The Association Between Vertical Equity and Presidential Voting Behavior and Taxpayers’ Compliance

Journal of Business Ethics - Since taking office, the President of the United States has consistently refused to make his tax returns available for public scrutiny. In so doing, he has broken with...     

Corporate climate-related voluntary disclosures: Does potential greenwash exist among Australian high emitters reports?

This study examines the issue of greenwashing among corporate high emitters subject to government scrutiny. To do so, we investigate the relationship between the actual environmental performance, measured as carbon emissions, of companies subject to the Australian National Greenhouse Energy Reporting Act 2007 (NGER) and their climate-related voluntary disclosures. To measure climate-related disclosure, we construct a climate-related disclosure index based on four prominent frameworks and score corporate report content against that Index. Using a sample of 150 companies with NGER emissions data for Years 2016 and 2017, a period that precedes the issuance of recommendations for disclosure by the Task Force on Climate-Related Financial Disclosure (TCFD), our regression results show a disappointingly low level of climate-related disclosure by most companies. We also find a negative relationship between climate-related disclosure and 1-year lagged environmental performance among companies exhibiting a year-on-year rise in carbon emissions. That is, our findings provide evidence of potential greenwashing by poor environmental performers, presumably to change negative perceptions by stakeholders, as predicted by socio-political theories. In contrast, we find no evidence of greenwashing among companies experiencing a year-on-year decrease in emissions.     

Accounting for time-dependent treatment use when developing a prognostic model from observational data: A review of methods

Failure to account for time-dependent treatment use when developing a prognostic model can result in biased future predictions. We reviewed currently available methods to account for treatment use when developing a prognostic model. First, we defined the estimands targeted by each method and examined their mechanisms of action with directed acyclic graphs (DAGs). Next, methods were implemented in data from 1,906 patients; 325 received selective β-blockers (SBBs) during follow-up. We demonstrated seven Cox regression modeling strategies: (a) ignoring SBB treatment; (b) excluding SBB users or (c) censoring them when treated; (d) inverse probability of treatment weighting after censoring (IPCW), including SBB treatment as (e) a binary or (f) a time-dependent covariate; and (g) marginal structural modeling (MSM). Using DAGs, we demonstrated IPCW and MSM have the best properties and target a similar estimand. In the case study, compared to (a), approaches (b) and (e) provided predictions that were 1% and 2% higher on average. Performance (c-statistic, Brier score, calibration slope) varied minimally between approaches. Our review of methods confirmed that ignoring treatment is theoretically inferior, but differences between the prediction models obtained using different methods can be modest in practice. Future simulation studies and applications are needed to assess the value of applying IPCW or MSM to adjust for treatments in different treatment and disease settings.     

Business students’ experiences of technology tools and applications in higher education

There has been much debate about the increasing use of technology in all learning, training and development contexts. In the case of higher education, although technology may not have fundamentally changed teaching practices, students report how several technologies and applications help with their learning, research and collaborative activities. Through a survey of business students at an Irish university, this paper examines their expectations and experiences of faculty usage of technology tools and applications. Although, on the surface, the findings provide some degree of confidence that students’ expectations of technology usage are being met, students would, nonetheless, like to see faculty make even greater, and more effective, use of technology in the classroom. The study provides faculty with examples from students of technology usage that supports their learning and these examples may be useful for faculty when designing and delivering their courses. Beyond the higher education context, the research highlights some broader practical implications for training and development in a corporate setting.     

Investors' Misweighting of Firm-Level Information and the Market's Expectations of Earnings

Prior studies use fundamental earnings forecasts to proxy for the market's expectations of earnings because analyst forecasts are biased and are available for only a subset of firms. We find that as a proxy for market expectations, fundamental forecasts contain systematic measurement errors analogous to those in analysts' biased forecasts. Therefore, these forecasts are not representative of investors' beliefs. The systematic measurement errors from using fundamental forecasts to proxy for market expectations occur because investors misweight the information in many firm-level variables when estimating future earnings, but fundamental forecasts are formed using the historically efficient weights on firm-level variables. Thus, we develop an alternative ex ante proxy for the market's expectations of future earnings (“the implied market forecast”) using the historical (and inefficient) weights, as reflected in stock returns, that the market places on firm-level variables. A trading strategy based on the implied market forecast error, which is measured as the difference between the implied market forecast and the fundamental forecast, generates excess returns of approximately 9 percent per year. These returns cannot be explained by investors' reliance on analysts' biased forecasts. Overall, our results reveal that market expectations differ from both fundamental forecasts and analysts' forecasts.