Les subventions des taux d'intérêt et le prix de la terre agricole au Québec

Cet article analyse les variables influençant le prix de la terre agricole au Québec et analyse, plus spécifiquement, l'impact des taux d'intérêt sur emprunt puisque ce dernier est subventionné au Québec. On propose un modèle tenant compte de l'activité agricole et de l'activité non agricole. Un impact positif des recettes agricoles sur le prix de la terre est principalement noté dans les régions près des centres urbains où l'agriculture est concentrée, soit en périphérie de Montréal et dans la région de Québec. Les sources de revenus non agricoles ont un impact significatif sur le prix des terres settlement dans la région la plus urbaine, soit prés de Montréal. Selon les coefficients de flexibilité calculés et les taux d'intérêt en vigueur au cours de la période étudiée, l'abolition des subventions du taux d'intérêt pourrait initier une chute de l'ordre de 15% a court terme et de 7% a long terme du prix de la terre dans les régions de Québec et de Montreal.     

Fractionalization

We provide new measures of ethnic, linguistic, and religious fractionalization for about 190 countries. These measures are more comprehensive than those previously used in the economics literature and we compare our new variables with those previously used. We also revisit the question of the effects of ethnic, linguistic, and religious heterogeneity on the quality of institutions and growth. We partly confirm and partly modify previous results. The patterns of cross-correlations between potential explanatory variables and their different degree of endogeneity makes it hard to make unqualified statements about competing explanations for economic growth and the quality of government. Our new data, which features the underlying group structure of ethnicities, religions and languages, also allows the computation of alternative measures of heterogeneity, and we turn to measures of polarization as an alternative to the commonly used index of fractionalization.     

A Monte Carlo study of growth regressions

Using Monte Carlo simulations, this paper evaluates the bias properties of estimators commonly used to estimate growth regressions derived from the Solow model. We explicitly allow for measurement error, country-specific fixed effects and regressor endogeneity. An OLS estimator applied to a single cross-section of variables averaged over time (the between estimator) performs best in terms of the extent of bias on each of the estimated coefficients. Fixed-effects and the Arellano–Bond GMM estimator overstate the speed of convergence under a wide variety of assumptions, while the between estimator understates it. Finally, fixed effects and Arellano–Bond bias towards zero the slope estimates on the human and physical capital accumulation variables, while the between estimator and the Blundell–Bond system GMM estimator bias these coefficients upwards.      

Involving consumers in peer‐facilitated home‐based food hygiene training

A community‐based food hygiene initiative was piloted in the Toxteth–Granby area of Liverpool between December 2001 and March 2002. The project aimed to increase awareness and behaviour, related to poor food purchase, storage and handling practices, by actively involving members of the local community in home‐based peer‐facilitated training. Facilitators (23) were actively recruited from within the Toxteth–Granby community, and undertook a 4‐day induction and training programme, which included successful completion of the CIEH Basic Food Hygiene course. Facilitators subsequently recruited and visited nearly 1000 households (992) within the Toxteth–Granby electoral ward, undertook an observation sheet, a detailed questionnaire, and spent approximately 1 hour delivering food hygiene training. Facilitators returned to all households within an 8‐week period, and completed further observation sheets and questionnaires, for use in a comparative analysis. Facilitators and householders received incentives for their involvement in the project. Analysis of the data collected showed that general awareness of a range of food hygiene issues was high (such as hand washing before handling food), and most participants (73.8%) were able to recognise the main symptoms of food borne disease. As a result of the home‐based hygiene training there were significant changes in knowledge, attitudes, and food handling practices, including the use of refrigerators, purchase of chilled and frozen foods, washing or peeling of fruits and vegetables, and the cooking of meat‐based products. The project demonstrates that it is possible for communities to improve their food hygiene awareness and food handling behaviours through home‐based peer‐facilitated training programmes, with minimal input from professionals.     

Online advertising and privacy

An online platform auctions an advertising slot. Several advertisers compete in the auction, and consumers differ in their preferences. Prior to the auction, the platform decides whether to allow advertisers to access information about consumers (disclosure) or not (privacy). Disclosure improves the match between advertisers and consumers but increases product prices, even without price‐discrimination. We provide conditions under which disclosure or privacy is privately and/or socially optimal. When advertisers compete on the downstream market, disclosure can lead to an increase or a decrease in product prices depending on the nature of the information.     

The democratic transition

Over the last two centuries, many countries experienced regime transitions toward democracy. We document this democratic transition over a long time horizon. We use historical time series of income, education and democracy levels from 1870 to 2000 to explore the economic factors associated with rising levels of democracy. We find that primary schooling, and to a weaker extent per capita income levels, are strong determinants of the quality of political institutions. We find little evidence of causality running the other way, from democracy to income or education.     

Enhancing claim classification with feature extraction from anomaly-detection-derived routine and peculiarity profiles

Usage-based insurance is becoming the new standard in vehicle insurance; it is therefore relevant to find efficient ways of using insureds' driving data. Applying anomaly detection (AD) to vehicles' trip summaries, we develop a method allowing to derive a “routine” and a “peculiarity” anomaly profile for each vehicle. To this end, AD algorithms are used to compute a routine and a peculiarity anomaly score for each trip a vehicle makes. The former measures the anomaly degree of the trip compared with the other trips made by the concerned vehicle, while the latter measures its anomaly degree compared with trips made by any vehicle. The resulting anomaly scores vectors are used as routine and peculiarity profiles. Features are then extracted from these profiles, for which we investigate the predictive power in the claim classification framework. Using real data, we find that features extracted from the vehicles' peculiarity profile improve the classification.