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In the academic debate, the tax–growth relationship is always a controversial one. This paper aims to investigate the relationship between tax structure and economic growth in India for the period 1980-2016. After controlling for total tax revenue share to GDP in the estimation model, the authors examine the long-run and short-run relationship between tax structure and growth in India.

Auto-regressive distributed lag (ARDL) model has been used in this study. This bound cointegration model has certain advantages to the traditional cointegration model. This study also applies the threshold cointegration test of Hansen and Seo (2002) for examining non-linearity in tax–growth nexus.

The analysis shows that income tax share, corporation tax share and excise tax share are harmful to growth in the long-run. While the custom share is enlarging the growth performance. Corporation tax share is also reducing growth in the short-run. Following the Pesaran et al. (2001) approach of ARDL bound testing, the authors find the existence of a long-run relationship between studied variables. However, this study does not find any existence of threshold effect in the tax–growth relationship for India.

Based on the empirical findings, the author suggests that the prime tax change, which has the potential to impact both long-run growth and short-run economic recovery is the reduction of corporate tax rate with sustainable revenue generation. It will definitely enlarge the foreign direct investment, saving and investment in India.

This study will be a contribution to the empirical literature by investigating “tax–growth” relationship in the Indian case. To the knowledge, this will be the first study to examine this relationship for India with a recent data set.

The purpose of this paper is to investigate the effects of exchange rate volatility, oil price return and COVID-19 cases on the stock market returns and volatility for selected emerging market economies. Additionally, this study compares the market performance in the emerging economies during the COVID-19 pandemic with the pre-COVID and global financial crisis (GFC) period.

The authors apply the arbitrage pricing theory to model the risk-return relationship between the risk-based factors (exchange rate volatility and COVID-19 cases) and stock market returns. By applying the exponential generalized autoregressive conditional heteroskedasticity model, the study captures the asymmetric volatility spillover from the stock markets to foreign exchange markets and vice versa.

Findings reveal that exchange rate volatility exerts a negative and significant effect on the market returns in Brazil (BOVESPA), Chile (S&P CLX IPSA), India (SENSEX), Mexico (S&P BMV IPC) and Russia (MOEX) during the coronavirus pandemic. Regarding the effect of oil price returns, the authors find a positive relationship between oil price and stock market returns across all the economies in the study. The market returns of Russia, India, Brazil and Peru appeared more volatile during the pandemic than the GFC period.

As the exchange rate volatility is causing higher risk and uncertainty in the stock market’s performance, the central bank’s effort to maintain a stabilizing effect on the exchange rate sale can be proven crucial for the economies under consideration. Emphasized should also be given to boost investors’ confidence in the stock market, and for this, the government policy actions in reducing the transmission of the disease are the need of the hour.

While a large volume of literature on stock market performance in times of COVID-19 has emerged from developed economies, this study adds to the literature by exploring the emerging economies’ stock market performance during the COVID-19 pandemic. Unlike previous literature, this study examines the volatility spillover between stock and exchange rate markets in the worst affected emerging economies during the crisis.

The primary purpose of this paper is to empirically investigate the impact of educational attainment on crime rates across 33 Indian states over the period 2001 to 2013. This paper also examines the role of various macroeconomic, socio-economic and demographic factors in determining the variation of crimes in India.

First, this paper provides a representative theoretical model and discusses the possible relationship between crime and education. Second, the paper applies a dynamic panel data (DPD) model to extract more precise, unbiased and reliable estimates of the effect of education in abating different crime rates. The main advantage of using the dynamic panel model is to address the problem of endogeneity in some regressors and capture the time persistent effect of education on crime.

Empirical findings reveal that a 1% increase in gross enrolment ratio leads to the reduction of total crime by 8%. However, a unique finding identifies a positive association between tertiary education and economic crime. This finding further goes against the general belief that criminals tend to be less educated than non-criminals.

This paper recommends that instead of punishment and mandatory law enforcement for offenders, increase in government expenditure and different educational attainment ratios can go a long way to combat crime in India, which has posed a serious threat to the stability of society. Furthermore, utilizing the information on offenders' educational attainment in examining the crime rates can be a future research agenda for policymakers.

This study contributes to the empirical debate of ‘crime-education nexus’ by examining the role of education on crime in India. This study is the first of its kind that focuses on the aspects of crime and education more recently and investigates the relationship between crime and education due to the recent changes in educational attainment ratios and crime rate.

The main purpose of this paper is to empirically investigate the effect of macroeconomic uncertainty on environmental degradation in India over the period 1971–2016. Additionally, this paper considers the role of financial development, energy consumption intensity and economic growth in explaining the variation of environmental degradation in India.

The authors applied the power generalized autoregressive conditional heteroskedasticity model to measure inflation volatility and used it as a proxy for macroeconomic uncertainty. From a methodological perspective, the authors employ the autoregressive distributive lag bound testing model to establish the long-run equilibrium association between the variables. The Toda–Yamamoto causality approach has been used to examine the direction of causality between the variables.

Findings suggest that macroeconomic uncertainty exerts a positive effect on carbon emissions, indicating that higher inflation volatility, as a proxy for macroeconomic uncertainty, hinders India's environmental quality. Financial development, economic growth and energy consumption intensity have also adversely impacted environmental quality.

The negative association between macroeconomic uncertainty and environmental degradation calls for some stringent policy actions. While formulating policies to promote growth and maintain stability, policymakers and government stakeholders should take into account the environmental effects of macroeconomic policies. There is a need to implement more environmental-friendly technologies in the financial sector that could reduce carbon emission.

To the best of the authors' knowledge, this study is the first that considers the role of macroeconomic uncertainty along with financial development and energy intensity in an emerging economy like India.