ty 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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