What drives support for self-driving car technology in the United States?

Abstract

Recent advances in automotive technology have made fully automated self-driving cars technologically feasible. Despite offering many benefits such as increased safety, improved fuel efficiency, and greater disability access, public support for self-driving cars remains low. While previous studies find that demographic factors such as age and sex influence self-driving car support, limited research has examined variables that are well known to predict public attitudes toward emerging technology. Using self-report data from a quota sample of American adults (N?=?1008), we find that age and sex are not significantly associated with support for self-driving car policies when controlling for these other variables. Instead, significant predictors of support included trust in automotive institutions and regulatory bodies, recognition of self-driving car benefits, positive affect toward self-driving cars, and a greater perception that human-driven cars are riskier than self-driving cars. Importantly, we also find that individualism is negatively associated with support. That is, people who value personal autonomy and limited government regulation may perceive policies encouraging self-driving car use as threatening to their worldviews. Altogether, our results suggest strategies for encouraging greater public support of self-driving vehicles while also forecasting potential barriers as this technology emerges as a fixture in transportation policy.     

Risk-type density diagrams by crash type on two-lane rural roads

The research presented here aims to plot density diagrams per road crash risk type to identify all possible scenarios where driving is less than safe. The starting point was the prediction of injury crash rate on horizontal homogeneous segments of two-lane rural roads for three main injurious crash types (head-on/side collisions, rear-end crashes, and single-vehicle run-off-road crashes) as observed on the network. A careful analysis of the database shows that a wide variety of factors appear to be influenced or associated with the crash dynamic, as follows: the road scenario (combination of infrastructure and environmental conditions found at the site at the time of the crash), mean lane width, the horizontal curvature indicator (measurement of the curvature change rate), and mean speed. Crashes recorded from 2003 to 2010, of which 1597 were injurious, and 645 resulted only in damage to property, were analyzed on more than 3700?km of road network in Southern Italy. Generalized estimating equations with a negative binomial distribution were implemented. Risk-type density charts were plotted to thoroughly identify all possible combinations of existing explicative variables producing hazardous conditions on the road. The different shades in the diagrams represent different ranges of injurious crash rates: the white band shows low levels, while a black band shows high values. It is not possible to consider working on an explanatory variable to reduce hazardous conditions on the road network without also considering how this variation might affect the influence of the remaining explanatory variables on crash phenomena and, consequently, on the predictive model. The risk maps make it possible to keep under control in a simple and immediate approach the way each variable as a result of variations of a part or of all.     

Do rating agencies’ decisions impact stock risks? Evidence from European markets

This article analyses the effect of rating agencies’ decisions on stock risks for European issuers concerning five kinds of events. Our approach is an extension of dummy variable regression event study methodology, using a GARCH(1,1) estimation to capture simultaneously the impact on both systematic and specific stock risks. This new methodology allows us to obtain both global results by categories of rating decisions and individual results, event by event. We document, globally, a positive impact of upgrading on systematic risk, a negative impact of rating confirmation on specific risk, and no significant impact in all other cases. Regarding event-by-event results, the proportion of rating actions exhibiting a significant effect on risk is almost always observed between 20% and 30%. The weak evidence of a global effect on systematic risk may be due to the lack of informational content of the rating decisions on the stocks’ risk, or the existence of rebalancing effects between systematic and idiosyncratic risks. Furthermore, it should be noticed that the decline in volatility in case of a rating affirmed is an insight of the certification role played by the agencies.     

Using artificial neural network and multivariate analysis techniques to evaluate road operating conditions

Regional paved roads are low volume roads with a prevalence of heavy traffic. In the world, these roads concern about 80% of the total road network; however, the traffic that affects these roads is about 20%. Since regional roads are characterized by weak demand, budget for their management/maintenance is very low. This produces considerable difficulties in the choice of strategies for maintenance planning and scheduling. For this reason, the recurring topics of research in this field deal with typical roads issues and aim to develop low cost tools and methods. The study proposes a decision support system to evaluate regional paved roads operating condition in relation to the hydrogeological situation. In particular, the system allows to evaluate in a quick and easy manner, the operating conditions of the road, through low-cost tools (i.e. using low economic resources). This is very useful in the case of LVRs because administrations for these roads have a limited budget. The procedure is developed on a regional paved roads network based on more than 80 roads located in Southern Italy. Data is collected by direct surveys in the field and is integrated with cartography and information available in road agency records. From data analysis, obtained using two different techniques, an easy and quick use procedure is made. In particular, Model 1 is built through multivariate analysis and Model 2 using the artificial neural network (ANN) technique. The results show the validity of the two models in Regional paved roads operating conditions estimation in relation to hydrogeological situations of sites. Both models show good reliability. In particular, the first model (Model 1) is characterized by a high level of significance (p < 0.01) and by a coefficient of determination equal to 0.82. Comparative tests between the second model (Model 2) on which standard tests cannot be performed for obvious reasons, and the first model (Model 1). The results show that the ANN model (model 2), characterized by lower residual, simulates more accurately than the second (Model 1).     

Business cycles,financial cycles and capital structure

We perform peridogram based cycle analysis of firm capital structure and find evidence that firms’ leverage is both persistent and cyclical. The cyclicality of leverage is supported by the trade-off, pecking order and market timing capital structure theories (Korajczyk and Levy in J Financ Econ 68:75–109, 2003; Bhamra et al. in Rev Financ Stud 23:645–703, 2010). Although market timing theory research supports persistence, previous literature dictates that the trade-off and pecking order theories may predict either persistent or mean reverting leverage. Our tests reject mean reversion in favor of persistent and cyclical leverage. We corroborate pecking order theory literature that predicts leverage is persistent. In these models, when firms’ investment spending is below earnings, leverage decreases. In addition, we examine whether firms change their capital structure as a result of business and financial cycles. Since financial cycles last longer than business cycles, financial cycles should have a long term effect on leverage. Our findings confirm the persistent leverage business cycle models that suggest firms change their capital structure due to financial and credit cycles (Jermann and Quadrini in Am Econ Rev 102:238–271, 2012; Azariadis et al. in Rev Econ Stud 83:1364–1405, 2016). We conclude that leverage is persistent due to the cyclicality of the financing decision.     

Persistence and the four‐factor model in the Australian funds market: a note

We investigate whether Australian fund managers are able to deliver persistent performance using Carhart’s (1997) four‐factor model. Short‐ and long‐term persistence is examined and the sample is also divided into unit trusts and superannuation funds. We do not find evidence of persistence in any sample of funds. We find that winner (loser) funds tend to hold past winner (loser) stocks. Winner and loser unit trusts both appear to have positive exposure to small stocks.     

Interaction of size,book‐to‐market and momentum effects in Australia

This study seeks to disentangle the effects of size, book‐to‐market and momentum on returns. Initial results show that each characteristic has a role in explaining returns, but that there is interaction between size and momentum, as well as between size and book‐to‐market. Three key findings emerge. First, the size premium is the strongest, particularly in the loser portfolios. Second, the value premium is generally limited to the smallest portfolios. Third, the momentum premium is evident for the large‐ and middle‐sized portfolios, but loser stocks significantly outperform winner stocks in the smallest size portfolio. When these interactions are controlled with multivariate regression, we find a significant negative average relation between size and returns, a significant positive average relation between book‐to‐market and returns, and a significant positive average relation between momentum and returns.     

The ERA: A Brave New World of Accountability for Australian University Accounting Schools

This study examines the potential impact of Excellence in Research for Australia (ERA) on Australian university accounting schools through a series of in-depth interviews with Heads of Schools. Using an institutional theory framework we find that the pending introduction of the ERA has brought about changes in school structures, processes and systems. A creeping isomorphism is apparent as evidenced by a sector-wide movement towards targeting publications in highly ranked North American journals. While participants were generally positive about the overall aims of the ERA many felt that it would marginalise non-mainstream research. Furthermore, they were of the opinion that the ERA would lead to a reduction in the standing of accounting schools within Australian universities relative to other disciplines.