Samuel BeckettS Waiting For Godot Essays - Theatre Of The Absurd

Samuel Beckett'S Waiting For Godot Nothing to be done, is one of the many phrases that is repeated again and again throughout Samuel Becketts Waiting For Godot. Godot is an existentialist play that reads like somewhat of a language poem. That is to say, Beckett is not interested in the reader interpreting his words, but simply listening to the words and viewing the actions of his perfectly mismatched characters. Beckett uses the standard Vaudevillian style to present a play that savors of the human condition. He repeats phrases, ideas and actions that has his audience come away with many different ideas about who we are and how beautiful our human existence is even in our desperation. The structure of Waiting For Godot is determined by Becketts use of repetition. This is demonstrated in the progression of dialogue and action in each of the two acts in Godot. The first thing an audience may notice about Waiting For Godot is that they are immediately set up for a comedy. The first two characters to appear on stage are Vladimir and Estragon, dressed in bowler hats and boots. These characters lend themselves to the same body types as Abbot and Costello. Vladimir is usually cast as tall and thin and Estragon just the opposite. Each character is involved in a comedic action from the plays beginning. Estragon is struggling with a tightly fitting boot that he just cannot seem to take off his foot. Vladimir is moving around bowlegged because of a bladder problem. From this beat on the characters move through a what amounts to a comedy routine. A day in the life of two hapless companions on a country road with a single tree. Beckett accomplishes two things by using this style of comedy. Comedy routines have a beginning and an ending. For Godot the routine begins at the opening of the play and ends at the intermission. Once the routine is over, it cannot continue. The routine must be done again. This creates the second act. The second act, though not an exact replication, is basically the first act repeated. The routine is put on again for the audience. The same chain of events: Estragon sleeps in a ditch, Vladimir meets him at the tree, they are visited by Pozzo and Lucky, and a boy comes to tell them that Godot will not be coming but will surely be there the following day. In this way repetition dictates the structure of the play. There is no climax in the play because the only thing the plot builds to is the coming of Godot. However, after the first act the audience has pretty much decided that Godot will never show up. It is not very long into the second act before one realizes that all they are really doing is wasting time, Waiting for...waiting. (50) By making the second act another show of the same routine, Beckett instills in us a feeling of our own waiting and daily routines. What is everyday for us but another of th e same act. Surely small things will change, but overall we seem to be living out the same day many times over. Another effect of repetition on the structure of Godot is the amount of characters in the play. As mentioned before, the play is set up like a Vaudeville routine. In order to maintain the integrity of the routine, the play must be based around these two characters. This leaves no room for extra characters that will get in the way of the act. To allow for the repetition of the routine to take place the cast must include only those characters who are necessary it. The idea that the two characters are simply passing time is evident in the dialogue. The aforementioned phrase, Nothing to be done, is one example of repetition in dialogue. In the first half-dozen pages of the play the phrase is repeated about four times. This emphasizes the phrase so that the audience will pick up on it. It allows the audience to realize that all these two characters have is the hope that Godot will show up. Until the time when Godot arrives, all they can do is pass

Determining Outliers in Statistics

Determining Outliers in Statistics Outliers are data values that differ greatly from the majority of a set of data. These values fall outside of an overall trend that is present in the data.  A careful examination of a set of data to look for outliers causes some difficulty. Although it is easy to see, possibly by use of a stemplot, that some values differ from the rest of the data, how much different does the value have to be to be considered an outlier?  We will look at a specific measurement that will give us an objective standard of what constitutes an outlier. Interquartile Range The interquartile range is what we can use to determine if an extreme value is indeed an outlier. The interquartile range is based upon part of the five-number summary of a data set, namely the first quartile and the third quartile. The calculation of the interquartile range involves a single arithmetic operation. All that we have to do to find the interquartile range is to subtract the first quartile from the third quartile. The resulting difference tells us how spread out the middle half of our data is. Determining Outliers Multiplying the interquartile range (IQR) by 1.5 will give us a way to determine whether a certain value is an outlier. If we subtract 1.5 x IQR from the first quartile, any data values that are less than this number are considered outliers. Similarly, if we add 1.5 x IQR to the third quartile, any data values that are greater than this number are considered outliers. Strong Outliers Some outliers show extreme deviation from the rest of a data set. In these cases we can take the steps from above, changing only the number that we multiply the IQR by, and define a certain type of outlier. If we subtract 3.0 x IQR from the first quartile, any point that is below this number is called a strong outlier. In the same way, the addition of 3.0 x IQR to the third quartile allows us to define strong outliers by looking at points which are greater than this number. Weak Outliers Besides strong outliers, there is another category for outliers. If a data value is an outlier, but not a strong outlier, then we say that the value is a weak outlier. We will look at these concepts by exploring a few examples. Example 1 First, suppose that we have the data set {1, 2, 2, 3, 3, 4, 5, 5, 9}. The number 9 certainly looks like it could be an outlier. It is much greater than any other value from the rest of the set. To objectively determine if 9 is an outlier, we use the above methods. The first quartile is 2 and the third quartile is 5, which means that the interquartile range is 3. We multiply the interquartile range by 1.5, obtaining 4.5, and then add this number to the third quartile. The result, 9.5, is greater than any of our data values. Therefore there are no outliers. Example 2 Now we look at the same data set as before, with the exception that the largest value is 10 rather than 9: {1, 2, 2, 3, 3, 4, 5, 5, 10}. The first quartile, third quartile, and interquartile range are identical to example 1. When we add 1.5 x IQR 4.5 to the third quartile, the sum is 9.5. Since 10 is greater than 9.5 it is considered an outlier. Is 10 a strong or weak outlier? For this, we need to look at 3 x IQR 9. When we add 9 to the third quartile, we end up with a sum of 14. Since 10 is not greater than 14, it is not a strong outlier. Thus we conclude that 10 is a weak outlier. Reasons for Identifying Outliers We always need to be on the lookout for outliers. Sometimes they are caused by an error. Other times outliers indicate the presence of a previously unknown phenomenon. Another reason that we need to be diligent about checking for outliers is because of all the descriptive statistics that are sensitive to outliers. The mean, standard deviation and correlation coefficient for paired data are just a few of these types of statistics.

A Detailed Break Down of a Teachers Job Description

A Detailed Break Down of a Teachers Job Description Teachers do much more than just teach. Their job descriptions are lengthy, much more than people realize. Most teachers work well after the final bell has ended. They take their work home with them. They spend several hours over the weekend working. Teaching is a difficult and misunderstood profession and requires a dedicated, patient, and willing person to keep up with all of the jobs demands. This article provides an in-depth look at a teacher’s job description.   A Teacher Must... A teacher must†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. have a comprehensive understanding of the content that they teach. They must continuously study and review new research within their content area. They must be able to break apart the foundations of new information and put into terms that their students can understand.A teacher must†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. develop weekly lesson plans that link their objectives with their required state standards. These plans must be engaging, dynamic, and interactive. These weekly plans must align strategically with their year-long lesson plans.A teacher must†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. always prepare a backup plan.  Even the most well-thought-out plans can fall apart. A teacher must be able to adapt and change on the fly according to their students’ needs.A teacher must†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. organize their classroom in such a way that it is student friendly and conducive to maximizing learning opportunities.A teacher must†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. decide whether or not a seating chart is appropriate. They must also decide when a change to that seating chart is necessary. A teacher must†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. decide on a behavior management plan for their classroom. They must adopt classroom rules, procedures, and expectation. They must practice their rules, procedures, and expectations on a daily basis. They must hold students accountable for their actions by determining an appropriate consequence when students fail to meet or follow those classroom rules, procedures, or expectations.A teacher must†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. attend and participate in all required district professional development.  They must learn the content being presented and figure out how to apply it to their classroom situation.A teacher must†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. attend and participate in optional professional development for areas that they recognize an individual weakness or an opportunity to learn something new. They do this because they want to grow and improve.A teacher must†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. spend time observing other teachers. They must have in-depth conversations with other educators. They must exchange ideas, ask for guidance, and be willing to listen to constructive criticism and advice. A teacher must†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. use the feedback from their evaluations as a driving force towards growth and improvement concentrating on areas that are scored lower.  They should ask the principal or evaluator for strategies or suggestions on how to improve those specific areas.A teacher must†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. grade and record every student’s papers in a timely manner. They must give their students timely feedback with suggestions for improvement. They must determine whether or not students have mastered a topic or are in need of re-teaching or remediation.A teacher must†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. develop and construct assessments and quizzes that align with classroom content and help determine if the lesson objectives are being met.A teacher must†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. break down data from assessments to self-assess whether or not how they are introducing the new content is successful or if changes need to be made.A teacher must†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. plan with other gr ade level and/or content level teachers determining common themes, objectives, and activities.A teacher must†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. keep the parents of their students informed of their progress on a regular basis. They must often communicate by routinely making phone calls, sending emails, having face-to-face conversations, and sending written notifications. A teacher must†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. find a way to engage parents in the learning process. They must keep parents actively involved with their child’s education by developing strategic cooperative learning opportunities.A teacher must†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. oversee classroom fundraising opportunities. They must follow all district procedures while tallying orders, submitting orders, counting money, turning in money, and sorting and distributing orders.A teacher must†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. serve as a sponsor for a class or club activity. As a sponsor, they must organize and oversee all of the activities. They must also attend all of the related activities and meetings.A teacher must†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. keep up with and study new instructional pedagogy. They must determine what is appropriate to utilize within their classroom and find a way to implement what they have learned in their daily lessons.A teacher must†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. keep up with the newest technological trends . They must become tech savvy to stay up with the digital generation. They must assess what technology would be advantageous to use in their classroom. A teacher must†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. organize and schedule all field trips in advance. They must follow all district protocol and get information out to parents in a timely manner.  They must create student activities that enhance the field trip and cement learning.A teacher must†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. develop emergency lesson plans and substitute plans for days that they have to miss work.A teacher must†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. attend extra-curricular activities. This demonstrates school pride and support for the students who participate in these events.A teacher must†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. sit on various committees to review and oversee critical aspects of the school such as budget, hiring new teachers, school safety, student health, and curriculum.A teacher must†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. monitor students while they are working independently. They must walk around the room, checking student progress, and assisting students who may not completely understand the assignment.A teacher mustâ⠂¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. develop whole group lessons that keep every student engaged. These lessons must consist of entertaining and content-based activities that help students learn key concepts, making connections to prior learning, and building towards topics that will be introduced in the future. A teacher must†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. gather, prepare, and distribute all the materials needed to complete a lesson prior to when class begins. It is often beneficial for the teacher to go through a practice run of the activity before doing it with the students.A teacher must†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. model newly introduced content or concepts to their students walking students through the proper steps to solve the problem prior to giving the students the opportunity to do it themselves.A teacher must†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. develop ways to differentiate instruction to challenge all students without frustrating them while still ensuring that every student meets their learning objective.A teacher must†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. develop guided practice activities for each lesson where the entire class is able to work out or solve problems together. This allows the teacher to check for understanding, clear up misconceptions, and determine if further instruction is needed before turning them loose o n independent practice.A teacher must†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. formulate sets of questions that require both higher level and lower level responses. Furthermore, they must ensure that they give every student the opportunity to participate in the discussion. Finally, they must give those students an appropriate wait time and rephrase questions when necessary. A teacher must†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. cover and monitor a wide variety of duties including breakfast, lunch, and recesses.A teacher must†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. return parent phone calls and hold parent conferences whenever a parent requests a meeting. These phone calls and meetings must be held during their planning period or before/after school.A teacher must†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. monitor the health and safety of all their students. They must look for signs of abuse or neglect. They must report it anytime that they believe a student is in any potential danger.A teacher must†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. develop and cultivate relationships with their students. They must build a trusting rapport with each student and one built on a foundation of mutual respect.A teacher must†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. must pause from lessons to take advantage of teachable moments. They must use these moments to teach their students valuable life lessons that can carry on with them throughout their life.A teacher mus t†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. must have empathy for every student. They must be willing to put themselves in their students’ shoes and realize that life is a struggle for many of them. They must care enough to show their students that getting an education can be a game changer for them. A teacher must†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. evaluate students and complete referrals for many individual needs and services including special education, speech-language, occupational therapy, or counseling.A teacher must†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. create a system for organization within their classroom. They must file, clean, straighten, and rearrange when necessary.A teacher must†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. utilize the Internet and social media to search for activities, lessons, and teaching resources that they can utilize within or supplement a lesson.A teacher must†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. make enough copies for their students. They must fix the copy machine when there is a paper jam, add new copy paper when it is empty, and change toner when necessary.A teacher must†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. must counsel students when they bring a personal issue to them. They must be a willing listener capable of giving students great life advice that can help lead them to the right decisions.A teacher must†¦Ã¢â‚¬ ¦ †¦. establish healthy working relationships with their co-workers. They must be willing to help them out, answer questions, and work together in a team environment. A teacher must†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. take on a leadership role once they establish themselves. They must be willing to serve as a mentor teacher to beginning teachers and serve in leadership areas as necessary.A teacher must†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. change the decoration on their bulletin boards, doors, and classroom at various points in the year.A teacher must†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. help students identify their individual strengths and weaknesses. They then must help them set goals and lead them on the path towards reaching those goals.A teacher must†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. develop and lead small group activities focused on helping students acquire missing skills in areas such as reading or math.A teacher must†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. be a role model who is always aware of their environment and does not allow themselves to be in a compromising situation.A teacher must†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. be willing to go the extra mile for their students offering tutoring or extended help for students who may be struggling.A teacher must†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. arrive at school early, stay late, and spend part of their weekend to ensure that they are prepared to teach their students.

Agenda Setting Essay Example | Topics and Well Written Essays - 1250 words

Agenda Setting - Essay Example As such, the input of the media in setting the agenda in such circumstances is vita since it helps decipher some of the prevailing political events at the time. The mass media is more likely to influence the opinions of an electorate during campaigns than the actual politician is. The structure of the media characterized with the various gatekeepers implies that decision of the mass media is informed therefore factual. The electorate therefore relies on the communications from the media. The elite are always more interested in politics that the illiterate in a society because politics of a country influences their wellbeing. Such a group considers the information they obtain from the media. The group relies on the media to initiate debates in social circles. The illiterate on the other hand believe the mass media content which often help them make their own personal decisions. The mass media gives intensified attention to specific issues in the society through repetitive coverage thus ensuring that the public discusses them at various stages. The article discusses the role of mass communication research in understanding the various effects of the mass media. The media continues to remain a vital section of the society that influences the actions, thoughts and feelings of the public. This implies that media is a vital aspect that does not only inform the public but influences the actions and thoughts of their target audience. By heightening research in the field, the scholar explains that the practitioners in the industry would discover new appropriate ways of ensuring that the media performs its functions effectively without harming the public. Mass communication research just as in any other discipline serves to develop new knowledge thereby informing the future of the profession. The book suggests specific features of the profession that requires effective research in order to uphold

Emerging Issues in Human Resource Management and Industrial Relations Essay

Emerging Issues in Human Resource Management and Industrial Relations - Essay Example Organizations have understood that it is the human resource which crafts a difference and offers competitive advantage (Buhler, 2010, p.1). In addition to that the productivity of an organization is purely dependent upon the employees (Chandramohan, 2008, p.1). Organizations have also started to put more stress on the management of its manpower. Human resources of an organization are the people who actually accomplish various tasks for the organization by applying their skills, knowledge and abilities. Human resources are also responsible to meet the various objectives of the organization. Human resource management (HRM) is a broader term which primarily encompasses the management of human resource within an organization. It can be defined as the process of managing and controlling the workforce of an organization by means of various activities. HRM is primarily responsible for carrying out a number of functions. Some of the vital tasks include:- †¢ Attracting potential candidates for a vacant position. †¢ Selection of workforce. †¢ Employee appraisal. †¢ Rewarding the employees. †¢ Training and further orientation of the employee (Werner and DeSimone, 2008, p.10). The aforementioned factors are only the basic function of HRM. Apart from that other functions of HRM include administering the organizational culture and leadership.

Unemployment as an Indicator of Macroeconomic Performance

Unemployment as an Indicator of Macroeconomic Performance The rate of unemployment is one of the most important indicators of macroeconomic performance. Unemployment arises due to the distortions in the supply of labor cause by the non-competitive wage differential. During the period from 1945 until at least 1968, unemployment rates in the major European economies were extremely low by todays standards. For instance in the United Kingdom, the average rate of unemployment for the entire period was about 1.8% of the labor force and in worst years it did not even exceed 2.5%. The main driving force was autonomous rather than policy related. These forces include waves of new products and processes, spread of trade and development around the world. However the cause of unemployment problem in Europe in comparison to the United States was their labor market institutions while the United States is far more superior due to the flexibility of their labor market. In this paper, determinants of unemployment in US are the concerns with economic growth as the main concern. Economic growth of a nation is the increase in a nations real output that occurs over time. In general, growth and unemployment are closely related as unemployment affects the growth rate through the scale of operation of an economy. Besides that, FDI inflow and inflation are taken into account altogether to identify the relationship towards the unemployment rate. 1.1 Background As unemployment is one of the most important economic indicators, the unemployment rate provides useful information such as how the labor market works as well as the percentage of human capital that is not used in the production process, which is especially crucial towards policy makers. Consequently, it is important to analyze the factors that impact the unemployment rate regardless short or long term perspective. The United States of America is a developed country which has one of the largest population and production in the world (Encyclopedia, 2010). As unemployment are explained by structural factors mainly by inflexible labor market. One may wonder the about the impact which economic growth, inflation and FDI have on the unemployment rate of the United States of America as the clutches of unemployment are hard to escape even for a develop country, especially for US which possesses by far the most flexible labor market. As a case study, the United States of America has been chosen as the research country. United States of America is reckoned to be particularly appropriate as United States of America labor market has proven by all accounts to be more dynamic in the sense of a higher level of job turnover, resulting in high vacancy levels at any point in time. Recently, unemployment rate in the United States of America has been found to be as high as 9.6% as of August 2010 compared to the 4. 1% ten years ago (Bureau of Labor Statistics, 2010). In the mean time the real GDP growth in 2000 was at 4.14% when the unemployment rate was 4.1% while the real GDP growth in 2003 was at 2.49% when the unemployment rate was 5.8% (Bureau of Labor Statistics, 2010). From here, it can be seen that unemployment rate moves in the opposite direction of economic growth, yet there were different versions of results concluded by different previous researchers. 1.2 Problem Statement Unemployment has been a famous macroeconomic variable that researchers tend to use to study on but even with so many researches carried out, some of the results obtained are not consistent with one and another. For instance, the debates among Monetarist and Keynesian views of unemployment as well as the new contributions of Lucass approach and new Keynesian Economics shows that there was no reason to account for growth in the unemployment model. However, a significant innovation occurred with Pissarides'(1990) formulation of an unemployment theory in equilibrium. In many previous attempts, he formalize a unique framework to study the labor market dynamic perspective, providing useful tools to analyze both long and short run unemployment. Pissarides also introduced a first link between long run unemployment and growth which matches the neoclassical framework of economic growth. ( Pissarides, 1990 Ch. II) In the case of US, its economy began its current economic recovery in December 2001. However, rather than experiencing employment growth, not only did the unemployment rate increase but the number of new jobs created in the economy actually declined significantly during the first year of the recovery (Seyfried). Thus this paper is conducted so as to affirm the relationship of economic growth has on the unemployment rate of the country. As some results obtained by past researchers showed that economic growth impacts unemployment whereas the others came to a conclusion that unemployment causes economic growth whereby the existence of Granger Causality relationship is quite possible. In this study, economic growth, inflation and FDI serves as explanatory variable to determine the relationship towards unemployment rate in the United States of America. 1.3 Objectives This study aims to investigate the determinants of unemployment rate in the United States of America with economic growth as the main concern in addition with inflation and FDI (foreign direct investment) to further assure that it is coherent with the results obtained from previous studies. 1.3.1 Specific objectives This paper aims to examine the relationship between economic growth, inflation and FDI towards the unemployment rate. On the other hand, this paper serves to probe further into the relationship between economic growth, FDI, and inflation towards unemployment to sustain the existence of granger causality relationship. 1.4 Significance of study The contribution of carrying out this study is to allow policy makers to have an insight of unemployment so as to allow them to decide on suitable policy that will help bring down the unemployment rate while sustaining appropriate inflation level and attract sufficient FDI inflow. The results generated will help provide insight to the nature of the relationship between economic growth, inflation, and FDI towards unemployment. It would be useful to policy makers to know the rate and relationship of economic growth as it is necessary to reduce the unemployment rate, or at least keeping it from rising. Moreover, in previous studies, FDI is found to have impacted the unemployment rate indirectly through spillover effects from economic growth. In this study, however, FDI is incorporated directly to affect unemployment growth; therefore the effectiveness of the implemented policy will be taken into account more effectively. CHAPTER 2: Literature Review 2.1 Conceptual Model According to Alexopolous (2003), in the case where there is technological growth in the economy, families will increase their investment in capital, which in turn increase the amount of family purchased consumption workers receive over time. As a result, firms optimally increase the wage rate proportionately in order to prevent workers from shirking on the job. Therefore, the rate of unemployment along the balanced growth path will not change over time, since the marginal product of labour and the marginal cost of labour grow at the same rate. Based on De Groot, in general, growth and unemployment are intimately related for two reasons. Unemployment affects the scale of operation of the economy and thereby the growth rate. Growth affects inter-temporal decisions of workers about where to allocate on the labor market once they are laid off, and thereby it affects equilibrium unemployment. According to Brecher (2007), rapid economic growth and FDI, accompanied by higher per capita income, usually increase output growth. Thus, domestic firms and foreign multinational corporations will demand more labour force with skills to create products. Hence, economic growth can promote future employment growth for labour force based on new Keynesian theory of the output-inflation tradeoff. Some studies found that overseas investment replaced domestic employment in developing countries; however, the same result did not happen in developed countries. Tremblay (2007) pointed out that based on classical economic theory, the Phillips Curve illustrated long-run tradeoff between unemployment and inflation. There is an inverse relationship between inflation and unemployment, that is saying inflation will rise when unemployment decrease and vice-versa. Futhermore, Luciano Fanti and Piero Manfredi (2003) mention that the neoclassical Solow model, which still provides excellent econometric fits and shows a globally stable positive growth equilibrium, but also shows two restrictive features as regards the scope of this paper: (1) it does not take into account the stylized fact of the existence of unemployment, which is generally not only positive but also strongly fluctuating; (2) in such a model fluctuations have never been endogenously determined Meanwhile, Martin Zagler (2006) noticed that the cost associated with economic growth is structural unemployment, as structural change destroys jobs in one firm and creates jobs in another. The source of unemployment is the rate of intra-sector structural change associated with faster economic growth. Besides, Bonatti (2007) says that an increase of the workers influence on the political process may raise the fraction of GDP allocated to finance the welfare state, thus leading to a higher unemployment rate and to a lower growth rate. The research work done by Chang (2007) noticed that when the degree of trade openness of Taiwan is larger, the unemployment rate of Taiwan will increase, this is because the young men and young women in Taiwan desire to extend their education in working age. According to Phillips (1998), the negative relationship between inflation and unemployment can be explained through governments expansionary policy to increase the consumption level of the citizens. As labor market tightens, unemployment rate will fall as money wages tended to rise more rapidly. Unemployment will then increase as government tries to control the inflation rate. This is because the increment in wages is closely related with the increase in price. Therefore, the trade-off between these two variables can be seen. 2.2 Methodology Effects panel regression methods were used by Zagler (2006) on the relationship between economic growth and unemployment. Moreover, Zagler (2006) checked his estimated model with the unit-root test to test the stationary of the model. In order to obtain information about the relationship between inflation and unemployment, the procedure of den Hann was employed by Bae (2006), which has the advantage as no assumptions about the order of integration in the variables of interest is required. The procedure estimates a vector regressions (VAR) model and analyzes the correlations of VAR forecast errors of inflation and unemployment at long horizons. Chang (2007) used vector autoregression method of variance decomposition and impulse response function analysis are applied to analyze various relationships among foreign direct investment (FDI), economic growth, unemployment and degree of openness in Taiwan. Besides that, he also uses the unit root test of augmented Dickey-Fuller (ADF and KPSS) test to examine the stationary properties of the economic time series. The appropriate lag-length in the ADF regression is selected by minimizing the Akaikes information criterion (AIC). He also uses co-integration test to determine whether there exists a long-run equilibrium relationship among variables and weak exogeneity, and multivariate Granger-causality test to determine their causal direction in the short-run between all variables. Besides, he also has applied the VAR technique of variance decomposition and impulse response function analysis to analyze various inter-relationships between FDI, unemployment rate and GDP variables in the case of Taiwan from the period of 1981 to 2003. Meanwhile, Eric Heyer, FrÃÆ' ©dÃÆ' ©ric ReynÃÆ' ¨s, Henri Sterdyniak (2006) present the results of the DF-GLS unit root test to test the growth rate of consumer price and also unemployment rate. 2.3 Empirical Result Zagler (2006) has carried out a research which empirically investigated the link between economic growth and unemployment, using micro econometric evidence for the United Kingdom. The results generated showed a significant and negative relationship between unemployment and economic growth. According to the result generated by Muscatelli and Tirelli (2001), it is proven that there is a negative relationship between economic growth and unemployment as Japan, Germany, Italy, France and Canada. This result is generally in favour of those theories which predict a negative linkage between unemployment on economioc growth Besides, Pehkonen (2000) stated that a fall in GDP has significant relationship with unemployment as a drop in the GDP in Finland leads to an increase in the unemployment since demand for labor have shrunk. Therefore, Pehkonen (2000) concluded that unemployment would increase as a result of a decrease in economic growth. Meanwhile, Mitra and Sato (2007) found that the major links between external scale economies and growth are perceived in terms of technical efficiency, and higher growth is taken to reduce the unemployment rate. Futhermore, Scahaik and Groot (1998) found that the unemployment and economic growth relationship in imperfect competition economy and different periods, where structural changes occur, has a negative correlation and effect of different degrees through testing the structural stability. Chang (2007) proved that economic growth as well as FDI have negative effects on unemployment as FDI are expected to generate economic growth by encouraging the expansion of trade and foreign investment. In addition, according to Solows growth theory, employment for labour force with skill can further promote economic growth and this can be verified by Taiwans economy model. Okuns law stating that reducing unemployment for labour force can promote further economic growth is then verified. Furthermore, unemployment is very sensitive to changes in GDP and vice versa, which does lend support that rising economic growth can obviously affect unemployment for labour force. shock of unemployment rate has negative effect on economic growth . He also mentions that the shocks in economic growth and FDI inflow decrease the unemployment rate. This means that rapid economic growth and FDI inflow, accompanied by higher per capita income can promote future employment growth for labour force. In the research study of Meckl (2001), correlation between growth and unemployment is shown to be positive if the research sector is of the high-wage sector in the economy, and negative if the research sector is the low-wage sector. Arico (2003) has already observed that the rate of growth is negatively related with the rate of unemployment. If the growth rate increases, it will decrease the net rate at which the stream of profits is discounted. For each firm the entry will result less costly. More vacancies will be created, reducing the unemployment rate. (Capitalization effect).On the other hands, It will reduce the life-time of each firm, by increasing the price for human capital. Each innovation will generate fewer vacancies than before. That will be reflected in an increase of the rate of unemployment. (Indirect creative destruction effect). Besides, Fanti and Manfredi (2003) has shown a negative relation between unemployment and growth , though we should also mention the positive relation between unemployment and growth obtained in the particular creative disruption context according to Schumpeters idea. Fanti and Manfredi alsomshows a surprising relation between unemployment and growth (via effects on population which is an endogenous engine of growth): this relation can be either positive or negative depending on the relative levels of cost of childrearing of workers and unemployed persons and the level of unemployment benefits. Meanwhile, Bonatti (2007) noticed that reduction of government transfers in favor of the workers allows decreasing the ratio of total tax revenues to GDP, thus monotonically increasing the growth rate and leading to a lower unemployment rate. CHAPTER 3: RESEARCH METHOD 3.1 Data Analysis 3.1.1 Unemployment Rate In this study, unemployment rate is the main study which was examine by using some explanatory variables. According to BLS, Bureau of Labor Statistics, (2009) those people who are with jobs can be considered as employed. On the other hand, a person will be classified as unemployed if they do not have a job, have actively looked for work in the prior 4 weeks, and are currently available for work. Dixon Shepherd (2002) stated that the unemployment rate can be considered as one of the most important indicators of macroeconomic performance in a country. The data of unemployment rate is obtained from the Bureau of Labor Statistics (BLS) which if measured in percentage from those people who are 16 years old and above from year 1970 to 2007. The method which BLS used to calculate the unemployment rate in United States is: X 100% 3.1.2 Real Gross Domestic Product Real gross domestic product (Real GDP) in a country can be measured by the total output value of goods and services which produced from the domestic labor in the country in a given year, expressed in base-year prices. In this study, it is expected that there is a negative relationship between the Real GDP and unemployment rate in United States. The source of the United States Real GDP data is from the World Bank World Development Indicators and International Financial Statistics of the IMF. On the other hand, the data obtained was converted to a 2005 base year. The formula to calculate the data of United States Real GDP is as below: 3.1.3 Foreign Direct Investment Foreign direct investment (FDI) is a kind of investment which is made to serve the business interest of the investor in a company which is in a different nation distinct from the investors country of origin. An example of FDI is a foreign company comes into a country to build or buy a factory and run a business there. Many economists believe that FDI is good for an economy, because it provides domestic job opportunities and increase domestic capital. In this study, net inflows of foreign direct investment in the measurement of current US Dollar are used. A net inflow of foreign direct investment is the total amount or value of the investment flow into United States from foreign investors to operate their business in United States and negative relationship between foreign direct investment and unemployment rate is expected in United States. 3.1.4 Consumer Price Index Consumer price index (CPI) is measured that examines the weighted average of prices of a basket of consumer goods and services in a country, such as transportation, food, rental fees and utilities fees. CPI is one of the measurements of inflation rate. According to Bureau of Labor Statistics (BLS), the prices for the goods and services used to calculate the CPI are collected in 87 urban areas in United States from about 23,000 retail and services establishments. The CPI data used in this study included all consumer items in United States from year 1970 to 2007. 3.2 Research Framework 3.2.1 Unemployment rate and Real Gross Domestic Product Based on the study, unemployment and real gross domestic product is expected to be negatively related. Edward (2007) stated there is a negative relationship between real gross domestic product and unemployment because of the theory of Okuns law. According to Okuns law, 1% increase in the unemployment rate will decrease GDP by 3%. However, Christopher (2010) said that, Okun coefficients can change over time because the relationship of unemployment to output growth depends on laws, technology, preferences, social customs, and demographics. 3.2.2 Unemployment and Consumer Price Index Consumer price index is one of the most frequently used statistics for identifying periods of inflation or deflation. This is because large rises in CPI during a short period of time typically denote periods of inflation. Therefore, we expect that there is an inverse relationship between the rate of unemployment and rate of inflation. According to the Phillips Curve theory, if the unemployment is high, inflation tends to be low. The diagram below shows the Phillips curve. Inflation Phillips curve Unemployment However, the result shows a positive relationship in our regression model. This problem will occur because of the multicolinearity problem in our regression model. But when one independent variable by one independent variable with the unemployment is tested, negative sign for consumer price index and unemployment are obtained. Bae (2006) stated that there is a positive long run relationship between unemployment and inflation. 3.2.3 Unemployment and Foreign Direct Investment In this study, inflow of foreign direct investment were expected to affect the unemployment rate significantly and expected that foreign direct investment has a negative long run relationship with unemployment. Foreign direct investment will increase job opportunities so, unemployment rate will decrease. Shu (2007) stated that FDI have negative effects on unemployment as FDI are expected to generate economic growth by encouraging the expansion of trade and foreign investment. 3.3 Econometric Methodology 3.3.1 Introduction This chapter consist the used of the method to examining the relationship between the unemployment and economic condition in United State by using the time series data ranging from the year 1970 to 2007. First, the result testing will start with the test of stationary by using Augmented Dickey-Fuller unit root test and proceed with the cointegration test. Secondly, the Multiple Regression Analysis and several ways to detect the assumption of the Classical Linear Regression Model (CLRM). The multicollinearity is used to test the correlation analysis. Breusch-Godfrey Serial Correlation LM Test is used to test the existence of serial autocorrelation, Autoregression Conditional Heteroscedasticity Test is used for testing the heteroscedasticity variance of error of the model and Ramsey RESET Test is used to detect the linearity regression and misspecification error. Unemployment = f (RGDP, FDI, CPI) RGDP = Real Gross Domestic Product FDI = Foreign Direct Investment CPI = Consumer Price Index The change in unemployment is our main study that we want to examine with using a few of variables which are RGDP (Real Gross Domestic Product), FDI (Foreign Direct Investment) and CPI (Consumer Price Index). y = ÃŽÂ ²0 + ÃŽÂ ²1Ln (RGDP) + ÃŽÂ ²2 (CPI) + ÃŽÂ ²3 (FDI) + Econometric Model with Expected Sign: = ÃŽÂ ²0 + ÃŽÂ ²1L (RGDP) + ÃŽÂ ²2 (CPI) + ÃŽÂ ²3(FDI) (-ve) (-ve) (-ve) Where +ve indicates that there is a postive relationship between the explanatory variable and dependent variable. On the other hand, -ve indicates that there is a negative relationship between the explanatory variable and dependent variable 3.3.2 Unit root A unit root test is used to examine whether a time series variable is stationary. In the model, T-statistic, F-statistic and R-squared are used to determine to ensure the validity of the test statistics is stationary. The result will become spurious regression problem if the non-stationary series in the ordinary least square (OLS) regression is used. Spurious regression result in high significant T-statistic and highly value for the coefficient of determination R-squared, and the R-square is larger than Durbin Watson. Therefore, if the stationary does not hold, estimate is not consistent and result will be misleading. To avoid the spurious regression problem, the Augmented Dickey-Fuller test (ADF) is used to examine the stationary of the variable. An Augmented Dickey-Fuller test (ADF) is used to test for a unit root in a time series sample. The Augmented Dickey-Fuller (ADF) statistic used in the test is a negative number. Therefore, the more negative value is, more power the rejection of the hypothesis that there is a unit root at some level of confidence. The equation for Augmented Dickey-Fuller (ADF) test Where ÃŽÂ ± is a constant, ÃŽÂ ² is the coefficient on a time trend and p is the lag order of the autoregressive process. ÃŽÂ ± = 0 and ÃŽÂ ² = 0 corresponds to modeling a random walk and ÃŽÂ ² = 0 corresponds to modeling a random walk drift. By including lags of the order p, the ADF formulation allows for higher-order autoregressive processes. This means that the lag length p needs to be determined when applying in the test. One possible approach is to test from high orders and examine the t-value on coefficients. The criterion such as the Akaike information criterion (AIC), Schwarz-Bayesian information criterion (SBIC) or the Hannan-Quinn information criterion (HQIC) test is used to examine the lag length. 3.3.3 Granger Causality The Granger Causality test indicates that a time series Y is said to be Granger caused by X if X helps the prediction of Y or equivalently if the coefficients on the lagged X are statistically significant. Granger Causality shows two-way causation in the case. X Granger causes Y and Y Granger causes X. It usually through a series of t-tests and F-tests on lagged values of X and lagged values of Y. 3.3.4 Multiple Regressions The ordinary least squares (OLS) or linear least squares are a method to examine the unknown parameters in a linear regression model. It is used to assume the distribance, ui. According to Gujarati (2003), ui stands for the normal distribution representing zero mean and constant variance, à Ã†â€™2 in the multiple regression models. With the normality assumption, OLS estimators 1, and 2 are linear functions of ui. Therefore, if ui are normally distributed, so 1,and 2 will make hypothesis testing more straightforward. OLS estimators of the partial regression coefficients are identical with the maximum likelihood (ML) estimators. There are the best linear unbiased estimators (BLUE). Besides, the least-square estimators are best unbiased estimators (BUE); it means that they have minimum variance in the entire class of unbiased estimators. 3.3.5 Multicollinearity Multicollinearity shows the two or more independent variables in a multiple regression model are highly linearly related. The multicollinearity test is perfect if the correlation between two independent variables is equal to 1 or -1. Multicollinearity will occur when there is a strong linear relationship among two or more independent variables. The equation below is refer the variables is perfectly multicollinear if there exist one or more exact linear relationships among some of the variables. Estimates for the parameters of the multiple regression equation is The ordinary least squares estimates include inverting the matrix XTX where, It indicate that if the linear relationship (perfect multicollinearity) is exactly with the independent variables, the rank of X is less than k+1 and the matrix XTX will not invertible. One of the detection of multicollinearity is used detection-tolerance or the variance inflation factor (VIF) for multicollinearity where R2j is the coefficient of determination of a regression of explanatory j on all the other explanators. Tolerances of less than 0.20 or 0.10 or a VIF of 5 or 10 and above reveal a multicollinearity problem. 3.3.6 Breusch-Godfrey Serial Correlation LM Test Breusch-Godfrey Serial Correlation LM test is a test of autocorrelation that is basically allows for nonstochastic regressors such as the lagged values of the regressand; higher-order autoregressive schemes such as AR (1), AR (2), etc and higher-order moving averages of white noise error terms such as t. Two variable regression models to illustrate the test, regressors can be added to the model and also lagged values of the regressand can be added to the model. Yt =ÃŽÂ ²1 +ÃŽÂ ²2Xt +ut The error term ut assume that the pth-order autoregressive, AR (p), Ut = ptut-1 + ptut-2 + à ¢Ã¢â€š ¬Ã‚ ¦+pput-p + t. where t.is a white noise error term. The null hypothesis H0 can be show as Ho: p1 = p2 = à ¢Ã¢â€š ¬Ã‚ ¦ = pp = 0 (no autocorrelation) At 5% significant level, if the computed p value of Chi-square is less than Chi-square tests, do not reject the null hypothesis, meaning that there is no autocorrelation problem. If computed p value of Chi-square is more than Chi-square tests, reject the null hypothesis, meaning that there is autocorrelation problem. 3.3.7 Autoregressive Conditional Heteroscedasticity Test In econometrics, Autoregressive Conditional Heteroskedasticity (ARCH) model assume that the variance of the current error term is related to the previos one. Autoregressive Conditional Heteroskedasticity model is used to model the time series with time-varying volatility such as stock price. 3.3.8 Specification error Ramsey Regression Equation Specification Error Test (Ramsey RESET test) is used to examine the specification error. The specification test for the linear regression model. More specifically, it is used to test the specification error in the equation. As the result, if the non-linear combinations of the independent variables have any power in explaining the dependent variable, means that the model is mis-specified. Consider the model Ã…Â · = E {y | à Ã¢â‚¬ ¡ } = ÃŽÂ ²Ãƒ Ã¢â‚¬ ¡ The Ramsey test is used to test whether the (ÃŽÂ ²1à Ã¢â‚¬ ¡)2, (ÃŽÂ ²2à Ã¢â‚¬ ¡)3à ¢Ã¢â€š ¬Ã‚ ¦,(ÃŽÂ ²k-1à Ã¢â‚¬ ¡)k has any power in explaining y. The Ramsey test is executed by calculate the following linear regression Ã…Â · = ÃŽÂ ²Ãƒ Ã¢â‚¬ ¡ + ÃŽÂ ²1Ã…Â ·2 +à ¢Ã¢â€š ¬Ã‚ ¦+ ÃŽÂ ²k-1Ã…Â ·k + ÃŽÂ µ After examine the test, the means of the F-test is to determine whether ÃŽÂ ²1 through ÃŽÂ ²k-1 are zero. If the null hypothesis reveals that all regression coefficients are zero, means that the null hypothesis cannot be reject, the Ramsey test is unable to detect any misspecification. If the null hypothesis is rejected, means that the model is misspecification. 3.3.9 Jarque-Bera Test of Normality Jarque-Bera test of normality is used to test the normally distributed. It is large-sample or an asymptotic test and based on the OLS. The test first calculates the skewness and kurtosis measures of the OLS residuals. JB = n Where the n = sample size, S = skewness coefficient, and K = kurtosis coefficient. The normally distributed variable, S is zero and K is three. Hence, the Jarque-Bera test of normality is a test of the joint hypothesis that S and K are zero and three, respectively. Therefore, the value of the Jaque-Bera statistic is expected to be zero. For the null hypothesis the residual is normally distributed, asymptotically (i.e., in large samples) the Jarque-Bera statistic gives the chi-square distribution with two degree of freedom showed by Jarque and Bera (Gujarati 2003) For the alternative hypothesis the residual is not normally distributed. At 5 significant levels, computed p value is less than Jarque-Bera statistic, we can reject the null hypothesis that the residual is not normally distributed whereas computed p value is more than Jarque-Bera statistic, we do not reject the null hypothesis that the residual is normally distributed. CHAPTER 4: RESEARCH RESULTS AND INTERPRETATION 4.1 Introduction This chapter consists of the results and interpretation of the relationship between Unemployment as an Indicator of Macroeconomic Performance Unemployment as an Indicator of Macroeconomic Performance The rate of unemployment is one of the most important indicators of macroeconomic performance. Unemployment arises due to the distortions in the supply of labor cause by the non-competitive wage differential. During the period from 1945 until at least 1968, unemployment rates in the major European economies were extremely low by todays standards. For instance in the United Kingdom, the average rate of unemployment for the entire period was about 1.8% of the labor force and in worst years it did not even exceed 2.5%. The main driving force was autonomous rather than policy related. These forces include waves of new products and processes, spread of trade and development around the world. However the cause of unemployment problem in Europe in comparison to the United States was their labor market institutions while the United States is far more superior due to the flexibility of their labor market. In this paper, determinants of unemployment in US are the concerns with economic growth as the main concern. Economic growth of a nation is the increase in a nations real output that occurs over time. In general, growth and unemployment are closely related as unemployment affects the growth rate through the scale of operation of an economy. Besides that, FDI inflow and inflation are taken into account altogether to identify the relationship towards the unemployment rate. 1.1 Background As unemployment is one of the most important economic indicators, the unemployment rate provides useful information such as how the labor market works as well as the percentage of human capital that is not used in the production process, which is especially crucial towards policy makers. Consequently, it is important to analyze the factors that impact the unemployment rate regardless short or long term perspective. The United States of America is a developed country which has one of the largest population and production in the world (Encyclopedia, 2010). As unemployment are explained by structural factors mainly by inflexible labor market. One may wonder the about the impact which economic growth, inflation and FDI have on the unemployment rate of the United States of America as the clutches of unemployment are hard to escape even for a develop country, especially for US which possesses by far the most flexible labor market. As a case study, the United States of America has been chosen as the research country. United States of America is reckoned to be particularly appropriate as United States of America labor market has proven by all accounts to be more dynamic in the sense of a higher level of job turnover, resulting in high vacancy levels at any point in time. Recently, unemployment rate in the United States of America has been found to be as high as 9.6% as of August 2010 compared to the 4. 1% ten years ago (Bureau of Labor Statistics, 2010). In the mean time the real GDP growth in 2000 was at 4.14% when the unemployment rate was 4.1% while the real GDP growth in 2003 was at 2.49% when the unemployment rate was 5.8% (Bureau of Labor Statistics, 2010). From here, it can be seen that unemployment rate moves in the opposite direction of economic growth, yet there were different versions of results concluded by different previous researchers. 1.2 Problem Statement Unemployment has been a famous macroeconomic variable that researchers tend to use to study on but even with so many researches carried out, some of the results obtained are not consistent with one and another. For instance, the debates among Monetarist and Keynesian views of unemployment as well as the new contributions of Lucass approach and new Keynesian Economics shows that there was no reason to account for growth in the unemployment model. However, a significant innovation occurred with Pissarides'(1990) formulation of an unemployment theory in equilibrium. In many previous attempts, he formalize a unique framework to study the labor market dynamic perspective, providing useful tools to analyze both long and short run unemployment. Pissarides also introduced a first link between long run unemployment and growth which matches the neoclassical framework of economic growth. ( Pissarides, 1990 Ch. II) In the case of US, its economy began its current economic recovery in December 2001. However, rather than experiencing employment growth, not only did the unemployment rate increase but the number of new jobs created in the economy actually declined significantly during the first year of the recovery (Seyfried). Thus this paper is conducted so as to affirm the relationship of economic growth has on the unemployment rate of the country. As some results obtained by past researchers showed that economic growth impacts unemployment whereas the others came to a conclusion that unemployment causes economic growth whereby the existence of Granger Causality relationship is quite possible. In this study, economic growth, inflation and FDI serves as explanatory variable to determine the relationship towards unemployment rate in the United States of America. 1.3 Objectives This study aims to investigate the determinants of unemployment rate in the United States of America with economic growth as the main concern in addition with inflation and FDI (foreign direct investment) to further assure that it is coherent with the results obtained from previous studies. 1.3.1 Specific objectives This paper aims to examine the relationship between economic growth, inflation and FDI towards the unemployment rate. On the other hand, this paper serves to probe further into the relationship between economic growth, FDI, and inflation towards unemployment to sustain the existence of granger causality relationship. 1.4 Significance of study The contribution of carrying out this study is to allow policy makers to have an insight of unemployment so as to allow them to decide on suitable policy that will help bring down the unemployment rate while sustaining appropriate inflation level and attract sufficient FDI inflow. The results generated will help provide insight to the nature of the relationship between economic growth, inflation, and FDI towards unemployment. It would be useful to policy makers to know the rate and relationship of economic growth as it is necessary to reduce the unemployment rate, or at least keeping it from rising. Moreover, in previous studies, FDI is found to have impacted the unemployment rate indirectly through spillover effects from economic growth. In this study, however, FDI is incorporated directly to affect unemployment growth; therefore the effectiveness of the implemented policy will be taken into account more effectively. CHAPTER 2: Literature Review 2.1 Conceptual Model According to Alexopolous (2003), in the case where there is technological growth in the economy, families will increase their investment in capital, which in turn increase the amount of family purchased consumption workers receive over time. As a result, firms optimally increase the wage rate proportionately in order to prevent workers from shirking on the job. Therefore, the rate of unemployment along the balanced growth path will not change over time, since the marginal product of labour and the marginal cost of labour grow at the same rate. Based on De Groot, in general, growth and unemployment are intimately related for two reasons. Unemployment affects the scale of operation of the economy and thereby the growth rate. Growth affects inter-temporal decisions of workers about where to allocate on the labor market once they are laid off, and thereby it affects equilibrium unemployment. According to Brecher (2007), rapid economic growth and FDI, accompanied by higher per capita income, usually increase output growth. Thus, domestic firms and foreign multinational corporations will demand more labour force with skills to create products. Hence, economic growth can promote future employment growth for labour force based on new Keynesian theory of the output-inflation tradeoff. Some studies found that overseas investment replaced domestic employment in developing countries; however, the same result did not happen in developed countries. Tremblay (2007) pointed out that based on classical economic theory, the Phillips Curve illustrated long-run tradeoff between unemployment and inflation. There is an inverse relationship between inflation and unemployment, that is saying inflation will rise when unemployment decrease and vice-versa. Futhermore, Luciano Fanti and Piero Manfredi (2003) mention that the neoclassical Solow model, which still provides excellent econometric fits and shows a globally stable positive growth equilibrium, but also shows two restrictive features as regards the scope of this paper: (1) it does not take into account the stylized fact of the existence of unemployment, which is generally not only positive but also strongly fluctuating; (2) in such a model fluctuations have never been endogenously determined Meanwhile, Martin Zagler (2006) noticed that the cost associated with economic growth is structural unemployment, as structural change destroys jobs in one firm and creates jobs in another. The source of unemployment is the rate of intra-sector structural change associated with faster economic growth. Besides, Bonatti (2007) says that an increase of the workers influence on the political process may raise the fraction of GDP allocated to finance the welfare state, thus leading to a higher unemployment rate and to a lower growth rate. The research work done by Chang (2007) noticed that when the degree of trade openness of Taiwan is larger, the unemployment rate of Taiwan will increase, this is because the young men and young women in Taiwan desire to extend their education in working age. According to Phillips (1998), the negative relationship between inflation and unemployment can be explained through governments expansionary policy to increase the consumption level of the citizens. As labor market tightens, unemployment rate will fall as money wages tended to rise more rapidly. Unemployment will then increase as government tries to control the inflation rate. This is because the increment in wages is closely related with the increase in price. Therefore, the trade-off between these two variables can be seen. 2.2 Methodology Effects panel regression methods were used by Zagler (2006) on the relationship between economic growth and unemployment. Moreover, Zagler (2006) checked his estimated model with the unit-root test to test the stationary of the model. In order to obtain information about the relationship between inflation and unemployment, the procedure of den Hann was employed by Bae (2006), which has the advantage as no assumptions about the order of integration in the variables of interest is required. The procedure estimates a vector regressions (VAR) model and analyzes the correlations of VAR forecast errors of inflation and unemployment at long horizons. Chang (2007) used vector autoregression method of variance decomposition and impulse response function analysis are applied to analyze various relationships among foreign direct investment (FDI), economic growth, unemployment and degree of openness in Taiwan. Besides that, he also uses the unit root test of augmented Dickey-Fuller (ADF and KPSS) test to examine the stationary properties of the economic time series. The appropriate lag-length in the ADF regression is selected by minimizing the Akaikes information criterion (AIC). He also uses co-integration test to determine whether there exists a long-run equilibrium relationship among variables and weak exogeneity, and multivariate Granger-causality test to determine their causal direction in the short-run between all variables. Besides, he also has applied the VAR technique of variance decomposition and impulse response function analysis to analyze various inter-relationships between FDI, unemployment rate and GDP variables in the case of Taiwan from the period of 1981 to 2003. Meanwhile, Eric Heyer, FrÃÆ' ©dÃÆ' ©ric ReynÃÆ' ¨s, Henri Sterdyniak (2006) present the results of the DF-GLS unit root test to test the growth rate of consumer price and also unemployment rate. 2.3 Empirical Result Zagler (2006) has carried out a research which empirically investigated the link between economic growth and unemployment, using micro econometric evidence for the United Kingdom. The results generated showed a significant and negative relationship between unemployment and economic growth. According to the result generated by Muscatelli and Tirelli (2001), it is proven that there is a negative relationship between economic growth and unemployment as Japan, Germany, Italy, France and Canada. This result is generally in favour of those theories which predict a negative linkage between unemployment on economioc growth Besides, Pehkonen (2000) stated that a fall in GDP has significant relationship with unemployment as a drop in the GDP in Finland leads to an increase in the unemployment since demand for labor have shrunk. Therefore, Pehkonen (2000) concluded that unemployment would increase as a result of a decrease in economic growth. Meanwhile, Mitra and Sato (2007) found that the major links between external scale economies and growth are perceived in terms of technical efficiency, and higher growth is taken to reduce the unemployment rate. Futhermore, Scahaik and Groot (1998) found that the unemployment and economic growth relationship in imperfect competition economy and different periods, where structural changes occur, has a negative correlation and effect of different degrees through testing the structural stability. Chang (2007) proved that economic growth as well as FDI have negative effects on unemployment as FDI are expected to generate economic growth by encouraging the expansion of trade and foreign investment. In addition, according to Solows growth theory, employment for labour force with skill can further promote economic growth and this can be verified by Taiwans economy model. Okuns law stating that reducing unemployment for labour force can promote further economic growth is then verified. Furthermore, unemployment is very sensitive to changes in GDP and vice versa, which does lend support that rising economic growth can obviously affect unemployment for labour force. shock of unemployment rate has negative effect on economic growth . He also mentions that the shocks in economic growth and FDI inflow decrease the unemployment rate. This means that rapid economic growth and FDI inflow, accompanied by higher per capita income can promote future employment growth for labour force. In the research study of Meckl (2001), correlation between growth and unemployment is shown to be positive if the research sector is of the high-wage sector in the economy, and negative if the research sector is the low-wage sector. Arico (2003) has already observed that the rate of growth is negatively related with the rate of unemployment. If the growth rate increases, it will decrease the net rate at which the stream of profits is discounted. For each firm the entry will result less costly. More vacancies will be created, reducing the unemployment rate. (Capitalization effect).On the other hands, It will reduce the life-time of each firm, by increasing the price for human capital. Each innovation will generate fewer vacancies than before. That will be reflected in an increase of the rate of unemployment. (Indirect creative destruction effect). Besides, Fanti and Manfredi (2003) has shown a negative relation between unemployment and growth , though we should also mention the positive relation between unemployment and growth obtained in the particular creative disruption context according to Schumpeters idea. Fanti and Manfredi alsomshows a surprising relation between unemployment and growth (via effects on population which is an endogenous engine of growth): this relation can be either positive or negative depending on the relative levels of cost of childrearing of workers and unemployed persons and the level of unemployment benefits. Meanwhile, Bonatti (2007) noticed that reduction of government transfers in favor of the workers allows decreasing the ratio of total tax revenues to GDP, thus monotonically increasing the growth rate and leading to a lower unemployment rate. CHAPTER 3: RESEARCH METHOD 3.1 Data Analysis 3.1.1 Unemployment Rate In this study, unemployment rate is the main study which was examine by using some explanatory variables. According to BLS, Bureau of Labor Statistics, (2009) those people who are with jobs can be considered as employed. On the other hand, a person will be classified as unemployed if they do not have a job, have actively looked for work in the prior 4 weeks, and are currently available for work. Dixon Shepherd (2002) stated that the unemployment rate can be considered as one of the most important indicators of macroeconomic performance in a country. The data of unemployment rate is obtained from the Bureau of Labor Statistics (BLS) which if measured in percentage from those people who are 16 years old and above from year 1970 to 2007. The method which BLS used to calculate the unemployment rate in United States is: X 100% 3.1.2 Real Gross Domestic Product Real gross domestic product (Real GDP) in a country can be measured by the total output value of goods and services which produced from the domestic labor in the country in a given year, expressed in base-year prices. In this study, it is expected that there is a negative relationship between the Real GDP and unemployment rate in United States. The source of the United States Real GDP data is from the World Bank World Development Indicators and International Financial Statistics of the IMF. On the other hand, the data obtained was converted to a 2005 base year. The formula to calculate the data of United States Real GDP is as below: 3.1.3 Foreign Direct Investment Foreign direct investment (FDI) is a kind of investment which is made to serve the business interest of the investor in a company which is in a different nation distinct from the investors country of origin. An example of FDI is a foreign company comes into a country to build or buy a factory and run a business there. Many economists believe that FDI is good for an economy, because it provides domestic job opportunities and increase domestic capital. In this study, net inflows of foreign direct investment in the measurement of current US Dollar are used. A net inflow of foreign direct investment is the total amount or value of the investment flow into United States from foreign investors to operate their business in United States and negative relationship between foreign direct investment and unemployment rate is expected in United States. 3.1.4 Consumer Price Index Consumer price index (CPI) is measured that examines the weighted average of prices of a basket of consumer goods and services in a country, such as transportation, food, rental fees and utilities fees. CPI is one of the measurements of inflation rate. According to Bureau of Labor Statistics (BLS), the prices for the goods and services used to calculate the CPI are collected in 87 urban areas in United States from about 23,000 retail and services establishments. The CPI data used in this study included all consumer items in United States from year 1970 to 2007. 3.2 Research Framework 3.2.1 Unemployment rate and Real Gross Domestic Product Based on the study, unemployment and real gross domestic product is expected to be negatively related. Edward (2007) stated there is a negative relationship between real gross domestic product and unemployment because of the theory of Okuns law. According to Okuns law, 1% increase in the unemployment rate will decrease GDP by 3%. However, Christopher (2010) said that, Okun coefficients can change over time because the relationship of unemployment to output growth depends on laws, technology, preferences, social customs, and demographics. 3.2.2 Unemployment and Consumer Price Index Consumer price index is one of the most frequently used statistics for identifying periods of inflation or deflation. This is because large rises in CPI during a short period of time typically denote periods of inflation. Therefore, we expect that there is an inverse relationship between the rate of unemployment and rate of inflation. According to the Phillips Curve theory, if the unemployment is high, inflation tends to be low. The diagram below shows the Phillips curve. Inflation Phillips curve Unemployment However, the result shows a positive relationship in our regression model. This problem will occur because of the multicolinearity problem in our regression model. But when one independent variable by one independent variable with the unemployment is tested, negative sign for consumer price index and unemployment are obtained. Bae (2006) stated that there is a positive long run relationship between unemployment and inflation. 3.2.3 Unemployment and Foreign Direct Investment In this study, inflow of foreign direct investment were expected to affect the unemployment rate significantly and expected that foreign direct investment has a negative long run relationship with unemployment. Foreign direct investment will increase job opportunities so, unemployment rate will decrease. Shu (2007) stated that FDI have negative effects on unemployment as FDI are expected to generate economic growth by encouraging the expansion of trade and foreign investment. 3.3 Econometric Methodology 3.3.1 Introduction This chapter consist the used of the method to examining the relationship between the unemployment and economic condition in United State by using the time series data ranging from the year 1970 to 2007. First, the result testing will start with the test of stationary by using Augmented Dickey-Fuller unit root test and proceed with the cointegration test. Secondly, the Multiple Regression Analysis and several ways to detect the assumption of the Classical Linear Regression Model (CLRM). The multicollinearity is used to test the correlation analysis. Breusch-Godfrey Serial Correlation LM Test is used to test the existence of serial autocorrelation, Autoregression Conditional Heteroscedasticity Test is used for testing the heteroscedasticity variance of error of the model and Ramsey RESET Test is used to detect the linearity regression and misspecification error. Unemployment = f (RGDP, FDI, CPI) RGDP = Real Gross Domestic Product FDI = Foreign Direct Investment CPI = Consumer Price Index The change in unemployment is our main study that we want to examine with using a few of variables which are RGDP (Real Gross Domestic Product), FDI (Foreign Direct Investment) and CPI (Consumer Price Index). y = ÃŽÂ ²0 + ÃŽÂ ²1Ln (RGDP) + ÃŽÂ ²2 (CPI) + ÃŽÂ ²3 (FDI) + Econometric Model with Expected Sign: = ÃŽÂ ²0 + ÃŽÂ ²1L (RGDP) + ÃŽÂ ²2 (CPI) + ÃŽÂ ²3(FDI) (-ve) (-ve) (-ve) Where +ve indicates that there is a postive relationship between the explanatory variable and dependent variable. On the other hand, -ve indicates that there is a negative relationship between the explanatory variable and dependent variable 3.3.2 Unit root A unit root test is used to examine whether a time series variable is stationary. In the model, T-statistic, F-statistic and R-squared are used to determine to ensure the validity of the test statistics is stationary. The result will become spurious regression problem if the non-stationary series in the ordinary least square (OLS) regression is used. Spurious regression result in high significant T-statistic and highly value for the coefficient of determination R-squared, and the R-square is larger than Durbin Watson. Therefore, if the stationary does not hold, estimate is not consistent and result will be misleading. To avoid the spurious regression problem, the Augmented Dickey-Fuller test (ADF) is used to examine the stationary of the variable. An Augmented Dickey-Fuller test (ADF) is used to test for a unit root in a time series sample. The Augmented Dickey-Fuller (ADF) statistic used in the test is a negative number. Therefore, the more negative value is, more power the rejection of the hypothesis that there is a unit root at some level of confidence. The equation for Augmented Dickey-Fuller (ADF) test Where ÃŽÂ ± is a constant, ÃŽÂ ² is the coefficient on a time trend and p is the lag order of the autoregressive process. ÃŽÂ ± = 0 and ÃŽÂ ² = 0 corresponds to modeling a random walk and ÃŽÂ ² = 0 corresponds to modeling a random walk drift. By including lags of the order p, the ADF formulation allows for higher-order autoregressive processes. This means that the lag length p needs to be determined when applying in the test. One possible approach is to test from high orders and examine the t-value on coefficients. The criterion such as the Akaike information criterion (AIC), Schwarz-Bayesian information criterion (SBIC) or the Hannan-Quinn information criterion (HQIC) test is used to examine the lag length. 3.3.3 Granger Causality The Granger Causality test indicates that a time series Y is said to be Granger caused by X if X helps the prediction of Y or equivalently if the coefficients on the lagged X are statistically significant. Granger Causality shows two-way causation in the case. X Granger causes Y and Y Granger causes X. It usually through a series of t-tests and F-tests on lagged values of X and lagged values of Y. 3.3.4 Multiple Regressions The ordinary least squares (OLS) or linear least squares are a method to examine the unknown parameters in a linear regression model. It is used to assume the distribance, ui. According to Gujarati (2003), ui stands for the normal distribution representing zero mean and constant variance, à Ã†â€™2 in the multiple regression models. With the normality assumption, OLS estimators 1, and 2 are linear functions of ui. Therefore, if ui are normally distributed, so 1,and 2 will make hypothesis testing more straightforward. OLS estimators of the partial regression coefficients are identical with the maximum likelihood (ML) estimators. There are the best linear unbiased estimators (BLUE). Besides, the least-square estimators are best unbiased estimators (BUE); it means that they have minimum variance in the entire class of unbiased estimators. 3.3.5 Multicollinearity Multicollinearity shows the two or more independent variables in a multiple regression model are highly linearly related. The multicollinearity test is perfect if the correlation between two independent variables is equal to 1 or -1. Multicollinearity will occur when there is a strong linear relationship among two or more independent variables. The equation below is refer the variables is perfectly multicollinear if there exist one or more exact linear relationships among some of the variables. Estimates for the parameters of the multiple regression equation is The ordinary least squares estimates include inverting the matrix XTX where, It indicate that if the linear relationship (perfect multicollinearity) is exactly with the independent variables, the rank of X is less than k+1 and the matrix XTX will not invertible. One of the detection of multicollinearity is used detection-tolerance or the variance inflation factor (VIF) for multicollinearity where R2j is the coefficient of determination of a regression of explanatory j on all the other explanators. Tolerances of less than 0.20 or 0.10 or a VIF of 5 or 10 and above reveal a multicollinearity problem. 3.3.6 Breusch-Godfrey Serial Correlation LM Test Breusch-Godfrey Serial Correlation LM test is a test of autocorrelation that is basically allows for nonstochastic regressors such as the lagged values of the regressand; higher-order autoregressive schemes such as AR (1), AR (2), etc and higher-order moving averages of white noise error terms such as t. Two variable regression models to illustrate the test, regressors can be added to the model and also lagged values of the regressand can be added to the model. Yt =ÃŽÂ ²1 +ÃŽÂ ²2Xt +ut The error term ut assume that the pth-order autoregressive, AR (p), Ut = ptut-1 + ptut-2 + à ¢Ã¢â€š ¬Ã‚ ¦+pput-p + t. where t.is a white noise error term. The null hypothesis H0 can be show as Ho: p1 = p2 = à ¢Ã¢â€š ¬Ã‚ ¦ = pp = 0 (no autocorrelation) At 5% significant level, if the computed p value of Chi-square is less than Chi-square tests, do not reject the null hypothesis, meaning that there is no autocorrelation problem. If computed p value of Chi-square is more than Chi-square tests, reject the null hypothesis, meaning that there is autocorrelation problem. 3.3.7 Autoregressive Conditional Heteroscedasticity Test In econometrics, Autoregressive Conditional Heteroskedasticity (ARCH) model assume that the variance of the current error term is related to the previos one. Autoregressive Conditional Heteroskedasticity model is used to model the time series with time-varying volatility such as stock price. 3.3.8 Specification error Ramsey Regression Equation Specification Error Test (Ramsey RESET test) is used to examine the specification error. The specification test for the linear regression model. More specifically, it is used to test the specification error in the equation. As the result, if the non-linear combinations of the independent variables have any power in explaining the dependent variable, means that the model is mis-specified. Consider the model Ã…Â · = E {y | à Ã¢â‚¬ ¡ } = ÃŽÂ ²Ãƒ Ã¢â‚¬ ¡ The Ramsey test is used to test whether the (ÃŽÂ ²1à Ã¢â‚¬ ¡)2, (ÃŽÂ ²2à Ã¢â‚¬ ¡)3à ¢Ã¢â€š ¬Ã‚ ¦,(ÃŽÂ ²k-1à Ã¢â‚¬ ¡)k has any power in explaining y. The Ramsey test is executed by calculate the following linear regression Ã…Â · = ÃŽÂ ²Ãƒ Ã¢â‚¬ ¡ + ÃŽÂ ²1Ã…Â ·2 +à ¢Ã¢â€š ¬Ã‚ ¦+ ÃŽÂ ²k-1Ã…Â ·k + ÃŽÂ µ After examine the test, the means of the F-test is to determine whether ÃŽÂ ²1 through ÃŽÂ ²k-1 are zero. If the null hypothesis reveals that all regression coefficients are zero, means that the null hypothesis cannot be reject, the Ramsey test is unable to detect any misspecification. If the null hypothesis is rejected, means that the model is misspecification. 3.3.9 Jarque-Bera Test of Normality Jarque-Bera test of normality is used to test the normally distributed. It is large-sample or an asymptotic test and based on the OLS. The test first calculates the skewness and kurtosis measures of the OLS residuals. JB = n Where the n = sample size, S = skewness coefficient, and K = kurtosis coefficient. The normally distributed variable, S is zero and K is three. Hence, the Jarque-Bera test of normality is a test of the joint hypothesis that S and K are zero and three, respectively. Therefore, the value of the Jaque-Bera statistic is expected to be zero. For the null hypothesis the residual is normally distributed, asymptotically (i.e., in large samples) the Jarque-Bera statistic gives the chi-square distribution with two degree of freedom showed by Jarque and Bera (Gujarati 2003) For the alternative hypothesis the residual is not normally distributed. At 5 significant levels, computed p value is less than Jarque-Bera statistic, we can reject the null hypothesis that the residual is not normally distributed whereas computed p value is more than Jarque-Bera statistic, we do not reject the null hypothesis that the residual is normally distributed. CHAPTER 4: RESEARCH RESULTS AND INTERPRETATION 4.1 Introduction This chapter consists of the results and interpretation of the relationship between

Initial Teaching Assignment Essay

In my role as a tutor of support teaching and learning in schools my responsibilities include: promoting cognitive elaboration *Cognitive psychology is concerned with the various mental activities which result in the acquisition and processing of information by the learner. It’s theories involve a perception of the learner as a purposive individual in continuous interaction with his social and psychological environment.( l.b.curzon (2003). teaching in further education. 6th ed. london: continuum. 35.) Holding a good knowledge of outside agencies that maybe used when an issue is outside of my knowledge or expertise. These may include N.S.P.C.C, medical teams including GP’s, health visitors ect, councillors, learning support workers, banks, building societies and the student finance England information for funding or loan advice, police, fire services and social services. All of these outside agencies could be used for supporting my learners and for them to use in their role as a teaching assistant as they are working with children and young people. I work towards promoting social and emotional development, encouraging learners and rewarding them during tasks, discussions, production of work whilst developing into a responsible teaching assistants. Being reliable is paramount to learners giving them a sense of belonging and security that I would always be there to discuss any issues or concerns with them, especially if the issue is a delicate one. Showing my learners that I can promote equality by letting them have every opportunity to attend and participate in every aspect of the lesson is also giving them opportunity to express their own ideas and personality. I myself am always looking to learn and gaining skills from learners is another way of learning and promoting diversity. Every learner is different and giving them opportunity to share their ways and knowledge and including these skills to improve their learning and adding to their new career in a positive way encourages diversity. Understand own responsibility for maintaining a safe and supportive learning environment.