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Arthur Anderson LLP Essay (Critical Writing)

Table of Contents Business Model Strategy Strategic Dilemma The Pros and Cons of the Entire Debacle The Consequences Reference List Business Model Many conscientious and assiduous business professionals presuppose that success is determined principally by their capacity to offer products and services, meet customer demands and requirements, and run their operations using effective and efficient techniques. However, in today’s dynamic, networked, and ever changing business environment, the business model has become a central tool of trade since it is inseparable from the product, process and operational approaches of a business enterprise in shaping how success is realized (Chesbrough, 2006, p. 18). More often than not, the difference between success and failure is thinly veiled in the type of business model adopted by an organization. Before its uneventful entry into questionable deals and fraud charges, Arthur Anderson LLP’s business model revolved around the concept of ‘thinking straight and talking straight,’ as proposed by its founder, Arthur Andersen (Smith
The story depicts the events of the mountain biking in which the author has participated in a small group. During the Nepal hike, they have found the half-frozen practically naked Sadhu. It was a puzzle for them what he had done so far from the safety caravan routes, but this man needed help. The first who came across the Sadhu was the New Zealander. He transmitted the Sadhu to the narrator and continued his way. The Sadhu was clothed and he came to life, but the problem of what to do with him still existed. No one of the climbers wanted to take responsibility upon himself, trying to shirk off the problem upon another. As a result, the Sadhu was left by the narrator, then by his friend Stephan. It is necessary to mention that Stephan, feeling the pang of conscience, has been trying to persuade the other members of the hiking to transmit the Sadhu either to the village or at least to the hub. Everyone refused, but the Sadhu was given food and drink. Later on, the author describes the conversation between him and his friend Stephan. This dialog is one of the key dilemmas of the story. In spite of the fact that both of them have left the half-frozen man alone, Stephan feels the pangs of the conscience. Stephan thinks that if the Sadhu dies it will be their fault. On the contrary, the narrator tries to persuade Stephan that they have done their best. Each member of the hiking did his bit. Somebody gave him food, another gave him clothes and so on. The narrator considered that they had been right leaving the Sadhu alone. He pointed out that the Sadhu would survive in any case, as he was strong enough to throw the stones into the dogs. However, both of them did not know whether the Sadhu would survive or not. Frankly speaking, the position of the narrator surprised me. As for me, in such a situation I would help the Sadhu. Otherwise, I would feel my guilty and all my journey would be doomed by it. With the passing of the years, the narrator contemplated again this episode. His attitude towards this event has changed. Several years ago, his actions seemed right and obvious for him. Now the ethical dilemma arises. He asks himself why they have acted in this way, and came to the conclusion that the reason for it was the absence of a strong leader in their company. The leader who was able to take the responsibility upon himself. Moreover, he recognized that within their group there was no mutual understanding and support. They had no sense of purpose or plan (McCoy, 1997). Get your 100% original paper on any topic done in as little as 3 hours Learn More The narrator is an investment banker and he has made for himself certain lessons from this situation. He remembered Stephans attempts to help the Sadhu, but Stephan had lacked the support from the group. As well as in business, in the complex situation, the individual loses himself if there is no support from the group. The narrator understands for himself that there is a difference between business ethics and the individual one. On the examples of huge corporations, he understands, that not always the interests of the whole company may be sacrificed for the sake of one person. However, to help the Sadhu or not still remains a personal ethical dilemma and everyone should decide it for himself independently. Reference List McCoy, B. (1997). The Parable of the Sadhu. Web.

Long Island Business Institute Blockchain Technology Discussion

Long Island Business Institute Blockchain Technology Discussion.

Industry experts believe blockchain is a technology that has the potential to affect the business of most IT professionals in the next five years. Pick an industry you feel will be most affected by blockchain and how blockchain may be used in that industry. As an IT manager, how would you embrace blockchain? For instance, how would training occur for your team, what strategies might you use, what security methods may you recommend be used?Your paper should meet the following requirements:Be approximately 2-3 pages in length, not including the required cover page and reference page.Follow APA7 guidelines. Your paper should include an introduction, a body with fully developed content, and a conclusion.Support your answers with the readings from the course and at least two scholarly journal articles to support your positions, claims, and observations, in addition to your textbook. Be clearly and well-written, concise, and logical, using excellent grammar and style techniques. You are being graded in part on the quality of your writing.Subject: Infotech Important in Strategic PlanBook: Attached PDF
Long Island Business Institute Blockchain Technology Discussion

San Diego Mesa College Applying Creative Skills in the Visual Art Discussion

python assignment help San Diego Mesa College Applying Creative Skills in the Visual Art Discussion.

The purpose of this assignment is to think more broadly about What Art Is (and maybe What Art Is Not!)Start with the fine arts: Painting, sculpture, architecture.What other kinds of art are there? (for example, Books, Stained-Glass Windows, Couture, Coins, Comics, Automotive design, Toy design, Garden design, Silverware, Furniture …) Now organize them. How would you arrange art objects in a museum, or in an introductory book like ours? Why? Give one or two examples of each kind of thing in each category. Give your thoughts, impressions and ideas on this medium. Does it interest you? Perhaps if your detailed list has a LOT of categories (e.g., High-Fashion design, Work clothes design, School clothes design, Uniforms design, Baby-clothes design, Baby-shoes design, Swimsuit design-Men, Swimsuit design-Women, Plus-size clothing design, Spacesuit design, Hospital protective gear design, Pet-suits design, Pet-harness/Leash design….), You could organize a list like the above under a larger category like “Clothing Design” … Which itself could be subordinate to just plain “Design”… This could be quite an outline! …Likewise with “Communications,” with subcategories like “Advertising”, “Public Service Announcements”, “Warning Signs”, “Lists of ingredients”, “Television visuals”, etc., etc. You could make a spreadsheet with scores from 1-5 for categories like, “Are you interested in this field?” … Or other ways to categorize these many forms of Art I shall give points according to how many items you can categorize, and how many you can rate. This could be worth an extra 20-30 points, if you put a lot of thought into it. Only five or ten if you do the bare minimum. …I always look kindly on essays with offbeat or clever ideas, turns of phrase, and surprising insights.
San Diego Mesa College Applying Creative Skills in the Visual Art Discussion

PLEASE USE ATTACHED TEMPLATE This assessment will focus on the concept of population health management. The focus of population

PLEASE USE ATTACHED TEMPLATE This assessment will focus on the concept of population health management. The focus of population health management is “value-based” care. Population health management utilizes a strategy of improving the delivery of care, containing healthcare costs, and improving the health of the overall community (Mullahy, 2016). Population health management is also connected to reimbursement, as healthcare systems are tasked with prevention of readmissions and working with community leaders to decrease/reduce the number of persons with conditions such as obesity and incidence of smoking (Mullahy, 2016). Mullahy, C. M. (2016). The case manager’s handbook (6th ed.) Burlington, MA: Jones and Bartlett. In this assessment, you are asked to use the Population Health Model (PHM) Template to create an approach to addressing the health needs of those individuals from your POI from a population-health focus, which will be your Population Health Plan (PHP). This means addressing the following: defining the population, identifying gaps in care, reviewing risks, plan for patient engagement, managing care, measuring outcomes, and identifying pertinent stakeholders. The successful completion of the assessment includes: Completion of Population Health Model Template A written discussion following the model, describing each aspect you have added to the model summary/conclusion paragraph scholarly references

Human Capital Migration and Economic Growth

Abstract Human Capital Migration, also known as the “Brain Drain,” has been a growing concern in the world. This study was conducted to understand the relationship between migration, human capital and economic growth to describe factors that impact “brain drain” within the United States. The researchers identified variables to support three different models based on a previous study that was conducted to compare 77 countries from around the world. The models are migration, human capital and growth. The variables analyzed for the migration model were not statistically significant. For human capital, there was a relationship between average life expectancy and human development index. In the growth model, unemployment rate played a role in relation to growth within the United States. Human Capital Migration and Economic Growth Introduction Human Capital Migration is the migration of highly skilled and/or educated individuals from one geographic region to another. For example, these individuals could be engineers, physicians, scientists, teachers or even doctors.[1] Human Capital Migration is also very commonly known as “Brain Drain” and has been a big issue and concern worldly for centuries and now it is an issue nationally within the United States. Due to this being a great issue of concern, governments have been trying to determine for years better ways to retain these highly skilled individuals, so they can attribute to the betterment of the economy in specific geographic locations. When trying the determine ways to retain individuals, it is important to determine what factors contribute to brain drain. In a 2005, a study published by the Journal of Applied Econometrics and International Development equations were formed to better understand the relationships migration (brain drain), human capital, and economic growth and to understand what economic variables directly affect them. The study created three models to determine if the relationships between certain variables had a strong relationship and contributed to over all human capital migration. [2] The purpose of this research is to test if economic growth indicators impact human capital migration within the United States. Therefore, it is important to determine which variables have a direct impact. This study will conduct a multiple regression to answer the following question: What economic growth factors play a role in Human Capital Migration? During the research, three models will be used similar to the study described above to answer the research question when applied to the United States population. The first model will be migration and the variables used will be unemployment rates, wages and per capita income, to calculate the migration to other states. The second model will be human capital, the variables used for comparison will be average life expectancy, education index and human development index. The third model is the growth model and the variables used will be, human development index, net rate of migration and unemployment rate. These three models will be constructed in this study and a multiple regression will be conducted on each one, to give an understanding if Economic Growth indicators Impact Human Capital Migration within the United States. Literature Review Dating back to the 18th century, economists have been studying the economic benefits of human capital overall. The study of human capital, whether it is lost or gained, has grown tremendously and has also broadly become a focus area for developing countries and developed countries. Human capital is an important benefactor in the determination of economic development. Human capital migration is also referenced as “brain drain” and “human capital flight” but is essentially the systematic loss of skilled, knowledgeable, well-educated individuals to other organizations, cities, states or even countries.[3] The term “brain drain” was initially coined by the Royal Society of London in 1963 describing migration of British scientists to US countries. The “brain drain” has become an economic concern amongst countries and now even individual states and policymakers in the US. Countries and states that fail to keep the skilled individuals within their borders or fail to attract them are at risk of economic stagnation. [4] Most scholarly articles and studies that view human capital migration and the causes seem to be conducted in other, usually lesser developed, countries as they are experiencing the “brain drain” to the United States. Several studies describe causes of human capital migration as “push” and “pull” factors. “Push” factors are negative characteristics of the home country usually from people migrating from lesser developed countries. “Pull” factors are characteristics that would attract the immigrant back to their home country. [5] A study was conducted in Taiwan to understand their issue of brain drain to the United States. It examined these “push” and “pull” factors of their native immigrants. The “pull” factors into the US are as follows: 1) Better teaching/research facilities, 2) Promising career prospects and better professional opportunities, 3) High Salary, 4) Strong job satisfaction and 5) Marital and family consideration. The “push” factors in Taiwan that explain their immigrants reasoning to not to return include the contrary to the pull factors such as: 1.) Low salary, 2.) Inadequate research facilities, 3) Few opportunities for career advancement, 4) Lack of political freedoms and, 5) Poor intellectual atmosphere. [6] Several other studies describing other countries, indicate similar, if not identical, factors. In a study looking at emigration of skilled Africans to industrialized countries, negative and positive impacts were described. Negative impacts included quantity of skilled workers decreased, increase in dependence on foreign aid such as financial or technical assistance, transfer in technology slows down and loss of money in income tax revenues and in potential contributions to GDP. Positive impacts were that migrants returned with new skills and house hold welfare increased.[7] There is vast literature for economic analysis and human migration, both independently. However, to intersect both literatures, the results are small. Bildirici (2005) examined 77 countries for the period 1990 – 2001 to determine the relationship of human capital, growth and brain drain.[8] They observed the effects of brain drain on economic growth. The countries were classified as developed (GDP index above 0.85), developing (GDP index between 0.50-0.85) and least developed. It is assumed that migration increased growth in the developed countries. Data obtained for their models were gathered from sources like Human Development Report and World Development Indicators. In their analysis brain drain is measured by migration rate, human capital is measured by human development index. Variables used in their model were, for example, average life expectancy, adult literacy rate, schooling rate, per capital income (pci), GDP index, human development index, and general population growth. Some of the results indicated that when there was a lack of employment, per capita income and wages increased and the frequency of migration decreased. It also discovered that when poverty increased, migration increases with it and that the reason why migration decreases as unemployment increases is that people cannot invest in human capital and cannot afford the cost of migration.4 Literature proposes that small countries or states face more problems in the loss of skilled individuals because they lose a larger portion of their skilled labor force or they are faced with more push factors. One study argues that economic growth of a small country can be a triggering event for the emigration of people. Increasing the GDP per capital can reduce the flow of emigrants from a country.[9] More research has been needed for the effects of human capital migration, not only into the states, but migration between states, as well. Some literature states that given the rise in economies like India and China, the United States is in need of high skilled immigrants, However, political leaders are in debates about the influx of low-skilled workers that are entering the US illegally so immigration itself has become a political nightmare. The number of skilled immigrants in the United States waiting to gain legal residence is now exceeding one million.[10] The US could be experiencing its first brain drain since fewer high-skilled workers are coming to the US. The economic growth that the US would be experiencing is occurring in China and India as mentioned above. This leads the researcher to evaluate the United States to compare relative migration, human capital and growth indicators to assess whether there is correlation between human capital migration and economic growth. Methodology In 2005, a study from the Journal of Applied Econometrics and International Development, titled Determinants of Human Capital Theory, Growth and Brain Drain; an Econometric Analysis for 77 Counties was conducted by Dr. Melike Bildirici, Melda Orcan, Seckin Sunnal, and Elcin Aykac to understand the relationship between human capital migration and growth in 77 counties around the world from the period 1990-2001. This study used data from many different data sets including Human Development Reports, World Development Report, and Financial Statistical Yearbook. The research provided three models that was used to understand the impact of Brain Drain. The models use various variables including average life expectancy, adult literacy rate, and population. This study’s research provided the base model for the purpose of understanding human capital migration and growth in relation to Brain Drain in the United States for this study. [11] This study was conducted to understand the relationship between human capital migration and economic growth with relation to Brain Drain within the United States. The “Determinants of Human Capital Theory, Growth and Brain Drain; an Econometric Analysis for 77 Countries” study’s research was used has the base for answering the following research question. [12] What economic growth factors play a role in Human Capital Migration? Each model was created and used to answer the research question proposed above to understand Brain Drain within the United States population. Therefore, a Migration, Human Capital, and Growth Model was created with varies independent variables, which are defined in Table 1 of the appendix. U.S. Population was used as the dependent variable. All variables were used as described in the literature. Below are the three models used within to answer the research question based on the literature. Migration Model: This model used the unemployment rates, wages index, per capita income and average life expectancy to calculate which variable was significance within the United States Migration. Human Capital Model: This model used education index, average life expectancy, and Human Development Index to calculate which variable was significance with United States Human Capital. Growth Model: This model used human development index, net rate of migration, and unemployment rate to calculate which variable was significance within the United States Economic. A multiple regression was conducted on all three models to understand the relationship between each model’s variable. The multiple regression determined which variables were significant to each model. The data for the variables was extracted from the World Bank database and U.S. Census Bureau. All independent variables and dependent variable data were used from 2010 through 2017, making a total of eight yearly data points for each variable. The statistical analyses were derived using Excel 2018 with the inclusion of Excel 2018 Add-In, Analysis TookPak. The multiple regression analyses were built to study the relationship between each variable within a model and determine which variables are significant to that model. Analysis The Migration Model, Human Capital Model, and Growth Model were analyzed using the multiple regression method, as detailed in the Determinants of Human Capital Theory, Growth and Brain Drain; an Econometric Analysis for 77 Countries study. The use of this method gave way to answering the research question and testing the thesis statement by analyzing which variable(s) were significant to each model. The following table shows the results of the multiple regression of each model. Table 2: Multiple Regression for Each Model F Value R Square Y Intercept P-Value Unemployment Rate Wage Index Per Capita Income Average Life Expectancy Education Index Human Development Index Net Rate of Migration Migration Model 334.83 .997 1734296496.48 0.16 .28 .07 .07 Human Capital Model 3019.05 .999 526630948.21 .00 .94 .01 Growth Model 218.04 .988 371149886.36 .05 .87 #NUM After conducting a multiple regression on each model, the test showed the following results for each model. Migration Model: The migration model was conducted to determine if there was a linear association between the U.S. population and unemployment rate, wage index, per capita income, and average life expectancy. The multiple regression showed the p-values of unemployment rate at .16, wage index at .28, per capita income at .07, and average life expectancy at .07. All p-values showed greater than the significance level of .05, thus it can be concluded, the F-test was not significant and fail to reject the null hypothesis, indicating evidence that there is no statistically significant between the variables and population. Human Capital Model: The human capital model was conducted to determine if there was a linear association between the U.S. population and average life expectancy, education index, and human development index. The multiple regression showed the p-value of average life expectancy at .00, education index at .94, and human development index at .01. Average life expectancy and human development index showed p-values less than the significance level of .05, therefore the F-Test was significant and reject the null hypothesis that these factors do play a role in brain drain within the U.S. Thus, there was a relationship between average life expectancy and human development index in relation to the U.S. population. Growth Model: The growth model was conducted to determine if there was a linear association between the U.S population and unemployment rate, human development index, and net rate of migration. When the multiple regression was conducted the first time, the results showed a #NUM for the p-value of net rate of migration due to data points missing. Missing data points is because the variable is reported every five years. Net Rate of Migration was removed from the multiple regression and a new multiple regression was conducted using the other two variables compared to U.S Population. The new multiple regression showed the p-value of unemployment rate at .05 and human development index at .87. Unemployment rate’s p-value showed less than the significance level of .05, therefore the F-Test was significant and reject the null hypothesis. Therefore, it can be concluded that unemployment rate does have a relationship with population. The purpose of this research was to understand if economic growth indicators impact human capital migration within the United States. Therefore, this research paper conducted a study to answer the following question: What economic growth factors play a role in human capital migration? The analysis provided information that showed that average life expectancy, human development, and unemployment rate are statistically significance to the U.S. population in relation to brain drain. Therefore, the hypothesis can be rejected, meaning Human Capital Migration impacts Economic Growth. Conclusion According to the results from the analysis, it was determined that there are certain economic variables that directly affect human capital migration. The migration model was the only one that did not reflect a strong relationship between U.S. population and unemployment rate, wage index and per capita income. Due to there not being a relationship with these variables, it can be concluded that these factors do not play a role in migration of people within the U.S. The human capital model was conducted to determine if there was a linear association between the U.S. population and average life expectancy, education index, and human development index. The multiple regression showed us that there was a strong relationship with average life expectancy and human development index in relation to the U.S. population. The growth model was conducted to determine if there was a linear association between the U.S. population and unemployment rate and between human development index and net rate of migration. When originally conducting a multiple regression, the variables compared to U.S. population, there was an error because one of the variables (net rate of migration) is only reported once every five years. Net rate of migration was then removed from the multiple regression and a new regression was tested. The new regression showed there was a strong relationship between unemployment compared to the U.S. population. In conclusion, it was determined that the research was able to solidify the thesis and answer our research questions. Through the analysis, it was determined that certain economic variables do effect human capital migration, which include unemployment rate, average life expectancy and human development index. Limitations As mentioned, there were several limitations encountered, when trying to determine if economic factors truly affect human capital migration. When attempting to obtain information of migration rate on a yearly basis, information could only be found for every five years. This cause an accurate analysis of this variable. This was an issue due to all of the other variables consisting of data that was collected yearly between the years of 2010-2018. The research also encountered the lack of literature that merges human capital migration with economic growth creating a limitation. There was much information on human capital migration and what exactly it is, but when it came to be determining the effects of economic factors caused by migration, there was a great lack of literature in this subject matter. Lastly, there was lack of prior studies on human capital migration within the United States. Most of the literature that could be found consisted of studies from abroad, that referenced developing countries. Further Study As far as conducting further study on this subject matter, there are a number of topics that can be considered to take this research a step further. First, would be to increase the number of years that was studied. This study only analyzed the data between the years of 2010 and 2018, which caused some limitations within the research due to the way some of the data is reported. Another way to take this study further, would be to apply the same analysis to individual states in the United States data and compare those finding to other states. Lastly, testing other variables such as education levels. More specifically, study individuals who have achieved graduate level degrees verses people have just a high school degree. This would all be great information to study, to further this research on human capital migration. Bibliography Bildirici, Orcan M, Sunal M, Aykac S, Elcin (2005). Determinants of human capital theory, growth and brain drain; an econometric analysis for 77 countries Applied Econometrics and International Development. AEID. Vol. 5-2 (2005). http://www.usc.es/economet/reviews/aeid526.pdf. Census Bureau. “Income”. United States Census Bureau. Accessed April 26, 2019. https://www.census.gov/topics/income-poverty/income/data.html Census Bureau. “Employment”. United States Census Bureau. Accessed April 26, 2019. https://www.census.gov/topics/income-poverty/income/data.html Census Bureau. “Population”. United States Census Bureau. Accessed April 26, 2019. https://www.census.gov/topics/population/data.html Chang, Shirley L. “Causes of Brain Drain and Solutions: The Taiwan Experience.” Studies in Comparative International Development 27, no. 1 (Spring 1992): 27. doi:10.1007/BF02687103. George J. Sefa Dei, and Alireza Asgharzadeh. 2002. “What Is to Be Done? A Look at Some Causes and Consequences of the African Brain Drain.” African Issues 30 (1): 31. doi:10.2307/1167087. Heery, Edmund, and Mike Noon. “Brain Drain [Human Capital Flight].” A Dictionary of Human Resource Management, 2017. doi:10.1093/acref/9780191827822.013.2023. Ibid. and David Ihrke, “United States Mover Rate at a New Record Low,” U.S. Census Bureau, Census Blogs, January 23, 2017, https://www.census.gov/newsroom/blogs/random-samplings/2017/01/mover-rate.html, accessed July 25, 2018. Rotte, Vogler M. (2000), “The Effects of Development on Migration: Theorectical Issues and New Empirical Evidence”, Journal of Population Economics. 13(3), 485- 508 Quinn, Zack. “Waiting in line: Why legal immigration can take decades”. Cronkite News. https://cronkitenews.azpbs.org/2016/11/28/waiting-turn-long-line-legal-immigration/ U.S. Census Bureau, Geographic Mobility: 2005 to 2010. December 2012, Table 2, https://www.census.gov/prod/2012pubs/p20-567.pdf, accessed April 19, 2019 World Bank. “World Development Indicators.” The World Bank Group. Accessed April 26, 2019. http://datatopics.worldbank.org/world-development-indicators/ Appendix Table 1: Data Defines Population: “All people, male and female child and adult, living in a given geographic area.” – (U.S. Census Bureau) Unemployment Rate: “Represents the number of unemployed people as a percentage of the civilian labor force.” – (U.S. Census Bureau) Wage Index: This is also known as Income Index. “GNI per capita (2011 PPP International $, using natural logarithm) expressed as an index using a minimum value of $100 and a maximum value $75,000.” – (World Bank) Per Capita Income: “Mean income computed for every, man, women, and child in a particular group. It is derived by diving the total income of a particular group by the total population.” – (U.S. Census Bureau) Average Life Expectancy: “An index using a minimum value of 20 years and a maximum value of 85 years.” – (World Bank) Education Index: “An average of mean years of schooling (of adults) and expected years of schooling (of children), both expressed as an index obtained by scaling with the corresponding maxima.” – (World Bank) Human Development Index: “A composite index measuring average achievement in three basic dimensions of human development – a long and healthy life, knowledge and a decent standard of living.” – (World Bank) Net Rate of Migration: “Net Rate of migration is the net total of migrants during the period, that is, the total number of immigrants less the annual number of emigrants, including both citizens and noncitizens. Data are five-year estimates.” – (World Bank) Table 2: Multiple Regression for Each Model F Value R Square Y Intercept P-Value Unemployment Rate Wage Index Per Capita Income Average Life Expectancy Education Index Human Development Index Net Rate of Migration Migration Model 334.83 .997 1734296496.48 0.16 .28 .07 .07 Human Capital Model 3019.05 .999 526630948.21 .00 .94 .01 Growth Model 218.04 .988 371149886.36 .05 .87 #NUM [1] Heery, Edmund, and Mike Noon. “Brain Drain [Human Capital Flight].” A Dictionary of Human Resource Management, 2017. doi:10.1093/acref/9780191827822.013.2023. [2] Chang, Shirley L. “Causes of Brain Drain and Solutions: The Taiwan Experience.” Studies in Comparative International Development 27, no. 1 (Spring 1992): 27. doi:10.1007/BF02687103. [3] Heery, Edmund, and Mike Noon. “Brain Drain [Human Capital Flight].” A Dictionary of Human Resource Management, 2017. doi:10.1093/acref/9780191827822.013.2023. [4] Heery, Edmund, and Mike Noon. “Brain Drain [Human Capital Flight].” A Dictionary of Human Resource Management, 2017. doi:10.1093/acref/9780191827822.013.2023. [5] Chang, Shirley L. “Causes of Brain Drain and Solutions: The Taiwan Experience.” Studies in Comparative International Development 27, no. 1 (Spring 1992): 27. doi:10.1007/BF02687103. [6] Chang, Shirley L. “Causes of Brain Drain and Solutions: The Taiwan Experience.” Studies in Comparative International Development 27, no. 1 (Spring 1992): 27. doi:10.1007/BF02687103. [7] George J. Sefa Dei, and Alireza Asgharzadeh. 2002. “What Is to Be Done? A Look at Some Causes and Consequences of the African Brain Drain.” African Issues 30 (1): 31. doi:10.2307/1167087. [8] Bildirici, Orcan M, Sunal M, Aykac S, Elcin (2005). Determinants of human capital theory, growth and brain drain; an econometric analysis for 77 countries Applied Econometrics and International Development. AEID. Vol. 5-2 (2005). http://www.usc.es/economet/reviews/aeid526.pdf. [9] Rotte, Vogler M. (2000), “The Effects of Development on Migration: Theorectical Issues and New Empirical Evidence”, Journal of Population Economics. 13(3), 485- 508 [10] Ibid. and David Ihrke, “United States Mover Rate at a New Record Low,” U.S. Census Bureau, Census Blogs, January 23, 2017, https://www.census.gov/newsroom/blogs/random-samplings/2017/01/mover-rate.html, accessed July 25, 2018. [11] Bildirici, Orcan M, Sunal M, Aykac S, Elcin (2005). Determinants of human capital theory, growth and brain drain; an econometric analysis for 77 countries Applied Econometrics and International Development. AEID. Vol. 5-2 (2005). http://www.usc.es/economet/reviews/aeid526.pdf. [12] Bildirici, Orcan M, Sunal M, Aykac S, Elcin (2005). Determinants of human capital theory, growth and brain drain; an econometric analysis for 77 countries Applied Econometrics and International Development. AEID. Vol. 5-2 (2005). http://www.usc.es/economet/reviews/aeid526.pdf.

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