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Mapping India’s Informal Economy: District-Level Insights

Prelims: Economy | ASUSE | Informal Sector | MoSPI
Mains: GS Paper III – Indian Economy, Employment, Inclusive Growth, Women-led Development
Keywords: ASUSE, Informal Economy, Unincorporated Sector, MoSPI, Women-led Development, Female Labour Participation, GVA, Informal Enterprises, Inclusive Growth, District-level Data

Why in News?

  • The Ministry of Statistics and Programme Implementation (MoSPI) has released, for the first time, district-level estimates of India’s informal economy based on the Annual Survey of Unincorporated Sector Enterprises (ASUSE), 2025.
  • The estimates cover 757 districts and provide detailed information on women’s participation, wages, enterprise ownership, employment and the geographical concentration of informal economic activity.

Background

  • The unincorporated non-agricultural sector includes enterprises engaged mainly in manufacturing, trade and other services that are not incorporated as formal companies.
  • Such enterprises constitute an important part of India’s economy by generating employment, supporting household incomes and encouraging small-scale entrepreneurship.
  • Informal workers often lack formal employment contracts, social security, stable wages and institutional protection. District-level estimates can therefore help governments design policies according to local economic conditions.

Key Findings of ASUSE 2025

1. Wide Regional Variation in Women’s Participation

Women account for around 29% of India’s informal workforce on average. In as many as 237 districts, women constitute at least one-third of informal workers. Some districts report exceptionally high levels of female participation.

Highest female participation:

  • Nirmal, Telangana – 78%
  • Imphal East, Manipur – 70%
  • Nizamabad, Telangana – 70%
  • Jagtial, Telangana – 69%
  • Bishnupur, Manipur – 66%

At the other extreme, participation remains very low in several districts.

Lowest female participation:

  • Srinagar, J&K – 11%
  • Hathras, Uttar Pradesh – 11%
  • Banaskantha, Gujarat – 11%
  • Marigaon, Assam – 9%
  • Rudraprayag, Uttarakhand – 7%

The data highlights major inter-district variations and shows that women’s participation depends considerably on local socio-economic conditions.

2. High Participation Does Not Always Mean High Wages

  • Greater female participation in informal employment does not automatically ensure better earnings.
  • Among districts where women make up at least half of the informal workforce, South West Khasi Hills in Meghalaya emerges as the best-paying district.
  • Annual emoluments per hired worker are around ₹1.71 lakh, compared with an all-India average of approximately ₹1.3 lakh.
  • This shows that employment policy must focus not only on creating jobs but also on improving their quality, productivity and remuneration.

3. Women’s Enterprise Ownership

  • The survey indicates a strong relationship between women’s workforce participation and female ownership of informal enterprises.
  • In Nirmal, which records the highest female workforce participation, nearly 80% of proprietary establishments are owned by women.
  • This suggests that women in some districts are participating in the informal economy not merely as workers but also as entrepreneurs and enterprise owners.

4. Informal Economic Activity Is Highly Concentrated

  • The geographical pattern changes significantly when informal activity is measured through the total number of workers, establishments and Gross Value Added.
  • North 24 Parganas, West Bengal, has the largest informal workforce, with approximately 21.3 lakh workers and around 16.6 lakh establishments.
  • The top 10 districts by number of establishments contribute around 11% of the sector’s total GVA, while the top 50 districts account for nearly one-third of its GVA.

This demonstrates that a relatively limited number of districts have emerged as major centres of informal economic activity.

Major Challenges

  • India’s informal sector continues to face low productivity, inadequate access to institutional finance, limited social security coverage and weak adoption of technology.
  • Women face additional constraints such as unpaid care responsibilities, mobility restrictions, skill gaps, limited ownership of productive assets and restricted market access.
  • Administrative changes and the creation of new districts may also affect geographical comparability of district-level datasets over time.

Way Forward

  • Government policy should combine employment generation with employment quality. Districts with high female participation should receive greater support for skilling, credit access, digitalisation and enterprise expansion.
  • Social-security coverage for informal workers should be strengthened alongside easier access to formal financial institutions.
  • District-level economic databases should also be regularly updated and integrated with local planning so that interventions can be tailored to regional needs rather than following a one-size-fits-all approach.

Prelims MCQs

Q. The Annual Survey of Unincorporated Sector Enterprises (ASUSE) is associated with which ministry?

A. Ministry of Labour and Employment
B. Ministry of Commerce and Industry
C. Ministry of Statistics and Programme Implementation
D. Ministry of Finance

Mains Question

“District-level mapping of India’s informal economy can improve both women-led development and evidence-based policymaking.” Discuss.

FAQs

What is ASUSE?

ASUSE is the Annual Survey of Unincorporated Sector Enterprises, which provides information on economic and operational characteristics of unincorporated non-agricultural enterprises.

Why is district-level informal economy data important?

It helps policymakers identify local differences in employment, wages, women’s participation, entrepreneurship and economic activity and design targeted interventions.

Which district has the largest informal workforce?

North 24 Parganas in West Bengal, with around 21.3 lakh informal workers, records the largest workforce in the available district-level estimates.

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