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Rising crude oil prices are likely to have an asymmetric and nonlinear negative impact on GDP growth. The purpose of this paper is to ask the following questions: Does the effect of a crude price shock depend on the position of crude price cycle, i.e. is the effect of price shock larger/smaller in periods of already elevated crude price? And, does the effect of crude price shock depend on the position of the economy in the business cycle, i.e. does the crude price shock affect growth differentially in periods of low/high growth?

The authors use a local linear projection (LLP) model to examine the asymmetric impact of crude price on GDP growth in an environment of high crude price. Next, a quantile regression model is used to account for differential impact on growth around high and low growth periods.

Results from the LLP model show that when oil price is above $70, each additional percentage point of increase in oil price results in a 20 basis point (bps) drop in quarterly GDP growth rate on average. The impact is felt between the third and sixth quarters. When oil prices rise above $80, the impact is similar, with a sharper drop in growth (30 bps). The exercise with quantile regression shows that the impact of an increase in crude prices on growth is almost double at lowest quantiles of growth compared with the median.

There is a growing literature that evaluates the impact of oil price in developing economies. However, nonlinearities in crude price-GDP growth dynamics have not received enough attention, especially during phases of elevated crude price or a growth downcycle. The authors believe that accounting for such effects is especially relevant in the present economic scenario of high oil prices because of geopolitical crises and a period of vulnerable growth because of supply chain issues arising out of the pandemic. Using recent data from oil-importing emerging market economies such as India, this paper fills a crucial gap in the literature.

The study provides medium-term estimates of recovery paths for Indian economy using a dynamic factor (DF)-based approach that employs data on high-frequency indicators à la Bhadury, Ghosh, and Kumar (2020). The DFs are used to analyze the post-pandemic recovery and convergence with its pre-COVID-19 trend for India between March 2021 and March 2022. A broad sectoral assessment of the impact of COVID-19 is also conducted. In addition, forward-looking measures based on stock returns are used to analyze the transmission of additional banking sector risks to the real sectors by constructing daily delta conditional value-at-risk (CoVaR) estimates. Our estimates based on the DFs suggest that the aggregate economic activities may catch up to the estimated pre-COVID trend by March 2021 predominantly driven by the growth in services sector. The industrial sector and consumer goods sector continue to show moderate signs of recovery. Our CoVaR estimates corroborate these findings. Banking sector transmission risk is among the lowest for services such as healthcare and information technology (IT), for both the lockdown period between March 25 and June 8, 2020, and for the latter months. The transmission risk continues to remain high for metal, oil and gas, and capital goods sector. Broadly, the evidence on forward-looking banking sector risk transmission for major sectors is in alignment with our finding on their recovery based on DF models, after easing of COVID-19 lockdown.

In the economic literature, a crisis has been thematically defined around bank runs, failure of large financial corporations, and financial distress. Section 1 summarizes our learnings about international banking crisis, in terms of the origin and impact of such crises. This provides us an international benchmark before we delve deeper into India's banking distress, its size and trends. Section 2 focuses on the twin-balance-sheet crisis in India. On one side, corporate firms recklessly overleveraged, resulting in excess capacities and business diversification. On the other side, banks, both private and public, fell prey to excessive and procyclical credit lending and improper monitoring. Overall, too many projects were left too weakly monitored. Separately, we have focused on two subsections, first, how the financial institutions in India have overstretched their credit-disposal limit during market upturns. Second, we found absence of any theoretically grounded approaches to determine the capital-adequacy ratios (CARs) for the banks. In Section 3, we have identified the steps taken so far by the Banking regulator and the Government to resolve the crisis. Further, we critically examine the role of Korea Asset Management Corporation (KAMCO) towards a successful non-performing assets (NPAs) resolution in South Korea. Few key takeaways include, (1) establishing a public asset-management company (AMC) focused on maximization of recoveries and resolution of stressed assets, (2) well-defined governance structure for the AMC ensuring it works on market principles, shielded from political interferences, and (3) realistic asset valuation and transfer price that ensures limited downside risks for the public AMC.

This study aims to delve into the influence of corporate social responsibility on the corporate brand performance of Indian business-to-business (B2B) companies.

The corporate social responsibility (CSR) practices have been measured through CSR disclosure index (CDI), generated by surveying annual reports/CSR reports/websites of 131 Indian B2B firms. The same was mapped to corporate brand performance of these firms, measured as customer-based corporate brand equity, which was measured through a questionnaire-survey of purchasing managers and users working in firms that are customers to the above-mentioned firms.

The result reveals the positive influence of CSR practices in shoring up corporate brand performance.

CDI has been developed based on CSR reporting across the stakeholder groups. However, the impact has been mapped onto one stakeholder category, the customer. The sample period was only one year, and the data is cross-sectional. Future studies may investigate the long-term effect of CSR using longitudinal data on larger data sets.

This study will encourage Indian B2B firms to practice CSR not only for conforming to the regulatory requirements but also as a strategic tool in strengthening the competitive advantage.

It is the first study of its kind to evaluate the imprint of corporate social responsibility, measured based on CSR reporting by firms, on corporate brand performance. It looks into the return earned by firms from the resources invested in CSR activities.

The economic value generated by a firm is determined by the efficient management of its resources within a given business environment. The Indian pharmaceutical industry is highly competitive and has attracted huge investments in research and development (R&D), including financing of biotechnological ventures, clinical trials, contract research activities in addition to traditional product development and filing of regulatory requirements. This study aims to identify the specific resources that are significant drivers of performance.

Data analysis uses panel regression based on an extended version of the Cobb-Douglas production function, where the dependent variable firm performance is measured using annual sales whilst the independent variables include labour, capital, R&D investments and marketing efforts. This study uses data spanning a period of 7 years (2012–2018) collected from 151 Indian pharmaceutical firms.

Contrary to the general understanding that R&D investments tend to create profitable opportunities, it is observed that R&D expenditures have a negative impact on sales in the short to medium time period. This study also highlights the finding that in addition to the positive impact of labour and capital, marketing efforts are more likely to have a greater positive influence on firm performance than R&D.

The uniqueness of the paper lies not only in the counterintuitive findings but also in the methodology used to capture the impact of the lagged effect of R&D investments on firm performance. Specifically, a regression model-based both on panel data and time-series averages is used to examine the said impact.