Department of Economics

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http://rcca.ndl.gov.in/handle/123456789/370

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Now showing 1 - 20 of 30
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    A Case Study of the Economic Impact of Mining in Goa: Perceptions of the Reserved and General Population of a Mining Dependent Locality
    (International Journal of Research in Social Sciences 2016 vol 6, 2016) Falleiro, Savio
    Mining has been an important industry in Goa for years. Operations of the industry were suspended due to orders of the Hon. Supreme Court of India. Much has been documented on the positive and negative effects of mining-the same often done by extreme pro and anti mining activists. The present paper is based on a field-based study involving households of a locality substantially dependent on mining being in close proximity to mining centres. Considering that the locality has a large number of people from ‘reserved’ backgrounds, the paper attempts to find if there was any significant association between the economic issues related to mining and the SC/ST, OBC and General backgrounds of the residents. The study which makes use of chi-square analysis, lists very significant findings, including those concerning the ‘reserved’ sections of population, and involving issues related to net economic effect of mining, health problems, government assistance for SC/ST and OBC sections etc.
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    A Cross-National Empirical Analysis of the Contribution of Fertility, Life Expectancy and Net Migration in Driving Contemporary and Future Population Ageing
    (Population and Economics, 2024) Falleiro, Savio
    Population ageing is an unprecedented phenomenon witnessed by nations globally. Given contradictory findings in the literature regarding the major drivers behind this phenomenon, this study presents a robust cross-national empirical analysis of the contributions of fertility, life expectancy, and net migration to driving contemporary and future population ageing. The study addresses endogeneity and serial correlation by employing dynamic cointegrating regressions, specifically Fully Modified Ordinary Least Squares (FMOLS) and Dynamic Ordinary Least Squares (DOLS), based on data extracted from the United Nations Population Division for 72 nations spanning two periods: 1960-2020 and 2020-2050. The results indicate that nations in advanced ageing transitions, primarily developed nations, experienced comparatively lower fertility rates and higher life expectancy rates than those in early stages, with fluctuations observed in net migration. A statistically significant longrun dynamic cointegrating relationship is found among the three major drivers and population ageing. Declining fertility has been the primary driver of global population ageing from 1960 to 2020, followed by increasing life expectancy and, lastly, net migration. These results remain robust across the four sub-panels of nations based on age-transition categories. Projections suggest that population ageing will persist as a reality. However, regression estimates indicate that life expectancy will surpass fertility to become the primary driver of ageing in the future. The study raises doubt about the rejuvenating role of migration as a solution to population ageing and underscores the importance of further research in this area
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    A Cross-National Empirical Analysis of the Contribution of Fertility, Life Expectancy and Net Migration in Driving Contemporary and Future Population Ageing
    (Population and Economics 8(4): 64–91, 2024) Falleiro, Savio
    Population ageing is an unprecedented phenomenon witnessed by nations globally. Given contradictory findings in the literature regarding the major drivers behind this phenomenon, this study presents a robust cross-national empirical analysis of the contributions of fertility, life expectancy, and net migration to driving contemporary and future population ageing. The study addresses endogeneity and serial correlation by employing dynamic cointegrating regressions, specifically Fully Modified Ordinary Least Squares (FMOLS) and Dynamic Ordinary Least Squares (DOLS), based on data extracted from the United Nations Population Division for 72 nations spanning two periods: 1960-2020 and 2020-2050. The results indicate that nations in advanced ageing transitions, primarily developed nations, experienced comparatively lower fertility rates and higher life expectancy rates than those in early stages, with fluctuations observed in net migration. A statistically significant longrun dynamic cointegrating relationship is found among the three major drivers and population ageing. Declining fertility has been the primary driver of global population ageing from 1960 to 2020, followed by increasing life expectancy and, lastly, net migration. These results remain robust across the four sub-panels of nations based on age-transition categories. Projections suggest that population ageing will persist as a reality. However, regression estimates indicate that life expectancy will surpass fertility to become the primary driver of ageing in the future. The study raises doubt about the rejuvenating role of migration as a solution to population ageing and underscores the importance of further research in this area.
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    A Generative AI-Driven Cloud Framework for Predictive Optimization in Smart Manufacturing Ecosystems
    (Innovations in Machine, Engineering, and Digital Conference (IMED) (pp. 1-6), 2026) Vaz, Sonia
    The ecosystems of smart manufacturing produce huge quantities of heterogeneous data through sensors, machines, and manufacturing processes that provide the prospects of predictive optimization and present the challenges associated with data scarcity and uncertainty. Conventional predictive models tend to use very few historical failure data and deterministic forecasts and therefore become less effective in dynamic industrial processes. The paper suggests a cloud-based architecture based on the generative AI model that combines generative modelling, predictive analytics, and scenario-based optimization to improve decision-making in smart manufacturing. Generative AI is used to simulate more real operational conditions in the future, such as infrequent failure cases, whereas deep learning models predict the health of the machine, its production rate, and quality. These projections are converted into prescriptive operations with the help of a cloud-based stochastic optimization engine that considers the limits of operations and uncertainty. Empirical test on an industrial testbed shows that the framework proposed is able to drastically decrease downtime and unplanned failures and increase throughput and energy efficiency when compared to rule-based systems and predictive only systems. The findings shape how effective is the integration of generative intelligence and cloud optimization of manufacturing performance enhancement due to its robust and scalable nature. The experimental results accelerate the productivity of nearly 22 percent, reduce energy consumption by 18 percent and result in a 28 percent decrease in unplanned downtime when compared to conventional optimization approaches. Furthermore, the results indicate that the framework is robust and adaptable to dynamic workload fluctuations and does not suffer from the impact of concept drift; thus, it is suitable for scalable and adaptive smart manufacturing environments.
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    A study of service quality in Indian public sector banks using modified SERVQUAL model
    (Cogent Business & Management, 2023) Vaz, Sonia
    Assessment of service quality has been widely utilized in the service sector, especially in the banking industry. The present study aims to understand the influence of service quality on customer loyalty in Indian public sector banks. The service quality is quantified with the help of a modified SERVQUAL model using dimensions Reliability, Assurance, Tangibles, Empathy, Responsiveness, Charges, and Convenience. Structural equation modelling (SEM) indicated that among all the dimensions, Assurance, Empathy, Responsiveness, and Tangible were found to have a significant relationship with service quality. The banks must focus on bringing in innovation in these parameters to maintain a high quality of service and achieve higher satisfaction, which subsequently develops customer trust towards the company. By bringing innovative changes to improve the service quality, the banks can also increase their competitive advantage and customer retention as service quality has a significant relationship with customer loyalty.
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    A study on health insurance of hoseholds in Varca
    (Gyana, 2019) Mendonca, Farah
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    AI-Powered FinTech Analytics for Transactional Transparency and Fraud Mitigation in Industrial IoT Ecosystems
    (Innovations in Machine, Engineering, and Digital Conference (IMED) (pp. 1-7), 2026) Vaz, Sonia
    The intensive introduction of Industrial Internet of Things (IIoT) technologies has already increased the quantity and complexity of machine-to-machine financial transactions, which has become a significant challenge in terms of the clarity of the story and exclusion of threats of fraud. The idea proposed in this paper is AI-based FinTech analytics that feature both supervised and non-supervised machine learning systems when detecting fraud in industrial transactional environments. Advanced feature engineering and imbalance-sensitive model evaluation metrics are used to improve detection accuracy and reliability. Explainable artificial intelligence methods have been used to foster regulatory trust. The experimental findings show that there is a high rate of fraud detection, a low rate of false alarms, and applicability in real time, which validates the efficiency of the proposed approach in light of safe and transparent IIoT-based financial systems. The superior performance of XGBoost (97.6%) is attributed to the optimization of the gradient for fraud detection.
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    An Analysis of LED Ban in Fishing Industry – Case Study of Cutbona Jetty (Goa)
    (ACTA SCIENTIFIC AGRICULTURE (ISSN: 2581-365X) 3.3 (2019): 04-10., 2018) Colaco, Vembly
    Economics of marine fishery depends upon the profitability of the trawler fishery, a recent technological up gradation in marine fishery is the introduction of LED lights. There are various studies put forth on the usage of LED –lights, some believe that usage of LED lights can have harmful effect on the marine fish population, while some perceive to be highly good and profitable. There are several technological institutions and researchers all over the world trying to analyze the technical aspect of light fishery. Countries that use led lights have shown a considerable increase in marine fish production, for eg led lights is highly used is Japan and Korea. The following will analyze the usage of LED and the link it has to profitability. As per the results, we found that respondents opposing LED-ban earned a larger profit compared to respondents supporting the Led-Ban. Thus, it can be said that, LED fishing is one factor contributing to increase in revenue. But care should be taken, that with the desire to earn high profits in the short-run, long term consequences should not be neglected. Thus, there is a need for strong policy framework, to have a sustainable fishing in future
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    Analysis of Backward and Forward Linkages Associated with Migrant Street Fish Vendors from Goa
    (Springer Nature, 2025. 285-291 Sustainable Digital Technology and Ethics in an Ever-Changing Environment: Volume 2, 2025) D’Souza, John Xavier
    The Indian Economy has always been characterized as an Agrarian Economy. A large number of families from the rural regions in India are still dependent on agriculture for livelihood. However with the passage of time the needs of the families have grown exponentially, making it difficult for them to satisfy those needs solely by farming. Hence people have resorted to migration. Employment can be considered as one of the core reasons for migration in India. Although there are numerous challenges faced by the migrants there are backward and forward linkages associated with them too. Goa also attracts a large number of migrants from different states, which are involved in street fish vending. The paper makes an attempt to examine the backward and forward linkages associated with migrant street fish vendors from Goa and also tries to provide a review of the challenges faced by migrant fish vendors. This paper is based on the Ph.D. study, concerning with the Migrant Street Fish Vendors from Goa.
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    Artificial intelligence driven sentiment analysis and market intelligence for strategic business decision making
    (ES Food and Agroforestry, 2025) Vaz, Sonia
    There is an increasing threat to agricultural productivity and food security because of the problems posed by climate change, limited resources, and inefficient supply chain management. The purpose of this study is to introduce AI-based forecasting and supply optimization network (AIFSO-Net), which is an artificial intelligence-driven network for supply optimization and forecasting. Dynamic optimization techniques and hybrid deep learning Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN–BiLSTM) are the two components that constitute this system. By utilizing data from multiple sources, including IoT sensors, satellite imaging, and meteorological records, the system can make predictions regarding crop yields and improve resource allocation in real time. Through the implementation of Predictive Reinforcement Learning (PRL), AIFSO-Net can improve the efficiency of its supply chain by adjusting to changes in the environment and market. The experimental results reveal that AIFSO-Net outperforms existing models, achieving a 12.8% improvement in forecasting accuracy and a 15.6% increase in supply chain efficiency. This is compared to standard methods such as transformers and genetic algorithm-based models. AIFSO-Net can improve agricultural forecasting, reduce post-harvest waste, and increase food security systems worldwide. In conclusion, AIFSO-Net offers a solution for sustainable agriculture that is both scalable and adaptable in addition to providing vital information for decision-making in agricultural situations that are always changing. The novelty of this work lies in the unified integration of hybrid deep learning, multi-source data fusion, and adaptive optimization (Dynamic Multi-Objective Optimization Algorithm (DMOOA) + PRL) within a single end-to-end framework for both crop forecasting and supply chain management, an aspect not addressed in the existing literature. This study contributes to sustainable agriculture and responsible resource management through accurate forecasting and adaptive supply chain optimization in alignment with Sustainable Development Goals 2 and Sustainable Development Goals 12.
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    Economics of Yoga: Multi-Level Healthy Gains
    (Asian Journal of Research in Social Sciences and Humanities, 2016) Falleiro, Savio
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    Federated and Explainable AI Models for Secure FinTech Transactions in Digital Manufacturing Supply Chains
    (Innovations in Machine, Engineering, and Digital Conference (IMED) (pp. 1-6), 2026) Vaz, Sonia
    Digital manufacturing supply chains are becoming increasingly dependent on inbuilt FinTech services to perform automated payments, invoicing, and settlements which presents sensitive financial and operational data to security and privacy threats. This article is an empirical paper concerning the application of Federated Learning (FL) and Explainable Artificial Intelligence (XAI) in securing FinTech transactions in decentralized manufacturing supply chains. The suggested framework will facilitate joint fraud and anomaly-related detection without exchanging raw data between supply-chain participants. Different privacy mechanisms such as client-level and secure aggregation are integrated to safeguard sensitive data and minimize the risks of inferences. Explainable AI methods are used such as SHAP, local surrogate models, to enable transparency and auditability as well as regulatory compliance. Experimental evidence has shown that federated models can attain almost centralized detection accuracy with much stronger privacy guarantees and explainability procedures can give insightful and interpretable information about model decisions. The paper identifies the trade-offs between accuracy, privacy, and computational overhead and concludes that federated and explainable AI provides a convenient, secure, and compliant solution to FinTech-enabled digital manufacturing ecosystems.
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    Impact analysis of e-learning on students of higher education institutions during COVID-19: A structural equation modelling approach
    (RECENT ADVANCES IN SCIENCES, ENGINEERING, INFORMATION TECHNOLOGY & MANAGEMENT 2782.1 (2023): 020198, 2023) Vaz, Sonia
    Classroom-based, face-to-face interactive teaching has been a conventional system for decades in higher education. Due to the spread of COVID-19, universities were forced to halt their educational programmes. As a result of integrating technology and education, e-learning has become a vital learning medium. As e-learning becomes progressively essential in education, there has been a significant increase in e-learning courses and programmes. E-learning systems play a critical role in today’s educational landscape and must be evaluated to ensure decisive delivery, pragmatic use, and a positive impact on learners. As a result of this extensive review of the existing literature and the development of a comprehensive model, different rates of success can be linked to different factors. The Technology Acceptance Model (TAM) and the User Satisfaction Model (USM) were both used to support our findings. PLS-SEM (Partial Least Squares–Structural Equation Modelling) was used to analyze data from 352 students who were participating in an e-learning course. Using this model, the study describes how learners’ self-regulation mechanisms and attitudes, variations in temperament, and extrinsic considerations such as technical assistance, development preparation, and usability of facilities influence the perceived ease of use and perceived value of electronic learning programmes
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    Impact analysis of e-learning on students of higher education institutions during COVID-19: A structural equation modelling approach
    (Journal of Computers, Mechanical and Management, 2022) Vaz, Sonia
    Staff members use tried-and-true procedures when completing workplace visits, delivering services, and completing client tasks. However, the COVID-19 pandemic compelled employers to change the work styles of individual employees to ensure good communication, work-life balance, and flexibility for employees while maintaining optimal work productivity levels. In addition, the World Health Organization established social separation guidelines to combat COVID-19. Thus, the pandemic challenged the work culture and resulted in employees being quarantined in their homes. As a result of this transformation, employees were encouraged to use digital tools to facilitate work-from-home opportunities. The current study analyzes employees' psychological and productive effects of work-from home culture. It also looks for coworker bonding threatened by this transformation and suggests a way to keep it intact. Through a thorough literature review, the authors developed a comprehensive model to assess the pandemic's impact on employees' lifestyles. The conceptual model was empirically tested by applying the model to data collected from 233 employees from various backgrounds. The model result was validated using Partial Least Squares Methods-Structural Equation Modeling. The inferences highlight the factors influencing employee morale and work culture and the parameters closely related to employee functioning in the organization that should not be affected.
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    Impact of consumer behaviour and core competency on brand attributes with respect to marketing mix
    (South Indian Journal of Social Science, 2023) Colaco, Vembly