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«Abstract We investigate the effects of a credit crunch in an economy where firms can operate a mature technology or restructure their activity and ...»

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Credit Crunches, Asset Prices and Technological Change

Luis Araujo

Michigan State University and São Paulo School of Economics-FGV

Raoul Minetti∗

Michigan State University

This Draft: November 2011

Abstract

We investigate the effects of a credit crunch in an economy where firms can operate a mature

technology or restructure their activity and adopt a new technology. We show that firms’ collateral and credit relationships ease firms’ access to credit and investment but can also inhibit firms’ restructuring. When this occurs, negative collateral or productivity shocks and the resulting drop in the price of collateral assets squeeze collateral-poor firms out of the credit market but foster the restructuring of collateral-rich firms. We characterize conditions under which such an increase in firms’ restructuring occurs within existing credit relationships or through their breakdown. The analysis reveals that the credit and asset market policies adopted during the recent credit crunch can promote investment but might also slow down a process of Shumpeterian restructuring in the credit market.

Keywords: Aggregate Restructuring, Collateral, Credit Relationships, Credit Crunch.

JEL Codes: E44.

1 Introduction The 2008-2010 financial crisis has put the spotlight on the role of the credit market in firms’ investment decisions. During the crisis, a major decline in the value of collateral assets, especially real estate, has put credit relationships between firms and lenders under strain, allegedly resulting into a drop in total credit and investment. The literature offers well-established theoretical arguments for interpreting these effects of a credit crunch. When entrepreneurs cannot fully commit to repay their lenders, the availability of collateralizable assets eases their access to credit (Kiyotaki and Moore, 1997). Ex post, after entrepreneurs default, lenders can repossess collateral and this compensates for the limited pledgeability of entrepreneurs’ ∗ Department of Economics, Michigan State University. E-mail: minetti@msu.edu. Phone: +1-517-355-7349. Address: 101 Marshall Hall, East Lansing, MI 48824-1038, USA. We wish to thank Satyajit Chatterjee, Luigi Guiso, Maurizio Iacopetta, Matteo Iacoviello, Rowena Pecchenino, Susan Chun Zhu and seminar participants at Bank of Italy/Ente Einaudi, Boston College, Central Michigan University, London School of Economics (FMG/ESRC Conference), Michigan State University, Stockholm School of Economics, Universidad Carlos III (Madrid), University of Michigan at Ann Arbor, Western Michigan University, the Federal Reserve Bank of Chicago “Summer Workshop on Money, Banking, Payments and Finance”, Midwest Economic Theory Meetings, Midwest Macroeconomics Meetings, the 2011 OFCE-SKEMA Workshop on Models with Credit Frictions and Money (Nice). All remaining errors are ours.

output; ex ante, lenders’ threat to repossess collateral deters entrepreneurs’ misbehavior. Credit relationships can enhance these benefits of collateral. For example, lenders who establish informationally intensive relationships with entrepreneurs can better monitor their assets and, hence, recover more value from asset repossession (Diamond and Rajan, 2001). In an aggregate perspective, an implication of these arguments is that shocks that erode the value of collateral assets or break credit relationships depress total investment by hindering firms’ access to external finance (Kiyotaki and Moore, 1997; Holmstrom and Tirole, 1997).

While useful to explain key mechanisms of transmission of the crisis, these arguments yield limited insights into the effects of a credit crunch on technological change. If one allows for technological change, two questions arise naturally: Do collateral and credit relationships ease entrepreneurs’ restructuring activity, meant as the upgrade from mature technologies to new ones? And therefore, in an aggregate perspective, do shocks that erode the value of collateral and break credit relationships depress aggregate restructuring as they allegedly depress investment? This paper takes a step towards addressing these questions. We study an economy where entrepreneurs operate a mature technology or restructure their activity and adopt a new technology.

Lenders, in turn, acquire information that is essential for repossessing and liquidating productive assets pledged as collateral when entrepreneurs default (as in Diamond and Rajan, 2001, for example). Lenders’ information on collateral assets eases entrepreneurs’ access to credit. However, their information renders lenders reluctant to finance entrepreneurs’ restructuring activity. In fact, the new technology has less assets pledgeable as collateral. Furthermore, the information on the assets of the mature technology is at least partially specific and non-transferable to the collateral assets of the new technology. Therefore, expecting that the information they have accumulated on mature collateral assets will go wasted if entrepreneurs upgrade to the new technology, lenders may hinder entrepreneurs’ restructuring efforts.

In this economy, entrepreneurs can form informationally intensive credit relationships with lenders to transfer them more information on collateral assets and obtain cheaper financing. Yet, the information accumulated within the relationships exacerbates lenders’ incentive to inhibit firms’ restructuring. When technological inertia arises, entrepreneurs can break their credit relationships and restructure. However, this wastes the information accumulated. Hence, these relationships and technological inertia can be long-lasting.





The resulting distribution of firms across collateral values replicates salient features of that obtained in previous general equilibrium models of the credit market (e.g., Holmstrom and Tirole, 1997). Collateral-poor firms lack access to credit because they cannot pledge enough expected returns to lenders, even when these obtain high quality information on collateral assets. Furthermore, firms with medium collateral value obtain credit from informed (relationship) lenders. The novelty consists of firms’ technology adoption. While firms with medium collateral value potentially restructure, collateral-rich firms with credit relationships preserve the mature technology. In fact, their lenders expect a large depreciation in the value of their information if the mature technology is abandoned in favor of the new technology.

We study the effects of contractions in the value of collateral assets and in productivity. Consider collateral shocks (the reasoning for productivity shocks is similar). Following the drop in the price of collateral assets, the credit relationships of collateral-poor firms break down because these firms can no longer pledge enough expected returns to lenders. The exclusion of collateral-poor firms from the credit market tends to reduce both total investment and aggregate restructuring. Consider next collateral-rich firms. The reduction in the asset price erodes the value of the information acquired by their lenders on mature collateral assets. This mitigates lenders’ technological inertia, allowing restructuring to occur within the relationships. This also increases the incentive of collateral-rich firms to deliberately break their credit relationships, borrow from new lenders and restructure. These effects work in the direction of fostering the restructuring of collateral-rich firms. If the increase in the restructuring of collateral-rich firms outweighs the drop in the restructuring of collateral-poor firms, the shock will cause a decline in total investment but an increase in total restructuring.

What are the consequences of the increased restructuring of collateral-rich firms for the credit market?

There is a credit regime in which lenders’ technological inertia is weak and/or firms derive large benefits from relationships: in this regime, collateral-rich firms restructure within their relationships. There is instead a credit regime in which lenders’ technological inertia is strong and/or firms derive small benefits from relationships: in this regime, collateral-rich firms restructure by breaking their relationships and borrowing from new lenders. Thus, depending on the credit regime, the increase in restructuring activity induced by the shock can entail a moderate or a major breakdown of credit relationships. This is also important for the impact of the shock on output, because the breakdown of relationships can depress output by raising asset liquidation costs.

In the last part of the paper, we investigate the effects of two unconventional policies carried out during the financial crisis: an intervention in the collateral asset market aimed at sustaining the asset price after the shock and a policy of direct lending to collateral-poor firms. We find that both policies foster total investment but may dampen the increase in the restructuring of collateral-rich firms during a credit crunch. The case of the direct lending policy is especially insightful. In our economy, the credit rationing of collateral-poor firms following the shock fosters the restructuring of collateral-rich firms by bringing down collateral asset demand and prices. A policy of direct lending dampens this effect.

This paper especially relates to two strands of literature. The first investigates the impact of a disruption in the financial structure on aggregate investment (for recent studies, see, e.g., Gertler and Karadi, 2010, and Gertler and Kiyotaki, 2010). We have discussed some key elements we share with Holmstrom and Tirole (1997). We also borrow properties of our modelling strategy from their paper, such as the focus on a finite horizon economy. Den Haan, Ramey and Watson (2003) and dell’Ariccia and Garibaldi (2001) are other related papers in this strand of literature. These studies analyze the breakdown of credit relationships that can be caused by a recession in economies with search frictions. While in these studies a breakdown of credit relationships depresses investment, in our economy it depresses investment but may also foster aggregate restructuring.

The second strand of literature analyzes the impact of recessions on firms’ restructuring. Most of this literature neglects the role of the credit market for aggregate restructuring. Caballero and Hammour (2004), Ramey (2004) and Barlevy (2003) are exceptions. These studies show that credit frictions can become more severe during recessions, hindering aggregate restructuring. Caballero and Hammour (2004) show that, because of credit frictions, production units can be destroyed at an excessive rate during a recession.

Furthermore, during the following recovery, the creation of new production units can be too slow and most of the recovery can occur via a slowdown of destruction. Ramey (2004) endogenizes financial managers’ project selection and shows that, if managers have empire-building incentives, during recessions they can discard efficient projects to preserve the size of their portfolios. Barlevy (2003) finds that credit frictions can reverse the “cleansing effect” of recessions by leading to the disruption of high-surplus production units rather than low-surplus ones. This paper endorses a view opposite to all these studies: while it negatively affects investment, the breakdown of informationally intensive credit relationships also mitigates lenders’ technological inertia.

The remainder of the paper is organized as follows. In Section 2, we outline and discuss the setup.

Section 3 solves for the equilibrium. In Section 4, we investigate the effects of shocks. Section 5 analyzes the robustness of the analysis. Section 6 considers the effect of policies in the asset and credit markets.

Section 7 concludes. The Appendix contains the main proofs while more technical proofs are relegated to a Supplement.

2 The Model This section describes the setup of the model. Table 1 summarizes the notation while Figure 1 illustrates the timing of events.

2.1 Agents, Goods and Technology Consider a four-date economy ( = 0 1 2 3) populated by a unit continuum of entrepreneurial firms and a continuum of investors of measure larger than one. There is a final consumption good, which can be produced and stored, and productive assets of two vintages, mature and new. Entrepreneurs have no endowment while each investor is initially endowed with an amount  of final good. All agents are risk neutral and consume on date 3.

Each entrepreneur can implement one indivisible project. On date 2, an entrepreneur can experience a technological innovation. If the innovation occurs, the entrepreneur chooses whether to restructure his activity, adopting a new technology, or operate a mature, less productive technology. In the innovation does not occur, the entrepreneur has to operate the mature technology. Under the mature (new) technology, on date 3 the entrepreneur transforms an amount    of final good into one unit of mature (new) assets.

With probability   12 the project succeeds and the mature (new) assets yield an output  ((1 + )) of final good; otherwise the project fails and the entrepreneur goes out of business. In this case, a fraction  () of mature (new) assets can be redeployed outside the firm.  captures the amount of collateralizable Date 0 Date 3 Date 1 Date 2

–  –  –

assets of an entrepreneur and is uniformly distributed across entrepreneurs over the domain [0 1].  ≤ 1 is a parameter that reflects the redeployability of new assets relative to mature assets.

On date 3, each entrepreneur still in business can reuse one unit of liquidated assets, obtaining an amount  of final good.  is uniformly distributed across entrepreneurs over the domain [0 ];  represents the aggregate productivity of liquidated assets.1

2.2 Credit Sector

–  –  –

assets (  ). Second, when a lender allows the innovation, she acquires less information on mature assets (  1).2 This reflects the idea that the lender has less opportunities - and with endogenous information acquisition, less incentives - to acquire information on a technology if the entrepreneur is working to abandon it. Consider next new assets: denoting the amount of information by , we let   = 0. Thus, a lender recovers less value from liquidating new assets than from liquidating mature assets - the normalization to zero is for simplicity.



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