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| Special Centennial Issue |
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No. 419 |
April 2025 |
Vol. CV (Part-IV) |
ISSN: 0019-5170 |
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Contents
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Financial Performance of Public and Private Sector
Banks in India: Has Covid-19 Made Any Difference?
Ananya Bhatia 1
Jagdeep Kumar 2
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The COVID-19 pandemic has significantly disrupted global
economies, and India, too, grappled with its repercussions,
witnessing potential implications on the performance and
operations of the banking sector. Thus, this study aims to
investigate the extent to which COVID-19 has influenced the
performance of banks within the Indian context. Using a
balanced panel data of 30 banks (12 public and 18 private)
spanning over 13 years (from 2010 to 2022), we employed a
random/fixed effect panel model to check the impact of the
bank-specific and macroeconomic variables on the bank’s
performance. For this purpose, Return on Assets (ROA) and
Return on Equity (ROE) are used as an indicator of financial
performance.
Our findings unveiled significant correlations between
Non-Performing Asset ratios, Credit-Deposit ratios, and
Fixed Assets to Total Assets ratios with both ROA and ROE.
Interestingly, bank-specific variables weren't significantly
impacted by COVID-19 and there was a noticeable increase
in the magnitude of coefficients during the pandemic.
Additionally, we observed that macroeconomic factors,
such as interest rates, became highly sensitive during the
pandemic period. In essence, despite the challenges posed
by the pandemic, banks exhibited resilience in maintaining
their performance. Moreover, our study can assist bankers
in identifying any weaknesses and taking precautionary steps
to enhance their financial standing during crises like
COVID-19.
Keywords: COVID-19, Financial Performance, ROA, Public
banks, Private Banks, Panel Data, Macro Economic
Variables.
- Research scholar, Economics Department, Maharishi Dayanand University, Rohtak,
124001.
E-mail: cananyabhatia.rs.eco@mdurohtak.ac.in
- Assistant professor, Economics Department, Maharishi Dayanand University, Rohtak,
124001. E-mail: jagdeep_dhy.eco@mdurohtak.ac.in
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Impact of Climate Change on Coconut Production
in Kerala: A Time Series (ARDL) Approach
Karnati Kiran Kumar1
Tinu Kurian 2
Naveen Kumar 3
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Climate change greatly influences global agriculture
productivity. Climatic fluctuation and the anticipated
changes in climate brought by global warming are harming
agriculture productivity. However, its directional impact and
intensity on agriculture varies from crop to crop. Few studies
have been undertaken to observe the influence of climate
change on the coconut crop compared to cereals and
legumes. The present study used time series data (1991 to
2020) to understand the various factors that impact the
coconut output. The Autoregressive Distributed Lag (ARDL)
model's error-correcting version is applied to understand the
short- and long-term effects of climate and other variables
on coconut output in the presence of a structural break. The
econometric findings of this study demonstrate that the area
under coconut and fertiliser is significant at a 5 per cent
level. Moreover, both variables have a positive impact on
coconut output. Climate variable like rainfall is significant at
a 5% level and maximum temperature is significant at a 1
per cent level. Finally, both variables have a negative effect
on the coconut output. This implies that climatic variables
such as rainfall and maximum temperature have a
detrimental influence on coconut output in Kerala. According
to the study, outspreading area and fertilizer boost coconut
output in the short run, whereas increasing maximum temperature and rainfall reduces it. However, none of the
factors were statistically significant in the long-term trends.
In this background, adaptive techniques in agriculture are
required to safeguard coconut output and to fulfil the evergrowing demands of the changing habits of human beings.
Keywords: Agriculture, Climate change, Output, Coconut,
Temperature, Fertiliser and Rainfall.
- Assistant Professor, Department of Economic Studies, School of Social Sciences, Central
University of Punjab, Bathinda, Punjab-151401,
E-mail: kirankumarkarnati40@gmail.com.
- Master of Arts in Economics, Department of Economic Studies, School of Social
Sciences, Central University of Punjab, Bathinda, Punjab-151401,
E-mail: tinujacobs09@gmail.com
- Ph.D. Scholar, Department of Economic Studies, School of Social Sciences, Central
University of Punjab, Bathinda, Punjab-151401,
E-mail: www.kumarnavin000@gmail.com
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Modelling Volatility based on GARCH-type Models:
Evidence from Indian Stock Market
Tejesh H R1
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Purpose: This study investigates the effectiveness of various
volatility models in capturing the dynamics of the Indian
stock market, specifically focusing on the returns of the BSE
Sensex Index and NSE Nifty Index. Due to the high volatility
commonly observed in emerging markets like India, the study
aims to compare the performance of different Generalized
Autoregressive Conditional Heteroskedasticity (GARCH)-
type models to improve forecasting accuracy.
Methodology: The study employed secondary data covering
a seventeen-year period from April 2008 to March 2024.
Monthly time series data of the Sensex and Nifty indices are
used in the analysis. The required data is obtained from the
official websites of BSE and NSE. The analysis is performed
using R-Studio version 2024.04.2+764.
Findings: The findings demonstrated that the ARMA (3,0)-
GARCH (2,1) model with normal distribution offers superior
forecasting performance for the Sensex return series. For the
Nifty return series, the ARMA (0,0)-TGARCH (2,1) model
demonstrated the best predictive accuracy. These findings
suggest that specific GARCH-type models can significantly
improve volatility forecasting in the Indian stock market.
Practical Implications: It is recommended that investors,
financial analysts, and corporate strategists consider the
ARMA (3,0)-GARCH (2,1) and ARMA (0,0)-TGARCH (2,1)
models for forecasting stock market returns in the Indian
market for most effective predations.
Originality: This research contributes to the field by offering
a comparative analysis of different GARCH-type models
within the context of the Indian stock market, an area less
explored in previous studies.
Keywords : volatility, GARCH, TGARCH, EGARCH, sensex,
and nifty.
JEL Classification Codes: C22, G17, G32
- Faculty of Commerce, SUBN Theosophical Women’s College, Hosapete - 583201,
Vijayanagara (Dist.), Karnataka, INDIA.
E-mail: hrtejesh@gmail.com
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Intensity of Labour Migration from Rural Farm
Households and Its Determinants: Insight from Assam
Mausumi Das 1
Mrinal Kanti Dutta 2
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Using primary data collected from a field survey of 284
households from four districts in Assam in north east India
and by applying the Double Hurdle model, the present study
examines the determinants of migration of labour from farm
households in the state along with intensity of migration. The
results from the applied model revealed that main
determinants pf migration at farm household level are age of
the household head, education of household head, household
size, dependency ratio, flood, monthly per capita
consumption expenditure and migration network. On the
other hand, the main determinants of the intensity of
migration are dependency ratio, own crop land and
migration network.
Keywords : migration, intensity of migration, double hurdle
model, Assam.
- Ph.D. Research Scholar, Department of Humanities and Social Sciences, IIT Guwahati,
North Guwahati-781039, Assam, India.
Email : das17611013@iitg.ac.in
- Professor, Department of Humanities and Social Sciences, IIT Guwahati, North
Guwahati.
Email: mkdutta@iitg.ac.in
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The Role of Education and Socio-Economic Status in
Determining Occupational Choice of Females in Rural
India: An Analysis Based on PLFS Data 2022–2023
Kashmiri Das
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The development of any nation is crucially dependent on the
efficient utilisation of its human resources. India is such a
country which no doubt enjoys a favourable demographic
dividend but also suffers from under utilisation of its female
work force despite a rise in their educational attainment and
fall in fertility. Females, particularly in rural India has
remained confined to agriculture sector where they engage
as subsidiary workers. Despite the growing significance of
the rural non-farm sector (RNFS), it is dominated by males
while participation of females is determined by several socioeconomic, cultural and demographic factors, thereby
widening the gap in their labour market participation.
However, with the start of the pandemic like Covid-19, some
studies point out that the gaps in labour force participation
between males and females will widen further in the postcovid year, while others demonstrate that participation of
females in the labour market is expected to increase due to
changing social norms that define the gendered pattern of
labour force participation. Our study therefore, contributes
to the extant literature by focusing not only on the
determinants of female’s non-farm employment participation
in the post-Covid period, but also highlights whether status
production is still prioritized or whether it is the skill and
human capital their determines their occupational choices.
The findings demonstrate that although maintenance of
status is prevalent but it applies only to married females. In
case of educated females, participation in non-farm
employment is more significant than maintenance of
household status. For low caste females, education facilitates them to engage in self-employed and casual non-farm
activities.
Keywords: Agriculture, rural non-farm sector, female
employment, rural India.
JEL Classification: J21, J46, J70, O15, R23.
- Guest Faculty, Department of Economics, Cotton University, Assam-781001. India.
Email: kashmirik93@gmail.com
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Determinants of Farmers Attitude Towards Adoption of
ICT for Agricultural Knowledge Management:
A study of farmers from Delhi-NCR
Rajesh Kumar* 1
Seema Singh 2
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Several empirical studies have shown that the intervention of
ICT services for information and knowledge management
(AKIM) for the farmers help them in taking informed
decisions and imparting skill required for their adoption of
modern farming practices. It is therefore very much essential
for the farmers to adopt and use multiple ICT sources in the
era of growing digitalisation to harness the benefit of
innovations in agricultural practices. Diverse individual,
economic, social and other factors influence the attitude and
the likelihood of acceptance of these ICT sources by the
farmers for their AKIM. This is an empirical study to analyse
these factors which influence the attitude and the likelihood
of acceptance of ICT sources by farmers for the purpose of
agricultural knowledge management. The study uses data
from primary survey of 667 farmers from three sub-regions:
Haryana, Rajasthan and Uttar Pradesh of Delhi-NCR and
estimates their ICT adoption level by constructing ICT use
index by using Principal Component Analysis (PCA).
Generalized ordered logistic regression model is used to
analyse factors influencing the adoption level of multiple ICT
sources and average marginal effects are calculated to
predict the likelihood of a farmer to be higher or lower
adopter of ICT for a marginal change in influencing factors.
Results of the study show that older, female and farmers of
lower social classes including minority have low ICT
adoption level. Factors such as age, gender and social class of the farmer negatively impacts likelihood of ICT adoption
by the farmers. Probability of a farmer to fall in high ICT
adoption group increases with education, training, size of
land holding and off farm income of the farmer.
Keywords: ICT, AKIM, Indian farmers, PCA, ICT use index,
Adoption, ologit, gologit.
- Assistant Professor, Lakshmibai College, University of Delhi, New Delhi.
Research Scholar, Delhi Technological University, New Delhi, India.
E-mail: rajeshkpoonia@lb.du.ac.in
- Professor of Economics, Department of Humanities, Delhi Technological University,
New Delhi, India.
E-mail: prof.seemasinghdtu@gmail.com
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Determinants of Land Ownership Disparities
Among Madhya Pradesh Households:
Insights for Sustainable Development
Ankit Singh1
Shweta Sudele2
Rekha Acharya3
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Rapid urbanization and unsustainable resource usage
pose a growing challenge to land ownership and use.
Madhya Pradesh's high population density (approximately
236 individuals per square kilometer, as reported by the
2011 census) mirrors broader patterns found throughout
India, where urbanization and agricultural adaptation are
critical responses to socioeconomic restrictions. The state's
ability to solve these challenges will be vital to sustaining
long-term growth in the face of increasing population
pressures. The number of landed households in Madhya
Pradesh has been steadily going down over the last few
decades. This is because of things like population growth
that breaks up families, urbanization that makes people less
dependent on agriculture, economic diversification into nonfarm activities, distress sales caused by agrarian crisis, and
government policies that favour industrial development over
agriculture. The Agricultural Census conducted by the
Ministry of Agriculture also highlights a decline in
operational holdings. Operational holdings' average size
dropped from 2.28 hectares to 1.08 hectares between 1970–
71 and 2015–16, indicating both fragmentation and
ownership loss. It has become clear that studying the extent
and reasons behind differences in landownership patterns is
essential to creating more informed land-use laws and practices. The factors that predict disparities in agricultural
land ownership among Madhya Pradesh households are
examined in this study. The results from Chi-square analysis
show that the households that own agricultural land are
significantly more likely to be poor or middle class (76 %),
rural dwellers (74 %), male-headed (86 %), living in
northern regions (64 %), and not educated beyond primary
school (63 %). Findings from the logistic regression analysis
indicate that the significant predictors of agricultural land
ownership include ownership of livestock with an odds ratio
(OR) of 3.33, place of residence (OR = 2.28), gender (OR =
0.55), wealth index (OR = 0.56), number of bedrooms (OR =
1.44), and educational attainment (OR = 0.96). This study
underlines the findings significance for sustainable
development, including the creation of cattle ranches, gender
equality, and poverty reduction.
- PhD Scholar, School of Economics, Devi Ahilya Vishwavidyalaya, Indore, Madhya
Pradesh,
E-mail: rajawatankit2013@gmail.com
- Assistant Professor, Shri Govindram Seksaria Institute of Technology and Science,
Indore, Madhya Pradesh,
E-mail: shwetas4699@gmail.com
- Professor, School of Economics, Chairman (BOS, Economics) & Director, Skill
Development, DDUKK, Devi Ahilya Vishwavidyalaya, Indore, Madhya Pradesh.
E-mail: mailforrekha@gmail.com
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Co-Integration & Causal Association Between Economic
Growth and Climate Variables: Indian Evidence
Subrata Roy1
Swati Singh2
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The present study has tried to examine the co-integrating
relationship along with the short-run dynamics among the
GSDP, temperature and precipitation of the Indian states
and Union Territories over a period from January 2010 to
December 2024 under the VECM framework. The study has
shown that there is absence of long-run equilibrium
relationship among the variables but there is presence of
short-run association. According to the Granger causality
test both uni-directional as well as bi-directional causality
have been found.
Keywords : GSDP, Temperature, Precipitation, VECM, Co-integration.
JEL Code: Q54; Q56; Q58; C21
- Associate Professor, Department of Commerce, Mahatma Gandhi Central
University, Motihari, East Champaran, Bihar-845401.
- Research Scholar, Department of Commerce, Mahatma Gandhi Central
University, Motihari, Bihar-845401.
E-mail: swatisingh221196@gmail.com
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