The study examines the nexus between financial development and economic growth in India during Q1: 1996 to Q3: 2018. This study employs time-series data of real GDP and ratio of broad money to GDP as a proxy for economic and financial development, respectively. The data are obtained from RBI database on the Indian economy. All variables are seasonally adjusted using X12-arima technique and expressed in natural logarithm form. Non-linear Autoregressive Distributed Lag (NARDL) bound test has been used to check for cointegrating relationship of these two variables. Empirical findings suggest that, unlike in the short run, in the long run financial development does impact economic growth positively. Further, a symmetric effect of positive and negative components of financial development is found for the Indian economy, whereas the effect of control variable like exchange rate and trade openness is in consonance with common economic intuition. Exchange rate is in consonance with intuitive economic logic that a fall in exchange rate makes exports cheaper and increases the quantity of export, which improves the balance of payment and leads to a rise in aggregate demand, hence improves economic growth. This paper contributes to the existing literature on India by breaking down financial indicator into positive and negative components to examine the finance-growth relationship.

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The present study seeks to examine the impact of microfinance and other socio-economic factors on women empowerment as viewed from their participation in decision making, income as well as employment generation activities. The findings of the study based on a field study conducted in two villages of Andhra Pradesh suggest that factors such as member and her husband's income, family size and frequency of Self help group meetings have overall positive influence on women empowerment. In case of income and employment generation activities, age and income of Self help group members, their household income and amount of loan are found to have positive effect.Jel Codes: D20; G34; L10

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This study aims to analyse the effectiveness of the lockdown measure taken to control the transmission of COVID-19 in India by examining the peak of the epidemic pre and post the adoption of stringent lockdown from 25 March 2020. Susceptibleexposed-infectious-recovered (SEIR) model has been developed to trace the peak of the outbreak. The study suggests that with the implementation of lockdown the peak of epidemic in India has delayed by two and a half month. Before lockdown peak was examined in end of May 2020 but post lockdown, it is expected to arrive in mid-August 2020. Thus lockdown measures has delayed the arrival of peak of epidemic which would be helpful in preparing the healthcare system in advance, to tackle worst situation if arises in future.

A structural vector autoregression (SVAR) model is proposed for analysing the impact of monetary policy stances on real variables in the Indian economy, in the context of its continuous exposure to global factors like oil price shocks and changes in global financial health. The empirical findings based on monthly data relating to the post-liberalisation period (April 1992-December 2012 suggest that contractionary monetary policy has had a considerable adverse impact on output for a year, which appears to be consistent with the prevailing economic outlook. But the same policy measure fuels inflation further for the first eight months in contrast to its expected decline, and the rise in inflation raises the issue of a 'price puzzle' in the Indian context. Monetary policy-induced exchange rate appreciation hampers industrial production due to outperformance of high import demand over export competitiveness and causes an increase in the average price level. Global factors like oil price shocks leave detrimental effects on output for a long time horizon of 10-12 months and raise inflation for nearly half a year, whereas an increase in the US federal funds rate results in a temporary decline in output growth of Indian industries.

This paper is an empirical attempt to assess the relations among business cycles, financial cycles, and economic uncertainty for India during the period January 2003 to January 2020. Empirical findings from the vector autoregression model suggest a bidirectional causal relationship between the real and financial markets. Further, the aforementioned markets are found to influence the uncertainty index in India. We employ the Granger causality test in the frequency domain which exhibits the dynamic nature of causal relations, while our novel fractal connectivity approach reveals the long‐run correlation structure among all three variables.

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