Executive Compensation and Financial Performance Measures: Evidence from Significant Financial Institutions

We survey the use of financial performance measures in determining executive pay among significant Australian financial institutions. We document evidence of the pervasiveness with which externally disclosed non‐GAAP (non‐Generally Accepted Accounting Principles) financial measures are also used internally to determine variable remuneration, with the apparent popularity of cash profit after tax in short‐term incentives plans. Our evidence also highlights the increasing use of peer group‐adjusted measures (e.g., relative cash earnings per share and return on equity ranking against a peer group) in determining longer‐run incentives, despite the fact that members of the peer group do not measure financial performance in a directly comparable manner. Detailed analysis of the four major trading banks (Australia and New Zealand Banking Group, Commonwealth Bank, National Australia Bank and Westpac) reveals differences in the way non‐GAAP earnings measures are calculated across the major banks, as well as some variation over time in the way individual banks measure performance. We also document evidence of non‐GAAP earnings restatements, with around 25% of non‐GAAP results subsequently being restated. These restatements are more likely to result in a downward revision of the initially reported non‐GAAP result than an upward revision. We therefore conclude that existing measures of financial performance used to determine senior executive compensation are not as ‘objective’, as might be assumed.     

A model of biased intermediation

We study situations in which consumers rely on a biased intermediary's advice when choosing among sellers. We introduce the notion that sellers' and consumers' payoffs can be congruent or conflicting, and show that this has important implications for the effects of bias. Under congruence, the firm benefiting from bias has an incentive to offer a better deal than its rival and consumers can be better‐off than under no bias. Under conflict, the favored firm offers lower utility, and bias harms consumers. We study various policies for dealing with bias and show that their efficacy also depends on whether the payoffs exhibit congruence or conflict.     

Household Determinants of the Adoption of Improved Cassava Varieties using DNA Fingerprinting to Identify Varieties in Farmer Fields: A Case Study in Colombia

We examine factors affecting the adoption of improved cassava varieties of 217 households in the Cauca Department in southwest Colombia. Using DNA fingerprinting through Single Nucleotide Polymorphisms (SNPs), we identified different cultivars in farmers fields. We also used this information to remove possible bias in the adoption model that could have resulted from a misclassification of improved varieties (IVs). As a result, we found that farmers substantially overestimate their use of IVs and there are important differences in the determinants of adoption between farmer self‐identification and DNA fingerprinting. This finding implies that the incorporation of DNA fingerprinting in IV adoption studies is important to ensure the accuracy of future agricultural economic research and the relevance of subsequent policy recommendations.     

Building relationship innovation in global collaborative partnerships: big data analytics and traditional organizational powers

This study examines how relationship innovation can be developed in global collaborative partnerships (alliances, joint ventures, mergers, and acquisitions). The recently emerging theory of big data analytics linked with traditional organizational powers has attracted a growing interest, but surprisingly little research has been devoted to this important and complex topic. Therefore, after developing the theoretical foundations, our study empirically quantifies the links between the theoretical constructs based on the data collected from chief executive officers, managing directors, and heads of departments who work in contemporary global data‐and‐information driven collaborative partnerships. The results from structural equation modeling indicate that the relationship innovation depends on the power of big data analytics and non‐mediated powers (NMP, expert and referent). The power of big data analytics also mediates the correlation between NMP and relationship innovation. However, mediated powers (coercive and manipulative) negatively affect the power of big data analytics and relationship innovation. The interaction effects further depict that analytically powered partnerships have better relationship innovation compared with those which focus less on the analytical power. Consequently, the contributions of this study provide a deeper understanding of mechanisms of how modern collaborative partnerships can use big data analytics and traditional organizational powers to co‐create relationship innovation.     

Target‐Setting,Pay for Performance,and Quality Improvement: A Case Study of Ontario Hospitals’ Quality‐Improvement Plans

This study examines whether difficult targets and quality indicators in executives' pay‐for‐performance (P4P) plans affect performance. The impact of target‐setting and P4P plans on quality improvement in the public sector is unclear. The Ontario government initiated the Quality Improvement Plan (QIP), which requires hospitals to set targets for quality indicators annually and link executive pay to target achievement since 2011. Analyzing Health Quality Ontario's database and hospitals' 2012–2013 QIPs, this study shows greater quality improvement in hospitals with difficult targets than hospitals with easy targets or without assigned targets; however, the positive impact disappears for high‐performance hospitals relative to their peers. We find no significant effect of the use of quality indicators in executives' P4P plans on quality improvement. Copyright © 2018 ASAC. Published by John Wiley & Sons, Ltd.     

Time series copulas for heteroskedastic data

We propose parametric copulas that capture serial dependence in stationary heteroskedastic time series. We suggest copulas for first‐order Markov series, and then extend them to higher orders and multivariate series. We derive the copula of a volatility proxy, based on which we propose new measures of volatility dependence, including co‐movement and spillover in multivariate series. In general, these depend upon the marginal distributions of the series. Using exchange rate returns, we show that the resulting copula models can capture their marginal distributions more accurately than univariate and multivariate generalized autoregressive conditional heteroskedasticity models, and produce more accurate value‐at‐risk forecasts.     

The equivalence of supporting software delegation and sales delegation

This paper considers a duopoly market with horizontally differentiated system goods to examine system owners' behaviors under supporting software delegation, in which owners of system firms use varieties of supporting software, coupled with profit, to evaluate their managers' performance. Supporting software delegation seems to induce managers to act more aggressively in price competition than sales delegation does; however, we prove that if two systems are compatible and the varieties of supporting software are determined by hardware owners' overall expenditure amount on software, then supporting software delegation is equivalent to sales delegation. Owners of system firms induce their managers to act less aggressively in hardware price competition by offering contracts with a negative weight on varieties of supporting software under supporting software delegation. We find that stronger network externalities do not reverse system owners' contracting behaviors under supporting software delegation. Finally, it is worth mentioning that hardware technologies are static in this paper. In other words, dynamic changes such as hardware evolution are not considered in our analysis.     

An innovation to improve health outcomes in Amajuba district,KwaZulu-Natal,South Africa

Systems’ thinking places high value on understanding the context. This study focused on the collection of disaggregated data in order to understand the context, to facilitate improvement of health outcomes. The aim of this article was to assess the implementation of municipal ward-based health data collection (disaggregated data) and health care workers’ perceptions of this data collection process. This cross-sectional study used mixed methods in Amajuba district. The participants were professional nurses at the Primary Health Care level. Of the 131 respondents, 123 (93.9%) collected municipal ward-based health data, and found it useful. Opportunities for improving data collection were identified. Disaggregation of the data at ward level contributes to a better understanding of the target population’s health, assists planning for health needs and enables provision of targeted interventions in order to improve health outcomes, to prevent financial regression and waste of health resources.     

EMPIRICAL PERFORMANCE OF GARCH MODELS WITH HEAVY‐TAILED INNOVATIONS

We introduce a new type of heavy‐tailed distribution, the normal reciprocal inverse Gaussian distribution (NRIG), to the GARCH and Glosten‐Jagannathan‐Runkle (1993) GARCH models, and compare its empirical performance with two other popular types of heavy‐tailed distribution, the Student's t distribution and the normal inverse Gaussian distribution (NIG), using a variety of asset return series. Our results illustrate that there is no overwhelmingly dominant distribution in fitting the data under the GARCH framework, although the NRIG distribution performs slightly better than the other two types of distribution. For market indexes series, it is important to introduce both GJR‐terms and the NRIG distribution to improve the models’ performance, but it is ambiguous for individual stock prices series. Our results also show the GJR‐GARCH NRIG model has practical advantages in quantitative risk management. Finally, the convergence of numerical solutions in maximum‐likelihood estimation of GARCH and GJR‐GARCH models with the three types of heavy‐tailed distribution is investigated.