The Traditional Role Of Domestic Servitude Under A...

Latin American women have faced centuries of gender discrimination and exploitation that have limited their participation in social, economic, and political endeavors. The traditional role of domestic servitude under a patriarchal society has occasionally included entering the general workforce in order to meet the economic needs of a family, but the continuous battle against gender biases has stifled their advancement. As a result of the constraints placed on women, many sought refuge in convents that sprang up across the continent in hopes of improving their quality of life. The convent offered women many new opportunities they would not otherwise have, but it too was fraught with internal discord based on gender, race, ethnicity and social stratification. By the mid-sixteenth century, most major cities in Latin America had at least one convent and over time larger cities such as Mexico City had as many as twenty. The generosity of wealthy benefactors often provided the necessary funds to keep older convents in operation or to build new ones. The Spanish monarchy also donated a considerable amount of funds for the maintenance of monasteries in order to keep a Catholic presence in the communities. Additionally, the influx of capital from the dowries of wealthy women of upper- and middle-class families entering the cloister would bolster the convent’s coffers and it was common for more than one daughter in a family become a nun. The convent also profited when the womenShow MoreRelatedEntrapment Of Household: Analysis Of â€Å"A Doll’S House†.1657 Words   |  7 Pagesinstitution of marriage in his plays. A Doll’s House presents the aftermath of nineteenth-century patriarchal husbandry like those in Susan Glaspell’s play, â€Å"Trifles†. 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What is your reaction when people stare or look at you in...

What is your reaction when people stare or look at you in elevator? People normally avoid facing others when they are in elevator, and it has been a norm that most people follow. The reason of doing this project is to see people’s reaction when others are breaking the norm of riding elevator. By doing this project we developed three steps of observations in elevator, which were examining people who follow the norm without violation, being an observer and a violator to watch the rider’s reaction when the norm is violated. Also, we would discuss about two main parts of the norm violation such as the process during the violation and the personal difficulties of breaking the norm. In the first step, my partner and I started observing the†¦show more content†¦In elevator with broken norm, Asians showed dislike when they being stared or watched, but they still avoided facing others, and Americans showed two different reactions such as evading eye contact and looking straight with greets. In this result, I speculated people’s reaction in the norm of elevator is not only causing by the different cultures, but also the reaction depends on how people’s moods are each days. In the third step, I exchanged the position with my partner. Being a violator to break the rule was a first time for me. To success this process of breaking the norm, I found hard time to break it up. The reactions of riders were showing annoyed and violated when I stared at them. However, I figured out there is age involving in the norm. When I saw the American parents with their children, the children didn’t turn their heads away but they kept looking at me. Therefore, the theory of mine that saying people follow the norm in elevator did not verify on children. During the observation, I discovered observer are also a violator because being an observer I had to look at the reaction of the riders and the violator, which it made me became a part of violator by facing and looking at others, but being as an observer was more easier than being a violator for me. As a violator, I had to tell myself to look at the ridersShow MoreRelatedInterview Questions and Answers16418 Words   |  66 Pagesquestion. But these few words can put you on the spot in a way no question can. Many quickly lose control of the interview during the most critical time- the first five minutes. This is not the time to go into a lengthy history or wander off in different directions. Your response should be focused and purposeful. Communicate a pattern of interests and skills that relate to the position in question. Consider your response to this question as a commercial that sells your autobiography. Provide an answerRead More65 Successful Harvard Business School Application Essays 2nd Edition 147256 Words   |  190 Pagesat St. Martins, Matthew Martz. x INTRODUCTION You are inspired, hopeful, accomplished, and eager. You seek per, sonal and professional advancement via an MBA that will prepare you for leadership challenges in any business field. You are aware, however, that Harvard Business School receives about ten thousand applications annually, and you are uncertain how to make your ap.... plication stand out. We understand. We have been in your shoes. This book seeks to demystifythe admissions processRead MoreProject Managment Case Studies214937 Words   |  860 Pagesdisclaim any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives orwritten sales materials. The advice and strategies contained herein may not be suitable for your situation. You should consult with a professional where appropriate. Neither the publisher nor author shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, conseq uential, or other damagesRead MoreAutobilography of Zlatan Ibrahimovic116934 Words   |  468 Pagesdream. I was playing in the best team in the world and had been welcomed by 70 000 people at the Camp Nou. I was walking on clouds. Well maybe not entirely, there were some bullshit in the papers. I was the bad boy and all that. I was difficult dealing with. But still, I was here. Helena and the kids were also good. We had a nice house in Esplugues de Llobregat and I felt fully charged. What could go wrong? Hey you, Guardiola said. Here in Barca we keep our feet down on the ground. Sure,Read MoreFundamentals of Hrm263904 Words   |  1056 Pagesresources, WileyPLUS gives you everything you need to personalize the teaching and learning experience.  » F i n d o u t h ow t o M A K E I T YO U R S  » www.wileyplus.com ALL THE HELP, RESOURCES, AND PERSONAL SUPPORT YOU AND YOUR STUDENTS NEED! 2-Minute Tutorials and all of the resources you your students need to get started www.wileyplus.com/firstday Student support from an experienced student user Ask your local representative for details! Collaborate with your colleagues, find a mentor

Estimation of Production Function of Public Sector Banks Free Essays

Project| Estimation of Production function of Public Sector Banks | | | Contents 1. INRODUCTION3 2. Methodology4 2. We will write a custom essay sample on Estimation of Production Function of Public Sector Banks or any similar topic only for you Order Now 1General Approach:4 2. 2Data Collection:4 2. 3Data Processing:5 2. 3. 1Nature of Banks:5 2. 3. 2Nature of Variables:5 2. 3. 3Assumptions in the treatment of Variables:5 2. 4Data Analysis:5 2. 4. 1Objective of the Analysis5 2. 4. 2Production Function Relationship:5 2. 5Limitation8 3. Data analysis and Results9 4. Conclusion15 5. Bibliography16 1. INRODUCTION The structure of the banking industry has undergone sweeping changes in the past two decades. In response to heightened competition from non-bank financial firms enabled by technological progress among other factors, banks have been expanding both the scale and scope of their operations, largely through consolidations. This merger wave coincides with extensive deregulation, which has removed restrictions on product offerings and interstate banking. These changes have motivated many studies. The estimation of bank productivity and returns to scale is of particular nterest because of its broad practical applications and important policy implications The Banking Sector is characterized by multiple inputs and outputs that are associated with various attributes, such as different types of deposits, loans, number of accounts, classes of employees and location of branches. Transformation in terms of moving from high operating cost, low productivity and high spread to being more efficient, p roductive and competitive has been an important challenge for the banking sector in India. Recent years have witnessed substantial research efforts that have been devoted to measuring the efficiency and productivity of the banking industry. However, assessment of performance of banks has been a problematic one because of the unresolved questions concerning inputs and outputs. In the absence of any coherent definitions, researchers have used a variety of inputs and outputs, mostly based on an intermediation or production approach. The study of the Indian banking sector is of special interest for multiple reasons. Besides being one of the fastest-growing emerging economies of the world, India has one of the largest state-owned banking systems and generates employment of around 1 million people. Secondly, the vast network of around 70,000 commercial bank branches provides the base of the finance-led growth and development process in India. Thus the issue of efficiency and productivity of banks in India is particularly important. In the aforementioned context we define productivity as a concept that involves the transformation of resources into final goods and services. Production function is a function that specifies the output of a firm, an industry, or an entire economy for all combinations of inputs. It indicates the highest output that a firm can produce for every specified combination of inputs. This function is an assumed technological relationship, based on the current state of engineering knowledge; it does not represent the result of economic choices, but rather is an externally given entity that influences economic decision-making. Almost all economic theories presuppose a production function, either on the firm level or the aggregate level. In this sense, the production function is one of the key concepts of mainstream neoclassical theories. In micro-economics, a production function is a function that specifies the output of a firm for all combinations of inputs. 2. Methodology 1 2 3. 1 General Approach: This section describes the general approach taken for the analysis of the Production function of the Public Sector Banks (PSBs) in India. A bank for its operation takes several inputs and generates several outputs. For e. g. the typical inputs are Employees, Capital for operation, Reserve Surplus, number of Branches, number of ATMs of a bank etc. Its output is typically the Loan (Advances), Interest Income etc. Since Multiple Regression is used so the production output is taken only one at a time. Also, only two input variable at a time is used, though several regression analysis have been done for different combinations of input and output to get the most reasonable and best approximate relationship. However, a bank uses any number of variables as input simultaneously. A bank measures its performance among other parameters on how much Loan or Credit it has disbursed in a fiscal year or how much Deposit it has collected from the customers etc. Though such data in isolation may not be a true estimate of the efficiency of the business because unregulated disbursal of loans may cause Non Performing Assets (NPAs) which will lower the Retained Earning of the Bank but since the report is concerned only with the Production function of the PSBs hence no comment will be made on this aspect. Similarly how competitively the Deposits have been taken will not be a subject matter of this report. The Methodology of the report is to be first gather relevant input/output data from authoritative source. The data so obtained are processed and any assumptions made for their subsequent analysis is clearly defined. In the next phase the data analysis is done wherein suitable regression technique is used to generate the relationship between the input variables and the Production output. Finally the Interpretation is done to assign the meaning to such endeavor. 3. 2 Data Collection: The data for the Public Sector Banks (PSB) in India for the following variables have been collected from the Reserve Bank of India’s (RBI) official website for the fiscal 2004-05 to 2008-09: Deposits * Capital * Loans Advances * Labour * Interest Income 3. 3 Data Processing: Nature of Banks: All the 20 Nationalised Banks including IDBI as well as all the Associate Banks of the State Bank of India have been considered for the study. Thus a total of 27 banks has been focussed from the fiscal 2004-05 to 2008-09 for their Production output vis-a-vis different inputs. Nature of Variables: For the Banking Se ctor there are few variables which are clearly treated as input variables and output (production) variables. Example includes Labour and Loan (Advances) as input variables and Interest Income as output variables. But their are variables like Deposits which are ambiguous in their treatment as either input or output. It is input because to disburse loan which is an output the bank requires deposits. It is this deposit which is finally disbursed as loan. However, Deposit is treated as Output because the performance of a Bank is measured among other parameters by how much Deposit it has been able to generate in a fiscal year. For our analysis we treat Deposits as Output/ Production variable. Assumptions in the treatment of Variables: 1. It is assumed that the cost of per unit Labour is constant and same across all banks. Thus we may take the Labour as a quantity across all banks as an input variable, without bothering about the variable wage rate for each labour i. e the Cost of Labour is a linear function of the quantity of Labour. 3. 4 Data Analysis: Objective of the Analysis The report wish to obtain the following objectives: * To establish a mathematical model of Production Function for PSBs in India. * To analyze the regression coefficients obtained vis-s-vis the PSBs’ input and output. To analyze the regression coefficients for specific banks over five years Production Function Relationship: To estimate the one variable Production output function for an economic entity the Cobb-Douglas Production Function is widely used. For the Banking industry the report establishes the relationship between the following input variables and the Production output variable: (A) For all the 20 Nationalised Banks (including IDBI) along with the Associate Banks of SBI, the following Regression Analysis is done across all the years starting from the fiscal 2004-05 to 2008-09. S. No| Input Variable1| Input Variable 2| Production Output| Across Time Period| Banks| 1| Labour| Capital| Deposit| 2004-05 to 2008-09| All PSBs| 2| Labour| Capital| Advances| 2004-05 to 2008-09| All PSBs| 3| Labour| Capital| Advances + Deposit| 2004-05 to 2008-09| All PSBs| 4| Labour| Capital| Interest Income| 2004-05 to 2008-09| All PSBs| The number observations made = Number of Production Functions * Number of Fiscal Years for which the observation is made = 4*5 =20 (B) Specific to the Largest Nationalised Bank as per capital viz. State Bank of India (SBI) and the Smallest PSB as per Capital viz. State Bank of Indore (SBIndore) were taken for regression analysis separately. The merger of  State Bank of Indore, the smallest associate bank of State Bank of India (SBI), was completed in the last week of August 2010, ut for our analysis we still continue to treat its data as separate from that of SBI. S. No| Input Variable1| Input Variable 2| Production Output| Across Time Period| Ban k| 1| Labour| Capital| Deposit| 2004-05 to 2008-09| SBI| 2| Labour| Capital| Advances| 2004-05 to 2008-09| SBI| 3| Labour| Capital| Advances + Deposit| 2004-05 to 2008-09| SBI| 4| Labour| Capital| Interest Income| 2004-05 to 2008-09| SBI| 5| Labour| Capital| Deposit| 2004-05 to 2008-09| SBIndore| 6| Labour| Capital| Advances| 2004-05 to 2008-09| SBIndore| 7| Labour| Capital| Advances + Deposit| 2004-05 to 2008-09| SBIndore| 8| Labour| Capital| Interest Income| 2004-05 to 2008-09| SBIndore| The number observations made = Number of Production Functions * Number of Fiscal Years for which the observation is made = 8*5 =40 3. 5. 1. 1 Multiple Regressions: For modelling and testing of multiple independent variables (or predictor variables), Multiple Regression is used. Since it is for only single dependent variable (or criterion variable) hence Multiple Regression is not a multivariate test. The model for a multiple regression takes the form:   y  =  ? 0  +  ? 1Ãâ€"1  +  ? 2Ãâ€"2  +  ? 3Ãâ€"3  + †¦.. +  ? And we wish to estimate the  ? 0,  ? 1,  ? 2, etc. by obtaining   ^ y1  =  b0  +  b1x1  +  b2x2  +  b3x3  + †¦.. Where the  b’s are termed as the â€Å"regression coefficients† and ? is the error or residual value. For 2 independent variables we fit the data for a plane. The beta values are used in measuring how effectively the predictor variable influences the criterion variable. R2, in multiple regression is the square of the measure of association which indicates the percent of overlap between the predictor variables and the criterion variable. 3. 5. 1. 2 Cobb-Douglas Production Function: The Production of an economic entity may be defined as a function of its inputs. In a general mathematical form, a production function can be defined as: P= f(X1,X2,X3,†¦Xn) Where: P = Production or output quantity X1,X2,X3,†¦Xn = Input variables such as Labour, raw material, capital etc. f() = function defining the relationship. This function may be a Linear Function of all input variables. It can also be a Product Function of all the individual variables with each variables weighted for a corresponding exponent. The Cobb-Douglas Production Function follows the latter approach and is as follows: P = A. L?. K? Where, P = Production or output quantity L = Labour (the number of employees) K = Capital (the monetary worth of all machinery, equipment, and buildings) A = Total factor productivity, a variable which accounts for effects on total output not explained by chosen inputs. ?, ? are the output elasticity of labour and capital, respectively. These values are constants. We assume ? , ? ;lt; 1 so that the firm has decreasing marginal products of labour and capital. The Multiple Regression is to be done using the Cobb-Douglas Production Function, then the said function needs to be in a the l inear form. To achieve linear scale the exponential Log of the Cobb-Douglas Production Function may be taken. Thus the following function is being used in the report for regression: Log (P) = a0 + ? *Log(L) + ? *Log(K) Thus the Input 1= Log(L), Input 2 = Log(K) and Output = Log(P) and Model Coefficients = ? , ? 3. 5. 1. 3 Return to Scale: Returns to scale refers to a technical property of production that examines changes in output subsequent to a proportional change in all inputs (where all inputs increase by a constant factor). If output increases by that same proportional change then there are constant returns to scale (CRTS). If output increases by less than that proportional change, there are decreasing returns to scale (DRS). If output increases by more than that proportion, there are increasing returns to scale (IRS). To summarise, it is as follows: ? + ? | Returns to scale| =1| constant| ;lt; 1| decreasing| ;gt; 1| increasing| 3. 5 Limitation * The correlation between labour expense and production across banks may be limited if the business model of the bank varies. For example banks who primary operate in larger   cities can produce more with a smaller workforce because of greater labour utilization while labour in far flung remote branches might be under utilized and may not contribute to production that efficiently. Hence we assume a linear utilisation of labour. * This correlation is limited because as technology is increasingly substituting labour in banks so a bank with smaller workforce but superior technology can still produce more. Different PSBs may differ on this aspect of technological implementation vis-a-vis their labour. * Our analysis has restricted inputs and outputs to very few variables. There can be other variables although the report has included the most important ones for the study. * In analysis of SBI and State bank of Indore we have taken only 5 data points for 5 years. This may limit the authenticity of analysis. We have chosen only two input case to estimate the production while other inputs are collectively taken i n intercept. * There is an assumption that the production function follows Cobb-Douglas Production estimation. Other Production estimation methods like Olley/Pakes and Levinshon/Pertin functions are not considered. * In the regression model, we have not factored in any smoothing techniques. * In the analysis of bank over the years the data may be misleading,banks over the year may with better technology produce more with lesser input this effect will lower their economies of scale in the given analysis, this is a wrong conclusion 3. Data analysis and Results We referred the website of RBI to get the data needed for our analysis. A total of 27 banks were taken for analysis and the data for these banks from the period 2004-05 to 2008-09 have been used for the analysis. We used the Cobb Douglas Function for the models, wherein Q = A * (Input1^ ? 1) * (Input2 ^ ? 2) The production functions thus attained provides us a view of the overall sector as a whole for the following the outputs. 1. Deposit 2. Advances 3. Deposit + Advances 4. Interest Income Further, we focussed on two banks, State Bank of India and State Bank of Indore, the largest and smallest in the sector in terms of capital, to understand the applicability of the product functions attained in the above study. Here, the data across the five years in the consideration were used to obtain the production functions for each of the input-output combinations mentioned above. The results have been summarized in the Table 1 below for the four different models taken for all the banks across five years and Table 2 for all the four models for 2 specific banks: Table 1: Case| Year| Intercept| ? ( Elasticity of Labour)| ? (Elasticity of Capital) | R2| Model 1:Input1: Labour Input2: Capital Output: Deposits| 2004-05| 0. 6431| 0. 7257| 0. 2440| 0. 9596| | 2005-06| 0. 8010| 0. 5535| 0. 4239| 0. 9802| | 2006-07| 0. 8944| 0. 5655| 0. 4017| 0. 9731| | 2007-08| 1. 2448| 0. 4426| 0. 676| 0. 9707| | 2008-09| 1. 2768| 0. 3591| 0. 5694| 0. 9685| Model 2:Input1: Labour Input2: Capital Output: Advances| 2004-05| 1. 0543| 0. 2347| 0. 6749| 0. 8900| | 2005-06| 0. 9721| 0. 1998| 0. 7609| 0. 9372| | 2006-07| 0. 9495| 0. 3228| 0. 6367| 0. 9448| | 2007-08| 1. 2994| 0. 2608| 0. 6275| 0. 9544| | 2008-09| 1. 2154| 0. 2486| 0. 6746| 0. 9641| Model 3:Input1: Labour Input2: Capital Output: Deposits + Advances| 2004-05| 1. 2041| 0. 4583| 0. 4768| 0. 9416| | 2005-06| 1. 2145| 0. 3679| 0. 5987| 0. 9695| | 2006-07| 1. 2331| 0. 4450| 0. 5174| 0. 9662| | 2007-08| 1. 5742| 0. 3575| 0. 5422| 0. 9663| | 2008-09| 1. 5500| 0. 3101| 0. 6157| 0. 9683| Model 4:Input1: Labour Input2: Capital Output: Interest Income| 2004-05| -0. 1461| 0. 5320| 0. 4036| 0. 9584| | 2005-06| -0. 0207| 0. 2972| 0. 6656| 0. 9610| | 2006-07| 0. 0246| 0. 3640| 0. 5843| 0. 9733| | 2007-08| 0. 3381| 0. 3250| 0. 5629| 0. 9639| | 2008-09| 0. 4347| 0. 2483| 0. 6411| 0. 9711| Table 2 State Bank of India| Case| Intercept| ? ( Elasticity of Labour)| ? (Elasticity of Capital) | R2| Input1: Labour Input2: Capital Output: Deposits| -3. 03105| 0. 978999| 0. 77501| 0. 976381| Input1: Labour Input2: Capital Output: Advances| 2. 773811| -0. 31806| 0. 972634| 0. 93499| Input1: Labour Input2: Capital Output: Deposits + Advances| -0. 37579| 0. 453894| 0. 852554| 0. 64079| Input1: Labour Input2: Capital Output: Interest Income| -3. 36783| 0. 872917| 0. 74153| 0. 996843| State Bank of Indore| Case| Intercept| ? ( Elasticity of Labour)| ? (Elasticity of Capital) | R2| Input1: Labour Input2: Capital Output: Deposits| 1. 693202| -0. 37172| 1. 310855| 0. 985134| Input1: Labour In put2: Capital Output: Advances| -3. 03629| 0. 124397| 2. 214496| 0. 938827| Input1: Labour Input2: Capital Output: Deposits + Advances| 0. 119414| -0. 21134| 1. 712892| 0. 966654| Input1: Labour Input2: Capital Output: Interest Income| 5. 081366| -1. 73671| 1. 552713| 0. 993676| The macro-economic factors in India definitely affect the performance of the banks. The various parameters like inflation, GDP affect the sentiment of the market in general, while the regulatory measures taken by RBI through changing CRR, SLR, repo and reverse repo rates effect a shift in the business outlook of the bank. Since these parameters keep on changing from time to time, we decided to have separate product functions for every year. This guards us against the negative impacts making an assumption of Ceteris Paribas in determining the product functions, where we might have a few more variables. But the correlation of those factors with the performance of the banks is not the motive of this study, and hence not in its scope. Also, while analyzing the performance of the banks, we have to keep in mind that, being in the public sector, their focus is not always on profit maximizing. Rather, the goal is often carrying out the social responsibilities like providing banking facilities at places where the venture might not be profitable, and hence not a feasible for the private sector to open branches at those places. Analysis and Results for the different models Model 1: Input variables: Labour (L), Capital (K) Output variable: Deposit The first graph below captures the variation in output with respect to change in labour and the second with respect to change in capital. A strong similarity in graph indicates that labour n capital can be almost perfect substitutes. If the graphs differ then they are not good substitutes Deposit is essentially an intermediate variable, here treated as an output. As expected, we see some variation in the results across the years. An interesting observation here is that the elasticity of labour decreases along the period under study. This is in keeping with the redundant labour created by the technical innovations of the operations reducing the productivity of labour. The policies of the Public sector bank do not allow them to reduce the input of labour suddenly. Also, the higher elasticity of capital for 2008-2009 indicates the mood of the market during the recession, where the safety of the bank deposits looked better when weighed against the risks and lower outputs of other avenues of investment. The high values of R2 point at the stability of the regression through which the production functions were attained. As the sum of Output Elasticity’s of Inputs (Labor and Capital) as ? +? value is close to unity, it implies that the Indian Public sector banks are in Economies of Scale. This is consistent with the earlier economic researches which imply the banking sector in general is in Economies of Scale (Increasing returns to scale). Model: 2 Input variables: Labour (L), capital (K) Output variable: Advances Here, again, we see that the R2 values are high indicating higher stability in the production functions. An interesting phenomenon that can be noticed in these results is in the relative stability of all three parameters across the years. The relative variation of the coefficients across the years is relatively low. Model: 3 Input variables: Labour (L), capital (K) Output variable: Deposit Advances Here, again, we see that the R2 values are high indicating higher stability in the production functions. An interesting phenomenon that can be noticed in these results is in the relative stability of all three parameters across the years. The relative variation of the coefficients across the years is relatively low. The economies of scale ? +? value is again close to unity and signifies that for all the different outputs there is an increasing scale of return. Model: 4 Input variables: Labour (L), capital (K) Output variable: Interest Income Again, we see a clear trend of declining elasticity of labour across the years, validating the observation made in case 1. The relatively higher elasticity of capital in 2008-09 indicates the stability and optimization of performance of the Indian banks in turbulent global scenario. For each of the banks under study, the income under both the heads, Interest and other, showed a steady rise. Analyzes for Specific banks: State bank of India and State bank of Indore All the above mentioned four models of input and output parameters where analyzed for State bank of India and State Bank of Indore for period of 5 Years . The below graphs are a couple of sample graphs of the analysis . All the graphs of the analysis are attached below. We must note a very interesting trend in the economies of scale (ie the sum of alpha n beta) in our result. The economy of scale for almost all the cases in the initial four analysis is slightly less than or almost equal to 1 but it is greater than 1 both for SBI and State bank of Indore respectively. This means that when we look at the overall sector the banks of larger size have almost proportionally large output as compared to their input but both in SBI and State bank of Indore the increase in output is disproportionally larger compared to increase in input. The Data used for the analysis and detailed regression analyses are attached below: The complete set of graphs created for all the models are as well attached below: 4. Conclusion The study focused on modeling the Production Function for public sector banks. The regression curves obtained from all the banks that were considered for production functions for Deposits, Advances, sum of Deposits and Advances and interest income. The coefficient of variation was above 90% in most of the cases which reinforces the assumption that the level of capital and labour count significantly explains the variation in output level. The sum of ? and ? , the parameters of the system, is nearly unity. This indicates that the industry has a production which exhibits constant returns to scale. For the analysis done on individual banks (SBI and State bank of India), the values of negative value of alpha  and beta indicate that the increase in labour or capital (as the case may) decreases the overall output of the bank. We have seen constant or slightly decreasing economies of scale across banks in any given year whereas SBIs have shown increasing scale of economy (;gt;1) over the years. To explore this issue further we had done a few more regression for some more banks for 5 years (5 data points). The analysis has thrown up very interesting conclusion, the economy of scale fluctuates by huge degree across various banks and overall it is negative. This happens when the bank is already utilizing more than the needed labour or capital for its given capacity and any further increase in it decreases the overall production . It can be concluded from this analysis that although overall it may not be desirable to have a large size bank, it is desirable to increase the size of both SBI and State bank of Indore as here the incremental return will outmatch the incremental investment as they have economies of scale greater than unity. Our results have been consistent with the previous research findings which state that banking industry has economies of scale i. e. output more than doubles with doubling of input. It was also observed that sum of output elasticity’s of factor inputs (? +? ) was greater for certain banks like SBI and State bank of Indore. 5. Bibliography * Microeconomics, 7th Edition. Robert S. Pindyck, Daniel L. Rubenfield, Prem L. Mehta. * http://en. wikipedia. org/wiki/Banking_in_India * How to cite Estimation of Production Function of Public Sector Banks, Papers

Light Rail in Manchester City for Improves Air- myassignmenthelp

Question: Discuss about theLight Rail in Manchester City for Improves Air Quality. Answer: Introduction The city of Manchester has continued to experience rapid growth over the years and is still one of fastest growing cities in the UK. This has put immense pressure on existing transport network. As a result of this, light rail transit (LRT) is a worthwhile project to implement in the city. The decision to build an LRT network covering an area of 15 km around the city will play a major role in reducing traffic congestion. Other benefits of LRT include: reduces pollution, increases property values, improves air quality, improves safety, enhances comfort, has lower per passenger operating costs, enhances development in the area, can run on different energy sources, is more reliable, has higher passenger capacity, has greater aesthetic, can operate effectively with other modes of transport, and improves health of passengers(Cervero Sullivan, 2011);(Hess Almeida, 2007); (Higgins, et al., 2014)(Li, et al., 2012)(MacDonald, et al., 2010); (Seo, et al., 2014) (Shang Zhang, 2013) (Topalovic , et al., 2012). In general, light rail system will bring a wide range of economic, social and environmental benefits to the city of Manchester. But for the benefits of LRT to be fully realized, it is very important to ensure that each stage of the project is done effectually. This report aims at discussing preliminary design; detailed design and testing, evaluation, validation and optimization phases of the project and critical human factors to LRT network. These elements are very crucial considering that the LRT network will be operating alongside existing modes of transportation and other land uses. When designing an LRT network, it is very important to visualize how it will be constructed, operated and maintained. This helps in ensuring that the final product created meets its objectives adequately. Therefore information contained in this report is very useful when carrying out preliminary design; detailed design and development; and system testing, evaluation, validation and optimization processes of an LRT project. Preliminary design phase This is a very crucial phase that follows the conceptual design phase. During this phase, the project team is tasked to demonstrate that the solution chosen from the conceptual design phase will meet all the project requirements, goals and objectives. Here, the team comprehensively analyzes the project concept and selected solution so as to ensure that they meet the design and performance specifications of the project and can be developed using available resources. The project team also identifies potential time and cost constraints. This process starts by identifying key components of the LRT network and how they will operate. These key components include: route of the LRT (including surface stretches and underground tunnels), number and sizes of lanes, alignments of the LRT (both vertical and horizontal), type and size of LRT vehicles, overhead catenary system, power systems, communication systems, traffic and signal systems, relay houses, boarding stations, stops, system software, etc. The team analyzes these subsystems by determining different specifications, including: system specifications (technical, operational, performance and support features of the system), product specification (qualitative and quantitative technical requirements of products that can be created offsite), process specifications (qualitative and quantitative technical requirements of services needed to complete functional requirements), and material specifications (technical requirements of materials to be used in creating the system). The main focus in this phase is on analyzing the functional requirements of the LRT networks subsystems and allocating resources for each subsystem. Each main function is split into sub-functions for easier analysis. The inputs and anticipated outputs, constraints and controls of the subsystems are also determined. By understanding all these items, it becomes easier for the project team to allocate resources appropriately. Therefore it is in this phase that the team will allocate the costs determined in conceptual design phase to specific subsystems of the LRT network (land purchase and survey; bridge, station and tracks; station infrastructure; communication and control signals; cables and electrical power; vehicles car sets; and labor). The design criteria used in preliminary design phase are: functional capability usability, interoperability, reliability, sustainability, producibility, maintainability, safety, security, supportability, serviceability, affordability and disposability. This criteria ensures that subsystems are designed by considering important factors throughout their lifecycle, i.e. from design stage to disposal. Successful completion of preliminary design phase requires all key stakeholders and professionals to work as a team and share their unique knowledge and experiences. This includes professionals from departments and/or fields such as design, environmental, manufacturing, quality, software, value, reliability, human factors, maintenance, logistics and safety/security. Every process completed in this phase is also evaluated and reviewed so as to identify other alternatives. Reviews of the preliminary designs created are also prepared for use in subsequent processes. Detailed design and development phases After establishing the technical specifications of all subsystems based on functional requirements of the LRT, the project team now goes ahead to create final designs of the subsystems and the entire system developed in preliminary design phase. This is done in the detailed design phase. In this phase, engineers, architects and designers use appropriate design software and engineering tools such as computer aided design (CAD) or computer-aided engineering (CAE) software, to create the designs(Blanchard Fabrycky, 2010). But before starting to create the final designs, necessary field studies are also carried out so as to collect useful data and information such as groundwater levels, soil characteristics, climatic conditions etc. This helps in determining the right types and sizes of different subsystems such as foundation type and materials of the light rail. Detailed design phase is an iterative process that continues from definition of the system to create designs that can be used to produce several similar products. Each system or component designed must have complete details to enable the manufacturer or contractor create it. The details are usually represented in form of design drawings (arrangement drawings, assembly drawings, connection drawings, construction drawings, control drawings, detail drawings, engineering drawings, installation drawings, logic drawings, numerical control drawings, piping drawings, schematic drawings, wiring/cable drawings and software drawings), electronic format or reports. Each design drawing is also reviewed by different professionals immediately it is completed so as to identify any errors (if any) or need for changes and/or improvement. After designing the subsystems and the entire system, the team prepares relevant documentations that entails design drawings, lists of components and materials, analy ses and reports. Data and information contained in these documents is also used to prepare bills of quantities (BoQs) for the project. The designs and documents prepared in this phase should enable the manufacturer or contractor to create the subsystems or system as a whole in the factory or on site(Goral, 2007). After detailed design phase follows development phase. This is where the designers create mock-ups, engineering models and prototype models so as to have realistic simulations and visualization of the configuration of the proposed LRT network and how it will work. Using the operating model created, the design team is able to demonstrate how the LRT will function and its expected performance. In this case, the operating model will show how LRT will reduce traffic congestion in the city of Manchester by facilitating easy and seamless movement of people from one place to another. These models are created using approved components and by following the required standards, codes and regulations. They are also tested to establish whether they meet all the requirements. System test, evaluation, validation and optimization Before the start of actual construction activities, it is very important to test, evaluate, validate and optimize the designed system. This is done so as to confirm that the system designs and models created meet all the necessary technical, functional, performance and other project requirements. The process of test and evaluation starts by testing individual parts then proceeding to subsystems and finally the entire system. After test and evaluation, these parts, subsystems and the entire system are validated, i.e. confirming that they meet the project requirements (technical, functional and operational specifications)(Luna, et al., 2013). It is important to note that the processes of testing, evaluating, validating and optimizing the system components are not established after the detailed design and development phases but during the conceptual design phase. This is where the scope of each test is determined, and required tools, equipment and personnel discussed. Doing so helps the project team to design the system and create models knowing the kind of tests they will be subjected. Even though it is not possible to establish the actual performance of a system until the final product is created, the findings obtained from the test and evaluation processes give a general impression of the expected performance of the system because these tests are performed in conditions that are customized to resemble real conditions. There are a number of tests that have to be performed on an LRT. Some of these include: structural tests (involves testing material characteristics and properties of various components), performance tests (entails testing individual parts of the system), reliability tests (involves testing the consistency of the system), environmental tests (involves testing the system when subjected to different environmental factors), maintainability tests (performed to determine maintenance needs of the system), support equipment tests (performed to ensure that all equipment are compatible), personnel test (carried out to ensure appropriate relationship between the system and people, including operators and users), software tests (carried out to ensure that the software performs the expected function efficiently), compatibility tests (performed to ensure that all subsystems have been integrated properly to form one complete system), noise and vibration tests and safety tests, among others(Cleghorn , 2009). System test, evaluation and validation processes have to be planned appropriately and in advance. After identifying relevant tests to be performed on a system, the required equipment, software, data collection methods, data analysis techniques, facilities, test-site and personnel should also be identified. The team should also plan for retesting if the components fail to meet minimum requirements on first testing. Components that fail to meet the necessary requirements get invalidated and therefore they have to be re-evaluated, corrected and changed or improved before the final product is created. Optimization is another very important process when design an LRT. This being a capital-intensive project, the government, key stakeholders and the general public expects to get maximum value for each dollar spent. As a result of this, the project team has to aim at optimizing every product created and process executed during the project. Generally, optimization is the process of seeking the best solution for each design problem. This is done using a variety of approaches such as differential calculus, function slope, partial differential, etc. In other words, the project team uses mathematical calculations to predict different aspects of the proposed LRT. This includes comparisons between costs and benefits of the project over a certain period of time. The ultimate goal of optimization is to analyze and compare different design alternatives so as to choose the best alternative that will meet all the project requirements at the lowest cost. Human factors Efficient operation of an LRT largely depends on human factors or elements put into consideration during the design process. This basically entails improving the interfaces between the light rail vehicles and the operators and users. For this reason, the design team has to consider all relevant human factors so that all the expected benefits of the LRT network can be realized by the residents of city of Manchester. The system should be designed to enhance usability and prevent abuse or misuse. There are three main categories of human factors as discussed below Anthropometric factors These are factors related to human bodys physical dimensions. It is important for the designers to ensure that drivers and the crew have adequate space to execute their tasks, jobs and duties effectively(Naweed Moody, 2015). The type and sizes of seats and other areas where they perform their functions should be adequate to prevent hindrances. Designers can create simulations of operating conditions so as to collect relevant experiment data or use data from past projects to know the right dimensions of various components. Sensory factors Sensory factors include sight/vision, hearing, touch/feeling, smell, etc. Operators of the light trains and/or vehicles should have sufficient horizontal and vertical fields so as to perform their jobs effectively and prevent accidents. The communication systems should be audible enough and the operators should not be affected by unnecessary noise. Workstations of the LRT should also provide a good sense of touch to the operators. Physiological factors These are environmental factors that affect operators of the light rail trains when on duty. They include extreme temperatures, humidity, noise, vibration, toxic substances, gas and radiation. All these can be avoided by ensuring proper design (layouts and materials). The system should be designed to ensure that operators of the light rail are not subjected to stresses, strain, trauma, fatigue, etc. that can reduce their operational efficiency(Mitra, et al., 2010). Besides considering human factors and ergonomics affecting personnel, the designers should also put in mind the needs of passengers. These include comfort, safety, health, reliability and affordability. Therefore it is important for the design team to consider the unique requirements of drivers, crew and passengers during operation phase of the LRT when designing the system. Conclusion and recommendations Many cities in different parts of the world have been able to ease traffic congestion, reduce carbon emissions and boost social and economic development through use of LRT networks. This is because LRT is a high-tech, efficient, reliable and flexible transportation mode with numerous benefits over others modes of transport. Therefore the city of Manchester stands to benefit a lot from an LRT project. Nevertheless, LRT can only attain its potential environmental, social and economic benefits if it is designed appropriately. All activities undertaken during preliminary design stage, detailed design and development stages, and system test, evaluation, validation and optimization stages are very critical and should be treated as such. These are stages where the LRT solution selected in conceptual design phase is demonstrated to be the best for the transportation problem in the city and relevant subsystems designed, tested, evaluated, validated, optimized and integrated to create one syst em. For this to be attained, it is very important for the client to develop precise project requirements and have necessary resources before bringing in other stakeholders to start designing the system. All tasks in the preliminary design stage, detailed design and development stages, and system test, evaluation, validation and optimization stages must also be performed by qualified personnel. Besides ensuring that the designs created meet all technical, functional and performance requirements of the project, designers should also consider human factors when developing the LRT network. These include anthropometric, human sensory and physiological factors. Additionally, all decisions should be made by considering their environmental, economic and social impacts. The stakeholders should also work on the project as a team through appropriate coordination, collaboration and consultation. Most importantly is to review every process completed before proceeding to the next stage. References Blanchard, b. Fabrycky, W., 2010. Systems engineering and analysis. 5th ed. New Jersey: Prentice Hall. Cervero, R. Sullivan, C., 2011. Green TODs: Marrying transit-oriented development and green urbanism. International Journal of Sustainable Development and World Ecology, 18(3), pp. 210-218. Cleghorn, D., 2009. Improving pedestrian and motorist safety along light rail alignments, Washington, D.C.: Transportation Research Board. Goral, J., 2007. Risk management in the conceptual design phase of building projects. Goteborg, Sweden : Chalmers University of Technology. Hess, D. Almeida, T., 2007. Impact of proximity to light rail rapid transit on station-area property values in Bufallo, New York. Urban Studies, 44(5/6), pp. 1041-1068. Higgins, C., Ferguson, M. Kanaroglou, P., 2014. Light railway and land use change: railway transit's role in reshaping and revitalizing cities. Journal of Public Transportation, 17(2), pp. 93-112. Li, L., Hu, J. Shao, D., 2012. Effects of accelerated development of urban rail transit in Shanghai before the World Expo on greenhouse gas emission reduction. China Environmental Science, 32(6), pp. 1141-1147. Luna, S. et al., 2013. Integration, verification, validation, test and evaluation (IVVTE) framework for system of systems (SoS). Procedia Computer Science, Volume 20, pp. 295-305. MacDonald, J. et al., 2010. The effect of light rail transit on body mass index and physical activity. American Journal of Preventive Medicine, 39(2), pp. 105-112. Mitra, B., Al Jubair, J., Cameron, P. Gabbe, B., 2010. Tram-related trauma in Melbourne, Victoria. Emergency Medicine Australia, 22(4), pp. 337-342. Naweed, A. Moody, H., 2015. A streetcar undesired: investigating ergonomics and human factors issues in the driver-cab interface of Australian trams. Urban Railway Transit, 1(3), pp. 149-158. Seo, K., Golub, A. Kuby, M., 2014. Combined impacts of highways and light rail transit on residential property values: a spatial hedonic price model for Phoenix, Arizona. Journal of Transport Geography, Volume 41, pp. 53-62. Shang, B. Zhang, X., 2013. Study of emission reduction: benefits of urbanrail transit. Procedia - Social and Behavioral Sciences, Volume 96, pp. 557-564. Topalovic, P., Carter, J., Topalovic, M. Krantzberg, G., 2012. Light rail transit in Hamilton: health, environmental and economic impact analysis. Social Indicators Research, 108(2), pp. 329-350.