PRE-BUDGET 2026 STARTUP EXPECTATIONS & CREDIT ACCESS: A Case Study of India’s Startup Ecosystem and Policy Gaps

 CASE-CUM-RESEARCH PAPER

PRE-BUDGET 2026 STARTUP EXPECTATIONS & CREDIT ACCESS:
A Case Study of India’s Startup Ecosystem and Policy Gaps

 

1. Abstract

India—now the world’s third-largest startup ecosystem, hosting 1.57 lakh+ registered startups and 117 unicorns—faces persistent constraints in early-stage funding, regulatory compliance, and operational incentives. Based on an ANI report (Jan 12, 2026), industry leaders from EV mobility, health, and HR call for sales-linked subsidies, expanded credit, tax incentives and GST threshold hikes.

This study integrates policy analysis of CGTMSE, MUDRA, and PMEGP with sector trends, credit data, and failure rates to assess the policy-market gap limiting startup growth and survivability. Findings reveal structural constraints in loan processing, regulatory overload, and insufficient innovation-linked financing.

Keywords: Startups, CGTMSE, MUDRA, PMEGP, Budget 2026, GST Threshold, Innovation Policy, MSME Financing, EV, Ecosystem Growth

 

2. Introduction

India’s startup boom reflects entrepreneurial momentum supported by digital adoption, demographic dividends, and policy frameworks such as Startup India, angel tax removal (2024), and Fund of Funds (Rs 10,000 crore corpus).

Yet, large segments struggle with:

• Capital access
• Regulatory burden
• Skill shortages
• High mortality rates (90% by year 10)

This paper analyses gaps between existing policy instruments and startup expectations ahead of Budget 2026.

 

3. Case Overview — ANI Interview Analysis

The ANI report captures perspectives from:

• Bharath Krishna Rao (Emobi): EV sales-linked manufacturing subsidies
• Abhinav H. Rao Kuchipudi (ParentVerse): Grants for impact & women-led ventures
• Devashish Sharma (Taggd): Employer-supported skilling + labor reform
• Avinash Deshmukh (iThrive): GST threshold ↑ to ₹1 crore
  These voices reflect systemic barriers across finance, taxation, skills & scale-up infrastructure.

 

4. Existing Schemes: Features vs Shortcomings

Table 1: Government Schemes & Startup Fit

Scheme	Key Features	Startup Limitations
CGTMSE	75–90% credit guarantee, loans to Rs 5 crore	Banks still seek collateral; slow approvals
MUDRA	Collateral-free loans up to Rs 10 lakh	Insufficient for R&D or scaling
PMEGP	15–35% subsidy for first-time entrepreneurs	Rural bias; heavy documentation
Fund of Funds	Rs 10,000 crore corpus via VC/PE	Mostly supports later-stage startups

Despite 60 lakh accounts worth Rs 3.55 lakh crore under CGTMSE, early-stage startups rarely benefit due to execution bottlenecks.

 

5. Ecosystem Challenges

1. Funding slowdowns
   2025 early-stage funding fell to approx. $10.5–11B, with 17–39% deal reduction
2. Failure risk
• 10% fail Year 1
• 45% fail Year 5
• 90% fail Year 10
3. Regulatory hurdles
   GST threshold (₹20 lakh service) burdens low-revenue startups
4. Skill and labor issues
   Four new labor codes exist, but skilling and employer offsets lag
5. Geographic imbalance
   75% capital flows to six metros, leaving tier 2–3 dry

 

6. Policy Expectations & Their Impact Potential

Demand	Expected Impact
Sales-linked subsidies for EV & hardware	Boost deep-tech manufacturing
Grant support for women-led & impact firms	Address inclusion + mortality rates
Employer-funded skilling incentives	Job readiness & gig workforce protection
GST threshold to ₹1 Cr	Reduce friction for micro-firms

These align strongly with Viksit Bharat 2047 goals.

 

7. Research Questions

1. How effectively do CGTMSE, MUDRA, and PMEGP address early-stage financial gaps?
2. Is GST exemption threshold a statistically significant barrier to startup survival?
3. Do sales-linked subsidies and skilling incentives improve scale-up performance?

Hypotheses

• H1: Access to collateral-free credit significantly increases startup survivability.
• H2: Raising GST threshold reduces compliance costs and improves profitability.
• H3: Non-loan grants outperform loans for tech/impact startups at seed stage.

 

8. Methodology

A mixed-methods approach:

1. Policy Analysis — Scheme guidelines, corpus utilization, timelines
2. Quantitative Analysis —
   Data sources: MCA21, DPIIT, CBIC, SIDBI, Invest India, DPIIT Startup Hub
• Loan sanction/approval ratio
• Portfolio NPAs
• City-wise & sector-wise adoption
3. Qualitative Interviews — founders, bankers, incubators
4. Comparative Study — US SBA loan guarantees, UK SEIS/EIS tax schemes

 

9. Key Metrics for Scheme Evaluation

CGTMSE

• Approval Rate = Loans Sanctioned / Loans Applied
• Time-to-Disbursement
• First-time borrower share
• Survival rate after 3 years

MUDRA

• Ticket size distribution (Shishu/Kishore/Tarun)
• Urban vs rural uptake
• Tech/startup share vs traditional MSME share

PMEGP

• Project completion rate
• Subsidy utilization %
• Employment generated per loan

Startup Ecosystem Health

• Seed vs Growth funding ratio
• Tier-3 geography penetration
• Patent filing intensity
• Women-led founder share

 

10. Findings & Discussion

• Design is strong; execution is weak
  Collateral-free policies fail when banks demand collateral anyway.
• Early-stage space remains under-served
  Small ticket sizes (<₹10 lakh) do not fit capital needs of tech hardware, biotech, and EV ventures.
• Compliance burden outweighs benefit
  GST filing, audit rules, and city permits raise operating costs.
• Skill mismatch persists
  Labor reform without skilling results in paperwork gains, not productivity gains.

Additional Analysis

Comparative Table: Indian Startup Ecosystem (2021–2025)

Year	Total Funding (Approx USD)	Deals (Approx)	Trend vs Prior Year	Startups Recognised
2021	$82 B (peak ecosystem boom)	~1,500+	Strong growth	~60,162 (20121)
2022	$129 B	~1,700+	Major boom	~86,704 (2022)
2023	~$45 B (sharp drop)	~800	Funding winter	~112,718 (2023)
2024	~$11.3 B	~1,448 (31%↓)	Stabilised, selective	~127,433+ (2024)
2025	~$10.5–11 B	~936–1,518*	Slight decline	~1.8–2.0 L+ startups

📌 Note:

• Some reported 2025 deal counts vary across sources (Inc42 notes 936+ deals; Tracxn/Reddit reports ~1,518 rounds with stage splits).
• Startup recognition numbers grew sharply — crossing ~1.8 lakh by mid-2025 and 2 lakh by end-2025.

 

📈 Trend Analysis (2021–2025)

1. Funding Lifecycle

• 2021–22: Exceptional boom driven by global VC influx, high valuations, and pandemic-era digital shift.
• 2023: Market correction/funding winter saw a sharp downturn (~65% drop from 2022).
• 2024–25: Funding stabilised at lower but sustainable levels (~$10–11B), with investors increasingly selective and focused on later-stage and quality growth opportunities.

Implication: Post-boom correction reflects global risk realignment — capital is less abundant but more discerning.

 

2. Deal Evolution

• 2021–22: High volume, broad sector coverage.
• 2023: Deal count halved alongside the funding drop.
• 2024–25: Deal counts recovered moderately but remain below peak, indicating fewer, larger and more selective rounds.

 

3. Ecosystem Expansion

• Despite funding volatility, startup creation/recognition accelerated, with ~2 lakh+ recognitions by end-2025 — almost a tripling from early decade counts.
• Women-led startup representation is rising and approaching near parity in registrations.

 

📊 Key Insight Patterns

➡️ Funding Downturn

• 2022 peak followed by a steep fall → a new “funding normal” around $10–11B by 2025.
• Equity funding suffers most; seed funding sharply declines while early-stage shows resilience — investors favor proven units.

Ecosystem Impact: Capital scarcity tightens runway, pushing startups toward efficiency and revenue discipline.

 

➡️ Deal Quality Over Quantity

• 2024 onwards shows fewer deals but larger individual sizes, especially in later stages and deep tech sectors.
• Seed funding contraction signals early stage risk aversion.

Policy Angle: Government incentives on early-stage risk capital could be essential — addressing this is part of the Budget 2026 demands.

 

➡️ Startup Count Growth vs Funding

• Startup formation continues even as funding resets — indicating entrepreneurial momentum > capital availability.
• This divergence highlights a financing gap for emerging ventures.

Policy Gap: Current schemes (CGTMSE, MUDRA, PMEGP) haven’t fully bridged seed and early-stage financing needs.

 

📌 Implications for CGTMSE / MUDRA / PMEGP (Expanded Analysis)

(Indicative, to integrate with funding trends above)

Credit Access and Startup Funding

• With funding dry up at seed and early stages, internal debt facilities like CGTMSE & MUDRA become more critical but are underutilised due to bank risk aversion and administrative friction.
• Startup leaders pushing Budget 2026 to expand sales-linked subsidies and threshold limits are responding to this market credit gap even as private funding contracts.

Consequence: Without streamlined credit schemes, many startups struggle to get bridging finance when private capital is constrained.

 

📌 Summary: 5-Year Comparative Analysis

Dimension	2021–22 Peak	2023 Crash	2024–25 Reset
Funding Volume	Very High	Very Low	Moderate, Selective
Deal Count	High	Low	Recovering
Startup Count	Growing	Growing	Rapid Growth
Private Risk Capital	Easy	Tight	Selective & quality-focused
Financing Gaps	Present	Widened	Persisting

 

11. Policy Recommendations

1. Fast-track guarantee approvals within 30 days
2. Automatic collateral acceptance under CGTMSE
3. GST threshold to ₹1 Cr for first 3 years
4. R&D + impact startup grant fund
5. Startup credit scoring using GST + UPI + cash flows
6. Tier-2/3 incubator funding priority

 

 

12. Conclusion

India is at an inflection point: policy intent is strong, but last-mile delivery is failing founders. Redesigning credit flows, simplifying compliance and linking subsidies to innovation, not collateral, can scale India’s transformation from a startup hub into a global innovation leader.

 

13. Teaching Notes

Learning Objectives

• Understand startup financing constraints in emerging markets
• Evaluate policy execution vs design
• Develop policy alternatives using evidence

Suggested Class Activities

1. Group debate: Raise GST threshold—Yes/No?
2. Data analytics: Use CGTMSE disbursement data to rank states
3. Policy simulation: Draft a new “Startup Credit Acceleration Scheme”

Discussion Questions

1. Why do collateral-free schemes still fail to reach eligible startups?
2. Do subsidies distort market discipline or encourage innovation?
3. Should India prioritize grants, loans, or equity funding for startups?

Assessment Task
Students submit a 1,500-word proposal recommending a Budget 2026 intervention with policy logic + measurable KPIs.

 

14. Suggested Data Sources

• DPIIT Startup India portal
• MCA-21 filings
• RBI & SIDBI reports
• CGTMSE annual credit reports
• CBIC GST analytics dashboard
• Startup Genome / Tracxn / Inc42 funding databases
• SEBI/IFSC regulatory filings
• World Bank Ease-of-Doing Business datasets