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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 present study aimed to examine the relationship between information and communication technologies (ICT) diffusion, financial development and economic growth in the panel of developing countries for 2005–2019.

The study employed the principal component analysis (PCA) to extract the index of ICT diffusion. First-generation panel unit root tests such as Levine Lin Chu (LLC), Im Pesaran Shin (IPS), Augmented Dickey-Fuller (ADF) and Phillips and Perron (PP) were employed to check the stationarity of the variables. Pedroni and Kao co-integration techniques were used to examine the existence of the long-run relationship, and co-integration coefficients were estimated using FMOLS and dynamic ordinary least squares (DOLS). The panel Granger causality approach examined the short-run and long-run causality.

The results confirmed that ICT diffusion, financial development and trade openness accelerate growth, whereas inflation dampens economic growth. Further, the causality test showed bidirectional causality between ICT growth and financial development growth but a unidirectional causality from financial development to ICT diffusion in developing countries.

The study recommends synchronizing public and private sector investment for a synergistic effect on ICT infrastructure and adequate investment in the financial sector to increase the growth rate in developing countries. Economic policies should be adopted toward incentives and subsidies to ensure affordable ICT services for disadvantaged communities. Also, training programs focussing on enhancing digital literacy to enable all segments of the population to use digital platforms for financial services are recommended.

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 main purpose of this paper is to analyze the role of information and communication technology (ICT) diffusion in women empowerment and in fostering the process of achieving the Sustainable Development Goals (SDGs) in South Asian Association for Regional Cooperation (SAARC) countries using panel data from 2005 to 2020.

An ICT diffusion index was constructed using principal component analysis (PCA). Further, the study uses econometric techniques robust to cross-sectional dependence (CSD) which include Pesaran's CSD tests, second-generation unit root test, Pedroni, Kao, Westerlund cointegration test, FMOLS, DCCE, Driscoll–Kraay (DK) regression, and D&H causality tests.

ICT diffusion and economic growth have a significant and favorable impact on women's empowerment. However, fertility rates and trade openness harm women's empowerment. In addition, the causality test results depict a bidirectional causal relationship between ICT and women empowerment and between growth and women empowerment. In addition, unidirectional causality is detected between education and women's empowerment. Overall, the findings indicate that expanding ICT and bridging the digital divide, particularly among women, can be effective in achieving empowerment-related SDGs.

To date, there are hardly any studies in SAARC context that empirically evaluate the link between ICT, women empowerment, and the issue of sustainability in a unified framework. Therefore, this study is unique in terms of conceptualization and methodological robustness in this context. The study will benefit policymakers and regulatory bodies to formulate appropriate policies to empower women and thereby attain the SDGs by 2030.

The present study examines the significance of financial inclusion in reducing income inequality in the Asian context.

This study uses panel estimation techniques such as the Pedroni cointegration test, Kao residual-based test, FMOLS, ARDL and Granger causality, a dataset consisting of the Gini coefficient index, three dimensions of financial inclusion measures and one added variable on financial depth, spanning from 2005 to 2019.

The study finds that in the long-run, income inequality disparity is highly influenced by financial inclusion indicators, such as the number of bank branches, deposit accounts, outstanding loans and domestic credit to the private sector. Whereas in the short run, disparities in income are unaffected by all the indicators of financial inclusion. Further, unidirectional causality from financial inclusion indicators to income inequality necessitates the need for policymakers to design policies and programs that would enhance access to financial services as an essential mechanism to reduce income disparity.

Studies based on a panel of Asian countries that have undergone impressive growth of financial inclusion initiatives since the past decade—but are still facing widening income inequality—are conspicuously rare in the literature. The empirical analysis fills this void by showing the significant role financial inclusion indicators play in steering the Asian economies toward income equality throughout the study period.

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.

While sustainable growth extends the use of resources, it is crucial to explore green growth (GG) that ensures growth sustainability through the adoption of renewable energy. Thus, this study is motivated to investigate the influence of renewable energy on GG in 19 emerging countries spanning a decade and a half (2000–2020). This study aims to provide a quantitative examination of how renewable energy contributes to sustainable economic growth.

This study uses advanced dynamic common correlated effect techniques to assess the long-term effectiveness of renewable energy on GG. Additionally, it uses Dumitrescu and Hurlin causality tests to identify synchronicity between the respective variables.

The findings of this study reveal that the adoption and utilisation of renewable energy effectively promote GG in emerging economies. However, in contrast, the significantly greater negative influence of trade openness on GG compared to renewable energy highlights the inadequacy and limited impact of cleaner energy alone.

To the best of the authors’ knowledge, existing literature predominantly focuses on investigating the relationship between renewable energy and economic growth, with only a limited number of studies exploring the impact on GG. To the best of the authors’ knowledge, this study would be the first to analyse this relationship in these emerging countries. Furthermore, previous estimation frameworks used in prior studies often overlook the crucial factor of cross-sectional dependence (CSD) among countries. Therefore, this study addresses this issue using a contemporary econometric approach that deals not only with CSD but other biases, like endogeneity, autocorrelation, small sample bias, etc.

This study aims to predict the Indian stock market (Nifty 50) by employing macroeconomic variables as input variables identified from the literature for two sub periods, i.e. the pre-coronavirus disease 2019 (COVID-19) (June 2011–February 2020) and during the COVID-19 (March 2020–June 2021).

Secondary data on macroeconomic variables and Nifty 50 index spanning a period of last ten years starting from 2011 to 2021 have been from various government and regulatory websites. Also, an artificial neural network (ANN) model was trained with the scaled conjugate gradient algorithm for predicting the National Stock exchange's (NSE) flagship index Nifty 50.

The findings of the study reveal that Scaled Conjugate Gradient (SCG) algorithm achieved 96.99% accuracy in predicting the Indian stock market in the pre-COVID-19 scenario. On the contrary, the proposed ANN model achieved 99.85% accuracy in during the COVID-19 period. The findings of this study have implications for investors, portfolio managers, domestic and foreign institution investors, etc.

The novelty of this study lies in the fact that are hardly any studies that forecasts the Indian stock market using artificial neural networks in the pre and during COVID-19 periods.

The aim of this paper is to analyse the spillover effects of microcredit on the economy of Ecuador, with a particular focus on its potential as a poverty alleviation mechanism.

To address our research questions, we take into account the distance between cantons (Ecuador’s own administrative distribution) by adopting a spatial autoregressive (SAR) model. To this end, a database will be constructed with macroeconomic information about the country, broken down by canton (administrative division of Ecuador), and in a 2019 cross section, with a total of 1,331 microcredit operations in all 221 of Ecuador’s cantons.

We find a positive effect of microcredit on these neighbouring regions in terms of wealth generation.

We acknowledge that this study is limited to Ecuadorian cantons. Nonetheless, it is crucial to emphasize that focussing on an under-represented developing country like Ecuador adds significant value to the research.

Facilitating access to microcredit is one of the main solutions to address the goals proposed in the sustainable development goals (SDGs).

Microcredit activity contributes to the creation of value and wealth in Ecuador, exerting a spillover effect in neighbouring areas that can generate positive multiplier effects and alleviate poverty. For all of the above reasons, our proposal for the country is to support and promote microcredit as one of the main solutions to address the goals proposed in the SDGs.

The novelty of this study lies in the use of spatial econometrics to observe the indirect effects of microcredit on the regions bordering the canton in which it was issued, thus examining the spatial effects of microcredit on wealth distribution.