ABSTRACT Self‐Help Group (SHG)‐Bank Linkage Programmes have emerged as an important mechanism for promoting rural financial inclusion by connecting underserved communities with formal banking institutions. Despite significant progress in expanding banking outreach, many rural households, particularly women and low‐income groups, continue to experience barriers in accessing formal credit, savings facilities, and financial services. This study examines the role of SHG‐Bank Linkage Programmes in strengthening financial inclusion through a conceptual analysis of existing research, institutional reports, and secondary evidence. The study focuses on four major dimensions: access to formal credit, savings behaviour and financial literacy, women's empowerment, and community‐level economic development. The analysis highlights that SHG‐bank partnerships reduce credit barriers through collective savings, peer accountability, and group‐based lending mechanisms. These programmes also contribute to women's economic participation by improving financial confidence, leadership capacity, and involvement in household and community decision‐making. However, challenges such as regional disparities, digital barriers, uneven SHG performance, and sustainability concerns remain significant. Based on the evidence reviewed, the study proposes an enhanced SHG‐Bank Linkage framework incorporating digital financial services, financial literacy initiatives, entrepreneurship support, and stronger institutional coordination. The proposed framework provides a conceptual pathway for strengthening inclusive and sustainable rural financial systems. The result shows that the enhanced version of the model achieves better financial reach by expanding credit access for SHG members from 58% to 85%. The future scope of SHG‐Bank linkage programmes lies in leveraging digital technologies to expand outreach and enhance financial literacy, thereby deepening financial inclusion in rural areas.
This research delves into the 'paradox of cash' that persists despite the proliferation of alternative payment methods, which have seemingly displaced paper currency as a medium of exchange. We examine the determinants of cash holdings and consumer payment preferences using novel payment diaries data from 2019 in India, a country known for its strong reliance on cash transactions. Despite recent policies aimed at reducing cash usage, we find that 94% of all transactions were carried out in cash. Cash holdings vary in response to well-established socio-demographic and economic factors, but also by behavioural parameters such as risk aversion, life satisfaction and attitudes toward tax evasion. The use of cash as a payment method varies by attitudes toward cash use and is non-linear in transaction size. These findings are robust to changes in the specification as well as to treating cash holdings as endogenous. We conclude by discussing the implications of our findings for policy, particularly in the context of promoting digital payments in India.
ABSTRACT In India's quest for sustainable development, the interplay among corporate green innovation, financial regulation, and green financing has gained increasing importance. Although existing literature emphasizes the critical role of green finance in promoting environmentally sustainable innovations, there is a notable gap in studies that examine the combined effects of green credit and financial regulation at the firm level. This study aims to bridge this gap by analyzing annual data from publicly traded Indian corporations over the period from 2012 to 2024. To explore these dynamics, we utilize a robust regression model that assesses the impact of green credit (GC) on corporate green innovation (CGI). Our analysis reveals that green finance plays a significant role in stimulating corporate green innovation, with financial regulation serving to further amplify this positive effect. Importantly, the impact of green finance is particularly pronounced among larger firms, underscoring its potential to catalyze sustainable transformation across all sectors of business in India.
We build on empirical evidence showing that international acquisitions exhibit path dependence and often unfold as sequences of deals, and argue that domestic and cross-border mergers are not necessarily isolated events. Specifically, we ask whether blocking a domestic merger that reduces consumer surplus in the short run could inadvertently preclude a subsequent cross-border acquisition (by the same parties) that would raise consumer surplus in both the home and foreign markets. To address this question, we develop a Cournot model with increasing marginal costs and show that moderate, complementary synergies can make hybrid mergers-those combining domestic and cross-border elements-both profitable and beneficial for consumer surplus, even when each merger on its own would not be viable and/or would harm consumers. Our analysis suggests that, in settings where such complementarities are plausible, the interaction and sequencing of domestic and cross-border mergers may be relevant for assessing their long-run welfare implications.
Predicting returns is a timeless and important economic topic. Fundamental analysts see identifying time-varying predictive factors as a key goal in asset pricing. This paper provides strong evidence of time-varying return predictability of the S&P 500 Index from 1929 to 2020, by proposing the construction of altered versions of the classical dividend-price (dp) and earnings-price (ep). Our study's primary objectives are to introduce a cyclical adjustment to the simple dp and ep, which would smooth dividends and earnings, to compare the predictive dynamics of these new ratios with their simple counterparts and to modify all ratios based on long-run equilibrium relationships that arise between the examined variables. We provide solid evidence that the cyclically-adjusted ratios perform better than the simple predictors in all forecasting horizons, both in and out of sample, and the modified variables capture more predictive components in returns. Through certain robustness checks including multivariate regression settings and evidence on excess and real return predictability, we thoroughly present the predictive components identified in our data set.
