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1 Part of this work was completed during the summer 2005 under the Graduate Research Programme of the Research Directorate of the European Central Bank, which I wish to thank for hospitality. 2 Monetary and Financial Analysis Department, Bank of Canada, 234 Wellington St., Ottawa, K1A 0G9, Ontario, Canada; e-mail: [email protected]; Homepage: http://www.bankofcanada.ca/ec/cmendicino/.
Studying the determinants of business cycle ‡uctuations is crucial for understanding the dynamics of modern economies. The aim of this paper is to examine how the degree of credit market development is related to business cycle ‡uctuations in industrialized countries. The paper presents some empirical facts and a model economy whose aim is to replicate the relation between credit market size and the volatility of output. First, OECD data are used to show that a negative and signi…cant relation exists between credit market size – as a proxy for the degree of credit market depth – and the propagation to output of variation in productivity. Credit market size substantially reduces the volatility of output driven by variations in productivity. Second, I present a model economy in which di¤erent degrees of credit market development, ceteris paribus, a¤ect the sensitivity of output to productivity shocks and thus its volatility over the business cycle. I use a stochastic dynamic general equilibrium model with collateral constraints a la Kiyotaki and Moore (1997) in which higher liquidation costs characterizes less developed credit systems. Existing literature dealing with credit markets has shown that credit frictions may be a powerful transmission mechanism that propagates and ampli…es shocks. This paper demonstrates that in a model with collateral constraints, movements in the relative price of capital and thus the reallocation of capital, substantially a¤ect the sensitivity of output to shocks. In accordance with the empirical …ndings, the model asserts that the propagation of variations in productivity to output is greater in economies with tighter credit markets. The reduction in productivity-driven output volatility implied by the model is closely related to the data.
As a result of macroeconomic, political and legal factors, credit markets signi…cantly di¤er among OECD countries1 . At the same time the volatility of the cyclical component of output shows a noticeable degree of variation across countries and time (Figure 1.a). Table 1 reports that credit market size is negatively and signi…cantly correlated with the volatility of output, consumption, investment, investment in residential properties and housing prices2 . Preliminary analysis on OECD data, indicating that smoother ‡uctuations are associated with greater sizes of the credit market, provide a reasonable ground to investigate the relation between credit market development and business cycle ‡uctuations in industrialized countries. The analysis is conducted in three steps. First, OECD data are used to document the relation between the degree of credit market development and macroeconomic ‡uctuations. Speci…cally, a greater size of the credit market, as a proxy for credit market depth, reduces the propagation of variations in productivity to output volatility. Second, I develop a two-sector business cycle model that links the degree of credit market development to the sensitivity of output to productivity shocks, and thus its volatility over the business cycle. Last, I compare the predictions of the model with the empirical …ndings. The model predicts a reduction in productivity-driven output volatility of about 20% that closely corresponds to the data evidence. Despite the stylized nature of the model, it mimics the data reasonably well both qualitatively and quantitatively. Model. The model is based on Kiyotaki and Moore (1997). To generate a reason for the existence of credit ‡ows, two types of agents are assumed, both of whom produce and consume the same type of goods using a physical asset. They di¤er in terms of discount factors, and consequently, impatient agents become borrowers. Credit constraints arise because lenders cannot force borrowers to repay. Thus, physical assets, are used not only as factors of production but also as loan collateral. My setup di¤ers from Kiyotaki and Moore (1997) framework, in that I use more standard assumptions as to preferences and 1 See e.g. La Porta, Lopes-de Silanes, Shleifer, Vishny (1997) and Djankov, Hart, McLiesh and Shleifer(2006), 2 See also …gures 1.a and 1.b. Correlations are computed for quarterly variables averaged over rolling three-year periods during the 1983-2004 a sample of 20 OECD countries.Volatility is measured as the standard deviation of the log detrended real variables. Hodrick-Prescott …lter are used to remove the estimated trend of the series.
technologies3 . Aggregate uncertainty is introduced into the model, so asset prices are not perfectly predicted by the agents. To be able to investigate the behavior of economies that di¤er in terms of access to credit …nancing, I allow for the existence of liquidation costs in modeling the collateral constraint4 . According to the Schumpeterian view, aggregate shocks generate an inter-…rm reallocation of resources, and evidence of this is well established as pertains to job ‡ows. Rampini and Eisfeldt (2005) have recently demonstrated the relevance of physical capital reallocation over the business cycle5 . In fact, in the USA the amount of capital reallocation represents approximately one quarter of total investment, and that depending on how capital reallocation is measured, between 1.4 and 5.5 of the capital stock turns over each year. Furthermore, the reallocation of existing productive assets among …rms (sales and acquisitions of property, plant, and equipment) is procyclical. The model presented in this paper generates a negative relationship between the degree of credit market development and output volatility giving a primary role to variations in relative prices and thus the reallocation of capital across …rms. When the economy is hit by a positive neutral productivity shock agents increase their capital expenditure, the relative price of capital rises and existing capital is thus reallocated to the production of the capital good. In economies with a greater access to the credit market, the productivity gap between the two groups of agents is smaller. Thus, following a productivity shock, less capital is redistributed to the more productive agents. The sensitivity of asset prices to the shock is reduced and consequently less capital is reallocated to the capital good production. As a result total production reacts by less to the shock. The magnitude of the e¤ect of credit market size on the reduction in the volatility of output induced by variations in productivity is in accordance with the empirical …ndings. 3 Kiyotaki and Moore assume that the agents are risk neutral and apart from using di¤erent discount factors, they also di¤er in their production technology. In my model, both groups of agents have a concave utility function and are generally identical, except that they have di¤erent subjective discount factors. 4 As in Aghion et al. (2005) collateral requirements serve as a proxy for the degree of credit market development. Tighter collateral constraints result in a smaller size of the credit market and thus, characterize economies with a less-developed credit market. 5 See also Maksimovic and Phillips (2001), Andreade, Mitchell, and Sta¤ord (2001), Schoar (2002), Jovanovic and Rousseau (2002). A few papers also examine the behavior of capital reallocation from a microeconomic point of view. Among the main results are that capital ‡ows from less productive to more productive …rms (Maksimovic and Phillips (2001)) and that gains derived from reallocation appear larger when productivity di¤erences are greater (Lang, Stulz, and Walkling (1989) and Servaes(1990)).
This results contribute signi…cantly to the debate concerning the ampli…cation role of collateral constraints. Cordoba and Ripoll (2004) show that adopting standard assumptions about preferences and technologies makes Kiyotaki and Moore’s model unable to generate persistent or ampli…ed shocks. Thus, their results call into question the quantitative relevance of credit frictions as a transmission mechanism. I document that in the model presented here, the magnitude of ampli…cation is related to the degree of credit rationing. The results of Cordoba and Ripoll hold only for economies with the least possible degree of credit rationing allowed by the model. Layout. The paper proceeds as follows. Section 2 discusses some empirical evidence. Section 3 presents the model, while section 4 discusses the solution method and calibration. Section 5 shows the steady-state implications of di¤erent degrees of credit rationing. Section 6 presents the dynamics of the model, and Section7 the relationship between credit market size and business cycle volatility. Section 8 investigates the importance of having two sectors of production in the model by comparing the results in terms of volatility with the one sector model version. Section 9 presents the conclusions of the study.
All OECD data used are obtained from the OECD database, while the data regarding private credit come from the IFS.
regression a set of control variables for other potential sources of business cycle volatility. A larger credit market does dampen the propagation of Solow residual volatility to output of about 13-18% depending on the control variables introduced in the regression. In table 2, I measure credit market size as a moving average over the three years. However, table 3 show that the result is robust independently of how credit market size is measured. In column 2, I measure credit market size as the beginning of the period value to emphasize how the established credit-to-GDP ratio a¤ects volatility in the following period. I also check the robustness of the relation, using the average over the all period sample, as a measure of credit market development that varies only in the cross-section and not over time (column 3). In all speci…cation the interaction between credit market size and the standard deviation of Solow residuals has a negative and signi…cant sign. Thus, across OECD countries, in the last twenty years, credit market size reduces the volatility of output induced by variations in productivity.
See Cordoba and Ripoll (2004) for a discussion of how di¤erent assumptions about production technology a¤ect the impact of technology shocks in the modeled economy. 8 In this way I avoid creating a rental market for capital, and make the model directly comparable to those of Kiyotaki and Moore (1997) and Cordoba and Ripoll (2004). 9 The assumption of decreasing returns in the production of invetment goods is equivalent to assume convex adjustment costs for investments.
and the size of the credit market.
10 In an economy in which the legal system is very e¢ cient the commitment problem vanishes and the borrowing constraint is not necessary any more.
is greater than or equal to the expected discounted marginal product of capital.
The price of capital is higher than the frictionless marginal tobin’s q for the borrowers.
the technology shocks according to standard values in the real business cycle literature14 . Table 4 summarizes the parameter values.
Lawrance (1991) estimates that the discount factors of poor households are in the 0.95 to 0.98 range, while according to Carroll and Samwick (1997), the empirical distribution of discount factors lies in the 0.91 to 0.99 interval. 13 See Cooley and Prescott (1995) or Prescott (1986). 14 For technology shock, see chapter 1 in Cooley and Prescott (1995) or Prescott 1986.
To solve for the recursive law of motion, I need to …nd the matrices P; Q; R; andS, so that the equilibrium described by these rules is stable. I solve this system using the undetermined coe¢ cients method of, for example, McCallum (1983), King, Plosser, and Rebelo (1987), Campbell (1994), and Uhlig (1995).15 .
does reduce the output loss.
See Uhlig (1995), A Toolkit for Analyzing Nonlinear Dynamic Stochastic Models Easily, for a description of the solution method.
in the production share of constrained agents, and consequently in total production, y ss . Hence, the amount of total capital, K ss , and consumption, C ss , are higher as well16 . In the steady state, asset price depends on the marginal productivity of capital and increases with 17 .
reproduce the same private credit-to-GDP as found in the data and thus, directly relate the theoretical results to the empirical …ndings.
value of < 1: Thus, the model can never the equivalent to the standard one-sector real business cycle model with a one-to-one trasformation rate between consumption and capital.
result, more-developed credit markets display reduced ampli…cation of productivity shocks on output21 . Looking at the decomposition of output, a larger credit market magni…es the reaction of consumption goods production while weakening the response of investment goods production. The di¤erence between the reactions of the two sectors is explained by the dynamics of the relative price of capital and thus the capital reallocation between the two sector. As shown in Figure 4.b (top panel), reducing credit market frictions lowers the sensitivity of asset prices to productivity shocks and consistently reduces the magnitude of capital reallocation. Given that in economies with a lower degree of credit rationing the productivity gap between lenders and borrowers is smaller, less capital is redistributed to the borrowers to …ll the gap. Thus, borrowers’demand for capital rises by less reducing the increase in the relative price of capital. So, it becomes less pro…table to reallocate capital to the production of investment goods. In economies with greater access to credit, ceteris paribus, less capital (as collateral) is needed to be able to borrow the same amount, so less capital is reallocated to the production of investment goods. This e¤ect contributes to the same shock having a weaker impact on total aggregate production. Since the decreased reaction of the capital production sector is greater than the ampli…cation of the shock in the consumption goods production, a larger credit market dampens the propagation of productivity shocks to output.
This …nding is in accordance with Calstrom and Fuerst’s (1997) results of a stronger impact of neutral technology shocks on output when a lower value or the monitoring cost in the …nancial contract is assumed.
setup, show that collateral constraints are unable to generate ampli…cation of productivity shocks. This …nding still holds in the model presented here. However, if we allow for di¤erent degrees of credit market development the magnitude of the initial ampli…cation impact varies with the credit market size. Thus, the ampli…cation of productivity shocks to output is greater in economies with tighter collateral constraints. Once we allow for to be lower than unity, the ampli…cation generated in the model is no longer negligible.
0.5 and unity. In both the model and the data there is a clear evidence that well developed credit markets induce smoother business cycle ‡uctuations. The reduction in productivitydriven output volatility implied by the established size of the credit market in the model is of the same magnitude as documented by the empirical …ndings reported in section 2. Thus, the model mimics the data very closely both qualitatively and quantitatively.
Since the initial impact of the shock would always be equal to the shock itself, we are now looking at the second-period e¤ect of the shock. 24 Regardless as to the shape of the capital reaction to technology shocks, the relationship between and the second impact of zt on yt assumes an inverted U shape; this is, of course, more pronounced when k2 z is not monotonic. 25 For further discussion on the ampli…cation role of collateral constraints refer to Mendicino (2006).
In this paper I revisit the relationship between the degree of credit market development and business cycle volatility. I present some evidence concerning the fact that industrialized countries with better-developed credit markets experience smoother business cycle ‡uctuations. I develop a two-sector business cycle model, built on that of Kiyotaki and Moore (1997), to investigate the contribution of credit market development to the decrease in macroeconomic volatility. To explain the behavior of economies that di¤er in terms of access to credit …nancing, I also allow for the existence of liquidation costs in modeling the collateral constraint. Relying on a business cycle model that takes into account di¤erent degrees of credit frictions, I demonstrate that tighter credit markets greatly amplify the propagation to output of variations in productivity. As a result, the reduction in productivity-driven output volatility implied by the model is closely related to the data.
Acknowledgments: I am indebted to Giancarlo Corsetti, Martin Floden, Zvi Hercowitz and Lars Ljungqvist for useful feedback on this project. I am also grateful to Francisco Covas, Ferre de Graeve, Nicola Gennaioli, Jesper Linde, Pietro Reichlin, Guido Rey and seminar participants at the SSE, Sverige Riksbank, EUI, ECB, Banco de Portugal, Magyar Nemzeti Bank, CEU, Bank of Canada, Federal Reserve Board, SUNY at Albany and HEC Montreal, 4th Macro Dynamics workshop, 2006 SED meeting and 7th conference Bank of Finland/CEPR for valuable discussions. I acknowledge …nancial support from the research grant of the Bankforkinstitute Foundation. This paper is built on the …rst chapter of my dissertation. The view expressed are my own and do not necessarly re‡ect the view of neither the European Central Bank nor the Bank of Canada. All errors are mine. First Draft: December 2004. This Draft: March 2007.
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(y), (c), (I), (ih), standard deviation of respectively detrended log real output, consumption, investment andinvestment in residential properties. (q) standard deviation of detrended log housing prices, credit stands for credit to the private sector as a share of gdp, is the ratio during the same period. Three years avagages Data on 20 OECD countries. Source: OECD. 1 and 5 per cent signi…cant coe¢ cients respectively one and two stars.
(0.502297) (0.426280) 2 R 0.583302 0.595468 0.586303 Countries 20 20 20 obs 140 140 140 Period 1983-04 1983-04 1983-04 Dependent Variable, (y), standard deviation of detrended log real output. Col. 1: Credit is the credit market size averaged over the 3 year period. Col. 2: Credit is the credit market size at the beginning of the period Col. 3: Credit is the credit market size averaged over the all period Controls: property rights, volatility of interest rate, terms of trade, cpi. Panel based on 3-year non-overlapping averages. Country and time-…xed e¤ects included. White-type robust standard errors in parenthesis, 5 and 10 per cent signi…cant coe¢ cients respectively in bold and italics.
γ Figure 2.a shows how the steady state productivity gap in total production between the two groups of agents varies with respect to γ.
Figure 2.b shows how the steady state values of the model's variables change with respect to the degree of credit market development γ.
γ Figure 2.c shows how the steady state values of the size of the credit market change with respect to the degree of credit market development γ.
Figure 3.a shows the response of total aggregate output to a 1% increase in productivity.
Figure 3.b shows the responses to a 1% increase in productivity.
Figure 3.c: responses of the model economy to an unexpected 1% increase in aggregate productivity. The units on the vertical axes are percentage deviations from the steady state, while on the horizontal axes are years.
Figure 4.c: first impact of the shock capital reallocation and asset prices.
Figure 5: standard deviations given a particular value for γ.
716 “Adjusting to the euro” by G. Fagan and V. Gaspar, January 2007. 717 “Discretion rather than rules? When is discretionary policy-making better than the timeless perspective?” by S. Sauer, January 2007. 718 “Drift and breaks in labor productivity” by L. Benati, January 2007. 719 “US imbalances: the role of technology and policy” by R. Bems, L. Dedola and F. Smets, January 2007. 720 “Real price wage rigidities in a model with matching frictions” by K. Kuester, February 2007. 721 “Are survey-based inflation expectations in the euro area informative?” by R. Mestre, February 2007. 722 “Shocks and frictions in US business cycles: a Bayesian DSGE approach” by F. Smets and R. Wouters, February 2007. 723 “Asset allocation by penalized least squares” by S. Manganelli, February 2007. 724 “The transmission of emerging market shocks to global equity markets” by L. Cuadro Sáez, M. Fratzscher and C. Thimann, February 2007. 725 ”Inflation forecasts, monetary policy and unemployment dynamics: evidence from the US and the euro area” by C. Altavilla and M. Ciccarelli, February 2007. 726 “Using intraday data to gauge financial market responses to Fed and ECB monetary policy decisions” by M. Andersson, February 2007. 727 “Price setting in the euro area: some stylised facts from individual producer price data” by P.Vermeulen, D. Dias, M. Dossche, E. Gautier, I. Hernando, R. Sabbatini and H. Stahl, February 2007. 728 “Price changes in Finland: some evidence from micro CPI data” by S. Kurri, February 2007. 729 “Fast micro and slow macro: can aggregation explain the persistence of inflation?” by F. Altissimo, B. Mojon and P. Zaffaroni, February 2007. 730 “What drives business cycles and international trade in emerging market economies?” by M. Sánchez, February 2007. 731 “International trade, technological shocks and spillovers in the labour market: a GVAR analysis of the US manufacturing sector” by P. Hiebert and I.Vansteenkiste, February 2007. 732 “Liquidity shocks and asset price boom/bust cycles” by R. Adalid and C. Detken, February 2007. 733 “Mortgage interest rate dispersion in the euro area” by C. Kok Sørensen and J.-D. Lichtenberger, February 2007. 734 “Inflation risk premia in the term structure of interest rates” by P. Hördahl and O. Tristani, February 2007. 735 “Market based compensation, price informativeness and short-term trading” by R. Calcagno and F. Heider, February 2007.
736 “Transaction costs and informational cascades in financial markets: theory and experimental evidence” by M. Cipriani and A. Guarino, February 2007. 737 “Structural balances and revenue windfalls: the role of asset prices revisited” by R. Morris and L. Schuknecht, March 2007. 738 “Commodity prices, money and inflation” by F. Browne and D. Cronin, March 2007. 739 “Exchange rate pass-through in emerging markets” by M. Ca’ Zorzi, E. Hahn and M. Sánchez, March 2007. 740 “Transition economy convergence in a two-country model: implications for monetary integration” by J. Brůha and J. Podpiera, March 2007. 741 “Sectoral money demand models for the euro area based on a common set of determinants” by J. von Landesberger, March 2007. 742 “The Eurosystem, the US Federal Reserve and the Bank of Japan: similarities and differences” by D. Gerdesmeier, F. P. Mongelli and B. Roffia, March 2007. 743 “Credit market and macroeconomic volatility” by C. Mendicino, March 2007.
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