The Value of a Statistical Life: a Meta-Analysis with a Mixed Effects Regression Model

The value of a statistical life (VSL) is a very controversial topic, but one which is essential to the optimization of governmental decisions. Indeed, our society faces any number of risks (health, transportation, work, etc.) and, as resources are limited, their complete elimination is impossible. The role of governments is to act as effectively as possible in reducing these risks. To do so, one must first determine the value that society is willing to pay in order to save a human life. However, we see a great variability in the values obtained from different studies. The source of this variability needs to be understood, in order to offer public decision-makers better guidance in choosing a value and to set clearer guidelines for future research on the topic. This article presents a meta-analysis based on 40 observations obtained from 37 studies (from nine different countries) which all use a hedonic wage method to calculate the VSL. Our meta-analysis is innovative in that it is the first to use the mixed effects regression model (Raudenbush, 1994) to analyze studies on the value of a statistical life. The outcome of our meta-analysis allows us to conclude that the variability found in the results studied stems in large part from differences in methodologies.Value of a statistical life, meta-analysis, mixed effects regression model, hedonic wage method, risk

Credit Spread Changes within Switching Regimes

Many empirical studies on credit spread determinants consider a single-regime model over the entire sample period and find limited explanatory power. We model the credit cycle independently from macroeconomic fundamentals using a Markov regime switching model. We show that accounting for endogenous credit cycles enhances the explanatory power of credit spread determinants. The single regime model cannot be improved when conditioning on the states of the NBER economic cycle. Furthermore, the regime-based model highlights a positive relation between credit spreads and the risk-free rate in the high regime. Inverted relations are also obtained for some other determinants.Credit spread, switching regimes, market risk, liquidity risk, default risk, credit cycle, NBER economic cycle

Detecting Regime Shifts in Corporate Credit Spreads

Using an innovative random regime shift detection methodology, we identify and confirm two distinct regime types in the dynamics of credit spreads: a level regime and a volatility regime. The level regime is long lived and shown to be linked to Federal Reserve policy and credit market conditions, whereas the volatility regime is short lived and, apart from recessionary periods, detected during major financial crises. Our methodology provides an independent way of supporting structural equilibrium models and points toward monetary and credit supply effects to account for the persistence of credit spreads and their predictive power over the business cycle.Credit spread regimes, level regimes, volatility regimes, credit cycle, economic cycle, monetary effect, credit supply effect

Poisson Models with Employer-Employee Unobserved Heterogeneity: An Application to Absence Data

We propose a parametric model based on the Poisson distribution that permits to take into account both unobserved worker and workplace heterogeneity as long as both effects are nested. By assuming that workplace and worker unobserved heterogeneity components follow a gamma and a Dirichlet distribution respectively, we obtain a closed form the unconditional density function. We estimate the model to obtain the determinants of absenteeism using linked employer-employee Canadian data from the Workplace and Employee Survey (2003). Coefficient estimates are interpreted in the framework of the typical labor-leisure model. We show that omitting unobserved heterogeneity on either side of the employment relationship leads to notable biases in the estimated coefficients. In particular, the impact of wages on absences is underestimated in simpler models.Absenteeism, Linked Employer-Employee Data, Employer-Employee Unobserved Heterogeneity, Count Data Models, Dirichlet Distribution

Poisson Models with Employer-Employee Unobserved Heterogeneity: An Application to Absence Data

We propose a parametric model based on the Poisson distribution that permits to take into account both unobserved worker and workplace heterogeneity as long as both effects are nested. By assuming that workplace and worker unobserved heterogeneity components follow a gamma and a Dirichlet distribution respectively, we obtain a closed form for the unconditional density function. We estimate the model to obtain the determinants of absenteeism using linked employer-employee Canadian data from the Workplace and Employee Survey (2003). Coefficient estimates are interpreted in the framework of the typical labor-leisure model. We show that omitting unobserved heterogeneity on either side of the employement relationship leads to notable biases in the estimated coefficients. In particular, the impact of wages on absences is underestimated in simpler models.Absenteeism; Linked Employer-Employee Data; Employer- Employee Unobserved Heterogeneity; Count Data Models; Dirichlet Distribution

Credit Spread Changes within Switching Regimes

Many empirical studies on credit spread determinants consider a single-regime model over the entire sample period and find limited explanatory power. We model the credit cycle independently from macroeconomic fundamentals using a Markov regime switching model. We show that accounting for endogenous credit cycles enhances the explanatory power of credit spread determinants. The single regime model cannot be improved when conditioning on the states of the NBER economic cycle. Furthermore, the regime-based model highlights a positive relation between credit spreads and the risk-free rate in the high regime. Inverted relations are also obtained for some other determinants

Modèle Bayésien de tarification de l’assurance des flottes de véhicules

Nous proposons un modèle paramétrique de tarification de l’assurance de véhicules routiers appartenant à une flotte. Les tables de primes qui y sont présentées tiennent compte des accidents passés des véhicules, des caractéristiques observables des véhicules et des flottes et des infractions au Code de la sécurité routière des conducteurs et des transporteurs. De plus, les primes sont ajustées en fonction des accidents accumulés par les flottes dans le temps. Il s’agit d’un modèle qui prend directement en compte des changements explicites des différentes composantes des probabilités d’accidents. Il représente une extension aux modèles d’assurance automobile de type bonus-malus pour les primes individuelles (Lemaire, 1985 ; Dionne et Vanasse, 1989 et 1992 ; Pinquet, 1997 et 1998 ; Frangos et Vrontos, 2001 ; Purcaru et Denuit, 2003). L’extension ajoute un effet flotte à l’effet véhicule pour tenir compte des caractéristiques ou des actions non observables des transporteurs sur les taux d’accidents des camions. Cette forme de tarification comporte plusieurs avantages. Elle permet de visualiser l’impact des comportements des propriétaires des flottes et des conducteurs des véhicules sur les taux d’accidents prédits et, par conséquent, sur les primes. Elle mesure l’influence des infractions et des accidents accumulés sur les primes d’assurance mais d’une façon différente. En effet, les effets des infractions sont obtenus via la composante de régression, alors que les effets des accidents proviennent des résidus non expliqués de la régression sur les accidents des camions via un modèle bayésien de tarification.We are proposing a parametric model to rate insurance for vehicles belonging to a fleet. The tables of premiums presented take into account past vehicle accidents, observable characteristics of the vehicles and fleets, and violations of the road-safety code committed by drivers and carriers. The premiums are also adjusted according to accidents accumulated by the fleets over time. The model proposed accounts directly for explicit changes in the various components of the probability of accidents. It represents an extension of bonus-malus-type automobile insurance models for individual premiums (Lemaire, 1985; Dionne and Vanasse, 1989 and 1992; Pinquet, 1997 and 1998; Frangos and Vrontos, 2001; Purcaru and Denuit, 2003). The extension adds a fleet effect to the vehicle effect so as to account for the impact that the unobservable characteristics or actions of carriers can have on truck accident rates. This form of rating makes it possible to visualize what impact the behaviors of owners and drivers can have on the predicted rate of accidents and, consequently, on premiums

SCAM et modélisation 3-D du canal TRPV5

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal

Étude des comportements de sécurité routière des propriétaires, exploitants et conducteurs des véhicules lourds au Québec

Le contenu de notre rapport consiste à : 1) Identifier les effets de l’application de la « Politique d’évaluation des PEVLs » sur la sécurité routière. 2) Inventorier les infractions commises par les conducteurs de véhicules lourds et par les PEVLs les plus courantes et leurs récurrences. Établir un lien statistique entre les types d’infraction des conducteurs de VLs et des PEVLs et les types d’accident. 3) Identifier et catégoriser les profils des conducteurs des VLs et des PEVLs et déterminer ceux qui sont les plus à risque sur le plan de la sécurité routière