The Credit-Driven Household Demand Channel

Atif Mian∗

Princeton University and NBER

Remarks at Nobel Symposium On “Money and Banking”Stockholm. May 26, 2018

A Striking Empirical Regularity

The great recession of 2008 left behind a striking empirical regularity that you can see in the left

panel of Figure 1. States within the U.S. that had a larger increase in household leverage between

2002 and 2007, ended up experiencing a much more severe recession between 2007 and 2010 as

measured by the increase in unemployment. Remarkably, we find exactly the same relationship

across countries. In particular, countries that had a larger increase in household leverage between

2002 and 2007, experienced a much more severe recession between 2007 and 2010 (right panel of

Figure 1).

One might complain here that I am conditioning on a recession in figure 1 and as such it is

not that surprising that leverage, conditional on a recession, hurts. However, as I will discuss, this

empirical regularity is not an artifact of cherry picking data, but is the result of a systematic force

that I am going to refer to as the credit-driven household demand channel.

The credit driven household demand channel has three stages. It starts with an expansion in

the supply of credit, i.e. for some reason, financial markets are willing to lend more to the same loan

applicant. This increase in credit supply fuels a boom that drives an outward shift in household

aggregate demand. However, the expanding credit boom also sows the seeds of its own destruction

and ultimately results in a macroeconomic slowdown.

I will present a wide range of empirical findings, both from the U.S. and around the world

and covering the last half century, that hopefully will convince the reader of the prominence of

credit-driven household demand channel. I will first present international evidence from business

∗I thank Michael Varley for excellent research assistance and Julis Rabinowitz Center for Public Policy and Financeat Princeton for financial support. Mian: (609) 258 6718, atif@princeton.edu.

1

cycles worldwide, including a new out-of-sample test of previous findings. I will then present results

from a natural experiment within the U.S. during the 1980s when states started deregulating their

banking systems. And finally i will discuss U.S. evidence from the Great Recession episode. The

last part of my remarks will be devoted to discussing the theoretical and policy implications of

these empirical findings.

International Evidence

I start with stuctural VAR evidence on credit shocks and their impact on business cycles world-

wide from Mian et al. (2017b). We use data from 30 mostly advanced countries over the last half

century and run VAR using log real GDP, and the two components of private credit to GDP, house-

hold credit to GDP and non-financial firm credit to GDP. Two points are worth noting. First, the

response of GDP to a household credit shock is a muted boom followed by a strong and persistent

slowdown that i have emphasized with an arrow (left panel of figure 2). Second, there is a clear

asymmetry in the response of GDP to household credit shock versus a shock to non-financial firm

credit. The latter does not produce the strong cyclical response (right panel of figure 2). This is

the first indication that a household credit-driven demand channel is important for understanding

business cycles.

Table 1 pools together evidence from Mian et al. (2017b,a) and highlights some noteworthy

elements of this credit-induced business cycle. First, household credit growth, but not non-financial

firm credit growth, is contemporaneously associated with “consumption booms, an increase in the

consumption to GDP ratio and a deterioration in the trade balance with the consumption share of

import goods rising significantly (columns 1 through 3).

This is suggestive of a household credit-driven local demand boom. As discussed in Mian et

al. (2017a), we can explicitly test for whether credit pushes local aggregate demand by testing if it

disproportionately expands non-tradable relative to tradable sector - both in terms of the relatize

size of the non-tradable sector and also in terms of relative price of non-tradable to tradable sector.

This hypothesis is confirmed by columns 4 and 5. There is expansion in non-tradable sector to

tradable sector employment (column 4) and increase in non-tradable to tradable sector prices

(column 5) when household credit expands. No such relationship holds for non-financial firm credit

that is more likely to operate on the supply-side of the economy. Column 6 summarizes the key

2

result I already highlighted in the VAR plot: growth in household credit predicts subsequent GDP

slowdown, whereas non-financial firm debt does not. Notice again the asymmetry between the

effects of household credit and non-financial firm credit throughout table 1.

The results in table 1 are based on a paper we wrote in 2015 and published in the QJE in 2017.

Since then, and just this month in fact, IMF has released a new global debt database that covers an

additional 105 countries with a breakdown of private credit into household credit and non-financial

firm credit. So we have done a literal out of sample test for the Nobel symposium in column 7 of

the core finding of Mian et al. (2017b). The results are essentially identical. As an example of a

recent country that experienced a credit-driven household demand channel boom-bust, interested

readers may look at the case of Brazil.

Figure 3 shows the non-parametric relationship between change in household debt to GDP

between year (t-4) and (t-1) and change in real GDP between t and (t+3). The non-parametric

relationship in the original 30-country sample of Mian et al. (2017b) is shown in blue, while the new

out-of-sample relationship is shown in red. The figure once again illustrates how similar the out

of sample result is. Figure 3 is also a more generalized version of the striking empirical regularity

that I highlighted in figure 1 for the Great Recession.

Another noteworthy feature of figure 3 is that the relationship is non-linear: reduction in house-

hold debt to GDP does not predict an increase in GDP growth. This non-linearity is consistent

with macroeconomic theory where frictions such as ZLB or downward wage rigidity only bind in

one direction.

Evidence From A Natural Experiment During the 1980’s

I will next present evidence from a natural experiment that produces a well-known plausibly

exogenous variation in credit supply expansion across U.S. states during the 1980s: the staggered

wave of banking deregulation across states. The left panel of figure 4 shows the first stage from

Mian et al. (2017a). States that deregulated earlier - for reasons unrelated to expected GDP trend

- experience a much larger expansion in bank credit. The increase in bank credit also resulted in a

much larger increase in household credit in early deregulating states. The right panel shows that

the credit supply expansion results in a, now familiar, boom-bust cycle. Early deregulating states

that see a faster expansion in credit, also experience a sharper decline in unemployment, but only

3

to be followed by a stronger subsequent recession.

Figure 5 shows that the credit-induced boom during the 1980s was driven by an expansion in

household demand. States more exposed to deregulation experienced a stronger expansion in non-

tradable employment, but no relative expansion in tradable employment, between 1982 and 1989.

Similarly, the price of non-tradable goods increased in states that were more exposed to banking

deregulation, but no such relationship exists for tradable sector prices (Figure 6). The combined

increase in non-tradable to tradable sector employment and non-tradable to tradable sector prices

suggests an expansion in local household demand driven by the increase in credit supply.

Figure 7 should look very familiar to the audience by now. It has the same empirical regular-

ity that we saw for the Great Recession in figure 1. States with a larger expansion in household

leverage from 1982 to 1989 experience a much more severe subsequent recession. Moreover, this

time the source of credit expansion is plausibly exogenous as it is driven by the staggered wave of

banking deregulation across U.S. states.

U.S. Evidence From The 2000’s

Evidence from the U.S. Great Recession episode is also quite supportive of the credit-driven

household demand channel. A number of authors have argued, some using very detail micro-

empirical evidence, that there was a large expansion in the supply of credit in the U.S.1. At the

macro level the expansion in credit supply can be seen from the fact that credit spreads in the

U.S. declined significantly prior to 2007, even as the quantity of all types of risky credit expanded

aggressively.

The expansion in credit again lead to an increase in household demand. This has been shown

by Di Maggio and Kermani (2017) who report that the non-tradable sector expanded relative to

tradable sector during the credit boom. However, ultimately rising household credit creates a

predictable collapse as we saw before. The severity of the household credit-driven collapse is driven

by Irving Fisher’s famous “debt deflation hypothesis”. There is an initial fall in demand, that leads

to a fall in employment and ignites a large fire-sale of houses. This feeds back to further reduction

in demand, thus amplifying the initial negative shock

1See e.g. Adelino et al. (2014); Demyanyk and Van Hemert (2011); Favara and Imbs (2015); Justiniano et al. (2015,2017); Keys et al. (2010); Levitin and Wachter (2012); Mian and Sufi (2009, 2017)

4

Figure 8 from Mian et al. (2013) shows the fall in spending, or local demand, against the fall in

household net worth at the county level between 2006 and 2009. Counties where households had

levered up more experienced a larger decline in net worth both because of the direct leverage effect

and because house prices fell more in these areas due to foreclosure fire sales. Counties experiencing

a larger reduction in household net worth experience a sharp contraction in local aggregate demand.

Figure 9 from Mian and Sufi (2014b) shows that the fall in local demand directly translates into

a loss in local employment. There is a stronger decline in non-tradable employment between 2007

and 2009 in counties with a stronger decline in household net worth. The focus on nontradable

employment is useful here since by definition nontradable employment must rely on local demand

for revenue. As such this strong positive relationship between change in nontradable employment

and change in household net worth reflects the direct impact and a ‘macro spillover’ of the drop in

local demand on employment.

It is interesting to contrast this result with a similar analysis using tradable employment. Unlike

for nontradable employment, there is no relationship between the local change in tradable employ-

ment and household net worth. This makes sense since tradable employment does not depend

exclusively on local demand for generating sales. The asymmetry in result between nontradable

and tradable employment further strengthens the interpretation that it is indeed local demand

shocks that are causing the change in local employment. Moreover the tradable employment is also

being affected by the demand shocks it is just that since those shocks are aggregate in nature –

they shift tradable employment downwards for all counties proportionately. A number of papers

have confirmed these effects in other contexts and using alternative empirical strategies2.

Macro Theory Implications Of The Credit-Driven Household Demand Channel

I have discussed a broad range of empirical evidence that describes the importance of the credit

driven household demand channel in practice, not only in the most recent 2008 global recession,

but even prior to that. I am next going to discuss the theoretical and policy implications of this

evidence.

A natural theoretical implication of empirical evidence on the importance of household credit

2See e.g. Andersen et al. (2014); Bahadir and Gumus (2016); Bunn and Rostom (2015); Drehmann et al. (2017);Giroud and Mueller (2017); Glick and Lansing (2010); IMF (2012, 2017); Di Maggio and Kermani (2017); Martinand Philippon (2014); Mian and Sufi (2010); Mian et al. (2017a,b); Verner and Gyongyosi (2017)

5

for macro aggregates is that heterogeneity matters, and in particular heterogeneity in the behavior

of borrower versus creditor households. For example, as right panel of figure 10 from Mian et al.

(2013) shows, marginal propensity to spend in response to a dollar decline in housing wealth is

much stronger for levered households than unlevered households. Similarly, the left panel of figure

10 from Mian and Sufi (2011) shows that during the boom phase, marginal propensity to borrow for

a dollar increase in home value was much stronger for low credit score individuals than high credit

score individuals. It is thus no longer sufficient to model aggregate dynamics using a representative

household economy. The empirical evidence suggests that macro aggregates fundamentally depend

on the covariance of shocks with the underlying household heterogeneity. Thus if a negative shock

falls disproportionately on levered households that have the highest marginal propensity to respond

then the net effect on the overall economy would be much stronger.

A number of recent papers in macroeconomic theory have emphasized such heterogeneity and

how it interacts with macro frictions such as zero lower bound constraint or downward nominal

wage rigidity 3. The work has also highlighted the presence of an aggregate demand externality, or a

pecuniary externality such that ex ante individual households will tend to over borrow from a macro

perspective. Individuals fail to fully internalize the negative future macroeconomic consequences of

their collective borrowing decisions leading to over-borrowing that may require macro-prudential

interventions.

The newer class of models rationalize why expansion in credit supply for the household sector

leads to a boom bust pattern. However, there is one potential problem. These models are based

on rational expectation and common belief assumptions. Thus the models suggest that market

participants should also predict the slowdown that follows a household credit boom. However

when we look at the data, there is no evidence that the market or households correctly predict a

subsequent slowdown during the boom. Infact, as figure 11 from Mian et al. (2017b) shows there

is evidence to the contrary. Growth in household credit predicts GDP forecasting errors by the

IMF and other professional forecastors. Models with heterogeneous beliefs or behavioral biases

can help address this issue, suggesting that these forces are also important to fully understand the

relationship between credit expansion and business cycles4.

3See e.g. Eggertsson and Krugman (2012); Farhi and Werning (2015); Guerrieri and Lorenzoni (2017); Huo andRıos-Rull (2016); Korinek and Simsek (2016); Lorenzoni (2008); Schmitt-Grohe and Uribe (2016)

4see e.g. Baron and Xiong (2016); Bordalo et al. (2017); Burnside et al. (2017); Geanakoplos (2010); Gennaioli et al.

6

So far I have focused on the credit driven household demand channel at the business cycle

frequency. However there is also a longer-term “super cycle in the background, driven by a persistent

expansion in credit as shown by the work of Jorda et al. (2016). Moreover, the super cycle is largely

driven by the growth in household credit, and has been accompanied by a strong decline in long-

term real interest rate. The latter fact suggests that the long term trends are driven by an expansion

in credit supply forces.

What might be the drivers of the increase in credit supply? And what are its longer term

economic consequences? The rise in global credit coincides with the rise in global inequality, par-

ticularly the top 1% versus the rest, and the appearance of a global savings glut. Since top incomes

save at a very high rate, channeling these savings into the financial sector is naturally going to

increase credit which can only be sustained at continually declining interest rates5. Indeed we find

in ongoing work, see figure 12, that the rise in household credit is concentrated in the bottom 99%

and not the 1%, while income gains since 1980’s have largely gone to the top 1%. How long can this

process continue without leading to liquidity trap-like situations and lower growth is an important

open question facing us today.

Policy Implications Of The Credit-Driven Household Demand Channel

The credit driven household demand channel has important implications for public policy. I

will particularly focus on implications for crisis response, monetary policy and macro prudential

policy.

Policy response to the 2008 crisis centered on interventions to promote provision of market

liquidity and public support for bank capital injections. However, a recession that is the result of

the credit-driven household demand channel requires that attention should be paid to repair and

restructure household balance sheets as well.

Our main criticism of the administration’s response to the 2008 crisis is that not sufficient

attention was paid to restructure loans for under-water and distressed homeowners. Similarly,

efforts should have been made to reduce the more than four million foreclosed homes that ended

up worsening the downturn considerably Mian et al. (2015).

(2012); Kindleberger (1978); Kindelberger and Aliber (2005); Krishnamurthy and Muir (2016); Lopez-Salido et al.(2017); Mian and Sufi (2018a); Minsky (2008); Nathanson and Zwick (2017)

5see e.g. Favilukis et al. (2012); Jorda et al. (2016); Kumhof et al. (2015)

7

Looking forward, there is a need to design better regulatory and financial framework that pro-

motes risk-sharing between creditors and debtors. We discuss this with relation to state-contingent

contracting in Mian and Sufi (2014a). As we discuss, moving in this direction requires institu-

tional changes, such as eliminating the favorable treatment of debt and re-designing bank capital

regulation.

The credit driven household demand channel is also one of the most powerful channels through

which monetary policy impacts the real economy. In normal times, expansionary monetary pol-

icy lowers rates and increases house prices, enabling high MPC constrained households to boost

spending, thus raising aggregate demand. A tightening cycle can work in reverse, making mone-

tary policy impotent as high MPC households fail to respond to monetary easing due to hightened

risk-aversion and borrowing constraints. For example, Di Maggio et al. (2017) show that lower

interest rates post-2008 were not passed-through to many constrained households who were unable

to refinance, thus putting a real drag on aggregate demand6. The failure of traditional monetary

policy tools to find traction in boosting aggregate demand means that alternative approaches need

to be considered.

Finally, a natural policy implication of credit-driven household demand channel is that ex-ante

macro-prudential policies that constrain household credit growth are useful. A number of countries,

most notably the U.K., have gone in this direction since 2008, putting limits on household credit

growth based on a combination of loan to value and debt to income constraints.

6For broader evidence on household demand channel constraining the efficacy of monetary policy, see Agarwal etal. (2017, 2018); Aladangady (2014); Baker (2018); Cloyne et al. (2017); Ganong and Noel (2017a,b); Jorda et al.(2014); Liu et al. (2018); Mian and Sufi (2018b, Forthcoming)

8

References

Adelino, Manuel, Antoinette Schoar, and Felipe Severino, “Credit supply and house prices:

Evidence from mortgage market segmentation,” Available at SSRN 1787252, 2014.

Agarwal, Sumit, Gene Amromin, Itzhak Ben-David, Souphala Chomsisengphet,

Tomasz Piskorski, and Amit Seru, “Policy Intervention in Debt Renegotiation: Evidence

from the Home Affordable Modification Program,” Journal of Political Economy, 2017, 125 (3).

, Souphala Chomsisengphet, Neale Mahoney, and Johannes Stroebel, “Do Banks Pass

Through Credit Expansions to Consumers Who Want to Borrow? Evidence from Credit Cards,”

Quarterly Journal of Economics, 2018, 133 (1), 129–90.

Aladangady, Aditya, “Homeowner Balance Sheets and Monetary Policy,” FEDS Working Paper,

Federal Reserve Board 2014.

Andersen, Asger L., Charlotte Duus, and Thais L. Jensen, “Household Debt and Consump-

tion During the Financial Crisis: Evidence from Danish Micro Data,” 2014. Working Paper.

Bahadir, Berrak and Inci Gumus, “Credit decomposition and business cycles in emerging

market economies,” Journal of International Economics, 2016, 103, 250–262.

Baker, Scott R, “Debt and the Response to Household Income Shocks: Validation and Application

of Linked Financial Account Data,” Journal of Political Economy, 2018.

Baron, Matthew and Wei Xiong, “Credit expansion and neglected crash risk,” National Bureau

of Economic Research, 2016.

Bordalo, Pedro, Nicola Gennaioli, and Andrei Shleifer, “Diagnostic Expectations and

Credit Cycles,” Journal of Finance, 2017.

Bunn, Philip and May Rostom, “Household Debt and Spending in the United Kingdom,” 2015.

Bank of England Working Paper.

Burnside, Craig, Martin Eichenbaum, and Sergio Rebelo, “Understanding Booms and

Busts in Housing Markets,” Journal of Political Economy, 2017, 124, 1088–1147.

9

Cloyne, James, Clodomiro Ferreira, and Paolo Surico, “Monetary Policy When Households

Have Debt: New Evidence on the Transmission Mechanism,” 2017.

Demyanyk, Yuliya and Otto Van Hemert, “Understanding the subprime mortgage crisis,”

Review of financial Studies, 2011, 24 (6), 1848–1880.

Di Maggio, Marco, Amir Kermani, Benjamin J. Keys, Tomasz Piskorski, Ramcharan

Rodney, Amit Seru, and Vincent Yao, “Interest Rate Pass-Through: Mortgage Rates,

Household Consumption, and Voluntary Deleveraging,” American Economic Review, 2017, 107

(11), 3550–88.

and , “Credit-induced boom and bust,” The Review of Financial Studies, November 2017,

30 (11), 3711–58.

Drehmann, Mathias, Mikael Juselius, and Anton Korinek, “Accounting for debt service:

the painful legacy of credit booms,” BIS Working Papers No 645 June 2017.

Eggertsson, Gauti B. and Paul Krugman, “Debt, Deleveraging, and the Liquidity Trap: A

Fisher-Minsky-Koo Approach,” Quarterly Journal of Economics, 2012, 127 (3), 1469–1513.

Farhi, Emmanuel and Ivan Werning, “A Theory of Macroprudential Policies in the Presence

of Nominal Rigidities,” 2015. Working Paper.

Favara, Giovanni and Jean Imbs, “Credit supply and the price of housing,” The American

Economic Review, 2015, 105 (3), 958–992.

Favilukis, Jack, David Kohn, Sydney C Ludvigson, and Stijn Van Nieuwerburgh, “In-

ternational capital flows and house prices: Theory and evidence,” in “Housing and the Financial

Crisis,” University of Chicago Press, 2012, pp. 235–299.

Ganong, Peter and Pascal Noel, “Consumer Spending During Unemployment: Positive and

Normative Implications,” 2017. Working Paper.

and , “The Effect of Debt on Default and Consumption: Evidence from Housing Policy in

the Great Recession,” 2017. Working Paper.

10

Geanakoplos, John, “The Leverage Cycle,” NBER Macroeconomics Annual 2009, Volume 24,

April 2010, pp. 1–65.

Gennaioli, Nicola, Andrei Shleifer, and Robert Vishny, “Neglected risks, financial innova-

tion, and financial fragility,” Journal of Financial Economics, 2012, 104 (3), 452–468.

Giroud, Xavier and Holger M. Mueller, “Firm Leverage, Consumer Demand, and Unemploy-

ment during the Great Recession,” Quarterly Journal of Economics, forthcoming, 2017.

Glick, Reuven and Kevin J. Lansing, “Global household leverage, house prices, and consump-

tion,” FRBSF Economic Letter, 2010, (11).

Guerrieri, Veronica and Guido Lorenzoni, “Credit crises, precautionary savings, and the

liquidity trap,” The Quarterly Journal of Economics, 2017, 132 (3), 1427–1467.

Huo, Zhen and Jose-Vıctor Rıos-Rull, “Financial Frictions, Asset Prices, and the Great

Recession,” 2016. Working Paper.

IMF, “Dealing with Household Debt,” in “World Economic Outlook, April” April 2012.

, “Household Debt and Financial Stability,” in “Global Financial Stability Report, October”

October 2017.

Jorda, Oscar, Moritz Schularick, and Alan M. Taylor, “Betting the House,” 2014. Federal

Reserve Bank of San Francisco Working Paper.

Jorda, Oscar, Moritz Schularick, and Alan M Taylor, “The great mortgaging: housing

finance, crises and business cycles,” Economic Policy, 2016, 31 (85), 107–152.

Justiniano, Alejandro, Giorgio E. Primiceri, and Andrea Tambalotti, “Credit Supply

and the Housing Boom,” Working Paper 20874, National Bureau of Economic Research January

2015.

, , and , “The Mortgage Rate Conundrum,” 2017. Working Paper.

Keys, Benjamin J, Tanmoy Mukherjee, Amit Seru, and Vikrant Vig, “Did securitization

lead to lax screening? Evidence from subprime loans,” The Quarterly journal of economics, 2010,

125 (1), 307–362.

11

Kindelberger, Charles P and Robert Z Aliber, Manias, panics and crashes: A history of

financial crises, Palgrave Macmillan, 2005.

Kindleberger, Charles, Manias, Panics and Crashes: A history of financial crises, New York,

Basic Books, 1978.

Korinek, Anton and Alp Simsek, “Liquidity trap and excessive leverage,” The American Eco-

nomic Review, 2016, 106 (3), 699–738.

Krishnamurthy, Arvind and Tyler Muir, “How credit cycles across a financial crisis,” Working

paper, 2016.

Kumhof, Michael, Romain Ranciere, and Pablo Winant, “Inequality, Leverage, and Crises,”

American Economic Review, 2015, 105, 1217–1245.

Levitin, Adam J. and Susan M. Wachter, “Explaining the Housing Bubble,” 2012. Working

Paper.

Liu, Ernest, Atif Mian, and Amir Sufi, “Low Interest Rates, Market Power and Productivity

Growth,” 2018.

Lopez-Salido, David, Jeremy C Stein, and Egon Zakrajsek, “Credit-market sentiment and

the business cycle,” The Quarterly Journal of Economics, 2017, p. qjx014.

Lorenzoni, Guido, “Inefficient Credit Booms,” The Review of Economic Studies, 2008, 75 (3),

809–833.

Martin, Philippe and Thomas Philippon, “Inspecting the Mechanism: Leverage and the

Great Recession in the Eurozone,” 2014. Working Paper.

Mian, Atif, Amir Sufi, and Francesco Trebbi, “Foreclosures, house prices, and the real

economy,” The Journal of Finance, 2015, 70 (6), 2587–2634.

and , “The Consequences of Mortgage Credit Expansion: Evidence from the U.S. Mortgage

Default Crisis,” The Quarterly Journal of Economics, 2009, 124 (4), pp. 1449–1496.

and , “Household Leverage and the Recession of 2007-09,” IMF Economic Review, 2010, 58

(1), 74–117.

12

and , “House Prices, Home Equity-Based Borrowing, and the US Household Leverage Crisis,”

American Economic Review, August 2011, 101 (5), 2132–56.

and , House of Debt: How They (and You) Caused the Great Recession and How We Can

Prevent It From Happening Again, University of Chicago Press: Chicago, 2014.

and , “What Explains the 2007–2009 Drop in Employment?,” Econometrica, 2014, 82 (6),

2197–2223.

and , “Fueling a Frenzy: Private Label Securitization and the Housing Cycle of 2000 to 2010,”

Working Paper 2018.

and , “Inequality, Surplus Savings and Credit Creation,” 2018.

and , “Finance and Business Cycles: The Credit-Driven Household Demand Channel,” Jour-

nal of Economic Perspectives, Forthcoming.

, Kamalesh Rao, and Amir Sufi, “Household Balance Sheets, Consumption, and the Eco-

nomic Slump,” The Quarterly Journal of Economics, 2013, 128 (4), 1687–1726.

Mian, Atif R, Amir Sufi, and Emil Verner, “Credit Supply and Business Cycle Ampli cation:

Evidence from Banking Deregulation in the 1980s,” 2017.

, , and , “Household debt and business cycles worldwide,” forthcoming, Quarterly Journal

of Economics, 2017.

and , “Household Debt and Defaults from 2000 to 2010: The Credit Supply View,” Evidence

and Innovation in Housing Law and Policy, 2017.

Minsky, Hyman P, Stabilizing an unstable economy, Vol. 1, McGraw-Hill New York, 2008.

Nathanson, Charles G. and Eric Zwick, “Arrested Development: Theory and Evidence of

Supply-Side Speculation in the Housing Market,” National Bureau of Economic Research June

2017.

Schmitt-Grohe, Stephanie and Martın Uribe, “Downward Nominal Wage Rigidity, Currency

Pegs, and Involuntary Unemployment,” Journal of Political Economy, 2016.

13

Verner, Emil and Gyozo Gyongyosi, “Household Debt Revaluation and the Real Economy:

Evidence from a Foreign Currency Debt Crisis,” 2017.

14

Figure 1: Introduction

AL

AK

AZ

AR

CA

COCT DE

DC

FLGA

HI

ID

IL

IN

IAKS

KYLA

ME

MD

MAMI

MN

MSMO

MT

NE

NV

NH

NJ

NMNY

NC

ND

OH

OK

OR

PA

RISC

SD

TNTX

UT

VT

VA

WAWV

WIWY

0

2

4

6

8

10

∆ U

nem

ploy

men

t, 20

07−

10

0 10 20 30 40 50 60∆ HH debt to GDP, 2002−07

Source: Mian and Sufi (IMF, 2010).

United States

AUSAUT

BELCAN

CHECZE

DEU

DNK

ESP

FINFRAGBR

GRC

HKG

HUN

IDN

IRL

ITA

JPNKOR

MEXNLDNOR

POL

PRT

SGP

SWE

THA

TUR

USA

−5

0

5

10

15

∆ U

nem

ploy

men

t, 20

07−

10

−20 0 20 40 60∆ HH debt to GDP, 2002−07

Source: Mian, Sufi, and Verner (QJE, 2017).

World

15

Figure 2: Effect of credit shock on GDP

−.8

−.6

−.4

−.2

0

.2

.4

.6

0 2 4 6 8 10Years after Shock

Source: Mian, Sufi, and Verner (QJE, 2017)

GDP Response to HH Debt Shock

−.8

−.6

−.4

−.2

0

.2

.4

.6

0 2 4 6 8 10Years after Shock

Source: Mian, Sufi, and Verner (QJE, 2017)

GDP Response to NF Debt Shock

16

Figure 3: Increase in household debt to GDP predicts GDP slowdown

−20

0

20

40

GD

P g

row

th, t

to t+

3

−10 0 10 20 30Household Debt to GDP Expansion, t−4 to t−1

MSV2017 30 CountriesIMF2018 Additional 105 Countries

Source: Mian, Sufi, and Verner (QJE, 2017).

17

Figure 4: Deregulation Experiment in the 1980s

100

150

200

250

1980 1982 1984 1986 1988 1990 1992

Early DeregulationLate Deregulation

Source: Mian, Sufi, and Verner (WP, 2018).

Total Bank Credit

50

60

70

80

90

100

1980 1982 1984 1986 1988 1990 1992Source: Mian, Sufi, and Verner (WP, 2018).

Unemployment Rate

18

Figure 5: Non-tradable vs Tradable Employment in the 1980s

AK

AL

AR

AZ

CA

CO

CT

DC

FL

GA

HI

IAID

IL

IN

KS

KY

LA

MA

MD

ME

MI

MN

MOMS

MT

NC

ND

NE

NH

NJ

NM

NV

NYOH

OK

ORPA

RI

SC

TN

TX

UT

VA

VT

WA

WI

WV

WY−0.10

0.00

0.10

0.20

0.30

0.40

−2 −1 0 1 2Deregulation exposure

Source: Mian, Sufi, and Verner (WP, 2018).

Non−Tradable Employment Growth, 82−89AK

ALAR

AZ

CA

CO

CTDC

FL GA

HI

IA

ID

IL

INKS

KY

LA

MA

MD

ME

MIMN

MO

MS

MT NCND

NENH

NJ

NM

NV

NY

OH

OK

OR

PA

RI

SC

TN

TX

UT

VA

VT

WA

WI

WVWY

−0.30

−0.20

−0.10

0.00

0.10

0.20

−2 −1 0 1 2Deregulation exposure

Source: Mian, Sufi, and Verner (WP, 2018).

Tradable Employment Growth, 82−89

19

Figure 6: Non-Tradable vs. Tradable Price in the 1980s

CA

CO

CT

FL

GAHI

ILIN

KS KY

MAMD

ME

MI

MNMO

NH

NJ

NY

OH

OR

PA

TX

WAWI

0.15

0.20

0.25

0.30

0.35

−2 −1 0 1 2Deregulation exposure

(Alaska excluded)Source: Mian, Sufi, and Verner (WP, 2018).

Non−tradable CPI Inflation, 84−89

CA

CO

CT

FLGA

HI

ILIN

KSKY

MA

MD

MEMI

MN

MONHNJ

NY

OHOR

PATX

WA

WI

0.05

0.10

0.15

0.20

0.25

−2 −1 0 1 2Deregulation exposure

(Alaska excluded)Source: Mian, Sufi, and Verner (WP, 2018).

Tradable CPI Inflation, 84−89

20

Figure 7: Increase in household leverage predicts 1990/91 recession

AK

ALAR

AZ

CA

CO

CTDC

FL

GAHI

IA

ID

ILIN

KSKYLA

MA

MDME

MI

MNMO

MSMT

NC

NDNE

NHNJ

NM

NV

NY

OH

OK

OR

PA

RI

SC

TN

TX

UT

VA

VT

WAWI

WV

WY

−2.00

0.00

2.00

4.00

6.00

∆ U

nem

ploy

men

t, 19

89−

92

−2 −1 0 1 2 3∆ HH Leverage, 1982−89

Source: Mian, Sufi, and Verner (WP, 2018).

21

Figure 8: Fall in demand during the great recession

−.4

−.2

0

.2

∆ E

xpen

ditu

re, 0

6−09

−.3 −.25 −.2 −.15 −.1 −.05 0 .05∆ HH net worth, 06−09

Source: Mian, Rao, and Sufi (QJE, 2013).

22

Figure 9: Local employment falls due to lack of demand

−.15

−.1

−.05

0

.05

.1

∆ T

rada

ble

Em

ploy

men

t, 07

−09

−.15

−.1

−.05

0

.05

.1

∆ N

on−

Tra

dabl

e E

mpl

oym

ent,

07−

09

−.3 −.25 −.2 −.15 −.1 −.05 0 .05∆ HH net worth, 06−09

Source: Mian and Sufi (ECMA, 2014).

−.15

−.1

−.05

0

.05

.1

∆ T

rada

ble

Em

ploy

men

t, 07

−09

−.15

−.1

−.05

0

.05

.1

∆ N

on−

Tra

dabl

e E

mpl

oym

ent,

07−

09

−.3 −.25 −.2 −.15 −.1 −.05 0 .05∆ HH net worth, 06−09

Source: Mian and Sufi (ECMA, 2014).

23

Figure 10: Heterogeneity in marginal propensity to borrow and marginal propensity to consume

0

.05

.1

.15

.2

.25

< 700 700 − 799 800 − 899 900 − 999

Source: Mian and Sufi (AER, 2011).

MPB out of Housing Wealth by Credit Score, ’02−06

0

.005

.01

.015

.02

.025

.03

<= 30% 30%−50% 50%−70% 70%−90% > 90%

Source: Mian, Rao, and Sufi (QJE, 2013).

MPC out of Housing Wealth by HH Leverage, ’06−09

24

Figure 11: Rising household leverage predicts forecasting errors

THA2002

HUN1993

NOR1994

THA2001FIN1996

HUN1994

FIN1997

CHE2009

SGP2007

NOR1996

DEU2009

SWE1996

NOR1995

HKG2007

NOR1993

DEU2008

SGP2008

FIN1998

FIN1995SWE1997

SWE1992

SWE1993

HKG2008NOR1997

NOR1992

THA2003

HKG2006

FIN1999SWE1995

DEU2007

SWE1994NOR1998

HUN1997

JPN2005HUN1998

HKG2009

CHE2008CZE1999

HUN1996JPN2003JPN2004

JPN2007

HKG2005

JPN2009SWE1998GBR1997

JPN2006

MEX1999

MEX2000

GBR1996

FRA1997

DEU1991

JPN2008

HUN1995

HUN1999

DEU2006

MEX2001

ITA1996

KOR2000

KOR2001ITA1997

CAN1996

DEU1992

DEU2005

SGP2009

GBR1995

MEX2002

CAN2002BEL2003NOR2001DEU1993

BEL2002

FRA1998

TUR2004

DEU1990

JPN2002ITA1998DEU2004

HKG1995

FIN1994

MEX1998TUR1997

THA2009

TUR1991FIN2000

DEU2003

CAN2001

ITA1992

CZE2000

TUR1990FRA1996GBR1998

TUR2003

ESP1993

NOR2002

CZE2001ITA1995HUN2000

BEL1996

TUR2002

FRA1999

TUR1992

THA2000CHE2007TUR1993

IDN2009

ESP1995

ESP1996

THA2008

JPN1999

GBR1994

CHE2003MEX2003

TUR2000

JPN1994

AUT2009

PRT1991

JPN1998FRA2001

BEL2004

ITA1991FRA2000

SGP2006

CAN1997

KOR2006

ESP1997

IDN2008

ESP1994BEL2001

AUS1993

TUR1995

TUR1999

KOR1999

PRT1992

ITA1990

NOR1999

TUR1996

BEL1997

USA1995

JPN1993ITA1994

NOR1991

CAN2003

CZE2002

FRA1995JPN2001ITA1999

AUS1991

TUR2001

USA1994

CAN1995SWE1999AUT1999HUN2001

MEX2004

FRA2002

JPN2000

AUT2004

FIN2001GBR1999USA1999

TUR1998

BEL2005BEL1995ITA1993

USA1998

MEX2005FRA2003

USA2000

HKG2004

JPN1995

FIN1993

ESP1992

CAN1998AUS1992

PRT1993AUS1990

SGP2001

MEX2006

AUT2003USA1993

IDN2007

POL2006

JPN1997

TUR1994

SWE1991NOR2000

MEX2009

THA2007

BEL1998

HKG1996

HKG2003

AUT2001THA2004

POL2002

POL2000MEX2007

POL2001

KOR1996

USA1996

PRT1990

AUT2000SWE2000

GRC1998

TUR2005FRA1993

FRA2004

USA1997

FIN2002CAN2000NOR2009BEL1994USA1992

BEL2000AUT2008

AUS1994

FIN2003

FRA1994

AUT2005ESP1998

USA1991

JPN1996

USA1990DEU1994

NOR2007

GRC1999

BEL1992

GBR1993

FRA1992

FIN1992

DEU2002

SGP2000

MEX2008

BEL1993

CZE2003

AUT2002

ITA2000

SWE2001USA2001BEL1999

KOR1997

GRC2000

SGP2005

POL1999

FRA1991

CZE2004

BEL1991BEL2006

HUN2002

KOR1998

FRA1990

CAN1994

HKG1997ESP1991

HKG2002

CAN1999

FRA2005

POL2005

IDN2006

SGP1999

NLD1994

ITA2003

USA2009

BEL1990ITA2001

TUR2009

IDN2005NLD2009GBR2000

ITA2004

SGP2003

KOR1994

CZE2006

KOR1995

JPN1992

HKG1994

ITA2002

CZE2005

POL2007

FIN1991

THA2006

DNK2002GBR2001

TUR2006

CAN2004

BEL2007

AUS2009

AUT2007

POL2004

AUT2006NOR2008

GRC2001

SWE2002

ITA2005

AUS1995SWE2003

FRA2006

KOR2007DEU2001

ITA2009

NOR2006

NOR2003SGP2002

FIN2009SGP1998

DEU1995

TUR2007

TUR2008GBR1992BEL2009POL2003

DNK2001THA2005BEL2008

SGP1995

PRT1994

FIN2004

CHE2006

KOR2005

USA2002ESP1999

SWE2004

HKG2001

HUN2003

DEU1998

NLD1995

GBR2008

CZE2007

FRA2009

KOR1993

FRA2007ESP1990

ITA2006

SWE2005

DEU1999

SWE1990

DEU1997CAN2005CAN1993THA1999

AUS1996CHE2005

FRA2008

CHE2004

DEU2000CAN2006

KOR2002

DEU1996GBR2009

JPN1991

CAN1992

AUS1998

SWE2008

NLD1996

DNK2000

CAN2007

CAN1991

AUS1997SWE2009

ITA2007

ITA2008

GBR2002

PRT1995

NOR1990

FIN1990

GRC2002

CAN1990

FIN2008

KOR2009

SGP1996

ESP2003

POL2008

HUN2007

SWE2006

AUS1999ESP2002

USA2008

SWE2007

CZE2008

CZE2009

FIN2005

SGP1997GBR1991

DNK2003

ESP2000

PRT2009CAN2008KOR1990

HUN2008

NLD1997

USA2007

USA2003

PRT1997

CAN2009

DNK1999

PRT1996

KOR2008

DNK1998KOR1992

HUN2004

GBR2007

NLD2008

KOR1991

ESP2004

ESP2001

SGP2004

FIN2007

NOR2005

AUS2001

GBR1990

ESP2009

GRC2003

NLD2003

GBR2006

AUS2000

HUN2005

AUS2002

HKG1998

HUN2006

THA1998

PRT1998

GBR2003

FIN2006

DNK2004

PRT2008

USA2006

NLD1998

GRC2004

JPN1990

THA1997

PRT2005

NLD2002

AUS2008

THA1996

HUN2009

PRT2007HKG2000

PRT2004

USA2005

DNK2005AUS2003

PRT2006

GRC2005NOR2004

THA1995

HKG1999

USA2004

POL2009

PRT2003

GBR2004KOR2004

NLD2007

NLD2004

PRT1999

ESP2005

AUS2007

GRC2009

NLD1999

DNK2006

GBR2005

GRC2006NLD2001

NLD2005

DNK2009

NLD2000

ESP2008

KOR2003AUS2004

GRC2007

PRT2002ESP2006PRT2000

NLD2006

GRC2008

AUS2006

DNK2008

DNK2007

ESP2007

PRT2001

AUS2005IRL2009

IRL2008

IRL2006

IRL2007

−30

−20

−10

0

10

20

IMF

WE

O t

to t+

3 G

DP

For

ecas

t Err

or

−10 0 10 20 30 40Household Debt to GDP Expansion, t−4 to t−1

Source: Mian, Sufi, and Verner (QJE, 2017)

25

Figure 12: Rising leverage concentrated in the bottom 99%

0

.5

1

1.5

2

Deb

t to

Inco

me

1960 1970 1980 1990 2000 2010Year

bot 99%top 1%

Source: Mian and Sufi (WP, 2018).

26

Table 1: International Evidence

MSV2017 30 Countries, 1962-2012 IMF2018

(1) (2) (3) (4) (5) (6) (7)

∆3CitYit

∆3NXitYit

∆3sMCit ∆3 ln

(LNTit

LTit

)∆3 ln

(PNTit

PTit

)∆3yi,t+4 ∆3yi,t+4

∆3dHHit 0.058∗ -0.15∗∗ 0.055∗ 0.36∗∗ 0.38∗∗ -0.34∗∗ -0.37∗

(0.024) (0.051) (0.025) (0.056) (0.097) (0.089) (0.17)

∆3dFit 0.038∗∗ -0.00036 -0.012 0.0085 -0.065 -0.032 -0.019∗∗

(0.012) (0.031) (0.021) (0.064) (0.059) (0.038) (0.0072)

Country fixed effects X X X X X X XR2 0.087 0.062 0.012 0.17 0.067 0.11 0.056Observations 816 832 858 639 670 840 964

Standard errors in parentheses

Source: Mian, Sufi, and Verner (QJE, 2017).+ p < 0.1, ∗ p < 0.05, ∗∗ p < 0.0127