An explanation of why shareholders' losses are so large after drug recalls

Recent studies show firms suffering drug recalls experience security losses many times larger than any reasonable measure of their direct cost. We discover that the implied standard deviation of stock returns from the Black-Scholes option pricing model significantly increases after a drug recall. The implied standard deviation provides a good proxy for the stock's ex ante beta. The higher systematic risk after a product recall must raise the discount rate used by investors. After a recall, stock prices are reduced in line with the lower expected future earnings and are further reduced because of a higher discount rate.     

INPUT–OUTPUT ANALYSIS: THE NEXT 25 YEARS

This year marks the 25th anniversary of the International Input–Output Association and the 25th volume of Economic Systems Research. To celebrate this anniversary, a group of eight experts provide their views on the future of input–output. Looking forward, they foresee progress in terms of data collections, methods, theory testing, and focus and scope.     

Prevention and management of product recalls in the processed food industry: a case study based on an exporter's perspective

During 1999–2003, the US Food and Drug Administration reported a total of 1307 processed food product recalls, most of which were avoidable. There are many areas of the processed food supply chain where significant exposure to risk exists. Additionally, there are systems that can be used in-house at manufacturing facilities, such as hazard analysis and critical control point (HACCP) and radio frequency identification (RFID) that can be very beneficial in recall avoidance. Effective employee training is another key point for consideration. In the event of a recall, a company must be prepared in advance with a detailed crisis management procedure. Companies should consider recall insurance, depending on their size and market dominance. Public relations strategies are critical in surviving a recall. Care must be taken to connect to the consumer and communicate a strong message. Findings from analysis suggest potential reduction of product recalls through recommended preventive measures including the use of HACCP and RFID systems.     

A Statistical Approach to Assess Referendum Results: the Venezuelan Recall Referendum 2004

This article presents a statistical approach to assess the coherence of official results of referendum processes. The statistical analysis described is divided in four phases, according to the methodology used and the corresponding results: (1) Initial Study, (2) Quantification of irregular certificates of election, (3) Identification of irregular voting centers and (4) Estimation of recall referendum results.
The technique of cluster analysis is applied to address the issue of heterogeneity of the parishes with respect to their political preferences.
The Venezuelan recall referendum 2004 is the case study we used to apply the proposed methodology, based on the data published by the "Consejo Nacional Electoral" (CNE-National Electoral Council). Finally, we present the conclusions of the study which we summarize as follows: The percentage of irregular certificates of election is between 22.2% and 26.5% of the total; 18% of the voting centers show an irregular voting pattern in their certificates of election, the votes corresponding to this irregularity are around 2,550,000; The result estimate, using the unbiased votes as representative of the population for the percentage of YES votes against President Chávez is 56.4% as opposed to the official result of 41%.     

Sudden Unintended Used‐Price Deceleration? The 2009–2010 Toyota Recalls

Using data from the vehicle resale market, I test consumer responsiveness to large‐scale product recalls that are caused by safety problems. The used‐vehicle prices of Toyotas are compared to the used‐vehicle prices of the other major domestic and foreign manufacturers. The results quantify the losses suffered by Toyota vehicle owners in secondary markets due to the 2009–2010 safety recalls of more than 9 million Toyota Motors vehicles. The treatment effect of a recall is measured using panel data with a difference‐in‐differences estimation approach that allows for time‐varying treatment effects and serial correlation. I find that this recall episode had negative effects in the resale market for automobiles that were quantitatively small (less than 2% of the vehicle’s resale value), statistically indistinguishable from zero, and short lived (did not persist beyond December 2009). A comparison with Audi’s recalls in the 1980s of vehicles with sudden unintended acceleration suggests that the extent to which a company’s reputation is established is more important than whether or not a company has a reputation for producing high‐quality products.     

CARBON EMISSION ACCOUNTING IN MRIO MODELS: THE TERRITORY VS. THE RESIDENCE PRINCIPLE

Consumption-based CO₂ emissions, which are commonly calculated by means of environmentally extended input–output analysis, are gaining wider recognition as a way to complement territorial emission inventories. Although their use has increased significantly in the last years, insufficient attention has been paid to the methodological soundness of the underlying environmental extension. This should follow the internationally agreed accounting rules of the System of Environmental-Economic Accounting, which addresses the activities undertaken by the residents of a country, independent from where these take place. Nonetheless, some footprint calculations use extensions that account for all the activities within the territory, which leads to methodological inconsistencies. Thus, this article introduces the most relevant conceptual differences between these accounting frameworks and shows the magnitude of the gap between them building on the data generated for the EXIOBASE model. It concludes that the differences are high for many countries and their magnitude is increasing over time.     

Evaluating a three-dimensional panel of point forecasts: The Bank of England Survey of External Forecasters

This article provides a first analysis of the forecasts of inflation and GDP growth obtained from the Bank of England's Survey of External Forecasters, considering both the survey average forecasts published in the quarterly Inflation Report, and the individual survey responses, recently made available by the Bank. These comprise a conventional incomplete panel dataset, with an additional dimension arising from the collection of forecasts at several horizons; both point forecasts and density forecasts are collected. The inflation forecasts show good performance in tests of unbiasedness and efficiency, albeit over a relatively calm period for the UK economy, and there is considerable individual heterogeneity. For GDP growth, inaccurate real-time data and their subsequent revisions are seen to cause serious difficulties for forecast construction and evaluation, although the forecasts are again unbiased. There is evidence that some forecasters have asymmetric loss functions.     

Veblen and the Political Economy of the Engineer

Abstract . Thorstein Veblen's case for a Technocracy, “The Engineers and the Price System,” has long posed an enigma: Why would a thinker as radical as Veblen align himself with a group as conservative as engineers? But engineers themselves had developed a political economy with important points in common with Veblen's analysis. Starting from their positions as technological experts in corporations, engineers came to believe that business methods were not efficient for production; this belief led them to develop systems of scientific management as an antidote to old-style management. Later, they expanded these ideas into a system of social management called Technocracy. This system of Technocracy represented an engineering effort at formulating an industrial democracy, with the cooperation of labor. Veblen was able to write a more systematic version of these ideas, because they fit in well with his own theoretical analysis.     

基于人才培养状态数据平台智能化动态仪表盘的开发与应用研究

摘要：状态数据采集平台全面展现了各高等职业院校人才培养水平和办学特色。如何有效地对平台中的大批量数据进行深入分析、研究和应用，高等职业教育领域的许多专家进行了较多的探索。传统数据分析方法耗时多、效率低，严重制约了平台功能的有效发挥。为了解决这一问题，有必要开发一种智能化动态分析软件，快捷、准确、形象、多维地对状态数据进行全面分析。本文通过分析不同对象的应用要求，开发了以高职状态数据采集平台为数据源的动态仪表盘软件，既有利于各级教育主管部门的科学决策，又有利于学生、教师、家长、用人单位和社会对高职教育的全面认识。     

Regional Inequalities in Consumption Patterns: A Multilevel Approach to the Case of Italy

The main aim of this paper is to evaluate the disparities in the Italian regions on the demand side. In more detail, an attempt will be made to find if the consumption behaviour of Italian households is different in the regions. With this in mind, Istat's 2000 Italian Family Budget data set was analysed. The data in question, which were collected through a two‐stage sample over Italy's 20 regions, contains information regarding the expenses of approximately 23,000 households. In this analysis, both households and regions are considered as units: households are nested in the regions so that the basic data structure is hierarchical. In order to take this hierarchical structure into account, a multilevel model was used, making it possible for parameters to vary randomly from region to region. The model in question also made it possible to consider heterogeneity across different groups (regions), such as stochastic variation. First, regional inequalities were tested using a simple model in which households constituted the first level of analysis and were grouped according to their region (the second level). As a second step, and in order to investigate the interaction between geographical context and income distribution, another model was used. This was cross‐classified by income and regions. The most relevant results showed that there is wide fragmentation of consumption behaviour and, at the same time, various differentiated types of behaviour in the regions under analysis. These territorial differentials become clear from income class and items of consumption.     

Explaining differences in labour market transitions between panel and life-course data in West-Germany

Panel and life-course data are ideally suited to unravelling labour market dynamics, but their designs differ, with potential consequences for the estimated relationships. To gauge the extent to which these two data designs produce dissimilar transition rates and the causation thereof, we use the German Life History Study and the German Socio-Economic Panel. Life-course data in particular suffer from recall effects due to memory bias causing understated transition probabilities. Panel data suffer from seam effects due to spurious transitions between statuses recalled in activity calendars that generate heaps at particular time points and cause overstated transition probabilities. We combine the two datasets and estimate multilevel (multistate) discrete-time models for event history data to model transitions between labour market states taking these factors into account. Though we find much lower transition rates in the life-course study, confirming the results of Solga (Qual Quant 35:291–309, 2001) in this Journal for East-Germany, part of the difference can be explained by short spells recall bias. The estimated models on exit, re-entry and job mobility on the combined datasets show indeed a negative retrospective design effect. Another specification that includes the length of the recall period shows no significant decrease in the transition probabilities with increasing length, suggesting that the negative design effect is due to other design differences.     

Research collaboration and R&D outsourcing: Different R&D personnel requirements in SMEs

The literature on ‘open’ innovation emphasises the need to engage in external knowledge relations in order to innovate. Particularly for SMEs, research cooperation and R&D outsourcing can offer possibilities to complement the often limited internal research resources. However, they also bring in their wake requirements in terms of absorptive capacity and managerial skills of the internal R&D personnel.The paper focuses on the different requirements in terms of availability and training of research managers and R&D experts for research cooperation versus R&D outsourcing in SMEs. An empirical analysis of micro-level data provided by the OECD business R&D survey for Belgium reveals that the relation between R&D personnel requirements and research collaboration and R&D outsourcing depends upon the SME size. Therefore, to study this subject appropriately a distinction between very small, small, and medium-sized firms is relevant. Very small firms engage significantly less in research cooperation than medium-sized firms and the propensity to engage in research cooperation is positively associated with the share of PhD holders among the research managers and R&D experts. For R&D outsourcing a lower involvement is noted in medium-sized firms, and the propensity to outsource increases with the formal qualification level of the R&D personnel and with R&D training. Among the SME, small firms are most engaged in research cooperation and in R&D outsourcing. In the case of research cooperation they rely on highly qualified experts. For R&D outsourcing activities both the presence of research managers and R&D experts is important.     

Two new mean–variance enhanced index tracking models based on uncertainty theory

The enhanced index tracking (EIT) problem is concerned with selecting a tracking portfolio that achieves an excess return over a given benchmark with a minimum tracking error. This paper explores the EIT problem by providing two new mean–variance EIT models based on uncertainty theory where stock returns are treated as uncertain variables instead of random variables and stock return distributions are estimated by experts instead of from historical data. First, this paper formulates an uncertain enhanced index tracking (UEIT) model and analyzes the characteristic of the UEIT frontier. Then to reduce the tracking portfolio’s risk, this paper adds a risk index (RI) constraint to the UEIT model and proposes a UEIT-RI model. Next, by comparing the UEIT and UEIT-RI models this paper gives the advantages of the two models. Investors can choose the model according to their preferences. Finally, this paper conducts numerical examples to illustrate the application of the two models and the analysis results.     

Applications of calendar instruments in social surveys: a review

Retrospective reports in survey interviews and questionnaires are subject to many types of recall error, which affect completeness, consistency, and dating accuracy. Concerns about this problem have led to the development of so-called calendar instruments, or timeline techniques. These aided recall procedures have been designed to help respondents gain better access to long-term memory by providing a graphical time frame in which life history information can be represented. In order to obtain more insights into the potential benefits of calendar methodology, this paper presents a review of the application of calendar instruments, their design characteristics and effects on data quality. Calendar techniques are currently used in a variety of fields, including life course research, epidemiology and family planning studies. Despite the growing interest in these new methods, their application often lacks sufficient theoretical foundation and little attention has been paid to their effectiveness. Several recent studies however, have demonstrated that in comparison to more traditional survey methods, calendar techniques can improve some aspects of data quality. While calendar instruments have been shown to be potentially beneficial to retrospective data quality, there is an apparent need for methodological research that generates more systematic knowledge about their application in social surveys.     

Biproportional Methods of Structural Change Analysis: A Typological Survey

Analysts often are interested in learning how much an exchange system has changed over time or how two different exchange systems differ. Identifying structural difference in exchange matrices can be performed using either 'directed' or 'undirected' methods. Directed methods are based on the computation and comparison of column- or row-normalizations of the matrices. The choice of row or column for the normalization implies a specific direction of the exchanges, so that the column-wise normalized results should not be compared to the row-wise normalized results. In this category fall the simple comparison of coefficient matrices and the causative method. Undirected methods do not impose such underlying constraints on exchanges. Hence, I present a set of undirected methods that can be used to compare structural matrices: the biproportional ordinary filter, the biproportional mean filter and the bi-Markovian filter. While doing so, I recall why the bicausative method must be dismissed. I then classify the methods according to their orientation and data needs, and illustrate how the results can differ from one method to the next using French tables for 1980 and 1997.     

The effects of feeding back experts’ own initial ratings in Delphi studies: A randomized trial

This study examined the effects of feeding back experts’ initial ratings on three Delphi outcome measures: (1) the percentage of items on which experts changed their opinion; (2) the degree to which experts changed their ratings towards the group response; and (3) the increase in the level of agreement among experts. Additionally, two conformity indices were developed. Within a real-world Delphi study, experts were randomly assigned to one of two conditions: either their initial ratings were included in feedback (IN) or excluded from feedback (EX). Results showed that experts in the EX condition changed their opinion relatively more often than experts in the IN condition. Results also suggested that experts in the EX condition changed their ratings to a greater degree towards the group response than experts in the IN condition. No difference between conditions was found regarding the increase in the level of agreement.     

The myth of purchasing professionals’ expertise. More evidence on whether computers can make better procurement decisions

In a previous experiment, we have shown that risk assessments of purchasing experts are certainly not better than that of subjects untrained in purchasing, and worse than the decisions made by formal models (J. Purchas. Supply Manage. 9 (2003) 191–198). Since both these results are rather counterintuitive, we conducted a series of experiments geared at replication and extension of these findings. These new experiments show that our previous results are robust, and reveal an additional finding that is both worrying and puzzling. It actually seems to be the case that for the purchasing decision tasks in our experiments, experts perform worse with growing experience. It therefore seems that, at least for the kinds of purchasing decisions under study, it does not make much sense to use expert judgments at all. However, we show that there is a way in which expert judgments can be used in combination with formal models to improve the predictive accuracy of purchasing predictions. In our case, superior predictions are made when we combine the prediction of a formal model with the prediction of the ‘average expert’, thereby combining the robust linear trends as encapsulated in the formal model with the more intuitive configural rules used by experts. We provide several explanations for this phenomenon.     

The use of multi-criteria decision-making methods in the integrated assessment of climate change: implications for IA practitioners

Integrated assessment (IA) considers interactions of physical, biological, and human systems in order to assess long-term consequences of environmental and energy policies such as limits on greenhouse gas emissions, and other strategies to negate climate change. Users of IA face the daunting task of interpreting large amounts of data and uncertainties. Multi-criteria decision-making (MCDM) methods can help users process IA data, understand policy tradeoffs, and learn how their value judgments affect decisions. We held a workshop during which climate change experts tested several MCDM methods for using IA outputs to rank hypothetical policies for abating greenhouse gas emissions. Participants also evaluated several methods for visualizing tradeoffs under both certainty and uncertainty cases. This paper explores potential roles for MCDM in IA identified during the workshop, along with implications for IA design and implementation. We summarize the workshops’ results regarding intertemporal discounting (a type of MCDM weighting judgment), visualization of impacts, how MCDM methods can help users to incorporate their background knowledge, and how MCDM can improve understanding of tradeoffs and the importance of value judgments. A key result is that the interest rates IA experts recommend for discounting future impacts depend strongly on what type of impact is being discounted, as well as upon the exact phrasing of questions used to elicit rates from the experts.