«Abstract We investigate the eﬀects of a credit crunch in an economy where ﬁrms can operate a mature technology or restructure their activity and ...»
Credit Crunches, Asset Prices and Technological Change
Michigan State University Michigan State University
This Draft: May 2011
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, a negative collateral shock 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 In the last two decades or so, a rich literature has investigated the role of credit in entrepreneurs’ investment decisions. In particular, a consensus has formed about the beneﬁts that two key features of the credit market, entrepreneurs’ collateral and credit relationships, have for investment. When entrepreneurs have limited ability to commit to repay their lenders, the availability of pledgeable assets eases their access to credit.
Lenders can repossess collateral and this may compensate for the limited pledgeability of output. Lenders can also deter entrepreneurs’ misbehavior by threatening to repossess collateral. Credit relationships enhance these beneﬁts of collateral. For example, lenders who have established close relationships with entrepreneurs can better monitor their collateral and, hence, obtain more value from its repossession. In an aggregate perspective, an implication of this view is that shocks that erode the value of collateral or break credit relationships can depress investment by hindering entrepreneurs’ access to ﬁnancing.
This view of the credit market is generally conﬁned to technologically static economies. If one allows for technological progress, 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? In this paper, we address these questions.
The intuition can be summed up as follows. In our economy, entrepreneurs operate mature technologies ∗ Department of Economics, Michigan State University. E-mail: firstname.lastname@example.org. Phone: +1-517-355-7349.
101 Marshall Hall, East Lansing, MI 48824-1038, USA. We wish to thank Satyajit Chatterjee, Luigi Guiso, Nobuhiro Kiyotaki, 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, Midwest Economic Theory Meetings, and Midwest Macroeconomics Meetings for helpful comments and discussions. All remaining errors are ours.
or restructure their activity and adopt new technologies. Lenders, in turn, learn information that is crucial for 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 conservative towards entrepreneurs’ restructuring. In fact, new technologies have less assets pledgeable as collateral. Furthermore, the information on the collateral assets of mature technologies is (partially) speciﬁc and non-transferable to the collateral assets of new technologies.
Therefore, lenders expect that the value of their information will depreciate if entrepreneurs restructure and may impede their restructuring eﬀort to prevent this.
In this economy, entrepreneurs can form credit relationships with lenders to transfer them more information on collateral assets and obtain cheaper ﬁnancing. Yet, because of the technological conservatism induced by lenders’ information, credit relationships favor technological inertia. When technological inertia arises, entrepreneurs can break their credit relationships, borrow from new lenders and restructure. However, this wastes the information accumulated within the relationships. Hence, these credit relationships and technological inertia can be long-lasting.
The distribution of ﬁrms across collateral values replicates salient features of that obtained in previous aggregate 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. 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, the conservatism of their lenders is strong because the lenders expect a large depreciation in the value of their information if the mature technology is abandoned in favor of the new technology.
We perturb this economy with an exogenous contraction in the value of collateral assets that reduces the asset price. 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. Consider next collateral-rich ﬁrms. The collateral shock erodes the value of the information of their lenders. This mitigates lenders’ conservatism, 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. Whether the restructuring occurs within existing relationships or through their breakdown depends on the credit regime. There is a credit regime in which lenders’ conservatism is weak and/or ﬁrms derive large beneﬁts from credit relationships: in this regime, collateral-rich ﬁrms restructure within their relationships. There is instead a credit regime in which lenders’ conservatism is strong and/or ﬁrms derive small beneﬁts from credit relationships: in this regime, collateral-rich ﬁrms restructure by breaking their relationships and borrowing from new lenders. Thus, depending on the credit regime, the surge in restructuring activity induced by the shock can entail a moderate or a major breakdown of credit relationships.
This is important for the aggregate impact of the shock, because the loss of asset liquidation skills that arises from a breakdown of credit relationships depresses output. Interestingly, we also show that in our model economy the credit rationing of collateral-poor ﬁrms may foster the restructuring of collateral-rich ﬁrms by bringing down asset demand and values.
The remainder of this paper unfolds as follows. In the next section, we relate the paper to the literature.
In Section 3, we outline and discuss the setup. Section 4 solves for the equilibrium. In Section 5, we investigate the eﬀects of a collateral shock. Section 6 concludes. Proofs are relegated to the Appendix.
2 Related Literature This paper especially relates to two strands of literature. The ﬁrst investigates the impact of recessions on the ﬁnancial structure and the consequences for aggregate investment. We have discussed the 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 highly tractable 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 papers analyze the breakdown of credit relationships that can be caused by a recession in economies with search frictions. While in these studies the breakdown of credit relationships depresses investment, in our economy this breakdown 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) shows 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 information-intensive credit relationships also mitigates the conservatism of lenders that inhibits restructuring.
3 The Model This section describes the setup of the model. Table 1 summarizes the notation while Figure 1 illustrates the timing of events.
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 restructures his activity and adopts a new technology; otherwise, he operates a mature, less productive 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 the project succeeds and the 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. ∈ [0 1] captures the amount of collateralizable assets of an entrepreneur and is uniformly distributed across entrepreneurs. ≤ 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
acquires information. Following an established literature (e.g., Aghion and Bolton, 1992; Rajan, 1992), we allow the lender to exert control over production opportunities. Precisely, on date 2 the lender can carry out a costless action that aﬀects the probability of the innovation: if she carries out this action, the innovation will occur with probability 1 − (0 1); otherwise, the innovation cannot occur.
The lender also acquires information as a by-product of her ﬁnancing activity. As in Diamond and Rajan (2001) and Habib and Jonsen (1999), information enables her to obtain more than other agents from the liquidation of the entrepreneur’s assets, that is, the lender “monitors” collateralizable assets. Precisely, the share of liquidation value that the lender obtains equals the amount of her information on the assets; the rest of the liquidation value is lost in the form of a (nominal or real) transaction cost. By contrary, we normalize to zero the net amount of ﬁnal good that any other agent obtains from asset liquidation.
Having characterized the nature of information, we have to specify its amount. We allow the entrepreneur to inﬂuence this amount by choosing the type of funding, relationship or transactional. Precisely, on date 0 each entrepreneur chooses whether to establish an informationally intensive credit relationship with his ﬁnancier on date 1 or simply obtain a transactional loan. Consider ﬁrst mature assets: () is the amount of information of a lender if she does (not) carry out the action for the innovation; furthermore, for a relationship lender = while for a transactional lender = . This speciﬁcation has two features.
First, because a credit relationship entails a long-run tie with the entrepreneur from date 0, a relationship lender learns more information about the entrepreneur’s assets ( ). Second, when a lender allows the innovation, she learns 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 learn information on a technology if the entrepreneur is working to abandon it. Consider next new assets: denoting the amount of information, we let = 0 Thus, a lender obtains less value from liquidating new assets than from liquidating mature assets - the normalization to zero is for simplicity. We will discuss this speciﬁcation shortly.