i arthshodh - university of rajasthan

i

ISSN 2230–7877

Arthshodh A Peer-reviewed bi-annual Journal of Department of Economics,

University of Rajasthan, Jaipur, India

Vol. VI No. 03 January–June , 2020

CONTENTS

ARTICLES Page No.

1. A Comparison of Determinants of Infant Mortality Rate between Desert and Non-Desert Districts of Rajasthan

Dr. M. R. Singariya

01-14

2. Impact of Coronavirus Pandemic (COVID-19) and 21 Day Nationwide Lockdown on Indian Economy

Dr. Rami Gaurang Dattubhai

15-36

3. Cost Benefit Analysis of Tomato Cultivation Under

Polyhouse in Haryana

Komal Malik & Vinay Kumar

37-44

4. Growth Analysis of Area, Production and Yield of Rapeseed and Mustard Crop in Rajasthan

Preeti Prasad, Rashmi Bhargava & S.K Kulshrestha

45-52

5. Inequality Re-examined Amidst Covid-19

Dr. G. L. Meena

53-70

6. Climate Change and Its Impact on Agricultural Production: An Evidence from India

Dr. Chitra Choudhary & Sumedha Bhatnagar

71-91

Accounting Studies i Volume 11 No. 1 May, 2013

Chief Editor’s Voice

A Forgettable 2020…

India’s rapid economic growth in recent decades has lifted the

country to become the world’s third-largest economy (in purchasing

power parity terms), while major economic reforms have helped

dramatically reduce poverty since 2002. As 2020 draws to a close, here

is a recap of how the Indian economy fared in a year upended by the

corona virus pandemic. From contracting by an unprecedented 23.9

per cent to plunging into a technical recession, the trajectory of India’s

economy saw a steep decline in 2020-primarily due to the Covid-19

pandemic. After reporting its first case in late January 2020 in the

southern state of Kerala, India introduced rigorous airport screenings

for the corona virus (COVID-19). The following weeks saw a quick

succession of events leading to a suspension of all travel in and out of

the country and announced country-wide lockdown by March 22.The

outbreak of the Covid-19 pandemic is an unprecedented shock to the

Indian economy. The economy was already in a parlous state before

Covid-19 struck. With the prolonged country-wide lockdown, global

economic downturn and associated disruption of demand and supply

chains, the economy is likely to face a protracted period of slowdown.

The magnitude of the economic impact will depend upon the

duration and severity of the health crisis, the duration of the

lockdown and the manner in which the situation unfolds once the

lockdown is lifted. While some of the effects of Covid crisis on the

economy are short term, many can have lasting impacts. The

ii Arthshodh

lockdowns have hugely impacted the supply-chain management and

sent the GDP and import export cycle plummeting. There are three

major areas of impact for Indian businesses which are linkages,

supply chain and macroeconomic factors. This is indeed the worst

recession since the Great Depression in the 1930s.

How the Government Responded to the biggest Crisis

All anecdotal evidence available, such as hundreds of

thousands of stranded migrant workers across the country,

suggested that the Medium, Small and Micro Enterprises (MSMEs)

were the worst casualty of Covid-19 induced lockdown. Hence, the

government laid its primary focus to lift the stressed MSME sector

with its relief packages, especially a massive increase in credit

guarantees to them. It essentially means that the government has

resorted to taking over the credit risk of MSMEs should they want to

remain in business. A credit guarantee by the government helps as it

assures the bank that its loan will be repaid by the government in

case the MSME falters.

Reserve Bank of India (RBI) has taken some necessary steps to

meet the crisis situation in the country. RBI came up with Business

Continuity Plan in the emerging situation and is sharing instructions,

by devising strategies between the staff member and other customers.

RBI has also started Open Market Operations from March 20 in the

form of purchase of an aggregate amount of Rs 10,000 crores of

government securities. There are no notified securities amount

mentioned, but RBI has a self-imposed ceiling of Rs 10,000 crore

wherein they have the sole right to decide the purchase of individual

securities, accept offer either less or higher than the amount of Rs

10,000 crores and accepting or rejecting the offer.

On May 12, the Prime Minister, announced a special

economic package of Rs 20 lakh crore (equivalent to 10% of India’s

GDP) with the aim of making the country independent against the

tough competition in the global supply chain and to help in

empowering the poor, labourers, migrants who have been adversely

Chief Editor iii

affected by COVID. The Atmanirbhar Bharat (Self-reliant India)

package, rolled out in several tranches to mitigate the biggest crisis

since 1979, reinforced the ‘fiscal conservatism’ ideology of the

government under Prime Minister–rather than large cash transfers,

the growth philosophy centres around creating an ecosystem that

aids domestic demand, incentivizes companies to generate jobs and

boost production, and simultaneously extends benefits to those in

severe distress.

India needs to continue implementing critical reforms in key

areas like health, labour, land, skills and finance to come out

stronger from the impact of Covid-19 pandemic, the World Bank

said these reforms should aim at enhancing productivity of the

Indian economy and spur private investments and exports. It also

provides a more in-depth analysis of selected economic and policy

issues and highlights the economic reforms that India has been

undertaking and needs to continue with in the medium to long-

term. Investing in infrastructure, labour, land and human capital

will give India the ability to navigate the uncertainties and be more

competitive as the world emerges from the pandemic. To put the

financial sector on a sounder footing, financial sector stability,

reforms in the non-banking finance company (NBFC) sector, deeper

capital market reforms, mainstreaming fintech to reach firms faster

and at a lower cost, and moving to a more strategic public-sector

footprint. The recent liquidity and performance issues in the

financial sector, exacerbated by the Covid-19 crisis, present

policymakers with a strong reason–and an opportunity–to accelerate

efforts towards building a more efficient, stable and market-oriented

financial system. It is encouraging that the government is moving to

a more selective and strategic public sector footprint in the financial

sector. International experience shows this can boost the banking

sector's ability to support credit, facilitate effective financial

intermediation and reduce fiscal exposure. The current crisis has

also brought to the forefront new economic opportunities in the

iv Arthshodh

areas of digital technology, retail, health-technology and education-

technology services besides global demand in areas such as

pharmaceuticals, medical equipment, and protective gear. These

opportunities can provide new growth levers for India.

S. S. Somra

Accounting Studies 1 Volume 11 No. 1 May, 2013

A Comparison of Determinants of Infant Mortality Rate between Desert and Non-Desert

Districts of Rajasthan

Dr. M. R. Singariya

Abstract

Pooled OLS and fixed effects panel regression models were used to

examine the determinants of infant mortality rate in Rajasthan using

district level data separately for twelve desert and twenty non desert

districts for the period of 1991 and 2001. Data were obtained from Human

Development Report Rajasthan, (An update 2008) published by Institute of

Development Studies Jaipur. In our empirical work, the explanatory

variables used are NSDP per capita, % share of urban population, % share

of primary sector employment, female literacy rate, female wok participation

rate, crude birth rate, total fertility rate and household access facilities like

electricity, safe drinking water and toilet.

The analysis seeks to determine which of socioeconomic variables

play an important role in reducing infant mortality rates. The results of

pooled data showed that the Crude birth rate has significant at 10% level

negative association in desert districts, while this association is reversed

and yet significant at 5% level was witnessed in non desert districts of

Rajasthan. This result increases our interest to investigate the present

application in depth. Fixed effects panel regression showed that IMR has

significant and negative association with CBR, household access electricity

and toilet facilities and positive association with female work participation

Associate Professor, Department of Economics, S D Government College

Beawar, Ajmer, Rajasthan, (Rajasthan).

2 Arthshodh

rate and share of urban population in the desert districts of Rajasthan.

While non desert districts analysis showed that higher female work

participation reduces the extent of IMR and this effect is statistically

significant (at 5%). This result is in accordance to earlier studies. Apart

from female work participation in reducing IMRs in non desert districts of

Rajasthan, household’s access toilet facility also seem to have significant

and negative influence on IMR. Higher levels of CBR, SPSE and SHOUP

are associated with higher levels of IMR in all 32 districts of Rajasthan. The

Double log Panel regression suggest that one percent increase in CBR and

SHOUP, on average increases IMR by 1.78% and 1.33% respectively in all

the districts of Rajasthan. So the policy implications of the findings are

clear and suggesting to control on CBR and urbanization or strengthening

health care facilities in urban areas of Rajasthan for reduction of IMR as

well as to capture Millennium Development Goals.

Key words: Infant Mortality Rate, Fixed effects panel, Desert, Rajasthan

Introduction

Rajasthan is situated in the northern part of India. It is the

largest state in India by area constituting 10.4 percent of the total

geographical area of India and it accounts for 5.5 percent of population

of India (Census of India, 2001). It is administratively divided in to 7

divisions and 32 districts. Recently, a new district has been carved out

in the state namely, Pratapgarh. Currently, there are 33 districts.

Topographically, deserts in the state constitute a large chunk of the

land mass, where the settlements are scattered and the density of

population is quite low.

There have been common and comparable state efforts of

development among all its districts, but different topography and

cultural practices have resulted in differences among desert and non-

desert districts in the state with respect to certain parameters. Thus it

was expected that there would be different influencing factors affecting

Infant Mortality Rate (IMR) in the desert and non-desert districts.

Though, the State has shown some progress on the human

development front with the human development index showing

A Comparison of Determinants of Infant Mortality Rate between Desert ....... 3

progress from a rank of 12 in 1981 to 9 in 2001 amongst the 15 major

States (National Human Development Report 2001), the Millennium

Development Goals (MDGs) of health are far behind the desired levels.

The Infant Mortality Rate (IMR), which is considered to be one of the

most sensitive indicators of human development lies at 63 infant deaths

per 1,000 live births compared to 53 per 1,000 live births in case of India

(SRS, 2008) Infant Mortality Rate (IMR) in the state has maintained near

stagnancy for most of the nineties. However, in the new millennium

decline in IMR was sharper. The aggregate IMR declined from 85 in

1995 to 80 in 2001 and further to 67 in 2004 (SRS Bulletin, April 2006).

In this study an attempt has been made to examine the

predictors of infant mortality rate in desert and non-desert districts of

Rajasthan. The specific objectives of this study are; to identify the

factors which are affecting infant mortality and to suggest viable

strategies to reduce the level of infant mortality in the state, so that an

effective measure can be used to meat out the stagnancy of IMR in the

state.

Method

This study looked at the following district level factors;

percentage of households access electricity, safe drinking water, toilet

facility, female literacy, female work participation, share of urban

population, share of primary sector employment, per capita net state

domestic product, crude birth rate, total fertility rate. The study assed

ten factors in all to describe socioeconomic characteristics and its

relationship with Infant mortality rate at district level. Furthermore we

run regressions separately for desert and non-desert districts to identify

those variables, out of the variables considered, which specifically

influence the response variables, the IMR in the desert and non desert

part of the state. The desert districts included in this study are Barmer,

Bikaner, Churu, Ganganagar, Hanumangarh, Jaisalmer, Jalore,

Jhunjhunu, Jodhpur, Nagaur, Pali and Sikar, mainly situated in the

northern-western part of the state. The non-desert districts which are

situated in southern-eastern part of the state and included in this study

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are Ajmer, Alwar, Banswara, Baran, Bharatpur, Bhilwara, Bundi,

Chittorgarh, Dausa, Dholpur, Dungarpur, Jaipur, Jhalawar, Karauli,

Kota, Rajsamand, Swai Madhopur, Sirohi, Tonk and Udaipur.

The data needed for the study have been collected from

Human Development Report Rajasthan, (an update 2008) published

by Institue of Development Studies Jaipur and Rajasthan Health

Scenario 2000, published by Indian Institute of Health Management

Research Jaipur. Pooled OLS and fixed effects panel regression

models have been employed for analysis in this paper. The infant

mortality rate has been used as dependent variable and ten

socioeconomic variables have been used as explanatory variables.

Results and Discussion

Table 1 gives the mean and standard deviation (SD) of the

eleven variables considered to describe socioeconomic status and

their links with IMR in desert and non desert districts of Rajasthan.

It is observed from the table 1, that there have been common and

comparable state efforts of development in the desert and non-desert

parts of the state, as households access electricity facility, share of

primary sector employment and female literacy rate matched well.

Table 1: Mean and SD of the considered variables in desert and

non-desert districts of the state

S. N.

Name of Variables

Description Non Desert Districts (20)

Desert Districts (12)

All Districts (32)

Mean S.D. Mean S.D. Mean S.D.

1 HAE % of H/H access electricity

42.81 15.22 42.8 15.59 42.81 15.24

2 HASWD % of H/H access safe drinking water

78.97 22.34 69.13 14.8 75.28 20.29

3 HATF % of H/H access toilet facility

18.68 9.67 29.25 20.84 22.64 15.59

4 CBR Crude Birth Rate 29.78 7.05 29.24 5.84 29.58 6.58

5 TFR Total Fertility Rate 4.02 1.14 3.97 1.12 4 1.12

6 FWPR Female Work Participation Rate

31.11 9.61 29.02 6.99 30.33 8.72

7 SPSE % share of Primary sector employment

69.33 11.92 69.69 8.38 69.46 10.66

8 SHOUP % share of urban

population

20.2 12.07 21.28 8.98 20.61 10.95


9 NSDP/c Per capita Net state

domestic product 8397 4494 8037 4525 8262 4473

10 FLR Female literacy rate 31.04 12.86 31.68 16.06 31.28 14.02

11 IMR Infant Mortality Rate 81.16 19.95 67.58 16.76 76.07 19.82

It is also observed from this table that there has been good

socioeconomic development in the desert; however provisions for

safe drinking water and per capita net state domestic product are

still lower in comparison to non-desert districts of the state. But

more interesting fact is that lower level of infant mortality is

witnessed with higher % of urban population and toilet facilities in

the desert part of the state. It is observed that state average statistics

is match well with non-desert districts.

Next, we examine the relationship between infant mortality

rate and female literacy rate pooling the data for the two periods,

1991 and 2001. The scatter plot with a trend line is exhibited in figure

1 for desert districts and figure 3 for non-desert districts separately.

It is amply evident that there is a negative association between IMR

and female literacy. The decline in IMR as female literacy increases is

higher in desert districts in comparison to non-desert districts. The

association between per capita NSDP and IMR in desert districts is

shown in figure 2 and in non desert districts is shown in figure 4.

The decline in IMR as per capita NSDP increases is not uniform in

desert and non-desert districts. The decline is higher in non-desert

districts.

6 Arthshodh

Our discussions on the correlations that exist between IMR

and female literacy or per capita NSDP-cannot be interpreted as a

cause-effect relationship, including also the possibility of two-way

causality among the above mentioned variables. We now develop an

econometric framework to examine the causal or simultaneity

among these variables.

Tablet 2: Results from Pooled OLS Estimation of IMR

S.N. Name of Variables

Desert Districts (12) Non Desert Districts (20) All Districts (32)

Coefficient t ratio Coefficient t ratio Coefficient t ratio

1 HAE 0.443 0.9176 -0.1263 -0.2532 -0.158 -0.4452

2 HASWD -0.007 -0.0145 -0.0279 -0.090 -0.018 -0.073

3 HATF -0.165 -0.435 -1.7836 -2.46** -0.526 -2.2**

4 CBR -4.379 -2.09 * 4.2932 2.221** 2.173 1.495

5 TFR 37.014 2.6** -26.0014 -2.044* -8.137 -0.77

6 FWPR 0.147 0.1287 -0.6220 -1.293 -0.219 -0.497

7 SPSE 1.046 1.16 -0.6101 -1.291 0.033 0.091

8 SHOUP 0.286 0.5902 0.9246 2.224** 0.478 1.502

9 NSDP/c 0.003 1.422 0.0003 0.1654 0.001 1.038

10 FLR -0.488 -1.047 -0.5213 -1.128 -0.403 -1.264

11 Constant -57.326 -0.556 155.517 2.191** 61.529 1.072

12 Observations 24.000 40.0000 64.000

13 R-Squared 0.685 0.5415 0.398

Note: - * significant at 1o% level, ** significant at 5% level and *** significant at 1%

level


Table 2 shows the results of pooled OLS regression of IMR on

set of explanatory variables for desert, non-desert and all 32 districts.

R2 % in the desert is 68.54 and in non-desert is 54.15, performance in

OLS regression model justifies the variable selection for to find out

determinants of IMR in desert and non-desert districts. The results of

pooled OLS regression shows that IMR has significant and negative

association with CBR and positive association with TFR in the desert

districts, where as this association is reversed and yet significant in

non desert districts of Rajasthan. It is also witnessed that IMR has

significant and negative association with % H/H access toilet

facilities and positive association with % urban population in non

desert districts.

Table 3: Fixed Effects Panel Data Regression Results of IMR

S.

N.

Name of

Variables Desert Districts (12) Non Desert Districts (20) All Districts (32)


1 HAE -25.525 -9.48* 0.846 0.9493 -0.471 -0.93

2 HASWD 25.687 9.392* 0.180 0.5815 0.097 0.4

3 HATF -16.086 -9.21* -2.596 -1.959* 0.791 1.83*

4 CBR -34.979 -9.51* 9.628 2.780** 4.576 2.449**

5 TFR 422.111 9.72* -91.759 -2.313** -24.910 -1.853*

6 FWPR 15.143 9.405* -1.275 -2.931** -0.864 -2.24**

7 SPSE 0.289 1.064 0.291 0.3484 1.105 2.034*

8 SHOUP 110.671 9.424* 0.332 0.06378 4.867 2.112**

9 NSDP/c 0.144 9.69 * 0.004 1.168 0.001 0.493

10 FLR 0.210 1.21 0.007 0.007422 -0.302 -0.526

11 Time Dummy -705.940 -9.78 * -114.970 -1.951* -5.600 -0.2076

12 Constant -4422.760 -9.66* 201.623 1.082 -111.790 -1.42

13 Observations 24 40 64

14 R-Squared 0.999 0.934 0.898

Note: * significant at 1o% level, ** significant at 5% level and *** significant at 1%

level

Because the OLS estimates do not control for unobserved

heterogeneity, they cannot be interpreted as casual effects. These

8 Arthshodh

results only tell us the signs and statistical significance of different

coefficients. Now we present estimated results for the fixed effects

model in table 3. The R-Squared figure has now gone up to .99 for

desert districts, .93 for non-desert districts and .897 for all districts.

Here also we see that our variables are of expected signs. The

coefficients of HAE & HATF for desert districts and HATF, CBR,

TFR and FWPR for non-desert districts are of expected and

significant sign in the fixed effects specification. The coefficient on

time trend is negative and statistically significant for both desert and

non-desert districts. We could interpret this result as the fact that

over the time there is an improvement in general awareness and

aspirations of the people, which are not captured from our

institutional and policy variables. So irrespective of the other factors,

there are some fundamental changes in the society over the decade

that is actually favourable to better outcome in reducing IMR.

Table 4: Fixed Effects Panel Data Regression Results of Log IMR

S.N. Name of

Variables

Desert Districts (12) Non Desert Districts (20) All Districts (32)


1 Log HAE -1.842 -13.2** 0.087 0.2739 -0.539 -1.533

2 Log HASWD -1.391 -6.996 * 0.384 2.574** 0.123 0.732

3 Log HATF 0.669 5.622 -0.667 -2.751** 0.027 0.1483

4 Log CBR -2.088 -8.477* 3.650 4.207*** 1.775 2.459**

5 Log TFR 0.721 3.715 -3.756 -4.023*** -1.279 -1.994*

6 Log FWPR -0.521 -3.085 -0.194 -2.776** -0.155 -1.982*

7 Log SPSE 0.698 3.334 -0.236 -0.4299 0.055 0.1251

8 Log SHOUP 0.952 3.772 0.754 1.11 1.325 2.166**

9 Log NSDP/c 0.726 8.777 * 0.188 0.4454 0.136 0.4488

10 Log FLR 0.630 8.527* -0.065 -0.1755 -0.018 -0.09

11 Time Dummy -0.837 -7.933 * -0.840 -1.269 -0.100 -0.238

12 Constant 8.656 4.306 -4.487 -0.8666 -3.220 -0.715

13 Observations 24 40 64

14 R-Squared 0.999 0.962 0.896

Note: * significant at 1o% level, ** significant at 5% level and *** significant at 1%

level


Finally, we present the fixed effects panel regression results

for double log specification with respect to IMR in desert and non-

desert districts of Rajasthan, the following observations are

particularly noteworthy.

(1) Higher the level of electricity and safe drinking water

facilities in desert districts of Rajasthan reduces the extent of

IMR and this effect is statistically significant. This result is in

accordance to earlier studies. A 1% increase in the percentage

of household’s access electricity facility in the desert districts

is associated with 1.84% decrease in IMRs. This result is

statistical significant at 5% level. While on average a 10%

increase in percentage of household’s access safe drinking

water facility is required to decrease the IMRs by 14 per

thousand in the twelve desert districts of Rajasthan. This

result is also significant at 10% level.

(2) Female literacy and per capita net state domestic product have a

positive and statistical significant at 10% level association with

IMR in desert districts. This unfortunate and unmatched result

is an outcome of the lower level of female literacy (particularly

in 1991) and Per capita NSDP existed in desert zone of

Rajasthan. Because of all high Per capita income generating

districts including Ganganagar, Hanumangarh, Kota, Ajmer,

Bhilwara and Rajsamand have witnessed high IMR as per HDI

Report of Rajasthan, an update 2008.

(3) Percentage of household’s access toilet facilities at home has

negative and statistically significant at 5% level influence on

IMR in non-desert twenty districts of Rajasthan. A 1% increase

in HATF is associated with a 0.66% drop in IMRs on average.

(4) Higher the levels of crude birth rates are associated with

higher levels of IMRs in non-desert districts of Rajasthan. A

1% increase in CBR leads to an increase in IMR by 3.65% on

average in non-desert districts of Rajasthan.

10 Arthshodh

(5) Higher female work participation reduces the extent of IMRs

in non-desert districts of Rajasthan and this result is

statistically significant at 5% level. This result is in accordance

to earlier studies. A mother who works outside the house has

greater resources, better access and information to health care

facilities and hence can take better care of child, implying

lower infant mortality rates in the household. A 1% increase

in female work participation rate is associated with 0.19%

decrease in IMRs in non desert twenty districts of Rajasthan,

mainly situated in southern-eastern part of the state.

(6) Though, there is negative influence of female literacy and

percentage share of primary sector employment was observed

with IMRs in non-desert districts, but it was not statistically

significant.

(7) Higher proportion of urban population in the total population

increases the extent of infant mortality in all the districts of

Rajasthan. The effect of urbanization (% urban population) on

IMR is positive and significant. There could be various other

factors that might influence IMR in urban areas like greater

level of pollution, over burden of population might widening

the gape between availability of health care facilities and its

requirements. On average 1% increase in SHOUP increases

IMRs by 1.33 % in all the districts of Rajasthan.

(8) Crude birth rate has highest and statistically significant

positive influence on IMRs in all the districts of Rajasthan. A

1% increase in CBR increases IMRs by 1.78% on average in all

the districts of Rajasthan.

(9) Female work participation rate has significant and negative

association with IMRs in southern-eastern districts of Rajasthan.

On average 6% increase in Female work participation rate is

required to reduce 10 infant deaths per thousand.


(10) Apart from female work participation rate female literacy also

seem to have negative influence on infant mortality but it is

not statistically significant in this equation.

Conclusion and Policy Suggestions

This study examines the determinants of infant mortality using

panel data of twelve desert and twenty non-desert districts of

Rajasthan for the period 1991-2001. In particular we have focused on

female literacy, female work participation rate, per capita NSDP,

demographic variables (CBR & TFR) and household access facility

variables (HAE, HASDW & HATF) which can be changed using policy.

We estimated the impact of these variables on IMRs using OLS, fixed

effects panel regression and its double log specification. The following

important findings emerge from our econometric analysis:

The descriptive analysis indicates that desert districts have

experienced lower level of IMR in spite of lower level of per

capita income, female work participation rate and percentage of

household access safe drinking water facilities.

The estimated results from fixed effects panel regression

showed that IMR has significant and negative association with

CBR, household access electricity and toilet facilities and

positive association with female work participation rate and

share of urban population in the desert districts of Rajasthan.

While non desert districts analysis showed that higher female

work participation reduces the extent of IMR and this effect is

statistically significant (at 5%). This result is in accordance to

earlier studies. Apart from female work participation in

reducing IMRs in non desert districts of Rajasthan, household’s

access toilet facility also seem to have significant and negative

influence on IMR.

Higher the level of electricity and safe drinking water facilities

in desert districts of Rajasthan reduces the extent of IMR and

this effect is statistically significant. This result is in accordance

12 Arthshodh

to earlier studies. A1% increase in the percentage of household’s

access electricity facility in the desert districts is associated with

1.84% decrease in IMRs. This result is statistical significant at 5%

level. While on average a 10% increase in percentage of

household’s access safe drinking water facility is required to

decrease the IMRs by 14 per thousand in the twelve desert

districts of Rajasthan. This result is also significant at 10% level.

Percentage of household’s access toilet facilities at home has

negative and statistically significant at 5% level influence on

IMR in non-desert twenty districts of Rajasthan. A 1% increase

in HATF is associated with a 0.66% drop in IMRs on average.

The findings of this study clearly demonstrate the role of

household facilities in reducing IMR is very high, in fact, much

higher than per capita NSDP and female literacy. Any increase

in electricity and safe drinking water facility in desert districts

and any increase in toilet facilities in non-desert districts would

have considerably high negative influence on IMR. Increasing

investment in household facilities is a required policy

intervention for reducing IMR. Economic variables like female

literacy and female work participation rate were found to be

negatively associated with IMRs. Policies promoting female

education and participation would also have the desirable effect

of reducing IMR. Higher the level of CBR and SHOUP show

high level of infant mortality suggesting to control on CBR and

urbanization or strengthening health care facilities in urban

areas of Rajasthan for reduction of IMR as well as to capture

Millennium Development Goals.

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Uttaranchal and Uttar pradesh. A district level Analysis, Institute

of Economics Growth

7 Hobcraft, JN and McDonald, JW and Rustein, SO (1985),

Demographicc determinanats of infant and early child mortality: A

comparative Analysis’, Population Studies 39(3), 363{385.

8 Kameswararao Avasarala (Jan 2009) : Quality of life Assessment at

Family planning adopter through user perspectives in the district

of Karim Nagar” publication by India Journal of community

medicine/Vol 34/issue-1/Janury 2009, P.P. 24.

9 Messias E. (1985), Income inequality, Illiteracy rate and life

expectancy in Brazil. American Journal of Public Health 93: 1294-6.

10 Nag, M. (1983), Impact of social and economic development on

mortality: Comparative study of Kerala and West Bengal’,

Economic and Political Weekly 18(19), 877{900.

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India”, financing and delivery of health care services in India P.P.

4,6

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management research, Jaipur. WHO collaborating centre for

district health system based on primary health care, 2001.

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2008). Published by Institute of devel. studies Jaipur PP no. 57-85

14 Arthshodh

14 Report (2001), various issues from directorate of Economics &

Statistics department of Government of Rajasthan, Jaipur.

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failure’, The Economic Journal 108(446), 1{25.

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India : A district level Analysis Population research Center Institute

of Economics Growth University Enclave, Delhi -8, Institute of

Economics Growth Discussion papers 44/2001,New Delhi.


Impact of Coronavirus Pandemic (COVID-19) and 21 Day Nationwide Lockdown on Indian Economy

Dr. Rami Gaurang Dattubhai1

Abstract

So far crores of people are infected and lakhs of people have died due

to spread of Coronavirus (COVID-19) in India. India hadwitnessed a rapid

spread of the infection due to Coronavirus, leading to the Government

putting the whole country on 21 day nationwide lockdown. Apart from this

Government of India declares COVID-19 a 'National Disaster',

establishing COVID-19 Economic Response Task Force, announcement of

‘Janata Curfew’, Relief package to help fight the COVID-19 outbreak and

measures taken by Reserve Bank of India, Emergency Response and Health

System Preparedness Package, etc. Due to the mass exodus of migrant

workers, vulnerability in rural areas and huge existence of unorganized

sector; Government is facing difficulties in implementing lockdown in

India. Government is facing several challenges viz. limited testing, lack of

strong and well equipped public healthcare, amount of elderly people and

high population density, less number of Personal Protective Equipment

(PPE) and ventilators while combating with COVID-19. Various national

and international rating agencies have significantly reduced growth rates

projections for the financial year 2020-21. As per FICCI survey, tourism,

hospitality and aviation are among the worst affected sectors that are facing

the maximum brunt of the present Coronavirus pandemic. Consumption is

Professor, Department of Economics, Veer Narmad South Gujarat University,

Surat, Gujarat, India. 1 Views expressed are personal and compiled from various sources.

16 Arthshodh

also getting impacted due to job losses and decline in income levels of

people, particularly the daily wage earners due to slowing activity in

several sectors. The coronavirus lockdown will have an adverse effect on the

MSMEs and agriculture sector in India. Due to lockdown it expected that

poverty, unemployment and inflation will increase in India. Government,

economic policy makers and planners have to formulate appropriate

economic policies and strategies once when the lockdown has lifted to

sustain and increase growth rates of different sectors of an economy

without compromising with the social welfare of different segments and

sections of society in days to come.

Keywords: Coronavirus, COVID-19, Rating Agencies, Industries and

Sectors, Indian Economy.

Introduction

The first three cases of Coronavirus pandemic in India were

reported on 30 January, 2020 in Kerala, all of whom were students

who had returned from Wuhan, China. So far over crores of people

have infected and around lakhs of people have died due to

pandemic spread of Coronavirus in India. There are huge variations

in Coronavirus infected persons across the various states and union

territories in India. Experts suggest the number of infections could

be a substantial underestimate, as India's testing rate is one of the

lowest in the world. The outbreak has been declared an epidemic in

various states and union territories, where provisions of

the Epidemic Diseases Act, 1897 have been invoked, and educational

institutions and many commercial establishments have been shut

down. India has suspended all tourist visas, as a majority of the

confirmed cases were linked to other countries. On 22 March 2020,

India observed a 14-hour voluntary public curfew at the insistence of

the Prime Minister Narendra Modi. Further, on 24 March, the prime

minister ordered a nationwide lockdown for 21 days, affecting the

entire 1.3 billion population of India. On 14 April, Prime

Minister Narendra Modi extended the nationwide lockdown until 3

May, with a conditional relaxation after 20 April for the regions

Impact of Coronavirus Pandemic (COVID-19) and 21 Day Nationwide ...... 17

where the spread has been contained. On 1 May, the Government of

India extended nationwide lockdown further by two weeks until 17

May. The Government divided the entire nation into three zones –

green zone, red zone and orange zone. On 17 May, nationwide

lockdown was further extended till 31 May by National Disaster

Management Authority.The transmission escalated in the month of

March, after several cases were reported all over the country, most

of which were linked to people with a travel history to affected

countries.

Measures taken by Government of India to reduce the spreading

of COVID-19

1 Declares COVID-19 A 'National Disaster': Amid the

coronavirus outbreak, the central government on March 14, 2020

declared COVID-19 as a national 'disaster' and announced to

provide ex-gratia relief of Rs 4 lakh to the families who died of

the virus. The move by the Centre would allow the states to

spend larger chunk of funds from the State Disaster Response

Fund (SDRF) to fight the pandemic (ABP News Bureau, 2020).

2 COVID-19 Economic Response Task Force:To deal with the

economic challenges caused by the pandemic, Prime

Minister Narendra Modi on March 19, 2020 announced the

creation of ‘COVID-19 Economic Response Task Force’ under

the Union Finance Minister Nirmala Sitharaman. The Task

Force will consult stakeholders, take feedback, on the basis of

which decisions will be taken to meet the challenges. The Task

Force will also ensure implementation of the decisions taken to

meet these challenges (Modi, N., 2020).

3 ‘Janata Curfew’:The Prime Minister Narendra Modi, in a

televised address to the nation (March 19, 2020), announced a

‘Janata Curfew’ from 7 am to 9 pm Sunday, March 22, 2020 to

stop the spread of coronavirus and pushes social distancing.

This nationwide voluntarily 14 hours self-quarantine exercise

https://en.wikipedia.org/wiki/Narendra_Modi

18 Arthshodh

led to a complete lockdown in various states with some even

resorting to Section 144 (Chandra, H. and Basu, M., 2020).

4 Nationwide lockdown for 21 days: Prior to this announcement,

numerous containment measures had already been imposed,

varying in intensity across the country, including travel

restrictions (complete restriction of incoming international

commercial passenger aircraft and some restrictions on

domestic travel including cancellation of domestic passenger air

traffic); closing educational establishments, gyms, museums,

and theatres; bans on mass gatherings; and encouraging firms to

promote remote work or work from home. On March 24, 2020,

the Government of India under Prime Minister Narendra

Modi ordered a nationwide lockdown for 21 days from March

25, 2020 to April 14, 2020, limiting movement of the entire 1.3

billion population of India as a preventive measure against

the 2020 coronavirus pandemic in India. The announcement

came in the backdrop of the COVID-19 outbreak and is intended

to enable the concept of ‘social distancing’ to contain the spread

of the virus. The order of lockdown was issued under the

Epidemic Diseases Act, 1897 and Disaster Management Act,

2005 (Wikipedia, 2020).

5 Relief package to help fight the Covid-19 outbreak: Finance

Minister Nirmana Sitharaman on March 26, 2020 announced a

relief package worth Rs 1.70 lakh crore (valued at approximately

0.8 percent of GDP)to help the nation's poor tackle the financial

difficulties arising from Covid-19 outbreak. The economic relief

package will focus primarily on migrant labourers and daily

wage labourers. The package includes a mix of food security

and direct cash transfer benefits which shield poor families

during the lockdown (India Today Web Desk, 2020).

The key elements of the package are: in-kind (food;

cooking gas) and cash transfers to lower-income households;

insurance coverage for workers in the healthcare sector; and


wage support to low-wage workers (in some cases for those still

working, and in other cases by easing the criteria for receiving

benefits in the event of job loss). These measures are in addition

to a previous commitment by Prime Minister Modi that an

additional 150 billion rupees (about 0.1 percent of GDP) will be

devoted to health infrastructure, including for testing facilities

for COVID-19, personal protective equipment, isolation beds,

ICU beds and ventilators. Several measures to ease the tax

compliance burden across a range of sectors have also been

announced, including postponing some tax-filing and other

compliance deadlines. Numerous state governments have also

announced measures to support the health and wellbeing of

lower-income households, primarily in the form of direct

transfers (free food rations and cashtransfers)—the magnitude

of these measures varies by state, but on aggregate measures

thus far amount to approximately 0.2 percent of India’s GDP

(IMF, 2020).

6 Measures taken by Reserve Bank of India: On March 27, 2020

the Reserve Bank of India (RBI) reduced the repo and reverse

repo rates by 75 and 90 basis points (bps) to 4.4 and 4.0 percent,

respectively, and announced liquidity measures to the tune of

3.7 trillion Rupees (1.8 percent of GDP) across three measures

comprising Long Term Repo Operations (LTROs), a cash

reserve ratio (CRR) cut of 100 bps, and an increase in marginal

standing facility (MSF) to 3 percent of the Statutory Liquidity

Ratio (SLR). Earlier in February, the CRR was exempted for all

retail loans to ease funding costs. The RBI has provided relief to

both borrowers and lenders, allowing companies a three-month

moratorium on loan repayments and the Securities and

Exchange Board of India temporarily relaxed the norms related

to debt default on rated instruments. At the same time, the

implementation of the net stable funding ratio and the last stage

of the phased-in implementation of the capital conservation

20 Arthshodh

buffers were delayed by six months. On April 1, the RBI created

a facility to help with state government's short-term liquidity

needs, and relaxed export repatriation limits. Earlier, the RBI

introduced regulatory measures to promote credit flows to the

retail sector and micro, small, and medium enterprises (MSMEs)

and provided regulatory forbearance on asset classification of

loans to MSMEs and real estate developers. CRR maintenance

for all additional retail loans has been exempted, and the

priority sector classification for bank loans to NBFCs has been

extended for on-lending for FY 2020/21. The RBI asked financial

institutions to assess the impact on their asset quality, liquidity,

and other parameters due to spread of COVID-19 and take

immediate contingency measures, including BCPs, to manage

the risks following the impact assessment (IMF, 2020).

7 Emergency Response and Health System Preparedness

Package: Government of India has announced significant

investments to the tune of Rs.15000 crores for 'India COVID-19

Emergency Response and Health System Preparedness

Package'. The funds sanctioned will be utilized for immediate

COVID-19 Emergency Response (amount of Rs.7774 crores) and

rest for medium-term support (1-4 years) to be provided under

mission mode approach. The key objectives of the package

include mounting emergency response to slow and limit

COVID-19 in India through the development of diagnostics and

COVID-19 dedicated treatment facilities, centralized

procurement of essential medical equipment and drugs

required for treatment of infected patients, strengthen and build

resilient National and State health systems to support

prevention and preparedness for future disease outbreaks,

setting up of laboratories and bolster surveillance activities, bio-

security preparedness, pandemic research and proactively

engage communities and conduct risk communication activities.

These interventions and initiatives would be implemented


under the overall umbrella of the Ministry of Health and Family

Welfare. Ministry of Health & Family Welfare (MoHFW) is

authorized to re-appropriate resources among components of

the package and among the various implementation agencies

(National Health Mission, Central Procurement, Railways,

Department of Health (PIB, 2020).

Difficulties in implementing lockdown and Challenges faced by India while combating COVID-19

Following are some difficulties faced by admiration of central,

state and local government in implementing nationwide lockdown

and challenges faced by India while combating pandemic spread of

Coronavirus (IA Staff, 2020).

The mass exodus of migrant workers: The announcement of

21-day lockdown with little to no time for preparation forced

migrant workers, who travel to the cities for work to commute to

their home states. This lockdown coincides with the harvest season,

the time when the migrant workers seek harvesting jobs in large

states. To return to their homes, these migrant workers walked a

long distance. However, they were stopped by authorities, which led

to them being stranded in large masses. In response to this crisis,

shelters and food were provided for these migrant laborers by the

state government, voluntary organizations and NGOs. Yet, their

accommodations do not provide for social distancing.

The vulnerability of rural India: Majority of the Indian

population live in rural areas. In comparison with urban areas in

India rural areas in India has limited access to the healthcare system,

making it difficult for these people to be tested and treated. This, as a

result, makes it highly difficult to monitor the spreading of

infections in these areas.

The ambiguity of the term ‘essential items’: The Centre has

exempted ‘essential items’ manufacturing in its 21-day lockdown

notification. However, there is no clear definition of the term ‘essential

items’, leading to states having different views on what is essential.

22 Arthshodh

Unorganized Sector: The people who would face the worst

impact of the lockdown would be those relying on the unorganized

sector, which amounts to about three-fourths of India’s working

population. These individuals are vulnerable because they do not have

financial security due to the lack of jobs during the time of lockdown.

Challenges faced by India while combating COVID-19

Limited testing: During early stage of the outbreak, the Indian

Council for Medical Research (ICMR) only allowed testing of those

who have travel history and those who have come in contact with them

and then have gone on to develop symptoms to be tested for COVID-

19. This led to India having one of the lowest testing rates in the world.

Also, initial tests didn’t specifically test for COVID-19 but just any

strain of Coronavirus, including SARS and MERS. As of March 24,

2020, India has just tested 18 individual per million people.

Lack of strong and well equipped public healthcare: In India,

states control their public healthcare system. The biggest states are the

most vulnerable as their healthcare may be overwhelmed due to their

dense population. Though the majority of healthcare is provided by

private hospitals; which are generally better-run and better equipped,

is costly and inaccessible for many. Beside, Government hospitals in

India especially in rural areas are ill-equipped to handle this situation.

This means that India’s healthcare is not well equipped to deal with

Stage-3 of the COVID-19 infection.

India’s elderly population and population density: Around

100 million people in India are over the age of 60. This is the age

group that is the most susceptible to the infection. This figure is

higher than Italy’s population, the country that is worst hit due to the

pandemic. India’s population density is about 450 inhabitants per

square kilometer. China, which has the world’s highest population,

has a density of 150 inhabitants per square kilometer. About 120000

people share 1 km2 in Mumbai, which is 12 times more than New

York City’s population density. Also, one-sixth of the population lives

in the slums, making them even more vulnerable to the infection.


Less number of Personal Protective Equipment (PPE) and

Ventilators: India has less number of PPE which provide protections

to medical professionals including doctors and nurses from

Coronavirus infections. Not only that there is a shortage of ventilators

also; which are useful to critical patients. As the number

of coronavirus (Covid-19) cases is on the rise in India, the central

government has estimated a steep rise in demand for personal

protective equipment (PPEs) and coronavirus diagnostic kit in the

coming months. The country would require an estimated 27 million

N95 masks, 15 million PPEs, 1.6 million diagnostic kits, and 50,000

ventilators by June 2020 (BS Web Team, 2020).

India’s growth rate projections by various national and

international rating agencies and institutions for the financial year

2020-21: Rating agencies, both national and international, are

unanimous that the COVID-19 pandemic will be an economic

catastrophe for India. Even though the country may not slip into a

recession, unlike the European countries, United States, or Asia-

Pacific that have stronger trade ties to China, analysts be certain of the

impact on India’s GDP growth will be significant. India is currently in

the midst of a 21-day lockdown that began on March 25, to contain

the spread of the coronavirus. The fallout of the move will spill over

to financial year 2020-21. In India, GDP growth is already at a decadal

low and any further dent in economic output will bring more pain to

workers who have seen their wages erode in recent times (Mathew,

P., 2020). Following table provide details of India’s growth rate

projections by various national and international rating agencies and

institutions for the financial year 2020-21.

Name of the rating agencies Growth rate projections for the

financial year 2020-21

Moody’s Investors Service, March 27, 2020 Reduced from 5.3% to 2.5%

Crisil, March 26, 2020 Reduced from 5.2% to 3.5%

Standard & Poor’s (S&P), March 31, 2020 Reduced from 5.2% to 3.5%

Fitch, April 3,2020 Reduced from 5.1% to 2.0%

24 Arthshodh

Name of the rating agencies Growth rate projections for the

financial year 2020-21

CARE Ratings Between 1.5 to 2.5%

KPMG, April 4,2020 Below 3%

Barclays, March 30, 2020 Reduced from 4.5% to 2.5%

India Ratings and Research (Ind-Ra), March 30,

2020

Reduced from 5.5% to 3.6%

Asian Development Bank, April 3, 2020 4%

Goldman Sachs, April 09, 2020 Reduced from 5.8% to 1.6%

Source: (Mathew, P., 2020), (PTI (1), 2020), (TPT Bureau, 2020), (BusinessToday.In.,

2020), (Noronha, G (1)., 2020), (Mukewar, P., 2020), (Kumar, C., 2020) , (Noronha,

G (2)., 2020) and (Mishra, P., 2020)

Based on India’s growth rate projections for the financial year

2020-21 by various national and international rating agencies and

institutions after the announcement of nationwide 21 day lockdown

due to the spread of Coronavirus; it is expected that Indian economy

will certainly and significantly slow down growth rates in near future.

Intensity and spread of Coronavirus to various parts of the country

viz. urban, semi urban and rural areas including metropolitan cities

and villages will significantly and adversely affects different segments

of the society and sectors of an economy. Government, economic

policy makers and planners have to formulate appropriate economic

policies and strategies to sustain and increase growth rates of

different sectors of an economy without compromising social welfare

of different segments and sections of society in days to come.

Impact of Coronavirus (COVID-19) on Demand and Supply

side as per survey conducted by FICCI

The rapid outbreak of deadly Coronavirus pandemic in the

country has not only led to a panic-like situation amongst the citizens,

but has also hit Indian economy - which was already reeling under a

significant slowdown over the past few quarters. The medical

rampant has presented fresh set of challenges for the country's

economy, causing severe disruptive impact on investment and


consumption demand. India’s economy, which was growing at a six-

year low rate of 4.7 % in the third quarter of the current fiscal, had

strong hopes of recovery in the fourth quarter. However, the new

Coronavirus epidemic has made the recovery extremely difficult in

the near to medium term. According to a survey conducted by FICCI,

the outbreak has assembled new roadblocks for the Indian economy

now, causing severe disruptive impact on both demand and supply

side elements which has the potential to derail India’s growth story

(FICCI, 2020).

Impact of COVID-19 on demand side: As per FICCI

survey, tourism, hospitality and aviation are among the worst

affected sectors that are facing the maximum brunt of the

present Coronavirus pandemic. Closing of cinema theaters and

declining footfall in shopping complexes have affected the retail

sector by impacting consumption of both essential and discretionary

items. Consumption is also getting impacted due to job losses and

decline in income levels of people, particularly the daily wage

earners due to slowing activity in several sectors including retail,

construction, entertainment and others. With widespread fear and

panic rapidly increasing among people across the country, overall

confidence level of consumers has dropped significantly, leading to

postponement of their purchasing decisions. Even the travel

restrictions imposed by Central government to prevent the spread of

Covid-19 in India have severely impacted the transport sector.

Impact of COVID-19 on supply side: Large scale shutdowns

of factories and resulting delay in supply of goods from China

have affected many Indian manufacturing sectors. According to the

FICCI report, sectors like automobiles, pharmaceuticals, electronics,

chemical products etc. are facing an imminent raw material and

component shortage. Besides having a negative impact on imports of

important raw materials, the slowdown in manufacturing activity in

China and other markets of Asia, Europe and the US is impacting

India’s exports to these countries as well.

26 Arthshodh

Labour Market: The sudden displacement of migrant labour

would have far-reaching impact on the Indian economy and states

should be prepared to deal with the consequences of behavioural

changes forced by the lockdown. The sudden lockdown and the

consequent shutdown of transport created a humanitarian crisis in

many states as panic-stricken migrant workers took to the highways

trying to walk hundreds of kilometers home. A number of migrant

workers who fled the big cities may never return, preferring to eke

out a living on their marginal farms or find work in nearby towns. It

would deprive industrial centers such as Gurugram, Surat and

Tiruppur of labour for a long period of time, likely raising the wage

burden on small- and medium-sized units struggling to crawl out of

recession. The Economic Survey 2016-17 had estimated that at least

nine million people migrate annually within the country, most of

them in search of work. While the top destination for migrants is

Delhi, followed by Mumbai, the southern states have become a

migrant magnet in recent years. The largest number of them sets off

from Bihar, UP, Bengal and Assam, often traveling more than 3,000

km to distant Kerala. There may be a second wave of home-coming of

migrant workers once the lockdown is lifted. Many who decided to

stay back are desperately waiting for transport to be available. They

would take off at the first opportunity. That would mean even if those

who left earlier decide to return, companies may find a shortage of

labour. The disruption could extend to farms which may also feel the

shortage as the kharif sowing season begins with the rains

(Narayanan, D., 2020).

Problems faced by Micro Small and Medium Enterprises

(MSME): Along with tackling healthcare on a war footing, the

government will have to pay attention to the brewing economic crisis.

According to CII data in a report released last year, the MSME sector

added 13-15 million jobs annually. It is vital that this sector, a key

component of the Indian economy, be protected during times of crisis.

However, even if global economies bounce back sooner than


expected, Indian MSMEs are likely to pay a high price. These

companies are too small to have enough of a cushion to last through a

pandemic like this one. Add to this the fact that many of these

companies have been asked to down shutters or curtail operations

while still paying employees and that’s apart from meeting costs for

taxes, power, and other utilities. In the wake of a pandemic like this

one, demand is likely to soar, while supply will be extremely weak.

Raw materials will likely be in short supply, as free trade will be

curtailed for a while. Wuhan, the center of the pandemic, is also one

of the largest auto hubs in the world. With Wuhan shut for months,

there’s going to be a huge shortage of components too. At this point in

time, China seems to have entered the post-peak period. According to

the WHO, this is when levels of the disease drop from the peak and

the process of recovery begins. Much of the rest of the world is still in

the early stages of the pandemic. This means China could get its

industries up and running in time to meet the global post-pandemic

demand. While that may be good news for a connected world, it

could be another severe blow to Indian MSMEs who manage to

survive. Given raw material, transportation, and labour issues that

manufacturers are likely to face, they are not going to be able to drop

their prices. China, with its head start, could still manage to get low-

cost products to the world, creating a massive competition issue for

Indian exporters (Mukewar, P., 2020).

Effects on Agricultural Sector: More than half of India's

workforce engages in farming, while agriculture contributes around

16% to the country's GDP. India is one of the world's largest producers

of crops like rice, wheat, sugarcane, cotton, vegetables and milk. The

coronavirus lockdown will have an adverse effect on the agriculture

sector in India.The sector is facing a lot of trouble with labourers and

movement of goods. As Rabi harvest season approaches, farmers

worry about their standing crops. Farmers growing wheat, mustard

and pulses already complained about their crops damage due to

untimely and heavy rainfall recently. This led to farmers fixing their

28 Arthshodh

crops but amid Coronavirus lockdown most of the labourers available

fled to their homes. As the restriction on movement of goods continues

amid the lockdown, the farmers are likely to feel the pinch in

their income. Moreover, farmers fear the sowing of summer season

crop as none of the shops selling seeds, fertilizers and other vital

inputs. Besides, several farm machines like combine and harvesters lie

stranded on highways as there is no one to operate them. Coronavirus

lockdown has impacted the supply chain of agricultural commodities.

By taking a toll on the loading and unloading of agricultural produce.

Also, the lockdown has hampered the movement of trucks carrying

essential commodities. Several cold storage and warehouse owners

complained regarding the dearth of laborers. Unwillingness to work

fearing police beating, many labourers are staying home or leaving for

their hometown. In all, the COVID-19 caused clampdown has caused

disruption and will eventually lead to a dip in farmer’s income (Kaur,

G., 2020). Central government will pay Rs 6,000 to each farmer in three

instalments in a year under the Prime Minister's Kisan Samman Nidhi

Yojana.

Government agri-research body Indian Council of Agricultural

Research (ICAR) is assessing the impact of Covid-19 lockdown on

agriculture and allied sectors and taking measures to minimize its

effect on the country’s food security. ICAR had issued crop-specific

advisories to farmers, asking them to take general precautions and

safety measures during harvesting, post-harvest operations, storage

and marketing of rabi crops. While the government has exempted

many agricultural operations from harvesting to movement of

produce to mandis from lockdown rules, the ICAR study will help the

government take further action (PTI (2), 2020).

Effects on Employment, Unemployment and Labour

Participation Rate: As per Centre for Monitoring Indian Economy

(CMIE) recent report; in March 2020, the labour participation rate fell

to an all-time low, the unemployment rate shot up sharply and the

employment rate fell to its all-time low. The employment rate fell to


an all-time low of 38.2 per cent in March 2020. The fall since January

2020 is particularly steep - almost spectacular. It seems to have

nosedived in March after having struggled to remain stable over the

past two years. Then, there is a precipitous fall. The Labour

Participation Rate in March 2020 was 41.9 per cent. It was 42.6 per

cent in February and 42.7 per cent in March 2019. This fall in the LPR

in March was the result of a sharp 9 million falls in the labour force -

from 443 million in January 2020 to 434 million in March 2020. Fall in

labour participation rate is largely associated with the national

shutdown to contain the spread of Coronavirus. But, this fall seems to

have happened even before the lockdown. It gets much worse as we

move into the lockdown. The unemployment rate in March was 8.7

per cent. This is the highest unemployment rate in 43 months, since

September 2016. The unemployment rate during this last week of

March, 2020 was 23.8 percent. Labour participation rate fell to 39

percent and the employment rate was a mere 30 percent. These are

very big variations and are subject to the usual sampling errors. It,

therefore, may not be very wise to focus on the magnitude of those

movements but on the certainty of the movements (Vyas, M., 2020).

Effects on Inflation: On the price scenario, slowdown in

demand and production activities, a sharp fall in the global price of

crude oil, and price decreases in other major commodities such as

energy, base metals and fertilizers among others are expected to exert

downward pressure on inflation. Dun & Bradstreet (D&B) expects the

CPI inflation to remain in the range of 6.5-6.7 per cent and WPI

inflation in the range of 2.35-2.5 per cent during March 2020 (PTI (3),

2020).

Effects on Poverty: As described above spread of Coronavirus

(COVID-19) and 21 day nationwide lockdown will have significant

effects on various industries and sectors in days to come. This will

lead to reduction in employment rate and labor participation in

agriculture sector, MSME and other unorganized sectors in India. Due

to this it is expected that people living below poverty line and level of

30 Arthshodh

absolute as well as relative poverty will increase substantially. Recent

United Nations (UN) report has estimated about 400 million people

working in the informal economy in India are at risk of falling deeper

into poverty due to the coronavirus crisis which is having

‘catastrophic consequences’ (PTI (4), 2020).

Way ahead: The coronavirus pandemic and nationwide

lockdown have adversely affected various sectors and industries in

India. This comes at a time when India’s economy and public

finances were already under substantial stress. India must think

about how to deal with the public health crisis and rebuild its

economy once lockdown is lifted.

After lockdown; restarting requires accurate data on infection

levels. The government must strengthen public healthcare

infrastructure, particularly in smaller towns and villages.

Government ensures more and rapid testing, rigorous

quarantines, availability of masks and PPE kits to health

professionals. Appropriate measures to identify and contain

new infections should be adopted.

So far there is no medicine or vaccine to protect against infection

of Coronavirus, boosting immunity of people is one of good

alternative. Government should distribute immunity booster to

everyone. Considering regional and cultural diversity in India,

localized (house made) immunity booster recommended by

homeopathy or Ayurveda can be recommended.

The areas with no Coronavirus cases and no migration of

workers should allow all sectors and services to operate. The

public transport will be partially restored and limited

movement on roads will be allowed. However, considering

possibility of further spread of Coronavirus pandemic people

have been asked to avoid any unnecessary travel.

Considering nature of Indian economy and present situation

where millions of people who have lost their jobs especially in


agriculture, MSME and unorganized sector. After the lock down

is lifted; more emphasis should be given to labour intensive

sectors from demand and employment perspective. Govt. can

boost scheme like MGNREGA and other employment oriented

programmes to enhance domestic demand. This will help

circulation flow in economy.

To boost agricultural sector farmers and farm labourers could be

allowed to work with reasonable safeguards as long as social

distancing norms are maintained. Harvesting season is on and

scarcity of labour has affected the agricultural sector; to ensure

maximum harvesting

MSME sector and small and tiny businesses may be affected

extremely by the ongoing lockdown and crisis. The government

should impose higher import duties on non-essential

commodities, raw materials and final product for a time being

which will give protection and boost to the domestic producers.

MSME sector should be given GST and Income tax relief for the

financial year 2020-21.

Government should provide low interest term loan to MSME

sector. Also, about six month extension should be given to

payment of existing EMI. This will help them to invest in this

difficult time and accumulate capital.

Government can expand the scope of MGNREGA by reducing

constraint of providing employment for stipulated days only.

For the time being this restriction can be removed. MGNREGA

workers should be used in agricultural sector for harvesting as

well as they will be provided employment in MSME sector

where higher skills are not essential. On one hand it will give

good grains whereas on another hand it will give employment

and wages to MGNREGA workers. This strategy will boost

demand and active supply chain in Indian economy.

32 Arthshodh

Direct transfers to households may reach most but not all, the

quantum of transfers seems inadequate to see a household

through a month. Government needs to ensure that the daily

wage earners, poor people and non-salaried workers will be

prevented from the pandemic of Coronavirus.

Significant and substantial reduction in growth rate projections

by various national and international rating agencies and

institutions will lead to demote in investor’s confidence that will

affect to a dropping exchange rates in this condition, and

substantial losses for our financial institutions. Limited fiscal

and financial resources are certainly a concern. Government

and Reserve Bank of India needs to revive entre financial system

for stimulating economic growth.

Government can collaborate more with NGO, philanthropists

and corporates; as they can play an effective role in creating

awareness among the public about the adverse effects of

Coronavirus infections. They are expecting to play a key role in

trying to contain the spread of Coronavirus and help in areas

such as patient care, support to governments, community

sensitization, hygiene promotion, distribution of food to needy

people and contact tracing. Government can also think to have

Public Private Partnership (PPP) mode for the same.

In restarting, e-commerce platforms and all its value chain

companies should be enabled so that supplies of essentials are

not affected, and people can stay at home.

In this situation, India can gain maximum benefit from

demographic dividend too. Healthy youth, stiff with

appropriate distancing at the workplace in aviation, hotel,

hospitality and tourism etc. for restarting.

Government should ensure to provide uninterrupted social

safety net to vulnerable sections of society along with orphans,

widows, elderly people, pensioners and persons with disabled

(PwD).


Conclusion

So far India has one of the lowest testing rates in the world;

increasing the number of tests and rapid strengthening of health care

system is a need of the hour. The Indian economy is likely to suffer

for a long time as even once the situation is controlled, it will take a

long time for everything to come normal. Government, economic

policy makers and planners have to formulate appropriate economic

policies and strategies once when the lockdown is lifted (sooner or

later) to sustain and increase growth rates of different sectors of an

economy without compromising with the social welfare of different

segments and sections of society in days to come.

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Cost Benefit Analysis of Tomato Cultivation Under Polyhouse in Haryana

Komal Malik

Vinay Kumar

Abstract

Tomato is one of the most extensively used and demanded vegetable crop because of its special nutritive value. Among all the vegetables, tomato is one of those few vegetables that remain in demand throughout the year. Therefore, the study was carried out with the objective of analyzing the viability of tomato cultivation under polyhouse in Haryana. Both primary and secondary data was used for conducting the study. Primary data was collected using a well structured questionnaire schedule. Purposive sampling was used to draw the sample in which a total of 180 farmers were choosen for the study. In order to analyze the results descriptive statistics like ratio, average and percentage were used. The study concluded that the cost benefit ratio for the crop of tomato cultivated under polyhouse is 1.32 which shows that tomato cultivation in polyhouse is viable for the farmers. Majority of the produce i.e. sixty percent of the tomato production was sold in regulated mandies. Therefore, it is necessary to continue research on Polyhouse so that viability can be increased and at the same time constraints can be wiped out.

Keywords: Polyhouse, Haryana, Constraints, Cost-Benefit.

Introduction

In the recent scenario, there has been a shift from traditional

farming towards horticulture farming. Among horticulture, the main

focus is upon vegetable crops as there demand is continuously

Assistant Professor, Department of Economics, Govt. College, Nalwa, Nagour,

Rajasthan. Assistant Professor, Department of Mathematics and Statistics, CCSHAU,

Hisar, Haryana.

38 Arthshodh

increasing among the consumers due to its nutritional value.

Tomato is considered as one of the most “protective food” due to its

nutritional value. Tomato (Lycopersicon esculentum) belongs to

the genus Lycopersicon under Solanaceae family. Tomato is a

good source of minerals, fiber, vitamins A, C and potassium. In

the Indian Culinary tradition, the most versatile and widely

used vegetable is tomato. Tomatoes are used for sauces, pickles,

puree, ketchup, soup and also as a salad. To various small and

marginal farmers, tomato acts as a good source of income. In

terms of production as well as area rank of India is second and

is after China. Haryana is one among the leading producer of

tomato as it stood at 12th rank with 392.36 thousand tonnes

(Anonymous, 2013) among all Indian States. In value addition

chain of processing tomato has very few competitors. Protected

cultivation is an improved agro technique being used worldwide to

register 3-4 times increase in production (Kumar, 2016). Tomato is

grown extensively in the plastic greenhouses for higher productivity

(Rana et al. 2014). Therefore, the present study has been conducted

with the following objectives:

1) To analyze the cost benefit ratio of cultivating tomato

under polyhouse in Haryana.

2) To analyze the marketing behavior of tomato cultivated

under polyhouse.

3) To find the major constraints of tomato cultivation under

polyhouse.

Materials and Methods

In order to conduct the present investigation, both primary as

well as secondary data has been analyzed. Primary data has been

collected through a well structured questionnaire schedule which

was then used for face to face interview for data collection. The

questionnaire was pre tested on a group of sample and the required

necessary changes has been incorporated into it. The questionnaire

Cost Benefit Analysis of Tomato Cultivation under Polyhone in Haryana 39

included both closed and open ended questions into it. Purposive

sampling was used to carry out the research. Districts of Bhiwani,

Sonipat and Rohtak were choose for study as these were the districts

having highest number of polyhouse in Haryana. A total of 180

farmers were choosen for the investigation. Secondary data was

collected from District Statistical Office and Department of

Horticulture, Haryana. The results of the study were analyzed using

Descriptive statistics like percentage, average, ratio etc. The gross

returns and net returns of tomato cultivation under polyhouse were

calculated by employing the following relationship.

GR = Yr.Pr

NR = GR – Total Cost

Where

GR = Gross returns per hectare of tomato in Rs/ha.

NR = Net returns per hectare of tomato in Rs/ha.

Yr = Yield of tomato in q/ha.

Pr = Price per quintal tomato in Rs/ha.

Results and Discussion

The per unit cost structure for tomato cultivation under

polyhouse is shown in the table1 formulated below:

Table 1: Per Unit Cost Structure of Tomato Cultivation ( /per acre)

S. No. Particulars Tomato (Cost Structure)

A Material Cost

1 Farm Yard Manure 9267.34

2 Seeds 25546.40

3 Plant Protection Chemical 13242.56

4 Fertilizers 5763.82

A-I Total Material Cost 53820.12

B Labour Cost

5 Land Preparation 5246.70

40 Arthshodh

6 Seed Bed Preparation & Sowing 5934.66

7 Irrigation 964.40

8 FYM & Fertilizer Application 1234.80

9 Hand Weeding 1236.50

10 Harvesting 52944.24

B-I Total Labour Cost 67561.30

11 Variable Cost( A-I + B-I) 121381.42

12 Interest on Working Capital 10317.42

13 Total Variable Cost ( 11+ 12 ) 131698.84

C Fixed & Other Cost

14 Rental value of land 30000

15 Interest on Fixed Capital 2850

16 Marketing Cost 16531.48

17 Management Charges 10726.64

C-1 Total Fixed &OtherCost 60108.12

Total Cost ( 13 + C-I) 191806.96

Source: Primary Survey

As the total cost of production for tomato was divided into

two parts; one was total variable cost and other one was total fixed

and other cost. Total variable cost has been further sub divided into

two major parts in which one was material cost and second one was

labour cost. The major portion of expenditure in material cost section

was spend upon seeds which constitutes an amount of 25546.40 per

acre. The highest expenditure among labour cost was made on

harvesting which were 52944.24 per acre. The addition of the total

labour cost and total material cost was variable cost. The variable

cost for the production of tomato under the polyhouse cultivation

was an amount of 121381.42 per acre. If we add the interest on

working capital an amount of 10317.42 per acre to the variable cost


calculated above, we get the total variable cost. The total variable

cost for tomato cultivation was 131698.84 per acre.

Among the fixed cost the maximum expenditure was upon

rent paid for the land. The average value of rent per acre was about

thirty thousand which was highest among all expenditures. One of

the probable reasons for such a high rental value of land was the

prosperity of Haryana state among the agriculture sector. The

summation of total variable cost and total fixed and other cost gives

us the total cost of tomato cultivation in polyhouse which was an

amount of 191806.96 per acre.

1.1. Return Structure of Tomato Cultivation

The per unit return structure of tomato cultivation in

polyhouse is shown in table2. The total yield that was obtained from

the field measuring one acre was 16.33 ton’s. The average price in

the market for the tomatoes which were cultivated in polyhouse

was 15.50 per kilogram.

Table 2: Per Unit Returns Structure in Tomato Cultivation ( / Acre)

Sr. No Particulars Return Structure

1 Total Yield(ton’s) 16.33 (rounded off)

2 Price (per kg) 15.50

3 Total Returns 253185.19

4 Total Cost 191806.96

5 Net Returns ( 3-4) 61378.23

C:B Ratio 1.32

When we multiply the total yield obtained from the field with

the per kg price, we get the total returns an amount of 253185.19

per acre for the tomato from the polyhouse. By subtracting the

amount of total cost i.e. 191806.96 from the total return of

253185.19, we got the net returns for the crop of tomato i.e.

61378.23 per acre. The table reveals that the cost benefit ratio for

42 Arthshodh

the crop of tomato cultivated under polyhouse is 1.32 which shows

that vegetable cultivation in polyhouse is beneficial for the farmers.

1.2. Marketing of Tomato

Marketing is one of the vital component that determines the

viability of any vegetable crop as if there are proper marketing

channels avilable for the product it will fetch higheramount of

remuneration to the farmer’s and hence higher level of profit. Therefore

the most considerable part is marketing aspect in the agriculture sector.

Table 3: Marketing channel for sale of produce of Tomato

Sale of the Produce Percentage

On Farm 4%

In Local Village Market 6%

Regulated Market / Mandi 60%

Sale in Retail Stores (i.e. Easy Day, Reliance Fresh, Grofers etc.)

11%

Hotel (on contract base) 19%

Processing Industry Nil

Other if any (specify) Nil

Total 100%

The table 3 formulated above shows the marketing channel

for the tomatoes cultivated in polyhouse. The table revealed that

four percent of the total produce was sold on the farm itself. Out of

the total produce six percent was sold in the local village market.

Next come was the regulated markets or mandis in which the

highest percentage was sold out as it constitutes for sixty percent of

the entire produce. Out of the total cultivated crop eleven percent

was sold to retail stores. The remaining nineteen percent was sold to

the hotels on the contract basis in which they make some informal

kind of contract with the farmers to take a required quantity on a

daily basis. Nothing was supplied to the processing industries.

1.3. Constraints of Polyhouse

Less durability of polyhouse cladding material and lack of

skilled labour were major production related constraints while


unorganized marketing system, lack of vegetable processing units, lack

of suitable cold storage facilities and lack of transportation facilities

were some major marketing related constraints faced by the farmers

cultivating tomato crop in polyhouse.

Conclusions

The importance of cost of cultivation cannot be ignored as it is

vital component in determining the viability of cultivating tomato

under polyhouse. The study concluded that the cost benefit ratio for

the crop of tomato cultivated under polyhouse is 1.32 which shows that

vegetable cultivation in polyhouse is viable for the farmers. Majority of

the produce i.e. sixty percent of the tomato production was sold in

regulated mandies which is a matter of concern as it will longer the

chain between the producer and the consumer which needs to be

reduced. Therefore, it is necessary to continue research on Polyhouse so

that viability can be increased and at the same time constraints can be

wiped out. New options for better marketing can be explored like

creation of special mandies for polyhouse crops, food processing

industries which directly purchase the crop from the farm. A lot has

been done so far and a huge remains to be explored and improved.

References

1. Cheema, D.S., Kaur, S., Srinivasan, R. and Kaur, S. (2010):

Monitoring of major pests on cucumber, sweet pepper and

tomato under net-house conditions in Punjab, Pest

management in horticultural ecosystems, 16(2): pp.148-155.

2. Gnanasekaran A. and Vijayalakshmi S. (2014): Economic

analysis of tomato cultivation in Dindigul district of Tamil

Nadu, International Journal of Science and research, 3 (12),

pp. 995-997.

3. Hazarika T.K. and Phookan D.B. (2005): Performance of

tomato cultivars for polyhouse cultivation during spring

summer in Assam, Indian Journal of Horticulture, 62(3):

pp.268-271

44 Arthshodh

4. Joseph A. and Muthuchamy I. (2014): Productivity, quality and Economics of Tomato (Lycopersicon esculentum Mill.) cultivation in aggregate hydroponics – A case study from Coimbatore region of Tamil Nadu, Indian Journal of Science and Technology, 7(8), pp. 1078-1086.

5. Kumar P., Chauhan R.S. and Grover R.K. (2016): Economics analysis of tomato cultivation under poly house and open field conditions in Haryana, India. Journal of Applied and Natural Science., 8(2): pp.846 – 848.

6. Lekshmi S.L. and Celine V.A. (2015): Evaluation of tomato hybrid for fruits, yield and quantity traits under polyhouse conditions, International Journal of Applied and Pure Science and Agriculture, 1(7), pp.58-62.

7. Murthy D. S., Prabhakar B. S., Hebbar S.S., Sreenives V. and Prabhakar M. (2016): Economic feasibility of vegetable production under polyhouse: a case study of capsicum and tomato, Journal of Horticulture Science, 4 (2):pp.148- 152.

8. Parvej M.R., Khan M.A.H. and Awal M.A. (2010): Phenological Development and Production Potentials of Tomato under Polyhouse Climate, Journal of Agricultural Sciences, 5(1): pp.19-31.

9. Rana, N., Kumar, M., Walia, A., Sharma S. (2014). Tomato Fruit Quality under Protected Environment and Open Field Conditions, International Journal of Bio-resource and Stress Management, 5(3): 422-426.

10. Sepat N.K., Sepat S.R., Sepat S. and Kumar A. (2013): Energy use efficiency and post analysis of tomato under green houses and open field production system at Nubra valley of Jammu and Kashmir, International Journal of Environmental Sciences. 3(4), pp. 1233-1241.

11. Wani K.P., Singh K.P., Amin A., Mushtaq F. and Dar Z.A.(2011): Protected cultivation of tomato, cucumber and capsicum under Kashmir valley condition, Asian Journal of Science and Technology, 1(4), pp. 56-61.


Growth Analysis of Area, Production and Yield of Rapeseed and Mustard Crop in Rajasthan

Preeti Prasad

Rashmi Bhargava

S.K Kulshrestha

Abstract

Rajasthan State is the desert dominant state even role of agriculture

plays a vital role in the economy of the state. Rajasthan is the higher producer

of mustard since it is a major crop of the state. The mustard crop covers

almost all districts even desert districts of the state so the area and production

have enhanced over the years in the state of this crop. Rajasthan has first

place in the area as well as the production of rapeseed-mustard crop and has

second place in productivity after Haryana. This paper deals with the area,

production, and yield growth of rapeseed-mustard crops over thirty-five

years. This paper shows that most of the districts of the state have positive

and significant growth in area, production, and yield of the crop.

Introduction

Mustard seeds were obtained from Channu-Daro of Harnapan

civilization. 2300–1750 B.C. (Allchin 1969). The Aryans used Brassica

species as spices and for oil. Thus it is clear that over a period of more

than 3500 years, mustard came to occupy an important place in the diet

of the Indian people as a source of oil and vegetation.

Research Scholar, Department of Economics, S.P.C Government College,

Ajmer, M.D.S.University. Ajmer, Rajasthan. Associate Professor, Department of Economics, S.P.C Government College,

Ajmer, Rajasthan. Assistant Professor in Economics, Vardhman Mahaveer Open University,

Kota, Rajasthan.

46 Arthshodh

The estimated area, production and yield of rapeseed-

mustard in the world was 36.59 million ha (mha), 72.37 million

tonnes (mt) and 1980 kg / ha respectively, during 2018–19. Globally,

India accounts for 19.8% and 9.8% of total production and

production (USDA). During the last eight years, productivity has

increased significantly from 1840 kg / ha in 2018–19 in 2010–11 and

production has also increased from 61.64 tonnes in 2010–11 to 72.42

million tonnes in 2018–19.

India is the fourth largest oilseed producing country in the

world, next only to USA, China and Brazil, harvesting about 25

million tons of oilseeds against the world production of 250 million

tons per annum. Since 1995, Indian share in world production of

oilseeds has been around 10 percent. Although, India is a major

producer of oilseeds, per capita oil consumption in India is only 10.6

kg/annum which is low compared to 12.5 kg/annum in China, 20.8

kg/annum in Japan, 21.3 kg/annum in Brazil and 48.0 kg/annum in

USA (Report on GPDP Project in Edible Oil Industry in India).

In Rapeseed-mustard, India has ranks second both in the

production (6.82 Mt) as well as in the area under cultivation (6.27 M ha)

of rapeseed-mustard in the world. Rapeseed-mustard is a rabi crop

predominantly grown in the states of Rajasthan, Uttar Pradesh,

Madhya Pradesh and Haryana. These states together contribute 4.90 M

ha of the area and produce 5.60 Mt of rapeseed-mustard. It is mainly

used as edible oil and medicine for burning. Its use is limited for

industrial purposes owing to high cost. Rajasthan is the leading

rapeseed-mustard producing state though its share has declined in

recent years. The production, area and yield of rapeseed-mustard seed

experienced a significant growth from 1985-1995, primarily due to the

increase in irrigated land and the availability of high-yielding seeds in

the country. This trend was partly reversed due to intermittent famine

conditions in some of the major rapeseed-mustard producing states,

such as Rajasthan. Jha et. al presented the comparison of area,

production and yield of mustard crop as in table 1.

Growth Analysis of Area, Production and Yield of Rapeseed .......... 47

Table 1: Period-Wise Growth Rates (%) In Area, Production and Yield of Major Rapeseed-Mustard Crop-Producing States: 1980-81

To 2008-09

State Area (M ha) Production (Mt) Yield (kg /ha)

Rajasthan 6.17 7.96 1.69

Uttar Pradesh -2.75 0 2.82

Haryana 4.62 5.43 2.05

Madhya Pradesh 3.73 6.61 2.53

Other states 1.26 2.25 1.32

India 3.87 4.18 2.26

Source: Jhaet. al (2012)

Objective of the study

The objective of the paper is to identify the growth rate of

area, production and yield during the period of study.

Literature Review

Jhaet. al (2012) analyzed that the major oilseed-producing

states, Rajasthan, Madhya Pradesh and Maharashtra have exhibited

the healthy growth rates in the area, production and productivity

during 1980-2009. Only a few states like Haryana, Madhya Pradesh,

Maharashtra, Rajasthan and West Bengal have increased the oilseeds

production through both area as well as productivity improvement.

Hedge (2012) estimated that projected Indian population of

1685 million by 2050, 17.84 Mt of vegetable oils is required to meet

the fat nutrition. This is equivalent to roughly 59.41 Mt of oilseeds. If

one assumes 25% of vegetable oils from crops other than annual

oilseeds, then the country needs to produce just 44.56 Mt of oilseeds

by 2050 to meet fat nutrition of the projected population. With full

adoption of currently available oilseed technologies, this level of

production could easily be achieved.

Kumrawat and Yadav (2018) proved the enhancement of

mustard crop in Bharatpur region over the years. Jain et. al (2016)

describes that fluctuating yield for oilseeds crops and the area and

yield instability of the mustard crop has been found declining overtime

48 Arthshodh

plausibly because of increase in irrigation facilities, location-specific

technologies and better input management.

Methodology

This study based on the time series data of area, production and

yield from the year 1980-81 to 2014-15 of district level. The secondary

data is collected from Agricultural Statistics of Rajasthan published by

the Directorate of Economics & Statistics, Rajasthan. The semi log

model and coefficient of variation have been used here to estimate the

district wise growth rate of area/ production/ yield in Rajasthan.

Semi-Log: The exponential equation is given by LnYi = α + βt + Ui This is fitted using OLS method

Here Yi = Area/Production/Yield inithyear (i= 1, 2, 3…N) α = Intercept, β = Regression coefficient, Ui = Residual term The parameters α and β are estimated by the least square

method. The collective effect of all explanatory variables on explained

variable is denoted by R2. It is called determination of coefficient.

R2 = 1-

The coefficient of determination (R2) has also been calculated

for the model.

The coefficient of determination (R2) has also been calculated

for the model.

Analysis of Area, Production and Yield

Rajasthan state has noticed highest growth rate in rapeseed –

mustard crop in term of area and production. It is clear from table 2

that production of rapeseed and mustard increased over the year in

most of the districts of Rajasthan. It is obvious that the highest growth

in production is found in Jaisalmer, Jhalawar, Kota, Bundi and

Jhunjhunu respectively. In which Jaisalmer, as well as Jhalawar growth

is 24 per cent and 23 per cent per annum and these are also statistically

significant. There are few districts in which growth is not statistically

significant although they have positive such as Ajmer, Jalore, Pali and


Sirohi district. Banswara district has negative growth during the period

of study which is minus four per cent. The area of rapeseed/mustard

crop has been increased over the year in the state since this is a most

edible crop not only in state but also in country so most of the farmers

are growing this crop. Many districts are such as Jaisalmer, Bundi,

Kota, Tonk and Churu are among the highest growing area district of

the crop. All district has been noticed positive growth except Banswara

where were negative growth rate in the area. There are some districts

where the growth rate in the area of this crop is near to zero such as

Pali, Sirohi and Udaipur. The yield of the mustard not much increased

during the period of study as compare to production and area. Some

district has three per cent growth rate such as Sikar, Jhunjhunu, Barmer

and Kota. Other district has growth rate less than 3 per cent. Some

districts have negative growth rate in such as Ajmer and Jaisalmer.

There is a district where the productivity growth rate near zero such as

Churu but it is not statistically significant. Some other district like

Bhilwara, Tonk and Bikaner where the growth rate is not statistically

significant.

Table 2 Production, Area and Productivity Growth of Rapeseed/ Mustard

Districts

Production Area Productivity

Growth Coefficient

R2 Growth

Coefficient R2

Growth Coefficient

R2

Ajmer 0.05(0.0672) 0.11 0.06(0.009) 0.22 -0.02(0.016) 0.18

Jaipur* 0.09(0.000) 0.6 0.08(0.000) 0.55 0.01(0.051) 0.13

Sikar 0.09(0.000) 0.74 0.06(0.000) 0.7 0.03(0.000) 0.44

Jhunjhunu 0.11(0.000) 0.79 0.08(0.000) 0.72 0.03(0.000) 0.53

Alwar 0.06(0.000) 0.77 0.04(0.000) 0.63 0.02(0.000) 0.48

Bharatpur 0.05(0.000) 0.62 0.03(0.000) 0.43 0.02(0.000) 0.45

Dholpur 0.05(0.000) 0.6 0.03(0.000) 0.42 0.02(0.001) 0.34

S.Madhopur* 0.08(0.000) 0.57 0.06(0.000) 0.54 0.02(0.003) 0.28

Bikaner 0.08(0.000) 0.61 0.06(0.000) 0.71 0.02(0.103) 0.09

Churu 0.11(0.000) 0.56 0.11(0.000) 0.65 0(0.956) 0

Ganganagar* 0.07(0.000) 0.74 0.05(0.000) 0.7 0.02(0.000) 0.36

Jodhpur 0.08(0.000) 0.75 0.05(0.000) 0.62 0.02(0.000) 0.5

Jaisalmer 0.24(0.000) 0.75 0.26(0.000) 0.81 -0.02(0.016) 0.25

Jalore 0.03(0.0552) 0.13 0(0.6973) 0.01 0.02(0.000) 0.57

Barmer 0.060.0018 0.3 0.05(0.006) 0.24 0.01(0.050) 0.13

Nagaur 0.07(0.000) 0.46 0.04(0.002) 0.29 0.03(0.000) 0.48

Pali 0(0.9283) 0 -0.01(0.631) 0.01 0.01(0.079) 0.11

Sirohi 0.01(0.6725) 0.01 0(0.909) 0 0.01(0.061) 0.12

Kota* 0.14(0.000) 0.72 0.11(0.000) 0.62 0.03(0.000) 0.72

50 Arthshodh

Bundi 0.12(0.000) 0.61 0.12(0.000) 0.6 0.02(0.004) 0.26

Jhalawar 0.23(0.000) 0.65 0.2(0.000) 0.61 0.02(0.003) 0.34

Tonk 0.11(0.000) 0.59 0.11(0.000) 0.51 0.01(0.101) 0.09

Banswara (-0.04(0.0026) 0.34 -0.08(0.000) 0.59 0.02(0.000) 0.5

Dungarpur 0.02(0.5398) 0.02 0(0.942) 0 0.02(0.015) 0.25

Udaipur* 0.04(0.023) 0.17 0.01(0.377) 0.03 0.02(0.000) 0.39

Bhilwara 0.08(0.0002) 0.41 0.09(0.000) 0.45 0.01(0.192) 0.06

Chittorgarh 0.1(0.000) 0.48 0.09(0.000) 0.41 0.02(0.003) 0.27

State 0.75(0.000) 0.75 0.05(0.000) 0.63 0.015(0.006) 0.31

Source: Authors’ Calculation

It is clear from the figure 1 that the growth rate in production

of mustard is different across the districts, some district growth rate

for the production of mustard is much higher such as Jaisalmer and

Jhalawar district, on the other hand, some districts have negative

growth rate like Banswara.

Fig 1 Production Growth of Rapeseed and Mustard

The figure 2 show that the area under the rapeseed/mustard

crops have growing trends in most of the districts of the state.

Jaisalmer is one of the districts where the area increased the fastest

growth rate among all districts. The reason being is that Indira

Gandhi takes over the district over the years. Agriculture potential

other districts such as Jhalawar, Kota, Bundi and Churu etc. increase

in this crop over the period of study. There are also some districts

where the area under this crop has not been increased such as

Dungarpur, Jaloreand Sirohi. A few districts where the growth rate

of the area has been negative in term of growth such as Banswara


and Pali. It is clear from the figure that the farmer is interested in

this type of crop in Rajasthan.

Fig.2 Area Growth of Rapeseed and Mustard

It can be drawn from figure 3 that the productivity of mustard

has been increased at the rate of two percent most of districts of the

state. The two districts such as Ajmer and Jaisalmer over the years.

There are four districts such as Jhunjhunu, Kota, Nagaur and Sikar

where the growth rate of yield three per cent per year.

Fig. 3 Productivity Growth Rate of Rapeseed and Mustard

Conclusion

This paper shows that there has been a good increase in both

area and production of mustard crop in different districts of

Rajasthan, but the growth rate in productivity is however positive

52 Arthshodh

but it has increased comparatively less. It has also been found from

this paper that despite Rajasthan State being a Desert State, both the

area and production of Mustard have increased in Desert districts.

Therefore, it can be said that this crop is being grown in good

quantity in all the districts except a few districts. Due to the

contribution of most districts to Mustard production, Rajasthan state

is also the first place in Mustard production in the entire country.

References

1 Jha, Girish Kumar, Suresh Pal, V C Mathur, GeetaBisaria, P

Anbukkani, R R Burman and S K Dubey(2012): “Edible Oilseeds

Supply and Demand Scenario in India: Implications for Policy”,

Indian Agricultural Research Institute, Director, IARI.

2 Nethrayini, K.R. and Mundinamani, S.M.(2013). Impact of Technology

Mission on Oilseeds and Pulses on Pulse Production in Karnataka.

International Research Journal of Agricultural Economics and

Statistics 4(2): 148-153.

3 Sharma, A.K., and Thomas, L.,(2013). Technology inputs and its

impact on farm profits: A case study of rapeseed mustard. Indian Res.

J. Ext. Edu. 13(3): 9- 14.

4 Swain, M., Problems and Prospects of Oilseeds Production in

Rajasthan: Special reference to rapeseed & mustard, AERC Report

submitted to Ministry of Agriculture, Government of India, New

Delhi (2013).

5 Kumrawat Meena and Yadav Manju(2018).Trends in Area,

Production, and Yield of Mustard crop in Bharatpur Region of

Rajasthan, International Journal of Engineering Development and

Research, Volume 6, (1), 315-321.

6 P.K. Jain, I.P. Singh and Anil Kumar(2005).Risk in Output Growth of

Oilseeds in the Rajasthan State: A Policy Perspective. Agricultural

Economics Research Review, Vol. 18 (Conference No.) 2005, 115-133


Inequality Re-examined Amidst Covid-19

Dr. G.L. Meena

Abstract

The policies of Liberalization, Privatization and Globalization put

India on the path of Higher Growth Rate (HGR) from the Hindu Growth

Rate(HGR). Then the important question at hand was had India been able to

distribute the benefit of that higher growth among its population evenly?

Value of Gini index being constantly higher signifies that the benefit of

growth has been reaped by a few only and around 176 million people are still

living below poverty line. It seems this inequality is not even realized during

the normal course of life however the hard times like Covid-19 uncovers the

difference in the standard of living of people. The main aim of the present

study is to put forth such inequalities prevailing in the day to day life of

Indian people particularly in the light of Covid-19. It seeks to examine the

inequalities based on gender, education, region and sector of employment.

The study finds that these inequalities are interconnected and cannot be

separated from each other. Thus, the policy makers do not seem to live up to

the expectations of the constitution makers where equality of status and of

opportunity had been envisaged to promote among all the citizens.

Keywords: HGR, Inequality, Covid-19.

Introduction

It is said that “charity begins at home” invigorated by that, an

unwarranted similar term can be coined for Indian societyi.e.

“inequality beginsat home”. According to the CIA World Factbook the

Assistant Professor, Dept. of Economics, University of Rajasthan, Jaipur,

(Rajasthan).

54 Arthshodh

value of Gini index, considered as a standard measure of inequality in

income distribution, was 35.2 for India in the year 2011 which is higher

than the other neighbouring countries like Pakistan, Bangladesh and

Nepal all being behind the country in economic growth. It clearly

signifies that India has not been able to distribute the benefits of higher

growth even equally among its population if not progressively.

Another unpleasant information lending support to this exclusionary

growth can be viewed in the percentage of people living below poverty

line. World Bank Poverty & Equity Brief states that between FY2011-12

and 2015 India witnessed a drastic decline in the poverty at the

international poverty line from 21.6 to 13.4 percent. However, the

absolute aspect of the picture narrates a different story as the number is

crossing 176 million. No society can claim to be poverty and inequality

free because these concepts are relative, however one can envisage a

decent life defined in terms of realisation by every citizen the

constitutional provisions of right to dignity, equality and livelihood

security. COVID-19 led situation has invoked resumption of such long-

overlooked discussions built on sharing of national prosperity, a decent

life ensured by constitutional provisions, exercising equality of rights

and unilateral decision making in a democratic state. As soon as the

lockdown was placed in by the central government of India a large

pool of stranded workers was on roads and then only many eyes

glared through a new India completely unsecured, in search of life

rather than livelihood. All of sudden it was realised that poverty and

inequality may be reducing over the years in degree but not in kind

and intensity. This revived many hidden questions like are we living in

a country which is divisive at various fronts ranging from home to

workplace? Is state doing justice with its role in securing life and

livelihood of people particularly in hard times of Covid-19? Do we see

appropriate participation by different strata of the society as agents in

policy formulation? All such pertinent questions necessitate a re-

examination of inequality prevailing in India at different levels.

Inequality Re-examined Amidst Covid – 19 55

In the quest of finding answers to these questions, the presents

study is an attempt to re-examine the inequalities prevailing in the

Indian society at different levels in the light of Covid-19 outbreak. After

introduction, second section presents an account of the data set used in

the study. Section third, being the core part of the study, provides a

detailed discussion of inequality in general and specifically with

reference to Covid-19. The final section concludes the study.

Data and Methodology

The main aim of this study is to put forth inequalities prevailing

in the day to day life of Indian people and although it’s not possible to

quantify each aspect of inequality. However, an attempt has been made

here to bear a pragmatic approach in analysing the issue to the possible

extent. The study is based on secondary data source. To serve that

purpose, various reports have been consulted like NSSO rounds on

Employment and Unemployment, Annual Report PFLS 2017-18, India

Wage Report -ILO, Report on Fifth Annual Employment-

Unemployment Survey, World Bank Poverty and Equity Brief to name

a few. Due to unavailability of data for the period mentioned, it was

not possible to stick to a unique reference period. Moreover, focus of

the study is more on the nature and kind of inequalities prevailing in

India rather than on its degree. Therefore, the reference periods,

deviating merely for 4-5 years, does not seem to make a difference to

the conclusions. In the present study the time period of 2011 has been

considered wherever census data is used and on the other hand

reference period of 2017-18 stands for NSSO data. This deviation is

permissible on the grounds that during this period no structural break

had been realised by Indian economy.

Inequalities in India

3.1 Gender imbalance: one of the major inequalities

prevailing in India can be seen in gender disparity. Constitutional

provisions impart equal status to women however that has not been

practiced so far. Both as a home maker as well as a factor of

production, their services have been undermined over the years. It

56 Arthshodh

will not be wrong to say that the life of an Indian woman begins

with the household chores while theman’swith the newspaper and a

cup of tea. Quantitative as well as qualitative aspect of gender

disparity in India presents a sad state of affairs. It will not be wrong

to say that quantitative aspect portrays the society’s psyche towards

women while qualitative one throws light on their being recipient of

unequal treatment in their life span. Using some most representative

parameters, an account of their state has been demonstrated below.

3.1.1 Quantitative Aspect:

To exhibit this aspect of gender disparity, sex ratio has been

used as a proxy since it directly deals with the number of females

which is measured in terms of 1000 males. Figure :1 depicts that sex

ratio is below 1000 in every state of the country except Kerala.

According to census 2011, male-female ratio in India is 1.06 males for

every female which is higher than the international average of 1.01.

the more striking feature is that child sex ratio (among 0-6 years age

group) is even worse with the figure of 1.09.

S

Source: Census of India, 2011.

A differenced sex ratio has been reflected by figure:2 where

the difference between the overall sex ratio and child sex ratio has

been found to be negative for all states except the five. it clearly


implies that preference for male child has been increasing over the

time. It also cements the belief that this gender imbalance will persist

even in the times to come.


One of the interesting facts is that the first child not being

male is one of the most critical factors in determining sex ratio. How

it ultimately affects the sex ratio is determined by the educational

attainment level of the parents in turn. In educated family tendency

is such that if first child happens to be a male, it negatively affects

sex ratio however nothing can be said explicitly for less educated or

uneducated families. Conversely, first child not being male has high

probability of affecting sex ratio positively irrespective of the nature

of families in terms of education. Although it’s not the result of their

desire to balance sex ratio rather of their preference to have male

child at any cost. It clearly hints at a social construction founded on

gender biasness, discriminating against women. Number based

discrimination is not as big an issue as the kind of treatment females

receive in India because sex ratio is imbalanced for almost each

country of the world with varying degrees. Therefore, an analysis of

qualitative aspect of gender disparity is required.

58 Arthshodh

3.1.2 Qualitative Aspect:

Here, by qualitative aspect we mean do the females enjoy the

benefits of equal status through their course of life? This has been

assessed by using three indicators reflecting the nature of status i.e.

literacy rate as a signal of social status, representation in assemblies

measuring their role in policy making and average level of wage rate

to check their status in economic spheres.

Figure-3 provides a picture of the gender gap in the literacy

rate in India where the excess of male literacy rate over the female

literacy rate has been shown. It can be easily noticed that the figure

is positive for all states of the country. Even at the national level,

male literacy rate is considerably higher than the female literacy rate

and it is around 17 percentage points. More than 30% females being

illiterate is a sign of their deprivation from the basic right of

education. It undermines their role in decision making and confines

them up to the household chores. Low female literacy is also one of

the critical factors causing high population growth in a already

highly populated country.


Second important indicator is the one which registers the role

of women in political spheres and that also defines their role in policy

making. As mentioned above, it has been measured as in terms of


their representation in legislative councils of Indian states and of the

country as a whole meaning that in state assemblies and in parliament

particularly in Lok Sabha. Figure:4 demonstrates that representation

of women in the state assemblies is very low, putting that in numbers

the highest percentage is 14 for three states i.e. Bihar, Haryana and

Rajasthan. Surprisingly these are the three states where literacy rate of

females is the lowest, sex ratio is the lowest and the differenced

literacy rate is the highest. Although it’s hard to find any correlation

between these two contradictory facts. Overall representation of

women in state assemblies is around 9 percent. There are two states,

Mizoram and Nagaland, where not a single woman is a part of

assembly. At national level, in the 17thLoksabha elections the

percentage of elected women representatives is around 14.3 % which

is very low not only in comparison of developed countries as shown

in table 1 but unfortunately also below the neighbouring countries

like Pakistan and Bangladesh. It is also at subpar with the other

member countries of BRICS. Thus, the results are alarming in the

sense that women’s role in policy making in India is less than the

countries which are at the advanced stage of development, the

countries which are lagging behind India in growth terms and the

countries which are almost at par with India in terms of growth. That

simply means status of women is more concerned with the societal

norms than with the economic prosperity.

Source: Chapter-5 Participation in Decision Making, Men and Women in India, 2017. pp:103-104.

60 Arthshodh

Table 1: Representation of Women in National Parliaments (Lower House)

(in %) India 14.3

Developed Countries

Italy 35.7

UK 32.0

Australia 30.0

US 23.6 Neighbouring Countries

Nepal 32.7

Pakistan 20.2

Bangladesh 20.7

Bhutan 14.9 BRICS

Brazil 15.0

Russian Federation 15.8

China 24.9

South Africa 42.7

Source:http://archive.ipu.org/wmn-e/classif.htm

One pleasant aspect of the picture is that 73rd amendment of

Indian constitution has mandated reservation of seats for women in

Panchayat Raj Institutions’ (PRIs)Elections also termed as local

elections. 20 states have made the provisions of 50 percent

reservation to women in PRIs. However, the crucial point here is to

see whether that mandate has been followed in letters only or in

spirit as well. Here a pragmatic approach suggests that the former

has overshadowed the latter which is exemplified in the stature of

women representative elected as head of the last unit of grassroot

level administration known as Sarpanch. The reality is that their

being elected as Sarpanch has evolved a new concept of “Sarpanch

Pati” (husband of sarpanch) where all her duties and powers are

exercised by her husband furthering his dominance over his wife

specifically and women in general in the power structure. The step

was welcome but implementation would remain poor until the

missing element from the policy making is realised and worked

http://archive.ipu.org/wmn-e/classif.htm


upon. Awareness generation and capacity building are the two most

important instruments in strengthening the position of any

vulnerable section of the society and equally important is their

timing. No doubt women’s position has been strengthened in the

documents but their literacy rate remains a big issue to be dealt with.

Government has put in efforts to build up their capacity however,

proper attention has not been paid to their education. Had the

Sarpanch been educated they would have discharged their duties at

their own discretion and better enjoyed the power.

The third important indicator highlights the relative economic

status of women and it has been measured by gender wage gap. It’s

worthwhile to mention here that the gender wage gap is defined as

the difference between median earnings of men and women relative

to median earnings of men. Figure 5 presents a sketch of the gender

wage gap in India. It is found to be 39% in the year 2011-12 which is

quite higher however, the positive side of the picture is that it has

been declining persistently from 1993-94. Although this gender wage

gap is “raw or unadjusted” meaning that the variables affecting the

wages from the background has not been controlled here. Education

level, age and other skills are the key factors in deciding the level of

wages thus the comparison without adjusting those variables cannot

said to be fully scientific.

Source: India Wage Report ILO, 2018.

62 Arthshodh

Therefore, the next figure:6 has been produced here which

deals with the difference in average level of daily wages gender wise

and that have also been categorised according to the employment

status so that the difference in the endowments can be better

captured. Figure manifests the considerable difference between the

average wages of the male and female across all the categories of

employment where wages of former exceeds the latter.

Source: India Wage Report ILO, 2018.

In addition to these facts, World Economic Forum published a

report on Global Gender Gap in 2020 which put forth some more

dimensions of the gender inequality. The report came up with the

calculation of global gender gap index along with the four more sub-

indices and one of them is the economic participation & opportunity

index which quantifies the economic status of women. In figure 7 it

is easily visible that there is a negative relationship between the

economic participation & opportunity and proportion of unpaid

works per day. Unpaid works has been measured as the proportion

of hours of work spent by female at home in care and volunteer

work to the hours of work spent by men for the same activities. In

India, this ratio has been found to be around 10 meaning that

women spend 10 times much time in these activities as done by men


leading to decline in their share in economic activities. Eventually

value of sub index for India is 0.354 and stands at 149th place among

the 153 countries of the world. The report has rightly figured out

that one of the dominant causes of gender inequality in India is the

fact that a major contribution is made by women in household

chores for which they are not paid. According to the report female

estimated earned income is mere one-fifth of maleincome, which is

also among the world’s lowest (144th).

Figure : 7 Economic Participation and Time Spent in Unpaid Domestic Work

Source: Global Gender Gap Report 2020, World Economic Forum. pp-14.

Above analysis made in terms of quantity as well as quality

clearly indicates that the condition of women in India is precarious.

3.2 Education Led Inequalities

Another side of inequality is the one that a person comes

across as soon as he comes in contact with the outer world by joining

the educational institutions. Education system in India gives rise to

the dynamic inequalities meaning that it appears as if the education

system is not only emanating the inequalities but also increasing

those. The most important trait of such inequality is that it is

64 Arthshodh

institutionalised. Two type of educational institutes exist in India:

Public and Private. Public sector run schools are basically either of

Hindi medium or of regional language while most of the schools in

the private sector provide vernacular education. If the discussion is

extended to the senior secondary level institutes, the results are quite

interesting in the sense that the students coming from the public

sector institutes will either become a part of skilled labour or

unskilled labour. If they become part of skilled labour force then

language medium will not erect any barrier in their becoming part of

organised formal sector. However, the bigger question is how much

is the share of this organised formal sector in total employment.

Table 3 indicates that the share of organised formal sector

employment in the distribution of total employment was 7.2 % in the

year 2011-12 and it has witnessed a minor increase of mere 0.7

percentage points in the span of six years from 2011-12 to 2017-18.

On the other hand, remaining ones, who could not become

part of organised formal sector, end up becoming part of unskilled

labour force. Needless to say, howinsecure the livelihood of these

labourers is. it’s not that the students from the private institutions

never face unemployment however their medium of language

qualifies them for several sophisticated jobs of private sector. If they

prefer organised formal sector jobs, they have the better

opportunities there asshown by their number in that sector. For

instance, figure:8 highlights the gap in selection between the

students who write UPSC exams in the two mediums (Hindi and

English) in India which are considered as the top most

administrative services in hierarchy. Figure depicts that the gap

between the Hindi and English medium students has been widening

continuously during the period of 10 years while the gap between

the total number of students and the students from English medium

has been reducing. Gap between the English medium and Hindi

medium students, who qualified for the same exam, was equal to

735 in the year 2008 which rose to 8233 in the year 2018.


Figure 8: Medium of writing of Examination of CandidatesAppeared in Civil Services (main) Examination.

Nu

mb

er

of

Stu

den

ts

18000

10000

8000

6000

4000

2000

0

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Year

Total

English

Hindi

2018

12000

14000

16000

Source: Annual Reports of Union Public Service Commission.

Disparity in the education system reflecting through the

difference in the medium of language is sufficient to realise that

education system itself is creating inequalities in the country.

3.3 Rural-Urban Gap

Another type of inequality pervasive in the country is based

on the region. Here regional disparity does not stand for the gap

between two states or two districts but for the gap between rural and

urban area. More than 60 % of the total population of India resides in

the rural areas but when it comes to the gap in the facilities available

in the rural and urban areas, the state of affairs is dismal. To present

a rough sketch of this gap, some indicators have been chosen here

showing the socio-economic status of the rural and urban life in

India. To reflect the four dimensions of development seven

indicators have been chosen and out of them five are positive and

two are negative. Higher value of positive indicator shows higher

level of development, on the other hand higher value of negative

indicator reflects lower level of development. For instance, average

infant mortality rate and % of households with kuchha house are

66 Arthshodh

two negative indicators in the sense that their value being higher is a

sign of backwardness. Following this notion, the observation of table

2 confirms the belief that “India lives in its villages but not the

facilities/amenities”. In case of all positive indicators, urban area

exceeds the rural area with a considerable margin and conversely,

rural area supersedes the urban for negative indicators and again

with the substantial difference. The issue of grave concern here is

that all chosen indicators represents the basic requirements for the

survival and their lack demands a quick action to justify the term

“Inclusive” prefixed to the development.

Table 2: Rural- Urban Gap in India (2011)

Dimensions of development

Indicator Rural Urban

Standard of living Average Daily Wages 175 384

Education Literacy Level 68.91 84.98

Health Average Infant Mortality Rate 48 29

Access to basic amenities

% of Households with electricity 55.7 93.1

% of households with toilet 17.6 52.8

% of households with improved source of drinking water

84.5 95

% of households with a Kachha House

19.1 2.5

Source: Census 2011 and India Wage Report ILO, 2018.

3.4 Formal-Informal Sector gap in India

The biggest example of inequality, Covid-19 has also played a

vital role in highlighting that, is the difference found in the returns of

the factors of production particularly between the two main factors

namely capital and labour as well as the difference between the two

categories of labour itself engaged in informal and formal sector. It

can also be termed as inter-factor and intra-factor inequality.

Workers engaged in the informal sector are with no written job

contract, paid leaves, health benefits and social security. Even in the

organised sector out of 13.2%, 5.2% labour is of informal nature


signalling at the rapid informalisation of the organised sector. Its

illusionary as it is indicative of the expansion of organised sector

however does not show its informalisation and such workers in the

organised sector has been termed as “contractual workers”. The table

given below briefs the employment structure in Indian economy.

Table 3: Distribution of Total Employment in India (%)

Worker 2011-12 2017-18

Unorganised Organised Total Unorganised Organised Total

Informal 82.6 9.8 92.4 85.5 5.2 90.7

Formal 0.4 7.2 7.6 1.3 7.9 9.3

Total 83.0 17.0 100.0 86.8 13.2 100.0

Source: Murthy, Ramana S V (2019). Measurement of Informal Economy-

_Indian Experience, IMF Seventh Statistical Forum. p.03.

Table indicates that the employment has expanded both in

the informal as well as formal sector however there is a considerable

degree of difference in the expansion as the former exceeds the later.

The worst part of the picture is that informal workers engaged in

unorganised sector not only constitutes more than 80% of the total

labour force but also has been increasing during 2011-12 to 2017-18.

The most striking thing is that this is the most vulnerable part of the

labour force. An in-depth analysis of the composition of informal

workers reveals that its major part belongs to the self-employed and

casual labour amounting to 51.9% and 27% respectively in 2017-18.

A closer look at the difference of the wage rate between the

formal and informal sector workers presents a sad state of affairs.

The real average daily wage of the organised sector worker was 430

in the year 2004-05 which grew to 513 in 2011-12 and on the other

hand the real wages accruing to the unorganised sector workers

during the same periods were 109 and 166 respectively. One of the

worst parts of the informalisation can also be viewed in the

distribution of informal workers by their status of employment. For

instance, regular wage/salaried workers are supposed to have some

68 Arthshodh

element of security in their jobs. However, their working conditions,

shown in the table below, tell a different story:

This is the workforce working in the informal sector and

which was stranded during Covid-19. Particularly this was the

casual labour and some own account workers & helpers in

household enterprises. As soon as the lockdown was placed in, they

were turned (forced) out of their jobs. Covid-19 has also highlighted

one more type of inequality prevailing in the country i.e. there has

always been a huge gap in the earnings of capital owners and the

wage earners. Here, the role of state also needs to be scrutinised

particularly in the context of Covid-19. On the one hand,

government has announced packages for the industries while on the

other hand the states are let free to unilaterally amend the labour

laws. It clearly indicates that in lieu of narrowing down gap between

the earning of capital and labour, state is stretching that gap.

Conclusion

The main aim of the present study is to highlight the

inequalities prevailing in the Indian society at various levels from

home to workplace. These inequalities have always been an inherent

part of life in the country however the hard times of Covid-19 has put

them forth in the public domain and suddenly made it a part of public

discussion. The important point is that the inequalities discussed in

the study are interconnected and cannot be separated from each

other. Education led inequalities are closely linked to the region-based

disparity and that in turn results in gap between formal and informal

sector employment. It’s not unfair to say that there exists a vicious

circle of inequality in the country and Covid-19 has just drawn the

attention of everybody towards it. Stranded labour on the roads

striving for their lives, puts a question mark over the spirit of

preamble to the constitution of India where equality of status and of

opportunity had been envisaged to promote among all the citizens. It

seems that labour is asking to the constitution makers and to the

government “are we the people of India”?


References

1 Ghosh, Madhusudan (2017). Infrastructure and Development in

Rural India. Margin-The Journal of Applied Economic

Research 11:3, pp.256-289.

2 International Labour Organization (2018). India Wage Report:

Wage Policies for Decent Work and Inclusive Growth.

3 Jensenius, Francesca R. (2016). Competing Inequalities? On the

Intersection of Gender and Ethnicity in Candidate Nominations in

Indian Elections. Government and Opposition, 51(3), 440-463.

doi:10.1017/gov.2016.8.

4 Labour Bureau (2016). Report on Fifth Annual Employment -

Unemployment Survey (2015-16). Ministry of Labour and

Employment, Government of India.

5 MoSPI (2017). Women and Men in India-2017: Participation in

Decision Making. Ministry of Statistics and Programme

Implementation, Government of India.

6 MoSPI (2017). Selected Socio-Economic Statistics: India 2017.

Ministry of Statistics and Programme Implementation,

Government of India.

7 Murthy, Ramana S V (2019). Measurement of Informal Economy

Indian Experience. IMF Seventh Statistical Forum.

8 Narayanan, Abhinav (2015). Informal Employment in India:

Voluntary Choice or a Result of Labour Market Segmentation?

Indian Journal of Labour Economics 58, pp.12-16.

9 NCEUS (2007). Report on Conditions of Work and Promotion of

Livelihoods in the Unorganised Sector. National Commission for

Enterprises in the Unorganised Sector, Government of India.

10 PLFS (2019). Annual Report, Periodic Labour Force Survey July

2017-June2018. Government of India: Ministry of Statistics and

Programme Implementation, National Statistical Office.

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11 The World Factbook, Central Intelligence Agency (CIA), US.

Retrieved from https://www.cia.gov/library/ publications/

the- world-factbook/rankorder/2172rank.html.

12 World Economic Forum (2020). Global Gender Gap Report 2020.

13 The World Bank (2020). Poverty & Equity Brief, South Asia,

India. Retrieved Fromhttps://databank.worldbank.org /data

/download/poverty/33EF03BB-9722-4AE2-ABC7-

AA2972D68AFE/Global_POVEQ_IND.pdf


Climate Change and Its Impact on Agricultural Production: An Evidence from India

Dr. Chitra Choudhary

Sumedha Bhatnagar

Abstract

In recent years, climate change is much talked about theme on

various platforms. Due to the drastic transformation in climate in past 26

years in form of change in rainfall pattern and temperature it has become a

major area of concern for many governmental organizations and policy

advisors as it impacts the development of an economy, directly or indirectly.

Climate change can be measured through the change in various factors

including rainfall pattern, temperature change, change in precipitation,

carbon emissions etc. Since agriculture sector that is the major driver of

any economy, specifically developing and less developed countries, is

largely dependent on the weather conditions of that country, change in

climate adversely impact agriculture and economy of that country. This

paper examines the impact of climate change on the agriculture production

of India. It also attempts to analyze the relationship between the climate

variables and the agricultural GDP on the country.

Key words:- Climate Change, Agricultural Production, Rainfall,

Temperature, Carbon-di-oxide.

Assistant Professor, Department of Economics, University of Rajasthan, Jaipur.

(Rajasthan). Research Scholar, Department of Humanities and Social Sciences, Malaviya

National Institute of Technology Jaipur, Rajasthan.

72 Arthshodh

Introduction

India is a developing economy and its economic development

is dependent on agriculture to a large extent for livelihood,

employments and overall economic growth (Stern 2006; Gollin, 2010).

After liberalization India faced acute famine and food scarcity for a

long period (Panagariya, 2005). Green revolution was the result of

persistent joint efforts that made the country self-sustainable and self-

reliant (Patnaik, 1990). Today, India is among the top exporters of rice

and other food grains (Dey, 2020). Geographically, India lies in the

South of Asia, due to its geographical positioning the overall climate

of the country is characterized as tropical in nature. According to

Koppen System, India hosts six major climatic subtypes that ranges

from arid desert in the west to the humid tropical region in the

southwest and island territories to alpine tundra and glaciers in the

north. The country faces varied, and unpredictable weather

conditions all around the year and extreme weather conditions can

possibly prevail in the country at the same point of time in two

different regions.

In recent years, climate change has been much talked about

theme on various platforms (Boutabba, 2014; Lobell and Asseng, 2017;

Guntukula, 2020). Due to the drastic transformation in climate in past

26 years in form of change in rainfall pattern and temperature it has

become a major area of concern for many governmental organizations

and policy advisors as it impacts the development of an economy,

directly or indirectly (Adams et. al 1990). Climate change can be

measured through the change in various factors including rainfall

pattern, temperature change, change in precipitation, carbon

emissions etc. (Auffhammer et. al, 2006; Auffhammer et. al, 2012).

Since agriculture sector that is the major driver of any economy,

specifically developing and less developed countries, is largely

dependent on the weather conditions of that country, change in

climate adversely impact agriculture and economy of that country

(Mendelson et. al 2006; Stern 2006; Kanwar, 2006; Fishman, 2011).

Climate Change and Its Impact on Agricultural Production : An ........ 73

According to the fourth assessment report of the Inter-

governmental Panel on Climate Change (IPCC), agriculture

production, including access to food, in many African countries is

projected to be severely compromised, it states that by 2020 the yield

from rain-fed agriculture could be reduced by up to 50%. Even

though, this is the debatable conclusion it, on broader view, it

demonstrates the risk to the crop yield of African countries due to

climate change. According to a report of Asian Development Bank

(2009), Asia and Pacific Regions have witnessed the rise in

temperature, this will impact the agriculture sector of this region as

37 percent of the total world emissions from agriculture production

are accumulated from Asia and the Pacific. The precipitation in India

is expected to rise by 10%- 12% and mean yearly temperature is

expected to rise by 3-6°C by 2100 (IPCC, 2014). The climate change is

likely to impact the agriculture production with expected fall of 10-

40% by 2100 (Agarwal, 2008). Even today, when technological

innovations and upgradation has taken place in the agriculture

sector, rainfall is the major factor that determines the overall

agriculture production of India thus highlighting the impact of

climate changes on the Indian economy (Lahiri and Roy, 1985; Sen

and Robert, 2004; Krishnamurthy, 2012).

The paper analyzes the impact of climate change into two

sections. First, it analyzes the impact on climate change variables on

the agriculture output and second, it studies the long-run

relationship between climate variables and agriculture GDP.

Additionally, the paper highlights the influence of fertilizer

consumption on agriculture output.

The paper is divided into 7 sections. Section 1 is the introduction

and it gives the brief overview of Indian agriculture and climate change

scenario. Section 2 is the literature review where the previous studies

are reviewed and based on which research gap is identified. The

section 3 is the methodology followed by findings of the study in

section 4. Section 5 and 6 gives the results and conclusion of the study.

74 Arthshodh

The last section gives the future scope of research and limitation of the

present study.

Literature Review

The economic impact of climate change on agriculture has been

studied extensively the world over and it the one debatable research

problem for the researchers (Mendelsohn et.al 1994; Auffhammer et.

al, 2006; Auffhammer et. al, 2012; Siddiqui et. al 2012). Climate change

can impact the economy in two ways; first by impacting the

production side of the economy and second by policy induced

abatement activities (Nordhaus, 1994). The effect of climate change on

the agriculture production was first studied by Arrehenius (1896),

who speculated the impact of concentration of atmospheric carbon

dioxide on the global temperature on the ground.

The impact of climate change on agriculture can be analysed in

two ways namely; through crop simulation approach (agronomic

simulations) that includes production function approach and

econometric modelling that also includes Ricardian analysis

(Mendelsohn et.al 1994; Sarker, et. al, 2014; Guntulula and Goyari

2020).The agronomic-economics is the study of physical impact that

are in the form of yield change and/or area changes, of climate

change. Whereas, in Ricardian approach farmers are assumed to

identify instantaneously and perfectly any change in climate, evaluate

all associated changes in market conditions, and then, modify their

action to maximize profits (Mendelsohn and Nordhaus, 1999; Seo et.

al 2008). Such assumptions are only viable in case when ergodic

agricultural system- i.e. where space and time are substitutable.

Schlenker (2006) estimated the impact of climate change on

the crop yield in the agriculture sector of the USA. The study

concluded that the increase in temperature impacted the farm value

but was nullified by the increase in agriculture production caused

due to high precipitation in the region. Thus, global warming in the

USA has very little impact on the agriculture sector. This,

highlighting that beginning of climate change may have small effect


for developed countries but in future the impact has tendency to

multiply. The agriculture sector of developing and underdeveloped

countries is more sensitive to the climate variability (Kumar and

Parikh, 2001). The latitudes are also one of the factors that impact of

climate change on a country Stern (2006). Paul, et. al (2009) identified

that there is a possibility that agriculture sector may harm the

climate of the region as, 14% of nitrite oxide and methane comes

from agriculture sector and another 18% is produced due to

deforestation for agriculture use.

In India, Kumar and Parikh (2001), applied a variant of

Ricardian approach and stated that 2° Celsius temperature rise and

7% increase in rainfall would lead to almost 8 % loss in farm level

net revenue. The study further highlighted that under doubled

carbon dioxide concentration, in later half of 21st century, the GDP

tends to decline by 1.4% to 3 % due to climate change. Kumar (2003)

extended the analysis to include climate variation in Ricardian

approach and estimated that 5% increase in climate variation along

with other climate change scenario would result in almost 10% drop

in the farm level net revenue.

To account social interaction between farmers, Polsky (2004)

introduced a spatial econometric specification of the Ricardian

model. Whereas, Schlenker et. al (2005) used spatial features to

arrive at efficient estimators of regression parameters. Both the

authors supported the importance of inter-farmer communication

and its significant influence on climate sensitivities.

Tobey et.al (1992), in his study concluded that even with

concurrent productivity losses in the major grain producing regions

of the world, global warming may not cause widespread disturbance

in the world agriculture sector. The interregional adjustments in the

production and consumption will serve as a buffer to the severity of

climate change impacts on the world agriculture and result in

relatively small impacts on domestic economies. According to Kaiser

and Crosson (1995), the impact of climate change at the farm level

76 Arthshodh

will depend on the magnitude of change in climatic variables and on

how successfully farmers adapt to the new climate.

Lahiri and Roy (1985) studied the supply response of rice

yields at the all India level and also included monthly rainfall. They

also argued that with the spread of HYVs post-mid 1960s (1965

onwards) Indian agriculture has become more rainfall dependent

has the water requirement has gone up and the spread of irrigation

has not been in pace with it. The study was further extended to other

foodgrains by Kanwar (2006) he studied the supply response using a

state level dataset and agreed with previous study that rainfall

considerably matters for the supply response. Auffhammer et al

(2012) and Auffhammer et al. (2006) explicitly studied the impact of

too little/ too much rainfall (akin to gamma rainfall) on the rice

yields using the state level panel dataset.

A study by Siddiqui et. al (2012) attempted to investigate the

impact of climate on four major crops of Pakistan namely; wheat,

rice, cotton and sugarcane. It incorporated scientific information on

the stages of development of each crop in order to assess the impact

of climate change on each stage of the crops. Fixed effect model was

applied to study the impact of variables on crop productivity. The

study concluded that the impact of change in temperature and

precipitation varies significantly with the timing and production

stages of the crop.

Gupta, Sen & Srinivasan (2012) estimated the impact of

climate change on foodgrain yields in India, namely rice and milltes.

They estimated a crop-specific agriculture-production function with

exogeneous climate variables. They eschewed crop simultation

approaches that rely on experimental data. The results stated that

change in rainfall and temperature pattern significantly impacted

the production of rice whereas for milltes, rainfall is the sole

determinant for the change the production pattern.


Guiteras (2009) examines the impact of temperature and

rainfall on combined yield (in money terms) for five major food and

one cash crop he found that climate can reduce yields by 4.5 to 9 per

cent in the medium-run (2010-39) and by as much as 25 per cent in

long-run (2070-2099) in the absence of long-run adaptation. His study

was criticized by Sarker et al. (2012) and by Krishnamurthy (2012) for

combining different crops which are differently impacted by climate

change. Fishman (2011) examined the district level panel and showed

the impact of intra-seasonal variability of rainfall on yields. He

concluded that the impact of climate change can be moderated by

spread of irrigation, but the effect varies with groundwater depletion.

Adams et al. used the output from two different climate

change models (Goddard Institute of Space Studies (GISS) and

Princeton Geophysical Fluid Dynamics Laboratory (GFDL) models)

to stimulate the agronomic and economic impacts of climate change

due to a doubling of atmospheric carbon dioxide. the results from

the study was contradictory to other studies, it found that acreage in

the southern United States generally decreased, while the acreage of

the Great lakes and northern Plains generally increased due to

climate change. It overstated the negative and/or understated the

positive economic impacts of the change.

The literature review highlights various models and

approaches adopted to analyse the impact of climate change on

different regions and different crops. A limited number of studies

have been conducted to empirically investigate the impact of climate

change on agriculture income in context of a developing countries

like India. Hence, present study is an attempt to empirically

investigate the impact of climate variables on the overall agriculture

production (GDP) of India during the period of 1960-2014.

Objectives and Hypothesis

Objectives:

1. To examine the impact of climate change on agriculture

production

78 Arthshodh

2. To observe the relationship between variables of climate

change and agricultural GDP

Hypothesis:

1. Climate change variables i.e. rainfall, temperature and carbon

dioxide emission have significant influence on agriculture

output.

2. A positive impact of rainfall and a negative effect of

temperature and carbon dioxide emission on the agriculture

output (AGDP).

3. Consumption of fertilizer has a significant influence on AGDP

Data Sources and Methodology

Data Source

Present study utilizes secondary and time- series data for the

analysis. The data has been congregated from the database of World

Bank. The time period of the study is from 1960-2014 and the variables

incorporated in the present analysis have been detailed as following:

(a) Dependent Variable-

Agricultural Gross Domestic Production (AGDP)

(b) Independent Variable-

Annual Temperature (Temp.)

Annual Average Rainfall (Rain.)

Carbon-di-oxide emission (Ce)

Consumption of Fertilizer (Cf)

Agriculture production is in crore rupees with the constant

price of the base year 2011-12. Temperature is in terms of degree

Celsius; carbon emissions are in terms of million tonnes and

fertilizer consumption is calculated in terms of kilo tonnes.

Methodology

The paper outlines that the agricultural production of a nation

is mostly influenced by variety of climatic and non-climatic

determinants. The main hypothesis is that the climate change


variables i.e. rainfall, temperature and carbon dioxide emission have

significant influence on agriculture output. A positive impact of

rainfall and a negative effect of temperature and carbon dioxide

emission on the agriculture output (AGDP) is hypothesized.

Additional hypothesis is that consumption of fertilizer also has a

significant influence on AGDP. After discussing the interaction of

various factors, it tries to explore the impact of climate change

variables on agricultural production and carries out time series

regression analysis. The study employs time-series regression

analysis on the selected variables

(i) Specification of the Working Model

In this study an attempt has been made to establish

relationship between agricultural gross domestic production and

climatic variables through following model;

AGDPt= α + β1 Tempt + β2 Raint+ β3 Cet+ β4 Cft+ εt

Where,

α = constant or intercept term

t = time or trend variable

εt = independent and identically distributed residual term

Before fitting the model, stationarity of time series variables is

checked using the unit root test.

Check for Stationarity of Variables

The approach to unit root testing implicitly assumes that the

time series that is to be tested can be written as:

Yt= Dt + zt+ εt

Where,

Dt=deterministic component (trend, seasonal components etc.)

zt = stochastic component

εt = stationary error process

The aim is to determine whether the stochastic component

contains a unit root or is stationary

80 Arthshodh

Given a time series data, Augmented Dickey-Fuller (ADF)

considers three differential-form autoregressive equations to detect

the presence of a unit root:

Yt is a random walk:

∆Yt = γYt-1 +

Yt is random walk with drift:

Yt is a random walk with drift around a stochastic trend:

Where,

t is the time or trend variable

α is the intercept constant called a drift

β is the coefficient on the time trend

γ is the coefficient presenting process root, i.e. the focus of testing

p is the lag order of the first difference autoregressive process

is an independent identically distributed residual term

The difference between the three equations concerns the

presence of the deterministic elements α (a drift term) and (a

linear time trend). The focus of testing is whether the coefficient γ

equal zero that infers the original series has a unit root.

Further, in order to observe relationship among above

described variables Joahnsen Cointegration Test is used.

Johansen Cointegration Test: Given a set of I (1) variables

{Xit…Xkt}. If there exists a linear combination of all variables with

vector β so that,

… Trend stationary

Then the x’s are cointegrated of order C (1, 1)


Cointegration in this paper is tested using Johansen

cointegration test also known as Johansen and Juselius (JJ) test. It has

two test statistics to check cointegration among the variables namely,

trace test and maximum Eigen value test. Trace test has a null

hypothesis that there are at most r cointegration vectors and

maximum Eigen value has a null hypothesis that there are r+1

cointegration vectors versus there are r cointegration vectors.

Findings

In order to regress the variables, stationarity test has been

conducted on all the variables. The ADF test for unit root is applied

for the statistical analysis. Schwarz Info Criterion (SIC) is used to

determine lag-length.

Table 1: Results of Unit root Test (source: author’s computation)

Augmented Dickey-Fuller Test

Variables Level/ First &

Second Difference

Without Trend With Trend

(t-

value)

(p-

value) (t- value)

(p-

value)

AGDP Level 4.76 1.00 0.71 0.99

First Difference -9.24 0.00 -11.93 0.00

Second Difference -6.71 0.00 -6.88 0.00

Temperature Level -1.48 0.54 -6.11 0.00



Rainfall Level -7.77 0.00 -7.83 0.00



Carbon

emission

Level 3.55 1.00 2.42 1.00

First Difference 0.25 0.97 -1.68 0.75


Fertilizer Level 3.84 1.00 -0.63 0.97



Ho is variable has a unit root

P<0.05 null hypothesis is rejected

82 Arthshodh

It is clear from the ADF results that AGDP and consumption

of fertilizer both are non-stationary at level and have stationary

process at first and second difference. Except level (without

intercept), temperature is found to be stationary in all cases. Rainfall

follows a stationary process at all levels. Carbon emission remains

non-stationary at level and at first difference, although becomes

stationary at second difference. In order to run a regression, all the

variables should be stationary at same level, hence analysis has been

carried out using variables at second difference.

Impact of Climate Variable on Agriculture GDP

After testing the stationarity of all the variables, time series

regression analysis is carried out to observe the impact of

independent variables (climate variables) on dependent variable

(AGDP). The result of simple regression is shown in table 2.

Table 2: Time-Series Regression Results

(Source: author’s computation) Dependent Variable: D (AGRICULTURE_GDP_IN_CRORE,2)

Variable Coefficient Std. Error t-Statistic Prob.

C 1424.963 2933.956 0.485680 0.6294

D (TEMPERATURE, 2) -16220.60 6164.654 -2.631226 0.0114

D (RAINFALL, 2) 972.5941 143.7659 6.765123 0.0000

D (CARBON EMISSION, 2) -0.141561 0.061842 -2.289069 0.0265

D (FERTILISER_CONSUMPTION, 2) 2.868969 3.074775 0.933066 0.3555

R-squared 0.652232 Mean dependent var 235.6717

Adjusted R-squared 0.623251 S.D. dependent var 34688.04

S.E. of regression 21291.48 Akaike info criterion 22.85959

Sum squared resid 2.18E+10 Schwarz criterion 23.04547

Log likelihood -600.7791 Hannan-Quinn criter. 22.93107

F-statistic 22.50574 Durbin-Watson stat 3.069413

Prob(F-statistic) 0.000000

Source: Computed

It is clearly reflected by the results that all the regressors have

expected hypothesized sign as described in methodology. Although,

except consumption of fertilizer all the climate change variables i.e.

temperature, rainfall and carbon emission are found to be significant.

Temperature has a negative impact on agricultural output, revealing


that if it goes by one degree then the agricultural output will decline

by Rs. 16220 crores. The coefficient value of rainfall shows a positive

impact, depicting that one additional milliliter(ml) of rainfall will

cause agricultural output to increase by Rs.972 crore. Carbon emission

has a negative influence on output from the agricultural sector i.e. on

an average an increase of one-unit emission of carbonwill cause

agricultural output to decrease by Rs. 0.14 crore. The results also

highlight that impact of fertilizer consumption is significant in the

model the plausible reason for the insignificance of the consumption

of fertilizer could be its underestimated figures.

Overall model fitness is suggested by F-statistics. Value of R2 is

65.22, exhibiting that 65 per cent of the variation in the regressand can

be explained by the regressors in the model. Diagnostic test shows

that there is no problem of heteroscedasticity, and multicollinearity.

Residuals are also normally distributed. Although, there exists a

problem of autocorrelation, but its magnitude is not much.

Long-Run Relationship between climate variables and agriculture GDP

Johansen Cointegration test has been applied for finding the

long-run relationship among the all climate variables and

agricultural GDP.

Table 3: Johansen Cointegration Test

Series: AGRICULTURE_GDP ANNUAL_TEMPERATURE

AVERAGE_RAINFALL CO2_EMISSION

Lags interval (in first differences): 1 to 2

Unrestricted Cointegration Rank Test (Trace)

Hypothesized Trace 0.05

No. of CE(s) Eigenvalue Statistic Critical Value Prob.**

None * 0.448062 62.82518 47.85613 0.0011

At most 1 * 0.284159 31.92054 29.79707 0.0280

At most 2 0.242971 14.53711 15.49471 0.0693

At most 3 0.001205 0.062691 3.841466 0.8023

84 Arthshodh

Trace test indicates 2 cointegrating eqn(s) at the 0.05 level

* denotes rejection of the hypothesis at the 0.05 level

**MacKinnon-Haug-Michelis (1999) p-values Unrestricted Cointegration Rank Test (Maximum Eigenvalue)

Hypothesized Max-Eigen 0.05

No. of CE(s) Eigenvalue Statistic Critical Value Prob.**

None * 0.448062 30.90464 27.58434 0.0180

At most 1 0.284159 17.38343 21.13162 0.1547

At most 2 * 0.242971 14.47442 14.26460 0.0463

At most 3 0.001205 0.062691 3.841466 0.8023

Max-eigenvalue test indicates 1 cointegrating eqn(s) at the 0.05 level

* denotes rejection of the hypothesis at the 0.05 level

**MacKinnon-Haug-Michelis (1999) p-values

Source: Computed

The results highlight that there exists a long run relationship

among agricultural GDP and all the climate variables.

Results

Temperature has a negative impact on agricultural output,

revealing that if it goes by one degree then the agricultural output

will decline by Rs. 16220 crores. The coefficient value of rainfall

shows a positive impact, depicting that one additional milliliter (ml)

of rainfall will cause agricultural output to increase by Rs.972 crore.

Carbon emission has a negative influence on output from the

agricultural sector i.e. on an average an increase of one-unit emission

of carbon will cause agricultural output to decrease by Rs. 0.14 crore.

Impact of fertilizer consumption is significant in the model the

plausible reason for the insignificance of the consumption of

fertilizer could be its underestimated figures. The results highlight

that there exists a long run relationship among agricultural GDP and

all the climate variables. Thus, validating the theory that the impact

of climate change can only be visible on the long-run growth of the

agriculture.


Conclusion

For majority of developing countries climate is an invisible

threat and if climate change response strategies were to be embraced

by these countries, it is imperative that such response strategies are

aligned to a development agenda. As according to Smit and Benhin

(2004), in order to mainstream climate change, it is important to pay

attention to climate change related issues that are presently

impacting the community, addressing the management or coping

strategies presently existing at local level and lastly revising the

policy structure that exists now to deal with these climatic issues i.e.

conducting impact assessment of the policies addressing the

measures of climate change adaptation and reformulating the policy

actions for more effective results to control the negative impact of

climate change. The results that all the regressors have expected

hypothesized sign as described in methodology. Impact of all the

climate change variables i.e. temperature, rainfall and carbon

emission are found to be significant. Temperature and Carbon

emission have negative impact on agricultural output and rainfall

shows a positive impact on output from the agricultural sector. The

impact of fertilizer consumption is significant in the model the

plausible reason for the insignificance of the consumption of

fertilizer could be its underestimated figures. The impact of climate

change is visible in long-run growth of agriculture thus, there is a

need to align climate change response strategies development

agenda. It is important to pay attention to climate change related

issues that are presently impacting the community, addressing the

management or coping strategies presently existing at local level

(Smith and Benhin (2004)). There is need to conducting impact

assessment of the policies addressing the measures of climate change

adaptation and reformulating the policy actions for more effective

results to control the negative impact of climate change.

86 Arthshodh

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90 Arthshodh

Appendix

Diagnostic tests for the Model

1) Multicollinearity

Variance Inflation Factors

Coefficient Uncentered Centered

Variable Variance VIF VIF

C 8608099. 1.006402 NA

D(ANNUAL_TEMPERAT

URE,2)

38002962 1.051612 1.051549

D(AVERAGE_RAINFALL,

2)

20668.64 1.287119 1.285981

D(CO2_EMISSION,2) 0.003824 1.133219 1.127241

D(CONSUMPTION_OF_F

ERTILISER,2)

9.454244 1.209599 1.209161

2) Autocorrelation

Breusch-Godfrey Serial Correlation LM Test:

F-statistic 22.61523 Prob. F(2,46) 0.0000

Obs*R-squared 26.27647 Prob. Chi-Square(2) 0.0000

3) Heteroskedasticity

Heteroskedasticity Test: Breusch-Pagan-Godfrey

F-statistic 0.836313 Prob. F(4,48) 0.5089

Obs*R-squared 3.453064 Prob. Chi-Square(4) 0.4851

Scaled explained

SS

3.706266 Prob. Chi-Square(4) 0.4472


4) Normality Test

0

2

4

6

8

10

12

14

-60000 -40000 -20000 0 20000 40000

Series: ResidualsSample 1962 2014Observations 53

Mean 1.17e-12Median -5399.618Maximum 50105.65Minimum -61615.95Std. Dev. 20456.19Skewness -0.014516Kurtosis 3.617166

Jarque-Bera 0.843001Probability 0.656062

Accounting Studies i Volume 11 No. 1 May, 2013

About Arthshodh

Arthshodh (ISSN 2230-7877) is a bi-annual Journal of Department of

Economics, University of Rajasthan, Jaipur, India.

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