Some thoughts on the role of private debt in determining the impact of a financial crisis


This article considers the role of private debt in determining the impact of a financial crisis. Data from OECD countries over the period 1981-2016 appears to show that high public debt levels aggravated crises. Recessions were deeper, and recoveries were weaker, when they were preceded by larger surges in household debt. Analogous observations are made for booms and busts in the housing market.


It is well documented that the Global Financial Crisis came after a large upward swing in the leverage cycle [1]. Although there is no generally accepted notion of how much debt is too much, the existing state of affairs warrants attention. Current global debt, at 225 percent of world GDP by recent IMF estimates [2], is at record levels and continues to rise (see Fig. 1).

The global crisis set in motion debt deflation dynamics a la Fisher-Minsky (1933;1982), which had the effect of rapidly inflating public debt-to-GDP. Following the recession, the initial response of many governments in Europe and elsewhere was to allow their debts to increase. This was achieved through two channels. The first channel consisted of the automatic stabilizers triggered by the recession-induced fall in government revenues. The second channel consisted of governments taking over private, mostly banking, debt that went or was about to go sour (De Grauwe, 2013).

Following such radical departures from past norms, a considerable amount of empirical work has emerged, with a number of researchers focusing their attention on the economic interplay between the macroeconomy and private debt.

The rest of this article is organised as follows: section two provides a review of the current research frontier, section three maps the evolution of private credit in the UK and other developed countries, section four reports the results of statistical estimates and provides robustness checks, section five reviews the most recent recent theoretical approaches, and section six concludes.

Current research frontier

Recent research, largely focusing on advanced economies, has identified meaningful connections between financial crises and rapid credit growth (Taylor, 2012), the critical role of expansion in global credit in general (Dell’Ariccia et al, 2012), and the role of expansion of credit to households in particular (Jorda et al, 2014).

Jorda et al (2011) develop the work of previous researchers [3] and show that credit growth acts as significant predictor of financial crises. Further studies (Schularick and Taylor, 2012; Jorda et al, 2013) show that, conditional on there being a recession, stronger credit growth tends to predict deeper recessions. The main findings of Jorda et al (2011) and Jorda et al (2014), namely that credit growth emerges as a significant predictor of financial instability, and that external imbalances have played an additional role, but more so in the pre-WWII era of low financialization than today, are illustrated in Figure 2 and Table 1.

Building on the work of Jorda et al (2011 and 2014), Mian et al (2016) identify negative dynamic correlations between changes in global household debt and global growth. They find that a rise in household sector debt by more than 1 standard deviation above trend is associated with downward pressure on GDP growth in the following three years.

Mian et al (2016) further argue that dramatic increases in private debt increase in likelihood of incidence in financial crises. They also demonstrate that increases in public debt have no effect [4] on the probability a financial crisis. The intuitive explanation they offer by way of support for this line of research is as follows. The distribution of debt matters because people in the lower half of the distribution have a much higher propensity to spend. For a given shock to their wealth, they pull back much more on their spending. Therefore, someone who has a significant level of debt can see their entire equity position wiped out should the house prices crash. This, as Mian et al (2016) argue, leads to a large drop of household spending in the aggregate. It sets off a cycle of higher unemployment, delinquent mortgages, and other deleterious effects that are problematic for the macroeconomy. This claim will be examined in further detail later in this article.

The evolution of private credit in the UK and other developed countries

Schularick and Taylor (2012)

Taking inspiration from Schularick and Taylor (2012), the next chapter outlines in general terms the evolution of credit in the UK and developed countries from a historical viewpoint. Let us start by observing that the share of total bank lending to the private non-financial sector in the British economy was around 16 per cent of GDP in 1880.

Examination of the data for the next century, excluding two world wars, reveals that this share increased gradually, rising to 63 per cent. This figure, which includes credit provided by non-bank institutions, plateaued at 63 per cent over the next three decades, remaining at 63 until 1980. What followed over next two decades was the liberalisation and globalisation of the financial sector. During this period, credit grew faster than the economy, and the stock of credit marked rapid growth, rising to 120 per cent by the early 1990s. Although in the early 1990s this trend was dampened by a recession, by 2009 it had reached 177 per cent of GDP. Similar upward trends are observed in advanced economies. This is illustrated graphically later in this article.

Between 1997 and 2007, the annualised growth rate of real credit was 7 per cent, more than double the rate of the real GDP growth, which was 3 per cent. Had we witnessed a continuation of this trend, the stock of credit would have been 200 per cent of GDP by 2012, and 300 per cent of GDP by 2023. The financial crisis and the recession that followed resulted in a sharp correction. Credit growth fell in real terms by around 2.5 per cent per annum in the period 2010-14. By 2014 the stock of credit fell from its peak of 177 per cent of GDP to 140 per cent, similar levels witnessed in 2002, where it continues to remain.

Since the majority of lending during the credit growth between the mid-1990s and the financial crisis was to UK households, we can witness that the debt to income ratios UK households follows a similar path to the stock of credit to GDP. In 1964, it was 54 per cent, remaining at this level until 1980. However, by 1990 it had grown to over 100 per cent. It rose again from the late 1990s, growing to 155 per cent by 2007.

It dropped to 135 per cent, its 2004 level, in 2012 and continues to remain at this level. Based on these observations, it is evident that the UK experienced a vigorous financial cycle during the period that preceded the crisis: there was a sustained build-up of credit to the private sector, after which a sharp correction put an end to some, but far from most, of the credit growth that predated this period.

Does private sector debt matter relative to the economy?

These observations are of crucial significance. Credit, as does debt, facilitates home purchases, investment, risk management, and consumption smoothing. In light of these observations, a natural question to ask, therefore, is as follows. If credit is such an essential building block of modern economic development,

what is the point at which credit intensity (i.e. credit to GDP) becomes problematic? Or is it even problematic at all? Indeed, a high level of private sector debt relative to the economy has often been interpreted as a sign of financial development, which in turn is beneficial for long-term growth. A large body of literature (see, for example, Rajan and Zingales, 1998; La Porta et al 2002; Rajan et al 2008; Bannerjee and Duflo, 2014) deals with this question and finds, by virtue of various theoretical arguments and empirical observations, that after an increase in credit intensity beyond certain levels of credit to GDP countries experience lower growth. For example, a number of cross-country empirical studies find that once a certain threshold is reached, larger public debt stifles potential growth, which suggests a non-monotonic and concave (“inverted U”-shape) relationship between public debt and economic growth (Arcand et al, 2012; Cecchetti et al, 2011; Clements et al, 2003, Kumar and Woo 2010; etc). Some have gone even further and calculated the debt-to-GDP turning point in this concave relationship to be approximately between 90 and 100 per cent (Checherita and Rother, 2010; Reinhart and Rogoff 2010a, 2010b).

As the reader no doubt will recall, the econometric analysis of Reinhart and Rogoff (2010a, 2010b) seemed to largely gloss over the empirical potential importance of private debt. On the other hand, Checherita and Rother (2010) did try to attempt to control for other potentially relevant variables, such as the stock of private debt. However, while doings so, they do not find the stock of private debt, defined as total domestic credit to the private sector, to be statistically significant in determining growth in their sample across any of their models. They therefore argued that its inclusion does not modify significantly the results for public debt. As I will argue, the argument advanced by Checherita and Rother (2010) does not hold empirically [5].

Does gross debt restrain economic activity?

Some well-cited and influential studies have asserted that gross debt may restrain economic activity. By way of example, two influential reports by McKinsey (2010, 2012) argue that having too much gross debt matters a great deal. They argue that adjustment, i.e. reducing net indebtedness, is needed. They argue that advanced economies need to “clear the way” for economic growth by reining in the rising levels of gross debt. This claim is worthy of examination in great detail. In particular, we may ask ourselves the following.

If true, what does this imply for economic performance, given that government debt surged in advanced economies as a result of the crisis, and gross debt has reached levels not seen in several decades? Given the weight of available evidence, it is difficult to accept on face value the findings of the McKinsey studies. For example, Fatas (2012) points out that focusing on gross debt is ”misleading”, since what is of consequence is net wealth, rather than gross debt. Let us take the example of Japan, the country with the largest debt to GDP ratio in the McKinsey reports. The Japanese economy is a net creditor to the rest of the world, a fact which reflects decades of current account surpluses. Similarly, the high Japanese stock of gross government debt is related to large private sector assets. Overall, it appears that there is no accepted wisdom regarding which mechanism, or if at all, gross debt restrains economic activity.

There are two other critical points that the McKinsey authors miss. First, is not the gross debt per se that policymakers ought to look at when trying to understand broader risks to the economy. What matters is financial stability. The McKinsey authors fail to ask themselves one key question. What are the economic implications if credit consistently keeps rising faster than GDP, and therefore the debt to income ratio continues its upward trajectory? Surely, it is precisely this pattern that is bound to signal broader economic risks: for the UK, for other advanced economies, and for emerging markets with coterminous economic scenarios.

Second, when McKinsey issue policy advice of reducing net indebtedness, their logic is at odds with the reality of where the broader risks to the economy actually lie. To illustrate this fundamental misunderstanding of modern economic dynamics by the McKinsey authors and their subsequent, arguably spurious, policy prescriptions let us consider two scenarios that roughly bookend the possibilities. Scenario one: debt is concentrated in households with low liquid financial assets and high MPC. Scenario two: debt is evenly distributed across the population. In the first scenario, the economy is more vulnerable than it would be if debt were more evenly distributed across the population. Under any scenario, for every financial debt there exists a corresponding financial asset, owned either by foreigners or domestic residents. For every borrower there exists a lender. What is the net indebtedness if not, by definition, zero? What is important, as I will continue to argue in this paper, is how the assets and liabilities are matched, and how the debt is distributed across the economy.

The 2007 financial crisis was not set off by the accumulation of debt by UK households. But the debt made our economy significantly vulnerable when the shock hit us. So what is the relationship between household debt and economic downturns? In this regard, there is considerable evidence pointing to the fact that household debt – especially when secured on housing assets – was at the heart of many financial crises in the past.

To this extent, Laeven and Valencia (2012) present arguments supporting the view that that the collapse of property prices features prominently the banking crises during the period 1970-2010, which has seen over 100 major banking crisis incidents throughout the global economy. Further, as a perspicuous study by Jorda, Schularick and Taylor (2015) demonstrates, the surge in credit-financed housing debt has emerged as the key contributor to banking crises for a large number of advanced economies since 1870. As their study correctly asserts, “…household debt to asset ratios have risen substantially in many countries.”

Financial stability risks have been increasingly linked to real estate lending booms which are typically followed by deeper recessions and slower recoveries. Housing finance has come to play a central role in the modern macroeconomy (Jorda et al, 2015). Supporting arguments have been advanced by Gourinchas and Obstfeld (2012), and Borio (2014).

Empirical estimation

The main empirical estimation is a cross-section and time-fixed-effects data model. I estimate this model using OECD data for 24 advanced economies for the period 1980-2016.

In this specification, the variable of interest is the level of Y [6]. Bust indicates a housing bust dummy variable that assumes the value 1 at the start of a housing bust. HighDebt is another dummy variable that assumes the value of 1 if the surge in the household ratio of debt-to-income was high in the three years before the bust. It is defined here as “high” if it was above the median for all housing busts across all economies. I include Time and country fixed effects are included in this specification to allow for country-specific trends and global shock. i (i = 1, …, N) denotes the country, t (t = 1, …, T) indicates the period, and u_{i,t} is a zero mean white noise-type error term. λ_{i} is an unknown parameter, µ is some intercept. Standard errors are clustered by economy.

Subsequent robustness checks, and the results of this statistical specification are reported in the section and table below.

Robustness checks

In this section I report several robustness checks carried out in order to evaluate the sensitivity of the empirical results reported earlier.

  1. These results are robust to controlling for other determinants, such as the Great Recession period of 2007 to 2009. Analogous results are observed by truncating the series in 2006.
  2. These empirical observations with regards to the more pronounced drops in economic activity are robust to the omittance of outliers. In this instance, Cook’s distance is used as a measure of outlier detection.
  3. The results are further robust to a different dynamic specification being used. As a robustness check, I have repeated the empirical exercise presented in the paper with four lags used in the baseline specification, instead of two.
  4. The results are also robust to an alternative estimation strategy. The alternative, and more demanding, estimation procedure involves the robust version of the GMM estimator, adapted from that described by Arellano and Bond (1991). This produces standard errors that are asymptotically robust to both heteroskedasticity and serial correlation [7]. Crucially, because of the limitations relating to the available panel sample size, using too many instruments may lead to biased results. To mitigate the potential issue of downward bias of the estimated asymptotic standard errors only recent values up to two lags are used [8].

The interested reader may note that Alfonso and Jalles (2012) tackle a comparable research question to the one presented in this paper, using a similar identification strategy, albeit with a much larger country sample and using a System GMM approach. While some may argue that there may be certain advantages to such an approach, another set of tools may be more appropriate for the job in hand. This is because of the fact that lags of the explanatory variables are used as relevant instruments. The System GMM technique requires additional restrictions. In particular, the deviations from long-run means must not be correlated with the fixed effects. I will argue, with support from evidence in Blundell, Bond, and Windmeijer (2000), that this assumption is much too strong, given that not all the countries are likely to be in the steady state.

Review of recent theoretical approaches

First, it must be noted that from a theoretical perspective, the literature is lagging behind empirical work in this area. There have been relatively few theoretical attempts to examine in great detail the interaction between public and private debt. Batini at al (2016) [9] make one such attempt. They use a DSGE model (calibrated to OECD data) to examine the role of fiscal policy in supporting private deleveraging. It builds on the Kiyotaki-Moore (1997) framework of credit cycles, and demonstrates how minor shocks to the economy are made significant by virtue of being amplified by credit restrictions, generating meaningful volatility in output. This model also incorporates borrowing constraints in the housing market a la Iacoviello (2005). Their model suggests that the high private debt poses more severe constraints on economic growth than what would be posed by public indebtedness. Their framework further suggests that public and private deleveraging forces combine in a deleterious way.

An alternative way to look at potential implications related to government debt reduction is to abstract from structural reasons that induce debt reduction and impose it exogenously. Using this approach, there is some available, albeit highly preliminary work by Romei (2016) and Scheer (2016) that looks to examine possible fiscal instruments for reduction of government debt in models of closed economies with nominal frictions. Both papers introduce monetary policy in the form of Taylor-type rules.

Andres et al (2016) use a model of small open economy to examine exogenous fiscal consolidation by virtue of several fiscal instruments in the context of private deleveraging within a monetary union. The presence of long term mortgages in the Andres et al (2016) model has a important outcomes relating to the way private spending adjusts to exogenous fiscal and other types of shocks. Non-linearities arise because of long-term debt contracts, in part due to multipliers associated to fiscal retrenchments. This establishes a theoretical link between the timing and intensity of fiscal adjustments, the duration of private deleveraging processes and, therefore, of recessions.

Concluding remarks

This blog article considered the role of private debt in determining the impact of a financial crisis. Using evidence from a panel dataset of OECD countries over the period 1981-2016, I argued that high public debt levels aggravated crises. Recessions were deeper, and recoveries were weaker, when they were preceeded by larger surges in household debt. Analogous observations were made for booms and busts in the housing market.

Housing bust episodes that came before more substantive run-ups in gross household debt correspond to busts that are deeper, recoveries that are weaker, and household deleveraging that is more conspicuous. The fall in economic activity is too discernible to be merely a symptom of a decline fall in house prices. Neither is it the result of banking crises alone. Instead, the severity of the contraction appears to be better explained by a coalescence of two processes: the decline in house prices and the pre-bust leverage. These observations are consistent with the predictions of recent theoretical models that attempt to explain how financial shocks might generate deep and prolonged recessions.


[1] See Geanakoplos, 2010; Eggertsson, 2010; Christiano et al, 2011; Woodford, 2011; Geanakoplos, 2012; Geanakoplos et al, 2012; and related literaure

[2] IMF Fiscal Monitor, October 2016.

[3] In particular, see work on global household leverage, house prices, and consumption by Glick and Lansing (2010)

[4] Greece appears to be a notable degenerate case to this empirical regularity.

[5] For avoidance of doubt, what I mean is this: the main thrust of the Checherita and Rother (2010) argument remains i.e. that there is a negative correlation between external debt and economic growth and that this correlation becomes particularly strong when debt reaches a certain threshold. But their secondary claim (i.e. that the stock of private debt, defined as total domestic credit to the private sector, fails to be statistically significant in determining growth) does not old any water once panel/cross sectional time series data is estimated using a more robust specification.

[6] I follow the standard approach in related literature, which is to take first differences to remove permanent unobserved heterogeneity, and to deploy lagged levels of the series as instruments for the endogenous and predetermined variables in first differences (Anderson and Hsiao, 1981; Holtz-Eakin, Newey and Rosen, 1988; Arellano and Bond, 1991; Blundell, Bond, and Windmeijer, 2000).

[7] See the discussion about this in Chapter 3 of Hayashi (2000)

[8] See

[9] Paper presented at Birkbeck’s inaugural BCAM Policy Talk on Friday 24 March 2017


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Notes from a Risk Management competition

Earlier this academic term, a team of graduate students from the Department of Economics, Mathematics, and Statistics took part in the PRMIA Risk Management Challenge. The Challenge is hosted every year in Europe and North America by the Professional Risk Managers’ International Association (PRMIA), the global professional and standard-setting body for the financial risk management industry.

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