Learning and the Form of Compensation

Using data from the PSID and an empirical setup similar to the one used in Altonji and Pierret (Q J Econ 116(1):313–350, 2001)’s paper on wages and employer learning, we find that the coefficient of a hard-to-observe correlate of productivity—parents’ educational attainment—in a wage regression increases more rapidly with experience in performance pay jobs than in nonperformance pay jobs. This result is driven entirely by bonus pay jobs as opposed to commission/piece rate jobs. In the latter, there is no evidence that the importance of parental education in the wage determination process increases over time. This is consistent with the notion that explicit pay-for-performance compensation schemes are, by design, revealing workers’ productivities and that employers need not infer anything about worker productivity when the payment is ex post as is the case for commissions and piece rates as opposed to having to set pay ex ante.     

Nonparametric regression estimation of conditional tails: the random covariate case

We present families of nonparametric estimators for the conditional tail index of a Pareto-type distribution in the presence of random covariates. These families are constructed from locally weighted sums of power transformations of excesses over a high threshold. The asymptotic properties of the proposed estimators are derived under some assumptions on the conditional response distribution, the weight function and the density function of the covariates. We also introduce bias-corrected versions of the estimators for the conditional tail index, and propose in this context a consistent estimator for the second-order tail parameter. The finite sample performance of some specific examples from our classes of estimators is illustrated with a small simulation experiment.     

Hierarchical Bayesian models to assess between- and within-batch variability of pathogen contamination in food

Assessing within-batch and between-batch variability is of major interest for risk assessors and risk managers in the context of microbiological contamination of food. For example, the ratio between the within-batch variability and the between-batch variability has a large impact on the results of a sampling plan. Here, we designed hierarchical Bayesian models to represent such variability. Compatible priors were built mathematically to obtain sound model comparisons. A numeric criterion is proposed to assess the contamination structure comparing the ability of the models to replicate grouped data at the batch level using a posterior predictive loss approach. Models were applied to two case studies: contamination by Listeria monocytogenes of pork breast used to produce diced bacon and contamination by the same microorganism on cold smoked salmon at the end of the process. In the first case study, a contamination structure clearly exists and is located at the batch level, that is, between batches variability is relatively strong, whereas in the second a structure also exists but is less marked.     

Metamodeling of aircraft infrared signature dispersion

Existing computer simulations of aircraft InfraRed Signature (IRS) do not account for the dispersion induced by uncertainty on input data such as aircraft aspect angles and meteorological conditions. As a result, they are of little use to estimate the detection performance of optronic systems: in that case, the scenario encompasses a lot of possible situations that must indeed be addressed, but cannot be singly simulated. In this paper, a three-step methodological approach for predicting simulated IRS dispersion of imperfectly known aircraft is proposed. The first step is a sensitivity analysis. The second step consists in a Quasi-Monte Carlo survey of the code output dispersion. In the last step, a neural network metamodel of the IRS simulation code is constructed. It will allow carrying out thorough computationally demanding tasks, such as those required for optimization of an optronic sensor. This method is illustrated in a typical scenario, namely an air-to-ground full-frontal attack by a generic combat aircraft, and gives satisfactory estimation of the infrared signature dispersion.     

OECD's ‘Better Life Index’: can any country be well ranked?

We critically review the Better Life Index (BLI) recently introduced by the Organization for Economic Co-operation and Development (OECD). We discuss methodological issues in the definition of the criteria used to rank the countries, as well as in their aggregation method. Moreover, we explore the unique option offered by the BLI to apply one's own weight set to 11 criteria. Although 16 countries can be ranked first by choosing ad hoc weightings, only Canada, Australia and Sweden do so over a substantial fraction of the parameter space defined by all possible weight sets. Furthermore, most pairwise comparisons between countries are insensitive to the choice of the weights. Therefore, the BLI establishes a hierarchy among the evaluated countries, independent of the chosen set of weights.     

Migrating Gender Inequalities? Immigrant Women's Participation in Political Survey Research

This study examines the participation of immigrant women in political surveys in Canada as a form of political participation. Investigating immigrant women's participation in the various components of the Canadian Election Studies, this study highlights the structuring impact of pre‐migration experiences with gender inequalities from two different perspectives. The larger the gender inequalities in immigrant women's country of origin, the lower their retention rate to the post‐election surveys, and the greater their propensity to provide non‐responses to political survey‐items. This study contributes to a better understanding of immigrant political integration and the related impact of pre‐migration experiences.     

Sampling Strategies for Evaluating the Rate of Adventitious Transgene Presence in Non‐Genetically Modified Crop Fields

According to E.U. regulations, the maximum allowable rate of adventitious transgene presence in non‐genetically modified (GM) crops is 0.9%. We compared four sampling methods for the detection of transgenic material in agricultural non‐GM maize fields: random sampling, stratified sampling, random sampling + ratio reweighting, random sampling + regression reweighting. Random sampling involves simply sampling maize grains from different locations selected at random from the field concerned. The stratified and reweighting sampling methods make use of an auxiliary variable corresponding to the output of a gene‐flow model (a zero‐inflated Poisson model) simulating cross‐pollination as a function of wind speed, wind direction, and distance to the closest GM maize field. With the stratified sampling method, an auxiliary variable is used to define several strata with contrasting transgene presence rates, and grains are then sampled at random from each stratum. With the two methods involving reweighting, grains are first sampled at random from various locations within the field, and the observations are then reweighted according to the auxiliary variable. Data collected from three maize fields were used to compare the four sampling methods, and the results were used to determine the extent to which transgene presence rate estimation was improved by the use of stratified and reweighting sampling methods. We found that transgene rate estimates were more accurate and that substantially smaller samples could be used with sampling strategies based on an auxiliary variable derived from a gene‐flow model.