Agriculture trade restrictiveness in Canada: How important are the cross effects?

A trade restrictiveness index (TRI) aggregates an entire protection structure into a single uniform measure that is consistent with trade theory and reflects the extent of policy interventions on trade or welfare. Although there are several variants of a TRI, all approaches aggregate protective measures using weights that depend on import demand and export supply elasticities; some studies ignore cross-price effects while others account for them. This study measures the degree of bias introduced by ignoring cross effects. It provides a practical approach to account for demand- and supply-side cross-price effects in a multi-commodity TRI setting. This approach is illustrated with a case study of distortions in the Canadian crop and livestock sector. Domestic demand and supply cross effects are approximated using a “constant differences of elasticities of substitution” functional form. On average, over the period 1996–2016, we find that cross-price effects do make a difference, and that including them makes the TRI 27% higher than an approach which ignores them. Furthermore, both TRI approaches produce indices that are higher and more variable than the OECD's percentage Producer Support Estimate (PSE) that measures policy transfers as a share of gross farm receipts. The fundamental differences between a TRI and PSE% is driven by market price support for milk.     

Arbitrators’ characteristics and decision-making records,gender of arbitrators and grievants,and the presence of legal counsel as predictors of arbitral outcomes

Labor arbitration helps define employee and employer rights and responsibilities. Evidence indicates that the advocates at arbitration tend to concern themselves with issues unrelated to the facts of the case or to the rights and responsibilities of the parties involved. More specifically, before the hearing, management and union advocates often dwell on issues such as the importance of the arbitrators’ personal characteristics and their decision-making history, the likely effects of the gender of arbitrators and grievants, and whether the presence of legal counsel impacts the arbitral outcomes. The purpose of this study is to examine whether these non-case-related factors actually can be associated with differing awards by arbitrators. This study may be the first in which the arbitrators’ decision-making history (award orientation) is a primary issue. Of all the issues studied, the arbitrator’s disciplinary award orientation is the only one with a consistent impact on arbitral outcomes. We conclude that the energy and resources expended by the advocates concerning non-case-related issues would be better spent in preparing their cases for arbitration and in examining the potential impact of arbitration on the rights and responsibilities of management and its employees.     

A Test of Technical Analysis: Matching Time Displaced Generalized Patterns

We use a least squares metric to match the return pattern of a target stock with that of an out‐of‐sample‐twin. The twin with the smallest metric is found by a comprehensive period‐by‐period search of stocks in the Center for Research in Security Prices data set extending back to 1926. If technical analysis has value, targets of twins producing the highest returns in the twin postperiod should also have the highest performance in the target postperiod. Using a randomly selected sample of 66,000 return patterns, we find higher means for targets corresponding to the highest returning twin quintile. We also use regressions to risk adjust target returns and find that twin returns in the postmatch period significantly predict risk‐adjusted target returns.     

Diversification of infrastructure projects for emergent and unknown non‐systematic risks

Allocating resources to competing large‐scale infrastructure projects involves multiple objectives. Traditional decision‐aiding methodologies focus on the trade‐offs among performance and resource objectives. Existing methodologies may fail to account for unknown and emergent risks that are typical of large‐scale infrastructure investment allocation problems. In modern portfolio theory, it is well known that a diversified portfolio can be very effective to reduce non‐systematic risks. The approach of diversification is equally important in choosing robust portfolios of infrastructure projects that may be subject to emergent and unknown risks. In this paper, we demonstrate a methodology to analyze and compare the diversification of portfolios of large‐scale infrastructure projects. We classify and explore several metrics of diversification and integrate them with risk and other performance objectives in a multiobjective approach. We test the new metrics and the methodology in a case study of hundreds of millions of dollars of infrastructure investments. The results suggest that the solutions that consider diversification are more robust to emergent risks, thus, identifying an opportunity to incorporate diversification‐based optimization methodologies to support a variety of problems involving large‐scale infrastructure investments.     

Predictive safety analytics: inferring aviation accident shaping factors and causation

This paper illustrates the development of an object-oriented Bayesian network (OOBN) to integrate the safety risks contributing to an in-flight loss-of-control aviation accident. With the creation of a probabilistic model, inferences about changes to the states of the accident shaping or causal factors can be drawn quantitatively. These predictive safety inferences derive from qualitative reasoning to conclusions based on data, assumptions, and/or premises, and enable an analyst to identify the most prominent causal factors leading to a risk factor prioritization. Such an approach facilitates a mitigation portfolio study and assessment. The model also facilitates the computation of sensitivity values based on perturbations to the estimates in the conditional probability tables. Such computations lead to identifying the most sensitive causal factors with respect to an accident probability. This approach may lead to vulnerability discovery of emerging causal factors for which mitigations do not yet exist that then informs possible future R&D efforts. To illustrate the benefits of an OOBN in a large and complex aviation accident model, the in-flight loss-of-control accident framework model is presented.