How Heavy Is A Job?: A Critical Survey of Job Evaluation as a Payment Device.

With salaries subjected to scrutiny more than ever, it is increasingly important that the process by which they are determined be understood and justifiable. Both public and private organisations now routinely rely on so-called “job evaluation” as a means of constructing an appropriate pay-scale and as such it is ever more necessary that we appreciate how this system works and that we recognise its limits. Only with such an understanding of the way in which salaries are set can we hope to have a meaningful discussion of their economic function. This paper aims to expound the details of job evaluation both in theory and in practice, and critically assess its shortcomings. In Section 1 below we describe the job evaluation system and in Section 2 we briefly outline the history and the usage of the system in both the private and the public sector. In Section 3 we theoretically analyse the often unstated but nonetheless implicit assumptions made by practitioners of the art of job evaluation. Section 4 applies the analysis of Section 3 to review a particular and important case study, namely The Senior Salaries Review of the Welsh Assembly 2004. Section 5 concludes.

Forecasting US bond default ratings allowing for previous and initial state dependence in an ordered probit model

In this paper we investigate the ability of a number of different ordered probit models to predict ratings based on firm-specific data on business and financial risks. We investigate models based on momentum, drift and ageing and compare them against alternatives that take into account the initial rating of the firm and its previous actual rating. Using data on US bond issuing firms rated by Fitch over the years 2000 to 2007 we compare the performance of these models in predicting the rating in-sample and out-of-sample using root mean squared errors, Diebold-Mariano tests of forecast performance and contingency tables. We conclude that initial and previous states have a substantial influence on rating prediction.