A Study on the Impact of Financial Inclusion for Inclusive Development

In India, the idea of a financially inclusive society is not new. The promotion of economic participation has risen to prominence as a social goal. Since the vast majority of Indians are still excluded from the benefits of inclusive development, achieving financial inclusion has become a significant and difficult task for the country's economy. The availability of credit is seen as a powerful tool for progress. Countries throughout the globe are working towards financial inclusion as a result of the impact that improved access to financial services has on Inclusive Growth.[1] Banking and other financial institutions serve as the backbone of every thriving economy. If we want to maintain and even increase the growth rate, we need to make sure that the economically disadvantaged are more represented in the process. India must take the road to financial inclusion if it wants to emerge as a major participant on the international stage.[2] The availability of financial resources is a powerful weapon in the struggle against poverty and for the expansion of economic opportunity. India is home to a sizable banking industry and a well-developed financial infrastructure. It is much easier for individuals and organisations to save money, invest, divide up risks, and make timely and cost-effective payments if they have access to a variety of credit and transaction services. There are clear correlations between the breadth and depth of the financial system and the expansion of the economy.[3-4] The term "financial inclusion" refers to the practise of guaranteeing low-income individuals quick and low-cost access to financial services and credit delivery. If we want low-income individuals to contribute to economic progress, we need to make sure they can easily access, use, and benefit from the official financial system.[5-6] Defining and measuring poverty is complex and nuanced. When a person's income and assets are insufficient to cover even their most basic needs, we classify that person as impoverished. It is crucial to accurately assess poverty in order to design, carry out, and evaluate successful anti-poverty initiatives. The term "inclusive growth" refers to the process through which the impoverished are able to participate in and reap the advantages of a country's economic expansion. Therefore, in order to achieve the primary goal of increasing the quality of living and decreasing disparities, it is necessary to take into account the significance of different socioeconomic programmes and policies, such as financial inclusion, for boosting economic development and upliftment of the poor. For a country like India, whose policies are always geared towards economic development in tandem with the eradication of poverty, an accurate measurement of poverty is crucial.[7-8] Financial inclusion as providing low-cost banking services to large underserved populations. Delivering an economy's financial system to its citizens is what Chakravathy calls "financial inclusion." As stated by the Rangarajan committee, "financial inclusion is the process of ensuring access to financial services and timely and adequate credit where needed by vulnerable groups such as weaker sections and low income groups at an affordable cost." [9] The field of development and policymakers has recently given more attention to financial inclusion. credit, remittances, and insurance. The fact that it can help reduce poverty and bring about social harmony and long-term economic progress has propelled it to the forefront of the development and policy communities.[10]

The current investigation is descriptive in character and uses a survey technique. The data is gathered using a structured, non-anonymized, pre-tested questionnaire. Individuals from ten different villages in the Ribhoi area make up the study's population.

Financial inclusion includes things like savings, loans, remittances, financial literacy, and insurance. Inclusion in the financial system is a determinant. There is a mediating effect between social and economic empowerment. Dependent variable inclusive growth

Due to the importance of primary data for this research, much attention was taken in developing the questionnaire. Primary data was gathered by questionnaires sent to residents of the communities. The survey is broken down into eight distinct sections. The first section is devoted to respondents' personal information, the second to their savings habits, and the third to the loans they have taken out. The respondents' use of remittances facilities is covered in Part 4, while the use of other financial goods like insurance and mutual funds is covered in Part 5. Financial literacy is covered in the sixth section, and the effect of financial inclusion on inclusive growth is discussed in the last section. The last section covers the holy trinity of Jan Dhan, Aadhaar, and mobile phones. The questionnaire section has free-form, multiple-choice, and Likert scale questions.

Sixty-two participants from each revenue block participated in the pilot project. Changes, additions, and deletions were made based on responses to the pilot study's questionnaire. Using Cornbach's Alpha, the reliability was determined to be 0.727, indicating 72.7% confidence in the results. An alpha value of 0.7 or higher is deemed adequate by most researchers (George and Mallery, 2001; Pallant, 2005), and a reliability coefficient of 0.5 or higher is generally seen as a good predictor of building dependability (Nunnally, 1976). The interview schedule for the survey and the development of creative research issues were informed primary data from respondents was prepared.

In the Ribhoi area, 10 villages over three blocks are selected using quota sampling. Respondents are selected from these rural areas using a judgement sampling technique.

The following formula is what the researcher used to decide on a sample size:

Where Z = Standardized value corresponding to a confidence level of 95% = 1.96 S = Sample SD from Pilot study of 62 samples = 0.632 E = Acceptable Error =5% = 0.05 Hence, Sample size =

= (1.96*0.0.632/0.05)2 = 614

To far, 656 answers have been collected from a total of 700 questionnaires distributed throughout 10 communities. 42 replies were determined to be biassed after careful examination. Since only 614 are useable, it is the precise number of people in the sample.

3.1 The fraction of the population that has one Those who indicate they have a savings account status are separated from those who do not. The following table shows a breakdown of the data in terms of frequency..

Yes 520 84.7 No 94 15.3

520 out of 614 respondents (84.7%) had a bank savings account based on the facts presented above. One-fifteenth (84/291) of those surveyed do not have a savings account at a major financial institution. 3.2 The amount of people who have had money deposited into their bank accounts via social assistance programmes In order to analyse the data, respondents are separated into groups depending on the types of social benefit programmes from which they have received money in their bank accounts. The following table provides a frequency distribution of the data. Table 3.2: Quantity of respondents with bank account deposits from social welfare programmes

OAP 48 9.2 NREGS 285 54.8 LPG 84 16.2

Others 103 19.8

The numbers in the table show that the majority of respondents (285) are receiving funds through the NREGS scheme (54.8%), followed by the respondents receiving benefits through the LPG scheme (16.2%), and finally the OAP category (9.2%).

between 1001 and 5000. The following table shows the frequency distribution of the aforementioned groupings. Table 3.3: Classification of Respondents by Keeping a Minimum Balance in Their Savings Account

0 -100 2 0.4 101 – 500 54 10.4 501 – 1000 338 65 1001 – 5000 126 24.2

The data in the table above reveals that just two respondents had a balance of zero to one hundred rupees in their savings accounts, while sixty-five percent of the total have balances of between five hundred and one thousand. Twenty-four percent (24/126) of respondents had bank balances of between $1,000 and $5,000. 3.4 Average number of family savings accounts The number of savings accounts held by a family or household is being used to categorise respondents. The results of the analysis are shown in the frequency distribution table below. Table 3.4: Households' average number of savings accounts

One 213 41 Two 217 41.7 Three 53 10.2 More than four 6 1.2

From the data shown above, we can conclude that 41.7% of American families have two savings accounts, while the remaining 62.3% have just one. Ten percent, or 53 respondents, have three savings accounts, and three percent, or 31 respondents, have four. There were 520 total respondents. Only six of those polled had more than four family savings accounts.

The following table displays the distribution of respondents into groups depending on whether or not they have secured the loan. Table 3.5: Loan status is used to divide respondents into four categories.

Yes 370 71.2 No 150 28.8 Total 520 100

From the data shown above, we can conclude that 370 (or 71.2% of the total) of the 520 respondents who filled out the survey have gotten a loan, whereas 150 (or 28.8%) have not.

Respondents were divided into four categories depending on how well they knew about the Pradhan Mantri Jan Dhan Yojana (PMJDY), a government-run savings programme designed to increase people's access to bank accounts. The following table provides frequency counts for the aforementioned groupings.

Yes 510 98.1 No 10 1.9

From the data shown above, it is apparent that the majority of respondents (510 out of 520) are familiar with the Pradhan Mantri Jan Dhan Yojana (PMJDY) programme, while just 10% are unaware of it. 3.6 Correlation between Respondents' Occupation and Their Savings Behaviour Table 3.7: There was a correlation between respondents' occupations and the amount they saved.

The data in the table above does not suggest any correlation between respondents' jobs and their savings rates 3.7 Correlation Between Respondents' Income Type and Their Savings Rate

Statistical Inference

Chi-square value 9.817 + df = 6

Daily 133 (54.30) 104 (42.4) 8(3.3) 245 Weekly 6 (54.5) 4 (36.4) 1 (9.10) 11 Monthly 63(63.60) 33(33.3) 3(3) 99 Seasonally 22 (44) 23(46) 5(10) 50

Based on the data shown above, it seems that respondents' savings rates are unrelated to their income distributions. 3.7 Correlation between Respondents' Income and the Amount Saved Table 3.8: shows a correlation between respondents' salary and the amount they saved. Income group Savings Less than 500 501 - 2500 Above 2500 Total Statistical Inference Up to 30000 123 ( 59.40) 75(36.20) 9(4.30) 207 Chi-square value 13.555 +

df = 8

30001 – 40000 38(46.90) 41(50.60) 2(2.50) 81 40001- 50000 3(30) 5(50) 2(20) 10 60000

Above

60000 59(56.7) 41(39.40) 4(3.8) 104

Total 224(55.30) 164(40.50) 17(4.20) 405 The data in the table above suggests that savers across all income brackets tend to put away around the same amount each year. 3.8 The Connection Between PMJDY and Knowing When to Avoid an Overdraft: Table 3.9: shows how familiarity with PMJDY overdrafts relates to the existence of a PMJDY account. Based on the data in the table above, we may predict that account holders are also likely to be familiar with the PMJDY overdraft account. Thus, we cannot accept H0.

Multiple regression analysis is used to make predictions about INCLUSIVE GROWTH. The value of a criteria may be estimated using multiple regression, a statistical method that takes into account many independent (or predictor) factors. Multi-factor analysis involves considering the relative importance of many variables at once. Modelling the connection between the explanatory and response variables is the main focus of multiple linear regression (MLR). The model for MLR, given ‗n‘ observations, is: yi = B0 + B1xi1 + B2xi2 + ... + Bpxip + Ei where i = 1, 2, ..., n Ho: There is no significant financial inclusion's effect on inclusive development (X2), individual growth (X3), and regional development (X4) are used as predictors of inclusive growth (Y) in this research. The non-significant variables are weeded out using a step-by-step regression approach. The study does not include one potential factor -- social empowerment(X2). The table demonstrates that the combined impact of the three factors on Inclusive Growth was 98.22 percent. Based on the R2 value, it seems that the aforementioned three factors have a significant impact on Inclusive Growth (the dependent variable). However, the factors contribute 96.3% to the dependent variable based on the corrected R square value of 0.963. Significant at the 1% level, the F value (3419.54) indicates that the model is well-fit. Table 3.10 : Financial inclusion-based multiple regressions for inclusive development

Coefficients t Sig

(Constant) -.784 .433 -1.812 .071

R = 0.982 R2 = 0.964

Adjusted R2 = 0.963 Individual growth (X3)

1.142 .088 .533 12.914 .000***

Economic empowerment

.421

.071

.238

5.930

.000*** 57.863*** (X1)

Regional growth (X4)

.638 .117 .220 5.435 .000***

The table shows that all three of the aforementioned factors have a major bearing on Inclusive development. greatest impact on inclusive growth ( =0.533, p0.01), followed by economic empowerment ( =0.238, p0.01) and regional development ( =0.220, p0.01). Increases in both manufacturing and the service sector activity—and hence in available jobs—are essential conditions for sustained economic expansion. As a result, the unemployment rate fell. The coefficients of the independent variables in the regression equation are shown in the Beta column of the table.

Y = 0.238 X1 + 0.533X3 + 0.220X4

This shows that development at the individual level, followed by economic empowerment and regional growth, has a significant impact on inclusive growth. This is a significant indicator that the primary objective of financial inclusion has been met: to increase low-income people's access to and use of the formal financial system in order to foster personal development and economic empowerment. The government's efforts to enhance economic prospects and provide equal chances to all members of society's most vulnerable groups have led to a rise in both the fixed and variable job options available to the public. Self-employment has been given a higher priority in government-sponsored programmes like PMEGP (prime minister's employment generation plan), Stand up India, and Pradhan mantri mudra yojana initiatives, which have opened doors for personal development. Subsidised interest rates on bank loans for farmers and a mandatory crop insurance plan (PMFBY) to protect against monsoon-related crop losses show that the government places a premium on the agricultural industry. Because of this, the unemployment rate has gone down (having a negative beta).

Method Of Measurement There are nine components to the illustrative measuring model. A random measurement mistake, represented by the corresponding error term, affects the accuracy of the three observed variables used to assess each component. They are all regressed into The First Model The early findings from the model show that the model does not well match the data. With 288 possible permutations, a chi-square of 786.725 indicates statistical significance. at p<0.05, χ²⁄df = 2.732; GFI=.901; AGFI= 0.870; RMSEA= 0.058.

Figure no 3.1 Initial Model Table 3.11: Fit Indices

x 2 - 786.725 Df - 288 x 2 significance p ≤ 0.05 .000 x 2 /df ≤2- 5.0 2.732

GFI ≥ 0.90 .901 AGFI >0.80 .870 RMSEA ≤0.08 .058

The model is unacceptable, as shown by the fit statistics. Because of this, the model has been adjusted.

Modification Index created the revamped version of the model. The Modification Index implies that the model fit might be statistically improved by correlating the error construct called "Remittances," as shown by the modification index. Items 22 and 23 are both tied to the same underlying "Individual" construct, as shown by the modification index's correlation of their respective error terms. The statistical fit of the model might be improved by correlating the error terms between the two variables. Therefore, it is statistically permissible and theoretically relevant to allow their error terms to be connected. The adjusted values of GFI (0.907), AGFI (0.876), and RMSEA (0.055) are consistent with an adequate fit to the data.

Figure 3.2: Modified Model

SEM assumes a correlation between exogenous variables. Savings, loans, remittances, and insurance are the four exogenous factors considered. The route map includes five endogenous variables: financial inclusion, economic empowerment, social empowerment, personal development, and regional growth. Error terms are required for each endogenous variable used in SEM. Therefore, e28, e29, e30, e31, and e32 are the five error terms. The model's endogenous variables are affected by the model's exogenous variables. The chi-square test for the theoretical structural model yielded a significant result (p0.05) of 863.099 with 304 degrees of freedom, suggesting a poor match. It has been said, however, that the chi square 2.839, GFI =.887, AGFI = 0.860, RMSEA = 0.060). The model has a construct reliability of 0.813. The statistics of the fit show that the theoretical model matches the data rather well.

Figure 3.4: Path Goal Model The route goal model suggests that savings, remittances, and insurance all have a direct impact on financial inclusion at the 5% level. At the 5% level, the impact of financial inclusion on economic independence is clear. There is a clear correlation between economic and social empowerment, and this correlation becomes substantial at the 5% level. Individual and regional development benefit directly from social empowerment, which is statistically significant at the 5% level. It is also investigated that the 5% level of significance of financial inclusion has an indirect influence on social empowerment. Individual and regional development are impacted indirectly, although economically empowering individuals has a major impact at the 5% level. It may be inferred that economic and social empowerment have mediated the inclusive development to a modest degree. relationship between financial inclusion and other variables, finding that savings, remittances, and insurance all have a significant role. It has been noticed that more access to financial services leads to greater economic independence. There is a clear correlation between economic and social empowerment. An individual's and a region's development are both impacted by social empowerment. The indirect link between financial inclusion and increased social agency is also explored. Individual and societal development benefit indirectly from economic empowerment. We infer that economic and social empowerment have mediated the inclusive development to a modest degree.

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Research Scholar, Mahatma Gandhi University, Byrnihat, Meghalaya E-Mail: kamzalian@gmail.com