Search results

One of the major goals of sustainable development is creating employment opportunity among all. Despite its largest demographic dividends, the whole world faces challenges in employment generation among youth. The growing number of unemployed youths is one of the important problems faced by developed as well-developing countries. Youth unemployment is the situation of young people who are looking for a job but cannot find a job in the age between 15 and 24. Mismatch between education and employability resulted in high unemployment rates among the youth. A key research question is that how we can bridge the gap and equip the youth for job field. Although eminent economists, newspapers, international statistical bodies continuously put fingers towards this vulnerability, research work in this field is really scant. On this backdrop, this chapter wants to explore the intensity of youth unemployment in India; keeping in mind, India has the largest youth population in the world. Based on data sources from World Development Indicators, the chapter studies the time series since globalisation to COVID periods. This chapter also tries to search the macroeconomic variables related anyway to the youth unemployment rate. As research methodology, we use vector autoregressive (VAR) Granger causality test. Based on our results, the author has concluded that human development index in India and GDP both ways causes each other. And youth unemployment rate in India causes HDI. However, our econometric investigations can be useful to better assessment of youth unemployment in India from liberalisation to pandemic. At the end of this chapter, some final considerations and policy implications on youth labour market dynamics are analysed and discussed.

This chapter addresses one of the most crucial areas for critical thinking: the morality of turbulent markets around the world. All of us are overwhelmed by such turbulent markets. Following Nassim Nicholas Taleb (2004, 2010), we distinguish between nonscalable industries (ordinary professions where income grows linearly, piecemeal or by marginal jumps) and scalable industries (extraordinary risk-prone professions where income grows in a nonlinear fashion, and by exponential jumps and fractures). Nonscalable industries generate tame and predictable markets of goods and services, while scalable industries regularly explode into behemoth virulent markets where rewards are disproportionately large compared to effort, and they are the major causes of turbulent financial markets that rock our world causing ever-widening inequities and inequalities. Part I describes both scalable and nonscalable markets in sufficient detail, including propensity of scalable industries to randomness, and the turbulent markets they create. Part II seeks understanding of moral responsibility of turbulent markets and discusses who should appropriate moral responsibility for turbulent markets and under what conditions. Part III synthesizes various theories of necessary and sufficient conditions for accepting or assigning moral responsibility. We also analyze the necessary and sufficient conditions for attribution of moral responsibility such as rationality, intentionality, autonomy or freedom, causality, accountability, and avoidability of various actors as moral agents or as moral persons. By grouping these conditions, we then derive some useful models for assigning moral responsibility to various entities such as individual executives, corporations, or joint bodies. We discuss the challenges and limitations of such models.

Classical unit root tests are known to suffer from potentially crippling size distortions, and a range of procedures have been proposed to attenuate this problem, including the use of bootstrap procedures. It is also known that the estimating equation’s functional form can affect the outcome of the test, and various model selection procedures have been proposed to overcome this limitation. In this chapter, the authors adopt a model averaging procedure to deal with model uncertainty at the testing stage. In addition, the authors leverage an automatic model-free dependent bootstrap procedure where the null is imposed by simple differencing (the block length is automatically determined using recent developments for bootstrapping dependent processes). Monte Carlo simulations indicate that this approach exhibits the lowest size distortions among its peers in settings that confound existing approaches, while it has superior power relative to those peers whose size distortions do not preclude their general use. The proposed approach is fully automatic, and there are no nuisance parameters that have to be set by the user, which ought to appeal to practitioners.

We form portfolios based on return and liquidity and examine the effects of liquidity and other risk factors on asset pricing in the Chinese stock market. Our results show that the past loser-and-illiquid stock portfolios tend to outperform the past winner-and-liquid stock portfolios in the 1–12 months holding period. The excess return is significantly associated with the market-wide liquidity factor even when we control the three Fama-French and momentum factors. Cross-sectionally, the liquidity beta significantly affects the excess return even with control of other risk betas and other traditional liquidity proxies.

‘Subjective well-being comprises of people's emotional responses, domain satisfactions, and global judgements of life satisfaction’ (Diener et al., 1999). The health-related Sustainable Development Goal (SDG-3) aims to ensure ‘good health and well-being’ for all, over the globe. The World Happiness Report (2022) reported a highly significant relation between the SDG-3 and the subjective well-being scores and hence for the improvement of citizen well-being suggested for a holistic approach to economic development. The present chapter examines the impact of global economic crisis 2008–09 on the subjective well-being using time series data for six selected countries for the time period 2004–2019. Considering the crisis as an ‘intervention’, this chapter performs interrupted time series analysis for single- and multiple-group (country) comparisons. The single group analysis finds that in the immediate year of crisis, there appeared to be significant decrease in subjective well-being, followed by a significant decrease in the annual trend subjective well-being relative to pre-crisis for most of the countries. In case of multiple group analysis, the regression results reveal that initial mean level difference between any country and remaining countries was significant for most of the countries. The difference in the subjective well-being trend between a particular country and remaining countries after initiation of the crisis compared to pre-crisis period has appeared to be significantly negative for all the countries considered in the analysis.

Purpose: Manufacturing has always been considered a backbone for economic growth. It has been considered an imperative sector in the growth of an economy. This study aims to trace the long-term relationship between gross domestic product (GDP) and manufacturing sector in the context of Indian economy.

Need for the study: According to research, the significance of the manufacturing sector is waning over time. This chapter studies the long-term relationship between the GDP, an indicator of growth, and the manufacturing sector. Over the last few decades, the contribution of manufacturing has been stagnant in the GDP of India.

Methodology: The decadal growth of various sectors in the GDP of India is studied using time series analysis. This study used the data released by the Ministry of Statistics and Programme Implementation (MOSPI) from 1950–1951 to 2013–2014. The long-term relationship between the sector of manufacturing and the GDP is examined through the augmented Dicky–Fuller (ADF) test and auto-regressive distributed lag (ARDL) models.

Findings: The findings suggest that in the Indian scenario, there is no relationship for an extended period between the GDP and the manufacturing sector, which calls for further policy implications.

Practical implications: India, while having the world’s fastest-growing economy, must continue to take steps to attain high growth rates and long-term sustainability by reducing obstacles to the expansion of the service sector in addition to manufacturing. Manufacturing-led services are to be boosted through policy interventions.

Due to its high leverage nature, a bank suffers vitally from the credit risk it inherently bears. As a result, managing credit is the ultimate responsibility of a bank. In this chapter, we examine how efficiently banks manage their credit risk via a powerful tool used widely in the decision/management science area called data envelopment analysis (DEA). Among various existing versions, our DEA is a two-stage, dynamic model that captures how each bank performs relative to its peer banks in terms of value creation and credit risk control. Using data from the largest 22 banks in the United States over the period of 1996 till 2013, we have identified leading banks such as First Bank systems and Bank of New York Mellon before and after mergers and acquisitions, respectively. With the goal of preventing financial crises such as the one that occurred in 2008, a conceptual model of credit risk reduction and management (CRR&M) is proposed in the final section of this study. Discussions on strategy formulations at both the individual bank level and the national level are provided. With the help of our two-stage DEA-based decision support systems and CRR&M-driven strategies, policy/decision-makers in a banking sector can identify improvement opportunities regarding value creation and risk mitigation. The effective tool and procedures presented in this work will help banks worldwide manage the unknown and become more resilient to potential credit crises in the 21st century.