The Missing Variables Distorting India’s Economic Policy Diagnosis

Trade credit, or inter-firm B2B credit, predates formal banking and remains an essential operating mechanism of commerce.

Millions of daily transactions and their repayment form a major working-capital base for trade and industry, influencing production, sales, inventories, receivables, payables, liquidity, bank finance, capex and operational efficiency. 

Communities such as the Marwaris built enduring trade and industrial networks on trade-credit relationships founded on trust, reputation and payment discipline. Trade credit itself, therefore, became a form of financial capital, even though it rarely appears with the same visibility as bank lending.

When firms sell on credit, whether their own funds, supplier credit or bank-funded, they effectively act as quasi-financial intermediaries. They extend working-capital credit to customers and, through suppliers, create a secondary layer of credit beyond direct bank lending.

The RBI’s 38-year corporate database covering 1985–2023 [Table 3], with about 2.38 million company-year observations, provides striking evidence. Sundry creditors consistently represented approximately 16–17% of sales, compared with bank working capital of roughly 8–14%. Supplier credit was therefore a dominant working-capital channel across periods and firm sizes.

The evidence is even stronger among the 24 Nifty-50 manufacturing companies examined for 2013–23. Sundry creditors amounted to 18.6% of sales, compared with only 3.3% for bank working capital.

Even India’s largest and most bank-served manufacturers rely heavily on supplier credit. Vendors effectively finance inventories that support production cycles, making inter-firm credit an integral part of the functioning of the formal economy.

Table 3: Trade Credit vs. Bank Working Capital: Corporate Evidence, FY1985–2023

Table: Corporate data points, FY1985–2023 — average sales, number of companies and working-capital ratios (% of sales). Sources: Compendium on Private Corporate Business Sector in India, RBI — Select Financial Statistics, 1950-51 to 2008-09 (Period 1); RBI's DBIE Database (Period 2); *CMIE Prowess (Nifty-50).

Historically, adhtiyas, or commission agents, and private moneylenders also absorbed short-term payment mismatches within trust-based commercial networks. Their withdrawal since the mid-2000s, accelerated by the disruption caused by COVID-19, removed an important liquidity shock absorber without a formal replacement.

The resulting vulnerability became visible during the pandemic.

The GAME–D&B Report of May 2022 found that delayed payments represented 65.7% of sales for micro-enterprises in 2020–21, compared with 25.2% for medium enterprises. Median debtor days for micro-enterprises reached 195 days against a statutory limit of 45 days. In all, ₹10.7 trillion was locked in delayed MSME payments, with 80% owed to micro and small enterprises.

This is not simply a question of delayed invoices. When payment delays spread through supply chains, trade credit becomes a transmission mechanism for financial stress. 

Policy Blind Spot
Seven decades of MSME credit policy have remained predominantly bank-centric.

Institution-building has been extensive, including SFCs, SIDBI, MUDRA, CGTMSE, NEF, Fund of Funds, priority-sector lending, TReDS, Samadhan and Section 43B(h). Yet MSME financial vulnerability remains widespread and the sector’s bank-credit gap continues to run into trillions.

COVID-19 did not create this fragility. It stress-tested an architecture in which the dominant trade-credit channel had received comparatively little institutional attention.

If trade credit meets around 80% of MSME working-capital needs, its role during stress, its effectiveness and the mechanisms required to strengthen it deserve systematic attention.

Trade credit is largely unsecured and often undocumented. It has no dedicated institution, comprehensive payment-discipline framework, regulator or sufficiently granular national dataset.

The bank bias is structural. The policy toolkits centre around collateral, formal balance sheets, NPAs and prudential ratios, while inter-firm credit remains largely outside the analytical framework. What is not measured cannot be effectively managed.

Trade credit also fits MSME realities differently from conventional bank finance. It is built on trust, relationships, repeat transactions, fast, need-based and revolving cash flows. Bank lending remains more dependent on collateral and documentation.

The result is an uneasy mismatch: bank credit receives continual policy engineering, while the financing mechanism embedded in millions of commercial relationships remains comparatively neglected.

Post-COVID stress exposed how B2B payment delays can cascade through supply chains outside conventional credit surveillance. The trade-credit ecosystem is therefore under structural stress, affecting liquidity, investment, productivity and growth.

This is not simply a credit-access problem. It is a problem of counterparty trust, payment discipline and visibility of financial obligations.

Trade credit needs its own data infrastructure, stress indicators and place in macro-financial surveillance. Another incremental bank credit scheme is unlikely to address the structural gap.

A systemic digital invoice-to-payment framework could. Linking invoices with verified payment dates could strengthen payment discipline, improve credit assessment, enable earlier detection of stress and make the largest yet least visible MSME financing channel measurable

Table 4: Currency-with-Public to GDP Ratio by Decade, 1970s–FY2026

Table: Average decadal ratio of Currency with Public (CwP) to GDP. *2010s figure excludes FY2017 (demonetisation) and FY2018 (remonetisation). Source: Handbook of Statistics on Indian Economy, RBI.

Import Paradox
There is another missing variable: the relationship between import transactions, cash demand and the informal financial system.

The RBI’s currency-demand model uses unit income elasticity as a benchmark. Yet rising financial inclusion, rapid digitalisation and restrictions on high-value cash transactions would reasonably suggest that effective elasticity should be below unity.

Instead, during 2001–26, average currency-demand elasticity was 1.18, while HDNs recorded an elasticity of 1.48. Currency with the public relative to GDP rose from 8.5% in the 1970s to 11.4% during 2021–26.

That divergence requires an explanation beyond conventional income-driven currency demand.

Chinese imports rose 171-fold between financial years of 2000 and 2025, with China’s share exceeding 16%. Yet the sustained rise appears not to have received sufficient transaction-level scrutiny for under-invoicing, misclassification and covert routing.

The undeclared component of imports, settled outside formal banking channels, offers a plausible additional explanation for unusually strong demand for high-denomination notes.

The denomination split reinforces the signal. HDNs elasticity was 1.48 compared with only 0.48 for non-HDNs, while China imports recorded an elasticity of 1.82 with respect to GDP, the highest among the variables examined.

Table 5: Currency-to-GDP Elasticity Divergence: HDNs vs. Non-HDNs vs. China Imports, FY2001–26

Table: Currency growth vs. GDP elasticity — HDNs, non-HDNs and China imports, FY2001-FY2026. Sources: RBI, Handbook of Statistics on the Indian Economy; 

The implications extend beyond currency demand.

Manufacturing IIP grew only 3.2% CAGR during 2012–26, while domestic production increasingly competed with importing, assembling and reselling. Competitive pressure weakened indigenous MSME clusters, investment appetite, resilience and movement up the value chain.

PLI, import substitution and export-promotion measures can also be weakened when under-valued imports erode the competitiveness of compliant domestic producers.

The non-oil China trade balance deteriorated from a $6.4 billion surplus in 2002-03 to approximately $112 billion deficit by 2025-26, adding external and foreign-exchange pressures.

Closer integration of the import surge, valuation anomalies and HDN demand could therefore have provided an earlier warning of the cumulative effects on manufacturing, MSMEs, investment, trade and external resilience.

The Structural Root
A recurring explanation is bounded rationality: policymakers work with the information, models and “good enough” readings available to them.

But there is another issue. Economic training and academic work can be strong in theory while providing limited exposure to the operational realities of businesses, banks and supply chains, particularly under stress.

It is akin to an MTech engineer who understands engines theoretically but, faced with a stalled one, struggles to identify a fault that an experienced mechanic diagnoses within minutes.

Or, more gently, many economists understand trade credit in the way one might study a cow: through papers and models without having seen the animal in operation. Trade credit is the buffalo standing beside it. It may look similar from a distance, but its behaviour and operating environment are different.

The problem is not the absence of theoretical knowledge. It is the failure to combine theory with operational familiarity.

None of this diminishes the genuine analytical strengths economists bring: rigour, modelling capability and command of theory and data. The concern is whether these strengths are combined with an understanding of how businesses, banks, suppliers and supply chains behave when conditions change.

Where that combination is missing, well-intentioned reforms can produce outcomes contrary to their intent, easing conditions for large, compliance-capable firms while leaving smaller and less visible businesses to absorb the shock.

Way Ahead
The answer is not to reject economic theory. It is to complete it by bringing the operational economy into the analytical field of view.

Reinvent big-data analytics at RBI and allied institutions, moving beyond compilation and mechanical reporting towards contextual analysis that surfaces ground-level signals alongside aggregate indicators.

Institutionalise trade credit as a tracked macro-financial variable, on a footing closer to bank credit. A GSTN-based framework capturing invoice due dates and actual payment dates could provide near-real-time information on payment stress and create automated consequences for chronic delays.

Bring practitioners, business-level economists and domain experts into policy design, adding operational intelligence that formal datasets may not capture.

Treat import mis-invoicing as a first-order variable in growth, savings and currency-demand analysis rather than as a peripheral trade-enforcement issue, given its potential bearing on capex, HDN demand and manufacturing competitiveness.

Calibrate policy timing to the quality of the diagnosis. Demonetisation illustrates the risk: an objective can be legitimate while the mechanism chosen to pursue it is poorly matched to the underlying problem.

The economic difficulties that followed these episodes had multiple causes: weak private capex, subdued manufacturing growth, currency pressures and a chronically credit-starved MSME sector despite periods of surplus system liquidity.

Yet a common thread runs through them.

The analytical framework was designed to see one layer of the economy while important activity was taking place in another.

The way forward is not less theory, but better-grounded theory. Economic frameworks must be broad enough to incorporate what businesses, banks and supply chains are already experiencing.

Taleb’s observation remains an appropriate warning: experts can fail to recognise what they do not know they are missing.

The cow and the buffalo must both be understood.

*This is Part II of a series examining the gaps between economic theory, financial data and the realities of business.

Part I explored how misdiagnosis can distort policy, examining India’s 2004–08 growth story and demonetisation, and how financialisation, cash flows and import-linked activity may have been overlooked in assessing the underlying economy.