多边贸易信贷抵消：算法与实证

[SQUEAKING] [RUSTLING] [CLICKING] TOMAŽ FLEISCHMAN: So thank you, everyone, for coming. So my name is Tomaž Fleischman. I'm coming from a company called Informal Systems, and I'm working with your professor here on a subject called MTCS, or Multilateral Trade Credit Set-off. So today, we will explore what this is in a series of small examples, and we will dive into details to understand what implications this has to the real world.

So the schedule for today. We will look at one of the problems that is prevalent on the market. This is late payment. We will try to understand what this is. Then we will look into how can we observe this through observing networks, obligation networks. Then we will formalize the MTCS as an algorithm, look how it works, and then we will look at the empirical setting. So how does it play out in the real-life situations?

And we will look at some things that are, let's say, challenges in this area. So the network topology. And what can we do further by injecting liquidity using these techniques? So this is the program for today. And let's start with definition of the late payment. So late payment, you might know something about it from the personal settings like friends owing you money. So in business setup, that means me offering someone my goods or services on credit, expecting payment later in, let's say, 30 days.

And when this does not happen, we have a late payment. And this late payment is not just, OK, it's delayed. So there are many, many issues concerning with this. So it raises cost because we have to finance this, that it is not paid. It depletes the cash reserves, and it depletes the cash reserves of those who are least capable of doing so. Small, vulnerable companies. So there are administrative costs, so we have to manage the late payments.

It's a drain on labor productivity because it's unnecessary work. It creates substantial distractions from the work. So this is just from the business point of view of an individual firm, it's a problem. Looking at systemically, so it might look nice, your firm might look nice on paper, but it's not because you have basically losses and not properly managed working capital because of late payment. It places a huge burden on small companies to finance this.

It causes unemployment. It causes bankruptcy. Actually, this is the major-- this is the number one reason for bankruptcies. It's killing otherwise profitable businesses. And all this basically creates huge barriers for small firms to enter. So as such, late payment is not just something that is not pleasant to see, it is an actual systemic problem that needs to be resolved. So how do we measure this? So how do we observe late payment?

So this is an example of a report. This is by Intrum Justitia. It's a firm that does this for European Union annually. This is just an example from their report, United Kingdom, year 2020. And what we see here, for example, the top chart is comparison of agreed terms and actual terms. And you see that agreed terms are always lower than the actual. So 21 to 31, 45 to 64. So this extra time needed to actually pay is actually late payment.

Then here on the middle, you see, from the size of the companies, how it looks like. So the larger companies more often demand from the users-- from their suppliers to extend the payment terms. And here on the bottom, it's this was just in the COVID, so you see that the sentiment went very sour during the COVID. So the expectations that there will be more late payment went up significantly during the COVID. So this is how you can see that late payment is there, and we can see, it's a prevalent problem.

It's not just UK. This is the latest data from 2024. And you see, across a number of European countries, you have this around 60-day payment, which is definitely above the agreed terms. And you can also see that this goes across the sectors. So it's not just manufacturing or services, it's basically everywhere. OK. So from a point of a firm, how does it look like. So let's look at this chart. This chart was prepared by the English Association of Chartered Accountants.

It's basically a toolkit for members of this association to estimate and then plan, how do I handle the late payment? And here, you see in this Venn diagram, that this-- try to sort the reasons. So you can have a very simple tactical reason, like, I just don't want to pay. It serves me well to pay later. There could be real trouble, like default-- I don't have means to pay. And there can be other stuff, like you are going beyond the regulatory practice, or maybe you have some administrative issues and you are just outside of the agreed payment terms.

The interesting one is here in the middle-- so if you mix everything, you get this, buyer default in bad faith, which basically means you did it on purpose. And unfortunately, this is happening, too. So the normal thing that happens is like this. So it starts with, OK, I don't want to pay. And then it goes into, oh, I demand that you approve that I pay later. And then from that, it goes into not just I demand that I pay later, I want a discount.

So this is a typical extortion scenario that happens all the time. And from the other side, I'm a little bit tight on money, I just don't want to pay right now. Then you get in all kinds of issues. And finally, you end up with the same time. So you are basically going into default and you have to-- you have to take action as a firm against non-performing customer. So what you can learn from this-- so this is a tool for a firm.

What you can learn from this, that late payment is a very complex issue. So for a firm, it takes a lot of effort to manage this. So it's-- it is not a trivial matter. So another way to measure this is to just take into the account all the late payments, but this is very difficult to do. So here, we have an example of a country-- it's Slovenia, it's my home country-- where we actually did it. So here, you have by years, '91 to '94.

Percent of GDP. And the blue line is reported late payment. So there was a mechanism set up. Firms would simply report, I'm late. Why would they report? Because this agency doing this also organized clearing of late payments. And the red line shows you, in a percentage of GDP, how much of the late payment was actually resolved through the mechanism. The other thing, you can observe this yellow bars, show you the annual change of the GDP.

So obviously, we were in bad shape in '91. And then getting better as the economy gets better, the late payment as a problem is diminished, so the reported late payment obligations go down. But this scheme-- so this is a very specific moment in time for Slovenia. Slovenia just became independent in '91-- we had war here, so this is a war economy result. And '92 is the first year of being a state and have everything function.

The interesting thing, the mechanism was set up by Parliament and monitored by Parliament, so all the data was public. Then in '95, things went under government, and then the data became unavailable. Then in year 2002, the mechanism went into a government agency and the data became public again. And we see, we were still doing fine with some nice GDP growth year by year. And the reported late payments going down until you-- I think you know this area.

So we have this financial crisis, 2008 and later. And you see the interesting jump, yeah. The late payments went up significantly, and so did the results. The similar thing you can observe here for the COVID. So by observing this graph, you might say, OK, but this mechanism is not really working. Why is it going down all the time? So there is some background information you need, and this is that, in this crisis, private firms establish their own clearing mechanisms, and currently, private firms clear more than the public agency, but private firms don't share the data.

But I know the biggest one is bigger than the public service right now. So we have this clea

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