Assessment of the Effect of Population and Diary Sampling Methods on Estimation of School‐Age Children Exposure to Fine Particles

Population and diary sampling methods are employed in exposure models to sample simulated individuals and their daily activity on each simulation day. Different sampling methods may lead to variations in estimated human exposure. In this study, two population sampling methods (stratified‐random and random‐random) and three diary sampling methods (random resampling, diversity and autocorrelation, and Markov‐chain cluster [MCC]) are evaluated. Their impacts on estimated children's exposure to ambient fine particulate matter (PM_2.5) are quantified via case studies for children in Wake County, NC for July 2002. The estimated mean daily average exposure is 12.9 μg/m³ for simulated children using the stratified population sampling method, and 12.2 μg/m³ using the random sampling method. These minor differences are caused by the random sampling among ages within census tracts. Among the three diary sampling methods, there are differences in the estimated number of individuals with multiple days of exposures exceeding a benchmark of concern of 25 μg/m³ due to differences in how multiday longitudinal diaries are estimated. The MCC method is relatively more conservative. In case studies evaluated here, the MCC method led to 10% higher estimation of the number of individuals with repeated exposures exceeding the benchmark. The comparisons help to identify and contrast the capabilities of each method and to offer insight regarding implications of method choice. Exposure simulation results are robust to the two population sampling methods evaluated, and are sensitive to the choice of method for simulating longitudinal diaries, particularly when analyzing results for specific microenvironments or for exposures exceeding a benchmark of concern.     

How Important is Selection? Experimental VS. Non‐Experimental Measures of the Income Gains from Migration

How much do migrants stand to gain in income from moving across borders? Answering this question is complicated by non‐random selection of migrants from the general population, which makes it hard to obtain an appropriate comparison group of non‐migrants. New Zealand allows a quota of Tongans to immigrate each year with a random ballot used to choose among the excess number of applicants. A unique survey conducted by the authors allows experimental estimates of the income gains from migration to be obtained by comparing the incomes of migrants to those who applied to migrate, but whose names were not drawn in the ballot, after allowing for the effect of non‐compliance among some of those whose names were drawn. We also conducted a survey of individuals who did not apply for the ballot. Comparing this non‐applicant group to the migrants enables assessment of the degree to which non‐experimental methods can provide an unbiased estimate of the income gains from migration. We find evidence of migrants being positively selected in terms of both observed and unobserved skills. As a result, non‐experimental methods other than instrumental variables are found to overstate the gains from migration by 20–82%, with difference‐in‐differences and bias‐adjusted matching estimators performing best among the alternatives to instrumental variables. (JEL: J61, F22, C21)     

Cost‐Effective Sampling and Analysis for Mycotoxins in a Cereal Batch

The presence of hazards (e.g., contaminants, pathogens) in food/feed, water, plants, or animals can lead to major economic losses related to human and animal health or the rejection of batches of food or feed. Monitoring these hazards is important but can lead to high costs. This study aimed to find the most cost‐effective sampling and analysis (S&A) plan in the cases of the mycotoxins deoxynivalenol (DON) in a wheat batch and aflatoxins (AFB₁) in a maize batch. An optimization model was constructed, maximizing the number of correct decisions for accepting/rejecting a batch of cereals, with a budget as major constraint. The decision variables were the choice of the analytical method: instrumental method (e.g., liquid chromatography combined with mass‐spectrometry (LC‐MS/MS)), enzyme‐linked‐immuno‐assay (ELISA), or lateral flow devices (LFD), the number of incremental samples collected from the batch, and the number of aliquots analyzed. S&A plans using ELISA showed to be slightly more cost effective than S&A plans using the other two analytical methods. However, for DON in wheat, the difference between the optimal S&A plans using the three different analytical methods was minimal. For AFB₁ in maize, the cost effectiveness of the S&A plan using instrumental methods or ELISA were comparable whereas the S&A plan considering onsite detection with LFDs was least cost effective. In case of nonofficial controls, which do not have to follow official regulations for sampling and analysis, onsite detection with ELISA for both AFB₁ in maize and DON in wheat, or with LFDs for DON in wheat, could provide cost‐effective alternatives.     

Optimization of the Aflatoxin Monitoring Costs along the Maize Supply Chain

An optimization model was used to gain insight into cost‐effective monitoring plans for aflatoxins along the maize supply chain. The model was based on a typical Dutch maize chain, with maize grown in the Black Sea region, and transported by ship to the Netherlands for use as an ingredient in compound feed for dairy cattle. Six different scenarios, with different aflatoxin concentrations at harvest and possible aflatoxin production during transport, were used. By minimizing the costs and using parameters such as the concentration, the variance of the sampling plan, and the monitoring and replacement costs, the model optimized the control points (CPs; e.g., after harvest, before or after transport by sea ship), the number of batches sampled at the CP, and the number of samples per batch. This optimization approach led to an end‐of‐chain aflatoxin concentration below the predetermined limit. The model showed that, when postharvest aflatoxin production was not possible, it was most cost‐effective to collect samples from all batches and replace contaminated batches directly after the harvest, since the replacement costs were the lowest at the origin of the chain. When there was aflatoxin production during storage, it was most cost‐effective to collect samples and replace contaminated batches after storage and transport to avoid the duplicate before and after monitoring and replacement costs. Further along the chain a contaminated batch is detected, the more stakeholders are involved, the more expensive the replacement costs and possible recall costs become.     

Likelihood Inference for Discretely Observed Nonlinear Diffusions

This paper is concerned with the Bayesian estimation of nonlinear stochastic differential equations when observations are discretely sampled. The estimation framework relies on the introduction of latent auxiliary data to complete the missing diffusion between each pair of measurements. Tuned Markov chain Monte Carlo (MCMC) methods based on the Metropolis‐Hastings algorithm, in conjunction with the Euler‐Maruyama discretization scheme, are used to sample the posterior distribution of the latent data and the model parameters. Techniques for computing the likelihood function, the marginal likelihood, and diagnostic measures (all based on the MCMC output) are developed. Examples using simulated and real data are presented and discussed in detail.     

EFFECTS OF RANDOM SHIFTS OF TESTING EQUIPMENT ON PROCESS CONTROL SYSTEM DESIGN AND SELECTION OF PROCESS CONTROL POLICIES*

This paper studies issues associated with designing process control systems when the testing equipment is subjected to random shifts. We consider a production process with two states: in control and out of control. The process may shift randomly to the out‐of‐control state over time. The process is monitored by periodically sampling finished items from the process. The equipment used to test sampled items also is assumed to have two states and may shift randomly during the testing process. We formulate a cost model for finding the optimal process control policy that minimizes the expected unit time cost. Numerical results show that shifts of the testing equipment may significantly affect the performance of a process control policy. We also studied the effects of the testing equipment's shifts on the selection of process control policies.     

Changing a Leopard's Spots: A New Research Direction for Organizational Culture in the Operations Management Field

Operations Management (OM) research on organizational culture has to change to be able to inform practice. Currently, organizational culture research in OM is largely confined to narrow topical and methodological niches and culture is most frequently used as an explanatory variable in quantitative, survey‐based research. We argue that the relegation of culture to this niche is due to self‐imposed methodological blinders that hobble the OM field. We then present four research imperatives to reinvigorate organizational culture research within our field. We urge OM scholars to view culture as a dynamic concept that can be influenced, to adopt alternative methods, to use non‐traditional data sources, and to rethink assumptions about dependent variables. We also identify gaps in the current knowledge and new research questions for the OM domain. We conclude that the field of OM could greatly expand its understanding of organizational culture and in so doing greatly improve business practice, but that to do so will require a change in the culture of the operations management research community.     

期权定价的蒙特卡罗模拟综合性方差减少技术

主要将重要性抽样技术处理特殊衍生证券定价问题的能力与控制变量技术、分层抽样技术简单灵活、易于应用的特点有机地结合起来,把分层抽样技术和控制变量技术引入重要性抽样模拟估计的分析框架,提出更为有效的关于期权定价蒙特卡罗模拟的综合性方差减少技术;并以基于算术型亚式期权定价为例,进行了实证模拟分析.     

Biogeographical Analysis of Chemical Co‐Occurrence Data to Identify Priorities for Mixtures Research

A challenge with multiple chemical risk assessment is the need to consider the joint behavior of chemicals in mixtures. To address this need, pharmacologists and toxicologists have developed methods over the years to evaluate and test chemical interaction. In practice, however, testing of chemical interaction more often comprises ad hoc binary combinations and rarely examines higher order combinations. One explanation for this practice is the belief that there are simply too many possible combinations of chemicals to consider. Indeed, under stochastic conditions the possible number of chemical combinations scales geometrically as the pool of chemicals increases. However, the occurrence of chemicals in the environment is determined by factors, economic in part, which favor some chemicals over others. We investigate methods from the field of biogeography, originally developed to study avian species co‐occurrence patterns, and adapt these approaches to examine chemical co‐occurrence. These methods were applied to a national survey of pesticide residues in 168 child care centers from across the country. Our findings show that pesticide co‐occurrence in the child care center was not random but highly structured, leading to the co‐occurrence of specific pesticide combinations. Thus, ecological studies of species co‐occurrence parallel the issue of chemical co‐occurrence at specific locations. Both are driven by processes that introduce structure in the pattern of co‐occurrence. We conclude that the biogeographical tools used to determine when this structure occurs in ecological studies are relevant to evaluations of pesticide mixtures for exposure and risk assessment.     

Marketing segmentation research—An application to bank services

The research reported here analyses data relating to bank services obtained by a stratified random sampling technique using stepwise linear regression and canonical correlation for the purpose of illustrating the application of segmentation principles to marketing research.     

Bootstrapping Realized Volatility

We propose bootstrap methods for a general class of nonlinear transformations of realized volatility which includes the raw version of realized volatility and its logarithmic transformation as special cases. We consider the independent and identically distributed (i.i.d.) bootstrap and the wild bootstrap (WB), and prove their first‐order asymptotic validity under general assumptions on the log‐price process that allow for drift and leverage effects. We derive Edgeworth expansions in a simpler model that rules out these effects. The i.i.d. bootstrap provides a second‐order asymptotic refinement when volatility is constant, but not otherwise. The WB yields a second‐order asymptotic refinement under stochastic volatility provided we choose the external random variable used to construct the WB data appropriately. None of these methods provides third‐order asymptotic refinements. Both methods improve upon the first‐order asymptotic theory in finite samples.     

The Effects of Random and Discrete Sampling when Estimating Continuous–Time Diffusions

High–frequency financial data are not only discretely sampled in time but the time separating successive observations is often random. We analyze the consequences of this dual feature of the data when estimating a continuous–time model. In particular, we measure the additional effects of the randomness of the sampling intervals over and beyond those due to the discreteness of the data. We also examine the effect of simply ignoring the sampling randomness. We find that in many situations the randomness of the sampling has a larger impact than the discreteness of the data.     

Asymptotic Properties of Weighted M-estimators for variable probability samples

I provide a systematic treatment of the asymptotic properties of weighted M-estimators under variable probability stratified sampling. The characterization of the sampling scheme and representation of the objective function allow for a straightforward analysis. Simple, consistent asymptotic variance matrix estimators are proposed for a large class of problems. When stratification is based on exogenous variables, I show that the unweighted M-estimator is more efficient than the weighted estimator under a generalized conditional information matrix equality. When population frequencies are known, a more efficient weighting is possible. I also show how the results carry over to multinomial sampling.     

Quantifying the Hurricane Catastrophe Risk to Offshore Wind Power

The U.S. Department of Energy has estimated that over 50 GW of offshore wind power will be required for the United States to generate 20% of its electricity from wind. Developers are actively planning offshore wind farms along the U.S. Atlantic and Gulf coasts and several leases have been signed for offshore sites. These planned projects are in areas that are sometimes struck by hurricanes. We present a method to estimate the catastrophe risk to offshore wind power using simulated hurricanes. Using this method, we estimate the fraction of offshore wind power simultaneously offline and the cumulative damage in a region. In Texas, the most vulnerable region we studied, 10% of offshore wind power could be offline simultaneously because of hurricane damage with a 100‐year return period and 6% could be destroyed in any 10‐year period. We also estimate the risks to single wind farms in four representative locations; we find the risks are significant but lower than those estimated in previously published results. Much of the hurricane risk to offshore wind turbines can be mitigated by designing turbines for higher maximum wind speeds, ensuring that turbine nacelles can turn quickly to track the wind direction even when grid power is lost, and building in areas with lower risk.     

Elective Patient Admission and Scheduling under Multiple Resource Constraints

We consider a patient admission problem to a hospital with multiple resource constraints (e.g., OR and beds) and a stochastic evolution of patient care requirements across multiple resources. There is a small but significant proportion of emergency patients who arrive randomly and have to be accepted at the hospital. However, the hospital needs to decide whether to accept, postpone, or even reject the admission from a random stream of non‐emergency elective patients. We formulate the control process as a Markov decision process to maximize expected contribution net of overbooking costs, develop bounds using approximate dynamic programming, and use them to construct heuristics. We test our methods on data from the Ronald Reagan UCLA Medical Center and find that our intuitive newsvendor‐based heuristic performs well across all scenarios.     

Unemployment and Earnings Assimilation of Immigrants

The regional unemployment elasticity of annual earnings for non‐OECD immigrants is found to be more than three times larger than for natives, using micro data covering all immigrants in Norway in 1990 and a random sample of natives. The decline in relative earnings of non‐OECD immigrants from 1980 to 1990 can largely be explained by the stronger impact of rising unemployment on immigrant earnings. These results highlight the importance of controlling for different period effects caused by fluctuating unemployment in panel studies of earnings assimilation among immigrants.     

Firm‐level Evidence on Gender Wage Discrimination in the Belgian Private Economy

In this paper we explore a matched employer–employee data set to investigate the presence of gender wage discrimination in the Belgian private economy labour market. Contrary to many existing papers, we analyse gender wage discrimination using an independent productivity measure. Using firm‐level data, we are able to compare direct estimates of a gender productivity differential with those of a gender wage differential. We take advantage of the panel structure to identify gender‐related differences from within‐firm variation. Moreover, inspired by recent developments in the production function estimation literature, we address the problem of endogeneity of the gender mix using a structural production function estimator alongside instrumental variable‐general method of moments (IV‐GMM) methods where lagged value of labour inputs are used as instruments. Our results suggest that there is no gender wage discrimination inside private firms located in Belgium, on the contrary.     

Non‐linear Effects of Comparison Income in Quit Decisions: Status versus Signal!

This research note utilizes German matched employer–employee data to investigate the relationship between mobility and relative wage positions within establishments for workers without university degrees. The main innovation involves the examination of non‐linear effects, because previous literature mainly analyses mean linear effects. Our random‐effects probit estimates of mobility suggest a non‐linear U‐shaped effect with respect to relative standing. This is plausible because workers in low relative wage positions might quit because of their low status and those in high relative wage positions because of their low career advancement opportunities. Consideration of non‐linearities, thus, is a major improvement for the analysis of the effects of relative wage positions.     

Stakeholders’ Perspective on Ecological Modeling in Environmental Risk Assessment of Pesticides: Challenges and Opportunities

The article closely examines the role of mechanistic effect models (e.g., population models) in the European environmental risk assessment (ERA) of pesticides. We studied perspectives of three stakeholder groups on population modeling in ERA of pesticides. Forty‐three in‐depth, semi‐structured interviews were conducted with stakeholders from regulatory authorities, industry, and academia all over Europe. The key informant approach was employed in recruiting our participants. They were first identified as key stakeholders in the field and then sampled by means of a purposive sampling, where each stakeholder identified as important by others was interviewed and asked to suggest another potential participant for our study. Our results show that participants, although having different institutional backgrounds often presented similar perspectives and concerns about modeling. Analysis of repeating ideas and keywords revealed that all stakeholders had very high and often contradicting expectations from models. Still, all three groups expected effect models to become integrated in future ERA of pesticides. Main hopes associated with effect models were to reduce the amount of expensive and complex testing and field monitoring, both at the product development stage, and as an aid to develop mitigation measures. Our analysis suggests that, although the needs of stakeholders often overlapped, subtle differences and lack of trust hinder the process of introducing mechanistic effect models into ERA.     

Competition and The Operational Performance of Hospitals: The Role of Hospital Objectives

We examine the effect of a hospital's objective (i.e., non‐profit vs. for‐profit) in hospital markets for elective care. Using game‐theoretic analysis and queueing models to capture the operational performance of hospitals, we compare the equilibrium behavior of three market settings in terms of such criteria as waiting times and patient costs from waiting and hospital payments. In the first setting, a monopoly, patients are served exclusively by a single non‐profit hospital; in the second, a homogeneous duopoly, patients are served by two competing non‐profit hospitals. In our third setting, a heterogeneous duopoly, the market is served by one non‐profit hospital and one for‐profit hospital. A non‐profit hospital provides free care to patients, although they may have to wait; for‐profit hospitals charge a fee to provide care with minimal waiting. A comparison between the monopolistic and each of the duopolistic settings reveals that the introduction of competition can hamper a hospital's ability to attain economies of scale and can also increase waiting times. Moreover, the presence of a for‐profit sector may be desirable only when the hospital market is sufficiently competitive. A comparison across the duopolistic settings indicates that the choice between homogeneous and heterogeneous competition depends on the patients' willingness to wait before receiving care and the reimbursement level of the non‐profit sector. When the public funder is not financially constrained, the presence of a for‐profit sector may allow the funder to lower both the financial costs of providing coverage and the total costs to patients. Finally, our analysis suggests that the public funder should exercise caution when using policy tools that support the for‐profit sector—for example, patient subsidies—because such tools may increase patient costs in the long run; it might be preferable to raise the non‐profit sector's level of reimbursement.