Assessing the Effect of Drivers’ Gender on Their Intention to Use Fully Automated Vehicles

Although fully automated vehicles (SAE level 5) are expected to acquire a major relevance for transportation dynamics by the next few years, the number of studies addressing their perceived benefits from the perspective of human factors remains substantially limited. This study aimed, firstly, to assess the relationships among drivers’ demographic factors, their assessment of five key features of automated vehicles (i.e., increased connectivity, reduced driving demands, fuel and trip-related efficiency, and safety improvements), and their intention to use them, and secondly, to test the predictive role of the feature’ valuations over usage intention, focusing on gender as a key differentiating factor. For this cross-sectional research, the data gathered from a sample of 856 licensed drivers (49.4% females, 50.6% males; M = 40.05 years), responding to an electronic survey, was analyzed. Demographic, driving-related data, and attitudinal factors were comparatively analyzed through robust tests and a bias-corrected Multi-Group Structural Equation Modeling (MGSEM) approach. Findings from this work suggest that drivers’ assessment of these AV features keep a significant set of multivariate relationships to their usage intention in the future. Additionally, and even though there are some few structural similarities, drivers’ intention to use an AV can be differentially explained according to their gender. So far, this research constitutes a first approximation to the intention of using AVs from a MGSEM gender-based approach, being these results of potential interest for researchers and practitioners from different fields, including automotive design, transport planning and road safety.

DO CEOS WITH SHARE PLEDGE SUPPRESS THE FIRMS’ INNOVATION?

It is a common phenomenon for corporate insiders to pledge their stock as collateral for personal loans in China. Using Chinese data, this paper examines the effects of CEOs’ share pledge on the firms’ future innovation output. Evidence suggests that the existence of CEOs with share pledge has a significantly negative effect on firms’ innovation output. The baseline results are consistent with a variety of robust tests. Furthermore, we propose the effect of CEOs’ share pledge works on the corporate innovation through the market value management channel. Finally, we find that the good corporate governance is a possible channel to relieve the agency cost on CEOs.

Robust Tests for the Mean Difference in Paired Data using Jackknife Resampling Technique

The paired sample t-test is a type of classical test statistics that is used to test the difference between two means in paired data, but it is not robust against the violation of the normality assumption. In this paper, some alternative robust tests are suggested by combining the Jackknife resampling with each of the Wilcoxon signed-rank test for small sample size and Wilcoxon signed-rank test for large sample size, using normal approximation. The Monte Carlo simulation experiments were employed to study the performance of the test statistics of each of these tests depending on the type one error rates and the power rates of the test statistics. All these tests were applied on different sample sizes generated from three distributions, represented by Bivariate normal distribution, contaminated Bivariate normal distribution, and Bivariate exponential distribution.

Self-regulated learning, academic achievement and socioeconomic context at the end of primary school

The increase of inequalities and the learning crisis due to COVID-19 pandemic has forced to review the role of education in the attainment of skills to learn throughout life. The purpose of this study is to investigate the incidence of the academic achievement on selfregulation strategies (forethought, inhibition and volitional inhibition), considering the socioeconomical context at the end of elementary school. The SRL strategies are assessed, from the perspective of students and teachers, triangulating measurement in different tasks. 67 students in their last year of primary education participated. The SRL measures were compared using robust tests considering high and low academic achievement and low and medium socioeconomic context (robust version of Welch’s test for two groups, Yuen’s test, and two-way ANOVA based on trimmed means and Winsorized variances). The academic achievement affects and significantly predicts the forethought strategy. In the low socioeconomical context, the students with a high academic achievement maximize their SRL. The modulating role of the school experience in self-regulation is discussed.

IDENTIFICATION ROBUST INFERENCE FOR MOMENTS-BASED ANALYSIS OF LINEAR DYNAMIC PANEL DATA MODELS

We use identification robust tests to show that difference (Dif), level (Lev), and nonlinear (NL) moment conditions, as proposed by Arellano and Bond (1991, Review of Economic Studies 58, 277–297), Ahn and Schmidt (1995, Journal of Econometrics 68, 5–27), Arellano and Bover (1995, Journal of Econometrics 68, 29–51), and Blundell and Bond (1998, Journal of Econometrics 87, 115–143) for the linear dynamic panel data model, do not separately identify the autoregressive parameter when its true value is close to one and the variance of the initial observations is large. We prove that combinations of these moment conditions, however, do so when there are more than three time series observations. This identification then solely results from a set of, so-called, robust moment conditions. These robust moments are spanned by the combined Dif, Lev, and NL moment conditions and only depend on differenced data. We show that, when only the robust moments contain identifying information on the autoregressive parameter, the discriminatory power of the Kleibergen (2005, Econometrica 73, 1103–1124) Lagrange multiplier (KLM) test using the combined moments is identical to the largest rejection frequencies that can be obtained from solely using the robust moments. This shows that the KLM test implicitly uses the robust moments when only they contain information on the autoregressive parameter.

Three essays on innovation, corporate control and household consumption-based asset pricing

We propose a novel consumption measure that has a daily frequency and is based on real time shopping data. Our measure explains the joint equity-premium‚ risk-free rate puzzle with a risk aversion coefficient much lower than any other consumption measures. It explains the cross-sectional variation of expected returns on various portfolios and is the only consumption measure that passes Kleibergen and Zhan (Journal of Finance, 2020) robust tests. Our model decomposes consumption shocks into different frequencies of volatility and shows that ignoring short-term dynamics and intra-annual fluctuations explains the much higher risk aversion from low-frequency consumption measures. At zip-code level daily consumption, (a) consumption in blue areas suggests higher risk aversion than that in red areas; (b) only Democratic consumption beta explains a variation of cross-sectional returns, and is more sensitive to overall industry performance.