Струкова Сборник дополнителных текст 2014
.pdfty investors in Australia’s far north. He vowed to keep up the pressure on Australia until it stopped taking such business.
The traditional image of a tax haven is a palm-fringed Caribbean island, a chillier outcrop in the English Channel or a European microstate such as Monaco or Liechtenstein. But offshore is not so much a geographical concept as a set of activities and offerings. What havens generally peddle is an escape from high taxes and strict regulation, along with easy incorporation and secrecy. Some of the biggest tax havens are in fact OECD economies, including America and Britain, that many would see as firmly onshore. They provide something the offshore islands cannot: a destination for money rather than a mere conduit, with first world capital markets and banks backstopped by large numbers of taxpayers.
Latin Americans have flocked to banks in Miami for decades, both for legitimate reasons of confidentiality (for instance, fears that details of wealth held at home could be leaked) and to dodge tax. A congressional investigator, asked where America keeps its dirtiest money, answers without hesitation: “Brickell” (Miami’s financial district). Can this party go on? Under new IRS rules, from last month America’s banks have had to report interest payments to non-residents. In some circumstances this information could be shared with 80 countries that have information exchange agreements with America. The regulations were bitterly opposed by Florida’s banks and politicians, who worried that Latin American depositors would flee in droves. They lost, victims of America’s need to offer some form of reciprocation as it presses foreign governments to provide details of Americans who hold money abroad. The scale of the withdrawals from Miami is not yet clear. America’s other offshore speciality is shell company registration. States such as Delaware and Nevada offer cheap, easy incorporation, with anonymity guaranteed. Registration agents do not even have to ask for ID, as they do in most tax havens. And what is not collected cannot be passed to the police, which is why criminals and debtors love American shells. Martin Kenney, a fraud-busting lawyer in the BVI, finds them harder to penetrate than vehicles in the Caribbean, where “there will at least be some sort of lead, even if only nominees, to help you start pounding through the layers.” Dodgy operators also like the air of legitimacy around an American company, and the ease with which shells can be used to open corporate bank accounts.
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Delaware is America’s incorporation giant, with 945,000 active entities. It makes so much money from company fees that it does not need to levy taxes on sales. Like some of the classic offshore havens, it is a small state with an economy that relies heavily on services for nonresidents. Its political class, left and right, is all in favour of crafting local laws to accommodate corporate customers. Registrations grew by an average of 7% a year in the decade to 2011, and anything that interferes with them is fought tooth and nail.
Delaware’s corporate spectrum is broad, with a few thousand public companies at one end, overseen by its world renowned Chancery Court, and hundreds of thousands of tiny, opaque LLCs (limited-liability corporations) and partnerships at the other. Delaware lawyers say the sleazy reputation of the smaller entities is unjustified, and that many LLCs are created by respectable companies for joint ventures and property transactions. Jeffrey Bullock, Delaware’s secretary of state, insists that it has struck the right balance between curbing criminality and “paying deference to the millions of legitimate businesspeople who benefit” from hassle free incorporation.
Some of the biggest tax havens are in fact OECD economies, including America and Britain, that many would see as firmly onshore.
But according to a World Bank database, American shells are the most popular corporate vehicles among perpetrators of large-scale corruption. An avid user was Viktor Bout, known as the “Merchant of Death”, a convicted arms smuggler. In a study last year three academics, led by Griffith University’s Mr. Sharman, approached shell company providers around the world posing as corrupt officials and money launderers. They found that OECD countries were less compliant than tax havens with international standards on corporate transparency, that America was among the least compliant, and that Delaware was one of the worst states (with not a single fully compliant response). Investigators joke that Delaware stands for “Dollars and Euros Laundered And Washed At Reasonable Expense”.
A federal bill supported by Barack Obama, which would force states to collect information on beneficial owners (the human sort rather than “legal persons” such as trusts), has been stalled for several years. The formidable antireform coalition includes the national lawyers’ association and the United States Chamber of Commerce.
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Fill the gaps using the words:
Taxpayers, balance, capital, onshore, Investigators
1.Some of the biggest tax havens are in fact OECD economies, including America and Britain, that many would see as firmly …
2.… joke that Delaware stands for “Dollars and Euros Laundered And Washed At Reasonable Expense”.
3.They provide something the offshore islands cannot: a destination for money rather than a mere conduit, with first world capital markets and banks backstopped by large numbers of …
4.Jeffrey Bullock, Delaware’s secretary of state, insists that it has struck the right … between curbing criminality and “paying deference to the millions of legitimate businesspeople who benefit” from hassle free incorporation.
5.They provide something the offshore islands cannot: a destination for money rather than a mere conduit, with first world markets and banks backstopped by large numbers of taxpayers.
Write down all words connected with economy
Make up your plan to this article
Reproduce the text using your list of the words and your plan
Unit 6
Warming up activities
What is it quality? Give a definition of it? What are the problems of high / low quality of the data?
Vocabulary:
data quality – качество данных
data entry – ввод данных, информационный вход benefit – выгода; польза; прибыль; преимущество revenue – доход
yield – прибыль, доход
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Look through the following text and answer the following questions:
1.Where do problems of Poor Quality Data come from?
2.Why do we need a high quality data?
3.How can data quality change company income/expenses?
4.May companies ignore the quality of its data?
Liability and Leverage. A Case for Data Quality
Philip Russom
April 2006 by The Data Warehousing Institute (TDWI).
When making a case for a data quality initiative or project, organizations cite both liability and leverage. They need to reduce costs by alleviating the liabilities of poor quality data or they want to increase revenue by leveraging the benefits of high quality data. Either way, the case can be compelling, such that most organizations claim a return on investments (ROI) in data quality.
Problems of Poor Quality Data
In the surveys of 2001 and 2005, TDWI asked, "Has your company suffered losses, problems or costs due to poor quality data?" Respondents answering yes grew from 44 percent in 2001 to 53 percent in 2005, which suggests that data quality problems are getting worse.
In the same period, however, respondents admitting that they "have- n't studied the issue" dropped from 43 percent to 36 percent. It is possible that the two trends cancel each other out, such that problems have not necessarily increased. Rather, more organizations now know from their own study that data quality problems are real and quantifiable. Averaging the two years together, 48.5 percent (or roughly half) of organizations now recognize the problem. Because this is far higher than the 12 percent denying any problem, we conclude that problems due to poor quality data are tangible across all industries and exist in quantity and severity sufficient to merit corrective attention.
Poor quality data creates problems on both sides of the fence between IT and business. Some problems are mostly technical in nature, such as extra time required for reconciling data (85 percent) or delays in deploying new systems (52 percent). Other problems are closer to busi-
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ness issues, such as customer dissatisfaction (69 percent), compliance problems (39 percent) and revenue loss (35 percent). Poor quality data can even cause problems with costs (67 percent) and credibility (77 percent).
Origins of Poor Quality Data
Survey responses show that problems unquestionably exist. But exactly where do they come from?
Problems originate in both IT and the business. Problems arise from technical issues (conversion projects, 46 percent; system errors, 25 percent), business processes (employee data entry, 75 percent; user expectations, 40 percent) and a mix of both (inconsistent terms, 75 percent). Problems even come from outside (customer data entry, 26 percent; external data, 38 percent). Hence, data quality is assaulted from all quarters, requiring great diligence from both IT and the business to keep its problems at bay, with both internal processes and external interactions.
Inconsistent data definition is a leading origin of data quality problems. Too often, the data itself is not wrong; it is just used wrongly. For example, multiple systems may each have a unique way of representing a customer. Application developers, integration specialists and knowledge workers regularly struggle to learn which representation is best for a given use. When good data is referenced wrongly, it can mislead business processes and corrupt databases downstream. With 75 percent of survey respondents pointing to this problem, it ties with data entry as the most common origin of data quality problems.
Data entry ties for worst place as an origin of data quality problems. This problem has been with us since the dawn of computing and is probably here to stay. The problem is lessened by user interfaces that require as little typing as possible, validation and cleansing prior to committing entered data, training for users, regular data audits and incentives for users to get it right.
Types of Data Prone to Quality Problems
Data about customers is the leading offender (74 percent). The state of customer data changes constantly as customers run up bills, pay bills, move to new addresses, change their names, get new phone numbers, change jobs, get raises, have children and so on. The customer is the
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most highly changeable entity in most organizations, along with equivalents such as the patient in health care, the citizen in government and the prospect in sales force automation. Unfortunately, every change is an opportunity for data to be entered incorrectly or to go out of date. Because customer data is often strewn across multiple systems, synchronizing it and resolving conflicting values are common data quality tasks.
Product data (43 percent) is in a distant second place after customer data. Defining product is challenging because it can take different forms, for example, as supplies that a manufacturer procures to assemble a larger product, the larger product produced by the manufacturer, products traveling through distribution channels and products available through a wholesaler or retailer. Note that this list constitutes a supply chain. In other organizations, the chain is not apparent; they simply acquire office supplies, medical supplies, military munitions and so on, which are consumed in the production of a service. Hence, one of the greatest challenges to assuring the quality of product data is to first define what "product" means in an organization.
Benefits of High Quality Data
Roughly half of respondents reported they "haven't studied the issue" of data quality benefits (49 percent), whereas the study shows that only one-third haven't studied its problems. With more time spent studying problems instead of benefits, data quality is clearly driven more by liability than leverage. Even so, benefits exist, and 41 percent claim to have derived them, compared to a mere 10 percent denying any benefit.
Awareness of Benefits from High Quality Data
The top three benefits of high quality data identified by respondents all relate directly to data warehousing, namely greater confidence in analytic systems (76 percent), less time spent reconciling data (70 percent) and a single version of the truth (69 percent). This is expected because data quality has a track record of success in data warehousing. Other benefits are more business driven, such as gains in customer satisfaction (57 percent), cost reduction (56 percent) and extra revenues (30 percent).
Data Quality ROI and Budget
TDWI's 2005 survey asked, "Does your company believe it can achieve a positive return on investment by investing in a data quality
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initiative?". Forty-three percent of respondents reported that their organization believes ROI is possible, whereas 19 percent do not. Thirty-eight percent admit they do not know. This is similar to the response given when TDWI asked this question in 2001: 40 percent, 19 percent and 41 percent, respectively. Based on the respondents' appraisal, ROI is a distinct possibility with data quality, though not an overwhelming probability.
Consistent with the recognized possibility of data quality ROI, a combined 80 percent of respondents report that data quality budgets will stay the same or increase, versus a miniscule four percent anticipating a budget cut. Some interviewees described their data quality initiative or team as a cost center, though it is in transition toward becoming a revenue center. Given users' growing budgets and belief that ROI is possible, investments in data quality are safe, growing and likely to yield a return in a reasonable amount of time.
The liabilities of poor quality data and the leveragability of high quality data should compel anyone to action. Organizations that depend on their data cannot afford to ignore its quality. Furthermore, data quality efforts are likely to yield a demonstrable return, and your peers in other organizations are increasing investments accordingly.
Fill the gaps using the words:
Customer, budget, liabilities, reduce, tangible
1.The … of poor quality data and the leveragability of high quality data should compel anyone to action.
2.The … is the most highly changeable entity in most organizations, along with equivalents such as the patient in health care, the citizen in government and the prospect in sales force automation.
3.Consistent with the recognized possibility of data quality ROI, a combined 80 percent of respondents report that data quality budgets will stay the same or increase, versus a miniscule four percent anticipating a … cut.
4.We conclude that problems due to poor quality data are … across all industries and exist in quantity and severity sufficient to merit corrective attention.
5.They need to … costs by alleviating the liabilities of poor quality data or they want to increase revenue by leveraging the benefits of high quality data.
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Write down all words connected with economy
Make up your plan to this article
Reproduce the text using your list of the words and your plan
Unit 7
Warming up activities
What do you know anything about offshore? Why is it popular? Why are offshores supposed to be suspicious?
Vocabulary:
offshore financial centers (OFCs) – оффшорные финансовые цен-
тры
tax authorities – налоговые органы
tax evasion schemes – схемы уклонения от уплаты налогов tax havens – налоговые гавани (территории с льготным налого-
обложением)
money laundering – отмывание денег (легализация)
dirty money – «грязные» деньги (полученные преступным путем) individuals – физические лица
Read the following text carefully and answer the following questions:
1.What kind of fraud are helped by offshores?
2.Where are tax heavens located generally?
3.Is the real reason offshore, not the countries with hart tax policies?
4.What are the offers help offshores to become less suspicious?
Storm survivors
Offshore financial centers have taken a battering recently, but they have shown remarkable resilience
Feb 16th 2013 | THE ECONOMIST
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A Belize bank account fronted by nominees that is owned by a shell company in the British Virgin Islands (BVI) that in turn is owned by a foundation in Panama. Over the past decade the bigger OFCs have cooperated more with foreign law-enforcement agencies, but progress is patchy, and offshore structures still crop up regularly in corruption and money-laundering cases. A recent example is the alleged use of Cayman companies as conduits for bribes to Saudis by a subsidiary of EADS, a European aerospace and defence company.
The scale of the offshore industry’s dirty money problem is hotly disputed. Economists at Global Financial Integrity, a research group founded by Raymond Baker, an authority on financial crime, reckon that developing countries alone suffered illicit financial outflows defined as money that is illegally earned, transferred or used of at least $5.9 trillion over the past ten years. Some say WHEN THE ECONOMIST INTELLIGENCE UNIT, a sister organization of this newspaper, published the first bound edition of “Tax Havens and Their Uses” in 1975, a queue several blocks long formed outside The Economist’s bookshop in London. Interest in offshore financial centres (OFCs) kept growing over the following twenty years as dozens of new havens popped up, often with help from lawyers based in Wall Street or the City of London. Tax authorities did little to intervene. Beginning in the mid 1970s, Jerome Schneider, a well-known “tax planner”, hawked various tax-evasion schemes with impunity for more than twenty years, even advertising in airline magazines.
This tolerance ended in the late 1990s, when prosecutors began to catch up with Mr. Schneider and his kind and the Organization for Economic Cooperation and Development (OECD), a rich country forum, declared war on “harmful tax competition”. Since then tax havens have been under sporadic attack, including two waves of blacklisting. In 2008-09 the G20 took up the cudgels, America put pressure on Swiss banks to reveal more about their customers and various tax authorities started paying for stolen information about offshore accounts.
Pressure on OFCs has since eased a little because they have all accepted, to differing degrees, that they need to exchange more information with their clients’ home countries. But they remain beleaguered as an increasingly confident band of “tax justice” campaigners pushes for more concerted action on tax evasion and avoidance, moneylaundering and the proceeds of corruption. Tax avoidance, the grey area
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between compliance and evasion, has shot up the political agenda. A recent cover of Private Eye, a British satirical magazine, caught the national mood, showing Santa Claus being booed for living offshore. Governments have been rushing out action plans. Britain has put tax compliance and corporate transparency at the top of its list of priorities for its presidency of the G8 this year. America’s media often suggest that Congress yank money back from tax havens to alleviate the nation’s fiscal woes.
The world has 50-60 active tax havens, mostly clustered in the Caribbean, parts of the United States (such as Delaware), Europe, SouthEast Asia and the Indian and Pacific oceans. They serve as domicile for more than 2m paper companies, thousands of banks, funds and insurers and at least half of all registered ships above 100 tonnes. The amount of money booked in those havens is unknowable, and so is the proportion that is illicit. The data gaps are “daunting”, says Gian Maria MilesiFerretti of the IMF. The Boston Consulting Group reckons that on paper roughly $8 trillion of private financial wealth out of a global total of $123 trillion sits offshore, but this excludes property, yachts and other fixed assets. James Henry, a former chief economist with McKinsey who advises the Tax Justice Network, a pressure group, believes the amount invested virtually tax-free offshore tops $21 trillion. His methodology is reasonably sophisticated but he admits his calculation is still “an exercise in night vision”.
Once commercial transactions are factored in, the likely total for offshore wealth balloons. Over 30% of global foreign direct investment is booked through havens. Mr. Milesi-Ferretti studied a group of 32 of them and found that international banks’ claims on these were of the same order as their claims on all emerging markets. Some OFCs are giants in certain kinds of business. The Cayman Islands (population 57,000) is the world’s leading hedge fund domicile. Bermuda (population 65,000) is number one in reinsurance.
These two are famous but in many ways atypical. Many of their smaller competitors are what Jason Sharman, of Griffith University in Australia, calls “aspirational havens”: islands that turned to finance to reduce their reliance on tourism and agriculture, but have never got beyond selling a few thousand offshore companies a year.
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