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PurposeThe objective of the research is to examine the impact of global governance and macroeconomic indicators on the lending capacity of banks in India.Design/methodology/approachEmploying a comprehensive time series dataset spanning from 1996 to 2022, we utilize the Nonlinear Autoregressive Distributed Lag model approach to investigate the short-run and long-run impact of government policy (GP) effectiveness, lending interest rates and remittance inflows (RI) on the lending capacity of banks in India.FindingsThe findings of the study indicate that lending interest rates have a statistically insignificant impact on lending capacity in the short term. However, in the long run, an increase in the lending interest rate leads to a decrease in lending capacity, whereas a decrease in the lending interest rate has a non-significant impact. On the other hand, the effectiveness of GPs affects both short-term and long-term lending capacity. In the short run, positive or negative changes in GP effectiveness lead to a decline in lending capacity. Whereas in the long run, a positive shock in GP effectiveness increases lending capacity, while a negative shock decreases it. Lastly, RI indicated no significant short-term impact on the lending capacity of the banks. Conversely, in the long run, a positive change in RI enhances lending capacity, whereas a negative change in RI reduces it, with a more pronounced effect.Originality/valueThe novelty of the study lies in the fact that it is a pioneering study that utilizes global governance and macroeconomic indicators to examine the impact on the lending capacity of banks and financial institutions in India. Moreover, the study adopts a non-linear approach to examine the relationship between the chosen variables, which enables an understanding of the impact of both positive and negative shocks on the dependent variable both in the short and long run. Lastly, the examination sheds light on the achievement of Sustainable Development Goal 8.10, which is related to financial inclusion and it is a major concern for a large developing nation like India.
PurposeThe objective of the research is to examine the impact of global governance and macroeconomic indicators on the lending capacity of banks in India.Design/methodology/approachEmploying a comprehensive time series dataset spanning from 1996 to 2022, we utilize the Nonlinear Autoregressive Distributed Lag model approach to investigate the short-run and long-run impact of government policy (GP) effectiveness, lending interest rates and remittance inflows (RI) on the lending capacity of banks in India.FindingsThe findings of the study indicate that lending interest rates have a statistically insignificant impact on lending capacity in the short term. However, in the long run, an increase in the lending interest rate leads to a decrease in lending capacity, whereas a decrease in the lending interest rate has a non-significant impact. On the other hand, the effectiveness of GPs affects both short-term and long-term lending capacity. In the short run, positive or negative changes in GP effectiveness lead to a decline in lending capacity. Whereas in the long run, a positive shock in GP effectiveness increases lending capacity, while a negative shock decreases it. Lastly, RI indicated no significant short-term impact on the lending capacity of the banks. Conversely, in the long run, a positive change in RI enhances lending capacity, whereas a negative change in RI reduces it, with a more pronounced effect.Originality/valueThe novelty of the study lies in the fact that it is a pioneering study that utilizes global governance and macroeconomic indicators to examine the impact on the lending capacity of banks and financial institutions in India. Moreover, the study adopts a non-linear approach to examine the relationship between the chosen variables, which enables an understanding of the impact of both positive and negative shocks on the dependent variable both in the short and long run. Lastly, the examination sheds light on the achievement of Sustainable Development Goal 8.10, which is related to financial inclusion and it is a major concern for a large developing nation like India.
Purpose 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. Design/methodology/approach 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. Findings 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. Originality/value 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.
Remittances have become a significant component of international capital flows, with millions of migrants sending billions of dollars back to their home countries annually. However, the way these outflows affect macroeconomic variables has not received sufficient attention in the literature, especially in the context of varying levels of financial development. Using time series data from 1987 to 2022 for the United Kingdom, this study examines the macroeconomic effects of remittance outflows and financial development. Our baseline estimation using the Autoregressive Distributed Lag model reveals heterogeneous impacts, as remittance outflows adversely affect economic growth but improve exchange rates. We find remittances do not have a significant effect on inflation or bank rates. The moderating effect of financial development analysis reveals a similar outcome. Our results suggest governments should consider stimulus policies that support investment in productive sectors to improve macroeconomic indicators and facilitate financial inclusion to enhance the adoption of growth strategies that promote remittances.
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