College Life Essay Example for Free

College Life Essay College is much different from my high school in so many ways. In my high school we was always on the same routine. We had four periods a day and first period started at 7:45 and we was on a block schedule meaning our classes was an hour and thirty minutes long. We had the same classes everyday so more learning during the day and less homework. Immediately after school we had football practice four days a week and played on Fridays. High school Is a good preparation for college, even though while in high school you will never expect what happens in college. In high school your parents were more involved in what go on in your daily life, whether you get in trouble in class or you get sick at lunch. College is an great experience I think everyone should have. In college you will learn responsibilities and how to take care of yourself. My life have change dramatically, went from seeing my family everyday to seeing them every three weeks maybe. The classes in college is much different than high school , there is no set schedule to do your homework or to study. This is where the responsible part come in, you are responsible whether you eat , sleep, study, have fun and even exercise. I didn’t mention being a student-athlete was hard as well, even though if I wasn’t an athlete I wouldn’t be a student. College not for everyone but if you have the opportunity at your grasp take advantage and make your family proud. Student-athletes have to set times like everyone else in college to complete there work and go to class but also they have to maintain there practice schedule daily. Being an athlete at Albany State is wonderful the fan base is so incredible and the odds are stack against us of making it pro. That’s why we are worked so hard and put to the test on the field and in the class room. Being a football player, practice start at 2:45 and end at 5:00 or so and some players have class after practice which is difficult to make some days but class is MANADTORY at Albany state. The coaches here are great and most of the coaches are alumni of this great school so they care about the organization deeply. Albany State Golden Rams is a Division ll power house which most teams underestimate and we show it to them on Saturdays. Here at Albany State Football players are respected not for what we do on the field but also of what we accomplish in the classrooms. I am very glad that I am able to attend Albany State University. Everything here is so overwhelming starting with The New Student Union Building . The food is amazing and the staff is amazing and caring. The games and the televisions are so entertaining and sitting in there is so relaxing and is a great study area. Campus life is the best of college experience meeting new people and socializing in the dorms. Here at Albany state are people that your going to be life long friends with. This campus is so secure thanks to the campus police. They are very concerned about the residents and love to protect and serve. If I had the choice to go to another institution I would not go Albany state is the life to live and the best college experience in the world. I appreciate what the staff, professors, coaches, police, RA’s , hall managers, and the Preside.

Determine the Empirical Formula of Magnesium Oxide by reacting a known mass of Magnesium with Oxygen Essay Example for Free

Determine the Empirical Formula of Magnesium Oxide by reacting a known mass of Magnesium with Oxygen Essay Safety Risk Assessment: Properly follow lab safety guidelines by wearing lab coats, gloves, and goggles and clear any personal belongings off the experiment area or the apparatus table. Gently handle glassware. Be careful when dealing with Bunsen burners. Make sure the crucible does not break while heating it as it might break due to the immense air pressure inside. Conclusion: Mg10O3 is the empirical formula we found through our experiment. Theoretically, the empirical formula is MgO. This is because Magnesium is a group 2 element so it would lose two electrons to form Mg2+ ion. Oxygen is a group 6 element so it would gain two electrons to form a O2- ion. So in theory, the empirical formula is MgO. Therefore we see were nowhere close obtaining a empirical formula to the theoretical answer though according to our uncertainty for Oxygen which is  ±3 it could be the oxygen we had weighed 5g and so therefore giving us a much accurate result of Mg10O2 which would go to a simplest ratio of Mg2O which would be closer to the theoretical answer. In addition to this according to our huge uncertainty the ratio could have also been 1:1 if the ratio would have led to 6:6. Though we see that the reason behind getting such an inaccurate answer is due to the incomplete combustion and due to not using a much more precise and accurate balance to weigh the apparatus and chemicals used.

Operations Management Of Customs Molds Commerce Essay

Operations Management Of Customs Molds Commerce Essay Custom Molds, Inc., manufactures custom-designed molds for plastic parts and produces custom-made plastic connectors for the electronics industry. Located in Tucson, Arizona, Custom Molds was founded by the father and son team of Tom and Mason Miller in 1975. Tom Miller, a mechanical engineer, had more than 20 years of experience in the connector industry with AMP, Inc., a large multinational producer of electronic connectors. Mason Miller had graduated from the University of Arizona in 1974 with joint degrees in chemistry and chemical engineering. The company was originally formed to provide manufacturers of electronic connectors with a source of high-quality, custom-designed molds for producing plastic parts. The market consisted mainly of the product design and development divisions of those manufacturers. Custom Molds worked closely with each customer to design and develop molds to be used in the customers product development processes. Thus, virtually every mold had to meet exacting standards and was somewhat unique. Orders for multiple molds would arrive when customers moved from the design and pilot-run stage of development to large-scale production of newly designed parts. As the years went by, Custom Molds reputation grew as a designer and fabricator of precision molds. Building on this reputation, the Millers decided to expand into the limited manufacture of plastic parts. Ingredient-mixing facilities and injection-molding equipment were added, and by the mid-1980s Custom Molds developed its reputation to include being a supplier of high-quality plastic parts. Because of limited capacity, the company concentrated its sales efforts on supplying parts that were used in limited quantities for research and development efforts and in pre-production pilot runs. Figure 3.13 Plant Layout PRODUCTION PROCESSES By 1985, operations at Custom Molds involved two distinct processes: one for fabricating molds and one for producing plastic parts. Although different, in many instances these two processes were linked, as when a customer would have Custom Molds both fabricate a mold and produce the necessary parts to support the customers RD efforts. All fabrication and production operations were housed in a single facility. The layout was characteristic of a typical job shop, with like processes and similar equipment grouped in various places in the plant. Figure 3.13 shows a schematic of the plant floor. Multiple pieces of various/ types of high-precision machinery, including milling, turning, cutting, and drilling equipment, were located in the mold-fabrication area. Fabricating molds is a skill-oriented, craftsman-driven process. When an order is received, a design team, comprising a design engineer and one of 13 master machinists, reviews the design specifications. Working closely with the customer, the team establishes the final specifications for the mold and gives them to the master machinist for fabrication. It is always the same machinist who was assigned to the design team. At the same time, the purchasing department is given a copy of the design specifications, from which it orders the appropriate raw materials and special tooling. The time needed to receive the ordered materials is usually three to four weeks. When the materials are received for a particular mold, the plant master scheduler reviews the workload of the assigned master machinist and schedules the mold for fabrication. Fabricating a mold takes from two to four weeks, depending on the amount of work the machinist already has scheduled. The fabrication process itself takes only three to five days. Upon completion, the mold is sent to the testing and inspection area, where it is used to produce a small number of parts on one of the injection molding machines. If the parts meet the design specifications established by the design team, the mold is passed on to be cleaned and polished. It is then packed and shipped to the customer. One day is spent inspecting and testing the mold and a second day cleaning, polishing, packing, and shipping it to the customer. If the parts made by the mold do not meet design specifications, the mold is returned to the master machinist for retooling and the process starts over. Currently, Custom Molds has a published lead time of nine weeks for delivery of custom-fabricated molds. The manufacturing process for plastic parts is somewhat different from that for mold fabrication. An order for parts may be received in conjunction with an order for a mold to be fabricated. ln instances where Custom Molds has previously fabricated the mold and maintains it in inventory, an order may be just for parts. If the mold is already available, the order is reviewed by a design engineer, who verifies the part and raw material specifications. If the design engineer has any questions concerning the specifications, the customer is contacted and any revisions to specifications are mutually worked out and agreed upon. Upon acceptance of the part and raw material specifications, raw material orders are placed and production is scheduled for the order. Chemicals and compounds that support plastic-parts manufacturing are typically ordered and received within one week. Upon receipt, the com- pounds are first dry-mixed and blended to achieve the correct composition. Then the mixture is wet-mixed to the desired consistency (called slurry) for injection into molding machines. When ready, the slurry is transferred to the injection molding area by an overhead pipeline and deposited in holding tanks adjacent to the injection machines. The entire mixing process takes only one day. When the slurry is staged and ready, the proper molds are secured from inventory or from the clean and polish operation if new molds were fabricated for the order and the parts are manufactured. Although different parts require different temperature and pressure settings, the time to produce a part is relatively constant. Custom Molds has the capacity to produce 5,000 parts per day in the injection-molding department; historically, however, the lead time for handling orders in this department has averaged one week. Upon completion of molding, the parts are taken to the cut and trim operation, where they are disconnected and leftover flashing is removed. After being inspected, the parts may be taken to assembly or transferred to the packing and shipping area for shipment to the customer. If assembly of the final parts is not required, the parts can be on their way to the customer two days after being molded. Sometimes the final product requires some assembly. Typically, this entails attaching metal leads to plastic connectors. If assembly is necessary, an additional three days is needed before the order can be shipped. Custom Molds is currently quoting a three-week lead time for parts not requiring fabricated molds. THE CHANGING ENVIRONMENT ln early 1991, Tom and Mason Miller began to realize that the electronics industry they supplied, along with their own business, was changing. Electronics manufacturers had traditionally used vertical integration into component-parts manufacturing to reduce costs and ensure a timely supply of parts. By the late 1980s, this trend had changed. Manufacturers were developing strategic partnerships with parts suppliers to ensure the timely delivery of high-quality, cost-effective parts. This approach allowed funds to be diverted to other uses that could provide a larger return on investment. The impact on Custom Molds could be seen in sales figures over the past three years. The sales mix was changing. Although the number of orders per year for mold fabrication remained virtually constant, orders for multiple, molds were declining, as shown in the following table: Number of orders Order size Molds 1988 Molds 1989 Molds 1990 1 80 74 72 2 60 70 75 3 40 51 55 4 5 6 5 5 3 5 4 6 4 8 5 7 2 0 1 8 10 6 4 9 11 8 5 10 15 10 5 TOTAL ORDERS 230 238 231 The reverse was true for plastic parts, for which the number of orders per year had declined but for which the order sizes were becoming larger, as illustrated in the following table: Number of orders Order size Parts 1988 Parts 1989 Parts 1990 50 100 93 70 100 70 72 65 150 40 30 35 200 36 34 38 250 25 27 25 500 10 12 14 750 1 3 5 1000 2 2 8 3000 1 4 9 5000 1 3 8 TOTAL ORDERS 286 280 277 During this same period Custom Molds began having delivery problems. Customers were complaining that parts orders were taking four to five weeks instead of the stated three weeks and that the delays were disrupting production schedules. When asked about the situation, the master scheduler said that determining when a particular order could be promised for delivery was very difficult. Bottlenecks were occurring during the production process, but where or when they would occur could not be predicted. They always seemed to be moving from one operation to another. Tom Miller thought that he had excess labor capacity in the mold-fabrication area. So, to help push through those orders that were behind schedule, he assigned one of the master machinists the job of identifying and expediting those late orders. However, that tactic did not seem to help much. Complaints about late deliveries were still being received. To add to the problems, two orders had been returned recently because of the number of defective parts. The Millers knew that something had to be done. The question was What? Questions 1. What are the major issues facing Tom and Mason Miller? 2. Identify the individual processes on a flow diagram. What are the competitive priorities for these processes and the changing nature of the industry? 3. What alternatives might the Millers pursue? What key factors should they consider as they evaluate these alternatives? Source: Krajewski Ritzman, Operations Management, 6th Edition Summary Custom Molds was founded by a father and son team in 1987 to provide high quality, custom-designed molds for manufacturers of electronic connectors, but later expanded into the production of plastic parts for the industry. In recent years, the changing environment of the electronics industry had a profound impact on the way Custom Molds conducts its business and manufacturing processes. The changing sales mix, coupled with delivery and quality problems, prompted the company to revise its business strategies to address the following issues: 1) Changing trends in the electronics manufacturing industry that caused changes in customer order needs 2) Unpredictable bottlenecks in the production environment 3) Quality issues resulting in defective parts 4) Delivery times 1. Major Issues Question 1 What are the major issues facing Tom and Mason Miller? 1.1 Changing Trends There were several issues facing the owners of the company. Firstly, the major issue is the electronics industry was changing in that manufacturers were developing strategic partnerships that allowed the delivery of high quality and cost effective parts. Also, the nature of their business had shifted in that the mix of sales had changed with the number of multiple orders declining and the demand for plastic parts increasing (Krajewski Ritzman, 2007). In comparing with mold fabrication and plastic part (see appendix), it is clear that plastic parts has a higher potential sales than mold fabrication on a larger order size. This will allow Tom and Mason to think whether it is best to eliminate mold fabrication and focus on more towards plastic parts because of the changing environment. 1.2 Production Process Issues faced by Custom Molds Inc.: à ¢Ã¢â€š ¬Ã‚ ¢ The delivery times on parts order were taking four to five weeks instead of the stated three weeks. à ¢Ã¢â€š ¬Ã‚ ¢ Number of defective products was on the rise. à ¢Ã¢â€š ¬Ã‚ ¢ Bottlenecks increased in the production process. à ¢Ã¢â€š ¬Ã‚ ¢ Changing strategies within their clients business needs changed order needs in an unexpected way. There are two distinct processes taking place in the same facility and each process serves different customer needs. Below is the analysis of each the processes (Mold Fabrication and Parts Manufacturing) along with recommendations for the same. 1. MOLD FABRICATION PROCESS: Mold fabrication is the core business of Custom Mold Inc., and the recommended process is shown in Exhibit 1. Mold Fabrication requires flexibility and quality; hence concept of Job Shop must be applied to streamline the process. Following are the Recommendations to do the same. à ¢Ã¢â€š ¬Ã‚ ¢ LAYOUT: Similar equipment or function must be grouped together and the layout of the equipment must be designed so as to minimize the material handling, cost and work in process inventories. Digital numerically controlled equipment should be used as it gives flexibility to change set-ups on the various machines quickly. This will allow Custom Mold Inc. to compete on quality, speed, customization and new product introduction. à ¢Ã¢â€š ¬Ã‚ ¢ STANDARDIZATION: To identify and eliminate bottlenecks, Custom Mold Inc must standardize all processes. This means that every task, every job, every event must be approached the same way each time it occurs. This includes a standard way of engineering, workholding, manufacturing and shipping. With standard processes, it will become easier to identify which areas are profitable and which are not. This will enable Custom Molds Inc to look t areas, which have the most variables and make them less variables. For example Fixturing / Workinholding is one of the biggest variables in every shop. In a year that has 8,760 hours, we spend 2,200 to provide high quality, custom-designed molds for manufacturers of electronic connectors, but later expanded into the production of plastic parts for the industry. In recent years, the changing environment of the electronics industry had a profound impact on the way Custom Molds conducts its business and manufacturing processes. The changing sales mix, coupled with delivery and quality problems, prompted the company to revise its business strategies to address to following issues: Custom Molds, Inc. was founded by a father and son team in 1987 to provide high à Ã‚ µÃƒâ€˜Ã¢â‚¬ ° to provide quality, custom-designed molds for manufacturers of electronic connectors, but later expanded into the production of plastic parts for the industry. In recent years, the changing environment of the electronics industry had a profound impact on the way Custom Molds conducts its business and manufacturing processes. The changing sales mix, coupled with delivery and quality problems, prompted the company to revise its business strategies to address to following issues: 1) Changing trends in the electronics manufacturing industry that caused changes in clients order needs 2) Unpredictable bottlenecks in the production environment 3) Quality issues resulting in defective parts 4) Delivery times promised to clients were not met Analysis 1) Process Inefficiencies Some of the issues presented above resulted from inefficiencies in the two distinct processes taking place in the same production facility at Custom Molds, namely the Molds Fabrication process and the Parts Production process (Exhibit 1a and b). The two processes serve different customer needs. Mold fabrication, a skill oriented and craftsman-driven process, requires flexibility and quality. Parts manufacturing, on the other hand, involves a more standardized process that competes on delivery and low cost. The margin for parts is also much smaller. In the mold fabrication process, the time needed to receive the ordered materials for each fabrication is usually 3-4 weeks. Only after the materials are received does the plant master scheduler review the workload of the assigned master machinist and schedule the mold for fabrication. The idle time between these two steps in the process significantly affects the lead time for delivery of custom-fabricated molds. The fabrication of a mold takes two to four weeks, depending on the amount of work the machinist already has scheduled,

Delegation of Tasks as a Manager Essay examples -- How To Delegate Eff

Objective: Identifying what job you want done The main purpose of delegation is to get the job done by someone else so that you, the manager, have more time for other, more difficult, tasks. To effectively delegate, you must give the entire authority of the task to the staff member you have selected to get the job done. This means not only reading instructions and filling out paperwork, but also the â€Å"decision making and changes which rely upon new information†. The staff member should be able to make decisions, whether good or bad, without referring back to the manager. By leaving the decisions to the delegated staff member, they use their own knowledge and initiative. The three key points to consider when delegating a task are: 1) They know what you want done- Explain the task clearly and make sure that you are understood. 2) They have the authority to achieve it- the selected staff member has the necessary abilities to do the job properly. 3) They know how to do it- the selected staff member has the necessary knowledge, or can obtain the knowledge, to do the job. Support the staff member without being overbearing. Allow the designated staff member to make their own decisions, but to feel reassured that you are there if you are needed, and remember to keep an open mind. Chances are, the staff member is not going to complete the assignment exactly the same way you would complete it. Their way may even end up being a more efficient way of getting the job done! Above all, make sure that you acknowledge and praise their efforts. Information: Implementing a Communication System To be successful, staff needs frequent communication with each othe... ... task that needs to be performed, and the expectations of the completed project. They must be able to communicate with the staff member, and leave the lines of communication open. A workable way to do this is to plan formalized meeting and scheduling times. They must know how to judge outcomes and use small mistakes or failures as learning tools upon which to grow. Finally, the manager must be willing and able to recognize achievements and congratulate on a job well done. Works Cited Blair, G. The Art of Management: The Essential Skills. Published by Chartwell Bratt. 2010 Culp, W. Journal of Management in Engineering: "Steps of Effective Delegation†. January 2014, Page 30 Jenks, J. and Kelly, J. Don’t Do, Delegate. Published by Bridles Ltd. 1986 Wright, R. (1996) Beyond Time Management: Business with a Purpose. Butterworth-Heineman

Marriage in Shakespeares Othello :: GCSE English Literature Coursework

Marriage in Othello  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Marriage is a part of life. Something that many people, if not everybody, look foward to. Marriage is a sacred thing, it is when two people dedicate their life to their love of their life. Your whole life revolves around it as evrything you do and evrything that happens affects your marriage. It is dedication, to live your whole life next to your partner making tough and easy decicions. There are going to be good times and there are going to be tough and difficult times. Regardless of what comes, you stick through it and side by side you support each other and stay together. Or do you? Many times the struggles and the pressure is too much for many marriages. The experiences that they go through is sometimes too much for a couple to handle and this causes a lot of stress and problems between the two. Sometimes the problems are so strong that it brakes up the marriage and they both go their seperate ways. Maybe their love wasn't strong enough. Maybe their commitment wasn't strong enough. Depending on your definition of love. Love can be very tricky sometimes. It can evenm blid a man from seing reality and it takes him far apart from the real world. He lives in a strange state of mind where what seems obvious to everybody else, can be overlooked by his or her love. Shakespeare uses jeoulousy as the biggest test of love in the book Othello. Is jeoulousy strong enogh to brake a marriage such as the one between Othello and Desdemona. They are both deeply in love with each other. How about love being blind? Was Emilia blind from reality because she was so in love with her husband Iago? We have learned the tragical ending of the book. Unfortunate ending for both marriages. Before discussing the ending, lets discuss the facts and the event that lead up to the ending. What was it that caused such a tragic ending? We already know that Desdemona was devoted to the love of her life, Othello, the General that made his friend Cassio Lt. So why would Iago want to brake up the marriage of Othello and Desdemona? Othello and Cassio were really close friends for many years. Cassio was the one that helped Othello get Desdemona's love. Iago, was also Othello's friend, and Othello did trust him a lot.

What Is History? :: essays research papers

What Is History? What is history? Where do I start? And who says it started there? I wasn’t there, does it matter? Can I accept whatever happens in another part of the world into my personal history, or just in the worlds history? What parts of history are most important? Who decides? What a question! The word history has many attached meanings to it, and the result is that the definition for history depends on who it is you are asking. But What is it? Dictionary.com states that, â€Å"history is a narrative of events; a story.† Everyone has stories. it goes without saying then that everyone has history. But what about looking at the world in a broader aspect. I think we could look at humans, in whole, and see that we all have a history; a social history. Also, what students mostly study in textbooks, and in lecture halls; political history. Therefore, history, in my terms, can be broken into three very different branches: Personal, Social, and political. A friend of mi ne unfortunately parted ways with a woman whom he devoted a long period of time to. Inquiring, as I often do, I ask for the details on their break up. I was given a response of, â€Å"Man, She’s history.† And my friend is exactly correct. This woman now lies within his personal history. Had this girl never came into my friends life, he could not claim her a part of his history; his past. We have defined history as, â€Å"a story,† and my friend can tell stories of him and his girl. (in fact lately that is all he talks about) The Vietnam war was a very important part in history. Actually, the boss at my present place of employment served a long period of time in the war. He was a grunt. I have asked him about it, trying to hear maybe a story or two of what it was like, but he doesn’t like to talk about it. this part of his history he shuts out. Many veterans are like this. I believe the reason being is that the war that is in his history is a very differen t war than we could ever read in books or hear about from professors. War is different for everyone; as is history itself. These few examples give way to the first area of history; personal history. This is the story that everyone has.

Unexpected Inflation

Unexpected In? ation and Redistribution of Wealth in Canada Cesaire A. Meh, Canadian Economic Analysis, and Yaz Terajima, Financial Stability One of the most important arguments in favour of price stability is that unexpected in? ation generates changes in the distribution of income and wealth among different economic agents. These redistributions occur because many loans in the economy are speci? ed in ? xed-dollar terms. Unexpected in? ation redistributes wealth from creditors to debtors by reducing the real value of nominal assets and liabilities. This article quanti? es the redistributional effects of unexpected in? ation in Canada.To this end, we ? rst provide comprehensive evidence of the nominal assets and liabilities of various economic sectors and household groups. We ? nd that the redistributional effects of unexpected in? ation are large even for episodes of low in? ation. The main winners are young, middleincome households, who are major holders of ? xed-rate mortgage deb t, and the government, since in? ation reduces the real burden of their debt for both groups. The losers are high-income households and middle-aged, middle-income households that hold long-term bonds and nonindexed pension wealth. T here is ongoing research on potential re? ements to monetary policy regimes in countries with low and stable in? ation. In Canada, for example, a systematic review of the current in? ationtargeting framework is underway (see the other articles in this issue). An issue that has received relatively less attention is the redistributional effects of unexpected in? ation. 1 Redistributional effects occur because many savings, investments, and loans in the economy are speci? ed in money terms (i. e. , not adjusted for in? ation); unexpected in? ation therefore redistributes wealth from lenders to borrowers by lowering the real value of nominal assets and liabilities. The analysis of these effects may be important since the welfare costs of in? ation depend not only on aggregate effects but also on potential redistributional consequences. Our calculations show that, even with an episode of low in? ation, the redistribution can be sizable. While this is a wealth transfer from one agent in the economy to another, a sense of who wins and who loses is essential in order to assess transitional costs and potential public support for reform. The goal of this article is to provide insight into the redistributional effects of in? tion in Canada. The article is a summary of the recent research of Meh and Terajima (2008). 3 The article proceeds as follows. The ? rst section documents nominal assets and liabilities (i. e. , ? nancial assets and liabilities that are denominated in Canadian dollars and not fully indexed to in? ation) held by different economic sectors and 1 2 . 3 In this article, we focus on in? ation that is either unexpected or partially unexpected. If in? ation were completely expected, the change in the real value of the nominal cl aim would be incorporated in the contract.Hence, there would not be any redistribution. On the other hand, lower-than-expected in? ation redistributes wealth from borrowers to lenders. Meh and Terajima (2008) build on Doepke and Schneider (2006) who document nominal assets and liabilities in the United States and develop a methodology to compute the redistribution of wealth caused by in? ation. UNEXPECTED INFLATION AND REDISTRIBUTION OF WEALTH IN CANADA BANK OF CANADA REVIEW SPRING 2009 43 household groups, while the second part describes the methodology used to compute the redistribution of wealth induced by unexpected in? ation.Using this methodology and the documented nominal positions, the third section quantitatively assesses the redistribution of wealth under episodes of low and moderate in? ation. The ? nal part of the article concludes. Nominal Assets and Liabilities Unexpected in? ation generates redistributions because most ? nancial assets and liabilities are speci? ed in money terms. For example, payments on ? xedrate mortgage contracts, bank deposits, non-indexed de? ned-bene? t pension plans,4 government and corporate bonds, and other types of loans are generally not adjusted for unexpected in? ation.Hence, when in? ation is high, the value of these assets and liabilities falls in terms of purchasing power, since the prices of other goods and services go up with in? ation, but payments on these ? nancial claims are ? xed. The extent of the changes in the purchasing power of ? nancial assets and liabilities also depends on the term to maturity, as we will show later on. In this section, we document Canadian holdings by type and maturity in various categories of assets and liabilities. Speci? cally, we look at asset and liability positions for three sectors: household, government, and non-residents. We also consider different groups of households. The objective is to show that, among these different groups of agents, holdings of nominal assets and liabilities differ in both qualitatively and quantitatively important ways. Given that these differences exist, there is potential for redistribution among them following in? ation shocks. (SFS). The NBSA documents the ownership of ? nancial and non-? nancial assets and liabilities by sector. We use the NBSA to compute the net asset and liability positions of the household, government, and foreign sectors.The SFS is a household survey data set on income and wealth. We use the 2005 wave (the latest available), involving about 5,000 households, with weights to produce Canadian aggregates. It provides a comprehensive picture of assets and liabilities. For the sake of consistency, we use the 2005 NBSA and focus our analyses on the year 2005. Categories of nominal assets and liabilities Following Doepke and Schneider (2006), nominal assets and liabilities are de? ned as all ? nancial claims that are denominated in Canadian dollars and not fully indexed to in? ation.We report net nominal positions (i. e. , assets minus liabilities) in four categories, de? ned as follows:6 †¢ Short-term – ? nancial assets and liabilities with a term to maturity less than or equal to one year (e. g. , domestic currency, bank deposits, consumer credit, and short-term paper) †¢ Mortgages – all mortgage claims †¢ Bonds – non-mortgage and non-pension nominal claims with maturity greater than one year, including government and corporate bonds and bank loans †¢ Pensions – employer pension plans without provisions for indexing bene? ts to the cost of living, including both de? ed-contribution plans and non-indexed de? ned-bene? t plans7 We distinguish among these categories because they differ in maturity structure. Differences in maturity will emerge as a key factor in assessing the extent of potential redistribution. Unexpected in? ation generates redistributions because most ? nancial assets and liabilities are speci? ed in money terms. Sect oral positions Data We use two main data sets, both provided by Statistics Canada: the National Balance Sheet Accounts (NBSA) and the Survey of Financial Security 4 5 Non-indexed de? ned-bene? pension plans are those where retirees receive ? xed payments not adjusted for in? ation. Since all businesses are owned by their shareholders, we allocate business sector portfolios across the three sectors, based on each sector’s equity holdings. Table 1 shows net positions in each category, as well as the overall net nominal position (NNP) for each sector. Positions are expressed relative to gross domestic product (GDP) in 2005. Positive numbers indicate net lending; negative numbers, net borrowing. 6 7 For more details, see Meh and Terajima (2008). Another type of plan is the indexed de? ed-bene? t plan. These plans are treated as real assets, since in? ation will not affect them. 44 UNEXPECTED INFLATION AND REDISTRIBUTION OF WEALTH IN CANADA BANK OF CANADA REVIEW SPRING 2009 We obs erve that households are the main net nominal lenders overall, with NNP at 40. 14 per cent of GDP. The government sector, at about 43 per cent of GDP, is the main counterparty borrowing from households. The foreign sector has a positive but small NNP of 2. 85 per cent of GDP. Households tend to lend through short-term claims, bonds, and pensions, and borrow through mortgages.The government sector borrows mainly through bonds; it also borrows through short-term claims and pensions. 8 The non-resident sector lends in mortgages and bonds and owes in pensions. 9 These observations suggest that households are the likely losers of unexpected in? ation, since it lowers the purchasing power of their lending (i. e. , savings). Table 1: Net Nominal Positions as a Percentage of GDP Sectors Short-term claims Mortgages Bonds Pensions NNP Households 12. 25 -11. 94 22. 14 17. 69 40. 14 Government -7. 60 3. 19 -29. 67 -8. 91 -42. 99 Non-residents -4. 65 8. 75 7. 53 -8. 79 2. 85Table 2: Nominal Posi tions as a Percentage of Net Worth by Age Age Cohort Under 36 36–45 Short-term claims Mortgages Bonds Pensions NNP 4. 83 -37. 95 -2. 63 -0. 05 -35. 80 -1. 01 -13. 57 4. 70 -1. 31 -11. 19 46–55 1. 48 0. 07 6. 50 5. 01 13. 06 56–65 2. 40 4. 48 7. 90 7. 36 22. 14 66–75 9. 00 3. 55 6. 70 8. 68 27. 93 Over 75 12. 27 3. 29 7. 68 8. 65 31. 89 Household groups We now look at the household sector in more detail, using the SFS data set. We examine three classes (low-income, middle-income, and high-income) and six age groups (under 36, 36–45, 46–55, 56–65, 66–75, and over 75) to observe differences within the sector. 0 Table 2 presents the overall positions for each age group as a percentage of the group’s net worth. We observe that the NNP increases with age, implying that households shift from being net borrowers to net lenders as they get older. Most of the borrowing of the young is from mortgages. With age, more lending (i. e. , saving) is observed in pensions and in liquid short-term claims. This implies that young households will gain from unexpected in? ation while older households will lose. Qualitatively, these patterns generally hold across different income classes, although with different magnitudes.Table 3 shows the positions of the three income classes, with the long-term category combining mortgages, bonds, and pensions. 11 The general pattern of â€Å"borrowing more when young and lending more with age† holds across different income classes. We observe, however, that levels of borrowing relative to their net worth among young middle-income and low-income households are relatively larger than they are for high-income households, mainly because the portfolios of low-income and middle-income households are concentrated in residential real estate (mortgages). This implies that while the young generally bene? from in? ation, bene? ts are likely concentrated among low-income and middleincome h ouseholds. Table 3: Nominal Positions as a Percentage of Net Worth by Age and Income Class Age Cohort Under 36 36–45 High-income Short-term claims Long-term claims Medium-income Short-term claims 5. 83 2. 24 -28. 71 4. 39 7. 01 5. 49 20. 55 9. 07 20. 29 14. 91 18. 97 3. 86 -6. 52 -3. 73 5. 89 -1. 97 18. 40 -2. 36 19. 89 8. 48 19. 03 8. 56 21. 26 46–55 56–65 66–75 Over 75 Long-term claims -95. 27 Low-income Short-term claims 18. 90 Long-term claims -71. 01 -0. 06 -27. 07 5. 04 -8. 30 13. 84 6. 89 12. 58 1. 7 10. 96 12. 79 8 The government sector is a borrower in pensions as it holds liabilities from employer pension plans to its employees. 9 The borrowing in pensions by the non-resident sector indirectly re? ects the pension liabilities of the business sector. As previously mentioned, we allocate business sector portfolios across the three sectors, based on each sector’s equity holdings. 10 The classes are de? ned based on a mix of income and wealth . For simplicity, we use the terms low-income, middle-income, and high-income to refer to each class. See Meh and Terajima (2008) for the details. 1 The distribution of households as well as that of net worth by age group and income class is shown in Meh and Terajima (2008). UNEXPECTED INFLATION AND REDISTRIBUTION OF WEALTH IN CANADA BANK OF CANADA REVIEW SPRING 2009 45 How In? ation Causes Redistribution Given the observed differences in nominal positions among households, government, and non-residents, unexpected in? ation should induce redistributions of real wealth. But how do we begin to identify the pattern and quantify the extent of the redistributions? The size of wealth redistribution depends on how economic agents adjust their expectations to in? tion surprises. We follow Doepke and Schneider (2006) by considering two scenarios that provide upper and lower bounds on the redistribution of wealth. The upper bound is captured by a â€Å"full-surprise† scenario (hereaft er FS). In this scenario, during several years of experiencing in? ation shocks, agents do not anticipate that shocks will continue in subsequent periods; nominal interest rates remain unchanged and the in? ation shock lowers the real value of nominal positions each period, regardless of the duration of these positions. Wealth redistribution from in? tion The goal of this section is to use the nominal positions documented above, combined with the methodology just described, to estimate the redistribution of wealth for an in? ation episode. Historically, in? ation episodes with different magnitudes lasting for extended periods have occurred. For example, between 2000 and 2004, the average in? ation rate in Canada was generally higher than the in? ation target rate of two per cent. To illustrate the in? ation-induced redistribution of wealth, we will consider a hypothetical in? ation episode that lasts ? e years with an in? ation shock of one per cent, starting in the benchmark year 2 005. 12 Redistribution across sectors Table 4 summarizes the sectoral present-value gains and losses induced by an in? ation episode with one per cent shocks that continue for ? ve years, beginning in 2005, under the FS and IA in? ation scenarios. Table 4: Redistribution of Wealth across Sectors as a Percentage of GDP, with a One Per Cent In? ation Shock Lasting Five Years Households Sectors Net Full-surprise scenario -1. 95 -1. 26 Gains 12. 53 7. 61 Losses -14. 48 -8. 86 2. 09 1. 49 -0. 14 -0. 3 Government Non-residents The size of wealth redistribution depends on how economic agents adjust their expectations to in? ation surprises. The lower bound is given by an â€Å"indexing ASAP† scenario (hereafter IA), where agents adjust their expectations after the initial shock to take into account the full duration of the shock. This scenario is also known as a gradual in? ation episode, since in? ation is partially anticipated. Under the IA scenario, the nominal yield curve is adj usted upwards to incorporate the in? ation shock. As a result, under the IA scenario, in? tion-induced gains or losses depend on the maturity of the nominal position. The position is â€Å"locked-in† at the pre-shock nominal interest rate until its maturity date but must be discounted using the new nominal rate, resulting in a lower present value. Intuitively, present-value gains or losses for a claim are larger under the FS scenario because all the positions are affected equally by the in? ation episode. Under the IA scenario, however, long-term positions are affected more drastically than shorter positions. Agents are able to mitigate their losses on instruments that mature before the in? tion episode ends. Our calculations are based on a present-value analysis, described in Box 1. Box 2 discusses how we assign terms to maturity for each category of claims. Indexing ASAP scenario It is apparent from the table that, under the two scenarios, the household sector loses, while the government sector wins. The household sector loss and the government gain are both large. Under FS, the household losses amount to 1. 95 per cent of GDP (or $26. 8 billion), while the government gain is 2. 09 per cent (roughly 5 per cent of NNP). The non-resident sector loses, but the loss is small, just 0. 4 per cent of GDP. To understand these ? ndings, recall that, under FS, gains and losses are directly proportional to the initial nominal positions. Since the household sector is the economy’s main lender and the government sector is the main borrower, it is not surprising that these sectors are the most dramatically affected by the shock under the FS scenario. 12 Under the current in? ation-targeting framework, in? ation has not exceeded expectations by one per cent for ? ve consecutive years. However, as a hypothetical scenario, we suppose price-level shocks that push in? tion to the upper bound of the range speci? ed in the current framework. The current annual in? ation target is two per cent with the target range extending from one to three per cent. 46 UNEXPECTED INFLATION AND REDISTRIBUTION OF WEALTH IN CANADA BANK OF CANADA REVIEW SPRING 2009 Box 1 Present-Value Analysis of Redistributions1 Full-surprise (FS) Scenario We start with an explanation of how unexpected in? ation changes the purchasing power of a nominal claim. Consider an -year, zero-coupon bond with a total nominal yield at time of . In the absence of unexpected in? tion, the present value of one dollar earned in periods through investment in this ? nancial claim is given by are then summed over all claims to derive the net redistribution. Indexing ASAP Scenario The indexing ASAP scenario corresponds to a onetime announcement at period that, starting from the current period , in? ation will be percent higher than expected during each period for the next periods. Assuming that the announcement is credible, bond markets will immediately revise their in? ation expectations and i ncorporate these updates into the nominal yield curve.Assuming that the real curve does not change after the shock and that the Fisher equation holds, the new nominal interest rate used to discount . Therefore, the present a claim is value, , of a claim under IA is , where indicates the exponential function to base . Suppose that at time , there is a one-time surprise increase in in? ation of per cent per year that lasts for periods. Under the FS scenario, since the in? ation shock in each subsequent period is unanticipated, market expectations do not adjust and the nominal term structure is unchanged.As a result, only a proportion, , of a position’s present value remains, and this proportion falls as the size and duration of the shock increase. The present value of , is thus given by this nominal claim under FS, This equation shows that the present value of a onedollar claim at time is independent of the term to maturity of that claim. The present-value gain or loss, , is gi ven by As can be seen from this equation, in contrast to the FS scenario, under IA, a ? nancial position of maturity will be affected only for the periods of its duration, before which the agent is assumed to reinvest at the pre-shock real yield.This is analogous to the agent’s reinvesting in a claim that offers a nominal rate of return that has been indexed to take the in? ation announcement into account. The present-value gain or loss of a claim of maturity under IA is given by: The net present value of gain or loss depends only on the size and duration of the shock and the initial nominal position. The gain is, indeed, proportional to the . pre-shock position, with a coef? cient of If , then there is a gain from the in? ation episode; otherwise, there is a loss. In order to derive the total gain or loss of an economic agent (e. g. , a sector r a household), is calculated for each claim with a term to maturity . The gains or losses 1 This methodology to calculate redistribu tion can be applied to compare the size of redistribution under different monetary policy regimes such as in? ation targeting and price-level targeting. This point is summarized in Crawford, Meh, and Terajima (this issue) and analyzed in detail in Meh, Rios-Rull, and Terajima (2008). Hence, under IA, the present-value gain or loss depends on (i) the size of the shock ( ), (ii) the duration of the shock ( ), (iii) the initial nominal position , and (iv) the maturity of the claim ( ).On the other hand, as mentioned above, the gain or loss under the FS scenario for any position is independent of its maturity. The IA scenario provides a lower bound for gain or loss on a claim, since it assumes full adjustment of expectations to the path of in? ation following the initial announcement. The total gain or loss of an economic agent is derived in the same way as in the FS scenario, based on the sum of the gains and losses from each claim. UNEXPECTED INFLATION AND REDISTRIBUTION OF WEALTH IN CANADA BANK OF CANADA REVIEW SPRING 2009 47 Box 2 Term-to-Maturity StructureIn this box, we describe how terms to maturity are determined for each claim. For ? nancial short-term claims, we assume that they all have one-year terms to maturity, such that we set = 1. For mortgages, we apply the distribution of ? xed-rate mortgages by term in 2005. 1 The distribution is obtained using the Canadian Financial Monitor data set from Ipsos Reid Canada, which is compiled from a household survey containing detailed mortgage information. Chart A presents the distribution of mortgages across terms of mortgages, weighted by outstanding balances. It shows that the most common term of Canadian ? ed-rate mortgages is ? ve years. Based on the fractions we obtain from Chart A, we assign a weight for each . For example, we assign a 60 per cent weight to . We take a similar approach for bonds. We derive a maturity distribution from quarterly data on the maturity and face value of federal government deb t. 2 Chart B shows the distribution from the fourth quarter of 2005. We assume that the distribution of terms to maturity for federal government bonds approximates that for all instruments in this category. For pensions, we focus on two types of pension plans: de? ned-contribution and non-indexed de? ned-bene? t plans.For de? ned-contribution plans, we assume that the average investment portfolio is approximated by the holdings of Trusteed Pension Plans. 3 The assets of Trusteed Pension Plans are given in the NBSA. We compute the distributions of these assets over terms to maturity and use them to assign weights to each value. For non-indexed de? ned-bene? t plans, we assume a ? xed stream of annual post-retirement payments. When calculating the present-value 1 The term of mortgage is the length of the current mortgage agreement. A mortgage can have a long amortization period, such as 30 years, with a shorter term, such as 5 years.When the term expires, a new term agreement can begi n at the prevailing interest rate. The term of mortgage, rather than the amortization period, is relevant for our analysis. These data were obtained from the Bank of Canada’s Communication, Auction and Reporting System database. See Meh and Terajima (2008) for more details. Trusteed Pension Plans hold approximately 70–75 per cent of employer pension plan assets. See Meh and Terajima (2008) for more details. gains and losses of pension assets, we apply the formulas in Box 1 to each payment, then sum all the gains or losses.In assigning the term to maturity of each payment, we set based on the difference between the current age of the household and the age at the time of the payment. Chart A: Distribution of Fixed-Rate Mortgages by Term % 70 60 50 40 30 20 10 0 Six months One year Two years Three to four years Five years Seven years Ten or more years Chart B: Distribution of Government Bonds by Term to Maturity % 15 10 5 0 1 yr. 10 yr. 20 yr. 30 yr. 2 3 48 UNEXPECTED INF LATION AND REDISTRIBUTION OF WEALTH IN CANADA BANK OF CANADA REVIEW SPRING 2009 It is also clear that gains and losses are generally smaller under IA.The household sector loss under IA is 1. 26 per cent of GDP (or $17. 3 billion), compared with 1. 95 per cent under FS. This change is driven by a reduction in the losses associated with the sector’s net savings in long-term bonds and pensions relative to the FS case. The change is offset somewhat, since instruments with a shorter maturity are less sensitive to gradual in? ation, and the gains associated with the sector’s net debt in mortgage markets shrink relative to the FS case. The government gain drops from about 2. 1 per cent of GDP under the FS scenario to about 1. 5 per cent under the IA scenario—i. . , it shrinks by almost one-third. This occurs because the government borrows through some bonds that have maturities of less than ? ve years. The non-resident sector’s losses, although small, increase f rom 0. 14 per cent of GDP under FS to 0. 23 per cent of GDP under IA. Finally, Table 4 shows gross redistributions for the household sector—i. e. , it distinguishes between losses associated with lending and gains associated with borrowing. It should be clear from these results that net calculations substantially understate how much wealth is shifted around. Under FS, the household sector gains 12. 3 per cent of GDP and loses 14. 48 per cent, implying a total gross redistribution of 27. 01 per cent of GDP. In other words, household wealth worth 27 per cent of GDP is reshuf? ed. Under IA, the total gross redistribution is 16. 47 per cent of GDP. Table 5: Redistribution of Wealth across Households as a Percentage of Net Worth by Age and Income Class, with a One Per Cent In? ation Shock Lasting Five Years Age group Under 36 Full-surprise scenario All High-income Middle-income Low-income Indexing ASAP scenario All High-income Middle-income Low-income 1. 66 0. 26 3. 91 2. 66 0. 44 -0. 18 1. 15 1. 15 -0. 54 -0. 74 -0. 3 0. 28 -0. 84 -0. 76 -0. 94 -0. 42 -0. 83 -0. 82 -0. 89 -0. 17 -0. 82 -0. 86 -0. 81 -0. 56 -0. 34 -0. 55 -0. 19 0. 14 1. 74 0. 13 4. 34 2. 53 0. 54 -0. 10 1. 28 1. 32 -0. 63 -0. 80 -0. 55 0. 16 -1. 07 -0. 85 -1. 26 -1. 01 -1. 36 -1. 34 -1. 42 -0. 69 -1. 55 -1. 45 -1. 64 -1. 15 -0. 53 -0. 68 -0. 42 -0. 16 36–45 46–55 56–65 66–75 Over 75 All Redistribution between household types Even though the household sector as a whole loses from surprise in? ation, the loss (or gain) is not uniform across different types of households. For different groups of households, we calculate the redistribution of wealth induced by the in? tion episode described above. Table 5 reports the present-value gains and losses as a percentage of the average net worth of each group for FS and IA. Overall, with respect to age categories, young households bene? t from in? ation and older households lose. On the income dimension, the right column of t he table indicates that high-income households lose the most and the loss declines as income becomes lower. Speci? cally, the main winners are young, middleincome households with large, ? xed-rate mortgage debts. Their gain as a proportion of mean net worth is large: 4. 34 per cent under FS and 3. 1 per cent under IA. The second group of winners is the young, lowincome group, who enjoy, on average, gains between 2. 53 per cent and 2. 66 per cent of their average net worth. The gains of the young low-income group come largely from their holdings of student loans and mortgage debt. Note that this group actually experiences greater gains under IA. As in the case for the non-resident sector, this occurs when there is a maturity mismatch. More speci? cally, while the gains associated with their net borrowing positions in bonds and mortgages do not vary much between in? tion scenarios, the losses associated with their savings in short-term instruments are mitigated under IA, since these c laims mature before the shock has ended. The main winners are young, middleincome households with large, ? xed-rate mortgage debts. More age groups among low-income housholds bene? t from the in? ation episode than those among the middle class or the high-income under FS. This is because low-income households remain net borrowers through to age 56, and therefore the youngest three groups among the low-income are winners. In general, older middle- and high-income households bear most of the losses under the two in? tion scenarios. More speci? cally, under the FS scenario, high- and middle-income households over age 75 are the sector’s greatest losers, with losses accounting for 1. 45 per cent and 1. 64 per cent, respectively, of their respective average net worth. These losses are UNEXPECTED INFLATION AND REDISTRIBUTION OF WEALTH IN CANADA BANK OF CANADA REVIEW SPRING 2009 49 mainly owing to their large positions in bonds and non-indexed de? ned-bene? t pensions. Table 5 also shows that most high-income households lose from the in? ation episode. Older middle- and high-income households bear most of the losses . . owing to their large positions in bonds and non-indexed de? ned-bene? t pensions. Conclusion In this article, we quantify the redistributional effects of unexpected in? ation in Canada. To this end, we ? rst provide comprehensive evidence of the nominal assets and liabilities of various economic sectors and household groups. We then conduct experiments examining the redistributional consequences of various in? ation episodes. The key ? nding is that the redistributional effects of unexpected in? ation are large even for episodes of low in? ation. For example, during an episode of low in? tion, where in? ation is one per cent above expectations for ? ve consecutive years, the loss of wealth among the household sector as a whole could amount to the equivalent of two per cent of GDP, or $27 billion. Among the main winners are young, middle-income households, who are major holders of ? xed-rate mortgage debt, and the government, since in? ation reduces the real burden of their debts. The losers are a combination of highincome households; middle-aged, middle-income households; and old households, who hold long-term bonds and non-indexed pension wealth.Non-indexed pension assets play an important role in the losses of old households. A natural question arising from these results is whether these redistributions have implications for the aggregate economy and welfare. These issues are analyzed in recent research by Meh, Rios-Rull, and Terajima (2008), whose ? ndings are also summarized in Crawford, Meh, and Terajima (this issue). Literature Cited Crawford, A. , C. A. Meh, and Y. Terajima. 2009. â€Å"Price-Level Uncertainty, Price-Level Targeting, and Nominal Debt Contracts. † Bank of Canada Review, (Spring): 31-41. Doepke, M. nd M. Schneider. 2006. â€Å"In? ation and the Redistribution of Nominal Wealth. † Journa l of Political Economy 114 (6): 1069–97. Meh, C. A. , J. -V. Rios-Rull, and Y. Terajima. 2008. â€Å"Aggregate and Welfare Effects of Redistribution of Wealth under In? ation and Price-Level Targeting. † Bank of Canada Working Paper No. 2008-31. Meh, C. A. and Y. Terajima. 2008. â€Å"In? ation, Nominal Portfolios, and Wealth Redistribution in Canada. † Bank of Canada Working Paper No. 2008-19. 50 UNEXPECTED INFLATION AND REDISTRIBUTION OF WEALTH IN CANADA BANK OF CANADA REVIEW SPRING 2009