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This paper aims to investigate the effect of economic policy uncertainty (EPU) shocks on Indian equity market sectors. The effect of domestic (Indian) and foreign (USA) EPU shocks is examined on ten major Bombay Stock Exchange sectors.

The study uses data covering the period from September 2005 to July 2023 and uses the methodology of quantile regression to investigate the heterogenous response of stock market sectors under diverse market conditions explained through the analysis of conditional quantiles distribution.

The results demonstrate that domestic and foreign EPU shocks negatively affect most of the sectors in bearish market conditions. Industrials, commodities, utilities, consumer discretionary and financial services are the most affected sectors by domestic EPU. However, the information technology sector is found to be immune to domestic EPU shocks but negatively affected by foreign EPU shocks. On the other hand, energy, financial services and fast-moving consumer goods sectors are found to be immune to foreign EPU shocks but are negatively affected by domestic EPU shocks.

Understanding the heterogeneous response of different sectors to EPU shocks could help investors and portfolio managers identify portfolio diversification opportunities.

This study makes an inaugural attempt to examine the responses of Indian stock market sectors to domestic and foreign EPU shocks using the approach of quantile regression and unveils the previously unexamined diverse reactions of Indian stock market sectors to EPU shocks originating from both India and USA.

The conflict in Russian Ukraine is a problem for the world economy because it hinders growth and drives up inflation when it is already high. The trade route between India and Russia is also impacted by the Russia-Ukraine crisis. This study aims to compile the most recent data on how the present global economic crisis is affecting it, with particular emphasis on the Indian economy.

This research develops a mathematical forecasting model to evaluate how the Russia-Ukraine crisis would affect the Indian economy when perturbations are applied to the major transport sectors. Input-output modeling (I-O model) and interval programing (IP) are the two precise methods used in the model. The inoperability I-O model developed by Wassily Leontief examines how disruption in one sector of the economy spreads to the other. To capture data uncertainties, IP has been added to IIM.

This study uses the forecasted inoperability value to analyze how the sectors are interconnected. Economic loss is used to determine the lowest and highest priority sectors due to the Russia-Ukraine crisis on the Indian economy. Furthermore, this study provides a decision-support conclusion for studying the sectors under various scenarios.

In future studies, other sectors could be added to study the Russian-Ukrainian crises’ effects on the Indian economy. Perturbation is only applied to transport sectors and could be applied to other sectors for studying the effects of the crisis. The availability of incomplete data is a significant concern in this study.

Russia-Ukraine conflict is a significant blow to the global economy and affects the global transportation network. This study discusses the application of the IIM-IP model to the Russia-Ukraine conflict. It also forecasts the values to examine how the crisis affected the Indian economy. This study uses a variety of scenarios to create a decision-support conclusion table that aids decision-makers in analyzing the Indian economy’s lowest and most affected sectors as a result of the crisis.

The purpose of this paper is to study the influence of inflated energy prices on the capital structure of Indian manufacturing corporations and to investigate whether the capital structure of Indian firms is driven by demand shocks or supply shocks during the study period.

After conducting a thorough review of the capital structure and inflation-based research studies, panel data-based regression model and correlation matrix have been used as statistical tools for Indian manufacturing sector available with the Centre for Monitoring Indian Economy Prowess database.

The results suggest that variables like the presence of inflated energy prices had adversely influenced the capital structure of Indian corporations. Not only this, the study also highlights that factors pertaining to the demand shock had induced Indian corporations to have higher debt levels in the capital structure.

This study has laid some ground work to explore the influence of inflation on capital structure of Indian firms upon which a more detailed evaluation could be based.

To the best of the authors’ knowledge, this study is the first that explores the influence of inflated energy prices on the capital structure of manufacturing firms in India by using the most recent data.

This paper aims to analyse the pattern of structural transformation and productivity growth of 15 major Indian states at a ten-sector level of disaggregation from 1983 to 2017.

The analysis has been carried over in a ten-sector disaggregated level through construction of the labour and output data from various micro data sets.

The majority of Indian states have bypassed the stage of industrialization, wherein labour previously engaged in agriculture has transitioned directly into the modern services sector while skipping the manufacturing. There are no sign of convergence of sectoral productivities and the heterogeneity among Indian states persists throughout the time period. The growth performance of states are not positively associated with the movement of labour across sectors as measured by the structural transformation index (STI). This goes against the narrative that structural transformation help reduce the misallocation of factors. Despite an increase in educational attainment of workers across all sectors, more than one-third of agricultural workers still remain either illiterate or lack formal schooling. Among sectors, construction (C) and trade, hotels and restaurants (THR) have absorbed the majority of workers who have left agricultural jobs. Finance, insurance, real estate and business services (FIRB), electricity, gas and water supply (EGWS) and mining and quarrying (MQ) are the three sectors that have seen significant gains in labour productivity during the study period.

To the best of the authors’ knowledge, this is the first attempt to analyse structural change and productivity growth in the Indian economy using Indian states as critical geographical marker. The results are new and add value to the literature.

The purpose of our research on blockchain technology is to unveil its immense potential, understand its applications and implications and identify opportunities to revolutionize existing systems and processes. This research aims to inspire the creation of new innovative solutions for industries. By harnessing blockchain technology, organizations can pinpoint key areas that could significantly benefit from its use, such as streamlining operations, providing secure and transparent digital solutions and fortifying data security.

This study presents a robust multi-criteria decision-making framework for assessing blockchain drivers in selected Indian industries. We initiated with an extensive literature review to identify potential drivers. We then sought the opinions of experts in the field to validate and refine our list. This meticulous process led us to identify 26 drivers, which we categorized into five main categories. Finally, we employed the Best-Worst Method to determine the relative importance of each criterion, ensuring a comprehensive and reliable assessment.

The authors have ranked the blockchain drivers based on their degree of importance using the Best-Worst Method. This study reveals the priority of BC implementation, with the retail industry identified as the most in need, followed by the Banking and Healthcare industries. Various critical factors are identified where blockchain technology could help reduce costs, increase efficiency and enable new innovative business models.

While this study acknowledges potential bias in driver assessment relying on literature and expert opinions, its findings carry significant practical implications. We have identified key areas where blockchain technology could be transformative by focusing on select industries. Future research should encompass other industries and real-world case studies for practical insights that could delve into the adoption challenges and benefits of blockchain technology in many other industries, thereby amplifying the relevance of our findings.

Blockchain is a groundbreaking, innovative technology with immense potential to revolutionize industries. Past research has explored the benefits and challenges of blockchain implementation in specific industries or sectors. This creates a gap in research regarding systematically classifying and ranking the importance of blockchain across different Indian industries. Our research seeks to address this gap by using advanced multi-criteria decision-making techniques. We aim to provide a comprehensive understanding of the significance of blockchain technology in critical Indian industries, offering valuable insights that can inform strategic decision-making and drive innovation in the country’s business landscape.

This study aims to examine empirically the nature of supply chain disruptions caused by the COVID-19 pandemic, particularly on the Indian automobile sector.

The authors evaluate the stock market performance of individual company and its quantitative relationship to certain variables related to company’s supply chain.

The authors analysed the company’s operations considering several ratios like asset intensity, company size, labour intensity and inventory to revenue.

The results of analysis can help the companies to understand how disruptions in the supply chain can affect the company’s operations and how it is perceived by the investors in the stock market.

Also, investors are benefitted, as they can understand how different companies with different operational characteristics react to global disruptions in supply chains, which in turn would help them to find better investment opportunities.

Although there is some literature available on the qualitative as well as quantitative analysis, the authors go further to analyse the impact of supply chain disruption on the stocks of the automobile sector.

We aim to analyze the capital structure heterogeneity for manufacturing and service sector firms. Additionally, we analyze the impact of the COVID-19 pandemic on the leverage adjustments of corporate firms.

This study applies the two-step system generalized method of moments (system-GMM) and panel data of 1,115 manufacturing and 482 service sector firms listed with the Bombay Stock Exchange (S&P BSE) from 2010 to 2023. We developed and analyzed three models. Model 1 analyzes the leverage determinants and speed of adjustment (SOA) for the manufacturing and service sectors. Model 2 evaluates the leverage SOA for various sub-sectors, and Model 3 analyzes the impact of the COVID-19 pandemic on the leverage SOA.

This study suggests the three following. First, the direction of leverage determinants suggests that manufacturing firms are highly tangible. In contrast, service sector firms are high-growth firms and recorded a higher SOA (12.01%) than manufacturing (9.09%). Second, analyzing the leverage heterogeneity, we found that SOA varies across the sub-sectors. For manufacturing, food and beverage sub-sector recorded the highest SOA (12.58%), while consumer durables reported the lowest (6.38%). Communication recorded the highest (24.15%) for services, while industrial services recorded the lowest (11.18%). Third, firms across sectors and sub-sectors increased their SOA during COVID-19 pandemic.

This in-depth analysis of leverage heterogeneity for different sectors and subsectors will assist policymakers, corporate managers and other stakeholders in making agile financial decisions.

The analysis of leverage heterogeneity for the manufacturing and service sector from the emerging Indian economy marks a novel contribution to existing literature.

Since the post-liberalization era, a noticeable structural change and transition in employment have unfolded within the Indian economy. Hence, the purpose of this paper is to understand employment transition and elucidate the evolving dynamics of rural economies and employment patterns from agriculture to more productive non-agricultural sectors. Additionally, the study investigates the underlying causes of socioeconomic disparities and their repercussions on employment trends.

To address the aforementioned issues, this study utilised secondary data from labour surveys conducted by the National Sample Survey Organisation spanning from 2004–05 to 2023. Initially, the study computed the magnitude of employment in both agriculture and non-agriculture sectors. Subsequently, the distribution of non-agricultural labour across various socioeconomic characteristics was estimated. Furthermore, a logistic regression model was employed to evaluate the impact of socioeconomic factors on employment choices. Finally, Fairlie’s decomposition model was applied to elucidate workers’ decisions to engage in non-agricultural sectors.

The study reveals a significant rise in rural non-agricultural employment, from 98.4 m in 2004–05 to 193.3 m in 2023, indicating changing job preferences. Notably, the construction and trade sectors emerge as significant drivers of this trend. However, self-employment and casual labour persist, highlighting job vulnerability. Additionally, women and marginalised individuals with low levels of education and socioeconomic status lag behind in non-agricultural employment.

This study makes a significant contribution by offering a thorough analysis of the employment transition from agriculture to non-agriculture over a span of two decades. It provides valuable insights into the evolving dynamics of employment trends.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-11-2023-0904.

In the context of an absence of studies examining the interrelationship between Indian construction industry and residential real estate sector, the study aims to develop and test a conceptual framework to stimulate construction industry through optimisation of housing market in India. The developed conceptual framework lays down a blueprint to assess the interaction between construction industry and housing market in other countries.

Means of stimulation of construction industry by residential real estate sector were identified. Housing market was examined to identify factors constituting consumer-centric delivery and consumer-empowered demand. Supply side of housing market was probed to identify underlying factors stifling housing delivery. The identified factors were put together to form the conceptual framework. A questionnaire was developed and administered to the delivery-side stakeholders of housing market.

The study demonstrates significant correlations between real estate investment-led construction industry output stimulation and consumer-centric residential real estate delivery. The deterrents to consumer-centric housing delivery have been ascertained to be having an impact on time, cost and scope of housing projects. Significant correlations have been ascertained between the deterrents. On the demand-side, skills, awareness and engagement of consumers are strongly correlated with each other. Affordability of housing is rightfully correlated with all the three means of stimulation of construction industry output.

Specific to the Indian context, the study presents and validates a novel conceptual framework aimed at stimulation of construction industry output through interventions in housing market.

The present paper aims to highlight how manufacturing expansions under conditions of increasing returns, which involve the growth of intermediate goods specializations, support advanced service employment. In addition, the increasing use of manufacturing products in services highlights additional, new service sector employment opportunities.

This paper investigates (1) the manufacturing and service interactions and (2) the investment behaviour in manufacturing using Auto-Regressive Distributed lags (ARDL) and Vector Autoregressive (VAR) models. The models allow for different specifications to study whether investment behaviour in manufacturing supports dynamic manufacturing and service interactions.

The results underpin how Kaldorian manufacturing as an engine of growth is still relevant in Indian growth and is key to achieving higher advanced employment, export-orientation and services and manufacturing nexus outcomes. What matters, though, is that manufacturing investments are to be guided mainly by intermediate goods specializations. The slowdown of these specializations, explaining the slowdown of manufacturing investment, is therefore, a concern.

A reinterpretation of manufacturing as an engine of growth in which primacy is given to investment behaviour in technical progress functions that can support the growth of specializations in manufacturing and such specialized service employment.