Cost benefit analysis is often expected or required for government projects. On the state and local level, the technique is useful for assessing the worthiness of a specific policy proposal. The accounting and analysis of large scale technical projects require specialized training in this manual, we offer a non-theoretical introduction to CBA, accessible to readers without an advanced statistical or economic background. The book is designed as a how-to primer in the basics of CBA for local officials who actually evaluate proposals or merely interpret reports using cost benefit analysis. A Reference List is provided for further study.

PurposePublic private partnerships (PPPs) centralize decision making into a hybrid type of firm, consisting of a government entity with a private firm, that is either a profit‐seeking or non‐profit entity, that initiates, constructs, maintains, or provides a service. The PPP model recognizes that both the public and the private sectors have certain comparative advantages in the performance of specific tasks. PPPs, grounded in cost/benefit analysis, have been used in Australia for decades and are presently being introduced in the USA as a form of innovate contracting. This paper aims to evaluate PPPs as a potentially transferable model for the delivery of public services. PPP firms are evaluated in terms of capital asset management, productive and allocative efficiency, transfer of risk between the public and private sectors, rights to the residual, and the public interest. A case study comparison of Fremantle Ports (Australia) and the Indiana Toll Road (USA) is employed to demonstrate PPP design and function.Design/methodology/approachA description and evaluation of public private partnerships (PPP) is presented and two original and primary case studies are reviewed.FindingsA PPP functioning as a monopoly provider of a common pool public asset approximates economic efficiency when user fees cover virtually full cost. Identifying optimal output and quality assessment is more challenging in the case of social goods in which the public goal is subsidy minimization and clients cannot assess quality. Best practices are helpful; they guarantee the PPP process, but not the outcome. All PPPs, in whatever country or industry, are vulnerable to bureaucratic expansion whenever they are given access to subsidized loans underwritten by taxpayers.Originality/valueThe two case studies in this paper are 100 percent original; they were examined in person by the authors, and the managers of the two entities were interviewed in Indiana (USA) and Fremantle, Western Australia.

Free market economists argue that national authorities avoid restrictions on the free movement of goods, services and financial capital between countries. Yet, countries continually choose to restrict the flow of capital both into and out of the country. Why is this done? Is it done to protect the domestic banking system, to control the domestic money supply, to manage the exchange rate, to provide stability for internal markets or to avoid wide swings in the availability of capital? Are these controls effective in precluding wide swings in a country's international trade balance? This article uses panel data in a logit model to analyse policy choice with respect to an international trade and/or investment regime. The goal is to identify choices effective in reducing the likelihood of a severe Balance of Trade Disturbance (BTD) and determine if the appropriate choice is related to per capita income (pci).

Ethics Out of Economics John Broome Cambridge, United Kingdom: Cambridge University Press, 1999 (267 pages)

The buzzword in the health care field today is prospective payment. Hospitals are phasing in a new system of Medicare reimbursement which covers some 26 million Americans; the system embodies the concept of the hospital knowing in advance exactly what the government will pay for a given patient (hence the term prospective).Hospitals which provide treatment under the fixed prospective rate may keep the change; those hospitals exceeding the prospective rate must absorb the costs themselves. Because many administrators believe such a prospective payment plan will not only cover Medicare patients (as it does now) but most hospital patients in the not-too-distant future, hospitals are beginning to use sophisticate systems to monitor individual physician resource consumption, payor activity and diagnostic trends. In short, the hospitals feel they are coming under new pressure to be cost-conscious.

Some doubt whether a pope can articulate, even for Christians, a coherent social vision. However, scholars dealing with political, social, and economic concerns benefit from an exposition of papal pastoral concerns; the Church is, after all, on the front line of social practice and grapples with the same issues as political, social, and economic theorists. In Caritas in Veritate ( CV ), tackles the politically ignored third-rail issues, outlining current social problems with traditional Vatican diplomacy. Our goal here is twofold: first, to show how CV challenges scholars to offer perspective on pathological factors impacting culture; second, to address those sections of CV dealing with economics that potentially are subject to misinterpretation. Policy issues concerning globalization, intermediate institutions, national sovereignty, international trade, and the environment are discussed. Barry Keating and Maryann O. Keating, Benedict XVI as Social Realist in Caritas in Veritate, Journal of Markets & Morality 14, no. 2 (Fall 2011): 345-358

There is a lingering suspicion that the market is unable to provide for quality or seeks some lowest common denominator of quality. This has led to the need for, according to some, government intervention to ensure quality in terms of minimum standards.Seldom observed or often only casually dealt with in the literature is the emergence, without coercion, of numerous market arrangements to provide consumers with information on product quality and/or help in processing that information. The market itself, in a number of ways, provides its own standards and enforcement of those standards. Explicit voluntary quality standards especially have received virtually no attention from academics. The most up-to-date industrial organization textbooks ignore the concept. Voluntary quality standards and the organizations which provide them, however, have not escaped the scrutiny of the Senate of the United States.

If one assumes that individuals who work in nonprofit institutions are no better or worse than others and hence operate in their own interest, an organization can be kept on track and be effective only if the incentives given to individuals in the ordinary performance of their duties reflect the original intent of the organization. Sponsors of not-for-profit institutions must take the time and make an effort to hold administrators to an objective function incorporating such goals.A model is presented describing two conflicts facing the managers of credit unions. Providing higher rates to large savers could lower cost and expand deposits but does not particularly conform with the democratic intent of credit union founders. Excluding the less creditworthy in favor of higher returns elsewhere will generate more revenue but also seems inconsistent with the original normative goals of credit unions.A data set for approximately 15,000 credit unions in 1985 is used to measure credit union behavior by size and type. It is argued here that the common bond under which a particular credit union operates acts as a constraint but not a brake on bureaucratic expansionary behavior.

Forecasts improve when the Index of Composite Leading Indicators is combined with the decomposition model, particularly during volatile times. (ProQuest: ... denotes formulae omitted.) Did you experience larger errors in your monthly forecasts last year? Did your forecasts deviate significantly from the actual when the recession started? Did your forecast users tell you that their own gut feelings and back-of-the-envelope forecasts were tracking better than your statistical forecasts? If your answer is yes to one or more of these questions, then it may be because your forecasts did not incorporate the cyclical variations in the economy. Businesses typically forecast 12 to 36 months in advance. The time series models we commonly use work well in identifying patterns during stable times, and do not work well during, at the beginning, or at the end of a recession. Generally when recession hits, demand falls dramatically. Before your time series models detect the dramatic drop in demand, a quarter or two have already passed! The result is an overstocked inventory and a loss of trust in your forecast numbers. The key to forecasting during the beginning or the end of a recession is to detect the oncoming cyclical change before it hits your business. Unfortunately, in the most commonly used time series forecasting models, it becomes apparent only after about three periods. As such, during the beginning or the end of a recession, time series models continue to over- or under- forecast, despite the fact they have worked well in the past. THE QUICK FIX One way to handle poor forecasts during a recession is to recalibrate the time series forecasts. This could be done in a variety of ways. One, reduce the period of the data used for creating a forecast. Instead of using 36 months of data, use 3 to 6 months of data. Perhaps you can use the data starting from when the recession emerged. The problem with this approach is that some of the time series models, such as ARIMA, require a large amount of data, and thus won't work well with short historical periods. However, there are models such as weighted moving averages and exponential smoothing that use relatively fewer data points, and thus can be adequate (but perhaps not ideal) candidate models for forecasting during a recession. This may be a temporary fix, but not a real solution. FORECASTING FAILURES A study by the Federal Reserve Bank of Philadelphia showed that forecasts prepared by professional forecasters are generally less accurate (actually, four times less accurate) when the U.S. economy enters or exits a recession compared to periods in which the economy is steadily growing. Your forecasts probably experience the same increase in forecast errors during those periods when the economy is entering or exiting a recession. The single most important reason is the failure of forecasters to predict the phase as well as the magnitude of the business cycle. Can business forecasters use time scries models to predict when the economy hits an upturn or downturn? They can by combining the classical time series decomposition model with the Composite Index of Leading Economic Indicators. DECOMPOSITION MODEL The simple concept behind the time series decomposition model is that each data series to be forecasted contains four components (or patterns): 1. Secular (or long-term) trend (T), 2. Seasonality (S), 3. Cyclicality (C), and 4. Random or Irregular Component (R). The component that is most important during an upswing and downswing is cyclicality, but it is this component that is missing in most time series models. How does the time series decomposition model work? Almost all the forecasting software available in the market include at least one version of the decomposition model, and in each case, they work in approximately the same way. First, the seasonality and the random movement are removed from the raw data by calculating moving averages. …

Data mining is a way to gain market intelligence from a huge amount of data ... the problem today is not the lack of data, but how to learn from it ... in data mining, the data tell the story, but it is up to you how to use that information. Data mining is used to search for valuable information from the mounds of data collected over time, which could be used in decision making. The information may be certain patterns and/or relationships that exist. With data mining, a retail store may find that certain products are sold more in one channel of distribution than in the others; certain products are sold together; certain products aie sold more in one geographical location than in others; and certain products are sold when a certain event occurs. Wal-Mart, for example, has found that the sales of beer increase when a hurricane is imminent. This means that they have to hold more than the usual supply o fbeerwhena hurricane is expected. With data mining, a financial analyst would like to know the characteristics of a company becoming insolvent; human resource managers would like to know the characteristics of a successful prospective employee; credit card departments would like to know whichpotential customers are more likely to pay back the debt and when a credit card is swiped, which transaction is fraudulent and which one is legitimate; direct marketers would like to know which customers purchase which types of products; booksellers like Amazon would like to know which customers purchase which types of books (fiction, detective stories, or any other kind); and so on. With this type of information available, decision makers will make better choices. Human resource people will hire the right individuals. Credit departments will target those prospective customers that are less prone to become delinquent an d'or less likely to involve in fraudulent activities. Direct marketers will target those customers that are more likely to purchase their products. With the insight gained from data mining, businesses may wish to re-configure their product offering and/or emphasize specific features of a product. These are not the only uses of data mining. Police use this tool to determine when and where a crime is likely to occur, and what would be the nature of that crime. Organized stock exchanges detect fraudulent activities with data mining. Pharmaceutical companies mine data to predict the efficacy of compounds as well as to uncover new chemical entities that maybe useful for a particular disease. The airline industry uses it to predict which flights are likely to be delayed (well before the flight is scheduled to depart). Weather analysts determine weather patterns with data mining to predict when there will be rain, sunshine, a hurricane, or snow. Nonprofit companies use data mining to predict the likelihood of individuals making a donation for a certain cause. The uses of data mining are far reaching and its benefits may be quite significant. DATAMININGIN HISTORICAL PERSPECTIVE The job of a data miner is to extract valuable information from the data available. The approach to finding information is to find patterns and relationships present in the data, which, of course, is not new. Indeed, man lias looked for patterns in almost every endeavor undertaken by mankind. Early man looked for patterns in the sky at night, in the movement of stars and planets, and in the weather. Modem man still hunts for patterns in early election returns, in global temperature changes, and in the sales data of new and matured products. Over the last 25 years or so, there has been a gradual evolution from data processing to data mining. In the 1960s, businesses routinely collected data and processed it using database management techniques that allowed an orderly listing and tabulation of the data as well as some query activity. On-line Transaction Processing (OLTP) became routine, data retrieval from stored data became faster and more efficient because of the availability of new and better storage devices, and data processing became quicker and more efficient because of advancements in computer technology. …

Economic literature and historical experience indicate a causal relationship between telecommunications and a country's development. This article, using structural microeconomic theory, outlines policies that will contribute to optimal accessibility and penetration of new telecommunication technology. Deregulation, privatization, licensing, and interconnection are discussed. Grass roots organizations and the urban/suburban “universal access” problem are included in a section on the digital divide.

As wealth and income increase, so too does the desire and ability to communicate with others. Suggests the opposite conclusion: that the link between telecommunications access and income levels is a causality that runs in the opposite direction – increased telecommunications access leads to increases in incomes. Discrimination against such access for the less developed countries is one of the great disparities of the twenty‐first century.

John Paul II’s vision of the social economy provides moral guidance to those seeking it. At the same time, it provokes market oriented free enterprise economists by its apparent lack of market understanding. Section one attempts to demonstrate how his vision expressed in Laborem Exercens conflicts with conservative free market economists. Section two deals with the moral logic embedded in conservative economic thought and suggests how John Paul II’s vision outlined in his three encyclicals on the social question enhances this perspective.

This article describes the use of an auction-trading market simulation in a 75-minute class to teach the different ways in which markets approach equilibrium.

From time to time, wisely or otherwise, one is tempted to resurrect old demons and wrestle with them anew. Could it be the case, for example, that mature contemporary firms in concentrated industries no longer experience a decay in their relatively high rates of return. And if they still do, does the greater variability in their profits suggest market contestability reflecting potential competition or underlying cost and demand conditions? After assuring that not just firms, but high-performing industries as a whole, experience quick and certain declines in relative rates of return, we turn to exploring which types of industries experience the greatest change in relative rank based on average rates of return.