Foundations of Economics.

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Suppose you are a CPA, and you have a corporate client that has been operating for several years. The company is considering expansion through reorganizations. The company currently has two (2) subsidiaries acquired through Type B reorganizations. The client has asked you for tax advice on the benefit of a Type A, C, or D reorganization over a Type B reorganization. Additional facts regarding the issues are reflected below.
The company currently files a consolidated income tax return with the two (2) subsidiaries acquired through a Type B reorganization.
ABC Corporation, a subsidiary targeted by the client for takeover, has substantial net operating losses.
XYZ Corporation and BB Corporation will be acquired as subsidiaries in the next six (6) months.
Use the Internet to research the rules and income tax laws regarding Types A, B, C, and D reorganizations and consolidated tax returns. Be sure to use the six (6) step tax research process in Chapter 1 that was demonstrated in Appendix A of your textbook as a guide for your written response.

Write a four to six (4-6) page paper in which you:
1. Compare the long-term tax benefits and advantages of each type of reorganization and recommend the type of reorganization that will be most beneficial to the client.
2. Suggest the type of reorganization the client should use for the ABC Corporation based on your research. Justify the response.
3. Propose a taxable acquisition structure for the client’s planned acquisitions over a nontaxable reorganization. Assess the value of a taxable transaction over a nontaxable reorganization for the client.
4. Examine the value and limitations of including the ABC Corporation if acquired as a wholly owned subsidiary in the consolidated return and provide a recommendation to your client. Support the recommendation with applicable research.
5. Create a scenario that will allow the client to reduce any disadvantages from filing a consolidated return as a member of a controlled group.
6. Use the six (6) step tax research process, located in Chapter 1 and demonstrated in Appendix A of the textbook, to record your research for communications to the client.

Sample Solution

i. Crime opportunities will increase
When one is using facial recognition to prevent crime, it will also create opportunities for criminals. Stalking and identity fraud, for instance, will be made much easier. Cameras with facial recognition technology can be used to keep track of people. These cameras do not have to be expensive, which will make it more tempting for criminals to buy them, and use them for the wrong purposes. The facial recognition technologies can provide stalkers with more data about their victims: they can be used to get to know a person’s weekly schedule or to keep track of when someone is going away. It provides critical information, which for instance tells a criminal if it is safe for them to break into a house.
These cameras can also be used to replace the user ID/password authentication method to access computer systems to obtain services in the name of another person. Even though the new methods can effectively distinguish the real face from fake photos by calculating the depth of the face, it is not that hard to break into a system that uses facial recognition. [3][8] US senator Al Franken has given his opinion on the problem of this topic in an open letter to the creators of an app that uses facial recognition (i.e. NameTag): “Unlike other biometric identifiers such as iris scans and fingerprints, facial recognition is designed to operate at a distance, without the knowledge or consent of the person being identified,” he wrote. “Individuals cannot reasonably prevent themselves from being identified by cameras that could be anywhere – on a lamp post, attached to an unmanned aerial vehicle or, now, integrated into the eyewear of a stranger.”. [9]
ii. Racial/ethnic bias
Recent research suggests that the algorithms behind facial-recognition technology may suffer from a racial or ethnic bias: many algorithms expose differences in accuracy across race, gender and other demographics [10].

It is shown in a study by P. J. Phillips [22] that algorithms developed in East Asia recognized Asian faces far more accurately than Caucasian faces. The exact opposite was true for algorithms developed in Europe and the United states. This implies that the conditions in which an algorithm is created can influence the accuracy of its results. A possible explanation for this is that the developer of an algorithm may program it to focus on facial appearances that are more easily distinguishable in some races than in others [10][22].

It is not only in the way the algorithm is programmed. It is also in the way the algo

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