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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.

As stated in the United Nations Global Assessment Report 2022 Concept Note, decision-makers everywhere need data and statistics that are accurate, timely, sufficiently disaggregated, relevant, accessible and easy to use. The purpose of this paper is to demonstrate scalable and replicable methods to advance and integrate the use of earth observation (EO), specifically ongoing efforts within the Group on Earth Observations (GEO) Work Programme and the Committee on Earth Observation Satellites (CEOS) Work Plan, to support risk-informed decision-making, based on documented national and subnational needs and requirements.

Promotion of open data sharing and geospatial technology solutions at national and subnational scales encourages the accelerated implementation of successful EO applications. These solutions may also be linked to specific Sendai Framework for Disaster Risk Reduction (DRR) 2015–2030 Global Targets that provide trusted answers to risk-oriented decision frameworks, as well as critical synergies between the Sendai Framework and the 2030 Agenda for Sustainable Development. This paper provides examples of these efforts in the form of platforms and knowledge hubs that leverage latest developments in analysis ready data and support evidence-based DRR measures.

The climate crisis is forcing countries to face unprecedented frequency and severity of disasters. At the same time, there are growing demands to respond to policy at the national and international level. EOs offer insights and intelligence for evidence-based policy development and decision-making to support key aspects of the Sendai Framework. The GEO DRR Working Group and CEOS Working Group Disasters are ideally placed to help national government agencies, particularly national Sendai focal points to learn more about EOs and understand their role in supporting DRR.

The unique perspective of EOs provide unrealized value to decision-makers addressing DRR. This paper highlights tangible methods and practices that leverage free and open source EO insights that can benefit all DRR practitioners.

The purpose of this paper is to improve the estimation of the production frontier in cases where outliers exist. We focus on the case when outliers appear above the true frontier due to measurement error.

The authors use stochastic data envelopment analysis (SDEA) to allow observed points above the frontier. They supplement SDEA with assumptions on the efficiency and show that the true frontier in the presence of outliers can be derived.

This paper finds that the authors’ maximum likelihood approach outperforms super-efficiency measures. Using simulations, this paper shows that SDEA is a useful model for outlier detection.

The model developed in this paper is original; the authors add distributional assumptions to derive the optimal quantile with SDEA to remove outliers. The authors believe that the value of the paper will lead to many citations because real-world data are often subject to outliers.

Existing literature in experimental accounting research suggests that accounting professionals and people with accounting backgrounds tend to have a lower level of moral reasoning and ethical development. Motivated by these findings, this paper aims to examine whether chief executive officers (CEOs) with accounting backgrounds have an impact on firms’ earnings management behavior and the level of accounting conservatism.

The authors classify CEOs into those with and without accounting backgrounds using BoardEx data. Using discretionary accruals from several different models, they do not find that CEOs with accounting backgrounds are more likely to engage in income-increasing accruals. However, the authors find that CEOs with accounting backgrounds exhibit lower levels of conservatism, proxied by C-scores and T-scores (Basu, 1997). This finding suggests that CEOs with accounting backgrounds recognize bad news more quickly than good news, consistent with the accounting principle of “anticipating all losses but anticipating no gains”.

The authors show that firms whose CEOs have accounting backgrounds exhibit lower levels of accounting conservatism. However, these firms do not exhibit higher levels of income-increasing discretionary accruals. This study documents the impact of CEOs’ educational backgrounds on firms’ accounting choices and confirms prior findings in experimental accounting research using large sample archival data.

This paper is the first study that investigates the impact of CEOs’ accounting backgrounds on firms’ financial reporting policy. The findings may have some policy implications. If accounting backgrounds of CEOs can make a significant difference on firms’ behavior, it is reasonable to make CEOs accountable for the quality of financial reporting. This paper is one of the first to empirically test inferences drawn by experimental accounting research. There has been a gap between archival and experimental accounting studies. The authors propose that interesting research questions can be addressed by filling in such a gap.

This paper aims to explore the design of management teams when the critical task facing individual managers is monitoring the performance of worker teams and producing performance measures under uncertain information environments.

The authors use a multi-agent LEN framework – linear contract, exponential utility and normal density – to model the incentive provision and organizational design.

The main lesson is that the use of performance measures under uncertainty is greatly affected by the potential for free-riding in the very monitoring activities which generate the measures to begin with. Accordingly, the value of having a management team, that is the incremental benefit of having a second manager, depends on the monitoring technology. Of particular importance are the potential free-riding in monitoring effort among multiple managers and synergies gained from having more than one manager, such as correlation among the performance measures produced or improvement due to splitting workers pool into separate groups for each manager to monitor separately.

The paper pushes this line of research further by explicitly modeling the endogenous process of signal generation within a rich economic environment. In this environment, number of workers being evaluated and number of managers who produce the signals are both endogenous. Furthermore, both workers and managers are subject to moral hazard problem. In particular, the managers suffer from potential free-riding problems but may benefit from synergistic forces due to team monitoring.