Regression analysis for equipment auditing
Purpose - To develop, test and implement a sampling strategy for equipment auditing for a Design/methodology/approach - Regression analysis is applied to auditing of equipment for a large US corporation. Empirical data and test data sets are used to evaluate the efficacy of using regression for auditing and to determine reasonable and efficient sample sizes to be employed across more than 5,000 locations. Findings - Regression is a viable and useful method for equipment auditing when there is anticipated high correlation between pre- and post-audit equipment value. Recommended sample size is dependent upon the size of the location as measured by total pieces of equipment. Decision rules combining acceptable tolerance limits, desired confidence level and sample size are provided. Research limitations/implications - The method, recommended sample sizes and decision rules are particularly applicable to instances where high correlation is expected between pre- and post-audit equipment values. Standard regression assumptions are not all met in all instances, especially with small sample sizes. Practical implications - The regression approach and model, sample size recommendations and decision rules for passing or failing an equipment audit described herein have been implemented at a Originality/value - This paper provides a practical and useful regression-based approach to sampling for equipment auditing. Recommended sample sizes and decision rules for passing or failing the audit are explicitly defined.
An Analysis of the Relative Power Characteristics of Analytical Procedures
The overall objective of this study is to analyze the relative effectiveness and efficiency of several analytical procedures. To accomplish this, we test the power characteristics of analytical procedures in simulated business and economic environments. The analytical procedures we test include the Martingale, Census X‐11, ARIMA, and stepwise regression expectation models. The power characteristics are measured by both positive and negative testing approaches, with and without accompanying tests of details, and with simple and dispersed error seeding patterns. The results suggest that the stepwise regression model outperforms X‐11, ARIMA, and Martingale models in discriminating between the decision risks at zero and material error for both testing approaches. All models seem to perform better in terms of the aggregate slope for higher degrees of economic stability. The stepwise procedure either outperforms the other procedures for the positive approach or comes close to doing so for the negative approach ...
Gender Pay Gap in the European Union Labor Market
The participation of women in the European Union labor market has been increasing over recent years, putting an end to the notion that women are a marginal part of the labor force. However, such increased integration of women into paid work has neither ended the traditional gender divide between jobs nor it translated into gender equality in terms of economic opportunity and economic participation in the labor market. The purpose of the paper is to briefly discuss the gender pay gap in the EU labor market. The paper defines gender pay gap and provides a brief theoretical overview of the underlying causes of such pay asymmetry between men and women. Statistics provided for EU 25 reveal that the current policy attempts to reduce such a gender asymmetry still lack improvement in practice. The paper concludes with selected policy suggestions aiming to make a progress in the issue of the gender equality in pay.