The Money Supply Process in India: Identification, Analysis and Estimation

A new specification is employed to test for the degree of endogeneity of commercial bank credit, and its response to structural variables relevant to the Indian context. Our specification allows us to both identify money supply in a single equation, and disentangle the contribution of the Central and the Commercial Banks to the money supply process. Bank credit reacted more to financial variables and had dissimilar responses to food and manufacturing prices and output. Instead of interest rates, sectoral returns played a major role. Monetary policy broadly succeeded in preventing an explosive growth in money supply and reined in inflationary expectations. But by targeting manufacturing prices it harmed real output. The estimated structure implies that it would be more efficient to target agricultural prices for inflation control. A monetary contraction should be completed earlier than in the past, and should coincide with a rise in food prices. Information available in the systematic structural features can be exploited in designing monetary policy.Money supply endogeneity, identification, information, sectoral prices

Thinking beyond Delhi’s odd-even plan

Today the trial of the AAP’s Odd-Even plan to alleviate congestion in India’s capital comes to an end. Varun Shridhar argues that regardless of whether it is viewed as a success, it is unlikely to prove effective in the long run. He writes that as the growing economy gives rise to more and more first-time car owners, the key to reducing congestion and air pollution lies in increased public transport patronage

Finding Trends in Big City Health Issues with Data Visualization

In recent years, data visualization has become one of the most effective tools to understand and identify unseen features of the large datasets available. An open source data set available for health issues for big cities across the United States was obtained. There are numerous indicators presented in the dataset including Demographics, Chronic Health Diseases, Social and Economic Factors, Food Safety, Mortality Rates, Cancer and Life Expectancy Rates. The dataset encompassed myriad of demographics as well as specific data for a number of US cities. The data was explored in different methods in Data points in terms of the demographic data available. These data visualizations could be used to understand and identify trends for providing improvement in vital areas of public health issues faced by these populated centers. The project employed R studio software. Multiple data visualization was created and discussed in detail