Uncertainty and Unemployment

This paper studies the impact of time‐varying idiosyncratic risk at the establishment level on unemployment fluctuations over 1972–2009. I build a tractable directed search model with firm dynamics and time‐varying idiosyncratic volatility. The model allows for endogenous separations, entry and exit, and job‐to‐job transitions. I show that the model can replicate salient features of the microeconomic behavior of firms and that the introduction of volatility improves the fit of the model for standard business cycle moments. In a series of counterfactual experiments, I show that time‐varying risk is important to account for the magnitude of fluctuations in aggregate unemployment for past U.S. recessions. Though the model can account for about 40% of the total increase in unemployment for the 2007–2009 recession, uncertainty alone is not sufficient to explain the magnitude and persistence of unemployment during that episode.     

CAN WE INFER HOSPITAL QUALITY FROM MEDICAL GRADUATES’ RESIDENCY CHOICES?

This paper uses a novel approach to infer hospital technical quality from revealed preferences over residency programs. Specifically, we use Spanish medical graduates’ residency choices made from 1995 to 2000. We start by estimating a model of medical graduates preferences that controls for hospital, proximity, specialty, and gender effects. We interpret the coefficients on the hospital dummy variables as measures of medical graduates’ preferences over hospitals. Our results show that graduates do indeed discriminate between hospitals and that their preferences correlate with hospital‐specific covariates arguably related to hospital training quality. We then show that preferences from medical graduates are positively and statistically significantly correlated with risk‐adjusted hospital rankings based on five alternative outcome measures. Finally, we construct reputation scores for each hospital using news story counts in three media outlets and find that medical graduates’ preferences are especially valuable for inference of hospital technical quality of care as they do not simply reflect well known reputation.     

Employer Changes and Wage Changes: Estimation with Measurement Error in a Binary Variable

Self‐reported tenure is often used to determine job changes. We show there are substantial inconsistencies in these responses; consequently, we risk misclassifying job changes as stays and vice versa. An estimator from Hausman et al. is applied to a job change model for Ireland, and we find that ignoring misclassification may substantially underestimate the true number of changes and lead to diminished covariate effects. The main contribution of the paper is to control for misclassification when estimating the wage effects of job mobility. A two‐step approach is adopted. We find ignoring misclassification leads to a significant downwards bias in the wage impact, and we provide an estimate that corrects for measurement error.     

Assessment of Seismic Loss Dependence Using Copula

The catastrophic nature of seismic risk is attributed to spatiotemporal correlation of seismic losses of buildings and infrastructure. For seismic risk management, such correlated seismic effects must be adequately taken into account, since they affect the probability distribution of aggregate seismic losses of spatially distributed structures significantly, and its upper tail behavior can be of particular importance. To investigate seismic loss dependence for two closely located portfolios of buildings, simulated seismic loss samples, which are obtained from a seismic risk model of spatially distributed buildings by taking spatiotemporally correlated ground motions into account, are employed. The characterization considers a loss frequency model that incorporates one dependent random component acting as a common shock to all buildings, and a copula‐based loss severity model, which facilitates the separate construction of marginal loss distribution functions and nonlinear copula function with upper tail dependence. The proposed method is applied to groups of wood‐frame buildings located in southwestern British Columbia. Analysis results indicate that the dependence structure of aggregate seismic losses can be adequately modeled by the right heavy tail copula or Gumbel copula, and that for the considered example, overall accuracy of the proposed method is satisfactory at probability levels of practical interest (at most 10% estimation error of fractiles of aggregate seismic loss). The developed statistical seismic loss model may be adopted in dynamic financial analysis for achieving faster evaluation with reasonable accuracy.     

A Flexible Hierarchical Bayesian Modeling Technique for Risk Analysis of Major Accidents

Safety analysis of rare events with potentially catastrophic consequences is challenged by data scarcity and uncertainty. Traditional causation‐based approaches, such as fault tree and event tree (used to model rare event), suffer from a number of weaknesses. These include the static structure of the event causation, lack of event occurrence data, and need for reliable prior information. In this study, a new hierarchical Bayesian modeling based technique is proposed to overcome these drawbacks. The proposed technique can be used as a flexible technique for risk analysis of major accidents. It enables both forward and backward analysis in quantitative reasoning and the treatment of interdependence among the model parameters. Source‐to‐source variability in data sources is also taken into account through a robust probabilistic safety analysis. The applicability of the proposed technique has been demonstrated through a case study in marine and offshore industry.     

An Assessment of the Cox Proportional Hazards Regression Model for Epidemiologic Studies

The basic assumptions of the Cox proportional hazards regression model are rarely questioned. This study addresses whether hazard ratio, i.e., relative risk (RR), estimates using the Cox model are biased when these assumptions are violated. We investigated also the dependence of RR estimates on temporal exposure characteristics, and how inadequate control for a strong, time‐dependent confounder affects RRs for a modest, correlated risk factor. In a realistic cohort of 500,000 adults constructed using the National Cancer Institute Smoking History Generator, we used the Cox model with increasing control of smoking to examine the impact on RRs for smoking and a correlated covariate X. The smoking‐associated RR was strongly modified by age. Pack‐years of smoking did not sufficiently control for its effects; simultaneous control for effect modification by age and time‐dependent cumulative exposure, exposure duration, and time since cessation improved model fit. Even then, residual confounding was evident in RR estimates for covariate X, for which spurious RRs ranged from 0.980 to 1.017 per unit increase. Use of the Cox model to control for a time‐dependent strong risk factor yields unreliable RR estimates unless detailed, time‐varying information is incorporated in analyses. Notwithstanding, residual confounding may bias estimated RRs for a modest risk factor.     

The Role of Firm Size and Performance Pay in Determining Employee Job Satisfaction Brief: Firm Size,Performance Pay,and Job Satisfaction

Abstract. Job satisfaction reflects the on‐the‐job utility of workers and has been found to influence both the behavior of workers and the productivity of firms. Performance pay remains popular and widely used to increase worker productivity and more generally align the objectives of workers and firms. Yet, its impact on job satisfaction is ambiguous. Whereas the increased earnings increase job satisfaction, the increased effort and risk decreases job satisfaction. This paper finds empirical evidence that on net performance pay increases job satisfaction but does so largely among union workers and males in larger firms.     

Impact of Acquired Immunity and Dose‐Dependent Probability of Illness on Quantitative Microbial Risk Assessment

Dose‐response models in microbial risk assessment consider two steps in the process ultimately leading to illness: from exposure to (asymptomatic) infection, and from infection to (symptomatic) illness. Most data and theoretical approaches are available for the exposure‐infection step; the infection‐illness step has received less attention. Furthermore, current microbial risk assessment models do not account for acquired immunity. These limitations may lead to biased risk estimates. We consider effects of both dose dependency of the conditional probability of illness given infection, and acquired immunity to risk estimates, and demonstrate their effects in a case study on exposure to Campylobacter jejuni. To account for acquired immunity in risk estimates, an inflation factor is proposed. The inflation factor depends on the relative rates of loss of protection over exposure. The conditional probability of illness given infection is based on a previously published model, accounting for the within‐host dynamics of illness. We find that at low (average) doses, the infection‐illness model has the greatest impact on risk estimates, whereas at higher (average) doses and/or increased exposure frequencies, the acquired immunity model has the greatest impact. The proposed models are strongly nonlinear, and reducing exposure is not expected to lead to a proportional decrease in risk and, under certain conditions, may even lead to an increase in risk. The impact of different dose‐response models on risk estimates is particularly pronounced when introducing heterogeneity in the population exposure distribution.     

Equilibrium Wage‐Tenure Contracts

In this study we consider a labor market matching model where firms post wage‐tenure contracts and workers, both employed and unemployed, search for new job opportunities. Given workers are risk averse, we establish there is a unique equilibrium in the environment considered. Although firms in the market make different offers in equilibrium, all post a wage‐tenure contract that implies a worker's wage increases smoothly with tenure at the firm. As firms make different offers, there is job turnover, as employed workers move jobs as the opportunity arises. This implies the increase in a worker's wage can be due to job‐to‐job movements as well as wage‐tenure effects. Further, there is a nondegenerate equilibrium distribution of initial wage offers that is differentiable on its support except for a mass point at the lowest initial wage. We also show that relevant characteristics of the equilibrium can be written as explicit functions of preferences and the other market parameters.     

Residential Location,Job Location,and Wages: Theory and Empirics

I develop a stylized partial on‐the‐job equilibrium search model that incorporates a spatial dimension. Workers reside on a circle and can move at a cost. Each point on the circle has a wage distribution. Implications about wages and job mobility are drawn from the model and tested on Danish matched employer–employee data. The model predictions hold true. I find that workers working farther away from their residence earn higher wages. When a worker is making a job‐to‐job transition where he/she changes workplace location he/she experiences a higher wage change than a worker making a job‐to‐job transition without changing workplace location. However, workers making a job‐to‐job transition that makes the workplace location closer to the residence experience a wage drop. Furthermore, low‐wage workers and workers with high transportation costs are more likely to make job‐to‐job transitions, but also residential moves.     

ON‐THE‐JOB SEARCH AND MORAL HAZARD

We analyze the interaction between intertemporal incentive contracts and search frictions associated with on‐the‐job search. In our model, agency problems call for wage contracts with deferred compensation. At the same time workers do on‐the‐job search. Deferred compensation improves workers' incentives to exert effort but distorts their on‐the‐job search decisions. We show that deferred compensation is less attractive when the value to the worker–firm pair of on‐the‐job search is high. Moreover, the interplay between search frictions and wage contracts creates feedback effects. If firms in equilibrium use contracts with deferred compensation, fewer firms with vacancies enter the on‐the‐job search market, and this in turn reduces the distortions created by deferred compensation. These feedback effects between the incentive contracts used and the activity level in the search markets can lead to multiple equilibria: a low‐turnover equilibrium where firms use deferred compensation, and a high‐turnover equilibrium where they do not. Furthermore, the model predicts that firms are more likely to use deferred compensation when search frictions are high and when the gains from on‐the‐job search are small.     

Risk Analysis of Dust Explosion Scenarios Using Bayesian Networks

In this study, a methodology has been proposed for risk analysis of dust explosion scenarios based on Bayesian network. Our methodology also benefits from a bow‐tie diagram to better represent the logical relationships existing among contributing factors and consequences of dust explosions. In this study, the risks of dust explosion scenarios are evaluated, taking into account common cause failures and dependencies among root events and possible consequences. Using a diagnostic analysis, dust particle properties, oxygen concentration, and safety training of staff are identified as the most critical root events leading to dust explosions. The probability adaptation concept is also used for sequential updating and thus learning from past dust explosion accidents, which is of great importance in dynamic risk assessment and management. We also apply the proposed methodology to a case study to model dust explosion scenarios, to estimate the envisaged risks, and to identify the vulnerable parts of the system that need additional safety measures.     

The Timing of Labour Reallocation and the Business Cycle: Evidence for The Netherlands

In this paper we study the cyclical behaviour of job and worker flows in The Netherlands. We find that job flows move counter‐cyclically while worker flows move acyclically. The persistence of newly created jobs is strongly pro‐cyclical while the persistence of destroyed jobs is strongly counter‐cyclical. We also examine the relative importance of aggregate and sectoral versus idiosyncratic shocks. We find both shocks to be negatively correlated and idiosyncratic shocks to be somewhat more important in explaining the time variation of job reallocation. Finally, we find that gross job flows account for about one‐third of the worker flows. The remainder reflect churning.     

Human resource development,organization development,organizational learning,and organizational effectiveness: all needed more than ever

In this study the Job Demand-Control model was used to study the quality of working life of Dutch secondary teachers. The Job Demand-Control model of Karasek is a theoretical model in which stress and learning are both considered as dependent variables which are influenced by three different task characteristics: job demands, job control, and social support. This model was tested for Dutch secondary teachers (n = 542). Results shed light on the relationship between stress and learning, on the one hand, and the effects of task characteristics on work stress and on work-based learning, on the other hand. It is concluded that the relationship between stress and learning is mediated by the amount of job control as the model predicts. However, the results also reveal that the Karasek model is better suited for explaining stress than for explaining learning. To explain work-based learning variables other than task characteristics have to be taken into account.     

Estimation of Unit Risk for Coke Oven Emissions

In 1984, based on epidemiological data on cohorts of coke oven workers, USEPA estimated a unit risk for lung cancer associated with continuous exposure from birth to 1 pg/m3 of coke oven emissions, of 6.2 × This risk assessment was based on information on the cohorts available through 1966. Follow-up of these cohorts has now been extended to 1982 and, moreover, individual job histories, which were not available in 1984, have been constructed. In this study, lung cancer mortality in these cohorts of coke oven workers with extended follow-up was analyzed using standard techniques of survival analysis and a new approach based on the two stage clonal expansion model of carcinogenesis. The latter approach allows the explicit consideration of detailed patterns of exposure of each individual in the cohort. The analyses used the extended follow-up data through 1982 and the detailed job histories now available. Based on these analyses, the best estimate of unit risk is 1.5 × with 95% confidence interval = 1.2 × 10-"1.8 X     

Monotonic Bayesian Semiparametric Benchmark Dose Analysis

Quantitative risk assessment proceeds by first estimating a dose‐response model and then inverting this model to estimate the dose that corresponds to some prespecified level of response. The parametric form of the dose‐response model often plays a large role in determining this dose. Consequently, the choice of the proper model is a major source of uncertainty when estimating such endpoints. While methods exist that attempt to incorporate the uncertainty by forming an estimate based upon all models considered, such methods may fail when the true model is on the edge of the space of models considered and cannot be formed from a weighted sum of constituent models. We propose a semiparametric model for dose‐response data as well as deriving a dose estimate associated with a particular response. In this model formulation, the only restriction on the model form is that it is monotonic. We use this model to estimate the dose‐response curve from a long‐term cancer bioassay, as well as compare this to methods currently used to account for model uncertainty. A small simulation study is conducted showing that the method is superior to model averaging when estimating exposure that arises from a quantal‐linear dose‐response mechanism, and is similar to these methods when investigating nonlinear dose‐response patterns.     

Estimation of a Benchmark Dose in the Presence or Absence of Hormesis Using Posterior Averaging

U.S. Environment Protection Agency benchmark doses for dichotomous cancer responses are often estimated using a multistage model based on a monotonic dose‐response assumption. To account for model uncertainty in the estimation process, several model averaging methods have been proposed for risk assessment. In this article, we extend the usual parameter space in the multistage model for monotonicity to allow for the possibility of a hormetic dose‐response relationship. Bayesian model averaging is used to estimate the benchmark dose and to provide posterior probabilities for monotonicity versus hormesis. Simulation studies show that the newly proposed method provides robust point and interval estimation of a benchmark dose in the presence or absence of hormesis. We also apply the method to two data sets on carcinogenic response of rats to 2,3,7,8‐tetrachlorodibenzo‐p‐dioxin.     

Time‐Series Models for Border Inspection Data

We propose a new modeling approach for inspection data that provides a more useful interpretation of the patterns of detections of invasive pests, using cargo inspection as a motivating example. Methods that are currently in use generally classify shipments according to their likelihood of carrying biosecurity risk material, given available historical and contextual data. Ideally, decisions regarding which cargo containers to inspect should be made in real time, and the models used should be able to focus efforts when the risk is higher. In this study, we propose a dynamic approach that treats the data as a time series in order to detect periods of high risk. A regulatory organization will respond differently to evidence of systematic problems than evidence of random problems, so testing for serial correlation is of major interest. We compare three models that account for various degrees of serial dependence within the data. First is the independence model where the prediction of the arrival of a risky shipment is made solely on the basis of contextual information. We also consider a Markov chain that allows dependence between successive observations, and a hidden Markov model that allows further dependence on past data. The predictive performance of the models is then evaluated using ROC and leakage curves. We illustrate this methodology on two sets of real inspection data.     

Workplace Job Satisfaction in Britain: Evidence from Linked Employer–Employee Data

The paper examines the nature of workplace job satisfaction in Britain using an ‘overall’ and domain‐specific job satisfaction outcomes from linked employer–employee data. A measure of aggregate job satisfaction alone might mask domain‐specific differences in satisfaction, something the combined approach in this paper addresses. As well as controlling for a rich set of correlates on employees and their workplaces, the paper deploys alternative empirical models that account for employee‐ and workplace‐level unobserved heterogeneity. The paper reports interesting results on the link between job satisfaction and observed as well as unobserved characteristics of employees and their workplaces.     

PhD Graduates with Post‐doctoral Qualification in the Private Sector: Does It Pay Off?

Abstract. In this paper, we address the question of the early careers of French PhD graduates in science and engineering sciences in 1996. Post‐doctoral training was initially developed for PhD graduates wishing to embark on a career in the public sector. However, a large proportion of post‐doctorate graduates turn to the private sector, and in particular to occupations that do not involve research. The question we raise is that of the wage premium on post‐doctoral training. To control for selection bias arising in the case where unobservable elements are correlated between participation and wages, we first estimate a treatment effect model. The main finding is that when selection bias is not controlled for, post‐doctoral participation increases earnings; however, when selection bias is controlled for, the participation in a post‐doctoral programme has no positive effect. With regards to this finding we show that post‐doctoral programmes play much more the role of a signal in the first stage of a career. This finding is also reinforced when we use a bivariate selection rule to control for the endogenous nature of having been recruited in the private sector.