Purpose: The purpose of this paper is to investigate the relationship between infrastructure development, rural–urban income inequality and poverty for BRICS economies. Design/methodology/approach: Pedroni’s panel co-integration test and panel dynamic ordinary least squares (PDOLS) have been used to carry out the analysis. Findings: The empirical findings confirm a long-run relationship among infrastructure development, poverty and rural–urban inequality. The PDOLS results suggest that both infrastructure development and economic growth lead to poverty reduction in BRICS. However, rural–urban income inequality aggravates poverty in these nations. The paper advocates for adopting policies aimed at strengthening infrastructure and achieving economic growth to reduce the current levels of poverty prevailing in the BRICS nations. Originality/value: Significant limitations exist in the literature in terms of not clearly defining the nature of relationship and interlinkages between infrastructure development, poverty and inequality, with regard to the BRICS nations. The available studies mainly focus on the relationship between infrastructure and growth, with the universal agreement being that these two are positively related. However, it is still not right to assume that economic growth attributable to infrastructure development will, therefore, subsequently lead to a reduction in inequality. This forms the basis for this study, that is, to critically examine the relationship between infrastructure development, inequality and poverty for BRICS nations.

Financial literacy plays a crucial role in shaping individual investment decisions by influencing susceptibility to behavioural biases such as heuristics, framing effects, cognitive illusions, and herding mentality. While most existing studies have examined financial literacy as a mediating factor, our study is among the first in the literature to analyse the role of behavioural biases as mediating factors in the relationship between financial literacy and investment decisions. Specifically, we investigate key biases, including overconfidence, herding, disposition effect, self-attribution, anchoring, availability, representativeness, and familiarity. Using purposive sampling, we collected 482 responses through a structured Likert scale questionnaire. The dataset underwent rigorous validation and reliability tests to ensure robustness. We employed Python-based statistical analysis and used Pearson’s correlation and mediation analysis to explore the relationships between financial literacy, behavioural biases, and investment decisions. With the help of these methods, we were able to uncover relationships and causal pathways which further our understanding of the role of behavioural biases in determining the impact of financial literacy on investment behaviour. The findings illustrate a notable positive correlation between investment decisions and financial literacy, implying that people with higher financial literacy levels possess greater and more rational financial decision-making capabilities. Other analyses have revealed that biases have a moderating effect on this relationship, showing another path through which financial literacy impacts behaviour at the level of the investor. By placing behavioural biases as mediating constructs, this research broadens the scope of investor psychology and the body of knowledge in behavioural finance, highlighting the need to change the approach to how financial literacy programs aimed at investors are structured and implemented.

Purpose: India is a developing nation where the marginal benefit of infrastructure development is tremendous. The purpose of this paper is to analyze the relationship between infrastructure development and poverty reduction for India using the yearly data from 1991 to 2015.Design/methodology/approach: The authors use the principal component analysis to construct indices for four major sub-sectors, namely, transport, water and sanitation, telecommunications and energy, falling under the broad infrastructure sector and then using these sectorwise indices, the authors construct an overall index which represents infrastructure development. The authors provide evidence on the link between infrastructure development and poverty reduction by using the auto regressive distributed lag (ARDL) bound testing approach.Findings: The ARDL test results suggest that infrastructure development and economic growth reduce poverty in both long run and short run. The causality test confirms that there is a positive and unidirectional causality running from infrastructure development to poverty reduction.Research limitations/implications: The study confirms that India’s Infrastructure development plays a vital role in reducing poverty and calls for the Indian Government to adopt economic policies which are aimed at developing and strengthening the infrastructure levels and bringing in more investment in the infrastructure sector in order to help the poor population by making them exposed to better opportunities of employment and income growth, thereby achieving the goal of poverty reduction.Originality/value: This paper is a fresh and unique attempt of its kind to empirically investigate the causal relationship between infrastructure development and poverty reduction in India using modern econometric techniques.

Purpose: This paper aims to suggest the preferred mode of financing for major sub-sectors of infrastructure: roads, seaports, telecommunication and energy by examining which mode of infrastructure financing – public, private or public–private partnership (PPP) – has the maximum positive impact on the overall GDP of India. The same exercise was carried out for the overall infrastructure sector by integrating data from all the four sub-sectors.Design/methodology/approach: The structural vector autoregressive approach was used with the period of analysis taken from 1995 to 2014. The stationary properties of the variables were checked by the Phillips–Perron unit root.Findings: The PPP mode of financing was found to make the maximum positive impact on the GDP of India. Considering the four sub-sectors individually, it was concluded that the private mode of financing in roads, energy and telecom sectors has the maximum positive impact on the GDP, while the PPP gives optimal benefit to the seaports sector.Practical implications: Results will aid the Indian Government and policymakers to efficiently design and develop their economic policies accordingly.Originality/value: The study is novel in a sense that it helps to address the lack of research into the area of infrastructure financing in India.

This study examines the efficiency of the overall Indian banking industry using Data Envelopment Analysis (DEA) and to perform a comparative efficiency analysis of public, private, and foreign banks using six varied forms. Also, providing ranks to the banks based on their efficiency. The study incorporates BCC output-oriented DEA model using a sample of 50 Indian banks (public banks = 17, private banks = 18, foreign banks = 15) for a period ranging from 2009-10 to 2018-19, hence incorporating the after-effects of the financial crisis and demonetization, this study uses panel data from 2009-10 to 2018-19. The results showed that most of the Indian banks fall on the efficient side or are near to full efficiency. However, public banks outperform private and foreign banks in terms of their average efficiency. Results also specify that the performance of banks is sensitive to input-output variables, units under evaluation, and choice of the model. The current study has just focused on the internal factors for analyzing the efficiency of Indian banks; however, certain external factors might also impact the banks’ efficiency.