Monetary Transmission & Small Firm Credit Rationing: The Stablecoin Opportunity to Raise Business Credit Flows

Abstract

Credit rationing, especially prevalent for smaller firms, impedes economic growth. A central bank-aligned not-for-profit managed business-to-business “stablecoin” (“synthetic central bank digital currency”) providing trade credit liquidity can provide additional monetary mass to mitigate small firm credit rationing. This raises growth by reducing monetary transmission imperfections consequent upon asymmetric information, commercial bank underwriting restrictions, market power dynamics, and regulatory distortion. A simple framework is developed to contextualise small firm credit rationing and associated monetary transmission imperfections with broader credit flows into both the real and monetary sectors. Evidence is presented regarding monetary transmission efficacy to firms, paving the way to proposing a business-to-business central bank-mediated “trade credit stablecoin” to improve business credit supply. In addition to providing additional (estimated at more than 10%) industrial and commercial (including smaller) firm financing, the envisaged trade credit stablecoin provides an additional monetary transmission channel for central banks to manage credit supply to the real economy to support economic activity and raise growth. Available to all firms, the trade credit stablecoin offers additional low-cost liquidity to firms, thereby offering policymakers an additional contra-cyclical monetary transmission instrument to support growth and, where necessary, reduce real economic disruption consequent upon financial system crises and liquidity events.

1. Introduction

This article draws upon varied monetary and Schumpeterian economic threads to provide the theoretical scaffolding to establish that a not-for-profit synthetic central bank digital currency stablecoin can help bridge an important gap in the real economy, especially small firm financing needs. Systemic stability is assured by integrating the stablecoin into the central banking “lender of last resort” framework through applying some lessons from previous fixed currency rate regimes to underpin the stablecoin’s fungibility with the fiat currency monetary system by means of volume “currency” control rather than interest rate price control.

The innovation in this article is to set a framework to integrate a stablecoin into the fiat monetary and commercial banking frameworks such as to provide central banks with an additional monetary transmission channel that directly drives credit to non-financial industrial and commercial, especially smaller, firms. This new credit is additive to existing commercial bank lending.

The stablecoin also reduces commercial bank information asymmetry constraints, as the information collected through its operation can help reduce information imperfections, which provides additional granular transaction and credit details. This is the case for smaller firms. Further, systemic stability for this new monetary instrument is assured by restricting fiat currency conversion flows to match the available fiat currency conversion monetary mass whilst maintaining a reserve buffer. The currency conversion mass is held as fiat cash either in commercial bank accounts or in a reserve account at the central bank. The central bank has the option to increase or reduce the reserve buffer per their sole decision. This provides central banks with a hybrid quantitative easing monetary transmission channel that bypasses conventional financial markets to directly reach the real economy. The new instrument, which is restricted in its application to settling business-to-business transactions, utilises known technologies, learns from previous fixed-rate currency conversion regimes to maintain fungibility, and builds upon previous community currency experiments to address real economy monetary needs without distorting financial markets.

2. Money and the Real Economy

Money and economic development are inextricably linked. In unfettered perfect markets, Adam Smith’s “invisible hand” balances supply and demand to establish equilibrium across all markets. Equilibrium in the credit supply market is established by the rate of interest. Theoretically, supply and demand operate perfectly, no institution has market power, there no regulation, and all actors have perfect knowledge and perfectly rational expectations for tomorrow. Exogenously created money and perfect “in equilibrium” markets determine all price levels.

Such perfection is barely recognisable in today’s real world. Money is created endogenously by commercial bank credit issued in response to demand with core interest rates set by central banks. Regulation, monopolies, and oligopolies exist and are imperfect; expectations and information flows distort markets as they reflect time and uncertainty. Money, in addition to being a transacting medium, is the intemporal asset that acts to both hedge unexpected events (the precautionary motive) and as liquidity for opportunistic arbitrage/investment across different asset classes to attain profit (the speculative motive). Both motives reflect the fear and greed imperatives that govern much human behaviour and that are subject to sudden expectational shifts that drive market events such as the 1929 financial crash, 2008 post–Lehman’s crisis, and March 2020 US asset price crash.

Banking institutions respond to credit demand (market need) both directly (through credit creation), and indirectly (through lending to private shadow bank lenders). They skew lending decisions based upon regulation and asset collateral, as banks seek to minimise potential losses rather than maximise firm-level enterprise value. Collateral reflects past successes that generate annuity income, whilst anticipated cash flows reflect an uncertain future synonymous with entrepreneurial expectations. “Credit rationing” (which empirically overly impacts smaller, less asset-rich firms), reflects this lender preference for safety over risk. In a dynamic innovating economy, previous success may not continue, so as time passes, mismatches between asset values and long-term underlying cashflows arise, with inevitable value realignment occurring though correction events (financial crises).

3. Cryptocurrencies

Cryptocurrencies lacking institutional and regulatory baggage arguably offer untainted “money” that both provides a speculative store of value and enables monetary exchange [1] (Chohan, 2017) not tainted by institutional and regulatory distortion. Yet crypto asset price volatility restricts transactional roles, as a key attribute of a “medium of exchange” is to maintain a reasonably stable value.

Addressing this, a stablecoin subset of crypto assets has emerged. This is backed by “off-chain” fiat financial assets or “on-chain” crypto assets, and the claim is that these assets assure a fixed conversion rate into a linked fiat currency such as the US dollar. This asset backing is intended to give stablecoins the required back up to avoid liquidity crisis runs. These liquidity crises may subsequently morph into a solvency crises as collateral asset prices fall in response to distress sales [2] (Baughman et al., 2022). Crypto exchange failures (such as FTX) and stablecoin backing controversies (such as with Tether) reflect the immaturity of both these payment instruments and their regulatory environment. An evolving body of literature addresses these concerns.

On the one side lies the question, What does fiat currency backing mean for a stable coin? Superficially transparent, the reserve backing for USDT Tether is, upon more detailed examination, less clear [3] (Barthélémy et al., 2023). Historically, a significant percentage was derived by holding newly issued commercial paper, where it seems [3] (Barthélémy et al., 2023) that this paper had been issued in return for the Tether stablecoin, most likely issued by Chinese firms [4] (De, 2023). Subsequently, Tether has shifted its reserve assets away from commercial paper, with about 78% now being US Treasury bills and associated “repo” and “reverse repo” arrangements [5] (BDO, 2024). These securities are held at fair value [6] (IFRS, 2023), so any significant stress in the US Treasury market will impact the value of the underlying asset backing. This change has also reduced any indirect support provided by Tether’s previous commercial paper purchase for the real economy.

A stablecoin liquidity stress event is likely to lead to contagion within both crypto markets and associated reserve asset markets [7] (Wu et al., 2023). A crypto exchange or stablecoin bankruptcy also causes significant contagion [8] (Martins, 2024). For stablecoins, the depth of these contagion effects are likely to be mitigated when there is a strong link to real economic activity that provides “natural” real economy cashflows to underpin the stablecoin. Further speculative trading will be minimised when there is a transaction pricing scheme that raises fiat currency conversion transaction costs in the event of a “dash to fiat cash” [9] (Bertsch, 2023).

To complete the picture, initial coin offerings offer firms a way to raise either fiat national or cryptocurrency by offering investors new crypto coins to be traded on crypto exchanges. Issuance is according to an unregulated white paper that specifies the business, potential coin owner dividends, and coin owner rights. With nearly 50% of investors losing their capital in these instruments within a year [10] (Hornuf et al., 2022), these have limited attractiveness to potential investors.

Unsurprisingly, crypto assets are attracting regulatory interest. For example, the Global Financial Stability Board is preparing regulatory frameworks [11] (Financial Stability Board, 2023). Concurrently, the US Congress is debating the bi-partisan Lummis-Gillibrand Payment Stablecoin Bill [12] (Lummis et al., 2024). Additionally, many central banks have started work toward issuing their own central bank digital currency implementations.

4. Financial vs. Real Economy

The contrast between desires to speculate to accumulate and to operate and innovate epitomises the difference between the financial and real economies. In the financial economy, be it crypto or conventional, actors extract value embedded in existing assets and associated cashflows by arbitraging between individual assets and asset classes to maximise returns. In contrast, in the real economy, firms operate, innovate, and invest to provide customers with goods and services, creating new value. There is some cross-over. For example, a financial economy firm that produces and sells market research generates value in the real economy, whilst trading profits sit within the financial economy.

The dichotomy between the financial and real economies is evident in both the post-2008 global financial crisis and COVID-19 pandemic “quantitative easing,” where central banks injected funds into financial market actors, expecting conventional monetary transmission to both drive real economy activity and stabilise financial markets [13] (Luck et al., 2020), [14] (Karfakis et al., 2023). Evidence from the period of 2008 to 2011 suggests that whilst financial markets healed, the real economy impact was small [15] (Joyce et al., 2014). In contrast, the real economy impact was much greater during the COVID-19 pandemic [13] (Luck et al., 2020), when quantitative easing was combined with state-led direct lending support schemes to drive credit to reach firms, especially smaller firms through existing commercial bank channels [16] (Calabrese et al., 2022) & [17] (Beck et al., 2022). As lending flows to firms rose under government schemes such as the US Paycheck Protection Plan [18] (US Treasury, 2023), they effectively bypassed the Basel regulatory risk-weighting framework [19] (Filomeni, et al., 2024) through state guarantee credit risk substitution. Loan volume grew strongly despite lending availability outside state-supported schemes falling [16] (Calabrese et al., 2022). The US Paycheck Protection plan take-up in particular demonstrated significantly underserved small firms’ appetite to borrow to cover working capital needs. Rising “shadow bank” private debt also indicates commercial firm funding constraints, this time impacting larger companies [20] (Block et al., 2023) The existence of an industrial and commercial firm credit rationing gap drives the two primary research questions addressed in this paper:

• What is the nature of the industrial and commercial credit rationing gap?
• Could a central bank-aligned stablecoin help reduce credit rationing?

To address these questions, this paper builds a simplistic, notational (emphatically not mathematical), transliteral monetary transmission framework to help explore monetary and credit transmission and credit rationing in real economy firms. Having identified the character of such a gap, the discussion introduces a new monetary transmission instrument, a business-to-business trade credit-focused, central bank-aligned stablecoin to reduce credit rationing, highlighting features that address some regulatory and design challenges [21] (Catalini et al., 2022). The instrument’s attributes are also designed to inherently drive its stability through embedding the stability generating characteristics previously highlighted from [9] (Bertsch, 2023). The concluding remarks summarise the findings and pose further research questions.

5. Materials & Methodology

5.1. Analytical Framework

Money is created via both commercial bank credit creation and quantitative eased money issued by central banks. Quantitative easing both supports financial market asset values and generally relaxes state debt financing constraints [22] (Cukierman, 2021) by indirectly funding state expenditure by procuring newly issued government debt. When, as in the pandemic, quantitative easing is used to support state commercial loan guarantees, it circumvents commercial bank regulation [23] (Vousinas, 2015) regarding bank capital and liquidity [24] (Penikas, 2023), thereby driving money into real economy firms. Credit rationing arises from information asymmetry [25] (Stiglitz & Weiss, 1981), regulatory constraints, and a lack of available asset collateral [26] (Kang et al., 2023). Basel bank regulations constrain firm lending flows, even if an individual firm’s default risk is lower than normal [27] (Vozzella et al., 2020). Rationed credit, which restricts growth, is especially applicable to smaller firms with fewer assets, notwithstanding that they can be socio-economic anchors to their locality [28] (Savlovschi et al., 2011).

The structure of the following sections is shown in Figure 1 below.

5.2. Theoretical Framework

Monetary transmission acts as the connective tissue between money mass changes and activity in both the financial and real economies. The Fisher quantity theory equation states:

MV = PT

(1)

where M = monetary stock, V = velocity of circulation, P = price level, T = number of transactions, often presented as GDP.

Causality across Equation (1) has been subject to much debate (summarised by Kaldor (1970) [29]) as to whether monetary growth is exogenous or endogenous. Notwithstanding, since the 1970s, money in the major Western economies has been increasingly created endogenously [30] (Bank of England, 2014) through commercial bank credit creation. Created credit reaches the real economy directly when made for real economy actors, and indirectly when it feeds through actors in the financial economy. Connections between financial and real economies (which are multiple and operate directly and indirectly) are itemised in Simmons et al. (2021) [31].

The Fisher (1911) equation [32] underpins the static circular monetary flow variously described by Schumpeter (1934) [33] and Keynes (1930) in his Treatise on Money [34]. In this static paradigm, the velocity of circulation (V) and number of transactions (T) are constant. Re-expressing this,

MψV_t = PθT

(2)

where constant ψ asserts stable velocity and θ a stable number of transactions, all within a single time period t. Being static (no growth and no time), relative prices in all markets are fixed over a time period t. This monetary condition underpins both Schumpeter’s Walrasian-type circular flow [35] (Minsky, 1988) and Friedman’s (1968) money neutrality [36].

Keynes (1930) [34] introduces a financial sector with a money-holding appetite aligned with trading (speculating) financial assets. Developing this theme, differing financial economy profit time horizons imply a decoupling in the financial economy velocity of circulation from the real economy, as variable speculative financial cycles operate distinctly and in different time periods from the wealth-generating real circulation. Equation (2) can thus be rewritten as follows:

MV = (M_RψV_R) + (M_FV_F)

(3)

PT = (P_RθT_R) + (P_FT_F)

(4)

where subscript R = real economy activity and subscript F = financial economy activity. Equations (3) and (4) can be consolidated into:

((M_RψV_R) + (M_FV_F)) = ((P_RθT_R) + (P_FT_F))

(5)

Equation (5) gives us a static (that is, without innovation, economies of scale, or productivity change) condition for natural real economy growth (reflecting population and capital formation growth within stable markets and a constant production function) and financial sector speculative variation over the financial economy monetary mass, velocity of circulation, and transaction volumes. Equation (5) is oversimplistic, as there are indirect credit and connection dynamics between the monetary and real economies, which are summarised in Simmons et al. (2021) [31]. Notwithstanding, Equation (5) reveals insights into the post-2008 pre-pandemic quantitative easing experience, where monetary mass changes materially impacted financial economy prices with minimal impact on real economic activity.

5.3. Monetary/Credit Transmission

Monetary conditions are transmitted to economic actors via interest rate changes, which rebalance loan supply and demand, subject to information asymmetries, regulatory distortions, market power, and financial innovations. Together, these deliver money to different sectors with variable transmission efficacy [37] (Boivin et al., 2010).

Equation (5) states that the money stock in the real economy matches the rate of output (the velocity of circulation and number of transactions are stable), whilst money stock changes enable dynamic transacting activity (and hence asset price changes) in the financial economy via changes in both the monetary mass and the velocity of circulation. Under Equation (5), any change in monetary mass to reach the real economy (assuming full capacity utilisation) will change prices and not GDP [36] (Friedman, 1968). Real impacts are limited to indirect transmission via the financial economy. In an “unfreeze” moment in the real economy, such as a Minsky moment [38] (Minsky, 1982), financial crisis occurs as asset values adjust to underlying real cashflows as the constants in Equation (5) temporarily cease to apply. Consequent shifts in money-holding appetite impact the real economy via both changing velocity and transaction volumes, thereby exhibiting Keynes’s “liquidity preference” [39] (Keynes, 1936).

Recapping, Equation (5) assumes perfect monetary transmission. Regulatory, information, and market power distortions invalidate this assumption, as they interrupt real economy credit flows, especially to smaller, younger firms with fragmented transaction histories and fewer assets to collateralise. Failing to meet credit underwriting criteria, these firms are credit rationed in contrast to well-functioning credit market segments where interest rates balance credit supply and demand market segments [37] (Bovin et al., 2010). Such rationing occurs both through denial of credit and through overly expensive credit pricing [40] (EU SAFE, 2023).

In credit-rationed segments, Adam Smith’s “invisible hand” is no longer neutral. Increases in interest rates and lending restriction negatively impact firm investment levels, whilst interest rate reductions and credit easing have less impact and take longer to work through [41] (Perez-Orive, 2023). Asymmetric behaviour distorts credit allocation, especially in respect to firms with new products and technologies that are transforming their productivity possibilities, as lending flows fail to reflect potential equilibrium between the interest rate and marginal productivity of capital [42] (Stiglitiz & Weiss 1992). Such distortions constrain activity for these firms, implying economic activity below its static natural equilibrium (e.g., Schumpeter’s (1934) circular flow [33]).

Making the transaction rate T variable in the real economy gives Equation (6), where, without credit rationing, the number of transactions can now change to reflect innovation, productivity improvements, and economies of scale.

((M_RψV_R) + (M_FV_F)) = ((P_RT_R) + (P_FT_F))

(6)

Real economy credit rationing associated with credit market asymmetry [42] (Stiglitiz & Weiss 1992) constrains T_R in Equation (6) and consequently, given the fixed V_R velocity of circulation, GDP growth is constrained. Unconstrained variations in T_R working in perfectly functioning markets ceteris paribus pull through unconstrained endogenous changes via commercial bank credit creation [30] (Bank of England, 2014), whereas credit rationing constrains changes in M_R, thereby restricting growth as firms experience working and fixed capital shortages. Removing these capital constraints either requires more capital be injected by the entrepreneur [43] (Hart et al., 1994) or entrepreneurs secure loans via personal guarantees [44] (FSB, 2013).

Credit flows are also impacted by lender characteristics. Those with large, stable depositor funding (mainly smaller US banks) tend to have higher small firm lending flows. Conversely, banks with substantial “zombie” commercial loan books (where the borrower can service but not repay the loan) may tend to roll loans over to give time for them to heal to avoid balance sheet write-offs in preference to new lending to commercial borrowers with healthier growth potential. Paradoxically, the regulatory caution that reduces short-term growth also potentially reduces longer-term crisis-induced financial system damage, thereby supporting longer-term GDP growth [45] (Ben Naceur et al., 2018). Outside the banking system but forming part of a firm’s monetary resources is unregulated trade credit [46] (Casey et al., 2014) and less regulated “shadow bank” lending, whose lending flows, similar to commercial bank lending [47] (De Bandt et al., 2023) & [48] (Durdu, 2023), are pro-cyclical.

Relaxing innovation, technical change, and productivity improvement assumptions reveal the dynamic “natural growth” rate g that represents potential productive capacity changes when both the value of T_R and the credit contribution M_R required to service any given value of T_R are in equilibrium, taking into account the technical change embedded by capital investment k that shifts production functions and associated total factor productivity. Such dynamics are accommodated by T_R when, following Keynes (1936) [39], with unconstrained firm lending equating the market interest rate for business loans to the marginal efficiency of capital (which in turn is an anticipatory expression of Wicksell’s (1898) natural rate of interest [49]), unconstrained credit growth is evoked to deliver natural growth rate g.

Credit rationing constraints that impact T_R reduce k capital formation, thereby reducing g, the dynamic rate of potential growth. Additionally, differing circulations in the real and financial economy drive mismatches between underlying cash flows and asset prices, which eventually incur instability, with resultant Minsky moment adjustments as they are bought back into line [38] (Minsky, 1982). In practical terms, credit rationing makes it moot as to whether Equations (5) or (6) rule, as such rationing impairs their operational efficacy.

5.4. Transmission Dynamics

Credit availability for an individual firm is summarised as:

N_E = f (S_E, A_E, X_E, R_E, Z_E, U_E)

(7)

where N = credit from all sources, E = an individual firm, S = size, A = net unencumbered assets, X = the lender underwriting criteria, R = risk officer regulatory components, Z = reputational (know your customer) risk, and U = uncertainty.

Lending criteria expand into vector c (c₁ collateral, c₂ sector lending limits, c₃ firm projected free cash flow, c₄ credit rating criteria, c₅ credit officer review criteria, c₆ current financial statements and tax returns, etc.)

c = (c₁, c₂, c₃, c₄, c₅, c₆, ….c_n)

(8)

Regulatory criteria expand into vector r (r₁ risk weightings, r₂ Basel Pillar II proxy, r₃ Basel Pillar III proxy, r₄ liquidity factor proxy).

r = (r₁, r₂, r₃, r₄, ….r_n)

(9)

Both absolute values and the relative weighting between elements within Equation (7) vary over the business cycle, as lender underwriting standards adapt to business conditions, expectations, sector lending limits, and regulatory pressures, thereby changing underlying cash flows and varying collateral values. Sum $\sum _1^{n}N$ represents the total available credit to all firms in the economy, and it fails to match demand if any component in N is rationed. Unconstrained or weakly constrained firms optimise funding cost to align with credit availability over the business cycle [50] (Becker et al., 2014), whilst constrained credit-rationed firms restrict output to meet available credit.

Assuming endogenous money, credit flows into a range of financial and real economy assets through a range of credit channels. Some channels reach, as per [51] (Tobin, 1965), financial actors, others real economy actors, and yet others reach both types of actors. Together, these form the monetary transmission universe Q_n with transmission channels (Q₁…_n).

Each channel contains multiple credit products, some unique and some available across multiple channels. Products interact across all transmission channels to establish multiple credit states, with either one (stability) or multiple states existing at a moment in time. “Stickiness” in shifting between states reflects market, regulatory, and institutional inertia that results in either (i) credit-rationed borrowers forced into ever riskier funding and/or (ii) borrower uncertainty rising. As a state shift starts, lenders/borrowers challenge existing credit paradigms, increasingly prioritising liquidity over profit. Yet, even in extreme instability, some (but not all) credit channel elements within each Q can remain relatively stable. In this melee of change, demand responds as risk and uncertainty [52] (Knight, 1921) weightings (that can themselves be unstable) change to reflect adaptive expectations [53] (Lo, 2005), with such change driven by real quantifiable criteria and/or postulant (rational to non-rational) expectations. Specific interest rates [54] (Hahn, 1947) that are attached to each loan offer reflect the state they are made within, with each state aligning to specific information asymmetry [25] (Stiglitz & Weiss, 1981), regulatory and underwriting constraints, and Knightian uncertainty [52] (Knight, 1921). Where a market clearing interest rate cannot be established rationally due to uncertainty and instability, credit rationing (either through price or quantity restriction) rules. Credit rationing in turn impedes demand, as borrowers do not apply for loans in fear that they will never be granted. Credit rationing incidence rises with the uncertainty associated with instability and state shifts, implying that the optimal design for an instrument to reduce credit rationing should ascribe certainty to all values in Equation (7) and associated vectors r and c.

5.5. The Monetary Transmission Universe

The current monetary transmission universe described in

Table 1. Monetary transmission universe.

Monetary Transmission Space	Credit Channel	Credit Product
Commercial Banking	⮚ Secured lending ⮚ Unsecured lending ⮚ Mortgage	⮚ Secured loan ⮚ Unsecured loan ⮚ Mortgage
Shadow Banking	⮚ Finance company ⮚ Fintech ⮚ Factoring company ⮚ Savings and loans ⮚ Trading leverage ⮚ Private debt ⮚ Venture/angel capital ⮚ Private equity ⮚ Bond syndication	⮚ Secured loan ⮚ Unsecured loan ⮚ Mortgage ⮚ Equity ⮚ Mezzanine debt ⮚ Bonds ⮚ Leverage limits (trading) ⮚ Settlement period (trading)
Crypto Assets	⮚ Initial coin offer ⮚ Coin mining	⮚ ICO ⮚ Crypto exchanges
Central Bank/State	⮚ Helicopter money from quantitative easing	⮚ Transfer payments ⮚ Tax refunds ⮚ State guarantees
Commercial	⮚ Trade credit	⮚ Invoice terms

provides a picture of existing arrangements to set the context for this paper.

5.6. Data Considerations

This paper’s main US sources are (i) the US Federal Reserve Z.1 National Accounts release [55] (US Fed, 2023), including the data extract [56] (US Fed, 2023) for non-financial corporate businesses prepared utilising low-level definitions [57] (US Fed, 2023); (ii) the Federal Reserve Bank of Kansas City Small Business Survey [58] (Kansas Fed, 2024); (iii) the Bureau of Economic Affairs GDP deflator [59] (BEA, 2024) to express values in 2017 constant prices and GDP [60] (BEA, 2024) series; (iii) broader industrial and commercial firm loan data from US Federal Reserve Schedule H.8 [61] (US Fed, 2024) and discontinued Schedule E.2 [62] (US Fed, 2024); (iv) the small firm GDP percentage from the Small Business Advocacy Bureau [63] (SBA, 2018); (v) the availability of credit reports to small business [64] (US Fed, 2017) & [65] (US Fed, 2022); and (vi) bank lending behaviour from the Federal Reserve Senior Loan Officer Opinion Survey [66] (US Fed, 2024). For the European Union, sources include (i) the European Investment Fund small business finance outlook [67] (Kraemer-Eis et al., 2023), (ii) the European Union “SAFE” survey [40] (EU SAFE, 2023) and [68] (EU SAFE, 2018), (iii) EU data on small business finance concerns [69] (EU Commission, 2014), (iv) EU banking authority regarding SME Basel risk weights [70] (EBA, 2016), and (v) the bank loan analysis for the EU [71] (Statista, 2023). For the UK, the foundation came from [72] (Cosh et al., 2008), with updates from [73] (BVA BDRC, 2022). Accounting definitions come from the standard in [74] (IAS, 2023) and the published Basel III framework [75] (BIS, 2017).

The results in this paper are constrained, as small firm lending data series suffer from inconsistent definition. The US Federal Reserve [62] (US Fed, 2024) reports small business loans as <USD 10k versus the <USD 1 million used by the Federal Deposit Insurance Corporation [76] (FDIC, 2024). Schedule Z.1 [55] (US Fed, 2023) uses the methodology in [76] (FDIC, 2024). By contrast, the especially helpful Federal Reserve Reports on Small Firm Lending [64] (US Fed, 2017) & [65] (US Fed, 2022) seemingly classify small business loans by registered corporation type rather than loan size. “S” corporations are small firms, “C” corporations are larger ones. Separate data recording collateral charges do not contain loan values, so these are “proxied” in a US small firm lending study [77] (Gopal et al., 2022). International data comes from [78] (IMF, 2024), plus local country-focused publications. Relevant data sources are referenced in each table and chart, together with, as appropriate, notes on the data presented.

This data inconsistency makes evaluating small firm growth constraints in respect to credit rationing challenging, with data that are available being inconsistently collected across the globe, leading to a lack of multi-country comprehensive longitudinal data for both small firm bank lending and small firm GDP. Analysis is further complicated by capital market distortions that make small firms overly dependent upon commercial bank financing. Notwithstanding, the global nature of banking regulations allows key trends to be discerned by using a mix of US, EU, and UK data.

5.7. Data Setting (Results)

Recalling Equation (6) ((M_RψV_R) + (M_FV_F)) = ((P_RT_R) + (P_FT_F)), real economy credit requirements M_RψV_R expresses the additional financial resources firms require to both maintain their existing operations and fund growth ((P_RT_R). Notably, the rate of change in T_R expresses the real economy growth rate and can itself be expressed as the sum of all value-adding activities by every real economy actor. These activities include the value-adding activity undertaken by smaller firms, so with the velocity of circulation V being stable, whether the monetary mass allocated to smaller firms is constrained ceteris paribus determines whether small firm activity and growth rates match growth potential. A stable or rising share of commercial bank lending to smaller firms makes no comment on whether such bank lending is sufficient to enable commercial growth. Even rising bank lending flows can accompany credit shortage and rationing if the potential growth in real transactions (△T_R) is not met by a matched growth in available finance (△M_R).

Significant short-term falls in smaller firm GDP contribution are an indicative proxy that credit is constrained, as the changes in industrial concentration associated with changes in market structure and technical progress (such as the introduction of new platform technologies), even when occurring rapidly, in the absence of systemic crisis, take many years to manifest themselves [79] (Klepper et al., 1990).

The smaller firm contribution to GDP has been falling in the USA, EU, and UK. In the US, smaller firms’ GDP share fell from 48% in 1998 to 43.5% in 2014, whilst concurrently, activity migrated towards lower-productivity “capital light” industries [63] (SBA, 2018). EU small firm contribution to EU GVA fell from 59% in 2010 [80] (EU Commission, 2011) to 51.8% in 2022 [67] (Kraemer-Eis et al., 2023), with EU small firms also exhibiting the same trend toward more capital light industries [81] (Chen et al., 2023). Lower capital intensity reduces productivity growth, as credit-constrained small firms reduce innovation, impacting total factor productivity [82] (Yu et al. 2021) and leading to reduced competitiveness and lower overall growth.

Table 2. Small firm commercial bank lending as percentage of GDP.

Year	USA	China	UK
2011	24.33	31.84	11.36
2012	24.47	35.09	10.29
2013	24.29	35.12	9.33
2014	24.58	36.29	8.97
2015	25.59	36.62	8.55
2016	26.31	36.00	8.29
2017	27.58	36.00	7.93
2018	29.59	35.56	7.69
2019	31.05	37.10	7.49
2020	33.68	41.67	10.11
2021	28.09	41.75	9.21
Av % Real GDP Growth 2011–2021
	2.3%	7.0%	1.4%

Sources: USA: Federal Reserve Financial Access Surveys [64] (US Fed, 2017) & [65] (US Fed, 2022); China: research report [83] (iResearch, 2021); UK: [84] (Hutton, 2022); China and UK: [78] (IMF, 2024); GDP growth: [85] (IMF, 2024). Notes: 1. UK only expresses SME activity as a % of turnover; USA and China are % GDP; 2. US expressed as % 2017 constant price GDP.

sets out small firm bank lending flows in the US, China, and the UK. No EU data are included due to a lack of accessible relevant longitudinal data.

Although correlation does not prove causation, Table 2 suggests some association between lending flows and growth rates. This suggestion is affirmed, as external bank finance is often used to fund capital investment [67] (Kraemer-Eis et al., 2023), suggesting that a restriction in long-term small firm credit leads to a restriction in capital investment, which in turn over time can be taken to explain the observed reduction in capital intensity already mentioned above.

The missing EU context in Table 2 can be inferred from a 2015 study that describes a fall in monthly lending flows to small firms from EUR 95 billion in mid-2008 to EUR 54 billion in 2014 [70] (EBA, 2016). Subsequent data suggest small firm lending flows recovered to between EUR 100 billion and EUR 110 billion per quarter after 2015, peaking in 2019 [67] (Kraemer-Eis et al., 2023:27), and then, notwithstanding a spike during the pandemic, gently falling in monetary terms thereafter. This implies a sharper downward trend in real lending when adjusted for post-pandemic inflation. Concurrently, the SME per capita productivity gap with large firms grew from EUR 18,000 in 2008 to more than EUR 32,000 in 2022 [67] (Kraemer-Eis et al., 2023: 4)

5.8. Small Firm Lending Landscape

The availability of finance to industrial and commercial firms has been a concern since the 1930s, although it is only relatively recently that the major economies have started to collect relevant data. Being survey-based rather than based on comprehensive longitudinal data that are reconcilable back to lender balance sheets, these data only offers a limited picture. Smaller firm credit rationing can be inferred by inspecting loan acceptance rates, as per

Table 3. US small firm loan access % loan applications.

Geography	2007			2018			2022
	Rejected Amount	Partial Amount	Full Amount	Rejected Amount	Partial Amount	Full Amount	Rejected Amount	Partial Amount	Full Amount
UK	15%	19%	66%	26%	10%	65%	48%	6%	46%
USA	44% *	21% *	35% *	26.5% †	NA	73.5 †	12%	28.2	59.9%
EU	15% ‡	15% ‡	70% ‡	6%	10%	74%	4%	20%	61%

Sources: UK: 2007 Cosh et al. [72] (Cosh et al., 2008); 2018 and 2022 [73] (BVA BDRC, 2022); USA: 2007 * 2014 data used as earliest available [86] (Fed System, 2015); 2018 ^† 2017 data used due to data availability [64] (US Fed, 2017) and 2022 [65] (US Fed, 2022). EU: 2007 ^‡ 2008 data used due to data availability [79] (EU Commission, 2011); 2018 [68] (EU SAFE, 2018); 2022 [40] (EU SAFE, 2023). Note: UK data for 2022 is an amalgam of success rates for bank loans and overdrafts weighted as category success rate applied to each percentage of loan applications to give an overall success rate OD_w1 + LO_w2 = BL, where OD = overdraft applications as a percentage of funding requests, LO = loan applications as a percentage of funding requests, BL = total bank lending, and w₁…w_n weights for application success rates.

below. Notably, less than ⅔ of requests are fully approved.

Figure 2 below portrays US commercial bank lending to industrial and commercial firms over a 20-year period, illustrating the gradual falling back of industrial and commercial lending as a percentage of GDP. US firms have progressively had less access to commercial bank credit to fund themselves, notably impacting smaller firms (commercial bank lending is significantly more important to smaller than larger firms).

The same trend is visible within the EU. Small firm use of bank overdrafts fell from nearly 40% in 2015 to around 28% in 2023, and that of bank loans fell from just over 20% to just over 12%. In contrast, smaller firm lease finance remained stable [67] (Kraemer-Eis et al., 2023) and the use of trade receivable use rose [40] (EU SAFE, 2023). Total EU small firm external funding in 2021 was EUR 6336 billion (author’s own estimate) based on [67] (Kraemer-Eis et al., 2023) & [71] (Statista, 2023), with bank lending being EUR 2471 billion [71] (Statista, 2023). In contrast, in the USA, bank lending recovered from 2011 to peak in 2018 and then started to fall again.

Data in all countries were distorted by the special lending programs undertaken during the pandemic, which raised state-supported commercial lending (private risk lending fell during the same 2020/2021 period). Notwithstanding, overall smaller firms report “tightening” credit conditions, and as external funding availability fell, the share small firms contributed to GDP fell and the productivity gap with large firms grew.

Table 3 and

Table 4. US dollar lending to non-financial firms 2011–2021.

2017 Constant Price
USD Billions	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
Total debt	6933	7153	7431	7745	8208	8519	8907	9383	9706	10,421	10,324
Industrial revenue and corporate bonds	4403	4674	4891	5102	5486	5706	5352	5396	5563	6108	5895
Mortgages	623	477	452	408	446	479	532	608	650	681	761
Bank loans not secured on real estate	684	758	824	884	959	996	893	974	989	1046	946
Commercial paper	126	138	152	188	181	185	205	190	187	124	122
Municipal securities	0	0	0	0	0	0	570	554	561	555	532
Other loans (shadow banks)	1096	1105	1114	1163	2099	1153	1354	1660	1758	1906	2068
MEMO trade credit	1926	1957	2031	2157	2099	2170	2352	2611	2793	2875	3189
Percentages of GDP
2017 Constant Price	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
Mortgages	3.6%	2.7%	2.5%	2.2%	2.3%	2.5%	2.7%	3.0%	3.1%	3.2%	3.5%
Bank loans not secured on real estate	3.9%	4.3%	4.6%	4.7%	5.0%	5.1%	4.5%	4.8%	4.8%	5.0%	4.3%
Industrial revenue and corporate bonds	25.4%	26.5%	27.2%	27.3%	28.9%	29.4%	26.7%	26.4%	26.9%	29.1%	27.1%
Commercial paper	0.7%	0.8%	0.8%	1.0%	1.0%	1.0%	1.0%	0.9%	0.9%	0.6%	0.6%
Municipal securities	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	2.8%	2.7%	2.7%	2.6%	2.4%
Other loans (shadow banks)	6.3%	6.3%	6.2%	6.2%	11.0%	5.9%	6.8%	8.1%	8.5%	9.1%	9.5%
Trade credit	11.1%	11.1%	11.3%	11.6%	11.0%	11.2%	11.7%	12.8%	13.5%	13.7%	14.7%

Sources: Fed Reserve Financial Access Surveys [64] (US Fed, 2017) & [65] (US Fed, 2022), [60] (BEA, 2024); footnote 5 page 7 [65] (US Fed, 2022) implies data for C corporations.

below present US lending flows since 2011 to (i) all industrial and commercial firms (Table 3) and (ii) small firms (Table 4).

Table 5. US dollar lending to small US firms 2011–2021.

2017 Constant Price
Billions USD	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
Total debt	4226	4321	4361	4589	4862	5104	5528	5692	5870	6260	6106
Mortgages	3059	3098	3128	3274	3451	3635	3915	4029	4208	4372	4352
Bank loans (not real estate secured)	980	1031	1037	1112	1204	1261	1376	1422	1417	1557	1390
Shadow bank loans (not real estate secured)	186	193	196	202	207	207	237	241	245	331	363
MEMO trade credit	525	526	553	555	603	653	588	581	614	579	598
Percent SME 2017 Constant Price GDP
Mortgages	40%	40%	40%	40%	42%	43%	45%	45%	47%	48%	46%
Bank loans (not real estate secured)	13%	13%	13%	14%	15%	15%	16%	16%	16%	17%	15%
Credit union supplied	1.2%	1.3%	1.3%	1.2%	1.3%	1.2%	1.4%	1.4%	1.4%	1.8%	1.9%
Finance company supplied	1.2%	1.3%	1.3%	1.2%	1.3%	1.2%	1.4%	1.4%	1.4%	1.8%	1.9%
Trade credit	7.0%	6.8%	7.1%	6.8%	7.3%	7.7%	6.7%	6.5%	6.8%	6.3%	6.3%
Total	55.9%	56.2%	55.8%	56.5%	58.8%	60.5%	63.4%	64.1%	65.3%	68.6%	64.6%

Sources: Federal Reserve Financial Access Surveys [64] (US Fed, 2017) & [65] (US Fed, 2022), [60] (BEA, 2024); footnote 5 page 7 [65] (US Fed, 2022) implies data for C corporations. Note: S corporations are used small businesses proprietorships and partnerships; C corporations are for larger firms. Referenced footnote is unclear; data results make clear that two different data collection sources were used. Nonfinancial entity assignment comes from the designated entity standard industrial classification held in the firm’s legal registration.

builds from these two tables to contrast lending flows between them, confirming that not only are lending flows lower for smaller firms, but also these firms utilise different credit products and different monetary transmission channels. In the US as in the EU, credit flows into the overall industrial and commercial loan market demonstrate changing monetary transmission as credit markets adapt (with increases in funding from bonds, shadow banks, and trade credit) to Basel III regulatory and other institutional changes.

Smaller firms have become increasingly dependent upon real estate mortgages, with minor reliance on shadow banking. Utilising a database of secured lending charges and transaction volumes (lending values are inferred but not in the database) suggests US small business lending is 95% secured, with the remaining 5% being made up of unsecured loans (3.6%) and credit card borrowing (1.4%). Significantly diverging from Federal Reserve data (Table 4), mortgage lending represents 22%. Asset-based lenders securing loans against equipment, deposit balances, and inventory prefer to fund mid-sized companies (≈40 employees, sales ≈ USD 8 million to USD 10 million), whilst Fintech lends smaller amounts to smaller firms (≈ 18 employees, sales ≈ USD 3 million) [77] (Gopal et al., 2022). Private debt providers are largely absent from the small firm segment [87] (Block et al., 2024). Smaller firms thus depend upon traditional commercial banks, operating leases, factoring receivables, and trade credit for external funding. Large firms also rely upon trade credit (Table 4 below) for a significant component of their external financing. Figure 3 below shows US bank lending officer (2020 pandemic excepted) credit criteria moving broadly in line with real GDP. Procyclical credit restriction constrains smaller firm resources during downturns, despite (as shown in

Table 6. Major differences in funding sources between small firms and industrial and commercial firms.

US Data 2017 Constant Price GDP	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
Mortgages as % GDP
Small firms	40.5%	40.3%	40.1%	40.3%	41.8%	43.1%	44.9%	45.4%	46.8%	47.9%	46.0%
All industrial and commercial firms	3.6%	2.7%	2.5%	2.2%	2.3%	2.5%	2.7%	3.0%	3.1%	3.2%	3.5%
Non-Real Estate Loans % GDP
Small firms	13.0%	13.4%	13.3%	13.7%	14.6%	14.9%	15.8%	16.0%	15.8%	17.1%	14.7%
All industrial and commercial firms	3.9%	4.3%	4.6%	4.7%	5.0%	5.1%	4.5%	4.8%	4.8%	5.0%	4.3%
Bonds as % GDP
Small firms	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
All industrial and commercial firms	25.4%	26.5%	27.2%	27.3%	28.9%	29.4%	26.7%	26.4%	26.9%	29.1%	27.1%
Trade Credit (Memo Estimate)
Small firms	7.0%	6.8%	7.1%	6.8%	7.3%	7.7%	6.7%	6.5%	6.8%	6.3%	6.3%
All industrial and commercial firms	11.1%	11.1%	11.3%	11.6%	11.0%	11.2%	11.7%	12.8%	13.5%	13.7%	14.7%

Sources: USA: Federal Reserve Financial Access Surveys [64] (US Fed, 2017) & [65] (US Fed, 2022), [60] (BEA, 2024).

below) downturns requiring that they grant more trade credit to larger firms.

Table 6 summarises the findings from Table 4 and Table 5 to illustrate how smaller firm funding in the US differs from large firms, with a much higher dependence upon commercial bank finance and no access to general capital (bond) markets. Even firm-granted trade credit is much lower to smaller firms. These results are amplified in the EU, as firms there have a higher dependency upon commercial bank lending [67] (Kraemer-Eis et al., 2023).

6. Discussion

Conventional monetary transmission theory asserts that money is mediated via commercial banks to deliver interest rate-priced credit that matches demand in both the asset-trading financial sector [34] (Keynes, 1930) and the wealth-generating real economy. Market imperfections such as information asymmetries, behavioural dynamics (greed and fear), regulatory diktats, and market actor power distort transmission to the real economy, leading to the imperfect credit supply and associated credit rationing, and constraining GDP and growth, reflecting a constrained real economy monetary mass M_R in Equation (6). Figure 4, as a snapshot balance sheet view, splits monetary transmission across financial and real economies.

Figure 4 highlights that the financial economy (under-reported, as it should also include financial leverage), with 49% of bank assets representing under 7.5% of US gross value added [88] (BEA, 2024), highlighting the mismatch in resource allocation between the financial and real sectors. Delving deeper, within the real economy, money transmission is delivered via various credit channels and credit instruments according to firm size, as shown in Figure 5 below.

The figure shows that smaller US firms account for 33% of external finance despite contibuting 43.5% of GDP, again suggesting that this sector is credit constrained. Note: Data in Figure 5 use the net wealth US Federal Schedule Z.1 [55] (2023), so they have different values than the gross bank lending from Schedule H.8 [61] (US Fed, 2024) used in Figure 4.

Developing the theme of “constrained” credit suggested by Figure 4 and Figure 5, and referencing Equation (7), small firms lacking public information, with fewer available assets, have higher uncertainty U and tougher underwriting X, driving tighter regulation r and lending criteria.

Reducing firm-level credit rationing, thereby enabling real economy GDP growth rather than relaxing commercial bank regulations, requires an additional financial instrument that obviates the uncertainty, underwriting, and regulatory distortions inherent in commercial bank lending practices that have resulted in disproportionate allocation to the financial and provide resources that directly reach firms. For such an instrument, the current Basel commercial bank regulatory constraints would be replaced by alternative regulatory arrangements that ensure it is systemically safe. This paper now seeks to lay out some design pointers for such an instrument.

6.1. Trade Credit—A Vital Source of Non-Bank External Credit

Table 4 and Table 5 indicate that a significant firm-level source of credit that is outside the commercial banking system is trade credit. Firms use trade credit (when a supplier allows a customer to defer invoice payment) to partly fund their operations. Its terms are set during pricing negotiations according to the power dynamics within the commercial relationship [89] (Wilson et al.,2022). Smaller firms often accede to larger firms’ (especially those with substantial supply chain weight) demands, either formally by agreeing to extended payment terms or informally by accepting the late payment of invoices. The higher levels of trade credit that large firms can secure (

Table 7. Calculated US small firm trade credit requirements.

Trade Credit	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
Small firms % GDP	7.0%	6.8%	7.1%	6.8%	7.3%	7.7%	6.7%	6.5%	6.8%	6.3%	6.3%
Large firms % GDP	11.1%	11.1%	11.3%	11.6%	11.0%	11.2%	11.7%	12.8%	13.5%	13.7%	14.7%
Difference	4.1%	4.2%	4.2%	4.7%	3.8%	3.4%	5.0%	6.2%	6.7%	7.4%	8.3%
Small firm current price GDP	6728	7008	7259	7581	7901	8109	8428	8890	9255	9262	9977
Unconstrained small firm trade credit (calc. from small firm GDP)	746	776	821	876	873	907	989	1137	1251	1268	1464
Actual small firm trade credit	483	494	528	537	588	646	593	599	642	615	675
Gap (est. additional funding requirement)	263	282	293	339	285	261	396	538	609	653	789

Source: author’s calculations from [22,23].

) are consequent upon the longer payment delays they can demand [90] (Lorentz et al., 2016). This difference in trade credit availability will be used to estimate the potential funding increment that a new payment instrument dedicated to delivering additional trade credit could imply. Such a new instrument will have both “additive” and substitution effects. This is consequent upon firms, especially smaller firms, bridging resultant funding gaps caused by the need to grant trade credit from a mixture of their own cash resources and external finance [91] (Crouzet et al., 2020). This reduces firm-level financial resources available for innovation and investment, with an additional consequence being to raise and prolong small firm recessionary damage [92] (Cerra et al., 2023). By juxtaposition, the judicious use of trade credit to nurture productive relationships raises small firms’ growth potential [93] (Lefebvre, 2023).

As an important source of industrial and commercial firm external finance [94] (McGuiness et al., 2016), within the EU, trade credit provides 15% of small firm external finance [67] (Kraemer-Eis et al., 2023), suggesting that EU small firm trade credit is ≈ EUR 371 billion. For context, invoice factoring for all EU firms provided ≈ EUR 310 million against unpaid receivables in 2022 [95] (EUF, 2024). Table 7 uses data from Table 3 and Table 4 above to estimate US trade credit need on the basis that firms, regardless of size, desire a similar trade credit ratio.

Table 8. Estimated trade credit stablecoin additional financial impact.

USD Billion Euro Converted at 1 EUR = USD 1.08	US					EU
	Existing	Un-Constrained	30%	50%	75%	Existing	Un-Constrained	30%	50%	75%
All firms	18,540	6.4%	10.7%	16.1%	4300	10%	17%	25%	18,540	6.4%
Small firms	6890	9.8%	16.3%	24.5%	2471	6%	9%	14%	6890	9.8%

Sources: author’s own calculations based upon USA: Federal Reserve Financial Access Survey [65] (US Fed, 2022) US Federal Schedule Z.1 [55], (US FED 2023) and EU: [67] (Kraemer-Eis et al., 2023) [71] (Statista, 2023).

below combines US and EU estimates for the increase in available financial resources for firms, which can either augment or replace existing financial resources.

Table 8 shows the unconstrained trade credit required for all industrial and commercial firms, and small business assuming both large and small firms have a similar appetite to use trade credit. The new financial instrument will both partially replace the existing resources used to fund trade credit and add additional capacity to remove credit constraints. Given the quantum and uncertainty involved in the calculation, the results are a “direction of travel” approximation.

In performing the calculations, for the US, values were taken from the 2021 column in Table 7 to calculate both large and small firm unconstrained credit requirements. For the EU, there is a lack of regularly published data on trade credit, so the calculations are based upon a recent estimate that suggests trade credit as 20% of sales for firms within the eurozone [96] (ECB, 2011: Chart A). Existing small firm credit trade credit usage estimates [67] (Kraemer-Eis et al., 2023) suggest that this amounts to 15% of GVA, suggesting that small firm trade credit is also significantly constrained in Europe. The difference between the percentage of GVA for small firms and for all industrial and commercial firms is used in Table 8 to calculate the unconstrained EU small firm trade credit requirement. The result is then assessed using a variable take-up factor of 30%, 50%, or 75% to calculate the potential additional financial resource contribution based upon firm appetite to use the new financial instrument. The results suggest substantial increases in available external finance from the new instrument ranging between USD 1.1 trillion and 2.0 trillion for the US, and from USD 437 billion to USD 1.0 trillion for the EU.

Table 9. Potential percentage increase in real economy lending flow.

Euro converted at 1 EUR = USD 1.08	USA				EU
	Total External Financing US	Firm Take-Up of New Instrument			Total External Financing EU	Firm Take-Up of New Instrument
		30%	50%	75%		30%	50%	75%
All firms	18,540	6.4%	10.7%	16.1%	4300	10%	17%	25%
Small firms	6890	9.8%	16.3%	24.5%	2471	6%	9%	14%

Sources: author’s own calculations based upon USA: Federal Reserve Financial Access Survey [65] (US Fed, 2022), US Federal Schedule Z.1 [55], (US FED 2023) and EU: [67] (Kraemer-Eis et al., 2023).

below expresses this as the potential percentage increase in real economy funding from introducing the new instrument. The higher impact in the EU reflects the greater use of bank credit by EU firms.

6.2. Stablecoin-Enabled Trade Credit

Turning to history for a moment, acute small firm credit rationing in both Austria in 1932 and in Sardinia post 2008 led to “community currency” experiments such as the 1932 Wörgl Schilling [97] (Richter, 2012) and Sardex [98] (Posnett, 2015) using adjunct currency financial instruments to deliver credit and boost firm-level economic activity. The Wörgl Schilling in particular faced unsurmountable challenges in integrating into the central bank framework, thereby limiting its fungibility into national currency.

A synthetic central bank currency (central bank-managed stablecoin), a trade credit stablecoin that extends the underlying concepts beneath the Wörgl Schilling and Sardex and that is restricted to being a settlement medium for business-to-business transactions can, if appropriately designed, provide a new source of quasi-money whose credit creation is partly demand-driven and partly central bank quantity-controlled. Being independent from the commercial banking system, it offers an independent monetary transmission channel that provides additional business-to-business credit for exclusive used by non-financial industrial and commercial firms. As an acceptable medium of exchanges for business-to-business transactions, its issuance adds to the real economy monetary mass M_R in Equation (6), thereby freeing in-firm financial resources to fund innovation and investment, and raise equity returns to help attract new equity capital.

As a business-to-business financial instrument, its usage is restricted to either paying other firms or being exchanged into fiat currency to pay external firms. The fixed exchange rate conversion process is throttled by available fiat currency reserves and underpinned by the central bank as lender of last resort. Stablecoin transfer to in-network suppliers is automatic and immediate upon customer invoice payment approval. The inbound fiat currency balance arises from participating firms requiring that external firms settle invoices in the stablecoin. External conversion is managed by the network administrator according to the constraints in

Table 10. Trade credit stablecoin fiat currency conversion events.

Event	Network Admin Transaction	Fiat Reserve	Currency Control	Charges
External firms purchase goods from network firm	External firm remits cash to purchase stablecoin to settle invoice	+	None	None
Network firm purchases goods from external firm	Network firm remits stablecoin that is exchanged for cash remitted to pay invoice	−	Yes	Yes
Network firm purchases goods from network firm	Balance is transferred from one member to another		None	Yes
Central bank boosts real economy money supply to firms	Central bank injects conversion liquidity in return for security over network manager loans to firms	+	None	None
Central bank withdraws cash	Central bank reduces conversion liquidity, reducing security over network manager loans	−	None	None
Network administrator builds reserve account	% Firm fee income hypothecated to reserve account	+	None	None

below.

The table shows how each transaction touching the stablecoin network (either within or external to the network) impacts the level of fiat currency (available for conversion to pay external firms) held by the stablecoin network. The table can be read in conjunction with Figure A1 and Figure A2 in the Appendix A of this article.

Summarising, under normal operation, firms within the stablecoin network pay each other using stablecoin. If a firm has insufficient coin to pay the invoices, operating in the same way as a commercial bank overdraft, the firm is automatically issued new stablecoin by the network administrator up to its credit limit. As stablecoin is received from other firms, the network administrator reduces any debit balance to zero before building a firm-level credit balance. The ability to convert stablecoin to fiat currency to pay external firm invoices obviates circulation imbalances that would otherwise lead to firm-level credit or debit imbalances accumulating according to intra-network flows [42] (Simmons et al., 2021). The imbalances would have a similar character to eurozone “Target 2” imbalances [99] (Cecioni et al., 2012). Conversion to fiat currency is restricted to the available fiat currency. In the absence of adequate fiat currency in this account, firms must pay external customers in fiat cash.

Fiat conversion depends upon central bank supervision to determine conversion flows (and associated fiat money supply impacts). In boom times, fiat cash balances are withdrawn from the network through mandatory reserve deposits into the network’s central bank account, whilst in a recession, the central bank provides loan cash through the same mechanism. The methodology is similar to the 1970s Bank of England special supplementary deposit corset [100] (Bank of England, 1982).

Figure 6 below outlines this high-level concept. Firms using the stablecoin are deemed to be within the community and so sit within the community currency boundary. Transactions between these firms are settled using the community currency, with the network administrator (a not-for-profit entity) acting as the coin issuer and exchange.

Within the network, stablecoin is issued up to assigned credit limits in response to payment transactions. Payment receipts first cancel negative balances and then build credit amounts.

Firms that are external to the network pay network firms in fiat currency, which the network administrator then converts to stablecoin and applies to the firms’ accounts as usual. The inbound fiat currency serves as a reservoir for network firms to pay external firms in fiat currency by converting stablecoin to fiat. The central bank regulates the network administrator and acts as a lender of last resort in the event of a systemic event. It can also manage fiat money conversion by calling in funds or depositing them into the reserve assets.

The central bank-regulated stablecoin thus becomes an additional monetary transmission and credit channel, more appropriately labelled a synthetic central bank digital currency [101] (Adrian et al., 2021). Acting contra-cyclically, it substantially improves overall monetary system efficacy by making substantial additional monetary mass available to industrial and commercial firms, reducing smaller firm credit rationing and in turn contributing significantly to long-term real economy growth.

7. Conclusions

Returning to the two primary research questions, (i) there is a credit rationing gap, and (ii) an appropriately designed stablecoin can help close this gap. To echo Milton Friedman [36], money matters. Economic activity and growth depend upon there being available money and credit in the real economy. Whilst generally, required interest rates throttled credit flows to larger firms in the real economy, smaller firms that make a crucial contribution to GDP can be, and often are, credit rationed. This credit rationing works pro-cyclically, accentuating recessionary damage.

A dedicated business-to-business stablecoin providing additional interest-free trade credit offers an additional transmission channel to allow central banks to target credit to reach wealth creators in the real economy. As a non-interest-bearing, non-speculative asset linked to real business transactions, moral hazard is contained, as this stablecoin is a loan to participating firms that must be repaid upon leaving the network or used to settle debts with other firms within the network. Run by a not-for-profit administrator, the stablecoin is supervised by the central bank to help deliver its given monetary policy stance.

The network and stablecoin activity provide additional information to commercial banks, thereby reducing small firm information asymmetry and aiding commercial bank credit decisions. One step away, but integrated into central bank monetary policy, this proposed stablecoin is an example of IMF economist Tobias Adrian’s synthetic central bank digital currency [101] (Adrian et al., 2021).

This article is a starting point. It sets a framework within which to understand monetary and credit transmission within the modern financialised economy that positions the not-for-profit trade credit-focused stablecoin outlined at a very high level in this paper. Detailed specification, piloting, and implementation are required.

Additionally, there is need for a consistent small business finance, credit, and output dataset that can be integrated into and reconciled with overall monetary and GDP statistics, thereby helping overcome existing data inconsistencies. Further, theoretical work on a dynamic transmission model is also required. The simplistic notation in this paper requires formalisation into a dynamic monetary transmission model that can then be subjected to econometric testing. One feature of such research may be to integrate concepts from this paper into instability and crisis analysis, with a special focus on the potential for multiple equilibria/disequilibria, tipping points, and crisis onset.