Did the Arab Spring lead to economic, institutional and sociopolitical changes? An empirical analysis through the perceptions of Egyptian youth

PurposeEgyptian youth played a central role in the Arab Spring (AS) uprisings, demanding bread, justice and opportunities. This paper examines the perceptions of young Egyptians about the AS through their responses on the economic, institutional and sociopolitical conditions in the pre-AS and post-AS periods.Design/methodology/approachThe empirical analysis relies on a micro-level dataset extracted from the SAHWA Youth Survey. The estimations are carried out through the bivariate ordered probit model.FindingsThe results reveal that the perceptions about the AS-related outcomes are not uniform, and that social values and ideological characteristics matter more than the standard socioeconomic attributes in comprehending the responses. They indicate that individuals with secularist, non-traditionalist and gender equality inclinations have generally formed more favorable perceptions about the AS-related changes. Also, the results suggest that the AS has generated propitious perceived conditions for further global connection, relative to the Arab and Islamic ties. They show that the perceptions of individuals who place credence in entrepreneurial attitude have been relatively unfavorable vis-à-vis the post-AS conditions.Originality/valueThe findings underscore the importance of comprehending the perceptions about the AS-related outcomes. They imply that the AS has produced “winners” and “losers,” and has laid down the basis for social transformations in Egypt.

Youths Interests in the Biosphere and Sensitivity to Nuclear Power Technology in the UAE: With Discussions on Open Innovation and Technological Convergence in Energy and Water Sectors

Nuclear power technology (NPT) perception and acceptance have globally emerged as the most critical questions for the successful integration of NPT into any national energy mix. In its combination with seawater desalination, NPT provides a sustainable alternative for the security and economic efficiency of both energy and fresh water supply, the latter of which has been identified as “the bloodstream of the biosphere”. Integrating econometric analysis into energy research with social science ramifications, this paper relies on bivariate ordered probit regression to study the impact of youths’ interests in the biosphere on their awareness and optimism toward NPT in the UAE. The model is estimated using maximum likelihood methods, with the results showing each level increase in UAE youths’ biospheric interests, to increase their NPT awareness by 13.5%, while conjointly reducing their optimistic expectations toward the technology by 2.4%. In addition, awareness and expectations about NPT are found to vary heterogeneously across the seven Emirates of the country. Moreover, accounting for all relevant factors (including respondents’ biospheric interests), formed expectations about NPT are not significantly shaped by NPT awareness. Given that the first unit of the UAE’s nuclear power plant “Barakah” just became operational in August 2020, our results provide important insights for evidence-based policy making to sustain the nascent nuclear energy program in the long run.

On the Influence of Land Use and Transit Network Attributes on the Generation of, and Relationship between, the Demand for Public Transit and Ride-Hailing Services in Toronto

In North American cities, the growing use of ride-hailing services, such as Uber and Lyft, has occurred at a time when public transit ridership has either stagnated or declined. Previous studies on the topic have found that the relationship between ride-hailing and public transit services tends to be context-specific in nature. In some cases, ride-hailing has been shown to complement public transit, while, in others, it can be a substitute. This paper takes an integrated approach to modeling the generation of demand for ride-hailing and public transit services, using trip-level ride-hailing data and data from a regional household travel survey. In addition, the relationship between the demand for ride-hailing and transit services is also explored. This paper uses the bivariate ordered probit model and the recursive regression model to study the role that built environment and socio-demographic attributes play in the generation of transit and ride-hailing demand. The model results reveal that there are several built environment attributes, such as transit accessibility and the density of commercial and recreational establishments, that influence the generation of both ride-hailing and transit demand. The results also indicate that the relationship between ride-hailing and transit services tends to be more complementary than substitutive in nature. This does not, however, mean that ride-hailing services are never used as a substitute for public transit in Toronto. The results of this study aim to provide transit agencies with a means of identifying locations where ridership may be threatened by ride-hailing services.

Investigation on the Injury Severity of Drivers in Rear-End Collisions Between Cars Using a Random Parameters Bivariate Ordered Probit Model

The existing studies on drivers’ injury severity include numerous statistical models that assess potential factors affecting the level of injury. These models should address specific concerns tailored to different crash characteristics. For rear-end crashes, potential correlation in injury severity may present between the two drivers involved in the same crash. Moreover, there may exist unobserved heterogeneity considering parameter effects, which may vary across both crashes and individuals. To address these concerns, a random parameters bivariate ordered probit model has been developed to examine factors affecting injury sustained by two drivers involved in the same rear-end crash between passenger cars. Taking both the within-crash correlation and unobserved heterogeneity into consideration, the proposed model outperforms the two separate ordered probit models with fixed parameters. The value of the correlation parameter demonstrates that there indeed exists significant correlation between two drivers’ injuries. Driver age, gender, vehicle, airbag or seat belt use, traffic flow, etc., are found to affect injury severity for both the two drivers. Some differences can also be found between the two drivers, such as the effect of light condition, crash season, crash position, etc. The approach utilized provides a possible use for dealing with similar injury severity analysis in future work.

Digital Media Consumption and Youth's Interests in Ecosystem Services, Sustainability and Science as a Means for Disease Prevention

Along with the advantages associated with access to information and fast communication, screen time from increased digital media consumption has recently been associated with adverse effects on youth well-being. To get a clearer picture of its value for global youth based sustainability initiatives, this study investigates the effects of increased digital media consumption on youth's interests in ecosystem services, sustainability and science as a means for disease prevention. We achieve this, using data on 187821 adolescent students from 50 countries worldwide. Methodologically, we rely on a mixed bivariate ordered probit representation of youth's joint interest in the biosphere (ecosystem services and sustainability) and science as a means for disease prevention, which we then estimate using Markov Chain Monte Carlo (MCMC) Methods.  We found that each level increase in adolescent students' reported frequency of news blogs visits and web browsing on broad science adversely affect their interests in ecosystem services, sustainability and science as a means for disease prevention. Although each level increase in youth's frequency of ecological website visits also reduces by 20% (with 95% CI [-0.36; -0.32]) their interests in the biosphere, it is found to increase however by 3% (with 95% CI [0.02; 0.05]) their interest science as a means for disease prevention. Overall, our results highlight heterogeneous effects of digital media consumption on adolescents' well-being in terms of their interests in ecosystem services, sustainability, and science as a means for disease prevention.

Factors Affecting the Adoption of Autonomous Vehicles for Commute Trips: An Analysis with the 2015 and 2017 Puget Sound Travel Surveys

The emergence of autonomous vehicles (AVs) may shape the future landscape of urban mobility. Although there is a growing literature on public opinions with regard to self-driving, little attention has been explicitly given to the commuters and their preferences for their commute trips. Using data from the 2015 and 2017 Puget Sound Regional Household Travel Studies, this study investigates the factors associated with employees’ propensity for using AVs. We develop a bivariate ordered probit model to jointly estimate the determinants of levels of interest in (i) commuting alone using AVs and (ii) commuting with others using shared AVs. Not surprisingly, it was found that current solo drivers are more likely to commute alone using AVs compared with other mode users. Significant differences were not found between current drivers and other commuters when it comes to the potential use of shared AVs. The results also reveal that, controlling for other factors, commuters surveyed in 2017 are less likely to be interested in shared AVs compared with their 2015 counterparts. The conclusion is that more planning efforts are needed to support the market penetration of shared AVs.

Estimation urban railway demand in Yogyakarta using Bivariate Ordered Probit Model

The urban railway system is believed to solve transportation problems caused by the high growth of private vehicles and urbanization. This study is going to analyze the potential demand for the urban railway in Yogyakarta, Indonesia based on bivariate ordered probit model. The survey of preference stated with 120 samples conducted in Yogyakarta. The model of train demand is distinguished between public transport users and private vehicle users using seven scenarios. In-train travel time, waiting time, tariff, and ticketing discount for students are four factors considered in the model. The demand model shows that in-train travel time is the most important factor influence for train demand. Meanwhile, the scenario result reveals that respondents except student are willing to pay more to obtain shorter travel time, students who use private vehicle are reluctant to shift into the train, and ticketing discount brings no effect to stimulate them to use the train.