horn
It is assumed that the stocks of horn held by the State (KNP, KZN and DEA) were not available for international trade from 2000-2016. The only horn that might have been exported during these years (apart from the horn derived from illegal hunting and pseudo-hunting) would have come from private sector stocks (Table 5 page 7). The fraction AS determines the amount of private horn stock available for trade and this is determined by the numeric optimiser. The best fit to the data suggests that the fraction of total stocks of horn traded is 20.5% in each year over the full period from 2000-2016. 4.
Volume of private sector horn traded
At this stage a key decision is made in the model (yellow diamond in Fig.3). For the year under consideration, if the amount traded from private horn stocks is greater than the adjusted demand (see above) then the demand is met by trade from the private horn stocks.12 If it is less, then the full amount available for trade is used, leaving a shortfall which is translated into a number of rhinos illegally killed.13 This is the box PREDICTED NUMBERS KILLED in Fig.3. The total stock of horn is adjusted every year to take into account the drawdown of horn traded by the private sector. The value of the horn generated by private sector trading is an illicit financial flow (IFF2). 5.
Goodness-of-fit
On the right-hand side of Fig.3 is the box containing the OFFICIAL RECORD OF NUMBERS KILLED which is adjusted by the finding factor for carcases in Kruger National Park (variable KNPFF) to give the ADJUSTED RECORD OF NUMBERS KILLED (Table 1 & Fig.1). The value of the horn generated by illegal hunting is an illicit financial flow (IFF1). The squared differences between the ESTIMATED NUMBERS KILLED (Ne) and the ADJUSTED RECORD OF NUMBERS KILLED (Na) in each year of the simulation are tabulated in the array SUM 2 OF SQUARED DIFFERENCES (Ne - Na) and summed to give the measure of the goodness-of-fit for any particular combination of the variables. When there is no resemblance of a correspondence between Ne and Na, the sum of squared differences typically is about 3 million. When there is a reasonable correspondence, the number reduces to less than 20,000.
12. One commentator has taken this statement to mean I am assuming perfect knowledge of information flow between the suppliers and demanders. All I am assuming is that, in any given year, if the illegal buyers in Africa can meet the demand (as set by their masters in Asia) by buying from private rhino owners who are willing to sell horn illegally they will do so. If available private stocks are insufficient to meet the demand, it will be met by illegal hunting. 13. The number of rhinos illegally killed is calculated by dividing the shortfall in weight by the average horn weight for rhino (3.9kg).
12
IV. RESULTS 1.
Goodness-of-fit
The arrays corresponding to the diagram of the model (Fig.3) are shown in Table 6 below. The combination of variables is such that the sum of the squared differences between the number of rhino predicted to be killed and the adjusted number actually killed is the minimum obtainable with the numeric optimiser. The correspondence between the two arrays might be described as a reasonable fit (Fig.4). It would be surprising if it were a ‘perfect’ fit for the reasons given in the last paragraph on page 10. Table 6: Best fit from the simulation model AS
0.205
Background Trade
76.991
Demand for horn in 2000 (kg)
321.0
DEMAND Estimated Pseudo Demand Hunting
Proportion of private stock available for trade
Average horn wt illegal hunting
SUPPLY
3.9
NUMBERS
Adjusted Demand
Available Stock
Private Trade
Shortfall
Estimated Nos killed
Actual Squared Nos killed Differences
Year
kg
kg
kg
kg
kg
kg
N
N
N
2000
321
0
321
411
321
77
20
10
95
2001
325
0
325
373
325
77
20
9
115
2002
338
0
337
337
337
77
20
35
233
2003
367
0
367
381
367
77
20
31
127
2004
431
65
366
444
366
77
20
14
33
2005
561
110
451
542
451
77
20
18
3
2006
800
305
495
662
495
77
20
33
176
2007
1,202
715
487
831
487
77
20
18
3
2008
1,821
330
1,491
1,078
1,078
413
106
116
102
2009
2,694
450
2,244
1,304
1,304
940
241
171
4,904
2010
3,817
725
3,092
1,615
1,615
1,477
379
430
2,630
2011
5,141
900
4,241
2,034
2,034
2,207
566
616
2,510
2012
6,804
450
6,354
2,594
2,594
3,760
964
951
172
2013
9,430
450
8,980
3,337
3,337
5,643
1,447
1,403
1,929
2014
11,178
450
10,728
4,072
4,072
6,656
1,707
1,766
3,520
2015
11,998
450
11,548
4,739
4,739
6,809
1,746
1,726
396
2016
12,269
450
11,819
5,355
5,355
6,464
1,657
1,641
270
Sum of squared differences Notes 1. Horn weights for pseudo hunting are from Table 4 2. Available stock is from Table 5 3. Actual numbers killed are from Table 2 with adjustments for a carcase finding factor of 0.6 for KNP.
13
17,218
Figure 4: Estimated and predicted numbers of rhino killed 2000-2016 2.
The relationship between supply and demand
The data shown in Fig.5 (next page) are also taken from Table 7. The ‘best-fit’ conditions indicate a demand of some 12 tonnes of horn in the year 2015. The critical rôle of the private sector trade over the period 2000-2013 needs emphasising. Assuming that the results of the model are not spurious, up until 2006 more than three-quarters of the demand was being met by the private illegal trade. Available private sector stocks were unable to cope with the increasing demand from 2008 onwards despite the increasing volume of horn available from dehorning operations on private land. From 2008 onwards virtually the only available supply of horn came from dehorning and that supply was more or less exhausted each year – however, because it was renewable, some 2-5 tonnes were available every year from 2008-2013. Accepting the assumption that rhinos are killed when demand exceeds supply, had there been no private sector trade in the year 2012, the number of rhinos that would have been killed to meet demand would have exceeded 2,000 – the (illegal) domestic trade reduced this to less than 1,000.
14
Figure 5: Demand and supply of rhino horn 2000-2016 The contribution of pseudo-hunting to meeting the demand was relatively minor. It reached a peak of 37% in 2007 and was 21% in 2006 but outside of these years it was around 10%. Stiles (pers.comm.) remarks that all of this horn would have reached the end market whereas the remainder would have been subject to seizures. The total weight of South African rhino horn seized from 2010-2015 was 1,109kg – 4.1% of the estimated horn taken by illegal hunting for the same period (Emslie et al. 2016, Table 5). 15
3.
Rate of change of demand
A key departure from the earlier work on this model (Martin 2014) is the use of a logistic curve to simulate the demand from 2000-2016. As might be expected, the rate of change in the demand (dD/dt) is low during the early years, increases steeply in the years 2010-2013 and declines in the final years. To some extent improved law enforcement may have contributed to the levelling off. The slight assymetry in the curve is caused by the transition from a higher value of the standard deviation in the years below the mean to a lower value for the years greater than the mean (page 10). Table 7. Rate of change (ROC) of demand 2001-2016 The rate of change (ROC) is shown as a percentage Year 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 ROC
4.
0.1
0.1
0.3
0.6
1.3
2.4
4.0
6.2
8.7
11.2
13.2
16.6
26.3
17.5
8.2
2.7
Effects of the moratorium on private trade
Before 2008, South Africans were legally allowed to trade rhino horn with each other, provided that a change of ownership certificate was issued at a DEA provincial office (see footnote 4). A moratorium on this trade was announced in 2008 (footnote 5). The first inference to be drawn from this is that the informal illegal trade from the private sector actually increased after the imposition of the moratorium. The second inference is that the moratorium was irrelevant to the amount of horn traded – which was determined largely by market forces. 5.
Carcase finding factor in Kruger National Park
The last of the variables that appears in Fig.3 is the finding factor for carcases in Kruger National Park. A finding factor of 0.6 was used for all of the preceding analyses (i.e. 40% of carcases were not found). Martin (2014) examined the effect of varying this finding factor on the goodness-offit for the optimum scenario (Fig.6). For values of the finding factor less than 0.5 the fit is very poor; values of the finding factor between 0.55 and 0.65 produce a good fit; and, for finding factor values greater than 0.7 the fit becomes progressively worse. No great significance should be attached to the results of this test .... it cannot be described as exhaustive.
Fig 6: Best fit for KNP carcase finding factor 16
6.
Illicit Financial Flows Three different types of illicit financial flow arise from the analysis. (1) The first is the value of the horns obtained from illegal hunting and illegal export. This is a simple theft of assets belonging to the people of South Africa. Some corruption is involved in facilitating the exports. (2) The second is the sale of rhino horn obtained from private sector stocks. The largest part of the stock is derived from dehorning operations. The legality of these sales is discussed in the Discussion section (page 19). (3) The last is the export of legal sport hunting trophies. I do not consider that it constitutes an illicit financial flow and it has been set as zero in Table 8 on the next page.
To estimate the magnitude of these financial flows requires the assumption of a price for rhino horn. Gao et al. (2016) give a wide range of prices for different rhino horn products at the end markets in China. The prices range from US$30,000-US$490,000/kg (Fig.7 below). Certain carved cups and containers realise prices between $300,000 and $500,000 per kg. A single rhino horn sceptre realised over US$200,000. Eight types of item realised prices between $100,000 and $200,000 per kg. Five types of item realised prices below $100,000 per kg. Amongst these were 19 uncarved rhino horns at a price of US$72,000 per kg each. This then is the end market price.
Figure 7. Types and Prices of rhino horn items (Gao et al. 2016 Table 2) 17
The price of rhino horn in the illegal market in South Africa is unlikely to be 20% of this value. Illegal hunters in the areas adjacent to KNP are receiving between US$3,000 and US$10,000 per kg for rhino horn (Danie Pienaar, Update on Kruger National Park Rhinos in Madders et al. 2014 page 32). The higher value – $10,000 – has been to calculate the value of rhino horn ‘at the farm gate’ in this study. In Table 8 below, the illicit financial flows have been derived by multiplying the weights of horn for illegal hunting (Table 3, p5), private horn trade and pseudo-hunting (Table 6, p13) by US$10,000. The total illicit financial flow is $703 million for the period 2000-2016 and the annual loss in 2016 is $123 million. The corrected values obtained by removing pseudo-hunting from the IFFs are shown in the final column. Table 8: Illicit Financial Flows All figures in US$ millions Illegal Hunting
Private trade
Pseudo-hunting PH
Totals
less PH
2016
64
54
5
123
118
2000-2016
351
293
59
703
644
In Table 9 below, we examine the scenario that would have prevailed if all private land stocks could have been legally legal traded from 2000-2016.14 We have retained the same demand function and have assumed that the horn weight contributed by pseudo-hunting is the same but it is no longer considered an illicit financial flow. The trade of private land stocks would have been able to meet the demand up until 2012 and only when the annual demand exceeds 9 tonnes from 2013 onwards are there losses of rhino to illegal hunting. The losses of rhino to illegal hunting from 2000-2016 in Table 8 are 9,000: the losses are reduced to less than 4,000 in the Table 9 scenario. The net loss of rhino from illegal hunting in 2016 is slightly over 1,000 which is sustainable. The net profit from legal trade from 2000-2016 amounts to $413 million. Table 9: Financial Flows under a legal trade scenario All figures in US$ millions Illegal Hunting
Private trade
Pseudohunting
2016
41
77
5
2000-2016
146
500
59
Legal trade
Net profit
82
41
559
413
These profits might be even higher if the buyers have confidence in the selling system and the supply. There would no longer be an incentive to stockpile horns. _______________
14. In Table 8 only 20% of private land stocks were available for trade.
18
VI. DISCUSSION In the introduction, the paucity of data underpinning these analyses was emphasised. However, a simulation model has been constructed which, with the use of a numeric optimiser, returns values for the key variables that provide a plausible explanation for the underlying processes driving the system – which is the aim of any investigation. The initial objective of the 2014 analysis was to test whether the imposition of a moratorium on domestic trade in 2008 might have given rise to the sharp escalation in illegal hunting which followed. The conclusion was that it did not. A more plausible explanation, backed up by the goodness-of-fit results, is that private sector horn stocks that had been meeting the escalating demand for rhino horn from 2000-2007 could no longer do so. Over the critical period beginning in 2008, the horn production from the private sector was increasing as a result of accelerated dehorning – ostensibly to reduce the threat to rhinos from illegal hunting but fortuitously providing valuable quantities of rhino horn. However, all of this stock was not available to the trade, i.e. there was a fair proportion of law-abiding citizens who did not engage in the trade. By 2016 the production from dehorning is estimated to have reached 8 tonnes per annum and private land stocks were some 26 tonnes. The best fit to the data is obtained when the proportion of the total private rhino stocks that were available for trade is around 20%. Had this proportion been higher fewer rhino would have been killed from 2008-2016. The conclusion is that the moratorium was irrelevant. What is not irrelevant is that Jacobsen’s (2013) observation “Less trade = more poaching” is vindicated. Had it not been for the significant amounts of horn entering the trade from the private sector from 2000-2013 considerably more rhino would have been killed. t’Sas-Rolfes (2012) observes – “Some commentators have blamed South Africans in the hunting and game ranching industry for ‘fuelling demand’ for rhino horn by playing a role in the illegal supply chain. I disagree with this view, which I believe to be a confusion of cause and effect. In fact, it is more likely that the South African game ranching industry played a role in delaying an inevitable resurgence of poaching activity, driven by Asian consumer demand.” It is not the aim of this paper to pass moral judgement on the private rhino farmers. Ian Parker (pers.comm.) has made the remark that “Good law must be workable”. In this case, it was not ‘good law’. Firstly, in the period prior to 2008 when domestic trade was legally permitted, very few permits were obtained for what was a significant trade. It is easy to understand why – few sellers of horn wanted to leave a paper trail. After the imposition of the moratorium, obviously the trickle of permits became zero. But, because it was very difficult to catch people engaged in the illegal trade, it continued – in fact, according to this analysis, it actually increased.
19
Rightly or wrongly, South African law has been amended to allow outright ownership of wild animals (van der Merwe & Rabie 1976, section 372).15 The constitution of South Africa provides powerful rights of private ownership and it is not surprising that private sellers of horn in South Africa felt justified in their activities. As a general principle in law, subsidiary legislation (rules and regulations) should not be used to subvert the provisions of higher legislation. The moratorium is one example of this but there are numerous others falling in the same category.16 The net effect of the many NORMS & STANDARDS and the TOPS regulations is that they reduce the incentives for private individuals to manage wildlife sustainably and to remain legal. The maximum rate at which white rhino horn production can increase is, not unsurprisingly, equal to the rate at which rhino populations can increase – about 9% under optimum rainfall and habitat conditions but less than 8% in semi-arid savannas.17 The major task for those implementing a legal trade in rhino horn will be to reduce the rate of increase in demand to less than 8% pa. In this 2016 analysis which uses a logistic curve to simulate demand, the demand will level off at slightly over 12 tonnes in 2020. Private sector stocks of horn will exceed this in 2021 if current rates of rhino population increase on private land and the current regime of dehorning are maintained. A legal trade in horn would provide all the incentives for this to happen. The implication of this is that demand can be met sustainably from private land stocks and there is no case for “demand reduction”. People advocating a legal trade in rhino horn point out that the stocks of rhino horn in State hands amount to about 20 tonnes and intuitively they believe that this will meet demand or at least give the legal trade a breathing space to begin influencing the demand for horn. An annual requirement for 12 tonnes of horn would exhaust State Land stocks in less than two years. The rate of accumulation of rhino horn stocks from 14,000 State Land rhinos where there is no dehorning is less than 1 tonne/year. The high level of demand is not an argument against a legal trade in horn. If rhino horn as a commodity can be traded in an open market18 where the interactions of price, supply and demand can operate effectively, then the chances of producing a stable market based on a sustainable supply of horn will be greatest. To reduce or eliminate IFF from horn exports a legal trade would be the optimum policy. ______________
15. Roman-Dutch law is based on the fundamental premise that the status of wild animals is res nullius, i.e. belonging to no-one. 16. e.g. A reduction in numbers of trophy hunting permits issued; Restrictions on the nationalities of persons to whom hunting permits are issued; and onerous constraints affecting the dehorning rhinos. 17. Hall Martin (et al 2008) estimated that stocks of white rhino on private land in South Africa increased at about 4.5% per annum from 2000-2008. 18.
... not one constrained by CITES to ‘approved’ buyers, or subject to quotas approved by the Parties or limited to intermittent and sporadic one-off sales.
20
References Amman K (2013). The Rhino and the Bling. Journalist’s article published on Facebook 10 August 2013. Project Africa Rhino. Bell RHV (1986). Adaptive Management. In: Conservation and Wildlife Management in Africa. Proc. Workshop sponsored by the US Peace Corps, Kasungu National Park, Malawi. Edited by RHV Bell and E McShane-Caluzi. Peace Corps, Washington, 1986. Condon T (2012-2013). RWN RHINO POACHING HOTLINE. timconwild conservation network
[email protected] Condon T (2013). RHINO WAR NEWS – Poaching Stats Update. timconwild conservation network 18 September 2013.
[email protected] Crookes DJ & JN Blignaut (2015). Debunking the myth that a legal trade will solve the rhino horn crisis: A system dynamics model for market demand. Journal for Nature Conservation 28 11–18 Di Minin E, J Laitila, F Montesino-Pouzols, N Leader-Williams, R Slotow, PS Goodman & A Moilanen (2015). Identification of policies for a sustainable legal trade in rhinoceros horn based on population projection and socioeconomic models. Conservation Biology, 29(2), 545–555 Emslie RH, T Milliken & B Talukdar (2012). African and Asian Rhinoceroses – Status, Conservation and Trade. CoP16, Doc. 54.2 Annex 2, 16 March 2013, CITES Secretariat, Geneva, Switzerland. Emslie RH, T Milliken, B Talukdar, S Ellis, K Adcock & MH Knight (compilers) (2016). African and Asian Rhinoceroses – Status, Conservation and Trade. Report from the IUCN Species Survival Commission (IUCN SSC) African and Asian Rhino Specialist Groups and TRAFFIC to the CITES Secretariat, CoP17 Doc.68 Annex 5. 21pp Ferreira SM, JM Botha & MC Emmett (2012). Anthropogenic influences on conservation values of white rhinoceros. PlOsONE 7(9), e45989. doi:10.1371/journal.pone.0045989 Gao Y, KJ Stoner, ATL Lee & SG Clark (2016). Rhino horn trade in China: An analysis of the art and antiques market. Biological Conservation 201: 343–347. Hall CMS (2012). An investigation into the financial feasibility of intensive commercial white rhino farming in South Africa: a strategic approach. Study submitted in partial fulfilment of the requirements for the degree of Bachelor of Industrial Engineering in the Faculty of Engineering, Built Environment and Information Technology, University of Pretoria, October 2012. 49pp Hall-Martin AJ, JG du Toit, PM Hitchins & MH Knight (2008). The 2008 survey of white rhinoceros, Ceratotherium simum simum, on private land in South Africa . Data collectors: J Ackermann, PM Hitchins, A Uys & K Vickery. Study carried out for WWF International. 66pp Holling CS (1976). Adaptive Environmental Assessment and Management. John Wiley, New York. Holling CS (2001). Understanding the Complexity of Economic, Ecological and Social Systems. Ecosystems 4: 390-405 Jacobsen T (2013). Table: Less trade = More poaching and Diagram: Rhino Horn Supply www.rhinodotcom.com 30 January 2013. Madders K, R Martin & J Sturgeon [Editors] (2014). Community Rhino Farms. Report of a Joint Workshop between Resource Africa and South African National Parks held in Skukuza, Kruger National Park, 19-21 September 2013. Publishers Resource Africa, Pretoria, South Africa. 48pp. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2851914
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Martin RB (2010). Background Study, WHITE RHINOCEROS Ceratotherium simum simum (Southern Subspecies). Consultancy for the Namibia Ministry of Environment and Tourism’s White Rhino Conservation Strategy for Namibia, May 2010. Martin RB (2010). A legal trade in rhino horn: Hobson’s Choice. http://www.rhinoresourcecenter.com/ pdf_files/133/1331766338.pdf Martin RB (2014). A Detective Story: Use of a Simulation Model and a Numeric Optimiser to Explore Levels of Illegal Trade in Rhino Horn in South Africa between 2000-2013. Draft paper prepared in collaboration with Tanya Jacobsen (RhinoDotCom) Martin RB (2016). Environmental Methodologies: Why Isn't the Use of Adaptive Management More General? Social Science Research Network. http://dx.doi.org/10.2139/ssrn.2840436 Milliken T & J Shaw (2012). The South Africa - Viet Nam Rhino Horn Trade Nexus: A deadly combination of institutional lapses, corrupt wildlife industry professionals and Asian crime syndicates. TRAFFIC, Johannesburg, South Africa. 180pp van der Merwe CG & MA Rabie (1976). Joubert - “The Law of South Africa” Volume 1. Butterworths, Durban 1976 Rademeyer J (2016). Tipping Point: Transnational organised crime and the ‘war’ on poaching. Part 1 of a 2-part investigation into rhino horn trafficking in Southern Africa, Global Initiative Against Transnational Organised Crime, Geneva, Switzerland. 56pp Ruitenbeek J & C Cartier (2001). The Invisible Wand: Adaptive Co-management as an Emergent Strategy in Complex Bio-Economic Systems. Occasional Paper No.34, Centre for International Forestry Research, Bogor, Indonesia. 47pp Trading Economics (2013). China GDP per capita PPP. http://www. tradingeconomics.com/china/ gdp-per-capita-ppp ‘t Sas-Rolfes M (2012). The Rhino Poaching Crisis: A Market Analysis. Rhino Resource Center, www.rhinoresourcecentre.com/pdf_files/133/1331370813.pdf
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ASSESSING THE EXTENT AND IMPACT OF ILLICIT FINANCIAL FLOWS IN THE WILDLIFE AND TOURISM SECTORS IN SOUTHERN AFRICA
Volume 4 Illegal Wildlife Trade in Selected Wildlife Species and
Illicit Financial Flows in Wildlife Tourism Daniel Stiles Resource Africa ___________________________________________________________________________ TABLE OF CONTENTS OBJECTIVES OF THE STUDY.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Illegal Wildlife Trade (IWT) .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wildlife Tourism. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methodology. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2 2 2 3
IWT AND IFF EVALUATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Lion parts .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Illegal Wildlife Trade . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Illicit Financial Flows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Pangolins.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 Illegal Wildlife Trade . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 Illicit Financial Flows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Crocodiles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Illegal Wildlife Trade . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Illicit Financial Flows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 Abalone. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 Illegal Wildlife Trade . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 Illicit Financial Flows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 Sharks and Rays.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 Illegal Wildlife Trade . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 Illicit Financial Flows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 Cycads. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 Illegal Trade .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 Illicit Financial Flows. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 WILDLIFE TOURISM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Photographic and Recreational Wildlife Tourism. . . . . . . . . . . . . . . . . . . . . . . . . . . . Valuation of the tourism industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Illicit Financial Flows. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
36 37 37 42
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 _______________
List of Tables 1. Numbers of lion body parts reported in the CTD as exported 2006-2014 . . . . . . . . . . . . . 5 2. Prices converted to USD of lion parts in South Africa . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 3. Estimated value of lion bone exports for four Southern African countries 2006-2014 . . 8 4. Direct, commercial exports of Crocodylus niloticus skins from producer countries . . . . 17 5. Value in USD of crocodile skin exports from Southern Africa 2006-2013 . . . . . . . . . . . 19 6. Total Allowable Catches (TACs) and catches for the abalone fishery for 1993-2013. . . 22 7. Prices of abalone products (USD) 2005-2016 in Southern Africa . . . . . . . . . . . . . . . . . . 23 8. Estimated quantities and values of abalone products exported from RSA 2007-2010 . . 25 9. Shark fin and meat exports and re-exports from South Africa and Namibia 2006-2011. 30 10. Cycad specimens exported from Southern Africa, 2006-2014 .. . . . . . . . . . . . . . . . . . . . 33 11. Tourism spending and international tourism arrivals in Southern Africa, 2006-2015 .. . 38 12. Total Leisure Travel and Tourism spending, 2006-2015 ($billions) . . . . . . . . . . . . . . . . 39 13. Proportion of Total Tourism related to Wildlife . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 List of Figures 1. Lion skeleton with a complete set of bones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2. A typical bag of lion bones as sold by game ranchers to dealers . . . . . . . . . . . . . . . . . . . . 7 3. Trade flow chart for lion parts from South Africa to Eastern Asia . . . . . . . . . . . . . . . . . 11 4. Temminck’s Ground Pangolin Smutsia temmincki . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 5. Main flows of pangolin seizures, 2007-July 2015 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 6. A typical crocodile farm in South Africa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 7. The three main types of crocodile skins that are exported . . . . . . . . . . . . . . . . . . . . . . . . 18 8. Crocodile meat cuts (Source: Ecotao 2016) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 9. Abalone range and fishing zones on the South Africa coastline . . . . . . . . . . . . . . . . . . . 21 10. Dried abalone for sale in Hong Kong .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 11. Estimated legal and illegal abalone catch from South Africa 2000-2014 . . . . . . . . . . . . 26 12. Smuggling illegal abalone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 13. Shark fin wholesaler in Guangzhou, China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 14. Processed raw shark fin for sale in Bangkok, Thailand at about $235/kg . . . . . . . . . . . . 29 15. The marine product catch and trade chain and points of control . . . . . . . . . . . . . . . . . . . 31 16. Requirement for shipping import and export documents .. . . . . . . . . . . . . . . . . . . . . . . . 31 17. Cycad seedlings for sale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 18. Cycad offsets for sale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 19. Tourists visit Southern Africa primarily to see the wildlife . . . . . . . . . . . . . . . . . . . . . . . 36 20. Leisure Travel & Tourism spending in Southern Africa 2006-2015 ($billions) . . . . . . . 40 ________________ 1
OBJECTIVES OF THE STUDY We have divided the Wildlife Trade and Tourism sector into three parts, each with its own objectives and methodology –
Illegal Wildlife Trade (IWT) Wildlife is the iconic natural resource of Sub-Saharan Africa. Unfortunately, wild animals and products derived from them have become a largely illegal multi-billion dollar business in Africa. There are hundreds of different species of animals and plants that are trafficked live or in derived product form. This short-term initial study cannot possibly be comprehensive in its scope, therefore certain high-value species have been selected for data collection and analysis to provide case study examples of what would be needed to be done for a longer term inclusive study. The case study species are – Elephant (Loxodonta africa) – Raw & worked ivory and sale of live animals .. Volume 2 Rhinoceros (Ceratotherium simum and Diceros bicornis) – Horn . . . . . . . . . . . . Volume 3 Volume 4 Lion (Panthera leo) – Bones, teeth, claws, skin and live animals Pangolin (Manis spp.) – Meat and scales Crocodiles (Crocodylus nilotica) – Skins and meat Abalone (Haliotis midae) – Meat and shell Sharks and Rays (members of the Subclass Elasmobranchii) – Meat and fins Cycads (Encephalartos spp.) – Live shoots, bulbs and seeds With each of these species the objectives are to – 1. Approximate the total production value in USD arising from the annual offtake 2. Attempt to determine the illegal portion value of the estimated production 3. Assess the quantitative value that might have been lost through IFFs 4. Identify the transfer methods, channels and actors involved in the IFFs and 5. Assess the impact on Southern African economies of the IFFs.
Wildlife Tourism Wildlife tourism includes both non-consumptive and consumptive uses of wildlife and, in many southern African localities, both activities involve local or international visitors and take place on the same categories of land – State Protected Areas, Private and Communal Lands. Photographic and Recreational Wildlife Tourism Entails observing and photographing primarily wild animals . . . . . . . . . . . . . . Volume 4 Trophy Hunting Entails hunting primarily wild animals . . . . . . . . . . . . . . . . . . . . . . . Not done at this stage Will be included in this volume subject to project extension and additional funding
2
The objectives for the two components of this subsector study are to – 1. Estimate the income generated by wildlife tourism in Southern Africa 2006 to 2015 2. Assess the quantitative value that might have been lost through IFFs 3. Identify the transfer methods, channels and actors involved in the IFFs and 4. Assess the impact on Southern African economies of the IFFs The overall objectives of the wildlife and tourism study are to – 1. Develop methodologies for collecting and analysing data at the private sector level to estimate IFFs. This has never been attempted for the wildlife tourism sector. 2. Combine the values of the three sub-sectors to arrive at a global value of the economic contribution of the wildlife and tourism sector to the Southern Africa economy. This will not be a complete valuation because only a relatively small sample of species is included in the IWT sub-sector and not all countries are included in the analysis of the other two sub-sectors. 3. Estimate the loss to the Southern African economy from IFFs in the wildlife and tourism sector, and 4. Assess the impact that this loss has on the Southern African economy. __________________ Methodology We have used a variety of different methodologies to arrive at economic valuations of the various constituents of the wildlife trade and tourism sub-sectors. Likewise, different methodologies have been employed to assess the value of the IFF component of the sub-sectors and to identify the patterns, actors, channels, and impact of illicit flows. Due to the variation in the methodologies employed, each component in this volume includes the methodology near the start of the subsection. ______________________
In all financial figures in the report the symbol “$” refers to United States dollars.
3
IWT AND IFF EVALUATION This section contains the estimates of actual income (converted to $) that has been derived from the various economic activities in the Illegal Wildlife Trade sub-sector. These figures are needed in order to then approximate the quantities that may have been diverted to IFFs and thus lost to Southern African economies. To understand how IFFs might function the drivers behind the trade are described along with both the legal and illegal trade methods. While there are some commonalities between all of the trade commodities and even some with wildlife tourism, each product and the wildlife tourism sector will be described separately.
Lion parts Illegal Wildlife Trade The African lion (Panthera leo) is listed on CITES Appendix II and therefore the parts of African lion can be traded internationally with export permits, subject to non-detriment findings. There was virtually no international trade from Southern Africa prior to about 2006, except for the export of trophies and other parts (skulls and skins) associated with trophy hunting, according to the CITES Trade Database (CTD). Lion parts, particularly skeletons and collections of bones began to be exported in large numbers to eastern Asia (Viet Nam, Laos and China) in 2009. The bones were destined for use in traditional medicine, acting as substitutes for scarce tiger (Panthera tigris) bones, the two related species having similarly shaped bones (Williams et al. 2015). Bauer et al. (2015) estimated that there were between 10,385 and 15,925 lions in Southern Africa. Southern Africa is the only region where trends in lion numbers are positive according to Bauer et al. (2015, supplementary material, Table 2). However, populations in Botswana and Zambia are inferred to have declined between 1993-2014 (Bauer et al. 2015, supplementary material, Table 3). In addition, all subpopulations in Namibia, Zambia and Zimbabwe number below 500 individuals (Bauer et al. 2015, supplementary material, Table 3). With regard to Mozambique, the authors note that the lion population has experienced a temporary boom that is unlikely to continue, possibly related to the increased opportunity to feed on the bodies of illegally hunted elephants which has increased dramatically in the country in recent years. With regard to South Africa, the wild population is small but inferred to have increased between 19932014 (Bauer et al. 2015, supplementary material, Table 3). African lions are traditionally used for medicinal, ceremonial and ritual purposes by a number of African communities. Lions are also considered a risk to livestock and human life in many communities, and are targeted as a result. Traditional use and persecution may play a significant role in the decline of some populations (Bauer et al. 2010). Significant commercial captive breeding operations exist, principally in South Africa. In its draft Biodiversity Management Plan for African Lion (Funston & Levendal 2014), the South African authorities estimate that there are currently ‘as many as 6,000 lions currently in over 200 breeding facilities’ in the country, approximately three times the number in South Africa’s national parks, and almost twice the estimated total number of wild lions in the country (3,155).
4
Methods To evaluate the income derived from lion parts, data were gathered primarily from the CTD, CITES reports and TRAFFIC reports concerning the prices and quantities of the various body parts, including whole bodies. Angola, Lesotho, Malawi, Mozambique, Swaziland and Zambia are not included in the analysis because sources indicated that trade of these products was insignificant in these countries. South Africa breeds the Asian tiger (Panthera tigris) in captivity and there is a small trade in live specimens and products. There are estimated to be more than 280 Tigers in at least 44 facilities in South Africa (Williams et al. 2015). The total size of the ex situ tiger population in South Africa is not known and is believed to be higher than estimated here from the partial survey that was conducted. This trade has potential to grow and data on tiger parts should also be included in future studies. The lion and tiger parts international trade are related, as the first largely derives from the second (Williams et al. 2015). Tiger parts are important in traditional East Asian medicine. As tiger parts have become increasingly expensive due to limited supply, East Asian traders have discovered that certain lion parts (bones, claws and teeth) are virtually indistinguishable from their corresponding tiger part, and are much cheaper in Southern Africa. Stoner & Krishnasamy (2016) note that in 2015 tiger seizures in China and Viet Nam occurred where the shipments originated from Africa, the parts of which can only have originated from captive sources. A map in the report indicates that South Africa and Angola were the origins of tiger part seizures in Asia. The products that were found to be traded are bodies, skeletons (with postcranial bones and skulls listed as separate products), skins, tails, teeth and claws. Of great interest are the commodities exported to Eastern Asia that are part of the extensive global carnivore trade and feed into the ‘tiger’ bone industry as substitutes. Since 1998 China, Viet Nam, Lao PDR, Myanmar and Thailand have imported increasing quantities of live lions, lion bodies, bones and skeletons. African lions (Panthera leo) are CITES Appendix II and can therefore be legally exported with a CITES export permit; no CITES import permit is required from the importing country. National laws vary concerning the ownership and trade of lion parts in Southern Africa, but this report will assume that if the transaction was recorded in the CTD that it was legal. See Kasiki & Hamunyela (2014) for a review of national laws. The number of the different parts was recorded for each year 2006 to 2014, prices in the respective countries were obtained on each if available in the literature, and the total value was calculated. Results Table 1. Numbers of lion body parts reported in the CTD as exported 2006-2014 Country South Africa Botswana Namibia Zimbabwe TOTAL
Bodies Skeletons
Skulls
Bones
Skin
Teeth
Claws
546
3,137
684
6,121
770
261
1,231
3
32
2 5 1 554
0 42 0 3,179
14 10 179 887
1,565kg 0 4 137 6,262
189 273 189 1,421
44 0 11 316
165 g 131 20 32 1,414
2 0 0 5
0 0 0 32
1,565kg
5
165 g
Tails Feet
A total of 14,070 lion parts, excluding trophies, were exported from Southern Africa between 2006-2014. Of this total, 10,882 were skeletal parts (i.e. bones). An additional 1,565 kg of bones were exported. Price data on lion parts are extremely sparse, limited mainly to skeletons and bones. Prices could only be found for South Africa. A summary of what could be found in the literature is shown in Table 2. Table 2. Prices converted to $ of lion parts in South Africa Year Skeleton (set) 2009 1,300 2011 1,480 2012 2013 1,260-1,560
Bones $/kg 72-96
Skulls 650
130 540-630
Sources: Williams et al. 2015; Rademeyer 2012
Although the dollar prices did not rise appreciably during the 2009-2013 period because of the depreciation of the value of the South African rand, local ZAR prices rose by approximately 30 per cent during this period. Calculating the value of the exported lion parts poses several difficulties. The skeleton of a lion with the various bones labelled is shown in Figure 1 below.
Figure 1. Lion skeleton with a complete set of bones (from Williams et al. 2015) 6
In an export as recorded in the CTD, the skull, teeth, feet, tail (caudal vertebrae), and claws may or may not be included in what is termed as a ‘skeleton’. Dealers in lion parts refer to the skeleton as a ‘set’, but often these are sold by the game rancher to the dealer with parts missing. A set will total approximately 240 to 280 bones. Most skeletons are the product of trophy hunting, and often the hunter will take the head and skin with him as trophies, and sometimes the claws. Unfortunately, no wholesale prices could be found for bodies, skins, teeth, claws or other parts. Retail prices for lion skin rugs in South Africa ranged from $1,280 to $1,975, but dealers who export would not pay these prices to game ranchers. According to Williams et al. (2015), the term ‘bodies’ was incorrectly used in place of ‘skeletons’ in 80 cases in the CTD that they could confirm by comparison with records provided by the South Africa Department of Environmental Affairs. We will assume that this is the case for all ‘bodies’ recorded in Table 1. This would certainly make sense, as whole bodies with the flesh and organs intact would have to be shipped frozen, adding considerable expense for the dealers. Figure 2. A typical bag of lion bones as sold by game ranchers to dealers (from Williams et al. 2015) Lion Aid (2012a,b,c) stated that in eastern Asian countries skeletons sold for $4,000 in 2010, $10,000-15,000 in 2012 and $15,000 in 2013. Bones sold for $300/kg in 2010 and $400-500 in 2013. In 2012-2013 therefore, the prices for skeletons and individual bones were three to ten times higher in eastern Asia than in South Africa, providing the motivation for lion bone dealers. The 2006-2014 income estimated from the sale of selected lion body parts, all of which were bones, is shown in Table 3 (next page). Since price data were not available for all parts, for all years or in all countries, certain assumptions needed to be made. The earliest price data are from 2009 and the most recent 2013. The range in prices for skeletons was $1,260-1,560, for bones $72-130/kg and for skulls $540-650 each. The average for each has been used: Skeletons $1,410; Bones $101/kg and Skulls $607.1 To calculate the weight of bones in which only the numbers were included in the CTD it was assumed that (1) the average skeleton without skull weighed 9 kg, (2) the average number of bones included in a weighed skeleton set was 120 and, thus, (3) the average bone weighed 75 gm. These assumptions were based on detailed studies carried out by Williams et al. (2015). They are necessarily inexact values and may be adjusted with future research. 1.
Given the depreciation in the value of the rand 2013-2016, the dollar prices used to calculate the values in Table 3 may be overestimates if applied to 2016 and beyond.
7
Table 3. Estimated value of lion bone exports for four Southern African countries 2006-2014 based on the number of bones given in Table 1 Country South Africa Botswana Namibia Zimbabwe TOTAL
Skeleton sets N 3,683 2 47 1 3,733
$
Bones kg
5,193,030 2,820 66,270 1,410 5,263,530
2,017 0 4 137 2,158
$
Skulls N
$
TOTAL $
204,424 0 30 1,040 204,594
684 14 10 179 887
415,188 8,498 6,070 108,653 538,409
5,812,642 11,318 72,370 111,103 6,007,433
The total value of approximately $6 million gained from lion bones since 2006 is relatively small. These bones gained the dealers when sold in eastern Asia at least $15 million and possibly up to $50 million (Lion Aid 2012a,b,c). The value of the exported lion skins, teeth, claws, feet and tails not included in Table 3 cannot be estimated because the literature does not provide prices at the supplier level. The skins are by far the most valuable. If we assume that a game rancher would sell a skin at between $500 to $1,000, the total value for the 1,421 skins exported would be between $710,500 and $1,421,000. The value of the other parts would be insignificant. It would be safe to say that not much more than $7 million worth of lion parts was exported from Southern Africa 2006-2014, ostensibly legally. Lion parts are also traded illegally. International wildlife crime networks operate in selected countries where desired live animals or products are found, often in developing countries where they can evade trade tariffs and environmental regulations by exploiting regional weaknesses in law enforcement, border and Customs control and the corruptibility of people within the public and private sector (Nellemann et al. 2016; UNODC 2016). The lion bone trade in Southern Africa is an example of such a network of dealers that operate both illegally and legally. The illegal trade in lions in Southern Africa usually involves regulated activities for which offenders are not in possession of permits to breed, keep, hunt, catch, sell, convey or export live animals or parts thereof. Cases involving illegal trade are detected, frequently at airports, when persons found in possession of lion parts do not have the necessary CITES permits. Incidents of confiscations, seizures and/or prosecutions from illegal international trade and possession of wildlife are reported annually in the CTD and by TRAFFIC (TRAFFIC 2015). A review of the CTD for South Africa, Mozambique, Botswana, Namibia, Zimbabwe and Zambia for confiscations/seizures 2006-2014 turned up extremely little, and not a single skeleton. Only one skull, 8 bones, 4 skins, 10 teeth and 197 claws were seized by importing countries, almost all in the USA. The USA also seized all 36 lion hunting trophies reported. Given that most of these items were probably for personal use and were seized simply because they were undocumented, the commercial value of this total is insignificant. Many countries do not include records of confiscations in their annual reports to CITES, while the USA has a long history of providing such information to CITES (Nowell & Pervushina 2014). While CITES seizure records are a useful measure of the scale of illegal trade and are suggestive of enforcement effort, more incidences of confiscations occur than are captured in the CTD (Williams et al. 2015). For example, Williams et al. (2015) found the following examples of illegal activity involving lions in Southern Africa – 8
•
Some exporters are allegedly selling re-used permits for about $100;
•
Poaching and illegal translocation: cases in South Africa when lions from Botswana are killed and cubs illegally smuggled to unknown farms in South Africa;
•
There have been attempts to smuggle rhino horns by wrapping them in lion skins from trophy hunts;
•
Tiger cubs have falsely been declared as dogs to avoid having to acquire a CITES permit;
•
Lion cubs have been declared as dogs in a container of live birds;
•
A large consignment of lion skeletons of mixed sex destined for eastern Asia was falsely declared as “samples and documents”;
•
Falsely declared quantities on the CITES permit;
•
Incorrect and/or forged documentation, re-used permits, and exporters offering to courier missing documentation;
•
Exports not accompanied by the original documentation and CITES permits;
•
Illegally hunted Tanzanian lions are known to transit through Mozambique; and
•
Some freight companies are willing to turn a blind eye to missing documents.
In spite of all of these incidents of alleged illegal behaviour, the value of the illicit component of lion part trade is no doubt less than the legal component, for the simple reason that it is legal to trade in lion parts if the extant national and international regulations and permitting procedures are followed correctly. Illicit Financial Flows IFFs can occur with legal exports of lion bones through under-reporting of export numbers, which will result in tax on profit evasion, or misinvoicing the value of the exported lion parts, which will also evade taxes. A common way for the number of bones to be underreported is to invoice and report a whole lion skeleton as one lion bone. For example, in July 2008 South Africa issued its first permit to export lion skeletons obtained from captive bred animals to Southeast Asia. The destination of the cargo was mistakenly recorded as Viet Nam instead of Lao PDR and the quantity recorded as 35 lion ‘bones’ instead of the ‘bones of 35 lions’ (i.e. 35 ‘skeletons’). A second permit was issued later in 2008, and the export destination and quantity were also incorrectly captured – this time as ‘15 bones’ to Viet Nam instead of ‘15 skeletons’ to Lao PDR (Williams et al. 2015). Therefore, by the end of 2008, permits to export 50 skeletons to Lao PDR had been issued. Instead of the reported 50 bones, between 12,000 and 14,000 bones were exported (240-280 bones per skeleton set). This marked the beginning of a steadily expanding trend of lion bone exports to Eastern Asia. The importer of both shipments was reportedly from the Bolikhamxay Province, Lao PDR. Five months prior to the permit for 35 skeletons being issued however, a permit had also been issued to an importer in Bolikhamxay Province to receive ‘10 skulls/skins’ and ‘20 floating bones’ (clavicles) – an amount that would have been derived from 10 lions. Given the date of this permit in early 2008, the Lao-based importer was probably making enquires about lion bones in South Africa in 2007 or earlier, but whether illegal exports of bones actually occurred then is a matter of speculation (Williams et al. 2015). 9
From 2008 to 2014 CITES permits issued to export lion bones totalled 3,179 skeletons (see Table 1). Lao PDR was the primary recipient of the bones (85%), followed by Viet Nam (13%). Permits issued to Thailand and China were reported beginning in 2011 in the CTD. If the mean mass of a lion skeleton is ± 9.28 kg (Williams et al. 2015), then the exports are equivalent to 29,501 kg – over 29.5 tonnes in seven years. Another method to effectively move IFFs from Southern Africa to foreign lands is to declare lion parts as hunting trophies (CITES Purpose Code H), but sell the parts commercially after import. The exporters would therefore not be subject to taxes on any profits made from the exports, as trophies are ‘non-commercial’. The CTD reports 717 lion trophy exports from Southern Africa 2006-2014 to China, Viet Nam, Lao PDR and Thailand, plus 739 kg of bones. Many of these ‘hunting trophies’ were described with the terms ‘skeletons’, ‘skulls’ and ‘bones’. It is likely that many if not most of these were for commercial purposes. From 2008 to 2010, Williams et al. (2015) found that 1,024 more CITES lion trophy export permits were issued than there were lions hunted in South Africa. Some of the trophies were likely not exported, even though permits were issued, but the balance would have been illegal commercial exports masquerading as non-commercial trophies. It should be noted that exports of lion trophies to Eastern Asia increased markedly after the 2006–2007 CITES measures to protect tigers and other Asian big cats were adopted and the motives for these exports are also in question. Lion parts trade flows from landowners in South Africa to Eastern Asia are shown in a schematic diagram (Fig. 3 next page). South Africa supplies over 95 per cent of the bones exported from Southern Africa (see Table 1). IFFs can occur at all links in the trade chain – landowners, bone agents, freight forwarders and handling agents – if they do not report to the revenue authorities the correct quantity and value of the lion parts, or the correct fees paid for the services. The definitions of the categories used in Fig. 3 are from Williams et al.( 2015) – Landowner: the owner of the facility that breeds and/or allows lion hunting on the property and thus has bones for sale. Not all landowners with lions will sell the bones, and there is anecdotal evidence that some farmers destroy carcasses after the lions have been hunted. Landowners are sometimes, but not always, listed as the exporters on the CITES permits. Bone agent: an enterprise or individual who sells the bones to a customer in Eastern Asia and who is usually listed as the exporter on the CITES permit and the consignor on a waybill. Bone agents mostly buy skeletons, skulls, skins and other parts from farmers and then prepare them for export. The agents must submit all the necessary paperwork to the freight forwarders before the export can proceed including the CITES permits, a taxidermy certificate from the State Veterinarian. The bone agent doesn’t necessarily own land and lions, but they may act as their own agent for skeletons from their own property. In terms of South African regulations, they could also be called ‘wildlife products traders’ since they engage in the business of acquiring and sourcing dead specimens of listed threatened or protected species with the express intention to trade the specimens for commercial purposes. Taxidermists may act as bone agents.
10
Figure 3. Trade flow chart for lion parts from South Africa to Eastern Asia (Source: Williams et al. 2015) Importer: the customer/buyer to whom the bone agent and/or landowner sells the bones is listed on the CITES permit as the importer, and on the airline waybill as the consignee. The importer most frequently reported by the Department of Environmental Affairs for 2009 and 2010 is the Xaysavang Company from Paksane, Lao PDR, owned by the notorious wildlife trafficker Vixay Kaosavang. He has also been implicated in rhino pseudo hunts and rhino horn smuggling (Rademeyer 2012). Freight forwarder: also known as forwarding and shipping agents, the forwarder is a person or company who organizes the transport and shipment of the bone consignment on behalf of the bone agent so that the cargo reaches the customer in the Eastern Asia. Forwarders contract with a particular carrier (e.g. an airline or shipping company) through a ground handling agent (GHA) to transport the consignment to the destination. First, however, the forwarder contacts the GSA (General Sales Agent) of the airline and buys cargo space for the consignment. The forwarders also prepare and process the necessary documentation including generating the air waybill. Thereafter, the forwarder takes the cargo to a GHA.
11
Ground handling agent (GHA): GHAs are responsible for handling cargo on behalf of a specific airline. An airline will only be the client of one GHA at a time. Some airlines such as South African Airlines and Lufthansa, however, are ‘self handling agents’– i.e. they do not require the services of a private GHA, and the freight forwarder can take the cargo directly to them (Airline 7 in Figure 3). Thus, the GHA takes the consignment of bones delivered to them by the forwarder, keeps it in a warehouse, and then takes the cargo to the aircraft once the necessary documentation has been generated, checked and the cargo inspected. If the GHA has been directed by the South African Revenue Service (SARS) to notify them of certain types of cargo, then a Customs representative from SARS will check the consignment. The GHA will also notify the relevant authorities if they notice anything suspect or if they do not understand the documentation. _________________ In a less linear trade flow the landowner could sell to more than one bone agent on different occasions. There is anecdotal evidence that the bone agents do not routinely choose the same freight forwarding company – possibly so as not to draw attention to their potentially controversial activities, but also to go with the carrier that has the cheaper rates. Consequently, consignments of lion parts could leave South Africa via different gateways and carriers at different times depending on the freight forwarder that the bone agent chooses. This scenario is not unlike wildlife traders in East Asia, for example, who are reported to change routes opportunistically to take advantage of new infrastructure, to reduce transaction costs or avoid detection by the authorities (UNODC 2013). Bone agents also consolidate parts bought from several different landowners and ship them in one consignment (Williams et al. 2015). Small numbers of lion parts enter the trade from wild and zoo mortality and poaching (Mouton 2013; CITES 2016a), but by far the main source is from captive-bred hunted lions. Apart from the Lao-based Xaysavang Export-Import Company (EIA 2014; Rademeyer 2012) not much is known about lion bone buyers in Viet Nam, Thailand and China. This is largely because the information is confidential. It seems that most of the consignments exported to Lao PDR go to the same addresses, but the name of the importer is allegedly not always the same. The Xaysavang company, however, is not the only importer of lion bones and there are, as yet, unnamed importers purchasing unknown quantities of bones and other parts in Viet Nam, Thailand and China. Because the financial and tax reporting records of companies or individuals that sell, buy and export lion bones are confidential, no estimate of IFFs associated with the legal trade can be made. However, based on CTD reports the illegal exports would be unlikely to exceed 15% of the legal trade, i.e. about $1 million for the period 2006-2014. The illegal trade in lion parts in Southern Africa usually involves activities for which offenders are not in possession of permits to breed, keep, hunt, catch, sell, convey or export live animals or parts thereof. Cases involving illegal trade are detected, frequently at airports, when persons found in possession of lion parts do not have the necessary CITES permits. Incidents of confiscations, seizures and/or prosecutions from illegal international trade and possession of wildlife are reported annually in the CTD and by TRAFFIC (TRAFFIC 2015).
12
Between 2006 and 2014 only 206 seizures were reported in the CTD of illegal lion parts imports from Southern Africa, and only 9 of these were termed bones or skulls. The U.S. made almost all of the seizures, with a few from New Zealand and one from the U.K. Smuggling of lion parts would appear to be very limited, probably because legal trade is relatively easy. The total value of the legal trade in lion parts in Southern Africa from 2006-2014 is estimated at about $7 million (page 8). Illegal exports (an IFF) are estimated to be about 15% of this figure, i.e. $1.05 million. An additional IFF arises from probable tax evasion and the activities discussed on the previous page for which we have allowed 10% of the total legal trade, i.e. $700,000, giving a total Illicit Financial Flow in lion parts of $1,750,000 for 2006-2014. ____________________
Pangolins Pangolins were included in this study because of recent claims that it is “the most trafficked mammal…” (Sutter 2014), that the scale of pangolin trafficking is “shocking” (Davies 2014) and “Chinese demand for the pangolin… is forcing the endangered animals closer to extinction” (Worldwatch Institute 2016). How are Southern African pangolins contributing to this trade? The African species of pangolin, also known as scaly anteaters, are Temminck’s Ground Pangolin (Smutsia temminckii), African White-bellied Pangolin (Phataginus tricuspis), Blackbellied Pangolin (Uromanis tetradactyla) and Giant Ground Pangolin (Smutsia gigantean). The former taxonomic nomenclature using the genus Manis spp. for all four is still seen in the literature. Only Temminck’s Ground Pangolin is found in Southern Africa. Pangolins roll up into a ball when threatened (Fig. 4).
Figure 4. Temminck’s Ground Pangolin Smutsia temmincki Illegal Wildlife Trade The animals are hunted for their meat, which is either consumed or traded as bushmeat, and for their scales, which are used for cultural and ethno-medicinal purposes, including in traditional African medicine, muti or juju (Challendar & Hywood 2012).
13
The increasing scarcity of pangolins in Asia has led to an escalation in market prices which is now driving the illegal poaching of African species for export (Challender et al. 2014; Zhao et al. 2014). Based on confiscations of internationally trafficked wildlife, whole pangolins and scales are most likely to be traded (Chandler & Hywood 2012). In Chinese pharmacopeia, roasted pangolin scale is believed to detoxify, relieve palsy and stimulate lactation (Zhao et al. 2014). In Viet Nam, the high prices obtained for pangolin meat have led to its consumption as a form of status (Shairpe et al. 2016; Newton et al. 2008). Pangolin skins used to be a major trade commodity, but this has apparently ceased (UNODC 2016). Following CITES CoP17 in October 2016, all species of pangolin are currently listed in Appendix I of CITES so that all international trade for commercial purposes is banned. Smutsia temminckii is classified as Vulnerable on the IUCN Red List of Threatened Species (Pietersen et al. 2014). Methods To evaluate the income derived from pangolin parts, data were gathered primarily from the CTD, CITES reports, UNODC and TRAFFIC reports concerning the prices and quantities of the various body parts, including whole bodies. Results Since 2006, the CTD holds almost no data on trade of pangolin specimens from Southern Africa. Only 43 items in 11 transactions are recorded, all of them involving Temminck’s Ground Pangolin (Smutsia temminckii). Although all of the items were from the wild (Source Code W), the exports were evidently allowed because they were either personal effects or for educational or scientific purposes. All of the exports but one were from Zambia. One export was 15 scales from Namibia to Zambia. Shepherd et al. (2016) reported 67 seizures of pangolins made in Zimbabwe 2010-2015 – an insignificant number. Much more trade in pangolins is recorded from East, Central and West Africa (CITES 2016b; UNODC 2016), and a CITES European Union report found that the majority of pangolin seizures in the EU in 2012 and 2013 were African pangolins with 85% involving pangolins illegally exported from West and Central Africa (SC65 Doc. 27 and SC65 Doc. 27.1 Annex 4). The EU also reported that 80 per cent of seized pangolin specimens were destined for China. Numerous seizures totalling thousands of kilograms of confiscated pangolin parts, primarily scales, have been recorded since 2013 in the EU, but none from Southern Africa (Fig. 5 next page). Challender et al. (2016) state that since 2009 there have been seizures involving pangolin derivatives implicating Angola, Mozambique, Zambia and Zimbabwe in Southern Africa, but no item descriptions, numbers or prices are given. Increasing trade to Asia may be facilitated by a growing Chinese presence on the continent as a result of growing economic links (Challender & Hywood 2012). The decline of Asian pangolin populations, and crucially, the increasing economic and development ties between East Asia and many African countries in recent years has resulted in a growing illegal trade in African pangolin parts to Asian markets (e.g. Gomez et al., 2016). Additionally, the price of pangolins has increased in some parts of Africa, especially in areas where species are becoming scarcer. In Nigeria the cost of pangolins has increased by 10 times from prices of five years ago.
14
Figure 5. Main flows of pangolin seizures, 2007-July 2015 Illicit Financial Flows The evidence thus far suggests that not enough export trade exists in pangolin products from Southern Africa to contribute meaningfully to IFFs in the sub-region. Most pangolin trade in Africa occurs in West, Central and East Africa (CITES 2016b; UNODC 2016). The amount that the Southern African economy loses from IFFs associated with the pangolin trade is effectively nil, though this could change in future if the wildlife trafficking networks currently engaged with other wildlife products decide to target the species. ____________________
Crocodiles Illegal Wildlife Trade The Nile crocodile, Crocodylus niloticus, is one of 23 crocodilian species globally and the only one found naturally in Southern Africa. The wild Nile crocodiles were hunted severely for their skins until 1975, when the species was listed on Appendix I at the first Conference of the Parties in Berne, Switzerland, and global commercial trade was outlawed. A number of countries in Southern Africa (and elsewhere) had started up crocodile farms prior to the CITES listing2 and have succeeded in having their crocodile populations moved to Appendix II in conformance with CITES Res. Conf. 11.16 (Rev.CoP15) in order to allow regulated international trade. These countries are Botswana, Malawi, Mozambique, Namibia, South Africa, Zambia and Zimbabwe (Caldwell 2015). Since then, illegal hunting of wild crocodiles for their skins has largely ceased.
2.
When Zimbabwe acceded to CITES in1981 it had already developed a crocodile ranching industry and entered a Reservation against the Appendix I listing of Crocodylus niloticus.
15
The IUCN/SSC Crocodile Specialist Group stated, “Despite predictions that legal trade would encourage illegal trade, an outstanding result of market-driven conservation of crocodilians is that illegal trade has all but been eradicated in the face of well-regulated legal trade” (IUCN 2016a). The African population is increasing even though the range is shrinking. Zimbabwe, South Africa and Zambia are the largest exporters of crocodile skins in Southern Africa and, indeed, in Africa as a whole. Crocodile farming is a major contributor to the global luxury market for designer handbags, shoes, belts and other leather accessories. It is highly valued for its boneless underbelly and soft leather. The European market orders over 100,000 crocodile skins from Africa every year. Asia is another big market for African crocodile skins, where it is used to produce non-branded leather products. The term ‘crocodile farm’ is used to describe any facility that breeds and/or grows crocodilians for commercial purposes. Strictly speaking, a ‘crocodile ranch’ is a facility that collects wild eggs, hatchlings and/or juveniles that have a low probability of surviving to adulthood, and growing them in captivity. From a CITES perspective, three production systems apply to crocodilians: ranching, captive breeding and wild harvest. As all species of crocodilian are listed on the CITES Appendices, international trade is regulated. Countries that are signatories to CITES, and which utilize wild crocodilian resources, must demonstrate that the use does not threaten the survival of the species (non-detriment). This typically involves some sort of monitoring of the wild population to assess the impacts of use, and regulation of products in trade. For example, all crocodilian skins in international trade must have a uniquely numbered, non-reusable tag attached to them - this allows ‘legal’ skins to be readily identified (IUCN 2016b).
Figure 6. A typical crocodile farm in South Africa
16
Methods Data were gathered from publications, reports and analyses of the CTD records. Results Crocodile skin exports from Southern African countries 2006-2013 are shown in Table 4 below (Source: Caldwell 2015). It is apparent that there are important discrepancies between the CTD entries and other sources. Table 4. Direct, commercial exports of Crocodylus niloticus skins from producer countries Country Botswana Malawi
Mozambique Namibia South Africa Zambia Zimbabwe Total
2006
2007
2008
2009
2010
2011
2012
2013
0
3,201
3,741
16,261
15,001
1,800
1,000
4,000
698
13,501
3,370
2,603
399
1,508
6,063
5,373
2,021
179
566
0
2,449
18,788
7,234
21,977
305
0
0
600
2
200
800
1,103
23,542
30,514
37,627
25,050
53,329
57,298
77,473
580,551
404,571
37,305
28,197
43,655
23,717
37,584
15,331
453,681
71,616
64,490
81,554
67,350
80,995
90,533
88,421
115,499
502,753
149,190
155,055
155,519
175,892
207,711
196,322 1,182,184
Figure derived from im porter-reported data Zim babwe data supplied by CFAZ (the Crocodile Farm ers Association of Zim babwe)
Comments on exports by Southern Africa range States (from Caldwell 2015) – Botswana: No commercial exports of skins were reported by Botswana between 1998 and 2010, however, South Africa reported importing skins from captive-bred individuals in 2008, 2009 and 2010, as well as 320 ranched skins in 2007. All were destined for South Africa, which appears to be the only country importing skins for commercial purposes from Botswana. Malawi: The import of 500 skins was reported by Germany in 2010 (of which 100 were wildsourced and the remainder ranched), while a total of 2,256 skins were reportedly imported by Germany and Singapore in 2011 (of which 96 were wild-sourced and the remainder ranched). In 2012, 3,549 skins (110 wild, 500 captive-bred and the remainder ranched) were reported as imports by Germany, Singapore and South Africa. All in 2013, apart from two wild skins reportedly exported to Australia, were from ranching operations. Mozambique: Of those exported 2011-2013, 1,694 were apparently wild-caught skins exported to Singapore and South Africa, with the remainder of ranched origin destined for France, Japan, Portugal, the Republic of Korea and Singapore. Namibia: Namibia reported exporting 200 ranched skins to South Africa in 2011, and a further 800 ranched skins to South Africa and one captive-bred skin to the Netherlands in 2012. In 2013 all exports were reported to be of captive-bred origin, a total of 1,103 exported to Israel, Italy and the Republic of Korea.
17
South Africa: Although there are no known commercial ranching operations in South Africa, as all of the registered producers are technically farms, 5,113 of the skins exported 2011-2013 were reportedly ranched. It is known that South Africa imports hatchling crocodiles from Mozambique therefore it seems likely that the ranched skins originated from Mozambique and were misreported as direct exports. For 2013, data in Table 4 are as reported by importing countries. Zambia: The vast majority of skins in 2011- 2013 were ranched and exported to Singapore followed by France and Japan. Zimbabwe: Exports of skins reported by Zimbabwe in its annual reports to the CTD are in most years substantially lower than those reported by importers and also the figures supplied by the Crocodile Farmers Association of Zimbabwe (CFAZ); the CFAZ figures have therefore been used in this analysis as a precautionary measure. In 2011, Zimbabwe’s annual report to CITES recorded the export of 22,557 skins whereas importers reported over 140,000 skins and CFAZ data indicate exports of 80,995 skins. In 2012 the Zimbabwe report indicated a higher figure than CFAZ, but cross-matching of the two reports indicated that several shipments of backstrips had been erroneously reported as whole skins in the annual report. In 2013 CFAZ reported exports of over 115,000 skins as opposed to the figure of only 91,000 in the CITES annual report. However it should be noted that not all skins exported from Zimbabwe are produced by CFAZ members and therefore it is likely that neither set of figures accurately represents a complete record of Zimbabwe’s skin exports; importers again reported over 100,000 skins from Zimbabwe in 2013. Prices for skins are variable, depending on the cut (see Figure 7 below), size and quality. Most skins are harvested from crocodiles after 4 or 5 years old, with the belly skin being the most valuable part. The two other types are termed ‘backstraps’, the central strip along the back, and ‘hornback’, the entire back minus the head. European and Japanese fashion houses send valuators to farm operations to value the quality. The highest quality can realise $150-300 per skin, down to about $100 for a good quality skin (Iwuoha 2015; Jacobson 2015). These skins after tanning and dyeing reach ten times the wet skin export price.
Figure 7. The three main types of crocodile skins that are exported 18
Averaging out all farmed skin sales to $125 each, the valuations of the exports given in Table 4 (page 17) are presented in Table 5 below. Table 5. Value in US$ of crocodile skin exports from Southern Africa 2006-2013 Country Botswana Malawi Mozambique Namibia South Africa
2006
2007
2008
2009
2010
2011
2012
2013
TOTALS
0
40,000
46,750
203,250
187,500
225,000
125,000
500,000
1,327,500
87,250
168,750
421,250
325,375
49,875
188,500
757,875
671,625
2,670,500
252,625
40,275
127,350
0
306,125
2,348,500
904,250
2,747,125
6,726,250
38,125
0
0
75,000
250
25,000
100,000
137,875
376,250
2,942,750
3,814,250
4,703,375
3,131,250
6,666,125
7,162,250
9,684,125
7,256,875
45,361,000
Zambia
5,057,125
4,663,125
3,524,625
5,456,875
4,214,625
4,698,000
1,916,375
5,671,000
35,201,750
Zimbabwe
8,952,000
8,061,250
10,194,250
8,418,750
10,124,375
11,319,125
11,052,625
14,437,375
82,559,750
17,331,881
16,789,657
19,019,608
17,612,509
21,550,885
25,968,386
24,542,262
31,423,888
174,239,076
Total
These values are conservative, assuming that the wild skins would be considerably less valuable than the carefully farmed and processed skins. A more accurate valuation could be obtained if the export records consistently reported the belly skin, backstrap and hornback breakdown by year and by country. Between 2006 and the end of 2013 approximately $174,239,076 was earned from crocodile skin exports from Southern Africa. Although highly variable year-to-year, the total annual exports 2014-2015 would most likely be in the $50-60 million range because the industry is expanding to meet global demand. Jacobson (2015) estimated that the South Africa crocodile industry netted ZAR 250 million a year (about $20 million in early 2015), but this included meat, live sales and domestic consumption. Iwuoha (2015) estimated that South Africa earned $12-16 million from skin exports annually and Zimbabwe $30 million a year. Either the number of skins exported is underreported, as Caldwell (2015) suggests, or the average value of $125 per skin used here should be increased substantially. According to the CTD, exactly 2,500 crocodile items have been seized/confiscated from 2006 to 2014 from Southern African countries. Zambia was the largest source followed by Zimbabwe, with skins, leather products and garments being the main product categories. All but 36 of the items were seized by the USA. These confiscations would have been worth perhaps $250,000 $500,000. Exports of Crocodylus niloticus meat, which originate mainly from South Africa, Zambia and Zimbabwe, increased steadily from less than two tonnes in 1992 to over 550 tonnes in 2007, but then decreased to less than 120 tonnes in 2009. Exports subsequently recovered and were around 250 tonnes in both 2011 and 2012, but 2013 showed a decline to under 130 tonnes (Caldwell 2015). The main destinations for C. niloticus meat 2011-2013 were Europe, Hong Kong SAR and China. Wholesale crocodile meat prices range from $7.70/kg to $16.50/kg in South Africa, depending on the cut. A full carcass goes for $7.70/kg, which weighs 10-12 kg (Ecotao 2016). The various crocodile meat cuts are shown in Figure 8 on the next page. Assuming the average frozen meat export price to be $10/kg, the exports from Southern Africa would have equalled $5.5 million in 2007 and $1.3 million in 2013. The total estimated value of meat exports 2006-2014 was approximately $25 million.
19
Figure 8. Crocodile meat cuts (Source: Ecotao 2016) Illicit Financial Flows The vast majority of crocodile skin and meat trade is legitimate and legal, with production and exports being made principally from farming enterprises. Annual skin exports for Southern Africa currently exceed $30 million, with almost half of the exports from Zimbabwe, followed by South Africa, Zambia and Mozambique, in that order (see Table 5). Crocodile meat exports vary greatly year-to-year, but average over the last ten years at approximately $2-3 million a year. IFFs could be made through trade misinvoicing, transfer pricing or even round-tripping, but without access to crocodile farming company records, including any offshore companies they may be affiliated with, it is impossible to assess what proportion of economic activity might be fraudulent. Between 2006 and 2014 some 1,191 crocodile skins or garments were seized as illegal imports, all in the U.S. (CTD records). South Africa was the source for 905 of them, the rest from Zimbabwe. They presumably were seized because they did not have CITES export permits, making them an IFF. The value would be approximately $150,000. No data could be found on illegally-hunted, wild crocodile skin exports but we have allowed a conservative figure of 5% of the legal skin export 2006-2014 to provide for the small illegal trade that must be taking place, i.e. $8.7 million. Our estimate for the legal exports of skin (2006-2013) is $174 million (Table 5) to which we have added a further $31 million (the legal production in 2013) to give an estimate of $205 million for the period 2006-2014. Meat exports (2006-2014) are a further $25m, giving the total legal exports of skin and meat as $230 million. Assuming that Tax Evasion is a conservative 10% of this amount, we could expect the total Illicit Financial Flows out of Southern Africa not to exceed about $23million (tax evasion) + 8.7 million (illegal hunting) = $31.7 million. ____________________ 20
Abalone In Africa, commercial abalone is found in the wild only in South Africa. Of the five abalone species found in South African waters, just one, the endemic Haliotis midae, is commercially exploited. A slow moving grazing mollusc, H. midae reaches sexual maturity after seven to nine years. It is a large sea snail reaching 23 cm in length encased in a hard shell called perlemoen in South Africa. It occupies shallow inshore waters from Cape Columbine on the country’s west coast as far as Port St Johns in the Eastern Cape, with greatest densities occurring in waters less than 10 metres deep (Figure 9).
Figure 9. Abalone range and fishing zones on the South Africa coastline (Source: De Greef K & S Raemaekers 2014)
Illegal Wildlife Trade It is only legal to harvest wild abalone in the western part of this range. Since 1986, the legal commercial harvesting area has been subdivided into seven fishing zones (A-G), with each allocated its own Total Allowable Catch (TAC) based on stock assessments and previous yields. Abalone yields have varied greatly over the years since records began to be kept in the 1950s. Production peaked in the 1960s at over 2,500 tonnes a year, but illegal harvesting overexploited the resource base and by the 2006/07 season it was down to only 75 tonnes (Table 6). The TAC is today about 150 tonnes, with Zones C and D closed, formerly the heart of the abalone commercial fishery (Prochazka 2014; De Greef & Raemakers 2014). 21
The overexploitation is due to illegal harvesting with virtually all of the catch exported to Hong Kong, Taiwan and China. In 2012 a conservative estimate was 1,723 tonnes of abalone illegally taken in South Africa, more than ten times the TAC. Between 2004 and 2013 the illegal harvest was over 20,000 tonnes (Burgener 2013; De Greef & Raemakers 2014). Abalone consumption in Southern Africa is insignificant. There are also 14 abalone farms on land in South Africa, which export from 50 to more than 200 tonnes each of meat and canned abalone to eastern Asia annually. Farming started up in the 1990s to address the falling wild production and rising demand and prices in eastern Asia (Du Plessis 2008). The farms are spread from the east coast around the south coast and up the west coast. The highest concentration of farms is in the Hermanus/Gansbaai area in the Western Cape where there are six farms, producing 75% of the total amount of product exported (Anon. 2010). Poached abalone is also known to be traded through Namibia and this poses enforcement challenges since there is currently one known legal commercial abalone aquaculture operation in Namibia producing and trading in H. midae. The South African abalone species is endemic and legitimate South African exporters have indicated that they do not export abalone, in any form, to other African countries. Furthermore, apart from the South African fishery and aquaculture production and the Namibia aquaculture operation, there is no other known legal commercial harvesting or trade in abalone in any of the other African countries (Burgener 2013). H. midae was listed in CITES Appendix III in 2007, but the trade regulations did not have the desired results, so it was delisted in 2010. Trade records in the CTD therefore are only available for 2007-2010 (De Greef & Raemakers 2014).
Table 6. Total Allowable Catch (TAC) Total Commercial Catch (TCC) and Total Recreational Catch (TRC) for the abalone fishery for 1993-2013 Season
TAC
TCC
TRC
1993/94
615
613
549
1994/95
615
616
446
1995/96
615
614
423
1996/97
550
537
429
1997/98
523
523
221
1998/99
515
482
127
1999/00
500
490
174
2000/01
433
368
95
2001/02
314
403
110
2002/03
226
296
102
2003/04
237
258
0
2004/05
223
204
0
2005/06
125
212
0
2006/07
75
110
0
2007/08
0
74
0
2008/09
150
0
0
2009/10
150
150
0
2010/11
150
152
0
2011/12
150
145
0
2012/13
150
–
0
Source: Prochazka (2014)
Methods Data on quantities and prices of abalone products were gathered from reports, publications and the CTD. The quantities of each product were estimated and multiplied by the prices.
22
Results The majority of the legal and illegal trade is in live, canned, dried and frozen abalone. There are no recorded incidents of illegal trade in live abalone, most of it is smuggled out in dried form. Consumers rehydrate it before cooking, much like dried mushrooms (Fig.10). When dried, abalone shrinks to one-tenth of its original size. A hundred tonnes of dried produce is thus equivalent to 1,000 tonnes of fresh abalone (Steinberg 2005).
Figure 10. Dried abalone for sale in Hong Kong (Credit: M. Burgener/TRAFFIC) The interior of the shell is composed of nacre, also known as mother-of-pearl, which can be cleaned and polished to be sold as an ornamental half-shell or used in jewellery (http://www.heartofabalone.co.za/#!harbour-shop/ck1v) The aquaculture farms sold 62 per cent live abalone and 33 per cent canned in 2008 (Du Plessis 2008), thus it is assumed that most of the live abalone exports reported in the CTD originates from these farms. South Africa is a leading producer of cultured abalone, contributing approximately 2% of global abalone production. There are 14 farms that produce approximately 1,200 tonnes of product per year. However, most farms are expanding and annual production is expected to climb to 2,500 tons within the next five years (Viking Aquaculture 2016) and to 5,000 tonnes by 2023 (Erasmus 2013). Prices of abalone products are shown in Table 7. Table 7. Prices of abalone products (USD) 2005-2016 in Southern Africa Product Live Dried Frozen Canned
2005 34-38/kg1
2009 34/kg2
2012 2014 3 35-45/kg 40/kg4
2016 156-335/kg5,6
1
25-35/can
45/kg2 25/can2
Sources: 1 - Du Plessis (2008); 2 - Cloete (2009); 3 - Erasmus (2012); 4 - Hopkinson (2014); 5 - Menges (2016); 6 - Pijoos (2016)
23
Regardless of the form in which abalone is sold–dried, fresh, frozen, etc.—the abalone price in shell is the same worldwide. The price for live abalone in 2005 was approximately $34-38 per kilogram. Frozen ($45/kg) and canned (US$ 25 per 425 g tin) abalone fetch good prices, and in some instances even higher than live abalone prices, however, losses in weight associated with shucking and evisceration, as well as processing costs, result in a lower price than live abalone per unit. There are advantages and disadvantages related to different abalone product forms (Du Plessis 2008; Cloete 2009). Abalone is sold in the South Africa legal export market with US dollar prices, so the price is unaffected by fluctuations in the value of the rand. The 2016 prices for dried abalone are simply press reports and so are not of high reliability. Several abalone farms were requested by email to provide prices of their various products, but none replied. Live abalone provides the most consistent price data of $34/kg to $45/kg for the ten years 2005-2014. $40/kg has been used as an average price in the value estimations. Dried and canned abalone is sold in different sizes and qualities and without detailed statistics showing the breakdown of the different types sold it is impossible to estimate an accurate value of sales. The shells sell for about $1,400 per tonne (Wikipedia 2016). A more serious problem is the fact that the CTD does not have categories for dried and canned abalone. The description categories used are ‘live’, ‘bodies’ and ‘meat’. In 2007, the first year of reporting to the CTD, the number of items is sometimes reported and other reports give the kilogram weight. A few assumptions were necessarily made to convert to kilograms, since prices are given in kilograms – ·
The average weight of a live exported abalone is 125 gm. The websites of abalone farms showed that they export live abalone ranging from 30 gm to 400 gm each. Medium size abalone (less than 115 gm out of the shell) are preferred and gain higher prices (De Greef & Raemakers 2014), thus it is assumed that the average price will be near this.
·
The average dry weight of abalone in 425 gm cans is 213 gm, while the dry weight in 850 gm cans is 425 gm of abalone meat (Viking Aquaculture 2016). The rest is water and the can. We will assume 300 gm as the average weight of meat per can.
·
Dried abalone is the most popular form in eastern Asia, if dried properly using approved methods, which the South African industry has mastered. It is also much cheaper to transport dried abalone. We will assume that 75 per cent of ‘meat’ and ‘bodies’ is dried and 25% is canned and frozen.
·
The average weight of a dried abalone is 10 per cent of a live one.
All of the trade transactions 2008-2010 were in kilograms and no ‘bodies’ were reported, only ‘live’ and ‘meat’. As above, 75 per cent of the meat was assumed to be dried while 25 per cent was assumed to be canned. The South Africa export data were compared to the import country data. The larger quantity was used when there was a discrepancy, assuming that underreporting would be advantageous for both duty and tax reasons. The quantities and values of exports were estimated as shown in Table 8.
24
Table 8. Estimated quantities and values of abalone products exported from South Africa as reported in the CITES Trade Database, 2007-2010 Product weights 2007-2010 in kilogrammes Product Live Dried Canned Totals
2007
2008
2009
2010
Value $
667,288 38,011 12,670
1,826,437 639,618 213,206
1,386,150 950,991 316,997
216,659 31,453 10,484
163,861,360 498,021,900 46,113,083
717,969
2,468,063
2,339,150
250,122
707,996,343
The total value of abalone product exports of almost $708 million for the four years can be considered an underestimate, given that 2007 and 2010 represent no doubt only partial reporting periods. The estimated exports in 2008 totalled $266,541,925 and 2009 totalled $343,120,778, which represent full reporting years. Most of these legal exports were from farmed abalone, with wild abalone making up a distinct minority. Given the expansion in production in the farms, legal exports today probably exceed $500 million annually. Based on Table 6, the commercial wild catch in 2005/2006 and 2006/2007 totalled 322 tonnes (322,000 kg) and 2011-2014 would have been approximately 450 tonnes (450,000kg). Add to this an estimated farm production of 700,000 kg in 2006 and 12 million kg 2011-2014, this totals 13,472,000 kg for the years missing in Table 8. Let’s assume that of this, 13 million kg is exported at an average of $40/kg. This totals $520 million. The total legal exports of abalone 2006-2014 therefore equal approximately $1.23 billion. The illegal exports of abalone, almost all in dried form, are more difficult to estimate because of the clandestine nature of the business. According to various sources, the smuggled export market is controlled by Chinese Triad groups that have been operating in South Africa since the 1980s (Gastrow 2001; Steinberg 2005). These Triads originated in Hong Kong and Taiwan and maintain ties with their origin. Most of the illegal exports therefore go there. De Greef & Raemakers (2014) estimate that 1,567 tonnes of illegal abalone were smuggled out of South Africa in 2012, with a total of 20,500 tonnes smuggled to Hong Kong alone 2004-2013 (an average of 2,050 tonnes per annum). Burgener (TRAFFIC, unpublished) collected import statistics in Hong Kong for January 2012 to October 2013. The figures showed that 181.5 tonnes of dried abalone had entered from illegal sources in Southern Africa in the 2012/2013 season, which converts to over 1,800 tonnes of live weight. During the same period, 117.5 tonnes (117,500 kg) of legal dried abalone was imported from South Africa (De Greef & Raemakers 2014), which converts to approximately 117,500,000 kg of live abalone.3 Steinberg (2005) found that the majority of illegal abalone is smuggled across land borders or on light aircraft from South Africa to neighbouring countries before being re exported to Hong Kong. Import data analyses prepared by TRAFFIC suggest that this is still the case (De Greef & Raemakers 2014).
3.
This 117,500,000 kg figure from 2012/2013 corresponds well with the increase in production from expanding farms that would be expected from the estimated 639,618 kg in 2008 and 950,991 kg in 2009 exported from South Africa (Table 8).
25
Illegal volumes of abalone have been decreasing in recent years in South Africa because of overexploitation, but we can assume an average of 2,000 tonnes live weight of abalone per annum from 2006 through 2014 were illegally exported (see Figure 11), which at $40/kg equals $80 million a year, or a total of $720 million for the nine years 2006-2014. The total of $1.95 billion of exports, both legal and illegal, between 2006-2014 make abalone one of the most lucrative of all wildlife products.
Figure 11. Estimated legal and illegal abalone catch from South Africa converted to live weight. (Source: Burgener 2015). Illicit Financial Flows The analysis above in this report estimated that in the 2006-2014 period approximately $1.23 billion of legal abalone and $720 million of illegal abalone were exported from Southern Africa, almost all of it originating in South Africa. The majority of the legal abalone was produced in 14 farms in South Africa and one in Namibia. Virtually all of the illegal abalone – 18,000 tonnes live weight – was taken illegally in the wild. The trend that will continue in future is for higher volumes to be produced and exported from the expanding legal farms and less to be illegally harvested and smuggled out from the wild as a result of overexploitation of the resource base. The $720 million worth of abalone smuggled out of Southern Africa between 2006-2014 (about $80 million per year) is a direct IFF loss to the Southern Africa economy, mainly South Africa. No duty or taxes were paid to governments on the income, and, because the actors involved in the smuggling have close connections with East Asia, it is reasonable to assume that much of the payment for the commodity remained in the purchasing destinations of Hong Kong SAR, Taiwan and China. A common method of smuggling is to mix abalone in with consignments labelled as sardines or other commodity that is not listed in CITES appendices (Fig.12 next page). 26
Figure 12. Smuggling illegal abalone (Source: TRAFFIC)
Potential IFFs associated with the 15 commercial farm enterprises could be occurring through trade misinvoicing, transfer pricing or round-tripping, but without access to abalone farming company records, including any offshore companies they may be affiliated with, it is impossible to assess what proportion of economic activity might be illegal. Based on a value of $1.23 billion for legal exports 2006-2014 (page 25) and, using the criterion that IFFs due to tax evasion could easily amount to 10% of this, $123 million has been added to the$720 million of illegally exported abalone to give a first-order estimate of $843 million for the Illicit Financial Flows arising from abalone. ________________
27
Sharks and Rays There are more than 500 species of sharks and biologically related rays and skates in the subclass Elasmobranchii of the Class Chondrichthyes (cartilagenous fishes). Their skeletons are made of cartilage and connective tissue and they have on average eight fins. Sharks range in length from 17 cm for the Dwarf Lanternshark up to 40 metres for the largest fish in the world the Whale Shark. Sharks are found in all of world’s oceans and seas down to a depth of about 2,000 metres. The two main products traded globally from sharks and rays are their fins and meat. There are local tourist markets for jaws and teeth, but these are relatively small and informal and would not contribute significantly to IFFs. Shark liver, liver oil, skin, cartilage and gill rakers are also sold, but data are not sufficient to quantify the market (Dent & Clarke 2015). In addition, the species of shark being traded is only rarely identified in trade records for shark meat and never for shark fins. Knowledge of the specific characteristics of domestic markets is also very limited, and there is little concrete information on such things as the types of products being marketed, the prices of these products at different points in the supply chain, the profile of the typical consumer, and the major demand drivers. Shark fin is used primarily in the preparation of shark fin soup, used in Chinese culture for special occasions (Fig.13, photo D.Stiles). It is very expensive and can cost $100 or more for a bowl in a restaurant. The fin actually has no taste, it is used to provide a gelatinous consistency, and other ingredients are added to give it flavour. Shark meat is considered of low quality and is correspondently much cheaper than the fins. The products have generally different markets, with fins going to countries with large Chinese populations (China, Hong Kong SAR, Taiwan, Singapore, Malaysia, Viet Nam, Thailand, USA). The largest meat markets are the Republic of Korea, Spain, Italy, Brazil and Uruguay (Dent & Clarke 2015). South Africa exports shark meat to Australia particularly for the fish and chips market (Da Silva & Burgener 2007).
Figure 13. Shark fin wholesaler in Guangzhou, China
28
Official FAO statistics conservatively put the average annual declared value of total world shark fin imports at $377.9 million per year from 2000 to 2011, with an average annual volume imported of 16,815 tonnes. This calculates to an average price of $22.50/kg. In 2011, the last year for which full global data are available, the total declared value of world exports was $438.6 million for 17,154 tonnes imported, an average of $25.60/kg. The corresponding 2000–2011 annual average figures for shark meat were 107,145 tonnes imported, worth $239.9 million, an average of $2.24/kg. In 2011 alone, the reported figures for total world imports of shark meat were $379.8 million and 121,641 tonnes for value and volume, respectively, an average price of $3.12/kg (Dent & Clarke 2015). Shark fins are approximately ten times more valuable than shark meat for fishermen (Fig.14 Photo: D. Stiles), which explains the motivation for shark finning, which is the practice of removing only the fins from a caught shark and throwing the body back into the water alive, where it quickly dies. Fishing boats have limited storage space and owners prefer to keep only the most valuable part of the catch. Figure 14. Processed raw shark fin for sale in Bangkok, Thailand at about $235/kg ' ___________________ There are more than 200 species of sharks, skates and rays identified in South African waters, of which 98 are caught in 12 fisheries: ten commercial, one recreational and the KwaZulu-Natal bather protection system. Approximately 4,000 tonnes per annum are landed. Target fisheries for sharks include the demersal longline, bather protection, commercial and recreational line and gillnet fisheries. By-catch fisheries include the inshore and offshore trawl, beach seine, tuna and swordfish pelagic longline (including the shark-directed vessels), mid-water trawl, hake longline and prawn trawl fisheries. Sharks are landed at virtually all Western Cape ports and many other ports along the South African coast (Prochazka 2014). The exports reported from South Africa for shark fin and meat 2006 to 2011 are shown in Table 9 (next page). Data were not available for other Southern African countries for shark fins, though statistics are available for shark meat for Namibia, and also shark fins only in 2012. Namibia exported 297 tonnes of fins to Singapore worth $4.5 million (Dent & Clarke 2015). If this figure is extrapolated as an average income for 2006-2011, the total would be $27 million. The average price was therefore a relatively low $15.15/kg. Hong Kong SAR took almost all of the shark fins. 29
Table 9. Shark fin and meat exports and re-exports from South Africa and Namibia, 2006-2011 Country
Year 2006
2007
2008
2009
2010
2011
Metric tonnes
Product
Total
Total Value $
South Africa
meat fins
1,126 142
894 1,154 1,822 1,172 1,039 82 186 174 115 89
7,207 12,972,000 788 17,730,000
Namibia
meat
2,314 2,744 1,803 2,368 3,333 3,314
15,876 28,576,800
Source: Dent & Clarke 2015
Illegal Wildlife Trade Shark meat export prices for Namibia 2006-2012 ranged from $1.08/kg to $2.34/kg, showing considerable variability. The average of $1.80/kg was used to estimate the legal value of meat exports from both Namibia and South Africa in Table 9. The average global export average of $22.50/kg has been used for shark fins, giving an estimate of shark fin exports from South Africa for 2006-2011 of about $17,730,000. To obtain an estimate for the value of shark fin exports from Namibia 2006-2011, the ratio of the value of fin exports to meat exports from South Africa (1.37 over the given period) has been applied to the meat exports from Namibia giving a figure of $39,056,678. The estimate for the gross total of legal exports of shark meat and fins from South Africa and Namibia 2006-2011 is therefore a total of $98,336,078. This has been scaled up from the 6 year period 2006-2011 by a factor of 1.333 to give a value of $147,504,117 for the nine year period 2006-2014. Data are not available for the two other Southern African countries with access to the sea (Mozambique and Angola), but neither country has a developed fisheries industry and most sharks fished in their waters go to other countries without being landed. Shark and ray ‘poaching’, that is, catches made by foreign fishing trawlers operating in Southern African waters are a direct IFF loss to the Southern Africa economy. These catches are never landed in a Southern African country but the fins and – less likely – the meat is transported to be sold offshore. We have assumed that the illegal trade in shark meat and fins from 20062014 is 15% of the gross total of legal exports of shark meat and fins from South Africa and Namibia 2006-2011 , i.e. $22,125,618. Illicit Financial Flows Export data on shark fins and meat are limited and out of date, with the figures only available up to 2011. The statistics for meat and fin exports 2006-2011 showed high variability in export quantities year to year. South Africa exported from 894 to 1,822 tonnes of shark meat a year and 82 to 186 tonnes of fins. Namibia exported from 1,803 to 3,333 tonnes of meat and in 2012, the only year for which data are available, exported 297 tonnes of fins to Singapore. On the next page, Figure 15 presents the trade chain of wild caught fish catch, which could apply to any fish and wild caught marine product, including those of sharks, abalone, lobsters, oysters, etc. To enable accurate valuation of the exports, good data would be needed primarily at the Import & Export point in the chain. To allow for an assessment of any IFFs generated by the trade, financial data would be needed at the points of Landing, Buyer, Processor and Trader. In addition, one would need to be able to compare the declared quantities and prices of each consignment exported and imported, as illustrated in Figure 16. These types of data are not freely available. 30
Figure 15. The marine product catch and trade chain and points of control (Source: TRAFFIC)
Figure 16. Requirement for shipping import and export documents Using the estimates in bold font on the previous page, the total legal and illegal exports from Southern Africa for the period 2006-2014 amount to almost $170 million of which illegal exports ($22 million) are about 15% of the legal exports. An additional IFF results from various methods of tax evasion in the legal trade which have been estimated at about 10% of the value of the legal trade, i.e. about US$15 million. The combined Illicit Financial Flows from illegal exports and tax evasion on legal exports is therefore estimated at about $37 million. _______________ 31
Cycads Cycads are gymnosperms, plants that have seeds but produce no flowers or fruit. They are often confused with palms or ferns because of their superficially similar leaves and trunks. Cycads are, however, unique, unrelated to any other group of living plants. They can live 1,000 years or more because they continue to produce new offshoots from the bulb at the base of the trunk. They originated over 200 million years ago and are very popular with collectors and landscapers because of their attractiveness, rarity and hardiness. Some adult specimens of rare species can attract $100,000 or more (Thamm 2014). Worldwide, there are 11 genera and over 340 described species, with some species as yet undescribed. IUCN reports that cycads are the most threatened plant group in the world (Rayner & Pires 2016). There are 49 indigenous Encephalartos species in Southern Africa and one species of Strangeria (S. eriopus). All of these cycad species are on CITES Appendix I, but are traded commercially using the A and D source codes (‘artificially propagated’ and ‘Appendix-I specimens bred in captivity for commercial purposes’ respectively).4 Thirty-eight of these species occur in South Africa, one in Angola, one in Malawi, 9 in Mozambique, 10 in Swaziland, one in Zambia and 3 in Zimbabwe. No wild cycads are found in Botswana or Namibia. Nine Encephalartos species in South Africa are extinct in the wild and others are threatened by excessive collecting. All legal trade in cycads is carried out by nurseries that propagate the plants for commercial purposes. These nurseries commonly raise and sell cycad species that are both native and nonnative to the countries in which they are located. In spite of legal sources being available, because of the high prices that the rarest species can fetch there are organized criminal networks that steal the plants in the wild and from botanical gardens, and even from the commercial nurseries (Smith 2014). Because of the cycad theft, TRAFFIC (2011) has called for a total ban on all legal cycad trade. The most common parts of the cycad that are sold for export are seedlings (germinated seeds) and suckers (stem offshoots, when planted called offsets), which are generally sold by the stem diameter in centimetres (Figs. 17 & 18). South Africa prohibits export of cycad plants exceeding 15cm in maximum dimension, except for the following dwarf species which cannot be exported if the stem diameter is more than 7cm: E.caffer, E.humilis, E.cupidus, E.cerinus and E.ngoyanus. Figure 17. Cycad seedlings for sale 4.
Some Southern African nursery websites claim that they are registered with CITES as artificial propagation facilities, but none are listed by CITES (https://cites.org/eng/common/reg/e_nu.html).
32
Figure 18. Cycad offsets for sale (Photo: Peter Heiblom) (Photo: http://www.minorgarden.com/) Methods Cycad nursery websites were searched for species and prices of products for sale, press reports provided information, as did websites of cycad societies and associations, and export numbers were obtained from the CTD. All categories except seeds recorded in the CTD were assumed to be either seedlings or suckers/offshoots. Results The numbers of seeds and live specimens exported from Southern Africa between 2006 and 2014 are shown in Table 10 below. The only exports not from South Africa were one live specimen each from Malawi and Namibia, both to South Africa, and 100 live specimens and 255 seeds from Swaziland to South Africa. Table 10. Cycad specimens exported from Southern Africa, 2006-2014 Plant Part
2006
2007
2008
2009
2010
2011
2012
2013
2014
Total
483
0
6,032
0
0
3,613
5,652
91
240
16,111
Seedlings & 8,221 Offshoots
12,177
13,845
16,462
9,473
7,894
5,344
4,588
11,038
89,041
Total
12,177
19,877
16,462
9,473
11,507
10,996
4,679
11,278
105,152
Seeds
8,704
33
Prices for seeds, seedlings and offshoots vary considerably according to species and variety. The rarest species and most popular varieties (usually ‘blue’) are the most expensive. Offshoots are also priced according to the diameter or height of the caudex (root stem or bulb). Females are more expensive than males. The price of seeds drops as more are bought. For example, one E. altensteinii can be priced at $12, 5 for $36, 10 for $60 and 50 for $250. A spreadsheet was created containing each of the 50 species found in Southern Africa and seven non-native species that were found for sale. Prices for seeds, seedlings and offshoots per cm were found on websites for 50 of the 57 species. There was considerable variation in pricing and sometimes groups of small seedlings of mixed species were sold as lots. The prices for hybrids were not used. The CTD does not distinguish seedlings from offshoots and in general seedlings have much lower prices than offshoots, so an average price for each species was guesstimated. From 2006-2014, more than 105,000 cycad specimens were reported in the CTD as exported from Southern Africa. Of these, more than 16,000 were seeds and the rest (>89,000) were seedlings or offsets (see Table 10). The total estimate for cycad seed, seedling and offshoots exported legally from 2006-2014 came to $10,741,112 or an average of $1.2 million annually. Illegal Trade In 2012, South Africa prohibited specific activities involving certain Encephalartos species that are listed on Appendix I of CITES, in terms of the National Environmental Management: Biodiversity Act, 2004 (Act No. 10 of 2004). The prohibition states that, unless required for conservation or enforcement purposes, the following activities involving wild specimens of Encephalartos species are prohibited – ! Collect, pluck, uproot, destroy ! Export from the Republic of South Africa, sell, trade, buy ! Receive, give, donate, accept, acquire, dispose ! Import into the Republic of South Africa, convey, move, translocate ! Possess, exercise physical control (except where permits were issued, prior to the publication of the prohibition notice, for plants that form part of legally obtained parental stock)5 The number of cycads smuggled out of Southern Africa is unknown. The smuggled cycads tend to be much larger and therefore much more valuable because many are collected as grown plants in the wild or from botanical gardens (Smith 2014; Thamm 2014; Rayner & Pires 2016). They can be shipped labelled as palm tree species that do not require CITES permits. The CTD reports small numbers of seizures, only 26 live specimens and 235 seeds from six species and one Encephalartos spp. seizure in seven incidents 2006-2014 from Southern Africa. Even if the total successfully smuggled to destination was ten times the seized number, this would only equal 260 live specimens and 2,350 seeds. These would be probably worth less than $50,000 in total for the common species. Adults of the rare species can command up to $100,000 each (Thamm 2014), but the numbers of these successfully smuggled out is completely unknown. Illegally exported cycads would be unlikely to exceed 15% of legal exports – about $1.6 million over the period 2006-2014. 5.
https://cites.org/sites/default/files/eng/cop/17/WorkingDocs/E-CoP17-58.pdf
34
Illicit Financial Flows The first component of illicit financial flows – that of direct theft and smuggling of cycads was estimated on the previous page at about $1.6 million over the period 2006-2014. Using the same approach as that used for the preceding species, an additional IFF arising from tax evasion equal to 10% of the total legal exports ($1.1 million over the period 2006-2014) needs to be added to the illegal exports to give an estimate of $2.7 million for the total Illicit Financial Flow in cycads for the period 2006-2014.
______________________
35
WILDLIFE TOURISM International tourism has been on a steady upward growth trend for many years now. In 2015 the United Nations World Tourism Organization (UNWTO) estimated that international tourist arrivals reached 1.184 billion, a 4.4% increase from 2014. Tourism accounts for 10 per cent of global GDP and one in eleven jobs (UNWTO 2016a). Africa received only 5% of these visitors, about 53 million arrivals, in 2015. Wildlife tourism is what attracts most visitors to Southern Africa (Fig.19). The types of activities are diverse, and include the classic safari experience of viewing wildlife in parks and reserves, bird-watching, scuba diving, whale or dolphin watching, visiting a zoo, wildlife conservancy or game ranch, big game fishing and trophy hunting.
Figure 19. Tourists visit Southern Africa primarily to see the wildlife (Photo: D. Stiles)
36
Photographic and Recreational Wildlife Tourism This section will concern only non-consumptive wildlife tourism in line with the definition of UNEP/CMS (2006). Analyzing the economic value of the wildlife watching tourism market segment in Southern Africa faces some of the following challenges – 1. The availability of national tourism statistics for African countries is quite limited and refers to the direct economic contribution of tourism. At the national level, data on international tourist arrivals and international tourism receipts are available for the majority of countries. However, data on employment or tourism industries or indicators on the average length of stay and the average expenditure per day are being reported for only a small number of African countries. In addition, Tourism Satellite Accounts (TSA) are only available for a limited number of African countries. South Africa was the only country in Southern Africa to have a TSA report. 2. Where data are available at national level, they mostly refer to the whole tourism sector, regardless of the different travel purposes. A few countries account indicators according to three different travel purposes, i.e. leisure, business, visiting friends and relatives (VFR) and others; but different segments of tourism such as beach tourism, nature tourism, cultural tourism or wildlife-related tourism are not identified. 3. Data on the tourism expenditure of wildlife tourism at the destination level are not collected systematically, or, where data are generated by registrations, surveys or studies, these are often not published. Valuation of the tourism industry Methods Data were taken from the UN World Tourism Organization (http://statistics.unwto.org/), the Africa Tourism Data Portal (http://tourismdataforafrica.org/), national tourism offices and published reports. The data reported in different sources for the same variables were not always consistent, so the figure that seemed most in line with other data points was chosen. The statistical variables chosen to report on here consist of – !
Total Leisure Travel & Tourism Spending
!
International tourism expenditures
!
International tourism number of arrivals
!
Per cent share of GDP
The total value of the economic income from wildlife tourism will be considered as 50% of the total amount reported as Total Leisure Travel & Tourism Spending (page 40). A dedicated field survey country-by-country to collect wildlife tourism only data would be required in which national tourism offices and a statistically valid random sample of tour operators were visited in order to generate an accurate valuation. The UNWTO (2016b) conducted a desk survey of wildlife tourism in Africa in 2014, but because of the small and non-random nature of the sample of respondents the information produced can be considered as indicative only. For example, the survey found that the average price per person per day of a standard wildlife watching tour was $243 and $753 for a luxury wildlife watching tour. A larger and more random sample (the respondents were companies that volunteered) would no doubt produce different results. Only four countries in Southern Africa responded. 37
The first variable includes both domestic and international tourism. Visitors often travel to foreign destinations for more than one reason. For example, they might add on a safari after a business meeting, or go whale-watching after attending a conference, thus mixing business with leisure. The data should be considered as approximations only. Results Tourism data from multiple sources for 2006-2015 is shown in Table 11. Lesotho and Swaziland were not included because the total income was less than the probable margin of error. Table 11. Tourism spending and international tourism arrivals in Southern Africa, 2006-2015 ANGOLA
U nits
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Total Leisure Travel & Tourism Spending
$ billions
0.73
1.26
1.75
2.08
1.76
1.97
2.10
2.51
2.91
3.40
International tourism expenditures
$ billions
0.393
0.473
0.447
0.719
0.646
0.706
1.234
International tourism num ber of arrivals
m illions
0.121
0.195
0.294
0.366
0.425
0.481
0.528
0.650
0.595
%
1.0
1.2
1.2
1.5
1.2
1.1
1.0
1.0
1.0
1.1
Percentage share of G D P
BOTSWANA
U nits
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Total Leisure Travel & Tourism Spending
$ billions
0.86
1. 00
0.96
0.90
1.02
1.09
1.09
1.19
1.31
1.43
International tourism expenditures
$ billions
0.285
0.284
0.240
0.231
0.026
0.036
0.036
0.113
International tourism num ber of arrivals
m illions
1.426
1.736
2.101
2.103
2.145
1.614
2.598 C
2.082 C
%
3.3
3.6
3.2
2.7
2.5
2.2
2.2
2.2
2.3
2.3
Percentage share of G D P
MALAWI
U nits
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Total Leisure Travel & Tourism Spending
$ billions
0.07
0.09
0.10
0.13
0.11
0.12
0.09
0.10
0.10
0.11
International tourism expenditures
$ billions
0.085
0.079
0.096
0.100
0.093
0.096
0.140
International tourism num ber of arrivals
m illions
0.638
0.735
0.742
0.755
0.746
0.767
0.770
2.598
2.082
%
1.3
1.5
1.4
1.6
1.3
1.2
1.2
1.3
1.2
1.1
Percentage share of G D P
MOZAMBIQUE
U nits
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Total Leisure Travel & Tourism Spending
$ billions
0.24
0.25
0.34
0.36
0.32
0.39
0.44
0.45
0.48
0.53
International tourism expenditures
$ billions
0.196
0.209
0.235
0.247
0.294
0.260
0.289
0.273
International tourism num ber of arrivals
m illions
0.664
0.771
1.193
1.461
1.718
2.013
2.206
1.970
%
1.9
1.8
1.9
2.1
1.9
1.8
1.8
1.7
1.7
1.6
Percentage share of G D P
NAMIBIA
U nits
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Total Leisure Travel & Tourism Spending
$ billions
0.68
0.83
0.79
0.81
1.03
1.18
1.20
1.34
1.55
1.76
International tourism expenditures
$ billions
0.118
0.132
0.114
0.356
0.383
0.461
0.435
0.374
International tourism num ber of arrivals
m illions
0.833
0.929
0.931
0.980
0.984
1.027
1.079
1.176
%
2.2
2.4
2.0
1.8
2.2
2.6
2.4
2.6
2.8
3.0
Percentage share of G D P
SOUTH AFRICA
U nits
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Total Leisure Travel & Tourism Spending
$ billions
13.03
13.97
12.24
11.88
16.21
17.43
17.76
18.83
20.70
22.53
International tourism expenditures
$ billions
5.230
6.103
6.905
6.420
8.139
8.397
8.542 B
9.914 B 10.262 B
International tourism num ber of arrivals A
m illions
8.509
9.208
9.729
10.098
11.575
12.496
13.796
15.155
15.092
15.052
%
2.2
2.2
2.0
1.9
2.0
1.9
2.1
2.1
2.1
2.1
Percentage share of G D P
ZAMBIA
U nits
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Total Leisure Travel & Tourism Spending
$ billions
0.22
0.26
0.32
0.25
0.32
0.36
0.38
0.42
0.46
0.50
International tourism expenditures
$ billions
0.097
0.098
0.107
0.083
0.128
0.140
0.441 E
0.540 E
International tourism num ber of arrivals
m illions
0.757
0.897
0.812
0.710
0.814
0.920 E
0.859 E
0.915 E
%
1.0
1.2
1.1
1.0
1.1
1.1
1.1
1.0
1.0
1.0
U nits
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
0.40
0.58
0.64
0.65
0.69
0.75
0.81
0.87
0.94
$ billions
0.338
0.365
0.294
0.523
0.634
0.662
0.749
0.856
0.827
m illions
2.287
2.506
1.956
2.017
2.239
2.423
1.794 D
1.832 D
1.880 D
%
5.1
4.8
6.1
5.9
4.8
4.4
4.5
4.4
4.3
Percentage share of G D P
ZIMBABWE Total Leisure Travel & Tourism Spending International tourism expenditures International tourism num ber of arrivals Percentage share of G D P
$ billions D
6.9
Sources: Tourism Data for Africa Portal; A: Lehohla (2016a); B: Lehohla (2016b); C: Buthali (2016); D: Govt. of Zimbabwe (2016); E: Govt. of Zambia; UNWTO 2016a,b)
38
The totals and averages for the Total Leisure Travel and Tourism spending and percentage of GDP variables for the ten-year period 2006-2015 which had complete data sets are shown in Table 12 below. Table 12. Total Leisure Travel and Tourism spending, 2006-2015 ($ billions) Country ANGOLA Total Leisure Travel & Tourism Spending Percentage share of GDP %
Total
Average
20.47
2.047 1.1
BOTSWANA Total Leisure Travel & Tourism Spending Percentage share of GDP %
10.85
1.085 2.65
MALAWI Total Leisure Travel & Tourism Spending Percentage share of GDP %
1.02
0.102 1.3
3.8 %
0.38 1.8
NAMIBIA Total Leisure Travel & Tourism Spending Percentage share of GDP %
11.17
1.117 2.4
SOUTH AFRICA Total Leisure Travel & Tourism Spending Percentage share of GDP %
164.58
16.458 2.2
ZAMBIA Total Leisure Travel & Tourism Spending Percentage share of GDP %
3.49
0.349 0.9
ZIMBABWE Total Leisure Travel & Tourism Spending Percentage share of GDP %
7.0
0.70 5.1
MOZAMBIQUE Total Leisure Travel & Tourism Spending Percentage share of GDP
The total amount of Leisure Travel and Tourism spending in most of Southern Africa 20062015 is estimated to be on the order of $222 billion, an average of $22.2 billion a year over the ten years. South Africa’s average of almost $16.5 billion per annum comprises about 74 per cent of the total. As Table 11 shows, however, the spending in the later years greatly exceeds that in the earlier years, shown graphically in Figure 20 (next page). Spending almost doubled over the ten years from $16.27 billion in 2006 to $31.2 billion in 2015.
39
Figure 20. Leisure Travel & Tourism spending in Southern Africa 2006-2015 ($billions) Sources in Table 11. As a percentage contribution to GDP, the average ranged from 1.1% in Angola to 5.1% in Zimbabwe (Table 12). The proportional contributions are a combination of the size of the tourism markets and other economic production sectors. In Angola, total tourism spending is much higher than in Zimbabwe, but because the GDP is dominated by the oil and gas sector, tourism is a relatively minor contributor, while Zimbabwe’s economy is much weaker in other sectors, so tourism gains a larger share of GDP, even with less spending. The figures given in Table 11 and Fig.20 are the total amount of Leisure Travel and Tourism spending in most of Southern Africa 2006-2015. Not all of this spending can be attributed to wildlife. In Table 13 on the next page we estimate the contribution of wildlife to Leisure Travel and Tourism spending by summing the proportions of wildlife-related activities in Zimbabwe (ZIMSTAT 2016, Table 4.6). Out of a sample of some 25,000 tourists leaving Zimbabwe in 2015, more than half (55%) had engaged in activities related to wildlife.6 The question then arises whether it is valid to apply this percentage to other countries in southern Africa. Zimbabwe may have more attractive wildlife resources than some of the other southern African countries. This is probably not true for Botswana with its Okavango Swamps and Namibia with its spectacular desert ecosystems. South Africa accounts for nearly threequarters of the tourism spending in southern Africa but its wildlife attractions are probably less spectacular than those of Botswana, Namibia and Zimbabwe. Without much better data these questions are difficult to answer. Still, it is a reasonable assumption that wildlife contributes at least half (50%) of the total tourism expenditure in southern Africa. All of the declared wildlife tourism income is from legal sources, but how IFFs may be generated will be dealt with in the next section. 6.
The estimate in Table12 for the average annual Zimbabwe Total Leisure Travel and Tourism spending is $0.7 billion. ZIMSTAT (2016, p32) estimates the tourism revenue for Zimbabwe in the year 2015-2016 as $0.8 billion. Given the increasing trend of tourism income in the region (Fig.20), these amounts are comparable.
40
Table 13. Proportion of Total Tourism related to Wildlife The figures given in the main body of the table are percentages of the numbers shown in the row “Num ber in sam ple ” Age Group 3
15-19
20-24
25-29
30-34
35-39
40-44
45-49
50-54
55-59
60-64
65-69
70-74
75+
Sightseeing
29.9
30.0
29.1
26.2
25.3
24.9
28.0
30.3
31.2
33.7
35.4
37.1
35.4
Hunting safari
0.5
0.2
0.3
0.3
0.2
0.3
0.4
0.7
0.4
0.5
0.4
0.6
0.0
Walking safari
7.5
5.9
5.5
4.5
3.5
5.0
4.4
5.7
5.8
6.4
5.0
5.9
7.4
Game drives/view
12.2
9.4
7.8
6.5
6.6
6.8
7.2
8.9
9.0
9.1
10.6
10.5
10.1
Water sport
5.5
5.2
4.0
3.0
2.2
2.6
2.8
3.4
2.6
2.0
1.3
1.5
0.7
Photographic safari
3.4
2.7
2.3
2.1
2.3
2.3
2.3
2.3
2.4
2.8
3.1
3.6
3.7
Business
3.1
8.5
11.7
16.1
19.5
18.8
17.4
12.3
10.2
7.1
3.9
1.3
3.4
Shopping
15.9
17.8
20.8
25.1
24.5
23.3
19.2
14.8
15.0
14.0
13.3
13.7
12.8
Historical places/Cultural interest
4.9
5.9
5.7
4.2
4.6
4.0
4.4
4.6
4.8
4.8
5.4
4.1
6.6
Boat cruises
7.3
7.9
8.0
6.9
6.9
6.9
8.8
10.8
13.1
14.9
16.6
17.4
16.5
Other specify
9.8
6.6
4.8
5.2
4.5
5.0
5.2
6.2
5.5
4.6
4.9
4.2
3.4
Total Percent
100
100
100
100
100
100
100
100
100
100
100
100
100
Number in sample
798
1,616
2,538
3,167
2,920
2,878
2,458
2,214
2,074
1,752
1,507
929
407
25,258
Wildlife-related numbers
485
907
1,345
1,473
1,308
1,330
1,256
1,300
1,284
1,181
1,071
698
298
13,934
Wildlife-related %
60.8
56.1
53.0
46.5
44.8
46.2
51.1
58.7
61.9
67.4
71.1
75.1
73.1
55.2
Activities Engaged
Rows shaded in GREEN are directly related to wildlife. The data source is ZIMSTAT (2016, Table 4.6)
41
Illicit Financial Flows The total income from tourism in the ten years 2006-2015 for Southern Africa was estimated to be on the order of $222 billion (Table 12 page 39). Adjusting this for receipts attributable to wildlife, the figure for 2006-2015 reduces to $111 billion. From 2015 onward tourism receipts will exceed $30 billion annually ($15 billion attributable to wildlife), approximately threequarters of this produced by the South Africa tourism sector. On average, wildlife tourism contributes a bit more than 1% to Southern Africa’s GDP per annum. There are many tricks tourism companies can use to minimize taxes and move or keep tourism proceeds offshore. Alvin Mosioma, executive director of the advocacy group Tax Justice Network–Africa, said tourism is a sector that is prone to questionable tax practices because it is almost impossible to nail down a market value for services. That makes it easy for companies involved in the industry to book profits and costs in a way that shifts their tax burdens to lowor no-tax jurisdictions (Fitzgibbon 2016). For example, wildlife tour enterprises operating in South Africa, Botswana, Namibia or any other country could headquarter their companies in the British Virgin Islands or Mauritius, and open company bank accounts in places such as the Isle of Man or Lichtenstein, where banking confidentiality is high. Such places are known as ‘tax havens’, because they assess little or no taxes on foreigners registered in their jurisdictions. The Panama Papers revealed that several African wildlife tourism operators have done this, using Mossack Fonseca to create the offshore companies and bank accounts (Fitzgibbon 2016). The tourism operators then proceed to use these offshore companies to market their safaris and collect the proceeds from clients into offshore banks. Only a small proportion of the funds actually enter Southern African countries for operating expenses, which are declared for tax purposes. Taxes are paid in the offshore jurisdictions where the companies are registered, but at much lower rates than in Southern African countries. One tax that is ‘above the line’ that tourism companies cannot avoid by using offshore companies is the bed tax, also known as the Transient Occupancy Tax (TOT) in some places. Hotels, safari camps, or wherever visitors may sleep, must declare the number of rooms and nights of occupancy and pay to government a certain predetermined fee as a tax. This is the main reason that lodgings require the guests to register at check-in. Theoretically, government tax authorities should be able to determine lodging income based on the bed nights and advertised room rates, but they rarely, if ever, do this. To reduce the tax burden on actual income derived from room and/or board payments, the proprietor can take out a ‘loan’ from the company that provides the clients overseas. This may be the operator’s own offshore company, or a third-party marketing company.7 The loan is purportedly capital to be used to invest in the local company, for example to purchase safari vehicles or to build a camp or lodge. The loan is repaid to the marketing company through them keeping most or all of the payments made by clients booking safari tours. This income never appears on the books in Africa, thus avoiding taxes. Since the money is repaying a loan, real or invented, it also does not appear in the accounting of the marketing company (J. Brooke, pers. comm. in Zimbabwe, 2015). Taxes can also be evaded by the usual practices involving misinvoicing, transfer pricing and round-tripping. 7.
Many companies in Europe, North America and elsewhere specialize as tour agents in selling the safaris, lodging and air flights of companies physically based in Africa. They make a commission on sales.
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The analysis of the wildlife tourism sector differs markedly from the analysis of illegal trade in wildlife products. There is no trade in wildlife species products involved in tourism ... which is essentially a service industry. Without access to the financial records of all of the companies involved in marketing and carrying out wildlife tourism, it is impossible to estimate accurately the amount that might end up as IFFs. However, the incentives to evade taxes and accumulate personal wealth are probably higher in the wildlife tourism industry than in the wildlife commodity trade industry. In the so-called 'Panama Papers' scandal, Fitzgibbon (2016) found at least 30 wildlife safari companies in Africa used offshore 'shell' companies created by Mossack Fonseca elsewhere in the world. For most of the wildlife species products analysed in this volume, a percentage of 10% of the value of the legal trade was used to estimate the potential IFFs resulting from private sector tax evasion. That percentage is probably too low for the tourism industry. Doubling the percentage (20%) gives the result that the Illicit Financial Flow arising from tax evasion in the wildlifebased tourism sector from 2006-2014 would be of the order of $22.2 billion. The annual loss would be about $2.5 billion. __________________
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