Examine how a federated model of statistical responsibility can improve the quality and coverage of India's official statistics. What coordination problems might arise?

MoSPI's Committee on Knowledge Systems, chaired by Ratan P. Watal, has proposed a federated model for measuring the knowledge economy [1]. In it, system integrators set a common framework, domain institutions (sectoral ministries) measure specialised parts, and key partners (States, regulators, academia) support them [1]. Spreading the work this way can widen coverage, but only if coordination is built into the design.

How a federated model improves quality and coverage

• Domain expertise: work is assigned by each institution's mandate and comparative strength [1]. Ministries closest to the data measure their own sectors.
• Coverage of new sectors: seven priority areas (human capital, R&D, creative economy, AI systems, data and databases, high-tech products and the digital economy) are measured as a family of thematic accounts, not one aggregate [1]. This builds on the Culture Satellite Account and the Digital Economy GDP study for 2022–23 [1].
• Comparability: a common framework consistent with SNA 2025 keeps results comparable with other countries. The UN Statistical Commission adopted SNA 2025 at its 56th session [2].
• Ground-level reach: naming States as key partners brings in their data on universities, skilling and local firms [1].
• Transparency: a public consultation runs until 12 December 2026 [1].

Coordination problems

• Inconsistent definitions: the boundary conditions are guiding principles, not rigid criteria [1]. Different agencies may classify the same product differently.
• Double counting: if separate agencies count data, the AI model and the AI's output, the same value may be counted more than once.
• Self-assessment bias: ministries are judged by the numbers they produce. The integrator provides a framework but has no stated power to audit them [1].
• Skewed priorities: the cost-effective measurability criterion favours areas that already have data [1]. Weak areas risk staying unmeasured. Measuring a gap also does not close it: GERD (R&D spending as a share of GDP) has fallen since 2009–10 and remains far below the 2% goal [3].
• Thin State role: States are partners, not domain institutions [1]. Without funding, regional gaps stay invisible.

A federated model improves the depth and reach of official statistics, but its credibility depends on coordination. MoSPI, as integrator, should publish methods before numbers and label untested accounts "experimental". Ministries should report each account alongside the budget they control. States need clear data-sharing roles and funding to go with them. In the spirit of cooperative federalism and SDG target 17.18 on high-quality data, this can make India's statistical system both wide-reaching and trustworthy.