By Dipak Kurmi
For more than a decade, the Make in India initiative has served as the ideological and economic lodestar for the nation’s developmental aspirations. The strategic objective remains as clear as it is ambitious: to pivot the Indian economy by escalating the share of manufacturing in the Gross Domestic Product from its stagnant baseline of 15-16% to a robust 25%. This vision is not merely rhetorical; it is backed by an unprecedented mobilization of fiscal resources, most notably the Production Linked Incentive (PLI) schemes. Spanning fourteen critical sectors and involving billions of dollars in subsidies, these policies aim to transform India into a global factory floor. From electronics and pharmaceuticals to advanced chemistry cells, the regulatory machinery is fully geared toward industrial expansion. However, a profound structural paradox threatens this momentum. While India aspires to lead the Fourth Industrial Revolution, its methods for measuring industrial success remain trapped in a previous era. The information gap between policy implementation and performance verification is widening, creating a blind spot that could undermine the very competitiveness the nation seeks to build.
The fundamental crisis lies in the staggering latency of official statistical reporting. Currently, the most comprehensive dataset available to policymakers and economists is the Annual Survey of Industries (ASI), which provides a granular look at the manufacturing landscape but arrives with a crippling lag of 18 to 24 months. By the time a researcher identifies a slump or a surge in a specific sub-sector, the economic reality on the ground has already shifted. Similarly, the Manufacturing Gross Value Added (GVA) data within the National Accounts Statistics (NAS) is only released approximately a year after the reference period and is notorious for undergoing multiple significant revisions. At the state level, where regional industrial corridors are managed, the delays are even more pronounced. While the Index of Industrial Production (IIP) offers monthly updates, it is frequently criticized for its limited coverage, high volatility, and reliance on outdated base years. For an economy that prides itself on digital transformation and real-time governance, relying on two-year-old data to steer modern industrial policy is a luxury that India can no longer afford.
In the search for a more agile and accurate diagnostic tool, electricity consumption emerges as a definitive, high-frequency proxy for industrial health. Manufacturing is, by its very nature, an energy-intensive endeavor where machines, assembly lines, and climate-controlled environments demand a constant flow of power. Unlike labor statistics, which are often obscured by informal employment, or capacity utilization figures, which are difficult to observe directly and prone to reporting bias, electricity usage is metered, continuous, and remarkably hard to misreport. Because this data is already recorded daily and monthly by distribution companies (DISCOMs), it offers a level of geographic and sectoral granularity that traditional surveys cannot match. The intuition here is simple but powerful: if the factories are humming, the meters are spinning. This transition from survey-based estimation to sensor-based reality represents a leap toward data-driven governance that matches the pace of global markets.
The validity of using electricity as an economic thermometer is well-documented on the international stage. During the height of the COVID-19 pandemic, when traditional data collection was paralyzed by lockdowns, European studies utilized high-frequency electricity market data to track economic contraction and subsequent recovery in near real-time. In China, sector-level power consumption patterns successfully mirrored factory shutdowns and provided an early signal of rebounding activity weeks before official bureaucratic statistics could confirm the trend. Furthermore, longitudinal studies in developing economies, particularly across West Africa and nations like Nigeria, have demonstrated a statistically significant long-run association between electricity intake and factory-based industrial output. These global precedents suggest that India would not be experimenting with an unproven theory, but rather adopting a best practice that has already been stress-tested in both advanced and emerging markets during periods of extreme volatility.
Domestic data within India reinforces this global evidence with remarkable consistency. Over the past fifteen years, all-India manufacturing GVA and electricity consumption have moved almost in lockstep, boasting a correlation coefficient of 0.99. This near-perfect alignment has persisted through various policy regimes, business cycles, and the unprecedented disruptions caused by the pandemic. However, moving from a mere observation of correlation to a functional, real-time estimation framework requires a sophisticated econometric approach. The raw data from industrial feeders and high-tension manufacturing connections, which are already in the hands of state DISCOMs, must be systematically mapped to the National Industrial Classification (NIC) codes. By aggregating this data weekly or monthly, and applying controls for industrial scale and long-run efficiency trends, the government can develop an early-warning system that provides a snapshot of GVA long before the official figures are finalized.
The effectiveness of this proxy is particularly evident when examining the data at the sub-national level. In states like Gujarat, characterized by a high manufacturing-to-GDP ratio and dense industrial clusters, the correlation between power and output stands at a formidable 0.96. Similar strengths are observed in other industrial hubs such as Maharashtra, Karnataka, and Uttar Pradesh, where the correlation remains around 0.9. Interestingly, the areas where the relationship weakens actually prove the reliability of the metric. In services-dominated economies like Delhi, or tourism-dependent regions with minimal industrial activity, the link between electricity and manufacturing GVA is naturally thinner. This heterogeneity is not a flaw; it is an informative feature that proves electricity works best as a monitoring tool precisely where manufacturing is economically significant. It allows policymakers to ignore the “noise” of the services sector and focus on the “signal” of the factory floor.
A deep dive into sectoral evidence further validates this approach, with food processing serving as a primary example. As a sector that is employment-intensive and deeply linked to the agricultural supply chain, food processing is central to India’s inflation management and export strategy. Under the Make in India framework, the PLI scheme has allocated 10,900 crore rupees to this sector to modernize capacity in ready-to-eat foods, processed fruits, and marine products. In this specific arena, electricity consumption tracks manufacturing output almost perfectly. Because these facilities rely on heavy refrigeration and automated processing lines, their power signatures are distinct and reliable. By contrast, the proxy is less effective for activities like repair services or specific petroleum-related industries where output value is driven by global price fluctuations rather than physical volume. Identifying these sectoral nuances allows for a more surgical application of the electricity-based measurement model.
To transform this insight into a national standard, a coordinated institutional effort is required. The Ministry of Statistics and Programme Implementation (MoSPI) should take the lead by constituting an expert group to design a formal measurement framework and data architecture. This process must bridge the gap between the Central Electricity Authority (CEA) and state-level DISCOMs, which currently maintain vast amounts of data in silos not designed for statistical integration. Standardizing the way DISCOMs record and share granular power data, indexed by NIC codes, would ensure comparability across state borders and facilitate a unified national industrial dashboard. Furthermore, supporting states to link electricity connection data with the administrative records held by the Chief Inspector of Factories would significantly strengthen the ASI sampling frame. This would eventually lead to better coverage of Micro, Small, and Medium Enterprises (MSMEs), which are often underrepresented in current delayed datasets.
As India moves toward a future defined by energy efficiency and a transition to cleaner power sources, the electricity intensity of manufacturing will inevitably evolve. Some might argue that as factories become more efficient, they will consume less power per unit of output, potentially decoupling the relationship between electricity and GVA. However, historical data suggests that even with significant efficiency gains, the timing and direction of electricity demand continue to track industrial cycles with high precision. The momentum of a nation’s growth is measured not just by the final destination, but by the speed and accuracy of the information used to navigate toward it. By integrating electricity consumption into the core of its industrial monitoring, India can bridge the information gap, ensuring that the billions invested in manufacturing are matched by a world-class, real-time understanding of the nation’s industrial pulse.
(the writer can be reached at dipakkurmiglpltd@gmail.com)
