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*Corresponding author: E-mail: [email protected];
ISSN: 2320-7027
Financial Inclusion of Farmers: A Case Study of Dhenkanal District of Odisha, India
Sharda Priyadarshini1, P. K. Singh1, O. P. Singh1 and Yash Gautam1*
1Department of Agricultural Economics, Institute of Agricultural Sciences, BHU, Varanasi-221005, India.
Authors’ contributions
This work was carried out in collaboration among all authors. Author SP designed the study, performed the statistical analysis, wrote the protocol, and wrote the first draft of the manuscript.
Authors PKS and OPS managed the analyses of the study. Author YG managed the literature searches. All authors read and approved the final manuscript.
Article Information
DOI: 10.9734/AJAEES/2020/v38i1230486 Editor(s):
(1)Dr. Roxana Plesa, University of Petrosani, Romania.
Reviewers:
(1)Putri Haryati Ibrahim, International Islamic University Malaysia, Malaysia.
(2)Keisuke Kokubun, Economic Research Institute, Japan.
Complete Peer review History:http://www.sdiarticle4.com/review-history/64868
Received 25 October 2020 Accepted 30 December 2020 Published 31 December 2020
ABSTRACT
Finance is essential for both economic growth and development of the country. Lack of access to finance for small, marginal farmers and weaker sections of the society has been recognized as a serious threat to economic progress especially in developing countries like India. Moreover, prolonged and persistent deprivation of banking services to a large segment of the population causing financial exclusion which leads to a decline in investment hindering economic development of the country. Thus, the need for inclusive growth comes in the picture of economic and social development of the society. The study was aimed at analysing the extent of financial inclusion among the farmers, a way to include the weaker and vulnerable section of society in the inclusive financial system which will make their present life better and secure with future plans. A multistage sampling technique was adopted in selecting one hundred farmers. Data were collected using survey schedule administered by the researcher. Descriptive statistical tools such as Garret’s ranking technique and inferential statistical tool such as multiple regression analysis were employed to analyse the data.
Original Research Article
Keywords: Financial inclusion; farmers; GDP; economic growth.
1. INTRODUCTION
India is one of major and fastest growing economy in the world after USA and China.
Growth of India during the six and half decades of independence has been remarkable. India is one of the bright spots with average Gross Domestic Product (GDP) growth around 8.26 percent in 2016, after that it was showing declining trend. In 2017, India’s GDP growth was 7.04 percent and it was further reduced to the level of 5.02 percent in 2019 [1]. Finance is essential for both economic growth and development of the country. A serious threat to economic progress especially in developing countries like India is lack of access to finance for small, marginal farmers and weaker sections of the society. Moreover, prolonged and persistent deprivation of banking services to a large segment of the population causing financial exclusion which leads to a decline in investment hindering economic development of the country.
Thus, the need for inclusive growth comes in the picture of economic and social development of the society. However, for attaining the objectives of inclusive growth, there is urgent need of resources, resource generation and their mobilization for financial inclusion is required.
Financial inclusion plays a very crucial role in the process of economic growth and it is useful to facilitate economic transaction, manage day today resources, improve quality of life, protect against vulnerability, make productivity enhancing investments and leverage assets [2].
Majority of population in India live in rural parts of the country. Hence, development of rural India is a key to the economic growth and development of the country. Financial inclusion is an innovative idea to connect the rural people with the banking habits and aimed at providing banking and all other financial services to in a fair, transparent and equitable manner at affordable cost to sections of underprivileged and low-income segments of society. These vulnerable sections of the society even found difficult to save and to plan for their future [3].
Thus, financial inclusion is a key element to achieve the success of inclusive growth and inclusive growth is very essential component for the development of the country. The relationship between financial inclusion and development using the index of financial inclusion identified the factors that are significantly associated with financial inclusion. Levels of human development
and financial inclusion in any country move closely with each other [4]. Finance has become an essential part of an economy for development of the society as well as economy of a nation.
Financial inclusion helps in improved and better sustainable economic and social development of the country. It enables and empowers the underprivileged, poor, women with a vision of making them self - sufficient and financially strong. As a large portion of rural population is still not included in the inclusive growth, the concept of financial inclusion becomes a challenge for the Indian economy. Inclusive growth is only possible if people from all the sections of the society would involve in the process of financial inclusion [5]. Credit is not only one of the critical inputs in agriculture but it is also important as effective means of development. A large number of agencies including co-operatives, Regional Rural Banks (RRBs), Commercial Banks (CBs), Self Help Groups (SHGs) and a well spread informal credit outlets together constitute the Indian rural credit delivery system. One of the objectives of the credit policy is to minimise the contribution of non-institutional sources, specially the moneylenders in the flow of agricultural credit. Its timely availability in the right quantity and at an affordable cost goes a long way in contributing wellbeing of people, especially for the weaker and excluding sections of the society.
The Incidence of Indebtedness (IOI) was about 31.4 and 22.4 percent among the rural and urban households respectively [6]. In the rural segment of India percentage of rural households indebted to institutional agencies was 17.2 percent as against 19.0 percent households to non- institutional agencies [7]. Therefore it shows that in rural areas, the expansion of the formal sector and various programs has not succeeded in supplanting the moneylenders as the dominant source of credit. In rural areas, the share of debt from the institutional and non-institutional credit agencies was 56 and 44 percent respectively, whereas in case of urban area, it was 85 and 15 percent respectively [6]. Thus, in rural area there is decreasing share of institutional credit in total credit. The NSSO has reported that institutional sources were responsible for providing only 56 percent of the total credit compared to the previous report which says 57 percent [8] and 64 percent [9]. However, despite various efforts, the spread of formal sources of credit especially in rural areas has been slow and was showing
declining trend. In rural areas, co-operative societies and commercial banks, jointly accounted for 50 percent of the outstanding cash debt, with co-operative societies (24.8%) accounted for a slightly lower share than the Banks (25.1%). Among the non-institutional credit agencies, professional moneylenders were found to be the most important source of finance (28.2%) in rural areas.
2. REVIEW OF LITERATURE
Sahu et al. [10] reported the literature on the rural credit market in India and found that peasant farm households were rationed in their access to subsidized formal credit. Because of a lack of infrastructure and poor access to institutional credit, such farmers were exploited by means of an interlocked market connecting informal credit. The research data from Kalahandi district in Orissa suggested that access to formal credit was limited in rural areas although there exists a high demand for it and a high degree of credit rationing by the formal lender occurs which contributed to the need for informal loans.
Chauke et al. [11] studied factors that affect smallholder farmer’s access to credit sources in the Capricorn District Municipality of Limpopo Province, South Africa. It was found that factors that contributed significantly to credit access were the need for credit, attitude towards risk, distance between lender and borrower, perception on loan repayment, perception on lending procedures and total value of assets.
They recommended the establishment of loans offices close to farmers and operated by officers familiar with farmers to reduce lending procedures, risks and educate them on perceptions on loan repayment.
Chithra and Selvam [12] studied the access to finance by the poor as a prerequisite for poverty reduction and sustainable economic development of a country. They attempted to measure the inter-state variations in the access to finance, using a composite Financial Inclusion Index (IFI). The analysis revealed that among the socio-economic factors, income, literacy and population were found to have significant association with the level of financial inclusion.
Siddik et al. [13] presented a study of financial inclusion in Bangladesh and attempted to identify the determinants of financial inclusion. The empirical findings showed that among the socio- geographic variables, rural population, household
size, and literacy rate, among the infrastructure variables, paved road networks, Internet, and among the banking variables, deposit penetration were found to be the significant determinants of financial inclusion.
Ulwodi and Muriu [14] examined barriers to financial inclusion across sub-Sahara Africa (SSA). Lower levels of income were associated with lower levels of access to formal account.
Similarly, literacy rate was found to have significant effect on the level of account ownership. Owning a debit card was more likely to increase account ownership. Another important barrier to account ownership was proximity to the nearest financial services.
Nath and Dhawan [15] found that financial inclusion helped individuals to economically grow strong. The study identified major factors like not enough income, lack of financial education, absence of required documents for due diligence process, convenience and trust in local money lenders over banks as few of the reasons for not being part of formal banking system.
3. RESEARCH METHODOLOGY
The Odisha state and Dhenkanal district was purposively selected for the present study because it was observed that, overall credit- deposit ratio was above 60 percent. Dhenkanal district is among the nine districts who have achieved Credit-Deposit ratio of more than 60 percent [16]. A multistage sampling technique with development blocks as first stage and village as second stage and farmers as third stage sampling unit was adopted. There are eight development blocks in the district, out of which Kamakhyanagar development block was selected randomly to observe the extent of financial inclusion among the farmers. After selection of development block, a list of all the villages was prepared and four villages viz., Jaganathpur, Anlabereni, Kantioputashahi, and Kotagara were selected randomly with the help of random table. After selection of villages, twenty-five farmers were randomly selected from each village making a total of one hundred farmers for the study. The period of the study was 2017-18 and the primary as well as secondary data were collected regarding the financial inclusion in the study area.
3.1 Analytical Tools
Descriptive statistics such as frequencies and percentage, Garrett's ranking technique and
inferential statistical tool such as multiple regressions were used in achieving the standard objectives of the study. The data were analysed and converted in to frequency and percentage and then expressed in tabular form.
3.1.1 Multiple regression model
Multiple regression model was used to find out the determinants of financial inclusion of the respondents. The model is specified as follows.
= + + + + +
Where, Y is the level of financial inclusion (percentage score), a is constant, X1 is income measured in rupee, X2 is farm size (hectare), X3 is distance from village to bank (Km) and X4 is land ownership (1 for own land otherwise 0). b1, b2, b3 and b4 are the parameters to be estimated and they determine the extent of financial inclusion among the farmers. In order to measure the dependent variable (Y), major components of financial inclusion i.e. access to credit, saving, insurance and bank account were considered.
Scores here computed and then converted to percentages.
3.1.2 Garrett’s ranking technique
Garrett’s ranking technique [17] was used to know the major problems faced by the farmers with regard to financial inclusion. In Garrett’s scoring technique, the respondents were asked to rank the factors or problems and these ranks were converted into percent position by using the following formula.
Percent position = 100* (Rij – 0.5) / Nj
Where, Rij is ranking given to the ith attribute by the jth sample respondents and Nj is total rank given by the jth sample
respondents
The percent position of each rank was converted into scores by referring to Table given by Garrett.
Then score of each rank was computed. Then for each factor, the scores of individual respondents were added together and divided by the total number of respondents for whom scores were added. These mean scores for all the factors were arranged in descending order and the constraints were ranked, the attributes with the highest mean value was considered as the most
important one and others followed in that order.
4. RESULTS AND DISCUSSION
4.1 Socio-economic Profile of the Respondents
The result of the study indicates that 17 percent of the respondents were between the age group of 21-35 years (Table 1). Out of total respondents, 44 percent of respondents were under the age group of 36-50 years, and 34 percent were between age group of 51-65 years.
This implies that majority of respondents were found between the age group of 36-65 years.
The implications of the study found that most of the respondents represent their active age group.
The study also depicts that out of the total respondents, 99 and 1.0 percent were under male and female category respectively. This implied that men were more interested for financial inclusion rather than women in the study area. Still women are reluctant to open an account and other services like credit, saving and insurance. It was also evident from the analysis that nine percent of respondents were without formal education, 31percent obtained primary education, 43 percent of the respondents attained secondary education, six percent of respondents attained higher secondary education, while 11 percent of the respondents were graduates and postgraduates. Results suggests that majority of the respondents (74 percent) have completed only primary and secondary education. Thus, there were low
literacy rate among the respondents. Though the respondents are not highly educated, they might not be interested in taking part the
banking activities like credit, saving, insurance which might limit their access to financial inclusion. The study also reveals about the family size of the respondents. Out of the total respondents, 62 percent respondents were
having family size of one to five, 34 percent respondents were having family size of
six to ten, three percent of respondents have large family size having (eleven to fifteen members) and only 1.0 percent has family size above 11. This revealed that majority of the respondents were having family size of one to five and this is the average size of a family of rural India [18].
Table 1. Socio-economic profile of respondents
Socio-economic parameters Category Frequency Percentage
Age of Respondents (years) 21 to 35 years 17 17.00
36 to 50 years 44 44.00
51 to 65 years 34 34.00
Above 65 years 05 05.00
Gender involved in dairying Male 99 99.00
Female 01 01.00
Educational level of the respondents’
No formal education 09 09.00
Primary 31 31.00
Secondary 43 43.00
Higher Secondary 06 06.00
Graduate and above 11 11.00
Family size Small (less than 5 members) 62 62.00
Medium(6-10 members) 34 34.00
Large(11 to 15) 01 01.00
Land holding Marginal farmers 39 39.00
Small farmers 47 47.00
Medium farmers 14 14.00
Annual income Rs30,000 – Rs1,30,000 88 88.00
Rs1,30,001 – Rs2,30,001 9 9.00
Rs2,30,002 – Rs3,30,002 2 2.00
Above Rs3,30002 1 1.00
Number of respondents having bank account
No bank account 3 3.00
Bank account 97 97.00
Credit access by respondents No credit access 36 36.00
Credit access 64 64.00
(Source: Field survey, 2018) Though the size of the family is the perfect size
of family, all the members must share the burden of credit among them and interested in taking credit. The study is in conformity with the findings of Kumar et al. [19] who reported that the quantum of institutional credit availed by the farming households was affected by a number of socio-demographic factors and among the factors, family size was one of them. The result presented in Table 1 also shows that 88 percent of the respondents were having annual income between Rs 30,000– Rs 1,30,000, 9 percent of the respondents were having their annual income between Rs1,30,001 to Rs 2,30,001 and only 1.0 percent of the respondents were earning annual income of above Rs 3,30,002.
Due to low income of the majority of the farmers, they must have low saving or no saving and they may also be afraid of taking credit and other financial services and their access to financial inclusion might be limited. The result was in conformity with Paramasivan and Ganeshkumar [3] who reported lack of sufficient income is an obstacle which excluded the people from accessing bank account, credit, saving and other financial services. Findings of the study was also similar to the study conducted by Ray [20] who
reported that lack of sufficient income was found to be the main reason for involuntary financial exclusion.
Out of the total respondents in the study area, 97 percent have bank accounts while the remaining three percent were not having bank account. The implications of the study revealed that account penetration was wide in the study area. This is in line with the findings of Sarma [21] who reported a similar result in Wayanad district of Kerala. And as far as credit access of the respondents is concern, 64 percent of the respondents in the study area were having access to credit and 36 percent respondents are not having access to the credit. This implies that though majority of respondents were access to credit but there were some portion of respondents who are still devoid of getting access to credit. Those who have access to credit, their attitude towards risk taking and investment in commercial crop were more than those who don’t have access to credit.
4.2 Determinants of Financial Inclusion The results of regression analysis and the determinants, affecting financial inclusion is
presented in Table 2. It was observed that, the dependent variable is financial inclusion which is a combination of four parameters such as credit, bank account, insurance and saving. The independent variables under study were total income (farm and non-farm income), farm size, and distance of the respondents to the financial institutions and ownership of land. The Cobb- Douglas production function was fitted to examine determinants of financial inclusion. It is evident from the analysis that income and land ownership were positive correlation and significantly affecting the financial inclusion.
This implies that one percent increase in income and ownership of land will increase the chances of financial inclusion by 0.23 percent and 0.10 percent respectively. Similar study by Pandey and Raman [22] and Chithra and Selvam [12]
reported that high income households have greater financial needs such as saving, borrowing, investment etc. Hence household income was found to have significant association with the level of financial inclusion. Though farm size was not significant but it was positively correlated to financial inclusion. It means larger the farm size, more will be the chances of financial inclusion. In the present study farm size was not significantly correlated with financial inclusion and this result disagrees with the findings of Kumar et al. [19] who reported that farm size emerged to be the major determinant
of financial inclusion. The distance of the respondent to the financial institutions, services or products were negatively correlated to financial inclusion. This implies as the distance increases the chances of financial inclusion decreases. Distance was found to be an insignificant relationship with financial inclusion.
The findings of this study disagree with Chauke et al. [11]. The value of R2 was found to be 0.70 which implies that 70 percent of variation in chances of financial inclusion is as a result of variables included in the regression model (X1, X2, X3, and X4).
4.3 Constraints of Financial Inclusion The constraints faced by respondents were obtained and analysed for their order of importance based on Garrett’s mean score values obtained. It is evident from the analysis that most of the respondents reported insufficient income as the major constraints which was ranked Ist faced by them. Repayment schedule, lack of security and low saving were ranked II, III and IV respectively. The reason behind the insufficient income of the respondents might be due to the small size of land holding (Table 3).
Mostly the respondents under study were marginal and small category with low marketable surplus and hence their income from farming was low. As the respondents’ primary occupation was Table 2. Determinants of financial inclusion
Variables Coefficient t-test Sig
Intercept (a) 0.60 1.24 0.21
Income (X1) 0.23** 2.17 0.03
Farm size (X2) 0.03 0.48 0.62
Distance from village to bank (X3) -0.05 -0.95 0.33
Land ownership (X4) 0.10** 2.27 0.02
R2 0.70
** Significant at 5 percent (0.05) level of significance Table 3. Constraints of financial inclusion
SI. No. Constraints Total score Garrett’s mean score Rank
1. Insufficient income 7040 70.40 I
2. Repayments schedule 6091 60.91 II
3. Security issues 6019 60.19 III
4. Low saving 5754 57.54 IV
5. Lack of awareness 4597 45.97 V
6. Distance 3993 39.93 VI
7. Tedious paper work 3695 36.95 VII
8. Delay in bank 2911 29.11 VIII
(Source: Field survey, 2018)
agriculture, they depend fully or partially on agriculture for their livelihood. Agriculture is uncertain because it is largely dependent on monsoon; hence farmers might not get sufficient income all the time. This finding is in conformity with Ray [20] who reported that lack of sufficient and regular income was found to be the main reasons for involuntary financial exclusion.
Similar result was also reported by Chattopadhyay [23]. Paramasivan and Ganeshkumar [3] reported that lack of sufficient income, act as an obstacle which excluded the people from accessing bank account, credit, saving and other financial services. On the other side, lack of awareness, distance, tedious paper work and delayed practice by banks were found to be the other constraints affecting financial inclusion. It was reported by Thangaraj [24] also.
The second most important constraints for financial inclusion in the study area was bank repayment schedule. In most of the cases, the repayment duration of the loan is fixed by the bank and it is not flexible in nature. But income received by the farmers from agriculture was very volatile in nature. Some years, farmers are not able to recover even the cost of cultivation of the crop and farmers are unable to repay the loan amount. Resulting to this, farmers in the study area having the fear that once loan is due, bank may take the possession of agricultural land or mortgage property.
The third most important constraints for financial inclusion in the study area was security issue.
Most of the farmers in the study area were categorised as marginal and small farmers and land holding size less than two hectares.
farmers are unable to get required amount of loan against their agricultural land for crop and term loan.
5. SUMMARY AND CONCLUSION
The income level was found to be low and the minimum per annum income was Rs 30, 000 resulting to this saving of respondents in the study area was very low. Majority of farmers were having their own land which increases the chance of financial inclusion. Bank account penetration was wide in the study area. The major determinants of financial inclusion were found to be income from agriculture. While getting the financial services, the farmers faced a lot of problems. Among the various problems faced by the respondents, insufficient income was found to be the major constraints in financial
inclusion process. The problems next to it reported by most of the respondents was repayment schedule followed by security issues etc.
Since literacy rate was found to be low in the study area, therefore financial literacy programme should be organised because it is being progressively linked with financial inclusion which would help in building informed customers and would result in win-win situation for all.
Majority of farmer belongs to small and marginal category; hence grass root level organizations like NGOs, farmers club should encourage vulnerable and weaker sections of the study area in the process of financial inclusion by their local participation. Insufficient income was found to be the key issue. Thus, availability of marketing facility for crop produce, timely procurement of agriculture produces, with remunerative prices and warehousing facility should be provided in order to enhance their income level. Since majority of farmers live in rural areas, rural infrastructure development like availability of electricity, improved connectivity through roads and telecommunications is an essential prerequisite for attaining greater financial inclusion and inclusive growth.
CONSENT
As per international standard or university standard, participant’s written consent has been collected and preserved by the authors.
COMPETING INTERESTS
Authors have declared that no competing interests exist.
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