NUSAP: a method to evaluate the quality of assumptions in quantitative microbial risk assessment

The Numeral Unit Spread Assessment Pedigree (NUSAP) system was implemented to evaluate assumptions in a quantitative microbial risk assessment (QMRA) model for Salmonella spp. in minced pork meat. This QMRA model allows the testing of mitigation strategies for the reduction of human salmonellosis and aims to serve as a basis for science‐based policy making. The NUSAP method was used to assess the subjective component of assumptions in the QMRA model by a set of four pedigree criteria: ‘the influence of situational limitations’, ‘plausibility’, ‘choice space’ and ‘the agreement among peers’. After identifying 13 key assumptions relevant for the QMRA model, a workshop was organized to assess the importance of these assumptions on the output of the QMRA. The quality of the assumptions was visualized using diagnostic and kite diagrams. The diagnostic diagram pinpointed assumptions with a high degree of subjectivity and a high ‘expected influence on the model results’ score. Examples of those assumptions that should be dealt with care are the assumptions regarding the concentration of Salmonella on the pig carcass at the beginning of the slaughter process and the assumptions related to the Salmonella prevalence in the slaughter process. The kite diagrams allowed a clear overview of the pedigree scores for each assumption as well as a representation of expert (dis)agreement. The evaluation of the assumptions using the NUSAP system enhanced the debate on the uncertainty and its communication in the results of a QMRA model. It highlighted the model’s strong and weak points and was helpful for redesigning critical modules. Since the evaluation of assumptions allows a more critical approach of the QMRA process, it is useful for policy makers as it aims to increase the transparency and acceptance of management decisions based on a QMRA model.     

Human capital,reservation wages and job competition: Heckman's lambda re-interpreted

This study integrates insights from three theories into a single model explaining the simultaneous distribution of employment and wages. Human capital theory is taken as the general framework, whereas search theory and the more recent ‘crowding’ or ‘job competition’ hypothesis are used to explain selectivity in employment and the resulting bias in wage regressions. An empirical test on Belgian data, using a two-stage probit-OLS model, indicates that the crowding theory dominates the search hypothesis for men. For women, it seems to be outweighed by relatively higher reservation wages, probably due to women's different behaviour with respect to family responsibilities.     

Number,Age Composition and School Achievements of Siblings in Two African Capital Cities

This paper uses biographical data from Dakar and Yaoundé, two large African cities, to study the link between number of siblings and school attainment. The data describe all fertility events experienced by parents and the number of siblings of each child over time. The average family size effect is estimated first. The family size effect at different ages is then estimated. The results show that, in Dakar, both the overall and age-specific effect of family size on education are negative and statistically significant. In Yaoundé, the overall effect is not significant, but negative effects at some schooling ages (between 14 and 17) are observed in this study. Finally, the negative impact of family size on school achievement seems to be driven more by elder siblings than by younger ones.     

Driven by distraction: investigating the effects of anxiety on driving performance using the Attentional Control Theory

This study investigates the effects of trait anxiety on self-reported driving behaviours through its negative impacts on Central Executive functions. Following a self-report study that found trait anxiety to be significantly related to driving behaviours, the present study extended the predictions of Eysenck and Calvo’s Attentional Control Theory, proposing that anxiety affects driving behaviours, in particular driving lapses, through its impact across the Central Executive. Seventy-five Australian drivers participated in the study, completing the Parametric Go/No-Go and n-back tasks, as well as the State-Trait Anxiety Inventory and the Driving Behaviour Questionnaire. While both trait anxiety and processing efficiency of the Central Executive was found to significantly predict driving lapses, trait anxiety remained a strong predictor of driving lapses after processing efficiency was controlled for. It is concluded that while processing efficiency of the central Executive is a key determinant of driving lapses, another Central Executive function that is closer to the driving lapses in the trait anxiety – driving lapses relationship may be needed. Suggestions regarding how to improve future trait anxiety – driving behaviours research are discussed.