Financial Inclusion Among Scheduled Tribe Population in Rural Areas of Tripura: A Primary Survey

Abstract

The present study investigates the degree of financial inclusion (FI) among tribal communities, specifically focusing on the rural tribal populations of the South Tripura district. More specifically, the study examines the level of access to and usage of formal financial services, including banking, credit and insurance, among scheduled tribes (ST) households. An appointed field investigator has collected primary data from 408 households belonging to the tribal community in the South District of Tripura through a structured interview schedule. We used the methodology suggested by the Reserve Bank of India (RBI) to compute the composite financial inclusion index (CFII) to assess the degree of FI among the ST population in the study area. The results indicate that most respondents demonstrate moderate to high levels of FI, with only a small percentage classified as having low FI. The study also confirms that FI varies based on education, income source type and income groups. Government and policymakers should enhance effective FI programmes, particularly in digital banking, mobile money services and microfinance. Improving digital infrastructure and reducing transaction costs can further increase accessibility for these communities.

Keywords

Financial inclusion scheduled tribe population rural area Tripura

Introduction

Inclusive growth has become a buzzword, and it is a key priority in the government’s agenda worldwide, as it aims to ensure that the benefits of economic progress are distributed equitably among all segments of society, with a special focus on marginalised and vulnerable groups. Financial inclusion (FI) is a crucial enabler of inclusive growth (Sahoo et al., 2017), as it provides access to financial services and opportunities for individuals and communities to participate in the economy. FI is the process of ensuring access to financial services, timely and adequate credit for vulnerable groups such as weaker sections and low-income groups at an affordable cost, Reserve Bank of India. ‘Broad access to financial services implies an absence of price and non-price barriers in the use of financial services’. Different authors have provided different definitions of FI. For instance, Rajan (2009) defines FI as the provision of universal access to a wide range of financial services at an affordable price. These encompass not only banking products, but also financial services such as insurance and equity products, which are all part of this category. According to Aggarwal (2014), FI aims to provide financial services at a reasonable cost to various marginalised and disadvantaged groups in society. Sahay et al. (2015) define FI as the access, usage and delivery of financial services at affordable costs to vulnerable segments of society. Sarma and Pais (2011) gave a comprehensive definition of FI based on several dimensions, including accessibility, availability and usage of the formal financial system for all members of an economy. The only way to address the isolation of the poor from the formal financial sector of the country is through FI. As a subset of economic inclusion, FI has the potential to improve the financial situations of the unprivileged and impoverished and enhance the quality of their living. A more resilient and stable real economy can be achieved through FI, which entails making risk management financial resources more accessible to individuals, households and small businesses (Cull et al., 2012).

According to Mohan (2006), FI is the polar opposite of financial exclusion. Thus, it is reasonable to infer that financial exclusion results from the lack of access to and willingness or ability to use a variety of financial services. It is strongly linked to poverty and is an outgrowth of the broader issue of social exclusion and imposes a substantial price tag on personal development and economic advancement. The factors that can lead to financial exclusion are access exclusion owing to geographical distance and risk management; price exclusion owing to unaffordability of financial products; condition exclusion due to inappropriate conditions that some people are forced to endure; marketing exclusion because of niche marketing and sales of financial products and self-exclusion resulting from people’s voluntary self-withdrawal from the formal system due to fear of rejection or psychological barriers (Kempson & Whyley, 1999; Mukherjee et al., 2019). In the economic development process, the financial exclusion of the tribal population and rural poor of the country has resulted in poverty, economic disparities, unemployment and income inequalities. Access to and involvement in the financial system can empower people, particularly those from lower socio-economic backgrounds, strata of society to gain social and economic empowerment (Mukherjee et al., 2019). Therefore, strengthening FI in the country has been one of the important developmental agendas of both the Government of India and the financial sector regulators (namely RBI, SEBI, IRDAI and PFRDA). The RBI adopted the term FI for the first time in its 2005–2006 annual policy statement, and since then, it has taken several initiatives to make this a reality (Majumdar & Gupta, 2013). However, India has a long history towards ensuring FI without the formal usage of the term. The journey of FI in India began in 1956 with the nationalisation of life insurance companies. This was followed by the nationalisation of banks in 1969 and 1980, and general insurance companies in 1972, mandatory priority sector lending by commercial banks and ‘no-frill accounts’ mandate in 2005–2006. Despite this, the status of FI in India is not up to expectations, and a sizable portion of the Indian populace remains out of the formal financial system. Therefore, the Government of India has adopted several schemes to boost FI and economic prosperity in the current century. The recent initiatives include Pradhan Mantri Jan-Dhan Yojana (PMJDY) to encourage saving and open access to credit, Pradhan Mantri Suraksha Bima Yojana (PMSBY) to provide accidental death or disability cover and Atal Pension Yojana (APY) to provide pension cover to subscribing bank account holders. Most recently, the RBI has also taken up two crucial initiatives, such as a national strategy for FI (2019–2024) to make financial services available, accessible and affordable to all citizens and a national strategy for financial education (2020–2025) to build a financially aware and empowered India. Thus, India has been on the verge of witnessing two decades of experimentation and implementation in the area of FI. As a basic measure of FI, the proportion of adults with an account in a formal financial institution rose from 35% in 2011 to 77% in 2021, which is on par with the global average. The eventual objective of FI is to eliminate poverty, eradicate income disparity and enhance economic empowerment.

However, despite consistent effort, the FI level remained low in the North East Region (NER) (Bhanot et al., 2012). According to CRISIL Inclusix (2018) score, the macro data shows that compared to the aggregate India, the FI status in NER is lower than the national level. It is believed that insufficient literacy and lack of knowledge among the tribal population have led to low levels of FI. Indian scheduled tribes (STs) have long encountered socio-economic obstacles (Guha et al., 2017). They have limited access to financial resources and the ability to improve their well-being. Due to their deprivation of modern banking and financial services, they are compelled to remain content with limited resources. Considering the above-mentioned facts, the government is striving hard to bring the maximum tribal population under a formal banking system by making them open accounts in banks or other financial institutions to encourage saving and facilitate the extension of credit. The present study investigates the financial literacy (FL), financial service usage, factors responsible for FI and demographic factors’ impact on FI among tribal community populations, with a particular focus on South Tripura district, considering the respondents from the rural areas. The study of FI in rural areas is important because the rural population contributes significantly to economic development, as observed by various studies (Hasan et al., 2020; Le et al., 2019). Lal (2018) reported that access to basic financial services such as savings, loans, insurance and credit helps to come out of poverty. By providing access to financial services to a diverse range of individuals, the entire financial system can experience significant growth and foster deeper connections within communities (Hasan et al., 2020; Rashidin et al., 2020) and be connected with the eradication of poverty (Chao et al., 2021). Mukherjee et al. (2019) found that Tripura is one of the six Indian states that demand urgent attention for the promotion of FI. The FL among the ST population in India is significantly poor. A significant proportion of the rural population belonging to STs, as many as 77% and 58% in urban areas, do not possess even the most fundamental financial knowledge, leaving them susceptible to financial abuse (Singh & Singh, 2023). Regulatory authorities with a prophetic approach, political interference, internet restrictions and an ineffective project evaluation mechanism are some of the obstacles to FI in Tripura (Maity, 2019; Maity & Sahu, 2022). This study is expected to fill that caveat in the existing literature.

Socio-economic Profile of Tripura

Tripura is the third smallest state in the country, bordered by the neighbouring country Bangladesh to the south, west and north and by the states of Assam and Mizoram to the east side. It is the second most populous state in the NER of India, following Assam. The economy of Tripura is predominantly agrarian and is marked by a significant rate of poverty coupled with low per capita income, limited capital formation, infrastructural bottlenecks, geographical remoteness and communication challenges, slow industrial progression and a high degree of unemployment. Approximately 74% of the state’s population resides in the rural areas. The state has the highest literacy rate (87.22% as per the 2011 census) among all states in India, placing third after Kerala and Mizoram in 2011. The literacy rate among the tribal population of the state is likewise remarkably high. According to the Census 2011 data, 79.23% of households in the state (78.19% in rural Tripura and 81.93% in urban Tripura) were availing banking services. There were 508 branches of commercial banks in the state in 2015–2016, which increased to 547 in the year 2021–2022. The state has a total of 548 ATMs as of March 2024, with the highest number in West Tripura (309) and the lowest in Unakoti (22).

Tribal Population Profile of Tripura

The north-eastern hilly terrain state of Tripura is characterised by diversity. It houses a substantial share of the country’s tribal population. The STs make up roughly one-third of the state’s population. According to the 2011 census, the state’s ST population was 1,166,813, accounting for 31.8% of the total population. The total number of ST men was 588,327, while the number of ST women was 578,486. With 19 distinct tribal communities—Tripura/Tripuri, Riang, Jamatia, Noatia, Uchai, Chakma, Mog, Lushai, Kuki, Halam, Munda, Kaur, Orang, Santal, Bhil, Bhutia, Chaimal, Garo, Khasia and Lepcha—the state boasts a rich cultural legacy. Every tribe has a distinct culture of its own. The Tripuris are the most populous and the most dominant tribe of the aforementioned sort. Table 1 shows the decadal variation in the percentage of the ST population to the total population of the state from 1881 to 2011. The proportion of the tribal population to the total population of the state has been decreasing. While more than half of the population in the state in the 1880s and 1990s were tribal, the percentage reduced to just 31.76% in 2011. The dwindling share of the tribal population over the decades can also be observed from the line chart in Figure 1. Bhattacharjee et al. (1996) observed that due to the continued influx of non-tribal individuals, which accelerated dramatically following the partition of India, the ratio of tribal people to the overall population has experienced a significant decline since 1941, resulting in their minority status.

Table 1.

Decadal Variation in Percentage of Scheduled Tribe (ST) Population to Total Population of the State.

Year	ST Population	Total Population	Percentage of ST to Total Population
1881	49,913	95,637	52.19
1891	70,219	137,442	51.09
1901	91,672	173,325	52.89
1911	111,316	229,613	48.48
1921	171,611	304,437	56.37
1931	192,219	382,450	50.26
1941	256,966	513,010	50.09
1951	235,482	639,029	36.85
1961	360,070	1,142,005	31.53
1971	450,544	1,556,342	28.94
1981	583,920	2,053,058	28.44
1991	853,345	2,757,205	30.94
2001	993,426	3,199,203	31.05
2011	1,166,813	3,673,917	31.76

Source: Census 2011 reports.

Figure 1.

Tribal Population Profile of Tripura.

Data and Methodology

This section outlines the approach and procedures employed to investigate the level of FI among the ST population of select rural areas of Tripura. This study aims to explore the current state of FI among the ST population.

Methodological aspects have been devised corresponding to the objectives of the study. The structured schedule has been prepared with an appropriate literature review for primary data collection. The first part of the questionnaire is related to the demographic factors of respondents. The second part is related to financial awareness based on dichotomous questions corresponding to the first objective. For the fulfilment of the second objective, the questionnaire is dedicated to exploring the degree of financial service usage by the respondents. The content validity of the questionnaire has been verified in discussion with academicians in the relevant field before starting the final data collection process. From a socio-economic point of view, the study of FI among the tribal populations of the South Tripura district has significant importance because the Tripuri community people are the largest tribal community representing more than 50% of tribal communities in Tripura. Therefore, the overall economic development of the state largely depends on the development of the tribal community, which is not possible without proper FI. A representative sample has been selected based on a stratified random sampling technique to conduct the research within the given time and cost. The required primary data were collected over 6 months by qualified field investigators. A personal interview was preferred over a self-administered questionnaire, considering the low level of education and awareness among the target respondents. The study calculated the FI index based on RBI methodology. The index summarises information on various aspects of FI into a single value ranging from 0 to 100. To measure the Composite Financial Inclusion Index (CFII), we first assessed Percentage. Positive Scores (PPS), which represent the proportion of positive responses among all questions (Debi et al., 2025). We calculated the PPS separately for three key dimensions of financial inclusion: Access to financial services (Access), Usage of financial services (Use) and Quality of Financial Services (Quality) (Paul & Debnath, 2025).

Access = (Total positive responses/Total questions) \dots 1 [0 \leq Access \leq 1]

(i)

Usage = (Total positive responses / Total questions) \dots 2 [0 \leq Usage \leq 1]

(ii)

Quality = (Total positive responses / Total questions) \dots 3 [0 \leq Quality \leq 1]

(iii)

Finally, we computed the Composite Financial Inclusion Index (CFII) following the methodology proposed by the Reserve Bank of India, wherein the highest weight is assigned to usage (45%), followed by access (35%) and quality (20%). This weighting scheme reflects the theoretical understanding that mere access to financial services is insufficient unless accompanied by their effective utilization. The approach is consistent with the broader financial inclusion literature and aligns with institutional frameworks developed by the Reserve Bank of India, which emphasize outreach, penetration, and usage of financial services. The selection of indicators, such as bank account ownership, access to credit, and the use of digital financial services, is theoretically grounded in financial intermediation theory. This perspective conceptualises financial systems as facilitators of efficient resource allocation, reduction of transaction costs, and enhancement of economic participation. Therefore, following equation estimates the CFII-

CFII = 0.45 X Usage + 0.35 X Access + 0.20 X Quality

(iv)

Composite Financial Inclusion Index (CFII), which is bounded between 0 and 1. Where 1 means the highest financial inclusion and 0 means the absence of financial inclusion. A total of 408 sample households were surveyed through a random sampling technique in the South District of Tripura as per the register of residence maintained by the Government of Tripura. Descriptive statistics are used to summarise the FI status among the tribal population in the surveyed district. To investigate the difference in FI between different demographic groups, we employed two non-parametric statistical tests: the Wilcoxon rank-sum test and the Kruskal–Wallis test.

Wilcoxon Rank-sum Test

The Wilcoxon rank-sum test, also known as the Mann–Whitney U test, is a non-parametric test used to compare the distribution of a continuous variable between two independent groups. In this study, we used the Wilcoxon rank-sum test to examine the difference in FI between two demographic groups, such as males and females or rural and urban residents.

Kruskal–Wallis Test

The Kruskal–Wallis test is a non-parametric test used to compare the distribution of a continuous variable among three or more independent groups. In this study, we used the Kruskal–Wallis test to examine the difference in FI among multiple demographic groups, such as different age groups, income levels or education levels.

Purpose of the Tests

The purpose of using these tests was to determine whether there are significant differences in FI between different demographic groups. By using these non-parametric tests, we can identify whether the observed differences in FI are statistically significant, which can inform our understanding of the factors that influence FI.

The Wilkinson rank-sum test and the Kruskal–Wallis test are useful for this study because they do not require normality of the data, which is often an assumption of parametric tests. Additionally, these tests are robust to outliers and can handle skewed distributions, making them suitable for analysing FI data.

Result and Discussion

The present chapter elucidates the findings derived from the study on FI among the ST population in select rural areas of Tripura. FI, an imperative for sustainable development, is pivotal in ensuring equitable access to financial services for marginalised communities. This study aims to unravel the extent of FI among the STs in Tripura, identifying critical gaps and highlighting the socio-economic implications of such disparities.

This chapter is organised into two sections: Results and Discussion. The Results section presents the empirical data collected through surveys and interviews, offering a quantitative and qualitative analysis of FI metrics. Key indicators such as access to banking services, utilisation of financial products and awareness levels are meticulously examined. The Discussion section interprets these findings, providing a contextual analysis correlating the data with broader socio-economic trends and policy frameworks. It also explores the challenges faced by the ST population in accessing financial services and proposes recommendations for enhancing FI in these regions.

Table 2 presents a summary of the socio-demographic profile of the respondents. Our study involved collecting data from 408 respondents from the ST population of the South District of Tripura. The sample consisted of 26% female and 74% male respondents; the majority of them belong to the Hindu community to the extent of 52.21%, followed by 24.26% Buddhist and 23.53% Christian, all of whom met the inclusion criteria of having a bank account and a basic idea of banking transactions. We employed a field investigator who collected responses through personal interviews using a standard set of questions that met the set objectives. Regarding family background, 68.87% of respondents have a nuclear family, and 31.13% are from a joint family structure. Most respondents were adults between the ages of 36 and 50. They had graduated from low-income families with a monthly income limit of ₹25,000, and only 6.13% of the respondents came from households with a monthly income of more than ₹50,000. These findings provide valuable insight into the demographics of the respondents, which can be useful for policymakers and stakeholders in developing targeted programmes and services to better serve the needs of this group.

Table 2.

Respondent Profile.

Variable	Category	Number	Percentage
Gender	Female	106	26.00
Gender	Male	302	74.00
Age group	Up to 35	88	21.57
	From 36 to 50	192	47.06
	Above 50	128	31.37
Religion	Hindu	213	52.21
	Christian	96	23.53
	Buddhist	99	24.26
Education	Up to 10th	94	23.04
	From 11th to graduation	139	34.07
	Post-graduation and above	175	42.89
Family type	Nuclear	281	68.87
Family type	Joint	127	31.13
Income source	Agriculture/horticulture	136	33.33
	Daily wages	90	22.06
	Business	84	20.59
	Service	98	24.02
Income	Up to 25,000	324	79.41
	From 25,001 to 50,000	59	14.46
	Above 50,000	25	6.13

Source: Primary survey.

Table 3 outlines the compelling summary of the composite financial inclusion index (CFII). The mean value of 0.7862 strongly suggests that financial inclusion index (FII) scores are moderately high and indicate a significant level of FI among the population. The low standard deviation of 0.0979 highlights that these scores are consistent across the board, showcasing a reliable level of inclusion. Furthermore, the skewness and kurtosis values reveal a slight left skew in the distribution, which implies fewer extreme outliers and a more favourable overall trend. This analysis serves as a powerful testament to the state of FI, deserving of focused attention.

Table 3.

Descriptive Statistics of Financial Inclusion Index (FII).

Variables	Financial Inclusion Index
Mean	0.7862
Maximum	0.9550
Minimum	0.3629
Standard deviation	0.0979
Coefficient of variance	0.1246
Skewness	−0.98179
Kurtosis	1.25585

Table 4 displays the distribution of respondents according to their CFII. The distribution of respondents based on their FII levels shows that a majority experience moderate to high FI. Out of the total respondents, 50.49% fall into the moderate FII category (scores between 0.51 and 0.80), while 48.04% demonstrate high FI (above 0.80). Only 1.47% are categorised under low FII (up to 0.50), indicating a small portion with limited access to financial resources. This pattern highlights significant progress in FI, with most individuals achieving moderate to strong financial access. However, the presence of a low FII group underscores the need for targeted interventions to bridge the gap and ensure universal inclusion. These findings suggest that while FI initiatives have been effective, further efforts are required to address the needs of the most excluded segments of the population.

Table 4.

Distribution of Respondents Based on Financial Inclusion Index (FII).

Levels	Range	Number	Percentage
Low FII	Up to 0.50	6	1.47
Moderate FII	0.51–0.80	206	50.49
High FII	Above 0.80	196	48.04

Source: Primary survey.

Table 5 describes respondents’ overall FL status and differences in FL based on socio-economic backgrounds. Overall, FL among the surveyed respondents is about 78.62%. The FII, based on various demographic factors, reveals interesting trends and disparities in FI. The overall average FII is 78.62%, reflecting a generally strong level of FI among respondents. Gender differences are minimal, with males (78.69%) having slightly higher inclusion than females (78.40%), indicating near-equitable access to financial resources. Age-wise, younger individuals (up to 35 years) exhibit the highest FII at 80.37%, while the inclusion declines for older groups, with those above 50 recording the lowest FII at 77.33%. Educational attainment strongly influences FI, as respondents with post-graduate education show the highest FII (82.00%) compared to those with education up to 10th grade (73.63%), emphasising the role of education in improving FL and access. Income also correlates with FII—respondents earning above ₹50,000 achieve the highest inclusion (84.48%), while those earning up to ₹25,000 have lower inclusion (77.63%). Similarly, those in business (84.06%) and service (81.28%) sectors report higher inclusion, while agricultural workers (75.61%) and daily wage earners (75.18%) lag behind. Family type also matters, as nuclear families (81.44%) demonstrate higher inclusion than joint families (77.34%). Religious differences show Buddhists with the highest FII (79.12%), followed by Hindus (78.93%) and Christians (77.40%). These findings underscore the need for targeted policy interventions, focusing on vulnerable groups such as the less-educated, lower-income earners, older individuals and those in informal sectors like agriculture and daily wages. Enhancing access to financial services for these groups can further close the gaps and promote equitable FI for all.

Table 5.

Financial Inclusion Index (FII) Based on Demographic Factors.

Variable	Category	Number	FII (%)
Average Financial Inclusion Index			78.62
Gender	Female	106	78.40
Gender	Male	302	78.69
Age group	Up to 35	88	80.37
	From 36 to 50	192	78.67
	Above 50	128	77.33
Religion	Hindu	213	78.93
	Christian	96	77.40
	Buddhist	99	79.12
Education	Up to 10th	94	73.63
	From 11th to graduation	139	77.74
	Post-graduation and above	175	82.00
Family type	Nuclear	281	81.44
Family type	Joint	127	77.34
Income source	Agriculture/horticulture	136	75.61
	Daily wages	90	75.18
	Business	84	84.06
	Service	98	81.28
Income	Up to 25,000	324	77.63
	From 25,001 to 50,000	59	82.45
	Above 50,000	25	84.48

Source: Primary survey.

To investigate the FI difference between male and female groups, we used the Wilcoxon Rank-Sum (Mann–Whitney) Test.

Table 6 summarises results from the Two-sample Wilcoxon Rank-sum (Mann–Whitney) test, which assesses whether there is a significant difference in FI scores between male and female respondents. The Wilcoxon rank-sum test results show no statistically significant difference in FI between males and females. With a p value of .4352—well above the conventional .05 threshold—we fail to reject the null hypothesis, indicating that gender is not a determining factor in FI levels in this sample. The implication is that policy interventions or programmes aimed at improving FI may not need to be gender-targeted in this specific context, and broader socio-economic variables could be more influential in driving disparities.

Table 6.

Wilcoxon Rank-sum for Financial Inclusion Between Gender.

Group	Observation	Rank Sum	Expected
Female	106	20,871	61,759
Male	302	62,565	21,677
Unadjusted variance 1,091,075.67 Adjustment for ties −24,232.43 Adjusted variance 1,066,843.24 H₀: FII2 (Gender = 0) = FII2 (Gender = 1) z = 0.780 Probability > \|z\| = 0.4352

Note: FII: Financial inclusion index.

The Kruskal–Wallis test results (in Table 7) suggest that there is no statistically significant difference in FI levels across age groups, with a p value of .0769, slightly above the conventional threshold of .05. While this result indicates that age is not a strong differentiator in FI in this dataset, the p value’s closeness to significance hints at a potential trend worth exploring further. The implication is that although current findings do not justify age-targeted interventions, researchers or policymakers may consider conducting a more detailed analysis—possibly with a larger sample or post hoc tests—to uncover subtle age-related patterns that could inform more refined strategies.

Table 7.

Kruskal–Wallis Test for Financial Inclusion Among Age Groups.

Age Group	Observation	Rank Sum
Up to 35	88	19,917.50
From 36 to 50	192	39,282.00
Above 50	128	24,236.50
χ² = 5.131 with 2 d.f. Probability = 0.0769

The Kruskal–Wallis test (Table 8) reveals a highly significant difference in FI across education levels, with a p value of .0001, indicating that individuals with higher educational attainment—particularly post-graduates—tend to have substantially greater access to or participation in financial systems. This suggests that education is a key determinant of FI. For policymakers, the implication is clear: efforts to improve FI should go hand in hand with initiatives that promote educational opportunities, especially in underserved communities. Tailored FL programmes and integrating financial education into school curricula could help bridge inclusion gaps and empower individuals with the knowledge and confidence to engage more fully with financial services.

Table 8.

Kruskal–Wallis Test for Financial Inclusion Among Education Groups.

Group	Observation	Rank Sum
Up to 10th	94	14,716.00
From 11th to graduation	139	26,258.00
Post-graduation and above	175	42,462.00
χ² = 36.276 with 2 d.f. Probability = 0.0001 χ² with ties = 37.100 with 2 d.f. Probability = 0.0001

The Wilcoxon rank-sum test indicates a statistically significant difference in FI between nuclear and joint families, with individuals from nuclear families showing notably higher FI scores (Table 9). The p value of .0001, well below the .05 threshold, confirms the robustness of this finding. This suggests that family structure is an influential factor in financial participation—potentially due to differences in financial decision-making autonomy, household responsibilities or access to resources. For policymakers, this underscores the need to consider family dynamics when designing inclusion strategies. Tailored programmes targeting joint family settings—such as shared decision-making workshops or joint-account literacy campaigns—could help bridge the participation gap and ensure equitable access across household types.

Table 9.

Wilcoxon Rank-sum for Financial Inclusion Based on Family Type.

Group	Observation	Rank Sum
Nuclear	281	53,293.50
Joint	127	30,142.50
χ² = 14.303 with 1 d.f. Probability = 0.0002 χ² with ties = 14.628 with 1 d.f. Probability = 0.0001

The Kruskal–Wallis test (see Table 10) highlights a statistically significant difference in FI across income groups, with a p value of .0016, well below the 0.05 threshold. Interestingly, individuals in the lowest income bracket (up to ₹25,000) recorded the highest rank sum, indicating greater engagement with financial services compared to higher-income groups. This may reflect the success of pro-poor FI initiatives targeting underserved populations. The policy implication is that while lower-income individuals are benefiting from inclusion efforts, there may be a need to broaden the definition and scope of FI to better capture the financial behaviours of middle- and higher-income segments, who may rely on alternative or informal financial channels not reflected in conventional indices.

Table 10.

Kruskal–Wallis Test for Financial Inclusion Based on Income Levels.

Group	Observation	Rank Sum
Up to 25,000	324	62,832.50
From 25,001 to 50,000	59	14,480.00
Above 50,000	25	6,123.50
χ² = 12.650 with 2 d.f. Probability = 0.0018 χ² with ties = 12.937 with 2 d.f. Probability = 0.0016

Table 11 presents the result of the Kruskal–Wallis test for FI based on Income Source. The Kruskal–Wallis test results indicate that FI varies significantly across income sources, with χ² values (χ²(3) = 50.143 without ties and χ²(3) = 51.282 with ties) and extremely low p values (.0001) confirming strong statistical significance. Among the groups, individuals relying on daily wages have the lowest rank sum, indicating they are the least financially included compared to those engaged in agriculture, business or service. This suggests that income source plays a critical role in determining access to and participation in financial systems. The implication is that policymakers and financial institutions need to design targeted interventions for daily wage earners, who are more vulnerable to exclusion, by improving access to affordable financial services, promoting literacy and tailoring products to their irregular income patterns. Addressing these disparities can enhance overall FI and contribute to reducing inequality across occupational groups.

Table 11.

Kruskal–Wallis Test for Financial Inclusion based on Income Source.

Group	Observation	Rank Sum
Agriculture/horticulture	136	22,707.00
Daily wages	90	15,571.00
Business	84	22,623.00
Service	98	22,535.00
χ² (3) = 50.143 Probability = 0.0001 χ² (3) with ties = 51.282 Probability = 0.0001

The Kruskal–Wallis test results presented in Table 12 in connection with FI based on religion indicate that there are no statistically significant differences among the three groups (Hindu, Christian, Buddhist). The χ² values (χ²(2) = 1.388 without ties and χ²(2) = 1.419 with ties) are very small, and the associated p values (.4996 and .4918) are much higher than the conventional threshold of .05. This means that FI levels are broadly similar across religious groups, and religion does not appear to be a determining factor in access to or participation in financial systems. This suggests that financial institutions and policymakers should focus on socio-economic variables (such as occupation, income stability or education) that have a stronger impact. The finding reinforces the idea that FI policies should remain religiously neutral, ensuring equal access across communities while prioritising groups disadvantaged by economic or occupational factors.

Table 12.

Kruskal–Wallis Test for Financial Inclusion based on Religion.

Group	Observation	Rank Sum
Hindu	213	44,235.00
Christian	96	18,449.50
Buddhist	99	20,751.50
χ² (2) = 1.388 Probability = 0.4996 χ² (2) with ties = 1.419 Probability = 0.4918

Conclusion and Implications

FI is critical to socio-economic development, particularly for marginalised communities, such as the ST population in rural areas. It ensures that individuals and businesses within these communities have access to essential and affordable financial products and services. These services include banking, credit, insurance and digital transactions, all offered in a manner that is sustainable and beneficial to their long-term economic health.

FI plays a pivotal role in advancing social equity, empowering individuals and fortifying communities by enabling them to engage more actively in economic activities. For marginalised groups, especially the ST population, FI serves as a vital element to mainstream economic systems, offering them expanded opportunities for savings, investment and access to credit. Furthermore, examining FI in rural areas is essential because the rural population contributes significantly to overall economic development and productivity.

The present study provides valuable insights into various aspects of FI, such as FL and the usage of financial services. It focuses particularly on the South Tripura district, analysing responses from individuals in rural settings. The study’s findings indicate that FI among the population is approximately 78.62%. The level of FI varies across different age groups, with younger respondents demonstrating higher FI compared to older respondents. Moreover, there is an absence of significant disparities in FI based on gender and religion. This is particularly encouraging, given that the RBI Report, 2023, highlights a trend where females typically possess lower FL levels compared to their male counterparts. The equal levels of FL observed among both male and female populations within the tribal communities of South Tripura lead to an overall higher level of FI in the region. In addition, the FII scores recorded in the study are moderately high, indicating a substantial degree of FI within the population. The findings reveal that the majority of respondents exhibit moderate to high levels of FI.

To conclude, this study highlights the multifaceted nature of FI among the tribal community in South Tripura, emphasising its significance not only for economic advancement but also for social empowerment and community development. Continued efforts to enhance FL and promote access to financial services will be critical in bridging the economic divide and fostering inclusive growth in these marginalised populations. Government and policymakers should prioritise the enhancement of effective FI programmes, with a particular focus on digital banking, mobile money services and microfinance options. Improving digital infrastructure and lowering transaction costs can further increase accessibility. However, it is important to recognise that the study’s findings may not fully represent the broader population. Future research could expand the scope by including diverse communities across different regions, which would allow for a more comprehensive understanding of FI challenges and opportunities.

Social Implications

The study’s findings on FI among the ST population of select rural areas of Tripura have several social implications: The study’s findings help increase financial awareness and literacy among the ST population, enabling them to make informed decisions about their financial resources. Understanding the FI needs of the ST population, efforts can be made to improve their financial management skills and practices. By promoting FI, the study’s findings contribute to improved livelihoods and economic well-being among the ST population. The study’s findings help address financial exclusion and promote FI among the ST population, reducing social and economic inequalities. By understanding the FI needs of the ST population, efforts can be made to promote inclusive growth and development. The study’s findings help empower marginalised communities, including the ST population, by providing them with access to financial services and promoting FL. By addressing financial exclusion and promoting FI, the study’s findings contribute to promoting social justice and equality.

Footnotes

Declaration of Conflict of Interests

The authors declare no potential conflict of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship and/or publication of this article: The authors acknowledge the grant received under the scheme of Studies on the Culture, History and Geography of the STs of India (Award Letter No. 02/69/2022-23/OBC/TS/RP dated 3 August 2023) from Indian Council of Social Science Research (ICSSR), New Delhi, India.

ORCID iD

Pranesh Debnath

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