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This paper aims to empirically examine the influence of macroeconomic and socioeconomic factors on improving financial inclusion in India, with a specific focus on two distinct indicators of financial inclusion.

This study has used a time-series data set covering the years 1996 to 2022, using a nonlinear autoregressive distributed lag methodology. This approach allows for the examination of both short- and long-run effects of key macroeconomic and socio-economic indicators, including GDP per capita growth, remittance inflows and the income share held by the lowest 20% of the population on the growth of two financial inclusion indicators: the number of commercial bank branches and ATMs per 100,000 adults.

Model-1 investigates how commercial bank branch growth affects financial inclusion. Positive remittance inflow growth and a rise in the income share of the bottom 20% both lead to increased financial inclusion in both the short and long term, with the effects being more pronounced in the long run. Conversely, negative effects of remittance inflow growth and a decline in GDP per capita growth lead to reduced financial inclusion, primarily affecting the long run. Focusing on ATM growth, Model-2 reveals that positive remittance inflow growth has the strongest impact on financial inclusion in the short term. While income share growth for the bottom 20% and GDP growth also positively influence financial inclusion, their effects become significant only in the long run. Conversely, a decline in GDP per capita growth hinders financial inclusion, primarily affecting the short run.

This study fills a gap in research on macroeconomic and socioeconomic factors influencing financial inclusion in India by examining the impact of GDP per capita growth, remittance inflows and the income share held by the lowest 20% of the population, an area relatively unexplored in the Indian context. Second, the study provides comprehensive distinct results for different financial inclusion indicators, offering valuable insights for policymakers. These findings are particularly relevant for policymakers working toward Sustainable Development Goal 8.10.1, as they can use the results to tailor policies that align with SDG objectives. Additionally, policymakers in other developing nations can benefit from this study’s findings to enhance financial inclusion in their respective countries.

This study aims to examine the influence of global and local structural factors on reducing poverty.

The research uses data spanning from 1996–2022 and uses the nonlinear autoregressive distributed lag model. This model allows for the assessment of the short and long-term effects of remittance inflow (RI) growth, control on corruption and employment rate (ER) on poverty reduction in India.

The research findings indicate that in the short run, an increase in international RIs, a higher control of corruption and a greater ERare associated with a decrease in poverty in India. Specifically, a positive change in RIs and control of corruption significantly reduces poverty, while an increase in the ERhas a substantial impact. However, in the long run, only a positive change in RIs and a higher control of corruption continue to significantly reduce poverty.

This study makes several significant contributions to the existing literature. First, it examines the simultaneous impact of three structural factors on poverty, offering insights into their combined long-term effects on the economy. Second, unlike previous studies, this research investigates the nonlinear effects on poverty, which is particularly relevant for developing nations. Lastly, this study aligns with the United Nations’ Sustainable Development Goal 1, which aims to end poverty in all its forms everywhere. The findings of this study are expected to assist Indian policymakers in formulating effective poverty eradication strategies and provide guidance for other developing nations facing similar challenges.