In the bustling markets of Guwahati, amid the cacophony of honking rickshaws, street-side tea stalls, and hand-pulled carts, lies the engine that keeps the city’s economy running its informal workforce. These workers street vendors, daily wage labourers, domestic helpers, public transport operators, and countless others contribute immensely to the urban economy, yet remain largely invisible in formal planning and policy discourse. What they all share in common is not just the absence of formal contracts or steady income, but a daily negotiation with financial uncertainty.

Across urban India, informal employment has become not just widespread but entrenched. It is marked by unregulated working conditions, an absence of job security, and little to no access to basic social protections like health insurance, pensions, or paid leave (ILO, 2018; NSSO, 2020). For cities like Guwahati often viewed as a gateway to the wider Northeast this reality is especially pronounced. The region’s socio-political complexity, coupled with underinvestment in formal economic infrastructure, has left a large segment of its urban population with no choice but to operate in the informal sector. For many, informal work isn’t a stepping stone it’s the destination.

Yet this form of employment, while flexible and immediate, is inherently fragile. Most informal workers survive on narrow margins. Their incomes fluctuate unpredictably. A single day off due to illness, family emergency, or political shutdown can derail the month’s budget. This chronic instability feeds what economists describe as financial fragility a condition where individuals or households are highly vulnerable to economic shocks because they lack savings, carry high levels of debt, or depend on income sources that are uncertain and irregular (Guérin et al., 2013; Lusardi et al., 2011).

But here's the thing: the lack of financial stability isn’t just about low income. It's compounded by exclusion from formal credit systems. Walk into any bank in Guwahati and ask what’s needed for a loan: permanent address proof, salary slips, IT returns, formal ID, sometimes even a guarantor. Now imagine a daily wage labourer who has none of those things who changes rented rooms every few months, earns in cash, pays no taxes, and can’t provide a payslip. These systemic entry barriers shut the door on thousands of workers, regardless of their repayment ability or intent (Rutherford, 2000; Demirguc-Kunt & Klapper, 2013).

With nowhere else to turn, they resort to informal financial arrangements borrowing from moneylenders, neighbours, shopkeepers, or savings groups. While these sources are faster and less bureaucratic, they often come with exploitative terms: high interest, social pressure, and no legal recourse in case of disputes (Collins et al., 2009). The result is a vicious cycle where financial distress feeds informality, and informality perpetuates financial distress.

In this context, Guwahati becomes more than just a case study it becomes a mirror. A city where modern infrastructure and digital payments coexist with manual labour and paper ledgers. Where state-backed financial inclusion schemes often fail to reach the people who need them most not because the schemes are flawed, but because they aren't designed with the realities of informal life in mind.

This study, therefore, sets out with a clear purpose: to investigate the impact of financial fragility and credit access barriers on urban informal employment in Guwahati, Assam. But it’s not just about diagnosing a problem. It’s about understanding how these two forces fragility and exclusion interact to shape the choices, constraints, and coping mechanisms of informal workers.

Specifically, it seeks to answer the following questions:

• How prevalent is financial fragility among informal workers in urban Guwahati?
• What are the key structural, procedural, and psychological barriers that hinder their access to formal credit?
• How do financial fragility and credit constraints influence the persistence, quality, and precariousness of informal employment?

To address these questions, the study sets out two primary objectives:

1. To comprehensively profile financial fragility among Guwahati’s urban informal workers capturing patterns in income stability, debt cycles, savings behavior, financial stress, and coping strategies.
2. To critically examine the barriers to accessing formal financial services including issues of documentation, collateral, literacy, institutional trust, and awareness of government schemes.

In doing so, the study hopes to do more than generate academic insights. It aims to bring visibility to a workforce often left out of policy narratives, and to lay the groundwork for practical, context-sensitive recommendations that can enable more secure, empowered, and financially resilient lives.

Because behind every informal worker in Guwahati is a story not just of hardship, but of ingenuity, survival, and quiet endurance. And if public policy is to matter, it must first learn to listen.

Profile of Urban Informal Workers in Guwahati

Socio-economic and Demographic Characteristics The urban informal workforce in Guwahati represents diverse socio-economic and demographic backgrounds. Respondents in this study included street vendors, public vehicle operators (auto-rickshaw, taxi, bus drivers), daily wage labourers, and domestic helpers (ILO, 2018; NSSO, 2020). The demographic profile revealed a substantial representation of individuals aged between 25 and 50 years, with a significant proportion supporting dependent family members (Chen, 2012). Gender-wise, the workforce comprised predominantly males, although female participation was notably substantial, particularly among domestic helpers and street vendors (Bhowmik, 2010). Education levels were varied, ranging from illiteracy to secondary school completion, with minimal representation of higher education (Planning Commission of India, 2014).

Occupational Structure and Earnings The occupational structure among informal workers in Guwahati indicated a dominance of street vending and daily wage labor, followed by public transport services and domestic work (ILO, 2018). Earnings varied considerably across occupations, with public vehicle operators generally reporting higher daily incomes relative to street vendors and domestic helpers. Daily wage labourers faced the greatest income volatility, often resulting from inconsistent employment opportunities (NSSO, 2020). Overall, respondents indicated highly variable income streams, often insufficient to support consistent household financial planning or savings (Collins et al., 2009).

Existing Sources of Credit and Coping Mechanisms Informal workers relied extensively on informal sources of credit, including moneylenders, relatives, and friends, due to barriers to formal financial institutions (Guérin et al., 2013). Credit was primarily utilized for immediate consumption needs, health emergencies, children's education, and small-scale business investments. Moneylenders were often preferred for their ease of access and lack of stringent documentation requirements, despite high-interest rates (Rutherford, 2000). Additionally, respondents employed various coping strategies, such as reducing household expenditures, borrowing informally, or temporarily diversifying their income sources, to manage financial shocks (Collins et al., 2009).

This chapter provides foundational insights necessary to understand the financial behaviours, vulnerabilities, and credit access challenges faced by informal workers in Guwahati. These profiles set the stage for a detailed examination of financial fragility and credit access barriers explored in subsequent chapters.

Informal employment remains a foundational yet precarious component of urban economies in developing countries. Although it provides livelihood opportunities, it is largely characterized by economic insecurity, lack of social protections, and limited upward mobility (ILO, 2018; NSSO, 2020). In India, informal occupations span a broad spectrum—from street vending and domestic work to transport and construction—each with unique risks and coping mechanisms (Chen, 2012; Bhowmik, 2010).

Financial fragility is widely acknowledged as a key vulnerability among informal workers. Lusardi et al. (2011) define it as the inability to absorb financial shocks due to inadequate savings, irregular income, or excessive debt. Guérin et al. (2013) and Collins et al. (2009) highlight that informal workers often operate outside the reach of formal financial systems, relying instead on informal credit networks that are costly and insecure. Recent Indian studies echo these concerns—Kavya and Yadav (2024) document the shift from savings to credit-driven consumption among low-income households, while RBI (2023) and NITI Aayog (2022) reports highlight regional disparities in financial inclusion, particularly in the Northeast.

Barriers to formal credit access have been consistently flagged across empirical studies. Demirguç-Kunt and Klapper (2013) and Rutherford (2000) point to stringent documentation requirements, low financial literacy, and limited institutional trust as key obstacles. State-level data from Assam’s Economic Survey (2023) also underscores how digital exclusion and lack of banking penetration continue to marginalize urban informal workers from formal credit systems.

Despite a growing body of literature on informal employment and financial vulnerability, significant gaps remain. Most research has focused either on large metro cities or rural India, with limited empirical insights from the Northeast. Furthermore, behavioral segmentation of financial fragility—how different types of informal workers cope, adapt, or fall through the cracks—remains underexplored.

This study addresses these gaps by applying a multidimensional framework to assess the intersection of financial fragility, credit exclusion, and informal work in Guwahati—offering both granular segmentation and regional relevance.

The present study adopts a descriptive and analytical research design to investigate financial fragility and barriers to credit access among urban informal workers in Guwahati, Assam. A mixed-methods approach, combining quantitative and qualitative methodologies, was utilized to comprehensively explore the complexity and multifaceted nature of informal employment, financial behaviours, and credit accessibility (Creswell & Creswell, 2017).

Target Population and Sampling Technique The target population for this study comprised informal workers including street vendors, public vehicle operators (auto-rickshaw, taxi, bus drivers), daily wage workers, and domestic helpers across Guwahati. To ensure broad socio-economic representation, Guwahati was divided geographically into four primary zones—North, South, East, and West—and a multistage stratified random sampling technique was employed to select a representative sample of 400 respondents across these occupational groups and zones. Primary data were collected over a three-month period, from November 2024 to February 2025, through both face-to-face structured surveys and semi-structured qualitative interviews administered by trained enumerators.

Sample Size Determination The sample size of 400 respondents was determined using Cochran’s formula for sample size estimation, ensuring statistically valid findings at a 95% confidence level and a 5% margin of error (Cochran, 1977). This sample size aligns with similar empirical studies on financial behavior and informality within urban settings, thereby enhancing the generalizability and reliability of the study's findings.

Data Collection Primary data collection involved structured surveys and semi-structured qualitative interviews. The structured survey included demographic details, financial behavior indicators (savings, debt levels, financial shocks), and perceived barriers to formal credit access. The qualitative interviews provided detailed narratives regarding participants' financial management practices, experiences with credit access, and coping mechanisms during financial shocks. The survey instrument underwent a pilot test, achieving a Cronbach's Alpha score of 0.84, indicating high internal consistency.

Analytical Framework Quantitative data analysis employed descriptive statistics, cross-tabulations, and regression analysis to investigate relationships between financial fragility, credit access barriers, and informal employment conditions using SPSS software. Prior to detailed analysis, assumption tests were conducted including the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy (>0.6) and Bartlett’s Test of Sphericity (p < 0.05), confirming data suitability for further statistical analysis.

Ethical Considerations All ethical guidelines were strictly followed, including obtaining informed consent, maintaining respondent anonymity, ensuring confidentiality of data, and clearly communicating study objectives and participant rights. Participants had the right to withdraw from the study at any stage without consequence.

Analysis and Findings

Descriptive Analysis

Descriptive statistics were applied to the responses collected from 400 informal workers in Guwahati to examine key financial indicators such as saving habits, access to credit, coping strategies, and perceived stress. The mean scores for saving behavior ranged from 2.6 to 3.1, suggesting a weak to moderate saving tendency. Standard deviation values (1.1–1.3) indicated high variability among workers, reflecting the diverse nature of informal employment.

For items measuring credit access (e.g., “I feel confident applying for a bank loan”), mean scores were consistently low (2.2–2.8), confirming widespread exclusion from formal financial services. On financial stress (e.g., “I feel anxious about my financial future”), the mean exceeded 3.6 with a median of 4, revealing persistent economic anxiety among a majority of respondents.

The responses used the entire range of the Likert scale (1 to 5), with interquartile ranges mostly between 2 and 4. This shows a balanced distribution, allowing for meaningful segmentation and regression analysis.

Cross-Tabulation Analysis

Cross-tabulations were performed between income groups, occupation types, and key financial behavior variables:

• Street vendors and domestic workers reported the highest incidence of informal borrowing (over 70%), primarily due to ineligibility for formal loans.
• Public transport workers were more likely to access semi-formal sources like NBFCs and cooperatives, especially for vehicle-related financing.
• Daily wage earners reported the lowest financial literacy and highest stress levels—82% felt unprepared for a financial emergency.

Income was strongly correlated with credit behavior. Workers earning below ₹10,000/month had near-zero formal credit access and heavily relied on friends, family, or moneylenders. Female workers reported higher levels of stress and lower access across all income brackets.

Regression Analysis: Predictors of Financial Fragility

To identify significant predictors of financial fragility, a multiple linear regression was conducted with financial fragility as the dependent variable (measured as a composite index from Likert-scale items).

Independent variables included:

• Access to formal credit (dummy coded: 1 = Yes, 0 = No)
• Monthly income
• Financial literacy level
• Employment type (categorical: wage labor, street vending, etc.)
• Coping strategy index (frequency of borrowing, expense cuts, side jobs)

Model Summary:

• R² = 0.497, Adjusted R² = 0.489
• F(5, 394) = 77.62, p < 0.001

Interpretation:

The results of the multiple linear regression analysis reveal that lack of access to formal credit is the most significant predictor of financial fragility among urban informal workers in Guwahati. With a standardized beta coefficient of 0.421 and a highly significant p-value (p < 0.001), it indicates that individuals who are excluded from formal credit systems are substantially more vulnerable to financial shocks. Monthly income is also a strong predictor, with a negative beta (–0.336), suggesting that higher income reduces financial fragility. Similarly, financial literacy plays a critical role; those with greater financial knowledge are less likely to exhibit fragile financial behavior (β = –0.284, p < 0.001). The analysis further shows that wage-based informal workers are significantly more fragile than their self-employed counterparts, likely due to the instability and lack of control associated with such employment. Lastly, while the coping strategy index shows a modest negative relationship with financial fragility (β = –0.112), its significance implies that while informal coping strategies such as borrowing or reducing expenses may temporarily cushion financial shocks, they are not sufficient to provide long-term financial resilience. Overall, the model underscores the compounded effects of structural exclusion, income insecurity, and limited financial knowledge in deepening financial fragility in the informal workforce.

Factor Analysis: Latent Dimensions of Financial Behavior

Interpretation: The rotated component matrix from the factor analysis identifies three distinct and interpretable dimensions underlying the financial behavior of urban informal workers in Guwahati.

Factor 1: Financial Fragility captures the day-to-day financial vulnerability faced by workers. Items with high loadings under this factor include difficulties in meeting basic household expenses, income disruptions from even short absences at work, frequent borrowing, and the need to delay essential expenses like bills or meals. These indicators reflect the precarious nature of informal employment, where even minor shocks can severely destabilize household finances.

Factor 2: Credit Access Barriers reflects systemic and psychological obstacles preventing informal workers from engaging with formal financial institutions. High-loading items include the absence of bank accounts, rejection due to lack of documentation, discomfort or fear when approaching banks, and limited understanding of credit procedures. This factor highlights not just physical exclusion but also a deep-seated lack of confidence and familiarity with formal credit systems.

Factor 3: Informal Coping Mechanisms reveals how workers navigate financial distress outside formal frameworks. Key behaviours include borrowing from personal networks, cutting down on essential expenses, taking up extra jobs, or selling household assets. These strategies indicate resilience and resourcefulness but also point to the absence of structured safety nets and sustainable solutions.

Together, these three factors present a comprehensive picture: informal workers are not only financially fragile but also structurally excluded from formal credit and heavily reliant on informal, short-term coping strategies. Addressing only one of these dimensions—say, financial literacy—without also tackling systemic access barriers and underlying fragility would leave large gaps in intervention efforts.

Cluster Analysis: Segmenting Informal Workers

Using k-means clustering on the factor scores, respondents were grouped into three clusters:

Cluster Interpretation:

• Cluster 0 – Stressed but Adaptive: High fragility, high coping, relatively informed but still excluded.
• Cluster 1 – Excluded but Stable: Structurally blocked from access, but stable due to savings or community networks.
• Cluster 2 – At-Risk and Unsupported: Moderate fragility, minimal coping. These workers are silently slipping into deeper insecurity.

Scatter plots and cluster-wise bar charts confirmed clear behavioral separation across groups.

Interpretation and Discussion

The findings of this study reveal a deeply stratified and nuanced financial landscape among urban informal workers in Guwahati. While informal employment is often viewed as a homogenous sector, the results demonstrate significant variability in financial experiences, access to credit, and coping capacities. The segmentation of respondents into distinct clusters—based on financial fragility, credit access barriers, and coping mechanisms—highlights the heterogeneity of vulnerabilities within the informal economy.

The regression analysis reinforces the central role of formal financial exclusion in shaping economic insecurity. Among the identified predictors, lack of access to formal credit emerged as the most significant contributor to financial fragility, followed closely by low income and limited financial literacy. These findings point to a structural bottleneck: despite targeted financial inclusion schemes, the informal workforce continues to remain excluded due to documentation barriers, institutional mistrust, and procedural complexity.

Importantly, while some respondents—particularly in Cluster 0—exhibited resilience through informal coping strategies such as borrowing from personal networks or taking on multiple jobs, these mechanisms are neither scalable nor sustainable. Cluster 2, characterized by moderate fragility and minimal coping capacity, appears particularly vulnerable to financial shocks and long-term instability. The silent deterioration observed in this group calls for urgent policy attention.

These results align with earlier research by Collins et al. (2009) and Lusardi et al. (2011), which documented the cyclical nature of poverty and financial exclusion. However, this study adds regional specificity by focusing on Northeast India—an area often underrepresented in national-level datasets. In doing so, it underscores the importance of geographically tailored interventions that respond to local constraints and behaviours, rather than relying solely on national financial inclusion templates.

Ultimately, the study calls for a shift from generalized interventions to differentiated, cluster-based policy design aimed at reducing financial fragility across diverse informal worker segments.

This study examined the impact of financial fragility and credit access barriers among informal workers in Guwahati, revealing the multidimensional and systemic nature of their economic vulnerability. Findings indicate that financial fragility is shaped by a combination of irregular income, inadequate savings, debt dependence, and limited access to formal financial institutions. Among all predictors, lack of access to formal credit emerged as the most significant contributor to financial fragility, followed by income insecurity and low financial literacy.

The segmentation analysis further demonstrates that the informal workforce is not a homogenous group. While some workers exhibit resilience through informal coping strategies, others—particularly those with minimal support systems—face growing financial instability. This diversity of experience highlights the need for differentiated and context-sensitive interventions.

Policy responses must move beyond conventional financial inclusion models. It is essential to design adaptive mechanisms such as simplified KYC norms, acceptance of alternative documentation, and promotion of community-based finance systems like Self-Help Groups (SHGs). For the most vulnerable groups, immediate support through emergency credit, insurance schemes, and welfare integration is crucial.

Moreover, financial fragility must be understood as a socio-economic issue that intersects with mental health, trust in institutions, and broader systemic exclusion. Addressing these concerns requires coordination among financial institutions, local governments, and social organizations.

In conclusion, financial inclusion should not be reduced to mere access to bank accounts or credit. Rather, it must encompass a broader commitment to building institutional trust, resilience, and sustainable livelihood pathways for those engaged in informal work. Recognizing and addressing the segmented realities of Guwahati’s informal sector is vital for fostering inclusive urban growth and long-term economic security.

Practical Implications

The findings of this study have several important practical implications for policymakers, financial institutions, and development practitioners seeking to improve the financial resilience of urban informal workers. First, a segmented approach to financial inclusion is essential—different worker clusters face different constraints and require tailored interventions. For example, Cluster 1, which is structurally excluded but financially stable, could benefit from low-documentation microcredit products, while Cluster 2, which is more fragile and unsupported, urgently needs access to emergency safety nets and insurance schemes. Second, there is a pressing need to simplify access protocols by modifying KYC requirements to accept alternative documentation such as utility bills, UPI transaction histories, or informal work records, enabling wider participation in formal financial systems. Third, promoting community-based finance models, such as informal savings groups and Self-Help Groups (SHGs), can create safe, trust-based financial spaces for excluded populations; government recognition and support of these models can help scale them more effectively. Fourth, integrating financial literacy with mental health services through urban wellness centers would offer holistic support, addressing not just financial ignorance but the stress and anxiety that often accompany financial precarity. Finally, aligning public policy with existing digital payment infrastructure, such as UPI and BPME schemes, can enable pre-approved, Aadhaar-linked microloans for informal workers, helping them transition into formal credit ecosystems without bureaucratic hurdles. Collectively, these interventions can bridge the gap between financial vulnerability and resilience, enabling informal workers to build stability, plan for the future, and contribute more securely to the urban economy.

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