Manufacturing feature-based cost management system: a case study in Brazil

Feature costing is a topic of recent discussion related to cost management systems (CMSs) aimed at understanding how product features influence production process costs. It raises as a key issue in mass customisation environments where a single product model can present hundreds of menu options. In this study, we explore the concept of feature costing beyond the use of activity-based costing (ABC). We propose a CMS based on features and common elements as costing objects instead of products. In our model, we use the unit of production effort method to organise the direct manufacturing costs, ABC method to address the indirect cost and the standard methods to compute raw materials costs. We illustrate the method in the production of bus seats in a Brazilian manufacturing company. This study gives different perspectives to the international academic community on the use of a different CMS used by many Brazilian companies.     

The Impact of Corporate Reputation on Firm Risk: A Panel Data Analysis of Spanish Quoted Firms

Researchers have traditionally addressed the influence of corporate reputation on firm performance, but have not considered the influence of corporate reputation on firm risk. This research develops hypotheses regarding the opposing influence of corporate reputation on a firm's systematic risk, unsystematic risk and total risk, as well as the moderation effect of firm size and industry concentration. Using a panel data method, these relationships are analysed, controlling for the effects of endogeneity, for a sample of Spanish quoted firms in the period 2001–2007. Specifically, two complementary analyses are performed. The first distinguishes firms included and not included in the MERCO index of the most reputable firms. The second analyses the impact of corporate reputation for the sub‐sample of most reputable firms. Being reputable reduces a firm's unsystematic risk and total risk, but increases systematic risk. In addition, firm size weakens these influences of corporate reputation on firm risk. However, among the most reputable firms, differences in reputation score have a lower effect on risk. Specifically, the corporate reputation level only influences firm unsystematic risk. It seems that what matters is not the degree of corporate reputation, but whether being or not being reputable is the question in terms of risk.     

Voting power measurement: a story of misreinvention

In this account of the history of voting-power measurement, we confine ourselves to the concept of a priori voting power. We show how the concept was re-invented several times and how the circumstances in which it was reinvented led to conceptual confusion as to the true meaning of what is being measured. In particular, power-as-influence was conflated with value in the sense of transferable utility cooperative game theory (power as share in constant total payoff). Influence was treated, improperly, as though it were transferable utility, and hence an additive and distributive quantity. We provide examples of the resulting misunderstanding and mis-directed criticism.     

Functional approaches for predicting land use with the temporal evolution of coarse resolution remote sensing data

The sensor SPOT 4/Végétation gives every day satellite images of Europe with medium spatial resolution, each pixel corresponding to an area of 1 r km 2 1 r km. Such data are useful to characterize the development of the vegetation at a large scale. The pixels, named "mixed' pixels, aggregate information of different crops and thus different themes of interest (wheat, corn, forest, …). We aim at estimating the land use when observing the temporal evolution of reflectances of mixed pixels. The statistical problem is to predict proportions with longitudinal covariates. We compared two functional approaches. The first relies on varying-time regression models and the second is an extension of the multilogit model for functional data. The comparison is achieved on a small area on which the land use is known. Satellite data were collected between March and August 1998. The functional multilogit model gives better predictions and the use of composite vegetation index is more efficient.     

Characterization of discrete populations through conditional expectations of order statistics

In this paper we give some properties of the expected values of any order statistic when one of its adjacent order statistics is known (order mean function) from a sequence of sizen of independent and identically distributed random variables with discrete distribution. Furthermore, we obtain the explicit expressions of the distribution from these order mean functions, and finally, we show the necessary and sufficient conditions for any real function to be an order mean function. We also add some examples of characterization of discrete distributions from the order mean functions. Partially supported by Consejería de Cultura y Educación (C.A.R.M.), under Grant PIB 95/90.     

Optimal exact design of double acceptance sampling plans by attributes

The design of double acceptance sampling (AS) plans for attributes based on the operating characteristic curve paradigm is usually addressed by enumeration algorithms. These AS plans may be non optimal regarding the sample size to inspect as they were obtained without the requirement that the constraints at the OC curve controlled points are not violated for minimum Average Sample Number (ASN) scenarios. An approach based on mathematical programming is proposed to systematically design double AS plans for attributes, where the characteristics controlled are modelled by binomial or Poisson distributions. Specifically, Mixed Integer Nonlinear Programming (MINLP) formulations are developed and combined with an enumeration algorithm that allows finding ASN minimax optimal plans. A theoretical result is developed with the purpose of assuring the global optimum design is reached by iteration where a convenient solver is used to find local optima. To validate the algorithm, we compare our results with those of tables commonly used for practical purposes, consider different rates of risk, and setups commonly used in Lot Quality Assurance Plans (LQAS) for health monitoring programmes. Finally, we compare AS plans determined for processes described by binomial and Poisson distributions.     

On the one parameter unit-Lindley distribution and its associated regression model for proportion data

In this paper considering an appropriate transformation on the Lindley distribution, we propose the unit-Lindley distribution and investigate some of its statistical properties. An important fact associated with this new distribution is that it is possible to obtain the analytical expression for bias correction of the maximum likelihood estimator. Moreover, it belongs to the exponential family. This distribution allows us to incorporate covariates directly in the mean and consequently to quantify their influences on the average of the response variable. Finally, a practical application is presented to show that our model fits much better than the Beta regression.     

Identification of average marginal effects under misspecification when covariates are normal

A previously known result in the econometrics literature is that when covariates of an underlying data generating process are jointly normally distributed, estimates from a nonlinear model that is misspecified as linear can be interpreted as average marginal effects. This has been shown for models with exogenous covariates and separability between covariates and errors. In this paper, we extend this identification result to a variety of more general cases, in particular for combinations of separable and nonseparable models under both exogeneity and endogeneity. So long as the underlying model belongs to one of these large classes of data generating processes, our results show that nothing else must be known about the true DGP—beyond normality of observable data, a testable assumption—in order for linear estimators to be interpretable as average marginal effects. We use simulation to explore the performance of these estimators using a misspecified linear model and show they perform well when the data are normal but can perform poorly when this is not the case.     

Neighborhood processes, self-efficacy, and adolescent mental health

Self-efficacy beliefs are central to mental health. Because adolescents' neighborhoods shape opportunities for experiences of control, predictability, and safety, we propose that neighborhood conditions are associated with adolescents' self-efficacy and, in turn, their internalizing problems (i.e., depression/anxiety symptoms). We tested these hypotheses using three waves of data from the Project on Human Development in Chicago Neighborhoods (N = 2,345). Results indicate that adolescents living in violent neighborhoods tended to report lower self-efficacy beliefs, partly because they were more likely to experience fear in their neighborhood. However, moving out of Chicago neighborhoods marked by violence and low collective efficacy to neighborhoods outside of the city was associated with adolescents' increased self-efficacy (vs. staying in such neighborhoods), an association explained by adolescents' school-related experiences. Finally, through self-efficacy, these neighborhood processes had an indirect association with adolescents' internalizing problems. Results partially support a model linking neighborhood conditions, cognitions about the self, and emotions.