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.

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.

The objective of this study is to empirically analyse the relationship between public infrastructure investment and economic growth for India using yearly data for its 28 states (excluding Telangana). We have taken six major sub-sectors falling under infrastructure sector: transport; education, sports, arts and culture; energy; medical and public health; telecommunication; and water supply and sanitation. We have aimed to analyse the efficiency of each of these sub-sectors using data envelopment analysis (DEA). For every state, we have used the public investment data from the state budget files as input while sector-specific infrastructural criterions and sector-wise revenue are taken as outputs. We have gone by the logic that a state’s particular sub-sector of infrastructure will be highly efficient if it is able to use up the investment allotted to it and create a stronger infrastructure as compared to other states, subsequently generating higher amount of revenues. For each sector, various infrastructural criteria were clubbed together using principal component analysis technique to construct a single infrastructure index (representing the sector-wise output). Further, DEA was applied to calculate efficiency for each Indian state and they were ranked based on their efficiency scores. The analysis tells us that policy-making and budget allocation may be done in accordance with standing performances of different states in various sectors and the goals of the respective state governments.

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The purpose of this article is to examine the impact of Global Financial Crisis on the Indian stock market which has been an issue of immense interest to time series analysts and econometricians around the world. We have conducted empirical analysis on daily stock returns of the top 20 companies listed on Bombay Stock Exchange (BSE) for the period 2001–2012. The study shows the presence of autoregressive conditional heteroskedasticity (ARCH) effect and volatility clustering during the study period. The BSE Sensex also depicts asymmetric volatility effect. Therefore, the standard generalised autoregressive conditional heteroskedasticity (GARCH) (0, 1) model provides the best description of return dynamics. Application of GARCH to daily stock returns of individual companies suggests that the companies illustrate high volatility for the period 2007–2009. Also, the returns from the portfolio of 20 companies reveal high volatility for the period 2007–2009. Hence, we prove that the Indian economy too has felt the impact of the global financial crisis. Finally, we conclude with some challenges for future research in this area.