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i
COVID-19: The New Economics for
Economies
Edited By
Dr. Bhola Khan,
Dr. Babagana Mala Musti
ii
Copyright © 2020 by
Dr. Bhola Khan & Dr. Babagana Mala Musti
ISBN 978-1-7438594-1-0-8
All rights reserved. No part of this publication may be reproduced, stored in a retrieval
system, or transmitted, in any form, electronic, mechanical, photocopying, recording or
other means, now known or hereafter invented, without the prior written permission of
the copyright owner. Except for brief quotations in critical reviews, articles or
presentations, upon which the author will be duly acknowledged.
Printed by
Kabod Limited
08030628933
Kabodlimited@gmail.com
iii
Preface
The outbreak of Covid-19 disease has, in most part of the world, started by January 2020.
However, it was first identified in Wuhan province of China in November 2019 and it was
identified as different from the other Corona virus family. In Nigeria, the first case was
recorded on 17th March 2020. In an effort to contain the spread of the disease, just as in
other parts of the world, Nigeria has imposed a strict social distancing and complete
lockdown. The fear of financial and economic crisis led to panic buying and hoarding for
future consumption. Consequently, economic activity in all sectors of the economy came
to standstill. Most industries lay off their staff. The effects on the poor citizen was so
enormous that the government had to introduce palliative packages to ease the impact of
the pandemic on households.
The aim of this edited book is to provide wider and comprehensive literature about the
present Corona virus which also known Covid-19 for the academics, Scholars, researchers,
students and industry professionals to understand the world economies and their problems
in general and Nigerian economy and its problems in specific. Current pandemic has almost
changed the current global scenarios such as Labour Market, Labour Policies and Laws,
Employment, Migration, Reverse Migration, Financial Development and Financial
Inclusion, International Trade Direction and Central Banking and Monetary Policies,
Environmental Changes, Covid-19 and its Impact on Tourism and Tourism Industry. This
edited book comprises of 28 Chapters and various issues are deliberated by the authors and
they tried to proffer a way forward in Covid-19 and Post-Covid-19 time. It will also help
us understand governments’ expansionary fiscal and monetary policies to combat the
impact of Covid-19 and the policies required to bring back their economy on track.
iv
Acknowledgement
First and foremost, we thank Almighty God that provided us the ability and patience to
undertake this work. Our sincere thanks and deep gratitude to all the contributors who
provided their scholarly work for this edited book. The contributors to the book are from
Nigeria, Morocco, Turkey and India. We must express our sincere thanks to the Vice-
Chancellor, Yobe State University, Prof Mala M. Daura for providing all kinds of support
for this book. We also express our sincere thanks to the Deputy Vice-Chancellors, the
Registrar, Bursar and entire Management of the University for their enormous support
during this work. The publication of this edited book is sponsored by Yobe State
University. We really appreciate, and we express our high esteemed to the University and
the Management.
v
Table of Content
Page No.
Chapter 1
The Role of the COVID-19 Pandemic and Oil Prices on the
US Stock Market in Different Volatility Regimes:
An MS ARMA GARCH Approach
Ayben Koy, Oğuz Şimşek 1
Chapter 2
The State of the Economy, Employment Conditions
and Reverse Migration in India: Challenges and Prospects
in the Times of COVID-19
Avanindra Nath Thakur 16
Chapter 3
COVID-19-Impact: Violence Against Women in India
Ve d P r a k as h 27
Chapter 4
The Impact of COVID-19 on Household Economy
and Consumption Preferences: An International Survey
Bilal Celik, Kemal Ozden, Senol Dane 39
Chapter 5
Women Empowerment and Minimization of COVID-19
Pandemic Effects on Households: An Empirical Investigation
Kolawole Subair, Yusuf Ismael 61
Chapter 6
An Appraisal of the Impact of COVID-19 Lockdown on
Hotels’ Business in Adamawa and Taraba States, Nigeria
Boniface, Godwin, Garba Ibrahim Sheka, Atiman Kasima Wilson 75
Chapter 7
COVID-19 and the Global Economy:
An Implication for Aggregate Macroeconomic
Performance and Budget Financing in Nigeria
Galli Shuaibu Musa, Muhammad Lawal Fatima 86
Chapter 8
COVID- 19 Crisis Through the Lens of Migrant Worker:
An Indian Perspective
Samit Mahore 97
vi
Chapter 9
Effect of Corona Virus Pandemic Outbreak on
Agricultural Production (Livestock Sub Sector) and
Food Security in Yobe State Nigeria
Isa Musa Mabu, Mohammed Bukar 108
Chapter 10
The Impact of COVID-19 on Crop Production and
Food Security in the North-Eastern Region of Nigeria
Mohammed B., Musa I. M, Gide S. 116
Chapter 11
Assessment of the Impact of Entrepreneurship Scheme
on Income Generation Among Youths in Yobe State
Hadiza Mali Bukar, Bulus James Ngada, Hussaini Ibn Mohammed 122
Chapter 12
COVID-19 Pandemic, Unemployment and Poverty in
Developing Economies: Nigeria as a Case Study
Mansur Abdullahi, Abdullahi Ibrahim Haruna 133
Chapter 13
MEADS and Logistics of International Trade
Mustapha Khiati, Badiaa Mechkour 149
Chapter 14
Ethical Issues and Challenges Faced by
Education Sector’s Stakeholders
Manish Sharma, Akriti Srivastava 157
Chapter 15
Impact of COVID-19 on Indian Economy with
Special Reference of Banking Sector
Oshin Ansari, H.K. Agrawal 166
Chapter 16
Psychosocial Impact of COVID-19 on General
Workers in Nigeria: A Review
Ali Garba Kolo, Mohammed A. N. A. Imam, Umar Saleh Baba 172
Chapter 17
An Overview of Economic Recession and Its Impact on
Gross Domestic Product (GDP) in the Global Economy
Y. Ebenezer 178
vii
Chapter 18
Relevance of Gandhian Economic Thoughts in the
Wake of Economic Downturn due to COVID-19 Crisis
Mitali Tiwari, Isha Yadav, Samreen Hussain 191
Chapter 19
Changing Dynamics of Industrial Relations in India
Manab Jyoti Gogoi 203
Chapter 20
Financial Inclusion: An Inevitable Solution to Fuel Indian Economy
Manish Sharma, Mohd. Akbar
207
Chapter 21
The Implications of Open Defecation on The
Rural Households of Yobe State, Nigeria
Baba-Adamu Mohammed, Jajere Ibrahim Ahmed 222
Chapter 22
Empirical Assessment of Socioeconomic Impact of
COVID-19 Pandemic on Nigerian Households
Ibrahim Inuwa Balarabe 234
Chapter 23
Impact of COVID-19 on Tourism Sector: An Overview
Jawahar Lal 243
Chapter 24
Environmental Changes and Its Interrelationship with
COVID-19: A Case Study of Assam
Chinmoyee Borpujari 252
Chapter 25
COVID-19 and Impact of Policies Measures on Indian Economy
Sarabjeet Kaur 259
Chapter 26
COVID-19 and Migrant Workers in India: An Appraisal
Raju Majhi 272
Chapter 27
The Economic and Health Impact of COVID-19 In Nigeria
Bhola Khan, Sabina Khanam, Babagana Mala Musti 290
Chapter 28
COVID-19: Challenges and Opportunities in Health Sector
Dr. Gulab Phalahari 297
viii
1
Chapter 1
The Role of the COVID-19 Pandemic and Oil Prices on the US Stock Market
in Different Volatility Regimes: An MS ARMA GARCH Approach
Ayben Koy, Oğuz Şimşek,
1Assoc. Prof. of Finance,
Istanbul Ticaret University,
Email: akoy@ticaret.edu.tr
2Research Assistant,
Istanbul Ticaret University,
Email: osimsek@ticaret.edu.tr
Abstract
While the concerns about the future were reflected in the asset prices in a very short time,
the reactions of the stock markets to the pandemic were seen as investors behaving very
nervously and selling the financial assets. This study examines the volatility structure of
the US stock market during the ongoing pandemic period with the help of Markov Regime
Switching Autoregressive Moving Average Generalized Autoregressive Conditional
Heteroskedasticity (MS ARMA GARCH) Models. The findings obtained from the study
provide evidence that oil has got a vital role in explaining the yield difference for SP500
index between different volatility regimes as the low volatility and the high volatility.
Keywords: SP500, COVID-19, Oil, Markov Regime Switching Model, MS ARMA
GARCH
JEL Codes: G10, G12, G15
Introduction
While the 2020 COVID-19 pandemic continues to spread throughout the World, the losses
the pandemic will cause are discussed in the basis of global, territorial and sectoral
respectively. These scenarios will become more known as the economic and social effects
of the pandemic occur over time. On the other hand, Russia-Saudi Arabia’s oil price war
after failing to reach an OPEC agreement resulted in a collapse of crude oil prices and a
stock market crash in March 2020. In the financial markets, while the concerns about the
future were reflected in the asset prices in a very short time, the reactions of the markets to
the pandemic were seen as investors behaving very nervously and selling the financial
assets. In 2020, losses in the US and European stock exchanges fell nearly 35% below their
COVID-19: The New Economics for Economies
2
highest level in history. The uncertainty led global investors to gold, which they saw as a
safe haven in past crises, and the most convertible currency, the US Dollar. While various
economic programs were announced by governments in the context of combating the
pandemic, there were visible rebounds in the markets as a result of important decisions
such as the liquidity-increasing decision affecting all financial markets at the global level,
such as the Fed's unlimited government bonds and mortgage-backed securities.
On the other hand, oil prices dropped very rapidly with decreasing demand. The pandemic
process and the slowdown in economies continued to lower this demand further and keep
the price down. Moreover, the price of the May futures contract for the West Texas
Intermediate / WTI type crude oil of the US fell to $ minus 37.63 USD per barrel on April
20. The decrease in oil demand and the concerns that the warehouses would fill brought
the price to minus levels. Energy prices have undergone major changes since the 1990s.
Oil prices, which tended to decline in the 1990s due to political and economic reasons, fell
to $12. With the trend that started in 2002, it rose to $140. In the last two decades, great
price changes continued to be observed in other energy prices, such as natural gas, in both
the spot and derivative markets. We will continue to see similar movements in energy
prices both from supply-demand and from the structures of financial markets.
This study examines the volatility structure of the US stock market during the pandemic
period. The pandemic first showed its effect on the slowdown in the world economy in oil
prices. Although the drop-in oil prices were due to a contraction in demand and excess
supply before the pandemic, it deepened with it. Following oil prices, major return losses
and declines occurred in developed and emerging stock markets, including the US stock
market. The fact that the USA has an important place among oil-producing countries brings
with it a more corrosive effect on the decline in oil prices for the US economy. Within the
scope of these relations, while examining the volatility structure of the SP500 index, which
provides information about the US stock markets, it has become important to add the
change in oil prices to the model. The number of cases of the pandemic that emerged in the
USA on January 21, 2020, is another variable in the study. While trying to explain the
change in the US stock market in parallel with the developments in the world with changes
in oil prices, it is another sub-subject of the study to investigate whether the increasing
number of cases in the country is important in explaining the volatility change in the stock
market.
Literature Review
In addition to its main impact areas such as disease and death, COVID-19 also has
significant social, economic, and financial impacts. While scientists all over the world use
drugs, vaccines and etc. to solve the pandemic, other researchers trying to understand the
effects of COVID-19 in various disciplines such as economy, banking, and tourism. When
COVID-19: The New Economics for Economies
3
looking at the studies in the field of economy and finance, it is seen that issues such as
production and consumption demands, government expenditures, loss of GDP, tourism,
and travel are examined, and various predictions have been made. Studies examining the
effect of COVID-19 on stock markets and financial markets are spreading rapidly in the
literature.
Studying with coronavirus data between 23 January 2020 and 13 March 2020, Zeren and
Hizarci (2020) use the sample of China, South Korea, Germany, Spain, and France, where
cases are frequently seen. The study examines the cointegration relationship between stock
market indices and the number of daily COVID-19 cases and deaths, and the structural
break unit root tests used showed that the structural break in the stock market indices
occurred in March. According to the findings of the cointegration analysis, they state that
there is a significant long-term relationship between the COVID-19-day total case and the
stock market indices of China, South Korea and Spain, but the relationship is not valid for
Italy, France, and Germany. As a result of the study, investors are recommended to invest
in cryptocurrencies, gold, and derivative instruments rather than stock market investments.
Like Zeren and Hızarcı (2020), Günay (2020) has also examined the cointegration between
financial markets and COVID-19. Firstly, analyzing the impacts of COVID-19's on stock
volatility is aimed at the study, the author also aims to investigate the correlation between
stock market indices of countries such as US, United Kingdom, Spain, Italy, China, and
Turkey. Using the data from 3 January 2005 and 3 April 2020, the author divided into the
data four sub-intervals. In the study where modified ICSS, dynamic conditionally
correlated multivariate GARCH, and DCC-MVFIGARCH models are used, it is reported
that all stock markets experienced structural breaks during the audited periods. However,
while the Turkish and Chinese stock markets do not show the structural breaks caused by
COVID-19, and also it has been stated that the highest increase in the joint movement is
seen between the Turkish and Chinese stock markets.
Examining whether the numbers of current COVID-19 cases and deaths and the number of
new COVID-19 cases and deaths affect stock returns Alber (2020), worked on six different
countries in which the highest number of total cases between 1 March 2020 and 10 April
2020. According to the result from China, France, Germany, İtaly, Spain, and USA, it is
concluded that stock market returns are sensitive to coronavirus cases more than deaths and
sensitive to cumulative coronavirus cases more than new cases.
Sansa (2020) analyze the effects of COVID-19 on the financial markets for China and the
USA. He uses the data of the Shangai Stock Exchange and Dow Johns Exchange as
dependent variables while using the confirmed case numbers as independent variables.
COVID-19: The New Economics for Economies
4
Covering the period 1-25 March 2020, findings from the study reveal a significant positive
association between confirmed COVID-19 cases and financial markets.
Examining the impact of the COVID-19 pandemic on the global economy using data
between January 21, 2020, and April 07, 2020, Şenol and Zeren (2020) use the number of
cases and deaths as independent variables, while Morgan Stanley Capital International
(MSCI) World Indices, Europe, Emerging Markets and They used G-7 countries as
independent variables. According to the results of the Fourier Cointegration test used
within the scope of the analysis of the study, it has been reported that there is a long-term
relationship between exchanges and COVID-19. In another study, examining country-
specific and systematic risks in financial markets under the COVID-19 outbreak, Zhang et
al. (2020) state that financial markets are following a dramatic movement at an
unprecedented scale. Research findings revealed that global market risks increased
significantly with the pandemic. Stock market responses on country basis have been
directly linked to the severity of the pandemic. Moreover, it is stated that the economic
losses associated with the pandemic increased the volatility in the markets and caused the
markets to become unpredictable.
Baret et al. (2020) argue that the pandemic generally causes loss of value in stock markets,
stocks, and bonds. Describing this as the proof that this situation pushes financial markets
and investments in a different direction, Baret et al. (2020) argue in detail that as of
February 21, 2020, stock, bond, and oil prices dropped significantly and all asset classes
were looking for a safe haven. Indicating that unlike other world markets, the Chinese
market remains strong and stable regardless of the COVID-19 outbreak, Xinhuan (2020,
p.1) reports in his study that the Chinese financial market remained more stable in general
compared to overseas markets despite the new COVID-19 outbreak.
Examining the effects of COVID-19 on the financial markets in terms of imports, Larry
(2020), state that imports almost stopped that worldwide imports are dependent on Chinese
production and the pandemic seriously stopped production possibilities. Therefore, it is
reported that COVID-19 is directly affected by the export economies of countries around
the world.
Data and Methodology
The daily data used in the study covers the period 01.02.2020 to 04.13.2020. The study
looks at a short period in which the covid19 pandemic caused stock market crashes in early
2020. Futures prices of both WTI crude oil (OIL) and SP500 index (SP500) are used instead
of spot prices to make analysis with larger data. Total cases (TC) used in the study are the
cases reported for the USA.
COVID-19: The New Economics for Economies
5
The low volatility and high volatility regimes of SP500 are analyzed by Markov Regime
Switching Autoregressive Moving Average Generalized Autoregressive Conditional
Heteroskedasticity (MS-ARMA-GARCH) Model. The MS-ARMA-GARCH models are
tested in OXmetrics. Before modeling the variables, the stationarity of the series is
investigated using the Augmented Dickey–Fuller (ADF), Philips–Perron (PP), and
Kwiatkowski-Philips-Schmidt-Shin (KPSS) unit root tests by E-Views. Test results
concluded that series in difference should be used. Besides, the AR(2) MA(2) model is
selected with the help of the automatic ARIMA forecasting function of E-Views.
The descriptive statistics for the variables in level and differences are shown in Table 1.
While OIL and SP500 skewed to the left (left-skewed) in level (-0.4568, -0.6241), and in
difference series (-1.1530, -0.5095); total cases is skewed to the right (right-skewed)
(2.6215, 3.3697). The kurtosis’ of OIL and SP500 in levels are less than 3, these series have
lighter tails than a normal distribution, or we can say these series have light-tailed
distributions. On the other hand, the kurtosis of other variables is greater than 3. Total cases
in level, and all variables in differences has got heavier-tailed distributions.
Table 1. Descriptive Statistics
OIL
TOTAL
CASES
SP500
D OIL
CHANGE IN
TOTAL
CASES
D SP500
Mean
43.4954
52985.55
3016.373
-0.5260
7534.743
-6.4645
Median
49.9200
15.5000
3230.750
-0.4000
1.0000
-0.7500
Maximum
63.2700
557571.0
3387.380
5.0100
97819.00
227.0000
Minimum
20.0900
0.0000
2220.500
-10.1500
0.0000
-279.7500
Std. Dev.
14.2600
127628.9
355.0131
2.0986
18380.82
90.1241
Skewness
-0.4568
2.6215
-0.6241
-1.1530
3.3697
-0.5095
Kurtosis
1.6311
8.9536
1.8338
9.0491
15.2606
4.9195
Jarque-Bera
8.3511
194.0485
8.9971
129.2208
603.5358
14.5617
Probability
0.0154
0.000000
0.0111
0.0000
0.0000
0.0007
Sum
3218.660
3920931.
223211.6
-38.9200
557571.0
-478.3700
Sum Sq. Dev.
14844.38
0.0000
9200503.
321.4992
0.0000
592931.6
Observations
74
74
74
74
74
74
Source: Author’s Calculations.
COVID-19: The New Economics for Economies
6
Methodology
In 1982, Engle developed the Autoregressive Conditional Heteroscedasticity model
(ARCH) to estimate the time-varying variance of financial assets. This model is generalized
as the Generalized Autoregressive Conditional Heteroskedasticity model (GARCH) by
Bollerslev in 1986. The GARCH class models, which still maintain their popularity with
the presentation of Engle and Bollerslev, describe the features of the financial time series
beyond the future volatility clusters, such as the extreme plausibility and thick tail. In the
GARCH model developed by Bollerslev (1986) and Taylor (1986), the variance of error
terms is affected both by their past values and by the values of their conditional variance:
𝜎!
"=𝛼#+𝛼$%𝑢!%$
"+𝛽σ&%$
"
(1)
The GARCH models are successful in modeling the variances in time series that have
continued to be developed to test different features of the series. The Thresholder
Generalized Autoregressive Conditional Variance (TGARCH) model developed by
Zakoian (1994), GJRGARCH (Glosten et al., 1993), Integrated GARCH (IGARGH)
(Nelson, 1990), Fractionally Integrated GARCH (FIEGARCH) (Baillie et al., 1996),
GARCH in mean (GARCH-M) (Engle et al., 1987) are among the main linear GARCH
models.
The exponential generalized autoregressive conditionally varying variance (EGARCH)
model developed by Nelson (1991) is the first GARCH model to analyze the nonlinearity
in a GARCH model. Logistics smooth transition GARCH (LSTGARCH) (Hgerud, 1997
and Gonzalez-Rivera, 1998), Volatility switching GARCH (SGARCH) (Fornari and Mele,
1997), Asymmetric nonlinear smooth transition GARCH (ANST-GARCH) (Anderson et
al., 1999) and Quadratic GARCH (QGARCH) (Sentana, 1995), are some of the other
nonlinear GARCH models.
This study investigates the volatility of SP500 index by implementing the Markov Regime
Switching GARCH (MS GARCH) models. The main structure of the MS GARCH model,
which analyzes the financial markets in terms of the low volatility and high volatility
markets, is created by Klaassen (1999). Kim (1993), Cai (1994), Hamilton and Susmel
(1994), and Dueker (1997) are the studies that developed the constraints of the MS GARCH
models.
MRS models are firstly implemented by Hamilton (1989). According to the MRS models,
the economy (market) is not directly observable, the time series variable can be observed
and the regime of the economy (s0) can be obtained in probabilities. If the last state is
known, it is also possible to estimate the following state s1 based on the probability of
regime-switching (Bildirici et al., 2010). The MRS model has got a time series process
dependent on an unobservable regime variable (st) (Krolzig, 2000). The regime-generating
COVID-19: The New Economics for Economies
7
process is an ergodic Markov chain (Krolzig 2000) (pij = Pr(st+1 = j|st = i); ∑
𝑃𝑖𝑗=1
'
()$
;
i,j ={1,..,m) and st follows an ergodic M-state Markov process with an irreducible transition
matrix:
𝐏=
/
𝑝$$ … 𝑝$'
… … …
𝑝'$ … 𝑝''
/ (2)
In a standard MS GARCH Model, there is a conditional mean, a conditional variance, a
regime process, and a conditional distribution (Marcucci, 2005 and Günay, 2015). The
methodology used in this study is the Markov Regime Switching Autoregressive Moving
Average Generalized Autoregressive Conditional Heteroskedasticity (MS-ARMA-
GARCH) Model.
While yt is the dependent variable of the ARMA model,
ℎ!
is the conditional variance, and
the parameters (
∅,𝜃,𝛼,𝛽)
are dependent on the state of Markov chain, the formulas in the
ARMA-GARCH model are as follows:
𝑦!=𝐶*!+𝜀!+ +
∑
%∅,(𝑆!)𝑦!%,
-
,)$ +%
∑
𝜃((𝑆!)ɛ!%(
'
()$
%%%%%%%%%%%%ℎ!
=
𝑤.!
+ ∑
%𝛼,(𝑆!)
/
,)$
𝜀!%,
"
+∑
%𝛽((𝑆!)
0
()$
ℎ!%(
𝜀!+ =%
>
ℎ!%∗%𝑢!
;
𝑢!++~%𝑁(0,1)
, (3)
Empirical Analysis
We employed MS-ARMA-GARCH models with two regimes to investigate the low
volatility (regime 0) and high volatility regimes (regime 1) in the SP500 stock index. The
estimation procedure implemented in the "Ox Metrics program" identifies regime 0 as the
low volatility and regime 1 as the high volatility. With the help of the ARMA models, the
regimes are also identified as expansion (regime 0) and recession (regime 1).
We formulated different ARMA models with different switching components as constant,
oil, and TC. As shown in Table 1, four of the seven models installed have got switching
TC variables, and four models have got switching oil variables. Among the seven different
models, four models are found significant. Only one of these four models that are found
significant contains the switching variable TC. However, three of the models with a
switching oil is significant.
COVID-19: The New Economics for Economies
8
Table 2: Models
SIGNIFICANT MODELS
INSIGNIFICANT MODELS
MODEL 1
MS_ARMA_GARCH(2, 2, 2) - C
(Switching Constant)
MODEL 2
MS_ARMA_GARCH(2, 2, 2) - CO (Switching
Constant and Oil)
MS_ARMA_GARCH(2, 2, 2) - CTC (Switching
Constant and Total Cases)
MODEL 3
MS_ARMA_GARCH(2, 2, 2) – O (Switching Oil)
MS_ARMA_GARCH(2, 2, 2) – CV (Switching
Constant and Total Cases)
MODEL 4
MS_ARMA_GARCH(2, 2, 2) – COTC (Switching
Constant, Oil and Total Cases)
MS_ARMA_GARCH(2, 2, 2) – OTC (Switching
Oil and Total Cases)
Source: Author’s Calculations.
The estimated equations for Model 1-2 are represented in Table 2. In the first model the
only switching variable in the ARMA equation is the constant. The second model have got
two switching variables as constant and oil.
According to Model 1, the coefficients of the autoregressive variables are positive (ar(1),
0.0777; ar(2), 0.2250), and the coefficients of the MA variables are negative (ma(1), -
0.2883; ma(2) -0.1012), and the variable with the highest positive coefficient is the second
autoregressive component. The constant in Model 1 is the only switching variable. In
regime 0 which is described as expansion or low volatility, the constant is 5.7819, and it
takes a value of -28.5710 in regime 1. This high difference explains the huge losses of
regime 1 of SP500.
In the ARMA equation of Model 2, the second autoregressive component is positive (ar(2),
0.1242), the first autoregressive component and the MA variables are negative (ar(1), -
0.1790; ma(1), -0.0721; ma(2) -0.0453). In Model 2, there are two switching variables as
constant and oil. The constant is positive in regime 0 (5.4900) and a negative in regime 1
(-8.0628). Oil plays an important role in explaining the regime change in the ARMA
equation of the second model. As can be seen from the coefficients, the role of oil in
explaining the return difference in the sp500 index in regime 1 is nearly three times in
regime 0 (oil(1), 5.7699; oil(2) 15.2146).
The variance equations have got three switching components as constant, alpha (the
residual’s square), and beta (conditional variance). It is seen in the variance equations for
both two models that the coefficients are higher in the high volatility regime (regime1) and
the vital role belongs to the constant (Model 1: constant (1), 24.1245; constant (2), 90.0796;
Model 2: constant (1), 23.6097, constant (2), 95.3600). The fact that the constant in the
high volatility regime is approximately four times the constant in the low volatility regime
draws attention to the volatility difference. It is also seen that the conditional variance in
COVID-19: The New Economics for Economies
9
the first model has a higher importance in explaining the volatility difference compared to
other models (2-4) (Model 1: beta(2), 0.2690; Model 2: beta(2), 0.0503; Model 3: beta(2),
0.1064; Model 4: beta(2), 0.0655).
Table 3: Equations, Probabilities and Information Criteria for Model 1 and Model 2
MODEL 1
MODEL 2
Regime (0)
Regime (1)
Regime (0)
Regime (1)
ARMA
Equation
ARMA
Equation
AR(1)
0.0777
AR(1)
-0.1790
AR(2)
0.2250
AR(2)
0.1242
MA(1)
-0.2883
MA(1)
-0.0721
MA(2)
-0.1012
MA(2)
-0.0453
Constant
5.7819
-28.5710
Constant
5.4900
-8.0628
Oil
5.7699
15.2146
Variance
Equation
Variance
Equation
Constant
24.1245
90.0796
Constant
23.6097
95.3600
Alpha
0.0000
0.1009
Alpha
0.0000
0.1187
Beta
0.0000
0.2690
Beta
0.0000
0.0503
Transition probabilities
Transition probabilities
p_{0|0}
0.9724
p_{0|0}
0.9723
p_{1|1}
1
p_{1|1}
1
Information Criterions
Information Criterions
log-likelihood
-392.2268
AIC
11.2563
SC
11.6674
log-likelihood
-388.5403
AIC
11.2095
SC
11.6838
Source: Author’s Calculations.
Table 3 shows the estimated equations for Model 3-4. Model 3 has got only one switching
variable in the ARMA equation as the oil. Model 4 has got three switching variables in
ARMA equation as constant, oil, and total cases. Although they are not very different from
in the other models in terms of value, the lowest AIC and SIC criteria are obtained in Model
3. Thus, Model 3 can be said to be stronger than other models.
In the ARMA equation of Model 3, one of the coefficients of the autoregressive and MA
are positive (ar(2), 0.0760; ma(1), 0.0283) and the other variables are negative (ar(1), -
0.2706; ma(2): -0.0230). The constant in Model 3 does not switch and it takes its lowest
positive value among the constants in other models (4.2324). The coefficient of oil the high
volatility regime is approximately four times the coefficient in the low volatility regime
(oil(1), 4.8628; oil(2), 16.6188). Defining the switching mechanism between regimes in the
COVID-19: The New Economics for Economies
10
ARMA model alone, oil is successful in determining the yield differences in the SP500
index.
While the ARMA equation of Model 4 is examined, it is seen that only the second
autoregressive component is positive (ar(2), 0.1943), and the first autoregressive
component and the MA variables are negative (ar(1), -0.0906; ma(1), -0.1832; ma(2) -
0.1482). Model 4 has got three switching variables in the ARMA equation, at the same time
it is the only model with a switching TC variable. The constant in the high volatility regime
is negative as in the Model 1 and Model 2, and it is approximately six times higher in
absolute value (constant(1), 7.0680; constant(2) -43.4210). The coefficients of oil show its
important role in determining the regimes as in the Model 2 and Model 3 (oil(1), 6.1287,
13.9931) and TC have got a significant effect too (TC(1), -0.0002, TC(2), 0.0017).
Variance equations for both Model 3 and Model 4 show similar results as Model 1 and
Model 2. The coefficients are higher in the high volatility regime (regime1) and the vital
role belongs to the constant (Model 1: constant (1), 24.6903; constant (2), 92.9959; Model
2: constant (1), 22.8972, constant (2), 86.3017). The constants in the high volatility regimes
are approximately four times the constant in the low volatility regimes. It is also seen that
the constant of alpha (the residual’s square) in the high volatility regime of Model 4 is
higher when compared to other models (1-3) (Model 1: alpha(2), 0.1092; Model 2:
alpha(2), 0.2178; Model 3: alpha(2), 0.1092; Model 4: beta(2), 0.2178).
Table 4: Equations, Probabilities and Information Criteria for Model 3 and Model 4
MODEL 3
MODEL 4
Regime (0)
Regime (1)
Regime (0)
Regime (1)
ARMA
Equation
ARMA
Equation
AR(1)
-0.2706
AR(1)
-0.0906
AR(2)
0.0760
AR(2)
0.1943
MA(1)
0.0283
MA(1)
-0.1832
MA(2)
-0.0230
MA(2)
-0.1482
Constant
4.2324
Constant
7.0680
-43.4210
Oil
4.8628
16.6188
Oil
6.1287
13.9931
Total Cases
-0.0002
0.0017
Variance
Equation
Variance
Equation
Constant
23.6903
92.9959
Constant
22.8972
86.3017
Alpha
0.0000
0.1092
Alpha
0.0000
0.2178
Beta
0.0000
0.1064
Beta
0.0000
0.0655
Transition probabilities
Transition probabilities
p_{0|0}
0.9724
p_{0|0}
0.9712
COVID-19: The New Economics for Economies
11
p_{1|1}
1
p_{1|1}
0.9826
Information Criterions
Information Criterions
log-likelihood
-388.7912
AIC
11.1886
SC
11.6313
log-likelihood
-386.5800
AIC
11.2383
SC
11.8075
Source: Author’s Calculations.
Figures 1-4 show the smoothed regime probabilities for Model 1-4.
In the upper left part of the figures, it can be examined how much the actual data is
overlapped with the 1-step-predictions. With the help of figures, it is seen that the success
of the 3rd model higher than other models is in line with the information criteria.
The shaded areas seen on the left bottom side of the figures correspond to low volatility
regimes (regime 0), and the shaded areas on the right bottom sides are related to high
volatility regimes (regime1).
In this study, it is seen on the figures that regime periods with high volatility include periods
in which the stock markets experienced collapse during the pandemic. During the sampling
period, the first regime switching from low volatility regime to high volatility in all models
occurred before the 40th observation. The only model in which the second regime
switching is experienced is the fourth model with the TC variable.
Figure 1: Model 1
DSP500 FT
1-step prediction
Fitted
Regime 0
0 20 40 60 80
-200
-100
0
100
200 DSP500 FT
1-step prediction
Fitted
Regime 0
r:DSP500FT(sc aled)
0 20 40 60 80
-2
-1
0
1
2r:DSP500FT(sc aled)
0 20 40 60 80
0.25
0.50
0.75
1.00 P[Regime 0] smoothed
0 20 40 60 80
0.25
0.50
0.75
1.00 P[Regime 1] smoothed
COVID-19: The New Economics for Economies
12
Figure 2: Model 2
Figure 3: Model 3
DSP50 0FT
1-step prediction
Fitted
Regime 0
0 20 40 60 80
-200
-100
0
100
200 DSP50 0FT
1-step prediction
Fitted
Regime 0
r:DSP500FT(sc aled)
0 20 40 60 80
-2
0
2
r:DSP500FT(sc aled)
0 20 40 60 80
0.25
0.50
0.75
1.00 P[Regime 0] smoothed
0 20 40 60 80
0.25
0.50
0.75
1.00 P[Regime 1] smoothed
DSP500F T
1-step prediction
Fitted
Regime 0
0 20 40 60 80
-200
-100
0
100
200 DSP500F T
1-step prediction
Fitted
Regime 0
r:DSP500FT(sc aled)
0 20 40 60 80
-2
0
2
r:DSP500FT(sc aled)
0 20 40 60 80
0.25
0.50
0.75
1.00 P[Regime 0] smoothed
0 20 40 60 80
0.25
0.50
0.75
1.00 P[Regime 1] smoothed
COVID-19: The New Economics for Economies
13
Figure 4: Model 4
Conclusion
This study examines the volatility structure of the US stock market during the pandemic
period with the help of MS ARMA GARCH Models to investigate the low volatility
(regime 0) and high volatility regimes (regime 1) in the SP500 stock index. We formulated
different ARMA models with different switching components as constant, oil, and TC.
Among different models, only one model contains the switching variable TC, but there are
three models contain a switching oil as a variable in the ARMA equation. Moreover, the
coefficients of oil in different regimes show that oil plays an important role in explaining
the regime changes in the ARMA equations. Also, lowest AIC and SIC criteria are obtained
in Model 3, which is the model that has got one switching variable in the ARMA equation
as oil. We can conclude that oil is the most important variable in explaining the yield
difference for the SP500 index between different volatility regimes as the low volatility
and the high volatility.
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16
Chapter 2
The State of the Economy, Employment Conditions and Reverse Migration in
India: Challenges and Prospects in the Times of COVID-19
Avanindra Nath Thakur,
Associate Professor,
Jindal School of Government and Public Policy,
O P Jindal Global University, Sonipat, Haryana, India,
Email: avanindra.cesp@gmail.com
Abstract
The outbreak of COVID-19 has happened in the time when the entire globe was already
struggling with recessionary tendencies and consequent upon the enormous extent of the
spread of the infections and following economic lock down has led to the situation with
little prospects of recovery in short span of time. In Indian scenario, the situation was even
worse primarily on three accounts; first the country’s health care system was completely
inadequate to handle such a level of pandemic therefore long lasting effect of pandemic
was not never beyond the expectation. secondly, an overwhelming proportion of India’s
working population was engaged in informal sector having hardly any social securities
and therefore remained largely vulnerable to any external shock such as like this. Thirdly,
for couple of years the health of the economy has been experiencing relative deteriorating
on many accounts and consequently the distress was quite clearly visible on employment
front of the country. The declaration of country wide complete lockdown triggered massive
reverse migration, closure of various production units, increased unemployment at massive
scale. Though the government later announced relief packages, given the extent of crises,
the packages has hardly expected to cater much. Thus, there is a desperate need to rethink
not only about the approach to address the crises led by Covid 19 in short terms such as
revaluation of economic relief packages but also there is an urgent need to rethink about
the entire development discourse of the country in terms of reprioritising agriculture and
other labour intensive sectors of the economy, bringing bout institutional changes such as
land redistribution etc., formalisation of workforce with the provision of adequate social
security and giving priority to health and education sector of the country in order to make
India resilient to such unforeseen crises in future.
Keywords: Informal Sector, Labour, Reverse Migration.
Introduction
For country like India generation of decent work and livelihood conditions has been among
the most important policy objectives of economic development since Independence.
COVID-19: The New Economics for Economies
17
However, on this front, spanning over more than seven decades of Independence Indian
achievement has not been satisfactory. The ‘dual economy’ model (Lewis, 1954) based on
relatively smooth transitions derived automatically through the market route has hardly
been realized so far. More precisely, a transition of workforce from low paid
informal/agriculture/primary to high paid/secondary or tertiary has remained nearly off the
track (Jha and Thakur 2013). Nearly 80 percent of the workforce in India is engaged in
informal sector with either very low level of social security or even worse without any kind
of social security. Evidently, vulnerability of the workforce in regard to any external shock
can easily be estimated to be very high in case of India.
In fact, with the advent of neo-liberal policies the overall performance in regard to the well-
being of labour has come under severe stress, especially in the context of provisions of
adequate and sustainable livelihood options for a large section of the rural population and
of rising number of informal urban labour. Factors like substantial compression of rural
development expenditures, increasing input prices, vulnerability to world market price
fluctuations due to greater openness, inadequate /non-existent crop insurance and
substantial weakening of the provisioning for credit, along with the governments’ apathy
to the demand for remunerative prices for farm produce are among the obvious causal
correlates of the contemporary agrarian crisis in the country (Patnaik, 2007; Roy, 2017,
Talule, 2020). The adverse effect of these policies was reflected in the substantial decline
in the absorption capacity of the agricultural sector and a continuous deterioration in the
quality of employment in the entire rural sector. The neoliberal policies with contractionary
fiscal and monetary regime led to the demand contraction in the economy and sectors such
as agriculture etc. witnessed sharp fall in their overall incomes (Jha and Thakur 2017).
Concentration of economic activities in a few hands led to the nearly stagnant formal sector
with nearly stagnant employment elasticities in most of the sectors of the economy. The
agrarian distress has led to the sharp fall in the purchasing power of the rural population in
general. Micro, Small, Medium enterprises also witnessed relative deterioration in their
overall performance over the years. Employment elasticities in the entire formal sector
remained very limited and thus creation of additional employment remained largely absent
(Papola and Sahu, 2012).
Besides, during last couple of years the some of the indicators of the economy has been
showing the sign of rising distress. Thus, outbreak of the Covid 19 and subsequent
announcement of economic lock down are bound to effect Indian economy adversely and
the impact can easily be predicted to be extremely severe in case of informal laborers
constituting overwhelming share of total country’s workforce. In this scenario, this paper
would examine that to what extent Indian economy has been affected by such outbreak and
what has been the possible impact on workforce of the country primarily on employment
front. Further, it would try to identity the challenges associated with providing quality
COVID-19: The New Economics for Economies
18
employment in this scenario and would also evaluate the prospects of recovery. While
explaining the prospects of recovery, this paper would also suggest some policy measures
in order to address those challenges in a given time frame.
State of the Economy and Employment Conditions on the Eve of Covid 19 Outbreak
State of the Economy:
Figure 1: The growth rates of various sectors since 2016
Source: Government of India, Economic Survey 2020.
Particularly since 2015, with further deepening of neo-liberal agenda in the economic
sphere and ignorance of agriculture sector in policy discourse led to the contraction in
overall demand and triggered further deterioration in the economic conditions of marginal
population in general and rural population in particular. Figure 1 shows that overall GDP
growth of the country has decelerated from 7.9 percent in 2016 to 4.9 percent in 2019. In
fact, India’s GDP growth had dropped to 4.5% in the July-September quarter of 2019-20
and for the entire fiscal year it remained lower than 5 percent. A sharp deceleration in
Agricultural growth has been witnessed since 2016 reflecting worsening of already existing
agrarian distress in country. In 2019 there has been the lowest growth in industry since
1991-92 equalling 2.6 percent. Further, since July 2019, Except for November, there has
been contraction in factory output. Capital goods and consumer goods output remained the
worst hit. The monthly collection of GST, stuck below the Rs 1 lakh crore mark since May
2019. A sharp deceleration in the construction sector was witnessed during 2019-20 and
the country's real estate sector witnessed one of the poorest years, faced with a poor housing
7.9
6.9 6.6
4.9
6.8
5
2.7 2.6
7.5
6
7.5
2.6
7.7 7.8
6.9
5.9
9.2
11.9
8.6 9.1
0
2
4
6
8
10
12
14
2016-17 2017-18 2018-2019 2019-2020
Growth Rates since 2016
GVA Agr+ Industry Trade+ Finance+ Community+
COVID-19: The New Economics for Economies
19
demand. As on date, according to rough estimates, there is an unsold inventory of around
450,000 housing units. External Sector was also showing a deteriorating trends. The
shrinkage of merchandise exports in the successive months of August (-6%), September (-
6.6%), October (-4.6 %), November (-0.34%) and December (-1.9%) for 2019 showed an
alarming trends (RBI Handbook). Similar was the trends of Import and overall trade during
the same period. The decline was evident in import of consumption goods indicating falling
demands in the economy. Indian rupees against the dollar touched the lowest even before
the lock down was announced. A trend of rising external debt was also evident since the
beginning of the fiscal year. Thus, before the outbreak of the Coid-19, the economic
condition of the country was not good enough to provide with reasonable resistance. The
year 2019-20 was already reflecting the trends of recession.
Employment Condition
Urban employment: A falling employment elasticities across different sectors coupled
with recent trend of deceleration in growth rate for various sectors of the economy has
strong negative implications for employment generation in the country (Basu and Das
2016). Quite recently, it is not that the capacity to absorb additional labour has halted in
the major sectors in Urban India, but also there has been enough trends showing an
expulsion of labour from the same. For instance, urban manufacturing sector has witnessed
a significant decline of 2.1 million jobs between 2013–14 and 2015–16 (Mehrotra et al
2014). Construction sector which accommodated large part migrant labour in urban areas
between 2006 to 2012 started losing its capacity to absorb additional labour during the more
recent period. In fact, half a million jobs were lost in this sector since 2013 in urban area
itself. However, some rise in employment in urban area was observed in the services sector
(by nearly 2.9 million) it remained largely confined to unorganised sector (Abraham, 2017).
Besides, there has been enough evidence of suppression of real wage in the urban sectors
since 2014 (Anand and Azad, 2019).
Farm Employment- As discussed earlier Agriculture sector remained the worst victim of
neo-liberal reforms and the situation was further exacerbated with further apathy of the
government since 2013. This has a strong bearing on employment conditions in the farm
sector. In fact, negative employment elasticity has been evident in the agricultural sector
since 2004 (Basu and Das 2016). Thus, expulsion of labour from the agriculture sector has
been visible since then. Between 2004-05 and 2009-10 nearly 20 million employments
were lost in agriculture (Himanshu, 2011 and Thomas 2012). Further, between 2009-10
and 2011-12 such decline was estimated to be around 13 million (Mehrotra et al, 2014).
The more recent estimates show that between 2012 to 2018 there has been further fall in
agriculture employment amounted to nearly 27.3 million (Abraham, 2017; Thomas 2020).
COVID-19: The New Economics for Economies
20
Nonfarm rural employment- the fall in agricultural employment would not have been a
matter of concern if ‘pull factor’ would have existed in non- farm sector. However, non-
farm sector has also come under severe stress in terms of providing additional employment.
For instance, one of important sources of non-farm employment i.e. rural manufacturing
has been witnessing falling share in total manufacturing from nearly 32% in 1994 to 22%
in 2010 and nearly 17% in 2015 (Papola and Sahu 2012; GoI 2016). Mainly two sectors
namely construction and trade witnessed increasing their share in rural non-farm
employment which remained largely informal (Basole, 2017). However, construction
sector which accounted to nearly 18.9 million additional employments between 2005 and
2012, provided only 1.6 million additional employments between 2012 and 2018 (Thomas,
2020). Further, deceleration in the growth of rural non- farm wage also indicates during
the recent period also confirm the same (Usami and Das 2017). Further, a visible rise in the
share of self-employed during 2004 to 2009 and from 2012 to 2015 were primarily
attributed to push factor from agriculture (Abraham 2017; Himanshu 2011; Jatav and Sen
2012) and mostly remained confined under the low paid informal sector.
The Impact of COVID-19 Outbreak
As a response to COVID-19, the government of India announced a complete lockdown of
the country since 24th of March. The announcement of lockdown was too sudden to give
preparation time for various stakeholders of the economy. The announcement of lockdown
happened in the time when the entire country was already struggling with the recessionary
tendencies as discussed earlier. Needless to say that the impact of such unplanned and
unprepared lockdown in a country like India where more than 85 percent of the workforce
and over 75 percent of output are routed in the informal sector, is expected to be severe.
The Impact on the Informal Economy and the Incidence of Reverse Migration
The Indian economy at the time of COVID-19 outbreak was not resilient enough to handle
any external shock. In this scenario the impact of lockdown on the economy was bound to
be disastrous and far more longer. In short run the unemployment rate shoot up, many
small industrial units closed down, supply chains disrupted, agriculture sector witnessed
further problems with crashing of prices, rise in the input costs, irregularity in the supply
of inputs, sharp fall in consumption of entire population living at the margin, rising
indebtedness particularly from the non-institutional sources, along with many more. Thus,
the entire informal sector comes under severe distress with the sudden announcement of
lockdown and consequently, a large number of workers rendered jobless and most of them
happened to be migrant labourers. Such lockdown has flagged off an enormously large
movement of labourers (reverse-migration) in the country.
There is no pan India level official record of such migration, the estimation of its actual
size is subject to speculations. There are various estimates of the possible extent of revere
migration, Amitabh Kundu and his team puts this number at nearly 18 million, Chinmay
COVID-19: The New Economics for Economies
21
Tumbe puts the figure at least 30 million and Shridhar Kundu puts this figure higher than
23 million
1
. Off these migrants a substantial number of migrants are coming from UP,
Bihar, MP and Odisha. Based on various state level information and based on media
coverage along with many field reports, it seems plausible to expect that the ‘Covid-19 led
lockdown’ might induce at least 35-40 million reverse migration including intra state and
circular migration. This indicates the extent to which Covid 19 and the policy responses to
it has generated the crises particularly in terms of loss of means of livelihood.
Rural Employment Under COVID-19 Outbreak
With the announcement of country wise complete lockdown there was hardly any casual
work available during the entire period of lockdown both in the agriculture and non-
agriculture sector. Casual labourers which constitutes an overwhelming proportion of total
rural workforce and rely only on daily wages for their livelihood remained totally devoid
of any income for the entire period of the lockdown. This in turn has affected the purchasing
power of the rural population and consequently the demand in the economy has fell down
sharply. With a sharp fall in the demand, a large number of non-farm units are expected to
lose their business. In fact, some field based observations (Thakur 2020a and Thakur
2020b) confirms these facts. One important implication of this is the sharp rise in labour
force participation rate in villages coupled with shrinking work opportunities and
consequently rise in unemployment rate in the economy. Thus, situation is expected to be
even worse with reverse migration in rural areas. Higher labour force participation is
expected from variety of reasons- Firstly, due to acute income deficiency a large number
of children and older population are expected to join the labour force who otherwise were
absent from job market; Secondly, many housewives are also expected to enter the labour
force in order to supplement their family income; Thirdly, many households earlier
engaged in non-farm activities are expected to quit their occupation subject to falling
demand in the economy and expected to enter in casual labour force; Fourthly, many small
and marginal farmers with loss in their income (due to higher input price and lower output
prices) are expected to supplement their income from wage employment and therefore
might seek wage employment. Lastly, a large section of these reverse migrant would join
labour force in their native villages as they could not sustain without work for long.
However, a sharp rise in the labour force participation rate with a sharp reduction in work
opportunities has already led to a sharp rise in employment rate at pan India level.
Employment Prospects in Urban Areas
As far as prospects of recovery on the employment front is concerned the situation might
be understood differently for destinations of migrations. At the destination of migration,
1
https://indianexpress.com/article/explained/coronavirus-how-many-migrant-workers-displaced-a-range-
of-estimates-6447840/ ; https://indianexpress.com/article/explained/coronavirus-india-lockdown-migran-
workers-mass-exodus-6348834/
COVID-19: The New Economics for Economies
22
the prospect of recovery is very bleak at least in near future for following reasons- Firstly,
an overwhelming proportion of units under the informal sectors usually have very small
capital and savings, for them it is most unlikely that they would sustain even for a week
under forced lock down. Secondly, a sizable proportion of these small units are highly
indebted and without regular selling they are very likely to discontinue their instalments.
The situation would be even worse if the loans are being taken from the non-institutional
sources with higher rate of interests. Thirdly, because of reverse migration, the average
wage in the destination might go up and it might render small units unviable. Similarly, a
scarcity of labourers in these urban areas might be responsible for under production for
relatively long period of times even after lock down is totally lifted. Besides, there is also
a possibility that these small units might lose their business in hands of large businessman
and in order to counter low profit rates monopolistic tendencies may arise in times to come.
A possible shift from small to large scale production even within the informal sector might
lead to lower employment elasticity in near future with lesser possibility of recovery of
employment. Fourthly, the demand in the economy has already became too low and even
if with some credit relief (or with other government support) they start their operation, there
is a high possibility that they might not be able to sell their produce adequately in the
market. Fifthly, with the possibility of rise in wage rate in the destination of migration along
with lower availability of labour, a shift towards more capital intensive production is also
not unexpected. This kind of responses is also likely in the agriculture sector in North
Indian region. If it happens in good scale, there might be the fall in long term demand for
labour in this region and the regaining employment might not be feasible in near future.
And lastly, there is no surety about how long these crises would continue. It is evident that
the large and organised units with reasonable resilience to counter such tendencies are
under severe stress and working at less than the potential output. Thus, even if lockdown
is completely lifted there is hardly any possibility of readjustment of absorbing migrants in
any sizable manner in their original destination of migration. So the employment condition
in urban sector with high probability would remain under distress unless a suitable support
packages is delivered for their recovery.
COVID-19 Relief Packages: Response of the Government and Possibility of Recovery
There is hardly any doubt that the Indian Economy has come under severe distress and the
impact on the workforce of such crises has been immense. The only possible way to get
out of such crises is to get reasonable support from the government. The government in
response to such crises announced a relief packages. Thus, it becomes an imperative to
analyse that to what extent these packages would be instrumental in triggering suitable
recovery in near future. In other words, one needs to explore whether the packages are
adequate enough to handle the present crises the country is facing.
COVID-19: The New Economics for Economies
23
Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA)
Given the higher demand for wage employment in rural areas one of the key policy targets
might be MGNREGA. In short run it can address the problem in two ways. First, it can
provide wage employment to the labourers and give them some relief in terms of cash at
hand. Second, with higher injection of liquidity in rural areas through the wage payments
under the scheme, purchasing power of the rural population can be enhanced. This in turn
might give some respite to the shopkeepers or other self -employed in non-agriculture
households in terms of rising demand of their goods in local areas. In general, higher
allocation of funds for MGNREGA can provide with the government a breathing space to
think about suitable long term policies. Thus, not beyond the expectation, the government
of India as part of relief package announced 40,0000 (nearly 61,500 crore allocation in the
2020-21 budget) crores additional allocation for MGNREGA on an immediate basis.
Though the increment apparently seems reasonable, many caveats are attached to it. First,
in the latest budget (2020-21) the central government had already cut the allocation of
MGNREGA by nearly 14 percent (nearly by 10000 crores) Second, in 2019-20 fiscal year,
(even with higher allocation in MNREGA) nearly 7.6 million workers who demanded for
job did not get it. This year, with such a massive cut in allocation there was higher
possibility of rising this gap during the present fiscal year. Thus, the extra allocation is only
sufficient to absorb additional 4 to 5 million labours in rural areas for the entire fiscal year.
This is completely inadequate not only for absorbing the reserve migrants (more than 30
million in size) but also insufficient to meet rising demand of work due to higher labour
force participation in rural areas due to lock down induced crises. Besides, the amount it is
going to disburse in rural areas would be far short of pushing the demand in the rural
economy in any substantial manner.
Garib Kalyan Rojgar Yojana
The scheme will work in a mission mode in 116 districts across 6 states namely Bihar,
Madhya Pradesh, Uttar Pradesh, Rajasthan, Jharkhand and Odisha that received the
maximum numbers of migrant workers back. The mission will be a convergent effort
between 12 different ministries and total Allocation is approximately 50,000 crores.
Though this a welcome step, nonetheless there are some limitations of this scheme which
are worth mentioning here. This scheme is only reorganisation of existing projects not
entirely a greenfield scheme there for creation of additional employment would be limited
to some extent. Secondly, the wage component of this scheme is not high enough to create
more wage funds. Moreover, the coverage of this scheme is lower both in terms of areas
and number of employment it could provide. Since it involves several departments and
ministries the administrative complications in implementing the projects efficiently would
be a tough task.
COVID-19: The New Economics for Economies
24
Package for the Agriculture Sector
A very attractive package worth Rs 1 lakh crore was announced as ‘Agri Infrastructure
Fund’ that will finance projects at the farm-gate and aggregation point for efficient post-
harvest management of crops. Besides this, schemes for micro food enterprises, cattle
vaccination, dairy sector, herbal plantation, beekeeping, and fruits and vegetables were also
announced. The Essential Commodities Act was announced to be amended to deregulate
trade in cereals, edible oils, oilseeds, pulses, onion and potato, and stock limits for these
will be imposed only in exceptional circumstances. It was announced to enact a central law
to permit barrier-free inter-State trade of farm commodities and ensure a legal framework
to facilitate contract farming. The support package for agriculture has some inherent
problems. The entire country’s economy is struggling with demand side contraction and
viability of new investments is highly questionable unless a demand recovery is done
properly. However, focus of the scheme is to provide loans for agribusinesses and for some
post- harvest measures which will be disbursed largely as loans to the private players to
create capacity. However, in situation like the present no investment would be viable if
demand is not raised to an adequate level. And the packages have hardly directed in that
direction. Essential commodities act amendment will have very limited impact on the
farmers as it is more related to middle man and traders selling agricultural commodities.
The most of the proposals presented by Finance Minister were long-term measures that are
unlikely to provide immediate relief caused by the national lockdown. There has been very
small allocation for government procurement and providing direct infrastructural
development under the public sector investment. Cash transfer has been very meagre which
amounted to only 6000 in three instalments. Provisions of input support, price support etc.
have been very limited. Even in long term no institutional reforms was mentioned. Thus,
the announce of relief packages in the agriculture sector is looking off the mark and it is
unlikely that it would trigger recovery of this sector in any significant manner.
Package for the Support of Micro, Small and Medium Enterprises (MSME)
A relief package was also announced for MSME sector as ‘Atmnirbhar’ package, Basic
aim of the packages as mentioned in the policy name itself is to use MSME sector as key
to make India self-reliant. Although the allocation of package was reasonable high, its
effectiveness is limited by the fact that it hardly gives focus on providing stress driven
demand constrained economy with adequate cash support. The package was primarily in
the form of concessional credit while the sector is constrained by infrastructural bottleneck,
lack of cash in their hand, high level of indebtedness, lack of competitiveness vis a vis large
manufacturing units, extremely poor condition of demand in the economy etc. Thus, the
package for MSME sector hardly seems effective in terms of reviving the sector both in
terms of output and employment recovery in near future.
COVID-19: The New Economics for Economies
25
Thus, on the whole the packages announced by the government of India is hardly enough
to put the economy at the recovery path. In should be put India on the recovery path it not
only important to enhance the government relief packages significantly but also there is an
urgent need to refocus the packages in giving priority to enhancing cash support to the
various stakeholders of the economy and to expand infrastructural support with enhanced
public sector investment on immediate basis.
Concluding Remarks and Policy Suggestions
Thus, it seems clear that the Indian economy needs a complete overhaul in order to get
recovery in near future. The relief packages are neither sufficient not in the right direction
to counter the distress the economy is witnessing now. Instead of providing concessional
credits to various stakeholder of the economy there is an urgent need to enhance cash
support along with a sharp enhancement in public investment in order to create better
infrastructure support for agriculture and MSME sector. The role of MGNREGA is
important both in terms of providing employment and raising the rural demand in general,
therefore a sharp rise in the allocation for MGNREGA is needed. It is needed to make
MGNREGA universal instead of one in a family and provision of working days is needed
to be increased at least from 100 to 200 days per year. Besides, providing adequate free
food to everyone should be continued for full fiscal year. Giving rural and agriculture
sector higher priority in terms of increased public investment is the key to improve
employment conditions in rural areas. Implementation of unfinished land reforms might
also be proved instrumental in this regard. The focus must be shifted from large industrial
houses led growth to higher labour intensive small units led development. Needless to say
that social sector such as health and education are key to any country’s long term
sustainable growth and India has not been doing great on this front. Stepping up public
investment in health and education is definitely need of the hour and must be realised
immediately.
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Jatav and Sen (2013), “Drivers of Non-Farm Employment in Rural India: Evidence from
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Publication, New Delhi.
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Employment Trends in the Indian Economy: 1993–94 to 2011–12,” Economic &
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27
Chapter 3
COVID-19-Impact: Violence Against Women in India
Ve d Pr ak as h
Assistant Director,
National Institute of Labour Economics
Research and Development (NILERD),
(Formerly known as Institute of Applied
Manpower Research (IAMR),
(NITI Aayog, Government of India),
Narela, Delhi110040
Email: ved_107@yahoo.co.in
Abstract
The present study covers the status of violence against women during the COVID-19
pandemic lockdown period in India. The analysis of this paper is framed based on available
data at website of the National Commission for Women (NCW), Government of India. This
suggests that the significant increase in the number of cases of different kinds of violence
against women during lock down period between March 2020 to July 2020 which result in
societal disturbance and impact upon Indian nationals. The list of selected violence against
women is presented in this work and discussed about the challenges faced by women. The
possible reasons of rising violence against women are- the joblessness of daily earners,
meager engagement in economic activities and stay at home with fear situations, etc. This
study reveals that violence against women during covid19 pandemic was found increased
in India.
Keywords: Violence against women; Domestic Violence, Lockdown impact in India.
Introduction
As we are aware that most of the Indian society worships the women in the form of goddess,
like Durga for powers, Lakshmi for wealth, and Saraswati for learning. As we see the look
back in the history of women empowerment during Vedic period, there was no gender
division in hunting, warfare, defense and political activities. The Girls were allowed to
educate similar to boys and need to pass through a period of Brahmcharya. The marital age
of women was not below 16 years. Many women made significant contribution to the
advancement of education, viz. Sulabha, Maitreyi, Vadava Prathitey, Vachaknavi, and
Gargi. Later, the patriarchy system was initiated in India in during the period of Atharva
Ve d a. H o w e ve r , th e s i g ni f i c an t i m p or t a n ce o f w o me n ( G a nd h a r i, K u n t i, Draupdi) may be
seen in the period of Mahabharta, as these women are known to decide the warfare.
COVID-19: The New Economics for Economies
28
During COVID-19 pandemic, with an intention to become educated and empowered like
men, in addition to learn domestic activities, females may be found more interested in
gating college education. Sometimes, as women get busier in getting college education,
they do not have sufficient time to learn traditional domestic and household activities, due
to this lapse; many females usually faced problems to carry out traditionally domestic work
and kitchen activities including qualitative cooking. Thus, married life of such women
became less valued (found unable to do traditional domestic work). However, in schools
and colleges, system more facilitates females to get training for self-protection in the
society vice to live jointly like a friend.
However, there is no doubt to say that women really work hard for development at every
level in the family from child birth to fulfill the desires of her family. In general, women
are applauded for this kind of devotion at every place. Usually, men always regard and
protect women everywhere she needs helps & support. Apart from this, it is true to say that
there may be few dominant men in the society who use women as an object & compel them
to perform humiliating tasks; Contrary to the fact that there may be found few self-focused
rarely performing women in the society, which is still under the lens of investigation.
The Indian society and traditional culture allow male to dominate in his family. Sometimes,
he may feel irritational for any unwanted cause created by the family women.
Unfortunately, man may be forced to commit domestic violence against his family women.
In order to protect self, in place of rational and evidentiary discussion, women may found
by using abusing insulting words loudly may be with an intension to humiliate to cause the
insult of men (as there are many laws for women protection) or family male for any kind
of demanding issue(s). Due to this, the feeling of humiliation and insulting of men or other
family member(s) may be one of the reasons of violence against women. However, it is a
matter of in-depth investigation, newspapers published many times of such kinds of
incidents. Such type of violence may include burns. In the present days, the domestic
violence is seen among the educated and well settled families.
In order to ensure the protection from COVID-19 virus, as per lockdown rules, Social
Distancing, zero movement in public place are characteristic ideas. Due to this, during the
lock down period, the news published in Hindustan, daily Hindi News Paper, published
from Delhi on 28th April 2020 disclose about unseen mismatch in behavioral aspects
amongst male and female felt in the families worldwide. As published in Hindustan, daily
Hindi News Paper, published from Delhi on 19th April 2020, change in unseen behavioral
aspects amongst male and female indirectly creates hidden conflicts between both men &
women
2
. During defensive lockdown from COVID-19 pandemic to protect and save human
2
Hindustan, daily Hindi News Paper, published from Delhi on 19th April, 2020.
COVID-19: The New Economics for Economies
29
life around the globe, several news has been published about increase in violence against
women Internationally.
In India due to COVID-19 pandemic, the lock down in India was implemented from 25th
March 2020, which was extended till July 2020. Some relaxations were allowed in the said
lock down period in various parts of India from time to time on the basis of requirement of
local population. During this lock down, most of the working populations including women
were not allowed to come out from their homes during the corona virus spread sensitive
time in India for the significant reason of their safety from COVID-19 with improved
immune system. Most of the economic activities were closed down during this period, and
dependent women became handicap due to zero income of her spouse, and could not able
to cook minimum foods for their children, and unwanted financial burden increased in the
household whose income was hand to mouth before implementing the said lock down in
India. Spouse dependency for money is the common issue for create domestic violence.
This economically dependency of women on her spouse was suggested as resource theory
by William Goode (1971). Use of alcohol frequently by family men is the other reason to
create unwanted violence in the poor families.
As far as domestic violence is concerned, in addition to women, men and other members
of family like children and old parents, etc., also faced many kinds of problems (including
physical assault) in their homes including unwanted ignorance & violence in their homes
for meager important reason(s). In such kind of conflict situation(s), earning male member
of the family may have felt the situation like sandwich. During COVID-19 pandemic lock
down period in India, if such earning male member of the family want to live in their homes,
wife and/or other family members may create unwanted disturbance, if such earning male
member of the family wants to come out from their homes for their peace of mind peace,
corona virus may attack on them. It may be stated here that there was no place to stay for
earning male members of the families, as he may have different kinds of issues to be settled
for own family survivals.
The National Commission of India (NCW) is a nodal organization of the Indian
Government which helps to cover all kinds of complaints received from women in India
for their safety and protection. As per NCW, violence against women are listed in India
such as Acid Attack, Bigamy / Polygamy, Cyber Crime against women, Denial of
Maternity Benefits to women, Domestic Violence, Dowry death, Gender Discrimination
including equal right to education & work, Harassment of married women/Dowry
harassment, Indecent Representation of Women, Outraging modesty of
women/Molestation, Police Apathy against women, Rape/Attempt to Rape, Right to
exercise choice in marriage/ Honour Crimes, Right to live with dignity, Sexual Assault,
Sexual Harassment of Women at workplace, Sexual Harassment, Stalking / Voyeurism,
COVID-19: The New Economics for Economies
30
Trafficking / Prostitution of women, etc. which is available on the website
(http://ncw.nic.in/ ) of National Commission for Women (NCW), Government of India.
As far as violence against women is concerned, there are many examples are exists which
reveals that male may also found victims of domestic violence and sexual assault too; but,
in general, as discussed in Book Review, The Second Sexism: Discrimination against men
and boys (2012), society consider such violence less seriously because of the prevailing
attitudes towards male member of the family, it is belief that men are fearless, sustain
greater pain, and are more capable of self-defence in compare to women. While men are
considered too responsible for harassment of women; likewise, women needs to be
responsible for discrimination of men. According to Lenore E. Walker, the domestic
violence is cyclic and having four phases, in which the abuser’s tension situations are the
cause of violence and peace.
We need parallel securities to protect female in the public place where women are working.
As women security needs more security personnel at public place, it may increase the cost
for securities for women at public places. Female housemaid who generally comes from
needy and low income families, in addition to taking specified jobs in low wages from such
housemaid, such housemaid also forced to face humiliation and various kinds of
harassment at their working place.
As most of the population was asked to stay at their home during lock down period in India,
and all male and female including their children (if any) and their dependent parents (if
any), to know the impact of lock down on violence against women in India in a non-privacy
environment which created during lock down period, the data collected from the NCW
website on the selected complaints and placed in the table (below), and the total incidents
against women given in the Figure-A (below) which shows significant changes in number
of complaints received in NCW on violence against women in India during January, 2019
to July, 2020.
COVID-19: The New Economics for Economies
31
Table1: Number and Nature of Selected Violence Complaints Registered (month-wise) in National
Commission of India during 2019 and 2020
Months
Bigamy /
Polygamy
Cyber
Crime
against
women
Dowry
death
Harassment
of married
women/
Dowry
harassment
Indecent
Representation
of Women
Other
Total
(1)
(2)
(3)
(4)
(5)
(6)
(8)
(9)
Jan, 2019
18
39
14
87
2
1145
1305
Feb, 2019
11
30
22
200
7
1035
1305
Mar, 2019
11
22
16
204
6
785
1044
Apr, 2019
14
35
13
228
7
809
1106
May, 2019
12
49
46
397
10
1203
1717
Jun, 2019
10
33
14
316
11
957
1341
Jul, 2019
11
43
31
361
14
1358
1818
Aug, 2019
14
61
49
468
11
2183
2786
Sep, 2019
11
40
56
468
13
1791
2379
Oct, 2019
9
35
38
391
7
1405
1885
Nov, 2019
9
35
32
368
7
1191
1642
Dec, 2019
21
37
42
275
9
1018
1402
Jan, 2020
10
32
32
267
2
1119
1462
Feb, 2020
7
21
17
221
1
1157
1424
Mar, 2020
6
37
18
203
1
1082
1347
Apr, 2020
6
55
9
62
1
667
800
May, 2020
18
73
27
159
8
1215
1500
June, 2020
14
103
27
273
3
1623
2043
July, 2020
23
110
49
493
1
2238
2914
Source: National Commission for Women (NCW)
Figure-A: Total Complaints about Cime against women Registered in NCW
during 2019 and 2020
1305
1305
1044
1106
1717
1341
1818
2786
2379
1885
1642
1402
1462
1424
1347
800
1500
2043
2914
0
500
1000
1500
2000
2500
3000
3500
Jan, 2019
Feb, 2019
Mar, 2019
Apr, 2 019
May, 2019
Jun, 2019
Jul, 2019
Aug, 201 9
Sep, 201 9
Oct, 2019
Nov, 2019
Dec, 2019
Jan, 2020
Feb, 2020
Mar, 2020
Apr, 2 020
May, 2020
June, 2020
July, 2020
Month and Yea r
Numb er o f Compl aints
COVID-19: The New Economics for Economies
32
Figure-A reflects that the number of complaints about Violence against women increased
during lock down period in India during May, 2020 to July, 2020 while some relaxations
were allowed in the said lock down. It reached to 2914 complaints in July, 2020 which
includes 1096 more complaints in compare to July, 2019. However, there was a significant
downfall may be seen in the complaints (only 800) registered in NCW during complete
lock down in India in April, 2020.
Due to limitation of the paper, only few selected violence against women are discussed in
this paper, Viz., (i) Bigamy / Polygamy, (ii) Cyber Crime against women, (iii) Dowry death,
(iv) Harassment of married women/ Dowry harassment, & (v) Indecent Representation of
Women.
Violence against women: bigamy / polygamy
Bigamy is Violence against women which is treated as punishable offence. This Violence
reduced in the beginning of the lock down India in March and April, 2020 but suddenly
increased three times in May, 2020 as seen in the Figure-B. Further to this, the number of
complaints reduced during June, 2020 but again it jumped to maximum number of this
incident during July, 2020. During 2019, 151 complaints of Bigamy / Polygamy were
registered in NCW, out of which only 87 complaints was registered during January to July,
2019. In compare to January, 2019 to July, 2019, 18 more complaints