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The purpose of this paper is to investigate how active labour market policy (ALMP) training programmes and hiring subsidies increase or decrease differences in the unemployment risk between lesser and higher educated people during an economic downturn. A focus is put on potential job competition dynamics and cumulative (dis)advantages of the lesser and higher educated.

The paper uses multi-level data. The fifth wave (2010) of the European Social Survey was used and combined with macro-level data on labour market policies of the OECD. The sample consisted of 18,172 observations in 19 countries.

The results show that higher levels of participation and spending on training policies are related to a smaller difference in the unemployment risks of the educational groups. Higher training policy intensity is associated with a lower unemployment risk for the lesser educated and a higher unemployment risk for the higher educated. This implies that the lesser educated are better able to withstand downward pressure from the higher educated, thereby, reducing downward displacement during an economic downturn. Hiring subsidies do not seem to be associated with the impact of education on unemployment.

The paper adds to the discussion on ALMP training and hiring subsidies that are primarily rooted in the human capital theory and signalling theory. Both theories ignore the social context of labour market behaviour. The job competition theory and cumulative (dis)advantage theory add to these theories by focussing on the relative position of individuals and the characteristics that accompany the social position of the individual.

Truck appointment systems have been applied in critical container ports in the United States due to their potential to improve handling operations. This paper aims to develop a truck appointment system to optimise the total cost experiencing at the entrance of container terminals by managing truck arrivals and the number of service gates satisfying a given level of service.

The approximation of M_t/G/n_t queuing model is applied and integrated into a cost optimisation model to identify (1) the number of arrival trucks allowed at each time slot and (2) the number of service gates operating at each time slot that ensure the average waiting time is less than a designated time threshold. The optimisation model is solved by the Genetic Algorithm and tested with a case study. Its effectiveness is identified by comparing the model's outcomes with observed data and other recent studies.

The results indicate that the developed truck appointment system can provide more than threefold and twofold reductions of the total cost experiencing at the terminal entrance compared to the actual data and results from previous research, respectively.

The proposed approach provides applicably coordinated truck plans and operating service gates efficiently to decrease congestion, emission and expenses.

Avoiding truck congestion and peaks in landside activity is one of the challenges to container terminal managers. The spreading of truck arrivals at terminals can be facilitated by widening the opening hours of terminals at the landside. Israel’s Ministry of Transport has instituted the “Good Night Program”, involving monetary incentives for importers and exporters who deliver containers to ports at night.

This paper aims to quantitatively examine the market utility resulting from shifting traffic from daytime to nighttime, and analyzes customer considerations regarding nighttime transportation.

The external utility found in the traffic-economics model is quite similar to the economic incentive given to customers. Therefore, a significant increase of the incentive is not feasible.

Furthermore, it seems that an incentive method by itself is not effective enough, and does not motivate customers to act and find creative solutions to the obstacles they face. To achieve a considerable change in nighttime transport to Israeli ports, more effective methods should be examined.

This paper aims to identify the level of contribution of different levels of education to remaining in unemployment as well as the transition from unemployment to employment in Egypt.

In this paper, transition probabilities matrix differentiated by gender, age groups, educational levels, marital status and place of residence based on worker flows across employment, unemployment and out of labor force states during the period 2012–2018 using Egypt Labor Market Panel Survey of 2018. The results point to the highly static nature of the Egyptian labor market. Employment and the out of labor force states are the least mobile among labor market states. This is because employment state is very desirable and the out of labor force is the largest labor market states, especially for females. Also, this study examines the impact of different educational levels separately on remaining in unemployment and transition from unemployment to employment state using eight binary logistic regression models.

The main results of transitions from unemployment to employment are relatively large for males, elder-age, uneducated workers as well as workers who are not married and urban residents, and the results of the logistic regression models consistent with the transition probabilities matrix results, except for few cases. Based on the above findings, there is enough evidence to accept the null hypothesis that no education has a positive significant impact to transition unemployed individuals from unemployment to employment, while less than intermediate as well as higher education have a negative significant impact to transition unemployed individuals from unemployment to employment.

This paper proposes to address the problem of the unemployment among highly educated which is much higher compared with illiterates and try to understand the impact of different levels of education separately on the transition from unemployment to employment, to help the policymakers to eradicate the gap between education and the demand of the labor market in Egypt.

The purpose of this paper is to develop a framework to evaluate and assess the performance of the COVID-19 vaccine distribution process that is sensitive to the unique supply-side and demand-side constraints exhibited in the US vaccine rollout.

A queuing framework that operates under two distinct regimes is formulated to analyze service rates that represent system capacity to vaccinate (under the first regime) and hesitancy-induced throughput (under the second regime). These supply- and hesitancy-constrained regimes form the focus of the present paper, as the former reflects the inherent ability of the nation in its various jurisdictions to mobilize, whereas the latter reflects a critical area for public policy to protect the population’s overall health and safety.

The two-regime framework analysis provides insights into the capacity to vaccinate and hesitancy-constrained demand, which is found to vary across the country primarily by politics and region. The framework also allows analysis of the end-to-end supply chain, where it is found that the ability to vaccinate was likely constrained by last-mile administration issues, rather than the capacity of the manufacturing and transportation steps of the supply chain.

This study presents a new framework to consider end-to-end supply chains as dynamic systems that exhibit different regimes because of unique supply- and demand-side characteristics and estimate rollout capacity and underlying determinants at the national, state and county levels.