Kmarts Downfall Essay Example | Topics and Well Written Essays - 750 words

Kmarts Downfall - Essay Example The competition model established by Porter is an analytical tool for studying industry behavior and corporate strategies. It is derived from industrial organization economics and includes five forces. These forces, in turn, determine the level of competition, and thus that profitability of a market. These five forces comprise of substitutes, competitors, new entering firms, bargaining power of suppliers and customers. The facts of this case study reveal that Kmart was facing intense competition from Wal-mart and Target. Wal-Mart initiated the movement of every day low prices, which was a more worthy substitute for products in Kmart. In addition, Wal-Mart utilized information technology to keep a record of sales in all of their stores and for ordering stocks of fast moving items. Wal-mart heavily invested in information technology by installing new registers with barcode scanners in every store during the 1970s and early 1980s, which fed the sales data into the back-end computers. This information, in turn, assisted them in planning future strategies, deciding which products reap more profit. Thus, they gained a competitive advantage. By 1983, Wal-Mart was able to receive goods for only two cents whereas Kmart had to pay five cents per dollar for getting goods to stores. This meant that Wal-Mart was in a position to sell products at a price three percent lesser in contrast to Kmart. Then, another c ompetitor Target began a new campaign in which they depicted themselves as a low-cost source of quality and style mart. They focused on merchandising. These attractive schemes took away Kmart’s market share. This demonstrates that Kmart was under intense pressure of substitutes, competitors, and lost customers. In 1987, Kmart undertook investments worth 1 million to modernize their systems.  

Sosial anthropology Essay Example | Topics and Well Written Essays - 4250 words

Sosial anthropology - Essay Example Led by Wired, dot com boosters were claiming that the Net was creating the free market only found up to then in the neo classical economics text books. Inspired by post modernist gurus, new media activists were convinced that humanity would liberate itself from corporate control by escaping into cyber space (Barbrook, 2005) Jumping forward from the 1990s to the year 2008 the above quote seems true to both sides. On one hand we have seen a huge shift in the way that people consume with more and more people choosing to sell and buy over the world wide web, vast amounts of profits generated for companies like Google who enjoyed in the â€Å"1990s an estimated growth of 100% per year.† (Coffman, K. G; Odlyzko, A. M. 1998) The internet market has continued to grow substantially year on year as technologies change and more and more parts of the world come on line. On the other hand, an extraordinary phenomena is taking place throughout the world which is almost totally contradictory to the doctrine of the free market and capitalism, and that is the culture of exchange and gift economy that is developing and becoming more possible through the use of the internet and social software. A gift economy can be defined as a means in which goods and services are exchanged without there being an agreement of a reciprocal return to the giver. Most anthropological academics, however would agree, although there is no implicit need for a return gift there is often an expectation of reciprocity. Marcel Mauss in his classic work "The Gift1" argues that gifts are never free and that the act of giving creates a social bond that requires obligation to reciprocate on part of the recipient. This reciprocity however is not however solely between two people but between group, tribes and communities. Further studies challenge the idea that reciprocity is an inevitable outcome of exchange and focus on the classification of different types of reciprocity. For the purpose of

Effects of Free Trade Agreements on Trade and Growth in US

Effects of Free Trade Agreements on Trade and Growth in US The Effects of Free Trade Agreements on Trade and Growth in American Countries: Evidence from the Gravity Model Approach Trade as a driver of growth and development is a concept that has been addressed from different perspectives or approaches for scholars and policy-makers. However, an integrative path was sealed with the creation of the World Trade Organization as the main tool to promote a more accessible and clear way to commerce between nations and was further strengthened by bilateral and multilateral FTAs, which continue developing and growing. In the current political scenario, the discussion between supporters of globalisation and detractors provides a compelling framework to study the real effects that Free Trade Agreements cause on the economic performance. While the first group affirms that FTAs enhances the markets and therefore, the economic growth and employment, the second group argues that the global market is damaging the small domestic economies. The present paper covers the increasing effects on trade that are expected by countries that engage in Free Trade Agreements, including bilateral or multilateral ones within American countries, in the context of the three central multilateral trade agreements in the continent (NAFTA, MERCOSUR, and The Pacific Alliance) and other relevant bilateral agreements. The main question to be addressed is whether the positive effects predicted by economic theory on trade when countries eliminate fares and other barriers to trade as part of an agreement effectively happen in the current context of the Americas. The hypothesis is that the implementation of Free Trade Agreements has a positive and significant impact on the trade flows between the American countries. Section 2 includes the theoretical framework behind the relation between trade and FTAs, Section 3 presents the model specification, Section 4 shows the estimation of the model and the econometric tests, the limitations of the theoret ical framework and the model specification are discussed in Section 5, and Section 6 concludes. The Gravity Model has its origins on Location Theory, as it was the main model to include the effects of distance on traded quantities. Isard and Peck (1954) acknowledged the importance of considering distance as a variable in trade analysis establishing the ground from which others such as Tinbergen (1962) and Pà ¶yhà ¶nen (1963) would build the Gravity theory to explain trade flows between countries, conducting the first econometric studies based on the gravity equation. The Gravity Model has proven to be extremely successful in ordering the observed variations in economic transactions and movement of factors. It is also distinguished for its representation of economic interaction in a multi-country world, where the distribution of goods and factors is driven by gravity forces that are conditional to the size of economic activities at each location (Anderson, 2010). In this way, trade between countries is positively related to countries sizes and negatively related to distance. Moreover, as a widely used analytical framework, the model can incorporate adjusting variables such as FTA to indicate the existence of Free Trade Agreements between the objective countries (Yang and Martinez-Zarzozo, 2014). Tinbergen (1962) suggests an economically insignificant average treatment effects of FTAs. However, numerous studies, such as Frankel (1997) on MERCOSUR, find a significant positive effect in line with the expected results. These contradictory outcomes emphasise the fragility of the estimation of FTAs treatment effects and are a clear signal that robustness should be tested. One of the central issues to be explored is the exogeneity of FTAs, since the presence of them, if endogenous, can provide seriously biased results. Baier and Bergstrand (2007) provide several important conclusions to be taken into consideration. They observe that using the standard cross-section gravity equation provides a downwards-biased result. Secondly, attributed to this bias, traditional FTAs effects are underestimated by around 75%-85%. Lastly, the authors demonstrate that the best estimates of the effect of FTAs on bilateral trade are achieved from a theoretically framed gravity equation using panel data with bilateral, country and time fixed effects or differenced panel data with country and time effects. As it is suggested by extensive literature, trade flows are better explained by the Gravity Model, which propose the Newtons Gravity concept to explain bilateral trade as an attraction force, influenced positively by the size of the economies involved in trading and negatively with the costs of transaction (Tinbergen, 1962 and PoÃÅ'ˆyhoÃÅ'ˆnen, 1963). As proxy variables of the size of the economy, the model uses GDP and population of both countries; and Distance between the countries as a proxy for transaction costs. Following the Newtons Gravity Equation, the model estimates: Where is the trade flows between a specific country pair, in other words, is the sum of exports from country to country plus exports from country to country . is the gross domestic product in country , is the population of country , is the GDP in country , is the population of country , and is the distance between the capital cities (as major economic centres) of countries and . To avoid spurious effects due to inflation and currency exchange rates, the variables , and are measured in 2010 constant US dollars. Moreover, recent literature has implemented an augmented version of the gravity model to evaluate other variables of interest related to trade flows. In this way, besides to include more time-sections to the analysis, a dummy for implemented FTAs is added to the explanatory variables, taking a value of 1 if there exist a fully in force agreement and 0 otherwise. For the purpose of this paper, an FTA is considered if it establishes 100% free trade, because many cooperation agreements in the Americas consider only certain sectors for free trade, and these are not the focus of this research. Including the dummy variable, transforming the gravity model using Logarithmic function, to accomplish the linearity-in-parameters assumption, and including the time sections, the model to estimate is: However, it is strongly likely that this model has problems of endogeneity and thus, the estimators are biased due to sampling selection and omitted variable bias, how it is suggested by the literature. However, the logic behind this biasedness is different to the literature review. For Baier and Bergstrand (2007), the parameter of interest would have a negative bias because countries will be more interested in implementing an FTA when the benefits of it are greater. Therefore, the authors conclude that a possible omitted variable would be Tariff Barriers. In this scenario, Tariff Barriers are negatively correlated with trade and positively with FTA, generating a negative bias. This is not the case for America. On the contrary, progressive lower barriers and an improving in the diplomatic relationships have finally pushed the creation of Free Trade areas and agreements. That is why, in this case, we suggest that the bias for the sample would be positive, since the possible omitted variables would be lower barriers and good diplomatic relationships, affecting the FTAs and the trade itself positively. To solve this problem, the literature suggests the use of Fixed Effects Panel Data strategy because this model can control for country-specific and invariant-in-time unobservable variables. Therefore, the model to estimate is: Where will be the identifier for the 29 different country-pair units. Since the Fixed Effects model reacts only to variant-in-time variables, the variable Distance is dropped from the model. This estimation allows controlling by characteristics related to the specific country-pair like diplomatic relationships, trade openness, institutions, and so on. However, there could be variables related to unobserved characteristics in time like trade trends and generalised willingness to trade and sign FTAs. For this reason, it is recommended to use time fixed effects to avoid endogeneity, through the next model: Where will be the identifier for the 13 different time sections. Since the scope of this paper is to evaluate the effect of the FTAs on American countries, the three biggest trade agreements in the continent (NAFTA, MERCOSUR, The Pacific Alliance) were taken as a research target, and their members were chosen as the population. The countries included by Trade Agreement are presented in Table 1: Table 1. Multilateral Trade Agreements in America Agreement Country Start Date North American Free Trade Agreement (NAFTA) Canada 01/01/94 Mexico 01/01/94 United States 01/01/94 Southern Common Market (MERCOSUR) Argentina 15/08/91 Bolivia 28/02/97 Brazil 15/08/91 Paraguay 15/08/91 Uruguay 15/08/91 MERCOSUR Chile Chile 01/10/96 The Pacific Alliance Chile 01/02/12 Colombia 01/02/12 Mexico 01/02/12 Peru 01/02/12 Source: Organization of American States (2016) However, if those countries were incorporated without taking into account other Free Trade Agreements between them or third countries, problems of sample selection bias would be created. For this reason, in addition to the mentioned free trade areas, bilateral FTA are considered, according to Table 2: Table 2. Bilateral Trade Agreements in the sample FTA Start Date Bolivia Mexico 07/06/10 Canada Chile 05/12/96 Canada Colombia 21/11/08 Canada Peru 29/05/08 Chile Mexico 01/08/99 Chile Panama 07/03/08 Chile Peru 01/03/09 Mexico Chile 01/08/99 Mexico Uruguay 15/07/04 Panama Canada 01/04/13 Panama Peru 01/05/12 United States Chile 01/01/04 United States Colombia 15/05/12 United States Panama 31/10/12 United States Peru 01/02/09 Source: Organization of American States (2016) As the model considers only one dummy variable, if a country-pair has two agreements in force (bilateral and trade area), it is considered the oldest one. Besides, it is important to point out that Venezuela (suspended member of MERCOSUR) was dropped from the list due to the lack of reliable information about trade flows. The information about bilateral trade flows was obtained from The World Banks World Integrated Trade Solution, and the other variables were constructed using information from the World Development Indicators. The database used to estimate the model has 29 country-pairs (cross-sectional units) and 13 time-sections since 1990 to 2014. The used database of bilateral trade drops 1996, leaving the database with one time-section less. Since it is one time-section of fourteen and according to our investigation, the missing information is not related to an event influencing trade flows and the time section is dropped for the entire observations, we have a low risk of biased estimators. Table 3 contains the descriptive statistics showed by the Statistical Software STATA ® for the variables in levels: Table 3. Descriptive statistics of relevant variables (in levels) Variable | Mean Std. Dev. Min Max | Observations ID overall | 25.45435 19.18174 1 74 | N = 460 between | 19.36072 1 74 | n = 29 within | 0 25.45435 25.45435 | T-bar = 15.8621 | | Exports overall | 4.55E+10 1.22E+11 1.45E+08 6.13E+11 | N = 460 between | 1.01E+11 4.22E+08 4.75E+11 | n = 29 within | 3.63E+10 -1.67E+11 2.48E+11 | T-bar = 15.8621 | | GDP_Exp overall | 1.02E+12 2.25E+12 9.96E+09 1.62E+13 | N = 460 between | 2.40E+12 1.37E+10 1.30E+13 | n = 29 within | 3.60E+11 -1.94E+12 4.21E+12 | T-bar = 15.8621 | | Pop~Exp overall | 6.71E+07 7.28E+07 2738125 3.19E+08 | N = 460 between | 7.16E+07 3324953 2.86E+08 | n = 29 within | 8255115 3.64E+07 9.97E+07 | T-bar = 15.8621 | | GDP_Imp overall | 6.78E+12 6.55E+12 9.96E+09 1.62E+13 | N = 460 between | 6.28E+12 1.61E+10 1.38E+13 | n = 29 within | 1.50E+12 2.71E+12 1.00E+13 | T-bar = 15.8621 | | Pop~Imp overall | 1.68E+08 1.31E+08 3201604 3.19E+08 | N = 460 between | 1.31E+08 3310046 2.95E+08 | n = 29 within | 1.42E+07 1.31E+08 2.00E+08 | T-bar = 15.8621 | | FTA overall | 0.5043478 0.5005254 0 1 | N = 460 between | 0.4360526 0 1 | n = 29 within | 0.2546286 -0.453985 1.393237 | T-bar = 15.8621 | | Distance overall | 3690.712 2529.406 213.02 8483.39 | N = 460 between | 2533.405 213.02 8483.39 | n = 29 within | 1.55E-12 3690.712 3690.712 | T-bar = 15.8621 However, since the estimations are calculated using a logarithmic transformation of the continuous variables, the descriptive statistics of the variables in natural logarithm are presented in Table 4: Table 4. Descriptive statistics of relevant variables (in logarithm) Variable | Mean Std. Dev. Min Max | Observations FTA overall | 0.5043478 0.5005254 0 1 | N = 460 between | 0.4360526 0 1 | n = 29 within | 0.2546286 -0.4539855 1.393237 | T-bar = 15.8621 | | lexports overall | 2.25E+01 1.84E+00 1.88E+01 27.14178 | N = 460 between | 1.64E+00 1.98E+01 26.85607 | n = 29 within | 5.17E-01 2.10E+01 23.84729 | T-bar = 15.8621 | | lGDP_ex overall | 2.65E+01 1.66E+00 2.30E+01 30.41464 | N = 460 between | 1.69E+00 2.33E+01 30.18564 | n = 29 within | 2.28E-01 2.58E+01 27.04886 | T-bar = 15.8621 | | lGDP_im overall | 2.77E+01 2.795514 2.30E+01 30.41464 | N = 460 between | 2.719438 2.35E+01 30.25019 | n = 29 within | 1.94E-01 2.70E+01 28.17505 | T-bar = 15.8621 | | lPop_ex overall | 1.75E+01 1.11E+00 1.48E+01 19.58041 | N = 460 between | 1.12E+00 1.50E+01 19.47142 | n = 29 within | 8.70E-02 1.72E+01 17.64414 | T-bar = 15.8621 | | lPop_im overall | 1.80E+01 1.821994 1.50E+01 19.58041 | N = 460 between | 1.826536 1.50E+01 19.50204 | n = 29 within | 8.05E-02 1.78E+01 18.19405 | T-bar = 15.8621 | | ldista~e overall | 7.89E+00 9.17E-01 5.36E+00 9.045865 | N = 460 between | 9.10E-01 5.36E+00 9.045865 | n = 29 within | 0.00E+00 7.89E+00 7.891049 | T-bar = 15.8621 Although using pooled OLS with the database will generate problems of endogeneity discussed further below, OLS estimation is made to have the first approach to the gravity model. Table 5 shows the obtained results: Table 5. Gravity Model estimated by OLS lexports | Coef. Std. Err. t P>|t| [95% Conf. Interval] lGDP_exp | 0.6340649 0.0394767 16.06 0 0.5564848 0.7116451 lGDP_imp | 0.4512511 0.0464715 9.71 0 0.3599247 0.5425775 lPop_exp | 0.2196251 0.0606458 3.62 0 0.1004432 0.3388071 lPop_imp | 0.5049373 0.0726212 6.95 0 0.362221 0.6476536 FTA | 0.5136195 0.0689928 7.44 0 0.3780338 0.6492052 ldistance | -0.9256142 0.0407673 -22.7 0 -1.005731 -0.8454978 _cons | -12.68833 0.7146799 -17.75 0 -14.09283 -11.28383 With a , the model behaves according to the literature and all variables are statistically significant using any level of significance. The variables measuring the mass of the economies are positive and distance is negative. Additionally, the variable of interest FTA is positive and statistically relevant, showing tha

How the Characters of the Scarlet Letter Represent Sin :: English Literature Essays

How the Characters of the Scarlet Letter Represent Sin Lexico Publishing Dictionary at Dictionary.com defines sin as; 1. A transgression of a religious or moral law, especially when deliberate, and 2. Something regarded as being shameful, deplorable, or utterly wrong. These who definitions cleary represent the sin in Nathaniel Hawthorne's The Scarlet Letter, through the characters Hester Prynne, her daughter Pearl, Dimmesdale the father, and Chillingworth, Hester's husband. Hester Prynne, the wearer of the famous scarlet letter that gave the novel it's name, is the story's source of the unforgivable sin that tears through the community of Boston in the 1600's. Hester's future and reputation in her small home town were changed forever after she was sentenced to wear the beautifuly embroidered scarlet letter "A" for the rest of her days in the village. This letter on her chest forces her to be a public outcast, and a symbol for everyone else around her to look at, as a sinner. As Hawthorne describes it, "It had the effect of a spell, taking her out of the ordinary relations with humanity and enclosing her in a sphere by herself" , it did just that. Hester soon realizes that she is in a world of her own now, and must deal with this punishment as she has brought it onto herself. Since the scarlet letter itself represents sin, it brings about her isolation from the world and shows her sin will affect her own livlihood. Also, things such as guilt and lonlines s are concequences of her sin, that she must learn to deal with. But probly the most important symbol of her sin is her daughter Pearl, as she is living evidence of the adultery between Hester and Dimmsedale. Pearl, the outcome of the relation between Hester Prynne and Reverend Dimmesdale, is the very embodiment of Hester. Pearl represents them same thing the scarlet letter represents in once sense, as both are bound to Hester forever and are both outcomes of her sin. Both Pearl and the letter A add to the anguish and pain that the scarlet letter offer from her mother's sin. Pearl is not just a reminder of the deed like the scarlet letter, but actually helps in the torturing of her mother without knowing it. As it did to Hester, the scarlet letter became a big part in Pearls life. Pearls attraction to it drove her mother into even more suffering.

Construction Process

INTRODUCTION Construction is a vast process where a lot of obstacles are faced. It can be because of the conditions of the terrain where a construction is on-going or because of the nature of the construction itself which causes the difficulties. So to overcome some of these or most of it there is always surveys done before a construction is started. Therefore a survey is a part of the construction process.And these surveys can be of different types depending on the situation of the construction process for example the surveys done before the start of the construction and at the surveys during the construction and after finishing the construction varies hence the purpose of these surveys as well as the instruments and the procedures for the surveys varies. Since then it is important to know some of the obstacles which we face at the different faces of constructions and how we deal with them to bring a solution to make the process of construction easier and safer.This assignment is a partial fulfilment of the Geomatic Engineering (ECV 3213) coursework, this will cover an explanatory report on how to overcome the obstructions to horizontal distance measurement using tape, permanent tests and adjustments for accuracy in theodolite, digital terrain modelling and verticality check / control for multi-story building works during construction. The report will discuss three examples of obstructions to horizontal distance measurement while using tape and how it is dealt to overcome this or solutions for these obstructions.For the permanent tests and adjustments for accuracy in theodolite, the collimation in azimuth, the spire and plate level tests will be taken as consideration and deliberated. Also more explanations on the remaining two items will be discussed. In the report purpose of the instruments or methods used, the procedures, the advantages and disadvantages, some practical applications with examples will be discussed. Further more relevant illustrations and sk etches will be included.The aim of the assignment is to carry out a literature search and read about the above mentioned four important items in the field of surveying and learn and understand the significance of these for the construction process hence for the surveying. The objective is to give the readers a clear cut Image of the topics and how it is practically applied in the field and provide the readers with relevant and understandable information.

Computer Network

Computer Network also called Network is a group of computers and other devices connected to each other to share resources electronically. Networks can be as small as two computers or many thousand computers that are connected to one another. These computers are usually connected to one another through wires, satellite, and modems. Each device connected to the network is called a â€Å"node†, and the computer that is connected to the network is called a workstation.There are several ways to connect networks together, the way the Network is laid out is called Topology, and there are several types of topologies, Bus Topology, Star Topology, Ring Topology. There are several types of network, Peer to Peer Network, Client/Server Network. Depending on the size of networks they are divided into several categories and have different terms that specify these networks, LAN (Local Area Network), MAN (Metropolitan Area Network), and WAN (Wide Area Network). One of the primary reasons to ne twork is to increase productivity and cut all the unnecessary costs.By connecting their office people can get the advantages of managing their data, all the data can be stored in a server computer instead of storing them in each workstations hard disk. This way you can easily back-up and manage your data. And whenever someone makes changes to files it will updated and everyone will have the access to the same updated file. The network administrator will decide to whom grant the access to the files. Another advantage of network is that it makes data transfer easy so you don’t have to transport files into Floppy disk, or USB device and walk over to your co-workers desk.Networking also will allow you to share the equipment like printer, if there are several hundred computers in an office building you don’t have to buy different printers for each computers instead you can just buy one or two printers that can be shared between all computers on a network. You can save on so ftware too many software companies offer businesses software deals that are more affordable than purchasing separate software license for each computer, it also can save time when the time to upgrade to a new version of a software you can just update it on a server instead of doing it on each computer.Another way of saving money is that you can share the internet too. As you can see networking computers has many advantages and it is recommended to network. As I mentioned above the way the networks are laid out are called topology, today you will come across the networks that laid out uniquely using one type of topology or combination of different types of topologies these kind of topologies are also called a â€Å"Hybrid Topology†:1. Bus.2. Star.3. Ring.The Bus Topology is older topology that is not seen that often and almost never used in modern networks.It is very easy to set up because all the computers are connected to each other using a single cable which is called a bac kbone or segment. Because all the computers connected to a bus network to one cable only one computer can send packets of data (which are electronic signals) at a time. Bus topologies have some advantages they are easy to set up, and since they are on one line of cable if one computer fails it does not crash the entire network. They are also very inexpensive to set up because less cable is used.Despite the advantages of a bus topology there disadvantages too. One of the disadvantages of a bus topology is that if the cable used breaks, the entire cable needs to be replaced, and if the cable is too long it will hard to find out where the cable is broken. The bus topology is also not very scalable if you have a small network it will be hard to expand it. It is also not very secure network. Even though the Bus topology is very easy to set up and inexpensive I t is very outdated, it should not be used in a modern networks.The Star Topology is a topology mostly used in modern networks alt hough it is an older topology too, many modifications has been made to it to handle all the modern networking needs. In a star topology all the computers are connected separately to the Hub or Switch, and all computers have their own cable. When a computer sends the packets of data to other computers in the network it is sent through the cable to the hub or switch, then it passes the data packet to other computers that are connected to it.Since the computers in a star topology are connected to the switch separately and all the computers have their own cables, in case it brakes it is easy to configure the problem. The computer that breaks down is the only computer that will not have access to the network, and all the other computer are not affected because they have their own cables connected to the switch. Another benefit the star topology has is that it is scalable and can be expanded anytime. The star topology is the most used topology in modern networks and it is recommended to u se the star topology if you are setting up a network.