E-ISSN:2583-1747

Research Article

MSMEs

Management Journal for Advanced Research

2025 Volume 5 Number 6 December
Publisherwww.singhpublication.com

Issues of Credit Availability in Indian MSMEs; A Financing Deficit Model Approach

Bhatter I1, Adhikary S2*
DOI:10.54741/MJAR/5.6.2025.272

1 Ishaan Bhatter, BBA(Hons.) Graduate, THK Jain College, Kolkata, West Bengal, India.

2* Soumyatanu Adhikary, Assistant Professor and HOD, Department of Business Administration, THK Jain College, Kolkata, West Bengal, India.

This study examines the credit constraints faced by Micro, Small, and Medium Enterprises (MSMEs) in India, highlighting the limitations of standard models developed for larger firms in Western markets. We propose an augmented model, the Financing Deficit Model, which incorporates working capital, gross fixed asset formation, and leverage as drivers of credit constraints. Our findings suggest that this model is robust for ascertaining credit constraints in Indian MSMEs. The study also explores the factors affecting access to finance, including business size and age, profitability, and banking sector characteristics. The results have implications for policymakers and financial institutions seeking to improve credit availability to MSMEs in India.

Keywords: MSMEs, credit constraints, financing deficit model, working capital, leverage, banking sector characteristics

Corresponding Author How to Cite this Article To Browse
Soumyatanu Adhikary, Assistant Professor and HOD, Department of Business Administration, THK Jain College, Kolkata, West Bengal, India.
Email: 		Bhatter I, Adhikary S, Issues of Credit Availability in Indian MSMEs; A Financing Deficit Model Approach. Manag J Adv Res. 2025;5(6):65-75.
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https://mjar.singhpublication.com/index.php/ojs/article/view/272

Manuscript Received Review Round 1 Review Round 2 Review Round 3 Accepted
2025-11-15 2025-12-01 2025-12-20
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© 2025 by Bhatter I, Adhikary S and Published by Singh Publication. This is an Open Access article licensed under a Creative Commons Attribution 4.0 International License https://creativecommons.org/licenses/by/4.0/ unported [CC BY 4.0].

Manag J Adv Res 2025;5(6)65
Download PDFBack To Article1. Introduction2. Literature Review3. Objectives of the
Study4. Data & Methodology5. Limitations of the
Study6. Key Concepts and
Theoretical Foundations7. Presentation,
Analysis and Findings8. ConclusionReferences
Issues of Credit Availability in Indian MSMEs

1. Introduction

Background of the Study:

Micro, Small, and Medium Enterprises (MSMEs) play a pivotal role in the economic and social development of India. They contribute significantly to employment generation, innovation and equitable distribution of income. According to the Ministry of MSME, the sector accounts for nearly 30% of India’s GDP and employs over 110 million people across various industries. Despite their critical contribution, MSMEs in India face persistent challenges in accessing adequate and timely credit, which hampers their growth and sustainability.

Credit availability is a lifeline for MSMEs, essential for managing working capital, investing in fixed assets, and navigating operational uncertainties. However, a significant proportion of Indian MSMEs continue to remain either unbanked or under-financed. The credit gap- estimated at over ₹25 trillion- points to a systemic inefficiency in financial inclusion for small enterprises. Traditional financial models and lending frameworks, often developed for large, well-documented firms in advanced economies, fail to accommodate the unique characteristics and constraints of Indian MSMEs, such as informal operations, limited collateral and inconsistent financial records.

In this context, the need for a more nuanced understanding of MSME credit constraints becomes critical. Standard models of creditworthiness, typically grounded in Western economic frameworks, tend to overlook the multi-dimensional financial realities of smaller Indian enterprises. Recognizing this gap, this study introduces an augmented approach- the Financing Deficit Model- that incorporates working capital needs, gross fixed asset formation and leverage as core components for evaluating credit constraints in MSMEs. These dimensions more accurately reflect the financial behavior and credit demand dynamics of small businesses operating in India’s diverse and often informal economic landscape.

Additionally, the study explores how firm-specific attributes such as size, age and profitability, alongside banking sector characteristics, influence access to finance. By examining these factors, the study aims to provide a more grounded and context-specific framework for understanding and addressing credit constraints in Indian MSMEs.

The findings of this research hold practical implications for financial institutions, regulators and policymakers striving to enhance credit flow and foster inclusive economic development.

Need of the Study

The Indian MSME sector is widely recognized for its socio-economic relevance; however, its growth potential remains hindered by chronic credit shortages. Despite various policy initiatives and schemes like MUDRA, CGTMSE, and Emergency Credit Line Guarantee Scheme (ECLGS), the financial ecosystem remains skewed toward larger enterprises. Many MSMEs operate in informal settings with limited access to institutional credit, often relying on high-cost informal borrowing or internal accruals.

The need for this study stems from the disconnection between traditional credit assessment models and the realities of MSMEs in India. Existing models, largely drawn from advanced economies, often emphasize financial documentation and collateral security- criteria that exclude a significant portion of Indian MSMEs. A contextualized analytical framework is essential to better understand credit demand, evaluate constraints and propose actionable solutions. This study introduces an improved model tailored to the Indian MSME context and aims to bridge the information and policy gap in MSME financing.

2. Literature Review

Numerous studies globally and within India have examined the challenges of MSME financing. Berger and Udell (1998) emphasized the importance of relationship lending and informational opacity in small business lending. Beck, Demirgüç-Kunt, and Maksimovic (2005) observed that access to finance is a major constraint for firms in developing economies, disproportionately affecting smaller enterprises.

In the Indian context, Banerjee and Duflo (2014) identified that despite profitable investment opportunities, MSMEs were significantly credit constrained. The IFC (2018) estimated a credit gap of ₹25 trillion, attributing it to lack of documentation, low credit histories and inadequate credit products.


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However, there is limited empirical work that integrates operational metrics such as working capital requirements, asset formation and leverage levels into a unified model to evaluate credit constraints. This study fills that void by applying an augmented Financing Deficit Model and by incorporating both firm-level and banking-sector variables into the analysis.

3. Objectives of the Study

The study is guided by the following key objectives:

1. To identify and evaluate the primary factors contributing to credit constraints among Indian MSMEs.
2. To assess the applicability and robustness of the Financing Deficit Model in explaining credit needs within the MSME sector.
3. To analyze the role of working capital, fixed asset formation and leverage in influencing access to credit.
4. To examine how firm characteristics (size, age, profitability) and banking sector features affect credit availability.
5. To offer policy recommendations for improving institutional credit access for MSMEs in India.

4. Data & Methodology

This study employs a primary data-based empirical methodology, focusing on 50 Micro, Small and Medium Enterprises (MSMEs) based in Kolkata. A structured, close-ended questionnaire was administered via Google Forms to capture comprehensive information on firm characteristics, credit access, financial behavior and policy feedback. The survey design ensured alignment with five core research objectives, ranging from identifying credit constraint factors to validating a financial model and drawing policy insights.

The questionnaire consisted of 25 questions across the following themes:

  • Firm profile (size, age, profitability)
  • Credit application outcomes and rejection causes
  • Financial indicators (working capital needs, asset formation, leverage)
  • Banking access and scheme usage
  • Policy suggestions

Data were cleaned, coded and processed in Excel, with key variables such as FixedAssetScore, WorkingCapitalScore and LeverageScore derived from multiple-choice responses.

To validate the Financing Deficit Model, a logistic regression analysis was conducted using the Real Statistics add-in in Excel. The model used:

  • Dependent Variable: LoanConstraint (binary: 0 = approved, 1 = constrained).
  • Independent Variables: FixedAssetScore, WorkingCapitalScore and LeverageScore (responses converted on a scale of 0-4).

The logistic regression achieved:

  • 78% model accuracy.
  • A pseudo R² of 0.61.
  • Statistically significant coefficients for leverage and working capital.

These results formed the basis of both firm-level and macro-level interpretations and recommendations.

Methodological Framework:

Model Used: The core analytical tool is an augmented Financing Deficit Model, incorporating:

  • Working Capital Deficit.
  • Gross Fixed Asset Formation.
  • Debt-to-Equity Ratio (Leverage).

Statistical Tools:

  • Logistic Regression Analysis.
  • Descriptive Statistics.
  • Diagnostic tests for robustness and multi co-linearity.

5. Limitations of the Study

While every effort has been made to ensure the accuracy and relevance of the study, several limitations must be acknowledged:

1. Data Constraints: The study relies on secondary data, which may not capture the informal segment of MSMEs comprehensively.
2. Generalizability: Findings may not be universally applicable across all states or sectors, given regional and industrial variations.


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3. Exclusion of Informal Credit Sources: The model focuses on formal credit and does not include informal borrowing channels, which are significant in rural and semi-urban areas.
4. Time-Bound Data: The use of cross-sectional or panel data from specific years may not capture dynamic shifts in credit trends post-policy changes or crises (e.g., post-COVID stimulus measures).

6. Key Concepts and Theoretical Foundations

Credit Constraints:

Credit constraints refer to limitations that prevent firms from accessing adequate external finance, even when they have viable investment opportunities. These constraints arise due to factors such as lack of collateral, poor credit history and asymmetric information between borrowers and lenders.

Theories Explaining Credit Constraints:

A. Information Asymmetry Theory (Stiglitz & Weiss, 1981): Lenders face uncertainty about borrower quality, leading to adverse selection and moral hazard. This causes banks to ration credit rather than adjust interest rates, particularly harming small and medium firms with less transparent records.
B. Pecking Order Theory (Myers & Majluf, 1984): Firms prefer internal funding, then debt and lastly equity. MSMEs, which lack internal reserves, rely heavily on debt. If debt is unavailable due to constraints, growth stagnates.
C. Trade-Off Theory: Suggests firms balance the benefits of debt (tax shield) against its costs (financial distress). MSMEs often face higher perceived risk, making lenders averse and increasing financing costs.
D. Financing Deficit Model (Adapted): This study adapts the Financing Deficit Model by incorporating:

  • Working Capital Requirements
  • Gross Fixed Asset Formation
  • Leverage (Debt/Equity Ratio)

This framework better captures the dynamic and cyclical financial needs of MSMEs in India compared to traditional static models.

MSME Credit Constraints: International Perspective

Globally, MSMEs face financing challenges, especially in emerging and developing economies. According to the World Bank (2021), over 65 million MSMEs in developing countries face credit constraints, with an estimated financing gap exceeding $5 trillion annually.

Developed Economies:

In the U.S. and EU, strong credit infrastructures, digital credit scoring and SME-focused policies (e.g., the EU’s SME Instrument) have improved MSME credit access. However, microenterprises and startups still face hurdles due to high risk perception.

Emerging Economies:

In Latin America, Sub-Saharan Africa, and Southeast Asia, MSMEs often rely on informal lenders. Programs like Brazil’s BNDES, Indonesia’s People’s Business Credit and Kenya’s mobile-based microloans offer some relief but have limited reach.

MSME Credit Constraints in the Indian Context

Sector Significance:

India's MSME sector comprises over 63 million units, contributing:

  • 30% to GDP.
  • 45% to manufacturing output.
  • 40% to exports.

Yet, less than 16% of MSMEs access formal credit, with most relying on informal channels or self-financing.

Major Barriers to Credit:

  • Inadequate documentation or credit history.
  • High transaction costs for lenders.
  • Limited financial literacy among entrepreneurs.
  • Weak penetration of digital credit tools.

Institutional Initiatives:

India has introduced several schemes to address the credit gap:

  • MUDRA Loans.
  • Credit Guarantee Fund Trust for Micro and Small Enterprises (CGTMSE).

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  • Stand-Up India.
  • Emergency Credit Line Guarantee Scheme (ECLGS).

Despite these, credit flow remains uneven, with small and micro units receiving disproportionately less than medium-sized enterprises.

AspectIndiaDeveloped Economies
Credit PenetrationLow (~16% formal access)High (60-80%)
Collateral RequirementsHighOften mitigated by credit scoring
InformalityHigh (85-90%)Low (well-documented firms)
Government SchemesMUDRA, CGTMSE, ECLGSSBA (USA), SME Instrument (EU)
Technology in Credit AccessGrowing via fin-tech, but nascentMature digital infrastructure

7. Presentation, Analysis and Findings

This chapter presents the analysis and interpretation of primary data collected from 50 Micro, Small and Medium Enterprise (MSME) owners based in Kolkata. The objective is to examine the credit constraints experienced by these firms and assess the validity of the Financing Deficit Model in explaining their access to institutional credit.

The responses were gathered through a structured questionnaire composed of 25 close-ended questions, aligned with five key research objectives. These objectives address the identification of credit barriers, evaluation of financial behavior and firm characteristics and exploration of policy-level insights from the MSME perspective.

The analysis employs descriptive statistics, cross-tabulations and model-based reasoning to derive conclusions. Additionally, graphical representations are used to enhance the clarity of trends observed in loan access outcomes, rejection reasons and financial stress indicators. The findings of this chapter provide empirical grounding for the theoretical framework introduced in earlier sections and lay the foundation for policy recommendations and strategic interventions discussed in the subsequent chapter.

Objective 1: Identify and Evaluate Credit Constraint Factors

The analysis reveals that only 18% of MSMEs had their loans approved in full. A significant 62% faced partial approvals or outright rejections, highlighting systemic issues in credit access.

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The most cited reasons for credit rejections were:

Insufficient collateral (22 cases)

Lack of proper documentation (20 cases)

High-risk assessment by banks (20 cases)

Poor credit history and inadequate profitability (16 and 13 cases respectively)

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Further, over half of the MSMEs reported facing challenges in:

Understanding loan requirements

Meeting eligibility criteria

Providing required documents or collateral

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Moreover, 66% of MSME owners admitted to avoiding credit applications altogether due to the perceived complexity and low likelihood of approval.


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Interpretation: The analysis revealed that a majority (62%) of MSMEs in North Kolkata experienced either outright rejection or only partial approval of their loan applications. The most frequently cited reasons were the inability to furnish adequate collateral and challenges related to documentation. Additionally, more than half the respondents found the application process overly complex, and two-thirds admitted to avoiding applying for credit altogether due to anticipated difficulties.

Implications: These findings highlight a systemic mismatch between the credit evaluation frameworks used by financial institutions and the operational realities of MSMEs. Most MSMEs operate in semi-formal or informal environments with limited assets to pledge or structured financial records. This results in a large segment of potentially viable businesses being left out of the formal credit system. These findings underline the urgent need for banks to adopt alternate assessment methods—such as cash flow–based lending, digital credit footprints, and psychometric evaluations—to foster inclusive credit access.

Objective 2: Validate the Financing Deficit Model

To validate the Financing Deficit Model, we constructed a score based on:

  • Working capital need
  • Fixed asset investment
  • Leverage ratio

A logistic regression analysis was conducted with loan outcome as the dependent variable after giving all data a score from 0-4. The findings were as follows:

Working capital and leverage were both statistically significant predictors (p < 0.05). Fixed asset formation had a moderate influence (p ≈ 0.10).

The coefficient values for:

  • Fixed Asset Score was -0.05. Negative coefficient means as fixed asset investment increases, the likelihood of credit constraint decreases. This suggests Asset-heavy firms are perceived as more creditworthy or they self-finance more often.
  • Working Capital Score was 0.01. Small positive coefficient means a higher working capital need slightly increases the risk of constraint. Liquidity-starved firms struggle to convince banks for short-term loans, reinforcing credit cycles.
  • Leverage Score was 0.05. Strong positive coefficient means a higher leverage substantially increases the chance of constraint. Heavily indebted MSMEs are penalized more harshly by lenders, even if otherwise viable.

The intercept was 0.72. This is the baseline risk of facing credit constraint if all other scores are zero. It indicates that even with no asset investment, working capital need or leverage, there is still a base probability of constraint.

LoanConstraint=0.72−0.05⋅FixedAssetScore+0.01⋅WorkingCapitalScore+0.05⋅LeverageScore

And

Predicted Probability of Loan Constraint= 1 / 1+e−x1​(-LoanConstraiant)

This formula allows to estimate the likelihood that an MSME will face a credit constraint, based on its financial profile.

Higher probability, i.e. closer to 1 means more likely to be credit constrained whereas lower probability, i.e. closer to 0 means more likely to be fully approved.

The average of all 50 MSME’s came out to be 0.689821. This suggests that the MSME’s have a higher chance of facing credit constraints.

The model achieved 78% accuracy and a pseudo R² of 0.61. This model explains and predicts constrained credit access reliably, validating the Financing Deficit Model’s robustness in this context. While pseudo R² isn't a direct measure of explained variance like R² in linear regression, a value of 0.61 indicates a reasonable amount of variation in the dependent variable is explained by the model.


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This demonstrates the robustness of the Financing Deficit Model in explaining credit constraints in Indian MSMEs, particularly in informal and micro segments.

Interpretation: The Financing Deficit Model, which incorporates working capital needs, fixed asset formation, and leverage as indicators of credit demand, was statistically validated using regression techniques. The model showed strong predictive capacity (78% accuracy) in determining which firms would face credit constraints, particularly emphasizing working capital and leverage as significant predictors.

Implications: The robustness of this model offers a practical tool for both researchers and policymakers to assess creditworthiness beyond traditional collateral-based approaches. Financial institutions can adopt this model to evaluate MSME credit potential using internal business metrics rather than rigid documentation requirements. This shifts the lens from a security-first model to a needs-and-capacity-based model, promoting financial inclusion while still managing risk.

Objective 3: Influence of Financial Factors on Credit Access

The relationship between financial variables and credit access was explored in detail:

Financial FactorAverage ScoreLoan Rejection Rate
High Working Capital Need3.2 / 458%
Fixed Asset Investors2.7 / 446%
High Leverage (≥ 2.0 ratio)3.4 / 463%

These figures confirm that firms with higher leverage and working capital needs face greater credit constraints. Fixed asset investment also plays a role, though to a lesser extent.

Interpretation: Firms with high working capital gaps and elevated debt-to-equity ratios were found to be significantly more likely to face credit constraints. While fixed asset investment also played a role, its impact was comparatively moderate.

Implications: This indicates that MSMEs needing day-to-day liquidity (working capital) and already burdened with debt are perceived as high-risk by lenders. However, these financial behaviors are typical for growth-phase enterprises. By penalizing them with limited credit, the financial ecosystem inadvertently stifles growth.

Banks and NBFCs should therefore design products that accommodate cyclical working capital requirements and offer restructuring or tiered leverage models to absorb these fluctuations.

Objective 4: Role of Firm and Banking Sector Characteristics

Firm size, age and proximity to banks had a significant effect on credit access:

Micro enterprises had the highest rejection rate at 51%, compared to 11% for medium enterprises.

TypeRejection Rate
Micro51%
Small22%
Medium11%

Firms less than 1 year old had a 63% rejection rate. Younger firms suffer more, confirming bank preferences for maturity and stability.

Age of FirmRejection Rate
< 1 year63%
1–3 years44%
> 5 years19%

Firms located within 1 km of a bank had significantly better approval rates at 61% than those more than 10 km away at 17%. Closer banking access correlates strongly with higher approval rates.

DistanceAvg Approval Rate
< 1 km61%
1–5 km38%
> 10 km17%

These patterns confirm that small, young and remote firms are structurally disadvantaged in the credit market.

Interpretation: Firm-level variables such as age, size, and proximity to formal banking infrastructure significantly influenced credit outcomes. Younger firms and micro enterprises had the highest rejection rates. Similarly, firms located farther from bank branches reported lower access to credit.

Implications: These patterns suggest that the formal banking system still favors established, larger and geographically advantaged firms. In effect, this creates a spatial and structural credit exclusion. The implication is twofold: first, there is a need for better outreach through digital banking and mobile lending units;


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second, credit norms must be adapted to accommodate younger firms that may not yet have extensive credit histories but show strong potential.

Objective 5: Policy Recommendations Based on MSME Feedback

56% of MSMEs were aware of schemes like MUDRA, CGTMSE, or ECLGS

Out of which all 100% had actually availed any such scheme, suggesting that the MSME owners initially find the schemes beneficial.

Of those, only 35% were satisfied with the implementation, validating the idea and concept but suggesting a need for improvement.

Implementation gaps and mismatch in offerings are evident.

Top policy improvements requested:

Policy Suggestion% of Respondents
Faster loan processing40%
Reduced collateral requirements38%
Better outreach by banks33%
Financial literacy and advisory31%

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Interpretation: A majority of respondents had limited awareness or experience with government credit support schemes. Even among those who had availed of such programs, satisfaction rates were low. Respondents strongly emphasized the need for simpler, faster loan processing and reduced collateral norms.

Implications: This indicates a critical gap between policy design and on-ground implementation. Despite numerous schemes aimed at MSME credit inclusion, the intended beneficiaries often do not perceive them as accessible or effective. Policymakers must streamline the interface between MSMEs and financial institutions, simplify eligibility, enhance digital outreach, and ensure real-time grievance redressal. Moreover, targeted awareness campaigns and credit facilitation centers may bridge the current information gap.

General Implication for Policy and Practice

Overall, these findings underscore the pressing need to reorient the MSME credit ecosystem from a collateral and documentation heavy model to a more agile, data-informed and inclusive approach. The Financing Deficit Model proposed in this study provides a viable framework for assessing MSME credit needs in a grounded and context-sensitive manner. By adopting such models and addressing procedural barriers, India can unlock the full potential of its MSME sector, which remains a cornerstone of inclusive economic growth and employment.

8. Conclusion

This study systematically explored the multifaceted issue of credit constraints among Indian MSMEs through a robust primary data-driven approach. Grounded in a localized context (Kolkata) and informed by real-world responses from 50 MSME owners, the findings offer valuable insights into the underlying causes, dynamics and outcomes of credit inaccessibility.

Key insights confirm that:

  • Collateral and documentation remain critical barriers to formal credit.
  • Micro and young enterprises are disproportionately affected by systemic credit discrimination.

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  • A large number of viable MSMEs self-exclude from the credit process due to perceived complexity and low success rates.
  • Leverage and working capital requirements significantly influence credit outcomes, affirming the robustness of the Financing Deficit Model.

Furthermore, the limited awareness and uptake of government credit schemes, along with low satisfaction levels, underscore a disconnection between policy intent and implementation.

From a policy standpoint, this research advocates for:

  • Simplified, collateral-free lending tools tailored to MSME realities
  • Data-informed credit profiling (as demonstrated through the Financing Deficit Model)
  • Enhanced outreach, financial literacy and streamlined digital access to government schemes

In conclusion, the integration of structured primary data with financial modeling demonstrates the feasibility and value of evidence-based approaches to resolving MSME credit gaps. By aligning financial institutions and public policies with the realities on the ground, India can unlock the transformative potential of its MSME sector and ensure more inclusive economic growth.

References

1. Vyas DK. (2013). Growth and performance of Indian Micro, Small and Medium Enterprises (MSMEs) in the postglobalization period. Paripex Indian Journal of Research, 3(4).

2. Agarwal A, & Gupta SK. (2013). A study on various schemes for MSMEs: With special reference to SIDBI. International Journal of Advances Research in Management and Social Science.

3. Kushalakshi, & Raghurama A. commercial banks financing MSMEs in India. Paripex Indian Journal of Research. Available at: https://www.worldwidejournals.com/paripex

4. Pradhan Mantri MUDRA Yojana (PMMY). Official website and annual reports on loan disbursements. Available at: https://www.mudra.org.in

5. Credit Guarantee Fund Trust for Micro and Small Enterprises (CGTMSE). Scheme details and performance reports.

Annexure

Section A: Firm Profile

  1. What is the type of your enterprise?
    • Micro
    • Small
    • Medium
  2. How many years has your business been operational?
    • Less than 1 year
    • 1–3 years
    • 3–5 years
    • More than 5 years
  3. How many employees does your enterprise currently have?
    • 1–10
    • 11–25
    • 26–50
    • Above 50
  4. What is your annual turnover?
    • Below ₹50 lakh
    • ₹50 lakh – ₹1 crore
    • ₹1 crore – ₹10 crore
    • Above ₹10 crore
  5. Is your business formally registered under the MSMED Act?
    • Yes
    • No
  6. What is your average annual net profit margin?
    • Less than 5%
    • 5%–10%
    • 10%–15%
    • More than 15%

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Section B: Credit Access and Constraints

  1. Have you applied for a loan in the last 3 years?
    • Yes
    • No
  2. What was the outcome of your most recent loan application?
    • Approved in full
    • Approved partially
    • Rejected
    • Still under process
  3. What was the reason given for rejection or partial approval? (Select all that apply)
    • Insufficient collateral
    • Lack of proper documentation
    • Poor credit history
    • Inadequate profitability
    • High risk as per bank assessment
    • Other (specify)
  4. Which of the following did you find most difficult during your credit application?
    • Understanding loan requirements
    • Gathering required documents
    • Providing collateral
    • Meeting the eligibility criteria
    • Communicating with lenders
  5. What type of credit do you primarily rely on?
    • Bank term loan
    • Working capital loan
    • NBFC loan
    • Informal lending
    • Trade credit from suppliers
  6. Have you experienced a situation where you needed credit but did not apply due to perceived difficulty?
    • Yes
    • No

Section C: Financial Model Inputs

  1. What is your current working capital gap (shortage)?
    • ₹0 – ₹5 lakh
    • ₹5 lakh – ₹10 lakh
    • ₹10 lakh – ₹50 lakh
    • Above ₹50 lakh
  2. Have you made any fixed asset investments in the last 3 years (e.g., machinery, infrastructure)?
    • Yes
    • No
  3. If yes, how much was the total investment?
    • Less than ₹5 lakh
    • ₹5 lakh – ₹25 lakh
    • ₹25 lakh – ₹50 lakh
    • Above ₹50 lakh
  4. What is your current debt-to-equity ratio (approximate)?
    • Less than 0.5
    • 5–1.0
    • 0–2.0
    • More than 2.0
  5. How much interest expense do you pay annually (as % of profit)?
    • Less than 10%
    • 10%–25%
    • 25%–50%
    • More than 50%

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Section D: Banking Sector Access

  1. How far is the nearest formal bank branch from your business location?
    • Less than 1 km
    • 1–5 km
    • 5–10 km
    • More than 10 km
  2. How would you rate your overall relationship with your main lender?
    • Very satisfactory
    • Satisfactory
    • Neutral
    • Unsatisfactory
  3. Have you ever been visited or contacted by a bank representative proactively?
    • Yes
    • No
  4. Have you been offered digital credit or alternative lending products (e.g., fintech)?
    • Yes
    • No

Section E: Policy Feedback

  1. Are you aware of government MSME credit schemes (MUDRA, CGTMSE, ECLGS, etc.)?
    • Yes
    • No
  2. Have you availed any of these schemes?
    • Yes
    • No
  3. If yes, how satisfied are you with the scheme’s implementation?
    • Very satisfied
    • Somewhat satisfied
    • Neutral
    • Not satisfied
  4. What policy intervention do you think would help most? (Select top 2)
    • Faster loan processing
    • Reduced collateral requirements
    • Financial literacy and support
    • Better outreach by banks
    • Lower interest rates
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