In order for managers to be able to estimate break-even numbers for budgeting purposes, historical total costs must be able to be separated into their fixed and variable cost components. There are generally two methods for teaching this: the high-low method, and the method of least squares regression. The high-low method is considered theoretically inferior to the method of least squares, yet it continues to be taught in accounting courses. The argument for its continuation has been that it is “quick and easy”. However, with the proliferation of electronic spreadsheets, this advantage can also be attributed to the method of least squares. The high-low method’s continued coverage in accounting textbooks would seem to indicate that educators feel that the results generated by each method are not significantly different. This paper compares these methods by using a bootstrapping technique. Bootstrapping facilitates the simulated generation of entire distributions from a sample and allows statistical comparisons to be made between the distributions. The results of this study indicate that the high-low method, while easy to use, may be giving results that are significantly different from results obtained from regression. Because students now have the ability to do regression easily and inexpensively using a spreadsheet, and because of the theoretical shortcomings of the high-low method, it may be that educators should discontinue using and teaching the high-low method altogether.
· Job creation in the new EU Member States (NMS) and the EU candidate countries remains low despite high GDP growth in most countries. However, there are significant differences in developments among these countries (most recently between Poland and the other new Member States). · Labour markets in the NMS/candidate countries differ significantly from those of the EU 15 countries in terms of employment rates, employment patterns and unemployment. There are persistently high shares of long-term unemployed, youth unemployment is twice as high as in the EU-15 and there are diverging trends in female and youth employment rates as compared to the EU-15. · The service sector (in particular market services) has become the main source of employment, but in some countries manufacturing employment growth has resumed as well. Job creation (in employment generating services and manufacturing industries) has matched job destruction (in other activities) at least in some of the NMS. · Services are dominated by low-skill sectors, while most high-skill sectors are still underdeveloped; the latter show, however, the most dynamic employment growth both in relative and absolute terms. · Future employment opportunities will arise primarily in market services - particularly in high-skill sectors, while jobs in transport and telecommunications will decline further. There is also scope for new job creation in communal services, especially in health and social services. · Stimulating the creation of part-time jobs - the share of which in total employment in the NMS is negligible compared with the EU-15 - would be conducive to increasing employment, particularly of women. · The educational composition of the NMS labour force is biased in favour of those with medium-level education (i.e. those who have completed upper secondary schooling or training); relative to the EU-15, the NMS have smaller shares of both people with tertiary education (the 'highly educated') and those at most with only basic schooling (the 'low-educated'). Within the group of 'medium-educated' there is a larger proportion who have completed vocational training programmes rather than general upper secondary education compared to the EU-15. · The employment rates of the highly educated in the NMS and the EU-15 are similar, rates for the medium-educated are also much the same in the more advanced NMS (the Czech Republic, Hungary, Slovenia and Slovakia) as in the EU-15 but somewhat lower in the other countries; but employment rates are very low (and, conversely, unemployment is high) for the low-educated (the two exceptions are Slovenia and Romania). · There are a number of structural features accounting for these employment rate differences the primary sector (largely agriculture) accounts for a high proportion of the employment of the low-educated and this sector has lost jobs on a massive scale in most NMS; furthermore there is an 'under-representation' of the low-educated in both industry and market services in the NMS compared to the EU-15. The medium-educated, on the other hand, are particularly strongly represented in the workforce in industry which in turn specializes (as compared with the EU-15) in medium-skill sectors. This provides job opportunities for the medium-educated; however, strong productivity catching-up in industry tends to reduce jobs. Finally, the highly educated are particularly in demand in the high-skill sectors of market services (financial and business services), which have expanded strongly both in the NMS and the EU-15, and in publicly provided services; in fact, the highly educated in the NMS are disproportionately employed in public services (compared to the EU-15) while there is relatively low employment of the highly educated in industry. · Overall, the very low employment rate of the low-educated in the NMS seems to be a function of three factors a high proportion employed in agriculture which is shrinking; a relatively weak representation in the labour-intensive, lower-skill sectors of industry and market services; and, linked to these two factors, a tendency to be out-competed for jobs by the medium-educated in a situation where the latter are in relatively abundant supply and there is much slack in the labour market. · An analysis of occupational structure in the NMS supports this picture there is, in comparison to the EU-15, a smaller share of low-skill manual jobs in agriculture, industry and market services, i.e. the type of jobs which could provide job opportunities for the low-educated. Also in public services, there is a relative under-representation of low-skilled non-manual jobs compared to the situation in the EU-15. This is consistent with the low-educated being substituted by the medium-educated. · Finally, an analysis of changes in the structure of the labour force over the more recent period (1998-2003) and of the younger age cohorts shows significant adjustments in the educational characteristics of the labour force towards an 'upgrading' in educational attainment; however, the speed of change on the demand side is such that the labour market position of the low-educated has deteriorated further. Furthermore, an age cohort analysis indicates that in a number of respects educational attainment levels are adjusting less in the NMS than in the EU-15.
This study has been prepared for the European Commission (Framework Contract B2/Entr/05/091) and is composed of five sections. The first three sections all deal with assessing the role of skills in the European economy Section 1 undertakes a number of econometric exercises to analyse the relationship between skills and two indicators of competitiveness, productivity growth and exports. This and the next section represent new research effort in that a disaggregated database (by NACE 2-digit industries) has been used to analyse this relationship. Section 2 extends the analysis towards the relationship between skills and economic growth by analysing the role of skills in the context of a growth accounting exercise where skill changes are separately identified in affecting the 'quality of labour services' and hence the contribution of labour input to value added. Again the analysis exploits the detailed, disaggregated database made recently available through the EU KLEMS project. Section 3 presents an overview of skill compositional changes in different groups of EU economies. We distinguish between EU Northern economies, EU South (composed of Greece, Portugal and Spain) and the New Member States (restricted to only four countries, the Czech Republic, Hungary, Slovakia and Slovenia, for data reasons). In this section aggregate, economy-wide skill upgrading is decomposed into 'within' and 'between' (industry) changes in skill composition and the results show interesting patterns distinguished for more advanced and catching-up types of economies. The last two sections move away from the topic of reviewing the impact of skills on economic performance and the tracking of changing skill demands in EU economies. In section 4, a literature overview is provided of empirical studies regarding returns to skill acquisition through schooling and training. The idea behind this section is that returns to schooling and training reflect both skill shortages and also provide the basis for decisions with regard to skill acquisition. Finally, section 5 presents a country-by-country overview of how information is gathered with regard to skill gaps in different EU economies. The methodologies and sources for assessing skill shortages are reviewed. These are a necessary ingredient into any attempt of designing policies in relation to skill planning and the design of schooling and training institutions. The section closes with a recommendation on useful extension of European-wide vacancy statistics.
· Job creation in the new EU Member States (NMS) and the EU candidate countries remains low despite high GDP growth in most countries. However, there are significant differences in developments among these countries (most recently between Poland and the other new Member States). · Labour markets in the NMS/candidate countries differ significantly from those of the EU 15 countries in terms of employment rates, employment patterns and unemployment. There are persistently high shares of long-term unemployed, youth unemployment is twice as high as in the EU-15 and there are diverging trends in female and youth employment rates as compared to the EU-15. · The service sector (in particular market services) has become the main source of employment, but in some countries manufacturing employment growth has resumed as well. Job creation (in employment generating services and manufacturing industries) has matched job destruction (in other activities) at least in some of the NMS. · Services are dominated by low-skill sectors, while most high-skill sectors are still underdeveloped; the latter show, however, the most dynamic employment growth both in relative and absolute terms. · Future employment opportunities will arise primarily in market services - particularly in high-skill sectors, while jobs in transport and telecommunications will decline further. There is also scope for new job creation in communal services, especially in health and social services. · Stimulating the creation of part-time jobs - the share of which in total employment in the NMS is negligible compared with the EU-15 - would be conducive to increasing employment, particularly of women. · The educational composition of the NMS labour force is biased in favour of those with medium-level education (i.e. those who have completed upper secondary schooling or training); relative to the EU-15, the NMS have smaller shares of both people with tertiary education (the 'highly educated') and those at most with only basic schooling (the 'low-educated'). Within the group of 'medium-educated' there is a larger proportion who have completed vocational training programmes rather than general upper secondary education compared to the EU-15. · The employment rates of the highly educated in the NMS and the EU-15 are similar, rates for the medium-educated are also much the same in the more advanced NMS (the Czech Republic, Hungary, Slovenia and Slovakia) as in the EU-15 but somewhat lower in the other countries; but employment rates are very low (and, conversely, unemployment is high) for the low-educated (the two exceptions are Slovenia and Romania). · There are a number of structural features accounting for these employment rate differences the primary sector (largely agriculture) accounts for a high proportion of the employment of the low-educated and this sector has lost jobs on a massive scale in most NMS; furthermore there is an 'under-representation' of the low-educated in both industry and market services in the NMS compared to the EU-15. The medium-educated, on the other hand, are particularly strongly represented in the workforce in industry which in turn specializes (as compared with the EU-15) in medium-skill sectors. This provides job opportunities for the medium-educated; however, strong productivity catching-up in industry tends to reduce jobs. Finally, the highly educated are particularly in demand in the high-skill sectors of market services (financial and business services), which have expanded strongly both in the NMS and the EU-15, and in publicly provided services; in fact, the highly educated in the NMS are disproportionately employed in public services (compared to the EU-15) while there is relatively low employment of the highly educated in industry. · Overall, the very low employment rate of the low-educated in the NMS seems to be a function of three factors a high proportion employed in agriculture which is shrinking; a relatively weak representation in the labour-intensive, lower-skill sectors of industry and market services; and, linked to these two factors, a tendency to be out-competed for jobs by the medium-educated in a situation where the latter are in relatively abundant supply and there is much slack in the labour market. · An analysis of occupational structure in the NMS supports this picture there is, in comparison to the EU-15, a smaller share of low-skill manual jobs in agriculture, industry and market services, i.e. the type of jobs which could provide job opportunities for the low-educated. Also in public services, there is a relative under-representation of low-skilled non-manual jobs compared to the situation in the EU-15. This is consistent with the low-educated being substituted by the medium-educated. · Finally, an analysis of changes in the structure of the labour force over the more recent period (1998-2003) and of the younger age cohorts shows significant adjustments in the educational characteristics of the labour force towards an 'upgrading' in educational attainment; however, the speed of change on the demand side is such that the labour market position of the low-educated has deteriorated further. Furthermore, an age cohort analysis indicates that in a number of respects educational attainment levels are adjusting less in the NMS than in the EU-15.
CASE DESCRIPTION Students often fail to understand that much of FASB's work does address not-for-profit entities. This case attempts to demonstrate to students differences between for-profit and notfor- profit and how SFACs impact theory underlying subsequent FASB standards on reporting. Thus, this case attempts to help students better understand basic principles and concepts that differ between for-profit and not-for-profit organizations. This case specifically addresses SFAC # 4 and SFASs 116 and 117. This case was designed to be used in a graduate theory or financial reporting class that has a nonprofit component. The case allows students to see through basic research how nonprofits fundamentally differ from for profit entities conceptually and theoretically. An instructor could also use this case in an undergraduate nonprofit class as a project to introduce students to parts of FASB's Conceptual Framework that relate to nonprofits, thus helping students to understand theory behind reporting in a nonprofit environment. Thus, this case can be used in either undergraduate or graduate classes depending on which of requirements instructor wishes students to complete. CASE SYNOPSIS In this case, you are asked to take role of Director of Fiscal Operations of a not-forprofit organization, Children and Family Service Center. The Trustees have hired you because of concerns that accounting records are not adequate. You are give ten areas of concern and asked to answer various questions related to these concerns. Thus, you attempt to determine appropriate treatment for each item. This case will help you to better understand basic principles and concepts that differ between for-profit and not-for-profit organizations. INSTRUCTORS' NOTES Solutions to Requirements 1. Explain what makes a not-for-profit entity distinct from a for-profit entity? You may wish to include in your discussion how Statement of Financial Accounting Concepts (SFAC) # 4 distinguishes two types of entities. According to SFAC # 4, major distinguishing characteristics of a not-for-profit entity from a for-profit entity are as follows: Receipts of significant amounts of resources are from resource providers who do not expect to receive either repayment or economic benefits proportionate to resources provided. These resource providers are interested in services organization provides. Operating purposes are not to provide goods and services at a profit or profit equivalent. A not-for-profit's purpose is to use resources to provide goods and services to its constituents and beneficiaries, and it is generally prohibited from distributing assets as dividends to its members, directors, officers, or others. Absence of defined ownership interests that can be sold, transferred, or redeemed, or that convey entitlement to a share of a residual distribution of resources in event of liquidation of organization (SFAC # 4, paragraph 6). 2. According to SFAS # 117, what is primary purpose of not-for-profit financial statements? Based on SFAC # 4, what are objectives of not-for-profit financial reporting? Compare these objectives to objectives of financial reporting for business enterprises described in SFAC # 1. What are similarities and dissimilarities? According to SFAS # 117, the primary purpose of financial statements is to provide relevant information to meet common interests of donors, members, creditors, and others who provide resources to not-for-profit organizations (SFAS # 117, paragraph 4). These users have common interest in assessing (a) services an organization provides and its ability to continue to provide those services and (b) how managers discharge their stewardship responsibilities and other aspects of their performance. According to SFAC # 4, objectives of financial reporting for nonbusiness are: To provide information that is useful to present and potential resource providers and other users in making rational decisions about allocation of resources to those (SFAC # 4, paragraph 35). …
The purpose of this paper is to determine why net income adjusted for depreciation and amortization (NOF) is a strong predictor of financial distress. This paper develops four‐state ordinal financial distress models lagged one, two, and three years before financial distress to test NOF, instead of using dichotomous bankrupt and nonbankrupt models. The results of this paper suggest that NOF is a strong predictor of financial distress because NOF is an alternative measure of economic income, not because NOF is a naive measure of operating cash flow. For this study, NOF is even a better measure of economic income than net income.
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