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This paper aims to study the impact of audit quality on the components of executive cash compensation. It is predicted that as audit quality improves, greater emphasis will be placed on the incentive components of cash compensation, and lower emphasis on the salary (fixed) component. Specifically, it is predicted that as audit quality enhances, greater emphasis will be placed on earnings and sales revenues in determining executive cash compensation. Using auditor specialization as a proxy for audit quality, empirical support is provided for all of our predictions.

This paper provides empirical support with agency theoretic predictions.

This paper developed the following hypotheses: H1 – in executive cash compensation, more weight is being placed on earnings-based measures as auditor specialization improves; H2 – in executive cash compensation, more weight is also being placed on sales revenues as auditor specialization improves; H3 – in executive cash compensation, salary levels decrease as auditor specialization improves; and H4 – the impact of auditor specialization on the weight on earnings, sales and the salary levels is lower in the post-Sarbanes–Oxley Act (SOX) period compared to pre-SOX period.

First, the article limits itself to cash compensation, while current executive compensation is largely made of equity. Second, the measure of audit quality used, ‘national level auditor specialization’, may not be as effective in the post-SOX era.

Compensation committees should pay attention to audit quality (in whatever way it may be proxied by) in determining executive compensation.

This is the first paper to show that audit quality not only improves the earnings response coefficient in firm valuation but also enhances the weight placed on earnings (and sales revenues) in executive compensation.

The purpose of this paper is to provide an outline of the major contributions in the literature on the determination of the least distance in data envelopment analysis (DEA). The focus herein is primarily on methodological developments. Specifically, attention is mainly paid to modeling aspects, computational features, the satisfaction of properties and duality. Finally, some promising avenues of future research on this topic are stated.

DEA is a methodology based on mathematical programming for the assessment of relative efficiency of a set of decision-making units (DMUs) that use several inputs to produce several outputs. DEA is classified in the literature as a non-parametric method because it does not assume a particular functional form for the underlying production function and presents, in this sense, some outstanding properties: the efficiency of firms may be evaluated independently on the market prices of the inputs used and outputs produced; it may be easily used with multiple inputs and outputs; a single score of efficiency for each assessed organization is obtained; this technique ranks organizations based on relative efficiency; and finally, it yields benchmarking information. DEA models provide both benchmarking information and efficiency scores for each of the evaluated units when it is applied to a dataset of observations and variables (inputs and outputs). Without a doubt, this benchmarking information gives DEA a distinct advantage over other efficiency methodologies, such as stochastic frontier analysis (SFA). Technical inefficiency is typically measured in DEA as the distance between the observed unit and a “benchmarking” target on the estimated piece-wise linear efficient frontier. The choice of this target is critical for assessing the potential performance of each DMU in the sample, as well as for providing information on how to increase its performance. However, traditional DEA models yield targets that are determined by the “furthest” efficient projection to the evaluated DMU. The projected point on the efficient frontier obtained as such may not be a representative projection for the judged unit, and consequently, some authors in the literature have suggested determining closest targets instead. The general argument behind this idea is that closer targets suggest directions of enhancement for the inputs and outputs of the inefficient units that may lead them to the efficiency with less effort. Indeed, authors like Aparicio et al. (2007) have shown, in an application on airlines, that it is possible to find substantial differences between the targets provided by applying the criterion used by the traditional DEA models, and those obtained when the criterion of closeness is utilized for determining projection points on the efficient frontier. The determination of closest targets is connected to the calculation of the least distance from the evaluated unit to the efficient frontier of the reference technology. In fact, the former is usually computed through solving mathematical programming models associated with minimizing some type of distance (e.g. Euclidean). In this particular respect, the main contribution in the literature is the paper by Briec (1998) on Hölder distance functions, where formally technical inefficiency to the “weakly” efficient frontier is defined through mathematical distances.

All the interesting features of the determination of closest targets from a benchmarking point of view have generated, in recent times, the increasing interest of researchers in the calculation of the least distance to evaluate technical inefficiency (Aparicio et al., 2014a). So, in this paper, we present a general classification of published contributions, mainly from a methodological perspective, and additionally, we indicate avenues for further research on this topic. The approaches that we cite in this paper differ in the way that the idea of similarity is made operative. Similarity is, in this sense, implemented as the closeness between the values of the inputs and/or outputs of the assessed units and those of the obtained projections on the frontier of the reference production possibility set. Similarity may be measured through multiple distances and efficiency measures. In turn, the aim is to globally minimize DEA model slacks to determine the closest efficient targets. However, as we will show later in the text, minimizing a mathematical distance in DEA is not an easy task, as it is equivalent to minimizing the distance to the complement of a polyhedral set, which is not a convex set. This complexity will justify the existence of different alternatives for solving these types of models.

As we are aware, this is the first survey in this topic.

This paper aims to examine the effect of the Sarbanes–Oxley Act (SOX), which was signed by President George W. Bush and came into effect on July 30, 2002, on firm productivity.

The authors use the total factor productivity (TFP) as our measure of firm productivity.

Analyzing annual firm-level data from the Compustat database for the period of 1991-2006, the authors find that firm productivity increases at a higher rate in the post-SOX period. The results indicate that, although firms incur significant costs in complying with the requirements of the SOX, they also benefit from these requirements as evidenced by the improved productivity over time post-SOX. There is also a shift in the output elasticities from capital toward labor. The SOX has a positive effect on the output elasticity of labor but a negative impact on that of capital.

The results have the following important implications. The SOX is a value-enhancing regulation in that it not only strengthens a firm’s corporate governance but also improves its productivity. However, compliance with the SOX can impose a long-term cost on firms: the decrease in the capital investment, leading to a decline in the output elasticity of capital. If this decline in the capital investment continues, it can have an adverse effect on firm productivity in the long term.

This paper extends the literature along the line of the actual operational effects of the SOX regulation by examining its effect on the productivity of firms.