Funding and Asset Allocation in Corporate Pension Plans: An Empirical Investigation

This paper contrasts and empirically tests two different views of corporate pension policy: the traditional view that pension funds are managed without regard to either corporate financial policy or the interests of the corporation and its shareholders, and the corporate financial perspective represented by the recent theoretical work of Black (1980), Sharpe (1916),Tepper (1981), and Treynor (1971), which stresses the potential effects of a firm's financial condition on its pension funding and asset allocation decisions. We find several pieces of evidence supporting the corporate financial perspective. First, we find that there is a significant inverse relationship between firms' profitability and the discount rates they choose tor eport their pension liabilities. In view of this we adjust all reported pension liabilities to a common discount rate assumption. We then find a significant positive relationship between firm profitability and the degree ofpension funding, as is consistent with the corporate financial perspective. We also find some evidence that firms facing higher risk and lower tax liabilities are less inclined to fully fund their pension plans. On the asset allocation question, we find that the distribution of plan assets invested in bonds is bi-modal, but that it does not tend to cluster around extreme portfolio configurations to the extent predicted by the corporate financial perspective. We also find that the percentage of plan assets invested in bonds in negatively related to both total size of plan and the proportion of unfunded liabilities.The latter relationship shows up particularly among the riskiest firms and is consistent with the corporate financial perspective on pension decisions.

Tracking Fecal Pollution Sources in the Upper Reaches of the Horse Creek Watershed in Aiken County, SC

The Horse Creek watershed in Aiken County, SC, is known for its history of high coliform pollution. Previous studies have identified one particular tributary, Sand River, as being a major contributor to the upper portions of the watershed, but the source(s) remain unknown. Sand River drains Hitchcock Woods, an urban forest that is heavily used by equestrians; is transected by both old and new sewer lines; and is surrounded by older homes, some of which depend upon aging septic systems. In addition, Sand River in Hitchcock Woods receives an enormous volume of stormflow from the downtown area during rain events. This study focused on fecal pollution in two of Sand River’s smaller tributaries, Calico Creek and Cuthbert Branch

The Law and Policy of People Analytics

Leading technology companies such as Google and Facebook have been experimenting with people analytics, a new data-driven approach to human resources management. People analytics is just one example of the new phenomenon of “big data,” in which analyses of huge sets of quantitative information are used to guide decisions. Applying big data to the workplace could lead to more effective outcomes, as in the Moneyball example, where the Oakland Athletics baseball franchise used statistics to assemble a winning team on a shoestring budget. Data may help firms determine which candidates to hire, how to help workers improve job performance, and how to predict when an employee might quit or should be fired. Despite being a nascent field, people analytics is already sweeping corporate America. Although cutting-edge businesses and academics have touted the possibilities of people analytics, the legal and ethical implications of these new technologies and practices have largely gone unexamined. This Article provides a comprehensive overview of people analytics from a law and policy perspective. We begin by exploring the history of prediction and data collection at work, including psychological and skills testing, and then turn to new techniques like data mining. From that background, we examine the new ways that technology is shaping methods of data collection, including innovative computer games as well as ID badges that record worker locations and the duration and intensity of conversations. The Article then discusses the legal implications of people analytics, focusing on workplace privacy and employment discrimination law. Our article ends with a call for additional disclosure and transparency regarding what information is being collected, how it should be handled, and how the information is used. While people analytics holds great promise, that promise can only be fulfilled if employees participate in the process, understand the nature of the metrics, and retain their identity and autonomy in the face of the data’s many narratives

The Law and Policy of People Analytics

Leading technology companies such as Google and Facebook have been experimenting with people analytics, a new data-driven approach to human resources management. People analytics is just one example of the new phenomenon of “big data,” in which analyses of huge sets of quantitative information are used to guide decisions. Applying big data to the workplace could lead to more effective outcomes, as in the Moneyball example, where the Oakland Athletics baseball franchise used statistics to assemble a winning team on a shoestring budget. Data may help firms determine which candidates to hire, how to help workers improve job performance, and how to predict when an employee might quit or should be fired. Despite being a nascent field, people analytics is already sweeping corporate America. Although cutting-edge businesses and academics have touted the possibilities of people analytics, the legal and ethical implications of these new technologies and practices have largely gone unexamined. This Article provides a comprehensive overview of people analytics from a law and policy perspective. We begin by exploring the history of prediction and data collection at work, including psychological and skills testing, and then turn to new techniques like data mining. From that background, we examine the new ways that technology is shaping methods of data collection, including innovative computer games as well as ID badges that record worker locations and the duration and intensity of conversations. The Article then discusses the legal implications of people analytics, focusing on workplace privacy and employment discrimination law. Our article ends with a call for additional disclosure and transparency regarding what information is being collected, how it should be handled, and how the information is used. While people analytics holds great promise, that promise can only be fulfilled if employees participate in the process, understand the nature of the metrics, and retain their identity and autonomy in the face of the data’s many narratives

The Law and Policy of People Analytics

(Excerpt) Recently, leading technology companies such as Google and IBM have started experimenting with people analytics, a new data-driven approach to human resources management. People analytics is just one example of the phenomenon of big data, in which analyses of huge sets of quantitative information are used to guide a variety of decisions. Applying big data to workplace situations could lead to more effective work outcomes, as in Moneyball, where the Oakland A\u27s baseball franchise used statistics to assemble a winning team on a shoestring budget. People analytics is the name given to this new approach to personnel management on a wider scale. Although people analytics is a nascent field, its implementation could transform the ways that employers approach HR decisions. Data may help firms determine which candidates to hire, how to help workers improve job performance, and how to predict when an employee might quit or should be fired. In addition, people analytics could provide insights on more quotidian issues like location of the employee offices and use of break times. The data that drives these decisions may be collected in new ways: through the use of innovative computer games, software that monitors employee electronic communications and activities, and devices such as ID badges that record worker locations and the tone of conversations. Data may also be collected from sources outside the employer which have been gathered for different purposes, like real estate records, or for undefined purposes, like Google searches. While people analytics has great potential, no one has yet comprehensively analyzed the employment law or business ethics implications of these new technologies or practices. To date, most of the discussion centers on the uses for the data, not on its effects or its interactions with the law of the workplace. This Article seeks to survey these effects and interactions. Part I provides an overview, reviewing the history of employment testing, defining data mining, and describing the most current trends in people analytics. Part II describes the use of computer games and other technology to gather information. Part III examines the implications of people analytics on workplace privacy norms and laws. Part IV discusses the impact on equal-opportunity norms; while more and better information should lead to more merit-based decisions, disparate impact or unconscious bias could still operate to harm already-marginalized workers. Part V concludes with normative observations and preliminary policy notes. As the field of people analytics continues to develop, we must keep the values of employee voice, transparency, and autonomy as guiding principles

Preserving Evolutionary History with Improved Confidence

We thank Faith (2019) and Mindell (2019) for their insightful perspectives on our study of the impact of phylogenetic imputation on the assessment of evolutionary distinctiveness (ED; Isaac et al., 2007). As Mindell highlights, the finding that ED scores for species on a phylogeny are remarkably robust despite having species missing from that phylogeny is encouraging; our results suggest that we can be confident in moving forward with prioritization of the species for which we have data. This is important because in some cases, for example, it may take considerable time to obtain samples from the missing species, resulting in further delay before the ED scores for those species already sampled can be used to inform management decisions. We cautioned, however, that the ED scores for those missing species may be imputed imprecisely, and so we gave guidelines for working with imputed species’ ED scores. With this in mind, we offer some additional thoughts resulting from the commentaries of Mindell and Faith