Top pay transparency in Europe: codes,convergence and clichés

Following the waves of corporate scandals and the increasing attention to corporate governance, the transparency of reporting on director-level pay has increased across Europe. This article examines the extent of convergence in laws, codes and corporate reporting practices in regard to director-level pay in France, Germany, Switzerland and the UK. The paper analyses annual reports and the websites of 23 companies, using institutional theory to discuss the coercive, normative and mimetic pressures for convergence that are revealed, not just in the laws and codes in each country, but also in the standardized wording in the relevant sections of reports which have become clichés.     

Testing time series data compatibility for benchmarking

Compatibility testing determines whether two series, say a sub-annual and an annual series, both of which are subject to sampling errors, can be considered suitable for benchmarking. We derive statistical tests and discuss the issues with their implementation. The results are illustrated using the artificial series from Denton (1971) and two empirical examples. A practical way of implementing the tests is also presented.     

Whose constituency? Representativeness of the Finnish Paper Workers' Union,innovation strategies,and outsourcing in the Finnish paper industry between 1980 and 2008

Outsourcing has been a conflictual issue in the Finnish paper industry, which reached a temporary solution in 2005. This article analyses the Paper Workers' Union's stance on outsourcing, arguing that it has been defending acquired rights and perhaps deliberately narrowed its representation of diverse constituencies over time.     

Assessment of Interjudge Reliability in the Open-Ended Questions Coding Process

In the process of coding open-ended questions, the evaluation of interjudge reliability is a critical issue. In this paper, using real data, the behavior of three coefficients of reliability among coders, Cohen’s K, Krippendorff’s α and Perreault and Leigh’s I _r are patterned, in terms of the number of judges involved and the categories of answer defined. The outcome underlines the importance of both variables in the valuations of interjudge reliability, as well as the higher adequacy of Perreault and Leigh’s I _r and Krippendorff’s α for marketing and opinion research.     

Hyperbolic Efficiency and Parametric Distance Functions: With Application to Spanish Savings Banks

Distance functions are gaining relevance as alternative representations of production technologies, with growing numbers of empirical applications being made in the productivity and efficiency field. Distance functions were initially defined on the input or output production possibility sets by Shephard (1953, 1970) and extended to a graph representation of the technology by Färe, Grosskopf and Lovell (1985) through their graph hyperbolic distance function. Since then, different techniques such as non parametric-DEA and parametric-SFA have been used to calculate these distance functions. However, in the latter case we know of no study in which the restriction to input or output orientation has been relaxed. What we propose is to overcome such restrictiveness on dimensionality by defining and estimating a parametric hyperbolic distance function which simultaneously allows for the maximum equiproportionate expansion of outputs and reduction of inputs. In particular, we introduce a translog hyperbolic specification that complies with the conventional properties that the hyperbolic distance function satisfies. Finally, to illustrate its applicability in efficiency analysis we implement it using a data set of Spanish savings banks.     

Looking Forward to Performance Improvement: A Field Test of the Feedforward Interview for Performance Management

This study examines the effectiveness of the feedforward interview for improving the job performance of employees relative to a traditional performance appraisal interview in a business equipment firm. Managers (n = 25) were randomly assigned to one of two conditions. Employees (n = 70) who engaged in a feedforward interview with their manager were observed by an anonymous peer to perform significantly better on the job four months later than employees (n = 75) who received the company's traditional performance appraisal interview. The finding that the feedforward intervention increased performance relative to the performance appraisal indicates that the effect is a relatively enduring one. The results suggest that the feedforward interview should prove useful for human resource managers who are searching for ways to increase the performance of their organization's human resources over and above the traditional performance appraisal. © 2014 Wiley Periodicals, Inc.     

Playing the synthesizer with Canadian data: Adding polls to a structural forecasting model

Election forecasting has become a fixture of election campaigns in a number of democracies. Structural modeling, the major approach to forecasting election results, relies on ‘fundamental’ economic and political variables to predict the incumbent’s vote share usually a few months in advance. Some political scientists contend that adding vote intention polls to these models—i.e., synthesizing ‘fundamental’ variables and polling information—can lead to important accuracy gains. In this paper, we look at the efficiency of different model specifications in predicting the Canadian federal elections from 1953 to 2015. We find that vote intention polls only allow modest accuracy gains late in the campaign. With this backdrop in mind, we then use different model specifications to make ex ante forecasts of the 2019 federal election. Our findings have a number of important implications for the forecasting discipline in Canada as they address the benefits of combining polls and ‘fundamental’ variables to predict election results; the efficiency of varying lag structures; and the issue of translating votes into seats.     

Forecasting recovery rates on non-performing loans with machine learning

We compare the performance of a wide set of regression techniques and machine-learning algorithms for predicting recovery rates on non-performing loans, using a private database from a European debt collection agency. We find that rule-based algorithms such as Cubist, boosted trees, and random forests perform significantly better than other approaches. In addition to loan contract specificities, predictors that refer to the bank recovery process — prior to the portfolio’s sale to a debt collector — are also shown to enhance forecasting performance. These variables, derived from the time series of contacts to defaulted clients and client reimbursements to the bank, help all algorithms better identify debtors with different repayment ability and/or commitment, and in general those with different recovery potential.