Good Booms, Bad Booms

Gary Gorton, Yale and NBER

Guillermo Ordoñez, Penn and NBER

1

Introduction

Financial crises not rare.

- 147 systemic crises since 1970.

- Occur in developed and emerging economies.

- Throughout history of market economies.

Credit booms usually precede financial crises.

We show that credit booms are also not rare.

- Over 50 years, on average a country spends 27 years in a boom,

12 of which were spent in a boom ending in a crisis.

Need macro models that incorporate crises and credit booms.

A crisis is not a “large shock.”

2

Introduction continued

But, some credit booms end in a crisis (bad booms) and other booms

do not (good booms).

Why?

What are the credit booms financing?

We show that: Total Factor Productivity and Labor Productivity

behave differently across good booms and bad booms.

Both booms start with a positive shock to TFP and LP growth. But TFP

and LP growth die off very quickly in bad booms.

3

Introduction continued

We present a model consistent with these facts.

In the model, there are credit booms; some end in a crisis, some do

not.

We do not rely on aggregate “shocks.”

But, there is technological change.

The seeds of a crisis are planted many years beforehand, related to

technological change.

Aggregate fluctuations related to low frequency phenomena.

4

Technology

Macro looks at high frequency movements, using the H-P filter.

Thinks of growth and cycles as conceptually separate.

RBC models need a negative contemporaneous “shock” to generate a

deviation from trend, a recession.

But, they cannot generate credit booms or crises.

There is a separate literature on TFP and growth (and another on

finance and growth).

5

In our sample the average length of a boom is 11 years.

6

Good Booms, Bad Booms: Empirical Evidence

Why do some booms end in a crisis while others do not?

We analyze a sample of 34 countries (17 advanced and 17 emerging

markets) over a 50 year span, 1960-2010.

Our credit measure is “domestic credit to the private sector divided by

GDP” (World Bank Macro Dataset).

TFP from Mendoza and Terrones (2012) and LP and other variables

from IMF Financial Statistics.

Crises: Laeven and Valencia (2012).

7

Definition of a Credit Boom

Other researchers detrend the credit measure. This defines how long

a boom is since longer booms affect the trend.

We do not want to impose any structure on booms.

We move forward through each country’s credit-GDP and define a

boom to be at least 3 consecutive years of positive annual growth

higher than 5%.

The boom ends whenever we observe at least two consecutive years

of credit growth of zero or less.

Results robust to changes in these thresholds.

8

Booms

Average duration of a boom: 11 years.

9

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Finding 1 (above figures): Significant differences across Good

Booms and Bad Booms. Confirmed in paper.

11

Finding 2: Credit growth is the best predictor of crises, but likelihood

mitigated by productivity growth.

Logit(𝐶𝑟𝑖𝑠𝑖𝑠𝑗,𝑡) = Φ(𝛼 + 𝛽Δ𝐶𝑟𝑒𝑑𝑖𝑡𝑗,𝑡−1)

12

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Finding 3: Credit booms start with a productivity shock.

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Finding 4: Are booms only about credit to households? No.

Repeat analysis with HH Credit. 33 booms of which 17 ended in a

crisis, compared to 87 and 34.

15

Finding 5: H-P filtering misses all this.

16

Summary

Credit booms are NOT rare and occur in both advanced and

emerging economies.

Booms are 11 years long on average.

Investment booms coincide with credit booms.

Booms start with a positive shock to TFP and LP growth.

But this shock dies out quickly in Bad Booms.

Growth of HH Credit highly correlated with other types of

credit growth.

Results not driven by Financial Crisis of 2007-2008.

These findings are not found when applying H-P filtering.

17

Model: Micro Foundations

Financial intermediation is about the provision of short-term debt: money.

Gorton and Pennacchi (1990): banks exist to create information-insensitive debt (riskless) for trading.

- Agents trade; need a security to protect against adverse selection.

- Liquidityinformation-insensitivity; but debt exogenous.

Dang, Gorton, Holmström (2013): debt is the optimal trading security because it is information-insensitive (not just riskless).

- Crisisfear of adverse selection reduces amount traded (and hence welfare). Crisis: info-insensitive->info-sensitive.

18

Model

Model is Gorton and Ordonez AER (2014) with two important

extensions (pointed out later).

Model is about bank creation of money, but banks and money

are abstracted from.

Firms Banks Households

Households need money

Banks produce money; collateralized

lending to firms

Receive loans;

Provide collateral

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Firms Banks Households

Households need money

Banks produce money; collateralized

lending to firms

Receive loans;

Provide collateral

Firms Households

Households: collateralized

lending to firms

Receive loans;

Provide collateral

20

Model continued

The collateral will be called “land.”

The “money” can be thought of as repo, which is

collateralized. Or, asset-backed commercial paper, etc.

21

Model continued

Two overlapping generations every period.

- Young/Households: Endowment and no labor.

- Old/Firms: Labor but no endowment.

Two goods that can be used to consume or produce.

- Numeraire (K): Perishable and reproducible.

- Land (X): Non-perishable and non-reproducible.

22

Land Collateral

Land type is unknown without info production.

Learning whether a unit of land if good or bad costs (in terms

of K) γl for lenders to learn and γb for borrowers to learn.

Good land: Generates C units of numeraire (only once).

Bad land: Generates 0 units of numeraire (only once).

Each unit of land has a common belief p of being good.

X = {C with probability p0 with probability (1 − p)

.

23

Firms that have land with a high enough probability that it is

good collateral, p, can raise funds in the loan market and

produce. We call them active firms.

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Firms

Continuum of mass 1 of risk neutral individuals/firms (old generation).

When old each has entrepreneurial ideas L* (no disutility) and no K.

A firm is a combination of labor, L*, a unit of land X, and numeraire K

(“capital”), to produce more numeraire:

Y = {A min{K, L} with probability q

0 with probability (1 − q)

where A>1.

Firms need to borrow K to produce. Optimal K*=L*.

Production is efficient, i.e., qA>1.

Y and q are nonverifiable.

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Technology

There are two types of projects available.

A fraction ψ has a high probability of success, qH, and the rest have a

low probability of success qL.

All projects are efficient: qHA>qLA>1, which implies that the optimal

scale of production is K*=L*.

A production opportunity set is defined by ψ.

Ψ arrives (exogenously). A different ψ may arrive later (but not in the

model).

26

Before approaching households for a loan, active firms are randomly

assigned to a queue to choose their project. When it is their turn to

pick, they pick the best available project.

So, the firm privately knows the quality, q, of its project.

Let η be the mass of active firms in the economy.

Lenders beliefs about the probability of success of any given firm are:

�̂�(𝜂) = {

𝑞𝐻 𝑖𝑓 𝜂 < 𝜓𝜓

𝜂𝑞𝐻 + (1 −

𝜓

𝜂)𝑞𝐿 𝑖𝑓 𝜂≥𝜓

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𝜂 is a function of time: 𝜂𝑡. In the credit boom, more and more firms

will become active.

Consequently, the average productivity of projects in the economy,

�̂�(𝜂), which is also the lenders’ beliefs about the probability of

success of a given firm, weakly declines with the mass of active firms,

η, and reaches a minimum when all firms are active, i.e., η=1.

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Households

Continuum of mass 1 of risk neutral households (young generation).

Each is born endowed with 𝐾 > 𝐾∗ of numeraire good and no L*.

They can lend K to firms and buy land X from firms.

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Market for land

At the end of a period:

- Match of a household with a firm (young with old).

- Negotiation power to the buyer (take-it-or-leave it offer).

- Price of land is pC.

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Lending market

At the beginning of the period:

- The output of firms is non-contractible.

- Firms can post a fraction x of land as collateral.

- Match of a household and a firm.

- Negotiation power to the borrower.

- Assume C>K*.

31

Information-Sensitive (IS) Debt

If information is produced then firms and lenders learn the true value

of the collateral.

If (risk neutral and competitive) lenders are producing information

then:

𝑝(�̂�(𝜂)𝑅𝐼𝑆𝑙 +(1-�̂�(𝜂))𝑥𝐼𝑆

𝑙 𝐶 − 𝐾) = 𝛾𝑙

Where: K is the loan size, 𝑅𝐼𝑆𝑙 is the face value of the debt and 𝑥𝐼𝑆

𝑙 is

the fraction of land posted as collateral.

Note that if 𝑅𝐼𝑆𝑙 > 𝑥𝐼𝑆

𝑙 𝐶, the firm will always default, handing over

collateral rather than repay debt.

If 𝑅𝐼𝑆𝑙 < 𝑥𝐼𝑆

𝑙 𝐶, then the firm will always sell the collateral at price 𝐶

and repay 𝑅𝐼𝑆𝑙 .

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So, 𝑅𝐼𝑆𝑙 = 𝑥𝐼𝑆

𝑙 𝐶 𝑥𝐼𝑆𝑙 =

𝑝𝐾+𝛾𝑙

𝑝𝐶≤ 1.

Note that, since the interest rates and the fraction of collateral posted

do not depend on q—because the debt is risk-free firms cannot

signal their q by offering to pay different interest rates.

This leads to expected profits:

𝐸(𝜋|𝑝, 𝑞, 𝐼𝑆, 𝑙) = max {𝑝𝐾∗(𝑞𝐴 − 1) − 𝛾𝑙 , 0}

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If 𝛾𝑏 < 𝛾𝑙, then firms choose to produce information. Then expected

profits are:

𝐸(𝜋|𝑝, 𝑞, 𝐼𝑆) = max {𝑝𝐾∗(𝑞𝐴 − 1) − min {𝛾𝑏(𝑞𝐴 − 1), 𝛾𝑙},0} (4)

Since the opportunity cost for firms to produce information

is 𝛾𝑏(𝑞𝐴 − 1).

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35

Information-Insensitive (II) Debt

Neither firms nor lenders produce information about the value of the

collateral.

Lenders set 𝑅𝐼𝐼 and x to break even:

�̂�(𝜂)𝑅𝐼𝐼 + (1 − �̂�(𝜂))𝑝𝑥𝐼𝐼𝐶 = 𝐾

Subject to 𝑅𝐼𝐼 = 𝑝𝑥𝐼𝐼𝐶. Then 𝑥𝐼𝐼 =𝐾

𝑝𝐶≤ 1.

Lenders want to deviate and produce information if expected gains

are greater than losses:

𝑝(�̂�(𝜂)𝑅𝐼𝐼 + (1 − �̂�(𝜂))𝑥𝐼𝐼𝐶 − 𝐾) > 𝛾𝑙 ⇒ (1 − 𝑝)(1 − �̂�(𝜂))𝐾 > 𝛾𝑙.

Lenders do not want to deviate if:

𝐾 <𝛾𝑙

(1−𝑝)(1−�̂�(𝜼)). (5)

36

From the point of view of the lenders, info-insensitive loans are such

that:

𝐾 < 𝐾𝑙(𝑝|�̂�(𝜂), 𝐼𝐼) = 𝑚𝑖𝑛{𝐾∗, 𝛾𝑙

(1−𝑝)(1−�̂�(𝜂)), 𝑝𝐶} **

The condition that guarantees that borrowers do not want to produce

info is:

𝑝(𝐾∗ − 𝛾𝑏)(𝑞𝐴 − 1) + (1 − 𝑝) min{𝐾, 𝐾∗ − 𝛾𝑏} (𝑞𝐴 − 1) < 𝐾(𝑞𝐴 − 1)

Or, in terms of loan size: 𝐾 > 𝐾𝑏(𝑝|�̂�(𝜂), 𝐼𝐼) ≡ 𝐾∗ − 𝛾𝑏 ##

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Combining ** and ## defines 𝑝∗such that 𝐾𝑙(𝑝∗) = 𝐾𝑏(𝑝∗) ---

depends on �̂�(𝜂).

I.e. info-insensitive debt is feasible only when the loan is: (1) above the

red dotted line, and (2) below the solid blue line.

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The cutoff 𝑝𝐻is the belief under which firms reduce borrowing, less

than the optimal 𝐾∗, to prevent info production, from eq. (5):

𝑝𝐻 = 1 −𝛾𝑙

𝐾∗(1 − �̂�(𝜂))

Note that 𝑝𝐻 is inversely related to �̂�(𝜂). As there are more active

firms, 𝑝𝐻 increases (moves to the right).

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Dynamics

Evolution of collateral value:

Each collateral is associated with one of three possible beliefs:

- 𝑝 = 0, if info produced and collateral is bad.

- 𝑝 = 1, if info produced and collateral is good.

- 𝑝 = �̂�, if no information was produced.

Assume that at t=0 all collateral qualities are known.

λ

1-λ

Collateral value remains unchanged.

Idiosyncratic shock: Collateral value

changes, becomes good with probability �̂�

.

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45

46

Define 𝜒 ≡ 𝜆�̂� + (1 − 𝜆). This is the fraction of active firms after

idiosyncratic shocks in a single period.

A fraction (1 − 𝜆) of all collateral suffers the shock and their

perceived quality, absent info production, is �̂� while a fraction 𝜆 of

the collateral known to be good, �̂�, remains good.

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Prop 1: 𝜒 such that 𝑝𝐹𝑙 (�̂�(𝜒)) < �̂� < 𝑝𝐹

ℎ(�̂�(𝜒)). Then stuck in the

IS range. Info is produced every period. Consumption is low.

Prop 2: Crisis.

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49

Recall that the cutoff 𝑝∗(�̂�(𝜂)) is monotonically increasing as �̂�(𝜂)

declines. Moving to the right in the figure.

More active firms reduces the perceived average quality of any

single firm.

Prop 2: �̂�(𝜒) is such that �̂� > 𝑝∗(�̂�(𝜂)) and �̂� < 𝑝∗(�̂�(1)). Then

Information Cycles.

Intuition: Starting from a date when there is perfect information,

such that there is no incentive to acquire information. At the start

the quality of the projects is high. But, then info decays over time

more and more firms get loans (credit boom), but the average

quality of the projects declines. At some point there is a switch to

producing information.

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Empirical Tests

During Bad Booms firms are increasingly likely to default. The

economy is increasingly fragile.

Fragility: Atkeson, Eisfeldt and Olivier-Weill (2013): 1/vol.

51

𝐿𝑜𝑔𝑖𝑡(𝐵𝑎𝑑𝐵𝑜𝑜𝑚𝑗,𝑡|𝐵𝑜𝑜𝑚𝑗,𝑡) = Φ(𝛼 +1

𝑣𝑜𝑙𝑗,𝑡−1)

52

TFP should be related to fragility.

Δ(𝑇𝐹𝑃)𝑗,𝑡 = 𝛼 + 𝛽Δ1

𝑣𝑜𝑙𝑗,𝑡−1+ 𝜀𝑗,𝑡

53

Conclusion

Current macro models rely heavily on “technology” shocks. But,

they cannot explain credit booms or crises.

Dynamics of “regular” cycles and systemic events? Technological

change is important for boom, recessions, and crises— possibly in

one unified model.

We do not rely on an exogenous “shock.” A crisis is not a big

“shock.”

54

Preview of “Aggregate Information Dynamics” (with Chousakos

and Ordonez)

Recessions/Good times defined differently.

The amount of information in the economy varies over time.

o Information measures based on stock prices.

More information is produced going into a recession with a crisis.

There is some feedback effect to investment.

o Investment moves from lowest quartile of firms to next highest

quartile (sorted by Tobin’s Q).