the authors, Sakshi Malik and Cheshta Kapuria, have analyzed empirically the impact of globalization and other macroeconomic variables on the quality of a country's governance using panel Ordinary Least Squares (fixed effects and random effects) and two-step system Generalized Method of Moments (GMM) on a panel dataset of 14 emerging Asian countries for the period 2005-2017. The results of the study indicate that social and political globalization, urbanization, and information and communication technology infrastructure influence governance favorably, while per capita growth and government consumption have no significant impact on the country's level of governance. The study also finds that social globalization coupled with economic globalization can lead to an improved level of governance. The study highlights that the improved quality of governance can be attained by blending cultural integration with capital flows.

In the second paper, "An Analysis of Political Transfers from the Center to the States in India", the authors, J S Darshini and K Gayithri, have examined the level and changing trends in the process of states' dependency on central transfers and also the economic and political factors influencing the flow of conditional or discretionary transfers to the 14 major Indian states using static panel model for the period 1981-82 to 2014-15. The study reveals that the successive Finance Commissions have gradually enhanced the share of states in the centralized divisible pool over a period of time. The study also reveals the overall empirical outcome that states with a larger fiscal space and Gross State Domestic Product (GSDP) growth were able to get more funds relative to the political factors during the first and third sub-periods. The study highlights that both the economic and political factors together influence the flow of discretionary transfers among states in India. In the third paper, "On the Informational Efficiency of the Cryptocurrency Market", the authors, Anoop S Kumar, Thota Nagaraju and Taufeeq Ajaz, have analyzed the weak form informational efficiency in the cryptocurrency markets using Cryptocurrency Market Index (CRIX) data. The statistical tests used in this study indicate evidence in support of weak form efficiency. The results of the proposed Adaptability Index (AI) reveal that cryptocurrency markets are adaptive in nature. The study also reveals that cryptocurrency markets exhibit fractal structure.

In the last paper, "Passenger Demand Forecasting in the Ridesharing Context: A Comparison of Statistical and Deep Learning Approaches", the author, Sanjay Fuloria, has forecasted the passenger demand in the ridesharing services in the short run, using exponential smoothing, multiple regression, and a neural network-based algorithm Long Short-Term Memory (LSTM). The study finds that LSTM models perform better in forecasting both for training and validation datasets. The study also reveals that multiple regression performs better than exponential smoothing in training set. In fact, multiple regression method is on a par with LSTM. The study highlights that overall LSTM performs better than the other two methods and could be used for demand forecasting for cabs in the platform economy.