Dynamic Costly State Verication


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1 Dynamic Costly State Verication Cheng Wang Department of Economics, Iowa State University, Ames, Iowa , USA ( Received: revised: Summary. I study a model of dynamic risk sharing with costly state verication (CSV). In the model, a risk neutral agententers an innitely repeated relationship with a risk averse agent. In each period, the risk averse agent receives a random income which is observed only by himself, unless the risk neutral agent engages in costly monitoring. I provide a set of characterizations for the optimal contract, and I show that CSV has interesting eects on the long run distribution of the agents' expected utilities. Keywords and Phrases: Dynamic Costly State Verication JEL Classication Numbers: 026, 35 This research was initially joint work with Bruce Smith. I thank Bruce for the inspiration. I thank Fernando Alvarez, Narayana Kocherlakota, Steve Spear, and an anonymous referee for helpful comments. I also thank seminar participants at Carnegie Mellon University, Federal Reserve Banks of Minneapolis, Federal Reserve bank of Richmond, Duke University, SUNY Bualo, University of Rochester, University of Pittsburgh, University of Western Ontario, the 998 Econometric Society winter meeting, the 999 SED meeting, and the 999 SITE workshop at Stanford University for discussions.
2 Introduction The model of costly state verication (CSV) (Townsend 979) has been used as a vehicle for studying a variety of issues related to investmentcontracting, banking, and macroeconomics. Atypical interpretation of the model is that it describes a nancial lending process where the borrower's income is not directly observed by the lender, unless the lender incurs a cost to monitor. In this paper, I extend the model of CSV to a fully dynamic setting. Specically, I study a problem of optimal dynamic contracting between a risk neutral agent (the lender) and a risk averse agent (the borrower). In each period, the risk averse agent receives a random income which isob served directly only by himself. The risk neutral agent can observe the risk averse agent's income realization by incurring a xed monitoring cost. Like Townsend, I focus on deterministic monitoring strategies. My model thus stands between the literature of CSV and the literature of (noncsv) dynamic contracting. 2 I will focus on two sets of questions. First, what is the structure of the optimal contract? In particular, what is the risk neutral agent's optimal monitoring strategy, and how does it depend on the parameters and the dynamics of the model? When comparisons are relevant, how dotheanswers to these questions dier between the dynamic and the static CSV models? Second, what are the eects of CSV on the long run distribution of the agents' consumption and utility? The second question is of interest partly because the existing noncsv dynamic contracting literature has focused a lot on the long run implications of dynamic contracts. 3 I proceed through a series of environments that are progressively more technically demanding. I rst consider the case of CARA utility function and two income levels. I then consider the case of CARA with more than two income levels. Finally, I study the model with noncara utilities. The characterizations of the optimal contract that I can obtain will be fairly complete for the rst case, they are much less complete for the latter cases. In quite a few situations I will have to rely on numerical computation for insights. In the case of CARA and two income levels, there is a critical level of the xed monitoring cost,, below which monitoring is optimal and above which monitoring is not optimal. When monitoring is optimal, rstbest risk sharing is obtained. When monitoring is not optimal, the optimal provision See, for example, Diamond (984), Gale and Hellwig (985), Williamson (986, 987), Bernanke and Gertler (989), Haubrich (989), Boyd and Smith (997). 2 This literature includes, among others, Green (987), Spear and Srivastava (987), Thomas and Worrall (989), Phelan and Townsend (990), Taub (990), Atkeson and Lucas (992), Wang (995), Kocherlakota (996), Cole and Kocherlakota (200), Aiyagari and Williamson (997), Fernandes and Phelan (998). 3 See Green (987), Atkeson and Lucas (992), Thomas and Worrall (990), Phelan (998).
3 of intertemporal incentives implies that on average, and relative to the rstbest allocation, the risk neutral agent's consumption must be postponed, while the risk averse agent's consumption must be moved up. Thus, as interest rate rises, it is more expensive for the risk neutral agent to nance the risk averse agent's relatively earlier consumption, making monitoring more ecient relative to nomonitoring. In other words, decreases as interest rate increases. This has interesting implications, especially for macroeconomists who seek to study CSV in dynamic general equilibrium settings. This implies that, depending on the cost of monitoring, a small change in interest rates can have profound eects on the form of the optimal contract and on the long run distribution of wealth between the agents. Specically, a small increase in interest rates can shift the optimal contract from monitoring to nomonitoring, making the risk neutral agent better o while the risk averse agent worse o in the long run. In the case of CARA with two income levels, I show that the eects of a change in risk aversion on the optimal monitoring policy is dierent in the dynamic model than in the static model. In the static model, the cuto monitoring cost is strictly increasing in the coecient of absolute risk aversion,. In the dynamic model, is increasing in only if is suciently small, and is decreasing in if is suciently large moreover, goes to zero as goes to innity. In the static model, the risk averse agent's consumption variability required byincentive compatibility is constantin. As risk aversion increases, this xed amount of consumption variability becomes increasingly costly to the risk neutral agent. Thus, an increase in strengthens the case for monitoring. In the dynamic model, there are twoforcesworking in dierent directions. On the one hand, an increase in implies that any xed amount of consumption variability becomes more costly, strengthening the case for monitoring. On the other hand, an increase in risk aversion reduces the amount of consumption variability that is needed for truthtelling in the absence of monitoring, due to a change in the risk averse agent's attitude towards intertemporal substitutions. This weakens the case for monitoring. The former force dominates when is suciently small, and the latter dominates for suciently large. This nonmonotonic relationship between monitoring and absolute risk aversion implies that when the risk averse agent's utility function takes a noncara form, the optimal monitoring policy should depend critically on and not be monotonic in the risk averse agent's expected utility (wealth). This, in turn, can generate interesting life cycle dynamics and have implications for the limiting distribution of the risk averse agent's consumption and expected utility. In a numerical example with log utility, I nd that monitoring is optimal only when the risk averse agent's expected utility falls between two cutos. Monitoring is not optimal when the risk averse agent's expected utility is suciently low (he is suciently risk averse) or when the risk averse agent's expected utility is suciently high (he is not 2
4 suciently risk averse). I show that this generates interesting dynamics and has important implications for the limiting distribution of the risk averse agent's consumption and expected utility. This paper is an extension of Smith and Wang (998) who study a twoperiodtwostateexponentialutility model of CSV, and focuses on the model's implications for deposit insurance. Chang (990) studies a two period model of CSV with risk neutral agents. Aiyagari and Alvarez (995) study the longrun implications of dynamic risk sharing with stochastic costly state verication in the context of unemployment insurance. The model is set up in Section 2. The optimal contract under CARA is characterized in Sections 3. Section 4 considers noncara utilities. Section 5 concludes the paper. 2 Model Time is discrete and lasts forever: t =0 2 ::: where t = 0 is the ex ante contracting period. There is one consumption good and there are two agents: a risk neutral agent and a risk averse agent. The risk neutral agent has P the following preferences: E t= 0 t; (c t ; t ) where c t is consumption, t ( 0) is monitoring eort, and 2 [0 ) is his discount factor. The risk averse agent's preferences are given by E 0 P t= t; v(c t ) where c t is consumption, 2 [0 ) is his discount factor.theriskaverse agent's period utility function v is strictly concave and continuously dierentiable. In each period t, the risk averse agent receives a random income of t. Assume t takes values from a nite set f 2 ::: N g, where < 2 < ::: < N,and t is i.i.d. across periods. Let i 2 (0 ) be the probability with which t takes the value of i : Probf t = i g = i, i = 2 ::: N,and P i i =. Assume that the probability distribution of the risk averse agent's random income is common knowledge between the two agents. However, each period, the realization of the risk averse agent's random income can be costlessly observed only by the risk averse agent himself. The risk neutral agent can observe the risk averse agent's current income in any period, but must incur a xed cost 0 in order to do so, where is interpreted as the cost of information acquisition. As in Townsend (979), I restrict attention in this paper to deterministic monitoring (verication) strategies, where the risk neutral agent monitors the risk averse agent's report of income with probability one or zero. 4 To insulate the model from any dynamics other 4 This assumption is made for mathematical convenience. Townsend (979) shows that stochastic monitoring dominates deterministic monitoring. Boyd and Smith (994) use a calibrated one period model of lending and borrowing with risk neutral agents to show that the gains from stochastic monitoring are likely to be small. I argue that the gains from stochastic monitoring should be even smaller in a dynamic model like this one, where the incentive role that monitoring plays in the static model can now be shared by the intertemporal component of the dynamic contract. See Mookherjee and Png (989) for 3
5 than those associated with the CSV problem, I assume that the consumption good cannot be stored across periods, and the risk averse agent hasno outside saving or borrowing opportunities. Note that if = 0 then there is complete information. On the other hand, if = or state verication is infeasible, then my model resembles the models of Green (987), and Thomas and Worrall (990), the key difference being that I allow asymmetric discounting between the agents. This dierence is important. It allows me to study for example the eects of achange in the interest rate that the risk neutral agent faces on the risk sharing dynamics and the optimal monitoring policy. 3 The Exponential Utility Function Throughout this section, I assume the risk averse agent's period utility function takes the CARA form: v(c)=; exp(;c) c2 R. 3. The FirstBest Contract In the case of complete information where = 0, The optimal allocation is rstbest and can be written: fb fc fb t g t= where cfb t is the risk averse agent's consumption in period t. The rstbest allocation solves max fctg P t= t; ; ; ct subject to P t= t; [; exp(;c t )] = w 0 where w 0 is the promised ex ante lifetime utility of the risk averse agent. It is straightforward to show that c fb t = ;t log( ) ; log(;w 0) + log( ; ): So c fb t depends linearly on t, and in a deterministic fashion: it increases in t if <, decreases in t if >, and it is constant int if =. Let w fb t; denote the risk averse agent's expected utility at the beginning of period t. Then it is easy to show that the law of motion for the risk averse agent's expected utility takes the following form: w fb t =( )wfb t; =( )t w 0 : () Thus over time the risk averse agent's expected utility also follows a deterministic path: wt fb is strictly decreasing and converges to ; if >,is increasing and converges to 0 if <, and remains constant if =. It also follows that ; exp[;c fb t ]=(; )wfb =(; ) t; wfb t : (2) This equation characterizes the intertemporal structure of the risk averse agent's consumption. The risk averse agent's current period utility isa optimal stochastic monitoring in a static model. See Aiyagari and Alvarez (995) and Monnet and Quintin (2002) for dynamic CSV models with stochastic monitoring. 4
6 constant fraction ( ; ) of his lifeime utility from next period onward, where the constant fraction is increasing in but decreasing in. An increase in () indicates that the risk neutral (averse) agent is relatively more patient and hence it is optimal to have his consumption postponed. 3.2 Costly State Verication Inow return to the case of costly state verication, i.e., the case where >0. I start by describing the extensive formofacontract. Let h t denote a history of all events that have taken place up to and including period t, t 0, where h 0 =, h t = fh h 2 ::: h t g, t, and h t =( t ~ t s t ) where t denotes the risk averse agent's report of income in period t s t is an indicator: s t =, if monitoring occurred in period t, and s t = 0 otherwise and lastly, ~ t denotes the riskneutral agent's information regarding the realization of t at the end of the period: t ~ = t if s t =, and ~ t = t if s t =0. As in Townsend (979), I assume that the risk averse agent will not misrepresent if he is indierent between truthtelling and misrepresentation. Thus conditional on the riskaverse agent submitting for state verication, I have ~ t = t.thus, without loss of generality, I can rewrite h t as h t =( t ) if s t =,andh t =( ~ t 0) if s t =0: Acontract is a sequence of functions that map from histories to currentperiod verication strategies and payments: = fs t (h t; ) M t (h t )g t= where S t (h t; ) is the risk neutral agent's verication strategy in period t, which is the set of (reported) states in which verication takes place, and M t (h t )ispayment fromtheriskaverse agent to the riskneutral agent in period t. Note that there are 2 N possible verication strategies for each period. Let S denote the set of all such verication strategies, and for each S t 2 S, let (S t ) denote the probability with which verication takes place, given strategy S t. That is, (S t )= P i 2S t i.for example, if N = 2, then there are four possible verication strategies: (i)s t =, (ii)s t = f g, (iii) S t = f 2 g (iv) S t = f 2 g and (S t )=0 2 and respectively. Following Green (987) and Spear and Srivastava (987), the extensive form contract can be represented recursively by using the risk averse agent's expected utility w as a state variable. Specically, the recursive form of takes the form fs(w) fm( i w) W( i w)g N i=g where S the risk neutral agent's monitoring policy, M is the payment scheme, and W is the law of motion for the state variable w. It is then straightforward to show that the optimal recursive contract is a solution to the following Bellman equation: for all w 2 (; 0), U (w) = subject to sup S(w) fm ( i w) W ( i w)g N i= NX i= i [M( i w)+u [W ( i w)]] ; [S(w)] S(w) M( i w) 2 R W ( i w) 2 (; 0) 8 i 2 (4)! (3) 5
7 v[; i + M( i w)] + W( i w) v[; i + M( j w)] + W( j w) NX i= 8 i 2 8 j 62 S(w) (5) i fv[; i + M( i w)] + W( i w)g = w: (6) Here, (5) is the incentive constraint, (6) is the \promisekeeping" constraint. The term [S(w)] represents the risk averse agent's expected cost of monitoring in the current period, given that his monitoring policy is S(w). Constraint (5) imposes that, given any history (i.e., for any w), and for any currentperiod realization of the risk averse agent's random income, the risk averse agent have no incentives to misrepresent. Suppose (5) is violated for some i 2, and j 62 S(w). Then whenever the risk averse agent receives i,hewillhave an incentive to report j and will not be caught. Remember if the risk averse agent's income is i,henever have incentives to report any j (6= i ) which lies in the verication region, knowing he would be caught in a state verication, and his true income would then be revealed anyway. Thus, as is clear from (5), monitoring reduces the number of constraints that an incentive compatible contract must satisfy. 5 The above functional equation can be solved almost analytically. Let S and let (S) max (m i w i ) N i= NX i= subject to m i > 0 w i > 0 8i, and i log m i + ; log w i exp(; i )m i + w i exp(; i )m j + w j 8 i 2 8 j 62 S (8) (7) NX i= i [exp(; i )m i + w i ]=: (9) Proposition Let fs (w) M ( i w) W ( i w)g denote an optimal contract. Then S (w) 2 argmax S [(S) ; (S)] M ( i w) = log(m i )+ log(;w) W ( i w)=wi w where fm i w i g maximizes the right hand side of (7) for S = S. Notice that S (w) is constant inw. In the remainder of the paper I will use S for S (w). Let C ( i w) i ; M ( i w) denote the optimal 5 Notice that constraint (5) covers the special cases where =0or =. Specically, = 0 implies it is optimal to set S(w) = for all w, so constraint (5) is never binding. In the case where =, S(w) =, for all w. Also note that for = 0, (5) implies, as in standard static models of costly state verication such as Townsend (979), that for all i j 62 S(w), M( i w)=m( j w): Thus, without monitoring, no risk sharing is feasible. 6
8 consumption scheme for the risk averse agent. Proposition shows that M ( i w) is monotone decreasing in w, and both C ( i w)andw ( i w) are monotone increasing in w. Another monotonicity property of the incentive compatible contract that is easy to obtain is: If i j 62 S,then i < j implies M ( i w) <M ( j w) and W ( i w) <W ( j w). Finally, note that the rst best allocation can also be represented recursively, using the risk averse agent's expected utility asastatevariable. Let fm fb ( i w) W fb ( i w)g denote the rstbest contract. 3.3 Two Income Levels In this section I characterize the optimal contract under the assumption that there are only two possible income levels, that is, N =2. Proposition 2 There exists > 0 such that the optimal verication strategy is given by: S = f g if and S = otherwise where (f g) ; ( ) where and ( ) (f g) + log( ; )+ X max i log m i + (m i w i ) i= 2 i= 2 ; log( ) ; log w i subject to m i > 0 w i > 0 (9), and exp(; 2 )m 2 + w 2 =exp(; 2 )m + w : Thus, it is optimal either to engage in no state verication [S = ], or to verify the state when reported income is low [S = f g]. Suppose S = f g, then rst best risk sharing is attained. Proposition 2 is an extension of a parallel result in Smith and Wang (998) for the static and two period models of CSV. A corollary of Proposition 2, and an extension of a parallel result in Wang and Smith (998) for the case of static CSV, is: changes in the values of and 2 aect the level of the critical monitoring cost only through changes in the value of ( 2 ; ). Moreover, is strictly increasing in ( 2 ; ). This is easy to prove. I need only use the following transformation in the problem that denes ( ): ~m i exp(; i )m i. The detailed proof is left for the reader. Therefore an increase in ( 2 ; ) strengthens the case for monitoring. This is intuitive. Everything else equal, an increase in ( 2 ; )makes truth telling more dicult to achieve, for now conditional on t = 2, the 7
9 temptation to report is greater. Mathematically, this means that all else equal, a higher value of ( 2 ; )makes the incentive constraint tighter. 6 Hence without monitoring, a greater amount of distortion in the risk averse agent's consumption is needed for truthtelling. Inowmove on to provide a characterization for the dynamics of the optimal contract under S =. Although this characterization is not directly about the optimal monitoring policy, it provides some of the underlining intuitions for an important result that will be contained in my next proposition. Proposition 3 Suppose S =. Then for all w, C ( 2 w) >C ( w) W ( 2 w) >W ( w) and v[c ( w)] > ( ; ) W ( w) (0) v[c ( 2 w)] = ( ; ) W ( 2 w) () X i W ( i w) < w: (2) Thus when S =, in order to provide incentives for truthtelling, two kinds of distortions occur in the riskaverse agent's consumption scheme relative to the rstbest consumption scheme. There is an intratemporal distortion across states of current income, there is also an intertemporal consumption distortion across the current and the future periods. Notice that the intertemporal distortion occurs only in the low income state. Since the risk averse agent never has an incentive toreport 2 when his income is,nointertemporal incentives are needed in state 2. Proposition 3 is not entirely new. Specically, equation (2) is an extension of a similar result in Green (987) and Thomas and Worrall (990) who deal with the case of =, although my proof of (2) (see Appendix) is much simpler and perhaps more intuitive. Proposition 3 indicates that monitoring has longrun implications for the agents' expected utilities. Under full risk sharing (which is attained when the monitoring cost is P suciently small), equations () and (2), when written recursively, imply i W fb ( i w)= w where W fb( i w) is the rstbest law of motion of the risk averse agent's expected utility. Thus equation (2) shows that with no monitoring, on average the optimal contract pushes the expected utility of the risk averse agent down and that of the risk neutral agent up, relative to the case of monitoring. I will return to the long run implications of monitoring for the agents' expected utilities in the later sections. The longrun implications of dynamic contracts have been a preoccupation of the dynamic contracting literature (Phelan (998)). 6 With the transformation ~m i = e ; i m i, the incentive constraint that is binding becomes ~m 2 + w 2 e ;( 2;) ~m + w. 8
10 Compared to the static CSV model, one advantage of the dynamic model is that it allows me to consider the eects of the agents' attitudes toward future consumption on the structure and dynamics of the optimal contract. Proposition 4 provides a result about the relationship between monitoring and the agents' discount factors that has no parallel in the static CSV Proposition 4 < 0 and! 0as! (ii) (iii) Suppose = =0 Proposition 4 says that as the risk neutral agent becomes more patient, decreases monotonically. In other words, everything else equal, a higher discount factor of the risk neutral agent makes monitoring less likely to be optimal. Moreover, xing the monitoring cost, a suciently high can always make monitoring nonoptimal. The story goes as follows. Remember that with no monitoring, the optimal contract implies that, relative to rstbest, the risk neutral agent must shift resources from the future to the present to nance the risk averse agent's current consumption. Now the cost of doing this is lower when is higher. Put dierently, a higher implies intertemporal incentives for truth telling are more readily available, monitoring is thus relatively more expensive, and so should be lower. Proposition 4 shows that is invariant in. Given the CARA utility function of the risk averse agent, the value function is in the log of w, and that the discount factor always enters the equations multiplicatively with w, the eects of a variation in the risk averse agent's discount factor are canceled out. However, intuitively, one might > 0 to hold. As increases, future consumption is more valuable for the risk averse agent, relative to current consumption. This in turn means that in order to persuade the risk averse agent togiveupany specied amount of future consumption, the risk neutral agent must pay a higher cost in terms of current consumption. That is, as increases, intertemporal incentives are more expensive, or, monitoring is relatively more ecient inachieving incentive compatibility, should thus be higher. The results I have obtained so far continue to hold if the risk averse agent's utility function takes the more general form v(c) =; exp(;c), where >0 is the coecient ofcara.now the question I want toask is: how would a change in the value of aect the structure of the optimal contract? In particular, how does the cuto monitoring cost depend on the value of? Proposition 5 In the one > 0: In the dynamic > 0 for suciently < 0 for suciently large (iii)! 0 as!: Thus there is a monotonic relationship between and in the static CSV model, but not in the dynamic model. Moreover, in the dynamic 9
11 model, the optimal demand for monitoring falls to zero as risk aversion becomes innitely great. Why? In the static model, suppose there is no monitoring. Then incentive compatibility requires M( w)=m( 2 w), and hence C( 2 w) ; C( w)= 2 ; where C( i w) denotes the risk averse agent's consumption in state i. This equation implies the risk averse agent's consumption variability is constant as increases. However, as risk aversion increases, this xed amount of consumption variability becomes increasingly costly to the risk neutral agent, holding constant the risk averse agent's expected utility. In other words, the benets of consumption smoothing, which can be provided by monitoring, increase as increases. Thus an increase in strengthens the case for monitoring. In the dynamic model, there are two mechanisms that work in dierent directions. On the one hand, as in the single period model, an increase in strengthens the case for monitoring, because consumption variability ismore costly. On the other hand, in the dynamic model, because of the new intertemporal dimension along which incentives can be created, the total utility variability that is needed for incentive compatibility is bounded, which in turn implies that as increases, the amount of consumption variability needed for truthtelling decreases, weakening the case for monitoring. Note that this second mechanism is completely shut down in the static model. To elaborate on how the second mechanism works, imagine a simple twoperiod setting where the rst period environment is the same as the stage environment inmy fully dynamic model, but the risk averse agent has no income in the second period. The agents consume in both periods, and there is no discounting across periods. With no monitoring, the optimization problem is subject to U nm (w) max X + i [log(v i )+log(w i )] v v 2 w w 2 v + w exp[;( ; 2 )]v 2 + w 2 (3) v 2 + w 2 exp[;( 2 ; )]v + w (4) (v + w )+ 2 (v 2 + w 2 )=w (5) where v i (w i ) is the risk averse agent's utility in the rst (second) period given that his rst period income is i. In stead of tackling the above problem directly, consider the following auxiliary problem: U nm (w) max X + [log(v i ) + log(w i )] v v 2 w w 2 subject to (3), (4), (5), and v = v 2 : Note that by imposing v = v 2 we have ruled out utility inequality across states in period as an incentive device. That is, the contract must rely entirely on intertemporal arrangements to obtain incentive compatibility. As is straightforward to show, in 0
12 this problem, at the optimum, constraint (3) is not binding, it holds that w 2 = v 2 and K()v = w = K()w 2 (6) where K() 2 ; exp[;( 2 ; )]: Here the rst equation of (6) characterizes the intertemporal inequality in the risk averse agent's utility that is required for incentive compatibility by the optimal contract, and the second equation characterizes the intratemporal inequality in the risk averse agent's period 2 utility. Notice that K 0 () > 0, so higher risk aversion implies higher utility inequality across states and periods. Moreover, K() is bounded between and 2. Thus total utility variability required for incentive compatibility is bounded as increases. Note that in the single period model, ;e ;C( w) = e ( 2; ) [;e ;C( 2 w) ] where e ( 2; )!as!, so utility variability isunbounded as increases. That utility variability in the optimal dynamic contract is bounded is essential for obtaining our result. I have ~c 2 ; c =~c ; c =c 2 ; c = log[k()] where ; exp(;c i )=w i and ; exp(;~c i )=v i. The expression log[k()] measures in a sense the minimum consumption variability required for truthtelling. Clearly, log[k()]= is increasing in for suciently small, and decreasing in for suciently large. Moreover, given K()! 2as!, log[k()]= converges to zero as!, implying that the optimal contract converges to the rst best as risk aversion goes to innity. To show that! 0as!formally, rewrite U nm (w): U nm (w) = max w w 2 [ log(;w ) + ( + 2 )log(;w 2 )] subject to (6) and w +(+ 2 )w 2 = w. Obviously, U nm (w) =U fb (w)+ Z() where U fb (w) is the risk neutral agent's value function in the case of complete information, U fb (w) = + 2 log(; w ) and 2 Z() ;max z z 2 [ log(z ) + ( + 2 ) log(z 2 )] subject to z +(+ 2 )z 2 =,andz = K()z 2 : But K()! 2as!, and hence Z() converges to a nite number as!,andso = U fb (w) ; U nm (w) U fb (w) ; Unm (w) = Z()! 0 as!: I provide a numerical example to further illustrate the relationship between and. Let =:0 2 =2:0 = 2 =0:5 = =0:90: Figure plots as a function of.
13 3.3. Interpretations Propositions 2, 4, and 5 show that the structure of the optimal contract is sensitive to the model's parameters. Suppose the risk neutral agent has access to a credit market where he can lend and borrow at a constant rateof interest equal to r and hence his discount factor is determined by +r : Then Propositions 2 and 4 imply that, depending on the monitoring cost, small changes in interest rates can have profound eects on the longrun distributions of the agents' expected utilities. This, in turn, suggests that if dynamic CSV is to be taken seriously for studying distributions in the long run, then it is important that it be imbedded in a general equilibrium framework that endogenizes the interest rate. In addition, Proposition 4 indicates that models that use dynamic CSV as a vehicle for studying macroeconomic quantities should also treat risk aversion carefully. One waytointerpret monitoring is to think of it as an essential feature of bank loans (intermediated lending). This interpretation implies that Propositions 4 and 5 maybe useful for thinking about the rm's choice between intermediated and unintermediated lendings. 7 For example, Proposition 4 implies that, depending on the monitoring cost, a small increase in interest rates can make borrowers to switch from borrowing directly from the credit market to intermediated bank loans. 8 A second way to think about Proposition 4 is to suppose = = 9, and think of as measuring the length of the credit relationship. Thus Proposition 4 asserts, in a sense, that a \shorter" credit relationship strengthens the case for monitoring. This prediction of our model seems consistent with the empirical evidence that bank loans have considerably shorter maturity than either private or public debt. 0 7 In practice, some business enterprises seek nancing from nancial intermediaries while others borrow directly from the credit market (e.g., commercial paper, corporate bond). A key distinction between the two nancing mechanisms is that nancial intermediaries often engage in extensive monitoring during the process of nancing, whereas typical individual lenders do not monitor, or do so much less. A theoretical explanation for this distinction is that monitoring of private information is more ecient whenitisdele gated to a nancial intermediary rather than when done repetitively by individual lenders (Diamond 984). The idea that banks are delegated monitors is central to the models of nancial intermediation based on costly state verication (e.g., Williamson 986, 987). Recent studies on the choice of the optimal nancing mechanism by Diamond (99) and Holmstrom and Tirole (997) have also taken seriously the notion that bank nancing is closely related to monitoring. In both papers, nancial intermediaries are modeled as monitors who can detect the borrower's choosing a bad project. 8 Does this have anything to say with respect to the nancial \disintermediation" that occurred in the U.S. during the early 90's when interest rates were historically low? 9 Consider the following interpretation of the common discount factor : Suppose the relationship between the two agents terminates exogenously with probability (; ) at the end of each period. That is, suppose is the probability with which the lending and borrowing relationship will survive into the next period. We can then compute the Obviously then expected length of the contract as follows L P t= tt; ( ; ) = ; L increases as increases. 0 James (987) reports that the longest bank loan is 2 years, less than the median 2
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