Mixed Levels of Uncertainty in Complex Policy Models

The characterization and treatment of uncertainty poses special challenges when modeling indeterminate or complex coupled systems such as those involved in the interactions between human activity, climate and the ecosystem. Uncertainty about model structure may become as, or more important than, uncertainty about parameter values. When uncertainty grows so large that prediction or optimization no longer makes sense, it may still be possible to use the model as a behavioral test bed to examine the relative robustness of alternative observational and behavioral strategies. When models must be run into portions of their phase space that are not well understood, different submodels may become unreliable at different rates. A common example involves running a time stepped model far into the future. Several strategies can be used to deal with such situations. The probability of model failure can be reported as a function of time. Possible alternative surprises can be assigned probabilities, modeled separately, and combined. Finally, through the use of subjective judgments, one may be able to combine, and over time shift between models, moving from more detailed to progressively simpler order-of-magnitude models, and perhaps ultimately, on to simple bounding analysis.     

Probabilistic Connotations of Carcinogen Hazard Classifications: Analysis of Survey Data for Anchoring Effects

A study was undertaken to test for anchoring effects when numerical probabilities were elicited for carcinogen hazard designations. Subjects were asked to assign probabilities to both probable and possible. The sequence was randomly varied so that half of the subjects evaluated probable first and half evaluated possible first. While there was no consensus on the numerical probabilistic meanings assigned to these verbal expressions, in general, probable was assigned a higher probability than possible and there were specific values that were assigned frequently, indicating some consistency in interpretation. There appeared to be a fairly constant scaling factor between the probabilities assigned to the designations. Anchoring was manifested in two ways: a smaller difference between the designations when they were evaluated in sequence than when they were evaluated in isolation, and assignment of readily accessible "benchmark" values such as 50% and 75%.     

社交网络个体活跃行为的大数据分析：从网络外部性的视角

用户是社交网络平台的基石,用户的活跃行为为平台创造内容、带来价值,因而用户活跃度一直是社交网络平台的关注重点。已有研究仅关注用户是否存在持续使用意向或行为,将持续使用行为定义为二元变量,未对持续使用行为细分,忽略了对用户活跃行为的研究。此外,社交网络中用户活跃行为之间有着不可忽视的相互影响,而受制于用户的关系网络数据难以获取,少有研究从该角度对社交网络中的用户行为进行研究。 将持续使用行为进一步细分为个体活跃和沉默行为,从网络外部性视角探讨同伴活跃行为对焦点个体活跃行为的影响以及个体网络规模与活跃行为的关系,并进一步探讨个体间关系强度和个体特征的调节作用。以超过百万用户的真实关系网络、个体特征信息和使用行为数据进行实证分析,通过Tobit模型验证上述关系。 研究结果表明,同伴活跃度和个体网络规模对个体活跃度有显著的正向作用,同时关系强度具有正向调节作用;个体特征在同伴活跃度与个体活跃度之间起调节作用,具体来说,女性用户和年轻用户受同伴活跃行为的影响更强。 从网络外部性的角度探讨用户活跃行为的影响因素,拓展了关于在线社交网络用户持续使用行为的研究和网络外部性测量方式,关注不同节点对于网络外部性贡献的差异性,对今后网络外部性的研究有一定的借鉴意义。基于中国数百万真实用户的数据集,结果更具普适性,同时反映出中国在线社交网络中用户的行为特征,可为今后的研究者提供参考。研究结果可以帮助社交网络平台的管理者更加了解用户活跃度的影响机理以及用户行为之间的内在联系,从而能以更科学合理的方式激发用户活跃度,保持社交网络平台的持续运营。     

Unemployment in Urban China: an Analysis of Survey Data from Shanghai

Abstract. We analyse the unemployment problem in the largest Chinese city, Shanghai, based on a survey of the unemployed conducted in late 1989. We find, as expected, that unemployment is particularly serious among school-leavers and contract and temporary workers, but also find a surprisingly high incidence of unemployment amongst permanent state employees and relatively long unemployment durations. The Shanghai unemployed rely heavily on occasional work for support, and only a small proportion-receive state benefit. We identify problems of interpretation stemming from the stock-sampling nature of the survey, and use non-parametric methods to reveal an underlying strong downward movement in individual re-employment probabilities.     

The Role of Emotion in Global Warming Policy Support and Opposition

Prior research has found that affect and affective imagery strongly influence public support for global warming. This article extends this literature by exploring the separate influence of discrete emotions. Utilizing a nationally representative survey in the United States, this study found that discrete emotions were stronger predictors of global warming policy support than cultural worldviews, negative affect, image associations, or sociodemographic variables. In particular, worry, interest, and hope were strongly associated with increased policy support. The results contribute to experiential theories of risk information processing and suggest that discrete emotions play a significant role in public support for climate change policy. Implications for climate change communication are also discussed.     

The Importance of Role Models and Demographic Context for Senior Women's Work Identity Development

The lack of senior female role models continues to be cited as a key barrier to women's career success. Yet there is little academic research into the gendered aspects of role modelling in organizations, or the utility of role models at a senior level. The paper starts with a review of papers examining the construction of role models in organizational settings. This leads to the inclusion of two related areas – organizational demographics as the contextual factor affecting the availability of role models and how they are perceived, and work identity formation as a possible key explanatory factor behind the link between the lack of senior female role models and the lack of career progression to top organizational levels. The literature looking at social theories of identity formation is then considered from a gender perspective. The key gaps identified are that while the behavioural value of role models has been well documented, a better understanding is needed of how gender and organizational demography influence the role modelling process. Importantly, the symbolic value and possibly other values of female role models in the identity construction of senior women require further in‐depth investigation. Finally, this review calls for a more integrated approach to the study of role models and work identity formation, pulling together literatures on organizational demography, the cognitive construal of role models and their importance for successful work identity formation in senior women.     

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.     

The Importance of Social Embeddedness: Churn Models at Mobile Providers

This article argues the importance of social embeddedness at mobile providers by examining the effects of customers’ network topological properties on churn probability—the probability of a customer switching from one telecommunication provider to another. This article uses data from regional snowball sampling—the only practically feasible network sampling method—to identify groups with significantly different churn ratios for customers with different network topological properties. Clear evidence indicates that individual network characteristics (node‐level metrics) have considerable impact on churn probabilities. The inclusion of network‐related measures in the churn model allows a longer‐term projection of churners and improves the predictive power of the model. With no possibility to carry out repeated sampling, sample stability was checked through simulation results. On the one hand, this article highlights the importance and effectiveness of the provider's tailored marketing campaigns by showing that customers targeted by direct marketing campaigns are less threatened by churn than nontargeted customers. On the other, this article shows that social embeddedness blocks the impact of the very same marketing efforts. This article forwards the idea that social embeddedness, also prevalent in vendor switching, can be extended to understanding the development of professional societies threatened by membership churn.     

Robust Priors in Nonlinear Panel Data Models

Many approaches to estimation of panel models are based on an average or integrated likelihood that assigns weights to different values of the individual effects. Fixed effects, random effects, and Bayesian approaches all fall into this category. We provide a characterization of the class of weights (or priors) that produce estimators that are first‐order unbiased. We show that such bias‐reducing weights will depend on the data in general unless an orthogonal reparameterization or an essentially equivalent condition is available. Two intuitively appealing weighting schemes are discussed. We argue that asymptotically valid confidence intervals can be read from the posterior distribution of the common parameters when N and T grow at the same rate. Next, we show that random effects estimators are not bias reducing in general and we discuss important exceptions. Moreover, the bias depends on the Kullback–Leibler distance between the population distribution of the effects and its best approximation in the random effects family. Finally, we show that, in general, standard random effects estimation of marginal effects is inconsistent for large T, whereas the posterior mean of the marginal effect is large‐T consistent, and we provide conditions for bias reduction. Some examples and Monte Carlo experiments illustrate the results.     

Efficiency Bounds for Missing Data Models With Semiparametric Restrictions

This paper shows that the semiparametric efficiency bound for a parameter identified by an unconditional moment restriction with data missing at random (MAR) coincides with that of a particular augmented moment condition problem. The augmented system consists of the inverse probability weighted (IPW) original moment restriction and an additional conditional moment restriction which exhausts all other implications of the MAR assumption. The paper also investigates the value of additional semiparametric restrictions on the conditional expectation function (CEF) of the original moment function given always observed covariates. In the program evaluation context, for example, such restrictions are implied by semiparametric models for the potential outcome CEFs given baseline covariates. The efficiency bound associated with this model is shown to also coincide with that of a particular moment condition problem. Some implications of these results for estimation are briefly discussed.     

Social Amplification of Wildfire Risk: The Role of Social Interactions and Information Sources

Wildfire is a persistent and growing threat across much of the western United States. Understanding how people living in fire‐prone areas perceive this threat is essential to the design of effective risk management policies. Drawing on the social amplification of risk framework, we develop a conceptual model of wildfire risk perceptions that incorporates the social processes that likely shape how individuals in fire‐prone areas come to understand this risk, highlighting the role of information sources and social interactions. We classify information sources as expert or nonexpert, and group social interactions according to two dimensions: formal versus informal, and generic versus fire‐specific. Using survey data from two Colorado counties, we empirically examine how information sources and social interactions relate to the perceived probability and perceived consequences of a wildfire. Our results suggest that social amplification processes play a role in shaping how individuals in this area perceive wildfire risk. A key finding is that both “vertical” (i.e., expert information sources and formal social interactions) and “horizontal” (i.e., nonexpert information and informal interactions) interactions are associated with perceived risk of experiencing a wildfire. We also find evidence of perceived “risk interdependency”—that is, homeowners’ perceptions of risk are higher when vegetation on neighboring properties is perceived to be dense. Incorporating social amplification processes into community‐based wildfire education programs and evaluating these programs’ effectiveness constitutes an area for future inquiry.     

Price Effects of the Minimum Wage: A Survey Data Analysis for the German Construction Sector

Utilizing the link between employment and price changes as a result of minimum wages, we use firm‐level data to evaluate the effect of minimum wage introduction in the German construction sector. In East Germany we find significant positive price effects that exclude the possibility of rising employment. Rather, the results indicate the existence of a competitive sector‐specific labour market, and thus declining employment. In contrast, we cannot find any significant price reaction for West Germany. This suggests that the implemented minimum wage in West Germany is too low in comparison to the predominantly paid wages and is hence not binding.     

Resourcing Social Enterprises: The Role of Socially Oriented Bootstrapping

Resource constraints are a challenge for social enterprises, prompting interest in innovative approaches to address these deficiencies. In this study, we contribute to the literature on resourcing practices in small, early stage, social enterprises by examining the role of socially oriented bootstrapping for resource access. We use data from eight UK social enterprises to reveal organizational practices – building credibility, leveraging persuasion and creating resource communities – that shape a diverse set of bootstrapping mechanisms to facilitate exchange relationships and to enable resource acquisition and mobilization. We argue that the unique position of social enterprises allows them to benefit from socially orientated bootstrapping and they display a complex approach that is shaped by a creative interplay of practices to support value creation through resource exchange.     

供应商选择模型与方法综述

供应商选择问题是学术界和工业界十分关注的优化决策问题,许多研究者进行了并且还在继续进行相关的研究工作。本文采用分类的方法归纳了供应商选择问题的研究进展,深入分析了在不同采购策略下供应商评价准则、供应商选择模型和优化方法。本文指出了各种研究方法的优缺点、适用范围、存在的问题,并且进一步指出未来研究的方向。     

Binary Response Models for Panel Data: Identification and Information

This paper considers a panel data model for predicting a binary outcome. The conditional probability of a positive response is obtained by evaluating a given distribution function (F) at a linear combination of the predictor variables. One of the predictor variables is unobserved. It is a random effect that varies across individuals but is constant over time. The semiparametric aspect is that the conditional distribution of the random effect, given the predictor variables, is unrestricted. This paper has two results. If the support of the observed predictor variables is bounded, then identification is possible only in the logistic case. Even if the support is unbounded, so that (from Manski (1987)) identification holds quite generally, the information bound is zero unless F is logistic. Hence consistent estimation at the standard pn rate is possible only in the logistic case.     

A Cautionary Note on Data Inputs and Visual Outputs in Social Network Analysis

Innovations in network visualization software over the last decade or so have been important to the popularization of social network analysis (SNA) among academics, consultants and managers. Indeed, there is a growing literature that seeks to demonstrate how ‘invisible social networks’ might be revealed and leveraged for ‘visible results’ through management interventions. However, the seductive power of the network graphic has distracted attention away from a variety of emerging and long recognized concerns in SNA. For example, weaknesses exist in data collection techniques that often rely on nominal boundary‐setting and respondent recall. Non‐response can also be highly problematic. Increasingly, email data are being employed, yet this represents a poor proxy for relationships and raises issues of privacy. In displaying relational data, visualizations typically reify and ossify the network. Yet, individual perceptions of a network can vary greatly from unified visualizations, and their structure is typically fleeting. The aim of this paper is to draw together the diffuse literature concerning data input and visual output issues in SNA, in order to raise awareness among management researchers and practitioners. In doing so, the nature and impact of such weaknesses are discussed, as are ways in which these might be resolved or mitigated.     

Supply Chain Models with Mutual Commitments and Implications for Social Responsibility

In today's increasingly globalized environment, more and more companies recognize the mutual dependence of supply chain partners in value creation. When making business decisions, they take into consideration their partners’ bottom line profitability, especially in emerging markets. The question is, is this kind of practice sustainable? This study makes an attempt to formalize this issue by examining a stylized two‐party supply chain model in which each player maximizes its own profit while making a certain commitment to its partner. We compare five different games between the two supply‐chain partners, which reflect different power positions of the players and different levels of commitment. We identify conditions under which both players are better off with mutual commitments than without, a situation we call win–win. We show that win–win can be achieved if and only if the mutual commitments are comparable. Thus, the recognition of mutual dependence of the supply chain members needs to be translated into reciprocal concerns. In addition, different players’ commitments play different roles but together they have a similar effect as a profit sharing contract. Finally, we discuss the implications of our findings in the context of socially responsible operations. In particular, our analyses show that it is possible to care about the supply chain partners’ bottom line without sacrificing one's own profitability, and our models can be used as a tool to determine the commitment levels by evaluating the predicted outcome.