Univariate ratio analysis development - Free Essay Example

Sample details Pages: 11 Words: 3261 Downloads: 10 Date added: 2017/06/26 Category Finance Essay Type Analytical essay Did you like this example? Prior to Poors publishing of assets, liabilities, and earnings information in the American Railroad Journals of 1849, credit-worthiness was assessed using qualitative data. Poors publications were the first instances of quantitative data being used to assess the risk of commercial lending. This continued until the early 1930s when the first academic studies were carried out to assess the quantitative differences in financial ratios between well performing and failing businesses (Altman 1968). Univariate Ratio analysis development Between 1930 and 1968, financial ratios were analysed using univariate methodology to establish differences between failing and non-failing companies. This methodology analysed many ratios but individually. However the model could not find relationships between multiple ratios. An example of this was The Bureau of Business Researchs study (1930). It used 24 ratios to analyse 29 failing manufacturing companies. The results were compared to look for similar financial ratio characteristics in the sample. This showed that failing firms had common financial ratio characteristics which meant that it was possible to see potential failure of a firm before bankruptcy occurred. The research found that decline in the following ratios suggested potential future bankruptcy. Working Capital to Total Assets Surplus and Reserves to Total Assets Net Worth to Fixed Assets Fixed Assets to Total Assets Current Ratio Net Worth to Total Assets Sales to Total Assets Cash to Tot al Assets (BBR, 1930) The key weakness of the BBR model was that only failing companies had been compared. There was now a need to compare companies that had failed against companies that were trading successfully in order to see a true difference in ratio performance. Fitzpatrick chose 13 ratios to compare 19 failed and 19 successful companies in order to established a benchmark for average performance. Subjects whose ratios were better than average were deemed successful and those that were below average were deemed unsuccessful. This gave more meaningful results than the BBR study as it made a comparison between failed and non-failed companies rather than comparing failing companies only. Fitzpatrick suggested the most significant ratios were Net Worth to Debt and Net Worth to Net Profits. (Fitzpatrick, 1932) In 1935 BBRs study was repeated using a larger sample. 183 bankrupt companies from numerous industries were sampled. This increased the models generalisability. The re search suggested that the Working Capital to Total Assets ratio was most significant when predicting bankruptcy and the key trend was that the Current Assets to Total Assets ratio fell when companies were failing. (Smith and Wanakor, 1935) These studies were useful for differentiating between bankrupt and non-bankrupt firms and suggested common financial trends of a failing businesses; but they could not predict how soon bankruptcy was likely to occur. However Merwin suggested that trends of reducing Net-Working Capital to Total Assets, Current ratio, and Net Worth to Total Debt could predict company weakness up to five years before bankruptcy (Merwin,1942). The majority of bankruptcy prediction research up to 1946 suggested that the Working Capital to Total Assets ratio and the Current ratios were the most significant indicators of financial distress (Bellovary et al, 2007). However Chudson suggested that bankruptcy prediction models developed for multiple industry types gave greater generalisability but were less accurate than an industry specific model when predicting the bankruptcy timeframe (Chudson, 1945). Don’t waste time! Our writers will create an original "Univariate ratio analysis development" essay for you Create order Multiple Discriminant Analysis Model Development By 1968 the financial academics decided that univariate analysis models were not as accurate at predicting bankruptcy as required and considered the use of statistical evidence of past company performance would be a viable replacement. While academics agreed liquidity, solvency, and profitability were effective, they could not agree on their order of significance. The main issue was that a company could have low profitability but have good liquidity. In this instance a univariate model might give an ambiguous result. (Altman 1968) To address these issues Altman developed a model that established the most significant ratios and gave a weighting to each one so that they could be multiplied together. This created one formula that categorized companies into bankrupt and non-bankrupt entities. The model linked five different ratios into one formula that classified its sample into two groupings according to their characteristics. This technique is known multiple discriminant analysis or MDA. Altman called it the Z-Score model (Altman 1968). Altmans Z-Score Development Methodology 33 bankrupt companies and 33 non-bankrupt companies were chosen from the U.S.A. manufacturing industry. The bankrupt companies had all filed under chapter 10 of the National Bankruptcy Act between 1946 and 1965. To ensure reliability, validity, and generalisability the non-bankrupt companies were chosen using paired sampling and stratified random techniques. They were stratified by asset size and industry. To ensure comparability between samples, non-bankrupt companies were chosen that had continuous trading results for the same time period as the bankrupts (Altman 1968). Altman classified the 22 most common ratios used to indicate financial stress into five categories. They were liquidity, leverage, solvency, profitability, and activity. Out of the 22 ratios, five were chosen for their statistical significance in each category. Computer analysis was then used to establish the most accurate weighting for each ratio (Altman 1968). Ratio Weighting Working Capital to Total Assets (X1) 0.012 Retained Earnings to Total Assets (X2) 0.014 Earnings before interest and tax to Total Assets (X3) 0.033 Market Value of Equity to Book value of total debt (X4) 0.006 Sales to total Assets (X5) 0.999 Z score function formula = 0.012(X1) + 0.014(X2) + 0.033(X3) + 0.006(X4) +0.999(X5) The following financial ratios were selected to create the Z-Score formula: The Significance of the five selected ratios X1, working capital / total assets ratio measures liquidity. The working capital is calculated by taking away the current liabilities from the current assets. If a company continually makes an operating loss, its current assets will be depleting in relation to its total assets, thus reducing liquidity. This was in agreement of Merwin, 1942 who suggested this was the strongest indicator of potential bankruptcy. (Altman 1968) X2, Retained Earnings / Total Assets This measures the companys accumulated profitability. By definition new companies will have less retained earnings than older companies; therefore they are more at risk of failure. Statistics have shown that younger companies are more likely to fail than older companies in real world situations, hence the significance of the ratio. (Altman 1968) X3, Earnings before Interest and Taxes / Total Assets. This ratio measures the companys asset productivity. It eliminates all tax and leverage so that the true earning potential of the companys assets can be measured. Assets are valued by their earning power, and since bankruptcy is defined as when total liabilities are in excess of valued assets, this ratio is of great importance. (Altman 1968) X4, Market value of Equity / Book value of total debt. This is in essence a debt to equity ratio, though it includes the current market value of all the companys shares inclusive of regular and preference shares. The debt part of the ratio includes both long term and current liabilities. This ratio is used to measure how much asset value can be depleted before the company becomes insolvent. Altman has added the market value element to the ratio where previous studies have not. He suggests that this is more effective at predicting bankruptcy than the univariate analysis of net worth to total debt ratio which had been the most commonly used prior to his study. (Altman 1968) X5, Sales / Total Assets This is the capital turnover ratio which measures the companys ability to generate sales from its assets. Individually it is of least significance; however its discriminate ability contribution is ranked second out of the five ratios used so is of great importance to the formula. (Altman 1968) Z-score Meanings After much research and modelling, Altman concluded that if a companys Z-score was above 2.99 it could be classed as definitively non-bankrupt. However if its Z-score was below1.81then it could be classed as definitively bankrupt. However if its Z-score fell between these two figures then it could not be definitively classed as being in either category. This was known as the grey area or zone of ignorance. Altman found that companies who scored within this grey area were open to mis-classification. He suggested that it was difficult to predict bankruptcy in a company whos Z-score fell within the grey area, particularly those that had recently started trading. To address this he developed guidelines for the classification of companies that Z-scored within this area. Eliminating the grey area Altman selected all the companies from his first sample that had fallen into the grey area and classified them by six Z-score ranges between 1.81 and 2.99. The results showed that as the Z-score increased from 1.8, the number of companies whos Z-score falls in each sub range decreases to two companies at 2.67 2.68 and then increases again as it reaches 2.99. Altman suggested that this shows the mid range of the grey area to be 2.67 2.68. Therefore 2.675 is the optimum Z-score value that discriminates between Bankrupt and Non-bankrupt companies. (Altman, 1968) The Z-Scores Accuracy at Predicting Bankruptcy Altman tested his Z-score model accuracy to five years too. The results showed the following: Year Prior to Bankruptcy Accuracy (%) 1 95% 2 72% 3 48% 4 29% 5 36% Altman suggested that on the basis of these results, his Z-score model was accurate at predicting bankruptcy up to two years before the company was bankrupt. However after the second year Altman suggests that the model is unreliable. The reason being that in year three there is less than a 50/50 chance of accurate prediction. Altmans Conclusions Altman concluded that as the companies came closer to bankruptcy, the ratios X1 to X5 showed increasing deterioration. Moreover there was massive deterioration during years three and two before eventual bankruptcy. Hence the Z-score also deteriorates as the company gets closer to bankruptcy. As all samples were from the manufacturing sector the model is most accurate when predicting bankruptcy in companies from this sector. Strengths and weaknesses of the Z-score model Before the Z-score model was developed, bankruptcy prediction models were univariate and not industry specific. Altman revolutionised bankruptcy prediction modelling and from 1968 MDA became the industry standard methodology. Altmans Z-score model has proven to be reliable and valid. He also developed other variations on the Z-Score model including one for non PLC manufacturing companies. On this basis the model is very versatile for predicting bankruptcy in numerous applications. However when developing the private company Z-Score model, Altman used unadjusted financial data the sample was small giving rise to reliability issues. Another possible problem is that the Z-score, was developed using American company data, so there may be generalisability issues when Z-scoring non U.S. companies. https://epublications.marquette.edu/cgi/viewcontent.cgi?article=1025context=account_fac https://www.exceluser.com/tools/zscore.htm However Mandru et al suggest that Z-score models are not suitable in modern credit risk analysis. They suggest that it does not consider the economic and business cycle impact and should consider qualitative variables as well as market trends, market share, and the quality of the companys products and management. Ultimately these variables are reflected in the companies share prices; hence why modern commercial credit risk analysts such as Moodys use this data within their computer modelling systems. https://www.wseas.us/e-library/conferences/2010/Cambridge/AIKED/AIKED-12.pdf Bankruptcy Prediction Model development after Altman Logit Probit Models Bankruptcy prediction modellers continued to use MDA through to the late 1980s. As MDA was being phased out, Logit and Probit analysis models were becoming more popular. They are both regression type analyses that use both univariate and multivariate techniques. The Logit analysis model estimates the probability of an event occurring. It uses a set of independant variables i.e. the financial ratios, and predicts an outcome in the form of a 1 (bankrupt positive) or a 0 (non-bankrupt). Probit analysis models produce a threshold level where if the companys score exceeds it, it is classed as bankrupt. https://www.iasri.res.in/ebook/EBADAT/6-Other%20Useful%20Techniques/5-Logit%20and%20Probit%20Analysis%20Lecture.pdf Neural Network Analysis The Neural network analysis for bankruptcy prediction has been developed since the late 1980s. It is an advanced version of the Logit and Probit regression analyses. Using computer simulation, the model acts like a human brain, creating neural pathways to remember previous events of bankruptcy. The programme then processes the companys data and compares it to the previous bankruptcy events. The result is a statistical probability of the company failing. Thus a quantitative credit risk value can be applied. An example of this type of analysis is a Back-Propagation neural network which can recognise patterns of business behaviour. The downside is that it requires an immense amount of data to be entered in order to train the system to successfully establish the ratio weightings. It also has seven parameters which makes it a complex task to ascertain the correct model structure to gain a meaningful outcome (Shin et al, 2005). https://www.iasri.res.in/ebook/EBADAT/6-Other%20Useful %20Techniques/5-Logit%20and%20Probit%20Analysis%20Lecture.pdf Support Vector Machines Support Vector Machines are another form of artificial intelligent bankruptcy prediction computer programme. They are closely related to the back-propagation neural network but can be used in a more generalised setting. Shin et al suggest they are better than the back-propagation neural networks because they only need two parameters and a much smaller data set to train the system to generate accurate predictions. Hence the SVM is more accurate and gives better generalisability than the BPN. (Shin et al, 2005) Mertons Expected Default Frequency Model However in 1974 Merton developed a new way of calculating credit risk. The emphasis here is placed on predicting when the borrower is likely to default on a repayment loan as opposed to the likelihood of bankruptcy. Though bankruptcy prediction is of utmost importance when calculating credit risk, a company is more likely to default on a loan before ultimate bankruptcy. So in the case of the lender, the risk of default could be construed as more important than the companys risk of bankruptcy; hence the development of the EDF model otherwise known as the Expected Default Frequency. EDF measures three values: The companys current market value (calculated by the market value of total assets) The level of liability in the company (risk to default before further lending) The current market value and its vulnerability to large changes form the external environment. (How volatile are the companys assets?) This was the first time that qualitative and quantitative data had been used to assess credit risk in one model. An improvement over Altmans Z-score and other previous models that used quantitative data only. In 1990 Kealhofer et al, co-founders of KMV the credit ratings agency bought the rights to use this model and called it the KMV-EDF model. Moodys analytics bought KMV in 2002 and the EDF model became the most widely accepted credit default model used today. It is now known as the Moodys KMV RISKCALC with updated versions being developed continuously. (Dwyer et al, 2004) Summing up A key issue with bankruptcy prediction models is the difficulty in comparing models. Karels and Prakesh, 1987 suggest that some studies define financial failure as an inability to pay financial obligations, whereas others define financial failure as filing for official bankruptcy. If this hasnt been defined properly throughout the research topic it is difficult to tell which model it is best to use in which circumstances. Accuracy is essential when predicting bankruptcy. If a company heading for bankruptcy is declared to be healthy there are many implications for the ratings agency such as lawsuits and loss of future clients. In terms of accuracy ratings the neural networks developed 100% accuracy at their peak of development and MDA models did not improve much further than Altman in 1968 at 95%. Although neural networks are 100% accurate, complex computer programmes are needed to run such prediction models. The reason Altmans Z-score model is still so popular is that an acco untant can reliably predict bankruptcy to the same degree of accuracy using only an excel spreadsheet. There is a general consensus throughout the literature that a model that can accurately predict bankruptcy at the earliest stage is the most valuable. There is a trend of models becoming less accurate as they predict further into the future. There is also a theme throughout the literature regarding the limitations of bankruptcy prediction models. The industry sector and time frame of the test subjects must be the same as the samples used to develop the model. If this rule is not kept to, the reliability of the model reduces. (Grice and Dugan, 2001) There have been over 150 bankruptcy prediction models developed with optimal accuracy. It has been suggested that new uses for existing models should be found and that new research should use existing models to be tested on real life situations, such as studying individual industries. https://epublications.marquette.edu/cgi/viewcon tent.cgi?article=1025context=account_fac The gap in the research The literature suggests that prior to Moodys RiskCalc 3.1; MDA and neural network analysis have proven to be the most accurate ways to predict the risk of credit default. However, due to the limitations of this studys scope and relevant resources; the use of Riskcalc 3.1 and neural network analysis is inappropriate. Therefore MDA will be used to analyse the risk of bankruptcy in the automotive industry, ranking firms in order of risk. As Altman revolutionized bankruptcy prediction by becoming the first developer to use MDA methodology, and that the accuracy of this model type did not improve greatly after this time, the Z-score model is most appropriate to analyse the risk of bankruptcy in my chosen industry. Another reason to use Altmans Z-score model is that Moodys riskcalc 3.1 expected default frequency value is derived from the companys share price and most recent financial statements. By using Altman 1968 we can eliminate the influence of the stock market and compare the re al financial performance within the automotive industry and rank them according to risk of bankruptcy on the basis of their published financial accounts only. The advantage of Altmans Z-score models is that accountants can use them to advise potential investors which companies within an individual industry are safest to invest in. Moreover they can accurately predict in ranked order how soon each company will become bankrupt. This information is most valuable in times of recession and global financial instability. With the current economic uncertainty and global financial market crisis, this information will prove invaluable to creditors and investors. Automotive Industry relevance During the recent global economic downturn, with exception to the banking industry, the largest global industry affected was the automotive industry. Hardest hit was General Motors which was nationalised by the United States federal government after chapter 11 bankruptcy. In 2010 this industry produced in excess of 60 million vehicles. Parts manufacturers situated in almost every country worldwide rely heavily on this industry for their livelihood. Many support industries are also reliant on the global automotive industry too. KPMGs 2010 global automotive industry study suggests that the industry is running at almost 40% over capacity and little is being done to address the problem. Worst affected is North America followed by Western Europe and Japan. The problem is being made worse by existing manufacturers moving into the emerging markets in Brazil, Russia, India, and China. It is predicted that these markets will become overcapacities within 5 years, with China becoming 20% o ver built by 2015. The current industry over capacity is unsustainable. The potential financial and human cost if car manufacturers started defaulting on liability repayments could be as catastrophic as the recent banking crisis. Bankruptcy risk assessment in this industry is extremely important to the lives of people worldwide. If we can rank this industry by Altmans Z-score we take away the influence of share trading, thus showing each companys true quantitative financial position and risk of bankruptcy with a high degree of accuracy and will be able to rank each manufacturer accordingly. (KPMG, 2011) Word Count 2953

Diversity And Public Administration By Harvey L. White And...

In the book entitled, Diversity and Public Administration by Harvey L. White and Mitchell F. Rice , White and Rice (2010) stated challenges will form from changes in demographics which are affecting the demands of delivery and visions of products and services. Since 2000, minority population has increased dramatically. In result of minority population increasing, public organizations have a more diverse work environment. In the workplace, diversity can be a benefit. According to the article entitled , â€Å"Advantages and Disadvantages of Diversity in the Workplace† by David Ingram, Ingram(2015) stated that by have a diverse workplace allows the company to utilize their employees’ cultural difference to strengthen the organizations’ productivity. Another advantage of diverse workplace is that it increase employee’s personal growth. By employees being expose to cultural difference and new ideas, this allows employees to have global perspective on how to conduct business (Ingram,2015). Diversity can also have disadvantages in the workforce. One of the disadvantages is the ability of employees to respect each other cultural differences. Diverse workplace has to endure several challenges and issues. One of the challenges of organizations having diverse working environment is gerontology. Managers concerns with workers being older is that they do not have ability to learn and adjust to new policies and methods. In the public organizations, age is a challenge becauseShow MoreRelatedStephen P. Robbins Timothy A. Judge (2011) Organizational Behaviour 15th Edition New Jersey: Prentice Hall393164 Words   |  1573 PagesTitle. 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Original Writing †Birkan Akin Free Essays

It was a dark night on a small island off the coast of Maine; I heard a shot, and a gunshot that echoed in my ears it felt like I was wounded. On the night of the gunshot a fisherman disappeared. The body was absent from the day of the gunshot, a young man known as Ryan Adams was accused of the murder. We will write a custom essay sample on Original Writing – Birkan Akin or any similar topic only for you Order Now Years later the fisherman was found, found for just a few seconds he was found by a young girl who was playing in the sea when she suddenly felt a hand. I was at the scene firstly it seemed like the girls was drowning, I went for her rescue thinking that she is drowning, but then a yellow raincoat flicked into my eyes. I screamed out â€Å"its him, its him it’s the fisherman that went missing at the gunshot scene†. I was in shock I couldn’t believe it, then I suddenly saw the fisherman moving he was getting, getting out of the sea. I screamed â€Å"he’s alive he’s alive† I saw for one moment and the he vanished, disappeared he was gone. I couldn’t even say stop, wait, don’t go†¦he was gone. The following day everybody in the island was talking about the return of the fisherman, but just for a few seconds. On the same day the young man Ryan Adams that was accused of murdering the fisherman was set free. A further investigation was in process the investigation was named as ‘who fired the gunshot?’ Since the day the fisherman reappeared he was never seen again. One night I went out to the seaside to catch some fish for dinner, the sky was pitch black I couldn’t see anything. When I was fishing I heard something, something that was thrown into the sea from a distance. I turned around and looked at the shaw it looked like I saw the fisherman it looked he had that same yellow raincoat I was sure it was him because it looked like he was getting closer to me. I packed up my equipment and headed towards my home. I had another look back and I saw the fisherman in distance, he was running, running towards me. When I looked carefully at him it looked like he was carrying something in his hand, it was hard to describe from such distance but as he got closer I started picturing it, it looked like a gun. I was shattered, scared in amazement. He was getting closer and closer and closer and closer, then I heard a gun shot it was echoing in my ears it sounded like the bullet was getting closer†¦then suddenly I flew out my bed and started screaming â€Å"agghhhhhhhhhhhhhhhh†¦its was a dream, a dream, a dream that know one would ever believe. How to cite Original Writing – Birkan Akin, Papers

Examine the Implications for the Organization of an Ageing Workforce in Australia free essay sample

Introduction Over the past decades, Australia, as a whole, has been facing the challenges of aging workforce which present difficulties not only for small business but also large enterprise. Proven by many researchers, niche capabilities and potential development of any organization are largely dependent on its employees. Geok, Pok and Noi [2002 pp9] claim that â€Å"the impact of graying population on country’s social and welfare systems, infrastructures as well as economic growth is substantial, several developed nations have been promoted and adopted social policies to address this specific issue†. Many business cases studies regard mature workers at the age of 45 and over are as ageing workforces who are believed to introduce difficulties in business planning and strategies to retain and sustain the balance in workforce within an organization. At the same time, it cannot be denied that the present of older generation within an organization is absolutely essential in term of providing necessary knowledge and working experiences to the younger generation worker. Therefore, it is important for organizations to examine the implications of Australian ageing workforce in order to achieve its targets and development. This paper will discuss the demographics of Australian population and workforce. Benefit of mature workforce within an organization will also be addressed. The consequences of ageing workforce will in turn be examined by in term of Human Resource management as well as cost and productivity level of the company. Finally, this study attempts to indicate certain management measures to maximize the productivity of ageing workforce. The demographics of Australian population and workforce Firstly, it is essential to examine the Australian population as ageing within population will most likely lead to the ageing in the workforce. It can be indentified that the current ageing population within Australia is primarily due to the rapid decreasing in the birth ratio. The fundamental reason behind such decreasing ratio is due the post World War II â€Å"baby boomer† generations that is entering to the age of retirement in the next ten years. In the mean time, birth rate is considerably lower within Australia. The proportion of people aged 65 years and over in Australia has witnessed an increase of 12. 9% between June 2004 and June 2009 [Australia Bureau of Statistics 2009 cat. no. 3235. 0]. In addition, there is an increase in the average life expectancy as a result of the improvements in health and advances in medical service. This emphasizes that there is currently a larger number of older people than ever before and according to Productivity Commission 2005, this trend will continue for several decades. Furthermore, the current generations tend to marry in a later age and have fewer children than previous generation. According to Australia Bureau of Statistics [cat. no. 3306. 0. 55. 001], the median age of males getting married in 2007 was at the age of 31. 6 and that of females was 29. 3. Both males and females median age at marriage has been increasing gradually over the past 20 years. Moreover, in recent decades, there has been tendency for women to delay childbearing until forties, not to mentioned some remained childless [Australia Institute of Health and Welfare cat. o. PER 50]. These have resulted in a rather low supply of young employees while older employees are retiring and leaving the workforce. Figure 1: Average age of full time workers and civilian population ages 15-64 years Source: Parliamentary Library Over the past two decades, workforce has been ageing faster than the general population. The chart indicates that the average age of the civilian population in 2004 is 2. 2 years greater than it was in 1994, rising from 36. 6 to 38. 8 years. On the other hand, an increase in the average of all full-time workers of 3. years, from 35. 9 to 39. 4 can be observed in figure 1. The workforce ageing faster than population is because of the participation of baby boomer women later in life. As a result of social reason such as financial pressure, there is an increase in the number of women in the labor market. The average age of full-time female worker has risen by 5. 6 to 38. 5 years [Parliament of Australia]. Women have been moving to part-time jobs that have more advantages for them than previous decades. Another factor which impacts on the ageing workforce is hat there is currently a trend for mature aged worker to keep working past their retirement age as they still possess the physical capability to make a significant contribution to the economic growth of Australia. Essentially, with the advancement in medical system, people are able to have better health care and in turn live longer life. Consequently, the working period of aging workers can be much longer as their ability and flexibility is still sufficient for the allocated tasks. This in turn affects the decision to retire as it is directly related to required physical capability. Current government’s policies and incentives reflected that the retirement decision, nowadays, depends not only on the organizations but also on individual. Benefits of a mature workforce Current status of ageing population is a controversial topic in society. This requires companies to pay more attention to older personnel resources. Majority of older employees believe that they are still at the peak of their job performance. Organizations with its main employees are matured workers; therefore, need to consider the benefit of its older generation staff in order to look for appropriate measures to attract and retain older workforce. Firstly, flexibility, maturity and patience are one of the most important benefits of the ageing workforce. With the older employee’s experiences, better quality work will be ensured which allow business to reduce a significant amount of production cost. In addition, older employees can help company maintain a stable workforce, reliability, and dedication to work. Secondly, the older generation workers have higher working morality in punctuality as well as ensuring the completion of assigned tasks. Moreover, honesty is the common characteristics of older workers. They understand the value of life, the causes and effects of working and social relationships as well as responsibility. As a result, older generation workers will always come to ensure the integrity of their action by dedicating it to the truth. Furthermore, the intangible value of older workers is meticulous, thorough, focused and attentive which can help companies to deliver a higher quality work package and in turn gaining better niche reputation in the market. Additionally, older workers are also valuable resources within a company as they have the ability to listen and understand the objective of a given task. Therefore, when they are allocated a duty, with certain working experiences, the mature workers know what needs to be done within the entire process to finish their job. They are likely to be more efficient and productive. Next, older employees tend to have more pride within their completed tasks in term of quality which can be difficult to find in younger employees. The younger employees are not attentive with the assigned work which can be completed without any further consideration on quality and improvement of their work. Comparatively, older employees will more likely to spend more time to measure and improve the quality of their given tasks. Older employees understand that working for a company that has meaningful life rather than getting a monthly wage. They are more likely to spend more time, while receive similar wages to complete their assigned tasks as well as fulfilling their goal in life Besides, performance and trust, mutual sharing ideas and advice have made older workers become more ideal. These years of experience working in various fields had given the employees a great insight on how to have the work done more efficiently, saving more time and money for the company [NSW Business Chamber]. Compared to younger workers, older workers with broader experience have greater ability to solve certain problems such as communication or technical errors that might arise during the completion of their task. With their good organizational, planning skills as well as competencies that are built over the years, the mature aged workers have the ability to face and troubleshoot errors without seeking advice from colleagues and high management. Moreover, through experience, older generation workforces also possess better communication skill to negotiate and persuade partners in order to achieve the objectives. These skills allow them to be flexible in a difficult situation. The better working practice and morality characterized older employees as essential resources which can lead to positive influence on other younger employees. Consequently, they can become excellent teachers, the typical example, helping to train other staff to be more reliable and efficient [NSW Business Chamber]. In general, business owner should no longer wonder on whether or not to recruit older workers and should recognize benefits of ageing workforce. The competencies and reliability of older workers ensure that their recruitment become truly more productive and efficient than hiring a younger labor force. With the buildup experience as well as the ability to think thoroughly, older employees should be considered for further business recruitment. A report from Queensland Government states that â€Å"Studies of older people in the workforce have also found that they are flexible in their working hours and conditions, and have good coping skills when faced with changes†. Companies who value to the reliability, competencies and working morality within their employees should consciously consider hiring mature workforce. Their contribution and performance can have positive impact on the company’s turnover and profit for years. Consequences of an aging workforce The aging workforce in Australia has caused certain economic and social concerns such as labor shortage when personnel resources reach retirement stage, difficulty in planning [Shacklock 2005] and aged care workforce issue [Spoehn 2008] and [Kryger 2005]. To begin with, the proportion of ageing population in Australia is increasing, in fact, people aged 65 years is predicted to take 25% of total population in 2045 [Spoehn 2008]. This may lead to labor crisis which is forecasted with a shortage of 1. 4 million skill workers in various industries within Australia [Hannah 2009]. In general, the labor shortage affects not only entrepreneurs but also the government. The shortage is mostly on scientific and technical fields which requires high level of knowledge as well as the necessary skilled and experience. According to [Hudson 2004], within 12 years from 1982 to 2000, the requirement of post-secondary increased more than 50%. Meanwhile, the Hudson study [2004] also showed that nearly 60% of Australian at the age of 17 studying year 12 did not go on to tertiary education in 2001. This information presents a long term a problem for Australian’s scientific and technical industry as it will not be having enough talent pool for further research and development. Essentially, this would lead to the degradation in Australia’s academy system, technical advancement and future development. To the government, high rate of retirement is possibly synonymous with social concern as well as welfare problems. In 2004, the Australian Federal Treasurer – Peter Costello called people to keep working longer since the national budget was suffered rising demand by high aging population [Eric 2005]. Moreover, in the term of human resource, companies are also facing difficulties in personnel planning when choosing the suitable candidates for any specific tasks. For companies to succeed in their respective market human resource planning plays a significant role in term of delegating tasks effectively and efficiently. According to various study, the longer the period of time an aging personnel works, the more difficult it is for human resource department to come up with a scheme to manage and sustain the workforce balance within a company. To clarify the aforementioned point, it is rather ambiguous for either human resource or management to predict the exact retirement day for an aging worker. If retirement day for aging worker is not clearly defined, human resource department may not be able to recruit the appropriate replacement in time to continue the delay works. This delay works would in turn introduce costs in every aspect of the company as productivity level is reduced and it takes time and effort to find and train suitable candidate for the vacancy. [Patrickson and Ranjin 2008]. Therefore, high level of collaboration as well as management had to be performed in order to produce an appropriate future personnel panning. It takes time and effort to find and test if one person is suitable for the vacancy in company or not. In term of salary and cost, due to the experience and responsibility senior employees possess, the company, by nature, will have to assign a relatively high portion of its turnover as salary for older generation worker [Hudson 2004]. Furthermore, aged care workforce issue is one other large amount of cost that Australian government has to solve. Generally, there is currently an imbalance of gender and age in workforce, the lack of health care staff as well as the variety of health care package amongst industries. High number of woman and old people in workforce may lead to increase of cost budget raised from maternity care, incident care, and retirement pension. As a result, companies and government had to allocate large sum of money for ageing workers. According to Spoehn [2008], community aged care rose 6 times from 1995 to 2003 and from 2001 to 2006 and this led to the aged care package went up to 48%. As mentioned above, having older eneration worker would introduce certain benefits into the company’s profile, however, with the rapid developing rate of technology, it was studied that the old worker may find it difficult to accept and adapt to new changes [Patrickson and Ranjin 2008]. For instance, various survey has been done and shown that worker in the age of 65 and over normally assume that new technology is ha rd to deal with. On the other hand, it seems rather easier for younger employee to accept changes and quickly adapt with the new technology in order to complete their tasks at a higher productivity level. Thus, having to train the older workers with new set of skills and technology will result in a large amount of cost as well as time which might be utilized for other aspect of the company. In fact, Patrickson and Ranjin [2008] concluded, human’s ability and flexibility weakens as they age, especially when they reach seventy. However, with the extensive working experience in the industry, it was shown by many research authors, working procedures and strategies resulting from ageing worker increase the effectiveness of the work when compared to the younger generation worker’s [Hudson 2004]. Managing an ageing workforce As a result of a continuing ageing workforce in Australia, it is necessary that all the involved parties such as the government, the trade union and firms implement different policies in order to maximize the benefits of an ageing workforce and reduce the negative aspects. The Australian government and the labor union could essentially cooperate to support mature aged workers. According to Patrickson and Ranzjin (2005), the government has enforced a policy that allows workers to receive higher pension welfare if they continue to work at the age of 65 and over. This incentive is done to encourage older employees to devote their experience to the economic growth for a longer period of time. Moreover, in 2005, the Queensland government has implemented the â€Å"recreation leave on half-pay† policy [Price and Colley 2007 p11]. Essentially, the working age of labor force can be lengthening by the extension of leave which provide the social balance for mature aged workers. Price and Colley [2007] also suggested that the Queensland public services have been supporting older workers to have a social balance between life and work by providing flexibility in the workplace location such as working at home. Apart from the direct assistance for workers, the government’s policies also aim to help employers. These legislations consist of statements revolving around the advantages of employing older workers such as the public acknowledgment for firm’s marketing plan [Patrickson and Ranzjin 2005]. In addition, the labor union has also assisted the government to enforce appropriate policies to an ageing workforce. An example of this is Australian Council of Trade Unions (ACTU)’s campaign to mobilize proper working hours. ACTU’s campaign has leaded the Queensland government to negotiate with enterprises and develop the workload management methods in order to monitor the employees’ issues [Price and Colley 2007]. Managing mature aged workforce is rather difficult for organizations and firms. It was indicated that managers must confront the conflict between the special values of older workers and the requirement sustaining the efficiency and productive capacity [Patrickson and Ranzjin 2005]. However, as it was mentioned above, mature aged workers can benefit firms with their experience while young workers might be dynamic with technology and innovation but tend to lack working experience. Therefore, manager should maintain an ageing diversity workforce. Moreover, firms and organizations should also encourage older workers continue to devote their working ability for longer period of time so that the experience of ageing employees can be best valued. There are four main human resource management strategies that managers can follow to keep older workers stay with their organizations. First of all, as older employees may have devoted to the companies for long time, it is necessary that the firms have a database of information about them such as their expectations and their opinion [Patrickson and Ranzjin 2005]. Although the elderly worker may not be able to acquire new knowledge as quickly as the younger generation do, ageing workers can still be retrained. In reality, according to Burke and Ng [2006], younger employees are likely to be disloyal and can quickly change their jobs because of their desire to grow higher in their career’s â€Å"ladder†. Meanwhile, older workers are possibly worth training as they will continue to contribute to the firms after they are trained. Furthermore, the employment of ageing workers with the aim to train other employees is probably a common but useful HR management strategy. For small and medium firms, this strategy which is also referred to as â€Å"on – the – job training (OJT)† will ensure that the transfer of experience and knowledge from the former generation to the new employees [Beaver and Hutchings 2005 p9]. This allows managers to not only utilize the experience of ageing workforce but at the same time reducing the cost of training new employees. Finally, since the problem of health is the major reason why older workers decide to retire, managers should take actions in order to improve workers’ well – being. For example, it was explained that the improvement in working condition to prevent occupational illnesses as well as a better assistance for workers who have health problems can increase the working age of the workforce [Ilmarinen 2006]. Conclusion Human resource is the most important key in maintaining productivity in a company in particular and in an economy in general. That is why employers and government is putting effort into encouraging people to remain at work as long as they can by introducing certain policies and incentives such as guarantying the health care, welfare, extra-education as well as increasing the role of labor union. Furthermore, within an organization, managers are responsible to create an equal opportunity during recruiting process for all workers regardless of age as well as the experience of the employee. It is necessary for human resource department to manage and sustain the balance in company’s workforce which ultimately will bring out the potential for future success. References BEAVER G and HUTCHINGS K [2005] â€Å"Training and developing an age diverse workforce in small medium enterprises: The need for a strategic approach† Education and Training Vol. 47 Issue 8/9 pp 592 – 604 BURKE R and NG E [2006] â€Å"The changing nature of work and organizations: implications for human resource management† Human Resource Management Review Vol. 6 Issue 2 June pp 86 – 94 FED: Australia Faces Shortage Of 100,000 Tradespeople, says MBA. [1 June]. AAP General News Wire,1. Retrieved March 29 2011, from Academic Research Library. 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