The direct and indirect effects of transportation infrastructure have a positive impact on the economic growth and development of a country. Apart from improving accessibility, infrastructure development brings along trade and investment opportunities to the previously unconnected regions. It also provides access to goods, services, and employment opportunities in these regions through the multiplier effect. In this paper, a panel of data is employed using the unit root, cointegration, and Granger Causality (GC) model to test whether causal linkages between economic growth and transportation infrastructure exist at national and provincial level. The findings suggest that in the short run, there is no causality between the two variables at the national level, however, a unidirectional causality from economic development to infrastructure investment exists in the long run. At the provincial level, bidirectional causality in the rich and much developed provinces exists, whereas a unidirectional GC exists from economic growth to transportation infrastructure in the underdeveloped provinces. The outcomes of the model shed light on the fact that infrastructure investment per se is not sufficient to boost the economic activity in the underdeveloped regions of Pakistan. A cointegrated investment package is needed, targeting not only infrastructure but also the social and technological development, which can help these regions to realize the promotion of economic growth in the long run.

The Coronavirus (COVID-19) outbreak has become one of the biggest threats to the global economy and financial markets. This study aims to analyze the effects of COVID-19 on 56 global stock indices from October 15, 2019 to August 7, 2020 by using a complex network method. Furthermore, the change of the network structure is analyzed in depth by dividing the stock markets into developed, emerging and frontier markets. The findings reveal a structural change in the form of node changes, reduced connectivity and significant differences in the topological characteristics of the network, due to COVID-19. A contagion effect is also identified in the network structure of emerging markets, with the nodes behaving synchronously. The findings also reveal substantial clustering and homogeneity in the world stock market network, based on geographic positioning. Besides, the number of positive correlations in the global stock indices increased during the outbreak. The stock markets of France and Germany seem to be the most relevant developed markets, while Taiwan and Slovenia have this relevance in emerging and frontier markets. The findings of this study help regulators and practitioners to design important strategies in the light of varying stock market dynamics during COVID-19.

We study the structural properties of Pakistan railway network (PRN), where railway stations are considered as nodes while edges are represented by trains directly linking two stations. The network displays small world properties and is assortative in nature. Based on betweenness and closeness centralities of the nodes, the most important cities are identified with respect to connectivity as this could help in identifying the potential congestion points in the network.

The structure and properties of public transportation networks have great implications in urban planning, public policies, and infectious disease control. This study contributes a weighted complex network analysis of travel routes on the national highway network of Pakistan. The network is responsible for handling 75 percent of the road traffic yet is largely inadequate, poor, and unreliable. The highway network displays small world properties and is assortative in nature. Based on the betweenness centrality of the nodes, the most important cities are identified as this could help in identifying the potential congestion points in the network. Keeping in view the strategic location of Pakistan, such a study is of practical importance and could provide opportunities for policy makers to improve the performance of the highway network.