Strengthening India’s Statistical Ecosystem – Explained

Statistical Ecosystem Latest News

• The Ministry of Statistics and Programme Implementation has announced plans to use data from the Annual Survey of Unincorporated Sector Enterprises and the Periodic Labour Force Survey to develop a District Domestic Product (DDP) framework for more accurate, district-level economic estimation.

MoSPI to Use ASUSE and PLFS Surveys for Accurate District Domestic Product Estimation

• The Ministry of Statistics and Programme Implementation (MoSPI) has announced a significant step toward improving India’s statistical architecture by integrating two major datasets to calculate the District Domestic Product: 
  • The Annual Survey of Unincorporated Sector Enterprises (ASUSE) and 
  • The Periodic Labour Force Survey (PLFS)
• This initiative aims to provide more accurate, district-level economic data and empower states to make evidence-based policy decisions.

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Context: Strengthening India’s Statistical Ecosystem

• At present, India’s national and state-level GDP data often fail to capture regional variations within districts. 
• Most District Domestic Product (DDP) estimates rely on top-down allocation methods, proportionately distributing state GDP based on outdated demographic indicators like population.
• This approach has long been criticised by experts who highlighted that the current method results in “near-identical growth rates for districts,” thus masking true inter-district disparities.
• Recognising this data gap, MoSPI announced that beginning January 2025, the ministry will work with state governments to introduce a bottom-up estimation model using detailed datasets from ASUSE and PLFS.

About ASUSE and PLFS

• Annual Survey of Unincorporated Sector Enterprises (ASUSE)
  • ASUSE captures detailed data on India’s vast unincorporated non-agricultural sector, covering manufacturing, trade, and services enterprises, including households, micro, and small units. 
  • This survey provides insights into the economic and operational characteristics of establishments that often remain outside the formal sector’s purview.
  • Earlier released annually, ASUSE now provides quarterly data for enhanced frequency and granularity. It serves as a critical input for understanding local enterprise activity, investment, and value addition patterns.
• Periodic Labour Force Survey (PLFS)
  • PLFS is conducted by the National Statistical Office (NSO) to measure employment, unemployment, and labor market participation across rural and urban areas. 
  • The survey is now conducted monthly, capturing dynamic trends in workforce participation, earnings, and occupational structures.
  • By combining ASUSE (enterprise data) and PLFS (labour data), the government aims to create a comprehensive database of district-level economic activities, bridging the enterprise and employment dimensions of local economies.

Purpose and Methodology for Estimating DDP

• The integration of ASUSE and PLFS will allow policymakers to capture real economic activity at the district level rather than relying on extrapolated state averages.
• Key features of the initiative include:
  • Bottom-up estimation: District-level data will be aggregated upward to form state and national accounts, reversing the current top-down allocation model.
  • Dual-sector coverage: The approach accounts for both enterprise activity (ASUSE) and labour participation (PLFS), ensuring holistic measurement of economic output.
  • Policy collaboration: MoSPI is working closely with state governments to align data collection frameworks with local administrative and planning needs.
  • Inclusion of informal sector: Since unincorporated enterprises and household-level activities form a large share of India’s economy, the new methodology ensures that informal sector output is adequately represented.

Complementary Statistical Initiatives

• The effort to refine DDP estimation is part of MoSPI’s broader agenda to modernise India’s statistical system. Several related initiatives are underway:
  • Annual Survey of Service Sector Enterprises (ASSSE): To be launched in January 2026, this will capture the dynamics of incorporated services such as IT, financial services, and logistics.
  • National Household Income Survey (NHIS): Scheduled for February 2026, it aims to measure income distribution, wealth, and inequality, complementing consumption and employment data.
  • Expanded data accessibility: MoSPI has identified over 250 datasets for improved public access, including data from GST, E-Vahan, and trade statistics, to enrich national accounts and research capacity.

Significance of District Domestic Product (DDP)

• The DDP represents the gross value added (GVA) within a district’s geographical boundaries. 
• It serves as a microeconomic counterpart to the state’s Gross State Domestic Product (GSDP).
• An accurate DDP framework can enable:
  • Targeted policy interventions by identifying lagging districts.
  • Evidence-based fiscal planning at local levels.
  • Better assessment of regional inequality and employment trends.
  • Alignment with decentralised planning under India’s federal structure.
• The move also aligns with the government’s vision of Viksit Bharat @2047, where data-driven governance is seen as central to inclusive development.

Challenges and the Way Forward

• While the initiative is promising, implementing district-level GDP estimation faces several challenges:
  • Data reliability: Unincorporated sector data can be difficult to capture consistently.
  • Coordination with states: States vary in statistical capacity and infrastructure.
  • Avoiding double-counting: Integrating enterprise and labour datasets requires precise harmonisation.
• Nonetheless, experts consider this reform a crucial step toward improving the granularity, reliability, and timeliness of economic data in India. 
• With states like Maharashtra, Tamil Nadu, and Karnataka already experimenting with DDP frameworks, MoSPI’s bottom-up model may soon standardise district-level measurement across the country.

Source: IE | FE