Trade in Hormone-Treated Beef

In the 1970s, scientists discovered how to synthesize certain hormones and use them to accelerate the growth rate of livestock animals, reduce the fat content of meat, and increase milk production. Bovine somatotropin (BST), a growth hormone produced by cattle, was first synthesized by the biotechnology firm Genentech. Injections of BST could be used to supplement an animal’s own hormone production and increase its growth rate. These hormones soon became popular among farmers, who found that they could cut costs and help satisfy consumer demands for leaner meat. Although these hormones occurred naturally in animals, consumer groups in several countries soon raised concerns about the practice. They argued that the use of hormone supplements was unnatural and that the health consequences of consuming hormone-treated meat were unknown but might include hormonal irregularities and cancer. The European Union responded to these concerns in 1989 by banning the use of growth-promoting hormones in the production of livestock and the importation of hormone-treated meat. The ban was controversial because a reasonable consensus existed among scientists that the hormones posed no health risk. Although the EU banned hormone-treated meat, many other countries did not, including big meat-producing countries such as Australia, Canada, New Zealand, and the United States. The use of hormones soon became widespread in these countries. According to trade officials outside the EU, the European ban constituted an unfair restraint on trade. As a result of this ban, exports of meat to the EU fell. For example, U.S. red meat exports to the EU declined from $231 million in 1988 to $98 million in 1994. The complaints of meat exporters were bolstered in 1995 when Codex Aliment Arius, the international food standards body of the UN’s Food and Agriculture Organization and the World Health Organization, approved the use of growth hormones. In making this decision, Codex reviewed the scientific literature and found no evidence of a link between the consumption of hormone- treated meat and human health problems, such as cancer. Fortified by such decisions, in 1995 the United States pressed the EU to drop the import ban on hormone-treated beef. The EU refused, citing “consumer concerns about food safety.” In response, both Canada and the United States in- dependently filed formal complaints with the World Trade Organization. The United States was joined in its complaint by a number of other countries, including Australia and New Zealand. The WTO created a trade panel of three independent experts. After reviewing evidence and hearing from a range of experts and representatives of both parties, the panel in May 1997 ruled that the EU ban on hormone-treated beef was illegal because it had no scientific justification. The EU immediately indicated it would appeal the finding to the WTO court of appeals. The WTO court heard the appeal in November 1997 and in February 1998 agreed with the findings of the trade panel that the EU had not presented any scientific evidence to justify the hormone ban. This ruling left the EU in a difficult position. Legally, the EU had to lift the ban or face punitive sanctions, but the ban had wide public support in Europe. The EU feared that lifting the ban could produce a consumer backlash. Instead the EU did nothing, so in February 1999 the United States asked the WTO for permission to impose punitive sanctions on the EU. The WTO responded by allowing the United States to impose punitive tariffs valued at $120 million on EU exports to the United States. The EU decided to accept these tariffs rather than lift the ban on hormone-treated beef, and as of 2010, the ban and punitive tariffs were still in place.

Read the Country Focus “Trade in Hormone-Treated Beef.” Applying the facts of the case, answer the following questions.

a) What was the main argument that the European Union used to ban importations of hormone-treated beef? Who benefited from this ban and who did not benefit from this ban? Explain how for each case.

b) What action did the European Union take when it realized that it could face punitive actions for imposing this ban? In your opinion, what were the advantages and disadvantages of taking this action?

c)   Based on the result of the EU-U.S. trade negotiations over hormone-, treated beef, what observations can you make about the realities of

international trade in terms of national sovereignty versus national benefit? Explain your answer.

In a recent analysis, we highlighted the higher risks COVID-19 poses for communities of color due to underlying health, social, and economic disparities. When we released that analysis, only a handful of states were reporting racial and ethnic data for confirmed coronavirus cases and deaths, but those data were already showing stark, disproportionate impacts for some groups of color. The Centers for Disease Control and Prevention (CDC) began reporting national data on confirmed coronavirus cases by race and ethnicity as of April 17, 2020. Similar to earlier state data, they suggest that the virus is having disproportionate effects, with Black people accounting for 34% of confirmed cases with known race/ethnicity compared to 13% of the total population as of April 20, 2020. However, race and ethnicity is missing or unspecified for nearly two-thirds (65%) of the CDC-reported cases, limiting the ability to interpret the data. In addition to the CDC data, a growing number of states have started reporting racial and ethnic data for cases and deaths, which provide further insight into how the virus is affecting communities across the country: As of April 15, 2020, 33 states, including DC, were reporting data on distribution of confirmed coronavirus cases and/or deaths by race/ethnicity. Our analysis of these data finds that they continue to paint a sobering picture of how the virus is disproportionately affecting communities of color, as described and illustrated below (Figure 1). These data will continue to evolve as states update their data and additional states begin reporting data by race and ethnicity. Going forward, we will update these data on a regular basis and add them to our State Data and Policy Actions to Address Coronavirus dashboard. n the majority of states reporting data, Black people accounted for a higher share of confirmed cases (in 20 of 31 states) and deaths (in 19 of 24 states) compared to their share of the total population. These disparities were particularly large in Wisconsin, where Black people made up a four-times higher share of confirmed cases (25% vs. 6%) and an over six-times higher share of deaths (39% vs. 6%) compared to their share of the total population. Similarly, in Kansas, Black people accounted for a three-times higher share of cases (17% vs. 6%) and an over five times higher share of deaths (33% vs. 6%) than their share of the total population. Other states where the share of deaths among Black people was at least twice as high as their share of the total population included Illinois, Michigan, Missouri, Arkansas and Indiana. Moreover, Black people accounted for over half of all deaths in DC (75%), Mississippi (66%), Louisiana (59%), Alabama (52%), and Georgia (51%). We also observed disparate impacts for Hispanic and Asian individuals in some states. In 6 of 26 states reporting data, Hispanic individuals made up a greater share of confirmed cases compared to their share of the total population, with the largest relative differences in Iowa (17% vs. 6%) and Wisconsin (12% vs. 7%). ). Asian people made up a higher share of cases or deaths relative to their share of the total population in a few states, although the differences generally are small. In Alabama, Asian people accounted for 4% of deaths compared to 1% of the total population. Although we identified fewer disparities for these groups compared to Black people, less states report data for these groups and states differ in how they report these data. For example, states vary in whether they include or exclude Hispanic individuals from racial categories and some report data for Asian people alone, while others combine Asian people with another racial group. Moreover, states do not provide data for subgroups of Asian people, which can mask disparities for subgroups who are at higher risk. Data remain largely unavailable for smaller groups, including people who are American Indian or Alaska Native (AIAN) and Native Hawaiian or Other Pacific Islander (NHOPI), limiting the ability to identify impacts for them. These groups are at high risk given large pre-existing disparities in health, social, and economic factors, and there are large disparities in some of the states where data are available. For example, AIAN people make up a larger share of confirmed cases compared to their share of the total population in New Mexico (37% vs. 9%), and AIAN individuals make up five times more deaths compared to their share of the total population in Arizona (21% vs. 4%). The Indian Health Service (IHS) also reports confirmed cases among IHS patients. However, not all AIAN people are able to access services through IHS, and IHS has historically been underfunded to meet the needs of AIAN people, so these data do not provide for a complete understanding of impacts for this group. Comprehensive nationwide data by race and ethnicity will be key to understanding how COVID-19 is affecting communities as well as shaping and targeting response efforts. While the majority of states are reporting racial and ethnic data, in many states, race and/or ethnicity is unknown for a significant share of cases and deaths. The unknown race share exceeds 20% for cases in 14 states and for deaths in 4 states. Moreover, as noted earlier, there are inconsistencies in how states report data that limit comparability across states. As such, the availability of comprehensive, consistent nationwide data disaggregated by race and ethnicity remains important for understanding the impact of COVID-19 across communities. Moreover, going forward, these data will be important to broader efforts to advance equity and address disparities that existed prior to COVID-19 and that will likely widen due to COVID-19.

IN 3 TO 4 SINGLE SPACED TYPED PARAGRAPHS 1. WHAT ARE THE KEY POINTS OF THIS ARTICLE, 2. IN YOUR OPINION WHAT ARE THE ROOT CAUSES OF THIS DISPARITY--BE SPECIFIC AND 3. WHAT STEPS WOULD YOU RECOMMEND TO THE CONGRESS OF THE UNITED STATES TO CLOSE THE GAP.

MBA - Managerial Economics

Discuss briefly the supply schedule and the various factors affecting the supply in the market.

Thanks

Bottlenecks and the founder effect are two different types of genetic drift. How do they differ? Provide an example of each

1 Explain gene flow?  Does gene flow lead to adaptive evolution?

2 Explain genetic drift / how does gene pool affect in small populations and preservation of rare species ?  

3 In what way can founder effect lead to genetic drift in a population?

4 Explain founder effect how gene pool change ?

5 Explain directional, disruptive and stabilizing of natural selection

6 What is a balanced polymorphism? and explain the heterozygote advantage in terms of sickle cell anemia.

Mark worked as route manager for United Trucks Pty Ltd in Queensland from 2003-17. A term of his contract was that if he should leave the company, he could not engage in the trucking industry in Queensland for five years. In 2018 he registered a company called Sunshine Trucks Pty Ltd. Mark owns 99% of the shares in the company. The other 1% is owned by his brother, Greg, whom he elected as sole director and CEO. Sunshine Trucks operates from Townsville and carries goods all over Queensland. Greg also signs a contract on behalf of the company, taking out a loan of $ 2 million from Grasping Bank in 2018 as start-up capital. The company did well during 2018, 2019 and the first half of 2020, but in July 2020 was not able to repay a loan instalment of $ 100 000 owing to Grasping Bank Ltd. Mark comes to you for advice after receiving two letters: One from United Trucks Pty Ltd requiring Sunshine Trucks Ltd to cease operating in Queensland, the other from Grasping Bank Ltd threatening to sue him for $ 100 000. Advise him, citing all relevant legal authority. Please note that you should assume that the restraint of trade clause in the contract that Mark had with United Trucks is valid under the law of contract. You should therefore not discuss that issue.

Advice Mark using ILAC.

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1. Cash point Ltd prepares quarterly budget as part of the budgetary control system. During the last quarter ending 31^st March 2020 the following information is accumulated concerning operations for the next quarter ending 30^th June 2020.

1. Sales which are all on credit will amount to Kshs 400,000 in April 2020 the same level as in February and March 2020, increasing at 10% per month for the next six months and thereafter at 5%
2. Sales credit terms are strictly 30 days after-sales. Past experience shows that this policy is effective in 75% of cases. Of the remainder, 20% will pay in subsequent month and 5% never pay up until after 180 days.
3. Inventories are maintained at equivalent of 15 days sales. On 31^st March 2020 the inventory level is estimated at Kshs125,000.
4. Goods are bought and paid for within 30 days. During March 2020, merchandise purchases amounted to Kshs 250,000
5. The unit selling price is Kshs 100 with a profit margin of 40% on sales.
6. The cash balance on 31^st March 2020 is Kshs 87,800.
7. Projected payment for expenses and other items are as follows

8. Other payments are to be made as follows:

• Half yearly rent at the rate of Kshs 8,000 per month will be paid in April and the next instalment in October 2020.
• Income tax amounting to Kshs 20,000 will be paid in June 2020 being installment tax for 2020.
• Some computer equipment will be acquired in April 2020 but paid for in May 2020 at a price of Ksh 90,000

Required

A cash budget for the three months April, May, and June 2020

Translation and Remeasurement of Account Balances

U.S. Industries has a subsidiary in Switzerland. The subsidiary’s financial statements are maintained in Swiss francs (CHF). Exchange rates ($/CHF) for selected dates are as follows:

The following items appear in the subsidiary’s trial balance at December 31, 2020:

1. Cash in bank, CHF4,000,000.

2. Inventory, CHF3,000,000. The inventory was acquired on November 30, 2020.

3. Machinery and equipment, CHF11,000,000. A review of the records indicates that the company bought equipment costing CHF5,000,000 in January 2018 (20 percent of this was sold in January 2020) and additional equipment costing CHF7,000,000 in January 2019. Ignore accumulated depreciation.

4. Depreciation expense on machinery and equipment, CHF1,100,000 (depreciated over ten years, straight-line basis).

Required

Calculate the dollar amount for each of the above items, assuming the functional currency of the Swiss subsidiary is

(a) the U.S. dollar and

(b) the Swiss franc.

Enter answers using all zeros (do not abbreviate to millions or thousands).

Traynor Corporation reports its 40 percent investment in Victor Company on its December 31, 2020 balance sheet at $14,608,000. Traynor acquired its interest in Victor on January 2, 2018 and uses the equity method to account for the investment. Victor’s assets and liabilities were fairly stated on January 2, 2018 except for unreported technology (5-year life) of $4 million. Victor reported net income of $1.2 million, $1.5 million, and $1.4 million, and paid dividends of $200,000, $250,000, and $230,000 in 2018, 2019, and 2020, respectively. There was no impairment of Traynor’s investment. Required How much did Traynor Corporation pay for its investment in Victor Company on January 2, 2018?

On 1 July 2018 Fraser Ltd acquired an item of equipment with an acquisition cost of $400,000. The equipment can be used for 8 years. On 30 June 2019, the end of financial year, the fair value of the equipment was $357,000. The equipment was sold for $330,000 on 1 January 2020. Non-current asset is depreciated evenly over the useful life and has no residual value. The company uses the revaluation model to record non-current asset. The income tax rate is 30%. Ignore GST.

Required: Prepare relevant journal entries to record non-current asset in 2018/2019 and 2019/2020 financial years in accordance with AASB 116 and AASB 136. (Narrations are required, tax effect entries are required.)