Digital-Credit Readiness of Street Vendors under PM SVANidhi: A Survey of Mumbai Suburban Markets (BMS Project)

Digital-Credit Readiness of Street Vendors under PM SVANidhi is a Sem-6 BMS research project (the Mumbai University 'black book') that surveys about 200 street vendors across four suburban Mumbai markets — Sion–Matunga, Kurla (LBS Marg), Ghatkopar and Mulund — to measure how ready they are to move from cash-box borrowing to formal, digitally accessed credit.

The policy hook is live. PM SVANidhi, the Ministry of Housing and Urban Affairs' scheme for street vendors, gives collateral-free working-capital loans in tranches (first tranche enhanced to ₹15,000, second to ₹25,000, third ₹50,000, per the Cabinet restructuring of August 2025), a 7% interest subsidy for timely repayment, cashback on digital transactions, and a new UPI-linked RuPay PM SVANidhi credit card with a ₹30,000 limit for vendors who repay the second tranche. Street vending itself is protected and regulated by the Street Vendors Act, 2014.

But a credit card and a UPI rail only matter if the vendor can actually use them. This project measures, vendor by vendor: scheme awareness and loan status, current borrowing (wholesaler udhaar, moneylender, SVANidhi tranche), digital-payment usage, a six-item digital-literacy checklist, perceived barriers (documentation, bank visits, fear of apps), and a composite digital-credit readiness score.

Analysis in Excel and IBM SPSS Statistics covers descriptives, chi-square (market × awareness), one-way ANOVA (literacy across age groups), Pearson correlation, multiple regression on the readiness score, and a binary logistic regression on credit-card willingness. The output is a market-wise readiness map with practical suggestions for the banks and self-help groups that digitise vendor lending.

Mumbai's street vendors sit at the sharp end of financial exclusion: their income is daily and cash-heavy, their working capital comes largely from wholesaler credit and informal lenders, and their legal identity as vendors — the certificate of vending under the Street Vendors Act, 2014 — only recently became a bridge to formal credit through PM SVANidhi. The scheme now offers enhanced tranches (₹15,000 / ₹25,000 / ₹50,000), a 7% interest subsidy, digital-transaction cashback and a UPI-linked RuPay credit card for vendors who repay the second tranche.

Yet loan sanction and digital usage are not the same as digital-credit readiness. A vendor may accept UPI payments but distrust banks, hold a smartphone but be unable to complete a KYC journey, or be eligible for the next tranche but unaware of it. No current study maps this readiness at market level in Mumbai's suburbs, and generic awareness camps treat all vendors alike.

This project therefore asks: how ready are suburban Mumbai's street vendors to borrow digitally under PM SVANidhi, what determines that readiness, and what specifically blocks the next step — awareness, literacy, documentation fear or borrowing habit? The answer defines where banks, SHGs and the Town Vending Committee should spend their effort first.

Abstract

This project studies whether street vendors in four suburban Mumbai markets are ready to borrow formally through digital channels under PM SVANidhi — the government's collateral-free working-capital scheme for street vendors, restructured in August 2025 with enhanced tranches and a UPI-linked RuPay credit card. A structured survey of ~200 vendors produces a readiness score analysed in SPSS through chi-square, ANOVA, correlation, multiple regression and logistic regression.

Introduction

Street vending is Mumbai's most visible informal occupation and a legally protected one: the Street Vendors Act, 2014 requires cities to survey vendors, issue certificates of vending and form Town Vending Committees. PM SVANidhi then sits on top of that legal identity, converting the certificate into a bank relationship — collateral-free tranches, interest subsidy, digital cashback and, since the 2025 restructuring, a UPI-linked credit card for vendors who move up the tranche ladder.

The scheme's success therefore depends on a chain: identity → bank account → loan → digital repayment capacity. Each link can fail at the counter — a vendor may have a certificate but fear documentation, have a bank account but no smartphone fluency, or accept UPI payments all day yet never consider borrowing digitally. Which link actually fails, and for whom, is what this study measures.

Existing system / literature gap

• Financial-inclusion research in India is usually measured at state or national level (RBI's National Strategy for Financial Inclusion targets access, usage and quality) — not at the level of one occupation on one street.
• Micro-credit studies focus on SHGs and MUDRA borrowers; street vendors appear mainly in livelihood and eviction studies, rarely in credit-readiness studies.
• The UPI-linked PM SVANidhi credit card is too new for published survey evidence; this project produces first-hand local data on willingness to adopt it.

The proposed study

A descriptive cross-sectional survey of ~200 vendors in four suburban markets, selected to mix railway-station footfall (Sion, Kurla, Ghatkopar) with residential catchments (Mulund). The bilingual questionnaire uses pictorial aids so literacy is never a barrier. Measured constructs: scheme awareness, tranche status, borrowing sources, UPI usage, digital literacy (6-item checklist), perceived barriers, and a composite readiness score. Hypotheses are tested in SPSS: chi-square (market × awareness), ANOVA (digital literacy across age bands), Pearson correlation (UPI maturity × readiness), multiple regression on readiness, and logistic regression on willingness to take the credit card. Findings are written up as a market-wise readiness map plus recommendations for banks, SHGs and the local Town Vending Committee.

Feasibility

• Technical: Excel and SPSS handle every test; Google Forms with offline paper backup survives field realities.
• Economic: travel by suburban train plus printing — under ₹2,000.
• Operational: vendors answer during non-peak hours in Marathi or Hindi in 10–12 minutes; the form collects no names, phone numbers or ID numbers, only a serial and market code.

In scope

• Street vendors with a fixed or semi-fixed pitch in four suburban Mumbai markets: Sion–Matunga, Kurla (LBS Marg), Ghatkopar and Mulund (~200 respondents).
• PM SVANidhi as the formal-credit channel: awareness, tranche status, repayment experience, digital repayment behaviour and willingness to take the UPI-linked credit card.
• Current informal borrowing (wholesaler udhaar, moneylenders, self-help groups) as reported by vendors.
• Statistical analysis of one survey wave in Excel and SPSS: descriptives, chi-square, ANOVA, correlation, multiple regression and one logistic regression.

Out of scope

• Vendors without any fixed or semi-fixed pitch (mobile hawkers moving across wards) — sampling them reliably needs a different design.
• Other micro-credit channels (MUDRA, SHG bank-linkage economics) beyond their role as current borrowing sources.
• borrowers' actual bank statements or credit-bureau data — nothing is collected from banks.
• Any collection of names, phone numbers, Aadhaar, PAN or vending-certificate numbers; the questionnaire uses serial numbers and market codes only.

Research design: descriptive, cross-sectional survey with hypothesis testing. Unit of study: the individual vendor's stall; unit of analysis: the questionnaire record.

Sampling: market-stratified systematic sampling. Four markets are purposively chosen to mix station footfall and residential catchments; within each market, stalls are numbered block-wise and every nth stall is approached until the quota (~50 per market, n ≈ 200) is met. One response per stall; vendors below 18 years excluded.

Instrument: a bilingual (English/Marathi) structured questionnaire with pictorial aids for every construct: Section A vendor profile (age band, gender, years vending, certificate-of-vending status); Section B borrowing sources and weekly credit need; Section C PM SVANidhi awareness and tranche status (aided recall with a one-page pictorial scheme card); Section D UPI usage and the six-item digital-literacy checklist (receives UPI payment, shows QR, checks bank SMS balance, pays a bill online, recharges phone via app, saves a contact's UPI ID); Section E barriers and willingness (documentation fear, bank-visit avoidance, app fear, interest willingness) on 5-point Likert scales, feeding the readiness score.

Pilot: 15 vendors at a non-sample market (Dadar) to check comprehension, timing and Cronbach's alpha for the Likert batteries (target ≥ 0.7).

Hypotheses: H1: market and awareness are associated (chi-square). H2: digital-literacy means differ across age bands (ANOVA). H3: UPI-usage maturity correlates positively with readiness (Pearson). H4: readiness is predicted by awareness, literacy, borrowing source and documentation fear (multiple regression). H5: the odds of credit-card willingness rise with tranche status and literacy (binary logistic regression).

Ethics: verbal informed consent in Marathi/Hindi; anonymity; right to skip; no identifiers; aggregate-only reporting.

The study runs as one pipeline from legal identity to a readiness map. The Street Vendors Act supplies the vendor's formal identity; PM SVANidhi converts that identity into a credit relationship; the survey measures whether vendors can actually climb that ladder digitally.

flowchart TD
  A["Street Vendors Act 2014: certificate of vending"] --> B["PM SVANidhi: tranches, subsidy, cashback, credit card"]
  B --> C["Research gap: readiness unknown at market level"]
  C --> D["Bilingual questionnaire with pictorial aids"]
  D --> E["Pilot 15 vendors; Cronbach alpha >= 0.7"]
  E --> F["4 suburban markets, systematic sample n = 200"]
  F --> G["Excel: coding, composite scores"]
  G --> H["SPSS: chi-square, ANOVA, correlation"]
  H --> I["Multiple regression: readiness score"]
  I --> J["Logistic regression: credit-card willingness"]
  J --> K["Market-wise readiness map"]
  K --> L["Actions for banks, SHGs and Town Vending Committee"]

Readiness framework

flowchart LR
  A["Scheme awareness"] --> R["Digital-credit readiness score"]
  B["Digital literacy (6 items)"] --> R
  C["UPI-usage maturity"] --> R
  D["Documentation fear"] --> R
  E["Tranche status"] --> R
  R --> W["Willingness: UPI-linked credit card (logistic)"]

Measurement notes. The readiness score is the mean of five Likert items (would apply online, comfortable with e-KYC, trusts SMS/app banking, would repay digitally, would recommend the scheme), reverse-coded where needed and checked for internal consistency in the pilot. The logistic model reports odds ratios with 95% confidence intervals, so each claim in the conclusions chapter maps to a test an examiner can rerun from the raw data.

• Panel design: repeat the survey six months after the credit-card rollout to move from stated willingness to observed adoption.
• Ward-level extension: add two island-city markets (Dadar, Colaba) to compare suburban and island-city readiness.
• Enumerator experiment: alternate pictorial-aid and text-only versions to measure how much the aids themselves change responses.
• Qualitative layer: ten vendor interviews on repayment anxiety and household cash flows to explain the barriers the survey only quantifies.
• Bank-side view: a short structured interview study with branch officers on SVANidhi appraisal bottlenecks to pair demand-side and supply-side evidence.