A Simulation Study of Quantitative Risk Assessment for Bivariate Continuous Outcomes

The neurotoxic effects of chemical agents are often investigated in controlled studies on rodents, with binary and continuous multiple endpoints routinely collected. One goal is to conduct quantitative risk assessment to determine safe dose levels. Yu and Catalano (2005) describe a method for quantitative risk assessment for bivariate continuous outcomes by extending a univariate method of percentile regression. The model is likelihood based and allows for separate dose‐response models for each outcome while accounting for the bivariate correlation. The approach to benchmark dose (BMD) estimation is analogous to that for quantal data without having to specify arbitrary cutoff values. In this article, we evaluate the behavior of the BMD relative to background rates, sample size, level of bivariate correlation, dose‐response trend, and distributional assumptions. Using simulations, we explore the effects of these factors on the resulting BMD and BMDL distributions. In addition, we illustrate our method with data from a neurotoxicity study of parathion exposure in rats.     

The Return Period Analysis of Natural Disasters with Statistical Modeling of Bivariate Joint Probability Distribution

New features of natural disasters have been observed over the last several years. The factors that influence the disasters’ formation mechanisms, regularity of occurrence and main characteristics have been revealed to be more complicated and diverse in nature than previously thought. As the uncertainty involved increases, the variables need to be examined further. This article discusses the importance and the shortage of multivariate analysis of natural disasters and presents a method to estimate the joint probability of the return periods and perform a risk analysis. Severe dust storms from 1990 to 2008 in Inner Mongolia were used as a case study to test this new methodology, as they are normal and recurring climatic phenomena on Earth. Based on the 79 investigated events and according to the dust storm definition with bivariate, the joint probability distribution of severe dust storms was established using the observed data of maximum wind speed and duration. The joint return periods of severe dust storms were calculated, and the relevant risk was analyzed according to the joint probability. The copula function is able to simulate severe dust storm disasters accurately. The joint return periods generated are closer to those observed in reality than the univariate return periods and thus have more value in severe dust storm disaster mitigation, strategy making, program design, and improvement of risk management. This research may prove useful in risk‐based decision making. The exploration of multivariate analysis methods can also lay the foundation for further applications in natural disaster risk analysis.     

Workers’ Risk Tolerance and Occupational Injuries

This study explores the relationship between individuals’ risk tolerance and occupational injuries. We analyze data from a national representative survey of U.S. workers that includes information about injuries, risk tolerance, cognitive and noncognitive attributes, and risky behaviors. We measure risk tolerance through questions regarding individuals’ willingness to gamble on their lifetime income. We estimate zero‐inflated count models to assess the role played by such measures on workers’ recurrent injuries. We discuss some implications of our results for future research and occupational safety policies. Our results highlight the concurrent and changing role played by individual, work, and environmental factors in explaining recurrent incidents. They show that risk tolerance affects recurrent injuries, although not in the direction that proponents of the concept of proneness would expect. Our measure of risk aversion shows that individuals who are somewhat more risk tolerant have fewer recurrent injuries than those who are risk averse. But the estimated relationship is U‐shaped, not monotonic and, therefore, not easy to predict. At the same time, we find that individuals’ “revealed risk preferences”—specific risky behaviors—are related to higher injury probabilities. Demanding working conditions, measures of socioeconomic status, health, and safety problems experienced by workers during their youth remain among the most important factors explaining the phenomena of recurrent injuries. So our results contribute also to the important debate about the relationship between health and socioeconomic status.     

Perception of Risk and the Attribution of Responsibility for Accidents

Accidents, one often hears, “happen”; we accept, and even expect, that they will be part of daily life. But in situations in which injury or death result, judgments of responsibility become critical. How might our perceptions of risk influence the ways in which we allocate responsibility for an accident? Drawing from attribution and risk perception theory, this study investigates how perceived controllability and desirability of risk, in addition to perceived danger and recreational risk‐taking, relate to attributions of responsibility for the cause of unintentional injury in a unique setting: U.S. national parks. Three parks, Mount Rainier, Olympic, and Delaware Water Gap, provide the setting for this survey‐based study, which considers how park visitors (N = 447) attribute responsibility for the cause of a hypothetical visitor accident. Results suggest that respondents tended to make more internal (i.e., related to characteristics of the victim), rather than external (i.e., related to characteristics of the park, or park management) attributions. As respondents viewed park‐related risk as controllable, they were more likely to attribute the cause of the accident to the victim. Moreover, among other significant variables, having experienced a similar accident predicted lower internal causal attribution. Opportunities for future research linking risk perception and attribution variables, as well as practical implications for the management of public outdoor settings, are presented.     

Robust Nonparametric Confidence Intervals for Regression‐Discontinuity Designs

In the regression‐discontinuity (RD) design, units are assigned to treatment based on whether their value of an observed covariate exceeds a known cutoff. In this design, local polynomial estimators are now routinely employed to construct confidence intervals for treatment effects. The performance of these confidence intervals in applications, however, may be seriously hampered by their sensitivity to the specific bandwidth employed. Available bandwidth selectors typically yield a “large” bandwidth, leading to data‐driven confidence intervals that may be biased, with empirical coverage well below their nominal target. We propose new theory‐based, more robust confidence interval estimators for average treatment effects at the cutoff in sharp RD, sharp kink RD, fuzzy RD, and fuzzy kink RD designs. Our proposed confidence intervals are constructed using a bias‐corrected RD estimator together with a novel standard error estimator. For practical implementation, we discuss mean squared error optimal bandwidths, which are by construction not valid for conventional confidence intervals but are valid with our robust approach, and consistent standard error estimators based on our new variance formulas. In a special case of practical interest, our procedure amounts to running a quadratic instead of a linear local regression. More generally, our results give a formal justification to simple inference procedures based on increasing the order of the local polynomial estimator employed. We find in a simulation study that our confidence intervals exhibit close‐to‐correct empirical coverage and good empirical interval length on average, remarkably improving upon the alternatives available in the literature. All results are readily available in R and STATA using our companion software packages described in Calonico, Cattaneo, and Titiunik (2014d, 2014b).     

Predictors of Parental Risk Perceptions: The Case of Child Pedestrian Injuries in School Context

The objective of this article is to explore the factors that influence parental risk perceptions of child pedestrian injuries in the elementary school context. Parents (n= 193) from six different schools responded to a questionnaire on road safety, including a measure of their risk perception. Results of bivariate analyses show that eight variables are significantly related to risk perception. Environmental variables, as we measure them, were not significant, contrary to our initial hypotheses. Only three variables, parent's gender, perceived primary source of danger, and sense of control remained significant in OLS regression analyses (adjusted R² of 0.16, F= 9.27; p= 0.00). Since parents’ perceptions of road risks are an important factor in their road safety practices and in their choice of transportation mode used for their child's journey to school, our analysis elucidates factors underlying these choices. Our results can help decisionmakers to design traffic injury prevention measures and to promote physical activity through the use of active modes of transport.     

Assessment of Catastrophic Risk Using Bayesian Network Constructed from Domain Knowledge and Spatial Data

Prediction of natural disasters and their consequences is difficult due to the uncertainties and complexity of multiple related factors. This article explores the use of domain knowledge and spatial data to construct a Bayesian network (BN) that facilitates the integration of multiple factors and quantification of uncertainties within a consistent system for assessment of catastrophic risk. A BN is chosen due to its advantages such as merging multiple source data and domain knowledge in a consistent system, learning from the data set, inference with missing data, and support of decision making. A key advantage of our methodology is the combination of domain knowledge and learning from the data to construct a robust network. To improve the assessment, we employ spatial data analysis and data mining to extend the training data set, select risk factors, and fine‐tune the network. Another major advantage of our methodology is the integration of an optimal discretizer, informative feature selector, learners, search strategies for local topologies, and Bayesian model averaging. These techniques all contribute to a robust prediction of risk probability of natural disasters. In the flood disaster's study, our methodology achieved a better probability of detection of high risk, a better precision, and a better ROC area compared with other methods, using both cross‐validation and prediction of catastrophic risk based on historic data. Our results suggest that BN is a good alternative for risk assessment and as a decision tool in the management of catastrophic risk.     

Managing Safety‐Related Disruptions: Evidence from the U.S. Nuclear Power Industry

Low‐probability, high‐impact events are difficult to manage. Firms may underinvest in risk assessments for low‐probability, high‐impact events because it is not easy to link the direct and indirect benefits of doing so. Scholarly research on the effectiveness of programs aimed at reducing such events faces the same challenge. In this article, we draw on comprehensive industry‐wide data from the U.S. nuclear power industry to explore the impact of conducting probabilistic risk assessment (PRA) on preventing safety‐related disruptions. We examine this using data from over 25,000 monthly event reports across 101 U.S. nuclear reactors from 1985 to 1998. Using Poisson fixed effects models with time trends, we find that the number of safety‐related disruptions reduced between 8% and 27% per month in periods after operators submitted their PRA in response to the Nuclear Regulatory Commission's Generic Letter 88‐20, which required all operators to conduct a PRA. One possible mechanism for this is that the adoption of PRA may have increased learning rates, lowering the rate of recurring events by 42%. We find that operators that completed their PRA before Generic Letter 88‐20 continued to experience safety improvements during 1990–1995. This suggests that revisiting PRA or conducting it again can be beneficial. Our results suggest that even in a highly safety‐conscious industry as nuclear utilities, a more formal approach to quantifying risk has its benefits.     

12 Entrepreneurship: A Field of Dreams?

Abstract

This paper has two objectives. We begin by contrasting two potential paths for future research in entrepreneurship. One is the establishment of an independent field of research with a clear jurisdiction, a common theoretical canon, and autonomy from related fields. The second is a phenomena‐based approach, in which scholars congregate around common interests in empirical phenomena but approach them with distinct disciplinary lenses. After discussing these alternatives and lobbying for the phenomena‐based approach, we then review some of the recent, discipline‐based research in economic and organizational sociology relevant to entrepreneurship, and identify significant gaps in that literature.     

The Crucial Role of Nomothetic and Idiographic Conceptions of Time: Interdisciplinary Collaboration in Nuclear Waste Management

The disposal of nuclear waste involves extensive time scales. Technical experts consider up to 1 million years for the disposal of spent fuel and high‐level waste in their safety assessment. Yet nuclear waste is not only a technical but also a so‐called sociotechnical problem and, therefore, requires interdisciplinary collaboration between technical, natural, social sciences, and the humanities in its management. Given that these disciplines differ in their language, epistemics, and interests, such collaboration might be problematic. Based on evidence from cognitive psychology, we suggest that, in particular, a concept like time is presumably critical and can be understood differently. This study explores how different scientific disciplines understand extensive time scales in general and then focuses on nuclear waste. Eighteen qualitative exploratory interviews were conducted with experts for time‐related phenomena of different disciplines, among them experts working in nuclear waste management. Analyses revealed two distinct conceptions of time corresponding to idiographic and nomothetic research approaches: scientists from the humanities and social sciences tend to have a more open, undetermined conception of time, whereas natural scientists tend to focus on a more determined conception that includes some undetermined aspects. Our analyses lead to reflections on potential difficulties for interdisciplinary teams in nuclear waste management. We focus on the understanding of the safety assessment, on potential implications for communication between experts from different disciplines (e.g., between experts from the humanities and engineering for risk assessment and risk communication), and we reflect on the roles of different disciplines in nuclear waste management.     

An Empirical Agent‐Based Model to Simulate the Adoption of Water Reuse Using the Social Amplification of Risk Framework

Water reuse can serve as a sustainable alternative water source for urban areas. However, the successful implementation of large‐scale water reuse projects depends on community acceptance. Because of the negative perceptions that are traditionally associated with reclaimed water, water reuse is often not considered in the development of urban water management plans. This study develops a simulation model for understanding community opinion dynamics surrounding the issue of water reuse, and how individual perceptions evolve within that context, which can help in the planning and decision‐making process. Based on the social amplification of risk framework, our agent‐based model simulates consumer perceptions, discussion patterns, and their adoption or rejection of water reuse. The model is based on the “risk publics” model, an empirical approach that uses the concept of belief clusters to explain the adoption of new technology. Each household is represented as an agent, and parameters that define their behavior and attributes are defined from survey data. Community‐level parameters—including social groups, relationships, and communication variables, also from survey data—are encoded to simulate the social processes that influence community opinion. The model demonstrates its capabilities to simulate opinion dynamics and consumer adoption of water reuse. In addition, based on empirical data, the model is applied to investigate water reuse behavior in different regions of the United States. Importantly, our results reveal that public opinion dynamics emerge differently based on membership in opinion clusters, frequency of discussion, and the structure of social networks.     

Assessing the Performance of a Classification‐Based Vulnerability Analysis Model

In this article, a classification model based on the majority rule sorting (MR‐Sort) method is employed to evaluate the vulnerability of safety‐critical systems with respect to malevolent intentional acts. The model is built on the basis of a (limited‐size) set of data representing (a priori known) vulnerability classification examples. The empirical construction of the classification model introduces a source of uncertainty into the vulnerability analysis process: a quantitative assessment of the performance of the classification model (in terms of accuracy and confidence in the assignments) is thus in order. Three different app oaches are here considered to this aim: (i) a model–retrieval‐based approach, (ii) the bootstrap method, and (iii) the leave‐one‐out cross‐validation technique. The analyses are presented with reference to an exemplificative case study involving the vulnerability assessment of nuclear power plants.     

Mitigating Sports Injury Risks Using Internet of Things and Analytics Approaches

Sport injuries restrict participation, impose a substantial economic burden, and can have persisting adverse effects on health‐related quality of life. The effective use of Internet of Things (IoT), when combined with analytics approaches, can improve player safety through identification of injury risk factors that can be addressed by targeted risk reduction training activities. Use of IoT devices can facilitate highly efficient quantification of relevant functional capabilities prior to sport participation, which could substantially advance the prevailing sport injury management paradigm. This study introduces a framework for using sensor‐derived IoT data to supplement other data for objective estimation of each individual college football player's level of injury risk, which is an approach to injury prevention that has not been previously reported. A cohort of 45 NCAA Division I‐FCS college players provided data in the form of self‐ratings of persisting effects of previous injuries and single‐leg postural stability test. Instantaneous change in body mass acceleration (jerk) during the test was quantified by a smartphone accelerometer, with data wirelessly transmitted to a secure cloud server. Injuries sustained from the beginning of practice sessions until the end of the 13‐game season were documented, along with the number of games played by each athlete over the course of a 13‐game season. Results demonstrate a strong prediction model. Our approach may have strong relevance to the estimation of injury risk for other physically demanding activities. Clearly, there is great potential for improvement of injury prevention initiatives through identification of individual athletes who possess elevated injury risk and targeted interventions.     

An Integrational Framework of Organizational Moral Development,Legitimacy, and Corporate Responsibility: A Longitudinal,Intersectoral Analysis of Citizenship Reports

In this article, we outline a unique conceptual framework connecting legitimacy types (Suchman, 1995 ), theories of corporate responsibility (Brummer, 1991 ), and levels of organizational moral development based on Kohlberg's ( 1971 ) moral development stages. In addition, based on Global Reporting Initiative (GRI) categories, we found empirical support for our framework, by content analyzing Fortune 500 corporate citizenship reports from four different industries (i.e., chemicals, motor vehicle/auto parts, pharmaceutical, and utilities), at three data points (i.e., 2002, 2007, and 2012). Our analysis indicates that motor vehicle/auto parts and chemicals industries are at a higher developmental level, and portray moral legitimacy along with social demandingness corporate responsibility in recent years; while the pharmaceutical and utilities sectors are at a lower developmental level, showing signs of pragmatic legitimacy, alongside classical and stakeholder corporate responsibility strategies. This article contributes to the current organizational moral development literature by developing and finding empirical support of a conceptual framework of organizational moral development, legitimacy, and corporate responsibility. In particular, our findings provide a deeper understanding of the differences in moral development levels across four focal industries over a 10‐year timespan.     

Exposure Assessment for Pesticide Intake from Multiple Food Products: A Bayesian Latent‐Variable Approach

Pesticide risk assessment for food products involves combining information from consumption and concentration data sets to estimate a distribution for the pesticide intake in a human population. Using this distribution one can obtain probabilities of individuals exceeding specified levels of pesticide intake. In this article, we present a probabilistic, Bayesian approach to modeling the daily consumptions of the pesticide Iprodione though multiple food products. Modeling data on food consumption and pesticide concentration poses a variety of problems, such as the large proportions of consumptions and concentrations that are recorded as zero, and correlation between the consumptions of different foods. We consider daily food consumption data from the Netherlands National Food Consumption Survey and concentration data collected by the Netherlands Ministry of Agriculture. We develop a multivariate latent‐Gaussian model for the consumption data that allows for correlated intakes between products. For the concentration data, we propose a univariate latent‐t model. We then combine predicted consumptions and concentrations from these models to obtain a distribution for individual daily Iprodione exposure. The latent‐variable models allow for both skewness and large numbers of zeros in the consumption and concentration data. The use of a probabilistic approach is intended to yield more robust estimates of high percentiles of the exposure distribution than an empirical approach. Bayesian inference is used to facilitate the treatment of data with a complex structure.     

Towards a Fuzzy Bayesian Network Based Approach for Safety Risk Analysis of Tunnel‐Induced Pipeline Damage

Tunneling excavation is bound to produce significant disturbances to surrounding environments, and the tunnel‐induced damage to adjacent underground buried pipelines is of considerable importance for geotechnical practice. A fuzzy Bayesian networks (FBNs) based approach for safety risk analysis is developed in this article with detailed step‐by‐step procedures, consisting of risk mechanism analysis, the FBN model establishment, fuzzification, FBN‐based inference, defuzzification, and decision making. In accordance with the failure mechanism analysis, a tunnel‐induced pipeline damage model is proposed to reveal the cause‐effect relationships between the pipeline damage and its influential variables. In terms of the fuzzification process, an expert confidence indicator is proposed to reveal the reliability of the data when determining the fuzzy probability of occurrence of basic events, with both the judgment ability level and the subjectivity reliability level taken into account. By means of the fuzzy Bayesian inference, the approach proposed in this article is capable of calculating the probability distribution of potential safety risks and identifying the most likely potential causes of accidents under both prior knowledge and given evidence circumstances. A case concerning the safety analysis of underground buried pipelines adjacent to the construction of the Wuhan Yangtze River Tunnel is presented. The results demonstrate the feasibility of the proposed FBN approach and its application potential. The proposed approach can be used as a decision tool to provide support for safety assurance and management in tunnel construction, and thus increase the likelihood of a successful project in a complex project environment.     

Ambiguity,Learning, and Asset Returns

We propose a novel generalized recursive smooth ambiguity model which permits a three‐way separation among risk aversion, ambiguity aversion, and intertemporal substitution. We apply this utility model to a consumption‐based asset‐pricing model in which consumption and dividends follow hidden Markov regime‐switching processes. Our calibrated model can match the mean equity premium, the mean risk‐free rate, and the volatility of the equity premium observed in the data. In addition, our model can generate a variety of dynamic asset‐pricing phenomena, including the procyclical variation of price–dividend ratios, the countercyclical variation of equity premia and equity volatility, the leverage effect, and the mean reversion of excess returns. The key intuition is that an ambiguity‐averse agent behaves pessimistically by attaching more weight to the pricing kernel in bad times when his continuation values are low.     

Institutional and Resource‐based Explanations for Subsidiary Performance

Addressing calls to integrate insights from institutional theory and the resource‐based view, we bring together dual theoretical explanations from institutional theory and the resource‐based view to examine the effectiveness of transfer of practice and human capital development as two routes to subsidiary performance. Our study of Hong Kong firms with subsidiaries in Mainland China shows that both routes positively affect subsidiary performance. However, our data show that our sampled firms struggled to successfully transfer practices from their parents. We attribute an explanation for this to the characteristics of practices as organizational capabilities in which transfer is made harder by the difficulty in replicating such capabilities. Consequently, developing subsidiary human capital is an important ally to practice transfer as a means to achieve superior subsidiary performance. Our results raise interesting questions about practice transfer and the resource‐based view relevant to future scholarly research.     

The Dynamics of Occupational Choice: Theory and Evidence

In this paper we provide new empirical and theoretical insights into the dynamics of occupational choice. In the empirical part of the paper we analyse West German time series data on the number of first‐year students in more than 50 fields. We show that this number is cyclical in many fields so that occupational choice is cyclical in many professions. In the theoretical part we develop a model that explains cycles in occupational choice. Unlike other models, it is not based on the unrealistic assumption that occupational choice is independent of the risk of unemployment associated with each occupation.