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Prospects of Financial Inclusion in Agricultural Insurance in India with Special Focus on Tamilnadu

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Agricultural insurance plays a predominant role in times of uncertainties. Agriculture in India suffers from various uncontrollable factors such as drought, floods, monsoon and crop failure. It a prerequisite to cover farmers from natural calamities and set back proper credit mechanism is followed in future seasons. To safeguard the farmers against uncertainties government have introduced many agricultural schemes all over the country. The uncertainties due to natural calamities and monsoon failure are properly regulated through various insurance schemes and policies. There are various schemes announced by government namely Pradhan Mantri Fasal Bima Yojana (PMFBY), National Agriculture Insurance Scheme (NAIS), Restructured Weather Based Crop Insurance Scheme (RWBCIS), and Modified National Agriculture Insurance Scheme (MNAIS). Thus, of above four the flagship insurance scheme at present is PMFBY & RWBCIS. PMFBY & RWBCIS is one flagship program that is performing well in many states in India. Government has taken initiatives to make the program successful in many deprived states of India and throughout India. This paper has incorporated the flagship insurance scheme PMFBY & RWBCIS. This study analyzes the benefits obtained through insurance schemes provided by government and how farmers have benefitted through it. Government role on combating agricultural problem is taking major structure during risky period. This study had used the secondary data set to measure the effectiveness of the scheme. This paper is an attempt to study the benefits attained by the farmers.
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Glady P & Ramesh /GJCR/ 9(3) 2022; 59-64
Global Journal of Current Research 59
Full Length Research Paper
Prospects of Financial Inclusion in Agricultural Insurance in India with
Special Focus on Tamilnadu
1Violet Glady. P, and 2Dr. K. Ramesh,
1- Research Scholar in Commerce, K.S.R. College of Arts and Science, Namakal, Tamilnadu. (Assistant Professor, Stella
Maris College. Chennai).
2-Professor, K.S.R. College of Arts and Science, Namakal, Tamilnadu, India.
ARTICLE INFORMATION ABSTRACT
Introduction
Agriculture primary source of income to majority of population in India and majority of population is dependent on
agriculture for their livelihood. Agriculture contributes above 17% of total GDP and provide employment for above 60%
of the population. But still agriculture in India suffer from various natural calamities and obsolete technology.
Government to overcome the uncertain situation of the farmers have implemented various crop insurance schemes so that
farmers can easily claim and fulfill their losses. For curbing this situation government have made numerous experiments
and launched various insurance schemes and policies. Government have previously launched few schemes namely:
Comprehensive Crop Insurance Scheme (1958), National Agriculture Insurance Scheme (1999), Weather Based Crop
Insurance Scheme (2007), Modified National Agriculture Insurance Scheme (2011) and Pradhan Mantri Fasal Bima
Yojana (2016). In addition, government have included the following schemes recently namely National Agriculture
Insurance Scheme (NAIS), Restructured Weather Based Crop Insurance Scheme (RWBCIS), and Modified National
Agriculture Insurance Scheme (MNAIS). Further, government have taken many steps to increase the investment in
agriculture, Agri based technologies, marketing development etc., in order to combat seasonal fluctuation and natural
calamities.
Table 1: Country profile on PMFBY
Total Farmers
1,65,88,401
Total Application
4,08,28,503
Loanee Applications
2,67,13,961
Non- Loanee Applications
1,41,14,542
Vol. 9. No. 3. 2022
©Copyright by CRDEEP Journals. All Rights Reserved.
Contents available at: http://www.crdeepjournal.org
Global Journal of Current Research (ISSN: 2320-2920) CIF: 3.269
A Quarterly Peer Reviewed Journal
DOI:
Corresponding Author:
Violet Glady P
Article history:
Received: 20-07-022
Revised: 22-07-2022
Accepted: 27-07-2022
Published: 29-07-2022
Key words:
Insurance Schemes,
Agriculture Problems,
Effectiveness.
Agricultural insurance plays a predominant role in times of uncertainties. Agriculture in India
suffers from various uncontrollable factors such as drought, floods, monsoon and crop
failure. It a prerequisite to cover farmers from natural calamities and set back proper credit
mechanism is followed in future seasons. To safeguard the farmers against uncertainties
government have introduced many agricultural schemes all over the country. The
uncertainties due to natural calamities and monsoon failure are properly regulated through
various insurance schemes and policies. There are various schemes announced by
government namely Pradhan Mantri Fasal Bima Yojana (PMFBY), National Agriculture
Insurance Scheme (NAIS), Restructured Weather Based Crop Insurance Scheme (RWBCIS),
and Modified National Agriculture Insurance Scheme (MNAIS). Thus, of above four the
flagship insurance scheme at present is PMFBY & RWBCIS. PMFBY & RWBCIS is one
flagship program that is performing well in many states in India. Government has taken
initiatives to make the program successful in many deprived states of India and throughout
India. This paper has incorporated the flagship insurance scheme PMFBY & RWBCIS. This
study analyzes the benefits obtained through insurance schemes provided by government
and how farmers have benefitted through it. Government role on combating agricultural
problem is taking major structure during risky period. This study had used the secondary
data set to measure the effectiveness of the scheme. This paper is an attempt to study the
benefits attained by the farmers.
Glady P & Ramesh /GJCR/ 9(3) 2022; 59-64
Global Journal of Current Research 60
Sum Insured
10,73,02,35,24,748
Area Insured
2,69,79,533 Hect.
Total Insurance Company
11
Total Bank Branches CSC VLE’s
36,123 49,683
Pradhan Mantri Fasal Bima Yojana (PMFBY)
PMFBY was lunched during Kharif 2016 onwards by Ministry of Agriculture and Farmers Welfare, New Delhi. PMFBY
have made several changes to meet the requirements of the farmers than the previous schemes in India. This scheme is
successful in India due to the approach made by the government and financial institutions. The scheme covers all the
farmers who are sanctioned with Seasonal Agricultural Operations (SAO), and loanee farmers for notified crops would
be covered compulsorily under this scheme.
Table 2: State-wise Number of Farmer Applications Enrolled under Pradhan Mantri Fasal Bima Yojana (PMFBY) in
India (2019 2020 to 2021 2022)
States/UT
2019-2020
2020-2021
2021-2022
(Provisional)
Andaman & Nicobar Islands
0.001
0.003
0.004
Assam
10.03
13.99
4.92
Chhattisgarh
40.18
51.6
58.31
Goa
0.01
0.001
0.001
Haryana
17.1
16.49
14.28
Himachal Pradesh
2.84
2.41
1.96
Karnataka
21.32
16.07
18.85
Kerala
0.58
0.76
0.77
Madhya Pradesh
88.15
82.78
81.49
Maharashtra
145.64
124.03
97.11
Manipur
0.03
-
0.03
Meghalaya
0.01
0.001
-
Odisha
48.79
97.54
74.78
Puducherry
0.12
0.1
0.29
Rajasthan
85.27
107.61
318.68
Sikkim
0
0.001
0.02
Tamil Nadu
38.93
56.91
49.28
Tripura
0.36
2.01
1.85
Uttar Pradesh
46.92
41.88
35.89
Uttarakhand
2.13
1.71
1.78
Others
73.95
-
0.66
India
622.35
615.9
760.95
Source: Lok Sabha Starred Question No. 193, Dated on 15.03.2022.
The main objective of the scheme is as follows:
- Aims at providing financial support to farmers at the times of distress due to natural calamities.
- To stable the income of farmers and to make them stay in farming.
- It motivates the farmers to adopt to innovative and modern techniques in agricultural practices.
- It protects farmers from production risk.
- It ensures proper flow of credit to agriculture sector which contribute food security and crop diversifications.
Restructured Weather Based Crop Insurance Scheme (RWBCIS)
Restructured Weather Based Crop Insurance Scheme Lunched in the year 2016 by Prime Minister. Nearly 12 states
implemented the scheme during kharif 2016 and 9 states implemented the scheme during Rabi 2016 2017. Nearly 15
lakhs farmers have been insured in the kharif 2016. It covers food crops like cereals, millets and pulses. It also covers oil
seeds, commercial and horticultural crops. The main objective of the scheme is to reduce the financial loss due to adverse
weather conditions, RWBCIS uses weather parameter as a “proxy” for crop yield for compensating the farmers for
deemed crop losses and Payout structures are developed to cover the losses due to weather fluctuations.
Need and Significance of the study:
1. Agriculture insurance is a every time need to avoid risk factors faced by farmers during uncertain conditions like
flood, drought, land-slides, fire, monsoon failure, crop failures etc.,
2. The study concentrates on government schemes like Pradhan Mantri Fasal Bima Yojana (PMFBY) and
Restructured Weather Based Crop Insurance schemes (RWBCIS) and its utility among farmers.
3. The research paper intends to analyse the farmers application insured and number of applications benefited. This
analysis extends the need to check the linearity between the application insured and benefitted under the scheme
PMFBY and RWBCIS.
Glady P & Ramesh /GJCR/ 9(3) 2022; 59-64
Global Journal of Current Research 61
Objectives of the Study:
1. To test central tendency for agriculture insurance scheme like PMFBY & RWBCIS throughout India for the
period 2016 2020
2. To study the linear relationship between the farmers applications insured and benefited for the period 2016
2020
3. To determine the association between the variables under PMFBY & RWBCIS in Tamilnadu for the period
2016 2020.
Literature review
Rajan Kumar Ghosh (2019), The report on Performance Evaluation of Pradhan Mantri Fasal Bima Yojana (PMFBY),
have identified that the state of Assam has highest level of awareness about the PMFBY under the loanee category.
Before the introduction of PMFBY assam loanee category farmers have not insured under the previous schemes.
Similarly, it is identified that the state of Assam, Bihar, Madhya Pradesh, Uttar Pradesh and Himachal Pradesh had very
limited number of loanee farmers insured under the previous schemes. Government have conducted awareness campaign
about PMFBY and it have highly benefited farmers in most of the state. Other agencies like banks and Panchayats helped
in spreading the awareness among the farmers about the scheme. The scheme was modified based on the suggestions
provided to meet the farmers need.
S.S. Raju & Ramesh Chand (2008), The study on Agricultural Insurance in India Problems and Prospects have identified
that risk is higher for farm income than the production. State wise record shows that the risk factor is less for the state
where irrigation is viable and few states face risk because of poor irrigation. In certain states farmers face high problem
of poor productivity and high risk in production due to poor irrigation. The situation is remaining the same in few states
even after introducing infrastructural facilities. Hence, it accelerates the need to device and extend the insurance product
to agriculture production.
Sidharth Sinha (2007), The study on Agriculture Insurance in India have identified that Crop Yield Insurance is
unsuccessful due to low coverage and high claims to premium ratio. The major fault with the scheme is design and
implementation. In this scheme most of the risk is borne by the government so the implementing agency receive less
intensives to analyse the problems and hazards. This makes the implementing agency to be highly dependent on
government for funds and to meet claims and eventually slow the process.
Materials and methods
The study focuses on Prospects of Financial Inclusion in Agricultural Insurance in India”. The data applied for this
research is obtained from the report prepared by insurance companies and verified by Department of Agriculture
Cooperation and Farmers Welfare, Government of India, from the period 2016 - 2020. The data is also pooled from
Indiastat.com. The data covers all the states in India. The testing and analysis concentrate on the state of Tamilnadu in
specific. This study paper consists of two main hypotheses. One hypothesis tests the linearity between the variables and
another hypothesis test the slope of the beta variable when β1 = 0 and β1 0. The regression model fitted for testing
farmers application benefited: y = β0 + β1 x + e, here we focus on β1, i.e., Farmers Applications Benefited.
β0, represents the intercept value. i.e., farmers applications insured. The hypothetical condition is tested using Excel data
analysis tool.
Results
Table 3: Central Tendency Measure (Mean) of PMFBY & RWBCIS. (Throughout India for the period 2016 2020)
2016 2017
2017 2018
2018 2019
2019 - 2020
Farmers application insured
21.67
20.50
22.15
19.55
Area insured
21.00
19.50
20.04
15.23
Sum insured
7454.19
252.73
208.09
112.13
Farmers share in premium
149.96
161.77
194.12
136.82
Gross premium
806.30
199.59
220.41
213.14
Reported claims
821.11
242.17
187.47
246.73
Paid claims
620.37
242.11
150.33
275.85
Farmers application benefited
5.81
6.76
9.00
9.31
The central tendency measure (mean) of 2016 2020 on Pradhan Mantri Fasal Bima Yojana and Restructured weather
Based Crop Insurance Scheme. The mean value shows that in the 2018 the application insures by farmers is 22.15 lakhs
and farmers share in premium is 194.12 highest when compared to 2016, 2017 and 2019. In the period 2019 the means
score on farmers application benefited is upward with 9.31 lakhs all over India. The mean value of reported and paid
claims is at peak during the period 2019. Farmer applications benefitted is high during the period 2019 with 9.31 lakhs.
Table 4: Deviation measure of PMFBY & RWBCIS. (Throughout India for the period 2016 2020)
2016 2017
2017 2018
2018 2019
2019 - 2020
Farmers application insured
31.866
28.473
34.215
29.508
Glady P & Ramesh /GJCR/ 9(3) 2022; 59-64
Global Journal of Current Research 62
Area insured
33.196
30.287
32.151
20.375
Sum insured
9775.410
322.152
316.525
191.679
Farmers share in premium
202.754
204.331
261.709
183.980
Gross premium
1211.570
280.384
332.185
324.334
Reported claims
956.232
328.057
293.169
296.437
Paid claims
955.088
328.053
277.029
351.533
Farmers application benefited
8.625
11.956
16.720
21.029
It is inferred from Table 4 that the deviation of farmers application insured is high during the period 2018 with 34.215
lakhs in India. The deviation was less during the period 2017. It is to be noted that the Farmers application benefited
deviation is more during the period 2019 and the deviation was less during the period 2016 with 21.029 lakhs and 8.625
lakhs respectively. The deviation on farmers share in premium and gross premium was high during the period 2018 with
261.709 lakhs and 332.185 lakhs respectively. The deviation on reported claim and paid claims was high during the
period 2016 with 956.232 lakhs and 955.088 lakhs respectively.
Table 5: Correlation analysis for Farmers Application Insured and Farmers Application Benefited:
H0: The population correlation coefficient is not significantly different from zero. There is no significant linear
relationship between farmers application insured and benefited. (ρ = 0)
Ha: The population correlation coefficient is significantly different from zero. There is significant linear relationship
between farmers application insured and benefited. (ρ ≠ 0)
Years
Particulars
Testing
FAI
FAB
2016 2017
FAI
Pearson Correlation
1
.897**
Sig. (2-tailed)
.000
N
27
27
FAB
Pearson Correlation
.897**
1
Sig. (2-tailed)
.000
N
27
27
2017 2018
FAI
Pearson Correlation
1
.895**
Sig. (2-tailed)
.000
N
26
25
FAB
Pearson Correlation
.895**
1
Sig. (2-tailed)
.000
N
25
25
2018 2019
FAI
Pearson Correlation
1
.893**
Sig. (2-tailed)
.000
N
26
24
FAB
Pearson Correlation
.893**
1
Sig. (2-tailed)
.000
N
24
24
2019 2020
FAI
Pearson Correlation
1
.942**
Sig. (2-tailed)
.000
N
22
16
FAB
Pearson Correlation
.942**
1
Sig. (2-tailed)
.000
N
16
16
**. Correlation is significant at the 0.01 level (2-tailed).
FAI Farmers Application Insured
FAB Farmers Application Benefitted
It is determined from the above table that there is a highly positive correlation between the two variables Farmers
Application Insured and Farmers Application Benefitted for all the period starting from 2016 to 2020 throughout India.
The positive correlation value for all the four period is r = 0.897, 0.895, 0.893 and 0.942 respectively. Therefore, it is
clear that the alternative hypothesis is accepted and there is a significant linear relationship between the two variables.
Thus, PMFBY & RWBCIS is highly beneficial to farmers and can provide safety measures at the time of uncertainties.
Table 6: Simple Regression Model of Farmers Applications Insured and Benefited under PMFBY & RWBCIS in
Tamilnadu for the period 2016 2020.
H0: β1 = 0, farmers insurance application not benefited in Tamilnadu under PMFBY & RWBCIS
Ha: β1 ≠ 0, farmers insurance application is benefited in Tamilnadu under PMFBY & RWBCIS
Regression Statistics
Multiple R
0.99201
R= Square Root of R2
R Square
0.984084
R2 = Coefficient of determination
Glady P & Ramesh /GJCR/ 9(3) 2022; 59-64
Global Journal of Current Research 63
Adjusted R Square
0.976126
Adjusted R2 used if more than one x variable
Standard Error
1.321334
This is the sample estimate of the standard
deviation of the error u
Observation (Tamilnadu Data)
4
Number of observations used in the regression
(n)
The Regression Statistics Table gives the overall goodness-of-fit measures: R2 = 0.984. Correlation between y and x is
0.99201 (when squared gives correlation squared = 0.984 = R2).
Table 7: Analysis of Variance between Farmers Applications Insured and Farmers Application Benefited under PMFBY
& RWBCIS in Tamilnadu for the period 2016 2020.
Df
SS
MS
F
Significance
F
Regression
1
215.8962714
215.8963
123.6574
0.007990067
Residual
2
3.491845304
1.745923
Total
3
219.3881168
R2 = 1 - Residual SS / Total SS (general formula for R2)
= 1 3.492/219.388 (from data in the ANOVA table)
= 0.984 (which equals R2 given in the regression Statistics table).
Table 8: Interpret Regression Coefficients Table on Farmers Applications Insured and Farmers Application Benefited
under PMFBY & RWBCIS in Tamilnadu for the period 2016 2020.
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper
95%
Intercept
2.0374
1.2859
1.5843
0.2539
-3.49555
7.57038
FAB
1.2343
0.1110
11.120
0.0079
0.75671
1.71188
- The regression model for farmers application benefited: y = β0 + β1 x + e, Here we focus on β1, i.e., Farmers
Applications Benefited. β0, represents the intercept value. i.e., farmers applications insured.
- The "Coefficient" gives the least squares estimates of β0 and β1 (Farmers Applications Insured and Benefited in
Tamilnadu from 2016 2019).
- The "Standard error" gives the standard errors (i.e. the estimated standard deviation between the samples) of the
least squares estimate of β0 and β1.
- The second row of the column "t Stat" gives the computed t-statistic as follows:H0: β0 =0 against Ha: β1 ≠0. The
coefficient divided by the standard error: here 1.2343 / 0.1110 = 11.120.
It is compared to a T distribution with (n-k) degrees of freedom where here n = 4 and k = 2.
- The column "P-value" gives for farmers application benefited are for H0: β1 = 0 against Ha: β1 ≠ 0. The columns
"Lower 95%" and "Upper 95%" values define a 95% confidence interval for β1.
Summary of the output:
The fitted line is y = 2.0374+1.2343*x
The slope coefficient has estimated standard error of 0.1110
The slope coefficient has t-statistic of 11.120
The slope coefficient has p-value of 0.0079
The 95% confidence interval for β1 is (0.75671, 0.71188).
There are 4 observations and 2 regressors (intercept and x) so in inference we use T (4-2) =T (2).
A measure of the fit of the model is R2 = 0.984
The standard error of the regression is 1.321
Findings and suggestions
1) It is found from the study that PMFBY & RWBCIS is a successful scheme in many states in India. It has
benefited farmers more than the predecessor schemes.
2) They study found that there is positive correlation between the two variables farmers application insured and
farmers application benefited under PMFBY & RWBCIS with reasonable farmers share in premium and gross
premium.
3) The scheme was successful in Tamilnadu with a positive regression model.
4) It is suggested from the study that certain schemes are not viable and should be restructured to meet the
requirement of the farmers at the time of natural and man-made calamities.
Conclusion
The study has highlighted the flagship government schemes like Pradhan Mantri Fasal Bima Yojana & Restructured
Weather Based Crop Insurance Scheme and noted that the program is implemented throughout India except few highly
rural states. The current scheme was highly beneficial than the predecessor schemes with proper area insured and proper
allocation of premium to the farmers. The program on weather-based crop insurance ensures a structured payout at the
time of natural calamities. It is found that the farmers from the states of Assam, Madhya Pradesh, Himachal Pradesh,
Glady P & Ramesh /GJCR/ 9(3) 2022; 59-64
Global Journal of Current Research 64
Bihar and Uttar Pradesh are highly aware of the scheme and insured under the loanee category. The scheme covers food
crops like cereals, millets and pulses. It also covers oil seeds, commercial and horticultural crops. The farmers share in
premium is reasonable and ensure them a stable farming even at the time of risk and uncertainties due to seasonal
fluctuations and calamities.
References
Barbara Illowsky & Susan Dean, Statistics at De Ansa College, Sourced from Openstax. Nationalinsurance.nic.co.in
Pradhan Mantri Fasal Bima Yojana, Ministry of Agriculture and Farmers Welfare. State Wise Business Statistics. 2016
2017 to 2019 2020.
Rajan Kumar Ghosh, 2019. Performance Evaluation of Pradhan Mantri Fasal Bima Yojana. Part III, Report, Center for
Management in Agriculture, IIMA.
S.S. Raju & Ramesh Chand,2008. Agricultural Insurance in India Problems and Prospects, National Center for
Agricultural Economics and Policy Research (ICAR), NCAP Working Paper No.8.
Sidharth Sinha, 2007. Agriculture Insurance in India, Center for Insurance and Risk Management, Working Paper Series,
IFMR.
ResearchGate has not been able to resolve any citations for this publication.
Performance Evaluation of Pradhan Mantri Fasal Bima Yojana
  • Rajan Kumar
Rajan Kumar Ghosh, 2019. Performance Evaluation of Pradhan Mantri Fasal Bima Yojana. Part III, Report, Center for Management in Agriculture, IIMA.