WPS8260


Policy Research Working Paper                   8260




    Macroeconomic Implications of Financial
               Imperfections
                              A Survey

                              Stijn Claessens
                              M. Ayhan Kose




Development Economics
Development Prospects Group
November 2017
Policy Research Working Paper 8260


  Abstract
 This paper surveys the theoretical and empirical literature on                     and propagate economic and financial shocks. The second
 the macroeconomic implications of financial imperfections.                         channel, which is associated with the supply side of finance,
 It focuses on two major channels through which financial                           emphasises the implications of changes in financial inter-
 imperfections can affect macroeconomic outcomes. The                               mediaries’ balance sheets for the supply of credit, liquidity
 first channel, which operates through the demand side                              and asset prices, and, consequently, for macroeconomic
 of finance and is captured by financial accelerator-type                           outcomes. These channels have been shown to be import-
 mechanisms, describes how changes in borrowers’ balance                            ant in explaining the linkages between the real economy
 sheets can affect their access to finance and thereby amplify                      and the financial sector. That said, many questions remain.




  This paper is a product of the Development Prospects Group, Development Economics. It is part of a larger effort by the
  World Bank to provide open access to its research and make a contribution to development policy discussions around the
  world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be
  contacted at Stijn.Claessens@bis.org and akose@worldbank.org.




         The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development
         issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the
         names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those
         of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and
         its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.


                                                       Produced by the Research Support Team
    Macroeconomic Implications of Financial Imperfections: A Survey


                                Stijn Claessens and M. Ayhan Kose




      JEL classifications: D53, E21, E32, E44, E51, F36, F44, F65, G01, G10, G12,
      G14, G15, G21
      Keywords: Asset prices, balance sheets, credit, financial accelerator, financial
      intermediation, financial linkages, international linkages, leverage, liquidity,
      macro-financial linkages, output, real-financial linkages.



  Claessens (Monetary and Economic Department, Bank for International Settlements; CEPR;
Stijn.Claessens@bis.org); Kose (Development Prospects Group, World Bank; Brookings Institution;
CEPR; CAMA; akose@worldbank.org). We are grateful to Olivier Blanchard for his helpful comments
at the early stages of this project of surveying the literature on macro-financial linkages when we were
with the Research Department of the International Monetary Fund. This paper has a companion,
“Asset prices and macroeconomic outcomes: a survey.” We would like to thank Boragan Aruoba,
Claudio Borio, Menzie Chinn, Ergys Islamaj, Jaime de Jesus Filho, Raju Huidrom, Atsushi Kawamoto,
Seong Tae Kim, Grace Li, Raoul Minetti, Ezgi Ozturk, Eswar Prasad, Lucio Sarno, Hyun Song Shin,
Kenneth Singleton, Naotaka Sugawara, Hui Tong, Kamil Yilmaz, Kei-Mu Yi, Boyang Zhang, Tianli
Zhao, and many colleagues and participants at seminars and conferences for useful comments and
inputs. Miyoko Asai, Ishita Dugar and Xinghao Gong provided excellent research assistance. Mark
Felsenthal, Sonja Fritz, Krista Hughes, Serge Jeanneau, Graeme Littler, Tracey Lookadoo, and Rosalie
Singson provided outstanding editorial help. The views expressed in this paper are those of the authors
and do not necessarily represent those of the institutions they are affiliated with or have been affiliated
with.


                                                                                        ii
ii
“...They [economists] turned a blind eye to the limitations of human rationality that often
lead to bubbles and busts; to the problems of institutions that run amok; to the imperfections
of markets – especially financial markets – that can cause the economy’s operating system to
undergo sudden, unpredictable crashes ...”
Paul Krugman (2009a)


““Hello, Paul, where have you been for the last 30 years?”... Pretty much all we have been
doing for 30 years is introducing flaws, frictions and new behaviors... The long literature on
financial crises and banking ... has also been doing exactly the same....”
John H. Cochrane (2011)


“I believe that during the last financial crisis, macroeconomists (and I include myself among
them) failed the country, and indeed the world. In September 2008, central bankers were in
desperate need of a playbook that offered a systematic plan of attack to deal with fast evolving
circumstances. Macroeconomics should have been able to provide that playbook. It could
not...”
Narayana Kocherlakota (2010)


“... What does concern me of my discipline, however, is that its current core – by which I
mainly mean the so-called dynamic stochastic general equilibrium (DSGE) approach – has
become so mesmerized with its own internal logic... This is dangerous for both methodological
and policy reasons... To be fair to our field, an enormous amount of work at the intersection
of macroeconomics and corporate finance has been chasing many of the issues that played a
central role during the current crisis... However, much of this literature belongs to the
periphery of macroeconomics rather than to its core...”
Ricardo Caballero (2010)


“One can safely argue that there is a hole in our knowledge of macro financial interactions;
one might also argue more controversially that economists have filled this hole with rocks as
opposed to diamonds; but it is harder to argue that the hole is empty.”
Ricardo Reis (2017)


“The financial crisis ... made it clear that the basic model, and even its DSGE cousins, had
other serious problems, that the financial sector was much more central to macroeconomics
than had been assumed...”
Olivier Blanchard (2017a)




                                               iii
                                             Table of contents

1. Introduction .................................................................................. 1

2. Financial imperfections: the demand side ...................................... 6
      A. Basic mechanisms .....................................................................6
      B.    Empirical evidence ...................................................................8

3. Financial imperfections in general equilibrium ............................. 11
      A. The financial accelerator ........................................................ 11
      B. Quantitative importance of the financial accelerator ............. 13

4. Financial market imperfections in open economies ....................... 17

5. Financial imperfections: the supply side ...................................... 19
      A. Bank lending channel ............................................................. 20
      B. Bank capital channel .............................................................. 22
      C. Leverage and liquidity channels ............................................. 23

6. Evidence relating to the supply side channels .............................. 25
      A. Bank lending channel ............................................................. 26
      B. Bank capital channel .............................................................. 27
      C. Leverage and liquidity channels ............................................. 29

7. Aggregate macro-financial linkages .............................................. 31
      A. Business and financial cycles .................................................. 32
      B. Linkages between business and financial cycles ..................... 34

8. Conclusions ................................................................................. 36
      A summary .................................................................................... 37
      What next? .................................................................................... 38

Appendix I. Research on macro-financial linkages: a brief history ...... 43

Appendix II. Financial accelerator mechanisms ................................. 48

Appendix III. Understanding liquidity and leverage cycles ................. 51

Appendix IV. Linkages between business and financial cycles: an
   overview of empirical studies ...................................................... 53
      Credit market cycles and business cycles ...................................... 53
      Cycles in asset (house and equity) prices and business cycles ...... 53
      Synchronisation of financial cycles ................................................ 55

References ......................................................................................... 77



                                                     iv
1. Introduction

The Great Financial Crisis (GFC) of 2007–09 confirmed the vital importance of advancing
our understanding of macro-financial linkages. The GFC was a bitter reminder of how sharp
fluctuations in asset prices, credit and capital flows can have a dramatic impact on the
financial position of households, corporations and sovereign nations.1 These fluctuations were
amplified by macro-financial linkages, bringing the global financial system to the brink of
collapse and leading to the deepest contraction in world output in more than half a century.
Moreover, these linkages have resulted in unprecedented challenges for fiscal, monetary and
financial sector policies.
    Macro-financial linkages centre on the two-way interactions between the real economy
and the financial sector. Shocks arising in the real economy can be propagated through
financial markets, thereby amplifying business cycles. Conversely, financial markets can be
the source of shocks, which, in turn, can lead to more pronounced macroeconomic fluctuations.
The global dimensions of these linkages can result in cross-border spillovers through both real
and financial channels.
    The crisis has led to a lively debate over the state of research on the role of financial
market imperfections in explaining macroeconomic fluctuations. Some argue that the crisis
showed that the profession did not pay sufficient attention to these linkages. Others, by
contrast, claim that they have been recognised for a long time and that substantial progress
has been made in understanding them. But most acknowledge that financial market
imperfections can often intensify fluctuations in the financial and real sectors. Yet, the absence
of a unifying framework to study the two-way interactions between the financial sector and
the real economy has limited the practical applications of existing knowledge and impeded
the formulation of policies.2
   This debate can be seen as a natural extension of the long-standing discussion about the
importance of financial market developments for the real economy (as described in detail in
Appendix I).3 The diverging paths followed by the fields of macroeconomics and finance are


1
  A large literature documents the various macro-financial linkages that have contributed to the
devastating impact of the GFC. Some of the important books on the topic include Krugman (2009b),
Sorkin (2009), Wessel (2009), Lewis (2010), Kose and Prasad (2010), Paulson (2010), Gorton (2012),
Turner (2012), Bernanke (2013), Blinder (2013), Claessens et al (2014), Geithner (2014), Mian and
Sufi (2014a), Wolf (2014), Farmer (2016), King (2016) and Taylor (2016). Lo (2012) reviews a set of
21 books on the GFC.
2
  We presented some quotes reflecting the flavour of this debate at the beginning of the survey.
Krugman (2009a) criticises the macroeconomics literature for its failure to recognise the strong
relationship between the financial sector and the real economy, while Cochrane (2011, 2017) responds
critically to Krugman’s views. Caballero (2010), Kocherlakota (2010), Taylor (2011), Romer (2016)
and Reis (2017) provide varying assessments of research on macroeconomics. Blanchard (2017a)
stresses the need for a broader class of macroeconomic models.
3
  Early surveys of the literature on macro-financial linkages include Gertler (1988), Bernanke (1993),
Lowe and Rohling (1993) and Bernanke et al (1996). Mankiw (2006) and Blanchard (2000, 2009) offer
more general reviews of the state of macroeconomics before the crisis. Recent (but more selective)
updates on macro-financial linkages include Gilchrist and Zakrajšek (2008), Matsuyama (2008),
Solimano (2010), BCBS (2011, 2012), Caprio (2011), Gertler and Kiyotaki (2011), Quadrini (2011),
Borio (2014) and Morley (2016), as well as papers in Friedman and Woodford (2011). Work related to
macro-financial linkages includes: Cochrane (2006) on financial markets and the real economy;
Brunnermeier (2001) and Cochrane (2005) on asset pricing; and Tirole (2006) on the role and impact



                                                  1
at the root of recent debates (Figure A1). The literature has exhibited an oscillating pattern
between integration and separation of financial and real economy issues. Early studies often
considered developments in the real economy and financial sector jointly but they resorted to
mostly qualitative approaches. Later studies, however, emphasised the separation of the real
sector from the financial sector and subscribed to the idea that the financial sector was no
more than a “veil” to the real economy. The corporate finance and asset pricing literatures
largely adopted the “efficient markets” paradigm. An influential branch of the macroeconomic
literature (following the real business cycle (RBC) approach) mostly focused on models that
do not account for financial imperfections and their potential role in shaping macro-financial
linkages.4
     Although progress has been slower than hoped for, the literature has been making a more
concerted effort over the past three decades to analyse the interactions between financial
markets and the real economy. A number of studies have emphasised the critical roles played
by financial factors for the real economy. Starting with Bernanke and Gertler (1989) –
followed by Carlstrom and Fuerst (1997), Kiyotaki and Moore (1997) and others – rigorous
analytical models have been developed. These models have been used for a variety of purposes,
including the analysis of the impact of monetary and fiscal policies on the real economy and
financial markets.
    This paper surveys the rapidly expanding literature on the implications of financial
market imperfections for macroeconomic outcomes. It attempts to contribute to the research
programme in at least four dimensions. First, it presents a broad perspective on theoretical
and empirical studies on the implications of financial market imperfections for macroeconomic
outcomes. Second, it emphasises the global dimensions of these linkages in light of the rapid
growth of international financial transactions and their critical role in the transmission of
cross-border shocks. Third, it summarises the main empirical features of the linkages between
the financial sector and the real economy. Finally, it attempts to identify gaps in the literature
in order to provide guidance for future studies.



of financial imperfections on corporate finance and other economic variables. Crowe et al (2010) and
Nowotny et al (2014) present collections of papers on macro-financial linkages. Brunnermeier et al
(2013) provide an analytical review of the literature on macro models with financial frictions. On the
supply side, Adrian and Shin (2010b) survey the literature on the changing role of financial institutions
and the growing importance of the shadow banking system. Gorton and Metrick (2013) and Pozsar et
al (2013) review the role of securitisation and shadow banking; Brunnermeier and Oehmke (2013) and
Scherbina and Schlusche (2014) review the literature on asset price bubbles; and Forbes (2012) reviews
the literature on asset price contagion. Blanchard (2017a) looks at the state of macroeconomics,
focusing on the need to include distortions other than nominal price rigidities, including financial
frictions. Kocherlakota (2016) shows how the predictions of real business cycle models significantly
change in the presence of small nominal rigidities and argues that these types of models are not useful
tools for analysis of business cycles. For a recent discussion of the need to incorporate financial frictions,
labour market frictions and household heterogeneity in benchmark macroeconomic models, see Ghironi
(2017). Obstfeld and Taylor (2017) consider the importance of finance in the context of the
international monetary system. The literature on law and finance also relates to the broad theme of
macro-financial linkages (see La Porta et al (2013) for a recent review) but more from a longer-run
developmental perspective.
4
   Claessens and Kose (2017) review research on the interactions between asset prices and
macroeconomic outcomes in models without financial market imperfections. In these studies, changes
in financial variables, such as asset prices, are associated with individual consumption and investment
decisions but there are no aggregate feedback mechanisms from financial to real variables and little
scope for macro-financial linkages.


                                                      2
    The survey focuses on two main channels through which financial market imperfections
can lead to macro-financial linkages. The first channel, largely operating through the demand
side of finance, describes how changes in borrowers’ balance sheets can amplify
macroeconomic fluctuations. The central idea underlying this channel is best captured by the
financial accelerator – an extensively studied propagation mechanism in a wide range of
models. The second channel, associated with the supply side of finance, emphasises the
importance of balance sheets of banks and other financial institutions in lending and liquidity
provision for the real economy.
    Given the large number of studies on the macroeconomic implications of financial
imperfections, a survey on the topic comes with a number of caveats. First, for presentational
purposes, we use the rough distinction between the demand and supply sides of finance,
reviewing each separately and analysing how they can lead to macro-financial linkages. The
demand and supply sides are of course interrelated as transactions are endogenous outcomes,
especially when they are considered in a general equilibrium framework. Nevertheless, this
rough demarcation allows us to classify many studies in a simple manner. Second, our
objective is to provide intuitive explanations of how financial frictions can lead to macro-
financial linkages. Hence, rather than delving into the details of certain models, we explain
the general ideas describing the workings of models and then summarise the relevant empirical
evidence for specific channels. In order to present a coherent review of this large body of
work, each section provides a self-contained summary of the specific literature.
    Third, macro-financial linkages ultimately originate at the microeconomic level. Hence,
whenever possible, we draw lessons from the theoretical and empirical work on the
microeconomic factors that are relevant for the behaviour of macroeconomic and financial
aggregates. Fourth, while many of the papers we review have policy relevance, we largely
stay away from directly addressing policy issues, including those related to monetary,
macroprudential, regulatory and crisis management policies. Finally, while we did our best to
include all the major studies on the topic, it is probably unavoidable that a survey of such a
rich literature would miss some contributions.
     Section 2 presents a brief review of the basic microeconomic mechanisms that could lead
to financial market imperfections on the demand side. It starts with a conceptual discussion
of how imperfections (financial frictions) stemming from information asymmetries and
enforcement difficulties affect the amount and costs of external financing available to firms
and households.5 Financial frictions can lead to deviations from the predictions of the standard
complete market models in terms of how (real and financial) resources are allocated.6 Models
incorporating financial frictions typically predict that access to external finance becomes
easier and the premium charged for such financial transactions decreases with the strength of
borrowers' balance sheets and net worth. This can lead to the amplification of (monetary,
financial and real) shocks as changes in net worth affect access to finance – and the use of
that finance – and subsequently influence consumption and investment.
   The section also reviews the empirical evidence on the importance of financial market
imperfections on the demand side. Studies have employed microeconomic (firm, household,
and sector-level) data to examine the role of imperfections in explaining the behaviour of


5
  Some of the pioneering papers on financial frictions include Akerlof (1970), Jensen and Meckling
(1976), Townsend (1979), Stiglitz and Weiss (1981), Bulow and Rogoff (1989) and Hart and Moore
(1994).
6
  Throughout this paper, the terms “financial market imperfections” and “financial frictions” are used
interchangeably. Financial frictions – in conjunction with a country’s legal, regulatory and tax system
– influence the design and evolution of financial contracts, markets and intermediaries.


                                                  3
households, firms and sectors over the business cycle. Some also analyse the importance of
imperfections in driving macroeconomic outcomes during specific episodes. Most studies find
that these imperfections tend to affect small firms and households the most, especially during
times of financial stress. Although many studies provide supporting evidence concerning the
roles played by imperfections, there is also a debate about their aggregate quantitative
importance.
    Section 3 reviews general equilibrium models that feature amplification mechanisms
operating through the demand side. These models show how financial accelerator-type effects
can arise when small shocks are propagated and amplified across the real economy through
their impact on access to finance. In these models, the interactions between access to external
financing and firms’ or households’ net worth or cash flows (or relevant asset prices) serve as
transmission mechanisms between the financial sector and the real economy. Small,
temporary shocks can amplify and spill over to other segments of the economy and generate
large, persistent fluctuations in consumption, investment and output.
    The past two decades have witnessed significant growth in research featuring financial
accelerator mechanisms. This research programme, which often uses dynamic stochastic
general equilibrium (DSGE) models, emphasises the important role played by shocks to
external financing and asset prices in amplifying business cycles. For example, it models that
changes in asset prices and net worth significantly influence household borrowing and
spending through their impact on households’ access to finance and the cost of such finance.
More recent work highlights how financial frictions can affect the allocation of resources across
a number of dimensions (firm heterogeneity, project choice, technological change, housing
market structure and the functioning of labour markets). Section 3 also reviews how the
financial accelerator mechanism has been incorporated into the analysis of the transmission
of monetary policy to the real economy.
     Section 4 looks at studies on the macroeconomic implications of financial imperfections
in the context of open economies. As is the cases for a household or firm, imperfections can
have an impact on net worth and affect a country’s ability to borrow. Research has considered
the importance of various imperfections using different types of open economy models. It has
found that, with contracts being more difficult to enforce and information asymmetries being
more prominent across borders, imperfections can be important for macroeconomic outcomes
in open economies. For example, the financial accelerator mechanism has been shown to be
quantitatively important in explaining the real effects of financial stress in open economy
models. Another strand of the literature examines the interactions between imperfections and
exchange rates, and considers the transmission of shocks in open economies through changes
in the external value of the collateral required for financing. Recent empirical work supports
the importance of global shocks to credit markets in leading to large cross-border spillovers
in real activity.
    Sections 5 and 6 present a summary of the amplification channels that operate largely on
the supply side of finance and the empirical evidence relating to the importance of these
channels (for discussions of the main functions of the financial system, see Levine (1997, 2005)
and Zingales (2015)). Some of the same financial sector imperfections that give rise to the
financial accelerator mechanism also affect the operations of financial intermediaries and
markets, ie the supply side of finance. Just as is the case for firms, financial institutions’
operations are affected by their net worth. Furthermore, financial intermediaries and markets
themselves are subject to various imperfections and related market failures. Importantly,
interactions among financial market participants can lead to aggregate developments on the




                                               4
supply side of finance. Together, these observations imply that the supply side can be a source
of shocks, amplification and propagation, leading, in turn, to macro-financial linkages.7
     Developments on the supply side can have a substantial influence on macroeconomic
outcomes through various mechanisms that can be classified under three major groups,
although there are significant overlaps between these groups. The first two groups focus on
banks’ special role in intermediation. The first includes the bank lending channel, a
mechanism traditionally identified in the literature as being particularly relevant for the
transmission of monetary policy. Notably, changes in the balance sheets of banks, especially
their liquidity, can affect the overall supply of financing. The second and related group is
associated with (changes in) bank capital. As capitalisation varies over the cycle, banks
expand or cut back lending, leading to aggregate procyclical effects. Both mechanisms matter
especially to those households and firms for which bank credit cannot easily be substituted
for.
     The third and most recent group of studies focuses on the financial system’s overall
leverage and liquidity. The GFC showed that leverage could build up to excessive levels
during upturns and drop sharply in downturns. This has important implications for the supply
of external financing, asset prices and, consequently, for the real economy. In addition,
providing liquidity is an important function of the financial system. The aggregate supply of
financing and liquidity to the private sector depends, however, on a complex set of
interactions between financial institutions, notably (but not only) through the interbank and
other financial markets. Fluctuations on the supply side can have a significant impact on
macroeconomic outcomes, with cycles of growing leverage, ample liquidity and rising asset
prices being followed by cycles of deleveraging and liquidity hoarding (especially during
periods of financial stress). More generally, a wide range of factors, including balance sheet
positions of households, non-financial enterprises and financial institutions, interactions
between those agents and financial markets, and access to information and ability to process
it, can affect asset prices and the supply of external financing.
     Empirical work shows that supply side shocks can affect the evolution of external
financing, asset prices and market liquidity, with the potential for feedback loops between
real and financial markets. A number of recent studies focus specifically on the linkages arising
from the first two supply side channels for the transmission mechanisms of monetary policy.
They document that the supply side can influence macroeconomic outcomes through an
amplification of credit supply and external financing during boom periods and deleveraging
and liquidity hoarding during periods of financial stress. A more recent strand documents the
role of such supply factors internationally.
    Section 7 reviews recent empirical studies that analyse aggregate linkages between the
real economy and the financial sector. Using long series of cross-country data, those studies
report a number of salient facts about the features of business and financial cycles and about
the interactions between the two. They document that financial cycles appear to play an
important role in shaping recessions and recoveries. In particular, recessions associated with


7
  Important contributions on the supply side include Diamond and Dybvig (1983), Gale and Hellwig
(1985), Calomiris and Kahn (1991), Holmström and Tirole (1997), Allen and Gale (1998) and Diamond
and Rajan (2001). More recent work includes Adrian and Shin (2008), Geanakoplos (2008) and
Brunnermeier and Pedersen (2009), Danielsson et al (2011), Gertler and Karadi (2011), Gertler and
Kiyotaki (2011), Goodhart et al (2012), He and Krishnamurthy (2012), Brunnermeier and Sannikov
(2014, 2016), Begenau (2016), Gertler et al (2016), Begenau and Landvoigt (2017) and Piazzesi and
Schneider (2017). Many recent models combine demand and supply side considerations (including
banks, shadow banks and payment and collateral services).


                                               5
financial disruptions are often longer and deeper than other recessions while, conversely,
recoveries associated with rapid growth in credit and house prices tend to be relatively
stronger. These results collectively point to the importance of the two-way linkages between
financial markets and the real economy.
    Section 8 concludes with a summary of the main messages, questions for future research
and policy issues that would require further work.



2. Financial imperfections: the demand side

This section reviews the basic mechanisms through which financial market imperfections on
the demand side can lead to macro-financial linkages. It starts with a conceptual discussion
of how the state of borrowers’ balance sheets affects their access to external financing at the
microeconomic level, ie at the levels of households and corporations. This is followed by a
summary of microeconomic and sectoral evidence supporting the importance of imperfections
on the demand side. Although most demand side studies support the role of imperfections,
their quantitative importance is still under debate.

A. Basic mechanisms
Financial market imperfections often stem from information asymmetries and enforcement
problems. Due to information asymmetries, lenders and investors know less about the
expected rate of return on prospective projects than firm managers or owners do. Moreover,
lenders and investors do not know everything about borrowers; for example, whether they
are well qualified, exerts sufficient effort or selects economically efficient projects (ie with
positive net present value). Since the economic prospects of projects, the financial position of
borrowers and the actual effort expended by borrowers cannot be observed perfectly ex-ante,
adverse selection and moral hazard problems arise. Lenders and investors may also face
difficulty in enforcing contracts ex-post. In part due to information asymmetries (eg to verify
the exact state of affairs of borrowers such as the actual effort they exerted) but also as the
legal and institutional frameworks may not allow for efficient ex-post settlements (eg as
collateral is hard to repossess) lenders may have to incur large costs and therefore refrain
from lending in the first place.
    These information asymmetries lead to transaction costs and incomplete financial markets
in the sense that not every worthwhile project is financed.8 In order to choose worthwhile

8
  As Quadrini (2011) notes in his review, the presence of financial frictions implies the absence of
complete trade in certain risks, that is, models with financial frictions feature missing markets, thus
limiting a full sharing of risk. Models (implicitly or explicitly) also assume heterogeneous agents since
there would otherwise be no reason to trade claims inter-temporally or intra-temporally. The fact that
markets are missing may be exogenously assumed or can arise from financial frictions, that is, forms
of market incompleteness due to information asymmetries and limited enforcement. There has been an
extensive theoretical literature on how such imperfections can lead to missing markets or incomplete
contracts, including in financial markets. Seminal corporate finance papers include Akerlof (1970),
Jaffee and Russell (1976), Jensen and Meckling (1976), Rothschild and Stiglitz (1976), Stiglitz and
Weiss (1981) and Myers and Majluf (1984). Tirole (2006) provides a textbook treatment of the role of
imperfections in corporate finance and discusses some of their macroeconomic implications. Meyers
(2015) reviews capital structure theories and how they have been tested. Samphantharak and



                                                   6
projects, avoid adverse selection and prevent shirking, lenders and investors will have to incur
transaction costs. In order to overcome monitoring problems, savers and principal investors
have to rely on agents (such as banks) for monitoring to be done on their behalf or employ
certain ex-post mechanisms (for example, costly state verification (Townsend (1979)). For
this to happen, they need to incur additional costs. These costs and other imperfections create,
in turn, a wedge between: i) what the expected value of a firm or project would be and how
much external financing it could obtain with no agency costs (ie under perfect information or
run by the principal); and ii) what the value of the firm would be under a particular external
financing arrangement.9 This wedge means a higher cost of external financing or can even
lead lenders to ration the external financing they provide ex-ante (Stiglitz and Weiss (1981)).
     Models incorporating these imperfections typically predict that access to external finance
is related to the strength of borrowers' balance sheets. Because of this link, most models do
not only predict that access to finance differs from what is predicted by models without
imperfections, but also that to the extent that financing is available, its amount depends on
borrowers' net worth – the value of assets less outstanding debt obligations – and the
collateral value of easily saleable assets, especially liquid assets (eg cash). Net worth and
collateral provides three types of assurance to investors. First, with skin in the game,
borrowers have better incentives to select profitable projects and work hard to deliver
successful results. Second, borrowers have assets to help repay the loan, ie the simple value
of recoverable collateral. Third, collateral can help investors screen out quality borrowers
from low quality ones or help trustworthy borrowers signal their quality (Bester (1987) and
Besanko and Thakor (1987)).
     Lenders may demand a premium over the risk-free rate to provide credit to borrowers.
This “external finance premium,” ie the difference between the cost of external funds and the
opportunity cost of internal funds, covers the costs incurred by financial intermediaries in
evaluating borrowers' prospects and monitoring their actions. It can also be seen as
compensating investors for the inefficiencies and risks induced by moral hazard and adverse
selection. If borrowers have limited net worth, their access to external financing can be fully
constrained, even if they have profitable investment projects. This is because lenders have
little confidence in their incentives to perform (and the lenders cannot screen out good
projects) and their ability to repay. This premium and constraint on external financing also
imply that deadweight losses can arise because not all profitable projects can obtain financing.
     Changes in borrowers’ net worth then affect their access to finance. Shocks, such as
fluctuations in asset prices or changes in economic prospects, influence the balance sheets of
borrowers. Given financial imperfections, resulting changes in net worth affect the volume of
external financing supplied and its cost. Specifically, as net worth rises, the volume of external
financing increases while its cost declines to a level that is comparable to the implicit cost of
internal funds. Conversely, as net worth declines, the volume of external financing falls while
its cost increases.
    The relationship between borrowers' net worth and their access to external financing
(and its cost) implies that the impact of shocks (monetary, real and financial) can be
amplified. This is an expected outcome as borrowers adjust their investment or consumption


Townsend (2009) discuss how finance affects household behaviour. Dewatripont and Tirole (1994) and
Freixas and Rochet (1997) present early analytical overviews of imperfections as they affect financial
institutions (see also Greenbaum et al (2016)).
9
  These imperfections also affect the formation of firms since a firm (as an organiser of activities) can
be a mechanism to internalise some of those constraints (Coase (1937)). Aghion and Holden (2011)
present a review of research on incomplete contracts and the theory of the firm.


                                                   7
plans in the face of changes in the volume and cost of external financing. However, the effects
of financial imperfections can be asymmetric over the business cycle. In fact, when net worth
is high (more likely to be the case during booms), the problems of adverse selection or moral
hazard are less relevant (as lenders’ collateral requirements are less binding). Conversely,
with adverse shocks, net worth-related constraints can suddenly become much more
significant. The asymmetric nature of shocks has been exploited by the empirical literature
to show the relevance of macro-financial linkages.
     The key mechanisms described above are also relevant to understanding the implications
of changes in household balance sheets. As is the case for corporations, the borrowing potential
of households also hinges on the strength of their balance sheets. This means that movements
in asset (such as house or equity) prices can lead to changes in household borrowing and
spending that are larger than what is suggested by conventional life-time wealth and
consumption effects (Claessens and Kose (2017)). Since housing represents a large part of
household net worth, movements in house prices can affect homeowners’ access to financing
and the external financing premium they face (Mian et al (2017)).

B. Empirical evidence
Numerous studies examine the empirical relevance of financial imperfections. Some employ
microeconomic (firm-, household- and sector-level) data while others consider specific events
or episodes. This section summarises the empirical evidence provided by microeconomic data
(largely focusing on corporate investment and household consumption). Additional work that
considers macroeconomic and time series evidence is presented in Section 7.
     Research documents a strong association between firm cash flow and investment. Theory
predicts that, in environments with no financial market imperfections, a corporation’s current
cash flow is immaterial to its investment decision. With imperfections, however, a
corporation’s cash flow can influence investment decisions because the corporation is subject
to an external finance premium that stems from financing constraints. As the cash flow
increases, so can investment because of the greater availability of internal funds and the lower
cost of such funds relative to external funds. Many studies using panel data report that
corporate cash flows are indeed correlated with investment decisions. This is especially true
for firms that are smaller, do not pay dividends or have poor credit ratings, exactly those
firms that are more likely to be subject to imperfections. The first influential paper that
presents empirical evidence of this link, by Fazzari et al (1988), has been followed by many
others.10
    The empirical evidence on the link between internal cash flows and investment shows
that imperfections play a significant role. Although other factors can also lead to such
linkages, studies using various techniques to control for such factors have confirmed the
relevance of imperfections.11 One confounding factor is that firms’ current cash flows can be
correlated with future profitability. Since prospective returns are relevant to current
investment decisions, this can generate a correlation (even without financial market frictions).
Evidence shows, however, that imperfections remain important. For example, Blanchard et

10
   Other studies include Hubbard (1998), Hoshi et al (1991), Whited (1992), Calomiris et al (1995),
Gross (1995) and Hubbard et al (1995). Stein (2003) presents a survey of theoretical and empirical
work regarding the determinants of firm-level investment dynamics.
11
   For example, Kaplan and Zingales (1997) criticise the approach taken by Fazzari et al (1988) because
they do not control for the endogeneity of financing constraints. See further Kaplan and Zingales (2000)
on why investment-cash flow sensitivities are not good indicators of financing constraints.


                                                   8
al (1994) find supporting evidence by analysing how firms adjust their investment in response
to “cash windfalls”. They argue that such windfalls are unrelated to future profitability.
     Other research, using different approaches, also emphasise the importance of financial
imperfections. Lamont (1997) analyses internal capital markets of oil companies and finds
that, in response to a sudden drop in oil prices, these companies significantly reduce their
non-oil investment compared with other companies. Since this approach controls for the
profitability of investments, it provides evidence of the importance of cash flows for
investment. A number of other studies, including those on the determinants of inventories
and employment, also point to the critical role of imperfections.12 Another strand of the
literature uses information extracted from CEO’s public statements and surveys to assess
directly whether firms are credit-constrained (Graham and Harvey (2001)).
     Imperfections are acutely important for small firms, particularly during times of financial
stress. Because such firms have more limited access to financial markets (because they have
less collateral or are less transparent to outside investors), imperfections are often more
relevant to them (Petersen and Rajan (1995)). Fazzari et al (1988)) find that investment is
indeed significantly more sensitive to current cash flows for new and small firms. In a related
study, Gertler and Hubbard (1988) report an inverse relationship between firm size and sales
variability that stems from imperfections. They also find that the effects of financial market
frictions on investment are asymmetric, with larger impacts during downturns than during
booms. Fort et al (2013) show that young (typically small) businesses are more sensitive to
the cycle than older/larger businesses. Campello et al (2010) and other papers document the
adverse implications of financial constraints during a financial crisis (see Peek and Rosengren
(2016) for a comparison of supply side effects between the GFC and previous crises).
     Other studies also examine the importance of internal cash flow and asset price
movements for the investment undertaken by different classes of firms. Gilchrist and
Himmelberg (1999), for example, document that internal cash flow is critical for investment,
especially for firms that are small, have limited access to credit markets and have relatively
weaker balance sheets. They find that investment is responsive to both fundamental and
financial factors, as predicted by the theory relating to financial frictions. Their estimates
show that financial factors increase the overall response of investment to an expansionary
shock by about 25% over the first few years following the initial shock. Chaney et al (2012)
and Gan (2007) find a significant impact of asset (real estate) prices on corporate investment
in the United States and Japan (see also Lin et al (2011) for the role of ownership structures
for financing constraints).
    Studies also report that small firms are more likely to be credit-constrained during periods
of contractionary monetary policy. Gertler and Gilchrist (1994) examine the behaviour of
small and large manufacturing firms in the United States during five periods of contractionary
monetary policy and one period of credit crunch. They document that small and large firms
behave differently during these periods, with the former group reducing its debt and the latter
one increasing it. Small firms experience larger declines in their inventories and sales than
large firms (Figure 1). They conclude that because large firms have easier access to credit,
the impact of adverse credit market shocks is less pronounced for them than it is for smaller
firms. Sharpe (1994) provides similar supporting evidence and concludes that the cyclicality
of a firm’s labour force is inversely related to its size.



12
 See for additional work, Cantor (1990), Blinder and Maccini (1991), Oliner and Rudebusch (1993),
Carpenter et al (1994) and Guiso et al (2013).


                                               9
    The behaviour of households is similarly affected by financial market imperfections. Due
to imperfections, including an inability to borrow against one’s life time income, households
can be subject to borrowing constraints and therefore undertake more precautionary saving.
This can make household consumption highly sensitive to fluctuations in transitory income.
A number of empirical studies suggest that changes in aggregate consumption are significantly
correlated with lagged or predictable changes in income or credit growth (Flavin (1981),
Campbell and Mankiw (1989, 1990) and Deaton (1992); see Jappelli and Pistaferri (2010) for
a review). Ludvigson (1999) shows a statistically significant correlation between consumption
growth and predictable credit growth.
    Changes in house prices affect household borrowing and spending substantially through
their impact on household net worth and cost of credit. Home equity is often a large part of
household net worth. A variety of micro-based empirical studies document that household
consumption is affected more by changes in house prices than what simple life-time
consumption models would predict (Claessens and Kose (2017)). Lamont and Stein (1999)
report that US households with weak balance sheets adjust their housing demand more
strongly in the face of income shocks, consistent with a role for borrowing constraints. Other
studies also show that households face an external finance premium, which is lower when
their financial position is stronger (Almeida et al (2006)).
     Financial imperfections associated with housing markets are shown to have implications
beyond their impact on individual households. Using regional-level data for the United States,
Mian and Sufi (2010) show that the local variation in house price appreciation in the 2000s,
and related subprime expansion and securitisation, led to a disassociation of local mortgage
credit from income as borrowing constraints became (excessively) relaxed. Credit extension
was driven by expectations of house price appreciation but this was followed by a wave of
mortgage defaults when prices started to decline (Figures 2 and 3). In the regions affected,
this triggered subsequent large adjustments in durables consumption (proxied by auto sales)
and a rapid increase in unemployment (Mian and Sufi (2014b) and Loutskina and Strahan
(2015); see also Benmelech et al (2017) on the important role of credit supply shocks in the
auto loan market during the GFC).
     However, some question the potential role of imperfections in explaining the behaviour of
households and firms. They argue that the close association between income and consumption
may not be entirely due to imperfections. Others point out the general difficulty in separating
real effects from balance sheet effects. Factors, such as the expected rate of return, while
difficult to measure, also affect firm and household access to finance. Some consequently argue
that changes in cash flows represent a set of factors that is different from the strength of
balance sheets (Eberly et al (2008)). Carroll (1997), for example, suggests that the excess
sensitivity of consumption growth to forecastable income growth is explained by non-
linearities of the marginal utility function rather than by borrowing constraints.
    The quantitative importance of financial frictions for small firms has been under scrutiny
as well. Chari et al (2008) challenge the findings of Gertler and Gilchrist (1994) and report
that the behaviour of small and large firms is not significantly different during US recessions
than it is during other times. Kudlyak and Sánchez (2017) consider how the debt, sales and
inventories of small and large firms evolved during the third quarter of 2008, a period of
elevated financial stress. They report that large firms experienced bigger declines in sales and
short-term debt than small ones (Table 1). They also document similar patterns for earlier
recessions. Others argue that even if smaller firms tend to face significant external finance
premia, the role of such firms in explaining business cycles may be small (Cummins et al
(2006)). Srinivasan (1986) shows that small- and medium-size manufacturing firms depend



                                              10
more on internal finance than large firms, potentially making the costs of external finance
less relevant in the aggregate.
    It has also been shown that large firms are affected by imperfections, especially during
times of financial stress. Even relatively large firms, including publicly-listed corporations,
have been shown to face external finance premia, as predicted by theories premised upon
imperfections (see Levin and Natalucci (2005) and Levin et al (2004)). When large firms suffer
from adverse shocks to their balance sheets, their investments are also negatively affected,
especially during recessionary and stressed periods (Aguiar (2005) and Gilchrist and Sim
(2007)). Almeida and Campello (2010) find that large firms can also face high external
financial costs.
    On balance, many studies confirm the role of imperfections for outcomes associated with
the behaviour of firms and households. This is particularly true of studies based on
microeconomic data. However, few studies cover the universe of firms or households and only
a small number of studies analyse the aggregate quantitative importance of financial frictions
in a rigorous fashion. Consequently, they are less clear about the aggregate impact of
imperfections. The next section turns therefore to studies that focus on the aggregate impact
of imperfections and how such imperfections give rise to the financial accelerator mechanism.



3. Financial imperfections in general equilibrium

This section describes how financial imperfections can lead to more pronounced
macroeconomic fluctuations. It first introduces the financial accelerator mechanism through
which financial imperfections amplify and propagate aggregate cyclical fluctuations. In
general, equilibrium models featuring this mechanism display that real, monetary and
financial shocks can have a magnified effect on the real economy because borrowers adjust
their investment in response to changes in external financing. The section then presents a
summary discussion of the empirical evidence on the quantitative importance of the various
types of financial accelerator mechanism, mostly in the context of DSGE models.

A. The financial accelerator
How does the financial accelerator work? The relationships described above, between the
amount of external financing and its cost (premium), on one hand, and the strength of
borrowers’ balance sheets and cash flow positions, on the other, lead to an amplification
mechanism where small shocks can result in large economy-wide adjustments to investment
and consumption. Since wealth is a state (or given) variable and economic agents cannot
quickly (or optimally) adjust their investment/saving plans (as they face costs of doing so),
this mechanism persists over time, causing short-lived shocks to real or financial variables to
have longer-lasting effects on the real economy. This propagation mechanism can also have
general equilibrium effects as individual agents’ actions affect other agents’ behaviour in a
mutually reinforcing fashion (see Figures 4A and 4B).13




13
 For surveys describing variants of the financial accelerator mechanism, see Antony and Broer (2010),
BCBS (2011), Coric (2011) and Quadrini (2011).



                                                 11
The basic mechanism
Although narratives of the propagation mechanisms had been around for a long time,
Bernanke and Gertler (1989) presented the first formal model featuring the financial
accelerator mechanism.14 In their model, a negative productivity shock weakens the cash flow
and balance sheet positions of corporations. In turn, this reduces their access to external
finance and increases the premium on such finance, as in standard corporate finance models.
One of the innovations of their model is the use of a dynamic framework. In particular, they
introduced an overlapping generations model in which only entrepreneurs could costlessly
observe the returns on their individual projects. But outside lenders have to incur a fixed cost
to observe those returns (this “costly state verification” mechanism was first developed by
Townsend (1979, 1988)). As firms’ balance sheets and cash flows worsen, they start facing
limits on their access to external finance in ensuing periods. This, in turn, leads them to
reduce investment even after the initial productivity shock dissipates, thus leading to a
persistence of shocks at the firm level.
    While movements in cash flows play a key role in driving changes in access to credit and
corporate balance sheets more generally in the model developed by Bernanke and Gertler
(1989), fluctuations in credit and asset prices can also play important roles in amplifying
shocks over time. For example, Kiyotaki and Moore (1997) focus on the role of asset prices.
In their analysis, declines in asset prices constrain the ability of corporations to obtain new
loans, with subsequent effects on investment and ultimately on output. They show that by
allowing for the endogenous determination of asset prices, a small negative shock leading to
an asset price decline gets amplified as it reduces the value of collateral for all borrowers and
thereby reduces the aggregate availability of loans. This further depresses demand for the
asset and its price, which then further reduces access to external financing.
    In these models, shocks persist and amplify over time and also spill over to other
corporations or sectors. The interactions between credit limits and cash flows or asset prices
become the transmission mechanism by which small or temporary shocks – whether from
technology, or other real factors, policies or income distribution – can generate large,
persistent fluctuations. In these models, durable assets play a dual role: not only are they
factors of production but they also serve as collateral.

Many dimensions of the financial accelerator
A number of studies have shown how various types of financial imperfection can lead to
different propagation and amplification mechanisms. Some models provide complementary
explanations of how such mechanisms can operate. In most models, cash flows, asset prices
and balance sheets tend to be depressed during recessions.15 Although the mechanisms (and/or
channels) vary – running from cash flow, default risk, capital allocation across firms,
technological change and information asymmetry to the functioning of labour and housing



14
   Earlier general equilibrium models often featured incomplete financial markets (rather than market
imperfections). Farmer (1984), for example, presents a setting where a complete set of futures markets
does not exist because traders have finite lives.
15
   Each model has its specific advantages and limitations. Some models assume that agents are short-
lived, as in the overlapping generations model of Bernanke and Gertler (1990) and Suarez and Sussman
(1997). The model by Kiyotaki and Moore (1997) is dynamic with long-lived agents but, as Suarez and
Sussman (1997) highlight, it rules out price indexation as a way of insuring against unanticipated
shocks.



                                                 12
markets – the overall impact of amplification on macroeconomic outcomes is quite similar.
Appendix II presents a summary of these mechanisms.16

B. Quantitative importance of the financial accelerator
Research, using a variety of approaches, documents the quantitative importance of the
financial accelerator mechanism. This sub-section looks at two distinct groups of studies. The
first employs DSGE models to evaluate the quantitative importance of the financial
accelerator for the business cycle. The second group considers the role of the financial
accelerator mechanism within the context of monetary policy. The sub-section concludes with
a discussion of studies that challenge the quantitative importance of the financial accelerator.

Studies employing DSGE models
In one of the first DSGE models with financial market imperfections, Carlstrom and Fuerst
(1997) show the importance of endogenous agency costs in accounting for business cycles.
They employ a setup featuring a financial accelerator mechanism with long-lived
entrepreneurs. Their model can replicate the empirical observation that output growth
displays a hump-shaped behaviour in response to negative shocks as households delay their
investment decisions until agency costs are at their lowest (several periods after the initial
shock).
    Bernanke et al (1999) represent the seminal DSGE model involving the financial
accelerator. They show that endogenous fluctuations in balance sheets can propagate the
impact of relatively small exogenous disturbances and lead to larger and longer-lasting effects
on the real economy. Looking at how monetary policy shocks can get amplified, they find
that the impact on investment of a 25 basis point decline in interest rates is almost doubled
by the financial accelerator because the reduction is reinforced by an additional decline in the
external finance premium. The initial response of output to such an interest rate decline is
also about 50% greater due to financial accelerator effects. It is also more persistent because
of agency problems between borrowers and lenders.
     DSGE models and their many variants show how adding imperfections can help explain
business cycles. Models including imperfections on the demand side – featuring external
finance premia, balance sheet constraints on borrowing and liquidity shortages – are able to
replicate to a significant degree the behaviour of key macroeconomic variables. Christiano et
al (2008), for example, show that financial factors play an important role in explaining
business cycles during the past two decades in the United States and Europe. Von Heideken
(2009) documents that the financial accelerator greatly improves the ability of standard
models, even those with an elaborate set of real and nominal frictions, to mimic the main
features of business cycles in the United States and the euro area. Using the Bernanke et al
(1999) financial accelerator framework, Nolan and Thoenissen (2009) show that shocks to the
efficiency of the financial sector play an important role in explaining business cycles in the
United States.17




16
   See also Bernanke and Gertler (2000) and Cecchetti et al (2000) for a discussion of different
mechanisms, including those working through asset prices.
17
   Other papers employing general equilibrium models (with the financial accelerator) discuss how
financial institutions fit into broader real activity, including Christiano et al (2003), Christensen and
Dib (2008) and De Graeve (2008).


                                                   13
     DSGE models combining microeconomic and asset price data with macroeconomic
variables, such as investment and consumption, further confirm the critical role of
imperfections. Gilchrist et al (2009) demonstrate the quantitative importance of imperfections
by examining credit spreads on the senior unsecured debt issued by a large panel of non-
financial firms. Estimating a DSGE model that links balance sheet conditions to the real
economy through movements in the external finance premium (using credit spreads as proxy),
they show that rising external finance premia are related to subsequent declines in investment
and output. They also show that credit market shocks contributed significantly to US
economic fluctuations during 1990–2008. In related studies, Gerali et al (2010) and Atta-
Mensah and Dib (2008) incorporate credit risk into standard DSGE models and quantify the
role of frictions in business cycle fluctuations.
     Studies using DSGE models have also shown how endogenous developments in housing
markets can magnify and transmit shocks. Aoki et al (2004) quantify the effects of shocks to
housing investment, housing prices and consumption in a model in which houses serve as
collateral to reduce borrowing-related agency costs. Campbell and Hercowitz (2009)
investigate the impact of mortgage market deregulation in a calibrated general equilibrium
framework with borrowing-constrained households. Iacoviello (2005) constructs a model in
which households’ collateral constraints are connected to real estate, and finds that collateral
and accelerator effects are critical in replicating the changes in consumption resulting from
movements in house prices.18 Aspachs-Bracons and Rabanal (2010) report that, while labour
market frictions are critical in accounting for the main features of housing cycles in Spain,
financial frictions associated with collateral constraints appear less important.
     Using a framework in which house prices and business investment are linked, recent
studies show how credit constraints affect macroeconomic fluctuations. For example, Liu et
al (2013) study the close relationship between land prices and business investment. They
focus on land prices because most of the fluctuations in house prices are driven by land prices
rather than by the cost of construction (Davis and Heathcote (2007)). They introduce land
as a collateral asset in firms’ credit constraints and identify a shock that drives most of the
observed fluctuations in land prices. Since firms are credit-constrained by land value, a shock
to housing demand originating in the household sector triggers competing demands for land
between the household and business sectors. This sets off a financial spiral that drives large
fluctuations in land prices and strong comovements between land prices and investment,
consumption and hours worked.
    Some other studies, using DSGE models, analyse the importance of disturbances in
housing markets in explaining certain features of business cycles. Monacelli (2009) shows that
a borrowing constraint, where durables play the role of collateral asset, improves on a
standard New Keynesian model’s ability to match the positive comovement of durable and
non-durable spending and the large response of durable spending to shocks. Davis and
Heathcote (2005) show how a multi-sector growth model – with housing affecting household
borrowing and spending – matches many empirical facts: consumption, residential investment

18
  Guerrieri and Iacoviello (2013) present a model with collateral constraints that displays asymmetric
responses to house price changes. In their model, collateral constraints become muted when housing
wealth is high (shocks to house prices lead to small and positive changes in consumption and hours
worked). However, collateral constraints become tight when housing wealth is low (shocks to house
prices translate into negative and large changes in consumption and hours worked). Kannan et al
(2012) consider the importance of credit constraints in driving the linkages between house prices and
macroeconomic fluctuations. Iacoviello and Pavan (2013) and Iacoviello (2004, 2015) also consider the
role of housing markets in explaining business cycles. Wachter et al (2014) present a collection of
papers on the role of housing markets during the GFC.


                                                 14
and nonresidential investment comove and residential investment is more than twice as
volatile as business investment.
    Research also shows how disturbances in housing markets can have differential effects on
the real economy depending on institutional and other country-specific features. Iacoviello
and Minetti (2006b) show that the impact of house prices on borrowing constraints is stronger
in countries with more liberalised credit markets. Iacoviello and Minetti (2008) explain the
intensity of the broad credit channel of monetary policy with variables capturing the efficiency
of housing finance and the type of institutions active in mortgage provision in four European
countries. Cardarelli et al (2008) show how housing finance and house price shocks relate to
business cycles in OECD countries and that spillovers from the housing sector to the rest of
the economy are larger in economies where it is easier to access mortgage credit and use
homes as collateral.

Financial accelerator and monetary policy
The financial accelerator mechanism is also critical in understanding one of the major channels
of monetary policy transmission. In addition to the direct interest rate channel (ie the effect
of interest rate changes on asset prices and, through related channels, consumption and
investment), monetary policy affects the real economy through its impact on the balance
sheets of corporations and households.19 This so-called “balance sheet channel of monetary
policy transmission” is closely related to the financial accelerator mechanism. Bernanke et al
(1999) and Cordoba and Ripoll (2004a), for example, extend the Kiyotaki and Moore (1997)
framework to environments with money and investigate the role of monetary policy.
    Changes in monetary policy can have much larger effects on real macroeconomic
aggregates than those resulting alone from traditional, direct interest rate and asset price
channels. Interest rate movements affect the external finance premium and the severity of
financing constraints faced by corporations and households because they influence cash flows
and balance sheets, including net worth (through asset price effects). To illustrate,
contractionary monetary policy is typically associated with a drop in asset prices and thus
results in a decline in the net worth of corporations and households. This leads to an increase
in their external finance premium and weakens their borrowing ability. This, in turn,
constrains their spending on investment and consumption (Bernanke and Gertler (1995)).
    The balance sheet channel of monetary transmission, also called the “broad credit
channel”, has been studied extensively. A number of papers consider different dimensions of
this channel in various settings (see Boivin et al (2011) for a summary of this literature).20
These papers typically find that monetary policy has an impact on the balance sheets of
borrowers and on the distribution of income between borrowers and lenders. A change in
policy rates can therefore have an effect on the real economy that is larger than what would
be suggested by the direct channels alone. Section 5 discusses a similar channel but in relation



19
   For a discussion of these other channels, see Claessens and Kose (2017). For a review of the relevance
of the corporate finance literature for monetary policy, see Trichet (2006).
20
   See, for example, Faia and Monacelli (2007), Iacoviello and Minetti (2008), Christiano et al (2008),
Carlstrom et al (2009), De Fiore and Tristani (2011), Eggertsson and Krugman (2012) and Cúrdia and
Woodford (2016). Woodford (2011) reviews this abundant literature. Considering balance sheet effects,
Taylor (2008) proposes a modified Taylor rule that adjusts the short-term interest rate to observed
increases in credit spreads. Kannan et al (2012) provide evidence of how the inclusion of house price
movements in the conduct of monetary policy can help stabilise the economy in the face of pressures
in the housing market.


                                                   15
to the supply side: the bank lending channel, which refers to the effect of monetary policy on
the supply of loans.

Debate about the importance of the financial accelerator mechanism
Some studies question the quantitative importance of the financial accelerator mechanism
and suggest that other mechanisms might be more important. Chari et al (2007) analyse
financial and other frictions with data for the US Great Depression and the 1982 recession.
Their results suggest that labour wedges – differences between what firms are willing to pay
given the marginal product of labour and what workers are willing to accept in wages given
their marginal rate of substitution vis-à-vis leisure – account for most of the fluctuations (see
also Buera and Moll (2015)).21 Meier and Muller (2006) estimate a model with a financial
accelerator for the United States, matching the impulse response functions after a monetary
policy shock. They claim that financial frictions do not play a significant role. Bacchetta and
Caminal (2000), using a stylistic model of credit markets, show that the impact of anticipated
productivity and fiscal or saving shocks on output fluctuations is usually not amplified but
may rather be dampened because of credit market imperfections.
     Other researchers also argue that the quantitative relevance of imperfections associated
with credit constraints, such as those studied by Kiyotaki and Moore (1997), can be small.
For example, Kocherlakota (2000) shows that the degree of amplification provided by credit
constraints depends crucially on the parameters of the economy. In a related paper, Cordoba
and Ripoll (2004b) argue that the amplification mechanism in Kiyotaki and Moore (1997)
relies heavily on their underlying assumptions. They consider a more standard setting and
argue that while collateral constraints can help amplify small unexpected shocks to the real
economy, their quantitative impact is small. In his review, Quadrini (2011) also highlights
the generally weak amplification of collateral-based financial accelerator models as regards
investment, suggesting instead to focus more closely on how financing constraints affect
working capital rather than investment.
    There has also been a vigorous debate about the importance of financial factors in
explaining the Great Depression. While Calomiris (1993) and Bernanke (1995), in their review
of various factors explaining the Great Depression, clearly come out favouring financial
market imperfections, others do not. For example, Cole and Ohanian (2004) and Ohanian
(2009) use general equilibrium models to show that labour policies can account for about 60%
of the drop in economic activity in the 1930s and that these policies began to reverse when
the economy resumed expansion in 1940. This suggests that financial factors did not play
such a large role.22 Chatterjee (2006) presents a short summary of recent studies, including
those employing various types of general equilibrium model. A reflection of this intense debate
can also be seen in the discussions on the sources of the post-GFC recession (eg Ohanian
(2010), Woodford (2010) and Caballero (2010)).




21
   Christiano and Davis (2006) claim that the result by Chari et al (2007) is not warranted if spillovers
across wedges are taken into consideration (see also Justiniano et al (2010, 2011)). Ajello (2016) finds
evidence that a significant fraction of US output and investment volatility is driven by shocks to
financial intermediation spreads.
22
   Using a standard New Keynesian model, Eggertsson (2012) argues that the New Deal policies of the
Great Depression were helpful in promoting the recovery. These policies were expansionary because
they changed expectations (from deflationary to inflationary), thus eliminating the deflationary spiral
of 1929–33. This made lending cheaper and stimulated demand.


                                                   16
4. Financial market imperfections in open economies

The macroeconomic implications of financial market imperfections have also been studied in
the context of open economy models. Similar to the case of a corporation or household in a
closed economy, a country’s ability to borrow is affected by its net worth because of
imperfections. Obstfeld and Rogoff (2002) argue that the relevance of imperfections is
probably even stronger in an open economy context because contracts are harder to enforce
and information asymmetries are greater than is the case in a closed economy setting. As a
result, limited pledgeability of output and limited verifiability of borrowers’ credit quality
and actions influence access to international finance more than domestic finance.
    The financial accelerator has been shown to be a quantitatively important mechanism in
explaining the real effects of financial stress in open economy models. Gertler et al (2007)
employ an open economy version of the model by Bernanke et al (1999) to analyse the
behaviour of the Korean economy during the 1997–98 financial crisis. They report that the
financial accelerator mechanism explains half of the reduction in output and that credit
market frictions amplify the adverse effects of the crisis on investment.
     Other research considers the relevance of imperfections in different open economy
contexts. Aghion et al (2004), Aoki et al (2010) and Ferraris and Minetti (2013) use general
equilibrium small open economy models with credit constraints to investigate the impact of
various forms of financial liberalisation (of the capital account or credit markets) for
fluctuations in output. Caballero and Krishnamurthy (1998, 2001), Paasche (2001) and
Schneider and Tornell (2004) show that sharp fluctuations in credit and asset markets
translate into boom-bust cycles in emerging market economies (EMEs) because of balance
sheet constraints. Matsuyama (2005) finds that differences in financial market imperfections
can lead to capital flowing from developing economies to advanced economies.
    An important channel through which shocks can affect macroeconomic fluctuations is the
external value of collateral required for financing. Mendoza (2010) constructs a small open
DSGE model to examine the implications of a variety of shocks – including imported input
prices, the “world interest rate” and productivity shocks – for real activity through collateral
constraints. His model shows that when borrowing levels are high relative to asset values,
shocks can be amplified (as in the debt-deflation mechanism of Fisher (1933)) and have a
large impact on output as the collateral constraint cuts access to working capital financing
(see Korinek and Mendoza (2014)). These findings help explain why the rapid slowdowns or
reversals of capital inflows observed in EMEs (“sudden stops”) are often followed by financial
stress (see Claessens and Kose (2014)).23
    Another strand of the literature examines the interactions between financial market
imperfections and exchange rates. Krugman (1999) and Aghion et al (2000) show that the
combination of imperfections and currency mismatches can lead to highly volatile business
cycle fluctuations, especially in EMEs. Céspedes et al (2004) use the financial accelerator

23
   Since some countries respond to these risks by building up foreign exchange reserves, such
precautionary holdings of foreign, liquid assets could turn sudden stops into low-probability events
nested within normal cycles, as observed in the data (Mendoza et al (2009), Borio and Disyatat (2011)).
Bianchi (2011) studies the implications of credit constraints for overborrowing in a small open economy
DSGE model and concludes that raising the cost of borrowing during tranquil times restores
constrained efficiency and significantly reduces the incidence and severity of financial crises.
Brunnermeier and Sannikov (2015) study a model in which short-term capital flows could be excessive
and be a source of financial stress. Kalantzis (2015) study a two-sector model in which large capital
inflows lead to financial crises.


                                                  17
construct of Bernanke et al (1999) in an open economy model, and find that a negative
external shock can have a magnified impact on output because of the effects of a real
devaluation on corporate sector balance sheets. In their model, devaluation lowers the real
value of assets and adversely affects entrepreneurs’ net worth. This leads to an increase in
the cost of external credit and, in turn, further constrains investment, thereby amplifying the
impact of the initial shock on the broader economy. Cook (2004), using a small open economy
model calibrated to reflect the characteristics of East Asian EMEs, shows that a combination
of currency mismatches and exchange rate depreciation can increase the cost of capital and
reduce investment by adversely impacting firms’ balance sheets.
    Other studies consider the role of different types of financial market imperfection in
general equilibrium multi-country settings. Backus et al (1994), Baxter and Crucini (1995),
Heathcote and Perri (2002), Kose and Yi (2001, 2006) and many others have built multi-
country models of international business cycles. Many such models, however, feature the
assumption of financial autarky (ie countries cannot trade financial assets). Kehoe and Perri
(2002) present a model in which the debt capacity of a country is tied to the value attributed
by the country to its future access to international financial markets. They show that this
mechanism can explain the cross-country output correlations observed in the data. Heathcote
and Perri (2013) review the literature on various puzzles related to international risk sharing
and allocative efficiency across countries and conclude that, even over the long run, allocations
appear inefficient because of capital market imperfections.
    The role of financial market imperfections in the transmission of business cycles has also
been a fertile area of study. Gilchrist et al (2002) consider a model in which firms face credit
constraints in borrowing both at home and abroad, which amplify the international
transmission of shocks. Iacoviello and Minetti (2006a) develop a DSGE model where firms
face a degree of slack with respect to credit constraints that differs according to whether they
deal with domestic versus foreign creditors. They argue that this helps capture the observed
comovements of output. Guerrieri et al (2012) examine the implications of default in a
currency union with a model comprising banks that are capital constrained.
     Recent research also examines the role of financial market imperfections in explaining the
highly synchronised nature of the GFC. Perri and Quadrini (forthcoming) find that the
recession that accompanied the GFC, and its global reach in particular, can be explained by
shocks in credit markets. Using a two-country DSGE model, they show that positive shocks
affect the real sector as they enhance the borrowing capacity of firms and thereby lead to
higher employment and production, although at a lower level of labour productivity. They
document that, when countries are financially integrated, country-specific shocks to credit
markets affect employment and production in other countries by creating significant business
cycle spillovers (see also Kalemli-Ozcan et al (2013) and Quadrini (2014)).24 Moreover, credit




24
   Devereux and Yetman (2010) and Dedola and Lombardo (2012) also examine how credit market
shocks in DSGE models with financial market imperfections can generate international business cycle
spillovers. Kollmann et al (2011) introduce a banking sector in an international business cycle model
and study how shocks to this sector can generate global spillovers. Bacchetta and van Wincoop (2016)
show that national business cycles can become highly synchronised when the world economy is hit by
a global panic shock. Rose and Spiegel (2010, 2011) and Kamin and DeMarco (2012) examine this
issue using empirical approaches.



                                                 18
shocks that are different from productivity shocks, tend to generate asymmetric business
cycles (ie contractions that are sharper than expansions) and more volatile asset prices.25
    Shocks originating in financial markets appear to be important in explaining global
business cycles, especially during periods of global recessions. Helbling et al (2011) analyse
the role of disturbances in global credit markets in explaining business cycles in G7 countries
using a set of VAR models. Their results indicate that these disturbances can have a
significant impact on output and other macroeconomic variables (Table 2). In their analysis,
credit shocks, for example, account for roughly 11% of the variance of global GDP. In
addition, they report that credit shocks account for about as large a share of fluctuations on
their own as standard productivity shocks. Credit shocks explain almost 10% of the variance
in global productivity and about 11% of the variations in inflation and interest rates. These
shares are also close to those obtained for productivity shocks.
     Helbling et al (2011) also undertake a series of counterfactual simulations to examine the
evolution of global GDP during the GFC and report that credit shocks played an important
role. Figure 5 shows the difference between the actual cumulative change in the demeaned
global GDP factor and the cumulative change in the simulated value in the absence of a
global credit shock during the period ranging from Q3 2007 to Q4 2009. The impact of the
shock clearly intensified as the recession spread from the United States to other advanced
economies. For example, without the credit shock, the global recession would have been about
10% milder, given the difference between actual and simulated cumulative growth in Q3 2009.
The bottom panel of Figure 5 compares the contributions of credit and productivity shocks
to cumulative global GDP growth based on counterfactual simulations. Credit shocks on their
own accounted for a larger share of the cumulative decline in the global GDP factor than
productivity shocks (for the role of shocks originating in credit markets, see also Huidrom
(2014), Bassett et al (2014) and López-Salido et al (2017)).



5. Financial imperfections: the supply side

The process of financial intermediation arises in part from attempts to overcome
imperfections, but it can itself also create amplification and propagation effects. The financial
accelerator mechanism discussed in the previous sections explains how changes in borrowers’
balance sheet and cash flow positions (and certain other features of borrowers) – the demand
side of finance – can affect their access to financing and thereby lead to an amplification and
propagation of shocks. Some of the same and other, similar imperfections also affect the
operations of financial intermediaries and markets – the supply side of finance. Together,
these imperfections imply that the supply side of finance can by itself be a source of shocks
and propagation, leading to specific macro-financial linkages.
    This section presents the three main supply side channels linking financial imperfections
to the real economy. The first sub-section analyses the role of bank lending in shaping
macroeconomic outcomes. Next, the implications of changes in bank balance sheets for the
real economy are considered. The third one looks at how the channels associated with
leverage, liquidity and other supply factors can affect real aggregates.



25
  Some recent studies emphasise the importance of various imperfections associated with financial
shocks, trade credit, and working capital in explaining the sharp decline of trade relative to output
during the GFC (Amiti and Weinstein (2011), Chor and Manova (2012) and Bems et al (2013)).


                                                 19
A. Bank lending channel
The bank lending channel, also referred to as the narrow credit channel, arises from the
special role played by banks in credit extension. As explained in the previous section, certain
asymmetric information problems are more likely to be prevalent among households and small
firms. This can limit their access to financial services, even when households have adequate
income or when firms have projects with reasonably high risk-adjusted returns. Banks invest
in information acquisition and monitoring and can thereby (partially) overcome the problems
arising from information asymmetry (and other “contracting” problems).26 However, during
this process, some households and smaller firms may become bank-dependent in that they are
unable to substitute with ease other forms of finance for bank loans (or whatever financial
services they obtain from a bank). Larger firms, by contrast, may be less affected by such
lock-in effects because they are less subject to information asymmetries and do not depend as
much on banks. In addition to retained earnings, they can finance investment by issuing
equities and bonds in capital markets or by raising other forms of external financing.27
    Liquidity provision, which takes the form of credit lines and backup facilities (targeted
at firms and including capital market instruments, such as commercial paper) is another
reason for the special role of banks. Indeed, banks play a special role in maturity
transformation and liquidity provision (see Diamond and Dybvig (1983) and Holmström and
Tirole (1997)). The traditional function of a bank is to borrow short (eg collect households
deposits) and lend long (eg extend loans to firms and mortgages to households). In doing so,
a bank provides valuable external financing. Banks also provide liquidity services to firms
and households. Through the raising of wholesale funds, for example, a bank can quickly
make liquidity available to corporations. Although other financial institutions perform similar
functions, the ability of banks to provide liquidity at short notice is not easily matched by
other forms of financial intermediation.
     The dependence of firms and households on banks for credit and liquidity has
consequences for the real economy. Since some firms and households cannot easily substitute
for bank loans and liquidity, banks play a central role in the propagation of economic
fluctuations. Real and financial shocks affecting banks’ ability to lend and provide liquidity
then influence the real sector. Shocks can arise from changes in regulation, supervision,
technology or preferences. For example, a regulatory change can require banks to keep higher
reserves. If they cannot raise funds quickly, banks may need to adjust their lending, an
adjustment that is more likely to affect bank-dependent borrowers, such as smaller firms and
households. When faced with an adverse shock, a change in lending is also more likely for
small banks because of their limited access to other forms of funding (such as certificates of
deposit). Since such banks are also more likely to have a higher proportion of bank-dependent
clients, it can again disproportionally affect smaller firms and households.


26
   Many studies examine the special roles of banks (Freixas and Rochet (2008) and BCBS (2016) review
the literature). For example, banks can screen potential borro wers, acquire information on firms’
collateral (Rajan and Winton (1995) and Diamond and Rajan (2001)) or directly monitor borrowers’
actions in order to prevent problems associated with moral hazard (Repullo and Suarez (2000) and
Holmström and Tirole (1997)). Earlier contributions include Brunner and Meltzer (1963) and Bernanke
(1983), and Rajan (1998) provides a comprehensive review of the functions of banks.
27
   Bernanke and Blinder (1988) provide a stylised discussion of the lending channel using an IS/LM
type framework. Stein (1998) provides a “micro-founded” adverse selection model of bank asset and
liability management that generates a lending channel. For an early overview of the theory and
empirical evidence relating to the bank lending channel, see Kashyap and Stein (1994).



                                                20
    A number of studies have formally analysed the possible general equilibrium effects of
the special role of banks. In Diamond and Rajan (2005), banks create value added because
they have superior skills in assessing entrepreneurs’ collateral and commit to using those skills
on behalf of investors by issuing demand deposits.28 Negative shocks can undermine this role,
shrinking the common pool of liquidity and thereby creating spill-overs to other banks and
exacerbating overall liquidity shortages. The interbank market, in particular the possibility
that it may freeze, is crucial to their model. Other research develops models that also analyse
the occurrence of interbank market freezes and the role of such freezes in inducing credit
crunches (Freixas and Jorge (2008) and Bruche and Suarez (2010)). Diamond and Rajan
(2011) construct a model showing that the possibility of future fire sales means that deep-
pocketed investors are willing to buy bank assets only at a low price. With banks preferring
to hold on to their assets, the credit crunch is exacerbated.29
    The dependence of firms on bank financing influences how monetary policy is transmitted
to the real economy through the bank lending channel.30 Monetary policy actions can affect
the ability of banks to lend since it influences the supply of funds that a bank has access to
– by affecting the availability of deposit funds and its funding costs more generally – and
consequently the amount of loans a bank can make. A monetary contraction, for example will
act to increase bank’s funding costs. This will then induce banks to reduce their supply of
loans. The decline in the supply of loans, if not offset by firms and households obtaining other
forms of financing, in turn, negatively impacts aggregate output because it constraints
households’ consumption and (small) firms’ investment. The bank lending channel can thus
explain why policy rate decisions affect the supply and cost of credit by more than the sole
impact of the policy rate move (see Claessens and Kose (2017) for a discussion of the interest
rate channel).
    The credit and liquidity roles of banks also have implications for banks’ organisation as
well as their regulation and supervision (see BCBS (2016) for a review). A unique aspect of
banks’ maturity transformation and liquidity provision process is their use of demand deposits
that are “redeemable” at par and on request.31 This makes banks vulnerable to liquidity runs


28
   Holmström and Tirole (1998) represents the pioneering study of the special role of banks in a general
equilibrium environment. For models that endogenise the superior skills of banks in collecting
entrepreneurs’ collateral, see Habib and Johnsen (1999) and Araujo and Minetti (2007).
29
   Gorton and Huang (2004) show how, in an environment in which private investors make inefficient
project choices (eg as they cannot accumulate the liquidity needed to buy the assets of distressed
financial institutions), the government can provide liquidity and help mitigate such inefficient choices.
Lorenzoni (2007) shows that competitive financial contracts can result in excessive borrowing ex ante
and excessive volatility ex post in an economy with financial frictions and hit by aggregate shocks.
30
   Early surveys of the literature on the bank lending channel include Bernanke (1993), Bernanke and
Gertler (1995), Cecchetti (1995), Hubbard (1995) and Peek and Rosengren (1995a). As discussed later,
there are also studies highlighting the importance of risk-taking by banks in their lending decisions (eg
Disyatat (2011) and Borio and Zhu (2012)).
31
   A number of studies (Calomiris and Kahn (1991), Kashyap et al (2002), Diamond and Rajan (2001)
and Huberman and Repullo (2014)) explain why banks fund themselves with short maturities given
those risks. These models generally rely on the disciplining features of short-term debt and the
beneficial tension between making illiquid loans to borrowers and providing liquidity on demand to
depositors. While other intermediaries, such as money market funds, are also vulnerable to runs, as
seen during the GFC (Schmidt et al (2016) and Covitz et al (2013)), they generally do not lend and
take short-term on-demand deposits at the same time. They are also thought to be “less special,” in
that their intermediation functions can be more easily replaced, although the GFC raised questions
about such an assumption.



                                                   21
(Diamond and Dybvig (1983)). While banks also have access to wholesale funding, as the
GFC has shown, this access can be subject to sudden withdrawals too (Gertler et al (2016)).
These unique credit and liquidity provision functions and the possibility of runs have
implications for the way banks are organised, governed and treated by the government
(including through regulation and supervision). It also has implications for the provision of
public safety nets and for crisis management.32

B. Bank capital channel
The health of a financial intermediary’s balance sheet can influence its lending and other
intermediation activities. Balance sheet positions, especially net worth, matter for financial
intermediaries just as is the case for non-financial corporations. Net worth has an impact on
financial intermediaries’ access to funds and their liquidity positions and thereby affects their
lending activities. Banks also need to satisfy capital adequacy requirements (whether market-
or regulation-driven). Given the costs associated with raising new capital quickly on the open
market, a bank’s net worth depends over the short run on changes in the quality of its loan
portfolio and the value of its other assets, including securities.
    Consequently, changes in the value of a bank’s assets will affect its access to and cost of
funding and its ability to make new loans. A decline in loan quality, for example, or a fall in
the value of tradable assets, can lead to a drop in a bank’s capital. This can make its funding
more costly or make its capital adequacy requirements binding, forcing the bank to shrink its
loan book. When these shocks take place simultaneously at many banks, they can lead to
systemic consequences, especially when alternative sources of external financing are limited.
     These effects can be a source of aggregate cyclical fluctuations through what has been
called the bank capital channel (Greenwald and Stiglitz (1993); see Borio and Zhu (2012) for
further references). When many banks are affected by the same capital shock, aggregate
effects can occur. For example, during a recession, the quality of bank loan portfolios tends
to weaken, adversely impacting banks’ balance sheets. In order to shore up their relative
capital positions (as desired by the market or to satisfy regulatory requirements) – but
unlikely to be able to raise capital quickly – banks may need to tighten their lending standards
and reduce the volume of risky credit they provide.33 Since borrowers who rely on banks for
their external funding needs have a limited set of alternatives, this can lead to a slowdown in
economic activity, or even a recession, with a higher proportion of non-performing loans and
deteriorating bank balance sheets. The decline in bank lending induced by such a “capital
crunch” can affect (and interact with) economic activity through various channels (see
Bernanke and Lown (1991), Holmström and Tirole (1997), Repullo and Suarez (2000) and




32
   Some of these issues are discussed further in Claessens and Kose (2014). Acharya et al (2011a, 2011b)
model “freezes” in the market for bank assets. In their models, depending on the information
environment and the nature of liquidation costs, small shocks can lead to sudden interruptions in
financial institutions’ ability to roll over their liabilities. Bank liquidity may also be countercyclical,
that is, inefficiently high during booms but excessively low during crises, making interventions to
resolve banking crises desirable ex post but not ex ante (Farhi and Tirole (2012)). See further Tirole
(2011) for a review of various aspects of illiquidity and Holmström and Tirole (2011) for an extensive
analysis of (private and public) forms of liquidity.
33
    Repullo and Suarez (2013) provide a model in which banks are subject to regulatory capital
requirements and have limited access to equity markets. Gorton and Winton (2017) present a general
equilibrium model to study the private and social costs of bank capital.


                                                    22
Van den Heuvel (2006, 2008)). With this mechanism, a strong link can arise between bank
capital, the supply of bank financing and macroeconomic outcomes.
     The interaction between bank capital and firm liquidity matters in various ways,
especially when firms are locked into credit relationships with banks. Den Haan et al (2003)
and Minetti (2007) show that a capital crunch can induce firms to abandon high quality
projects or break up credit relationships. Thus, a capital crunch depresses not only the volume
of investment but also its average productivity. Chen (2001) shows that a capital crunch can
cause a drop in asset prices (eg real estate), which can, in turn, have feedback effects,
including a contraction in bank capital. Minetti and Peng (2013) investigate the mechanics
of the bank capital channel in an open economy model (calibrated for Argentina) and show
that real interest rate shocks generate large fluctuations in output and real estate prices.
     Some recent studies employing DSGE models help gauge the quantitative importance of
the bank capital channel. Gertler and Kiyotaki (2011, 2015) and Gertler and Karadi (2011,
2013) develop models that exhibit moral hazard in the financial sector and thus provide a
role for bank capital. They find that, under reasonable assumptions about efficiency costs,
banks limit their deposit taking in response to a decline in net worth. These studies also
explore how unconventional monetary policy (UMP) – specifically direct intervention in credit
markets – can attenuate the bank capital channel. Christiano and Ikeda (2013) show how
these and other models with financial frictions allow for quantitative analyses of the channels
by which unconventional policies can affect financial and economic outcomes in times of
financial stress. They find that the net welfare benefits of such intervention during a financial
crisis is large and increases with the severity of a crisis.34
     The bank capital channel also matters for the conduct of monetary policy. Monetary
policy may have a limited impact (including through the bank lending channel) when
shortfalls in bank capital constrain loan supply and already dampen economic activity. The
potency of the bank capital channel also hinges on the degree to which non-bank financial
institutions may be capital-constrained (or otherwise) themselves and the extent to which
firms and households are bank-dependent (Gilchrist and Zakrajšek (2012)). Even well
developed and adequately capitalised non-banks may not be able to offset a decline in banks’
supply of loans since their financing can be imperfect substitutes for bank loans. This means
that, in practice, central banks consider the state of all intermediaries’ balance sheets (even
though they tend to focus on those of banks).

C. Leverage and liquidity channels
Leverage, defined as the ratio of total assets to shareholder equity, has received much
attention recently because of its role in the GFC. Fluctuations in the leverage of financial
institutions (and other agents) relate to changes in asset prices through both simple
accounting and the behaviour of agents. The basic accounting relationship between
movements in asset values and changes in leverage is negative, ie rising asset prices boosts
net worth and thereby makes measured leverage drop, ie leverage is countercyclical. Similar
to the financial accelerator mechanism, this means that financial institutions and other agents




34
  There is a large literature on the effectiveness of UMPs that uses various approaches, see Eggertsson
and Woodford (2003), Krishnamurthy and Vissing-Jorgensen (2011), Farmer (2012), Woodford (2012),
Bauer and Rudebusch (2014), Baumeister and Benati (2013), Swanson and Williams (2014), Arteta et
al (2015, 2016), Farmer and Zabczyk (2016), and Borio and Zabai (2016).


                                                  23
can fund themselves easier in times of rising asset prices, and consequently lend more to
others, even without raising their leverage.
     In practice, as Adrian and Shin (2008) show, leverage is not countercyclical (or even
acyclical), but procyclical, at least for broker-dealers; it increases when asset prices rise and
falls when asset prices decline. And, in financial markets, as Geanakoplos (2010) shows,
margins (or haircuts), which dictate the share of financing available for a unit of collateral,
are procyclical too, ie lower during booms and higher in busts. While the exact mechanisms
remain unclear, many attribute this procyclicality to perverse incentive structures, incomplete
corporate governance arrangements, herding behaviour and other agency issues (see Borio et
al (2001); Claessens and Kodres (2017)).
     As leverage fluctuates, it affects the supply of financing. When leverage is high, supply
can be expected to be more ample since it means that intermediaries face fewer constraints
in credit extension. Conversely, when leverage is low, financing tends to be more constrained.
When a number of financial institutions exhibit acylical or procyclical leverage rather than
countercyclical leverage, aggregate financing and liquidity conditions are affected. This
behaviour leads to a feedback effect: stronger balance sheets fuel greater demand for assets
and this, in turn, raises asset prices and further strengthens balance sheets and demand for
assets.
     Consequently, since there is more, rather than less – as in other markets – buying of an
asset when its price rises, leverage does not necessarily decrease during an asset price boom
and can even increase (Drehmann and Juselius (2012) document that, while in practice
increases in market values outstrip debt increases at the aggregate and sectoral level, there is
procyclicality). Conversely, during a bust, the mechanism works in reverse, balance sheets
weaken due to asset price drops, leverage decreases and pressures arise to curtail the supply
of financing. In turn, this leads to additional declines in asset prices, possibly affecting a
broader array of institutions and activities, further weakening balance sheets and reducing
leverage. These reductions in leverage can also be associated with increases in asset price
volatility, which is in part related to the arrival of adverse information (Fostel and
Geanakoplos (2012)). Figures 6A and 6B summarise conceptually the mechanisms and
dynamics of the leverage cycle during upward and downward phases.35
     The leverage channel operates in ways that are very similar to the bank capital channel
(see Adrian and Shin (2011a)). The difference is that the aggregate leverage channel is not
limited to banks or related to the special nature of banking. Rather, it can operate at the
level of the overall financial system, when the various actors (including hedge funds,
institutional and other investors) experience limits on their ability to undertake transactions.
It can also affect the so-called shadow banking system.36


35
   The leverage cycle can relate to the presence of asset price bubbles, which can be rational or
irrational. Either type can be welfare enhancing or reducing (see further Claessens and Kose (2017) on
asset bubbles). Nuño and Thomas (2017) document that leverage has contributed more than equity to
fluctuations in total assets and that it is positively correlated with assets and GDP but negatively
correlated with equity (see also Halling et al (2016)). He and Krishnamurthy (2013) develop a model
of financial intermediaries to study the linkage between risk premia and leverage.
36
   See Adrian and Ashcraft (2016) for an overview of the shadow banking system, its growth and
functioning; Claessens et al (2012b) for a review of the functions performed by shadow banking systems;
Gennaioli et al (2013), Gertler et al (2016) and Begenau and Landvoigt (2017) for analytical models
of shadow banking; and Gorton and Ordoñez (2013) on how an economy can become fragile if it relies
extensively on privately-produced safe assets, such as those generated by shadow banking. See further



                                                  24
     The leverage channel can lead to more pronounced financial and business cycles, possibly
associated with bubbles and other asset price anomalies. Because of the net worth and other
related balance sheet channels, changes in leverage affect asset prices and influence the
availability of external financing for all types of borrower. In other words, the degree of
leverage becomes an indicator of the buoyancy of external financing and risk-taking. Through
feedback effects, such as changes in asset prices, the leverage channel can then lead to stronger
two-way linkages between the real and financial sectors. The leverage channel is also closely
related to the overall state of liquidity, with asset prices possibly deviating from
“fundamentals” over the leverage cycle (see Shleifer and Vishny (1997) and further Appendix
III).37
    Recent studies examine the interaction between leverage and boom-bust cycles through
the lens of externalities. Lorenzoni (2008) and Jeanne and Korinek (2010) model how firms
that set leverage during booms do not account for the impact that their leverage has on the
price of collateral assets during busts (see also Dávilla and Korinek (2017)). Such externalities
can, in turn, lead to excess leverage. Other studies emphasise the role of strategic interactions
and complementarities among banks in pursing collectively risky strategies ex ante (Farhi
and Tirole (2012)) and inducing credit crunches ex post (Rajan (1994) and Gorton and He
(2008)).38



6. Evidence relating to the supply side channels

The supply side of finance can have a significant influence on macroeconomic outcomes
through various mechanisms. Empirical evidence suggests that the behaviour of financial
institutions can have an impact on the supply of external financing and overall liquidity.
Studies also show that supply side factors can affect the evolution of asset prices, with the
potential for virtuous and vicious feedback loops between real and financial markets. In
addition, recent research documents that the supply side can influence macroeconomic
outcomes through deleveraging and liquidity hoarding, especially during periods of financial
stress. While it is hard to separate empirically the roles of different channels – liquidity
shortfalls, for example, are often related to adverse shocks to capitalisation – this section
attempts to survey the empirical literature relating to these three channels.




Gorton (2016) and Golec and Perotti (2017) for reviews of the literature on safe assets with a domestic
focus and Gourinchas and Rey (2016) for an analysis of the role and effects of safe assets globally.
37
   Bruno and Shin (2015) study the dynamics linking monetary policy and bank leverage. They
construct a model of the risk-taking channel of monetary policy in an international context. The model
rests on a feedback loop between global banks’ increased leverage and capital flows amid currency
appreciation for capital recipient economies. It shows that adjustments to leverage act as a linchpin
between fluctuations in risk-taking and monetary transmission.
38
   Other research aimed at understanding how externalities can carry the seeds of ensuing busts includes
Dell’Ariccia and Marquez (2006) who show how lending standards tend to weaken during booms as
adverse selection is less severe and lenders find it optimal to weaken screening and lending standards
(with the objective of trading quality for market share). This leads to deteriorating portfolios, lower
profits and a higher probability of a downward correction.



                                                  25
A. Bank lending channel
The bank lending channel has been extensively studied empirically, at least as regards the
effect of changes in bank liquidity conditions. There is intense debate about whether this
channel can be identified with macroeconomic data – given the difficulty of separating factors
driving demand from those driving supply. Some studies look at credit market indicators,
such as the ratio between bank lending and commercial paper, showing that tighter monetary
policy leads to a decline of the ratio (Kashyap et al (1993) and Ludvigson (1998)). Oliner and
Rudebusch (1996) argue that a change in the mix of finance can capture the bigger decline
in the amount of credit granted to small firms compared with large firms. Others question
the potency of the bank lending channel in relation to monetary policy, especially for the
United States. Some studies argue that since banks can accumulate deposits by issuing money
market liabilities, such as certificates of deposits, monetary policy has a limited impact on
bank lending (see Romer and Romer (1989) and Ramey (1993)). A number of studies, though,
find evidence supporting the relevance of the bank lending channel.39
    Other work finds that the bank lending channel plays a role for small banks but has a
limited overall impact. Kashyap and Stein (2000), using US bank data, find the impact of
monetary policy on lending to be stronger for banks with less liquid balance sheets, with the
pattern largely attributable to smaller banks. This evidence supports the bank lending channel
since these banks are likely to have fewer external financing options. Others question this
view (Bernanke (2007)). Given the growing depth and variety of capital markets, they argue
that even small banks have gained access to a multitude of funding sources in addition to
retail deposits. Moreover, even if the bank lending channel is important for smaller banks,
these banks constitute only a minor share of total US lending. Consistently, Lown and Morgan
(2002) report results suggesting that while bank lending may play an important role in
macroeconomic fluctuations, the magnitude of the bank lending channel for monetary policy
changes may be quite small.
     Empirical evidence also suggests that the importance of the transmission channels varies
by type of loans and changes over time. Recent studies of the bank lending channel attempt
to establish which types of bank loan are more likely to be affected by nominal or real shocks.
Den Haan et al (2007), employing a reduced-form VAR model to identify monetary policy
shocks, find that after a monetary tightening, real estate and consumer loans decline sharply
while commercial and industrial (C&I) loans respond positively and often significantly. By
contrast, after a non-monetary negative shock, C&I loans decline sharply, while real estate
and consumer loans display no decrease. Boivin et al (2011) report that US transmission
channels have evolved over time due to structural changes in the economy, particularly in
credit markets, and changes in the relationship between monetary policy and expectations
formation. As a consequence, monetary policy innovations have had a more muted effect on
real activity and inflation in recent decades relative to before 1980.
    The potency of this channel also varies across countries and appears to be more influential
in bank-dominated systems. In market-based systems, such as those of the United States and
the United Kingdom, where the role of capital markets is relatively important, firms and
households enjoy a variety of external financing alternatives whereas in bank-based systems,
such as those of Germany and Japan, fewer options exist. This implies that the bank lending
channel is expected to be more influential.



39
 See Gertler and Gilchrist (1993, 1994), Friedman and Kuttner (1993), Kashyap and Stein (1995),
Peek and Rosengren (1995b) and Kakes and Sturm (2002).


                                              26
    Evidence from outside the United States appears indeed to be more clearly supportive of
the bank lending channel. Research relating to some European countries show that banks
play a more prominent role in financial intermediation (see Ehrmann et al (2003) and
Iacoviello and Minetti (2008)). Jiménez et al (2012) use Spanish data on loan applications
and loans granted and find that tighter monetary conditions and worse economic conditions
weaken loan extension (especially to firms). This is also the case for lending from banks with
lower capital or liquidity ratios. Their results suggest that firms cannot offset the impact of
credit restrictions by simply switching to other banks (or other forms of financing). The
channel might be weakening over time though, as many financial systems have become more
market-based (Altunbas et al (2010), Claessens (2016)).

B. Bank capital channel
A number of studies find empirical support for the bank capital channel, especially during
periods of substantial capital shortage, leading to so-called credit crunches. For example,
Lown and Morgan (2006) show for the United States that surveys of senior loan officers,
which partly reflect supply conditions, provide significant explanatory power for US real
activity. De Bondt et al (2010) show how similar lending surveys, especially those relating to
enterprises, are a significant leading indicator of bank credit and real GDP growth in the euro
area. Some studies report that credit losses at commercial banks had some, albeit not large,
regional effects on the US recovery from the 1990–91 recession (Bernanke and Lown (1991),
Hancock and Wilcox (1993, 1994), Berger and Udell (1994) and Peek and Rosengren (1994)).40
These studies found multipliers, that is the effect of a 1% change in bank capital on the
percent change in lending, ranged from 1.5 to 2.7. Ashcraft (2006) also found some small
effects of variations in commercial bank loans on real activity in normal times. Gambacorta
and Marques-Ibanez (2011) show that weaker banks in the United States and Europe
restricted loan supply more strongly during the GFC than other banks.
    Other studies document varying effects. Bayoumi and Melander (2008) employ a VAR
model and report that an exogenous fall in the bank capital/asset ratio of one percentage
point reduces real US GDP by some 1.5% through weaker credit availability. Moreover, an
exogenous fall in demand of 1% of GDP is gradually magnified to about 2% through financial
feedback effects. Greenlaw et al (2008) regress the log difference of GDP on the lagged four
quarter (log) change of domestic non-financial debt, using as instruments TED spreads and
lending standards. They find a change in credit growth of 1% to affect real GDP growth by
about 0.34% in the short run and 0.47% in the long run. By contrast, Berrospide and Edge
(2010), who update studies from the early 1990s using panel regression techniques, find a
modest effect on lending: capital shortfalls affect the extension of new loans with a range of
0.7% to 1.2% and not significant changes in GDP growth (however, in a VAR setting they
do find bank capital shocks to affect GDP growth up to 2.75%). Francis and Osborne (2010)
also report a smaller effect for UK banks.
     Effects can vary by bank capitalisation and by the state of the business/financial cycles.
Bernanke (1992) and Meh and Moran (2010) find that the health of banks plays an important
role during recessions and subsequent recoveries because bank capital can (or cannot) cushion
shocks (see also Berger and Bouwman (2013)). Other studies also find a greater role for bank
capital during periods of significant credit losses or outright shortages of bank capital. Some


40
  Hancock and Wilcox (1994) investigate the impact of the bank capital channel on the US housing
market and find significant effects of the early 1990s capital crunch on commercial and residential real
estate activity.


                                                  27
report that during such periods, weakly capitalised banks limit their lending much more than
highly capitalised banks (Peek and Rosengren (1995b) and Woo (2003)). In a related paper,
Ashcraft (2005) finds, however, that it is the closure of commercial banks rather than shocks
to their capital base that leads to large and persistent negative effects on real output. He
reports a decline in real income growth of about 3% to 6% in counties where subsidiaries of
failed US banks are located.
     Sectoral and event-based studies provide more direct (and sometimes clearer causal)
evidence linking bank capital to economic fluctuations. Calomiris and Mason (2004) find that
bank loan supply shocks have an impact on local area income over the 1930–32 period, using
as instrument variables measured at the end of 1929 (before the Great Depression produced
changes in bank loan foreclosures and net worth). Peek and Rosengren (1997, 2000) find a
response of up to 3% in semiannual lending growth by Japanese branches in the United States
in response to a decline of one percentage point in the capital of parent banks, which, in turn,
has consequences for real activity. Since they also use instrumental variables for lending (asset
quality and bank capitalisation of Japanese parent banks as well as changes in land prices in
Japan), they can claim evidence of causality (see also Haltenhof et al (2014)).
    Firm-focused and other microeconomic research also lends support to the important role
played by bank capital. In particular, loans from banks that have a weak capital base are
more sensitive to changes in market interest rates than loans from better capitalised banks
(Kishan and Opiela (2000, 2006) and Gambacorta (2005)). Conversely, bank capital matters
for the conduct of monetary policy (Gambacorta and Shin, forthcoming). In addition,
evidence suggests an inverse relationship between bank capital and the interest rate charged
on loans, even after accounting for the characteristics of borrowers, banks and various
contract terms (Hubbard et al (2002)). Using firm-specific data on the use of bank debt and
public bond financing from 1990 to 2014, Becker and Ivashina (2014) show that the close link
between bank credit supply and business cycle evolution is driven by external
financing/supply effects and especially impacts small firms. Conversely, Laeven and Valencia
(2013) and Giannetti and Simonov (2013) find important positive effects of bank
recapitalisations on the growth of firms’ real value added and borrowing.
    Some recent studies with DSGE models have incorporated capital shortfall shocks to the
supply of finance. Jermann and Quadrini (2012), after documenting the cyclical properties of
US firms' financial flows, show how adding financial shocks to a model with standard
productivity shocks can much better explain movements in real and financial variables,
including during periods of financial stress. Financial imperfection arises in their setup from
the limited ability of firms to borrow (due to an enforcement constraint). Gilchrist and
Zakrajšek (2011) show in a DSGE model how credit spreads for financial institutions, likely
related to their soundness, significantly impact US business cycles during the period 1985–
2010 (Figure 7).41




41
  The potency of the bank capital channel also depends on accounting standards and the recognition
of capital losses. In particular, the speed at which loan losses are recognised in banks’ balance sheets
and, consequently, their capital positions, determines in part the pace at which banks may amplify
negative aggregate shocks by cutting back on lending. At the same time, the lack of prompt recognition
of loan losses may distort banks’ incentives, inducing them to direct funds to inefficient borrowers.
Caballero et al (2008), for example, find evidence that the limited recognition of capital depletion in
Japanese banks slowed Japan’s recovery from the early 1990s recession.


                                                  28
C. Leverage and liquidity channels
Financial system leverage is often procyclical. There is much empirical evidence relating to
leverage and liquidity mechanisms during booms, including the recent ones in advanced
countries. During the early to mid-2000s, the rapid increase in asset prices in the United
States and in other advanced countries led to more abundant liquidity and allowed for greater
financial sector leverage. This, in turn, led to a greater supply of external financing and
further asset price increases, creating a virtuous cycle, with increasing asset prices and higher
collateral values. For the United States, Adrian and Shin (2008, 2011b) show that this
procyclical behaviour of leverage was more prevalent among broker-dealers, while households,
non-financial non-farm firms and commercial banks exhibited less or no cyclicality (Figure
8).
    Some of these effects also operate in an international context. Shin (2012), Gourinchas
(2011) and Rey (2015) highlight how changes in liquidity intermediated globally by banks
(and interacting with global imbalances and monetary policy in key countries, notably the
United States) can lead to more pronounced and synchronised national cycles, as witnessed
very clearly before, during and after the GFC.42
     There is also ample evidence pertaining to the leverage cycle during busts, which relates
to the increase in margins (haircuts) charged on collateralised lending. In the fall of 2008, as
the cycle swung down, asset prices declined and financial institutions incurred large capital
losses. Funding and leverage constraints forced institutions to sell off (securitised) assets. Not
just investment banks, but also commercial banks that relied less on core deposits and equity
financing, had to cut back lending as their funding and balance sheet positions were strained
(Cornet et al (2011)). These fire sales were associated with higher margins (or haircuts).
Geanakoplos (2010) shows that haircuts on repurchase agreements (repos) increased from
10% at the end of 2006 to more than 40% when the GFC started (see also Gorton and Metrick
(2010)). The sharp increase in haircuts meant that banks had less collateral to offer and had
to absorb more losses. In turn, this forced banks and other financial institutions to shed
assets, further depressing prices, which led to even greater capital and funding problems.43
    Variations in the supply of external financing can also show up in the debt and equity
issuance of non-financial firms. Covas and Den Haan (2011) document that most size-sorted
categories of US firms display a procyclical issuance pattern of debt and equity, with the
procyclicality decreasing with firm size.44 Research also reports that initial public offering
(IPO) markets can be “hot”, with periods of heavy issuance or “cold”, with a dearth of


42
   See also Cerutti et al (2017a) on the role of global factors in driving capital flows and Cerutti et al
(2015) on how dependence on specific lenders and investors can affect countries’ exposure to such
global factors. See also Cerutti et al (2017b) for a recent empirical assessment. Landau (2013) and
Hartmann (2017) provide general reviews of global and international liquidity, as provided by the
private sector or by central banks, and how it relates to the designs of the international monetary and
financial systems.
43
   A fire sale spiral, as first pointed out by Stiglitz (1982) and Geanakoplos and Polemarchakis (1986),
creates a negative externality and a possible rationale for regulation. Because each institution does not
bear the full cost of its own actions, it will not fully take into account the price impact of its own fire
sales on asset prices. See further Brunnermeier and Pedersen (2009), Gorton (2010) and Choi and Cook
(2012) for models of fire sales. Brunnermeier and Oehmke (2013) review the related literature on
bubbles.
44
   More generally, heterogeneity among firms (and households) and specific patterns of external
financing are likely to be important factors in explaining why financial frictions can lead to relatively
large effects on business cycles (Zetlin-Jones and Shourideh (2017)).


                                                    29
offerings (see Ritter (1984)). This cyclicality seems in part related to the state of investor
supply. Helwege and Liang (2004) show that hot markets are largely driven by greater
investor optimism rather than by changes in adverse selection costs, managerial optimism or
technology. More generally, the supply of external financing, with related effects on asset
prices, seems to vary for reasons that are unrelated to the real economy. While often not
explicitly investigated, some of these variations seem to have large real consequences (see
Titman (2013), for an overview of how various shocks to debt and equity markets and other
market segments can affect real activity, among others, through corporate investment-related
externalities).
     The impact of procyclical leverage on the real economy can be especially perverse in times
of stress, when financial institutions and markets cut back on financing and asset prices drop
sharply. Almeida et al (2012) show how the GFC led to a reduction in investment for firms
for which long-term debt happened to mature in the third quarter of 2007 (of several
percentage points relative other firms). Using a DSGE model, Mendoza (2005) shows that as
leverage drops from 15% to 11% during a crisis, a 2% wealth-neutral shock leads to about a
4% drop in consumption and investment and a 1.3% decline in output (see also Adrian et al
(2012) and Adrian and Shin (2014)).
     These variations seem to relate to countries’ institutional environment. For example,
countries, with market-based financial systems tend to exhibit greater cyclicality in leverage.
In market-based systems, the effective use of collateralisation and the development of more
sophisticated risk management and information-sharing mechanisms mean that leverage can
be increased with greater ease. In bank-oriented systems, in contrast, leverage is more
restricted, in part due to regulations. Consequently, leverage and asset price cycles more likely
arise in market-based systems (IMF (2009)). As changes in leverage and liquidity within the
financial system affect the real economy, shocks to asset prices can consequently have a
greater real impact.
    Because short-term collateralised borrowing is the chief tool used by financial institutions
to adjust their leverage, the leverage channel relates to (and affects) monetary policy. Adrian
and Shin (2008) show that repos and reverse repos transactions, in which borrowers provide
securities as collateral, are used heavily to adjust leverage. Since the growth of repo
transactions is closely associated with the ease or restrictiveness of monetary policy, a strong
connection arises between monetary policy and liquidity. When monetary policy is “loose”
(“tight”), there is more likely to be rapid (slow) growth in repos and financial market liquidity
tends to be high (low). Furthermore, as Geanakoplos (2003) shows, not only does leverage
display endogenous cycles but the interest rate becomes endogenous. With both leverage and
interest rates adjusting, this can lead to further procyclical supply side behaviour (see
Geanakoplos (2010) for a review).
     Both monetary policy and macroeconomic conditions appear to affect the risk appetite of
financial intermediaries and their supply of credit. Adrian et al (2010b) study the links
between the growth of financial intermediaries’ balance sheets, the macroeconomic risk
premium and output in the United States. The empirical behaviour of the macroeconomic
risk premium tracks closely that of the term spread of interest rates and of the premium
charged to more risky credits. They also develop a measure of intermediary risk appetite
using changes in balance sheet quantities. In response to shocks to risk appetite, the
macroeconomic risk premium and output exhibit significant and persistent changes. Higher
risk appetite is associated with a decline in the risk premium and a pick up in output (Figure




                                               30
9).45 Adrian and Duarte (2016) model how these interactions can make the financial system
more vulnerable to negative shocks and lead to highly nonlinear dynamics that can adversely
affect the real economy (see also Aikman et al (2016) for a review of the various possible links
between financial vulnerabilities, monetary policy and macroeconomic developments).
    However, the relationship between the monetary policy stance and risk-taking is complex.
De Nicolò et al (2010) model how monetary policy easing can induce greater risk-taking
through a search for yield. At the same time, they show that there can be another effect at
work if financial intermediaries operate with limited liability. In their model, at least in the
short run when bank capital is fixed, high charter-value (well capitalised) banks increase risk-
taking if the policy rate is low and low charter-value (poorly capitalised) banks do the opposite
as they try to preserve their capital. On balance, the effects of monetary policy on risk-taking
depend on intermediaries’ degree of limited liability and financial health. Empirical evidence,
while still partial, supports these complex interactions. For example, empirical evidence for
the United States by De Nicolò et al (2010) broadly supports the prediction that a low policy
rate is associated with greater risk-taking by banks as the riskiness of their loans is higher
when interest rates are lower (Figure 10).46



7. Aggregate macro-financial linkages

The previous sections documented that imperfections on the demand and supply sides of
finance could be associated with pronounced fluctuations in the real economy.
Complementary to this literature has been long standing research that provides important
insights into the general patterns of aggregate macro-financial linkages (see the overview of
this literature in Appendix IV). Since it is hard to identify the direction of causality between
changes in financial markets and fluctuations in real activity, and whether demand or supply
channels are the main factors, many of these studies have taken a largely agnostic approach.
    Until recently, this research programme did not present a comprehensive perspective on
business and financial cycles. This was for at least two major reasons. First, most studies
consider only selected aspects of business and financial cycles. For example, many examined
the implications of booms in asset prices or credit only rather than considering the full
financial cycle. Second, research tended to focus on case studies or used small country samples.

45
   See further Adrian et al (2016) on the relevance of leverage for macroeconomic modelling (and macro-
financial linkages). They show that a parsimonious model using detrended dealer leverage as a “price -
of-risk” variable performs well in time series and cross-sectional tests for a wide variety of equity and
bond portfolios (at least better than models that use intermediary net worth as a state variable) and
in comparison to benchmark asset pricing models.
46
   For the effects of monetary policy on risk-taking, including when interest rates are particularly low,
see further Dell’Ariccia and Marquez (2013), Dell’Ariccia et al (2014), Jimenez et al (2014), Valencia
(2014), Ioannidou et al (2015), and Borio and Hofmann (2017). Posen (2009), Bean et al (2010) and
Bernanke (2010) argue otherwise. Adrian and Shin (2008, 2011a) and Adrian et al (2010a) analyse how
the risk-taking channel works in the United States. Some other recent studies providing further
microeconomic evidence for the presence of the risk-taking channel domestically and internationally
include Maddaloni and Peydro (2011), Altunbas et al (2014), Bruno and Shin (2015, 2017), Morais et
al (2015), Dell’Ariccia et al (2017), and Domanski, Shin and Sushko (2017). See also Rajan (2005 ) and
Hanson and Stein (2015) for arguments and models linking low interest rates to search-for-yield motives
for investors other than banks. For a review of studies of the effects of low interest rates on financial
institutions' profitability and capitalization and risk-taking, see European Systemic Risk Board (2016).



                                                   31
Although the literature on financial crises has employed broader samples, the identification
of crises has often suffered from analytical drawbacks and the analysis has limited itself to a
single phase of the cycle, the aftermath of a crisis.47
     Some recent studies, however, document the major features of macro-financial linkages
using rich cross-country databases covering a long period of time. In this section, we present
a summary of the findings of this work.48 The section begins with an overview of the
methodology and data sets used in these studies. This is followed by a discussion of the
stylised facts that emerge from the data. The last sub-section considers the main properties
of the linkages between business and financial cycles.
    A number of salient facts emerge about the features of business and financial cycles and
their interactions over different phases. First, financial cycles are often more pronounced than
business cycles, with financial downturns deeper and more intense than recessions. Second,
financial cycles can build on each other and become amplified. For example, credit downturns
that overlap with house price busts tend to be longer and deeper than other credit downturns.
Third, financial cycles appear to play an important role in shaping recessions and recoveries.
In particular, recessions associated with financial disruptions, notably house price busts, are
often longer and deeper than other recessions. Conversely, recoveries associated with rapid
growth in credit and house prices tend to be stronger than other recoveries.

A. Business and financial cycles
A number of methodologies have been developed to characterise business cycles. The findings
reported in this section are based on the “classical” definition of a business cycle, which
focuses on changes in levels of economic activity. The definition goes back to the pioneering
work of Burns and Mitchell (1946) who laid the methodological foundations for the analysis
of business cycles in the United States. Moreover, it constitutes the guiding principle of the
Business Cycle Dating Committees of the National Bureau of Economic Research (NBER)
and the Centre for Economic Policy Research (CEPR) in determining the turning points of
US and European business cycles.49


47
   Claessens and Kose (2014) review a number of studies that focus specifically on periods of financial
stress and crisis and the behaviour of real and financial variables during such events. Reinhart and
Rogoff (2008, 2009a, 2009b, 2014) review the characteristics of various types of financial crisis for many
countries over a long period. Boissay et al (2016) analyse the links between credit booms, (interbank)
liquidity and banking crises (see also Allen et al (2011)).
48
   The notion of financial cycles was empirically documented in early studies for smaller samples of
countries and periods of time, such as Borio et al (1994) and Borio and Lowe (2002), and refined in
subsequent work, including Drehmann et al (2012), Aikman et al (2015) and Juselius and Drehmann
(2015). This section draws on Claessens et al (2009, 2011, 2012a) which provide detailed empirical
analyses of the interactions between business and financial cycles.
49
   An alternative methodology would be to consider how economic activity fluctuates around a trend
and then to identify a “growth cycle” as a deviation from this trend (Stock and Watson (1999)). While
several studies have use detrended series (and their second moments, such as volatility and correlation)
to study the various aspects of cycles, it is well known that the results of these studies have depended
on the choice of detrending methodology (Canova (1998)). The advantage of turning points identified
by using the classical methodology is that they are robust to the inclusion of newly available data. In
other methodologies, the addition of new data can affect the estimated trend and thus the identification
of a growth cycle. Fatas and Mihov (2013) analyse different approaches for the dating of recoveries
using US data. See also Ng and Wright (2013) for a survey of business cycles facts and methodologies.



                                                   32
    The classical dating methodology distinguishes three phases of cycles: recessions,
expansions and recoveries. It assumes that a recession begins just after the economy reaches
a peak and ends as it reaches a trough. An expansion begins just after a trough and ends at
the next peak. A complete business cycle has two phases, recession (from peak to trough) and
expansion (from trough to peak). Together with these two phases, recoveries from recessions
have been studied. The recovery phase is the early part of an expansion and is usually defined
as the time it takes for output to return from its lowest point to the level it reached just
before the decline began. An alternative definition considers the increase in output four
quarters after the trough. Given the complementary nature of these two definitions of the
recovery phase, both of them are used here.
    Financial cycles are identified by employing the same methodology. However, different
terms are used to describe them: the recovery phase is called an “upturn” and the contraction
a “downturn”. These two phases provide rather well defined time windows for considering
the evolution of financial cycles. In what follows, we study the main features of business and
financial cycles, considering, in particular, their duration, amplitude and synchronisation.50

Business cycles
Recessions can be long, deep and costly. A typical recession lasts close to four quarters while
a recovery last about five quarters (Figure 11).51 The typical decline in output from peak to
trough, the recession’s amplitude, is about 3% for the full sample and the typical cumulative
output loss amounts to about 5%. The amplitude of a recovery, defined as the increase in the
first four quarters following the trough, is typically about 4%. Although most recessions
(recoveries) are associated with moderate declines (increases) in output, there can be much
larger changes in activity as well.
    Business cycles in EMEs are more pronounced than those in advanced economies. In
particular, the median decline in output during recessions is much larger in EMEs (4.9%)
than in advanced economies (2.2%) and recoveries in EMEs are twice as strong as those in
advanced countries. In terms of cumulative loss, recessions in EMEs are almost three times
more costly than those in advanced economies. These findings suggest that macroeconomic
developments, policy factors and institutional characteristics, including possibly the degree of
financial frictions, potentially affect the evolution of business cycles in different countries.
    Business cycles are highly synchronised across countries. For some observers, the global
nature of the GFC, during which many economies experienced a recession at the same time,
was surprising. However, this is not so unusual because recessions and recoveries are often
synchronised across countries. Recessions in many advanced economies, for example, were
concentrated in four periods over the past 40 years – the mid-1970s, the early 1980s, the early

50
   The results reported in this section are based on a large database that comprises a total of 44
countries: 21 advanced OECD economies and 23 EMEs. For the former group, the data coverage ranges
from Q1 1960 to Q4 2010 while for the latter it ranges from Q1 1978 to Q4 2010 (because quarterly
data series are less consistently available prior to 1978). In order to study business cycles, GDP is used
because that variable is the best available measure of economic activity. Financial cycles are studied
considering three distinct but interdependent market segments: credit, housing and equity. See further
Claessens et al (2012a).
51
   Claessens et al (2012a) identify 243 recessions and 245 recoveries. The number of recessions and
recoveries differs slightly because of the timing of events. There are 804 complete financial cycles over
the period Q1 1960 to Q4 2010. The sample features 253 downturns in credit, 183 in house prices and
443 in equity prices; and 220, 155 and 429 upturns in credit, house and equity prices, respectively.
Since equity prices are more volatile than credit and house prices, they feature naturally more often in
downturns and upturns than the other financial variables.


                                                   33
1990s and 2008–09 – and often coincided with global shocks, such as increases in oil prices
and interest rates (Figure 12). Such synchronised recessions tend to be deeper than other
types of recession.

Financial cycles
Financial cycles are often longer and more pronounced than business cycles, with financial
downturns particularly deeper and longer than recessions. Downturns (upturns) of financial
cycles tend to be longer than recessions (recoveries) (Figures 13 and 14). Episodes of house
price downturns, in contrast, persist for about eight quarters and other financial downturns
last around six quarters. A typical financial downturn corresponds to about a 6% decline in
credit, 6%–7% fall in house prices and 30% decline in equity prices. Upturns are often longer
than downturns, with the strength of upturns to differ across financial markets. Equity price
upturns are the sharpest, some 26%. Financial cycles are also more intense than business
cycles, ie financial variables adjust much more quickly than real ones, as shown by their slope
coefficient. These findings are consistent with various studies documenting that asset prices
are more volatile than economic fundamentals (see Shiller (1981, 2003) and Campbell (2003)).
     The main features of financial downturns vary across EMEs and advanced economies.
While not necessarily longer, financial downturns in EMEs are much sharper than in advanced
countries. Credit contractions last about the same in both groups but are one-third deeper in
EMEs. Equity downturns last as long in both groups but upturns are much shorter in EMEs.
Comparisons between mean and medians show that the distributions of duration and
amplitude of the phases of financial cycles are also more skewed to the right in EMEs than
in advanced economies. These differences indicate that factors possibly related to the presence
of financial frictions could affect financial cycles.
     Financial cycles also tend to feed off of each other and become amplified. The likelihood
of a credit downturn (or upturn) taking place goes up substantially if there is also a disruption
(or boom) in house prices. There are also strong interactions between financial cycles. Credit
downturns that overlap with house price busts tend to be longer and deeper than other credit
downturns. Similarly, a typical credit upturn becomes 30% longer and twice as large when it
coincides with a housing boom. This suggests that feedback effects play a role as disruptions
in one market aggravate the problems in another, probably because of collateral constraints
and complementarities between credit and housing finance. Moreover, globally synchronised
financial downturns are often longer and deeper, especially for credit and equity markets.
During highly synchronised equity market downturns, for example, prices drop by about 30%
compared with some 18% for other downturns.

B. Linkages between business and financial cycles
Recent research using cross-country data has revealed important links between business and
financial cycles. Claessens et al (2012a) use a comprehensive database for a large sample of
advanced economies and EMEs over a long period of time to provide a broad empirical
characterisation of macro-financial linkages. They report three main results. First, business
cycles are more closely synchronised with credit and house price cycles than with equity price
cycles. Second, financial cycles appear to play an important role in determining recessions
and recoveries and shaping the features of business cycles more generally. In particular,
recessions are more likely to coincide with financial disruptions while recoveries are more
likely to be associated with booms. Third, recessions associated with some forms of financial
disruption, notably house price busts, are often longer and deeper than other recessions.
Conversely, recoveries associated with rapid growth in credit and house prices tend to be


                                               34
stronger. These results collectively highlight the importance of macro-financial linkages,
especially those involving developments in credit and housing markets, for the real economy.

Synchronisation and likelihood of cycles
Business cycles often move in tandem with financial cycles, especially with credit and house
price cycles. One can study the degree of synchronisation between business and financial
cycles by using the concordance statistic (Table 3).52 Cycles in output and credit appear to
be the most highly synchronised, with a median (mean) synchronisation of 0.81 (0.78). This
means that cycles in output and credit are typically in the same phase about 80% of the time.
The concordance statistic for cycles in output and house prices, 0.68 (0.64), is lower than that
for output and credit but still slightly higher than that for output and equity prices, 0.58
(0.60). This reinforces the common finding that developments in credit and housing markets
could be key in driving macro-financial linkages.
    There are also differences in concordance between advanced economies and EMEs.
Advanced economies typically display a higher degree of synchronisation of output, credit
and house prices than EMEs. This may reflect the more developed nature of advanced country
financial markets with the result that fluctuations in credit, house prices and other financial
variables are more important to the real economy. It may also indicate that EMEs are more
often affected by global shocks operation through international capital flows, including
through actions of their residents (see for example Forbes and Warnock (2012) and Caballero
and Simsek (2016)).
     The likelihood of recessions and recoveries varies with the presence of financial disruptions
or booms. The unconditional probability of being in a recession or a recovery in any given
quarter is about 19%. However, if there is a financial disruption episode in the same quarter,
the probability of a recession increases substantially, to 35% to 38%. Similarly, if a credit
(house price) boom is already underway, the probability of experiencing a recovery rises to
roughly 57% (43%). Rapid growth in equity prices is, however, not associated with greater
likelihood of a recovery in the real economy.

Interactions between cycles
Recessions accompanied by financial disruptions tend to be longer and deeper than other
recessions. In particular, recessions associated with asset price busts are significantly longer
than recessions without such disruptions (Figure 15). Recessions with severe asset price busts
as well as credit crunches result in significantly larger drops in output and, correspondingly,
greater cumulative output losses relative to those without such episodes.53
    A recession associated with one type of financial disruption is often accompanied by
broader stress in other financial markets. For example, recessions accompanied by credit
crunches result in a significant decline in credit as well as substantial drops in house and
equity prices. One can also analyse recessions accompanied by combinations of credit crunches
and asset busts. Although the number of such episodes is small, a recession associated with a

52
   The concordance statistic provides a measure of the fraction of time that the two series are in the
same phase of their respective cycles. The series are perfectly procyclical (countercyclical) if the
concordance index is equal to unity (zero).
53
   For a deeper perspective, it is useful to consider additional measures of credit and asset prices. For
example, some papers (Chari et al (2008) and Cohen-Cole et al (2008)) highlight the importance of
going beyond aggregate measures (for example, by differentiating credit to corporations from credit to
households) to study the dynamics of credit markets. Unfortunately, such disaggregated series are not
available for a large number of countries over long periods.


                                                   35
credit crunch and an asset price bust often leads to a larger cumulative output loss than a
recession with only a credit crunch or an asset price bust.
    Just as recessions associated with financial disruptions are longer and deeper, recoveries
associated with credit or house price booms are shorter and stronger. With respect to
duration, recoveries coinciding with house price booms tend to be significantly shorter (Figure
16). Moreover, recoveries associated with credit and house price booms are often stronger and
faster than those without such booms. By contrast, recoveries combined with booms in equity
markets do not appear to be different from those without such episodes, confirming the
somewhat limited role of equity markets in macro-financial linkages.
    These stylised facts describing the aggregate linkages between business and financial
cycles are also supported by the results of panel regressions incorporating a wide range of
explanatory variables. In particular, changes in house prices tend to play a critical role in
determining the duration and cost of recessions (Claessens et al (2012a)). The results are also
consistent with the findings of recent empirical studies emphasising the importance of house
price dynamics in shaping business cycles (Cecchetti (2008), Leamer (2007), IMF (2008), and
Muellbauer (2007)).54
     Why are recessions associated with house price busts more costly? First, housing
represents a large share of wealth for most households and, consequently, price adjustments
affect consumption and output more strongly (Claessens and Kose (2017)). By contrast,
equity ownership is relatively less common and typically more highly concentrated among
wealthy households who likely make much smaller adjustments to their consumption during
the various phases of the financial cycle (and recessions and recoveries). Housing wealth has
indeed been found to have a larger effect on consumption than equity wealth (Carroll et al
(2011)). Second, equity prices are more volatile than house prices, implying that changes in
house prices are more likely to be (perceived to be) permanent than those of equity prices
(Cecchetti (2008) and Kishor (2007)). With more permanent wealth changes, households
adjust their consumption more strongly when house prices increase (decline), leading to larger
increases (declines) in output during recoveries (recessions) that are associated with house
price booms (busts).



8. Conclusions

The GFC was a painful reminder of the importance of macro-financial linkages. These linkages
centre on the two-way interactions between the real economy and the financial sector.
Imperfections in financial markets can intensify these linkages and lead to gyrations in the
financial sector and the real economy. Global dimensions of these linkages can result in
spillovers across borders through both real and financial channels.
    Research on macro-financial linkages has a long tradition, but has become a central topic
only over the past three decades. This paper reviews this rich literature and presents a broad
perspective on theoretical and empirical work. The survey considers the two channels – the

54
  Analytical models also support this notion. Using the financial accelerator mechanism presented in
Section 5, for example, some studies (Aoki et al (2004) and Iacoviello (2005)) use DSGE models to
show specifically how endogenous developments in housing markets can magnify and transmit various
types of shock to the real economy and find quantitatively large effects. Mian and Sufi (2010, 2014)
provide empirical evidence at the regional level for the real economy effects of mortgage credit
expansion in the United States.


                                                36
demand and supply sides – through which financial imperfections can affect macroeconomic
outcomes. The demand side channel, largely captured by financial accelerator-type
mechanisms, describes how, through changes in the balance sheets of borrowers, financial
markets can amplify macroeconomic fluctuations. The supply side channel emphasises the
importance of changes in financial intermediaries’ balance sheets for the availability of
external financing and liquidity, the role of financial markets in the determination of asset
prices and the implications of those factors for the real economy.

A summary
The literature has made significant progress in understanding the demand side of macro-
financial linkages. Many models now feature amplification mechanisms operating through the
demand side. These models show how a financial accelerator-type mechanism can lead to the
propagation and amplification of small (real or financial) shocks across the real economy
(through their impact on access to finance). A number of models that incorporate financial
accelerator mechanisms show the importance of changes in asset prices and other financial
shocks in driving movements in borrowers’ net worth and access to finance, leading to
fluctuations in aggregate activity.
    These analytical findings are also supported by empirical studies. In particular, extensive
evidence documents how the state of borrowers’ balance sheets affects their access to external
finance. Demand side imperfections in financial markets have been shown to lead to an
amplification of shocks (monetary, real and financial) because changes in the net worth of
borrowers affect their access to finance and, therefore, to consumption, investment and
output. Empirical studies confirm that these imperfections tend to affect small firms and
households particularly strongly, especially during periods of financial stress. Although most
findings support the roles played by financial imperfections, there is nevertheless an ongoing
debate about the quantitative importance of the financial accelerator.
     The GFC has shifted attention to the critical role played by amplification channels
operating through the supply side of finance. Earlier theoretical work on the bank lending
channel analysed the possible general equilibrium effects arising from the special role of banks
in financial intermediation. Empirical studies documented how the dependence of some firms
on bank financing influences the transmission of monetary policy to the real economy. Since
the GFC, there has been a broader recognition that the supply side of finance (beyond the
specific role played by banks for some firms) can be a source of shocks, amplification and
propagation.
     This recognition has led to a number of studies on modelling the supply side of finance.
Although the literature is still in its early stages, recent work has analysed the roles played
by bank lending, bank capital and financial markets more generally – inter alia through their
impact on asset prices, liquidity and leverage – in shaping macroeconomic outcomes. This
work has brought out the critical role importance of the leverage channel in leading to more
pronounced financial and business cycles. Related, given the importance of banks’ credit and
liquidity provisioning roles, studies have provided insights into how to consider and adapt
banks’ internal organisation, and their regulation and supervision in general equilibrium
settings.
    Recent empirical studies confirm that shocks associated with the supply side of finance
can affect the evolution of external financing and asset prices, with the potential for feedback
loops between real and financial markets. New tests of the bank lending channel based on
variables, like the size of banks, types of loan, and certain other specific features of the



                                              37
financial system, show its importance as a channel of monetary transmission. Other work
shows that the potency of the bank capital channel varies over business and financial cycles
(especially during credit crunches). Recent work also examines the procyclical nature of
financial system leverage, asset prices and liquidity, providing evidence relating to their
impact on real aggregates, especially in times of financial stress.
    In addition, a number of empirical studies on aggregate macro-financial linkages
document the importance of developments in financial markets for the real economy. In
particular, cycles in various financial market segments (equity, housing and credit) appear to
play an important role in shaping recessions and recoveries. Recessions associated with
financial disruptions are often longer and deeper than other recessions while, conversely,
recoveries associated with rapid growth in credit and house prices tend to be relatively
stronger.

What next?

The topic of macro-financial linkages promises to remain an exciting area of research, given
the many open questions and significant policy interest. A list of potential future areas of
research, by no means exhaustive, is provided next.
    Data issues. There are large data gaps. The lack of adequate time series data on important
financial and macroeconomic variables has been a severe limitation for researchers. Although
the prices of many traded assets are widely available (including for equities and bonds), gaps
remain with respect to higher frequency and longer-dated series relating to the housing market
and aggregate credit. Comprehensive cross-country databases pertaining to business and
financial cycles have only recently been constructed. Researchers would also benefit from
better access to granular data on external financing and credit, and on the balance sheets of
firms, banks and other financial intermediaries. Such data are essential to the exploration of
the links between the financial sector and the real economy and to the assessment of the
systemic risks that can arise from these linkages (see the papers collected in Brunnermeier
and Krishnamurthy (2014) on the general data needs required for research on macro-financial
linkages).
     Data deficiencies are especially significant at the international level. There is a dearth of
information at the aggregate and granular levels about bilateral exposures – between two
countries as well as between two financial institutions, the cross-border activities of banks,
institutional investors, hedge funds and other market participants (see further Cerutti et al
(2014), Borio (2013), Tarashev et al (2016) and Heath and Bese Goksu (2017)). Moreover,
existing data sources are often not comparable because they are compiled under different
regimes. While some recent data collection efforts have been underway, such as those under
the G20 Data Gap Initiative (FSB-IMF (2016)), progress in this area has been slow, including
on obtaining data on the world’s largest financial institutions, the so-called global systemically
important financial institutions (G-SIFIs).
    Demand side channels. Although research on the demand side channels is much richer
than that on the supply side, many questions remain open. DSGE models, including those
with financial market imperfections and financial accelerator-type amplification mechanisms,
have been widely used by policy institutions. Yet, when calibrated with reasonable parameters
and tested with realistic shocks, the quantitative importance of the financial accelerator in
explaining real activity appears to be limited. Models still face difficulties in accounting for
heterogeneity among agents and for the asymmetries and non-linearities that arise from
macro-financial linkages, especially when adverse shocks hit borrowers’ net worth and curtail
their access to external financing. Little attention has been devoted to the role of the debt


                                               38
service ratio as leading indicators of household consumption (Juselius and Drehmann (2015)
and Drehmann et al (2017)).55
     Some fundamental aspects associated with the demand side channels are still being
debated. For example, questions surrounding the quantitative importance of financial market
imperfections for small firms should be answered, as some argue that there is little difference
between small and large firms. While many empirical studies find that the effects of financial
conditions vary by type of economic agent, including households, firms, financial
intermediaries and sovereign entities, many of those do not present rigorous tests for specific
financial imperfections. To illustrate, the GFC has highlighted the role of house prices in
affecting consumption and broader macroeconomic outcomes, but the exact channels by which
this takes place are not yet well established. Consequently, the implications of empirical
findings for regulation, institutional reform or other policy design issues require additional
work.
     Supply side channels. Since the GFC, few question the relevance of supply side factors
for macroeconomic outcomes. That said, the theoretical literature still falls short of providing
realistic models. While some models show how supply side dynamics can lead to macro-
financial linkages, they are still rudimentary in their treatment of the financial system. They
tend to focus on banks rather than on the financial sector as a whole. And even when banks
are included, their treatment is highly stylised. For example, many models simply assume the
presence of banks, but do attempt to justify their existence, eg whether banks arise to
overcome information asymmetries or because they are “special” in other ways.
     Models often assume that banks are homogenous while in practice large banks operate
very differently from small ones. There is often no distinction between liquidity and solvency
risks, and the interaction between such risks is not always explicitly modelled. Moreover, the
banks’ choice of assets, including which sectors they lend to, is often imposed a priori rather
than being endogenously determined. Models typically ignore many parts of the financial
system (other than banking) and are unable to account for gross positions or intra-financial
system transactions (such as interbank claims or transactions between banks and capital
markets).56
    A more realistic representation of the supply side of finance requires richer models that
account for the heterogeneity of banks and capture the behaviour of other financial
intermediaries. It also means modelling how banks, non-bank financial institutions and
markets are linked to each other (such as through shadow banking activities) as well as how
such institutions and markets are linked to the international financial system. Furthermore,
more sophisticated approaches to modelling of the intricate linkages between financial
imperfections, labour markets and real activity are sorely needed. Any advances in this area
would require, among others, overcoming the general “linear” structure of DSGE models,



55
   Several surveys discuss advances in the modelling of heterogeneous agents, news shocks, financial
crises, bubbles and systemic risk (Heathcote et al (2009), Lorenzoni (2011, 2014), and Brunnermeier
and Oehmke (2013)). Another strand of the literature uses ad hoc borrowing constraints to model
financial imperfections in environments with a continuum of households (Huggett (1993), Aiyagari
(1994), Krusell and Smith (1998)), which is particularly useful in studying distributional issues. Moll
(2014) studies an environment in which financial frictions lead to misallocation of resources.
56
   See Acemoglu et al (2015) and Boissay et al (2016) for analysis focusing on effects of networks and
links among financial institutions, including through the interbank market. See Freixas et al (2011) for
analysis on monetary and prudential policies in the interbank markets. See Dou et al (2017) for a
review of the role of macro-financial interactions in dynamics models used at central banks.


                                                  39
which has proved to be a hindrance to the analysis of financial turmoil given that “non-
linear” effects, such as liquidity shortages, fire sales and deleveraging, are prevalent.
     More empirical work is also needed on the roles of various asset markets, including credit,
housing and equity markets. Empirical studies need to provide a better understanding of the
potential role of supply side channels through the operations of bank, non-bank financial
institutions and financial markets. The roles played by institutional and other factors (such
as benchmark-based compensation contracts, competition among financial institutions and
the states of general liquidity and capitalisation) in driving the leverage cycle of banks and
other financial institutions are in great part a mystery. The roles of collateral and margin
constraints during boom and fire sale periods need deeper analysis. Interbank markets,
especially during periods of financial turmoil, are surprisingly little analysed. Research also
needs to focus on identifying the best measures (price, quantity or a combination of both)
that characterise the linkages between supply side financial market imperfections and
macroeconomic outcomes. This could also help answer some basic questions, such as which
quantitative variables are better in predicting fluctuations in macro-financial linkages and
systemic risk.
     Global implications of financial imperfections. The rapid spread of disruptions from US
financial markets to other countries during the GFC has led to a number of questions about
the cross-border transmission of real and financial shocks. Understanding the reasons for the
collapse of global trade and financial flows during the height of the crisis and the implications
of this for the real sector is likely to be a significant area of research. More work on the
international spillovers of financial shocks, the roles played by multinational financial
intermediaries and the synchronisation of business and financial cycles is also needed. On the
theory front, open economy models could do a better job in assessing the cross-border
implications of financial imperfections, considering the influence of both demand and supply
side channels.
     Policy challenges. As noted in the introduction, the GFC has generated an intense debate
in the economics profession about the state of research on the importance of financial market
imperfections in explaining macroeconomic fluctuations. Some argue that the crisis showed
that the profession had not paid sufficient attention to these linkages. Others claim that these
linkages had been recognised for a long time and that substantial progress had been made in
understanding them. Our survey shows that the profession has indeed produced valuable
research spanning a wide spectrum of issues. However, challenges remain, notably with respect
to how the findings may best translate into policy. More research is required in order to arrive
at a solid understanding of the issues and help guide policy decisions.57
     Policy challenges stemming from the GFC require a much better integration of “core”
research with empirical findings and the operational aspects of policy design. Since the GFC,
many new regulations have been adopted (FSB (2017)). Questions remain, however, about
what may be the “optimal” design of the financial system (Claessens (2016)). And related to
this is the preferred design of the regulatory infrastructure, an issue that is not often formally


57
  Bernanke (2010), Blanchard et al (2010, 2013), Caballero (2010), Kocherlakota (2010), Woodford
(2010), Turner (2012), Claessens et al (2014), Kose and Terrones (2015), Blanchard and Summers
(2017) and several papers in the Fall 2010 issue of the Journal of Economic Perspectives look at how
the GFC may have influenced research, including the literature on the intersection between
macroeconomics and finance. Blanchard et al (2012, 2016) and Akerlof et al (2014) discuss how
economic policies have been reassessed by economists since the GFC. Gopinath (2017) provides a
review of the recent macroeconomic policy-related work in international economics.



                                                40
addressed when adopting new regulations (see Claessens and Kodres (2017) for a review).
One set of questions, for example, relates to the best configuration of capital adequacy and
liquidity requirements for commercial banks.58 Other questions focus on how to best monitor
and, perhaps, regulate the shadow banking system (see Claessens et al (2015) for a collection
of papers on the subject).
    The interactions between monetary policy and financial imperfections also require work.
For example, the conduct monetary policy in the presence of financial frictions and the zero
lower bound demands further analysis (eg Brunnermeier and Sannikov (2016) and Rogoff
(2017)). A better assessment of the role played by monetary policy during a liquidity trap
and the implications of UMPs are of strong policy interest (eg Korinek and Simsek (2016)
and Del Negro et al (2017)). Related is the need for a better understanding of the role of
financial factors in determining the real interest rate, including in the context of a possible
global secular stagnation scenario. Some work on this has pointed at debt overhang in driving
low interest rates, including for open economies.59 While recent research has advanced our
understanding of the basic issues, more work is warranted.
    A broad area of further research involves the design of macroprudential policies (see
Claessens (2015) and Adrian (2017) for analytical reviews and IMF-FSB-BIS (2016) for a
review of policies). Following initial work at the BIS (see Galati and Moessner (2013)), there
is now a widespread recognition of the importance of imperfections, externalities and specific
market features as motivating factors for macroprudential policy.60 While progress is being
made, the conceptual frameworks underpinning proposals for specific policies still require
much more work, particularly given the non-linearities involved (see Mendoza (2016)).
    Furthermore, the empirical work to date has often used aggregate data, which does not
always allow for specific policy recommendations incorporating all relevant trade-offs, notably
the costs of macroprudential policies and the possibility of regulatory adaption and arbitrage.
For example, empirical studies on how specific macroprudential policy tools, such as
borrowing limits on housing finance or countercyclical capital buffers, may affect banks’
overall risk-taking have been limited and their results are still preliminary (see Acharya et al
(2017) and Auer and Ongena (2017) for early work). More research could help in guiding the
design of macroprudential policies.
    There is also a vigorous debate on the effectiveness of macroprudential policies and other
regulatory measures to cope with large fluctuations in asset and credit markets, and whether
monetary policy is perhaps also needed, as reflected in the contrasting views on the costs and
benefits of “leaning against the wind” (compare Svensson (2016, 2017) and Adrian and Liang
(2016)). More research on the linkages between monetary policy and asset price dynamics,
and related financial systemic risks, is thus definitely needed. The global dimension of


58
   See, for example, Dewatripont and Tirole (2012), Stein (2012), Goodhart et al (2013), Admati and
Hellwig (2014), Fender and Lewrick (2016), Kara and Ozsoy (2016) and Kashyap et al (2017).
59
   See Eggertsson and Krugman (2012) and Eggertsson et al (2016); see also Borio (2017) on the possible
role of monetary policy in affecting the real interest rate.
60
   See the early contributions by Crockett (2000), Borio (2003) and Knight (2006) (see also Clement
(2010)). Subsequently, the BIS and other related entities have conducted much research on various
aspects of macroprudential policy. The Committee on the Global Financial System (CGFS), for
example, reported on operational aspects in 2010, 2011 and 2012 (CGFS (2010, 2012) and CGSG
(2011)). An influential, post-GFC take was Brunnermeier et al (2009). Recent work on macroprudential
policy includes Bianchi et al (2012, 2016), Farhi and Werning (2016) and Bianchi and Mendoza
(forthcoming).



                                                  41
financial cycles is also an area that is subject to debate, with varying views of the quantitative
importance of the global financial cycle (Rey (2015), Cerutti et al (2017b) and Ha et al
(2017)). Related, more work on the global consequences of monetary policies is needed to help
guide the design of such policies, including for small open economies, which often use a
combination of exchange rate, foreign exchange reserves, macroprudential and capital flow
management policies.61 Future research also needs to focus on the interaction between
financial policies and fiscal policy.




61
     See Jeanne (2014, 2016), Pereira da Silva (2016) and Agénor et al (2017).


                                                    42
Appendix I. Research on macro-financial linkages: a brief history

Research on macro-financial linkages has a long history. The Great Depression created much
interest in such linkages but this interest faded away over the next few decades. There has
been a resurgence of interest since the early 1980s, with the introduction of rigorous general
equilibrium models that have provided rich theoretical insights. Such insights have been
complemented by the results of empirical studies at the macroeconomic and microeconomic
levels. This appendix provides a brief review of the evolution of this literature (see Figure A1
for a schematic presentation).
    The study of credit cycles, which precedes that of business cycles, goes back at least to
Mills (1867) but, as just mentioned, the Great Depression was the primary motivation for the
early qualitative work on the role of financial factors in shaping macroeconomic outcomes.
Fisher (1933) provided a descriptive account of the relationship between the high leverage of
borrowers and the severity of the downturn during the Great Depression. His “debt-deflation”
mechanism was the first elegant narrative showing how a decline in net worth induced by a
drop in prices, ie deflation, could lead borrowers to reduce their spending and investment,
which, in turn, could cause activity to contract and result in a vicious cycle of falling output
and deflation. Haberler (1937) reviewed early studies of business cycle fluctuations, focusing
on the so-called monetary, over-investment, under-consumption, and psychological theories.
     The literature that followed, however, turned its attention to the role of money, rather
than credit, as the critical financial variable. While Keynes (1936) also brought out financial
developments, eg as he discussed how the confidence of borrowers and lenders could changes
in ways nor easily explained with economic models (‘animal spirits”). He focussed more on
the importance of money for the real economy. Armed with insights from the liquidity
preference hypothesis, the early “Keynesian” models paid special attention to the mechanisms
linking money to real activity, including the multiplier mechanism and the role of fiscal policy
(Hicks (1937), Modigliani (1944) and Tobin (1958)). The “monetarist” school, on the other
hand, insisted on the importance of monetary rather than real factors (Friedman (1956),
Friedman and Schwartz (1963) and Tobin (1969)).
    Later studies documented the critical role of financial intermediation in economic
development and macroeconomic fluctuations but these did not lead to a fundamental shift
in thinking. Gurley and Shaw (1955) showed that economic development and financial
sophistication go hand in hand. Others, including Cameron (1961), Shaw (1973) and
McKinnon (1973), also highlighted the importance of finance for development, contrasting
among others the East Asian and Latin American experiences. Their main argument was that
a country’s overall “financial capacity,” ie its financial system’s ability to provide credit, was
more relevant to the real economy than money. In the early stages of financial development,
money could be important but it becomes less relevant in more developed systems,
particularly as a measure of credit availability. Instead, banks increasingly use non-deposit
sources of funding and non-bank intermediaries provide alternative sources of financing. This
view, while also advanced by many financial historians (eg Goldsmith (1969)), did not prevail
and research on finance and macroeconomics followed separate paths.
    New analytical insights were gained during the 1950s with the applications of portfolio
theory. A major breakthrough was the introduction of portfolio theory by Markowitz (1952),
which inspired a large literature. However, it did not pay much attention to financial frictions.
Tobin (1969) improved the understanding of how asset valuation, a key area of finance, affects
investment. Through his concept of the “q” valuation ratio, he established a direct
relationship between developments in equity markets and investment. With few exceptions,



                                               43
however, these contributions paid little attention to the various forms of financial
intermediation or to their imperfections.
    The separation of finance and macroeconomics became increasingly pronounced after the
introduction of the “irrelevance of financing structure” theorem of Modigliani and Miller
(1958). The theorem provided a case for the independence of firm valuation and investment
from financing structures and suggested more generally a decoupling of real economic activity
from financial intermediation. A number of developments accelerated this separation. First,
the Arrow-Debreu theorem (which shows that, in the presence of contingent claims that span
every possible state of the world, allowing agents to insure against any event, it becomes
much easier for agents to make choices), upon which Modigliani and Miller based their work.
Second the methodological advances of the 1970s, notably with respect to asset pricing
(Merton (1973)) and derivatives modelling (Black and Scholes (1973)). And, lastly, the
emergence of the rational expectations paradigm (see below). Together they gave traction to
the concept of “efficient financial markets” (Fama (1970, 1991)). This work put less emphasis
on the role of banks as markets were thought to be able to efficiently provide most financial
services (notably, of the twenty chapters in the Handbook of the Economics of Finance (2003),
only one (Constantinides et al (2003)) was on financial intermediation).
    The growing popularity of work based on the assumption of efficient and frictionless
financial markets coincided with a shift in the macroeconomic literature. Researchers focused
increasingly on the real side of the economy, using models with little role for financial variables
(see Chari and Kehoe (2006) for a review). The precursor in macroeconomics to efficient
financial markets was the rational expectations paradigm (Muth (1961) and Lucas (1976)),
which assumes agent make choices consistent with models involving uncertainty and full
information. It further reinforced the drive for quantitative models with fully optimising
agents acting mostly in frictionless worlds. Although the widely used vector autoregression
(VAR) models, first proposed by Sims (1972), partially shifted attention back to money as a
key financial aggregate, at least in applied policy work, the broader literature concentrated
mainly on real variables (Lucas (1975) and Kydland and Prescott (1982)). Research on
monetary policy stressed the importance of central bank independence (Sargent and Wallace
(1976)) and focused on inflation targeting (Bernanke and Mishkin (1997) and Clarida et al
(1999)).
     Contributions to the macroeconomic literature over this period paid little attention to
financial intermediation. The class of so-called New Keynesian macroeconomic models (ie
using microeconomic foundations that assume some price or wage "stickiness", leading to a
slow adjustment of real variables to shocks) that emerged included various real and nominal
frictions but did not properly account for financial imperfections (Smets and Wouters (2003)).
Indeed, Gertler (1988) began his overview of the subject with a powerful observation: “most
of macroeconomic theory presumes that the financial system functions smoothly and smoothly
enough to justify abstracting from financial considerations. This dictum applies to modern
theory” (see also Blanchard (1990)).
    Some authors paid attention to the banking system, but mostly because it issued money,
not because of its financial intermediation function. Bank runs (Diamond and Dybvig (1983))
and asset price bubbles (Blanchard and Watson (1982)) were studied but these did not
become central to macroeconomic research. Minsky (1982, 1986) drew attention to the
endogenous build-up of financial vulnerabilities. However, his work was largely qualitative
and remained peripheral (see also Kindleberger (1996) and Borio and Lowe (2004)). Overall,
the lack of interest in banking and finance seems to have been related to the less volatile
nature of business cycle, especially after the mid-1980s, the era of the “Great Moderation”
(Blanchard (2009)).


                                                44
    That said, interest in macro-financial linkages slowly picked up in the early 1970s. On
the microeconomic theory front, research on the effects of asymmetric information and
incentives, building on earlier work, notably Akerlof (1970), provided new insights. Many
authors analysed the nature of optimal contracts with unobservable information, principal-
agent problems or other imperfections (Jensen and Meckling (1976), Townsend (1979),
Stiglitz and Weiss (1981), Williamson (1987)); including specifically for financial institutions
(Gale and Hellwig (1985), Calomiris and Kahn (1991) and Holmström and Tirole (1997)).
Following Shiller (1981), some researchers focused on deviations from the efficient markets
hypothesis. Fazzari et al (1988) documented how corporate cash flows correlate with
investment decisions, contradicting the q theory and providing evidence of financial
imperfections.
    Following work by pioneers in behavioural economics (eg Kahneman and Tversky (1979)),
Thaler in a series of influential papers (eg De Bondt and Thaler (1985)) started the field of
behavioural finance. This branch analyses how investor psychology, in conjunction with limits
to arbitrage, can affect prices in financial markets. Building on Thaler’s insights, Shleifer and
Vishny (1997), drew attention to the limits to arbitrage in asset markets. Many contributions
since then have showed how individuals’ behaviour deviates from the standard paradigm (see
Barberis and Thaler (2003), Thaler (2005) and Hirshleifer (2015) for reviews).
     New empirical work also studied the importance of macro-financial linkages. Notably,
Mishkin (1978) and Bernanke (1983) documented the critical role of financial factors in
explaining the severity and persistence of the Great Depression. Mishkin argued that
household balance sheet positions significantly impact consumer demand and Bernanke
showed that a worsening of bank and corporate balance sheet positions leads to a more severe
debt crisis. Other empirical studies put greater emphasis on the role of financial markets and
institutions in shaping aggregate economic outcomes (Eckstein and Sinai (1986) and Brunner
and Meltzer (1988)). Bernanke and Blinder (1988), Kashyap et al (1993) and others
demonstrated the importance of the bank lending channel. A number of studies considered
specific episodes of credit crunches in the United States and other countries and analysed the
role of financial institutions in driving business cycles (Sinai (1992)). Importantly, with better
data, the (causal) links between financial development and longer-term macroeconomic
outcomes were documented (Goldsmith (1987), Fry (1988) and King and Levine (1993)).
    Over the past three decades, more rigorous analytical models have investigated the
linkages between financial markets and the real economy. Some of these focus on amplification
mechanisms, collectively known as “the financial accelerator”, which operates through the
demand side of financial transactions (Bernanke and Gertler (1989), Carlstrom and Fuerst
(1997), Kiyotaki and Moore (1997) and Bernanke et al (1999)). These models show how
accelerator effects arise when small shocks, real or financial, are propagated and amplified
across the real economy as they lead to changes in access to finance. More recent theoretical
and empirical research has illustrated the importance of amplification channels operating on
the supply side, including through financial institutions and markets (Brunnermeier and
Pedersen (2009), Adrian and Shin (2008) and Geanakoplos (2008)). New models that include
both demand and supply types of macro-financial linkage (Brunnermeier and Sannikov
(2014), Gertler and Kiyotaki (2011), Williamson (2012) and Dávila and Korinek (2017)) have
been developed.




                                               45
                            Figure A1. Evolution of research on macro-financial linkages
                       Macroeconomics                                                             Finance
         Studies                              Approach                          Approach                            Studies

                                                                                                                Mills (1867)
      Fisher (`33)                        Qualitative discussions of macro-financial linkages
                                                                                                               Haberler (`37)
      Keynes (`36)                                    Narratives of debt-deflation
                                                                                                               Marschak (`38)




                                                                                                              Gurley-Shaw (`55)
     Hicks (`37)                                                                Financial
                                                                                                               Goldsmith (`69)
    Modigliani (`44)                    Keynesian approach                   intermediation
                                                                                                                 Shaw (`73)
     Tobin (`58)                                                          Financial repression
                                                                                                               Cameron (`67)




                                                                            Portfolio theory
                                                                                                              Markowitz (`52)
   Friedman-Schwartz                                                        Capital structure
                                        Monetarist approach                                                 Modigliani-Miller (`58)
         (`63)                                                                irrelevance
                                                                                                                Tobin (`69)
                                                                                 Q ratio



     Sims (`72, `80)                            VARs
       Lucas (`76)                          Lucas critique
                                                                            Efficient markets                    Fama (`70)
  Sargent-Wallace (`75)                 Monetary arithmetic
                                                                              Asset pricing                     Merton (`73)
      Sargent (`81)                     Rational expectations
                                                                          Derivatives-arbitrage              Black-Scholes (`73)
 Kydland-Prescott (`82)                  Real business cycle
King-Plosser-Rebelo (`88)                      models




                                                                   46
                               Macroeconomics                                                                        Finance
               Studies                                   Approach                              Approach                                      Studies

     Mishkin (`78); Minsky (`82)
          Bernanke (`83)
                                                     Lessons from the                        Information                                 Akerlof (`70)
       Abel-Blanchard (`86)
          Goldsmith (`87)                            Great Depression                         asymmetry                             Jensen-Meckling (`76)
              Fry (`88)                              Rational bubbles                       Agency issues                               Townsend (`79)
     Mankiw and Romer (`91)                         Role of money and                        Enforcement                              Stiglitz-Weiss (`81)
         King-Levine (`93)                          banks for economy                      Optimal contracts
        Kindleberger (`96)


       Bernanke-Blinder (`88)                                                                                                        Diamond-Dybvig (`83)
                                                                                           Corporate finance
       Bernanke-Gertler (`89)                      Financial accelerator                                                                Williamson (`88)
                                                                                                Financial
     Kashyap-Stein-Wilcox (’93)                          theories                                                                     Calomiris-Kahn (`91)
                                                                                             intermediation                            Holmström-Tirole
       Carlstrom-Fuerst (`97)                     Bank lending channel
                                                                                         Private public liquidity                           (`97, `98)
        Kiyotaki-Moore (`97)

                                                                                                                                    Kahneman-Tversky (`79)
      Brunner-Meltzer (`88)                                                                 Bubbles, crises                                Shiller (`81)
                                                  New Keynesian DSGE
  Bernanke-Gertler-Gilchrist (`99)                                                       Market imperfections                             Minsky (`86)
                                                          models
       Smets-Wouters (`03)                                                                Limits of arbitrage                    Fazzari-Hubbard-Petersen (`88)
                                                   Inflation targeting                                                                Shleifer-Vishny (`97)
         Woodford (`03)                                                                  Behavioural finance
                                                                                                                                           Thaler (`05)


                                                   Financial frictions in                Leverage and supply                          Adrian-Shin (`08)
   Mendoza (`10); Woodford (`10)                                                                                                      Geanakoplos (`08)
                                                       DSGE models                          Leverage cycles
       Gertler-Kiyotaki (`11)
                                                   Models with money,                    Liquidity, asset prices,                Brunnermeier-Pedersen (`09)
    Brunnermeier-Sannikov (`16)
                                                    liquidity, leverage                         fire sales                           He-Krishnamurthy (`13)

Notes: This diagram presents a rough representation of the evolution of research on macro-financial linkages. Early interest in such linkages was triggered by the
Great Depression. However, it remained largely qualitative. Although research became more analytical over time, it moved away from the analysis of financial
market imperfections, especially over the period 1950–80. Since then, the literature has made significant progress in capturing the effects of microeconomic
imperfections. More recently, it has combined theoretical insights from macroeconomics and finance in general equilibrium models. It has also produced many
empirical studies at the micro- and macroeconomic levels. This diagram includes only a select set of studies because of space constraints. We summarize many
other studies in the main text.



                                                                                 47
Appendix II. Financial accelerator mechanisms

Although there are many variants of the financial accelerator mechanism, they tend to
describe similar channels of transmission and propagation. A number of theoretical studies
show that the mechanism can play an important role in accentuating macroeconomic
fluctuations. This appendix presents a summary of the various mechanisms found in the
literature (see Quadrini (2011) for a systematic analytical review of the causes of financial
frictions and the types of accelerator model; Gerke et al (2013) also compare the various
models that feature a financial accelerator mechanism and collateral constraints).
     One mechanism works through changes in cash flows that depend on the overall state of
the economy. Greenwald and Stiglitz (1993) develop a dynamic model in which changes in
corporate cash flows play a critical role in propagating financial market disturbances to real
sector variables, including employment and inventories. Another mechanism acts through
changes in the intensity of adverse selection in credit markets. Azariadis and Smith (1998)
show that the presence of adverse selection can create indeterminacy, with the economy
fluctuating between Walrasian and credit rationing regimes, and with cyclical downturns
exhibiting declines in real interest rates and increases in credit rationing (see also Mankiw
(1986)).
     Changes in default probabilities over the cycle can also amplify cyclical fluctuations.
During the upswing of a cycle, default probabilities decline, allowing investors to lend greater
sums of money, as predicted by the Stiglitz-Weiss model (1981) of lending under asymmetric
information and moral hazard. This fuels the upswing. On the downswing, the mechanism
works in reverse, leading to sharp declines in available external financing. The general
equilibrium implication of this mechanism is modelled by Suarez and Sussman (1997) using
an overlapping generations model. In particular, during booms old entrepreneurs sell larger
quantities and prices fall, implying that young entrepreneurs must rely to a greater extent on
external sources of financing. Since external financing can generate excessive risk-taking,
booms are often followed by higher failure rates. Fire sales by bankrupt corporations during
such periods then lead to asset price declines, which, in turn, generate macroeconomic
fluctuations.
     The allocation of capital across heterogeneous firms can, in the presence of financial
imperfections, also create procyclicality. Bernanke and Gertler (1989) and Kiyotaki and
Moore (1997) focus on the impact of financial imperfections on aggregate investment and
employment but neglect the effects of the reallocation of inputs across heterogeneous firms.
Recent studies try to fill this gap. Eisfeldt and Rampini (2006) find that the costs of capital
reallocation, such as those induced by financial frictions, need to be countercyclical to be
consistent with the cyclical dynamics of capital reallocation and productivity dispersion. Moll
(2014), in a model where entrepreneurs are subject to borrowing constraints and idiosyncratic
productivity shocks, shows with plant-level panel data that financial frictions can explain
aggregate productivity losses in two EMEs that are 20% larger than in the United States. In
related research, Khan and Thomas (2013), using a DSGE model in which capital reallocation
across firms is distorted by frictions, show that a shock can be amplified and propagated
through disruptions to the distribution of capital across firms. Herrera et al (2011), in their
study of US firms and the dynamic properties of gross credit flows relative to macroeconomic
variables, find that financial frictions can impact aggregate productivity by hindering the
inter-firm reallocation of credit.
   Project choice and technological change can also lead to financial accelerator mechanisms.
The literature on the accelerator often emphasises the impact of imperfections on the volume



                                              48
of investment or consumption. However, imperfections can also propagate and amplify shocks
by triggering changes in the quality and productivity of projects undertaken. Some studies
argue that financial frictions that are made worse by recessions induce firms to switch to
lower quality and lower productivity projects (Barlevy (2003)). Aghion et al (2010) show how
recession-induced credit constraints can make firms choose short-term investments with low
liquidity risk, making the share of long-term but more productive projects procyclical. In
other studies, though, recessions have a cleansing effect, stimulating the adoption of more
productive technologies. Araujo and Minetti (2011) document that, while firms' collateral
and credit relationships ease their access to credit and investment, such relationships can also
inhibit restructuring activity (ie the transition to new and more productive technologies).
    Financial imperfections affecting large firms also can result in accelerator-type effects.
While the literature generally stresses that small firms are especially exposed to financial
imperfections, frictions can also affect large firms. As is well documented in the corporate
finance literature, large firms, especially those that are widely held, face more acute agency
problems. Managers of large firms can have incentives to allocate cash inefficiently, for
example, by empire building (Jensen and Meckling (1976) and Jensen (1986)). Dow et al
(2005) integrate this problem into a dynamic equilibrium model and show how cash flow
shocks can affect investment and be propagated over time.
    Philippon (2006) models how managers tend to overinvest and how shareholders show
greater tolerance for such behaviour more during booms, with the cyclical behaviour related
to the quality of governance. Martin and Ventura (2011) consider a financial accelerator
model in which bubbles in asset prices drive changes in borrowers’ net worth and the tightness
of credit constraints (see also Martin and Ventura (2015)). Note also that firm demand for
external financing appears to deviate from the behaviour predicted by standard models (Baker
and Wurgler (2013) survey theory and evidence on behavioural corporate finance).
     Information asymmetries can be a source of asset price booms, bubbles and busts. Models
à la Kiyotaki and Moore (1997) assume that agents participating in asset markets are equally
informed. However, financial frictions can also generate confusion about the fundamental
value of assets. When agents are asymmetrically informed about the fundamental value of
assets, Yuan (2005) shows that credit constraints can contribute to uncertainty and
exacerbate the volatility of asset prices. Iacoviello and Minetti (2010) show that opening an
economy to foreign agents (such as lenders) that have less information about traded collateral
assets, can lead to higher volatility of asset prices, credit and output. Gorton and Ordoñez
(2013, 2014) show that when an economy relies on informationally-insensitive debt, a credit
boom can follow during which firms with low quality collateral start borrowing. Since this is
associated with a more fragile environment, a small shock can trigger a large change in the
information set, leading to a drop in asset prices and a sharp decline in output and
consumption. A distinct but related area of research explores the role of distortions, such as
moral hazard or weak corporate governance, in generating asset price bubbles (Allen and Gale
(2000) and Barlevy (2014)).
    Moreover, financial imperfections can propagate adverse shocks through labour markets,
for instance, by hindering the job search process. Motivated by the GFC, Sterk (2015)
presents a business cycle model in which a fall in house prices reduces geographical mobility
because credit-constrained homeowners experience a decline in their home equity, creating
distortions in the labour market. The model can explain much of the joint cyclical fluctuations
in US housing and labour market variables, including the events that took place in 2009.
Andrés et al (2010) explore a similar propagation channel involving credit frictions and labour
markets. They are able to explain the comovements of US house prices with output,
investment and consumption. Burnside et al (2016) provide a model in which agents’


                                              49
interactions create “fads” about asset price prospects. This process, in turn, generates boom-
busts cycles or protracted booms that are similar to those observed for house prices.




                                             50
Appendix III. Understanding liquidity and leverage cycles

Liquidity, a key concept for macroeconomic and financial developments, is difficult to define,
in part as it is multi-faceted (see further Holmström and Tirole (2011) and Tirole (2011)).
Liquidity is often considered in relation to the price of credit. It can correspondingly be
measured by short-term interest rates, with lower rates being associated with ampler liquidity.
While the underlying theoretical motivations are not always entirely clear, some studies also
employ quantity-based measures – such as the aggregate quantity of money or “excess” money
growth (money growth less nominal GDP growth). Both concepts tend to move in the same
direction. They are also closely related to the monetary transmission channels of interest rates
and asset prices.
     Narrower definitions of liquidity in the finance literature relate to the tradability of
specific assets while broader definitions refer to banks’ role in liquidity provision. A liquid
asset is said to have the following features: it can be sold rapidly, with minimal loss of value
(close to the true present value of its discounted cash flows) within a short period of time
(minutes or hours). Conversely, an illiquid asset is not readily saleable. This type of liquidity
depends on various factors. For example, an asset can be illiquid because of uncertainty about
its value or because there is no market in which it can easily be traded. Liquidity creation is
also seen as a core function of banks (see Bouwman (2014) for a review of the literature on
(private) liquidity creation by commercial banks and its regulation).
     Recently, the literature has introduced new classifications of liquidity formally.
Specifically, Brunnermeier and Pedersen (2009) categorise liquidity into two forms: market
and funding liquidity (among practitioners these concepts had been familiar for some time;
see for example Borio (2000, 2004)). Market liquidity is defined as the ease with which money
can be raised by selling assets at reasonable prices. A liquid (or deep) market is one with
willing buyers and sellers at all times for large quantities and with orders that are not strongly
influencing prices (the probability that the next trade is executed at a price equal or close to
the last one is high; see Vayanos and Wang (2013) for a review of the theoretical and empirical
literature on market liquidity).
    Funding liquidity describes the ease with which financial institutions, investors or
arbitrageurs can obtain funding. It is high, ie markets are “awash with liquidity”, when it is
easy to raise money. Funding liquidity is affected by the strength of fund-seekers’ balance
sheets and cash flows. This strength is, in turn, affected by asset prices: when collateral values
are high (and/or rising) and margins are low, funding liquidity can be ampler (as in the case
of repos). As such, market and funding liquidity are closely related. And, there is a strong
parallel to the financial accelerator mechanism of Kiyotaki and Moore (1997) that focuses on
how changes in asset prices affect firms’ ability to raise external financing.
     Liquidity can be influenced by two distinct leverage spirals: the valuation and the
margin/haircut spirals, both of which relate to the soundness and funding positions of
financial institutions. The valuation spiral is driven by asset price effects. If many financial
institutions suffer a similar shock – say a drop in the value of mortgage-backed securities –
all of them have to cut back their asset positions. This depresses asset prices further, leading
to an additional erosion of capital, which then forces institutions to cut back on their positions
even more. With mark-to-market accounting rules and market discipline, leveraged financial
institutions cannot defer these losses individually. Moreover, when markets are illiquid, selling
assets depresses prices further.
     The margin/haircut spiral can come on top of the valuation spiral. It arises when many
institutions finance their asset positions with (short-term) borrowed money (repos) and have


                                               51
to put up margins in cash or are imposed a discount (haircut) on the assets they provide as
collateral. These margins/haircuts increase in times of price declines – as lenders want better
protection – and thereby lead to a general tightening of lending conditions (margins and
haircuts implicitly determine the maximum leverage that a financial institution can adopt).
The margin/haircut spiral then reinforces the valuation spiral in forcing institutions to reduce
their leverage.
     These mutually reinforcing effects create virtuous or vicious cycles, with real economic
impacts. Brunnermeier and Pedersen (2009), Adrian and Shin (2008, 2010a) and Geanakoplos
(2010) point out how these mechanisms can affect liquidity and leverage, which, in turn,
affect financial and economic cycles. During a virtuous cycle, these mechanisms can lead to
rising asset prices (even bubbles). In a vicious cycle, they can create fire sales. Importantly,
these cycles can be triggered by relatively small shocks. In particular, even a temporary lack
of liquidity may create adverse effects for a highly leveraged financial institution. Liquidity
shocks can also be aggravated through various channels, including the hoarding of funds, runs
on financial institutions and network effects (via counterparty credit risk). Because of these
spirals, small shocks can force the economy into a process of deleveraging and fire sales. This
can have a substantial impact on the real economy, as happened during the Asian financial
crisis of the late 1990s and the GFC (see Shleifer and Vishny (2011) for a review of the
literature on fire sales and macroeconomics).




                                              52
Appendix IV. Linkages between business and financial cycles: an
overview of empirical studies

Using various methodologies and measures to proxy cycles, a number of studies have
examined the features of business and financial cycles and the aggregate linkages between
such cycles. They have pointed out the procyclical nature of financial markets and provided
the broad patterns describing the linkages between business and financial cycles. This
Appendix reviews these studies for the three most important market segments: credit, equity
and housing.62

Credit market cycles and business cycles

The study of credit cycles has a history that goes back to Mills (1867) at least. Most of the
early work in this area employed qualitative approaches and considered the extreme versions
of these cycles: booms and busts (or crunches) (see Keynes (1936), Galbraith (1954), Shiller
(1989, 2000) and Sinai (1993); Niehans (1992) reviews very early work on credit cycles (Juglar
(1862)). A number of studies also consider specific credit crunches in the United States and
other countries (see Wojnilower (1980, 1985), Owens and Schreft (1995), Cantor and
Wenninger (1993) and Helbling et al (2011)). Using US data going back to 1875, Bordo and
Haubrich (2010) document that credit disruptions appear to exacerbate cyclical downturns.
A number of studies also consider the important role played by credit in driving business
cycles. Using VAR models, Meeks (2012) examines the role of credit shocks in explaining US
business cycles and finds that such shocks play an important role during financial crises but
a somewhat smaller role during “normal” business cycles.
    Recent studies apply a variety of quantitative approaches to cross-country data to analyse
episodes of credit booms and crunches. Mendoza and Terrones (2008), for example, use a
“thresholds method” to identify credit booms in 48 countries over the period 1960–2006. They
find that booms generally coincide with above-trend growth in output, consumption and
investment during the build-up phase and below-trend growth of those variables in the
unwinding phase. During the build-up phase, a surge in private capital inflows is accompanied
by a deterioration of current account positions (see also Gourinchas et al (2001), Schularick
and Taylor (2012)). Other researchers (such as Castro and Kubota (2013) and Dell’Ariccia
et al (2016)) also study the determinants of credit booms’ length.

Cycles in asset (house and equity) prices and business cycles

Booms and busts in asset prices have also been a major area of research. Borio and Lowe
(2002), using an aggregate index of asset price (equities, and residential and commercial
property), define booms as periods during which asset prices deviate from their trends by
specified amounts. They also consider the interaction between developments in asset prices
and credit. They report that there are substantial declines in house prices and residential
investment during housing busts (after episodes of booms) in 16 advanced economies. This


62
  For additional references on the literature covering the linkages between business and financial cycles
see Rebelo (2005), Claessens et al (2009, 2011, 2012a), Siregar and Lim (2011), Guarda and Jeanfils
(2012), Hubrich et al (2013), Borio (2014), De Rezende (2014), Große Steffen (2015), Hubrich and
Tetlow (2015), Kose and Terrones (2015), Hartmann et al (2015), Abbate (2016), Abildgren (2016),
Jordà et al (2016, 2017), Bluwstein (2017) and Gandré (2017).


                                                   53
work builds on earlier contributions, including in Bank for International Settlements (1993),
and Borio et al. (1994), and has since been deepened in a number of ways (see further
Hofmann (2001) and Davis and Zhu (2004)). Similarly, Detken and Smets (2004) identify
between 1970 and 2002 38 house price booms in 18 OECD countries on the basis of prices
exceeding trend growth rates by at least 10% (see also Adalid and Detken (2007)). They
emphasise the importance of joint fluctuations in house prices and credit over the boom-bust
cycles in asset prices.
    Others have focused on boom and bust episodes in house and equity prices. However,
because they employed different methodologies and data sets, their findings have been difficult
to compare. Bordo and Jeanne (2002) analysed episodes of booms and busts in house and
equity prices for OECD countries and documented that more than one in every two house
price booms ended up with a bust, against one for every six equity price booms. Using OECD
data for 18 countries from 1970 to 2009, Burnside et al (2016) found that the amplitude of
typical house price booms and busts was 54% and 29%, with a median length of four and five
years, respectively. They also report that booms are not always followed by busts.
     A number of other studies have borrowed methods widely employed in the business cycle
literature to study financial cycles. Following Harding and Pagan (2002), rigorous approaches
to documenting financial cycles have been used. For example, Pagan and Sossounov (2003)
identify “bear” and “bull” phases in equity markets using formal methods of business cycle
dating for US monthly data over the 1835–1997 period. They report that while the duration
of bear markets is about 15 months, it is around 25 months for bull markets. Bear markets
are characterised by about a 30% decline in equity prices and bull markets by about a 40%
increase.
    Ohn et al (2004) examine the “duration dependence” exhibited by bull and bear markets
in the United States and report that both phases show positive dependence. Using the same
methodology, Edwards et al (2003) find that the cyclical properties of equity prices in EMEs
change following periods of financial liberalisation. Kaminsky and Schmukler (2008) report
that equity price cycles in EMEs tend to become more volatile after liberalisation. Drehmann
et al (2012) show that the length and amplitude of financial cycles have increased markedly
since the mid-1980s and that cyclical peaks are very closely associated with financial crises.
    Other research focuses on the cyclical properties of house prices. Although cyclicality is
common, the duration and amplitude of housing cycles vary widely across geographical areas
and time (Cunningham and Kolet (2011) and Hall et al (2006)). This, in turn, reflects
variations in demand and supply conditions, the characteristics of housing finance and the
sources of linkage between housing and real activity. Igan and Loungani (2012) study the
characteristics of house price cycles in advanced economies and find that long-run price
dynamics are mostly driven by local fundamental factors, such as demographics and
construction costs, although movements in such fundamentals – and credit conditions – can
create short-run deviations from equilibrium paths.
    Some studies consider the linkages between business and asset price cycles (Breitung and
Eickmeier (2014), Cicarelli et al (2016) and Prieto et al (2016)). A central finding of these
studies is that house price cycles tend to have an especially close relationship with business
cycles. Based on evidence for 27 countries, Cecchetti (2008) finds that housing booms worsen
growth prospects while equity booms have little impact on the expected mean and variance
of macroeconomic performance (although they do aggravate adverse outcomes). Cecchetti
and Li (2008) study the impact of booms in equity and house prices on extreme fluctuations
in output and the price level. They find that equity and housing booms are both associated
with significantly worse growth and inflation prospects over a three-year horizon. Leamer
(2007) finds that there are strong linkages between various aspects of housing market and


                                              54
business cycles in the United States. Ha et al (2017) find evidence of global cycles specific to
financial variables. They also find that shocks to house and equity prices have spillover effects
on macroeconomic aggregates (see also Cotter et al (2017)).

Synchronisation of financial cycles

Some studies document the extent of cyclical synchronisation and lead-lag relationships in
the financial markets of various countries. Goodhart and Hofmann (2008) analyse the degree
of synchronisation between house prices and credit movements – where these two variables
may comove because a change in housing wealth has collateral effects which affect both credit
demand and supply or changes in credit supply affect house price fluctuations. They show
that the effects of shocks to money and credit are stronger when house prices are booming.
Borio and McGuire (2004) report that peaks in housing prices lag peaks in equity prices by
up to two years, with the lag length negatively related to changes in short-term interest rates.
Hirata et al (2012) analyse the synchronisation of house prices across countries and their
interactions with other financial variables (see also Cesa-Bianchi (2013)). Using a dynamic
factor model, Rey (2015) and Miranda-Agrippino and Rey (2015) find that a common factor
drives a sizeable portion of variations in asset prices globally and capital flows. Cerutti et al
(2017a), however, question the quantitative importance of global factors for capital flows.




                                               55
  Table 1. Behaviour of small and large firms during recessions and tight
                              money periods
                            (in percent change)
                                        Sales                    Inventories          Short-term Debt

                               Large            Small         Large      Small       Large        Small


2007-2009 Recession1/          -24.62           -20.24        -14.18     -15.36      -38.24      -19.83


2001 Recession2/               -14.64           -6.92         -12.65     -9.28       -35.95      -12.13


All Recessions pre-20013/      -7.59            -10.15        -5.67      -9.11       -24.48      -12.62


Tight Money Dates 4/            -5.5            -9.39          -3.3      -7.78       -14.15      -11.45

Source: Kudlyak and Sánchez (2017).
Note: The table shows the difference between the minimum value of the detrended series in an interval of 12
quarters following the episode and the peak value of the series.
1/
   The peak of the recession is Q4 2007.
2/
   The peak of the recession is Q1 2001.
3/
   The peaks of the recessions covered are: Q4 1969, Q4 1973, Q1 1980, Q3 1981 and Q3 1990.
4/
   The periods of tight money are Q2 1966, Q4 1968, Q2 1974, Q3 1978, Q4 1979, Q4 1988 and Q2 1994.




                                                         56
            Table 2. Variance decomposition: VAR with global factors
            (fraction of variance explained by respective shock, in percent)
                 Forecast
                                                                     Interest                Credit     Default
Shocks            horizon       GDP      Productivity Inflation                  Credit
                                                                      Rates                 Spread       Rates
                (quarters)

Credit               1           8.9           6.5          6.8        9.9         14.6        9.2        15.7
                     4           9.7           8.8          9.2        10.1        13.9        9.5        14.9
                     8           10.6         10.3          10.6       10.5        12.5       10.9        14.2
                    12           10.8         10.4          10.9       10.8        12.1       11.1        13.9


Productivity         1           9.3           7.1          23.5       9.1         9.1         8.5        10.7
                     4           10.5          9.4          19.6       10.3        11.4        9.9        12.2
                     8           12.1         11.0          16.6       11.8        13.3       11.4        12.5
                    12           12.3         11.4          16.3       12.3        14.5       11.8        12.5
Source: Helbling et al (2011).
Note: The roles of credit and productivity shocks in explaining global business cycles are shown using a VAR
model that includes the estimated global factor of each variable and US credit spreads and default rates. The
table reports the fraction of the forecast error variance of these variables that is explained by global credit and
productivity shocks for different forecast horizons. Though both shocks are identified simultaneously, the
variance decompositions need not add up to 100% because other potentially unidentified shocks make up the
rest of the variance. Credit is measured by the aggregate claims on the private sector of deposit money banks
and is obtained from the International Financial Statistics (IFS) of the IMF. The default rate series correspond
to the monthly rates for US speculative-grade corporate bonds rated by Moody's Investor Service. GDP data
are chained volume series from the OECD. The interest rates correspond to nominal short-term government
bill rates and are taken from IFS. Labour productivity is defined as real GDP per hours worked and is obtained
from the OECD. Inflation corresponds to the change in each country's CPI. The sample includes G7 countries
for the period Q1 1988 to Q4 2009.




                                                       57
     Table 3. Synchronisation of business and financial cycles
                       (concordance index)
                                             All          Advanced        Emerging
                                          Countries       Economies        Markets

    Output and Credit Cycles
      Mean                                   0.78           0.82**           0.74
      Median                                 0.81            0.83            0.76
      Standard Deviation                     0.10            0.06            1.13


    Output and House Price Cycles
      Mean                                   0.64           0.67**           0.54
      Median                                 0.68            0.70            0.50
      Standard Deviation                     0.12            0.15            0.15


    Output and Equity Price Cycles
      Mean                                   0.59           0.57***          0.62
      Median                                 0.58           0.57***          0.64
      Standard Deviation                     0.06            0.08            0.05
Note: Each cell represents the concordance statistic for the corresponding two cycles.
Concordance is calculated as the fraction of time that two cycles are in the same phase. ***
and ** imply significance at the 1% and 5% levels, respectively. Significance refers to the
difference between advanced economy and emerging markets (means and medians only).




                                            58
                    Figure 1. Behaviour of large and small firms
                           (around periods of tight money)
     0


    -3


    -6


    -9


   -12
                    Large       Small

   -15
                      Sales                     Inventories                Short-term Debt
Source: Kudlyak and Sánchez (2017).
Note: The figure presents the difference between the minimum value of the detrended series in an interval
of 12 quarters following an episode of tight money and the peak value of the series. Tight money periods
are: Q2 1966, Q4 1968, Q2 1974, Q3 1978, Q4 1979, Q4 1988 and Q2 1994 based on the historical record
analysed in Romer and Romer (1989). Small firms are defined as those at or below the 30th percentile of
assets and large firms as those above the 30th percentile.




                                                  59
  Figure 2. Default rates and growth of house prices in countries with low
                          and high leverage growth




Source: Mian and Sufi (2010).
Note: High-leverage growth counties are defined as the top 10% of counties in terms of the increase in the debt
to income ratio from Q4 2002 to Q4 2006. Low-leverage growth counties are in the bottom 10% of the same
measure. The left panel plots the change in the default rate for high- and low-leverage growth countries and the
right panel plots the growth rate for high- and low-leverage growth countries.




                                                      60
Figure 3. Auto sales, new home building and unemployment rates in high-
                    and low-leverage growth countries




Source: Mian and Sufi (2010).
Note: High-leverage growth countries are defined as the top 10% of countries by the increase in the debt to
income ratio from Q4 2002 to Q4 2006. Low-leverage growth countries are in the bottom 10% based on the
same measure. The left panel plots the growth in auto sales, the middle panel plots the growth in new housing
permits, and the right panel plots the change in the unemployment rate.




                                                    61
            Figure 4A. Financial accelerator mechanism: demand side
                                 (virtuous circle)




Note: The chart depicts the financial accelerator mechanism by which shocks to the economy may be amplified
by changes in access to external financing, which then translates into changes in economic agents’ investment
and consumption spending. In turn, these changes are propagated and reinforced as asset prices and economic
activity fluctuate, which then affects the demand and availability of external financing. This creates further
feedback loops that are propagated through financial markets and the real economy.




                                                     62
             Figure 4B. Financial accelerator mechanism: supply side
                                  (virtuous circle)




Note: The chart depicts both the demand (as in Figure 4A) and the supply sides of the financial accelerator
mechanism. As financial institutions’ balance sheets and profitability increase and asset prices rise, the
assessment of economic prospects is viewed more positively and the supply of external financing expands. This
then translates into changes in investment that enhance the feedback loops between financial markets and the
real economy.




                                                    63
                  Figure 5. Evolution of global GDP: 2007:3–2009:4
                                        A. Role of credit shock
        5


        0


       -5


      -10


      -15


      -20
                           Actual             Counterfactual

      -25
             2007:3 2007:4 2008:1 2008:2 2008:3 2008:4 2009:1 2009:2 2009:3 2009:4

                     B. Contributions of credit and productivity shocks
       2.5


       2.0
                         Credit
       1.5               Productivity


       1.0


       0.5


       0.0


      -0.5
              2007:3 2007:4 2008:1 2008:2 2008:3 2008:4 2009:1 2009:2 2009:3 2009:4
Source: Helbling et al (2011).
Note: Panel A compares the results of counterfactual simulations for the global GDP factor during the GFC.
The solid line represents the actual global GDP factor and the dashed line represents the counterfactual when
the global credit shock is set to zero during the period considered. Panel B compares the contributions of credit
and productivity shocks to cumulative global GDP growth based on the counterfactual simulations. The bars
represent the median difference. A positive (negative) bar captures how the decrease in the global GDP factor
would have been smaller (greater) in the absence of the respective shocks. Credit is defined as the aggregate
claims on the private sector by deposit money banks and is obtained from the IFS. Labour productivity is
defined as real GDP per hours worked and is obtained from the OECD.




                                                      64
Figure 6A. Conceptual representation of liquidity and leverage cycles: gains
                             (virtuous circle)




Source: Brunnermeier and Pedersen (2009).
Note: The figure depicts a situation where initial gains trigger a virtuous loop of increased asset prices and
further gains. The underlying mechanism is similar to that of the financial accelerator where higher asset prices
lead to increases in capitalisation, which then enhances the demand for assets, (further) driving up prices. The
mechanism is in part reinforced by lower margins/haircuts on assets used as collateral, which allows for greater
leverage.




                                                      65
   Figure 6B. Conceptual representation of liquidity and leverage cycles:
                                   losses
                              (vicious circle)




Source: Brunnermeier and Pedersen (2009).
Note: The figure depicts a situation where initial losses initiate a vicious circle of declining asset prices and
losses. The underlying mechanism is that of a fire sale, which is essentially the forced sale of an asset at a
dislocated, low price. It is in part triggered by an increase in the margins/haircuts on assets used as collateral,
which allows for reduced leverage.




                                                       66
             Figure 7. Macroeconomic implications of financial shocks




Source: Gilchrist and Zakrajšek (2011).
Note: The figure depicts the impulse response function of a nine variable VAR model to a one standard deviation
orthogonalised shock to the financial bond premium over the period Q1 1985 to Q2 2010. Shaded bands denote
95% confidence intervals based on 1000 bootstrap replications.




                                                     67
                                                                      Figure 8. Total assets and leverage
                                          (leverage and asset growth move together for securities brokers and dealers)
                                                          A. Households                                        B. Non-financial non-farm corporations
                                          8                                                                                                    6




                                                                                                     Total Assets Growth (Percent Quarterly)
Total Asset Growth (Percent Quarterly)
                                                                                                                                               5
                                          6
                                                                                                                                               4
                                          4
                                                                                                                                               3

                                          2                                                                                                    2

                                                                                                                                               1
                                          0
                                                                                                                                               0
                                         -2
                                                                                                                                               -1

                                         -4                                                                                                    -2
                                              -1        -0.5     0      0.5      1       1.5                                                        -2.5      -1.5    -0.5     0.5     1.5       2.5
                                                     Leverage Growth (Percent Quarterly)                                                                   Leverage Growth (Percent Quarterly)

                                                     C. Commercial banks                                                                        D. Securities brokers and dealers
                                         6                                                                                                     40
                                                                                                    Total Asset Growth (Percent Quarterly)
                                         5
Total Asset Growth (Percent Quarterly)




                                                                                                                                               30

                                         4
                                                                                                                                               20
                                         3
                                                                                                                                               10
                                         2
                                                                                                                                                    0
                                         1
                                                                                                                                               -10
                                         0

                                         -1                                                                                                    -20

                                         -2                                                                                                    -30
                                              -50       -30     -10      10      30       50                                                            -60     -40    -20      0       20       40
                                                    Leverage Growth (Percent Quarterly)                                                                   Leverage Growth (Percent Quarterly)

Source: Adrian and Shin (2008).
Note: Panel A plots the quarterly changes in total household assets to quarterly changes in household leverage as
extracted from the US Financial Accounts (formerly called Flow of Funds). Panel B plots the change in leverage
and change in total assets of non-financial, non-farm corporations drawn from the US Flow of Funds Accounts.
Panel C plots the changes in leverage against the changes in the total assets of US commercial banks. Panel D
plots the same for US securities brokers and dealers. The data are from 1963 to 2006.




                                                                                               68
                 Figure 9. Impulse responses to a risk appetite shock
                                     (in percent)




Source: Adrian et al (2010b).
Note: Stronger risk appetite leads to an expansion of intermediaries’ balance sheets and a compression of credit
spreads. The response of the macroeconomic risk premium peaks at four quarters and then subsequently reverts
slowly towards zero. However, the significance of the risk appetite shock on the macroeconomic premium is
fairly persistent and only becomes insignificant after about six quarters.




                                                      69
               Figure 10. Monetary conditions and bank risk-taking




Source: Dell’Ariccia et al (2014).
Note: Simple OLS regression of a risk measure of bank lending and the real federal funds rate for all banks.
The dependent variable is the risk of bank loans, which is based on an index ranging from 1 to 5. The measure
is based on quarterly data over the period Q2 1997 to Q3 2009 and is taken from the Federal Reserve’s Survey
of Terms of Business Lending.




                                                    70
                          Figure 11. Recessions and recoveries:
                         duration, amplitude and cumulative loss
                                                Duration
                                              (in quarters)
  8
           Recessions

  6        Recoveries

                                                              **
  4

  2

  0
                Full Sample                 Advanced Economies            Emerging Market Economies

                                               Amplitude
                                               (in percent)
 8
           Recessions
 6
           Recoveries
 4                                                            ***
 2
 0
-2
-4                                               ***
-6
                Full Sample                 Advanced Economies            Emerging Market Economies

                                 Cumulative loss from recessions
                                               (in percent)
     0

 -2

 -4
                                                        ***
 -6

 -8

-10
                 Full Sample                 Advanced Economies            Emerging Market Economies
Source: Claessens et al (2012a).
Note: All the statistics except for those relating to duration correspond to sample medians. For duration, the
means are shown. The duration of a recession is the number of quarters that have elapsed between the peak
and the trough. The duration of a recovery is the time taken to attain the level of output reached at the
previous peak. The amplitude of a recession is the decline in output from peak to trough. The amplitude of a
recovery is the one-year change in output after the trough. The cumulative loss combines information about
the duration and the amplitude to measure the overall cost of a recession. ***, ** and * imply significance at
the 1%, 5% and 10% levels, respectively. Significance refers to the difference between advanced economies and
EMEs.




                                                       71
                         Figure 12. Synchronisation of recessions
                                       (in percent)
   100


     80


     60


     40


     20


      0
          1960   1965    1970     1975     1980     1985    1990     1995     2000     2005     2010
Source: Claessens et al (2012a).
Note: The share of countries experiencing a recession is presented. The figure includes completed as well as
ongoing episodes. The sample contains quarterly data for 21 advanced economies over the period Q2 1960 to
Q4 2010.




                                                    72
                                   Figure 13. Financial downturns:
                                    duration, amplitude and slope
                                                Duration
                                             (in quarters)
  21
              Downturns
  18                                                        ***
              Crunches/Busts
  15
  12                         ***                                                            ***
   9
   6
   3
   0
                    Credit                        House Price                      Equity Price

                                               Amplitude
                                              (in percent)
  0
-10
-20
-30
                             ***                            ***
-40
-50                  Downturns
-60                  Crunches/Busts
                                                                                             ***
-70
                    Credit                        House Price                      Equity Price

                                                  Slope
                                              (in percent)
   0

 -2
                                                            ***
 -4                          ***

 -6
                                                                                              ***
                     Downturns
 -8
                     Crunches/Busts
-10
                    Credit                        House Price                       Equity Price
Source: Claessens et al (2012a).
Note: the amplitude and slope statistics correspond to sample medians. For duration, the means are shown.
Duration is the number of quarters between peak and trough. Amplitude is based on the decline in each
variable during the downturn. Slope is the amplitude divided by the duration. Busts (crunches) are the worst
25% of downturns calculated by the amplitude. ***, ** and * imply significance at the 1%, 5% and 10% levels,
respectively. Significance refers to the difference between busts (crunches) and other financial downturns.




                                                    73
                                  Figure 14. Financial upturns:
                                  duration, amplitude and slope
                                                   Duration
                                                (in quarters)
21
18
                    Upturns       Booms
15
12
 9                           *                                                                    ***

 6
 3
 0
                    Credit                          House Price                          Equity Price

                                                  Amplitude
                                                 (in percent)
70
                                                                                                   ***
60
                    Upturns       Booms
50
40
30
20                         ***
                                                               ***
10
  0
                    Credit                           House Price                         Equity Price

                                                     Slope
                                                 (in percent)
12
                                                                                                   ***
10
                   Upturns       Booms
 8
 6
 4                         ***
                                                                ***
 2
 0
                    Credit                           House Price                         Equity Price
Source: Claessens et al (2012a).
Note: The amplitude and slope correspond to sample medians. For duration, the means are shown. Duration
is the time it takes to attain the level of the previous peak. Amplitude is the change in one year after the
trough of each variable. Slope is the amplitude from the trough to the period where the financial variable
reaches its last peak, divided by duration. Booms are the top 25% of upturns calculated by the amplitude.
***, ** and * imply significance at the 1%, 5%, and 10% levels, respectively. Significance refers to the difference
between financial booms and other financial upturns.




                                                       74
                     Figure 15. Recessions with financial disruptions:
                        duration, amplitude and cumulative loss
                                                Duration
                                              (in quarters)
 6
                 Recessions associated with                 Recessions associated without
 5                                                ***
                                                                                    **
 4
 3
 2
 1
 0
             Credit Crunches                  House Price Busts                 Equity Price Busts

                                               Amplitude
                                               (in percent)
 0

-1

-2

-3                                                **

-4               *                                                               ***
                      Recessions associated with                 Recessions associated without
-5
             Credit Crunches                  House Price Busts                  Equity Price Busts

                                            Cumulative loss
                                               (in percent)
     0

 -2

 -4

 -6

 -8                                          ***                                     ***
                  **
                  Recessions associated with                Recessions associated without
-10
              Credit Crunches                  House Price Busts                  Equity Price Busts
Source: Claessens et al (2012a).
Note: The amplitude and cumulative loss statistics correspond to sample medians. Duration corresponds to
the sample mean. Disruptions are the worst 25% of downturns as represented by the amplitude. ***, ** and
* imply significance at the 1%, 5% and 10% levels, respectively. Significance refers to the difference between
recessions with and without financial disruptions. For other definitions, see the notes to Figures 13 and 14.




                                                       75
                       Figure 16. Recoveries with financial booms:
                             duration, amplitude and slope
                                                 Duration
                                               (in quarters)
6
                      Recoveries associated with                Recoveries associated without
5
4
3
                                                   ***
2
1
0
              Credit Booms                    House Price Booms                  Equity Price Booms

                                                Amplitude
                                               (in percent)
10
                ***                                                 Recoveries associated with
 8
                                                                    Recoveries associated without
                                                   ***
 6

 4

 2

 0
               Credit Booms                    House Price Booms                  Equity Price Booms

                                                    Slope
                                               (in percent)
3
                                                                    Recoveries associated with
                ***                                                 Recoveries associated without
2
                                                   ***
1


0
              Credit Booms                    House Price Booms                  Equity Price Booms
Source: Claessens et al (2012a).
Note: The amplitude and slope correspond to the sample medians. Duration corresponds to the sample means.
Booms are the highest 25% of upturns by amplitude. ***, ** and * imply significance at the 1%, 5% and 10%
levels, respectively. Significance refers to the difference between recoveries with and without booms. For other
definitions, see the notes to Figures 13 and 14.




                                                         76
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