Credit booms – defined as periods of rapid credit growth – are a common phenomenon in both advanced and emerging economies (Mendoza and Terrones 2008, Bakker et al. 2012). They are generally accompanied by astrong macroeconomic performance, including high asset prices and high rates of investment and GDP growth. However, the conventional wisdom is to view them with suspicion. First, credit booms are often perceived to fuel resource misallocation – high asset prices and a positive economic outlook may lead to a relaxation of lending standards and, consequently, to the funding of relatively inefficient activities (Gopinath et al. 2017). As the old banker maxim goes, ‘bad loans are made in good times.’ Second, credit booms often end in crises that are followed by protracted periods of low growth (Schularick and Taylor 2012, Krishnamurthy and Muir 2017).
This conventional wisdom raises important questions. What determines the allocation of resources during credit booms? How does this allocation shape the macroeconomic effects of credit booms, and of their demise? And finally, are all credit booms alike? In a recent paper, we develop a new theory of information production during credit booms to address these questions, exploiting US data to provide new empirical evidence of the theory's main predictions (Asriyan et al. 2018).
A theory of information production during credit booms
We study an economy that is populated by entrepreneurs and lenders. Entrepreneurs have access to long-lived investment projects but need external funding to undertake them; lenders, instead, have resources but they lack the ability to run investment projects. Absent any friction, this would not be a problem, as lenders could simply provide credit to entrepreneurs with productive investment opportunities. We introduce a friction, however, by assuming that some projects enable entrepreneurs to divert resources to private consumption.
If they are to break even, lenders need to protect themselves against such diversion by entrepreneurs. They have two ways of doing so.
- The first is collateralisation. Entrepreneurs are endowed with assets, and lenders can ask them to retain ‘skin in the game’ by posting these assets as collateral.
- The second is costly screening. Lenders may engage in costly information production to ensure that the projects undertaken by entrepreneurs do not permit resource diversion. We make two assumptions regarding screening. First, the cost of screening an individual project in any given period is increasing in the economy's aggregate amount of screening in that period. This assumption captures the intuitive notion that there is an increasing cost of producing information in any given period due, for instance, to some fixed underlying factor. Second, the information generated through screening is long-lived, and it accompanies the project throughout its life.
The key insight of the model is that, in equilibrium, the relative intensity of collateralisation versus screening depends on the scarcity of entrepreneurial collateral (e.g. real estate). When the price of collateral is low, lenders rely largely on screening. Since only few investment projects can be funded via collateralisation, the return to investment at the margin (and thus to screening) is high. This raises the equilibrium level of screening and thus the amount of information on existing projects. When the price of collateral is instead high, the equilibrium mix of screening to collateralisation is low. In this case, since many investment projects can be funded via collateralisation, the marginal return to investment is low. This reduces equilibrium screening and thus the amount of information on existing projects.
This insight has powerful implications for the effects of collateral-driven credit booms. When the economy enters a collateral boom, the price of collateral rises and credit, investment, and output all expand together. But, for the reasons outlined above, lenders rely more on collateralisation and less on screening. Even as the economy booms, therefore, the amount of information on existing projects falls – in this sense, the boom is accompanied by a ‘depletion’ of information. When the boom ends and the price of collateral falls, credit, investment, and output fall as well, but they do so for two reasons:
- all else being equal, the scarcity of collateral means that lenders must increase their reliance on costly screening; and
- this need for screening is especially strong because information has been ‘depleted’ during the boom.
For these reasons, the end of a collateral boom is accompanied by a large crash and a slow recovery – that is, a transitory ‘undershooting’ of economic activity relative to its new long-run level. In a nutshell, collateral booms end in deep crises.
Besides this general insight, the model sheds light on three key debates regarding credit booms and their macroeconomic effects:
- First, the theory shows that not all credit booms are alike. Richter et al. (2017) and Gorton and Ordonez (2016) have recently referred to ‘good’ and ‘bad’ booms, depending on whether they end in crisis or not. Through the lens of our model, the defining feature of booms lies in the shock that drives them. In particular, unlike collateral-driven booms, productivity-driven booms do not generate information depletion. By raising the return to investment, an increase in productivity actually raises equilibrium screening and information production. Thus, the end of productivity-driven booms does not exhibit a deep crisis with an undershooting of economic activity. This has clear implications for the design of macroprudential policies, questioning the desirability of ‘one-size-fits-all’ policies to control credit booms (e.g. policies that target the credit gap). To the extent that they are desirable, such policies should not treat all credit booms in the same way.
- Second, the model speaks to the recent literature on asset price bubbles (Martin and Ventura 2018). In essence, one can interpret collateral-driven booms as the result of bubbles, which raise collateral and/or market liquidity but do not affect economic fundamentals. Under this interpretation, the model highlights a hitherto unexplored cost of bubbles that surfaces when they burst – while they last, they deplete information on existing projects.
- Third, the model also shows why credit booms can lead to resource misallocation. By reducing information, collateral booms raise dispersion in the productivity of investment. There is a positive counterpart to this increase in dispersion, however, as the economy saves on information costs.
Finally, we study the normative properties of our economy. Intuitively, it may seem that market participants produce too little information during booms – if less information were depleted during booms, the busts would be less severe and recoveries would be faster. We show, however, that this intuition is incorrect. Since agents are rational, they correctly anticipate the value of information in future states of nature. Thus, even in the midst of a collateral boom, agents understand that when the bust comes, screened projects will be very valuable and they will be able to appropriate this value. If anything, we find that, due to pecuniary externalities resulting from market prices affecting financial constraints, agents produce too much information! For information generation to be sub-optimally low, we argue that there must be additional distortions that prevent agents from fully internalising the social return to information production, such as external economies in the screening technology or frictions in the market for projects.
We test three central predictions of the theory on US firm-level data from COMPUSTAT. First, as is standard in the presence of financial frictions, the theory predicts that a rise in collateral values should coincide with an increase in investment and output. Second, and more central to the theory, an increase in collateral values should lead to information depletion – that is, to a decline in screened investment. Finally, the theory predicts that a decline in collateral values should reduce investment and output, the more so when the amount of information on existing projects is lower (i.e. the share of past investment that has been screened).
Testing the empirical relevance of the model's main predictions is nontrivial for at least two reasons.
First, all three predictions refer to the effect of collateral values on the amount and composition of investment. Assessing this in the data requires identifying changes in collateral values that are orthogonal to other economic conditions, such as productivity, which may affect investment on their own. We deal with this by following Chaney et al. (2012) and estimating the impact of real estate prices on corporate investment using instrumental variables.
Second, the main prediction of the model is that an increase in net worth or collateral reduces the economy's reliance on screening, so that there is less information on existing projects. Assessing this in the data would require a measure of the informational content of investment, which we do not observe. We proceed instead by focusing on ﬁrm-level measures of information that are commonly used in the economics and ﬁnance literature. In particular, we use three alternative measures of information at the firm level: the bid-ask spread on the firm's stock, the number of financial analysts that follow the firm, and the ratio of intangible assets to tangible fixed assets of the sector in which the firm operates.
Our empirical results are consistent with the main predictions of the model. First, a firm's investment is increasing in the value of its real estate. Second, this effect is stronger for firms on which there is less information, as measured through the bid-ask spread, the number of analysts covering the firm, or the ratio of intangible to tangible assets of the sector in which the firm operates. Finally, to assess how the distribution of investment during the boom affects the severity of the subsequent bust, we analyse evidence at the state level during the recent housing boom and bust in the US. We find that, at the state level, investment during the bust years (2007–2012) is negatively correlated with the share of investment that was undertaken by ‘low-information’ firms during the boom (2001–2006).
More broadly, our theory is consistent with various strands of stylised evidence. First, there is ample evidence showing that investment is positively correlated with collateral values (Peek and Rosengren 2000, Gan 2007, Chaney et al. 2012). Second, there is also evidence that lending standards, and in particular lenders' information on borrowers, deteriorates during booms (Asea and Blomberg 1998, Keys et al. 2010). Third, and focusing more specifically on collateral booms, Doerr (2018) finds that the US housing boom of the 2000s led to a reallocation of capital and labour to less productive firms. Fourth, there is evidence that credit booms that are accompanied by house price booms (Richter et al. 2017) and that are characterised by low productivity growth (Gorton and Ordonez 2016) are more likely to end in crises. All of these findings are consistent with the model's main predictions.
Authors’ note: The views expressed in this column are those of the authors and do not necessarily reflect the views of the ECB.
Asea, P K and B Blomberg (1998), “Lending cycles," Journal of Econometrics 83(1-2): 89–128.
Asriyan, V, L Laeven and A Martin (2018), “Collateral booms and information depletion,” CEPR, Discussion paper no 13340.
Bakker, B, G Dell'Ariccia, L Laeven, J Vandenbussche, D Igan and H Tong (2012), “Policies for macro-financial stability: How to deal with credit booms," IMF Staff Discussion Note no 12/06.
Chaney, T, D Sraer and D Thesmar (2012), “The collateral channel: How real estate shocks affect corporate investment," American Economic Review 102(6): 2381–2409.
Doerr, S (2018), “Collateral, reallocation, and aggregate productivity: Evidence from the US Housing Boom," Unpublished paper.
Gan, J (2007), “The real effects of asset market bubbles: Loan-and firm-level evidence of a lending channel," Review of Financial Studies 20(6): 1941–1973.
Gopinath, G, S Kalemli- Ozcan, L Karabarbounis and C Villegas-Sanchez (2017), “Capital allocation and productivity in South Europe," Quarterly Journal of Economics 132(4): 1915–1967.
Gorton, G and G Ordonez (2016), “Good booms, bad booms," NBER, Working paper 22008.
Keys, B J, T Mukherjee, A Seru and V Vig (2010), “Did securitization lead to lax screening? Evidence from subprime loans," Quarterly Journal of Economics 125(1): 307–362.
Krishnamurthy, A and T Muir (2017), “How credit cycles across a financial crisis," NBER, Working paper 2385.
Martin, A and J Ventura (2018), “The macroeconomics of rational bubbles: a user's guide," Annual Review of Economics 10: 505–539.
Mendoza, E and M Terrones (2008), “An anatomy of credit booms: Evidence from macro aggregates and micro data," NBER, Working paper 14049.
Peek, J and E S Rosengren (2000), “Collateral damage: Effects of the Japanese bank crisis on real activity in the United States," American Economic Review 90(1): 30–45.
Richter, B, M Schularick and P Wachtel (2017), “When to lean against the wind," CEPR, Discussion Paper No. 12188.