Six-state review

Year-to-date revenues for the first four months of FY2003 were above their FY2002 level in most New England states. Hit hard by dramatically diminished tax receipts and/or increased spending pressures, all six states closed FY2002 with deficits that had to be eliminated by end-of-the-year fiscal measures. The improved revenue collections for the first four months of FY2003 were welcome. General revenues were up in Connecticut, Maine, New Hampshire, Rhode Island, and Vermont. Sales tax receipts, driven in large part by car purchases induced by manufacturers' offers of zero percent financing, were up in Connecticut, Maine, Rhode Island, and Vermont; meals and rooms tax receipts were up in New Hampshire. Personal income tax collections were mixed; although higher in Maine and Vermont, they were down slightly in Rhode Island and sharply in Connecticut and Massachusetts.Deficit financing - New England ; Revenue

Preparing for the storm: rainy day funds in New England

Rainy day funds have played an important role in alleviating the current state fiscal crisis. This article examines the benefits of these funds, the various ways in which they can be structured, and the differences in the structure and use of these funds in New England.Deficit financing - New England ; Fiscal policy - New England

Securitizing tobacco settlements: the basics, the benefits, the risks

In this time of fiscal hardship for American states, governors and legislators across the country are searching for ways to make up for revenue shortfalls. Partly because of the severity of the revenue crisis, innovative and unconventional means of raising cash have begun to surface. One of these approaches, the topic of this article, is the securitization of tobacco settlement revenues.Tobacco industry ; Fiscal policy - New England ; Deficit financing - New England ; Bonds - New England

Issues in economics

Budget crises in the six New England states have prompted spending cuts, leaving taxpayers to ask, "Where does the state spend our money?"State finance - New England ; Fiscal policy - New England

Estimating risk when zero events have been observed

Assessing the risk of complications or adverse events following an intervention presents challenges when they have not yet occurred. Suppose, for instance, a chronic shortage of cardiac telemetry beds has prompted a hospital to implement a new policy that places low-risk patients admitted to ‘rule out myocardial infarction’ in regular ward beds (ie, with no telemetry). After 6 months and the admission of 100 such patients, no cardiac arrests or other untoward events have occurred. This absence of harm (ie, zero adverse events) indicates a low risk, but clearly we cannot infer a risk of zero on the basis of only 100 patients. But, what can we say about the true underlying risk

Dealing with deficits: how New England States are managing the fiscal crisis

Following the economic boom of the late 1990s, the current recessionary period has caused revenues to decline and demand for government services to rise. Now, government executives and lawmakers, faced with persistent budget deficits, are struggling with difficult decisions: "What cuts should we make?" "Could we, should we, raise taxes?" "How deeply do we dip into quickly diminishing reserves?"...Deficit financing - New England ; Economic conditions - New England

Historical Exploration - Learning Lessons from the Past to Inform the Future

This report examines a number of exploration campaigns that have taken place during the last 700 years, and considers them from a risk perspective. The explorations are those led by Christopher Columbus, Sir Walter Raleigh, John Franklin, Sir Ernest Shackleton, the Company of Scotland to Darien and the Apollo project undertaken by NASA. To provide a wider context for investigating the selected exploration campaigns, we seek ways of finding analogies at mission, programmatic and strategic levels and thereby to develop common themes. Ultimately, the purpose of the study is to understand how risk has shaped past explorations, in order to learn lessons for the future. From this, we begin to identify and develop tools for assessing strategic risk in future explorations. Figure 0.1 (see Page 6) summarizes the key inputs used to shape the study, the process and the results, and provides a graphical overview of the methodology used in the project. The first step was to identify the potential cases that could be assessed and to create criteria for selection. These criteria were collaboratively developed through discussion with a Business Historian. From this, six cases were identified as meeting our key criteria. Preliminary analysis of two of the cases allowed us to develop an evaluation framework that was used across all six cases to ensure consistency. This framework was revised and developed further as all six cases were analyzed. A narrative and summary statistics were created for each exploration case studied, in addition to a method for visualizing the important dimensions that capture major events. These Risk Experience Diagrams illustrate how the realizations of events, linked to different types of risks, have influenced the historical development of each exploration campaign. From these diagrams, we can begin to compare risks across each of the cases using a common framework. In addition, exploration risks were classified in terms of mission, program and strategic risks. From this, a Venn diagram and Belief Network were developed to identify how different exploration risks interacted. These diagrams allow us to quickly view the key risk drivers and their interactions in each of the historical cases. By looking at the context in which individual missions take place we have been able to observe the dynamics within an exploration campaign, and gain an understanding of how these interact with influences from stakeholders and competitors. A qualitative model has been created to capture how these factors interact, and are further challenged by unwanted events such as mission failures and competitor successes. This Dynamic Systemic Risk Model is generic and applies broadly to all the exploration ventures studied. This model is an amalgamation of a System Dynamics model, hence incorporating the natural feedback loops within each exploration mission, and a risk model, in order to ensure that the unforeseen events that may occur can be incorporated into the modeling. Finally, an overview is given of the motivational drivers and summaries are presented of the overall costs borne in each exploration venture. An important observation is that all the cases - with the exception of Apollo - were failures in terms of meeting their original objectives. However, despite this, several were strategic successes and indeed changed goals as needed in an entrepreneurial way. The Risk Experience Diagrams developed for each case were used to quantitatively assess which risks were realized most often during our case studies and to draw comparisons at mission, program and strategic levels. In addition, using the Risk Experience Diagrams and the narrative of each case, specific lessons for future exploration were identified. There are three key conclusions to this study: Analyses of historical cases have shown that there exists a set of generic risk classes. This set of risk classes cover mission, program and strategic levels, and includes all the risks encountered in the cases studied. At mission level these are Leadership Decisions, Internal Events and External Events; at program level these are Lack of Learning, Resourcing and Mission Failure; at Strategic Level they are Programmatic Failure, Stakeholder Perception and Goal Change. In addition there are two further risks that impact at all levels: Self-Interest of Actors, and False Model. There is no reason to believe that these risk classes will not be applicable to future exploration and colonization campaigns. We have deliberately selected a range of different exploration and colonization campaigns, taking place between the 15th Century and the 20th Century. The generic risk framework is able to describe the significant types of risk for these missions. Furthermore, many of these risks relate to how human beings interact and learn lessons to guide their future behavior. Although we are better schooled than our forebears and are technically further advanced, there is no reason to think we are fundamentally better at identifying, prioritizing and controlling these classes of risk. Modern risk modeling techniques are capable of addressing mission and program risk but are not as well suited to strategic risk. We have observed that strategic risks are prevalent throughout historic exploration and colonization campaigns. However, systematic approaches do not exist at the moment to analyze such risks. A risk-informed approach to understanding what happened in the past helps us guard against the danger of assuming that those events were inevitable, and highlights those chance events that produced the history that the world experienced. In turn, it allows us to learn more clearly from the past about the way our modern risk modeling techniques might help us to manage the future - and also bring to light those areas where they may not. This study has been retrospective. Based on this analysis, the potential for developing the work in a prospective way by applying the risk models to future campaigns is discussed. Follow on work from this study will focus on creating a portfolio of tools for assessing strategic and programmatic risk

Six-state review

New England states are facing their worst fiscal crises in at least a decade. General revenues, especially those from the personal income tax, fell sharply in every New England state except New Hampshire in FY2002. All six states closed the fiscal year with deficits. Although preliminary reports suggest that FY2003 revenue collections in some states may be up from last year, deficits are still expected throughout the region. In response, all six states are cutting expenditures, drawing down reserve accounts, and/or raising taxes and fees.Deficit financing - New England ; Economic conditions - New England

Multi-sports training in English Soccer Academies: A survey exploring practices, practitioner perspectives, and barriers to use.

Despite interest in multi-sports training as a strategy to enhance long-term player development in soccer, current practice within English professional soccer academies is not well understood. This study explored the use of multi-sports training by English professional soccer academies (i.e., if multi-sports training was used, how often, session duration, activity type and age-group variations), and practitioners' perspectives with respect to player development (i.e., perceived physical/sociological benefits of; and barriers to use) using an online survey. One practitioner per club, per age-category (i.e., Foundation [<9 to <12 years], Youth [<13 to <16 years] and Professional [<18 to <23 years]) was permitted to respond. Sixty practitioners responded, of which, 48% worked with U18-U23 players; 42% with U13-14 and U15-16 players, respectively; 20% with U9-10 players and 33% with U11-12 players. Half (n = 30) of the respondents used multi-sports training for a total of 1 (IQR 1-2) session and 30 (13 to 60) minutes per week. Respondents “agreed” or “strongly agreed” that multi-sports training improved physical outcomes, reduced injury risk, helped to avoid early professionalism, and improved communication and problem-solving. Several barriers to inclusion were identified, such as limited training time, lack of equipment and attrition from other staff and players. In summary, although multi-sports training was used by only ~50% of practitioners, indicating that many clubs specialize with deliberate soccer practice, most practitioners perceived multi-sports training to be beneficial to player development

Saddlepoint approximation for the generalized inverse Gaussian Lévy process

The generalized inverse Gaussian (GIG) Lévy process is a limit of compound Poisson processes, including the stationary gamma process and the stationary inverse Gaussian process as special cases. However, fitting the GIG Lévy process to data is computationally intractable due to the fact that the marginal distribution of the GIG Lévy process is not convolution-closed. The current work reveals that the marginal distribution of the GIG Lévy process admits a simple yet extremely accurate saddlepoint approximation. Particularly, we prove that if the order parameter of the GIG distribution is greater than or equal to −1, the marginal distribution can be approximated accurately — no need to normalize the saddlepoint density. Accordingly, maximum likelihood estimation is simple and quick, random number generation from the marginal distribution is straightforward by using Monte Carlo methods, and goodness-of-fit testing is undemanding to perform. Therefore, major numerical impediments to the application of the GIG Lévy process are removed. We demonstrate the accuracy of the saddlepoint approximation via various experimental setups