Psychological Analysis of Girl Interupted free essay sample

Running Head: BPD in Girl Interrupted Borderline Personality Disorder in Girl Interrupted Girl Interrupted is a movie based on Susanna Kaysen’s novel, which was inspired by her stay in a mental institution in the 1960s. Incidentally the main character, played by Winona Ryder, is named Susanna. She is eighteen years old and begins the movie by reflecting back on the events leading up to her visit to the psychologist. She has just graduated from high school and other than being an aspiring writer, has no plans for her life. In addition she has flashbacks of her attempted suicide, though she denies taking a bottle of aspirin with vodka. When she was brought to the hospital they found bruises on her wrist, but she claimed she had no bones in her wrist. The psychologist concluded that Susanna needed some rest and sent her to Claymore, which is a private mental institution. We will write a custom essay sample on Psychological Analysis of Girl Interupted or any similar topic specifically for you Do Not WasteYour Time HIRE WRITER Only 13.90 / page In the institution, the psychiatrist diagnosis’s Susanna with borderline personality disorder. While in the institution Susanna meets various other women with disorders ranging from eating disorders to psychopaths. After two years she is released from the institution and deemed clear. There are many theories behind why people develop such personality disorders. The theories take psychoanalytic, contemporary, and/or developmental approaches to discover the various reasons why people develop with given personalities. According to Otto Kernberg, there are two developmental tasks an individual must accomplish. Failing to accomplish a certain developmental task often corresponds with an increased risk in developing certain personality disorders. The first developmental task is psychic clarification of self and other. Failing to complete this task causes one to not be able to differentiate one’s own experience from those of others. Not accomplishing the second task, overcoming splitting, results in an increased risk to develop a borderline personality. Kernberg theorized that borderline personality disorder is formulated by the failure to develop in childhood. In addition, Kernberg takes some of Freud’s view points and further theorizes that people who suffer from this disorder have an impaired ego. Freud defines one’s ego as a part of the mind which constrains the id to reality, and is the â€Å"referee† between the superego and id. People with borderline personality disorder externally scatter and internally contradict their concept of themselves. Kernberg states, borderlines can describe themselves for five hours without your getting a realistic picture of what theyre like. Furthermore, borderline personality disorder is clinically defined as ones instability which is evident in their relationships, emotions, behavior and image of themselves. Persons with such disorder may have feelings of abandonment. Often in efforts to manipulate people back into relationships, they take part in self-manipulating behavior or suicidal attempts. Suicide is sometimes attempted impulsively by persons in periods of extreme depression. In addition people often experience strong emotions and strong desires for intimacy. Susanna suffers greatly from these along with other symptoms of borderline personality disorder. One can see from the opening scene in her attempted suicide, that that was one of the leading symptoms. In her out reach for attention she overdosed with a bottle of aspirin along with a bottle of vodka. She also claimed that she had no bones in her hand, which could have been further self-mutilation or another delusional disorder. In addition she has a flashback of her intimate relations with the father of a girl from her high school, along with another man she meets at a club. When she is not around the type of intimacy she utterly wants, she develops a very close relationship with Lisa, who is a patient of the mental institution. This strong desire to always be in a relationship is another prime symptom of this particular personality disorder. The reason for her development of her disorder was caused in early childhood. Both of Susanna’s parents were more focused on leading the perfect suburban life, and paid little attention to her. Thus from such neglect, they never realized that she was being sexual abused by their neighbor, who occasionally babysat her when her parents were out. Susanna began her troubles because she began to become unable to differentiate what was correct and what was not, further leading her to have the inability to distinguish right from wrong. She had no one to turn to and helping her develop is a crucial stage, according to Kernberg. In conclusion, Otto Kernberg’s theory of the two crucial developmental factors in childhood and the consequences of failing such tasks, appear to be appropriate theories behind the onset of Susanna’s borderline personality disorder. Although this theory has its limitations because it is not always necessary that this particular disorder is developed in early childhood. There are various theories looking into the biological factors that could predetermine whether someone will develop this disorder. In addition, there is always the question as to why Susanna was able to overcome this disorder, and why others cannot, thus further brining into play genetics/biology. All in all, the mind is endless and everyday people are coming up with new theories and reasons behind why people are who they are and do what they do.

Estimating a demand function for Fruit and Vegetables The WritePass Journal

Estimating a demand function for Fruit and Vegetables Introduction Estimating a demand function for Fruit and Vegetables IntroductionLiterature ReviewFunctional FormChoosing the Functional FormTesting individual parametersFurther TestsHomogeneitySlutskyInterpretation Of The Preferred ModelRegression equation for the preferred modelInterpretation of ElasticitiesConclusionReferencesRelated Introduction In this project I will examine the quarterly data set for FTVG20 from Ruritania between 1981 and 2010. I will find a functional form which best fits the data and then test for insignificant variables, structural breaks, seasonality and homogeneity. I will use Slutsky’s equation to calculate the income and substitution effects and then interpret the model. The social, economic, geographic and economic characteristics of Ruritania are not known. The data set shows that the quantity demanded of fruit and vegetables (QFTVG20) is dependent on the following variables: Table 1 Variable Explanation PMTFH Price of meat and fish PFTVG Price of fruit and vegetables PTEA Price of tea PCOFF Price of coffee PBEER Price of beer PWINE Price of wine PLEIS Price of leisure PTRAV Price of travel PALLOTH Price of all other goods INCOME Income Literature Review Ruel, Minot and Smith use household expenditure surveys in 10 Sub-Saharan African countries and used a Working-Lessor functional form to find that the main determinants of demand are per capita expenditure, household size, households headed by a female, education and location (urban vs rural). A study by Seale considered the effect of price and income on the demand for different food categories. They found that the food budget share of fruit and vegetable consumption is 10-25%, which is much higher than that of Ruritania. They calculated the expenditure elasticity of fruit and vegetables for low income countries (LICs) to be 0.636, middle-income countries (MICs) 0.514 and high-income countries (HICs) 0.281. The Frisch own-price elasticity of demand was -0.514 in LICs, -0.416 in MICs and -0.227 in HICs. There have been several studies considering non-economic factors that contribute to the demand for fruit and vegetables. A study by Nayga found that demand depends on socio-demographic factors such as location, age, family structure, ethnicity, children and education, whilst Pollard, Kirk and Cade find social desirability, habits, sensory appeal, convenience and advertising to be explanatory variables. Block’s research in Indonesia finds that mothers with nutritional knowledge spend a greater proportion of their food budget on foods rich in nutrients and minerals, such as fruit and vegetables. Functional Form Studenmund says that ‘choice of a functional form is a vital part of the specification of that equation.’ He goes on to mention that the use of Ordinary Least Squares means that the equation should be linear in the parameters rather than variables. In determining a demand function for fruit and vegetables I will consider the following functional forms: Table 2 Model Functional form Linear QFTVG20 = b0 + b1PMTFH + b2PFTVG + b3PTEA + b4PCOFF + b5PBEER + b6PWINE + b7PLEIS + b8PTRAV + b9PALLOTH + b10INCOME + et Log-Log ln(QFTVG20) = b0 + b1ln(PMTFH) + b2ln(PFTVG) + b3ln(PTEA) + b4ln(PCOFF) + b5ln(PBEER) + b6ln(PWINE) + b7ln(PLEIS) + b8ln(PTRAV) + b9ln(PALLOTH) + b10ln(INCOME) + et Log-Linear ln(QFTVG20) = b0 + b1PMTFH + b2PFTVG + b3PTEA + b4PCOFF + b5PBEER + b6PWINE + b7PLEIS + b8PTRAV + b9PALLOTH + b10INCOME + et Linear-Log QFTVG20 = b0 + b1ln(PMTFH) + b2ln(PFTVG) + b3ln(PTEA) + b4ln(PCOFF) + b5ln(PBEER) + b6ln(PWINE) + b7ln(PLEIS) + b8ln(PTRAV) + b9ln(PALLOTH) + b10ln(INCOME) + et In determining which functional form is preferable and which variables are significant, I will use the statistical tests detailed below: Table 3 Test It tests for Null Hypothesis Alternative Hypothesis F Test Significance of overall regression, individual and joint parameters H0: Test statistic critical valueModel is insignificant HA: Test statistic critical valueModel is significant R2 Proportion of variation in sample data explained by the regression n/a n/a Ramsey RESET (RR) Misspecification of the model and omitted variables H0: Test statistic critical valueModel is adequate and there is no misspecification HA: Test statistic critical valueModel is inadequate and can be improved Jarque-Bera (JB) Normality of the error term H0: Test statistic critical valueThe error term is normally distributed HA: Test statistic critical valueThe error term is not normally distributed Whites (WT) Heteroscedasticity H0: Test statistic critical valueThere is homoscedasticity HA: Test statistic critical valueThere is heteroscedasticity Breusch-Godfrey (BG) Higher order autocorrelation H0: Test statistic critical valueThere is no autocorrelation HA: Test statistic critical valueThere is autocorrelation Durbin-Watson (DW) First order autocorrelation H0: Test statistic upper boundThere is no autocorrelation HA: Test statistic lower boundThere is autocorrelation Additionally, when :Lower bound Test statistic Upper boundThe test for autocorrelation is inconclusive Changes In Demand Roberta Cook’s research has shown that per capita fruit and vegetables consumption (pounds) in the United States has increased by 12.4% from 1976-2006. Interestingly, in the same period there was a 28% reduction in the amount of citrus fruits consumed but growth was boosted by non-citrus fruits and vegetables. Cook suggests that the increase in demand is due to changes in lifestyle such as the large increase in the number of two-income households. This has led to a focus on cooking quickly therefore using more fresh produce. The scatter plot below shows the change in quantity demanded for fruit and vegetables in Ruritania over the time period 1981 to 2010. Quantity demanded was constant between 1980 and 1991 before increasing exponentially. The data does not follow the results of Cook’s research but I am able to predict that the data will fit either a log-log or log-linear model. Choosing the Functional Form From considering the four functional forms I obtained the following test results which are in line with my predictions: Table 4 Statistical Test Critical value at 5% significance level Linear Log-Log Log-Linear Linear-Log F Test 1.91 62.86* 157.9* 152* 52.81* R2 n/a 0.852233 0.935444 0.933081 0.828901 Ramsey RESET (RR) 3.92 31.982 0.0028123* 1.7429* 33.845 Jarque-Bera (JB) 5.99 41.046 0.25565* 0.093059* 31.152 Whites (WT) 1.64 2.0512 1.0271* 0.93069* 2.1058 Durbin-Watson (DW) DU= 1.898DL = 1.462 1.32 2.05* 2.03* 1.27 Breusch-Godfrey (BG) 11.07 19.306 7.8052* 2.6625* 23.394          Although the linear model and the linear-log model pass the F-test, only 85% and 83% of the variation in the data is explained by the respective regression model. Both models also fail the Breusch-Godfrey test, Durbin-Watson test, White’s Test, Jarque Bera test and the Ramsey RESET test. From these results I can conclude that the demand function for fruit and vegetables is not in linear or linear-log form. The log-log functional form and the log-linear functional form both explain around 93.5% of the data, which is relatively high. They both pass the T-test, Durbin-Watson test, White’s Test, Jarque Bera test, Breusch-Godfrey test and the Ramsey RESET test at 5%. Although they both pass the same tests, the log-log form passes the Ramsey RESET test at 0.0028123 whilst the log-linear form passes at 1.7429. Since the log-log model passes this more satisfactorily, the model will have a lower chance of misspecification. Additionally, a log-log model allows easier interpretation as elasticity is constant and equal to b at every point.   I will therefore choose the log-log functional form as the demand function for fruit and vegetables. For analysis, if an independent variable changes by 1% whilst other independent variables are held constant, then the dependant variable will change by the b value of the independent variable. Testing individual parameters Having identified the preferred functional form, I will now test the significance of individual parameters at a 5% significance level. Calculated using a 2-tailed T-test H0: b0 = 0 H1: b0 ≠ 0 Test statistic (t) = b0 b0  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   T(N-2)  Ã‚  Ã‚  Ã‚   where N = 120 so T(118) Se(b0) If   tc ≠¤ t ≠¤ tc   fail to reject the null hypothesis and b0 is not significant If  Ã‚   t ≠¥ tc   or t ≠¤ -tc reject the null hypothesis and b0 is significant Table 5 Parameter Coefficient Standard Error Test Statistic Intercept 3.35508 2.655 1.264 ln(PMTFH) -0.294682 0.1787 -1.649 ln(PFTVG) -0.576745 0.2888 -1.997* ln(PTEA) -0.524011 0.2585 -2.027* ln(PCOFF) 0.0219395 0.2631 0.083 ln(PBEER) 0.115732 0.2336 0.495 ln(PWINE) -0.191003 0.3137 -0.609 ln(PLEIS) 0.169363 0.1732 0.978 ln(PTRAV) 0.197812 0.1532 1.291 ln(PALLOTH) 2.31373 0.5330 4.341* ln(INCOME) -0.669523 0.2149 -3.116* The critical values for the t – test are +/- 1.98. From the t-test I have found that only four of the parameters are significant at a 5% significance level. They are: price of fruit and vegetables, price of tea, price of all other goods and the level of income. Since the price of meat and fish, intercept and price of travel are close to the critical value, I will keep these in the model. I will now run a second regression excluding the variables: price of coffee, price of beer, price of wine and price of leisure, and will use more t-tests to determine which of the parameters are significant. The results are shown in the table below. Table 6 Parameter Coefficient Standard Error Test Statistic Intercept 2.53817 1.678 1.513 ln(PMTFH) -0.145648 0.1367 -1.065 ln(PFTVG) -0.613409 0.2411 -2.544* ln(PTEA) -0.518472 0.1684 -3.079* ln(PTRAV) 0.125349 0.1283 0.977 ln(PALLOTH)    2.66028 0.2728 9.752* ln(INCOME)    -0.662872 0.1934 -3.427* Whilst the intercept is still insignificant, I will continue to include it in the model as removing it can create bias in the regression.The price of meat and fish and the price of travel are still insignificant in this regression so I will remove them from the model. The restricted regression model has the functional form: ln(QFTVG20) = b0 + b2ln(PFTVG) + b3ln(PTEA) + b9ln(PALLOTH) + b10ln(INCOME) + et To ensure the removal of the six parameters improves the model, I will run an F-test on the restricted model: F = (SSRR-SSUR)/r (SSUR)/n-k Where r = number of restrictions in the model, n = number of observations, k = number of parameters in the unrestricted model (including the intercept) The null hypothesis is: H0: b1 = b4 = b5 = b6 = b7 = b8 = 0 HA: Null hypothesis is untrue At 5% significance level, critical value F(6,109) = 2.18 F = (16.7224302 – 16.0433624)/6 (16.0433624)/120-11 F = 0.7689409526 2.18 Since the test statistic is less that the critical value, I fail to reject the null hypothesis so the variables are collectively insignificant and can now be removed. Further Tests I will consider whether there are structural breaks and seasonal changes. Structural Breaks I have chosen to graph QFTVG20 over time rather than lnQFTVG20 as there is a marked increase in fruit and vegetables consumption after 1998 which does not appear on the graph for lnFTVG. This increase in consumption may be due to a structural change. I will therefore split the regression model into two, and carry out a Chow Test, where: H0 = no structural change HA = structural change Chow Test n1 = number of observations in the first regression n2 = number of observations in the second regression k = number of parameters including the constant SSRR = RSS from original model SSUR = RSS from regression 1 + RSS from regression 2 Table 7 Time Period Number of observations Residual sum of squares 1981 – 1998 72 10.7905333 1999 – 2010 48 5.82816287 1981 2010 120 16.7224302 F = (16.7224302 – 10.7905333 – 5.82816287)/5  Ã‚   = 0.1373241701 (10.7905333 + 5.82816287)/(72 + 48 – 25) At a 5% significance level, the critical value is F(5,110) = 2.29 Since 0.137 2.29 I fail to reject the null hypothesis and can conclude that there is no structural change when tested at the 5% significance level. Seasonal Dummy Variables Since fruit and vegetables grow on a seasonal basis, it is prudent to include seasonal dummy variables to see whether the data follows seasonality. To do this, I will create four dummy variables, however, I will only include three dummy variables so as to avoid falling into the dummy variable trap. This avoids obtaining perfect multicollinearity. The three dummies refer to the difference between themselves and the omitted (reference) dummy variable. With the inclusion of three dummy variables, the model becomes: ln(QFTVG20) = b0 + b2ln(PFTVG) + b3ln(PTEA) + b9ln(PALLOTH) + b10ln(INCOME) + baD1 + bbD2 + bcD3 + et Table 8 Quarter Parameter Coefficient Estimated standard error Test statistic Significant at 5% (critical value +/- 1.98 1 D1 -0.0217025 0.09880 -0.220 No 2 D2 0.0908533 0.09877 0.920 No 3 D3 0.111825 0.09875 1.13 No This shows that the dummy variables are insignificant at 5% significance level. To remove the dummy variables, I run an F-test to check for the combined significance. H0: ba = bb = bc = 0 HA: H0 is not true F = (SSRR-SSUR)/r  Ã‚     Ã‚  ~ F(r, n-k) (SSUR)/n-k F = (16.7224302 – 16.3332741)/3   Ã‚  = 0.8890750414 16.3332741/(120 – 8) At 5% significance level, the critical value for F(3,112) is 2.68. Since 0.889 2.68 I fail to reject the null hypothesis. From this, it can be seen that at the 5% significance level, there is no evidence of seasonality. I can now remove the seasonal dummy variables. Homogeneity A demand function is homogenous if when both prices and income are doubled, the optimal quantities demanded do not change. H0: b2 + b3 + b9 + b10 = 0 HA: b2 + b3 + b9 + b10 ≠  0 If H0 is true, the equation can be rearranged as: b10 = b2 b3 b9 The regression model thus becomes: ln(QFTVG20) = b0 + b2ln(PFTVG) + b3ln(PTEA) + b9ln(PALLOTH) + (- b2 b3 b9)ln(INCOME) From logarithmic rules, the equation can be written as: Ln(QFTVG20)= b0 + b2ln(PFTVG/INCOME) + b3ln(PTEA/INCOME) + b9ln(PALLOTH/INCOME) F = (SSRR-SSUR)/r (SSUR)/n-k F = (17.3810772 16.7224302)/1   = 4.529509413 16.7224302/(120-5) The critical value for F(1,115) is 3.92. Since 4.5295 3.92 I reject the null hypothesis and conclude that demand is not homogenous, it exhibits heterogeneity. Laitinen has undertaken a study which concludes that the test of homogeneity is ‘seriously biased’ towards rejecting the null hypothesis. This leads me to believe that my result is acceptable and could be due to this, or the money illusion, where consumers mistake changes in nominal values to be changes in real values. Slutsky The Slutsky equation shows how a price change can lead to an income effect and a substitution effect. To calculate the price elasticity of demand I multiply through by P/Q and multiply the last term by I/I giving: This means: Price elasticity of demand = substitution effect – (income elasticity x fraction of income spent) From table 10 it can be seen that the income elasticity of demand is -0.470995 and price elasticity of demand of fruit and vegetables is -0.626791. The fraction of income spent on fruit and vegetables is 3%. Income effect = -0.470995 x 0.03 = -0.01412985 Substitution effect = -0.626791 -0.01412985 = -0.61266115 Since income elasticity of demand is negative, this means that fruit and vegetables are inferior goods. The substitution effect must always be negative. Interpretation Of The Preferred Model Having identified that there are no structural breaks in the model and that there is no evidence of seasonality, I can run a third regression with all the insignificant variables removed. The demand function is determined by: ln(QFTVG20) = b0 + b2ln(PFTVG) + b3ln(PTEA) + b9ln(PALLOTH) + b10ln(INCOME) + et The restricted regression model gives the following results to the aforementioned diagnostic tests: Table 9 Statistical Test Critical value at 5% significance level Log Log (restricted) Log Log (unrestricted) F Test 1.91 398.5* 157.9* R2 n/a 0.932711 0.935444 Ramsey RESET (RR) 3.92 0.26863* 0.0028123* Jarque-Bera (JB) 5.99 0.52542* 0.25565* Whites (WT) 1.64 0.36082* 1.0271* Durbin-Watson (DW) upper 1.898lower 1.462 2.01* 2.05* Breusch-Godfrey (BG) 11.07 4.8690* 7.8052* * Significant at 5% significance level The restricted log-log model passes every test carried out and passes the F test and White’s Test more satisfactorily than the unrestricted log-log model. I will now run further t-tests and consider whether the remaining variables are still significant. The results are shown in the table below. Table 10 Parameter Coefficient Standard Error Test Statistic Constant 0.814700 1.125 0.724 ln(PFTVG) -0.626791 0.2407 -2.604* ln(PTEA) -0.579563 0.1616 -3.586* ln(PALLOTH) 2.80783 0.2372 11.837* ln(INCOME) -0.470995 0.1353 -3.481* The table shows that all the remaining parameters (except the constant) are significant at a 5% significance level. Regression equation for the preferred model ln(QFTVG20) = 0.814700 0.626791ln(PFTVG) 0.579563ln(PTEA) + 2.80783ln(PALLOTH) 0.470995ln(INCOME) The equation suggests that fruit and vegetables are inferior goods as the coefficient for income is negative. This means that as income increases, the demand for fruit and vegetables decrease. Interpretation of Elasticities Table 11 Parameter Coefficient Interpretation Constant 0.814700 Autonomous LPFTVG -0.626791 Own price inelastic LPTEA -0.579563 Complement LPALLOTH 2.80783 Substitute LINCOME -0.470995 FTVG20 is income inelastic and is an inferior good. Constant – represents the value that is predicted for the dependant variable when all the independent variables are equal to zero. LPFTVG – A 1% increase in price will lead to a 0.626791% fall in quantity demand of fruit and vegetables. The average own-price elasticity for fresh fruit from 10 studies combined by Durham and Eales is -0.6 which is very close to the elasticity I have found. LPTEA – A 1% increase in price of tea will lead to a fall in demand of FTVG20 of 0.579563%. This could be due to fruit and tea being consumed together, for example, as part of breakfast. LPALLOTH – a 1 % increase in the price of all other goods will cause a 2.80783% increase in demand for fruit and vegetables LINCOME – A 1% increase in income means the demand for fruit and vegetables will fall by 0.470995%. From this I can conclude that fruit and vegetables are inferior goods. Purcell and Raunikar found that at lower incomes, fruit and vegetables are normal goods but at higher incomes they are inferior goods. They also found that green vegetables are inferior goods for all levels of income from 1958-62. Their results correspond to a recent study (2007) by Ruel, Minot and Smith, who found that in 10 (relatively poor) African countries the average income-elasticity of demand for fruit and vegetables was 0.766, i.e. fruit and vegetables are normal goods for low-income countries. Conclusion In this project I have estimated a demand function for fruit and vegetables (20) in Ruritania. Through using diagnostic tests and regression analysis I have found it to be a log-log model. I was able to remove insignificant variables leaving independent variables of price of fruit and vegetables, tea, all other goods and income. I then tested the data for seasonality and structural breaks and found no evidence of seasonality or structural breaks between 1981 and 2010. I found the data to be heterogeneous and justified this with reference to Laitinen’s research. Using Slutsky’s equation, I found that fruit and vegetables are inferior goods. To improve the model I could separate the demand for fruit and vegetables to see whether they both remain inferior goods. It would also be interesting to consider socioeconomic factors, such as those studied by Nayga. Additionally, since a large proportion of demand for fruit is made up of the demand for juice, it would useful to consider the demand of whole fruit and vegetables rather than that pressed into juice. These factors combined may improve the model so that a proportion of the remaining 6.6% of the data fits my regression model. References Ashworth, J. Durham Economics Lecture Notes Bath Lecture Notes: www.people.bath.ac.uk/bm232/EC50161/Dummy%20Variables.ppt Block, S., ‘Maternal Nutritional Knowledge and the Demand for Micronutrient Rich Foods: Evidence From Indonesia’ Cook, R. ‘U.S. Per Capita Fruit and Vegetables Consumption’ Cook, R. ‘Some Key Changes In U.S. Consumption Patterns’ Durham, C. Eales, J. ‘Demand Elasticities For Fresh Fruit and the Retail Level’ Greenwood, S. ‘Consumer Trends for the New Millennium Impact Fresh-cut Produce’ Han, T., Wahl, T. ‘China’s Rural Demand For Fruit and Vegetables’ Griffiths, W., Judge, G. ‘Undergraduate Economics’ Laitinen, K. ‘Why is demand homogeneity so often rejected?’ Nau, F. ‘Additional Notes On Regression Analysis’ Duke Fuqua Business School Nayga. ‘Determinants of US Household Expenditures on Fruit and Vegetables. A Note and Update.’ Nicholson, W. ‘Microeconomic Theory: Basic Principles and Extensions’ Purcell, J.C., Raunikar, R. ‘Quantity-Income Elasticities For Foods By Level of Income’ Journal of Farm Economics, December 1967 Ruel, M.T., Minot, N., Smith, L. ‘Patterns and Determinants of Fruit and Vegetable Consumption In Sub-Saharan Africa: A Multicountry Comparison’   International Food Policy Research Institute, 2005 Seale, J., Regmi, A., Bernstein, J. ‘International Evidence on Food Consumption Patterns’ Studenmund, A. ‘Using Econometrics’ Wang, X. Durham Economics Lecture Notes

Chandler's claims for superiority of managerial capitalism' are Essay

Chandler's claims for superiority of managerial capitalism' are specific to a particular time and place and cannot be generalized disscuss - Essay Example But in Scale and Scope the essential thesis is that between the 1850s and the 1880s the transportation and communications networks established the technological and organizational base for the exploitation Ð ¾f economies Ð ¾f scale and scope in the processes Ð ¾f production and distribution. (p. 58) The entrepreneurial response in distribution preceded that in production because innovation in distribution was primarily organizational, not technological. The reasons for the decline Ð ¾f commission agents and the growth Ð ¾f full line, full service wholesalers and mass retailers is not entirely clear from Chandlers analysis.[1] Many Ð ¾f the names Ð ¾f the mass retailers that emerged after the Civil War are still familiar today and include Macys, Lord & Taylor, Strawbridge & Clothier, John Wanamaker, Marshall Field, and Emporium. Montgomery Ward and Sears Roebuck came to dominate the rural market, relying heavily on mail-order operations. These houses built administrative systems to handle more transactions in a day than most traditional merchants could handle in a lifetime. The laying down Ð ¾f railroad and telegraph systems precipitated a wave Ð ¾f industrial innovation in Western Europe and the United States far more wide ranging than that which had occurred in Britain at the end Ð ¾f the eighteenth century. This wave has been properly termed by historians the Second Industrial Revolution. . . (p. 62) and involved systemic innovations in oil refining, steel, machinery, glass, artificial dyes, fibres, fertilizers, and food processing. But for the potential Ð ¾f these innovations to be realized, entrepreneurs had to make the three pronged investment. [2] In industries where only one or two pioneering enterprises made the three pronged investment, these enterprises quickly dominated the market. More Ð ¾ften, however, the modern industrial enterprise in the United States appeared after merger or acquisition (p.