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.

Okun’s law – A meta analysis

This paper seeks to identify whether there is a representative empirical Okun’s Law coefficient (OLC) and to measure its size. We carry out a meta regression analysis on a sample of 269 estimates of the OLC to uncover reasons for differences in empirical results and to estimate the ‘true’ OLC. On statistical (and other) grounds, we find it appropriate to investigate two separate subsamples, using respectively (some measure of) unemployment or output as dependent variable. Our results can be summarized as follows. First, there is evidence of type II publication bias in both sub-samples, but a type I bias is present only among the papers using some measure of unemployment as the dependent variable. Second, after correction for publication bias, authentic and statistically significant OLC effects are present in both sub-samples. Third, bias-corrected estimated true OLCs are significantly lower (in absolute value) with models using some measure of unemployment as the dependent variable. Using a bivariate MRA approach, the estimated true effects are -0.25 for the unemployment sub-sample and -0.61 for the output-sub sample; with a multivariate MRA methodology, the estimated true effects are -0.40 and -1.02 for the unemployment and the output-sub samples respectively.