We focus on the timely issue of Content Delivery Network (CDN) resource management. We introduce a multi-stage scheme that leverages solutions from the capital market. The mechanism of Stock Options (SOs) is used to address a potential scarcity of resources, not adequately addressed by other predictive mechanisms previously introduced by the authors. Using a Predictive Reservation Scheme (PRS), network resources offered by a CDN are monitored through established techniques (Kernel Regression Estimators) in a given time frame. Next, a Secondary Market (SM) significantly reduces resource waste by allowing the fast exchange of unused (remaining) resources granted to Origin Servers (OSs). This exchange occurs either by implementing socially optimal practices or by allowing automatic electronic auctions at the end of the day (EoD) or shorter time intervals. Finally, we further enhance our Load Prediction Mechanism (LPM); SOs are purchased and exercised, depending on the lack of resources at the EoD. As a result, OSs may acquire resources (if required) at a standard price. The effectiveness of the proposed stock market-based CDN resource management framework further improves.

This paper investigates the extent to which the structure of the interaction network between suppliers and buyers affects equilibrium price heterogeneity. An incomplete interaction structure leads to uneven information flows and different information bases for consumers which is then taken into account by price setting producers. This results in heterogenous prices even if producers are identical in all other respects. The complete interaction network serves as a special case resembling standard monopolistic competition models. We show that a slight deviation from the complete network results in heterogeneous prices, although this heterogeneity becomes economically significant only under sparse or small networks. Sparsity as the main determinant of price heterogeneity dominates network asymmetry: relatively dense networks show minimal price dispersion even if its degree distribution follows a power law.

This paper adds some formal research to the success of ongoing efforts to combat the COVID-19 pandemic by examining the drivers of the administration and delivery efficiency of coronavirus vaccines. For this purpose, we use data from the 50 US states and place the formal analysis in the context of socio-economic drivers of vaccinations. Results show that state-economic prosperity and rural population aid vaccine administration and delivery efficiency. Delivery efficiency improves in states with more nursing homes per capita, in states with more COVID-19 deaths, and with more health workers. A subset of health workers, including physicians and nurses, did not significantly impact administration or efficiency. On the other hand, vaccination efficiency was lower in states with a centralized public health agency. States with a larger share of the elderly population and those with Democrats as governors were no different from others with regard to vaccinations. Robustness checks are performed using vaccination data from a more recent period. Finally, a state’s legacy of corrupt activity, across two different time dimensions, is broadly consistent with the greasing effects of corruption. Some policy implications based on the evolving data are discussed.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11066-021-09148-w.