Art History Essay Example for Free

Art History Essay I have had to date a career in event and music management. I studied drama at Manchester Met University and then left to work in event and music management. I have had a passion for art since childhood. Over the past three years i have sought to use this enthusiasm to good effect. I focused on discovering new talent and learning about the process of creation and also the curating of exhibitions. This lead to me creating a pop up exhibition for students at Central Saint Martins in a vacant space on the Portland estate in Marylebone. I also worked in collaboration with commercial clients; Art related fashion Installations at The Saint Martin’s lane hotel alongside the Opera Gallery on Bond Street and also in New York for the Morgan’s hotel group. I started up an event management company with two other people. We parted company and i used the quite challenging period after this to rethink my ideas and what i wanted to do with my life. I read around the subject of Art History extensively during this period. I am now assisting my father in his emerging market advisory company. i have persuaded him to consider developing the cultural industries side of the business with a focus on exploring market opportunities in Africa in relation to art. I want to underpin this with increasing my academic and practical knowledge of contemporary art. In the past i have completed number of courses at Central Saint Martin’s school of arts in art politics and also an introduction into curating. In the weekly classes it gave me the basic framework and understanding of contemporary art, artists and museums. Within the curating course you learnt to build ideas on exhibition making and also gain an understanding of historical models of the past that really aided me in my professional collaborations. These courses have motivated me to this point and really cemented the idea of studying in more depth and looking at the business of Art as a career. I am currently attending Morley College and studying a short course in Japanese Prints and French art in the middle ages which are both coming to an end. I looked at the Sotheby’s course in contemporary art and it seems a natural progression from what I have done in the past and will aid to my further studies in the history of art this year and a career in the arts.

Stock Market Performance and Economic Activity Relationship

Stock Market Performance and Economic Activity Relationship Introduction The debate of whether stock market is associated with economic growth or the stock market can be served as the economic indicator to predict future. According to many economists stock market can be a reason for the future recession if there is a huge decrease in the stock price or vice versa. However, there are evidence of controversial issue about the ability of prediction from the stock market is not reliable if there is a situation like 1987 stock market crashed followed by the economic recession and 1997 financial crises. (Stock market and economic growth in Malaysia: causality test). The aim of the study is to find the relation between the stock market performance and the real economic activity in case of four countries The UK, The USA, Malaysia and Japan. With my limited knowledge I have tried to find out the role of financial development in stimulating economic growth. A lot of economists have different view about stock market development and the economic growth. If we focus on some related literature published on this topic one question arises: Is economic development is affected by stock market development? Even though there are lots of debate on some are saying that stock market can help the economy but the effect of stock market in the economy especially in the economy is very little. Ross Levine suggested in his paper published in 1998 that recent evidence suggested stock market can really give a boom to economic growth. (REFERENCE) It is not really possible to measure the growth by simply looking at the ups and down in the stock market indicator and by looking at the rates of growth in GDP. A lot of things can cause in the growth of stock market like changes in the banking system, foreign participation in the in the financial market may participate strongly. Apparently it seems that these developments can cause development of stock market followed by the good economic growth. But to check the accuracy one required to follow an appropriate method which would meaningfully measure whether stock price is really effecting the economic growth or not? In my work I have tried to find out the co integrating relationship between Stock price and GDP and tried to check if there is a long run and short run relationship between the stock price and GDP. The method used for the studies is Engle Granger co integration method. To do this I have used ADF (Augmented Dickey Fuller Test) to check for the stationary behaviour of the variables and then I have performed the Engle Granger Engle Granger co integration method followed by residual based error correction model. To check for the short run relationship I have used 2nd stage Engle Granger co integration method. To check the causal effect of the four countries stock market and economic growth I used Granger Causality Method. In this paper I have reviewed some studies of scholars which I have discussed on the literature review part. This paper contains five parts Part two is about the literature based on the past wok of scholars. Part Three discussed about the Data. Part four is about the methodology, Results are discussed on part five and part six is all about the summary and conclusion of the whole study. In my work I have founded there is no long run relationship between stock market and economic growth in all four countries. In addition there is no causal relation between stock index yield and the national economy growth rate. The empirical results of the thesis concludes that the possibility of seemingly abnormal relationship between the stock index and national economy of these for countries. Literature Review: Stock market contributes to economic growth in different ways either directly or indirectly. The functions of stock market are savings mobilization, Liquidity creation, and Risk diversification, keep control on disintermediation, information gaining and enhanced incentive for corporate control. The relationship between stock market and economic growth has become an issue of extensive analysis. There is always a question whether the stock market directly influence economic growth. A lot of research and results shows that there is a strong relationship between stock market and economic growth. Evidence on whether financial development causes growth help to reconcile these views. If we go back to the study of Schumpeter (1912) his studies emphasizes the positive influence on the development of a countrys financial sector on the level and the potential risk of losses caused by the adverse selection and moral hazard or transaction costs are argued by him how necessary the rate of growth argues that financial sectors provides of reallocating capital to minimize the potential losses. Empirical evidence from king and Levine (1983) show that the level of financial intermediation is good predictor of long run rates of growth, capital accumulation and productivity. Enhanced liquidity of financial market leads to financial development and investors can easily diversify their risk by creating their portfolio in different investments with higher investment. Demiurgic and Maksimovic (1996) have found positive causal effects of financial development on economic growth in line with the ‘supply leading hypothesis. According to his studies countries with better financial system has a smooth functioning stock market tend to grow much faster as they have access to much needed funds for financially constrained economic enterprises by the large efficient banks. Related research was done for the past three decades focusing on the role of financial development in stimulating economic growth they never considered about the stock market. An empirical study by Ming Men and Rui on Stock market index and economic growth in China suggest that possible reason of apparent abnormal relationship between the stock Index and national economy in china. Apparent abnormal relationship may be because of the following reason inconsistency of Chinese GDP with the structure of its stock market, role played by private sector in growth of GDP and disequilibrium of finance structure etc. The study was done using the cointegration method and Granger causality test, the overall finding of the study is Chinese finance market is not playing an important role in economic development. (Men M 2006 China paper). An article by Indrani Chakraborti based on the case of India presented in a seminar in kolkata in October, 2006 provides some information about the existence of long run stable relationship between stosk market capitalization, bank credit and growth rate of real GDP. She used the concept of the granger causality after using both the Engle-Granger and Johansen technique. In her study she found GDP is co-integrated with financial depth, Volatility in the stock market and GDP growth is co integrated with all the findings the paper explain that the in an overall sense, economic growth is the reson for financial development in India.(Chakraboty Indrani). Few writers from Malaysia found that stock market does help to predict future economy. Stock market is associated with economic growth play as a source for new private capital. Causal relationship between the stock market and economic growth which was done by using the formal test for causality by C.J. Granger and yearly Malaysia data for the period 1977-2006. The result from the study explain that future prediction is possible by stock market. A study focused on the relationship between stock market performance and real economic activity in Turkey. The study shows existence of a long run relationship between real economic activity and stock prices†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦ Result from the study pointed out that economic activity increases after a shock in stock prices and then declines in Turkish market from the second quarter and a unitary (Turkish paper) An international time series analysis from 1980-1990 by By RAGHURAM G. RAJAN AND LUIGI ZINGALES shows some evidence of the relation between stock market and economic growth. This paper describes whether economic growth is facilitated by financial development. He found that financial development has strong effect on economic growth. (Rajan and Zingales, 1998) The study of Ross LEVINE AND SARA ZERVOS on finding out the long run relationship between stock market and bank suggest a positive effect both the variables has positive effect on economic growth. International integration and volatility is not properly effected by capital stock market. And private save saving rates are not at all affected by these financial indicators. (Levine and Zervos 1998) Belgium Stock market study with economic development shows the positive long run relationship between both the variables. In case of Belgium the evidences are quiet strong that Economic growth is caused by the development of the stock market. It is more focused between the period 1873 and 1935, basically this period is considered as the period of rapid industrialization in Belgium. The importance of the stock market in Belgium is more pronounced after liberalization of the stock market in 1867-1873. The time varying nature of the link between stock market development and economic growth is explained by the institutional change in the stock exchange. They also tried to find out the relationship to the universal banking system. Before 1873 the economic growth was based on the banking system and after 1873 stock market took the place. (Stock Market Development and economic growth in Belgium, Stijin Van Nieuwerburg, Ludo Cuyvers, Frans Buelens July 5, 2005) Senior economist of the World Banks Policy research department Ross Levine has discussed about Stock market in his paper Stock Markets: A Spur to economic growth on the impact of development. Less risky investments are possible in liquid equity market and it attracts the savers to acquire an asset, equity. As they can sell it quickly when they need access to their savings, and if they want to alter their portfolio. Though many long term investment is required for the profitable investment. But reluctance of the investors towards long term investment as they dont have the access to their savings easily. Permanent access to capital is raised by the companies through equity issues as they are facilitating longer term, more profitable investments and prospect of long term economic growth is enhanced as liquid market improves the allocation of capital. The empirical evidence from the study strongly suggests that greater stock markets create more liquidity or at least continue economic gro wth. (Levine. R A spur to economic Growth) Another paper was focused on the linkages between financial development and economic growth using TYDL model for the empirical exercises by Purna Chandra Padhan suggests that both stock price and economic activity are integrated of order one and Johansen-Juselias Coin-integration tests for this study found one co integrating vector exists. It is proved by the spurious relation rule in this study the existence of at least one direction of causality. He described that bi-directional causality between stock price and economic growth meaning that economic activity can be enhanced by well developed stock exchange and vice-versa. ( Title:  The nexus between stock market and economic activity: an empirical analysis for India Author(s): Purna Chandra Padhan Journal: International Journal of Social Economics Year: 2007 Volume: 34 Issue: 10  Page: 741 – 753 DOI: 10.1108/03068290710816874 Publisher: Emerald Group Publishing Limited) Chee Keong Choong (Universiti Tunku Abdul Rahman Malaysia) Zulkornain Yusop (Universiti Putra Malaysia) Siong Hook Law (Universiti Putra Malaysia) Venus Liew Khim Sen (Universiti Putra Malaysia) Date of creation: 23 Jul 2003 tried to find out the importance of the causal relationship of Financial development and economic growth. The findings of their study usin autoregressive Distributed lag (ARDL) describes about the positive long run impact on economic growth Granger causality also suggest same results. However, another study on Iran by N. Shahnoushi, A.G Daneshvar, E Shori and M. Motalebi 2008 Financial development is not considered as an effective factor to the economic growth. The study was focused on the causal relationship between the financial development and economic growth. Time series data used for the study from the period 1961-2004. Granger causality shows there is no co integrating relationship between financial development and economic growth in Iran only the economical growth leads to financial development. Establishing link between savings and investment is very much important and financial market provides that. Transient or lasting growth is the ultimate affect of the financial market. Economic growth can be influenced by financial market by improving the productivity of the capital, Investment to firms can be channelled and greater capital accumulation by increasing savings. To ensure the stability of the financial market potential regulation is important due to asymmetric information, especially at the time of financial liberalization. (Economic Development and Financial Market Tosson Nabil Deabes Moderm Academy for technology aand computer sciences; MAM November 2004 Economic Development Financial Market Working Paper No. 2 ) Data: The empirical analysis was carried out using the quarterly data for The UK, The USA, Japan and Malaysia. The data were collected from the DataStream for the period 1993I to 2008III. Economic growth is measured as the growth rate of gross domestic product (GDP) of each country with the help of stock prices SP. For the software processing I used Eviews 6.0 for the planned regression in order to get the results. The empirical analysis is done from the quarterly data from the stock market indices and the and the GDP between the first quarter of 1993 and the fourth quarter of 2008. All the data has been extracted from the data stream and expressed in US$. The data for Japan share price is from Tokyo Stock Exchange. Malaysias Share price is form Kuala Lumpur Composite Index, UKs is from UK FT all share price index and USA share price is taken from the DOW Jones industrial share price index. The nature of the Data is series used for the time series regression. List of Variables: UGDP UK GDP USP UK Share price LUGDP Log of UK GDP LUSP Log of UK Share price USGDP USA GDP USSP USA (DOW Jones) Share price LUSGDP Log of USA GDP LUSSP Log of USA Share price MGDP Malaysia GDP MSP Malaysia Share price LMGDP Log of Malaysia GDP LMSP Log of Malaysia Share price JGDP Japan GDP JSP Japan Share Price LJGDP Log of Japan GDP LJSP Log of Japan Share price Methodology: Engle and Granger (1987) first established the cointegration method. It is a method of measuring long term diversification based on data. Linear combination of two non stationary series shows that they are integrated in order one I(1) that is stationary. And this is a co integrated series. Cointegration Long term common random trend between non stationary time series. The linear combination of both the nonstationary series can be stationary if both the variables are integrated in same order. Cointegration is a very powerful approach in the long term analysis because a common stochastic trend is shared in cointegration that mean two series will not drift separately too much. They might deviate from each other but in the long run but eventually the will revert back in the long run. If there is very low correlation between the series still the series can be co-integrated as high correlation is not implied in cointegration. The reason for choosing the method as it will allow us to check the move between the variable in the long run even there might be a divergence in the short run. The first step in the analysis is check each index series whether the series for the presence of unit root which shows whether the series is non stationary. The method that I followed to do this is Augmented Dickey Fuller Test (ADF). I proceed the Granger cointegration technique 1987 when the stationary requirements are met. Cointegration long term common stochastic trend between nonstationary time series. If non-stationary series x and yare both integrated of same order and there is a linear combination of them that is stationary, they are called cointegrated series. A common stochastic trend is shared in Cointegration. It follows that these two series will not drift apart too much, meaning that even they may deviate from each other in the short-term, they will revert to the long-run equilibrium. This fact makes cointegration a very powerful approach for the long-term analyses. Meanwhile, cointegration does not imply high correlation; two series can be co integrated and yet have very low correlations. Cointegration tests allow us to determine whether financial variables of different national markets move together over the long run, while providing for the possibility of short-run divergence. The first step in the analysis is to test each index series for the presence of unit roots, which shows whether the series are nonstationary. All the series must be nonstationarity and integrated of the same order. To do this, we apply both the Augmented Dickey-Fuller (ADF) test. Once the stationarity requirements are met, we proceed Granger bivariate cointegration (1987) procedure. 30 International Research Journal of Finance and Economics Issue 24 (2009) Series Stationary Test: In this study I have used Augmented Dickey Fuller Test (ADF) to test the stationarity of variables. ADF is test for unit root where I have checked the Unit root and strong negative numbers of unit root is being rejected by the null hypothesis (level of significance). The following regression for the unit root test in Eviews: Is the white noise error tem. Is the difference operator. , () () Here with the test we can find the estimates of are equal to zero or not. Y is said to be stationary if the cumulative distribution of the ADF statistics by showing that if the calculated ratio of the coefficient is less than the critical value according to Fuller (1976). If we accept the Ho the sequence is predicted to be having unit root and if Ho is rejected then we can say that the series doesnt have unit root. This proves that the series is stationary. The co –integration test can only be performed if both the sequences are all integrated of order I (1). Cointegration Test: According to Engle and Granger (1987) to check for cointegration if both the variables and are integrated with order one the proposed method for cointegration residual-based test for cointegration (Engle-Granger method). So from the above method we can find the equation By regressing with And after that and is denoted as the estimated regression coefficient vectors. Then, = – is representing the estimated residual vector. If the residual is itegrated with zero that means the series for the residual is stationary, and and are then co integrated. An in this situation (1, -) is called co-integrating vector. Therefore is a co integrating equation, so, from it we can say that there is long run relationship between and. Granger causality test: Granger causality test is applied if the relationship is lagged between the two variables to determine the direction of relation in statistical term. It gives information about the short term relationship between the variables. In terms of conceptual definition causality is consist of different ideas, this concept produce a relation between caused and results were agreed upon. Aristo defines that there exist a link between causes and results and without causes these results are impossible. And this strong relationship. Some economists believe that the idea of causality is the mix of both theoretical and explanation and statistical concept. The frontline operational definition of causality is given by some economist, but Granger is the one who provided the information to understand it correctly and completely. Granger s operational causality definition depends of below hypotheses, Next cannot be the reason of past. 1. Next cannot be reason of past. Certain causality is possible only with past causes present time or future time. Cause is always to be come true before the result. In addition, this makes time lagged between causes and results. 2. Causality can be determined only stochastic process. It is not possible to determine the causality between two deterministic processes. After 1990s, Granger and Engle contributed to time series literature importantly. On these developments about time series analysis, some variations were done with Granger Causality test. According to this, possible long-term relationship would be tested and if 20 variables were co-integrated, long-term regression error equation s lagged value would be included in Granger Error Correction model as error correction term. Thus, Granger Causality test should be applied. If there is no co-integration between the variables, it can be continued with Granger Causality Test without including error correction terms. If there is a co-integration between the variables, Granger Causality Test will be failed and it will be certainly necessary to be included error correction term into the models. Granger Causality Test, which depends on time series data, is made by the estimation of the equations below with Least Squares Method (LSM). Xt = + j t j X + i t i Y + Ut Yt = + j t j Y + j t j X + Ut In Granger Causality test, there are three possible situations that one directional causality from x to y or y to x, opposite direction between x and y or one affect to other and independency of x and y each other. This situation changes according to chosen of null hypothesis and lagged values randomly in equations above whose parameters are whether equal to zero or not. According to researches, randomly choice makes causality incline to deviations importantly. To understand this test clearly it can be talked about below equation; t (LNGDP) = 0 + t inii (LNGDP)1+ t I nii (LND1)1+ Ut To apply Granger Causality test under null hypothesis, which illustrates coefficients of financial deepening variables (LND1) are meaningful (equal to zero) and then F-statistics can be calculated. If null hypothesis is not rejected then it is possible to say that Granger causality test accepts that financial deepening causes economic growth. The direction can be either negative or positive (Granger and Engle, 1987). Indicators of the economic growth and the financial deepening are variables, which are used for Granger Causality test. Moreover, this test can determine the effects of one variable on the other. Test result for Unit Root: Augmented Dickey Fuller Model (ADF) is used to test the stationary of each variable. Null and alternative hypothesis describes about the investigation of unit root. If the null is accepted and alternative is rejected then the variable non stationary behaviour and vice versa is stationary. Form the result of Augmented Dickey Fuller test of the four countries variables (Log GDP and Log Share price) shows that the entire variable has unit root at level which proves that the series is not stationary. However, the result from the first difference shows the significance at 1%, 5% and 10% critical value and found to be stationary behaviour. Therefore, it suggests that all the variables are integrated of order one. Variables level/1st Difference Augmented Dickey Fuller Statistic(ADF) test Japan Conclusion t statistic value With Trend t statistic value With trend and Intercept 1% 5% 10% 1% 5% 10% GDP Level -2.653258 -3.522887     -2.901779 -2.588280   -2.693600   -4.088713   -3.472558 -3.163450 1st Difference -9.053185 -3.524233   -2.902358 -2.588587 -9.003482   -4.090602   -3.473447 -3.163967 Share Price Level   -2.116137 -3.522887     -2.901779 -2.588280   -2.203273   -4.088713   -3.472558 -3.163450 1st Difference   -6.899295 -3.524233   -2.902358 -2.588587   -6.844396   -4.090602   -3.473447 -3.163967 Table 01: Unit root test for stationary Japan If we have a look on the unit root test for the variables GDP and Share price to find out the stationary behaviour the Augmented Dickey Fuller Test with intercept and with intercept and trend in level and first difference. The t statistic value with trend is -2.653258 which is higher than the critical values in 1%, 5% and 10% critical value. The same applies with intercept and trend as the t statistic value -2.693600 is higher than the critical value in all the level of critical value. So from the nature of stationary behaviour we can say in level GDP shows nonstationary behaviour. And the first difference LnGDP is integrated with order one. In case of LnSP the results with intercept and with intercept trend in level are -2.116137 and -2.203273 which is higher than the critical values shows non stationary behaviour as they are higher than the critical value. The unit root test for the variables at first difference shows stationary as the t statistic value is than the critical value i n all level and they are integrated in order one. Variables level/1st Difference Augmented Dickey Fuller Statistic(ADF) test Malaysia Conclusion t statistic value With Trend t statistic value With trend and Intercept 1% 5% 10% 1% 5% 10% GDP Level -1.195020 -3.522887     -2.901779 -2.588280 -1.933335   -4.088713   -3.472558 -3.163450 1st Difference -5.951843 -3.524233   -2.902358 -2.588587 -5.923595   -4.090602   -3.473447 -3.163967 Share Price Level   -1.900406 -3.522887     -2.901779 -2.588280   -1.891183   -4.088713   -3.472558 -3.163450 1st Difference   -7.842122 -3.524233   -2.902358 -2.588587   -7.779757   -4.090602   -3.473447 -3.163967 The unit root test result for LMGDP and LMSP values presented in natural logarithm. And the level values with intercept and with intercept and trend for LMGDP is -1.195020 and -1.93335 respectively. The values are higher than the critical value means the series has non stationary behaviour. On the other hand the 1st difference values with intercept and with intercept and trend are -5.951843 and -5.923595 respectively. The 1st difference values are integrated with order one. And in the same way I did the ADF test to check for Stationary behaviour of LMSP in level and first difference with intercept and trend. The values in level are -1.900406 and -1.891183 with intercept and trend us higher than the critical value and the series is not integrated with order one. The first difference t statistic values are -7.842122 and -7.779757 with intercept and with intercept and trend respectively are less than the critical value in both the case implies that the series is integrated with order on e. Variables level/1st Difference Augmented Dickey Fuller Statistic(ADF) test UK Conclusion t statistic value With Trend t statistic value With trend and Intercept 1% 5% 10% 1% 5% 10% GDP Level -0.690866 -3.522887     -2.901779 -2.588280 -2.377333   -4.088713   -3.472558 -3.163450 1st Difference -7.474388 -3.524233   -2.902358 -2.588587 -7.439027   -4.090602   -3.473447 -3.163967 Share Price Level -1.711599 -3.522887     -2.901779 -2.588280 -1.261546   -4.088713   -3.472558 -3.163450 1st Difference -7.254574 -3.524233   -2.902358 -2.588587 -7.391821   -4.090602   -3.473447 -3.163967 The results from Augmented Dickey Fuller test (ADF) for UK GDP in level with intercept and with intercept and trend is –0.690866 and -2.377333 respectively. Both the values in level are higher than the critical value and are integrated in order 0 shows non stationary behaviour. The t statistic values in 1st difference with intercept and with intercept and trend are -7.474388 and -7.439207 respectively. Which suggest that the critical values are less than the critical values in 1%, 5% and 10% level. So from the above hypothesis it can be said that it series is integrated with order one. When I performed the unit root test using the same method the series in level with intercept and with intercept and trend the values in are -1.711599 and -1.261546 respectively. The values are higher than the critical values implies that they are not integrated in order one shows non stationary behaviour. However, the 1st difference value of log natural share price is -7.254573 and -7.391821 wit h intercept and with intercept and trend respectively. So from the result we can say that the series is integrated in order one in both the cases with intercept and with intercept and trend. So the series in first difference is stationary. Variables level/1st Difference Augmented Dickey Fuller Statistic(ADF) test USA Conclusion t statistic value With Trend t statistic value With trend and Intercept 1% 5% 10% 1% 5% 10% GDP Level -3.244801 -3.522887     -2.901779 -2.588280   2.866507   -4.088713   -3.472558 -3.163450 1st Difference -5.010864 -3.524233   -2.902358 -2.588587 -5.010864   -4.090602   -3.473447 -3.163967 Share Price Level -2.074732 -3.522887     -2.901779 -2.588280 -0.359637   -4.088713   -3.472558 -3.163450 1st Difference -8.181234 -3.524233   -2.902358 -2.588587 -8.735399   -4.090602   -3.473447 -3.163967 Augmented Dickey Fuller Statistic in case of the variable of USA LUSSP and LUGDP I have used the same method using intercept and intercept and trend in level and first difference. Th Stock Market Performance and Economic Activity Relationship Stock Market Performance and Economic Activity Relationship Introduction The debate of whether stock market is associated with economic growth or the stock market can be served as the economic indicator to predict future. According to many economists stock market can be a reason for the future recession if there is a huge decrease in the stock price or vice versa. However, there are evidence of controversial issue about the ability of prediction from the stock market is not reliable if there is a situation like 1987 stock market crashed followed by the economic recession and 1997 financial crises. (Stock market and economic growth in Malaysia: causality test). The aim of the study is to find the relation between the stock market performance and the real economic activity in case of four countries The UK, The USA, Malaysia and Japan. With my limited knowledge I have tried to find out the role of financial development in stimulating economic growth. A lot of economists have different view about stock market development and the economic growth. If we focus on some related literature published on this topic one question arises: Is economic development is affected by stock market development? Even though there are lots of debate on some are saying that stock market can help the economy but the effect of stock market in the economy especially in the economy is very little. Ross Levine suggested in his paper published in 1998 that recent evidence suggested stock market can really give a boom to economic growth. (REFERENCE) It is not really possible to measure the growth by simply looking at the ups and down in the stock market indicator and by looking at the rates of growth in GDP. A lot of things can cause in the growth of stock market like changes in the banking system, foreign participation in the in the financial market may participate strongly. Apparently it seems that these developments can cause development of stock market followed by the good economic growth. But to check the accuracy one required to follow an appropriate method which would meaningfully measure whether stock price is really effecting the economic growth or not? In my work I have tried to find out the co integrating relationship between Stock price and GDP and tried to check if there is a long run and short run relationship between the stock price and GDP. The method used for the studies is Engle Granger co integration method. To do this I have used ADF (Augmented Dickey Fuller Test) to check for the stationary behaviour of the variables and then I have performed the Engle Granger Engle Granger co integration method followed by residual based error correction model. To check for the short run relationship I have used 2nd stage Engle Granger co integration method. To check the causal effect of the four countries stock market and economic growth I used Granger Causality Method. In this paper I have reviewed some studies of scholars which I have discussed on the literature review part. This paper contains five parts Part two is about the literature based on the past wok of scholars. Part Three discussed about the Data. Part four is about the methodology, Results are discussed on part five and part six is all about the summary and conclusion of the whole study. In my work I have founded there is no long run relationship between stock market and economic growth in all four countries. In addition there is no causal relation between stock index yield and the national economy growth rate. The empirical results of the thesis concludes that the possibility of seemingly abnormal relationship between the stock index and national economy of these for countries. Literature Review: Stock market contributes to economic growth in different ways either directly or indirectly. The functions of stock market are savings mobilization, Liquidity creation, and Risk diversification, keep control on disintermediation, information gaining and enhanced incentive for corporate control. The relationship between stock market and economic growth has become an issue of extensive analysis. There is always a question whether the stock market directly influence economic growth. A lot of research and results shows that there is a strong relationship between stock market and economic growth. Evidence on whether financial development causes growth help to reconcile these views. If we go back to the study of Schumpeter (1912) his studies emphasizes the positive influence on the development of a countrys financial sector on the level and the potential risk of losses caused by the adverse selection and moral hazard or transaction costs are argued by him how necessary the rate of growth argues that financial sectors provides of reallocating capital to minimize the potential losses. Empirical evidence from king and Levine (1983) show that the level of financial intermediation is good predictor of long run rates of growth, capital accumulation and productivity. Enhanced liquidity of financial market leads to financial development and investors can easily diversify their risk by creating their portfolio in different investments with higher investment. Demiurgic and Maksimovic (1996) have found positive causal effects of financial development on economic growth in line with the ‘supply leading hypothesis. According to his studies countries with better financial system has a smooth functioning stock market tend to grow much faster as they have access to much needed funds for financially constrained economic enterprises by the large efficient banks. Related research was done for the past three decades focusing on the role of financial development in stimulating economic growth they never considered about the stock market. An empirical study by Ming Men and Rui on Stock market index and economic growth in China suggest that possible reason of apparent abnormal relationship between the stock Index and national economy in china. Apparent abnormal relationship may be because of the following reason inconsistency of Chinese GDP with the structure of its stock market, role played by private sector in growth of GDP and disequilibrium of finance structure etc. The study was done using the cointegration method and Granger causality test, the overall finding of the study is Chinese finance market is not playing an important role in economic development. (Men M 2006 China paper). An article by Indrani Chakraborti based on the case of India presented in a seminar in kolkata in October, 2006 provides some information about the existence of long run stable relationship between stosk market capitalization, bank credit and growth rate of real GDP. She used the concept of the granger causality after using both the Engle-Granger and Johansen technique. In her study she found GDP is co-integrated with financial depth, Volatility in the stock market and GDP growth is co integrated with all the findings the paper explain that the in an overall sense, economic growth is the reson for financial development in India.(Chakraboty Indrani). Few writers from Malaysia found that stock market does help to predict future economy. Stock market is associated with economic growth play as a source for new private capital. Causal relationship between the stock market and economic growth which was done by using the formal test for causality by C.J. Granger and yearly Malaysia data for the period 1977-2006. The result from the study explain that future prediction is possible by stock market. A study focused on the relationship between stock market performance and real economic activity in Turkey. The study shows existence of a long run relationship between real economic activity and stock prices†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦ Result from the study pointed out that economic activity increases after a shock in stock prices and then declines in Turkish market from the second quarter and a unitary (Turkish paper) An international time series analysis from 1980-1990 by By RAGHURAM G. RAJAN AND LUIGI ZINGALES shows some evidence of the relation between stock market and economic growth. This paper describes whether economic growth is facilitated by financial development. He found that financial development has strong effect on economic growth. (Rajan and Zingales, 1998) The study of Ross LEVINE AND SARA ZERVOS on finding out the long run relationship between stock market and bank suggest a positive effect both the variables has positive effect on economic growth. International integration and volatility is not properly effected by capital stock market. And private save saving rates are not at all affected by these financial indicators. (Levine and Zervos 1998) Belgium Stock market study with economic development shows the positive long run relationship between both the variables. In case of Belgium the evidences are quiet strong that Economic growth is caused by the development of the stock market. It is more focused between the period 1873 and 1935, basically this period is considered as the period of rapid industrialization in Belgium. The importance of the stock market in Belgium is more pronounced after liberalization of the stock market in 1867-1873. The time varying nature of the link between stock market development and economic growth is explained by the institutional change in the stock exchange. They also tried to find out the relationship to the universal banking system. Before 1873 the economic growth was based on the banking system and after 1873 stock market took the place. (Stock Market Development and economic growth in Belgium, Stijin Van Nieuwerburg, Ludo Cuyvers, Frans Buelens July 5, 2005) Senior economist of the World Banks Policy research department Ross Levine has discussed about Stock market in his paper Stock Markets: A Spur to economic growth on the impact of development. Less risky investments are possible in liquid equity market and it attracts the savers to acquire an asset, equity. As they can sell it quickly when they need access to their savings, and if they want to alter their portfolio. Though many long term investment is required for the profitable investment. But reluctance of the investors towards long term investment as they dont have the access to their savings easily. Permanent access to capital is raised by the companies through equity issues as they are facilitating longer term, more profitable investments and prospect of long term economic growth is enhanced as liquid market improves the allocation of capital. The empirical evidence from the study strongly suggests that greater stock markets create more liquidity or at least continue economic gro wth. (Levine. R A spur to economic Growth) Another paper was focused on the linkages between financial development and economic growth using TYDL model for the empirical exercises by Purna Chandra Padhan suggests that both stock price and economic activity are integrated of order one and Johansen-Juselias Coin-integration tests for this study found one co integrating vector exists. It is proved by the spurious relation rule in this study the existence of at least one direction of causality. He described that bi-directional causality between stock price and economic growth meaning that economic activity can be enhanced by well developed stock exchange and vice-versa. ( Title:  The nexus between stock market and economic activity: an empirical analysis for India Author(s): Purna Chandra Padhan Journal: International Journal of Social Economics Year: 2007 Volume: 34 Issue: 10  Page: 741 – 753 DOI: 10.1108/03068290710816874 Publisher: Emerald Group Publishing Limited) Chee Keong Choong (Universiti Tunku Abdul Rahman Malaysia) Zulkornain Yusop (Universiti Putra Malaysia) Siong Hook Law (Universiti Putra Malaysia) Venus Liew Khim Sen (Universiti Putra Malaysia) Date of creation: 23 Jul 2003 tried to find out the importance of the causal relationship of Financial development and economic growth. The findings of their study usin autoregressive Distributed lag (ARDL) describes about the positive long run impact on economic growth Granger causality also suggest same results. However, another study on Iran by N. Shahnoushi, A.G Daneshvar, E Shori and M. Motalebi 2008 Financial development is not considered as an effective factor to the economic growth. The study was focused on the causal relationship between the financial development and economic growth. Time series data used for the study from the period 1961-2004. Granger causality shows there is no co integrating relationship between financial development and economic growth in Iran only the economical growth leads to financial development. Establishing link between savings and investment is very much important and financial market provides that. Transient or lasting growth is the ultimate affect of the financial market. Economic growth can be influenced by financial market by improving the productivity of the capital, Investment to firms can be channelled and greater capital accumulation by increasing savings. To ensure the stability of the financial market potential regulation is important due to asymmetric information, especially at the time of financial liberalization. (Economic Development and Financial Market Tosson Nabil Deabes Moderm Academy for technology aand computer sciences; MAM November 2004 Economic Development Financial Market Working Paper No. 2 ) Data: The empirical analysis was carried out using the quarterly data for The UK, The USA, Japan and Malaysia. The data were collected from the DataStream for the period 1993I to 2008III. Economic growth is measured as the growth rate of gross domestic product (GDP) of each country with the help of stock prices SP. For the software processing I used Eviews 6.0 for the planned regression in order to get the results. The empirical analysis is done from the quarterly data from the stock market indices and the and the GDP between the first quarter of 1993 and the fourth quarter of 2008. All the data has been extracted from the data stream and expressed in US$. The data for Japan share price is from Tokyo Stock Exchange. Malaysias Share price is form Kuala Lumpur Composite Index, UKs is from UK FT all share price index and USA share price is taken from the DOW Jones industrial share price index. The nature of the Data is series used for the time series regression. List of Variables: UGDP UK GDP USP UK Share price LUGDP Log of UK GDP LUSP Log of UK Share price USGDP USA GDP USSP USA (DOW Jones) Share price LUSGDP Log of USA GDP LUSSP Log of USA Share price MGDP Malaysia GDP MSP Malaysia Share price LMGDP Log of Malaysia GDP LMSP Log of Malaysia Share price JGDP Japan GDP JSP Japan Share Price LJGDP Log of Japan GDP LJSP Log of Japan Share price Methodology: Engle and Granger (1987) first established the cointegration method. It is a method of measuring long term diversification based on data. Linear combination of two non stationary series shows that they are integrated in order one I(1) that is stationary. And this is a co integrated series. Cointegration Long term common random trend between non stationary time series. The linear combination of both the nonstationary series can be stationary if both the variables are integrated in same order. Cointegration is a very powerful approach in the long term analysis because a common stochastic trend is shared in cointegration that mean two series will not drift separately too much. They might deviate from each other but in the long run but eventually the will revert back in the long run. If there is very low correlation between the series still the series can be co-integrated as high correlation is not implied in cointegration. The reason for choosing the method as it will allow us to check the move between the variable in the long run even there might be a divergence in the short run. The first step in the analysis is check each index series whether the series for the presence of unit root which shows whether the series is non stationary. The method that I followed to do this is Augmented Dickey Fuller Test (ADF). I proceed the Granger cointegration technique 1987 when the stationary requirements are met. Cointegration long term common stochastic trend between nonstationary time series. If non-stationary series x and yare both integrated of same order and there is a linear combination of them that is stationary, they are called cointegrated series. A common stochastic trend is shared in Cointegration. It follows that these two series will not drift apart too much, meaning that even they may deviate from each other in the short-term, they will revert to the long-run equilibrium. This fact makes cointegration a very powerful approach for the long-term analyses. Meanwhile, cointegration does not imply high correlation; two series can be co integrated and yet have very low correlations. Cointegration tests allow us to determine whether financial variables of different national markets move together over the long run, while providing for the possibility of short-run divergence. The first step in the analysis is to test each index series for the presence of unit roots, which shows whether the series are nonstationary. All the series must be nonstationarity and integrated of the same order. To do this, we apply both the Augmented Dickey-Fuller (ADF) test. Once the stationarity requirements are met, we proceed Granger bivariate cointegration (1987) procedure. 30 International Research Journal of Finance and Economics Issue 24 (2009) Series Stationary Test: In this study I have used Augmented Dickey Fuller Test (ADF) to test the stationarity of variables. ADF is test for unit root where I have checked the Unit root and strong negative numbers of unit root is being rejected by the null hypothesis (level of significance). The following regression for the unit root test in Eviews: Is the white noise error tem. Is the difference operator. , () () Here with the test we can find the estimates of are equal to zero or not. Y is said to be stationary if the cumulative distribution of the ADF statistics by showing that if the calculated ratio of the coefficient is less than the critical value according to Fuller (1976). If we accept the Ho the sequence is predicted to be having unit root and if Ho is rejected then we can say that the series doesnt have unit root. This proves that the series is stationary. The co –integration test can only be performed if both the sequences are all integrated of order I (1). Cointegration Test: According to Engle and Granger (1987) to check for cointegration if both the variables and are integrated with order one the proposed method for cointegration residual-based test for cointegration (Engle-Granger method). So from the above method we can find the equation By regressing with And after that and is denoted as the estimated regression coefficient vectors. Then, = – is representing the estimated residual vector. If the residual is itegrated with zero that means the series for the residual is stationary, and and are then co integrated. An in this situation (1, -) is called co-integrating vector. Therefore is a co integrating equation, so, from it we can say that there is long run relationship between and. Granger causality test: Granger causality test is applied if the relationship is lagged between the two variables to determine the direction of relation in statistical term. It gives information about the short term relationship between the variables. In terms of conceptual definition causality is consist of different ideas, this concept produce a relation between caused and results were agreed upon. Aristo defines that there exist a link between causes and results and without causes these results are impossible. And this strong relationship. Some economists believe that the idea of causality is the mix of both theoretical and explanation and statistical concept. The frontline operational definition of causality is given by some economist, but Granger is the one who provided the information to understand it correctly and completely. Granger s operational causality definition depends of below hypotheses, Next cannot be the reason of past. 1. Next cannot be reason of past. Certain causality is possible only with past causes present time or future time. Cause is always to be come true before the result. In addition, this makes time lagged between causes and results. 2. Causality can be determined only stochastic process. It is not possible to determine the causality between two deterministic processes. After 1990s, Granger and Engle contributed to time series literature importantly. On these developments about time series analysis, some variations were done with Granger Causality test. According to this, possible long-term relationship would be tested and if 20 variables were co-integrated, long-term regression error equation s lagged value would be included in Granger Error Correction model as error correction term. Thus, Granger Causality test should be applied. If there is no co-integration between the variables, it can be continued with Granger Causality Test without including error correction terms. If there is a co-integration between the variables, Granger Causality Test will be failed and it will be certainly necessary to be included error correction term into the models. Granger Causality Test, which depends on time series data, is made by the estimation of the equations below with Least Squares Method (LSM). Xt = + j t j X + i t i Y + Ut Yt = + j t j Y + j t j X + Ut In Granger Causality test, there are three possible situations that one directional causality from x to y or y to x, opposite direction between x and y or one affect to other and independency of x and y each other. This situation changes according to chosen of null hypothesis and lagged values randomly in equations above whose parameters are whether equal to zero or not. According to researches, randomly choice makes causality incline to deviations importantly. To understand this test clearly it can be talked about below equation; t (LNGDP) = 0 + t inii (LNGDP)1+ t I nii (LND1)1+ Ut To apply Granger Causality test under null hypothesis, which illustrates coefficients of financial deepening variables (LND1) are meaningful (equal to zero) and then F-statistics can be calculated. If null hypothesis is not rejected then it is possible to say that Granger causality test accepts that financial deepening causes economic growth. The direction can be either negative or positive (Granger and Engle, 1987). Indicators of the economic growth and the financial deepening are variables, which are used for Granger Causality test. Moreover, this test can determine the effects of one variable on the other. Test result for Unit Root: Augmented Dickey Fuller Model (ADF) is used to test the stationary of each variable. Null and alternative hypothesis describes about the investigation of unit root. If the null is accepted and alternative is rejected then the variable non stationary behaviour and vice versa is stationary. Form the result of Augmented Dickey Fuller test of the four countries variables (Log GDP and Log Share price) shows that the entire variable has unit root at level which proves that the series is not stationary. However, the result from the first difference shows the significance at 1%, 5% and 10% critical value and found to be stationary behaviour. Therefore, it suggests that all the variables are integrated of order one. Variables level/1st Difference Augmented Dickey Fuller Statistic(ADF) test Japan Conclusion t statistic value With Trend t statistic value With trend and Intercept 1% 5% 10% 1% 5% 10% GDP Level -2.653258 -3.522887     -2.901779 -2.588280   -2.693600   -4.088713   -3.472558 -3.163450 1st Difference -9.053185 -3.524233   -2.902358 -2.588587 -9.003482   -4.090602   -3.473447 -3.163967 Share Price Level   -2.116137 -3.522887     -2.901779 -2.588280   -2.203273   -4.088713   -3.472558 -3.163450 1st Difference   -6.899295 -3.524233   -2.902358 -2.588587   -6.844396   -4.090602   -3.473447 -3.163967 Table 01: Unit root test for stationary Japan If we have a look on the unit root test for the variables GDP and Share price to find out the stationary behaviour the Augmented Dickey Fuller Test with intercept and with intercept and trend in level and first difference. The t statistic value with trend is -2.653258 which is higher than the critical values in 1%, 5% and 10% critical value. The same applies with intercept and trend as the t statistic value -2.693600 is higher than the critical value in all the level of critical value. So from the nature of stationary behaviour we can say in level GDP shows nonstationary behaviour. And the first difference LnGDP is integrated with order one. In case of LnSP the results with intercept and with intercept trend in level are -2.116137 and -2.203273 which is higher than the critical values shows non stationary behaviour as they are higher than the critical value. The unit root test for the variables at first difference shows stationary as the t statistic value is than the critical value i n all level and they are integrated in order one. Variables level/1st Difference Augmented Dickey Fuller Statistic(ADF) test Malaysia Conclusion t statistic value With Trend t statistic value With trend and Intercept 1% 5% 10% 1% 5% 10% GDP Level -1.195020 -3.522887     -2.901779 -2.588280 -1.933335   -4.088713   -3.472558 -3.163450 1st Difference -5.951843 -3.524233   -2.902358 -2.588587 -5.923595   -4.090602   -3.473447 -3.163967 Share Price Level   -1.900406 -3.522887     -2.901779 -2.588280   -1.891183   -4.088713   -3.472558 -3.163450 1st Difference   -7.842122 -3.524233   -2.902358 -2.588587   -7.779757   -4.090602   -3.473447 -3.163967 The unit root test result for LMGDP and LMSP values presented in natural logarithm. And the level values with intercept and with intercept and trend for LMGDP is -1.195020 and -1.93335 respectively. The values are higher than the critical value means the series has non stationary behaviour. On the other hand the 1st difference values with intercept and with intercept and trend are -5.951843 and -5.923595 respectively. The 1st difference values are integrated with order one. And in the same way I did the ADF test to check for Stationary behaviour of LMSP in level and first difference with intercept and trend. The values in level are -1.900406 and -1.891183 with intercept and trend us higher than the critical value and the series is not integrated with order one. The first difference t statistic values are -7.842122 and -7.779757 with intercept and with intercept and trend respectively are less than the critical value in both the case implies that the series is integrated with order on e. Variables level/1st Difference Augmented Dickey Fuller Statistic(ADF) test UK Conclusion t statistic value With Trend t statistic value With trend and Intercept 1% 5% 10% 1% 5% 10% GDP Level -0.690866 -3.522887     -2.901779 -2.588280 -2.377333   -4.088713   -3.472558 -3.163450 1st Difference -7.474388 -3.524233   -2.902358 -2.588587 -7.439027   -4.090602   -3.473447 -3.163967 Share Price Level -1.711599 -3.522887     -2.901779 -2.588280 -1.261546   -4.088713   -3.472558 -3.163450 1st Difference -7.254574 -3.524233   -2.902358 -2.588587 -7.391821   -4.090602   -3.473447 -3.163967 The results from Augmented Dickey Fuller test (ADF) for UK GDP in level with intercept and with intercept and trend is –0.690866 and -2.377333 respectively. Both the values in level are higher than the critical value and are integrated in order 0 shows non stationary behaviour. The t statistic values in 1st difference with intercept and with intercept and trend are -7.474388 and -7.439207 respectively. Which suggest that the critical values are less than the critical values in 1%, 5% and 10% level. So from the above hypothesis it can be said that it series is integrated with order one. When I performed the unit root test using the same method the series in level with intercept and with intercept and trend the values in are -1.711599 and -1.261546 respectively. The values are higher than the critical values implies that they are not integrated in order one shows non stationary behaviour. However, the 1st difference value of log natural share price is -7.254573 and -7.391821 wit h intercept and with intercept and trend respectively. So from the result we can say that the series is integrated in order one in both the cases with intercept and with intercept and trend. So the series in first difference is stationary. Variables level/1st Difference Augmented Dickey Fuller Statistic(ADF) test USA Conclusion t statistic value With Trend t statistic value With trend and Intercept 1% 5% 10% 1% 5% 10% GDP Level -3.244801 -3.522887     -2.901779 -2.588280   2.866507   -4.088713   -3.472558 -3.163450 1st Difference -5.010864 -3.524233   -2.902358 -2.588587 -5.010864   -4.090602   -3.473447 -3.163967 Share Price Level -2.074732 -3.522887     -2.901779 -2.588280 -0.359637   -4.088713   -3.472558 -3.163450 1st Difference -8.181234 -3.524233   -2.902358 -2.588587 -8.735399   -4.090602   -3.473447 -3.163967 Augmented Dickey Fuller Statistic in case of the variable of USA LUSSP and LUGDP I have used the same method using intercept and intercept and trend in level and first difference. Th

Contingency Theories of Organizations

Contingency Theories of Organizations Part 2 OB: What is the core argument of contingency theories of organizations? Discuss giving examples from at least one such theory. Evaluate the claims of this theory and assess its relevance for organizations today. Organizations operate in many different environments and it is vital to assess how they influence their structures. Effective and efficient organizing has become increasingly important in the modern world characterized by rapid changes. Contingency approaches emphasize that in order for organizations to succeed they must adopt a structure suitable for the environment in which they operate. There are many forms of contingency theory. In general, contingency theories are a class of behavioral theory that claim that there is no best way to organize a corporation and the organizational structure of the company. An organizational or leadership style that is effective in some situations may not be successful in others. Therefore, the best way of organizing the company, is contingent upon the internal and external situation of the company. External environments influence organizations in a varied number of ways. Critical external factors include, but are not limited to, the size of the organization, labor markets, availability and cost of capital, competitors, governmental laws and policies, managerial assumptions about employees, strategies, technologies used, etc. The main ideas of contingency theory are: * There is no universal or one best way to manage * The design of organizations and its subsystems must fit with the environment * Effective organizations not only have a proper fit with the environment but also between its subsystems * The needs of an organization are better satisfied when it is properly designed and the management style is appropriate both to the tasks undertaken and the nature of the work group. Several contingency approaches were developed simultaneously in the late 1960s. The emergence of the theory was the result of criticisms of the classical theories such as Webers bureaucracy (Weber, 1946) and Taylors scientific management (Taylor, 1911) which had failed because they neglected that management style and organizational structure were influenced by various aspects of the environment: the contingency factors. The contingency approach originated with the work of Joan Woodward (1958), who declared that successful organizations in different industries with different technologies were characterized by different organizational structures. In this essay I will discuss three influential contingency theories, those of Burns and Stalker (1961), Lawrence and Lorsch (1967) and Fiedler (1967). Tom Burns and Graham Stalker in their 1961 book, The Management of Innovation studied about 20 Scottish and British electronics companies operating in increasingly competitive and innovative technological markets. Their findings demonstrated that organizations operating in stable environments are very different from those which have to face a changing and dynamic environment. The authors have discovered that differences in the way firms approached change and innovation related to the values and mission of the firms. Burns and Stalker classified the firms into 2 categories on the basis of their managerial structures and practices: mechanistic and organic. The authors found that mechanistic organizations, also called bureaucracies, are suited for relatively stable environmental conditions. Such organizations are clearly programmed, strictly controlled and hierarchically structured. Often they do not have mission and vision statements, and instead depend on established rules for guidance, measuring success by the degree to which staff conforms to process and procedure. Organizational tasks are typically broken down into specialized activities. Individuals are responsible for their specific functions in a relative isolation from the overall organizational goal. The organic organizations are more likely to exist under unstable environmental conditions. Organic organizations are orientated towards results, have a flat organization structure instead of a hierarchy, and little structure in terms of process and rules. They focus on results and employees receive positive rewards for creative and pragmatic contributions. Given these conditions it becomes necessary to review and redefine the responsibilities, methods, inter-role relationships, and even goals on a continual basis. Burns and Stalker emphasized that each system is appropriate under its own specific conditions. Neither system was superior to the other under all situations. Since the 1960s much of writings in organization theories field is a constant debate between the machine/organ analogies, and attempts to develop growth models of how simple mechanistic forms can grow into the more complex organic forms. Another significant study to demonstrate the relationships between environmental characteristics and effective organizational structures was conducted by Paul Lawrence and Jay Lorsch (1967). They studied ten US firms in three separate industries (plastics, food, containers) that confronted varying degrees of uncertainty, complexity and change. The researchers found that successful firms in each industry had a different degree of differentiation. The firms operating in uncertain, complex, rapidly changing environments had more highly differentiated internal structures: sales, production and RD departments. Such organizations require the greater need for suitable mechanisms for integrating and resolving conflicts between ranges of segments. Successful firms in more homogeneous and stable environment were more formalized and hierarchical in their forms. Authors concluded that successful firms must have internal structures as complex as environments in which they operate. This seminal work of Lawrence and Lorsch refined the contingency theory by demonstrating that different markets and technological environments require different kinds of organizations, and that subunits or functional departments within an organization might be managed in different ways, due to variations resulting from their sub-environments. Their view is ecological those organizations that can best adapt to the environment will survive. Managerial leadership has influenced organizational activities in many ways. These influences include motivating subordinates, budgeting scarce resources, and serving as a source of communication. Contingency theories of leadership argue that no single leadership style is effective in all circumstances, but the leadership styles are contingent on the organizational and situational context. Fred Fiedlers theory (1967) is the earliest and most extensively researched is also known as contingency model of leadership effectiveness. Fiedlers ideas originated from trait and behavioral models by stating that performance of the group is dependent on the leaders psychological orientation and on three contextual variables: group atmosphere, task structure, and leaders power position. The contingency model underlines the importance of both the leaders personality and the situation in which that leader operates. The first major factor in Fiedlers theory is known as the leadership style. This is the consistent system of interaction that takes place between a leader and work group. In order to classify leadership styles, Fiedler has developed an index called the Least-Preferred Coworker (LPC) scale. To get an LPC score a leader is asked to think of co-workers with whom he/she has ever worked and choose the one with whom the work was the most difficult. Then this person is rated on a number of eight-point bipolar scales (friendly/unfriendly, hostile/supportive, etc.). The responses to these scales are summed and averaged: a high LPC score suggests that the leader has a human relations orientation, while a low LPC score indicates a task orientation. The second major factor in Fiedlers theory is known as situational favorableness or environmental variable. This basically is defined as the degree a situation enables a leader to exert influence over a group. Fiedler then extends his analysis by focusing on three key situational factors, which are leader-member, task structure and position power. For leader-member relations, Fiedler maintains that the leader will have more influence if they maintain good relationships with group members who like, respect, and trust them, than if they do not. Fiedler explains that task structure is the second most important factor in determining structural favorableness. He contends that highly structured tasks, which specify how a job is to be done in detail provide a leader with more influences over group actions than do unstructured tasks. Finally, as for position power, leads who have the power to hire and fire, discipline and reward, have more power than those who do not. For example, the head of a department has more power than a file clerk. By classifying a group according to three variables, it is possible to identify eight different group situations or leadership style. These eight different possible combinations were then classified as either task orientation or relationship orientated. Several implications can be derived from Fiedlers findings. First, it is not accurate to speak of effective and ineffective leaders. Fiedler goes on by suggesting that there are only leader who perform better in some situations, but not all situations. Second, almost anyone can be a leader by carefully selecting those situations that match his or her leadership style. Lastly, the effectiveness of a leader can be improved by designing the job to fit the manager. For instance, by increasing or decreasing a leaders position power, changing the structure of a task, or influencing leader-member relations, an organization can alter a situation to better fit a leaders style. The following aspects can be considered as strengths of Fiedlers theory: it is predictive and supported by a lot of empirical research, it does not require that people be effective in all situations and provides a way to assess leader style that could be useful to an organization. However among its weaknesses are the fact that it is cumbersome to use, it doesnt explain what to do when there is a mismatch between style and situation and it doesnt take into account situational variables, like training and experience, which also have an impact in a leaders effectiveness. Finally, there is some doubt whether the LPC is a true measure of leadership style. In summary, the essence of contingency theory is that best practices depend on the contingencies of the situation. Contingency theory is often called the à ¢Ã¢â€š ¬Ã…“it all dependsà ¢Ã¢â€š ¬Ã‚  theory, because when a contingency theorist is being asked for an answer, the typical response will be that it all depends. While this may sound simplistic, assessing the contingencies on which decisions depend can be a very complex. Contingency theorists try to identify and measure the conditions under which things will likely occur. Considering that organizations should attain both external and internal fit to achieve superior performance, at the same time, the processes of strategy formulation and implementation are not separable activities; there is a need for an integrative approach that incorporates both schools of thought. The appropriate management style and organizational structure depend on the environmental context of the organization concerned. The ability to manage change is now recognized as a core organizational competence. References: 1. Fineman, S., Sims, D. Gabriel, Y. (2005) Organizing and organizations , London, Sage. 2. Smith, M. J. (1984). Contingency rules theory, context, and compliance behaviors. Human Communication Research, 10, 489-512. 3. Burns, T., Stalker, M. (1961). The Management of Innovation, 3rd Edition, 1994, Oxford University Press 4. Lawrence, P. R., Lorsch, J. W. (1967). Organization and Environment. Cambridge, MA: Harvard University Press. 5. Fiedler, F. E. (1964). A Contingency Model of Leadership Effectiveness. Advances in Experimental Social Psychology (Vol.1). 149-190. New York: Academic Press. Burnes, B. (1996), No such thing as à ¢Ã¢â€š ¬Ã‚ ¦ a à ¢Ã¢â€š ¬Ã…“one best wayà ¢Ã¢â€š ¬Ã‚  to manage organizational change. Management decision, Vol. 34, Issue 10, pp. 10-18

The Tormented Genius of Edgar Allan Poe Essay -- Literary Analysis

It has been said that one cannot be truly great till they have experienced hardship. This, perhaps, is the reason that Edgar Allan Poe is thought to be one of the greatest story tellers in all of history. His life was not sprinkled with tragedy, but completely drowned in it. From the beginning of Poe’s life till the very end, he was, according to The Haunted Man by Phillip Lindsay, â€Å"born to live in nightmares† and that Poe’s life â€Å"might [as] well have been one of [Poe’s] own creations (Lindsay 2).† Death, hardship, and betrayal followed him wherever he travelled, causing him to become a depressed alcoholic along the way. It is widely believed by literary critics that â€Å"had he not been this tortured creature seeking a coffin for a bridal-couch he would not have written the extraordinary and sometimes great tales that he did write (Lindsay 2).† Poe’s traumatic experiences with death, disease, and the people around him helpe d to shape two of his most famous stories: â€Å"The Masque of the Red Death† and â€Å"The Fall of the House of Usher† In Poe’s story â€Å"The Masque of the Red Death†, the characters cannot escape death, no matter how hard they try, in the same way that Poe and the people he loved could not escape. In the story, the prince Prospero’s kingdom is overwhelmed with â€Å"the red death†, much like Poe’s life was ravaged by tuberculosis. The prince attempts to lock out the disease by hiding away in his castle, avoiding it for several months, only to still be claimed by it at the end, brought in by an unwelcomed guest. Likewise, When Poe’s wife Virginia was in the worst of her sickness, they moved, hiding away in warmer weather with the vain hope that she would somehow survive. The red death is a disease much like tuberculosis in its sy... ....'" Literature Resource Center. Studies in Short Fiction 30.2, 1993. Web. Hutchisson, James M. Poe. Jackson: University of Mississippi, 2005. Print. Kalasky, Ed. Drew. The Fall of the House of Usher by Edgar Allan Poe. Vol. 22. Literature Criticism Online. Web. Lawrence, D.H. "The Fall of the House of Usher." Short Story Criticism. Vol. 22. 289-93. Literature Criticism Online. Web. Lindsay, Philip. The Haunted Man; a Portrait of Edgar Allan Poe. New York: Philosophical Library, 1954. Print. May, Charles E. Edgar Allan Po: A Study of the Short Fiction. Vol. 28. New York: Twayne, 1991. Print. Twayne's Studies in Short Fiction Ser. Patterson, R. "Once upon a Midnight Dreary: The Life and Addictions of Edgar Allan Poe." CMAJ.JAMC. 15 Oct. 1992. Web. Poe, Edgar Allan, and Philip Van Doren Stern. The Portable Edgar Allan Poe. New York: Penguin, 1973. Print.

Olubowale Victor Akintimehin: Stage Name Wale Essay -- Musicians

When most musicians become famous it is common that they lose touch with their culture, family, and become obsessed with making money. Many come in with a certain singing or rapping style but change what makes them unique for a record deal. In the hip-hop scene rappers start to dress themselves in many expensive designer clothing labels and diamond accessories, however, for Nigerian-American hip-hop MC Olubowale Victor Akintimehin, stage name Wale, this is a different story. Unlike many rappers, Wale is noteworthy and respected for the exact opposite: he embraces his culture and is interested in becoming famous for just simply being himself. Wale was born in Northwest Washington, D.C. September 21, 1984 to Nigerian immigrants who first arrived five years prior. After 10 years, their family then moved to Gaithersburg, Maryland where Wale found himself attending seven different high schools in both the DC and Maryland area. "My first high school was a predominantly black school, [and] then I went to a predominantly white school, and then back again. I think that helped me cultivate an open mind about most things in life†¦I kind of understand the plight of all people, from understanding all those different environments," he says in his biography on his official website www.ralphfolarin.com. In 2001, Wale graduated from Quince Orchard High School and later attended Robert Morris and Virginia State University on football scholarships. However after transferring a third time to Bowie State University Wale decided to drop out and aspire on the journey for a potential recording career. In 2003-2004, Wale got his first air time with his song, "Rhyme of the Century," on a local radio station which placed him in the "Unsigned Hype" column ... ...azine-says-he-loves-dark-skinned-pretty-girls-0 http://dimewars.com/Video/Wale-Talks-Nigerian-Pride---Fear-Of-Lightskinned-Girls.aspx?bcmediaid=2d817e9c-5618-42a5-9f65-e7402c28b65c http://www.jprotege.com/wale-nigerian-day-parade/ http://www.getmusic.com.au/wale/biography http://questionmarkmag.com/2011/08/wale-works-with-african-artistes-for-charity-gig/ http://www.rap-up.com/2009/08/08/wales-attention-gets-sidetracked/ http://afrofusionlounge.wordpress.com/2010/11/29/bunny-mack-interview-no-qualms-about-wales-sample-of-his-classic-song/ http://killerboombox.com/6485/audio/heaters/wale-no-one-be-like-you http://www.migrationinformation.org/Profiles/display.cfm?ID=788 http://www.census.gov/population/international/ http://www.fco.gov.uk/en/travel-and-living-abroad/travel-advice-by-country/country-profile/sub-saharan-africa/nigeria?profile=intRelations&pg=4 Olubowale Victor Akintimehin: Stage Name Wale Essay -- Musicians When most musicians become famous it is common that they lose touch with their culture, family, and become obsessed with making money. Many come in with a certain singing or rapping style but change what makes them unique for a record deal. In the hip-hop scene rappers start to dress themselves in many expensive designer clothing labels and diamond accessories, however, for Nigerian-American hip-hop MC Olubowale Victor Akintimehin, stage name Wale, this is a different story. Unlike many rappers, Wale is noteworthy and respected for the exact opposite: he embraces his culture and is interested in becoming famous for just simply being himself. Wale was born in Northwest Washington, D.C. September 21, 1984 to Nigerian immigrants who first arrived five years prior. After 10 years, their family then moved to Gaithersburg, Maryland where Wale found himself attending seven different high schools in both the DC and Maryland area. "My first high school was a predominantly black school, [and] then I went to a predominantly white school, and then back again. I think that helped me cultivate an open mind about most things in life†¦I kind of understand the plight of all people, from understanding all those different environments," he says in his biography on his official website www.ralphfolarin.com. In 2001, Wale graduated from Quince Orchard High School and later attended Robert Morris and Virginia State University on football scholarships. However after transferring a third time to Bowie State University Wale decided to drop out and aspire on the journey for a potential recording career. In 2003-2004, Wale got his first air time with his song, "Rhyme of the Century," on a local radio station which placed him in the "Unsigned Hype" column ... ...azine-says-he-loves-dark-skinned-pretty-girls-0 http://dimewars.com/Video/Wale-Talks-Nigerian-Pride---Fear-Of-Lightskinned-Girls.aspx?bcmediaid=2d817e9c-5618-42a5-9f65-e7402c28b65c http://www.jprotege.com/wale-nigerian-day-parade/ http://www.getmusic.com.au/wale/biography http://questionmarkmag.com/2011/08/wale-works-with-african-artistes-for-charity-gig/ http://www.rap-up.com/2009/08/08/wales-attention-gets-sidetracked/ http://afrofusionlounge.wordpress.com/2010/11/29/bunny-mack-interview-no-qualms-about-wales-sample-of-his-classic-song/ http://killerboombox.com/6485/audio/heaters/wale-no-one-be-like-you http://www.migrationinformation.org/Profiles/display.cfm?ID=788 http://www.census.gov/population/international/ http://www.fco.gov.uk/en/travel-and-living-abroad/travel-advice-by-country/country-profile/sub-saharan-africa/nigeria?profile=intRelations&pg=4

Emergency Medical service Essay

The purpose of this report is to socially analyze the first assignment that has already been conducted. This report is divided into two parts, the first part reflects on the matter of the first assignment to identify and list significant issues discussed in the assignment. The second part performs a social analysis of the identified problems of the Emergency Medical Service (EMS) organization by using a number of sources related to the subject. Personal reflection of first assignment In the first assignment, my task was to analyse and construct a common KADS model for an Emergency Medical service (EMS) situated in Netherlands. EMS was facing difficulties in serving the large area around its location in a proper and efficient way. Many efficiency related issues were arising regarding the functioning of EMS. On analysis it became apparent that these difficulties were related to three types of delays that were encountered: †¢ Patient delay †¢ GP arrival delay †¢ Treatment delay In order to overcome these difficulties, some solutions were recommended. Analysing real life organizations and making recommendations for betterment is not an easy task and relatively new for me therefore, lots of effort was put in gathering the relevant information, understanding the job descriptions and requirements. Then in depth analysis and thorough study of the working of organization was performed to reach the proper conclusions and the recommendations made were also tested for efficiency and betterment. The common KADS methodology was employed to perform the analysis and resolve the problems regarding the real life data of organizations which was a new concept for me and common KADS requires analysis and modelling to be performed through tables that depict many aspects of organization like problems and opportunities, solutions available and the process and working of the organization. Application of these methodologies to improve the efficiency of working of an organization and finding solution is different and new but with constant effort I was able to grasp the general idea of application of common KADS and making use of the common KADS methodology tables were used for analysis of EMS organization and solution was found to resolve the difficulties faced by the organization in its working. The example of Ice cream case study provided by the lecturer went a long way in helping me understand the idea of common KADS and how we perform analysis of real life organization data using the common KADS methodology. On reflection I find common KADS methodology interesting to study and I fully understand now the worth of this tool in making in depth analysis of organizations to find solutions for betterment in an organization. Common KADS is an efficient tool to use in group projects for data analysis and to make inferences regarding the state of organizations. Social analysis: In the previous assignment Common KADS modelling and analysis was applied to the EMS organization and three problem areas were identified: patient delay, GP arrival delay and treatment delay. In this section we will analyse the above identified problems and we shall define social theories to gain in depth understanding of these problems. The first problem identified is the patient delay. Patient delay is the average waiting time of the patient before calling the GP. This estimated time for the EMS organization is one hour. Reducing this delay can play a major role in decreasing the rate of death due to patient delay. In Netherlands monarchy system of government is established where a policy exists in which an individual or a function is in authority and controls all other persons under him in a company or organization. In this situation most people are trained to work under one person’s authority without any interference (Davenport & Prusak 1997). In this kind of monarch system of government implemented in Netherlands, people often own little responsibility as they are more prone to listening than action. Therefore, most of the people are hesitant in calling the GP even when they feel sick, until they are quite sure of their ailing condition. This patient delay problem can be solved by starting awareness programs in public to make them take due action quickly and contact the GP when they fall ill. According to Nonaka and konno(1998, pp42) the tacit knowledge can be exchanged between the individuals through a lot of activities such as being together, spending time and written or verbal instructions which is called the socialization. Moreover, this awareness is considered as a tacit knowledge so it can be imparted to the general public by distributing some educational materials and starting some training programmes that provide heuristic lessons to the public. As a result, the people will become aware of the danger of waiting too long before calling the GP and how it may affect their health. The disqualifications of the GP to make the right decision at the right time could be the result of the lack of education or the lack of training and experience. To overcome the problems related to poor diagnosis of the GP the EMS organisation needs to recruit more experienced and qualified GP staff or train existing GP by arranging different work shops and seminars where the GP could be further educated through the exchange of experience between the staff. The third problem identified during analysis was the treatment delay. In the treatment delay another 25 minutes are wasted before treating the patient after arrival and diagnosis at the hospital unless the patient is diagnosed to have AMI. The reason of this delay can be associated with the relationship between the paramedic and the doctor. As mentioned before, the system of government in Netherlands is a monarchy system. This system is covering all the levels of governmental organisations where the paramedic level comes under the GP level. As a result of this structure, the paramedic examines the patient before the GP as he was with the patient in the ambulance before reaching the hospital. The GP should listen to the paramedic’s account of patient condition and involve the paramedic in making the treatment decision regarding the patient. To find a solution to this problem the system of dealing with the levels in hierarchical structure must be changed. For example, the EMS should implement the federalism system which involves representative democracy which would help in eradicating the problems faced due to a weak central government and a high level of local autonomy (Davenport & Prusak 1997). Conclusion Therefore after conducting the above reflection on my previous assignment and performing social analysis I find myself better acquainted with the common KADS methodology. It is a worthy tool in analysing and resolving the problems of any organization. As in the case of Emergency Medical Service (EMS) this tool was helpful in identifying the major delay problems faced by the patients and provided many alternative solutions for the betterment of EMS service available for the patients.

Diaphragm: A Closer Look

Medieval ContraceptionIn the early medieval times, women who don’t want to bear children used lemon halves to stop sperm form going to the insides of the uterus. The highly acidic lemon juice serves as a spermicide, destroying the sperm upon contact. This sparked the idea of creating the modern day diaphragm, a contraceptive used by women.The DiaphragmThe modern diaphragm is invented by a physician from Germany, Dr. Wilhelm Mensnga in 1880. It is a rubber contraceptive shaped like a dome, which is fit inside the vagina. It employs a barrier method of contraception, wherein it blocks the entry of the sperm into the uterus. It is filled with spermicide before it is put in place inside the vagina. It is made of soft and flexible material for easier insertion. In order to ensure comfort and ease, the woman should be fitted for a diaphragm by a specialist (â€Å"Diaphragm (Contraceptive)†).The diaphragm fits inside the vagina and blocks the opening to the uterus. It prevents the sperm from accessing entrance to the uterus.   If ever there are some sperm that swims over the edge of the diaphragm, the spermicidal cream or jelly kills that sperm right away. This device is not an efficient birth control method if you do not use spermicidal cream or jelly. Every time a woman plans to have intercourse, she needs to use a diaphragm in order to avoid getting pregnant.The diaphragm should be put in place inside the vagina moments before sexual intercourse and should stay there for 6 to 8 hours with respect to the time of the man’s last ejaculation. This is to ensure that the spermicide would do its work. It is then gently removed, washed in soapy, warm water and returned to its case so it can be ready for another use. The lifespan of a diaphragm device is from six months to two years, depending on the usage (â€Å"Diaphragm Contraceptive Device†).Diaphragm vs. Other ContraceptivesDiaphragms are usually mistaken with Cervical Caps. The cervical c ap was invented in 1860 but is not approved by Food and Drug Administration in the US, while Diaphragms surfaced in 1880. Cervical caps are usually smaller, shaped like a thimble and is directly positioned over the cervix. Women with deep vaginas would find it rather hard to put cervical caps properly, because their position of their cervix is too far back and is hard to reach. When this is the case, using a diaphragm is preferable to ensure comfort to the women.Diaphragms have negative and positive sides when you compare it with condoms. Using a diaphragm means you have to be sure of sexual intercourse because you need to put it in advance. After that, sexual intercourse could then be made anytime within the next few hours, without bothering to stop in order to apply and replace condom. It assures of a more intimate interaction because there are no abnormal sensations that you could get when you opt for condom. It provides all the natural physical sensation of having sex for men an d women alike (Allen).Advantages and Disadvantages in using DiaphragmThe diaphragm device is relatively cheap. This is because diaphragm can be used for moths without needing to replace or buy a new one. All you need to provide is the spermicidal cream of jelly. It is an effective birth control method when used correctly. There are a few or no side effects from using a diaphragm.It is unusual to experience a problem with using diaphragm. There are some people who are allergic to spermicidal creams and jellies. It can cause skin irritation and itching. When this happens, try to switch brands of spermicidal cream or jelly. If it still persists, then it is time to consult your doctor.Failure Rate for DiaphragmWhen used with a spermicide, the effectivity of the Diaphragm could be from 84% to 95%. Failure is sometimes caused by carelessness, either by improper fitting or insertion of the device. Some also got pregnant because they didn’t use spermicide jelly or cream (Silverberg). The Goal of the DiaphragmJust like other contraceptives and birth control methods, the diaphragm aims to reduce the likelihood of being pregnant or having a baby. Along with the goal of contraception, the diaphragm also promises an enjoyable sexual intercourse where safety and comfort is considered.