Using Industry Average Multiples For Valuation Finance Essay

Using Industry Average Multiples For Valuation Finance Essay Valuation of equity shares of a company is an important exercise and is performed on multiple occasions, be it investment decision in a particular company, merger, acquisition, restructuring, public issue, etc. Using industry average multiple is a common practice, especially when an unlisted security is to be valued. The study looks at eight industries and attempts to derive (a) which is the most stable industry average multiple by using the statistical tool coefficient of variation and (b) which would be the most important financial performance parameter, which could be driving multiple of a particular security within the industry by using statistical tool of coefficient of correlation. Executive Summary A company will get valued/re-valued on multiple occasions such as raising capital, sale of business, swap of shares, issue of stock options, etc. Valuation of publicly traded securities is quite straightforward and often regulated for different events, while valuation of thinly traded or un-traded securities requires some special approaches. There are three main approaches to security valuation such as discounted cash flows, asset based valuation and comparables. Comparables are regarded as one of the most useful and practical method. Ideal approach within comparables is to find out a publicly traded company which is exactly like the company being valued and adopt an appropriate multiple as valuation metric. Finding such a company is a challenge. Even if a company is financially alike, many non-financial factors such as general market reputation, stock liquidity, etc. could be influenced its valuation of a particular stock. Experts often use industry average multiples to counter this anomaly. They could be used on a stand-alone basis or along-with a set of exact comparables. The articles analyses the concept of industry multiples in eight industries: Private sector banks, Public sector banks, General food processing, Agri Inputs, Edible Oil, Rice, Sugar, Plantations (tea, coffee, flowers) and Auto-components and tries to answer two questions: Which is the most appropriate industry average multiple? The criterion used is co-efficient of variation. Multiples used are Market Capitalisation (MCap) / Profit After Tax, Enterprise Value (EV) / Earnings Before Interest Taxes Depreciation and Ammortisation (EBITDA), MCap/Book Value, MCap/Sales Which factor is the major driver of a multiple in a particular industry? The author has calculated co-efficient of correlation between different multiples and factors like revenues, 5 year revenue growth, margins, total assets, provisions, Return on Equity (ROE), Net worth. EV/EBITDA was the most stable multiple followed by Mcap/PAT (similar to P/E ratio). Revenue, net-worth and margins were important drivers. Keywords: Industry average multiple, valuation, market capitalization, book value, coefficient of variation/correlation Background There are many situations wherein a company will get valued/re-valued such as raising capital, sale of business, swap of shares, issue of stock options, etc. While, valuation is easy and fairly regulated (SEBI, the regulator in India has defined how a security is to be valued for different purposes) for a publicly traded company, valuation of a thinly traded or un-traded securities requires some special approaches. At times, analysts also value a well-traded company to determine whether it is value fair or if there is any possible up-side. Different approaches to valuation are as described below: Comparables Asset Value EBITDA PAT Book Value Sales, etc. Equity Value DCF Figure 1 Different valuation methods Asset Value: Asset based approaches such as book value (asset less liabilities as reflected in books of accounts) and realizable value (market value of asset less liabilities) are more relevant when the company/vehicle is wound-up or dissolved in any manner. Discounted Cash Flow (Discounted Cash Flow to the Firm): Discounted cash flow is, theoretically, the best valuation method. The company calculates its projected financial performance. These projections and their assumptions are vetted against market factors, expert opinions. Once the parties are confident with projections, cash flows of the company (called Cash Flow to the Firm) are calculated as follows: EBIT X (1-Tax Rate) Less Working Capital Changes Less Capital Expenditure Add Depreciation. An important component of DCF based valuation is the Terminal Value. Last year in the projection period is capitalized as: Cash flow in terminal year X (1+ perennial growth rate) / (WACC perennial growth rate). This is again discounted to calculate present value of terminal cash flow. This approach is well recognized, but is not widely used due to the following limitations: The model involves a number of assumptions (i) Entire set of assumptions going into calculation of financial projections, (ii) Market risk premium, (iii) Long term growth rate, etc. which makes it very subjective. The method does not work with firms which have un-utilised assets, are in the process of re-structuring, which do not have positive operating cash flows, etc. Comparables: One of the most preferred methods of valuing a company is comparing it with a publicly traded company of similar nature called relative valuation. It is also the most intuitive method we practice it in pricing almost everything real estate, items of daily usage, etc. In relative valuation, the value of an asset is derived from the pricing of comparable assets, standardized using a common variable such as earnings, cash flows, book value or revenues. (Damodaran on Valuation: Security Analysis for Investment and Corporate Finance, by Ashwath Damodaran, Wiley Finance) A publicly traded peer is identified and compared to the company under consideration in terms of various valuation parameters like Price to Earnings, Price to Book, Price to Sales, Enterprise Value / EBITDA which ever is applicable and accordingly the value of the company/security under consideration can be calculated, e.g. If a comparable company is traded at 15 times its earnings, the earnings of the company under consideration are multiplied by 15 to calculate its value. The approach is fairly simple, however, the challenge lies in finding an exact comparable. There can be many differentiating factors, and some of them could be quite stark. The pricing of the publicly traded peer would also be influenced by many non-objective factors like: general market perception, promoter reputation, adverse market rumors, low liquidity in specific stock, low level of public holding, etc. In light of these, many analysts and industry experts use industry-average multiples, on a stand-alone basis as well as to moderate/rationalize multiples of an individual or group of comparables. This brings us to the questions which the article intends to ponder over: Which bench-mark should be used? Every industry has two or three popular benchmarks, which appropriately capture financial and operative strengths, such as the tea gardens are valued at certain times of their sales, so are football clubs. Manufacturing industries are valued at certain time of their EBITDA or PAT as the case may be. However, if an industry average is to be used, high degree of variability in the multiple will compromise its reliability. Another question is what drives a companys valuation. The range in multiples in many industries tends to be quite high. Some tangible financial factor could be an important driver/differentiator for a company. Which would be the driver in a particular industry? The article attempts to answer these questions via an exercise on 214 companies in 8 different industries. The author has: Chosen 8 industries based on his past work experience Selected different publicly listed companies in each industry Derived their multiples and financial parameters from various databases Checked the variability of industry averages of multiples by using the statistical tool co-efficient of variation to answer the first question (most reliable benchmark) Run correlation between a particular industry relevant bench-mark such as 5 year growth, margins, etc. and the multiple e.g. correlation between P/E ratios and book size in banking industry to answer the second question. The breakup of companies across industries is as follows: Table 1 Sectors and number of companies used in analysis Industry No of companies Private sector banks 14 Public sector banks 23 General food processing 16 Agri Inputs 8 Edible Oil 17 Rice 7 Sugar 17 Plantations (tea, coffee, flowers) 17 Auto-components 85 Total 214 The following multiples were used: Market Capitalisation (MCap) / Profit After Tax, Enterprise Value (EV) / Earnings Before Interest Taxes Depreciation and Ammortisation (EBITDA), MCap/Book Value, MCap/Sales. Mcap/PAT is similar to more commonly used Price to Earnings per share (P/E), and Mcap/Book Value is similar to Price to Book value per share (P/B). The following financial performance parameters were selected for analysis: Revenues of latest available financial year, 5 year revenue growth, margins (PAT margin for banks and EBITDA margins for others), total assets, provisions, Return on Equity (ROE), Net worth Analysis Private Sector Banks The following banks were analysed within private sector banks: HDFC Bank Ltd., ICICI Bank Limited, Axis Bank Limited, IndusInd Bank Limited, Yes Bank Ltd, Federal Bank Limited, ING Vysya Bank Limited, The Jammu Kashmir Bank Limited, Karur Vysya Bank Ltd., South Indian Bank Limited, City Union Bank Ltd., Karnataka Bank Ltd, Development Credit Bank Ltd., Lakshmi Vilas Bank Limited. Table 2 Results of private sector banks Banks (private) Multiple Parameter Mcap/PAT Mcap/Assets Mcap/Sales Mcap/Book Value Mean 8.40 0.09 0.88 1.19 StdEv 5.43 0.08 0.82 0.93 Coeff of Variation 0.65 0.99 0.93 0.78 Correlation between multiple parameter Revenue 0.10 0.08 0.09 0.09 Past 5 year growth 0.20 0.34 0.36 0.48 Margin 0.32 0.59 0.61 0.63 Total Assets 0.07 0.06 0.06 0.07 Provisions -0.05 -0.10 -0.09 -0.07 ROE 0.01 0.32 0.34 0.43 Net Worth 0.18 0.18 0.18 0.16 MCap/PAT, similar to Price to Earnings showed maximum stability. Margin (calculated as PAT/Revenue) showed maximum correlation with MCap/PAT, followed by high growth rate. The MCap/Book value Price to Book in popular parlance and Return On Equity showed the maximum correlation across all multiples and parameters. Margin and ROE showed maximum correlation with MCap/PAT. Public Sector Banks Public sector banks tend to have different operating objectives and are often valued differently compared to private sector banks. Mcap/PAT of public sector banks is 5.41 v/s 8.40 as observed in private sector banks. The following public sector banks were analysed: Indian Overseas Bank, Andhra Bank, Corporation Bank, Central Bank Of India, UCO Bank, Dena Bank, Bank of Maharashtra, State Bank of Bikaner and Jaipur, State Bank of Travancore, State Bank of Mysore, United Bank of India, Punjab Sind Bank. Table 3 Results of public sector banks Banks (public) Multiple Parameter Mcap/PAT Mcap/Assets Mcap/Sales Mcap/Book Value Mean 5.41 0.04 0.45 0.69 StdEv 1.36 0.01 0.15 0.16 Coeff of Variation 0.25 0.29 0.32 0.23 Correlation between multiple parameter Revenue 0.34 0.61 0.65 0.60 Past 5 year growth -0.05 0.12 0.13 0.07 Margin -0.46 0.79 0.81 0.76 Total Assets 0.31 0.62 0.70 0.63 Provisions 0.44 0.51 0.50 0.46 ROE -0.64 0.57 0.57 0.69 Net Worth 0.29 0.70 0.75 0.65 Public sector banks showed a different trend in variability of multiples. The book value multiple seems to show the least variation around mean as compared to Mcap/PAT observed in private banks. Within the book value multiple, margins show the highest correlation of 0.76 followed by ROE, 0.69. Food processing Food processing falls into manufacturing domain. EV/EBITDA multiple is introduced in place of the Total Assets multiple is relevant to the banking and NBFC company wherein income is primarily driven by book size. EV/EBITDA is one of the most popular multiples in manufacturing sector. It captures the operating strength of a company (EBITDA) v/s Enterprise Value. Enterprise value is a debt and cash neutral metric, calculated by Market Capitalisation + Debt Cash. Table 4 Results of food processing (general) Food Processing Multiple Parameter Mcap/PAT EV/EBITDA Mcap/Sales Mcap/Book Value Mean 18.50 9.39 0.97 4.21 StdEv 14.09 6.64 1.61 7.36 Coeff of Variation 0.76 0.71 1.65 1.75 Correlation between multiple parameter Revenue 0.39 0.64 0.49 0.75 Past 5 year growth -0.34 -0.27 -0.17 -0.14 EBITDA Margin 0.02 0.20 0.57 0.26 ROE 0.50 0.80 0.75 0.90 Net Worth 0.06 0.31 0.34 0.34 EV/EBITDA shows the lowest variation around mean (0.71). ROE is the most important driver for this multiple (0.8 correlation), followed by revenue. The following companies were considered for analysis in food processing: Hatson Agro Products REI Agro, Heritage Foods, KSE Limited, Nestle India Ltd., Glaxo SmithKline, Britannia Industries, Zydus Wellness, DFM Foods Ltd., Vadilal Industries, Himalya International, ADF Foods, Anik Industries, Srinivasa Hatcheries, Flex Foods, Bambino Agro, Foods and Inns, Tasty Bite Eatables, Freshtrop Fruits, Temptation Foods, Chordia Food Products. Vadilal Enterprises, Sita Shree Food Products, Simran Farms, Venkys (India), Waterbase. The companies belonged to multiple sub-sectors like dairy, poultry, consumer goods, ice creams, frozen food, etc . Agri Inputs Agri inputs included seed, special fertilizers and some special input companies in food processing industries. The larger fertilizer companies, which fall more into chemicals domain were not considered. The following companies were anlysed: Sukhjit Starch Chemicals, Narmada Gelatines, Sakuma Exports, Vidhi Dyestuffs, Saboo Sodium Chloro, Kaveri Seed, Advanta India, Basant Agro Tech. In agri inputs also, EV/EBITDA showed maximum stability, followed by MCap/PAT. EBITDA margin showed highest correlation with EV/EBITDA. Table 5 Results of specialised agri inputs Agri Input Multiple Parameter Mcap/PAT EV/EBITDA Mcap/Sales Mcap/Book Value Mean 11.27 8.53 0.98 1.68 StdEv 9.64 5.11 1.28 1.87 Coeff of Variation 0.86 0.60 1.30 1.11 Correlation between multiple parameter Revenue -0.36 0.25 0.04 0.11 Past 5 year growth -0.18 0.25 0.26 0.40 EBITDA Margin -0.88 -0.08 0.71 0.11 ROE 0.02 -0.03 0.40 0.48 Net Worth 0.33 0.66 0.48 0.53 Edible Oil: Edible oil is a special segment within food processing. The sector is characterized by high level of imports, benchmarking with international prices, low regulations compared to commodities like rice and pulses, etc. The following companies were anlysed: Ruchi Soya Industries, Sanwaria Agro Oils, Rasoya Proteins, Gujarat Ambuja Exports, Jayant Agro-Organics, JVL Agro Industries, Vippy Industries Limited, Vimal Oil Foods, Raj Oil Mills, BCL Industries, Hind Industries, Kriti Nutrients, Vijay Solvex, Sam Industries, Modi Naturals, Natraj Proteins, Poona Dal Oil Industries Table 6 Results of edible oil Edible Oil Multiple Parameter Mcap/PAT EV/EBITDA Mcap/Sales Mcap/Book Value Mean 11.10 6.43 0.21 1.53 StdEv 9.77 4.18 0.27 2.20 Coeff of Variation 0.88 0.65 1.31 1.44 Correlation between multiple parameter Revenue 0.43 -0.12 -0.12 -0.03 Past 5 year growth -0.28 -0.36 0.99 -0.20 EBITDA Margin -0.01 -0.03 0.51 0.16 ROE -0.20 -0.12 0.58 0.61 Net Worth 0.38 -0.14 -0.11 -0.04 EV/EBITDA showed the maximum stability, however, none of the parameters showed any reasonable correlation with the parameter. EV/EBITDA was followed by Mcap/PAT with 0.88 coefficient of variation. This factor showed relatively higher correlation with revenue followed by Net Worth. Rice Rice is also a typical sector within food processing. Most of the publicly traded rice companies have focused on basmati rice. Basmati is a famous variety of aromatic rice and has large export market in the middle east, Europe and US. The following companies were analysed: Khushi Ram Behari La, Usher Agro, LT Food, Lakshmi Energy and Foods, Emmsons International, Chaman Lal Setia Exports, GRM Overseas. The sector showed better stability of Mcap/PAT followed by Mcap/Book Value. Within Mcap/PAT EBITDA margin showed the highest correlation at 0.86. Table 7 Results of rice Rice Multiple Parameter Mcap/PAT EV/EBITDA Mcap/Sales Mcap/Book Value Mean 6.12 7.94 0.16 0.68 StdEv 2.27 3.95 0.13 0.30 Coeff of Variation 0.37 0.50 0.83 0.44 Correlation between multiple parameter Revenue 0.42 0.68 0.16 0.17 Past 5 year growth -0.70 0.47 -0.92 -0.98 EBITDA Margin 0.86 -0.60 0.59 -0.25 ROE -0.77 0.12 0.11 0.73 Net Worth 1.00 -0.17 0.55 -0.30 Sugar: Sugar is one of the largest organized sectors in agri processing. The sector has many large companies like Renuka Sugars, Bajaj Hindustan, etc. The sector also has some typical features like minimum procurement price, cyclical production, concentrated production in Asia and South America, etc. The following companies were analysed: E.I.D. Parry, Bajaj Hindusthan, Bannari Amman Sugars, Triveni Engineering, Andhra Sugars, Dhampur Sugar Mills, KCP Sugar, Ponni Sugars (Erode), Ugar Sugar Works, Dalmia Bharat Sugar, Thiru Arooran Sugars, Sri Chamundeswari, Piccadily Agro, Vishnu Sugar Mills, Kesar Enterprises, Piccadily Sugars, Indian Sucrose EV/EBITDA showed lowest co-efficient of variation (0.44). The multiple showed highest correlation with net worth, followed by revenue. Table 8 Results of sugar Sugar Multiple Parameter Mcap/PAT EV/EBITDA Mcap/Sales Mcap/Book Value Mean 14.38 6.90 0.35 0.80 StdEv 14.79 3.07 0.20 0.39 Coeff of Variation 1.03 0.44 0.56 0.48 Correlation between multiple parameter Revenue -0.01 0.20 0.00 0.19 Past 5 year growth -0.26 -0.04 -0.42 0.23 EBITDA Margin -0.51 -0.43 0.49 0.18 ROE -0.65 -0.69 0.44 0.61 Net Worth -0.01 0.44 0.08 0.01 Plantations Tea and Coffee are another specialized area in agri and food industries. The sector has stakes of many large FMCG companies like Tata Tea, Unilever, etc. This sector also has special policies, farming conditions, competitive factors. For the purpose of this analysis, flowers have also been analysed together with tea and coffee. The following companies for part of this analysis: Karuturi Global, Neha International, Pochiraju Industries, Tata Global Beverage, McLeod Russel India, Tata Coffee, CCL Products India, Warren Tea, Dhunseri Petrochem, Goodricke Group, Jayshree Tea, Assam Company India, Harrisons Malayalam, Russell India, United Nilgiri Tea, Joonktollee Tea, Diana Tea. Here also, EV/EBITDA showed minimum coefficient of variation, followed by Mcap/Sales. Revenue and net worth showed the highest correlation with EV/EBITDA. Table 9 Results of plantation (tea, coffee, flowers) Plantation (tea, coffee flowers) Multiple Parameter Mcap/PAT EV/EBITDA Mcap/Sales Mcap/Book Value Mean 15.17 9.60 1.09 1.13 StdEv 13.19 5.70 0.81 0.87 Coeff of Variation 0.87 0.59 0.75 0.76 Correlation between multiple parameter Revenue 0.22 0.33 0.09 0.27 Past 5 year growth -0.47 -0.38 -0.19 -0.43 EBITDA Margin -0.34 -0.42 0.20 -0.11 ROE -0.37 -0.27 0.20 0.54 Net Worth 0.18 0.29 0.16 0.21 Auto components Auto components industry comprises of a large number of specialized players focusing on different segments of auto industry. Major segments and their composition in total industry size are: Engine parts 31% Drive transmission and steering parts 19% Body and Chassis 12% Suspension and braking parts 12% Equipments 10% Electrical parts 9% Miscellaneous 7% The industry is estimated at USD 43.5 billion in FY 2011-12. (Auto Components Manufacturers Association of India) The following companies were anlaysed in the industry: Bosch, Cummins India, Exide Industries, Motherson Sumi Systems, WABCO, Amtek India, Kirloskar, Amtek Auto Limited, Federal-Mogul, Sundram Fasteners, Wheels India, Shanthi Gears, NRB Bearings, Automotive Axles, Mahindra Forgings, Commercial Engineers, Banco Products, Jamna Auto Industries, Fairfield Atlas, Gabriel India, Lumax Industries, Sundaram-Clayton, India Motor Parts, Saint-Gobain, Steel Strips Wheels, Setco Automotive, Minda Industries, Suprajit Engineering, Rane Holdings, ZF Steering Gear, Munjal Showa, Sona Koyo Steering, Munjal Auto, Lumax Auto Technology, Autoline Industries, India Nippon, FIEM Industries, L. G. Balakrishnan, Subros, Pricol, Hindustan Composites, Ucal Fuel Systems, Rane Madras, Rico Auto Industries, Jay Bharat Maruti, Shivam Autotech, Omax Autos, IST, Bimetal Bearings, Rane Engine Valves, REIL Electricals, Rane Brake Lining, Precision Pipes, Automotive Stampings, Harita Seating, JMT Auto, Alicon Castalloy, JBM Auto, Bharat Gears, Menon Pistons, Talbros Automotive, Triton Valves, Aunde India, Clutch Auto, Pix Transmissions, Bharat Seats, Lakshmi Precision, Menon Bearings, Simmonds Marshall, Kar Mobiles, IP Rings, Jay Ushin, Gujarat Automotive, Competent Automobile, Lumax Automotive Systems, Autolite India, ANG Industries, Hindustan Hardy, Raunaq Automotive, Remsons Industries, Porwall Auto Components, Spectra Industries, Kew Industries, Jagan Lamps, Coventry Coil-O Matic. In this industry again, EV/EBITDA is the most stable multiple. EV/EBITDA shows maximum correlation with revenue and net-worth. Table 10 Results of auto-components Auto Components Multiple Parameter Mcap/PAT EV/EBITDA Mcap/Sales Mcap/Book Value Mean 12.47 6.04 0.67 1.62 StdEv 13.09 4.65 0.93 1.67 Coeff of Variation 1.05 0.77 1.40 1.03 Correlation between multiple parameter Revenue 0.19 0.35 0.18 0.31 Past 5 year growth -0.04 0.05 -0.05 0.09 EBITDA Margin 0.03 0.06 0.47 0.12 ROE -0.31 0.04 0.21 0.45 Net Worth 0.13 0.35 0.30 0.24 Inferences: The most stable multiples across different industries and their respective coefficients of correlations with different financial parameters were as follows: Table 11 Summary of trends Coefficient of variation Correlation Industry Co-efficient of Variation Multiple Highest Correlation Second highest Correlation Private sector banks 0.65 MCAP/PAT Margin 0.32 Past 5 year growth 0.20 Public sector banks 0.23 P/B Margin 0.76 ROE 0.69 General food processing 0.71 EV/EBITDA ROE 0.80 Revenue 0.64 Agri Inputs 0.60 EV/EBITDA Net worth 0.66 Revenue 0.25 Edible Oil 0.88 MCAP/PAT Revenue 0.43 Net worth 0.38 Rice 0.37 MCAP/PAT Net worth 1.00 EBITDA margin 0.86 Sugar 0.44 EV/EBITDA Net worth 0.44 Revenue 0.20 Plantations (tea, coffee, flowers) 0.59 EV/EBITDA Revenue 0.33 Revenue 0.29 Auto-components 0.77 EV/EBITDA Revenue 0.35 Revenue 0.35 *In edible oil, lower coefficient was observed in EV/EBITDA. P/E was chosen because EV/EBITDA showed no correlation with any of the parameters studied. Co-efficient of variation was minimum in public sector banks and highest in auto-components. Industry multiple of public sector banks, hence, stands as the most reliable industry multiple among the industries observed. The co-efficient would be high if there is considerable heterogeneity within the industry in terms of size, profitability, product portfolio, promoter background, etc. Earnings based multiples EV/EBITDA and P/E showed minimum coefficient of variation in all industries, except public sector banks, which showed Mcap to Book Value as the most stable multiple. Considering the correlations observed with the most stable multiple, we can infer that: net margins are the main drivers of multiples in banks (both public and private) among the parameters observed, ROE was most influential in food processing and edible oil plantations and auto-components seem to be driven by revenue vis-Ã  -vis other parameters observed and agri inputs, rice and sugar were influenced by net-worth of respective companies. The following table shows the maximum correlation observed in a particular industry. Table 12 Maximum correlations across industries Industry Maximum Correlation Relationships Private sector banks 0.63 ROE and Mcap/Book Value Public sector banks 0.81 PAT Margin and Mcap/Book Value General food processing 0.90 ROE and Mcap/Book Value Agri Inputs 0.71 EBITDA margin and Mcap/Sales Edible Oil 0.99 5 year growth and Mcap/Sales Rice 1.00 Net-worth and Mcap/PAT Sugar 0.61 ROE and Mcap/Book Value Plantations (tea, coffee, flowers) 0.54 ROE and Mcap/Book Value Auto-components 0.47 EBITDA margin and Mcap/Sales ROE and Mcap/Book Value showed highest correlation in four out of nine industries, followed by EBITDA margin and Mcap/Sales. The results were quite intuitive a company generating higher returns on invested capital (ROE), or a company operating at a higher margin should be valued more than its peers. Table 13 Results of general correlation analysis Parameter Mcap/PAT

asia pop. :: essays research papers

Right now there is a major problem involving the population of South Asia. In India’s best years just about half of the population was properly fed because the population was so enormous. Not to add that the foods they get are fruits, vegetables, and rice. This is not a way to live. Also AIDS is a pretty big problem in India. It is estimated that one in five adults will have been diagnosed with the AIDS virus by the year 2003. Because prostitution is legal in parts if India the AIDS virus can spread very quickly. Besides the AIDS rate skyrocketing, the birthrate does too. I have created a plan worldwide to help India and other countries that need help. In my plan most of the Funding will be provided by a simple tax. I plan on raising tobacco costs by 25 cents. Right now the United States makes 400 billion sales in tobacco a year. That means a lot of money would be available after my 25-cent tax. That tax money would go towards India’s government for education. Phase two of my plan I plan on making prostitution illegal in India. That would cut India’s AIDS population by one third. Officers will enforce the streets and the government would not have to pay extra because of the tobacco increase. Tobacco money will build new jails and hire more officers. This will also provide more jobs. Prostitution crimes will receive a minimum of two years in jail (for the first offense). Phase three takes place next year. It is a law that permits families to have no more than one child. The family will receive two thousand dollars for having only one child. If the family has more than one child the family will have to pay a heavy tax of fifteen thousand dollars. If the family cannot afford to pay the tax the father and mother are forced to alternate turns in jail for a minimum of three years. This plan is not harming the individual because they are harming themselves my having children. The plan will be announced now and promoted for future notice. This way next year the plan can take effect. New jails will be built for the people that do not pay the child tax. The next phase of my plan is to educate the country. More tobacco money will be spent on educating all of India.

Imprisonment and Social Control Essay -- Prison Justice

Imprisonment is one of the primary ways in which social control may be achieved; the Sage Dictionary of Criminology defines social control as a concept used to describe all the ways in which conformity may be achieved. Throughout time imprisonment and its ideas around social control have varied. Imprisonment has not always been used for punishment, nor has it always thought about the prisoners themselves. However when looking at imprisonment it is important to consider the new penology. Therefore, it needs to be clear what the new penology is. The new penology is said, not to be about punishing individuals or about rehabilitating them, but about identifying and managing unruly groups in society. It is concerned with the managerial processes, not the individual’s behaviour or even community organisation. All in all, its goal is to make crime tolerable, not to eliminate it entirely. (Feeley, M and Simon, J). Therefore the New Penology is not about the reform of individuals but the control of populations as a whole, with imprisonment focusing on particular offenders who are defined as ‘persistent’ or ‘high rate’. In light of this, the history of imprisonment, the purposes of imprisonment and indeed the question of whether it works as a form of social control or not all need to be addressed, as well as looking into the critics of the new penology. Imprisonment has a number of purposes, the first being punishment, which brings with it the idea of retribution and revenge. The second purpose is incapacitation, this looks at the protection of society and the length of time the individual must serve in prison. Deterrence is the third purpose; it attempts to prevent the individual committing any future crime and goes some way to deter ... ...ology. Devon: Willan Publishing, pp 684-713. Sampson, R, and Laub, J. (1933), ‘Individual Factors in Crime’, in Newburn, T. Criminology. Devon: Willan Publishing, pp. 843. Shichor, D. (1997) ‘Three Strikes as a Public Policy: The Convergence of the New Penology and the McDonaldization of Punishment’, Crime Delinquency, (43), pp. 470-492. Spelman, W. (2000) ‘What Recent Studies Do (and Dont) Tell Us About Imprisonment and Crime’. In Michael, T. Crime and Justice: A Review of Research. (3). Chicargo: University of Chicargo Press. Wilson, D. (2006) ‘Social Control’, The Sage Dictionary of Criminology: 391-392. London: Sage Publications. Woolfe, H. And Tumim, S. (1991). ‘Official Aims of Imprisonment’, in Cavadino, M. and Dignan, J. The Penal System: An Introduction. London: Sage Publications. Zedner, L (2004). Criminal Justice. New York, USA: Oxford University.

Write a Set of Instructions Explaining in Objective Terms

It is very important to pass down the formal rituals of the town to next generations. In order to pass down the ritual, the community will need a large black box to keep the folded papers together when the lottery starts, one folded paper with large black dot on, a stool to put the large black box on, and piles of stone. Remember the ritual might vary slightly from other communities, but the ritual is an important part of our society's history and its present. In our society, a lottery will be held every year, on July 27th at noon, since there are only 300 people; it is possible to finish the lottery before the late lunch.The ritual of the society is not only a tradition, but also a ritual to guarantee our success every harvesting season. Remember the quote â€Å"lottery in June, corn be heavy soon†. Steps in the ritual process are shown below. The lottery can be divided into a preparatory stage, lottery stage, and finishing stage. A preparatory stage takes a day or more. A ni ght before the June 27th, the lottery official should make a list of the heads of the families, and the members of each household in each family. Second, based on the number of the people on the list, the lottery official has to make lottery ballots.Remember, one ballot should be marked with a black circle. All ballots must be same size, and are folded in a same way. Third, if all the ballots are done, put the ballots inside the black box. On the following day, before the lottery starts, lottery official has to gather up the children and make them to collect stones and put it in a pile next to the town square where the lottery is held. After telling children, remind the people about the lottery through the announcements. Then, when everyone has gathered, the lottery official enters with a wooden black box, followed by a postmaster carrying a stool.Place the stool at the center of the square and put the black box on it. Second stage is before the lottery stage. After everyone have ga thered, remind people about the rules of the lottery; wife draws with the husband’s family, if there is somebody in absence, a substitute will choose the ballot to fill in the missing person, and everyone takes only one folded paper. The third stage is Lottery stage; when the lottery official had finished going over the rules of the ritual, proper swearing-in by the postmaster takes place; the official starts the recital of perfunctory tuneless chant.After all the perfunctory steps of the lottery are done, start the lottery immediately. The lottery official should read the names of the head of the families. When their family name is called, representing one's family, a head of the household approaches the black box and chooses one folded paper from the box. When every man representing each family had drawn, open the paper and check which family has won the first-round lottery. Check the number of family members of the the family that had drawn the paper with the black dot.Aft er counting the number of the family members, make a set of ballots just for the family that had drawn the paper with the black dot (make sure every member of the family would pick one folded paper from the box, and one of the folded papers inside the box is marked with a black dot). When the lottery official had finished making new sets of ballots for the second round lottery, call each member of the family into the box, and make everyone take one folded paper from the box.Remind each family member to not open the folded paper until everyone had finished choosing the folded paper. After every member of the family had picked their choice of folded paper, tell the participants to open the paper. Announce the winner of he lottery. The last step is finishing stage. Start the ritual of the lottery. Use stones that boy had gathered up. Stone the winner till the death. Announce the end of the ritual lottery, and dismiss the crowd. The lottery official takes the box and put the box away un til the next lottery in the next year.

Development of Haiti 2010

Haiti is the poorest country in the Western Hemisphere with 80% of the population living under the poverty line and 54% in horrible poverty. Two-thirds of all Haitians depend on the agricultural sector, mainly small-scale subsistence farming, and remain at risk to damage from frequent natural disasters as well as the country's widespread growth of deforestation (much of the remaining forested land is being cleared for agriculture and used as fuel).While the economy has recovered in recent years, registering positive growth since 2005, four tropical storms in 2008 along with the recent storm that had hit Haiti this year in 2010 severely damaged the transportation, communications, and agricultural areas. Larger scale agricultural products in Haiti include coffee, mangos, sugarcane, rice, corn, sorghum and wood. Although industry is small, sugar refining, textiles and some assembly are common in Haiti. The economic inequality in Haiti is comparatively high. Expenditure distributions are highly slanted with the majority of expenditures at the low end.The GDP (gross domestic product) per capita in Haiti as of 2009 is $1,300. The number of the unemployed in Haiti is 3. 643 million people. The labor force rates in Haiti by occupation, for agriculture it is 66%, for services it is 25%, and for industry it is only 9%. In Haiti, those who can read and write are usually 15 and older. Typical males can read and write more so than girls, but only by a small percentage: males are 54. 8% literate and females are 51. 2% literate. Haiti has 15,200 primary schools, of which 90% are non-public and managed by the communities, religious organizations.The enrollment rate for primary school is 67%, and fewer than 30% reach 6th grade. Secondary schools enroll 20% of eligible-age children. Although, public education is free, private and unsophisticated schools provide around 75% of educational programs offered and less than 65% of those eligible for primary education are actually enrol led. Only 63% of those enrolled will complete primary school. Although Haitians place a high value on education, few can afford to send their children to secondary school. Remittances sent by Haitians living abroad are important in contributing to educational costs.Haiti meets most international human rights standards. In practice, however, many provisions are not respected. The government’s human rights record is poor. Political killings, kidnapping, torture, and unlawful custody are common unofficial practices. Medical facilities in Haiti are in short supply and for the most part they are all very poor quality; outside the capital standards are even lower than in Port-au-Prince. Medical care in Port-au-Prince is limited, and the level of community sanitation is extremely low. Life-threatening emergencies often require evacuation by air ambulance at the patient's expense.Doctors and hospitals often expect immediate cash payment for health services. The degree of risk in Hait i is quite high; half of the children in Haiti are unvaccinated and just 40% of the population has access to basic health care. Even before the 2010 earthquake, nearly half the causes of deaths have been attributed to HIV/AIDS, respiratory infections, meningitis and diarrheal diseases, including cholera and typhoid. Ninety percent of Haiti’s children suffer from waterborne diseases and intestinal parasites. Approximately 5% of Haiti's adult population is infected with HIV.Cases of tuberculosis in Haiti are more than ten times as high as those in other Latin American countries. Also, around 30,000 people in Haiti suffer each year from malaria. Environmental widespread growth of deforestation in Haiti as well as, soil erosion, poor supply of drinkable water, biodiversity, climate change, and desertification are some main causes as to why Haiti is such a poor and lacking country today. The forests that once covered the entire country have now been reduced to 4% of the total land area. Haiti loses 3% of its forests every year.Deforestation has had a disastrous effect on soil fertility, because the steep hillsides on which so many Haitian farmers work are particularly at risk to erosion. Another environmental factor that faces Haiti is the unplanned and unsustainable timber harvesting, agricultural clearing, and livestock cultivation that has thrown Haiti’s environment into crisis, creating the effects of hurricanes and floods on the already unstable country. Haiti’s transportation is not at all well; although they have 14 airports in Haiti, only 4 of them are paved and the other 10 are not.The road total mileage in Haiti is about 2,585 miles, only 628 miles of it is paved and 1,957 miles is unpaved. Haiti has only two main highways that run from one end of the country to the other. In the past Haiti used railroads, but today they are no longer in use due to other forms of transportation that have become available. The birth rate in Haiti is 24 . 92 births per 1,000 people of the population, and the death rate is 32. 31 deaths per 1,000 people of the population as of 2010. The infant mortality rate total is 77. 26% deaths per 1,000 live births; males have a higher death rate than females. Males having 81. deaths per 1,000 live births and females having 73. 07 deaths per 1,000 live births. Life expectancy of the total population is only 29. 93 years, males only having 29. 61 years and females living until around age 30. The reason for such a high mortality rate is due to AIDS; this can result in lower life expectancy, higher infant mortality, higher death rates, lower population growth rates, and changes in the distribution of population by age and sex than would otherwise be expected.Works Cited CIA. â€Å"CIA – The World Factbook. † Welcome to the CIA Web Site — Central Intelligence Agency. 27 Oct. 2010. Web. 05 Nov. 010. . Nicolas, Marc-Charles. â€Å"Facts about Haiti, About Haiti, Data and Populat ion of Haiti, Haiti Crime Report, Haiti Superficie, Haiti Superficy. † Haitisurf. com- Haiti Website, Haitian Website, Top Haitian Website – Haiti Tourism – Haiti Vacations. 2008. Web. 05 Nov. 2010. . Rival, Antonio. â€Å"Culture of Haiti – Traditional, History, People, Clothing, Traditions, Women, Beliefs, Food, Customs, Family, Social, Dress, Marriage, Men, Life, Population, Religion, Rituals. † Countries and Their Cultures. Web. 05 Nov. 2010. . U. S. Library of Congress. â€Å"Haiti – GEOGRAPHY. † Country Studies. Web. 17 Nov. 2010. .

A Plot Summary of a Movie About Cuban Refugees in America

A Plot Summary of a Movie About Cuban Refugees in America In 1980, Fidel Castro sent hundreds of thousands of Cubans from his country on rafts to America. Castro not only sent everyday people to America on boats; he used this as a chance to clear out his crowded jail cells. There was an estimated 25,000 former Cuban inmates that arrived in Florida. Tony Montana is one of the masses of criminals sent by Cuba to America. His only chance to get a green card was to kill Rebenga, a former political leader from Cuba. Montana had no trouble executing the former communist leader. Tony moves to Miami and agrees to do a job for a big time cocaine dealer, Frank Lopez. Frank noticed the good job Tony did for him and in turn wants Montana to continue working for him. Lopez sends him to Bolivia to work on a deal with Sosa, their cocaine supplier. While there Tony seizes an opportunity to go into business for himself. Tony shoots his way to the top of a drug crime family, killing both friend and foe alike. Finally after murdering his boss and marrying his bosss girlfriend, hes crowned drug kingpin. Tony starts having legal problems and rather than do time, his supplier Sosa offered to take care of the court case if Tony would take an assassin with him to New York to eliminate one of his enemies. This was the begging of the end for Tony Montana. The man who was supposed to be eliminated had his wife and kids in the car with him. Tony was against killing women and children but the assassin wouldn’t listen to him; so Tony shot him. Tonys decline was caused by his twisted sense of brotherhood. When Sosa learned of this he was furious. He sent a small army of men to Tony’s home to kill him. Tony didn’t go down easy, but in the end he still went down. Drugs in this movie portrayed money and power. Whoever distributed the drugs had control of the others around him who didn’t. The main goal of many characters in this movie was to get people under you so they can do your dirty work. All major characters in this movie were obsessed with money drugs, and women. Tony’s philosophy was: â€Å"First you get the money, then you get the power, and then you get the women. The unhealthy messages about drugs conveyed in this movie suggest that they are a quick way to make cash but can ultimately be someone’s downfall. Tony’s drug control led to other problems. He couldn’t get his money laundered from his normal banker because the more money Tony gave the banker, the higher the interest charge he would receive in return. Tony’s best friend set up a meeting with a different banker; who happened to be an undercover police officer. Drugs are also portrayed as a way to get women. Elvira, the woman of Tony’s obsession, went from one boyfriend to another based on who could supply her cocaine addiction. In one particular scene Frank tells Tony the two key rules to success in the drug business. Rule number one is to â€Å"never underestimate the other guys greed.† Rule number two is to â€Å"never get high on your own supply.† This came back to haunt Tony because later in the movie Frank hired two hitters to take Tony out right during a performance by octavio the clown. Just when octavio was waddling around the stage, the hitters opened fire. Luckily for Tony, they werent great shots. Tony managed to evade them and escaped through the door. Tony knew it was Frank. The only thing to do was to strike back. So, he went right over to Franks. Frank denied any involvement. He said it was the Diaz brothers. Tony wasnt fooled and Frank cracked. He admitted to what he did and knelt down and groveled at Tonys feet. Tony ordered his death. The last scene was the most memorable because Tony had crossed Sosa, the powerful cocaine supplier and Sosa sent his army to execute Tony. Tony was so doped up on cocaine that he took seven or eight shots in the chest and didn’t feel any pain. He made the statement: â€Å"Your bullets can’t hurt me.† It took a shotgun blast in the back to finally kill Tony. Tony’s death was as violent as his life. I would definitely recommend this movie to others, especially if they’re the kind of person who enjoys violent, action-packed movies. was very realistic and shows how a Cuban refugee could have climbed up ladder to become a very powerful drug dealer. This film was very violent and bloody. Compared to some films today, the violence isn’t as bad as people thought it was in 1983. One of my favorite scenes in the movie is when Tony’s friend Angel is getting cut into pieces with a chainsaw. The blood splatters everywhere. The music in this film sometimes seemed lifeless and boring. When you take into consideration that it was made almost eighteen years ago, the music doesn’t sound too bad. I think Frank Lopez’s first rule of how to succeed applies to every aspect of your business life. Because no matter how bad you may want something, there’s always going to be someone else who wants it more than you.

Just Desert Essay Example | Topics and Well Written Essays - 750 words

Just Desert - Essay Example The Just desert model suggests that retribution justifies punishment because individuals deserve what they received for past deeds. Under the just desert theory the punishment should be the same for all people who commit the same crime. But the idea is that does just desert punishments stops the offenders to repeat it again Has it created the peace balance in the society and is it beneficial for us in general Is the Punishment based on deterrence or incapacitation wrong In a research (Kevin M. Carlsmith and John M. Darley and Paul H. Robinson; Journal of Personality and Social Psychology 2002, Vol. 83, No. 2, 284-299) they came to know people are in favor of deterrence at macro level, but when it comes to individuals, people favor just desert because they consider it right and wrong doers deserve it. "The task of a just deserts theorist, then, is to assess the magnitude of the harm and to devise a punishment that is proportionate in severity, if not in kind. Kant (1952) recommended censure proportionate to a perpetrator's "internal wickedness," a quantity that may be approximated by society's sense of moral outrage over the crime". After researching for quite sometime, I came to know that many are in favor of just desert. They suggest that just desert results in social Control. When there are established criminal justice punishments in society and people know the degrees of punishment they will have to suffer if they committed any wrong deeds, people tend to think before doing anything. Because they are aware of retribution, and they know that punishment will be same for all levels of people without being bias, they feel just and are less likely to commit serious crimes. But some argue that being blind to class difference doesn't lead towards just. Ehrlich (1938: 363) pointed out that the more the rich and poor are dealt with according to the same legal propositions, the more the advantage of the rich is increased'. Galanter (1975: 363) puts it more beautifully. "The sailor over board and the shark are both swimmers, but only one is in the swimming business". Geertz (1983: 217) says that "there are number of f acts about the way the world works, mostly facts about the distribution of power, which prevent punishment being imposed on the most deserving of it. A policy of attempting punishment of all those who deserve it (and who can be caught) has the effect of increasing injustice, worsening tendencies to punish most where desert is least. This is because for the tendency for the law to be 'the most powerful where least needed, a sprinkler system that turns off when the fire gets too hot'." Some argue that the theory is fair to the offender if the punishment fits the crime; same punishment of all offenders for the same crime, etc which give people the sense of just. People know that it does not authorize selecting a criminal for particularly cruel punishment by random drawing, even if this would expend fewer overall social resources than imposing lower and proportionate punishment on all similar offenders, which is referred to as the consequentiality theory. Another benefit is that in just desert, people are punished according to the seriousness of the crime.