General Happiness Equation Using Econometric Models Of Panel Data Methods Philosophy Essay

General Happiness Equation Using Econometric Models Of Panel Data Methods Philosophy Essay This study presents a general happiness equation using econometric models of panel data methods. The model tries to observe and estimate the relationship between income and happiness after controlling for other factors. With advanced methods, we also test for the presence of personality bias and whether it correlates with income. Finally, we provide some analysis of our estimation results and briefly discuss alternative approaches in the literature. Introduction Empirical research on human happiness have only recently in the last few decades received serious attention from both economists and non-economists. The lack of national-level representative survey data and the difficulty to apply econometric techniques were the stumbling blocks for further research in the past. With the establishments of national socio-economic panel surveys as well as technological advancements that gave birth to neat econometric software packages, the literature experienced a surge in the amount of research as well as the popularity drawn to these works. Things began to look brighter and brighter, and as a result came the birth of a new field called happiness economics. What happiness economists typically try to do is to estimate what they call happiness equations. Using econometric techniques, they could test for a causal link between income and happiness. After controlling for other factors that can cause happiness (eg. education, marital status, disability, unemployment etc.), early work which used simple cross sectional methods suggest a positive and statistically significant correlation. To run Ordinary Least Squares (OLS) regressions on cross sectional data sounds decent, but is in actual fact highly inadequate. What if happiness is also caused by another factor that is unobservable in the data, such as personality? Could it be that ones happiness strongly depends on who he is as a person? On face value, it seems plausible or at least interesting to suggest that peoples capacity to be happy vary from individual to individual. Perhaps some people are born extrovert and optimistic, and as a result tend to be happier than others even if they have less income than them. Then simple OLS will suffer from an omitted variable bias problem, which causes one or more of its classical assumptions to be violated and hence estimates to be biased. To solve this problem of unobserved heterogeneity bias, we can use panel data and propose a fixed effects model. We can run a pooled OLS regression on panel data, but it would still be susceptible to the omitted variable bias problem. Firstly, we can think of the personality variable as a time-constant effect. By exploiting the nature of panel data, which follows the same individual over time, we can eliminate this unobserved time-constant effect by doing some transformation on the data. The simplest way is to perform first-differencing. Namely, we take observations on an individual for two time periods and we calculate the differences. Then we run an OLS regression on these transformed values. In effect, we have removed all unobserved time-constant variables not only limited to personality. Maybe an individuals thumbprints or DNA may be correlated with happiness, we do not know for sure. But the elegance of first-differencing makes it sure that we remove all nuisance unobserved time -constant variables that disturb our primary goal. Through transforming the data in such a way that we are now dealing with relative rather than absolute values, we have also mitigated the problem of heterogeneous scaling in subjective responses. Every individual have their own perception on the happiness score. A score of 7 may be others score of 6, and so on. This would make interpersonal (cross-sectional) comparisons meaningless, and is part of the reason why in the past empirical work on this literature have been viewed with scepticism by many economists. By reasonably assuming that a persons metric or perception is time-invariant, this issue is dealt with in a fixed effects model. There are other advanced transformation techniques that uses data on multiple time periods. One technique performs a time-demeaning transformation on the data. Again, all unobserved time-constant variables will be eliminated. But for details presented later, OLS regression on these transformed values provides more efficient estimators than on the first-differenced values for our purposes. Estimators that result from this method are called fixed effects (FE) estimators. While the fixed effects model allows for arbitrary correlation between the explanatory variables and the unobserved time-constant effect, a random effects model explicitly assumes that there is no such correlation. Estimation on this model is typically done by transforming the data using a method of quasi-demeaning, and then a Generalised Least Squares (GLS) regression is run on the transformed values. The resulting estimators are called random effects (RE) estimators. How these techniques are performed as well as the intuition behind them is explained with technical detail in Section 3. Why we may want to use a random effects model over a fixed effects model is because we may believe that personality has no effect on any of the independent variables, including income. If this is true, then using FE estimators will result in relatively inefficient estimates than RE estimators. But intuitively, personality is likely to be correlated with the ability to make money, and thus income. Studies have shown that happy people tend to earn more in general (eg. see Lyubomirsky et al. 2005). If this were true, simple pooled OLS methods will lead to inaccurate estimates where the effect of income on happiness will be overstated or biased upwards. The fixed effects model allows for this correlation, and is thus more widely accepted in the literature to fit the data better. Lastly, can we test for this assumption? Is the unobserved time-constant variable correlated with any of the explanatory variables? Which model fits the data better? We can do what is called a Hausman test, which tests for statistically significant differences in the coefficients on the time-varying explanatory variables between fixed effects and random effects. The intuition and decision rule on which model to accept will be described in detail later. For comparison, we present the results for pooled OLS, FE and RE estimations together. Although this approach is one of the most popular one in the literature when it comes to estimating happiness equations, there are other alternatives ways. Powdthavee (2009)s work was quite similar to this study, but in addition he used a method of instrumental variables (IV) which involved using another variable to instrument for income. Happiness equations may suffer from the problem of simultaneity, whereby the causal link between happiness and income runs both ways. To address this, he used data on the proportion of household members whose payslip has been shown to the interviewer as the instrument for income. He reasoned that household income is bound to be measured more accurately with a higher proportion of household members showing their payslip. With this direct correlation, as well as reasonably assuming that this proportion has little correlation with happiness, it would allow for an estimation based on an exogenous income effect. Besides his work, other work (eg. Frijters et al. 2004, Gardner Oswald 2007) has attempted to address the endogeneity effect more directly using different types of exogeneous income effects. Another line of thinking interprets the happiness scores as ordinal rather than cardinal. Here, simple OLS estimation would be inadequate. One solution to this would be to use ordered latent response models. Winkelmann (2004) was one example of this in which he performed an ordered probit regression with multiple random effects on subjective well-being data in Germany. To date, there is no statistical software package that could implement a fixed effects ordered probit regression. An alternative to this would be to convert the happiness scoring scale into a (0,1) dummy, thereby roughly cutting the sample into half, and then estimate by conditional logit regression, as attempted by Winkelmann Winkelmann (1998) and later Powdthavee (2009). However, their work combined with Ferrer-i-Carbonell Frijters (2004) seems to suggest that it makes no difference qualitatively whether to assume cardinality or ordinality on the happiness scores. There is no one perfect model that can address all the problems. We believe that the FE RE approach, not only simple, is also elegant and easier to understand. Coefficient estimates can be interpreted easily and the approach also addresses the most important of problems in the estimation, especially that of unobserved heterogeneity bias. Although bias in happiness equations come from many different sources, it is our belief that this source is one of the major ones and is easily removed using simple techniques. Data We use data from the British Household Panel Survey (BHPS), a widely used data source for empirical studies in the UK. The BHPS surveys a nationally representative sample of the UK population aged 16 and above. The survey interviews both individual respondents and households as a whole every year in waves since 1991. To date has been 18 waves in total. Survey questions are comprehensive and they include income, marital status, employment status, health, opinions on social attitudes and so on. The data set is also an unbalanced panel; there is entry into and exit from the panel. Data can be obtained through the UK Data Archive website. Our dependent variable, happiness, uses data on the question of individual life satisfaction. From Wave 6 onwards, the survey included a question which asks respondents to rate how satisfied they are with their lives from a rating of 1 (very dissatisfied) to 7 (very satisfied). This question is strategically located at the end of the survey after respondents had been asked about their household and individual responses in order to avoid any framing effects of a particular event dominating responses to the LS question. For ease of representation, we now refer to happiness as life satisfaction (LS). For income, we use data on the total household net income, deflated by consumer price index and equivalised using the Modified-OECD equivalence scale. The initial value is worked out through responses in the Household Finance section which includes question on sources and amount of incomes received in a year. Inflation would seriously distort our estimation and so is accounted for. Equivalisation involves dividing the total household net income by a value worked out according to an equivalence scale. For example, a household with two adults would have their total household income divided by 1.5. The more adults are there in the household, the higher this value would be. Children would add relatively less to the value than adults. This method would provide an equivalent household income variable, which would account for the fact that different household sizes enjoy different standards of living on the same level of income per household member. Due to economies of scale in consumption, a household with three adults would typically have needs more than triple than that of a single member household. Equivalisation would make comparisons between households a lot fairer or more accurate. Lastly, we use the log form. We use data on the years 2002-2006 (Waves 12-16). There are in total [unconfirmed] respondents with [unconfirmed] observations that have nonmissing information on LS. Descriptive statistics are provided in the Appendix section. Econometric Method We denote as our dependent variable. We have explanatory (binary and non-binary) variables which includes income, employment status, marital status and so on. There are respondents , where . A simple pooled cross-section model would look like (1) where the first subscript denotes the cross-sectional units, the second denotes the time period and the third denotes the explanatory variables. As mentioned earlier, this simple model does not address the issue of unobserved heterogeneity bias. To see why, we can view the unobserved variables affecting the dependent variable, or the error, as consisting of two parts; a time-constant (the heterogeneity bias) and time-varying component. (2) Thus if we regress by simple pooled OLS, we obtain (3) Here one of the key assumptions for OLS estimation to be unbiased has been violated, since the error term is correlated with . The above model is called a fixed effects model. The variable captures all unobserved, time-constant factors that affect . In our analysis, personality falls under this variable. is the idiosyncratic error that represents other unobserved factors that change over time and affect . The simplest method to eliminate is as follows. First, we write the equation for two years as By subtracting the equation on the first period from the second, we obtain (4) where denotes the change from to . In effect, we have transformed the model in such a way that we are only dealing with relative rather than absolute values. This technique is called first-differencing. We can then proceed to estimate the equation at (4) via OLS. Essentially, the error term here is no longer correlated with , as the time-constant effect has been differenced away or minused out of the equation. However this is only the case if and only if the strict exogeneity assumption holds. This assumption requires that the idiosyncratic error at each time, is uncorrelated with the explanatory variables in every time period. If this holds, then OLS estimation will be unbiased. A more popular transformation technique in the literature is the time-demeaning method. Again, we begin from equation (3), and using (2) we rewrite it as (5) Then we perform the following transformation. First, we average (5) over time, giving (6) where and so on. Next, we subtract (6) from (5) for every time period, giving or (7) where is the time-demeaned value of LS, and so on. Essentially again, has disappeared from the equation. With these new, transformed values, we can then use standard OLS estimation. Conditions for unbiasedness remain the same as in the first-differencing method, including the strict exogeneity assumption. As mentioned earlier, the resulting estimators are called FE estimators. In our analysis, we decided to use FE over first-differencing. It is important to state why we do this. The reasoning is as follows. When , their estimation is fundamentally the same. When , both estimations are still unbiased (and in fact consistent), but they differ in terms of relative efficiency. The crucial point to note here is the degree of serial correlation between the idiosyncratic errors, . When there is no serial correlation, FE is more efficient than first-differencing. We have confidence that we have included sufficient controls for other factors in our happiness equation, so that whatever that is left in the error term should be minimal and serially uncorrelated. In addition, FE is safer in the sense that if the strict exogeneity assumption is somehow violated, the bias tends to zero at the rate whereas the bias in first-differencing does not depend on T. With multiple time periods, FE can exploit this fact and be better than first-differencing. Another reason why FE i s more popular is that it is easier to implement in standard statistical software packages, and is even more so when we have an unbalanced panel. With multiple time periods, the first-differencing transformation requires more computation and is less elegant overall than FE. As mentioned earlier, if is uncorrelated with each explanatory variable in every time period, the transformation in FE will lead to inefficient estimators. We can use a random effects model to address this. We begin from (5), writing it as (8) with an intercept explicitly included. This is so that, without loss of generality, we can make the assumption that has zero mean. The other fundamental assumption is that is uncorrelated with each explanatory variable at every time period, or (9) With (9), the equation at (8) is called a random effects model. If the assumption at (9) holds, even simple cross section OLS estimation will provide us with consistent results. With multiple time periods, pooled OLS can be even better and also still achieve consistency. However, because is in the composite error from (2), then the are serially correlated across time. The correlation between two time periods will be (10) where and . This correlation can be quite substantial, and thus causes standard errors in pooled OLS estimation to be incorrect. To solve this problem, we can use the method of Generalized Least Squares (GLS). First, we transform the data in a way that eliminates serial correlation in the errors. We define a constant as . (11) Then in a similar way to the FE transformation, we quasi-demean the data for each variable, or, (12) where is the quasi-demeaned value of LS, and so on. takes a value between zero and one. As mentioned earlier, estimations on these values produce RE estimators. This transformation basically subtracts a fraction of the time average. That fraction, from (11), depends on , and . We can see here that FE and pooled OLS are in fact a special cases of RE; in FE, and in pooled OLS, . In a way, measures how much of the unobserved effect is kept in the error term. Now that the errors are serially uncorrelated, we can proceed by feasible GLS estimation. This will give us consistent estimators with large N and fixed T, which is suitable for our data set. To summarize, if we believe that personality is an unobserved heterogeneous factor affecting LS then pooled OLS will give us biased estimators. To address this issue, we can use a fixed effects or random effects model. In the former case, we prefer the FE transformation over first-differencing. The choice between FE and RE depends on whether this factor is also correlated with one of our explanatory variables. We think that personality may be correlated with income. If so, then we use the transformation in FE to completely remove it. If this factor is uncorrelated with all explanatory variables at all time periods, then we do a transformation in RE to partially remove it as a complete removal will lead to inefficient estimates. In this scenario, RE is still better or more efficient than pooled OLS because of the serial correlation problem. An additional characteristic that RE has over FE is that RE allows for time-constant explanatory variables in the regression equation. Remember in FE that every variable is time-demeaned; so variables like gender (does not vary) as well as age (varies very little) will not provide us with useful information. In RE, these variables are only quasi-demeaned, so we can still include these variables in our estimation. Estimation Results We produce results for estimation by pooled OLS, FE and RE. Besides our key explanatory income variable, other control variables are included in the regression. They are gender, age, marital status,

Case Study Nissin Essay

I. Synopsis (summary of the case including theoretical context of the problem) II. 2nd part A. Time Context (the time when the problem was noted) B. Case Viewpoint (indentification of the real owner of the problem) C. Statement of the Problem (in a gap or difficulty that deter or prevents the company from achieving its objectives D. Statement of the Objectives (ends or results that you would like to accomplish) E. Statement of the Areas of Consideration (Facts of the case in outline form) F. Statement of the Alternative Courses of Action (A choice between two or more possible solution to solve the problem.) (minimum acad requirements should be two with advantages and disadvantages for each alternative). G. Statement of Conclusion (the Final decision) H. Statement of Recommendation (Plans of action presented in Tabular form indicating activities, assigned person or department and target date of completion) Case Study I. Synopsis The Study is about the company, Monde Nissin where all of the heads of the department comprising the said company were gathered to reiview and discuss the year’s performance for its instant noodle line. The said meeting was initiated by the demand analyst of the said company. He/She reported that their sales growth over that past year has been a single digit from years 2006 to 2008, with respect to the previous years which was year 2003 to year 2005. They discussed the problem as to how did their sales growth decline, some of the department heads reported that the decline was due to increase in price of their product. The increase in product price was due to the increase of imported wheat, the price of dollar is still unstable, and the addition of the brand new warehouse. They also said the population in the Philippines  decreased consumption of instant noodles and other staples due to increase in prices of goods. Some also said that the population in the country today is more concerned with their health and wellness, some people are also environmentally conscious and some people also want products that has variability. With these constraints, the department heads concluded that they should produce a new product which features health and wellness but the introduction of a new product in the market will require high cost, because they will recalibrate their facilities because their facilities are not flexible therefore incurring high cost. II. A. Time Context The problem was noted in year 2008. B. Case Viewpoint The main problem of the Case Study is why the sales growth of the Company declined and how to make their sales growth rise again. C. Statement of the Problem The company is faced with many problems or challenges, these are: a. The imported wheat price is rising b. The dollar remains to be unstable c. The costs they are incurring because of the new warehouse d. The decreasing consumption of the public with their product due to increase of the prices of goods e. The increasing demand for products that has Health and Wellness benefits, products that are good for the environment and products that has variability D. Statement of the Objectives f. To help the company increase its sales growth E. Statement of the Areas of Consideration g. The company has to consider the increase in price of imported wheat h. The dollar that is still unstable i. The decrease of the public in buying their product due to increase of price of goods j. The increasing demand for products that has Health and Wellness benefits, products that are good for the environment and products that has variability.

Biography of Francesco Clemente, Neo-Expressionist Artist

Francesco Clemente (born March 23, 1952) is an Italian artist most closely associated with the Neo-Expressionist movement. His work reacts against Conceptual and Minimalist Art by returning to figurative ideas and techniques from the past. His work is influenced by other cultures, most strongly that of India, and he frequently collaborates with artists and filmmakers. Fast Facts: Francesco Clemente Occupation: ArtistKnown For: Key figure in the Neo-Expressionist artistic movement Born: March 23, 1952 in Naples, ItalyEducation: University of RomeSelected Works: Name (1983), Alba (1997), The Sopranos (2008)Notable Quote: When I look at a drawing of a person, I look at that person as living. Early Life and Career Born into an aristocratic family, Francesco Clemente grew up in Naples, Italy. He studied architecture at the University of Rome. He has spoken about a philosophical crisis that he experienced as a student. He felt deeply the fact that all people, including himself, would eventually die, and he believed he had no specific separate identity or consciousness from others. He said, I believe there is such a thing as an imagination shared by the different contemplative traditions. Self-Portrait (1991). Sally Larson (CC BY-SA 3.0) Clementes first solo exhibition took place in Rome in 1971. His works explored the concept of identity. He studied with Italian conceptual artist Alighiero Boetti and met American artist Cy Twombly, who lived in Italy. Boetti and Clemente traveled to India in 1973. There, Clemente encountered the Indian Buddhist concept of anatman, or lack of self, which became a central thematic element in his work. He opened a studio in Madras, India, and created his 1981 series of gouache paintings titled Francesco Clemente Pinxit while working with painters in the Indian states of Orissa and Jaipur. In 1982, Clemente moved to New York City, where he quickly became a fixture of the art scene. Since then, he has lived primarily in three different cities: Naples, Italy; Varanasi, India; and New York City. Neo-Expressionism Francesco Clemente became part of what was known as the Transavanguardi or Transavantgarde movement among artists in Italy. In the U.S., the movement is considered part of the broader Neo-Expressionist movement. It is a sharp reaction to Conceptual and Minimalist Art. The Neo-Expressionists returned to figurative art, symbolism, and an exploration of emotions in their works. Neo-Expressionism emerged in the late 1970s and began to dominate the art market for the first half of the 1980s. The movement received sharp criticism for the omission or marginalization of female artists in favor of all-male shows. Clemente was at the center of sometimes-heated discussions about Neo-Expressionism and its authenticity. With its relative lack of political content, some observers criticized the movement for being inherently conservative and market-focused instead of concerned with the creation of art itself. Clemente responded that he didnt feel it was necessary to tamper with reality in his work and said that he preferred to present the world as it truly exists. One of Clementes best-known Neo-Expressionist works is his 1983 piece entitled Name. The vividly-colored painting depicts a man, who looks similar to Clemente, staring out at the viewer. There are small versions of the man inside his ear, eye sockets, and his mouth. Another significant portrait in Clementes career is his 1997 painting titled Alba, featuring the artists wife. She is a frequent subject for his paintings. In the portrait, she is reclining in a slightly uncomfortable pose. The image feels like it is squeezed into the frame, giving the viewer a claustrophobic sensation. Many of Clementes portraits have a similarly distorted, almost uncomfortable style. Collaborations In the 1980s, Francesco Clemente began a series of collaborations with other artists, poets, and filmmakers. One of the first of those was a 1983 project with Andy Warhol and Jean-Michel Basquiat. The artists each began their own individual paintings, then swapped so that the next artist could add their own content. The result was a series of canvases full of dramatic flourishes that are instantly recognizable as belonging to an individual artist; these flourishes collide into and overlap each other. In 1983, Clemente began his first project with poet Allen Ginsberg. One of their three collaborative works is the book White Shroud, with illustrations by Francesco Clemente. In the 1990s, Clemente worked with the poet Robert Creeley on a series of books. Another joint project was Clementes 2008 work with New Yorks Metropolitan Opera. He first worked with the renowned opera company when he created a large banner for the Philip Glass opera Satyagraha. Later in the year, Clemente created a series of paintings called The Sopranos: portraits of the divas featured in the Metropolitan Operas 2008-2009 season. They were created over a four-month period and featured the singers in their stage roles. Film and TV Appearances Francesco Clemente began his association with the film industry in 1997, when he made a cameo appearance as a hypnotherapist in Good Will Hunting. In 1998, Clemente created approximately two hundred paintings for director Alfonso Cuarons adaptation of Charles Dickens classic Great Expectations. In 2016, Clemente appeared in a film by independent writer, director, and actor Adam Green titled Adam Greens Aladdin. In the reworking of the Arabian Nights story, Aladdins dysfunctional family lives in an average American city ruled by a corrupt sultan. Francesco Clemente appears as the genie, Mustafa. Clemente is a frequent subject of TV interviews. One of the best-known is an extended interview with Charlie Rose in 2008 from his self-titled PBS show. Legacy and Influence Clementes work often defies specific characterization. Although he uses figural techniques associated with Neo-Expressionism, his pieces are not always intensely emotional in content. He eagerly embraces inspiration from artistic traditions other than his own. He encourages other artists to experiment boldly with media and techniques that are new to them. Travels, everyday life, and study in India heavily influence Francesco Clementes work. He has avidly studied Indian spiritual texts, and he began studying the Sanskrit language in New York in 1981. In 1995, he took a trip to Mount Abu in the Himalayas and painted a watercolor a day for fifty-one consecutive days. The Solomon R. Guggenheim Museum in New York City organized a major retrospective of Clementes work in 2000. Another retrospective at the Irish Museum of Modern Art in Dublin followed in 2004. Source Dennison, Lisa. Clemente. Guggenheim Museum Publications, 2000.

The Use Of Ibe Provided Services For Special Education

Ibe provided services outside of school, such as at their own house, hospital, or institution (Gibb Dyches, 2016, p. 82). Just like different placements, there are different services that can be provided for those individuals who have an IEP. These services are provided by special educators that are licensed along with para-educators under a licensed educator’s supervision. The IEP team must figure out which services will help the specific individual achieve their measureable annual goal. Some services might include services for special education, supplementary services and aids, program modifications, supports, and related services (Gibb Dyches, 2016, p. 92). V. Student Participation with Nondisabled Under the IDEA, special education is supposed to be conducted in the LRE. Because of this, all students with disabilities are expected to participate in a general classroom and participate in the same activities as their nondisabled classmates. By doing this, it ensures that these individuals are not being excluded. Having these students participate in a general classroom and participate in the same activities as their classmates also provides the individuals with the added benefits that these opportunities provide along with their teachers’ content. The activities that the individuals with special needs are expected to participate in include both extracurricular and nonacademic activities. If for some reason the IEP team determines that an individual it notShow MoreRelatedPreparing Regular Education Teachers For Address The Diverse Needs Of Children With Special Needs2409 Words   |  10 PagesPreparing regular education teacher s to address the diverse needs of children with special needs in inclusive set up. Rationale Sri Lanka has accepted inclusive education as a policy which shows different education reforms. Education reforms in 1997 supported the philosophy and practice of Inclusive Education. According to the concept of inclusive education, the responsibility of addressing the needs of all children has to be taken by regular education teachers. But the issues of addressing theRead MoreIgbo Dictionary129408 Words   |  518 Pagesmanuscript. 5. Has limited scientific names and technical vocabulary. The most striking feature of Igwe is that because it includes words from many dialects, it symbolises the aspiration and nasalisation that are distinctive for some Igbo dialects and thus uses a very wide array of consonant symbols. Both dictionaries have many more headwords than the present manuscript because the Williamson dictionary tends to include all derived forms under a single headword whereas Echeruo and Igwe list derived formsRead MoreSmart Home Technology10920 Words   |  44 PagesBowe r and Clarissa Martin. Institute for a Broadband-Enabled Society Level 4, Building 193 The University of Melbourne, Victoria 3010, Australia ISBN 978 0 7340 4781 6  © The University of Melbourne 2012 This work is copyright. Apart from any use as permitted under the Copyright Act 1968, no part may be produced by any process without prior written permission from the University of Melbourne. 2 Executive Summary Australia, along with many parts of the world, has an ageing populationRead MoreThe Ethiopian Financial Sector Reform29124 Words   |  117 Pagesin Economics OCTOBER, 2009 i Acknowledgement The author of this thesis acknowledges the support and technical assistance from many sources. I am grateful to my thesis advisor, Professor Teshome Mulat, who has read the manuscript and provided valuable comments. My thanks also goes to Ato Kagnew Wolde, Ato Tegenu Hailu, Ato Atnafu G/Meskel and Staff of Commercial Bank of Ethiopia; without whose encouragement and support, this paper would have not been completed timely. I am also indebtedRead MoreValue Creation and Enhancement: Back to the Future22107 Words   |  89 Pagessection, still holds, it is the forecasts of earnings, net capital expenditures and working capital that will yield these cash flows. One of the most significant inputs into any valuation is the expected growth rate in operating income. While one could use past growth or consider analyst forecasts to make this 1 In practical terms, this firm will have to raise external financing, either from debt or equity or both, to cover the excess reinvestment. 5 6 estimate, the fundamentals that driveRead MoreMandinka Empire21578 Words   |  87 PagesHistory in Africa, Volume 32, 2005, pp. 321-369 (Article) Published by African Studies Association DOI: 10.1353/hia.2005.0021 For additional information about this article http://muse.jhu.edu/journals/hia/summary/v032/32.1schaffer.html Access Provided by your local institution at 03/10/13 1:43PM GMT BOUND TO AFRICA: THE MANDINKA LEGACY IN THE NEW WORLD MATT SCHAFFER I I offer here a theory of â€Å"cultural convergence,† as a corollary to Darwin’s natural selection, regarding how slave Creoles