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Unemployment Insurance and Macro-Financial (In)Stability

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Yavuz Arslan discusses the (de)stabilizing effects of Unemployment Insurance

Image courtesy of interviewee. May 7, 2024

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This video video discusses the analysis of unemployment insurance (UI) and its role as an automatic stabilizer during economic downturns, particularly in the context of past crises such as the 2008 financial crisis and COVID-19. The authors highlight the need for automatic stabilizers—policies that do not require legislative approval and react automatically to economic changes, such as increased unemployment.

This video video outlines two key mechanisms introduced in the study:

1. **Household Balance Sheet Effects**: When unemployment insurance becomes more generous, households tend to save less and borrow more, particularly in the mortgage market. This video video leads to increased debt levels, which can heighten vulnerability during economic shocks as households may be more likely to default on their mortgages.

2. **Bank Balance Sheet Effects**: More generous unemployment insurance results in banks having riskier mortgage portfolios, as they may offer lower down payment requirements due to decreased income risk. This video video increases the likelihood of defaults, particularly during economic downturns when house prices and incomes decrease.

The authors argue that while unemployment insurance may support unemployed individuals, it also negatively impacts the stability of the broader economy due to higher indebtedness among households and increased risk for banks. Thus, they claim that the stabilizing effects of unemployment insurance could be outweighed by these adverse consequences, suggesting that relying solely on UI during crises is insufficient.

As a solution, the text advocates for integrating macroprudential policies—such as stricter lending standards alongside unemployment insurance—to mitigate these negative effects while still providing social safety nets. Future research may explore similar effects of other social policies, such as health insurance and progressive taxation. Overall, the findings suggest that policymakers should avoid over-relying on unemployment insurance and consider a broader range of fiscal interventions.

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X-TIMESTAMP-MAP:LOCAL=00:00:00.000,MPEGTS=900000

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We wanted to analyze, whether unemploy insurance,

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and maybe similar policies, stabilize economic downturn.

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So we were interested this topic because especially

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after like 2009, CRI eight crisis in 2008,

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global one financial crisis, and also

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after COVID-19, you know, the,

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there was like a problem with the fiscal policy.

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So because, you know, the, you know, they, they have to,

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vote for legislations, so that, you know,

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they can actually extend the fiscal policy

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to smooth down downturns.

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And because of these problems with the voting,

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and, you know, when will, when will it happen,

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we let be approved this kind of pro problems.

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A lot of policy institutions, including IMF

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and like similar, similar institutions as well

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as like top economists have been arguing

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that maybe we should revert to automatic stabilizers.

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So whatever, what are automatic stabilizers?

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They're automatic in the sense that, you know,

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they don't need legislation vote.

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Uh, so that, you know, they are, you know,

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when something happens in the economy,

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when the economies goes south, then, you know,

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these policies kick in.

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Well, in the case of, unemployment insurance policies,

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like let's say you are in the 2008

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and you see that unemployment increases to five to 6%, 7%,

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8%, then,

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automatically transfers increase to the unemployed people.

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So you don't need any vault.

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So it is like they are implemented in the economic system,

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economic system, so that, you know, unemployed people

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directly increase, their income.

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So they're automatic in that sense.

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And why, why are they called stab stabilizers?

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Uh, because the idea is that when these policies kick in,

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then, unemployed people will have,

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smoother income.

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So they, their income will not decline much as a result.

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They will not have to reduce their consumption much.

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Then, you know, as a society, as a,

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as an aggregate equitable, as a macro economy,

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then they will need to de, aggregate consumption

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and aggregate demand with decline less so

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that it'll be stabilizing.

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So it's automatic and stabilizers.

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So these are the mechanisms that,

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people have been thinking about.

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Uh, of course, the, there unemployment issues,

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policies also have some side effects.

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So the side effect is that, you know, people

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who are unemployed will search less,

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and as a result, they will stay

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for unemployed for a longer time.

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So this is the side effect.

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It has been studied in the economic, you know,

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economic literature and empirical and theoretically,

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and the magnitude of these side effects are not, you know,

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very well pinned down so that, you know,

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their people find some large

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effects and also some small effects.

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So you asked about the Bailey chatty formula.

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So Bailey chatty formula, but if they do is they actually

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weigh in the cost

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and benefits of this unemployment issues, benefits.

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So the benefits are, as I mentioned, like they liquid

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smooth co income, ter consumption,

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and the cost is unemployment, unemployment,

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longer unemployment.

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But we doing our paper is

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that we introduced two new mechanisms,

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which is not in the formula

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or the ones that I mentioned about you.

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The two, the two mechanisms.

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We argue that they are strong enough

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to overturn the stabilizing effects of an implementations.

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So the two, the two mechanisms are that, so when,

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the idea here is that when unemployment insurance becomes,

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more generous, we argue that it weakens the households

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and the bank balances.

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So why is that? So what happens is that, you know,

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if unemployment increase becomes more generous income,

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realistic lines, if income realistic lines, people need

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to save less in financial assets.

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So this is the first mechanism,

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but this, this was already found in the literature so that,

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you know, people save less because of the lower precautions.

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What's new? Our model, our study is that we also show

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that household borrow more.

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Uh, especially in the mortgage market, we find that,

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because of this lower income risk households,

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consumers want to own more housing,

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and they borrow more in the mortgage market,

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and they can do it because,

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there is no less income risk.

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So this is the first thing. So,

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household have less savings,

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and in particular, they have less liquid savings

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because they don't need much liquidity,

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because, you know, their income stream is, smoother.

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They don't need much liquidity.

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Instead, they want to own housing,

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and they do this by borrowing more in the mortgage market.

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The second thing is that bank balances also become weaker.

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I mean, this, this might be obvious.

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The, the, because you know, the assets, the liability

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of the households is, is the, the assets

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of the banks, right?

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So that, you know, if mortgage borrowing

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or the households increase, but also,

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mortgage assets in the bank balance is also increase.

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So now we have banks which have more mortgages,

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which we know from the 2008 crisis that might be a problem.

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And moreover, since individual income risk is lower,

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ris, their default risk is also lower, right?

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I mean, unemployed, they have zero income.

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They will probably, people will probably default,

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but example, now everyone knows this, what

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what banks do is they want you

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to put more down payment if income risk is higher.

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And the opposite side is

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that if the unemployment issues becomes more generous,

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they will not ask that much down payment.

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Okay? So right now, actually, we have an economy

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where mortgage borrowing is higher.

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At the same time down payments are also lower, so

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that each mortgage is actually, relatively riskier.

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So when a, when a bad thing happens in the economy,

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so you know, economy goes south,

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and the downturn is, you know, a sharp downturn,

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then we have households which have mortgage, which is

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and lower down payments.

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So that now the, and the banks with more mortgages

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and lower down payments riskier than the riskier mortgages.

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Now, these two effects, we argue

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that they will actually make the things worse

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and will dominate the cushioning effects

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of the unemploy issues.

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So we provide both theoretical

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and empirical evidence that,

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these mechanisms can be strong enough

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to overturn the stabilizing effects our people raise.

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You know, couple of strengths of the literature,

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but I want to only talk about, like the major three

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of the papers, three papers,

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that relates very close to ours.

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So the first paper is an econometric paper

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by Alistair McKay and Cord Race.

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Uh, so it's a 2000, I think 16 paper.

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So they build an generically model, pet Agent ally modeled

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that features, income risk, unemployment,

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insurance, unemployment.

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And they mostly focus on the two mechanisms

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that I mentioned in the paper.

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Uh, not, not our mechanisms,

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but you know, the income smoothing,

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income smoothing effects, unemployment insurance, as well

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as the unemployment effects.

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And they find that, I mean, especially

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for unemployment insurance,

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it stabilizes economic downturn.

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So there's no, the, it's kind of,

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it works like an automatic stabilizer.

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So what that model doesn't have is the, the borrowing

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and the leverage and the balance sheet effects

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that you mentioned in the paper.

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So, if for example, we, we show that, you know,

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without those mechanisms actually, our model

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and our framework also stabilizes.

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Uh, but once you take into these effects, then, this,

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the, the economies become, less stable

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with more general, more unemployment issues spans.

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There are two other papers

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that study the problem empirically so empirically,

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but there's this, and one of them is diman

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and care money paper and not publish it,

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but it is very well known paper.

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So these papers use cross cross-sectional variation across

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unemployment insurance in the United States,

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and they study whether the regions,

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which have more general unemployment insurance are,

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did better, does better in terms of economic stability,

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and they find that it is the case

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and other paper, in an Eco American economic review by,

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David Hu Sue Matsa.

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And in 2018, they look at individual level data,

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and they show that,

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if unemployment insurance is more generous,

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unemployed people default less on their mortgages,

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and they have other findings.

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But, relating to our work, that's the case.

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What we argue is that, cross-sectional identification,

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captures only some part of the effects

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of unemployment insurance policies.

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So once, for example, when once we adjust our model

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to fit their people's empirical frameworks, then we show

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that our model also captures their findings so that,

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you know, if you, for example, neglect the systemic effects

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of unemploy insurance, then we also find

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that the economy is more stable.

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However, it's kind of a,

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an incomplete picture.

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So if you neglect this, economy by effects, systemic

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by effects, or in the case of, the AR paper

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that I mentioned, you by hu they find

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that it affects the unemployed, it, it,

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it lowers the foreclosures of unemployed.

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However, you know, we show that, you know, okay,

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in our framework as well,

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unemployed people also benefit from the,

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unemployment issues perhaps.

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But when you look at the employed, which is the big majority

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of the people, then you just find the opposite effects.

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And in our case, employed people are like 95%

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of the population, and even small effects dominates

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the effects on unemployed.

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So I think, this could summarize, how our paper fits

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to the, the most relevant, literature in the

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stabilization effects of the unemployment.

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So we have two, sets of evidence.

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So first, we develop a generally covid model,

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heterogene generally covid model,

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and provide evidence from that model.

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And second, we also provide some empirical

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evidence from the United States.

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Okay? So first, let me de describe our framework.

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Uh, our framework is an heterogeneous agent,

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overlapping generation model for the household side.

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In the household side, this is overlapping generation, so

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that, you know, households, enter into the economy,

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like at the age of eight 18, they work until 65.

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Then, from 65 to 25, they are retired. Okay?

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So when they're working, they,

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they face two times two types of risk.

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The first is an income risk employee.

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People also face income risk, so that, you know,

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sometimes you earn a lot

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and sometimes you are less so that, you know,

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you don't know exactly how much you are going

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to earn even if you are employed.

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The second risk is an unemployment risk.

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Unemployment risk is, age dependent as in the data.

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So like, you know, younger people,

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face a higher risk compared to older people.

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And once they're unemployed, people can,

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can get unemployment issues.

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So this is the part where we introduce the unemploy issues,

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benefits, and how it influences income.

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Risk people,

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can save in, financial assets

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to smooth their income risk as a precaution model.

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Uh, they, they can do it,

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and second houses like consumption and housing, okay?

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So, you know, they can, they choose how much to save,

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how much to consume, and then whether they want

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to own a house or not, they can rent a house as well.

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I mean, but housing, you know, if they want

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to own housing are expensive.

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Houses are expensive. So like, you know,

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if a person doesn't have a house, if, if, if,

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if a person wants to own a house, they may want to,

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borrow via mortgages, long-term mortgages.

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So this is a one critical thing that in our model is that

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terms, the terms of mortgage contracts are indigence so

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that, you know, it is chosen by households

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and the banks with the coordination of the banks.

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So consider a household, they have an income risk,

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they have an unemployment risk,

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and unemploy unemployment issues is given.

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So they know the situation,

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and the banks also know the situation.

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So that, you know, what, what happens is that, you know,

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banks, over households, a many of contracts, let,

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let me, let me give an example.

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They say if you put 30% down payment,

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then your interest rate will be like 2%.

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If you put 20% down payment, your interest rate will be 3%,

12:12.745 --> 12:15.285
and if you put 10%, then interest will be 4%.

12:15.385 --> 12:17.125
So this kind of a, you know,

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of course in the comp computational equilibrium,

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we find we do this in a very much, finer, set

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of points when unemployment insurance increases,

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individual default risk status,

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keeping everything constantly declines so

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that the banks also know this.

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So they say that if in economies

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with more generous unemployment insurance, they say that,

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okay, households will be less likely default

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because they have an a more generous unemployment insurance.

12:43.975 --> 12:46.795
So what they will do is for each corresponding down payment

12:46.795 --> 12:50.335
requirement, they will offer a lower interest rate so

12:50.335 --> 12:52.975
that you know, the mortgage terms that the banks offering

12:52.975 --> 12:54.855
to the households will be loser, you know,

12:55.025 --> 12:57.855
loser in the sense that they will be easier conditions.

12:58.515 --> 13:00.175
So what happens then is, you know, of course

13:00.775 --> 13:03.295
interest rates are declining since the mortgage interest

13:03.295 --> 13:05.535
rates are declining, households will choose,

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lower down payment requirements and more, mortgage.

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So in equilibrium, the amount of mortgages will increase,

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and the amount of the size

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of the D payments will also lower.

13:17.005 --> 13:20.225
So this is actually a, a critical component of the analysis.

13:20.885 --> 13:23.545
So we have banks, banks are offering these mortgages.

13:23.695 --> 13:25.025
They also lend to firms,

13:25.125 --> 13:27.345
but I'm going, I'm not going to in detail, you know,

13:27.345 --> 13:28.985
because they're not that much interested.

13:28.985 --> 13:30.465
Firms are not that much interesting.

13:30.885 --> 13:33.825
Uh, they're important, but I mean, in terms of, the UI

13:33.825 --> 13:35.545
and interaction, they're not much interesting.

13:36.085 --> 13:39.585
So now the banks will offer losing mortgages.

13:39.775 --> 13:41.025
They will also lend to firms,

13:41.725 --> 13:43.985
and they finance these operations,

13:44.045 --> 13:46.505
by borrowing from investors at an extent,

13:46.685 --> 13:48.145
ex exogenous interest rate.

13:49.085 --> 13:52.465
But here we model, the banking,

13:52.465 --> 13:54.745
balance sheet problems according to G

13:54.845 --> 13:57.065
and American economic paper.

13:57.065 --> 13:58.545
So they know banks also can default,

13:59.005 --> 14:00.945
but now, you know, the investors know that,

14:00.945 --> 14:02.025
okay, banks can default.

14:02.575 --> 14:05.145
Then they also limit their lending to banks.

14:05.485 --> 14:07.345
So that, you know, there is bank lending

14:07.405 --> 14:09.505
as a result is a kind of a,

14:09.565 --> 14:10.865
is dependent on the bank network.

14:11.495 --> 14:13.865
This is kind of a, a very common way

14:13.865 --> 14:15.545
of modeling bank balance sheet problems.

14:16.165 --> 14:18.465
So now we have households,

14:18.465 --> 14:20.545
because of, this mortgage decisions,

14:20.575 --> 14:21.705
they have a balance sheet.

14:22.085 --> 14:23.265
And because of this, you know,

14:23.645 --> 14:25.265
UI affecting their mortgage terms

14:25.265 --> 14:26.745
and conditions, they have a balance sheet.

14:27.045 --> 14:28.465
And then we have banks holding

14:28.465 --> 14:29.865
these mortgages in their balance sheets.

14:30.375 --> 14:32.905
This is, the modeling environment,

14:32.905 --> 14:34.425
but net also say a couple

14:34.425 --> 14:36.545
of offers about the empirical setup,

14:36.615 --> 14:39.465
because we also, I think, provide important evidence from,

14:39.525 --> 14:42.585
the writing, the, the data from the United States.

14:43.205 --> 14:45.945
Uh, the advantage of US data is that,

14:46.015 --> 14:49.665
each United States state, decides on its,

14:49.805 --> 14:52.085
the generosity of the unemployment issues benefits.

14:52.985 --> 14:56.125
So as a result, we have a chance to analyze

14:56.745 --> 15:00.205
how UI affects, mortgage borrowing.

15:00.945 --> 15:03.685
Uh, then we also have data on,

15:04.635 --> 15:06.445
very county level house price data,

15:06.675 --> 15:08.205
loan data, the loaning data.

15:08.705 --> 15:12.365
And we can also analyze how much, these variables are,

15:12.625 --> 15:16.365
stable, in response to shocks to interest rate, so

15:16.365 --> 15:18.685
that, you know, we have a chance to, analyze

15:18.945 --> 15:22.205
how our results in the model, compare to results,

15:22.265 --> 15:24.255
in the data I would like to go,

15:24.615 --> 15:27.695
I will submit the findings in the model, then I will,

15:27.695 --> 15:29.655
switch to the, you know, what we find in the data.

15:30.475 --> 15:32.535
So in the model first what we do is, you know,

15:32.535 --> 15:35.695
we calibrate this, this model to the US data, you know, how,

15:36.115 --> 15:38.575
how the balance sheets look like in the data.

15:38.795 --> 15:40.895
So like, you know, we compare our models,

15:40.895 --> 15:44.015
steady state results, with the US data, like how

15:44.155 --> 15:48.575
of balance sheet, how much, how home ownership rate,

15:48.575 --> 15:49.655
those kind of variables.

15:49.955 --> 15:53.375
We also match the data bank balance sheet data with the,

15:53.515 --> 15:54.855
US bank balance sheets data.

15:55.565 --> 15:57.025
So we, we do this

15:57.045 --> 15:59.105
and to study the steady state effects,

15:59.105 --> 16:02.185
what we do is we solve the model,

16:03.465 --> 16:04.725
across different,

16:05.045 --> 16:07.405
unemployment insurance levels in the steady state so that,

16:07.405 --> 16:11.165
you know, we compare, the steady state

16:11.165 --> 16:14.245
of the economies from 20% unemployment insurance,

16:14.455 --> 16:18.125
generosity to 60%, unemployment insurance generosity,

16:18.545 --> 16:22.245
and 60% meaning that if a person gets unemployed, he

16:22.245 --> 16:25.205
or she will get, 60% of previous income,

16:25.745 --> 16:26.765
in that economy.

16:27.105 --> 16:30.365
Uh, and if it is 20%, he will,

16:30.365 --> 16:33.765
she will get 20% his past income, in the steady state so

16:33.765 --> 16:36.645
that, you know, unemployment issues become, various.

16:37.265 --> 16:40.565
And we show that first, mortgage borrowing increases.

16:40.675 --> 16:42.005
Like, let me look at the numbers.

16:42.105 --> 16:44.205
So for example, in the 20% economy,

16:44.305 --> 16:47.485
the mortgage borrowing is like 76% of GDP.

16:47.945 --> 16:52.125
In the 60% economy, it is like 83% loan,

16:52.125 --> 16:54.205
down payment requirements, amount

16:54.725 --> 16:58.845
C down payment payments decline from 10.4% to 8.4%,

16:59.745 --> 17:03.125
and the liquid asset savings decline by 3.2%.

17:03.265 --> 17:06.485
So as UI becomes more generous,

17:06.765 --> 17:09.485
mortgage borrowing increases down payment declines,

17:10.025 --> 17:12.885
and the financial assets also in, declines.

17:13.105 --> 17:15.365
So these are the, balance sheet mechanisms

17:15.365 --> 17:16.405
that, we mentioned.

17:17.015 --> 17:18.405
These are the steady state effects.

17:18.435 --> 17:19.445
What we want to do is,

17:19.445 --> 17:21.085
and of course it's not our main question,

17:21.085 --> 17:23.165
these are the mechanisms that we are thinking about.

17:23.585 --> 17:27.885
But to study whether unemployment insurance, increases,

17:28.025 --> 17:32.445
stability or decreases stability, we introduce to,

17:32.685 --> 17:34.285
a boom bust scenario in our model.

17:34.545 --> 17:36.405
So first we generate a boom,

17:37.235 --> 17:39.085
then we generate a bust in the economy.

17:39.665 --> 17:41.765
And the shock that generates, the boom

17:41.765 --> 17:45.645
and the bust is first interest rates decline, from,

17:45.655 --> 17:49.045
let's say 96 to 2000 interest rates decline.

17:49.385 --> 17:50.765
Uh, like the one in the data.

17:51.555 --> 17:53.525
Then, you know, people expected it

17:53.525 --> 17:54.605
was, it'll stay like that.

17:54.905 --> 17:59.135
But in 2008, suddenly interest rate goes back increases.

18:00.735 --> 18:02.875
And at the same time, we also introduced like an

18:03.035 --> 18:05.595
unemployment shock to make, to make our wound

18:05.595 --> 18:06.995
bust scenario similar to the data.

18:07.895 --> 18:12.045
So what we find is that unemployment in,

18:12.065 --> 18:16.405
the boas center, boomba cycle is a larger in the economies

18:16.405 --> 18:18.005
with higher unemployment insurance economi.

18:18.385 --> 18:21.325
So, and now I'm, I will talk, the mechanisms behind it.

18:22.815 --> 18:25.435
So when, think about, now, let's think,

18:25.435 --> 18:26.955
let's focus first on the household scale.

18:27.575 --> 18:31.595
So suppose, there's a shock in all these economies

18:31.705 --> 18:34.315
that house prices and income declines by the same amount.

18:36.085 --> 18:37.425
Now, in the economies

18:37.425 --> 18:39.425
with more generous unemployment insurance,

18:39.965 --> 18:41.465
people are more indebted.

18:42.625 --> 18:43.845
Now, house prices decline

18:43.845 --> 18:47.165
and they have more leverage given the same house price

18:47.165 --> 18:49.325
decline, and same income decline.

18:49.815 --> 18:52.005
Since h households have more leverage,

18:52.475 --> 18:53.485
they have a more incentive

18:53.585 --> 18:56.285
to default on their mortgages, okay?

18:56.435 --> 18:58.165
Same amount of decline in house prices,

18:58.715 --> 19:00.085
same amount of decline in inc.

19:00.265 --> 19:02.605
In, in incre, income in across UIs

19:03.115 --> 19:05.885
will push more households to default

19:06.505 --> 19:08.885
in more generous U economies because they are more inducted.

19:09.025 --> 19:10.045
So this is the first thing.

19:11.025 --> 19:13.285
Now, let's think about the bank down sheet.

19:14.545 --> 19:16.265
Remember, during the bus period,

19:16.615 --> 19:18.705
what generates a decline in,

19:18.885 --> 19:21.025
the bust is increasing interest rates.

19:21.805 --> 19:25.655
So now in the higher, more generous UI economies,

19:25.665 --> 19:27.615
banks have more mortgages in their balance sheets.

19:28.455 --> 19:29.455
Mortgages are long-term assets.

19:29.755 --> 19:31.815
So when, when interest rates increase,

19:32.115 --> 19:34.095
and they are, you know, we model, the mortgages,

19:34.095 --> 19:35.015
fixed rate mortgages, which

19:35.015 --> 19:35.935
is the case in the United States.

19:36.595 --> 19:40.135
So when, when interest rate increases, the value

19:40.135 --> 19:41.135
of the mortgages decline,

19:42.905 --> 19:46.485
and in more UI economies, there are more mortgages.

19:46.945 --> 19:49.525
So that, you know, increasing interest rates lower the

19:49.525 --> 19:53.365
bank balance sheets more in more UI economies

19:53.365 --> 19:54.405
because they have more mortgages.

19:54.405 --> 19:57.205
This is the first thing. The second thing is from,

19:57.205 --> 19:58.765
it's coming from the household balance sheet channel.

19:59.215 --> 20:02.525
Since house prices decline, it generates more

20:03.645 --> 20:05.715
increase in foreclosures, okay?

20:05.775 --> 20:08.475
So foreclosures, a high foreclosure

20:09.745 --> 20:11.165
in more generous u economies.

20:11.945 --> 20:13.725
And because of this valuation effects,

20:13.725 --> 20:16.405
because of the higher interest rates, now banks have

20:16.405 --> 20:20.445
to reduce their credit more in more generous U icons,

20:21.125 --> 20:22.985
and we show that these are strong enough to,

20:22.985 --> 20:24.425
to overturn the stabilizing effects.

20:25.485 --> 20:28.825
The second thing that the most, the other important thing is

20:28.825 --> 20:33.365
that we find that unemployment insurance benefits

20:33.925 --> 20:34.945
helped unemployed.

20:35.245 --> 20:37.185
So if you, for example, if you have a heterogene surgeon

20:37.185 --> 20:40.985
model, if you compare how unemployed people do

20:41.525 --> 20:42.865
in different UI economies

20:42.865 --> 20:46.505
and implementations, economies, unemployed people, the,

20:46.645 --> 20:51.285
the ratio of, for the, the ra, the rising foreclosures,

20:52.025 --> 20:54.365
in among unemployed in higher

20:54.885 --> 20:56.925
unemployment insurance economies is lower,

20:57.625 --> 20:58.845
compared to the lower.

20:59.265 --> 21:01.405
So this is the one that, you know,

21:01.405 --> 21:04.245
the empirical literature find so far, so that, you know,

21:04.375 --> 21:07.045
among unemployed, we see that actually yes,

21:07.565 --> 21:09.405
unemployment insurance effect, helps them.

21:09.785 --> 21:12.845
But to remember, unemployed people are only like six, seven,

21:12.945 --> 21:14.605
10% at the worst case scenario.

21:15.425 --> 21:17.005
Now, if you look at the employed people,

21:17.665 --> 21:19.445
the thing just reverse, remember.

21:19.505 --> 21:23.205
So employee people do not get the unemployment Ben benefits,

21:23.625 --> 21:25.805
and they don't benefit from more generous ui.

21:26.155 --> 21:30.475
However, ex example, they,

21:30.905 --> 21:32.715
they are affected by these policies

21:32.715 --> 21:36.155
because they know that if they are employ unemployed,

21:36.155 --> 21:37.475
they will get the benefits so that, you know,

21:37.475 --> 21:38.635
the balances are weaker

21:39.305 --> 21:41.835
because they, they expected income risk is lower.

21:42.095 --> 21:43.675
Now, they, they don't get the benefits.

21:43.865 --> 21:46.235
However, they suffer from their balance sheet effects

21:46.615 --> 21:48.675
and the results, the consumption declines more

21:48.675 --> 21:50.075
and foreclosures increases more.

21:51.555 --> 21:56.475
Uh, one thing that we do is actually, we want

21:56.475 --> 21:58.675
to, we want to analyze, which one,

21:58.675 --> 22:01.675
which balances are important, how much, okay, so bank,

22:01.895 --> 22:03.075
is it the bank balance sheets

22:03.175 --> 22:04.315
or the household balance sheets?

22:05.015 --> 22:07.635
So what we can easily do is, which is,

22:07.635 --> 22:09.475
relatively straightforward, is,

22:09.535 --> 22:11.315
we can close the bank balance sheet channel

22:12.265 --> 22:14.125
and see how much of the results

22:14.125 --> 22:15.605
that we have are coming from the banks,

22:15.985 --> 22:18.365
and how much is the, are the effects are coming from the,

22:18.465 --> 22:19.525
households.

22:20.355 --> 22:23.885
Once we close the bank balance channel, what we find is

22:23.885 --> 22:27.845
that consumptions become, consumption becomes more stable.

22:29.295 --> 22:31.715
So a higher in, consumption is more,

22:31.715 --> 22:34.195
becomes more stable in more generous UI economies

22:34.195 --> 22:35.315
and IOUs economies.

22:36.065 --> 22:38.555
However, housing and the mortgages

22:38.775 --> 22:43.515
and foreclosures becomes still stays, unstable.

22:44.705 --> 22:48.275
Okay? So that, you know, households, balance sheets, we,

22:48.335 --> 22:50.035
we actually qu try to quantify

22:50.455 --> 22:53.075
and we find it like they're almost equally important.

22:53.375 --> 22:55.595
And the household balance sheets are important more in long

22:55.595 --> 22:57.275
term than balance sheets are more

22:57.275 --> 22:58.475
important in the short term.

23:00.025 --> 23:02.275
This is also important in the sense that, you know,

23:02.275 --> 23:04.115
if you look at the cross-sectional analysis,

23:04.115 --> 23:07.035
where they analyzed how UI effects,

23:08.515 --> 23:11.755
regional stability, those papers, those that kind

23:11.755 --> 23:16.195
of research empirical setup cannot, identify the effects

23:16.335 --> 23:18.595
of, UI on the banking sector.

23:18.595 --> 23:20.675
Because, you know, when banking sector goes bad,

23:20.985 --> 23:22.195
it's an economy wide thing.

23:22.195 --> 23:25.295
It's not regional. So, they have

23:25.295 --> 23:27.735
to just look at the regional effects in the cross-section

23:28.055 --> 23:30.575
analysis by neglecting the bank's balance sheets.

23:30.875 --> 23:34.895
And what we find is that actually confirming their view,

23:34.955 --> 23:36.335
so that, you know, we say that, okay,

23:36.335 --> 23:38.775
if you close the bank balance channel,

23:38.845 --> 23:40.695
then we have some stability in consumption,

23:41.535 --> 23:44.785
however, it'll be actually missing an important component.

23:44.845 --> 23:48.405
Uh, is a systemic part coming from the bank balance sheets.

23:49.375 --> 23:51.725
Lemme go to the setup.

23:52.615 --> 23:55.955
So what we do here is that, we use Honda data.

23:56.335 --> 24:00.115
Uh, Honda data is a very detailed, mortgage level data,

24:00.895 --> 24:05.555
and we use, state level,

24:06.495 --> 24:08.315
ui, unemployment insurance, generosities,

24:09.015 --> 24:11.315
and first we show that in the data as well.

24:11.865 --> 24:14.315
Once unemployment insurance becomes more generous,

24:15.065 --> 24:16.795
long income ratio increases.

24:17.335 --> 24:19.115
So this is relatively a new finding.

24:19.375 --> 24:21.595
Uh, i, I haven't seen that much in the literature.

24:21.815 --> 24:24.355
So that this is, remember our main story is

24:24.355 --> 24:26.875
that unemployment insurances, insurance be,

24:27.515 --> 24:30.035
vehicles, balance sheets and household balance sheets,

24:30.575 --> 24:34.195
and yet we didn't show much empirical, evidence now,

24:34.195 --> 24:36.195
with the hundred data, but we show is

24:36.195 --> 24:37.835
that this is indeed the case in the data.

24:38.295 --> 24:40.955
And quantitatively our estimates are very similar to the,

24:40.955 --> 24:42.115
what we find in the model.

24:42.545 --> 24:44.075
This is the first empirical thing,

24:45.035 --> 24:46.295
but this is yet a correlation.

24:46.675 --> 24:48.735
Uh, and this has a, it is not like a causal relationship

24:48.735 --> 24:52.135
that people, will look for for causation.

24:52.135 --> 24:55.895
What we do is, there is a surprise, shortening in,

24:56.035 --> 24:57.935
UI generous in Missouri.

24:58.275 --> 25:01.375
So there was kind of a, people, other papers use this

25:01.375 --> 25:02.815
as a kind of an exogenous shock,

25:03.275 --> 25:07.095
and we show that actually compared to similar states,

25:07.125 --> 25:11.135
once a UI becomes, less generous in Missouri,

25:11.135 --> 25:14.175
long income ratios, become, smaller so

25:14.175 --> 25:16.935
that it's like a more causal, identification.

25:18.075 --> 25:20.695
And remember in the model, which, which we argued is that,

25:20.995 --> 25:23.895
even after closing the bank balance channel,

25:24.115 --> 25:25.615
our results for housing

25:25.675 --> 25:28.455
and the mortgages, stay, with even

25:28.455 --> 25:29.495
with the household balances.

25:30.235 --> 25:33.775
So for that, what we do is we, form,

25:33.835 --> 25:36.975
we generate the data, at the county level,

25:37.185 --> 25:40.295
house prices and mortgage credit, data.

25:40.875 --> 25:43.935
And we want to analyze how, mortgage credit

25:43.935 --> 25:46.495
and house prices react to long-term interest rates.

25:46.875 --> 25:48.735
So for the, for the housing market,

25:48.845 --> 25:51.535
this is the most important variable that we know.

25:51.915 --> 25:54.215
So what we do is that we show is that,

25:54.215 --> 25:58.095
once in long-term interest rate changes, economies

25:58.095 --> 26:00.295
with more generous and implement insurance benefits,

26:00.875 --> 26:03.935
reacts more, to, changes in interest rates.

26:03.995 --> 26:05.575
So house prices decline more

26:06.235 --> 26:09.095
and mortgage crisis decline more than interest rates,

26:09.455 --> 26:11.015
increases more in more generous U economies.

26:11.015 --> 26:14.295
So this is, kind of, we find it interesting.

26:14.355 --> 26:17.375
The result is that, and because UI doesn't really stabilize

26:17.635 --> 26:20.175
an implement issue, when I say ui, it's implement insurance,

26:20.555 --> 26:23.855
it doesn't stabilize, economic downturn

26:24.075 --> 26:27.095
or, how did the housing market suppose you are a

26:27.095 --> 26:30.295
policymaker, and that something like a covid,

26:30.595 --> 26:32.095
or 2008 crisis happens.

26:33.315 --> 26:36.575
So now would you just,

26:37.885 --> 26:39.215
feel happy that,

26:39.755 --> 26:42.655
you have a good mplementation benefits and

26:42.685 --> 26:45.485
Just let the mplementation benefits do the job

26:46.265 --> 26:48.045
Our finding says you shouldn't.

26:48.235 --> 26:51.245
Okay? So that, you know, still there is a big room

26:51.245 --> 26:52.685
for discretionary fiscal policy.

26:52.745 --> 26:54.245
We want to say that this is the first thing.

26:55.465 --> 26:57.805
And the second thing is that maybe you know how to avoid,

26:57.825 --> 27:00.285
how can we avoid this kind of step stabilizing effects?

27:00.285 --> 27:03.245
Because we are not saying against we,

27:03.245 --> 27:05.765
we are not against unemploy issues, benefits, let me say

27:05.765 --> 27:07.365
that, because we are aware

27:07.365 --> 27:10.085
that it has a huge social benefits to the society.

27:10.505 --> 27:12.805
So we don't want to say that or you have to just cancel it.

27:12.905 --> 27:13.925
No, we don't say that.

27:14.345 --> 27:17.325
But what we say is that maybe, we might,

27:17.505 --> 27:21.325
or the policy makers might, collaborate these policies,

27:21.465 --> 27:25.085
in, with macro prudential policies so that,

27:25.085 --> 27:28.125
you know, maybe, they should actually,

27:28.425 --> 27:30.845
put some like extra long to value restrictions,

27:30.845 --> 27:32.965
minimum down payment requirements such

27:32.965 --> 27:36.885
and such so that these side effects can be, overcome.

27:37.625 --> 27:41.125
The finally, I mean, we mostly focus on unemploy issues,

27:41.365 --> 27:45.085
benefits, however, these findings can go, up

27:45.085 --> 27:48.765
to other policies as well, like, health insurance,

27:49.265 --> 27:52.325
pro, you know, progressive income taxation.

27:52.825 --> 27:54.765
You can, think all social policies

27:54.955 --> 27:59.085
that might ensure households to, against, idiosyncratic

27:59.085 --> 28:00.245
or macroeconomic shocks.

28:00.785 --> 28:03.565
Uh, so that, you know, that those kind

28:03.565 --> 28:06.805
of policies might also affect, similar effects similar

28:06.805 --> 28:09.005
to the UI and implementations policies.

28:09.005 --> 28:11.925
So that, I mean, for future work, you asked,

28:11.925 --> 28:13.845
maybe we can, we, we are planning to,

28:13.995 --> 28:15.525
explore those policies as well.

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