A background note can be accessed here: MoSPI’s Index of Service Production
The proposed Index of Service Production (ISP) relies heavily on GST data and administrative records to generate monthly indicators for the formal services sector. To what extent does this data architecture risk systematically under-representing India’s large informal services economy?
The proposed Index of Service Production (ISP) marks an important shift toward high-frequency measurement of India’s services economy through GST data and digital administrative records. However, its architecture is structurally oriented toward the formal economy, creating potential “informality blind spots.” Since the ISP is expected to cover around 70 percent of the formal services sector, enterprises below the GST threshold and several GST-exempt activities remain outside its core measurement framework. Together, these segments still account for a substantial share of services gross value added (GVA).
This raises an interpretive challenge in a dual economy. Rising GST turnover may partly reflect a redistribution of market share from informal firms to formal firms, rather than a net increase in aggregate output. In such cases, the ISP may capture the pace of formalisation more effectively than the full trajectory of sectoral expansion.
The issue becomes sharper during periods of economic stress, when informal and formal service activities often diverge significantly. Because the ISP depends heavily on digital and administrative data trails, it may provide timely visibility into organised services while under-capturing contraction, labour displacement, or recovery dynamics within the informal economy. This is particularly relevant for labour-intensive service activities where informal employment remains dominant.
The methodology converts nominal GST turnover into volume indices using deflators such as Service Producer Price Indices (SPPIs). How robust is this approach in capturing real service sector output, given the heterogeneity of services and pricing dynamics?
The ISP’s methodology follows an internationally accepted approach already used in economies such as the US, the UK, Europe, and Japan, where nominal service-sector turnover is converted into volume estimates through deflation techniques. In sectors where physical “units” of output are difficult to define, statistical systems often estimate real output by adjusting turnover using Service Producer Price Indices (SPPIs) or related price measures.
The challenge lies in the heterogeneity of services. Unlike manufacturing, service output frequently combines pricing changes, quality improvements, technological shifts, and variations in labour intensity. For example, higher IT services turnover may reflect increased billing rates, greater deployment of AI-enabled solutions, or a move toward more complex, high-value services, rather than a simple rise in output volume.
The robustness of the ISP will therefore depend heavily on the sophistication of its deflators. Broad CPI- or WPI-linked adjustments may not adequately distinguish inflation from productivity or quality improvements. International practice increasingly relies on granular and quality-adjusted pricing methods, including hedonic approaches and business-to-business service price tracking, to avoid overstating or understating real activity in complex service sectors.
The ISP adopts a “tri-source” model combining GST data, administrative datasets, and enterprise surveys across over 40 service sub-sectors. How does this integration affect statistical consistency and reliability across sectors?
The ISP’s “tri-source” framework combines GST turnover data, administrative records, and enterprise surveys across more than 40 service sub-sectors. GST datasets are intended to track market-facing activities such as trade, transport, and hospitality, while administrative databases support measurement in regulated sectors like banking and insurance. Enterprise surveys such as ASISSE fill gaps in sectors including education and health.
This integration significantly expands statistical coverage compared to earlier attempts at service-sector measurement and strengthens the possibility of generating a more frequent and policy-relevant indicator for services activity. At the same time, combining multiple data systems introduces important consistency challenges. Each source differs in frequency, reporting structure, and operational definition of “output,” requiring extensive reconciliation and benchmarking.
Reliability will therefore depend not only on data availability but also on institutional coordination and statistical harmonisation within the National Statistical Office (NSO). Preventing double-counting, addressing missing observations, and aligning monthly ISP estimates with quarterly and annual GDP series will require robust integration protocols. International alignment with OECD and Eurostat standards also becomes important to improve comparability and reduce the scale of subsequent GDP revisions.