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The purpose of this study is to examine linkage between exchange rate, stock return and interest rate for India.

Using monthly data from January 2000 to December 2014, this study has scrutinized the linkage between exchange rate, stock return and interest rate using maximum overlap discrete wavelet transform (MODWT) which is very much appropriate when the variables are discrete in nature.

Our major findings indicate that the empirical relationship between these variables is not significant at lower scales. As we go on higher scales, there is a clear linkage between them, and three markets are associated with each other. Moreover, the direction and type of the relationship depends on the frequency bands, and finally with the help of Granger causality tests, we established a lead/lag relationship between stock price, exchange rate and interest rate.

The linkage between stock market, foreign exchange market and money market in case of emerging countries like India is more relevant because negative or positive shocks affecting one market may be transmitted quickly to another through contagious effect.

Little attention has been given to examine the link between stock return, exchange rate and interest rate in India. This study adopts a more sophisticated MODWT approach for examining the cross-correlation and causality.

The purpose of this paper is to examine the convergence analysis of public debt among Indian states using annual data from 1990‒1991 to 2014‒2015.

The paper tests this hypothesis using club convergence technique propounded by Phillips and Sul (2007).

The results reveal the existence of debt divergence for overall Indian states. States are formed into four clubs on the basis of their level of debt, and three clubs support the hypothesis of club convergence. Further, the total public debt decomposes into three compositions such as market loans, bank loans and loans and advances from the central government. The existence of convergence is found for market loans and bank loans; however, the presence of divergence is found in case of loans and advances for overall states.

Since public debt plays an important role for fiscal health of the Indian states, findings of this study suggest to squeeze the fiscal consolidation further for Indian states whose debts as a percentage to gross state domestic product are on the higher side. Further, the examination of debt convergence helps to manage debt level among the states because heavy dependence on public debt could retard investment and economic growth.

Whereas bulk of empirical studies emphasize on examining the linkage between public debt and economic growth, and issue on debt sustainability across Indian states, examination of convergence of debt and its compositions (markets borrowings, bank loans and loans and advances from the central government) among the Indian states is scanty.

The purpose of this study is to examine the fiscal sustainability issue by dividing the fiscal deficit into high and low regimes using the quarterly data from 1997: Q1 to 2013: Q3. Further, we obtain the optimum level of public debt at which fiscal sustainability can be achieved.

This study uses the Markov Switching-Vector Error Correction Model (MS-VECM) for examining fiscal sustainability and threshold regression model to obtain the optimum level of debt.

The results derived from MS-VECM reveal the evidence in favor of fiscal sustainability during low fiscal deficit periods. Similarly, using a threshold regression model, the optimum public debt as a percentage to GDP seems to be around 21 per cent on a quarterly basis, beyond this level, public debt hurts economic growth.

From the policy front, the government of India should cut down the fiscal deficit only if debt reaches beyond a threshold level.

Noting that the vast literature has focused on examining the fiscal sustainability in India, the novelty of this study is to examine the fiscal sustainability by considering high and low deficits regimes using a non-linear approach.

This paper aims to investigate whether improvement in human capital can foster energy conservation by reducing the energy consumption in India using annual data from 1980 to 2014. Further, this study examines the relationship between human capital and various forms of energy consumption such as electricity, coal, natural gas, hydrocarbon gas and petroleum consumption.

To attain the objective, the study investigates this relation through the auto-regressive distributed lag model (ARDL) technique to find a long-run and short-run relationship. Second, to check the robustness of the results, the authors use alternative econometric methods such as dynamic ordinary least squares and fully modified dynamic ordinary least squares.

The results reveal a negative relationship between human capital and energy consumption, which implies that improvement in human capital lowers the energy consumption and various forms energy consumption, except for petroleum consumption. The results derived from ARDL show that there exists a long-run and short-run association between human capital and energy consumption. The results are consistent across the econometric techniques.

Because G20 countries including India aim at reducing carbon emission to a certain level, this study provides an insight that by emphasizing on human capital, India can reduce energy consumption, which would foster energy conservation.

To the best of the authors’ knowledge, this the first study in India which attempts to examine the effect of human capital on energy consumption and its various forms.

The purpose of this research is to evaluate corruption in the context of India, spanning the period between 1988 and 2021. Additionally, it aims to provide an in-depth comprehension of the factors that drive its prevalence and to propose policy directives for addressing these underlying issues.

The study instead of relying on perception-based measures, takes a distinct approach by formulating a corruption index derived from reported instances, thus ensuring a more objective assessment. Furthermore, we employ stochastic frontier analysis to tackle the issue of under-reporting within the corruption index based on reported cases. Subsequently, an auto regressive distributed lag (ARDL) methodology is applied to ascertain the principal drivers of corruption, encompassing both long and short factors.

This study reveals that corruption in India is notably influenced by economic growth and income inequality. Conversely, government effectiveness and globalization display a tendency to mitigate corruption. However, our rigorous analysis demonstrates that financial development does not wield a substantial influence in our study. Moreover, our inquiry uncovers a nonlinear relationship between economic growth and corruption. Additionally, we ascertain that the long run and short run impacts of corruption remain relatively stable across both models utilized in our study.

This study differs from previous research in the subsequent manners. Primarily, we employed an objective measure to formulate the corruption index, coupled with addressing the underreporting issues via stochastic frontier analysis. Moreover, this study pioneers the identification of a non-linear relationship between corruption and economic growth within the Indian context, a facet unexplored in previous investigations.

The purpose of the paper is to examine the impact of exchange rate misalignment on economic growth in India using annual data from 1980 to 2014.

First, misalignment is measured, which is defined as the deviations of the actual real exchange rate (RER) from its equilibrium level. The equilibrium real exchange rate (ERER) is estimated using the auto-regressive distributed lag (ARDL) model by considering key macroeconomic fundamentals of the determinants of RER. Zivot and Andrews’ unit root with structural break is used to test the stationarity property of data. The impact of exchange rate misalignment on economic growth has been examined using ARDL and variance decomposition techniques.

Our results find an overvaluation of the exchange rate till 2000, and thereafter, an undervaluation of the exchange rate prevails in India. Further, the result indicates that an increase in exchange rate misalignment leads to a decrease in economic growth and vice versa. Moreover, a positive misalignment (overvaluation) hurts the economic growth and a negative misalignment (undervaluation) promotes the economic growth.

From the policy perspective, the results highlight that India needs to maintain an appropriate exchange rate which can reduce the RER misalignment. It is better for the Reserve Bank of India (RBI)’s intervention to smoothen the fluctuations of the exchange rate to avoid the inefficiency in the allocation of resources. However, to minimize the RER misalignment, the intervention should be conducted only in the short run.

The study contributes to the existing literature by estimating the exchange rate misalignment for India and its impact on economic growth.

This paper aims to examine the relationship between global value chains (GVCs) and domestic value-added content (DVA) in a panel of 58 countries for the period 2005–2015.

First, the authors quantify the refined measures of GVC linkages by using the Borin and Mancini (2019) decomposition technique. Second, the authors apply the feasible generalised least squares method to test the relationship between GVCs and DVA empirically.

First, the authors find that GVC links are crucial to the enhancement of DVA. Second, a study at the sectoral level reveals that GVC links in the primary sector raise DVA whilst reducing DVA in the services sector. Third, the authors find that only upstream activities enhance value-added content. Fourth, the authors note the augmenting role played by national policies in mediating the gains associated with GVCs. Finally, the authors note that the outcomes associated with GVCs are consistent when the sample of countries is divided into groups based on income.

The results lead us to urge policymakers to promote greater integration of business activities into GVCs to reap their benefits.

This paper contributes to the research on the impact of GVCs on DVA by emphasising the significance of the types of GVC activities and policies that improve DVA.

This paper investigates the per-capita output club convergence in case of 120 countries for the period 1995–2015. Further, we disaggregate per-capita output into three broad sectors such as agriculture, industry, and service and investigate the convergence hypothesis.

The paper tests this hypothesis using the Phillips and Sul panel club convergence technique.

Our findings are as follows: (1) our results indicate the evidence of output divergence for the full sample; (2) when countries are divided into different clubs, the results exhibit the sign of per capita output club convergence both for aggregate and three major sectors. Further, this study confirms that industry's per capita output is the main driver for aggregate per-capita output club convergence in case of club 1. For club 2, agriculture's per capita output is a primary source for aggregate per capita output club convergence. Likewise, in the case of clubs 3 and 4, we find the service sector's per capita output is the main component for aggregate per-capita output club convergence; (3) both the service and industry sectors are major drivers for aggregate per-capita output club convergence.

This study suggests to the policymaker that sector-specific policies need to be adopted to boost the per-capita output growth by improving the performance of each of the sectors across the countries.

Notwithstanding, there are many studies that examine the output convergence using a notion of beta and sigma convergence, but studies regarding per capita output club convergence both at the aggregate and sectoral level are scanty.

The purpose of this paper is to examine whether innovation plays a significant role in the total factor productivity (TFP) growth in India at an aggregate level.

This study first estimates the TFP growth using a growth accounting framework. In the second stage, the authors examine the long-run and short-run impact of innovation on TFP growth using the ARDL bound testing approach.

The results indicate a cointegrating relationship between innovation and TFP growth. Further, coefficients of long-run elasticity show that the increase in overall innovation activities improves the TFP growth. Other factors such as human capital, financial development and FDI do not affect the TFP growth in the long run; however, these variables significantly affect the productivity growth in the short run.

Findings of the study suggest that the innovation-friendly policies such as the strengthening of intellectual property rights, R&D subsidies and innovation rebates may spur the productivity growth, and hence, good growth and prosperity as well.

Having devoted a large volume of literature to address the sources of economic growth, the present study focuses on the determinants of TFP growth in India which may fall in similar category but differ in several angles: First, the authors construct a TFP index using a growth accounting framework. Second, the authors construct an innovation index using principal component analysis which is new to the literature and also an innovation index. Third, given the scanty innovation activities in low developed countries like India and its widening role in the contemporary literature, special emphasis will be given to this aspect. Finally, the effect of the examined relationship on TFP growth in the long run and short run provides several implications for policy purpose to the developing nations like India.

The purpose of this paper is to measure and investigate the determinants of corruption across Indian states.

This research begins by developing a corruption index (CI) based on official data on corruption cases. Second, the authors also create an adjusted corruption index (ACI) using the stochastic frontier modelling approach to address corruption case under-reporting. Third, they use a panel feasible generalised least square (FGLS) technique to discover corruption determinants.

The results show that approximately 77% of corruption cases in India go under-reported, which is a major concern. The results also show that per capita income, government spending, law and order and urbanisation are the important factors affecting corruption at the regional level.

The findings of the study emphasise the need to address the huge under-reporting of corruption data. From a policy perspective, the governments need to emphasise factors like per capita income, government spending, law and order and urbanisation to tackle corruption in India.

Corruption is a complex global phenomenon, and several studies have conducted detailed research on the causes of corruption at all levels (regional and cross national), but this study differs from previous studies in the following ways. First, the authors used a more objective measure of corruption by developing a CI at the state level. Second, the study uses stochastic frontier analysis, which is novel in the literature on corruption analysis, to address the most critical component of under-reporting in corruption data. Finally, the study attempts to examine the factors of corruption at the regional level, which is again innovative in nature.