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The main aim of this study is to explore the role of multi-dimensional human capital on the economic growth of the Indian economy.

The study used the methodology given by World Bank, 2018) in calculating the human capital index (HCI). The HCI has been constructed at a regional level for all 28 Indian states and 8 Union Territories (UTs) for the period of 2015–2016. The study explored the linkages between HCI and per capita gross state domestic product (PGSDP). The study further employed OLS (Ordinary Least Square) for overall significance and Spearmen’s Rank correlation coefficient test for establishing the linkage between HCI and PGSDP.

The results indicate that quality education, expected year of schooling, and infant mortality rate play a significant role in the improvement of HCI which further impacts the productivity rate of the upcoming generation and the inclusive growth of the country. The findings show that Mizoram, Chandigarh and Kerala are better performing states while the Bihar and Uttar Pradesh are the worst performers. The results also show that there is a positive and statistically significant correlation between PGSDP and HCI and its components. Further, the results show that public expenditure on health and education has significant effect on HCI.

The results of this study would be useful for policymakers to identify the determinants and improve the position of Indian states in HCI. The results show that policymakers should focus on quality education and health to improve the productivity of future generation workers for sustainable and inclusive growth.

The study is the pioneering study to analyze the state-wise HCI in India using methods mentioned by the World Bank. Unlike previous studies, variables such as expected year of schooling, under-5 mortality rates and survival rates are constructed more pragmatically.

The main purpose of the present research is to explore the possible effectiveness of information and communication technology (ICT), infrastructure development, exchange rate and governance on inbound tourism demand using time series data in India.

The stationarity of the variables is checked by using the ADF, PP and KPSS unit root tests. The paper uses the Bayer-Hanck and auto-regressive distributed lag (ARDL) bounds testing approach to cointegration to examine the existence of long-run relationships; the error-correction mechanism for the short-run dynamics and the vector error correction method (VECM) to test the direction of causality.

The findings of the research indicate the presence of cointegration among the variables. Further, long-run results indicate infrastructure development, word-of-mouth and ICT have a positive and significant linkage with international tourist arrivals in India. However, ICT has a positive and significant effect on tourist arrivals in the short run as well. The VECM results indicate long-run unidirectional causality from infrastructure, ICT, governance and exchange rate to tourist arrivals.

This study implies that inbound tourism demand in India can be augmented by improving infrastructure, governance quality and ICT penetration. For an emerging country like India, this may have far-reaching implications for sustaining and improving tourism sector growth.

This paper is the first of its kind to empirically examine the impact of ICT, infrastructure and governance quality in India using modern econometric techniques. Inbound tourism demand research aids government and policymakers in developing effective public policies that would reposition India to gain from a highly competitive global tourism industry.

The purpose of this paper is to examine the relationship between tourism sector development and poverty reduction in India using annual data from 1970 to 2018. The paper attempts to answer the critical question: Is tourism pro-poor in India?

Stationarity properties of the series are checked by using the ADF unit root test. The paper uses the Auto Regressive Distributed Lag (ARDL) bound testing approach to cointegration to examine the existence of long-run relationships; error-correction mechanism for the short-run dynamics, and Granger non-causality test to test the direction of causality.

The cointegration test confirms a long-run relationship between tourism development and poverty reduction for India. The ARDL test results suggest that tourism development and economic growth reduces poverty in both the long run and the short run. Furthermore, inflation had a negative and significant short-run impact on the poverty reduction variable. The causality test confirms that there is a positive and unidirectional causality running from tourism development to poverty reduction confirming that tourism development is pro-poor in India.

This study implies that poverty in India can be reduced by tourism sector growth and price stability. For a fast-growing economy with respect to economic growth and tourism sector growth, this may have far-reaching implications toward inclusive growth in India.

This paper is the first of its kind to empirically examine the causal relationship between tourism sector development and poverty reduction in India using modern econometric techniques.

The study aims to analyze the gender-wise perception of the agricultural households toward climate change and the adaption measures taken by these households, especially women, to mitigate climate changes.

Purposive random sampling technique is used to collect primary data from a pilot survey conducted in two semi-arid districts of Rajasthan, India. Data mainly focused on analyzing the gender-based perception and adaptation strategies undertaken toward climate change. And descriptive statistics are used for analysis.

The study found that both the gender are aware of the climatic changes. Deforestation increased population, change in living standards, urbanization and industrialization contribute to climate changes. The women are employing limited adaptation strategies to mitigate the climatic stress compared to males.

This is a pilot study; hence, it has an insufficient sample size for the detailed statistical analysis. Further, it is only limited to two semi-arid districts of Rajasthan.

This pioneering study highlights gender-wise differences in perception and adaptation strategies undertaken in this region. The study suggests raising awareness about climate change and providing credit facilities for undertaking adaptation measures to reduce agricultural households' vulnerability, particularly enhancing women's adaptive capacity to climate change.

The purpose of this paper is to assess the sustainability of current accounts for five major South Asian economies, namely, India, Pakistan, Bangladesh, Sri Lanka and Nepal, for the period 1985–2016.

The study employs the intertemporal solvency model of Hakkio and Rush (1991) and Husted (1992). Autoregressive Distributed Lag bounds test, Gregory and Hansen’s test and Carrion-i-Silvestre and Sanso’s test are used to assess the cointegration between current account inflows and outflows. The coefficients of long-run relationship are obtained using dynamic ordinary least squares. Besides the econometric investigation, the study also examines some other indicators such as the composition of current account, size of external debt, etc., to shed further light on the sustainability of current accounts.

The study finds support for the long-run relationship between the current account outflows and inflows for all the countries. The estimates of slope coefficient indicate strong sustainability in case of India, Bangladesh and Nepal, whereas weak sustainability holds for Sri Lanka and Pakistan underscoring the need for policy interventions. In a comparative perspective, the current accounts in India, Nepal and Bangladesh conform more to a sustainable behavior in terms of the size of deficits, external debt stock and compliance to the intertemporal budget constraint.

The study employs econometric techniques allowing for structural breaks in the assessment of current account sustainability. Besides using the intertemporal model, the study also examines factors such as composition of current accounts, size of external debts, etc., to evaluate sustainability.

The main purpose of the present research is to analyze the relationship between technological development, financial development and economic growth in India in a non-linear and asymmetric framework.

The study employs the nonlinear autoregressive distributed lags model (NARDL) and Hetemi J asymmetric causality tests to explore nonlinearities in the dynamic interaction among the variables. The stationarity properties of data are checked by using Ng–Perron and ADF structural break unit root tests. The unit root test confirms that the variables are non-stationarity in level and are differenced stationary.

The study finds that there is a cointegrating relationship between technological development, financial development and economic growth in the long run. The findings suggest that a positive shock in technological development increases economic growth (coefficient value 1.497 at 1% significance level) and a negative shock will harm economic performance (coefficient value −0.519 at 1% significance level). A long-term positives shock in financial development boosts the economy (coefficient value 1.08 at 5% significance level) and negative shock hampers the economic performance (coefficient value −1.09 at 5% significance level). The asymmetric causality test result confirms bi-directional causality between technological development and economic growth and unidirectional causality from negative economic growth to negative technological development and bi-directional causality between economic growth and financial development, unidirectional negative financial development to economic growth.

The results of this research can significantly facilitate stakeholders and policymakers in devising short-term as well as long-term policies for financial development and technological innovation to achieve sustainable long-run economic growth in India.

This paper is the first of its kind to empirically examine the cointegrating and causal relationship between technology, financial development and economic growth in India using non-linear asymmetric cointegration and causality tests.

The main purpose of this paper is to examine the role of population aging in determining the health care expenditure (HCE) in India over the period 1981 to 2018.

While establishing the linkage between population aging and HCE, the study has used economic growth, urbanization and CO2 emissions as control variables and used the autoregressive distributed lag (ARDL) approach to cointegration and VECM based Granger causality approach to estimate both the long-run and short-run relationships among the variables.

The results of the ARDL bounds test showed that there is a stable and long-run relationship among the variables. The long-run and short-run coefficients reveal that population aging and income per capita exert a statistically significant and positive effect on per capita HCE in India. The VECM causality evidence shows that there is a presence of short-run causality from economic growth and population aging to per capita HCE, urbanization to environmental degradation and further from aging to urbanization. However, the long-run causality evidence confirms unidirectional causality from population aging to the per capita HCE.

The research findings could be improved by considering the changes in mortality rate over time because of other environmental factors such as air pollution, among others as control variables. Various other variables affecting the health of an aged person could be considered for better research outcome which is not included in the present study because of the paucity of data. However, the present research findings would certainly serve effective policy instrument aiming at maximizing health gains that are highly associated with the elderly population and economic growth towards achieving sustainable development in India.

The uniqueness of the present study lies in its estimation where the relationship between population aging and HCE is looked at while considering the impact of other environmental factors separately. The causal relationship is shown among the variables using updated econometrics time-series techniques. The study tried to resolve the ambiguity associated with the relationship between aging and HCE at a macro level.

The present study aims to construct and compare Composite Environmental Sustainability Index (CESI) for 20 emerging countries for the period 1990–2020.

The study constructs CESI using the principal component analysis (PCA). Furthermore, for the preparation of index weights, varimax rotation is used to get component loadings.

The study finds that the overall CESI values lies between 2 and 4.8 for the 20 emerging countries considered in the study. This study depicts a diverse picture of environmental sustainability among emerging countries. The study also shows the trend of CESI values from 1990 to 2020. The bottom three countries whose CESI is very low compared to others are Iran, South Africa and Saudi Arabia. However, Brazil, Columbia and Chile are top three highest scorers in 2020.

The study contributes to the literature by constructing a composite index comprising of three sub-indices to measure the environmental sustainability of an economy. These sub-indices include seven indicators that are more inclusive and comprehensive. To the authors' knowledge, this is a pioneering attempt in the construction of the index for emerging countries.

The purpose of this paper is to examine the nexus among economic growth, nonrenewable energy consumption and renewable energy consumption in India over the period 1971-2017.

This study uses nonlinear autoregressive distributed lags model and asymmetric causality test to explore nonlinearities in the dynamic interaction among the variables.

The findings indicate that the impact of nonrenewable energy consumption and renewable energy consumption on the economic growth is asymmetric in both long run and short run. In long run, a positive shock in nonrenewable energy consumption and renewable energy consumption exerts a positive impact on growth. However, the negative shocks in nonrenewable energy consumption produce larger negative effects on the growth. The results of nonlinear causality test indicate a unidirectional causality from nonrenewable energy consumption and renewable energy consumption to economic growth and thus support “growth hypothesis” in context of India.

The findings imply that policy measures to discourage nonrenewable energy consumption may produce deflationary effects on economic growth in India. Further, the findings demonstrate the potential role of renewable energy consumption in promoting economic growth.

To the best of the authors’ knowledge, this study is the first attempt to explore nonlinearities in the relationship between economic growth and the components of energy consumption in terms of renewable and nonrenewable energy consumption.

The purpose of the present study is to examine the inter-relationship between the multi-dimensional poverty and water poverty using household level data for Indian states.

A modified water poverty index (MWPI) for both rural and urban households was created using the five components approach and various quantifiable proxies. Principal component analysis (PCA) has been used for the construction of MWPI. Multidimensionality of poverty (MPI) is measured using the Alkire and Foster methodology. Further, the study has utilized correlation and Tobit regression analysis to show the relationship between MWPI and MPI.

The empirical findings suggest that there is a positive and significant relationship between multidimensional poverty and water poverty, with the extent of relationship being greater in rural areas. The results show that in rural areas all the components of water poverty has significant impact on multidimensional poverty, whereas in urban areas except use component all others have significant impact on multidimensional poverty. Further, components of multidimensional poverty were also found to be significantly impacting water poverty.

The study suggests that policymakers cannot treat both forms of poverty in isolation. If India aims to reduce poverty, then it needs to pay significant attention to improving water conditions.

This is a pioneering attempt to construct water poverty index at the household level while accounting for micro-level differences for Indian economy. It highlights that water poverty leads to multi-dimensional poverty and vice-versa.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-12-2021-0731.