In the autumn of 2020, several members of Mutual Credit Services launched a loose symposium with the not-too-catchy identifier ‘Circular Trade Analytics’.
What we wanted to do was to develop tools and techniques that enabled us to look at the relationships and dynamics in economies which were practicing various mechanisms we identified as ‘circular’ in character – Continuous Clearing and Mutual Credit in particular.
A small but wide-ranging group began to meet fortnightly and it became clear that there was significant work to be done – that despite the enormous worldwide output of economics-related papers, books, articles and analytical tools, there seemed to be very little that was applicable to the kinds of networks we are setting out to build – and that what work does exist tends toward the socio-economic, rather than the quantitative.
We know that we need to be to able quantify benefits for members, to be able to understand when trading networks are healthy or not, and what the correlates are of these conditions, how to advise network managers as to how flow and velocity of trade may be encouraged – these and and many other questions will need good answers if we are to build out and support thriving networks.
It seemed as though, despite the work already achieved by attendees, such as the output from Grassroots Economics, there was little to build upon, and a great deal to do.
And then we were introduced to a new paper, by Tomaž Fleischman, with Paolo Dini and Giuseppe Littera; Liquidity-Saving through Obligation-Clearing and Mutual Credit: An Effective Monetary Innovation for SMEs in Times of Crisis, and it was as if someone had turned the light on.
It turns out that, unbeknown to most, a significant body of technique and experience has been developing in Slovenia over decades, for the analysis of trading networks at scale, and in particular, the discovery of ‘cyclic structures’ within the networks – the very circularity that we are wanting to understand.
Although remarkable work has been done, to significant effect (for instance, the Slovenian economy was helped to bounce back from the 2008 financial crisis through an invoice clearing mechanism that allowed almost 10% of the country’s trade transactions to be settled without money changing hands), this paper is the first time that these capacities have been demonstrated in a published paper.
Continuous Clearing and Mutual Credit
In this first paper, the authors demonstrate the remarkable impact that two key “new economy” approaches – namely, Continuous Clearing and Mutual Credit – can have on business finances.
The paper analyses a large number of business-to-business trade transactions (over 130,000, totalling >€30M, for the year 2019), looking at the impact of different approaches to the settlement of these transactions on the fiat balance-sheet positions of the participant firms. At any point, each business will have a trade balance of outstanding ‘accounts payable’ (to creditors) and ‘accounts receivable’ (from debtors).
The default position, of course, is that each invoice is settled individually with a bank transfer. This is the trade world as we know it, with all its attendant risks and impacts – late payments, defaults, invoice factoring, expensive overdraft finance and the rest. This is known in the jargon as ‘Real Time Gross Settlement’ – and is taken in the paper as the ‘baseline’ for comparison.
The graph shows the distribution of final balances for each individual business represented in the data set. This demonstrates a fairly wide distribution of outcomes, with businesses of small and large turnover represented among those with net deficits, balanced outcomes and net surpluses, and – as expected – a preponderance of firms with the largest net balance positions (whether +ve or -ve) having relatively large turnover.
The first mechanism analysed is ‘invoice clearing’. Within a sufficiently large set of transactions, it is possible to discover ‘cyclic’ patterns which connect ‘loops’ of transactions together – meaning that all of these can be ‘offset’ – resolved without any bank-money transfers [A owes B, B owes C, C owes D, D owes E, E owes A]. Such offsetting does not change the eventual balance position of the firm, but does reduce the amount of bank currency it needs to conduct its trade, and thus improves cash-flow and availability of working capital.
Using the ‘cycle detection’ algorithm developed in Slovenia in the 1970s (a significant piece of work in itself, since this is a computationally expensive ‘np hard’ problem, further complicated by the fact that the cycles are not ‘disjoint’ – to use the term from the paper – but deeply interconnected), the authors show that, for a significant proportion of participating firms, in a sufficiently diverse network, a notable proportion of their trade can be ‘netted off’ in this way. For a significant proportion of participating firms, applying this mechanism could halve the amount of bank-money needed across the year, while the ‘across the board’ average would be about a quarter.
Note that this computationally tricky approach can be avoided by the simple expedient of all participants submitting invoice data to a ‘central clearing’ ledger. This ledger simply adds up all credits and debits for each member to produce a ‘cleared balance’ – the net sum owed to or by a firm in respect of the whole network. At the end of each clearing period, each firm would either pay or receive a single bank-money transfer from the central clearing account to achieve settlement for all invoices.
This simple approach is used in the Trade Credit Clubs model , and can be straightforwardly integrated into a firm’s accounting software by treating central clearing as if it were a bank account.
The paper goes on to analyse the impact for firms of adding a further mechanism – Mutual Credit.
This can be simply understood as a pooled trade credit arrangement. Each firm agrees, in respect of the network, to allow a certain amount of value to be settled using an accounting unit internal to the network – a Mutual Credit Unit (MCU). This reduces the amount of bank-money needed still further, as non-cleared balances at the end of each clearing period need not be paid in full, but simply brought back within acceptable limits. The network, through the aggregate commitments of all of its members, is effectively providing its own money supply.
The paper assumed that each firm has access to ‘rolling trade-credit’ amounting to 2% of annual turnover (the rule applied in the Sardex network, from which the transaction data was sourced), and shows that the impact of this is approximately equal to the impact of the Obligation Clearing mechanism – a reduction of the order of 25% of bank-money needed to clear obligations at the end of the trading period.
The Trade Credit Club model includes this mechanism, too, but rather than imposing a blanket credit limit on participating firms, offers a process whereby each firm proposes both the maximum credit balance it is prepared to hold and the maximum credit it wishes to access within the network. Once all members have made proposals, an optimisation routine will be used to set workable balance limits. This process can be repeated at intervals, so that liquidity provided by the network can respond to changing conditions.
Combining both mechanisms
Finally, the authors show that it is not only possible to combine both mechanisms, but that the impact is additive, so that an approximately 50% reduction in bank-money requirements is achieved in the context studied.
In discussing other large transaction data sets with Tomaž Fleischman, it seems that provision of internal liquidity within the network at the level of around 20% of the total turnover can allow for essentially all trade within the network to be settled without need for bank-money.
Alongside the quantitative findings outlined above – which are dramatic enough in themselves, the paper also provides some fascinating history with a potted history of the development of the Slovenian system.
In the section with the heading ‘History and Context’, a rather dry and minimal account is given of the routine usage of centralised obligation clearing by the Slovenian government from 1991 onwards, and the contribution of this mechanism to the weathering of economic turbulence in the country. This graphic shows the counter-cyclical take-up of this mechanism during the credit crunch that followed the 2008 crisis.
What is not described in the paper, but which came up in conversation with Tomaž is the attempts by the EU to convince the Slovenian authorities to stop using this mechanism when they were negotiating to join the Eurozone, despite the fact that it had made a major contribution to saving their economy on several occasions. Although the Slovenians refused, they did have to accept restrictions on the operation and scope of the mechanism. The result of this is that the clearing mechanism is not utilised to the full. It should also be noted that there is no Mutual Credit liquidity supply into the Slovenian system.
… and that’s not all!
The above outline covers only my interpretation of the most immediate findings from the paper, but it repays close reading, so I do encourage you to spend some time with the full text.
Some other nuggets include:
- some rather lovely graphical network mapping images (which are themselves just a taster for watching Tomaž demonstrate them ‘live’),
- useful discussion of the character and effects of cycles in trade networks – in particular around the risk that such cycles pose in credit squeeze situations – since they can cause a ‘freeze’ that extends throughout an economy – where no-one can pay anyone else
- the software used for cycle detection is also described in some detail.
And finally, an eye opener …
The section on ‘Liquidity Saving Mechanisms’ was an eye opener. It makes it clear that various mechanisms to provide liquidity into money systems are well-understood in the banking sector. ‘Clearing and Settlement Systems from Around the World: A Qualitative Analysis’, referred to in the paper, documents the extensive use of netting-off mechanisms used in bank payments systems around the world. Again, some reading between the lines and looking at referenced material makes it clear that the advantages of these mechanisms are not the subject of public discussion or made available outside the banking sector or multinational corporates.
In addition to the clearing approaches used, it is clear that many of these systems include close analogues to mutual credit mechanisms (albeit of restricted scope) when they incorporate mechanisms described as “ … liquidity reservations, transaction prioritization and timing, and active queue management … ”.
It’s pretty obvious why these practices are kept ‘under wraps’ – the business model of commercial banks depends upon making access to money costly. Offering liquidity saving mechanisms to networks of businesses which allow them to diminish their need for bank finance is not something banks are likely to promote.
There are a number of references to follow up here, but the most important learning so far from this section for me has been that the mechanisms underlying ‘Mutual Credit’ and ‘Continuous Clearing’ are not in themselves either ‘novel’ or ‘radical’. That the innovation we provide is not in the mechanics, but in their implementation – in bringing them to a different context. Bringing them out from the financial management back rooms of banks and corporates, and into the service of the value producing economy. This theme is explored further in a companion piece to this post – ‘Complementary’ economics won’t do it.