Die Anderen als sozialer Kontext. Zur Bedeutung strategischer Interaktion

An action is defined as “strategic” when the consequences of ego’s action depend on the action of alter. Situations of strategic interaction are numerous in daily life, business, and politics. Pioneers like Erving Goffman or Raymond Boudon recognized the importance of strategic interaction in sociological analysis long ago. Other peoples’ opportunities of actions form ego’s strategic context. The dynamics of the impact of the strategic context on ego’s action can be modeled and analyzed by means of game theory. We will discuss three examples of strategic interaction models: “Diffusion of responsibility”, Boudon’s “logic of relative frustration”, and the problem of social exchange and trust. We demonstrate the effects of the strategic context on the opportunities and beliefs of actors. In contrast to non-strategic rational choice theory, beliefs and opportunities are not assumed as exogenous. The analysis of the strategic context contributes to a better understanding of the micro-level effects and the macro-level implications. However, the strict rationality requirements of game models are often violated. In these situations, evolutionary models based on principles of learning and adaptions are more adequate than models based on assumptions of strict rationality.     

The effect of accounting standards on Big Bath behavior

This paper investigates the influence of accounting standards on systematic earnings understatement. To isolate the effect of reporting regulations from those of other institutional drivers of earnings management, Holthausen (2003) proposes analyzing reporting practices in a country undergoing a transition in accounting standards. We respond to this call by analyzing earnings management practices of large German-listed companies before and after their transitions from German GAAP to IFRS. Specifically, we focus on Big Bath behavior following Chief Financial Officer turnovers between 1999 and 2006. Our findings provide evidence of the effect of accounting standards on the existence of this particular earnings understatement pattern. However, while we detect Big Bath behavior for our sample of German GAAP firms, we find no such indication for those firms reporting under IFRS. Controlling for alternative explanations such as self-selection bias does not change our findings. We conclude that accounting standards seem to have a considerable influence on earnings management behavior, independent of managers’ and auditors’ incentives.     

(Un)Gleichheit durch soziale Schlie?ung

The concept of social closure is frequently used in recent debates about wage inequality. The following article shows that social closure generates not only inequality but equality, as well. Economic, segmentational and closure theoretical arguments shed light on the different relations of an employee to his employer in different labour market structures. Properties of the employee like vocational training, seniority or gender lose their impact on wages the more closed a labour market is. These hypotheses are tested with the 2006 BIBB/BAuA-Employment-Survey. Results support a deep effect of social closure in the German labour market.     

Sensitivity Analysis Using Risk Measures

In a quantitative model with uncertain inputs, the uncertainty of the output can be summarized by a risk measure. We propose a sensitivity analysis method based on derivatives of the output risk measure, in the direction of model inputs. This produces a global sensitivity measure, explicitly linking sensitivity and uncertainty analyses. We focus on the case of distortion risk measures, defined as weighted averages of output percentiles, and prove a representation of the sensitivity measure that can be evaluated on a Monte Carlo sample, as a weighted average of gradients over the input space. When the analytical model is unknown or hard to work with, nonparametric techniques are used for gradient estimation. This process is demonstrated through the example of a nonlinear insurance loss model. Furthermore, the proposed framework is extended in order to measure sensitivity to constant model parameters, uncertain statistical parameters, and random factors driving dependence between model inputs.     

Choosing summary statistics by least angle regression for approximate Bayesian computation

Bayesian statistical inference relies on the posterior distribution. Depending on the model, the posterior can be more or less difficult to derive. In recent years, there has been a lot of interest in complex settings where the likelihood is analytically intractable. In such situations, approximate Bayesian computation (ABC) provides an attractive way of carrying out Bayesian inference. For obtaining reliable posterior estimates however, it is important to keep the approximation errors small in ABC. The choice of an appropriate set of summary statistics plays a crucial role in this effort. Here, we report the development of a new algorithm that is based on least angle regression for choosing summary statistics. In two population genetic examples, the performance of the new algorithm is better than a previously proposed approach that uses partial least squares.     

Multiple hypothesis tests based On conditional differences in means

Many hypothesis tests are univariate tests and cannot cope with multiple hypothesis without an auxiliary procedure as e. g. the Bonferroni-Holm-procedure. At the same time, there is an urgent need for testing multiple hypothesis due to the very simple existing methods as the Bonferroni-correction or the Bonferroni-Holm-procedure, which suffers from a very small local significance level to detect statistical inferences or the drawback that logical and statistical dependencies among the test statistics are not used, whereby its detection is NP-hard. In honour of this occasion, we present a multiple hypothesis test for i.i.d. random variables based on conditional differences in means, which is capable to cope with multiple hypothesis and does not suffer on such drawbacks as the Bonferroni-correction or the Bonferroni-Holm-procedure. Thereby, the computation time can be neglected.     

CCR or BCC: what if we are in the wrong model?

The CCR model by Charnes et al. (Eur J Oper Res 2:429–444, 1978) together with the BCC model by Banker et al. (Manag Sci 30:1078–1091, 1984) are the most popular approaches of measuring efficiency among a group of decision making units, DMUs, in data envelopment analysis, DEA. The right choice of a DEA model—CCR or BCC—often, if not always, is a difficult decision. To evaluate a DMU’s efficiency for both models might be helpful, but it does not always capture the essential issues at stake. In this paper we propose a comparative analysis of both concepts: How does activity scaling under constant BCC-efficiency influence CCR-efficiency. And inversely, how does BCC-efficiency behave when activity scaling under constant CCR-efficiency is applied. Such findings of mutual effects improve a DMU’s ability to reassess upsizing and downsizing of activities. Moreover, it allows for exact calculations of the resulting economic effects, and these effects give new insights beyond classical DEA. Finally, scale efficiency turns out to be the ideal concept to control these activity changes, rather than just CCR- or BCC-efficiency. We use a little numerical example to emphasize advantages of the new concept and sketch the new findings for a theater scenery.