HISTORICAL EVIDENCE ON LINKS BETWEEN
PRIVATE CREDIT AND
FINANCIAL CRISES
MONETARY POLICY, FINANCIAL MARKETS AND
THE EUROPEAN CRISIS
- SEMINAR PAPER -
Germany, 2015
Written by
Christian Juncher Lunde
Humboldt Universität zu Berlin
Matriculation Number: 426109
Supervisor: Dr. Marcel Fratzscher
1
Contents
1 Introduction
2
2 Literature on credit and it’s role in the economy
3
2.1
Historical evidence on the role of credit . . . . . . . . . . . . . . . . . . . .
3 Discussion of relevant literature
5
7
3.1
New era in finance capitalism, so what? . . . . . . . . . . . . . . . . . . . .
7
3.2
Better policy responses but same outcome, how is that? . . . . . . . . . . .
8
3.3
Are financial crises really credit booms gone bust? . . . . . . . . . . . . . .
9
4 How should policy makers react to the evidence?
10
5 Research ideas
12
5.1
Idea 1: clarifying the role of credit by adding asset prices in the puzzle . . 13
5.2
Idea 2: Income heterogeneity and risk creation . . . . . . . . . . . . . . . . 14
6 Conclusion
15
7 Appendix
17
8 References
19
1
1
Introduction
During and after the global financial crisis, there has been a renewed interest in the role of
credit in the macroeconomy and a desire to question old ways of viewing things. Through
time, there have been a lot of different ideas of how credit1 might affect the economy2 .
Due to the different ways of viewing at credit and it’s possible links to the real economy,
one could find it hard to characterize the role of credit. In this paper i therefore turn to the
historical evidence and look at the linkages between credit and financial crises. Clarifying
these dynamics relevant for e.g. the great recession in 2007 might help us prevent a future
financial crisis of the same magnitude.
There exists different strands of literature studying the links between financial indicators
and the real economy. As this paper focuses on credit in the form of private debt, focus is
given to historical work by Moritz Schularick and Alan M. Taylor (2012). From here we
focus on mainly 3 results; (i) credit aggregates have decoupled from money aggregates,
creating two eras of finance capitalism, (ii) after WW2 monetary responses to financial
crises have been more aggresive, but output costs remains high (iii) and finally that credit
growth have significant predictability with regards to crises, strengthening the argument
that financial crises are credit booms gone bust.
Back when credit and money aggregates was two sides of the same coin, monetary policy
had a greater influence on liabilities. However, ever since WW2 there has globally been
a trend of deregulation and financial innovations like wholesale funding, all which contributed to a relative decline of safe and liquid assets on banks balance sheets. Hereby
creating fragility in the financial sector. Moreover, the better policy responses to crises
haven’t meant reduced real costs during crisis. This is to a large extent due to the increase
1
When this paper uses the term ’credit’, what is meant is private debt in the form of aggregated bank
loans.
2
For example we have the money view, which is associated with works by Milton Friedman and Anna
J. Schwartz, where the banks role in credit creation from bank loans are not important. Moreover we
have the irrelevance view, based on ideas of Franco Modigliani and Merton Miller, who argued that the
financial structure do not affect real economic decisions. Finally we also have the credit view, based on
works by Frederic S. Mishkin, S. Bernanke and more, where bank credit plays a larger role then just the
level of bank money. By this saying that the asset side, leverage and composition of the banks balance
sheet may have macroeconomic implications.
2
in the counterfactual (larger financial sector). In this discussion is also the problem of
reversed causality, where the aggressive monetary policy during crises can contribute to
increased risk-taking. Finally the correlation between crises and credit seems to lack a
clear causality story. Thus creating risk of omitted variables in the robustness test of the
baseline model.
Policy relevance is discussed for the historical evidence. With regards to the policy makers
loss of control with liabilities, other measurement to control the non-monetary liabilities
are recommended (capital requirements). When looking at policy responses after crises
then strong monetary responses are still recommended, also having the risk of risk of
reversed causality in mind. It is also discussed how policy makers can use the credit
linkages in preemptive monetary policy, where not only the size of credit but also the
type of credit needs to be taken into account.
Finally I raise two ideas for further research. From different strands in the literature,
a link between assets prices, credit and crises is suggested. Therefore it seems vital to
bring asset prices into the puzzle3 . Furthermore, it is important not to only rely on macro
statistics on credit, but also examine the heterogenity of privat credit. Therefore research
on the impact of heterogenity in debt/income ratios is suggested.
2
Literature on credit and it’s role in the economy
The role of credit in the macroeconomy has been target to a lot of debate through the
years. Some would say, that the role of money and credit is of no real significance to the
real economy (Friedman, Schwartz, Modgliani, Miller and more), while others believes
that these sizes is indeed able to affect the real economy (Minsky, Kindleberger, Schumpeter and more). I therefore want to look into what the facts tells us, which is where the
historical evidence comes into the picture.
With respect to comparing financial data to the real economy, important work has been
done by Reinhart and Reinhart (2008), where they, among other things, look at whether
capital inflow bonanzas are associated with higher likelihood of economic crises (in both
3
The authors due run the baseline model including equity prices, but other measures of asset prices,
like real estate, is desired.
3
developed and emerging markets).
Despite the critique of the article4 , also Reinhart and Rogoff (2010) gives us important
tools on how to investigate links between the financial factors and the real economy.
Even though both working papers gives important methodology on how financial data
affects the real economy, the first article has a focus on international capital flows and the
latter on public debt5 . Therefore, another article, while same in spirit, has a more appropriate focus for the questions regarding private debt (credit), with issues i wish to clarify.
The paper ’Credit Booms Gone Bust: Monetary Policy, Leverage Cycles and Financial
Crises, 1870-2008’ by Moritz Schularick and Alan M. Taylor (2012) has a larger focus on
the role of credit in the real economy (including stylized facts for credit aggregates, policy
responses to crises and credits involvement in financial crises).
The article uses a historical dataset for 14 countries over 140 years and contains indicators
for the macroeconomy, a dummy for financial crises6 and, as a new contribution to the
literature, also data regarding aggregated bank loans and total bank assets. The data only
concerns developed countries and obviously the focus is on the long run. Other articles
pay more attention to emerging markets, where the story often is that financial crises
are linked to currency instability or sovereign debt problems (Reinhart and Rogoff 2008).
Only looking at developed countries also mean that data is less affected by the credibility
questions and institutional weaknesses that emerging markets often face.
4
The article ’Growth in a Time of Debt’ has been target for a lot of critique, not only for an initially
spreadsheet error creating misleading results, but also in more general terms by Paul Krugman who
argues against some magic public debt threshold at around 90 percent, where public debt is argued not
to do any good for the growth in the economy. Hence, the debate is especially about the effectiveness of
austerity in fiscal policy for debt-burdened economies.
5
When looking for policy-relevance regarding the historic developments of credit, it is important to
distinguish private debt from public debt. In the following, credit will refer to private debt, since the
credit aggregates in focus are bank loans and total bank assets. Thus, conclusions made by Reinhart and
Rogoff concerning a negative correlation between economic growth and public debt is not that relevant
for this papers discussion.
6
Its important to distinguish financial crises from economic crises, where the definition of financial
crises is made according to Reinhart and Rogoff (2009) and involves 79 systematic country-specific crises
over the 140 year period. Several things influences whether a crisis is defined as financial or not, some of
them regards bank runs, bailout packages and bankruptcy.
4
2.1
Historical evidence on the role of credit
Many important and relevant results can be found from the article by Taylor and Schularick (2009). I will in the following clarify the results most relevant for this paper.
Looking at the average global trends over time for credit and money aggregates, one
striking result is that money and credit aggregates tend to behave differently over time,
cf. figure 17 . More precise one could talk of ’two eras of finance capitalism’. Where in the
first era, credit aggregates (bank loans and bank assets8 ) pre-WW2 has a roughly stable
relationship to money (defending Friedmans focus on money, since money and credit
tended to be two sides of the same coin), but post-WW2, and especially after 1970, this
relationship no longer exists. Instead we see a decoupling of money and credit aggregates.
This story can also be seen from figure 1X in appendix.
Figure 1: Decoupling of Money and Credit Aggregates (Year effects)
Source: Schularick & Taylor (2012)
Knowing the general development of credit since 1870, the next step is to know what
happens with real variables, credit and money aggregates during the time of crises. Focusing on the two eras of finance capitalism it is found, that there are different dynamics
7
Wanting to look at global trends, we use the country fixed effect regression xit = ai + bt + eit ,
wherefrom we plot the estimated year effects bt . In other words, we show the mean from the predicted
time effects from fixed country effect regressions for the dependent variable). Doing this averaging will
mask cyclical variation and give us the desired trend.
8
Bank loans is by authors defined as domestic currency lending by domestic banks to domestic households and non-financial corporations(excluding lending within the financial system. While bank assets is
sum of all balance sheet assets of banks with national residency.
5
during crises before and after WW2, as seen in figure 2. Pre-WW2 we see a large fall in
bank loans (leverage) compared to normal periods. But post-WW2 we see a much more
supported money growth from central banks, which again stops the lending from freezing.
Moreover, the lack of support in money growth pre-WW2 often gave deflation in several
periods, whereas post-WW2 the inflation was kept stable. Looking at history, it seems
like policy makers have learned from the great depression and has been reacting more
aggressive to crises after WW2.
However, when looking at the real side of the economy, the financial crises hurts output
and investment approximately as much in both eras, as seen from figure 2X in appendix.
Thus, the fact that policy makers has prevented fall in leverage post-WW2, didn’t mean
reduced output costs.
Figure 2: Aggregates (Post Crisis Periods Relative to Normal)
Source: Schularick & Taylor (2012)
Being aware of the historic rise of credit and the still-present output costs during crises, the
authors look at whether or not credit could in itself be a source to financial instability.
This is done by testing whether or not lagged credit aggregates can predict financial
crises. A basic forecasting framework is used, where baseline for testing is the following
logit probabilistic model9
logit(pit ) = b0i + b1 (L) DlogCREDITit + b2 (L) Xit + eit
9
(1)
With similar coefficient results, the logit is here preferred to the OLS linear probability mainly because
of the probability being bounded between 0 and 1 in logit, which is not the case for the OLS.
6
Where L is the lag operator (Schularick and Taylor generally uses 5 lags), DlogCREDIT
is the difference in the bank loans used as credit aggregate and X is a vector with additional variables.
This relatively simple forecasting turns out to have very significant coefficients with regards to past credit growth forecasting financial crises, as seen from table 1X in appendix.
In the baseline model X is omitted, but if replaced by various real and nominal variables,
the predictability from credit is still very significant. Also, as expected from figure 1,
bank loans and broad money doesn’t have same level of predictability. Thus, credit outperforms broad money10 in forecasting, especially when looking at data post-WW2. That
lagged credit growth is indeed significant when forecasting can also be seen from table 1X
in appendix. Moreover, there seems to be some ’size-effect’, meaning that
Loans
GP D
seems to
have some predictive power. This means, ceteris paribus11 , that countries with growth in
the magnitude of the financial sector is more likely to witness domestic financial crises.
3
Discussion of relevant literature
To summarize, we have clarified that (i) credit aggregates have decoupled from money
aggregates, creating two eras of finance capitalism, (ii) after WW2 there has been more
aggressive monetary responses, but still high output costs following a crisis and (iii) credit
growth have significant predictability with regards to crises, strengthening the argument
that financial crises are credit booms gone bust.
3.1
New era in finance capitalism, so what?
When looking at figure 1, it is easy to see the different trends pre- and post-WW2.
Questions that then comes to mind is; why and how did this decoupling of money and
credit happen? Here one must remember what is included in the broad money base (M2),
which in general is currency in circulation, overnight deposits, deposits with short agreed
maturity (<2 years) and deposits redeemable at a short period (<3 months). This means,
that when bank loans decouples from broad money, credit has to come from somewhere
10
Broad money is the monetary aggregate M2 or M3 (differs between countries), while narrow money
is M0 or M1.
11
The ceteris paribus is actually fullfilled in the baseline model, due to the fact that it also includes
the country specific fixed effect (even though not significant).
7
else. This is indeed what we see from data, namely that banks has been increasing their
leverage. This is done by wholesale funding, debt securities etc. In other words banks
has been going from insured deposits to uninsured lending (Ratnovski and Huang, 2010).
This was not possible in during the Bretton Woods system just after WW2 because of
very strict regulation. However, after the end of the Bretton Woods system there has
globally been a tendency for deregulation (Jordá et al 2014). This together with financial
innovation has made it possible for banks to leverage on a high scale, which is what we see
in figure 1 from 1970 and onwards. Summing up, banks have had a relative decline of safe
and liquid assets on their balance sheets. Moreover, credit creation and financial stability
in the financial sector has become more dependent on the private financial markets and
less dependent on the monetary liabilities controlled by central banks. This is also some
of the dynamics discussed, when talking about credit as a propagator of shocks.
3.2
Better policy responses but same outcome, how is that?
When looking at figure 2, it is easy to see the difference in the monetary responses preand post-WW2. In the early period, there wasn’t much expansionary monetary policy
by the central banks, which made way for large decreases in broad money and lending.
This was not the case for the second era, where monetary policy was more aggressive,
which stabilized lending and inflation in time of crises. This development has of course
been influenced by the global trend of converting from gold to fiat money and having a
central bank as a lender-of-last-resort. Knowing this, the development in figure 2 is really
not that surprising. Especially when thinking about the fact, that the people in favour
of fiat money often argues that such a system is needed to protect the financial sector
against the peril of financial crises (Selgin, 1989). So policy makers learned from the
great depression and a system was created wherein central banks have more possibility
to counteract financial crises. The huge increase in bank assets over GDP in figure 1, is
expressing how the financial sector has become a more and more significant part of the
economy. Which presumably is the main reason why the real output costs have remained
large. In other words, the counterfactual have changed quite dramatically, as the financial
sector today is a more vital part of the economy.
One dilemma regarding the aggressive policy responses, is the potential reverse causality:
that the larger implicit policy intervention during crises and the expectants of rescue op8
erations have contributed to the large increase in leverage in the banking sector. Critiques
of the aggressive policy might argue, that policy makers have been bailing out finance,
while failing to protect the real economy. Some of this criticism seems legit, as systematically big banks most likely take into account, that there is a large probability of rescue
operations, monetary easing and the like during severe financial crises. Because not only
do we see more aggressive monetary policy after WW2, but historical evidence also shows
that financial policy have become considerably more aggressive in the times of financial
crises (Jordá et al, 2012).
If the argument, that more aggressive policy-responses contributes to the build up of
credit, is accepted, then the role of monetary and fiscal policy becomes much more complicated. On the one hand, the policy makers have the ability to dampen crises in the
short run, but doing so, they might build up risk of financial crises in the future. The
latter coming from aggressive monetary policy motivating increased lending, moral hazard
and risk-taking, which then influences the risk of future financial crises (if the hypothesis
regarding credit booms gone bust are true).
3.3
Are financial crises really credit booms gone bust?
When looking at table 1X in appendix, it becomes clear that in the baseline model of
equation (1), lagged credit growth truly has significant predictive power. In similar papers
analysing the effect of public debt on growth, the question of reversed causality is often
present. When for example looking at correlation between public debt and growth, one
could imagine the public expenditures to rise in times of economic distress, creating some
level of reversed causality. At first, there does not seem to be a causality problem here,
since the current probability of crises is forecasted by lagged credit aggregates. This fact
together with the significant results does indeed tempt one to agree with the hypothesis
that financial crises are credit booms gone bust.
However, the authors does not give a clear theoretical foundation for their findings12 .
Without having a clear-cut explanation on how intense credit growth indeed raises probability of financial crises, one cannot be certain that the regression does not ommit crucial
12
They do relate their empirical work to the hypothesis of crises being credit booms gone wrong. A
hypothesis influenced by the works of Minsky, Kindleberger and others. However, a clear-cut modelframework with all relevant variables, that fits to data, is still to be made.
9
variables. In other words, one cannot reject the idea of having false inferences of causality
because of a third omitted variable that influences both risk and credit. If this was the
case, it could thin out the significance of credit13 . Having in mind the robustness tests
in the article, one could still question whether or not the true causality dynamics have
been found14 . To use the authors own words, then "they are able to tell if a storm is
coming, but they don’t know why it is brewing"(INET, 2012). Hence, studying which factors that triggers the increase in credit should be investigated further. Here one suspect
could be asset prices (like housing prices), which also is a central part of theory regarding
boom-and-bust-cycles (Minsky, 1986)15 . Moreover, a variable that could be interesting to
look at is the heterogeneity in private debt over yearly income, cf. discussion in the next
section.
The evidence, that credit is a vital part of the dynamics during a financial crisis, was also
manifested by Jordá et al. in the article ’credit bites back’ (2012). In this article the
authors find, that crises preceded by large growth in credit, has a clear tendency of giving
more severe recessions when comparing to normal crises. Which tells, that credit not only
matters in creation of crisis, but also the depth of a crisis. Hence, there is evidence that
’credit bites back’. One could ask, if credit affecting crises is a demand or supply story.
Again this is not clear, but one could imagine that in leveraged recessions the interest
rate tends to fall sharper (INET, 2012), which would speak of a demand of credit driven
explanation. But this question still remains relatively open.
4
How should policy makers react to the evidence?
The different historical evidence motivates different policy discussions. In this section i
connect the preceded discussions with liability policy, monetary policy after a crisis and
preemptive monetary policy.
13
This danger is of course always present when making regressions and the like, but not having a clear
theoretical foundation makes it easier to miss variables crucial for the understanding of the true causality.
14
If other variables should turn out being relevant, this would mean that the vector with additional
variables, X, should not be put equal to 0.
15
Schularick and Taylor is limited by data, and therefore only make robustness test against equity
prices, which doesn’t add much to the predictability of the model.
10
Liability policy
Monetary policy can, to some extent, control the money aggregates in an economy. The
decoupling of money and credit therefore has the consequence, that the central banks
has a smaller influence on the liabilities in the economy. Moreover, that banks have
been putting more weight on wholesale funding (uninsured), means their assets generally
becomes less safe and liquid. If private debt really can predict and strengthen shocks, as
empirics tells us, there could be good reasons to make debt less attractive. Because of
the loss of control with liabilities, some liability policy could be a good idea. Here it is
relevant to mention capital requirements and capital ratios, which might be able to reduce
risk of future financial crises and dampen the financial instability16 . Evidence has also
been found, that banks who relies much on wholesale funding do indeed decrease their
supply of credit more than average banks, in the years after a financial crisis (IMF, 2012).
Monetary activity during crisis
A more aggressive monetary and fiscal policy after WW2 might have been a factor for
the global trend of increasing leverage in the banking sector. Hence, some tradeoff of
financial stability in the short and long run could be present when making monetary
policy. How strong the link between aggressive policy and risks of future crises remains an
open question. The link from; aggressive policy responses → credit and risk motivation
→ increased risk of crises, could be very low. Having the current evidence in mind,
weakening the monetary policy responses because of feared reversed causality should not
be recommended. So still more aggressive policy responses, compared to the restrictive
policy during the great depression, seems recommendable.
Preemptive monetary policy
As stated earlier, there seems to be historical evidence suggesting that credit does have
an important role to play in central bank policy17 . But the literature is far from anything
close to a Taylor rule giving mechanical suggestions on how to react on changes in credit.
16
This should only be seen as part of the solution, as data shows that even in periods with high capital
ratios, financial crises have been present. For example in the time around the great depression where
capital ratio levels around 20-30 wasn’t unusual, i.e. a level along the line to what is proposed today.
17
Here assuming that the central bank cares for financial stability and are not blinded on e.g. inflation
targeting.
11
Looking at the historical evidence, it contrary seems recommendable that decisions should
pay close attention to the development of credit and the type of credit, as emphasized by
the following.
Without giving any definitive answers, one could recommend policy makers to have the
following logic, when monitoring credit. Lets consider a case, where credit has been
increasing over some time. When central banks observes this, they should be motivated
to dampen the activity on the lending market, ceteris paribus, and thereby decrease the
risk of a future financial crisis. This could of course be done by increasing the pressure
on interest rates18 . Here one must remember, that non-monetary liabilities has become a
significant part of the lending system. So even though the central banks can still affect
credit19 , their effectiveness has decreased compared to pre-WW2. Hence, making debt less
attractive with capital requirements needs to happen as well, before seeing an effective
dampening of the aggregated lending market.
One should always be careful before dampening lending due to the possible productive
investments one might be dampening as well. Here developments in lending could e.g.
be influenced by technical developments creating productive and profitable investments.
Hence, one should not look to one-sided at lending. Policy makers can however defend
dampening the lending market if the credit trend is due to non-productive factors. This
could for example be excessive risk taking from the systematically important banks or
over-investment from corporations being over-optimistic about the future. Again, policy
makers being wary and not just giving into mechanical responses is of great importance.
This being especially important, as many of the models used by central banks today do
not include financial market indicators of any kind (INET, 2010).
5
Research ideas
In the aftermath of the previous discussion, many questions are left unanswered and
open for further research. Here is a few questions one could wish to clarify; level of
18
As discussed earlier, a possible omitted variable is some measure of asset prices (like housing prices).
But even if the significant predictability of credit comes from it’s relationship with asset prices, then
development in housing prices would be dampen as well, as the cost of loans would increase.
19
Affecting the credit has e.g. been done through open market operations like LTRO and TLRTO
made by the European Central Bank.
12
reversed causality from aggressive policy responses (financial instability from systemically
risk)20 , how to practically implement credit in monetary decisions, credit-growth-inflation
linkages, and finally measuring the costs of crises more accurately (net effect from boom
and bust).
All relevant problems worth researching. However, i have choosed to look further at
two other research ideas. The first regards the validity of the ’credit booms gone bust’
conclusion, the dynamics behind this result and possible omitted variables. The second
idea looks at the possibility of using a quite different factor, namely an average ratio for
private debt over private income, as a predictor for financial crises.
5.1
Idea 1: clarifying the role of credit by adding asset prices
in the puzzle
Motivation
A question raised in the discussion, was the possibility of omitted variables in our baseline model. The evidence shows strong correlation between credit and the risk of future
financial crisis. But clear-cut theory is yet to be done, which increases the risk of missing
crucial variables in the regression. A relevant variable to look at in this regard, is different
measures of asset prices21 . This relates to theory regarding asset bubbles and how literature describes mechanisms that makes asset prices swing away from benchmark levels
(Frydman and Goldberg, 2013). As an example of asset bubbles, it’s obvious to mention
the recent bubble in the American housing sector. It is however debatable, whether an
increase in fundamental housing prices might have caused increased lending, or increased
possibilities of lending caused an bubble on the housing market. The latter causality is
probably the right one for the recent subprime crisis we have seen in USA, namely that
leverage acceleration gives acceleration in asset prices. However uncertainty remains,
which is why research of credits true predictive power should be investigated further.
20
Related to this is the evidence of some correlation between the size of the financial sector and the
risk of a financial crisis. Clarifying the evidence and causality could lead to the conclusion that bank
taxes is needed. Such bank taxes could finance future bank packages during crises and being modelled
only to tax the most risky liabilities on the banks asset sheets.
21
Another important part of the history regarding financial instability and risk of crisis could be some
measure of politically institutions (Kumhof, 2010).
13
Not only would asset prices be interesting to bring into the picture, but also data going
beyond credit aggregates. By this meaning data splitting lending up into different subcategories. In the perfect world one could desire lending being put in groups of consumption
lending, mortgage lending and lending for more productive purposes. It seems fairly plausible, that the different types of lending gives different implications with regards to risk
of a future crisis. Such data not being within our reach, I will in the next section focus
on including asset prices in the puzzle.
The research in practice and possible responses on the results
One possible approach Collect data for the 14 countries for different measures of asset
prices, e.g. housing prices. With this data run regression (1), using the lagged development
in asset prices data as additional explaining variables in the equation. The interaction
of asset prices and credit should also be investigated22 . Should credit still be the most
vital part in predicting future financial crisis, this would strengthen the weight policy
makers should put on the preemptive monetary policy as described in the previous section,
wherein observation of credit and types of credit is important for decisions regarding
monetary policy.
5.2
Idea 2: Income heterogeneity and risk creation
Motivation
Schularick and Taylor have managed to create an impressive dataset, which they mainly
uses to investigate the role of credit in times of crises. There are however many other
strands of literature, who looks at other factors creating and propagating financial crises.
Merging the dataset with other relevant factors therefore seems obvious for further research. A target for this research could be the hypothesis of inequality being a source for
financial instability. Kumhof and Rancière (2010) have already found empirical evidence
that inequality did rise before the great depression in 1929 and the great recession in
200723 . Together with the rising inequality, they also found that the debt/income ratio
22
This could technically be done by adding the term ∆log(loans/P ) x asset prices/P , for example
when looking at a 5-year moving average.
23
Also Rajan, Krugman and Reich have been writing about the link between household indebtedness,
income equality and financial crises.
14
preceding the financial crisis was rising for persons in the bottom 95% wealth distribution,
while decreasing for the top 5%. This can be seen in figure 3X in appendix. Moreover,
they create a DSGE model able to formally argument for their findings24 . However, collecting data and creating a factor which could express the developments in debt/income
for low-income groups is yet to be done.
The research in practice and possible responses on the results
One possible approach: Collecting data regarding debt/income ratios for different groups
in society (e.g. real income deciles), with the purpose of seeing if developments in debt/income
heterogeneity influences risk of financial instability. With this data and the forecasting framework described by equation (1), one could take 5-years lagged development in
debt/income ratios as additional explaining variables.
Result from this regression could affect the preemptive monetary policy25 and not least
the arguments for redistribution. Hence, redistribution policies preventing high indebtedness in low-income households could reduce financial instability. Such a redistribution
could also be desirable compared to post-crisis bailouts and debt restructuring (Kumhof
and Rancière, 2010).
6
Conclusion
Over the last 140 years a lot have happened with credit and its place in the economy. Not
only has private credit decoupled from the broad money base, it has increased in a large
scale, making the financial sector a much more influential part of the economy. The partly
changing nature of credit, together with the historic lack of financial factors in monetary
models makes a foundation for discussion. Having in mind the significant predictability
that credit posses, it seems necessary to take abnormal volatility in this factor serious
when making preemptive monetary policy.
24
The dynamics is, that increasing inequality motivated low-income earners to increase their
debt/income ratio, as to limit their loss of consumption. With low-income taking loans from high-income,
the financial sector increased in size together with financial fragility.
25
An interaction test, as mentioned in research idea 1, could arguably also be relevant. This from
the hypothesis that inequality makes the policy makers encourage easy credit to keep demand and job
creation robust despite stagnating incomes (Rajan, 2010).
15
With which tools and how much the central banks should react still seems open for
debate. This paper however tries to elucidate some of the important problems policy
makers face when making this choice. With regards to the results from Schularick and
Taylors research, then further studying is recommended. This could first of all be done by
clarifying the story of why crisis should be credit booms gone bust. A story where asset
prices might have an important role to play. Furthermore should advantage be taken of
the data available. This could be done by investigating not just the role of aggregated
credit, but also the role of heterogeneity in private debt.
16
7
Appendix
Figure 1X: Relative to Broad Money (Year effects)
Source: Schularick & Taylor (2012)
Figure 2X: Real Variables (Post Crisis Periods Relative to Normal)
Source: Schularick & Taylor (2012)
17
Table 1X: Baseline Model and Alternative Measures of Money and Credit
Source: Schularick & Taylor (2012)
Figure 3X: Debt to Income Ratios
Source: Kumhof & Rancière (2010)
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