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New GDP series (base year 2022–23)

22 Mar 2026 GS 3 Economy
New GDP series (base year 2022–23) Click to view full image

Introduction

  • A new GDP series with base year 2022–23 has been released in the public domain.

  • Released on February 27, 2026 by the Ministry of Statistics and Programme Implementation (MoSPI).

  • Provides GDP estimates and related aggregates for:

    • 2022–23

    • 2023–24

    • 2024–25

  • Objective:

    • To provide a more accurate and realistic picture of the Indian economy

    • To overcome limitations of the outdated 2011–12 base year

GDP size (current prices, ₹ lakh crore)

  • 2022–23 → 261.18

  • 2023–24 → 289.84

  • 2024–25 → 318.07 (first revised estimate)

  • These estimates are 3–4% lower than earlier estimates based on the old series

Sectoral composition (GVA, 2024–25)

  • Primary sector 21.4%

  • Secondary sector 25.8%

  • Tertiary sector 52.9%

Key observation:

  • Tertiary sector dominates the economy

  • Structural pattern consistent with a service-led economy

Growth trends (Manufacturing sector)

  • Real GVA growth:

    • 2023–24 → 12.7%

    • 2024–25 → 9.3%

  • Indicates strong industrial performance (>9%) in both years

Expenditure side (GDP)

  • Private Final Consumption Expenditure (PFCE) share ≈ 56%

  • Applies to both:

    • Current prices

    • Constant prices

  • Indicates consumption-driven nature of Indian economy

Major methodological refinements

1. Treatment of multi-activity enterprises

  • GVA now apportioned across different activities

  • Based on revenue share data (MGT 7/7A)

  • Earlier: Entire GVA assigned to single dominant activity

2. Adjustment for non-reporting companies

  • Use of blown-up factorat:

    • Industry × size class level

  • Based on paid-up capital

  • Captures contribution of:

    • Active companies that did not file returns

3. Expanded corporate coverage

  • Inclusion of Limited Liability Partnerships (LLPs)

  • Data source: Ministry of Corporate Affairs (MCA)

4. Household sector estimation

  • GVA estimated as:

    • GVAPW (ASUSE) × number of workers (PLFS)

    • GVA per worker (GVAPW)

  • Uses:

    • Annual Survey of Unincorporated Sector Enterprises (ASUSE)

    • Periodic Labour Force Survey (PLFS)

  • Earlier method:

    • Base year GVA extrapolated using indicators

Improvements in estimation techniques

1. Real GVA estimation

  • Expanded use of:

    • Double deflation

    • Volume extrapolation

  • Aligns estimates with international standards

2. PFCE estimation

  • Benchmark estimates for 2022–23 derived using:

    • Household Consumption Expenditure Survey (HCES 2022–23)

  • Particularly for:

    • Widely consumed items

    • Low income elasticity goods

Challenges in the new GDP series

1. Private corporate sector issues

  • GVA compiled using MCA database

  • Problem:

    • Data available only at enterprise level

    • Difficult to allocate GVA across States

  • Affects:

    • Gross State Value Added (GSVA) estimation

2. Limitations of ASI data

  • Earlier method:

    • State-wise GVA allocated using ASI shares

  • New method:

    • Uses ASI + GST data

Major issue:

  • Inadequate ASI frame

Evidence:

  • MCA companies (manufacturing): 135,802

  • ASI factories: 67,649

Implication:

  • ASI-based shares may:

    • Not reflect reality

    • Distort State GDP estimates

3. Suggested improvements

  • Improve ASI sampling frame using:

    • MCA database

    • GST database

  • Conduct:

    • Properly designed sample surveys of active companies

  • Goal:

    • Better State-wise GVA estimation

Household sector issues

Method

  • GVA = GVAPW (ASUSE) × workers (PLFS)

Problem: Volatility in ASUSE data

Example 1 (Rubber & plastic products)

  • 2021–22 → ₹163,078

  • 2022–23 → ₹255,447

  • 2023–24 → ₹201,930

Example 2 (Bihar manufacturing)

  • ₹89,638 → ₹117,021 → ₹100,101

  • Indicates:

    • Unstable annual estimates

Solutions suggested

  • Use of 3-year moving average (except base year)

  • Explore:

    • Rotating panel design in ASUSE

    • Similar to PLFS (overlapping samples)

Conclusion

  • Key improvements:

    • Better methodology

    • Wider data coverage

    • Improved estimation techniques

  • Key requirements ahead:

    • Strengthen ASI database

    • Refine ASUSE methodology

  • Outcome:

    • More reliable GDP and GSDP estimates

Prelims Practice MCQs

Q. Consider the following statements regarding sectoral composition in the new GDP series (2024–25):

  1. Tertiary sector has the highest share in GVA.

  2. Primary sector contributes more than the secondary sector.

  3. Secondary sector contributes more than one-fourth of total GVA.

Which of the statements given above is/are correct?

(a) 1 and 3 only
(b) 1 only
(c) 2 and 3 only
(d) 1, 2 and 3

Answer: (a) 1 and 3 only

Explanation:

  • Tertiary = 52.9% → highest (Correct)

  • Primary = 21.4%, Secondary = 25.8% → Primary is NOT higher (Statement 2 wrong)

  • Secondary > 25% → Correct

Q. Which of the following methodological changes has been introduced in the new GDP series?

  1. Allocation of entire GVA of enterprises to their major activity

  2. Use of MGT 7/7A data for multi-activity enterprises

  3. Inclusion of LLP data using MCA database

  4. Use of ASUSE and PLFS for household sector estimation

Select the correct answer:

(a) 2, 3 and 4 only
(b) 1 and 2 only
(c) 1, 3 and 4 only
(d) 2 and 4 only

Answer: (a) 2, 3 and 4 only

Explanation:

  • Statement 1: Old method (wrong)

  • Statement 2: New improvement (Correct)

  • Statement 3: LLP coverage added (Correct)

  • Statement 4: ASUSE + PLFS used (Correct)

Q. With reference to estimation techniques in the new GDP series, consider the following:

  1. Double deflation method is expanded.

  2. Volume extrapolation is used for real GVA estimation.

  3. PFCE estimates rely exclusively on income tax data.

Which of the statements is/are correct?

(a) 1 and 2 only
(b) 2 and 3 only
(c) 1 only
(d) 1, 2 and 3

Answer: (a) 1 and 2 only

Explanation:

  • Statement 1: Correct → Improved estimation method

  • Statement 2: Correct

  • Statement 3: Incorrect → PFCE uses HCES data, not tax data



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