The Red de Semillas Libres: Contesting Biohegemony in Colombia

The Red de Semillas Libres (Network of Free Seeds) in Colombia contests the expansion of, and dominant narratives on, agricultural biotechnology and intellectual property rights (IPRs) protections on seed – or what has been called ‘biohegemony’. We argue that despite its challenges, the Network contests ‘biohegemony’ through lawsuits, supporting ‘seed sovereignty’, and reframing the often taken‐for‐granted discourse on local seed varieties as raw material and a resource to be ‘discovered’, ‘invented’ and commodified by industry and Western‐based technoscience. Based on ethnographic research, we extend the concept of biohegemony to include struggle and contestation by examining how the Network pursues seed sovereignty.     

Evaluating training effectiveness in India: Exploring the relationship between training components,metacognition and learning outcomes

This study aims to examine the key training factors that significantly impact the learning outcomes of trainees. A comprehensive framework is proposed to investigate the interrelationships among various training components, trainer performance, training usefulness, and their effects on metacognition and learning. Empirical testing of the model is conducted using the partial least squares structural equation modelling approach, analyzing data collected from 322 public servants in North India. The findings reveal that almost all the focal factors comprising a training programme, such as the usefulness of training, trainer performance and metacognition, positively influence trainees' learning. While training agencies should consider all components of the training programme, the perceived usefulness, trainer performance and trainees' metacognition are particularly influential factors in facilitating learning. Training components and trainer performance predict the usefulness of training. This study integrates all focal factors of a training programme into a comprehensive framework using employee data, explaining a significant portion (45%–64%) of the variance in trainees' learning. These findings emphasize the importance of addressing these focal factors thoughtfully when designing and implementing effective training programmes.     

Accounting Year Effects Modeling in the Stochastic Chain Ladder Reserving Method

Abstract

In almost all stochastic claims reserving models one assumes that accident years are independent. In practice this assumption is violated most of the time. Typical examples are claims inflation and accounting year effects that influence all accident years simultaneously. We study a Bayesian chain ladder model that allows for accounting (calendar) year effects modeling. A case study of a general liability dataset shows that such accounting year effects contribute substantially to the prediction uncertainty and therefore need a careful treatment within a risk management and solvency framework.     

Interactions between Brands and CO Labels: The Case of “Bavarian Beer” and “Munich Beer” – Application of a Conditional Logit Model

ABSTRACT

For the past four decades ‘country-of-origin’ (CO) research has been discussing the value of designations such as ‘Made in Germany’ or ‘Made in USA.’ However, published studies have not usually investigated brand-specific effects of ‘country-of-origin’ (CO labels). This article employs a choice experiment in order to analyse the impact of a CO label applied to branded food products. For this purpose, a nationwide survey was carried out in Germany to measure the effect of the geographical indications ‘Bavarian beer’ and ‘Munich Beer’ on brand choice. The results indicate that weak unknown brands can benefit especially from the labeling of geographical indications.     

The Time of Recovery and the Maximum Severity of Ruin in a Sparre Andersen Model

Abstract

Phase-type distributions are one of the most general classes of distributions permitting a Markovian interpretation. Sparre Andersen risk models with phase-type claim interarrival times or phase-type claims can be analyzed using Markovian techniques, and results can be expressed in compact matrix forms. Computations involved are readily programmable in practice.

This paper studies some quantities associated with the first passage time and the time of ruin in a Sparre Andersen risk model with phase-type interclaim times. In an earlier discussion the present author obtained a matrix expression for the Laplace transform of the first time that the surplus process reaches a given target from the initial surplus. Using this result, we analyze (1) the Laplace transform of the recovery time after ruin, (2) the probability that the surplus attains a certain level before ruin, and (3) the distribution of the maximum severity of ruin. We also give a matrix expression for the expected discounted dividend payments prior to ruin for the Sparre Andersen model in the presence of a constant dividend barrier.     

Assessing Consumer Fraud Risk in Insurance Claims

Abstract

We present an unsupervised learning method for classifying consumer insurance claims according to their suspiciousness of fraud versus nonfraud. The predictor variables contained within a claim file that are used in this analysis can be binary, ordinal categorical, or continuous variates. They are constructed such that the ordinal position of the response to the predictor variable bears a monotonic relationship with the fraud suspicion of the claim. Thus, although no individual variable is of itself assumed to be determinative of fraud, each of the individual variables gives a “hint” or indication as to the suspiciousness of fraud for the overall claim file. The presented method statistically concatenates the totality of these “hints” to make an overall assessment of the ranking of fraud risk for the claim files without using any a priori fraud-classified or -labeled subset of data. We first present a scoring method for the predictor variables that puts all the variables (whether binary “red flag indicators,” ordinal categorical variables with different categories of possible response values, or continuous variables) onto a common –1 to 1 scale for comparison and further use. This allows us to aggregate variables with disparate numbers of potential values. We next show how to concatenate the individual variables and obtain a measure of variable worth for fraud detection, and then how to obtain an overall holistic claim file suspicion value capable of being used to rank the claim files for determining which claims to pay and the order in which to investigate claims further for fraud. The proposed method provides three useful outputs not usually available with other unsupervised methods: (1) an ordinal measure of overall claim file fraud suspicion level, (2) a measure of the importance of each individual predictor variable in determining the overall suspicion levels of claims, and (3) a classification function capable of being applied to existing claims as well as new incoming claims. The overall claim file score is also available to be correlated with exogenous variables such as claimant demographics or highvolume physician or lawyer involvement. We illustrate that the incorporation of continuous variables in their continuous form helps classification and that the method has internal and external validity via empirical analysis of real data sets. A detailed application to automobile bodily injury fraud detection is presented.     

Fair Terms and Fair Pricing for Multiple Warrant Issues

Abstract

Some firms utilize one or more tranches of warrant issues to supplement their capital base. Unlike exchange-traded options, the exercise of warrants requires the issuance of stock by the company, resulting in a form of dilution. Some previous studies of warrant valuation relied on “the value of the firm,” which is nonobservable, making it difficult to apply the corresponding valuation formula. This paper derives closed-form formulas to value single and multiple tranches of warrants based on the underlying stock price, its volatility, and other known parameter values. The paper first establishes the equivalence of the Black-Scholes formula for both call options and warrants in the case of a single tranche. Thereafter, it considers the impact on the value of previously issued warrants that results when a new tranche of warrants is subsequently issued, showing in each case that fair treatment of the first-issued warrant holders requires an adjustment (due to dilution) in the terms of those warrants and a corresponding modification in the warrants’ value once a second tranche of warrants is issued. To promote such fair treatment, terms of a warrant indenture would specify the nature of the adjustment required when future warrants are issued or exercised, analogous to the antidilution terms related, for example, to stock dividends. Unlike multiple issues of traded options, which are valued independently of one another, multiple warrant issues will be shown to have prices dependent on other warrants outstanding. Also examined is the sensitivity of the fair-value adjustment to changes in the underlying variables, and the theoretical fair-value prices are compared with Black-Scholes prices and with market prices of warrants in the case of two publicly traded companies, each with two warrant issues outstanding. As warrant issues modify the equity structure of a firm, the methodology of valuing warrants presented here will be useful to investment actuaries in situations in which a comprehensive market value for all of a firm’s securities is called for. In addition, risk management practices may sometimes include the use of warrant transactions to hedge stock positions similar to the way that call options are used for that purpose. This may include hedging the risk in equity-linked insurance contracts when the equity position includes stock in companies that have one or more warrant issues that are traded. The methods developed here are also applicable to multiple issues of executive stock options (ESOs) or to combinations of warrant issues and ESOs.