Audit personnel salaries and audit quality

This study examines the relation between audit personnel salaries and office-level audit quality. We measure audit personnel salaries at the associate, senior, and manager ranks for Big 4 audit offices from 2004 to 2013, using unique individual-auditor-level data obtained from the U.S. Department of Labor. We find that offices that pay lower salaries have a higher percentage of clients that experience restatements. In related analyses, we also find lower levels of audit quality when audit employees are paid less, relative to other lines of service in accounting firms. Finally, we document positive and significant associations between salary and fees, suggesting that audit offices pass some of the cost of higher labor onto their clients. Overall, our findings provide important initial evidence on the role of audit salary and its relation to audit quality and audit fees.     

Trading off learning and performance: Exploration and exploitation at work

Employees are increasingly given control over how they learn, and their choices for training are diverse and varied, yet employees must balance competing demands. On one hand, they are expected to be increasingly efficient in their current job duties – on the other hand, they are expected to develop new skills and competencies that enable them to adapt and respond to changing job demands. Drawing from the organizational learning literature, we propose a model of worker and work characteristics that inform choices between two mindsets related to learning at work. The first mindset is exploration, or the pursuit of learning outside one’s current knowledge domain; the second mindset is exploitation, the refinement/deepening of one’s existing knowledge stock focusing on the task at hand. We further propose that these strategic choices, or trade-offs, influence employee learning and performance in unique ways, with different implications for both routine and adaptive performance. Finally, we incorporate the notions of feedback loops and risk assessments that influence ongoing decisions between exploration and exploitation mindsets. Recommendations for future research and extensions of the theoretical model are also proposed.     

Reluctant Generalised Additive Modelling

Sparse generalised additive models (GAMs) are an extension of sparse generalised linear models that allow a model's prediction to vary non-linearly with an input variable. This enables the data analyst build more accurate models, especially when the linearity assumption is known to be a poor approximation of reality. Motivated by reluctant interaction modelling, we propose a multi-stage algorithm, called reluctant generalised additive modelling (RGAM), that can fit sparse GAMs at scale. It is guided by the principle that, if all else is equal, one should prefer a linear feature over a non-linear feature. Unlike existing methods for sparse GAMs, RGAM can be extended easily to binary, count and survival data. We demonstrate the method's effectiveness on real and simulated examples.     

A Guide to Information on Australian Resource Projects

This article provides a guide to economic information on Australian resource projects. Three sources are described in some detail and their differences, strengths and weaknesses are discussed. As the data are under‐researched, some examples of how they can be used in economic analysis are mentioned.     

Seatbelt use among drivers in an urban city in Nigeria: an observational study

Studies from parts of Nigeria reported low compliance with seatbelt. This study sought to establish driver seatbelt use in Enugu, Nigeria by gender, vehicle type/use and time of day. Observations were done day and night at randomly selected locations. Data were analysed with SPSS version 15. Differences in response were checked with chi-square for trend. Confidence interval was 95% and P value < 0.05 was regarded as significant. Average compliance was 37.6% for the 510 males (85%) and 90 females (15%) observed. It was 74.8% in the day and 0.3% at night. Among males, 218 (42.7%) wore seatbelt while 9 (10%) females did. For commercial drivers 159 (65.2%) complied while 68 (19.1%) private drivers did. Truck drivers had 100% compliance while sports utility vehicle drivers had the lowest (18.8%). There is poor seatbelt compliance in Enugu, Nigeria and need for educational campaigns and stricter enforcement.     

Dimensions of entrepreneurial orientation and small and medium enterprise performance in emerging economies

The removal of trade barriers has encouraged the entry of new competitors into formerly protected markets. This situation creates pressure on many small and medium enterprises (SMEs) in emerging economies such as Tanzania. Using a survey method and cross-sectional research design, the research examines three dimensions of entrepreneurial orientation (EO), namely: pro-activeness, risk-taking and competitive aggressiveness. Understanding their relationships and variance may help to improve our ability to explain SME performance. The findings contribute to how SME performance in emerging economies can be enhanced to enable SMEs to face challenges posed by competitor influx in the context of an open market economy. The findings indicate a strong relationship between EO dimensions and performance, with risk-taking and competitive aggressiveness moderating the effect of pro-activeness. The proposed model could predict 72% of the variance explained in SME performance.     

Are batteries ready for plug-in hybrid buyers?

The notion persists that battery technology and cost remain as barriers to commercialization of electric-drive passenger vehicles. Within the context of starting a market for plug-in hybrid electric vehicles (PHEVs), we explore two aspects of the purported problem: (1) PHEV performance goals and (2) the abilities of present and near-term battery chemistries to meet the resulting technological requirements. We summarize evidence stating that battery technologies do not meet the requirements that flow from three sets of influential PHEV goals due to inherent trade-offs among power, energy, longevity, cost, and safety. However, we also show that part of this battery problem is that those influential goals are overly ambitious compared to goals derived from consumers’ PHEV designs. We elicited PHEV designs from potential early buyers among U.S. new car buyers; most of those who are interested in a PHEV are interested in less technologically advanced PHEVs than assumed by experts. Using respondents’ PHEV designs, we derive peak power density and energy density requirements and show that current battery chemistries can meet them. By assuming too aggressive PHEV goals, existing policy initiatives, battery research, and vehicle development programs mischaracterize the batteries needed to start commercializing PHEVs. To answer the question whether batteries are ready for PHEVs, we must first answer the question, “whose PHEVs?”     

A Brief Survey of Modern Optimization for Statisticians

Modern computational statistics is turning more and more to high‐dimensional optimization to handle the deluge of big data. Once a model is formulated, its parameters can be estimated by optimization. Because model parsimony is important, models routinely include non‐differentiable penalty terms such as the lasso. This sober reality complicates minimization and maximization. Our broad survey stresses a few important principles in algorithm design. Rather than view these principles in isolation, it is more productive to mix and match them. A few well‐chosen examples illustrate this point. Algorithm derivation is also emphasized, and theory is downplayed, particularly the abstractions of the convex calculus. Thus, our survey should be useful and accessible to a broad audience.