CSR and Accounting: Drawing on Weber and Aristotle to Rethink Generally Accepted Accounting Principles

The purpose of this article is to discuss and provide an alternative, less materialist–individualist approach to interpret the four assumptions of generally accepted accounting principles: economic entity, unit measure, periodic reporting, and going concern. The article draws from and builds on arguments first developed by Weber and Aristotle to demonstrate how a materialist–individualist moral point of view influences the conventional interpretation of the four basic assumptions for generally accepted accounting principles. We then propose an ideal‐type conceptual framework upon which to critique mainstream accounting theory and to develop alternative accounting theory that balances multiple forms of well‐being (including financial, but also social, physical, spiritual, and ecological well‐being) for multiple stakeholders (including owners, employees, customers, suppliers, competitors, neighbors, future generations, and so forth).     

Societal Risk Perception Among African Villagers Without Access to the Media

The impact of the media on people's risk perception was assessed by comparing risk ratings obtained from African villagers without access to the media with risk ratings obtained from African city-dwellers with access to the media and risk ratings obtained from French participants. The overall mean risk judgment observed among the Togolese villagers was lower than the mean rating observed among the Togolese city-dwellers, and lower than the mean rating observed among the French. The linear association observed between the Togolese villagers' ratings and the Togolese city-dweller ratings and the French ratings was moderate. The impact of the media on risk perception was estimated to be an increase of about 15% of the overall mean ratings, and to about 31% of the variance of the mean ratings. This impact was independent of educational level.     

A note on influence diagnostics in AR(1) time series models

The purpose of this paper is to develop influence diagnostics for AR(1) models under the innovative and the data perturbation schemes. There are four main contributions. First, we derive analytical expressions for the slope and curvature statistics. Second, we establish a relationship between the slope and curvature showing that the standardised slope and standardised curvature are equal for the innovative perturbation scheme, and these vectors are nearly identical for several values of the autoregressive parameter, for the data perturbation scheme. Third, we present a connection between the influence statistics and the tests for outlier detection. Fourth, for the innovative perturbation scheme, we derive the asymptotic distribution of a new influence statistic, whereas for the data perturbation scheme, the distribution of the influence statistics is obtained via Monte Carlo simulation. We additionally discuss practical guidelines for the use of local influence statistics, which are illustrated on a chemical process data set.     

It Takes Two to Tango: Couples’ Happiness and Childbearing

The existing literature has so far considered the role of the individual’s subjective well-being on fertility, neglecting the importance of the partner’s well-being. Using data from the British Household Panel Survey (BHPS) and event history models estimated separately by parity, we find that in a couple, women’s happiness matters more than that of the male partner in terms of having the first child. Specifically, we observe that couples in which either partner is happier than usual are more at risk of having the first child, but the effect is stronger with higher happiness of the woman. For the transition to the second child, we find that couples in which the woman declares to be happier or less happy than usual have a lower risk of childbirth. We, moreover, find support for a multiplicative effect of partners’ SWB on the decision to have a first child. Our results show that failing to acknowledge that the subjective well-being of both partners matters for the inherently joint decision making of childbearing can lead to an incomplete view of how subjective well-being affects fertility.     

The choice of time interval in seasonal adjustment: A heuristic approach

A typical problem of the seasonal adjustment procedures arises when the series to be adjusted is subject to structural breaks. In fact, using the full span of the series can result in a biased estimation of the “true” seasonally adjusted series, with unclear evidence showed by the usual diagnostic tests. In these cases the researcher has to decide where to cut-off the observed series to obtain a homogeneous span; this is generally performed by a simple visual inspection of the graph of the series and/or using a-priori information about the occurrence of the break. In this paper we propose a statistical criterion based on a distance measure between filters, evaluating its performance with Monte Carlo experiments. The first results of this work have been presented at the XL scientific meeting of the Italian statistical society, Florence, 26–28 April 2000, benefiting of the discussion arisen there; a preliminary version of this paper circulated as ISAE working paper No. 21/2001 with the title “The Choice of Time Interval in Seasonal Adjustment: Characterization and Tools”. We thank an anonymous referee for precious suggestions. The authors are solely responsible of any remaining error.     

A spatiotemporal model for Mexico City ozone levels

Summary.  We consider hourly readings of concentrations of ozone over Mexico City and propose a model for spatial as well as temporal interpolation and prediction. The model is based on a time-varying regression of the observed readings on air temperature. Such a regression requires interpolated values of temperature at locations and times where readings are not available. These are obtained from a time-varying spatiotemporal model that is coupled to the model for the ozone readings. Two location-dependent harmonic components are added to account for the main periodicities that ozone presents during a given day and that are not explained through the covariate. The model incorporates spatial covariance structure for the observations and the parameters that define the harmonic components. Using the dynamic linear model framework, we show how to compute smoothed means and predictive values for ozone. We illustrate the methodology on data from September 1997.     

Bayesian deconvolution of oil well test data using Gaussian processes

We use Bayesian methods to infer an unobserved function that is convolved with a known kernel. Our method is based on the assumption that the function of interest is a Gaussian process and, assuming a particular correlation structure, the resulting convolution is also a Gaussian process. This fact is used to obtain inferences regarding the unobserved process, effectively providing a deconvolution method. We apply the methodology to the problem of estimating the parameters of an oil reservoir from well-test pressure data. Here, the unknown process describes the structure of the well. Applications to data from Mexican oil wells show very accurate results.     

New Results for Computing Blaker’s Exact Confidence Interval for One Parameter Discrete Distributions

The authors state new general results for computing Blaker’s exact confidence interval limits for usual one-parameter discrete distributions. Specific results for implementing an accurate and fast algorithm are made explicit for the binomial, negative binomial, Poisson and hypergeometric model.