Role of Kisan Credit Card (KCC) Scheme in Financial Inclusion: A Study of Farm Households
Sanjay Kumar Hooda
Indira Gandhi University Meerpur Rewari, Haryana,
Somjit
Indira Gandhi University Meerpur Rewari, Haryana,
somjitlohchab1983@gmail.com
Abstract
Financial inclusion is a critical issue for a country's social and economic development. Since the early twenty century, the Reserve Bank of India and the Government of India has taken various steps for financial inclusion, especially for rural area namely customer service centers, credit counseling centers, and the Kisan Credit Card (KCC). The present study was carried out to determine the effect of the Kisan Credit Card (KCC) scheme on farm households in both states Haryana and Punjab. We selected a total number of 240 respondents equally from both states. In this study, the researchers analyzed the age-wised perception of farmers and their impact on farm households, which have been playing a key role in the development of the agriculture base. The researchers have found that 30.40% of farmers were thinks positively about the KCC scheme in the age group of 31-40 years in both states. KCC scheme has been found very useful for an agricultural loan or crop loan. The significant differences in the perception of farm households toward the KCC scheme between both states have not been found.
KEYWORDS: Kisan Credit Card (KCC), Farm households, Financial Inclusion Action Plan (FIAP), Direct Benefit Transfer (DBT), and Financial Consumer Protection and Financial Literacy (FCP&FL).
INTRODUCTION
The Reserve Bank of India and the Government of India have made their efforts constantly for achieving financial inclusion in the country. The government is continually encouraging banks and other financial institutions to expand their banking activities in rural areas of the country. Efforts have been made to develop an equitable financial system for the economic development of the country. Financial inclusion services are part and partial of the inclusive growth approach required for the economic development of an economy and assist in ensuring that everyone has access to the required finance (Ashwani Kumar & H. Gupta, 2019). G20 Financial Inclusion Action Plan (FIAP) (2017) is one of the crucial papers related to the financial inclusion movement in the country as it seeks to improve access to effective and secure financial services. It is also one of the most important guiding texts for the movement of financial inclusion as it works to provide safe financial services to two billion people (Busch, M. O.et al, 2017). The Global Partnership for Financial Inclusion (GPFI) is implemented in the four subgroups: (i) SME Finance, (ii) Regulation and Standard-Setting Bodies, (iii) Financial Consumer Protection and Financial Literacy, and (iv) Market and Payment Systems (Timermann, B., & Gmehling, P., 2017). Financial inclusion is a challenge for the Indian economy as the larger part of the rural population is still waiting for inclusive growth. The Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) Scheme and the Aadhar Scheme are all initiatives taken by the government of India. Turning to farms, the farmers are using the Kisan Credit Card Scheme to get cash or term credit for many related operations such as pump sets, field expansion, planting, drip irrigation, etc. (Kumar Bijoy 2018). To increase the country's entire growth, India's citizens should be educated and aware of the uses of financial inclusion supportive policies, which have been implemented from time to time by the central and state governments. This approach is designed to assist them in making better financial decisions and establishing the greatest platform for saving and investing habits which would lead to India's progressive economic growth and long-term economic development. Our government should therefore provide official credit outlets as well as inexpensive access to financial services for filling up the gap in public financial subsidies and welfare programmers (Hardarshan Kaur & N. K. Dhaliwal, 2019). In this way, the Indian government should quantitatively and qualitatively enhance the banking sector while maintaining sanity on all fronts. There is a need for launching novel lending tools to help farmers, leading to the introduction of the Kisan Credit Card Scheme. The scheme began in 1998, after the Shri R.V. Gupta committee's recommendations that which are very important from the financial inclusion of the farmer's point of view (Anurag Kumar Jha, 2020). Therefore, from the above discussion, it reflects that KCC Scheme is fulfilling two-fold objectives- to provide sufficient and timely credit needs of farmers, and to provide a mechanism to get a formal banking system for enhancing the financial inclusion of farmers. Therefore, there is a close relationship between K.C.C. Scheme and financial inclusion (Anuwarul Hoda et al., 2021). Dangi Neha and Kumar Pawan (2013) in their study, “Current Situation of Financial Inclusion in India and Its future vision” depicted the financial inclusion in India as well as the complete world. The said study was conducted based on a survey method and the World Bank anthologized this study. They found that 21% of individuals were from poor income groups and only 26% of women were included. Two percent of Indian adults have a credit card and 2% of Indian adults have an outstanding mortgage loan. Wherein 50% of the world population, 38% belongs to the poorest income group, 47% were women, 15% of the adult had a credit card, and 7% of adults had an outstanding mortgage loan. KCC progress report shows that Rupees 27 million and 1600 millions KCC amount were outstanding in March 2011 and 2012 respectively. The total number of 30 million Kisan credit cards increased in this period and the amount outstanding was Rs. 2068. Qasim Mir Suhail et al., (2014) carried out their research on financial inclusion in Jammu and Kashmir. The banking service started in 2 phases in J&K. 1st phase started for more than 2000 population in the village. For this purpose, 795 numbers of villages were identified where no banking services and facilities were available but after 16 December 2013, banking services and banking facilities have been started and a 99% target was achieved in this phase. 2nd phase started for the villages having a population of less than 2000 wherein 5582 total numbers of villages were identified in J & K. and 2331 villages were covered by banking services and facilities. They achieved the 41% of the total target during the 2nd phase by end of December 2013. The target for sanction of total KCC was 7,22,212 for J&K. out of which 5,55,512 KCC were issued and Rs. 310152 crores amount was disbursed which was noted as 71% of the desired target. Samantara, Samir R. (2015) revealed in his study on the growth of agriculture production and issuing KCC in Arunachal Pradesh. From the period 2000-01 to 2010-11 the average growth rate was 2.34% found in the production, whereas 1.14% growth was seen in issuing of KCC during the same time duration. The researcher found that the KCC Scheme has proved to lifeline for production in the agriculture sector. Shah Puri & Prof. D. Medha, (2018) depicted in their study the awareness of different types of agriculture term loans. The result shows that more than 50% of farmers were aware of all types of agriculture loans. However, 100% of farmers were aware of the KCC scheme in the Pune district. Hardarshan Kaur et al., (2018) analyze their study on the performance of the KCC scheme by the regional rural bank of Punjab. There were three regional rural banks in Punjab, namely Malwa Gramin bank, Satluj Gramin bank, and Panjab Gramin bank. The growth of Malwa Gramin Bank was seen highest of all. Sanction of the KCC was 23.57% and the amount of sanction was 43.61. Sanction of KCC was the same as 23.57 but disbursement of the amount was highest with a sanction of 45.10 Crore. By Staluj Gramin bank, the sanction of the KCC was 10.12 but the issue of the KCC was 9.40, and the sanction of the KCC and disbursement of amount were 11.17 crore. The performance of the bank regarding sanction and disbursement was noted as the lowest as compared to others. In Malwa Gramin Bank, disbursement per KCC amount is 1.33 Crore. The lowest difference of 1.33 crore was seen of outstanding amount in Malwa Gramin bank and the highest difference of 2.22 of outstanding amount was found in Punjab Garmin bank.
Research Gap: After reviewing the literature related to the role of the Kisan Credit Card (KCC) Scheme in financial inclusion in India, it was found that various research studies have been carried out to study the efficiency, performance, efficacy, effectiveness, and socio-economic profile of Kisan Credit Card holders. These researches were conducted in different parts of India apart from Northern India. No study related to K.C.C.'s perception of households towards the quality of K C.C and its impact on demographic characteristics of farm households has been found especially in Northern states of India. Thus, there was a need to conduct a study on K.C.C Scheme concerning financial inclusion. Accordingly, the present study entitled "Role of Kisan Credit Card (KCC) Scheme in Financial Inclusion”. This study would also help in understanding the status the financial inclusion of farmhouses holds.
Objectives of the study
Research Methodology
Research Design: Descriptive survey method has been used in the present study.
Sample Size: To determine the effect of the Kisan Credit Card (KCC) scheme on farm households in both states Haryana and Punjab, we selected a total number of 240 respondents selected equally from both states. In this study, the researchers analyzed the age-wised perception of farmers and their impact on farm households.
Selection of Sample: The process of selection of the sample areas is defined in the following manner. Three dimensions were chosen from the Punjab state and two dimensions were selected from the state of Haryana through the use of the Systematic Random Sampling technique on the foundation of agriculture development. Thereafter, two blocks were selected from each district. After it, two villages were selected from each block based on the Simple Random Sampling technique. For analyzing the impact of the K.C.C. Scheme in pursuance of banking habits of farms households, influence of the K.C.C. Scheme on financial inclusion of selected households.
Sampling Technique: Multi-Stage sampling, consisting of purposive, simple random sampling and chain base sampling techniques has been used for selecting the sample for this study.
Hypotheses
H01: There exists no significant difference in perception of farm households, towards the KCC schemes across ages in Haryana and Punjab States.
H02: There exists no significant difference in the impact of the KCC scheme on farm households towards the KCC schemes across ages in Haryana and Punjab States.
Table 1.1 Detail of KCC holders
|
Age in Years |
Frequency |
Percentage |
Cumulative frequency |
|
18-30 |
37 |
15.40 |
15.40 |
|
31-40 |
73 |
30.40 |
45.80 |
|
41-50 |
63 |
26.30 |
72.10 |
|
51 and above |
67 |
27.90 |
100.00 |
|
|
240 |
100.00 |
- |
|
Annual Income |
|
|
|
|
1 Lac to 3 Lacs |
68 |
28.3 |
28.3 |
|
3Lacs to 5 Lacs |
108 |
45.0 |
73.3 |
|
More than 5 Lacs |
64 |
26.7 |
100.0 |
|
Total |
240 |
100.0 |
|
|
Occupation |
|
|
|
|
Farmer |
164 |
68.3 |
68.3 |
|
Service |
15 |
6.3 |
74.6 |
|
Business Man |
35 |
14.6 |
89.2 |
|
Farmers and Businessman |
26 |
10.8 |
100.0 |
|
Total |
240 |
100.0 |
|
|
Pattern of Loan |
|
|
|
|
Formal Loan |
118 |
49.2 |
49.2 |
|
Informal Loan |
10 |
4.2 |
53.3 |
|
Both (Formal and Informal) |
112 |
46.7 |
100.0 |
|
Total |
240 |
100.0 |
|
|
Types of Farmers |
|
|
|
|
Marginal Farmer |
53 |
22.1 |
22.1 |
|
Small Farmer |
91 |
37.9 |
60.0 |
|
Semi Medium Farmer |
69 |
28.8 |
88.8 |
|
Medium Farmer |
16 |
6.7 |
95.4 |
|
Large Farmer |
11 |
4.6 |
100.0 |
|
Total |
240 |
100.0 |
|
After analyzing the age-wise interpretation of the results as given in table 1.1, it has been found that 37 respondents are from the age group of 18-30 years ( 15.40%), 73 respondents belonging to the age group of 31-40 years with 30.40%, and 63 Nos. (26.30%) of the respondents are belonging to the age group 41-50 years and 67 respondents are belonging to the age group above 50 years i.e. (27.90%). Thus, the result can be concluded in light of the above data that the highest numbers of K.C.C holders are from the age group of 31-40 years of age having 30.40% and the lowest numbers of K.C.C holders are from the age group of 18-30 years with 15.40% of the total population of the study. In the rest of the age groups of 41-50 years and above 50 years, the numbers of K.C.C holders are approximately the same. Hence, age-wised variation is there in KCC holders in both states of Haryana and Punjab.
Descriptive Statistics towards Perception of K.C.C Schemes across Demographical Characteristics
After analyzing and depicting the results given in table 1.2 that it is noted that the farm households of the age group of 18-30 years have given more preference to p14 in Haryana state and Punjab state; they have given more preference to p17 towards the K.C.C schemes. The farm households in the age group of 31-40 years have given more preference to p9 in the Haryana and Punjab States; they have given more preference to p17 towards the K.C.C. schemes. The farm households of age group 41-50 years have given more preference to p14 in Haryana state and Punjab state; they have given more preference to p15 towards the K.C.C schemes. The farm households in the age group above 51 years have given more preference to p15 in Haryana and Punjab states; they have given more preference to p9 towards K.C.C schemes.
Table 1.2: Descriptive Statistics towards Perception of K.C.C Schemes across Aging factors.
|
AQ
K.C.C Attributes |
18 -30 years (N= 37 ) |
31-40 years (N=73 )
|
41-50 years (N= 63 ) |
Above 51 years (N= 67 ) |
Total (N= 240 )
|
|||||||||||||
|
Hry. |
Pb. |
Hry. |
Pb.
|
Hry. |
Pb. |
Hry. |
Pb. |
Total |
||||||||||
|
M |
S.D |
M |
S.D |
M |
S.D |
M |
S.D |
M |
S.D |
M |
S.D |
M |
S.D |
M |
S.D |
M |
S.D |
|
|
P1 |
1.769 |
0.599 |
1.500 |
0.510 |
2.052 |
.803 |
1.571 |
0.502 |
1.687 |
0.780 |
1.548 |
0.675 |
1.750 |
0.722 |
1.566 |
0.501 |
1.691 |
0.675 |
|
P2 |
1.659 |
0.854 |
2.791 |
1.102 |
2.394 |
1.174 |
2.942 |
1.083 |
2.093 |
0.995 |
2.709 |
1.160 |
2.648 |
1.110 |
3.100 |
1.184 |
2.604 |
1.152 |
|
P3 |
2.769 |
1.235 |
2.291 |
0.806 |
2.447 |
1.031 |
2.228 |
0.689 |
2.343 |
0.865 |
2.354 |
0.797 |
2.405 |
1.012 |
2.500 |
0.973 |
2.391 |
0.912 |
|
P4 |
3.076 |
1.115 |
2.541 |
0.883 |
2.342 |
1.020 |
2.828 |
1.014 |
2.718 |
1.113 |
2.580 |
1.025 |
2.945 |
1.129 |
2.700 |
1.118 |
2.691 |
1.061 |
|
P5 |
2.461 |
1.265 |
3.333 |
0.916 |
2.684 |
1.296 |
2.885 |
1.105 |
2.718 |
1.419 |
2.483 |
0.995 |
3.027 |
1.092 |
2.666 |
1.184 |
2.795 |
1.180 |
|
P6 |
3.307 |
1.548 |
3.083 |
1.176 |
3.026 |
1.404 |
2.657 |
1.186 |
3.187 |
1.255 |
3.548 |
1.150 |
3.135 |
1.417 |
3.133 |
1.547 |
3.112 |
1.335 |
|
P7 |
1.923 |
1.037 |
1.916 |
0.775 |
2.157 |
1.241 |
1.942 |
1.136 |
2.250 |
1.295 |
2.193 |
0.980 |
2.162 |
1.142 |
2.233 |
1.072 |
2.116 |
1.106 |
|
P8 |
2.153 |
1.573 |
2.083 |
1.212 |
2.131 |
1.234 |
2.171 |
1.271 |
2.468 |
1.565 |
1.806 |
0.749 |
1.405 |
0.864 |
2.333 |
1.184 |
2.050 |
1.229 |
|
P9 |
2.615 |
1.325 |
3.166 |
1.167 |
3.315 |
1.357 |
3.342 |
1.370 |
2.937 |
1.268 |
3.387 |
1.229 |
3.324 |
1.510 |
3.466 |
1.074 |
3.245 |
1.304 |
|
P10 |
3.155 |
0.987 |
2.125 |
0.946 |
3.131 |
1.255 |
2.142 |
0.772 |
2.875 |
1.237 |
2.645 |
1.112 |
2.513 |
1.044 |
2.566 |
1.250 |
2.625 |
1.139 |
|
P11 |
2.461 |
0.965 |
1.791 |
1.102 |
1.973 |
0.999 |
1.771 |
1.262 |
1.937 |
0.981 |
1.709 |
1.188 |
1.973 |
1.189 |
1.666 |
1.212 |
1.875 |
1.128 |
|
P12 |
1.615 |
1.043 |
1.500 |
0.834 |
1.894 |
1.180 |
1.142 |
0.429 |
1.718 |
0.924 |
1.645 |
1.305 |
1.621 |
1.063 |
1.800 |
1.423 |
1.620 |
1.075 |
|
P13 |
2.233 |
0.832 |
2.500 |
0.932 |
2.868 |
1.094 |
2.257 |
0.980 |
2.562 |
1.075 |
2.225 |
0.920 |
2.054 |
.9702 |
2.901 |
1.155 |
2.462 |
1.046 |
|
P14 |
3.307 |
0.947 |
3.083 |
1.138 |
2.973 |
1.026 |
3.000 |
1.137 |
3.343 |
1.065 |
3.129 |
1.024 |
3.405 |
1.091 |
2.903 |
1.093 |
3.133 |
1.074 |
|
P15 |
2.076 |
1.116 |
3.166 |
1.274 |
3.026 |
1.404 |
2.828 |
1.124 |
3.031 |
1.149 |
3.580 |
0.885 |
3.783 |
1.272 |
3.233 |
1.135 |
3.175 |
1.241 |
|
P16 |
2.153 |
1.344 |
3.291 |
1.041 |
2.447 |
1.288 |
3.314 |
1.231 |
2.468 |
1.190 |
3.387 |
1.174 |
2.594 |
1.383 |
3.100 |
1.241 |
2.870 |
1.295 |
|
P17 |
3.076 |
1.382 |
3.541 |
1.215 |
3.000 |
1.065 |
3.371 |
1.214 |
3.156 |
1.194 |
3.548 |
1.090 |
3.054 |
1.223 |
3.433 |
1.006 |
3.266 |
1.162 |
Source: Primary data
Table 1.3 ANOVA Statistics on Perception towards K.C.C Schemes across Age in Hr. & Pb...
|
Test
Att. |
State of Haryana |
|||||||
|
Levene. Stat. |
Sig. (2-tailed) |
F- Value |
Sig. (2-tailed). |
Welch Stat. |
Sig. (2-tailed) |
Stat. Sig. (Yes/ No) |
Accepted/ Not Accepted |
|
|
P1 |
0.807 |
0.492 |
1.631 |
0.186 |
- |
- |
No |
Accepted |
|
P2 |
3.224 |
0.025 |
- |
- |
3.780 |
0.016* |
Yes |
Not Accepted |
|
P3 |
0.848 |
0.474 |
0.575 |
0.633 |
- |
- |
No |
Accepted |
|
P4 |
0.932 |
0.428 |
2.512 |
0.062 |
- |
- |
No |
Accepted |
|
P5 |
1.020 |
0.387 |
0.836 |
0.477 |
- |
- |
No |
Accepted |
|
P6 |
0.845 |
0.472 |
0.160 |
0.923 |
- |
- |
No |
Accepted |
|
P7 |
0.509 |
0.677 |
0.226 |
0.878 |
- |
- |
No |
Accepted |
|
P8 |
6.552 |
0.000 |
- |
- |
5.474 |
0.003* |
Yes |
Not Accepted |
|
P9 |
1.896 |
0.134 |
1.279 |
0.085 |
- |
- |
No |
Accepted |
|
P10 |
1.010 |
0.394 |
2.059 |
0.110 |
- |
- |
No |
Accepted |
|
P11 |
1.232 |
0.301 |
0.873 |
0.457 |
- |
- |
No |
Accepted |
|
P12 |
0.392 |
0.754 |
0.481 |
0.696 |
- |
- |
No |
Accepted |
|
P13 |
1.383 |
0.251 |
4.248 |
0.007* |
- |
- |
Yes |
Not Accepted |
|
P14 |
0.939 |
0.424 |
1.243 |
0.297 |
- |
- |
No |
Accepted |
|
P15 |
2.287 |
0.082 |
6.376 |
0.000* |
- |
- |
Yes |
Not Accepted |
|
P16 |
0.471 |
0.703 |
0.373 |
0.772 |
- |
- |
No |
Accepted |
|
P17 |
1.003 |
0.394 |
0.103 |
0.958 |
- |
- |
No |
Accepted |
Continue…….
|
Test
Att. |
State of Punjab
|
|||||||
|
Levene. Stat. |
Sig. (2-tailed) |
F- Value |
Sig. (2-tailed). |
Welch Stat. |
Sig. (2-tailed) |
Stat. Sig. (Y/N) |
Accepted/ Not Accepted |
|
|
P1 |
1.188 |
0.317 |
0.092 |
0.964 |
- |
- |
No |
Accepted |
|
P2 |
0.084 |
0.969 |
0.691 |
0.559 |
- |
- |
No |
Accepted |
|
P3 |
1.472 |
0.226 |
0.629 |
0.597 |
- |
- |
No |
Accepted |
|
P4 |
0.956 |
0.416 |
0.495 |
0.686 |
- |
- |
No |
Accepted |
|
P5 |
0.953 |
0.418 |
3.146 |
0.028 |
- |
- |
No |
Accepted |
|
P6 |
2.538 |
0.060 |
2.688 |
0.050 |
- |
- |
No |
Accepted |
|
P7 |
0.768 |
0.514 |
0.774 |
0.511 |
- |
- |
No |
Accepted |
|
P8 |
4.324 |
0.006 |
|
|
1.688 |
0.179 |
No |
Accepted |
|
P9 |
2.545 |
0.060 |
0.280 |
0.840 |
- |
- |
No |
Accepted |
|
P10 |
6.222 |
0.001 |
- |
- |
2.158 |
0.102 |
No |
Accepted |
|
P11 |
0.098 |
0.961 |
0.065 |
0.978 |
- |
- |
No |
Accepted |
|
P12 |
8.895 |
0.000 |
- |
- |
3.770 |
0.016* |
Yes |
Not Accepted |
|
P13 |
2.112 |
0.102 |
2.975 |
0.035 |
- |
- |
No |
Accepted |
|
P14 |
0.352 |
0.788 |
0.251 |
0.861 |
- |
- |
No |
Accepted |
|
P15 |
5.121 |
0.002 |
- |
.- |
3.048 |
0.035* |
Yes |
Not Accepted |
|
P16 |
1.166 |
0.326 |
0.326 |
0.807 |
- |
- |
No |
Accepted |
|
P17 |
0.562 |
0.641 |
0.180 |
0.910 |
- |
- |
No |
Accepted |
Source: Primary data
Table 1.4 Age-wise impact of K.C.C Schemes on Farm House Holds in Haryana and Punjab States.
|
Model 1 (Farm House Holds’ Age)
|
Haryana |
Punjab |
||||||
|
Un-sta’dized Coffi’t Beta |
Standardized Coffi’t Beta |
t- value |
Sig. 2-tailed |
Un-sta’dized Coffi’t. Beta |
Sta’dized Coffi’t Beta |
t- value |
Sig. 2-tailed |
|
|
(Constant) |
2.353 |
- |
3.978 |
0.000 |
|
|
0.655 |
0.514 |
|
IMP01 |
0.123 |
0.113 |
0.966 |
0.336 |
0.721 |
0.160 |
1.267 |
0.208 |
|
IMP02 |
0.184 |
0.180 |
1.172 |
0.244 |
0.225 |
0.189 |
1.298 |
0.197 |
|
IMP03 |
0.138 |
0.134 |
0.930 |
0.354 |
0.300 |
0.213 |
1.497 |
0.138 |
|
IMP04 |
0.112 |
0.130 |
1.204 |
0.231 |
0.301 |
0.275 |
1.973 |
0.051 |
|
IMP05 |
0.051 |
0.058 |
0.533 |
0.595 |
0.371 |
0.173 |
1.569 |
0.120 |
|
IMP06 |
0.096 |
0.111 |
0.895 |
0.373 |
0.170 |
0.068 |
0.549 |
0.584 |
|
IMP07 |
0.086 |
0.088 |
0.831 |
0.408 |
0.092 |
0.084 |
0.626 |
0.533 |
|
IMP08 |
0.122 |
0.027 |
0.258 |
0.797 |
0.108 |
0.184 |
1.872 |
0.064 |
|
IMP09 |
0.128 |
0.165 |
1.489 |
0.140 |
0.165 |
0.044 |
0.383 |
0.703 |
|
IMP10 |
0.061 |
0.074 |
0.788 |
0.432 |
0.053 |
0.032 |
0.287 |
0.775 |
|
IMP11 |
0.075 |
0.085 |
0.734 |
0.465 |
0.032 |
0.307 |
2.335 |
0.022 |
|
IMP12 |
0.118 |
0.131 |
1.229 |
0.222 |
0.311 |
0.124 |
0.954 |
0.343 |
|
IMP13 |
0.079 |
0.082 |
0.768 |
0.445 |
0.099 |
0.013 |
0.102 |
0.919 |
|
IMP14 |
0.115 |
0.151 |
1.512 |
0.134 |
0.020 |
0.081 |
0.722 |
0.472 |
|
IMP15 |
0.023 |
0.027 |
0.285 |
0.776 |
0.077 |
0.035 |
0.329 |
0.743 |
|
IMP16 |
0.030 |
0.031 |
0.350 |
0.727 |
0.058 |
0.036 |
0.301 |
0.764 |
|
IMP17 |
0.198 |
0.266 |
2.633 |
0.010 |
0.059 |
0.022 |
0.205 |
0.838 |
|
IMP18 |
0.195 |
0.224 |
2.120 |
0.037 |
0.018 |
0.084 |
0.633 |
0.529 |
|
IMP19 |
0.085 |
0.099 |
0.837 |
0.405 |
0.078 |
0.049 |
0.445 |
0.657 |
|
IMP20 |
0.015 |
0.016 |
0.162 |
0.872 |
0.094 |
0.088 |
0.771 |
0.442 |
|
IMP21 |
0.172 |
0.254 |
2.700 |
0.008 |
0.101 |
0.002 |
0.015 |
0.988 |
|
IMP22 |
0.178 |
0.174 |
1.871 |
0.064 |
0.001
|
0.186 |
1.737 |
.086 |
|
R |
0.629 |
0.491 |
||||||
|
R2 |
0.395 |
0241 |
||||||
|
F-value |
2.883 |
1.400 |
||||||
|
Stat. Sig. |
Yes |
Yes |
||||||
|
Acc./ Not Acc’td |
Accepted partially |
Accepted partially |
||||||
The calculated F-value in the case of Haryana state is 2.883 and in the case of Punjab state, it is 1.400, which is greater than the tabulated value of ANOVA i.e. [F (21, 119) = 3.68, p<0.05 and F (21, 119) = 3.67, p<0.05] respectively which expresses that the impact of on age of farm households is statistically significant in Haryana and Punjab States.
The findings of the regression model suggest the fitness of model 1 as the F-value has been found significant at a 0.05 level of significance in both types of states. From the above analysis, it is also observed that IMP-17, IMP-18, and IMP-21 in the Haryana state are significant at a 0.05 percent level of significance and the rests are not significant at a 0.05 percent level of significance. In the Punjab state, IMP-11 is significant and the rest are not significant at a 0.05 percent level of significance.
It is summarized that the K.C.C Schemes have an impact on farm households in Haryana and Punjab States. The regression equation for responsibilities assigned is as under:
y1=b0+b1x1+b2x2+b3x3+b4x4+b5x5+b6x6+ b7x7+b8x8+b9x9+b10x10+b11x11+b12x12+ b13x13+b14x14+b15x15+b16x16+b17x17+b18x18+ b19x19+b20x20+b21x21
y1= Dependent variable i.e impact of KCC scheme on age.
b0= y-intercept
b1=IMP 1 (KCC scheme has improved my economic condition.)
b2=IMP2 (KCC scheme has improved the living standard of my family)
b3=IMP 3(KCC scheme has improved my social status)
b4=IMP 4(KCC scheme has improved the educational standard of my family)
b5=IMP 5(KCC scheme has improved other activities like the purchasing of machinery and helped in the lease in / lease out of agricultural equipment)
b6=IMP 6(Crop production has been improved with the help of the KCC scheme)
b7=IMP 7(KCC scheme has a direct link with agriculture development)
b8=IMP 8( Physical labor in agricultural activities has decreased after taking a loan under the KCC scheme)
b9=IMP 9 (The use of machinery is increasing after taking a loan under the KCC scheme).
b10=IMP 10 (Due to loan under the KCC scheme the capacity to store grains has increased)
b11=IMP 11(The K.C.C. scheme has provided a road map for future farming planning)
b12=IMP 12 (I feel relief in financial pressure after the KCC loan)
bI3=MP 13 (The K.C.C. scheme has decreased level of mental stress)
b14=IMP 14 (I never thought of suicide due to financial pressure)
b15=IMP 15 (Personal accident insurance under the KCC scheme is helpful for my family)
b16=IMP 16 (Insurance is mandatory for all KCC holders)
b17=IMP 17 (Accidental insurance of Rup. 50000 physical disability insurance of 25000 is a good provision under the KCC scheme)
b18=IMP 18 = (I am aware of the procedure of the Fasal Bima Yojana)
b19= IMP 19 (My bank provides sufficient information about Fasal Bima Yojana)
b20=IMP 20 (The Fasal Bima Yojana is farmer oriented in terms of settlement of claim)
b21=IMP 21 (My bank does not share any information with me regarding the Fasal Bima Yojana.
B22=IMP 22 (KCC scheme has contributed towards batter management of potential resources like land water management, livestock, and fisheries)
(Where, b-regression coefficient & x-independent variables)
The regression equation is been used to validate regression model 1 regarding the age-wised impact of K.C.C schemes on farm households. The effects of each of these paths are tested by regression analysis where each causing variable is taken as an independent variable and the resultant variable is taken as a dependent variable in the equation. The regression equation proved a significant difference in farm household opinions for all paths.
So, the regression equation for Haryana and Punjab States is as under:
|
Haryana |
Punjab |
|
y1=2.353+0.123x1+0.184x2+0.138x3+0.112x4+0.051x5+0.196x6+0.086x7+0.122x8+0.128x9+0.061x10+0.075x11+0.118x12+0.079x13+0.115x14+0.029x15+0.030x16+0.158x17+0.195x18+0.085x19+0.015x20+0.172x21+0.178x22 Model 1 indicates that if IMP-1 is increased by 1, then the impact of the KCC scheme is estimating to increase by 0.123 points if all other independent variables are constant. Similarly, IMP-1 to IMP-22 has increased by 1 then the KCC scheme on the age of farmhouse hold is estimated to be increased by 0.184,0.128,0.112,0.051,0.096,0.086,0.122,0.075,0.118,0.079,0.115,0.023,0.030,0.198,0.195,0.085,0.015,0.172 and 0.178 respectively, if all other variables remain unchanged. |
y1=2.308+0.721x1+0.225x2+0.300x3+0.301x4+0.571x5+0.120x6+0.092x7+0.108x8+0.165x9+0.053x10+0.0032x11+0.311x12+0.099x13+0.020x14+0.077x15+0.58x16+0.059x17+0.018x18+0.078x19+0.094x20+0.901x21+0.101x22 Model 1 indicates that IMP-1 increased by 1 then the Impact of the KCC scheme is estimating to be increase by 0.721points if all other independent variables are constant. Similarly, are increased by 1 then impact of KCC scheme is estimated to be increased by 0.225,0.300,0.301,0.371,0.170,0.92,0.108,0.185,0.053,0.032,0.311,0.093,0.022,0.077,0.052,0.059,0.018,0.078,0.094,0.101 ,and 0.101 respectively, if all other variables remain unchanged. |
Findings and Conclusion of the study
Suggestions of the study
The whole process of the KCC scheme was noted as a crucial factor in determining the financial inclusion of the farmers with help of studying the agricultural credit in which banking sectors play a crucial role in helping the farmers through increasing production of agriculture as well as borrowing powers (Anuwarul Hoda et al., 2021). The services provided by the commercial banks for meeting the credit requirements of farmers were insufficient to fulfill their actual needs. Due to an increase or decrease in the rural farmer’s credit, it was then observed that the commercial banks should have inculcated saving habits instead of providing credit facilities to the farmers cheaply (Murari P. Sharma, 2016). Thus, there is a gap in research and further scope in the area of K.C.C schemes. There is a requirement of making the process of the KCC scheme easy and financial inclusion lawfully as it will be helpful for the poor farmers (Dharmendra Mehta et al., 2016).
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