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Financial Crises and the Global Supply Network

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How do financial crises affect the organization and growth of multinational enterprises, particularly their affiliate networks and performance?

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This video is a transcript of a discussion about financial crises and their impact on global supply chains, led by Sergi. Key points discussed include:

1. **Understanding Financial Crises**: Sergi addresses the ambiguity in defining financial crises compared to normal recessions, highlighting that they involve disruptions to the financial sector, affecting access to credit, consumption, and investment.

2. **Context of the Study**: The research focuses on the impact of financial crises on global supply chains, examining how multinational firms and their subsidiaries are affected differently based on their financial contexts.

3. **Research Methodology**: The study analyzes data from various countries during the Eurozone financial crisis, focusing on changes in risk premiums to measure the crisis's intensity and how it affects networks of suppliers.

4. **Findings**:
- Financially hit networks saw a decline in the number of affiliates and had significant negative effects on revenue and employment.
- The network structure became geographically smaller, with firms reducing their exposure to larger countries during crises.
- Credit constraints were a primary mechanism influencing these outcomes, especially for more leveraged firms.

5. **Conclusions and Policy Implications**: The research concludes that financial crises distort global supply chains, which can have significant welfare implications. It emphasizes the need for a robust financial system to mitigate the negative effects of credit constraints on production networks.

6. **Future Research Directions**: The study suggests that understanding how financial shocks evolve and impact network structures is crucial and proposes that future work should explore these dynamics in greater depth to better understand international trade and financial interdependencies.

The discussion underscores the interconnectedness of financial health and supply chain dynamics, with broader implications for policy and economic resilience.

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X-TIMESTAMP-MAP=LOCAL:00:00:00.000,MPEGTS:90000

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Okay, so it's the 15th of October.

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We've just come up to, 12:00 PM in,

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in the uk we're doing financial crises

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and the Global Supply Network, Sergi.

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Thanks for talking to us. Okay.

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So, I mean, the audience will know

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what a financial crisis is,

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but they may not know how that differs

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from normal recessions.

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Can you maybe speak to that and what your context was?

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Yeah, so let, let me start by saying that there is no,

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I know, objective or clear definition of a financial crisis.

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So when, when, when a country experience, a band run,

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currency crisis, a sovereign default

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or a sudden stop, this episode is leveled as,

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a current financial crisis.

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Uh, but also, notice that,

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even within this type of crisis, sometimes, more, more

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of one of these events happen at the same, at the same time,

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and also also one leads to another.

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So it's kind of bit of a mess.

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So I, I always like to use the, the like to quote the, the,

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the first sentence of the book of Anna Carina

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to describe, a crisis.

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So all happy families are like each

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and happy family is different in its own way.

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You know, that means all financial crisis are

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basically different.

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And to give you an example, so if, if you,

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you think about the, the,

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the most recent global financial crisis, which is the,

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the great conversation in the,

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just started in the la late two thousands

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and just pick a couple of countries, for example,

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Spain and, and the us.

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So they, they look similar

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because the origin of the cows of the crisis were,

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actually quite, quite similar.

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So they both had a, a large house in Baba, which was funded

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by, by private debt,

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and it was also, funded

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by capital inflow from, from abroad.

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Then the housing bubble burst.

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It, it led to a province in the financial sector

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and a, a way of distraction for, for homeowner.

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But the consequences were very different in the

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US and, and in Spain.

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So just to give you a, a number, the GDP per capita in,

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in the US recover pre-crisis level in just a couple

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of years, and in, in Spain it took six years.

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So there were also, problems in,

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in Spain we were related to the, the Eurozone,

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but also were, some enforce error for part of the, the,

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the Spanish government.

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So it's difficult to talk about Francis in general

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because they're all different,

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but if I had to say something generally, I would say

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that financial prices are different from normal crisis

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because a financial crisis is associated with some damage

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to the financial sector, which implies that both houses

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and firms have, problems to have access to, to credit,

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which leads to lower consumption

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and lower, investment

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and some more prolonged, financial crisis in terms

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of financial possessions, in terms of lower and,

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and output and lower employment.

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And we, we know that also empirically from, from the work

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of, insure ulaan

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and tailor, who assembled a data set with several

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countries for different periods.

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So now I, we know that this is empirically true

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that financial crisis are,

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are different than, than normal crisis

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Follow up. And I'll ask you

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one question just then, where, where do,

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what is your work about?

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What is your work's context? What, what is the aims of your

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Yeah, so in, in this, in this, what we want

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to do is we want, wanna examine, which is the fact

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of financial crisis on the global supply chain.

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Uh, so more specifically, we're gonna to look at,

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a network of, of affiliates.

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So the, the affiliate may or may not be a supplier.

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Uh, so this world relates

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to three different, literatures.

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So first the literature on, the long run effects

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of financial crisis, in, in, in this, in this case, it,

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it relates to the, to the work as I I mentioned of,

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insure and, and company.

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And we share with them the, the view

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that you need a long panel to be able to see all the,

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the effects because it takes time for the effects to, to,

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to, to appear in the, in the data.

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One main difference is that,

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this studies usually look at the aggregate

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effects, so at the country level.

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So we're doing this, whether the effects is different

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across firms within the, the court.

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That's where we depart from this literature.

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Then there is a second literature, which is

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directly looks at how multinational firms

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react when there is a financial crisis.

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So here i I would like to cite, a couple of papers.

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So one is by Kino, Chenko and ez,

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and they show that during this great recession,

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multinational films,

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ri was lower than than domestic, ones.

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So main difference is

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that our interest is not comparing multinational

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and domestic, which has been shown before,

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but looking at called the, the network

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of these suppliers may, may change over over time.

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Then a closer paper is one of our far

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and chain where they show is that also during the,

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the rate recession, if an affiliate is owned by a,

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by a foreign firm, so a film is owned by a foreign film, it,

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it copes better with a crisis than domestic fear.

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That is the, the ownership kind of matter.

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So here, the main difference is that in our case,

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it could also matter the country

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of the pattern of the owner.

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So let you give you an example.

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A Spanish film owned by a German film may come differently,

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the financial crisis than a Spanish film

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owned by an Italian film.

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So that's something that we want

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to take into account in our analysis.

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And then finally, the fair literature that we relate on

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is the one that wants to understand the

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geographical composition of the global supply chain.

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So here, there is a, a very nice, review of Andra

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and in the Handbook of International Economics.

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And so there are different reasons.

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So the one that is relevant for us is the one that says

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that the quality of al institutions matter.

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So when a, when a parent chooses

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to locate production in country

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or contribute, it matters, which is the,

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the financial development of the, the quarter.

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And the the reason is, quite a straightforward.

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So if the, the affiliate has no access to,

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to bank credit, it may need credit from the parent,

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and then by the parent giving credit to the affiliate,

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it may distort the incentive of the affiliate to to produce,

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high quality good.

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So there will be a trade off between which type

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of countries you go

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and which type of goods you decide to offshore.

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But to the best of my knowledge, I think we are the,

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the first to empirical examine, which is the effect

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of financial crisis on, on the network of, suppliers.

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Well explained, really well explained. Okay.

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How did you do that then? Can you speak to your design

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and your, your data set and methods?

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So, so yeah, so our, our, crisis is going

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to be the financial disruption that happened in the Eurozone

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during the, the great, recession.

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So, and some of these papers that I mentioned

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before, they only look to what happens in one country.

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So they can basically do a before and

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after crisis kind of comparison.

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In our case, it's quite interested in this

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location across countries.

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We cannot do the same major size.

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So what we need to do is to have like a ranking

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of in intensity

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of the crisis in different different countries.

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And for doing that, we're going to look at the evolution

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of the risk premium across countries.

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So, and a risk premium, we define it as everyone does,

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as the difference between the 10 year government deal

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of a country with respect to, to Germany.

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And if you look at this evolution of the risk team across

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countries in Euro zone, you will see that from the nineties

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until 2007, it was very stable

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and close to zero in August, 2007, it starts increasing

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until July, 2012 when it goes back slowly

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to the pre 2007 levels.

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So what happened in July, 2012 is the whatever it takes

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a speech of of Mario

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and Dragon, what is less, well known is

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what happened in August, 2007.

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And so, and this is the, when BNP Parva, decided

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to close some through funds,

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which basically collapsed in the securitization markets

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and which was what the banks were using to obtain funds.

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So even though there is a popular narrative

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that the crisis has started with the collapse

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of Leman Brothers, now there is a growing consensus.

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And if you look at the financial data, it's very clear

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that the problems started one year earlier, so in,

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in the August, 2007.

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So what we do is we, we, we say that the extent

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or the intensity of the financial crisis is a difference in

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this premium between July, 2012 and August, 2007.

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And then if you look at the banking,

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you see what you will expect.

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So the, the peaks are up in the ranking.

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So Portugal, Italy, Ireland, Christmas, Spain,

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and in the bottom you find,

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the core Arizona countries.

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And even in negative numbers you find the uk, for example.

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So then what, what we, what we do, so when we have this,

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ranking at the country level, we need to assign a chalk

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to each multinational fair.

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And for doing that, we need the location

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of all the affiliates, of the, the network,

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assuming we have that what we complete is an, an average

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of the change in that this premium in all the locations,

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in all the countries where this network was, was present.

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So now let me go to a bit bit to the, to the data.

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So, this, this premium is from the OCD database,

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which is, public.

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But then what we need for this project is to be able to,

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to follow the entire network of the, the firm.

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Uh, and how that is, is, thanks to a a

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we have is is Bruno me.

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And what he did also in, in previous work was

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to use the annual versions of Abell,

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which is the counterpart of Orbi for, for Europe

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to construct, time series of para,

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affiliates link.

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So we went up with a, a panel of 39 countries,

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29 of each are, European.

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And, the panel is span from 2003

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to 2015.

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So we have, some years

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before the crisis to, to be able to check

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that nothing was happening before and some years also

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after to again, fully account for the, the effects

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of financial, crisis and,

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and then what we do economically.

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So we, we kind of do the, the, the standard,

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now becomes a standard.

235
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This local projections ensure that.

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00:11:43.735 --> 00:11:47.595
So basically we're able to, to track the evolution of,

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more financially hit networks over over time. Fantastic.

238
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Do you need a glass of water

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before we get into the second half,

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00:11:55.745 --> 00:11:56.745
or are you okay to keep going?

241
00:11:57.955 --> 00:11:59.295
Yes. Or maybe, yeah, of course.

242
00:12:17.015 --> 00:12:19.265
Yeah, right. Okay. So we're gonna do the findings.

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00:12:19.685 --> 00:12:22.385
So sometimes on the findings, we do follow up questions just

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to sort of flesh it out, but,

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we, I'm sure it'd be great.

246
00:12:24.985 --> 00:12:26.065
You've been very good so far.

247
00:12:26.125 --> 00:12:27.705
Uh, so I'm just gonna go to, editors.

248
00:12:27.705 --> 00:12:29.185
This is the, what are the findings of the paper?

249
00:12:30.315 --> 00:12:32.535
So we have like two sets of, of findings.

250
00:12:32.955 --> 00:12:34.615
So on the first,

251
00:12:34.615 --> 00:12:36.775
what we do is we describe which are the effects

252
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of financial crisis,

253
00:12:38.715 --> 00:12:40.655
and in the second part, we try

254
00:12:40.655 --> 00:12:43.455
to give some evidence on a particular mechanism

255
00:12:43.925 --> 00:12:45.455
that expenses result.

256
00:12:45.455 --> 00:12:46.855
So let me start from the beginning.

257
00:12:47.435 --> 00:12:49.855
So we have like four main main findings.

258
00:12:50.275 --> 00:12:52.255
So the first is that we, we show

259
00:12:52.255 --> 00:12:54.975
that the growth in the number of affiliates

260
00:12:55.965 --> 00:12:58.845
declines in more financially hit networks.

261
00:12:59.585 --> 00:13:00.725
And we, we find that the,

262
00:13:00.725 --> 00:13:03.045
the effect is negative from the, the start of the crisis.

263
00:13:03.585 --> 00:13:05.165
And this is a negative trend,

264
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but the effects only become significant after 2009.

265
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So starting in 2010, which is more what we were expecting

266
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because it takes some time to observe, an effect.

267
00:13:16.915 --> 00:13:19.925
Then the second, result is that we observe is that

268
00:13:20.455 --> 00:13:24.085
these effects are driven by vertical, relationship

269
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between the affiliate

270
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and the, the part, vertical relationship means that

271
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what the affiliate is doing is producing,

272
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is basically an input that is used by the, by the pattern.

273
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So when we look at the horizontal relationship, which is

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that, that there is no least this link of production,

275
00:13:40.945 --> 00:13:42.725
we find no, no effects.

276
00:13:43.835 --> 00:13:47.405
Then our, our fair, our fair result is that we show

277
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that the, the, the network becomes geographically smaller

278
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and we have different indicators for that.

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So for example, the distance between the para

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and the affiliates declines in more

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financially hidden networks.

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We also find that the difference

283
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between affiliates also declines,

284
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and also we find that the,

285
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the more financially hit networks, the decide to reduce

286
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exposure to the big, countries.

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And then finally, the last, the last, result is

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that's also compared to the literature.

289
00:14:20.605 --> 00:14:22.645
They also focus on revenues and employment.

290
00:14:22.645 --> 00:14:23.645
We can also do the, the same,

291
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and we show that the parents in more financial hit networks

292
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experience a substantial decline in both and revenues

293
00:14:31.425 --> 00:14:34.085
and unemployment growth during this, this period.

294
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And then the, let me go to the second part,

295
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which is the mechanism.

296
00:14:38.505 --> 00:14:41.885
So given that is a financial crisis, we pick

297
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and we were suspecting that, credit constraints

298
00:14:45.105 --> 00:14:47.645
or Fran constraints may be playing a role on that.

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And the reason is the following.

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So imagine you have, an affiliate,

301
00:14:52.705 --> 00:14:55.165
and in the quantity of affiliate there is a, a crisis.

302
00:14:55.825 --> 00:14:58.165
So now this affiliate is, is losing access

303
00:14:58.185 --> 00:14:59.405
to bank, bank credit.

304
00:14:59.945 --> 00:15:02.925
So he may need finding from the, from the parent.

305
00:15:03.865 --> 00:15:06.045
Now, if the parent has financial

306
00:15:06.285 --> 00:15:07.885
capacity, there will be no problem.

307
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He can lend money to the, the affiliate

308
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and everything is fine.

309
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But if the pattern has financial constraints,

310
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it will have a problem to do that,

311
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and the pattern may decide to block the link and,

312
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and let the, the affiliate fail.

313
00:15:23.025 --> 00:15:24.925
So we have different ways to test that.

314
00:15:25.345 --> 00:15:29.925
So one is that we compare the effect of this,

315
00:15:29.995 --> 00:15:34.325
financial, shock depending on the initial level

316
00:15:35.025 --> 00:15:36.325
of the leverage of the panel.

317
00:15:36.825 --> 00:15:40.605
And what we observe is that if the parents have low leverage

318
00:15:40.785 --> 00:15:43.805
to begin with, the financial crisis has no effect

319
00:15:44.105 --> 00:15:45.165
on the, on the panel.

320
00:15:46.265 --> 00:15:50.245
And if the parent is very ly, is very ed

321
00:15:50.245 --> 00:15:53.245
or is very ly constrained, the effects are very negative.

322
00:15:53.305 --> 00:15:55.685
And, and there, so basically all the effects

323
00:15:55.685 --> 00:15:58.165
that we are commending in the first part is

324
00:15:58.165 --> 00:16:01.645
because there are very leveraged, parents, which is kind

325
00:16:01.645 --> 00:16:03.205
of similar to one of our results

326
00:16:03.345 --> 00:16:06.125
of the work offshore than company at the country level.

327
00:16:06.185 --> 00:16:09.805
So they, they always also emphasize that the problems of

328
00:16:10.365 --> 00:16:11.645
countries facing a financial crisis

329
00:16:12.265 --> 00:16:15.005
are exacerbated if they have accumulated a lot

330
00:16:15.005 --> 00:16:16.725
of debt prior to the crisis.

331
00:16:16.785 --> 00:16:18.445
So that's is kind of, similar.

332
00:16:19.155 --> 00:16:22.965
Then the, the second piece of evidence that we, we have is

333
00:16:22.965 --> 00:16:26.045
that we look at which are the characteristics

334
00:16:26.305 --> 00:16:29.565
of the affiliates that are being dropped from the network.

335
00:16:31.025 --> 00:16:34.005
So what we find is, for example, that the leverage

336
00:16:34.025 --> 00:16:37.445
of the affiliate in itself doesn't explain

337
00:16:37.875 --> 00:16:39.565
that they are being dropped from the network.

338
00:16:40.155 --> 00:16:43.845
What it's important is that interaction between the leverage

339
00:16:43.865 --> 00:16:48.165
of the affiliate and being in a network financially hit.

340
00:16:48.745 --> 00:16:50.925
So if I'm in affiliate very leveraged,

341
00:16:51.065 --> 00:16:53.965
and I'm in a network, which is hit by the crisis,

342
00:16:54.465 --> 00:16:56.645
I'm more likely to be dropped from the, the network.

343
00:16:57.505 --> 00:16:59.965
And then we also show that these effects are also

344
00:17:00.615 --> 00:17:03.045
exacerbate if the pattern is very leveraged.

345
00:17:03.305 --> 00:17:05.525
So it means that the worst thing that can happen

346
00:17:05.585 --> 00:17:07.565
for an affiliate is that I'm leveraged,

347
00:17:08.065 --> 00:17:09.165
my pattern is leveraged,

348
00:17:09.225 --> 00:17:13.005
and I have a network that is, hit by the ential crisis.

349
00:17:13.725 --> 00:17:14.945
So this, these are the, the mentors

350
00:17:15.055 --> 00:17:16.185
also of our, of our paper,

351
00:17:16.605 --> 00:17:18.305
You've given me nothing to do.

352
00:17:18.585 --> 00:17:20.065
I normally follow up and ask about

353
00:17:20.065 --> 00:17:21.425
mechanism, really well explained.

354
00:17:21.635 --> 00:17:22.705
Let's get to the final question.

355
00:17:22.815 --> 00:17:24.425
What are your conclusions, implications,

356
00:17:24.425 --> 00:17:25.665
and areas for future resell?

357
00:17:26.865 --> 00:17:29.165
So, I will say that our main conclusion is first

358
00:17:29.185 --> 00:17:30.805
to show that financial crisis

359
00:17:31.355 --> 00:17:33.005
distort the global supply chain.

360
00:17:33.825 --> 00:17:36.645
And this may have welfare implications that were not, a,

361
00:17:36.765 --> 00:17:38.285
a study examine before

362
00:17:38.285 --> 00:17:40.765
because, so you will think that

363
00:17:41.425 --> 00:17:44.645
the production follow some conditions.

364
00:17:44.665 --> 00:17:47.365
So we saying that distorting

365
00:17:50.045 --> 00:17:54.025
supply chain, then our, our second, conclusion is

366
00:17:54.025 --> 00:17:58.225
that affiliates, may be drop from a network,

367
00:17:58.615 --> 00:18:02.865
even if the country is not financial, financially hit.

368
00:18:03.285 --> 00:18:08.265
So that being in, in, in the bad network may lead to,

369
00:18:08.285 --> 00:18:10.305
that affiliate being being dropped.

370
00:18:10.445 --> 00:18:13.745
And that's an also an important, result.

371
00:18:14.175 --> 00:18:17.105
Then another, a more policy implication is that

372
00:18:17.105 --> 00:18:19.865
what we're showing here is that the, the credit constraints

373
00:18:20.405 --> 00:18:23.185
are shaping this international operation of production.

374
00:18:23.805 --> 00:18:25.945
So it highlights the, the important

375
00:18:26.005 --> 00:18:29.505
of having a very healthier Brazilian, financial system.

376
00:18:29.645 --> 00:18:32.225
So it, it goes beyond the standard consideration.

377
00:18:32.245 --> 00:18:34.465
Why, why do you want a good financial system?

378
00:18:34.565 --> 00:18:37.825
So it also here for this global supply chain structure.

379
00:18:38.405 --> 00:18:41.745
And then for, for future research, that there was a very,

380
00:18:41.965 --> 00:18:46.345
important paper of c chenko that they,

381
00:18:46.455 --> 00:18:51.065
they emphasize that business cycle shocks propagate across

382
00:18:51.425 --> 00:18:54.985
countries in part through the network of, of suppliers.

383
00:18:55.845 --> 00:19:00.305
Uh, but in this work, the network is taken as, as given

384
00:19:00.485 --> 00:19:03.425
and constant Here, what we're showing is that the, the,

385
00:19:03.685 --> 00:19:07.225
the shocks itself change the, the, the network.

386
00:19:07.965 --> 00:19:10.745
So including that the network change

387
00:19:10.745 --> 00:19:12.905
with the chalk may be important to understand

388
00:19:13.445 --> 00:19:15.465
how shocks propagate across countries

389
00:19:16.005 --> 00:19:18.785
and also why this propagation may,

390
00:19:18.925 --> 00:19:20.305
may change over over time,

391
00:19:21.125 --> 00:19:23.585
but believe that for, for future, message.

392
00:19:23.975 --> 00:19:27.025
Fantastic. Oh, really good surgery. That's a wrap, sir.

393
00:19:27.025 --> 00:19:27.785
Let me too, about your next steps.

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